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Signal function three-dimensional

In three-dimensional experiments, two different 2D experiments are combined, so three frequency coordinates are involved. In general, the 3D experiment may be made up of the preparation, evolution (mixing periods of the first 2D experiment, combined with the evolution t ), mixing, and detection ( ) periods of the second 2D experiment. The 3D signals are therefore recorded as a function of two variable evolution times, t and <2, and the detection time %. This is illustrated in Fig. 6.1. [Pg.346]

Fig. 1.7 Two-dimensional objects in position three individual objects. Likewise, the Fourier space (top row) and their Fourier transform, transform is additive and the signal functions corresponding to the shape of the signal func- corresponding to each of the objects are shown tion S(kXr ky) (bottom row). The actual object, a for comparison, letter i inside a circle, is shown as the sum of... Fig. 1.7 Two-dimensional objects in position three individual objects. Likewise, the Fourier space (top row) and their Fourier transform, transform is additive and the signal functions corresponding to the shape of the signal func- corresponding to each of the objects are shown tion S(kXr ky) (bottom row). The actual object, a for comparison, letter i inside a circle, is shown as the sum of...
Successive separation steps, e.g. in two-dimensional chromatography (two-dimensional thin-layer chromatography) that result in three-dimensional signal functions y = f(ziyz2)y as schematically shown in Fig. 3.4(v). [Pg.81]

Two-dimensional excitation experiments (two wavelengths excitation in fluorescence spectroscopy or two frequency experiments in 2D-NMR) also generate three-dimensional signal functions. [Pg.81]

Time-dependent analytical measurements, which give three-dimensional information of the type y = f(zy t) as shown schematically in Fig. 3.11a. The same characteristic holds for distribution analysis in one spatial direction, i.e., line scans, y = f(zylx). Such signal functions are frequently represented in form of multiple diagrams as shown in Fig. 3.11b. [Pg.81]

Owing to their pivotal role in mammalian signal transduction, there has been an intense interest in the enzymes of the PLC superfamily. Progress toward understanding the mechanism, structure, and function of PI-PLCs from both bacterial and mammalian sources has been particularly impressive [12-15]. Several PI-PLCs have been isolated and cloned, and a number of high resolution, three-dimensional X-ray structures are available [16-19]. In contrast to the advances that have been made with mammalian PI-PLC isoenzymes, their PC-PLC counterparts are poorly characterized. Studies with mammalian PC-PLCs have typically been conducted with partially purified enzymes, and there has not been a report of the isolation of a pure, eukaryotic PC-PLC. To circumvent the currently intractable problems associated with mammalian PC-PLCs, PLCs from bacterial sources have been sought as potentially useful models. [Pg.134]

Computation of this function results in a three-dimensional plot for which one axis is time delay (or range), the second is Doppler frequency or radial velocity and the third is the output power of the matched filter (usually normalised to unity). The extent of the ambiguity function peak in the Tr and the fd dimensions determines the range and Doppler resolutions respectively. As we are using the directly received signal only we term this self-ambiguity as there is no inclusion of any system geometry dependence on the transmitter and receiver locations. [Pg.12]

Vieira AV, Lamaze C, Schmid SL (1996) Control of EGF receptor signaling by clathrin-mediated endocytosis. Science 274 2086-2089 Vijay-Kumar S, Bugg CE. Wilkinson KD, Cook WJ (1985) Three-dimensional structure of ubiquitin at 28 A resolution. Proc Natl Acad Sd USA 82 3582-3585 Wang HR, Kania M, Baumeister W. Nederlof PM (1997) Import of human and Thermoplasma 20S proteasomes into nudei of HeLa cells requires functional NLS sequences. Eur J Cell Biol 73 105-113... [Pg.159]

Fig. 8. Schematic representation of aberrant mFas, which is expected to attenuate the Fas-mediated signaling. Aberrant mFas is functionally and structurally classified into main three types (a) the membrane-binding decoy receptor, (b) the membrane-binding decorative receptor, and (c) the membrane-unstable or soluble receptor. Although both mFas in models (a) and (b) are normally fixed on the membrane, the mFas in the former can bind Fas ligand, but is defective for trimerization, whereas the mFas in the latter would have no ability to bind Fas ligand in vivo because of conformational alteration. Like model (a), the mFas in model (c) can be reactive for Fas ligand, but it cannot transduce the apoptotic signal into the cytoplasmic death cascade because of incomplete trimerization due to an abnormal TM domain or truncation of the 1C domain. The hatched and jagged markings indicate deduced alterations of amino acid sequence or three-dimensional structure, respectively. Fig. 8. Schematic representation of aberrant mFas, which is expected to attenuate the Fas-mediated signaling. Aberrant mFas is functionally and structurally classified into main three types (a) the membrane-binding decoy receptor, (b) the membrane-binding decorative receptor, and (c) the membrane-unstable or soluble receptor. Although both mFas in models (a) and (b) are normally fixed on the membrane, the mFas in the former can bind Fas ligand, but is defective for trimerization, whereas the mFas in the latter would have no ability to bind Fas ligand in vivo because of conformational alteration. Like model (a), the mFas in model (c) can be reactive for Fas ligand, but it cannot transduce the apoptotic signal into the cytoplasmic death cascade because of incomplete trimerization due to an abnormal TM domain or truncation of the 1C domain. The hatched and jagged markings indicate deduced alterations of amino acid sequence or three-dimensional structure, respectively.
Because we can measure—or reliably estimate—all three of these brain functions, we can construct a three-dimensional model representing (1) the energy level of the brain and its component parts (Factor A, for Activation) (2) the input-output gating status of the brain, including its internal signaling systems (Factor I, for Information Source) and (3) the modulatory status of the brain, which is determined by those chemical systems that determine the mode of processing to which the information is subjected (Factor M, for Modulation). [Pg.7]

Figure 8. Three-dimensional representation of the time evolution of the IR chemiluminescence spectra following the IRMPD of CH2F2 in the presence of O atoms. Conditions were 28.5mTorr CH2F2, 12.0mTorr O atoms, 5.09 Torr total pressure, unapodized FWHM resolution of 6.04 cm 1, Nyquist wavenumber 7901.4 cm"1 with the signal obtained for 1 shot per sampling point. The data were digitized at 30 /is resolution, but are shown here with 150/is between spectra and have been corrected for the instrument function. Emission from HF near 4000 cm-1 and CO near 2000 cm-1 is clearly seen. Reproduced with permission from Ref. 40. Figure 8. Three-dimensional representation of the time evolution of the IR chemiluminescence spectra following the IRMPD of CH2F2 in the presence of O atoms. Conditions were 28.5mTorr CH2F2, 12.0mTorr O atoms, 5.09 Torr total pressure, unapodized FWHM resolution of 6.04 cm 1, Nyquist wavenumber 7901.4 cm"1 with the signal obtained for 1 shot per sampling point. The data were digitized at 30 /is resolution, but are shown here with 150/is between spectra and have been corrected for the instrument function. Emission from HF near 4000 cm-1 and CO near 2000 cm-1 is clearly seen. Reproduced with permission from Ref. 40.

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See also in sourсe #XX -- [ Pg.55 ]

See also in sourсe #XX -- [ Pg.55 ]




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Function three dimensional

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