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Sample records for identify predictive surrogate

  1. Multiple surrogates for prediction and optimization

    NASA Astrophysics Data System (ADS)

    Viana, Felipe A. C.

    2011-12-01

    Statistical modeling of computer experiments embraces the set of methodologies for generating a surrogate model (also known as metamodel or response surface approximation) used to replace an expensive simulation code. The aim of surrogate modeling is to construct an approximation of a response of interest based on a limited number of expensive simulations. Nevertheless, after years of intensive research on the field, surrogate-based analysis and optimization is still a struggle to achieve maximum accuracy for a given number of simulations. In this dissertation, we have taken advantage of multiple surrogates to address the issues that we face when we (i) want to build an accurate surrogate model under limited computational budget, (ii) use the surrogate for constrained optimization and the exact analysis shows that the solution is infeasible, and (iii) use the surrogate for global optimization and do not know where to place a set of points in which we are most likely to have improvement. In terms of prediction accuracy, we have found that multiple surrogates work as insurance against poorly fitted models. Additionally, we propose the use of safety margins to conservatively compensate for fitting errors associated with surrogates. We were able to estimate the safety margin for a specific conservativeness level, and we found that it is possible to select a surrogate with the best compromise between conservativeness and loss of accuracy. In terms of optimization, we proposed two strategies for enabling surrogate-based global optimization with parallel function evaluations. The first one is based on the simultaneous use of multiple surrogates (a set of surrogates collaboratively provide multiple points). The second strategy uses a single surrogate and one cheap to evaluate criterion (probability of improvement) for multiple point selection approximation. In both cases, we found that we could successfully speed up the optimization convergence without clear penalties as

  2. APPLICATION OF A TIERED SURROGATE APPROACH TO IDENTIFY TOXICITY SURROGATES FOR HUMAN HEALTH RISK ASSESSMENT

    EPA Science Inventory

    APPLICATION OF A TIERED SURROGATE APPROACH TO IDENTIFY TOXICITY SURROGATES FOR HUMAN HEALTH RISK ASSESSMENT. P.R. Dodmane1, L.E. Lizarraga1, J.P. Kaiser2, S.C. Wesselkamper2, Q.J. Zhao2. 1ORISE Participant, U.S. EPA, National Center for Environmental Assessment (NCEA), Cincinnati...

  3. Identifying experimental surrogates for Bacillus anthracis spores: a review

    PubMed Central

    2010-01-01

    Bacillus anthracis, the causative agent of anthrax, is a proven biological weapon. In order to study this threat, a number of experimental surrogates have been used over the past 70 years. However, not all surrogates are appropriate for B. anthracis, especially when investigating transport, fate and survival. Although B. atrophaeus has been widely used as a B. anthracis surrogate, the two species do not always behave identically in transport and survival models. Therefore, we devised a scheme to identify a more appropriate surrogate for B. anthracis. Our selection criteria included risk of use (pathogenicity), phylogenetic relationship, morphology and comparative survivability when challenged with biocides. Although our knowledge of certain parameters remains incomplete, especially with regards to comparisons of spore longevity under natural conditions, we found that B. thuringiensis provided the best overall fit as a non-pathogenic surrogate for B. anthracis. Thus, we suggest focusing on this surrogate in future experiments of spore fate and transport modelling. PMID:21092338

  4. Statistical identifiability and the surrogate endpoint problem, with application to vaccine trials

    PubMed Central

    2013-01-01

    Summary Given a randomized treatment Z, a clinical outcome Y, and a biomarker S measured some fixed time after Z is administered, we may be interested in addressing the surrogate endpoint problem by evaluating whether S can be used to reliably predict the effect of Z on Y. Several recent proposals for the statistical evaluation of surrogate value have been based on the framework of principal stratification. In this paper, we consider two principal stratification estimands: joint risks and marginal risks. Joint risks measure causal associations of treatment effects on S and Y, providing insight into the surrogate value of the biomarker, but are not statistically identifiable from vaccine trial data. While marginal risks do not measure causal associations of treatment effects, they nevertheless provide guidance for future research, and we describe a data collection scheme and assumptions under which the marginal risks are statistically identifiable. We show how different sets of assumptions affect the identifiability of these estimands; in particular, we depart from previous work by considering the consequences of relaxing the assumption of no individual treatment effects on Y before S is measured. Based on algebraic relationships between joint and marginal risks, we propose a sensitivity analysis approach for assessment of surrogate value, and show that in many cases the surrogate value of a biomarker may be hard to establish, even when the sample size is large. PMID:20105158

  5. The value of surrogate endpoints for predicting real-world survival across five cancer types.

    PubMed

    Shafrin, Jason; Brookmeyer, Ron; Peneva, Desi; Park, Jinhee; Zhang, Jie; Figlin, Robert A; Lakdawalla, Darius N

    2016-04-01

    Objective It is unclear how well different outcome measures in randomized controlled trials (RCTs) perform in predicting real-world cancer survival. We assess the ability of RCT overall survival (OS) and surrogate endpoints - progression-free survival (PFS) and time to progression (TTP) - to predict real-world OS across five cancers. Methods We identified 20 treatments and 31 indications for breast, colorectal, lung, ovarian, and pancreatic cancer that had a phase III RCT reporting median OS and median PFS or TTP. Median real-world OS was determined using a Kaplan-Meier estimator applied to patients in the Surveillance and Epidemiology End Results (SEER)-Medicare database (1991-2010). Performance of RCT OS and PFS/TTP in predicting real-world OS was measured using t-tests, median absolute prediction error, and R(2) from linear regressions. Results Among 72,600 SEER-Medicare patients similar to RCT participants, median survival was 5.9 months for trial surrogates, 14.1 months for trial OS, and 13.4 months for real-world OS. For this sample, regression models using clinical trial OS and trial surrogates as independent variables predicted real-world OS significantly better than models using surrogates alone (P = 0.026). Among all real-world patients using sample treatments (N = 309,182), however, adding trial OS did not improve predictive power over predictions based on surrogates alone (P = 0.194). Results were qualitatively similar using median absolute prediction error and R(2) metrics. Conclusions Among the five tumor types investigated, trial OS and surrogates were each independently valuable in predicting real-world OS outcomes for patients similar to trial participants. In broader real-world populations, however, trial OS added little incremental value over surrogates alone. PMID:26743800

  6. Fast Prediction and Evaluation of Gravitational Waveforms Using Surrogate Models

    NASA Astrophysics Data System (ADS)

    Field, Scott E.; Galley, Chad R.; Hesthaven, Jan S.; Kaye, Jason; Tiglio, Manuel

    2014-07-01

    We propose a solution to the problem of quickly and accurately predicting gravitational waveforms within any given physical model. The method is relevant for both real-time applications and more traditional scenarios where the generation of waveforms using standard methods can be prohibitively expensive. Our approach is based on three offline steps resulting in an accurate reduced order model in both parameter and physical dimensions that can be used as a surrogate for the true or fiducial waveform family. First, a set of m parameter values is determined using a greedy algorithm from which a reduced basis representation is constructed. Second, these m parameters induce the selection of m time values for interpolating a waveform time series using an empirical interpolant that is built for the fiducial waveform family. Third, a fit in the parameter dimension is performed for the waveform's value at each of these m times. The cost of predicting L waveform time samples for a generic parameter choice is of order O(mL+mcfit) online operations, where cfit denotes the fitting function operation count and, typically, m ≪L. The result is a compact, computationally efficient, and accurate surrogate model that retains the original physics of the fiducial waveform family while also being fast to evaluate. We generate accurate surrogate models for effective-one-body waveforms of nonspinning binary black hole coalescences with durations as long as 105M, mass ratios from 1 to 10, and for multiple spherical harmonic modes. We find that these surrogates are more than 3 orders of magnitude faster to evaluate as compared to the cost of generating effective-one-body waveforms in standard ways. Surrogate model building for other waveform families and models follows the same steps and has the same low computational online scaling cost. For expensive numerical simulations of binary black hole coalescences, we thus anticipate extremely large speedups in generating new waveforms with a

  7. Identifying Novel Clinical Surrogates to Assess Human Bone Fracture Toughness.

    PubMed

    Granke, Mathilde; Makowski, Alexander J; Uppuganti, Sasidhar; Does, Mark D; Nyman, Jeffry S

    2015-07-01

    Fracture risk does not solely depend on strength but also on fracture toughness; ie, the ability of bone material to resist crack initiation and propagation. Because resistance to crack growth largely depends on bone properties at the tissue level, including collagen characteristics, current X-ray based assessment tools may not be suitable to identify age-related, disease-related, or treatment-related changes in fracture toughness. To identify useful clinical surrogates that could improve the assessment of fracture resistance, we investigated the potential of (1)H nuclear magnetic resonance spectroscopy (NMR) and reference point indentation (RPI) to explain age-related variance in fracture toughness. Harvested from cadaveric femurs (62 human donors), single-edge notched beam (SENB) specimens of cortical bone underwent fracture toughness testing (R-curve method). NMR-derived bound water showed the strongest correlation with fracture toughness properties (r = 0.63 for crack initiation, r = 0.35 for crack growth, and r = 0.45 for overall fracture toughness; p < 0.01). Multivariate analyses indicated that the age-related decrease in different fracture toughness properties were best explained by a combination of NMR properties including pore water and RPI-derived tissue stiffness with age as a significant covariate (adjusted R(2)  = 53.3%, 23.9%, and 35.2% for crack initiation, crack growth, and overall toughness, respectively; p < 0.001). These findings reflect the existence of many contributors to fracture toughness and emphasize the utility of a multimodal assessment of fracture resistance. Exploring the mechanistic origin of fracture toughness, glycation-mediated nonenzymatic collagen crosslinks and intracortical porosity are possible determinants of bone fracture toughness and could explain the sensitivity of NMR to changes in fracture toughness. Assuming fracture toughness is clinically important to the ability of bone to resist fracture

  8. Identifying novel clinical surrogates to assess human bone fracture toughness

    PubMed Central

    Granke, Mathilde; Makowski, Alexander J; Uppuganti, Sasidhar; Does, Mark D; Nyman, Jeffry S

    2015-01-01

    Fracture risk does not solely depend on strength but also on fracture toughness, i.e. the ability of bone material to resist crack initiation and propagation. Because resistance to crack growth largely depends on bone properties at the tissue level including collagen characteristics, current X-ray based assessment tools may not be suitable to identify age-, disease-, or treatment-related changes in fracture toughness. To identify useful clinical surrogates that could improve the assessment of fracture resistance, we investigated the potential of 1H nuclear magnetic resonance spectroscopy (NMR) and reference point indentation (RPI) to explain age-related variance in fracture toughness. Harvested from cadaveric femurs (62 human donors), single-edge notched beam (SENB) specimens of cortical bone underwent fracture toughness testing (R-curve method). NMR-derived bound water showed the strongest correlation with fracture toughness properties (r=0.63 for crack initiation, r=0.35 for crack growth, and r=0.45 for overall fracture toughness; p<0.01). Multivariate analyses indicated that the age-related decrease in different fracture toughness properties were best explained by a combination of NMR properties including pore water and RPI-derived tissue stiffness with age as a significant covariate (adjusted R2 = 53.3%, 23.9%, and 35.2% for crack initiation, crack growth, and overall toughness, respectively; p<0.001). These findings reflect the existence of many contributors to fracture toughness and emphasize the utility of a multimodal assessment of fracture resistance. Exploring the mechanistic origin of fracture toughness, glycation-mediated, non-enzymatic collagen crosslinks and intra-cortical porosity are possible determinants of bone fracture toughness and could explain the sensitivity of NMR to changes in fracture toughness. Assuming fracture toughness is clinically important to the ability of bone to resist fracture, our results suggest that improvements in fracture

  9. On identified predictive control

    NASA Technical Reports Server (NTRS)

    Bialasiewicz, Jan T.

    1993-01-01

    Self-tuning control algorithms are potential successors to manually tuned PID controllers traditionally used in process control applications. A very attractive design method for self-tuning controllers, which has been developed over recent years, is the long-range predictive control (LRPC). The success of LRPC is due to its effectiveness with plants of unknown order and dead-time which may be simultaneously nonminimum phase and unstable or have multiple lightly damped poles (as in the case of flexible structures or flexible robot arms). LRPC is a receding horizon strategy and can be, in general terms, summarized as follows. Using assumed long-range (or multi-step) cost function the optimal control law is found in terms of unknown parameters of the predictor model of the process, current input-output sequence, and future reference signal sequence. The common approach is to assume that the input-output process model is known or separately identified and then to find the parameters of the predictor model. Once these are known, the optimal control law determines control signal at the current time t which is applied at the process input and the whole procedure is repeated at the next time instant. Most of the recent research in this field is apparently centered around the LRPC formulation developed by Clarke et al., known as generalized predictive control (GPC). GPC uses ARIMAX/CARIMA model of the process in its input-output formulation. In this paper, the GPC formulation is used but the process predictor model is derived from the state space formulation of the ARIMAX model and is directly identified over the receding horizon, i.e., using current input-output sequence. The underlying technique in the design of identified predictive control (IPC) algorithm is the identification algorithm of observer/Kalman filter Markov parameters developed by Juang et al. at NASA Langley Research Center and successfully applied to identification of flexible structures.

  10. Combining endangered plants and animals as surrogates to identify priority conservation areas in Yunnan, China

    PubMed Central

    Yang, Feiling; Hu, Jinming; Wu, Ruidong

    2016-01-01

    Suitable surrogates are critical for identifying optimal priority conservation areas (PCAs) to protect regional biodiversity. This study explored the efficiency of using endangered plants and animals as surrogates for identifying PCAs at the county level in Yunnan, southwest China. We ran the Dobson algorithm under three surrogate scenarios at 75% and 100% conservation levels and identified four types of PCAs. Assessment of the protection efficiencies of the four types of PCAs showed that endangered plants had higher surrogacy values than endangered animals but that the two were not substitutable; coupled endangered plants and animals as surrogates yielded a higher surrogacy value than endangered plants or animals as surrogates; the plant-animal priority areas (PAPAs) was the optimal among the four types of PCAs for conserving both endangered plants and animals in Yunnan. PAPAs could well represent overall species diversity distribution patterns and overlap with critical biogeographical regions in Yunnan. Fourteen priority units in PAPAs should be urgently considered as optimizing Yunnan’s protected area system. The spatial pattern of PAPAs at the 100% conservation level could be conceptualized into three connected conservation belts, providing a valuable reference for optimizing the layout of the in situ protected area system in Yunnan. PMID:27538537

  11. Combining endangered plants and animals as surrogates to identify priority conservation areas in Yunnan, China.

    PubMed

    Yang, Feiling; Hu, Jinming; Wu, Ruidong

    2016-01-01

    Suitable surrogates are critical for identifying optimal priority conservation areas (PCAs) to protect regional biodiversity. This study explored the efficiency of using endangered plants and animals as surrogates for identifying PCAs at the county level in Yunnan, southwest China. We ran the Dobson algorithm under three surrogate scenarios at 75% and 100% conservation levels and identified four types of PCAs. Assessment of the protection efficiencies of the four types of PCAs showed that endangered plants had higher surrogacy values than endangered animals but that the two were not substitutable; coupled endangered plants and animals as surrogates yielded a higher surrogacy value than endangered plants or animals as surrogates; the plant-animal priority areas (PAPAs) was the optimal among the four types of PCAs for conserving both endangered plants and animals in Yunnan. PAPAs could well represent overall species diversity distribution patterns and overlap with critical biogeographical regions in Yunnan. Fourteen priority units in PAPAs should be urgently considered as optimizing Yunnan's protected area system. The spatial pattern of PAPAs at the 100% conservation level could be conceptualized into three connected conservation belts, providing a valuable reference for optimizing the layout of the in situ protected area system in Yunnan. PMID:27538537

  12. Inaccuracies in predicting human norovirus inactivation using surrogate viruses

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Human norovirus (NoV) cannot be propagated in cell culture, so virus inactivation studies, including processing interventions, are generally performed on virus surrogates that may be readily quantified in the laboratory. However, there are fundamental differences in many closely related viruses, di...

  13. Fast and Accurate Prediction of Numerical Relativity Waveforms from Binary Black Hole Coalescences Using Surrogate Models

    NASA Astrophysics Data System (ADS)

    Blackman, Jonathan; Field, Scott E.; Galley, Chad R.; Szilágyi, Béla; Scheel, Mark A.; Tiglio, Manuel; Hemberger, Daniel A.

    2015-09-01

    Simulating a binary black hole coalescence by solving Einstein's equations is computationally expensive, requiring days to months of supercomputing time. Using reduced order modeling techniques, we construct an accurate surrogate model, which is evaluated in a millisecond to a second, for numerical relativity (NR) waveforms from nonspinning binary black hole coalescences with mass ratios in [1, 10] and durations corresponding to about 15 orbits before merger. We assess the model's uncertainty and show that our modeling strategy predicts NR waveforms not used for the surrogate's training with errors nearly as small as the numerical error of the NR code. Our model includes all spherical-harmonic -2Yℓm waveform modes resolved by the NR code up to ℓ=8 . We compare our surrogate model to effective one body waveforms from 50 M⊙ to 300 M⊙ for advanced LIGO detectors and find that the surrogate is always more faithful (by at least an order of magnitude in most cases).

  14. Ensemble of surrogates-based optimization for identifying an optimal surfactant-enhanced aquifer remediation strategy at heterogeneous DNAPL-contaminated sites

    NASA Astrophysics Data System (ADS)

    Jiang, Xue; Lu, Wenxi; Hou, Zeyu; Zhao, Haiqing; Na, Jin

    2015-11-01

    The purpose of this study was to identify an optimal surfactant-enhanced aquifer remediation (SEAR) strategy for aquifers contaminated by dense non-aqueous phase liquid (DNAPL) based on an ensemble of surrogates-based optimization technique. A saturated heterogeneous medium contaminated by nitrobenzene was selected as case study. A new kind of surrogate-based SEAR optimization employing an ensemble surrogate (ES) model together with a genetic algorithm (GA) is presented. Four methods, namely radial basis function artificial neural network (RBFANN), kriging (KRG), support vector regression (SVR), and kernel extreme learning machines (KELM), were used to create four individual surrogate models, which were then compared. The comparison enabled us to select the two most accurate models (KELM and KRG) to establish an ES model of the SEAR simulation model, and the developed ES model as well as these four stand-alone surrogate models was compared. The results showed that the average relative error of the average nitrobenzene removal rates between the ES model and the simulation model for 20 test samples was 0.8%, which is a high approximation accuracy, and which indicates that the ES model provides more accurate predictions than the stand-alone surrogate models. Then, a nonlinear optimization model was formulated for the minimum cost, and the developed ES model was embedded into this optimization model as a constrained condition. Besides, GA was used to solve the optimization model to provide the optimal SEAR strategy. The developed ensemble surrogate-optimization approach was effective in seeking a cost-effective SEAR strategy for heterogeneous DNAPL-contaminated sites. This research is expected to enrich and develop the theoretical and technical implications for the analysis of remediation strategy optimization of DNAPL-contaminated aquifers.

  15. Surrogate Modeling of High-Fidelity Fracture Simulations for Real-Time Residual Strength Predictions

    NASA Technical Reports Server (NTRS)

    Spear, Ashley D.; Priest, Amanda R.; Veilleux, Michael G.; Ingraffea, Anthony R.; Hochhalter, Jacob D.

    2011-01-01

    A surrogate model methodology is described for predicting in real time the residual strength of flight structures with discrete-source damage. Starting with design of experiment, an artificial neural network is developed that takes as input discrete-source damage parameters and outputs a prediction of the structural residual strength. Target residual strength values used to train the artificial neural network are derived from 3D finite element-based fracture simulations. A residual strength test of a metallic, integrally-stiffened panel is simulated to show that crack growth and residual strength are determined more accurately in discrete-source damage cases by using an elastic-plastic fracture framework rather than a linear-elastic fracture mechanics-based method. Improving accuracy of the residual strength training data would, in turn, improve accuracy of the surrogate model. When combined, the surrogate model methodology and high-fidelity fracture simulation framework provide useful tools for adaptive flight technology.

  16. Surrogate Modeling of High-Fidelity Fracture Simulations for Real-Time Residual Strength Predictions

    NASA Technical Reports Server (NTRS)

    Spear, Ashley D.; Priest, Amanda R.; Veilleux, Michael G.; Ingraffea, Anthony R.; Hochhalter, Jacob D.

    2011-01-01

    A surrogate model methodology is described for predicting, during flight, the residual strength of aircraft structures that sustain discrete-source damage. Starting with design of experiment, an artificial neural network is developed that takes as input discrete-source damage parameters and outputs a prediction of the structural residual strength. Target residual strength values used to train the artificial neural network are derived from 3D finite element-based fracture simulations. Two ductile fracture simulations are presented to show that crack growth and residual strength are determined more accurately in discrete-source damage cases by using an elastic-plastic fracture framework rather than a linear-elastic fracture mechanics-based method. Improving accuracy of the residual strength training data does, in turn, improve accuracy of the surrogate model. When combined, the surrogate model methodology and high fidelity fracture simulation framework provide useful tools for adaptive flight technology.

  17. Fast and Accurate Prediction of Numerical Relativity Waveforms from Binary Black Hole Coalescences Using Surrogate Models.

    PubMed

    Blackman, Jonathan; Field, Scott E; Galley, Chad R; Szilágyi, Béla; Scheel, Mark A; Tiglio, Manuel; Hemberger, Daniel A

    2015-09-18

    Simulating a binary black hole coalescence by solving Einstein's equations is computationally expensive, requiring days to months of supercomputing time. Using reduced order modeling techniques, we construct an accurate surrogate model, which is evaluated in a millisecond to a second, for numerical relativity (NR) waveforms from nonspinning binary black hole coalescences with mass ratios in [1, 10] and durations corresponding to about 15 orbits before merger. We assess the model's uncertainty and show that our modeling strategy predicts NR waveforms not used for the surrogate's training with errors nearly as small as the numerical error of the NR code. Our model includes all spherical-harmonic _{-2}Y_{ℓm} waveform modes resolved by the NR code up to ℓ=8. We compare our surrogate model to effective one body waveforms from 50M_{⊙} to 300M_{⊙} for advanced LIGO detectors and find that the surrogate is always more faithful (by at least an order of magnitude in most cases). PMID:26430979

  18. Predicting trace organic compound breakthrough in granular activated carbon using fluorescence and UV absorbance as surrogates.

    PubMed

    Anumol, Tarun; Sgroi, Massimiliano; Park, Minkyu; Roccaro, Paolo; Snyder, Shane A

    2015-06-01

    This study investigated the applicability of bulk organic parameters like dissolved organic carbon (DOC), UV absorbance at 254 nm (UV254), and total fluorescence (TF) to act as surrogates in predicting trace organic compound (TOrC) removal by granular activated carbon in water reuse applications. Using rapid small-scale column testing, empirical linear correlations for thirteen TOrCs were determined with DOC, UV254, and TF in four wastewater effluents. Linear correlations (R(2) > 0.7) were obtained for eight TOrCs in each water quality in the UV254 model, while ten TOrCs had R(2) > 0.7 in the TF model. Conversely, DOC was shown to be a poor surrogate for TOrC breakthrough prediction. When the data from all four water qualities was combined, good linear correlations were still obtained with TF having higher R(2) than UV254 especially for TOrCs with log Dow>1. Excellent linear relationship (R(2) > 0.9) between log Dow and the removal of TOrC at 0% surrogate removal (y-intercept) were obtained for the five neutral TOrCs tested in this study. Positively charged TOrCs had enhanced removals due to electrostatic interactions with negatively charged GAC that caused them to deviate from removals that would be expected with their log Dow. Application of the empirical linear correlation models to full-scale samples provided good results for six of seven TOrCs (except meprobamate) tested when comparing predicted TOrC removal by UV254 and TF with actual removals for GAC in all the five samples tested. Surrogate predictions using UV254 and TF provide valuable tools for rapid or on-line monitoring of GAC performance and can result in cost savings by extended GAC run times as compared to using DOC breakthrough to trigger regeneration or replacement. PMID:25792436

  19. Experimental verification of ion stopping power prediction from dual energy CT data in tissue surrogates.

    PubMed

    Hünemohr, Nora; Krauss, Bernhard; Tremmel, Christoph; Ackermann, Benjamin; Jäkel, Oliver; Greilich, Steffen

    2014-01-01

    We present an experimental verification of stopping-power-ratio (SPR) prediction from dual energy CT (DECT) with potential use for dose planning in proton and ion therapy. The approach is based on DECT images converted to electron density relative to water ϱe/ϱe, w and effective atomic number Zeff. To establish a parameterization of the I-value by Zeff, 71 tabulated tissue compositions were used. For the experimental assessment of the method we scanned 20 materials (tissue surrogates, polymers, aluminum, titanium) at 80/140Sn kVp and 100/140Sn kVp (Sn: additional tin filtration) and computed the ϱe/ϱe, w and Zeff with a purely image based algorithm. Thereby, we found that ϱe/ϱe, w (Zeff) could be determined with an accuracy of 0.4% (1.7%) for the tissue surrogates with known elemental compositions. SPRs were predicted from DECT images for all 20 materials using the presented approach and were compared to measured water-equivalent path lengths (closely related to SPR). For the tissue surrogates the presented DECT approach was found to predict the experimental values within 0.6%, for aluminum and titanium within an accuracy of 1.7% and 9.4% (from 16-bit reconstructed DECT images). PMID:24334601

  20. Surrogate modelling and optimization using shape-preserving response prediction: A review

    NASA Astrophysics Data System (ADS)

    Leifsson, Leifur; Koziel, Slawomir

    2016-03-01

    Computer simulation models are ubiquitous in modern engineering design. In many cases, they are the only way to evaluate a given design with sufficient fidelity. Unfortunately, an added computational expense is associated with higher fidelity models. Moreover, the systems being considered are often highly nonlinear and may feature a large number of designable parameters. Therefore, it may be impractical to solve the design problem with conventional optimization algorithms. A promising approach to alleviate these difficulties is surrogate-based optimization (SBO). Among proven SBO techniques, the methods utilizing surrogates constructed from corrected physics-based low-fidelity models are, in many cases, the most efficient. This article reviews a particular technique of this type, namely, shape-preserving response prediction (SPRP), which works on the level of the model responses to correct the underlying low-fidelity models. The formulation and limitations of SPRP are discussed. Applications to several engineering design problems are provided.

  1. Measure profile surrogates: A method to validate the performance of epileptic seizure prediction algorithms

    NASA Astrophysics Data System (ADS)

    Kreuz, Thomas; Andrzejak, Ralph G.; Mormann, Florian; Kraskov, Alexander; Stögbauer, Harald; Elger, Christian E.; Lehnertz, Klaus; Grassberger, Peter

    2004-06-01

    In a growing number of publications it is claimed that epileptic seizures can be predicted by analyzing the electroencephalogram (EEG) with different characterizing measures. However, many of these studies suffer from a severe lack of statistical validation. Only rarely are results passed to a statistical test and verified against some null hypothesis H0 in order to quantify their significance. In this paper we propose a method to statistically validate the performance of measures used to predict epileptic seizures. From measure profiles rendered by applying a moving-window technique to the electroencephalogram we first generate an ensemble of surrogates by a constrained randomization using simulated annealing. Subsequently the seizure prediction algorithm is applied to the original measure profile and to the surrogates. If detectable changes before seizure onset exist, highest performance values should be obtained for the original measure profiles and the null hypothesis. “The measure is not suited for seizure prediction” can be rejected. We demonstrate our method by applying two measures of synchronization to a quasicontinuous EEG recording and by evaluating their predictive performance using a straightforward seizure prediction statistics. We would like to stress that the proposed method is rather universal and can be applied to many other prediction and detection problems.

  2. Development of surrogate correlation models to predict trace organic contaminant oxidation and microbial inactivation during ozonation.

    PubMed

    Gerrity, Daniel; Gamage, Sujanie; Jones, Darryl; Korshin, Gregory V; Lee, Yunho; Pisarenko, Aleksey; Trenholm, Rebecca A; von Gunten, Urs; Wert, Eric C; Snyder, Shane A

    2012-12-01

    The performance of ozonation in wastewater depends on water quality and the ability to form hydroxyl radicals (·OH) to meet disinfection or contaminant transformation objectives. Since there are no on-line methods to assess ozone and ·OH exposure in wastewater, many agencies are now embracing indicator frameworks and surrogate monitoring for regulatory compliance. Two of the most promising surrogate parameters for ozone-based treatment of secondary and tertiary wastewater effluents are differential UV(254) absorbance (ΔUV(254)) and total fluorescence (ΔTF). In the current study, empirical correlations for ΔUV(254) and ΔTF were developed for the oxidation of 18 trace organic contaminants (TOrCs), including 1,4-dioxane, atenolol, atrazine, bisphenol A, carbamazepine, diclofenac, gemfibrozil, ibuprofen, meprobamate, naproxen, N,N-diethyl-meta-toluamide (DEET), para-chlorobenzoic acid (pCBA), phenytoin, primidone, sulfamethoxazole, triclosan, trimethoprim, and tris-(2-chloroethyl)-phosphate (TCEP) (R(2) = 0.50-0.83) and the inactivation of three microbial surrogates, including Escherichia coli, MS2, and Bacillus subtilis spores (R(2) = 0.46-0.78). Nine wastewaters were tested in laboratory systems, and eight wastewaters were evaluated at pilot- and full-scale. A predictive model for OH exposure based on ΔUV(254) or ΔTF was also proposed. PMID:23062789

  3. Development and application of a green fluorescent protein (GFP) expressing E. coli O103 surrogate for tracking contamination through grinding and identifying persistent points of contamination

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Objective: To 1.) develop and validate an easily trackable E. coli O157:H7/non-O157 STEC surrogate that can be detected to the same level of sensitivity as E. coli O157:H7; and 2.) apply the trackable surrogate to model contamination passage through grinding and identify points where contamination ...

  4. SU-C-BRF-07: A Pattern Fusion Algorithm for Multi-Step Ahead Prediction of Surrogate Motion

    SciTech Connect

    Zawisza, I; Yan, H; Yin, F

    2014-06-15

    Purpose: To assure that tumor motion is within the radiation field during high-dose and high-precision radiosurgery, real-time imaging and surrogate monitoring are employed. These methods are useful in providing real-time tumor/surrogate motion but no future information is available. In order to anticipate future tumor/surrogate motion and track target location precisely, an algorithm is developed and investigated for estimating surrogate motion multiple-steps ahead. Methods: The study utilized a one-dimensional surrogate motion signal divided into three components: (a) training component containing the primary data including the first frame to the beginning of the input subsequence; (b) input subsequence component of the surrogate signal used as input to the prediction algorithm: (c) output subsequence component is the remaining signal used as the known output of the prediction algorithm for validation. The prediction algorithm consists of three major steps: (1) extracting subsequences from training component which best-match the input subsequence according to given criterion; (2) calculating weighting factors from these best-matched subsequence; (3) collecting the proceeding parts of the subsequences and combining them together with assigned weighting factors to form output. The prediction algorithm was examined for several patients, and its performance is assessed based on the correlation between prediction and known output. Results: Respiratory motion data was collected for 20 patients using the RPM system. The output subsequence is the last 50 samples (∼2 seconds) of a surrogate signal, and the input subsequence was 100 (∼3 seconds) frames prior to the output subsequence. Based on the analysis of correlation coefficient between predicted and known output subsequence, the average correlation is 0.9644±0.0394 and 0.9789±0.0239 for equal-weighting and relative-weighting strategies, respectively. Conclusion: Preliminary results indicate that the prediction

  5. Evaluation of a bacterial bioluminescence bioassay as a predictive surrogate for mysid chronic estimator tests with produced water

    SciTech Connect

    Korenaga, G.L.; Stine, E.R.; Henry, L.R.

    1995-12-01

    Toxicity limits are appearing more frequently in permits for produced water discharges. A bioluminescent bacteria bioassay has been proposed as a screening tool to predict toxicity in higher organisms, which are more expensive and require longer testing times. Before such a surrogate screen can be used, a correlation must be demonstrated between toxicity in the surrogate and the species of interest. This paper describes tests comparing produced water toxicity in the bioluminescent bacteria test and in the myoid shrimp chronic estimator test, which is frequently required in Gulf of Mexico discharge Under these test conditions, the bacteria test was not adequately predictive of produced water chronic toxicity to the myoid shrimp.

  6. Serum creatinine level, a surrogate of muscle mass, predicts mortality in critically ill patients.

    PubMed

    Thongprayoon, Charat; Cheungpasitporn, Wisit; Kashani, Kianoush

    2016-05-01

    Serum creatinine (SCr) has been widely used to estimate glomerular filtration rate (GFR). Creatinine generation could be reduced in the setting of low skeletal muscle mass. Thus, SCr has also been used as a surrogate of muscle mass. Low muscle mass is associated with reduced survival in hospitalized patients, especially in the intensive care unit (ICU) settings. Recently, studies have demonstrated high mortality in ICU patients with low admission SCr levels, reflecting that low muscle mass or malnutrition, are associated with increased mortality. However, SCr levels can also be influenced by multiple GFR- and non-GFR-related factors including age, diet, exercise, stress, pregnancy, and kidney disease. Imaging techniques, such as computed tomography (CT) and ultrasound, have recently been studied for muscle mass assessment and demonstrated promising data. This article aims to present the perspectives of the uses of SCr and other methods for prediction of muscle mass and outcomes of ICU patients. PMID:27162688

  7. Serum creatinine level, a surrogate of muscle mass, predicts mortality in critically ill patients

    PubMed Central

    Thongprayoon, Charat; Cheungpasitporn, Wisit

    2016-01-01

    Serum creatinine (SCr) has been widely used to estimate glomerular filtration rate (GFR). Creatinine generation could be reduced in the setting of low skeletal muscle mass. Thus, SCr has also been used as a surrogate of muscle mass. Low muscle mass is associated with reduced survival in hospitalized patients, especially in the intensive care unit (ICU) settings. Recently, studies have demonstrated high mortality in ICU patients with low admission SCr levels, reflecting that low muscle mass or malnutrition, are associated with increased mortality. However, SCr levels can also be influenced by multiple GFR- and non-GFR-related factors including age, diet, exercise, stress, pregnancy, and kidney disease. Imaging techniques, such as computed tomography (CT) and ultrasound, have recently been studied for muscle mass assessment and demonstrated promising data. This article aims to present the perspectives of the uses of SCr and other methods for prediction of muscle mass and outcomes of ICU patients. PMID:27162688

  8. Waterlow score as a surrogate marker for predicting adverse outcome in acute pancreatitis.

    PubMed

    Gillick, K; Elbeltagi, H; Bhattacharya, S

    2016-01-01

    Introduction Introduced originally to stratify risk for developing decubitus ulcers, the Waterlow scoring system is recorded routinely for surgical admissions. It is a composite score, reflecting patients' general condition and co-morbidities. The aim of this study was to investigate whether the Waterlow score can be used as an independent surrogate marker to predict severity and adverse outcome in acute pancreatitis. Methods In this retrospective analysis, a consecutive cohort was studied of 250 patients presenting with acute pancreatitis, all of whom had their Waterlow score calculated on admission. Primary outcome measures were length of hospital stay and mortality. Secondary outcome measures included rate of intensive care unit (ICU) admission and development of complications such as peripancreatic free fluid, pancreatic necrosis and pseudocyst formation. Correlation of the Waterlow score with some known markers of disease severity and outcomes was also analysed. Results The Waterlow score correlated strongly with the most commonly used marker of disease severity, the Glasgow score (analysis of variance, p=0.0012). Inpatient mortality, rate of ICU admission and length of hospital stay increased with a higher Waterlow score (Mann-Whitney U test, p=0.0007, p=0.049 and p=0.0002 respectively). There was, however, no significant association between the Waterlow score and the incidence of three known complications of pancreatitis: presence of peripancreatic fluid, pancreatic pseudocyst formation and pancreatic necrosis. Receiver operating characteristic curve analysis demonstrated good predictive power of the Waterlow score for mortality (area under the curve [AUC]: 0.73), ICU admission (AUC: 0.65) and length of stay >7 days (AUC: 0.64). This is comparable with the predictive power of the Glasgow score and C-reactive protein. Conclusions The Waterlow score for patients admitted with acute pancreatitis could provide a useful tool in prospective assessment of disease

  9. A Kriging surrogate model coupled in simulation-optimization approach for identifying release history of groundwater sources

    NASA Astrophysics Data System (ADS)

    Zhao, Ying; Lu, Wenxi; Xiao, Chuanning

    2016-02-01

    As the incidence frequency of groundwater pollution increases, many methods that identify source characteristics of pollutants are being developed. In this study, a simulation-optimization approach was applied to determine the duration and magnitude of pollutant sources. Such problems are time consuming because thousands of simulation models are required to run the optimization model. To address this challenge, the Kriging surrogate model was proposed to increase computational efficiency. Accuracy, time consumption, and the robustness of the Kriging model were tested on both homogenous and non-uniform media, as well as steady-state and transient flow and transport conditions. The results of three hypothetical cases demonstrate that the Kriging model has the ability to solve groundwater contaminant source problems that could occur during field site source identification problems with a high degree of accuracy and short computation times and is thus very robust.

  10. Predicting analysis time in events-driven clinical trials using accumulating time-to-event surrogate information.

    PubMed

    Wang, Jianming; Ke, Chunlei; Yu, Zhinuan; Fu, Lei; Dornseif, Bruce

    2016-05-01

    For clinical trials with time-to-event endpoints, predicting the accrual of the events of interest with precision is critical in determining the timing of interim and final analyses. For example, overall survival (OS) is often chosen as the primary efficacy endpoint in oncology studies, with planned interim and final analyses at a pre-specified number of deaths. Often, correlated surrogate information, such as time-to-progression (TTP) and progression-free survival, are also collected as secondary efficacy endpoints. It would be appealing to borrow strength from the surrogate information to improve the precision of the analysis time prediction. Currently available methods in the literature for predicting analysis timings do not consider utilizing the surrogate information. In this article, using OS and TTP as an example, a general parametric model for OS and TTP is proposed, with the assumption that disease progression could change the course of the overall survival. Progression-free survival, related both to OS and TTP, will be handled separately, as it can be derived from OS and TTP. The authors seek to develop a prediction procedure using a Bayesian method and provide detailed implementation strategies under certain assumptions. Simulations are performed to evaluate the performance of the proposed method. An application to a real study is also provided. Copyright © 2015 John Wiley & Sons, Ltd. PMID:26689725

  11. Quantitative prediction of respiratory tidal volume based on the external torso volume change: a potential volumetric surrogate

    NASA Astrophysics Data System (ADS)

    Li, Guang; Arora, Naveen C.; Xie, Huchen; Ning, Holly; Lu, Wei; Low, Daniel; Citrin, Deborah; Kaushal, Aradhana; Zach, Leor; Camphausen, Kevin; Miller, Robert W.

    2009-04-01

    An external respiratory surrogate that not only highly correlates with but also quantitatively predicts internal tidal volume should be useful in guiding four-dimensional computed tomography (4DCT), as well as 4D radiation therapy (4DRT). A volumetric surrogate should have advantages over external fiducial point(s) for monitoring respiration-induced motion of the torso, which deforms in synchronization with a patient-specific breathing pattern. This study establishes a linear relationship between the external torso volume change (TVC) and lung air volume change (AVC) by validating a proposed volume conservation hypothesis (TVC = AVC) throughout the respiratory cycle using 4DCT and spirometry. Fourteen patients' torso 4DCT images and corresponding spirometric tidal volumes were acquired to examine this hypothesis. The 4DCT images were acquired using dual surrogates in ciné mode and amplitude-based binning in 12 respiratory stages, minimizing residual motion artifacts. Torso and lung volumes were calculated using threshold-based segmentation algorithms and volume changes were calculated relative to the full-exhalation stage. The TVC and AVC, as functions of respiratory stages, were compared, showing a high correlation (r = 0.992 ± 0.005, p < 0.0001) as well as a linear relationship (slope = 1.027 ± 0.061, R2 = 0.980) without phase shift. The AVC was also compared to the spirometric tidal volumes, showing a similar linearity (slope = 1.030 ± 0.092, R2 = 0.947). In contrast, the thoracic and abdominal heights measured from 4DCT showed relatively low correlation (0.28 ± 0.44 and 0.82 ± 0.30, respectively) and location-dependent phase shifts. This novel approach establishes the foundation for developing an external volumetric respiratory surrogate.

