检索结果-内蒙古大学图书馆

computational prediction and validation of specific EmbR binding site on PknH

Synthetic and Systems Biotechnology 2021年第4期6卷 429-436页

作者： Insung Na Huanqin Dai Hantian Li Anvita Gupta David Kreda Powell Zhang Xiangyin Chen Lixin Zhang Gil Alterovitz Computational Health Informatics Program Boston Children’s Hospital/Harvard Medical SchoolBostonMA 02115USA Department of Medicine Brigham and Women’s Hospital/Harvard Medical SchoolBostonMA 02115USA State Key Laboratory of Mycology Institute of MicrobiologyBeijing100101 China Department of Computer Sciences Stanford UniversityStanford CA 94305USA Center for Biomedical Informatics Harvard Medical SchoolBostonMA 02115USA State Key Laboratory of Bioreactor Engineering East China University of Science and TechnologyShanghaiChina Department of Biology The Johns Hopkins UniversityBaltimoreMD 21218USA Lexington High School BostonMA 02115USA

Tuberculosis drug resistance continues to threaten global health but the underline molecular mechanisms are not ***(EMB),one of the well-known first-line drugs in tuberculosis treatment is,unfortunately,not free from drug resistance *** studies have shown that some genetic mutations in Mycobacterium tuberculosis(Mtb)EmbR,and EmbC/A/B genes cause EMB ***-PknH pair controls embC/A/B operon,which encodes EmbC/A/B genes,and EMB interacts with EmbA/B ***,the EmbR binding site on PknH was *** conducted molecular simulation on the EmbR-peptides binding structures and discovered phosphorylated PknH 273-280(N′-HEALS^(P)DPD-C′)makesβstrand with the EmbR FHA domain,asβ-MoRF(MoRF;molecular recognition feature)does at its binding *** bond number analysis also supported the peptides’β-MoRF forming activity at the EmbR FHA ***,we discovered that previously known phosphorylation residues might have their chronological order according to the phosphorylation *** discovery validated that Mtb PknH 273-280(N′-HEALSDPD-C′)has reliable EmbR binding *** approach is revolutionary in the computer-aided drug discovery field,because it is the first trial to discover the protein-protein interaction site,and find binding partner in nature from this site.

关键词： Disorder-to-order transition Protein intrinsic disorder Binding site prediction Drug resistance Molecular simulation

来源：评论

学校读者我要写书评

暂无评论

The use of research activities as learning instrument in electrical engineering and computer science graduations

The use of research activities as learning instrument in ele...

引用

Frontiers in Education (FIE) Conference

作者： C.A.P.S. Martins M.B. Carvalho C.V. Pousa D.O. Penha Computer Science Department and Graduate Program in Electrical Engineering (PPGEE) Computational and Digital System Laboratoty (LSDC) Pontifical Catholic University of Minas Gerais Belo Horizonte Minas Gerais Brazil PUC-Minas PPGEE LSDC Belo Horizonte Minas Gerais Brazil Computer Science Department LSDC PUC-Minas Belo Horizonte Minas Gerais Brazil

In this paper we analyze the use of research activities as learning instrument in electrical engineering and computer science. This pedagogic approach was applied in undergraduate disciplines, undergraduate teaching assistance and undergraduate research projects. Our main goals are optimize the learning process using research and motivate the use of research activities as learning instrument.

关键词： Instruments Computer science Education Performance analysis Electrical engineering Learning systems Proposals Humans Microprocessors Cache memory

来源：评论

学校读者我要写书评

暂无评论

Dynamical charge structure factor of a one-dimensional ionic Hubbard model in the low-energy region

arXiv

引用

arXiv 2018年

作者： Komaki, Yuhei Iwase, Yuma Yanagimatsu, Shogo Muta, Yoshiyuki Maeshima, Nobuya Hino, Ken-ichi Doctoral Program in Materials Science Graduate School of Pure and Applied Sciences University of Tsukuba Tsukuba Ibaraki305-8573 Japan College of Engineering Sciences University of Tsukuba Tsukuba305-8573 Japan Center for Computational Sciences University of Tsukuba Tsukuba305-8577 Japan Division of Materials Science University of Tsukuba Tsukuba305-8573 Japan

We present a numerical study of the charge dynamical structure factor N(k, ω) of a one-dimensional (1D) ionic Hubbard model in the Mott insulator (MI) phase. We show that the low-energy spectrum of N(k, ω) is expressed in terms of the spin operators for the spin degrees of freedom. Numerical results for the spin degrees of freedom, obtained by the Lanczos diagonalization method, well reproduce the low-energy spectrum of N(k, ω) of the 1D ionic Hubbard model. In addition, we show that these spectral peaks probe the dispersion of the spin-singlet excitations of the system and are observed in the wide parameter region of the MI phase. Copyright © 2018, 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

