Tuning of the electronic characteristics of ZnO nanowire field effect transistors by proton irradiation.

We demonstrated a controllable tuning of the electronic characteristics of ZnO nanowire field effect transistors (FETs) using a high-energy proton beam. After a short proton irradiation time, the threshold voltage shifted to the negative gate bias direction with an increase in the electrical conductance, whereas the threshold voltage shifted to the positive gate bias direction with a decrease in the electrical conductance after a long proton irradiation time. The electrical characteristics of two different types of ZnO nanowires FET device structures in which the ZnO nanowires are placed on the substrate or suspended above the substrate and photoluminescence (PL) studies of the ZnO nanowires provide substantial evidence that the experimental observations result from the irradiation-induced charges in the bulk SiO(2) and at the SiO(2)/ZnO nanowire interface, which can be explained by a surface-band-bending model in terms of gate electric field modulation. Our study on the proton-irradiation-mediated functionalization can be potentially interesting not only for understanding the proton irradiation effects on nanoscale devices, but also for creating the property-tailored nanoscale devices.

A robust Bayesian two-sample test for detecting intervals of differential gene expression in microarray time series.

Understanding the regulatory mechanisms that are responsible for an organism's response to environmental change is an important issue in molecular biology. A first and important step towards this goal is to detect genes whose expression levels are affected by altered external conditions. A range of methods to test for differential gene expression, both in static as well as in time-course experiments, have been proposed. While these tests answer the question whether a gene is differentially expressed, they do not explicitly address the question when a gene is differentially expressed, although this information may provide insights into the course and causal structure of regulatory programs. In this article, we propose a two-sample test for identifying intervals of differential gene expression in microarray time series. Our approach is based on Gaussian process regression, can deal with arbitrary numbers of replicates, and is robust with respect to outliers. We apply our algorithm to study the response of Arabidopsis thaliana genes to an infection by a fungal pathogen using a microarray time series dataset covering 30,336 gene probes at 24 observed time points. In classification experiments, our test compares favorably with existing methods and provides additional insights into time-dependent differential expression.