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494P Evaluation of transcriptome forward computational strategies to improve molecular diagnosis in rare pediatric neuromuscular disease

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Neuromuscular Disorders 2024年 43卷 104441.331-104441.331页

作者： Silverstein, S. Ganapathy, K. Donkervoort, S. Moy, J. Uapinyoying, B. Gorokhova, S. Ganesh, V. Weisburd, B. Orbach, R. Foley, A. Mohammadi, P. Adams, D. Bonnemann, C. Neuromuscular and Neurogenetics Disorders of Childhood Section NINDS NIH Bethesda USA Undiagnosed Diseases Program NIH Bethesda USA Rutgers New Jersey Medical School Newark USA Department of Integrative Structural and Computational Biology Scripps Research Institute La Jolla USA Graduate College of Computing and Data Science Boston University Boston USA Center for Genetic Medicine Research Children's Research Institute Children's National Hospital Washington DC USA Aix Marseille Univ INSERM MMG Marseille France Department of Medical Genetics Timone Children's Hospital Marseille France The Broad Institute of MIT and Harvard Boston USA Center for Immunity and Immunotherapies Seattle Children's Research Institute Seattle USA Department of Pediatrics University of Washington School of Medicine Seattle USA Department of Genome Sciences University of Washington Seattle USA

Pediatric neuromuscular diseases are a genetically and clinically heterogenous group of disorders of which 30-60% remain molecularly undiagnosed even after evaluation with exome or genome sequencing. Estimates for added diagnostic benefit of transcriptomic sequencing range between 7-32% across different disease groups. Recently, open-source computational tools designed to systematically survey transcriptomic sequencing data for aberrant events have become available. In this study we assess the sensitivity of DROP (OUTRIDER, FRASER and MAE), MINTIE, LeafcutterMD, and ANEVA-H to detect known outliers in pediatric neuromuscular cohorts in order to identify the optimal set of tools for diagnostic use. Each tool selected utilizes a different statistical strategy and experimental design geared for rare disease. Tools were run on three transcriptome cohorts from the NINDS Neuromuscular and Neurogenetic Diseases of Childhood Section: muscle tissue (n=71), cycloheximide treated fibroblasts (n=79) and untreated fibroblasts (n=37). For splicing tools, MINTIE is the best performing tool (recall rate = 9/21, 42%), followed by FRASER (recall rate = 16/41, 36%), then LeafcutterMD (recall rate = 0/41, 0%). For expression outliers, OUTRIDER detects 6% of true positives (1/15), while the addition of healthy GTEx controls does not improve detection. For monoallelic expression, MAE and ANEVA-H will be compared. Additional evaluations to identify the optimal RNA library preparation methodology for these tools is also in progress. Thus far, OUTRIDER yielded one new candidate diagnosis of TRIP4 related disease. Analysis for new candidate variants is ongoing. Based on these results, and those of ongoing experiments, we will provide recommendations for the use of these tools in diagnostic pipelines for rare pediatric neuromuscular disease.

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arXiv

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arXiv 2019年

作者： Liao, Hao Liu, Ming-Kai Mariani, Manuel Sebastian Zhou, Mingyang Wu, Xingtong National Engineering Laboratory for Big Data System Computing Technology Guangdong Province Key Laboratory of Popular High Performance Computers College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China Chengdu610051 China URPP Social Networks Universität Zürich CH-8050 Switzerland

When investigating the spreading of a piece of information or the diffusion of an innovation, we often lack information on the underlying propagation network. Reconstructing the hidden propagation paths based on the observed diffusion process is a challenging problem which has recently attracted attention from diverse research fields. To address this reconstruction problem, based on static similarity metrics commonly used in the link prediction literature, we introduce new node-node temporal similarity metrics. The new metrics take as input the time-series of multiple independent spreading processes, based on the hypothesis that two nodes are more likely to be connected if they were often infected at similar points in time. This hypothesis is implemented by introducing a time-lag function which penalizes distant infection times. We find that the choice of this time-lag strongly affects the metrics' reconstruction accuracy, depending on the network's clustering coefficient and we provide an extensive comparative analysis of static and temporal similarity metrics for network reconstruction. Our findings shed new light on the notion of similarity between pairs of nodes in complex networks. Copyright © 2019, The Authors. All rights reserved.

