Positive Deviance

Heckert, Druann Maria; Heckert, D. Alex

doi:10.1002/9781118701386.ch5

Cited by 11 publications

(10 citation statements)

References 35 publications

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“…To produce trajectories from raw sptPALM movies, we used a custom sptPALM tracking pipeline publicly available on GitHub ( https://github.com/alecheckert/quot , swh:1:rev:1adf7a0574c62f38140f1dec2d14555bfc03b9a7 , Heckert, 2022b ). All localization and tracking for this article was performed with the following settings:…”

Section: Methodsmentioning

confidence: 99%

Recovering mixtures of fast-diffusing states from short single-particle trajectories

Heckert

Dahal

Tijan

et al. 2022

eLife

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Single-particle tracking (SPT) directly measures the dynamics of proteins in living cells and is a powerful tool to dissect molecular mechanisms of cellular regulation. Interpretation of SPT with fast-diffusing proteins in mammalian cells, however, is complicated by technical limitations imposed by fast image acquisition. These limitations include short trajectory length due to photobleaching and shallow depth of field, high localization error due to the low photon budget imposed by short integration times, and cell-to-cell variability. To address these issues, we investigated methods inspired by Bayesian nonparametrics to infer distributions of state parameters from SPT data with short trajectories, variable localization precision, and absence of prior knowledge about the number of underlying states. We discuss the advantages and disadvantages of these approaches relative to other frameworks for SPT analysis.

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Section: Methodsmentioning

confidence: 99%

Recovering mixtures of fast-diffusing states from short single-particle trajectories

Heckert

Dahal

Tijan

et al. 2022

eLife

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“…The distribution of the data set was analyzed in order to obtain a meaningful relationship between inputs and output. A normal probability plot as a normality test, one of the best ways for determining the distribution of the data especially for a less number of experiments [21], was obtained for our data as shown in Fig. 3.…”

Section: Resultsmentioning

confidence: 96%

Analysis of an inverted square SRR via design of experiment (DoE) approach

Yarici

Öztürk

2021

Journal of Electrical Engineering

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A design of experiment (DoE) study is presented based on an investigation of the influences of the chosen geometric parameters of an Inverted Square Split Ring Resonator on its resonance frequency. A statistical software was used to determine DoE steps and the values of chosen geometrical parameters for the experiments. The determined experiments were carried out by making simulations with electromagnetic design software. The resonator simulation outputs were analyzed by using normality tests and tools of the statistical software. By using these analyses, mainly a 2-level full factorial DoE approach, the effects of the geometrical parameters (input factors), and their interactions on the resonance frequency (response factor) were presented. In the light of our findings, this study proposes a promising path for microwave studies with several advantages such as being able to understand the dynamics of an optimized RF resonator device system, designing these kinds of devices with a few experiments, and increasing the time efficiency via reducing the number of attempts.

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“…Main Effects Plots are the primary statistical graphics tool for determining the most important factors in a multifactor system 27 . For a given response, the mean response is plotted (vertically) vs each of the five factors and their two (or three) settings (horizontally).…”

Section: Discussionmentioning

confidence: 99%

“…It is good practice to carry out alternative estimation and testing procedures to ensure that our scientific conclusions are not dependent on the particular statistical methodology employed. In this section a parallel analysis to the Main Effects Plot based on a “Block Plot” approach 29 is used to assess (for a given response) (a) whether a factor effect is statistically significant (yes/no), and (b) whether that (yes/no) significance conclusion is consistently true over all of the conditions/combinations of the remaining factors. If consistent, then our factor conclusion is robust (which is very desirable); if not consistent, then our factor conclusion depends on settings of other factors and hence there are interactions (which, if existent, are very desirable to uncover).…”

Section: Discussionmentioning

confidence: 99%

Effects of upholstery materials on the burning behavior of real‐scale residential upholstered furniture mock‐ups

et al. 2020

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Fire spread and growth on real-scale four cushion mock-ups of residential upholstered furniture (RUF) were investigated with the goal of identifying whether changes in five classes of materials (barrier, flexible polyurethane foam, polyester fiber wrap, upholstery fabric, and sewing thread), referred to as factors, resulted in statistically significant changes in burning behavior. A fractional factorial experimental design plus practical considerations yielded a test matrix with 20 material combinations. Experiments were repeated a minimum of two times. Measurements included fire spread rates derived from video recordings and heat release rates (HRRs). A total of 13 experimental parameters (3 based on the videos and 10 on the HRR results), referred to as responses, characterized the measurements. Statistical analyses based on Main Effects Plots (main effects) and Block Plots (main effects and factor interactions) were used. The results showed that three of the factors resulted in statistically significant effects on varying numbers of the 13 responses. The Barrier and Fabric factors had the strongest main effects with roughly comparable magnitudes. Foam was statistically significant for fewer of the responses and its overall strength was weaker than for Barrier and Fabric. No statistically significant main effects were identified for Wrap or Thread. Multiple two-term interactions between factors were identified as being statistically significant. The Barrier*Fabric interaction resulted in the highest number of and strongest statistically significant effects. The existence of two-term interactions means that it will be necessary to consider their effects in approaches designed to predict the burning behavior of RUF.

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Positive Deviance

Cited by 11 publications

References 35 publications

Recovering mixtures of fast-diffusing states from short single-particle trajectories

Recovering mixtures of fast-diffusing states from short single-particle trajectories

Analysis of an inverted square SRR via design of experiment (DoE) approach

Effects of upholstery materials on the burning behavior of real‐scale residential upholstered furniture mock‐ups

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