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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. Assessing a surrogate predictive value: a causal inference approach.

    PubMed

    Alonso, Ariel; Van der Elst, Wim; Meyvisch, Paul

    2017-03-30

    Several methods have been developed for the evaluation of surrogate endpoints within the causal-inference and meta-analytic paradigms. In both paradigms, much effort has been made to assess the capacity of the surrogate to predict the causal treatment effect on the true endpoint. In the present work, the so-called surrogate predictive function (SPF) is introduced for that purpose, using potential outcomes. The relationship between the SPF and the individual causal association, a new metric of surrogacy recently proposed in the literature, is studied in detail. It is shown that the SPF, in conjunction with the individual causal association, can offer an appealing quantification of the surrogate predictive value. However, neither the distribution of the potential outcomes nor the SPF are identifiable from the data. These identifiability issues are tackled using a two-step procedure. In the first step, the region of the parametric space of the distribution of the potential outcomes, compatible with the data at hand, is geometrically characterized. Further, in a second step, a Monte Carlo approach is used to study the behavior of the SPF on the previous region. The method is illustrated using data from a clinical trial involving schizophrenic patients and a newly developed and user friendly R package Surrogate is provided to carry out the validation exercise. Copyright © 2016 John Wiley & Sons, Ltd.

  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. 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

    2016-01-01

    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. 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. 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. 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.

  5. 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

  6. Surrogate models for identifying robust, high yield regions of parameter space for ICF implosion simulations

    NASA Astrophysics Data System (ADS)

    Humbird, Kelli; Peterson, J. Luc; Brandon, Scott; Field, John; Nora, Ryan; Spears, Brian

    2016-10-01

    Next-generation supercomputer architecture and in-transit data analysis have been used to create a large collection of 2-D ICF capsule implosion simulations. The database includes metrics for approximately 60,000 implosions, with x-ray images and detailed physics parameters available for over 20,000 simulations. To map and explore this large database, surrogate models for numerous quantities of interest are built using supervised machine learning algorithms. Response surfaces constructed using the predictive capabilities of the surrogates allow for continuous exploration of parameter space without requiring additional simulations. High performing regions of the input space are identified to guide the design of future experiments. In particular, a model for the yield built using a random forest regression algorithm has a cross validation score of 94.3% and is consistently conservative for high yield predictions. The model is used to search for robust volumes of parameter space where high yields are expected, even given variations in other input parameters. Surrogates for additional quantities of interest relevant to ignition are used to further characterize the high yield regions. This work performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344, Lawrence Livermore National Security, LLC. LLNL-ABS-697277.

  7. 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

  8. 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

  9. Effectiveness of Biological Surrogates for Predicting Patterns of Marine Biodiversity: A Global Meta-Analysis

    PubMed Central

    Mellin, Camille; Delean, Steve; Caley, Julian; Edgar, Graham; Meekan, Mark; Pitcher, Roland; Przeslawski, Rachel; Williams, Alan; Bradshaw, Corey

    2011-01-01

    The use of biological surrogates as proxies for biodiversity patterns is gaining popularity, particularly in marine systems where field surveys can be expensive and species richness high. Yet, uncertainty regarding their applicability remains because of inconsistency of definitions, a lack of standard methods for estimating effectiveness, and variable spatial scales considered. We present a Bayesian meta-analysis of the effectiveness of biological surrogates in marine ecosystems. Surrogate effectiveness was defined both as the proportion of surrogacy tests where predictions based on surrogates were better than random (i.e., low probability of making a Type I error; P) and as the predictability of targets using surrogates (R2). A total of 264 published surrogacy tests combined with prior probabilities elicited from eight international experts demonstrated that the habitat, spatial scale, type of surrogate and statistical method used all influenced surrogate effectiveness, at least according to either P or R2. The type of surrogate used (higher-taxa, cross-taxa or subset taxa) was the best predictor of P, with the higher-taxa surrogates outperforming all others. The marine habitat was the best predictor of R2, with particularly low predictability in tropical reefs. Surrogate effectiveness was greatest for higher-taxa surrogates at a <10-km spatial scale, in low-complexity marine habitats such as soft bottoms, and using multivariate-based methods. Comparisons with terrestrial studies in terms of the methods used to study surrogates revealed that marine applications still ignore some problems with several widely used statistical approaches to surrogacy. Our study provides a benchmark for the reliable use of biological surrogates in marine ecosystems, and highlights directions for future development of biological surrogates in predicting biodiversity. PMID:21695119

  10. 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.

  11. Identifying family members who may struggle in the role of surrogate decision maker.

    PubMed

    Majesko, Alyssa; Hong, Seo Yeon; Weissfeld, Lisa; White, Douglas B

    2012-08-01

    Although acting as a surrogate decision maker can be highly distressing for some family members of intensive care unit patients, little is known about whether there are modifiable risk factors for the occurrence of such difficulties. To identify: 1) factors associated with lower levels of confidence among family members to function as surrogates and 2) whether the quality of clinician-family communication is associated with the timing of decisions to forego life support. We conducted a prospective study of 230 surrogate decision makers for incapacitated, mechanically ventilated patients at high risk of death in four intensive care units at University of California San Francisco Medical Center from 2006 to 2007. Surrogates completed a questionnaire addressing their perceived ability to act as a surrogate and the quality of their communication with physicians. We used clustered multivariate logistic regression to identify predictors of low levels of perceived ability to act as a surrogate and a Cox proportional hazard model to determine whether quality of communication was associated with the timing of decisions to withdraw life support. There was substantial variability in family members' confidence to act as surrogate decision makers, with 27% rating their perceived ability as 7 or lower on a 10-point scale. Independent predictors of lower role confidence were the lack of prior experience as a surrogate (odds ratio 2.2, 95% confidence interval [1.04-4.46], p=.04), no prior discussions with the patient about treatment preferences (odds ratio 3.7, 95% confidence interval [1.79-7.76], p<.001), and poor quality of communication with the ICU physician (odds ratio 1.2, 95% confidence interval [1.09-1.35] p<.001). Higher quality physician-family communication was associated with a significantly shorter duration of life-sustaining treatment among patients who died (β=0.11, p=.001). Family members without prior experience as a surrogate and those who had not engaged in

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

    PubMed

    Yang, Feiling; Hu, Jinming; Wu, Ruidong

    2016-08-19

    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.

  13. 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

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

    NASA Astrophysics Data System (ADS)

    Yang, Feiling; Hu, Jinming; Wu, Ruidong

    2016-08-01

    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.

  15. Surrogate measures and consistent surrogates.

    PubMed

    Vanderweele, Tyler J

    2013-09-01

    Surrogates which allow one to predict the effect of the treatment on the outcome of interest from the effect of the treatment on the surrogate are of importance when it is difficult or expensive to measure the primary outcome. Unfortunately, the use of such surrogates can give rise to paradoxical situations in which the effect of the treatment on the surrogate is positive, the surrogate and outcome are strongly positively correlated, but the effect of the treatment on the outcome is negative, a phenomenon sometimes referred to as the "surrogate paradox." New results are given for consistent surrogates that extend the existing literature on sufficient conditions that ensure the surrogate paradox is not manifest. Specifically, it is shown that for the surrogate paradox to be manifest it must be the case that either there is (i) a direct effect of treatment on the outcome not through the surrogate and in the opposite direction as that through the surrogate or (ii) confounding for the effect of the surrogate on the outcome, or (iii) a lack of transitivity so that treatment does not positively affect the surrogate for all the same individuals for whom the surrogate positively affects the outcome. The conditions for consistent surrogates and the results of the article are important because they allow investigators to predict the direction of the effect of the treatment on the outcome simply from the direction of the effect of the treatment on the surrogate. These results on consistent surrogates are then related to the four approaches to surrogate outcomes described by Joffe and Greene (2009, Biometrics 65, 530-538) to assess whether the standard criteria used by these approaches to assess whether a surrogate is "good" suffice to avoid the surrogate paradox.

  16. Surrogate measures and consistent surrogates

    PubMed Central

    VanderWeele, Tyler J.

    2014-01-01

    Summary Surrogates which allow one to predict the effect of the treatment on the outcome of interest from the effect of the treatment on the surrogate are of importance when it is difficult or expensive to measure the primary outcome. Unfortunately, the use of such surrogates can give rise to paradoxical situations in which the effect of the treatment on the surrogate is positive, the surrogate and outcome are strongly positively correlated, but the effect of the treatment on the outcome is negative, a phenomenon sometimes referred to as the "surrogate paradox." New results are given for consistent surrogates that extend the existing literature on sufficient conditions that ensure the surrogate paradox is not manifest. Specifically, it is shown that for the surrogate paradox to beman.est it must be the case that either there is (i) a direct effect of treatment on the outcome not through the surrogate and in the opposite direction as that through the surrogate or (ii) confounding for the effect of the surrogate on the outcome, or (iii) a lack of transitivity so that treatment does not positively affect the surrogate for all the same individuals for which the surrogate positively affects the outcome. The conditions for consistent surrogates and the results of the paper are important because they allow investigators to predict the direction of the effect of the treatment on the outcome simply from the direction of the effect of the treatment on the surrogate. These results on consistent surrogates are then related to the four approaches to surrogate outcomes described by Joffe and Greene (2009, Biometrics 65, 530–538) to assess whether the standard criterion used by these approaches to assess whether a surrogate is "good" suffices to avoid the surrogate paradox. PMID:24073861

  17. 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.

  18. 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.

  19. Development of Fluorescence Surrogates to Predict the Photochemical Transformation of Pharmaceuticals in Wastewater Effluents.

    PubMed

    Yan, Shuwen; Yao, Bo; Lian, Lushi; Lu, Xinchen; Snyder, Shane A; Li, Rui; Song, Weihua

    2017-03-07

    The photochemical transformation of pharmaceutical and personal care products (PPCPs) in wastewater effluents is an emerging concern for environmental scientists. In the current study, the photodegradation of 29 PPCPs was examined in effluents under simulated solar irradiation. Direct photodegradation, triplet state effluent organic matter ((3)EfOM*)-mediated and hydroxyl radical (HO(•))-mediated degradation are three major pathways in the removal process. With the photodegradation of trace levels of PPCPs, the excitation-emission matrix (EEM) fluorescence intensities of the effluents were also gradually reduced. Therefore, fluorescence peaks have been identified, for the first time, as appropriate surrogates to assess the photodegradation of PPCPs. The humic-like fluorescence peak is linked to direct photolysis-labile PPCPs, such as naproxen, ronidazole, diclofenac, ornidazole, tinidazole, chloramphenicol, flumequine, ciprofloxacin, methadone, and dimetridazole. The tyrosine-like EEM peak is associated with HO(•)/CO3(•-)-labile PPCPs, such as trimethoprim, ibuprofen, gemfibrozil, atenolol, carbamazepine, and cephalexin. The tryptophan-like peak is associated with (3)EfOM*-labile PPCPs, such as clenbuterol, metoprolol, venlafaxine, bisphenol A, propranolol, ractopamine, salbutamol, roxithromycin, clarithromycin, azithromycin, famotidine, terbutaline, and erythromycin. The reduction in EEM fluorescence correlates well with the removal of PPCPs, allowing a model to be constructed. The solar-driven removal of EEM fluorescence was applied to predict the attenuation of 11 PPCPs in five field samples. A close correlation between the predicted results and the experimental results suggests that fluorescence may be a suitable surrogate for monitoring the solar-driven photodegradation of PPCPs in effluents.

  20. Ensemble of Surrogates-based Optimization for Identifying an Optimal Surfactant-enhanced Aquifer Remediation Strategy at Heterogeneous DNAPL-contaminated Sites

    NASA Astrophysics Data System (ADS)

    Lu, W., Sr.; Xin, X.; Luo, J.; Jiang, X.; Zhang, Y.; Zhao, Y.; Chen, M.; Hou, Z.; Ouyang, Q.

    2015-12-01

    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.

  1. 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.

  2. 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).

  3. 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

    2014-01-06

    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).

  4. 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.

  5. Biomarker Surrogates Do Not Accurately Predict Sputum Eosinophils and Neutrophils in Asthma

    PubMed Central

    Hastie, Annette T.; Moore, Wendy C.; Li, Huashi; Rector, Brian M.; Ortega, Victor E.; Pascual, Rodolfo M.; Peters, Stephen P.; Meyers, Deborah A.; Bleecker, Eugene R.

    2013-01-01

    Background Sputum eosinophils (Eos) are a strong predictor of airway inflammation, exacerbations, and aid asthma management, whereas sputum neutrophils (Neu) indicate a different severe asthma phenotype, potentially less responsive to TH2-targeted therapy. Variables such as blood Eos, total IgE, fractional exhaled nitric oxide (FeNO) or FEV1% predicted, may predict airway Eos, while age, FEV1%predicted, or blood Neu may predict sputum Neu. Availability and ease of measurement are useful characteristics, but accuracy in predicting airway Eos and Neu, individually or combined, is not established. Objectives To determine whether blood Eos, FeNO, and IgE accurately predict sputum eosinophils, and age, FEV1% predicted, and blood Neu accurately predict sputum neutrophils (Neu). Methods Subjects in the Wake Forest Severe Asthma Research Program (N=328) were characterized by blood and sputum cells, healthcare utilization, lung function, FeNO, and IgE. Multiple analytical techniques were utilized. Results Despite significant association with sputum Eos, blood Eos, FeNO and total IgE did not accurately predict sputum Eos, and combinations of these variables failed to improve prediction. Age, FEV1%predicted and blood Neu were similarly unsatisfactory for prediction of sputum Neu. Factor analysis and stepwise selection found FeNO, IgE and FEV1% predicted, but not blood Eos, correctly predicted 69% of sputum Eospredicted 64% of sputum Neupredict both sputum Eos and Neu accurately assigned only 41% of samples. Conclusion Despite statistically significant associations FeNO, IgE, blood Eos and Neu, FEV1%predicted, and age are poor surrogates, separately and combined, for accurately predicting sputum eosinophils and neutrophils. PMID:23706399

  6. 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

    USDA-ARS?s Scientific Manuscript database

    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 ...

  7. Evaluating Candidate Principal Surrogate Endpoints

    PubMed Central

    Gilbert, Peter B.; Hudgens, Michael G.

    2009-01-01

    Summary Frangakis and Rubin (2002, Biometrics 58, 21–29) proposed a new definition of a surrogate endpoint (a “principal” surrogate) based on causal effects. We introduce an estimand for evaluating a principal surrogate, the causal effect predictiveness (CEP) surface, which quantifies how well causal treatment effects on the biomarker predict causal treatment effects on the clinical endpoint. Although the CEP surface is not identifiable due to missing potential outcomes, it can be identified by incorporating a baseline covariate(s) that predicts the biomarker. Given case–cohort sampling of such a baseline predictor and the biomarker in a large blinded randomized clinical trial, we develop an estimated likelihood method for estimating the CEP surface. This estimation assesses the “surrogate value” of the biomarker for reliably predicting clinical treatment effects for the same or similar setting as the trial. A CEP surface plot provides a way to compare the surrogate value of multiple biomarkers. The approach is illustrated by the problem of assessing an immune response to a vaccine as a surrogate endpoint for infection. PMID:18363776

  8. 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

  9. 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.

  10. 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.

  11. Surrogate molecular markers for IGHV mutational status in chronic lymphocytic leukemia for predicting time to first treatment.

    PubMed

    Morabito, Fortunato; Cutrona, Giovanna; Mosca, Laura; D'Anca, Marianna; Matis, Serena; Gentile, Massimo; Vigna, Ernesto; Colombo, Monica; Recchia, Anna Grazia; Bossio, Sabrina; De Stefano, Laura; Maura, Francesco; Manzoni, Martina; Ilariucci, Fiorella; Consoli, Ugo; Vincelli, Iolanda; Musolino, Caterina; Cortelezzi, Agostino; Molica, Stefano; Ferrarini, Manlio; Neri, Antonino

    2015-08-01

    ZAP-70 is a marker of clinical outcome in chronic lymphocytic leukemia (CLL), however its assessment suffers from a lack of standardization consensus. To identify novel markers able to surrogate IGHV mutational status, CD19(+)CD5(+)-B-lymphocytes from 216 patients enrolled in a prospective study (ClinicalTrial.gov Identifier:NCT00917540), underwent gene expression profiling. Samples were split into CLL-Training (n=102) and CLL-Validation (n=114) sets, and an independent supervised analysis for IGHV mutational status was performed considering all genes with gene expression equal or above that of ZAP-70. Thirty-one genes (23 up- and 8 down-regulated) and 23 genes (18 up- and 5 down-regulated) satisfied these criteria in the CLL-Training and CLL-Validation sets, respectively, and 20 common genes (15 up and 5 down) were found to be differentially regulated in both sets. Two (SNORA70F, NRIP1) of the down-regulated and 6 (SEPT10, ZNF667, TGFBR3, MBOAT1, LPL, CRY1) of the up-regulated genes were significantly associated with a reduced risk of disease progression in both sets. Forcing the afore-mentioned genes in a Cox multivariate model together with IGHV mutational status, only CRY1 (HR=2.3, 95% CI: 1.1-4.9, P=.027) and MBOAT1 (HR=2.1, 95% CI: 1.1-3.7, P=.018) retained their independent prognostic impact, supporting the hypothesis that these genes may potentially act as surrogates for predicting IGHV mutational status.

  12. Survival of patients with gastrointestinal cancers can be predicted by a surrogate microRNA signature for cancer stem-like cells marked by DCLK1 kinase

    PubMed Central

    Weygant, Nathaniel; Ge, Yang; Qu, Dongfeng; Kaddis, John S.; Berry, William L.; May, Randal; Chandrakesan, Parthasarathy; Bannerman-Menson, Edwin; Vega, Kenneth J.; Tomasek, James J.; Bronze, Michael S.; An, Guangyu; Houchen, Courtney W.

    2016-01-01

    DCLK1 is a gastrointestinal (GI) tuft cell kinase that has been investigated as a biomarker of cancer stem-like cells in colon and pancreatic cancers. However, its utility as a biomarker may be limited in principle by signal instability and dilution in heterogeneous tumors, where the proliferation of diverse tumor cell lineages obscures the direct measurement of DCLK1 activity. To address this issue, we explored the definition of a microRNA signature as a surrogate biomarker for DCLK1 in cancer stem-like cells. Utilizing RNA/miRNA sequencing datasets from the Cancer Genome Atlas, we identified a surrogate 15-miRNA expression signature for DCLK1 activity across several GI cancers, including colon, pancreatic and stomach cancers. Notably, Cox regression and Kaplan-Meier analysis demonstrated that this signature could predict the survival of patients with these cancers. Moreover, we identified patient subgroups that predicted the clinical utility of this DCLK1 surrogate biomarker. Our findings greatly strengthen the clinical significance for DCLK1 expression across GI cancers. Further, they provide an initial guidepost toward the development of improved prognostic biomarkers or companion biomarkers for DCLK1-targeted therapies to eradicate cancer stem-like cells in these malignancies. PMID:27287716

  13. Predictive Model for Inactivation of Feline Calicivirus, a Norovirus Surrogate, by Heat and High Hydrostatic Pressure▿

    PubMed Central

    Buckow, Roman; Isbarn, Sonja; Knorr, Dietrich; Heinz, Volker; Lehmacher, Anselm

    2008-01-01

    Noroviruses, which are members of the Caliciviridae family, represent the leading cause of nonbacterial gastroenteritis in developed countries; such norovirus infections result in high economic costs for health protection. Person-to-person contact, contaminated water, and foods, especially raw shellfish, vegetables, and fruits, can transmit noroviruses. We inactivated feline calicivirus, a surrogate for the nonculturable norovirus, in cell culture medium and mineral water by heat and high hydrostatic pressure. Incubation at ambient pressure and 75°C for 2 min as well as treatment at 450 MPa and 15°C for 1 min inactivated more than 7 log10 PFU of calicivirus per ml in cell culture medium or mineral water. The heat and pressure time-inactivation curves obtained with the calicivirus showed tailing in the logarithmic scale. Modeling by nth-order kinetics of the virus inactivation was successful in predicting the inactivation of the infective virus particles. The developed model enables the prediction of the calicivirus reduction in response to pressures up to 500 MPa, temperatures ranging from 5 to 75°C, and various treatment times. We suggest high pressure for processing of foods to reduce the health threat posed by noroviruses. PMID:18156330

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

    PubMed Central

    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

  15. Predicting Treatment Effect from Surrogate Endpoints and Historical Trials | Division of Cancer Prevention

    Cancer.gov

    By Stuart G. Baker, 2017 Introduction This software fits a zero-intercept random effects linear model to data on surrogate and true endpoints in previous trials. Requirement:  Mathematica Version 11 or later. |

  16. 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.

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

    PubMed

    Zhao, Ying; Lu, Wenxi; Xiao, Chuanning

    2016-01-01

    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.

  18. Individual Identifiability Predicts Population Identifiability in Forensic Microsatellite Markers.

    PubMed

    Algee-Hewitt, Bridget F B; Edge, Michael D; Kim, Jaehee; Li, Jun Z; Rosenberg, Noah A

    2016-04-04

    Highly polymorphic genetic markers with significant potential for distinguishing individual identity are used as a standard tool in forensic testing [1, 2]. At the same time, population-genetic studies have suggested that genetically diverse markers with high individual identifiability also confer information about genetic ancestry [3-6]. The dual influence of polymorphism levels on ancestry inference and forensic desirability suggests that forensically useful marker sets with high levels of individual identifiability might also possess substantial ancestry information. We study a standard forensic marker set-the 13 CODIS loci used in the United States and elsewhere [2, 7-9]-together with 779 additional microsatellites [10], using direct population structure inference to test whether markers with substantial individual identifiability also produce considerable information about ancestry. Despite having been selected for individual identification and not for ancestry inference [11], the CODIS markers generate nontrivial model-based clustering patterns similar to those of other sets of 13 tetranucleotide microsatellites. Although the CODIS markers have relatively low values of the F(ST) divergence statistic, their high heterozygosities produce greater ancestry inference potential than is possessed by less heterozygous marker sets. More generally, we observe that marker sets with greater individual identifiability also tend toward greater population identifiability. We conclude that population identifiability regularly follows as a byproduct of the use of highly polymorphic forensic markers. Our findings have implications for the design of new forensic marker sets and for evaluations of the extent to which individual characteristics beyond identification might be predicted from current and future forensic data.

  19. Can Serum Albumin Level and Total Lymphocyte Count be Surrogates for Malnutrition to Predict Wound Complications After Total Knee Arthroplasty?

    PubMed

    Morey, Vivek M; Song, Young Dong; Whang, Ji Sup; Kang, Yeon Gwi; Kim, Tae Kyun

    2016-06-01

    Although the serum albumin level and total lymphocyte count (TLC) have been reported as valid and reliable markers for defining malnutrition, their cutoff levels and predictive values for wound complications in patients undergoing total knee arthroplasty (TKA) remain questionable. A total of 3169 TKAs performed between April 2003 and December 2013 were retrospectively reviewed. We determined the prevalence of malnutrition on applying different definitions, with various cutoff values of serum albumin and TLC and analyzed the variations in outcome. The differences between groups with and without malnutrition in terms of functional outcome and complications were determined using Student's t test and analysis of variance. Multivariate logistic regression analysis was conducted to identify the independent risk factors. Among all the patients (N = 3169), the serum albumin level and TLC varied widely, with means of 4.1 g/dL and 2189 cells/mm(3), respectively. The prevalence of malnutrition (21%) as per the conventional definition (serum albumin level <3.5 g/dL or a serum TLC <1500 cells/mm(3)) dropped to only 1.6% when malnutrition was defined as serum albumin <3.5 g/dL "and" TLC <1500/mm(3), indicating a very small overlap between the 2 markers. No differences were observed between 2 groups in functional outcomes and incidence of wound complications. Our findings call into question the values of serum albumin level and TLC as a surrogate of malnutrition for predicting wound complications after TKA. Copyright © 2015 Elsevier Inc. All rights reserved.

  20. Suitability of Organic Matter Surrogates to Predict Trihalomethane Formation in Drinking Water Sources

    PubMed Central

    Pifer, Ashley D.; Fairey, Julian L.

    2014-01-01

    Abstract Broadly applicable disinfection by-product (DBP) precursor surrogate parameters could be leveraged at drinking water treatment plants (DWTPs) to curb formation of regulated DBPs, such as trihalomethanes (THMs). In this study, dissolved organic carbon (DOC), ultraviolet absorbance at 254 nm (UV254), fluorescence excitation/emission wavelength pairs (IEx/Em), and the maximum fluorescence intensities (FMAX) of components from parallel factor (PARAFAC) analysis were evaluated as total THM formation potential (TTHMFP) precursor surrogate parameters. A diverse set of source waters from eleven DWTPs located within watersheds underlain by six different soil orders were coagulated with alum at pH 6, 7, and 8, resulting in 44 sample waters. DOC, UV254, IEx/Em, and FMAX values were measured to characterize dissolved organic matter in raw and treated waters and THMs were quantified following formation potential tests with free chlorine. For the 44 sample waters, the linear TTHMFP correlation with UV254 was stronger (r2=0.89) than I240/562 (r2=0.81, the strongest surrogate parameter from excitation/emission matrix pair picking), FMAX from a humic/fulvic acid-like PARAFAC component (r2=0.78), and DOC (r2=0.75). Results indicate that UV254 was the most accurate TTHMFP precursor surrogate parameter assessed for a diverse group of raw and alum-coagulated waters. PMID:24669183

  1. Suitability of Organic Matter Surrogates to Predict Trihalomethane Formation in Drinking Water Sources.

    PubMed

    Pifer, Ashley D; Fairey, Julian L

    2014-03-01

    Broadly applicable disinfection by-product (DBP) precursor surrogate parameters could be leveraged at drinking water treatment plants (DWTPs) to curb formation of regulated DBPs, such as trihalomethanes (THMs). In this study, dissolved organic carbon (DOC), ultraviolet absorbance at 254 nm (UV254), fluorescence excitation/emission wavelength pairs (IEx/Em), and the maximum fluorescence intensities (FMAX) of components from parallel factor (PARAFAC) analysis were evaluated as total THM formation potential (TTHMFP) precursor surrogate parameters. A diverse set of source waters from eleven DWTPs located within watersheds underlain by six different soil orders were coagulated with alum at pH 6, 7, and 8, resulting in 44 sample waters. DOC, UV254, IEx/Em, and FMAX values were measured to characterize dissolved organic matter in raw and treated waters and THMs were quantified following formation potential tests with free chlorine. For the 44 sample waters, the linear TTHMFP correlation with UV254 was stronger (r(2)=0.89) than I240/562 (r(2)=0.81, the strongest surrogate parameter from excitation/emission matrix pair picking), FMAX from a humic/fulvic acid-like PARAFAC component (r(2)=0.78), and DOC (r(2)=0.75). Results indicate that UV254 was the most accurate TTHMFP precursor surrogate parameter assessed for a diverse group of raw and alum-coagulated waters.

  2. 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.

  3. Identifying cryptic diversity with predictive phylogeography.

    PubMed

    Espíndola, Anahí; Ruffley, Megan; Smith, Megan L; Carstens, Bryan C; Tank, David C; Sullivan, Jack

    2016-10-26

    Identifying units of biological diversity is a major goal of organismal biology. An increasing literature has focused on the importance of cryptic diversity, defined as the presence of deeply diverged lineages within a single species. While most discoveries of cryptic lineages proceed on a taxon-by-taxon basis, rapid assessments of biodiversity are needed to inform conservation policy and decision-making. Here, we introduce a predictive framework for phylogeography that allows rapidly identifying cryptic diversity. Our approach proceeds by collecting environmental, taxonomic and genetic data from codistributed taxa with known phylogeographic histories. We define these taxa as a reference set, and categorize them as either harbouring or lacking cryptic diversity. We then build a random forest classifier that allows us to predict which other taxa endemic to the same biome are likely to contain cryptic diversity. We apply this framework to data from two sets of disjunct ecosystems known to harbour taxa with cryptic diversity: the mesic temperate forests of the Pacific Northwest of North America and the arid lands of Southwestern North America. The predictive approach presented here is accurate, with prediction accuracies placed between 65% and 98.79% depending of the ecosystem. This seems to indicate that our method can be successfully used to address ecosystem-level questions about cryptic diversity. Further, our application for the prediction of the cryptic/non-cryptic nature of unknown species is easily applicable and provides results that agree with recent discoveries from those systems. Our results demonstrate that the transition of phylogeography from a descriptive to a predictive discipline is possible and effective. © 2016 The Author(s).

  4. Surrogate gas proxy prediction model for Delta 14C-based measurements of fossil fuel-CO2

    NASA Astrophysics Data System (ADS)

    Coakley, K. J.; Miller, J. B.; Montzka, S. A.; Sweeney, C.; Miller, B.

    2016-12-01

    The measured {}14}C {:12} {C isotopic ratio ofatmospheric CO2 (and its associated derived Δ 14Cvalue) is an ideal tracer for determination of the fossil fuelderived CO2 enhancement contributing to any atmosphericCO2 measurement (Cff). Given enough such measurements,independent top-down estimation of US fossil fuel- CO2emissions should be possible. However, the number of Δ 14Cmeasurements is presently constrained by cost, available samplevolume, and availability of mass spectrometer measurement facilities.Δ 14C is therefore measured in just a small fraction ofsamples obtained by flask air sampling networks around the world.Here, we develop a Projection Pursuit Regression model topredict Cff as a function of multiple surrogate gases acquiredwithin the NOAA/ESRL Global Greenhouse Gas Reference Network (GGGRN).The surrogates consist of measured enhancements of various anthropogenictrace gases, including CO, SF6, and halo- andhydro-carbons acquired in vertical airborne sampling profiles nearCape May, NJ and Portsmouth, NH from 2005 through 2010. Modelperformance is quantified based on predicted values correspondingto test data excluded from the model building process. Chi-squarehypothesis test analysis indicates that these predictions andcorresponding observations are consistent given our uncertaintybudget which accounts for random effects and one particular systematiceffect. To account for the possibility of additional systematiceffects, we incorporate another component of uncertainty into ourbudget. Provided that these estimates are of comparable qualityto Δ 14C -based estimates, we expect an improved determinationof fossil fuel-CO2 emissions.

  5. 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.

  6. The St. Gallen surrogate classification for breast cancer subtypes successfully predicts tumor presenting features, nodal involvement, recurrence patterns and disease free survival.

    PubMed

    Vasconcelos, Ines; Hussainzada, Afsana; Berger, Stefan; Fietze, Ellen; Linke, Jörg; Siedentopf, Friederike; Schoenegg, Winfried

    2016-10-01

    To evaluate how the St. Gallen intrinsic subtype classification for breast cancer surrogates predicts disease features, recurrence patterns and disease free survival. Subtypes were classified by immunohistochemical staining according to St. Gallen subtypes classification in a 5-tyre system: luminal A, luminal B HER2-neu negative, luminal B HER2-neu positive, HER2-neu non luminal or basal-like. Data were obtained from the records of patients with invasive breast cancer treated at our institution. Recurrence data and site of first recurrence were recorded. The chi(2) test, analysis of variance, and multivariate logistic regression analysis were used to determine associations between surrogates and clinicopathologic variables. A total of 2.984 tumors were classifiable into surrogate subtypes. Significant differences in age, tumor size, nodal involvement, nuclear grade, multicentric/multifocal disease (MF/MC), lymphovascular invasion, and extensive intraductal component (EIC) were observed among surrogates (p < 0.0001). After adjusting for confounding factors surrogates remained predictive of nodal involvement (luminal B HER2-neu pos. OR = 1.49 p = 0.009, non-luminal HER2-neu pos. OR = 1.61 p = 0.015 and basal-like OR = 0.60, p = 0.002) while HER2-neu positivity remained predictive of EIC (OR = 3.10, p < 0.0001) and MF/MC (OR = 1.45, p = 0.02). Recurrence rates differed among the surrogates and were time-dependent (p = 0.001) and site-specific (p < 0.0001). The St. Gallen 5-tyre surrogate classification for breast cancer subtypes accurately predicts breast cancer presenting features (with emphasis on prediction of nodal involvement), recurrence patterns and disease free survival. Copyright © 2016 Elsevier Ltd. All rights reserved.

  7. Identifying Predictive Markers for Personalized Treatment Selection

    PubMed Central

    Shen, Yuanyuan

    2017-01-01

    Summary It is now well recognized that the effectiveness and potential risk of a treatment often vary by patient subgroups. Although trial-and-error and one-size-fits-all approaches to treatment selection remains a common practice, much recent focus has been placed on individualized treatment selection based on patient information (La Thangue and Kerr, 2011; Ong et al., 2012). Genetic and molecular markers are becoming increasingly available to guide treatment selection for various diseases including HIV and breast cancer (Mallal et al., 2008; Zujewski and Kamin, 2008). In recent years, many statistical procedures for developing individualized treatment rules (ITRs) have been proposed. However, less focus has been given to efficient selection of predictive biomarkers for treatment selection. The standard Wald test for interactions between treatment and the set of markers of interest may not work well when the marker effects are non-linear. Furthermore, interaction based test is scale dependent and may fail to capture markers useful for predicting individualized treatment differences. In this paper, we propose to overcome these difficulties by developing a kernel machine (KM) score test that can efficiently identify markers predictive of treatment difference. Simulation studies show that our proposed KM based score test is more powerful than the Wald test when there is non-linear effect among the predictors and when the outcome is binary with non-linear link functions. Furthermore, when there is high-correlation among predictors and when the number of predictors is not small, our method also over-performs Wald test. The proposed method is illustrated with two randomized clinical trials. PMID:26999054

  8. Using a Surrogate Test of Math Skills to Predict Performance of Non-Traditional Accounting Students.

    ERIC Educational Resources Information Center

    Schiff, Jonathan B.

    1989-01-01

    A study was designed to predict student performance in the introductory accounting course, specifically the performance of nontraditional students. A predictive instrument to measure mathematical abilities was employed; outcomes were compared to individuals' final course grade. Results indicate that a brief math skills test can predict performance…

  9. Development of a surrogate model based on patient weight, bone mass and geometry to predict femoral neck strains and fracture loads.

    PubMed

    Taylor, Mark; Perilli, Egon; Martelli, Saulo

    2017-04-11

    Osteoporosis and related bone fractures are an increasing global burden in our ageing society. Areal bone mineral density assessed through dual energy X-ray absorptiometry (DEXA), the clinically accepted and most used method, is not sufficient to assess fracture risk individually. Finite element (FE) modelling has shown improvements in prediction of fracture risk, better than aBMD from DEXA, but is not practical for widespread clinical use. The aim of this study was to develop an adaptive neural network (ANN)-based surrogate model to predict femoral neck strains and fracture loads obtained from a previously developed population-based FE model. The surrogate model performance was assessed in simulating two loading conditions: the stance phase of gait and a fall. The surrogate model successfully predicted strains estimated by FE (r(2)=0.90-0.98 for level gait load case, r(2)=0.92-0.96 for the fall load case). Moreover, an ANN model based on three measurements obtainable in clinics (femoral neck length (level gait) or maximum femoral neck diameter (fall), femoral neck bone mass, body weight) was able to give reasonable predictions (r(2)=0.84-0.94) for all of the strain metrics and the estimated femoral neck fracture load. Overall, the surrogate model has potential for clinical applications as they are based on simple measures of geometry and bone mass which can be derived from DEXA images, accurately predicting FE model outcomes, with advantages over FE models as they are quicker and easier to perform.

  10. Serum midkine as a surrogate biomarker for metastatic prediction in differentiated thyroid cancer patients with positive thyroglobulin antibody

    PubMed Central

    Jia, Qiang; Meng, Zhaowei; Xu, Ke; He, Xianghui; Tan, Jian; Zhang, Guizhi; Li, Xue; Liu, Na; Hu, Tianpeng; Zhou, Pingping; Wang, Sen; Upadhyaya, Arun; Liu, Xiaoxia; Wang, Huiying; Zhang, Chunmei

    2017-01-01

    Serum thyroglobulin (Tg) is the main post-operative tumor biomarker for patients with differentiated thyroid cancer (DTC). However, the presence of thyroglobulin antibodies (TgAb) can interfere with Tg level and invalidate the test. In this study, we aimed to investigate the predicative value of midkine (MK) as a cancer biomarker for DTC patients with positive TgAb before the first 131I therapy. MK levels were measured by enzyme-linked immunosorbent assay in 151 recruited DTC patients after exercising strict inclusion and exclusion criteria. There were 28 TgAb positive DTC patients with metastases and 123 DTC patients without metastases. The value of pre-131I-ablative MK to predict metastasis was assessed by receiver operating characteristic (ROC) curves in these two groups of patients. MK levels in the TgAb positive DTC patients were significantly higher than the DTC patients without metastases. ROC showed good predictability of MK, with an area under the curve of 0.856 (P < 0.001), and a diagnostic accuracy of 83% at the optimal cut-off value of 550 pg/ml. In conclusion, we show that MK can potentially be used as a surrogate biomarker for predicting DTC metastases when Tg is not suitable due to TgAb positivity. PMID:28240744

  11. Statistical model based shape prediction from a combination of direct observations and various surrogates: application to orthopaedic research.

    PubMed

    Blanc, Rémi; Seiler, Christof; Székely, Gabor; Nolte, Lutz-Peter; Reyes, Mauricio

    2012-08-01

    In computer-assisted orthopaedic surgery, recovering three-dimensional patient-specific anatomy from incomplete information has been focus of interest due to several factors such as less invasive surgical procedures, reduced radiation doses, and rapid intra-operative updates of the anatomy. The aim of this paper is to report results obtained combining statistical shape modeling and multivariate regression techniques for predicting bone shape from clinically and surgically relevant predictors, including sparse observations of the bone surface but also morphometric and anthropometric information. Different state of the art methods such as partial least square regression, principal component regression, canonical correlation analysis, and non-parametric kernel-based regression are compared. Clinically relevant surrogate variables and combinations are investigated on a database of 142 femur and 154 tibia shapes obtained from CT images. The results are evaluated using cross validation to quantify the prediction error. The proposed approach enables to characterize the added value of different predictors in a quantitative and localized fashion. Results indicate that complementary sources of information can be efficiently exploited to improve the accuracy of shape prediction. Copyright © 2012 Elsevier B.V. All rights reserved.