  12. Statistical surrogate models for prediction of high-consequence climate change.

    SciTech Connect

    Constantine, Paul; Field, Richard V., Jr.; Boslough, Mark Bruce Elrick

    2011-09-01

    In safety engineering, performance metrics are defined using probabilistic risk assessments focused on the low-probability, high-consequence tail of the distribution of possible events, as opposed to best estimates based on central tendencies. We frame the climate change problem and its associated risks in a similar manner. To properly explore the tails of the distribution requires extensive sampling, which is not possible with existing coupled atmospheric models due to the high computational cost of each simulation. We therefore propose the use of specialized statistical surrogate models (SSMs) for the purpose of exploring the probability law of various climate variables of interest. A SSM is different than a deterministic surrogate model in that it represents each climate variable of interest as a space/time random field. The SSM can be calibrated to available spatial and temporal data from existing climate databases, e.g., the Program for Climate Model Diagnosis and Intercomparison (PCMDI), or to a collection of outputs from a General Circulation Model (GCM), e.g., the Community Earth System Model (CESM) and its predecessors. Because of its reduced size and complexity, the realization of a large number of independent model outputs from a SSM becomes computationally straightforward, so that quantifying the risk associated with low-probability, high-consequence climate events becomes feasible. A Bayesian framework is developed to provide quantitative measures of confidence, via Bayesian credible intervals, in the use of the proposed approach to assess these risks.

  13. Utility of surrogate markers for the prediction of relapses in inflammatory bowel diseases.

    PubMed

    Musci, Jason Orlando Dimitri; Cornish, Jack Stephen; Däbritz, Jan

    2016-06-01

    Patients with diagnosed inflammatory bowel disease (IBD) will commonly experience a clinical relapse in spite of a prolonged therapy-induced period of clinical remission. The current methods of assessing subclinical levels of low-grade inflammation which predispose patients to relapse are not optimal when considering both cost and patient comfort. Over the past few decades, much investigation has discovered that proteins such as calprotectin that are released from inflammatory cells are capable of indicating disease activity. Along with C-reactive protein and erythrocyte sedimentation rate, calprotectin has now become part of the current methodology for assessing IBD activity. More recently, research has identified that other fecal and serum biomarkers such as lactoferrin, S100A12, GM-CSF autoantibodies, α1-antitrypsin, eosinophil-derived proteins, and cytokine concentrations have variable degrees of utility in monitoring gastrointestinal tract inflammation. In order to provide direction toward novel methods of predicting relapse in IBD, we provide an up-to-date review of these biomarkers and their potential utility in the prediction of clinical relapse, given their observed activities during various stages of clinical remission. PMID:26975751

  14. Surrogate gas prediction model as a proxy for Δ14C-based measurements of fossil fuel CO2

    NASA Astrophysics Data System (ADS)

    Coakley, Kevin J.; Miller, John B.; Montzka, Stephen A.; Sweeney, Colm; Miller, Ben R.

    2016-06-01

    The measured 14C:12C isotopic ratio of atmospheric CO2 (and its associated derived Δ14C value) is an ideal tracer for determination of the fossil fuel derived CO2 enhancement contributing to any atmospheric CO2 measurement (Cff). Given enough such measurements, independent top-down estimation of U.S. fossil fuel CO2 emissions should be possible. However, the number of Δ14C measurements is presently constrained by cost, available sample volume, and availability of mass spectrometer measurement facilities. Δ14C is therefore measured in just a small fraction of samples obtained by flask air sampling networks around the world. Here we develop a projection pursuit regression (PPR) model to predict Cff as a function of multiple surrogate gases acquired within the NOAA/Earth System Research Laboratory (ESRL) Global Greenhouse Gas Reference Network (GGGRN). The surrogates consist of measured enhancements of various anthropogenic trace gases, including CO, SF6, and halocarbon and hydrocarbon acquired in vertical airborne sampling profiles near Cape May, NJ and Portsmouth, NH from 2005 to 2010. Model performance for these sites is quantified based on predicted values corresponding to test data excluded from the model building process. Chi-square hypothesis test analysis indicates that these predictions and corresponding observations are consistent given our uncertainty budget which accounts for random effects and one particular systematic effect. However, quantification of the combined uncertainty of the prediction due to all relevant systematic effects is difficult because of the limited range of the observations and their relatively high fractional uncertainties at the sampling sites considered here. To account for the possibility of additional systematic effects, we incorporate another component of uncertainty into our budget. Expanding the number of Δ14C measurements in the NOAA GGGRN and building new PPR models at additional sites would improve our understanding of

  15. Investigation of a breathing surrogate prediction algorithm for prospective pulmonary gating

    SciTech Connect

    White, Benjamin M.; Low, Daniel A.; Zhao Tianyu; Wuenschel, Sara; Lu, Wei; Lamb, James M.; Mutic, Sasa; Bradley, Jeffrey D.; El Naqa, Issam

    2011-03-15

    Purpose: A major challenge of four dimensional computed tomography (4DCT) in treatment planning and delivery has been the lack of respiration amplitude and phase reproducibility during image acquisition. The implementation of a prospective gating algorithm would ensure that images would be acquired only during user-specified breathing phases. This study describes the development and testing of an autoregressive moving average (ARMA) model for human respiratory phase prediction under quiet respiration conditions. Methods: A total of 47 4DCT patient datasets and synchronized respiration records was utilized in this study. Three datasets were used in model development and were removed from further evaluation of the ARMA model. The remaining 44 patient datasets were evaluated with the ARMA model for prediction time steps from 50 to 1000 ms in increments of 50 and 100 ms. Thirty-five of these datasets were further used to provide a comparison between the proposed ARMA model and a commercial algorithm with a prediction time step of 240 ms. Results: The optimal number of parameters for the ARMA model was based on three datasets reserved for model development. Prediction error was found to increase as the prediction time step increased. The minimum prediction time step required for prospective gating was selected to be half of the gantry rotation period. The maximum prediction time step with a conservative 95% confidence criterion was found to be 0.3 s. The ARMA model predicted peak inhalation and peak exhalation phases significantly better than the commercial algorithm. Furthermore, the commercial algorithm had numerous instances of missed breath cycles and falsely predicted breath cycles, while the proposed model did not have these errors. Conclusions: An ARMA model has been successfully applied to predict human respiratory phase occurrence. For a typical CT scanner gantry rotation period of 0.4 s (0.2 s prediction time step), the absolute error was relatively small, 0

  16. Building Chinese wind data for Wind Erosion Prediction System using surrogate US data

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Wind erosion is a global problem, especially in arid and semiarid regions of the world, which leads to land degradation and atmosphere pollution. The process-based Wind Erosion Prediction System (WEPS), developed by the USDA, is capable of simulating the windblown soil loss with changing weather and...

  17. The Low Fall as a Surrogate Marker of Frailty Predicts Long-Term Mortality in Older Trauma Patients

    PubMed Central

    Wong, Ting Hway; Nguyen, Hai V.; Chiu, Ming Terk; Chow, Khuan Yew; Ong, Marcus Eng Hock; Lim, Gek Hsiang; Nadkarni, Nivedita Vikas; Bautista, Dianne Carrol Tan; Cheng, Jolene Yu Xuan; Loo, Lynette Mee Ann; Seow, Dennis Chuen Chai

    2015-01-01

    Background Frailty is associated with adverse outcomes including disability, mortality and risk of falls. Trauma registries capture a broad range of injuries. However, frail patients who fall comprise a large proportion of the injuries occurring in ageing populations and are likely to have different outcomes compared to non-frail injured patients. The effect of frail fallers on mortality is under-explored but potentially significant. Currently, many trauma registries define low falls as less than three metres, a height that is likely to include non-frailty falls. We hypothesized that the low fall from less than 0.5 metres, including same-level falls, is a surrogate marker of frailty and predicts long-term mortality in older trauma patients. Methods Using data from the Singapore National Trauma Registry, 2011–2013, matched till September 2014 to the death registry, we analysed adults aged over 45 admitted via the emergency department in public hospitals sustaining blunt injuries with an injury severity score (ISS) of 9 or more, excluding isolated hip fractures from same-level falls in the over 65. Patients injured by a low fall were compared to patients injured by high fall and other blunt mechanisms. Logistic regression was used to analyze 12-month mortality, controlling for mechanism of injury, ISS, revised trauma score (RTS), co-morbidities, gender, age and age-gender interaction. Different low fall height definitions, adjusting for injury regions, and analyzing the entire adult cohort were used in sensitivity analyses and did not change our findings. Results Of the 8111 adults in our cohort, patients who suffered low falls were more likely to die of causes unrelated to their injuries (p<0.001), compared to other blunt trauma and higher fall heights. They were at higher risk of 12-month mortality (OR 1.75, 95% CI 1.18–2.58, p = 0.005), independent of ISS, RTS, age, gender, age-gender interaction and co-morbidities. Falls that were higher than 0.5m did not

  18. Quality of Communication in the ICU and Surrogate's Understanding of Prognosis

    PubMed Central

    Chiarchiaro, Jared; Buddadhumaruk, Praewpannarai; Arnold, Robert M.; White, Douglas B.

    2014-01-01

    Objective Although misperceptions about prognosis by surrogates in ICUs are common and influence treatment decisions, there is no validated, practical way to measure the effectiveness of prognostic communication. Surrogates' subjective ratings of quality of communication have been used in other domains as markers of effectiveness of communication. We sought to determine whether surrogates' subjective ratings of the quality of prognostic communication predicts accurate expectation about prognosis by surrogates. Design We performed a cross sectional cohort study. Surrogates rated the quality of prognostic communication by survey. Physicians and surrogates gave their percentage estimate of patient survival on ICU day 3 on a 0-100 probability scale. We defined discordance about prognosis as a difference in the physician's and surrogate's estimates of ≥ +/-20%. We used multi-level logistic regression modeling to account for clustering under physicians and patients and adjust for confounders. Patients 275 patients with acute respiratory distress syndrome at high risk of death or severe functional impairment, their 546 surrogate decision makers, and their 150 physicians. Measurements and Main Results There was no predictive utility of surrogates' ratings of the quality of communication about prognosis to identify inaccurate expectations about prognosis. (OR 1.04 +/- 0.07, p 0.54). Surrogates' subjective ratings of the quality of communication about prognosis were high, as assessed with a variety of questions. Discordant prognostic estimates were present in 63.5% (95% CI: 59.0%-67.9%) of physician-surrogate pairs. Conclusions Although most surrogates rate the quality of prognostic communication highly, inaccurate expectations about prognosis are common among surrogates. Surrogates' ratings of the quality of prognostic communication do not reliably predict an accurate expectation about prognosis. PMID:25687030

  19. Performance predictions of a tubular SOFC operating on a partially reformed JP-8 surrogate

    NASA Astrophysics Data System (ADS)

    Gupta, Gaurav K.; Marda, Jonathan R.; Dean, Anthony M.; Colclasure, Andrew M.; Zhu, Huayang; Kee, Robert J.

    This paper uses chemically reacting flow models to explore the effect of upstream JP-8 steam reforming on the performance of a tubular, anode-supported, solid-oxide fuel cell. In all cases studied in this paper, a steam-carbon ratio of 3 is used for the reformer inlet. However, by varying the reformer temperature, the methane concentration in the reformate stream can be varied. In this study methane mole fractions are varied between 0 and 20%, on a dry basis. The methane mole fraction is found to have a substantial effect on fuel-cell efficiency, power density, and heat-release profiles. The paper also explores the effects of internal reforming chemistry and electrochemical charge transfer on the gas-phase kinetics and propensity for deposit formation. A detailed reaction mechanism is used to describe methane steam reforming on Ni within the anode, while a detailed gas-phase mechanism is used to predict the gas-phase composition in the fuel channel.

  20. Information Packet on Surrogate Parents.

    ERIC Educational Resources Information Center

    Moore, Jean J.; Mason, Doris M.

    The information packet focuses on the role of the surrogate parent with emphasis on the rights of the handicapped child as mandated by P.L. 94-142, the Education for All Handicapped Children Act. Included are the following: a discussion of 10 surrogate parent issues identified through a literature search and survey of five states (Connecticut,…

  1. Projection in surrogate decisions about life-sustaining medical treatments.

    PubMed

    Fagerlin, A; Ditto, P H; Danks, J H; Houts, R M; Smucker, W D

    2001-05-01

    To honor the wishes of an incapacitated patient, surrogate decision makers must predict the treatment decisions patients would make for themselves if able. Social psychological research, however, suggests that surrogates' own treatment preferences may influence their predictions of others' preferences. In 2 studies (1 involving 60 college student surrogates and a parent, the other involving 361 elderly outpatients and their chosen surrogate decision maker), surrogates predicted whether a close other would want life-sustaining treatment in hypothetical end-of-life scenarios and stated their own treatment preferences in the same scenarios. Surrogate predictions more closely resembled surrogates' own treatment wishes than they did the wishes of the individual they were trying to predict. Although the majority of prediction errors reflected inaccurate use of surrogates' own treatment preferences, projection was also found to result in accurate prediction more often than counterprojective predictions. The rationality and accuracy of projection in surrogate decision making is discussed. PMID:11403214

  2. Structure-Based Prediction of Drug Distribution Across the Headgroup and Core Strata of a Phospholipid Bilayer Using Surrogate Phases

    PubMed Central

    2015-01-01

    Solvation of drugs in the core (C) and headgroup (H) strata of phospholipid bilayers affects their physiological transport rates and accumulation. These characteristics, especially a complete drug distribution profile across the bilayer strata, are tedious to obtain experimentally, to the point that even simplified preferred locations are only available for a few dozen compounds. Recently, we showed that the partition coefficient (P) values in the system of hydrated diacetyl phosphatidylcholine (DAcPC) and n-hexadecane (C16), as surrogates of the H- and C-strata of the bilayer composed of the most abundant mammalian phospholipid, PC, agree well with the preferred bilayer location of compounds. High P values are typical for lipophiles accumulating in the core, and low P values are characteristic of cephalophiles preferring the headgroups. This simple pattern does not hold for most compounds, which usually have more even distribution and may also accumulate at the H/C interface. To model complete distribution, the correlates of solvation energies are needed for each drug state in the bilayer: (1) for the H-stratum it is the DAcPC/W P value, calculated as the ratio of the C16/W and C16/DAcPC (W for water) P values; (2) for the C-stratum, the C16/W P value; (3) for the H/C interface, the P values for all plausible molecular poses are characterized using the fragment DAcPC/W and C16/W solvation parameters for the parts of the molecule embedded in the H- and C-strata, respectively. The correlates, each scaled by two Collander coefficients, were used in a nonlinear, mass-balance based model of intrabilayer distribution, which was applied to the easily measurable overall P values of compounds in the DMPC (M = myristoyl) bilayers and monolayers as the dependent variables. The calibrated model for 107 neutral compounds explains 94% of experimental variance, achieves similar cross-validation levels, and agrees well with the nontrivial, experimentally determined bilayer

  3. A comparison of lung motion measured using implanted electromagnetic transponders and motion algorithmically predicted using external surrogates as an alternative to respiratory correlated CT imaging

    NASA Astrophysics Data System (ADS)

    Lechleiter, Kristen M.; Low, Daniel A.; Chaudhari, Amir; Lu, Wei; Hubenschmidt, James P.; Mayse, Martin L.; Dimmer, Steven C.; Bradley, Jeffrey D.; Parikh, Parag J.

    2007-03-01

    Three-dimensional volumetric imaging correlated with respiration (4DCT) typically utilizes external breathing surrogates and phase-based models to determine lung tissue motion. However, 4DCT requires time consuming post-processing and the relationship between external breathing surrogates and lung tissue motion is not clearly defined. This study compares algorithms using external respiratory motion surrogates as predictors of internal lung motion tracked in real-time by electromagnetic transponders (Calypso® Medical Technologies) implanted in a canine model. Simultaneous spirometry, bellows, and transponder positions measurements were acquired during free breathing and variable ventilation respiratory patterns. Functions of phase, amplitude, tidal volume, and airflow were examined by least-squares regression analysis to determine which algorithm provided the best estimate of internal motion. The cosine phase model performed the worst of all models analyzed (R2 = 31.6%, free breathing, and R2 = 14.9%, variable ventilation). All algorithms performed better during free breathing than during variable ventilation measurements. The 5D model of tidal volume and airflow predicted transponder location better than amplitude or either of the two phasebased models analyzed, with correlation coefficients of 66.1% and 64.4% for free breathing and variable ventilation respectively. Real-time implanted transponder based measurements provide a direct method for determining lung tissue location. Current phase-based or amplitude-based respiratory motion algorithms cannot as accurately predict lung tissue motion in an irregularly breathing subject as a model including tidal volume and airflow. Further work is necessary to quantify the long term stability of prediction capabilities using amplitude and phase based algorithms for multiple lung tumor positions over time.

  4. Protein signatures as potential surrogate biomarkers for stratification and prediction of treatment response in chronic myeloid leukemia patients

    PubMed Central

    Alaiya, Ayodele A.; Aljurf, Mahmoud; Shinwari, Zakia; Almohareb, Fahad; Malhan, Hafiz; Alzahrani, Hazzaa; Owaidah, Tarek; Fox, Jonathan; Alsharif, Fahad; Mohamed, Said Y.; Rasheed, Walid; Aldawsari, Ghuzayel; Hanbali, Amr; Ahmed, Syed Osman; Chaudhri, Naeem

    2016-01-01

    There is unmet need for prediction of treatment response for chronic myeloid leukemia (CML) patients. The present study aims to identify disease-specific/disease-associated protein biomarkers detectable in bone marrow and peripheral blood for objective prediction of individual’s best treatment options and prognostic monitoring of CML patients. Bone marrow plasma (BMP) and peripheral blood plasma (PBP) samples from newly-diagnosed chronic-phase CML patients were subjected to expression-proteomics using quantitative two-dimensional gel electrophoresis (2-DE) and label-free liquid chromatography tandem mass spectrometry (LC-MS/MS). Analysis of 2-DE protein fingerprints preceding therapy commencement accurately predicts 13 individuals that achieved major molecular response (MMR) at 6 months from 12 subjects without MMR (No-MMR). Results were independently validated using LC-MS/MS analysis of BMP and PBP from patients that have more than 24 months followed-up. One hundred and sixty-four and 138 proteins with significant differential expression profiles were identified from PBP and BMP, respectively and only 54 proteins overlap between the two datasets. The protein panels also discriminates accurately patients that stay on imatinib treatment from patients ultimately needing alternative treatment. Among the identified proteins are TYRO3, a member of TAM family of receptor tyrosine kinases (RTKs), the S100A8, and MYC and all of which have been implicated in CML. Our findings indicate analyses of a panel of protein signatures is capable of objective prediction of molecular response and therapy choice for CML patients at diagnosis as ‘personalized-medicine-model’. PMID:27573699

  5. Protein signatures as potential surrogate biomarkers for stratification and prediction of treatment response in chronic myeloid leukemia patients.

    PubMed

    Alaiya, Ayodele A; Aljurf, Mahmoud; Shinwari, Zakia; Almohareb, Fahad; Malhan, Hafiz; Alzahrani, Hazzaa; Owaidah, Tarek; Fox, Jonathan; Alsharif, Fahad; Mohamed, Said Y; Rasheed, Walid; Aldawsari, Ghuzayel; Hanbali, Amr; Ahmed, Syed Osman; Chaudhri, Naeem

    2016-09-01

    There is unmet need for prediction of treatment response for chronic myeloid leukemia (CML) patients. The present study aims to identify disease-specific/disease-associated protein biomarkers detectable in bone marrow and peripheral blood for objective prediction of individual's best treatment options and prognostic monitoring of CML patients. Bone marrow plasma (BMP) and peripheral blood plasma (PBP) samples from newly-diagnosed chronic-phase CML patients were subjected to expression-proteomics using quantitative two-dimensional gel electrophoresis (2-DE) and label-free liquid chromatography tandem mass spectrometry (LC-MS/MS). Analysis of 2-DE protein fingerprints preceding therapy commencement accurately predicts 13 individuals that achieved major molecular response (MMR) at 6 months from 12 subjects without MMR (No-MMR). Results were independently validated using LC-MS/MS analysis of BMP and PBP from patients that have more than 24 months followed-up. One hundred and sixty-four and 138 proteins with significant differential expression profiles were identified from PBP and BMP, respectively and only 54 proteins overlap between the two datasets. The protein panels also discriminates accurately patients that stay on imatinib treatment from patients ultimately needing alternative treatment. Among the identified proteins are TYRO3, a member of TAM family of receptor tyrosine kinases (RTKs), the S100A8, and MYC and all of which have been implicated in CML. Our findings indicate analyses of a panel of protein signatures is capable of objective prediction of molecular response and therapy choice for CML patients at diagnosis as 'personalized-medicine-model'. PMID:27573699

  6. Software Surrogate

    NASA Technical Reports Server (NTRS)

    1999-01-01

    In 1994, Blackboard Technology received a NASA Phase I SBIR award entitled "A Blackboard-Based Framework for Mixed-Initiative, Crewed- Space-System Applications." This research continued in Phase II at JSC, where a generic architecture was developed in which a software surrogate serves as the operator's representative in the fast-paced realm of nearly autonomous, intelligent systems. This SBIR research effort addressed the need to support human-operator monitoring and intervention with intelligent systems such as those being developed for NASA's crewed space program.

  7. Predicting missing links and identifying spurious links via likelihood analysis.

    PubMed

    Pan, Liming; Zhou, Tao; Lü, Linyuan; Hu, Chin-Kun

    2016-01-01

    Real network data is often incomplete and noisy, where link prediction algorithms and spurious link identification algorithms can be applied. Thus far, it lacks a general method to transform network organizing mechanisms to link prediction algorithms. Here we use an algorithmic framework where a network's probability is calculated according to a predefined structural Hamiltonian that takes into account the network organizing principles, and a non-observed link is scored by the conditional probability of adding the link to the observed network. Extensive numerical simulations show that the proposed algorithm has remarkably higher accuracy than the state-of-the-art methods in uncovering missing links and identifying spurious links in many complex biological and social networks. Such method also finds applications in exploring the underlying network evolutionary mechanisms. PMID:26961965

  8. Predicting missing links and identifying spurious links via likelihood analysis

    NASA Astrophysics Data System (ADS)

    Pan, Liming; Zhou, Tao; Lü, Linyuan; Hu, Chin-Kun

    2016-03-01

    Real network data is often incomplete and noisy, where link prediction algorithms and spurious link identification algorithms can be applied. Thus far, it lacks a general method to transform network organizing mechanisms to link prediction algorithms. Here we use an algorithmic framework where a network’s probability is calculated according to a predefined structural Hamiltonian that takes into account the network organizing principles, and a non-observed link is scored by the conditional probability of adding the link to the observed network. Extensive numerical simulations show that the proposed algorithm has remarkably higher accuracy than the state-of-the-art methods in uncovering missing links and identifying spurious links in many complex biological and social networks. Such method also finds applications in exploring the underlying network evolutionary mechanisms.

  9. Predicting missing links and identifying spurious links via likelihood analysis

    PubMed Central

    Pan, Liming; Zhou, Tao; Lü, Linyuan; Hu, Chin-Kun

    2016-01-01

    Real network data is often incomplete and noisy, where link prediction algorithms and spurious link identification algorithms can be applied. Thus far, it lacks a general method to transform network organizing mechanisms to link prediction algorithms. Here we use an algorithmic framework where a network’s probability is calculated according to a predefined structural Hamiltonian that takes into account the network organizing principles, and a non-observed link is scored by the conditional probability of adding the link to the observed network. Extensive numerical simulations show that the proposed algorithm has remarkably higher accuracy than the state-of-the-art methods in uncovering missing links and identifying spurious links in many complex biological and social networks. Such method also finds applications in exploring the underlying network evolutionary mechanisms. PMID:26961965

  10. Identifying predictive morphologic features of malignancy in eyelid lesions

    PubMed Central

    Leung, Christina; Johnson, Davin; Pang, Renee; Kratky, Vladimir

    2015-01-01

    Abstract Objective To determine features of eyelid lesions most predictive of malignancy, and to design a key to assist general practitioners in the triaging of such lesions. Design Prospective observational study. Setting Department of Ophthalmology at Queen’s University in Kingston, Ont. Participants A total of 199 consecutive periocular lesions requiring biopsy or excision were included. Main outcome measures First, potential features suggestive of malignancy for eyelid lesions were identified based on a survey sent to Canadian oculoplastic surgeons. The sensitivity, specificity, and odds ratios (ORs) of these features were then determined using 199 consecutive photographed eyelid lesions of patients who presented to the Department of Ophthalmology and underwent biopsy or excision. A triage key was then created based on the features with the highest ORs, and it was pilot-tested by a group of medical students. Results Of the 199 lesions included, 161 (80.9%) were benign and 38 (19.1%) were malignant. The 3 features with the highest ORs in predicting malignancy were infiltration (OR = 18.2, P < .01), ulceration (OR = 14.7, P < .01), and loss of eyelashes (OR = 6.0, P < .01). The acronym LUI (loss of eyelashes, ulceration, infiltration) was created to assist in memory recall. After watching a video describing the LUI triage key, the mean total score of a group of medical students for correctly identifying malignant lesions increased from 46% to 70% (P < .001). Conclusion Differentiating benign from malignant eyelid lesions can be difficult even for experienced physicians. The LUI triage key provides physicians with an evidence-based, easy-to-remember system for assisting in the triaging of these lesions. PMID:25756148

  11. A new framework for selecting environmental surrogates.

    PubMed

    Lindenmayer, David; Pierson, Jennifer; Barton, Philip; Beger, Maria; Branquinho, Cristina; Calhoun, Aram; Caro, Tim; Greig, Hamish; Gross, John; Heino, Jani; Hunter, Malcolm; Lane, Peter; Longo, Catherine; Martin, Kathy; McDowell, William H; Mellin, Camille; Salo, Hanna; Tulloch, Ayesha; Westgate, Martin

    2015-12-15

    Surrogate concepts are used in all sub-disciplines of environmental science. However, controversy remains regarding the extent to which surrogates are useful for resolving environmental problems. Here, we argue that conflicts about the utility of surrogates (and the related concepts of indicators and proxies) often reflect context-specific differences in trade-offs between measurement accuracy and practical constraints. By examining different approaches for selecting and applying surrogates, we identify five trade-offs that correspond to key points of contention in the application of surrogates. We then present an 8-step Adaptive Surrogacy Framework that incorporates cross-disciplinary perspectives from a wide spectrum of the environmental sciences, aiming to unify surrogate concepts across disciplines and applications. Our synthesis of the science of surrogates is intended as a first step towards fully leveraging knowledge accumulated across disciplines, thus consolidating lessons learned so that they may be accessible to all those operating in different fields, yet facing similar hurdles. PMID:26298409

  12. Surrogate motherhood.

    PubMed

    Chang, Curtis Li-ming

    2004-03-01

    A "surrogate mother" is a woman who, for financial or other reasons, agrees to bear a child for another woman who is incapable to conceive herself. In other words, she is a "substitute mother" that conceives, gestates and delivers a baby on behalf of another woman who is subsequently to be seen as the "real" (social and legal) mother of the child. Though the practice of surrogacy has already become a big market in western countries, it has also generated countless challenges for the law because it adds a third dimension to the meaning of motherhood. Like adoption, surrogacy separates the role of rearing mother from what the law has called the natural mother, but gestational surrogacy breaks the latter down into the roles of genetic mother and birth mother, leaving two women with biological connections to the child. Because surrogacy tends to commodify and dehumanize people, and because of all its legal, social, and psychological complications, it is obviously not wise to accept surrogacy as an alternative way of procreation. PMID:15460596

  13. Use of surrogate outcomes in US FDA drug approvals, 2003–2012: a survey

    PubMed Central

    Yu, Tsung; Hsu, Yea-Jen; Fain, Kevin M; Boyd, Cynthia M; Holbrook, Janet T; Puhan, Milo A

    2015-01-01

    Objective To evaluate, across a spectrum of diseases, how often surrogate outcomes are used as a basis for drug approvals by the US Food and Drug Administration (FDA), and whether and how the rationale for using treatment effects on surrogates as predictors of treatment effects on patient-centred outcomes is discussed. Study design and setting We used the Drugs@FDA website to identify drug approvals produced from 2003 to 2012 by the FDA. We focused on four diseases (chronic obstructive pulmonary disease (COPD), type 1 or 2 diabetes, glaucoma and osteoporosis) for which surrogates are commonly used in trials. We reviewed the drug labels and medical reviews to provide empirical evidence on how surrogate outcomes are handled by the FDA. Results Of 1043 approvals screened, 58 (6%) were for the four diseases of interest. Most drugs for COPD (7/9, 78%), diabetes (26/26, 100%) and glaucoma (9/9, 100%) were approved based on surrogates while for osteoporosis, most drugs (10/14, 71%) were also approved for patient-centred outcomes (fractures). The rationale for using surrogates was discussed in 11 of the 43 (26%) drug approvals based on surrogates. In these drug approvals, we found drug approvals for diabetes are more likely than the other examined conditions to contain a discussion of trial evidence demonstrating that treatment effects on surrogate outcomes predict treatment effects on patient-centred outcomes. Conclusions Our results suggest that the FDA did not use a consistent approach to address surrogates in assessing the benefits and harms of drugs for COPD, type 1 or 2 diabetes, glaucoma and osteoporosis. For evaluating new drugs, patient-centred outcomes should be chosen whenever possible. If the use of surrogate outcomes is necessary, then a consistent approach is important to review the evidence for surrogacy and consider surrogate's usage in the treatment and population under study. PMID:26614616

  14. Surrogate consent to non-beneficial research: erring on the right side when substituted judgments may be inaccurate.

    PubMed

    Johansson, Mats; Broström, Linus

    2016-04-01

    Part of the standard protection of decisionally incapacitated research subjects is a prohibition against enrolling them unless surrogate decision makers authorize it. A common view is that surrogates primarily ought to make their decisions based on what the decisionally incapacitated subject would have wanted regarding research participation. However, empirical studies indicate that surrogate predictions about such preferences are not very accurate. The focus of this article is the significance of surrogate accuracy in the context of research that is not expected to benefit the research subject. We identify three morally relevant asymmetries between being enrolled and not being enrolled in such non-beneficial research, and conclude that when there is a non-negligible probability that surrogates' predictions are wrong, it will generally be better to err on the side of not authorizing enrollment. PMID:27130296

  15. A Surrogate Approach to the Experimental Optimization of Multielement Airfoils

    NASA Technical Reports Server (NTRS)

    Otto, John C.; Landman, Drew; Patera, Anthony T.

    1996-01-01

    The incorporation of experimental test data into the optimization process is accomplished through the use of Bayesian-validated surrogates. In the surrogate approach, a surrogate for the experiment (e.g., a response surface) serves in the optimization process. The validation step of the framework provides a qualitative assessment of the surrogate quality, and bounds the surrogate-for-experiment error on designs "near" surrogate-predicted optimal designs. The utility of the framework is demonstrated through its application to the experimental selection of the trailing edge ap position to achieve a design lift coefficient for a three-element airfoil.

  16. Identifying Future Scientists: Predicting Persistence into Research Training

    ERIC Educational Resources Information Center

    McGee, Richard; Keller, Jill L.

    2007-01-01

    This study used semistructured interviews and grounded theory to look for characteristics among college undergraduates that predicted persistence into Ph.D. and M.D./Ph.D. training. Participants in the summer undergraduate and postbaccalaureate research programs at the Mayo Clinic College of Medicine were interviewed at the start, near the end,…

  17. Use of a simple anthropometric measurement to predict birth weight. WHO Collaborative Study of Birth Weight Surrogates.

    PubMed Central

    1993-01-01

    Low-birth-weight babies are most at risk of infant mortality. Unfortunately, in many developing countries it is not possible to weigh babies accurately because of the lack of robust scales. This article describes the results of a WHO Collaborative Study to investigate whether birth weight can be predicted accurately using chest circumference and/or arm circumference. The implications of the results for paediatric practice in developing countries are discussed. PMID:8490977

  18. Identifying future scientists: predicting persistence into research training.

    PubMed

    McGee, Richard; Keller, Jill L

    2007-01-01

    This study used semistructured interviews and grounded theory to look for characteristics among college undergraduates that predicted persistence into Ph.D. and M.D./Ph.D. training. Participants in the summer undergraduate and postbaccalaureate research programs at the Mayo Clinic College of Medicine were interviewed at the start, near the end, and 8-12 months after their research experience. Of more than 200 themes considered, five characteristics predicted those students who went on to Ph.D. and M.D./Ph.D. training or to M.D. training intending to do research: 1) Curiosity to discover the unknown, 2) Enjoyment of problem solving, 3) A high level of independence, 4) The desire to help others indirectly through research, and 5) A flexible, minimally structured approach to the future. Web-based surveys with different students confirmed the high frequency of curiosity and/or problem solving as the primary reason students planned research careers. No evidence was found for differences among men, women, and minority and nonminority students. Although these results seem logical compared with successful scientists, their constancy, predictive capabilities, and sharp contrast to students who chose clinical medicine were striking. These results provide important insights into selection and motivation of potential biomedical scientists and the early experiences that will motivate them toward research careers. PMID:18056303

  19. Identifying Future Scientists: Predicting Persistence into Research Training

    PubMed Central

    2007-01-01

    This study used semistructured interviews and grounded theory to look for characteristics among college undergraduates that predicted persistence into Ph.D. and M.D./Ph.D. training. Participants in the summer undergraduate and postbaccalaureate research programs at the Mayo Clinic College of Medicine were interviewed at the start, near the end, and 8–12 months after their research experience. Of more than 200 themes considered, five characteristics predicted those students who went on to Ph.D. and M.D./Ph.D. training or to M.D. training intending to do research: 1) Curiosity to discover the unknown, 2) Enjoyment of problem solving, 3) A high level of independence, 4) The desire to help others indirectly through research, and 5) A flexible, minimally structured approach to the future. Web-based surveys with different students confirmed the high frequency of curiosity and/or problem solving as the primary reason students planned research careers. No evidence was found for differences among men, women, and minority and nonminority students. Although these results seem logical compared with successful scientists, their constancy, predictive capabilities, and sharp contrast to students who chose clinical medicine were striking. These results provide important insights into selection and motivation of potential biomedical scientists and the early experiences that will motivate them toward research careers. PMID:18056303

  20. Assessing the Surrogate Susceptibility of Oxacillin and Cefoxitin for Commonly Utilized Parenteral Agents against Methicillin-Susceptible Staphylococcus aureus: Focus on Ceftriaxone Discordance between Predictive Susceptibility and in Vivo Exposures

    PubMed Central

    Kang, Nayon; Housman, Seth T.; Nicolau, David P.

    2015-01-01

    Susceptibility testing with the use of surrogate agents is common among clinical microbiology laboratories. One such example is oxacillin and cefoxitin for β-lactams against methicillin-susceptible Staphylococcus aureus (MSSA). This study aimed to assess the surrogate predictive value (SPV) of oxacillin and cefoxitin for the susceptibility of commonly utilized parenteral β-lactams against MSSA as well as to evaluate the concordance between predictive susceptibility testing and the in vivo exposures for ceftriaxone. Broth microdilution MICs were determined for cefazolin, cefoxitin, ceftaroline, ceftriaxone, nafcillin, and oxacillin against a national collection of 1238 MSSA from US hospitals. Pharmacodynamic profiling was utilized to establish a clinical breakpoint for commonly utilized doses of ceftriaxone. Oxacillin had good SPVs for all the β-lactams tested, whereas cefoxitin produced unacceptable major errors for all four agents and thus appears to be an unacceptable susceptibility surrogate. While oxacillin is an adequate surrogate based on the currently defined laboratory criteria, our data also suggest that caution should be exercised when incorporating this testing approach in the clinical setting in view of the fact that the MIC distribution of MSSA coupled with the commonly utilized low doses of ceftriaxone may result in inadequate in vivo exposures against this pathogen. PMID:26264030

  1. Assessing the Surrogate Susceptibility of Oxacillin and Cefoxitin for Commonly Utilized Parenteral Agents against Methicillin-Susceptible Staphylococcus aureus: Focus on Ceftriaxone Discordance between Predictive Susceptibility and in Vivo Exposures.

    PubMed

    Kang, Nayon; Housman, Seth T; Nicolau, David P

    2015-01-01

    Susceptibility testing with the use of surrogate agents is common among clinical microbiology laboratories. One such example is oxacillin and cefoxitin for β-lactams against methicillin-susceptible Staphylococcus aureus (MSSA). This study aimed to assess the surrogate predictive value (SPV) of oxacillin and cefoxitin for the susceptibility of commonly utilized parenteral β-lactams against MSSA as well as to evaluate the concordance between predictive susceptibility testing and the in vivo exposures for ceftriaxone. Broth microdilution MICs were determined for cefazolin, cefoxitin, ceftaroline, ceftriaxone, nafcillin, and oxacillin against a national collection of 1238 MSSA from US hospitals. Pharmacodynamic profiling was utilized to establish a clinical breakpoint for commonly utilized doses of ceftriaxone. Oxacillin had good SPVs for all the β-lactams tested, whereas cefoxitin produced unacceptable major errors for all four agents and thus appears to be an unacceptable susceptibility surrogate. While oxacillin is an adequate surrogate based on the currently defined laboratory criteria, our data also suggest that caution should be exercised when incorporating this testing approach in the clinical setting in view of the fact that the MIC distribution of MSSA coupled with the commonly utilized low doses of ceftriaxone may result in inadequate in vivo exposures against this pathogen. PMID:26264030

  2. Evaluation of Non-Viral Surrogate Markers as Predictive Indicators for Monitoring Progression of Human Immunodeficiency Virus Infection: An Eight-Year Analysis in a Regional Center.

    PubMed

    Rafatpanah, Houshang; Essmailian, Leila; Hedayati-Moghaddam, Mohammad Reza; Vakili, Rosita; Norouzi, Mehdi; Sarvghad, Mohammad Reza; Hosseinpour, Ali Mohammad; Sharebiani, Hiva; Rezaee, S A Rahim

    2016-01-01

    Suitable methods for clinical monitoring of HIV-infected patients are crucial in resource-poor settings. Demographic data, clinical staging, and laboratory findings for 112 asymptomatic subjects positive for HIV were assessed at the first admission and the last visit from 2002 to 2010. Cox regression analysis showed hemoglobin (Hb) (HR = 0.643, P = 0.021) to be a predictive indicator for disease progression, while CD4, CD8, and platelet counts showed low HRs, despite having significant probability values. Hb and total lymphocyte count (TLC) rapidly declined from stage II to III (10.9 and 29.6%, respectively). Reduced CD4 and platelet counts and Hb during stage I were associated with disease progression, and TLC was correlated with CD4 counts at the last follow-up (P < 0.001). However, WHO TLC cutoff of 1,200 cell/mm(3) had 26.1% sensitivity and 98.6% specificity. ROC curve analysis suggested that a TLC cutoff of 1,800 cell/mm(3) was more reliable in this region. Statistical analysis and data mining findings showed that Hb and TLC, and their rapid decline from stage II to III, in addition to reduced platelet count, could be valuable markers for a surrogate algorithm for monitoring of HIV-infected subjects and starting anti-viral therapy in the absence of sophisticated detection assays. PMID:25971319

  3. Feasibility of High-Power Diode Laser Array Surrogate to Support Development of Predictive Laser Lethality Model

    SciTech Connect

    Lowdermilk, W H; Rubenchik, A M; Springer, H K

    2011-01-13

    Predictive modeling and simulation of high power laser-target interactions is sufficiently undeveloped that full-scale, field testing is required to assess lethality of military directed-energy (DE) systems. The cost and complexity of such testing programs severely limit the ability to vary and optimize parameters of the interaction. Thus development of advanced simulation tools, validated by experiments under well-controlled and diagnosed laboratory conditions that are able to provide detailed physics insight into the laser-target interaction and reduce requirements for full-scale testing will accelerate development of DE weapon systems. The ultimate goal is a comprehensive end-to-end simulation capability, from targeting and firing the laser system through laser-target interaction and dispersal of target debris; a 'Stockpile Science' - like capability for DE weapon systems. To support development of advanced modeling and simulation tools requires laboratory experiments to generate laser-target interaction data. Until now, to make relevant measurements required construction and operation of very high power and complex lasers, which are themselves costly and often unique devices, operating in dedicated facilities that don't permit experiments on targets containing energetic materials. High power diode laser arrays, pioneered by LLNL, provide a way to circumvent this limitation, as such arrays capable of delivering irradiances characteristic of De weapon requires are self-contained, compact, light weight and thus easily transportable to facilities, such as the High Explosives Applications Facility (HEAF) at Lawrence Livermore National Laboratory (LLNL) where testing with energetic materials can be performed. The purpose of this study was to establish the feasibility of using such arrays to support future development of advanced laser lethality and vulnerability simulation codes through providing data for materials characterization and laser-material interaction

  4. Proteomic analysis of Plasmodium falciparum induced alterations in humans from different endemic regions of India to decipher malaria pathogenesis and identify surrogate markers of severity.

