Thermoelastic dissipation in MEMS/NEMS flexural mode resonators.

Understanding the energy dissipation mechanisms in single-crystal silicon MEMS/NEMS resonators are particularly important to maximizing an important figure of merit relevant for miniature sensor and signal processing applications: the Quality factor (Q) of resonance. This paper discusses thermoelastic dissipation (TED) as the dominant internal-friction mechanism in flexural mode MEMS/NEMS resonators. Criteria for optimizing the geometrical design of flexural mode MEMS/NEMS resonators are theoretically established with a view towards minimizing the TED for single-crystal silicon MEMS/NEMS flexural mode resonators.

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.