Adjusting for Confounding by Neighborhood Using a Proportional Odds Model and Complex Survey Data

In social epidemiology, an individual\u27s neighborhood is considered to be an important determinant of health behaviors, mediators, and outcomes. Consequently, when investigating health disparities, researchers may wish to adjust for confounding by unmeasured neighborhood factors, such as local availability of health facilities or cultural predispositions. With a simple random sample and a binary outcome, a conditional logistic regression analysis that treats individuals within a neighborhood as a matched set is a natural method to use. The authors present a generalization of this method for ordinal outcomes and complex sampling designs. The method is based on a proportional odds model and is very simple to program using standard software such as SAS PROC SURVEYLOGISTIC (SAS Institute Inc., Cary, North Carolina). The authors applied the method to analyze racial/ethnic differences in dental preventative care, using 2008 Florida Behavioral Risk Factor Surveillance System survey data. The ordinal outcome represented time since last dental cleaning, and the authors adjusted for individual-level confounding by gender, age, education, and health insurance coverage. The authors compared results with and without additional adjustment for confounding by neighborhood, operationalized as zip code. The authors found that adjustment for confounding by neighborhood greatly affected the results in this example

TRA-940: ASSESSING CURRENT AND FUTURE MACKENZIE RIVER FREIGHT VOLUMES IN THE CONTEXT OF CLIMATE CHANGE IMPACTS

The Mackenzie River is a major freight transportation route that connects many remote communities in the Northwest Territories and parts of Nunavut to southern Canada’s transportation network. The river is only navigable during the summer months, from mid-June until sometime in late-September to mid-October, when it is clear of ice. However, the water conditions of the river have changed significantly in recent years. Although water levels always decrease as the delivery season moves into fall, these reductions have been occurring much faster, in turn reducing barge loading capacities as well as operational speeds. In addition, based on simulations of ice breakup and water volumes in the Mackenzie River basin, the sailing season opening dates are anticipated to shift earlier in the future. In the end, the main impact of climate change on river transport is not definitive events but rather, increased variability in events. This research aims to account for those abovementioned climate changes in the freight volume scheduling process, and conducts a numerical analysis based on the projections of future water conditions from climate simulation models as well as predicted freight volumes from time-series analysis and forecast models. The results of the numerical analysis can help local government and waterway transportation companies to better understand how freight scheduling strategies could account for climate changes that affect regional waterway transportation and, hence, optimize their operational schedules to take advantage of good water conditions while reducing financial cost

Large-scale Molecular Dynamics Simulation with Forward Flux Sampling on Hadoop

Simulating rare events is extremely difficulty and requires massive computational resources and complex data processing workflow, which is determined by the nature of stochastic systems. To help computational scientists discover hard scientific problems in this area, we built a large-scale molecular dynamics simulation framework integrated with forward flux sampling (FFS) technique on Hadoop ecosystem. In this project, we port the customized FFS workflow to underlying MapReduce-based computing pipeline by using dataflow-driven design pattern and Gromacs application. The early works show that our framework is able to provide a scalable, fault-tolerance and efficient rare events simulation environment over varieties of computing infrastructures, while preserving the flexibility of the original scientific application

Dual RL: Unification and New Methods for Reinforcement and Imitation Learning

The goal of reinforcement learning (RL) is to maximize the expected cumulative return. It has been shown that this objective can be represented by an optimization problem of the state-action visitation distribution under linear constraints. The dual problem of this formulation, which we refer to as dual RL, is unconstrained and easier to optimize. We show that several state-of-the-art off-policy deep reinforcement learning (RL) algorithms, under both online and offline, RL and imitation learning (IL) settings, can be viewed as dual RL approaches in a unified framework. This unification provides a common ground to study and identify the components that contribute to the success of these methods and also reveals the common shortcomings across methods with new insights for improvement. Our analysis shows that prior off-policy imitation learning methods are based on an unrealistic coverage assumption and are minimizing a particular f-divergence between the visitation distributions of the learned policy and the expert policy. We propose a new method using a simple modification to the dual RL framework that allows for performant imitation learning with arbitrary off-policy data to obtain near-expert performance, without learning a discriminator. Further, by framing a recent SOTA offline RL method XQL in the dual RL framework, we propose alternative choices to replace the Gumbel regression loss, which achieve improved performance and resolve the training instability issue of XQL. Project code and details can be found at this https://hari-sikchi.github.io/dual-rl.Comment: 46 pages. Under revie

An Infrastructure to Support Data Integration and Curation for Higher Educational Research

The recent challenges for higher education call for research that can offer a comprehensive understanding about the performance and efficiency of higher education institutions in their three primary missions: research, education, and service. In other for this to happen, it is necessary for researchers to have access to a multitude of data sources.However, due to the nature of their academic training, many higher education practitioners do not have access to expertise in working with different data sources. In this work, we describe a design and implementation for an infrastructure that will bring together the tools and the data to provide access to researchers in the field of higher education institutional research. The infrastructure will include integration and curation for data from different sources, embedded statistical environment, high performance computational back-end, and extensibility for future Big Data and unstructured data

Predictors of Neonatal Abstinence Syndrome in Buprenorphine Exposed Newborn: Can Cord Blood Buprenorphine Metabolite Levels Help?

