检索结果-内蒙古大学图书馆

scTenifoldXct: A semi-supervised method for predicting cell-cell interactions and mapping cellular communication graphs

引用

Cell systems 2023年第4期14卷 302-311.e4页

作者： Yongjian Yang Guanxun Li Yan Zhong Qian Xu Yu-Te Lin Cristhian Roman-Vicharra Robert S Chapkin James J Cai Department of Electrical and Computer Engineering Texas A&M University College Station TX 77843 USA. Department of Statistics Texas A&M University College Station TX 77843 USA. Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE School of Statistics East China Normal University 3663 North Zhongshan Road Shanghai 200062 China. Department of Veterinary Integrative Biosciences Texas A&M University College Station TX 77843 USA. Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan. Department of Nutrition and the Program in Integrative Nutrition & Complex Diseases Texas A&M University College Station TX 77843 USA. Electronic address: r-chapkin@tamu.edu. Department of Electrical and Computer Engineering Texas A&M University College Station TX 77843 USA Department of Veterinary Integrative Biosciences Texas A&M University College Station TX 77843 USA Interdisciplinary Program of Genetics Texas A&M University College Station TX 77843 USA. Electronic address: jcai@tamu.edu.

We present scTenifoldXct, a semi-supervised computational tool for detecting ligand-receptor (LR)-mediated cell-cell interactions and mapping cellular communication graphs. Our method is based on manifold alignment, using LR pairs as inter-data correspondences to embed ligand and receptor genes expressed in interacting cells into a unified latent space. Neural networks are employed to minimize the distance between corresponding genes while preserving the structure of gene regression networks. We apply scTenifoldXct to real datasets for testing and demonstrate that our method detects interactions with high consistency compared with other methods. More importantly, scTenifoldXct uncovers weak but biologically relevant interactions overlooked by other methods. We also demonstrate how scTenifoldXct can be used to compare different samples, such as healthy vs. diseased and wild type vs. knockout, to identify differential interactions, thereby revealing functional implications associated with changes in cellular communication status.

关键词： cell-cell interaction cellular communication gene regression network machine learning manifold alignment neural networks scRNA-seq single-cell RNA sequencing

来源：评论

学校读者我要写书评

暂无评论

Two-dimensional Bose-Hubbard model for helium on graphene

arXiv

引用

arXiv 2021年

作者： Yu, Jiangyong Lauricella, Ethan Elsayed, Mohamed Shepherd, Kenneth Nichols, Nathan S. Lombardi, Todd Kim, Sang Wook Wexler, Carlos Vanegas, Juan M. Lakoba, Taras Kotov, Valeri N. Del Maestro, Adrian Department of Physics University of Vermont BurlingtonVT05405 United States Materials Science Program University of Vermont BurlingtonVT05405 United States Department of Physics and Astronomy University of Missouri ColumbiaMO65211 United States Department of Mathematics & Statistics University of Vermont BurlingtonVT05405 United States Department of Physics and Astronomy University of Tennessee KnoxvilleTN37996 United States Min H. Kao Department of Electrical Engineering and Computer Science University of Tennessee KnoxvilleTN37996 United States

An exciting development in the field of correlated systems is the possibility of realizing two-dimensional (2D) phases of quantum matter. For a systems of bosons, an example of strong correlations manifesting themselves in a 2D environment is provided by helium adsorbed on graphene. We construct the effective Bose-Hubbard model for this system which involves hard-core bosons (U ≈ ∞), repulsive nearest-neighbor (V > 0) and small attractive (V0 4He adsorbed on graphene deep in the commensurate solid phase with 1/3 of the sites on the dual triangular lattice occupied. Because the parameters of the effective Bose-Hubbard model are very sensitive to the exact lattice structure, this opens up an avenue to tune quantum phase transitions in this solid-state system. Copyright © 2021, The Authors. All rights reserved.

