Do Humans Optimally Exploit Redundancy to Control Step Variability in Walking?

It is widely accepted that humans and animals minimize energetic cost while walking. While such principles predict average behavior, they do not explain the variability observed in walking. For robust performance, walking movements must adapt at each step, not just on average. Here, we propose an analytical framework that reconciles issues of optimality, redundancy, and stochasticity. For human treadmill walking, we defined a goal function to formulate a precise mathematical definition of one possible control strategy: maintain constant speed at each stride. We recorded stride times and stride lengths from healthy subjects walking at five speeds. The specified goal function yielded a decomposition of stride-to-stride variations into new gait variables explicitly related to achieving the hypothesized strategy. Subjects exhibited greatly decreased variability for goal-relevant gait fluctuations directly related to achieving this strategy, but far greater variability for goal-irrelevant fluctuations. More importantly, humans immediately corrected goal-relevant deviations at each successive stride, while allowing goal-irrelevant deviations to persist across multiple strides. To demonstrate that this was not the only strategy people could have used to successfully accomplish the task, we created three surrogate data sets. Each tested a specific alternative hypothesis that subjects used a different strategy that made no reference to the hypothesized goal function. Humans did not adopt any of these viable alternative strategies. Finally, we developed a sequence of stochastic control models of stride-to-stride variability for walking, based on the Minimum Intervention Principle. We demonstrate that healthy humans are not precisely “optimal,” but instead consistently slightly over-correct small deviations in walking speed at each stride. Our results reveal a new governing principle for regulating stride-to-stride fluctuations in human walking that acts independently of, but in parallel with, minimizing energetic cost. Thus, humans exploit task redundancies to achieve robust control while minimizing effort and allowing potentially beneficial motor variability

Screening data for: A key mammalian cholesterol synthesis enzyme, squalene monooxygenase, is allosterically stabilized by its substrate

To identify compounds that modulate squalene monooxygenase (SM) stability, screening of FDA-approved drug library was performed with HEK293 cells stably expressing SM-emerald luciferase fusion protein. For experimental details, see the accompanying article.The data is a table with the following columns:position, [ROW].[COLUMN].[PLATE ID];name, name of compounds;conc, concentration of the compounds in µM;type, types of the treatment (conotrol "ctrl" or "library");plate, plate id (number);row, row number (A to H) within 96-well plates;column, column number (1 to 12) within 96-well plates;luc_10, luminescence data for 10 µM treatment screen;luc_1, luminescence data for 1 µM treatment screen;nluc_10, luminecence data for 10 µM treatment screen, normalized in a plate-wise manner;nluc_1, luminecence data for 1 µM treatment screen, normalized in a plate-wise manner;rsn_10, robust signal-to-noise ratio (robust Z score) for 10 µM screen;rsn_1, robust signal-to-noise ratio (robust Z score) for 1 µM screen.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

Three-variable reversible Gray–Scott model

Even though the field of nonequilibrium thermodynamics has been popular and its importance has been suggested by Demirel and Sandler [J. Phys. Chem. B 108, 31 (2004)], there are only a few investigations of reaction-diffusion systems from the aspect of thermodynamics. A possible reason is that model equations are complicated and difficult to analyze because the corresponding chemical reactions need to be reversible for thermodynamical calculations. Here, we introduce a simple model for calculation of entropy production rate: a three-variable reversible Gray–Scott model. The rate of entropy production in self-replicating pattern formation is calculated, and the results are compared with those reported based on the Brusselator model in the context of biological cell division