Biomedical subjects

David E Goldberg

Publications and source records attributed to David E Goldberg.

4 recordsLinked to original sources

PubMed2002

Inosine induces axonal rewiring and improves behavioral outcome after stroke.

Cerebral infarct (stroke) often causes devastating and irreversible losses of function, in part because of the brain's limited capacity for anatomical reorganization. The purine nucleoside inosine has previously been shown to induce neurons to express a set of growth-associated proteins and to extend axons in culture and in vivo. We show here that in adult rats with unilateral cortical infarcts, inosine stimulated neurons on the undamaged side of the brain to extend new projections to denervated areas of the midbrain and spinal cord. This growth was paralleled by improved performance on several behavioral measures.

Animals↗

PubMed2002

Inosine stimulates axon growth in vitro and in the adult CNS.

Unlike mammals, lower vertebrates can regenerate their optic nerves and certain other CNS pathways throughout life. To identify the molecular bases of this phenomenon, we developed a cell culture model and found that goldfish retinal ganglion cells will regenerate their axons in response to the purine nucleoside inosine. Inosine acts through a direct intracellular mechanism and induces many of the changes in gene expression that underlie regenerative growth in vivo, e.g., upregulation of GAP-43, T alpha-1 tubulin, and the cell-adhesion molecule, L1. N-kinase, a 47-49-kDa serine-threonine kinase, may mediate the effects of inosine and serve as part of the modular signal transduction pathway that controls axon growth. In vivo, inosine stimulates extensive axon growth in the mature rat corticospinal tract. Following unilateral transection of the corticospinal tract, inosine applied to the intact sensorimotor cortex stimulated layer 5 pyramidal cells to upregulate GAP-43 expression and to sprout axon collaterals. These collaterals crossed the midline at the level of the cervical enlargement and reinnervated regions whose normal connections had been served. Further understanding of the molecular changes that lie upstream and downstream of N-kinase may lead to new insights into the control of axon growth and to novel methods to improve functional outcome in patients with CNS injury.

Animals↗

PubMed2002

Network random keys - a tree representation scheme for genetic and evolutionary algorithms.

When using genetic and evolutionary algorithms for network design, choosing a good representation scheme for the construction of the genotype is important for algorithm performance. One of the most common representation schemes for networks is the characteristic vector representation. However, with encoding trees, and using crossover and mutation, invalid individuals occur that are either under- or over-specified. When constructing the offspring or repairing the invalid individuals that do not represent a tree, it is impossible to distinguish between the importance of the links that should be used. These problems can be overcome by transferring the concept of random keys from scheduling and ordering problems to the encoding of trees. This paper investigates the performance of a simple genetic algorithm (SGA) using network random keys (NetKeys) for the one-max tree and a real-world problem. The comparison between the network random keys and the characteristic vector encoding shows that despite the effects of stealth mutation, which favors the characteristic vector representation, selectorecombinative SGAs with NetKeys have some advantages for small and easy optimization problems. With more complex problems, SGAs with network random keys significantly outperform SGAs using characteristic vectors. This paper shows that random keys can be used for the encoding of trees, and that genetic algorithms using network random keys are able to solve complex tree problems much faster than when using the characteristic vector. Users should therefore be encouraged to use network random keys for the representation of trees.

Algorithms↗

PubMed2002

Spin-flip symmetry and synchronization.

In the context of optimization by evolutionary algorithms (EAs), epistasis, deception, and scaling are well-known examples of problem difficulty characteristics. The presence of one such characteristic in the representation of a search problem indicates a certain type of difficulty the EA is to encounter during its search for globally optimal configurations. In this paper, we claim that the occurrence of symmetry in the representation is another problem difficulty characteristic and discuss one particular form, spin-flip symmetry, characterized by fitness invariant permutations on the alphabet. Its usual effect on unspecialized EAs, premature convergence due to synchronization problems, is discussed in detail. We discuss five different ways to specialize EAs to cope with the symmetry: adapting the genetic operators, changing the fitness function, using a niching technique, using a distributed EA, and attaching a highly redundant genotype-phenotype mapping.

Algorithms↗