Rapid characterization of folding and binding interactions with thermolabile ligands by DSC

2016 ◽  
Vol 52 (92) ◽  
pp. 13471-13474 ◽  
Author(s):  
R. W. Harkness ◽  
S. Slavkovic ◽  
P. E. Johnson ◽  
A. K. Mittermaier
Keyword(s):  
New Method ◽  
Binding Interactions ◽  
Kinetic Information ◽  
Rapid Characterization

A new method to characterize interactions between macromolecules and thermolabile ligands by DSC yields stability, affinity, and kinetic information.

New method for rapid characterization of molecular shapes: applications in drug design

1993 ◽  
Vol 33 (1) ◽  
pp. 79-85 ◽  
Author(s):  
R. Nilakantan ◽  
N. Bauman ◽  
R. Venkataraghavan
Keyword(s):  
Drug Design ◽  
New Method ◽  
Rapid Characterization

ChemInform Abstract: New Method for Rapid Characterization of Molecular Shapes: Applications in Drug Design.

ChemInform ◽  
2010 ◽  
Vol 24 (19) ◽  
pp. no-no
Author(s):  
R. NILAKANTAN ◽  
N. BAUMAN ◽  
R. VENKATARAGHAVAN
Keyword(s):  
Drug Design ◽  
New Method ◽  
Rapid Characterization

scholarly journals A new method for the rapid characterization of root growth and distribution using digital image correlation

2018 ◽  
Vol 218 (2) ◽  
pp. 835-846 ◽  
Author(s):  
Tan Bao ◽  
Garrett W. Melenka ◽  
Megan K. Ljubotina ◽  
Jason P. Carey ◽  
James F. Cahill
Keyword(s):  
Digital Image Correlation ◽  
Root Growth ◽  
Digital Image ◽  
Image Correlation ◽  
New Method ◽  
Rapid Characterization

A New Method for Rapid Characterization of the Folding Pathways of Multidisulfide-Containing Proteins

2004 ◽  
Vol 126 (12) ◽  
pp. 3720-3721 ◽  
Author(s):  
Ervin Welker ◽  
Laura Hathaway ◽  
Harold A. Scheraga
Keyword(s):  
New Method ◽  
Folding Pathways ◽  
Rapid Characterization

A new method for approximation of closed convex subsets of B(H)

Filomat ◽  
2017 ◽  
Vol 31 (19) ◽  
pp. 6005-6013
Author(s):  
Mahdi Iranmanesh ◽  
Fatemeh Soleimany
Keyword(s):  
Best Approximation ◽  
Numerical Range ◽  
New Method ◽  
C Algebra ◽  
Convex Subsets

In this paper we use the concept of numerical range to characterize best approximation points in closed convex subsets of B(H): Finally by using this method we give also a useful characterization of best approximation in closed convex subsets of a C*-algebra A.

On a generalization of the Rényi–Srivastava characterization of the Poisson law

2021 ◽  
Vol 58 (1) ◽  
pp. 68-82
Author(s):  
Jean-Renaud Pycke
Keyword(s):  
Life Insurance ◽  
Collective Model ◽  
New Method ◽  
Bell Polynomials ◽  
Explicit Formulas ◽  
Compound Distributions ◽  
Poisson Law ◽  
Pierre Loti

AbstractWe give a new method of proof for a result of D. Pierre-Loti-Viaud and P. Boulongne which can be seen as a generalization of a characterization of Poisson law due to Rényi and Srivastava. We also provide explicit formulas, in terms of Bell polynomials, for the moments of the compound distributions occurring in the extended collective model in non-life insurance.

scholarly journals An evaluation of the InDevR FluChip-8G insight microarray assay in characterizing influenza a viruses

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Emily S. Bailey ◽  
Xinye Wang ◽  
Mai-juan Ma ◽  
Guo-lin Wang ◽  
Gregory C. Gray
Keyword(s):  
Influenza A ◽  
Influenza Viruses ◽  
Time Range ◽  
Influenza B ◽  
Influenza A Viruses ◽  
Laboratory Methods ◽  
Duke University ◽  
Multiple Viral Strains ◽  
Rapid Characterization

AbstractInfluenza viruses are an important cause of disease in both humans and animals, and their detection and characterization can take weeks. In this study, we sought to compare classical virology techniques with a new rapid microarray method for the detection and characterization of a very diverse, panel of animal, environmental, and human clinical or field specimens that were molecularly positive for influenza A alone (n = 111), influenza B alone (n = 3), both viruses (n = 13), or influenza negative (n = 2) viruses. All influenza virus positive samples in this study were first subtyped by traditional laboratory methods, and later evaluated using the FluChip-8G Insight Assay (InDevR Inc. Boulder, CO) in laboratories at Duke University (USA) or at Duke Kunshan University (China). The FluChip-8G Insight multiplexed assay agreed with classical virologic techniques 59 (54.1%) of 109 influenza A-positive, 3 (100%) of the 3 influenza B-positive, 0 (0%) of 10 both influenza A- and B-positive samples, 75% of 24 environmental samples including those positive for H1, H3, H7, H9, N1, and N9 strains, and 80% of 22 avian influenza samples. It had difficulty with avian N6 types and swine H3 and N2 influenza specimens. The FluChip-8G Insight assay performed well with most human, environmental, and animal samples, but had some difficulty with samples containing multiple viral strains and with specific animal influenza strains. As classical virology methods are often iterative and can take weeks, the FluChip-8G Insight Assay rapid results (time range 8 to 12 h) offers considerable time savings. As the FluChip-8G analysis algorithm is expected to improve over time with addition of new subtypes and sample matrices, the FluChip-8G Insight Assay has considerable promise for rapid characterization of novel influenza viruses affecting humans or animals.

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