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.

Improved models for drop-on-demand ink-jet shortening

A previous 1-D model for the shortening of an unbroken drop-on-demand ink-jet ligament has been extended to the case of an arbitrary attached tail mass, and can also include extensional viscosity (which has ∼ 2% effect) as well as linear elasticity in the fluid. Predictions from the improved model are shown to be very similar to results from 2-D axisymmetric numerical simulations of DoD ink-jet ligaments and also to the results of recent experiments on Newtonian fluids jetted without satellite formation.

Centrifugal modelling of the landslides triggering mechanism in layered fill slopes

Concerns over loosely compacted fill slopes stability in Hong Kong arouse in the past few decades, since the Sau Mau Ping disasters in 1972 and 1976. Research conducted on loose fill slopes in the past few years aimed to understand the failure mechanisms of a loosely compacted fill slope. Recently, layering effect has been hypothesised to be a possible condition in the fill slopes leading to a fast flowslide triggered by a rise of water table. Centrifuge experiments were conducted to investigate the layering effect on a model granular slope and hence to determine the triggering mechanisms of seepage induced slope failure. Test results showed that slope failure can be easily triggered in layered fill model slopes when seepage is restricted and localised pore water pressure is allowed to build up within the slope. © 2006 Taylor & Francis Group, London.

Material-Based Construction Site Image Retrieval

The technological advancements in digital imaging, the widespread popularity of digital cameras, and the increasing demand by owners and contractors for detailed and complete site photograph logs have triggered an ever-increasing growth in the rate of construction image data collection, with thousands of images being stored for each project. However, the sheer volume of images and the difficulties in accurately and manually indexing them have generated a pressing need for methods that can index and retrieve images with minimal or no user intervention. This paper reports recent developments from research efforts in the indexing and retrieval of construction site images in architecture, engineering, construction, and facilities management image database systems. The limitations and benefits of the existing methodologies will be presented, as well as an explanation of the reasons for the development of a novel image retrieval approach that not only can recognize construction materials within the image content in order to index images, but also can be compatible with existing retrieval methods, enabling enhanced results.