Cluster spectra

Figure Cl.1.4. Photoelectron spectra of V, ,(A= 17, 27, 43, and 65) at 6.42 eV photon energy, compared to tire bulk photoelectron spectmm of V(100) surface at 21.21 eV photon energy. The cluster spectra reveal tire appearance of bulk features at and how tire cluster spectral features evolve toward tire bulk. The bulk spectmm is referenced to tire Fenni level. Wu H, Desai S R and Wang L S 1996 Phys. Rev. Lett. 77 2436, figure 2.

These results are also in accord with earlier UV-visible absorption results [57, 59] on AU55 dissolved in dichloromethane, as shown in Fig. 8. The bump seen in the cluster spectra is always very weak. 

Fig. 4.3. Dendrogram resulting from cluster analysis containing 91 spectra from 15 tree species (see also Table 4.2). Cluster analysis was done on first derivatives over the spectral range 380 cm-1 to 1700 cm-1). The distance matrix was calculated using Euclidean distance and Ward s algorithm was applied for clustering. Spectra were measured after decomposition of carotenoid molecules with 633 nm irradiation. For example, spectra of each species are shown in Fig. 4.1. Reprinted with permission from [52]...

Reasonable noise in the spectral data does not affect the clustering process. In this respect, cluster analysis is much more stable than other methods of multivariate analysis, such as principal component analysis (PCA), in which an increasing amount of noise is accumulated in the less relevant clusters. The mean cluster spectra can be extracted and used for the interpretation of the chemical or biochemical differences between clusters. HCA, per se, is ill-suited for a diagnostic algorithm. We have used the spectra from clusters to train artificial neural networks (ANNs), which may serve as supervised methods for final analysis. This process, which requires hundreds or thousands of spectra from each spectral class, is presently ongoing, and validated and blinded analyses, based on these efforts, will be reported. 

The mean cluster spectra shown in Fig. 9.4(d) not only differ in intensity quite strongly, as pointed out before, but they show small frequency shifts in the amide I and II peaks which are, most likely, the cause for different cluster memberships. In order to correlate the spectral differences with cellular features, we have used biochemical stains (incorporation of bromodeoxyuracil and an immuno-histochemical stain for cycline E) to establish the exact stage of the cells within the cell cycle. 

Figure 5.8c shows a confocal depth profile of the same urothelial cell, taken along the red dashed line in Figure 5.8b. This profile indicates that the cell is much thicker (ca. 8 pm) than a squamous cell, which explains its much higher IR absorbance. The Raman mean cluster spectra observed for the different regions are...

From the progressive evolution of transient absorption spectra after the pulse, the growth mechanism of Agl clusters from oligomers to nanoparticles was proposed. The band maximum in the UV is red-shifted at increasing time according to the nuclearity-depencence of the cluster spectra (Figure 15). 

Figure 5.10 illustrates PCA results for the distinction of the three anatomical regions. In this figure, blue represents spectra of cells from the cheek, red from the mouth floor, and green from the tongue. In comparison to previous results, the separation between the three classes was not quite as crisp, although the mean cluster spectra reported here are virtually identical to those of the prior study, albeit with a slightly better S/N ratio because of the increased number of spectra. 

Figure 6.20 Average cluster spectra obtained from P. trkhocarpa (lumen excluded). Spectra were vertically offset for clarity. Colors of the spectra correspond to different cell wall layers with the same color...

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