The identification of molecular motors that modulate the neuronal cytoskeleton has been elusive. Here, we show that a molecular motor protein, myosin Va, is present in high proportions in the cytoskeleton of mouse CNS and peripheral nerves. Immunoelectron microscopy, coimmunoprecipitation, and blot overlay analyses demonstrate that myosin Va in axons associates with neurofilaments, and that the NF-L subunit is its major ligand. A physiological association is indicated by observations that the level of myosin Va is reduced in axons of NF-L–null mice lacking neurofilaments and increased in mice overexpressing NF-L, but unchanged in NF-H–null mice. In vivo pulse-labeled myosin Va advances along axons at slow transport rates overlapping with those of neurofilament proteins and actin, both of which coimmunoprecipitate with myosin Va. Eliminating neurofilaments from mice selectively accelerates myosin Va translocation and redistributes myosin Va to the actin-rich subaxolemma and membranous organelles. Finally, peripheral axons of dilute-lethal mice, lacking functional myosin Va, display selectively increased neurofilament number and levels of neurofilament proteins without altering axon caliber. These results identify myosin Va as a neurofilament-associated protein, and show that this association is essential to establish the normal distribution, axonal transport, and content of myosin Va, and the proper numbers of neurofilaments in axons.

Reverse convex programs generally have disconnected feasible regions. Basic solutions are defined and properties of the latter and of the convex hull of the feasible region are derived. Solution procedures are discussed and a cutting plane algorithm is developed. IntroductionA constraint h ( x ) >1 0 is called a reverse convex constraint if h is pseudo-convex. Optimization problems with several such constraints generally have disconnected feasible regions. To our knowledge, problems of this form were first studied by Rosen [13], in a control theoretic setting, and subsequently, in an engineering design setting, by Avriel and Williams [1,2]. These authors developed a procedure for finding a Kuhn-Tucker point and this procedure, in a more general setting, has been proven to converge to such a point by Meyer [12] from whom the term "reverse convex" is taken. As shown by Avriel and Williams, such constraints arise in geometric programming when some coefficients of product terms are negative, a likely occurrence in engineering design problems. Bansal and Jacobsen [4,5] and Hillestad [9] show that such problems arise when there are b u d g e t constraints which reflect economies-of-scale. It is also of interest to note that problems with 0-1 restrictions can be cast into the reverse convex form. For instance, the constraint x i = 0 or 1 can be rewritten as -x i + x~ >t 0, 0 < x i • 1. Ueing [ 16] has developed a combinatorial procedure for reverse convex problems and this procedure is expanded upon in this paper.

Abstract. A constraint g ( x )>1 0 is said to be a reverse convex constraint if the function g is continuous and strictly quasi-convex. The feasible regions for linear programs with an additional reverse convex constraint are generally non-convex and disconnected. It is shown that the convex hull of the feasible region is a convex polytope and, as a result, there is an optimal solution on an edge of the polytope defined by only the linear constraints. The only possible edges which can contain such an optimal solution are characterized in relation to the best feasible vertex of the polytope defined by only the linear constraints. This characterization then provides a finite algorithm for finding a globally optimal solution.