Bayesian Methods in High-Dimensional Sparse Mediation Analysis

Causal mediation analysis aims to examine the role of a mediator or a group of mediators that lie in the pathway between an exposure and an outcome. Recent biomedical studies often involve a large number of potential mediators, typically a large ensemble of biomarkers that are measured via high-throughput technologies. The goal of my dissertation is to develop novel statistical methods that can accommodate and leverage high-dimensional mediators in mediation analysis. We provide an overview of mediation analysis and an outline of our work in Chapter I. We elaborate our methodological developments in the following chapters. In Chapter II, we develop a Bayesian inference method using continuous shrinkage priors to simultaneously analyze high-dimensional mediators. Simulations demonstrate that our method improves the power of global mediation analysis compared to simpler alternatives and has decent performance to identify true non-null contributions to the mediation effects of the pathway. The Bayesian method also helps us to understand the structure of the composite null cases for inactive mediators in the pathway. We applied our method to Multi-Ethnic Study of Atherosclerosis (MESA) and identified DNA methylation regions that may actively mediate the effect of socioeconomic status (SES) on cardiometabolic outcomes. In Chapter III, we develop methods to directly perform targeted penalization of the natural indirect effect (NIE) in a Bayesian paradigm. Specifically, we develop two novel prior models for identification of the NIEs in high-dimensional mediation analysis, both with a joint distribution on the coefficients of the exposure-mediator and mediator-outcome models: (a) four-component Gaussian mixture prior, and (b) product threshold Gaussian prior. By jointly modeling the two parameters that contribute to the NIE, the proposed methods enable penalization on their product in a targeted way. Resultant inference can take into account the four-component composite structure underlying the indirect effect. We show through extensive simulations that the proposed methods improve both selection and estimation accuracy compared to other existing or alternative shrinkage/penalization based methods. We applied our methods to two ongoing epidemiological studies: the MESA and the LIFECODES birth cohort. The identified active mediators reveal important biological pathways that may be useful for understanding disease mechanism. In Chapter IV, we further extend the Gaussian mixture method in Chapter III to explicitly incorporate the useful correlation structural information among mediators in the model building process. Instead of assuming independent prior for each mediator as in our previous methods, we propose to (a) jointly model the mixing probabilities for correlated mediator selection, or (b) jointly model the group indicators by a Potts distribution, both adding the possible grouping effect across mediators through another layer in the Bayesian hierarchy. We develop efficient sampling algorithms under non-conjugate priors and large state space. Various simulations demonstrate that our methods enable effective identification of active mediators with high correlations, which could be missed using independent priors. The proposed methods also suggest new mediation findings in the LIFECODES and MESA data applications.PHDBiostatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163093/1/yanys_1.pd

Bayesian shrinkage estimation of high dimensional causal mediation effects in omics studies

Causal mediation analysis aims to examine the role of a mediator or a group of mediators that lie in the pathway between an exposure and an outcome. Recent biomedical studies often involve a large number of potential mediators based on high‐throughput technologies. Most of the current analytic methods focus on settings with one or a moderate number of potential mediators. With the expanding growth of ‐omics data, joint analysis of molecular‐level genomics data with epidemiological data through mediation analysis is becoming more common. However, such joint analysis requires methods that can simultaneously accommodate high‐dimensional mediators and that are currently lacking. To address this problem, we develop a Bayesian inference method using continuous shrinkage priors to extend previous causal mediation analysis techniques to a high‐dimensional setting. Simulations demonstrate that our method improves the power of global mediation analysis compared to simpler alternatives and has decent performance to identify true nonnull contributions to the mediation effects of the pathway. The Bayesian method also helps us to understand the structure of the composite null cases for inactive mediators in the pathway. We applied our method to Multi‐Ethnic Study of Atherosclerosis and identified DNA methylation regions that may actively mediate the effect of socioeconomic status on cardiometabolic outcomes.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/162770/3/biom13189.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/162770/2/biom13189-sup-0001-SuppMat.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/162770/1/biom13189_am.pd

