Acetate Causes Alcohol Hangover Headache in Rats

Background: The mechanism of veisalgia cephalgia or hangover headache is unknown. Despite a lack of mechanistic studies, there are a number of theories positing congeners, dehydration, or the ethanol metabolite acetaldehyde as causes of hangover headache. Methods: We used a chronic headache model to examine how pure ethanol produces increased sensitivity for nociceptive behaviors in normally hydrated rats. Results: Ethanol initially decreased sensitivity to mechanical stimuli on the face (analgesia), followed 4 to 6 hours later by inflammatory pain. Inhibiting alcohol dehydrogenase extended the analgesia whereas inhibiting aldehyde dehydrogenase decreased analgesia. Neither treatment had nociceptive effects. Direct administration of acetate increased nociceptive behaviors suggesting that acetate, not acetaldehyde, accumulation results in hangover-like hypersensitivity in our model. Since adenosine accumulation is a result of acetate formation, we administered an adenosine antagonist that blocked hypersensitivity. Discussion: Our study shows that acetate contributes to hangover headache. These findings provide insight into the mechanism of hangover headache and the mechanism of headache induction

Multi-robot path planning for a swarm of robots that can both fly and drive

© 2017 IEEE. The multi-robot path planning problem has been extensively studied for the cases of flying and driving vehicles. However, path planning for the case of vehicles that can both fly and drive has not yet been considered. Driving robots, while stable and energy efficient, are limited to mostly flat terrain. Quadcopters, on the other hand, are agile and highly mobile but have low energy efficiency and limited battery life. Combining a quadcopter with a driving mechanism presents a path planning challenge by enabling the selection of paths based off of both time and energy consumption. In this paper, we introduce a framework for multi-robot path planning for a swarm of flying-and-driving vehicles. By putting a lightweight driving platform on a quadcopter, we create a robust vehicle with an energy efficient driving mode and an agile flight mode. We extend two algorithms, priority planning with Safe Interval Path Planning and a multi-commodity network flow ILP, to accommodate multimodal locomotion, and we show that these algorithms can indeed plan collision-free paths for flying-and-driving vehicles on 3D graphs. Finally, we demonstrate that our system is able to plan paths and control the motions of 8 of our vehicles in a miniature town