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Spatial proteomics in three-dimensional intact specimens

四川生理科学杂志 2022年第12期44卷 2202-2202页

作者： Harsharan Singh Bhatia Insititute for Tissue Engineering and Regenerative Medicine (iTERM) Helmholtz Zentrum München 85764 Neuherberg Germany Institute for Stroke and Dementia Research Klinikum der Universität München Ludwig-Maximilians University Munich 81377 Munich Germany Department for Proteomics and Signal Transduction Max Planck Institute of Biochemistry 82152 Martinsried Germany Boehringer Ingelheim Pharma GmbH & Co. KG Drug Discovery Sciences Birkendorfer Str. 65 D-88400 Biberach Riss Germany Graduate School of Neuroscience (GSN) 82152 Munich Germany Munich Cluster for Systems Neurology (SyNergy) 81377 Munich Germany Center for Translational Cancer Research (TranslaTUM) of the TUM 81675 Munich Germany Image-Based Biomedical Modeling Department of Informatics Technical University of Munich 85748 Garching Germany Helmholtz AI Helmholtz Zentrum München 85764 Neuherberg Germany Biomedical Image Analysis Group Department of Computing Imperial College London London SW7 2AZ UK Department for Quantitative Biomedicine University of Zurich 8006 Zurich Switzerland Department of Neuroradiology Klinikum rechts der Isar 81675 Munich Germany Munich Medical Research School (MMRS) 80336 Munich Germany Institute for Diabetes and Cancer Helmholz Zentrum München 85764 Neuherberg Germany Institute for Diabetes and Obesity Helmholz Zentrum München 85764 Neuherberg Germany German Center for Diabetes Research Helmholz Zentrum München 85764 Neuherberg Germany German Center for Neurodegenerative Diseases (DZNE) 81377 Munich Germany Neuroproteomics School of Medicine Klinikum Rechts der Isar Technical University of Munich 81675 Munich Germany Institute of Anatomy University of Leipzig 04109 Leipzig Germany Institute of Computational Biology Helmholz Zentrum München 85764 Neuherberg Germany TUM School of Life Sciences Weihenstephan Technical University of Munich 85354 Freising Germany Department of Mathematics Technical University of Munich 85748 Garching Germany NNF Center for Protein Res

Spatial molecular profiling of complex tissues is essential to investigate cellular function in physiological and pathological ***,methods for molecular analysis of large biological specimens imaged in 3D are ***,we present DISCO-MS,a technology that combines whole-organ/whole-organism clearing and imaging,deep-learning-based image analysis,robotic tissue extraction,and ultra-high-sensitivity mass spectrometry.

关键词： Spatial combine analysis

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Thermal Stress and Reliability Analysis of TSV-Based 3-D ICs With a Novel Adaptive Strategy Finite Element Method

Thermal Stress and Reliability Analysis of TSV-Based 3-D ICs...

引用

作者： Zhou, Hao Zhu, Hengliang Cui, Tao Pan, David Z. Zhou, Dian Zeng, Xuan State Key Laboratory of ASIC and Systems Department of Microelectronics Fudan University Shanghai200433 China State Key Laboratory of Scientific and Engineering Computing National Center for Mathematics and Interdisciplinary Sciences Institute of Computational Mathematics Academy of Mathematics and System Sciences Chinese Academy of Sciences Beijing100190 China Department of Electrical and Computer Engineering University of Texas at Austin AustinTX78712 United States Department of Electrical Engineering University of Texas at Dallas RichardsonTX75080 United States

