Brown University
Graduate School Dissertation Defense Information
Brown University Center for Statistical Sciences Seminar
Bloomberg School of Public Health, Biostatistics Department | |
*Lectureship Series on Statistical Genetics Bioinformatics |
Abstract: In this talk I will give some examples of why I think it is important that statisticians be involved in preprocessing of microarray data. I will then describe a specific example related to preprocessing Affymetrix GeneChip high density oligonucleotide array raw data. High density oligonucleotide expression array technology is widely used in many areas of biomedical research for quantitative and highly parallel measurements of gene expression. Affymetrix GeneChip arrays are the most popular. In this technology each gene is typically represented by a set of 11-20 pairs of oligonucleotides separately referred to as probes. Typically 12,000 to 20,000 probe sets are arrayed on a silicon chip. RNA samples are prepared, labeled and hybridized to the arrays. Arrays are then scanned, and images produced and analyzed to obtain an intensity value for each probe. These intensities quantify the extent of the hybridization between the labeled target sample and the oligonucleotide probe. A final step to obtain expression measures is to summarize the probe intensities for a given gene in order to quantify the amount of the corresponding mRNA species in the sample. Using two extensive spike-in studies and a dilution study, we performed a careful assessment of the method of summarizing probe level data provided by the current version of the Affymetrix Microarray Suite (MAS 5.0). We found that the performance of the Affymetrix technology can be greatly improved by the use of expression measures derived from empirically motivated statistical models. The advantages of a new expression measure are assessed through bias, variance, sensitivity, and specificity. In particular, the improvements achieved by a 10-fold decrease in variability for low expression levels are demonstrated.
*Sponsored by the C.V. Starr Foundation Lectureship Series Fund
**Co-sponsored by The Center for Genetics and Genomics
A paper describing this example can be found on the web: http://www.biostat.jhsph.edu/~ririzarr/papers
Brown University
Joint Materials/Solid Mechanics Seminar Series
Can We Predict Friction and Failure Using Nanotribology | |
Abstract: The design of reliable MEMS devices that involve sliding s urfaces requires a predictive capability for friction and wear across length scales. In this study, we use atomic force microscopy to resolve roughness features of silicon MEMS surfaces from the nm-to-um scale, in addition to deriving nano-scale frictional constitutive behavior for single asperity contacts. We then derive surface roughness parameters that are used as inputs to predict the interfacial mechanics from generalized models based on the Greenwood-Williamson approach using transformation methods developed by McCool. We also employ a fractal approach in which we derive parameters that are optimized to match the measured topography. These methods allow us to determine parameters including as the real contact area and peak pressures as a function of the applied load. Combined with the frictional constitutive behavior, we can predict frictional behavior for a complex interface. Our predictions are compared with actual experimental results from a MEMS friction test device to determine the extent to which our multi-scale model, based on experimental inputs, can reliably predict friction and failure in MEMS devices.
The Fluids, Thermal and Chemical Processes Group of the
Division of Engineering and
The Center for Fluid Mechanics Seminar
UMR- 6607, Ecole Polytechnique de l'Université de Nantes, Rue Christian Pauc BP 90604, F-44306 Nantes cedex 3, France | |
Abstract: Görtler vortices which appear due to the streamline curvature in a boundary layer modify drastically momentum and heat transfer. In this seminar we report on extensive measurements of velocity and temperature fields as well as wall heat transfer rate in a concave boundary layer heated with a constant wall heat flux. We have shown that wall heat transfer rate can be increased as much as 320% over that of flat-plate boundary layer. This increase is due to the modification of the flow field by non-linear growth of perturbations; no heat transfer enhancement was detected in the linear growth region. Down-wash region has larger spanwise extent and steeper temperature gradients compared with up-wash region. Therefore, the heat transfer enhancement balance is positive.
Görtler instability and, as such heat transfer enhancement by Görtler instability is initial conditions-dependent. In particular up-stream perturbation wavelength and amplitude affect downstream non-linear enhancement of the heat transfer rate. Higher amplitudes and larger wavenumber of the upstream perturbations increase heat transfer rate of non-linear Görtler vortices.
Brown Analysis Seminar
Brown University Division of Engineering Seminar
Special Stochastic Systems Seminar
Abstract: We study the asymptotic behaviors, in the zero noise limit, of solutions to Schr\"odinger's functional equations and of a class of h-path processes, and give a new proof of the existence of the minimizer of Monge's problem with a quadratic cost.
Scientific Computing Seminar
Abstract: Groundwater flow in natural porous media, as well as the diffusion of heat through composite materials, is often uncertain because the medium is highly heterogeneous in space but only sparsely sampled. In the first part of this talk, I'll review relevant mathematical and hydrologic topics, and then I'll focus on steady-state flow through saturated, non-uniform media. On continuum scales, flow through aporous medium is usually described by Darcy's Equation, which relates the distribution of the system state, hydraulic pressure head, to the medium's hydraulic conductivity. It has become common to quantify uncertainty in groundwater flow models by treating hydraulic conductivity and head as random fields. Conductivity is usually assumed to be statistically uniform (or stationary) in space, and uncertainty about it is propagated through the averaged Darcy Equation to estimate statistics of head. While the stationarity assumption is convenient for developing a theory, most porous media are far from uniform. Instead, they are usually composed of distinct facies, or blocks, of internally uniform materials. The physical features of a natural composite medium suggest a probability model whose components are 1) a random geometry that defines the probable locations of blocks, and 2) the random distribution of conductivity within a block. Examples of such composites include stratifiedaquifers, fractured media with distinct fracture zones, and relatively impermeable lenses embedded in highly permeable material. Many other natural phenomena exhibit similar block structure at common scales of observation: the spatial distribution of vegetation classes in a landscape, of fuel loads within a forest, and of soil types are additional important examples. After summarizing the statistics of flow in composite media, I'll conclude with some open questions.
PDE Seminar
Department of Mathematics Colloquium
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