Evaluation of Rhodotorula growth on solid substrate via a linear mixed effects model

The growth of Rhodotorula glutinis and Rhodotorula mucilaginosa was studied under optimal and stress cultivation conditions at 10°C and 20°C for 14 days. The method of image analysis was used to determine the size of colonies. The linear mixed effects model implemented in the statistical program S-PLUS was applied to analyse the repeated measurements. Two-phase kinetics was confirmed and the mean growth rates in the second linear phase under various stress conditions were estimated. The results indicated a higher growth rate of R. mucilaginosa than was that of R. glutinis under all cultivation conditions. The highest growth rate of was observed during the cultivation of R. mucilaginosa in media with 2% of NaCl at 20°C. The impact of neglecting the fact that repeated data are not independent and using the classical regression model instead of the mixed effects model was demonstrated through the comparison of the confidence intervals for the parameters based on both approaches. While the point estimates of the corresponding parameters were similar, the width of the confidence intervals differed substantially.

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Stem Damage Modifies the Impact of Wind on Norway Spruces

Forests ◽

10.3390/f11040463 ◽

2020 ◽

Vol 11 (4) ◽

pp. 463 ◽

Cited By ~ 6

Author(s):

Guntars Snepsts ◽

Mara Kitenberga ◽

Didzis Elferts ◽

Janis Donis ◽

Aris Jansons

Keyword(s):

Tree Height ◽

Mixed Effects ◽

Mixed Effects Model ◽

Wind Damage ◽

Stand Age ◽

Bark Stripping ◽

Linear Mixed Effects Model ◽

Linear Mixed Effects ◽

Stem Damage ◽

The Impact

Bark stripping caused by cervids can have a long-lasting negative effect on tree vitality. Such trees of low vitality might be more susceptible to other disturbances. The amplifying effects of disturbance interactions can cause significantly more damage to forest ecosystems than the individual effects of each disturbance. Therefore, this study aimed to assess the impact of bark stripping (stem damage) on the probability of wind damage and snapping height for Norway spruces (Picea Abies (L.) H. Karst.). In this study, we used the Latvian National Forest Inventory data from the period 2004–2018. In the analysis, we used data based on 32,856 trees. To analyse the data, we implemented a Bayesian binary logistic generalised linear mixed-effects model and the linear mixed-effects model. Our results showed that stem damage significantly increased the probability of wind damage and affected the snapping height of Norway spruces. Similarly, root damage, the slenderness ratio, the stand age, the stand density, the soil type, and the dominant tree species had a significant influence on the probability of wind damage. In both periods, trees with stem damage had significantly (p < 0.05) higher probability (odd ratio 1.68) to be wind damaged than trees without stem damage. The stem damaged Norway spruce trees snapped in the first 25% of the tree height, while trees without stem damage snapped around half (50%) of the tree height. Our results show that stem damage significantly alters the effect of wind damage on Norway spruces, suggesting that such damage must be incorporated into wind-risk assessment models.

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Analyzing Repeated Measurements while Accounting for Derivative Tracking, Varying Within-subject Variance, and Autocorrelation: The Xtmixediou Command

The Stata Journal Promoting communications on statistics and Stata ◽

10.1177/1536867x1701700303 ◽

2017 ◽

Vol 17 (3) ◽

pp. 573-599

Author(s):

Rachael A. Hughes ◽

Michael G. Kenward ◽

Jonathan A. C. Sterne ◽

Kate Tilling

Keyword(s):

