A small GTP-binding protein dissociates from synaptic vesicles during exocytosis.

Low-molecular-weight GTP-binding proteins are strong candidates for regulators of membrane traffic. In yeast, mutations in the sec4 or ypt1 genes encoding small GTP-binding proteins inhibit constitutive membrane flow at the plasma membrane or Golgi complex, respectively. It has been suggested that membrane fusion-fission events are regulated by cycling of small GTP-binding proteins between a membrane-bound and free state, but although most of these small proteins are found in both soluble and tightly membrane-bound forms, there is no direct evidence to support such cycling. In rat brain a small GTP-binding protein, rab3A, is exclusively associated with synaptic vesicles, the secretory organelles of nerve terminals. Here we use isolated nerve terminals to study the fate of rab3A during synaptic vesicle exocytosis. We find that rab3A dissociates quantitatively from the vesicle membrane after Ca2(+)-dependent exocytosis and that this dissociation is partially reversible during recovery after stimulation. These results are direct evidence for an association-dissociation cycle of a small GTP-binding protein during traffic of its host membrane.     

Subset threshold autoregression

We develop in this paper an efficient way to select the best subset threshold autoregressive model. The proposed method uses a stochastic search idea. Differing from most conventional approaches, our method does not require us to fix the delay or the threshold parameters in advance. By adopting the Markov chain Monte Carlo techniques, we can identify the best subset model from a very large of number of possible models, and at the same time estimate the unknown parameters. A simulation experiment shows that the method is very effective. In its application to the US unemployment rate, the stochastic search method successfully selects lag one as the time delay and five best models from more than 4000 choices. Copyright © 2003 John Wiley & Sons, Ltd.     

The Huntington's disease candidate region exhibits many different haplotypes.

Analysis of 78 Huntington's disease (HD) chromosomes with multi-allele markers revealed 26 different haplotypes, suggesting a variety of independent HD mutations. The most frequent haplotype, accounting for about one third of disease chromosomes, suggests that the disease gene is between D4S182 and D4S180. However, the paucity of an expected class of chromosomes that can be related to this major haplotype by assuming single crossovers may reflect the operation of other mechanisms in creating haplotype diversity. Some of these mechanisms sustain alternative scenarios that do not require a multiple mutational origin for HD and/or its positioning between D4S182 and D4S180.     

ROLE OF α-PSEUDO-UNIVEX FUNCTIONS IN VECTOR VARIATIONAL-LIKE INEQUALITY PROBLEMS

In this paper, we introduce a new class of generalized convex function, namely, a-pseudounivex function, by combining the concepts of pseudo-univex and α-invex functions. Further, we establish some relationships between vector variational-like inequality problems and vector optimization problems under the assumptions of α-pseudo-univex functions. Results obtained in this paper present a refinement and improvement of previously known results.