Ca(2+) binding to c-state of adenine nucleotide translocase (ANT)-surrounding cardiolipins enhances (ANT)-Cys(56) relative mobility: A computational-based mitochondrial permeability transition study

The oxidation of critical cysteines/related thiols of adenine nucleotide translocase (ANT) is believed to be an important event of the Ca(2+)-induced mitochondrial permeability transition (MPT), a process mediated by a cyclosporine A/ADP-sensitive permeability transition pores (PTP) opening. We addressed the ANT-Cys(56) relative mobility status resulting from the interaction of ANT/surrounding cardiolipins with Ca(2+) and/or ADP by means of computational chemistry analysis (Molecular Interaction Fields and Molecular Dynamics studies), supported by classic mitochondrial swelling assays. The following events were predicted: (i) Ca(2+) interacts preferentially with the ANT surrounding cardiolipins bound to the H4 helix of translocase, (ii) weakens the cardiolipins/ANT interactions and (iii) destabilizes the initial ANT-Cys(56) residue increasing its relative mobility. The binding of ADP that stabilizes the conformation ""m"" of ANT and/or cardiolipin, respectively to H5 and H4 helices, could stabilize their contacts with the short helix h56 that includes Cys(56), accounting for reducing its relative mobility. The results suggest that Ca(2+) binding to adenine nucleotide translocase (ANT)-surrounding cardiolipins in c-state of the translocase enhances (ANT)-Cys(56) relative mobility and that this may constitute a potential critical step of Ca(2+)-induced PTP opening. (C) 2009 Elsevier B.V. All rights reserved.

Saturation mutagenesis of the beta subunit of the human granulocyte-macrophage colony-stimulating factor receptor shows clustering of constitutive mutations, activation of ERK MAP kinase and STAT pathways, and differential beta subunit tyrosine phosphoryl

The high-affinity receptors for human granulocyte-macrophage colony-stimulating factor (GM-CSF), interleukin-1 (IL-3), and IL-5 are heterodimeric complexes consisting of cytokine-specific alpha subunits and a common signal-transducing beta subunit (h beta c). We have previously demonstrated the oncogenic potential of this group of receptors by identifying constitutively activating point mutations in the extracellular and transmembrane domains of h beta c. We report here a comprehensive screen of the entire h beta c molecule that has led to the identification of additional constitutive point mutations by virtue of their ability to confer factor independence on murine FDC-P1 cells. These mutations were clustered exclusively in a central region of h beta c that encompasses the extracellular membrane-proximal domain, transmembrane domain, and membrane-proximal region of the cytoplasmic domain. Interestingly, most h beta c mutants exhibited cell type-specific constitutive activity, with only two transmembrane domain mutants able to confer factor independence on both murine FDC-P1 and BAF-B03 cells. Examination of the biochemical properties of these mutants in FDC-P1 cells indicated that MAP kinase (ERK1/2), STAT, and JAK2 signaling molecules were constitutively activated. In contrast, only some of the mutant beta subunits were constitutively tyrosine phosphorylated. Taken together; these results highlight key regions involved in h beta c activation, dissociate h beta c tyrosine phosphorylation from MAP kinase and STAT activation, and suggest the involvement of distinct mechanisms by which proliferative signals can be generated by h beta c. (C) 1998 by The American Society of Hematology.

A spectral clustering algorithm for manufacturing cell formation

A graph clustering algorithm constructs groups of closely related parts and machines separately. After they are matched for the least intercell moves, a refining process runs on the initial cell formation to decrease the number of intercell moves. A simple modification of this main approach can deal with some practical constraints, such as the popular constraint of bounding the maximum number of machines in a cell. Our approach makes a big improvement in the computational time. More importantly, improvement is seen in the number of intercell moves when the computational results were compared with best known solutions from the literature. (C) 2009 Elsevier Ltd. All rights reserved.

Molecular evidence of horizontal transmission of hepatitis C virus within couples

Hepatitis C virus (HCV) transmission has decreased with the adoption of universal blood donor screening and social policies to reduce the risk of infection in intravenous drug users, but remains a worldwide health problem. The objective of this study was to evaluate the phylogenetic relationships among sequences from different HCV genomic regions from sexual partners of infected patients. Nine couples with a stable relationship and without other risk factors for HCV infection and 42 control patients were selected, and the NS3 and NS5B regions were analysed. Phylogenetic analysis showed that viruses from five of the couples had a common origin, clustering in the same monophyletic group, with bootstrap values greater than 70. For the other couples, monophyletic groups were observed, but without bootstrap support. Thus, using two different viral genome regions, a common source of infection was observed in both members of five couples. These data strongly support HCV transmission within couples.

A Bayesian approach to fuzzy hypotheses testing for the estimation of optimal age for vaccination against measles

Fuzzy Bayesian tests were performed to evaluate whether the mother`s seroprevalence and children`s seroconversion to measles vaccine could be considered as ""high"" or ""low"". The results of the tests were aggregated into a fuzzy rule-based model structure, which would allow an expert to influence the model results. The linguistic model was developed considering four input variables. As the model output, we obtain the recommended age-specific vaccine coverage. The inputs of the fuzzy rules are fuzzy sets and the outputs are constant functions, performing the simplest Takagi-Sugeno-Kang model. This fuzzy approach is compared to a classical one, where the classical Bayes test was performed. Although the fuzzy and classical performances were similar, the fuzzy approach was more detailed and revealed important differences. In addition to taking into account subjective information in the form of fuzzy hypotheses it can be intuitively grasped by the decision maker. Finally, we show that the Bayesian test of fuzzy hypotheses is an interesting approach from the theoretical point of view, in the sense that it combines two complementary areas of investigation, normally seen as competitive. (C) 2007 IMACS. Published by Elsevier B.V. All rights reserved.

