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3D imaging lipidometry in single cell by in-flow holographic tomography

Opto-Electronic Advances 2023年第1期6卷 10-25页

作者： Daniele Pirone Daniele Sirico Lisa Miccio Vittorio Bianco Martina Mugnano Danila del Giudice Gianandrea Pasquinelli Sabrina Valente Silvia Lemma Luisa Iommarini Ivana Kurelac Pasquale Memmolo Pietro Ferraro CNR-ISASI Institute of Applied Sciences and Intelligent Systems“E.Caianiello”Via Campi Flegrei 34PozzuoliNapoli 80078Italy Department of Electrical Engineering and Information Technologies University of Naples“FedericoⅡ”via Claudio 21Napoli 80125Italy Department of Chemical Materials and Production Engineering of the University of Naples FedericoⅡPiazzale Tecchio 80Napoli 80125Italy Department of Mathematics and Physics University of Campania“Luigi Vanvitelli”Caserta 81100Italy Biotechnology and Methods in Laboratory Medicine Department of ExperimentalDiagnostic and Specialty Medicine(DIMES)University of BolognaBologna 40126Italy Subcellular Nephro-Vascular Diagnostic Program Pathology Unit S.Orsola IRCCSUniversity of BolognaBologna 40126Italy Unit of Medical Genetics Department of Medical and Surgical Sciences(DIMEC)University of BolognaVia Massarenti 9Bologna 40138Italy Study and Research Center on Gynecological Neoplasias Department of Medical and Surgical Sciences(DIMEC)University of BolognaVia Massarenti 9Bologna 40138Italy Center for Applied Biomedical Research(CRBA) University of BolognaBologna 40138Italy Department of Pharmacy and Biotechnology(FABIT) University of BolognaBologna 40126Italy

The most recent discoveries in the biochemical field are highlighting the increasingly important role of lipid droplets(LDs)in several regulatory mechanisms in living *** are dynamic organelles and therefore their complete characterization in terms of number,size,spatial positioning and relative distribution in the cell volume can shed light on the roles played by *** now,fluorescence microscopy and transmission electron microscopy are assessed as the gold standard methods for identifying LDs due to their high sensitivity and ***,such methods generally only provide 2D assays and partial ***,both can be destructive and with low productivity,thus limiting analysis of large cell numbers in a *** we demonstrate for the first time the capability of 3D visualization and the full LD characterization in high-throughput with a tomographic phase-contrast flow-cytometer,by using ovarian cancer cells and monocyte cell lines as models.A strategy for retrieving significant parameters on spatial correlations and LD 3D positioning inside each cell volume is *** information gathered by this new method could allow more in depth understanding and lead to new discoveries on how LDs are correlated to cellular functions.

关键词： lipid droplets label-free phase-contrast imaging in-flow tomography 3D imaging

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Fault Diagnosis of Wind Energy Conversion Systems Using Gaussian Process Regression-based Multi-Class Random Forest

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IFAC-PapersOnLine 2022年第6期55卷 127-132页

作者： Majdi Mansouri Radhia Fezai Mohamed Trabelsi Hajji Mansour Hazem Nounou Mohamed Nounou Electrical and Computer Engineering Program Texas A&M University at Qatar Doha Qatar Department of Mathematics and Sciences Prince Sultan University Riyadh 11586 Saudi Arabia Research Laboratory of Automation Signal Processing and Image National Engineering School of Monastir 5019 Tunisia Electronic and Communications Engineering Department Kuwait College of Science and Technology P. O. Box 27235 Kuwait Higher Institute of Applied Science and Technology of Kasserine University of Kairouan PO Box 471 1200 Kasserine Tunisia Chemical Engineering Program Texas A&M University at Qatar Doha Qatar

