Browsing by Author "Greenwood, Mark C."

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An Alternative to the Carnegie Classifications: Identifying Similar Institutions with Structural Equation Models and Clustering
(2019-01) Harmon, Paul; McKnight, Sarah; Hildreth, Laura; Godwin, Ian; Greenwood, Mark C.
The Carnegie Classification of Institutions of Higher Education is a commonly used framework for institutional classification that classifies doctoral-granting schools into three groups based on research productivity. Despite its wide use, the Carnegie methodology involves several shortcomings, including a lack of thorough documentation, subjectively placed thresholds between institutions, and a methodology that is not completely reproducible. We describe the methodology of the 2015 and 2018 updates to the classification and propose an alternative method of classification using the same data that relies on structural equation modeling (SEM) of latent factors rather than principal component-based indices of productivity. In contrast to the Carnegie methodology, we use SEM to obtain a single factor score for each school based on latent metrics of research productivity. Classifications are then made using a univariate model-based clustering algorithm as opposed to subjective thresholding, as is done in the Carnegie methodology. Finally, we present a Shiny web application that demonstrates sensitivity of both the Carnegie Classification and SEM-based classification of a selected university and generates a table of peer institutions in line with the stated goals of the Carnegie Classification.
Bioenergy and Breast Cancer: A report on tumor growth and metastasis
(2016) Running, Alice; Greenwood, Mark C.; Hildreth, Laura; Schmidt, Jade
As many as 80% of the 296,000 women and 2,240 men diagnosed with breast cancer in the United States will seek out complementary and alternative medicine (CAM) treatments. One such therapy is Healing Touch (HT), recognized by the National Center for Complementary and Integrative Health (NCCIH) as a treatment modality. Using a multiple experimental groups design, fifty-six six- to eight-week-old Balb/c mice were injected with 4T1 breast cancer tumor cells and randomly divided into intervention and positive control groups. Five days after tumor cell injection, mice in the intervention groups received HT either daily or every other day for 10 minutes by one HT practitioner. At 15 days after tumor cell injection, tumor size was measured, and metastasis was evaluated by a medical pathologist after necropsy. Tumor size did not differ significantly among the groups (F(3,52) = 0.75, p value = 0.53). The presence of metastasis did not differ across groups (chi-square(3) = 3.902, p = 0.272) or when compared within an organ (liver: chi-square(3) = 2.507, p = 0.474; lungs: chi-square(3) = 3.804, ; spleen: chi-square(3) = 0.595, p = 0.898). However, these results did indicate a moderate, though insignificant, positive impact of HT and highlight the need for continued research into dose, length of treatment, and measurable outcomes (tumor size, metastasis) to provide evidence to suggest application for nursing care.
Characterization of synovial fluid metabolomic phenotypes of cartilage morphological changes associated with osteoarthritis
(2019-08) Carlson, Alyssa K.; Rawle, Rachel A.; Wallace, Cameron W.; Brooks, Ellen G.; Adams, Erik; Greenwood, Mark C.; Olmer, Merissa; Lotz, Martin K.; Bothner, Brian; June, Ronald K.
"Objective Osteoarthritis (OA) is a multifactorial disease with etiological heterogeneity. The objective of this study was to classify OA subgroups by generating metabolomic phenotypes from human synovial fluid. Design: Post mortem synovial fluids (n = 75) were analyzed by high performance-liquid chromatography mass spectrometry (LC-MS) to measure changes in the global metabolome. Comparisons of healthy (grade 0), early OA (grades I-II), and late OA (grades III-IV) donor populations were considered to reveal phenotypes throughout disease progression. Results: Global metabolomic profiles in synovial fluid were distinct between healthy, early OA, and late OA donors. Pathways differentially activated among these groups included structural deterioration, glycerophospholipid metabolism, inflammation, central energy metabolism, oxidative stress, and vitamin metabolism. Within disease states (early and late OA), subgroups of donors revealed distinct phenotypes. Synovial fluid metabolomic phenotypes exhibited increased inflammation (early and late OA), oxidative stress (late OA), or structural deterioration (early and late OA) in the synovial fluid. Conclusion: These results revealed distinct metabolic phenotypes in human synovial fluid, provide insight into pathogenesis, represent novel biomarkers, and can move toward developing personalized interventions for subgroups of OA patients.
