Biomarker Detection in Association Studies: Modeling SNPs Simultaneously via Logistic ANOVA

In genome-wide association studies, the primary task is to detect biomarkers in the form of Single Nucleotide Polymorphisms (SNPs) that have nontrivial associations with a disease phenotype and some other important clinical/environmental factors. However, the extremely large number of SNPs comparing to the sample size inhibits application of classical methods such as the multiple logistic regression. Currently the most commonly used approach is still to analyze one SNP at a time. In this pa- per, we propose to consider the genotypes of the SNPs simultaneously via a logistic analysis of variance (ANOVA) model, which expresses the logit transformed mean of SNP genotypes as the summation of the SNP effects, effects of the disease phenotype and/or other clinical variables, and the interaction effects. We use a reduced-rank representation of the interaction-effect matrix for dimensionality reduction, and employ the L1-penalty in a penalized likelihood framework to filter out the SNPs that have no associations. We develop a Majorization-Minimization algorithm for computational implementation. In addition, we propose a modified BIC criterion to select the penalty parameters and determine the rank number. The proposed method is applied to a Multiple Sclerosis data set and simulated data sets and shows promise in biomarker detection

Teleportation of the one-qubit state in decoherence environments

We study standard quantum teleportation of one-qubit state for the situation in which the channel is subject to decoherence, and where the evolution of the channel state is ruled by a master equation in the Lindblad form. A detailed calculation reveals that the quality of teleportation is determined by both the entanglement and the purity of the channel state, and only the optimal matching of them ensures the highest fidelity of standard quantum teleportation. Also our results demonstrated that the decoherence induces distortion of the Bloch sphere for the output state with different rates in different directions, which implies that different input states will be teleported with different fidelities.Comment: 17 pages, 10 figure

A K-fold averaging cross-validation procedure

Cross-validation (CV) type of methods have been widely used to facilitate model estimation and variable selection. In this work, we suggest a new K-fold CV procedure to select a candidate ‘optimal’ model from each hold-out fold and average the K candidate ‘optimal’ models to obtain the ultimate model. Due to the averaging effect, the variance of the proposed estimates can be significantly reduced. This new procedure results in more stable and efficient parameter estimation than the classical K-fold CV procedure. In addition, we show the asymptotic equivalence between the proposed and classical CV procedures in the linear regression setting. We also demonstrate the broad applicability of the proposed procedure via two examples of parameter sparsity regularisation and quantile smoothing splines modelling. We illustrate the promise of the proposed method through simulations and a real data example

Electrical Characterization of PbZr0.4Ti0.6O3 Capacitors

We have conducted a careful study of current-voltage (I-V) characteristics in fully integrated commercial PbZr0.4Ti0.6O3 thin film capacitors with Pt bottom and Ir/IrO2 top electrodes. Highly reproducible steady state I-V were obtained at various temperatures over two decades in voltage from current-time data and analyzed in terms of several common transport models including space charge limited conduction, Schottky thermionic emission under full and partial depletion and Poole-Frenkel conduction, showing that the later is the most plausible leakage mechanism in these high quality films. In addition, ferroelectric hysteresis loops and capacitance-voltage data were obtained over a large range of temperatures and discussed in terms of a modified Landau-Ginzburg-Devonshire theory accounting for space charge effects.Comment: 17 pages, 7 figure

Synchrotron Mössbauer spectroscopic study of ferropericlase at high pressures and temperatures

The electronic spin state of Fe^(2+) in ferropericlase, (Mg_(0.75)Fe_(0.25))O, transitions from a high-spin (spin unpaired) to low-spin (spin paired) state within the Earth’s mid-lower mantle region. To better understand the local electronic environment of high-spin Fe^(2+) ions in ferropericlase near the transition, we obtained synchrotron Mössbauer spectra (SMS) of (Mg_(0.75),Fe_(0.25))O in externally heated and laser-heated diamond anvil cells at relevant high pressures and temperatures. Results show that the quadrupole splitting (QS) of the dominant high-spin Fe^(2+) site decreases with increasing temperature at static high pressure. The QS values at constant pressure are fitted to a temperature-dependent Boltzmann distribution model, which permits estimation of the crystal-field splitting energy (Δ_3) between the d_(xy_ and d_(xz) or d_(zy) orbitals of the t_(2g) states in a distorted octahedral Fe^(2+) site. The derived Δ_3 increases from approximately 36 meV at 1 GPa to 95 meV at 40 GPa, revealing that both high pressure and high temperature have significant effects on the 3d electronic shells of Fe^(2+) in ferropericlase. The SMS spectra collected from the laser-heated diamond cells within the time window of 146 ns also indicate that QS significantly decreases at very high temperatures. A larger splitting of the energy levels at high temperatures and pressures should broaden the spin crossover in ferropericlase because the degeneracy of energy levels is partially lifted. Our results provide information on the hyperfine parameters and crystal-field splitting energy of high-spin Fe^(2+) in ferropericlase at high pressures and temperatures, relevant to the electronic structure of iron in oxides in the deep lower mantle

Disengagement of motor cortex from movement control during long-term learning.

Motor learning involves reorganization of the primary motor cortex (M1). However, it remains unclear how the involvement of M1 in movement control changes during long-term learning. To address this, we trained mice in a forelimb-based motor task over months and performed optogenetic inactivation and two-photon calcium imaging in M1 during the long-term training. We found that M1 inactivation impaired the forelimb movements in the early and middle stages, but not in the late stage, indicating that the movements that initially required M1 became independent of M1. As previously shown, M1 population activity became more consistent across trials from the early to middle stage while task performance rapidly improved. However, from the middle to late stage, M1 population activity became again variable despite consistent expert behaviors. This later decline in activity consistency suggests dissociation between M1 and movements. These findings suggest that long-term motor learning can disengage M1 from movement control