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

    PubMed Central

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

    2016-01-01

    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 US 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 ask 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/ESRL Global Greenhouse Gas Reference Network (GGGRN). The surrogates consist of measured enhancements of various anthropogenic trace gases, including CO, SF6, and halo- and hydrocarbons acquired in vertical airborne sampling profiles near Cape May, NJ and Portsmouth, NH from 2005 through 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 uncertainties and potentially

  13. Surrogate gas prediction model as a proxy for Δ(14)C-based measurements of fossil fuel-CO2.

    PubMed

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

    2016-06-27

    The measured (14)C:(12)C isotopic ratio of atmospheric CO2 (and its associated derived Δ(14)C 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 US fossil fuel-CO2 emissions should be possible. However, the number of Δ(14)C measurements is presently constrained by cost, available sample volume, and availability of mass spectrometer measurement facilities. Δ(14)C is therefore measured in just a small fraction of samples obtained by ask 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/ESRL Global Greenhouse Gas Reference Network (GGGRN). The surrogates consist of measured enhancements of various anthropogenic trace gases, including CO, SF6, and halo- and hydrocarbons acquired in vertical airborne sampling profiles near Cape May, NJ and Portsmouth, NH from 2005 through 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 Δ(14)C measurements in the NOAA GGGRN and building new PPR models at additional sites would improve our understanding of uncertainties and

  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. Early Fiber Number Ratio Is a Surrogate of Corticospinal Tract Integrity and Predicts Motor Recovery After Stroke.

    PubMed

    Bigourdan, Antoine; Munsch, Fanny; Coupé, Pierrick; Guttmann, Charles R G; Sagnier, Sharmila; Renou, Pauline; Debruxelles, Sabrina; Poli, Mathilde; Dousset, Vincent; Sibon, Igor; Tourdias, Thomas

    2016-04-01

    The contribution of imaging metrics to predict poststroke motor recovery needs to be clarified. We tested the added value of early diffusion tensor imaging (DTI) of the corticospinal tract toward predicting long-term motor recovery. One hundred seventeen patients were prospectively assessed at 24 to 72 hours and 1 year after ischemic stroke with diffusion tensor imaging and motor scores (Fugl-Meyer). The initial fiber number ratio (iFNr) and final fiber number ratio were computed as the number of streamlines along the affected corticospinal tract normalized to the unaffected side and were compared with each other. The prediction of motor recovery (ΔFugl-Meyer) was first modeled using initial Fugl-Meyer and iFNr. Multivariate ordinal logistic regression models were also used to study the association of iFNr, initial Fugl-Meyer, age, and stroke volume with Fugl-Meyer at 1 year. The iFNr correlated with the final fiber number ratio at 1 year (r=0.70; P<0.0001). The initial Fugl-Meyer strongly predicted motor recovery (≈73% of initial impairment) for all patients except those with initial severe stroke (Fugl-Meyer<50). For these severe patients (n=26), initial Fugl-Meyer was not correlated with motor recovery (R(2)=0.13; p=ns), whereas iFNr showed strong correlation (R(2)=0.56; P<0.0001). In multivariate analysis, the iFNr was an independent predictor of motor outcome (β=2.601; 95% confidence interval=0.304-5.110; P=0.031), improving prediction compared with using only initial Fugl-Meyer, age, and stroke volume (P=0.026). Early measurement of FNr at 24 to 72 hours poststroke is a surrogate marker of corticospinal tract integrity and provides independent prediction of motor outcome at 1 year especially for patients with severe initial impairment. © 2016 American Heart Association, Inc.

  16. Surrogate Robot

    NASA Image and Video Library

    2014-08-21

    The Surrogate robot Surge, built at NASA Jet Propulsion Laboratory in Pasadena, CA., is being developed in order to extend humanity reach into hazardous environments to perform tasks such as using environmental test equipment.

  17. 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

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

    USDA-ARS?s Scientific Manuscript database

    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...

  19. A comparative analysis of the pedestrian injury risk predicted by mechanical impactors and post mortem human surrogates.

    PubMed

    Kerrigan, Jason R; Crandall, Jeff R; Deng, Bing

    2008-11-01

    The objective of this study is to compare the risk of injury to pedestrians involved in vehicle-pedestrian impacts as predicted by two different types of risk assessment tools: the pedestrian subsystem impactors recommended by the European Enhanced Vehicle-Safety Committee (EEVC) and post-mortem human surrogates (PMHS). Seven replicate full-scale vehicle-pedestrian impact tests were performed with PMHS and a mid-sized sedan travelling at 40 km/h. The PMHS were instrumented with six-degree-of-freedom sensor cubes and sensor data were transformed and translated to predict impact kinematics at the head center of gravity, proximal tibiae, and knee joints. Single EEVC WG 17/EuroNCAP adult headform, upper legform and lower legform impactor tests of the same vehicle were selected for comparison based on the proximity of their impact locations to that of the PMHS. The PMHS experienced higher HIC values (1830/2160) and lower impact velocities (8.5/7.5 m/s) than the impactor (1532 and 11.1 m/s) in impacts at the lower fourth of the windshield. The lower legform impactor (31 degrees) and PMHS (right: 25-40 degrees, and left: 24-39 degrees) predicted similar maximum knee bending angles. Some PMHS tibial accelerations (114-613 g) exceeded the proposed acceptance criteria (150-200 g) in both the absence and presence of distal tibial fracture, with the impactor predicting a similar result (335 g). The upper legform impactor test resulted in bending moments (361 Nm) and forces (6.3 kN) exceeding the acceptance criteria, while PMHS sustained pelvic injuries in 6 out of 7 tests.

  20. Handgrip dynamometry: a surrogate marker of malnutrition to predict the prognosis in alcoholic liver disease

    PubMed Central

    Gaikwad, Nitin Rangrao; Gupta, Sudhir Jagdishprasad; Samarth, Amol Rajendra; Sankalecha, Tushar Hiralal

    2016-01-01

    Background The aim of the study was to determine the utility of handgrip dynamometry (HGD) in predicting short term mortality and complications in alcoholic liver disease. Methods Patients with alcoholic liver disease were included and nutritional assessment was done using the Subjective Global Assessment (SGA), HGD and other conventional parameters. Mortality rates and complications were compared to nutritional status. Results 80 patients were included in the study. Mean age of patients was 43.06±10.03 years. 69 patients survived and 11 patients died within the 3 month study duration. Handgrip strength (HGS) was higher in SGA A (28.76±5.48 kg) than SGA B (22.43±4.95 kg) and SGA C (16.78±3.83 kg) (P=<0.001). Number of complications including spontaneous bacterial Peritonitis, gastrointestinal bleeding and encephalopathy in SGA C group were 66.66%, in SGA B 20.75% and SGA A 10%. Mean HGS was significantly higher in the survivors (24.23±5.86) compared to non-survivors (18.04±4.82) (P=0.0011). There was a strong negative correlation between the HGS and Child-Pugh score (P=<0.0012). Multivariate logistic regression analysis to assess the risk factors for death showed handgrip to be in the suggestive significance range (P=0.072). The sensitivity of HGS was 88.41% in predicting short term mortality. Conclusions HGS correlates with Child-Pugh score in predicting short term mortality. HGD is a simple, inexpensive and sensitive tool for assessing the nutritional status in alcoholic liver disease and can reliably predict its complications and survival. PMID:27708519

  1. Quality of communication in the ICU and surrogate's understanding of prognosis.

    PubMed

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

    2015-03-01

    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 predict accurate expectation about prognosis by surrogates. 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 greater than or equal to ±20%. We used multilevel logistic regression modeling to account for clustering under physicians and patients and adjust for confounders. Medical-surgical, trauma, cardiac, and neurologic ICUs of five U.S. academic medical centers located in California, Pennsylvania, Washington, North Carolina, and Massachusetts. Two hundred seventy-five patients with acute respiratory distress syndrome at high risk of death or severe functional impairment, their 546 surrogate decision makers, and their 150 physicians. None. There was no predictive utility of surrogates' ratings of the quality of communication about prognosis to identify inaccurate expectations about prognosis (odds ratio, 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. Although most surrogates rate the quality of prognostic communication high, inaccurate expectations about prognosis are common among surrogates. Surrogates' ratings of the quality of

  2. Serum creatinine level, a surrogate of muscle mass, predicts mortality in peritoneal dialysis patients.

    PubMed

    Park, Jongha; Mehrotra, Rajnish; Rhee, Connie M; Molnar, Miklos Z; Lukowsky, Lilia R; Patel, Sapna S; Nissenson, Allen R; Kopple, Joel D; Kovesdy, Csaba P; Kalantar-Zadeh, Kamyar

    2013-08-01

    In hemodialysis patients, higher serum creatinine (Cr) concentration represents larger muscle mass and predicts greater survival. However, this association remains uncertain in peritoneal dialysis (PD) patients. In a cohort of 10 896 PD patients enrolled from 1 July 2001 to 30 June 2006, the association of baseline serum Cr level and change during the first 3 months after enrollment with all-cause mortality was examined. The cohort mean ± SD age was 55 ± 15 years old and included 52% women, 24% African-Americans and 48% diabetics. Compared with patients with serum Cr levels of 8.0-9.9 mg/dL, patients with serum Cr levels of <4.0 mg/dL and 4.0-5.9 mg/dL had higher risks of death {HR 1.36 [95% confidence interval (95% CI) 1.19-1.55] and 1.19 (1.08-1.31), respectively} whereas patients with serum Cr levels of 10.0-11.9 mg/dL, 12.0-13.9 mg/dL and ≥14.0 mg/dL had lower risks of death (HR 0.88 [95% CI 0.79-0.97], 0.71 [0.62-0.81] and 0.64 [0.55-0.75], respectively) in the fully adjusted model. Decrease in serum Cr level over 1.0 mg/dL during the 3 months predicted an increased risk of death additionally. The serum Cr-mortality association was robust in patients with PD treatment duration of ≥12 months, but was not observed in those with PD duration of <3 months. Muscle mass reflected in serum Cr level may be associated with survival even in PD patients. However, the serum Cr-mortality association is attenuated in the early period of PD treatment, suggesting competing effect of muscle mass versus residual renal function on mortality.

  3. Prediction of ignition implosion performance using measurements of Low-deuterium surrogates

    NASA Astrophysics Data System (ADS)

    Spears, B. K.; Brandon, S.; Clark, D.; Cerjan, C.; Edwards, J.; Landen, O.; Lindl, J.; Haan, S.; Hatchett, S.; Salmonson, J.; Springer, P.; Weber, S. V.; Wilson, D.

    2010-08-01

    The National Ignition Campaign (NIC) will use non-igniting "THD" capsules with cryogenic ice layers to study and optimize the hydrodynamic assembly of the fuel without burn. These capsules are characterized by the ratios of T:H:D. The species ratios are set with two goals in mind: (1) control T:D in order to adjust the nuclear energy production and (2) preserve the average atomic number of the fuel at 2.5 to maintain hydrodynamic similarity with the DT ignition capsule. We have developed an experimentally observable ignition threshold factor (ITFX) that uses measurements from THD experiments to predict the performance of DT ignition implosions. It was developed and tested on multiple large databases of 2D radhydro simulations. Each of the thousands of simulations includes twin DT and THD simulations with a variety of physical failure mechanisms - drive asymmetry, capsule roughness, continuum mixing, fabrication errors, among others. The results of our numerical database and the ITFX metric have allowed us to develop an experimental estimate of the probability of DT ignition based on THD experiments. The analysis accounts for both diagnostic precision and the effects of a finite number of shots. The NIC expects to field a combination of diagnostics and experimental attempts that result in a 15 to 20 percent uncertainty in the experimentally inferred probability of ignition. This work was completed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.

  4. DGT as surrogate of biomonitors for predicting the bioavailability of copper in freshwaters: an ex situ validation study.

    PubMed

    Ferreira, Daniel; Ciffroy, Philippe; Tusseau-Vuillemin, Marie-Hélène; Bourgeault, Adeline; Garnier, Jean-Marie

    2013-04-01

    The present report is the companion study of our previous study in which we investigated the impact of the dissolved organic matter, water cationic composition and pH on the bioavailability and the bioaccumulation of copper (Cu) in aquatic mosses (Fontinalis antipyretica). The impact had been assessed under laboratory controlled conditions and modelled using a two-compartment model calibrated under a wide range of water compositions (Ferreira et al., 2008, 2009). Herein are reported the validation stage of the abovementioned approach for contrasted geochemical field conditions. Experiments were performed with aquatic mosses that were exposed for 7d to two nominal Cu concentrations (5 and 15μgL(-1)) in a flow-through field microcosm supplied with four contrasting natural waters. At the end of the exposure period, a 6-fold difference in the bioaccumulated Cu contamination levels was found among the four deployment sites, suggesting a significant control of the water quality on the metal bioaccumulation by aquatic mosses. In parallel, the so-called 'labile' Cu concentration for the same four field conditions was determined using a DGT device (Diffusive Gradient in Thin film). By coupling these DGT measurements and a cation competition model involving Ca(2+), Mg(2+), Na(+) and H(+), the time-dependent Cu concentrations in aquatic mosses were predicted; these simulation results were compared to the actual bioaccumulation of Cu in mosses. We found that any bioaccumulation model that ignores water characteristics is not suitable to predict the Cu accumulation by aquatic mosses under various water quality conditions. Instead, we found that our approach integrating DGT measurements and cationic composition was able to reproduce the Cu bioaccumulation kinetics by aquatic mosses for a wide range of water quality conditions. In conclusion, the DGT approach was demonstrated to be a dynamic in situ measuring technique that can be used as a surrogate of bioindicators if the

  5. 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,…

  6. The surrogate's authority.

    PubMed

    Lindemann, Hilde; Nelson, James Lindemann

    2014-04-01

    The authority of surrogates-often close family members-to make treatment decisions for previously capacitated patients is said to come from their knowledge of the patient, which they are to draw on as they exercise substituted judgment on the patient's behalf. However, proxy accuracy studies call this authority into question, hence the Patient Preference Predictor (PPP). We identify two problems with contemporary understandings of the surrogate's role. The first is with the assumption that knowledge of the patient entails knowledge of what the patient's choice of treatment would be. The second is with the assumption that a good decision reproduces the content of that choice. If we are right, then the PPP, helpful though it might be in guiding surrogates' decisions, nevertheless would hold them to the wrong standards and in that way could add to, rather than relieve, the stress they experience as they try to do their job.

  7. Prediction of naphthenic acid species degradation by kinetic and surrogate models during the ozonation of oil sands process-affected water.

    PubMed

    Islam, Md Shahinoor; Moreira, Jesús; Chelme-Ayala, Pamela; Gamal El-Din, Mohamed

    2014-09-15

    Oil sands process-affected water (OSPW) is a complex mixture of organic and inorganic contaminants, and suspended solids, generated by the oil sands industry during the bitumen extraction process. OSPW contains a large number of structurally diverse organic compounds, and due to variability of the water quality of different OSPW matrices, there is a need to select a group of easily measured surrogate parameters for monitoring and treatment process control. In this study, kinetic and surrogate correlation models were developed to predict the degradation of naphthenic acids (NAs) species during the ozonation of OSPW. Additionally, the speciation and distribution of classical and oxidized NA species in raw and ozonated OSPW were also examined. The structure-reactivity of NA species indicated that the reactivity of individual NA species increased as the carbon and hydrogen deficiency numbers increased. The kinetic parameters obtained in this study allowed calculating the evolution of the concentrations of the acid-extractable fraction (AEF), chemical oxygen demand (COD), and NA distributions for a given ozonation process. High correlations between the AEF and COD and NA species were found, suggesting that AEF and COD can be used as surrogate parameters to predict the degradation of NAs during the ozonation of OSPW. Copyright © 2014 Elsevier B.V. All rights reserved.

  8. Tracking contamination through ground beef production and identifying points of recontamination using a novel green fluorescent protein (GFP) expressing, E. coli O103, non-pathogenic surrogate

    USDA-ARS?s Scientific Manuscript database

    Introduction: Commonly, ground beef processors conduct studies to model contaminant flow through their production systems using surrogate organisms. Typical surrogate organisms may not behave as Escherichia coli O157:H7 during grinding and are not easy to detect at very low levels. Purpose: Develop...

  9. 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.

  10. 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

  11. Structure-based prediction of drug distribution across the headgroup and core strata of a phospholipid bilayer using surrogate phases.

    PubMed

    Natesan, Senthil; Lukacova, Viera; Peng, Ming; Subramaniam, Rajesh; Lynch, Sandra; Wang, Zhanbin; Tandlich, Roman; Balaz, Stefan

    2014-10-06

    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

  12. Comprehensive Profiling of Radiosensitive Human Cell Lines with DNA Damage Response Assays Identifies the Neutral Comet Assay as a Potential Surrogate for Clonogenic Survival

    PubMed Central

    Nahas, Shareef A.; Davies, Robert; Fike, Francesca; Nakamura, Kotoka; Du, Liutao; Kayali, Refik; Martin, Nathan T.; Concannon, Patrick; Gatti, Richard A.

    2015-01-01

    In an effort to explore the possible causes of human radiosensitivity and identify more rapid assays for cellular radiosensitivity, we interrogated a set of assays that evaluate cellular functions involved in recognition and repair of DNA double-strand breaks: (1) neutral comet assay, (2) radiation-induced γ-H2AX focus formation, (3) the temporal kinetics of structural maintenance of chromosomes 1 phosphorylation, (4) intra-S-phase checkpoint integrity, and (5) mitochondrial respiration. We characterized a unique panel of 19 “radiosensitive” human lymphoblastoid cell lines from individuals with undiagnosed diseases suggestive of a DNA repair disorder. Radiosensitivity was defined by reduced cellular survival using a clonogenic survival assay. Each assay identified cell lines with defects in DNA damage response functions. The highest concordance rate observed, 89% (17/19), was between an abnormal neutral comet assay and reduced survival by the colony survival assay. Our data also suggested that the neutral comet assay would be a more rapid surrogate for analyzing DNA repair/processing disorders. PMID:21962002

  13. 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.

  14. Use of biotinylated plasmid DNA as a surrogate for HSV DNA to identify proteins that repress or activate viral gene expression.

    PubMed

    Mallon, Stephen; Wakim, Bassam T; Roizman, Bernard

    2012-12-18

    ICP0, a key herpes simplex virus regulatory protein, functions first in the nucleus and then in the cytoplasm. The duration of its nuclear sojourn in cells transfected with DNA and then infected is related to the quantity of transfected DNA. Furthermore, ICP0 transactivates both viral genes and genes encoded by the transfected DNA. The data support the hypothesis that ICP0 is retained in the nucleus until it completes the replacement of repressive chromatin with effector proteins that enable transcription of both DNA templates.To identify the effector proteins, we transfected cells with biotinylated DNA encoding a nonviral gene and then infected the cells with wild-type virus. Proteins bound to transfected biotinylated plasmid recovered from mock-treated and infected cells were identified using mass spectrometry followed by appropriate database search. The transfected DNA from mock-infected cells yielded proteins associated with repression, whereas DNA recovered from infected cells included proteins known to enable transcription and proteins that have not been previously associated with that role. To test the hypothesis that the proteins hitherto not known to associate with viral gene expression are nevertheless essential, we tested the role of the DEAD-box helicase Ddx17. We report that Ddx17 plays a critical role in the expression of early and late viral genes. Thus, biotinylated DNA recovered from transfected infected cells can function as a surrogate for viral DNA and is a rich source of proteins that play a role in viral gene expression but which have not been previously identified in that role.

  15. Toward a Psychology of Surrogate Decision Making.

    PubMed

    Tunney, Richard J; Ziegler, Fenja V

    2015-11-01

    In everyday life, many of the decisions that we make are made on behalf of other people. A growing body of research suggests that we often, but not always, make different decisions on behalf of other people than the other person would choose. This is problematic in the practical case of legally designated surrogate decision makers, who may not meet the substituted judgment standard. Here, we review evidence from studies of surrogate decision making and examine the extent to which surrogate decision making accurately predicts the recipient's wishes, or if it is an incomplete or distorted application of the surrogate's own decision-making processes. We find no existing domain-general model of surrogate decision making. We propose a framework by which surrogate decision making can be assessed and a novel domain-general theory as a unifying explanatory concept for surrogate decisions.

  16. SitesIdentify: a protein functional site prediction tool

    PubMed Central

    2009-01-01

    Background The rate of protein structures being deposited in the Protein Data Bank surpasses the capacity to experimentally characterise them and therefore computational methods to analyse these structures have become increasingly important. Identifying the region of the protein most likely to be involved in function is useful in order to gain information about its potential role. There are many available approaches to predict functional site, but many are not made available via a publicly-accessible application. Results Here we present a functional site prediction tool (SitesIdentify), based on combining sequence conservation information with geometry-based cleft identification, that is freely available via a web-server. We have shown that SitesIdentify compares favourably to other functional site prediction tools in a comparison of seven methods on a non-redundant set of 237 enzymes with annotated active sites. Conclusion SitesIdentify is able to produce comparable accuracy in predicting functional sites to its closest available counterpart, but in addition achieves improved accuracy for proteins with few characterised homologues. SitesIdentify is available via a webserver at http://www.manchester.ac.uk/bioinformatics/sitesidentify/ PMID:19922660

  17. Identify the dominant variables to predict stream water temperature

    NASA Astrophysics Data System (ADS)

    Chien, H.; Flagler, J.

    2016-12-01

    Stream water temperature is a critical variable controlling water quality and the health of aquatic ecosystems. Accurate prediction of water temperature and the assessment of the impacts of environmental variables on water temperature variation are critical for water resources management, particularly in the context of water quality and aquatic ecosystem sustainability. The objective of this study is to measure stream water temperature and air temperature and to examine the importance of streamflow on stream water temperature prediction. The measured stream water temperature and air temperature will be used to test two hypotheses: 1) streamflow is a relatively more important factor than air temperature in regulating water temperature, and 2) by combining air temperature and streamflow data stream water temperature can be more accurately estimated. Water and air temperature data loggers are placed at two USGS stream gauge stations #01362357and #01362370, located in the upper Esopus Creek watershed in Phonecia, NY. The ARIMA (autoregressive integrated moving average) time series model is used to analyze the measured water temperature data, identify the dominant environmental variables, and predict the water temperature with identified dominant variable. The preliminary results show that streamflow is not a significant variable in predicting stream water temperature at both USGS gauge stations. Daily mean air temperature is sufficient to predict stream water temperature at this site scale.

  18. 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

  19. A novel anti-Vpre-B antibody identifies immunoglobulin-surrogate receptors on the surface of human pro-B cells

    PubMed Central

    1996-01-01

    Vpre-B and lambda 5 genes, respectively, encode V-like and C-like domains of a surrogate immunoglobulin light chain (psi L). Such psi L complex is expressed in early progenitor B (pro-B) cells, before conventional immunoglobulin heavy (microH) and light (L) chains are produced. We raised a wide panel of monoclonal antibodies (mAbs) against soluble recombinant Vpre-B proteins to study early events in human B cell development. One of these antibodies, B-MAD688, labeled surrogate Ig-complexes on the surface of microH- pro-B cell lines and normal bone marrow cells in immunofluorescence assays. Immunoprecipitations using surface-labeled pro-B cells and B-MAD688 mAb indicated that human psi L is associated with high molecular weight components homologous to the surrogate heavy (psi H) chains described in mouse. Using B-MAD688 and SLC2 mAbs, we were able to distinguish between psi H psi L and microH psi L complexes on the surface of human pro-B and later precursor, pre-B, cells. The finding of psi H psi L complexes in mouse and man lead us to hypothesize a role for psi H- containing receptors in B cell development. PMID:8676092

  20. A predictive approach to identify genes differentially expressed

    NASA Astrophysics Data System (ADS)

    Saraiva, Erlandson F.; Louzada, Francisco; Milan, Luís A.; Meira, Silvana; Cobre, Juliana

    2012-10-01

    The main objective of gene expression data analysis is to identify genes that present significant changes in expression levels between a treatment and a control biological condition. In this paper, we propose a Bayesian approach to identify genes differentially expressed calculating credibility intervals from predictive densities which are constructed using sampled mean treatment effect from all genes in study excluding the treatment effect of genes previously identified with statistical evidence for difference. We compare our Bayesian approach with the standard ones based on the use of the t-test and modified t-tests via a simulation study, using small sample sizes which are common in gene expression data analysis. Results obtained indicate that the proposed approach performs better than standard ones, especially for cases with mean differences and increases in treatment variance in relation to control variance. We also apply the methodologies to a publicly available data set on Escherichia coli bacteria.

  1. Mortality predictability of body size and muscle mass surrogates in Asian vs white and African American hemodialysis patients.

    PubMed

    Park, Jongha; Jin, Dong Chan; Molnar, Miklos Z; Dukkipati, Ramanath; Kim, Yong-Lim; Jing, Jennie; Levin, Nathan W; Nissenson, Allen R; Lee, Jong Soo; Kalantar-Zadeh, Kamyar

    2013-05-01

    To determine whether the association of body size and muscle mass with survival among patients undergoing long-term hemodialysis (HD) is consistent across race, especially in East Asian vs white and African American patients. Using data from 20,818 patients from South Korea who underwent HD from February 1, 2001, to June 30, 2009, and 20,000 matched patients from the United States (10,000 whites and 10,000 African Americans) who underwent HD from July 1, 2001, to June 30, 2006, we compared mortality associations of baseline body mass index (BMI) and serum creatinine level as likely surrogates of obesity and muscle mass across the 3 races. In Korean HD patients, higher BMI together with higher serum creatinine levels were associated with greater survival, as previously reported from US and European studies. In the matched cohort (10,000 patients from each of the 3 races), mortality risks were lower across higher BMI and serum creatinine levels, and these associations were similar in all 3 races (reference groups: patients with BMI >25.0 kg/m(2) or serum creatinine >12 mg/dL in each race). White, African American, and Korean patients with BMI levels of 18.5 kg/m(2) or less (underweight) had 78%, 79%, and 57% higher mortality risk, respectively, and white, African American, and Korean patients with serum creatinine levels of 6.0 mg/dL or less had 108%, 87%, and 78% higher mortality, respectively. This study shows that race does not modify the association of higher body size and muscle mass with greater survival in HD patients. Given the consistency of the obesity paradox, which may be related to a mitigated effect of protein-energy wasting on mortality irrespective of racial disparities, nutritional support to improve survival should be tested in HD patients of all races. Copyright © 2013 Mayo Foundation for Medical Education and Research. Published by Elsevier Inc. All rights reserved.

  2. 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.

  3. 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

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

    PubMed

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

    2016-03-10

    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.

  5. Total lymphocyte count as a surrogate marker to predict CD4 count in human immunodeficiency virus-infected children: a retrospective evaluation.

    PubMed

    Wang, Yuming; Li, Yuqian; Wang, Chongjian; Liang, Shuying; Guo, Jinling; Li, Zizhao; Zhang, Meixi; Li, Wenjie

    2012-01-01

    A retrospective study was conducted, and 576 human immunodeficiency virus-infected children with total lymphocyte count (TLC) and CD4 count were recruited from China. Spearman rank order correlation and receiver-operating characteristic were used. An overall positive correlation was noted between TLC and CD4 count (prehighly active antiretroviral therapy [pre-HAART], r = 0.789, 6 months of HAART, r = 0.642, 12 months of HAART, r = 0.691, P = 0.001). TLC ≤ 2600 cells/mm(3) predicted a CD4 count of ≤ 350 cells/mm(3) with 82.9% sensitivity, 79.6% specificity pre-HAART. Meanwhile, the optimum prediction for CD4 count of ≤ 350 cells/mm(3) was a TLC of ≤ 2400 cells/mm at 6 months (73.6% sensitivity and 74.1% specificity) and 12 months (81.7% sensitivity and 76.5% specificity) of HAART. TLC can be used as a surrogate marker for predicting CD4 count of human immunodeficiency virus-infected children before and during HAART in resource-limited countries.

  6. 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.

  7. Subarachnoid hemorrhage admissions retrospectively identified using a prediction model

    PubMed Central

    McIntyre, Lauralyn; Fergusson, Dean; Turgeon, Alexis; dos Santos, Marlise P.; Lum, Cheemun; Chassé, Michaël; Sinclair, John; Forster, Alan; van Walraven, Carl

    2016-01-01

    Objective: To create an accurate prediction model using variables collected in widely available health administrative data records to identify hospitalizations for primary subarachnoid hemorrhage (SAH). Methods: A previously established complete cohort of consecutive primary SAH patients was combined with a random sample of control hospitalizations. Chi-square recursive partitioning was used to derive and internally validate a model to predict the probability that a patient had primary SAH (due to aneurysm or arteriovenous malformation) using health administrative data. Results: A total of 10,322 hospitalizations with 631 having primary SAH (6.1%) were included in the study (5,122 derivation, 5,200 validation). In the validation patients, our recursive partitioning algorithm had a sensitivity of 96.5% (95% confidence interval [CI] 93.9–98.0), a specificity of 99.8% (95% CI 99.6–99.9), and a positive likelihood ratio of 483 (95% CI 254–879). In this population, patients meeting criteria for the algorithm had a probability of 45% of truly having primary SAH. Conclusions: Routinely collected health administrative data can be used to accurately identify hospitalized patients with a high probability of having a primary SAH. This algorithm may allow, upon validation, an easy and accurate method to create validated cohorts of primary SAH from either ruptured aneurysm or arteriovenous malformation. PMID:27629096

  8. Surrogate endpoint analysis: an exercise in extrapolation.

    PubMed

    Baker, Stuart G; Kramer, Barnett S

    2013-03-06

    Surrogate endpoints offer the hope of smaller or shorter cancer trials. It is, however, important to realize they come at the cost of an unverifiable extrapolation that could lead to misleading conclusions. With cancer prevention, the focus is on hypothesis testing in small surrogate endpoint trials before deciding whether to proceed to a large prevention trial. However, it is not generally appreciated that a small surrogate endpoint trial is highly sensitive to a deviation from the key Prentice criterion needed for the hypothesis-testing extrapolation. With cancer treatment, the focus is on estimation using historical trials with both surrogate and true endpoints to predict treatment effect based on the surrogate endpoint in a new trial. Successively leaving out one historical trial and computing the predicted treatment effect in the left-out trial yields a standard error multiplier that summarizes the increased uncertainty in estimation extrapolation. If this increased uncertainty is acceptable, three additional extrapolation issues (biological mechanism, treatment following observation of the surrogate endpoint, and side effects following observation of the surrogate endpoint) need to be considered. In summary, when using surrogate endpoint analyses, an appreciation of the problems of extrapolation is crucial.

  9. 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

  10. The [(13)c]glucose breath test is a reliable method to identify insulin resistance in Mexican adults without diabetes: comparison with other insulin resistance surrogates.

    PubMed

    Maldonado-Hernández, Jorge; Martínez-Basila, Azucena; Matute-González, María Guadalupe; López-Alarcón, Mardia

    2014-06-01

    Insulin resistance (IR) precedes type 2 diabetes, but tests used to detect it in clinical settings reported poor reproducibility. We assessed the reliability of the [(13)C]glucose breath test ((13)C-GBT) in a sample of subjects without diabetes. Repeatability of the test was compared with that of other IR surrogates derived from the fasting or oral glucose tolerance test (OGTT). Eighty-six healthy volunteers received an oral load of 75 g of glucose in 150 mL of water followed by 1.5 mg/kg of [U-(13)C]glucose in 50 mL of water. Breath and blood samples were collected at baseline and at 10, 20, 30, 60, 90, 120, 150, and 180 min following the glucose load; the same procedure was repeated within 1 week. The enrichment of breath (13)CO2 was measured by ratio mass spectrometry and expressed as percentage oxidized dose at a given time period. Intrasubject variability was assessed with Bland-Altman plots and coefficients of variation (CVs). The overall CV of the (13)C-GBT was 12.99±11.61%, compared with 18.42% of fasting insulin, 19.44% for homeostasis model assessment, 17.06% of the composite insulin sensitivity index, and 29.99% for insulin in the 2-h oral glucose tolerance test. The variability of the (13)C-GBT tended to be higher in lean (17.40%) than in overweight (10.17%) and obese (12.61%) individuals. The variability of the (13)C-GBT is lower than that of other IR surrogates, making it a reproducible method to estimate insulin sensitivity in overweight and obese adults without diabetes. Because the individuals did not have diabetes but were within a high range of insulin sensitivity, the test should have application in clinical and population-based studies, given the evidence for the utility and limitations of this surrogate.

  11. 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

  12. A surrogate for topical delivery in human skin: silicone membranes.

    PubMed

    Sloan, Kenneth B; Synovec, Jennifer; Ketha, Hemamalini

    2013-02-01

    We have identified, for any surrogate membrane and human skin in vitro, the maximum flux through the membrane (output) should be measured if a correlation between the two is to be obtained. We also identified from an analysis of the passive permeation process that molecular weight, lipid and aqueous solubilities (which are easily measured) constitute the physicochemical properties of the active (input), upon which prediction of flux through the surrogate membrane and through skin in vitro should be based. Besides providing the bases for predicting flux, changes in these physicochemical properties can be easily implemented by those wishing to optimize new cosmetics or topical products. Maximum flux values through silicone membrane (n = 70) and through human skin in vitro (n = 52) have been collected and a good correlation between the flux through human skin in vitro and flux through silicone membrane (for the same molecules) was found.

  13. Doubt and belief in physicians' ability to prognosticate during critical illness: the perspective of surrogate decision makers.

    PubMed

    Zier, Lucas S; Burack, Jeffrey H; Micco, Guy; Chipman, Anne K; Frank, James A; Luce, John M; White, Douglas B

    2008-08-01

    Although discussing a prognosis is a duty of physicians caring for critically ill patients, little is known about surrogate decision-makers' beliefs about physicians' ability to prognosticate. We sought to determine: 1) surrogates' beliefs about whether physicians can accurately prognosticate for critically ill patients; and 2) how individuals use prognostic information in their role as surrogate decision-makers. Multicenter study in intensive care units of a public hospital, a tertiary care hospital, and a veterans' hospital. We conducted semistructured interviews with 50 surrogate decision-makers of critically ill patients. We analyzed the interview transcripts using grounded theory methods to inductively develop a framework to describe surrogates' beliefs about physicians' ability to prognosticate. Validation methods included triangulation by multidisciplinary analysis and member checking. Overall, 88% (44 of 50) of surrogates expressed doubt about physicians' ability to prognosticate for critically ill patients. Four distinct themes emerged that explained surrogates' doubts about prognostic accuracy: a belief that God could alter the course of the illness, a belief that predicting the future is inherently uncertain, prior experiences where physicians' prognostications were inaccurate, and experiences with prognostication during the patient's intensive care unit stay. Participants also identified several factors that led to belief in physicians' prognostications, such as receiving similar prognostic estimates from multiple physicians and prior experiences with accurate prognostication. Surrogates' doubts about prognostic accuracy did not prevent them from wanting prognostic information. Instead, most surrogate decision-makers view physicians' prognostications as rough estimates that are valuable in informing decisions, but are not determinative. Surrogates identified the act of prognostic disclosure as a key step in preparing emotionally and practically for the

  14. 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.

  15. 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

  16. Surrogate Poster Artist Concept

    NASA Image and Video Library

    2015-03-11

    This artist's concept shows Surrogate, a robot that could one day assist in disasters or hazardous situations such as a dangerous chemical laboratory. Surrogate was designed and built at the Jet Propulsion Laboratory in Pasadena, California. Its components came from RoboSimian, another JPL-built robot designed for disaster relief and mitigation (see PIA19313). Surrogate rolls on a track rather than moving on its limbs. http://photojournal.jpl.nasa.gov/catalog/PIA19314

  17. Evaluating deceased donor registries: identifying predictive factors of donor designation.

    PubMed

    Hajhosseini, Babak; Stewart, Bryan; Tan, Jane C; Busque, Stephan; Melcher, Marc L

    2013-03-01

    The objectives of this study were to evaluate and compare the performance of the deceased donor registries of the 50 states and the District of Columbia and to identify possible predictive factors of donor designation. Data were collected retrospectively by Donate Life America using a questionnaire sent to Donor Designation Collaborative state teams between 2007 and 2010. By the end of 2010, there were 94,669,081 designated donors nationwide. This accounted for 39.8 per cent of the U.S. population aged 18 years and over. The number of designated organ donors and registry-authorized recovered donors increased each year; however, the total number of recovered donors in 2010 was the lowest since 2004. Donor designation rate was significantly higher when license applicants were verbally questioned at the Department of Motor Vehicles (DMV) regarding their willingness to register as a donor and when DMV applicants were not given an option on DMV application forms to contribute money to support organ donation, compared with not being questioned verbally, and being offered an option to contribute money. State registries continue to increase the total number of designated organ donors; however, the current availability of organs remains insufficient to meet the demand. These data suggest that DMV applicants who are approached verbally regarding their willingness to register as a donor and not given an option on DMV application forms to contribute money to support organ donation might be more likely to designate themselves to be a donor.

  18. 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

    2015-09-08

    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.

  19. 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

  20. Evaluating surrogate marker information using censored data.

    PubMed

    Parast, Layla; Cai, Tianxi; Tian, Lu

    2017-01-15

    Given the long follow-up periods that are often required for treatment or intervention studies, the potential to use surrogate markers to decrease the required follow-up time is a very attractive goal. However, previous studies have shown that using inadequate markers or making inappropriate assumptions about the relationship between the primary outcome and surrogate marker can lead to inaccurate conclusions regarding the treatment effect. Currently available methods for identifying and validating surrogate markers tend to rely on restrictive model assumptions and/or focus on uncensored outcomes. The ability to use such methods in practice when the primary outcome of interest is a time-to-event outcome is difficult because of censoring and missing surrogate information among those who experience the primary outcome before surrogate marker measurement. In this paper, we propose a novel definition of the proportion of treatment effect explained by surrogate information collected up to a specified time in the setting of a time-to-event primary outcome. Our proposed approach accommodates a setting where individuals may experience the primary outcome before the surrogate marker is measured. We propose a robust non-parametric procedure to estimate the defined quantity using censored data and use a perturbation-resampling procedure for variance estimation. Simulation studies demonstrate that the proposed procedures perform well in finite samples. We illustrate the proposed procedures by investigating two potential surrogate markers for diabetes using data from the Diabetes Prevention Program. Copyright © 2017 John Wiley & Sons, Ltd.

  1. Surrogate Endpoint Evaluation: Principal Stratification Criteria and the Prentice Definition.

    PubMed

    Gilbert, Peter B; Gabriel, Erin E; Huang, Ying; Chan, Ivan S F

    2015-09-01

    A common problem of interest within a randomized clinical trial is the evaluation of an inexpensive response endpoint as a valid surrogate endpoint for a clinical endpoint, where a chief purpose of a valid surrogate is to provide a way to make correct inferences on clinical treatment effects in future studies without needing to collect the clinical endpoint data. Within the principal stratification framework for addressing this problem based on data from a single randomized clinical efficacy trial, a variety of definitions and criteria for a good surrogate endpoint have been proposed, all based on or closely related to the "principal effects" or "causal effect predictiveness (CEP)" surface. We discuss CEP-based criteria for a useful surrogate endpoint, including (1) the meaning and relative importance of proposed criteria including average causal necessity (ACN), average causal sufficiency (ACS), and large clinical effect modification; (2) the relationship between these criteria and the Prentice definition of a valid surrogate endpoint; and (3) the relationship between these criteria and the consistency criterion (i.e., assurance against the "surrogate paradox"). This includes the result that ACN plus a strong version of ACS generally do not imply the Prentice definition nor the consistency criterion, but they do have these implications in special cases. Moreover, the converse does not hold except in a special case with a binary candidate surrogate. The results highlight that assumptions about the treatment effect on the clinical endpoint before the candidate surrogate is measured are influential for the ability to draw conclusions about the Prentice definition or consistency. In addition, we emphasize that in some scenarios that occur commonly in practice, the principal strata sub-populations for inference are identifiable from the observable data, in which cases the principal stratification framework has relatively high utility for the purpose of effect

  2. Surrogate Endpoint Evaluation: Principal Stratification Criteria and the Prentice Definition

    PubMed Central

    Gilbert, Peter B.; Gabriel, Erin E.; Huang, Ying; Chan, Ivan S.F.

