Effects of Cannabinoids on Caffeine Contractures in Slow and Fast Skeletal Muscle Fibers of the Frog

The effect of cannabinoids on caffeine contractures was investigated in slow and fast skeletal muscle fibers using isometric tension recording. In slow muscle fibers, WIN 55,212-2 (10 and 5 μM) caused a decrease in tension. These doses reduced maximum tension to 67.43 ± 8.07% (P = 0.02, n = 5) and 79.4 ± 14.11% (P = 0.007, n = 5) compared to control, respectively. Tension-time integral was reduced to 58.37 ± 7.17% and 75.10 ± 3.60% (P = 0.002, n = 5), respectively. Using the CB1 cannabinoid receptor agonist ACPA (1 μM) reduced the maximum tension of caffeine contractures by 68.70 ± 11.63% (P = 0.01, n = 5); tension-time integral was reduced by 66.82 ± 6.89% (P = 0.02, n = 5) compared to controls. When the CB1 receptor antagonist AM281 was coapplied with ACPA, it reversed the effect of ACPA on caffeine-evoked tension. In slow and fast muscle fibers incubated with the pertussis toxin, ACPA had no effect on tension evoked by caffeine. In fast muscle fibers, ACPA (1 μM) also decreased tension; the maximum tension was reduced by 56.48 ± 3.4% (P = 0.001, n = 4), and tension-time integral was reduced by 57.81 ± 2.6% (P = 0.006, n = 4). This ACPA effect was not statistically significant with respect to the reduction in tension in slow muscle fibers. Moreover, we detected the presence of mRNA for the cannabinoid CB1 receptor on fast and slow skeletal muscle fibers, which was significantly higher in fast compared to slow muscle fiber expression. In conclusion, our results suggest that in the slow and fast muscle fibers of the frog cannabinoids diminish caffeine-evoked tension through a receptor-mediated mechanism

The king's artists The Royal Academy of Arts as a 'national institution', c1768-1820

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Meta Monte-Carlo Tree Search for Automatic Opening Book Generation

Modern game playing programs use opening books in order to perform better. Generating opening books automatically in combination with an αβ program has been well studied. A challenge is to generate automatically an opening book for the new Monte-Carlo Tree Search (MCTS) algorithms. In this article, we tackle this issue by combining two level of MCTS. The resulting algorithm is called Meta-MCTS. Instead of applying a simulation strategy, it uses an MCTS program to play a simulated game. We describe two Meta-MCTS algorithms: the first one, Quasi Best-First, favors exploitation. The second one, Beta Distribution Sampling, favors exploration. Our approach is generic and can be used for general game playing. Itwill be particularly usefulwhen there is off-line time avail- able. In order to evaluate the performances of these algorithms, we generated and tested 99 Go opening books