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arXiv 2024年

作者： Lutsker, Guy Sapir, Gal Shilo, Smadar Merino, Jordi Godneva, Anastasia Greenfield, Jerry R. Samocha-Bonet, Dorit Dhir, Raja Gude, Francisco Mannor, Shie Meirom, Eli Chechik, Gal Rossman, Hagai Segal, Eran Department of Computer Science and Applied Mathematics Weizmann Institute of Science Rehovot Israel Department of Molecular Cell Biology Weizmann Institute of Science Rehovot Israel NVIDIA Tel Aviv Israel Pheno.AI Tel-Aviv Israel Faculty of Medical and Health Sciences Tel Aviv University Tel-Aviv Israel The Jesse Z and Sara Lea Shafer Institute for Endocrinology and Diabetes National Center for Childhood Diabetes Schneider Children’s Medical Center of Israel Petah Tikva Israel Novo Nordisk Foundation Center for Basic Metabolic Research University of Copenhagen Copenhagen Denmark Diabetes Unit Endocrine Division Massachusetts General Hospital BostonMA United States Center for Genomic Medicine Massachusetts General Hospital BostonMA United States Clinical Diabetes Appetite and Metabolism Lab Garvan Institute of Medical Research Sydney Australia St Vincent’s Clinical School University of NSW Sydney Australia Department of Endocrinology and Diabetes St Vincent’s Hospital Sydney Australia University of Zurich Davos Switzerland Department of Medicine University of Santiago de Compostela Spain Concepción Arenal Primary Care Center Santiago de Compostela Spain Mohamed bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates

Recent advances in self-supervised learning enabled novel medical AI models, known as foundation models (FMs), offer great potential for better characterizing health from diverse biomedical data. Continuous glucose monitoring (CGM) provides rich, temporal data on glycemic patterns, but its full potential for predicting broader health outcomes remains underutilized. Here, we introduce GluFormer, a generative foundation model for CGM data designed to learn glycemic patterns and produce representations that reflect aspects of metabolic health. Trained on over 10 million CGM measurements from 10,812 adults, primarily without diabetes, GluFormer uses autoregressive token prediction to capture longitudinal glucose dynamics. We show that GluFormer generalizes to 19 external cohorts (n=6,044) spanning different ethnicities and ages, 5 different countries, 8 different CGM devices, and diverse pathophysiological states (e.g., prediabetes, type 1 & type 2 diabetes, gestational diabetes, and obesity). Our results suggest that GluFormer’s representations can improve upon current CGM metrics, such as the Glucose Management Indicator (GMI), for forecasting clinical measures. In individuals with prediabetes, GluFormer stratifies those likely to experience clinically significant increases in HbA1C% over a two-year period - outperforming baseline HbA1C%. In a longitudinal study of 580 adults with CGM data and 12-year follow-up, GluFormer identifies individuals at elevated risk of developing diabetes more effectively than blood HbA1C%, capturing 66% of all new-onset diabetes diagnoses in the top quartile versus 7% in the bottom quartile. Similarly, 69% of cardiovascular-death events occurred in the top quartile and none in the bottom quartile, indicating GluFormer-based stratification could capture additional risk signals not reflected by traditional glycemic metrics. We also show that CGM representations from pre-intervention periods in Randomized Clinical Trials (RCTs) outperform we

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Trajectory inference across multiple conditions with condiments: differential topology, progression, differentiation, and expression.

Research Square

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Research Square 2021年

作者： de Bézieux, Hector Roux Van den Berge, Koen Street, Kelly Dudoit, Sandrine Division of Biostatistics School of Public Health University of California BerkeleyCA United States Center for Computational Biology University of California BerkeleyCA United States Department of Statistics University of California BerkeleyCA United States Department of Applied Mathematics Computer Science and Statistics Ghent University Ghent Belgium Department of Data Sciences Dana-Farber Cancer Institute BostonMA United States Department of Biostatistics Harvard T.H. Chan School of Public Health BostonMA United States

In single-cell RNA-sequencing (scRNA-seq), gene expression is assessed individually for each cell, allowing the investigation of developmental processes, such as embryogenesis and cellular differentiation and regeneration, at unprecedented resolutions. In such dynamic biological systems, grouping cells into discrete groups is not reflective of the biology. Cellular states rather form a continuum, e.g., for the differentiation of stem cells into mature cell types. This process is often represented via a trajectory in a reduced-dimensional representation of the scRNA-seq dataset. While many methods have been suggested for trajectory inference, it is often unclear how to handle multiple biological groups or conditions, e.g., inferring and comparing the differentiation trajectories of wild-type and knock-out stem cell populations. In this manuscript, we present a method for the estimation and downstream interpretation of cell trajectories across multiple conditions. Our framework allows the interpretation of differences between conditions at the trajectory, cell population, and gene expression levels. We start by integrating datasets from multiple conditions into a single trajectory. By comparing the conditions along the trajectory’s path, we can detect large-scale changes, indicative of differential progression. We also demonstrate how to detect subtler changes by finding genes that exhibit different behaviors between these conditions along a differentiation path. © 2021, CC BY.

