The limited capability to regenerate new neurons following injuries of the central neural system（CNS）still remains a major challenge for basic and clinical neuroscience.Neural stem cells（NSCs）could nearly have the...The limited capability to regenerate new neurons following injuries of the central neural system（CNS）still remains a major challenge for basic and clinical neuroscience.Neural stem cells（NSCs）could nearly have the potential to differentiate into all kinds of neural cells in vitro.展开更多
Background Dairy cows’lactation performance is the outcome of the crosstalk between ruminal microbial metabo-lism and host metabolism.However,it is still unclear to what extent the rumen microbiome and its metabolite...Background Dairy cows’lactation performance is the outcome of the crosstalk between ruminal microbial metabo-lism and host metabolism.However,it is still unclear to what extent the rumen microbiome and its metabolites,as well as the host metabolism,contribute to regulating the milk protein yield(MPY).Methods The rumen fluid,serum and milk of 12 Holstein cows with the same diet(45%coarseness ratio),parity(2–3 fetuses)and lactation days(120–150 d)were used for the microbiome and metabolome analysis.Rumen metabolism(rumen metabolome)and host metabolism(blood and milk metabolome)were connected using a weighted gene co-expression network(WGCNA)and the structural equation model(SEM)analyses.Results Two different ruminal enterotypes,with abundant Prevotella and Ruminococcus,were identified as type1 and type2.Of these,a higher MPY was found in cows with ruminal type2.Interestingly,[Ruminococcus]gauvreauii group and norank_f_Ruminococcaceae(the differential bacteria)were the hub genera of the network.In addition,differential ruminal,serum and milk metabolome between enterotypes were identified,where the cows with type2 had higher L-tyrosine of rumen,ornithine and L-tryptophan of serum,and tetrahydroneopterin,palmitoyl-L-carnitine,S-lactoylglutathione of milk,which could provide more energy and substrate for MPY.Further,based on the identi-fied modules of ruminal microbiome,as well as ruminal serum and milk metabolome using WGCNA,the SEM analysis indicated that the key ruminal microbial module1,which contains the hub genera of the network([Ruminococcus]gauvreauii group and norank_f_Ruminococcaceae)and high abundance of bacteria(Prevotella and Ruminococcus),could regulate the MPY by module7 of rumen,module2 of blood,and module7 of milk,which contained L-tyrosine and L-tryptophan.Therefore,in order to more clearly reveal the process of rumen bacterial regulation of MPY,we established the path of SEM based on the L-tyrosine,L-tryptophan and related components.The SEM based on the metabolites suggested that[Ruminococcus]gauvreauii group could inhibit the energy supply of serum tryptophan to MPY by milk S-lactoylglutathione,which could enhance pyruvate metabolism.Norank_f_Ruminococcaceae could increase the ruminal L-tyrosine,which could provide the substrate for MPY.Conclusion Our results indicated that the represented enterotype genera of Prevotella and Ruminococcus,and the hub genera of[Ruminococcus]gauvreauii group and norank_f_Ruminococcaceae could regulate milk protein synthesis by affecting the ruminal L-tyrosine and L-tryptophan.Moreover,the combined analysis of enterotype,WGCNA and SEM could be used to connect rumen microbial metabolism with host metabolism,which provides a fundamental understanding of the crosstalk between host and microorganisms in regulating the synthesis of milk composition.展开更多
The goal of this research is to introduce the simulation studies of the vector-host disease nonlinear system(VHDNS)along with the numerical treatment of artificial neural networks(ANNs)techniques supported by Levenber...The goal of this research is to introduce the simulation studies of the vector-host disease nonlinear system(VHDNS)along with the numerical treatment of artificial neural networks(ANNs)techniques supported by Levenberg-Marquardt backpropagation(LMQBP),known as ANNs-LMQBP.This mechanism is physically appropriate,where the number of infected people is increasing along with the limited health services.Furthermore,the biological effects have fadingmemories and exhibit transition behavior.Initially,the model is developed by considering the two and three categories for the humans and the vector species.The VHDNS is constructed with five classes,susceptible humans