    PubMed

    Ray, Sandipan; Kumar, Vipin; Bhave, Amruta; Singh, Vaidhvi; Gogtay, Nithya J; Thatte, Urmila M; Talukdar, Arunansu; Kochar, Sanjay K; Patankar, Swati; Srivastava, Sanjeeva

    2015-09-01

    India significantly contributes to the global malaria burden and has the largest population in the world at risk of malaria. This study aims to analyze alterations in the human serum proteome as a consequence of non-severe and severe infections by the malaria parasite Plasmodium falciparum to identify markers related to disease severity and to obtain mechanistic insights about disease pathogenesis and host immune responses. In discovery phase of the study, a comprehensive quantitative proteomic analysis was performed using gel-based (2D-DIGE) and gel-free (iTRAQ) techniques on two independent mass spectrometry platforms (ESI-Q-TOF and Q-Exactive mass spectrometry), and selected targets were validated by ELISA. Proteins showing altered serum abundance in falciparum malaria patients revealed the modulation of different physiological pathways including chemokine and cytokine signaling, IL-12 signaling and production in macrophages, complement cascades, blood coagulation, and protein ubiquitination pathways. Some muscle related and cytoskeletal proteins such as titin and galectin-3-binding protein were found to be up-regulated in severe malaria patients. Hemoglobin levels and platelet counts were also found to be drastically lower in severe malaria patients. Identified proteins including serum amyloid A, C-reactive protein, apolipoprotein E and haptoglobin, which exhibited sequential alterations in their serum abundance in different severity levels of malaria, could serve as potential predictive markers for disease severity. To the best of our information, we report here the first comprehensive analysis describing the serum proteomic alterations observed in severe P. falciparum infected patients from different malaria endemic regions of India. This article is part of a Special Issue entitled: Proteomics in India. PMID:25982387

  5. The oxidation of a gasoline surrogate in the negative temperature coefficient region

    SciTech Connect

    Lenhert, David B.; Miller, David L.; Cernansky, Nicholas P.; Owens, Kevin G.

    2009-03-15

    This experimental study investigated the preignition reactivity behavior of a gasoline surrogate in a pressurized flow reactor over the low and intermediate temperature regime (600-800 K) at elevated pressure (8 atm). The surrogate mixture, a volumetric blend of 4.6% 1-pentene, 31.8% toluene, 14.0% n-heptane, and 49.6% 2,2,4-trimethyl-pentane (iso-octane), was shown to reproduce the low and intermediate temperature reactivity of full boiling range fuels in a previous study. Each of the surrogate components were examined individually to identify the major intermediate species in order to improve existing kinetic models, where appropriate, and to provide a basis for examining constituent interactions in the surrogate mixture. n-Heptane and 1-pentene started reacting at 630 K and 640 K, respectively, and both fuels exhibited a strong negative temperature coefficient (NTC) behavior starting at 700 and 710 K, respectively. Iso-octane showed a small level of reactivity at 630 K and a weak NTC behavior starting at 665 K. Neat toluene was unreactive at these temperatures. The surrogate started reacting at 630 K and exhibited a strong NTC behavior starting at 693 K. The extent of fuel consumption varied for each of the surrogate constituents and was related to their general autoignition behavior. Most of the intermediates identified during the surrogate oxidation were species observed during the oxidation of the neat constituents; however, the surrogate mixture did exhibit a significant increase in intermediates associated with iso-octane oxidation, but not from n-heptane. While neat toluene was unreactive at these temperatures, in the mixture it reacted with the radical pool generated by the other surrogate components, forming benzaldehyde, benzene, phenol, and ethyl-benzene. The observed n-heptane, iso-octane, and surrogate oxidation behavior was compared to predictions using existing kinetic models. The n-heptane model reasonably predicted the disappearance of the fuel

  6. Surrogate screening models for the low physical activity criterion of frailty

    PubMed Central

    Eckel, Sandrah P.; Bandeen-Roche, Karen; Chaves, Paulo H.M.; Fried, Linda P.; Louis, Thomas A.

    2012-01-01

    Background and Aims Low physical activity, one of five criteria in a validated clinical phenotype of frailty, is assessed by a standardized, semi-quantitative questionnaire on up to 20 leisure time activities. Because of the time demanded to collect the interview data, it has been challenging to translate to studies other than the Cardiovascular Health Study (CHS), for which it was developed. Considering subsets of activities, we identified and evaluated streamlined surrogate assessment methods and compared them to one implemented in the Women’s Health and Aging Study (WHAS). Methods Using data on men and women ages 65 and older from the CHS, we applied logistic regression models to rank activities by “relative influence” in predicting low physical activity. We considered subsets of the most influential activities as inputs to potential surrogate models (logistic regressions). We evaluated predictive accuracy and predictive validity using the area under receiver operating characteristic curves and assessed criterion validity using proportional hazards models relating frailty status (defined using the surrogate) to mortality. Results Walking for exercise and moderately strenuous household chores were highly influential for both genders. Women required fewer activities than men for accurate classification. The WHAS model (8 CHS activities) was an effective surrogate, but a surrogate using 6 activities (walking, chores, gardening, general exercise, mowing and golfing) was also highly predictive. Conclusions We recommend a 6 activity questionnaire to assess physical activity for men and women. If efficiency is essential and the study involves only women, fewer activities can be included. PMID:21993168

  7. Jet Pump Design Optimization by Multi-Surrogate Modeling

    NASA Astrophysics Data System (ADS)

    Mohan, S.; Samad, A.

    2015-01-01

    A basic approach to reduce the design and optimization time via surrogate modeling is to select a right type of surrogate model for a particular problem, where the model should have better accuracy and prediction capability. A multi-surrogate approach can protect a designer to select a wrong surrogate having high uncertainty in the optimal zone of the design space. Numerical analysis and optimization of a jet pump via multi-surrogate modeling have been reported in this work. Design variables including area ratio, mixing tube length to diameter ratio and setback ratio were introduced to increase the hydraulic efficiency of the jet pump. Reynolds-averaged Navier-Stokes equations were solved and responses were computed. Among different surrogate models, Sheppard function based surrogate shows better accuracy in data fitting while the radial basis neural network produced highest enhanced efficiency. The efficiency enhancement was due to the reduction of losses in the flow passage.

  8. Development and Validation of a Clinic-Based Prediction Tool to Identify Female Athletes at High Risk for Anterior Cruciate Ligament Injury

    PubMed Central

    Myer, Gregory D.; Ford, Kevin R.; Khoury, Jane; Succop, Paul; Hewett, Timothy E.

    2012-01-01

    Background Prospective measures of high knee abduction moment (KAM) during landing identify female athletes at high risk for anterior cruciate ligament injury. Laboratory-based measurements demonstrate 90% accuracy in prediction of high KAM. Clinic-based prediction algorithms that employ correlates derived from laboratory-based measurements also demonstrate high accuracy for prediction of high KAM mechanics during landing. Hypotheses Clinic-based measures derived from highly predictive laboratory-based models are valid for the accurate prediction of high KAM status, and simultaneous measurements using laboratory-based and clinic-based techniques highly correlate. Study Design Cohort study (diagnosis); Level of evidence, 2. Methods One hundred female athletes (basketball, soccer, volleyball players) were tested using laboratory-based measures to confirm the validity of identified laboratory-based correlate variables to clinic-based measures included in a prediction algorithm to determine high KAM status. To analyze selected clinic-based surrogate predictors, another cohort of 20 female athletes was simultaneously tested with both clinic-based and laboratory-based measures. Results The prediction model (odds ratio: 95% confidence interval), derived from laboratory-based surrogates including (1) knee valgus motion (1.59: 1.17-2.16 cm), (2) knee flexion range of motion (0.94: 0.89°-1.00°), (3) body mass (0.98: 0.94-1.03 kg), (4) tibia length (1.55: 1.20-2.07 cm), and (5) quadriceps-to-hamstrings ratio (1.70: 0.48%-6.0%), predicted high KAM status with 84% sensitivity and 67% specificity (P < .001). Clinic-based techniques that used a calibrated physician’s scale, a standard measuring tape, standard camcorder, ImageJ software, and an isokinetic dynamometer showed high correlation (knee valgus motion, r = .87; knee flexion range of motion, r = .95; and tibia length, r = .98) to simultaneous laboratory-based measurements. Body mass and quadriceps-to-hamstrings ratio

  9. Surrogate waveform models

    NASA Astrophysics Data System (ADS)

    Blackman, Jonathan; Field, Scott; Galley, Chad; Scheel, Mark; Szilagyi, Bela; Tiglio, Manuel

    2015-04-01

    With the advanced detector era just around the corner, there is a strong need for fast and accurate models of gravitational waveforms from compact binary coalescence. Fast surrogate models can be built out of an accurate but slow waveform model with minimal to no loss in accuracy, but may require a large number of evaluations of the underlying model. This may be prohibitively expensive if the underlying is extremely slow, for example if we wish to build a surrogate for numerical relativity. We examine alternate choices to building surrogate models which allow for a more sparse set of input waveforms. Research supported in part by NSERC.

  10. Surrogate clinical endpoints to predict overall survival in non-small cell lung cancer trials—are we in a new era?

    PubMed Central

    Wang, Xiaofei; Ready, Neal E.

    2015-01-01

    Surrogate endpoints for clinical trials in oncology offer an alternative metric for measuring clinical benefit, allowing for shorter trial duration, smaller patient cohorts, and single arm design. The correlation of surrogate endpoints with overall survival (OS) in therapeutic studies is a central consideration to their validity. The Food and Drug Administration (FDA) recently published an analysis of fourteen clinical trials in advanced non-small cell lung cancer (NSCLC), and discovered a strong association between response rate and progression free survival. Furthermore, a correlation between response rate and OS is demonstrated when analyzing the experimental treatment arm separately, minimizing bias from patient crossover. We also highlight multiple, important considerations when using response as an endpoint in clinical trials involving NSCLC patients. PMID:26798592

  11. Identifying developmental vascular disruptor compounds using a predictive signature and alternative toxicity models

    EPA Science Inventory

    Identifying Developmental Vascular Disruptor Compounds Using a Predictive Signature and Alternative Toxicity Models Presenting Author: Tamara Tal Affiliation: U.S. EPA/ORD/ISTD, RTP, NC, USA Chemically induced vascular toxicity during embryonic development can result in a wide...

  12. Intersecting pentagons as surrogate for identifying the use of mini mental state examination in assessment of dementia in a largely illiterate population

    PubMed Central

    Raina, SK; Maria, A; Chander, V; Raina, S

    2015-01-01

    Background and Rationale: The mini-mental state evaluation (MMSE) is often used to identify patients with dementia. One component of the MMSE is the intersecting pentagon copying (IPC) test, which may be difficult to be used in an illiterate population. Materials and Methods: A post hoc analysis on an elderly population (60 years and above) from Himachal Pradesh was carried out. The data of only 1,513 elderly individuals out of a total of 2,000 participants with a score of more than 26 (nondemented) out of a possible score of 30 on cognitive battery available were used. The scores on the IPC were evaluated and their association with some demographic variables was also assessed. Results: Illiterate participants, female participants, those with greater age, and the rural/tribal population groups faced the most difficulty in drawing the intersecting pentagons and even greater difficulty in drawing them correctly. Discussion: The IPC presents challenges for people who are illiterate and the scoring method needs to be addressed and changed particularly when the test is used in largely illiterate populations. PMID:26440395

  13. Assessing the Validity of Surrogate Outcomes for ESRD: A Meta-Analysis.

    PubMed

    Jun, Min; Turin, Tanvir Chowdhury; Woodward, Mark; Perkovic, Vlado; Lambers Heerspink, Hiddo J; Manns, Braden J; Tonelli, Marcello; Hemmelgarn, Brenda R

    2015-09-01

    Validation of current and promising surrogate outcomes for ESRD in randomized controlled trials (RCTs) has been limited. We conducted a systematic review and meta-analysis of RCTs to further inform the ability of surrogate outcomes for ESRD to predict the efficacy of various interventions on ESRD. MEDLINE, EMBASE, and CENTRAL (from inception through September 2013) were searched. All RCTs in adults with proteinuria, diabetes, or CKD stages 1-4 or renal transplant recipients reporting ≥10 ESRD events and a surrogate outcome (change in proteinuria or doubling of serum creatinine [DSCR]) for ESRD during a ≥1-year follow-up were included. Two reviewers abstracted trial characteristics and outcome data independently. To assess the correlation between the surrogate outcomes and ESRD, we determined the treatment effect ratio (TER), defined as the ratio of the treatment effects on ESRD and the effects on the change in surrogate outcomes. TERs close to 1 indicate greater agreement between ESRD and the surrogate, and these ratios were pooled across interventions. We identified 27 trials (97,458 participants; 4187 participants with ESRD). Seven trials reported the effects on change in proteinuria and showed consistent effects for proteinuria and ESRD (TER, 0.82; 95% confidence interval, 0.59 to 1.16), with minimal heterogeneity. Twenty trials reported on DSCR. Treatment effects on DSCR were consistent with the effects on ESRD (TER, 0.98; 95% confidence interval, 0.85 to 1.14), with moderate heterogeneity. In conclusion, DSCR is generally a good surrogate for ESRD, whereas data on proteinuria were limited. Further assessment of the surrogacy of proteinuria using prospective RCTs is warranted. PMID:25556165

  14. MRI Surrogates for Molecular Subgroups of Medulloblastoma

    PubMed Central

    Perreault, S.; Ramaswamy, V.; Achrol, A.S.; Chao, K.; Liu, T.T.; Shih, D.; Remke, M.; Schubert, S.; Bouffet, E.; Fisher, P.G.; Partap, S.; Vogel, H.; Taylor, M.D.; Cho, Y.J.; Yeom, K.W.

    2016-01-01

    BACKGROUND AND PURPOSE Recently identified molecular subgroups of medulloblastoma have shown potential for improved risk stratification. We hypothesized that distinct MR imaging features can predict these subgroups. MATERIALS AND METHODS All patients with a diagnosis of medulloblastoma at one institution, with both pretherapy MR imaging and surgical tissue, served as the discovery cohort (n = 47). MR imaging features were assessed by 3 blinded neuroradiologists. NanoString-based assay of tumor tissues was conducted to classify the tumors into the 4 established molecular subgroups (wingless, sonic hedgehog, group 3, and group 4). A second pediatric medulloblastoma cohort (n = 52) from an independent institution was used for validation of the MR imaging features predictive of the molecular subtypes. RESULTS Logistic regression analysis within the discovery cohort revealed tumor location (P < .001) and enhancement pattern (P = .001) to be significant predictors of medulloblastoma subgroups. Stereospecific computational analyses confirmed that group 3 and 4 tumors predominated within the midline fourth ventricle (100%, P = .007), wingless tumors were localized to the cerebellar peduncle/cerebellopontine angle cistern with a positive predictive value of 100% (95% CI, 30%–100%), and sonic hedgehog tumors arose in the cerebellar hemispheres with a positive predictive value of 100% (95% CI, 59%–100%). Midline group 4 tumors presented with minimal/no enhancement with a positive predictive value of 91% (95% CI, 59%–98%). When we used the MR imaging feature–based regression model, 66% of medulloblastomas were correctly predicted in the discovery cohort, and 65%, in the validation cohort. CONCLUSIONS Tumor location and enhancement pattern were predictive of molecular subgroups of pediatric medulloblastoma and may potentially serve as a surrogate for genomic testing. PMID:24831600

  15. Plutonium radiation surrogate

    DOEpatents

    Frank, Michael I.

    2010-02-02

    A self-contained source of gamma-ray and neutron radiation suitable for use as a radiation surrogate for weapons-grade plutonium is described. The source generates a radiation spectrum similar to that of weapons-grade plutonium at 5% energy resolution between 59 and 2614 keV, but contains no special nuclear material and emits little .alpha.-particle radiation. The weapons-grade plutonium radiation surrogate also emits neutrons having fluxes commensurate with the gamma-radiation intensities employed.

  16. GeneValidator: identify problems with protein-coding gene predictions

    PubMed Central

    Drăgan, Monica-Andreea; Moghul, Ismail; Priyam, Anurag; Bustos, Claudio; Wurm, Yannick

    2016-01-01

    Summary: Genomes of emerging model organisms are now being sequenced at very low cost. However, obtaining accurate gene predictions remains challenging: even the best gene prediction algorithms make substantial errors and can jeopardize subsequent analyses. Therefore, many predicted genes must be time-consumingly visually inspected and manually curated. We developed GeneValidator (GV) to automatically identify problematic gene predictions and to aid manual curation. For each gene, GV performs multiple analyses based on comparisons to gene sequences from large databases. The resulting report identifies problematic gene predictions and includes extensive statistics and graphs for each prediction to guide manual curation efforts. GV thus accelerates and enhances the work of biocurators and researchers who need accurate gene predictions from newly sequenced genomes. Availability and implementation: GV can be used through a web interface or in the command-line. GV is open-source (AGPL), available at https://wurmlab.github.io/tools/genevalidator. Contact: y.wurm@qmul.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online. PMID:26787666

  17. Detailed Kinetic Modeling of Gasoline Surrogate Mixtures

    SciTech Connect

    Mehl, M; Curran, H J; Pitz, W J; Westbrook, C K

    2009-03-09

    Real fuels are complex mixtures of thousands of hydrocarbon compounds including linear and branched paraffins, naphthenes, olefins and aromatics. It is generally agreed that their behavior can be effectively reproduced by simpler fuel surrogates containing a limited number of components. In this work, a recently revised version of the kinetic model by the authors is used to analyze the combustion behavior of several components relevant to gasoline surrogate formulation. Particular attention is devoted to linear and branched saturated hydrocarbons (PRF mixtures), olefins (1-hexene) and aromatics (toluene). Model predictions for pure components, binary mixtures and multi-component gasoline surrogates are compared with recent experimental information collected in rapid compression machine, shock tube and jet stirred reactors covering a wide range of conditions pertinent to internal combustion engines. Simulation results are discussed focusing attention on the mixing effects of the fuel components.

  18. [Immunological surrogate endpoints to evaluate vaccine efficacy].

    PubMed

    Jin, Pengfei; Li, Jingxin; Zhou, Yang; Zhu, Fengcai

    2015-12-01

    An immunological surrogate endpoints is a vaccine-induced immune response (either humoral or cellular immune) that predicts protection against clinical endpoints (infection or disease), and can be used to evaluate vaccine efficacy in clinical vaccine trials. Compared with field efficacy trials observing clinical endpoints, immunological vaccine trials could reduce the sample size or shorten the duration of a trial, which promote the license and development of new candidate vaccines. For these reasons, establishing immunological surrogate endpoints is one of 14 Grand Challenges of Global Health of the National Institutes of Health (NIH) and the Bill and Melinda Gates Foundation. From two parts of definition and statistical methods for evaluation of surrogate endpoints, this review provides a more comprehensive description. PMID:26887309

  19. The Surrogate Self

    ERIC Educational Resources Information Center

    Gunnison, Hugh

    1976-01-01

    The use of the "surrogate self" in counseling is a simple Gestalt-like role-playing technique (Perls 1969) that can be especially effective when the client has begun to see the counselor as a trusted, caring, and understanding person. The role-playing is described. (Author/EJT)

  20. Using complete genome comparisons to identify sequences whose presence accurately predicts clinically important phenotypes.

    PubMed

    Hall, Barry G; Cardenas, Heliodoro; Barlow, Miriam

    2013-01-01

    In clinical settings it is often important to know not just the identity of a microorganism, but also the danger posed by that particular strain. For instance, Escherichia coli can range from being a harmless commensal to being a very dangerous enterohemorrhagic (EHEC) strain. Determining pathogenic phenotypes can be both time consuming and expensive. Here we propose a simple, rapid, and inexpensive method of predicting pathogenic phenotypes on the basis of the presence or absence of short homologous DNA segments in an isolate. Our method compares completely sequenced genomes without the necessity of genome alignments in order to identify the presence or absence of the segments to produce an automatic alignment of the binary string that describes each genome. Analysis of the segment alignment allows identification of those segments whose presence strongly predicts a phenotype. Clinical application of the method requires nothing more that PCR amplification of each of the set of predictive segments. Here we apply the method to identifying EHEC strains of E. coli and to distinguishing E. coli from Shigella. We show in silico that with as few as 8 predictive sequences, if even three of those predictive sequences are amplified the probability of being EHEC or Shigella is >0.99. The method is thus very robust to the occasional amplification failure for spurious reasons. Experimentally, we apply the method to screening a set of 98 isolates to distinguishing E. coli from Shigella, and EHEC from non-EHEC E. coli strains and show that all isolates are correctly identified. PMID:23935901

  1. Trajectories of Substance Use Disorders in Youth: Identifying and Predicting Group Memberships

    ERIC Educational Resources Information Center

    Lee, Chih-Yuan S.; Winters, Ken C.; Wall, Melanie M.

    2010-01-01

    This study used latent class regression to identify latent trajectory classes based on individuals' diagnostic course of substance use disorders (SUDs) from late adolescence to early adulthood as well as to examine whether several psychosocial risk factors predicted the trajectory class membership. The study sample consisted of 310 individuals…

  2. Predicting Recidivism Among Adjudicated Delinquents: A Model to Identify High Risk Offenders.

    ERIC Educational Resources Information Center

    Grenier, Charles E.; Roundtree, George A.

    1987-01-01

    Studied 100 clients of the East Baton Rouge Parish Family Court to develop predictive model for identifying offenders at high risk for recidivism. Found important predictor variables to be presence of delinquent siblings or friends, school problems, type of offense committed, and gender of delinquent. (Author/NB)

  3. Using Predictive Modelling to Identify Students at Risk of Poor University Outcomes

    ERIC Educational Resources Information Center

    Jia, Pengfei; Maloney, Tim

    2015-01-01

    Predictive modelling is used to identify students at risk of failing their first-year courses and not returning to university in the second year. Our aim is twofold. Firstly, we want to understand the factors that lead to poor first-year experiences at university. Secondly, we want to develop simple, low-cost tools that would allow universities to…

  4. Use of NMR and NMR Prediction Software to Identify Components in Red Bull Energy Drinks

    ERIC Educational Resources Information Center

    Simpson, Andre J.; Shirzadi, Azadeh; Burrow, Timothy E.; Dicks, Andrew P.; Lefebvre, Brent; Corrin, Tricia

    2009-01-01

    A laboratory experiment designed as part of an upper-level undergraduate analytical chemistry course is described. Students investigate two popular soft drinks (Red Bull Energy Drink and sugar-free Red Bull Energy Drink) by NMR spectroscopy. With assistance of modern NMR prediction software they identify and quantify major components in each…

  5. Sooting characteristics of surrogates for jet fuels

    SciTech Connect

    Mensch, Amy; Santoro, Robert J.; Litzinger, Thomas A.; Lee, S.-Y.

    2010-06-15

    Currently, modeling the combustion of aviation fuels, such as JP-8 and JetA, is not feasible due to the complexity and compositional variation of these practical fuels. Surrogate fuel mixtures, composed of a few pure hydrocarbon compounds, are a key step toward modeling the combustion of practical aviation fuels. For the surrogate to simulate the practical fuel, the composition must be designed to reproduce certain pre-designated chemical parameters such as sooting tendency, H/C ratio, autoignition, as well as physical parameters such as boiling range and density. In this study, we focused only on the sooting characteristics based on the Threshold Soot Index (TSI). New measurements of TSI values derived from the smoke point along with other sooting tendency data from the literature have been combined to develop a set of recommended TSI values for pure compounds used to make surrogate mixtures. When formulating the surrogate fuel mixtures, the TSI values of the components are used to predict the TSI of the mixture. To verify the empirical mixture rule for TSI, the TSI values of several binary mixtures of candidate surrogate components were measured. Binary mixtures were also used to derive a TSI for iso-cetane, which had not previously been measured, and to verify the TSI for 1-methylnaphthalene, which had a low smoke point and large relative uncertainty as a pure compound. Lastly, surrogate mixtures containing three components were tested to see how well the measured TSI values matched the predicted values, and to demonstrate that a target value for TSI can be maintained using various components, while also holding the H/C ratio constant. (author)

  6. Multidimensional Scaling of Video Surrogates.

    ERIC Educational Resources Information Center

    Goodrum, Abby A.

    2001-01-01

    Four types of video surrogates were compared under two tasks. Multidimensional scaling was used to map dimensional dispersions of users' judgments of similarity between videos and surrogates. Congruence between these maps was used to evaluate representativeness of each surrogate type. Congruence was greater for image-based than for text-based…

  7. Predicting performance at medical school: can we identify at-risk students?

    PubMed Central

    Shaban, Sami; McLean, Michelle

    2011-01-01

    Background The purpose of this study was to examine the predictive potential of multiple indicators (eg, preadmission scores, unit, module and clerkship grades, course and examination scores) on academic performance at medical school, with a view to identifying students at risk. Methods An analysis was undertaken of medical student grades in a 6-year medical school program at the Faculty of Medicine and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates, over the past 14 years. Results While high school scores were significantly (P < 0.001) correlated with the final integrated examination, predictability was only 6.8%. Scores for the United Arab Emirates university placement assessment (Common Educational Proficiency Assessment) were only slightly more promising as predictors with 14.9% predictability for the final integrated examination. Each unit or module in the first four years was highly correlated with the next unit or module, with 25%–60% predictability. Course examination scores (end of years 2, 4, and 6) were significantly correlated (P < 0.001) with the average scores in that 2-year period (59.3%, 64.8%, and 55.8% predictability, respectively). Final integrated examination scores were significantly correlated (P < 0.001) with National Board of Medical Examiners scores (35% predictability). Multivariate linear regression identified key grades with the greatest predictability of the final integrated examination score at three stages in the program. Conclusion This study has demonstrated that it may be possible to identify “at-risk” students relatively early in their studies through continuous data archiving and regular analysis. The data analysis techniques used in this study are not unique to this institution. PMID:23745085

  8. High-throughput respirometric assay identifies predictive toxicophore of mitochondrial injury

    PubMed Central

    Wills, Lauren P.; Beeson, Gyda C.; Trager, Richard E.; Lindsey, Christopher C.; Beeson, Craig C.; Peterson, Yuri K.; Schnellmann, Rick G.

    2014-01-01

    Many environmental chemicals and drugs negatively affect human health through deleterious effects on mitochondrial function. Currently there is no chemical library of mitochondrial toxicants, and no reliable methods for predicting mitochondrial toxicity. We hypothesized that discrete toxicophores defined by distinct chemical entities can identify previously unidentified mitochondrial toxicants. We used a respirometric assay to screen 1760 compounds (5 μM) from the LOPAC and ChemBridge DIVERSet libraries. Thirty-one of the assayed compounds decreased uncoupled respiration, a stress test for mitochondrial dysfunction, prior to a decrease in cell viability and reduced the oxygen consumption rate in isolated mitochondria. The mitochondrial toxicants were grouped by chemical similarity and two clusters containing four compounds each were identified. Cheminformatic analysis of one of the clusters identified previously uncharacterized mitochondrial toxicants from the ChemBridge DIVERSet. This approach will enable the identification of mitochondrial toxicants and advance the prediction of mitochondrial toxicity for both drug discovery and risk assessment. PMID:23811330

  9. Characterization of Potential Surrogates for Produce Pathogens

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Background Escherichia coli (E. coli) is commonly used as a surrogate for pathogens in research to identify sources of agricultural contamination and to characterize how pathogens persist on plant surfaces. However, E. coli strains are highly diverse, exhibiting differences in physical, chemical and...

  10. Nitrate Salt Surrogate Blending Scoping Test Plan

    SciTech Connect

    Anast, Kurt Roy

    2015-11-13

    Test blending equipment identified in the “Engineering Options Assessment Report: Nitrate Salt Waste Stream Processing”. Determine if the equipment will provide adequate mixing of zeolite and surrogate salt/Swheat stream; optimize equipment type and operational sequencing; impact of baffles and inserts on mixing performance; and means of validating mixing performance

  11. Temporal Effects in Trend Prediction: Identifying the Most Popular Nodes in the Future

    PubMed Central

    Zhou, Yanbo; Zeng, An; Wang, Wei-Hong

    2015-01-01

    Prediction is an important problem in different science domains. In this paper, we focus on trend prediction in complex networks, i.e. to identify the most popular nodes in the future. Due to the preferential attachment mechanism in real systems, nodes’ recent degree and cumulative degree have been successfully applied to design trend prediction methods. Here we took into account more detailed information about the network evolution and proposed a temporal-based predictor (TBP). The TBP predicts the future trend by the node strength in the weighted network with the link weight equal to its exponential aging. Three data sets with time information are used to test the performance of the new method. We find that TBP have high general accuracy in predicting the future most popular nodes. More importantly, it can identify many potential objects with low popularity in the past but high popularity in the future. The effect of the decay speed in the exponential aging on the results is discussed in detail. PMID:25806810

  12. Identifying the predictable and unpredictable patterns of spring-to-autumn precipitation over eastern China

    NASA Astrophysics Data System (ADS)

    Ying, Kairan; Zheng, Xiaogu; Zhao, Tianbao; Frederiksen, Carsten S.; Quan, Xiao-Wei

    2016-06-01

    The patterns of interannual variability that arise from the slow (potentially predictable) and fast or intraseasonal (unpredictable) components of seasonal mean precipitation over eastern China are examined, based on observations from a network of 106 stations for the period 1951-2004. The analysis is done by using a variance decomposition method that allows identification of the sources of the predictability and the prediction uncertainty, from March-April-May (MAM) to September-October-November (SON). The average potential predictability (ratio of slow-to-total variance) of eastern China precipitation is generally moderate, with the highest value of 0.18 in June-July-August (JJA) and lowest value of 0.12 in April-May-June (AMJ). The leading predictable precipitation mode is significantly related to one-season-lead SST anomalies in the area of the Kuroshio Current during AMJ-to-JJA, the Indian-western Pacific SST in July-August-September (JAS), and the eastern tropical Pacific SST in MAM and SON. The prolonged linear trends, which are seen in the principal component time series associated with the second or third predictable precipitation modes in MJJ-to-ASO, also serve as a source of predictability for seasonal precipitation over eastern China. The predictive characteristics of the atmospheric circulation-precipitation relationship indicate that the western Pacific subtropical high plays a key role in eastern China precipitation. In addition, teleconnection patterns that are significantly related to the predictable precipitation component are also identified. The leading/second unpredictable precipitation modes from MAM to SON all show a monopole/dipole structure, which are accompanied by wavy circulation patterns that are related to intraseasonal events.

  13. Identifying combinations of risk and protective factors predicting physical activity change in high school students.

    PubMed

    Dunton, Genevieve Fridlund; Atienza, Audie A; Tscherne, James; Rodriguez, Daniel

    2011-02-01

    Research sought to identify combinations of risk and protective factors predicting change in physical activity (PA) over one year in high school students. Adolescents (N = 344; M = 15.7 years) participated in a longitudinal study with assessment of demographics, substance use/smoking exposure, height and weight, psychological factors, and PA in 10th and 11th grade. PA participation in 11th grade was greatest for adolescents who engaged in PA and had high sports competence (78%), and least for adolescents who did not engage in or enjoy PA (13%) in 10th grade. Identifying adolescent subgroups at risk for decreasing PA can inform the development of tailored interventions. PMID:21467595

  14. kmer-SVM: a web server for identifying predictive regulatory sequence features in genomic data sets

    PubMed Central

    Fletez-Brant, Christopher; Lee, Dongwon; McCallion, Andrew S.; Beer, Michael A.

    2013-01-01

    Massively parallel sequencing technologies have made the generation of genomic data sets a routine component of many biological investigations. For example, Chromatin immunoprecipitation followed by sequence assays detect genomic regions bound (directly or indirectly) by specific factors, and DNase-seq identifies regions of open chromatin. A major bottleneck in the interpretation of these data is the identification of the underlying DNA sequence code that defines, and ultimately facilitates prediction of, these transcription factor (TF) bound or open chromatin regions. We have recently developed a novel computational methodology, which uses a support vector machine (SVM) with kmer sequence features (kmer-SVM) to identify predictive combinations of short transcription factor-binding sites, which determine the tissue specificity of these genomic assays (Lee, Karchin and Beer, Discriminative prediction of mammalian enhancers from DNA sequence. Genome Res. 2011; 21:2167–80). This regulatory information can (i) give confidence in genomic experiments by recovering previously known binding sites, and (ii) reveal novel sequence features for subsequent experimental testing of cooperative mechanisms. Here, we describe the development and implementation of a web server to allow the broader research community to independently apply our kmer-SVM to analyze and interpret their genomic datasets. We analyze five recently published data sets and demonstrate how this tool identifies accessory factors and repressive sequence elements. kmer-SVM is available at http://kmersvm.beerlab.org. PMID:23771147

  15. Accurate structure prediction of peptide–MHC complexes for identifying highly immunogenic antigens

    SciTech Connect

    Park, Min-Sun; Park, Sung Yong; Miller, Keith R.; Collins, Edward J.; Lee, Ha Youn

    2013-11-01

    Designing an optimal HIV-1 vaccine faces the challenge of identifying antigens that induce a broad immune capacity. One factor to control the breadth of T cell responses is the surface morphology of a peptide–MHC complex. Here, we present an in silico protocol for predicting peptide–MHC structure. A robust signature of a conformational transition was identified during all-atom molecular dynamics, which results in a model with high accuracy. A large test set was used in constructing our protocol and we went another step further using a blind test with a wild-type peptide and two highly immunogenic mutants, which predicted substantial conformational changes in both mutants. The center residues at position five of the analogs were configured to be accessible to solvent, forming a prominent surface, while the residue of the wild-type peptide was to point laterally toward the side of the binding cleft. We then experimentally determined the structures of the blind test set, using high resolution of X-ray crystallography, which verified predicted conformational changes. Our observation strongly supports a positive association of the surface morphology of a peptide–MHC complex to its immunogenicity. Our study offers the prospect of enhancing immunogenicity of vaccines by identifying MHC binding immunogens.

  16. Nonspinning numerical relativity waveform surrogates: assessing the model

    NASA Astrophysics Data System (ADS)

    Field, Scott; Blackman, Jonathan; Galley, Chad; Scheel, Mark; Szilagyi, Bela; Tiglio, Manuel

    2015-04-01

    Recently, multi-modal gravitational waveform surrogate models have been built directly from data numerically generated by the Spectral Einstein Code (SpEC). I will describe ways in which the surrogate model error can be quantified. This task, in turn, requires (i) characterizing differences between waveforms computed by SpEC with those predicted by the surrogate model and (ii) estimating errors associated with the SpEC waveforms from which the surrogate is built. Both pieces can have numerous sources of numerical and systematic errors. We make an attempt to study the most dominant error sources and, ultimately, the surrogate model's fidelity. These investigations yield information about the surrogate model's uncertainty as a function of time (or frequency) and parameter, and could be useful in parameter estimation studies which seek to incorporate model error. Finally, I will conclude by comparing the numerical relativity surrogate model to other inspiral-merger-ringdown models. A companion talk will cover the building of multi-modal surrogate models.

  17. High-throughput respirometric assay identifies predictive toxicophore of mitochondrial injury

    SciTech Connect

    Wills, Lauren P.; Beeson, Gyda C.; Trager, Richard E.; Lindsey, Christopher C.; Beeson, Craig C.; Peterson, Yuri K.; Schnellmann, Rick G.

    2013-10-15

    Many environmental chemicals and drugs negatively affect human health through deleterious effects on mitochondrial function. Currently there is no chemical library of mitochondrial toxicants, and no reliable methods for predicting mitochondrial toxicity. We hypothesized that discrete toxicophores defined by distinct chemical entities can identify previously unidentified mitochondrial toxicants. We used a respirometric assay to screen 1760 compounds (5 μM) from the LOPAC and ChemBridge DIVERSet libraries. Thirty-one of the assayed compounds decreased uncoupled respiration, a stress test for mitochondrial dysfunction, prior to a decrease in cell viability and reduced the oxygen consumption rate in isolated mitochondria. The mitochondrial toxicants were grouped by chemical similarity and two clusters containing four compounds each were identified. Cheminformatic analysis of one of the clusters identified previously uncharacterized mitochondrial toxicants from the ChemBridge DIVERSet. This approach will enable the identification of mitochondrial toxicants and advance the prediction of mitochondrial toxicity for both drug discovery and risk assessment. - Highlights: • Respirometric assay conducted in RPTC to create mitochondrial toxicant database. • Chemically similar mitochondrial toxicants aligned as mitochondrial toxicophores • Mitochondrial toxicophore identifies five novel mitochondrial toxicants.

  18. Reduced cost mission design using surrogate models

    NASA Astrophysics Data System (ADS)

    Feldhacker, Juliana D.; Jones, Brandon A.; Doostan, Alireza; Hampton, Jerrad

    2016-01-01

    This paper uses surrogate models to reduce the computational cost associated with spacecraft mission design in three-body dynamical systems. Sampling-based least squares regression is used to project the system response onto a set of orthogonal bases, providing a representation of the ΔV required for rendezvous as a reduced-order surrogate model. Models are presented for mid-field rendezvous of spacecraft in orbits in the Earth-Moon circular restricted three-body problem, including a halo orbit about the Earth-Moon L2 libration point (EML-2) and a distant retrograde orbit (DRO) about the Moon. In each case, the initial position of the spacecraft, the time of flight, and the separation between the chaser and the target vehicles are all considered as design inputs. The results show that sample sizes on the order of 102 are sufficient to produce accurate surrogates, with RMS errors reaching 0.2 m/s for the halo orbit and falling below 0.01 m/s for the DRO. A single function call to the resulting surrogate is up to two orders of magnitude faster than computing the same solution using full fidelity propagators. The expansion coefficients solved for in the surrogates are then used to conduct a global sensitivity analysis of the ΔV on each of the input parameters, which identifies the separation between the spacecraft as the primary contributor to the ΔV cost. Finally, the models are demonstrated to be useful for cheap evaluation of the cost function in constrained optimization problems seeking to minimize the ΔV required for rendezvous. These surrogate models show significant advantages for mission design in three-body systems, in terms of both computational cost and capabilities, over traditional Monte Carlo methods.

  19. Bayesian Calibration of the Community Land Model using Surrogates

    SciTech Connect

    Ray, Jaideep; Hou, Zhangshuan; Huang, Maoyi; Sargsyan, K.; Swiler, Laura P.

    2015-01-01

    We present results from the Bayesian calibration of hydrological parameters of the Community Land Model (CLM), which is often used in climate simulations and Earth system models. A statistical inverse problem is formulated for three hydrological parameters, conditioned on observations of latent heat surface fluxes over 48 months. Our calibration method uses polynomial and Gaussian process surrogates of the CLM, and solves the parameter estimation problem using a Markov chain Monte Carlo sampler. Posterior probability densities for the parameters are developed for two sites with different soil and vegetation covers. Our method also allows us to examine the structural error in CLM under two error models. We find that accurate surrogate models can be created for CLM in most cases. The posterior distributions lead to better prediction than the default parameter values in CLM. Climatologically averaging the observations does not modify the parameters’ distributions significantly. The structural error model reveals a correlation time-scale which can potentially be used to identify physical processes that could be contributing to it. While the calibrated CLM has a higher predictive skill, the calibration is under-dispersive.

