Plant biology is experiencing a renewed interest in the mechanistic underpinnings and evolution of phenotypic plasticity that calls for a re-evaluation of how we analyse phenotypic responses to a ...rapidly changing climate. We suggest that dissecting plant plasticity in response to increasing temperature needs an approach that can represent plasticity over multiple environments, and considers both population-level responses and the variation between genotypes in their response. Here, we outline how a random regression mixed model framework can be applied to plastic traits that show linear or nonlinear responses to temperature. Random regressions provide a powerful and efficient means of characterising plasticity and its variation. Although they have been used widely in other fields, they have only recently been implemented in plant evolutionary ecology. We outline their structure and provide an example tutorial of their implementation.
Our work aims to investigate methods for solving the mixed-model assembly line balancing problem (MALBP) under uncertainty with the objective of minimizing the number of workstations. Specifically, ...we model task processing time as fuzzy stochastic variables (FRVs) due to the inherent uncertainties and variations in the manufacturing environment. Additionally, we introduce a ranking method for FRVs and propose a mathematical model to address MALBP. The recently developed Red Fox Optimization (RFO) algorithm is also discretized for the first time to support solving this problem. Finally, matheuristic algorithms combine a metaheuristic such as the popular Genetic Algorithm (GA), Particle Swarm Optimization (PSO), or the Discretized Red Fox Optimization (DRFO) algorithm with the Mixed-Integer Programming (MIP) model to generate the best solution in a reasonable time. Our comparative results demonstrate that the GA-MIP combination outperforms the others in both objective value and computational time.
•We are the first to consider the Assembly Line Balancing Problem with fuzzy random processing time.•a new method to rank fuzzy stochastic processing times is introduced by comparing interval values.•The problem is formulated as chance-constrained programming model.•The recently developed Red Fox Optimization (RFO) algorithm is also discretized for the first time to support solution process.•A matheuristic combines a metaheuristic and the MIP model to search for better solutions for large-sized problems.•The metaheuristic algorithm to generate the starting solution for the MIP model to shorten finding the optimal solution.•Our results are nearly the same quality as the exact solution of the MIP model in a much shorter time for small-sized instances. MGA matheuristic yields significantly better performance than others in both terms of objective value and computational time for large-sized problems.
Evaluating the genotype (G) by management practice (M) interaction in agronomic experimentation is essential to help grain growers optimise the desired trait of interest (e.g. grain yield). However, ...the approach is complicated by interaction effects with environmental factors that differ across sites and seasons. Popular statistical methods for modelling the genotype by environment (G × E) interaction are limited as they neither provide a biological understanding of how environmental factors impact on the G × E interaction, nor assess how different management practices influence the G × E interaction. These limitations may be addressed by incorporating environmental covariates (ECs) into the modelling process to better explain why differences exist in the optimal genotype by management practice combination across environments.
A novel statistical methodology is proposed that incorporates ECs to explore genotype by environment by management practice (G × E × M) interactions in agronomic multi-environment trial studies.
A predictive linear mixed model is proposed that incorporates site and season specific ECs into a commonly used G × E interaction framework. The model is extended to include the effect of continuously varying agronomic management practices, whilst allowing for non-linear trait responses and complex variance structures. The methodology is applied to a multi-environment dataset exploring yield response to established plant density in a series of sorghum agronomy trials.
Results indicated that the grain yield of sorghum genotypes would be optimised in environments that have (i) high total plant available water and photo-thermal quotient around flowering, (ii) low pre-flowering radiation and evapotranspiration and (iii) achieved flowering at an optimal time. Under this set of optimal G × E conditions, a high established plant density further optimised grain yield.
The proposed methodology successfully incorporated ECs to better understand G × E × M interactions in agronomic field trials, enabling predictions to be made in an untested or future environment and linking the statistical analysis to crop-ecophysiology principles.
This work will improve the generalisations agronomists can draw from experimental studies, enhancing the biological understanding of the analysis results and allowing for the development of more targeted and robust recommendations for agronomic practices.
