The COVID-19 pandemic has caused severe global disasters,highlighting the importance of understanding the details and trends of epidemic transmission in order to introduce efficient intervention measures.While the wid...The COVID-19 pandemic has caused severe global disasters,highlighting the importance of understanding the details and trends of epidemic transmission in order to introduce efficient intervention measures.While the widely used deterministic compartmental models have qualitatively presented continuous “analytical” insight and captured some transmission features,their treatment usually lacks spatiotemporal variation.Here,we propose a stochastic individual dynamical(SID)model to mimic the random and heterogeneous nature of epidemic propagation.The SID model provides a unifying framework for representing the spatiotemporal variations of epidemic development by tracking the movements of each individual.Using this model,we reproduce the infection curves for COVID-19 cases in different areas globally and find the local dynamics and heterogeneity at the individual level that affect the disease outbreak.The macroscopic trend of virus spreading is clearly illustrated from the microscopic perspective,enabling a quantitative assessment of different interventions.Seemingly,this model is also applicable to studying stochastic processes at the “meter scale”,e.g.,human society’s collective dynamics.展开更多
The tumor suppressor p53 mediates the cellular response to various stresses. It was experimentally shown that the concentration of p53 can show oscillations with short or long periods upon DNA damage. The underlying m...The tumor suppressor p53 mediates the cellular response to various stresses. It was experimentally shown that the concentration of p53 can show oscillations with short or long periods upon DNA damage. The underlying mechanism for this phenomenon is still not fully understood. Here, we construct a network model comprising the ATM-p53-Wip1 and p53-Mdm2 negative feedback loops and ATM autoactivation. We recapitulate the typical features of p53 oscillations including p53 birhythmicity. We show the dependence of p53 birhythmicity on various factors such as the phosphorylation status of ATM. We also perform stochastic simulation and find the noise-induced transitions between two modes of p53 oscillation, which increases the p53 variability in both the amplitude and period. These results suggest that p53 birhythmicity enhances the responsiveness of p53 network, which may facilitate its tumor suppressive function.展开更多
RNAs play crucial and versatile roles in biological processes. Computational prediction approaches can help to understand RNA structures and their stabilizing factors, thus providing information on their functions, an...RNAs play crucial and versatile roles in biological processes. Computational prediction approaches can help to understand RNA structures and their stabilizing factors, thus providing information on their functions, and facilitating the design of new RNAs. Machine learning (ML) techniques have made tremendous progress in many fields in the past few years. Although their usage in protein-related fields has a long history, the use of ML methods in predicting RNA tertiary structures is new and rare. Here, we review the recent advances of using ML methods on RNA structure predictions and discuss the advantages and limitation, the difficulties and potentials of these approaches when applied in the field.展开更多
In quantum information technology,crucial information is regularly encoded in different quantum states.To extract information,the identification of one state from the others is inevitable.However,if the states are non...In quantum information technology,crucial information is regularly encoded in different quantum states.To extract information,the identification of one state from the others is inevitable.However,if the states are nonorthogonal and unknown,this task will become awesomely tricky,especially when our resources are also limited.Here,we introduce the quantum stochastic neural network(QSNN),and show its capability to accomplish the binary discrimination of quantum states.After a handful of optimizing iterations,the QSNN achieves a success probability close to the theoretical optimum,no matter whether the states are pure or mixed.Other than binary discrimination,the QSNN is also applied to classify an unknown set of states into two types:entangled ones and separable ones.After training with four samples,it can classify a number of states with acceptable accuracy.Our results suggest that the QSNN has the great potential to process unknown quantum states in quantum information.展开更多
Network virtualization is important for elastic optical networks(EONs)because of more flexible service provisioning.To ensure guaranteed quality of service(QoS)for each virtual elastic optical network(VEON),clients us...Network virtualization is important for elastic optical networks(EONs)because of more flexible service provisioning.To ensure guaranteed quality of service(QoS)for each virtual elastic optical network(VEON),clients usually request network resources from a network operator based on their bandwidth requirements predicted from historical traffic demands.However,this may not be efficient as the actual traffic demands of users always fluctuate.To tackle this,we propose a new VEON service provisioning scheme,called SATP,which consists of three stages,i.e.,spectrum assignment(SA),spectrum trading(ST),and spectrum purchasing(SP).Unlike conventional once-for-all VEON service provisioning approaches,the SATP scheme first allocates spectrum resources to VEONs according to their predicted bandwidth requirements with a satisfaction ratio α(0<α≤1).Then,to minimize service degradation on VEONs which are short of assigned spectra for their peak traffic periods,the scheme allows VEONs to trade spectra with each other according to their actual bandwidth requirements.Finally,it allows VEON clients to purchase extra spectrum resources from a network operator if the spectrum resources are still insufficient.To optimize this entire process,we formulate the problem as a mixed integer linear programming(MILP)model and also develop efficient heuristic algorithms for each stage to handle large test scenarios.Simulations are conducted under different test conditions for both static and dynamic traffic demand scenarios.Results show that the proposed SATP scheme is efficient and can achieve significant performance improvement under both static and dynamic scenarios.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.22273034)the Frontiers Science Center for Critical Earth Material Cycling of Nanjing University。
文摘The COVID-19 pandemic has caused severe global disasters,highlighting the importance of understanding the details and trends of epidemic transmission in order to introduce efficient intervention measures.While the widely used deterministic compartmental models have qualitatively presented continuous “analytical” insight and captured some transmission features,their treatment usually lacks spatiotemporal variation.Here,we propose a stochastic individual dynamical(SID)model to mimic the random and heterogeneous nature of epidemic propagation.The SID model provides a unifying framework for representing the spatiotemporal variations of epidemic development by tracking the movements of each individual.Using this model,we reproduce the infection curves for COVID-19 cases in different areas globally and find the local dynamics and heterogeneity at the individual level that affect the disease outbreak.The macroscopic trend of virus spreading is clearly illustrated from the microscopic perspective,enabling a quantitative assessment of different interventions.Seemingly,this model is also applicable to studying stochastic processes at the “meter scale”,e.g.,human society’s collective dynamics.
