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A pretrained transformer model for decoding individual glucose dynamics from continuous glucose monitoring data

National science Review 2025年第5期 362-381页

作者： Yurun Lu Dan Liu Zhongming Liang Rui Liu Pei Chen Yitong Liu Jiachen Li Zhanying Feng Lei M.Li Bin Sheng Weiping Jia Luonan Chen Huating Li Yong Wang Center for Excellence in Mathematical Sciences National Center for Mathematics and Interdisciplinary Sciences Hua Loo-Keng Center for Mathematical Sciences Key Laboratory of Management Decision and Information System Academy of Mathematics and Systems ScienceChinese Academy of Sciences School of Mathematics University of Chinese Academy of SciencesChinese Academy of Sciences Department of Endocrinology and Metabolism Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghai Diabetes InstituteShanghai Clinical Center for Diabetes Shanghai Key Laboratory of Diabetes Mellitus Key Laboratory of Systems Health Science of Zhejiang Province School of Life ScienceHangzhou Institute for Advanced StudyUniversity of Chinese Academy of Sciences BGI-Research School of Mathematics South China University of Technology Department of Statistics Department of Biomedical Data Science Bio-X ProgramStanford University Department of Computer Science and Engineering Shanghai Jiao Tong University State Key Laboratory of Cell Biology Center for Excellence in Molecular Cell Science Shanghai Institute of Biochemistry and Cell Biology Chinese Academy of Sciences Guangdong Institute of Intelligence Science and Technology Pazhou Laboratory (Huangpu)

Continuous glucose monitoring(CGM) technology has grown rapidly to track real-time blood glucose levels and trends with improved sensor accuracy. The ease of use and wide availability of CGM will facilitate safe and effective decision making for diabetes management. Here, we developed an attention-based deep learning model, CGMformer, pretrained on a well-controlled and diverse corpus of CGM data to represent individual's intrinsic metabolic state and enable clinical applications. During pretraining, CGMformer encodes glucose dynamics including glucose level, fluctuation, hyperglycemia, and hypoglycemia into latent space with self-supervised learning. It shows generalizability in imputing glucose value across five external datasets with different populations and metabolic states(MAE = 3.7 mg/d L). We then fine-tuned CGMformer towards a diverse panel of downstream tasks in the screening of diabetes and its complications using task-specific data, which demonstrated a consistently boosted predictive accuracy over direct fine-tuning on a single task(AUROC = 0.914 for type 2 diabetes(T2D) screening and 0.741 for complication screening). By learning an intrinsic representation of an individual's glucose dynamics,CGMformer classifies non-diabetic individuals into six clusters with elevated T2D risks, and identifies a specific cluster with lean body-shape but high risk of glucose metabolism disorders, which is overlooked by traditional glucose measurements. Furthermore, CGMformer achieves high accuracy in predicting an individual's postprandial glucose response with dietary modelling(Pearson correlation coefficient = 0.763)and helps personalized dietary recommendations. Overall, CGMformer pretrains a transformer neural network architecture to learn an intrinsic representation by borrowing information from a large amount of daily glucose profiles, and demonstrates predictive capabilities fine-tuned towards a broad range of downstream applications, holding promise for the ear

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Local Number Fluctuations in Hyperuniform and Nonhyperuniform Systems: Higher-Order Moments and Distribution Functions

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Physical Review X 2021年第2期11卷 021028-021028页

作者： Salvatore Torquato Jaeuk Kim Michael A. Klatt Department of Physics Princeton University Princeton New Jersey 08544 USA Department of Chemistry Princeton Institute for the Science and Technology of Materials and Program in Applied and Computational Mathematics Princeton University Princeton New Jersey 08544 USA Institut für Theoretische Physik FAU Erlangen-Nürnberg Staudtstr. 7 91058 Erlangen Germany

