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CellBiAge: Improved single-cell age classification using data binarization

Cell Reports 2023年第12期42卷 113500页

作者： Yu, Doudou Li, Manlin Linghu, Guanjie Hu, Yihuan Hajdarovic, Kaitlyn H. Wang, An Singh, Ritambhara Webb, Ashley E. Molecular Biology Cell Biology and Biochemistry Graduate Program Brown University Providence 02912 RI United States Data Science Institute Brown University Providence 02912 RI United States Neuroscience Graduate Program Brown University Providence 02912 RI United States Department of Applied Mathematics & Statistics Johns Hopkins University Baltimore 21218 MD United States Department of Computer Science Brown University Providence 02912 RI United States Center for Computational Molecular Biology Brown University Providence 02912 RI United States Department of Molecular Biology Cell Biology and Biochemistry Brown University Providence 02912 RI United States Center on the Biology of Aging Brown University Providence 02912 RI United States Carney Institute for Brain Science Brown University Providence 02912 RI United States Center for Translational Neuroscience Brown University Providence 02912 RI United States

Aging is a major risk factor for many diseases. Accurate methods for predicting age in specific cell types are essential to understand the heterogeneity of aging and to assess rejuvenation strategies. However, classifying organismal age at single-cell resolution using transcriptomics is challenging due to sparsity and noise. Here, we developed CellBiAge, a robust and easy-to-implement machine learning pipeline, to classify the age of single cells in the mouse brain using single-cell transcriptomics. We show that binarization of gene expression values for the top highly variable genes significantly improved test performance across different models, techniques, sexes, and brain regions, with potential age-related genes identified for model prediction. Additionally, we demonstrate CellBiAge's ability to capture exercise-induced rejuvenation in neural stem cells. This study provides a broadly applicable approach for robust classification of organismal age of single cells in the mouse brain, which may aid in understanding the aging process and evaluating rejuvenation methods. © 2023 The Authors

关键词： aging brain CP: Cell biology hypothalamus machine learning single-cell RNA-seq

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Active learning of uniformly accurate interatomic potentials for materials simulation

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Physical Review Materials 2019年第2期3卷 023804-023804页

作者： Linfeng Zhang De-Ye Lin Han Wang Roberto Car Weinan E Department of Mathematics and Program in Applied and Computational Mathematics Princeton University Princeton New Jersey 08544 USA and Beijing Institute of Big Data Research Beijing 100871 People's Republic of China

An active learning procedure called deep potential generator (DP-GEN) is proposed for the construction of accurate and transferable machine learning-based models of the potential energy surface (PES) for the molecular modeling of materials. This procedure consists of three main components: exploration, generation of accurate reference data, and training. Application to the sample systems of Al, Mg, and Al-Mg alloys demonstrates that DP-GEN can produce uniformly accurate PES models with a minimal number of reference data.

关键词： Atomic & molecular structure Potential energy surfaces Alloys Amorphous materials Liquids Density functional theory First-principles calculations Molecular dynamics

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Quality of internal representation shapes learning performance in feedback neural networks

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Physical Review Research 2021年第1期3卷 013176-013176页

作者： Lee Susman Francesca Mastrogiuseppe Naama Brenner Omri Barak Interdisciplinary Program in Applied Mathematics Technion Israel Institute of Technology Haifa 32000 Israel Network Biology Research Laboratories Technion Israel Institute of Technology Haifa 32000 Israel Gatsby Computational Neuroscience Unit University College London London W1T 4JG United Kingdom Department of Chemical Engineering Technion Israel Institute of Technology Haifa 32000 Israel Rappaport Faculty of Medicine Technion Israel Institute of Technology Haifa 32000 Israel

A fundamental feature of complex biological systems is the ability to form feedback interactions with their environment. A prominent model for studying such interactions is reservoir computing, where learning acts on low-dimensional bottlenecks. Despite the simplicity of this learning scheme, the factors contributing to or hindering the success of training in reservoir networks are in general not well understood. In this work, we study nonlinear feedback networks trained to generate a sinusoidal signal, and analyze how learning performance is shaped by the interplay between internal network dynamics and target properties. By performing exact mathematical analysis of linearized networks, we predict that learning performance is maximized when the target is characterized by an optimal, intermediate frequency which monotonically decreases with the strength of the internal reservoir connectivity. At the optimal frequency, the reservoir representation of the target signal is high-dimensional, desynchronized, and thus maximally robust to noise. We show that our predictions successfully capture the qualitative behavior of performance in nonlinear networks. Moreover, we find that the relationship between internal representations and performance can be further exploited in trained nonlinear networks to explain behaviors which do not have a linear counterpart. Our results indicate that a major determinant of learning success is the quality of the internal representation of the target, which in turn is shaped by an interplay between parameters controlling the internal network and those defining the task.

