检索结果-内蒙古大学图书馆

25th IEEE International Symposium on Safety, Security, and Rescue Robotics (IEEE SSRR)

作者： Salathe, Marco Quiter, Brian J. Bandstra, Mark S. Chen, Xin Negut, Victor Folsom, Micah Weber, Gunther H. Greulich, Christopher Swinney, Mathew Prins, Nicholas Archer, Daniel E. Lawrence Berkeley Natl Lab Appl Nucl Phys Program One Cyclotron Rd Berkeley CA 94720 USA Lawrence Berkeley Natl Lab Comp Sci Res Div Machine Learning & Analyt Grp Berkeley CA USA Oak Ridge Natl Lab POB 2008 Oak Ridge TN 37831 USA

ISBN: (纸本)9781665456807

A handheld system developed to digitize a contextual understanding of the scene at a chemical, biological, radiological, nuclear and/or explosives (CBRNE) events is described. The system uses LiDAR and cameras to create a colorized 3D model of the environment, which helps domain experts that are supporting responders in the field. To generate the digitized model, a responder scans any suspicious objects and the surroundings by carrying the system through the scene. The scanning system provides a real-time user interface to inform the user about scanning progress and to indicate any areas that may have been missed either by the LiDAR sensors or the cameras. Currently, the collected data are post-processed on a different device, building a colorized triangular mesh of the encountered scene, with the intention of moving this pipeline to the scanner at a later point. The mesh is sufficiently compressed to be sent over a reduced bandwidth connection to a remote analyst. Furthermore, the system tracks fiducial markers attached to diagnostic equipment that is placed around the suspicious object. The resulting tracking information can be transmitted to remote analysts to further facilitate their supporting efforts. The paper will discuss the system's design, software components, the user interface used for scanning a scene, the necessary procedures for calibration of the sensors, and the processing steps of the resulting data. The discussion will close by evaluating the system's performance on 11 scenes.

关键词： User interfaces

来源：评论

学校读者我要写书评

暂无评论

A machine learning approach to infer on-street parking occupancy based on parking meter transactions 23

A machine learning approach to infer on-street parking occup...

引用

23rd IEEE International Conference on Intelligent Transportation Systems (ITSC)

作者： Sonntag, Jonas Schmidt-Thieme, Lars Univ Hildesheim Volkswagen Financial Serv Researchers Program Informat Syst & Machine Learning Lab D-31141 Hildesheim Germany Univ Hildesheim Informat Syst & Machine Learning Lab D-31141 Hildesheim Germany

ISBN: (纸本)9781728141497

Cruising for parking is not only stressful task for most drivers but also increases congestion and emissions. Therefore smart parking guidance systems are gaining increasing interest from researchers and city councils. These systems mostly rely on expensive and not well scalable technology like real time parking sensors or camera systems. In this paper we propose a deep learning architecture that predicts the current number of parking cars at different locations based on digital meter payment transactions. We outperform simple baseline models as well as a state of the art probabilistic approach from the literature. Transactional data does not directly translate to parking occupancy since not all people stick to their paid duration or pay at all. We therefore discuss the reliability of our method on different datasets and spatial granularities. Although our model is not as reliable as sensor data, especially for small parking zones, we find that our methodology provides an inexpensive way of inferring on-street parking occupancy and enable meaningful smart parking services.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Rigid and Non-Rigid Motion Compensation in Weight-Bearing CBCT of the Knee Using Simulated Inertial Measurements

引用

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 2022年第5期69卷 1608-1619页

作者： Maier, Jennifer Nitschke, Marlies Choi, Jang-Hwan Gold, Garry Fahrig, Rebecca Eskofier, Bjoern M. Maier, Andreas Friedrich Alexander Univ Erlangen Nurnberg FAU Pattern Recognit Lab D-91058 Erlangen Germany Friedrich Alexander Univ Erlangen Nurnberg FAU Machine Learning & Data Analyt Lab D-91052 Erlangen Germany Ewha Womans Univ Grad Program Syst Hlth Sci & Engn Div Mech & Biomed Engn Seoul South Korea Stanford Univ Sch Med Dept Radiol Stanford CA 94305 USA Siemens Healthcare GmbH Erlangen Germany

Objective: Involuntary subject motion is the main source of artifacts in weight-bearing cone-beam CT of the knee. To achieve image quality for clinical diagnosis, the motion needs to be compensated. We propose to use inertial measurement units (IMUs) attached to the leg for motion estimation. Methods: We perform a simulation study using real motion recorded with an optical tracking system. Three IMU-based correction approaches are evaluated, namely rigid motion correction, non-rigid 2D projection deformation and non-rigid 3D dynamic reconstruction. We present an initialization process based on the system geometry. With an IMU noise simulation, we investigate the applicability of the proposed methods in real applications. Results: All proposed IMU-based approaches correct motion at least as good as a state-of-the-art marker-based approach. The structural similarity index and the root mean squared error between motion-free and motion corrected volumes are improved by 24-35% and 78-85%, respectively, compared with the uncorrected case. The noise analysis shows that the noise levels of commercially available IMUs need to be improved by a factor of 10(5) which is currently only achieved by specialized hardware not robust enough for the application. Conclusion: Our simulation study confirms the feasibility of this novel approach and defines improvements necessary for a real application. Significance: The presented work lays the foundation for IMU-based motion compensation in cone-beam CT of the knee and creates valuable insights for future developments.

