Geographic Information Systems-Based Approaches to Study Congressional Redistricting in the United States

The ability for the citizens of a nation to determine their own representation has long been regarded as one of the most critical objectives of any electoral system. Without having the assurance of equality in representation, the fundamental nature and operation of the political system is severely undermined. Given the centuries of institutional reforms and population changes in the American system, Congressional Redistricting stands as an institution whereby this promise of effective representation can either be fulfilled or denied. The broad set of processes that encapsulate Congres- sional Redistricting have been discussed, experimented, and modified to achieve clear objectives and have long been understood to be important. Questions remain about how the dynamics which link all of these processes operate and what impact the real- ities of Congressional Redistricting hold for representation in the American system. This dissertation examines three aspects of how Congressional Redistricting in the Untied States operates in accordance with the principle of “One Person, One Vote.” By utilizing data and data analysis techniques of Geographic Information Systems (GIS), this dissertation seeks to address how Congressional Redistricting impacts the principle of one person, one vote from the standpoint of legislator accountability, redistricting institutions, and the promise of effective minority representation.

Understanding GPU programming for statistical computation: Studies in massively parallel massive mixtures

This article describes advances in statistical computation for large-scale data analysis in structured Bayesian mixture models via graphics processing unit (GPU) programming. The developments are partly motivated by computational challenges arising in fitting models of increasing heterogeneity to increasingly large datasets. An example context concerns common biological studies using high-throughput technologies generating many, very large datasets and requiring increasingly high-dimensional mixture models with large numbers of mixture components.We outline important strategies and processes for GPU computation in Bayesian simulation and optimization approaches, give examples of the benefits of GPU implementations in terms of processing speed and scale-up in ability to analyze large datasets, and provide a detailed, tutorial-style exposition that will benefit readers interested in developing GPU-based approaches in other statistical models. Novel, GPU-oriented approaches to modifying existing algorithms software design can lead to vast speed-up and, critically, enable statistical analyses that presently will not be performed due to compute time limitations in traditional computational environments. Supplementalmaterials are provided with all source code, example data, and details that will enable readers to implement and explore the GPU approach in this mixture modeling context. © 2010 American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America.

Gene selection using iterative feature elimination random forests for survival outcomes.

Although many feature selection methods for classification have been developed, there is a need to identify genes in high-dimensional data with censored survival outcomes. Traditional methods for gene selection in classification problems have several drawbacks. First, the majority of the gene selection approaches for classification are single-gene based. Second, many of the gene selection procedures are not embedded within the algorithm itself. The technique of random forests has been found to perform well in high-dimensional data settings with survival outcomes. It also has an embedded feature to identify variables of importance. Therefore, it is an ideal candidate for gene selection in high-dimensional data with survival outcomes. In this paper, we develop a novel method based on the random forests to identify a set of prognostic genes. We compare our method with several machine learning methods and various node split criteria using several real data sets. Our method performed well in both simulations and real data analysis.Additionally, we have shown the advantages of our approach over single-gene-based approaches. Our method incorporates multivariate correlations in microarray data for survival outcomes. The described method allows us to better utilize the information available from microarray data with survival outcomes.

RIR-MAPLE deposition of plasmonic silver nanoparticles

© 2016, Springer-Verlag Berlin Heidelberg.Nanoparticles are being explored in many different applications due to the unique properties offered by quantum effects. To broaden the scope of these applications, the deposition of nanoparticles onto substrates in a simple and controlled way is highly desired. In this study, we use resonant infrared matrix-assisted pulsed laser evaporation (RIR-MAPLE) for the deposition of metallic, silver nanoparticles for plasmonic applications. We find that RIR-MAPLE, a simple and versatile approach, is able to deposit silver nanoparticles as large as 80 nm onto different substrates with good adhesion, regardless of substrate properties. In addition, the nanoparticle surface coverage of the substrates, which result from the random distribution of nanoparticles across the substrate per laser pulse, can be simply and precisely controlled by RIR-MAPLE. Polymer films of poly(3-hexylthiophene-2,5-diyl) (P3HT) are also deposited by RIR-MAPLE on top of the deposited silver nanoparticles in order to demonstrate enhanced absorption due to the localized surface plasmon resonance effect. The reported features of RIR-MAPLE nanoparticle deposition indicate that this tool can enable efficient processing of nanoparticle thin films for applications that require specific substrates or configurations that are not easily achieved using solution-based approaches.

Component-wise gradient boosting and false discovery control in survival analysis with high-dimensional covariates.

