GeneMCL in microarray analysis

Accurately and reliably identifying the actual number of clusters present with a dataset of gene expression profiles, when no additional information on cluster structure is available, is a problem addressed by few algorithms. GeneMCL transforms microarray analysis data into a graph consisting of nodes connected by edges, where the nodes represent genes, and the edges represent the similarity in expression of those genes, as given by a proximity measurement. This measurement is taken to be the Pearson correlation coefficient combined with a local non-linear rescaling step. The resulting graph is input to the Markov Cluster (MCL) algorithm, which is an elegant, deterministic, non-specific and scalable method, which models stochastic flow through the graph. The algorithm is inherently affected by any cluster structure present, and rapidly decomposes a graph into cohesive clusters. The potential of the GeneMCL algorithm is demonstrated with a 5730 gene subset (IGS) of the Van't Veer breast cancer database, for which the clusterings are shown to reflect underlying biological mechanisms. (c) 2005 Elsevier Ltd. All rights reserved.

Cupins: the most functionally diverse protein superfamily?

The cupin superfamily of proteins, named on the basis of a conserved β-barrel fold (‘cupa’ is the Latin term for a small barrel), was originally discovered using a conserved motif found within germin and germin-like proteins from higher plants. Previous analysis of cupins had identified some 18 different functional classes that range from single-domain bacterial enzymes such as isomerases and epimerases involved in the modification of cell wall carbohydrates, through to two-domain bicupins such as the desiccation-tolerant seed storage globulins, and multidomain transcription factors including one linked to the nodulation response in legumes. Recent advances in comparative genomics, and the resolution of many more 3-D structures have now revealed that the largest subset of the cupin superfamily is the 2-oxyglutarate-Fe2+ dependent dioxygenases. The substrates for this subclass of enzyme are many and varied and in total amount to probably 50–100 different biochemical reactions, including several involved in plant growth and development. Although the majority of enzymatic cupins contain iron as an active site metal, other members contain either copper, zinc, cobalt, nickel or manganese ions as a cofactor, with each cofactor allowing a different type of chemistry to occur within the conserved tertiary structure. This review discusses the range of structures and functions found in this most diverse of superfamilies.

The phylogeography of salmonid proliferative kidney disease in Europe and North America

Salmonid proliferative kidney disease (PKD) is caused by the myxozoan Tetracapsuloides bryosalmonae. Given the serious and apparently growing impact of PKD on farmed and wild salmonids, we undertook a phylogeographic study to gain insights into the history of genealogical lineages of T. bryosalmonae in Europe and North America, and to determine if the global expansion of rainbow trout farming has spread the disease. Phylogenetic analyses of internal transcribed spacer 1 sequences revealed a clade composed of all North American sequences plus a subset of Italian and French sequences. High genetic diversity in North America and the absence of genotypes diagnostic of the North American clade in the rest of Europe imply that southern Europe was colonized by immigration from North America; however, sequence divergence suggests that this colonization substantially pre-dated fisheries activities. Furthermore, the lack of southern European lineages in the rest of Europe, despite widespread rainbow trout farming, indicates that T. bryosalmonae is not transported through fisheries activities. This result strikingly contrasts with the commonness of fisheries-related introductions of other pathogens and parasites and indicates that fishes may be dead-end hosts. Our results also demonstrate that European strains of T. bryosalmonae infect and induce PKD in rainbow trout introduced to Europe.

Pharmacological characterisation of the agonist radioligand binding site of 5-HT2A, 5-HT2B and 5-HT2C receptors