    2015-01-01

    A common problem of interest within a randomized clinical trial is the evaluation of an inexpensive response endpoint as a valid surrogate endpoint for a clinical endpoint, where a chief purpose of a valid surrogate is to provide a way to make correct inferences on clinical treatment effects in future studies without needing to collect the clinical endpoint data. Within the principal stratification framework for addressing this problem based on data from a single randomized clinical efficacy trial, a variety of definitions and criteria for a good surrogate endpoint have been proposed, all based on or closely related to the “principal effects” or “causal effect predictiveness (CEP)” surface. We discuss CEP-based criteria for a useful surrogate endpoint, including (1) the meaning and relative importance of proposed criteria including average causal necessity (ACN), average causal sufficiency (ACS), and large clinical effect modification; (2) the relationship between these criteria and the Prentice definition of a valid surrogate endpoint; and (3) the relationship between these criteria and the consistency criterion (i.e., assurance against the “surrogate paradox”). This includes the result that ACN plus a strong version of ACS generally do not imply the Prentice definition nor the consistency criterion, but they do have these implications in special cases. Moreover, the converse does not hold except in a special case with a binary candidate surrogate. The results highlight that assumptions about the treatment effect on the clinical endpoint before the candidate surrogate is measured are influential for the ability to draw conclusions about the Prentice definition or consistency. In addition, we emphasize that in some scenarios that occur commonly in practice, the principal strata sub-populations for inference are identifiable from the observable data, in which cases the principal stratification framework has relatively high utility for the purpose of

  3. Non-invasive combined surrogates of remifentanil blood concentrations with relevance to analgesia.

    PubMed

    Lötsch, Jörn; Skarke, Carsten; Darimont, Jutta; Zimmermann, Michael; Bräutigam, Lutz; Geisslinger, Gerd; Ultsch, Alfred; Oertel, Bruno G

    2013-10-01

    Surrogates may provide easy and quick access to information about pharmacological parameters of interest that can be directly measured only with difficulty. Surrogates have been proposed for opioid blood concentrations to replace invasive sampling, serving as a basis for target-controlled infusion systems to optimize analgesia. We aimed at identifying surrogates of remifentanil steady-state blood concentrations with relevance for its clinical, in particular, analgesic, effects. A "single ascending dose" study design assessed concentration-dependent effects of remifentanil in a double-blind randomized fashion in 16 healthy volunteers. Remifentanil was administered by means of computerized infusion aimed at steady-state effect-site concentrations of 0, 1.2, 1.8, 2.4, 3, 3.6, 4.8, and 6 ng/ml (one concentration per subject, two subjects per concentration). Arterial remifentanil blood concentrations were measured during apparent steady state. Pharmacodynamic parameters were measured at baseline and during steady-state conditions. Potential surrogate parameters included the pupil diameter, the amplitude of pupil light reflex, and the performance in a visual tracking task. Clinical parameters were analgesia to experimental pain, nausea, tiredness, and visual acuity. Remifentanil blood concentrations were well predicted by its effects on the pupil light reflex amplitude, better than by its miotic effects. However, the best prediction for both remifentanil blood concentrations and analgesic effects was obtained using a combination of three surrogate parameters (pupil diameter, light reflex amplitude, and tracking performance). This combination of pharmacodynamic parameters provided even better predictions of analgesia than could be obtained using the measured opioid blood concentrations. Developing surrogates only for opioid blood concentrations is insufficient when opioid effects are the final goal. Combining pharmacodynamic surrogate parameters seems to provide a

  4. 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,…

  5. Jet Pump Design Optimization by Multi-Surrogate Modeling

    NASA Astrophysics Data System (ADS)

    Mohan, S.; Samad, A.

    2014-09-01

    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.

  6. 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.

  7. 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

  8. 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.

  9. Ethical frameworks for surrogates' end-of-life planning experiences.

    PubMed

    Kim, Hyejin; Deatrick, Janet A; Ulrich, Connie M

    2017-02-01

    Despite the growing body of knowledge about surrogate decision making, we know very little about the use of ethical frameworks (including ethical theories, principles, and concepts) to understand surrogates' day-to-day experiences in end-of-life care planning for incapacitated adults. This qualitative systematic review was conducted to identify the types of ethical frameworks used to address surrogates' experiences in end-of-life care planning for incapacitated adults as well as the most common themes or patterns found in surrogate decision-making research. Seven research papers explicitly identified ethical theories, principles, or concepts, such as autonomy, substituted judgment, and best interest standards as guidelines for the research. Surrogate decision making themes included the responsibilities and goals of being a surrogate, factors influencing surrogates' decision making, outcomes for surrogates, and an overarching theme of "wanting to do the right thing" for their loved one and/or themselves. Understanding the complexity of surrogates' experiences of end-of-life care planning is beyond the scope of conventional ethical frameworks. Ethical frameworks that address individuality and contextual variations related to decision making may more appropriately guide surrogate decision-making research that explores surrogates' end-of-life care planning experiences.

  10. Use of fluconazole as a surrogate marker to predict susceptibility and resistance to voriconazole among 13,338 clinical isolates of Candida spp. Tested by clinical and laboratory standards institute-recommended broth microdilution methods.

    PubMed

    Pfaller, M A; Messer, S A; Boyken, L; Rice, C; Tendolkar, S; Hollis, R J; Diekema, D J

    2007-01-01

    Clinical laboratories frequently face the problem of delayed availability of commercially prepared approved reagents for performing susceptibility testing of new antimicrobials. Although this problem is encountered more often with antibacterial agents, it is also an issue with antifungal agents. A current example is voriconazole, a new triazole antifungal with an expanded spectrum and potency against Candida spp., Aspergillus spp., and other opportunistic fungal pathogens. The present study addresses the use of fluconazole as a surrogate marker to predict the susceptibility of Candida spp. to voriconazole. Reference broth microdilution MIC results for 13,338 strains of Candida spp. isolated from more than 200 medical centers worldwide were used. Voriconazole MICs and interpretive categories (susceptible, < or =1 microg/ml; susceptible dose dependent, 2 microg/ml; resistant, > or =4 microg/ml) were compared with those of fluconazole by regression statistics and error rate bounding analyses. For all 13,338 isolates, the absolute categorical agreement was 91.6% (false susceptible or very major error [VME], 0.0%). Since voriconazole is 16- to 32-fold more potent than fluconazole, the performance of fluconazole as a surrogate marker for voriconazole susceptibility was improved by designating those isolates with fluconazole MICs of < or =32 microg/ml as being susceptible to voriconazole, resulting in a categorical agreement of 97% with 0.1% VME. Clinical laboratories performing antifungal susceptibility testing of fluconazole against Candida spp. can reliably use these results as surrogate markers until commercial FDA-approved voriconazole susceptibility tests become available.

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

    PubMed

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

    2015-01-01

    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. 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. 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. 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.

  12. Surrogate end points in secondary analyses of cardiovascular trials.

    PubMed

    Buhr, Kevin A

    2012-01-01

    A surrogate end point is one that is used as a substitute for a clinical end point of more direct interest, usually for reasons of practicality, and that is expected to predict clinical benefit. Surrogate end points play a critical role in the advancement of all medical research, and cardiovascular (CV) research in particular. However, the relationship between a surrogate end point and its clinical end point is usually complex, and there are many examples where results based on surrogates have proved to be misleading. Secondary analyses of existing clinical trial data are likely to involve surrogate end points, if only because clinical end points will have been extensively studied as part of the primary analysis of a trial large enough to collect useful clinical end point data. Validation of a surrogate end point is a laudable goal for a secondary analysis of a large clinical end point trial (or meta-analysis of multiple smaller trials), and the result may be an important new tool for further study of a class of compounds in a particular disease context. Secondary analyses using surrogate end points may also provide new insight into disease or treatment mechanism, but as with any surrogate end point analysis, the results can mislead, and the existing literature is heavy on application and light on methodology. Surrogate end points often substitute efficiency for clarity, and while many interesting and potentially informative secondary analyses of CV trials will involve surrogates, results are likely to be ambiguous and should be interpreted with care.

  13. TU-AB-303-02: A Novel Surrogate to Identify Anatomical Changes During Radiotherapy of Head and Neck Cancer Patients

    SciTech Connect

    Gros, S; Roeske, J; Surucu, M

    2015-06-15

    Purpose: To develop a novel method to monitor external anatomical changes in head and neck cancer patients in order to help guide adaptive radiotherapy decisions. Methods: The method, developed in MATLAB, reveals internal anatomical changes based on variations observed in external anatomy. Weekly kV-CBCT scans from 11 Head and neck patients were retrospectively analyzed. The pre-processing step first corrects each CBCT for artifacts and removes pixels from the immobilization mask to produce an accurate external contour of the patient’s skin. After registering the CBCTs to the initial planning CT, the external contours from each CBCT (CBCTn) are transferred to the first week — reference — CBCT{sub 1}. Contour radii, defined as the distances between an external contour and the central pixel of each CBCT slice, are calculated for each scan at angular increments of 1 degree. The changes in external anatomy are then quantified by the difference in radial distance between the external contours of CBCT1 and CBCTn. The radial difference is finally displayed on a 2D intensity map (angle vs radial distance difference) in order to highlight regions of interests with significant changes. Results: The 2D radial difference maps provided qualitative and quantitative information, such as the location and the magnitude of external contour divergences and the rate at which these deviations occur. With this method, anatomical changes due to tumor volume shrinkage and patient weight loss were clearly identified and could be correlated with the under-dosage of targets or over-dosage of OARs. Conclusion: This novel method provides an efficient tool to visualize 3D external anatomical modification on a single 2D map. It quickly pinpoints the location of differences in anatomy during the course of radiotherapy, which can help determine if a treatment plan needs to be adapted.

  14. Plutonium radiation surrogate

    DOEpatents

    Frank, Michael I [Dublin, CA

    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.

  15. Use of anidulafungin as a surrogate marker to predict susceptibility and resistance to caspofungin among 4,290 clinical isolates of Candida by using CLSI methods and interpretive criteria.

    PubMed

    Pfaller, Michael A; Diekema, Daniel J; Jones, Ronald N; Castanheira, Mariana

    2014-09-01

    This study addressed the application of anidulafungin as a surrogate marker to predict the susceptibility of Candida to caspofungin due to unacceptably high interlaboratory variation of caspofungin MIC values. CLSI reference broth microdilution methods and species-specific interpretive criteria were used to test 4,290 strains of Candida (eight species), including 71 strains with documented fks mutations. Caspofungin MIC values were compared with those of anidulafungin to determine the percentage of categorical agreement (CA) and very major (VME), major (ME), and minor error rates, as well as the ability to detect fks mutants. For all 4,290 isolates the CA was 97.1% (0.2% VME and ME, 2.5% minor errors) using anidulafungin as the surrogate. Among the 62 isolates of Candida albicans (4 isolates), C. tropicalis (5 isolates), C. krusei (4 isolates), C. kefyr (2 isolates), and C. glabrata (47 isolates) that were nonsusceptible (NS; either intermediate [I] or resistant [R]) to both caspofungin and anidulafungin, 52 (83.8%) contained a mutation in fks1 or fks2. Eight mutants of C. glabrata, two of C. albicans, and one each of C. tropicalis and C. krusei were classified as susceptible (S) to both antifungal agents. The remaining 7 mutants (2 C. albicans and 5 C. glabrata) were susceptible to one of the agents and either intermediate or resistant to the other. Using the epidemiological cutoff value (ECV) of 0.12 μg/ml for both caspofungin and anidulafungin to differentiate wild-type (WT) from non-WT strains of C. glabrata, 42 of the 55 (76.4%) C. glabrata mutants were non-WT and 8 of the 55 (14.5%) were WT for both agents (90.9% concordance). Anidulafungin can accurately serve as a surrogate marker to predict S and R of Candida to caspofungin.

  16. Use of micafungin as a surrogate marker to predict susceptibility and resistance to caspofungin among 3,764 clinical isolates of Candida by use of CLSI methods and interpretive criteria.

    PubMed

    Pfaller, Michael A; Messer, Shawn A; Diekema, Daniel J; Jones, Ronald N; Castanheira, Mariana

    2014-01-01

    Due to unacceptably high interlaboratory variation in caspofungin MIC values, we evaluated the use of micafungin as a surrogate marker to predict the susceptibility of Candida spp. to caspofungin using reference methods and species-specific interpretive criteria. The MIC results for 3,764 strains of Candida (eight species), including 73 strains with fks mutations, were used. Caspofungin MIC values and species-specific interpretive criteria were compared with those of micafungin to determine the percent categorical agreement (%CA) and very major error (VME), major error (ME), and minor error rates as well as their ability to detect fks mutant strains of Candida albicans (11 mutants), Candida tropicalis (4 mutants), Candida krusei (3 mutants), and Candida glabrata (55 mutants). Overall, the %CA was 98.8% (0.2% VMEs and MEs, 0.8% minor errors) using micafungin as the surrogate marker. Among the 60 isolates of C. albicans (9 isolates), C. tropicalis (5 isolates), C. krusei (2 isolates), and C. glabrata (44 isolates) that were nonsusceptible (either intermediate or resistant) to both caspofungin and micafungin, 54 (90.0%) contained a mutation in fks1 or fks2. An additional 10 C. glabrata mutants, two C. albicans mutants, and one mutant each of C. tropicalis and C. krusei were classified as susceptible to both antifungal agents. Using the epidemiological cutoff values (ECVs) of 0.12 μg/ml for caspofungin and 0.03 μg/ml for micafungin to differentiate wild-type (WT) from non-WT strains of C. glabrata, 80% of the C. glabrata mutants were non-WT for both agents (96% concordance). Micafungin may serve as an acceptable surrogate marker for the prediction of susceptibility and resistance of Candida to caspofungin.

  17. Estimating Comparable Scores Using Surrogate Variables.

    ERIC Educational Resources Information Center

    Liou, Michelle; Cheng, Philip E.; Li, Ming-Yen

    2001-01-01

    Studied the possibility of using surrogate variables, such as school grades, test scores, or examinee background, as replacements for common terms predicting sample-selection bias between groups. Proposed a general model for estimating complete data (fitted) distributions through covariates and estimated model parameters using the EM algorithm.…

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

    PubMed Central

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

    2015-01-01

    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

  19. 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.

  20. 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…

  1. 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.

  2. [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.

  3. 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)

  4. Stochastic surrogate Hamiltonian

    NASA Astrophysics Data System (ADS)

    Katz, Gil; Gelman, David; Ratner, Mark A.; Kosloff, Ronnie

    2008-07-01

    The surrogate Hamiltonian is a general scheme to simulate the many body quantum dynamics composed of a primary system coupled to a bath. The method has been based on a representative bath Hamiltonian composed of two-level systems that is able to mimic the true system-bath dynamics up to a prespecified time. The original surrogate Hamiltonian method is limited to short time dynamics since the size of the Hilbert space required to obtain convergence grows exponentially with time. By randomly swapping bath modes with a secondary thermal reservoir, the method can simulate quantum dynamics of the primary system from short times to thermal equilibrium. By averaging a small number of realizations converged values of the system observables are obtained avoiding the exponential increase in resources. The method is demonstrated for the equilibration of a molecular oscillator with a thermal bath.

  5. Stochastic surrogate Hamiltonian

    SciTech Connect

    Katz, Gil; Kosloff, Ronnie; Gelman, David; Ratner, Mark A.

    2008-07-21

    The surrogate Hamiltonian is a general scheme to simulate the many body quantum dynamics composed of a primary system coupled to a bath. The method has been based on a representative bath Hamiltonian composed of two-level systems that is able to mimic the true system-bath dynamics up to a prespecified time. The original surrogate Hamiltonian method is limited to short time dynamics since the size of the Hilbert space required to obtain convergence grows exponentially with time. By randomly swapping bath modes with a secondary thermal reservoir, the method can simulate quantum dynamics of the primary system from short times to thermal equilibrium. By averaging a small number of realizations converged values of the system observables are obtained avoiding the exponential increase in resources. The method is demonstrated for the equilibration of a molecular oscillator with a thermal bath.

  6. The physician-surrogate relationship.

    PubMed

    Torke, Alexia M; Alexander, G Caleb; Lantos, John; Siegler, Mark

    2007-06-11

    The physician-patient relationship is a cornerstone of the medical encounter and has been analyzed extensively. But in many cases, this relationship is altered because patients are unable to make decisions for themselves. In such cases, physicians rely on surrogates, who are often asked to "speak for the patient." This view overlooks the fundamental fact that the surrogate decision maker cannot be just a passive spokesperson for the patient but is also an active agent who develops a complex relationship with the physician. Although there has been much analysis of the ethical guidelines by which surrogates should make decisions, there has been little previous analysis of the special features of the physician-surrogate relationship. Such an analysis seems crucial as the population ages and life-sustaining technologies improve, which is likely to make surrogate decision making even more common. We outline key issues affecting the physician-surrogate relationship and provide guidance for physicians who are making decisions with surrogates.

  7. 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

  8. Characterization of Potential Surrogates for Produce Pathogens

    USDA-ARS?s Scientific Manuscript database

    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...

  9. The Prognostic Nutritional Index Predicts Survival and Identifies Aggressiveness of Gastric Cancer.

    PubMed

    Eo, Wan Kyu; Chang, Hye Jung; Suh, Jungho; Ahn, Jin; Shin, Jeong; Hur, Joon-Young; Kim, Gou Young; Lee, Sookyung; Park, Sora; Lee, Sanghun

    2015-01-01

    Nutritional status has been associated with long-term outcomes in cancer patients. The prognostic nutritional index (PNI) is calculated by serum albumin concentration and absolute lymphocyte count, and it may be a surrogate biomarker for nutritional status and possibly predicts overall survival (OS) of gastric cancer. We evaluated the value of the PNI as a predictor for disease-free survival (DFS) in addition to OS in a cohort of 314 gastric cancer patients who underwent curative surgical resection. There were 77 patients in PNI-low group (PNI ≤ 47.3) and 237 patients in PNI-high group (PNI > 47.3). With a median follow-up of 36.5 mo, 5-yr DFS rates in PNI-low group and PNI-high group were 63.5% and 83.6% and 5-yr OS rates in PNI-low group and PNI-high group were 63.5% and 88.4%, respectively (DFS, P < 0.0001; OS, P < 0.0001). In the multivariate analysis, the only predictors for DFS were PNI, tumor-node-metastasis (TNM) stage, and perineural invasion, whereas the only predictors for OS were PNI, age, TNM stage, and perineural invasion. In addition, the PNI was independent of various inflammatory markers. In conclusion, the PNI is an independent prognostic factor for both DFS and OS, and provides additional prognostic information beyond pathologic parameters.

  10. IFN-γ/TNF-α ratio in response to immuno proteomically identified human T-cell antigens of Mycobacterium tuberculosis - The most suitable surrogate biomarker for latent TB infection.

    PubMed

    Prabhavathi, Maddineni; Pathakumari, Balaji; Raja, Alamelu

    2015-08-01

    The enormous reservoir of latent TB infection (LTBI) poses a major hurdle for global TB control. The existing Tuberculin skin test (TST) and IFN-γ release assays (IGRAs) are found to be suboptimal for LTBI diagnosis. Previously we had taken an immunoproteomic approach and identified 10 protein fractions (contains 16 proteins), which are solely recognized by LTBI. In a cohort of 40 pulmonary TB patients (PTB) and 35 healthy household contacts (HHC), IFN-γ and TNF-α response were measured against 16 antigens by using 1:10 diluted whole blood assay. Among all the antigens, IFN-γ response to Rv2626c has shown positivity of 88.57% in HHC and 7.5% in PTB group. IFN-γ response to combination of Rv2626c + Rv3716c has demonstrated 100% positivity in HHC and 17.5% positivity in PTB respectively. Compared to individual cytokines (i.e. IFN-γ and TNF-α), ratio of IFN-γ/TNF-α has shown promising results for diagnosis of LTBI. IFN-γ/TNF-α ratio against Rv3716c and TrxC has exhibited a positivity of 94.29% in HHC and 5% in PTB group. Accession of Rv2626c and Rv3716c may improve the diagnostic performance of existing QFT-GIT. Independent of QFT-GIT assay, ratio of IFN-γ/TNF-α in response to either Rv3716c or TrxC may acts as suitable surrogate biomarker for LTBI.

  11. Robustness of surrogates of biodiversity in marine benthic communities.

    PubMed

    Magierowski, Regina H; Johnson, Craig R

    2006-12-01

    The usefulness of surrogates to estimate complex variables describing community structure, such as the various components of biodiversity, is long established. Most attention has been given to surrogates of species richness and species diversity and has focused on identifying a subset of taxa as a surrogate of total community richness or diversity. In adopting a surrogate measure, it is assumed that the relationship between the surrogate(s) and total richness or diversity is consistent in both space and time. These assumptions are rarely examined explicitly. We examined the robustness of potential surrogates of familial richness and multivariate community structure for macrofauna communities inhabiting artificial kelp holdfasts by comparing among communities of dissimilar ages and among communities established at different times of the year. This is important because most benthic "landscapes" will be a mosaic of patches reflecting different intensities, frequencies, and timing of disturbances. The total abundance of organisms and familial richness of crustaceans or polychaetes were all good predictors of total familial richness (R2 > 0.68). In contrast, while the familial richness of other groups, such as mollusks and echinoderms, were well correlated with total familial richness for communities at an early stage of development, the strength of these relationships declined with community age. For multivariate community structure, carefully selected subsets of approximately 10% of the total taxa yielded similar patterns to the total suite of taxa, irrespective of the age of the community. Thus, useful surrogates of both familial richness and multivariate community structure can be identified for this type of community. However, the choice of technique for selecting surrogate taxa largely depends on the nature of the pilot data available, and careful selection is required to ensure that surrogates perform consistently across different-aged communities. While the

  12. 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...

  13. 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...

  14. 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

  15. Immune signature of metastatic breast cancer: Identifying predictive markers of immunotherapy response.

    PubMed

    Kim, Ji-Yeon; Lee, Eunjin; Park, Kyunghee; Park, Woong-Yang; Jung, Hae Hyun; Ahn, Jin Seok; Im, Young-Hyuck; Park, Yeon Hee

    2017-07-18

    In breast cancer (BC), up to 10-20% patients were known to have clinical benefit with immune checkpoint inhibitors, and biomarkers are needed for optimal use of this multi-potential therapeutic strategy. Accordingly, we conducted an experiment to identify expression of genes associated with immune checkpoints that represent potential targets of cancer immunotherapy. We performed whole-transcriptome sequencing and whole-exome sequencing using 37 refractory BC specimens. In the immune pathway gene set expression analysis, we found that HER2 expression and previous taxane treatment were positively correlated with high expression of immune gene set expression (p = 0.070 and 0.008, respectively). The nine genes associated with immune checkpoints - PDCD1(PD-1), CD274(PD-L1), CD276(B7-H3), CTLA-4, IDO1, LAG3, VTCN1, HAVCR2, and TNFRSF4(OX40) - interacted with each other. In addition, HER2 expression also affected the expression levels of these genes (p = 0.044). Lastly, expression of immune checkpoint genes and tissue-infiltrating lymphocytes were positively correlated in metastatic BCs (p < 0.001). In conclusion, we suggest that HER2 expression and previous taxane treatment are potential surrogate markers for high expression of immune checkpoint genes and immune pathway gene sets. Further study of the BC immune signature with large-scale, translational data sets is warranted.

  16. Biomarkers and surrogate endpoints in glaucoma clinical trials.

    PubMed

    Medeiros, Felipe A

    2015-05-01

    Surrogate endpoints are often used as replacements for true clinically relevant endpoints in several areas of medicine, as they enable faster and less expensive clinical trials. However, without proper validation, the use of surrogates may lead to incorrect conclusions about the efficacy and safety of treatments. This article reviews the general requirements for validating surrogate endpoints and provides a critical assessment of the use of intraocular pressure (IOP), visual fields, and structural measurements of the optic nerve as surrogate endpoints in glaucoma clinical trials. A valid surrogate endpoint must be able to predict the clinically relevant endpoint and fully capture the effect of an intervention on that endpoint. Despite its widespread use in clinical trials, no proper validation of IOP as a surrogate endpoint has ever been conducted for any class of IOP-lowering treatments. Evidence has accumulated with regard to the role of imaging measurements of optic nerve damage as surrogate endpoints in glaucoma. These measurements are predictive of functional losses in the disease and may explain, at least in part, treatment effects on clinically relevant endpoints. The use of composite endpoints in glaucoma trials may overcome weaknesses of the use of structural or functional endpoints in isolation. Unless research is dedicated to fully develop and validate suitable endpoints that can be used in glaucoma clinical trials, we run the risk of inappropriate judgments about the value of new therapies.

  17. 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.

  18. 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.

  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. 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…

  1. 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

  2. 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

    2016-04-07

    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

  3. 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

  4. 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

  5. Predictive value of weight-for-age to identify overweight children.

    PubMed

    Stettler, Nicolas; Zomorrodi, Arezoo; Posner, Jill C

    2007-12-01

    The objective was to assess the predictive value of weight-for-age to identify overweight children and adolescents in the unusual research or public health situations where height is not available to calculate BMI. Data from the National Health and Nutrition Examination Survey 1999 to 2004 were used to calculate the sensitivity, specificity, and positive and negative predictive values of selected weight-for-age cut-off points to identify overweight children and adolescents (as defined by BMI >or=95th percentile). Positive and negative predictive values are dependent on prevalence and are reported here for this study population only. The 50th and 75th weight-for-age percentiles had good sensitivity (100% and 99.6%, respectively), but poor positive predictive value (23.7% and 37.0%, respectively), while the 95th and 97th percentiles had reasonable positive predictive value (80.3% and 91.5%, respectively), but limited sensitivity (82.0% and 66.7%, respectively) to identify overweight subjects. The properties of weight-for-age percentiles to identify overweight subjects differed between sex, age, and race/ethnicity but remain within a relatively narrow range. No single weight-for-age cut-off point was found to identify overweight children and adolescents with acceptable values for all properties and, therefore, cannot be used in the clinical setting. Furthermore, the positive predictive values reported here may be lower in populations with a lower prevalence of obesity. However, in unusual research or public health situations where height is not available, such as existing databases, weight-for-age percentiles may be useful to target limited resources to groups more likely to include overweight children and adolescents than the general population.

  6. Strategies to design clinical studies to identify predictive biomarkers in cancer research.

    PubMed

    Perez-Gracia, Jose Luis; Sanmamed, Miguel F; Bosch, Ana; Patiño-Garcia, Ana; Schalper, Kurt A; Segura, Victor; Bellmunt, Joaquim; Tabernero, Josep; Sweeney, Christopher J; Choueiri, Toni K; Martín, Miguel; Fusco, Juan Pablo; Rodriguez-Ruiz, Maria Esperanza; Calvo, Alfonso; Prior, Celia; Paz-Ares, Luis; Pio, Ruben; Gonzalez-Billalabeitia, Enrique; Gonzalez Hernandez, Alvaro; Páez, David; Piulats, Jose María; Gurpide, Alfonso; Andueza, Mapi; de Velasco, Guillermo; Pazo, Roberto; Grande, Enrique; Nicolas, Pilar; Abad-Santos, Francisco; Garcia-Donas, Jesus; Castellano, Daniel; Pajares, María J; Suarez, Cristina; Colomer, Ramon; Montuenga, Luis M; Melero, Ignacio

    2017-02-01

    The discovery of reliable biomarkers to predict efficacy and toxicity of anticancer drugs remains one of the key challenges in cancer research. Despite its relevance, no efficient study designs to identify promising candidate biomarkers have been established. This has led to the proliferation of a myriad of exploratory studies using dissimilar strategies, most of which fail to identify any promising targets and are seldom validated. The lack of a proper methodology also determines that many anti-cancer drugs are developed below their potential, due to failure to identify predictive biomarkers. While some drugs will be systematically administered to many patients who will not benefit from them, leading to unnecessary toxicities and costs, others will never reach registration due to our inability to identify the specific patient population in which they are active. Despite these drawbacks, a limited number of outstanding predictive biomarkers have been successfully identified and validated, and have changed the standard practice of oncology. In this manuscript, a multidisciplinary panel reviews how those key biomarkers were identified and, based on those experiences, proposes a methodological framework-the DESIGN guidelines-to standardize the clinical design of biomarker identification studies and to develop future research in this pivotal field.

  7. Predictive performance of the Short Time Exposure test for identifying eye irritation potential of chemical mixtures.

    PubMed

    Saito, Kazutoshi; Miyazawa, Masaaki; Nukada, Yuko; Ei, Kyo; Abo, Takayuki; Sakaguchi, Hitoshi

    2015-04-01

    The Short Time Exposure (STE) test is an in vitro eye irritation test based on the cytotoxicity in SIRC cells (rabbit corneal cell line) following a 5 min treatment of chemicals. This study evaluated the predictive performance of the STE test to identify the globally harmonized system (GHS) Not Classified category and other irritant categories (i.e., GHS Category 1 or 2) when used to test 40 chemical mixtures that included irritants. The STE test correctly identified 30 tested mixtures classified as GHS irritant categories and 5 out of 10 tested mixtures classified as GHS Not Classified. The sensitivity, specificity, positive predictivity, negative predictivity, and overall accuracy of the STE test were 100% (30/30), 50% (5/10), 86% (25/30), 100% (5/5), and 88% (35/40), respectively. These predictive performances were comparative to or greater than those in other in vitro eye irritation tests that have been accepted as test guideline by the Organisation for Economic Co-operation and Development. This suggests that the STE test has sufficient predictivity for identifying the eye irritation potential of chemical mixtures. Since no false negatives in this study were found, this indicates that the STE test is applicable as a part of the bottom-up approach. Copyright © 2015 Elsevier Ltd. All rights reserved.

  8. A Review of Surrogate Assisted Multiobjective Evolutionary Algorithms.

    PubMed

    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.

  9. 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

  10. 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…

  11. Identifying and Validating Selection Tools for Predicting Officer Performance and Retention

    DTIC Science & Technology

    2017-05-01

    Research Note 2017-01 Identifying and Validating Selection Tools for Predicting Officer Performance and Retention...Teresa L. Russell, Editor Cheryl J. Paullin, Editor Human Resources Research Organization Peter J. Legree, Editor Robert N. Kilcullen, Editor...Mark C. Young, Editor U.S. Army Research Institute May 2017 United States Army Research Institute for the Behavioral and Social

  12. 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…

  13. 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…

  14. 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…

  15. 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…

  16. 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…

  17. Identifying the singleplex and multiplex proteins based on transductive learning for protein subcellular localization prediction.

    PubMed

    Cao, Junzhe; Liu, Wenqi; He, Jianjun; Gu, Hong

    2013-07-01

    A new method is proposed to identify whether a query protein is singleplex or multiplex for improving the quality of protein subcellular localization prediction. Based on the transductive learning technique, this approach utilizes the information from the both query proteins and known proteins to estimate the subcellular location number of every query protein so that the singleplex and multiplex proteins can be recognized and distinguished. Each query protein is then dealt with by a targeted single-label or multi-label predictor to achieve a high-accuracy prediction result. We assess the performance of the proposed approach by applying it to three groups of protein sequences datasets. Simulation experiments show that the proposed approach can effectively identify the singleplex and multiplex proteins. Through a comparison, the reliably of this method for enhancing the power of predicting protein subcellular localization can also be verified.

  18. 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

  19. A predictive model identifies patients most likely to have inadequate bowel preparation for colonoscopy.

    PubMed

    Hassan, Cesare; Fuccio, Lorenzo; Bruno, Mario; Pagano, Nico; Spada, Cristiano; Carrara, Silvia; Giordanino, Chiara; Rondonotti, Emanuele; Curcio, Gabriele; Dulbecco, Pietro; Fabbri, Carlo; Della Casa, Domenico; Maiero, Stefania; Simone, Adriana; Iacopini, Federico; Feliciangeli, Giuseppe; Manes, Gianpiero; Rinaldi, Antonio; Zullo, Angelo; Rogai, Francesca; Repici, Alessandro

    2012-05-01

    An inadequate level of bowel preparation can affect the efficacy and safety of colonoscopy. Although some factors have been associated with outcome, there is no strategy to identify patients at high risk for inadequate preparation. We searched for factors associated with an inadequate level of preparation and tested the validity of a predictive clinical rule based on these factors. We performed a prospective study of 2811 consecutive patients who underwent colonoscopy examinations at 18 medical centers; clinical and demographic data were collected before the colonoscopy. Bowel preparation was classified as adequate or inadequate; 925 patients (33%) were found to have inadequate preparation. Multivariate analysis was used to identify factors associated with inadequate preparation, which were expressed as odds ratio (OR) and used to build a predictive model. Factors associated with inadequate bowel preparation included being overweight (OR, 1.5), male sex (OR, 1.2), a high body mass index (OR, 1.1), older age (OR, 1.01), previous colorectal surgery (OR, 1.6), cirrhosis (OR, 5), Parkinson disease (OR, 3.2), diabetes (OR, 1.8), and positive results in a fecal occult test (OR, 0.6). These factors predicted which patients would have inadequate cleansing with 60% sensitivity, 59% specificity, 41% positive predictive value, and 76% negative predictive value; they had an under the receiver operating characteristic curve value of 0.63. Assuming 100% efficacy of a hypothetical regimen to address patients predicted to be at risk of inadequate preparation, the rate would decrease from 33% to 13%. We identified factors associated with inadequate bowel preparation for colonoscopy and used these to build an accurate predictive model. Copyright © 2012 AGA Institute. Published by Elsevier Inc. All rights reserved.

  20. Surrogate alcohol drinking in Estonia.

    PubMed

    Pärna, Kersti; Leon, David A

    2011-08-01

    Surrogate, nonbeverage alcohols, provide a cheap and concentrated source of ethanol for drinking that has been associated with premature mortality. The aim of this study was to provide the first estimate of the prevalence of surrogate alcohol consumption in a national population sample of Estonia. The Estonian Health Interview Survey conducted in 2006 to 2007 was a nationally representative sample of the population aged 15 to 84 years (N = 6,370). The age-standardized percentage prevalences of ever having drunk surrogates were estimated. The association of age, ethnicity, and education with the prevalence of surrogate drinking was estimated using logistic regression. Of all respondents who reported drinking at least once in their lifetime (N = 5,423), 65% had consumed alcohol during the previous 4 weeks. In this group (N = 3,525), the age-standardized prevalence rate of surrogate drinking was 1.4% (2.3% men, 0.3% women). Among men, surrogate drinking was rare under the age of 35 years (0.3%). Ethnicity and education were both related to surrogate drinking: relative to Estonian men, non-Estonians (mainly Russians) had an odds ratio (OR) for surrogate drinking (adjusted for age and education) of 2.58 (95% CI 1.41, 4.72), while relative to those with higher education those with secondary education had an OR (adjusted for age and ethnicity) of 2.28 (0.78, 6.67) and those with basic education an OR of 3.91 (1.29, 11.84). Surrogate alcohols are drunk in Estonia, particularly among men. This behavior shows pronounced variation in prevalence by ethnicity and education. Reducing consumption of these substances needs to be part of any strategy to reduce the burden of alcohol-related problems in Estonia today. Copyright © 2011 by the Research Society on Alcoholism.

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

    PubMed

    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.

  2. 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

  3. 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

  4. A python analytical pipeline to identify prohormone precursors and predict prohormone cleavage sites.

    PubMed

    Southey, Bruce R; Sweedler, Jonathan V; Rodriguez-Zas, Sandra L

    2008-01-01

    Neuropeptides and hormones are signaling molecules that support cell-cell communication in the central nervous system. Experimentally characterizing neuropeptides requires significant efforts because of the complex and variable processing of prohormone precursor proteins into neuropeptides and hormones. We demonstrate the power and flexibility of the Python language to develop components of an bioinformatic analytical pipeline to identify precursors from genomic data and to predict cleavage as these precursors are en route to the final bioactive peptides. We identified 75 precursors in the rhesus genome, predicted cleavage sites using support vector machines and compared the rhesus predictions to putative assignments based on homology to human sequences. The correct classification rate of cleavage using the support vector machines was over 97% for both human and rhesus data sets. The functionality of Python has been important to develop and maintain NeuroPred (http://neuroproteomics.scs.uiuc.edu/neuropred.html), a user-centered web application for the neuroscience community that provides cleavage site prediction from a wide range of models, precision and accuracy statistics, post-translational modifications, and the molecular mass of potential peptides. The combined results illustrate the suitability of the Python language to implement an all-inclusive bioinformatics approach to predict neuropeptides that encompasses a large number of interdependent steps, from scanning genomes for precursor genes to identification of potential bioactive neuropeptides.

  5. Temporal effects in trend prediction: identifying the most popular nodes in the future.

    PubMed

    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.

  6. 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…

  7. 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

    2017-05-01

    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.

  8. Predicting urinary creatinine excretion and its usefulness to identify incomplete 24 h urine collections.

    PubMed

    De Keyzer, Willem; Huybrechts, Inge; Dekkers, Arnold L M; Geelen, Anouk; Crispim, Sandra; Hulshof, Paul J M; Andersen, Lene F; Řehůřková, Irena; Ruprich, Jiří; Volatier, Jean-Luc; Van Maele, Georges; Slimani, Nadia; van't Veer, Pieter; de Boer, Evelien; De Henauw, Stefaan

    2012-09-28

    Studies using 24 h urine collections need to incorporate ways to validate the completeness of the urine samples. Models to predict urinary creatinine excretion (UCE) have been developed for this purpose; however, information on their usefulness to identify incomplete urine collections is limited. We aimed to develop a model for predicting UCE and to assess the performance of a creatinine index using para-aminobenzoic acid (PABA) as a reference. Data were taken from the European Food Consumption Validation study comprising two non-consecutive 24 h urine collections from 600 subjects in five European countries. Data from one collection were used to build a multiple linear regression model to predict UCE, and data from the other collection were used for performance testing of a creatinine index-based strategy to identify incomplete collections. Multiple linear regression (n 458) of UCE showed a significant positive association for body weight (β = 0·07), the interaction term sex × weight (β = 0·09, reference women) and protein intake (β = 0·02). A significant negative association was found for age (β = -0·09) and sex (β = -3·14, reference women). An index of observed-to-predicted creatinine resulted in a sensitivity to identify incomplete collections of 0·06 (95 % CI 0·01, 0·20) and 0·11 (95 % CI 0·03, 0·22) in men and women, respectively. Specificity was 0·97 (95 % CI 0·97, 0·98) in men and 0·98 (95 % CI 0·98, 0·99) in women. The present study shows that UCE can be predicted from weight, age and sex. However, the results revealed that a creatinine index based on these predictions is not sufficiently sensitive to exclude incomplete 24 h urine collections.

