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The Energy Return on Investment of Whole-Energy Systems: Application to Belgium

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Biophysical economics and sustainability 2022年第4期7卷 12页

作者： Jonathan Dumas Antoine Dubois Paolo Thiran Pierre Jacques Francesco Contino Bertrand Cornélusse Gauthier Limpens Departments of Computer Science and Electrical Engineering Liege University Place du 20-Août 7 4000 Liege Belgium. Institute of Mechanics Materials and Civil Engineering Catholic University of Louvain Place de l'Université 1 1348 Ottignies-Louvain-la-Neuve Belgium.

Planning the defossilization of energy systems while maintaining access to abundant primary energy resources is a non-trivial multi-objective problem encompassing economic, technical, environmental, and social aspects. However, most long-term policies consider the cost of the system as the leading indicator in the energy system models to decrease the carbon footprint. This paper is the first to develop a novel approach by adding a surrogate indicator for the social and economic aspects, the (EROI), in a whole-energy system optimization model. In addition, we conducted a global sensitivity analysis to identify the main parameters driving the EROI uncertainty. This method is illustrated in the 2035 Belgian energy system for several greenhouse gas (GHG) emissions targets. Nevertheless, it can be applied to any worldwide or country energy system. The main results are threefold when the GHG emissions are reduced by 80%: (i) the EROI decreases from 8.9 to 3.9; (ii) the imported renewable gas (methane) represents 60 % of the system primary energy mix; (iii) the sensitivity analysis reveals this fuel drives 67% of the variation of the EROI. These results raise questions about meeting the climate targets without adverse socio-economic impact, demonstrating the importance of considering the EROI in energy system models.

关键词： Energy return on energy investment Energy transition EnergyScope TD Polynomial chaos expansion Sensitivity analysis Whole-energy system

In silico identification of drug candidates against COVID-19

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Informatics in Medicine Unlocked 2020年 21卷 100461页

作者： Wu, Yifei Chang, Kuan Y. Lou, Lei Edwards, Lorette G. Doma, Bly K. Xie, Zhong-Ru Computational Drug Discovery Laboratory School of Electrical and Computer Engineering College of Engineering University of Georgia Athens 30602 GA United States Department of Computer Science and Engineering National Taiwan Ocean University Keelung 202 Taiwan The Franklin College of Arts and Sciences University of Georgia Athens 30602 GA United States

The COVID-19 pandemic has caused unprecedented health and economic crisis throughout the world. However, there is no effective medication or therapeutic strategy for treatment of this disease currently. Here, to elucidate the inhibitory effects, we first tested binding affinities of 11 HIV-1 protease inhibitors or their pharmacoenhancers docked onto SARS-CoV-2 main protease (M^pro), and 12 nucleotide-analog inhibitors docked onto RNA dependent RNA polymerase (RdRp). To further obtain the effective drug candidates, we screened 728 approved drugs via virtual screening on SARS-CoV-2 M^pro. Our results demonstrate that remdesivir shows the best binding energy on RdRp and saquinvir is the best inhibitor of M^pro. Based on the binding energies, we also list 10 top-ranked approved drugs which can be potential inhibitors for M^pro. Overall, our results do not only propose drug candidates for further experiments and clinical trials but also pave the way for future lead optimization and drug design. © 2020 The Author(s)

关键词： COVID-19 Drug repurposing Ligand-protein docking Main protease Remdesivir RNA-dependent RNA polymerase Virtual screening

Tracking and Failure Analysis of Explanted Neuromodulation Devices

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Neuromodulation : journal of the International Neuromodulation Society 2025年第4期28卷 700-702页

作者： Tess L Veuthey Deborah Duricka Dario J Englot David A Provenzano David M Dickerson Sanket Dhruva Christopher Miranda Loftus Nina Meizu-Nwaba David Prescott McMullen Richard B North Pierre D'Haese Peter E Konrad Lawrence Poree Department of Neurology and Neurological Sciences Stanford Medicine Stanford CA USA. Washington Wyoming Alaska Montana and Idaho School of Medical Education Anchorage AK USA. Departments of Neurological Surgery & Electrical Engineering Neurology Radiology & Radiological Sciences Computer Science and Biomedical Engineering Vanderbilt Health Nashville TN USA. Pain Diagnostics and Interventional Care Pittsburgh PA USA. Anesthesia Pain Management Services NorthShore University Health System Chicago IL USA. Department of Internal Medicine University for California San Francisco San Francisco CA USA. Office of Neurological and Physical Medicine Devices OHT5 OPEQ CDRH FDA Silver Springs MD USA. Department of Neurosurgery Anesthesiology and Critical Care Medicine (ret.) Johns Hopkins University School of Medicine Baltimore MD USA. Rockefeller Neuroscience Institute West Virginia University Morgantown WV USA. Dept of Neurosurgery West Virginia University Morgantown WV USA. Department of Anesthesia and Perioperative Care Division of Pain Medicine University of California San Francisco CA USA. Electronic address: Lawrence.Poree@UCSF.edu.

