Soluble factors released from platelets can modulate the immune response of leukocytes. We and others have recently found that T lymphocytes with bound platelets have reduced proliferation and IFN-γ and IL-17 production. Thus, we speculate that if we induce the binding of platelets to lymphocytes, we will be able to regulate the inflammatory response. When we cocultured platelets with lymphocytes at different ratios, we were able to increase the percentage of lymphocytes with bound platelets. The coculture of platelets with lymphocytes in the presence of stimulation decreased the production of IFN-γ and TNF-α, T cell proliferation, and the expression of CD25, PD-L1, and SLAM. However, this coculture increased CD39 expression. All of these effects were dependent on the dose of platelets and operated indistinctly with platelets from different healthy donors. When platelets were cocultured in the same compartment with lymphocytes, we observed less IFN-γ and TNF-α production and T lymphocyte proliferation than in cultures with platelets separated from lymphocytes by a 0.4-μm pore size filter. The binding of platelets to lymphocytes was blocked with anti-P-selectin Abs, and when this occurred we observed higher IFN-γ and TNF-α production than in nonblocked conditions. The cocultures of platelets with synovial fluid cells from rheumatoid arthritis patients reduced inflammatory cytokine production and increased IL-10 production. These results suggest that platelet binding to lymphocytes effectively regulates T lymphocyte function. This mechanism could be easily applied to reduce inflammatory responses.
Basic aerodynamic coefficients are modeled as functions of angle of attack, speed brake deflection angle, Mach number, and side slip angle. Most of the aerodynamic parameters can be well-fitted using polynomial functions. We previously demonstrated that a neural network is a fast, reliable way of predicting aerodynamic coefficients. We encountered few under fitted andor over fitted results during prediction. The training data for the neural network are derived from wind tunnel test measurements and numerical simulations. The basic questions that arise are: how many training data points are required to produce an eiticient neural network prediction, and which type of transfer functions should be used between the input-hidden layer and hidden-output layer. In this paper, a comparative study of the efficiency of neural iietwork prediction based 0:: different transfer functions and training dataset sizes is presented. T!ie results of the neural network prediction reflect the sensitivity of the architecture, transfer functions, and training dataset size.
The Columbia Accident Investigation Board issued a major recommendation to NASA. Prior to return to flight, NASA should develop and implement a comprehensive inspection plan to determine the structural integrity of all Reinforced Carbon-Carbon (RCC) system components. This inspection plan should take advantage of advanced non-destructive inspection technology. This paper describes a non-intrusive technology with a micro-flying robot to continuously monitor inside a space vehicle for any stress related fissures, cracks and foreign material embedded in walls, tubes etc.
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