Spatial analysis for interval-valued data.

Symbolic data analysis deals with complex data with symbolic objects, such as lists, histograms, and intervals. Spatial analysis for symbolic data is relatively underexplored. To fill the gap, this paper proposes a statistical framework for spatial interval-valued data (SIVD) analysis.

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.

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.

Inpatient Mortality and 30-Day Readmission Rates Associated with Troponin Testing in Patients without Acute Myocardial Infarction.

Objective: Troponin values above the threshold established to diagnose acute myocardial infarction (AMI; >99th percentile) are commonly detected in patients with diagnoses other than AMI. The objective of this study was to compare inpatient mortality and 30-day readmission rate in patients with troponin I (TnI) above and below the 99th percentile in those with type 1 AMI and type 2 myocardial injury.

Methods: Between January 1, 2016 and December 31, 2016, there were 56,895 inpatient hospitalizations; of these 14,326 (25.

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.

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.

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.

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.

Resistance Training-Induced Elevations in Muscular Strength in Trained Men Are Maintained After 2 Weeks of Detraining and Not Differentially Affected by Whey Protein Supplementation.

Hwang, PS, Andre, TL, McKinley-Barnard, SK, Morales Marroquín, FE, Gann, JJ, Song, JJ, and Willoughby, DS. Resistance training-induced elevations in muscular strength in trained men are maintained after 2 weeks of detraining and not differentially affected by whey protein supplementation. J Strength Cond Res 31(4): 869-881, 2017-Resistance training (RT) with nutritional strategies incorporating whey protein intake postexercise can stimulate muscle protein synthesis and elicit hypertrophy.

Eight Weeks of Phosphatidic Acid Supplementation in Conjunction with Resistance Training Does Not Differentially Affect Body Composition and Muscle Strength in Resistance-Trained Men.

This study attempted to determine the effects of eight weeks of resistance training (RT) combined with phosphatidic acid (PA) supplementation at a dose of either 250 mg or 375 mg on body composition and muscle size and strength. Twenty-eight resistance-trained men were randomly assigned to ingest 375 mg [PA375 (n = 9)] or 250 mg [PA250 (n = 9)] of PA or 375 mg of placebo [PLC (n = 10)] daily for eight weeks with RT. Supplements were ingested 60 minutes prior to RT and in the morning on non-RT days.

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.

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.

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.

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.

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).

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.

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.

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.

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.

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.

Bayesian meta-analysis models for microarray data: a comparative study.

Background: With the growing abundance of microarray data, statistical methods are increasingly needed to integrate results across studies. Two common approaches for meta-analysis of microarrays include either combining gene expression measures across studies or combining summaries such as p-values, probabilities or ranks. Here, we compare two Bayesian meta-analysis models that are analogous to these methods.

Bayesian models for pooling microarray studies with multiple sources of replications.

Background: Biologists often conduct multiple but different cDNA microarray studies that all target the same biological system or pathway. Within each study, replicate slides within repeated identical experiments are often produced. Pooling information across studies can help more accurately identify true target genes.

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.