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Gene expression imputation across multiple brain regions provides insights into schizophrenia risk (vol 51, pg 659, 2019)

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NATURE GENETICS 2019年第6期51卷 1068-1068页

作者： Huckins, Laura M. Dobbyn, Amanda Ruderfer, Douglas M. Hoffman, Gabriel Wang, Weiqing Pardinas, Antonio F. Rajagopal, Veera M. Als, Thomas D. Nguyen, Hoang T. Girdhar, Kiran Boocock, James Roussos, Panos Fromer, Menachem Kramer, Robin Domenici, Enrico Gamazon, Eric R. Purcell, Shaun Demontis, Ditte Borglum, Anders D. Walters, James T. R. O'Donovan, Michael C. Sullivan, Patrick Owen, Michael J. Devlin, Bernie Sieberts, Solveig K. Cox, Nancy J. Im, Hae Kyung Sklar, Pamela Stahl, Eli A. Pamela Sklar Division of Psychiatric Genomics Icahn School of Medicine at Mount Sinai New York NY USA Department of Genetics and Genomics Icahn School of Medicine at Mount Sinai New York NY USA Department of Psychiatry Icahn School of Medicine at Mount Sinai New York NY USA Icahn Institute for Genomics and Multiscale Biology Icahn School of Medicine at Mount Sinai New York NY USA Vanderbilt University Medical Center Nashville TN USA MRC Centre for Neuropsychiatric Genetics and Genomics Cardiff University Cardiff UK Department of Biomedicine Aarhus University Aarhus Denmark The Lundbeck Foundation Initiative for Integrative Psychiatric Research iPSYCH Denmark Center for Integrative Sequencing Aarhus University Aarhus Denmark Department of Human Genetics David Geffen School of Medicine University of California Los Angeles Los Angeles CA USA Human Brain Collection Core National Institute of Mental Health Bethesda MD USA Laboratory of Neurogenomic Biomarkers Centre for Integrative Biology (CIBIO) University of Trento Trento Italy Clare Hall University of Cambridge Cambridge UK University of North Carolina at Chapel Hill Chapel Hill NC USA Karolinska Institutet Stockholm Sweden Department of Psychiatry University of Pittsburgh Pittsburgh PA USA Systems Biology Sage Bionetworks Seattle WA USA Section of Genetic Medicine Department of Medicine University of Chicago Chicago IL USA Integrated Technology Research Laboratories Pharmaceutical Research Division Takeda Pharmaceutical Company Limited Fujisawa Japan Neuropsychiatry Section Department of Psychiatry Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Neuropsychiatric Signaling Program Department of Psychiatry Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Psychiatry JJ Peters Virginia Medical Center Bronx NY USA Department of Neuroscience Icahn School of Medicine at Mount Sinai New York New York NY USA Friedman Brain Institute

Transcriptomic imputation approaches combine eQTL reference panels with large-scale genotype data in order to test associations between disease and gene expression. These genic associations could elucidate signals in complex genome-wide association study (GWAS) loci and may disentangle the role of different tissues in disease development. We used the largest eQTL reference panel for the dorso-lateral prefrontal cortex (DLPFC) to create a set of gene expression predictors and demonstrate their utility. We applied DLPFC and 12 GTEx-brain predictors to 40,299 schizophrenia cases and 65,264 matched controls for a large transcriptomic imputation study of schizophrenia. We identified 413 genic associations across 13 brain regions. Stepwise conditioning identified 67 non-MHC genes, of which 14 did not fall within previous GWAS loci. We identified 36 significantly enriched pathways, including hexosaminidase-A deficiency, and multiple porphyric disorder pathways. We investigated developmental expression patterns among the 67 non-MHC genes and identified specific groups of pre- and postnatal expression.

关键词： Gene expression Genome-wide association studies Schizophrenia Transcriptomics

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Multi-symplectic method for the coupled Schr(o)dinger-KdV equations

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中国物理B（英文版） 2014年第8期23卷 226-232页

作者： Zhang Hong Song Song-He Zhou Wei-En Chen Xu-Dong Department of Mathematics and System Science College of Science National University of Defense Technology Changsha 410073 China Department of Mathematics and System Science College of Science National University of Defense Technology Changsha 410073 China State Key Laboratory of High Performance Computing National University of Defense Technology Changsha 410073 China

In this paper, we present a multi-symplectic Hamiltonian formulation of the coupled Schr?dinger-KdV equations (CSKE) with periodic boundary conditions. Then we develop a novel multi-symplectic Fourier pseudospectral (MSFP) scheme for the CSKE. In numerical experiments, we compare the MSFP method with the Crank–Nicholson (CN) method. Our results show high accuracy, effectiveness, and good ability of conserving the invariants of the MSFP method.

