scholarly journals Taxon incompleteness and discrete time bins affect character change rates in simulated data

2020 ◽  
Vol 16 (11) ◽  
pp. 20200418
Author(s):  
Jorge R. Flores
Keyword(s):  
Discrete Time ◽  
Evolutionary Dynamics ◽  
Simulated Data ◽  
Phenotypic Change ◽  
Change Rate ◽  
Arbitrary Time ◽  
Tree Length ◽  
The Impact

Estimating how fast or slow morphology evolves through time (phenotypic change rate, PR) has become common in macroevolutionary studies and has been important for clarifying key evolutionary events. However, the inclusion of incompletely scored taxa (e.g. fossils) and variable lengths of discrete arbitrary time bins could affect PR estimates and potentially mask real PR patterns. Here, the impact of taxon incompleteness (unscored data) on PR estimates is assessed in simulated data. Three different time bin series were likewise evaluated: bins evenly spanning the tree length (i), a shorter middle bin and longer first and third bins (ii), and a longer middle bin and shorter first and third bins (iii). The results indicate that PR values decrease as taxon incompleteness increases. Statistically significant PR values, and the dispersion among PR values, depended on the time bins. These outcomes imply that taxon incompleteness can undermine our capacity to infer morphology evolutionary dynamics and that these estimates are also influenced by our choice of discrete time bins. More importantly, the present results stress the need for a better approach to deal with taxon incompleteness and arbitrary discrete time bins.

scholarly journals TAXON INCOMPLETENESS AND DISCRETE TIME BINS AFFECT CHARACTER CHANGE RATES IN SIMULATED DATA

2020 ◽  
Author(s):  
Jorge R. Flores
Keyword(s):  
Discrete Time ◽  
Evolutionary Dynamics ◽  
Simulated Data ◽  
Phenotypic Change ◽  
Change Rate ◽  
Tree Length ◽  
The Impact

AbstractEstimating how fast or slow morphology evolves through time (phenotypic change rate; PR) has become common in macroevolutionary studies, and has been important for clarifying key evolutionary events. However, the inclusion of incompletely scored taxa (e.g., fossils) could affect PR estimates and potentially mask real PR patterns. This effect being also affected by the usage of arbitrary discrete time bins. Here, the impact of taxon incompleteness (unscored data) on PR estimates is assessed in simulated data. Three different time bin series were likewise evaluated: bins evenly spanning the tree length (i), a shorter middle bin (ii), and a longer middle bin (iii). The results indicate that PR values decrease as taxon incompleteness increases. Statistical significant PR values, and the dispersion among PR values, depended on the time bins. These outcomes imply that taxon incompleteness can undermine our capacity to infer morphology evolutionary dynamics and that these estimates are also influenced by our choice of discrete time bins. More importantly, the present results stress the need for a better approach to deal with taxon incompleteness and arbitrary discrete time bins.

Analyzing the Impact of Healthy Behavior on Weight Change with a Provable Mathematical Model (Preprint)

2020 ◽  
Author(s):  
Ayan Chatterjee ◽  
Ram Bajpai ◽  
Pankaj Khatiwada
Keyword(s):  
Physical Activity ◽  
Mathematical Model ◽  
Health Behavior ◽  
Weight Change ◽  
Simulated Data ◽  
Negative Behavior ◽  
Healthy Behavior ◽  
Healthy Habits ◽  
Lifestyle Diseases ◽  
The Impact