基金Project supported by the Natural Science Foundation of the Jiangsu Higher Education Institutions of China(Grant Nos.16KJB180007 and 16KJB140014)the Special Foundation of Theoretical Physics Research Program of China(Grant No.11547025)the National Natural Science Foundation of China(Grant No.11574139)
文摘The tumor suppressor p53 mediates the cellular response to various stresses. It was experimentally shown that the concentration of p53 can show oscillations with short or long periods upon DNA damage. The underlying mechanism for this phenomenon is still not fully understood. Here, we construct a network model comprising the ATM-p53-Wip1 and p53-Mdm2 negative feedback loops and ATM autoactivation. We recapitulate the typical features of p53 oscillations including p53 birhythmicity. We show the dependence of p53 birhythmicity on various factors such as the phosphorylation status of ATM. We also perform stochastic simulation and find the noise-induced transitions between two modes of p53 oscillation, which increases the p53 variability in both the amplitude and period. These results suggest that p53 birhythmicity enhances the responsiveness of p53 network, which may facilitate its tumor suppressive function.
基金Project supported by the National Natural Science Foundation of China (Grant Nos. 11774158, 11974173, 11774157, and 11934008)。
文摘RNAs play crucial and versatile roles in biological processes. Computational prediction approaches can help to understand RNA structures and their stabilizing factors, thus providing information on their functions, and facilitating the design of new RNAs. Machine learning (ML) techniques have made tremendous progress in many fields in the past few years. Although their usage in protein-related fields has a long history, the use of ML methods in predicting RNA tertiary structures is new and rare. Here, we review the recent advances of using ML methods on RNA structure predictions and discuss the advantages and limitation, the difficulties and potentials of these approaches when applied in the field.
基金supported by the National Key R&D Program of China (Grant No. 2017YFA0303703)the National Natural Science Foundation of China (Grant No. 12175104)
文摘In quantum information technology,crucial information is regularly encoded in different quantum states.To extract information,the identification of one state from the others is inevitable.However,if the states are nonorthogonal and unknown,this task will become awesomely tricky,especially when our resources are also limited.Here,we introduce the quantum stochastic neural network(QSNN),and show its capability to accomplish the binary discrimination of quantum states.After a handful of optimizing iterations,the QSNN achieves a success probability close to the theoretical optimum,no matter whether the states are pure or mixed.Other than binary discrimination,the QSNN is also applied to classify an unknown set of states into two types:entangled ones and separable ones.After training with four samples,it can classify a number of states with acceptable accuracy.Our results suggest that the QSNN has the great potential to process unknown quantum states in quantum information.
基金National Key R&D Program China under Grant 2018YFB1801701National Natural Science Foundation of China(NSFC)under Grant 61671313the Priority Academic Program Development of Jiangsu Higher Education Institutions。
文摘Network virtualization is important for elastic optical networks(EONs)because of more flexible service provisioning.To ensure guaranteed quality of service(QoS)for each virtual elastic optical network(VEON),clients usually request network resources from a network operator based on their bandwidth requirements predicted from historical traffic demands.However,this may not be efficient as the actual traffic demands of users always fluctuate.To tackle this,we propose a new VEON service provisioning scheme,called SATP,which consists of three stages,i.e.,spectrum assignment(SA),spectrum trading(ST),and spectrum purchasing(SP).Unlike conventional once-for-all VEON service provisioning approaches,the SATP scheme first allocates spectrum resources to VEONs according to their predicted bandwidth requirements with a satisfaction ratio α(0<α≤1).Then,to minimize service degradation on VEONs which are short of assigned spectra for their peak traffic periods,the scheme allows VEONs to trade spectra with each other according to their actual bandwidth requirements.Finally,it allows VEON clients to purchase extra spectrum resources from a network operator if the spectrum resources are still insufficient.To optimize this entire process,we formulate the problem as a mixed integer linear programming(MILP)model and also develop efficient heuristic algorithms for each stage to handle large test scenarios.Simulations are conducted under different test conditions for both static and dynamic traffic demand scenarios.Results show that the proposed SATP scheme is efficient and can achieve significant performance improvement under both static and dynamic scenarios.
