scholarly journals A Bayesian Non-parametric Mixed-Effects Model of Microbial Phenotypes

2019 ◽  
Author(s):  
Peter D. Tonner ◽  
Cynthia L. Darnell ◽  
Francesca M.L. Bushell ◽  
Peter A. Lund ◽  
Amy K. Schmid ◽  
...  
Keyword(s):  
Population Growth ◽  
Random Effects ◽  
Growth Conditions ◽  
Growth Behavior ◽  
Parametric Models ◽  
Growth Data ◽  
Latent Growth ◽  
Growth Curve Models ◽  
Microbial Response ◽  
Non Parametric

AbstractSubstantive changes in gene expression, metabolism, and the proteome are manifested in overall changes in microbial population growth. Quantifying how microbes grow is therefore fundamental to areas such as genetics, bioengineering, and food safety. Traditional parametric growth curve models capture the population growth behavior through a set of summarizing parameters. However, estimation of these parameters from data is confounded by random effects such as experimental variability, batch effects or differences in experimental material. A systematic statistical method to identify and correct for such confounding effects in population growth data is not currently available. Further, our previous work has demonstrated that parametric models are insufficient to explain and predict microbial response under non-standard growth conditions. Here we develop a hierarchical Bayesian non-parametric model of population growth that identifies the latent growth behavior and response to perturbation, while simultaneously correcting for random effects in the data. This model enables more accurate estimates of the biological effect of interest, while better accounting for the uncertainty due to technical variation. Additionally, modeling hierarchical variation provides estimates of the relative impact of various confounding effects on measured population growth.

scholarly journals A Bayesian non-parametric mixed-effects model of microbial growth curves

2020 ◽  
Vol 16 (10) ◽  
pp. e1008366
Author(s):  
Peter D. Tonner ◽  
Cynthia L. Darnell ◽  
Francesca M. L. Bushell ◽  
Peter A. Lund ◽  
Amy K. Schmid ◽  
...  
Keyword(s):  
Population Growth ◽  
Random Effects ◽  
Growth Curves ◽  
Growth Conditions ◽  
Growth Behavior ◽  
Parametric Models ◽  
Growth Data ◽  
Growth Curve Models ◽  
Microbial Response ◽  
Non Parametric

Substantive changes in gene expression, metabolism, and the proteome are manifested in overall changes in microbial population growth. Quantifying how microbes grow is therefore fundamental to areas such as genetics, bioengineering, and food safety. Traditional parametric growth curve models capture the population growth behavior through a set of summarizing parameters. However, estimation of these parameters from data is confounded by random effects such as experimental variability, batch effects or differences in experimental material. A systematic statistical method to identify and correct for such confounding effects in population growth data is not currently available. Further, our previous work has demonstrated that parametric models are insufficient to explain and predict microbial response under non-standard growth conditions. Here we develop a hierarchical Bayesian non-parametric model of population growth that identifies the latent growth behavior and response to perturbation, while simultaneously correcting for random effects in the data. This model enables more accurate estimates of the biological effect of interest, while better accounting for the uncertainty due to technical variation. Additionally, modeling hierarchical variation provides estimates of the relative impact of various confounding effects on measured population growth.

scholarly journals Bioactive Compounds, Antioxidant Activity and Growth Behavior in Lettuce Cultivars Grown under Field and Greenhouse Conditions

Proceedings ◽  
2020 ◽  
Vol 70 (1) ◽  
pp. 52
Author(s):  
Melisa Lanza Volpe ◽  
Verónica C. Soto Vargas ◽  
Anabel Morón ◽  
Roxana E. González
Keyword(s):  
Antioxidant Activity ◽  
Bioactive Compounds ◽  
Antioxidant Activities ◽  
Growth Conditions ◽  
Growth Behavior ◽  
Total Phenolic ◽  
Spectrophotometric Methods ◽  
Leafy Greens ◽  
Growing Conditions ◽  
Field Production

Lettuce (Lactuca sativa L.) is one of the most important leafy greens worldwide. The nutritional value of its edible leaf depends on different factors including type and growing conditions. The aim was to determine the bioactive compounds content, antioxidant activity and growth behavior of twenty-two lettuce genotypes, cultivated under field and greenhouse conditions. Total phenolic compound, chlorophylls, carotenoids, anthocyanin contents and antioxidant activities were analyzed by spectrophotometric methods. Data were analyzed by analysis of variance (ANOVA). Significant differences between bioactive compounds, antioxidant activity and growth behavior were found among cultivars and morphological types, for both growth conditions. Carotenoid and chlorophyll content was higher in greenhouse conditions for all genotypes. In field production, butterhead and iceberg lettuces showed lower content of these bioactive compounds. The red-pigmented Falbala cultivar from field production showed the highest level of polyphenols and anthocyanin. Meanwhile, in greenhouse conditions, the oak leaf cultivar Grenadine displayed the highest concentration of these phenolic compounds. The iceberg type lettuce showed the lowest percentages of antioxidant activity in both environments. The results showed the effect of growing conditions and the high variability in lettuce bioactive compounds content and antioxidant activity among the different types.