关键词： Information services

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A survey on edge computing systems and tools

arXiv

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arXiv 2019年

作者： Liu, Fang Tang, Guoming Li, Youhuizi Cai, Zhiping Zhang, Xingzhou Zhou, Tongqing School of Data and Computer Science Sun Yat-sen University GuangzhouGuangdong China Key Laboratory of Science and Technology on Information System Engineering National University of Defense Technology ChangshaHunan China School of Compute Science and Technology Hangzhou Dianzi University China College of Computer National University of Defense Technology ChangshaHunan China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences China

—Driven by the visions of Internet of Things and 5G communications, the edge computing systems integrate computing, storage and network resources at the edge of the network to provide computing infrastructure, enabling developers to quickly develop and deploy edge applications. Nowadays the edge computing systems have received widespread attention in both industry and academia. To explore new research opportunities and assist users in selecting suitable edge computing systems for specific applications, this survey paper provides a comprehensive overview of the existing edge computing systems and introduces representative projects. A comparison of open source tools is presented according to their applicability. Finally, we highlight energy efficiency and deep learning optimization of edge computing systems. Open issues for analyzing and designing an edge computing system are also studied in this survey. Copyright © 2019, The Authors. All rights reserved.

关键词： Energy efficiency

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Multi-scale quantum harmonic oscillator algorithm with individual stabilization strategy 9th

Multi-scale quantum harmonic oscillator algorithm with indiv...

引用

9th International Conference on Swarm Intelligence, ICSI 2018

作者： Wang, Peng Li, Bo Jin, Jin Mu, Lei Xin, Gang Huang, Yan Ye, Xing Gui College of Computer Science and Technology Southwest Minzu University Chengdu610225 China Chengdu Institution of Computer Application of China Academy of Science Chengdu610041 China University of Chinese Academy of Sciences Beijing100049 China Huaiyin Normal University Huaiyin223300 China Cloud Computing and Big Data Center China Unicom Fujian Branch Fuzhou350007 China

ISBN: (纸本)9783319938141

Multi-scale quantum harmonic oscillator algorithm (MQHOA) is a novel global optimization algorithm inspired by wave function of quantum mechanics. In this paper, a MQHOA with individual stabilization strategy (IS-MQHOA) is proposed utilizing the individual steady criterion instead of the group statistics. The proposed strategy is more rigorous for the particles in the energy level stabilization process. A more efficient search takes place in the search space made by the particles and improves the exploration ability and the robustness of the algorithm. To verify its performance, numerical experiments are conducted to compare the proposed algorithm with the state-of-the-art SPSO2011 and QPSO. The experimental results show the superiority of the proposed approach on benchmark functions. © Springer International Publishing AG, part of Springer Nature 2018.

关键词： Stabilization

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U-Net-and-a-half: Convolutional network for biomedical image segmentation using multiple expert-driven annotations

arXiv

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arXiv 2021年

作者： Zhang, Yichi Kers, Jesper Cassol, Clarissa A. Roelofs, Joris J. Idrees, Najia Farber, Alik Haroon, Samir Daly, Kevin P. Ganguli, Suvranu Chitalia, Vipul C. Kolachalama, Vijaya B. Section of Computational Biomedicine Department of Medicine Boston University School of Medicine BostonMA02118 United States Department of Pathology Academic Medical Center Meibergdreef 9 Amsterdam1105 AZ Netherlands Department of Pathology Leiden University Medical Center Albinusdreef 2 Leiden2333 ZA Netherlands Arkana Laboratories Little RockAR72211 United States Amsterdam Cardiovascular Sciences University of Amsterdam Netherlands Renal Section Department of Medicine Boston University School of Medicine BostonMA02118 United States Department of Surgery Boston University School of Medicine BostonMA02118 United States Department of Radiology Boston University School of Medicine BostonMA02118 United States Veterans Affairs Boston Healthcare System BostonMA02118 United States Department of Computer Science College of Arts and Sciences Boston University MA02215 United States University of Amsterdam Science Park 904 Amsterdam1098 XH Netherlands Faculty of Computing and Data Sciences Boston University BostonMA02215 United States