Brain-network clustering via kernel-ARMA modeling and the Grassmannian

arXiv

引用

arXiv 2019年

作者： Ye, Cong Slavakis, Konstantinos Patil, Pratik V. Muldoon, Sarah F. Medaglia, John NY14260 United States Department of Mathematics Computational and Data-Enabled Science and Engineering Program University at Buffalo SUNY NY14260 United States Department of Psychology Drexel University PA19104 United States Perelman School of Medicine University of Pennsylvania PA19104 United States

Recent advances in neuroscience and in the technology of functional magnetic resonance imaging (fMRI) and electro-encephalography (EEG) have propelled a growing interest in brain-network clustering via time-series analysis. Notwithstanding, most of the brain-network clustering methods revolve around state clustering and/or node clustering (a.k.a. community detection or topology inference) within states. This work answers first the need of capturing non-linear nodal dependencies by bringing forth a novel feature-extraction mechanism via kernel autoregressive-moving-average modeling. The extracted features are mapped to the Grassmann manifold (Grassmannian), which consists of all linear subspaces of a fixed rank. By virtue of the Riemannian geometry of the Grassmannian, a unifying clustering framework is offered to tackle all possible clustering problems in a network: Cluster multiple states, detect communities within states, and even identify/track subnetwork state sequences. The effectiveness of the proposed approach is underlined by extensive numerical tests on synthetic and real fMRI/EEG data which demonstrate that the advocated learning method compares favorably versus several state-of-the-art clustering schemes. Copyright © 2019, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

来源：评论

学校读者我要写书评

暂无评论

An accurate front capturing scheme for tumor growth models with a free boundary limit

arXiv

引用

arXiv 2017年

作者： Liu, Jian-Guo Tang, Min Wang, Li Zhou, Zhennan Department of Mathematics Department of Physics Duke University Department of Mathematics Institute of Natural Sciences MOE LSC Shanghai Jiao Tong University Department of Mathematics Computational and Data-Enabled Science and Engineering Program State University of New York Buffalo United States Department of Mathematics Duke University

We consider a class of tumor growth models under the combined effects of density-dependent pressure and cell multiplication, with a free boundary model as its singular limit when the pressure-density relationship becomes highly nonlinear. In particular, the constitutive law connecting pressure p and density ρ is p(ρ) = mm−1 ρm−1, and when m 1, the cell density ρ may evolve its support due to a pressure-driven geometric motion with sharp interface along the boundary of its support. The nonlinearity and degeneracy in the diffusion bring great challenges in numerical simulations, let alone the capturing of the singular free boundary limit. Prior to the present paper, there is lack of standard mechanism to numerically capture the front propagation speed as m 1. In this paper, we develope a numerical scheme based on a novel prediction-correction reformulation that can accurately approximate the front propagation even when the nonlinearity is extremely strong. We show that the semi-discrete scheme naturally connects to the free boundary limit equation as m → ∞, and with proper spacial discretization, the fully discrete scheme has improved stability, preserves positivity, and implements without nonlinear solvers. Finally, extensive numerical examples in both one and two dimensions are provided to verify the claimed properties and showcase good performance in various applications.35K55, 35B25, 76D27, 92C50 Copyright © 2017, The Authors. All rights reserved.

关键词： Tumors

来源：评论

学校读者我要写书评

暂无评论

Unsupervised particle sorting for high-resolution single-particle cryo-EM

arXiv

引用

arXiv 2019年

作者： Zhou, Ye Moscovich, Amit Bendory, Tamir Bartesaghi, Alberto Department of Computer Science Duke University Durham United States Program in Applied and Computational Mathematics Princeton University United States School of Electrical Engineering Tel Aviv University Tel Aviv Israel Department of Biochemistry Duke University School of Medicine Durham United States Department of Electrical and Computer Engineering Duke University Durham United States

Single-particle cryo-Electron Microscopy (EM) has become a popular technique for determining the structure of challenging biomolecules that are inaccessible to other technologies. Recent advances in automation, both in data collection and data processing, have significantly lowered the barrier for non-expert users to successfully execute the structure determination workflow. Many critical data processing steps, however, still require expert user intervention in order to converge to the correct high-resolution structure. In particular, strategies to identify homogeneous populations of particles rely heavily on subjective criteria that are not always consistent or reproducible among different users. Here, we explore the use of unsupervised strategies for particle sorting that are compatible with the autonomous operation of the image processing pipeline. More specifically, we show that particles can be successfully sorted based on a simple statistical model for the distribution of scores assigned during refinement. This represents an important step towards the development of automated workflows for protein structure determination using single-particle *** Codes 62P10 Copyright © 2019, The Authors. All rights reserved.

