SMERED: A Bayesian Approach to Graphical Record Linkage and De-duplication

We propose a novel unsupervised approach for linking records across arbitrarily many files, while simultaneously detecting duplicate records within files. Our key innovation is to represent the pattern of links between records as a {\em bipartite} graph, in which records are directly linked to latent true individuals, and only indirectly linked to other records. This flexible new representation of the linkage structure naturally allows us to estimate the attributes of the unique observable people in the population, calculate $k$-way posterior probabilities of matches across records, and propagate the uncertainty of record linkage into later analyses. Our linkage structure lends itself to an efficient, linear-time, hybrid Markov chain Monte Carlo algorithm, which overcomes many obstacles encountered by previously proposed methods of record linkage, despite the high dimensional parameter space. We assess our results on real and simulated data.

Dual-energy computed tomography with advanced postimage acquisition data processing: improved determination of urinary stone composition.

INTRODUCTION: The characterization of urinary calculi using noninvasive methods has the potential to affect clinical management. CT remains the gold standard for diagnosis of urinary calculi, but has not reliably differentiated varying stone compositions. Dual-energy CT (DECT) has emerged as a technology to improve CT characterization of anatomic structures. This study aims to assess the ability of DECT to accurately discriminate between different types of urinary calculi in an in vitro model using novel postimage acquisition data processing techniques. METHODS: Fifty urinary calculi were assessed, of which 44 had >or=60% composition of one component. DECT was performed utilizing 64-slice multidetector CT. The attenuation profiles of the lower-energy (DECT-Low) and higher-energy (DECT-High) datasets were used to investigate whether differences could be seen between different stone compositions. RESULTS: Postimage acquisition processing allowed for identification of the main different chemical compositions of urinary calculi: brushite, calcium oxalate-calcium phosphate, struvite, cystine, and uric acid. Statistical analysis demonstrated that this processing identified all stone compositions without obvious graphical overlap. CONCLUSION: Dual-energy multidetector CT with postprocessing techniques allows for accurate discrimination among the main different subtypes of urinary calculi in an in vitro model. The ability to better detect stone composition may have implications in determining the optimum clinical treatment modality for urinary calculi from noninvasive, preprocedure radiological assessment.

A versatile diffractive maskless lithography for single-shot and serial microfabrication.

We demonstrate a diffractive maskless lithographic system that is capable of rapidly performing both serial and single-shot micropatterning. Utilizing the diffractive properties of phase holograms displayed on a spatial light modulator, arbitrary intensity distributions were produced to form two and three dimensional micropatterns/structures in a variety of substrates. A straightforward graphical user interface was implemented to allow users to load templates and change patterning modes within the span of a few minutes. A minimum resolution of approximately 700 nm is demonstrated for both patterning modes, which compares favorably to the 232 nm resolution limit predicted by the Rayleigh criterion. The presented method is rapid and adaptable, allowing for the parallel fabrication of microstructures in photoresist as well as the fabrication of protein microstructures that retain functional activity.

Adaptive Mixture Modelling Metropolis Methods for Bayesian Analysis of Non-linear State-Space Models.

We describe a strategy for Markov chain Monte Carlo analysis of non-linear, non-Gaussian state-space models involving batch analysis for inference on dynamic, latent state variables and fixed model parameters. The key innovation is a Metropolis-Hastings method for the time series of state variables based on sequential approximation of filtering and smoothing densities using normal mixtures. These mixtures are propagated through the non-linearities using an accurate, local mixture approximation method, and we use a regenerating procedure to deal with potential degeneracy of mixture components. This provides accurate, direct approximations to sequential filtering and retrospective smoothing distributions, and hence a useful construction of global Metropolis proposal distributions for simulation of posteriors for the set of states. This analysis is embedded within a Gibbs sampler to include uncertain fixed parameters. We give an example motivated by an application in systems biology. Supplemental materials provide an example based on a stochastic volatility model as well as MATLAB code.

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.

Same-sex gaze attraction influences mate-choice copying in humans.

Mate-choice copying occurs when animals rely on the mating choices of others to inform their own mating decisions. The proximate mechanisms underlying mate-choice copying remain unknown. To address this question, we tracked the gaze of men and women as they viewed a series of photographs in which a potential mate was pictured beside an opposite-sex partner; the participants then indicated their willingness to engage in a long-term relationship with each potential mate. We found that both men and women expressed more interest in engaging in a relationship with a potential mate if that mate was paired with an attractive partner. Men and women's attention to partners varied with partner attractiveness and this gaze attraction influenced their subsequent mate choices. These results highlight the prevalence of non-independent mate choice in humans and implicate social attention and reward circuitry in these decisions.

Bayesian Analysis of Latent Threshold Dynamic Models

We discuss a general approach to dynamic sparsity modeling in multivariate time series analysis. Time-varying parameters are linked to latent processes that are thresholded to induce zero values adaptively, providing natural mechanisms for dynamic variable inclusion/selection. We discuss Bayesian model specification, analysis and prediction in dynamic regressions, time-varying vector autoregressions, and multivariate volatility models using latent thresholding. Application to a topical macroeconomic time series problem illustrates some of the benefits of the approach in terms of statistical and economic interpretations as well as improved predictions. Supplementary materials for this article are available online. © 2013 Copyright Taylor and Francis Group, LLC.

Physical and economic potential of geological CO2 storage in saline aquifers.

