Statistical significance of variables driving systematic variation

There are a number of well-established methods such as principal components analysis (PCA) for automatically capturing systematic variation due to latent variables in large-scale genomic data. PCA and related methods may directly provide a quantitative characterization of a complex biological variable that is otherwise difficult to precisely define or model. An unsolved problem in this context is how to systematically identify the genomic variables that are drivers of systematic variation captured by PCA. Principal components (and other estimates of systematic variation) are directly constructed from the genomic variables themselves, making measures of statistical significance artificially inflated when using conventional methods due to over-fitting. We introduce a new approach called the jackstraw that allows one to accurately identify genomic variables that are statistically significantly associated with any subset or linear combination of principal components (PCs). The proposed method can greatly simplify complex significance testing problems encountered in genomics and can be utilized to identify the genomic variables significantly associated with latent variables. Using simulation, we demonstrate that our method attains accurate measures of statistical significance over a range of relevant scenarios. We consider yeast cell-cycle gene expression data, and show that the proposed method can be used to straightforwardly identify statistically significant genes that are cell-cycle regulated. We also analyze gene expression data from post-trauma patients, allowing the gene expression data to provide a molecularly-driven phenotype. We find a greater enrichment for inflammatory-related gene sets compared to using a clinically defined phenotype. The proposed method provides a useful bridge between large-scale quantifications of systematic variation and gene-level significance analyses.Comment: 35 pages, 1 table, 6 main figures, 7 supplementary figure

Consistent Estimation of Low-Dimensional Latent Structure in High-Dimensional Data

We consider the problem of extracting a low-dimensional, linear latent variable structure from high-dimensional random variables. Specifically, we show that under mild conditions and when this structure manifests itself as a linear space that spans the conditional means, it is possible to consistently recover the structure using only information up to the second moments of these random variables. This finding, specialized to one-parameter exponential families whose variance function is quadratic in their means, allows for the derivation of an explicit estimator of such latent structure. This approach serves as a latent variable model estimator and as a tool for dimension reduction for a high-dimensional matrix of data composed of many related variables. Our theoretical results are verified by simulation studies and an application to genomic data

Effects of electrostatic correlations on electrokinetic phenomena

Classical theory of the electric double layer is based on the fundamental assumption of a dilute solution of point ions. There are a number of situations such as high applied voltages, high concentration of electrolytes, systems with multivalent ions, or solvent-free ionic liquids where the classical theory is often applied but the fundamental assumptions cannot be justified. Perhaps the most basic assumption underlying continuum models in electrokinetics is the mean-field approximation, that the electric field acting on each discrete ion is self-consistently determined by the local mean charge density. This paper considers situations where the mean-field approximation breaks down and electrostatic correlations become important. A fourth-order modified Poisson equation is developed that accounts for electrostatic correlations and captures the essential features in a simple continuum framework. The theory is derived variationally as a gradient approximation for non-local electrostatics, in which the dielectric permittivity becomes a differential operator. The only new parameter is a characteristic length scale for correlated ion pairs. The model is able to capture subtle aspects of more detailed simulations based on Monte Carlo, molecular dynamics, or density functional theory and allows for the straightforward calculation of electrokinetic flows in correlated liquids, for the first time. Departures from classical Helmholtz-Smoluchowski theory are controlled by the dimensionless ratio of the correlation length to the Debye screening length. Charge-density oscillations tend to reduce electro-osmotic flow and streaming current, and over-screening of the surface charge can lead to flow reversal. These effects also help to explain the apparent charge-induced thickening of double layers in induced-charge electrokinetic phenomena

Observations of far-infrared fine structure lines: o III88.35 micrometer and oI 63.2 micrometer

Observations of the O III 88.35 micrometer line and the O I63.2 micrometer were made with a far infrared spectrometer. The sources M17, NGC 7538, and W51 were mapped in the O III line with 1 arc minute resolution and the emission is found to be quite widespread. In all cases the peak of the emission coincides with the maximum radio continuum. The far infrared continuum was mapped simultaneously and in M17, NGC 7538, and W51 the continuum peak is found to be distinct from the center of ionization. The O III line was also detected in W3, W49, and in a number of positions in the Orion nebula. Upper limits were obtained on NGS 7027, NGC 6572, DR21, G29.9-0.0 and M82. The 63.2 micrometer O I line was detected in M17, M42, and marginally in DR21. A partial map of M42 in this line shows that most of the emission observed arises from the Trapezium and from the bright optical bar to the southeast

Rationale for tau aggregation inhibitor therapy in Alzheimer's disease and other tauopathies

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Rip/singularity free cosmology models with bulk viscosity

In this paper we present two concrete models of non-perfect fluid with bulk viscosity to interpret the observed cosmic accelerating expansion phenomena, avoiding the introduction of exotic dark energy. The first model we inspect has a viscosity of the form ${\zeta} = {\zeta}_0 + ({\zeta}_1-{\zeta}_2q)H$ by taking into account of the decelerating parameter q, and the other model is of the form ${\zeta} = {\zeta}_0 + {\zeta}_1H + {\zeta}_2H^2$. We give out the exact solutions of such models and further constrain them with the latest Union2 data as well as the currently observed Hubble-parameter dataset (OHD), then we discuss the fate of universe evolution in these models, which confronts neither future singularity nor little/pseudo rip. From the resulting curves by best fittings we find a much more flexible evolution processing due to the presence of viscosity while being consistent with the observational data in the region of data fitting. With the bulk viscosity considered, a more realistic universe scenario is characterized comparable with the {\Lambda}CDM model but without introducing the mysterious dark energy.Comment: 9 pages, 6 figures, submitted to EPJ-