Thermomechanical stress is one of the most important issues in performance and reliability analysis of through silicon via-based 3-D integrated circuits (3-D ICs), where an accurate numerical approach is generally needed to produce stress models and identify weak points in the structure. In this paper, we propose a knowledge-oriented nonuniform (KONU) refinement strategy for 3-D IC stress simulation under the framework of a parallel adaptive finite element method (FEM), and apply it in 3-D IC stress and reliability analysis. Parallel adaptive FEM is promising for solving large-scale problems due to its high accuracy and parallel efficiency. It produces refined meshes based on the a posteriori error analysis, which has the quasi-optimal convergence rate for solving the problem. It has high parallel efficiency, which makes it suitable for handling large and complex structures in 3-D ICs. The KONU refinement strategy developed in this paper can efficiently reduce the number of refinement iterations in parallel adaptive FEM for 3-D IC thermomechanical stress simulation and improves the computational efficiency. It is demonstrated in this paper through several examples that parallel adaptive FEM for thermomechanical stress evaluation can be widely applied in 3-D IC reliability analysis, where accurate stress simulation and modeling is exceptionally important to produce accurate results. © 2018 IEEE.

关键词： Reliability analysis

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Assessing Trustworthy AI in Times of COVID-19: Deep Learning for Predicting a Multiregional Score Conveying the Degree of Lung Compromise in COVID-19 Patients

IEEE Transactions on Technology and Society

引用

IEEE Transactions on Technology and Society 2022年第4期3卷 272-289页

作者： Allahabadi, Himanshi Amann, Julia Balot, Isabelle Beretta, Andrea Binkley, Charles Bozenhard, Jonas Bruneault, Frederick Brusseau, James Candemir, Sema Cappellini, Luca Alessandro Chakraborty, Subrata Cherciu, Nicoleta Cociancig, Christina Coffee, Megan Ek, Irene Espinosa-Leal, Leonardo Farina, Davide Fieux-Castagnet, Genevieve Frauenfelder, Thomas Gallucci, Alessio Giuliani, Guya Golda, Adam Van Halem, Irmhild Hildt, Elisabeth Holm, Sune Kararigas, Georgios Krier, Sebastien A. Kuhne, Ulrich Lizzi, Francesca Madai, Vince I. Markus, Aniek F. Masis, Serg Mathez, Emilie Wiinblad Mureddu, Francesco Neri, Emanuele Osika, Walter Ozols, Matiss Panigutti, Cecilia Parent, Brendan Pratesi, Francesca Moreno-Sanchez, Pedro A. Sartor, Giovanni Savardi, Mattia Signoroni, Alberto Sormunen, Hanna-Maria Spezzatti, Andy Srivastava, Adarsh Stephansen, Annette F. Theng, Lau Bee Tithi, Jesmin Jahan Tuominen, Jarno Umbrello, Steven Vaccher, Filippo Vetter, Dennis Westerlund, Magnus Wurth, Renee Zicari, Roberto V. Ey Netherlands Enterprise Intelligence Department Amsterdam1083 HP Netherlands Eth Zurich Health Ethics and Policy Lab Department of Health Sciences and Technology Zürich8092 Switzerland Center for Diplomatic and Strategic Studies Postgraduate Studies in Diplomacy and International Relations Paris75015 France Pisa56124 Italy Hackensack Meridian Health Bioethics Center EdisonNJ08820 United States University of Oxford Faculty of Philosophy OxfordOX2 6GG United Kingdom Collège André- Laurendeau Philosophie Department MontrealQCH8N 2J4 Canada Université du Québec À Montréal École des Médias MontrealQCH2L 2C4 Canada Pace University Philosophy Department New YorkNY10038 United States The Ohio State University Wexner Medical Center Department of Radiology ColumbusOH43210 United States Humanitas Research Hospital Department of Radiology Milan20089 Italy Humanitas University Department of Biomedical Sciences Milan20089 Italy University of New England Faculty of Science Agriculture Business and Law ArmidaleNSW2351 Australia University of Technology Sydney Faculty of Engineering and Information Technology SydneyNSW2007 Australia Scuola Superiore Sant'Anna European Centre of Excellence on the Regulation of Robotics and Ai Pisa56127 Italy University of Bremen Group of Computer Architecture Bremen28359 Germany New York University Grossman School of Medicine Division of Infectious Diseases and Immunology Department of Medicine New YorkNY10016 United States Digital Institute Ai Research Section Stockholm16731 Sweden Arcada University of Applied Sciences Department of Business Management and Analytics Helsinki00550 Finland University of Brescia Radiological Sciences and Public Health Department of Medical and Surgical Specialties Brescia25121 Italy Sncf Reseau Sa Ethique Groupe La Plaine93418 France Institute of Diagnostic and Interventional Radiology University Hospital Zurich Zürich8091 Switzerland Eindhoven University of Tech

