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Item Time Series Analysis of Mosquito Population Data(Entomological Society of America, 1973) Hacker, Carl S.; Scott, David W.; Thompson, James R.A statistical technique, time series analysis, has been introduced, allowing the development of objective and quantitative insights into the behavior of relative mosquito densities as a function of time. This method is particularly useful for detecting periodicities in mosquito densities as well as the relationship between meteorological phenomena and mosquito densities.Item AIDS: The mismanagement of an epidemic(Maxwell Pergamon Macmillan, 1989) Thompson, J.R.An argument is made that, so far from being a disease which is unstoppable in its epidemic consequences, AIDS has produced an epidemic, which owes its present virulence to sociological configurations of rather recent existence. Instead of a vigorous attack on the transmission chain of the epidemic, the emphasis of public health policy has been on finding a vaccine and/or a cure of the disease which produces the epidemic. By means of a simple model, it is argued that by simply closing businesses catering to high contact rate anal sex, e.g. sexually oriented bathhouses, the American public health establishment might have avoided most of the tragic consequences of the present epidemic.Item Marketplace Competition in the Personal Computer Industry*(Wiley, 1992) Bridges, Eileen; Ensor, Katherine B.; Thompson, James R.A decision regarding development and introduction of a potential new product depends, in part, on the intensity of compeitition anticipated in the marketplace. In the case of a technology-based product such as a personal computer (PC), the number of competing products may be very dynamic and consequently uncertain. We address this problem by modeling growth in the number of new PCs as a stochastic counting process, incorporating product entries and exits. We demonstrate how to use the resulting model to forecast competition five years in advance.Item A Post Keynesian Analysis of the Black-Scholes Option Pricing Model(Rice University, 1999) Thompson, J.R.; Williams, E.E.Item The Age of Tukey(2001) Thompson, James R.Item Why We All Held Our Breath When the Market Reopened(Rice University, 2003) Findlay, M.C.; Williams, E.E.; Thompson, J.R.Item Nobels for nonsense(2005) Thompson, James R.; Baggett, L. Scott; Wojciechowski, William C.; Williams, Edward E.Item Market truths: theory versus empirical simulations(2006) Wojciechowski, William C.; Thompson, James R.Item Making an R package(2010-11-09) Wickham, HadleyItem A systems biology approach reveals common metastatic pathways in osteosarcoma(BioMed Central, 2012) Flores, Ricardo J.; Li, Yiting; Yu, Alexander; Shen, Jianhe; Rao, Pulivarthi H.; Lau, Serrine S.; Vannucci, Marina; Lau, Ching C.; Man, Tsz-KwongBackground: Osteosarcoma (OS) is the most common malignant bone tumor in children and adolescents. The survival rate of patients with metastatic disease remains very dismal. Nevertheless, metastasis is a complex process and a single-level analysis is not likely to identify its key biological determinants. In this study, we used a systems biology approach to identify common metastatic pathways that are jointly supported by both mRNA and protein expression data in two distinct human metastatic OS models. Results: mRNA expression microarray and N-linked glycoproteomic analyses were performed on two commonly used isogenic pairs of human metastatic OS cell lines, namely HOS/143B and SaOS-2/LM7. Pathway analysis of the differentially regulated genes and glycoproteins separately revealed pathways associated to metastasis including cell cycle regulation, immune response, and epithelial-to-mesenchymal-transition. However, no common significant pathway was found at both genomic and proteomic levels between the two metastatic models, suggesting a very different biological nature of the cell lines. To address this issue, we used a topological significance analysis based on a “shortest-path” algorithm to identify topological nodes, which uncovered additional biological information with respect to the genomic and glycoproteomic profiles but remained hidden from the direct analyses. Pathway analysis of the significant topological nodes revealed a striking concordance between the models and identified significant common pathways, including “Cytoskeleton remodeling/TGF/WNT”, “Cytoskeleton remodeling/Cytoskeleton remodeling”, and “Cell adhesion/Chemokines and adhesion”. Of these, the “Cytoskeleton remodeling/TGF/WNT” was the top ranked common pathway from the topological analysis of the genomic and proteomic profiles in the two metastatic models. The up-regulation of proteins in the “Cytoskeleton remodeling/TGF/WNT” pathway in the SaOS-2/LM7 and HOS/143B models was further validated using an orthogonal Reverse Phase Protein Array platform. Conclusions: In this study, we used a systems biology approach by integrating genomic and proteomic data to identify key and common metastatic mechanisms in OS. The use of the topological analysis revealed hidden biological pathways that are known to play critical roles in metastasis. Wnt signaling has been previously implicated in OS and other tumors, and inhibitors of Wnt signaling pathways are available for clinical testing. Further characterization of this common pathway and other topological pathways identified from this study may lead to a novel therapeutic strategy for the treatment of metastatic OS.Item Hyaluronan turnover and hypoxic brown adipocytic differentiation are co-localized with ossification in calcified human aortic valves(Elsevier, 2012) Stephens, Elizabeth H.; Saltarrelli, Jerome G. Jr.; Balaoing, Liezl R.; Baggett, L. Scott; Nandi, Indrajit; Anderson, Kristin M.; Morrisett, Joel D.; Reardon, Michael J.; Simpson, Melanie A.; Weigel, Paul H.; Olmsted-Davis, Elizabeth A.; Davis, Alan R.; Grande-Allen, K. JaneThe calcification process in aortic stenosis has garnered considerable interest but only limited investigation into selected signaling pathways. This study investigated mechanisms related to hypoxia, hyaluronan homeostasis, brown adipocytic differentiation, and ossification within calcified valves. Surgically explanted calcified aortic valves (nᅠ=ᅠ14) were immunostained for markers relevant to these mechanisms and evaluated in the center (NodCtr) and edge (NodEdge) of the calcified nodule (NodCtr), tissue directly surrounding nodule (NodSurr); center and tissue surrounding small モprenodulesヤ (PreNod, PreNodSurr); and normal fibrosa layer (CollFibr). Pearson correlations were determined between staining intensities of markers within regions. Ossification markers primarily localized to NodCtr and NodEdge, along with markers related to hyaluronan turnover and hypoxia. Markers of brown adipocytic differentiation were frequently co-localized with markers of hypoxia. In NodCtr and NodSurr, brown fat and ossification markers correlated with hyaluronidase-1, whereas these markers, as well as hypoxia, correlated with hyaluronan synthases in NodEdge. The protein product of tumor necrosis factor-? stimulated gene-6 strongly correlated with ossification markers and hyaluronidase in the regions surrounding the nodules (NodSurr, PreNodSurr). In conclusion, this study suggests roles for hyaluronan homeostasis and the promotion of hypoxia by cells demonstrating brown fat markers in calcific aortic valve disease.Item Accuracy of optical spectroscopy for the detection of cervical intraepithelial neoplasia without colposcopic tissue information; a step toward automation for low resource settings(Society of Photo-Optical Instrumentation Engineers, 2012-04) Yamal, Jose-Miguel; Zewdie, Getie A.; Cox, Dennis D.; Atkinson, E. Neely; Cantor, Scott B.; MacAulay, Calum; Davies, Kalatu; Adewole, Isaac; Buys, Timon P. H.; Follen, MicheleOptical spectroscopy has been proposed as an accurate and low-cost alternative for detection of cervical intraepithelial neoplasia. We previously published an algorithm using optical spectroscopy as an adjunct to colposcopy and found good accuracy (sensitivity ¼ 1.00 [95% confidence interval ðCIÞ ¼ 0.92 to 1.00], specificity ¼ 0.71 [95% CI ¼ 0.62 to 0.79]). Those results used measurements taken by expert colposcopists as well as the colposcopy diagnosis. In this study, we trained and tested an algorithm for the detection of cervical intraepithelial neoplasia (i.e., identifying those patients who had histology reading CIN 2 or worse) that did not include the colposcopic diagnosis. Furthermore, we explored the interaction between spectroscopy and colposcopy, examining the importance of probe placement expertise. The colposcopic diagnosis-independent spectroscopy algorithm had a sensitivity of 0.98 (95% CI ¼ 0.89 to 1.00) and a specificity of 0.62 (95% CI ¼ 0.52 to 0.71). The difference in the partial area under the ROC curves between spectroscopy with and without the colposcopic diagnosis was statistically significant at the patient level (p ¼ 0.05) but not the site level (p ¼ 0.13). The results suggest that the device has high accuracy over a wide range of provider accuracy and hence could plausibly be implemented by providers with limited training.Item Neural Networks of Colored Sequence Synesthesia(Society for Neuroscience, 2013) Tomson, Steffie N.; Narayan, Manjari; Allen, Genevera I.; Eagleman, David M.Synesthesia is a condition in which normal stimuli can trigger anomalous associations. In this