检索结果-内蒙古大学图书馆

A new method for multiancestry polygenic prediction improves performance across diverse populations

Nature Genetics 2023年第10期55卷 1757-1768页

作者： Zhang, Haoyu Zhan, Jianan Jin, Jin Zhang, Jingning Lu, Wenxuan Zhao, Ruzhang Ahearn, Thomas U. Yu, Zhi O’Connell, Jared Jiang, Yunxuan Chen, Tony Okuhara, Dayne Aslibekyan, Stella Auton, Adam Babalola, Elizabeth Bell, Robert K. Bielenberg, Jessica Bryc, Katarzyna Bullis, Emily Coker, Daniella Partida, Gabriel Cuellar Dhamija, Devika Das, Sayantan Elson, Sarah L. Eriksson, Nicholas Filshtein, Teresa Fitch, Alison Fletez-Brant, Kipper Fontanillas, Pierre Freyman, Will Granka, Julie M. Heilbron, Karl Hernandez, Alejandro Hicks, Barry Hinds, David A. Jewett, Ethan M. Kukar, Katelyn Kwong, Alan Lin, Keng-Han Llamas, Bianca A. Lowe, Maya McCreight, Jey C. McIntyre, Matthew H. Micheletti, Steven J. Moreno, Meghan E. Nandakumar, Priyanka Nguyen, Dominique T. Noblin, Elizabeth S. Petrakovitz, Aaron A. Poznik, G. David Reynoso, Alexandra Schumacher, Morgan Shastri, Anjali J. Shelton, Janie F. Shi, Jingchunzi Shringarpure, Suyash Su, Qiaojuan Jane Tat, Susana A. Tchakouté, Christophe Toukam Tran, Vinh Tung, Joyce Y. Wang, Xin Wang, Wei Weldon, Catherine H. Wilton, Peter Wong, Corinna D. Garcia-Closas, Montserrat Lin, Xihong Koelsch, Bertram L. Chatterjee, Nilanjan Division of Cancer Epidemiology and Genetics National Cancer Institute Bethesda MD United States Department of Biostatistics Harvard T.H. Chan School of Public Health Boston MA United States 23andMe Inc. Sunnyvale CA United States Department of Biostatistics Johns Hopkins Bloomberg School of Public Health Baltimore MD United States Department of Biostatistics Epidemiology and Informatics University of Pennsylvania Philadelphia PA United States Department of Applied Mathematics and Statistics Johns Hopkins University Baltimore MD United States Program in Medical and Population Genetics Broad Institute of MIT and Harvard Cambridge MA United States Booz Allen Hamilton Inc. McLean VA United States Division of Genetics and Epidemiology Institute of Cancer Research London United Kingdom Department of Statistics Harvard University Cambridge MA United States Department of Oncology School of Medicine Johns Hopkins University Baltimore MD United States

Polygenic risk scores (PRSs) increasingly predict complex traits;however, suboptimal performance in non-European populations raise concerns about clinical applications and health inequities. We developed CT-SLEB, a powerful and scalable method to calculate PRSs, using ancestry-specific genome-wide association study summary statistics from multiancestry training samples, integrating clumping and thresholding, empirical Bayes and superlearning. We evaluated CT-SLEB and nine alternative methods with large-scale simulated genome-wide association studies (~19 million common variants) and datasets from 23andMe, Inc., the Global Lipids Genetics Consortium, All of Us and UK Biobank, involving 5.1 million individuals of diverse ancestry, with 1.18 million individuals from four non-European populations across 13 complex traits. Results demonstrated that CT-SLEB significantly improves PRS performance in non-European populations compared with simple alternatives, with comparable or superior performance to a recent, computationally intensive method. Moreover, our simulation studies offered insights into sample size requirements and SNP density effects on multiancestry risk prediction. © 2023, The Author(s), under exclusive licence to Springer Nature America, Inc.

关键词： Genome-wide association studies Preventive medicine

来源：评论

学校读者我要写书评

暂无评论

Drone-borne calibration pulser for radio observatories detecting ultra-high-energy cosmic rays and neutrinos 38

Drone-borne calibration pulser for radio observatories detec...

