Development of mixed linear models to analyze and describe the impact of malathion on the larval growth of Megaselia scalaris (Diptera: Phoridae) under various feeding media and environmental conditions.

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.

A longitudinal study of the influence of air pollutants on children: a robust multivariate approach.

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).

Variability in rat weight gain during development.

The rat is one of the most employed animal models in biomedicine. Traditionally, weight gain has been utilized to gauge development and compare across species. Numerous studies have conducted longitudinal analyses of rat development, with emphasis on weight gain analysis. Given the high variability in these patterns, experimental data from a single laboratory may not be reliable for generalized estimation. This study aimed to analyze the effect of different factors on the pattern of weight gain during rat development. A literature survey was conducted to compile a database comprising nearly 300 data points of age and weight from 15 longitudinal studies. The database comprised both pre- and postnatal data. Utilizing the Gompertz equation, the data was analyzed to formulate a comprehensive model describing rat development. Differences in growth patterns became increasingly evident at later developmental stages, when significant differences in the maximum asymptote between sexes and strains were reached.

Multi-trait selection in multi-environments for performance and stability in cassava genotypes.

Genotype-environment interaction (GEI) presents challenges when aiming to select optimal cassava genotypes, often due to biased genetic estimates. Various strategies have been proposed to address the need for simultaneous improvements in multiple traits, while accounting for performance and yield stability. Among these methods are mean performance and stability (MPS) and the multi-trait mean performance and stability index (MTMPS), both utilizing linear mixed models. This study's objective was to assess genetic variation and GEI effects on fresh root yield (FRY), along with three primary and three secondary traits. A comprehensive evaluation of 22 genotypes was conducted using a randomized complete block design with three replicates across 47 distinct environments (year x location) in Brazil. The broad-sense heritability (H2) averaged 0.37 for primary traits and 0.44 for secondary traits, with plot-based heritability (hmÉ¡2) consistently exceeding 0.90 for all traits. The high extent of GEI variance (σÉ¡xe2) demonstrates the GEI effect on the expression of these traits. The dominant analytic factor (FA3) accounted for over 85% of the total variance, and the communality (ɧ) surpassed 87% for all traits. These values collectively suggest a substantial capacity for genetic variance explanation. In Cluster 1, composed of remarkably productive and stable genotypes for primary traits, genotypes BRS Novo Horizonte and BR11-34-69 emerged as prime candidates for FRY enhancement, while BRS Novo Horizonte and BR12-107-002 were indicated for optimizing dry matter content. Moreover, MTMPS, employing a selection intensity of 30%, identified seven genotypes distinguished by heightened stability. This selection encompassed innovative genotypes chosen based on regression variance index (Sdi2, R2, and RMSE) considerations for multiple traits. In essence, incorporating methodologies that account for stability and productive performance can significantly bolster the credibility of recommendations for novel cassava cultivars.

Genotype-by-socioeconomic status interaction influences heart disease risk scores and carotid artery thickness in Mexican Americans: the predominant role of education in comparison to household income and socioeconomic index.

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.

Inference of differentially expressed genes using generalized linear mixed models in a pairwise fashion.

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.

A finite mixture mixed proportion regression model for classification problems in longitudinal voting data.

Continuous clustered proportion data often arise in various areas of the social and political sciences where the response variable of interest is a proportion (or percentage). An example is the behavior of the proportion of voters favorable to a political party in municipalities (or cities) of a country over time. This behavior can be different depending on the region of the country, giving rise to groups (or clusters) with similar profiles. For this kind of data, we propose a finite mixture of a random effects regression model based on the L-Logistic distribution. A Markov chain Monte Carlo algorithm is tailored to obtain posterior distributions of the unknown quantities of interest through a Bayesian approach. To illustrate the proposed method, with emphasis on analysis of clusters, we analyze the proportion of votes for a political party in presidential elections in different municipalities observed over time, and then identify groups according to electoral behavior at different levels of favorable votes.

Disentangling crucial factors of the pandemic in Brazil: Effect of lockdown restrictions on mental health.

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.

Mean platelet volume as a predictor of platelet count recovery in dengue patients.

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.

lme4GS: An R-Package for Genomic Selection.

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.

Trajectories of Systolic Blood Pressure in Children: Risk Factors and Cardiometabolic Correlates.

