Charging of radioactive and environmental airborne particles.

The charging of various airborne particles was investigated using single-particle levitation and charge-balance equations. Though radioactive decay and triboelectrification can induce charging, it is typically assumed that the aerosols in a radioactive plume will not carry significant charge at steady state since atmospheric particles can have their charge neutralized through the capture of adjacent counter-ions (i.e., diffusion charging). To assess this assumption, we directly measured the surface charge and charge density of various triboelectrically charged aerosols including radioactive uranium oxide (<1 µm), urban dust, Arizona desert dust, hydrophilic and hydrophobic silica nanoparticles, and graphene oxide powders using an electric field-assisted particle levitator in air. Of these particles, uranium oxide aerosols exhibited the highest surface charge density. Charge balance equations were employed to predict the average charge gained from radioactive decay as a function of time and to evaluate the effects of diffusion charging on triboelectrically charged radioactive and non-radioactive particles in the atmosphere. Simulation results show that particles, initially charged through triboelectrification, can be quickly discharged by diffusion charging in the absence of radioactive decay. Nevertheless, simulation results also indicate that particles can be strongly charged when they carry radionuclides. These experimental and simulation results suggest that radioactive decay can induce strong particle charging that may potentially affect atmospheric transport of airborne radionuclides.

A Bayesian Hierarchical Spatial Model to Correct for Misreporting in Count Data: Application to State-Level COVID-19 Data in the United States.

The COVID-19 pandemic that began at the end of 2019 has caused hundreds of millions of infections and millions of deaths worldwide. COVID-19 posed a threat to human health and profoundly impacted the global economy and people's lifestyles. The United States is one of the countries severely affected by the disease. Evidence shows that the spread of COVID-19 was significantly underestimated in the early stages, which prevented governments from adopting effective interventions promptly to curb the spread of the disease. This paper adopts a Bayesian hierarchical model to study the under-reporting of COVID-19 at the state level in the United States as of the end of April 2020. The model examines the effects of different covariates on the under-reporting and accurate incidence rates and considers spatial dependency. In addition to under-reporting (false negatives), we also explore the impact of over-reporting (false positives). Adjusting for misclassification requires adding additional parameters that are not directly identified by the observed data. Informative priors are required. We discuss prior elicitation and include R functions that convert expert information into the appropriate prior distribution.

Fine-scale spatiotemporal air pollution analysis using mobile monitors on Google Street View vehicles.

People are increasingly concerned with understanding their personal environment, including possible exposure to harmful air pollutants. In order to make informed decisions on their day-to-day activities, they are interested in real-time information on a localized scale. Publicly available, fine-scale, high-quality air pollution measurements acquired using mobile monitors represent a paradigm shift in measurement technologies. A methodological framework utilizing these increasingly fine-scale measurements to provide real-time air pollution maps and short-term air quality forecasts on a fine-resolution spatial scale could prove to be instrumental in increasing public awareness and understanding. The Google Street View study provides a unique source of data with spatial and temporal complexities, with the potential to provide information about commuter exposure and hot spots within city streets with high traffic. We develop a computationally efficient spatiotemporal model for these data and use the model to make short-term forecasts and high-resolution maps of current air pollution levels. We also show via an experiment that mobile networks can provide more nuanced information than an equally-sized fixed-location network. This modeling framework has important real-world implications in understanding citizens' personal environments, as data production and real-time availability continue to be driven by the ongoing development and improvement of mobile measurement technologies.

Bayesian Correction for Misclassification in Multilevel Count Data Models.

Covariate misclassification is well known to yield biased estimates in single level regression models. The impact on hierarchical count models has been less studied. A fully Bayesian approach to modeling both the misclassified covariate and the hierarchical response is proposed. Models with a single diagnostic test and with multiple diagnostic tests are considered. Simulation studies show the ability of the proposed model to appropriately account for the misclassification by reducing bias and improving performance of interval estimators. A real data example further demonstrated the consequences of ignoring the misclassification. Ignoring misclassification yielded a model that indicated there was a significant, positive impact on the number of children of females who observed spousal abuse between their parents. When the misclassification was accounted for, the relationship switched to negative, but not significant. Ignoring misclassification in standard linear and generalized linear models is well known to lead to biased results. We provide an approach to extend misclassification modeling to the important area of hierarchical generalized linear models.