引用

38th International Cosmic Ray Conference, ICRC 2023

作者： Nam, J. Kuo, C.-Y. Shin, B.K. Chen, P. Chen, Y. Hsu, S.-Y. Huang, J.-J. Huang, M.-H.A. Leung, C.H. Liu, T.-C. Wang, S.-H. Wang, M.-Z. Wang, Y.-H. Besson, D. Novikov, A. Hornhuber, C. Young, R. Department of Physics National Taiwan University Taipei Taiwan Leung Center for Cosmology and Particle Astrophysics National Taiwan University Taipei Taiwan Ulsan National Institute of Science and Technology Ulsan Korea Republic of Department of Energy and Resources National United University Miaoli Taiwan Department of Electrophysics National Yang Ming Chiao Tung University Hsinchu300 Taiwan Department of applied physics National PingTung university PingTung900 Taiwan Chung Cheng Institute of Technology National Defense University TaoYuan335 Taiwan Undergraduate Degree Program of Systems Engineering and Technology National Yang Ming Chiao Tung University Hsinchu300 Taiwan Department of Physics and Astronomy University of Kansas Lawrence United States Instrumentation Design Lab University of Kansas Lawrence United States Moscow Russia Institute of Astronomy and Astrophysics Academia Sinica Astronomy-Mathematics Building No. 1 Sec. 4 Roosevelt Rd. Taipei Taiwan

We report on the development of a drone-borne calibration pulser (cal-pulser) for radio observatories detecting ultra-high energy (UHE) cosmic rays and neutrinos. This system allows us to calibrate the radio detector in its field of view regardless of the accessibility of the site. The system is compact and sufficiently lightweight to be attached to a commercial drone. It consists of a solid-state high-power pulse generator, a programmable attenuator, a transmitting antenna, and a differential GPS (D-GPS) module. The drone, with the ∼1.3 kg payload, has a typical flight time of around 25 minutes, corresponding to a maximum flight distance of around 10 km. After developing the first version of the system in 2019, it successfully demonstrated its calibration performance with the TAROGE stations in Taiwan and the TAROGE-M station in Mt. Melbourne, Antarctica. In 2022, the system was upgraded with a dual-band D-GPS for more reliable operation and more accurate determination of the coordinates of the cal-pulser. In addition to the cal-pulser, we investigated aerial photogrammetry using the built-in camera and the D-GPS, which is an efficient method to determine the coordinates of the detector. For objects within 10 m of the drone, an accuracy of a few millimeter was achieved. These drone-borne cal-pulser and the photogrammetry have become essential calibration procedures in the TAROGE-M experiment. We suggest these approaches may find application in many other radio experiments that detect UHE cosmic rays and neutrinos, such as ARIANNA, BEACON, GRAND, as well as IceCube-Gen2. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Cosmic rays

来源：评论

学校读者我要写书评

暂无评论

Quantum distance to uncontrollability and quantum speed limits

引用

Physical Review A 2022年第4期105卷 042402-042402页

作者： Daniel Burgarth Jeff Borggaard Zoltán Zimborás Center for Engineered Quantum Systems School of Mathematical and Physical Sciences Macquarie University 2109 New South Wales Australia Interdisciplinary Center for Applied Mathematics and the Department of Mathematics at Virginia Tech Blacksburg Virginia 24061 USA Wigner Research Centre for Physics H-1525 P.O.Box 49 Budapest Hungary BME-MTA Lendület Quantum Information Theory Research Group H-1535 P.O.Box 888 Budapest Hungary Mathematical Institute Budapest University of Technology and Economics H-1111 Budapest Hungary

Distance to uncontrollability is a crucial concept in classical control theory. Here, we introduce quantum distance to uncontrollability as a measure of how close a universal quantum system is to a nonuniversal one. This allows us to provide a quantitative version of the quantum speed limit, decomposing the bound into geometric and dynamical components. We consider several physical examples including globally controlled solid state qubits, scrambling of quantum information, and a cross-Kerr system, showing that the quantum distance to uncontrollability provides a precise meaning to spectral crowding, weak interactions, and other bottlenecks to universality. We suggest that this measure should be taken into consideration in the design of quantum technology.