OBJECTIVE: To identify systolic blood pressure (SBP) percentile trajectories in children and to describe the early-life risk factors and cardiometabolic correlates of those trajectories. STUDY DESIGN: Using age-, sex-, and height-specific SBP percentiles based on the American Academy of Pediatrics reference, we examined SBP trajectories using latent class mixed models from ages 3 to 8 years (n = 844) from the Growing Up in Singapore Towards healthy Outcomes-study, a Singaporean mother-offspring cohort study. We analyzed associations between SBP trajectories and early-life risk factors using multinomial logistic regression and differences across trajectories in cardiometabolic outcomes using multiple linear regression. RESULTS: Children were classified into 1 of 4 SBP percentile trajectories: "low increasing" (15%), "high stable" (47%), "high decreasing" (20%), and "low stable" (18%). Maternal hypertension during early pregnancy was a predictor of the "high stable" and "low increasing" SBP trajectories. Rapid child weight gain in the first 2 years of life was only associated with the "high stable" trajectory. Compared with children in the "low stable" trajectory, children in the "high stable" SBP trajectory had greater body mass index z scores, sum of skinfold thicknesses, waist circumference from ages 3 to 8 years, and abdominal adipose tissue (milliliters) at 4.5 years (adjusted mean difference [95% CI]: superficial and deep subcutaneous abdominal adipose tissue: 115.2 [48.1-182.3] and 85.5 [35.2-135.8]). Their fat mass (kilograms) (1.3 [0.6-2.0]), triglyceride levels (mmol/L) (0.10 [0.02-0.18]), and homeostasis model assessment of insulin resistance (0.28 [0.11 0.46]) at age 6 years were also greater but not their arterial thickness and stiffness. CONCLUSIONS: Reducing maternal blood pressure during pregnancy and infant weight gain in the first 2 years of life might help to prevent the development of high SBP.

The differential genetic signatures related to climatic landscapes for jaguars and pumas on a continental scale.

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.

Temporal dynamics and the influence of environmental variables on the prevalence of avian influenza virus in main wetlands in central Chile.

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.

Cross-country differences in age trends in alcohol consumption among older adults: a cross-sectional study of individuals aged 50 years and older in 22 countries.

BACKGROUND AND AIMS: Age-related changes in physiological, metabolic and medication profiles make alcohol consumption likely to be more harmful among older than younger adults. This study aimed to estimate cross-national variation in the quantity and patterns of drinking throughout older age, and to investigate country-level variables explaining cross-national variation in consumption for individuals aged 50 years and older. DESIGN: Cross-sectional observational study using previously harmonized survey data. SETTING: Twenty-two countries surveyed in 2010 or the closest available year. PARTICIPANTS: A total of 106 180 adults aged 50 years and over. MEASUREMENTS: Cross-national variation in age trends were estimated for two outcomes: weekly number of standard drink units (SDUs) and patterns of alcohol consumption (never, ever, occasional, moderate and heavy drinking). Human Development Index and average prices of vodka were used as country-level variables moderating age-related declines in drinking. FINDINGS: Alcohol consumption was negatively associated with age (risk ratio = 0.98; 95% confidence interval = 0.97, 0.99; P-value < 0.001), but there was substantial cross-country variation in the age-related differences in alcohol consumption [likelihood ratio (LR) test P-value < 0.001], even after adjusting for the composition of populations. Countries' development level and alcohol prices explained 31% of cross-country variability in SDUs (LR test P-value < 0.001) but did not explain cross-country variability in the prevalence of heavy drinkers. CONCLUSIONS: Use and harmful use of alcohol among older adults appears to vary widely across age and countries. This variation can be partly explained both by the country-specific composition of populations and country-level contextual factors such as development level and alcohol prices.

Assessment of individual competence: a sequential mixed model 1 / Avaliação da competência individual: um modelo sequencial misto

Aims: the assessment of individual competence in medical education is about finding a balance between having sufficient resources to make valid and reliable decisions and not using more resources than necessary. Sequential assessment, in which more resources are used for borderline performing candidates than for poorly or clearly satisfactorily performing candidates, can be used to achieve that balance. Although sequential assessment is commonly associated with larger groups of candidates to be assessed, in many practical settings numbers of candidates may be small. Objective: this article presents a single case design with a statistical model for the assessment of individual competence that can be used regardless of the number of candidates. Method: a worked example of a solution that can be used for an individual candidate, using simulated data in the zero-cost Open Source statistical program R version 4.0.5, is provided. Results: the aforementioned solution provides statistics that can be used to make pass/fail decisions at the level of the individual candidate as well as to make decisions regarding the length and timing of an exam (or parts thereof) for the individual candidate. Conclusion: the solution provided can help to reduce resources needed for assessment to a considerable extent while maximizing resources for borderline candidates. This facilitates both decision making and cost reduction in assessment.