  20. Bayesian calibration of the Community Land Model using surrogates

    SciTech Connect

    Ray, Jaideep; Hou, Zhangshuan; Huang, Maoyi; Swiler, Laura Painton

    2014-02-01

    We present results from the Bayesian calibration of hydrological parameters of the Community Land Model (CLM), which is often used in climate simulations and Earth system models. A statistical inverse problem is formulated for three hydrological parameters, conditional on observations of latent heat surface fluxes over 48 months. Our calibration method uses polynomial and Gaussian process surrogates of the CLM, and solves the parameter estimation problem using a Markov chain Monte Carlo sampler. Posterior probability densities for the parameters are developed for two sites with different soil and vegetation covers. Our method also allows us to examine the structural error in CLM under two error models. We find that surrogate models can be created for CLM in most cases. The posterior distributions are more predictive than the default parameter values in CLM. Climatologically averaging the observations does not modify the parameters' distributions significantly. The structural error model reveals a correlation time-scale which can be used to identify the physical process that could be contributing to it. While the calibrated CLM has a higher predictive skill, the calibration is under-dispersive.

  1. Surrogate Reservoir Model

    NASA Astrophysics Data System (ADS)

    Mohaghegh, Shahab

    2010-05-01

    Surrogate Reservoir Model (SRM) is new solution for fast track, comprehensive reservoir analysis (solving both direct and inverse problems) using existing reservoir simulation models. SRM is defined as a replica of the full field reservoir simulation model that runs and provides accurate results in real-time (one simulation run takes only a fraction of a second). SRM mimics the capabilities of a full field model with high accuracy. Reservoir simulation is the industry standard for reservoir management. It is used in all phases of field development in the oil and gas industry. The routine of simulation studies calls for integration of static and dynamic measurements into the reservoir model. Full field reservoir simulation models have become the major source of information for analysis, prediction and decision making. Large prolific fields usually go through several versions (updates) of their model. Each new version usually is a major improvement over the previous version. The updated model includes the latest available information incorporated along with adjustments that usually are the result of single-well or multi-well history matching. As the number of reservoir layers (thickness of the formations) increases, the number of cells representing the model approaches several millions. As the reservoir models grow in size, so does the time that is required for each run. Schemes such as grid computing and parallel processing helps to a certain degree but do not provide the required speed for tasks such as: field development strategies using comprehensive reservoir analysis, solving the inverse problem for injection/production optimization, quantifying uncertainties associated with the geological model and real-time optimization and decision making. These types of analyses require hundreds or thousands of runs. Furthermore, with the new push for smart fields in the oil/gas industry that is a natural growth of smart completion and smart wells, the need for real time

  2. A Proteomic Approach Identifies Candidate Early Biomarkers to Predict Severe Dengue in Children

    PubMed Central

    Nhi, Dang My; Huy, Nguyen Tien; Ohyama, Kaname; Kimura, Daisuke; Lan, Nguyen Thi Phuong; Uchida, Leo; Thuong, Nguyen Van; Nhon, Cao Thi My; Phuc, Le Hong; Mai, Nguyen Thi; Mizukami, Shusaku; Bao, Lam Quoc; Doan, Nguyen Ngoc; Binh, Nguyen Van Thanh; Quang, Luong Chan; Karbwang, Juntra; Yui, Katsuyuki; Morita, Kouichi; Huong, Vu Thi Que; Hirayama, Kenji

    2016-01-01

    Background Severe dengue with severe plasma leakage (SD-SPL) is the most frequent of dengue severe form. Plasma biomarkers for early predictive diagnosis of SD-SPL are required in the primary clinics for the prevention of dengue death. Methodology Among 63 confirmed dengue pediatric patients recruited, hospital based longitudinal study detected six SD-SPL and ten dengue with warning sign (DWS). To identify the specific proteins increased or decreased in the SD-SPL plasma obtained 6–48 hours before the shock compared with the DWS, the isobaric tags for relative and absolute quantification (iTRAQ) technology was performed using four patients each group. Validation was undertaken in 6 SD-SPL and 10 DWS patients. Principal findings Nineteen plasma proteins exhibited significantly different relative concentrations (p<0.05), with five over-expressed and fourteen under-expressed in SD-SPL compared with DWS. The individual protein was classified to either blood coagulation, vascular regulation, cellular transport-related processes or immune response. The immunoblot quantification showed angiotensinogen and antithrombin III significantly increased in SD-SPL whole plasma of early stage compared with DWS subjects. Even using this small number of samples, antithrombin III predicted SD-SPL before shock occurrence with accuracy. Conclusion Proteins identified here may serve as candidate predictive markers to diagnose SD-SPL for timely clinical management. Since the number of subjects are small, so further studies are needed to confirm all these biomarkers. PMID:26895439

  3. Predicting Disease Risk, Identifying Stakeholders, and Informing Control Strategies: A Case Study of Anthrax in Montana.

    PubMed

    Morris, Lillian R; Blackburn, Jason K

    2016-06-01

    Infectious diseases that affect wildlife and livestock are challenging to manage and can lead to large-scale die-offs, economic losses, and threats to human health. The management of infectious diseases in wildlife and livestock is made easier with knowledge of disease risk across space and identifying stakeholders associated with high-risk landscapes. This study focuses on anthrax, caused by the bacterium Bacillus anthracis, risk to wildlife and livestock in Montana. There is a history of anthrax in Montana, but the spatial extent of disease risk and subsequent wildlife species at risk are not known. Our objective was to predict the potential geographic distribution of anthrax risk across Montana, identify wildlife species at risk and their distributions, and define stakeholders. We used an ecological niche model to predict the potential distribution of anthrax risk. We overlaid susceptible wildlife species distributions and land ownership delineations on our risk map. We found that there was an extensive region across Montana predicted as potential anthrax risk. These potentially risky landscapes overlapped the ranges of all 6 ungulate species considered in the analysis and livestock grazing allotments, and this overlap was on public and private land for all species. Our findings suggest that there is the potential for a multi-species anthrax outbreak on multiple landscapes across Montana. Our potential anthrax risk map can be used to prioritize landscapes for surveillance and for implementing livestock vaccination programs. PMID:27169560

  4. A predicted protein interactome identifies conserved global networks and disease resistance subnetworks in maize

    PubMed Central

    Musungu, Bryan; Bhatnagar, Deepak; Brown, Robert L.; Fakhoury, Ahmad M.; Geisler, Matt

    2015-01-01

    Interactomes are genome-wide roadmaps of protein-protein interactions. They have been produced for humans, yeast, the fruit fly, and Arabidopsis thaliana and have become invaluable tools for generating and testing hypotheses. A predicted interactome for Zea mays (PiZeaM) is presented here as an aid to the research community for this valuable crop species. PiZeaM was built using a proven method of interologs (interacting orthologs) that were identified using both one-to-one and many-to-many orthology between genomes of maize and reference species. Where both maize orthologs occurred for an experimentally determined interaction in the reference species, we predicted a likely interaction in maize. A total of 49,026 unique interactions for 6004 maize proteins were predicted. These interactions are enriched for processes that are evolutionarily conserved, but include many otherwise poorly annotated proteins in maize. The predicted maize interactions were further analyzed by comparing annotation of interacting proteins, including different layers of ontology. A map of pairwise gene co-expression was also generated and compared to predicted interactions. Two global subnetworks were constructed for highly conserved interactions. These subnetworks showed clear clustering of proteins by function. Another subnetwork was created for disease response using a bait and prey strategy to capture interacting partners for proteins that respond to other organisms. Closer examination of this subnetwork revealed the connectivity between biotic and abiotic hormone stress pathways. We believe PiZeaM will provide a useful tool for the prediction of protein function and analysis of pathways for Z. mays researchers and is presented in this paper as a reference tool for the exploration of protein interactions in maize. PMID:26089837

  5. Towards predictive data-driven simulations of wildfire spread - Part I: Reduced-cost Ensemble Kalman Filter based on a Polynomial Chaos surrogate model for parameter estimation

    NASA Astrophysics Data System (ADS)

    Rochoux, M. C.; Ricci, S.; Lucor, D.; Cuenot, B.; Trouvé, A.

    2014-11-01

    This paper is the first part in a series of two articles and presents a data-driven wildfire simulator for forecasting wildfire spread scenarios, at a reduced computational cost that is consistent with operational systems. The prototype simulator features the following components: an Eulerian front propagation solver FIREFLY that adopts a regional-scale modeling viewpoint, treats wildfires as surface propagating fronts, and uses a description of the local rate of fire spread (ROS) as a function of environmental conditions based on Rothermel's model; a series of airborne-like observations of the fire front positions; and a data assimilation (DA) algorithm based on an ensemble Kalman filter (EnKF) for parameter estimation. This stochastic algorithm partly accounts for the nonlinearities between the input parameters of the semi-empirical ROS model and the fire front position, and is sequentially applied to provide a spatially uniform correction to wind and biomass fuel parameters as observations become available. A wildfire spread simulator combined with an ensemble-based DA algorithm is therefore a promising approach to reduce uncertainties in the forecast position of the fire front and to introduce a paradigm-shift in the wildfire emergency response. In order to reduce the computational cost of the EnKF algorithm, a surrogate model based on a polynomial chaos (PC) expansion is used in place of the forward model FIREFLY in the resulting hybrid PC-EnKF algorithm. The performance of EnKF and PC-EnKF is assessed on synthetically generated simple configurations of fire spread to provide valuable information and insight on the benefits of the PC-EnKF approach, as well as on a controlled grassland fire experiment. The results indicate that the proposed PC-EnKF algorithm features similar performance to the standard EnKF algorithm, but at a much reduced computational cost. In particular, the re-analysis and forecast skills of DA strongly relate to the spatial and temporal

  6. Towards predictive data-driven simulations of wildfire spread - Part I: Reduced-cost Ensemble Kalman Filter based on a Polynomial Chaos surrogate model for parameter estimation

    NASA Astrophysics Data System (ADS)

    Rochoux, M. C.; Ricci, S.; Lucor, D.; Cuenot, B.; Trouvé, A.

    2014-05-01

    This paper is the first part in a series of two articles and presents a data-driven wildfire simulator for forecasting wildfire spread scenarios, at a reduced computational cost that is consistent with operational systems. The prototype simulator features the following components: a level-set-based fire propagation solver FIREFLY that adopts a regional-scale modeling viewpoint, treats wildfires as surface propagating fronts, and uses a description of the local rate of fire spread (ROS) as a function of environmental conditions based on Rothermel's model; a series of airborne-like observations of the fire front positions; and a data assimilation algorithm based on an ensemble Kalman filter (EnKF) for parameter estimation. This stochastic algorithm partly accounts for the non-linearities between the input parameters of the semi-empirical ROS model and the fire front position, and is sequentially applied to provide a spatially-uniform correction to wind and biomass fuel parameters as observations become available. A wildfire spread simulator combined with an ensemble-based data assimilation algorithm is therefore a promising approach to reduce uncertainties in the forecast position of the fire front and to introduce a paradigm-shift in the wildfire emergency response. In order to reduce the computational cost of the EnKF algorithm, a surrogate model based on a polynomial chaos (PC) expansion is used in place of the forward model FIREFLY in the resulting hybrid PC-EnKF algorithm. The performance of EnKF and PC-EnKF is assessed on synthetically-generated simple configurations of fire spread to provide valuable information and insight on the benefits of the PC-EnKF approach as well as on a controlled grassland fire experiment. The results indicate that the proposed PC-EnKF algorithm features similar performance to the standard EnKF algorithm, but at a much reduced computational cost. In particular, the re-analysis and forecast skills of data assimilation strongly relate

  7. Human mesenchymal stromal cells: identifying assays to predict potency for therapeutic selection.

    PubMed

    Deskins, Desirae L; Bastakoty, Dikshya; Saraswati, Sarika; Shinar, Andrew; Holt, Ginger E; Young, Pampee P

    2013-02-01

    Multipotent mesenchymal stromal cells (MSCs) have the potential to repair and regenerate damaged tissues, making them attractive candidates for cell-based therapies. To maximize efficacy of MSCs, prediction of their therapeutic abilities must be made so that only the best cells will be used. Our goal was to identify feasible and reproducible in vitro assays to predict MSC potency. We generated cell lines from 10 normal human bone marrow samples and used the International Society for Cellular Therapy's minimal criteria to define them as MSCs: plastic adherence, appropriate surface marker expression, and trilineage differentiation. Each MSC line was further characterized by its growth, proliferation, and viability as determined by cell count, bromodeoxyuridine incorporation, and cellular ATP levels, respectively. To determine whether these tests reliably predict the therapeutic aptitude of the MSCs, several lines were implanted in vivo to examine their capacity to engraft and form granulation tissue in a well-established murine wound model using polyvinyl alcohol sponges. Long-term engraftment of MSCs in the sponges was quantified through the presence of the human-specific Alu gene in sponge sections. Sections were also stained for proliferating cells, vascularity, and granulation tissue formation to determine successful engraftment and repair. We found that high performance in a combination of the in vitro tests accurately predicted which lines functioned well in vivo. These findings suggest that reliable and reproducible in vitro assays may be used to measure the functional potential of MSCs for therapeutic use. PMID:23362238

  8. Human plasma metabolomics for identifying differential metabolites and predicting molecular subtypes of breast cancer

    PubMed Central

    Chen, Zhuo; Li, Jin; Liu, Qun; Alolga, Raphael N; Chen, Yan; Lai, Mao-De; Li, Ping; Zhu, Wei; Qi, Lian-Wen

    2016-01-01

    Purpose This work aims to identify differential metabolites and predicting molecular subtypes of breast cancer (BC). Methods Plasma samples were collected from 96 BC patients and 79 normal participants. Metabolic profiles were determined by liquid chromatography-mass spectrometry and gas chromatography-mass spectrometry based on multivariate statistical data analysis. Results We observed 64 differential metabolites between BC and normal group. Compared to human epidermal growth factor receptor 2 (HER2)-negative patients, HER2-positive group showed elevated aerobic glycolysis, gluconeogenesis, and increased fatty acid biosynthesis with reduced Krebs cycle. Compared with estrogen receptor (ER)-negative group, ER-positive patients showed elevated alanine, aspartate and glutamate metabolism, decreased glycerolipid catabolism, and enhanced purine metabolism. A panel of 8 differential metabolites, including carnitine, lysophosphatidylcholine (20:4), proline, alanine, lysophosphatidylcholine (16:1), glycochenodeoxycholic acid, valine, and 2-octenedioic acid, was identified for the classification of BC subtypes. These markers showed potential diagnostic value with average area under the curve at 0.925 (95% CI 0.867-0.983) for the training set (n=51) and 0.893 (95% CI 0.847-0.939) for the test set (n=45). Conclusion Human plasma metabolomics is useful in identifying differential metabolites and predicting breast cancer subtypes. PMID:26848530

  9. Revisiting photodynamic therapy dosimetry: reductionist & surrogate approaches to facilitate clinical success.

    PubMed

    Pogue, Brian W; Elliott, Jonathan T; Kanick, Stephen C; Davis, Scott C; Samkoe, Kimberley S; Maytin, Edward V; Pereira, Stephen P; Hasan, Tayyaba

    2016-04-01

    Photodynamic therapy (PDT) can be a highly complex treatment, with many parameters influencing treatment efficacy. The extent to which dosimetry is used to monitor and standardize treatment delivery varies widely, ranging from measurement of a single surrogate marker to comprehensive approaches that aim to measure or estimate as many relevant parameters as possible. Today, most clinical PDT treatments are still administered with little more than application of a prescribed drug dose and timed light delivery, and thus the role of patient-specific dosimetry has not reached widespread clinical adoption. This disconnect is at least partly due to the inherent conflict between the need to measure and understand multiple parameters in vivo in order to optimize treatment, and the need for expedience in the clinic and in the regulatory and commercialization process. Thus, a methodical approach to selecting primary dosimetry metrics is required at each stage of translation of a treatment procedure, moving from complex measurements to understand PDT mechanisms in pre-clinical and early phase I trials, towards the identification and application of essential dose-limiting and/or surrogate measurements in phase II/III trials. If successful, identifying the essential and/or reliable surrogate dosimetry measurements should help facilitate increased adoption of clinical PDT. In this paper, examples of essential dosimetry points and surrogate dosimetry tools that may be implemented in phase II/III trials are discussed. For example, the treatment efficacy as limited by light penetration in interstitial PDT may be predicted by the amount of contrast uptake in CT, and so this could be utilized as a surrogate dosimetry measurement to prescribe light doses based upon pre-treatment contrast. Success of clinical ALA-based skin lesion treatment is predicted almost uniquely by the explicit or implicit measurements of photosensitizer and photobleaching, yet the individualization of treatment

  10. Revisiting photodynamic therapy dosimetry: reductionist & surrogate approaches to facilitate clinical success

    NASA Astrophysics Data System (ADS)

    Pogue, Brian W.; Elliott, Jonathan T.; Kanick, Stephen C.; Davis, Scott C.; Samkoe, Kimberley S.; Maytin, Edward V.; Pereira, Stephen P.; Hasan, Tayyaba

    2016-04-01

    Photodynamic therapy (PDT) can be a highly complex treatment, with many parameters influencing treatment efficacy. The extent to which dosimetry is used to monitor and standardize treatment delivery varies widely, ranging from measurement of a single surrogate marker to comprehensive approaches that aim to measure or estimate as many relevant parameters as possible. Today, most clinical PDT treatments are still administered with little more than application of a prescribed drug dose and timed light delivery, and thus the role of patient-specific dosimetry has not reached widespread clinical adoption. This disconnect is at least partly due to the inherent conflict between the need to measure and understand multiple parameters in vivo in order to optimize treatment, and the need for expedience in the clinic and in the regulatory and commercialization process. Thus, a methodical approach to selecting primary dosimetry metrics is required at each stage of translation of a treatment procedure, moving from complex measurements to understand PDT mechanisms in pre-clinical and early phase I trials, towards the identification and application of essential dose-limiting and/or surrogate measurements in phase II/III trials. If successful, identifying the essential and/or reliable surrogate dosimetry measurements should help facilitate increased adoption of clinical PDT. In this paper, examples of essential dosimetry points and surrogate dosimetry tools that may be implemented in phase II/III trials are discussed. For example, the treatment efficacy as limited by light penetration in interstitial PDT may be predicted by the amount of contrast uptake in CT, and so this could be utilized as a surrogate dosimetry measurement to prescribe light doses based upon pre-treatment contrast. Success of clinical ALA-based skin lesion treatment is predicted almost uniquely by the explicit or implicit measurements of photosensitizer and photobleaching, yet the individualization of treatment

  11. Comparison of Surrogate Markers as Measures of Uncomplicated Insulin Resistance in Korean Adults

    PubMed Central

    Kim, Tae Jong; Kim, Hye Jung; Kim, Young Bae; Lee, Jee Yon; Lee, Hye Sun; Hong, Jung Hwa

    2016-01-01

    Background Metabolic syndrome (MS) is known to increase the risk of various cardiometabolic diseases and in-sulin resistance (IR) has known to have central role in the development of MS. Many surrogate indices of IR have been proposed and the detection of MS might be a suitable model for assessing the accuracy of surrogate indices. The aims of our study are to invest the most appropriate index by assessment of the diagnostic capacity of IR among each surrogate index and identifying cut-off values for discriminating uncomplicated MS in Korean adults. Methods A cross-sectional study was performed, assessing 294 Korean adults, 85 of whom were diagnosed with uncomplicated MS. The sensitivities and specificities of five surrogate IR indices were compared to discriminate MS from healthy subjects; these included fasting serum insulin, homeostasis model assessment–insulin resistance index, quantitative insulin sensitivity check index, McAuley index, and Disse index. Correlations between each index value were assessed using Pearson's and Spearman's correlation methods. Results The McAuley index showed the highest area under the curve (0.85), specificity (86.12%), accuracy (82.31%), positive predictive value (68.13%), and negative predictive value (88.67%) to distinguish MS, with a cut-off point of 5.3 defined. Correlation coefficients of the five indices showed that the McAuley index had the strongest correlation with IR. Conclusion The McAuley index showed the best accuracy in the detection of MS as a surrogate marker of IR. To establish more effective and accurate standards of measuring IR, comprehensive and multi-scaled studies are required. PMID:27274391

  12. Precision Oncology: Identifying Predictive Biomarkers for the Treatment of Metastatic Renal Cell Carcinoma

    PubMed Central

    Modi, Parth K.; Farber, Nicholas J.; Singer, Eric A.

    2016-01-01

    The recent FDA approval of multiple new pharmaceutical agents for metastatic renal cell carcinoma (RCC) has left physicians with several options for first- and second- line therapy. With limited head-to-head comparisons, however, there is a paucity of evidence to recommend the use of one agent over another. To address this knowledge gap, Voss et al. identified serum biomarkers from specimens collected during the RECORD-3 trial, a comparative study of first-line sunitinib versus first-line everolimus. Of the biomarkers identified, the 5 most strongly associated with first-line everolimus progression-free survival (PFS1L) were combined to form a composite biomarker score (CBS). The CBS was significantly associated with everolimus PFS1L in multivariate regression analysis. This study is an example of the additional value offered by a randomized trial with prospective biospecimen collection and a significant step towards identifying predictive biomarkers for the treatment of metastatic RCC. As further comparative trials are performed, it will be essential that biomarkers are appropriately identified and validated in order to further the goal of precision oncology.

  13. Identifying the necessary and sufficient number of risk factors for predicting academic failure.

    PubMed

    Lucio, Robert; Hunt, Elizabeth; Bornovalova, Marina

    2012-03-01

    Identifying the point at which individuals become at risk for academic failure (grade point average [GPA] < 2.0) involves an understanding of which and how many factors contribute to poor outcomes. School-related factors appear to be among the many factors that significantly impact academic success or failure. This study focused on 12 school-related factors. Using a thorough 5-step process, we identified which unique risk factors place one at risk for academic failure. Academic engagement, academic expectations, academic self-efficacy, homework completion, school relevance, school safety, teacher relationships (positive relationship), grade retention, school mobility, and school misbehaviors (negative relationship) were uniquely related to GPA even after controlling for all relevant covariates. Next, a receiver operating characteristic curve was used to determine a cutoff point for determining how many risk factors predict academic failure (GPA < 2.0). Results yielded a cutoff point of 2 risk factors for predicting academic failure, which provides a way for early identification of individuals who are at risk. Further implications of these findings are discussed. PMID:22182300

  14. Parcel-scale urban coastal flood prediction: Identifying critical data and forcing requirements

    NASA Astrophysics Data System (ADS)

    Gallien, T.; Sanders, B. F.

    2012-12-01

    Coastal flooding represents a significant socio-economic and humanitarian threat to urbanized lowlands throughout the world. In California, sea levels are projected to rise 1-1.4 meters in the next century. Numerous coastal communities are currently at risk of flooding during high tides or large wave events and a significant body of evidence suggests climate change will exacerbate flooding in these low lying, and often highly populated, areas. Flood prediction in urbanized embayments pose a number of challenges including water level characterization, appropriate representation of both weir-like (i.e. wall) overflow and wave runup/overtopping volumes and the need for highly accurate local data and site knowledge. In addition, a paucity of high quality validation data fundamentally obstructs predictive flood modeling efforts. Here, a Southern California coastal community which benefits from two unique flood event validation data sets is modeled in context of current and future sea level scenarios. The uncalibrated hydrodynamic model resolves critical urban infrastructure and includes essential dynamic processes such as tidal amplification, weir-like overflow and spatially distributed wave overtopping volumes. Results identify data and forcing requirements that are essential to accurate parcel-scale (individual home or street) flood prediction in defended urban terrain.

  15. Father involvement: Identifying and predicting family members' shared and unique perceptions.

    PubMed

    Dyer, W Justin; Day, Randal D; Harper, James M

    2014-08-01

    Father involvement research has typically not recognized that reports of involvement contain at least two components: 1 reflecting a view of father involvement that is broadly recognized in the family, and another reflecting each reporter's unique perceptions. Using a longitudinal sample of 302 families, this study provides a first examination of shared and unique views of father involvement (engagement and warmth) from the perspectives of fathers, children, and mothers. This study also identifies influences on these shared and unique perspectives. Father involvement reports were obtained when the child was 12 and 14 years old. Mother reports overlapped more with the shared view than father or child reports. This suggests the mother's view may be more in line with broadly recognized father involvement. Regarding antecedents, for fathers' unique view, a compensatory model partially explains results; that is, negative aspects of family life were positively associated with fathers' unique view. Children's unique view of engagement may partially reflect a sentiment override with father antisocial behaviors being predictive. Mothers' unique view of engagement was predicted by father and mother work hours and her unique view of warmth was predicted by depression and maternal gatekeeping. Taken, together finding suggests a far more nuanced view of father involvement should be considered. PMID:25000130

  16. Environmental diversity as a surrogate for species representation.

    PubMed

    Beier, Paul; de Albuquerque, Fábio Suzart

    2015-10-01

    Because many species have not been described and most species ranges have not been mapped, conservation planners often use surrogates for conservation planning, but evidence for surrogate effectiveness is weak. Surrogates are well-mapped features such as soil types, landforms, occurrences of an easily observed taxon (discrete surrogates), and well-mapped environmental conditions (continuous surrogate). In the context of reserve selection, the idea is that a set of sites selected to span diversity in the surrogate will efficiently represent most species. Environmental diversity (ED) is a rarely used surrogate that selects sites to efficiently span multivariate ordination space. Because it selects across continuous environmental space, ED should perform better than discrete surrogates (which necessarily ignore within-bin and between-bin heterogeneity). Despite this theoretical advantage, ED appears to have performed poorly in previous tests of its ability to identify 50 × 50 km cells that represented vertebrates in Western Europe. Using an improved implementation of ED, we retested ED on Western European birds, mammals, reptiles, amphibians, and combined terrestrial vertebrates. We also tested ED on data sets for plants of Zimbabwe, birds of Spain, and birds of Arizona (United States). Sites selected using ED represented European mammals no better than randomly selected cells, but they represented species in the other 7 data sets with 20% to 84% effectiveness. This far exceeds the performance in previous tests of ED, and exceeds the performance of most discrete surrogates. We believe ED performed poorly in previous tests because those tests considered only a few candidate explanatory variables and used suboptimal forms of ED's selection algorithm. We suggest future work on ED focus on analyses at finer grain sizes more relevant to conservation decisions, explore the effect of selecting the explanatory variables most associated with species turnover, and investigate

  17. Cerium as a surrogate in the plutonium immobilization waste form

    NASA Astrophysics Data System (ADS)

    Marra, James Christopher

    In the aftermath of the Cold War, approximately 50 tonnes (MT) of weapons useable plutonium (Pu) has been identified as excess. The U.S. Department of Energy (DOE) has decided that at least a portion of this material will be immobilized in a titanate-based ceramic for final disposal in a geologic repository. The baseline formulation was designed to produce a ceramic consisting primarily of a highly substituted pyrochlore with minor amounts of brannerite and hafnia-substituted rutile. Since development studies with actual actinide materials is difficult, surrogates have been used to facilitate testing. Cerium has routinely been used as an actinide surrogate in actinide chemistry and processing studies. Although cerium appeared as an adequate physical surrogate for powder handling and general processing studies, cerium was found to act significantly different from a chemical perspective in the Pu ceramic form. The reduction of cerium at elevated temperatures caused different reaction paths toward densification of the respective forms resulting in different phase assemblages and microstructural features. Single-phase fabrication studies and cerium oxidation state analyses were performed to further quantify these behavioral differences. These studies indicated that the major phases in the final phase assemblages contained point defects likely leading to their stability. Additionally, thermochemical arguments predicted that the predominant pyrochlore phase in the ceramic was metastable. The apparent metastabilty associated with primary phase in the Pu ceramic form indicated that additional studies must be performed to evaluate the thermodynamic properties of these compounds. Moreover, the metastability of this predominant phase must be considered in assessment of long-term behavior (e.g. radiation stability) of this ceramic.

  18. A Review of Surrogate Assisted Multiobjective Evolutionary Algorithms

    PubMed Central

    Díaz-Manríquez, Alan; Toscano, Gregorio; Barron-Zambrano, Jose Hugo; Tello-Leal, Edgar

    2016-01-01

    Multiobjective evolutionary algorithms have incorporated surrogate models in order to reduce the number of required evaluations to approximate the Pareto front of computationally expensive multiobjective optimization problems. Currently, few works have reviewed the state of the art in this topic. However, the existing reviews have focused on classifying the evolutionary multiobjective optimization algorithms with respect to the type of underlying surrogate model. In this paper, we center our focus on classifying multiobjective evolutionary algorithms with respect to their integration with surrogate models. This interaction has led us to classify similar approaches and identify advantages and disadvantages of each class. PMID:27382366

  19. Surrogate Endpoints in Suicide Research

    ERIC Educational Resources Information Center

    Wortzel, Hal S.; Gutierrez, Peter M.; Homaifar, Beeta Y.; Breshears, Ryan E.; Harwood, Jeri E.

    2010-01-01

    Surrogate endpoints frequently substitute for rare outcomes in research. The ability to learn about completed suicides by investigating more readily available and proximate outcomes, such as suicide attempts, has obvious appeal. However, concerns with surrogates from the statistical science perspective exist, and mounting evidence from…

  20. Sulfur hexafluoride as a surrogate

    SciTech Connect

    Taylor, P.H.; Chadbourne, J.F.

    1987-06-01

    A viable chemical surrogate for monitoring the effectiveness of hazardous waste incinerators must include high thermal stability and low toxicity among its characteristics. The relationship between sulfur hexafluoride (SF6) and hazardous constituent thermal stability for a mixture of chlorinated hydrocarbons indicates that SF6 has the potential to satisfy the basic requirements of a chemical surrogate for hazardous waste incineration.

  1. Actual and Perceived Gender Differences in the Accuracy of Surrogate Decisions about Life-Sustaining Medical Treatment among Older Spouses

    ERIC Educational Resources Information Center

    Zettel-Watson, Laura; Ditto, Peter H.; Danks, Joseph H.; Smucker, William D.

    2008-01-01

    This study examined the influence of surrogate gender on the accuracy of substituted judgments about the use of life-sustaining treatment in a sample of 249 older adults and their self-selected surrogate decision-makers. Overall, wives were more accurate than husbands at predicting their spouses' treatment wishes. Surrogates' perceptions of their…

  2. Identifying influential data points in hydrological model calibration and their impact on streamflow predictions

    NASA Astrophysics Data System (ADS)

    Wright, David; Thyer, Mark; Westra, Seth

    2015-04-01

    Highly influential data points are those that have a disproportionately large impact on model performance, parameters and predictions. However, in current hydrological modelling practice the relative influence of individual data points on hydrological model calibration is not commonly evaluated. This presentation illustrates and evaluates several influence diagnostics tools that hydrological modellers can use to assess the relative influence of data. The feasibility and importance of including influence detection diagnostics as a standard tool in hydrological model calibration is discussed. Two classes of influence diagnostics are evaluated: (1) computationally demanding numerical "case deletion" diagnostics; and (2) computationally efficient analytical diagnostics, based on Cook's distance. These diagnostics are compared against hydrologically orientated diagnostics that describe changes in the model parameters (measured through the Mahalanobis distance), performance (objective function displacement) and predictions (mean and maximum streamflow). These influence diagnostics are applied to two case studies: a stage/discharge rating curve model, and a conceptual rainfall-runoff model (GR4J). Removing a single data point from the calibration resulted in differences to mean flow predictions of up to 6% for the rating curve model, and differences to mean and maximum flow predictions of up to 10% and 17%, respectively, for the hydrological model. When using the Nash-Sutcliffe efficiency in calibration, the computationally cheaper Cook's distance metrics produce similar results to the case-deletion metrics at a fraction of the computational cost. However, Cooks distance is adapted from linear regression with inherit assumptions on the data and is therefore less flexible than case deletion. Influential point detection diagnostics show great potential to improve current hydrological modelling practices by identifying highly influential data points. The findings of this

  3. BFH-OST, a new predictive screening tool for identifying osteoporosis in postmenopausal Han Chinese women

    PubMed Central

    Ma, Zhao; Yang, Yong; Lin, JiSheng; Zhang, XiaoDong; Meng, Qian; Wang, BingQiang; Fei, Qi

    2016-01-01

    Purpose To develop a simple new clinical screening tool to identify primary osteoporosis by dual-energy X-ray absorptiometry (DXA) in postmenopausal women and to compare its validity with the Osteoporosis Self-Assessment Tool for Asians (OSTA) in a Han Chinese population. Methods A cross-sectional study was conducted, enrolling 1,721 community-dwelling postmenopausal Han Chinese women. All the subjects completed a structured questionnaire and had their bone mineral density measured using DXA. Using logistic regression analysis, we assessed the ability of numerous potential risk factors examined in the questionnaire to identify women with osteoporosis. Based on this analysis, we build a new predictive model, the Beijing Friendship Hospital Osteoporosis Self-Assessment Tool (BFH-OST). Receiver operating characteristic curves were generated to compare the validity of the new model and OSTA in identifying postmenopausal women at increased risk of primary osteoporosis as defined according to the World Health Organization criteria. Results At screening, it was found that of the 1,721 subjects with DXA, 22.66% had osteoporosis and a further 47.36% had osteopenia. Of the items screened in the questionnaire, it was found that age, weight, height, body mass index, personal history of fracture after the age of 45 years, history of fragility fracture in either parent, current smoking, and consumption of three of more alcoholic drinks per day were all predictive of osteoporosis. However, age at menarche and menopause, years since menopause, and number of pregnancies and live births were irrelevant in this study. The logistic regression analysis and item reduction yielded a final tool (BFH-OST) based on age, body weight, height, and history of fracture after the age of 45 years. The BFH-OST index (cutoff =9.1), which performed better than OSTA, had a sensitivity of 73.6% and a specificity of 72.7% for identifying osteoporosis, with an area under the receiver operating

  4. Higher-order Lie symmetries in identifiability and predictability analysis of dynamic models.

    PubMed

    Merkt, Benjamin; Timmer, Jens; Kaschek, Daniel

    2015-07-01

    Parameter estimation in ordinary differential equations (ODEs) has manifold applications not only in physics but also in the life sciences. When estimating the ODE parameters from experimentally observed data, the modeler is frequently concerned with the question of parameter identifiability. The source of parameter nonidentifiability is tightly related to Lie group symmetries. In the present work, we establish a direct search algorithm for the determination of admitted Lie group symmetries. We clarify the relationship between admitted symmetries and parameter nonidentifiability. The proposed algorithm is applied to illustrative toy models as well as a data-based ODE model of the NFκB signaling pathway. We find that besides translations and scaling transformations also higher-order transformations play a role. Enabled by the knowledge about the explicit underlying symmetry transformations, we show how models with nonidentifiable parameters can still be employed to make reliable predictions. PMID:26274260

  5. Higher-order Lie symmetries in identifiability and predictability analysis of dynamic models

    NASA Astrophysics Data System (ADS)

    Merkt, Benjamin; Timmer, Jens; Kaschek, Daniel

    2015-07-01

    Parameter estimation in ordinary differential equations (ODEs) has manifold applications not only in physics but also in the life sciences. When estimating the ODE parameters from experimentally observed data, the modeler is frequently concerned with the question of parameter identifiability. The source of parameter nonidentifiability is tightly related to Lie group symmetries. In the present work, we establish a direct search algorithm for the determination of admitted Lie group symmetries. We clarify the relationship between admitted symmetries and parameter nonidentifiability. The proposed algorithm is applied to illustrative toy models as well as a data-based ODE model of the NFκ B signaling pathway. We find that besides translations and scaling transformations also higher-order transformations play a role. Enabled by the knowledge about the explicit underlying symmetry transformations, we show how models with nonidentifiable parameters can still be employed to make reliable predictions.

  6. Prediction trajectory of moving target based on parameter identify in RLS filtering with forget factor

    NASA Astrophysics Data System (ADS)

    Yin, Yili; Tian, Yan; Li, Zhang

    2015-10-01

    A moving target should be missing from a photoelectric theodolite tracker, when the clouds and other special conditions encountered in the course of a theodolite tracking a moving object, and this condition should cause the interruption of tracking process. In view of this problem, an algorithm based on the frame of parameter identification and rolling prediction to trajectory was presented to predicting the target trajectory when it missing. Firstly, the article makes a specification of photoelectric theodolite and it operating mechanism detailed. The reasons of flying target imaging disappear from the field of theodolite telescope and the traditional solution to this problem, the least square curve fitting of trajectory quadratic function of time, were narrated secondly. The algorithm based on recursive least square with forget factor, identify the parameters of target motion using the data of position from single theodolite, then the forecasting trajectory of moving targets was presented afterwards ,in the filtering approach of past data rolling smooth with the weight of last procedure. By simulation with tracking moving targets synthetic corner from a real tracking routine of photoelectric theodolite, the algorithm was testified, and the simulation of curve fitting a quadratic function of time was compared at the last part.

  7. Lipid profiling identifies a triacylglycerol signature of insulin resistance and improves diabetes prediction in humans

    PubMed Central

    Rhee, Eugene P.; Cheng, Susan; Larson, Martin G.; Walford, Geoffrey A.; Lewis, Gregory D.; McCabe, Elizabeth; Yang, Elaine; Farrell, Laurie; Fox, Caroline S.; O’Donnell, Christopher J.; Carr, Steven A.; Vasan, Ramachandran S.; Florez, Jose C.; Clish, Clary B.; Wang, Thomas J.; Gerszten, Robert E.