In the present study we compared the affinity of various drugs for the high affinity "agonist-preferring" binding site of human recombinant 5-HT2A, 5-HT2B and 5-HT2C receptors stably expressed in monoclonal mammalian cell lines. To ensure that the "agonist-preferring" conformation of the receptor was preferentially labelled in competition binding experiments, saturation analysis was conducted using antagonist and agonist radiolabels at each receptor. Antagonist radiolabels ([H-3]-ketanserin for 5-HT2A receptor and [H-3]-mesulergine for 5-HT2B and 5-HT2C receptor) bound to a larger population of receptors in each preparation than the corresponding agonist radiolabel ([I-125]-DOI for 5-HT2A receptor binding and [H-3]-5-HT for 5-HT2B and 5-HT2C receptor binding). Competition experiments were subsequently conducted against appropriate concentrations of the agonist radiolabels bound to the "agonist-preferring" subset of receptors in each preparation. These studies confirmed that there are a number of highly selective antagonists available to investigate 5-HT2 receptor subtype function (for example, MDL 100907, RS-127445 and RS-102221 for 5-HT2A, 5-HT2B and 5-HT2C receptors respectively). There remains, however, a lack of highly selective agonists. (-)DOI is potent and moderately selective for 5-HT2A receptors, BW723C86 has poor selectivity for human 5-HT2B receptors, while Org 37684 and VER-3323 display some selectivity for the 5-HT2C receptor. We report for the first time in a single study, the selectivity of numerous serotonergic drugs for 5-HT2 receptors from the same species, in mammalian cell lines and using, exclusively, agonist radiolabels. The results indicate the importance of defining the selectivity of pharmacological tools, which may have been over-estimated in the past, and highlights the need to find more selective agonists to investigate 5-HT2 receptor pharmacology.

Expression of AmphiCoe, an amphioxus COE/EBF gene, in the developing central nervous system and epidermal sensory neurons

The COE/EBF gene family marks a subset of prospective neurons in the vertebrate central and peripheral. nervous system; including neurons deriving from some ectodermal placodes. Since placodes are often considered unique to vertebrates, we have characterised an amphioxus COE/EBF gene with the aim of using it as a marker to examine the timing and location of peripheral neuron differentiation. A single COE/EBF family member, AmphiCoe, was isolated from the amphioxus Branchiostoma floridae: AmphiCoe lies basal to the vertebrate COE/EBF genes in molecular phylogenetic analysis, suggesting that the duplications that formed the vertebrate COE/EBF family were specific to the vertebrate lineage. AmphiCoe is expressed in the central nervous system and in a small number of scattered ectodermal cells on the flanks of neurulae stage embryos. These cells become at least largely recessed beneath the ectoderm. Scanning electron microscopy was used to examine embryos in which the ectoderm had been partially peeled away. This revealed that these cells have neuronal morphology, and we infer that they are the precursors of epidermal primary sensory neurons. These characters lead us to suggest that differentiation of some ectodermal cells into sensory neurons with a tendency to sink beneath the embryonic surface represents a primitive feature that has become incorporated into placodes during vertebrate evolution. (C) 2004 Wiley-Liss, Inc.

Investigation of the mechanism of agonist and inverse agonist action at D-2 dopamine receptors

This study investigated, for the D-2 dopamine receptor, the relation between the ability of agonists and inverse agonists to stabilise different states of the receptor and their relative efficacies. K-i values for agonists were determined in competition, versus the binding of the antagonist [H-3]spiperone. Competition data were fitted best by a two-binding site model (with the exception of bromocriptine, for which a one-binding site model provided the best fit) and agonist affinities for the higher (K-h) (G protein-coupled) and lower affinity (K-l) (G protein-uncoupled) sites determined. Ki values for agonists were also determined in competition versus the binding of the agonist [H-3]N-propylnorapomorphine (NPA) to provide a second estimate of K-h,. Maximal agonist effects (E-max) and their potencies (EC50) were determined from concentration-response curves for agonist stimulation of guanosine-5'-O-(3-[S-32] thiotriphosphate) ([S-35]GTPgammaS) binding. The ability of agonists to stabilise the G protein-coupled state of the receptor (K-l/K-h, determined from ligand-binding assays) did not correlate with either of two measures of relative efficacy (relative E-max, Kl/EC50) of agonists determined in [S-35]GTPgammaS-binding assays, when the data for all of the compounds tested were analysed For a subset of compounds, however, there was a relation between K-l/K-h and E-max.. Competition-binding data versus [H-3]spiperone and [H-3]NPA for a range of inverse agonists were fitted best by a one-binding site model. K-i values for the inverse agonists tested were slightly lower in competition versus [H-3]NPA compared to [H-3]spiperone. These data do not provide support for the idea that inverse agonists act by binding preferentially to the ground state of the receptor. (C) 2004 Elsevier Inc. All rights reserved.