关键词： CT reconstruction inertial measurements motion compensation noise non-rigid motion

来源：评论

学校读者我要写书评

暂无评论

GENE REGULATORY NETWORK INFERENCE IN THE PRESENCE OF DROPOUTS: A CAUSAL VIEW

arXiv

引用

arXiv 2024年

作者： Dai, Haoyue Ng, Ignavier Luo, Gongxu Spirtes, Peter Stojanov, Petar Zhang, Kun Department of Philosophy Carnegie Mellon University United States Machine Learning Department Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates Cancer Program Eric and Wendy Schmidt Center Broad Institute of MIT and Harvard United States

Gene regulatory network inference (GRNI) is a challenging problem, particularly owing to the presence of zeros in single-cell RNA sequencing data: some are biological zeros representing no gene expression, while some others are technical zeros arising from the sequencing procedure (aka dropouts), which may bias GRNI by distorting the joint distribution of the measured gene expressions. Existing approaches typically handle dropout error via imputation, which may introduce spurious relations as the true joint distribution is generally unidentifiable. To tackle this issue, we introduce a causal graphical model to characterize the dropout mechanism, namely, Causal Dropout Model. We provide a simple yet effective theoretical result: interestingly, the conditional independence (CI) relations in the data with dropouts, after deleting the samples with zero values (regardless if technical or not) for the conditioned variables, are asymptotically identical to the CI relations in the original data without dropouts. This particular test-wise deletion procedure, in which we perform CI tests on the samples without zeros for the conditioned variables, can be seamlessly integrated with existing structure learning approaches including constraint-based and greedy score-based methods, thus giving rise to a principled framework for GRNI in the presence of dropouts. We further show that the causal dropout model can be validated from data, and many existing statistical models to handle dropouts fit into our model as specific parametric instances. Empirical evaluation on synthetic, curated, and real-world experimental transcriptomic data comprehensively demonstrate the efficacy of our method. Copyright © 2024, The Authors. All rights reserved.

关键词： Gene expression

来源：评论

学校读者我要写书评

暂无评论

Topological data analysis of pattern formation of human induced pluripotent stem cell colonies

引用

SCIENTIFIC REPORTS 2025年第1期15卷 1-18页

作者： Hartsock, Iryna Park, Eunbi Toppen, Jack Bubenik, Peter Dimitrova, Elena S. Kemp, Melissa L. Cruz, Daniel A. H Lee Moffitt Canc Ctr & Res Inst Dept Machine Learning Tampa FL 33612 USA Georgia Inst Technol Sch Biol Sci Atlanta GA 30332 USA MIT Program Computat & Syst Biol Cambridge MA 02139 USA Univ Florida Dept Math Gainesville FL 32611 USA Calif Polytech State Univ San Luis Obispo Math Dept San Luis Obispo CA 93407 USA Georgia Inst Technol Wallace H Coulter Dept Biomed Engn Atlanta GA 30332 USA Emory Univ Atlanta GA 30332 USA Univ Florida Dept Med Gainesville FL 32611 USA

Understanding the multicellular organization of stem cells is vital for determining the mechanisms that coordinate cell fate decision-making during differentiation;these mechanisms range from neighbor-to-neighbor communication to tissue-level biochemical gradients. Current methods for quantifying multicellular patterning tend to capture the spatial properties of cell colonies at a fixed scale and typically rely on human annotation. We present a computational pipeline that utilizes topological data analysis to generate quantitative, multiscale descriptors which capture the shape of data extracted from 2D multichannel microscopy images. By applying our pipeline to certain stem cell colonies, we detected subtle differences in patterning that reflect distinct spatial organization associated with loss of pluripotency. These results yield insight into putative directed cellular organization and morphogen-mediated, neighbor-to-neighbor signaling. Because of its broad applicability to immunofluorescence microscopy images, our pipeline is well-positioned to serve as a general-purpose tool for the quantitative study of multicellular pattern formation.

