scholarly journals Optimal designs for asymmetric sigmoidal response curves in bioassays and immunoassays

2019 ◽  
Vol 29 (2) ◽  
pp. 421-436
Author(s):  
Seung Won Hyun ◽  
Weng Kee Wong ◽  
Yarong Yang
Keyword(s):  
Statistical Inference ◽  
Software Package ◽  
Logistic Models ◽  
Experimental Designs ◽  
Optimal Designs ◽  
Model Parameters ◽  
Response Curves ◽  
Different Types ◽  
User Friendly

The 5-parameter logistic (5PL) model is frequently used to model and analyze responses from bioassays and immunoassays which can be skewed. Various types of optimal experimental designs for 2, 3 and 4-parameter logistic models have been reported but not for the more complicated 5PL model. We construct different types of optimal designs for studying various features of the 5PL model and show that commonly used designs in bioassays and immunoassays are generally inefficient for statistical inference. To facilitate use of such designs in practice, we create a user-friendly software package to generate various tailor-made optimal designs for the 5PL model and evaluate robustness properties of a design under a variation of criteria, model forms and misspecification in the nominal values of the model parameters.

scholarly journals Testing parallelism for the four-parameter logistic model with D-optimal design

2020 ◽  
Vol 15 (3) ◽  
pp. 273-284
Author(s):  
Lin Ying ◽  
Hyun Seung Won
Keyword(s):  
Optimal Design ◽  
Logistic Model ◽  
Test Preparation ◽  
Test Sample ◽  
Optimal Designs ◽  
Model Parameters ◽  
Reference Standard ◽  
Response Curves ◽  
Classical Design ◽  
Intersection Union Test

In order to determine the potency of the test preparation relative to the standard preparation, it is often important to test parallelism between a pair of dose-response curves of reference standard and test sample. Optimal designs are known to be more powerful in testing parallelism as compared to classical designs. In this study, D-optimal design was implemented to study the parallelism and compare+ its performance with a classical design. We modified D-optimal design to test the parallelism in the four-parameter logistic (4PL) model using Intersection-Union Test (IUT). IUT method is appropriate when the null hypothesis is expressed as a union of sets, and by using this method complicated tests involving several parameters are easily constructed. Since D-optimal design minimizes the variances of model parameters, it can bring more power to the IUT test. A simulation study will be presented to compare the empirical properties of the two different designs.

Dose-response Curves in the Fibrin Plate Assay Fibrinolytic Activity of Proteases

1974 ◽  
Vol 32 (02/03) ◽  
pp. 356-365 ◽  
Author(s):  
F Haverkate ◽  
D. W Traas
Keyword(s):  
Dose Response ◽  
Fibrinolytic Activity ◽  
Enzyme Concentration ◽  
Response Curves ◽  
Agar Gel ◽  
Porcine Tissue ◽  
Plate Assay ◽  
Fibrin Plate ◽  
Different Types ◽  
Dose Response Curves

SummaryIn the fibrin plate assay different types of relationships between the dose of applied proteolytic enzyme and the response have been previously reported. This study was undertaken to determine whether a generally valid relationship might exist.Trypsin, chymotrypsin, papain, the plasminogen activator urokinase and all of the microbial proteases investigated, including brinase gave a linear relationship between the logarithm of the enzyme concentration and the diameter of the circular lysed zone. A similar linearity of dose-response curves has frequently been found by investigators who used enzyme plate assays with substrates different from fibrin incorporated in an agar gel. Consequently, it seems that this linearity of dose-response curves is generally valid for the fibrin plate assay as well as for other enzyme plate bioassays.Both human plasmin and porcine tissue activator of plasminogen showed deviations from linearity of semi-logarithmic dose-response curves in the fibrin plate assay.