    2011-01-01

    Dyslipidemia is an independent risk factor for type 2 diabetes, although exactly which of the many plasma lipids contribute to this remains unclear. We therefore investigated whether lipid profiling can inform diabetes prediction by performing liquid chromatography/mass spectrometry–based lipid profiling in 189 individuals who developed type 2 diabetes and 189 matched disease-free individuals, with over 12 years of follow up in the Framingham Heart Study. We found that lipids of lower carbon number and double bond content were associated with an increased risk of diabetes, whereas lipids of higher carbon number and double bond content were associated with decreased risk. This pattern was strongest for triacylglycerols (TAGs) and persisted after multivariable adjustment for age, sex, BMI, fasting glucose, fasting insulin, total triglycerides, and HDL cholesterol. A combination of 2 TAGs further improved diabetes prediction. To explore potential mechanisms that modulate the distribution of plasma lipids, we performed lipid profiling during oral glucose tolerance testing, pharmacologic interventions, and acute exercise testing. Levels of TAGs associated with increased risk for diabetes decreased in response to insulin action and were elevated in the setting of insulin resistance. Conversely, levels of TAGs associated with decreased diabetes risk rose in response to insulin and were poorly correlated with insulin resistance. These studies identify a relationship between lipid acyl chain content and diabetes risk and demonstrate how lipid profiling could aid in clinical risk assessment. PMID:21403394

  8. Identifying and tracking pedestrians based on sensor fusion and motion stability predictions.

    PubMed

    Musleh, Basam; García, Fernando; Otamendi, Javier; Armingol, José Maria; de la Escalera, Arturo

    2010-01-01

    The lack of trustworthy sensors makes development of Advanced Driver Assistance System (ADAS) applications a tough task. It is necessary to develop intelligent systems by combining reliable sensors and real-time algorithms to send the proper, accurate messages to the drivers. In this article, an application to detect and predict the movement of pedestrians in order to prevent an imminent collision has been developed and tested under real conditions. The proposed application, first, accurately measures the position of obstacles using a two-sensor hybrid fusion approach: a stereo camera vision system and a laser scanner. Second, it correctly identifies pedestrians using intelligent algorithms based on polylines and pattern recognition related to leg positions (laser subsystem) and dense disparity maps and u-v disparity (vision subsystem). Third, it uses statistical validation gates and confidence regions to track the pedestrian within the detection zones of the sensors and predict their position in the upcoming frames. The intelligent sensor application has been experimentally tested with success while tracking pedestrians that cross and move in zigzag fashion in front of a vehicle. PMID:22163639

  9. Identifying and Tracking Pedestrians Based on Sensor Fusion and Motion Stability Predictions

    PubMed Central

    Musleh, Basam; García, Fernando; Otamendi, Javier; Armingol, José Mª; de la Escalera, Arturo

    2010-01-01

    The lack of trustworthy sensors makes development of Advanced Driver Assistance System (ADAS) applications a tough task. It is necessary to develop intelligent systems by combining reliable sensors and real-time algorithms to send the proper, accurate messages to the drivers. In this article, an application to detect and predict the movement of pedestrians in order to prevent an imminent collision has been developed and tested under real conditions. The proposed application, first, accurately measures the position of obstacles using a two-sensor hybrid fusion approach: a stereo camera vision system and a laser scanner. Second, it correctly identifies pedestrians using intelligent algorithms based on polylines and pattern recognition related to leg positions (laser subsystem) and dense disparity maps and u-v disparity (vision subsystem). Third, it uses statistical validation gates and confidence regions to track the pedestrian within the detection zones of the sensors and predict their position in the upcoming frames. The intelligent sensor application has been experimentally tested with success while tracking pedestrians that cross and move in zigzag fashion in front of a vehicle. PMID:22163639

  10. An Evaluation of Two Internal Surrogates for Determining the Three-Dimensional Position of Peripheral Lung Tumors

    SciTech Connect

    Spoelstra, Femke Soernsen de Koste, John R. van; Vincent, Andrew; Cuijpers, Johan P.; Slotman, Ben J.; Senan, Suresh

    2009-06-01

    Purpose: Both carina and diaphragm positions have been used as surrogates during respiratory-gated radiotherapy. We studied the correlation of both surrogates with three-dimensional (3D) tumor position. Methods and Materials: A total of 59 repeat artifact-free four-dimensional (4D) computed tomography (CT) scans, acquired during uncoached breathing, were identified in 23 patients with Stage I lung cancer. Repeat scans were co-registered to the initial 4D CT scan, and tumor, carina, and ipsilateral diaphragm were manually contoured in all phases of each 4D CT data set. Correlation between positions of carina and diaphragm with 3D tumor position was studied by use of log-likelihood ratio statistics. Models to predict 3D tumor position from internal surrogates at end inspiration (EI) and end expiration (EE) were developed, and model accuracy was tested by calculating SDs of differences between predicted and actual tumor positions. Results: Motion of both the carina and diaphragm significantly correlated with tumor motion, but log-likelihood ratios indicated that the carina was more predictive for tumor position. When craniocaudal tumor position was predicted by use of craniocaudal carina positions, the SDs of the differences between the predicted and observed positions were 2.2 mm and 2.4 mm at EI and EE, respectively. The corresponding SDs derived with the diaphragm positions were 3.7 mm and 3.9 mm at EI and EE, respectively. Prediction errors in the other directions were comparable. Prediction accuracy was similar at EI and EE. Conclusions: The carina is a better surrogate of 3D tumor position than diaphragm position. Because residual prediction errors were observed in this analysis, additional studies will be performed using audio-coached scans.

  11. Surrogate mothers: the legal issues.

    PubMed

    Mady, T M

    1981-01-01

    Increasing numbers of couples have benefitted from, or may be considering use of, the surrogate mother procedure. In this procedure, a couple, usually a husband and wife, enters into a contract with a surrogate mother. Under the terms of the contract, the surrogate mother is artificially inseminated, bears a child, and relinquishes all rights regarding that child to the semen donor and his wife. In exchange for bearing a child, the surrogate mother often receives a fee. In light of such increased use of the procedure, the issue of whether or not the arrangement is legal has particular importance. Questions of legality involve possible violations of criminal baby-selling statutes. Issues of whether adoption is necessary and whether the child is legitimate also are inherent in the surrogate mother arrangement. This Note argues that these questions should be resolved in favor of finding no impediment to the use of the surrogate mother procedure, at least within certain guidelines. However, even in the absence of legal impediment, detailed contracts and thorough medical screening for genetic, physical and psychological problems would further eliminate ambiguities regarding liability. In addition, the Note concludes that legislation should be enacted to deal with the legal ambiguities of the surrogate mother arrangement. This legislation should regulate the parties that enter into such an arrangement and the rights and responsibilities of these parties. PMID:7332012

  12. miRTar Hunter: A Prediction System for Identifying Human microRNA Target Sites

    PubMed Central

    Park, Kiejung; Kim, Ki-Bong

    2013-01-01

    MicroRNAs (miRNAs) are important regulators of gene expression and play crucial roles in many biological processes including apoptosis, differentiation, development, and tumorigenesis. Recent estimates suggest that more than 50% of human protein coding genes may be regulated by miRNAs and that each miRNA may bind to 300–400 target genes. Approximately 1, 000 human miRNAs have been identified so far with each having up to hundreds of unique target mRNAs. However, the targets for a majority of these miRNAs have not been identified due to the lack of large-scale experimental detection techniques. Experimental detection of miRNA target sites is a costly and time-consuming process, even though identification of miRNA targets is critical to unraveling their functions in various biological processes. To identify miRNA targets, we developed miRTar Hunter, a novel computational approach for predicting target sites regardless of the presence or absence of a seed match or evolutionary sequence conservation. Our approach is based on a dynamic programming algorithm that incorporates more sequence-specific features and reflects the properties of various types of target sites that determine diverse aspects of complementarities between miRNAs and their targets. We evaluated the performance of our algorithm on 532 known human miRNA:target pairs and 59 experimentally-verified negative miRNA:target pairs, and also compared our method with three popular programs for 481 miRNA:target pairs. miRTar Hunter outperformed three popular existing algorithms in terms of recall and precision, indicating that our unique scheme to quantify the determinants of complementary sites is effective at detecting miRNA targets. miRTar Hunter is now available at http://203.230.194.162/~kbkim. PMID:23475422

  13. Surrogate markers of long-term outcome in primary total hip arthroplasty

    PubMed Central

    Malak, T. T.; Broomfield, J. A. J.; Palmer, A. J. R.; Hopewell, S.; Carr, A.; Brown, C.; Prieto-Alhambra, D.

    2016-01-01

    Objectives High failure rates of metal-on-metal hip arthroplasty implants have highlighted the need for more careful introduction and monitoring of new implants and for the evaluation of the safety of medical devices. The National Joint Registry and other regulatory services are unable to detect failing implants at an early enough stage. We aimed to identify validated surrogate markers of long-term outcome in patients undergoing primary total hip arthroplasty (THA). Methods We conducted a systematic review of studies evaluating surrogate markers for predicting long-term outcome in primary THA. Long-term outcome was defined as revision rate of an implant at ten years according to National Institute of Health and Care Excellence guidelines. We conducted a search of Medline and Embase (OVID) databases. Separate search strategies were devised for the Cochrane database and Google Scholar. Each search was performed to include articles from the date of their inception to June 8, 2015. Results Our search strategy identified 1082 studies of which 115 studies were included for full article review. Following review, 17 articles were found that investigated surrogate markers of long-term outcome. These included one systematic review, one randomised control trial (RCT), one case control study and 13 case series. Validated surrogate markers included Radiostereometric Analysis (RSA) and Einzel-Bild-Röntgen-Analyse (EBRA), each measuring implant migration and wear. We identified five RSA studies (one systematic review and four case series) and four EBRA studies (one RCT and three case series). Patient Reported Outcome Measures (PROMs) at six months have been investigated but have not been validated against long-term outcomes. Conclusions This systematic review identified two validated surrogate markers of long-term primary THA outcome: RSA and EBRA, each measuring implant migration and wear. We recommend the consideration of RSA in the pre-market testing of new implants. EBRA can

  14. Electrocardiogram PR Interval Is a Surrogate Marker to Predict New Occurrence of Atrial Fibrillation in Patients with Frequent Premature Atrial Contractions.

    PubMed

    Chun, Kwang Jin; Hwang, Jin Kyung; Choi, So Ra; Park, Seung-Jung; On, Young Keun; Kim, June Soo; Park, Kyoung-Min

    2016-04-01

    The clinical significance of prolonged PR interval has not been evaluated in patients with frequent premature atrial contractions (PACs). We investigated whether prolonged PR interval could predict new occurrence of atrial fibrillation (AF) in patients with frequent PACs. We retrospectively analyzed 684 patients with frequent PACs (> 100 PACs/day) who performed repeated 24-hour Holter monitoring. Prolonged PR interval was defined as longer than 200 msec. Among 684 patients, 626 patients had normal PR intervals (group A) and 58 patients had prolonged PR intervals (group B). After a mean follow-up of 59.3 months, 14 patients (24.1%) in group B developed AF compared to 50 patients (8.0%) in group A (P < 0.001). Cox regression analysis showed that prolonged PR interval (hazard ratio [HR], 1.950; 95% CI, 1.029-3.698; P = 0.041), age (HR, 1.033; 95% CI, 1.006-1.060; P = 0.015), and left atrial (LA) dimension (HR, 1.061; 95% CI, 1.012-1.112; P = 0.015) were associated with AF occurrence. Prolonged PR interval, advanced age, and enlarged LA dimension are independent risk factors of AF occurrence in patients with frequent PACs. PMID:27051234

  15. Electrocardiogram PR Interval Is a Surrogate Marker to Predict New Occurrence of Atrial Fibrillation in Patients with Frequent Premature Atrial Contractions

    PubMed Central

    Hwang, Jin Kyung; Choi, So Ra; Park, Seung-Jung; Kim, June Soo

    2016-01-01

    The clinical significance of prolonged PR interval has not been evaluated in patients with frequent premature atrial contractions (PACs). We investigated whether prolonged PR interval could predict new occurrence of atrial fibrillation (AF) in patients with frequent PACs. We retrospectively analyzed 684 patients with frequent PACs (> 100 PACs/day) who performed repeated 24-hour Holter monitoring. Prolonged PR interval was defined as longer than 200 msec. Among 684 patients, 626 patients had normal PR intervals (group A) and 58 patients had prolonged PR intervals (group B). After a mean follow-up of 59.3 months, 14 patients (24.1%) in group B developed AF compared to 50 patients (8.0%) in group A (P < 0.001). Cox regression analysis showed that prolonged PR interval (hazard ratio [HR], 1.950; 95% CI, 1.029–3.698; P = 0.041), age (HR, 1.033; 95% CI, 1.006–1.060; P = 0.015), and left atrial (LA) dimension (HR, 1.061; 95% CI, 1.012–1.112; P = 0.015) were associated with AF occurrence. Prolonged PR interval, advanced age, and enlarged LA dimension are independent risk factors of AF occurrence in patients with frequent PACs. PMID:27051234

  16. Evolving issues in surrogate motherhood.

    PubMed

    Erlen, J A; Holzman, I R

    1990-01-01

    Surrogate mothering is an arrangement whereby a woman who gives birth to an infant intends--through a contractual agreement--to give that baby to another couple. The recent Baby M case in the United States has raised numerous legal concerns causing many legislative bodies to consider possible statutes to regulate or prohibit surrogacy. The competing interests among and between the individuals involved in this relationship (i.e., the surrogate mother, the couple, the baby, and society) suggest various ethical issues related to benefits, risks, and autonomy. Legal and ethical concerns surrounding the technologically possible procedure of surrogate motherhood are discussed. PMID:2391288

  17. On the processes generating latitudinal richness gradients: identifying diagnostic patterns and predictions

    SciTech Connect

    Hurlbert, Allen H.; Stegen, James C.

    2014-12-02

    Many processes have been put forward to explain the latitudinal gradient in species richness. Here, we use a simulation model to examine four of the most common hypotheses and identify patterns that might be diagnostic of those four hypotheses. The hypotheses examined include (1) tropical niche conservatism, or the idea that the tropics are more diverse because a tropical clade origin has allowed more time for diversification in the tropics and has resulted in few species adapted to extra-tropical climates. (2) The productivity, or energetic constraints, hypothesis suggests that species richness is limited by the amount of biologically available energy in a region. (3) The tropical stability hypothesis argues that major climatic fluctuations and glacial cycles in extratropical regions have led to greater extinction rates and less opportunity for specialization relative to the tropics. (4) Finally, the speciation rates hypothesis suggests that the latitudinal richness gradient arises from a parallel gradient in rates of speciation. We found that tropical niche conservatism can be distinguished from the other three scenarios by phylogenies which are more balanced than expected, no relationship between mean root distance and richness across regions, and a homogeneous rate of speciation across clades and through time. The energy gradient, speciation gradient, and disturbance gradient scenarios all exhibited phylogenies which were more imbalanced than expected, showed a negative relationship between mean root distance and richness, and diversity-dependence of speciation rate estimates through time. Using Bayesian Analysis of Macroevolutionary Mixtures on the simulated phylogenies, we found that the relationship between speciation rates and latitude could distinguish among these three scenarios. We emphasize the importance of considering multiple hypotheses and focusing on diagnostic predictions instead of predictions that are consistent with more than one hypothesis.

  18. Improved mutation tagging with gene identifiers applied to membrane protein stability prediction

    PubMed Central

    Winnenburg, Rainer; Plake, Conrad; Schroeder, Michael

    2009-01-01

    Background The automated retrieval and integration of information about protein point mutations in combination with structure, domain and interaction data from literature and databases promises to be a valuable approach to study structure-function relationships in biomedical data sets. Results We developed a rule- and regular expression-based protein point mutation retrieval pipeline for PubMed abstracts, which shows an F-measure of 87% for the mutation retrieval task on a benchmark dataset. In order to link mutations to their proteins, we utilize a named entity recognition algorithm for the identification of gene names co-occurring in the abstract, and establish links based on sequence checks. Vice versa, we could show that gene recognition improved from 77% to 91% F-measure when considering mutation information given in the text. To demonstrate practical relevance, we utilize mutation information from text to evaluate a novel solvation energy based model for the prediction of stabilizing regions in membrane proteins. For five G protein-coupled receptors we identified 35 relevant single mutations and associated phenotypes, of which none had been annotated in the UniProt or PDB database. In 71% reported phenotypes were in compliance with the model predictions, supporting a relation between mutations and stability issues in membrane proteins. Conclusion We present a reliable approach for the retrieval of protein mutations from PubMed abstracts for any set of genes or proteins of interest. We further demonstrate how amino acid substitution information from text can be utilized for protein structure stability studies on the basis of a novel energy model. PMID:19758467

  19. On the processes generating latitudinal richness gradients: identifying diagnostic patterns and predictions

    PubMed Central

    Hurlbert, Allen H.; Stegen, James C.

    2014-01-01

    We use a simulation model to examine four of the most common hypotheses for the latitudinal richness gradient and identify patterns that might be diagnostic of those four hypotheses. The hypotheses examined include (1) tropical niche conservatism, or the idea that the tropics are more diverse because a tropical clade origin has allowed more time for diversification in the tropics and has resulted in few species adapted to extra-tropical climates. (2) The ecological limits hypothesis suggests that species richness is limited by the amount of biologically available energy in a region. (3) The speciation rates hypothesis suggests that the latitudinal gradient arises from a gradient in speciation rates. (4) Finally, the tropical stability hypothesis argues that climatic fluctuations and glacial cycles in extratropical regions have led to greater extinction rates and less opportunity for specialization relative to the tropics. We found that tropical niche conservatism can be distinguished from the other three scenarios by phylogenies which are more balanced than expected, no relationship between mean root distance (MRD) and richness across regions, and a homogeneous rate of speciation across clades and through time. The energy gradient, speciation gradient, and disturbance gradient scenarios all produced phylogenies which were more imbalanced than expected, showed a negative relationship between MRD and richness, and diversity-dependence of speciation rate estimates through time. We found that the relationship between speciation rates and latitude could distinguish among these three scenarios, with no relation expected under the ecological limits hypothesis, a negative relationship expected under the speciation rates hypothesis, and a positive relationship expected under the tropical stability hypothesis. We emphasize the importance of considering multiple hypotheses and focusing on diagnostic predictions instead of predictions that are consistent with multiple

  20. PREDICT-PD: Identifying risk of Parkinson's disease in the community: methods and baseline results

    PubMed Central

    Noyce, Alastair J; Bestwick, Jonathan P; Silveira-Moriyama, Laura; Hawkes, Christopher H; Knowles, Charles H; Hardy, John; Giovannoni, Gavin; Nageshwaran, Saiji; Osborne, Curtis; Lees, Andrew J; Schrag, Anette

    2014-01-01

    Objectives To present methods and baseline results for an online screening tool to identify increased risk for Parkinson's disease (PD) in the UK population. Methods Risk estimates for future PD were derived from the results of a systematic review of risk factors and early features of PD. Participants aged 60–80 years without PD were recruited by self-referral. They completed an online survey (including family history, non-motor symptoms and lifestyle factors), a keyboard-tapping task and the University of Pennsylvania Smell Identification Test. Risk scores were calculated based on survey answers. Preliminary support for the validity of this algorithm was assessed by comparing those estimated to be higher risk for PD with those at lower risk using proxies, including smell loss, REM-sleep behaviour disorder and reduced tapping speed, and by assessing associations in the whole group. Results 1324 eligible participants completed the survey and 1146 undertook the keyboard-tapping task. Smell tests were sent to 1065 participants. Comparing the 100 highest-risk participants and 100 lowest-risk participants, median University of Pennsylvania Smell Identification Test scores were 30/40 versus 33/40 (p<0.001), mean number of key taps in 30 s were 55 versus 58 (p=0.045), and 24% versus 10% scored above cut-off for REM-sleep behaviour disorder (p=0.008). Regression analyses showed increasing risk scores were associated with worse scores in the three proxies across the whole group (p≤0.001). Conclusions PREDICT-PD is the first study to systematically combine risk factors for PD in the general population. Validity to predict risk of PD will be tested through longitudinal follow-up of incident PD diagnosis. PMID:23828833

  1. Numeracy and Literacy Independently Predict Patients’ Ability to Identify Out-of-Range Test Results

    PubMed Central

    Exe, Nicole L; Witteman, Holly O

    2014-01-01

    .12-1.58, P=.001). Predicted probabilities suggested 77% of higher numeracy and health literacy participants, but only 38% of lower numeracy and literacy participants, could correctly identify the hemoglobin A1c levels as outside the reference range. Correct identification reduced perceived blood glucose control (mean difference 1.68-1.71 points on a 0-10 scale, P<.001). For participants with diabetes, increased health literacy reduced the likelihood of calling one’s doctor when hemoglobin A1c=7.1% (OR 0.66 per unit, 95% CI 0.52-0.82, P<.001) and increased numeracy increased intention to call when hemoglobin A1c=8.4% (OR 1.36 per unit, 95% CI 1.10-1.69, P=.005). Conclusions Limited health literacy and numeracy skills are significant barriers to basic use of laboratory test result data as currently presented in some EHR portals. Regarding contacting their doctor, less numerate and literate participants with diabetes appear insensitive to the hemoglobin A1c level shown, whereas highly numerate and literate participants with diabetes appear very sensitive. Alternate approaches appear necessary to make test results more meaningful. PMID:25135688

  2. Anaerobic biodegradation of surrogate naphthenic acids.

    PubMed

    Clothier, Lindsay N; Gieg, Lisa M

    2016-03-01

    Surface bitumen extraction from the Alberta's oil sands region generates large settling basins known as tailings ponds. The oil sands process-affected water (OSPW) stored in these ponds contain solid and residual bitumen-associated compounds including naphthenic acids (NAs) that can potentially be biodedgraded by indigenous tailings microorganisms. While the biodegradation of some NAs is known to occur under aerobic conditions, little is understood about anaerobic NA biodegradation even though tailings ponds are mainly anoxic. Here, we investigated the potential for anaerobic NA biodegradation by indigenous tailings microorganisms. Enrichment cultures were established from anoxic tailings that were amended with 5 single-ringed surrogate NAs or acid-extractable organics (AEO) from OSPW and incubated under nitrate-, sulfate-, iron-reducing, and methanogenic conditions. Surrogate NA depletion was observed under all anaerobic conditions tested to varying extents, correlating to losses in the respective electron acceptor (sulfate or nitrate) or the production of predicted products (Fe(II) or methane). Tailings-containing cultures incubated under the different electron-accepting conditions resulted in the enrichment and putative identification of microbial community members that may function in metabolizing surrogate NAs under the various anoxic conditions. In addition, more complex NAs (in the form of AEO) was observed to drive sulfate and iron reduction relative to controls. Overall, this study has shown that simple surrogate NAs can be biodegraded under a variety of anoxic conditions, a key first step in understanding the potential anaerobic metabolism of NAs in oil sands tailings ponds and other industrial wastewaters. PMID:26724449

  3. Learning impairments identified early in life are predictive of future impairments associated with aging

    PubMed Central

    Hullinger, Rikki; Burger, Corinna

    2016-01-01

    The Morris water maze (MWM) behavioral paradigm is commonly used to measure spatial learning and memory in rodents. It is widely accepted that performance in the MWM declines with age. However, young rats ubiquitously perform very well on established versions of the water maze, suggesting that more challenging tasks may be required to reveal subtle differences in young animals. Therefore, we have used a one-day water maze and novel object recognition to test whether more sensitive paradigms of memory in young animals could identify subtle cognitive impairments early in life that might become accentuated later with senescence. We have found that these two tasks reliably separate young rats into inferior and superior learners, are highly correlated, and that performance on these tasks early in life is predictive of performance at 12 months of age. Furthermore, we have found that repeated training in this task selectively improves the performance of inferior learners, suggesting that behavioral training from an early age may provide a buffer against age-related cognitive decline. PMID:26283528

  4. Identifying Precipitation Types Using Dual-Polarization-Based Radar and Numerical Weather Prediction Model Data

    NASA Astrophysics Data System (ADS)

    Seo, B. C.; Bradley, A.; Krajewski, W. F.

    2015-12-01

    The recent upgrade of dual-polarization with NEXRAD radars has assisted in improving the characterization of microphysical processes in precipitation and thus has enabled precipitation estimation based on the identified precipitation types. While this polarimetric capability promises the potential for the enhanced accuracy in quantitative precipitation estimation (QPE), recent studies show that the polarimetric estimates are still affected by uncertainties arising from the radar beam geometry/sampling space associated with the vertical variability of precipitation. The authors, first of all, focus on evaluating the NEXRAD hydrometeor classification product using ground reference data (e.g., ASOS) that provide simple categories of the observed precipitation types (e.g., rain, snow, and freezing rain). They also investigate classification uncertainty features caused by the variability of precipitation between the ground and the altitudes where radar samples. Since this variability is closely related to the atmospheric conditions (e.g., temperature) at near surface, useful information (e.g., critical thickness and temperature profile) that is not available in radar observations is retrieved from the numerical weather prediction (NWP) model data such as Rapid Refresh (RAP)/High Resolution Rapid Refresh (HRRR). The NWP retrieved information and polarimetric radar data are used together to improve the accuracy of precipitation type identification at near surface. The authors highlight major improvements and discuss limitations in the real-time application.

  5. Molecular dissection of valproic acid effects in acute myeloid leukemia identifies predictive networks.

    PubMed

    Rücker, Frank G; Lang, Katharina M; Fütterer, Markus; Komarica, Vladimir; Schmid, Mathias; Döhner, Hartmut; Schlenk, Richard F; Döhner, Konstanze; Knudsen, Steen; Bullinger, Lars

    2016-07-01

    Histone deacetylase inhibitors (HDACIs) like valproic acid (VPA) display activity in leukemia models and induce tumor-selective cytotoxicity against acute myeloid leukemia (AML) blasts. As there are limited data on HDACIs effects, we aimed to dissect VPA effects in vitro using myeloid cell lines with the idea to integrate findings with in vivo data from AML patients treated with VPA additionally to intensive chemotherapy (n = 12). By gene expression profiling we identified an in vitro VPA response signature enriched for genes/pathways known to be implicated in cell cycle arrest, apoptosis, and DNA repair. Following VPA treatment in vivo, gene expression changes in AML patients showed concordant results with the in vitro VPA response despite concomitant intensive chemotherapy. Comparative miRNA profiling revealed VPA-associated miRNA expression changes likely contributing to a VPA-induced reversion of deregulated gene expression. In addition, we were able to define markers predicting VPA response in vivo such as CXCR4 and LBH. These could be validated in an independent cohort of VPA and intensive chemotherapy treated AML patients (n = 114) in which they were inversely correlated with relapse-free survival. In summary, our data provide new insights into the molecular mechanisms of VPA in myeloid blasts, which might be useful in further advancing HDAC inhibition based treatment approaches in AML. PMID:27309669

  6. Identifying Psychosocial Variables That Predict Safer Sex Intentions in Adolescents and Young Adults.

    PubMed

    Brüll, Phil; Ruiter, Robert A C; Wiers, Reinout W; Kok, Gerjo

    2016-01-01

    Young people are especially vulnerable to sexually transmitted infections (STIs). The triad of deliberate and effective safer sex behavior encompasses condom use, combined with additional information about a partner's sexual health, and the kind of sex acts usually performed. To identify psychosocial predictors of young people's intentions to have safer sex, as related to this triad, we conducted an online study with 211 sexually active participants aged between 18 and 24 years. Predictors [i.e., perceived behavioral control (PBC), subjective norms, and intention] taken from Fishbein and Ajzen's Reasoned Action Approach (RAA), were combined with more distal variables (e.g., behavioral inhibition, sensation seeking, parental monitoring, and knowledge about STIs). Beyond the highly predictive power of RAA variables, additional variance was explained by the number of instances of unprotected sexual intercourse (SI) during the last 12 months and reasons for using barrier protection during first SI. In particular, past condom non-use behavior moderated PBC related to intended condom use. Further, various distal variables showed significant univariate associations with intentions related to the three behaviors of interest. It may, therefore, be helpful to include measures of past behavior as well as certain additional distal variables in future safer sex programs designed to promote health-sustaining sexual behavior. PMID:27148520

  7. Mass Spectrometry-Based Metabolomics Identifies Longitudinal Urinary Metabolite Profiles Predictive of Radiation-Induced Cancer.

    PubMed

    Cook, John A; Chandramouli, Gadisetti V R; Anver, Miriam R; Sowers, Anastasia L; Thetford, Angela; Krausz, Kristopher W; Gonzalez, Frank J; Mitchell, James B; Patterson, Andrew D

    2016-03-15

    Nonlethal exposure to ionizing radiation (IR) is a public concern due to its known carcinogenic effects. Although latency periods for IR-induced neoplasms are relatively long, the ability to detect cancer as early as possible is highly advantageous for effective therapeutic intervention. Therefore, we hypothesized that metabolites in the urine from mice exposed to total body radiation (TBI) would predict for the presence of cancer before a palpable mass was detected. In this study, we exposed mice to 0 or 5.4 Gy TBI, collected urine samples periodically over 1 year, and assayed urine metabolites by using mass spectrometry. Longitudinal data analysis within the first year post-TBI revealed that cancers, including hematopoietic, solid, and benign neoplasms, could be distinguished by unique urinary signatures as early as 3 months post-TBI. Furthermore, a distinction among different types of malignancies could be clearly delineated as early as 3 months post-TBI for hematopoietic neoplasms, 6 months for solid neoplasms, and by 1 year for benign neoplasms. Moreover, the feature profile for radiation-exposed mice 6 months post-TBI was found to be similar to nonirradiated control mice at 18 months, suggesting that TBI accelerates aging. These results demonstrate that urine feature profiles following TBI can identify cancers in mice prior to macroscopic detection, with important implications for the early diagnosis and treatment. PMID:26880804

  8. Identifying Psychosocial Variables That Predict Safer Sex Intentions in Adolescents and Young Adults

    PubMed Central

    Brüll, Phil; Ruiter, Robert A. C.; Wiers, Reinout W.; Kok, Gerjo

    2016-01-01

    Young people are especially vulnerable to sexually transmitted infections (STIs). The triad of deliberate and effective safer sex behavior encompasses condom use, combined with additional information about a partner’s sexual health, and the kind of sex acts usually performed. To identify psychosocial predictors of young people’s intentions to have safer sex, as related to this triad, we conducted an online study with 211 sexually active participants aged between 18 and 24 years. Predictors [i.e., perceived behavioral control (PBC), subjective norms, and intention] taken from Fishbein and Ajzen’s Reasoned Action Approach (RAA), were combined with more distal variables (e.g., behavioral inhibition, sensation seeking, parental monitoring, and knowledge about STIs). Beyond the highly predictive power of RAA variables, additional variance was explained by the number of instances of unprotected sexual intercourse (SI) during the last 12 months and reasons for using barrier protection during first SI. In particular, past condom non-use behavior moderated PBC related to intended condom use. Further, various distal variables showed significant univariate associations with intentions related to the three behaviors of interest. It may, therefore, be helpful to include measures of past behavior as well as certain additional distal variables in future safer sex programs designed to promote health-sustaining sexual behavior. PMID:27148520

  9. The Creation of Surrogate Models for Fast Estimation of Complex Model Outcomes

    PubMed Central

    Pruett, W. Andrew; Hester, Robert L.

    2016-01-01

    A surrogate model is a black box model that reproduces the output of another more complex model at a single time point. This is to be distinguished from the method of surrogate data, used in time series. The purpose of a surrogate is to reduce the time necessary for a computation at the cost of rigor and generality. We describe a method of constructing surrogates in the form of support vector machine (SVM) regressions for the purpose of exploring the parameter space of physiological models. Our focus is on the methodology of surrogate creation and accuracy assessment in comparison to the original model. This is done in the context of a simulation of hemorrhage in one model, “Small”, and renal denervation in another, HumMod. In both cases, the surrogate predicts the drop in mean arterial pressure following the intervention. We asked three questions concerning surrogate models: (1) how many training examples are necessary to obtain an accurate surrogate, (2) is surrogate accuracy homogeneous, and (3) how much can computation time be reduced when using a surrogate. We found the minimum training set size that would guarantee maximal accuracy was widely variable, but could be algorithmically generated. The average error for the pressure response to the protocols was -0.05±2.47 in Small, and -0.3 +/- 3.94 mmHg in HumMod. In the Small model, error grew with actual pressure drop, and in HumMod, larger pressure drops were overestimated by the surrogates. Surrogate use resulted in a 6 order of magnitude decrease in computation time. These results suggest surrogate modeling is a valuable tool for generating predictions of an integrative model’s behavior on densely sampled subsets of its parameter space. PMID:27258010

  10. Statistical evaluation of surrogate endpoints with examples from cancer clinical trials.

    PubMed

    Buyse, Marc; Molenberghs, Geert; Paoletti, Xavier; Oba, Koji; Alonso, Ariel; Van der Elst, Wim; Burzykowski, Tomasz

    2016-01-01

    A surrogate endpoint is intended to replace a clinical endpoint for the evaluation of new treatments when it can be measured more cheaply, more conveniently, more frequently, or earlier than that clinical endpoint. A surrogate endpoint is expected to predict clinical benefit, harm, or lack of these. Besides the biological plausibility of a surrogate, a quantitative assessment of the strength of evidence for surrogacy requires the demonstration of the prognostic value of the surrogate for the clinical outcome, and evidence that treatment effects on the surrogate reliably predict treatment effects on the clinical outcome. We focus on these two conditions, and outline the statistical approaches that have been proposed to assess the extent to which these conditions are fulfilled. When data are available from a single trial, one can assess the "individual level association" between the surrogate and the true endpoint. When data are available from several trials, one can additionally assess the "trial level association" between the treatment effect on the surrogate and the treatment effect on the true endpoint. In the latter case, the "surrogate threshold effect" can be estimated as the minimum effect on the surrogate endpoint that predicts a statistically significant effect on the clinical endpoint. All these concepts are discussed in the context of randomized clinical trials in oncology, and illustrated with two meta-analyses in gastric cancer. PMID:25682941

  11. Melancholic depression prediction by identifying representative features in metabolic and microarray profiles with missing values.

    PubMed

    Nie, Zhi; Yang, Tao; Liu, Yashu; Li, Qingyang; Narayan, Vaibhav A; Wittenberg, Gayle; Ye, Jieping

    2015-01-01

    Recent studies have revealed that melancholic depression, one major subtype of depression, is closely associated with the concentration of some metabolites and biological functions of certain genes and pathways. Meanwhile, recent advances in biotechnologies have allowed us to collect a large amount of genomic data, e.g., metabolites and microarray gene expression. With such a huge amount of information available, one approach that can give us new insights into the understanding of the fundamental biology underlying melancholic depression is to build disease status prediction models using classification or regression methods. However, the existence of strong empirical correlations, e.g., those exhibited by genes sharing the same biological pathway in microarray profiles, tremendously limits the performance of these methods. Furthermore, the occurrence of missing values which are ubiquitous in biomedical applications further complicates the problem. In this paper, we hypothesize that the problem of missing values might in some way benefit from the correlation between the variables and propose a method to learn a compressed set of representative features through an adapted version of sparse coding which is capable of identifying correlated variables and addressing the issue of missing values simultaneously. An efficient algorithm is also developed to solve the proposed formulation. We apply the proposed method on metabolic and microarray profiles collected from a group of subjects consisting of both patients with melancholic depression and healthy controls. Results show that the proposed method can not only produce meaningful clusters of variables but also generate a set of representative features that achieve superior classification performance over those generated by traditional clustering and data imputation techniques. In particular, on both datasets, we found that in comparison with the competing algorithms, the representative features learned by the proposed

  12. MELANCHOLIC DEPRESSION PREDICTION BY IDENTIFYING REPRESENTATIVE FEATURES IN METABOLIC AND MICROARRAY PROFILES WITH MISSING VALUES

    PubMed Central

    Nie, Zhi; Yang, Tao; Liu, Yashu; Lin, Binbin; Li, Qingyang; Narayan, Vaibhav A; Wittenberg, Gayle; Ye, Jieping

    2014-01-01

    Recent studies have revealed that melancholic depression, one major subtype of depression, is closely associated with the concentration of some metabolites and biological functions of certain genes and pathways. Meanwhile, recent advances in biotechnologies have allowed us to collect a large amount of genomic data, e.g., metabolites and microarray gene expression. With such a huge amount of information available, one approach that can give us new insights into the understanding of the fundamental biology underlying melancholic depression is to build disease status prediction models using classification or regression methods. However, the existence of strong empirical correlations, e.g., those exhibited by genes sharing the same biological pathway in microarray profiles, tremendously limits the performance of these methods. Furthermore, the occurrence of missing values which are ubiquitous in biomedical applications further complicates the problem. In this paper, we hypothesize that the problem of missing values might in some way benefit from the correlation between the variables and propose a method to learn a compressed set of representative features through an adapted version of sparse coding which is capable of identifying correlated variables and addressing the issue of missing values simultaneously. An efficient algorithm is also developed to solve the proposed formulation. We apply the proposed method on metabolic and microarray profiles collected from a group of subjects consisting of both patients with melancholic depression and healthy controls. Results show that the proposed method can not only produce meaningful clusters of variables but also generate a set of representative features that achieve superior classification performance over those generated by traditional clustering and data imputation techniques. In particular, on both datasets, we found that in comparison with the competing algorithms, the representative features learned by the proposed

  13. Predicting 3-year outcomes of early-identified children with hearing impairment

    PubMed Central

    Ching, T.Y.C.; Day, J.; Seeto, M.; Dillon, H.; Marnane, V.; Street, L.

    2013-01-01

    Problem/Objectives Permanent childhood hearing loss has major negative impacts on children’s health and development. To improve outcomes, universal newborn hearing screening (UNHS) has been implemented widely. However, high-quality evidence on its efficacy was lacking. To address this evidence gap, we conducted the Longitudinal Outcomes of Children with Hearing Impairment (LOCHI) study to directly compare outcomes of early- and late-identified children. This paper investigates whether early performance measured shortly after initial amplification predicts language development at 3 years of age. Methodology This is a prospective, population-based study. We assessed outcomes at 6- and 12-months after amplification, and then at 3 and 5 years of age. Main outcome measures included directly-assessed language, receptive vocabulary, speech production; and parent-reported functional performance in everyday life. A range of demographic and audiological information was also collected at evaluation intervals. Results About 450 children participated, and 3-year outcomes scores were available for 356 participants. Multiple regression analysis revealed that early language scores or functional performance ratings were significant predictors of 3-year outcomes. Other significant predictors included the presence or absence of additional disabilities, severity of hearing loss, and age at cochlear implant activation. Conclusions Early performance, either directly-assessed language ability (PLS-4) or parent-reported functional ratings (PEACH), were significant predictors of 3-year outcomes; along with presence or absence of additional disabilities, severity of hearing loss, and age at CI activation. Earlier implantation is possible with early detection of hearing loss via UNHS. Monitoring performance after initial amplification allows preventive strategies to be implemented early to improve outcomes. PMID:24383228

  14. Psychosocial aspects of surrogate motherhood.

    PubMed

    van den Akker, Olga B A

    2007-01-01

    This review addresses the psychosocial research carried out on surrogacy triads (surrogate mothers, commissioning mothers and offspring) and shows that research has focused on a number of specific issues: attachment and disclosure to surrogate offspring; experiences, characteristics and motivations of surrogate mothers; and changes in profiles of the commissioning/intended mothers. Virtually all studies have used highly selected samples making generalizations difficult. There have been a notable lack of theory, no interventions and only a handful of longitudinal studies or studies comparing different populations. Few studies have specifically questioned the meaning of and need for a family or the influence and impact that professionals, treatment availability and financial factors have on the choices made for surrogate and intended mothers. Societal attitudes have changed somewhat; however, according to public opinion, women giving up babies still fall outside the acceptable remit. Surrogate and intended mothers appear to reconcile their unusual choice through a process of cognitive restructuring, and the success or failure of this cognitive appraisal affects people's willingness to be open and honest about their choices. Normal population surveys, on the contrary, are less accepting of third party reproduction; they have no personal need to reconsider and hence maintain their original normative cognitively consonant state. PMID:16936307

  15. Relaxation rates for inverse power law particle interactions and their variable hard sphere surrogates

    NASA Astrophysics Data System (ADS)

    Rubinstein, Robert

    2015-11-01

    It is well known that collision models based on an assumed intermolecular potential (IPL, LJ, ...) can be successfully replaced by simplified surrogates (VHS, VSS, VS, ...) in DSMC calculations. But these surrogates only reproduce certain gross properties of the molecular model, for example, the temperature dependence of the viscosity; they do not approximate, and even mis-state, the details of the particle interactions. The success of the simplified models in problems at finite Knudsen number, where the Navier-Stokes approximation is not valid, may therefore seem surprising. To understand this success in a very special case, we showed that the first seven relaxation rates of the linearized Boltzmann equation for Maxwellian molecules are well approximated by the corresponding relaxation rates of its VHS surrogate. We will show that this analysis can be extended in somewhat less generality to IPL interactions, and to some extent to more realistic models including LJ. We believe that this analysis can help address the more general problem of identifying the properties of the collision model that dominate the predictions of the Boltzmann equation.

  16. Surrogate insulin-producing cells

    PubMed Central

    Wong, Adrianne L.; Hwa, Albert; Hellman, Dov

    2012-01-01

    Diabetes, a large and growing worldwide health concern, affects the functional mass of the pancreatic beta cell, which in turn affects the glucose regulation of the body. Successful transplantation of cadaveric islets and pancreata for patients with uncontrolled type 1 diabetes has provided proof-of-concept for the development of commercial cell therapy approaches to treat diabetes. Three broad issues must be addressed before surrogate insulin-producing cells can become a reality: the development of a surrogate beta-cell source, immunoprotection, and translation. Cell therapy for diabetes is a real possibility, but many questions remain; through the collaborative efforts of multiple stakeholders this may become a reality. PMID:22891077

  17. Identifying the causes of the poor decadal climate prediction skill over the North Pacific

    NASA Astrophysics Data System (ADS)

    Guemas, V.; Doblas-Reyes, F. J.; Lienert, F.; Soufflet, Y.; Du, H.