关键词： Pluripotent stem cells Microscopy images Cell pattern formation Topological data analysis Persistence landscapes

来源：评论

学校读者我要写书评

暂无评论

DeepGenePrior: A deep learning model for prioritizing genes affected by copy number variants

引用

PLOS COMPUTATIONAL BIOLOGY 2023年第7期19卷 e1011249-e1011249页

作者： Rahaie, Zahra Rabiee, Hamid R. Alinejad-Rokny, Hamid Sharif Univ Technol Dept Comp Engn BCB Grp DML Tehran Iran UNSW Sydney UNSW Biomed Machine Learning Lab BML Grad Sch Biomed Engn Sydney Australia Macquarie Univ Ctr Appl Artificial Intelligence Hlth Data Analyt Program Sydney Australia

The genetic etiology of brain disorders is highly heterogeneous, characterized by abnormalities in the development of the central nervous system that lead to diminished physical or intellectual capabilities. The process of determining which gene drives disease, known as "gene prioritization," is not entirely understood. Genome-wide searches for gene-disease associations are still underdeveloped due to reliance on previous discoveries and evidence sources with false positive or negative relations. This paper introduces DeepGenePrior, a model based on deep neural networks that prioritizes candidate genes in genetic diseases. Using the well-studied Variational AutoEncoder (VAE), we developed a score to measure the impact of genes on target diseases. Unlike other methods that use prior data to select candidate genes, based on the "guilt by association" principle and auxiliary data sources like protein networks, our study exclusively employs copy number variants (CNVs) for gene prioritization. By analyzing CNVs from 74,811 individuals with autism, schizophrenia, and developmental delay, we identified genes that best distinguish cases from controls. Our findings indicate a 12% increase in fold enrichment in brain-expressed genes compared to previous studies and a 15% increase in genes associated with mouse nervous system phenotypes. Furthermore, we identified common deletions in ZDHHC8, DGCR5, and CATG00000022283 among the top genes related to all three disorders, suggesting a common etiology among these clinically distinct conditions. DeepGenePrior is publicly available online at to address obstacles in existing gene prioritization studies identifying candidate genes.

关键词： Gene ontologies Schizophrenia Autism spectrum disorder Autism Ontologies Nervous system machine learning Phenotypes

来源：评论

学校读者我要写书评

暂无评论

learning characteristics of graph neural networks predicting protein-ligand affinities

引用

NATURE machine INTELLIGENCE 2023年第12期5卷 1427-1436页

作者： Mastropietro, Andrea Pasculli, Giuseppe Bajorath, Juergen Sapienza Univ Rome Dept Comp Control & Management Engn Antonio Rubert Rome Italy Rhein Friedrich Wilhelms Univ Dept Life Sci Informat LIMES Program Unit Chem Biol & Med Chem B IT Bonn Germany Lamarr Inst Machine Learning & Artificial Intellig Bonn Germany

In drug design, compound potency prediction is a popular machine learning application. Graph neural networks (GNNs) predict ligand affinity from graph representations of protein-ligand interactions typically extracted from X-ray structures. Despite some promising findings leading to claims that GNNs can learn details of protein-ligand interactions, such predictions are also controversially viewed. For example, evidence has been presented that GNNs might not learn protein-ligand interactions but memorize ligand and protein training data instead. We have carried out affinity predictions with six GNN architectures on community-standard datasets and rationalized the predictions using explainable artificial intelligence. The results confirm a strong influence of ligand-but not protein-memorization during GNN learning and also show that some GNN architectures increasingly prioritize interaction information for predicting high affinities. Thus, while GNNs do not comprehensively account for protein-ligand interactions and physical reality, depending on the model, they balance ligand memorization with learning of interaction patterns. Graph neural networks have proved useful in modelling proteins and their ligand interactions, but it is not clear whether the patterns they identify have biological relevance or whether interactions are merely memorized. Mastropietro et al. use a Shapley value-based method to identify important edges in protein interaction graphs, enabling explanatory analysis of the model mechanisms.

关键词： Ligands

来源：评论

学校读者我要写书评

暂无评论

Long-term exposure to PM2.5 and mortality in a national cohort in South Korea: effect modification by community deprivation, medical infrastructure, and greenness

引用

BMC PUBLIC HEALTH 2024年第1期24卷 1页

作者： Byun, Garam Kim, Sera Choi, Yongsoo Kim, Ayoung Lee, Jong-Tae Bell, Michelle L. Yale Univ Sch Environm New Haven CT 06511 USA Korea Univ Dept Publ Hlth Sci Interdisciplinary Program Precis Publ Hlth Grad Sch Seoul 02841 South Korea Seoul Natl Univ Grad Sch Publ Hlth Dept Publ Hlth Sci 1 Gwanak ro Seoul 08826 South Korea Seoul Natl Univ Ai Machine learning Stat Collaborat Res Ensemble A Seoul South Korea Pusan Natl Univ Seoul South Korea Korea Univ Coll Hlth Sci Sch Hlth Policy & Management Hana Sci Hall145 Anam ro Seoul 02841 South Korea

