Kir6.2-K ATP channels alter glycolytic flux to modulate cortical activity, arousal, and sleep-wake homeostasis.

Metabolism plays an important role in the maintenance of vigilance states (e.g. wake, NREM, and REM). Brain lactate fluctuations are a biomarker of sleep. Increased interstitial fluid (ISF) lactate levels are necessary for arousal and wake-associated behaviors, while decreased ISF lactate is required for sleep. ATP-sensitive potassium (K ATP ) channels couple glucose-lactate metabolism with neuronal excitability. Therefore, we explored how deletion of neuronal K ATP channel activity (Kir6.2-/- mice) affected the relationship between glycolytic flux, neuronal activity, and sleep/wake homeostasis. Kir6.2-/- mice shunt glucose towards glycolysis, reduce neurotransmitter synthesis, dampen cortical EEG activity, and decrease arousal. Kir6.2-/- mice spent more time awake at the onset of the light period due to altered ISF lactate dynamics. Together, we show that Kir6.2-K ATP channels act as metabolic sensors to gate arousal by maintaining the metabolic stability of each vigilance state and providing the metabolic flexibility to transition between states. Highlights: Glycolytic flux is necessary for neurotransmitter synthesis. In its absence, neuronal activity is compromised causing changes in arousal and vigilance states despite sufficient energy availability. With Kir6.2-K ATP channel deficiency, the ability to both maintain and shift between different vigilance states is compromised due to changes in glucose utilization. Kir6.2-K ATP channels are metabolic sensors under circadian control that gate arousal and sleep/wake transitions.

Highest fusion performance without harmful edge energy bursts in tokamak.

The path of tokamak fusion and International thermonuclear experimental reactor (ITER) is maintaining high-performance plasma to produce sufficient fusion power. This effort is hindered by the transient energy burst arising from the instabilities at the boundary of plasmas. Conventional 3D magnetic perturbations used to suppress these instabilities often degrade fusion performance and increase the risk of other instabilities. This study presents an innovative 3D field optimization approach that leverages machine learning and real-time adaptability to overcome these challenges. Implemented in the DIII-D and KSTAR tokamaks, this method has consistently achieved reactor-relevant core confinement and the highest fusion performance without triggering damaging bursts. This is enabled by advances in the physics understanding of self-organized transport in the plasma edge and machine learning techniques to optimize the 3D field spectrum. The success of automated, real-time adaptive control of such complex systems paves the way for maximizing fusion efficiency in ITER and beyond while minimizing damage to device components.