A novel network information framework quantifies muscle interactions, characterises their task-relevance, and identifies networks of muscles with similar and complementary functional roles.

The human brain encodes coordinated patterns of joint movements – synergies – to increase the efficiency with which it can control complex hand movements.

Populations of spinal cord neurons innervate motoneurons controlling local and distant synergist and antagonist muscles.

Learning to reach in the sensorimotor loop, and the required neural dynamics, can be potentially explained by simple principles.

Muscle synergies are able to identify muscular adaptation that results from feelings of joint instability, whereas tibiofemoral kinematics are sensitive for detecting acute instability events during functional activities.

The ability of intrinsically disordered proteins to protect sensitive enzymes during drying can be tuned through interactions with cosolutes.

Human infants can use various muscle activations as soon as birth to produce rhythmic leg movements, but the strategy underlying this variable output seems to change between the first months of life and toddlerhood.

Myomaker is activated on muscle stem cells to promote their fusion with myofibers, which is essential for induction of pro-growth signaling pathways and physiological muscle hypertrophy.

Context-dependent optimization of Gli-binding site occupancy, in the presence of Hand2, is critical for modulating tissue-specific transcriptional output within tissues that lack an obvious Hedgehog morphogen gradient.

Leptin acts in the paraventricular nucleus to slowly increase sympathetic nerve activity to skeletal muscle and brown adipose tissue via induction of its own receptor in TRH glutamatergic neurons.