BACKGROUND Lifestyle diseases are the primary cause of death worldwide. The gradual growth of negative behavior in humans due to physical inactivity, unhealthy habit, and improper nutrition expedites lifestyle diseases. In this study, we develop a mathematical model to analyze the impact of regular physical activity, healthy habits, and a proper diet on weight change, targeting obesity as a case study. Followed by, we design an algorithm for the verification of the proposed mathematical model with simulated data of artificial participants. OBJECTIVE This study intends to analyze the effect of healthy behavior (physical activity, healthy habits, and proper dietary pattern) on weight change with a proposed mathematical model and its verification with an algorithm where personalized habits are designed to change dynamically based on the rule. METHODS We developed a weight-change mathematical model as a function of activity, habit, and nutrition with the first law of thermodynamics, basal metabolic rate (BMR), total daily energy expenditure (TDEE), and body-mass-index (BMI) to establish a relationship between health behavior and weight change. Followed by, we verified the model with simulated data. RESULTS The proposed provable mathematical model showed a strong relationship between health behavior and weight change. We verified the mathematical model with the proposed algorithm using simulated data following the necessary constraints. The adoption of BMR and TDEE calculation following Harris-Benedict’s equation has increased the model's accuracy under defined settings. CONCLUSIONS This study helped us understand the impact of healthy behavior on obesity and overweight with numeric implications and the importance of adopting a healthy lifestyle abstaining from negative behavior change.

Applying the risk principle to optimize accuracy and equity in correctional risk assessment: Results From a Simulation

2021 ◽  
pp. 0306624X2098652
Author(s):  
Grant Duwe
Keyword(s):  
Risk Assessment ◽  
Predictive Accuracy ◽  
Ethnic Disparities ◽  
Simulated Data ◽  
Risk Assessments ◽  
Risk Level ◽  
Risk Levels ◽  
Risk Principle ◽  
Changes In Risk ◽  
The Impact

As the use of risk assessments for correctional populations has grown, so has concern that these instruments exacerbate existing racial and ethnic disparities. While much of the attention arising from this concern has focused on how algorithms are designed, relatively little consideration has been given to how risk assessments are used. To this end, the present study tests whether application of the risk principle would help preserve predictive accuracy while, at the same time, mitigate disparities. Using a sample of 9,529 inmates released from Minnesota prisons who had been assessed multiple times during their confinement on a fully-automated risk assessment, this study relies on both actual and simulated data to examine the impact of program assignment decisions on changes in risk level from intake to release. The findings showed that while the risk principle was used in practice to some extent, the simulated results showed that greater adherence to the risk principle would increase reductions in risk levels and minimize the disparities observed at intake. The simulated data further revealed the most favorable outcomes would be achieved by not only applying the risk principle, but also by expanding program capacity for the higher-risk inmates in order to adequately reduce their risk.

scholarly journals A Traveler’s Guide to the Multiverse: Promises, Pitfalls, and a Framework for the Evaluation of Analytic Decisions

2021 ◽  
Vol 4 (1) ◽  
pp. 251524592095492
Author(s):  
Marco Del Giudice ◽  
Steven W. Gangestad
Keyword(s):  
Degrees Of Freedom ◽  
A Priori ◽  
Simulated Data ◽  
Style Analysis ◽  
Data Set ◽  
Scant Attention ◽  
Equivalence Type ◽  
The Impact ◽  
Biased Estimates

Decisions made by researchers while analyzing data (e.g., how to measure variables, how to handle outliers) are sometimes arbitrary, without an objective justification for choosing one alternative over another. Multiverse-style methods (e.g., specification curve, vibration of effects) estimate an effect across an entire set of possible specifications to expose the impact of hidden degrees of freedom and/or obtain robust, less biased estimates of the effect of interest. However, if specifications are not truly arbitrary, multiverse-style analyses can produce misleading results, potentially hiding meaningful effects within a mass of poorly justified alternatives. So far, a key question has received scant attention: How does one decide whether alternatives are arbitrary? We offer a framework and conceptual tools for doing so. We discuss three kinds of a priori nonequivalence among alternatives—measurement nonequivalence, effect nonequivalence, and power/precision nonequivalence. The criteria we review lead to three decision scenarios: Type E decisions (principled equivalence), Type N decisions (principled nonequivalence), and Type U decisions (uncertainty). In uncertain scenarios, multiverse-style analysis should be conducted in a deliberately exploratory fashion. The framework is discussed with reference to published examples and illustrated with the help of a simulated data set. Our framework will help researchers reap the benefits of multiverse-style methods while avoiding their pitfalls.

scholarly journals The importance of species interactions in eco-evolutionary community dynamics under climate change