A Non Parametric Model for Magneto Rheological Dampers

1999 ◽  
Author(s):  
Mehdi Ahmadian ◽  
Xubin Song
Keyword(s):  
Test Data ◽  
Mr Damper ◽  
Parametric Model ◽  
Parametric Models ◽  
Magnetic Saturation ◽  
Mr Dampers ◽  
Electro Magnetic ◽  
Magneto Rheological ◽  
Force Characteristics ◽  
Non Parametric

Abstract A non-parametric model for magneto-rheological (MR) dampers is presented. After discussing the merits of parametric and non-parametric models for MR dampers, the test data for a MR damper is used to develop a non-parametric model. The results of the model are compared with the test data to illustrate the accuracy of the model. The comparison shows that the non-parametric model is able to accurately predict the damper force characteristics, including the damper non-linearity and electro-magnetic saturation. It is further shown that the parametric model can be numerically solved more efficiently than the parametric models.

Bayesian non-parametric models for regional prevalence estimation

2008 ◽  
Vol 35 (5) ◽  
pp. 567-582 ◽  
Author(s):  
Adam J. Branscum ◽  
Timothy E. Hanson ◽  
Ian A. Gardner
Keyword(s):  
Parametric Models ◽  
Prevalence Estimation ◽  
Non Parametric

scholarly journals The Mindful Adolescent: Developmental Changes in Nonreactivity to Inner Experiences during the Transition to High School, and Relations to Stress and Positive Affect

2018 ◽  
Author(s):  
Brian Galla ◽  
Eli Tsukayama ◽  
Daeun Park ◽  
Alisa Yu ◽  
Angela Duckworth
Keyword(s):  
High School ◽  
Middle School ◽  
Positive Affect ◽  
Perceived Stress ◽  
Growth Curve ◽  
Developmental Change ◽  
Latent Growth Curve ◽  
Latent Growth ◽  
Growth Curve Models ◽  
Latent Growth Curve Models

Little is known about the naturalistic development of mindfulness in adolescence, and whether changes in this mental faculty are associated with perceived stress and emotional well-being. The current longitudinal study examined the development of one dimension of mindfulness, nonreactivity to inner experience, in a racially and socioeconomically diverse sample (N = 1,657) during the transition from middle school to high school. Students participated in up to four assessment waves, from fall of 8th grade through spring of 9th grade, during which they completed self-report measures assessing nonreactivity, perceived stress, and positive affect. Latent growth curve models indicated that levels of nonreactivity increased linearly during the two-year study period. Developmental change in nonreactivity varied minimally by gender, socioeconomic status, and race/ethnicity. Parallel process latent growth curve models showed that changes in nonreactivity were associated with concomitant reductions in perceived stress and increases in positive affect. Random intercept cross-lagged panel models showed that within-person nonreactivity prospectively predicted changes in perceived stress and positive affect. This is the first study to track naturalistic developmental change in mindfulness during adolescence. Results suggest that the nonreactivity dimension of mindfulness may boost resilience during the transition from middle school to high school.

scholarly journals Bayesian Power Equivalence in Latent Growth Curve Models

2019 ◽  
Author(s):  
Angelika Stefan ◽  
Timo von Oertzen
Keyword(s):  
Social Sciences ◽  
Growth Curve ◽  
Gold Standard ◽  
Latent Structure ◽  
Latent Growth Curve ◽  
Latent Growth ◽  
Growth Curve Models ◽  
Latent Growth Curve Models ◽  
The Social ◽  
Design Factors

Longitudinal studies are the gold standard for research on time-dependentphenomena in the social sciences. However, they often entail high costs dueto multiple measurement occasions and a long overall study duration. It istherefore useful to optimize these design factors while maintaining a highinformativeness of the design. Von Oertzen and Brandmaier (2013) appliedpower equivalence to show that Latent Growth Curve Models (LGCMs)with different design factors can have the same power for likelihood-ratiotests on the latent structure. In this paper, we show that the notion ofpower equivalence can be extended to Bayesian hypothesis tests of the latentstructure constants. Specifically, we show that the results of a Bayes FactorDesign Analysis (BFDA; Schönbrodt & Wagenmakers, 2018) of two powerequivalent LGCMs are equivalent. This will be useful for researchers whoaim to plan for compelling evidence instead of frequentist power and providesa contribution towards more efficient procedures for BFDA.

Non-parametric lactation curves

1989 ◽  
Vol 48 (2) ◽  
pp. 331-339 ◽  
Author(s):  
D. A. Elston ◽  
C. A. Glasbey ◽  
D. R. Neilson
Keyword(s):  
Parametric Method ◽  
Parametric Models ◽  
Summary Statistics ◽  
Parametric Curves ◽  
Biased Estimation ◽  
Derivatives Of ◽  
Non Parametric

ABSTRACTLactation curves are fitted to data as a preliminary to estimating summary statistics. Two widely quoted curves are atbe-ct (Wood, 1967) and a(1 - e-bt) - ct (Cobby and Le Du, 1978), each of which has three parameters. Restriction to either of these curves imposes limitations on the fit to the data and can result in biased estimation of summary statistics. Alternatively, lactation curves can be generated by the use of a non-parametric method which requires only weak assumptions about the signs of derivatives of the curves. Because the non-parametric curves are more flexible, estimates of summary statistics are less likely to be biased than those based on parametric models. Use of the non-parametric curves is particularly advantageous around the time of peak yield, where the curves of Wood and Cobby and Le Du are known to fit data poorly.

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