Identifying progressive imaging genetic patterns via multi-task sparse canonical correlation analysis: a longitudinal study of the ADNI cohort

Motivation Identifying the genetic basis of the brain structure, function and disorder by using the imaging quantitative traits (QTs) as endophenotypes is an important task in brain science. Brain QTs often change over time while the disorder progresses and thus understanding how the genetic factors play roles on the progressive brain QT changes is of great importance and meaning. Most existing imaging genetics methods only analyze the baseline neuroimaging data, and thus those longitudinal imaging data across multiple time points containing important disease progression information are omitted. Results We propose a novel temporal imaging genetic model which performs the multi-task sparse canonical correlation analysis (T-MTSCCA). Our model uses longitudinal neuroimaging data to uncover that how single nucleotide polymorphisms (SNPs) play roles on affecting brain QTs over the time. Incorporating the relationship of the longitudinal imaging data and that within SNPs, T-MTSCCA could identify a trajectory of progressive imaging genetic patterns over the time. We propose an efficient algorithm to solve the problem and show its convergence. We evaluate T-MTSCCA on 408 subjects from the Alzheimer’s Disease Neuroimaging Initiative database with longitudinal magnetic resonance imaging data and genetic data available. The experimental results show that T-MTSCCA performs either better than or equally to the state-of-the-art methods. In particular, T-MTSCCA could identify higher canonical correlation coefficients and capture clearer canonical weight patterns. This suggests that T-MTSCCA identifies time-consistent and time-dependent SNPs and imaging QTs, which further help understand the genetic basis of the brain QT changes over the time during the disease progression. Availability and implementation The software and simulation data are publicly available at https://github.com/dulei323/TMTSCCA. Supplementary information Supplementary data are available at Bioinformatics online

Negative differential thermal resistance and thermal transistor

We report on the first model of a thermal transistor to control heat flow. Like its electronic counterpart, our thermal transistor is a three-terminal device with the important feature that the current through the two terminals can be controlled by small changes in the temperature or in the current through the third terminal. This control feature allows us to switch the device between "off" (insulating) and "on" (conducting) states or to amplify a small current. The thermal transistor model is possible because of the negative differential thermal resistance.Comment: 4 pages, 4 figures. SHortened. To appear in Applied Physics Letter

Template epitaxial growth of thermoelectric Bi/BiSb superlattice nanowires by charge-controlled pulse electrodeposition

© The Electrochemical Society, Inc. 2009. All rights reserved. Except as provided under U.S. copyright law, this work may not be reproduced, resold, distributed, or modified without the express permission of The Electrochemical Society (ECS). The archival version of this work was published in The Journal of The Electrochemical Society, 156(9), 2009.Bi/BiSb superlattice nanowires (SLNWs) with a controllable and very small bilayer thickness and a sharp segment interface were grown by adopting a charge-controlled pulse electrodeposition. The deposition parameters were optimized to ensure an epitaxial growth of the SLNWs with a preferential orientation. The segment length and bilayer thickness of the SLNWs can be controlled simply by changing the modulating time, and the consistency of the segment length can be well maintained by our approach. The Bravais law in the electrodeposited nanowires is verified by the SLNW structure. The current–voltage measurement shows that the SLNWs have good electrical conductance, particularly those with a smaller bilayer thickness. The Bi/BiSb SLNWs might have excellent thermoelectric performances.National Natural Science Foundation of China and the National Major Project of Fundamental Research for Nanomaterials and Nanostructures

Fast Multi-Task SCCA Learning with Feature Selection for Multi-Modal Brain Imaging Genetics

Brain imaging genetics studies the genetic basis of brain structures and functions via integrating both genotypic data such as single nucleotide polymorphism (SNP) and imaging quantitative traits (QTs). In this area, both multi-task learning (MTL) and sparse canonical correlation analysis (SCCA) methods are widely used since they are superior to those independent and pairwise univariate analyses. MTL methods generally incorporate a few of QTs and are not designed for feature selection from a large number of QTs; while existing SCCA methods typically employ only one modality of QTs to study its association with SNPs. Both MTL and SCCA encounter computational challenges as the number of SNPs increases. In this paper, combining the merits of MTL and SCCA, we propose a novel multi-task SCCA (MTSCCA) learning framework to identify bi-multivariate associations between SNPs and multi-modal imaging QTs. MTSCCA could make use of the complementary information carried by different imaging modalities. Using the G2,1-norm regularization, MTSCCA treats all SNPs in the same group together to enforce sparsity at the group level. The l2,1-norm penalty is used to jointly select features across multiple tasks for SNPs, and across multiple modalities for QTs. A fast optimization algorithm is proposed using the grouping information of SNPs. Compared with conventional SCCA methods, MTSCCA obtains improved performance regarding both correlation coefficients and canonical weights patterns. In addition, our method runs very fast and is easy-to-implement, and thus could provide a powerful tool for genome-wide brain-wide imaging genetic studies

A new complexity bound for the least-squares problem

AbstractFor a least-squares problem of m degree polynomial regression with n measured values (n ≥ m + 1), the traditional methods need O(n2m) arithmetic operations. We prove that the arithmetic complexity of this problem does not exceed O(nlog22m)

Comparative genomics approaches accurately predict deleterious variants in plants

Recent advances in genome resequencing have led to increased interest in prediction of the functional consequences of genetic variants. Variants at phylogenetically conserved sites are of particular interest, because they are more likely than variants at phylogenetically variable sites to have deleterious effects on fitness and contribute to phenotypic variation. Numerous comparative genomic approaches have been developed to predict deleterious variants, but the approaches are nearly always assessed based on their ability to identify known disease-causing mutations in humans. Determining the accuracy of deleterious variant predictions in nonhuman species is important to understanding evolution, domestication, and potentially to improving crop quality and yield. To examine our ability to predict deleterious variants in plants we generated a curated database of 2,910 Arabidopsis thaliana mutants with known phenotypes. We evaluated seven approaches and found that while all performed well, their relative ranking differed from prior benchmarks in humans. We conclude that deleterious mutations can be reliably predicted in A. thaliana and likely other plant species, but that the relative performance of various approaches does not necessarily translate from one species to another

Effect of chromatic dispersion induced chirp on the temporal coherence property of individual beam from spontaneous four wave mixing

Temporal coherence of individual signal or idler beam, determined by the spectral correlation property of photon pairs, is important for realizing quantum interference among independent sources. To understand the effect of chirp on the temporal coherence property, two series of experiments are investigated by introducing different amount of chirp into either the pulsed pump or individual signal (idler) beam. In the first one, based on spontaneous four wave mixing in a piece of optical fiber, the intensity correlation function of the filtered individual signal beam, which characterizes the degree of temporal coherence, is measured as a function of the chirp of pump. The results demonstrate that the chirp of pump pulses decreases the degree of temporal coherence. In the second one, a Hong-Ou-Mandel type two-photon interference experiment with the signal beams generated in two different fibers is carried out. The results illustrate that the chirp of individual beam does not change the temporal coherence degree, but affect the temporal mode matching. To achieve high visibility, apart from improving the coherence degree by minimizing the chirp of pump, mode matching should be optimized by managing the chirps of individual beams.Comment: 17pages, 4figure

Thermal memory: a storage of phononic information

Memory is an indispensable element for computer besides logic gates. In this Letter we report a model of thermal memory. We demonstrate via numerical simulation that thermal (phononic) information stored in the memory can be retained for a long time without being lost and more importantly can be read out without being destroyed. The possibility of experimental realization is also discussed.Comment: 5 pages, 3 figures