Search

Post-traumatic stress disorder (PTSD) is a common and debilitating psychiatric disorder that may occur in individuals exposed to traumatic events such as accidents, interpersonal violence, war, combat, or natural disasters. Additionally, PTSD has been implicated in the development of a variety of chronic conditions including cardiovascular and metabolic diseases, suggesting that the biological alterations of the disorder can manifest themselves as chronic diseases in those suffering from PTSD. The biological underpinnings of the disorder are not well understood. Gene expression studies can illuminate the complex physiology of PTSD reflecting the embodiment of trauma, i.e. the process in which traumatic experiences in our social environments are manifested in our body by genomic mechanisms. To date, gene expression studies that examine the whole transcriptome are scarce and limited to single-timepoint assessments. Here we applied a transcriptome-wide gene expression screen with RNA-sequencing to whole blood samples to elucidate the gene expression signatures associated with the development of PTSD. The study participants (N=72, of whom 21 eventually developed PTSD) are a trauma exposed subsample of participants enrolled in a longitudinal and prospective cohort study of adults living in Detroit, Michigan. PTSD was assessed in a structured telephone interview and whole blood samples were taken both before and after trauma exposure. We found 45 differentially expressed genes associated with PTSD development with an estimated log2fold change > 1.5 at a nominal p value (pPAX6,TSPAN7,PXDN,VWC2,SULF1andNFATC4were also ubiquitously expressed in all brain regions examined. Subsequent gene set enrichment analysis identified several pathways relating to brain and immune functioning to be enriched in individuals developing PTSD. Longitudinal sampling provides a promising mean to elucidate the pathophysiology underlying the embodiment of trauma.

The mRNA-seq data analysis is a powerful technology for inferring information from biological systems of interest. Specifically, the sequenced RNA fragments are aligned with genomic reference sequences, and we count the number of sequence fragments corresponding to each gene for each condition. A gene is identified as differentially expressed (DE) if the difference in its count numbers between conditions is statistically significant. Several statistical analysis methods have been developed to detect DE genes based on RNA-seq data. However, the existing methods could suffer decreasing power to identify DE genes arising from overdispersion and limited sample size. We propose a new differential expression analysis procedure: heterogeneous overdispersion genes testing (DEHOGT) based on heterogeneous overdispersion modeling and a post-hoc inference procedure. DEHOGT integrates sample information from all conditions and provides a more flexible and adaptive overdispersion modeling for the RNA-seq read count. DEHOGT adopts a gene-wise estimation scheme to enhance the detection power of differentially expressed genes. DEHOGT is tested on the synthetic RNA-seq read count data and outperforms two popular existing methods, DESeq and EdgeR, in detecting DE genes. We apply the proposed method to a test dataset using RNAseq data from microglial cells. DEHOGT tends to detect more differently expressed genes potentially related to microglial cells under different stress hormones treatments.

Crowdsourcing has emerged as an alternative solution for collecting large scale labels. However, the majority of recruited workers are not domain experts, so their contributed labels could be noisy. In this article, we propose a two-stage model to predict the true labels for multicategory classification tasks in crowdsourcing. In the first stage, we fit the observed labels with a latent factor model and incorporate subgroup structures for both tasks and workers through a multi-centroid grouping penalty. Group-specific rotations are introduced to align workers with different task categories to solve multicategory crowdsourcing tasks. In the second stage, we propose a concordance-based approach to identify high-quality worker subgroups who are relied upon to assign labels to tasks. In theory, we show the estimation consistency of the latent factors and the prediction consistency of the proposed method. The simulation studies show that the proposed method outperforms the existing competitive methods, assuming the subgroup structures within tasks and workers. We also demonstrate the application of the proposed method to real world problems and show its superiority. Supplementary materials for this article are available online.

Multi-dimensional tensor data has gained increasing attention in recent years, especially in biomedical imaging analyses. However, most existing tensor models are only based on the mean information...

This article provides an overview of tensors, their properties, and their applications in statistics. Tensors, also known as multidimensional arrays, are generalizations of matrices to higher orders and are useful data representation architectures. We first review basic tensor concepts and decompositions, and then we elaborate traditional and recent applications of tensors in the fields of recommender systems and imaging analysis. We also illustrate tensors for network data and explore the relations among interacting units in a complex network system. Some canonical tensor computational algorithms and available software libraries are provided for various tensor decompositions. Future research directions, including tensors in deep learning, are also discussed.

