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This study is aimed to investigate whether Mabuterol (Mab) inhibits proliferation of airway smooth muscle cells (ASMCs) induced by platelet-derived growth factor BB (PDGF-BB) and how far it is related to mitochondrial fission/fusion and intracellular calcium if it comes into play. To explore the mechanism of Mab's antagonizing the proliferation, Mdivi-1, DRP1 inhibitor, which has an inhibitory effect on mitochondrial fission, is used to compare with Mab. Cell viability was measured by either MTT or CCK-8. The inhibitory effect of Mab on S phase of ASM cell cycle induced by PDGF-BB was analyzed by flow cytometry (FCM). Fluo-3/AM, Ca2+ fluorescent probe, was used to detect Ca2+ fluorescence intensity by inverted microscope and flow cytometry. The gene expression of Drp-1 and Mfn-2 was observed with Real time PCR and the proteins of Drp-1, Mfn-2, PCNA and cyclin D1 were assessed by Western Blot. Mab and Mdivi-1 both suppressed the proliferation induced by PDGF-BB. The results from inverted microscope and flow cytometry showed that Mab inhibited [Ca2+]i in rat ASMCs induced by PDGF-BB. Cell cycle concept map illustrated that Mab significantly controlled the S phase of ASM cell cycle induced by PDGF-BB. As a consequence, Real time PCR and Western blot revealed the fact that Mab decreased the expression of Drp-1 mRNA and protein, and promoted the expression of Mfn-2 mRNA and protein. These findings suggested that Mab placed restrictions on the proliferation of rat ASMCs induced by PDGF-BB and the mechanism might be associated with the intracellular calcium inhibited and the mitochondrial fission/fusion regulated by Mab. Mab inhibited the proliferation of ASMCs induced by PDGF-BB Mab suppressed the increase of intracellular Ca2+ induced by PDGF-BB Mab suppressed the expression of Drp-1 and promoted that of Mfn-2 Mechanisms of Mab' action may be related to its regulating fission and fusion of mitochondria [ABSTRACT FROM AUTHOR]

• A novel data-driven mathematical model which fills the gap in the solution to the DOPF problem under high uncertainty. The proposed data-driven mathematical model lets the model has the ability that can autonomously learn the distribution characteristic of uncertain variables. and effectively prevents the over-fitting data phenomenon. • The first to put forward the mathematical expression between linear ACOPF and uncertain variables and to give the data-driven model a formal mathematical expression too. Our work fills the gap that current data-driven methods based on big data have no accurate mathematical model. • Our model significantly reduces the computational burden. This improvement solves the phenomenon that standard data-driven algorithms require extreme demand in the aspect of computer hardware. The uncertainty caused by the distributed generations(DG) with inconspicuous patterns has been an essential subject in the optimization scheduling for the distribution network. We propose a novel data-driven approach to deal with the dynamic optimal power flow(DOPF) problem which contains uncertain variables with their unknown probability distribution. The data-driven model is made to learn the joint probability distribution of the uncertain variables and use robust optimization(RO) to solve the multi-stage stochastic linear DOPF by averaging the worst case from each uncertainty set. In contrast to the motivation for traditional RO to find solutions that perform well on the worst-case realization, our proposed approach adds robustness to the historical data as a tool to avoid overfitting as the number of data points tends to infinity. The application verification for the AC OPF problem is presented for the IEEE-33 system. The simulation verifies the feasibility and robustness of the proposed approach and its results are compared with those of other data-driven stochastic optimization methods. We prove that the proposed approach can effectively solve the overvoltage problem caused by the high permeability of photovoltaic generation and achieve a better out-of-sample performance guarantee, and also has obvious economic advantages over other data-driven methods. [ABSTRACT FROM AUTHOR]

• A novel POMDP modelling method for reactive power optimization of the ADN which fills the gap in the solution to the optimization problem with significant data uncertainty. • Introduce a technique for transforming the POMDP model into the belief state which can be easily solved by the RL algorithm. • Use a misestimated state probability vector to consider data uncertainty in the belief state update process. • Match multiple agents with multiple reactive compensation devices and adopt a novel multi-agent actor-attention-critic algorithm to solve the proposed belief MDP model. We present an innovative partially observable Markov decision process (POMDP) modelling method for the reactive power optimization process of the active distribution network (ADN) under the high permeability of the distributed generation. This model is tolerant of the uncertainty resulting from data uncertainty. We believe that the belief state space in the POMDP model corresponds to the state space in the Markov decision process (MDP) model, and we apply the multi-agent actor-attention-critic (MAAC) reinforcement learning (RL) algorithm to the proposed model. This technique extracts the most effective information with the highest quality from the huge historical measurement database, hence enhancing the learning effectiveness of agents and the stability of the optimization strategy. We simulate reactive power optimization in a modified IEEE-33 nodes ADN and a modified IEEE-123nodes ADN. The simulation demonstrates the stability and economic superiority of the proposed approach under varying degrees of data uncertainty relative to previous RL algorithms based on the MDP model. The simulation demonstrates that the proposed POMDP model is more appropriate for the real operation of the partially observable distribution network than the MDP models. And the optimal strategy obtained by the proposed MAAC algorithm is reliable with deteriorating data quality. [ABSTRACT FROM AUTHOR]