A Generative Shape Compositional Framework to Synthesise Populations of Virtual Chimaeras

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arXiv 2022年

作者： Dou, Haoran Virtanen, Seppo Ravikumar, Nishant Frangi, Alejandro F. The Center for Computational Imaging and Simulation Technologies in Biomedicine within the School of Computing The University of Leeds LeedsLS2 9JT United Kingdom The Christabel Pankhurst Institute Division of Informatics Imaging and Data Sciences University of Manchester ManchesterM1 3BB United Kingdom The Department of Computer Science University of Manchester ManchesterM1 3BB United Kingdom Departments of Electrical Engineering and Cardiovascular Sciences KU Leuven Leuven Belgium Alan Turing Institute London United Kingdom

Generating virtual organ populations that capture sufficient variability while remaining plausible is essential to conduct in-silico trials of medical devices. However, not all anatomical shapes of interest are always available for each individual in a population. The imaging examinations and modalities used can vary between subjects depending on their individualised clinical pathways. Different imaging modalities may have various fields of view, are sensitive to signals from other tissues/organs, or both. Hence, missing/partially overlapping anatomical information is often available across individuals. We introduce a generative shape model for multipart anatomical structures, learnable from sets of unpaired datasets, i.e., where each substructure in the shape assembly comes from datasets with missing or partially overlapping substructures from disjoint subjects of the same population. The proposed generative model can synthesise complete multipart shape assemblies coined virtual chimaeras. We applied this framework to build virtual chimaeras from databases of whole heart shape assemblies that each contribute samples for heart substructures. Specifically, we propose a graph neural network-based generative shape compositional framework which comprises two components, a part-aware generative shape model which captures the variability in shape observed for each structure of interest in the training population, and a spatial composition network which assembles/composes the structures synthesised by the former into multipart shape assemblies (viz. virtual chimaeras). We also propose a novel self-supervised learning scheme that enables the spatial composition network to be trained with partially overlapping data and weak labels. We trained and validated our approach using shapes of cardiac structures derived from cardiac magnetic resonance images in the UK Biobank. When trained with complete and partially overlapping data, our approach significantly outperforms a PCA-base

关键词： Magnetic resonance

Feature selection for huge data via minipatch learning

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arXiv 2020年

作者： Yao, Tianyi Allen, Genevera I. Department of Statistics Rice University HoustonTX United States Departments of Electrical and Computer Engineering Statistics and Computer Science Rice University HoustonTX United States Jan and Dan Duncan Neurological Research Institute Baylor College of Medicine HoustonTX United States

Feature selection often leads to increased model interpretability, faster computation, and improved model performance by discarding irrelevant or redundant features. While feature selection is a well-studied problem with many widely-used techniques, there are typically two key challenges: i) many existing approaches become computationally intractable in huge-data settings with millions of observations and features;and ii) the statistical accuracy of selected features degrades in high-noise, high-correlation settings, thus hindering reliable model interpretation. We tackle these problems by proposing Stable Minipatch Selection (STAMPS) and Adaptive STAMPS (AdaSTAMPS). These are meta-algorithms that build ensembles of selection events of base feature selectors trained on many tiny, (adaptively-chosen) random subsets of both the observations and features of the data, which we call minipatches. Our approaches are general and can be employed with a variety of existing feature selection strategies and machine learning techniques. In addition, we provide theoretical insights on STAMPS and empirically demonstrate that our approaches, especially AdaSTAMPS, dominate competing methods in terms of feature selection accuracy and computational time. © 2020, CC BY-NC-SA.