Sh(t),infected humans Ih(t),recovered humans Rh(t),infected vectors Iv(t),and susceptible vector Sv(t)based system of the fractional-order nonlinear ordinary differential equations.To solve the number of variations of the VHDNS,the numerical simulations are performed using the stochastic ANNs-LMQBP.The achieved numerical solutions for solving the VHDNS using the stochastic ANNs-LMQBP have been described for training,verifying,and testing data to decrease the mean square error(MSE).An extensive analysis is provided using the correlation studies,MSE,error histograms(EHs),state transitions(STs),and regression to observe the accuracy,efficiency,expertise,and aptitude of the computing ANNs-LMQBP.展开更多
The smart distribution network(SDN)is integrat ing increasing distributed generation(DG)and energy storage(ES).Hosting capacity evaluation is important for SDN plan ning with DG.DG and ES are usually invested by users...The smart distribution network(SDN)is integrat ing increasing distributed generation(DG)and energy storage(ES).Hosting capacity evaluation is important for SDN plan ning with DG.DG and ES are usually invested by users or a third party,and they may form friendly microgrids(MGs)and operate independently.Traditional centralized dispatching meth od no longer suits for hosting capacity evaluation of SDN.A quick hosting capacity evaluation method based on distributed optimal dispatching is proposed.Firstly,a multi-objective DG hosting capacity evaluation model is established,and the host ing capacity for DG is determined by the optimal DG planning schemes.The steady-state security region method is applied to speed up the solving process of the DG hosting capacity evalua tion model.Then,the optimal dispatching models are estab lished for MG and SDN respectively to realize the operating simulation.Under the distributed dispatching strategy,the dual-side optimal operation of SDN-MGs can be realized by several iterations of power exchange requirement.Finally,an SDN with four MGs is conducted considering multiple flexible resources.It shows that the DG hosting capacity of SDN oversteps the sum of the maximum active power demand and the rated branch capacity.Besides,the annual DG electricity oversteps the maximum active power demand value.展开更多
基金supported by National Program on Key Basic Research Project(973 Programs 2015CB755605)National Natural Science Foundation of China(81471312)
文摘The limited capability to regenerate new neurons following injuries of the central neural system（CNS）still remains a major challenge for basic and clinical neuroscience.Neural stem cells（NSCs）could nearly have the potential to differentiate into all kinds of neural cells in vitro.
基金the National Natural Science Foundation of China(32272829,32072761,31902184)Shaanxi Provincial Science and Technology Association Young Talents Lifting Program Project(20220203).
文摘Background Dairy cows’lactation performance is the outcome of the crosstalk between ruminal microbial metabo-lism and host metabolism.However,it is still unclear to what extent the rumen microbiome and its metabolites,as well as the host metabolism,contribute to regulating the milk protein yield(MPY).Methods The rumen fluid,serum and milk of 12 Holstein cows with the same diet(45%coarseness ratio),parity(2–3 fetuses)and lactation days(120–150 d)were used for the microbiome and metabolome analysis.Rumen metabolism(rumen metabolome)and host metabolism(blood and milk metabolome)were connected using a weighted gene co-expression network(WGCNA)and the structural equation model(SEM)analyses.Results Two different ruminal enterotypes,with abundant Prevotella and Ruminococcus,were identified as type1 and type2.Of these,a higher MPY was found in cows with ruminal type2.Interestingly,[Ruminococcus]gauvreauii group and norank_f_Ruminococcaceae(the differential bacteria)were the hub genera of the network.In addition,differential ruminal,serum and milk metabolome between enterotypes were identified,where the cows with type2 had higher L-tyrosine of rumen,ornithine and L-tryptophan of serum,and tetrahydroneopterin,palmitoyl-L-carnitine,S-lactoylglutathione of milk,which could provide more energy and substrate for MPY.Further,based on the identi-fied modules of ruminal microbiome,as well as ruminal serum and milk metabolome using WGCNA,the SEM analysis indicated that the key ruminal microbial module1,which contains the hub genera of the