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interferes with synthesis of dermal components is twofold UVA irradiation increases the synthesis of a nonfunctional latent TGF- protein, and UVA wavelengths downregulate the expression of TbRII by broblasts within 8 hours of UV exposure99,102,103 The effect of UVA irradiation is a 90% reduction in skin broblast TbRII activation, which diminishes procollagen synthesis and directly contributes to the aged appearance of photodamaged skin102 TbRII mRNA expression is also diminished after UVA irradiation Total production reduced by 40% when compared to nonUVA exposed tissue Skin treated with TGF- 1 after UVA exposure results in a 20% increase in TbRII expression when compared to controls treated with UVA alone This suggests that topical TGF- 1 may act as a self-protecting inducer of the TbRII receptor, abrogating UVA-induced reduction in TbRII expression99 UVA irradiation reduces procollagen production by 75% The addition of TGF- 1 after UV irradiation blunts this reduction in collagen synthesis99 This data supports the speculation that quantitative reduction of collagen synthesis in photodamaged skin, accounted for by UV-induced changes in TGF/TbRII signaling, is a process that can be modi ed by the addition of TGF- 1 TGF- is a small growth factor protein with potent keratinocyte mitogenic activity TGF- binds to keratinocyte epidermal growth factor receptors (EGFRs) to stimulate keratinocyte proliferation TGFis produced by wounded keratinocytes that are exposed to human serum Fibrin contained within serum is the physiologic stimulus for keratinocyte synthesis of TGF- 104 TGFacts in an autocrine fashion to induce keratinocyte proliferation as well as keratinocyte expression of surface EGFR TGF- appears to be the strongest mitogen for keratinocytes, surpassing the activity of epidermal growth factor, keratinocyte growth factor, and TGF- 105 Epidermal growth factor receptor (EGFR) is a widely expressed cell surface transmembrane receptor with intrinsic protein-tyrosine kinase activity106,107 EGFR ligands include amphiregulin, betacellulin, epiregulin, heparin-binding EGF-like growth factor, and TGF- 108 Ligand binding to EGFR induces receptor dimerization and autophosphorylation, two steps necessary for EGFR activity106,107 The EGFR mediates cell-speci c anabolic or catabolic effects EGFR activation promotes keratinocyte proliferation106,108 and has been shown to be effective in reducing the healing time in partial-thickness skin wounds108 111 However, activated EGFR also acts as a negative growth factor stimulating the degradation of collagen and elastin through induction of ker-.

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computed from these variables will also be stochastic variables Therefore, accurate stochastic system modeling and analysis is critical to navigation system design Accurate characterization of model and variable properties and analysis of their a ect on the system de nes the analysis of the overall system performance When purchasing or designing sensors, the designer naturally searches for the best sensors that t within the project budget However, any use of an adjective such as best requires further consideration of its de nition in this context For example, for the acceleration sensor, the best sensor would have no noise (ie, 1 (t) = 0) and no bias (ie, b(t) = 0); however, this is typically not possible under application conditions Instead, thermal uctuations and component variation will cause both quantities to be nonzero To surpass the competition, each sensor manufacturer has the incentive to remove, to the extent possible, all the predictable components of sensor error (eg, nonlinearities, temperature dependent characteristics) Therefore, the remaining sensor error is non-deterministic and will be accommodated within the navigation system design by stochastic process modeling and state estimation Knowing this, the sensor manufacturer designs the sensor so that the characteristics that de ne the random nature of the sensor are time-invariant Also, the sensor manufacturer will typically supply the parameters that quantify the stochastic nature of the sensor A brief discussion of sensor speci cations is presented in Section 492 4 discusses the various topics from the theory of random variables and stochastic processes that must be understood for the design and analysis of navigation systems This discussion includes de nitions of basic stochastic process concepts, discussion of dynamic systems with stochastic inputs, de nition of various stochastic process models that regularly appear in navigation systems, and discussion of the time variation of the mean and variance of the state of a linear stochastic state space system 4 concludes with a discussion of state estimation as it relates to linear stochastic state space systems and the characterization of their state estimation accuracy through covariance analysis The example of Section 111 is considered again in Section 493-494 using the quantitative methods of 4.

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