关键词： Hubbard model

来源：评论

学校读者我要写书评

暂无评论

Spiral wave dynamics in a neuronal network model

arXiv

引用

arXiv 2024年

作者： Souza, Diogo L.M. Borges, Fernando S. Gabrick, Enrique C. Bentivoglio, Lucas E. Protachevicz, Paulo R. dos Santos, Vagner Viana, Ricardo L. Caldas, Ibere L. Iarosz, Kelly C. Batista, Antonio M. Kurths, Jürgen Graduate Program in Sciences State University of Ponta Grossa PR Ponta Grossa84030-900 Brazil Department of Physiology and Pharmacology State University of New York Downstate Health Sciences University BrooklynNY United States Center for Mathematics Computation and Cognition Federal University of ABC SP São Bernardo do Campo09606-045 Brazil Department of Physics Humboldt University Berlin Newtonstraße 15 Berlin12489 Germany Potsdam Institute for Climate Impact Research Telegrafenberg A31 Potsdam14473 Germany Institute of Physics University of São Paulo SP São Paulo05508-090 Brazil Department of Physics Federal University of Paraná PR Curitiba81530-000 Brazil Exact and Natural Sciences and Engineering UNIFATEB University Center PR Telêmaco Borba84266-010 Brazil Department of Physics State University of Ponta Grossa PR Ponta Grossa84030-900 Brazil Department of Mathematics and Statistics State University of Ponta Grossa PR Ponta Grossa84030-900 Brazil

Spiral waves are spatial-temporal patterns that can emerge in different systems as heart tissues, chemical oscillators, ecological networks and the brain. These waves have been identified in the neocortex of turtles, rats, and humans, particularly during sleep-like states. Although their functions in cognitive activities remain until now poorly understood, these patterns are related to cortical activity modulation and contribute to cortical processing. In this work, we construct a neuronal network layer based on the spatial distribution of pyramidal neurons. Our main goal is to investigate how local connectivity and coupling strength are associated with the emergence of spiral waves. Therefore, we propose a trustworthy method capable of detecting different wave patterns, based on local and global phase order parameters. As a result, we find that the range of connection radius (R) plays a crucial role in the appearance of spiral waves. For R syn) greatly influences the pattern transitions for higher R, where spikes and bursts firing patterns can be observed in spiral and non-spiral waves. Finally, we show that for some values of R and gsyn bistable states of wave patterns are obtained. © 2024, CC BY.

关键词： Network layers

来源：评论

学校读者我要写书评

暂无评论

Design and Application Data Collection for integrated evaluation and monitoring

引用

IOP Conference Series: Materials Science and engineering 2021年第1期1088卷

作者： Muhammad Hidayatullah Agus Teguh Wahyudi Shinta Esabella Titi Andriani G Gunawan Faculty of Engineering Universitas Teknologi Sumbawa Indonesia. 84371 Office of Informatics and Statistics Communication Sumbawa Regency Indonesia. Physics Education Study Program Faculty of Teacher Training and Education Universitas Mataram Indonesia. 83125

The process of data collection of tax subjects and objects at the Regional Revenue Agency (BAPENDA) of Sumbawa Regency is still manual, where the subject and tax object data are still managed in the form of records on bookkeeping media and office application office media applications such as Microsoft Excel. The need for an application design as a form of digitizing a manual process in the governance system to improve the effectiveness and efficiency of the agency. The research aims to develop Tax Object Subject Data Collection Applications. The application is built with Web-based programming, using the PHP programming Language and Sublime Text as Tools Editor and MySQL as a data storage medium. The application built can present data collection features related to tax object subject data such as companies, restaurants, hotels, parking and ground water data and can display reports in the form of data related to tax object and object data collection to facilitate the monitoring process in BAPENDA Sumbawa Regency.