关键词： RNA

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Unbiased Estimation of the Gradient of the Log-Likelihood in Inverse Problems

arXiv

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arXiv 2020年

作者： Jasra, B.Y. Ajay Law, Kody J.H. Deng, L.U. Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia Department of Mathematics University of Manchester ManchesterM13 9PL United Kingdom Department of Statistics & Applied Probability National University of Singapore Singapore117546 Singapore

We consider the problem of estimating a parameter θ ∈ Θ ⊆ Rdθ associated to a Bayesian inverse problem. Treating the unknown initial condition as a nuisance parameter, typically one must resort to a numerical approximation of gradient of the log-likelihood and also adopt a discretization of the problem in space and/or time. We develop a new methodology to unbiasedly estimate the gradient of the log-likelihood with respect to the unknown parameter, i.e. the expectation of the estimate has no discretization bias. Such a property is not only useful for estimation in terms of the original stochastic model of interest, but can be used in stochastic gradient algorithms which benefit from unbiased estimates. Under appropriate assumptions, we prove that our estimator is not only unbiased but of finite variance. In addition, when implemented on a single processor, we show that the cost to achieve a given level of error is comparable to multilevel Monte Carlo methods, both practically and theoretically. However, the new algorithm provides the possibility for parallel computation on arbitrarily many processors without any loss of efficiency, asymptotically. In practice, this means any precision can be achieved in a fixed, finite constant time, provided that enough processors are available. Copyright © 2020, The Authors. All rights reserved.

关键词： Parameter estimation

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A Dibenzo[g,p]chrysene-Based Organic Semiconductor with Small Exciton Binding Energy via Molecular Aggregation

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Angewandte Chemie 2024年第42期136卷

作者： Hiroki Mori Dr. Seihou Jinnai Yasushi Hosoda Prof. Azusa Muraoka Prof. Ken-ichi Nakayama Prof. Akinori Saeki Prof. Yutaka Ie The Institute of Scientific and Industrial Research (SANKEN) Osaka University 8-1 Mihogaoka Ibaraki Osaka 567-0047 Japan Innovative Catalysis Science Division Institute for Open and Transdisciplinary Research Initiatives (ICS-OTRI) Osaka University 2-1 Yamadaoka Suita Osaka 565-0871 Japan Department of Mathematics Physics and Computer Science Japan Women's University 2-8-1 Mejirodai Bunkyo-ku Tokyo 112-8681 Japan Department of Applied Chemistry Graduate School of Engineering Osaka University 2-1 Yamadaoka Suita Osaka 565-0871 Japan

Exciton binding energy ( E b ) is understood as the energy required to dissociate an exciton in free-charge carriers, and is known to be an important parameter in determining the performance of organic opto-electronic devices. However, the development of a molecular design to achieve a small level of E b in the solid state continues to lag behind. Here, to investigate the relationship between aggregation and E b , star-shaped π-conjugated compounds DBC-RD and TPE-RD were developed using dibenzo[ g , p ]chrysene (DBC) and tetraphenylethylene (TPE). Theoretical calculations and physical measurements in solution showed no apparent differences between DBC-RD and TPE-RD, indicating that these molecules possess similar properties on a single-molecule level. By contrast, pristine films incorporating these molecules showed significantly different levels of electron affinity, ionization potential, and optical gap. Also, DBC-RD had a smaller E b value of 0.24 eV compared with that of TPE-RD (0.42 eV). However, these molecules showed similar E b values under dispersed conditions, which suggested that the decreased E b of DBC-RD in pristine film is induced by molecular aggregation. By comparison with TPE-RD, DBC-RD showed superior performances in single-component organic solar cells and organic photocatalysts. These results indicate that a molecular design suitable for aggregation is important to decrease the E b in films.