Background Buprenorphine is a semi-synthetic opioid used for the treatment of opioid dependence. Opioid use, including buprenorphine, has been increasing in recent years, in the general population and in pregnant women. Consequently, there has been a rise in frequency of neonatal abstinence syndrome (NAS), associated with buprenorphine use during pregnancy. The purpose of this study was to investigate correlations between buprenorphine and buprenorphine-metabolite concentrations in cord blood and onset of NAS in buprenorphine exposed newborns. Methods Nineteen (19) newborns who met inclusion criteria were followed after birth until discharge in a double-blind non-intervention study, after maternal consent. Cord blood and tissue samples were collected and analyzed by liquid chromatography–mass spectrometry (LC–MS) for buprenorphine and metabolites. Simple and multiple logistic regressions were used to examine relationships between buprenorphine and buprenorphine metabolite concentrations in cord blood and onset of NAS, need for morphine therapy, and length of stay. Results Each increase in 5 ng/ml level of norbuprenorphine in cord blood increases odds of requiring treatment by morphine 2.5 times. Each increase in 5 ng/ml of buprenorphine-glucuronide decreases odds of receiving morphine by 57.7 %. Along with concentration of buprenorphine metabolites, birth weight and gestational age also play important roles, but not maternal buprenorphine dose. Conclusions LC–MS analysis of cord blood concentrations of buprenorphine and metabolites is an effective way to examine drug and metabolite levels in the infant at birth. Cord blood concentrations of the active norbuprenorphine metabolite and the inactive buprenorphine-glucuronide metabolite show promise in predicting necessity of treatment of NAS. These finding have implications in improving patient care and reducing healthcare costs if confirmed in a larger sample

Reduced Levels of mGlu2 Receptors within the Prelimbic Cortex Are Not Associated with Elevated Glutamate Transmission or High Alcohol Drinking

Background A Grm2 cys407* stop codon mutation, which results in a loss of the metabotropic glutamate 2 (mGlu2) receptor protein, was identified as being associated with high alcohol drinking by alcohol-preferring (P) rats. The objectives of the current study were to characterize the effects of reduced levels of mGlu2 receptors on glutamate transmission and alcohol drinking. Methods Quantitative no-net-flux microdialysis was used to test the hypothesis that basal extracellular glutamate levels in the prelimbic (PL) cortex and nucleus accumbens shell (NACsh) will be higher in P than Wistar rats. A lentiviral-delivered short-hairpin RNA (shRNA)-mediated knockdown was used to test the hypothesis that reduced levels of mGlu2 receptors within the PL cortex will increase voluntary alcohol drinking by Wistar rats. A linear regression analysis was used to test the hypothesis that there will be a significant correlation between the Grm2 cys407* mutation and level of alcohol intake. Results Extracellular glutamate concentrations within the PL cortex (3.6 ± 0.6 vs. 6.4 ± 0.6 μM) and NACsh (3.2 ± 0.4 vs. 6.6 ± 0.6 μM) were significantly lower in female P than female Wistar rats. Western blot detected the presence of mGlu2 receptors in these regions of female Wistar rats, but not female P rats. Micro-infusion of shRNAs into the PL cortex significantly reduced local mGlu2 receptor levels (by 40%), but did not alter voluntary alcohol drinking in male Wistar rats. In addition, there was no significant correlation between the Grm2 mutation and alcohol intake in 36 rodent lines (r = 0.29, p > 0.05). Conclusions Collectively, these results suggest a lack of association between the loss of mGlu2 receptors and glutamate transmission in the NACsh and PL cortex of female P rats, and between the level of mGlu2 receptors in the PL cortex and alcohol drinking of male Wistar rats

Progesterone Metabolites Produced by Cytochrome P450 3A Modulate Uterine Contractility in a Murine Model

Objective: We seek to characterize the effect of progesterone metabolites on spontaneous and oxytocin-induced uterine contractility. Study Design: Spontaneous contractility was studied in mouse uterine horns after treatment with progesterone, 2α-hydroxyprogesterone, 6β-hydroxyprogesterone (6β-OHP), 16α-hydroxyprogesterone (16α-OHP), or 17-hydroxyprogesterone caproate (17-OHPC) at 10−9 to 10−6 mol/L. Uterine horns were exposed to progestins (10−6 mol/L), followed by increasing concentrations of oxytocin (1-100 nmol/L) to study oxytocin-induced contractility. Contraction parameters were compared for each progestin and matched vehicle control using repeated measures 2-way analysis of variance. In vitro metabolism of progesterone by recombinant cytochrome P450 3A (CYP3A) microsomes (3A5, 3A5, and 3A7) identified major metabolites. Results: Oxytocin-induced contractile frequency was decreased by 16α-OHP (P = .03) and increased by 6β-OHP (P = .05). Progesterone and 17-OHPC decreased oxytocin-induced contractile force (P = .02 and P = .04, respectively) and frequency (P = .02 and P = .03, respectively). Only progesterone decreased spontaneous contractile force (P = .02). Production of 16α-OHP and 6β-OHP metabolites were confirmed in all CYP3A isoforms tested. Conclusion: Progesterone metabolites produced by maternal or fetal CYP3A enzymes influence uterine contractility