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Forensic entomology plays a crucial role in estimating the minimum postmortem interval through the study of insect larvae found at crime scenes. The precision of this estimation relies on various biotic and abiotic elements that simultaneously influence insect growth and development, encompassing factors such as temperature, humidity, photoperiod, diet, and the existence of xenobiotics in decomposing tissues. Despite numerous studies on the influence of these factors, including the impact of xenobiotics, there are currently no robust tools available for making corrections to this estimation considering concurrently all variables. In an attempt to propose an exploratory and descriptive statistical model to analyze the simultaneous effect and interaction of different variables on larval growth, this study aimed to compare the effect of malathion on the growth of Megaselia scalaris (Loew, 1866) (Diptera: Phoridae) raised in malathion-spiked porcine muscle, under controlled and uncontrolled temperature and humidity conditions (environmental conditions). Larvae were also reared using various growth media. A split-plot design that combined crossed and nested factors was employed; 2 linear mixed models were developed to assess the relationships between the variables. The model provides valuable insights into the complex interactions among xenobiotics, growth media, and environmental conditions in the size and development of M. scalaris.

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This paper aims to evaluate the statistical association between exposure to air pollution and forced expiratory volume in the first second (FEV1) in both asthmatic and non-asthmatic children and teenagers, in which the response variable FEV1 was repeatedly measured on a monthly basis, characterizing a longitudinal experiment. Due to the nature of the data, an robust linear mixed model (RLMM), combined with a robust principal component analysis (RPCA), is proposed to handle the multicollinearity among the covariates and the impact of extreme observations (high levels of air contaminants) on the estimates. The Huber and Tukey loss functions are considered to obtain robust estimators of the parameters in the linear mixed model (LMM). A finite sample size investigation is conducted under the scenario where the covariates follow linear time series models with and without additive outliers (AO). The impact of the time-correlation and the outliers on the estimates of the fixed effect parameters in the LMM is investigated. In the real data analysis, the robust model strategy evidenced that RPCA exhibits three principal component (PC), mainly related to relative humidity (Hmd), particulate matter with a diameter smaller than 10 µm (PM10) and particulate matter with a diameter smaller than 2.5 µm (PM2.5).

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Background: Socioeconomic status (SES) is a potent environmental determinant of health. To our knowledge, no assessment of genotype-environment interaction has been conducted to consider the joint effects of socioeconomic status and genetics on risk for cardiovascular disease (CVD). We analyzed Mexican American Family Studies (MAFS) data to evaluate the hypothesis that genotype-by-environment interaction (GxE) is an important determinant of variation in CVD risk factors. Methods: We employed a linear mixed model to investigate GxE in Mexican American extended families. We studied two proxies for CVD [Pooled Cohort Equation Risk Scores/Framingham Risk Scores (FRS/PCRS) and carotid artery intima-media thickness (CA-IMT)] in relation to socioeconomic status as determined by Duncan's Socioeconomic Index (SEI), years of education, and household income. Results: We calculated heritability for FRS/PCRS and carotid artery intima-media thickness. There was evidence of GxE due to additive genetic variance heterogeneity and genetic correlation for FRS, PCRS, and CA-IMT measures for education (environment) but not for household income or SEI. Conclusion: The genetic effects underlying CVD are dynamically modulated at the lower end of the SES spectrum. There is a significant change in the genetic architecture underlying the major components of CVD in response to changes in education.