关键词： organic semiconductors organic solar cells organic photocatalysts exciton binding energies dielectric constants

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Navigating the challenges in creating complex data systems: a development philosophy

arXiv

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arXiv 2022年

作者： Dittmer, Sören Roberts, Michael Gilbey, Julian Biguri, Ander Preller, Jacobus Rudd, James H.F. Aston, John A.D. Schönlieb, Carola-Bibiane Selby, Ian Breger, Anna Thorpe, Matthew Weir-McCall, Jonathan R. Gkrania-Klotsas, Effrossyni Korhonen, Anna Jefferson, Emily Langs, Georg Yang, Guang Prosch, Helmut Stanczuk, Jan Tang, Jing Babar, Judith Sánchez, Lorena Escudero Teare, Philip Patel, Mishal Wassin, Marcel Holzer, Markus Walton, Nicholas Lió, Pietro Shadbahr, Tolou Sala, Evis Department of Applied Mathematics and Theoretical Physics University of Cambridge Cambridge United Kingdom Addenbrooke’s Hospital Cambridge University Hospitals NHS Trust Cambridge United Kingdom Department of Medicine University of Cambridge Cambridge United Kingdom Department of Pure Mathematics and Mathematical Statistics University of Cambridge Cambridge United Kingdom ZeTeM University of Bremen Bremen Germany Department of Radiology University of Cambridge Cambridge United Kingdom Faculty of Mathematics University of Vienna Austria Department of Mathematics University of Manchester Manchester United Kingdom Royal Papworth Hospital Cambridge Royal Papworth Hospital NHS Foundation Trust Cambridge United Kingdom Language Technology Laboratory University of Cambridge Cambridge United Kingdom Population Health and Genomics School of Medicine University of Dundee Dundee United Kingdom Department of Biomedical Imaging and Image-guided Therapy Computational Imaging Research Lab Medical University of Vienna Vienna Austria National Heart and Lung Institute Imperial College London London United Kingdom Research Program in Systems Oncology Faculty of Medicine University of Helsinki Helsinki Finland Data Science & Artificial Intelligence AstraZeneca Cambridge United Kingdom Clinical Pharmacology & Safety Sciences AstraZeneca Cambridge United Kingdom Contextflow GmbH Vienna Austria Institute of Astronomy University of Cambridge Cambridge United Kingdom Department of Computer Science and Technology University of Cambridge Cambridge United Kingdom

In this perspective, we argue that despite the democratization of powerful tools for data science and machine learning over the last decade, developing the code for a trustworthy and effective data science system (DSS) is getting harder. Perverse incentives and a lack of widespread software engineering (SE) skills are among many root causes we identify that naturally give rise to the current systemic crisis in reproducibility of DSSs. We analyze why SE and building large complex systems is, in general, hard. Based on these insights, we identify how SE addresses those difficulties and how we can apply and generalize SE methods to construct DSSs that are fit for purpose. We advocate two key development philosophies, namely that one should incrementally grow – not biphasically plan and build – DSSs, and one should always employ two types of feedback loops during development: one which tests the code’s correctness and another that evaluates the code’s efficacy. Copyright © 2022, The Authors. All rights reserved.

关键词： Software engineering

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*** - A plug-and-play solution for online systems medicine and network-based drug repurposing

arXiv

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arXiv 2023年

作者： Maier, Andreas Hartung, Michael Abovsky, Mark Adamowicz, Klaudia Bader, Gary D. Baier, Sylvie Blumenthal, David B. Chen, Jing Elkjaer, Maria L. Garcia-Hernandez, Carlos Helmy, Mohamed Hoffmann, Markus Jurisica, Igor Kotlyar, Max Lazareva, Olga Levi, Hagai List, Markus Lobentanzer, Sebastian Loscalzo, Joseph Malod-Dognin, Noel Manz, Quirin Matschinske, Julian Mee, Miles Oubounyt, Mhaned Pico, Alexander R. Pillich, Rudolf T. Poschenrieder, Julian M. Pratt, Dexter Pržulj, Nataša Sadegh, Sepideh Saez-Rodriguez, Julio Sarkar, Suryadipto Shaked, Gideon Shamir, Ron Trummer, Nico Turhan, Ugur Wang, Ruisheng Zolotareva, Olga Baumbach, Jan Institute for Computational Systems Biology University of Hamburg Hamburg Germany Division of Orthopaedic Surgery Schroeder Arthritis Institute Data Science Discovery Centre Osteoarthritis Research Program Krembil Research Institute UHN Toronto Canada Data Science Discovery Centre for Chronic Diseases Krembil Research Institute University Health Network 60 Leonard Avenue 5KD-407 TorontoONM5T 0S8 Canada Department of Molecular Genetics University of Toronto TorontoON Canada The Donnelly Centre University of Toronto TorontoON Canada Department of Computer Science University of Toronto TorontoON Canada Princess Margaret Cancer Centre University Health Network TorontoON Canada The Lunenfeld-Tanenbaum Research Institute Mount Sinai Hospital TorontoON Canada TUM School of Life Sciences Technical University of Munich Munich Germany Erlangen91052 Germany Department of Medicine University of California San Diego 9500 Gilman Drive La Jolla CA92093 United States Department of Neurology Odense University Hospital Odense Denmark Institute of Clinical Research University of Southern Denmark Odense Denmark Institute of Molecular Medicine University of Southern Denmark Odense Denmark Barcelona08034 Spain Institute for Advanced Study Lichtenbergstrasse 2a GarchingD-85748 Germany Technical University of Munich Germany National Institute of Diabetes Digestive and Kidney Diseases BethesdaMD20892 United States Departments of Medical Biophysics and Computer Science University of Toronto Toronto Canada Institute of Neuroimmunology Slovak Academy of Sciences Bratislava Slovakia Heidelberg69120 Germany Heidelberg Germany European Molecular Biology Laboratory Genome Biology Unit Heidelberg69117 Germany Blavatnik School of Computer Science Tel-Aviv University Tel-Aviv Israel Heidelberg University Faculty of Medicine Heidelberg University Hospital Institute for Computational Biomedicine Bioquant Heidelberg Germany Department of Medicine Bri

