In many applications,flow measurements are usually sparse and possibly noisy.The reconstruction of a high-resolution flow field from limited and imperfect flow information is significant yet challenging.In this work,w...In many applications,flow measurements are usually sparse and possibly noisy.The reconstruction of a high-resolution flow field from limited and imperfect flow information is significant yet challenging.In this work,we propose an innovative physics-constrained Bayesian deep learning approach to reconstruct flow fields from sparse,noisy velocity data,where equationbased constraints are imposed through the likelihood function and uncertainty of the reconstructed flow can be estimated.Specifically,a Bayesian deep neural network is trained on sparse measurement data to capture the flow field.In the meantime,the violation of physical laws will be penalized on a large number of spatiotemporal points where measurements are not available.A non-parametric variational inference approach is applied to enable efficient physicsconstrained Bayesian learning.Several test cases on idealized vascular flows with synthetic measurement data are studied to demonstrate the merit of the proposed method.展开更多
The current understanding of lactate extends from its origins as a byproduct of glycolysis to its role in tumor metabolism,as identified by studies on the Warburg effect.The lactate shuttle hypothesis suggests that la...The current understanding of lactate extends from its origins as a byproduct of glycolysis to its role in tumor metabolism,as identified by studies on the Warburg effect.The lactate shuttle hypothesis suggests that lactate plays an important role as a bridging signaling molecule that coordinates signaling among different cells,organs and tissues.Lactylation is a posttranslational modification initially reported by Professor Yingming Zhao’s research group in 2019.Subsequent studies confirmed that lactylation is a vital component of lactate function and is involved in tumor proliferation,neural excitation,inflammation and other biological processes.An indispensable substance for various physiological cellular functions,lactate plays a regulatory role in different aspects of energy metabolism and signal transduction.Therefore,a comprehensive review and summary of lactate is presented to clarify the role of lactate in disease and to provide a reference and direction for future research.This review offers a systematic overview of lactate homeostasis and its roles in physiological and pathological processes,as well as a comprehensive overview of the effects of lactylation in various diseases,particularly inflammation and cancer.展开更多
Clinical practice has shown that Parkin is the major causative gene found in an autosomal recessive juvenile parkin-sonism(AR-JP)via Parkin mutations and that the Parkin protein is the core expression product of the P...Clinical practice has shown that Parkin is the major causative gene found in an autosomal recessive juvenile parkin-sonism(AR-JP)via Parkin mutations and that the Parkin protein is the core expression product of the Parkin gene,which itself belongs to an E3 ubiquitin ligase.Since the discovery of the Parkin gene in the late 1990s,researchers in many countries have begun extensive research on this gene and found that in addition to AR-JP,the Parkin gene is associated with many diseases,including type 2 diabetes,leprosy,Alzheimer’s,autism,and cancer.Recent studies have found that the loss or dysfunction of Parkin has a certain relationship with tumorigenesis.In general,the Parkin gene,a well-established tumor suppressor,is deficient and mutated in a variety of malignancies.Parkin overexpres-sion inhibits tumor cell growth and promotes apoptosis.However,the functions of Parkin in tumorigenesis and its regulatory mechanisms are still not fully understood.This article describes the structure,functions,and post-transla-tional modifications of Parkin,and summarizes the recent advances in the tumor suppressive function of Parkin and its underlying mechanisms.展开更多
Phonon Boltzmann transport equation(BTE)is a key tool for modeling multiscale phonon transport,which is critical to the thermal management of miniaturized integrated circuits,but assumptions about the system temperatu...Phonon Boltzmann transport equation(BTE)is a key tool for modeling multiscale phonon transport,which is critical to the thermal management of miniaturized integrated circuits,but assumptions about the system temperatures(i.e.,small temperature gradients)are usually made to ensure that it is computationally tractable.To include the effects of large temperature non-equilibrium,we demonstrate a data-free deep learning scheme,physics-informed neural network(PINN),for solving stationary,mode-resolved phonon BTE with arbitrary temperature gradients.This scheme uses the temperature-dependent phonon relaxation times and learns the solutions in parameterized spaces with both length scale and temperature gradient treated as input variables.Numerical experiments suggest that the proposed PINN can accurately predict phonon transport(from 1D to 3D)under arbitrary temperature gradients.Moreover,the proposed scheme shows great promise in simulating device-level phonon heat conduction efficiently and can be potentially used for thermal design.展开更多
基金support from the National Science Foundation (Grant CMMI-1934300)Defense Advanced Research Projects Agency (DARPA) under the Physics of Artificial Intelligence (PAI) program (Grant HR00111890034)partial funding support by graduate fellowship from China Scholarship Council (CSC) in this effort
文摘In many applications,flow measurements are usually sparse and possibly noisy.The reconstruction of a high-resolution flow field from limited and imperfect flow information is significant yet challenging.In this work,we propose an innovative physics-constrained Bayesian deep learning approach to reconstruct flow fields from sparse,noisy velocity data,where equationbased constraints are imposed through the likelihood function and uncertainty of the reconstructed flow can be estimated.Specifically,a Bayesian deep neural network is trained on sparse measurement data to capture the flow field.In the meantime,the violation of physical laws will be penalized on a large number of spatiotemporal points where measurements are not available.A non-parametric variational inference approach is applied to enable efficient physicsconstrained Bayesian learning.Several test cases on idealized vascular flows with synthetic measurement data are studied to demonstrate the merit of the proposed method.
