We describe a more complex myosin cross-bridge model that uses multiple springs to replicate myosin’s force-generating
power stroke and account for the effects of lattice spacing and radial force. The four springs which comprise this model (the 4sXB) correspond to the mechanically relevant portions of myosin’s structure. As occurs in vivo, the 4sXB’s state-transition kinetics and force-production dynamics vary with lattice spacing. Additionally, we describe a simpler two-spring cross-bridge (2sXB) model which produces results similar to those of the 4sXB model. Unlike the 4sXB model, the 2sXB model requires no iterative techniques, making it more computationally efficient. The rate at which both multi-spring cross-bridges bind and generate force decreases as lattice spacing grows. The axial force generated
by each cross-bridge BMS-754807 manufacturer as it undergoes a power stroke increases as lattice spacing grows. The radial force that a cross-bridge produces as it undergoes a power stroke varies from expansive to compressive as lattice spacing increases. Importantly, these results mirror those for intact, contracting muscle force production.”
“A strong coupling between local charging and the specific volume of a polymer surface was exploited for topographic patterning. The charges were deposited locally using an atomic force microscope (AFM) tip sliding over the surface at moderate bias voltages of up to 5 V. The same tip was Selleck Caspase inhibitor used to measure both topography (using the AFM imaging mode) and charge (using the Kelvin Probe Force Microscopy method). The height of the obtained structures can reach several nanometers. With an estimated depth of the charge of 1 to 10 nm, this corresponds to an increase of specific volume of 10 to 100%. It is shown that the structures and the charges can be erased Cell Cycle inhibitor independently from each other. The charging is discussed in the context of molecular rearrangements necessary to store charge. (C) 2011
American Institute of Physics. [doi:10.1063/1.3600211]“
“We estimated genetic parameters for egg production in different periods by means of random regression models, aiming at selection based on partial egg production from a generation of layers. The production was evaluated for each individual by recording the number of eggs produced from 20 to 70 weeks of age, with partial records taken every three weeks for a total of 17 periods. The covariance functions were estimated with a random regression model by the restricted maximum likelihood method. A model composed of third-order polynomials for the additive effect, ninth-order polynomials for the permanent environment, and a residual variance structure with five distinct classes, was found to be most suitable for adjusting the egg production data for laying hens. The heritability estimates varied from 0.04 to 0.14. The genetic correlations were all positive, varying from 0.10 to 0.99.