Exercise-induced changes in stress hormones and cell adhesion molecules in obese men.

PURPOSE: The current study examined the relationship between exercise-induced changes in stress hormones (epinephrine, norepinephrine, and cortisol) and vascular inflammatory markers (soluble intracellular adhesion molecule-1 [sICAM-1], soluble endothelial selectin [sE-selectin], and soluble vascular adhesion molecule-1 [sVCAM-1]) in obese men over a 24-hour period following exercise at lower and higher intensity. PATIENTS AND METHODS: Fifteen physically inactive, obese, college-aged men performed a single bout of cycling exercise at lower and higher intensities (lower intensity: 50% of maximal heart rate, and higher intensity: 80% of maximal heart rate) in random order. Overnight fasting blood samples were collected at baseline, immediately postexercise (IPE), 1-hour PE (1-h PE), and 24-hour PE. Changes in stress hormones and inflammatory markers were analyzed with a repeated-measures analysis of variance using Bonferroni multiple comparisons and a linear regression analysis (p<0.05). RESULTS: sICAM-1, sVCAM-1, epinephrine, and norepinephrine did not change over time, while sE-selectin was significantly lower at 1-h PE (10.25±1.07 ng/mL, p=0.04) than at baseline (12.22±1.39 ng/mL). Cortisol and sICAM-1 were negatively related at 1-h PE following lower-intensity exercise (r2=0.34, p=0.02), whereas cortisol and sVCAM-1 were positively related at IPE following higher-intensity exercise (r2=0.36, p=0.02). CONCLUSION: Regardless of intensity, an acute bout of aerobic exercise may lower sE-selectin in sedentary obese men. Responses of cortisol are dependent on exercise intensity, and cortisol may be a key stress hormone playing a major role in regulating sICAM-1 and sVCAM-1.

The H3K27me3-demethylase KDM6A is suppressed in breast cancer stem-like cells, and enables the resolution of bivalency during the mesenchymal-epithelial transition.

The deposition of the activating H3K4me3 and repressive H3K27me3 histone modifications within the same promoter, forming a so-called bivalent domain, maintains gene expression in a repressed but transcription-ready state. We recently reported a significantly increased incidence of bivalency following an epithelial-mesenchymal transition (EMT), a process associated with the initiation of the metastatic cascade. The reverse process, known as the mesenchymal-epithelial transition (MET), is necessary for efficient colonization. Here, we identify numerous genes associated with differentiation, proliferation and intercellular adhesion that are repressed through the acquisition of bivalency during EMT, and re-expressed following MET. The majority of EMT-associated bivalent domains arise through H3K27me3 deposition at H3K4me3-marked promoters. Accordingly, we show that the expression of the H3K27me3-demethylase KDM6A is reduced in cells that have undergone EMT, stem-like subpopulations of mammary cell lines and stem cell-enriched triple-negative breast cancers. Importantly, KDM6A levels are restored following MET, concomitant with CDH1/E-cadherin reactivation through H3K27me3 removal. Moreover, inhibition of KDM6A, using the H3K27me3-demethylase inhibitor GSK-J4, prevents the re-expression of bivalent genes during MET. Our findings implicate KDM6A in the resolution of bivalency accompanying MET, and suggest KDM6A inhibition as a viable strategy to suppress metastasis formation in breast cancer.

Temporal trends in lipid screening and therapy among youth from 2002 to 2012.

BACKGROUND: Pediatric lipid management recommendations have evolved from selective screening to universal screening to identify and target therapy for genetic dyslipidemias. Data on the success of the selective screening guidelines for lipid testing, dyslipidemia detection, and lipid management are conflicting. OBJECTIVE: To determine temporal trends in lipid testing, dyslipidemia categories and pharmacotherapy in a cohort of 653,642 individual youth aged 2 to 20 years from 2002 to 2012. METHODS: Summary data on lipid test results, lipid-lowering medicine (LLM) dispensings, and International Classification of Diseases, Ninth Revision diagnoses were compiled from the virtual data warehouses of 5 sites in the Cardiovascular Research Network. Temporal trends were determined using linear regression. RESULTS: Among the average 255,160 ± 25,506 children enrolled each year, lipid testing declined from 16% in 2002 to 11% in 2012 (P < .001 for trend). Among the entire population, the proportion newly detected each year with low-density lipoprotein cholesterol >190 mg/dL, a value commonly used to define familial hypercholesterolemia, increased over time from 0.03% to 0.06% (P = .03 for trend). There was no significant change over time in the proportion of the yearly population initiated on LLM or statins specifically (0.045 ± 0.009%, P = .59 [LLM] and 0.028 ± 0.006%, P = .25 [statin]). CONCLUSIONS: Although lipid testing declined during 2002 to 2012, the detection of familial hypercholesterolemia-level low-density lipoprotein cholesterol increased. Despite this increased detection, pharmacotherapy did not increase over time. These findings highlight the need to enhance lipid screening and management strategies in high-risk youth.