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Background: Technological advances involving RNA-Seq and Bioinformatics allow quantifying the transcriptional levels of genes in cells, tissues, and cell lines, permitting the identification of Differentially Expressed Genes (DEGs). DESeq2 and edgeR are well-established computational tools used for this purpose and they are based upon generalized linear models (GLMs) that consider only fixed effects in modeling. However, the inclusion of random effects reduces the risk of missing potential DEGs that may be essential in the context of the biological phenomenon under investigation. The generalized linear mixed models (GLMM) can be used to include both effects. Methods: We present DEGRE (Differentially Expressed Genes with Random Effects), a user-friendly tool capable of inferring DEGs where fixed and random effects on individuals are considered in the experimental design of RNA-Seq research. DEGRE preprocesses the raw matrices before fitting GLMMs on the genes and the derived regression coefficients are analyzed using the Wald statistical test. DEGRE offers the Benjamini-Hochberg or Bonferroni techniques for P-value adjustment. Results: The datasets used for DEGRE assessment were simulated with known identification of DEGs. These have fixed effects, and the random effects were estimated and inserted to measure the impact of experimental designs with high biological variability. For DEGs' inference, preprocessing effectively prepares the data and retains overdispersed genes. The biological coefficient of variation is inferred from the counting matrices to assess variability before and after the preprocessing. The DEGRE is computationally validated through its performance by the simulation of counting matrices, which have biological variability related to fixed and random effects. DEGRE also provides improved assessment measures for detecting DEGs in cases with higher biological variability. We show that the preprocessing established here effectively removes technical variation from those matrices. This tool also detects new potential candidate DEGs in the transcriptome data of patients with bipolar disorder, presenting a promising tool to detect more relevant genes. Conclusions: DEGRE provides data preprocessing and applies GLMMs for DEGs' inference. The preprocessing allows efficient remotion of genes that could impact the inference. Also, the computational and biological validation of DEGRE has shown to be promising in identifying possible DEGs in experiments derived from complex experimental designs. This tool may help handle random effects on individuals in the inference of DEGs and presents a potential for discovering new interesting DEGs for further biological investigation.

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Quarantine and social distance restrictions have been enforced worldwide to reduce the spread of coronavirus disease 2019 (COVID-19). The effects of these measures on mental health are recognised, but remaining unclear, is whether these effects are a consequence of the virus itself or policies that are enforced to prevent it. The present study investigated the impact of lockdown restrictions on anxiety and depression at two different times in 2020. Data were collected from 118 participants from all regions of Brazil. After easing quarantine restrictions in the second half of 2020, two natural groups were formed. One group included participants who voluntarily remained at home (n = 73). The other group consisted of those who decided to leave home (n = 45). A linear mixed model was used to determine the effects of group and time and their interaction. The McNemar test was used to determine intragroup differences in perceptions and concerns about COVID-19. Logistic regression identified predictors of high and stable depression and anxiety. None of the factors or their interactions was significant. Indicators of depression and anxiety remained stable over time, regardless of whether the participants left home or remained at home. Significantly, a strong and stable agreement with quarantine was found. The participants agreed that COVID-19 was a threat to public health. Political orientation was a predictor of high and stable levels of depression but not anxiety. Participants who self-identified as liberal politically were at a greater risk of developing depression. The results suggest that the lockdown policy did not contribute to disruptions of mental health, which instead was a consequence of the pandemic and virus itself. We also found wide and strong support amongst the participants for lockdown and mandatory stay-at-home policies.

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BACKGROUND: Thrombocytopenia is a marker of severity in dengue, and its resolution predicts clinical improvement. The objective was to evaluate mean platelet volume (MPV) trajectories as a predictor of platelet count (PC) recovery in dengue patients. METHODS: An observational, longitudinal and analytical study was conducted at Fundación Valle del Lili (Cali, Colombia). Patients diagnosed with dengue during 2016-2020 were included. The association between PC and the covariates was evaluated using simple linear, quadratic and non-parametric spline smoothing regression models. A longitudinal linear mixed model was adjusted and then validated for PC measurements. RESULTS: A total of 71 patients were included. The median age was 27 y, 38.5% were women and half had dengue with warning signs. A statistically significant PC decrease was observed when MPV was 13.87 fL and 4.46 d from the onset of symptoms, while PC displayed a significant constant increase with neutrophils count. Then, PC recovery was achieved with an MPV of 13.58 fL, 4.5 d from the onset of symptoms and a minimum neutrophils count of 150 µL. CONCLUSION: MPV may be a predictor of PC recovery in dengue patients. PC recovery is expected when a patient has an MPV of 13.58 fL, an onset time of 4.5 d and a neutrophils count of 150 µL.

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Genomic selection (GS) is a technology used for genetic improvement, and it has many advantages over phenotype-based selection. There are several statistical models that adequately approach the statistical challenges in GS, such as in linear mixed models (LMMs). An active area of research is the development of software for fitting LMMs mainly used to make genome-based predictions. The lme4 is the standard package for fitting linear and generalized LMMs in the R-package, but its use for genetic analysis is limited because it does not allow the correlation between individuals or groups of individuals to be defined. This article describes the new lme4GS package for R, which is focused on fitting LMMs with covariance structures defined by the user, bandwidth selection, and genomic prediction. The new package is focused on genomic prediction of the models used in GS and can fit LMMs using different variance-covariance matrices. Several examples of GS models are presented using this package as well as the analysis using real data.

