scholarly journals Power-Law Kinetics of Tracer Washout from Physiological Systems

10.1114/1.105 ◽  
1998 ◽  
Vol 26 (5) ◽  
pp. 775-779 ◽  
Author(s):  
Daniel A. Beard ◽  
James B. Bassingthwaighte
1997 ◽  
Vol 24 (7) ◽  
pp. 495-499 ◽  
Author(s):  
J. P. Poirier ◽  
A. G. Duba

2016 ◽  
Vol 270 ◽  
pp. 31-42 ◽  
Author(s):  
Kilian Kobl ◽  
Sébastien Thomas ◽  
Yvan Zimmermann ◽  
Ksenia Parkhomenko ◽  
Anne-Cécile Roger

2018 ◽  
Vol 115 (3) ◽  
pp. 513-518 ◽  
Author(s):  
Iris Grossman-Haham ◽  
Gabriel Rosenblum ◽  
Trishool Namani ◽  
Hagen Hofmann

Protein dynamics are typically captured well by rate equations that predict exponential decays for two-state reactions. Here, we describe a remarkable exception. The electron-transfer enzyme quiescin sulfhydryl oxidase (QSOX), a natural fusion of two functionally distinct domains, switches between open- and closed-domain arrangements with apparent power-law kinetics. Using single-molecule FRET experiments on time scales from nanoseconds to milliseconds, we show that the unusual open-close kinetics results from slow sampling of an ensemble of disordered domain orientations. While substrate accelerates the kinetics, thus suggesting a substrate-induced switch to an alternative free energy landscape of the enzyme, the power-law behavior is also preserved upon electron load. Our results show that the slow sampling of open conformers is caused by a variety of interdomain interactions that imply a rugged free energy landscape, thus providing a generic mechanism for dynamic disorder in multidomain enzymes.


1988 ◽  
Vol 27 (1) ◽  
pp. 191-194 ◽  
Author(s):  
Patrick L. Mills ◽  
Steven Lai ◽  
Milorad P. Dudukovic ◽  
Palghat A. Ramachandran

2000 ◽  
Vol 112 (7) ◽  
pp. 3117-3120 ◽  
Author(s):  
M. Kuno ◽  
D. P. Fromm ◽  
H. F. Hamann ◽  
A. Gallagher ◽  
D. J. Nesbitt

1992 ◽  
Vol 46 (8) ◽  
pp. 5024-5027 ◽  
Author(s):  
Peter J. Eng ◽  
Peter W. Stephens ◽  
Teddy Tse

Author(s):  
Bart Smeets ◽  
Rodrigo Watté ◽  
Herman Ramon

AbstractWe analyze the temporal evolution of accumulated hospitalization cases due to COVID-19 in Belgium. The increase of hospitalization cases is consistent with an initial exponential phase, and a subsequent power law growth. For the latter, we estimate a power law exponent of ≈ 2.2, which is consistent with growth kinetics of COVID-19 in China and indicative of the underlying small world network structure of the epidemic. Finally, we fit an SIR-X model to the experimental data and estimate the effect of containment policies in comparison to their effect in China. This model suggests that the base reproduction rate has been significantly reduced, but that the number of susceptible individuals that is isolated from infection is very small. Based on the SIR-X model fit, we analyze the COVID-19 mortality and the number of patients requiring ICU treatment over time.


2017 ◽  
Vol 56 (2) ◽  
pp. 358-394 ◽  
Author(s):  
Dylan Antonio S. J. Talabis ◽  
Carlene Perpetua P. Arceo ◽  
Eduardo R. Mendoza
Keyword(s):  

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