Using Ontology Fingerprints to evaluate genome-wide association study results

We describe an approach to characterize genes or phenotypes via ontology fingerprints which are composed of Gene Ontology (GO) terms overrepresented among those PubMed abstracts linked to the genes or phenotypes. We then quantify the biological relevance between genes and phenotypes by comparing their ontology fingerprints to calculate a similarity score. We validated this approach by correctly identifying genes belong to their biological pathways with high accuracy, and applied this approach to evaluate GWA study by ranking genes associated with the lipid concentrations in plasma as well as to prioritize genes within linkage disequilibrium (LD) block. We found that the genes with highest scores were: ABCA1, LPL, and CETP for HDL; LDLR, APOE and APOB for LDL; and LPL, APOA1 and APOB for triglyceride. In addition, we identified some top ranked genes linking to lipid metabolism from the literature even in cases where such knowledge was not reflected in current annotation of these genes. These results demonstrate that ontology fingerprints can be used effectively to prioritize genes from GWA studies for experimental validation

Current-Driven Magnetic Excitations in Permalloy-Based Multilayer Nanopillars

We study current-driven magnetization switching in nanofabricated Ni84Fe16/Cu/Ni84Fe16 trilayers at 295 K and 4.2 K. The shape of the hysteretic switching diagram at low magnetic field changes from 295 K to 4.2 K. The reversible behavior at higher field involves two phenomena, a threshold current for magnetic excitations closely correlated with the switching current, and a peak in differential resistance characterized by telegraph noise, with average period that decreases exponentially with current and shifts with temperature. We interpret both static and dynamic results at 295 K and 4.2 K in terms of thermal activation over a potential barrier, with a current dependent effective magnetic temperature.Comment: 4 pages, 4 Figure

Consistent Differential Expression Pattern (CDEP) on microarray to identify genes related to metastatic behavior

<p>Abstract</p> <p>Background</p> <p>To utilize the large volume of gene expression information generated from different microarray experiments, several meta-analysis techniques have been developed. Despite these efforts, there remain significant challenges to effectively increasing the statistical power and decreasing the Type I error rate while pooling the heterogeneous datasets from public resources. The objective of this study is to develop a novel meta-analysis approach, Consistent Differential Expression Pattern (CDEP), to identify genes with common differential expression patterns across different datasets.</p> <p>Results</p> <p>We combined False Discovery Rate (FDR) estimation and the non-parametric RankProd approach to estimate the Type I error rate in each microarray dataset of the meta-analysis. These Type I error rates from all datasets were then used to identify genes with common differential expression patterns. Our simulation study showed that CDEP achieved higher statistical power and maintained low Type I error rate when compared with two recently proposed meta-analysis approaches. We applied CDEP to analyze microarray data from different laboratories that compared transcription profiles between metastatic and primary cancer of different types. Many genes identified as differentially expressed consistently across different cancer types are in pathways related to metastatic behavior, such as ECM-receptor interaction, focal adhesion, and blood vessel development. We also identified novel genes such as <it>AMIGO2</it>, <it>Gem</it>, and <it>CXCL11 </it>that have not been shown to associate with, but may play roles in, metastasis.</p> <p>Conclusions</p> <p>CDEP is a flexible approach that borrows information from each dataset in a meta-analysis in order to identify genes being differentially expressed consistently. We have shown that CDEP can gain higher statistical power than other existing approaches under a variety of settings considered in the simulation study, suggesting its robustness and insensitivity to data variation commonly associated with microarray experiments.</p> <p><b>Availability</b>: CDEP is implemented in R and freely available at: <url>http://genomebioinfo.musc.edu/CDEP/</url></p> <p><b>Contact</b>: [email protected]</p

Signaling network prediction by the Ontology Fingerprint enhanced Bayesian network

Abstract Background Despite large amounts of available genomic and proteomic data, predicting the structure and response of signaling networks is still a significant challenge. While statistical method such as Bayesian network has been explored to meet this challenge, employing existing biological knowledge for network prediction is difficult. The objective of this study is to develop a novel approach that integrates prior biological knowledge in the form of the Ontology Fingerprint to infer cell-type-specific signaling networks via data-driven Bayesian network learning; and to further use the trained model to predict cellular responses. Results We applied our novel approach to address the Predictive Signaling Network Modeling challenge of the fourth (2009) Dialog for Reverse Engineering Assessment's and Methods (DREAM4) competition. The challenge results showed that our method accurately captured signal transduction of a network of protein kinases and phosphoproteins in that the predicted protein phosphorylation levels under all experimental conditions were highly correlated (R2 = 0.93) with the observed results. Based on the evaluation of the DREAM4 organizer, our team was ranked as one of the top five best performers in predicting network structure and protein phosphorylation activity under test conditions. Conclusions Bayesian network can be used to simulate the propagation of signals in cellular systems. Incorporating the Ontology Fingerprint as prior biological knowledge allows us to efficiently infer concise signaling network structure and to accurately predict cellular responses.http://deepblue.lib.umich.edu/bitstream/2027.42/109490/1/12918_2012_Article_989.pd