Daily Step Count Profile Data for 61 Days [dataset]
(2016-04) Meyer, Elijah S.; Greenwood, Mark C.; Tran, Tan
Equine Glucose Data [dataset]
(Montana State University ScholarWorks, 2016-04) Weeding, Jennifer L.; Greenwood, Mark C.; Moreaux, Shannon; Nichols, Jyme
Functional Analysis of Variance for Association Studies
(Public Library of Science, 2014) Greenwood, Mark C.; Vsevolozhskaya, Olga; Zaykin, Dmitri; Wei, Changshuai; Lu, Qing
While progress has been made in identifying common genetic variants associated with human diseases, for most of common complex diseases, the identified genetic variants only account for a small proportion of heritability. Challenges remain in finding additional unknown genetic variants predisposing to complex diseases. With the advance in next-generation sequencing technologies, sequencing studies have become commonplace in genetic research. The ongoing exome-sequencing and whole-genome-sequencing studies generate a massive amount of sequencing variants and allow researchers to comprehensively investigate their role in human diseases. The discovery of new disease-associated variants can be enhanced by utilizing powerful and computationally efficient statistical methods. In this paper, we propose a functional analysis of variance (FANOVA) method for testing an association of sequence variants in a genomic region with a qualitative trait. The FANOVA has a number of advantages: (1) it tests for a joint effect of gene variants, including both common and rare; (2) it fully utilizes linkage disequilibrium and genetic position information; and (3) allows for either protective or risk-increasing causal variants. Through simulations, we show that FANOVA outperform two popularly used methods – SKAT and a previously proposed method based on functional linear models (FLM), – especially if a sample size of a study is small and/or sequence variants have low to moderate effects. We conduct an empirical study by applying three methods (FANOVA, SKAT and FLM) to sequencing data from Dallas Heart Study. While SKAT and FLM respectively detected ANGPTL 4 and ANGPTL 3 associated with obesity, FANOVA was able to identify both genes associated with obesity.
Intermediate Statistics with R
(2014-01) Greenwood, Mark C.
Introductory statistics courses prepare students to think statistically but cover relatively few statistical methods. Building on the basic statistical thinking emphasized in an introductory course, a second course in statistics at the undergraduate level can explore a large number of statistical methods. This text covers more advanced graphical summaries, One-Way ANOVA with pair-wise comparisons, Two-Way ANOVA, Chi-square testing, and simple and multiple linear regression models. Models with interactions are discussed in the Two-Way ANOVA and multiple linear regression setting with categorical explanatory variables. Randomization-based inferences are used to introduce new parametric distributions and to enhance understanding of what evidence against the null hypothesis “looks like”. Throughout, the use of the statistical software R via Rstudio is emphasized with all useful code and data sets provided within the text. This is Version 3.1 of the book.
Meta-analysis of the Age-Dependent Efficacy of Multiple Sclerosis Treatments
(2017-11) Weideman, Ann Marie; Tapia-Maltos, Marco Aurelio; Johnson, Kory; Greenwood, Mark C.; Bielekova, Bibiana
Objective: To perform a meta-analysis of randomized, blinded, multiple sclerosis (MS) clinical trials, to test the hypothesis that efficacy of immunomodulatory disease-modifying therapies (DMTs) on MS disability progression is strongly dependent on age. Methods: We performed a literature search with pre-defined criteria and extracted relevant features from 38 clinical trials that assessed efficacy of DMTs on disability progression. We fit a linear regression, weighted for trial sample size, and duration, to examine the hypothesis that age has a defining effect on the therapeutic efficacy of immunomodulatory DMTs. Results: More than 28,000 MS subjects participating in trials of 13 categories of immunomodulatory drugs are included in the meta-analysis. The efficacy of immunomodulatory DMTs on MS disability strongly decreased with advancing age (R-2= 0.6757, p = 6.39e-09). Inclusion of baseline EDSS did not significantly improve the model. The regression predicts zero efficacy beyond approximately age 53 years. The comparative efficacy rank derived from the regression residuals differentiates high-and low-efficacy drugs. High-efficacy drugs outperform low-efficacy drugs in inhibiting MS disability only for patients younger than 40.5 years. Conclusion: The meta-analysis supports the notion that progressive MS is simply a later stage of the MS disease process and that age is an essential modifier of a drug efficacy. Higher efficacy treatments exert their benefit over lower efficacy treatments only during early stages of MS, and, after age 53, the model suggests that there is no predicted benefit to receiving immunomodulatory DMTs for the average MS patient.