  9. Feasibility Analysis of Incorporating In-Vitro Toxicokinetic Data as a Surrogate for In-Vivo Data for Read-across Predictions (ASCCT meeting)

    EPA Science Inventory

    The underlying principle of read-across is that biological activity is a function of physical and structural properties of chemicals. Analogs are typically identified on the basis of structural similarity and subsequently evaluated for their use in read-across on the basis of the...

  10. Using individualized predictive disease modeling to identify patients with the potential to benefit from a disease management program for diabetes mellitus.

    PubMed

    Weber, Christian; Neeser, Kurt

    2006-08-01

    Diabetes is an increasing health problem, but efforts to handle this pandemic by disease management programs (DMP) have shown conflicting results. Our hypothesis is that, in addition to a program's content and setting, the choice of the right patients is crucial to a program's efficacy and effectiveness. We used individualized predictive disease modeling (IPDM) on a cohort of 918 patients with type 2 diabetes to identify those patients with the greatest potential to benefit from inclusion in a DMP. A portion of the patients (4.7%) did not have even a theoretical potential for an increase in life expectancy and would therefore be unlikely to benefit from a DMP. Approximately 16.1% had an increase in life expectancy of less than half a year. Stratification of the entire cohort by surrogate parameters like preventable 10-year costs or gain in life expectancy was much more effective than stratification by classical clinical parameters such as high HbA1c level. Preventable costs increased up to 50.6% (or 1,010 per patient (1 = US dollars 1.28), p < 0.01) and life expectancy increased up to 54.8% (or 2.3 years, p < 0.01). IPDM is a valuable strategy to identify those patients with the greatest potential to avoid diabetes-related complications and thus can improve the overall effectiveness and efficacy of DMPs for diabetes mellitus.

  11. MISO - Mixed Integer Surrogate Optimization

    SciTech Connect

    Mueller, Juliane

    2016-01-20

    MISO is an optimization framework for solving computationally expensive mixed-integer, black-box, global optimization problems. MISO uses surrogate models to approximate the computationally expensive objective function. Hence, derivative information, which is generally unavailable for black-box simulation objective functions, is not needed. MISO allows the user to choose the initial experimental design strategy, the type of surrogate model, and the sampling strategy.

  12. Surrogate end points save lives

    PubMed Central

    Vinden, Christopher

    2017-01-01

    Summary Patient-centric markers are important, and when they can be conveniently measured they should dominate research questions. However, when the research question pertains to serious or potentially fatal illnesses and it will take years or even decades to answer with patient-centric outcomes, then a pragmatic approach based on common sense and surrogate markers should be adopted. This commentary discusses the important role that surrogate markers can play in medical research. PMID:28338466

  13. Surrogate data--a secure way to share corporate data.

    PubMed

    Tetko, Igor V; Abagyan, Ruben; Oprea, Tudor I

    2005-01-01

    The privacy of chemical structure is of paramount importance for the industrial sector, in particular for the pharmaceutical industry. At the same time, companies handle large amounts of physico-chemical and biological data that could be shared in order to improve our molecular understanding of pharmacokinetic and toxicological properties, which could lead to improved predictivity and shorten the development time for drugs, in particular in the early phases of drug discovery. The current study provides some theoretical limits on the information required to produce reverse engineering of molecules from generated descriptors and demonstrates that the information content of molecules can be as low as less than one bit per atom. Thus theoretically just one descriptor can be used to completely disclose the molecular structure. Instead of sharing descriptors, we propose to share surrogate data. The sharing of surrogate data is nothing else but sharing of reliably predicted molecules. The use of surrogate data can provide the same information as the original set. We consider the practical application of this idea to predict lipophilicity of chemical compounds and we demonstrate that surrogate and real (original) data provides similar prediction ability. Thus, our proposed strategy makes it possible not only to share descriptors, but also complete collections of surrogate molecules without the danger of disclosing the underlying molecular structures.

  14. Surrogate data a secure way to share corporate data

    NASA Astrophysics Data System (ADS)

    Tetko, Igor V.; Abagyan, Ruben; Oprea, Tudor I.

    2005-09-01

    The privacy of chemical structure is of paramount importance for the industrial sector, in particular for the pharmaceutical industry. At the same time, companies handle large amounts of physico-chemical and biological data that could be shared in order to improve our molecular understanding of pharmacokinetic and toxicological properties, which could lead to improved predictivity and shorten the development time for drugs, in particular in the early phases of drug discovery. The current study provides some theoretical limits on the information required to produce reverse engineering of molecules from generated descriptors and demonstrates that the information content of molecules can be as low as less than one bit per atom. Thus theoretically just one descriptor can be used to completely disclose the molecular structure. Instead of sharing descriptors, we propose to share surrogate data. The sharing of surrogate data is nothing else but sharing of reliably predicted molecules. The use of surrogate data can provide the same information as the original set. We consider the practical application of this idea to predict lipophilicity of chemical compounds and we demonstrate that surrogate and real (original) data provides similar prediction ability. Thus, our proposed strategy makes it possible not only to share descriptors, but also complete collections of surrogate molecules without the danger of disclosing the underlying molecular structures.

  15. Dynamical model of surrogate reactions

    SciTech Connect

    Aritomo, Y.; Chiba, S.; Nishio, K.

    2011-08-15

    A new dynamical model is developed to describe the whole process of surrogate reactions: Transfer of several nucleons at an initial stage, thermal equilibration of residues leading to washing out of shell effects, and decay of populated compound nuclei are treated in a unified framework. Multidimensional Langevin equations are employed to describe time evolution of collective coordinates with a time-dependent potential energy surface corresponding to different stages of surrogate reactions. The new model is capable of calculating spin distributions of the compound nuclei, one of the most important quantities in the surrogate technique. Furthermore, various observables of surrogate reactions can be calculated, for example, energy and angular distribution of ejectile and mass distributions of fission fragments. These features are important to assess validity of the proposed model itself, to understand mechanisms of the surrogate reactions, and to determine unknown parameters of the model. It is found that spin distributions of compound nuclei produced in {sup 18}O+{sup 238}U{yields}{sup 16}O+{sup 240}*U and {sup 18}O+{sup 236}U{yields}{sup 16}O+{sup 238}*U reactions are equivalent and much less than 10({h_bar}/2{pi}) and therefore satisfy conditions proposed by Chiba and Iwamoto [Phys. Rev. C 81, 044604 (2010)] if they are used as a pair in the surrogate ratio method.

  16. On the importance of identifying, characterizing, and predicting fundamental phenomena towards microbial electrochemistry applications.

    PubMed

    Torres, César Iván

    2014-06-01

    The development of microbial electrochemistry research toward technological applications has increased significantly in the past years, leading to many process configurations. This short review focuses on the need to identify and characterize the fundamental phenomena that control the performance of microbial electrochemical cells (MXCs). Specifically, it discusses the importance of recent efforts to discover and characterize novel microorganisms for MXC applications, as well as recent developments to understand transport limitations in MXCs. As we increase our understanding of how MXCs operate, it is imperative to continue modeling efforts in order to effectively predict their performance, design efficient MXC technologies, and implement them commercially. Thus, the success of MXC technologies largely depends on the path of identifying, understanding, and predicting fundamental phenomena that determine MXC performance. Copyright © 2013 Elsevier Ltd. All rights reserved.

  17. 34 CFR 303.422 - Surrogate parents.

    Code of Federal Regulations, 2013 CFR

    2013-07-01

    ... 34 Education 2 2013-07-01 2013-07-01 false Surrogate parents. 303.422 Section 303.422 Education... DISABILITIES Procedural Safeguards Surrogate Parents § 303.422 Surrogate parents. (a) General. Each lead agency... under paragraph (a) of this section, includes the assignment of an individual to act as a surrogate...

  18. 34 CFR 303.422 - Surrogate parents.

    Code of Federal Regulations, 2014 CFR

    2014-07-01

    ... 34 Education 2 2014-07-01 2013-07-01 true Surrogate parents. 303.422 Section 303.422 Education... DISABILITIES Procedural Safeguards Surrogate Parents § 303.422 Surrogate parents. (a) General. Each lead agency... under paragraph (a) of this section, includes the assignment of an individual to act as a surrogate...

  19. 34 CFR 303.422 - Surrogate parents.

    Code of Federal Regulations, 2012 CFR

    2012-07-01

    ... 34 Education 2 2012-07-01 2012-07-01 false Surrogate parents. 303.422 Section 303.422 Education... DISABILITIES Procedural Safeguards Surrogate Parents § 303.422 Surrogate parents. (a) General. Each lead agency... under paragraph (a) of this section, includes the assignment of an individual to act as a surrogate...

  20. 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. Copyright © 2015 Elsevier Ltd. All rights reserved.

  1. Clinical prediction model to identify vulnerable patients in ambulatory surgery: towards optimal medical decision-making.

    PubMed

    Mijderwijk, Herjan; Stolker, Robert Jan; Duivenvoorden, Hugo J; Klimek, Markus; Steyerberg, Ewout W

    2016-09-01

    Ambulatory surgery patients are at risk of adverse psychological outcomes such as anxiety, aggression, fatigue, and depression. We developed and validated a clinical prediction model to identify patients who were vulnerable to these psychological outcome parameters. We prospectively assessed 383 mixed ambulatory surgery patients for psychological vulnerability, defined as the presence of anxiety (state/trait), aggression (state/trait), fatigue, and depression seven days after surgery. Three psychological vulnerability categories were considered-i.e., none, one, or multiple poor scores, defined as a score exceeding one standard deviation above the mean for each single outcome according to normative data. The following determinants were assessed preoperatively: sociodemographic (age, sex, level of education, employment status, marital status, having children, religion, nationality), medical (heart rate and body mass index), and psychological variables (self-esteem and self-efficacy), in addition to anxiety, aggression, fatigue, and depression. A prediction model was constructed using ordinal polytomous logistic regression analysis, and bootstrapping was applied for internal validation. The ordinal c-index (ORC) quantified the discriminative ability of the model, in addition to measures for overall model performance (Nagelkerke's R (2) ). In this population, 137 (36%) patients were identified as being psychologically vulnerable after surgery for at least one of the psychological outcomes. The most parsimonious and optimal prediction model combined sociodemographic variables (level of education, having children, and nationality) with psychological variables (trait anxiety, state/trait aggression, fatigue, and depression). Model performance was promising: R (2)  = 30% and ORC = 0.76 after correction for optimism. This study identified a substantial group of vulnerable patients in ambulatory surgery. The proposed clinical prediction model could allow healthcare

  2. Validation and Refinement of a Prediction Rule to Identify Children at Low Risk for Acute Appendicitis

    PubMed Central

    Kharbanda, Anupam B; Dudley, Nanette C; Bajaj, Lalit; Stevenson, Michelle D; Macias, Charles G; Mittal, Manoj K; Bachur, Richard G; Bennett, Jonathan E; Sinclair, Kelly; Huang, Craig; Dayan, Peter S

    2013-01-01

    Objective To validate and refine a clinical prediction rule to identify which children with acute abdominal pain are at low risk for appendicitis (Low Risk Appendicitis Rule). Design Prospective, multi-center cross-sectional study. Setting Ten pediatric hospital emergency departments. Participants Children 3–18 years old who presented with suspected appendicitis from May 2009 – April 2010. Main Outcome Measures The test performance of the Low Risk Appendicitis Rule. Results Among 2625 patients enrolled, 1018 (38.8%; 95% confidence interval [CI] 36.9% – 40.7%) had appendicitis. Validation of the rule resulted in a sensitivity of 95.5% (95% CI 93.9 – 96.7%), specificity of 36.3% (33.9 – 38.9%) and negative predictive value (NPV) of 92.7% (90.1 – 94.6%). Theoretical application would have identified 573 (24%) as low risk, misclassifying 42 patients (4.5%; 95% CI 3.4% – 6.1%) with appendicitis. We refined the prediction rule, resulting in a model that identified patients at low risk if: a) absolute neutrophil count (ANC) ≤ 6.75 × 103/µL and no maximal tenderness in right lower quadrant (RLQ) or b) ANC ≤ 6.75 × 103/µL, maximal tenderness in the RLQ but no abdominal pain with walking/jumping or coughing. This refined rule had a sensitivity of 98.1% (97.0 – 98.9%), specificity of 23.7% (21.7 – 25.9%) and NPV of 95.3% (92.3 – 97.0%). Conclusions We have validated and refined a simple clinical prediction rule for pediatric appendicitis. For patients identified as low risk, clinicians should consider alternative strategies such as observation or ultrasound, rather than proceed to immediate imaging with CT. PMID:22869405

  3. The overexpression of p16 is not a surrogate marker for high-risk human papilloma virus genotypes and predicts clinical outcomes for vulvar cancer.

    PubMed

    Sznurkowski, Jacek J; Żawrocki, Anton; Biernat, Wojciech

    2016-07-13

    We aimed to evaluate the correlation between p16(ink4a)-overexpression and high risk (hr)HPV-DNA in vulvar squamous cell carcinoma (vSCC) tumors as well as the impact of both biomarkers on the prognosis of vSCC patients. PCR-detection of (hr)HPV-DNA and immunohistochemical staining for p16(ink4a) were conducted in 85 vSCC tumors. Survival analyses included the Kaplan-Meier method, log-rank test and Cox proportional hazards model. p16(ink4a)-overexpression and (hr)HPV-DNA were detected in 35 and 37 of the 85 tumors, respectively. Among the 35 p16(ink4a)-positive tumors, 10 lacked (hr)HPV-DNA (29 %). Among the 50 p16(ink4a)-negative tumors, (hr)HPV-DNA was detected in 12 cases (24 %). The median follow-up was 89.20 months (range 1.7-189.5 months). P16(ink4a)-overexpression, but not (hr)HPV-DNA positivity of the primary tumor, was correlated with prolonged overall survival (OS) (p = 0.009). P16(ink4a)-overexpression predicted a better response to radiotherapy (p < 0.001). Univariate analysis has demonstrated that age (p = 0.025), tumor grade (p = 0.001), lymph node metastasis (p < 0.001), FIGO stage (p < 0.001), p16(ink4a)-overexpression (p = 0.022), and adjuvant RTX (p < 0.001) were prognostic factors for OS. Multivariate analysis has demonstrated that lymph node metastasis (HR 1-2.74, 95 % CI 1.50-5.02, p = 0.019), tumor grade (HR 1-2.80, 95 % CI 1.33-5.90, p = 0.007) and p16(ink4a)-overexpression (HR 1-2.11, 95 % CI 1.13-3.95, p = 0.001) are independent prognostic factors. The discovered overlap suggests the use of p16(ink4a) in combination with HPV-DNA detection as an ancillary test for future research and clinical studies in vSCC. The prognostic and predictive value of p16(ink4a)-overexpression should be tested in larger cohort studies.

  4. 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.

  5. Validation of a Predictive Model to Identify Patients at High Risk for Hospital Readmission.

    PubMed

    Spiva, LeeAnna; Hand, Marti; VanBrackle, Lewis; McVay, Frank

    2016-01-01

    Hospital readmission is an adverse patient outcome that is serious, common, and costly. For hospitals, identifying patients at risk for hospital readmission is a priority to reduce costs and improve care. The purposes were to validate a predictive algorithm to identify patients at a high risk for preventable hospital readmission within 30 days after discharge and determine if additional risk factors enhance readmission predictability. A retrospective study was conducted on a randomized sample of 598 patients discharged from a Southeast community hospital. Data were collected from the organization's database and manually abstracted from the electronic medical record using a structured tool. Two separate logistic regression models were fit for the probability of readmission within 30 days after discharge. The first model used the LACE index as the predictor variable, and the second model used the LACE index with additional risk factors. The two models were compared to determine if additional risk factors increased the model's predictive ability. The results indicate both models have reasonable prognostic capability. The LACE index with additional risk factors did little to improve prognostication, while adding to the model's complexity. Findings support the use of the LACE index as a practical tool to identify patients at risk for readmission.

  6. Surrogate model based iterative ensemble smoother for subsurface flow data assimilation

    NASA Astrophysics Data System (ADS)

    Chang, Haibin; Liao, Qinzhuo; Zhang, Dongxiao

    2017-02-01

    Subsurface geological formation properties often involve some degree of uncertainty. Thus, for most conditions, uncertainty quantification and data assimilation are necessary for predicting subsurface flow. The surrogate model based method is one common type of uncertainty quantification method, in which a surrogate model is constructed for approximating the relationship between model output and model input. Based on the prediction ability, the constructed surrogate model can be utilized for performing data assimilation. In this work, we develop an algorithm for implementing an iterative ensemble smoother (ES) using the surrogate model. We first derive an iterative ES scheme using a regular routine. In order to utilize surrogate models, we then borrow the idea of Chen and Oliver (2013) to modify the Hessian, and further develop an independent parameter based iterative ES formula. Finally, we establish the algorithm for the implementation of iterative ES using surrogate models. Two surrogate models, the PCE surrogate and the interpolation surrogate, are introduced for illustration. The performances of the proposed algorithm are tested by synthetic cases. The results show that satisfactory data assimilation results can be obtained by using surrogate models that have sufficient accuracy.

  7. How Surrogates Decide: A Secondary Data Analysis of Decision-Making Principles Used by the Surrogates of Hospitalized Older Adults.

    PubMed

    Devnani, Rohit; Slaven, James E; Bosslet, Gabriel T; Montz, Kianna; Inger, Lev; Burke, Emily S; Torke, Alexia M

    2017-08-24

    Many hospitalized adults do not have the capacity to make their own health care decisions and thus require a surrogate decision-maker. While the ethical standard suggests that decisions should focus on a patient's preferences, our study explores the principles that surrogates consider most important when making decisions for older hospitalized patients. We sought to determine how frequently surrogate decision-makers prioritized patient preferences in decision-making and what factors may predict their doing so. We performed a secondary data analysis of a study conducted at three local hospitals that surveyed surrogate decision-makers for hospitalized patients 65 years of age and older. Surrogates rated the importance of 16 decision-making principles and selected the one that was most important. We divided the surrogates into two groups: those who prioritized patient preferences and those who prioritized patient well-being. We analyzed the two groups for differences in knowledge of patient preferences, presence of advance directives, and psychological outcomes. A total of 362 surrogates rated an average of six principles as being extremely important in decision-making; 77.8% of surrogates selected a patient well-being principle as the most important, whereas only 21.1% selected a patient preferences principle. Advance directives were more common to the patient preferences group than the patient well-being group (61.3% vs. 44.9%; 95% CI: 1.01-3.18; p = 0.04), whereas having conversations with the patient about their health care preferences was not a significant predictor of surrogate group identity (81.3% vs. 67.4%; 95% CI: 0.39-1.14; p = 0.14). We found no differences between the two groups regarding surrogate anxiety, depression, or decisional conflict. While surrogates considered many factors, they focused more often on patient well-being than on patient preferences, in contravention of our current ethical framework. Surrogates more commonly prioritized

  8. CAsubtype: An R Package to Identify Gene Sets Predictive of Cancer Subtypes and Clinical Outcomes.

    PubMed

    Kong, Hualei; Tong, Pan; Zhao, Xiaodong; Sun, Jielin; Li, Hua

    2017-01-21

    In the past decade, molecular classification of cancer has gained high popularity owing to its high predictive power on clinical outcomes as compared with traditional methods commonly used in clinical practice. In particular, using gene expression profiles, recent studies have successfully identified a number of gene sets for the delineation of cancer subtypes that are associated with distinct prognosis. However, identification of such gene sets remains a laborious task due to the lack of tools with flexibility, integration and ease of use. To reduce the burden, we have developed an R package, CAsubtype, to efficiently identify gene sets predictive of cancer subtypes and clinical outcomes. By integrating more than 13,000 annotated gene sets, CAsubtype provides a comprehensive repertoire of candidates for new cancer subtype identification. For easy data access, CAsubtype further includes the gene expression and clinical data of more than 2000 cancer patients from TCGA. CAsubtype first employs principal component analysis to identify gene sets (from user-provided or package-integrated ones) with robust principal components representing significantly large variation between cancer samples. Based on these principal components, CAsubtype visualizes the sample distribution in low-dimensional space for better understanding of the distinction between samples and classifies samples into subgroups with prevalent clustering algorithms. Finally, CAsubtype performs survival analysis to compare the clinical outcomes between the identified subgroups, assessing their clinical value as potentially novel cancer subtypes. In conclusion, CAsubtype is a flexible and well-integrated tool in the R environment to identify gene sets for cancer subtype identification and clinical outcome prediction. Its simple R commands and comprehensive data sets enable efficient examination of the clinical value of any given gene set, thus facilitating hypothesis generating and testing in biological and

  9. Clinical prediction rule to identify high-risk inpatients for adverse drug events: the JADE Study.

    PubMed

    Sakuma, Mio; Bates, David W; Morimoto, Takeshi

    2012-11-01

    Adverse drug events (ADEs) are common health problems worldwide. Developing a prediction rule to identify patients at high risk for ADEs to prevent or ameliorate ADEs could be one attractive strategy. The Japan Adverse Drug Events (JADE) study is a prospective cohort study including 3459 participants. We randomly divided the JADE study cohort into the derivation and the validation sets, using an automated random digit generator. We calculated the probabilities of ADE in each patient in the validation set after applying the prediction rule developed in the derivation set. The actual incidence and area under the receiver operating characteristic curve (AUC) in the validation set were compared with those in the derivation set to evaluate the prognostic ability of our developed prediction rule. The developed prediction rule included eight independent risk factors. Each patient in the validation set was classified into three categories of risk for the ADEs according to the probability of ADEs calculated by the developed prediction rule. Eight percent (137/1730) of patients in the validation set fell into the high-risk group, and 35% of this group (48/137) had at least one ADE. The AUC in the validation set was 0.63 (95%CI 0.60-0.66), and the performance to discriminate the probability of ADE was similar (p = 0.08) compared with that in the derivation set. This prediction rule had the modest predictive ability and could help physicians and other healthcare professionals to make an estimation of patients at high risk for ADEs. Copyright © 2012 John Wiley & Sons, Ltd.

  10. 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.

  11. Financial Surrogate Decision Making: Lessons from Applied Experimental Philosophy.

    PubMed

    Feltz, Adam

    2016-09-20

    An estimated 1 in 4 elderly Americans need a surrogate to make decisions at least once in their lives. With an aging population, that number is almost certainly going to increase. This paper focuses on financial surrogate decision making. To illustrate some of the empirical and moral implications associated with financial surrogate decision making, two experiments suggest that default choice settings can predictably influence some surrogate financial decision making. Experiment 1 suggested that when making hypothetical financial decisions, surrogates tended to stay with default settings (OR = 4.37, 95% CI 1.52, 12.48). Experiment 2 replicated and extended this finding suggesting that in a different context (OR = 2.27, 95% CI 1.1, 4.65). Experiment 2 also suggested that those who were more numerate were less likely to be influenced by default settings than the less numerate, but only when the decision is whether to "opt in" (p = .05). These data highlight the importance of a recent debate about "nudging." Defaults are common methods to nudge people to make desirable choices while allowing the liberty to choose otherwise. Some of the ethics of using default settings to nudge surrogate decision makers are discussed.

  12. Surrogate-assisted network analysis of nonlinear time series

    NASA Astrophysics Data System (ADS)

    Laut, Ingo; Räth, Christoph

    2016-10-01

    The performance of recurrence networks and symbolic networks to detect weak nonlinearities in time series is compared to the nonlinear prediction error. For the synthetic data of the Lorenz system, the network measures show a comparable performance. In the case of relatively short and noisy real-world data from active galactic nuclei, the nonlinear prediction error yields more robust results than the network measures. The tests are based on surrogate data sets. The correlations in the Fourier phases of data sets from some surrogate generating algorithms are also examined. The phase correlations are shown to have an impact on the performance of the tests for nonlinearity.

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

    PubMed

    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.

  14. 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

  15. 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

  16. Ago2 Immunoprecipitation Identifies Predicted MicroRNAs in Human Embryonic Stem Cells and Neural Precursors

    PubMed Central

    Swerdel, Mavis R.; Moore, Jennifer C.; Cohen, Rick I.; Wu, Hao; Sun, Yi E.; Hart, Ronald P.

    2009-01-01

    Background MicroRNAs are required for maintenance of pluripotency as well as differentiation, but since more microRNAs have been computationally predicted in genome than have been found, there are likely to be undiscovered microRNAs expressed early in stem cell differentiation. Methodology/Principal Findings SOLiD ultra-deep sequencing identified >107 unique small RNAs from human embryonic stem cells (hESC) and neural-restricted precursors that were fit to a model of microRNA biogenesis to computationally predict 818 new microRNA genes. These predicted genomic loci are associated with chromatin patterns of modified histones that are predictive of regulated gene expression. 146 of the predicted microRNAs were enriched in Ago2-containing complexes along with 609 known microRNAs, demonstrating association with a functional RISC complex. This Ago2 IP-selected subset was consistently expressed in four independent hESC lines and exhibited complex patterns of regulation over development similar to previously-known microRNAs, including pluripotency-specific expression in both hESC and iPS cells. More than 30% of the Ago2 IP-enriched predicted microRNAs are new members of existing families since they share seed sequences with known microRNAs. Conclusions/Significance Extending the classic definition of microRNAs, this large number of new microRNA genes, the majority of which are less conserved than their canonical counterparts, likely represent evolutionarily recent regulators of early differentiation. The enrichment in Ago2 containing complexes, the presence of chromatin marks indicative of regulated gene expression, and differential expression over development all support the identification of 146 new microRNAs active during early hESC differentiation. PMID:19784364

  17. 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.

  18. 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

  19. Surrogate-assisted feature extraction for high-throughput phenotyping.

    PubMed

    Yu, Sheng; Chakrabortty, Abhishek; Liao, Katherine P; Cai, Tianrun; Ananthakrishnan, Ashwin N; Gainer, Vivian S; Churchill, Susanne E; Szolovits, Peter; Murphy, Shawn N; Kohane, Isaac S; Cai, Tianxi

    2017-04-01

    Phenotyping algorithms are capable of accurately identifying patients with specific phenotypes from within electronic medical records systems. However, developing phenotyping algorithms in a scalable way remains a challenge due to the extensive human resources required. This paper introduces a high-throughput unsupervised feature selection method, which improves the robustness and scalability of electronic medical record phenotyping without compromising its accuracy. The proposed Surrogate-Assisted Feature Extraction (SAFE) method selects candidate features from a pool of comprehensive medical concepts found in publicly available knowledge sources. The target phenotype's International Classification of Diseases, Ninth Revision and natural language processing counts, acting as noisy surrogates to the gold-standard labels, are used to create silver-standard labels. Candidate features highly predictive of the silver-standard labels are selected as the final features. Algorithms were trained to identify patients with coronary artery disease, rheumatoid arthritis, Crohn's disease, and ulcerative colitis using various numbers of labels to compare the performance of features selected by SAFE, a previously published automated feature extraction for phenotyping procedure, and domain experts. The out-of-sample area under the receiver operating characteristic curve and F -score from SAFE algorithms were remarkably higher than those from the other two, especially at small label sizes. SAFE advances high-throughput phenotyping methods by automatically selecting a succinct set of informative features for algorithm training, which in turn reduces overfitting and the needed number of gold-standard labels. SAFE also potentially identifies important features missed by automated feature extraction for phenotyping or experts.

  20. 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.

  1. Regulatory element-based prediction identifies new susceptibility regulatory variants for osteoporosis.

    PubMed

    Yao, Shi; Guo, Yan; Dong, Shan-Shan; Hao, Ruo-Han; Chen, Xiao-Feng; Chen, Yi-Xiao; Chen, Jia-Bin; Tian, Qing; Deng, Hong-Wen; Yang, Tie-Lin

    2017-08-01

    Despite genome-wide association studies (GWASs) have identified many susceptibility genes for osteoporosis, it still leaves a large part of missing heritability to be discovered. Integrating regulatory information and GWASs could offer new insights into the biological link between the susceptibility SNPs and osteoporosis. We generated five machine learning classifiers with osteoporosis-associated variants and regulatory features data. We gained the optimal classifier and predicted genome-wide SNPs to discover susceptibility regulatory variants. We further utilized Genetic Factors for Osteoporosis Consortium (GEFOS) and three in-house GWASs samples to validate the associations for predicted positive SNPs. The random forest classifier performed best among all machine learning methods with the F1 score of 0.8871. Using the optimized model, we predicted 37,584 candidate SNPs for osteoporosis. According to the meta-analysis results, a list of regulatory variants was significantly associated with osteoporosis after multiple testing corrections and contributed to the expression of known osteoporosis-associated protein-coding genes. In summary, combining GWASs and regulatory elements through machine learning could provide additional information for understanding the mechanism of osteoporosis. The regulatory variants we predicted will provide novel targets for etiology research and treatment of osteoporosis.

  2. Predictive modeling using a nationally representative database to identify patients at risk of developing microalbuminuria.

    PubMed

    Villa-Zapata, Lorenzo; Warholak, Terri; Slack, Marion; Malone, Daniel; Murcko, Anita; Runger, George; Levengood, Michael

    2016-02-01

    Predictive models allow clinicians to identify higher- and lower-risk patients and make targeted treatment decisions. Microalbuminuria (MA) is a condition whose presence is understood to be an early marker for cardiovascular disease. The aims of this study were to develop a patient data-driven predictive model and a risk-score assessment to improve the identification of MA. The 2007-2008 National Health and Nutrition Examination Survey (NHANES) was utilized to create a predictive model. The dataset was split into thirds; one-third was used to develop the model, while the other two-thirds were utilized for internal validation. The 2012-2013 NHANES was used as an external validation database. Multivariate logistic regression was performed to create the model. Performance was evaluated using three criteria: (1) receiver operating characteristic curves; (2) pseudo-R (2) values; and (3) goodness of fit (Hosmer-Lemeshow). The model was then used to develop a risk-score chart. A model was developed using variables for which there was a significant relationship. Variables included were systolic blood pressure, fasting glucose, C-reactive protein, blood urea nitrogen, and alcohol consumption. The model performed well, and no significant differences were observed when utilized in the validation datasets. A risk score was developed, and the probability of developing MA for each score was calculated. The predictive model provides new evidence about variables related with MA and may be used by clinicians to identify at-risk patients and to tailor treatment. The risk score developed may allow clinicians to measure a patient's MA risk.

  3. [Predicting individual risk of high healthcare cost to identify complex chronic patients].

    PubMed

    Coderch, Jordi; Sánchez-Pérez, Inma; Ibern, Pere; Carreras, Marc; Pérez-Berruezo, Xavier; Inoriza, José M

    2014-01-01

    To develop a predictive model for the risk of high consumption of healthcare resources, and assess the ability of the model to identify complex chronic patients. A cross-sectional study was performed within a healthcare management organization by using individual data from 2 consecutive years (88,795 people). The dependent variable consisted of healthcare costs above the 95th percentile (P95), including all services provided by the organization and pharmaceutical consumption outside of the institution. The predictive variables were age, sex, morbidity-based on clinical risk groups (CRG)-and selected data from previous utilization (use of hospitalization, use of high-cost drugs in ambulatory care, pharmaceutical expenditure). A univariate descriptive analysis was performed. We constructed a logistic regression model with a 95% confidence level and analyzed sensitivity, specificity, positive predictive values (PPV), and the area under the ROC curve (AUC). Individuals incurring costs >P95 accumulated 44% of total healthcare costs and were concentrated in ACRG3 (aggregated CRG level 3) categories related to multiple chronic diseases. All variables were statistically significant except for sex. The model had a sensitivity of 48.4% (CI: 46.9%-49.8%), specificity of 97.2% (CI: 97.0%-97.3%), PPV of 46.5% (CI: 45.0%-47.9%), and an AUC of 0.897 (CI: 0.892 to 0.902). High consumption of healthcare resources is associated with complex chronic morbidity. A model based on age, morbidity, and prior utilization is able to predict high-cost risk and identify a target population requiring proactive care. Copyright © 2013 SESPAS. Published by Elsevier Espana. All rights reserved.

  4. Scope and Outcomes of Surrogate Decision Making Among Hospitalized Older Adults

    PubMed Central

    Torke, Alexia M.; Sachs, Greg A.; Helft, Paul R.; Montz, Kianna; Hui, Siu L.; Slaven, James E.; Callahan, Christopher M.

    2014-01-01

    Importance Hospitalized older adults often lack decisional capacity, but outside of the intensive care unit (ICU) and end-of-life care settings, little is known about the frequency of decision making by family members or other surrogates or its implications for hospital care. Objective To describe the scope of surrogate decision making, the hospital course and outcomes for older adults. Design Prospective, observational study. Setting Medical and Medical ICU services of two hospitals in one Midwest city. Participants 1083 hospitalized older adults identified by their physicians as requiring major medical decisions. Measures Clinical characteristics, hospital outcomes, nature of major medical decisions and surrogate involvement. Results Based on physician reports at 48 hours of hospitalization, 47.4% (44.4%–50.4%) of older adults required at least some surrogate involvement including 23.0% (20.6% – 25.6%) with all decisions made by a surrogate. Among patients who required a surrogate for at least one decision within 48 hours, 57.2% required decisions about life sustaining care (mostly addressing code status), 48.6% about procedures and surgeries and 46.9% about discharge planning. Patients who required a surrogate experienced a more complex hospital course with greater use of ventilators (2.5% patients who made decisions, 13.2% patients who required any surrogate decisions, p<0.0001), artificial nutrition (1.7% patient, 14.4% surrogate, p<0.0001) and greater length of stay (median 6 days patient, 7 days surrogate, p<0.0001). They were more likely to be discharged to an extended care facility (21.2% patient, 40.9% surrogate, p<0.0001), and had higher hospital mortality (0.0% patient; 5.9% surrogate, p<0.0001). Most surrogates were daughters (58.9%), sons (25.0%) or spouses (20.6%). Overall, only 7.4% had a living will and 25.0% a health care representative document in the medical record. Conclusion Surrogate decision making occurs for nearly half of hospitalized

  5. 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. PMID:27540511

  6. School absenteeism among children and its correlates: a predictive model for identifying absentees.

    PubMed

    Uppal, Preena; Paul, Premila; Sreenivas, V

    2010-11-01

    To determine the magnitude of absenteeism and its correlates and to develop a model to predict absenteeism in school children. A cross-sectional study. three government schools in Delhi. 704 students, aged 10 to15 years. students were registered and interviewed using a pre designed questionnaire. The frequency and causes of school absenteeism were ascertained by school records, leave applications and one months recall. The factors were subjected to univariate analysis and a stepwise multiple logistic regression analysis and a predictive model was developed. The average absenteeism of a student over 6 months was 14.3±10.2 days (95% CI 13.5 -15.0). 48% children absented themselves for more than two days per month on an average. The main factors associated with school absenteeism were younger age, male sex, increasing birth order, lower levels of parental education and income, school truancy, school phobia and family reasons. The discriminating ability of the predictive model developed was 92.4% it is possible to identify potential absentees in school children.

  7. Identifying predictive features in drug response using machine learning: opportunities and challenges.

    PubMed

    Vidyasagar, Mathukumalli

    2015-01-01

    This article reviews several techniques from machine learning that can be used to study the problem of identifying a small number of features, from among tens of thousands of measured features, that can accurately predict a drug response. Prediction problems are divided into two categories: sparse classification and sparse regression. In classification, the clinical parameter to be predicted is binary, whereas in regression, the parameter is a real number. Well-known methods for both classes of problems are briefly discussed. These include the SVM (support vector machine) for classification and various algorithms such as ridge regression, LASSO (least absolute shrinkage and selection operator), and EN (elastic net) for regression. In addition, several well-established methods that do not directly fall into machine learning theory are also reviewed, including neural networks, PAM (pattern analysis for microarrays), SAM (significance analysis for microarrays), GSEA (gene set enrichment analysis), and k-means clustering. Several references indicative of the application of these methods to cancer biology are discussed.

  8. 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.

  9. 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.

  10. [Development of a predictive model of death or urgent hospitalization to identify frail elderly].

    PubMed

    Pandolfi, Paolo; Collina, Natalina; Marzaroli, Paolo; Stivanello, Elisa; Musti, Muriel Assunta; Giansante, Chiara; Perlangeli, Vincenza; Pizzi, Lorenzo; De Lisio, Sara; Francia, Fausto

    2016-01-01

    to develop and validate a predictive model of mortality or emergency hospitalization in all subjects aged 65 years and over. cohort study based on 9 different databases linked with each other. the model was developed on the population aged 65 years and over resident at 01.01.2011 for at least two years in the city of Bologna (Emilia-Romagna Region, Northern Italy); 96,000 persons were included. the outcome was defined in case of emergency hospitalization or death during the one-year follow-up and studied with a logistic regression model. The predictive ability of the model was evaluated by using the area under the Roc curve, the Hosmer-Lemeshow test, and the Brier score in the derivation sample (2/3 of the population). These tests were repeated in the validation sample (1/3 of the population) and in the population of Bologna aged 65 years and over on 01.01.2012, after applying the coefficients of the variables obtained in the derivation model. By using the regression coefficients, a frailty index (risk score) was calculated for each subject later categorized in risk classes. the model is composed of 28 variables and has good predictive abilities. The area under the Roc curve of the derivation sample is 0.77, the Hosmer-Lemeshow test is not significant, and the Brier score is 0.11. Similar performances are obtained in the other two samples. With increasing risk class, the mean age, number of hospitalizations, emergency room service consultations, and multiple drug prescriptions increase, while the average income decreases. the model has good predictive ability. The frailty index can be used to support a proactive medicine and stratify the population, plan clinical and preventive activities or identify the potential beneficiaries of specific health promotion projects.

  11. The predictive capabilities of a novel cardiovascular magnetic resonance derived marker of cardiopulmonary reserve on established prognostic surrogate markers in patients with pulmonary vascular disease: results of a longitudinal pilot study.