关键词： coupled Schr(o)dinger-KdV equations multi-symplectic Fourier pseudospectral method

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Average vector field methods for the coupled Schr(o)dinger-KdV equations

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中国物理B（英文版） 2014年第7期23卷 242-250页

作者： Zhang Hong Song Song-He Chen Xu-Dong Zhou Wei-En Department of Mathematics and System Science College of Science National University of Defense Technology Changsha 410073 China Department of Mathematics and System Science College of Science National University of Defense Technology Changsha 410073 China State Key Laboratory of High Performance Computing National University of Defense Technology Changsha 410073 China

The energy preserving average vector field (AVF) method is applied to the coupled Schr?dinger–KdV equations. Two energy preserving schemes are constructed by using Fourier pseudospectral method in space direction discretization. In order to accelerate our simulation, the split-step technique is used. The numerical experiments show that the non-splitting scheme and splitting scheme are both effective, and have excellent long time numerical behavior. The comparisons show thatthesplittingschemeisfasterthanthenon-splittingscheme, butitisnotasgoodasthenon-splittingschemeinpreserving the invariants.

关键词： coupled Schr(o)dinger-KdV equations average vector field method splitting method Fourier pseudospectral method

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Antenna calibration method for MMSE-based network MIMO system

Antenna calibration method for MMSE-based network MIMO syste...

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IEEE International Conference on Communications in China Workshops (ICCC)

作者： Qiang Liu Xin Su Jie Zeng Lili Liu Tiejun Lv Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts and Telecommunications Beijing China Tsinghua National Laboratory for Information Science and Technology Tsinghua University Beijing China Beijing University of Posts and Telecommunications Beijing Beijing CN

ISBN: (纸本)9781467397025

In this paper, we propose a new channel reciprocity calibration method based on minimum mean square error (MMSE) in network multiple-input and multiple-output (MIMO) system. Network MIMO system can mitigate the inter-cell interference by joint precoding techniques. The system is assumed to operate in time division duplex (TDD). However, the generalized channeles do not only consist of free-space electromagnetic channel, but also include transceiver modules, the mismatches between the transceiver modules of the antennas destroy the channel reciprocity. Therefore the mismatches should be calibrated before channel reciprocity can be utilized. An effective method for calculating the calibration coefficients in the network MIMO system whose BSs are equipped with multiple antennas is proposed. The network MIMO system is divided into M independent centralized MIMO systems and one distributed MIMO system, then the calibration coefficients of the two systems can be combined to calculate the calibration coefficients of the network MIMO system. The validity of the method is proved by the simulation results.

关键词： Antennas Calibration MIMO Manganese Transceivers Channel estimation Downlink

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A survey on distributed compressed sensing： theory and applications

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Frontiers of Computer Science 2014年第6期8卷 893-904页

作者： Hongpeng YIN Jinxing LI Yi CHAI Simon X. YANG College of Automation Chongqing University Chongqing 400030 China Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education Chongqing 400030 China State Key Laboratory of Power Transmission Equipment and System Security and New Technology Chongqing 400030 China School of Engineering University of Guelph Guelph N1G 2W1 Canada

The compressed sensing （CS） theory makes sample rate relate to signal structure and content. CS samples and compresses the signal with far below Nyquist sampling frequency simultaneously. However, CS only considers the intra-signal correlations, without taking the correlations of the multi-signals into account. Distributed compressed sensing （DCS） is an extension of CS that takes advantage of both the inter- and intra-signal correlations, which is wildly used as a powerful method for the multi-signals sensing and compression in many fields. In this paper, the characteristics and related works of DCS are reviewed. The framework of DCS is introduced. As DCS＇s main portions, sparse representation, measurement matrix selection, and joint reconstruction are classified and summarized. The applications of DCS are also categorized and discussed. Finally, the conclusion remarks and the further research works are provided.

关键词： compressed sensing distributed compressed sensing sparse representation measurement matrix joint reconstruction joint sparsity model

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Convolution-based means of gradient for fast eye center localization

Convolution-based means of gradient for fast eye center loca...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Hai-Bin Cai Hui Yu Chun-Yan Yao Shen-Yong Chen Hong-Hai Liu School of Computing University of of Portsmouth UK College of Computer Science Zhejiang University of Technology China College of Mechanical Engineering Zhejiang University of Technology China State Key Laboratory of Mechanical System and Vibration Shanghai Jiao Tong University China

ISBN: (纸本)9781467372220

Localizing eye center is primary challenge for application-s involving gaze estimation, face recognition and human machine interaction. The challenge is caused by significant variability of eye appearance in illumination, shape, color, viewing angle and dynamics, and computation related issues. In this paper, we propose a convolution-based means of gradient method to efficiently and accurately locate the eye center in low resolution images. Priority of enhancing its computation is achieved by the use of FFT transform and fewer identified pixels of circular boundary of potential eye centres. The proposed algorithm is validated in the research database platform of BioID face database. The experimental results confirm that the proposed outperforms the-state-of-art methods and its potential in real-time eye gaze tracking related applications.

