Virial Expansions for a Binary Mixture Model and for a Related One‐Component Model

1972 ◽  
Vol 57 (10) ◽  
pp. 4484-4492 ◽  
Author(s):  
J. P. Straley ◽  
M. A. Cotter ◽  
Tae‐Journ Lie ◽  
B. Widom
Keyword(s):  
Binary Mixture ◽  
Mixture Model ◽  
Component Model

scholarly journals Surface and capillary transitions in an associating binary mixture model

2003 ◽  
Vol 67 (4) ◽  
Author(s):  
J. M. Romero-Enrique ◽  
L. F. Rull ◽  
U. Marini Bettolo Marconi
Keyword(s):  
Binary Mixture ◽  
Mixture Model

scholarly journals JAMMING DYNAMICS IN GRAIN MIXTURES: AN EXTENDED HYDRODYNAMIC APPROACH

2007 ◽  
Vol 07 (02) ◽  
pp. L163-L167
Author(s):  
SUPURNA SINHA
Keyword(s):  
Binary Mixture ◽  
Mixture Model ◽  
Hard Sphere ◽  
Point Of View ◽  
The Novel ◽  
Hydrodynamic Approach ◽  
Granular Mixtures ◽  
Small Grains ◽  
Extended Hydrodynamics

We study jamming in granular mixtures from the novel point of view of extended hydrodynamics. Using a hard sphere binary mixture model we predict that a few large grains are expected to get caged more effectively in a matrix of small grains compared to a few small grains in a matrix of larger ones. A similar effect has been experimentally seen in the context of colloidal mixtures.

scholarly journals The potential energy landscape in the Lennard-Jones binary mixture model

2003 ◽  
Vol 15 (11) ◽  
pp. S1227-S1236 ◽  
Author(s):  
M Sampoli ◽  
P Benassi ◽  
R Eramo ◽  
L Angelani ◽  
G Ruocco
Keyword(s):  
Binary Mixture ◽  
Potential Energy ◽  
Mixture Model ◽  
Energy Landscape ◽  
Lennard Jones ◽  
Potential Energy Landscape

scholarly journals Bayesian Binary Mixture Models as a Flexible Alternative to Cut-Off Analysis of ELISA Results, a Case Study of Seoul Orthohantavirus

Viruses ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 1155
Author(s):  
Arno Swart ◽  
Miriam Maas ◽  
Ankje de Vries ◽  
Tryntsje Cuperus ◽  
Marieke Opsteegh
Keyword(s):  
Binary Mixture ◽  
Mixture Models ◽  
Mixture Model ◽  
Optical Density ◽  
Quantitative Polymerase Chain Reaction ◽  
Optimal Solution ◽  
Enzyme Linked Immunosorbent Assay ◽  
Analysis Tool ◽  
Distribution Of Values ◽  
Serological Data

Serological assays, such as the enzyme-linked immunosorbent assay (ELISA), are popular tools for establishing the seroprevalence of various infectious diseases in humans and animals. In the ELISA, the optical density is measured and gives an indication of the antibody level. However, there is variability in optical density values for individuals that have been exposed to the pathogen of interest, as well as individuals that have not been exposed. In general, the distribution of values that can be expected for these two categories partly overlap. Often, a cut-off value is determined to decide which individuals should be considered seropositive or seronegative. However, the classical cut-off approach based on a putative threshold ignores heterogeneity in immune response in the population and is thus not the optimal solution for the analysis of serological data. A binary mixture model does include this heterogeneity, offers measures of uncertainty and the direct estimation of seroprevalence without the need for correction based on sensitivity and specificity. Furthermore, the probability of being seropositive can be estimated for individual samples, and both continuous and categorical covariates (risk-factors) can be included in the analysis. Using ELISA results from rats tested for the Seoul orthohantavirus, we compared the classical cut-off method with a binary mixture model set in a Bayesian framework. We show that it performs similarly or better than cut-off methods, by comparing with real-time quantitative polymerase chain reaction (RT-qPCR) results. We therefore recommend binary mixture models as an analysis tool over classical cut-off methods. An example code is included to facilitate the practical use of binary mixture models in everyday practice.

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