Group Inverse-Gamma Gamma Shrinkage for Sparse Regression with Block-Correlated Predictors

Heavy-tailed continuous shrinkage priors, such as the horseshoe prior, are widely used for sparse estimation problems. However, there is limited work extending these priors to predictors with grouping structures. Of particular interest in this article, is regression coefficient estimation where pockets of high collinearity in the covariate space are contained within known covariate groupings. To assuage variance inflation due to multicollinearity we propose the group inverse-gamma gamma (GIGG) prior, a heavy-tailed prior that can trade-off between local and group shrinkage in a data adaptive fashion. A special case of the GIGG prior is the group horseshoe prior, whose shrinkage profile is correlated within-group such that the regression coefficients marginally have exact horseshoe regularization. We show posterior consistency for regression coefficients in linear regression models and posterior concentration results for mean parameters in sparse normal means models. The full conditional distributions corresponding to GIGG regression can be derived in closed form, leading to straightforward posterior computation. We show that GIGG regression results in low mean-squared error across a wide range of correlation structures and within-group signal densities via simulation. We apply GIGG regression to data from the National Health and Nutrition Examination Survey for associating environmental exposures with liver functionality.Comment: 44 pages, 4 figure

Novel Likelihood Ratio Tests for Screening Gene‐Gene and Gene‐Environment Interactions With Unbalanced Repeated‐Measures Data

There has been extensive literature on modeling gene‐gene interaction (GGI) and gene‐environment interaction (GEI) in case‐control studies with limited literature on statistical methods for GGI and GEI in longitudinal cohort studies. We borrow ideas from the classical two‐way analysis of variance literature to address the issue of robust modeling of interactions in repeated‐measures studies. While classical interaction models proposed by Tukey and Mandel have interaction structures as a function of main effects, a newer class of models, additive main effects and multiplicative interaction (AMMI) models, do not have similar restrictive assumptions on the interaction structure. AMMI entails a singular value decomposition of the cell residual matrix after fitting the additive main effects and has been shown to perform well across various interaction structures. We consider these models for testing GGI and GEI from two perspectives: likelihood ratio test based on cell means and a regression‐based approach using individual observations. Simulation results indicate that both approaches for AMMI models lead to valid tests in terms of maintaining the type I error rate, with the regression approach having better power properties. The performance of these models was evaluated across different interaction structures and 12 common epistasis patterns. In summary, AMMI model is robust with respect to misspecified interaction structure and is a useful screening tool for interaction even in the absence of main effects. We use the proposed methods to examine the interplay between the hemochromatosis gene and cumulative lead exposure on pulse pressure in the Normative Aging Study.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/99643/1/gepi21744-sup-0001-si.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/99643/2/gepi21744.pd

Improving estimation and prediction in linear regression incorporating external information from an established reduced model

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/143779/1/sim7600_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/143779/2/sim7600.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/143779/3/sim7600-sup-0001-Supplementary.pd

Effects of Air Pollution on Heart Rate Variability: The VA Normative Aging Study

Reduced heart rate variability (HRV), a marker of poor cardiac autonomic function, has been associated with air pollution, especially fine particulate matter [< 2.5 μm in aerodynamic diameter (PM(2.5))]. We examined the relationship between HRV [standard deviation of normal-to-normal intervals (SDNN), power in high frequency (HF) and low frequency (LF), and LF:HF ratio] and ambient air pollutants in 497 men from the Normative Aging Study in greater Boston, Massachusetts, seen between November 2000 and October 2003. We examined 4-hr, 24-hr, and 48-hr moving averages of air pollution (PM(2.5), particle number concentration, black carbon, ozone, nitrogen dioxide, sulfur dioxide, carbon monoxide). Controlling for potential confounders, HF decreased 20.8% [95% confidence interval (CI), 4.6–34.2%] and LF:HF ratio increased 18.6% (95% CI, 4.1–35.2%) per SD (8 μg/m(3)) increase in 48-hr PM(2.5). LF was reduced by 11.5% (95% CI, 0.4–21.3%) per SD (13 ppb) increment in 4-hr O(3). The associations between HRV and PM(2.5) and O(3) were stronger in people with ischemic heart disease (IHD) and hypertension. The associations observed between SDNN and LF and PM(2.5) were stronger in people with diabetes. People using calcium-channel blockers and beta-blockers had lower associations between O(3) and PM(2.5) with LF. No effect modification by other cardiac medications was found. Exposures to PM(2.5) and O(3) are associated with decreased HRV, and history of IHD, hypertension, and diabetes may confer susceptibility to autonomic dysfunction by air pollution