Bayesian Hierarchical Models for High-Dimensional Mediation Analysis with Coordinated Selection of Correlated Mediators

We consider Bayesian high-dimensional mediation analysis to identify among a large set of correlated potential mediators the active ones that mediate the effect from an exposure variable to an outcome of interest. Correlations among mediators are commonly observed in modern data analysis; examples include the activated voxels within connected regions in brain image data, regulatory signals driven by gene networks in genome data and correlated exposure data from the same source. When correlations are present among active mediators, mediation analysis that fails to account for such correlation can be sub-optimal and may lead to a loss of power in identifying active mediators. Building upon a recent high-dimensional mediation analysis framework, we propose two Bayesian hierarchical models, one with a Gaussian mixture prior that enables correlated mediator selection and the other with a Potts mixture prior that accounts for the correlation among active mediators in mediation analysis. We develop efficient sampling algorithms for both methods. Various simulations demonstrate that our methods enable effective identification of correlated active mediators, which could be missed by using existing methods that assume prior independence among active mediators. The proposed methods are applied to the LIFECODES birth cohort and the Multi-Ethnic Study of Atherosclerosis (MESA) and identified new active mediators with important biological implications

Bayesian Sparse Mediation Analysis with Targeted Penalization of Natural Indirect Effects

Causal mediation analysis aims to characterize an exposure's effect on an outcome and quantify the indirect effect that acts through a given mediator or a group of mediators of interest. With the increasing availability of measurements on a large number of potential mediators, like the epigenome or the microbiome, new statistical methods are needed to simultaneously accommodate high-dimensional mediators while directly target penalization of the natural indirect effect (NIE) for active mediator identification. Here, we develop two novel prior models for identification of active mediators in high-dimensional mediation analysis through penalizing NIEs in a Bayesian paradigm. Both methods specify a joint prior distribution on the exposure-mediator effect and mediator-outcome effect with either (a) a four-component Gaussian mixture prior or (b) a product threshold Gaussian prior. By jointly modeling the two parameters that contribute to the NIE, the proposed methods enable penalization on their product in a targeted way. Resultant inference can take into account the four-component composite structure underlying the NIE. We show through simulations that the proposed methods improve both selection and estimation accuracy compared to other competing methods. We applied our methods for an in-depth analysis of two ongoing epidemiologic studies: the Multi-Ethnic Study of Atherosclerosis (MESA) and the LIFECODES birth cohort. The identified active mediators in both studies reveal important biological pathways for understanding disease mechanisms

Landau Quantization of Massless Dirac Fermions in Topological Insulator

The recent theoretical prediction and experimental realization of topological insulators (TI) has generated intense interest in this new state of quantum matter. The surface states of a three-dimensional (3D) TI such as Bi_2Te_3, Bi_2Se_3 and Sb_2Te_3 consist of a single massless Dirac cones. Crossing of the two surface state branches with opposite spins in the materials is fully protected by the time reversal (TR) symmetry at the Dirac points, which cannot be destroyed by any TR invariant perturbation. Recent advances in thin-film growth have permitted this unique two-dimensional electron system (2DES) to be probed by scanning tunneling microscopy (STM) and spectroscopy (STS). The intriguing TR symmetry protected topological states were revealed in STM experiments where the backscattering induced by non-magnetic impurities was forbidden. Here we report the Landau quantization of the topological surface states in Bi_2Se_3 in magnetic field by using STM/STS. The direct observation of the discrete Landau levels (LLs) strongly supports the 2D nature of the topological states and gives direct proof of the nondegenerate structure of LLs in TI. We demonstrate the linear dispersion of the massless Dirac fermions by the square-root dependence of LLs on magnetic field. The formation of LLs implies the high mobility of the 2DES, which has been predicted to lead to topological magneto-electric effect of the TI.Comment: 15 pages, 4 figure

A Lightweight Attention-Based Network towards Distracted Driving Behavior Recognition

