Prenatal exposure to fine particulate matter PM2.5 and small for gestational age: a Bayesian model for area-based data in Milan

If geostatistical observations are continuous but can not be modeled by the Gaussian distribution, a more appropriate model for these data may be the transformed Gaussian model. In transformed Gaussian models it is assumed that the random field of interest is a nonlinear transformation of a Gaussian random field (GRF). For example, [9] propose the Bayesian transformed Gaussian model where they use the Box-Cox family of power transformation [3] on the observations and show that prediction for unobserved random fieldsin Bayesianposterior predictive distribution where uncertainty about the transformation parameter is taken into account. More recently, [5] consider maximum likelihood estimation of the parameters and a “plug-in” method of prediction for transformed Gaussian model with Box-Cox family of transformations. Both [9] and [5] consider spatial prediction of rainfall to illustrate their model and method of analysis. A review of the Bayesian transformed Gaussian random fields model is given in [8]. See also [6] who discusses several issues regarding the formulation and interpretation of transformed Gaussian random field models, including the approximate nature of the model for positive data based on Box-Cox family of transformations, and the interpretation of the model parameters.

Objective: Air particulate matter concentrations in Changchun City, Jilin Province, may change around the autumn heating day. The aim of this study was to provide data references for environmental protection, detection and regulation in Changchun. Methods: Atmospheric particulate matter samples were collected using an airborne particulate matter sampler on the roof top of the Civil Engineering Teaching Hall on the campus of Jilin University of Construction; free settling dust of Atmospheric particulate matter was collected using metal trays. Atmospheric particulate matter concentrations were analysed by manual detection methods (weight method), carbonaceous fractions by total organic carbon analyser, and atmospheric fallout material composition and crystal structure by XRD diffractometer. The physicochemical properties of fine particulate matter around the autumn heating day in Changchun were investigated. Conclusions: (1) The daily average concentrations of various types of atmospheric particulate matter PM1, PM2.5 and PM10 generally increased after the start of the heating period. However, air quality is influenced by a combination of meteorological factors, of which emissions of air pollutants from urban heat generating plants during the heating period is only one aspect. So there is a situation where the average daily concentration of atmospheric particulate matter is lower after heating than before. (2) Analysis of the atmospheric its particulate matter PM2.5 samples collected around the heating day showed that the daily average concentrations of organic carbon (OC) and elemental carbon (EC) of atmospheric its particulate matter PM2.5 increased significantly after the heating day. (3) There was no significant difference in the main components of atmospheric dust fall before and after the heating day in Changchun, with the main components being crystalline SiO2 and a small Number of impurities.

The objective of this current research is to model the experimental data on the effectiveness of an incentive-based weight reduction method by using Bayesian hierarchical growth models. Three Bayesian hierarchical growth models are proposed, namely parametric Bayesian hierarchical growth model with correlated intercept and slope random effects model, parametric Bayesian hierarchical growth model with no correlated intercept and slope random effects model and semi-parametric Bayesian hierarchical growth model with Dirichlet process mixture prior model. The data is obtained from forty eight (48) students who had participated in an experiment on weight reduction method. The students were divided equally into two groups: single and pair groups. The experiment was carried out over the period of three months with a weight reading session for every two weeks. At the end of the study, we had six repeated measures of each student’s weight in kg and some measures of covariates and factors. Our results showed that the best model for the above data based on the Bayesian fit indexes and the models’ flexibility is the semi-parametric Bayesian hierarchical growth model with Dirichlet process mixture prior model. The results of the semi-parametric model showed that the ‘growth’ or reduction rates of the weight reduction experiment relate to the students’ gender, height in cm, experimental group (single or pair) and time in term of weeks.

Prenatal and early postnatal exposure to ambient particulate matter and early childhood neurodevelopment: A birth cohort study

Recent advances in big data and analytics research have provided a wealth of large data sets that are too big to be analyzed in their entirety, due to restrictions on computer memory or storage size. New Bayesian methods have been developed for data sets that are large only due to large sample sizes. These methods partition big data sets into subsets and perform independent Bayesian Markov chain Monte Carlo analyses on the subsets. The methods then combine the independent subset posterior samples to estimate a posterior density given the full data set. These approaches were shown to be effective for Bayesian models including logistic regression models, Gaussian mixture models and hierarchical models. Here, we introduce the R package parallelMCMCcombine which carries out four of these techniques for combining independent subset posterior samples. We illustrate each of the methods using a Bayesian logistic regression model for simulation data and a Bayesian Gamma model for real data; we also demonstrate features and capabilities of the R package. The package assumes the user has carried out the Bayesian analysis and has produced the independent subposterior samples outside of the package. The methods are primarily suited to models with unknown parameters of fixed dimension that exist in continuous parameter spaces. We envision this tool will allow researchers to explore the various methods for their specific applications and will assist future progress in this rapidly developing field.