    PubMed

    Baillie, Timothy J; Sidharta, Samuel; Steele, Peter M; Worthley, Stephen G; Willoughby, Scott; Teo, Karen; Sanders, Prashanthan; Nicholls, Stephen J; Worthley, Matthew I

    2017-01-09

    No unified method exists to effectively predict and monitor progression of pulmonary arterial hypertension (PAH). We assessed the longitudinal relationship between a novel marker of cardiopulmonary reserve and established prognostic surrogate markers in patients with pulmonary vascular disease. Twenty participants with confirmed (n = 14) or at high risk (n = 6) for PAH underwent cardiovascular magnetic resonance (CMR) at baseline and after ~6 months of guideline-appropriate management. Ten PAH participants underwent RHC within 48 h of each CMR. RHC (mean pulmonary arterial pressure, mPAP; pulmonary vascular resistance index, PVRI; cardiac index, CI) and phase-contrast CMR (mean pulmonary arterial blood flow velocity, meanPAvel) measurements were taken at rest and during continuous adenosine infusion (70/140/210 mcg/kg/min). Initial meanPAvel's (rest and hyperemic) were correlated with validated surrogate prognostic parameters (CMR: RV ejection fraction, RVEF; RV end systolic volume indexed, RVESVI; RHC: PVRI, CI; biomarker: NT-pro brain natriuretic peptide, NTpBNP; clinical: 6-min walk distance, 6MWD), a measure of pulmonary arterial stiffness (elastic modulus) and volumetric estimation of RV ventriculoarterial (VA) coupling. Changes in meanPAvel's were correlated with changes in comparator parameters over time. At initial assessment, meanPAvel at rest correlated significantly with PVRI (inversely), CI (positively) and elastic modulus (inversely) (R (2) > 0.37,P < 0.05 for all), whereas meanPAvel at peak hyperemia correlated significantly with PVRI, RVEF, RVESVI, 6MWD, elastic modulus and VA coupling (R (2) > 0.30,P < 0.05 for all). Neither resting or hyperemia-derived meanPAvel correlated with NTpBNP levels. Initial meanPAvel at rest correlated significantly with RVEF, RVESVI, CI and VA coupling at follow up assessment (R (2) > 0.2,P < 0.05 for all) and initial meanPAvel at peak hyperemia correlated with RVEF, RVESVI, PVRI and VA

  12. Computational Gene Expression Modeling Identifies Salivary Biomarker Analysis that Predict Oral Feeding Readiness in the Newborn

    PubMed Central

    Maron, Jill L.; Hwang, Jooyeon S.; Pathak, Subash; Ruthazer, Robin; Russell, Ruby L.; Alterovitz, Gil

    2014-01-01

    Objective To combine mathematical modeling of salivary gene expression microarray data and systems biology annotation with RT-qPCR amplification to identify (phase I) and validate (phase II) salivary biomarker analysis for the prediction of oral feeding readiness in preterm infants. Study design Comparative whole transcriptome microarray analysis from 12 preterm newborns pre- and post-oral feeding success was used for computational modeling and systems biology analysis to identify potential salivary transcripts associated with oral feeding success (phase I). Selected gene expression biomarkers (15 from computational modeling; 6 evidence-based; and 3 reference) were evaluated by RT-qPCR amplification on 400 salivary samples from successful (n=200) and unsuccessful (n=200) oral feeders (phase II). Genes, alone and in combination, were evaluated by a multivariate analysis controlling for sex and post-conceptional age (PCA) to determine the probability that newborns achieved successful oral feeding. Results Advancing post-conceptional age (p < 0.001) and female sex (p = 0.05) positively predicted an infant’s ability to feed orally. A combination of five genes, NPY2R (hunger signaling), AMPK (energy homeostasis), PLXNA1 (olfactory neurogenesis), NPHP4 (visual behavior) and WNT3 (facial development), in addition to PCA and sex, demonstrated good accuracy for determining feeding success (AUROC = 0.78). Conclusions We have identified objective and biologically relevant salivary biomarkers that noninvasively assess a newborn’s developing brain, sensory and facial development as they relate to oral feeding success. Understanding the mechanisms that underlie the development of oral feeding readiness through translational and computational methods may improve clinical decision making while decreasing morbidities and health care costs. PMID:25620512

  13. Surrogate Nuclear Reactions using STARS

    SciTech Connect

    Bernstein, L A; Burke, J T; Church, J A; Ahle, L; Cooper, J R; Hoffman, R D; Moody, K; Punyon, J; Schiller, A; Algin, E; Plettner, C; Ai, H; Beausang, C W; Casten, R F; Hughes, R; Ricard-McCutchan, E; Meyer, D; Ressler, J J; Caggiano, J A; Zamfir, N V; Amro, H; Heinz, A; Fallon, P; McMahan, M A; Macchiavelli, A O; Phair, L W

    2004-10-26

    The results from two surrogate reaction experiments using the STARS (Silicon Telescope Array for Reaction Studies) spectrometer are presented. The surrogate method involves measuring the particle and/or {gamma}-ray decay probabilities of excited nuclei populated via a direct reaction. These probabilities can then be used to deduce neutron-induced reaction cross sections that lead to the same compound nuclei. In the first experiment STARS coupled to the GAMMASPHERE {gamma}-ray spectrometer successfully reproduce surrogate (n,{gamma}), (n,n'{gamma}) and (n,2n{gamma}) cross sections on {sup 155,156}Gd using Gd {sup 3}He-induced reactions. In the second series of experiments an energetic deuteron beam from the ESTU tandem at the Wright Nuclear Structure Lab at Yale University was used to obtain the ratio of fission probabilities for {sup 238}U/ {sup 236}U and {sup 237}U/ {sup 239}U populated using the {sup 236,238}U(d,d'f) and {sup 236,238}U(d,pf) reactions. Results from these experiments are presented and the implications for the surrogate reaction technique are discussed.

  14. Detection and prediction limits for identifying highly confusable drug names from experimental data.

    PubMed

    Lambert, Bruce L; Bhaumik, Runa; Zhao, Weihan; Bhaumik, Dulal K

    2016-01-01

    Confusions between drug names that look and sound alike are common, costly, harmful, and difficult to prevent. One prevention strategy is to screen proposed new drug names for confusability before approving them. Widespread acceptance of preapproval tests of confusability is compromised by the lack of experimental designs and statistical methods to support valid inferences about whether a proposed new name is unacceptably confusing. One way of identifying confusing names is to conduct memory and perception experiments on a set of drug names which would include both the new name and a set of control names (e.g., names already on the market). The experiment would yield an observed error rate for every name. Inferences about the acceptability of the new name can be made by comparing the error rate of the new name to the distribution of error rates of the control names. We describe four memory and perception experiments on drug names, carried out using clinicians as participants. Each experiment included drug names designated as test and control names. We demonstrate how to use a combination of logistic regression, Poisson prediction limits, and highly assured credible intervals to identify and apply a threshold for identifying unacceptably confusing names. Our models show an excellent fit to the data. These experimental designs and analytic methods should be useful in the preapproval testing of proposed new drug names and in similar regulatory scenarios where it is necessary to draw inferences about the comparative safety or effectiveness of new vs. old products.

  15. Acutely-bereaved Surrogates' Stories about the Decision to Limit Life Support in the ICU

    PubMed Central

    Nunez, Eduardo R.; Schenker, Yael; Joel, Ian D.; Reynolds, Charles F.; Dew, Mary Amanda; Arnold, Robert M.; Barnato, Amber E.

    2015-01-01

    Objective Participating in a decision to limit life support for a loved one in the intensive care unit (ICU) is associated with adverse mental health consequences for surrogate decision makers. We sought to describe acutely-bereaved surrogates' experiences surrounding this decision. Design and setting Secondary analysis of interviews with surrogates approximately 4 weeks after a patient's death in one of 6 ICUs at 4 hospitals in Pittsburgh, Pennsylvania. Subjects Adults who participated in decisions about life support in the ICU. Interventions n/a Measurements We collected participant demographics, prior advance care planning, and decision control preferences. We used qualitative content analysis of transcribed interviews to identify themes in surrogates' experiences. Main results The 23 participants included the spouse (n=7), child/step-child (7), sibling (5), parent (3), or other relation (1) of the deceased patient. Their mean age was 55, 61% were women, all were white, 74% had prior treatment preferences discussions with the patient and 43% of patients had written advance directives. 15/23 (65%) surrogates preferred an active decision-making role, 8/23 (35%) preferred to share responsibility with the physician and no surrogates preferred a passive role. Surrogates report that key stressors in the ICU are the uncertainty and witnessed or empathic suffering. These factors contributed to surrogates' sense of helplessness in the ICU. Involvement in the decision to limit life support allowed surrogates to regain a sense of agency by making a decision consistent with the patient's wishes and values, counteracting surrogates' helplessness and ending the uncertainty and suffering. Conclusions In this all-white sample of surrogates with non-passive decision control preferences from a single US region, participating in decision making allowed surrogates to regain control, counteract feelings of helplessness, and end their empathic suffering. While prior research

  16. 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.

  17. Surrogate endpoints for EDSS worsening in multiple sclerosis. A meta-analytic approach.

    PubMed

    Sormani, M P; Bonzano, L; Roccatagliata, L; Mancardi, G L; Uccelli, A; Bruzzi, P

    2010-07-27

    To evaluate whether the effects on potential surrogate endpoints, such as MRI markers and relapses, observed in trials of experimental treatments are able to predict the effects of these treatments on disability progression as defined in relapsing-remitting multiple sclerosis (RRMS) trials. We used a pooled analysis of all the published randomized controlled clinical trials in RRMS reporting data on Expanded Disability Status Scale (EDSS) worsening and relapses or MRI lesions or both. We extracted data on relapses, MRI lesions, and the proportion of progressing patients. A regression analysis weighted on trial size and duration was performed to study the relationship between the treatment effect observed in each trial on relapses and MRI lesions and the observed treatment effect on EDSS worsening. A set of 19 randomized double-blind controlled trials in RRMS were identified, for a total of 44 arms, 25 contrasts, and 10,009 patients. A significant correlation was found between the effect of treatments on relapses and the effect of treatments on EDSS worsening: the adjusted R(2) value of the weighted regression was 0.71. The correlation between the treatment effect on MRI lesions and EDSS worsening was slightly weaker (R(2) = 0.57) but significant. These findings support the use of commonly used surrogate markers of EDSS worsening as endpoints in multiple sclerosis clinical trials. Further research is warranted to validate surrogate endpoints at the individual level rather than at the trial level, to draw important conclusions in the management of the individual patient.

  18. 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

  19. Objectively identifying landmark use and predicting flight trajectories of the homing pigeon using Gaussian processes

    PubMed Central

    Mann, Richard; Freeman, Robin; Osborne, Michael; Garnett, Roman; Armstrong, Chris; Meade, Jessica; Biro, Dora; Guilford, Tim; Roberts, Stephen

    2011-01-01

    Pigeons home along idiosyncratic habitual routes from familiar locations. It has been suggested that memorized visual landmarks underpin this route learning. However, the inability to experimentally alter the landscape on large scales has hindered the discovery of the particular features to which birds attend. Here, we present a method for objectively classifying the most informative regions of animal paths. We apply this method to flight trajectories from homing pigeons to identify probable locations of salient visual landmarks. We construct and apply a Gaussian process model of flight trajectory generation for pigeons trained to home from specific release sites. The model shows increasing predictive power as the birds become familiar with the sites, mirroring the animal's learning process. We subsequently find that the most informative elements of the flight trajectories coincide with landscape features that have previously been suggested as important components of the homing task. PMID:20656739

  20. 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

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

    PubMed

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

    2016-01-01

    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. 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. 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 characteristic curve of 0.797. BFH

  2. Could tumor characteristics identified by colonoscopy predict the locally advanced rectal carcinoma?

    PubMed

    Wang, Hao; Cao, Fu-ao; Gong, Hai-feng; Zheng, Jian-ming; Fu, Chuan-gang

    2010-09-01

    Neoadjuvant chemoradiation is now considered the standard care for locally advanced rectal carcinoma (T3-4 or/and N1-2 lesions), but the accuracy of staging examinations including endorectal ultrasonography (ERUS) and MRI is far from excellent. In addition, the above staging equipment or professionals who perform the examinations may not be available in some hospitals, while preoperative colonoscopy and biopsy are usually obtainable in most hospitals. The objective of the present study was to investigate the clinical and pathological characteristics of locally advanced rectal carcinoma and identify candidates for neoadjuvant chemoradiation. This was a retrospective study. Patients who were treated for rectal cancer at Changhai Hospital from January 1999 to July 2008 were identified from our prospectively collected database. Statistical analysis was performed using SPSS Software System (version 15.0). The Mann-Whitney test, chi-square test and multivariate Logistic regression analysis were performed. A total of 1005 cases were included in this research, of which 761 cases were identified as locally advanced rectal carcinoma depending on postoperative TNM staging. The results of multivariate Logistic regression analysis indicated seven independent risk factors that could be used to predict a locally advanced rectal carcinoma independently: a high grade (including poor differentiation and undifferentiation) (OR: 3.856; 95% CI: 2.064 to 7.204; P = 0.000); large tumor size (OR: 2.455; 95% CI: 1.755 to 3.436; P = 0.000); elevated preoperative serum CEA level (OR: 1.823; 95% CI: 1.309 to 2.537; P = 0.000); non-polypoid tumor type (OR: 1.758; 95% CI: 1.273 to 2.427; P = 0.001); the absence of synchronous polyps (OR: 1.602; 95% CI: 1.103 to 2.327; P = 0.013); the absence of blood in stool (OR: 1.659; 95% CI: 1.049 to 2.624; P = 0.030); and a greater circumferential tumor extent (OR: 1.813; 95% CI: 1.055 to 3.113; P = 0.031). Based on these findings, a Logistic equation was

  3. PREDICT-PD: An online approach to prospectively identify risk indicators of Parkinson's disease.

    PubMed

    Noyce, Alastair J; R'Bibo, Lea; Peress, Luisa; Bestwick, Jonathan P; Adams-Carr, Kerala L; Mencacci, Niccolo E; Hawkes, Christopher H; Masters, Joseph M; Wood, Nicholas; Hardy, John; Giovannoni, Gavin; Lees, Andrew J; Schrag, Anette

    2017-02-01

    A number of early features can precede the diagnosis of Parkinson's disease (PD). To test an online, evidence-based algorithm to identify risk indicators of PD in the UK population. Participants aged 60 to 80 years without PD completed an online survey and keyboard-tapping task annually over 3 years, and underwent smell tests and genotyping for glucocerebrosidase (GBA) and leucine-rich repeat kinase 2 (LRRK2) mutations. Risk scores were calculated based on the results of a systematic review of risk factors and early features of PD, and individuals were grouped into higher (above 15th centile), medium, and lower risk groups (below 85th centile). Previously defined indicators of increased risk of PD ("intermediate markers"), including smell loss, rapid eye movement-sleep behavior disorder, and finger-tapping speed, and incident PD were used as outcomes. The correlation of risk scores with intermediate markers and movement of individuals between risk groups was assessed each year and prospectively. Exploratory Cox regression analyses with incident PD as the dependent variable were performed. A total of 1323 participants were recruited at baseline and >79% completed assessments each year. Annual risk scores were correlated with intermediate markers of PD each year and baseline scores were correlated with intermediate markers during follow-up (all P values < 0.001). Incident PD diagnoses during follow-up were significantly associated with baseline risk score (hazard ratio = 4.39, P = .045). GBA variants or G2019S LRRK2 mutations were found in 47 participants, and the predictive power for incident PD was improved by the addition of genetic variants to risk scores. The online PREDICT-PD algorithm is a unique and simple method to identify indicators of PD risk. © 2017 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society. © 2016 International Parkinson and Movement Disorder

  4. High-risk carotid plaques identified by CT-angiogram can predict acute myocardial infarction.

    PubMed

    Mosleh, Wassim; Adib, Keenan; Natdanai, Punnanithinont; Carmona-Rubio, Andres; Karki, Roshan; Paily, Jacienta; Ahmed, Mohamed Abdel-Aal; Vakkalanka, Sujit; Madam, Narasa; Gudleski, Gregory D; Chung, Charles; Sharma, Umesh C

    2017-04-01

    Prior studies identified the incremental value of non-invasive imaging by CT-angiogram (CTA) to detect high-risk coronary atherosclerotic plaques. Due to their superficial locations, larger calibers and motion-free imaging, the carotid arteries provide the best anatomic access for the non-invasive characterization of atherosclerotic plaques. We aim to assess the ability of predicting obstructive coronary artery disease (CAD) or acute myocardial infarction (MI) based on high-risk carotid plaque features identified by CTA. We retrospectively examined carotid CTAs of 492 patients that presented with acute stroke to characterize the atherosclerotic plaques of the carotid arteries and examined development of acute MI and obstructive CAD within 12-months. Carotid lesions were defined in terms of calcifications (large or speckled), presence of low-attenuation plaques, positive remodeling, and presence of napkin ring sign. Adjusted relative risks were calculated for each plaque features. Patients with speckled (<3 mm) calcifications and/or larger calcifications on CTA had a higher risk of developing an MI and/or obstructive CAD within 1 year compared to patients without (adjusted RR of 7.51, 95%CI 1.26-73.42, P = 0.001). Patients with low-attenuation plaques on CTA had a higher risk of developing an MI and/or obstructive CAD within 1 year than patients without (adjusted RR of 2.73, 95%CI 1.19-8.50, P = 0.021). Presence of carotid calcifications and low-attenuation plaques also portended higher sensitivity (100 and 79.17%, respectively) for the development of acute MI. Presence of carotid calcifications and low-attenuation plaques can predict the risk of developing acute MI and/or obstructive CAD within 12-months. Given their high sensitivity, their absence can reliably exclude 12-month events.

  5. Prenatal Features Predictive of Robin Sequence Identified by Fetal Magnetic Resonance Imaging.

    PubMed

    Rogers-Vizena, Carolyn R; Mulliken, John B; Daniels, Kimberly M; Estroff, Judy A

    2016-06-01

    Prenatal magnetic resonance imaging is increasingly used to detect congenital anomalies. The purpose of this study was to determine whether prenatal magnetic resonance imaging accurately characterizes features predictive of postnatal Robin sequence so that possible airway compromise and feeding difficulty at birth can be anticipated. The authors retrospectively identified pregnant women who underwent fetal magnetic resonance imaging between 2002 and 2014 and were found to be carrying a fetus with micrognathia. Micrognathia was subjectively categorized as minor, moderate, or severe. Pregnancy outcome was determined as follows: intrauterine fetal demise, elective termination, early neonatal death, or viable infant. Postnatal findings of micrognathia, Robin sequence, and associated anomalies were compared to prenatal findings. Micrognathia was identified in 123 fetuses. Fifty-two pregnancies (42.3 percent) produced a viable infant. The remainder resulted in termination in the fetal period or death shortly after birth resulting from unrelated causes. For infants who lived, prenatal micrognathia was categorized as minor (55.1 percent), moderate (30.6 percent), or severe (14.3 percent). Forty-two percent of neonates with minor prenatal micrognathia had postnatal micrognathia; however, only 11.1 percent had Robin sequence. All neonates with moderate fetal micrognathia had postnatal micrognathia, and the majority had Robin sequence (86.7 percent). All newborns with severe micrognathia had Robin sequence and all prenatally diagnosed with glossoptosis had Robin sequence. Prenatal findings of moderate or severe micrognathia or glossoptosis are predictive of postnatal Robin sequence, thus expediting appropriate perinatal management of airway and feeding problems. Diagnostic, IV.

  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.

  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. 34 CFR 303.406 - Surrogate parents.

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... 34 Education 2 2010-07-01 2010-07-01 false Surrogate parents. 303.406 Section 303.406 Education... DISABILITIES Procedural Safeguards General § 303.406 Surrogate parents. (a) General. Each lead agency shall... paragraph (a) of this section, includes the assignment of an individual to act as a surrogate for the...

  11. 34 CFR 300.519 - Surrogate parents.

    Code of Federal Regulations, 2014 CFR

    2014-07-01

    ... 34 Education 2 2014-07-01 2013-07-01 true Surrogate parents. 300.519 Section 300.519 Education... DISABILITIES Procedural Safeguards Due Process Procedures for Parents and Children § 300.519 Surrogate parents... paragraph (a) of this section include the assignment of an individual to act as a surrogate for the...

  12. 34 CFR 300.519 - Surrogate parents.

    Code of Federal Regulations, 2011 CFR

    2011-07-01

    ... 34 Education 2 2011-07-01 2010-07-01 true Surrogate parents. 300.519 Section 300.519 Education... DISABILITIES Procedural Safeguards Due Process Procedures for Parents and Children § 300.519 Surrogate parents... paragraph (a) of this section include the assignment of an individual to act as a surrogate for the...

  13. 34 CFR 300.519 - Surrogate parents.

    Code of Federal Regulations, 2013 CFR

    2013-07-01

    ... 34 Education 2 2013-07-01 2013-07-01 false Surrogate parents. 300.519 Section 300.519 Education... DISABILITIES Procedural Safeguards Due Process Procedures for Parents and Children § 300.519 Surrogate parents... paragraph (a) of this section include the assignment of an individual to act as a surrogate for the...

  14. 34 CFR 300.519 - Surrogate parents.

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... 34 Education 2 2010-07-01 2010-07-01 false Surrogate parents. 300.519 Section 300.519 Education... DISABILITIES Procedural Safeguards Due Process Procedures for Parents and Children § 300.519 Surrogate parents... paragraph (a) of this section include the assignment of an individual to act as a surrogate for the...

  15. 34 CFR 300.519 - Surrogate parents.

    Code of Federal Regulations, 2012 CFR

    2012-07-01

    ... 34 Education 2 2012-07-01 2012-07-01 false Surrogate parents. 300.519 Section 300.519 Education... DISABILITIES Procedural Safeguards Due Process Procedures for Parents and Children § 300.519 Surrogate parents... paragraph (a) of this section include the assignment of an individual to act as a surrogate for the...

  16. 34 CFR 303.406 - Surrogate parents.

    Code of Federal Regulations, 2011 CFR

    2011-07-01

    ... 34 Education 2 2011-07-01 2010-07-01 true Surrogate parents. 303.406 Section 303.406 Education... DISABILITIES Procedural Safeguards General § 303.406 Surrogate parents. (a) General. Each lead agency shall... paragraph (a) of this section, includes the assignment of an individual to act as a surrogate for the...

  17. 77 FR 34788 - Surrogate Foreign Corporations

    Federal Register 2010, 2011, 2012, 2013, 2014

    2012-06-12

    ... Internal Revenue Service 26 CFR Part 1 RIN 1545-BF47 Surrogate Foreign Corporations AGENCY: Internal... regulations regarding whether a foreign corporation is treated as a surrogate foreign corporation. The final... corporation as a surrogate foreign corporation (2006 temporary regulations). A notice of proposed...

  18. The market for surrogate motherhood contracts.

    PubMed

    Hewitson, G

    1997-09-01

    Surrogate motherhood is a controversial subject, and has not previously been formally modelled by economists. In this paper, a neoclassical model of the market for surrogate motherhood contracts is developed, based on the utility maximizing decisions of potential surrogate mothers and commissioning parties. The presence of both altruistic and self-interested behaviour generates unusual market outcomes.

  19. 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

  20. Surrogate Modeling of Deformable Joint Contact using Artificial Neural Networks

    PubMed Central

    Eskinazi, Ilan; Fregly, Benjamin J.

    2016-01-01

    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

  1. 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.

  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. Comparison between surrogate indexes of insulin sensitivity/resistance and hyperinsulinemic euglycemic clamp estimates in rats

    PubMed Central

    Muniyappa, Ranganath; Chen, Hui; Muzumdar, Radhika H.; Einstein, Francine H.; Yan, Xu; Yue, Lilly Q.; Barzilai, Nir

    2009-01-01

    Assessing insulin resistance in rodent models gives insight into mechanisms that cause type 2 diabetes and the metabolic syndrome. The hyperinsulinemic euglycemic glucose clamp, the reference standard for measuring insulin sensitivity in humans and animals, is labor intensive and technically demanding. A number of simple surrogate indexes of insulin sensitivity/resistance have been developed and validated primarily for use in large human studies. These same surrogates are also frequently used in rodent studies. However, in general, these indexes have not been rigorously evaluated in animals. In a recent validation study in mice, we demonstrated that surrogates have a weaker correlation with glucose clamp estimates of insulin sensitivity/resistance than in humans. This may be due to increased technical difficulties in mice and/or intrinsic differences between human and rodent physiology. To help distinguish among these possibilities, in the present study, using data from rats substantially larger than mice, we compared the clamp glucose infusion rate (GIR) with surrogate indexes, including QUICKI, HOMA, 1/HOMA, log (HOMA), and 1/fasting insulin. All surrogates were modestly correlated with GIR (r = 0.34–0.40). Calibration analyses of surrogates adjusted for body weight demonstrated similar predictive accuracy for GIR among all surrogates. We conclude that linear correlations of surrogate indexes with clamp estimates and predictive accuracy of surrogate indexes in rats are similar to those in mice (but not as substantial as in humans). This additional rat study (taken with the previous mouse study) suggests that application of surrogate insulin sensitivity indexes developed for humans may not be appropriate for determining primary outcomes in rodent studies due to intrinsic differences in metabolic physiology. However, use of surrogates may be appropriate in rodents, where feasibility of clamps is an obstacle and measurement of insulin sensitivity is a secondary

  4. 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.

  5. Biodegradation of naphthenic acid surrogates by axenic cultures.

    PubMed

    Yue, Siqing; Ramsay, Bruce A; Ramsay, Juliana A

    2015-07-01

    This is the first study to report that bacteria from the genera Ochrobactrum, Brevundimonas and Bacillus can be isolated by growth on naphthenic acids (NAs) extracted from oil sands process water (OSPW). These pure cultures were screened for their ability to use a range of aliphatic, cyclic and aromatic NA surrogates in 96-well microtiter plates using water-soluble tetrazolium redox dyes (Biolog Redox Dye H) as the indicator of metabolic activity. Of the three cultures, Ochrobactrum showed most metabolic activity on the widest range of NA surrogates. Brevundomonas and especially Ochrobactrum had higher metabolic activity on polycyclic aromatic compounds than other classes of NA surrogates. Bacillus also oxidized a wide range of NA surrogates but not as well as Ochrobactrum. Using this method to characterize NA utilisation, one can identify which NAs or NA classes in OSPW are more readily degraded. Since aromatic NAs have been shown to have an estrogenic effect and polycyclic monoaromatic compounds have been suggested to pose the greatest environmental threat among the NAs, these bacterial genera may play an important role in detoxification of OSPW. Furthermore, this study demonstrates that bacteria belonging to the genera Ochrobactrum and Bacillus can also degrade surrogates of tricyclic NAs.

  6. A Predictive Model to Identify Patients With Fecal Incontinence Based on High-Definition Anorectal Manometry.

    PubMed

    Zifan, Ali; Ledgerwood-Lee, Melissa; Mittal, Ravinder K

    2016-12-01

    Three-dimensional high-definition anorectal manometry (3D-HDAM) is used to assess anal sphincter function; it determines profiles of regional pressure distribution along the length and circumference of the anal canal. There is no consensus, however, on the best way to analyze data from 3D-HDAM to distinguish healthy individuals from persons with sphincter dysfunction. We developed a computer analysis system to analyze 3D-HDAM data and to aid in the diagnosis and assessment of patients with fecal incontinence (FI). In a prospective study, we performed 3D-HDAM analysis of 24 asymptomatic healthy subjects (control subjects; all women; mean age, 39 ± 10 years) and 24 patients with symptoms of FI (all women; mean age, 58 ± 13 years). Patients completed a standardized questionnaire (FI severity index) to score the severity of FI symptoms. We developed and evaluated a robust prediction model to distinguish patients with FI from control subjects using linear discriminant, quadratic discriminant, and logistic regression analyses. In addition to collecting pressure information from the HDAM data, we assessed regional features based on shape characteristics and the anal sphincter pressure symmetry index. The combination of pressure values, anal sphincter area, and reflective symmetry values was identified in patients with FI versus control subjects with an area under the curve value of 1.0. In logistic regression analyses using different predictors, the model identified patients with FI with an area under the curve value of 0.96 (interquartile range, 0.22). In discriminant analysis, results were classified with a minimum error of 0.02, calculated using 10-fold cross-validation; different combinations of predictors produced median classification errors of 0.16 in linear discriminant analysis (interquartile range, 0.25) and 0.08 in quadratic discriminant analysis (interquartile range, 0.25). We developed and validated a novel prediction model to analyze 3D-HDAM data. This

  7. Convergence of Mutation and Epigenetic Alterations Identifies Common Genes in Cancer That Predict for Poor Prognosis

    PubMed Central

    Chan, Timothy A; Glockner, Sabine; Yi, Joo Mi; Chen, Wei; Van Neste, Leander; Cope, Leslie; Herman, James G; Velculescu, Victor; Schuebel, Kornel E; Ahuja, Nita; Baylin, Stephen B

    2008-01-01

    Background The identification and characterization of tumor suppressor genes has enhanced our understanding of the biology of cancer and enabled the development of new diagnostic and therapeutic modalities. Whereas in past decades, a handful of tumor suppressors have been slowly identified using techniques such as linkage analysis, large-scale sequencing of the cancer genome has enabled the rapid identification of a large number of genes that are mutated in cancer. However, determining which of these many genes play key roles in cancer development has proven challenging. Specifically, recent sequencing of human breast and colon cancers has revealed a large number of somatic gene mutations, but virtually all are heterozygous, occur at low frequency, and are tumor-type specific. We hypothesize that key tumor suppressor genes in cancer may be subject to mutation or hypermethylation. Methods and Findings Here, we show that combined genetic and epigenetic analysis of these genes reveals many with a higher putative tumor suppressor status than would otherwise be appreciated. At least 36 of the 189 genes newly recognized to be mutated are targets of promoter CpG island hypermethylation, often in both colon and breast cancer cell lines. Analyses of primary tumors show that 18 of these genes are hypermethylated strictly in primary cancers and often with an incidence that is much higher than for the mutations and which is not restricted to a single tumor-type. In the identical breast cancer cell lines in which the mutations were identified, hypermethylation is usually, but not always, mutually exclusive from genetic changes for a given tumor, and there is a high incidence of concomitant loss of expression. Sixteen out of 18 (89%) of these genes map to loci deleted in human cancers. Lastly, and most importantly, the reduced expression of a subset of these genes strongly correlates with poor clinical outcome. Conclusions Using an unbiased genome-wide approach, our analysis has

  8. 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

  9. 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

  10. 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

  11. 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.

  12. 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. ©2016 American Association for Cancer Research.

  13. 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

    2016-07-02

    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.

  14. Identifying endpoints to predict the influence of immunosuppression on long-term kidney graft survival.

    PubMed

    Srinivas, Titte R; Oppenheimer, Federico

    2015-07-01

    Identifying a short-term endpoint for use in clinical trials that accurately reflects the influence of specific immunosuppressive regimens on long-term kidney graft survival is challenging. The number, timing, type (T-cell-mediated or antibody mediated), and severity of biopsy-proven acute rejection (BPAR) episodes in terms of histological changes and functional impact are highly influential for graft prognosis, and a crude measure of overall acute rejection incidence alone is unlikely to be a robust predictor of graft outcome. A series of studies has shown remarkably consistent results in terms of the cutoff point for one-yr renal function which predicts poor long-term graft survival, indicating that a threshold of 50 mL/min/1.73 m(2) is likely to be appropriate. Estimated glomerular filtration rate at one yr post-transplant discriminates effectively among immunosuppressive regimens with regard to graft survival, primarily calcineurin inhibitor reduction strategies. Several other factors that can affect graft survival, such as pathological changes in the graft, may be partly influenced by the immunosuppressive regimen, but the contribution of drug therapy is difficult to define. A combined approach in which both treated BPAR and renal function at one yr are used to assess novel immunosuppressive regimens appears to be promising as the emphasis shifts toward sustaining kidney allograft survival over the long term. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

  15. Is blood pressure reduction a valid surrogate endpoint for stroke prevention? an analysis incorporating a systematic review of randomised controlled trials, a by-trial weighted errors-in-variables regression, the surrogate threshold effect (STE) and the biomarker-surrogacy (BioSurrogate) evaluation schema (BSES)

    PubMed Central

    2012-01-01

    Background Blood pressure is considered to be a leading example of a valid surrogate endpoint. The aims of this study were to (i) formally evaluate systolic and diastolic blood pressure reduction as a surrogate endpoint for stroke prevention and (ii) determine what blood pressure reduction would predict a stroke benefit. Methods We identified randomised trials of at least six months duration comparing any pharmacologic anti-hypertensive treatment to placebo or no treatment, and reporting baseline blood pressure, on-trial blood pressure, and fatal and non-fatal stroke. Trials with fewer than five strokes in at least one arm were excluded. Errors-in-variables weighted least squares regression modelled the reduction in stroke as a function of systolic blood pressure reduction and diastolic blood pressure reduction respectively. The lower 95% prediction band was used to determine the minimum systolic blood pressure and diastolic blood pressure difference, the surrogate threshold effect (STE), below which there would be no predicted stroke benefit. The STE was used to generate the surrogate threshold effect proportion (STEP), a surrogacy metric, which with the R-squared trial-level association was used to evaluate blood pressure as a surrogate endpoint for stroke using the Biomarker-Surrogacy Evaluation Schema (BSES3). Results In 18 qualifying trials representing all pharmacologic drug classes of antihypertensives, assuming a reliability coefficient of 0.9, the surrogate threshold effect for a stroke benefit was 7.1 mmHg for systolic blood pressure and 2.4 mmHg for diastolic blood pressure. The trial-level association was 0.41 and 0.64 and the STEP was 66% and 78% for systolic and diastolic blood pressure respectively. The STE and STEP were more robust to measurement error in the independent variable than R-squared trial-level associations. Using the BSES3, assuming a reliability coefficient of 0.9, systolic blood pressure was a B + grade and diastolic blood pressure

  16. Predictive value of magnetic resonance for identifying neurovascular compressions in trigeminal neuralgia.

    PubMed

    Ruiz-Juretschke, F; Guzmán-de-Villoria, J G; García-Leal, R; Sañudo, J R

    2017-05-23

    Microvascular decompression (MVD) is accepted as the only aetiological surgical treatment for refractory classic trigeminal neuralgia (TN). There is therefore increasing interest in establishing the diagnostic and prognostic value of identifying neurovascular compressions (NVC) using preoperative high-resolution three-dimensional magnetic resonance (MRI) in patients with classic TN who are candidates for surgery. This observational study includes a series of 74 consecutive patients with classic TN treated with MVD. All patients underwent a preoperative three-dimensional high-resolution MRI with DRIVE sequences to diagnose presence of NVC, as well as the degree, cause, and location of compressions. MRI results were analysed by doctors blinded to surgical findings and subsequently compared to those findings. After a minimum follow-up time of six months, we assessed the surgical outcome and graded it on the Barrow Neurological Institute pain intensity score (BNI score). The prognostic value of the preoperative MRI was estimated using binary logistic regression. Preoperative DRIVE MRI sequences showed a sensitivity of 95% and a specificity of 87%, with a 98% positive predictive value and a 70% negative predictive value. Moreover, Cohen's kappa (CK) indicated a good level of agreement between radiological and surgical findings regarding presence of NVC (CK 0.75), type of compression (CK 0.74) and the site of compression (CK 0.72), with only moderate agreement as to the degree of compression (CK 0.48). After a mean follow-up of 29 months (range 6-100 months), 81% of the patients reported pain control with or without medication (BNI score i-iiiI). Patients with an excellent surgical outcome, i.e. without pain and off medication (BNI score i), made up 66% of the total at the end of follow-up. Univariate analysis using binary logistic regression showed that a diagnosis of NVC on the preoperative MRI was a favorable prognostic factor that significantly increased the odds of

  17. 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

  18. 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

  19. Critical care physicians’ approaches to negotiating with surrogate decision makers: a qualitative study

    PubMed Central

    Brush, David R.; Brown, Crystal E.; Alexander, G. Caleb

    2013-01-01

    Objective To describe how critical care physicians manage conflicts with surrogates about withdrawing or withholding patients’ life support. Design Qualitative analysis of key informant interviews with critical care physicians during 2010. We transcribed interviews verbatim and used grounded theory to code and revise a taxonomy of themes and to identify illustrative quotes. Setting 3 academic medical centers, 1 academic-affiliated medical center and 4 private practice groups or private hospitals in a large Midwestern city Subjects 14 critical care physicians Measurements and main results Physicians reported tailoring their approach to address specific reasons for disagreement with surrogates. Five common approaches were identified: (1) building trust, (2) educating and informing, (3) providing surrogates more time, (4) adjusting surrogate and physician roles, and (5) highlighting specific values. When mistrust was an issue, physicians endeavored to build a more trusting relationship with the surrogate before re-addressing decision making. Physicians also reported correcting misunderstandings by providing targeted education, and some reported highlighting specific patient, surrogate, or physician values that they hoped would guide surrogates to agree with them. When surrogates struggled with decision making roles, physicians attempted to reinforce the concept of substituted judgment. Physicians noted that some surrogates needed time to “come to terms” with the patent’s illness before agreeing with physicians. Many physicians had witnessed colleagues negotiate in ways they found objectionable, such as providing misleading information, injecting their own values into the negotiation, or behaving unprofessionally towards surrogates. While some physicians viewed their efforts to encourage surrogates’ agreement as persuasive, others strongly denied persuading surrogates and described their actions as “guiding” or “negotiating.” Conclusions Physicians

  20. Evaluation of a surrogate marker: validity and efficiency.

    PubMed

    Qu, Yongming

    2013-05-30

    Statistical validation of a surrogate endpoint or surrogate marker has been studied for three decades. However, there is still no consensus on how to evaluate surrogate endpoints or surrogate markers. There is much confusion on which method should be used for the evaluation of a surrogate endpoint or surrogate marker. In this article, we clarify the statistical definitions of the surrogate endpoint and surrogate marker and introduce the concept of the validity and efficiency of a surrogate marker. We suggest the proportion of information gain can be used to evaluate the validity of a surrogate marker.

  1. Review of surrogate modeling in water resources

    NASA Astrophysics Data System (ADS)

    Razavi, Saman; Tolson, Bryan A.; Burn, Donald H.

    2012-07-01

    Surrogate modeling, also called metamodeling, has evolved and been extensively used over the past decades. A wide variety of methods and tools have been introduced for surrogate modeling aiming to develop and utilize computationally more efficient surrogates of high-fidelity models mostly in optimization frameworks. This paper reviews, analyzes, and categorizes research efforts on surrogate modeling and applications with an emphasis on the research accomplished in the water resources field. The review analyzes 48 references on surrogate modeling arising from water resources and also screens out more than 100 references from the broader research community. Two broad families of surrogates namely response surface surrogates, which are statistical or empirical data-driven models emulating the high-fidelity model responses, and lower-fidelity physically based surrogates, which are simplified models of the original system, are detailed in this paper. Taxonomies on surrogate modeling frameworks, practical details, advances, challenges, and limitations are outlined. Important observations and some guidance for surrogate modeling decisions are provided along with a list of important future research directions that would benefit the common sampling and search (optimization) analyses found in water resources.