Development of deep learning systems for biomedical segmentation often requires access to expert-driven, manually annotated datasets. If more than a single expert is involved in the annotation of the same images, then the inter-expert agreement is not necessarily perfect, and no single expert's annotation can precisely capture the so-called ground truth of the regions of interest on all images. Also, it is not trivial to generate a reference estimate using annotations from multiple experts. Here we present a deep neural network, defined as U-Net-and-a-half, which can simultaneously learn from annotations performed by multiple experts on the same set of images. U-Net-and-a-half contains a convolutional encoder to generate features from the input images, multiple decoders that allow simultaneous learning from image masks obtained from annotations that were independently generated by multiple experts, and a shared lowdimensional feature space. To demonstrate the applicability of our framework, we used two distinct datasets from digital pathology and radiology, respectively. Specifically, we trained two separate models using pathologist-driven annotations of glomeruli on whole slide images of human kidney biopsies (n=10 patients), and radiologist-driven annotations of lumen crosssections of human arteriovenous fistulae obtained from intravascular ultrasound images (n=10 patients), respectively. The models based on U-Net-and-a-half exceeded the performance of the traditional U-Net models trained on single expert annotations alone, thus expanding the scope of multitask learning in the context of biomedical image *** Codes 68U10 © 2021, CC BY.

关键词： Deep neural networks

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Environment scan of generative AI infrastructure for clinical and translational science

Npj health systems

引用

Npj health systems 2025年第1期2卷 4页

作者： Betina Idnay Zihan Xu William G Adams Mohammad Adibuzzaman Nicholas R Anderson Neil Bahroos Douglas S Bell Cody Bumgardner Thomas Campion Mario Castro James J Cimino I Glenn Cohen David Dorr Peter L Elkin Jungwei W Fan Todd Ferris David J Foran David Hanauer Mike Hogarth Kun Huang Jayashree Kalpathy-Cramer Manoj Kandpal Niranjan S Karnik Avnish Katoch Albert M Lai Christophe G Lambert Lang Li Christopher Lindsell Jinze Liu Zhiyong Lu Yuan Luo Peter McGarvey Eneida A Mendonca Parsa Mirhaji Shawn Murphy John D Osborne Ioannis C Paschalidis Paul A Harris Fred Prior Nicholas J Shaheen Nawar Shara Ida Sim Umberto Tachinardi Lemuel R Waitman Rosalind J Wright Adrian H Zai Kai Zheng Sandra Soo-Jin Lee Bradley A Malin Karthik Natarajan W Nicholson Price Ii Rui Zhang Yiye Zhang Hua Xu Jiang Bian Chunhua Weng Yifan Peng Department of Biomedical Informatics Columbia University Irving Medical Center New York NY USA. Department of Population Health Sciences Weill Cornell Medicine New York NY USA. Department of Pediatrics Boston Medical Center Boston MA USA Chobanian & Avedisian School of Medicine Boston University Boston MA USA. Oregon Clinical and Translational Research Institute Oregon Health and Science University Portland OR USA. Department of Public Health Sciences University of California Davis Davis CA USA. Keck School of Medicine University of Southern California Los Angeles CA USA. Department of Medicine David Geffen School of Medicine University of California Los Angeles Los Angeles CA USA. Department of Pathology and Laboratory Medicine University of Kentucky College of Medicine Lexington KY USA. Clinical and Translational Science Center Weill Cornell Medicine New York NY USA. Division of Pulmonary Critical Care and Sleep Medicine University of Kansas School of Medicine Kansas City KS USA. Department of Biomedical Informatics and Data Science Heersink School of Medicine University of Alabama Birmingham AL USA. Harvard Law School Petrie-Flom Center for Health Law Policy Biotechnology and Bioethics Harvard University Cambridge MA USA. Department of Biomedical Informatics University at Buffalo Buffalo NY USA. Center for Clinical and Translational Science Mayo Clinic Rochester MN USA. Technology and Digital Solutions Stanford Medicine Stanford University Stanford CA USA. Center for Biomedical Informatics Rutgers Cancer Institute New Brunswick NJ USA. Department of Learning Health Sciences University of Michigan Medical School Ann Arbor MI USA. Altman Clinical and Translational Research Institute (ACTRI) University of California San Diego La Jolla CA USA. Department of Biostatistics and Health Data Science School of Medicine Indiana University Indianapolis IN USA. Department of Ophthalmology CCTSI University of Colorado Aurora CO USA. Center for Clinic