关键词： Inverse problems

来源：评论

学校读者我要写书评

暂无评论

Scalable privacy-preserving cancer type prediction with homomorphic encryption

arXiv

引用

arXiv 2022年

作者： Sarkar, Esha Chielle, Eduardo Gursoy, Gamze Chen, Leo Gerstein, Mark Maniatakos, Michail Tandon School of Engineering New York University Brooklyn United States Department of Electrical and Computer Engineering New York University Abu Dhabi Abu Dhabi United Arab Emirates Program in Computational Biology and Bioinformatics Yale University New Haven United States Department of Computer Science Yale University New Haven United States

Machine Learning (ML) alleviates the challenges of high-dimensional data analysis and improves decision making in critical applications like healthcare. Effective cancer/tumor classification from high-dimensional genetic mutation data can be useful for cancer diagnosis and treatment, if the distinguishable patterns between cancer/tumor types are identified. At the same time, analysis of high-dimensional data is computationally expensive and is often outsourced to cloud services. Privacy concerns in outsourced ML, especially in the field of genetics, motivate the use of encrypted computation, like Homomorphic Encryption (HE). But restrictive overheads of encrypted computation deter its usage. In this work, we explore the challenges of privacy preserving cancer detection using a real-world dataset consisting of more than 2 million genetic information for several cancer types. Since the data is inherently high-dimensional, we explore smaller ML models for cancer prediction to enable fast inference in the privacy preserving domain. We develop a solution for privacy preserving cancer inference which first leverages the domain knowledge on somatic mutations to efficiently encode genetic mutations and then uses statistical tests for feature selection. Our logistic regression model, built using our novel encoding scheme, achieves 0.98 micro-average area under curve with 13% higher test accuracy than similar studies. We exhaustively test our model’s predictive capabilities by analyzing the genes used by the model. Furthermore, we propose a fast matrix multiplication algorithm that can efficiently handle high-dimensional data. Experimental results show that, even with 40,000 features, our proposed matrix multiplication algorithm can speed up concurrent inference of multiple individuals by approximately 10x and inference of a single individual by approximately 550x, in comparison to standard matrix multiplication. © 2022, CC BY.

关键词： Clustering algorithms

来源：评论

学校读者我要写书评

暂无评论

Slater Insulator in Iridate Perovskites with Strong Spin-Orbit Coupling

引用

Physical Review Letters 2016年第17期117卷 176603-176603页

作者： Q. Cui J.-G. Cheng W. Fan A. E. Taylor S. Calder M. A. McGuire J.-Q. Yan D. Meyers X. Li Y. Q. Cai Y. Y. Jiao Y. Choi D. Haskel H. Gotou Y. Uwatoko J. Chakhalian A. D. Christianson S. Yunoki J. B. Goodenough J.-S. Zhou Beijing National Laboratory for Condensed Matter Physics and Institute of Physics Chinese Academy of Sciences Beijing 100190 China Materials Science and Engineering Program University of Texas at Austin Austin Texas 78712 USA Computational Condensed Matter Physical Laboratory RIKEN Wako Saitama 351-0198 Japan Quantum Condensed Matter Division Oak Ridge National Laboratory Tennessee 37831 USA Materials Science and Technology Division Oak Ridge National Laboratory Tennessee 37831 USA Department of Materials Science and Engineering University of Tennessee Knoxville Tennessee 37996 USA Department of Physics University of Arkansas Fayetteville Arkansas 72701 USA Advanced Photon Source Argonne National Laboratory Argonne Illinois 60439 USA Institute for Solid State Physics University of Tokyo 5-1-5 Kashiwanoha Chiba 277-8581 Japan Department of Physics and Astronomy Rutgers University 136 Frelinghuysen Road Piscataway New Jersey 08854 USA Department of Physics and Astronomy University of Tennessee Knoxville Tennessee 37966 USA Computational Materials Science Research Team RIKEN Advanced Institute for Computational Science (AICS) Kobe Hyogo 650-0047 Japan Computational Quantum Matter Research Team RIKEN Center for Emergent Matter Science (CEMS) Wako Saitama 351-0198 Japan