  2. 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

  3. 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

  4. Identifying influential metrics in the combined metrics approach of fault prediction.

    PubMed

    Goyal, Rinkaj; Chandra, Pravin; Singh, Yogesh

    2013-01-01

    Fault prediction is a pre-eminent area of empirical software engineering which has witnessed a huge surge over the last couple of decades. In the development of a fault prediction model, combination of metrics results in better explanatory power of the model. Since the metrics used in combination are often correlated, and do not have an additive effect, the impact of a metric on another i.e. interaction should be taken into account. The effect of interaction in developing regression based fault prediction models is uncommon in software engineering; however two terms and three term interactions are analyzed in detail in social and behavioral sciences. Beyond three terms interactions are scarce, because interaction effects at such a high level are difficult to interpret. From our earlier findings (Softw Qual Prof 15(3):15-23) we statistically establish the pertinence of considering the interaction between metrics resulting in a considerable improvement in the explanatory power of the corresponding predictive model. However, in the aforesaid approach, the number of variables involved in fault prediction also shows a simultaneous increment with interaction. Furthermore, the interacting variables do not contribute equally to the prediction capability of the model. This study contributes towards the development of an efficient predictive model involving interaction among predictive variables with a reduced set of influential terms, obtained by applying stepwise regression.

  5. 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.

  6. 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.

    2008-04-17

    Over the past three years we have studied various surrogate reactions (d,p), ({sup 3}He,t), ({alpha},{alpha}{sup '}) 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.

  7. Tractable Experiment Design via Mathematical Surrogates

    SciTech Connect

    Williams, Brian J.

    2016-02-29

    This presentation summarizes the development and implementation of quantitative design criteria motivated by targeted inference objectives for identifying new, potentially expensive computational or physical experiments. The first application is concerned with estimating features of quantities of interest arising from complex computational models, such as quantiles or failure probabilities. A sequential strategy is proposed for iterative refinement of the importance distributions used to efficiently sample the uncertain inputs to the computational model. In the second application, effective use of mathematical surrogates is investigated to help alleviate the analytical and numerical intractability often associated with Bayesian experiment design. This approach allows for the incorporation of prior information into the design process without the need for gross simplification of the design criterion. Illustrative examples of both design problems will be presented as an argument for the relevance of these research problems.

  8. A cross-cultural study on surrogate mother's empathy and maternal-foetal attachment.

    PubMed

    Lorenceau, Ellen Schenkel; Mazzucca, Luis; Tisseron, Serge; Pizitz, Todd D

    2015-06-01

    Traditional and gestational surrogate mothers assist infertile couples by carrying their children. In 2005, a meta-analysis on surrogacy was conducted but no study had examined empathy and maternal-foetal attachment of surrogate mothers. Assessments of surrogate mothers show no sign of psychopathology, but one study showed differences on several MMPI-2 scales compared to a normative sample: surrogate mothers identified with stereotypically masculine traits such as assertiveness and competition. They had a higher self-esteem and lower levels of anxiety and depression. To determine if there is a difference in empathy and maternal-foetal attachment of surrogate mothers compared to a comparison group of mothers. Three groups of European traditional and gestational surrogate mothers (n=10), Anglo-Saxon traditional and gestational surrogate mothers (n=34) and a European normative sample of mothers (n=32) completed four published psychometric instruments: the Interpersonal Reactivity Index (empathy index), the Hospital Anxiety and Depressions Scale and the MC20, a social desirability scale. Pregnant surrogate mothers filled the Maternal Antenatal Attachment Scale (n=11). Statistical non-parametric analyses of variance were conducted. Depending on cultural background, surrogate mothers present differences in terms of empathy, anxiety and depression, social desirability and quality of attachment to the foetus compared to a normative sample. Environment plays a role for traditional and gestational surrogacy. Surrogate mothers of both groups are less anxious and depressed than normative samples. Maternal-foetal attachment is strong with a slightly lower quality of attachment. Surrogate mother's empathy indexes are similar to normative samples, sometimes higher. Copyright © 2014 Australian College of Midwives. Published by Elsevier Ltd. All rights reserved.

  9. Evaluation of different approaches for identifying optimal sites to predict mean hillslope soil moisture content

    NASA Astrophysics Data System (ADS)

    Liao, Kaihua; Zhou, Zhiwen; Lai, Xiaoming; Zhu, Qing; Feng, Huihui

    2017-04-01

    The identification of representative soil moisture sampling sites is important for the validation of remotely sensed mean soil moisture in a certain area and ground-based soil moisture measurements in catchment or hillslope hydrological studies. Numerous approaches have been developed to identify optimal sites for predicting mean soil moisture. Each method has certain advantages and disadvantages, but they have rarely been evaluated and compared. In our study, surface (0-20 cm) soil moisture data from January 2013 to March 2016 (a total of 43 sampling days) were collected at 77 sampling sites on a mixed land-use (tea and bamboo) hillslope in the hilly area of Taihu Lake Basin, China. A total of 10 methods (temporal stability (TS) analyses based on 2 indices, K-means clustering based on 6 kinds of inputs and 2 random sampling strategies) were evaluated for determining optimal sampling sites for mean soil moisture estimation. They were TS analyses based on the smallest index of temporal stability (ITS, a combination of the mean relative difference and standard deviation of relative difference (SDRD)) and based on the smallest SDRD, K-means clustering based on soil properties and terrain indices (EFs), repeated soil moisture measurements (Theta), EFs plus one-time soil moisture data (EFsTheta), and the principal components derived from EFs (EFs-PCA), Theta (Theta-PCA), and EFsTheta (EFsTheta-PCA), and global and stratified random sampling strategies. Results showed that the TS based on the smallest ITS was better (RMSE = 0.023 m3 m-3) than that based on the smallest SDRD (RMSE = 0.034 m3 m-3). The K-means clustering based on EFsTheta (-PCA) was better (RMSE <0.020 m3 m-3) than these based on EFs (-PCA) and Theta (-PCA). The sampling design stratified by the land use was more efficient than the global random method. Forty and 60 sampling sites are needed for stratified sampling and global sampling respectively to make their performances comparable to the best K

  10. Use of Surrogate Outcomes in Nephrology Research.

    PubMed

    Samuels, Joshua

    2016-11-01

    Clinical trials are large and expensive and could require exceedingly long-term follow-up for subjects to reach clinically meaningful end points. To combat these methodologic issues, researchers sometimes use biomarkers as surrogate end points. A biomarker is an objectively measured characteristic that is indicative of some underlying phenomenon or process, while a surrogate is a biomarker that "takes the place" of a clinically meaningful outcome, usually earlier in the disease process. This paper reviews the history, strengths, and weaknesses of surrogate outcome use in clinical research and then discusses potential surrogate outcomes in nephrology research.

  11. Biodiesel surrogates: achieving performance demands.

    PubMed

    Sarin, Rakesh; Kumar, Ravindra; Srivastav, Bhawana; Puri, S K; Tuli, D K; Malhotra, R K; Kumar, Anand

    2009-06-01

    Synthesis of surrogate molecules is particularly useful for generating in sight of structural-activity relationships, understanding processes and improving the performance. In order to improve upon the physico-chemical properties of biodiesel, methyl, ethyl, isopropyl and n-butyl esters of beta-branched fatty acid have been synthesized, initiating from beta-branched alcohols. Beta-branched alcohols upon oxidation gave corresponding acids, which were converted to their esters. The synthesized esters have substantially better oxidative stability, exhibited by Rancimat oxidation induction period of more than 24 h. The cloud point of synthesized esters is < -36 degrees C, pour point is < -42 degrees C and CFPP is < -21 degrees C, which is substantially better than fatty acid methyl esters. Besides achieving the objective of better oxidative stability and improved low temperature properties, the synthesized surrogate esters have viscosity in the range of 4.2-4.6 cSt at 40 degrees C, meeting the international diesel and biodiesel standards. The cetane number of synthesized esters is 62-69, which is much better than diesel and biodiesel. The blends of the synthesized esters in diesel at 5% and 10% meet Indian standards of diesel.

  12. Algorithms for generating surrogate data for sparsely quantized time series

    NASA Astrophysics Data System (ADS)

    Suzuki, Tomoya; Ikeguchi, Tohru; Suzuki, Masuo

    2007-07-01

    The method of surrogate data is frequently used for a statistical examination of nonlinear properties underlying original data. If surrogate data sets are generated by a null hypothesis that the data are derived by a linear process, a rejection of the hypothesis means that the original data have more complex properties. However, we found that if an algorithm for generating surrogate data, for example, amplitude adjusted Fourier transformed, is applied to sparsely quantized data, there are large discrepancies between their power spectrum and that of the original data in lower frequency regions. We performed some simulations to confirm that these errors often lead to false rejections. In this paper, in order to prevent such drawbacks, we advance an extended hypothesis, and propose two improved algorithms for generating surrogate data that reduce the discrepancies of the power spectra. We also confirm the validity of the two improved algorithms with numerical simulations by showing that the extended null hypothesis can be rejected if the time series is produced from chaotic dynamical systems. Finally, we applied these algorithms for analyzing financial tick data as a real example; then we showed that the extended null hypothesis cannot be rejected because the nonlinear statistics or nonlinear prediction errors exhibited are the same as those of the original financial tick time series.

  13. Health information-seeking on behalf of others: characteristics of "surrogate seekers".

    PubMed

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

    2015-03-01

    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.

  14. Comparison of surrogate models with different methods in groundwater remediation process

    NASA Astrophysics Data System (ADS)

    Luo, Jiannan; Lu, Wenxi

    2014-10-01

    Surrogate modelling is an effective tool for reducing computational burden of simulation optimization. In this article, polynomial regression (PR), radial basis function artificial neural network (RBFANN), and kriging methods were compared for building surrogate models of a multiphase flow simulation model in a simplified nitrobenzene contaminated aquifer remediation problem. In the model accuracy analysis process, a 10-fold cross validation method was adopted to evaluate the approximation accuracy of the three surrogate models. The results demonstrated that: RBFANN surrogate model and kriging surrogate model had acceptable approximation accuracy, and further that kriging model's approximation accuracy was slightly higher than RBFANN model. However, the PR model demonstrated unacceptably poor approximation accuracy. Therefore, the RBFANN and kriging surrogates were selected and used in the optimization process to identify the most cost-effective remediation strategy at a nitrobenzene-contaminated site. The optimal remediation costs obtained with the two surrogate-based optimization models were similar, and had similar computational burden. These two surrogate-based optimization models are efficient tools for optimal groundwater remediation strategy identification.

  15. Using the Agricultural Environment to Select Better Surrogates for Foodborne Pathogens Associated with Fresh Produce

    USDA-ARS?s Scientific Manuscript database

    Despite the best efforts of industry and regulatory agencies to identify and implement good agricultural practices (GAPs) that eliminate pathogen contamination, significant produce associated outbreaks continue to occur. Identification of nonpathogenic surrogates for common produce-associated foodbo...

  16. 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.

  17. Identifying a predictive model for response to atypical antipsychotic monotherapy treatment in south Indian schizophrenia patients.

    PubMed

    Gupta, Meenal; Moily, Nagaraj S; Kaur, Harpreet; Jajodia, Ajay; Jain, Sanjeev; Kukreti, Ritushree

    2013-08-01

    Atypical antipsychotic (AAP) drugs are the preferred choice of treatment for schizophrenia patients. Patients who do not show favorable response to AAP monotherapy are subjected to random prolonged therapeutic treatment with AAP multitherapy, typical antipsychotics or a combination of both. Therefore, prior identification of patients' response to drugs can be an important step in providing efficacious and safe therapeutic treatment. We thus attempted to elucidate a genetic signature which could predict patients' response to AAP monotherapy. Our logistic regression analyses indicated the probability that 76% patients carrying combination of four SNPs will not show favorable response to AAP therapy. The robustness of this prediction model was assessed using repeated 10-fold cross validation method, and the results across n-fold cross-validations (mean accuracy=71.91%; 95%CI=71.47-72.35) suggest high accuracy and reliability of the prediction model. Further validations of these results in large sample sets are likely to establish their clinical applicability.

  18. Predicting conduct problems: can high-risk children be identified in kindergarten and grade 1?

    PubMed

    Bennett, K J; Lipman, E L; Brown, S; Racine, Y; Boyle, M H; Offord, D R

    1999-08-01

    Externalizing behavior symptoms (EBS) in childhood are a strong predictor of future conduct problems. This study evaluated their predictive accuracy using logistic regression and receiver operating characteristic curve techniques. EBS, alone and in combination with other child and familial risk factors, were used to predict conduct problems 30 months later in a nonclinic population of kindergartners and Grade 1 children. The sensitivity (Sn) and positive predictive value (PPV) of EBS alone were below preset criteria of > or = 50% for each (prevalence < or = 15%). Sn and PPV increased when other child and familial factors were combined with symptoms but did not exceed the preset criteria. From a developmental perspective, substantial stability of EBS exists over time. However, from the perspective of prevention science, significant levels of misclassification will occur when EBS are used to designate high-risk status under the low-prevalence conditions of normal populations.

  19. Which prediction models best identify additional axillary disease after a positive sentinel node biopsy for breast cancer?

    PubMed

    Berrang, Tanya S; Lesperance, Mary; Truong, Pauline T; Walter, Caroline; Hayashi, Allen H; Olivotto, Ivo A

    2012-06-01

    To determine which web-based model best identifies women at low risk of further axillary disease after a positive sentinel lymph node (SLN+) biopsy. 673 women with T1-2cN0M0 SNB+ breast cancer who underwent completion axillary dissection (AxD) were identified. A subgroup not eligible to avoid AxD as part of the Z0011 study was defined (Z0011 exclusion group). Predicted risk of further axillary disease was generated using seven web-based models. "Low risk" was defined as a ≤10% risk of further axillary disease. False negative ("low risk" prediction but AxD+) rates (FNRs), area under the receiver operating characteristic curve (AUC), and Brier score were determined for each model. 6 of 7 models identified "low risk" patients but FNRs ranged from 14 to 30%. The Stanford and Memorial Sloan-Kettering (MSKCC) models had the best FNRs. FNRs were lower with SLN micrometastasis (7-15%) and higher in the Z0011 exclusion group (21-41%). All models under-predicted further nodal disease in low risk patients and over-predicted in higher-risk patients. The Stanford and MSKCC models were able to identify women with SLN micrometastasis with a ≤10% FNR. Models were not able to accurately identify low risk women from a cohort that would have been excluded from Z0011.

  20. Can Falls Risk Prediction Tools Correctly Identify Fall-Prone Elderly Rehabilitation Inpatients? A Systematic Review and Meta-Analysis

    PubMed Central

    da Costa, Bruno Roza; Rutjes, Anne Wilhelmina Saskia; Mendy, Angelico; Freund-Heritage, Rosalie; Vieira, Edgar Ramos

    2012-01-01

    Background Falls of elderly people may cause permanent disability or death. Particularly susceptible are elderly patients in rehabilitation hospitals. We systematically reviewed the literature to identify falls prediction tools available for assessing elderly inpatients in rehabilitation hospitals. Methods and Findings We searched six electronic databases using comprehensive search strategies developed for each database. Estimates of sensitivity and specificity were plotted in ROC space graphs and pooled across studies. Our search identified three studies which assessed the prediction properties of falls prediction tools in a total of 754 elderly inpatients in rehabilitation hospitals. Only the STRATIFY tool was assessed in all three studies; the other identified tools (PJC-FRAT and DOWNTON) were assessed by a single study. For a STRATIFY cut-score of two, pooled sensitivity was 73% (95%CI 63 to 81%) and pooled specificity was 42% (95%CI 34 to 51%). An indirect comparison of the tools across studies indicated that the DOWNTON tool has the highest sensitivity (92%), while the PJC-FRAT offers the best balance between sensitivity and specificity (73% and 75%, respectively). All studies presented major methodological limitations. Conclusions We did not identify any tool which had an optimal balance between sensitivity and specificity, or which were clearly better than a simple clinical judgment of risk of falling. The limited number of identified studies with major methodological limitations impairs sound conclusions on the usefulness of falls risk prediction tools in geriatric rehabilitation hospitals. PMID:22815914

  1. 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. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  2. 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)

  3. Failure to Validate a Multivariable Clinical Prediction Model to Identify Pediatric Intensive Care Unit Patients at High Risk for Candidemia.

    PubMed

    Fisher, Brian T; Ross, Rachael K; Roilides, Emmanuel; Palazzi, Debra L; Abzug, Mark J; Hoffman, Jill A; Berman, David M; Prasad, Priya A; Localio, A Russell; Steinbach, William J; Vogiatzi, Lambrini; Dutta, Ankhi; Zaoutis, Theoklis E

    2016-12-01

    We attempted to validate a previously derived clinical prediction rule for candidemia in the pediatric intensive care unit. This multicenter case control study did not identify significant association of candidemia with most of the previously identified predictors. Additional study in larger cohorts with other predictor variables is needed. © The Author 2015. Published by Oxford University Press on behalf of the Pediatric Infectious Diseases Society. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  4. Research priorities in biomarkers and surrogate end-points.

    PubMed

    Aronson, Jeffrey K

    2012-06-01

    Ideal tests of the effects of therapeutic interventions measure the desired outcomes; however, the desired outcomes are not always easily measured or may be long-term objectives. Biomarkers and surrogate end-points are often cheaper and easier to measure and can be measured over a shorter time span. They can be used in screening, diagnosing, staging, and monitoring diseases, in monitoring responses to interventions, and in various aspects of drug discovery and development. They can be extrinsic to the body or intrinsic, and can relate to any point in the pharmacological chain, at the molecular, cellular, tissue, or organ level. Problems arise when the relation between the pathophysiology of the disease and the mechanism of action of the intervention is not properly understood; when adverse effects obviate therapeutic effects; when confounding factors, such as other drugs, alter the surrogate independently of the final end-point; when a biomarker persists after resolution of the disease; and when the concentration-effect curves for the effects of an intervention on the primary outcome and the surrogate are different. Use of biomarkers may also be hindered by poor reproducibility of measurement techniques. Challenges for clinical pharmacologists are to devise biomarker tests that are reliable, reproducible, sensitive, and specific, and surrogate end-points that are associated with the clinical outcomes of concern and useful. A robust taxonomy is needed of the relations that link the pathophysiology of disease, the mechanisms of action of interventions and their adverse effects, the desired clinical outcomes, and the surrogate end-points that predict them.

  5. Using Standardized Tests to Identify Prior Knowledge Necessary for Success in Algebra: A Predictive Analysis

    ERIC Educational Resources Information Center

    Jensen, Jennifer

    2014-01-01

    This study sought to determine if there is a relationship between students' scores on the eighth-grade Indiana State Test of Education Progress Plus (ISTEP+) exam and success on Indiana's Algebra End-of-Course Assessment (ECA). Additionally, it sought to determine if algebra success could be significantly predicted by the achievement in one or…

  6. Using Standardized Tests to Identify Prior Knowledge Necessary for Success in Algebra: A Predictive Analysis

    ERIC Educational Resources Information Center

    Jensen, Jennifer

    2014-01-01

    This study sought to determine if there is a relationship between students' scores on the eighth-grade Indiana State Test of Education Progress Plus (ISTEP+) exam and success on Indiana's Algebra End-of-Course Assessment (ECA). Additionally, it sought to determine if algebra success could be significantly predicted by the achievement in one or…

  7. Matrix metalloproteinase multiplex screening identifies increased MMP-2 urine concentrations in women predicted to develop preeclampsia.

    PubMed

    Martinez-Fierro, Margarita L; Perez-Favila, Aurelio; Garza-Veloz, Idalia; Espinoza-Juarez, Marcela A; Avila-Carrasco, Lorena; Delgado-Enciso, Ivan; Ortiz-Castro, Yolanda; Cardenas-Vargas, Edith; Cid-Baez, Miguel A; Ramirez-Santoyo, Rosa M; Cervantes-Kardasch, Victor H; Rodriguez-Sanchez, Iram P; Badillo-Almaraz, Jose I; Castañeda-Miranda, Rodrigo; Solis-Sanchez, Luis O; Ortiz-Rodriguez, Jose M

    2017-01-25

    Preeclampsia, a pregnancy disorder characterized by hypertension and proteinuria, represents the leading cause of fetal and maternal morbidity and mortality in developing countries. The identification of novel and accurate biomarkers that are predictive of preeclampsia is necessary to improve the prognosis of patients with preeclampsia. The objective of this study is to evaluate the usefulness of nine urinary metalloproteinases to predict the risk of preeclampsia development. MMP-1, MMP-2, MMP-3, MMP-7, MMP-8, MMP-9, MMP-10, MMP-12 and MMP-13 were analyzed in urine (early-pregnancy) from 17 women predicted to develop preeclampsia and 48 controls using the Bio-Plex Pro-Human MMP panel (Bio-Rad, Hercules, CA). Urinary MMP-2 showed differences between groups which allowed us to calculate an increased risk for PE development of up to 20 times among the study population. Increased urinary concentration of MMP-2 at 12 and 16 weeks of gestation predicted an increased risk of developing preeclampsia in the study population.

  8. Interpretations, perspectives and intentions in surrogate motherhood

    PubMed Central

    van Zyl, L.; van Niekerk, A.

    2000-01-01

    In this paper we examine the questions "What does it mean to be a surrogate mother?" and "What would be an appropriate perspective for a surrogate mother to have on her pregnancy?" In response to the objection that such contracts are alienating or dehumanising since they require women to suppress their evolving perspective on their pregnancies, liberal supporters of surrogate motherhood argue that the freedom to contract includes the freedom to enter a contract to bear a child for an infertile couple. After entering the contract the surrogate may not be free to interpret her pregnancy as that of a non-surrogate mother, but there is more than one appropriate way of interpreting one's pregnancy. To restrict or ban surrogacy contracts would be to prohibit women from making other particular interpretations of their pregnancies they may wish to make, requiring them to live up to a culturally constituted image of ideal motherhood. We examine three interpretations of a "surrogate pregnancy" that are implicit in the views and arguments put forward by ethicists, surrogacy agencies, and surrogate mothers themselves. We hope to show that our concern in this regard goes beyond the view that surrogacy contracts deny or suppress the natural, instinctive or conventional interpretation of pregnancy. Key Words: Surrogate motherhood • parental rights and responsibilities PMID:11055048

  9. 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…

  10. The interactive surrogate travel system.

    PubMed

    Nakajima, I; Ichimura, A; Juzoji, H; Mugita, K

    1999-01-01

    The Interactive Surrogate Travel (IST) system is based on the super-miniaturized system of virtual technology, Cave Automatic Virtual Environment (CAVE). Using bilateral virtual reality (VR-to-VR) communications, IST enables the testing of subjects via interactive communications. It appears that IST will find practical applications in the near future. We examined the utility of IST in medical treatment and psychiatric tests. Psychiatric symptoms reflect human pathos, which in turn are greatly influenced by culture. If these culture-bound symptoms can be adequately communicated between providers and clients of different cultures, we can develop effective telepsychiatric services across different societies and cultures. IST requires high-speed transmission and gigabyte circuits. A pilot project tested the utility of IST (through the use of optical fiber communications on earth) as a basis for experiments via the Gigabit satellite, to be launched in the year 2002.

  11. 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.

  12. Identifying predictive factors for long-term complications following button battery impactions: A case series and literature review.

    PubMed

    Eliason, Michael J; Melzer, Jonathan M; Winters, Jessica R; Gallagher, Thomas Q

    2016-08-01

    To complement a case series review of button battery impactions managed at our single military tertiary care center with a thorough literature review of laboratory research and clinical cases to develop a protocol to optimize patient care. Specifically, to identify predictive factors of long-term complications which can be used by the pediatric otolaryngologist to guide patient management after button battery impactions. A retrospective review of the Department of Defense's electronic medical record systems was conducted to identify patients with button battery ingestions and then characterize their treatment course. A thorough literature review complemented the lessons learned to identify potentially predictive clinical measures for long-term complications. Eight patients were identified as being treated for button battery impaction in the aerodigestive tract with two sustaining long-term complications. The median age of the patients treated was 33 months old and the median estimated time of impaction in the aerodigestive tract prior to removal was 10.5 h. Time of impaction, anatomic direction of the battery's negative pole, and identifying specific battery parameters were identified as factors that may be employed to predict sequelae. Based on case reviews, advancements in battery manufacturing, and laboratory research, there are distinct clinical factors that should be assessed at the time of initial therapy to guide follow-up management to minimize potential catastrophic sequelae of button battery ingestion. Published by Elsevier Ireland Ltd.

  13. 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.

  14. Conservative Surrogate Model Using Weighted Kriging Variance for Sampling-Based RBDO

    DTIC Science & Technology

    2011-06-01

    Prediction Intervals,” Scandinavian J Stat, Vol. 30, pp. 175-192, 2003. [7] F.A.C. Viana , V . Picheny, and R.T. Haftka, "Using cross validation to...problems. Viana et al. [7] used cross-validation to estimate the safety margin for the conservative surrogate model. While the cross-validation...Design Optimization,” accepted by AIAA Journal, 2011. [4] V . Picheny., “Improving Accuracy and Compensating for Uncertainty in Surrogate Modeling

  15. Effectiveness of Biodiversity Surrogates for Conservation Planning: Different Measures of Effectiveness Generate a Kaleidoscope of Variation

    PubMed Central

    Grantham, Hedley S.; Pressey, Robert L.; Wells, Jessie A.; Beattie, Andrew J.

    2010-01-01

    Conservation planners represent many aspects of biodiversity by using surrogates with spatial distributions readily observed or quantified, but tests of their effectiveness have produced varied and conflicting results. We identified four factors likely to have a strong influence on the apparent effectiveness of surrogates: (1) the choice of surrogate; (2) differences among study regions, which might be large and unquantified (3) the test method, that is, how effectiveness is quantified, and (4) the test features that the surrogates are intended to represent. Analysis of an unusually rich dataset enabled us, for the first time, to disentangle these factors and to compare their individual and interacting influences. Using two data-rich regions, we estimated effectiveness using five alternative methods: two forms of incidental representation, two forms of species accumulation index and irreplaceability correlation, to assess the performance of ‘forest ecosystems’ and ‘environmental units’ as surrogates for six groups of threatened species—the test features—mammals, birds, reptiles, frogs, plants and all of these combined. Four methods tested the effectiveness of the surrogates by selecting areas for conservation of the surrogates then estimating how effective those areas were at representing test features. One method measured the spatial match between conservation priorities for surrogates and test features. For methods that selected conservation areas, we measured effectiveness using two analytical approaches: (1) when representation targets for the surrogates were achieved (incidental representation), or (2) progressively as areas were selected (species accumulation index). We estimated the spatial correlation of conservation priorities using an index known as summed irreplaceability. In general, the effectiveness of surrogates for our taxa (mostly threatened species) was low, although environmental units tended to be more effective than forest

  16. 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.

  17. Improving probabilistic prediction of daily streamflow by identifying Pareto optimal approaches for modeling heteroscedastic residual errors

    NASA Astrophysics Data System (ADS)

    McInerney, David; Thyer, Mark; Kavetski, Dmitri; Lerat, Julien; Kuczera, George

    2017-03-01

    Reliable and precise probabilistic prediction of daily catchment-scale streamflow requires statistical characterization of residual errors of hydrological models. This study focuses on approaches for representing error heteroscedasticity with respect to simulated streamflow, i.e., the pattern of larger errors in higher streamflow predictions. We evaluate eight common residual error schemes, including standard and weighted least squares, the Box-Cox transformation (with fixed and calibrated power parameter λ) and the log-sinh transformation. Case studies include 17 perennial and 6 ephemeral catchments in Australia and the United States, and two lumped hydrological models. Performance is quantified using predictive reliability, precision, and volumetric bias metrics. We find the choice of heteroscedastic error modeling approach significantly impacts on predictive performance, though no single scheme simultaneously optimizes all performance metrics. The set of Pareto optimal schemes, reflecting performance trade-offs, comprises Box-Cox schemes with λ of 0.2 and 0.5, and the log scheme (λ = 0, perennial catchments only). These schemes significantly outperform even the average-performing remaining schemes (e.g., across ephemeral catchments, median precision tightens from 105% to 40% of observed streamflow, and median biases decrease from 25% to 4%). Theoretical interpretations of empirical results highlight the importance of capturing the skew/kurtosis of raw residuals and reproducing zero flows. Paradoxically, calibration of λ is often counterproductive: in perennial catchments, it tends to overfit low flows at the expense of abysmal precision in high flows. The log-sinh transformation is dominated by the simpler Pareto optimal schemes listed above. Recommendations for researchers and practitioners seeking robust residual error schemes for practical work are provided.

  18. Improving probabilistic prediction of daily streamflow by identifying Pareto optimal approaches for modelling heteroscedastic residual errors

    NASA Astrophysics Data System (ADS)

    David, McInerney; Mark, Thyer; Dmitri, Kavetski; George, Kuczera

    2017-04-01

    This study provides guidance to hydrological researchers which enables them to provide probabilistic predictions of daily streamflow with the best reliability and precision for different catchment types (e.g. high/low degree of ephemerality). Reliable and precise probabilistic prediction of daily catchment-scale streamflow requires statistical characterization of residual errors of hydrological models. It is commonly known that hydrological model residual errors are heteroscedastic, i.e. there is a pattern of larger errors in higher streamflow predictions. Although multiple approaches exist for representing this heteroscedasticity, few studies have undertaken a comprehensive evaluation and comparison of these approaches. This study fills this research gap by evaluating 8 common residual error schemes, including standard and weighted least squares, the Box-Cox transformation (with fixed and calibrated power parameter, lambda) and the log-sinh transformation. Case studies include 17 perennial and 6 ephemeral catchments in Australia and USA, and two lumped hydrological models. We find the choice of heteroscedastic error modelling approach significantly impacts on predictive performance, though no single scheme simultaneously optimizes all performance metrics. The set of Pareto optimal schemes, reflecting performance trade-offs, comprises Box-Cox schemes with lambda of 0.2 and 0.5, and the log scheme (lambda=0, perennial catchments only). These schemes significantly outperform even the average-performing remaining schemes (e.g., across ephemeral catchments, median precision tightens from 105% to 40% of observed streamflow, and median biases decrease from 25% to 4%). Theoretical interpretations of empirical results highlight the importance of capturing the skew/kurtosis of raw residuals and reproducing zero flows. Recommendations for researchers and practitioners seeking robust residual error schemes for practical work are provided.

  19. [Prospective study of pollen dispersal prediction and identifying the usefulness of different parameters].

    PubMed

    Maeda, Masanori; Maguchi, Shiro; Nakamaru, Yuji; Takagi, Dai; Fukuda, Satoshi

    2006-05-01

    Birch pollen is the major pollen allergen in Hokkaido, Northern Japan. We reported a Betula masting model based on the resource budget model hypothesis. In addition to weather conditions, cumulative hours of sunlight and mean temperature from May to July of the previous year, this model used the amount of annual pollen dispersed in previous and penultimate years as a parameter based on data from 1990 to 2000. We compared the predicted and observed amount of pollen dispersed for 3 years from 2001 to 2003 and evaluated the usefulness of each parameter in this model. Birch pollen was measured using the Durham sampler at the Hokkaido University Graduate School of Medicine Research Institute in Sapporo. The difference between predicted and observed amounts of pollen dispersal was about 200-500 grains cm(-2). The annual pollen dispersed in the previous year was found to be the most useful parameter. This model is useful in predicting whether the amount of birch pollen will be less than average, about average, more than average, or much more than average.

  20. Surrogate Decision-Makers' Perspectives on Discussing Prognosis in the Face of Uncertainty

    PubMed Central

    Evans, Leah R.; Boyd, Elizabeth A.; Malvar, Grace; Apatira, Latifat; Luce, John M.; Lo, Bernard; White, Douglas B.

    2009-01-01

    Rationale: Many physicians are reluctant to discuss a patient's prognosis when there is significant prognostic uncertainty. Objectives: We sought to understand surrogate decision makers' views regarding whether physicians should discuss prognosis in the face of uncertainty. Methods: We conducted semi-structured interviews with 179 surrogates for 142 incapacitated patients at high risk of death in four intensive care units at an academic medical center. The interviews explored surrogates' attitudes about whether physicians should discuss prognosis when they cannot be certain their prognostic estimates are correct. We used constant comparative methods to analyze the transcripts. Validation methods included triangulation by multidisciplinary analysis and member checking. Measurements and Main Results: Eighty-seven percent (155/179) of surrogates wanted physicians to discuss an uncertain prognosis. We identified five main reasons for this, including surrogates' belief that prognostic uncertainty is unavoidable, that physicians are their only source for prognostic information, and that discussing prognostic uncertainty leaves room for realistic hope, increases surrogates' trust in the physician, and signals a need to prepare for possible bereavement. Twelve percent (22/179) of surrogates felt that discussions about an uncertain prognosis should be avoided. The main explanation was that it is not worth the potential emotional distress if the prognostications are incorrect. Surrogates suggested that physicians should explicitly discuss uncertainty when prognosticating. Conclusions: The majority of surrogates of patients that are critically ill want physicians to disclose their prognostic estimates even if they cannot be certain they are correct. This stems from surrogates' belief that prognostic uncertainty is simultaneously unavoidable and acceptable. PMID:18931332

  1. Significance of including a surrogate arousal for sleep apnea-hypopnea syndrome diagnosis by respiratory polygraphy.

    PubMed

    Masa, Juan F; Corral, Jaime; Gomez de Terreros, Javier; Duran-Cantolla, Joaquin; Cabello, Marta; Hernández-Blasco, Luis; Monasterio, Carmen; Alonso, Alberto; Chiner, Eusebi; Aizpuru, Felipe; Zamorano, Jose; Cano, Ricardo; Montserrat, Jose M; Garcia-Ledesma, Estefania; Pereira, Ricardo; Cancelo, Laura; Martinez, Angeles; Sacristan, Lirios; Salord, Neus; Carrera, Miguel; Sancho-Chust, José N; Embid, Cristina

    2013-02-01

    Respiratory polygraphy is an accepted alternative to polysomnography (PSG) for sleep apnea/hypopnea syndrome (SAHS) diagnosis, although it underestimates the apnea-hypopnea index (AHI) because respiratory polygraphy cannot identify arousals. We performed a multicentric, randomized, blinded crossover study to determine the agreement between home respiratory polygraphy (HRP) and PSG, and between simultaneous respiratory polygraphy (respiratory polygraphy with PSG) (SimultRP) and PSG by means of 2 AHI scoring protocols with or without hyperventilation following flow reduction considered as a surrogate arousal. We included suspected SAHS patients from 8 hospitals. They were assigned to home and hospital protocols at random. We determined the agreement between respiratory polygraphy AHI and PSG AHI scorings using Bland and Altman plots and diagnostic agreement using receiver operating characteristic (ROC) curves. The agreement in therapeutic decisions (continuous positive airway pressure treatment or not) between HRP and PSG scorings was done with likelihood ratios and post-test probability calculations. Of 366 randomized patients, 342 completed the protocol. AHI from HRP scorings (with and without surrogate arousal) had similar agreement with PSG. AHI from SimultRP with surrogate arousal scoring had better agreement with PSG than AHI from SimultRP without surrogate arousal. HRP with surrogate arousal scoring had slightly worse ROC curves than HRP without surrogate arousal, and the opposite was true for SimultRP scorings. HRP with surrogate arousal showed slightly better agreement with PSG in therapeutic decisions than for HRP without surrogate arousal. Incorporating a surrogate arousal measure into HRP did not substantially increase its agreement with PSG when compared with the usual procedure (HRP without surrogate arousal).

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

    PubMed

    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

    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. 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. 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. 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. 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.

  3. Surrogate mobility and orientation affect the early neurobehavioral development of infant rhesus macaques (Macaca mulatta).

    PubMed

    Dettmer, Amanda M; Ruggiero, Angela M; Novak, Melinda A; Meyer, Jerrold S; Suomi, Stephen J

    2008-05-01

    A biological mother's movement appears necessary for optimal development in infant monkeys. However, nursery-reared monkeys are typically provided with inanimate surrogate mothers that move very little. The purpose of this study was to evaluate the effects of a novel, highly mobile surrogate mother on motor development, exploration, and reactions to novelty. Six infant rhesus macaques (Macaca mulatta) were reared on mobile hanging surrogates (MS) and compared to six infants reared on standard stationary rocking surrogates (RS) and to 9-15 infants reared with their biological mothers (MR) for early developmental outcome. We predicted that MS infants would develop more similarly to MR infants than RS infants. In neonatal assessments conducted at Day 30, both MS and MR infants showed more highly developed motor activity than RS infants on measures of grasping (p = .009), coordination (p = .038), spontaneous crawl (p = .009), and balance (p = .003). At 2-3 months of age, both MS and MR infants displayed higher levels of exploration in the home cage than RS infants (p = .016). In a novel situation in which only MS and RS infants were tested, MS infants spent less time near their surrogates in the first five minutes of the test session than RS infants (p = .05), indicating a higher level of comfort. Collectively, these results suggest that when nursery-rearing of infant monkeys is necessary, a mobile hanging surrogate may encourage more normative development of gross motor skills and exploratory behavior and may serve as a useful alternative to stationary or rocking surrogates.

  4. Uncertainty in the Bayesian meta-analysis of normally distributed surrogate endpoints.

    PubMed

    Bujkiewicz, Sylwia; Thompson, John R; Spata, Enti; Abrams, Keith R

    2015-08-13

    We investigate the effect of the choice of parameterisation of meta-analytic models and related uncertainty on the validation of surrogate endpoints. Different meta-analytical approaches take into account different levels of uncertainty which may impact on the accuracy of the predictions of treatment effect on the target outcome from the treatment effect on a surrogate endpoint obtained from these models. A range of Bayesian as well as frequentist meta-analytical methods are implemented using illustrative examples in relapsing-remitting multiple sclerosis, where the treatment effect on disability worsening is the primary outcome of interest in healthcare evaluation, while the effect on relapse rate is considered as a potential surrogate to the effect on disability progression, and in gastric cancer, where the disease-free survival has been shown to be a good surrogate endpoint to the overall survival. Sensitivity analysis was carried out to assess the impact of distributional assumptions on the predictions. Also, sensitivity to modelling assumptions and performance of the models were investigated by simulation. Although different methods can predict mean true outcome almost equally well, inclusion of uncertainty around all relevant parameters of the model may lead to less certain and hence more conservative predictions. When investigating endpoints as candidate surrogate outcomes, a careful choice of the meta-analytical approach has to be made. Models underestimating the uncertainty of available evidence may lead to overoptimistic predictions which can then have an effect on decisions made based on such predictions. © The Author(s) 2015.

