Implantation of a scaffold following bulbectomy induces laminar organization of regenerating olfactory axons

Primary olfactory axons expressing different odorant receptors are interspersed within the olfactory nerve. However, upon reaching the outer nerve fiber layer of the olfactory bulb they defasciculate, sort out, and refasciculate prior to targeting glomeruli in fixed topographic positions. While odorant receptors are crucial for the final targeting of axons to glomeruli, it is unclear what directs the formation of the nerve fiber and glomerular layers of the olfactory bulb. While the olfactory bulb itself may provide instructive cues for the development of these layers, it is also possible that the incoming axons may simply require the presence of a physical scaffold to establish the outer laminar cytoarchitecture. In order to begin to understand the underlying role of the olfactory bulb in development of the outer layers of the olfactory bulb, we physically ablated the olfactory bulbs in OMP-IRES-LacZ and P2-IRES-tau-LacZ neonatal mice and replaced them with artificial biological scaffolds molded into the shape of an olfactory bulb. Regenerating axons projected around the edge of the cranial cavity at the periphery of the artificial scaffold and were able to form an olfactory nerve fiber layer and, to some extent, a glomerular layer. Our results reveal that olfactory axons are able to form rudimentary cytoarchitectonic layers if they are provided with an appropriately shaped biological scaffold. Thus, the olfactory bulb does not appear to provide any tropic substance that either attracts regenerating olfactory axons into the cranial cavity or induces these axons to form a plexus around its outer surface. (c) 2006 Elsevier B.V. All rights reserved.

A bayesian artificial neural network method to characterise laminar defects using dynamic measurements

This paper reports on the development of an artificial neural network (ANN) method to detect laminar defects following the pattern matching approach utilizing dynamic measurement. Although structural health monitoring (SHM) using ANN has attracted much attention in the last decade, the problem of how to select the optimal class of ANN models has not been investigated in great depth. It turns out that the lack of a rigorous ANN design methodology is one of the main reasons for the delay in the successful application of the promising technique in SHM. In this paper, a Bayesian method is applied in the selection of the optimal class of ANN models for a given set of input/target training data. The ANN design method is demonstrated for the case of the detection and characterisation of laminar defects in carbon fibre-reinforced beams using flexural vibration data for beams with and without non-symmetric delamination damage.