关键词：

来源：评论

学校读者我要写书评

暂无评论

DeePN2: A deep learning-based non-Newtonian hydrodynamic model

arXiv

引用

arXiv 2021年

作者： Fang, Lidong Ge, Pei Zhang, Lei Weinan, E. Lei, Huan Department of Computational Mathematics Science and Engineering Michigan State University MI48824 United States School of Mathematical Sciences Institute of Natural Sciences and MOE-LSC Shanghai Jiao Tong University 800 Dongchuan Road Shanghai200240 China Center for Machine Learning Research School of Mathematical Sciences Peking University Beijing100871 China AI for Science Institute Beijing100080 China Department of Mathematics and Program in Applied and Computational Mathematics Princeton University NJ08544 United States Department of Statistics and Probability Michigan State University MI48824 United States

A long standing problem in the modeling of non-Newtonian hydrodynamics of polymeric flows is the availability of reliable and interpretable hydrodynamic models that faithfully encode the underlying micro-scale polymer dynamics. The main complication arises from the long polymer relaxation time, the complex molecular structure and heterogeneous interaction. DeePN2, a deep learning-based non-Newtonian hydrodynamic model, has been proposed and has shown some success in systematically passing the micro-scale structural mechanics information to the macro-scale hydrodynamics for suspensions with simple polymer conformation and bond potential. The model retains a multi-scaled nature by mapping the polymer configurations into a set of symmetry-preserving macro-scale features. The extended constitutive laws for these macro-scale features can be directly learned from the kinetics of their micro-scale counterparts. In this paper, we develop DeePN2 using more complex micro-structural models. We show that DeePN2 can faithfully capture the broadly overlooked viscoelastic differences arising from the specific molecular structural mechanics without human intervention. © 2021, CC BY.

关键词： Non Newtonian flow

来源：评论

学校读者我要写书评

暂无评论

Classification of EEG-based brain connectivity networks in schizophrenia using a multi-domain connectome convolutional neural network

arXiv

引用

arXiv 2019年

作者： Phang, Chun-Ren Ting, Chee-Ming Noman, Fuad Ombao, Hernando School of Biomedical Engineering & Health Sciences Universiti Teknologi Malaysia Skudai Johor81310 Malaysia Statistics Program King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia

Objective: We exploit altered patterns in brain functional connectivity as features for automatic discriminative analysis of neuropsychiatric patients. Deep learning methods have been introduced to functional network classification only very recently for fMRI, and the proposed architectures essentially focused on a single type of connectivity measure. Methods: We propose a deep convolutional neural network (CNN) framework for classification of electroencephalogram (EEG)-derived brain connectome in schizophrenia (SZ). To capture complementary aspects of disrupted connectivity in SZ, we explore combination of various connectivity features consisting of time and frequency-domain metrics of effective connectivity based on vector autoregressive model and partial directed coherence, and complex network measures of network topology. We design a novel multi-domain connectome CNN (MDC-CNN) based on a parallel ensemble of 1D and 2D CNNs to integrate the features from various domains and dimensions using different fusion strategies. Results: Hierarchical latent representations learned by the multiple convolutional layers from EEG connectivity reveal apparent group differences between SZ and healthy controls (HC). Results on a large resting-state EEG dataset show that the proposed CNNs significantly outperform traditional support vector machine classifiers. The MDC-CNN with combined connectivity features further improves performance over single-domain CNNs using individual features, achieving remarkable accuracy of 93.06% with a decision-level fusion. Conclusion: The proposed MDC-CNN by integrating information from diverse brain connectivity descriptors is able to accurately discriminate SZ from HC. Significance: The new framework is potentially useful for developing diagnostic tools for SZ and other disorders. Copyright © 2019, The Authors. All rights reserved.

关键词： Electroencephalography

来源：评论

学校读者我要写书评

暂无评论

Modeling the number of confirmed cases of COVID-19 in East Java using negative binomial regression based on least square spline estimator

引用

AIP Conference Proceedings 2024年第1期3083卷

作者： Arip Ramadan Nur Chamidah I. Nyoman Budiantara Student of Doctoral Study Program of Mathematics and Natural Sciences Faculty of Science and Technology Airlangga University Surabaya 60115 Indonesia. Department of Information System School of Industrial and System Engineering Telkom University Surabaya 60231 Indonesia. Department of Mathematics Faculty of Science and Technology Airlangga University Surabaya 60115 Indonesia. Research Group of Statistical Modeling in Life Science Faculty of Science and Technology Airlangga University Surabaya 60115 Indonesia. Department of Statistics Faculty of Sciences and Data Analytics Sepuluh Nopember Institute of Technology Surabaya 60111 Indonesia.