Distracted driving is currently a global issue causing fatal traffic crashes and injuries. Although deep learning has achieved significant success in various fields, it still faces the trade-off between computation cost and overall accuracy in the field of distracted driving behavior recognition. This paper addresses this problem and proposes a novel lightweight attention-based (LWANet) network for image classification tasks. To reduce the computation cost and trainable parameters, we replace standard convolution layers with depthwise separable convolutions and optimize the classic VGG16 architecture by 98.16% trainable parameters reduction. Inspired by the attention mechanism in cognitive science, a lightweight inverted residual attention module (IRAM) is proposed to simulate human attention, extract more specific features, and improve the overall accuracy. LWANet achieved an accuracy of 99.37% on Statefarm’s dataset and 98.45% on American University in Cairo’s dataset. With only 1.22 M trainable parameters and a model file size of 4.68 MB, the quantitative experimental results demonstrate that the proposed LWANet obtains state-of-the-art overall performance in deep learning-based distracted driving behavior recognition

How does one-sided versus two-sided customer orientation affect B2B platform’s innovation : Differential effects with top management team status

International audienceA platform is a triadic exchanging system with two-sided customers, it faces heterogeneous or even conflicting consumer needs from both sides. According to the theory of inventive problem solving, needs' contradictions are an important source of innovation. Adopting different customer orientation (CO) strategies will affect the platform's utilization of customer needs and thus influence the results of innovation. Our study divides platform CO into one-sided CO and two-sided CO and compares their relative effects on a platform's radical and incremental innovation. We also assess whether the impacts are conditional on the platform's top management team's (TMT) formal and informal status. Archival data from 126 electronic platforms revealed that two-sided CO promotes both radical and incremental innovation, but one-sided CO influences incremental innovation. Furthermore, TMT formal status positively moderates the impact of CO on radical innovation, but TMT informal status negatively moderates the effect of CO on incremental innovation.<br/

Caffeine-Induced Sleep Restriction Alters the Gut Microbiome and Fecal Metabolic Profiles in Mice

Insufficient sleep is becoming increasingly common and contributes to many health issues. To combat sleepiness, caffeine is consumed daily worldwide. Thus, caffeine consumption and sleep restriction often occur in succession. The gut microbiome can be rapidly affected by either one’s sleep status or caffeine intake, whereas the synergistic effects of a persistent caffeine-induced sleep restriction remain unclear. In this study, we investigated the impact of a chronic caffeine-induced sleep restriction on the gut microbiome and its metabolic profiles in mice. Our results revealed that the proportion of Firmicutes and Bacteroidetes was not altered, while the abundance of Proteobacteria and Actinobacteria was significantly decreased. In addition, the content of the lipids was abundant and significantly increased. A pathway analysis of the differential metabolites suggested that numerous metabolic pathways were affected, and the glycerophospholipid metabolism was most significantly altered. Combined analysis revealed that the metabolism was significantly affected by variations in the abundance and function of the intestinal microorganisms and was closely relevant to Proteobacteria and Actinobacteria. In conclusion, a long-term caffeine-induced sleep restriction affected the diversity and composition of the intestinal microbiota in mice, and substantially altered the metabolic profiles of the gut microbiome. This may represent a novel mechanism by which an unhealthy lifestyle such as mistimed coffee breaks lead to or exacerbates disease

Influence of Growth Velocity on the Separation of Primary Silicon in Solidified Al-Si Hypereutectic Alloy Driven by a Pulsed Electric Current

Investigating the separation of the primary silicon phase in Al-Si hypereutectic alloys is of high importance for the production of solar grade silicon. The present paper focuses on the effect of growth velocity on the electric current pulse (ECP)-induced separation of primary silicon in a directionally solidified Al-20.5 wt % Si hypereutectic alloy. Experimental results show that lower growth velocity promotes the enrichment tendency of primary silicon at the bottom region of the sample. The maximum measured area percentage of segregated primary silicon in the sample solidified at the growth velocity of 4 μm/s is as high as 82.6%, whereas the corresponding value is only 59% in the sample solidified at the growth velocity of 24 μm/s. This is attributed to the fact that the stronger forced flow is generated to promote the precipitation of primary silicon accompanied by a higher concentration of electric current in the mushy zone under the application of a slower growth velocity