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Anna Åkesson ◽  
Alva Curtsdotter ◽  
Anna Eklöf ◽  
Bo Ebenman ◽  
Jon Norberg ◽  
...  
Keyword(s):  
Climate Change ◽  
Species Interactions ◽  
Community Dynamics ◽  
Evolutionary Dynamics ◽  
Negative Impact ◽  
Trophic Levels ◽  
Temperature Dependent ◽  
Modeling Framework ◽  
Impact Of Climate Change ◽  
The Impact

AbstractEco-evolutionary dynamics are essential in shaping the biological response of communities to ongoing climate change. Here we develop a spatially explicit eco-evolutionary framework which features more detailed species interactions, integrating evolution and dispersal. We include species interactions within and between trophic levels, and additionally, we incorporate the feature that species’ interspecific competition might change due to increasing temperatures and affect the impact of climate change on ecological communities. Our modeling framework captures previously reported ecological responses to climate change, and also reveals two key results. First, interactions between trophic levels as well as temperature-dependent competition within a trophic level mitigate the negative impact of climate change on biodiversity, emphasizing the importance of understanding biotic interactions in shaping climate change impact. Second, our trait-based perspective reveals a strong positive relationship between the within-community variation in preferred temperatures and the capacity to respond to climate change. Temperature-dependent competition consistently results both in higher trait variation and more responsive communities to altered climatic conditions. Our study demonstrates the importance of species interactions in an eco-evolutionary setting, further expanding our knowledge of the interplay between ecological and evolutionary processes.

scholarly journals Modeling Chickenpox Dynamics with a Discrete Time Bayesian Stochastic Compartmental Model

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
A. Corberán-Vallet ◽  
F. J. Santonja ◽  
M. Jornet-Sanz ◽  
R.-J. Villanueva
Keyword(s):  
Discrete Time ◽  
Compartmental Model ◽  
Predictive Distribution ◽  
Model Parameters ◽  
Immunization Program ◽  
Posterior Predictive Distribution ◽  
Vaccination Strategies ◽  
Proposed Model ◽  
Routine Immunization Program ◽  
The Impact

We present a Bayesian stochastic susceptible-exposed-infectious-recovered model in discrete time to understand chickenpox transmission in the Valencian Community, Spain. During the last decades, different strategies have been introduced in the routine immunization program in order to reduce the impact of this disease, which remains a public health’s great concern. Under this scenario, a model capable of explaining closely the dynamics of chickenpox under the different vaccination strategies is of utter importance to assess their effectiveness. The proposed model takes into account both heterogeneous mixing of individuals in the population and the inherent stochasticity in the transmission of the disease. As shown in a comparative study, these assumptions are fundamental to describe properly the evolution of the disease. The Bayesian analysis of the model allows us to calculate the posterior distribution of the model parameters and the posterior predictive distribution of chickenpox incidence, which facilitates the computation of point forecasts and prediction intervals.

scholarly journals Volume change behaviour and microstructure of stabilized loess under cyclic freeze–thaw conditions

2016 ◽  
Vol 43 (10) ◽  
pp. 865-874 ◽  
Author(s):  
Sheng-lin Wang ◽  
Qing-feng Lv ◽  
Hassan Baaj ◽  
Xiao-yuan Li ◽  
Yan-xu Zhao
Keyword(s):  
Fly Ash ◽  
Volume Change ◽  
Cement Content ◽  
Frost Heave ◽  
Equivalent Diameter ◽  
Surface Porosity ◽  
Change Rate ◽  
Freeze Thaw ◽  
Significant Damage ◽  
The Impact

Freeze–thaw action is considered to be one of the most destructive actions that can induce significant damage in stabilized subgrades in seasonally frozen loess areas. Laboratory tests including frost heave – thaw shrinkage and microstructure change during freeze–thaw cycles were conducted to evaluate the volume change rate of loess stabilized with cement, lime, and fly ash under the impact of cyclic freeze–thaw conditions. The loess specimens collapsed after eight freeze–thaw cycles (192 h), but most stabilized loess specimens had no visible damage after all freeze–thaw cycles were completed. All of the stabilized loess samples underwent a much smaller volume change than the loess alone after the freeze–thaw cycles. Although surface porosity and equivalent diameter of stabilized loess samples increased, the stabilized loess can retain its microstructure during freeze–thaw cycles when the cement content was less than 6%. To ensure freeze–thaw resistance of stabilized loess subgrades, the mix proportions of the three additives was recommended to be 4 to 5% cement, 6% lime, and 10% fly ash.