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Modern and paleoclimate changes may have altered species dynamics by shifting species' niche suitability over space and time. We analyze whether the current genetic structure and isolation of the two large American felids, jaguar (Panthera onca) and puma (Puma concolor), are mediated by changes in climatic suitability and connection routes over modern and paleoclimatic landscapes. We estimate species distribution under 5 climatic landscapes (modern, Holocene, last maximum glaciations [LMG], average suitability, and climatic instability) and correlate them with individuals' genetic isolation through causal modeling on a resemblance matrix. Both species exhibit genetic isolation patterns correlated with LMG climatic suitability, suggesting that these areas may have worked as "allele refuges." However, the jaguar showed higher vulnerability to climate changes, responding to modern climatic suitability and connection routes, whereas the puma showed a continuous and gradual transition of genetic variation. Despite differential responsiveness to climate change, both species are subjected to the climatic effects on genetic configuration, which may make them susceptible to future climatic changes, since these are progressing faster and with higher intensity than changes in the paleoclimate. Thus, the effects of climatic changes should be considered in the design of conservation strategies to ensure evolutionary and demographic processes mediated by gene flow for both species.

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Although wild birds are considered the main reservoir of the influenza A virus (IAV) in nature, empirical investigations exploring the interaction between the IAV prevalence in these populations and environmental drivers remain scarce. Chile has a coastline of more than 4000 kilometres with hundreds of wetlands, which are important habitats for both resident and inter-hemispheric migratory species. The aim of this study was to characterize the temporal dynamics of IAV in main wetlands in central Chile and to assess the influence of environmental variables on AIV prevalence. For that purpose, four wetlands were studied from September 2015 to June 2018. Fresh faecal samples of wild birds were collected for IAV detection by real-time RT-PCR. Furthermore, a count of wild birds present at the site was performed and environmental variables, such as temperature, rainfall, vegetation coverage (Normalized Difference Vegetation Index (NDVI)) and water body size, were determined. A generalized linear mixed model was built to assess the association between IAV prevalence and explanatory variables. An overall prevalence of 4.28% ± 0.28% was detected with important fluctuations among seasons, being greater during summer (OR = 4.87, 95% CI 2.11 to 11.21) and fall (OR = 2.59, 95% CI 1.12 to 5.97). Prevalence was positively associated with minimum temperature for the month of sampling and negatively associated with water body size measured two months before sampling, and NDVI measured three months before sampling. These results contribute to the understanding of IAV ecological drivers in Chilean wetlands providing important considerations for the global surveillance of IAV.

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Gene-environment (GE) interaction has important implications in the etiology of complex diseases that are caused by a combination of genetic factors and environment variables. Several authors have developed GE analysis in the context of independent subjects or longitudinal data using a gene-set. In this paper, we propose to analyze GE interaction for discrete and continuous phenotypes in family studies by incorporating the relatedness among the relatives for each family into a generalized linear mixed model (GLMM) and by using a gene-based variance component test. In addition, we deal with collinearity problems arising from linkage disequilibrium among single nucleotide polymorphisms (SNPs) by considering their coefficients as random effects under the null model estimation. We show that the best linear unbiased predictor (BLUP) of such random effects in the GLMM is equivalent to the ridge regression estimator. This equivalence provides a simple method to estimate the ridge penalty parameter in comparison to other computationally-demanding estimation approaches based on cross-validation schemes. We evaluated the proposed test using simulation studies and applied it to real data from the Baependi Heart Study consisting of 76 families. Using our approach, we identified an interaction between BMI and the Peroxisome Proliferator Activated Receptor Gamma (PPARG) gene associated with diabetes.

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A fundamental and important challenge in modern datasets of ever increasing dimensionality is variable selection, which has taken on renewed interest recently due to the growth of biological and medical datasets with complex, non-i.i.d. structures. Naïvely applying classical variable selection methods such as the Lasso to such datasets may lead to a large number of false discoveries. Motivated by genome-wide association studies in genetics, we study the problem of variable selection for datasets arising from multiple subpopulations, when this underlying population structure is unknown to the researcher. We propose a unified framework for sparse variable selection that adaptively corrects for population structure via a low-rank linear mixed model. Most importantly, the proposed method does not require prior knowledge of individual relationships in the data and adaptively selects a covariance structure of the correct complexity. Through extensive experiments, we illustrate the effectiveness of this framework over existing methods. Further, we test our method on three different genomic datasets from plants, mice, and humans, and discuss the knowledge we discover with our model.