基金supported by The National Natural Science Foundation of China(grant no.81870331)The Natural Science Foundation of Shandong Province(grant no.ZR2020MH045)The Qingdao Municipal Science and Technology Bureau Project(grant no.21-1-4-rkjk-12-nsh).
文摘The current understanding of lactate extends from its origins as a byproduct of glycolysis to its role in tumor metabolism,as identified by studies on the Warburg effect.The lactate shuttle hypothesis suggests that lactate plays an important role as a bridging signaling molecule that coordinates signaling among different cells,organs and tissues.Lactylation is a posttranslational modification initially reported by Professor Yingming Zhao’s research group in 2019.Subsequent studies confirmed that lactylation is a vital component of lactate function and is involved in tumor proliferation,neural excitation,inflammation and other biological processes.An indispensable substance for various physiological cellular functions,lactate plays a regulatory role in different aspects of energy metabolism and signal transduction.Therefore,a comprehensive review and summary of lactate is presented to clarify the role of lactate in disease and to provide a reference and direction for future research.This review offers a systematic overview of lactate homeostasis and its roles in physiological and pathological processes,as well as a comprehensive overview of the effects of lactylation in various diseases,particularly inflammation and cancer.
基金This work was supported by the National Natural Science Foundation of China(81622005)Beijing Natural Science Foundation(7172213).
文摘Clinical practice has shown that Parkin is the major causative gene found in an autosomal recessive juvenile parkin-sonism(AR-JP)via Parkin mutations and that the Parkin protein is the core expression product of the Parkin gene,which itself belongs to an E3 ubiquitin ligase.Since the discovery of the Parkin gene in the late 1990s,researchers in many countries have begun extensive research on this gene and found that in addition to AR-JP,the Parkin gene is associated with many diseases,including type 2 diabetes,leprosy,Alzheimer’s,autism,and cancer.Recent studies have found that the loss or dysfunction of Parkin has a certain relationship with tumorigenesis.In general,the Parkin gene,a well-established tumor suppressor,is deficient and mutated in a variety of malignancies.Parkin overexpres-sion inhibits tumor cell growth and promotes apoptosis.However,the functions of Parkin in tumorigenesis and its regulatory mechanisms are still not fully understood.This article describes the structure,functions,and post-transla-tional modifications of Parkin,and summarizes the recent advances in the tumor suppressive function of Parkin and its underlying mechanisms.
基金The authors would like to thank ONR MURI(N00014-18-1-2429)for the financial support.The simulations are supported by the Notre Dame Center for Research ComputingNSF through the eXtreme Science and Engineering Discovery Environment(XSEDE)computing resources provided by Texas Advanced Computing Center(TACC)Stampede II under grant number TG-CTS100078This work is also supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.NRF-2021R1C1C1006251).
文摘Phonon Boltzmann transport equation(BTE)is a key tool for modeling multiscale phonon transport,which is critical to the thermal management of miniaturized integrated circuits,but assumptions about the system temperatures(i.e.,small temperature gradients)are usually made to ensure that it is computationally tractable.To include the effects of large temperature non-equilibrium,we demonstrate a data-free deep learning scheme,physics-informed neural network(PINN),for solving stationary,mode-resolved phonon BTE with arbitrary temperature gradients.This scheme uses the temperature-dependent phonon relaxation times and learns the solutions in parameterized spaces with both length scale and temperature gradient treated as input variables.Numerical experiments suggest that the proposed PINN can accurately predict phonon transport(from 1D to 3D)under arbitrary temperature gradients.Moreover,the proposed scheme shows great promise in simulating device-level phonon heat conduction efficiently and can be potentially used for thermal design.