关键词： Learning theory Artificial neural networks Dynamical systems Neuronal network models

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Empowering Precision Medicine: AI-Driven Schizophrenia Diagnosis via EEG Signals: A Comprehensive Review from 2002-2023

arXiv

引用

arXiv 2023年

作者： Jafari, Mahboobeh Sadeghi, Delaram Shoeibi, Afshin Alinejad-Rokny, Hamid Beheshti, Amin García, David López Chen, Zhaolin Acharya, U. Rajendra Gorriz, Juan M. Internship in BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney SydneyNSW2052 Australia Data Science and Computational Intelligence Institute University of Granada Spain BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney SydneyNSW2052 Australia UNSW Data Science Hub The University of New South Wales SydneyNSW2052 Australia Health Data Analytics Program Centre for Applied Artificial Intelligence Macquarie University Sydney2109 Australia Data Science Lab School of Computing Macquarie University SydneyNSW2109 Australia Monash University Melbourne Australia School of Mathematics Physics and Computing University of Southern Queensland Springfield Australia Department of Psychiatry University of Cambridge United Kingdom

Schizophrenia (SZ) is a prevalent mental disorder characterized by cognitive, emotional, and behavioral changes. Symptoms of SZ include hallucinations, illusions, delusions, lack of motivation, and difficulties in concentration. While the exact causes of SZ remain unproven, factors such as brain injuries, stress, and psychotropic drugs have been implicated in its development. SZ can be classified into different types, including paranoid, disorganized, catatonic, undifferentiated, and residual. Diagnosing SZ involves employing various tools, including clinical interviews, physical examinations, psychological evaluations, the Diagnostic and Statistical Manual of Mental Disorders (DSM), and neuroimaging techniques. Electroencephalography (EEG) recording is a significant functional neuroimaging modality that provides valuable insights into brain function during SZ. However, EEG signal analysis poses challenges for neurologists and scientists due to the presence of artifacts, long-term recordings, and the utilization of multiple channels. To address these challenges, researchers have introduced artificial intelligence (AI) techniques, encompassing conventional machine learning (ML) and deep learning (DL) methods, to aid in SZ diagnosis. This study reviews papers focused on SZ diagnosis utilizing EEG signals and AI methods. The introduction section provides a comprehensive explanation of SZ diagnosis methods and intervention techniques. Subsequently, review papers in this field are discussed, followed by an introduction to the AI methods employed for SZ diagnosis and a summary of relevant papers presented in tabular form. Additionally, this study reports on the most significant challenges encountered in SZ diagnosis, as identified through a review of papers in this field. Future directions to overcome these challenges are also addressed. The discussion section examines the specific details of each paper, culminating in the presentation of conclusions and findings. © 2023,

关键词： Electroencephalography

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Superposition and mimicking theorems for conditional McKean-Vlasov equations

arXiv

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arXiv 2020年

作者： Lacker, Daniel Shkolnikov, Mykhaylo Zhang, Jiacheng IEOR Department Columbia University New YorkNY10027 United States ORFE Department Bendheim Center for Finance Program in Applied & Computational Mathematics Princeton University PrincetonNJ08544 United States ORFE Department Princeton University PrincetonNJ08544 United States

We consider conditional McKean-Vlasov stochastic differential equations (SDEs), such as the ones arising in the large-system limit of mean field games and particle systems with mean field interactions when common noise is present. The conditional time-marginals of the solutions to these SDEs satisfy non-linear stochastic partial differential equations (SPDEs) of the second order, whereas the laws of the conditional time-marginals follow Fokker-Planck equations on the space of probability measures. We prove two superposition principles: The first establishes that any solution of the SPDE can be lifted to a solution of the conditional McKean-Vlasov SDE, and the second guarantees that any solution of the Fokker-Planck equation on the space of probability measures can be lifted to a solution of the SPDE. We use these results to obtain a mimicking theorem which shows that the conditional time-marginals of an Itô process can be emulated by those of a solution to a conditional McKean-Vlasov SDE with Markovian coefficients. This yields, in particular, a tool for converting open-loop controls into Markovian ones in the context of controlled McKean-Vlasov *** Codes 60H10, 60H15, 35Q84, 93E20 Copyright © 2020, The Authors. All rights reserved.