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The intraclass correlation is commonly used with clustered data. It is often estimated based on fitting a model to hierarchical data and it leads, in turn, to several concepts such as reliability, heritability, inter-rater agreement, etc. For data where linear models can be used, such measures can be defined as ratios of variance components. Matters are more difficult for non-Gaussian outcomes. The focus here is on count and time-to-event outcomes where so-called combined models are used, extending generalized linear mixed models, to describe the data. These models combine normal and gamma random effects to allow for both correlation due to data hierarchies as well as for overdispersion. Furthermore, because the models admit closed-form expressions for the means, variances, higher moments, and even the joint marginal distribution, it is demonstrated that closed forms of intraclass correlations exist. The proposed methodology is illustrated using data from agricultural and livestock studies.

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O presente trabalho objetivou estimar parâmetros, correlações e ganhos genéticos para caracteres de crescimento e forma, em um teste de progênies deEucalyptus camaldulensis na região centro-oeste do Brasil. Aos três anos de idade, as progênies foram avaliadas quanto aos caracteres: altura total (ALTT), altura comercial (ALTC), diâmetro à altura do peito (DAP) e forma de fuste (FF). A análise de deviance detectou diferenças significativas para os caracteres ALTC, DAP e FF. As estimativas das herdabilidades individuais foram de baixa magnitude para ALTT (0,10) e DAP (0,16), porém, ALTC (0,18) e FF (0,25) apresentaram valores de média a alta magnitude. Os coeficientes de variação genética individual (CVgi%) variaram de 8,59% para FF a 15,91% para ALTC. As correlações fenotípicas e genéticas preditas foram positivas e de alta magnitude entre ALTT e ALTC (0,80 e 0,82, respectivamente) e ALTT e DAP (0,85 e 0,86, respectivamente), indicando que a seleção indireta pode ser utilizada para essas associações. A seleção individual se mostrou superior, quando comparada à seleção entre e dentro. Os valores encontrados indicaram perspectivas de progressos genéticos com seleção baseada nos caracteres avaliados.(AU)

This study aimed to estimate parameters, correlations and genetic gain for growth and shape traits in a progeny trial using Eucalyptus camaldulensisin Central Brazil. When it was three years old, progenies were evaluated for the following traits: total height (ALTT), commercial height (ALTC), diameter at breast height (DAP) and stem form (FF). Deviance analysis detected significant differences for ALTC, DAP and FF. Estimates of individual heritability showed low magnitude for ALTT (0.10) and DAP (0.16). However, ALTC (0.18) and FF (0.25), showed median to high magnitude values. Individual genetic variation coefficients (CVgi%), ranged from 8.59% (FF) to 15.91% (ALTC). Predicted phenotypic and genetic correlations were positive and of high magnitude between ALTT and ALTC (0.80 and 0.82) as well as between ALTT and DAP (0.85 and 0.86), indicating that indirect selection can be used for these associations. Individual selection showed to be superior when compared to selection between and within. Found values indicated perspectives of genetic progress with selection based on the evaluated characters.(AU)

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O presente trabalho objetivou estimar parâmetros, correlações e ganhos genéticos para caracteres de crescimento e forma, em um teste de progênies deEucalyptus camaldulensisna região centro-oeste do Brasil. Aos três anos de idade, as progênies foram avaliadas quanto aos caracteres: altura total (ALTT), altura comercial (ALTC), diâmetro à altura do peito (DAP) e forma de fuste (FF). A análise de deviance detectou diferenças significativas para os caracteres ALTC, DAP e FF. As estimativas das herdabilidades individuais foram de baixa magnitude para ALTT (0,10) e DAP (0,16), porém, ALTC (0,18) e FF (0,25) apresentaram valores de média a alta magnitude. Os coeficientes de variação genética individual (CVgi%) variaram de 8,59% para FF a 15,91% para ALTC. As correlações fenotípicas e genéticas preditas foram positivas e de alta magnitude entre ALTT e ALTC (0,80 e 0,82, respectivamente) e ALTT e DAP (0,85 e 0,86, respectivamente), indicando que a seleção indireta pode ser utilizada para essas associações. A seleção individual se mostrou superior, quando comparada à seleção entre e dentro. Os valores encontrados indicaram perspectivas de progressos genéticos com seleção baseada nos caracteres avaliados.

