Vibration-reduced anxiety-like behavior relies on ameliorating abnormalities of the somatosensory cortex and medial prefrontal cortex

Tibetan singing bowls emit low-frequency sounds and produce perceptible harmonic tones and vibrations through manual tapping.The sounds the singing bowls produce have been shown to enhance relaxation and reduce anxiety.However,the underlying mechanism remains unclear.In this study,we used chronic restraint stress or sleep deprivation to establish mouse models of anxiety that exhibit anxiety-like behaviors.We then supplied treatment with singing bowls in a bottomless cage placed on the top of a cushion.We found that unlike in humans,the combination of harmonic tones and vibrations did not improve anxietylike behaviors in mice,while individual vibration components did.Additionally,the vibration of singing bowls increased the level of N-methyl-D-aspartate receptor 1 in the somatosensory cortex and prefrontal cortex of the mice,decreased the level ofγ-aminobutyric acid A(GABA)receptorα1 subtype,reduced the level of CaMKII in the prefrontal cortex,and increased the number of GABAergic interneurons.At the same time,electrophysiological tests showed that the vibration of singing bowls significantly reduced the abnormal low-frequency gamma oscillation peak frequency in the medial prefrontal cortex caused by stress restraint pressure and sleep deprivation.Results from this study indicate that the vibration of singing bowls can alleviate anxiety-like behaviors by reducing abnormal molecular and electrophysiological events in somatosensory and medial prefrontal cortex.

Efficient and effective Bayesian network local structure learning

In this paper, we propose a more efficient Bayesian network structure learning algorithm under the framework of score based local learning （SLL）. Our algorithm significantly improves computational efficiency by restricting the neighbors of each variable to a small subset of candidates and storing necessary information to uncover the spouses, at the same time guaranteeing to find the optimal neighbor set in the same sense as SLL. The algorithm is the- oretically sound in the sense that it is optimal in the limit of large sample size. Empirical results testify its improved speed without loss of quality in the learned structures.