Q-optimal experimental designs and close to them experimental designs for polynomial regression on the interval

2020 ◽  
Vol 86 (5) ◽  
pp. 65-72
Author(s):  
Yu. D. Grigoriev
Keyword(s):  
Optimal Design ◽  
Polynomial Regression ◽  
Direct Consequence ◽  
Experimental Designs ◽  
Optimal Designs ◽  
Software Systems ◽  
General Character ◽  
Support Points ◽  
Optimal Weights ◽  
Universal Expression

The problem of constructing Q-optimal experimental designs for polynomial regression on the interval [–1, 1] is considered. It is shown that well-known Malyutov – Fedorov designs using D-optimal designs (so-called Legendre spectrum) are other than Q-optimal designs. This statement is a direct consequence of Shabados remark which disproved the Erdős hypothesis that the spectrum (support points) of saturated D-optimal designs for polynomial regression on a segment appeared to be support points of saturated Q-optimal designs. We present a saturated exact Q-optimal design for polynomial regression with s = 3 which proves the Shabados notion and then extend this statement to approximate designs. It is shown that when s = 3, 4 the Malyutov – Fedorov theorem on approximate Q-optimal design is also incorrect, though it still stands for s = 1, 2. The Malyutov – Fedorov designs with Legendre spectrum are considered from the standpoint of their proximity to Q-optimal designs. Case studies revealed that they are close enough for small degrees s of polynomial regression. A universal expression for Q-optimal distribution of the weights pi for support points xi for an arbitrary spectrum is derived. The expression is used to tabulate the distribution of weights for Malyutov – Fedorov designs at s = 3, ..., 6. The general character of the obtained expression is noted for Q-optimal weights with A-optimal weight distribution (Pukelsheim distribution) for the same problem statement. In conclusion a brief recommendation on the numerical construction of Q-optimal designs is given. It is noted that in this case in addition to conventional numerical methods some software systems of symbolic computations using methods of resultants and elimination theory can be successfully applied. The examples of Q-optimal designs considered in the paper are constructed using precisely these methods.

A user-friendly general-purpose predictive software package for food safety

2011 ◽  
Vol 104 (2) ◽  
pp. 173-185 ◽  
Author(s):  
Amit Halder ◽  
Ashish Dhall ◽  
Ashim K. Datta ◽  
D. Glenn Black ◽  
P.M. Davidson ◽  
...  
Keyword(s):  
Food Safety ◽  
Software Package ◽  
General Purpose ◽  
User Friendly ◽  
Predictive Software

A General-Purpose Software for Menu Design of CAD Projects

1993 ◽  
Author(s):  
Y. C. Pao
Keyword(s):  
Software Package ◽  
Computer Programs ◽  
General Purpose ◽  
Final Report ◽  
Sample Design ◽  
Main Menu ◽  
Sample Application ◽  
User Friendly ◽  
Four Bar Linkage ◽  
General Purpose Software

Abstract A software package MenuCAD has been developed for the general need of designing menu-driven, user-friendly CAD computer programs. The main menu is formatted similar to the major contents in the final report of the design project including Contents, Analysis, Sample Design Cases, Illustrations and Tables, References, and Program Listings. Sub-menus are further divided into items delineating the steps involved in the design. Screen help messages are provided for design of the main menu and sub-menus interactively and for applying the arrow keys on the keyboard to select a sub-menus and a particular item in the sub-menu in order to execute a desired design step. MenuCAD builds the framework, its user has to supplement with a subroutine ExecItem for describing the special features and for directing how each design step should be executed in the project. A CAD design of four-bar linkage project is presented as a sample application of this package.

scholarly journals The Optimization of Hybrid Power Systems with Renewable Energy and Hydrogen Generation

Energies ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 1948 ◽  
Author(s):  
Fu-Cheng Wang ◽  
Yi-Shao Hsiao ◽  
Yi-Zhe Yang
Keyword(s):  
Power Systems ◽  
System Performance ◽  
Hydrogen Generation ◽  
System Optimization ◽  
Energy Sources ◽  
Model Parameters ◽  
Power Model ◽  
Hybrid Power Systems ◽  
Hybrid Power ◽  
Different Types