关键词： Quantum control

来源：评论

学校读者我要写书评

暂无评论

Local Order Metrics for Two-Phase Media Across Length Scales

arXiv

引用

arXiv 2022年

作者： Torquato, Salvatore Skolnick, Murray Kim, Jaeuk Department of Chemistry Department of Physics Princeton Institute for the Science and Technology of Materials Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Department of Chemistry Princeton University PrincetonNJ08544 United States McKetta Department of Chemical Engineering University of Texas at Austin AustinTX78712 United States

The capacity to devise order metrics for microstructures of multiphase heterogeneous media is a highly challenging task, given the richness of the possible geometries and topologies of the phases that can arise. This investigation initiates a program to formulate order metrics to characterize the degree of order/disorder of the microstructures of two-phase media in d-dimensional Euclidean space Rd across length scales. In particular, we propose the use of the local volume-fraction variance σV2 (R) associated with a spherical window of radius R as an order metric. We determine σV2 (R) as a function of R for 22 different models across the first three space dimensions, including both hyperuniform and nonhyperuniform systems with varying degrees of short- and long-range order. We find that the local volume-fraction variance as well as asymptotic coefficients and integral measures derived from it provide reasonably robust and sensitive order metrics to categorize disordered and ordered two-phase media across all length scales. © 2022, CC BY-SA.

关键词： Volume fraction

来源：评论

学校读者我要写书评

暂无评论

Feature selection using stochastic gates 37

Feature selection using stochastic gates

引用

37th International Conference on Machine Learning, ICML 2020

作者： Yamada, Yutaro Lindenbaum, Ofir Negahban, Sahand Kluger, Yuval Department of Statistics and Data Science Yale University CT United States Program in Applied Mathematics Yale University CT United States School of Medicine Yale University CT United States

ISBN: (纸本)9781713821120

Feature selection problems have been extensively studied in the setting of linear estimation (e.g. LASSO), but less emphasis has been placed on feature selection for non-linear functions. In this study, we propose a method for feature selection in neural network estimation problems. The new procedure is based on probabilistic relaxation of the `0 norm of features, or the count of the number of selected features. Our `0-based regularization relies on a continuous relaxation of the Bernoulli distribution;such relaxation allows our model to learn the parameters of the approximate Bernoulli distributions via gradient descent. The proposed framework simultaneously learns either a nonlinear regression or classification function while selecting a small subset of features. We provide an information-theoretic justification for incorporating Bernoulli distribution into feature selection. Furthermore, we evaluate our method using synthetic and real-life data to demonstrate that our approach outperforms other commonly used methods in both predictive performance and feature selection. Copyright 2020 by the author(s).

关键词： Feature Selection

来源：评论

学校读者我要写书评

暂无评论

On the Parameter Combinations that Matter and on Those that do Not

arXiv

引用

arXiv 2021年

作者： Evangelou, Nikolaos Wichrowski, Noah J. Kevrekidis, George A. Dietrich, Felix Kooshkbaghi, Mahdi McFann, Sarah Kevrekidis, Ioannis G. Department of Chemical and Biomolecular Engineering Johns Hopkins University United States Department of Applied Mathematics and Statistics Johns Hopkins University United States Department of Mathematics and Statistics University of Massachusetts Amherst United States Department of Informatics Technical University of Munich Germany The Program in Applied and Computational Mathematics Princeton University United States Department of Chemical and Biological Engineering Princeton University United States Lewis-Sigler Institute for Integrative Genomics Princeton University United States

We present a data-driven approach to characterizing nonidentifiability of a model’s parameters and illustrate it through dynamic as well as steady kinetic models. By employing Diffusion Maps and their extensions, we discover the minimal combinations of parameters required to characterize the output behavior of a chemical system: a set of effective parameters for the model. Furthermore, we introduce and use a Conformal Autoencoder Neural Network technique, as well as a kernel-based Jointly Smooth Function technique, to disentangle the redundant parameter combinations that do not affect the output behavior from the ones that do. We discuss the interpretability of our data-driven effective parameters, and demonstrate the utility of the approach both for behavior prediction and parameter estimation. In the latter task, it becomes important to describe level sets in parameter space that are consistent with a particular output behavior. We validate our approach on a model of multisite phosphorylation, where a reduced set of effective parameters (nonlinear combinations of the physical ones) has previously been established *** Codes 37E99 (Primary), 68T07 (Secondary) Copyright © 2021, The Authors. All rights reserved.