BackgroundThe ChIP-chip technology has been used in a wide range of biomedical studies, such as identification of human transcription factor binding sites, investigation of DNA methylation, and investigation of histone modifications in animals and plants. Various methods have been proposed in the literature for analyzing the ChIP-chip data, such as the sliding window methods, the hidden Markov model-based methods, and Bayesian methods. Although, due to the integrated consideration of uncertainty of the models and model parameters, Bayesian methods can potentially work better than the other two classes of methods, the existing Bayesian methods do not perform satisfactorily. They usually require multiple replicates or some extra experimental information to parametrize the model, and long CPU time due to involving of MCMC simulations.ResultsIn this paper, we propose a Bayesian latent model for the ChIP-chip data. The new model mainly differs from the existing Bayesian models, such as the joint deconvolution model, the hierarchical gamma mixture model, and the Bayesian hierarchical model, in two respects. Firstly, it works on the difference between the averaged treatment and control samples. This enables the use of a simple model for the data, which avoids the probe-specific effect and the sample (control/treatment) effect. As a consequence, this enables an efficient MCMC simulation of the posterior distribution of the model, and also makes the model more robust to the outliers. Secondly, it models the neighboring dependence of probes by introducing a latent indicator vector. A truncated Poisson prior distribution is assumed for the latent indicator variable, with the rationale being justified at length.ConclusionThe Bayesian latent method is successfully applied to real and ten simulated datasets, with comparisons with some of the existing Bayesian methods, hidden Markov model methods, and sliding window methods. The numerical results indicate that the Bayesian latent method can outperform other methods, especially when the data contain outliers.

Objective: To assess the association between airborne particulate matter (PM) exposure and the development of asthma in children, a systematic review and meta-analysis that included nearly 10 years of related literature was conducted. Study Design: The study investigators conducted a systematic review of relevant research articles published between March 2013 and March 2023, which were accessible through several medical literature data bases of. Random-effects meta-analyses were used to analyze the effects of PM on childhood asthma. Subgroup analyses, including exposure period, type of PM, regional factors, and study type, were also used. Odds ratio (OR) and 95% confidence intervals (CI) were used to represent the estimated effect of the population. Publication bias was assessed by using the Egger test and funnel plot. Data analyses were performed using statistical analysis software and a systematic review management tool. Results: A total of 15,365 articles were identified, of which 19 studies were included in this meta-analysis. The results showed that PM exposure was positively correlated with asthma in children, with the overall random-effects risk estimates of OR 1.10 (95% CI, 1.07-1.13). In stratified analyses, PM exposure was found to be a risk factor for the development of childhood asthma. Both prenatal and postnatal PM exposure were associated with an increased risk of asthma in children, but prenatal exposure was associated with a greater increase in risk than postnatal exposure, with an effect estimate OR of 1.21 (95% CI, 1.02-1.43). In the analysis of different PM types, the OR of PM2.5 (PM < 2.5 μm in diameter) exposure was OR 1.10 (95% CI, 1.05-1.15), and no association was found between PM10 (PM < 10 μm in diameter), coarse PM (PM with an aerodynamic diameter between 2.5 and 10 μm), and black carbon BC (diameter of 0.01-0.05 μm) exposure. In different regional analyses, the effects of PM exposure on childhood asthma risk were OR 1.15 (95% CI, 1.13-1.17) in South America and OR 1.02 (95% CI, 1.01-1.03) in Asia, but no association was found in Europe and North America. In addition, the results of different study types only found that the literature that used the time-series research method had a significant association with OR 1.03 (95% CI, 1.02-1.04), whereas the literature that used the cohort study method had no statistical difference. Conclusion: Exposure to airborne PM increased the risk of asthma in children. Both prenatal and postnatal PM exposure was associated with an increased risk of childhood asthma, but prenatal PM exposure was associated with a greater increase than postnatal PM exposure.

Fetal growth can be affected by gestational exposure to air pollution. The aim of the study was to determine the association between prematurity and low birth weight (LBW) with gestational exposure to PM2.5 and PM10 particulate matter in Chileans newborns. This cross-sectional analytical study included 595,369 newborns. Data were extracted from the live newborn records of the Chilean Ministry of Health. Sex, gestational age, birth weight, and living variables were analyzed. We used the Air Quality Information System of the Chilean Ministry of the Environment to obtain mean PM2.5 and PM10 emissions. A multivariate logistic regression model was performed with STATA 15.0 software at α < 0.05. Prevalence was 7.4% prematurity and 5.5% LBW. Mean PM2.5 and PM10 concentrations were 25.5 µg/m3 and 55.3 µg/m3, respectively. PM2.5 was associated with an increased the risk of LBW (OR: 1.031; 95%CI: 1.004–1.059) when exposure occurred in the second trimester, while PM10 affected the whole pregnancy. In addition, PM10 exposure in any gestational trimester was associated with an increased the risk of prematurity. The PM10 particulate matter was associated with both prematurity and LBW in all of the trimesters of exposure. The PM2.5 particulate matter was only associated with LBW when exposure occurred in the second gestational trimester.