•Incorporates environmental covariates and management in the statistical analysis.•Can identify key environmental covariates contributing to the G × E × M interaction.•Non-linear trait response to an environmental covariate can be modelled.•Environmental covariates can predict performance in future environments.
When (meta-)analyzing single-case experimental design (SCED) studies by means of hierarchical or multilevel modeling, applied researchers almost exclusively rely on the linear mixed model (LMM). This ...type of model assumes that the residuals are normally distributed. However, very often SCED studies consider outcomes of a discrete rather than a continuous nature, like counts, percentages or rates. In those cases the normality assumption does not hold. The LMM can be extended into a generalized linear mixed model (GLMM), which can account for the discrete nature of SCED count data. In this simulation study, we look at the effects of misspecifying an LMM for SCED count data simulated according to a GLMM. We compare the performance of a misspecified LMM and of a GLMM in terms of goodness of fit, fixed effect parameter recovery, type I error rate, and power. Because the LMM and the GLMM do not estimate identical fixed effects, we provide a transformation to compare the fixed effect parameter recovery. The results show that, compared to the GLMM, the LMM has worse performance in terms of goodness of fit and power. Performance in terms of fixed effect parameter recovery is equally good for both models, and in terms of type I error rate the LMM performs better than the GLMM. Finally, we provide some guidelines for applied researchers about aspects to consider when using an LMM for analyzing SCED count data.
The COVID-19 pandemic and government measures implemented to counter the spread of the infection may be a major stressor affecting the psychological health of university students. This study aimed to ...explore how anxiety symptoms changed during the pandemic.
676 students (76% females) at Zurich University of Applied Sciences participated in the first (T0) and second (T1) survey waves. Anxiety symptoms were assessed using the Generalized Anxiety Disorder-Scale-7 (GAD-7). Risk and protective factors (e.g., COVID-19-related variables) were examined.
GAD-7 scores decreased significantly from T0 to T1 (mean change: -0.446, SE = 0.132, 95% CI: -0.706, -0.186,
= -3.371,
= 659,
= 0.001). Participants with moderate-to-severe anxiety score were 20.2 and 15.6% at T0 and T1, respectively. The following positively predicted anxiety: older age, female gender, non-Swiss nationality, loneliness, participants' concern about their own health, and interaction between time and participants' concern about their own health. Resilience and social support negatively predicted anxiety.
Our findings provide information for public health measures and psychological interventions supporting the mental health of university students during the COVID-19 emergency.
Previous studies have reported that some important loci are missed in single-locus genome-wide association studies (GWAS), especially because of the large phenotypic error in field experiments. To ...solve this issue, multi-locus GWAS methods have been recommended. However, only a few software packages for multi-locus GWAS are available. Therefore, we developed an R software named mrMLM v4.0.2. This software integrates mrMLM, FASTmrMLM, FASTmrEMMA, pLARmEB, pKWmEB, and ISIS EM-BLASSO methods developed by our lab. There are four components in mrMLM v4.0.2, including dataset input, parameter setting, software running, and result output. The fread function in data.table is used to quickly read datasets, especially big datasets, and the doParallel package is used to conduct parallel computation using multiple CPUs. In addition, the graphical user interface software mrMLM.GUI v4.0.2, built upon Shiny, is also available. To confirm the correctness of the aforementioned programs, all the methods in mrMLM v4.0.2 and three widely-used methods were used to analyze real and simulated datasets. The results confirm the superior performance of mrMLM v4.0.2 to other methods currently available. False positive rates are effectively controlled, albeit with a less stringent significance threshold. mrMLM v4.0.2 is publicly available at BioCode (https://bigd.big.ac.cn/biocode/tools/BT007077) or R (https://cran.r-project.org/web/packages/mrMLM.GUI/index.html) as an open-source software.