Introdução: a avaliação da competência individual na educação médica consiste em encontrar um equilíbrio entre ter recursos suficientes para tomar decisões válidas e confiáveis e não usar mais recursos do que o necessário. A avaliação sequencial, na qual mais recursos são usados para candidatos limítrofes do que para candidatos com desempenho insatisfatório ou claramente satisfatório, pode ser usada para atingir esse equilíbrio. Embora a avaliação sequencial seja comumente associada a grupos maiores de candidatos a serem avaliados, em muitos ambientes práticos, o número de candidatos pode ser pequeno. Objetivo: este artigo apresenta um desenho de caso único com um modelo estatístico de avaliação de competência individual que pode ser utilizado independentemente do número de candidatos. Método: é fornecido um exemplo prático de uma solução que pode ser usada para um candidato individual, usando dados simulados no programa estatístico Open Source de custo zero R versão 4.0.5. Resultados: a solução mencionada fornece estatísticas que podem ser usadas para tomar decisões individuais de aprovação/reprovação para cada candidato, bem como para tomar decisões individualizadas sobre a duração e o tempo de um exame (ou partes dele) para um candidato. Conclusão: a solução fornecida pode ajudar a reduzir consideravelmente os recursos necessários para a avaliação, ao mesmo tempo que maximiza os recursos para os candidatos limítrofes. Isso facilita a tomada de decisões e a redução de custos na avaliação.

Small numbers are an opportunity, not a problem / Números pequenos são uma oportunidade, não um problema

Aims: outcomes of research in education and training are partly a function of the context in which that study takes place, the questions we ask, and what is feasible. Many questions are about learning, which involves repeated measurements in a particular time window, and the practical context is usually such that offering an intervention to some but not to all learners does not make sense or is unethical. For quality assurance and other purposes, education and training centers may have very locally oriented questions that they seek to answer, such as whether an intervention can be considered effective in their context of small numbers of learners. While the rationale behind the design and outcomes of this kind of studies may be of interest to a much wider community, for example to study the transferability of findings to other contexts, people are often discouraged to report on the outcomes of such studies at conferences or in educational research journals. The aim of this paper is to counter that discouragement and instead encourage people to see small numbers as an opportunity instead of as a problem. Method: a worked example of a parametric and a non-parametric method for this type of situation, using simulated data in the zero-cost Open Source statistical program R version 4.0.5. Results: contrary to the non-parametric method, the parametric method can provide estimates of intervention effectiveness for the individual participant, account for trends in different phases of a study. However, the non-parametric method provides a solution in several situations where the parametric method should be used. Conclusion: Given the costs of research, the lessons to be learned from research, and statistical methods available, small numbers should be considered an opportunity, not a problem.

Objetivo: os resultados da pesquisa em educação e treinamento são, em parte, uma função do contexto em que esse estudo ocorre, das perguntas que fazemos e do que é viável. Muitas perguntas são sobre a aprendizagem, que envolve medições repetidas em uma janela de tempo específica, e o contexto prático, geralmente, é tal, que oferecer uma intervenção a alguns, mas não a todos os alunos, não faz sentido ou é antiético. Para garantia de qualidade e outros propósitos, os centros de educação e treinamento podem ter perguntas orientadas localmente que procuram responder, como, por exemplo, se uma intervenção pode ser considerada eficaz em seu contexto de pequeno número de alunos. Embora a justificativa por trás do projeto e dos resultados deste tipo de estudos possa ser do interesse de uma comunidade muito mais ampla, por exemplo, para estudar a possibilidade de transferência de resultados para outros contextos, as pessoas são frequentemente desencorajadas a relatar os resultados de tais estudos em conferências ou em revistas de pesquisa educacional. O objetivo deste artigo é combater esse desânimo e, em vez disso, incentivar as pessoas a verem os pequenos números como uma oportunidade em vez de um problema. Método: realizado um exemplo de método paramétrico e não paramétrico para este tipo de situação, utilizando dados simulados no programa estatístico Open Source R versão 4.0.5 de custo zero. Resultados: ao contrário do método não paramétrico, o método paramétrico pode fornecer estimativas da eficácia da intervenção para o participante individual, levando em conta as tendências em diferentes fases de um estudo. No entanto, o método não paramétrico fornece uma solução em várias situações, onde o método paramétrico deve ser usado. Conclusão: dados os custos da pesquisa, as lições a serem aprendidas com a pesquisa e os métodos estatísticos disponíveis, pequenos números devem ser considerados uma oportunidade, não um problema.

Learning genetic and environmental graphical models from family data.