In recent decades, the development of new drugs has become increasingly expensive and inefficient, and the molecular mechanisms of most pharmaceuticals remain poorly understood. In response, computational systems and network medicine tools have emerged to identify potential drug repurposing candidates. However, these tools often require complex installation and lack intuitive visual network mining capabilities. To tackle these challenges, we introduce ***, a platform that assists specialized computational medicine tools in becoming user-friendly, web-based utilities for drug repurposing. With just three lines of code, *** turns any systems biology software into an interactive web tool for modeling and analyzing complex protein-drug-disease networks. Demonstrating its broad adaptability, *** has been successfully integrated with 21 computational systems medicine tools. Available at https://***, *** has significant potential for streamlining the drug discovery process, allowing researchers to focus on essential aspects of pharmaceutical treatment research. © 2023, CC BY.

关键词： Data visualization

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International Workshop on Applications of Probability and Statistics to Biology,July 11-13,2019--In Honor of Professor Minping Qian’s 80th Birthday

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Quantitative Biology 2020年第2期8卷 177-186页

作者： Minghua Deng Jianfeng Feng Hong Qian Lin Wan Fengzhu Sun Center for Quantitative Biology Peking UniversityBeijing 100871China LMAM Center for Statistical ScienceSchool of Mathematical SciencesPeking UniversityBeijing 100871China Institute of Science and Technology for Brain-inspired Intelligence Fudan UniversityShanghai 200433China School of Mathematical Sciences Fudan UniversityShanghai 200433China Centre for Computational Systems Biology Fudan UniversityShanghai 200433China Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence at Fudan University Ministry of EducationShanghai 200433China Department of Computer Science University of WarwickCoventryCV47ALUnited Kingdom Department of Applied Mathematics University of WashingtonSeattleWA 98195USA NCMIS Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100049China Quantitative and Computational Biology Program University of Southern CaliforniaLos AngelesCA 90089USA

The International Workshop on Applications of Probability and Statistics to Biology(APSB)was successfully held in Shanghai,China,July 11-13,*** workshop was hosted by the Institute of science and Technology for Brain-inspired Intelligence(ISTBI)at Fudan University,and in honor of the 80th birthday of *** Qian of Peking *** of the twenty eight speakers were former students or close collaborators of ***;and there were over eighty participants from all over China and United States.

关键词： Probability speakers ping

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Invertible neural networks for uncertainty Quantification in photoacoustic imaging

arXiv

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arXiv 2020年

作者： Nölke, Jan-Hinrich Adler, Tim Gröhl, Janek Kirchner, Thomas Ardizzone, Lynton Rother, Carsten Köthe, Ullrich Maier-Hein, Lena Division of Computer Assisted Medical Interventions German Cancer Research Center Heidelberg Germany Faculty of Physics and Astronomy Heidelberg University Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Institute of Applied Physics University of Bern Bern Switzerland Visual Learning Lab HCI IWR Heidelberg Germany Medical Faculty Heidelberg University Heidelberg Germany

