Elevated-temperature polyol-based colloidal-chemistry approach allows for the development of size-tunable (50 and 86 nm) assemblies of maghemite iso-oriented nanocrystals, with enhanced magnetization. 1H-Nuclear Magnetic Resonance (NMR) relaxometric
experiments show that the ferrimagnetic cluster-like colloidal entities exhibit a remarkable enhancement (4 to 5 times) in the transverse relaxivity, if compared to that of the superparamagnetic contrast agent Endorem, over an extended frequency range (1-60 MHz). The marked increase of the transverse relaxivity r2 at a clinical magnetic field strength (1.41 T), which is 405.1 and 508.3 mM-1 s-1 for small and large assemblies respectively, allows to relate the observed response to the raised intra-aggregate magnetic material volume fraction. Furthermore, cell tests with murine fibroblast culture medium confirmed the cell viability in presence of the clusters. We discuss the NMR dispersion profiles on the basis of relaxivity models to highlight the magneto-structural characteristics of the materials for improved T2-weighted magnetic resonance images.
In most gene expression data, the number of training samples is very small compared to the large number of genes involved in the experiments. However, among the large amount of genes, only a small fraction is effective for performing a certain task.
Furthermore, a small subset of genes is desirable in developing gene expression based diagnostic tools for delivering reliable and understandable results. With the gene selection results, the cost of biological experiment and decision can be greatly reduced by analyzing only the marker genes. An important application of gene expression data in functional genomics is to classify samples according to their gene expression profiles. Feature selection (FS) is a process which attempts to select more informative features. It is one of the important steps in knowledge discovery. Conventional supervised FS methods evaluate various feature subsets using an evaluation function or metric to select only those features which are related to the decision classes of the data under consideration. This paper studies a feature selection method based on rough set theory. Further K-Means, Fuzzy C-Means (FCM) algorithm have implemented for the reduced feature set without considering class labels. Then the obtained results are compared with the original class labels. Back Propagation Network (BPN) has also been used for classification. Then the performance of K-Means, FCM, and BPN are analyzed through the confusion matrix. It is found that the BPN is performing well comparatively.