Climate change is increasing the frequency and intensity of drought events in many boreal forests. Trees are sessile organisms with a long generation time, which makes them vulnerable to fast climate ...change and hinders fast adaptations. Therefore, it is important to know how forests cope with drought stress and to explore the genetic basis of these reactions. We investigated three natural populations of white spruce (Picea glauca) in Alaska, located at one drought‐limited and two cold‐limited treelines with a paired plot design of one forest and one treeline plot. We obtained individual increment cores from 458 trees and climate data to assess dendrophenotypes, in particular the growth reaction to drought stress. To explore the genetic basis of these dendrophenotypes, we genotyped the individual trees at 3000 single nucleotide polymorphisms in candidate genes and performed genotype–phenotype association analysis using linear mixed models and Bayesian sparse linear mixed models. Growth reaction to drought stress differed in contrasting treeline populations. Therefore, the populations are likely to be unevenly affected by climate change. We identified 40 genes associated with dendrophenotypic traits that differed among the treeline populations. Most genes were identified in the drought‐limited site, indicating comparatively strong selection pressure of drought‐tolerant phenotypes. Contrasting patterns of drought‐associated genes among sampled sites and in comparison to Canadian populations in a previous study suggest that drought adaptation acts on a local scale. Our results highlight genes that are associated with wood traits which in turn are critical for the establishment and persistence of future forests under climate change.
•Species type, mycorrhizal association, and N-fixing ability all significantly affected soil C.•Forest floor C stock was higher under coniferous, ECM, or non-N-fixing trees.•Mineral soil C ...concentration and stock were higher under broadleaved, AM, or N-fixing trees.•Tree species effects on soil C were mediated by latitude, MAT, MAP, and forest stand age.
Selection of appropriate tree species is an important forest management decision that may affect sequestration of carbon (C) in soil. However, information about tree species effects on soil C stocks at the global scale remains unclear. Here, we quantitatively synthesized 850 observations from field studies that were conducted in a common garden or monoculture plantations to assess how tree species type (broadleaf vs. conifer), mycorrhizal association (arbuscular mycorrhizal (AM) vs. ectomycorrhizal (ECM)), and N-fixing ability (N-fixing vs. non-N-fixing), directly and indirectly, affect topsoil (with a median depth of 10 cm) C concentration and stock, and how such effects were influenced by environmental factors such as geographical location and climate. We found that (1) tree species type, mycorrhizal association, and N-fixing ability were all important factors affecting soil C, with lower forest floor C stocks under broadleaved (44%), AM (39%), or N-fixing (28%) trees respectively, but higher mineral soil C concentration (11%, 22%, and 156%) and stock (9%, 10%, and 6%) under broadleaved, AM, and N-fixing trees respectively; (2) tree species type, mycorrhizal association, and N-fixing ability affected forest floor C stock and mineral soil C concentration and stock directly or indirectly through impacting soil properties such as microbial biomass C and nitrogen; (3) tree species effects on mineral soil C concentration and stock were mediated by latitude, MAT, MAP, and forest stand age. These results reveal how tree species and their specific traits influence forest floor C stock and mineral soil C concentration and stock at a global scale. Insights into the underlying mechanisms of tree species effects found in our study would be useful to inform tree species selection in forest management or afforestation aiming to sequester more atmospheric C in soil for mitigation of climate change.
As large-scale studies of gene expression with multiple sources of biological and technical variation become widely adopted, characterizing these drivers of variation becomes essential to ...understanding disease biology and regulatory genetics.
We describe a statistical and visualization framework, variancePartition, to prioritize drivers of variation based on a genome-wide summary, and identify genes that deviate from the genome-wide trend. Using a linear mixed model, variancePartition quantifies variation in each expression trait attributable to differences in disease status, sex, cell or tissue type, ancestry, genetic background, experimental stimulus, or technical variables. Analysis of four large-scale transcriptome profiling datasets illustrates that variancePartition recovers striking patterns of biological and technical variation that are reproducible across multiple datasets.
Our open source software, variancePartition, enables rapid interpretation of complex gene expression studies as well as other high-throughput genomics assays. variancePartition is available from Bioconductor: http://bioconductor.org/packages/variancePartition .
PDF
