Kinematic coordinations capture learning during human-exoskeleton interaction.

Human-exoskeleton interactions have the potential to bring about changes in human behavior for physical rehabilitation or skill augmentation. Despite significant advances in the design and control of these robots, their application to human training remains limited. The key obstacles to the design of such training paradigms are the prediction of human-exoskeleton interaction effects and the selection of interaction control to affect human behavior.

The Role of c-Jun and Autocrine Signaling Loops in the Control of Repair Schwann Cells and Regeneration.

After nerve injury, both Schwann cells and neurons switch to pro-regenerative states. For Schwann cells, this involves reprogramming of myelin and Remak cells to repair Schwann cells that provide the signals and mechanisms needed for the survival of injured neurons, myelin clearance, axonal regeneration and target reinnervation. Because functional repair cells are essential for regeneration, it is unfortunate that their phenotype is not robust.

Comparing Plan Recognition Algorithms Through Standard Plan Libraries.

Plan recognition deals with reasoning about the goals and execution process of an actor, given observations of its actions. It is one of the fundamental problems of AI, applicable to many domains, from user interfaces to cyber-security. Despite the prevalence of these approaches, they lack a standard representation, and have not been compared using a common testbed.

Failures of nerve regeneration caused by aging or chronic denervation are rescued by restoring Schwann cell c-Jun.

After nerve injury, myelin and Remak Schwann cells reprogram to repair cells specialized for regeneration. Normally providing strong regenerative support, these cells fail in aging animals, and during chronic denervation that results from slow axon growth. This impairs axonal regeneration and causes significant clinical problems.