This study aimed to estimate parameters, correlations and genetic gain for growth and shape traits in a progeny trial using Eucalyptus camaldulensisin Central Brazil. When it was three years old, progenies were evaluated for the following traits: total height (ALTT), commercial height (ALTC), diameter at breast height (DAP) and stem form (FF). Deviance analysis detected significant differences for ALTC, DAP and FF. Estimates of individual heritability showed low magnitude for ALTT (0.10) and DAP (0.16). However, ALTC (0.18) and FF (0.25), showed median to high magnitude values. Individual genetic variation coefficients (CVgi%), ranged from 8.59% (FF) to 15.91% (ALTC). Predicted phenotypic and genetic correlations were positive and of high magnitude between ALTT and ALTC (0.80 and 0.82) as well as between ALTT and DAP (0.85 and 0.86), indicating that indirect selection can be used for these associations. Individual selection showed to be superior when compared to selection between and within. Found values indicated perspectives of genetic progress with selection based on the evaluated characters.

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Compositional analysis is a requisite component of the substantial equivalence framework utilized to assess genetically modified (GM) crop safety. Statistical differences in composition data between GM and non-GM crops require a context in which to determine biological relevance. This context is provided by surveying the natural variation of key nutrient and antinutrient levels within the crop population with a history of safe use. Data accumulated from various genotypes with a history of safe use cultivated in relevant commercial crop-growing environments over multiple seasons are discussed as the appropriate data representative of this natural variation. A model-based parametric tolerance interval approach, which accounts for the correlated and unbalanced data structure of cumulative historical data collected from multisite field studies conducted over multiple seasons, is presented. This paper promotes the application of this tolerance interval approach to generate reference ranges for evaluation of the biological relevance of statistical differences identified during substantial equivalence assessment of a GM crop.

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The genetic composition of the resident Symbiodinium endosymbionts can strongly modulate the physiological performance of reef-building corals. Here, we used quantitative metabarcoding to investigate Symbiodinium genetic diversity in two species of mountainous star corals, Orbicella franksi and Orbicella faveolata, from two reefs separated by 19 km of deep water. We aimed to determine if the frequency of different symbiont genotypes varied with respect to coral host species or geographic location. Our results demonstrate that across the two reefs both coral species contained seven haplotypes of Symbiodinium, all identifiable as clade B and most closely related to type B1. Five of these haplotypes have not been previously described and may be endemic to the Flower Garden Banks. No significant differences in symbiont composition were detected between the two coral species. However, significant quantitative differences were detected between the east and west banks for three background haplotypes comprising 0.1%-10% of the total. The quantitative metabarcoding approach described here can help to sensitively characterize cryptic genetic diversity of Symbiodinium and potentially contribute to the understanding of physiological variations among coral populations.

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Objetivou-se com este trabalho comparar modelos de predição de plantas sobreviventes de Eucalyptus grandis. Utilizaram-se os seguintes modelos: modelo linear misto com os dados transformados, utilizando-se as transformações angular e BOX-COX; modelo linear generalizado misto com distribuição binomial e funções de ligação logística, probit e complemento log-log; modelo linear generalizado misto com distribuição Poisson e função de ligação logarítmica. Os dados são provenientes de um experimento em blocos ao acaso, para avaliação de progênies maternas de Eucalyptus grandis, aos 5 anos de idade, em que a variável resposta são plantas sobreviventes. Para comparação dos efeitos entre os modelos foram estimadas as correlações de Spearman e aplicado o teste de permutação de Fisher. Foi possível concluir que, o modelo linear generalizado misto com distribuição Poisson e função de ligação logarítmica se ajustou mal aos dados e que as estimativas para os efeitos fixos e predição para os efeitos aleatórios, não se diferenciaram entre os demais modelos estudados.

The objective of this work was to compare models for prediction of the survival of plants of Eucalyptus grandis. The following models were used: linear mixed model with the transformed data, by utilizing the angular transformations and BOX-COX; generalized linear mixed model with binomial distribution and logistic functions, probit and complement log-log links; generalized linear mixed model with Poisson distribution and logarithmic link function. The data came from a randomized block experiment for evaluation of Eucalyptus grandis maternal progenies at five years old, in which the variable response are surviving plants. For comparison of the effects among the models the correlations of Spearman were estimated and the test of permutation of Fisher was applied. It was possible to conclude that: the generalized linear mixed model with Poisson distribution and logarithmic link function misadjusted to the data; the estimates for the fixed effects and prediction for the random effects did not differ among the to other studied models.