We present an algorithm that generates natural and intuitive deformations via direct manipulation for a wide range of shape representations and editing scenarios. Our method builds a space deformation represented by a collection of affine transformations organized in a graph structure. One transformation is associated with each graph node and applies a deformation to the nearby space. Positional constraints are specified on the points of an embedded object. As the user manipulates the constraints, a nonlinear minimization problem is solved to find optimal values for the affine transformations. Feature preservation is encoded directly in the objective function by measuring the deviation of each transformation from a true rotation. This algorithm addresses the problem of "embedded deformation" since it deforms space through direct manipulation of objects embedded within it, while preserving the embedded objects' features. We demonstrate our method by editing meshes, polygon soups, mesh animations, and animated particle systems.

Measured cTnT concentrations were chronically elevated in the majority of patients with skeletal myopathies, whereas cTnI elevation was rare. Our data indicate that cross-reaction of the cTnT immunoassay with skeletal muscle troponin isoforms was the likely cause.

We present an algorithm that generates natural and intuitive deformations via direct manipulation for a wide range of shape representations and editing scenarios. Our method builds a space deformation represented by a collection of affine transformations organized in a graph structure. One transformation is associated with each graph node and applies a deformation to the nearby space. Positional constraints are specified on the points of an embedded object. As the user manipulates the constraints, a nonlinear minimization problem is solved to find optimal values for the affine transformations. Feature preservation is encoded directly in the objective function by measuring the deviation of each transformation from a true rotation. This algorithm addresses the problem of "embedded deformation" since it deforms space through direct manipulation of objects embedded within it, while preserving the embedded objects' features. We demonstrate our method by editing meshes, polygon soups, mesh animations, and animated particle systems.

We aimed to determine the relationship between level of injury, completeness of injury, resting as well as exercise hemodynamics, and endurance performance in athletes with spinal cord injury (SCI). Twenty-three elite male paracycling athletes (C3-T8) were assessed for neurological level/completeness of injury, autonomic completeness of injury, resting cardiovascular function, and time to complete a 17.3-km World Championship time-trial test. A subset were also fitted with heart rate (HR) monitors and their cycles were fitted with a global positioning systems device (n = 15). Thoracic SCI exhibited higher seated systolic blood pressure along with superior time-trial performance compared with cervical SCI (all P < 0.01). When further stratified by autonomic completeness of injury, the four athletes with cervical autonomic incomplete SCI exhibited a faster time-trial time and a higher average speed compared with cervical autonomic complete SCI (all P < 0.042). Maximum and average HR also tended to be higher in cervical autonomic incomplete vs autonomic complete. There were no differences in time-trial time, HR, or speed between thoracic autonomic complete vs incomplete SCI. In conclusion, autonomic completeness of injury and the consequent ability of the cardiovascular system to respond to exercise appear to be a critical determinant of endurance performance in elite athletes with cervical SCI.

We present an algorithm that generates natural and intuitive deformations via direct manipulation for a wide range of shape representations and editing scenarios. Our method builds a space deformation represented by a collection of affine transformations organized in a graph structure. One transformation is associated with each graph node and applies a deformation to the nearby space. Positional constraints are specified on the points of an embedded object. As the user manipulates the constraints, a nonlinear minimization problem is solved to find optimal values for the affine transformations. Feature preservation is encoded directly in the objective function by measuring the deviation of each transformation from a true rotation. This algorithm addresses the problem of "embedded deformation" since it deforms space through direct manipulation of objects embedded within it, while preserving the embedded objects' features. We demonstrate our method by editing meshes, polygon soups, mesh animations, and animated particle systems.