This work proposes a new fault diagnosis approach for a wind energy conversion (WEC) system. The proposed technique merges the benefits of feature extraction based on Gaussian Process Regression (GPR) and Multi-Class Random Forest (MCRF)-based fault classification where instances are classified into one or more classes. In the developed GPR-MCRF approach, the nonlinear statistical features including the mean vector M GPR and the variance matrix C GPR are computed using the GPR model with aim of extracting the most relevant features from the WEC system. Then, these features are introduced to the RF classifier for classification and diagnosis purposes. Therefore, the application of the GPR-MCRF technique for WEC systems aims to enhance the use of the classical raw data-based MCRF and diagnosis accuracy. Three kinds of faults (wear-out, open-circuit, and short-circuit faults) are considered in this work. Different case studies are investigated in order to illustrate the effectiveness and robustness of the developed technique compared to the state-of-the-art methods. The obtained results show that the the developed GPR-MCRF technique is an effective feature extraction and fault diagnosis technique for WEC systems.

关键词： Random Forest (RF) Multi-Class Gaussian Process Regression (GPR) Fault Diagnosis Wind Energy Conversion Systems

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An Immersed Peridynamics Model of Fluid-Structure Interaction Accounting for Material Damage and Failure

SSRN

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SSRN 2022年

作者： Kim, Keon Ho Bhalla, Amneet Pal Singh Griffith, Boyce E. Department of Mathematics University of North Carolina Chapel HillNC United States Department of Mechanical Engineering San Diego State University San DiegoCA United States Departments of Mathematics Applied Physical Sciences and Biomedical Engineering University of North Carolina Chapel HillNC United States Carolina Center for Interdisciplinary Applied Mathematics University of North Carolina Chapel HillNC United States Computational Medicine Program University of North Carolina Chapel HillNC United States McAllister Heart Institute University of North Carolina Chapel HillNC United States

This paper develops and benchmarks an immersed peridynamics method to simulate the deformation, damage, and failure of hyperelastic materials within a fluid-structure interaction framework. The immersed peridynamics method describes an incompressible structure immersed in a viscous incompressible fluid. It expresses the momentum equation and incompressibility constraint in Eulerian form, and it describes the structural motion and resultant forces in Lagrangian form. Coupling between Eulerian and Lagrangian variables is achieved by integral transforms with Dirac delta function kernels, as in standard immersed boundary (IB) methods. The major difference between our approach and conventional IB methods is that we use peridynamics, instead of classical continuum mechanics, to determine the structural forces. We focus on non-ordinary state-based peridynamic material descriptions that allow us to use a constitutive correspondence framework that can leverage well characterized nonlinear constitutive models of soft materials. The convergence and accuracy of our approach are compared to both conventional and immersed finite element methods using widely used benchmark problems of nonlinear incompressible elasticity. We demonstrate that the immersed peridynamics method yields comparable accuracy with similar numbers of structural degrees of freedom for several choices of the size of the peridynamic horizon. We also demonstrate that the method can generate grid-converged simulations of fluid-driven material damage growth, crack formation and propagation, and rupture under large deformations. © 2022, The Authors. All rights reserved.

关键词： Fluid structure interaction

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Detection of features from the internet of things customer attitudes in the hotel industry using a deep neural network model

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Measurement: Sensors 2022年 22卷

作者： Rajesh, Sudha Abd Algani, Yousef Methkal Al Ansari, Mohammed Saleh Balachander, Bhuvaneswari Raj, Roop Muda, Iskandar Kiran Bala, B. Balaji, S. Dept. of Computational Intelligence College of Engineering and Technology School of Computing SRMIST Kattankulathur Chennai India Department of Mathematics Sakhnin College Israel College of Engineering Department of Chemical Engineering University of Bahrain Bahrain Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602105 India Lecturer in Economics Education Department Government of Haryana Haryana Panchkula India Department of Doctoral Program Faculty Economic and Business Universitas Sumatera Utara 20222 Jl. Prof TM Hanafiah 12 USU Campus Padang bulan Medan20155 Indonesia Department of Artificial Intelligence and Data Science K.Ramakrishnan College of Engineering Tamil Nadu Trichy India Department of CSE Panimalar Engineering College Chennai India Department of Mathematics The Arab Academic College for Education in Israel-Haifa Israel