network([Ruminococcus]gauvreauii group and norank_f_Ruminococcaceae)and high abundance of bacteria(Prevotella and Ruminococcus),could regulate the MPY by module7 of rumen,module2 of blood,and module7 of milk,which contained L-tyrosine and L-tryptophan.Therefore,in order to more clearly reveal the process of rumen bacterial regulation of MPY,we established the path of SEM based on the L-tyrosine,L-tryptophan and related components.The SEM based on the metabolites suggested that[Ruminococcus]gauvreauii group could inhibit the energy supply of serum tryptophan to MPY by milk S-lactoylglutathione,which could enhance pyruvate metabolism.Norank_f_Ruminococcaceae could increase the ruminal L-tyrosine,which could provide the substrate for MPY.Conclusion Our results indicated that the represented enterotype genera of Prevotella and Ruminococcus,and the hub genera of[Ruminococcus]gauvreauii group and norank_f_Ruminococcaceae could regulate milk protein synthesis by affecting the ruminal L-tyrosine and L-tryptophan.Moreover,the combined analysis of enterotype,WGCNA and SEM could be used to connect rumen microbial metabolism with host metabolism,which provides a fundamental understanding of the crosstalk between host and microorganisms in regulating the synthesis of milk composition.
基金funded by National Research Council of Thailand(NRCT)and Khon Kaen University:N42A650291。
文摘The goal of this research is to introduce the simulation studies of the vector-host disease nonlinear system(VHDNS)along with the numerical treatment of artificial neural networks(ANNs)techniques supported by Levenberg-Marquardt backpropagation(LMQBP),known as ANNs-LMQBP.This mechanism is physically appropriate,where the number of infected people is increasing along with the limited health services.Furthermore,the biological effects have fadingmemories and exhibit transition behavior.Initially,the model is developed by considering the two and three categories for the humans and the vector species.The VHDNS is constructed with five classes,susceptible humans Sh(t),infected humans Ih(t),recovered humans Rh(t),infected vectors Iv(t),and susceptible vector Sv(t)based system of the fractional-order nonlinear ordinary differential equations.To solve the number of variations of the VHDNS,the numerical simulations are performed using the stochastic ANNs-LMQBP.The achieved numerical solutions for solving the VHDNS using the stochastic ANNs-LMQBP have been described for training,verifying,and testing data to decrease the mean square error(MSE).An extensive analysis is provided using the correlation studies,MSE,error histograms(EHs),state transitions(STs),and regression to observe the accuracy,efficiency,expertise,and aptitude of the computing ANNs-LMQBP.
基金supported in part by the State Grid Scientific and Technological Projects of China(No.SGTYHT/21-JS-223)in part by the National Natural Science Foundation of China(No.52277118),in part by the Tianjin Science and Technology Planning Project(No.22ZLGCGX00050)in part by the 67th Postdoctoral Fund and Independent Innovation Fund of Tianjin University in 2021.
文摘The smart distribution network(SDN)is integrat ing increasing distributed generation(DG)and energy storage(ES).Hosting capacity evaluation is important for SDN plan ning with DG.DG and ES are usually invested by users or a third party,and they may form friendly microgrids(MGs)and operate independently.Traditional centralized dispatching meth od no longer suits for hosting capacity evaluation of SDN.A quick hosting capacity evaluation method based on distributed optimal dispatching is proposed.Firstly,a multi-objective DG hosting capacity evaluation model is established,and the host ing capacity for DG is determined by the optimal DG planning schemes.The steady-state security region method is applied to speed up the solving process of the DG hosting capacity evalua tion model.Then,the optimal dispatching models are estab lished for MG and SDN respectively to realize the operating simulation.Under the distributed dispatching strategy,the dual-side optimal operation of SDN-MGs can be realized by several iterations of power exchange requirement.Finally,an SDN with four MGs is conducted considering multiple flexible resources.It shows that the DG hosting capacity of SDN oversteps the sum of the maximum active power demand and the rated branch capacity.Besides,the annual DG electricity oversteps the maximum active power demand value.
