Exploring the quality of protein structural models from a Bayesian perspective.

We explore how ideas and practices common in Bayesian modeling can be applied to help assess the quality of 3D protein structural models. The basic premise of our approach is that the evaluation of a Bayesian statistical model's fit may reveal aspects of the quality of a structure when the fitted data is related to protein structural properties. Therefore, we fit a Bayesian hierarchical linear regression model to experimental and theoretical 13 Cα chemical shifts. Then, we propose two complementary approaches for the evaluation of such fitting: (a) in terms of the expected differences between experimental and posterior predicted values; (b) in terms of the leave-one-out cross-validation point-wise predictive accuracy. Finally, we present visualizations that can help interpret these evaluations. The analyses presented in this article are aimed to aid in detecting problematic residues in protein structures. The code developed for this work is available on: https://github.com/BIOS-IMASL/Hierarchical-Bayes-NMR-Validation.

Spatiotemporal distribution and socioeconomic disparities of dengue, chikungunya and Zika in two Latin American cities from 2007 to 2017.

OBJECTIVE: To assess the presence, pattern and magnitude of socioeconomic inequalities on dengue, chikungunya and Zika in Latin America, accounting for their spatiotemporal distribution. METHODS: Using longitudinal surveillance data (reported arboviruses) from Fortaleza, Brazil and Medellin, Colombia (2007-2017), we fit Bayesian hierarchical models with structured random effects to estimate: (i) spatiotemporally adjusted incidence rates; (ii) Relative Concentration Index and Absolute Concentration Index of inequality; (iii) temporal trends in RCIs; and (iv) socioeconomic-specific estimates of disease distribution. The spatial analysis was conducted at the neighbourhood level (urban settings). The socioeconomic measures were the median monthly household income (MMHI) for Brazil and the Socio-Economic Strata index (SES) in Colombia. RESULTS: There were 281 426 notified arboviral cases in Fortaleza and 40 887 in Medellin. We observed greater concentration of dengue among residents of low socioeconomic neighbourhoods in both cities: Relative Concentration Index = -0.12 (95% CI = -0.13, -0.10) in Fortaleza and Relative Concentration Index = -0.04 (95% CI = -0.05, -0.03) in Medellin. The magnitude of inequalities varied over time across sites and was larger during outbreaks. We identified a non-monotonic association between disease rates and socioeconomic measures, especially for chikungunya, that changed over time. The Relative Concentration Index and Absolute Concentration Index showed few if any inequalities for Zika. The socioeconomic-specific model showed increased disease rates at MMHI below US$400 in Brazil and at SES-index below level four, in Colombia. CONCLUSIONS: We provide robust quantitative estimates of socioeconomic inequalities in arboviruses for two Latin American cities. Our findings could inform policymaking by identifying spatial hotspots for arboviruses and targeting strategies to decrease disparities at the local level.

Force of infection of Helicobacter pylori in Mexico: evidence from a national survey using a hierarchical Bayesian model.

Helicobacter pylori (H. pylori) is present in the stomach of half of the world's population. The force of infection describes the rate at which susceptibles acquire infection. In this article, we estimated the age-specific force of infection of H. pylori in Mexico. Data came from a national H. pylori seroepidemiology survey collected in Mexico in 1987-88. We modelled the number of individuals with H. pylori at a given age as a binomial random variable. We assumed that the cumulative risk of infection by a given age follows a modified exponential catalytic model, allowing some fraction of the population to remain uninfected. The cumulative risk of infection was modelled for each state in Mexico and were shrunk towards the overall national cumulative risk curve using Bayesian hierarchical models. The proportion of the population that can be infected (i.e. susceptible population) is 85.9% (95% credible interval (CR) 84.3%-87.5%). The constant rate of infection per year of age among the susceptible population is 0.092 (95% CR 0.084-0.100). The estimated force of infection was highest at birth 0.079 (95% CR 0.071-0.087) decreasing to zero as age increases. This Bayesian hierarchical model allows stable estimation of state-specific force of infection by pooling information between the states, resulting in more realistic estimates.