Increased efficiency of luminescent solar concentrators after application of organic wavelength selective mirrors

Organic wavelength-selective mirrors are used to reduce the loss of emitted photons through the surface of a luminescent solar concentrator (LSC). A theoretical calculation suggests that application of a 400 nm broad reflector on top of an LSC containing BASF Lumogen Red 305 as a luminophore can reflect 91% of all surface emitted photons back into the device. Used in this way, such broad reflectors could increase the edge-emission efficiency of the LSC by up to 66%. Similarly, 175 nm broad reflectors could increase efficiency up to 45%. Measurements demonstrate more limited effectiveness and dependency on the peak absorbance of the LSC. At higher absorbance, the increased number of internal re-absorption events reduces the effectiveness of the reflectors, leading to a maximum increase in LSC efficiency of ~5% for an LSC with a peak absorbance of 1. Reducing re-absorption by reducing dye concentration or the coverage of the luminophore coating results in an increase in LSC efficiency of up to 30% and 27%, respectively

Electron-phonon renormalization of electronic band gaps of semiconductors: Isotopically enriched silicon

Photoluminescence and wavelength-modulated transmission spectra displaying phonon-assisted indirect excitonic transitions in isotopically enriched Si-28, Si-29, Si-30, as well as in natural Si, have yielded the isotopic mass (M) dependence of the indirect excitonic gap (E-gx) and the relevant phonon frequencies. Interpreting these measurements on the basis of a phenomenological theory for (partial derivativeE(gx)/partial derivativeM), we deduce E-gx(M=infinity)=(1213.8+/-1.2) meV, the purely electronic value in the absence of electron-phonon interaction and volume changes associated with anharmonicity

Growth conditions, structure, and superconductivity of pure and metal-doped FeTe1-xSex single crystals

Superconducting single crystals of pure FeTe1 xSex and FeTe0.65Se0.35 doped with Co, Ni, Cu, Mn, Zn, Mo, Cd, In, Pb, Hg, V, Ga, Mg, Al, Ti, Cr, Sr or Nd into Fe ions site have been grown applying Bridgman's method. It has been found that the sharpness of transition to the superconducting state in FeTe1 xSex is evidently inversely correlated with crystallographic quality of the crystals. Among all of the studied dopants only Co, Ni and Cu substitute Fe ions in FeTe0.65Se0.35 crystals. The remaining examined ions do not incorporate into the crystal structure. Nevertheless, they form inclusions together with selenium, tellurium and/or iron, what changes the chemical composition of host matrix and therefore influences Tc value. Small disorder introduced into magnetic sublattice, by partial replacement of Fe ions by slight amount of nonmagnetic ions of Cu (~ 1.5 at%) or by magnetic ions of Ni (~ 2 at%) and Co (~5 at%) with spin value different than that of Fe ion, completely suppresses superconductivity in FeTe1 xSex system. This indicates that even if superconductivity is observed in the system containing magnetic ions it can not survive when the disorder in magnetic ions sublattice is introduced, most likely because of magnetic scattering of Cooper pairs.Comment: 18 pages, 12 figures, 3 table

Magnetic unipolar features in con- ductivity of point contacts between normal and ferromagnetic d-metals (Co, Ni, Fe)

In nanocontacts between normal and ferromagnetic metals (N--F) abrupt changes of the order of 1% are detected in differential resistance, dV/dI(V), versus bias voltage, V, on achieving of high current densities, ~10^9 A/cm^2. These features in dV/dI(V) are observed when the electron flow is directed from the nonmagnetic metal into the ferromagnet and connected with magnetization excitations in the ferromagnet induced by the current. Applying an external magnetic field leads to a shift of the observed features to higher biasing current, confirming the magnetic nature of the effect. Such effects are observed for the non-ballistic (not spectral) regime of current flow in the nanocontacts. Thus, the current induced magneto-conductance effects in multilayered N--F structures (nanopillars) extensively studied in the recent literature have much more general character and can be stimulated by elastic electron scattering at single N--F interfaces.Comment: 10 pages, 9 figs., presented on NATO ARW: Electron Correlation In New Materials And Nanosystems (19-23 Sept. 2005, Yalta, Ukraine

Magnetization relaxation in (Ga,Mn)As ferromagnetic semiconductors

We describe a theory of Mn local-moment magnetization relaxation due to p-d kinetic-exchange coupling with the itinerant-spin subsystem in the ferromagnetic semiconductor (Ga,Mn)As alloy. The theoretical Gilbert damping coefficient implied by this mechanism is calculated as a function of Mn moment density, hole concentration, and quasiparticle lifetime. Comparison with experimental ferromagnetic resonance data suggests that in annealed strongly metallic samples, p-d coupling contributes significantly to the damping rate of the magnetization precession at low temperatures. By combining the theoretical Gilbert coefficient with the values of the magnetic anisotropy energy, we estimate that the typical critical current for spin-transfer magnetization switching in all-semiconductor trilayer devices can be as low as $\sim 10^{5} {\rm A cm}^{-2}$.Comment: 4 pages, 2 figures, submitted to Rapid Communication