study,weexploit synesthesia to understand how the synesthetic experience can be explained by subtle changes in network properties. Of the many forms of synesthesia, we focus on colored sequence synesthesia, a form in which colors are associated with overlearned sequences, such as numbers and letters (graphemes). Previous studies have characterized synesthesia using resting-state connectivity or stimulus-driven analyses, but it remains unclear how network properties change as synesthetes move from one condition to another. To address this gap, we used functional MRI in humans to identify grapheme-specific brain regions, thereby constructing a functional “synesthetic” network. We then explored functional connectivity of color and grapheme regions during a synesthesia-inducing fMRI paradigm involving rest, auditory grapheme stimulation, and audiovisual grapheme stimulation. Using Markov networks to represent direct relationships between regions, we found that synesthetes had more connections during rest and auditory conditions. We then expanded the network space to include 90 anatomical regions, revealing that synesthetes tightly cluster in visual regions, whereas controls cluster in parietal and frontal regions. Together, these results suggest that synesthetes have increased connectivity between grapheme and color regions, and that synesthetes use visual regions to a greater extent than controls when presented with dynamic grapheme stimulation. These data suggest that synesthesia is better characterized by studying global network dynamics than by individual properties of a single brain region.Item Regularized partial least squares with an application to NMR spectroscopy(John Wiley & Sons, Inc., 2013) Allen, Genevera I.; Peterson, Christine; Vannucci, Marina; Maletic-Savatic, MirjanaHigh-dimensional data common in genomics, proteomics, and chemometrics often contains complicated correlation structures. Recently, partial least squares (PLS) and Sparse PLS methods have gained attention in these areas as dimension reduction techniques in the context of supervised data analysis. We introduce a framework for Regularized PLS by solving a relaxation of the SIMPLS optimization problem with penalties on the PLS loadings vectors. Our approach enjoys many advantages including flexibility, general penalties, easy interpretation of results, and fast computation in high-dimensional settings. We also outline extensions of our methods leading to novel methods for non-negative PLS and generalized PLS, an adoption of PLS for structured data. We demonstrate the utility of our methods through simulations and a case study on proton Nuclear Magnetic Resonance (NMR) spectroscopy data.Item Some Things Economists Know That Just Aren't So(Rice University, 2013) Thompson, J.R.; Baggett, L.S.; Wojciechowski, W.C.Item An Integrative Bayesian Modeling Approach to Imaging Genetics(Taylor & Francis, 2013) Stingo, Francesco C.; Guindani, Michele; Vannucci, Marina; Calhoun, Vince D.Item Molecular pathway identification using biological network-regularized logistic models(BioMed Central, 2013) Zhang, Wen; Wan, Ying-wooi; Allen, Genevera I.; Pang, Kaifang; Anderson, Matthew L.; Liu, ZhandongBackground: Selecting genes and pathways indicative of disease is a central problem in computational biology. This problem is especially challenging when parsing multi-dimensional genomic data. A number of tools, such as L1-norm based regularization and its extensions elastic net and fused lasso, have been introduced to deal with this challenge. However, these approaches tend to ignore the vast amount of a priori biological network information curated in the literature. Results: We propose the use of graph Laplacian regularized logistic regression to integrate biological networks into disease classification and pathway association problems. Simulation studies demonstrate that the performance of the proposed algorithm is superior to elastic net and lasso analyses. Utility of this algorithm is also validated by its ability to reliably differentiate breast cancer subtypes using a large breast cancer dataset recently generated by the Cancer Genome Atlas (TCGA) consortium. Many of the protein-protein interaction modules identified by our approach are further supported by evidence published in the literature. Source code of the proposed algorithm is freely available at http://www.github.com/zhandong/Logit-Lapnet. Conclusion: Logistic regression with graph Laplacian regularization is an effective algorithm for identifying key pathways and modules associated with disease subtypes. With the rapid expansion of our knowledge of biological regulatory networks, this approach will become more accurate and increasingly useful for mining