scholarly journals An evaluation of the interpretability and predictive performance of the BayesR model for genomic prediction

2020 ◽  
Author(s):  
Fanny Mollandin ◽  
Andrea Rau ◽  
Pascal Croiseau
Keyword(s):  
Linkage Disequilibrium ◽  
Qtl Mapping ◽  
Effect Size ◽  
Genomic Prediction ◽  
Simulated Data ◽  
Predictive Performance ◽  
Real Data ◽  
Sliding Windows ◽  
Size Classes ◽  
The Impact

ABSTRACTTechnological advances and decreasing costs have led to the rise of increasingly dense genotyping data, making feasible the identification of potential causal markers. Custom genotyping chips, which combine medium-density genotypes with a custom genotype panel, can capitalize on these candidates to potentially yield improved accuracy and interpretability in genomic prediction. A particularly promising model to this end is BayesR, which divides markers into four effect size classes. BayesR has been shown to yield accurate predictions and promise for quantitative trait loci (QTL) mapping in real data applications, but an extensive benchmarking in simulated data is currently lacking. Based on a set of real genotypes, we generated simulated data under a variety of genetic architectures, phenotype heritabilities, and we evaluated the impact of excluding or including causal markers among the genotypes. We define several statistical criteria for QTL mapping, including several based on sliding windows to account for linkage disequilibrium. We compare and contrast these statistics and their ability to accurately prioritize known causal markers. Overall, we confirm the strong predictive performance for BayesR in moderately to highly heritable traits, particularly for 50k custom data. In cases of low heritability or weak linkage disequilibrium with the causal marker in 50k genotypes, QTL mapping is a challenge, regardless of the criterion used. BayesR is a promising approach to simultaneously obtain accurate predictions and interpretable classifications of SNPs into effect size classes. We illustrated the performance of BayesR in a variety of simulation scenarios, and compared the advantages and limitations of each.

scholarly journals Genome context influences evolutionary flexibility of nearly identical type III effectors in two phytopathogenic Pseudomonads

2021 ◽  
Author(s):  
David A Baltrus ◽  
Qian Feng ◽  
Brian H Kvitko
Keyword(s):  
Pseudomonas Syringae ◽  
Evolutionary Dynamics ◽  
Effector Proteins ◽  
In Planta ◽  
Genomic Context ◽  
Type Iii Effectors ◽  
Type Iii ◽  
Genome Context ◽  
The Impact ◽  
Multiple Type

Integrative Conjugative Elements (ICEs) are replicons that can insert and excise from chromosomal locations in a site specific manner, can conjugate across strains, and which often carry a variety of genes useful for bacterial growth and survival under specific conditions. Although ICEs have been identified and vetted within certain clades of the agricultural pathogen Pseudomonas syringae, the impact of ICE carriage and transfer across the entire P. syringae species complex remains underexplored. Here we identify and vet an ICE (PmaICE-DQ) from P. syringae pv. maculicola ES4326, a strain commonly used for laboratory virulence experiments, demonstrate that this element can excise and conjugate across strains, and contains loci encoding multiple type III effector proteins. Moreover, genome context suggests that another ICE (PmaICE-AOAB) is highly similar in comparison with and found immediately adjacent to PmaICE-DQ within the chromosome of strain ES4326, and also contains multiple type III effectors. Lastly, we present passage data from in planta experiments that suggests that genomic plasticity associated with ICEs may enable strains to more rapidly lose type III effectors that trigger R-gene mediated resistance in comparison to strains where nearly isogenic effectors are not present in ICEs. Taken together, our study sheds light on a set of ICE elements from P. syringae pv. maculicola ES4326 and highlights how genomic context may lead to different evolutionary dynamics for shared virulence genes between strains.

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