The local number variance σ2(R) associated with a spherical sampling window of radius R enables a classification of many-particle systems in d-dimensional Euclidean space Rd according to the degree to which large-scale density fluctuations are suppressed, resulting in a demarcation between hyperuniform and nonhyperuniform phyla. To more completely characterize density fluctuations, we carry out an extensive study of higher-order moments or cumulants, including the skewness γ1(R), excess kurtosis γ2(R), and the corresponding probability distribution function P[N(R)] of a large family of models across the first three space dimensions, including both hyperuniform and nonhyperuniform systems with varying degrees of short- and long-range order. To carry out this comprehensive program, we derive new theoretical results that apply to general point processes, and we conduct high-precision numerical studies. Specifically, we derive explicit closed-form integral expressions for γ1(R) and γ2(R) that encode structural information up to three-body and four-body correlation functions, respectively. We also derive rigorous bounds on γ1(R), γ2(R), and P[N(R)] for general point processes and corresponding exact results for general packings of identical spheres. High-quality simulation data for γ1(R), γ2(R), and P[N(R)] are generated for each model. We also ascertain the proximity of P[N(R)] to the normal distribution via a novel Gaussian “distance” metric l2(R). Among all models, the convergence to a central limit theorem (CLT) is generally fastest for the disordered hyperuniform processes in two or higher dimensions such that γ1(R)∼l2(R)∼R−(d+1)/2 and γ2(R)∼R−(d+1) for large R. The convergence to a CLT is slower for standard nonhyperuniform models and slowest for the “antihyperuniform” model studied here. We prove that one-dimensional hyperuniform systems of class I or any d-dimensional lattice cannot obey a CLT. Remarkably, we discover a type of universality in that, for all of ou

关键词： Exotic phases of matter Fluctuations & noise Nonequilibrium statistical mechanics Surface & interfacial phenomena

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Gap Sensitivity Reveals Universal Behaviors in Optimized Photonic Crystal and Disordered Networks

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Physical Review Letters 2021年第3期127卷 037401-037401页

作者： Michael A. Klatt Paul J. Steinhardt Salvatore Torquato Department of Physics Princeton University Princeton New Jersey 08544 USA Institut für Theoretische Physik University of Erlangen-Nürnberg Staudtstr. 7 91058 Erlangen Germany Department of Chemistry Princeton Institute for the Science and Technology of Materialsand Program in Applied and Computational Mathematics Princeton University Princeton New Jersey 08544 USA

Through an extensive series of high-precision numerical computations of the optimal complete photonic band gap (PBG) as a function of dielectric contrast α for a variety of crystal and disordered heterostructures, we reveal striking universal behaviors of the gap sensitivity S(α)≡dΔ(α)/dα, the first derivative of the optimal gap-to-midgap ratio Δ(α). In particular, for all our crystal networks, S(α) takes a universal form that is well approximated by the analytic formula for a 1D quarter-wave stack, SQWS(α). Even more surprisingly, the values of S(α) for our disordered networks converge to SQWS(α) for sufficiently large α. A deeper understanding of the simplicity of this universal behavior may provide fundamental insights about PBG formation and guidance in the design of novel photonic heterostructures.

关键词： Photonic crystals Diamond structure Networks & random structures Random & disordered media Band structure methods

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Viscosity in water from first-principles and deep-neural-network simulations

arXiv

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arXiv 2022年

作者： Malosso, Cesare Zhang, Linfeng Car, Roberto Baroni, Stefano Tisi, Davide SISSA - Scuola Internazionale Superiore di Studi Avanzati Trieste34136 Italy Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States DP Technology Beijing100080 China Department of Chemistry Department of Physics Princeton Institute for the Science and Technology of Materials Princeton University PrincetonNJ08544 United States CNR Istituto Officina dei Materiali SISSA Unit Trieste34136 Italy

We report on an extensive study of the viscosity of liquid water at near-ambient conditions, performed within the Green-Kubo theory of linear response and equilibrium ab initio molecular dynamics (AIMD), based on density-functional theory (DFT). In order to cope with the long simulation times necessary to achieve an acceptable statistical accuracy, our ab initio approach is enhanced with deep-neural-network potentials (NNP). This approach is first validated against AIMD results, obtained by using the Perdew-Burke-Ernzerhof (PBE) exchange-correlation functional and paying careful attention to crucial, yet often overlooked, aspects of the statistical data analysis. Then, we train a second NNP to a dataset generated from the Strongly Constrained and Appropriately Normed (SCAN) functional. Once the error resulting from the imperfect prediction of the melting line is offset by referring the simulated temperature to the theoretical melting one, our SCAN predictions of the shear viscosity of water are in very good agreement with experiments. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep neural networks