Identifying ecological barriers in marine environment: The case study of Dasyatis marianae.

Ecological barriers are important determinants of the evolution and distributions marine organisms, and a challenge for evolutionary ecologists seeking to understand population structure in the sea. Dasyatis marianae is an endemic Brazilian species that indicates certain restrictions on its distribution probably due to marine barriers. In this study, Bayesian hierarchical spatial models, jointly with environmental and occurrence species data, are used to identify, which elements could generate these barriers on Dasyatis marianae distribution. Results show that salinity and temperature are the most important drivers that play an essential role to limit the distribution of this species. Indeed, low salinity values restrict Dasyatis marianae distribution in the north of the Brazilian coast, while in the south are colder temperatures. These results highlight the need to better define the distribution of marine species, especially for the ones affected by ecological barriers that are more sensitive to environmental changes.

Efecto de una capacitación sobre los puntajes de la prueba de admisión de la Universidad de Costa Rica: una aproximación bayesiana

Se usó un diseño cuasi-experimental con pre y post-test para estimar el efecto de una capacitación para la prueba de admisión de la Universidad Costa Rica, un test estandarizado que mide habilidades de razonamiento en contextos verbales y matemáticos. Cuatro colegios públicos del área metropolitana central del país participaron en el estudio, asignándose dos de ellos aleatoriamente al grupo de intervención y los otros dos al grupo de control, con 61 estudiantes en el primer grupo y 80 en el segundo. La intervención consistió de 5 sesiones de capacitación de 3 horas, utilizando como guía un manual desarrollado por una experta pedagoga, con enfoque constructivista. Las medidas antes y después fueron formas reducidas de la prueba de admisión 2014. La variable dependiente fue la diferencia entre ambas mediciones. El efecto de la capacitación fue de 3.5 puntos porcentuales y significativo, y se estimó utilizando un modelo bayesiano de regresión multinivel.

A quasi-experimental design with pre and post- test was used to estimate training effects for the University of Costa Rica's admission test, a standardized exam that measures reasoning abilities in mathematical and verbal contexts. Four secondary public schools from the metropolitan central area of the country participated in the study; two of them were randomly assigned to the intervention group and the other two to the control group, with 61 students in the first group and 80 in the second. The intervention consisted of five three-hour training sessions, using a written guide developed by a pedagogy expert with a constructivist approach. Before and after measures were reduced test forms of the real admission test from the year 2014. The dependent variable was the difference between the two measures. The effect of the training was estimated using a multilevel Bayesian regression model with a significant magnitude of 3.5 percentage points.

Geographical distribution of alcohol-attributable mortality in Chile: a Bayesian spatial analysis.

OBJECTIVE: To describe the distribution of alcohol-attributable mortality (AAM) at the local level (345 municipalities) in Chile, including fully and partially attributable causes in 2009. METHODS: AAM was estimated for the population 15years of age and older using per capita alcohol consumption combined with survey estimates. The effect of alcohol on each cause of death was extracted from the published scientific literature. We used Bayesian hierarchical models to smooth the Standardized Mortality Ratio for each municipality for six groups of causes related to alcohol consumption (total, neuro-psychiatric, cardiovascular, cancer, injuries and other causes). RESULTS: The percentage of municipalities with high risk for any group of causes in each region ranges from 0% to 87.0%. Municipalities with high risk were concentrated in south-central and southern Chile for all groups of causes related to alcohol. CONCLUSIONS: AAM risk shows marked geographic concentrations, mainly in south-central and southern regions of Chile. This combination of methods for small-area estimates of AAM is a powerful tool to identify high risk regions and associated factors, and may be used to inform local policies and programs.

Small area associations between social context and alcohol-attributable mortality in a middle income country.