关键词： Parameter estimation

来源：评论

学校读者我要写书评

暂无评论

A sharp interface Lagrangian-Eulerian method for flexible-body fluid-structure interaction

arXiv

引用

arXiv 2022年

作者： Kolahdouz, Ebrahim M. Wells, David R. Rossi, Simone Aycock, Kenneth I. Craven, Brent A. Griffith, Boyce E. Center for Computational Biology Flatiron Institute Simons Foundation New YorkNY United States Department of Mathematics University of North Carolina Chapel HillNC United States Division of Applied Mechanics Office of Science and Engineering Laboratories Center for Devices and Radiological Health United States Food and Drug Administration Silver SpringMD United States Departments of Mathematics and Biomedical Engineering University of North Carolina Chapel HillNC United States Carolina Center for Interdisciplinary Applied Mathematics University of North Carolina Chapel HillNC United States Computational Medicine Program University of North Carolina Chapel HillNC United States McAllister Heart Institute University of North Carolina Chapel HillNC United States

This paper introduces a sharp-interface approach to simulating fluid-structure interaction (FSI) involving flexible bodies described by general nonlinear material models and across a broad range of mass density ratios. This new flexible-body immersed Lagrangian-Eulerian (ILE) scheme extends our prior work on integrating partitioned and immersed approaches to rigid-body FSI. Our numerical approach incorporates the geometrical and domain solution flexibility of the immersed boundary (IB) method with an accuracy comparable to body-fitted approaches that sharply resolve flows and stresses up to the fluid-structure interface. Unlike many IB methods, our ILE formulation uses distinct momentum equations for the fluid and solid subregions with a Dirichlet-Neumann coupling strategy that connects fluid and solid subproblems through simple interface conditions. As in earlier work, we use approximate Lagrange multiplier forces to treat the kinematic interface conditions along the fluid-structure interface. This penalty approach simplifies the linear solvers needed by our formulation by introducing two representations of the fluid-structure interface, one that moves with the fluid and another that moves with the structure, that are connected by stiff springs. This approach also enables the use of multi-rate time stepping, which allows us to use different time step sizes for the fluid and structure subproblems. Our fluid solver relies on an immersed interface method (IIM) for discrete surfaces to impose stress jump conditions along complex interfaces while enabling the use of fast structured-grid solvers for the incompressible Navier-Stokes equations. The dynamics of the volumetric structural mesh are determined using a standard finite element approach to large-deformation nonlinear elasticity via a nearly incompressible solid mechanics formulation. This formulation also readily accommodates compressible structures with a constant total volume, and it can handle fully compressibl

关键词： Fluid structure interaction

来源：评论

学校读者我要写书评

暂无评论

The Manifold Scattering Transform for High-Dimensional Point Cloud Data

arXiv

引用

arXiv 2022年

作者： Chew, Joyce Steach, Holly R. Viswanath, Siddharth Wu, Hau-Tieng Hirn, Matthew Needell, Deanna Krishnaswamy, Smita Perlmutter, Michael UCLA Department of Mathematics Los AngelesCA United States Yale University Department of Genetics New HavenCT United States UC Irvine Department of Computer Science IrvineCA United States Duke University Department of Mathematics DurhamNC United States Duke University Department of Statistical Science DurhamNC United States Michigan State University Department of Mathematics East Lansing United States Michigan State University Department of CMSE East Lansing United States Yale University Department of Computer Science New HavenCT United States Yale University Applied Math Program New HavenCT United States

The manifold scattering transform is a deep feature extractor for data defined on a Riemannian manifold. It is one of the first examples of extending convolutional neural network-like operators to general manifolds. The initial work on this model focused primarily on its theoretical stability and invariance properties but did not provide methods for its numerical implementation except in the case of two-dimensional surfaces with predefined meshes. In this work, we present practical schemes, based on the theory of diffusion maps, for implementing the manifold scattering transform to datasets arising in naturalistic systems, such as single cell genetics, where the data is a high-dimensional point cloud modeled as lying on a low-dimensional manifold. We show that our methods are effective for signal classification and manifold classification *** Codes 68T07 Copyright © 2022, The Authors. All rights reserved.