The East Java Province has experienced a significant surge in number of confirmed cases of COVID-19. This study endeavors to investigate the potential correlation between weather conditions and the incidence of number of confirmed cases of COVID-19 in East Java. To achieve this, a nonparametric regression model, specifically, the Negative Binomial Regression (NBR) model based on the least squares spline estimator, was utilized. The outcomes of the study indicate that the Mean Absolute Percentage Error (MAPE) of nonparametic regression model is 0.30. Meanwhile, the MAPE for the parametric regression model is 0.34. The results show that a nonparametric regression model approach is better than parametric regression model approach. The study establishes that the truncated spline estimator based NBR model represents the best fit, with an MLCV value of -256.71. The findings of the study suggest that a temperature less than 21.75°C is associated with a decrease of 13.28 number of confirmed cases of COVID-19 per each 1°C increase, while a temperature between 21.75°C and 25.78°C is linked to an increase of 6.85 number of confirmed cases of COVID-19 per each 1°C increase. In contrast, a temperature greater than 25.78°C is associated with a decrease of 139.42 number of confirmed cases of COVID-19 per each 1°C increase. Similarly, a wind speed less than 5.57 m/s is related to a decrease of 12.99 number of confirmed cases of COVID-19 per each 1 m/s increase, whereas a wind speed between 5.57 m/s and 8.99 m/s is associated with a decrease of 10.29 number of confirmed cases of COVID-19 per each 1 m/s increase. Furthermore, a wind speed greater than 8.99 m/s is linked to a decrease of 19.16 number of confirmed cases of COVID-19 per each 1 m/s increase. The study provides evidence that higher temperatures and wind speeds result in a slower rise in the incidence of the number of confirmed cases of COVID-19. Consequently, it is recommended that the local government remains vigilant du

关键词：

来源：评论

学校读者我要写书评

暂无评论

Which ads to show? Advertisement image assessment with auxiliary information via multi-step modality fusion

arXiv

引用

arXiv 2019年

作者： Park, Kyung-Wha Lee, JungHoon Kwon, Sunyoung Ha, Jung-Woo Kim, Kyung-Min Zhang, Byoung-Tak Interdisciplinary Program in Neuroscience Seoul National University Statistics and Actuarial Science Soongsil University Clova AI Research NAVER Corp Department of Computer Science and Engineering Seoul National University Surromind Robotics

Assessing aesthetic preference is a fundamental task related to human cognition. It can also contribute to various practical applications such as image creation for online advertisements. Despite crucial influences of image quality, auxiliary information of ad images such as tags and target subjects can also determine image preference. Existing studies mainly focus on images and thus are less useful for advertisement scenarios where rich auxiliary data are available. Here we propose a modality fusion-based neural network that evaluates the aesthetic preference of images with auxiliary information. Our method fully utilizes auxiliary data by introducing multi-step modality fusion using both conditional batch normalization-based low-level and attention-based high-level fusion mechanisms, inspired by the findings from statistical analyses on real advertisement data. Our approach achieved state-of-the-art performance on the AVA dataset, a widely used dataset for aesthetic assessment. Besides, the proposed method is evaluated on large-scale real-world advertisement image data with rich auxiliary attributes, providing promising preference prediction results. Through extensive experiments, we investigate how image and auxiliary information together influence click-through rate. Copyright © 2019, The Authors. All rights reserved.