Background Long-term exposure to PM2.5 has been linked to increased mortality risk. However, limited studies have examined the potential modifying effect of community-level characteristics on this association, particularly in Asian contexts. This study aimed to estimate the effects of long-term exposure to PM2.5 on mortality in South Korea and to examine whether community-level deprivation, medical infrastructure, and greenness modify these associations. Methods We conducted a nationwide cohort study using the National Health Insurance Service-National Sample Cohort. A total of 394,701 participants aged 30 years or older in 2006 were followed until 2019. Based on modelled PM2.5 concentrations, 1 to 3-year and 5-year moving averages of PM2.5 concentrations were assigned to each participant at the district level. Time-varying Cox proportional-hazards models were used to estimate the association between PM2.5 and non-accidental, circulatory, and respiratory mortality. We further conducted stratified analysis by community-level deprivation index, medical index, and normalized difference vegetation index to represent greenness. Results PM2.5 exposure, based on 5-year moving averages, was positively associated with non-accidental (Hazard ratio, HR: 1.10, 95% Confidence Interval, CI: 1.01, 1.20, per 10 mu g/m(3) increase) and circulatory mortality (HR: 1.22, 95% CI: 1.01, 1.47). The 1-year moving average of PM2.5 was associated with respiratory mortality (HR: 1.33, 95% CI: 1.05, 1.67). We observed higher associations between PM2.5 and mortality in communities with higher deprivation and limited medical infrastructure. Communities with higher greenness showed lower risk for circulatory mortality but higher risk for respiratory mortality in association with PM2.5. Conclusions Our study found mortality effects of long-term PM2.5 exposure and underlined the role of community-level factors in modifying these association. These findings highlight the importance of considering so

关键词： PM2.5 Mortality Cohort study Deprivation index Medical infrastructure Greenness

来源：评论

学校读者我要写书评

暂无评论

Single-cell RNA-sequencing and subcellular spatial transcriptomics facilitate the translation of liver microphysiological systems for regulatory application

引用

Journal of Pharmaceutical Analysis 2023年第7期13卷 691-693页

作者： Dan Li Zhou Fang Qiang Shi Nicholas Zhang Binsheng Gong Weida Tong Ahmet F.Coskun Joshua Xu Division of Bioinformatics and Biostatistics National Center for Toxicological ResearchU.S.Food and Drug AdministrationJeffersonAR72079USA Wallace H.Coulter Department of Biomedical Engineering Georgia Institute of Technology and Emory UniversityAtlantaGA30332USA Machine Learning Graduate Program Georgia Institute of TechnologyAtlantaGA30332USA Division of Systems Biology National Center for Toxicological ResearchU.S.Food and Drug AdministrationJeffersonAR72079USA Interdisciplinary Bioengineering Graduate Program Georgia Institute of TechnologyAtlantaGA30332USA Parker H.Petit Institute for Bioengineering and Bioscience Georgia Institute of TechnologyAtlantaGA30332USA

Reducing the use of animal models in drug development and safety assessment has long been supported by the *** and Drug Administration(FDA).The report by Royal Society for the Prevention of Cruelty to Animals indicates that in 2020,experiments involved the use of over 100 million animals,with the United States leading the list by utilizing 20 million *** ethical considerations associated with animal testing and the costs in terms of time and money,animal models are not always effective in predicting human reactions to drug *** animal testing has been the traditional method for assessing the safety and efficacy of drugs.

关键词： money utilizing testing

来源：评论

学校读者我要写书评

暂无评论

Meta-learning for transformer-based prediction of potent compounds

引用

SCIENTIFIC REPORTS 2023年第1期13卷 1-13页

作者： Chen, Hengwei Bajorath, Juergen Rhein Friedrich Wilhelms Univ Lamarr Inst Machine Learning & Artificial Intelli Dept Life Sci Informat & Data Sci LIMES Program Unit Chem Biol & Med ChemB IT Friedrich Hirzebruch Allee 5-6 Bonn D-53115 Germany

For many machine learning applications in drug discovery, only limited amounts of training data are available. This typically applies to compound design and activity prediction and often restricts machine learning, especially deep learning. For low-data applications, specialized learning strategies can be considered to limit required training data. Among these is meta-learning that attempts to enable learning in low-data regimes by combining outputs of different models and utilizing meta-data from these predictions. However, in drug discovery settings, meta-learning is still in its infancy. In this study, we have explored meta-learning for the prediction of potent compounds via generative design using transformer models. For different activity classes, meta-learning models were derived to predict highly potent compounds from weakly potent templates in the presence of varying amounts of fine-tuning data and compared to other transformers developed for this task. Meta-learning consistently led to statistically significant improvements in model performance, in particular, when fine-tuning data were limited. Moreover, meta-learning models generated target compounds with higher potency and larger potency differences between templates and targets than other transformers, indicating their potential for low-data compound design.

关键词：

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：