Many challenging problems in biomedical research rely on understanding how variables are associated with each other and influenced by genetic and environmental factors. Probabilistic graphical models (PGMs) are widely acknowledged as a very natural and formal language to describe relationships among variables and have been extensively used for studying complex diseases and traits. In this work, we propose methods that leverage observational Gaussian family data for learning a decomposition of undirected and directed acyclic PGMs according to the influence of genetic and environmental factors. Many structure learning algorithms are strongly based on a conditional independence test. For independent measurements of normally distributed variables, conditional independence can be tested through standard tests for zero partial correlation. In family data, the assumption of independent measurements does not hold since related individuals are correlated due to mainly genetic factors. Based on univariate polygenic linear mixed models, we propose tests that account for the familial dependence structure and allow us to assess the significance of the partial correlation due to genetic (between-family) factors and due to other factors, denoted here as environmental (within-family) factors, separately. Then, we extend standard structure learning algorithms, including the IC/PC and the really fast causal inference (RFCI) algorithms, to Gaussian family data. The algorithms learn the most likely PGM and its decomposition into two components, one explained by genetic factors and the other by environmental factors. The proposed methods are evaluated by simulation studies and applied to the Genetic Analysis Workshop 13 simulated dataset, which captures significant features of the Framingham Heart Study.

Procedures for identification of superior progenies in successive generations of evaluation in common bean

When breeding the common bean in Brazil, the best progenies are chosen, normally, from solely the generation under analysis at the conclusion of the evaluation, without considering what occurred in the past. However, a number of recently published studies show that if an evaluation were to consider all relevant generations, the gain from selection could be higher, especially when an index that involves information from the population that gave rise to the progenies is used. Thus, the aim of this study was to compare three selection procedures in the evaluation of successive generations and to discuss the implications of the progeny × environment interaction in terms of success of selection. Cycle XV progenies from a bean recurrent selection program were used. The traits evaluated were grain yield, plant architecture and grain type. Analysis of variance was carried out and the variance components and heritabilities were estimated. The same analyses were made using mixed models. A selection index weighted by the effect of populations and progenies within populations (WSI) was also obtained. We estimated the correlations between the classification of the progenies using the three procedures and the coincidence of the best progenies evaluated in S0:4 with the progenies in the previous generations. We found that the classification of the progenies by the BLUP's and WSI did not expressively differ from that obtained when using only the mean, even when a number of generations were considered in the selection. None of the procedures used effectively mitigated the effect of the progeny × environment interaction.

Procedures for identification of superior progenies in successive generations of evaluation in common bean

When breeding the common bean in Brazil, the best progenies are chosen, normally, from solely the generation under analysis at the conclusion of the evaluation, without considering what occurred in the past. However, a number of recently published studies show that if an evaluation were to consider all relevant generations, the gain from selection could be higher, especially when an index that involves information from the population that gave rise to the progenies is used. Thus, the aim of this study was to compare three selection procedures in the evaluation of successive generations and to discuss the implications of the progeny × environment interaction in terms of success of selection. Cycle XV progenies from a bean recurrent selection program were used. The traits evaluated were grain yield, plant architecture and grain type. Analysis of variance was carried out and the variance components and heritabilities were estimated. The same analyses were made using mixed models. A selection index weighted by the effect of populations and progenies within populations (WSI) was also obtained. We estimated the correlations between the classification of the progenies using the three procedures and the coincidence of the best progenies evaluated in S0:4 with the progenies in the previous generations. We found that the classification of the progenies by the BLUP's and WSI did not expressively differ from that obtained when using only the mean, even when a number of generations were considered in the selection. None of the procedures used effectively mitigated the effect of the progeny × environment interaction.(AU)

Impact evaluation of camera enforcement for traffic violations in Cali, Colombia, 2008-2014.

INTRODUCTION: Cameras for detecting traffic violations have been used as a measure to improve road safety in different countries around the world. In Cali, Colombia, fixed cameras were installed in March 2012 on a number of roads and intersections. All camera devices are capable of detecting simultaneously the following traffic violations: driving over the speed limit, running a red light or stop sign, violation of the traffic ban schedule, and blocking the pedestrian crosswalk. OBJECTIVE: To evaluate the impact of camera enforcement of traffic violations in Cali, Colombia. METHODS: A quasi-experimental difference-in-differences study with before and after measurements and a comparison group was conducted. We observed 38 intervention areas and 50 comparison areas (250 m radius), during 42 months before and 34 months after the installation of cameras. Effects were estimated with mixed negative binomial regression models. RESULTS: In intervention areas, after 12 months, there was a reduction of 19.2% of all crashes and a 24.7% reduction of injury and fatal crashes. In comparison areas, this reduction was 15.0% for all crashes and 20.1% for injury and fatal crashes. After adjusted comparisons, intervention sites outperformed comparison sites with an additional yearly reduction of 5.3% (p = 0.045) for all crashes. CONCLUSIONS: The use of cameras for detecting traffic violations seems to have a positive effect on the reduction of crashes in intervention areas. A beneficial spillover effect was found as well in comparison areas; but more evaluations are needed.