Shaolin Li | PubMed Health

PubMed2003

Regulation of intermuscular electrical coupling by the Caenorhabditis elegans innexin inx-6.

The innexins represent a highly conserved protein family, the members of which make up the structural components of gap junctions in invertebrates. We have isolated and characterized a Caenorhabditis elegans gene inx-6 that encodes a new member of the innexin family required for the electrical coupling of pharyngeal muscles. inx-6(rr5) mutants complete embryogenesis without detectable abnormalities at restrictive temperature but fail to initiate postembryonic development after hatching. inx-6 is expressed in the pharynx at all larval stages, and an INX-6::GFP fusion protein showed a punctate expression pattern characteristic of gap junction proteins localized to plasma membrane plaques. Video recording and electropharyngeograms revealed that in inx-6(rr5) mutants the anterior pharyngeal (procorpus) muscles were electrically coupled to a lesser degree than the posterior metacorpus muscles, which caused a premature relaxation in the anterior pharynx and interfered with feeding. Dye-coupling experiments indicate that the gap junctions that link the procorpus to the metacorpus are functionally compromised in inx-6(rr5) mutants. We also show that another C. elegans innexin, EAT-5, can partially substitute for INX-6 function in vivo, underscoring their likely analogous function.

Amino Acid Sequence↗

PubMed2002

Prediction trees with soft nodes for binary outcomes.

Consider the problem of predicting the occurrence of an event, the onset of diabetes mellitus, say, from a vector of continuous and discrete predictors. We propose a new algorithm for the construction of a tree-structured predictor for the event of interest, which uses a new approach for dealing with continuous predictors. The novelty is that the tree uses splits for continuous variables. This means that at each node an individual goes to the right branch with a certain probability, function of a predictor. The predictor as well as the particular shape of the function is chosen from the data by the proposed algorithm. We evaluate its performance on several real data sets, in particular comparing it with a standard tree-growing algorithm. We also present an analysis of a well-known data set, the Pima Indian diabetes data set, to illustrate the application of the method in biostatistics.

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