This study reports a comprehensive environmental scan of the generative AI (GenAI) infrastructure in the national network for clinical and translational science across 36 institutions supported by the CTSA Program led by the National Center for Advancing Translational sciences (NCATS) of the National Institutes of Health (NIH) at the United States. Key findings indicate a diverse range of institutional strategies, with most organizations in the experimental phase of GenAI deployment. The results underscore the need for a more coordinated approach to GenAI governance, emphasizing collaboration among senior leaders, clinicians, information technology staff, and researchers. Our analysis reveals that 53% of institutions identified data security as a primary concern, followed by lack of clinician trust (50%) and AI bias (44%), which must be addressed to ensure the ethical and effective implementation of GenAI technologies.

关键词： Health services Public health

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PnP-AdaNet: Plug-and-play adversarial domain adaptation network with a benchmark at cross-modality cardiac segmentation

arXiv

引用

arXiv 2018年

作者： Dou, Qi Ouyang, Cheng Chen, Cheng Chen, Hao Glocker, Ben Zhuang, Xiahai Heng, Pheng-Ann Department of Computer Science and Engineering Chinese University of Hong Kong Department of Computing Imperial College London Department of Electrical Engineering and Computer Science University of Michigan Department of Computing Imperial College London Department of Computer Science and Engineering Chinese University of Hong Kong Imsight Medical Technology Co Ltd. China Department of Computing Imperial College London School of Data Science Fudan University China

Deep convolutional networks have demonstrated the state-of-the-art performance on various medical image computing tasks. Leveraging images from different modalities for the same analysis task holds clinical benefits. However, the generalization capability of deep models on test data with different distributions remain as a major challenge. In this paper, we propose the PnP-AdaNet (plug-and-play adversarial domain adaptation network) for adapting segmentation networks between different modalities of medical images, e.g., MRI and CT. We propose to tackle the significant domain shift by aligning the feature spaces of source and target domains in an unsupervised manner. Specifically, a domain adaptation module flexibly replaces the early encoder layers of the source network, and the higher layers are shared between domains. With adversarial learning, we build two discriminators whose inputs are respectively multi-level features and predicted segmentation masks. We have validated our domain adaptation method on cardiac structure segmentation in unpaired MRI and CT. The experimental results with comprehensive ablation studies demonstrate the excellent efficacy of our proposed PnP-AdaNet. Moreover, we introduce a novel benchmark on the cardiac dataset for the task of unsupervised cross-modality domain adaptation. We will make our code and database publicly available, aiming to promote future studies on this challenging yet important research topic in medical imaging. Copyright © 2018, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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A compact frozen-spin trap for the search for the electric dipole moment of the muon