  5. Identifying predictive clinical characteristics of the treatment efficacy of mirtazapine monotherapy for major depressive disorder

    PubMed Central

    Tsutsumi, Takahiro; Sugawara, Hiroko; Ito, Ryoko; Asano, Mizuho; Shimizu, Satoru; Ishigooka, Jun; Nishimura, Katsuji

    2016-01-01

    Background Mirtazapine, which is classified as a noradrenergic and specific serotonergic antidepressant, is widely prescribed for the treatment of major depressive disorder. The potential predictive factors of the efficacy of mirtazapine and the tolerability based on the incidence of oversedation and jitteriness/anxiety syndrome were evaluated. Patients and methods Patients with major depressive disorder were retrospectively investigated. Study subjects comprised 68 patients with depression who received mirtazapine as an initial antidepressant at the Department of Psychiatry of the Tokyo Women’s Medical University Hospital from September 2009 to March 2013. The efficacy of mirtazapine monotherapy was evaluated based on the Clinical Global Impression Improvement score. Clinical characteristics were compared between remission and nonremission groups to determine the factors predicting the efficacy. Moreover, discontinuation rates due to adverse effects, including oversedation and jitteriness/anxiety syndrome, were examined, and the effects of confounding factors were evaluated. Results The remission rate of mirtazapine monotherapy was 36.8% among the 68 enrolled subjects. The mean final doses in the remission and nonremission groups were 27.6±13.5 mg and 26.0±14.1 mg, respectively, and there was no significant difference between them. Multiple logistic analyses revealed that the absence of guilt (odds ratio [OR] =0.15; 95% CI [1.66–37.24], P=0.006) and the presence of psychomotor retardation (OR =4.30; 95% CI [1.30–16.60], P=0.016) were significantly related to the efficacy of mirtazapine monotherapy. The discontinuation rates due to oversedation and jitteriness/anxiety syndrome were 13.2% and 11.8%, respectively. Age did not differ significantly between patients with or without oversedation or jitteriness/anxiety syndrome (P=0.078 and P=0.579, respectively). Conclusion The absence of guilt and the presence of psychomotor retardation may predict the efficacy

  6. Evaluation of public cancer datasets and signatures identifies TP53 mutant signatures with robust prognostic and predictive value.

    PubMed

    Lehmann, Brian David; Ding, Yan; Viox, Daniel Joseph; Jiang, Ming; Zheng, Yi; Liao, Wang; Chen, Xi; Xiang, Wei; Yi, Yajun

    2015-03-26

    Systematic analysis of cancer gene-expression patterns using high-throughput transcriptional profiling technologies has led to the discovery and publication of hundreds of gene-expression signatures. However, few public signature values have been cross-validated over multiple studies for the prediction of cancer prognosis and chemosensitivity in the neoadjuvant setting. To analyze the prognostic and predictive values of publicly available signatures, we have implemented a systematic method for high-throughput and efficient validation of a large number of datasets and gene-expression signatures. Using this method, we performed a meta-analysis including 351 publicly available signatures, 37,000 random signatures, and 31 breast cancer datasets. Survival analyses and pathologic responses were used to assess prediction of prognosis, chemoresponsiveness, and chemo-drug sensitivity. Among 31 breast cancer datasets and 351 public signatures, we identified 22 validation datasets, two robust prognostic signatures (BRmet50 and PMID18271932Sig33) in breast cancer and one signature (PMID20813035Sig137) specific for prognosis prediction in patients with ER-negative tumors. The 22 validation datasets demonstrated enhanced ability to distinguish cancer gene profiles from random gene profiles. Both prognostic signatures are composed of genes associated with TP53 mutations and were able to stratify the good and poor prognostic groups successfully in 82%and 68% of the 22 validation datasets, respectively. We then assessed the abilities of the two signatures to predict treatment responses of breast cancer patients treated with commonly used chemotherapeutic regimens. Both BRmet50 and PMID18271932Sig33 retrospectively identified those patients with an insensitive response to neoadjuvant chemotherapy (mean positive predictive values 85%-88%). Among those patients predicted to be treatment sensitive, distant relapse-free survival (DRFS) was improved (negative predictive values 87

  7. Identifying the Factors That Predict Degree Completion for Entirely Online Community College Students

    ERIC Educational Resources Information Center

    Brock, Kishia R.

    2014-01-01

    The purpose of this research study was to identify demographic characteristics, academic factors, and student behaviors that contributed to successful degree and certificate completion for entirely online, nontraditional undergraduate students at a large community college. A discrete-time event history analysis was used to model the retention of…

  8. 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…

  9. 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'…

  10. 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"…

  11. 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.

  12. 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

  13. Validation and comparison of SCAP as a predictive score for identifying low-risk patients in community-acquired pneumonia.

    PubMed

    España, Pedro P; Capelastegui, Alberto; Quintana, José M; Bilbao, Amaia; Diez, Rosa; Pascual, Silvia; Esteban, Cristóbal; Zalacaín, Rafael; Menendez, Rosario; Torres, Antoni

    2010-02-01

    (1) To validate the Severe Community Acquired Pneumonia (SCAP) score in predicting 30-day mortality. (2) To validate its ability to identifying patients at low risk of death. (3) To compare it against the Pneumonia Severity Index (PSI), and the British Thoracic Society's CURB-65 rules. The SCAP score was validated to predict 30-day mortality in an internal validation cohort of consecutive adult patients seen in one hospital. Consecutive inpatients from other three hospitals were used to externally validate the score and compare the SCAP with the PSI and CURB-65. The discriminatory power of these rules to predict 30-day mortality was tested by the Area under Curve (AUC), and their predictive accuracy with the sensitivity, specificity and predictive values. The 30-day mortality rate increased directly with increasing SCAP score (class 0: 0.5%, to class 4: 66.5% risk) in the internal validation cohort, and from 1.3% to 29.2% in external cohort (P<0.001) with an AUC of 0.83 and 0.75, respectively (P=0.024). The SCAP score identified 62.4% (95% IC 58.8-66.0) low-risk patients, 52.5% (95% IC 48.8-56.2) the PSI and 46.2% (95% CI 42.5-49.9) the CURB-65 in the external cohort. Patients classified as low risk by the three rules had similar 30-day mortality (SCAP: 2.5%, PSI: 1.6% and CURB-65: 2.7%). The SCAP is valid to predict 30-day mortality among low-risk patients and identifies a larger proportion of patients as low-risk than the other studied rules. Copyright 2009. Published by Elsevier Ltd.

  14. 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.

  15. Interpretations, perspectives and intentions in surrogate motherhood.

    PubMed

    van Zyl, L; van Niekerk, A

    2000-10-01

    In this paper we examine the questions "What does it mean to be a surrogate mother?" and "What would be an appropriate perspective for a surrogate mother to have on her pregnancy?" In response to the objection that such contracts are alienating or dehumanising since they require women to suppress their evolving perspective on their pregnancies, liberal supporters of surrogate motherhood argue that the freedom to contract includes the freedom to enter a contract to bear a child for an infertile couple. After entering the contract the surrogate may not be free to interpret her pregnancy as that of a non-surrogate mother, but there is more than one appropriate way of interpreting one's pregnancy. To restrict or ban surrogacy contracts would be to prohibit women from making other particular interpretations of their pregnancies they may wish to make, requiring them to live up to a culturally constituted image of ideal motherhood. We examine three interpretations of a "surrogate pregnancy" that are implicit in the views and arguments put forward by ethicists, surrogacy agencies, and surrogate mothers themselves. We hope to show that our concern in this regard goes beyond the view that surrogacy contracts deny or suppress the natural, instinctive or conventional interpretation of pregnancy.

  16. Identifying High-Risk Patients without Labeled Training Data: Anomaly Detection Methodologies to Predict Adverse Outcomes

    PubMed Central

    Syed, Zeeshan; Saeed, Mohammed; Rubinfeld, Ilan

    2010-01-01

    For many clinical conditions, only a small number of patients experience adverse outcomes. Developing risk stratification algorithms for these conditions typically requires collecting large volumes of data to capture enough positive and negative for training. This process is slow, expensive, and may not be appropriate for new phenomena. In this paper, we explore different anomaly detection approaches to identify high-risk patients as cases that lie in sparse regions of the feature space. We study three broad categories of anomaly detection methods: classification-based, nearest neighbor-based, and clustering-based techniques. When evaluated on data from the National Surgical Quality Improvement Program (NSQIP), these methods were able to successfully identify patients at an elevated risk of mortality and rare morbidities following inpatient surgical procedures. PMID:21347083

  17. Identifying High-Risk Patients without Labeled Training Data: Anomaly Detection Methodologies to Predict Adverse Outcomes.

    PubMed

    Syed, Zeeshan; Saeed, Mohammed; Rubinfeld, Ilan

    2010-11-13

    For many clinical conditions, only a small number of patients experience adverse outcomes. Developing risk stratification algorithms for these conditions typically requires collecting large volumes of data to capture enough positive and negative for training. This process is slow, expensive, and may not be appropriate for new phenomena. In this paper, we explore different anomaly detection approaches to identify high-risk patients as cases that lie in sparse regions of the feature space. We study three broad categories of anomaly detection methods: classification-based, nearest neighbor-based, and clustering-based techniques. When evaluated on data from the National Surgical Quality Improvement Program (NSQIP), these methods were able to successfully identify patients at an elevated risk of mortality and rare morbidities following inpatient surgical procedures.

  18. Verification of the surrogate ratio method

    SciTech Connect

    Chiba, Satoshi; Iwamoto, Osamu

    2010-04-15

    Effects of difference in the spin and parity distributions for the surrogate and neutron-induced reactions are investigated. Without assuming specific (schematic) spin-parity distributions, it was found that the surrogate ratio method can be employed to determine neutron fission and capture cross sections if (1) weak Weisskopf-Ewing condition (defined in this paper) is satisfied, (2) there exist two surrogate reactions whose spin-parity distributions of the decaying nuclei are almost equivalent, and (3) difference of the representative spin values between the neutron-induced and surrogate reactions is no much larger than 10(Planck constant/2pi). If these conditions are satisfied, we need not know the spin-parity distributions populated by the surrogate method. Instead, we should just select a pair of surrogate reactions which will populate the similar spin-parity distributions, using targets having similar structure and reactions having the similar reaction mechanisms. Achievable accuracy is estimated to be around 5% and 10% for fission and capture channels, respectively, for nuclei of the Uranium region. The surrogate absolute method may be applicable to determination of fission cross sections with a caution. However, there will be little hope to apply this method for capture cross section measurements unless the spin-parity distributions in the neutron-induced and surrogate reactions are fairly close to each other (which is implausible) or the difference can be corrected theoretically. The surrogate ratio method was shown also to be a robust method in the presence of breakup reactions without assuming specific breakup reaction mechanisms.

  19. Computational methods using genome-wide association studies to predict radiotherapy complications and to identify correlative molecular processes

    NASA Astrophysics Data System (ADS)

    Oh, Jung Hun; Kerns, Sarah; Ostrer, Harry; Powell, Simon N.; Rosenstein, Barry; Deasy, Joseph O.

    2017-02-01

    The biological cause of clinically observed variability of normal tissue damage following radiotherapy is poorly understood. We hypothesized that machine/statistical learning methods using single nucleotide polymorphism (SNP)-based genome-wide association studies (GWAS) would identify groups of patients of differing complication risk, and furthermore could be used to identify key biological sources of variability. We developed a novel learning algorithm, called pre-conditioned random forest regression (PRFR), to construct polygenic risk models using hundreds of SNPs, thereby capturing genomic features that confer small differential risk. Predictive models were trained and validated on a cohort of 368 prostate cancer patients for two post-radiotherapy clinical endpoints: late rectal bleeding and erectile dysfunction. The proposed method results in better predictive performance compared with existing computational methods. Gene ontology enrichment analysis and protein-protein interaction network analysis are used to identify key biological processes and proteins that were plausible based on other published studies. In conclusion, we confirm that novel machine learning methods can produce large predictive models (hundreds of SNPs), yielding clinically useful risk stratification models, as well as identifying important underlying biological processes in the radiation damage and tissue repair process. The methods are generally applicable to GWAS data and are not specific to radiotherapy endpoints.

  20. Computational methods using genome-wide association studies to predict radiotherapy complications and to identify correlative molecular processes

    PubMed Central

    Oh, Jung Hun; Kerns, Sarah; Ostrer, Harry; Powell, Simon N.; Rosenstein, Barry; Deasy, Joseph O.

    2017-01-01

    The biological cause of clinically observed variability of normal tissue damage following radiotherapy is poorly understood. We hypothesized that machine/statistical learning methods using single nucleotide polymorphism (SNP)-based genome-wide association studies (GWAS) would identify groups of patients of differing complication risk, and furthermore could be used to identify key biological sources of variability. We developed a novel learning algorithm, called pre-conditioned random forest regression (PRFR), to construct polygenic risk models using hundreds of SNPs, thereby capturing genomic features that confer small differential risk. Predictive models were trained and validated on a cohort of 368 prostate cancer patients for two post-radiotherapy clinical endpoints: late rectal bleeding and erectile dysfunction. The proposed method results in better predictive performance compared with existing computational methods. Gene ontology enrichment analysis and protein-protein interaction network analysis are used to identify key biological processes and proteins that were plausible based on other published studies. In conclusion, we confirm that novel machine learning methods can produce large predictive models (hundreds of SNPs), yielding clinically useful risk stratification models, as well as identifying important underlying biological processes in the radiation damage and tissue repair process. The methods are generally applicable to GWAS data and are not specific to radiotherapy endpoints. PMID:28233873

  1. Predictive models for identifying the binding activity of structurally diverse chemicals to human pregnane X receptor.

    PubMed

    Yin, Cen; Yang, Xianhai; Wei, Mengbi; Liu, Huihui

    2017-07-12

    Toxic chemicals entered into human body would undergo a series of metabolism, transport and excretion, and the key roles played in there processes were metabolizing enzymes, which was regulated by the pregnane X receptor (PXR). However, some chemicals in environment could activate or antagonize human pregnane X receptor, thereby leading to a disturbance of normal physiological systems. In this study, based on a larger number of 2724 structurally diverse chemicals, we developed qualitative classification models by the k-nearest neighbor method. Moreover, the logarithm of 20 and 50% effective concentrations (log EC 20 and log EC 50) was used to establish quantitative structure-activity relationship (QSAR) models. With the classification model, two descriptors were enough to establish acceptable models, with the sensitivity, specificity, and accuracy being larger than 0.7, highlighting a high classification performance of the models. With two QSAR models, the statistics parameters with the correlation coefficient (R (2)) of 0.702-0.749 and the cross-validation and external validation coefficient (Q (2)) of 0.643-0.712, this indicated that the models complied with the criteria proposed in previous studies, i.e., R (2) > 0.6, Q (2) > 0.5. The small root mean square error (RMSE) of 0.254-0.414 and the good consistency between observed and predicted values proved satisfactory goodness of fit, robustness, and predictive ability of the developed QSAR models. Additionally, the applicability domains were characterized by the Euclidean distance-based approach and Williams plot, and results indicated that the current models had a wide applicability domain, which especially included a few classes of environmental contaminant, those that were not included in the previous models.

  2. ESA-UbiSite: accurate prediction of human ubiquitination sites by identifying a set of effective negatives.

    PubMed

    Wang, Jyun-Rong; Huang, Wen-Lin; Tsai, Ming-Ju; Hsu, Kai-Ti; Huang, Hui-Ling; Ho, Shinn-Ying

    2017-03-01

    Numerous ubiquitination sites remain undiscovered because of the limitations of mass spectrometry-based methods. Existing prediction methods use randomly selected non-validated sites as non-ubiquitination sites to train ubiquitination site prediction models. We propose an evolutionary screening algorithm (ESA) to select effective negatives among non-validated sites and an ESA-based prediction method, ESA-UbiSite, to identify human ubiquitination sites. The ESA selects non-validated sites least likely to be ubiquitination sites as training negatives. Moreover, the ESA and ESA-UbiSite use a set of well-selected physicochemical properties together with a support vector machine for accurate prediction. Experimental results show that ESA-UbiSite with effective negatives achieved 0.92 test accuracy and a Matthews's correlation coefficient of 0.48, better than existing prediction methods. The ESA increased ESA-UbiSite's test accuracy from 0.75 to 0.92 and can improve other post-translational modification site prediction methods. An ESA-UbiSite-based web server has been established at http://iclab.life.nctu.edu.tw/iclab_webtools/ESAUbiSite/ . syho@mail.nctu.edu.tw. Supplementary data are available at Bioinformatics online.

  3. 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%.

  4. 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.

  5. Gene expression profiling identifies genes predictive of oral squamous cell carcinoma.

    PubMed

    Chen, Chu; Méndez, Eduardo; Houck, John; Fan, Wenhong; Lohavanichbutr, Pawadee; Doody, Dave; Yueh, Bevan; Futran, Neal D; Upton, Melissa; Farwell, D Gregory; Schwartz, Stephen M; Zhao, Lue Ping

    2008-08-01

    Oral squamous cell carcinoma (OSCC) is associated with substantial mortality and morbidity. To identify potential biomarkers for the early detection of invasive OSCC, we compared the gene expressions of incident primary OSCC, oral dysplasia, and clinically normal oral tissue from surgical patients without head and neck cancer or preneoplastic oral lesions (controls), using Affymetrix U133 2.0 Plus arrays. We identified 131 differentially expressed probe sets using a training set of 119 OSCC patients and 35 controls. Forward and stepwise logistic regression analyses identified 10 successive combinations of genes which expression differentiated OSCC from controls. The best model included LAMC2, encoding laminin-gamma2 chain, and COL4A1, encoding collagen, type IV alpha1 chain. Subsequent modeling without these two markers showed that COL1A1, encoding collagen, type I alpha1 chain, and PADI1, encoding peptidyl arginine deiminase, type 1, could also distinguish OSCC from controls. We validated these two models using an internal independent testing set of 48 invasive OSCC and 10 controls and an external testing set of 42 head and neck squamous cell carcinoma cases and 14 controls (GEO GSE6791), with sensitivity and specificity above 95%. These two models were also able to distinguish dysplasia (n = 17) from control (n = 35) tissue. Differential expression of these four genes was confirmed by quantitative reverse transcription-PCR. If confirmed in larger studies, the proposed models may hold promise for monitoring local recurrence at surgical margins and the development of second primary oral cancer in patients with OSCC.

  6. Mathematical and experimental approaches to identify and predict the effects of chemotherapy on neuroglial precursors

    PubMed Central

    Hyrien, Ollivier; Dietrich, Jörg; Noble, Mark

    2010-01-01

    The adverse effects of chemotherapy on normal cells of the body create substantial clinical problems for many cancer patients. Relatively little is known, however, about the effects, other than promotion of cell death, of such agents on the function of normal precursor cells critical in tissue homeostasis and repair. We have combined mathematical and experimental analyses to identify the effects of sublethal doses of chemotherapy on glial precursor cells of the central nervous system (CNS). We modeled the temporal development of a population of precursor and terminally differentiated cells exposed to sublethal doses of carmustine (BCNU), a classical alkylating chemotherapeutic agent used in treatment of gliomas and non-Hodgkin’s lymphomas, as a multi-type age-dependent branching process. We fitted our model to data from in vitro clonal experiments using the method of pseudo-likelihood. This approach identifies several novel drug effects, including modification of the cell cycle length, the time between division and differentiation, and alteration in the probability of undergoing self-renewal division in precursor cells. These changes of precursor cell function in the chemotherapy-exposed brain may have profound clinical implications. Major Findings We applied our computational approach to analyze the effects of BCNU on clonal cultures of oligodendrocyte progenitor cells – one of the best-characterized neural progenitor cells in the mammalian brain. Our analysis reveals that transient exposures to BCNU increased the cell cycle length of progenitor cells and decreased their time to differentiation, while also decreasing the likelihood that they will undergo self-renewing divisions. By investigating the behavior of our mathematical model we demonstrate that precursor cell populations should recover spontaneously from transient modifications of the timing of division and of differentiation, but such recovery will not happen after alteration of cell fate. These

  7. Metabolomics to identify biomarkers and as a predictive tool in inflammatory diseases.

    PubMed

    Jutley, Gurpreet Singh; Young, Stephen P

    2015-12-01

    There is an overwhelming need for a simple, reliable tool that aids clinicians in diagnosing, assessing disease activity and treating rheumatic conditions. Identification of biomarkers in partially understood inflammatory disorders has long been sought after as the Holy Grail of Rheumatology. Given the complex nature of inflammatory conditions, it has been difficult to earmark the potential biomarkers. Metabolomics, however, is promising in providing new insights into inflammatory conditions and also identifying such biomarkers. Metabolomic studies have generally revealed increased energy requirements for by-products of a hypoxic environment, leading to a characteristic metabolic fingerprint. Here, we discuss the significance of such studies and their potential as a biomarker.

  8. Utility of percentage of births to teenagers as a surrogate for the teen birth rate.

    PubMed Central

    Gould, J; Blackwell, T; Heilig, C; Axley, M

    1998-01-01

    OBJECTIVES: The teen birth rate is commonly used in comparing regional variation in teen pregnancies, but local teen birth rates are not always available. In this study the percentage of all births that are to teens was evaluated for its utility as a surrogate for the teen birth rate. METHODS: Rank correlation and sensitivity and specificity analyses were used. RESULTS: The Spearman rank correlations between percentage of teen births (PTB) and teen birth rate (TBR) were .995, .906, and .841 for the 3 age groups suggesting that it may be reasonable to employ PTB to prioritize zip codes. Zip codes with upper quartile levels of percentages of teen births identified zip codes with upper quartile levels of TBR with a sensitivity of 83.8%, 68.8%, and 65%; a false-positive rate of 2.1%, 8.6%, and 10%; and a positive predictive value of 89.3%, 67.6%, and 67.5% for the age groups 10 through 14, 15 through 17, and 18 through 19 years. CONCLUSIONS: The percentage of births to teens is a useful surrogate for teen birth rate in California, especially among younger teenagers. PMID:9618618

  9. 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.

  10. 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.

  11. 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

  12. Heat-health warning systems: a comparison of the predictive capacity of different approaches to identifying dangerously hot days.

    PubMed

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

    2010-06-01

    We compared the ability of several heat-health warning systems to predict days of heat-associated mortality using common data sets. 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. 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. 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.

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

    NASA Astrophysics Data System (ADS)

    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.

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

    PubMed

    Zhao, Tingting; Ruan, Dan

    2016-09-07

    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.

  15. Identifying and cultivating superforecasters as a method of improving probabilistic predictions.

    PubMed

    Mellers, Barbara; Stone, Eric; Murray, Terry; Minster, Angela; Rohrbaugh, Nick; Bishop, Michael; Chen, Eva; Baker, Joshua; Hou, Yuan; Horowitz, Michael; Ungar, Lyle; Tetlock, Philip

    2015-05-01

    Across a wide range of tasks, research has shown that people make poor probabilistic predictions of future events. Recently, the U.S. Intelligence Community sponsored a series of forecasting tournaments designed to explore the best strategies for generating accurate subjective probability estimates of geopolitical events. In this article, we describe the winning strategy: culling off top performers each year and assigning them into elite teams of superforecasters. Defying expectations of regression toward the mean 2 years in a row, superforecasters maintained high accuracy across hundreds of questions and a wide array of topics. We find support for four mutually reinforcing explanations of superforecaster performance: (a) cognitive abilities and styles, (b) task-specific skills, (c) motivation and commitment, and (d) enriched environments. These findings suggest that superforecasters are partly discovered and partly created-and that the high-performance incentives of tournaments highlight aspects of human judgment that would not come to light in laboratory paradigms focused on typical performance. © The Author(s) 2015.

  16. Regression Trees Identify Relevant Interactions: Can This Improve the Predictive Performance of Risk Adjustment?

    PubMed

    Buchner, Florian; Wasem, Jürgen; Schillo, Sonja

    2017-01-01

    Risk equalization formulas have been refined since their introduction about two decades ago. Because of the complexity and the abundance of possible interactions between the variables used, hardly any interactions are considered. A regression tree is used to systematically search for interactions, a methodologically new approach in risk equalization. Analyses are based on a data set of nearly 2.9 million individuals from a major German social health insurer. A two-step approach is applied: In the first step a regression tree is built on the basis of the learning data set. Terminal nodes characterized by more than one morbidity-group-split represent interaction effects of different morbidity groups. In the second step the 'traditional' weighted least squares regression equation is expanded by adding interaction terms for all interactions detected by the tree, and regression coefficients are recalculated. The resulting risk adjustment formula shows an improvement in the adjusted R(2) from 25.43% to 25.81% on the evaluation data set. Predictive ratios are calculated for subgroups affected by the interactions. The R(2) improvement detected is only marginal. According to the sample level performance measures used, not involving a considerable number of morbidity interactions forms no relevant loss in accuracy. Copyright © 2015 John Wiley & Sons, Ltd.

  17. Prospective validation of a predictive model that identifies homeless people at risk of re-presentation to the emergency department.

    PubMed

    Moore, Gaye; Hepworth, Graham; Weiland, Tracey; Manias, Elizabeth; Gerdtz, Marie Frances; Kelaher, Margaret; Dunt, David

    2012-02-01

    To prospectively evaluate the accuracy of a predictive model to identify homeless people at risk of representation to an emergency department. A prospective cohort analysis utilised one month of data from a Principal Referral Hospital in Melbourne, Australia. All visits involving people classified as homeless were included, excluding those who died. Homelessness was defined as living on the streets, in crisis accommodation, in boarding houses or residing in unstable housing. Rates of re-presentation, defined as the total number of visits to the same emergency department within 28 days of discharge from hospital, were measured. Performance of the risk screening tool was assessed by calculating sensitivity, specificity, positive and negative predictive values and likelihood ratios. Over the study period (April 1, 2009 to April 30, 2009), 3298 presentations from 2888 individuals were recorded. The homeless population accounted for 10% (n=327) of all visits and 7% (n=211) of all patients. A total of 90 (43%) homeless people re-presented to the emergency department. The predictive model included nine variables and achieved 98% (CI, 0.92-0.99) sensitivity and 66% (CI, 0.57-0.74) specificity. The positive predictive value was 68% and the negative predictive value was 98%. The positive likelihood ratio 2.9 (CI, 2.2-3.7) and the negative likelihood ratio was 0.03 (CI, 0.01-0.13). The high emergency department re-presentation rate for people who were homeless identifies unresolved psychosocial health needs. The emergency department remains a vital access point for homeless people, particularly after hours. The risk screening tool is key to identify medical and social aspects of a homeless patient's presentation to assist early identification and referral. Copyright © 2012 College of Emergency Nursing Australasia Ltd. Published by Elsevier Ltd. All rights reserved.

  18. Identifying the source of unknown microcystin genes and predicting microcystin variants by comparing genes within uncultured cyanobacterial cells.

    PubMed

    Allender, Christopher J; LeCleir, Gary R; Rinta-Kanto, Johanna M; Small, Randall L; Satchwell, Michael F; Boyer, Gregory L; Wilhelm, Steven W

    2009-06-01

    While multiple phylogenetic markers have been used in the culture-independent study of microcystin-producing cyanobacteria, in only a few instances have multiple markers been studied within individual cells, and in all cases these studies have been conducted with cultured isolates. Here, we isolate and evaluate large DNA fragments (>6 kb) encompassing two genes involved in microcystin biosynthesis (mcyA2 and mcyB1) and use them to identify the source of gene fragments found in water samples. Further investigation of these gene loci from individual cyanobacterial cells allowed for improved analysis of the genetic diversity within microcystin producers as well as a method to predict microcystin variants for individuals. These efforts have also identified the source of the novel mcyA genotype previously termed Microcystis-like that is pervasive in the Laurentian Great Lakes and they predict the microcystin variant(s) that it produces.

  19. Fasting plasma glucose and hemoglobin A1c in identifying and predicting diabetes: the strong heart study.

    PubMed

    Wang, Wenyu; Lee, Elisa T; Howard, Barbara V; Fabsitz, Richard R; Devereux, Richard B; Welty, Thomas K

    2011-02-01

    To compare fasting plasma glucose (FPG) and HbA(1c) in identifying and predicting type 2 diabetes in a population with high rates of diabetes. Diabetes was defined as an FPG level ≥ 126 mg/dL or an HbA(1c) level ≥ 6.5%. Data collected from the baseline and second exams (1989-1995) of the Strong Heart Study were used. RESULTS For cases of diabetes identified by FPG ≥ 126 mg/dL, using HbA(1c) ≥ 6.5% at the initial and 4-year follow-up diabetes screenings (or in identifying incident cases in 4 years) among undiagnosed participants left 46% and 59% of cases of diabetes undetected, respectively, whereas for cases identified by HbA(1c) ≥ 6.5%, using FPG ≥ 126 mg/dL left 11% and 59% unidentified, respectively. Age, waist circumference, urinary albumin-to-creatinine ratio, and baseline FPG and HbA(1c) levels were common significant risk factors for incident diabetes defined by either FPG or HbA(1c); triglyceride levels were significant for diabetes defined by HbA(1c) alone, and blood pressure and sibling history of diabetes were significant for diabetes defined by FPG alone. Using both the baseline FPG and HbA(1c) in diabetes prediction identified more people at risk than using either measure alone. CONCLUSIONS Among undiagnosed participants, using HbA(1c) alone in initial diabetes screening identifies fewer cases of diabetes than FPG, and using either FPG or HbA(1c) alone cannot effectively identify diabetes in a 4-year periodic successive diabetes screening or incident cases of diabetes in 4 years. Using both criteria may identify more people at risk. The proposed models using the commonly available clinical measures can be applied to assessing the risk of incident diabetes using either criterion.

  20. Longitudinal Patterns of Spending Enhance the Ability to Predict Costly Patients: A Novel Approach to Identify Patients for Cost Containment.

    PubMed

    Lauffenburger, Julie C; Franklin, Jessica M; Krumme, Alexis A; Shrank, William H; Brennan, Troyen A; Matlin, Olga S; Spettell, Claire M; Brill, Gregory; Choudhry, Niteesh K

    2017-01-01

    With rising health spending, predicting costs is essential to identify patients for interventions. Many of the existing approaches have moderate predictive ability, which may result, in part, from not considering potentially meaningful changes in spending over time. Group-based trajectory modeling could be used to classify patients into dynamic long-term spending patterns. To classify patients by their spending patterns over a 1-year period and to assess the ability of models to predict patients in the highest spending trajectory and the top 5% of annual spending using prior-year predictors. We identified all fully insured adult members enrolled in a large US nationwide insurer and used medical and prescription data from 2009 to 2011. Group-based trajectory modeling was used to classify patients by their spending patterns over a 1-year period. We assessed the predictive ability of models that categorized patients in the top fifth percentile of annual spending and in the highest spending trajectory, using logistic regression and split-sample validation. Models were estimated using investigator-specified variables and a proprietary risk-adjustment method. Among 998,651 patients, in the best-performing model, prediction was strong for patients in the highest trajectory group (C-statistic: 0.86; R: 0.47). The C-statistic of being in the top fifth percentile of spending in the best-performing model was 0.82 (R: 0.26). Approaches using nonproprietary investigator-specified methods performed almost as well as other risk-adjustment methods (C-statistic: 0.81 vs. 0.82). Trajectory modeling may be a useful way to predict costly patients that could be implementable by payers to improve cost-containment efforts.

  1. Adolescent suicide: characterizing the need and identifying the predictive factors for preventive consultation or hospitalization in a rural community setting.

    PubMed

    Nair, Muttathu K C; Russell, Paul S S; Shankar, Satya R; Subramaniam, Vinod S; Nazeema, Suma; Mammen, Priya; Chembagam, Neethu

    2013-01-01

    Studies from India consistently document the highest suicide rates in the world, and the majority of completed suicides had been within adolescents. To characterize the need and identify the predictive factors for preventive consultation or hospitalization for adolescent suicide in a community setting. We prospectively collected data from 500 adolescents in a rural South Indian community with independent, trained raters. The need for suicide prevention was measured with the SAD PERSONS scale, socio-economic status with the Modified Kuppusamy Scale, depression and anxiety disorders with the Beck Depression Inventory and the Screen for Child Anxiety Related Emotional Disorders, respectively. The relationship between predictors and the need for preventive action was analyzed with univariate and multivariate regression analyses and a predictive model was built. Of those investigated, 2% and 0.6% required emergency consultation and hospitalization, respectively. Males needed more preventive action (p=0.04). Age (OR=3.40, p=0.07), gender (OR=3.13, p=0.05), presence of anxiety (OR=16.35, p=0.001), or depressive (OR=42.59, p=0.001) disorder independently predicted a need for protective action and, together, contributed to a parsimonious predictive model. The majority of adolescents in the community do not require preventive steps to address suicide risk. These predictors could identify the high-risk adolescents for suicide prevention and reduce the burden of care in the community.

  2. How to Identify High-Risk APS Patients: Clinical Utility and Predictive Values of Validated Scores.

    PubMed

    Oku, Kenji; Amengual, Olga; Yasuda, Shinsuke; Atsumi, Tatsuya

    2017-08-01

    Antiphospholipid syndrome (APS) is a clinical disorder characterised by thrombosis and/or pregnancy morbidity in the persistence of antiphospholipid (aPL) antibodies that are pathogenic and have pro-coagulant activities. Thrombosis in APS tends to recur and require prophylaxis; however, the stereotypical treatment for APS patients is inadequate and stratification of the thrombotic risks is important as aPL are prevalently observed in various diseases or elderly population. It is previously known that the multiple positive aPL or high titre aPL correlate to thrombotic events. To progress the stratification of thrombotic risks in APS patients and to quantitatively analyse those risks, antiphospholipid score (aPL-S) and the Global Anti-phospholipid Syndrome Score (GAPSS) were defined. These scores were raised from the large patient cohort data and either aPL profile classified in detail (aPL-S) or simplified aPL profile with classical thrombotic risk factors (GAPSS) was put into a scoring system. Both the aPL-S and GAPSS have shown a degree of accuracy in identifying high-risk APS patients, especially those at a high risk of thrombosis. However, there are several areas requiring improvement, or at least that clinicians should be aware of, before these instruments are applied in clinical practice. One such issue is standardisation of the aPL tests, including general testing of phosphatidylserine-dependent antiprothrombin antibodies (aPS/PT). Additionally, clinicians may need to be aware of the patient's medical history, particularly with respect to the incidence of SLE, which influences the cutoff value for identifying high-risk patients.

  3. Surrogate endpoints and emerging surrogate endpoints for risk reduction of cardiovascular disease.

    PubMed

    Rasnake, Crystal M; Trumbo, Paula R; Heinonen, Therese M

    2008-02-01

    This article reviews surrogate endpoints and emerging biomarkers that were discussed at the annual "Cardiovascular Biomarkers and Surrogate Endpoints" symposium cosponsored by the US Food and Drug Administration (FDA) and the Montreal Heart Institute. The FDA's Center for Food Safety and Applied Nutrition (CFSAN) uses surrogate endpoints in its scientific review of a substance/disease relationship for a health claim. CFSAN currently recognizes three validated surrogate endpoints: blood pressure, blood total cholesterol, and blood low-density lipoprotein (LDL) concentration in its review of a health claim for cardiovascular disease (CVD). Numerous potential surrogate endpoints of CVD are being evaluated as the pathophysiology of heart disease is becoming better understood. However, these emerging biomarkers need to be validated as surrogate endpoints before they are used by CFSAN in the evaluation of a CVD health claim.

  4. 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

  5. Surrogate mothers: whose baby is it?

    PubMed

    Cohen, B

    1984-01-01

    Advances in medical technology offer infertile couples who wish to raise children alternatives to adoption. The increasing number of surrogate mother contracts creates a myriad of legal issues surrounding the rights of the natural mother, the natural father and the child that is produced. In this Article, the Author discusses the legal issues and rights of the parties under the Constitution, the surrogate contract and family law principles. The Author proposes that courts should consider a surrogate contract as a revocable prebirth agreement which allows the natural mother to keep the child if she chooses. In addition, the Author advocates an interpretation of the statutes forbidding baby selling that would prohibit surrogate contracts in which the mother is paid a fee for the child.

  6. Summarizing the field of surrogate modeling research

    NASA Astrophysics Data System (ADS)

    Schultz, Colin

    2012-10-01

    As computer simulations of complex physical interactions grow, so do the time and expense required to operate them. Mirroring the development of such full-scale models has been the related field of surrogate modeling or metamodeling. Surrogate models take a variety of forms, but their shared goal is to provide a numerical output similar to that of a fully complex physical model while minimizing the computational time and cost required to calculate the result. With an eye toward introducing those unfamiliar with the practices and pitfalls of surrogate modeling to the topic and with a focus on its applications to water resources research, Razavi et al. prepared a systematic review of the surrogate modeling literature.

  7. 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. Copyright © 2014 Elsevier Ltd. All rights reserved.

  8. Identifying high risk individuals for targeted lung cancer screening: Independent validation of the PLCOm2012 risk prediction tool.

    PubMed

    Weber, Marianne; Yap, Sarsha; Goldsbury, David; Manners, David; Tammemagi, Martin; Marshall, Henry; Brims, Fraser; McWilliams, Annette; Fong, Kwun; Kang, Yoon Jung; Caruana, Michael; Banks, Emily; Canfell, Karen

    2017-07-15

    Lung cancer screening with computerised tomography holds promise, but optimising the balance of benefits and harms via selection of a high risk population is critical. PLCOm2012 is a logistic regression model based on U.S. data, incorporating sociodemographic and health factors, which predicts 6-year lung cancer risk among ever-smokers, and thus may better predict those who might benefit from screening than criteria based solely on age and smoking history. We aimed to validate the performance of PLCOm2012 in predicting lung cancer outcomes in a cohort of Australian smokers. Predicted risk of lung cancer was calculated using PLCOm2012 applied to baseline data from 95,882 ever-smokers aged ≥45 years in the 45 and Up Study (2006-2009). Predictions were compared to lung cancer outcomes captured to June 2014 via linkage to population-wide health databases; a total of 1,035 subsequent lung cancer diagnoses were identified. PLCOm2012 had good discrimination (area under the receiver-operating-characteristic-curve; AUC 0.80, 95%CI 0.78-0.81) and excellent calibration (mean and 90th percentiles of absolute risk difference between observed and predicted outcomes: 0.006 and 0.016, respectively). Sensitivity (69.4%, 95%CI, 65.6-73.0%) of the PLCOm2012 criteria in the 55-74 year age group for predicting lung cancers was greater than that using criteria based on ≥30 pack-years smoking and ≤15 years quit (57.3%, 53.3-61.3%; p < 0.0001), but specificity was lower (72.0%, 71.7-72.4% versus 75.2%, 74.8-75.6%, respectively; p < 0.0001). Targeting high risk people for lung cancer screening using PLCOm2012 might improve the balance of benefits versus harms, and cost-effectiveness of lung cancer screening. © 2017 UICC.

  9. 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

  10. [Definition and validation of a predictive model to identify patients with chronic obstructive pulmonary disease (COPD) from administrative databases].