Emergency department (ED) crowding is a universal health issue that affects the efficiency of hospital management and patient care quality. ED crowding frequently occurs when a request for a ward-b...

This paper is motivated from an HIV‐1 drug resistance study where we encounter three analytical challenges: to analyze data with an informative subsample, to take into account the weak signals, and to detect important signals and also conduct statistical inference. We start with an initial estimation method, which adopts a penalized pairwise conditional likelihood approach for variable selection. This initial estimator incorporates the informative subsample issue. To accounting for the effect of weak signals, we use a key idea of partial ridge regression. We also propose a one‐step estimation method for each of the signal coefficients and then construct confidence intervals accordingly. We apply the proposed method to the Stanford HIV‐1 drug resistance study and compare the results with existing approaches. We also conduct comprehensive simulation studies to demonstrate the superior performance of our proposed method.

In recent years, subgroup analysis has emerged as an important tool to identify unknown subgroup memberships. However, subgroup analysis is still under-studied for longitudinal data. In this paper, we propose a structured mixed-effects approach for longitudinal data to model subgroup distribution and identify subgroup membership simultaneously. In the proposed structured mixed-effects model, the heterogeneous treatment effect is modeled as a random effect from a two-component mixture model, while the membership of the mixture model is incorporated using a logistic model with respect to some covariates. One advantage of our approach is that we are able to derive the estimation of the treatment effects through an EM-type algorithm which keeps the subgroup membership unchanged over time. Our numerical studies and real data example demonstrate that the proposed model outperforms other competing methods.

Introduction Growing evidence shows the important role of sleep on cardiovascular health in pregnant women with nearly 50% self-reporting poor sleep quality and 30% insufficient sleep. These sleep disturbances have been associated with adverse perinatal outcomes (e.g., hypertension, preterm births). Many studies have only used self-reported questionnaires during pregnancy. However, a few studies have utilized sleep labs and found that subjective and objective assessments of sleep varied from poor to low associations. The aim of this study was to examine the longitudinal relationship between objective sleep and cardiovascular parameters in pregnant women. Methods Seventeen healthy pregnant women with 88% self-identifying as Hispanic were consented to participate in the longitudinal observation study. Their average age was 27.8 years (SD=4.48). A validated Oura ring was used to daily monitor objective sleep and cardiovascular parameters across the pregnancy, including resting heart rate (HR), heart rate variability (HRV) indicated by RMSSD (root mean square of successive differences) of interbeat intervals, deep, light, and Rapid EYE Movement (REM) sleep, awake time and sleep onset latency. Mixed effects models were conducted to analyze daily changing patterns of these parameters using R-4.2.0 for Mac. Results Over the pregnancy, when deep and light sleep increased by 1 hour, heart rate decreased by 5.55 bpm (p< 0.001) and 1.32 bpm (p = 0.01), respectively; when REM sleep increased by 1 hour, heart rate increased by 2.15 bpm (p =0.049). Conversely, when deep and light sleep increased by 1 hour, RMSSD increased by 10.32 ms (p< 0.001) and 2.93 ms (p< 0.02), respectively; when REM sleep increased by 1 hour, RMSSD decreased by 7.27 ms (p=0.001). There were no significant associations between HR/RMSSD and awake time/sleep onset latency. Conclusion This study appears to be the first to investigate the longitudinal relationship between sleep and cardiovascular parameters among primarily Hispanic pregnant women in the U.S. in a non-lab setting. Our study provides preliminary evidence that longer deep/light sleep and shorter REM sleep may improve cardiovascular health among pregnant women. Support (if any) National Science Foundation grants including the Smart and Connected Communities (CNS: 1831918) and Collaborative Research: Integrative Heterogeneous Learning for Intensive Complex Longitudinal Data (DMS: 2210640).

