Person-Specific Methods for Characterizing the Course and Temporal Dynamics of Concussion Symptomatology: A Pilot Study.

Better characterization of acute concussion symptomatology is needed in order to advance clinical and scientific understanding of persistent concussion symptoms. This paper aims to illustrate a novel framework for conceptualizing, collecting, and analyzing concussion symptom data. To that end, we describe the temporal and structural dynamics of acute concussion symptoms at the individual-patient level. Ten recently concussion adolescents and young adults completed 20 days of ecological momentary assessment (EMA) of post-concussion symptoms. Follow-up assessments were completed at 3 months post-injury. Network modeling revealed marked heterogeneity across participants. In the overall sample, temporal patterns explained the most variance in light sensitivity (48%) and the least variance in vomiting (5%). About half of the participants had symptom networks that were sparse after controlling for temporal variation. The other individualized symptom networks were densely interconnected clusters of symptoms. Networks were highly idiosyncratic in nature, yet emotional symptoms (nervousness, emotional, sadness), cognitive symptoms (mental fogginess, slowness), and symptoms of hyperacusis (sensitivity to light, sensitivity to noise) tended to cluster together across participants. Person-specific analytic techniques revealed a number of idiosyncratic features of post-concussion symptomatology. We propose applying this framework to future research to better understand individual differences in concussion recovery

interAdapt -- An Interactive Tool for Designing and Evaluating Randomized Trials with Adaptive Enrollment Criteria

The interAdapt R package is designed to be used by statisticians and clinical investigators to plan randomized trials. It can be used to determine if certain adaptive designs offer tangible benefits compared to standard designs, in the context of investigators' specific trial goals and constraints. Specifically, interAdapt compares the performance of trial designs with adaptive enrollment criteria versus standard (non-adaptive) group sequential trial designs. Performance is compared in terms of power, expected trial duration, and expected sample size. Users can either work directly in the R console, or with a user-friendly shiny application that requires no programming experience. Several added features are available when using the shiny application. For example, the application allows users to immediately download the results of the performance comparison as a csv-table, or as a printable, html-based report.Comment: 14 pages, 2 figures (software screenshots); v2 includes command line function descriptio

Fast, Exact Bootstrap Principal Component Analysis for p>1 million

Many have suggested a bootstrap procedure for estimating the sampling variability of principal component analysis (PCA) results. However, when the number of measurements per subject ($p$) is much larger than the number of subjects ($n$), the challenge of calculating and storing the leading principal components from each bootstrap sample can be computationally infeasible. To address this, we outline methods for fast, exact calculation of bootstrap principal components, eigenvalues, and scores. Our methods leverage the fact that all bootstrap samples occupy the same $n$-dimensional subspace as the original sample. As a result, all bootstrap principal components are limited to the same $n$-dimensional subspace and can be efficiently represented by their low dimensional coordinates in that subspace. Several uncertainty metrics can be computed solely based on the bootstrap distribution of these low dimensional coordinates, without calculating or storing the $p$-dimensional bootstrap components. Fast bootstrap PCA is applied to a dataset of sleep electroencephalogram (EEG) recordings ($p=900$, $n=392$), and to a dataset of brain magnetic resonance images (MRIs) ($p\approx$ 3 million, $n=352$). For the brain MRI dataset, our method allows for standard errors for the first 3 principal components based on 1000 bootstrap samples to be calculated on a standard laptop in 47 minutes, as opposed to approximately 4 days with standard methods.Comment: 25 pages, including 9 figures and link to R package. 2014-05-14 update: final formatting edits for journal submission, condensed figure

A randomized trial in a massive online open course shows people don't know what a statistically significant relationship looks like, but they can learn

Scatterplots are the most common way for statisticians, scientists, and the public to visually detect relationships between measured variables. At the same time, and despite widely publicized controversy, P-values remain the most commonly used measure to statistically justify relationships identified between variables. Here we measure the ability to detect statistically significant relationships from scatterplots in a randomized trial of 2,039 students in a statistics massive open online course (MOOC). Each subject was shown a random set of scatterplots and asked to visually determine if the underlying relationships were statistically significant at the P < 0.05 level. Subjects correctly classified only 47.4% (95% CI: 45.1%-49.7%) of statistically significant relationships, and 74.6% (95% CI: 72.5%-76.6%) of non-significant relationships. Adding visual aids such as a best fit line or scatterplot smooth increased the probability a relationship was called significant, regardless of whether the relationship was actually significant. Classification of statistically significant relationships improved on repeat attempts of the survey, although classification of non-significant relationships did not. Our results suggest: (1) that evidence-based data analysis can be used to identify weaknesses in theoretical procedures in the hands of average users, (2) data analysts can be trained to improve detection of statistically significant results with practice, but (3) data analysts have incorrect intuition about what statistically significant relationships look like, particularly for small effects. We have built a web tool for people to compare scatterplots with their corresponding p-values which is available here: http://glimmer.rstudio.com/afisher/EDA/.Comment: 7 pages, including 2 figures and 1 tabl

