Rat neurosphere cells protect axotomized rat retinal ganglion cells and facilitate their regeneration

We investigated the ability of a population of rat neural stem and precursor cells derived from rat embryonic spinal cord to protect injured neurons in the rat central nervous system (CNS). The neonatal rat optic pathway was used as a model of CNS injury, whereby retinal ganglion cells (RGCs) were axotomized by lesion of the lateral geniculate nucleus one day after birth. Neural stem and precursor cells derived from expanded neurospheres (NS) were transplanted into the lesion site at the time of injury. Application of Fast Blue tracer dye to the lesion site demonstrated that significant numbers of RGCs survived at 4 and 8 weeks in animals that received a transplant, with an average of 28% survival, though in some individual cases survival was greater than 50%. No RGCs survived in animals that received a lesion alone. Furthermore, labeled RGCs were also observed when Fast Blue was applied to the superior colliculus (SC) at 4 weeks, suggesting that neurosphere cells also facilitated RGC to regenerate to their normal target. Transplanted cells did not migrate or express neural markers after transplantation, and secreted several neurotrophic factors in vitro. We conclude that NS cells can protect injured CNS neurons and promote their regeneration. These effects are not attributable to cell replacement, and may be mediated via secretion of neurotrophic factors. Thus, neuroprotection by stem cell populations may be a more viable approach for treatment of CNS disorders than cell replacement therapy.

Investigation into the performance of an algorithm for deriving concise descriptions of optic flow fields for real-time control

This paper describes a region-based algorithm for deriving a concise description of a first order optical flow field. The algorithm described achieves performance improvements over existing algorithms without compromising the accuracy of the flow field values calculated. These improvements are brought about by not computing the entire flow field between two consecutive images, but by considering only the flow vectors of a selected subset of the images. The algorithm is presented in the context of a project to balance a bipedal robot using visual information.