arXiv

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arXiv 2025年

作者： Adelmann, A. Bainbridge, A.R. Bailey, I. Baldini, A. Basnet, S. Berger, N. Calzolaio, C. Caminada, L. Cavoto, G. Cei, F. Chakraborty, R. Barajas, C. Chavez Chiappini, M. Crivellin, A. Dutsov, C. Ebrahimi, A. Francesconi, M. Galli, L. Gallucci, G. Giovannozzi, M. Goyal, H. Grassi, M. Gurgone, A. Hildebrandt, M. Hoferichter, M. Höhl, D. Hu, T. Hume, T. Jaeger, J.A. Juknevicius, P. Kästli, H.C. Keshavarzi, A. Khaw, K.S. Kirch, K. Kozlinskiy, A. Lancaster, M. Märkisch, B. Morvaj, L. Papa, A. Paraliev, M. Pasciuto, D. Price, J. Renga, F. Sakurai, M. Sanz-Becerra, D. Schmidt-Wellenburg, P. Shang, Y.Z. Takeuchi, Y. Tegano, M.E. Teubner, T. Trillaud, F. Uglietti, D. Vasilkova, D. Venturini, A. Vitali, B. Voena, C. Vossebeld, J. Wauters, F. Wong, G.M. Zeng, Y. ETH Zürich Zürich8093 Switzerland PSI Center for Scientific Computing Theory and Data PSI Villigen5232 Switzerland ASTeC STFC Daresbury Laboratory Sci-Tech Daresbury WarringtonWA4 4AD United Kingdom Cockcroft Institute Sci-Tech Daresbury WarringtonWA4 4AD United Kingdom Lancaster University Lancaster United Kingdom University of Pisa INFN Pisa Italy Tsung-Dao Lee Institute Shanghai Jiao Tong University Shanghai China School of Physics and Astronomy Shanghai Jiao Tong University Shanghai China PRISMA+ Cluster of Excellence Institute of Nuclear Physics Johannes Gutenberg University Mainz Mainz Germany PSI Center for Accelerator Science and Engineering PSI Villigen5232 Switzerland Physik-Institut Universität Zürich Winterthurerstrasse 190 ZürichCH–8057 Switzerland PSI Center for Neutron and Muon Sciences PSI Villigen5232 Switzerland Sapienza Università di Roma Dip. di Fisica P.le A. Moro 2 Roma00185 Italy Instituto Nazionale di Fisica Nucleare Sez. di Roma P.le A. Moro 2 Roma00185 Italy University of Liverpool Liverpool United Kingdom CERN Beams Department Geneva1211 Switzerland Albert Einstein Center for Fundamental Physics Institute for Theoretical Physics University of Bern Sidlerstrasse 5 Bern3012 Switzerland Department of Physics and Astronomy University of Manchester Manchester United Kingdom School of Natural Sciences Technical University of Munich Garching Germany University College London London United Kingdom Instituto de Ingeniería Universidad Nacional Autónoma de México Cuidad de México Mexico Swiss Plasma Center École Polytechnique Fédérale de Lausanne Villigen Switzerland

The electric dipole moments (EDM) of fundamental particles inherently violate parity (P) and time-reversal (T) symmetries. By virtue of the CPT theorem in quantum field theory, the latter also implies the violation of the combined charge-conjugation and parity (CP) symmetry. We aim to measure the EDM of the muon using the frozen-spin technique within a compact storage trap. This method exploits the high effective electric field, E ≈ 165 MV/m, experienced in the rest frame of the muon with a momentum of about 23 MeV/c when it passes through a solenoidal magnetic field of |B⃗| = 2.5 T. In this paper, we outline the fundamental considerations for a muon EDM search and present a conceptual design for a demonstration experiment to be conducted at secondary muon beamlines of the Paul Scherrer Institute in Switzerland. In Phase I, with an anticipated data acquisition period of 200 days, the expected sensitivity to a muon EDM is σ(d) ≤ 4 × 10−21 e·cm. In a subsequent phase, Phase II, we propose to improve the sensitivity to σ(d) ≤ 6 × 10−23 e·cm using a dedicated instrument installed on a different beamline that produces muons of momentum 125 MeV/c. © 2025, CC BY.

关键词： Electric dipole moments

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A new machine learning framework for understanding the link etween cannabis use and first-episode psychosis 12

A new machine learning framework for understanding the link ...

引用

12th Annual Conference on Health Informatics Meets eHealth, eHealth 2018

作者： Alghamdi, Wajdi Stamate, Daniel Stahl, Daniel Zamyatin, Alexander Murray, Robin Di Forti, Marta Data Science and Soft Computing Lab Department of Computing Goldsmiths University of London United Kingdom Department of Biostatistics and Health Informatics Institute of Psychiatry Psychology and Neuroscience King's College London United Kingdom Faculty of Informatics Department of Applied Informatics National Research Tomsk State University Russia Department of Psychosis Studies Institute of Psychiatry Psychology and Neuroscience King's College London United Kingdom MRC Social Genetic and Developmental Psychiatry Centre Institute of Psychiatry Psychology and Neuroscience King's College London United Kingdom

ISBN: (纸本)9781614998570

Lately, several studies started to investigate the existence of links between cannabis use and psychotic disorders. This work proposes a refined Machine Learning framework for understanding the links between cannabis use and 1st episode psychosis. The novel framework concerns extracting predictive patterns from clinical data using optimised and post-processed models based on Gaussian Processes, Support Vector Machines, and Neural Networks algorithms. The cannabis use attributes' predictive power is investigated, and we demonstrate statistically and with ROC analysis that their presence in the dataset enhances the prediction performance of the models with respect to models built on data without these specific attributes. © 2018 The authors and IOS Press.