Polymer-Ionic Liquid Hybrid Electrolytes for Lithium Batteries

Intellectual Merit: The goal of this dissertation is to investigate the electrochemical properties and microstructure of thin film polymer electrolytes with enhanced electrochemical performance. Solid electrolyte architectures have been produced by blending novel room temperature ionic liquid (RTIL) chemistries with ionically conductive polymer matrices. A variety of microstructure and electrical characterization tools have been employed to understand the hybrid electrolyte's performance. Lithium-ion batteries are limited because of the safety of the electrolyte. The current generation of batteries uses organic solvents to conduct lithium between the electrodes. Occasionally, the low boiling point and high combustibility of these solvents lead to pressure build ups and fires within cells. Additionally, there are issues with electrolyte loss and decreased performance that must be accounted for in daily use. Thus, interest in replacing this system with a solid polymer electrolyte that can match the properties of an organic solvent is of great interest in battery research. However, a polymer electrolyte by itself is incapable of meeting the performance characteristics, and thus by adding an RTIL it has met the necessary threshold values. With the development of the novel sulfur based ionic liquid compounds, improved performance characteristics were realized for the polymer electrolyte. The synthesized RTILs were blended with ionically conductive polymer matrices (polyethylene oxide (PEO) or block copolymers of PEO) to produce solid electrolytes. Such shape-conforming materials could be lead to unique battery morphologies, but more importantly the safety of these new batteries will greatly exceeds those based on traditional organic carbonate electrolytes. Broader Impacts: The broader impact of this research is that it will ultimately help push forward an attractive alternative to carbonate based liquid electrolyte systems. Development of these alternatives has been slow; however bypassing the current commercial options will lead to not only safer and more powerful batteries. The polymer electrolyte system offers flexibility in both mechanical properties and product design. In due course, this will lead to batteries unlike any currently available on the market. RTILs offer quite an attractive option and the electrochemical understanding of novel architectures based upon sulfur will lead to further potential uses for these compounds

The impact of regulatory changes on the providers of treatment for opioid dependence

In 2000, changes in federal law allowed physicians to receive waivers to use narcotic medications, such as buprenorphine, for treatment of opioid dependence. As of 2006, physicians have been allowed to treat up to 100 patients after spending one year at a 30-patient limit. Physicians may choose to discontinue use of buprenorphine after the patient has successfully discontinued use of the substance of abuse ( withdrawal ), or physicians can keep patients on buprenorphine indefinitely ( maintenance ). The model in this dissertation assumes that demand for treatment of opioid dependence is exogenous but that demand for maintenance treatment can be induced by the physician. Using data from quarterly surveys of physicians from 2006 to 2010, this dissertation analyzes the impact of the higher caseload limit on the number of patients and the treatment path chosen by the physician. It finds support for the conclusion that physicians treat more patients after an increase in the caseload limit. The impact is particularly strong for maintenance, suggesting that the caseload limit discourages maintenance treatment. The dissertation also finds that this effect is stronger for physicians in primary-care type specialties

Dry matter intake and methane emissions of beef cattle grazing tall fescue pastures

A two-year study was conducted to use methane (CH4) production as an indicator of beef cattle efficiency on tall fescue (Festuca arundinacea) pasture management systems and to evaluate the importance of dry matter intake (DMI) among different tall fescue systems At Blount Unit, two steers on two pastures each of endophyte (Neotyphodium coenophialum) infected (E+) tall fescue, of endophyte free (E-) tall fescue, of E+/E-(1:1 ratio), and of E+/clover (Trifolium repens) were used to determine CH4 and DMI At Holston Unit, four steers and four cow/calf pairs on one pasture each of a best management practices (BMP) pasture system and of an unimproved pasture (UIP) system were used to determine CH4 and DMI Grazing occurred from March to September in 1997 and 1998. At Blount Unit, steers on E+ pasture gained less (P\u3c 0.05) weight than those on the E- and E+/clover pastures and consumed less (P \u3c 0.05) forage than on all other treatments There were no differences in ADG and DMI, except that cows consumed more (P \u3c 0 05) forage than steers at Holston Unit Animals on the BMP produced less (P\u3c 0.05) CH4 than the UIP. Cows produced more (P \u3c 0.05) CH4 than steers The E+/clover and BMP pasture systems were lower (P \u3c 0.05) in ADF and NDF and higher (P \u3c 0 05) in CP and IVDMD than the other pasture systems within their respective unit The presence of clover in E+ tall fescue increased forage quality, DMI,and ADG over that of E+ tall fescue This coupled with other management strategies may reduce CH4 production