Myo3A, One of Two Class III Myosin Genes Expressed in Vertebrate Retina, Is Localized to the Calycal Processes of Rod and Cone Photoreceptors and Is Expressed in the Sacculus

The striped bass has two retina-expressed class III myosin genes, each composed of a kinase, motor, and tail domain. We report the cloning, sequence analysis, and expression patterns of the long (Myo3A) and short (Myo3B) class III myosins, as well as cellular localization and biochemical characterization of the long isoform, Myo3A. Myo3A (209 kDa) is expressed in the retina, brain, testis, and sacculus, and Myo3B (155 kDa) is expressed in the retina, intestine, and testis. The tails of these two isoforms contain two highly conserved domains, 3THDI and 3THDII. Whereas Myo3B has three IQ motifs, Myo3A has nine IQ motifs, four in its neck and five in its tail domain. Myo3A localizes to actin filament bundles of photoreceptors and is concentrated in the calycal processes. An anti-Myo3A antibody decorates the actin cytoskeleton of rod inner/outer segments, and this labeling is reduced by the presence of ATP. The ATP-sensitive actin association is a feature characteristic of myosin motors. The numerous IQ motifs may play a structural or signaling role in the Myo3A, and its localization to calycal processes indicates that this myosin mediates a local function at this site in vertebrate photoreceptors

Asset pricing with dynamic programming

The study of asset price characteristics of stochastic growth models such as the risk-free interest rate, equity premium, and the Sharpe-ratio has been limited by the lack of global and accurate methods to solve dynamic optimization models. In this paper, a stochastic version of a dynamic programming method with adaptive grid scheme is applied to compute the asset price characteristics of a stochastic growth model. The stochastic growth model is of the type as developed by [Brock and Mirman (1972), Journal of Economic Theory, 4, 479–513 and Brock (1979), Part I: The growth model (pp. 165–190). New York: Academic Press; The economies of information and uncertainty (pp. 165–192). Chicago: University of Chicago Press. (1982). It has become the baseline model in the stochastic dynamic general equilibrium literature. In a first step, in order to test our procedure, it is applied to this basic stochastic growth model for which the optimal consumption and asset prices can analytically be computed. Since, as shown, our method produces only negligible errors, as compared to the analytical solution, in a second step, we apply it to more elaborate stochastic growth models with adjustment costs and habit formation. In the latter model preferences are not time separable and past consumption acts as a constraint on current consumption. This model gives rise to an additional state variable. We here too apply our stochastic version of a dynamic programming method with adaptive grid scheme to compute the above mentioned asset price characteristics. We show that our method is very suitable to be used as solution technique for such models with more complicated decision structure. Copyright Springer Science+Business Media, LLC 2007Stochastic growth models, Asset pricing, Stochastic dynamic programming, Adaptive grid, C60, C61, C63, D90, G12,