Multi-scale clustering of functional data with application to hydraulic gradients in wetlands
(Columbia University, New York, 2011) Greenwood, Mark C.; Soida, Richard S.; Sharp, Julia L.; Peck, Rory G.; Rosenberry, Donald O.
A new set of methods are developed to perform cluster analysis of functions, motivated by a data set consisting of hydraulic gradients at several locations distributed across a wetland complex. The methods build on previous work on clustering of functions, such as Tarpey and Kinateder (2003) and Hitchcock et al. (2007), but explore functions generated from an additive model decomposition (Wood, 2006) of the original time se- ries. Our decomposition targets two aspects of the series, using an adaptive smoother for the trend and circular spline for the diurnal variation in the series. Different measures for comparing locations are discussed, including a method for efficiently clustering time series that are of different lengths using a functional data approach. The complicated nature of these wetlands are highlighted by the shifting group memberships depending on which scale of variation and year of the study are considered.
NeurEx: digitalized neurological examination offers a novel high-resolution disability scale
(2018-10) Kosa, Peter; Barbour, Christopher; Wichman, Alison; Sandford, Mary; Greenwood, Mark C.; Bielekova, Bibiana
Objective To develop a sensitive neurological disability scale for broad utilization in clinical practice. Methods We employed advances of mobile computing to develop an iPad-based App for convenient documentation of the neurological examination into a secure, cloud-linked database. We included features present in four traditional neuroimmunological disability scales and codified their automatic computation. By combining spatial distribution of the neurological deficit with quantitative or semiquantitative rating of its severity we developed a new summary score (called NeurEx; ranging from 0 to 1349 with minimal measurable change of 0.25) and compared its performance with clinician- and App-computed traditional clinical scales. Results In the cross-sectional comparison of 906 neurological examinations, the variance between App-computed and clinician-scored disability scales was comparable to the variance between rating of the identical neurological examination by multiple sclerosis (MS)-trained clinicians. By eliminating rating ambiguity, App-computed scales achieved greater accuracy in measuring disability progression over time (n = 191 patients studied over 880.6 patient-years). The NeurEx score had no apparent ceiling effect and more than 200-fold higher sensitivity for detecting a measurable yearly disability progression (i.e., median progression slope of 8.13 relative to minimum detectable change of 0.25) than Expanded Disability Status Scale (EDSS) with a median yearly progression slope of 0.071 that is lower than the minimal measurable change on EDSS of 0.5. Interpretation NeurEx can be used as a highly sensitive outcome measure in neuroimmunology. The App can be easily modified for use in other areas of neurology and it can bridge private practice practitioners to academic centers in multicenter research studies.
New Multiple Sclerosis Disease Severity Scale Predicts Future Accumulation of Disability
(2017-11) Weideman, Ann Marie; Barbour, Christopher; Tapia-Maltos, Marco Aurelio; Tran, Tan; Jackson, Kayla; Kosa, Peter; Komori, Mika; Wichman, Alison; Johnson, Kory; Greenwood, Mark C.; Bielekova, Bibiana
The search for the genetic foundation of multiple sclerosis (MS) severity remains elusive. It is, in fact, controversial whether MS severity is a stable feature that predicts future disability progression. If MS severity is not stable, it is unlikely that genotype decisively determines disability progression. An alternative explanation tested here is that the apparent instability of MS severity is caused by inaccuracies of its current measurement. We applied statistical learning techniques to a 902 patient-years longitudinal cohort of MS patients, divided into training (n = 133) and validation (n = 68) sub-cohorts, to test four hypotheses: (1) there is intra-individual stability in the rate of accumulation of MS-related disability, which is also influenced by extrinsic factors. (2) Previous results from observational studies are negatively affected by the insensitive nature of the Expanded Disability Status Scale (EDSS). The EDSS-based MS Severity Score (MSSS) is further disadvantaged by the inability to reliably measure MS onset and, consequently, disease duration (DD). (3) Replacing EDSS with a sensitive scale, i.e., Combinatorial Weight-Adjusted Disability Score (CombiWISE), and substituting age for DD will significantly improve predictions of future accumulation of disability. (4) Adjusting measured disability for the efficacy of administered therapies and other relevant external features will further strengthen predictions of future MS course. The result is a MS disease severity scale (MS-DSS) derived by conceptual advancements of MSSS and a statistical learning method called gradient boosting machines (GBM). MS-DSS greatly outperforms MSSS and the recently developed Age Related MS Severity Score in predicting future disability progression. In an independent validation cohort, MS-DSS measured at the first clinic visit correlated significantly with subsequent therapy-adjusted progression slopes (r = 0.5448, p = 1.56e-06) measured by CombiWISE. To facilitate widespread use of MS-DSS, we developed a free, interactive web application that calculates all aspects of MS-DSS and its contributing scales from user-provided raw data. MS-DSS represents a much-needed tool for genotype-phenotype correlations, for identifying biological processes that underlie MS progression, and for aiding therapeutic decisions.