Development of a 4D hand gripping aid using a knitted shape memory alloy and evaluation of finger-bending angles in elderly women

As the global population ages, there is an increasing demand for physical assistive devices for the elderly. This study aimed to develop and evaluate a wearable gripping aid for elderly women to assist in their handgrip ability. We developed an actuator module for the hand-gripping aid using a 4D knitted shape memory alloy and attached to a flexible nylon glove. At baseline, we measured the bending angles of the knitted shape memory alloy and the subjects fingers while gripping. The bending angles of the gripping aid demonstrated similar hand mobility to those of elderly women in real life. We also found that SMA modules attached to a glove could implement the bending angle when gripping a ball derived from the index and middle fingers of elderly women. The finding could help to develop hand products that could be worn on the hand of the elderly by realizing the bending motion of each finger. The outcomes of this study suggest the practical potential of this wearable device as an effective hand-gripping aid for the elderly, based on a novel 4D material and ergonomic design approach.This work was supported by Seoul National University Research Grant in 2021 and the National Research Foundation of Korea (NRF) grant funded by the Korean Government (MSIT) (2016R1A5A1938472)

Monitoring Coastal Chlorophyll-a Concentrations in Coastal Areas Using Machine Learning Models

Harmful algal blooms have negatively affected the aquaculture industry and aquatic ecosystems globally. Remote sensing using satellite sensor systems has been applied on large spatial scales with high temporal resolutions for effective monitoring of harmful algal blooms in coastal waters. However, oceanic color satellites have limitations, such as low spatial resolution of sensor systems and the optical complexity of coastal waters. In this study, bands 1 to 4, obtained from Landsat-8 Operational Land Imager satellite images, were used to evaluate the performance of empirical ocean chlorophyll algorithms using machine learning techniques. Artificial neural network and support vector machine techniques were used to develop an optimal chlorophyll-a model. Four-band, four-band-ratio, and mixed reflectance datasets were tested to select the appropriate input dataset for estimating chlorophyll-a concentration using the two machine learning models. While the ocean chlorophyll algorithm application on Landsat-8 Operational Land Imager showed relatively low performance, the machine learning methods showed improved performance during both the training and validation steps. The artificial neural network and support vector machine demonstrated a similar level of prediction accuracy. Overall, the support vector machine showed slightly superior performance to that of the artificial neural network during the validation step. This study provides practical information about effective monitoring systems for coastal algal blooms

Does the Kyphotic Change Decrease the Risk of Fall?

ObjectivesFalls are a major problem in the elderly. Age-related degeneration of the human balance system increases the risk of falls. Kyphosis is a common condition of curvature of the upper spine in the elderly and its development occurs through degenerative change. However, relatively little is known about the effect of kyphotic changes on balance in the elderly. The aim of this study is to investigate the influence of kyphosis on the balance strategy through use of the motor control test (MCT) in computerized dynamic posturography.MethodsFifty healthy subjects who were not affected by other medical disorders that could affect gait or balance were enrolled in the study. By simulation of kyphotic condition through change of the angles of the line connecting the shoulder to the hip and the ankle axis by approximately 30°, the latency and amplitude of the MCT were measured in upright and kyphotic condition.ResultsIn the kyphotic condition, latency was shortened in backward movement. In forward movement, latency was shortened only in large stimulation. The amplitude in forward movement was decreased in kyphotic condition. However, the change of amplitude was not significant in large intensity backward movement in the same condition.ConclusionKyphotic condition decreases the latency of MCT, especially in backward movement. These findings imply that kyphotic condition may serve as a protective factor against falls