Enhanced classification for high-throughput data with an optimal projection and hybrid classifier.

High-throughput screening technologies recently developed allow scientists to conduct millions of biological and medical tests simultaneously and rapidly. A major bottleneck for the analysis is to reduce the inherent high dimensionality for subsequent analysis. Principal Component Analysis (PCA) is a popular tool for dimensionality reduction by selecting typically a few Principal Components (PCs) ranked by their variances, eigenvalues. Since this selection approach is not always effective in reducing dimensionality, we consider a different ranking criterion, the canonical variate criterion. To further enhance the classification performance, we propose an integrated classification framework to combine the criterion and two hybrid classification methods and compare with several popular classification methods using leave-one-out cross-validation. For illustration, three real high-throughput data sets are considered and analysed to illustrate the methods.

Assessing the detection capacity of microarrays as bio/nanosensing platforms.

Microarray is one of the most powerful detection systems with multiplexing and high throughput capability. It has significant potential as a versatile biosensing platform for environmental monitoring, pathogen detection, medical therapeutics, and drug screening to name a few. To date, however, microarray applications are still limited to preliminary screening of genome-scale transcription profiling or gene ontology analysis. Expanding the utility of microarrays as a detection tool for various biological and biomedical applications requires information about performance such as the limits of detection and quantification, which are considered as an essential information to decide the detection sensitivity of sensing devices. Here we present a calibration design that integrates detection limit theory and linear dynamic range to obtain a performance index of microarray detection platform using oligonucleotide arrays as a model system. Two different types of limits of detection and quantification are proposed by the prediction or tolerance interval for two common cyanine fluorescence dyes, Cy3 and Cy5. Besides oligonucleotide, the proposed method can be generalized to other microarray formats with various biomolecules such as complementary DNA, protein, peptide, carbohydrate, tissue, or other small biomolecules. Also, it can be easily applied to other fluorescence dyes for further dye chemistry improvement.

Understanding mechanisms of vitiligo development in Smyth line of chickens by transcriptomic microarray analysis of evolving autoimmune lesions.

BACKGROUND: The Smyth line (SL) of chicken is an excellent avian model for human autoimmune vitiligo. The etiology of vitiligo is complicated and far from clear. In order to better understand critical components leading to vitiligo development, cDNA microarray technology was used to compare gene expression profiles in the target tissue (the growing feather) of SL chickens at different vitiligo (SLV) states. RESULTS: Compared to the reference sample, which was from Brown line chickens (the parental control), 395, 522, 524 and 526 out of the 44 k genes were differentially expressed (DE) (P ≤ 0.05) in feather samples collected from SL chickens that never developed SLV (NV), from SLV chickens prior to SLV onset (EV), during active loss of pigmentation (AV), and after complete loss of melanocytes (CV). Comparisons of gene expression levels within SL samples (NV, EV, AV and CV) revealed 206 DE genes, which could be categorized into immune system-, melanocyte-, stress-, and apoptosis-related genes based on the biological functions of their corresponding proteins. The autoimmune nature of SLV was supported by predominant presence of immune system related DE genes and their remarkably elevated expression in AV samples compared to NV, EV and/or CV samples. Melanocyte loss was confirmed by decreased expression of genes for melanocyte related proteins in AV and CV samples compared to NV and EV samples. In addition, SLV development was also accompanied by altered expression of genes associated with disturbed redox status and apoptosis. Ingenuity Pathway Analysis of DE genes provided functional interpretations involving but not limited to innate and adaptive immune response, oxidative stress and cell death. CONCLUSIONS: The microarray results provided comprehensive information at the transcriptome level supporting the multifactorial etiology of vitiligo, where together with apparent inflammatory/innate immune activity and oxidative stress, the adaptive immune response plays a predominant role in melanocyte loss.