    2012-10-01

    While the North Pacific region has a strong influence on North American and Asian climate, it is also the area with the worst performance in several state-of-the-art decadal climate predictions in terms of correlation and root mean square error scores. The failure to represent two major warm sea surface temperature events occurring around 1963 and 1968 largely contributes to this poor skill. The magnitude of these events competes with the largest observed temperature anomalies in the twenty-first century that might be associated with the long-term warming. Understanding the causes of these major warm events is thus of primary concern to improve prediction of North Pacific, North American and Asian climate. The 1963 warm event stemmed from the propagation of a warm ocean heat content anomaly along the Kuroshio-Oyashio extension. The 1968 warm event originated from the upward transfer of a warm water mass centered at 200 m depth. For being associated with long-lived ocean heat content anomalies, we expect those events to be, at least partially, predictable. Biases in ocean mixing processes present in many climate prediction models seem to explain the inability to predict these two major events. Such currently unpredictable warm events, if occurring again in the next decade, would substantially enhance the effect of long-term warming in the region.

  18. Predicting crash risk and identifying crash precursors on Korean expressways using loop detector data.

    PubMed

    Kwak, Ho-Chan; Kho, Seungyoung

    2016-03-01

    In order to improve traffic safety on expressways, it is important to develop proactive safety management strategies with consideration for segment types and traffic flow states because crash mechanisms have some differences by each condition. The primary objective of this study is to develop real-time crash risk prediction models for different segment types and traffic flow states on expressways. The mainline of expressways is divided into basic segment and ramp vicinity, and the traffic flow states are classified into uncongested and congested conditions. Also, Korean expressways have irregular intervals between loop detector stations. Therefore, we investigated on the effect and application of the detector stations at irregular intervals for the crash risk prediction on expressways. The most significant traffic variables were selected by conditional logistic regression analysis which could control confounding factors. Based on the selected traffic variables, separate models to predict crash risk were developed using genetic programming technique. The model estimation results showed that the traffic flow characteristics leading to crashes are differed by segment type and traffic flow state. Especially, the variables related to the intervals between detector stations had a significant influence on crash risk prediction under the uncongested condition. Finally, compared with the single model for all crashes and the logistic models used in previous studies, the proposed models showed higher prediction performance. The results of this study can be applied to develop more effective proactive safety management strategies for different segment types and traffic flow states on expressways with loop detector stations at irregular intervals. PMID:26710266

  19. Shape Optimization by Bayesian-Validated Computer-Simulation Surrogates

    NASA Technical Reports Server (NTRS)

    Patera, Anthony T.

    1997-01-01

    A nonparametric-validated, surrogate approach to optimization has been applied to the computational optimization of eddy-promoter heat exchangers and to the experimental optimization of a multielement airfoil. In addition to the baseline surrogate framework, a surrogate-Pareto framework has been applied to the two-criteria, eddy-promoter design problem. The Pareto analysis improves the predictability of the surrogate results, preserves generality, and provides a means to rapidly determine design trade-offs. Significant contributions have been made in the geometric description used for the eddy-promoter inclusions as well as to the surrogate framework itself. A level-set based, geometric description has been developed to define the shape of the eddy-promoter inclusions. The level-set technique allows for topology changes (from single-body,eddy-promoter configurations to two-body configurations) without requiring any additional logic. The continuity of the output responses for input variations that cross the boundary between topologies has been demonstrated. Input-output continuity is required for the straightforward application of surrogate techniques in which simplified, interpolative models are fitted through a construction set of data. The surrogate framework developed previously has been extended in a number of ways. First, the formulation for a general, two-output, two-performance metric problem is presented. Surrogates are constructed and validated for the outputs. The performance metrics can be functions of both outputs, as well as explicitly of the inputs, and serve to characterize the design preferences. By segregating the outputs and the performance metrics, an additional level of flexibility is provided to the designer. The validated outputs can be used in future design studies and the error estimates provided by the output validation step still apply, and require no additional appeals to the expensive analysis. Second, a candidate-based a posteriori

  20. Computationally efficient calibration of WATCLASS Hydrologic models using surrogate optimization

    NASA Astrophysics Data System (ADS)

    Kamali, M.; Ponnambalam, K.; Soulis, E. D.

    2007-07-01

    In this approach, exploration of the cost function space was performed with an inexpensive surrogate function, not the expensive original function. The Design and Analysis of Computer Experiments(DACE) surrogate function, which is one type of approximate models, which takes correlation function for error was employed. The results for Monte Carlo Sampling, Latin Hypercube Sampling and Design and Analysis of Computer Experiments(DACE) approximate model have been compared. The results show that DACE model has a good potential for predicting the trend of simulation results. The case study of this document was WATCLASS hydrologic model calibration on Smokey-River watershed.

  1. Evaluating two sparse grid surrogates and two adaptation criteria for groundwater Bayesian uncertainty quantification

    NASA Astrophysics Data System (ADS)

    Zeng, Xiankui; Ye, Ming; Burkardt, John; Wu, Jichun; Wang, Dong; Zhu, Xiaobin

    2016-04-01

    Sparse grid (SG) stochastic collocation methods have been recently used to build accurate but cheap-to-run surrogates for groundwater models to reduce the computational burden of Bayesian uncertainty analysis. The surrogates can be built for either a log-likelihood function or state variables such as hydraulic head and solute concentration. Using a synthetic groundwater flow model, this study evaluates the log-likelihood and head surrogates in terms of the computational cost of building them, the accuracy of the surrogates, and the accuracy of the distributions of model parameters and predictions obtained using the surrogates. The head surrogates outperform the log-likelihood surrogates for the following four reasons: (1) the shape of the head response surface is smoother than that of the log-likelihood response surface in parameter space, (2) the head variation is smaller than the log-likelihood variation in parameter space, (3) the interpolation error of the head surrogates does not accumulate to be larger than the interpolation error of the log-likelihood surrogates, and (4) the model simulations needed for building one head surrogate can be recycled for building others. For both log-likelihood and head surrogates, adaptive sparse grids are built using two indicators: absolute error and relative error. The adaptive head surrogates are insensitive to the error indicators, because the ratio between the two indicators is hydraulic head, which has small variation in the parameter space. The adaptive log-likelihood surrogates based on the relative error indicators outperform those based on the absolute error indicators, because adaptation based on the relative error indicator puts more sparse-grid nodes in the areas in the parameter space where the log-likelihood is high. While our numerical study suggests building state-variable surrogates and using the relative error indicator for building log-likelihood surrogates, selecting appropriate type of surrogates and

  2. Dynamic impact indentation of hydrated biological tissues and tissue surrogate gels

    NASA Astrophysics Data System (ADS)

    Ilke Kalcioglu, Z.; Qu, Meng; Strawhecker, Kenneth E.; Shazly, Tarek; Edelman, Elazer; VanLandingham, Mark R.; Smith, James F.; Van Vliet, Krystyn J.

    2011-03-01

    For both materials engineering research and applied biomedicine, a growing need exists to quantify mechanical behaviour of tissues under defined hydration and loading conditions. In particular, characterisation under dynamic contact-loading conditions can enable quantitative predictions of deformation due to high rate 'impact' events typical of industrial accidents and ballistic insults. The impact indentation responses were examined of both hydrated tissues and candidate tissue surrogate materials. The goals of this work were to determine the mechanical response of fully hydrated soft tissues under defined dynamic loading conditions, and to identify design principles by which synthetic, air-stable polymers could mimic those responses. Soft tissues from two organs (liver and heart), a commercially available tissue surrogate gel (Perma-Gel™) and three styrenic block copolymer gels were investigated. Impact indentation enabled quantification of resistance to penetration and energy dissipative constants under the rates and energy densities of interest for tissue surrogate applications. These analyses indicated that the energy dissipation capacity under dynamic impact increased with increasing diblock concentration in the styrenic gels. Under the impact rates employed (2 mm/s to 20 mm/s, corresponding to approximate strain energy densities from 0.4 kJ/m3 to 20 kJ/m3), the energy dissipation capacities of fully hydrated soft tissues were ultimately well matched by a 50/50 triblock/diblock composition that is stable in ambient environments. More generally, the methodologies detailed here facilitate further optimisation of impact energy dissipation capacity of polymer-based tissue surrogate materials, either in air or in fluids.

  3. An Evaluation of the Predictive Validity of Confidence Ratings in Identifying Functional Behavioral Assessment Hypothesis Statements

    ERIC Educational Resources Information Center

    Borgmeier, Chris; Horner, Robert H.

    2006-01-01

    Faced with limited resources, schools require tools that increase the accuracy and efficiency of functional behavioral assessment. Yarbrough and Carr (2000) provided evidence that informant confidence ratings of the likelihood of problem behavior in specific situations offered a promising tool for predicting the accuracy of function-based…

  4. Paper Highlight: Biomarker Identified for Predicting Early Prostate Cancer Aggressiveness — Site

    Cancer.gov

    A team led by Cory Abate-Shen, Michael Shen, and Andrea Califano at Columbia University found that measuring the expression levels of three genes associated with aging can be used to predict the aggressiveness of seemingly low-risk prostate cancer.

  5. Bayesian calibration of the Community Land Model using surrogates

    NASA Astrophysics Data System (ADS)

    Ray, J.; Sargsyan, K.; Huang, M.; Hou, Z.

    2012-12-01

    We present results from a calibration effort of the Community Land Model (CLM) using surrogates. Three parameters, governing subsurface runoff and groundwater dynamics, were targeted and calibrated to observations from the Missouri Ozark Ameriflux tower site (US-Moz) spanning 1996-2004. We adopt a Bayesian approach for calibration where the parameters were estimated as probability distributions to account for the uncertainty due to modelling and observation errors. The model fitting was performed using an adaptive Markov chain Monte Carlo method. Since the sampling-based calibration of CLM could be computationally expensive, we first developed surrogates as alternatives to the CLM. The three-dimensional parameter space was sampled and CLM was used to produce monthly averaged predictions of runoff and latent/sensible heat fluxes. Multiple polynomial "trend" models were proposed, fitted to the CLM simulations via regression, and tested for over-fitting. A quadratic model was ultimately selected and bias-corrected using the universal kriging approach, to produce surrogates with errors less than 10% at any arbitrary point in the parameter-space. This "trend+kriged" model was then used as an inexpensive CLM surrogate, in an MCMC sampler, to solve the calibration problem. Joint densities were developed for the parameters, along with an estimate of the structural error of the surrogates.

  6. IDENTIFYING AND PREDICTING DIVING PLUME BEHAVIOR AT GROUNDWATER SITES CONTAINING MTBE: PART 1 SUPPLEMENTAL FUNDING FOR ACTIVITIES IN FY 2002

    EPA Science Inventory

    This work will complete work began under Identifying and Predicting Plume Diving Behavior at Groundwater Sites Containing MTBE: Part 1. As of September 2001, ORD Staff and ORD Contractors have characterized dividing MTBE plumes at Spring Green, Wisconsin; Milford, Michigan; and ...

  7. Surrogate modeling of deformable joint contact using artificial neural networks.

    PubMed

    Eskinazi, Ilan; Fregly, Benjamin J

    2015-09-01

    Deformable joint contact models can be used to estimate loading conditions for cartilage-cartilage, implant-implant, human-orthotic, and foot-ground interactions. However, contact evaluations are often so expensive computationally that they can be prohibitive for simulations or optimizations requiring thousands or even millions of contact evaluations. To overcome this limitation, we developed a novel surrogate contact modeling method based on artificial neural networks (ANNs). The method uses special sampling techniques to gather input-output data points from an original (slow) contact model in multiple domains of input space, where each domain represents a different physical situation likely to be encountered. For each contact force and torque output by the original contact model, a multi-layer feed-forward ANN is defined, trained, and incorporated into a surrogate contact model. As an evaluation problem, we created an ANN-based surrogate contact model of an artificial tibiofemoral joint using over 75,000 evaluations of a fine-grid elastic foundation (EF) contact model. The surrogate contact model computed contact forces and torques about 1000 times faster than a less accurate coarse grid EF contact model. Furthermore, the surrogate contact model was seven times more accurate than the coarse grid EF contact model within the input domain of a walking motion. For larger input domains, the surrogate contact model showed the expected trend of increasing error with increasing domain size. In addition, the surrogate contact model was able to identify out-of-contact situations with high accuracy. Computational contact models created using our proposed ANN approach may remove an important computational bottleneck from musculoskeletal simulations or optimizations incorporating deformable joint contact models. PMID:26220591

  8. Taxi-Aware Map: Identifying and Predicting Vacant Taxis in the City

    NASA Astrophysics Data System (ADS)

    Phithakkitnukoon, Santi; Veloso, Marco; Bento, Carlos; Biderman, Assaf; Ratti, Carlo

    Knowing where vacant taxis are and will be at a given time and location helps the users in daily planning and scheduling, as well as the taxi service providers in dispatching. In this paper, we present a predictive model for the number of vacant taxis in a given area based on time of the day, day of the week, and weather condition. The history is used to build the prior probability distributions for our inference engine, which is based on the naïve Bayesian classifier with developed error-based learning algorithm and method for detecting adequacy of historical data using mutual information. Based on 150 taxis in Lisbon, Portugal, we are able to predict for each hour with the overall error rate of 0.8 taxis per 1x1 km2 area.

  9. Predictive markers of capecitabine sensitivity identified from the expression profile of pyrimidine nucleoside-metabolizing enzymes.

    PubMed

    Yasuno, Hideyuki; Kurasawa, Mitsue; Yanagisawa, Mieko; Sato, Yasuko; Harada, Naoki; Mori, Kazushige

    2013-02-01

    Molecular markers predicting sensitivity to anticancer drugs are important and useful not only for selecting potential responders but also for developing new combinations. In the present study, we analyzed the difference in the sensitivity of xenograft models to capecitabine (Xeloda®), 5'-deoxy-5-fluorouridine (5'-DFUR, doxifluridine, Furtulon®) and 5-FU by comparing the mRNA levels of 12 pyrimidine nucleoside-metabolizing enzymes. Amounts of mRNA in the tumor tissues of 80 xenograft models were determined by real-time RT-PCR and mutual correlations were examined. A clustering analysis revealed that the 12 enzymes were divided into two groups; one group consisted of 8 enzymes, including orotate phosphoribosyl transferase (OPRT), TMP kinase (TMPK) and UMP kinase (UMPK), and was related to the de novo synthesis pathway for nucleotides, with mRNA expression levels showing significant mutual correlation. In the other group, 4 enzymes, including thymidine phosphorylase (TP) and dihydropyrimidine dehydrogenase (DPD), were involved in the salvage/degradation pathway of the nucleotides, and the mRNA levels of this group were dispersed more widely than that of the de novo group. Antitumor activity was assessed in 24 xenograft models for each drug. The antitumor activity of capecitabine and 5'-DFUR correlated significantly with the mRNA levels of TP and with the TP/DPD ratio, whereas the activity of 5-FU correlated significantly with OPRT, TMPK, UMPK and CD. In a stepwise regression analysis, TP and DPD were found to be independent predictive factors of sensitivity to capecitabine and 5'-DFUR, and UMPK was predictive of sensitivity to 5-FU. These results indicate that the predictive factors for sensitivity to capecitabine and 5'-DFUR in xenograft models may be different from those for 5-FU, suggesting that these drugs may have different responders in clinical usage. PMID:23229803

  10. Predicting the Risk of Clostridium difficile Infection upon Admission: A Score to Identify Patients for Antimicrobial Stewardship Efforts

    PubMed Central

    Kuntz, Jennifer L; Smith, David H; Petrik, Amanda F; Yang, Xiuhai; Thorp, Micah L; Barton, Tracy; Barton, Karen; Labreche, Matthew; Spindel, Steven J; Johnson, Eric S

    2016-01-01

    Introduction: Increasing morbidity and health care costs related to Clostridium difficile infection (CDI) have heightened interest in methods to identify patients who would most benefit from interventions to mitigate the likelihood of CDI. Objective: To develop a risk score that can be calculated upon hospital admission and used by antimicrobial stewards, including pharmacists and clinicians, to identify patients at risk for CDI who would benefit from enhanced antibiotic review and patient education. Methods: We assembled a cohort of Kaiser Permanente Northwest patients with a hospital admission from July 1, 2005, through December 30, 2012, and identified CDI in the six months following hospital admission. Using Cox regression, we constructed a score to identify patients at high risk for CDI on the basis of preadmission characteristics. We calculated and plotted the observed six-month CDI risk for each decile of predicted risk. Results: We identified 721 CDIs following 54,186 hospital admissions—a 6-month incidence of 13.3 CDIs/1000 patient admissions. Patients with the highest predicted risk of CDI had an observed incidence of 53 CDIs/1000 patient admissions. The score differentiated between patients who do and do not develop CDI, with values for the extended C-statistic of 0.75. Predicted risk for CDI agreed closely with observed risk. Conclusion: Our risk score accurately predicted six-month risk for CDI using preadmission characteristics. Accurate predictions among the highest-risk patient subgroups allow for the identification of patients who could be targeted for and who would likely benefit from review of inpatient antibiotic use or enhanced educational efforts at the time of discharge planning. PMID:26845084

  11. Surviving Surrogate Decision-Making: What Helps and Hampers the Experience of Making Medical Decisions for Others

    PubMed Central

    Starks, Helene; Taylor, Janelle S.; Hopley, Elizabeth K.; Fryer-Edwards, Kelly

    2007-01-01

    BACKGROUND A majority of end-of-life medical decisions are made by surrogate decision-makers who have varying degrees of preparation and comfort with their role. Having a seriously ill family member is stressful for surrogates. Moreover, most clinicians have had little training in working effectively with surrogates. OBJECTIVES To better understand the challenges of decision-making from the surrogate’s perspective. DESIGN Semistructured telephone interview study of the experience of surrogate decision-making. PARTICIPANTS Fifty designated surrogates with previous decision-making experience. APPROACH We asked surrogates to describe and reflect on their experience of making medical decisions for others. After coding transcripts, we conducted a content analysis to identify and categorize factors that made decision-making more or less difficult for surrogates. RESULTS Surrogates identified four types of factors: (1) surrogate characteristics and life circumstances (such as coping strategies and competing responsibilities), (2) surrogates’ social networks (such as intrafamily discord about the “right” decision), (3) surrogate–patient relationships and communication (such as difficulties with honoring known preferences), and (4) surrogate–clinician communication and relationship (such as interacting with a single physician whom the surrogate recognizes as the clinical spokesperson vs. many clinicians). CONCLUSIONS These data provide insights into the challenges that surrogates encounter when making decisions for loved ones and indicate areas where clinicians could intervene to facilitate the process of surrogate decision-making. Clinicians may want to include surrogates in advance care planning prior to decision-making, identify and address surrogate stressors during decision-making, and designate one person to communicate information about the patient’s condition, prognosis, and treatment options. PMID:17619223

  12. Predicting General Academic Performance and Identifying the Differential Contribution of Participating Variables Using Artificial Neural Networks

    ERIC Educational Resources Information Center

    Musso, Mariel F.; Kyndt, Eva; Cascallar, Eduardo C.; Dochy, Filip

    2013-01-01

    Many studies have explored the contribution of different factors from diverse theoretical perspectives to the explanation of academic performance. These factors have been identified as having important implications not only for the study of learning processes, but also as tools for improving curriculum designs, tutorial systems, and students'…

  13. Predicting the Unpredictable? Identifying High-Risk versus Low-Risk Parents with Intellectual Disabilities

    ERIC Educational Resources Information Center

    McGaw, Sue; Scully, Tamara; Pritchard, Colin

    2010-01-01

    Objectives: This study set out to identify risk factors affecting parents with intellectual disabilities (IDs) by determining: (i) whether perception of family support differs between parents with IDs, referring professionals, and a specialist parenting service; (ii) whether multivariate familial and demographic factors differentiates "high-risk"…

  14. Predicting School Readiness for Low-Income Children with Disability Risks Identified Early

    ERIC Educational Resources Information Center

    Jeon, Hyun-Joo; Peterson, Carla A.; Wall, Shavaun; Carta, Judith J.; Luze, Gayle; Eshbaugh, Elaine M.; Swanson, Mark

    2011-01-01

    This study examined school readiness at kindergarten entry for low-income children whose disability indicators were identified before age 3. Data were collected as part of the Early Head Start Research and Evaluation Longitudinal Follow-Up study. Children who had suspected developmental delays and did not receive Part C services had lower…

  15. Identifying the role of Wilms tumor 1 associated protein in cancer prediction using integrative genomic analyses.

    PubMed

    Wu, Li-Sheng; Qian, Jia-Yi; Wang, Minghai; Yang, Haiwei

    2016-09-01

    The Wilms tumor suppressor, WT1 was first identified due to its essential role in the normal development of the human genitourinary system. Wilms tumor 1 associated protein (WTAP) was subsequently revealed to interact with WT1 using yeast two-hybrid screening. The present study identified 44 complete WTAP genes in the genomes of vertebrates, including fish, amphibians, birds and mammals. The vertebrate WTAP proteins clustered into the primate, rodent and teleost lineages using phylogenetic tree analysis. From 1,347 available SNPs in the human WTAP gene, 19 were identified to cause missense mutations. WTAP was expressed in bladder, blood, brain, breast, colorectal, esophagus, eye, head and neck, lung, ovarian, prostate, skin and soft tissue cancers. A total of 17 out of 328 microarrays demonstrated an association between WTAP gene expression and cancer prognosis. However, the association between WTAP gene expression and prognosis varied in distinct types of cancer, and even in identical types of cancer from separate microarray databases. By searching the Catalogue of Somatic Mutations in Cancer database, 65 somatic mutations were identified in the human WTAP gene from the cancer tissue samples. These results suggest that the function of WTAP in tumor formation may be multidimensional. Furthermore, signal transducer and activator of transcription 1, forkhead box protein O1, interferon regulatory factor 1, glucocorticoid receptor and peroxisome proliferator-activated receptor γ transcription factor binding sites were identified in the upstream (promoter) region of the human WTAP gene, suggesting that these transcription factors may be involved in WTAP functions in tumor formation. PMID:27430156

  16. On the use of abiotic surrogates to describe marine benthic biodiversity

    NASA Astrophysics Data System (ADS)

    McArthur, M. A.; Brooke, B. P.; Przeslawski, R.; Ryan, D. A.; Lucieer, V. L.; Nichol, S.; McCallum, A. W.; Mellin, C.; Cresswell, I. D.; Radke, L. C.

    2010-06-01

    are not direct drivers of biodiversity patterns but often correspond with driving gradients and can be of some use in prediction. In such cases it would be better to identify what the spatial variable is acting as a proxy for so boundaries for that variable are not overlooked. The utility of these potential surrogates vary across spatial scales, quality of data, and management needs. A continued focus on surrogate research will address the need of marine scientists and resource managers worldwide for accurate and robust predictions, extending from simple measures of diversity to species distributions and patterns of assemblage.

  17. Prospective large-scale field study generates predictive model identifying major contributors to colony losses.

    PubMed

    Kielmanowicz, Merav Gleit; Inberg, Alex; Lerner, Inbar Maayan; Golani, Yael; Brown, Nicholas; Turner, Catherine Louise; Hayes, Gerald J R; Ballam, Joan M

    2015-04-01

    Over the last decade, unusually high losses of colonies have been reported by beekeepers across the USA. Multiple factors such as Varroa destructor, bee viruses, Nosema ceranae, weather, beekeeping practices, nutrition, and pesticides have been shown to contribute to colony losses. Here we describe a large-scale controlled trial, in which different bee pathogens, bee population, and weather conditions across winter were monitored at three locations across the USA. In order to minimize influence of various known contributing factors and their interaction, the hives in the study were not treated with antibiotics or miticides. Additionally, the hives were kept at one location and were not exposed to potential stress factors associated with migration. Our results show that a linear association between load of viruses (DWV or IAPV) in Varroa and bees is present at high Varroa infestation levels (>3 mites per 100 bees). The collection of comprehensive data allowed us to draw a predictive model of colony losses and to show that Varroa destructor, along with bee viruses, mainly DWV replication, contributes to approximately 70% of colony losses. This correlation further supports the claim that insufficient control of the virus-vectoring Varroa mite would result in increased hive loss. The predictive model also indicates that a single factor may not be sufficient to trigger colony losses, whereas a combination of stressors appears to impact hive health. PMID:25875764

  18. Prospective Large-Scale Field Study Generates Predictive Model Identifying Major Contributors to Colony Losses

    PubMed Central

    Kielmanowicz, Merav Gleit; Inberg, Alex; Lerner, Inbar Maayan; Golani, Yael; Brown, Nicholas; Turner, Catherine Louise; Hayes, Gerald J. R.; Ballam, Joan M.

    2015-01-01

    Over the last decade, unusually high losses of colonies have been reported by beekeepers across the USA. Multiple factors such as Varroa destructor, bee viruses, Nosema ceranae, weather, beekeeping practices, nutrition, and pesticides have been shown to contribute to colony losses. Here we describe a large-scale controlled trial, in which different bee pathogens, bee population, and weather conditions across winter were monitored at three locations across the USA. In order to minimize influence of various known contributing factors and their interaction, the hives in the study were not treated with antibiotics or miticides. Additionally, the hives were kept at one location and were not exposed to potential stress factors associated with migration. Our results show that a linear association between load of viruses (DWV or IAPV) in Varroa and bees is present at high Varroa infestation levels (>3 mites per 100 bees). The collection of comprehensive data allowed us to draw a predictive model of colony losses and to show that Varroa destructor, along with bee viruses, mainly DWV replication, contributes to approximately 70% of colony losses. This correlation further supports the claim that insufficient control of the virus-vectoring Varroa mite would result in increased hive loss. The predictive model also indicates that a single factor may not be sufficient to trigger colony losses, whereas a combination of stressors appears to impact hive health. PMID:25875764

  19. Surrogate motherhood: attachment, attitudes and social support.

    PubMed

    Fischer, S; Gillman, I

    1991-02-01

    In recent years, there has been a revival of interest in the procedure of using a surrogate mother to help infertile couples have a child. One of the issues brought into public awareness by the Baby M case, where the surrogate mother refused to give up the baby to the biological father, has been the nature of the attachment of the surrogate mother to the fetus. Thus far, research has not addressed this issue of attachment as well as it has considered other variables involved in the process of surrogacy. The current exploratory study focuses on differences between two groups of pregnant women - surrogate mothers and nonsurrogate mothers - in the degree and quality of attachment, attitudes toward pregnancy, and social support. An understanding of what pregnancy signifies for surrogate mothers is developed, based on objective measures and informal interviews with surrogate and nonsurrogate mothers. The implications of the various phenomena associated with surrogate motherhood are also considered. PMID:2023971

  20. Identifying Similar Patterns of Structural Flexibility in Proteins by Disorder Prediction and Dynamic Programming

    PubMed Central

    Petrovich, Aidan; Borne, Adam; Uversky, Vladimir N.; Xue, Bin

    2015-01-01

    Computational methods are prevailing in identifying protein intrinsic disorder. The results from predictors are often given as per-residue disorder scores. The scores describe the disorder propensity of amino acids of a protein and can be further represented as a disorder curve. Many proteins share similar patterns in their disorder curves. The similar patterns are often associated with similar functions and evolutionary origins. Therefore, finding and characterizing specific patterns of disorder curves provides a unique and attractive perspective of studying the function of intrinsically disordered proteins. In this study, we developed a new computational tool named IDalign using dynamic programming. This tool is able to identify similar patterns among disorder curves, as well as to present the distribution of intrinsic disorder in query proteins. The disorder-based information generated by IDalign is significantly different from the information retrieved from classical sequence alignments. This tool can also be used to infer functions of disordered regions and disordered proteins. The web server of IDalign is available at (http://labs.cas.usf.edu/bioinfo/service.html). PMID:26086829

  1. Applying psychological theory to evidence-based clinical practice: identifying factors predictive of taking intra-oral radiographs.

    PubMed

    Bonetti, Debbie; Pitts, Nigel B; Eccles, Martin; Grimshaw, Jeremy; Johnston, Marie; Steen, Nick; Glidewell, Liz; Thomas, Ruth; Maclennan, Graeme; Clarkson, Jan E; Walker, Anne

    2006-10-01

    enabled the creation of a methodology that can be replicated for identifying factors predictive of clinical behaviour and for the design and choice of interventions to modify practice as new evidence emerges. PMID:16843579

  2. Commentary: Surrogate Decisionmaking and Communication.

    PubMed

    Mukherjee, Debjani

    2016-07-01

    Mr. Hope's family's expectations and his staff's concerns raise important issues about surrogate decisionmaking, communication regarding prognosis, and staff angst. Unfortunately, Mr. Hope himself is unable to reliably understand and communicate his preferences, especially for complex medical decisions, so the ethics consultant is left to negotiate the disagreement between his family and his healthcare providers, who presumably both believe they are acting in his best interest. PMID:27348843

  3. Gene expression signature-based chemical genomic prediction identifies a novel class of HSP90 pathway modulators.

    PubMed

    Hieronymus, Haley; Lamb, Justin; Ross, Kenneth N; Peng, Xiao P; Clement, Cristina; Rodina, Anna; Nieto, Maria; Du, Jinyan; Stegmaier, Kimberly; Raj, Srilakshmi M; Maloney, Katherine N; Clardy, Jon; Hahn, William C; Chiosis, Gabriela; Golub, Todd R

    2006-10-01

    Although androgen receptor (AR)-mediated signaling is central to prostate cancer, the ability to modulate AR signaling states is limited. Here we establish a chemical genomic approach for discovery and target prediction of modulators of cancer phenotypes, as exemplified by AR signaling. We first identify AR activation inhibitors, including a group of structurally related compounds comprising celastrol, gedunin, and derivatives. To develop an in silico approach for target pathway identification, we apply a gene expression-based analysis that classifies HSP90 inhibitors as having similar activity to celastrol and gedunin. Validating this prediction, we demonstrate that celastrol and gedunin inhibit HSP90 activity and HSP90 clients, including AR. Broadly, this work identifies new modes of HSP90 modulation through a gene expression-based strategy. PMID:17010675

  4. Commentary: The Reluctant Surrogate.

    PubMed

    Foreman, Thomas

    2016-04-01

    An individual's hesitance or outright refusal to function as a substitute decisionmaker creates a number of challenges for treating teams, as is highlighted by the case of KS. It is not uncommon for individuals who suddenly find themselves in the role of substitute decisionmaker (SDM) to experience feelings of inadequacy or of being overwhelmed. The natural apprehension that comes with realizing, or being informed, that you are now responsible for providing or refusing consent on behalf of a loved one is often exacerbated by the accompanying circumstances. Even though there are movements afoot to encourage and support advance care planning and the inclusion of those who will become SDMs in conversations about values and wishes, there is still much work to be done. Although the case as presented does not provide information regarding what, if any, processes have taken place prior to the current hospital admission with regard to including the patient's sister in discussions about future circumstances, it is clear that the sister feels unprepared to assume the role being thrust upon her. What, then, does a clinical ethics consultation have to offer in such situations? The following discussion highlights three ways in which ethics consultation can be of value to both the treating team and the identified SDM: ethics consultation (1) helps the care team and SDM navigate the regulatory landscape, (2) supports the treating team, and (3) supports the SDM. PMID:26957458

  5. An empirical assessment and comparison of species-based and habitat-based surrogates: a case study of forest vertebrates and large old trees.

    PubMed

    Lindenmayer, David B; Barton, Philip S; Lane, Peter W; Westgate, Martin J; McBurney, Lachlan; Blair, David; Gibbons, Philip; Likens, Gene E

    2014-01-01

    A holy grail of conservation is to find simple but reliable measures of environmental change to guide management. For example, particular species or particular habitat attributes are often used as proxies for the abundance or diversity of a subset of other taxa. However, the efficacy of such kinds of species-based surrogates and habitat-based surrogates is rarely assessed, nor are different kinds of surrogates compared in terms of their relative effectiveness. We use 30-year datasets on arboreal marsupials and vegetation structure to quantify the effectiveness of: (1) the abundance of a particular species of arboreal marsupial as a species-based surrogate for other arboreal marsupial taxa, (2) hollow-bearing tree abundance as a habitat-based surrogate for arboreal marsupial abundance, and (3) a combination of species- and habitat-based surrogates. We also quantify the robustness of species-based and habitat-based surrogates over time. We then use the same approach to model overall species richness of arboreal marsupials. We show that a species-based surrogate can appear to be a valid surrogate until a habitat-based surrogate is co-examined, after which the effectiveness of the former is lost. The addition of a species-based surrogate to a habitat-based surrogate made little difference in explaining arboreal marsupial abundance, but altered the co-occurrence relationship between species. Hence, there was limited value in simultaneously using a combination of kinds of surrogates. The habitat-based surrogate also generally performed significantly better and was easier and less costly to gather than the species-based surrogate. We found that over 30 years of study, the relationships which underpinned the habitat-based surrogate generally remained positive but variable over time. Our work highlights why it is important to compare the effectiveness of different broad classes of surrogates and identify situations when either species- or habitat-based surrogates are likely

  6. An Empirical Assessment and Comparison of Species-Based and Habitat-Based Surrogates: A Case Study of Forest Vertebrates and Large Old Trees

    PubMed Central

    Lindenmayer, David B.; Barton, Philip S.; Lane, Peter W.; Westgate, Martin J.; McBurney, Lachlan; Blair, David; Gibbons, Philip; Likens, Gene E.

    2014-01-01

    A holy grail of conservation is to find simple but reliable measures of environmental change to guide management. For example, particular species or particular habitat attributes are often used as proxies for the abundance or diversity of a subset of other taxa. However, the efficacy of such kinds of species-based surrogates and habitat-based surrogates is rarely assessed, nor are different kinds of surrogates compared in terms of their relative effectiveness. We use 30-year datasets on arboreal marsupials and vegetation structure to quantify the effectiveness of: (1) the abundance of a particular species of arboreal marsupial as a species-based surrogate for other arboreal marsupial taxa, (2) hollow-bearing tree abundance as a habitat-based surrogate for arboreal marsupial abundance, and (3) a combination of species- and habitat-based surrogates. We also quantify the robustness of species-based and habitat-based surrogates over time. We then use the same approach to model overall species richness of arboreal marsupials. We show that a species-based surrogate can appear to be a valid surrogate until a habitat-based surrogate is co-examined, after which the effectiveness of the former is lost. The addition of a species-based surrogate to a habitat-based surrogate made little difference in explaining arboreal marsupial abundance, but altered the co-occurrence relationship between species. Hence, there was limited value in simultaneously using a combination of kinds of surrogates. The habitat-based surrogate also generally performed significantly better and was easier and less costly to gather than the species-based surrogate. We found that over 30 years of study, the relationships which underpinned the habitat-based surrogate generally remained positive but variable over time. Our work highlights why it is important to compare the effectiveness of different broad classes of surrogates and identify situations when either species- or habitat-based surrogates are likely

  7. Evaluating a surrogate endpoint at three levels, with application to vaccine development

    PubMed Central

    Gilbert, Peter B.; Qin, Li; Self, Steven G.

    2009-01-01

    SUMMARY Identification of an immune response to vaccination that reliably predicts protection from clinically significant infection, i.e. an immunological surrogate endpoint, is a primary goal of vaccine research. Using this problem of evaluating an immunological surrogate as an illustration, we describe a hierarchy of three criteria for a valid surrogate endpoint and statistical analysis frameworks for evaluating them. Based on a placebo-controlled vaccine efficacy trial, the first level entails assessing the correlation of an immune response with a study endpoint in the study groups, and the second level entails evaluating an immune response as a surrogate for the study endpoint that can be used for predicting vaccine efficacy for a setting similar to that of the vaccine trial. We show that baseline covariates, innovative study design, and a potential outcomes formulation can be helpful for this assessment. The third level entails validation of a surrogate endpoint via meta-analysis, where the goal is to evaluate how well the immune response can be used to predict vaccine efficacy for new settings (building bridges). A simulated vaccine trial and two example vaccine trials are presented, one supporting that certain anti-influenza antibody levels are an excellent surrogate for influenza illness and another supporting that certain anti-HIV antibody levels are not useful as a surrogate for HIV infection. PMID:17979212

  8. Functional connectivity classification of autism identifies highly predictive brain features but falls short of biomarker standards

    PubMed Central

    Plitt, Mark; Barnes, Kelly Anne; Martin, Alex

    2014-01-01

    Objectives Autism spectrum disorders (ASD) are diagnosed based on early-manifesting clinical symptoms, including markedly impaired social communication. We assessed the viability of resting-state functional MRI (rs-fMRI) connectivity measures as diagnostic biomarkers for ASD and investigated which connectivity features are predictive of a diagnosis. Methods Rs-fMRI scans from 59 high functioning males with ASD and 59 age- and IQ-matched typically developing (TD) males were used to build a series of machine learning classifiers. Classification features were obtained using 3 sets of brain regions. Another set of classifiers was built from participants' scores on behavioral metrics. An additional age and IQ-matched cohort of 178 individuals (89 ASD; 89 TD) from the Autism Brain Imaging Data Exchange (ABIDE) open-access dataset (http://fcon_1000.projects.nitrc.org/indi/abide/) were included for replication. Results High classification accuracy was achieved through several rs-fMRI methods (peak accuracy 76.67%). However, classification via behavioral measures consistently surpassed rs-fMRI classifiers (peak accuracy 95.19%). The class probability estimates, P(ASD|fMRI data), from brain-based classifiers significantly correlated with scores on a measure of social functioning, the Social Responsiveness Scale (SRS), as did the most informative features from 2 of the 3 sets of brain-based features. The most informative connections predominantly originated from regions strongly associated with social functioning. Conclusions While individuals can be classified as having ASD with statistically significant accuracy from their rs-fMRI scans alone, this method falls short of biomarker standards. Classification methods provided further evidence that ASD functional connectivity is characterized by dysfunction of large-scale functional networks, particularly those involved in social information processing. PMID:25685703

  9. Biomarkers and Surrogate Endpoints In Clinical Trials

    PubMed Central

    Fleming, Thomas R.; Powers, John H

    2012-01-01

    One of the most important considerations in designing clinical trials is the choice of outcome measures. These outcome measures could be clinically meaningful endpoints that are direct measures of how patients feel, function and survive. Alternatively, indirect measures, such as biomarkers that include physical signs of disease, laboratory measures and radiological tests, often are considered as replacement endpoints or “surrogates” for clinically meaningful endpoints. We discuss the definitions of clinically meaningful endpoints and surrogate endpoints, and provide examples from recent clinical trials. We provide insight into why indirect measures such as biomarkers may fail to provide reliable evidence about the benefit-to-risk profile of interventions. We also discuss the nature of evidence that is important in assessing whether treatment effects on a biomarker reliably predict effects on a clinically meaningful endpoint, and provide insights into why this reliability is specific to the context of use of the biomarker. . PMID:22711298

  10. Bedload-surrogate monitoring technologies

    USGS Publications Warehouse

    Gray, John R.; Laronne, Jonathan B.; Marr, Jeffrey D.G.

    2010-01-01

    Advances in technologies for quantifying bedload fluxes and in some cases bedload size distributions in rivers show promise toward supplanting traditional physical samplers and sampling methods predicated on the collection and analysis of physical bedload samples. Four workshops held from 2002 to 2007 directly or peripherally addressed bedload-surrogate technologies, and results from these workshops have been compiled to evaluate the state-of-the-art in bedload monitoring. Papers from the 2007 workshop are published for the first time with this report. Selected research and publications since the 2007 workshop also are presented. Traditional samplers used for some or all of the last eight decades include box or basket samplers, pan or tray samplers, pressure-difference samplers, and trough or pit samplers. Although still useful, the future niche of these devices may be as a means for calibrating bedload-surrogate technologies operating with active- and passive-type sensors, in many cases continuously and automatically at a river site. Active sensors include acoustic Doppler current profilers (ADCPs), sonar, radar, and smart sensors. Passive sensors include geophones (pipes or plates) in direct contact with the streambed, hydrophones deployed in the water column, impact columns, and magnetic detection. The ADCP for sand and geophones for gravel are currently the most developed techniques, several of which have been calibrated under both laboratory and field conditions. Although none of the bedload-surrogate technologies described herein are broadly accepted for use in large-scale monitoring programs, several are under evaluation. The benefits of verifying and operationally deploying selected bedload-surrogate monitoring technologies could be considerable, providing for more frequent and consistent, less expensive, and arguably more accurate bedload data obtained with reduced personal risk for use in managing the world's sedimentary resources. Twenty-six papers are

  11. Predicting Fish Growth Potential and Identifying Water Quality Constraints: A Spatially-Explicit Bioenergetics Approach

    NASA Astrophysics Data System (ADS)

    Budy, Phaedra; Baker, Matthew; Dahle, Samuel K.

