The endocannabinoid system (ECS) refers to a widespread signaling system and its alteration is implicated in a growing number of human diseases. Cannabinoid receptors (CBRs) are highly expressed in the central nervous system and many peripheral tissues. Evidence suggests that CB1Rs are expressed in human and murine skeletal muscle mainly in the cell membrane, but a subpopulation is present also in the mitochondria. However, very little is known about the latter population. To date, the connection between the function of CB1Rs and the regulation of intracellular Ca2+ signaling has not been investigated yet. Tamoxifen-inducible skeletal muscle-specific conditional CB1 knock-down (skmCB1-KD, hereafter referred to as Cre+/−) mice were used in this study for functional and morphological analysis. After confirming CB1R down-regulation on the mRNA and protein level, we performed in vitro muscle force measurements and found that peak twitch, tetanus, and fatigue were decreased significantly in Cre+/− mice. Resting intracellular calcium concentration, voltage dependence of the calcium transients as well as the activity dependent mitochondrial calcium uptake were essentially unaltered by Cnr1 gene manipulation. Nevertheless, we found striking differences in the ultrastructural architecture of the mitochondrial network of muscle tissue from the Cre+/− mice. Our results suggest a role of CB1Rs in maintaining physiological muscle function and morphology. Targeting ECS could be a potential tool in certain diseases, including muscular dystrophies where increased endocannabinoid levels have already been described.

The remodelling of the extracellular matrix plays an important role in skeletal muscle development and regeneration. Syndecan-4 is a cell surface proteoglycan crucial for muscle differentiation. Syndecan-4−/− mice have been reported to be unable to regenerate following muscle damage. To investigate the consequences of the decreased expression of Syndecan-4, we have studied the in vivo and in vitro muscle performance and the excitation–contraction coupling machinery in young and aged Syndecan-4+/− (SDC4) mice. In vivo grip force was decreased significantly as well as the average and maximal speed of voluntary running in SDC4 mice, regardless of their age. The maximal in vitro twitch force was reduced in both EDL and soleus muscles from young and aged SDC4 mice. Ca2+ release from the sarcoplasmic reticulum decreased significantly in the FDB fibres of young SDC4 mice, while its voltage dependence was unchanged regardless of age. These findings were present in muscles from young and aged mice as well. On C2C12 murine skeletal muscle cells, we have also found altered calcium homeostasis upon Syndecan-4 silencing. The decreased expression of Syndecan-4 leads to reduced skeletal muscle performance in mice and altered motility in C2C12 myoblasts via altered calcium homeostasis. The altered muscle force performance develops at an early age and is maintained throughout the life course of the animal until old age.

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This article presents our generative model for rhythm action games together with applications in business operation. Rhythm action games are video games in which the player is challenged to issue commands at the right timings during a music session. The timings are rendered in the chart, which consists of visual symbols, called notes, flying through the screen. We introduce our deep generative model, GenéLive!, which outperforms the state-of-the-art model by taking into account musical structures through beats and temporal scales. Thanks to its favorable performance, GenéLive! was put into operation at KLab Inc., a Japan-based video game developer, and reduced the business cost of chart generation by as much as half. The application target included the phenomenal "Love Live!", which has more than 10 million users across Asia and beyond, and is one of the few rhythm action franchises that has led the online era of the genre. In this article, we evaluate the generative performance of GenéLive! using production datasets at KLab as well as open datasets for reproducibility, while the model continues to operate in their business. Our code and the model, tuned and trained using a supercomputer, are publicly available.