This paper discusses the optimization of hybrid power systems, which consist of solar cells, wind turbines, fuel cells, hydrogen electrolysis, chemical hydrogen generation, and batteries. Because hybrid power systems have multiple energy sources and utilize different types of storage, we first developed a general hybrid power model using the Matlab/SimPowerSystemTM, and then tuned model parameters based on the experimental results. This model was subsequently applied to predict the responses of four different hybrid power systems for three typical loads, without conducting individual experiments. Furthermore, cost and reliability indexes were defined to evaluate system performance and to derive optimal system layouts. Finally, the impacts of hydrogen costs on system optimization was discussed. In the future, the developed method could be applied to design customized hybrid power systems.

scholarly journals Factors associated with 56-day non-return rate in dairy cattle

2011 ◽  
Vol 46 (6) ◽  
pp. 648-654 ◽  
Author(s):  
Ramiro Fouz ◽  
Fernando Gandoy ◽  
María Luisa Sanjuán ◽  
Eduardo Yus ◽  
Francisco Javier Diéguez
Keyword(s):  
Logistic Models ◽  
Herd Size ◽  
Return Rate ◽  
Dairy Herds ◽  
Model Parameters ◽  
North West ◽  
Factors Associated ◽  
Lactating Cows ◽  
Service Conception ◽  
Major Factors

The objective of this work was to identify factors associated with the 56-day non-return rate (56-NRR) in dairy herds in the Galician region, Spain, and to estimate it for individual Holstein bulls. The experiment was carried out in herds originated from North-West Spain, from September 2008 to August 2009. Data of the 76,440 first inseminations performed during this period were gathered. Candidate factors were tested for their association with the 56-NRR by using a logistic model (binomial). Afterwards, 37 sires with a minimum of 150 first performed inseminations were individually evaluated. Logistic models were also estimated for each bull, and predicted individual 56-NRR rate values were calculated as a solution for the model parameters. Logistic regression found four major factors associated with 56-NRR in lactating cows: age at insemination, days from calving to insemination, milk production level at the time of insemination, and herd size. First-service conception rate, when a particular sire was used, was higher for heifers (0.71) than for lactating cows (0.52). Non-return rates were highly variable among bulls. Asignificant part of the herd-level variation of 56-NRR of Holstein cattle seems attributable to the service sire. High correlation level between observed and predicted 56-NRR was found.

scholarly journals Cell Bioprinting: The 3D-Bioplotter™ Case

Materials ◽  
2019 ◽  
Vol 12 (23) ◽  
pp. 4005 ◽  
Author(s):  
Angelats Lobo ◽  
Ginestra
Keyword(s):  
3D Structure ◽  
Three Dimensional ◽  
Petri Dish ◽  
Two Dimensions ◽  
Point Of View ◽  
Lack Of Information ◽  
Synthetic Materials ◽  
Different Types ◽  
Available Information ◽  
User Friendly

The classic cell culture involves the use of support in two dimensions, such as a well plate or a Petri dish, that allows the culture of different types of cells. However, this technique does not mimic the natural microenvironment where the cells are exposed to. To solve that, three-dimensional bioprinting techniques were implemented, which involves the use of biopolymers and/or synthetic materials and cells. Because of a lack of information between data sources, the objective of this review paper is, to sum up, all the available information on the topic of bioprinting and to help researchers with the problematics with 3D bioprinters, such as the 3D-Bioplotter™. The 3D-Bioplotter™ has been used in the pre-clinical field since 2000 and could allow the printing of more than one material at the same time, and therefore to increase the complexity of the 3D structure manufactured. It is also very precise with maximum flexibility and a user-friendly and stable software that allows the optimization of the bioprinting process on the technological point of view. Different applications have resulted from the research on this field, mainly focused on regenerative medicine, but the lack of information and/or the possible misunderstandings between papers makes the reproducibility of the tests difficult. Nowadays, the 3D Bioprinting is evolving into another technology called 4D Bioprinting, which promises to be the next step in the bioprinting field and might promote great applications in the future.

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