    2011-10-01

    Anthropogenic impairment of water bodies represents a global environmental concern, yet few attempts have successfully linked fish performance to thermal habitat suitability and fewer have distinguished co-varying water quality constraints. We interfaced fish bioenergetics, field measurements, and Thermal Remote Imaging to generate a spatially-explicit, high-resolution surface of fish growth potential, and next employed a structured hypothesis to detect relationships among measures of fish performance and co-varying water quality constraints. Our thermal surface of fish performance captured the amount and spatial-temporal arrangement of thermally-suitable habitat for three focal species in an extremely heterogeneous reservoir, but interpretation of this pattern was initially confounded by seasonal covariation of water residence time and water quality. Subsequent path analysis revealed that in terms of seasonal patterns in growth potential, catfish and walleye responded to temperature, positively and negatively, respectively; crappie and walleye responded to eutrophy (negatively). At the high eutrophy levels observed in this system, some desired fishes appear to suffer from excessive cultural eutrophication within the context of elevated temperatures whereas others appear to be largely unaffected or even enhanced. Our overall findings do not lead to the conclusion that this system is degraded by pollution; however, they do highlight the need to use a sensitive focal species in the process of determining allowable nutrient loading and as integrators of habitat suitability across multiple spatial and temporal scales. We provide an integrated approach useful for quantifying fish growth potential and identifying water quality constraints on fish performance at spatial scales appropriate for whole-system management.

  12. Predictive Modelling to Identify Near-Shore, Fine-Scale Seabird Distributions during the Breeding Season.

    PubMed

    Warwick-Evans, Victoria C; Atkinson, Philip W; Robinson, Leonie A; Green, Jonathan A

    2016-01-01

    During the breeding season seabirds are constrained to coastal areas and are restricted in their movements, spending much of their time in near-shore waters either loafing or foraging. However, in using these areas they may be threatened by anthropogenic activities such as fishing, watersports and coastal developments including marine renewable energy installations. Although many studies describe large scale interactions between seabirds and the environment, the drivers behind near-shore, fine-scale distributions are not well understood. For example, Alderney is an important breeding ground for many species of seabird and has a diversity of human uses of the marine environment, thus providing an ideal location to investigate the near-shore fine-scale interactions between seabirds and the environment. We used vantage point observations of seabird distribution, collected during the 2013 breeding season in order to identify and quantify some of the environmental variables affecting the near-shore, fine-scale distribution of seabirds in Alderney's coastal waters. We validate the models with observation data collected in 2014 and show that water depth, distance to the intertidal zone, and distance to the nearest seabird nest are key predictors in the distribution of Alderney's seabirds. AUC values for each species suggest that these models perform well, although the model for shags performed better than those for auks and gulls. While further unexplained underlying localised variation in the environmental conditions will undoubtedly effect the fine-scale distribution of seabirds in near-shore waters we demonstrate the potential of this approach in marine planning and decision making. PMID:27031616

  13. Predictive Modelling to Identify Near-Shore, Fine-Scale Seabird Distributions during the Breeding Season

    PubMed Central

    Warwick-Evans, Victoria C.; Atkinson, Philip W.; Robinson, Leonie A.; Green, Jonathan A.

    2016-01-01

    During the breeding season seabirds are constrained to coastal areas and are restricted in their movements, spending much of their time in near-shore waters either loafing or foraging. However, in using these areas they may be threatened by anthropogenic activities such as fishing, watersports and coastal developments including marine renewable energy installations. Although many studies describe large scale interactions between seabirds and the environment, the drivers behind near-shore, fine-scale distributions are not well understood. For example, Alderney is an important breeding ground for many species of seabird and has a diversity of human uses of the marine environment, thus providing an ideal location to investigate the near-shore fine-scale interactions between seabirds and the environment. We used vantage point observations of seabird distribution, collected during the 2013 breeding season in order to identify and quantify some of the environmental variables affecting the near-shore, fine-scale distribution of seabirds in Alderney’s coastal waters. We validate the models with observation data collected in 2014 and show that water depth, distance to the intertidal zone, and distance to the nearest seabird nest are key predictors in the distribution of Alderney’s seabirds. AUC values for each species suggest that these models perform well, although the model for shags performed better than those for auks and gulls. While further unexplained underlying localised variation in the environmental conditions will undoubtedly effect the fine-scale distribution of seabirds in near-shore waters we demonstrate the potential of this approach in marine planning and decision making. PMID:27031616

  14. Realtime Prediction in Disturbed Landscapes: Identifying Highest Priority Disturbance Characteristics Impacting Streamflow Response in a CONUS-Scale Operational Model

    NASA Astrophysics Data System (ADS)

    Dugger, A. L.; Gochis, D. J.; Yu, W.; McCreight, J. L.; Barlage, M. J.

    2015-12-01

    The "next generation" of hydrologic prediction systems - targeting unified, process-based, real-time prediction of the total water cycle - bring with them an increased need for real-time land surface characterization. Climatologically-derived estimates may perform well under stationary conditions, however disturbance can significantly alter hydrologic behavior and may be poorly represented by mean historical conditions. Fortunately, remote sensing and on-the-ground observation networks are collecting snapshots of these land characteristics over an increasing fraction of the globe. Given the computing constraints of operating a large-domain, real-time prediction system, to take advantage of these data streams we need a way to prioritize which landscape characteristics are most important to hydrologic prediction post-disturbance. To address this need, we setup a model experiment over the contiguous US using the community WRF-Hydro system with the NoahMP land surface model to assess the value of incorporating various aspects of disturbed landscapes into a real-time streamflow prediction model. WRF-Hydro will serve as the initial operational model for the US National Weather Service's new national water prediction effort, so use of WRF-Hydro allows us to leverage both an existing CONUS-scale model implementation and a short research-to-operations path. We first identify USGS GAGES-II basins that experienced more than 25% forest loss between 2000 and 2013. Based on basin disturbance type, geophysical setting, and climate regime, we formulate a conceptual model of which "disturbed" landscape characteristics we expect to dominate streamflow response. We test our conceptual model using WRF-Hydro by modeling a baseline (no disturbance) case, and then bringing in empirically-derived model state shifts representing key disturbance characteristics (e.g., leaf area index, rooting depth, overland roughness, surface detention). For each state update and each basin, we quantify

  15. Predicting microRNA modulation in human prostate cancer using a simple String IDentifier (SID1.0).

    PubMed

    Albertini, Maria C; Olivieri, Fabiola; Lazzarini, Raffaella; Pilolli, Francesca; Galli, Francesco; Spada, Giorgio; Accorsi, Augusto; Rippo, Maria R; Procopio, Antonio D

    2011-08-01

    To make faster and efficient the identification of mRNA targets common to more than one miRNA, and to identify new miRNAs modulated in specific pathways, a computer program identified as SID1.0 (simple String IDentifier) was developed and successfully applied in the identification of deregulated miRNAs in prostate cancer cells. This computationally inexpensive Fortran program is based on the strategy of exhaustive search and specifically designed to screen shared data (target genes, miRNAs and pathways) available from PicTar and DIANA-MicroT 3.0 databases. As far as we know this is the first software designed to filter data retrieved from available miRNA databases. SID1.0 takes advantage of the standard Fortran intrinsic functions for manipulating text strings and requires ASCII input files. In order to demonstrate SID1.0 applicability, some miRNAs expected from the literature to associate with cancerogenesis (miR-125b, miR-148a and miR-141), were randomly identified as main entries for SID1.0 to explore matching sequences of mRNA targets and also to explore KEGG pathways for the presence of ID codes of targeted genes. Besides genes and pathways already described in the literature, SID1.0 has proven to useful for predicting other genes involved in prostate carcinoma. These latter were used to identify new deregulated miRNAs: miR-141, miR-148a, miR-19a and miR-19b. Prediction data were preliminary confirmed by expression analysis of the identified miRNAs in androgen-dependent (LNCaP) and independent (PC3) prostate carcinoma cell lines and in normal prostatic epithelial cells (PrEC). PMID:21334455

  16. Mechanical Properties of K Basin Sludge Constituents and Their Surrogates

    SciTech Connect

    Delegard, Calvin H.; Schmidt, Andrew J.; Chenault, Jeffrey W.

    2004-12-06

    A survey of the technical literature was performed to summarize the mechanical properties of inorganic components in K Basins sludge. The components included gibbsite, ferrihydrite, lepidocrocite and goethite, hematite, quartz, anorthite, calcite, basalt, Zircaloy, aluminum, and, in particular, irradiated uranium metal and uranium dioxide. Review of the technical literature showed that information on the hardness of uranium metal at irradiation exposures similar to those experienced by the N Reactor fuel present in the K Basins (typically up to 3000 MWd/t) were not available. Measurements therefore were performed to determine the hardness of coupons taken from three irradiated N Reactor uranium metal fuel elements taken from K Basins. Hardness values averaged 30 {+-} 8 Rockwell C units, similar to values previously reported for uranium irradiated to {approx}1200 MWd/t. The physical properties of candidate uranium metal and uranium dioxide surrogates were gathered and compared. Surrogates having properties closest to those of irradiated uranium metal appear to be alloys of tungsten. The surrogate for uranium dioxide, present both as particles and agglomerates in actual K Basin sludge, likely requires two materials. Cerium oxide, CeO2, was identified as a surrogate of the smaller UO2 particles while steel grit was identified for the UO2 agglomerates.

  17. Health information seeking on behalf of others: Characteristics of ‘surrogate seekers’

    PubMed Central

    Cutrona, Sarah L.; Mazor, Kathleen M.; Vieux, Sana N.; Luger, Tana M.; Volkman, Julie E.; Finney Rutten, Lila J.

    2014-01-01

    Understanding the behaviors of surrogate-seekers (those who seek health information for others) may guide efforts to improve health information transmission. We used 2011–2012 data from the Health Information National Trends Survey to describe behaviors of online surrogate-seekers. Respondents were asked about use of the Internet for surrogate-seeking over the prior 12 months. Data were weighted to calculate population estimates. Two-thirds (66.6%) reported surrogate-seeking. Compared to those who sought health information online for only themselves, surrogate-seekers were more likely to live in households with others (weighted percent 89.4% vs. 82.5% of self-seekers; p < 0.05); no significant differences in sex, race, income or education were observed. Surrogate-seekers were more likely to report activities requiring user-generated content: email communication with healthcare providers; visits to social networking sites to read and share about medical topics and participation in online health support groups. On multivariate analysis, those who had looked online for healthcare providers were more likely to be surrogate-seekers (OR 1.67, 95% CI 1.08–2.59). In addition to seeking health information, surrogate-seekers create and pass along communications that may influence medical care decisions. Research is needed to identify ways to facilitate transmission of accurate health information. PMID:24989816

  18. Many amino acid substitution variants identified in DNA repair genes during human population screenings are predicted to impact protein function

    SciTech Connect

    Xi, T; Jones, I M; Mohrenweiser, H W

    2003-11-03

    Over 520 different amino acid substitution variants have been previously identified in the systematic screening of 91 human DNA repair genes for sequence variation. Two algorithms were employed to predict the impact of these amino acid substitutions on protein activity. Sorting Intolerant From Tolerant (SIFT) classified 226 of 508 variants (44%) as ''Intolerant''. Polymorphism Phenotyping (PolyPhen) classed 165 of 489 amino acid substitutions (34%) as ''Probably or Possibly Damaging''. Another 9-15% of the variants were classed as ''Potentially Intolerant or Damaging''. The results from the two algorithms are highly associated, with concordance in predicted impact observed for {approx}62% of the variants. Twenty one to thirty one percent of the variant proteins are predicted to exhibit reduced activity by both algorithms. These variants occur at slightly lower individual allele frequency than do the variants classified as ''Tolerant'' or ''Benign''. Both algorithms correctly predicted the impact of 26 functionally characterized amino acid substitutions in the APE1 protein on biochemical activity, with one exception. It is concluded that a substantial fraction of the missense variants observed in the general human population are functionally relevant. These variants are expected to be the molecular genetic and biochemical basis for the associations of reduced DNA repair capacity phenotypes with elevated cancer risk.

  19. Surrogate Reactions in the Actinide Region

    SciTech Connect

    Burke, J T; Bernstein, L A; Scielzo, N D; Bleuel, D L; Lesher, S R; Escher, J; Ahle, L; Dietrich, F S; Hoffman, R D; Norman, E B; Sheets, S A; Phair, L; Fallon, P; Clark, R M; Gibelin, J; Jewett, C; Lee, I Y; Macchiavelli, A O; McMahan, M A; Moretto, L G; Rodriguez-Vieitez, E; Wiedeking, M; Lyles, B F; Beausang, C W; Allmond, J M; Ai, H; Cizewski, J A; Hatarik, R; O'Malley, P D; Swan, T

    2008-01-30

    Over the past three years we have studied various surrogate reactions (d,p), ({sup 3}He,t), ({alpha},{alpha}{prime}) on several uranium isotopes {sup 234}U, {sup 235}U, {sup 236}U, and {sup 238}U. An overview of the STARS/LIBERACE surrogate research program as it pertains to the actinides is discussed. A summary of results to date will be presented along with a discussion of experimental difficulties encountered in surrogate experiments and future research directions.

  20. A Network-Based Model of Oncogenic Collaboration for Prediction of Drug Sensitivity

    PubMed Central

    Laderas, Ted G.; Heiser, Laura M.; Sönmez, Kemal

    2015-01-01

    Tumorigenesis is a multi-step process, involving the acquisition of multiple oncogenic mutations that transform cells, resulting in systemic dysregulation that enables proliferation, invasion, and other cancer hallmarks. The goal of precision medicine is to identify therapeutically-actionable mutations from large-scale omic datasets. However, the multiplicity of oncogenes required for transformation, known as oncogenic collaboration, makes assigning effective treatments difficult. Motivated by this observation, we propose a new type of oncogenic collaboration where mutations in genes that interact with an oncogene may contribute to the oncogene’s deleterious potential, a new genomic feature that we term “surrogate oncogenes.” Surrogate oncogenes are representatives of these mutated subnetworks that interact with oncogenes. By mapping mutations to a protein–protein interaction network, we determine the significance of the observed distribution using permutation-based methods. For a panel of 38 breast cancer cell lines, we identified a significant number of surrogate oncogenes in known oncogenes such as BRCA1 and ESR1, lending credence to this approach. In addition, using Random Forest Classifiers, we show that these significant surrogate oncogenes predict drug sensitivity for 74 drugs in the breast cancer cell lines with a mean error rate of 30.9%. Additionally, we show that surrogate oncogenes are predictive of survival in patients. The surrogate oncogene framework incorporates unique or rare mutations from a single sample, and therefore has the potential to integrate patient-unique mutations into drug sensitivity predictions, suggesting a new direction in precision medicine and drug development. Additionally, we show the prevalence of significant surrogate oncogenes in multiple cancers from The Cancer Genome Atlas, suggesting that surrogate oncogenes may be a useful genomic feature for guiding pancancer analyses and assigning therapies across many tissue

  1. SVM-based prediction of propeptide cleavage sites in spider toxins identifies toxin innovation in an Australian tarantula.

    PubMed

    Wong, Emily S W; Hardy, Margaret C; Wood, David; Bailey, Timothy; King, Glenn F

    2013-01-01

    Spider neurotoxins are commonly used as pharmacological tools and are a popular source of novel compounds with therapeutic and agrochemical potential. Since venom peptides are inherently toxic, the host spider must employ strategies to avoid adverse effects prior to venom use. It is partly for this reason that most spider toxins encode a protective proregion that upon enzymatic cleavage is excised from the mature peptide. In order to identify the mature toxin sequence directly from toxin transcripts, without resorting to protein sequencing, the propeptide cleavage site in the toxin precursor must be predicted bioinformatically. We evaluated different machine learning strategies (support vector machines, hidden Markov model and decision tree) and developed an algorithm (SpiderP) for prediction of propeptide cleavage sites in spider toxins. Our strategy uses a support vector machine (SVM) framework that combines both local and global sequence information. Our method is superior or comparable to current tools for prediction of propeptide sequences in spider toxins. Evaluation of the SVM method on an independent test set of known toxin sequences yielded 96% sensitivity and 100% specificity. Furthermore, we sequenced five novel peptides (not used to train the final predictor) from the venom of the Australian tarantula Selenotypus plumipes to test the accuracy of the predictor and found 80% sensitivity and 99.6% 8-mer specificity. Finally, we used the predictor together with homology information to predict and characterize seven groups of novel toxins from the deeply sequenced venom gland transcriptome of S. plumipes, which revealed structural complexity and innovations in the evolution of the toxins. The precursor prediction tool (SpiderP) is freely available on ArachnoServer (http://www.arachnoserver.org/spiderP.html), a web portal to a comprehensive relational database of spider toxins. All training data, test data, and scripts used are available from the Spider

  2. SVM-Based Prediction of Propeptide Cleavage Sites in Spider Toxins Identifies Toxin Innovation in an Australian Tarantula

    PubMed Central

    Wong, Emily S. W.; Hardy, Margaret C.; Wood, David; Bailey, Timothy; King, Glenn F.

    2013-01-01

    Spider neurotoxins are commonly used as pharmacological tools and are a popular source of novel compounds with therapeutic and agrochemical potential. Since venom peptides are inherently toxic, the host spider must employ strategies to avoid adverse effects prior to venom use. It is partly for this reason that most spider toxins encode a protective proregion that upon enzymatic cleavage is excised from the mature peptide. In order to identify the mature toxin sequence directly from toxin transcripts, without resorting to protein sequencing, the propeptide cleavage site in the toxin precursor must be predicted bioinformatically. We evaluated different machine learning strategies (support vector machines, hidden Markov model and decision tree) and developed an algorithm (SpiderP) for prediction of propeptide cleavage sites in spider toxins. Our strategy uses a support vector machine (SVM) framework that combines both local and global sequence information. Our method is superior or comparable to current tools for prediction of propeptide sequences in spider toxins. Evaluation of the SVM method on an independent test set of known toxin sequences yielded 96% sensitivity and 100% specificity. Furthermore, we sequenced five novel peptides (not used to train the final predictor) from the venom of the Australian tarantula Selenotypus plumipes to test the accuracy of the predictor and found 80% sensitivity and 99.6% 8-mer specificity. Finally, we used the predictor together with homology information to predict and characterize seven groups of novel toxins from the deeply sequenced venom gland transcriptome of S. plumipes, which revealed structural complexity and innovations in the evolution of the toxins. The precursor prediction tool (SpiderP) is freely available on ArachnoServer (http://www.arachnoserver.org/spiderP.html), a web portal to a comprehensive relational database of spider toxins. All training data, test data, and scripts used are available from the Spider

  3. The diaphragm as an anatomic surrogate for lung tumor motion

    NASA Astrophysics Data System (ADS)

    Cerviño, Laura I.; Chao, Alvin K. Y.; Sandhu, Ajay; Jiang, Steve B.

    2009-06-01

    Lung tumor motion due to respiration poses a challenge in the application of modern three-dimensional conformal radiotherapy. Direct tracking of the lung tumor during radiation therapy is very difficult without implanted fiducial markers. Indirect tracking relies on the correlation of the tumor's motion and the surrogate's motion. The present paper presents an analysis of the correlation between tumor motion and diaphragm motion in order to evaluate the potential use of diaphragm as a surrogate for tumor motion. We have analyzed the correlation between diaphragm motion and superior-inferior lung tumor motion in 32 fluoroscopic image sequences from ten lung cancer patients. A simple linear model and a more complex linear model that accounts for phase delays between the two motions have been used. Results show that the diaphragm is a good surrogate for tumor motion prediction for most patients, resulting in an average correlation factor of 0.94 and 0.98 with each model respectively. The model that accounts for delays leads to an average localization prediction error of 0.8 mm and an error at the 95% confidence level of 2.1 mm. However, for one patient studied, the correlation is much weaker compared to other patients. This indicates that, before using diaphragm for lung tumor prediction, the correlation should be examined on a patient-by-patient basis.

  4. Prognostic breast cancer signature identified from 3D culture model accurately predicts clinical outcome across independent datasets

    SciTech Connect

    Martin, Katherine J.; Patrick, Denis R.; Bissell, Mina J.; Fournier, Marcia V.

    2008-10-20

    One of the major tenets in breast cancer research is that early detection is vital for patient survival by increasing treatment options. To that end, we have previously used a novel unsupervised approach to identify a set of genes whose expression predicts prognosis of breast cancer patients. The predictive genes were selected in a well-defined three dimensional (3D) cell culture model of non-malignant human mammary epithelial cell morphogenesis as down-regulated during breast epithelial cell acinar formation and cell cycle arrest. Here we examine the ability of this gene signature (3D-signature) to predict prognosis in three independent breast cancer microarray datasets having 295, 286, and 118 samples, respectively. Our results show that the 3D-signature accurately predicts prognosis in three unrelated patient datasets. At 10 years, the probability of positive outcome was 52, 51, and 47 percent in the group with a poor-prognosis signature and 91, 75, and 71 percent in the group with a good-prognosis signature for the three datasets, respectively (Kaplan-Meier survival analysis, p<0.05). Hazard ratios for poor outcome were 5.5 (95% CI 3.0 to 12.2, p<0.0001), 2.4 (95% CI 1.6 to 3.6, p<0.0001) and 1.9 (95% CI 1.1 to 3.2, p = 0.016) and remained significant for the two larger datasets when corrected for estrogen receptor (ER) status. Hence the 3D-signature accurately predicts breast cancer outcome in both ER-positive and ER-negative tumors, though individual genes differed in their prognostic ability in the two subtypes. Genes that were prognostic in ER+ patients are AURKA, CEP55, RRM2, EPHA2, FGFBP1, and VRK1, while genes prognostic in ER patients include ACTB, FOXM1 and SERPINE2 (Kaplan-Meier p<0.05). Multivariable Cox regression analysis in the largest dataset showed that the 3D-signature was a strong independent factor in predicting breast cancer outcome. The 3D-signature accurately predicts breast cancer outcome across multiple datasets and holds prognostic

  5. Heat–Health Warning Systems: A Comparison of the Predictive Capacity of Different Approaches to Identifying Dangerously Hot Days

    PubMed Central

    Sheridan, Scott C.; Allen, Michael J.; Pascal, Mathilde; Laaidi, Karine; Yagouti, Abderrahmane; Bickis, Ugis; Tobias, Aurelio; Bourque, Denis; Armstrong, Ben G.; Kosatsky, Tom

    2010-01-01

    Objectives. We compared the ability of several heat–health warning systems to predict days of heat-associated mortality using common data sets. Methods. Heat–health warning systems initiate emergency public health interventions once forecasts have identified weather conditions to breach predetermined trigger levels. We examined 4 commonly used trigger-setting approaches: (1) synoptic classification, (2) epidemiologic assessment of the temperature–mortality relationship, (3) temperature–humidity index, and (4) physiologic classification. We applied each approach in Chicago, Illinois; London, United Kingdom; Madrid, Spain; and Montreal, Canada, to identify days expected to be associated with the highest heat-related mortality. Results. We found little agreement across the approaches in which days were identified as most dangerous. In general, days identified by temperature–mortality assessment were associated with the highest excess mortality. Conclusions. Triggering of alert days and ultimately the initiation of emergency responses by a heat–health warning system varies significantly across approaches adopted to establish triggers. PMID:20395585

  6. A surrogate-based uncertainty quantification with quantifiable errors

    SciTech Connect

    Bang, Y.; Abdel-Khalik, H. S.

    2012-07-01

    Surrogate models are often employed to reduce the computational cost required to complete uncertainty quantification, where one is interested in propagating input parameters uncertainties throughout a complex engineering model to estimate responses uncertainties. An improved surrogate construction approach is introduced here which places a premium on reducing the associated computational cost. Unlike existing methods where the surrogate is constructed first, then employed to propagate uncertainties, the new approach combines both sensitivity and uncertainty information to render further reduction in the computational cost. Mathematically, the reduction is described by a range finding algorithm that identifies a subspace in the parameters space, whereby parameters uncertainties orthogonal to the subspace contribute negligible amount to the propagated uncertainties. Moreover, the error resulting from the reduction can be upper-bounded. The new approach is demonstrated using a realistic nuclear assembly model and compared to existing methods in terms of computational cost and accuracy of uncertainties. Although we believe the algorithm is general, it will be applied here for linear-based surrogates and Gaussian parameters uncertainties. The generalization to nonlinear models will be detailed in a separate article. (authors)

  7. Evaluation of bone surrogates for indirect and direct ballistic fractures.

    PubMed

    Bir, Cynthia; Andrecovich, Chris; DeMaio, Marlene; Dougherty, Paul J

    2016-04-01

    The mechanism of injury for fractures to long bones has been studied for both direct ballistic loading as well as indirect. However, the majority of these studies have been conducted on both post-mortem human subjects (PMHS) and animal surrogates which have constraints in terms of storage, preparation and testing. The identification of a validated bone surrogate for use in forensic, medical and engineering testing would provide the ability to investigate ballistic loading without these constraints. Two specific bone surrogates, Sawbones and Synbone, were evaluated in comparison to PMHS for both direct and indirect ballistic loading. For the direct loading, the mean velocity to produce fracture was 121 ± 19 m/s for the PMHS, which was statistically different from the Sawbones (140 ± 7 m/s) and Synbone (146 ± 3 m/s). The average distance to fracture in the indirect loading was .70 cm for the PMHS. The Synbone had a statistically similar average distance to fracture (.61 cm, p=0.54) however the Sawbones average distance to fracture was statistically different (.41 cm, p<0.05). Fractures patterns were found to be comparable to the PMHS for tests conducted with Synbones, however the input parameters were slightly varied to produce similar results. The fractures patterns with the Sawbones were not found to be as comparable to the PMHS. An ideal bone surrogate for ballistic testing was not identified and future work is warranted. PMID:26867672

  8. Cell-Line Selectivity Improves the Predictive Power of Pharmacogenomic Analyses and Helps Identify NADPH as Biomarker for Ferroptosis Sensitivity.

    PubMed

    Shimada, Kenichi; Hayano, Miki; Pagano, Nen C; Stockwell, Brent R

    2016-02-18

    Precision medicine in oncology requires not only identification of cancer-associated mutations but also effective drugs for each cancer genotype, which is still a largely unsolved problem. One approach for the latter challenge has been large-scale testing of small molecules in genetically characterized cell lines. We hypothesized that compounds with high cell-line-selective lethality exhibited consistent results across such pharmacogenomic studies. We analyzed the compound sensitivity data of 6,259 lethal compounds from the NCI-60 project. A total of 2,565 cell-line-selective lethal compounds were identified and grouped into 18 clusters based on their median growth inhibitory GI50 profiles across the 60 cell lines, which were shown to represent distinct mechanisms of action. Further transcriptome analysis revealed a biomarker, NADPH abundance, for predicting sensitivity to ferroptosis-inducing compounds, which we experimentally validated. In summary, incorporating cell-line-selectivity filters improves the predictive power of pharmacogenomic analyses and enables discovery of biomarkers that predict the sensitivity of cells to specific cell death inducers. PMID:26853626

  9. Comparing human norovirus surrogates: murine norovirus and Tulane virus.

    PubMed

    Hirneisen, Kirsten A; Kniel, Kalmia E

    2013-01-01

    Viral surrogates are widely used by researchers to predict human norovirus behavior. Murine norovirus (MNV) is currently accepted as the best surrogate and is assumed to mimic the survival and inactivation of human noroviruses. Recently, a new calicivirus, the Tulane virus (TV), was discovered, and its potential as a human norovirus surrogate is being explored. This study aimed to compare the behavior of the two potential surrogates under varying treatments of pH (2.0 to 10.0), chlorine (0.2 to 2,000 ppm), heat (50 to 75°C), and survival in tap water at room (20°C) and refrigeration (4°C) temperatures for up to 30 days. Viral infectivity was determined by the plaque assay for both MNV and TV. There was no significant difference between the inactivation of MNV and TV in all heat treatments, and for both MNV and TV survival in tap water at 20°C over 30 days. At 4°C, MNV remained infectious over 30 days at a titer of approximately 5 log PFU/ml, whereas TV titers decreased significantly by 5 days. MNV was more pH stable, as TV titers were reduced significantly at pH 2.0, 9.0, and 10.0, as compared with pH 7.0, whereas MNV titers were only significantly reduced at pH 10.0. After chlorine treatment, there was no significant difference in virus with the exception of at 2 ppm, where TV decreased significantly compared with MNV. Compared with TV, MNV is likely a better surrogate for human noroviruses, as MNV persisted over a wider range of pH values, at 2 ppm of chlorine, and without a loss of titer at 4°C. PMID:23317870

  10. Advance Care Planning Beyond Advance Directives: Perspectives from Patients and Surrogates

    PubMed Central

    McMahan, Ryan; Knight, Sara J.; Fried, Terri R.; Sudore, Rebecca L.

    2014-01-01

    Context Advance care planning (ACP) has focused on documenting life-sustaining treatment preferences in advance directives (ADs). ADs alone may be insufficient to prepare diverse patients and surrogates for complex medical decisions. Objectives To understand what steps best prepare patients and surrogates for decision making. Methods We conducted 13 English/Spanish focus groups with participants from a Veterans Affairs and county hospital and the community. Seven groups included patients (n=38) aged ≥65 years, who reported making serious medical decisions. Six separate groups included surrogates (n=31), aged ≥18 years, who made decisions for others. Semi-structured focus groups asked what activities best prepared participants for decision making. Two investigators independently coded data and performed thematic content analysis. Disputes were resolved by consensus. Results Mean±SD patient age was 78±8 years and 61% were non-white. Mean±SD surrogate age was 57±10 years and 91% were non-white. Qualitative analysis identified four overarching themes about how to best prepare for decision making: 1) identify values based on past experiences and quality of life, 2) choose surrogates wisely and verify they understand their role, 3) decide whether to grant leeway in surrogate decision making, and 4) inform other family and friends of one's wishes to prevent conflict. Conclusion Beyond ADs, patients and surrogates recommend several additional steps to prepare for medical decision making including using past experiences to identify values, verifying the surrogate understands their role, deciding whether to grant surrogates leeway, and informing other family and friends of one's wishes. Future ACP interventions should consider incorporating these additional ACP activities. PMID:23200188

  11. RNAseq versus genome-predicted transcriptomes: a large population of novel transcripts identified in an Illumina-454 Hydra transcriptome

    PubMed Central

    2013-01-01

    Background Evolutionary studies benefit from deep sequencing technologies that generate genomic and transcriptomic sequences from a variety of organisms. Genome sequencing and RNAseq have complementary strengths. In this study, we present the assembly of the most complete Hydra transcriptome to date along with a comparative analysis of the specific features of RNAseq and genome-predicted transcriptomes currently available in the freshwater hydrozoan Hydra vulgaris. Results To produce an accurate and extensive Hydra transcriptome, we combined Illumina and 454 Titanium reads, giving the primacy to Illumina over 454 reads to correct homopolymer errors. This strategy yielded an RNAseq transcriptome that contains 48’909 unique sequences including splice variants, representing approximately 24’450 distinct genes. Comparative analysis to the available genome-predicted transcriptomes identified 10’597 novel Hydra transcripts that encode 529 evolutionarily-conserved proteins. The annotation of 170 human orthologs points to critical functions in protein biosynthesis, FGF and TOR signaling, vesicle transport, immunity, cell cycle regulation, cell death, mitochondrial metabolism, transcription and chromatin regulation. However, a majority of these novel transcripts encodes short ORFs, at least 767 of them corresponding to pseudogenes. This RNAseq transcriptome also lacks 11’270 predicted transcripts that correspond either to silent genes or to genes expressed below the detection level of this study. Conclusions We established a simple and powerful strategy to combine Illumina and 454 reads and we produced, with genome assistance, an extensive and accurate Hydra transcriptome. The comparative analysis of the RNAseq transcriptome with genome-predicted transcriptomes lead to the identification of large populations of novel as well as missing transcripts that might reflect Hydra-specific evolutionary events. PMID:23530871

  12. The Impact of Climate Change on Indigenous Arabica Coffee (Coffea arabica): Predicting Future Trends and Identifying Priorities

    PubMed Central

    Gole, Tadesse Woldemariam; Baena, Susana

    2012-01-01

    Precise modelling of the influence of climate change on Arabica coffee is limited; there are no data available for indigenous populations of this species. In this study we model the present and future predicted distribution of indigenous Arabica, and identify priorities in order to facilitate appropriate decision making for conservation, monitoring and future research. Using distribution data we perform bioclimatic modelling and examine future distribution with the HadCM3 climate model for three emission scenarios (A1B, A2A, B2A) over three time intervals (2020, 2050, 2080). The models show a profoundly negative influence on indigenous Arabica. In a locality analysis the most favourable outcome is a c. 65% reduction in the number of pre-existing bioclimatically suitable localities, and at worst an almost 100% reduction, by 2080. In an area analysis the most favourable outcome is a 38% reduction in suitable bioclimatic space, and the least favourable a c. 90% reduction, by 2080. Based on known occurrences and ecological tolerances of Arabica, bioclimatic unsuitability would place populations in peril, leading to severe stress and a high risk of extinction. This study establishes a fundamental baseline for assessing the consequences of climate change on wild populations of Arabica coffee. Specifically, it: (1) identifies and categorizes localities and areas that are predicted to be under threat from climate change now and in the short- to medium-term (2020–2050), representing assessment priorities for ex situ conservation; (2) identifies ‘core localities’ that could have the potential to withstand climate change until at least 2080, and therefore serve as long-term in situ storehouses for coffee genetic resources; (3) provides the location and characterization of target locations (populations) for on-the-ground monitoring of climate change influence. Arabica coffee is confimed as a climate sensitivite species, supporting data and inference that existing

  13. The Reparative Motive in Surrogate Mothers.

    ERIC Educational Resources Information Center

    Kanefield, Linda

    1999-01-01

    Explores the motivations of surrogate mothers, focusing on underlying reparative motive--to compensate for or repair an earlier loss or sense of damage. Provides an overview of the typical surrogate's characteristics and personality, discusses the theoretical underpinnings of the reparative motive, and considers the tension between reparation and…

  14. Applying psychological theories to evidence-based clinical practice: Identifying factors predictive of managing upper respiratory tract infections without antibiotics

    PubMed Central

    Eccles, Martin P; Grimshaw, Jeremy M; Johnston, Marie; Steen, Nick; Pitts, Nigel B; Thomas, Ruth; Glidewell, Elizabeth; Maclennan, Graeme; Bonetti, Debbie; Walker, Anne

    2007-01-01

    use of antibiotics made significantly fewer scenario-based decisions to prescribe. In the cross theory analysis, perceived behavioural control (TPB), evidence of habitual behaviour (OLT), CS-SRM cause (chance/bad luck), and intention entered the equation, together explaining 36% of the variance. When predicting intention, at the theory level, the proportion of variance explained was: TPB, 30%; SCT, 29%; CS-SRM 27%; OLT, 43%. GPs who reported that they had already decided to change their management to try to avoid the use of antibiotics had a significantly higher intention to manage URTIs without prescribing antibiotics. In the cross theory analysis, OLT evidence of habitual behaviour, TPB attitudes, risk perception, CS-SRM control by doctor, TPB perceived behavioural control and CS-SRM control by treatment entered the equation, together explaining 49% of the variance in intention. Conclusion The study provides evidence that psychological models can be useful in understanding and predicting clinical behaviour. Taking a theory-based approach enables the creation of a replicable methodology for identifying factors that predict clinical behaviour. However, a number of conceptual and methodological challenges remain. PMID:17683558

  15. Identifying Individuals at High Risk of Psychosis: Predictive Utility of Support Vector Machine using Structural and Functional MRI Data.

    PubMed

    Valli, Isabel; Marquand, Andre F; Mechelli, Andrea; Raffin, Marie; Allen, Paul; Seal, Marc L; McGuire, Philip

    2016-01-01

    The identification of individuals at high risk of developing psychosis is entirely based on clinical assessment, associated with limited predictive potential. There is, therefore, increasing interest in the development of biological markers that could be used in clinical practice for this purpose. We studied 25 individuals with an at-risk mental state for psychosis and 25 healthy controls using structural MRI, and functional MRI in conjunction with a verbal memory task. Data were analyzed using a standard univariate analysis, and with support vector machine (SVM), a multivariate pattern recognition technique that enables statistical inferences to be made at the level of the individual, yielding results with high translational potential. The application of SVM to structural MRI data permitted the identification of individuals at high risk of psychosis with a sensitivity of 68% and a specificity of 76%, resulting in an accuracy of 72% (p < 0.001). Univariate volumetric between-group differences did not reach statistical significance. By contrast, the univariate fMRI analysis identified between-group differences (p < 0.05 corrected), while the application of SVM to the same data did not. Since SVM is well suited at identifying the pattern of abnormality that distinguishes two groups, whereas univariate methods are more likely to identify regions that individually are most different between two groups, our results suggest the presence of focal functional abnormalities in the context of a diffuse pattern of structural abnormalities in individuals at high clinical risk of psychosis. PMID:27092086

  16. Identifying Individuals at High Risk of Psychosis: Predictive Utility of Support Vector Machine using Structural and Functional MRI Data

    PubMed Central

    Valli, Isabel; Marquand, Andre F.; Mechelli, Andrea; Raffin, Marie; Allen, Paul; Seal, Marc L.; McGuire, Philip

    2016-01-01

    The identification of individuals at high risk of developing psychosis is entirely based on clinical assessment, associated with limited predictive potential. There is, therefore, increasing interest in the development of biological markers that could be used in clinical practice for this purpose. We studied 25 individuals with an at-risk mental state for psychosis and 25 healthy controls using structural MRI, and functional MRI in conjunction with a verbal memory task. Data were analyzed using a standard univariate analysis, and with support vector machine (SVM), a multivariate pattern recognition technique that enables statistical inferences to be made at the level of the individual, yielding results with high translational potential. The application of SVM to structural MRI data permitted the identification of individuals at high risk of psychosis with a sensitivity of 68% and a specificity of 76%, resulting in an accuracy of 72% (p < 0.001). Univariate volumetric between-group differences did not reach statistical significance. By contrast, the univariate fMRI analysis identified between-group differences (p < 0.05 corrected), while the application of SVM to the same data did not. Since SVM is well suited at identifying the pattern of abnormality that distinguishes two groups, whereas univariate methods are more likely to identify regions that individually are most different between two groups, our results suggest the presence of focal functional abnormalities in the context of a diffuse pattern of structural abnormalities in individuals at high clinical risk of psychosis. PMID:27092086

  17. On Using Surrogates with Genetic Programming.

    PubMed

    Hildebrandt, Torsten; Branke, Jürgen

    2015-01-01

    One way to accelerate evolutionary algorithms with expensive fitness evaluations is to combine them with surrogate models. Surrogate models are efficiently computable approximations of the fitness function, derived by means of statistical or machine learning techniques from samples of fully evaluated solutions. But these models usually require a numerical representation, and therefore cannot be used with the tree representation of genetic programming (GP). In this paper, we present a new way to use surrogate models with GP. Rather than using the genotype directly as input to the surrogate model, we propose using a phenotypic characterization. This phenotypic characterization can be computed efficiently and allows us to define approximate measures of equivalence and similarity. Using a stochastic, dynamic job shop scenario as an example of simulation-based GP with an expensive fitness evaluation, we show how these ideas can be used to construct surrogate models and improve the convergence speed and solution quality of GP. PMID:24967694

  18. Surrogate Guderley Test Problem Definition

    SciTech Connect

    Ramsey, Scott D.; Shashkov, Mikhail J.

    2012-07-06

    The surrogate Guderley problem (SGP) is a 'spherical shock tube' (or 'spherical driven implosion') designed to ease the notoriously subtle initialization of the true Guderley problem, while still maintaining a high degree of fidelity. In this problem (similar to the Guderley problem), an infinitely strong shock wave forms and converges in one-dimensional (1D) cylindrical or spherical symmetry through a polytropic gas with arbitrary adiabatic index {gamma}, uniform density {rho}{sub 0}, zero velocity, and negligible pre-shock pressure and specific internal energy (SIE). This shock proceeds to focus on the point or axis of symmetry at r = 0 (resulting in ostensibly infinite pressure, velocity, etc.) and reflect back out into the incoming perturbed gas.