Tourism and the hotel business have benefited greatly from the use of digital social networking. Using social big data research, the application of deep learning seems to have been beneficial in a marketing strategies and customer preference estimate. Recognizing human psychology, which is critical to industrial success, has benefited greatly from digital technology and social media. The Internet of Things (IoT) provides a chance for a hotel sector to improve the customer experience although lowering operational expenses. The ratings are determined by the following factors: Value, Apartment, Location, Hygiene, Front Office, Facilities, Professional Service, Internet, and Parking. Traditional techniques which anticipate hotel evaluations through minimal precision add difficulty to the rating assessment. As a result, efficient deep learning algorithms are employed to evaluate reviews designed to help consumers in selecting better hotels. To predict qualities, multiple classification techniques, including convolutional neural network-based deep learning (CNN-DL) and support vector machine (SVM) network-based deep learning, were used in this research. The system examines system efficiency by using the TripAdvisor website, this is a well American database. The research results reveal that the CNN-DL method outperforms another method in terms of classification efficiency and failure *** graphical results could also be utilized to enhance the effectiveness of the suggested model and offer insights into response tactics, demonstrating the study's academic and conceptual achievements. Although it is feasible to conclude from such research that the possibility of IoT within the hotel industry has not yet been fully investigated, as researchers commonly speculate on using IoT for implementations that might quickly be of involvement to a hotel sector, but refuse to recognize that possibility as a massive market. © 2022 The Author(s)

关键词： Hotels

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Unbiased Estimation using a Class of Diffusion Processes

arXiv

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arXiv 2022年

作者： Ruzayqat, Hamza Beskos, Alexandros Crisan, Dan Jasra, Ajay Kantas, Nikolas Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 KSA Saudi Arabia Department of Statistical Science University College London LondonWC1E 6BT United Kingdom Department of Mathematics Imperial College London LondonSW7 2AZ United Kingdom

We study the problem of unbiased estimation of expectations with respect to (w.r.t.) π a given, general probability measure on (d, B(d)) that is absolutely continuous with respect to a standard Gaussian measure. We focus on simulation associated to a particular class of diffusion processes, sometimes termed the Schrödinger-Föllmer Sampler, which is a simulation technique that approximates the law of a particular diffusion bridge process {Xt}tϵ[0,1]on d, d ϵ 0. This latter process is constructed such that, starting at X0= 0, one has X1∼ π. Typically, the drift of the diffusion is intractable and, even if it were not, exact sampling of the associated diffusion is not possible. As a result, [10, 16] consider a stochastic Euler-Maruyama scheme that allows the development of biased estimators for expectations w.r.t. π. We show that for this methodology to achieve a mean square error of O(ϵ2), for arbitrary ϵ > 0, the associated cost is O(ϵ-5). We then introduce an alternative approach that provides unbiased estimates of expectations w.r.t. π, that is, it does not suffer from the time discretization bias or the bias related with the approximation of the drift function. We prove that to achieve a mean square error of O(ϵ2), the associated cost (which is random) is, with high probability, O(ϵ-2| log(ϵ)|2+δ), for any δ > 0. We implement our method on several examples including Bayesian inverse *** Codes 60J60, 62D05, 65C40 © 2022, CC BY.

关键词： Diffusion

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Quantifying the Structure of Disordered Materials

arXiv

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arXiv 2022年

作者： Hardin, Thomas J. Chandross, Michael Meena, Rahul Fajardo, Spencer Giovanis, Dimitris Kevrekidis, Yannis Falk, Michael Shields, Michael Computational Materials and Data Science Department Sandia National Laboratories United States Department of Civil Engineering Johns Hopkins University United States Department of Materials Science and Engineering Johns Hopkins University United States Department of Applied Mathematics and Statistics Johns Hopkins University United States Department of Chemical and Biomolecular Engineering Johns Hopkins University United States Department of Mechanical Engineering Johns Hopkins University United States Department of Physics and Astronomy Johns Hopkins University United States Hopkins Extreme Materials Institute Johns Hopkins University United States