Proteomic analysis of redox- and ErbB2-dependent changes in mammary luminal epithelial cells using cysteine- and lysine-labelling two-dimensional difference gel electrophoresis

Differential protein expression analysis based on modification of selected amino acids with labelling reagents has become the major method of choice for quantitative proteomics. One such methodology, two-dimensional difference gel electrophoresis (2-D DIGE), uses a matched set of fluorescent N-hydroxysuccinimidyl (NHS) ester cyanine dyes to label lysine residues in different samples which can be run simultaneously on the same gels. Here we report the use of iodoacetylated cyanine (ICy) dyes (for labelling of cysteine thiols, for 2-D DIGE-based redox proteomics. Characterisation of ICy dye labelling in relation to its stoichiometry, sensitivity and specificity is described, as well as comparison of ICy dye with NHS-Cy dye labelling and several protein staining methods. We have optimised conditions for labelling of nonreduced, denatured samples and report increased sensitivity for a subset of thiol-containing proteins, allowing accurate monitoring of redox-dependent thiol modifications and expression changes, Cysteine labelling was then combined with lysine labelling in a multiplex 2-D DIGE proteomic study of redox-dependent and ErbB2-dependent changes in epithelial cells exposed to oxidative stress. This study identifies differentially modified proteins involved in cellular redox regulation, protein folding, proliferative suppression, glycolysis and cytoskeletal organisation, revealing the complexity of the response to oxidative stress and the impact that overexpression of ErbB2 has on this response.

Pseudo-neglect in Huntington's disease correlates with decreased angular gyrus density

Visuospatial attentional bias was examined in Huntington's disease (HID) patients with mild disease, asymptomatic gene-positive patients and controls. No group differences were found on the grey scales task (which is a non-motor task of visuospatial attentional bias), although patients' trinucleotide (CAG) repeat length correlated with increasing leftward bias. On the line bisection task, symptomatic patients made significantly larger leftward bisection errors relative to controls, who showed the normal slight degree of leftward error (pseudo-neglect). The asymptomatic group showed a trend for greater leftward error than controls. A subset of participants went on to have structural MRI, which showed a correlation between increased leftward error on the line bisection task and reduced density in the angular gyrus area (BA39) bilaterally. This finding is consistent with recent literature suggesting a critical role for the angular gyrus in the lateralization of visuospatial attention.

Investigation into the performance of an algorithm for deriving concise descriptions of optic flow fields for real-time control

This paper describes a region-based algorithm for deriving a concise description of a first order optical flow field. The algorithm described achieves performance improvements over existing algorithms without compromising the accuracy of the flow field values calculated. These improvements are brought about by not computing the entire flow field between two consecutive images, but by considering only the flow vectors of a selected subset of the images. The algorithm is presented in the context of a project to balance a bipedal robot using visual information.

Perspex machine VIII: axioms of transreal arithmetic

Transreal arithmetic is a total arithmetic that contains real arithmetic, but which has no arithmetical exceptions. It allows the specification of the Universal Perspex Machine which unifies geometry with the Turing Machine. Here we axiomatise the algebraic structure of transreal arithmetic so that it provides a total arithmetic on any appropriate set of numbers. This opens up the possibility of specifying a version of floating-point arithmetic that does not have any arithmetical exceptions and in which every number is a first-class citizen. We find that literal numbers in the axioms are distinct. In other words, the axiomatisation does not require special axioms to force non-triviality. It follows that transreal arithmetic must be defined on a set of numbers that contains{-8,-1,0,1,8,&pphi;} as a proper subset. We note that the axioms have been shown to be consistent by machine proof.