This article's main contributions are twofold: 1) to demonstrate how to apply the general European Union's High-Level Expert Group's (EU HLEG) guidelines for trustworthy AI in practice for the domain of healthcare and 2) to investigate the research question of what does 'trustworthy AI' mean at the time of the COVID-19 pandemic. To this end, we present the results of a post-hoc self-assessment to evaluate the trustworthiness of an AI system for predicting a multiregional score conveying the degree of lung compromise in COVID-19 patients, developed and verified by an interdisciplinary team with members from academia, public hospitals, and industry in time of pandemic. The AI system aims to help radiologists to estimate and communicate the severity of damage in a patient's lung from Chest X-rays. It has been experimentally deployed in the radiology department of the ASST Spedali Civili clinic in Brescia, Italy, since December 2020 during pandemic time. The methodology we have applied for our post-hoc assessment, called Z-Inspection®, uses sociotechnical scenarios to identify ethical, technical, and domain-specific issues in the use of the AI system in the context of the pandemic. © 2020 IEEE.

关键词： COVID-19

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Multilevel and multifidelity uncertainty quantification for cardiovascular hemodynamics

arXiv

引用

arXiv 2019年

作者： Fleeter, Casey M. Geraci, Gianluca Schiavazzi, Daniele E. Kahn, Andrew M. Marsden, Alison L. Institute for Computational and Mathematical Engineering Stanford University StanfordCA United States Center for Computing Research Sandia National Laboratories AlbuquerqueNM United States Department of Applied and Computational Mathematics and Statistics University of Notre Dame Notre DameIN United States Department of Medicine University of California San Diego San diegoCA United States Departments of Pediatrics and Bioengineering Stanford University StanfordCA United States

Standard approaches for uncertainty quantification in cardiovascular modeling pose challenges due to the large number of uncertain inputs and the significant computational cost of realistic three-dimensional simulations. We propose an efficient uncertainty quantification framework utilizing a multilevel multifidelity Monte Carlo (MLMF) estimator to improve the accuracy of hemodynamic quantities of interest while maintaining reasonable computational cost. This is achieved by leveraging three cardiovascular model fidelities, each with varying spatial resolution to rigorously quantify the variability in hemodynamic outputs. We employ two low-fidelity models (zero- and one-dimensional) to construct several different estimators. Our goal is to investigate and compare the efficiency of estimators built from combinations of these two low-fidelity model alternatives and our high-fidelity three-dimensional models. We demonstrate this framework on healthy and diseased models of aortic and coronary anatomy, including uncertainties in material property and boundary condition parameters. Our goal is to demonstrate that for this application it is possible to accelerate the convergence of the estimators by utilizing a MLMF paradigm. Therefore, we compare our approach to single fidelity Monte Carlo estimators and to a multilevel Monte Carlo approach based only on three-dimensional simulations, but leveraging multiple spatial resolutions. We demonstrate significant, on the order of 10 to 100 times, reduction in total computational cost with the MLMF estimators. We also examine the differing properties of the MLMF estimators in healthy versus diseased models, as well as global versus local quantities of interest. As expected, global quantities such as outlet pressure and flow show larger reductions than local quantities, such as those relating to wall shear stress, as the latter rely more heavily on the highest fidelity model evaluations. Similarly, healthy models show larger reducti

关键词： Monte Carlo methods

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An adaptive multi-fidelity pc-based ensemble kalman inversion for inverse problems

arXiv

引用

arXiv 2018年

作者： Yan, Liang Zhou, Tao Department of Mathematics Southeast University Nanjing210096 China LSEC Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China