关键词： Image fusion

来源：评论

学校读者我要写书评

暂无评论

Artificial intelligence for modelling infectious disease epidemics

引用

Nature 2025年第8051期638卷 623-635页

作者： Kraemer, Moritz U. G. Tsui, Joseph L.-H. Chang, Serina Y. Lytras, Spyros Khurana, Mark P. Vanderslott, Samantha Bajaj, Sumali Scheidwasser, Neil Curran-Sebastian, Jacob Liam Semenova, Elizaveta Zhang, Mengyan Unwin, H. Juliette T. Watson, Oliver J. Mills, Cathal Dasgupta, Abhishek Ferretti, Luca Scarpino, Samuel V. Koua, Etien Morgan, Oliver Tegally, Houriiyah Paquet, Ulrich Moutsianas, Loukas Fraser, Christophe Ferguson, Neil M. Topol, Eric J. Duchêne, David A. Stadler, Tanja Kingori, Patricia Parker, Michael J. Dominici, Francesca Shadbolt, Nigel Suchard, Marc A. Ratmann, Oliver Flaxman, Seth Holmes, Edward C. Gomez-Rodriguez, Manuel Schölkopf, Bernhard Donnelly, Christl A. Pybus, Oliver G. Cauchemez, Simon Bhatt, Samir Pandemic Sciences Institute University of Oxford Oxford United Kingdom Department of Biology University of Oxford Oxford United Kingdom Department of Electrical Engineering and Computer Science University of California Berkeley Berkeley CA United States UCSF UC Berkeley Joint Program in Computational Precision Health Berkeley CA United States Division of Systems Virology Department of Microbiology and Immunology The Institute of Medical Science The University of Tokyo Tokyo Japan Section of Epidemiology Department of Public Health University of Copenhagen Copenhagen Denmark Oxford Vaccine Group University of Oxford and NIHR Oxford Biomedical Research Centre Oxford United Kingdom Department of Epidemiology and Biostatistics Imperial College London London United Kingdom Department of Computer Science University of Oxford Oxford United Kingdom School of Mathematics University of Bristol Bristol United Kingdom MRC Centre for Global Infectious Disease Analysis School of Public Health Imperial College London London United Kingdom Department of Statistics University of Oxford Oxford United Kingdom Doctoral Training Centre University of Oxford Oxford United Kingdom Institute for Experiential AI Northeastern University MA Boston Thailand Santa Fe Institute Santa Fe NM United States World Health Organization Regional Office for Africa Brazzaville Congo WHO Hub for Pandemic and Epidemic Intelligence Health Emergencies Programme World Health Organization Berlin Germany Centre for Epidemic Response and Innovation (CERI) School for Data Science and Computational Thinking Stellenbosch University Stellenbosch South Africa African Institute for Mathematical Sciences (AIMS) South Africa Muizenberg Cape Town South Africa Genomics England London United Kingdom Scripps Research La Jolla CA United States Department of Biosystems Science and Engineering ETH Zürich Basel Switzerland Swiss Institute of Bioinformatics Lausanne Switzerland The Ethox Centre Nuffield

Infectious disease threats to individual and public health are numerous, varied and frequently unexpected. Artificial intelligence (AI) and related technologies, which are already supporting human decision making in economics, medicine and social science, have the potential to transform the scope and power of infectious disease epidemiology. Here we consider the application to infectious disease modelling of AI systems that combine machine learning, computational statistics, information retrieval and data science. We first outline how recent advances in AI can accelerate breakthroughs in answering key epidemiological questions and we discuss specific AI methods that can be applied to routinely collected infectious disease surveillance data. Second, we elaborate on the social context of AI for infectious disease epidemiology, including issues such as explainability, safety, accountability and ethics. Finally, we summarize some limitations of AI applications in this field and provide recommendations for how infectious disease epidemiology can harness most effectively current and future developments in AI. © Springer Nature Limited 2025.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Inferring a network from dynamical signals at its nodes

arXiv

引用

arXiv 2020年

作者： Weistuch, Corey Agozzino, Luca Mujica-Parodi, Lilianne R. Dill, Ken Laufer Center for Physical and Quantitative Biology Stony Brook University Department of Applied Mathematics and Statistics Stony Brook University Department of Physics and Astronomy Stony Brook University Department of Biomedical Engineering Stony Brook University Program in Neuroscience Stony Brook University Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Harvard Medical School Department of Chemistry Stony Brook University

We give an approximate solution to the difficult inverse problem of inferring the topology of an unknown network from given time-dependent signals at the nodes. For example, we measure signals from individual neurons in the brain, and infer how they are inter-connected. We use Maximum Caliber as an inference principle. The combinatorial challenge of high-dimensional data is handled using two different approximations to the pairwise couplings. We show two proofs of principle: in a nonlinear genetic toggle switch circuit, and in a toy neural network. Copyright © 2020, The Authors. All rights reserved.

关键词： Toys

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：