Sparse kernel density estimator using orthogonal regression based on D-optimality experimental design

A novel sparse kernel density estimator is derived based on a regression approach, which selects a very small subset of significant kernels by means of the D-optimality experimental design criterion using an orthogonal forward selection procedure. The weights of the resulting sparse kernel model are calculated using the multiplicative nonnegative quadratic programming algorithm. The proposed method is computationally attractive, in comparison with many existing kernel density estimation algorithms. Our numerical results also show that the proposed method compares favourably with other existing methods, in terms of both test accuracy and model sparsity, for constructing kernel density estimates.

Ratio selection for classification models

This paper is concerned with the selection of inputs for classification models based on ratios of measured quantities. For this purpose, all possible ratios are built from the quantities involved and variable selection techniques are used to choose a convenient subset of ratios. In this context, two selection techniques are proposed: one based on a pre-selection procedure and another based on a genetic algorithm. In an example involving the financial distress prediction of companies, the models obtained from ratios selected by the proposed techniques compare favorably to a model using ratios usually found in the financial distress literature.

Sparse model identification using orthogonal forward regression with basis pursuit and D-optimality

An efficient model identification algorithm for a large class of linear-in-the-parameters models is introduced that simultaneously optimises the model approximation ability, sparsity and robustness. The derived model parameters in each forward regression step are initially estimated via the orthogonal least squares (OLS), followed by being tuned with a new gradient-descent learning algorithm based on the basis pursuit that minimises the l(1) norm of the parameter estimate vector. The model subset selection cost function includes a D-optimality design criterion that maximises the determinant of the design matrix of the subset to ensure model robustness and to enable the model selection procedure to automatically terminate at a sparse model. The proposed approach is based on the forward OLS algorithm using the modified Gram-Schmidt procedure. Both the parameter tuning procedure, based on basis pursuit, and the model selection criterion, based on the D-optimality that is effective in ensuring model robustness, are integrated with the forward regression. As a consequence the inherent computational efficiency associated with the conventional forward OLS approach is maintained in the proposed algorithm. Examples demonstrate the effectiveness of the new approach.

Fault tolerance of Mobius cubes under two forbidden fault set models

An n-dimensional Mobius cube, 0MQ(n) or 1MQ(n), is a variation of n-dimensional cube Q(n) which possesses many attractive properties such as significantly smaller communication delay and stronger graph-embedding capabilities. In some practical situations, the fault tolerance of a distributed memory multiprocessor system can be measured more precisely by the connectivity of the underlying graph under forbidden fault set models. This article addresses the connectivity of 0MQ(n)/1MQ(n), under two typical forbidden fault set models. We first prove that the connectivity of 0MQ(n)/1MQ(n) is 2n - 2 when the fault set does not contain the neighborhood of any vertex as a subset. We then prove that the connectivity of 0MQ(n)/1MQ(n) is 3n - 5 provided that the neighborhood of any vertex as well as that of any edge cannot fail simultaneously These results demonstrate that 0MQ(n)/1MQ(n) has the same connectivity as Q(n) under either of the previous assumptions.

Semantic-associative visual content labelling and retrieval: A multimodal approach

A novel framework referred to as collaterally confirmed labelling (CCL) is proposed, aiming at localising the visual semantics to regions of interest in images with textual keywords. Both the primary image and collateral textual modalities are exploited in a mutually co-referencing and complementary fashion. The collateral content and context-based knowledge is used to bias the mapping from the low-level region-based visual primitives to the high-level visual concepts defined in a visual vocabulary. We introduce the notion of collateral context, which is represented as a co-occurrence matrix of the visual keywords. A collaborative mapping scheme is devised using statistical methods like Gaussian distribution or Euclidean distance together with collateral content and context-driven inference mechanism. We introduce a novel high-level visual content descriptor that is devised for performing semantic-based image classification and retrieval. The proposed image feature vector model is fundamentally underpinned by the CCL framework. Two different high-level image feature vector models are developed based on the CCL labelling of results for the purposes of image data clustering and retrieval, respectively. A subset of the Corel image collection has been used for evaluating our proposed method. The experimental results to-date already indicate that the proposed semantic-based visual content descriptors outperform both traditional visual and textual image feature models. (C) 2007 Elsevier B.V. All rights reserved.