Skeletal muscle in vertebrates is derived from somites, epithelial structures of the paraxial mesoderm, yet many unrelated reports describe the occasional appearance of myogenic cells from tissues of nonsomite origin, suggesting either transdifferentiation or the persistence of a multipotent progenitor. Here, we show that clonable skeletal myogenic cells are present in the embryonic dorsal aorta of mouse embryos. This finding is based on a detailed clonal analysis of different tissue anlagen at various developmental stages. In vitro, these myogenic cells show the same morphology as satellite cells derived from adult skeletal muscle, and express a number of myogenic and endothelial markers. Surprisingly, the latter are also expressed by adult satellite cells. Furthermore, it is possible to clone myogenic cells from limbs of mutant c-Met−/− embryos, which lack appendicular muscles, but have a normal vascular system. Upon transplantation, aorta-derived myogenic cells participate in postnatal muscle growth and regeneration, and fuse with resident satellite cells.The potential of the vascular system to generate skeletal muscle cells may explain observations of nonsomite skeletal myogenesis and raises the possibility that a subset of satellite cells may derive from the vascular system.
Being able to predict the failure of materials based on structural information is a fundamental issue with enormous practical and industrial relevance for the monitoring of devices and components. Thanks to recent advances in deep learning, accurate failure predictions are becoming possible even for strongly disordered solids, but the sheer number of parameters used in the process renders a physical interpretation of the results impossible. Here we address this issue and use machine learning methods to predict the failure of simulated two dimensional silica glasses from their initial undeformed structure. We then exploit Gradient-weighted Class Activation Mapping (Grad-CAM) to build attention maps associated with the predictions, and we demonstrate that these maps are amenable to physical interpretation in terms of topological defects and local potential energies. We show that our predictions can be transferred to samples with different shape or size than those used in training, as well as to experimental images. Our strategy illustrates how artificial neural networks trained with numerical simulation results can provide interpretable predictions of the behavior of experimentally measured structures.
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