Design and function of superfast muscles : new insights into the physiology of skeletal muscle

First published online as a Review in Advance on October 24, 2005. (Some corrections may occur before final publication online and in print)Author Posting. © Annual Reviews, 2005. This article is posted here by permission of Annual Reviews for personal use, not for redistribution. The definitive version was published in Annual Review of Physiology 68 (2006): 22.1-22.29, doi:10.1146/annurev.physiol.68.040104.105418.Superfast muscles of vertebrates power sound production. The fastest, the swimbladder muscle of toadfish, generates mechanical power at frequencies in excess of 200 Hz. To operate at these frequencies, the speed of relaxation has had to increase approximately 50-fold. This increase is accomplished by modifications of three kinetic traits: (a) a fast calcium transient due to extremely high concentration of sarcoplasmic reticulum (SR)-Ca2+ pumps and parvalbumin, (b) fast off-rate of Ca2+ from troponin C due to an alteration in troponin, and (c) fast cross-bridge detachment rate constant (g, 50 times faster than that in rabbit fast-twitch muscle) due to an alteration in myosin. Although these three modifications permit swimbladder muscle to generate mechanical work at high frequencies (where locomotor muscles cannot), it comes with a cost: The high g causes a large reduction in attached force-generating cross-bridges, making the swimbladder incapable of powering low-frequency locomotory movements. Hence the locomotory and sound-producing muscles have mutually exclusive designs.This work was made possible by support from NIH grants AR38404 and AR46125 as well as the University of Pennsylvania Research Foundation

On the methodological unification in electroencephalography

BACKGROUND: This paper presents results of a pursuit of a repeatable and objective methodology of analysis of the electroencephalographic (EEG) time series. METHODS: Adaptive time-frequency approximations of EEG are discussed in the light of the available experimental and theoretical evidence, and applicability in various experimental and clinical setups. RESULTS: Four lemmas and three conjectures support the following conclusion. CONCLUSION: Adaptive time-frequency approximations of signals unify most of the univariate computational approaches to EEG analysis, and offer compatibility with its traditional (visual) analysis, used in clinical applications