Effect of formulation variables on the prediction of release from microparticles with experimental design

2006 ◽  
Vol 102 (5) ◽  
pp. 4546-4553 ◽  
Author(s):  
A. Luzardo-Alvarez ◽  
S. Almeida-Prieto ◽  
F. Fraga-López ◽  
F. Otero-Espinar ◽  
E. Rodriguez-Núñez ◽  
...  
Keyword(s):  
Experimental Design ◽  
Formulation Variables

Application of statistical experimental design to study the formulation variables influencing the coating process of lidocaine liposomes

2007 ◽  
Vol 337 (1-2) ◽  
pp. 336-345 ◽  
Author(s):  
M.L. González-Rodríguez ◽  
L.B. Barros ◽  
J. Palma ◽  
P.L. González-Rodríguez ◽  
A.M. Rabasco
Keyword(s):  
Experimental Design ◽  
Coating Process ◽  
Statistical Experimental Design ◽  
Formulation Variables

Optimization of Formulation Variables of Benzocaine Liposomes using Experimental Design

2008 ◽  
Vol 18 (2) ◽  
pp. 113-125 ◽  
Author(s):  
Paola Mura ◽  
Gaetano Capasso ◽  
Francesca Maestrelli ◽  
Sandra Furlanetto
Keyword(s):  
Experimental Design ◽  
Formulation Variables

Didanosine extended-release matrix tablets: optimization of formulation variables using statistical experimental design

2002 ◽  
Vol 237 (1-2) ◽  
pp. 107-118 ◽  
Author(s):  
Carla Sánchez-Lafuente ◽  
Sandra Furlanetto ◽  
Mercedes Fernández-Arévalo ◽  
Josefa Alvarez-Fuentes ◽  
Antonio M. Rabasco ◽  
...  
Keyword(s):  
Experimental Design ◽  
Extended Release ◽  
Matrix Tablets ◽  
Statistical Experimental Design ◽  
Formulation Variables

Identifying suboptimalities with factorial model comparison

2018 ◽  
Vol 41 ◽  
Author(s):  
Wei Ji Ma
Keyword(s):  
Experimental Design ◽  
Model Comparison ◽  
Process Models ◽  
Multiple Forms ◽  
Factorial Experimental Design ◽  
Factorial Model ◽  
Multiple Components ◽  
The Many

AbstractGiven the many types of suboptimality in perception, I ask how one should test for multiple forms of suboptimality at the same time – or, more generally, how one should compare process models that can differ in any or all of the multiple components. In analogy to factorial experimental design, I advocate for factorial model comparison.

Combining integrated systems-biology approaches with intervention-based experimental design provides a higher-resolution path forward for microbiome research

2019 ◽  
Vol 42 ◽  
Author(s):  
J. Alfredo Blakeley-Ruiz ◽  
Carlee S. McClintock ◽  
Ralph Lydic ◽  
Helen A. Baghdoyan ◽  
James J. Choo ◽  
...  
Keyword(s):  
Systems Biology ◽  
High Resolution ◽  
Experimental Design ◽  
Integrated Systems ◽  
Microbiome Research ◽  
Constructive Criticism

Abstract The Hooks et al. review of microbiota-gut-brain (MGB) literature provides a constructive criticism of the general approaches encompassing MGB research. This commentary extends their review by: (a) highlighting capabilities of advanced systems-biology “-omics” techniques for microbiome research and (b) recommending that combining these high-resolution techniques with intervention-based experimental design may be the path forward for future MGB research.

Statistics and parallaxes

1978 ◽  
Vol 48 ◽  
pp. 7-29
Author(s):  
T. E. Lutz
Keyword(s):  
Experimental Design ◽  
Statistical Methods ◽  
Review Paper ◽  
Systematic Errors ◽  
Past Experience ◽  
Random Errors ◽  
Trigonometric Parallaxes ◽  
Systematic And Random Errors

This review paper deals with the use of statistical methods to evaluate systematic and random errors associated with trigonometric parallaxes. First, systematic errors which arise when using trigonometric parallaxes to calibrate luminosity systems are discussed. Next, determination of the external errors of parallax measurement are reviewed. Observatory corrections are discussed. Schilt’s point, that as the causes of these systematic differences between observatories are not known the computed corrections can not be applied appropriately, is emphasized. However, modern parallax work is sufficiently accurate that it is necessary to determine observatory corrections if full use is to be made of the potential precision of the data. To this end, it is suggested that a prior experimental design is required. Past experience has shown that accidental overlap of observing programs will not suffice to determine observatory corrections which are meaningful.

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