Task-related changes in degree centrality and local coherence of the posterior cingulate cortex after major cardiac surgery in older adults.

OBJECTIVES: Older adults often display postoperative cognitive decline (POCD) after surgery, yet it is unclear to what extent functional connectivity (FC) alterations may underlie these deficits. We examined for postoperative voxel-wise FC changes in response to increased working memory load demands in cardiac surgery patients and nonsurgical controls. EXPERIMENTAL DESIGN: Older cardiac surgery patients (n = 25) completed a verbal N-back working memory task during MRI scanning and cognitive testing before and 6 weeks after surgery; nonsurgical controls with cardiac disease (n = 26) underwent these assessments at identical time intervals. We measured postoperative changes in degree centrality, the number of edges attached to a brain node, and local coherence, the temporal homogeneity of regional functional correlations, using voxel-wise graph theory-based FC metrics. Group × time differences were evaluated in these FC metrics associated with increased N-back working memory load (2-back > 1-back), using a two-stage partitioned variance, mixed ANCOVA. PRINCIPAL OBSERVATIONS: Cardiac surgery patients demonstrated postoperative working memory load-related degree centrality increases in the left dorsal posterior cingulate cortex (dPCC; p < .001, cluster p-FWE < .05). The dPCC also showed a postoperative increase in working memory load-associated local coherence (p < .001, cluster p-FWE < .05). dPCC degree centrality and local coherence increases were inversely associated with global cognitive change in surgery patients (p < .01), but not in controls. CONCLUSIONS: Cardiac surgery patients showed postoperative increases in working memory load-associated degree centrality and local coherence of the dPCC that were inversely associated with postoperative global cognitive outcomes and independent of perioperative cerebrovascular damage.

Notes on the overlap measure as an alternative to the Youden index: How are they related?

The receiver operating characteristic (ROC) curve is frequently used to evaluate and compare diagnostic tests. As one of the ROC summary indices, the Youden index measures the effectiveness of a diagnostic marker and enables the selection of an optimal threshold value (cut-off point) for the marker. Recently, the overlap coefficient, which captures the similarity between 2 distributions directly, has been considered as an alternative index for determining the diagnostic performance of markers. In this case, a larger overlap indicates worse diagnostic accuracy, and vice versa. This paper provides a graphical demonstration and mathematical derivation of the relationship between the Youden index and the overlap coefficient and states their advantages over the most popular diagnostic measure, the area under the ROC curve. Furthermore, we outline the differences between the Youden index and overlap coefficient and identify situations in which the overlap coefficient outperforms the Youden index. Numerical examples and real data analysis are provided.

Reverse engineering gene networks using global-local shrinkage rules.

Inferring gene regulatory networks from high-throughput 'omics' data has proven to be a computationally demanding task of critical importance. Frequently, the classical methods break down owing to the curse of dimensionality, and popular strategies to overcome this are typically based on regularized versions of the classical methods. However, these approaches rely on loss functions that may not be robust and usually do not allow for the incorporation of prior information in a straightforward way. Fully Bayesian methods are equipped to handle both of these shortcomings quite naturally, and they offer the potential for improvements in network structure learning. We propose a Bayesian hierarchical model to reconstruct gene regulatory networks from time-series gene expression data, such as those common in perturbation experiments of biological systems. The proposed methodology uses global-local shrinkage priors for posterior selection of regulatory edges and relaxes the common normal likelihood assumption in order to allow for heavy-tailed data, which were shown in several of the cited references to severely impact network inference. We provide a sufficient condition for posterior propriety and derive an efficient Markov chain Monte Carlo via Gibbs sampling in the electronic supplementary material. We describe a novel way to detect multiple scales based on the corresponding posterior quantities. Finally, we demonstrate the performance of our approach in a simulation study and compare it with existing methods on real data from a T-cell activation study.

Mobile farm clinic outreach to address health conditions among Latino migrant farmworkers in Georgia.

Agricultural labor involves exposure to many occupational hazards, some of which can lead to chronic health conditions. The purpose of this study was to conduct an occupational health needs assessment of illnesses and work-related injuries among a Latino migrant farmworker population (recruited to harvest Vidalia onions) in South Georgia. Study data included survey responses from 100 farmworkers attending mobile farm clinics in 2010 at their worker housing residences, supplemented by medical diagnoses data from the same clinics collected over 3 years (2009-2011) for 1161 farmworkers at six different farms. From the survey, the main health problems reported were hypertension (25%), eye problems (12%), musculoskeletal problems (11%), diabetes (10%), and depression (7%). In multivariate analyses, depression scores were associated with having a history of musculoskeletal problems (p = .002). According to the mobile farm clinic data, the most common medical diagnoses included back pain (11.8%), hypertension (11.4%), musculoskeletal problems (11.3%), gastrointestinal disorders (8.6%), eye problems (7.2%), dermatitis or rash (7.0%), and tinea or fungal skin infections (5.6%). The study identified eye and musculoskeletal problems as the major occupational health conditions for this population of farmworkers.

Rheological study of the mixture of acetaminophen and polyethylene oxide for hot-melt extrusion application.

There is a growing interest of extrusion drug and polymer together to manufacture various solid dosages. In those cases, the drug's release profiles are greatly affected by the miscibility of two materials. The goal of this study is to test the drug's solubility in molten polymer and obtain the mixture's rheological properties for the purpose of optimizing the extrusion process. The dynamic and steady viscosities of APAP-PEO mixture were determined using oscillatory and capillary rheometers. The curves of viscosity vs. drug loading generally have a "V" shape, and the minimal point gives the APAP's solubility in PEO. The test results suggest that different dynamic methods lead to essentially the same solubility data. At high shear rates, the mixtures show shear thinning behavior and the viscosity becomes less sensitive to the drug loading. In other words, it is desirable to use a low shear rate in order to deduce the drug's solubility in polymer from the viscosity data. On the other hand, viscosity data at high shear rates are more representative of the materials' rheological properties during extrusion.