Comparing Gaussian graphical models with the posterior predictive distribution and Bayesian model selection

Gaussian graphical models are commonly used to characterize conditional (in)dependence structures (i.e., partial correlation networks) of psychological constructs. Recently attention has shifted from estimating single networks to those from various subpopulations. The focus is primarily to detect differences or demonstrate replicability. We introduce two novel Bayesian methods for comparing networks that explicitly address these aims. The first is based on the posterior predictive distribution, with a symmetric version of Kullback-Leibler divergence as the discrepancy measure, that tests differences between two (or more) multivariate normal distributions. The second approach makes use of Bayesian model comparison, with the Bayes factor, and allows for gaining evidence for invariant network structures. This overcomes limitations of current approaches in the literature that use classical hypothesis testing, where it is only possible to determine whether groups are significantly different from each other. With simulation we show the posterior predictive method is approximately calibrated under the null hypothesis (alpha = .05) and has more power to detect differences than alternative approaches. We then examine the necessary sample sizes for detecting invariant network structures with Bayesian hypothesis testing, in addition to how this is influenced by the choice of prior distribution. The methods are applied to posttraumatic stress disorder symptoms that were measured in 4 groups. We end by summarizing our major contribution, that is proposing 2 novel methods for comparing Gaussian graphical models (GGMs), which extends beyond the social-behavioral sciences. The methods have been implemented in the R package BGGM. Translational Abstract Gaussian graphical models are becoming popular in the social-behavioral sciences. Recently attention has shifted from estimating single networks to those from various subpopulations (e.g., males vs. females). We introduce Bayesian methodology for comparing networks estimated from any number of groups. The first approach is based on the posterior predictive distribution and it allows for determining whether networks are different from one another. This is ideal for testing the null hypothesis of group equality, say, in the context of testing for network replicability (or lack thereof). The second approach is based on Bayesian hypothesis testing and it allows for gaining evidence for network invariances or equality of partial correlations for any number of groups. This is ideal for focusing on specific aspects of the network such as individual partial correlations. In a series of simulations and illustrative examples we demonstrate the utility of the proposed methodology for comparing Gaussian graphical models. The methods have been implemented in the R package BGGM

A High Resolution Study of the Slowly Contracting, Starless Core L1544

We present interferometric observations of N2H+(1--0) in the starless, dense core L1544 in Taurus. Red-shifted self-absorption, indicative of inward motions, is found toward the center of an elongated core. The data are fit by a non-spherical model consisting of two isothermal, rotating, centrally condensed layers. Through a hybrid global-individual fit to the spectra, we map the variation of infall speed at scales ~1400AU and find values ~0.08 km/s around the core center. The inward motions are small in comparison to thermal, rotational, and gravitational speeds but are large enough to suggest that L1544 is very close to forming a star.Comment: 11 pages, 2 figures Accepted for publication in Astrophysical Journal Letter

Agreement between bovine respiratory disease scoring systems for pre-weaned dairy calves.

Clinical scoring systems have been proposed for respiratory disease diagnosis in calves, including the Wisconsin (WI) system (McGuirk in 2008) which uses five clinical signs, each partitioned into four levels of severity. Recently, we developed the California (CA) bovine respiratory disease (BRD) scoring system requiring less calf handling and consisting of six clinical signs, each classified as normal or abnormal. The objective of this study was to estimate the on-farm agreement between the WI and the CA scoring systems. A total of 100 calves were enrolled on a CA dairy and assessed for BRD case status using the two scoring systems simultaneously. The Kappa coefficient of agreement between these two systems was estimated to be 0.85, which indicated excellent agreement beyond chance. The simpler design and reduced calf handling required by the CA BRD scoring system may make it advantageous for on-farm use

Why Do They Do It? A Case Study of Factors Influencing Part-Time Faculty to Seek Employment at a Community College

The purpose of this qualitative study was to discover the motivational factors influencing part-time faculty employment within the community college from the perspective of the part-time faculty. The study examined these reported motivational factors for differences influenced by age, gender, and employment status. A survey was distributed to a random sample of part-time faculty members at a large metropolitan community college in the Southeastern United States. Participants were asked to respond to categorical demographic questions and survey questions to determine workplace satisfaction. Three open-ended questions were presented to obtain in-depth information about the motivational factors leading adjunct faculty to seek employment at the community college. Findings reveal that motivation is a result of interest in working within a discipline, working with students, and achieving personal satisfaction

`NMR Crystallization': in-situ NMR techniques for time-resolved monitoring of crystallization processes

Solid-state NMR spectroscopy is a well-established and versatile technique for studying structural and dynamic properties of solids, and there is considerable potential to exploit the power and versatility of solid-state NMR for in-situ studies of chemical processes. However, a number of technical challenges are associated with adapting this technique for in-situ studies, depending on the process of interest. Recently, an in-situ solid-state NMR strategy for monitoring the evolution of crystallization processes has been developed and has proven to be a promising approach for identifying the sequence of distinct solid forms present as a function of time during crystallization from solution, and for the discovery of new polymorphs. The latest development of this technique, called “CLASSIC” NMR, allows simultaneous measurement of both liquid-state and solid-state NMR spectra as a function of time, thus yielding complementary information on the evolution of both the liquid phase and the solid phase during crystallization from solution. This article gives an overview of the range of NMR strategies that are currently available for in-situ studies of crystallization processes, with examples of applications that highlight the potential of these strategies to deepen our understanding of crystallization phenomena

Multivariate ToF-SIMS image analysis of polymer microarrays and protein adsorption

The complexity of hyperspectral time of flight secondary ion mass spectrometry (ToF-SIMS) datasets makes their subsequent analysis and interpretation challenging, and is often an impasse to the identification of trends and differences within large sample-sets. The application of multivariate data analysis has become a routine method to successfully deconvolute and analyze objectively these datasets. The advent of high-resolution large area ToF-SIMS imaging capability has enlarged further the data handling challenges. In this work, a modified multivariate curve resolution image analysis of a polymer microarray containing 70 different poly(meth)acrylate type spots (over a 9.2 × 9.2 mm area) is presented. This analysis distinguished key differences within the polymer library such as the differentiation between acrylate and methacrylate polymers and variance specific to side groups. Partial least squares (PLS) regression analysis was performed to identify correlations between the ToF-SIMS surface chemistry and the protein adsorption. PLS analysis identified a number of chemical moieties correlating with high or low protein adsorption, including ions derived from the polymer backbone and polyethylene glycol side-groups. The retrospective validation of the findings from the PLS analysis was also performed using the secondary ion images for those ions found to significantly contribute to high or low protein adsorption

A Neurospora crassa mutant which overaccumulates carotenoid pigments

A Neurospora crassa mutant which overaccumulates carotenoid pigment