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Temporal networks provide a unifying understanding of the evolution of cooperation

arXiv

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arXiv 2023年

作者： Li, Aming Meng, Yao Zhou, Lei Masuda, Naoki Wang, Long Center for Systems and Control College of Engineering Peking University Beijing100871 China Center for Multi-Agent Research Institute for Artificial Intelligence Peking University Beijing100871 China School of Automation Beijing Institute of Technology Beijing100081 China Department of Mathematics State University of New York at Buffalo BuffaloNY14260 United States Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo BuffaloNY14260 United States

Understanding the evolution of cooperation in structured populations represented by networks is a problem of long research interest, and a most fundamental and widespread property of social networks related to cooperation phenomena is that the node’s degree (i.e., number of edges connected to the node) is heterogeneously distributed. Previous results indicate that static heterogeneous (i.e., degree-heterogeneous) networks promote cooperation in stationarity compared to static regular (i.e., degree-homogeneous) networks if equilibrium dynamics starting from many cooperators and defectors is employed. However, the above conclusion reverses if we employ non-equilibrium stochastic processes to measure the fixation probability for cooperation, i.e., the probability that a single cooperator successfully invades a population. Here we resolve this conundrum by analyzing the fixation of cooperation on temporal (i.e., time-varying) networks. We theoretically prove and numerically confirm that on both synthetic and empirical networks, contrary to the case of static networks, temporal heterogeneous networks can promote cooperation more than temporal regular networks in terms of the fixation probability of cooperation. Given that the same conclusion is known for the equilibrium fraction of cooperators on temporal networks, the present results provide a unified understanding of the effect of temporal degree heterogeneity on promoting cooperation across two main analytical frameworks, i.e., equilibrium and non-equilibrium ones. Copyright © 2023, The Authors. All rights reserved.

关键词： Heterogeneous networks

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Numerical Modeling of Currents Circulation in Balikpapan Bay during Oil Spill Event on March 31, 2018 2

Numerical Modeling of Currents Circulation in Balikpapan Bay...

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2nd Maritime science and Advanced technology: Marine science and technology in Framework of The Sustainable Development Goals, MSAT 2019

作者： Nur, A.A. Radjawane, I.M. Suprijo, T. Mandang, I. Department of Earth Science Faculty of Earth Science and Technology Bandung Institute of Technology Jalan Ganesha No 10 Bandung40132 Indonesia Hydro-Atmosphere Environment Research Group Physical Oceanography and Computational Modeling Laboratory Program Study of Physics Faculty of Mathematics and Natural Sciences Mulawarman University Jalan Barong Tongkok No 04 Samarinda75123 Indonesia

The coastal area in Balikpapan Bay and its surrounding area has been devastated after the burst of an underwater oil pipe at the bay on March 31st, 2018, and the crude oil still continues spreading for a few days later. A three-dimensional hydrodynamic model is used to simulate the currents dynamic and investigate the influence of the current circulation on the spreading of the oil spill in Balikpapan Bay from March 31th to April 15th, 2018, to cover the event i.e. several weeks after the oil spill incident. Model results are validated by calculating the RMSE, MAPE and model skill using water level between available observation data at Semayang Port, Balikpapan station and numerical model from October 1st, 2012 to January 1st, 2013. Verification result of tidal elevation data from observation and model prediction shows a good agreement with RMSE = 7.8 cm, MAPE = 14.3% and model skill = 0.995. Surface currents circulation in Balikpapan Bay can be distinguished by the currents pattern on the spring tide and neap tide condition. During the spring tide condition, the surface currents mostly move to the east after coming out from the bay. However, the surface currents are strongly going southward after come out from the bay on the neap tide condition. Based on the satellite images captured for the next days after the event, the spreading pattern of the oil spill seems to be matched to the pattern of surface currents circulation on the spring tide condition. From the analysis of the model result, it shows that the currents circulation playing the main role to disperse the oil spill in Balikpapan Bay towards the Makassar Strait. © Published under licence by IOP Publishing Ltd.