关键词： Feature extraction

Supervised Convex Clustering

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arXiv 2020年

作者： Wang, Minjie Yao, Tianyi Allen, Genevera I. Department of Statistics Rice University HoustonTX United States Departments of Electrical and Computer Engineering Statistics and Computer Science Rice University HoustonTX United States Jan and Dan Duncan Neurological Research Institute Baylor College of Medicine HoustonTX United States

Clustering has long been a popular unsupervised learning approach to identify groups of similar objects and discover patterns from unlabeled data in many applications. Yet, coming up with meaningful interpretations of the estimated clusters has often been challenging precisely due to its unsupervised nature. Meanwhile, in many real-world scenarios, there are some noisy supervising auxiliary variables, for instance, subjective diagnostic opinions, that are related to the observed heterogeneity of the unlabeled data. By leveraging information from both supervising auxiliary variables and unlabeled data, we seek to uncover more scientifically interpretable group structures that may be hidden by completely unsupervised analyses. In this work, we propose and develop a new statistical pattern discovery method named Supervised Convex Clustering (SCC) that borrows strength from both information sources and guides towards finding more interpretable patterns via a joint convex fusion penalty. We develop several extensions of SCC to integrate different types of supervising auxiliary variables, to adjust for additional covariates, and to find biclusters. We demonstrate the practical advantages of SCC through simulations and a case study on Alzheimer’s Disease genomics. Specifically, we discover new candidate genes as well as new subtypes of Alzheimer’s Disease that can potentially lead to better understanding of the underlying genetic mechanisms responsible for the observed heterogeneity of cognitive decline in older adults. Copyright © 2020, The Authors. All rights reserved.

关键词： Machine learning

Extracting robust and accurate features via a robust information bottleneck

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arXiv 2019年

作者： Pensia, Ankit Jog, Varun Loh, Po-Ling Departments of Computer Science University of Wisconsin-Madison Department of Statistics University of Wisconsin-Madison Departments of Electrical and Computer Engineering University of Wisconsin-Madison Departments of Statistics Columbia University

We propose a novel strategy for extracting features in supervised learning that can be used to construct a classifier which is more robust to small perturbations in the input space. Our method builds upon the idea of the information bottleneck by introducing an additional penalty term that encourages the Fisher information of the extracted features to be small, when parametrized by the inputs. By tuning the regularization parameter, we can explicitly trade off the opposing desiderata of robustness and accuracy when constructing a classifier. We derive the optimal solution to the robust information bottleneck when the inputs and outputs are jointly Gaussian, proving that the optimally robust features are also jointly Gaussian in that setting. Furthermore, we propose a method for optimizing a variational bound on the robust information bottleneck objective in general settings using stochastic gradient descent, which may be implemented efficiently in neural networks. Our experimental results for synthetic and real data sets show that the proposed feature extraction method indeed produces classifiers with increased robustness to perturbations. Copyright © 2019, The Authors. All rights reserved.

关键词： Fisher information matrix

Arbitrary-time error suppression for Markovian adiabatic quantum computing using stabilizer subspace codes

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Physical Review A 2019年第2期100卷 022326-022326页

作者： Daniel A. Lidar Departments of Electrical & Computer Engineering Chemistry Physics & Astronomy and Center for Quantum Information Science & Technology University of Southern California Los Angeles California 90089 USA

Adiabatic quantum computing (AQC) can be protected against thermal excitations via an encoding into error-detecting codes, supplemented with an energy penalty formed from a sum of commuting Hamiltonian terms. Earlier work showed that it is possible to suppress the initial thermally induced excitation out of the encoded ground state, in the case of local Markovian environments, by using an energy-penalty strength that grows only logarithmically in system size, at a fixed temperature. The question of whether this result applies beyond the initial time was left open. Here we answer this in the affirmative. We show that thermal excitations out of the encoded ground state can be suppressed at arbitrary times under the additional assumption that the total evolution time is polynomial in the system size. Thus, computational problems that can be solved efficiently by using AQC in a closed system setting can still be solved efficiently subject to coupling to a thermal environment. Our construction uses stabilizer subspace codes, which require at least 4-local interactions to achieve this result.

关键词： Adiabatic quantum optimization Quantum computation Quantum error correction

Erratum to "Leading edge of the a-wave of the electroretinogram and sodium iodate-induced age-related macular degeneration: A Model" [J. Theor. Biol. 592 (2024) 01-17/111879]

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Journal of theoretical biology 2024年 593卷 111912页

作者： Deepak K Pattanaik Vasudevan Lakshminarayanan Nachieketa K Sharma Amir Prasad Sahu Siksha 'O' Anusandhan University Bhubaneswar OR 751030 India. Electronic address: pattanaikdeepak85@***. School of Optometry and Vision Science and Departments of Physics Electrical and Computer Engineering and System Design Engineering University of Waterloo Waterloo Canada. Electronic address: Vengu@uwaterloo.ca. Siksha 'O' Anusandhan University Bhubaneswar OR 751030 India. Electronic address: nachiketasharma@soa.ac.in. Centurion University of Technology and Management Bhubaneswar OR India. Electronic address: amirprasadsahoo@***.