MEAN FLOW EFFECTS OF THE LOW-WAVENUMBER PRESSURE SPECTRUM ON A FLEXIBLE SURFACE.

The Lighthill theory is extended so that it may be used to determine the flow noise induced by a turbulent boundary layer over a plane homogeneous flexible surface. The influence of the surface properties and the mean flow on the sound generation is brought out explicitly through the use of a Green function. The form of the low-wavenumber wall-pressure spectrum on a rigid surface with an arbitrary mean flow profile is determined. The effect of a coating layer is investigated.

Some characteristics of thin shear layers in homogeneous turbulent flow.

Tomographic particle image velocimetry measurements of homogeneous isotropic turbulence that have been made in a large mixing tank facility at Cambridge are analysed in order to characterize thin highly sheared regions that have been observed. The results indicate that such regions coincide with regions of high enstrophy, dissipation and stretching. Large velocity jumps are observed across the width of these regions. The thickness of the shear layers seems to scale with the Taylor microscale, as has been suggested previously. The results discussed here concentrate on examining individual realizations rather than statistics of these regions.

Gene function prediction from synthetic lethality networks via ranking on demand.

MOTIVATION: Synthetic lethal interactions represent pairs of genes whose individual mutations are not lethal, while the double mutation of both genes does incur lethality. Several studies have shown a correlation between functional similarity of genes and their distances in networks based on synthetic lethal interactions. However, there is a lack of algorithms for predicting gene function from synthetic lethality interaction networks. RESULTS: In this article, we present a novel technique called kernelROD for gene function prediction from synthetic lethal interaction networks based on kernel machines. We apply our novel algorithm to Gene Ontology functional annotation prediction in yeast. Our experiments show that our method leads to improved gene function prediction compared with state-of-the-art competitors and that combining genetic and congruence networks leads to a further improvement in prediction accuracy.