Durable interest in developing a framework for the detailed structure of glassy materials has produced numerous structural descriptors that trade off between general applicability and interpretability. However, none approach the combination of simplicity and wide-ranging predictive power of the lattice-grain-defect framework for crystalline materials. Working from the hypothesis that the local atomic environments of a glassy material are constrained by enthalpy minimization to a low-dimensional manifold in atomic coordinate space, we develop a novel generalized distance function, the Gaussian Integral Inner Product (GIIP) distance, in connection with agglomerative clustering and diffusion maps, to parameterize that manifold. Applying this approach to a two-dimensional model crystal and a three-dimensional binary model metallic glass results in parameters interpretable as coordination number, composition, volumetric strain, and local symmetry. In particular, we show that a more slowly quenched glass has a higher degree of local tetrahedral symmetry at the expense of cyclic symmetry. While these descriptors require post-hoc interpretation, they minimize bias rooted in crystalline materials science and illuminate a range of structural trends that might otherwise be missed. © 2022, CC BY.

关键词： Metallic glass

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A Nodal Immersed Finite Element-Finite Difference Method

SSRN

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SSRN 2022年

作者： Wells, David Vadala-Roth, Ben Lee, Jae Ho Griffith, Boyce E. Department of Mathematics University of North Carolina Chapel HillNC United States U.S. Army Corps of Engineers Engineer Research and Development Center Coastal and Hydraulic Laboratory VicksburgMS United States Department of Mechanical Engineering Institute for Computational Medicine Johns Hopkins University BaltimoreMD United States Departments of Mathematics Applied Physical Sciences and Biomedical Engineering University of North Carolina Chapel HillNC United States Carolina Center for Interdisciplinary Applied Mathematics University of North Carolina Chapel HillNC United States Computational Medicine Program University of North Carolina Chapel HillNC United States McAllister Heart Institute University of North Carolina Chapel HillNC United States

The immersed finite element-finite difference (IFED) method is a computational approach to modeling interactions between a fluid and an immersed structure. The IFED method uses a finite element (FE) method to approximate the stresses and forces on a structural mesh and a finite difference (FD) method to approximate the momentum of the entire fluid-structure system on a Cartesian grid. The fundamental approach used by this method follows the immersed boundary framework for modeling fluid-structure interaction (FSI), in which a force spreading operator prolongs structural forces to a Cartesian grid, and a velocity interpolation operator restricts a velocity field defined on that grid back onto the structural mesh. With an FE structural mechanics framework, force spreading first requires that the force itself be projected onto the finite element space. Similarly, velocity interpolation requires projecting velocity data onto the FE basis functions. Consequently, evaluating either coupling operator requires solving a matrix equation at every time step. Mass lumping, in which the projection matrices are replaced by diagonal approximations, has the potential to accelerate this method considerably. This paper provides both numerical and computational analyses of the effects of this replacement for evaluating the force projection and for the IFED coupling operators. Constructing the coupling operators also requires determining the locations on the structure mesh where the forces and velocities are sampled. Here we show that sampling the forces and velocities at the nodes of the structural mesh is equivalent to using lumped mass matrices in the IFED coupling operators. A key theoretical result of our analysis is that if both of these approaches are used together, the IFED method permits the use of lumped mass matrices derived from nodal quadrature rules for any standard interpolatory element. This is different from standard FE methods, which require specialized treatments to

关键词： Fluid structure interaction

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Machine learning based non-Newtonian fluid model with molecular fidelity

arXiv

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arXiv 2020年

作者： Lei, Huan Wu, Lei Weinan, E. Department of Computational Mathematics Science & Engineering Department of Statistics & Probability Michigan State University MI48824 United States Department of Mathematics and Program in Applied and Computational Mathematics Princeton University NJ08544 United States

We introduce a machine-learning-based framework for constructing continuum non-Newtonian fluid dynamics model directly from a micro-scale description. Polymer solution is used as an example to demonstrate the essential ideas. To faithfully retain molecular fidelity, we establish a micro-macro correspondence via a set of encoders for the micro-scale polymer configurations and their macro-scale counterparts, a set of nonlinear conformation tensors. The dynamics of these conformation tensors can be derived from the micro-scale model and the relevant terms can be parametrized using machine learning. The final model, named the deep non-Newtonian model (DeePN2), takes the form of conventional non-Newtonian fluid dynamics models, with a new form of the objective tensor derivative. Numerical results demonstrate the accuracy of DeePN2. Copyright © 2020, The Authors. All rights reserved.