关键词： Convolution Eye center location Gradients Iris center location Convolution Eye Gradient mean gradient Human machine interaction

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Research of a Novel Flash P2P Network Traffic Prediction Algorithm

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Procedia Computer Science 2015年 55卷 1293-1301页

作者： Ji Yimu Yuan Yongge Zhao Chuanxin Jiang Chenchen Wang RuChuan School of Computer Science Nanjing University of Posts and Telecommunications Nanjing 210023 China Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing 210023 China Guangdong Key Laboratory of Popular High Performance Computers Shenzhen Key Laboratory of Service Computing and Applications Shenzhen 518060 China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Network Nanjing University of Posts and Telecommunications Nanjing 210023 China School of Mathematics and Computer Sciences Anhui Normal University Wuhu 241000 China

To improve the quality of service and network performance of the FlashP2P video-on-demand, the prediction FlashP2P network traffic flow is very useful to t control the network video traffic. In this paper, a novel prediction algorithm to forecast the traffic rate of the Flash P2P video is proposed. This method is based on the combination of the local mean decomposition (LMD) and the generalized autoregressive conditional heteroscedasticity (GARCH). LMD is used to decompose the original long-related flow into the summation of the short-related flow. Then, GRACH is utilized to predict the short-related flow. The developed algorithm is tested on a university's campus network. The predicted results show that our proposed method can achieve higher accuracy than those obtained by existing algorithms, such as EMD-ARMA(Empirical Mode Decomposition and Auto-Regressive and Moving Average Model) and WNN(Wavelet Neural Network), while keeping lower computational complexity.

关键词： Self-similarity LMD GARCH Traffic prediction

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Explicit Multi-Symplectic Splitting Methods for the Nonlinear Dirac Equation

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Advances in Applied Mathematics and Mechanics 2014年第4期6卷 494-514页

作者： Yaming Chen Songhe Song Huajun Zhu Department of Mathematics and System Science and State Key Laboratory of High Performance Computing National University of Defense TechnologyChangsha 410073China State Key Laboratory of Aerodynamics China Aerodynamics Research and Development CenterMianyang 621000China

In this paper,we propose two new explicit multi-symplectic splitting methods for the nonlinear Dirac(NLD)*** on its multi-symplectic formulation,the NLD equation is split into one linear multi-symplectic system and one nonlinear infinite Hamiltonian *** multi-symplectic Fourier pseudospectral method and multi-symplectic Preissmann scheme are employed to discretize the linear subproblem,*** the nonlinear subsystem is solved by a symplectic ***,a composition method is applied to obtain the final schemes for the NLD *** find that the two proposed schemes preserve the total symplecticity and can be solved *** experiments are presented to show the effectiveness of the proposed methods.

关键词： Nonlinear Dirac equation multi-symplectic method splitting method explicit method

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New tight upper bounds on the capacity for general deterministic dissemination in wireless ad hoc networks

New tight upper bounds on the capacity for general determini...

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IEEE International Conference on Performance, computing and Communications (IPCCC)

作者： Cheng Wang Jieren Zhou Tianci Liu Lu Shao Huiya Yan Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai China Department of Computer Science Tongji University Shanghai China School of Software Tsinghua University Beijing China Mathematics Department University of Wisconsin-La Crosse La Crosse WI

In this paper, we study capacity scaling laws of the deterministic dissemination (DD) in random wireless networks under the generalized physical model (GphyM). This is truly not a new topic. Our motivation to readdress this issue is two-fold: Firstly, we aim to propose a more general result to unify the network capacity for general homogeneous random models by investigating the impacts of different parameters of the system on the network capacity. Secondly, we target to close the open gaps between the upper and the lower bounds on the network capacity in the literature. We derive the general upper bounds on the capacity for the arbitrary case of (λ, nd, ns) by introducing the Poisson Boolean model of continuum percolation, where λ, nd, and ns are the general node density, the number of destinations for each session, and the number of sessions, respectively. We prove that the derived upper bounds are tight according to the existing general lower bounds constructed in the literature.

关键词： Roads Mathematics Unicast Signal to noise ratio Interference

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Incorporation of Ordinal Optimization into Learning Automata for High Learning Efficiency

Incorporation of Ordinal Optimization into Learning Automata...

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IEEE International Conference on Automation Science and Engineering

作者： JunQi Zhang Cheng Wang Di Zang MengChu Zhou Department of Computer Science and Technology the Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Department of Electrical and Computer Engineering New Jersey Institute of Technology

ISBN: (纸本)9781467381840

Learning automata (LA) represent important leaning mechanisms with applications in automated system design, biological system modeling, computer vision, and transportation. They play the critical roles in modeling a process as well as generating the appropriate signal to control it. They update their action probabilities in accordance with the inputs received from the environment and can improve their own performance during operations. The action probability vector in LA takes charge of two functions: 1) The cost of convergence, i.e., the size of sampling budget;2) The allocation of sampling budget among actions to identify the optimal one. These two intertwined functions lead to a problem: The sampling budget mostly goes to the currently estimated optimal action due to its high action probability regardless whether it can help identify the real optimal action or not. This work proposes a new class of LA that separates the allocation of sampling budget from the action probability vector. It uses the action probability vector to determine the size of sampling budget and then uses Optimal computing Budget Allocation (OCBA) to accomplish the allocation of sampling budget in a way that maximizes the probability of identifying the true optimal action. Simulation results verify its significant speedup ranging from 10.93% to 65.94% over the best existing LA algorithms.

关键词： Convergence Optimization Resource management Learning automata Computational modeling Pursuit algorithms Accuracy

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