Molluscan metabolomic analysis is essential for the understanding of the regulatory mechanism of aquatic invertebrate in response to hepatotoxic microcystins(MCs)stress.To understand the system responses of the gastro...Molluscan metabolomic analysis is essential for the understanding of the regulatory mechanism of aquatic invertebrate in response to hepatotoxic microcystins(MCs)stress.To understand the system responses of the gastropod to MC exposure,metabolomic alterations caused by two strains(MC-producing and non MC-producing)of Microcystis aeruginosa were characterized indiff erent biological matrices(hepatopancreas and muscle)of Bellamya aeruginosa(Gastropoda)using 1 H nuclear magnetic resonance(NMR)spectroscopy combined with MCs detections after exposure for 1,7,and 14 d.Although ELISA analysis showed that no MCs was detected in both tissues after non MC-producing M.aeruginosa exposure,MCs concentrations were increasing in the hepatopancreas(from 1.29±0.48μg/g to 3.17±0.11μg/g)and foot muscle(from 0.07±0.02μg/g to 0.21±0.08μg/g)after 14-d exposure of MC-producing M.aeruginosa.Meanwhile,we observed that MC induced signifi cant increase in creatine,a variety of amino acids(leucine,isoleucine,valine,threonine,alanine,methionine,glutamate,aspartate,and lysine),carboxylic acids(lactate,acetate,and D-3-hydroxybutyrate),and choline and its derivatives(phosphocholine and glycerophosphocholine)but decreased the energy substance(lipids,glucose,and glycogen)in the hepatopancreas.However,no signifi cant metabolite diff erences were observed in the muscle between MC-producing and non MC-producing cyanobacteria treated groups.These results suggest that MC exposure may cause hepatic energy expenditure accompanied with various metabolic disorders that involve lipid metabolism,protein catabolism,osmoregulation,glycolysis,glycogenolysis,and tricarboxylic acid(TCA)cycle.Moreover,metabolic perturbation was aggravated as the level of accumulated MCs raised over time in the MC-producing cyanobacteria treatment.These fi ndings indicated that MCs accumulation might lead to oxidative-stress-mediated damage of mitochondria functions.展开更多
This paper focuses on the online distributed optimization problem based on multi-agent systems. In this problem, each agent can only access its own cost function and a convex set, and can only exchange local state inf...This paper focuses on the online distributed optimization problem based on multi-agent systems. In this problem, each agent can only access its own cost function and a convex set, and can only exchange local state information with its current neighbors through a time-varying digraph. In addition, the agents do not have access to the information about the current cost functions until decisions are made. Different from most existing works on online distributed optimization, here we consider the case where the cost functions are strongly pseudoconvex and real gradients of the cost functions are not available. To handle this problem, a random gradient-free online distributed algorithm involving the multi-point gradient estimator is proposed. Of particular interest is that under the proposed algorithm, each agent only uses the estimation information of gradients instead of the real gradient information to make decisions. The dynamic regret is employed to measure the proposed algorithm. We prove that if the cumulative deviation of the minimizer sequence grows within a certain rate, then the expectation of dynamic regret increases sublinearly. Finally, a simulation example is given to corroborate the validity of our results.展开更多
In this paper,the problem of online distributed optimization subject to a convex set is studied via a network of agents.Each agent only has access to a noisy gradient of its own objective function,and can communicate ...In this paper,the problem of online distributed optimization subject to a convex set is studied via a network of agents.Each agent only has access to a noisy gradient of its own objective function,and can communicate with its neighbors via a network.To handle this problem,an online distributed stochastic mirror descent algorithm is proposed.Existing works on online distributed algorithms involving stochastic gradients only provide the expectation bounds of the regrets.Different from them,we study the high probability bound of the regrets,i.e.,the sublinear bound of the regret is characterized by the natural logarithm of the failure probability's inverse.Under mild assumptions on the graph connectivity,we prove that the dynamic regret grows sublinearly with a high probability if the deviation in the minimizer sequence is sublinear with the square root of the time horizon.Finally,a simulation is provided to demonstrate the effectiveness of our theoretical results.展开更多
This paper studies an online distributed optimization problem over multi-agent systems.In this problem,the goal of agents is to cooperatively minimize the sum of locally dynamic cost functions.Different from most exis...This paper studies an online distributed optimization problem over multi-agent systems.In this problem,the goal of agents is to cooperatively minimize the sum of locally dynamic cost functions.Different from most existing works on distributed optimization,here we consider the case where the cost function is strongly pseudoconvex and real gradients of objective functions are not available.To handle this problem,an online zeroth-order stochastic optimization algorithm involving the single-point gradient estimator is proposed.Under the algorithm,each agent only has access to the information associated with its own cost function and the estimate of the gradient,and exchange local state information with its immediate neighbors via a time-varying digraph.The performance of the algorithm is measured by the expectation of dynamic regret.Under mild assumptions on graphs,we prove that if the cumulative deviation of minimizer sequence grows within a certain rate,then the expectation of dynamic regret grows sublinearly.Finally,a simulation example is given to illustrate the validity of our results.展开更多