Physicochemical and biological dynamics in a coastal Antarctic lake as it transitions from frozen to open water
(2013-03) Dieser, Markus; Foreman, Christine M.; Jaros, C.; Lisle, John T.; Greenwood, Mark C.; Laybourn-Parry, Johanna; Miller, P. L.; Chin, Yu-Ping; McKnight, Diane M.
Pony Lake, at Cape Royds, Antarctica, is a shallow, eutrophic, coastal lake that freezes solid in the winter. Changes in Pony Lake's physicochemical parameters and microbial community were studied during the transition from ice to open water. Due to rising water temperatures, the progressive melt of the ice column and the gradual mixing of basal brines into the remaining water column, Pony Lake evolved physically and chemically over the course of the summer, thereby affecting the microbial community composition. Temperature, pH, conductivity, nutrients and major ion concentrations reached their maximum in January. Pony Lake was colonized by bacteria, viruses, phytoflagellates, ciliates, and a small number of rotifers. Primary and bacterial production were highest in mid-December (2.66 mg C 1-1 d-1 and 30.5 Âµg C 1-1 d-1, respectively). A 16S rRNA gene analysis of the bacterioplankton revealed 34 unique sequences dominated by members of the ÃŸ- and y-proteobacteria lineages. Cluster analyses on denaturing gradient gel electrophoresis (DGGE) banding patterns and community structure indicated a shift in the dominant members of the microbial community during the transition from winter ice, to early, and late summer lakewater. Our data demonstrate that temporal changes in physicochemical parameters during the summer months determine community dynamics and mediate changes in microbial species composition.
Prokaryotes in the WAIS Divide ice core reflect source and transport changes between Last Glacial Maximum and the early Holocene
(2018-05) Santibanez, Pamela A.; Maselli, Olivia J.; Greenwood, Mark C.; Grieman, Mackenzie M.; Saltzman, Eric S.; McConnell, Joseph R.; Priscu, John C.
We present the first long-term, highly resolved prokaryotic cell concentration record obtained from a polar ice core. This record, obtained from the West Antarctic Ice Sheet (WAIS) Divide (WD) ice core, spanned from the Last Glacial Maximum (LGM) to the early Holocene (EH) and showed distinct fluctuations in prokaryotic cell concentration coincident with major climatic states. The time series also revealed a ~1,500-year periodicity with greater amplitude during the Last Deglaciation (LDG). Higher prokaryotic cell concentration and lower variability occurred during the LGM and EH than during the LDG. A seven-fold decrease in prokaryotic cell concentration coincided with the LGM/LDG transition and the global 19 ka meltwater pulse. Statistical models revealed significant relationships between the prokaryotic cell record and tracers of both marine (sea-salt sodium [ssNa]) and burning emissions (black carbon [BC]). Collectively, these models, together with visual observations and methanosulfidic acid (MSA) measurements, indicated that the temporal variability in concentration of airborne prokaryotic cells reflected changes in marine/sea-ice regional environments of the WAIS. Our data revealed that variations in source and transport were the most likely processes producing the significant temporal variations in WD prokaryotic cell concentrations. This record provided strong evidence that airborne prokaryotic cell deposition differed during the LGM, LDG and EH, and that these changes in cell densities could be explained by different environmental conditions during each of these climatic periods. Our observations provide the first ice core time-series evidence for a prokaryotic response to long-term climatic and environmental processes.
