Characterizing the acoustic impedance and attenuation of biocompatible elastomers: An optimal design of experiments approach

2020 ◽  
Vol 148 (4) ◽  
pp. 2741-2741
Author(s):  
Valerie Rennoll ◽  
Ian M. McLane ◽  
Mounya Elhilali ◽  
James West
Keyword(s):  
Optimal Design ◽  
Design Of Experiments ◽  
Acoustic Impedance ◽  
Optimal Design Of Experiments

scholarly journals Maximal Power Tests for Detecting Defects in Meiotic Recombination

Genetics ◽  
2002 ◽  
Vol 161 (3) ◽  
pp. 1333-1337
Author(s):  
Thomas I Milac ◽  
Frederick R Adler ◽  
Gerald R Smith
Keyword(s):  
Optimal Design ◽  
Design Of Experiments ◽  
Meiotic Recombination ◽  
Region Of Interest ◽  
Genetic Distances ◽  
Genomic Region ◽  
Maximal Power ◽  
Optimal Design Of Experiments ◽  
Crossover Interference ◽  
Single Interval

Abstract We have determined the marker separations (genetic distances) that maximize the probability, or power, of detecting meiotic recombination deficiency when only a limited number of meiotic progeny can be assayed. We find that the optimal marker separation is as large as 30–100 cM in many cases. Provided the appropriate marker separation is used, small reductions in recombination potential (as little as 50%) can be detected by assaying a single interval in as few as 100 progeny. If recombination is uniformly altered across the genomic region of interest, the same sensitivity can be obtained by assaying multiple independent intervals in correspondingly fewer progeny. A reduction or abolition of crossover interference, with or without a reduction of recombination proficiency, can be detected with similar sensitivity. We present a set of graphs that display the optimal marker separation and the number of meiotic progeny that must be assayed to detect a given recombination deficiency in the presence of various levels of crossover interference. These results will aid the optimal design of experiments to detect meiotic recombination deficiency in any organism.

A New Practical Route to ZSM-5 Nanoparticles and Optimization of Synthetic Parameters through D-optimal Design of Experiments

2015 ◽  
Vol 62 (9) ◽  
pp. 817-825 ◽  
Author(s):  
Saeed Soltanali ◽  
Rouein Halladj ◽  
Alimorad Rashidi ◽  
Mansour Bazmi ◽  
Saeed Khodabakhshi
Keyword(s):  
Optimal Design ◽  
Design Of Experiments ◽  
Optimal Design Of Experiments

Optimal design of experiments for estimating the time of death in forensic medicine

2018 ◽  
Vol 34 (12) ◽  
pp. 125005 ◽  
Author(s):  
Martin Weiser ◽  
Yvonne Freytag ◽  
Bodo Erdmann ◽  
Michael Hubig ◽  
Gita Mall
Keyword(s):  
Optimal Design ◽  
Design Of Experiments ◽  
Forensic Medicine ◽  
Time Of Death ◽  
Optimal Design Of Experiments

scholarly journals Optimal Design of Experiments in the Presence of Interference

10.1596/29656 ◽  
2018 ◽  
Author(s):  
Sarah Baird ◽  
J. Aislinn Bohren ◽  
Craig McIntosh ◽  
Berk Ozler
Keyword(s):  
Optimal Design ◽  
Design Of Experiments ◽  
Optimal Design Of Experiments

Optimal Design of Experiments in the Presence of Interference

2018 ◽  
Vol 100 (5) ◽  
pp. 844-860 ◽  
Author(s):  
Sarah Baird ◽  
J. Aislinn Bohren ◽  
Craig McIntosh ◽  
Berk Özler
Keyword(s):  
Optimal Design ◽  
Design Of Experiments ◽  
Optimal Design Of Experiments

Optimal design of experiments for effective modeling of atrazine degradation in soils

2021 ◽  
Author(s):  
Luciana Chavez Rodriguez ◽  
Ana González-Nicolás ◽  
Brian Ingalls ◽  
Wolfgang Nowak ◽  
Thilo Streck ◽  
...  
Keyword(s):  
Optimal Design ◽  
Design Of Experiments ◽  
Environmental Fate ◽  
Mitigation Strategies ◽  
Model Discrimination ◽  
Degradation Pathways ◽  
Optimal Design Of Experiments ◽  
First Order ◽  
Bacterial Consortia ◽  
Improved Model

<p>The natural degradation pathways of the herbicide atrazine (AT) are highly complex. These pathways involve the metabolic activity of several bacterial guilds (that use AT as a source of carbon, nitrogen or both) and abiotic degradation mechanisms. The co-occurrence of multiple degradation pathways, combined with challenges in quantifying bacterial guilds and relevant intermediate metabolites, has led to the development of competing model formulations, which all represent valid descriptions of the fate of AT. A proper understanding of the fate of this complex compound is needed to develop effective management and mitigation strategies.</p><p>Here, we propose a model discrimination process in combination with prospective optimal design of experiments. We performed Monte-Carlo simulations using a first-order model that reflects a simple reaction chain of complete AT degradation and a set of Monod-based model variants that consider different bacterial consortia and degradation pathways. We used a Bayesian statistical analysis of these simulation ensembles to simulate virtual degradation experiments and chemical analysis strategies, thus obtaining predictions on the utility of experiments to deliver conclusive data for model discrimination. To do so, we defined different experimental protocols including a combination of: i) the metabolites to measure (AT, metabolites and CO<sub>2</sub>), ii) sampling frequency (sampling every day, every two days and every four days), iii) features difficult to quantify (specific bacterial guilds). As a statistical metric to measure the conclusiveness of these virtual experiments, we used the so-called energy distance.</p><p>Our results show that simulated AT degradation pathways following first-order reaction chains can be clearly distinguished from simulations using Monod-based models. Within the Monod-based models, we detected three clusters of models that differ in the number of bacterial guilds involved in AT degradation. Experimental designs considering main AT metabolites and sampling frequencies of once every two or four days at durations of 50 or 100 days provided the most informative data to discriminate models. Including measurements of bacterial guilds only slightly improved model discrimination. Our study highlights that environmental fate studies should prioritize measuring metabolites to elucidate active AT degradation pathways in soil and identify robust model formulations supporting risk assessment and mitigation strategies. </p>

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