955 resultados para MESH REFINEMENT


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Recent advances in multidimensional NMR methodology have permitted solution structures of proteins in excess of 250 residues to be solved. In this paper, we discuss several methods of structure refinement that promise to increase the accuracy of macromolecular structures determined by NMR. These methods include the use of a conformational database potential and direct refinement against three-bond coupling constants, secondary 13C shifts, 1H shifts, T1/T2 ratios, and residual dipolar couplings. The latter two measurements provide long range restraints that are not accessible by other solution NMR parameters.

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Mitral/tufted cells (M/T cells) and granule cells form reciprocal dendrodendritic synapses in the main olfactory bulb; the granule cell is excited by glutamate from the M/T cell and in turn inhibits M/T cells by gamma-aminobutyrate. The trans-synaptically excited granule cell is thought to induce lateral inhibition in neighboring M/T cells and to refine olfactory information. It remains, however, elusive how significantly and specifically this synaptic regulation contributes to the discrimination of different olfactory stimuli. This investigation concerns the mechanism of olfactory discrimination by single unit recordings of responses to a series of normal aliphatic aldehydes from individual rabbit M/T cells. This analysis revealed that inhibitory responses are evoked in a M/T cell by a defined subset of odor molecules with structures closely related to the excitatory odor molecules. Furthermore, blockade of the reciprocal synaptic transmission by the glutamate receptor antagonist or the gamma-aminobutyrate receptor antagonist markedly suppressed the odor-evoked inhibition, indicating that the inhibitory responses are evoked by lateral inhibition via the reciprocal synaptic transmission. The synaptic regulation in the olfactory bulb thus greatly enhances the tuning specificity of odor responses and would contribute to discrimination of olfactory information.

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In this paper we present different error measurements with the aim to evaluate the quality of the approximations generated by the GNG3D method for mesh simplification. The first phase of this method consists on the execution of the GNG3D algorithm, described in the paper. The primary goal of this phase is to obtain a simplified set of vertices representing the best approximation of the original 3D object. In the reconstruction phase we use the information provided by the optimization algorithm to reconstruct the faces thus obtaining the optimized mesh. The implementation of three error functions, named Eavg, Emax, Esur, permitts us to control the error of the simplified model, as it is shown in the examples studied.

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In this paper we present a study of the computational cost of the GNG3D algorithm for mesh optimization. This algorithm has been implemented taking as a basis a new method which is based on neural networks and consists on two differentiated phases: an optimization phase and a reconstruction phase. The optimization phase is developed applying an optimization algorithm based on the Growing Neural Gas model, which constitutes an unsupervised incremental clustering algorithm. The primary goal of this phase is to obtain a simplified set of vertices representing the best approximation of the original 3D object. In the reconstruction phase we use the information provided by the optimization algorithm to reconstruct the faces thus obtaining the optimized mesh. The computational cost of both phases is calculated, showing some examples.

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National Highway Traffic Safety Administration, Washington, D.C.

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National Highway Traffic Safety Administration, Washington, D.C.

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National Highway Traffic Safety Administration, Washington, D.C.

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National Highway Traffic Safety Administration, Washington, D.C.

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Bibliography: leaf 33.

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Bound in full brown paper covered boards with title in black on white paper labels mounted on spine.

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Mode of access: Internet.

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Shoemaker 23209.