关键词： eHealth First-episode psychosis Gaussian processes Machine learning Neural networks Support vector machine

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The NEID Earth Twin Survey. I. Confirmation of a 31-day planet orbiting HD86728

arXiv

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arXiv 2024年

作者： Gupta, Arvind F. Luhn, Jacob K. Wright, Jason T. Mahadevan, Suvrath Robertson, Paul Krolikowski, Daniel M. Ford, Eric B. Cañas, Caleb I. Halverson, Samuel Lin, Andrea S.J. Kanodia, Shubham Fitzmaurice, Evan Gilbertson, Christian Bender, Chad F. Blake, Cullen H. Dong, Jiayin Giovinazzi, Mark R. Logsdon, Sarah E. Monson, Andrew Ninan, Joe P. Rajagopal, Jayadev Roy, Arpita Schwab, Christian Stefánsson, Gu mundur U.S. National Science Foundation National Optical-Infrared Astronomy Research Laboratory 950 N. Cherry Ave. TucsonAZ85719 United States Department of Physics & Astronomy The University of California Irvine IrvineCA92697 United States Department of Astronomy & Astrophysics The Pennsylvania State University 525 Davey Laboratory 251 Pollock Road University ParkPA16802 United States Center for Exoplanets and Habitable Worlds The Pennsylvania State University 525 Davey Laboratory 251 Pollock Road University ParkPA16802 United States Penn State Extraterrestrial Intelligence Center The Pennsylvania State University 525 Davey Laboratory 251 Pollock Road University ParkPA16802 United States Steward Observatory The University of Arizona 933 N. Cherry Ave TucsonAZ85721 United States Institute for Computational and Data Sciences The Pennsylvania State University University ParkPA16802 United States Center for Astrostatistics 525 Davey Laboratory The Pennsylvania State University University ParkPA16802 United States NASA Goddard Space Flight Center GreenbeltMD20771 United States Jet Propulsion Laboratory California Institute of Technology 4800 Oak Grove Drive PasadenaCA91109 United States Earth and Planets Laboratory Carnegie Institution for Science 5241 Broad Branch Road NW WashingtonDC20015 United States Center for Computing Research Sandia National Laboratories 1450 Innovation Pkwy SE AlbuquerqueNM87123 United States Department of Physics and Astronomy University of Pennsylvania 209 S 33rd St PhiladelphiaPA19104 United States Center for Computational Astrophysics Flatiron Institute 162 Fifth Avenue New YorkNY10010 United States Department of Astronomy University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Physics & Astronomy Amherst College 25 East Drive AmherstMA01003 United States Department of Astronomy and Astrophysics Tata Institute of Fundamental Research Homi Bhabha Road Colaba Mumbai4000

With close to three years of observations in hand, the NEID Earth Twin Survey (NETS) is starting to unearth new astrophysical signals for a curated sample of bright, radial velocity (RV)-quiet stars. We present the discovery of the first NETS exoplanet, HD86728 b, a mp sin i = 9.16+0.55 -0.56 M⊕ planet on a circular, P = 31.1503+0.0062 -0.0066 d orbit, thereby confirming a candidate signal identified by Hirsch et al. (2021). We confirm the planetary origin of the detected signal, which has a semi-amplitude of just K = 1.91+0.11 -0.12 m s-1, via careful analysis of the NEID RVs and spectral activity indicators, and we constrain the mass and orbit via fits to NEID and archival RV measurements. The host star is intrinsically quiet at the ∼ 1 m s-1 level, with the majority of this variability likely stemming from short-timescale granulation. HD86728 b is among the small fraction of exoplanets with similar masses and periods that have no known planetary siblings. © 2024, CC BY-NC-ND.

关键词： Stars

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