Fuzzy clustering as a means of selecting representative conformers and molecular alignments

This paper describes the first application of fuzzy c-means clustering for the selection of representatives from assemblies of conformations or alignments. In case of alignments, their quality is taken into account using a weighted c-means scheme, developed in this work. The performance of fuzzy cluster validity measures, such as compactness, partition function, and entropy, are studied on several examples, but the visual 3D representation of data points is shown to be most beneficial in determining the optimum number of clusters. Fuzzy clustering is expected to perform better than crisp clustering methods in cases where there are a significant number of "outliers", such as in molecular dynamics simulations and molecular alignments.     

Synthesis and antiviral evaluation of some carbonucleoside analogues

1,2‐O‐Isopropylidene‐α‐L‐threofuranosyl heterocyclic derivatives were synthesized from 1,2‐O‐iso‐propylidene‐α‐D‐xilopentadialdo‐1,4‐furanose and tested for antiviral activity against herpes simplex virus type 1, dengue virus type 2 and Junin virus. For comparative propose, the antiviral activity of some of their pyranosyl analogues were also tested. The furanosyl derivatives showed to be moderate inhibitors of Junin virus and, in general, proved to be more effective than the pyranosyl analogues.     

Study of traffic-emitted lead pollution of soil and plants using different fuzzy clustering algorithms

We discuss the clustering of 234 environmental samples resulting from an extensive monitoring program concerning soil lead content, plant lead content, traffic density, and distance from the road at different sampling locations in former East Germany. Considering the structure of data and the unsatisfactory results obtained applying classical clustering and principal component analysis, it appeared evident that fuzzy clustering could be one of the best solutions. In the following order we used different fuzzy clustering algorithms, namely, the fuzzy c-means (FCM) algorithm, the Gustafson–Kessel (GK) algorithm, which may detect clusters of ellipsoidal shapes in data by introducing an adaptive distance norm for each cluster, and the fuzzy c-varieties (FCV) algorithm, which was developed for recognition of r-dimensional linear varieties in high-dimensional data (lines, planes or hyperplanes). Fuzzy clustering with convex combination of point prototypes and different multidimensional linear prototypes is also discussed and applied for the first time in analytical chemistry (environmetrics). The results obtained in this study show the advantages of the FCV and GK algorithms over the FCM algorithm. The performance of each algorithm is illustrated by graphs and evaluated by the values of some conventional cluster validity indices. The values of the validity indices are in very good agreement with the quality of the clustering results. Figure Projection of all samples on the plane defined by the membership degrees to cluster A₂, and A₄ obtained using Fuzzy c-varieties (FCV) algorithm (expression of objective function and distance enclosed)