关键词： Numerical methods

来源：评论

学校读者我要写书评

暂无评论

Microfluidic droplet detection using synthetic data generated by a Generative Adversarial Network

引用

Engineering Applications of Artificial Intelligence 2025年 156卷

作者： Karimzadeh, Marziyeh Meybodi, Mohsen Alambardar Shams, Mohammadreza Goli-Malekabadi, Zahra Department of Applied Mathematics and Computer Science Faculty of Mathematics and Statistics University of Isfahan Isfahan81746-73441 Iran Department of Computer Engineering Shahreza Campus University of Isfahan Isfahan Iran Department of Medical Technology and Tissue Engineering School of Life science Engineering College of Interdisciplinary Science and Technology University of Tehran Tehran Iran

Cellular heterogeneity, even among genetically identical cells, results in variations in their properties and behaviors, making single-cell analysis crucial for obtaining detailed insights. However, isolating single cells from a cell population poses major challenges, as conventional laboratory techniques often risk cell damage and involve complex procedures. Droplet microfluidics has emerged as a promising approach for encapsulating cells, particularly single cells, into individual droplets without causing harm. Despite this, factors like cell sedimentation and aggregation can reduce encapsulation efficiency and lead to deviations from the expected Poisson distribution. To address these challenges, leveraging artificial intelligence and deep learning to monitor, detect, and regulate encapsulation conditions in real-time is critical for enhancing system performance. However, deep learning models require substantial training data, and issues like microfluidic channel clogging and the scarcity of certain cell types often limit data availability. To overcome this limitation, researchers are turning to synthetic data generation to supplement training datasets and address data scarcity challenges effectively. This study emphasizes the potential of integrating synthetic data with cutting-edge deep learning techniques to enhance the accuracy and efficiency of single-cell analysis within droplet microfluidic systems. A diverse dataset integrating synthetic and real images was used to train the YOLOv8s model for automated detection and classification of microfluidic droplets, enhancing accuracy and system performance. The model trained on a combination of real and synthetic data outperformed the one trained using conventional data augmentation methods, achieving an mAP0.5 of 98% due to the increased diversity of training images. It also demonstrated faster and more stable training. Additionally, the YOLOv8 network, with a detection rate of approximately 2338 droplets per sec

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Unbiased Estimation using a Class of Diffusion Processes

arXiv

引用

arXiv 2022年

作者： Ruzayqat, Hamza Beskos, Alexandros Crisan, Dan Jasra, Ajay Kantas, Nikolas Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 KSA Saudi Arabia Department of Statistical Science University College London LondonWC1E 6BT United Kingdom Department of Mathematics Imperial College London LondonSW7 2AZ United Kingdom

We study the problem of unbiased estimation of expectations with respect to (w.r.t.) π a given, general probability measure on (d, B(d)) that is absolutely continuous with respect to a standard Gaussian measure. We focus on simulation associated to a particular class of diffusion processes, sometimes termed the Schrödinger-Föllmer Sampler, which is a simulation technique that approximates the law of a particular diffusion bridge process {Xt}tϵ[0,1]on d, d ϵ 0. This latter process is constructed such that, starting at X0= 0, one has X1∼ π. Typically, the drift of the diffusion is intractable and, even if it were not, exact sampling of the associated diffusion is not possible. As a result, [10, 16] consider a stochastic Euler-Maruyama scheme that allows the development of biased estimators for expectations w.r.t. π. We show that for this methodology to achieve a mean square error of O(ϵ2), for arbitrary ϵ > 0, the associated cost is O(ϵ-5). We then introduce an alternative approach that provides unbiased estimates of expectations w.r.t. π, that is, it does not suffer from the time discretization bias or the bias related with the approximation of the drift function. We prove that to achieve a mean square error of O(ϵ2), the associated cost (which is random) is, with high probability, O(ϵ-2| log(ϵ)|2+δ), for any δ > 0. We implement our method on several examples including Bayesian inverse *** Codes 60J60, 62D05, 65C40 © 2022, CC BY.

关键词： Diffusion

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：