Even as tough new government standards loom for two air pollutants—ozone and particulate matter—a new study is suggesting that the chemical coupling of these substances may complicate efforts to reduce their levels in the atmosphere. Chemical engineering professor John H . Seinfeld and colleagues at California Institute of Technology and the University of California, Irvine, report that a model of data from a severe smog episode in Southern California shows that reduction of the common chemical precursors shared by both pollutants did not produce a corresponding reduction in atmospheric particulates [ Science , 277, 116 (1997)]. In addition, the scientists found that atmospheric ammonia, ubiquitous in rural areas, including the East Los Angeles basin, was essential for particulate formation. The study comes at a particularly crucial moment: In response to increasing awareness of the detrimental public health effects of ozone and fine particles in the atmosphere, the Environmental Protection Agency has app...

The authors undertook research on the proper strategy of operation of educational building with gravitational ventilation in historic city center with high concentration of particulate matter PM10 and PM 2.5. In this facility the momentary increase in carbon dioxide concentration is often very high, and at the same time health requirements regarding atmospheric aerosol should be absolutely met. That is why long-term measurements of PM concentration outside and inside, as well as carbon dioxide concentration inside were carried out. CO2 was used also as a tracer gas for measurement of air change intensity. The article presents the first results of these tests and a correlation that occurs between the external and internal concentration of particulate matter PM 10 and PM 2.5. Due to a significant filtration effect of the external building envelope and particle deposition a potential conflict between required gravitational ventilation intensity and internal air pollution with particulate matters was partially reduced.

New Bayesian cohort models designed to resolve the identification problem in cohort analysis are proposed in this paper. At first, the basic cohort model which represents the statistical structure of time-series social survey data in terms of age, period and cohort effects is explained. The logit cohort model for qualitative data from a binomial distribution and the normal-type cohort model for quantitative data from a normal distribution are considered as two special cases of the basic model. In order to overcome the identification problem in cohort analysis, a Bayesian approach is adopted, based on the assumption that the effect parameters change gradually. A Bayesian information criterion ABIC is introduced for the selection of the optimal model. This approach is so flexible that both the logit and the normal-type cohort models can be made applicable, not only to standard cohort tables but also to general cohort tables in which the range of age group is not equal to the interval between periods. The practical utility of the proposed models is demonstrated by analysing two data sets from the literature on cohort analysis.

In discrete choice experiments, patients are presented with sets of health states described by various attributes and asked to make choices from among them. Discrete choice experiments allow health care researchers to study the preferences of individual patients by eliciting trade-offs between different aspects of health-related quality of life. However, many discrete choice experiments yield data with incomplete ranking information and sparsity due to the limited number of choice sets presented to each patient, making it challenging to estimate patient preferences. Moreover, methods to identify outliers in discrete choice data are lacking. We develop a Bayesian hierarchical random effects rank-ordered multinomial logit model for discrete choice data. Missing ranks are accounted for by marginalizing over all possible permutations of unranked alternatives to estimate individual patient preferences, which are modeled as a function of patient covariates. We provide a Bayesian version of relative attribute importance, and adapt the use of the conditional predictive ordinate to identify outlying choice sets and outlying individuals with unusual preferences compared to the population. The model is applied to data from a study using a discrete choice experiment to estimate individual patient preferences for health states related to prostate cancer treatment.

Brain decoding studies have demonstrated that viewed image categories can be estimated from human functional magnetic resonance imaging (fMRI) activity. However, there are still limitations with the estimation performance because of the characteristics of fMRI data and the employment of only one modality extracted from viewed images. In this paper, we propose a multi-view Bayesian generative model for multi-subject fMRI data to estimate viewed image categories from fMRI activity. The proposed method derives effective representations of fMRI activity by utilizing multi-subject fMRI data. In addition, we associate fMRI activity with multiple modalities, i.e., visual features and semantic features extracted from viewed images. Experimental results show that the proposed method outperforms existing state-of-the-art methods of brain decoding.

Functional magnetic resonance imaging (fMRI) is a new technique for studying the workings of the active human brain. During an fMRI experiment, a sequence of magnetic resonance images is acquired while the subject performs specific behavioral tasks. Changes in the measured signal can be used to identify and characterize the brain activity resulting from task performance. The data obtained from an fMRI experiment are a realization of a complex spatiotemporal process with many sources of variation, both biological and technological. This article describes a nonlinear Bayesian hierarchical model for fMRI data and presents inferential methods that enable investigators to directly target their scientific questions of interest, many of which are inaccessible to current methods. The article describes optimization and posterior sampling techniques to fit the model, both of which must be applied many thousands of times for a single dataset. The model is used to analyze data from a psychological experiment and to test a specific prediction of a cognitive theory.

In recent years there has been great interest in making inference for gene expression data collected over time. In this article, we describe a Bayesian hierarchical mixture model for partitioning such data. While conventional approaches cluster the observed data, we assume a nonparametric, random walk model, and partition on the basis of the parameters of this model. The model is flexible and can be tuned to the specific context, respects the order of observations within each curve, acknowledges measurement error, and allows prior knowledge on parameters to be incorporated. The number of partitions may also be treated as unknown, and inferred from the data, in which case computation is carried out via a birth-death Markov chain Monte Carlo algorithm. We first examine the behavior of the model on simulated data, along with a comparison with more conventional approaches, and then analyze meiotic expression data collected over time on fission yeast genes.