Multispectral photoacoustic imaging (PAI) is an emerging imaging modality which enables the recovery of functional tissue parameters such as blood oxygenation. However, the underlying inverse problems are potentially ill-posed, meaning that radically different tissue properties may - in theory - yield comparable measurements. In this work, we present a new approach for handling this specific type of uncertainty by leveraging the concept of conditional invertible neural networks (cINNs). Specifically, we propose going beyond commonly used point estimates for tissue oxygenation and converting single-pixel initial pressure spectra to the full posterior probability density. This way, the inherent ambiguity of a problem can be encoded with multiple modes in the output. Based on the presented architecture, we demonstrate two use cases which leverage this information to not only detect and quantify but also to compensate for uncertainties: (1) photoacoustic device design and (2) optimization of photoacoustic image acquisition. Our in silico studies demonstrate the potential of the proposed methodology to become an important building block for uncertainty-aware reconstruction of physiological parameters with PAI. Copyright © 2020, The Authors. All rights reserved.

关键词： Photoacoustic effect

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Magnetically Tuned Continuous Transition from Weak to Strong Coupling in Terahertz Magnon Polaritons

arXiv

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arXiv 2022年

作者： Baydin, Andrey Hayashida, Kenji Makihara, Takuma Tay, Fuyang Ma, Xiaoxuan Ren, Wei Ma, Guohong Noe, G. Timothy Katayama, Ikufumi Takeda, Jun Nojiri, Hiroyuki Cao, Shixun Bamba, Motoaki Kono, Junichiro Department of Electrical and Computer Engineering Rice University HoustonTX77005 United States The Smalley-Curl Institute Rice University HoustonTX77005 United States Division of Applied Physics Graduate School of Engineering Hokkaido University Hokkaido Sapporo060-8628 Japan Department of Physics and Astronomy Rice University HoustonTX77005 United States Applied Physics Graduate Program Smalley-Curl Institute Rice University HoustonTX77005 United States Department of Physics International Center of Quantum and Molecular Structures Materials Genome Institute Shanghai University Shanghai200444 China Department of Physics Graduate School of Engineering Science Yokohama National University Yokohama240-8501 Japan Institute for Materials Research Tohoku University Sendai980-8577 Japan The Hakubi Center for Advanced Research Kyoto University Kyoto606-8502 Japan Department of Physics I Kyoto University Kyoto606-8502 Japan PRESTO Japan Science and Technology Agency Kawaguchi332-0012 Japan Department of Materials Science and NanoEngineering Rice University HoustonTX77005 United States

Depending on the relative rates of coupling and dissipation, a light-matter coupled system is either in the weak- or strong-coupling regime. Here, we present a unique system where the coupling rate continuously increases with an externally applied magnetic field while the dissipation rate remains constant, allowing us to monitor a weak-to-strong coupling transition as a function of magnetic field. We observed a Rabi splitting of a terahertz magnon mode in yttrium orthoferrite above a threshold magnetic field of ∼14 T. Based on a microscopic theoretical model, we show that with increasing magnetic field the magnons transition into magnon polaritons through an exceptional point, which will open up new opportunities for in situ control of non-Hermitian systems. Copyright © 2022, The Authors. All rights reserved.

关键词： Polariton

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Spectral/hp element methods:Recent developments, applications, and perspectives

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Journal of Hydrodynamics 2018年第1期30卷 1-22页

作者： Hui Xu Chris D.Cantwell Carlos Monteserin Claes Eskilsson Allan P.Engsig-Karup Spencer J.Sherwin Department of Aeronautics Imperial College London Department of Applied Mathematics and Computer Science Technical University of Denmark Department of Civil Engineering Aalborg University Division Safety and Transport Research Institutes of Sweden (RISE) Center for Energy Resources Engineering (CERE) Technical University of Denmark

The spectral/hp element method combines the geometric flexibility of the classical h-type finite element technique with the desirable numerical properties of spectral methods, employing high-degree piecewise polynomial basis functions on coarse finite element-type meshes. The spatial approximation is based upon orthogonal polynomials, such as Legendre or Chebychev polynomials,modified to accommodate a C~0-continuous expansion. Computationally and theoretically, by increasing the polynomial order p,high-precision solutions and fast convergence can be obtained and, in particular, under certain regularity assumptions an exponential reduction in approximation error between numerical and exact solutions can be achieved. This method has now been applied in many simulation studies of both fundamental and practical engineering flows. This paper briefly describes the formulation of the spectral/hp element method and provides an overview of its application to computational fluid dynamics. In particular, it focuses on the use of the spectral/hp element method in transitional flows and ocean engineering. Finally, some of the major challenges to be overcome in order to use the spectral/hp element method in more complex science and engineering applications are discussed.

关键词： High-precision spectral/hp elements continuous Galerkin method discontinuous Galerkin method implicit large eddy simulation

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