A fuzzy max-flow min-cut theorem

Interval-valued versions of the max-flow min-cut theorem and Karp-Edmonds algorithm are developed and provide robustness estimates for flows in networks in an imprecise or uncertain environment. These results are extended to networks with fuzzy capacities and flows. (C) 2001 Elsevier Science B.V. All rights reserved.

Heat of desorption measured by means of electrical heat excitation

A new method of measuring heat of desorption is proposed in this Letter. The principle of the method is to measure the amount of mass released when a controlled amount of energy is supplied directly to a solid adsorbent. This is in contrast to conventional methods such as microcalorimetry, where heat released upon adsorption is measured. In this method, a quantified heat supply is generated by passing a de current through a carbon pellet, which is equilibrated with a gas phase confined in a closed vessel. As a consequence of the heating, the particle temperature is increased, resulting in partial desorption of adsorbed molecules. The variations of pellet temperature and the system pressure with respect to time are used to determine the heat of desorption as a function of loading.

Quantification of sulfur and sulfur-containing compounds in wastewaters by means of a combination of liquid chromatographic methods

Low-micromolar concentrations of sulfite, thiosulfate and sulfide, present in synthetic wastewater or anaerobic digester effluent, were quantified by means of derivatization with monobromobimane, followed by HPLC separation with fluorescence detection. The concentration of elemental sulfur was determined, after its extraction with chloroform from the derivatized sample, by HPLC with UV detection. Recoveries of sulfide (both matrices), and of thiosulfate and sulfite (synthetic wastewater) were between 98 and 103%. The in-run RSDs on separate derivatizations were 13 and 19% for sulfite (two tests), between 1.5 and 6.6% for thiosulfate (two tests) and between 4.1 and 7.7% for sulfide (three tests). Response factors for derivatives of sulfide and thiosulfate, but not sulfite, were steady over a 13-month period during which 730 samples were analysed. Dithionate and tetrathionate did not seem to be detectable with this method. The distinctness of the elemental sulfur and the derivatizing-agent peaks was improved considerably by detecting elution at 297 instead of 263 nm. (C) 2002 Elsevier Science B.V. All rights reserved.

Brief note on the variation of constants formula for fuzzy differential equations

This note gives a theory of state transition matrices for linear systems of fuzzy differential equations. This is used to give a fuzzy version of the classical variation of constants formula. A simple example of a time-independent control system is used to illustrate the methods. While similar problems to the crisp case arise for time-dependent systems, in time-independent cases the calculations are elementary solutions of eigenvalue-eigenvector problems. In particular, for nonnegative or nonpositive matrices, the problems at each level set, can easily be solved in MATLAB to give the level sets of the fuzzy solution. (C) 2002 Elsevier Science B.V. All rights reserved.

A mixture model-based approach to the clustering of microarray expression data

Motivation: This paper introduces the software EMMIX-GENE that has been developed for the specific purpose of a model-based approach to the clustering of microarray expression data, in particular, of tissue samples on a very large number of genes. The latter is a nonstandard problem in parametric cluster analysis because the dimension of the feature space (the number of genes) is typically much greater than the number of tissues. A feasible approach is provided by first selecting a subset of the genes relevant for the clustering of the tissue samples by fitting mixtures of t distributions to rank the genes in order of increasing size of the likelihood ratio statistic for the test of one versus two components in the mixture model. The imposition of a threshold on the likelihood ratio statistic used in conjunction with a threshold on the size of a cluster allows the selection of a relevant set of genes. However, even this reduced set of genes will usually be too large for a normal mixture model to be fitted directly to the tissues, and so the use of mixtures of factor analyzers is exploited to reduce effectively the dimension of the feature space of genes. Results: The usefulness of the EMMIX-GENE approach for the clustering of tissue samples is demonstrated on two well-known data sets on colon and leukaemia tissues. For both data sets, relevant subsets of the genes are able to be selected that reveal interesting clusterings of the tissues that are either consistent with the external classification of the tissues or with background and biological knowledge of these sets.

Model-based clustering in gene expression microarrays: An application to breast cancer data

In microarray studies, the application of clustering techniques is often used to derive meaningful insights into the data. In the past, hierarchical methods have been the primary clustering tool employed to perform this task. The hierarchical algorithms have been mainly applied heuristically to these cluster analysis problems. Further, a major limitation of these methods is their inability to determine the number of clusters. Thus there is a need for a model-based approach to these. clustering problems. To this end, McLachlan et al. [7] developed a mixture model-based algorithm (EMMIX-GENE) for the clustering of tissue samples. To further investigate the EMMIX-GENE procedure as a model-based -approach, we present a case study involving the application of EMMIX-GENE to the breast cancer data as studied recently in van 't Veer et al. [10]. Our analysis considers the problem of clustering the tissue samples on the basis of the genes which is a non-standard problem because the number of genes greatly exceed the number of tissue samples. We demonstrate how EMMIX-GENE can be useful in reducing the initial set of genes down to a more computationally manageable size. The results from this analysis also emphasise the difficulty associated with the task of separating two tissue groups on the basis of a particular subset of genes. These results also shed light on why supervised methods have such a high misallocation error rate for the breast cancer data.