MOTIVATION: Technological advances that allow routine identification of high-dimensional risk factors have led to high demand for statistical techniques that enable full utilization of these rich sources of information for genetics studies. Variable selection for censored outcome data as well as control of false discoveries (i.e. inclusion of irrelevant variables) in the presence of high-dimensional predictors present serious challenges. This article develops a computationally feasible method based on boosting and stability selection. Specifically, we modified the component-wise gradient boosting to improve the computational feasibility and introduced random permutation in stability selection for controlling false discoveries. RESULTS: We have proposed a high-dimensional variable selection method by incorporating stability selection to control false discovery. Comparisons between the proposed method and the commonly used univariate and Lasso approaches for variable selection reveal that the proposed method yields fewer false discoveries. The proposed method is applied to study the associations of 2339 common single-nucleotide polymorphisms (SNPs) with overall survival among cutaneous melanoma (CM) patients. The results have confirmed that BRCA2 pathway SNPs are likely to be associated with overall survival, as reported by previous literature. Moreover, we have identified several new Fanconi anemia (FA) pathway SNPs that are likely to modulate survival of CM patients. AVAILABILITY AND IMPLEMENTATION: The related source code and documents are freely available at https://sites.google.com/site/bestumich/issues. CONTACT: yili@umich.edu.

Non-Pharmacological Approaches for Pain Management in Sickle Cell Disease: Development of a Mindfulness-Based Intervention

Background: Sickle Cell Disease (SCD) is a genetic hematological disorder that affects more than 7 million people globally (NHLBI, 2009). It is estimated that 50% of adults with SCD experience pain on most days, with 1/3 experiencing chronic pain daily (Smith et al., 2008). Persons with SCD also experience higher levels of pain catastrophizing (feelings of helplessness, pain rumination and magnification) than other chronic pain conditions, which is associated with increases in pain intensity, pain behavior, analgesic consumption, frequency and duration of hospital visits, and with reduced daily activities (Sullivan, Bishop, & Pivik, 1995; Keefe et al., 2000; Gil et al., 1992 & 1993). Therefore effective interventions are needed that can successfully be used manage pain and pain-related outcomes (e.g., pain catastrophizing) in persons with SCD. A review of the literature demonstrated limited information regarding the feasibility and efficacy of non-pharmacological approaches for pain in persons with SCD, finding an average effect size of .33 on pain reduction across measurable non-pharmacological studies. Second, a prospective study on persons with SCD that received care for a vaso-occlusive crisis (VOC; N = 95) found: (1) high levels of patient reported depression (29%) and anxiety (34%), and (2) that unemployment was significantly associated with increased frequency of acute care encounters and hospital admissions per person. Research suggests that one promising category of non-pharmacological interventions for managing both physical and affective components of pain are Mindfulness-based Interventions (MBIs; Thompson et al., 2010; Cox et al., 2013). The primary goal of this dissertation was thus to develop and test the feasibility, acceptability, and efficacy of a telephonic MBI for pain catastrophizing in persons with SCD and chronic pain.

Methods: First, a telephonic MBI was developed through an informal process that involved iterative feedback from patients, clinical experts in SCD and pain management, social workers, psychologists, and mindfulness clinicians. Through this process, relevant topics and skills were selected to adapt in each MBI session. Second, a pilot randomized controlled trial was conducted to test the feasibility, acceptability, and efficacy of the telephonic MBI for pain catastrophizing in persons with SCD and chronic pain. Acceptability and feasibility were determined by assessment of recruitment, attrition, dropout, and refusal rates (including refusal reasons), along with semi-structured interviews with nine randomly selected patients at the end of study. Participants completed assessments at baseline, Week 1, 3, and 6 to assess efficacy of the intervention on decreasing pain catastrophizing and other pain-related outcomes.

Results: A telephonic MBI is feasible and acceptable for persons with SCD and chronic pain. Seventy-eight patients with SCD and chronic pain were approached, and 76% (N = 60) were enrolled and randomized. The MBI attendance rate, approximately 57% of participants completing at least four mindfulness sessions, was deemed acceptable, and participants that received the telephonic MBI described it as acceptable, easy to access, and consume in post-intervention interviews. The amount of missing data was undesirable (MBI condition, 40%; control condition, 25%), but fell within the range of expected missing outcome data for a RCT with multiple follow-up assessments. Efficacy of the MBI on pain catastrophizing could not be determined due to small sample size and degree of missing data, but trajectory analyses conducted for the MBI condition only trended in the right direction and pain catastrophizing approached statistically significance.

Conclusion: Overall results showed that at telephonic group-based MBI is acceptable and feasible for persons with SCD and chronic pain. Though the study was not able to determine treatment efficacy nor powered to detect a statistically significant difference between conditions, participants (1) described the intervention as acceptable, and (2) the observed effect sizes for the MBI condition demonstrated large effects of the MBI on pain catastrophizing, mental health, and physical health. Replication of this MBI study with a larger sample size, active control group, and additional assessments at the end of each week (e.g., Week 1 through Week 6) is needed to determine treatment efficacy. Many lessons were learned that will guide the development of future studies including which MBI strategies were most helpful, methods to encourage continued participation, and how to improve data capture.