关键词： Oil spills

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Universal relationship of compression shocks in two-dimensional Yukawa systems

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Physical Review E 2020年第1期101卷 013203-013203页

作者： Wei Lin M. S. Murillo Yan Feng Center for Soft Condensed Matter Physics and Interdisciplinary Research School of Physical Science and Technology Soochow University Suzhou 215006 China Department of Computational Mathematics Science and Engineering Michigan State University East Lansing Michigan 48824 USA

Using molecular dynamical simulations, compressional shocks in two-dimensional (2D) dusty plasmas are quantitatively investigated under various conditions. A universal relationship between the thermal and the drift velocities after shocks is discovered in 2D Yukawa systems. Using the equation of state of 2D Yukawa liquids, and the obtained pressure from the Rankine-Hugoniot relation, an analytical relation between the thermal and the drift velocities is derived, which well agrees with the discovered universal relationship for various conditions.

关键词： Dusty or complex plasma Plasma crystals Strongly-coupled plasmas

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Comparison of volume transport in the Halmahera Sea between la Nina 2011 and El Nino 2015 events based on numerical model 2

Comparison of volume transport in the Halmahera Sea between ...

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2nd Maritime science and Advanced technology: Marine science and technology in Framework of The Sustainable Development Goals, MSAT 2019

作者： Riza, Muhammad Putri, Mutiara R. Mandang, Idris Department of Earth Science Faculty of Earth Science and Technology Bandung Institute of Technology Jalan Ganesha No 10 Bandung40132 Indonesia Hydro-Atmosphere Environment Research Group Physical Oceanography and Computational Modeling Laboratory Program Study of Physics Faculty of Mathematics and Natural Sciences Mulawarman University Jalan Barong Tongkok No 04 Samarinda75123 Indonesia

Variations of volume transport in the Halmahera Sea are strongly influenced by the El Nino Southern Oscillation (ENSO). Based on the Southern Oscillation Index (SOI), in 2011 La Nina event took place with a strength of 3.02, while in 2015 El Nino occurred with a strength of -2.6. This paper discusses the variation of transport volume caused by the ENSO phenomenon based on the results of the Regional Ocean Model System (ROMS). On the Halmahera Sea at a latitude of 0.3°S with a width of 67 km and a depth of down to 200 m, net volume transport always moves southward. The largest volume transport in La Nina 2011 occurred in September-October, which was -8.9 Sv. Meanwhile, in El Nino 2015 the largest volume transport occurred in July-August, which was equal to -4.9 Sv. The cross correlation coefficient between volume transport and SOI in 2011 and 2015 was r = 0.55 and r = 0.61 respectively, where these results indicate a strong relationship. © Published under licence by IOP Publishing Ltd.

关键词： Numerical models

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Effects of concurrency on epidemic spreading in Markovian temporal networks

arXiv

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arXiv 2022年

作者： Liu, Ruodan Ogura, Masaki Reis, Elohim Fonseca Dos Masuda, Naoki Department of Mathematics State University of New York at Buffalo BuffaloNY14260-2900 United States Graduate School of Information Science and Technology Osaka University Osaka Suita565-0871 Japan Computational and Data-Enabled Sciences and Engineering Program State University of New York at Buffalo BuffaloNY14260-5030 United States Faculty of Science and Engineering Waseda University Tokyo169-8555 Japan

The concurrency of edges, quantified by the number of edges that share a common node at a given time point, may be an important determinant of epidemic processes in temporal networks. We propose theoretically tractable Markovian temporal network models in which each edge flips between the active and inactive states in continuous time. The different models have different amounts of concurrency while we can tune the models to share the same statistics of edge activation and deactivation (and hence the fraction of time for which each edge is active) and the structure of the aggregate (i.e., static) network. We analytically calculate the amount of concurrency of edges sharing a node for each model. We then numerically study effects of concurrency on epidemic spreading in the stochastic susceptible-infectious-susceptible and susceptible-infectious-recovered dynamics on the proposed temporal network models. We find that the concurrency enhances epidemic spreading near the epidemic threshold while this effect is small in many cases. Furthermore, when the infection rate is substantially larger than the epidemic threshold, the concurrency suppresses epidemic spreading in a majority of cases. In sum, our numerical simulations suggest that the impact of concurrency on enhancing epidemic spreading within our model is consistently present near the epidemic threshold but modest. The proposed temporal network models are expected to be useful for investigating effects of concurrency on various collective dynamics on networks including both infectious and other dynamics. Copyright © 2022, The Authors. All rights reserved.

