Jun Izawa | PubMed Health

PubMed2005

[Antigenic analysis of patients with asthma contracting by exposure to different species of pet hamsters].

BACKGROUND: Hamster-specific IgE antibodies as measured by currently available CAP-RAST tests are used to diagnose allergies associated with exposure to pet hamsters. Among patients with asthma attributed to hamsters, only about 60% are positive for hamster-specific IgE antibodies. We examined whether the results of CAP-RAST tests are related to differences in species-related antigens. METHODS: Currently available mixed specific IgE antibodies against European hamster and Golden hamster antigens (e84) were combined with Djungarian hamster epidermis to produce 3-species mixed specific antibodies. Correlations between results obtained with these mixed antibodies, those obtained with e84 and Djungarian hamster specific IgE antibody were examined in 20 patients with asthma who kept hamsters as pets. In addition, immunoblotting was used to analyze antigens in 9 patients. RESULTS: There was no correlation between results obtained with e84 and those obtained with Djungarian hamster antibody. Results obtained with 3-species mixed specific IgE antibodies positively correlated with those obtained with Djungarian hamster antibody. A specific binding protein for 67-kDa protein were found in patients who tested positive for e84 and Djungarian hamster antibody. A specific binding protein for 20-kDa protein were found in patients positive for only Djungarian hamster antibody. CONCLUSION: The main allergens differ among Golden hamsters, European hamsters, and dwarf Djungarian hamsters.

Adult↗

PubMed2004

Biological arm motion through reinforcement learning.

The present paper discusses an optimal learning control method using reinforcement learning for biological systems with a redundant actuator. It is difficult to apply reinforcement learning to biological control systems because of the redundancy in muscle activation space. We solve this problem with the following method. First, we divide the control input space into two subspaces according to a priority order of learning and restrict the search noise for reinforcement learning to the first priority subspace. Then the constraint is reduced as the learning progresses, with the search space extending to the second priority subspace. The higher priority subspace is designed so that the impedance of the arm can be high. A smooth reaching motion is obtained through reinforcement learning without any previous knowledge of the arm's dynamics.

Animals↗