BACKGROUND: Little is known about the association between alcohol-attributable mortality and small area socioeconomic variables when considering causes both wholly and partially attributable to alcohol. METHODS: An ecological study was conducted of the entire Chilean population aged 15 and older in 345 municipalities nationwide between 2004 and 2009. Deaths were attributed to alcohol consumption either wholly or partially, along with the estimated attributable fractions for each specified cause. Each municipality was characterized according to its average income and educational attainment. Estimates of the ecological associations were produced using a hierarchical Bayesian model, separating out deaths caused by alcohol and dividing them into seven groups of causes. RESULTS: Alcohol-attributable mortality risk showed an inverse association with income and education at the ecological level. A one-quintile increase in income was associated with an average decrease in risk of 10% (CI 95%: 10-20%) for cardiovascular deaths, 8% (6-10%) for intentional injuries and 7% (3-11%) for unintentional injuries. No associations were found between deaths due to cancers and other causes with income and education. CONCLUSIONS: Municipalities with lower income and education have higher risk of alcohol-attributable mortality in Chile.

Modelos jerárquicos espacio temporales para mapear riesgos relativos de dengue, en el Municipio Girardot, Estado Aragua, Venezuela / Spatio-temporal hierarchical models for mapping relative risks of dengue in the Municipality of Girardot, Aragua State, Venezuela

Los modelos Bayesianos jerárquicos espaciotemporales han sido usados en el mapeo de enfermedades, estudios de contaminación ambiental, contaminación industrial, entre muchos otros. Bajo esta metodología, los datos están asociados con un punto en una localidad E y con un instante de tiempo t. El objetivo de este trabajo es modelar el riesgo relativo de contraer dengue en el municipio Girardot del estado Aragua, Venezuela, durante el periodo epidemiológico del año 2009. Se proponen tres estructuras de modelos, un Binomial que toma en cuenta la variabilidad en el conteo de la ocurrencia de la enfermedad en las parroquias del municipio. Una segunda propuesta incluye un modelo Binomial como primer nivel de jerarquía, más un segundo nivel que introduce el efecto espacial, el efecto temporal y la interacción espacio-tiempo. Finalmente, un tercer modelo espacial que combina el modelo Poisson en el primer nivel de jerarquía para el número de casos, y en el segundo nivel de jerarquía se relaciona el riesgo relativo con las covariables a través de la función logaritmo más un efecto aleatorio. Los datos fueron recopilados por semanas y clasificados de acuerdo a las parroquias del municipio. Se utilizó el criterio de información de deviancia (DIC) para seleccionar el mejor modelo, resultando el modelo Poisson el más adecuado para representar el riesgo relativo de contraer dengue en la zona bajo estudio, confirmando que los patrones de alto riesgo se encuentran en las parroquias ubicadas al sur y suroeste del municipio Girardot, colindando algunas de ellas con el lago de Valencia.

Hierarchical Bayesian space-time models have been used in the mapping of disease, studies of environmental pollution and industrial pollution, among many others. Under this methodology, the data is associated with point in a locality E and an instant in time t. The aim of this work is to model the relative risk of dengue in Girardot Municipality, Aragua State, Venezuela, during the epidemic period 2009. In that sense, we propose three models. First, a binomial model that measures the variability in the count of occurrence of the disease in the parishes of the municipality. A second model includes the binomial model as a first hierarchical level, plus a second level which introduces the spatial effect, the temporal effect and spacetime interaction. Finally, a third spatial model that follows a Poisson model at the first level of hierarchy for the number of cases, and in the second level of hierarchy relates the relative risk associated with covariates through the logarithm function over a random effect. Data were collected for weeks and classified according to the parishes of the municipality. The Deviance Information Criterion (DIC) was used to select the best model. The Poisson model was best suited to represent the relative risk of contracting dengue in the area under study, showing that high-risk patterns were found in the parishes located in the south and southwest of the Girardot municipality, some of them bordering the lake of Valencia.