Significance of trends toward earlier snowmelt runoff, Columbia and Missouri Basin headwaters, western United States
(American Geophysical Union, 2007-08) Moore, Johnnie N.; Harper, Joel T.; Greenwood, Mark C.
We assess changes in runoff timing over the last 55 years at 21 gages unaffected by human influences, in the headwaters of the Columbia-Missouri Rivers. Linear regression models and tests for significance that control for “false discoveries” of many tests, combined with a conceptual runoff response model, were used to examine the detailed structure of spring runoff timing. We conclude that only about one third of the gages exhibit significant trends with time but over half of the gages tested show significant relationships with discharge. Therefore, runoff timing is more significantly correlated with annual discharge than with time. This result differs from previous studies of runoff in the western USA that equate linear time trends to a response to global warming. Our results imply that predicting future snowmelt runoff in the northern Rockies will require linking climate mechanisms controlling precipitation, rather than projecting response to simple linear increases in temperature.
Statistical methods for detecting groups of patterns in daily step count activity profiles
(2016) Meyer, Elijah S.; Tran, Tan; Greenwood, Mark C.
The growth of wearable technology is apparent. A total of 2.7 million wearable bands were shipped worldwide in the first quarter of 2014 (Sullivan, 2014). Of this number, Fitbit, a company focused on developing and manufacturing compact, wireless wearable technology devices accounted for half of the shipments alone. A Fitbit device can track steps, distance, calories burned, floors climbed, active minutes, and sleep patterns and provides regular users with daily totals or totals over 15-minute intervals throughout the day. The motivation of staying fit coupled with the convenience of collecting the data through a simple wrist band is fueling this industry. The utility of activity tracking for improved health outcomes is left for other researchers. Here we focus on whether the focus on daily summaries is making dissimilar days look similar, and whether we can group days in such a way that balances total activity as well as timing of that activity.
Supporting Information for "Significance of trends toward earlier snowmelt runoff, Columbia and Missouri Basin headwaters, western United States"
(American Geophysical Union, 2007-08) Moore, Johnnie N.; Harper, Joel T.; Greenwood, Mark C.
This is auxiliary material for this article contains one text file, four tables, and three figures from the article "Significance of trends toward earlier snowmelt runoff, Columbia and Missouri Basin headwaters, western United States" from Geophysical Research Letters on the 28 August 2007. http://onlinelibrary.wiley.com/doi/10.1029/2007GL031022/full
When a habitat freezes solid: Microorganisms over-winter within the ice column of a coastal Antarctic lake
(2011-03) Foreman, Christine M.; Dieser, Markus; Greenwood, Mark C.; Cory, R. M.; Laybourn-Parry, Johanna; Lisle, John T.; Jaros, C.; Miller, P. L.; Chin, Yu-Ping; McKnight, Diane M.
A major impediment to understanding the biology of microorganisms inhabiting Antarctic environments is the logistical constraint of conducting field work primarily during the summer season. However, organisms that persist throughout the year encounter severe environmental changes between seasons. In an attempt to bridge this gap, we collected ice core samples from Pony Lake in early November 2004 when the lake was frozen solid to its base, providing an archive for the biological and chemical processes that occurred during winter freezeup. The ice contained bacteria and virus-like particles, while flagellated algae and ciliates over-wintered in the form of inactive cysts and spores. Both bacteria and algae were metabolically active in the ice core melt water. Bacterial production ranged from 1.8 to 37.9 μg C L−1 day−1. Upon encountering favorable growth conditions in the melt water, primary production ranged from 51 to 931 μg C L−1 day−1. Because of the strong H2S odor and the presence of closely related anaerobic organisms assigned to Pony Lake bacterial 16S rRNA gene clones, we hypothesize that the microbial assemblage was strongly affected by oxygen gradients, which ultimately restricted the majority of phylotypes to distinct strata within the ice column. This study provides evidence that the microbial community over-winters in the ice column of Pony Lake and returns to a highly active metabolic state when spring melt is initiated.