Carbon sequestration in sandstone saline reservoirs holds great potential for mitigating climate change, but its storage potential and cost per ton of avoided CO2 emissions are uncertain. We develop a general model to determine the maximum theoretical constraints on both storage potential and injection rate and use it to characterize the economic viability of geosequestration in sandstone saline aquifers. When applied to a representative set of aquifer characteristics, the model yields results that compare favorably with pilot projects currently underway. Over a range of reservoir properties, maximum effective storage peaks at an optimal depth of 1600 m, at which point 0.18-0.31 metric tons can be stored per cubic meter of bulk volume of reservoir. Maximum modeled injection rates predict minima for storage costs in a typical basin in the range of $2-7/ ton CO2 (2005 U.S.$) depending on depth and basin characteristics in our base-case scenario. Because the properties of natural reservoirs in the United States vary substantially, storage costs could in some cases be lower or higher by orders of magnitude. We conclude that available geosequestration capacity exhibits a wide range of technological and economic attractiveness. Like traditional projects in the extractive industries, geosequestration capacity should be exploited starting with the low-cost storage options first then moving gradually up the supply curve.

Phenex: ontological annotation of phenotypic diversity.

BACKGROUND: Phenotypic differences among species have long been systematically itemized and described by biologists in the process of investigating phylogenetic relationships and trait evolution. Traditionally, these descriptions have been expressed in natural language within the context of individual journal publications or monographs. As such, this rich store of phenotype data has been largely unavailable for statistical and computational comparisons across studies or integration with other biological knowledge. METHODOLOGY/PRINCIPAL FINDINGS: Here we describe Phenex, a platform-independent desktop application designed to facilitate efficient and consistent annotation of phenotypic similarities and differences using Entity-Quality syntax, drawing on terms from community ontologies for anatomical entities, phenotypic qualities, and taxonomic names. Phenex can be configured to load only those ontologies pertinent to a taxonomic group of interest. The graphical user interface was optimized for evolutionary biologists accustomed to working with lists of taxa, characters, character states, and character-by-taxon matrices. CONCLUSIONS/SIGNIFICANCE: Annotation of phenotypic data using ontologies and globally unique taxonomic identifiers will allow biologists to integrate phenotypic data from different organisms and studies, leveraging decades of work in systematics and comparative morphology.

An evaluation of remifentanil-sevoflurane response surface models in patients emerging from anesthesia: model improvement using effect-site sevoflurane concentrations.

INTRODUCTION: We previously reported models that characterized the synergistic interaction between remifentanil and sevoflurane in blunting responses to verbal and painful stimuli. This preliminary study evaluated the ability of these models to predict a return of responsiveness during emergence from anesthesia and a response to tibial pressure when patients required analgesics in the recovery room. We hypothesized that model predictions would be consistent with observed responses. We also hypothesized that under non-steady-state conditions, accounting for the lag time between sevoflurane effect-site concentration (Ce) and end-tidal (ET) concentration would improve predictions. METHODS: Twenty patients received a sevoflurane, remifentanil, and fentanyl anesthetic. Two model predictions of responsiveness were recorded at emergence: an ET-based and a Ce-based prediction. Similarly, 2 predictions of a response to noxious stimuli were recorded when patients first required analgesics in the recovery room. Model predictions were compared with observations with graphical and temporal analyses. RESULTS: While patients were anesthetized, model predictions indicated a high likelihood that patients would be unresponsive (> or = 99%). However, after termination of the anesthetic, models exhibited a wide range of predictions at emergence (1%-97%). Although wide, the Ce-based predictions of responsiveness were better distributed over a percentage ranking of observations than the ET-based predictions. For the ET-based model, 45% of the patients awoke within 2 min of the 50% model predicted probability of unresponsiveness and 65% awoke within 4 min. For the Ce-based model, 45% of the patients awoke within 1 min of the 50% model predicted probability of unresponsiveness and 85% awoke within 3.2 min. Predictions of a response to a painful stimulus in the recovery room were similar for the Ce- and ET-based models. DISCUSSION: Results confirmed, in part, our study hypothesis; accounting for the lag time between Ce and ET sevoflurane concentrations improved model predictions of responsiveness but had no effect on predicting a response to a noxious stimulus in the recovery room. These models may be useful in predicting events of clinical interest but large-scale evaluations with numerous patients are needed to better characterize model performance.

A LabVIEW Platform for Preclinical Imaging Using Digital Subtraction Angiography and Micro-CT.

CT and digital subtraction angiography (DSA) are ubiquitous in the clinic. Their preclinical equivalents are valuable imaging methods for studying disease models and treatment. We have developed a dual source/detector X-ray imaging system that we have used for both micro-CT and DSA studies in rodents. The control of such a complex imaging system requires substantial software development for which we use the graphical language LabVIEW (National Instruments, Austin, TX, USA). This paper focuses on a LabVIEW platform that we have developed to enable anatomical and functional imaging with micro-CT and DSA. Our LabVIEW applications integrate and control all the elements of our system including a dual source/detector X-ray system, a mechanical ventilator, a physiological monitor, and a power microinjector for the vascular delivery of X-ray contrast agents. Various applications allow cardiac- and respiratory-gated acquisitions for both DSA and micro-CT studies. Our results illustrate the application of DSA for cardiopulmonary studies and vascular imaging of the liver and coronary arteries. We also show how DSA can be used for functional imaging of the kidney. Finally, the power of 4D micro-CT imaging using both prospective and retrospective gating is shown for cardiac imaging.