Inhibition of p38 MAPK Signaling Regulates the Expression of EAAT2 in the Brains of Epileptic Rats

Seizures induce the release of excitatory amino acids (EAAs) from the intracellular fluid to the extracellular fluid, and the released EAAs primarily comprise glutamic acid (Glu) and asparaginic acid (Asp). Glu neurotransmission functions via EAA transporters (EAATs) to maintain low concentrations of Glu in the extracellular space and avoid excitotoxicity. EAAT2, the most abundant Glu transporter subtype in the central nervous system (CNS), plays a key role in the regulation of glutamate transmission. Previous studies have shown that SB203580 promotes EAAT2 expression by inhibiting the p38 mitogen-activated protein kinase (MAPK) signaling pathway, but whether SB203580 upregulates EAAT2 expression in epileptic rats is unknown. This study demonstrated that EAAT2 expression was increased in the brain tissue of epileptic rats. Intraperitoneal injection of a specific inhibitor of p38 MAPK, SB203580, reduced the time to the first epileptic seizure and attenuated the seizure severity. In addition, SB203580 treatment increased the EAAT2 expression levels in the brain tissue of epileptic rats. These results suggest that SB203580 could regulate epileptic seizures via EAAT2

Adaptive event-triggered control based on heuristic dynamic programming for nonlinear discrete-time systems

This paper presents the design of a novel adaptive event-triggered control method based on the heuristic dynamic programming (HDP) technique for nonlinear discrete-time systems with unknown system dynamics. In the proposed method, the control law is only updated when the event-triggered condition is violated. Compared with the periodic updates in the traditional adaptive dynamic programming (ADP) control, the proposed method can reduce the computation and transmission cost. An actor-critic framework is used to learn the optimal event-triggered control law and the value function. Furthermore, a model network is designed to estimate the system state vector. The main contribution of this paper is to design a new trigger threshold for discrete-time systems. A detailed Lyapunov stability analysis shows that our proposed event-triggered controller can asymptotically stabilize the discrete-time systems. Finally, we test our method on two different discrete-time systems, and the simulation results are included

Event-Triggered Adaptive Dynamic Programming for Continuous-Time Systems with Control Constraints

In this paper, an event-triggered near optimal control structure is developed for nonlinear continuous-time systems with control constraints. Due to the saturating actuators, a nonquadratic cost function is introduced and the Hamilton-Jacobi-Bellman (HJB) equation for constrained nonlinear continuous-time systems is formulated. In order to solve the HJB equation, an actor-critic framework is presented. The critic network is used to approximate the cost function and the action network is used to estimate the optimal control law. In addition, in the proposed method, the control signal is transmitted in an aperiodic manner to reduce the computational and the transmission cost. Both the networks are only updated at the trigger instants decided by the event-triggered condition. Detailed Lyapunov analysis is provided to guarantee that the closed-loop event-triggered system is ultimately bounded. Three case studies are used to demonstrate the effectiveness of the proposed method

Predictive event-triggered control based on heuristic dynamic programming for nonlinear continuous-time systems

In this paper, a novel predictive event-triggered control method based on heuristic dynamic programming (HDP) algorithm is developed for nonlinear continuous-time systems. A model network is used to estimate the system state vector, so that the event-triggered instant is available to predict one step ahead of time. Furthermore, an actor-critic structure is used to approximate the optimal event-triggered control law and performance index function. Although event-triggered adaptive dynamic programming (ADP) has been investigated in the community before, to our best knowledge, this is the first study of using a \u27predictive\u27 approach through a model network to design the event-triggered ADP. This is the key contribution of this work. Compared to the existing event-triggered ADP methods, our simulations demonstrate that the predictive event-triggered approach can achieve improved control performance and lower computational cost in comparison with the existing methods

miR-378 Activates the Pyruvate-PEP Futile Cycle and Enhances Lipolysis to Ameliorate Obesity in Mice

Obesity has been linked to many health problems, such as diabetes. However, there is no drug that effectively treats obesity. Here, we reveal that miR-378 transgenic mice display reduced fat mass, enhanced lipolysis, and increased energy expenditure. Notably, administering AgomiR-378 prevents and ameliorates obesity in mice. We also found that the energy deficiency seen in miR-378 transgenic mice was due to impaired glucose metabolism. This impairment was caused by an activated pyruvate-PEP futile cycle via the miR-378-Akt1-FoxO1-PEPCK pathway in skeletal muscle and enhanced lipolysis in adipose tissues mediated by miR-378-SCD1. Our findings demonstrate that activating the pyruvate-PEP futile cycle in skeletal muscle is the primary cause of elevated lipolysis in adipose tissues of miR-378 transgenic mice, and it helps orchestrate the crosstalk between muscle and fat to control energy homeostasis in mice. Thus, miR-378 may serve as a promising agent for preventing and treating obesity in humans

Manganese/Cobalt Bimetal Nanoparticles Encapsulated in Nitrogen-Rich Graphene Sheets for Efficient Oxygen Reduction Reaction Electrocatalysis

It is of vital importance to search for a nonprecious metal based sustainable and efficient oxygen reduction reaction (ORR) electrocatalyst for the next generation of energy conversion and storage technology. We herein report a hybrid bimetal material composed of MnO/Co nanoparticles encapsulated in nitrogen-rich graphene nanosheets (MnO/Co–N–G) as a high performance ORR catalyst in alkaline electrolyte. The MnO/Co–N–G catalyst is derived from Mn²⁺, Co²⁺ incorporated polydopamine (PDA) coated graphene oxide (GO) sheets via a carbonization process. The morphology, structure, and composition properties of as-prepared MnO/Co–N–G catalyst are systematically investigated. Electrochemical measurements show that the MnO/Co–N–G catalyst exhibits excellent ORR activity superior to commercial Pt/C, featuring higher limiting current density, better methanol resistance, and excellent long-term durability in alkaline solution. The bimetal nanoparticles are believed to be responsible for the impressive ORR activity of the catalyst

Metal-support interaction boosted electrocatalysis of ultrasmall iridium nanoparticles supported on nitrogen doped graphene for highly efficient water electrolysis in acidic and alkaline media

Metal-support interaction boosted electrocatalysis of ultrasmall iridium nanoparticles supported on nitrogen doped graphene for highly efficient water electrolysis in acidic and alkaline medi

Mesoporous Hollow Nitrogen-Doped Carbon Nanospheres with Embedded MnFe₂O₄/Fe Hybrid Nanoparticles as Efficient Bifunctional Oxygen Electrocatalysts in Alkaline Media

Exploring sustainable and efficient electrocatalysts for oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) is necessary for the development of fuel cells and metal–air batteries. Herein, we report a bimetal Fe/Mn–N–C material composed of spinel MnFe₂O₄/metallic Fe hybrid nanoparticles encapsulated in N-doped mesoporous hollow carbon nanospheres as an excellent bifunctional ORR/OER electrocatalyst in alkaline electrolyte. The Fe/Mn–N–C catalyst is synthesized via pyrolysis of bimetal ion-incorporated polydopamine nanospheres and shows impressive ORR electrocatalytic activity superior to Pt/C and good OER activity close to RuO₂ catalyst in alkaline environment. When tested in Zn–air battery, the Fe/Mn–N–C catalyst demonstrates excellent ultimate performance including power density, durability, and cycling. This work reports the bimetal Fe/Mn–N–C as a highly efficient bifunctional electrocatalyst and may afford useful insights into the design of sustainable transition-metal-based high-performance electrocatalysts