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- Berg, Otto, et al.
(författare)
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Phosphatidylinositol-specific phospholipase C forms different complexes with monodisperse and micellar phosphatidylcholine
- 2004
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Ingår i: Biochemistry. - : American Chemical Society (ACS). - 0006-2960 .- 1520-4995. ; 43:7, s. 2080-2090
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Tidskriftsartikel (refereegranskat)abstract
- Phosphatidylinositol-specific phospholipase C (PI-PLC) from Bacillus cereus forms a premicellar complex E# with monodisperse diheptanoylphosphatidylcholine (DC7PC) that is distinguishable from the E* complex formed with micelles. Results are interpreted with the assumption that in both cases amphiphiles bind to the interfacial binding surface (i-face) of PI-PLC but not to the active site. Isothermal calorimetry and fluorescence titration results for the binding of monodisperse DC7PC give an apparent dissociation constant of K2 = 0.2 mM with Hill coefficient of 2. The gel-permeation, spectroscopic, and probe partitioning behaviors of E# are distinct from those of the E* complex. The aggregation and partitioning behaviors suggest that the acyl chains in E# but not in E* remain exposed to the aqueous phase. The free (E) and complexed (E# and E*) forms of PI-PLC, each with distinct spectroscopic signatures, readily equilibrate with changing DC7PC concentration. The underlying equilibria are modeled and their significance for the states of the PI-PLC under monomer kinetic conditions is discussed to suggest that the Michaelis−Menten complex formed with monodisperse DC7PC is likely to be E#S or its aggregate rather than the classical monodisperse ES complex.
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| 5. |
- Jain, SC, et al.
(författare)
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Trap filled limit of conducting organic materials
- 2002
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Ingår i: Journal of Applied Physics. - : American Institute of Physics. - 0021-8979 .- 1089-7550. ; 92:7, s. 3752-3754
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Tidskriftsartikel (refereegranskat)abstract
- According to the approximate theory of transport in a conducting organic material containing exponential traps, ln J versus ln V plots are straight lines with slope l=T-C/T, where T-C is the characteristic temperature of the trap distribution. It is assumed in this theory that the concentration p(t) of trapped holes is much larger than the concentration p of free holes. Our experiments and recent literature results show that at high applied voltages the observed ln J versus ln V plots deviate from the straight lines and bend down. The numerical solution presented in this article shows that at high voltages the contribution of p to the space charge does not remain negligible. Calculated ln J versus ln V plots do bend down consistent with our experimental results. The current approaches the trap-filled limit asymptotically as the applied voltage approaches infinity.
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| 7. |
- van der Zwaag, B.J., et al.
(författare)
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Extracting Knowledge from Neural Networks in Image Processing
- 2003
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Ingår i: Innovations in Knowledge Engineering. ; , s. 107-127
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- Despite their success-story, artificial neural networks have one major disadvantagecompared to other techniques: the inability to explain comprehensively how a trainedneural network reaches its output; neural networks are not only (incorrectly) seen as a “magic tool” but possibly even more as a mysterious “black box.” Although much research has already been done to “open the box,” there is a notable hiatus in known publications on analysis of neural networks. So far, mainly sensitivity analysis and rule extraction methods have been used to analyze neural networks. However, these can only be applied in a limited subset of the problem domains where neural network solutions are encountered. In this chapter we propose a wider applicable method which, for a given problem domain, involves identifying basic functions with which users in that domain are already familiar, and describing trained neural networks, or parts thereof, in terms of those basic functions. This will provide a comprehensible description of the neural network’s function and, depending on the chosen base functions, it may also provide an insight into the neural network’s inner “reasoning.” To illustrate our method, the elements of a feedforward-backpropagation neural network, that has been trained to detect edges in images, are described in terms of differential operators of various orders and with various angles of operation. The results are then compared with image filters known from literature, which we analyzed in the same way.
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