  19. The nematode Caenorhabditis elegans as a tool to predict chemical activity on mammalian development and identify mechanisms influencing toxicological outcome

    PubMed Central

    Harlow, Philippa H.; Perry, Simon J.; Widdison, Stephanie; Daniels, Shannon; Bondo, Eddie; Lamberth, Clemens; Currie, Richard A.; Flemming, Anthony J.

    2016-01-01

    To determine whether a C. elegans bioassay could predict mammalian developmental activity, we selected diverse compounds known and known not to elicit such activity and measured their effect on C. elegans egg viability. 89% of compounds that reduced C. elegans egg viability also had mammalian developmental activity. Conversely only 25% of compounds found not to reduce egg viability in C. elegans were also inactive in mammals. We conclude that the C. elegans egg viability assay is an accurate positive predictor, but an inaccurate negative predictor, of mammalian developmental activity. We then evaluated C. elegans as a tool to identify mechanisms affecting toxicological outcomes among related compounds. The difference in developmental activity of structurally related fungicides in C. elegans correlated with their rate of metabolism. Knockdown of the cytochrome P450s cyp-35A3 and cyp-35A4 increased the toxicity to C. elegans of the least developmentally active compounds to the level of the most developmentally active. This indicated that these P450s were involved in the greater rate of metabolism of the less toxic of these compounds. We conclude that C. elegans based approaches can predict mammalian developmental activity and can yield plausible hypotheses for factors affecting the biological potency of compounds in mammals. PMID:26987796

  20. Predictive factors of brain death in severe stroke patients identified by organ procurement and transplant coordination in Lorrain, France.

    PubMed

    Humbertjean, Lisa; Mione, Gioia; Fay, Renaud; Durin, Laurent; Planel, Sophie; Lacour, Jean-Christophe; Enea, Ana-Maria; Richard, Sébastien

    2016-03-01

    There are no established predictive factors to identify patients at the acute phase of severe stroke with a high probability of presenting brain death (BD). We retrospectively collected clinical and paraclinical data of consecutive patients at the acute phase of severe stroke with a potential progression to BD through the hospital organ procurement and transplant coordination system in five centres in Lorrain (France) between 1 January 2012 and 31 December 2013. Final endpoint was adjudicated BD. Of 400 included patients, 91 (23%) presented adjudicated BD. Initial Glasgow Coma Scale score ≤6 (P = 0.008), herniation (P = 0.009), hydrocephalus (P = 0.019), initial systolic blood pressure >150 mmHg (P = 0.002), past history of alcohol abuse (P = 0.019) and stroke volume >65 ml (P = 0.040) were significantly associated with BD progression. Two prognostic scores for stroke with unquantifiable or quantifiable volume were built according to the number of risk factors presented. Following internal validation, the respective bias-corrected predictive performance (c-index) of the two scores was 72% (95% confidence interval: 67-78%) and 77% (95% confidence interval: 72-82%). These scores could form the basis of a simple tool of six criteria to help physicians make the difficult decision of intensive care unit management to preserve organs in potential donors. PMID:26402792

  1. A general framework to learn surrogate relevance criterion for atlas based image segmentation.

    PubMed

    Zhao, Tingting; Ruan, Dan

    2016-09-01

    Multi-atlas based image segmentation sees great opportunities in the big data era but also faces unprecedented challenges in identifying positive contributors from extensive heterogeneous data. To assess data relevance, image similarity criteria based on various image features widely serve as surrogates for the inaccessible geometric agreement criteria. This paper proposes a general framework to learn image based surrogate relevance criteria to better mimic the behaviors of segmentation based oracle geometric relevance. The validity of its general rationale is verified in the specific context of fusion set selection for image segmentation. More specifically, we first present a unified formulation for surrogate relevance criteria and model the neighborhood relationship among atlases based on the oracle relevance knowledge. Surrogates are then trained to be small for geometrically relevant neighbors and large for irrelevant remotes to the given targets. The proposed surrogate learning framework is verified in corpus callosum segmentation. The learned surrogates demonstrate superiority in inferring the underlying oracle value and selecting relevant fusion set, compared to benchmark surrogates. PMID:27524268

  2. A computational tool for identifying minimotifs in protein-protein interactions and improving the accuracy of minimotif predictions.

    PubMed

    Rajasekaran, Sanguthevar; Merlin, Jerlin Camilus; Kundeti, Vamsi; Mi, Tian; Oommen, Aaron; Vyas, Jay; Alaniz, Izua; Chung, Keith; Chowdhury, Farah; Deverasatty, Sandeep; Irvey, Tenisha M; Lacambacal, David; Lara, Darlene; Panchangam, Subhasree; Rathnayake, Viraj; Watts, Paula; Schiller, Martin R

    2011-01-01

    Protein-protein interactions are important to understanding cell functions; however, our theoretical understanding is limited. There is a general discontinuity between the well-accepted physical and chemical forces that drive protein-protein interactions and the large collections of identified protein-protein interactions in various databases. Minimotifs are short functional peptide sequences that provide a basis to bridge this gap in knowledge. However, there is no systematic way to study minimotifs in the context of protein-protein interactions or vice versa. Here we have engineered a set of algorithms that can be used to identify minimotifs in known protein-protein interactions and implemented this for use by scientists in Minimotif Miner. By globally testing these algorithms on verified data and on 100 individual proteins as test cases, we demonstrate the utility of these new computation tools. This tool also can be used to reduce false-positive predictions in the discovery of novel minimotifs. The statistical significance of these algorithms is demonstrated by an ROC analysis (P = 0.001). PMID:20938975

  3. Comparison of predicted and derived measures of volatile organic compounds inside four relocatable classrooms due to identified interior finish sources

    SciTech Connect

    Hodgson, Alfred T.; Shendell, Derek G.; Fisk, William J.; Apte, Michael G.

    2003-06-01

    Indoor exposures to toxic and odorous volatile organic compounds (VOCs) are of general concern. Recently, VOCs in portable or relocatable classrooms (RCs) have received particular attention. However, very little was known about indoor environmental quality (IEQ) and the sources, composition, and indoor concentrations of VOCs in RCs. This project task focused on developing and demonstrating a process for selecting interior finish materials for RCs that have relatively low impacts with respect to their emissions of toxic and odorous VOCs. This task was part of a larger project to demonstrate the potential for simultaneous improvements in IEQ and energy efficiency in four new RCs equipped both with a continuously ventilating advanced heating, ventilating, and air conditioning system (HVAC) and a standard HVAC system. These HVACs were operated on alternate weeks. One RC per pair was constructed with standard interior finish materials, and the other included alternate interior materials identified in our prior laboratory study to have low VOC emissions. The RCs were sited in side-by-side pairs at two elementary schools in distinct northern California climate zones. Classroom VOC emission rates (mg hr{sup -1}) and concentrations were predicted based on VOC emission factors ({micro}g m{sup -2} hr{sup -1}) measured for individual materials in the laboratory, the quantities of installed materials and design ventilation rates. Predicted emission rates were compared to values derived from classroom measurements of VOC concentrations and ventilation rates made at pre-occupancy, eight weeks, and 27 weeks. Predicted concentrations were compared to measured integrated VOC indoor minus outdoor concentrations during school hours in the fall cooling season with the advanced HVAC operated. These measured concentrations also were compared between standard and material-modified RCs. Our combined laboratory and field process proved effective by correctly predicting that IEQ impacts of

  4. Hypothesis test for synchronization: Twin surrogates revisited

    NASA Astrophysics Data System (ADS)

    Romano, M. Carmen; Thiel, Marco; Kurths, Jürgen; Mergenthaler, Konstantin; Engbert, Ralf

    2009-03-01

    The method of twin surrogates has been introduced to test for phase synchronization of complex systems in the case of passive experiments. In this paper we derive new analytical expressions for the number of twins depending on the size of the neighborhood, as well as on the length of the trajectory. This allows us to determine the optimal parameters for the generation of twin surrogates. Furthermore, we determine the quality of the twin surrogates with respect to several linear and nonlinear statistics depending on the parameters of the method. In the second part of the paper we perform a hypothesis test for phase synchronization in the case of experimental data from fixational eye movements. These miniature eye movements have been shown to play a central role in neural information processing underlying the perception of static visual scenes. The high number of data sets (21 subjects and 30 trials per person) allows us to compare the generated twin surrogates with the "natural" surrogates that correspond to the different trials. We show that the generated twin surrogates reproduce very well all linear and nonlinear characteristics of the underlying experimental system. The synchronization analysis of fixational eye movements by means of twin surrogates reveals that the synchronization between the left and right eye is significant, indicating that either the centers in the brain stem generating fixational eye movements are closely linked, or, alternatively that there is only one center controlling both eyes.

  5. Development of a minimal kinase ensemble receptor (MKER) for surrogate AutoShim.

    PubMed

    Mukherjee, Prasenjit; Martin, Eric

    2011-10-24

    The target-tailored 3-D virtual screening (VS) method "Surrogate AutoShim" adds pharmacophoric shims to a 16-kinase crystal structure "Universal Kinase Ensemble Receptor" (UKER) to generate highly predictive, target-customized docking models. Predocking a corporate archive of millions of compounds into the 16-structure ensemble takes months. However, since the 16 UKER structures are always the same, docking need only be done once. The predocked results are then "shimmed" to reproduce experimental training data for any number of additional kinases far more accurately than conventional docking. Training new kinase models and predicting activity for millions of predocked compounds against dozens of kinases takes only hours. However reducing the predocking time would make the method even more advantageous. Sequential Floating Forward Search (SFFS) was employed to rationally identify a reduced subset using only 8 of the 16 structures, a "Minimal Kinase Ensemble Receptor" (MKER) that preserved the predictive accuracy for 20 kinase models. Furthermore, a performance evaluation of this subset on an extended set of 52 kinase targets and 100,000 compounds showed statistical model performance comparable to the original UKER. The MKER has halved the time for predocking large databases of internal and commercial compounds. For ad hoc virtual libraries, where predocking is not possible, 2- or 3-kinases "Approximate Kinase Ensemble Receptors" (AKER) were also identified with only a modest loss of prediction accuracy. PMID:21848284

  6. Benchmarking a surrogate reaction for neutron capture

    SciTech Connect

    Hatarik, R.; Cizewski, J. A.; Hatarik, A. M.; O'Malley, P. D.; Bernstein, L. A.; Bleuel, D. L.; Burke, J. T.; Escher, J. E.; Lesher, S. R.; Gibelin, J.; Phair, L.; Rodriguez-Vieitez, E.; Goldblum, B. L.; Swan, T.; Wiedeking, M.

    2010-01-15

    {sup 171,173}Yb(d,p{gamma}) reactions are measured, with the goal of extracting the neutron capture cross-section ratio as a function of the neutron energy using the external surrogate ratio method. The cross-section ratios obtained are compared to the known neutron capture cross sections. Although the Weisskopf-Ewing limit is demonstrated not to apply for these low neutron energies, a prescription for deducing surrogate cross sections is presented. The surrogate cross-section ratios deduced from the {sup 171,173}Yb(d,p{gamma}) measurements agree with the neutron capture results within 15%.

  7. Efficient Calibration of Computationally Intensive Groundwater Models through Surrogate Modelling with Lower Levels of Fidelity

    NASA Astrophysics Data System (ADS)

    Razavi, S.; Anderson, D.; Martin, P.; MacMillan, G.; Tolson, B.; Gabriel, C.; Zhang, B.

    2012-12-01

    Many sophisticated groundwater models tend to be computationally intensive as they rigorously represent detailed scientific knowledge about the groundwater systems. Calibration (model inversion), which is a vital step of groundwater model development, can require hundreds or thousands of model evaluations (runs) for different sets of parameters and as such demand prohibitively large computational time and resources. One common strategy to circumvent this computational burden is surrogate modelling which is concerned with developing and utilizing fast-to-run surrogates of the original computationally intensive models (also called fine models). Surrogates can be either based on statistical and data-driven models such as kriging and neural networks or simplified physically-based models with lower fidelity to the original system (also called coarse models). Fidelity in this context refers to the degree of the realism of a simulation model. This research initially investigates different strategies for developing lower-fidelity surrogates of a fine groundwater model and their combinations. These strategies include coarsening the fine model, relaxing the numerical convergence criteria, and simplifying the model geological conceptualisation. Trade-offs between model efficiency and fidelity (accuracy) are of special interest. A methodological framework is developed for coordinating the original fine model with its lower-fidelity surrogates with the objective of efficiently calibrating the parameters of the original model. This framework is capable of mapping the original model parameters to the corresponding surrogate model parameters and also mapping the surrogate model response for the given parameters to the original model response. This framework is general in that it can be used with different optimization and/or uncertainty analysis techniques available for groundwater model calibration and parameter/predictive uncertainty assessment. A real-world computationally

  8. Evaluation of a simulation-based surrogate safety metric.

    PubMed

    Wang, Chen; Stamatiadis, Nikiforos

    2014-10-01

    The development of surrogate safety measures is essential due to the problems of availability and quality of historical crash data. The Aggregate Conflict Propensity Metric (ACPM) is a surrogate metric recently proposed and it is based on conflict studies and traffic simulations. ACPM is expected to be capable of assessing the relative safety levels of traffic facilities and/or treatments in order to help traffic engineers to select appropriate treatments based on traffic safety estimates. This paper presents three experimental tests conducted to evaluate the reliability of ACPM. In each test, ACPM is compared to a traditional conflict indicator in terms of identifying and ranking safety of traffic conditions under various traffic volumes based on traffic simulations. ACPM shows its strength and reliability in all three tests, as it provides results highly consistent with the Highway Safety Manual. The experimental tests indicate that ACPM is a promising surrogate safety measure that can appropriately identify relative safety among traffic treatments and/or facilities and provide traffic engineers with useful information on potential safety impact. PMID:24892875

  9. Surrogate oracles, generalized dependency and simpler models

    NASA Technical Reports Server (NTRS)

    Wilson, Larry

    1990-01-01

    Software reliability models require the sequence of interfailure times from the debugging process as input. It was previously illustrated that using data from replicated debugging could greatly improve reliability predictions. However, inexpensive replication of the debugging process requires the existence of a cheap, fast error detector. Laboratory experiments can be designed around a gold version which is used as an oracle or around an n-version error detector. Unfortunately, software developers can not be expected to have an oracle or to bear the expense of n-versions. A generic technique is being investigated for approximating replicated data by using the partially debugged software as a difference detector. It is believed that the failure rate of each fault has significant dependence on the presence or absence of other faults. Thus, in order to discuss a failure rate for a known fault, the presence or absence of each of the other known faults needs to be specified. Also, in simpler models which use shorter input sequences without sacrificing accuracy are of interest. In fact, a possible gain in performance is conjectured. To investigate these propositions, NASA computers running LIC (RTI) versions are used to generate data. This data will be used to label the debugging graph associated with each version. These labeled graphs will be used to test the utility of a surrogate oracle, to analyze the dependent nature of fault failure rates and to explore the feasibility of reliability models which use the data of only the most recent failures.

  10. Predicting the spatial distribution of a seabird community to identify priority conservation areas in the Timor Sea.

    PubMed

    Lavers, Jennifer L; Miller, Mark G R; Carter, Michael J; Swann, George; Clarke, Rohan H

    2014-12-01

    Understanding spatial and temporal variability in the distribution of species is fundamental to the conservation of marine and terrestrial ecosystems. To support strategic decision making aimed at sustainable management of the oceans, such as the establishment of protected areas for marine wildlife, we identified areas predicted to support multispecies seabird aggregations in the Timor Sea. We developed species distribution models for 21 seabird species based on at-sea survey observations from 2000-2013 and oceanographic variables (e.g., bathymetry). We applied 4 statistical modeling techniques and combined the results into an ensemble model with robust performance. The ensemble model predicted the probability of seabird occurrence in areas where few or no surveys had been conducted and demonstrated 3 areas of high seabird richness that varied little between seasons. These were located within 150 km of Adele Island, Ashmore Reef, and the Lacepede Islands, 3 of the largest aggregations of breeding seabirds in Australia. Although these breeding islands were foci for high species richness, model performance was greatest for 3 nonbreeding migratory species that would have been overlooked had regional monitoring been restricted to islands. Our results indicate many seabird hotspots in the Timor Sea occur outside existing reserves (e.g., Ashmore Reef Marine Reserve), where shipping, fisheries, and offshore development likely pose a threat to resident and migratory populations. Our results highlight the need to expand marine spatial planning efforts to ensure biodiversity assets are appropriately represented in marine reserves. Correspondingly, our results support the designation of at least 4 new important bird areas, for example, surrounding Adele Island and Ashmore Reef. PMID:24976050

  11. Identifying bereaved subjects at risk of complicated grief: Predictive value of questionnaire items in a cohort study

    PubMed Central

    2011-01-01

    Background Bereavement is a condition which most people experience several times during their lives. A small but noteworthy proportion of bereaved individuals experience a syndrome of prolonged psychological distress in relation to bereavement. The aim of the study was to develop a clinical tool to identify bereaved individuals who had a prognosis of complicated grief and to propose a model for a screening tool to identify those at risk of complicated grief applicable among bereaved patients in general practice and palliative care. Methods We examined the responses of 276 newly bereaved individuals to a variety of standardised and ad hoc questionnaire items eight weeks post loss. Inventory of Complicated Grief (ICG-R) was used as a gold standard of distress at six months after bereavement. Receiver operating characteristic (ROC) curves analysis was performed for all scales and items regarding ICG-R score. Sensitivity, specificity and area under curve (AUC) were calculated for scales and items with the most promising ROC curve analyses. Results Beck's Depression Inventory (BDI) was the scale with the highest AUC (0.83) and adding a single item question ('Even while my relative was dying, I felt a sense of purpose in my life') gave a sensitivity of 80% and specificity of 75%. The positive/negative predictive values for this combination of questions were 70% and 85%, respectively. With this screening tool bereaved people could be categorized into three groups where group 1 had 7%, group 2 had 23% and group 3 had 64% propensity of suffering from complicated grief six months post loss. Conclusions This study shows that the BDI in combination with a single item question eight weeks post loss may be used for clinical screening for risk of developing complicated grief after six months. The feasibility and clinical implications of the screening tool has to be tested in a clinical setting. PMID:21575239

  12. Progress in Chemical Kinetic Modeling for Surrogate Fuels

    SciTech Connect

    Pitz, W J; Westbrook, C K; Herbinet, O; Silke, E J

    2008-06-06

    Gasoline, diesel, and other alternative transportation fuels contain hundreds to thousands of compounds. It is currently not possible to represent all these compounds in detailed chemical kinetic models. Instead, these fuels are represented by surrogate fuel models which contain a limited number of representative compounds. We have been extending the list of compounds for detailed chemical models that are available for use in fuel surrogate models. Detailed models for components with larger and more complicated fuel molecular structures are now available. These advancements are allowing a more accurate representation of practical and alternative fuels. We have developed detailed chemical kinetic models for fuels with higher molecular weight fuel molecules such as n-hexadecane (C16). Also, we can consider more complicated fuel molecular structures like cyclic alkanes and aromatics that are found in practical fuels. For alternative fuels, the capability to model large biodiesel fuels that have ester structures is becoming available. These newly addressed cyclic and ester structures in fuels profoundly affect the reaction rate of the fuel predicted by the model. Finally, these surrogate fuel models contain large numbers of species and reactions and must be reduced for use in multi-dimensional models for spark-ignition, HCCI and diesel engines.

  13. Coupled simulation-optimization model for coastal aquifer management using genetic programming-based ensemble surrogate models and multiple-realization optimization

    NASA Astrophysics Data System (ADS)

    Sreekanth, J.; Datta, Bithin

    2011-04-01

    Approximation surrogates are used to substitute the numerical simulation model within optimization algorithms in order to reduce the computational burden on the coupled simulation-optimization methodology. Practical utility of the surrogate-based simulation-optimization have been limited mainly due to the uncertainty in surrogate model simulations. We develop a surrogate-based coupled simulation-optimization methodology for deriving optimal extraction strategies for coastal aquifer management considering the predictive uncertainty of the surrogate model. Optimization models considering two conflicting objectives are solved using a multiobjective genetic algorithm. Objectives of maximizing the pumping from production wells and minimizing the barrier well pumping for hydraulic control of saltwater intrusion are considered. Density-dependent flow and transport simulation model FEMWATER is used to generate input-output patterns of groundwater extraction rates and resulting salinity levels. The nonparametric bootstrap method is used to generate different realizations of this data set. These realizations are used to train different surrogate models using genetic programming for predicting the salinity intrusion in coastal aquifers. The predictive uncertainty of these surrogate models is quantified and ensemble of surrogate models is used in the multiple-realization optimization model to derive the optimal extraction strategies. The multiple realizations refer to the salinity predictions using different surrogate models in the ensemble. Optimal solutions are obtained for different reliability levels of the surrogate models. The solutions are compared against the solutions obtained using a chance-constrained optimization formulation and single-surrogate-based model. The ensemble-based approach is found to provide reliable solutions for coastal aquifer management while retaining the advantage of surrogate models in reducing computational burden.

  14. Surrogate Motherhood II: Reflections after "Baby M."

    ERIC Educational Resources Information Center

    Schwartz, Lita Linzer

    1988-01-01

    Discusses the "Baby M" surrogate motherhood case which has produced heated debate in popular media, legal publications, and other professional journals. Summarizes arguments offered and reasoning behind actions of judiciary. (Author/ABL)

  15. Using the integrative model of behavioral prediction to identify promising message strategies to promote healthy sleep behavior among college students.

    PubMed

    Robbins, Rebecca; Niederdeppe, Jeff

    2015-01-01

    This research used the Integrative Model of Behavioral Prediction (IMBP) to examine cognitive predictors of intentions to engage in healthy sleep behavior among a population of college students. In doing so, we identify promising message strategies to increase healthy sleep behavior during college. In Phase 1, members of a small sample of undergraduates (n = 31) were asked to describe their beliefs about expected outcomes, norms, and perceived behavioral control associated with sleep on an open-ended questionnaire. We analyzed these qualitative responses to create a closed-ended survey about sleep-related attitudes, perceived norms, control beliefs, behavioral intentions, and behavior. In Phase 2, a larger sample of undergraduate students (n = 365) completed the survey. Attitudes and perceived behavioral control were the strongest predictors of both intentions to engage in sleep behavior and self-reported sleep behavior. Control beliefs associated with time management and stress also had substantial room to change, suggesting their potential as message strategies to better promote healthy sleep behavior in college. We conclude with a broader discussion of the study's implications for message design and intervention. PMID:24446927

  16. Surrogate burns in deactivation furnace system.

    PubMed

    Shah, J K

    1999-05-14

    The deactivation furnace system at the Deseret Chemical Depot in Utah is designed for processing explosive components from munitions containing nerve and mustard agents. The system was installed during the period of 1989 through 1993. The Utah Division of Solid and Hazardous Waste (UDSHW) required that trial burns be conducted using surrogate chemicals prior to introducing chemical agents into the system. The selected surrogate chemicals were monochlorobenzene and hexachloroethane based on the criteria established by the UDSHW. Three surrogate runs were conducted in October, 1995. The gaseous emissions and liquid and solid effluents were sampled and analyzed using approved EPA methods. The trial burns demonstrated the desirable destruction and removal efficiency for the selected surrogate chemicals. The pollution abatement system demonstrated the desired scrubbing efficiency for acid gases generated during incineration of chlorinated surrogate chemicals. The particulate removal efficiency during the trial burns was also considerably higher than required by regulations. After comprehensive review of the performance of the deactivation furnace system during the surrogate trial burns, UDSHW approved introduction of GB nerve agent into the system to prepare it for agent trial burns. PMID:10334826

  17. Augmented trial designs for evaluation of principal surrogates.

    PubMed

    Gabriel, Erin E; Follmann, Dean

    2016-07-01

    Observation of counterfactual intermediate responses, and evaluation of them as candidate surrogates, is complicated in a standard randomized trial as half of the responses are systematically missing by design. Although some augmentation procedures exist for obtaining counterfactual responses, they are specific to vaccine trials. We outline extensions to the existing augmentations and suggest augmentations of three trial designs outside the setting of vaccines. We outline the assumptions needed to identify the causal estimands of interest under each augmented design, under which standard likelihood-based methods can be used to evaluate intermediate responses as principal surrogates. Two of these designs, crossover and individual stepped-wedge, allow for the observation of clinical endpoints under both treatment and control for some subset of subjects and can therefore improve efficiency over standard parallel trial designs. The third, the treatment run-in design, allows for the observation of a baseline measure that may be as useful a surrogate as the true counterfactual intermediate response. As the evaluation methods rely on several assumptions, we also outline a remediation analysis, which can be used to help overcome assumption violations. We illustrate our suggested methods in an example from a drug-resistant tuberculosis treatment trial. PMID:26825099

  18. Predictive Value of C-Reactive Protein (CRP) in Identifying Fatal Outcome and Deep Infections in Staphylococcus aureus Bacteremia

    PubMed Central

    Ruotsalainen, Eeva; Rintala, Esa M.; Järvinen, Asko

    2016-01-01

    Introduction Clear cut-off levels could aid clinicians in identifying patients with a risk of fatal outcomes or complications such as deep infection foci in Staphylococcus aureus bacteremia (SAB). Cut-off levels for widely used clinical follow-up parameters including serum C-reactive protein (CRP) levels and white blood cell counts (WBC) have not been previously studied. Methods 430 adult SAB patients in Finland took part in prospective multicentre study in which their CRP levels and WBC counts were measured on the day of the positive blood culture, every other day during the first week, twice a week during hospitalization and at 30 days. Receiver operating characteristic (ROC) analysis was used to evaluate the prognostic value of CRP and WBC on the day of the positive blood culture and at days 4, 7, and 14 in predicting mortality and the presence of deep infections at 30 days. Adjusted hazard ratios (HR) for CRP level and WBC count cut-off values for mortality were calculated by the Cox regression analysis and adjusted odds ratios (OR) for cut-off values to predict the presence of deep infection by the binary logistic regression analysis. Results The succumbing patients could be distinguished from the survivors, starting on day 4 after the positive blood culture, by higher CRP levels. Cut-off values of CRP for day 30 mortality in adjusted analysis, that significantly predicted fatal outcome were at day 4 CRP >103 mg/L with sensitivity of 77%, specificity of 55%, and HR of 3.5 (95% CI, 1.2–10.3; p = 0.024), at day 14 CRP >61 mg/L with a sensitivity of 82%, specificity of 80% and HR of 3.6 (95% CI, 1.1–10.3; p<0.039) and cut-off value of WBC at day 14 >8.6 x109/L was prognostic with sensitivity of 77%, specificity of 78% and HR of 8.2 (95% CI, 2.9–23.1; p<0.0001). Cut-off values for deep infection in adjusted analysis were on the day of the positive blood culture CRP >108 mg/L with sensitivity of 77%, specificity of 60%, and HR of 2.6 (95% CI, 1.3–4.9; p = 0

  19. Dynamic Contrast-Enhanced MRI of Cervical Cancers: Temporal Percentile Screening of Contrast Enhancement Identifies Parameters for Prediction of Chemoradioresistance

    SciTech Connect

    Andersen, Erlend K.F.; Hole, Knut Hakon; Lund, Kjersti V.; Sundfor, Kolbein; Kristensen, Gunnar B.; Lyng, Heidi; Malinen, Eirik

    2012-03-01

    Purpose: To systematically screen the tumor contrast enhancement of locally advanced cervical cancers to assess the prognostic value of two descriptive parameters derived from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Methods and Materials: This study included a prospectively collected cohort of 81 patients who underwent DCE-MRI with gadopentetate dimeglumine before chemoradiotherapy. The following descriptive DCE-MRI parameters were extracted voxel by voxel and presented as histograms for each time point in the dynamic series: normalized relative signal increase (nRSI) and normalized area under the curve (nAUC). The first to 100th percentiles of the histograms were included in a log-rank survival test, resulting in p value and relative risk maps of all percentile-time intervals for each DCE-MRI parameter. The maps were used to evaluate the robustness of the individual percentile-time pairs and to construct prognostic parameters. Clinical endpoints were locoregional control and progression-free survival. The study was approved by the institutional ethics committee. Results: The p value maps of nRSI and nAUC showed a large continuous region of percentile-time pairs that were significantly associated with locoregional control (p < 0.05). These parameters had prognostic impact independent of tumor stage, volume, and lymph node status on multivariate analysis. Only a small percentile-time interval of nRSI was associated with progression-free survival. Conclusions: The percentile-time screening identified DCE-MRI parameters that predict long-term locoregional control after chemoradiotherapy of cervical cancer.

  20. Estimating alcohol involvement in trauma patients: search for a surrogate.

    PubMed

    Treno, A J; Cooper, K; Roeper, P

    1994-12-01

    This study explores the potential for the development of a surrogate for alcohol-involved traumatic injury. It presents a bivariate probit analysis that simultaneously models likelihoods of patients being tested for blood alcohol content (BAC) and having positive BACs given testing using 17,356 adult trauma cases selected from the California Regional Trauma Registry. It concludes that patient and injury characteristics predict both testing and BAC, and that a weighting scheme may be profitably used to determine changes in levels of alcohol-involved trauma in populations over time in the absence of empirical measurement of BAC. PMID:7695022

  1. Coastal aquifer management under parameter uncertainty: Ensemble surrogate modeling based simulation-optimization

    NASA Astrophysics Data System (ADS)

    Janardhanan, S.; Datta, B.

    2011-12-01

    saltwater intrusion are considered. The salinity levels resulting at strategic locations due to these pumping are predicted using the ensemble surrogates and are constrained to be within pre-specified levels. Different realizations of the concentration values are obtained from the ensemble predictions corresponding to each candidate solution of pumping. Reliability concept is incorporated as the percent of the total number of surrogate models which satisfy the imposed constraints. The methodology was applied to a realistic coastal aquifer system in Burdekin delta area in Australia. It was found that all optimal solutions corresponding to a reliability level of 0.99 satisfy all the constraints and as reducing reliability level decreases the constraint violation increases. Thus ensemble surrogate model based simulation-optimization was found to be useful in deriving multi-objective optimal pumping strategies for coastal aquifers under parameter uncertainty.

  2. Detailed chemical kinetic oxidation mechanism for a biodiesel surrogate

    SciTech Connect

    Herbinet, O; Pitz, W J; Westbrook, C K

    2007-09-17

    A detailed chemical kinetic mechanism has been developed and used to study the oxidation of methyl decanoate, a surrogate for biodiesel fuels. This model has been built by following the rules established by Curran et al. for the oxidation of n-heptane and it includes all the reactions known to be pertinent to both low and high temperatures. Computed results have been compared with methyl decanoate experiments in an engine and oxidation of rapeseed oil methyl esters in a jet stirred reactor. An important feature of this mechanism is its ability to reproduce the early formation of carbon dioxide that is unique to biofuels and due to the presence of the ester group in the reactant. The model also predicts ignition delay times and OH profiles very close to observed values in shock tube experiments fueled by n-decane. These model capabilities indicate that large n-alkanes can be good surrogates for large methyl esters and biodiesel fuels to predict overall reactivity, but some kinetic details, including early CO2 production from biodiesel fuels, can be predicted only by a detailed kinetic mechanism for a true methyl ester fuel. The present methyl decanoate mechanism provides a realistic kinetic tool for simulation of biodiesel fuels.

  3. Detailed chemical kinetic oxidation mechanism for a biodiesel surrogate

    SciTech Connect

    Herbinet, O; Pitz, W J; Westbrook, C K

    2007-09-20

    A detailed chemical kinetic mechanism has been developed and used to study the oxidation of methyl decanoate, a surrogate for biodiesel fuels. This model has been built by following the rules established by Curran et al. for the oxidation of n-heptane and it includes all the reactions known to be pertinent to both low and high temperatures. Computed results have been compared with methyl decanoate experiments in an engine and oxidation of rapeseed oil methyl esters in a jet stirred reactor. An important feature of this mechanism is its ability to reproduce the early formation of carbon dioxide that is unique to biofuels and due to the presence of the ester group in the reactant. The model also predicts ignition delay times and OH profiles very close to observed values in shock tube experiments fueled by n-decane. These model capabilities indicate that large n-alkanes can be good surrogates for large methyl esters and biodiesel fuels to predict overall reactivity, but some kinetic details, including early CO{sub 2} production from biodiesel fuels, can be predicted only by a detailed kinetic mechanism for a true methyl ester fuel. The present methyl decanoate mechanism provides a realistic kinetic tool for simulation of biodiesel fuels.

  4. Bayesian meta-analytical methods to incorporate multiple surrogate endpoints in drug development process.

    PubMed

    Bujkiewicz, Sylwia; Thompson, John R; Riley, Richard D; Abrams, Keith R

    2016-03-30

    A number of meta-analytical methods have been proposed that aim to evaluate surrogate endpoints. Bivariate meta-analytical methods can be used to predict the treatment effect for the final outcome from the treatment effect estimate measured on the surrogate endpoint while taking into account the uncertainty around the effect estimate for the surrogate endpoint. In this paper, extensions to multivariate models are developed aiming to include multiple surrogate endpoints with the potential benefit of reducing the uncertainty when making predictions. In this Bayesian multivariate meta-analytic framework, the between-study variability is modelled in a formulation of a product of normal univariate distributions. This formulation is particularly convenient for including multiple surrogate endpoints and flexible for modelling the outcomes which can be surrogate endpoints to the final outcome and potentially to one another. Two models are proposed, first, using an unstructured between-study covariance matrix by assuming the treatment effects on all outcomes are correlated and second, using a structured between-study covariance matrix by assuming treatment effects on some of the outcomes are conditionally independent. While the two models are developed for the summary data on a study level, the individual-level association is taken into account by the use of the Prentice's criteria (obtained from individual patient data) to inform the within study correlations in the models. The modelling techniques are investigated using an example in relapsing remitting multiple sclerosis where the disability worsening is the final outcome, while relapse rate and MRI lesions are potential surrogates to the disability progression. PMID:26530518

  5. Skin penetration surrogate for the evaluation of less lethal kinetic energy munitions.

    PubMed

    Bir, Cynthia A; Resslar, Marianne; Stewart, Shelby

    2012-07-10

    Although the benefits of the use of less lethal kinetic energy munitions are numerous, there is a need to evaluate the munitions prior to deployment to ensure their intended effect. The objective of the current research was to validate a surrogate that could be used to predict the risk of penetration of these devices. Existing data from biomechanical testing with post-mortem human specimens (PMHS) served as the foundation for this research. Development of the surrogate involved simulating the various layers of the skin and underlying soft tissues using a combination of materials. A standardized 12-gauge impactor was used to assess each combination. The energy density that resulted in a 50% risk of penetration for the anterior thorax region (23.99 J/cm(2)) from the previous research was matched using a specific combination of layers. Twelve various combinations of materials were tested with the 50% risk of penetration determined. The final validated surrogate consisted of a Laceration Assessment Layer (LAL) of natural chamois and .6 cm of closed-cell foam over a Penetration Assessment Layer (PAL) of 20% ordnance gelatin. This surrogate predicted a 50% risk of penetration at 23.88 J/cm(2). Injury risk curves for the PMHS and surrogate development work are presented. PMID:22405483

  6. Surrogate-based Analysis and Optimization

    NASA Technical Reports Server (NTRS)

    Queipo, Nestor V.; Haftka, Raphael T.; Shyy, Wei; Goel, Tushar; Vaidyanathan, Raj; Tucker, P. Kevin

    2005-01-01

    A major challenge to the successful full-scale development of modem aerospace systems is to address competing objectives such as improved performance, reduced costs, and enhanced safety. Accurate, high-fidelity models are typically time consuming and computationally expensive. Furthermore, informed decisions should be made with an understanding of the impact (global sensitivity) of the design variables on the different objectives. In this context, the so-called surrogate-based approach for analysis and optimization can play a very valuable role. The surrogates are constructed using data drawn from high-fidelity models, and provide fast approximations of the objectives and constraints at new design points, thereby making sensitivity and optimization studies feasible. This paper provides a comprehensive discussion of the fundamental issues that arise in surrogate-based analysis and optimization (SBAO), highlighting concepts, methods, techniques, as well as practical implications. The issues addressed include the selection of the loss function and regularization criteria for constructing the surrogates, design of experiments, surrogate selection and construction, sensitivity analysis, convergence, and optimization. The multi-objective optimal design of a liquid rocket injector is presented to highlight the state of the art and to help guide future efforts.

  7. Global Optimization Using Mixed Surrogates and Space Elimination in Computationally Intensive Engineering Designs

    NASA Astrophysics Data System (ADS)

    Younis, Adel; Dong, Zuomin

    2012-07-01

    Surrogate-based modeling is an effective search method for global design optimization over well-defined areas using complex and computationally intensive analysis and simulation tools. However, indentifying the appreciate surrogate models and their suitable areas remains a challenge that requires extensive human intervention. In this work, a new global optimization algorithm, namely Mixed Surrogate and Space Elimination (MSSE) method, is introduced. Representative surrogate models, including Quadratic Response Surface, Radial Basis function, and Kriging, are mixed with different weight ratios to form an adaptive metamodel with best tested performance. The approach divides the field of interest into several unimodal regions; identifies and ranks the regions that likely contain the global minimum; fits the weighted surrogate models over each promising region using additional design experiment data points from Latin Hypercube Designs and adjusts the weights according to the performance of each model; identifies its minimum and removes the processed region; and moves to the next most promising region until all regions are processed and the global optimum is identified. The proposed algorithm was tested using several benchmark problems for global optimization and compared with several widely used space exploration global optimization algorithms, showing reduced computation efforts, robust performance and comparable search accuracy, making the proposed method an excellent tool for computationally intensive global design optimization problems.

  8. Legal regulation of surrogate motherhood in Israel.

    PubMed

    Frenkel, D A

    2001-01-01

    The Israeli Law on surrogate motherhood demands a preconception agreement to include payments to be made to the surrogate mother. Surrogacy arrangements with family members are forbidden. Commercial surrogacy is allowed and encouraged. The Law causes many problems. Validity of consent given by surrogate mothers is doubtful. Possible future psychological harm are ignored. There is a danger of "commodification" of children. Abusing women of low socio-economic status as breeding machines may be another outcome. No clear responsibility is imposed on the "intended parents" for an impaired child. The law ignores possibility of divorce or death of the "intended parents" before the child's birth. Splitting motherhood is another social problem that has to be dealt with. So far the sperm of the husband from the "intended parents" has to be used, but further steps may follow. It is not certain that a policy of "positive eugenics" will not develop. PMID:11817392

  9. Parents and children: "surrogate" paradigm of modernity.

    PubMed

    Adam, Archimandrite; Akhaladze, Vakhtang

    2011-06-01

    The article provides an overview of surrogate motherhood--one of many currently available forms of Assisted Reproductive Technologies for couples who find themselves unable to conceive a child on their own. Within the years of its existence surrogate motherhood managed to accumulate lots of bioethical problems, paradoxes, dilemmas and collisions. Author represents some of them. Also the legal, moral and religious implications of surrogacy are addressed. The religious perspective from the Orthodox Christian, Catholic, Jewish, Hinduism, and Islamic points of view are provided. The author concludes that surrogate motherhood is not only the answer to childlessness but it supports metamorphosis of traditional attitude towards such human value as it is a family. PMID:21778532

  10. Attitudes toward surrogate motherhood in Canada.

    PubMed

    Krishnan, V

    1994-01-01

    The issue of surrogate motherhood captured worldwide attention with the Baby M case in the United States. Some medical practitioners now claim that the surrogate arrangement may be the answer for certain women who are unable to conceive children naturally. Feminist activists are highly critical about the issue. In her landmark book The Mother Machine, Corea (1985) called surrogates "breeders," women whose bodies are being used by men. Lawyers and ethicists debate whether surrogacy is baby selling or not, and religious fundamentalists have condemned any form of procreation outside the "normal" or "natural" form of sexual relations within a marriage. But what do Canadian women think about commercial surrogacy? Findings pertaining to this issue from the latest national fertility survey of 5,315 women in the reproductive ages of 18-49 are reported. PMID:8056650