The perovskite SrIrO3 is an exotic narrow-band metal owing to a confluence of the strengths of the spin-orbit coupling (SOC) and the electron-electron correlations. It has been proposed that topological and magnetic insulating phases can be achieved by tuning the SOC, Hubbard interactions, and/or lattice symmetry. Here, we report that the substitution of nonmagnetic, isovalent Sn4+ for Ir4+ in the SrIr1−xSnxO3 perovskites synthesized under high pressure leads to a metal-insulator transition to an antiferromagnetic (AF) phase at TN≥225 K. The continuous change of the cell volume as detected by x-ray diffraction and the λ-shape transition of the specific heat on cooling through TN demonstrate that the metal-insulator transition is of second order. Neutron powder diffraction results indicate that the Sn substitution enlarges an octahedral-site distortion that reduces the SOC relative to the spin-spin exchange interaction and results in the type-G AF spin ordering below TN. Measurement of high-temperature magnetic susceptibility shows the evolution of magnetic coupling in the paramagnetic phase typical of weak itinerant-electron magnetism in the Sn-substituted samples. A reduced structural symmetry in the magnetically ordered phase leads to an electron gap opening at the Brillouin zone boundary below TN in the same way as proposed by Slater.

关键词： Metal-insulator transition Spin-orbit coupling Iridates Perovskites

来源：评论

学校读者我要写书评

暂无评论

Tuning cooperative behavior in games with nonlinear opinion dynamics

arXiv

引用

arXiv 2021年

作者： Park, Shinkyu Bizyaeva, Anastasia Kawakatsu, Mari Franci, Alessio Leonard, Naomi Ehrich Computer Electrical and Mathematical Science and Engineering Division Thuwal23955-6900 Saudi Arabia Department of Mechanical and Aerospace Engineering Princeton University PrincetonNJ08544 United States Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Mathematics Department National Autonomous University of Mexico Mexico City04510 Mexico

We examine the tuning of cooperative behavior in repeated multi-agent games using an analytically tractable, continuous-time, nonlinear model of opinion dynamics. Each modeled agent updates its real-valued opinion about each available strategy in response to payoffs and other agent opinions, as observed over a network. We show how the model provides a principled and systematic means to investigate behavior of agents that select strategies using rationality and reciprocity, key features of human decision-making in social dilemmas. For two-strategy games, we use bifurcation analysis to prove conditions for the bistability of two equilibria and conditions for the first (second) equilibrium to reflect all agents favoring the first (second) strategy. We prove how model parameters, e.g., level of attention to opinions of others (reciprocity), network structure, and payoffs, influence dynamics and, notably, the size of the region of attraction to each stable equilibrium. We provide insights by examining the tuning of the bistability of mutual cooperation and mutual defection and their regions of attraction for the repeated prisoner's dilemma and the repeated multi-agent public goods game. Our results generalize to games with more strategies, heterogeneity, and additional feedback dynamics, such as those designed to elicit cooperation. Copyright © 2021, The Authors. All rights reserved.

关键词： Bifurcation (mathematics)

来源：评论

学校读者我要写书评

暂无评论

Deep potentials for materials science

材料展望（英文）

引用

材料展望（英文） 2022年第2期 89-115页

作者： Tongqi Wen Linfeng Zhang Han Wang Weinan E David J Srolovitz Department of Mechanical Engineering The University of Hong KongHong KongHong Kong Special Administrative Region of China DP Technology BeijingPeople's Republic of China AI for Science Institute BeijingPeople's Republic of China Laboratory of Computational Physics Institute of Applied Physics and Computational MathematicsBeijingPeople's Republic of China HEDPS CAPTCollege of EngineeringPeking UniversityBeijingPeople's Republic of China AI for Science Institute BeijingPeople's Republic of China School of Mathematical Sciences Peking UniversityBeijingPeople's Republic of China Department of Mathematics and Program in Applied and Computational Mathematics Princeton UniversityPrincetonNJUnited States of America Department of Mechanical Engineering The University of Hong KongHong KongHong Kong Special Administrative Region of China International Digital Economy Academy(IDEA) ShenzhenPeople's Republic of China

To fill the gap between accurate(and expensive)ab initio calculations and efficient atomistic simulations based on empirical interatomic potentials,a new class of descriptions of atomic interactions has emerged and been widely applied;*** learning potentials(MLPs).One recently developed type of MLP is the deep potential(DP)*** this review,we provide an introduction to DP methods in computational materials *** theory underlying the DP method is presented along with a step-by-step introduction to their development and *** also review materials applications of DPs in a wide range of materials *** DP Library provides a platform for the development of DPs and a database of extant *** discuss the accuracy and efficiency of DPs compared with ab initio methods and empirical potentials.

关键词： deep potential atomistic simulation machine learning potential neural network

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：