Transcriptional profiling of host gene expression in chicken embryo lung cells infected with laryngotracheitis virus.

BACKGROUND: Infection by infectious laryngotracheitis virus (ILTV; gallid herpesvirus 1) causes acute respiratory diseases in chickens often with high mortality. To better understand host-ILTV interactions at the host transcriptional level, a microarray analysis was performed using 4 x 44 K Agilent chicken custom oligo microarrays. RESULTS: Microarrays were hybridized using the two color hybridization method with total RNA extracted from ILTV infected chicken embryo lung cells at 0, 1, 3, 5, and 7 days post infection (dpi). Results showed that 789 genes were differentially expressed in response to ILTV infection that include genes involved in the immune system (cytokines, chemokines, MHC, and NF-kappaB), cell cycle regulation (cyclin B2, CDK1, and CKI3), matrix metalloproteinases (MMPs) and cellular metabolism. Differential expression for 20 out of 789 genes were confirmed by quantitative reverse transcription-PCR (qRT-PCR). A bioinformatics tool (Ingenuity Pathway Analysis) used to analyze biological functions and pathways on the group of 789 differentially expressed genes revealed that 21 possible gene networks with intermolecular connections among 275 functionally identified genes. These 275 genes were classified into a number of functional groups that included cancer, genetic disorder, cellular growth and proliferation, and cell death. CONCLUSION: The results of this study provide comprehensive knowledge on global gene expression, and biological functionalities of differentially expressed genes in chicken embryo lung cells in response to ILTV infections.

Allelic based gene-gene interactions in rheumatoid arthritis.

The detection of gene-gene interaction is an important approach to understand the etiology of rheumatoid arthritis (RA). The goal of this study is to identify gene-gene interaction of SNPs at the allelic level contributing to RA using real data sets (Problem 1) of North American Rheumatoid Arthritis Consortium (NARAC) provided by Genetic Analysis Workshop 16 (GAW16). We applied our novel method that can detect the interaction by a definition of nonrandom association of alleles that occurs when the contribution to RA of a particular allele inherited in one gene depends on a particular allele inherited at other unlinked genes. Starting with 639 single-nucleotide polymorphisms (SNPs) from 26 candidate genes, we identified ten two-way interacting genes and one case of three-way interacting genes. SNP rs2476601 on PTPN22 interacts with rs2306772 on SLC22A4, which interacts with rs881372 on TRAF1 and rs2900180 on C5, respectively. SNP rs2900180 on C5 interacts with rs2242720 on RUNX1, which interacts with rs881375 on TRAF1. Furthermore, rs2476601 on PTPN22 also interacts with three SNPs (rs2905325, rs1476482, and rs2106549) in linkage disequilibrium (LD) on IL6. The other three SNPs (rs2961280, rs2961283, and rs2905308) in LD on IL6 interact with two SNPs (rs477515 and rs2516049) on HLA-DRB1. SNPs rs660895 and rs532098 on HLA-DRB1 interact with rs2834779 and four SNPs in LD on RUNX1. Three-way interacting genes of rs10229203 on IL6, rs4816502 on RUNX1, and rs10818500 on C5 were also detected.

Classification for high-throughput data with an optimal subset of principal components.

High-throughput data have been widely used in biological and medical studies to discover gene and protein functions. Due to the high dimensionality, principal component analysis (PCA) is often involved for data dimension reduction. However, when a few principal components (PCs) are selected for dimension reduction or considered for dimension determination, they are typically ranked by their variances, eigenvalues. However, this approach is not always effective in subsequent multivariate analysis, particularly classification. To maximize information from data with a subset of the components, we apply a different ranking criterion, canonical variate criterion, which considers within- and between-group variance rather than total variance in the classical criterion. Four prevalent classification methods are considered and compared using leave-one-out cross-validation. These methods are illustrated with three real high-throughput data sets, two microarray data sets and a nuclear magnetic resonance spectra data set.

Optimal classification for time-course gene expression data using functional data analysis.