The ensemble Kalman inversion (EKI), as a derivative-free methodology, has been widely used in the parameter estimation of inverse problems. Unfortunately, its cost may become moderately large for systems described by high dimensional nonlinear PDEs, as EKI requires a relatively large ensemble size to guarantee its performance. In this paper, we propose an adaptive multi-fidelity polynomial chaos (PC) based EKI technique to address this challenge. Our new strategy combines a large number of low-order PC surrogate model evaluations and a small number of high-fidelity forward model evaluations, yielding a multi-fidelity approach. Especially, we present a new approach that adaptively constructs and refines a multi-fidelity PC surrogate during the EKI simulation. Since the forward model evaluations are only required for updating the low-order multifidelity PC model, whose number can be much smaller than the total ensemble size of the classic EKI, the entire computational costs are thus significantly reduced. The new algorithm was tested through the two-dimensional time fractional inverse diffusion problems and demonstrated great effectiveness in comparison with PC based EKI and classic EKI. Copyright © 2018, The Authors. All rights reserved.

关键词： Differential equations

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Supervised learning for quantum maximum entropy estimation

arXiv

引用

arXiv 2020年

作者： Cao, Ningping Xie, Jie Zhang, Aonan Hou, Shi-Yao Zhang, Lijian Zeng, Bei Department of Mathematics & Statistics University of Guelph GuelphONN1G 2W1 Canada Institute for Quantum Computing University of Waterloo WaterlooONN2L 3G1 Canada National Laboratory of Solid State Microstructures College of Engineering and Applied Sciences School of Physics Nanjing University Nanjing210093 China Collaborative Innovation Center of Advanced Microstructures Nanjing University Nanjing210093 China College of Physics and Electronic Engineering Center for Computational Sciences Sichuan Normal University Chengdu610068 China Department of Physics Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong

For an unknown quantum state ρ, a set of measurement outcomes provides partial information. The principe of maximum entropy states that the density matrix which best represents the current knowlege is the one with the maximum entropy. However, the maximum entropy estimation (MEE) is hard to calculate in general. In this work, we develop a method that outputs an approximation to the MEE from limited measurement results by using supervised learning techniques. The method estimates the MEE of theoretical data of several different quantum systems with high fidelities. We also test our method experimentally with a photonic set-up, which exhibits high fidelities (all greater than 99.7%) and robustness against experimental errors. In comparison with previous known optimization algorithms for MEE, our method provides adequate fidelities with high efficiency. Copyright © 2020, The Authors. All rights reserved.

关键词： Quantum optics

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A foundation model for atomistic materials chemistry