Optimization Algorithm ◽

Repeated Measures ◽

Covariance Structure ◽

Likelihood Estimation ◽

Mixed Effects ◽

Repeated Measurements ◽

Mixed Effects Model ◽

Linear Mixed Effects Model ◽

Linear Mixed Effects ◽

Restricted Maximum Likelihood Estimation

Linear mixed-effects models are commonly used to model trajectories of repeated measures of biomarkers of disease. Taylor, Cumberland, and Sy (1994, Journal of the American Statistical Association 89: 727–736) proposed a linear mixed-effects model with an added integrated Ornstein–Uhlenbeck (IOU) process (linear mixed-effects IOU model). This allows for autocorrelation, changing within-subject variance, and the incorporation of derivative tracking (that is, how much a subject tends to maintain the same trajectory for extended periods of time). They argued that the covariance structure induced by the stochastic process in this model was interpretable and more biologically plausible than the standard linear mixed-effects model. However, their model is rarely used, partly because of the lack of available software. In this article, we present the new command xtmixediou, which fits the linear mixed-effects IOU model and its special case, the linear mixed-effects Brownian motion model. The model is fit to balanced and unbalanced data using restricted maximum-likelihood estimation, where the optimization algorithm is the Newton–Raphson, Fisher scoring, or average information algorithm, or any combination of these. To aid convergence, xtmixediou allows the user to change the method for deriving the starting values for optimization, the optimization algorithm, and the parameterization of the IOU process. We also provide a predict command to generate predictions under the model. We illustrate xtmixediou and predict with a simulated example of repeated biomarker measurements from HIV-positive patients.

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The Impact of COVID-19 on Students' Marks: A Bayesian Hierarchical Modeling Approach

10.21203/rs.3.rs-73814/v2 ◽

2021 ◽

Author(s):

Jabed Tomal ◽

Saeed Rahmati ◽

Shirin Boroushaki ◽

Lingling Jin ◽

Ehsan Ahmed

Keyword(s):

Cognitive Skills ◽

Missing Values ◽

Hierarchical Modeling ◽

Mixed Effects ◽

Mixed Effects Model ◽

Linear Mixed Effects Model ◽

Bayesian Hierarchical ◽

Linear Mixed Effects ◽

Face To Face Learning ◽

The Impact

Abstract Due to COVID-19, universities across Canada were forced to undergo a transition from classroom-based face-to-face learning and invigilated assessments to online-based learning and non-invigilated assessments. This study attempts to empirically measure the impact of COVID-19 on students’ marks from eleven science, technology, engineering, and mathematics (STEM) courses using a Bayesian linear mixed eﬀects model ﬁtted to longitudinal data. The Bayesian linear mixed eﬀects model is designed for this application which allows student-speciﬁc error variances to vary. The novel Bayesian missing value imputation method is ﬂexible which seamlessly generates missing values given complete data. We observed an increase in overall average marks for the courses requiring lower-level cognitive skills according to Bloom’s Taxonomy and a decrease in marks for the courses requiring higher-level cognitive skills, where larger changes in marks were observed for the underachieving students. About half of the disengaged students who did not participate in any course assessments after the transition to online delivery were in special support.

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The Impact of COVID-19 on Students' Marks: A Bayesian Hierarchical Modeling Approach

10.21203/rs.3.rs-73814/v1 ◽

2020 ◽

Author(s):

Jabed Tomal ◽

Saeed Rahmati ◽

Shirin Boroushaki ◽

Lingling Jin ◽

Ehsan Ahmed

Keyword(s):

Cognitive Skills ◽

Missing Values ◽

Hierarchical Modeling ◽

Mixed Effects ◽

Mixed Effects Model ◽

Linear Mixed Effects Model ◽

Bayesian Hierarchical ◽

Linear Mixed Effects ◽

Face To Face Learning ◽

The Impact

Abstract Due to COVID-19, universities across Canada were forced to undergo a transition from classroom-based face-to-face learning and invigilated assessments to online-based learning and non-invigilated assessments. This study attempts to empirically measure the impacts of COVID-19 on students’ marks from eleven science, technology, engineering, and mathematics (STEM) courses using a Bayesian linear mixed effects model fitted to longitudinal data. The Bayesian linear mixed effects model is designed for this application which allows student-specific error variances to vary. The novel Bayesian missing value imputation method is flexible which seamlessly generates missing values given complete data. We observed an increase in overall average marks for the courses requiring lower-level cognitive skills according to Bloom's Taxonomy and a decrease in marks for the courses requiring higher-level cognitive skills, where larger changes in marks were observed for the underachieving students. About half of the disengaged students who did not participate in any course assessments after the transition to online delivery were in special support.

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