Classification problems have received considerable attention in biological and medical applications. In particular, classification methods combining to microarray technology play an important role in diagnosing and predicting disease, such as cancer, in medical research. Primary objective in classification is to build an optimal classifier based on the training sample in order to predict unknown class in the test sample. In this paper, we propose a unified approach for optimal gene classification with conjunction with functional principal component analysis (FPCA) in functional data analysis (FNDA) framework to classify time-course gene expression profiles based on information from the patterns. To derive an optimal classifier in FNDA, we also propose to find optimal number of bases in the smoothing step and functional principal components in FPCA using a cross-validation technique, and compare the performance of some popular classification techniques in the proposed setting. We illustrate the propose method with a simulation study and a real world data analysis.

A novel wavelet-based thresholding method for the pre-processing of mass spectrometry data that accounts for heterogeneous noise.

In recent years there has been an increased interest in using protein mass spectroscopy to discriminate diseased from healthy individuals with the aim of discovering molecular markers for disease. A crucial step before any statistical analysis is the pre-processing of the mass spectrometry data. Statistical results are typically strongly affected by the specific pre-processing techniques used. One important pre-processing step is the removal of chemical and instrumental noise from the mass spectra. Wavelet denoising techniques are a standard method for denoising. Existing techniques, however, do not accommodate errors that vary across the mass spectrum, but instead assume a homogeneous error structure. In this paper we propose a novel wavelet denoising approach that deals with heterogeneous errors by incorporating a variance change point detection method in the thresholding procedure. We study our method on real and simulated mass spectrometry data and show that it improves on performances of peak detection methods.

Clustering of time-course gene expression data using functional data analysis.

Clustering of gene expression data collected across time is receiving growing attention in the biological literature since time-course experiments allow one to understand dynamic biological processes and identify genes governed by the same processes. It is believed that genes demonstrating similar expression profiles over time might give an informative insight into how underlying biological mechanisms work. In this paper, we propose a method based on functional data analysis (FNDA) to cluster time-dependent gene expression profiles. Consideration of clustering problems using the FNDA setting provides ways to take time dependency into account by using basis function expansion to describe the partially observed curves. We also discuss how to choose the number of bases in the basis function expansion in FNDA. A synthetic cycle data and a real data are used to demonstrate the proposed method and some comparisons between the proposed and existing approaches using the adjusted Rand indices are made.

Bayesian ranking of sites for engineering safety improvements: decision parameter, treatability concept, statistical criterion, and spatial dependence.

In recent years, there has been a renewed interest in applying statistical ranking criteria to identify sites on a road network, which potentially present high traffic crash risks or are over-represented in certain type of crashes, for further engineering evaluation and safety improvement. This requires that good estimates of ranks of crash risks be obtained at individual intersections or road segments, or some analysis zones. The nature of this site ranking problem in roadway safety is related to two well-established statistical problems known as the small area (or domain) estimation problem and the disease mapping problem. The former arises in the context of providing estimates using sample survey data for a small geographical area or a small socio-demographic group in a large area, while the latter stems from estimating rare disease incidences for typically small geographical areas. The statistical problem is such that direct estimates of certain parameters associated with a site (or a group of sites) with adequate precision cannot be produced, due to a small available sample size, the rareness of the event of interest, and/or a small exposed population or sub-population in question. Model based approaches have offered several advantages to these estimation problems, including increased precision by "borrowing strengths" across the various sites based on available auxiliary variables, including their relative locations in space. Within the model based approach, generalized linear mixed models (GLMM) have played key roles in addressing these problems for many years. The objective of the study, on which this paper is based, was to explore some of the issues raised in recent roadway safety studies regarding ranking methodologies in light of the recent statistical development in space-time GLMM. First, general ranking approaches are reviewed, which include naïve or raw crash-risk ranking, scan based ranking, and model based ranking. Through simulations, the limitation of using the naïve approach in ranking is illustrated. Second, following the model based approach, the choice of decision parameters and consideration of treatability are discussed. Third, several statistical ranking criteria that have been used in biomedical, health, and other scientific studies are presented from a Bayesian perspective. Their applications in roadway safety are then demonstrated using two data sets: one for individual urban intersections and one for rural two-lane roads at the county level. As part of the demonstration, it is shown how multivariate spatial GLMM can be used to model traffic crashes of several injury severity types simultaneously and how the model can be used within a Bayesian framework to rank sites by crash cost per vehicle-mile traveled (instead of by crash frequency rate). Finally, the significant impact of spatial effects on the overall model goodness-of-fit and site ranking performances are discussed for the two data sets examined. The paper is concluded with a discussion on possible directions in which the study can be extended.