Zinc oxide nanostructures and high electron mobility nanocomposite thin film transistors

This paper reports on the synthesis of zinc oxide (ZnO) nanostructures and examines the performance of nanocomposite thin-film transistors (TFTs) fabricated using ZnO dispersed in both n- and p-type polymer host matrices. The ZnO nanostructures considered here comprise nanowires and tetrapods and were synthesized using vapor phase deposition techniques involving the carbothermal reduction of solid-phase zinc-containing compounds. Measurement results of nanocomposite TFTs based on dispersion of ZnO nanorods in an n-type organic semiconductor ([6, 6]-phenyl-C61-butyric acid methyl ester) show electron field-effect mobilities in the range 0.3-0.6 cm2V-1 s-1. representing an approximate enhancement by as much as a factor of 40 from the pristine state. The on/off current ratio of the nanocomposite TFTs approach 106 at saturation with off-currents on the order of 10 pA. The results presented here, although preliminary, show a highly promising enhancement for realization of high-performance solution-processable n-type organic TFTs. © 2008 IEEE.

Independent component analysis of microarray data in the study of endometrial cancer.

Gene microarray technology is highly effective in screening for differential gene expression and has hence become a popular tool in the molecular investigation of cancer. When applied to tumours, molecular characteristics may be correlated with clinical features such as response to chemotherapy. Exploitation of the huge amount of data generated by microarrays is difficult, however, and constitutes a major challenge in the advancement of this methodology. Independent component analysis (ICA), a modern statistical method, allows us to better understand data in such complex and noisy measurement environments. The technique has the potential to significantly increase the quality of the resulting data and improve the biological validity of subsequent analysis. We performed microarray experiments on 31 postmenopausal endometrial biopsies, comprising 11 benign and 20 malignant samples. We compared ICA to the established methods of principal component analysis (PCA), Cyber-T, and SAM. We show that ICA generated patterns that clearly characterized the malignant samples studied, in contrast to PCA. Moreover, ICA improved the biological validity of the genes identified as differentially expressed in endometrial carcinoma, compared to those found by Cyber-T and SAM. In particular, several genes involved in lipid metabolism that are differentially expressed in endometrial carcinoma were only found using this method. This report highlights the potential of ICA in the analysis of microarray data.