关键词： Fokker Planck equation

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Two-Stage Maximum Score Estimator

arXiv

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arXiv 2020年

作者： Gao, Wayne Yuan Xu, Sheng Xu, Kan Department of Economics University of Pennsylvania 133 S 36th St. PhiladelphiaPA19104 United States The Program in Applied and Computational Mathematics Princeton University Fine Hall Washington Road PrincetonNJ08544 United States

This paper considers the asymptotic theory of a semiparametric M-estimator that is generally applicable to models that satisfy a monotonicity condition in one or several parametric indexes. We call this estimator the two-stage maximum score (TSMS) estimator, since our estimator involves a first-stage nonparametric regression when applied to the binary choice model of Manski (1975, 1985). We characterize the asymptotic distribution of the TSMS estimator, which features phase transitions depending on the dimension of the first-stage estimation. Effectively, the first-stage nonparametric estimator serves as an imperfect smoothing function on a non-smooth criterion function, leading to the pivotality of the first-stage estimation error with respect to the second-stage convergence rate and asymptotic distribution. Copyright © 2020, The Authors. All rights reserved.

关键词： Functions

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Models for plasma kinetics during simultaneous therapeutic plasma exchange and extracorporeal membrane oxygenation

arXiv

引用

arXiv 2020年

作者： Puelz, Charles Danial, Zach Marinaro, Jon Raval, Jay Griffith, Boyce E. Peskin, Charles S. Courant Institute of Mathematical Sciences New York University Department of Emergency Medicine University of New Mexico Department of Pathology University of New Mexico Departments of Mathematics Applied Physical Sciences and Biomedical Engineering University of North Carolina Carolina Center for Interdisciplinary Applied Mathematics University of North Carolina Computational Medicine Program University of North Carolina McAllister Heart Institute University of North Carolina

This paper focuses on the derivation and simulation of mathematical models describing new plasma fraction in blood for patients undergoing simultaneous extracorporeal membrane oxygenation and therapeutic plasma exchange. Models for plasma exchange with either veno–arterial or veno–venous extracorporeal membrane oxygenation are considered. Two classes of models are derived for each case, one in the form of an algebraic delay equation and another in the form of a system of delay differential equations. In special cases, our models reduce to single compartment ones for plasma exchange that have been validated with experimental data [15]. We also show that the algebraic delay equations are forward Euler discretizations of the delay differential equations, with timesteps equal to transit times through model compartments. Numerical simulations are performed to compare different model types, to investigate the impact of plasma device port switching on the efficiency of the exchange process, and to study the sensitivity of the models to their parameters. Copyright © 2020, The Authors. All rights reserved.

关键词： Respiratory therapy

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Isotope effects on molecular structures and electronic properties of liquid water via deep potential molecular dynamics based on SCAN functional

arXiv

引用

arXiv 2020年

作者： Xu, Jianhang Zhang, Chunyi Zhang, Linfeng Chen, Mohan Santra, Biswajit Wu, Xifan Department of Physics Temple University PhiladelphiaPA19122 United States Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States CAPT HEDPS College of Engineering Peking University Beijing100871 China Institute for Computational Molecular Science Temple University PhiladelphiaPA19122 United States

Feynman path-integral deep potential molecular dynamics (PI-DPMD) calculations have been employed to study both light (H2O) and heavy water (D2O) within the isothermal–isobaric ensemble. In particular, the deep neural network is trained based on ab initio data obtained from the strongly constrained and appropriately normed (SCAN) exchange-correlation functional. Because of the lighter mass of hydrogen than deuteron, the properties of light water is more influenced by nuclear quantum effect than those of heavy water. Clear isotope effects are observed and analyzed in terms of hydrogen-bond structure and electronic properties of water that are closely associated with experimental observables. The molecular structures of both liquid H2O and D2O agree well with the data extracted from scattering experiments. The delicate isotope effects on radial distribution functions and angular distribution functions are well reproduced as well. Our approach demonstrates that deep neural network combined with SCAN functional based ab initio molecular dynamics provides an accurate theoretical tool for modeling water and its isotope effects. Copyright © 2020, The Authors. All rights reserved.