关键词： Dynamics

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The potency of hydrothermally prepared sulfated silica (SO4/SiO2) as a heterogeneous acid catalyst for ethanol dehydration into diethyl ether

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Chemosphere 2023年第1期341卷

作者： Pratika, Remi Ayu Wijaya, Karna Utami, Maisari Mulijani, Sri Patah, Aep Alarifi, Saud Ram Mani, Ravishankar Kumar Yadav, Krishna Ravindran, Balasubramani Chung, Woo Jin Chang, Soon Woong Munusamy-Ramanujam, Ganesh Study Program of Chemistry Faculty of Mathematics and Natural Sciences Universitas Palangka Raya Palangka Raya Indonesia Department of Chemistry Faculty of Mathematics and Natural Sciences Universitas Gadjah Mada Yogyakarta55281 Indonesia Department of Chemistry Faculty of Mathematics and Natural Sciences Universitas Islam Indonesia Yogyakarta55584 Indonesia Department of Chemistry Faculty of Mathematics and Natural Sciences Institut Pertanian Bogor Bogor Indonesia Department of Chemistry Faculty of Mathematics and Natural Sciences Institut Teknologi Bandung Bandung Indonesia Department of Zoology College of Science King Saud University P.O. Box 2455 Riyadh11451 Saudi Arabia Department of Pharmaceutical Biology Faculty of Pharmaceutical Sciences UCSI University Cheras Taman Connaught Kuala Lumpur56000 Malaysia Faculty of Science and Technology Madhyanchal Professional University Ratibad Bhopal462044 India Environmental and Atmospheric Sciences Research Group Scientific Research Center Al-Ayen University Thi-Qar Nasiriyah 64001 Iraq Department of Environmental Energy and Engineering Kyonggi University Yeongtong-Gu Gyeonggi-Do Suwon16227 Korea Republic of Department of Medical Biotechnology and Integrative Physiology Institute of Biotechnology Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Thandalam Tamil Nadu Chennai602105 India Molecular Biology and Immunobiology Division Interdisciplinary Institute of Indian System of Medicine SRM-IST Tamil Nadu Kattankulathur603203 India

The dehydration of ethanol into diethyl ether over a SO4/SiO2 catalyst was investigated. The SO4/SiO2 catalysts were prepared by the sulfation method using 1, 2, and 3 M of sulfuric acid (SS1, SS2, and SS3) via hydrothermal treatment. This study is focused on the synthesis of a SO4/SiO2 catalyst with high total acidity that can be subsequently utilized to convert ethanol into diethyl ether. The total acidity test revealed that the sulfation process increased the total acidity of SiO2. The SS2 catalyst (with 2 M sulfuric acid) displayed the highest total acidity of 7.77 mmol/g, whereas the SiO2 total acidity was only 0.11 mmol/g. Meanwhile, the SS3 catalyst (with 3 M sulfuric acid) has a lower total acidity of 7.09 mmol/g due to the distribution of sulfate groups on the surface having reached its optimum condition. The crystallinity and structure of the SS2 catalyst were not affected by the hydrothermal treatment or the sulfate process on silica. Furthermore, The SS2 catalyst characteristics in the presence of sulfate lead to a flaky surface in the morphology and non-uniform particle size. In addition, the surface area and pore volume of the SS2 catalyst decreased (482.56–172.26 m2/g) and (0.297–0.253 cc/g), respectively, because of the presence of sulfate on the silica surface. The SS2 catalyst's pore shape information explains the formation of non-uniform pore sizes and shapes. Finally, the activity and selectivity of SO4/SiO2 catalysts in the conversion of ethanol to diethyl ether yielded the highest ethanol conversion of 70.01% and diethyl ether product of 9.05% from the SS2 catalyst (the catalyst with the highest total acidity). Variations in temperature reaction conditions (175–225 °C) show an optimum reaction temperature to produce diethyl ether at 200 °C (11.36%). © 2023 Elsevier Ltd

关键词： Silica

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