arXiv

引用

arXiv 2023年

作者： Batatia, Ilyes Benner, Philipp Chiang, Yuan Elena, Alin M. Kovács, Dávid P. Riebesell, Janosh Advincula, Xavier R. Asta, Mark Avaylon, Matthew Baldwin, William J. Berger, Fabian Bernstein, Noam Bhowmik, Arghya Blau, Samuel M. Cărare, Vlad Darby, James P. De, Sandip Pia, Flaviano Della Deringer, Volker L. Elijošius, Rokas El-Machachi, Zakariya Falcioni, Fabio Fako, Edvin Ferrari, Andrea C. Genreith-Schriever, Annalena George, Janine Goodall, Rhys E.A. Grey, Clare P. Grigorev, Petr Han, Shuang Handley, Will Heenen, Hendrik H. Hermansson, Kersti Holm, Christian Hofmann, Stephan Jaafar, Jad Jakob, Konstantin S. Jung, Hyunwook Kapil, Venkat Kaplan, Aaron D. Karimitari, Nima Kermode, James R. Kroupa, Namu Kullgren, Jolla Kuner, Matthew C. Kuryla, Domantas Liepuoniute, Guoda Margraf, Johannes T. Magdău, Ioan-Bogdan Michaelides, Angelos Harry Moore, J. Naik, Aakash A. Niblett, Samuel P. Norwood, Sam Walton O’Neill, Niamh Ortner, Christoph Persson, Kristin A. Reuter, Karsten Rosen, Andrew S. Schaaf, Lars L. Schran, Christoph Shi, Benjamin X. Sivonxay, Eric Stenczel, Tamás K. Svahn, Viktor Sutton, Christopher Swinburne, Thomas D. Tilly, Jules van der Oord, Cas Vargas, Santiago Varga-Umbrich, Eszter Vegge, Tejs Vondrák, Martin Wang, Yangshuai Witt, William C. Zills, Fabian Csányi, Gábor Engineering Laboratory University of Cambridge Trumpington St and JJ Thomson Ave Cambridge United Kingdom Berlin Germany Department of Materials Science and Engineering University of California BerkeleyCA94720 United States Materials Sciences Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Mathematics Department University of British Columbia 1984 Mathematics Rd VancouverBCV6T 1Z2 Canada Institute of Condensed Matter Theory and Solid State Optics Friedrich Schiller University Jena Germany Molecular Foundry Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Bayreuth Germany Fritz-Haber-Institute of the Max-Planck-Society Berlin Germany Energy Technologies Area Lawrence Berkeley National Laboratory BerkeleyCA94720 United States U. S. Naval Research Laboratory WashingtonDC20375 United States Yusuf Hamied Department of Chemistry University of Cambridge Lensfield Road Cambridge United Kingdom Cavendish Laboratory University of Cambridge J. J. Thomson Ave Cambridge United Kingdom Department of Materials Science and Metallurgy University of Cambridge 27 Charles Babbage Road CambridgeCB3 0FS United Kingdom Chemix Inc. SunnyvaleCA94085 United States Inorganic Chemistry Laboratory Department of Chemistry University of Oxford OxfordOX1 3QR United Kingdom Scientific Computing Department Science and Technology Facilities Council Daresbury Laboratory Keckwick Lane DaresburyWA4 4AD United Kingdom BASF SE Carl-Bosch-Straße 38 Ludwigshafen67056 Germany Kavli Institute for Cosmology University of Cambridge Madingley Road CambridgeCB3 0HA United Kingdom Department of Chemistry and Biochemistry University of South Carolina South Carolina29208 United States Lennard-Jones Centre University of Cambridge Trinity Ln CambridgeCB2 1TN United Kingdom Institute for Computational Physics University of Stuttgart Stuttgart70569 Germany Department of Chemistry–Ångström Uppsala University Box 538 Uppsala

Machine-learned force fields have transformed the atomistic modelling of materials by enabling simulations of ab initio quality on unprecedented time and length scales. However, they are currently limited by: (i) the significant computational and human effort that must go into development and validation of potentials for each particular system of interest;and (ii) a general lack of transferability from one chemical system to the next. Here, using the state-of-the-art MACE architecture we introduce a single general-purpose ML model, trained on a public database of 150k inorganic crystals, that is capable of running stable molecular dynamics on molecules and materials. We demonstrate the power of the MACE-MP-0 model — and its qualitative and at times quantitative accuracy — on a diverse set problems in the physical sciences, including the properties of solids, liquids, gases, chemical reactions, interfaces and even the dynamics of a small protein. The model can be applied out of the box and as a starting or "foundation model" for any atomistic system of interest and is thus a step towards democratising the revolution of ML force fields by lowering the barriers to entry. © 2023, CC BY-NC-ND.

关键词： Molecular dynamics

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Incentivizing wi-fi network crowdsourcing: A contract theoretic approach

arXiv

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arXiv 2018年

作者： Ma, Qian Gao, Lin Liu, Ya-Feng Huang, Jianwei Department of Information Engineering Chinese University of Hong Kong Hong Kong School of Electronic and Information Engineering Harbin Institute of Technology Shenzhen China State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences China