基金Supported by the National Natural Science Foundation of China(Nos.42077219,31302192)the Natural Science Foundation of Zhejiang Province(No.LQ20C190003)+2 种基金the Ningbo Municipal Natural Science Foundation(No.2019A610421)the Ningbo Public Welfare Technology Application Research Project(No.202002N3049)the Hangzhou Municipal Agriculture and Social Development Project(No.2020ZDSJ0697),the Fundamental Research Funds for the Provincial Universities of Zhejiang(No.SJLY2020011),and the K.C.Wong Magna Fund in Ningbo University。
文摘Molluscan metabolomic analysis is essential for the understanding of the regulatory mechanism of aquatic invertebrate in response to hepatotoxic microcystins(MCs)stress.To understand the system responses of the gastropod to MC exposure,metabolomic alterations caused by two strains(MC-producing and non MC-producing)of Microcystis aeruginosa were characterized indiff erent biological matrices(hepatopancreas and muscle)of Bellamya aeruginosa(Gastropoda)using 1 H nuclear magnetic resonance(NMR)spectroscopy combined with MCs detections after exposure for 1,7,and 14 d.Although ELISA analysis showed that no MCs was detected in both tissues after non MC-producing M.aeruginosa exposure,MCs concentrations were increasing in the hepatopancreas(from 1.29±0.48μg/g to 3.17±0.11μg/g)and foot muscle(from 0.07±0.02μg/g to 0.21±0.08μg/g)after 14-d exposure of MC-producing M.aeruginosa.Meanwhile,we observed that MC induced signifi cant increase in creatine,a variety of amino acids(leucine,isoleucine,valine,threonine,alanine,methionine,glutamate,aspartate,and lysine),carboxylic acids(lactate,acetate,and D-3-hydroxybutyrate),and choline and its derivatives(phosphocholine and glycerophosphocholine)but decreased the energy substance(lipids,glucose,and glycogen)in the hepatopancreas.However,no signifi cant metabolite diff erences were observed in the muscle between MC-producing and non MC-producing cyanobacteria treated groups.These results suggest that MC exposure may cause hepatic energy expenditure accompanied with various metabolic disorders that involve lipid metabolism,protein catabolism,osmoregulation,glycolysis,glycogenolysis,and tricarboxylic acid(TCA)cycle.Moreover,metabolic perturbation was aggravated as the level of accumulated MCs raised over time in the MC-producing cyanobacteria treatment.These fi ndings indicated that MCs accumulation might lead to oxidative-stress-mediated damage of mitochondria functions.
基金supported by the National Natural Science Foundation of China(Nos.62103169,51875380)the China Postdoctoral Science Foundation(No.2021M691313).
文摘This paper focuses on the online distributed optimization problem based on multi-agent systems. In this problem, each agent can only access its own cost function and a convex set, and can only exchange local state information with its current neighbors through a time-varying digraph. In addition, the agents do not have access to the information about the current cost functions until decisions are made. Different from most existing works on online distributed optimization, here we consider the case where the cost functions are strongly pseudoconvex and real gradients of the cost functions are not available. To handle this problem, a random gradient-free online distributed algorithm involving the multi-point gradient estimator is proposed. Of particular interest is that under the proposed algorithm, each agent only uses the estimation information of gradients instead of the real gradient information to make decisions. The dynamic regret is employed to measure the proposed algorithm. We prove that if the cumulative deviation of the minimizer sequence grows within a certain rate, then the expectation of dynamic regret increases sublinearly. Finally, a simulation example is given to corroborate the validity of our results.
文摘In this paper,the problem of online distributed optimization subject to a convex set is studied via a network of agents.Each agent only has access to a noisy gradient of its own objective function,and can communicate with its neighbors via a network.To handle this problem,an online distributed stochastic mirror descent algorithm is proposed.Existing works on online distributed algorithms involving stochastic gradients only provide the expectation bounds of the regrets.Different from them,we study the high probability bound of the regrets,i.e.,the sublinear bound of the regret is characterized by the natural logarithm of the failure probability's inverse.Under mild assumptions on the graph connectivity,we prove that the dynamic regret grows sublinearly with a high probability if the deviation in the minimizer sequence is sublinear with the square root of the time horizon.Finally,a simulation is provided to demonstrate the effectiveness of our theoretical results.
基金Supported by National Natural Science Foundation of China(62103169,51875380)China Postdoctoral Science Foundation(2021M691313)。
文摘This paper studies an online distributed optimization problem over multi-agent systems.In this problem,the goal of agents is to cooperatively minimize the sum of locally dynamic cost functions.Different from most existing works on distributed optimization,here we consider the case where the cost function is strongly pseudoconvex and real gradients of objective functions are not available.To handle this problem,an online zeroth-order stochastic optimization algorithm involving the single-point gradient estimator is proposed.Under the algorithm,each agent only has access to the information associated with its own cost function and the estimate of the gradient,and exchange local state information with its immediate neighbors via a time-varying digraph.The performance of the algorithm is measured by the expectation of dynamic regret.Under mild assumptions on graphs,we prove that if the cumulative deviation of minimizer sequence grows within a certain rate,then the expectation of dynamic regret grows sublinearly.Finally,a simulation example is given to illustrate the validity of our results.