DNA methylation (DNAm) has been suggested to play a critical role in post-traumatic stress disorder (PTSD), through mediating the relationship between trauma and PTSD. However, this underlying mechanism of PTSD for African Americans still remains unknown. To fill this gap, in this paper, we investigate how DNAm mediates the effects of traumatic experiences on PTSD symptoms in the Detroit Neighborhood Health Study (DNHS) (2008–2013) which involves primarily African Americans adults. To achieve this, we develop a new mediation analysis approach for high-dimensional potential DNAm mediators. A key novelty of our method is that we consider heterogeneity in mediation effects across sub-populations. Specifically, mediators in different sub-populations could have opposite effects on the outcome, and thus could be difficult to identify under a traditional homogeneous model framework. In contrast, the proposed method can estimate heterogeneous mediation effects and identifies sub-populations in which individuals share similar effects. Simulation studies demonstrate that the proposed method outperforms existing methods for both homogeneous and heterogeneous data. We also present our mediation analysis results of a dataset with 125 participants and more than 450, 000 CpG sites from the DNHS study. The proposed method finds three sub-groups of subjects and identifies DNAm mediators corresponding to genes such as HSP90AA1 and NFATC1 which have been linked to PTSD symptoms in literature. Our finding could be useful in future finer-grained investigation of PTSD mechanism and in the development of new treatments for PTSD.

Link prediction infers potential links from observed networks, and is one of the essential problems in network analyses. In contrast to traditional graph representation modeling which only predicts two-way pairwise relations, we propose a novel tensor-based joint network embedding approach on simultaneously encoding pairwise links and hyperlinks onto a latent space, which captures the dependency between pairwise and multi-way links in inferring potential unobserved hyperlinks. The major advantage of the proposed embedding procedure is that it incorporates both the pairwise relationships and subgroup-wise structure among nodes to capture richer network information. In addition, the proposed method introduces a hierarchical dependency among links to infer potential hyperlinks, and leads to better link prediction. In theory we establish the estimation consistency for the proposed embedding approach, and provide a faster convergence rate compared to link prediction utilizing pairwise links or hyperlinks only. Numerical studies on both simulation settings and Facebook ego-networks indicate that the proposed method improves both hyperlink and pairwise link prediction accuracy compared to existing link prediction algorithms., 35 pages

Recent advances in mobile health (mHealth) technology provide an effective way to monitor individuals' health statuses and deliver just-in-time personalized interventions. However, the practical use of mHealth technology raises unique challenges to existing methodologies on learning an optimal dynamic treatment regime. Many mHealth applications involve decision-making with large numbers of intervention options and under an infinite time horizon setting where the number of decision stages diverges to infinity. In addition, temporary medication shortages may cause optimal treatments to be unavailable, while it is unclear what alternatives can be used. To address these challenges, we propose a Proximal Temporal consistency Learning (pT-Learning) framework to estimate an optimal regime that is adaptively adjusted between deterministic and stochastic sparse policy models. The resulting minimax estimator avoids the double sampling issue in the existing algorithms. It can be further simplified and can easily incorporate off-policy data without mismatched distribution corrections. We study theoretical properties of the sparse policy and establish finite-sample bounds on the excess risk and performance error. The proposed method is provided in our proximalDTR package and is evaluated through extensive simulation studies and the OhioT1DM mHealth dataset.

Mediation analysis plays a major role in identifying significant mediators in the pathway between environmental exposures and health outcomes. With advanced data collection technology for large-scale studies, there has been growing research interest in developing methodology for high-dimensional mediation analysis. In this paper we present HIMA2, an extension of the HIMA method (Zhang in Bioinformatics 32:3150–3154, 2016). First, the proposed HIMA2 reduces the dimension of mediators to a manageable level based on the sure independence screening (SIS) method (Fan in J R Stat Soc Ser B 70:849–911, 2008). Second, a de-biased Lasso procedure is implemented for estimating regression parameters. Third, we use a multiple-testing procedure to accurately control the false discovery rate (FDR) when testing high-dimensional mediation hypotheses. We demonstrate its practical performance using Monte Carlo simulation studies and apply our method to identify DNA methylation markers which mediate the pathway from smoking to reduced lung function in the Coronary Artery Risk Development in Young Adults (CARDIA) Study.

In this article, we propose a heterogeneous modeling framework which achieves individual-wise feature selection and heterogeneous covariates’ effects subgrouping simultaneously. In contrast to conv...