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Image analysis resolution

The three-dimensional structure of the sodium channel (from electric eel) was determined at 19-A resolution using cryo-electron microscopy and single-particle image analysis. The sodium channel has a bell-shaped outer surface of 135 A in height, 100 A in side length at the square bottom, and 65 A in diameter of the spherical top. An interesting finding is that there are several inner cavities connected to outer orifices. [Pg.1305]

Rosenberg, M. F., Caliaghan, R., Ford, R. C., Higgins, C. F., Structure of the multidrug resistance P-glyco-protein to 2.5 nm resolution determined by electron microscopy and image analysis, J. Biol. Chem. 1997, 272, 10685-10694. [Pg.487]

High Resolution Transmission Electron Microscopy (HRTEM, Philips CM20, 200 kV) was applied to get structural and nanotextural information on the fibers, by imaging the profile of the aromatic carbon layers in the 002-lattice fringe mode. A carbon fiber coated with pyrolytic carbon was incorporated in epoxy resin and a transverse section obtained by ultramicrotomy was deposited on a holey carbon film. An in-house made image analysis procedure was used to get quantitative data on the composite. [Pg.255]

HIGH RESOLUTION TRANSMISSION ELECTRON MICROSCOPY IMAGE ANALYSIS OF DISORDERED CARBONS USED FOR ELECTROCHEMICAL STORAGE OF ENERGY... [Pg.421]

Figure 30-17 (A) Two-dimensional map of the 260-kDa a subunit of the voltage-gated Na+ channel from the electric eel Electrophorus e/ecfns.438 441 (B) Image of the sodium channel protein obtained by cryo-electron microscopy and image analysis at 1.9 nm resolution. In this side view the protein appears to be bell-shaped with a height of 13.5 nm, a square bottom (cytoplasmic surface) 10 nm on a side, and a hemispherical top with a diameter of 6.5 nm. (C) Bottom view of the protein. (D) Axial section which cuts the bottom, as viewed in (C), approximately along a diagonal. From Sato et al.438 Notice the cavities (dark) and domain structures (light). The black arrow marks a constriction between upper (extracelllar) and lower (cytoplasmic) cavities. White lines indicate approximate position of the lipid bilayer. From Sato et al.i38 Courtesy of Chikara Sato. Figure 30-17 (A) Two-dimensional map of the 260-kDa a subunit of the voltage-gated Na+ channel from the electric eel Electrophorus e/ecfns.438 441 (B) Image of the sodium channel protein obtained by cryo-electron microscopy and image analysis at 1.9 nm resolution. In this side view the protein appears to be bell-shaped with a height of 13.5 nm, a square bottom (cytoplasmic surface) 10 nm on a side, and a hemispherical top with a diameter of 6.5 nm. (C) Bottom view of the protein. (D) Axial section which cuts the bottom, as viewed in (C), approximately along a diagonal. From Sato et al.438 Notice the cavities (dark) and domain structures (light). The black arrow marks a constriction between upper (extracelllar) and lower (cytoplasmic) cavities. White lines indicate approximate position of the lipid bilayer. From Sato et al.i38 Courtesy of Chikara Sato.
Snel M, Fuller M (2010) High-spatial resolution matrix-assisted laser desorption ionization imaging analysis of glucosylceramide in spleen sections from a mouse model of gaucher disease. Anal Chem 82(9) 3664-3670. doi 10.1021/ac902939k... [Pg.420]

Exploration of a data set before resolution is a golden rule fully applicable to image analysis. In this context, there are two important domains of information in the data set the spectral domain and the spatial domain. Using a method for the selection of pure variables like SIMPLISMA [53], we can select the pixels with the most dissimilar spectra. As in the resolution of other types of data sets, these spectra are good initial estimates to start the constrained optimization of matrices C and ST. The spatial dimension of an image is what makes these types of measurement different from other chemical data sets, since it provides local information about the sample through pixel-to-pixel spectral variations. This local character can be exploited with chemometric tools based on local-rank analysis, like FSMW-EFA [30, 31], explained in Section 11.3. [Pg.463]


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