Crowdsourced wireless community network enables individual users to share their private Wi-Fi access points (APs) with each other, hence can achieve a large Wi-Fi coverage with a small deployment cost via crowdsourcing. This paper presents a novel contract-based incentive framework to incentivize such a Wi-Fi network crowdsourcing under incomplete information (where each user has certain private information such as mobility pattern and Wi-Fi access quality). In the proposed framework, the network operator designs and offers a set of contract items to users, each consisting of a Wi-Fi access price (that a user can charge others for accessing his AP) and a subscription fee (that a user needs to pay the operator for joining the community). Different from the existing contracts in the literature, in our contract model each user's best choice depends not only on his private information but also on other users' choices. This greatly complicates the contract design, as the operator needs to analyze the equilibrium choices of all users, rather than the best choice of each single user. We first derive the feasible contract that guarantees the users' truthful information disclosure based on the equilibrium analysis of user choice, and then derive the optimal (and feasible) contract that yields the maximal profit for the operator. Our analysis shows that a user who provides a higher Wi-Fi access quality is more likely to choose a higher Wi-Fi access price and subscription fee, regardless of the user mobility pattern. Simulation results further show that when increasing the average Wi-Fi access quality of users, the operator can gain more profit, but (counter-intuitively) offer lower Wi-Fi access prices and subscription fees for users. Copyright © 2018, The Authors. All rights reserved.

关键词： Crowdsourcing

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A Gradient enhanced 1-minimization for sparse approximation of polynomial chaos expansions

arXiv

引用

arXiv 2018年

作者： Guo, Ling Narayan, Akil Zhou, Tao Department of Mathematics Shanghai Normal University Shanghai China Mathematics Department and Scientific Computing and Imaging Institute University of Utah Salt Lake CityUT84112 United States Institute of Computational Mathematics and Scientific Engineering Computing Amss Chinese Academy of Sciences Beijing China

We investigate a gradient-enhanced 1-minimization for constructing sparse polynomial chaos expansions. In addition to function evaluations, measurements of the function gradient is also included to accelerate the identification of expansion coeficients. By designing appropriate preconditioners to the measurement matrix, we show gradient-enhanced 1 minimization leads to stable and accurate coeficient recovery. The framework for designing preconditioners is quite general and it applies to recover of functions whose domain is bounded or unbounded. Comparisons between the gradient enhanced approach and the standard 1-minimization are also presented and numerical examples suggest that the inclusion of derivative information can guarantee sparse recovery at a reduced computational cost. Copyright © 2018, The Authors. All rights reserved.

关键词： Recovery

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NMRCloudQ： a quantum cloud experience on a nuclear magnetic resonance quantum computer

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Science Bulletin 2018年第1期63卷 17-23页

作者： Tao Xin Shilin Huang Sirui Lu Keren Li Zhihuang Luo Zhangqi Yin Jun Li Dawei Lu Guilu Long Bei Zeng Department of Physics Tsinghua University Beijing 100084 China Center for Quantum Information Institute for Interdiscriplinary Information Sciences Tsinghua University Belting 100084 China Beijing Computational Science Research Center Beijing 100193 China Institute for Quantum Computing University of Waterloo waterloo N2L 3C1 Ontario Canada Department of Physics and Institute for Quantum Science and Engineering Southern University of Science and Technology Shenzhen 518055 China The Innovative Center of Quantum Matter Beijing 100084 China Department of Mathematics and Statistics University of Guelph Guelph N1C 2W1 Ontario Canada

Cloud-based quantum computing is anticipated to be the most useful and reachable form for public users to experience with the power of quantum. As initial attempts, IBM Q has launched influential cloud services on a superconducting quantum processor in 2016, but no other platforms has followed up yet. Here,we report our new cloud quantum computing service – NMRCloud Q(http://***/zh-hans/),where nuclear magnetic resonance, one of the pioneer platforms with mature techniques in experimental quantum computing, plays as the role of implementing computing tasks. Our service provides a comprehensive software environment preconfigured with a list of quantum information processing packages,and aims to be freely accessible to either amateurs that look forward to keeping pace with this quantum era or professionals that are interested in carrying out real quantum computing experiments in person. In our current version, four qubits are already usable with in average 99.10% single-qubit gate fidelity and 97.15% two-qubit fidelity via randomized benchmaking tests. Improved control precisions as well as a new seven-qubit processor are also in preparation and will be available later.

关键词： Quantum cloud Nuclear magnetic resonance Gradient ascent pulse engineering Randomized benchmarking

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