Dissecting high-dimensional phenotypes with bayesian sparse factor analysis of genetic covariance matrices.

Quantitative genetic studies that model complex, multivariate phenotypes are important for both evolutionary prediction and artificial selection. For example, changes in gene expression can provide insight into developmental and physiological mechanisms that link genotype and phenotype. However, classical analytical techniques are poorly suited to quantitative genetic studies of gene expression where the number of traits assayed per individual can reach many thousand. Here, we derive a Bayesian genetic sparse factor model for estimating the genetic covariance matrix (G-matrix) of high-dimensional traits, such as gene expression, in a mixed-effects model. The key idea of our model is that we need consider only G-matrices that are biologically plausible. An organism's entire phenotype is the result of processes that are modular and have limited complexity. This implies that the G-matrix will be highly structured. In particular, we assume that a limited number of intermediate traits (or factors, e.g., variations in development or physiology) control the variation in the high-dimensional phenotype, and that each of these intermediate traits is sparse - affecting only a few observed traits. The advantages of this approach are twofold. First, sparse factors are interpretable and provide biological insight into mechanisms underlying the genetic architecture. Second, enforcing sparsity helps prevent sampling errors from swamping out the true signal in high-dimensional data. We demonstrate the advantages of our model on simulated data and in an analysis of a published Drosophila melanogaster gene expression data set

Bayesian Sparse Factor Analysis of Genetic Covariance Matrices

Quantitative genetic studies that model complex, multivariate phenotypes are important for both evolutionary prediction and artificial selection. For example, changes in gene expression can provide insight into developmental and physiological mechanisms that link genotype and phenotype. However, classical analytical techniques are poorly suited to quantitative genetic studies of gene expression where the number of traits assayed per individual can reach many thousand. Here, we derive a Bayesian genetic sparse factor model for estimating the genetic covariance matrix (G-matrix) of high-dimensional traits, such as gene expression, in a mixed effects model. The key idea of our model is that we need only consider G-matrices that are biologically plausible. An organism's entire phenotype is the result of processes that are modular and have limited complexity. This implies that the G-matrix will be highly structured. In particular, we assume that a limited number of intermediate traits (or factors, e.g., variations in development or physiology) control the variation in the high-dimensional phenotype, and that each of these intermediate traits is sparse -- affecting only a few observed traits. The advantages of this approach are two-fold. First, sparse factors are interpretable and provide biological insight into mechanisms underlying the genetic architecture. Second, enforcing sparsity helps prevent sampling errors from swamping out the true signal in high-dimensional data. We demonstrate the advantages of our model on simulated data and in an analysis of a published Drosophila melanogaster gene expression data set.Comment: 35 pages, 7 figure

Soft X-ray Photoemission Studies of the HfO2/SiO2/Si System

Cataloged from PDF version of article.Soft x-ray photoelectron spectroscopy with synchrotron radiation was employed to study the valence-band offsets for the HfO2/SiO2/Si and HfO2/SiOxNy/Si systems. We obtained a valence-band offset difference of -1.05+/-0.1 eV between HfO2 (in HfO2/15 Angstrom SiO2/Si) and SiO2 (in 15 Angstrom SiO2/Si). There is no measurable difference between the HfO2 valence-band maximum positions of the HfO2/10 Angstrom SiOxNy/Si and HfO2/15 Angstrom SiO2/Si systems. (C) 2002 American Institute of Physics

Frequency-tunable metamaterials using broadside-coupled split ring resonators

We present frequency tunable metamaterial designs at terahertz (THz) frequencies using broadside-coupled split ring resonator (BC-SRR) arrays. Frequency tuning, arising from changes in near field coupling, is obtained by in-plane horizontal or vertical displacements of the two SRR layers. For electrical excitation, the resonance frequency continuously redshifts as a function of displacement. The maximum frequency shift occurs for displacement of half a unit cell, with vertical displacement resulting in a shift of 663 GHz (51% of f0) and horizontal displacement yielding a shift of 270 GHz (20% of f0). We also discuss the significant differences in tuning that arise for electrical excitation in comparison to magnetic excitation of BC-SRRs

Novel monoclonal antibodies detect Smad-Interacting Protein 1 (SIP1) in the cytoplasm of human cells from multiple tumor tissue arrays

Cataloged from PDF version of article.Smad-interacting protein 1 (SIP1, also known as ZEB2) represses the transcription of E-cadherin and mediates epithelial-mesenchymal transition in development and tumor metastasis. Due to the lack of human SIP1-specific antibodies, its expression in human tumor tissues has not been studied in detail by immunohistochemistry. Hence, we generated two anti-SIP1 monoclonal antibodies, clones 1C6 and 6E5, with IgG1 and IgG2a isotypes, respectively. The specificity of these antibodies was shown by Western blotting studies using siRNA mediated downregulation of SIP1 and ZEB1 in a human osteosarcoma cell line. In the same context, we also compared them with 5 commercially available SIP1 antibodies. Antibody specificity was further verified in an inducible cell line system by immunofluorescence. By using both antibodies, we evaluated the tissue expression of SIP1 in paraffin-embedded tissue microarrays consisting of 22 normal and 101 tumoral tissues of kidney, colon, stomach, lung, esophagus, uterus, rectum, breast and liver. Interestingly, SIP1 predominantly displayed a cytoplasmic expression, while the nuclear localization of SIP1 was observed in only 6 cases. Strong expression of SIP1 was found in distal tubules of kidney, glandular epithelial cells of stomach and hepatocytes, implicating a co-expression of SIP1 and E-cadherin. Squamous epithelium of the esophagus and surface epithelium of colon and rectum were stained with moderate to weak intensity. Normal uterus, breast and lung tissues remained completely negative. By comparison with their normal tissues, we observed SIP1 overexpression in cancers of the kidney, breast, lung and uterus. However, SIP1 expression was found to be downregulated in tumors from colon, rectum, esophagus, liver and stomach tissues. Finally we did nuclear/cytoplasmic fractionation in 3 carcinoma cell lines and detected SIP1 in both fractions, nucleus being the dominant one. To our best knowledge, this is the first comprehensive immunohistochemical study of the expression of SIP1 in a series of human cancers. Our finding that SIP1 is not exclusively localized to nucleus suggests that the subcellular localization of SIP1 is regulated in normal and tumor tissues. These novel monoclonal antibodies may help elucidate the role of SIP1 in tumor development. © 2010 Elsevier Inc

Differential Privacy for Sequential Algorithms

We study the differential privacy of sequential statistical inference and learning algorithms that are characterized by random termination time. Using the two examples: sequential probability ratio test and sequential empirical risk minimization, we show that the number of steps such algorithms execute before termination can jeopardize the differential privacy of the input data in a similar fashion as their outputs, and it is impossible to use the usual Laplace mechanism to achieve standard differentially private in these examples. To remedy this, we propose a notion of weak differential privacy and demonstrate its equivalence to the standard case for large i.i.d. samples. We show that using the Laplace mechanism, weak differential privacy can be achieved for both the sequential probability ratio test and the sequential empirical risk minimization with proper performance guarantees. Finally, we provide preliminary experimental results on the Breast Cancer Wisconsin (Diagnostic) and Landsat Satellite Data Sets from the UCI repository