Stochastic model for gel permeation separation of polymers

1968 ◽  
Vol 6 (3) ◽  
pp. 517-527 ◽  
Author(s):  
Jack B. Carmichael
Keyword(s):  
Stochastic Model ◽  
Gel Permeation ◽  
Separation Of Polymers

Influence of the structure of macroporous glasses on the separation of polymers by gel permeation chromatography

1973 ◽  
Vol 77 (1) ◽  
pp. 149-159 ◽  
Author(s):  
S.P. Zhdanov ◽  
B.G. Belenkii ◽  
P.P. Nefedov ◽  
E.V. Koromaldi ◽  
M.A. Kuznetsova
Keyword(s):  
Gel Permeation Chromatography ◽  
Gel Permeation ◽  
Separation Of Polymers

A Fruitful Stochastic Model

1964 ◽  
Vol 9 (7) ◽  
pp. 273-276
Author(s):  
ANATOL RAPOPORT
Keyword(s):  
Stochastic Model

A Stochastic Model for Evolution of Altruistic Genes

1996 ◽  
Vol 6 (4) ◽  
pp. 445-453 ◽  
Author(s):  
Roberta Donato
Keyword(s):  
Stochastic Model

A Stochastic Model for the Inheritance of the Cancer Proneness Phenotype

1987 ◽  
Vol 26 (03) ◽  
pp. 117-123
Author(s):  
P. Tautu ◽  
G. Wagner
Keyword(s):  
Stochastic Model ◽  
Gaussian Process ◽  
Covariance Function ◽  
Neoplastic Disease ◽  
Disease Phenotype ◽  
Probabilistic Representation ◽  
Temporal Behaviour ◽  
Continuous Trait ◽  
Continuous Parameter ◽  
Level Zero

SummaryA continuous parameter, stationary Gaussian process is introduced as a first approach to the probabilistic representation of the phenotype inheritance process. With some specific assumptions about the components of the covariance function, it may describe the temporal behaviour of the “cancer-proneness phenotype” (CPF) as a quantitative continuous trait. Upcrossing a fixed level (“threshold”) u and reaching level zero are the extremes of the Gaussian process considered; it is assumed that they might be interpreted as the transformation of CPF into a “neoplastic disease phenotype” or as the non-proneness to cancer, respectively.

Aggregated, Conformationally Changed Fibrinogen Exposes the Stimulating Sites for t-PA-Catalysed Plasminogen Activation

1996 ◽  
Vol 75 (02) ◽  
pp. 326-331 ◽  
Author(s):  
Unni Haddeland ◽  
Knut Sletten ◽  
Anne Bennick ◽  
Willem Nieuwenhuizen ◽  
Frank Brosstad
Keyword(s):  
Conformational Changes ◽  
Gel Permeation Chromatography ◽  
Tissue Type ◽  
Plasminogen Activation ◽  
Chromogenic Substrate ◽  
Type Plasminogen Activator ◽  
Gel Permeation ◽  
Self Association ◽  
Fibrin Monomers ◽  
Stimulation Of

SummaryThe present paper shows that conformationally changed fibrinogen can expose the sites Aα-(148-160) and γ-(312-324) involved in stimulation of the tissue-type plasminogen activator (t-PA)-catalysed plasminogen activation. The exposure of the stimulating sites was determined by ELISA using mABs directed to these sites, and was shown to coincide with stimulation of t-PA-catalysed plasminogen activation as assessed in an assay using a chromogenic substrate for plasmin. Gel permeation chromatography of fibrinogen conformationally changed by heat (46.5° C for 25 min) demonstrated the presence of both aggregated and monomeric fibrinogen. The aggregated fibrinogen, but not the monomeric fibrinogen, had exposed the epitopes Aα-(148-160) and γ-(312-324) involved in t-PA-stimulation. Fibrinogen subjected to heat in the presence of 3 mM of the tetrapeptide GPRP neither aggregates nor exposes the rate-enhancing sites. Thus, aggregation and exposure of t-PA-stimulating sites in fibrinogen seem to be related phenomena, and it is tempting to believe that the exposure of stimulating sites is a consequence of the conformational changes that occur during aggregation, or self-association. Fibrin monomers kept in a monomeric state by a final GPRP concentration of 3 mM do not expose the epitopes Aα-(148-160) and γ-(312-324) involved in t-PA-stimulation, whereas dilution of GPRP to a concentration that is no longer anti-polymerizing, results in exposure of these sites. Consequently, the exposure of t-PA-stimulating sites in fibrin as well is due to the conformational changes that occur during selfassociation.

Stochastic Model of Traffic Jam and Traffic Signal Control

2011 ◽  
Vol 131 (2) ◽  
pp. 303-310
Author(s):  
Ji-Sun Shin ◽  
Cheng-You Cui ◽  
Tae-Hong Lee ◽  
Hee-hyol Lee
Keyword(s):  
Stochastic Model ◽  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Traffic Jam
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