关键词： Viscous flow

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Spatial atlas of the mouse central nervous system at molecular resolution (vol 622, pg 552, 2023)

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NATURE 2024年第7993期625卷 E6-E6页

作者： Shi, Hailing He, Yichun Zhou, Yiming Huang, Jiahao Maher, Kamal Wang, Brandon Tang, Zefang Luo, Shuchen Tan, Peng Wu, Morgan Lin, Zuwan Ren, Jingyi Thapa, Yaman Tang, Xin Chan, Ken Y. Deverman, Benjamin E. Shen, Hao Liu, Albert Liu, Jia Wang, Xiao Broad Institute of MIT and Harvard Cambridge MA USA Department of Chemistry Massachusetts Institute of Technology Cambridge MA USA John A. Paulson School of Engineering and Applied Sciences Harvard University Boston MA USA Computational and Systems Biology PhD Program Massachusetts Institute of Technology Cambridge MA USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA USA Department of Biology Massachusetts Institute of Technology Cambridge MA USA Klarman Cell Observatory Broad Institute of MIT and Harvard Cambridge MA USA Department of Chemistry and Chemical Biology Harvard University Cambridge MA USA Stanley Center for Psychiatric Research Broad Institute of MIT and Harvard Cambridge MA USA

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Quantum Linear System Solvers: A Survey of Algorithms and Applications

arXiv

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arXiv 2024年

作者： Morales, Mauro E.S. Pira, Lirandë Schleich, Philipp Koor, Kelvin Costa, Pedro C.S. An, Dong Aspuru-Guzik, Alán Lin, Lin Rebentrost, Patrick Berry, Dominic W. Joint Center for Quantum Information and Computer Science University of Maryland United States Centre for Quantum Software and Information University of Technology Sydney Australia Centre for Quantum Technologies National University of Singapore Singapore Department of Computer Science University of Toronto Canada Vector Institute for Artificial Intelligence Toronto Canada ContinoQuantum Sydney Australia Beijing International Center for Mathematical Research Peking University Beijing China Department of Chemistry University of Toronto Canada Canadian Institute for Advanced Research Toronto Canada Acceleration Consortium Toronto Canada Department of Chemical Engineering & Applied Chemistry University of Toronto Toronto Canada Department of Materials Science & Engineering University of Toronto Toronto Canada Lebovic Fellow Canadian Institute for Advanced Research Toronto Canada Department of Mathematics University of California Berkeley United States Applied Mathematics and Computational Research Division Lawrence Berkeley National Laboratory Berkeley United States Department of Computer Science National University of Singapore Singapore School of Mathematical and Physical Sciences Macquarie University Sydney Australia

Solving linear systems of equations plays a fundamental role in numerous computational problems from different fields of science. The widespread use of numerical methods to solve these systems motivates investigating the feasibility of solving linear systems problems using quantum computers. In this work, we provide a survey of the main advances in quantum linear systems algorithms, together with some applications. We summarize and analyze the main ideas behind some of the algorithms for the quantum linear systems problem in the literature. The analysis begins by examining the Harrow-Hassidim-Lloyd (HHL) solver. We note its limitations and reliance on computationally expensive quantum methods, then highlight subsequent research efforts which aimed to address these limitations and optimize runtime efficiency and precision via various paradigms. We focus in particular on the post-HHL enhancements which have paved the way towards optimal lower bounds with respect to error tolerance and condition number. By doing so, we propose a taxonomy that categorizes these studies. Furthermore, by contextualizing these developments within the broader landscape of quantum computing, we explore the foundational work that have inspired and informed their development, as well as subsequent refinements. Finally, we discuss the potential applications of these algorithms in differential equations, quantum machine learning, and many-body physics. © 2024, CC BY.

关键词： Quantum computers

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