Search

Hydrodynamic theories effectively describe many-body systems out of equilibrium in terms of a few macroscopic parameters. However, such parameters are difficult to determine from microscopic information. Seldom is this challenge more apparent than in active matter, where the hydrodynamic parameters are in fact fields that encode the distribution of energy-injecting microscopic components. Here, we use active nematics to demonstrate that neural networks can map out the spatiotemporal variation of multiple hydrodynamic parameters and forecast the chaotic dynamics of these systems. We analyze biofilament/molecularmotor experiments with microtubule/kinesin and actin/myosin complexes as computer vision problems. Our algorithms can determine how activity and elastic moduli change as a function of space and time, as well as adenosine triphosphate (ATP) or motor concentration. The only input needed is the orientation of the biofilaments and not the coupled velocity field which is harder to access in experiments. We can also forecast the evolution of these chaotic many-body systems solely from image sequences of their past using a combination of autoencoders and recurrent neural networks with residual architecture. In realistic experimental setups for which the initial conditions are not perfectly known, our physics-inspired machine-learning algorithms can surpass deterministic simulations. Our study paves the way for artificial-intelligence characterization and control of coupled chaotic fields in diverse physical and biological systems, even in the absence of knowledge of the underlying dynamics. [ABSTRACT FROM AUTHOR]

Uncoupling protein 1 (UCP1) is localized on the inner mitochondrial membrane and generates heat by uncoupling ATP synthesis from proton transit across the inner membrane. UCP1 is a key element of nonshivering thermogenesis and is most likely important in the regulation of body adiposity. Pigs (Artiodactyl family Suidae) lack a functional UCP1 gene, resulting in poor thermoregulation and susceptibility to cold, which is an economic and pig welfare issue owing to neonatal mortality. Pigs also have a tendency toward fat accumulation, which may be linked to their lack of UCP1, and thus influences the efficiency of pig production. Here, we report application of a CRISPR/Cas9-mediated, homologous recombination (HR)-independent approach to efficiently insert mouse adiponectin-UCP1 into the porcine endogenous UCP1 locus. The resultant UCP1 knock-in (KI) pigs showed an improved ability to maintain body temperature during acute cold exposure, but they did not have alterations in physical activity levels or total daily energy expenditure (DEE). Furthermore, ectopic UCP1 expression in white adipose tissue (WAT) dramatically decreased fat deposition by 4.89% (P < 0.01), consequently increasing carcass lean percentage (CLP; P < 0.05). Mechanism studies indicated that the loss of fat upon UCP1 activation in WAT was linked to elevated lipolysis. UCP1 KI pigs are a potentially valuable resource for agricultural production through their combination of cold adaptation, which improves pig welfare and reduces economic losses, with reduced fat deposition and increased lean meat production. [ABSTRACT FROM AUTHOR]