关键词： Electronic properties

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Orthogonal die random measures, primes, and applications

arXiv

引用

arXiv 2020年

作者： Bastian, Caleb Deen Rempala, Grzegorz A. Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Massive Dynamics PrincetonNJ08542 United States Department of Mathematics Ohio State University ColumbusOH43210 United States Division of Biostatistics Ohio State University ColumbusOH43210 United States

We show that the sequence of thinned uniform random counting measures converges weakly to the Poisson random measure. We call such measures ‘orthogonal dice’ due to their natural connection with certain counting problems and link the sizes of orthogonal dice with the primes by constructing the largest known prime orthogonal die. We give many examples of possible use of the construct of orthogonal dice in various areas of applications including gambling, cosmology, random matrices, approximation theory and circuits. The gambling example reveals for instance that fair six-sided die numbered {1, 2, 3, 4, 5, 6} generate negative covariance in point representations of hands across players, in contrast to seven-sided dice numbered {1, 2, 3, 4, 5, 6, 7} that generate zero covariance and are orthogonal. The cosmology example suggests that a ‘supermassive’ orthogonal die underlies the galaxy point patterns of the Universe. The random matrix application reveals that the variance of the spectral gap of the Gaussian orthogonal ensemble exhibits non-monotone scaling with thinning for the Dirac (empirical) random measure, in contrast to the orthogonal dice where the spectral gap variance is monotone. Finally, the approximation application shows that the discrete Legendre polynomials converge to the Charlier polynomials in Lp for integers p ≥ 1 whereas the electronic circuit/shot noise application identifies electric current as the Ornstein-Uhlenbeck process driven by an orthogonal die random *** Codes 60G57, 60G55, 60F05 Copyright © 2020, The Authors. All rights reserved.

关键词： Poisson distribution

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The impact of cosmic ray feedback during the epoch of reionisation

arXiv

引用

arXiv 2025年

作者： Farcy, Marion Rosdahl, Joakim Dubois, Yohan Blaizot, Jérémy Martin-Alvarez, Sergio Haehnelt, Martin Kimm, Taysun Teyssier, Romain Institute for Physics Laboratory for Galaxy Evolution and Spectral Modelling EPFL Observatoire de Sauverny Chemin Pegasi 51 Versoix1290 Switzerland Centre de Recherche Astrophysique de Lyon CNRS UMR 5574 Univ. Lyon Ens de Lyon 9 avenue Charles André Saint-Genis-LavalF-69230 France Institut d’Astrophysique de Paris CNRS UMR 7095 Sorbonne Université 98 bis bd Arago Paris75014 France Stanford University StanfordCA94305 United States Kavli Institute for Cosmology Institute of Astronomy Madingley Road CambridgeCB3 0HA United Kingdom Department of Astronomy Yonsei University 50 Yonsei-ro Seodaemun-gu Seoul03722 Korea Republic of Department of Astrophysical Sciences Princeton University Peyton Hall PrincetonNJ08544 United States Program in Applied and Computational Mathematics Princeton University Fine Hall Washington Road PrincetonNJ08544-1000 United States

Galaxies form and evolve via a multitude of complex physics. In this work, we investigate the role of cosmic ray (CR) feedback in galaxy evolution and reionisation, by examining its impact on the escape of ionising radiation from galaxies. For this purpose, we present two sphinx cosmological radiation-magneto-hydrodynamics simulations, allowing for the first time a study of the impact of CR feedback on thousands of resolved galaxies during the Epoch of Reionisation (EoR). The simulations differ in their feedback prescriptions: one adopts a calibrated strong supernova (SN) feedback, while the other simulation reduces the strength of SN feedback and includes CR feedback instead. We show that both comparably regulate star formation, reasonably match observations of high-redshift UV luminosity functions, and produce a similar amount of hydrogen ionising photons. In contrast to the model with strong SN feedback, the model with CRs lead to incomplete reionisation, which is in strong disagreement with observational estimates of the reionisation history. This is due to CR feedback shaping the ISM differently, filling with gas the low density cavities otherwise carved by SN explosions. As a result, this reduces the escape of ionising photons, at any halo mass, and primarily in the close vicinity of the stars. Our study indicates that CR feedback regulates galaxy growth during the EoR, but negatively affects reionisation, a tension which paves the way for further exploration and refinement of existing galaxy formation and feedback models. Such improvements are crucial in order to capture and understand the process of reionisation and the underlying evolution of galaxies through cosmic time. © 2025, CC BY.

关键词： Cosmic rays

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