transcriptomic, epi-genomic, and other types of genome wide association studies.Item Inferring metabolic networks using the Bayesian adaptive graphical lasso with informative priors(International Press, 2013) Peterson, Christine; Vannucci, Marina; Karakas, Cemal; Choi, William; Ma, Lihua; Maletic-Savatic, MirjanaMetabolic processes are essential for cellular function and survival. We are interested in inferring a metabolic network in activated microglia, a major neuroimmune cell in the brain responsible for the neuroinflammation associated with neurological diseases, based on a set of quantified metabolites. To achieve this, we apply the Bayesian adaptive graphical lasso with informative priors that incorporate known relationships between covariates. To encourage sparsity, the Bayesian graphical lasso places double exponential priors on the off-diagonal entries of the precision matrix. The Bayesian adaptive graphical lasso allows each double exponential prior to have a unique shrinkage parameter. These shrinkage parameters share a common gamma hyperprior. We extend this model to create an informative prior structure by formulating tailored hyperpriors on the shrinkage parameters. By choosing parameter values for each hyperprior that shift probability mass toward zero for nodes that are close together in a reference network, we encourage edges between covariates with known relationships. This approach can improve the reliability of network inference when the sample size is small relative to the number of parameters to be estimated. When applied to the data on activated microglia, the inferred network includes both known relationships and associations of potential interest for further investigation.Item Small median tumor diameter at cure threshold (<20 mm) among aggressive non-small cell lung cancers in male smokers predicts both chest X-ray and CT screening outcomes in a novel simulation framework(UICC, 2013) Goldwasser, Deborah L.; Kimmel, MarekThe effectiveness of population-wide lung cancer screening strategies depends on the underlying natural course of lung cancer. We evaluate the expected stage distribution in the Mayo CT screening study under an existing simulation model of non-small cell lung cancer (NSCLC) progression calibrated to the Mayo lung project (MLP). Within a likelihood framework, we evaluate whether the probability of 5-year NSCLC survival conditional on tumor diameter at detection depends significantly on screening detection modality, namely chest X-ray and computed tomography. We describe a novel simulation framework in which tumor progression depends on cellular proliferation and mutation within a stem cell compartment of the tumor. We fit this model to randomized trial data from the MLP and produce estimates of the median radiologic size at the cure threshold. We examine the goodness of model fit with respect to radiologic tumor size and 5-year NSCLC survival among incident cancers in both the MLP and Mayo CT studies. An existing model of NSCLC progression under-predicts the number of advanced-stage incident NSCLCs among males in the Mayo CT study (p-value = 0.004). The probability of 5-year NSCLC survival conditional on tumor diameter depends significantly on detection modality (p-value = 0.0312). In our new model, selected solution sets having a median tumor diameter of 16.2ヨ22.1 mm at cure threshold among aggressive NSCLCs predict both MLP and Mayo CT outcomes. We conclude that the median lung tumor diameter at cure threshold among aggressive NSCLCs in male smokers may be small (<20 mm).Item Robust fitting of a Weibull model with optional censoring(Elsevier, 2013) Yang, Jingjing; Scott, David W.The Weibull family is widely used to model failure data, or lifetime data, although the classical two-parameter Weibull distribution is limited to positive data and monotone failure rate. The parameters of the Weibull model are commonly obtained by maximum likelihood estimation; however, it is well-known that this estimator is not robust when dealing with contaminated data. A new robust procedure is introduced to fit a Weibull model by using L2 distance, i.e. integrated square distance, of the Weibull probability density function. The Weibull model is augmented with a weight parameter to robustly deal with contaminated data. Results comparing a maximum likelihood estimator with an L2 estimator are given in this article, based on both simulated and real data sets. It is shown that this new L2 parametric estimation method is more robust and does a better job than maximum likelihood in the newly proposed Weibull model when data are contaminated. The same preference for L2 distance criterion and the new Weibull model also happens for right-censored data with contamination.