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Biomedical subjects

B T Zhang

Publications and source records attributed to B T Zhang.

2 recordsLinked to original sources

Mitogen induced proliferation of isolated adult mouse Schwann cells.

The proliferation of neonatal Schwann cells (SCs) in response to mitogenic agents has been well analyzed in vitro, but a limited range of mitogens have been defined. We investigated whether three identified neonatal SC mitogens [glial growth factor (GGF), platelet-derived growth factor BB (PDGF-BB), and basic fibroblast growth factor (bFGF)] are required to stimulate mitosis of adult SCs. Adult SCs were isolated from mouse sciatic nerves by mechanical and chemical dissociation, following three experimental steps: 1) culturing the dissociated cells for 24 hr in 10% FCS-F12 medium, 2) culturing these cells in serum-free medium for the next 48 hr, and 3) purifying adult SCs by differential adhesion. We describe a new method for preparation of SCs from peripheral nerves of adult mouse that provides 99.5% pure SCs populations at cell yields of greater than 3 x 10(3) cells/mg of starting nerve wet weight within 5 culture days. Although mitosis of SCs in culture in response to mitogens requires the presence of serum, the complex nature of serum renders difficult a complete analysis of mitogens required for SCs DNA synthesis, so we examined the proliferating response of adult SCs to GGF, PDGF-BB, and bFGF in serum-free medium. GGF alone had mitogenicity for adult SCs in a dose-dependent manner, and synergistic activation coupling with forskolin was not observed. Neither PDGF-BB nor bFGF was mitogenic for adult SCs when used alone or with forskolin.(ABSTRACT TRUNCATED AT 250 WORDS)

Aging

Accelerated learning by active example selection.

Much previous work on training multilayer neural networks has attempted to speed up the backpropagation algorithm using more sophisticated weight modification rules, whereby all the given training examples are used in a random or predetermined sequence. In this paper we investigate an alternative approach in which the learning proceeds on an increasing number of selected training examples, starting with a small training set. We derive a measure of criticality of examples and present an incremental learning algorithm that uses this measure to select a critical subset of given examples for solving the particular task. Our experimental results suggest that the method can significantly improve training speed and generalization performance in many real applications of neural networks. This method can be used in conjunction with other variations of gradient descent algorithms.

Algorithms