Cluster identifiers

For example, clusters identified by IR spectra and extraction as Ir4(CO)i2 on y-Al203 were found by EXAFS spectroscopy to have an Ir-Ir coordination number of nearly 3, consistent with the tetrahedral structure of the metal frame EXAFS spectroscopy produces the equivalent result for sohd Ir4(CO)i2 [27]. EXAFS spectroscopy is the most appropriate method for determination of framework structures of supported clusters, but it is limited by the errors to clusters with at most about six metal atoms. Thus, it has been used to determine frameworks that are triangular (EXAFS first-shell metal-metal coordination number of 2), tetrahedral (EXAFS first-shell metal-metal coordination number of 3), and octahedral (EXAFS first-shell metal-metal... 

Among the many carbon clusters identified spectroscopically (from C40 to C2oo)> a few have been structurally characterized. For instance, Figure 12 illustrates the molecular structures of C60, C70, C76, C78, C80 and C82 (in their most stable isomeric forms).9... 

Prediction of BOD value. In the ten clusters Identified by the K-means clustering procedure, two clusters were represented by chemicals with only low BOD values and one cluster with nearly all (18 of 19 or 95 %) high BOD values (Table III). Therefore, no discrimination was attempted within these clusters. In the remaining clusters there were 202 high BOD chemicals and 97 low BOD chemicals. Of these, approximately 75 % (152 of 202) were correctly classified Into the high BOD class, while 73 Z (71 of 97) were correctly classified Into the low BOD class. Using both the clustering and discrimination analyses, 77 % (170 of 220) and 78 % (93 of 120) of the chemicals In the data base were correctly classified. Within each of the clusters, between 2 and 4 molecular connectivity Indices were used In the final discriminant functions to separate the two classes of BOD. Within each cluster a different combination of variables were used as discriminators. Because of Che exploratory nature of this analysis, we lowered the F-ratlo Inclusion level Co 1.0. In several of the clusters, the F-ratlos for variables Included In Che discriminant functions were subsequently small(e.g., < 4.0). 

Figure 1. Pyridazinone structures belonging to clusters identified by the method of Ward (partition index of 1.97).

Clustering can be viewed as a density estimation problem. The basic premise used in such an estimation is that in addition to the observed variables (i.e., descriptors) for each compound there exists an unobserved variable indicating the cluster membership. The observed variables are assumed to arrive from a mixture model, and the mixture labels (cluster identifiers) are hidden. The task is to find parameters associated with the mixture model that maximize the likelihood of the observed variables given the model. The probability distribution specified by each cluster can take any form. Although mixture model methods have found little use in chemical applications to date, they are mentioned here for completeness and because they are obvious candidates for use in the future. 

Catalytic properties of supported clusters identified as primarily Ira or Ir6 were reported by Xu et al [15], who investigated a structure-insensitive reaction, toluene hydrogenation. The support was NaY zeolite or, for comparison, MgO. EXAFS spectroscopy (Table 3) showed that the first-shell Ir-Ir coordination numbers characterizing both the fresh and used MgO-supported catalysts made by decarbonylation of supported [Ir4(CO)i2] or [HIr4(CO)ii] are indistinguishable fi om 3, the value for a tetrahedron, as in [Ir4(CO)i2] and [HIr4(CO)n]. The decarbonylated clusters retained or nearly retained this metal frame. EXAFS data show that the decarbonylated Ire clusters had metal fi ames indistinguishable from the octahedra of the precursor hexairidium carbonyls, indicated by the coordination number of approximately 4. 

In relatively simple cases, in which only two or three variables are measured for each sample, the data can usually be examined visually and any clustering identified by eye. As the number of variates increases, however, this is rarely possible and many scatter plots, between all possible pairs of variates, would need to be produced in order to identify major clusters, and even then clusters could be missed. To address this problem, many numerical clustering techniques have been developed, and the techniques themselves have been classified. For our purposes the methods considered belong to one of the following types. 

This method has been applied to the identification of the metabolic product of a cryptic sesquiterpenoid biosynthetic pathway discovered by analysis of the S. coelicolor genome sequence, although it turned out that it was involved in the biosynthesis of a natural product known to be produced by other Streptomyces species." ° It has also been used to identify the products of a cryptic gene cluster identified in the complete genome sequence of Bacillus halodurans C-125 that was predicted to direct the biosynthesis of a two-component lantibiotic named haloduracin, which was identified in the culture supernatants of B. halodurans as well (Figure 9) (see Chapter 5.08)." ... 

Sample type (TCL) Average no. of detected peptide ions No. of protein clusters identified No. of phosphoprotein clusters (peptide sequences)... 

All reactions in Fig. 24 afford products in which the coordination number at the unique subsite is 5 or 6. Of product clusters 49-55, 50 is the first organometallic derivative of a [4Fe-4Sp cluster, and 49, 51, and 55 are the initial examples of six-coordinate subsites. Another example of a substituted cluster identified by H NMR is provided by the spectrum in Fig. 25. Here 1 equivalent of tacn (1,4,7-triazacyclononane) reacts quantitatively with 37 to afford 49, the 4-Me, 5-H, and 6-Me resonances of which are substantially shifted from their positions in the chloride cluster. The spectrum is indicative of trigonal symmetry. Certain of the clusters 40-55 are considered in subsequent sections. 

Polymer-bound rhodium clusters were used for catalytic hydrogenations of a,/3-unsaturated aldehydes to allylic alcohols. Amination of chloromethylated polystyrene with 2-(2-(dimethylamino)ethoxy)ethanol gave an amine-functionalized polymer. Using the aminated polystyrene and Rh6(CO)i6 in the presence of H2 and CO or CO and water, various a,/ -unsaturated aldehydes were chemoselectively hydrogenated to give allylic alcohols in high yields, generally >95% conversion and 80-100% selectivity, at 303 K. Under the reaction conditions, a number of anionic clusters form, which can be recovered as ions paired to the ammonium cations of the polymer. Clusters identified by... 

Fig. 5. Map of eas gene cluster identified on the long arm of chromosome 2 in the sequenced genome of Aspergillus fumigatus. Arrows indicate direction of transcription. Black arrows indicate orthologues of genes in the C. purpurea eas cluster. Gray arrows indicate genes present in A. fumigatus, but not identified in the C. purpurea eas cluster.

The 2-naphthonate moiety is most likely of polyketide origin, but the exact nature of the nascent linear polyketide intermediate and its subsequent folding pattern to afford the 2-naphthonate backbone cannot be predicted in the absence of isotope labeling experiments. Since the biosynthesis of 1-naphthoic acid moiety in 301 is catalyzed by the iterative type I PKSs, and a close examination of the oifs within the 302 cluster identified, in addition to kedE that encodes the enediyne PKS, kedU38 that resides in the middle of the 15-gene type I PKS locus and encodes a type I PKS with a similar domain organization as in 301, it was proposed that KedL138 catalyzes the formation of a nascent intermediate, which is further modified by the other activities within the type I PKS locus to yield 3,6,8-trihydroxy-2-naphthoic acid as a key intermediate. 

A. We projected the 42-dimensional space of the bispectrum—which corresponds to j < 4—to the two-dimensional plane and clustered the points using the k-means algorithm [18]. In Fig. 2.7, we show the result of the principle component analysis. Different colours are assigned to each cluster identified by the k-means method, and we coloured the atoms with respect to the cluster they belong. This example demonstrates that the bispectmm can be used to identify atomic environments in an automatic way. 

Figure 16. Demulsifier performance data superimposed on the clusters identified in Figure 15. These data points correspond to jar tests where separated oil quality was less than 0.2% BSW. In field testing of...

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