    PubMed

    Belleudi, Valeria; Agabiti, Nera; Kirchmayer, Ursula; Cascini, Silvia; Bauleo, Lisa; Berardini, Ludovica; Pinnarelli, Luigi; Stafoggia, Massimo; Fusco, Danilo; Arcà, Massimo; Davoli, Marina; Perucci, Carlo Alberto

    2012-01-01

    To develop and validate a predictive model for the identification of patients with Chronic Obstructive Pulmonary Disease (COPD) among the resident population of the Lazio region, using information available in the regional administrative systems (SIS) as well as clinical data of a panel of COPD patients. All residents in the Lazio region over 40 years of age in 2007 (2,625,102 inhabitants) The predictive model was developed through record linkage of health care related consumption patterns among 428 panel patients with confirmed COPD diagnosis in 2006 and a control group of patients without COPD (selection from outpatients specialized health care registry, 1:4). Hospital admission for COPD was defined a priori to be sufficient to identify a COPD patient. For all other panel patients and controls, specific drug use (minimum 2 prescriptions during 12 months) and hospitalization for respiratory causes during the past 9 years were retrieved and compared between panel and control patients. COPD associated factors were selected through a Bootstrap- Stepwise (BS) procedure. The predictive model was validated through internal (cross-validation-bootstrap) and external validation (comparison with external COPD patients with confirmed diagnosis), and through comparison with other COPD identification approaches. The BS procedure identified the following predictors of COPD: consumption of beta 2 agonists, anticholinergics, corticosteroids, oxygen, and previous hospitalization for respiratory failure. For each patient, the expected probability of being affected by COPD was estimated. Depending on the cut-point of expected probability, sensibility ranged from 74.5% to 99.6% and specificity from 37.8% to 86.2%. Using the 0.30 cut-point, the model succeeded in identifying 67% of patients with diagnosis of COPD confirmed with spirometry. The predictive performance increased with increasing COPD severity. Prevalence of COPD turned out to be 7.8 %. The age-specific estimation was

  11. 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.

  12. Surrogate utility estimation by long-term partners and unfamiliar dyads.

    PubMed

    Tunney, Richard J; Ziegler, Fenja V

    2015-01-01

    To what extent are people able to make predictions about other people's preferences and values?We report two experiments that present a novel method assessing some of the basic processes in surrogate decision-making, namely surrogate-utility estimation. In each experiment participants formed dyads who were asked to assign utilities to health related items and commodity items, and to predict their partner's utility judgments for the same items. In experiment one we showed that older adults in long-term relationships were able to accurately predict their partner's wishes. In experiment two we showed that younger adults who were relatively unfamiliar with one another were also able to predict other people's wishes. Crucially we demonstrated that these judgments were accurate even after partialling out each participant's own preferences indicating that in order to make surrogate utility estimations people engage in perspective-taking rather than simple anchoring and adjustment, suggesting that utility estimation is not the cause of inaccuracy in surrogate decision-making. The data and implications are discussed with respect to theories of surrogate decision-making.

  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. 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.

  15. 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.

  16. 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.

    2014-12-01

    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

  17. 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

  18. Large-scale candidate gene study to identify genetic risk factors predictive of paliperidone treatment response in patients with schizophrenia.

    PubMed

    Wang, Dai; Fu, Dong-Jing; Wu, Xiaodong; Shapiro, Alice; Favis, Reyna; Savitz, Adam; Chung, Hedy; Alphs, Larry; Gopal, Srihari; Haas, Magali; Cohen, Nadine; Li, Qingqin

    2015-04-01

    Clinical response to antipsychotic medications can vary markedly in patients with schizophrenia. Identifying genetic variants associated with treatment response could help optimize patient care and outcome. To this end, we carried out a large-scale candidate gene study to identify genetic risk factors predictive of paliperidone efficacy. A central nervous system custom chip containing single nucleotide polymorphisms from 1204 candidate genes was utilized to genotype a discovery cohort of 684 schizophrenia patients from four clinical studies of paliperidone extended-release and paliperidone palmitate. Variants predictive of paliperidone efficacy were identified and further tested in four independent replication cohorts of schizophrenic patients (N=2856). We identified an SNP in ERBB4 that may contribute toward differential treatment response to paliperidone. The association trended in the same direction as the discovery cohort in two of the four replication cohorts, but ultimately did not survive multiple testing corrections. The association was not replicated in the other two independent cohorts. We also report several SNPs in well-known schizophrenia candidate genes that show suggestive associations with paliperidone efficacy. These preliminary findings suggest that genetic variation in the ERBB4 gene may differentially affect treatment response to paliperidone in individuals with schizophrenia. They implicate the neuregulin 1 (NRG1)-ErbB4 pathway for modulating antipsychotic response. However, these findings were not robustly reproduced in replication cohorts.

  19. 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.

  20. An unbiased lipidomics approach identifies early second trimester lipids predictive of Maternal Glycemic Traits and Gestational Diabetes Mellitus

    PubMed Central

    Lu, Liangjian; Koulman, Albert; Petry, Clive J.; Jenkins, Benjamin; Matthews, Lee; Hughes, Ieuan A.; Acerini, Carlo L.; Ong, Ken K.; Dunger, David B.

    2016-01-01

    Objective To investigate the relationship between early second trimester serum lipidomic variation and maternal glycemic traits at 28 weeks, and to identify predictive lipid biomarkers for Gestational Diabetes (GDM). Research Design and Methods Prospective study of 817 pregnant women (Discovery cohort, n=200; Validation cohort, n=617) who provided an early second trimester serum sample, and underwent oral glucose tolerance testing (OGTT) at 28 weeks. In the discovery cohort, lipids were measured using direct infusion mass spectrometry, and correlated with OGTT results. Variable Importance in Projection (VIP) scores were used to identify candidate lipid biomarkers. Candidate biomarkers were measured in the validation cohort using Liquid Chromatography- Mass Spectrometry, and tested for associations with OGTT results and GDM status. Results Early second trimester lipidomic variation was associated with 1-hour post-load glucose levels, but not with fasting plasma glucose. Of the 13 lipid species identified by VIP scores, 10 had nominally significant associations with post-load glucose levels. In the validation cohort, 5 of these 10 lipids had significant associations with post-load glucose levels independent of maternal age and BMI, i.e. TG(51:1), TG(48:1), PC(32:1), PCae(40:3) and PCae(40:4). All except the last were also associated with maternal GDM status. Together, these 4 lipid biomarkers had moderate ability to predict GDM (Area under curve (AUC)= 0.71±0.04, p=4.85×10-7), and improved the prediction of GDM by age and BMI alone from AUC 0.69 to AUC 0.74. Conclusions Specific early second trimester lipid biomarkers can predict maternal GDM status independent of maternal age and BMI, potentially enhancing risk factor-based screening. PMID:27703025

  1. Proteomic analysis to identify biomarkers in the primary tumour that predict response to neoadjuvant chemotherapy in liver metastases.

    PubMed

    Sutton, Paul; Evans, Jonathan; Jones, Robert; Malik, Hassan; Vimalachandran, Dale; Palmer, Daniel; Goldring, Chris; Kitteringham, Neil

    2015-02-26

    Colorectal cancer is the fourth commonest cancer in the UK, and the second commonest cause of cancer-related death. A knowledge of the biological phenotype of colorectal liver metastases would be invaluable to inform clinical decision making; however, deriving this information from the metastatic lesions is not feasible until after resection. We aimed to use proteomic analysis to identify biomarkers in the primary tumour that predict response to neoadjuvant chemotherapy in liver metastases. Fresh tissue from both primary colorectal tumour and liver metastases from 17 patients was subjected to proteomic analysis using isobaric tagging for relative quantification. Data were analysed with Protein Pilot (Ab Sciex, Framingham, MA, USA), with stratification of patients into those showing low or high response to chemotherapy permitting the identification of potential predictive biomarkers. These markers were subsequently validated by immunohistochemistry on a tissue microarray of 63 patients. We identified 5768 discrete proteins. Five of them predicted histopathological response to fluorouracil-based chemotherapy regimens, of which the FAD binding protein NQO1 was subsequently validated by immunohistochemistry. When compared with the chemotherapeutic agent alone, knockdown of the corresponding gene with small interfering RNA decreased cell viability when co-incubated with fluorouracil (77·1% vs 46·6%, p=0·037) and irinotecan (41·7% vs 24·4%, p=0·006). Similar results were also seen after inhibition of protein activity by pretreating cells with dicoumarol. These results show that proteomic sequencing of matched metastatic colorectal cancer samples is feasible, with high protein coverage. The high degree of similarity between the primary and secondary proteomes suggests that primary tissue is predictive of the metastatic phenotype. NQO1 expression in the primary tumour predicts response to neoadjuvant chemotherapy in the liver metastases, and inhibition of this

  2. Identifying Thoracic Malignancies Through Pleural Fluid Biomarkers: A Predictive Multivariate Model.

    PubMed

    Porcel, José M; Esquerda, Aureli; Martínez-Alonso, Montserrat; Bielsa, Silvia; Salud, Antonieta

    2016-03-01

    The diagnosis of malignant pleural effusions may be challenging when cytological examination of aspirated pleural fluid is equivocal or noncontributory. The purpose of this study was to identify protein candidate biomarkers differentially expressed in the pleural fluid of patients with mesothelioma, lung adenocarcinoma, lymphoma, and tuberculosis (TB).A multiplex protein biochip comprising 120 biomarkers was used to determine the pleural fluid protein profile of 29 mesotheliomas, 29 lung adenocarcinomas, 12 lymphomas, and 35 tuberculosis. The relative abundance of these predetermined biomarkers among groups served to establish the differential diagnosis of: malignant versus benign (TB) effusions, lung adenocarcinoma versus mesothelioma, and lymphoma versus TB. The selected putative markers were validated using widely available commercial techniques in an independent sample of 102 patients.Significant differences were found in the protein expressions of metalloproteinase-9 (MMP-9), cathepsin-B, C-reactive protein, and chondroitin sulfate between malignant and TB effusions. When integrated into a scoring model, these proteins yielded 85% sensitivity, 100% specificity, and an area under the curve (AUC) of 0.98 for labeling malignancy in the verification sample. For lung adenocarcinoma-mesothelioma discrimination, combining CA19-9, CA15-3, and kallikrein-12 had maximal discriminatory capacity (65% sensitivity, 100% specificity, AUC 0.94); figures which also refer to the validation set. Last, cathepsin-B in isolation was only moderately useful (sensitivity 89%, specificity 62%, AUC 0.75) in separating lymphomatous and TB effusions. However, this last differentiation improved significantly when cathepsin-B was used with respect to the patient's age (sensitivity 72%, specificity 100%, AUC 0.94).In conclusion, panels of 4 (i.e., MMP-9, cathepsin-B, C-reactive protein, chondroitin sulfate), or 3 (i.e., CA19-9, CA15-3, kallikrein-12) different protein biomarkers on pleural

  3. Improved Prediction of Cardiovascular Disease Based on a Panel of Single Nucleotide Polymorphisms Identified Through Genome-Wide Association Studies

    PubMed Central

    Davies, Robert W.; Dandona, Sonny; Stewart, Alexandre F.R.; Chen, Li; Ellis, Stephan G.; Tang, W.H. Wilson; Hazen, Stanley L.; Roberts, Robert; McPherson, Ruth; Wells, George A.

    2011-01-01

    Background Genome-wide association studies (GWAS) have identified single-nucleotide polymorphisms (SNPs) at multiple loci that are significantly associated with coronary artery disease (CAD) risk. In this study, we sought to determine and compare the predictive capabilities of 9p21.3 alone and a panel of SNPs identified and replicated through GWAS for CAD. Methods and Results We used the Ottawa Heart Genomics Study (OHGS) (3323 cases, 2319 control subjects) and the Wellcome Trust Case Control Consortium (WTCCC) (1926 cases, 2938 control subjects) data sets. We compared the ability of allele counting, logistic regression, and support vector machines. Two sets of SNPs, 9p21.3 alone and a set of 12 SNPs identified by GWAS and through a model-fitting procedure, were considered. Performance was assessed by measuring area under the curve (AUC) for OHGS using 10-fold cross-validation and WTCCC as a replication set. AUC for logistic regression using OHGS increased significantly from 0.555 to 0.608 (P=3.59×10–14) for 9p21.3 versus the 12 SNPs, respectively. This difference remained when traditional risk factors were considered in a subgroup of OHGS (1388 cases, 2038 control subjects), with AUC increasing from 0.804 to 0.809 (P=0.037). The added predictive value over and above the traditional risk factors was not significant for 9p21.3 (AUC 0.801 versus 0.804, P=0.097) but was for the 12 SNPs (AUC 0.801 versus 0.809, P=0.0073). Performance was similar between OHGS and WTCCC. Logistic regression outperformed both support vector machines and allele counting. Conclusions Using the collective of 12 SNPs confers significantly greater predictive capabilities for CAD than 9p21.3, whether traditional risks are or are not considered. More accurate models probably will evolve as additional CAD-associated SNPs are identified. PMID:20729558

  4. Prediction of DNA binding motifs from 3D models of transcription factors; identifying TLX3 regulated genes.

    PubMed

    Pujato, Mario; Kieken, Fabien; Skiles, Amanda A; Tapinos, Nikos; Fiser, Andras

    2014-12-16

    Proper cell functioning depends on the precise spatio-temporal expression of its genetic material. Gene expression is controlled to a great extent by sequence-specific transcription factors (TFs). Our current knowledge on where and how TFs bind and associate to regulate gene expression is incomplete. A structure-based computational algorithm (TF2DNA) is developed to identify binding specificities of TFs. The method constructs homology models of TFs bound to DNA and assesses the relative binding affinity for all possible DNA sequences using a knowledge-based potential, after optimization in a molecular mechanics force field. TF2DNA predictions were benchmarked against experimentally determined binding motifs. Success rates range from 45% to 81% and primarily depend on the sequence identity of aligned target sequences and template structures, TF2DNA was used to predict 1321 motifs for 1825 putative human TF proteins, facilitating the reconstruction of most of the human gene regulatory network. As an illustration, the predicted DNA binding site for the poorly characterized T-cell leukemia homeobox 3 (TLX3) TF was confirmed with gel shift assay experiments. TLX3 motif searches in human promoter regions identified a group of genes enriched in functions relating to hematopoiesis, tissue morphology, endocrine system and connective tissue development and function. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

  5. Identifying Future Drinkers: Behavioral Analysis of Monkeys Initiating Drinking to Intoxication is Predictive of Future Drinking Classification.

    PubMed

    Baker, Erich J; Walter, Nicole A R; Salo, Alex; Rivas Perea, Pablo; Moore, Sharon; Gonzales, Steven; Grant, Kathleen A

    2017-03-01

    The Monkey Alcohol Tissue Research Resource (MATRR) is a repository and analytics platform for detailed data derived from well-documented nonhuman primate (NHP) alcohol self-administration studies. This macaque model has demonstrated categorical drinking norms reflective of human drinking populations, resulting in consumption pattern classifications of very heavy drinking (VHD), heavy drinking (HD), binge drinking (BD), and low drinking (LD) individuals. Here, we expand on previous findings that suggest ethanol drinking patterns during initial drinking to intoxication can reliably predict future drinking category assignment. The classification strategy uses a machine-learning approach to examine an extensive set of daily drinking attributes during 90 sessions of induction across 7 cohorts of 5 to 8 monkeys for a total of 50 animals. A Random Forest classifier is employed to accurately predict categorical drinking after 12 months of self-administration. Predictive outcome accuracy is approximately 78% when classes are aggregated into 2 groups, "LD and BD" and "HD and VHD." A subsequent 2-step classification model distinguishes individual LD and BD categories with 90% accuracy and between HD and VHD categories with 95% accuracy. Average 4-category classification accuracy is 74%, and provides putative distinguishing behavioral characteristics between groupings. We demonstrate that data derived from the induction phase of this ethanol self-administration protocol have significant predictive power for future ethanol consumption patterns. Importantly, numerous predictive factors are longitudinal, measuring the change of drinking patterns through 3 stages of induction. Factors during induction that predict future heavy drinkers include being younger at the time of first intoxication and developing a shorter latency to first ethanol drink. Overall, this analysis identifies predictive characteristics in future very heavy drinkers that optimize intoxication, such as having

  6. 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.

  7. Proteomics of Genetically Engineered Mouse Mammary Tumors Identifies Fatty Acid Metabolism Members as Potential Predictive Markers for Cisplatin Resistance*

    PubMed Central

    Warmoes, Marc; Jaspers, Janneke E.; Xu, Guotai; Sampadi, Bharath K.; Pham, Thang V.; Knol, Jaco C.; Piersma, Sander R.; Boven, Epie; Jonkers, Jos; Rottenberg, Sven; Jimenez, Connie R.

    2013-01-01

    In contrast to various signatures that predict the prognosis of breast cancer patients, markers that predict chemotherapy response are still elusive. To detect such predictive biomarkers, we investigated early changes in protein expression using two mouse models for distinct breast cancer subtypes who have a differential knock-out status for the breast cancer 1, early onset (Brca1) gene. The proteome of cisplatin-sensitive BRCA1-deficient mammary tumors was compared with that of cisplatin-resistant mammary tumors resembling pleomorphic invasive lobular carcinoma. The analyses were performed 24 h after administration of the maximum tolerable dose of cisplatin. At this time point, drug-sensitive BRCA1-deficient tumors showed DNA damage, but cells were largely viable. By applying paired statistics and quantitative filtering, we identified highly discriminatory markers for the sensitive and resistant model. Proteins up-regulated in the sensitive model are involved in centrosome organization, chromosome condensation, homology-directed DNA repair, and nucleotide metabolism. Major discriminatory markers that were up-regulated in the resistant model were predominantly involved in fatty acid metabolism, such as fatty-acid synthase. Specific inhibition of fatty-acid synthase sensitized resistant cells to cisplatin. Our data suggest that exploring the functional link between the DNA damage response and cancer metabolism shortly after the initial treatment may be a useful strategy to predict the efficacy of cisplatin. PMID:23397111

  8. Dimensions and Role-Specific Mediators of Surrogate Trust in the ICU

    PubMed Central

    Hutchison, Paul J.; McLaughlin, Katie; Corbridge, Tom; Michelson, Kelly N.; Emanuel, Linda; Sporn, Peter H. S.; Crowley-Matoka, Megan

    2016-01-01

    Objective In the ICU, discussions between clinicians and surrogate decision makers are often accompanied by conflict about a patient’s prognosis or care plan. Trust plays a role in limiting conflict, but little is known about the determinants of trust in the ICU. We sought to identify the dimensions of trust and clinician behaviors conducive to trust formation in the ICU. Design Prospective qualitative study. Setting Medical ICU of a major urban university hospital. Subjects Surrogate decision makers of intubated, mechanically ventilated patients in the medical ICU. Measurements and Main Results Semistructured interviews focused on surrogates’ general experiences in the ICU and on their trust in the clinicians caring for the patient. Interviews were audio-recorded, transcribed verbatim, and coded by two reviewers. Constant comparison was used to identify themes pertaining to trust. Thirty surrogate interviews revealed five dimensions of trust in ICU clinicians: technical competence, communication, honesty, benevolence, and interpersonal skills. Most surrogates emphasized the role of nurses in trust formation, frequently citing their technical competence. Trust in physicians was most commonly related to honesty and the quality of their communication with surrogates. Conclusions Interventions to improve trust in the ICU should be role-specific, since surrogate expectations are different for physicians and nurses with regard to behaviors relevant to trust. Further research is needed to confirm our findings and explore the impact of trust modification on clinician-family conflict. PMID:27513360

  9. Predictive value of dobutamine echocardiography and positron emission tomography in identifying hibernating myocardium in patients with postischaemic heart failure

    PubMed Central

    Pagano, D; Bonser, R; Townend, J; Ordoubadi, F; Lorenzoni, R; Camici, P

    1998-01-01

    Objective—To compare the predictive value of dobutamine echocardiography (DE) and positron emission tomography (PET) in identifying reversible chronic left ventricular (LV) dysfunction (hibernating myocardium) in patients with coronary artery disease (CAD) and overt heart failure.
Patients—30 patients (four women) with CAD and heart failure undergoing coronary artery bypass grafting (CABG).
Methods—Myocardial viability was assessed with DE (5 and 10 µg/kg/min) and PET with [18F] 2-fluoro-2-deoxy-D-glucose (FDG) under hyperinsulinaemic euglycaemic clamp. Regional (echo) and global LV function (MUGA) were assessed at baseline and six months after CABG.
Results—192 of the 336 (57%) dysfunctional LV segments improved function following CABG (hibernating) and the LV ejection fraction (EF) increased from 23(7) to 32(9)% (p < 0.0001) (in 17 patients > 5%). DE and PET had similar positive predictive values (68% and 66%) in the identification of hibernating myocardium, but DE had a significantly lower negative predictive value than PET (54% v 96%; p < 0.0001). A significant linear correlation was found between the number of PET viable segments and the changes in EF following CABG (r = 0.65; p = 0.0001). Stepwise logistic regression identified the number of PET viable segments as an independent predictor of improvement in EF > 5%, whereas the number of DE viable segments, the baseline LVEF, and wall motion were not.
Conclusions—DE has a higher false negative rate than PET in identifying recoverable LV dysfunction in patients with severe postischaemic heart failure. The amount of PET viable myocardium correlates with the functional outcome following CABG.

 Keywords: dobutamine echocardiography;  positron emission tomography;  coronary artery disease;  heart failure;  hibernating myocardium PMID:9602663

  10. Engagement in Advance Care Planning and Surrogates' Knowledge of Patients' Treatment Goals.

    PubMed

    Fried, Terri R; Zenoni, Maria; Iannone, Lynne; O'Leary, John; Fenton, Brenda T

    2017-08-01

    A key objective of advance care planning (ACP) is improving surrogates' knowledge of patients' treatment goals. Little is known about whether ACP outside of a trial accomplishes this. The objective was to examine patient and surrogate reports of ACP engagement and associations with surrogate knowledge of goals. Cohort study SETTING: Primary care in a Veterans Affairs Medical Center. 350 community-dwelling veterans age ≥55 years and the individual they would choose to make medical decisions on their behalf, interviewed separately. Treatment goals were assessed by veterans' ratings of 3 health states: severe physical disability, cognitive disability, and pain, as an acceptable or unacceptable result of treatment for severe illness. Surrogates had knowledge if they correctly predicted all 3 responses. Veterans and surrogates were asked about living will and health care proxy completion and communication about life-sustaining treatment and quality versus quantity of life (QOL). Over 40% of dyads agreed that the veteran had not completed a living will or health care proxy and that there was no QOL communication. For each activity, sizeable proportions (18-34%) disagreed about participation. In dyads who agreed QOL communication had occurred, 30% of surrogates had knowledge, compared to 21% in dyads who agreed communication had not occurred and 15% in dyads who disagreed (P = .01). This relationship persisted in multivariable analysis. Agreement about other ACP activities was not associated with knowledge. Disagreement about ACP participation was common. Agreement about communication regarding QOL was modestly associated with surrogate knowledge of treatment goals. Eliciting surrogates' perspectives is critical to ACP. Even dyads who agree about participation may need additional support for successful engagement. © 2017, Copyright the Authors Journal compilation © 2017, The American Geriatrics Society.

  11. Genomic Models of Short-Term Exposure Accurately Predict Long-Term Chemical Carcinogenicity and Identify Putative Mechanisms of Action

    PubMed Central

    Gusenleitner, Daniel; Auerbach, Scott S.; Melia, Tisha; Gómez, Harold F.; Sherr, David H.; Monti, Stefano

    2014-01-01

    Background Despite an overall decrease in incidence of and mortality from cancer, about 40% of Americans will be diagnosed with the disease in their lifetime, and around 20% will die of it. Current approaches to test carcinogenic chemicals adopt the 2-year rodent bioassay, which is costly and time-consuming. As a result, fewer than 2% of the chemicals on the market have actually been tested. However, evidence accumulated to date suggests that gene expression profiles from model organisms exposed to chemical compounds reflect underlying mechanisms of action, and that these toxicogenomic models could be used in the prediction of chemical carcinogenicity. Results In this study, we used a rat-based microarray dataset from the NTP DrugMatrix Database to test the ability of toxicogenomics to model carcinogenicity. We analyzed 1,221 gene-expression profiles obtained from rats treated with 127 well-characterized compounds, including genotoxic and non-genotoxic carcinogens. We built a classifier that predicts a chemical's carcinogenic potential with an AUC of 0.78, and validated it on an independent dataset from the Japanese Toxicogenomics Project consisting of 2,065 profiles from 72 compounds. Finally, we identified differentially expressed genes associated with chemical carcinogenesis, and developed novel data-driven approaches for the molecular characterization of the response to chemical stressors. Conclusion Here, we validate a toxicogenomic approach to predict carcinogenicity and provide strong evidence that, with a larger set of compounds, we should be able to improve the sensitivity and specificity of the predictions. We found that the prediction of carcinogenicity is tissue-dependent and that the results also confirm and expand upon previous studies implicating DNA damage, the peroxisome proliferator-activated receptor, the aryl hydrocarbon receptor, and regenerative pathology in the response to carcinogen exposure. PMID:25058030

  12. 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

  13. 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

  14. Using Video Images to Improve the Accuracy of Surrogate Decision-Making: A Randomized Controlled Trial

    PubMed Central

    Volandes, Angelo E.; Mitchell, Susan L.; Gillick, Muriel R.; Chang, Yuchiao; Paasche-Orlow, Michael K.

    2009-01-01

    Introduction When patients are unable to make important end-of-life decisions, doctors ask surrogate decision makers to provide insight into patients’ preferences. Unfortunately, multiple studies have shown that surrogates’ knowledge of patient preferences is poor. We hypothesized that a video decision tool would improve concordance between patients and their surrogates for end-of-life preferences. Objective To compare the concordance of preferences among elderly patients and their surrogates listening to only a verbal description of advanced dementia or viewing a video decision support tool of the disease after hearing the verbal description. Methods This was a randomized controlled trial of a convenience sample of community-dwelling elderly subjects (≥65 years) and their surrogates, and was conducted at 2 geriatric clinics affiliated with 2 academic medical centers in Boston. The study was conducted between September 1, 2007, and May 30, 2008. Random assignment of patient and surrogate dyads was to either a verbal narrative or a video decision support tool after the verbal narrative. End points were goals of care chosen by the patient and predicted goals of care by the surrogate. Goals of care included life-prolonging care (CPR, mechanical ventilation), limited care (hospitalization, antibiotics, but not CPR), and comfort care (only treatment to relieve symptoms). The primary outcome measure was the concordance rate of preferences between patients and their surrogates. Results A total of 14 pairs of patients and their surrogates were randomized to verbal narrative (n = 6) or video after verbal narrative (n = 8). Among the 6 patients receiving only the verbal narrative, 3 (50%) preferred comfort care, 1 (17%) chose limited care, and 2 (33%) desired life-prolonging care. Among the surrogates for these patients, only 2 correctly chose what their loved one would want if in a state of advanced dementia, yielding a concordance rate of 33%. Among the 8 patients

  15. 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

  16. 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.

  17. 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.

  18. 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.

  19. Detailed chemical kinetic oxidation mechanism for a biodiesel surrogate

    SciTech Connect

    Herbinet, Olivier; Pitz, William J.; Westbrook, Charles K.

    2008-08-15

    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 and co-workers 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. (author)

  20. Surrogate end points for overall survival in breast cancer trials: A review.

    PubMed

    Fiteni, Frédéric; Bonnetain, Franck

    2016-10-01

    Our aim was to review the studies which assessed potential surrogate endpoints for overall survival (OS) in breast cancer trials. A Literature search in PubMed database of studies which assessed potential surrogate endpoints for OS in breast cancer trials was conducted. The surrogacy was assessed with the German institute of Quality and efficiency in Health Care's (IWQiG) framework and the Fleming hierarchy. Thirteen studies were identified. At the neoadjuvant setting, two individual patient data (IPD) meta-analyses and one aggregate data meta-analysis assessing surrogacy of pathological complete response (PCR) were identified. Trial-level association was calculated in one study and the squared correlation was 0.24. Therefore PCR was not judged to be valid surrogate for OS at the neoadjuvant setting according to the IWQiG framework and Fleming hierarchy. At the adjuvant setting, one meta-analysis on aggregate data was identified. 2-year DFS was not judged to be valid surrogate for OS at the neoadjuvant setting according to the IWQiG framework and Fleming hierarchy. At the metastatic setting, six meta-analyses based on aggregate data, three IPD meta-analyses and one retrospective study were identified. Within the IPD meta-analyses, at the trial-level association the squared correlation between the potential surrogates and OS ranged from 0.10 to 0.57 and no endpoint was judged to be valid surrogate for OS at the metastatic setting. The level of evidence available supporting a relationship between OS and potential surrogate endpoints in breast cancer trials is low.

  1. 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

  2. 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.

  3. Deciphering HLA-I motifs across HLA peptidomes improves neo-antigen predictions and identifies allostery regulating HLA specificity.

    PubMed

    Bassani-Sternberg, Michal; Chong, Chloé; Guillaume, Philippe; Solleder, Marthe; Pak, HuiSong; Gannon, Philippe O; Kandalaft, Lana E; Coukos, George; Gfeller, David

    2017-08-01

    The precise identification of Human Leukocyte Antigen class I (HLA-I) binding motifs plays a central role in our ability to understand and predict (neo-)antigen presentation in infectious diseases and cancer. Here, by exploiting co-occurrence of HLA-I alleles across ten newly generated as well as forty public HLA peptidomics datasets comprising more than 115,000 unique peptides, we show that we can rapidly and accurately identify many HLA-I binding motifs and map them to their corresponding alleles without any a priori knowledge of HLA-I binding specificity. Our approach recapitulates and refines known motifs for 43 of the most frequent alleles, uncovers new motifs for 9 alleles that up to now had less than five known ligands and provides a scalable framework to incorporate additional HLA peptidomics studies in the future. The refined motifs improve neo-antigen and cancer testis antigen predictions, indicating that unbiased HLA peptidomics data are ideal for in silico predictions of neo-antigens from tumor exome sequencing data. The new motifs further reveal distant modulation of the binding specificity at P2 for some HLA-I alleles by residues in the HLA-I binding site but outside of the B-pocket and we unravel the underlying mechanisms by protein structure analysis, mutagenesis and in vitro binding assays.

  4. Cell-line selectivity improves the predictive power of pharmacogenomic analyses and helps identify NADPH as biomarker for ferroptosis sensitivity

    PubMed Central

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

    2016-01-01

    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. 2,565 cell-line-selective lethal compounds were identified and grouped into 18 clusters based on their 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

  5. Who Makes It to the End?: A Novel Predictive Model for Identifying Surgical Residents at Risk for Attrition.

    PubMed

    Yeo, Heather L; Abelson, Jonathan S; Mao, Jialin; Lewis, Frank; Michelassi, Fabrizio; Bell, Richard; Sedrakyan, Art; Sosa, Julie A

    2017-09-01

    We present 8-year follow-up data from the intern class of 2007 to 2008 using a novel, nonparametric predictive model to identify those residents who are at greatest risk of not completing their training. Nearly 1 in every 4 categorical general surgery residents does not complete training. There has been no study at a national level to identify individual resident and programmatic factors that can be used to accurately anticipate which residents are most at risk of attrition out. A cross-sectional survey of categorical general surgery interns was conducted between June and August 2007. Intern data including demographics, attendance at US or Canadian medical school, proximity of family members, and presence of family members in medicine were de-identified and linked with American Board of Surgery data to determine residency completion and program characteristics. A Classification and Regression Tree analysis was performed to identify groups at greatest risk for non-completion. Of 1048 interns, 870 completed the initial survey (response rate 83%), 836 of which had linkage data (96%). Also, 672 residents had evidence of completion of residency (noncompletion rate 20%). On Classification and Regression Tree analysis, sex was the independent factor most strongly associated with attrition. The lowest noncompletion rate for men was among interns at small community programs who were White, non-Hispanic, and married (6%). The lowest noncompletion rate for women was among interns training at smaller academic programs (11%). This is the first longitudinal cohort study to identify factors at the start of training that put residents at risk for not completing training. Data from this study offer a method to identify interns at higher risk for attrition at the start of training, and next steps would be to create and test interventions in a directed fashion.

  6. 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.

  7. 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.

  8. Development of Surrogate Spinal Cords for the Evaluation of Electrode Arrays Used in Intraspinal Implants

    PubMed Central

    Cheng, Cheng; Kmech, Jonn; Mushahwar, Vivian K.

    2013-01-01

    We report the development of a surrogate spinal cord for evaluating the mechanical suitability of electrode arrays for intraspinal implants. The mechanical and interfacial properties of candidate materials (including silicone elastomers and gelatin hydrogels) for the surrogate cord were tested. The elastic modulus was characterized using dynamic mechanical analysis, and compared with values of actual human spinal cords from the literature. Forces required to indent the surrogate cords to specified depths were measured to obtain values under static conditions. Importantly, to quantify surface properties in addition to mechanical properties normally considered, interfacial frictional forces were measured by pulling a needle out of each cord at a controlled rate. The measured forces were then compared to those obtained from rat spinal cords. Formaldehyde-crosslinked gelatin, 12 wt% in water, was identified as the most suitable material for the construction of surrogate spinal cords. To demonstrate the utility of surrogate spinal cords in evaluating the behavior of various electrode arrays, cords were implanted with two types of intraspinal electrode arrays (one made of individual microwires and another of microwires anchored with a solid base), and cord deformation under elongation was evaluated. The results demonstrate that the surrogate model simulates the mechanical and interfacial properties of the spinal cord, and enables in vitro screening of intraspinal implants. PMID:23358939

  9. Development of a clinical prediction rule to identify patients with neck pain likely to benefit from cervical traction and exercise

    PubMed Central

    Petersen, Evan J.; Smith, Tracy A.; Cowan, James E.; Rendeiro, Daniel G.; Deyle, Gail D.; Childs, John D.

    2009-01-01

    The objective of the study was to develop a clinical prediction rule (CPR) to identify patients with neck pain likely to improve with cervical traction. The study design included prospective cohort of patients with neck pain referred to physical therapy. Development of a CPR will assist clinicians in classifying patients with neck pain likely to benefit from cervical traction. Eighty patients with neck pain received a standardized examination and then completed six sessions of intermittent cervical traction and cervical strengthening exercises twice weekly for 3 weeks. Patient outcome was classified at the end of treatment, based on perceived recovery according to the global rating of change. Patients who achieved a change ≥+6 (“A great deal better” or “A very great deal better”) were classified as having a successful outcome. Univariate analyses (t tests and chi-square) were conducted on historical and physical examination items to determine potential predictors of successful outcome. Variables with a significance level of P ≤ 0.15 were retained as potential prediction variables. Sensitivity, specificity and positive and negative likelihood ratios (LRs) were then calculated for all variables with a significant relationship with the reference criterion of successful outcome. Potential predictor variables were entered into a step-wise logistic regression model to determine the most accurate set of clinical examination items for prediction of treatment success. Sixty-eight patients (38 female) were included in data analysis of which 30 had a successful outcome. A CPR with five variables was identified: (1) patient reported peripheralization with lower cervical spine (C4–7) mobility testing; (2) positive shoulder abduction test; (3) age ≥55; (4) positive upper limb tension test A; and (5) positive neck distraction test. Having at least three out of five predictors present resulted in a +LR equal to 4.81 (95% CI = 2.17–11.4), increasing the

  10. Human surrogates for injury biomechanics research.

    PubMed

    Crandall, J R; Bose, D; Forman, J; Untaroiu, C D; Arregui-Dalmases, C; Shaw, C G; Kerrigan, J R

    2011-04-01

    This article reviews the attributes of the human surrogates most commonly used in injury biomechanics research. In particular, the merits of human cadavers, human volunteers, animals, dummies, and computational models are assessed relative to their ability to characterize the living human response and injury in an impact environment. Although data obtained from these surrogates have enabled biomechanical engineers and designers to develop effective injury countermeasures for occupants and pedestrians involved in crashes, the magnitude of the traffic safety problem necessitates expanded efforts in research and development. This article makes the case that while there are limitations and challenges associated with any particular surrogate, each provides a critical and necessary component in the continued quest to reduce crash-related injuries and fatalities. Copyright © 2011 Wiley-Liss, Inc.

  11. Genome context as a predictive tool for identifying regulatory targets of the TetR family transcriptional regulators.

    PubMed

    Ahn, Sang Kyun; Cuthbertson, Leslie; Nodwell, Justin R

    2012-01-01

    TetR family transcriptional regulators (TFRs) are found in most bacteria and archea. Most of the family members that have been investigated to date are repressors of their target genes, and the majority of these, like the well-characterized protein TetR, regulate genes that encode transmembrane efflux pumps. In many cases repression by TFR proteins is reversed through the direct binding of a small-molecule ligand. The number of TFRs in the public database has grown rapidly as a result of genome sequencing and there are now thousands of family members; however virtually nothing is known about the biology and biochemistry they regulate. Generally applicable methods for predicting their regulatory targets would assist efforts to characterize the family. Here, we investigate chromosomal context of 372 TFRs from three Streptomyces species. We find that the majority (250 TFRs) are transcribed divergently from one neighboring gene, as is the case for TetR and its target tetA. We explore predicted target gene product identity and intergenic separation to see which either correlates with a direct regulatory relationship. While intergenic separation is a critical factor in regulatory prediction the identity of the putative target gene product is not. Our data suggest that those TFRs that are <200 bp from their divergently oriented neighbors are most likely to regulate them. These target genes include membrane proteins (26% of which 22% are probable membrane-associated pumps), enzymes (60%), other proteins such as transcriptional regulators (1%), and proteins having no predictive sequence motifs (13%). In addition to establishing a solid foundation for identifying targets for TFRs of unknown function, our analysis demonstrates a much greater diversity of TFR-regulated biochemical functions.

  12. 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

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

    PubMed

    Wenger, Yvan; Galliot, Brigitte

    2013-03-25

    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. 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. 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.

  14. Applying psychological theories to evidence-based clinical practice: identifying factors predictive of placing preventive fissure sealants.

    PubMed

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

    2010-04-08

    Psychological models are used to understand and predict behaviour in a wide range of settings, but have not been consistently applied to health professional behaviours, and the contribution of differing theories is not clear. This study explored the usefulness of a range of models to predict an evidence-based behaviour -- the placing of fissure sealants. Measures were collected by postal questionnaire from a random sample of general dental practitioners (GDPs) in Scotland. Outcomes were behavioural simulation (scenario decision-making), and behavioural intention. Predictor variables were from the Theory of Planned Behaviour (TPB), Social Cognitive Theory (SCT), Common Sense Self-regulation Model (CS-SRM), Operant Learning Theory (OLT), Implementation Intention (II), Stage Model, and knowledge (a non-theoretical construct). Multiple regression analysis was used to examine the predictive value of each theoretical model individually. Significant constructs from all theories were then entered into a 'cross theory' stepwise regression analysis to investigate their combined predictive value. Behavioural simulation - theory level variance explained was: TPB 31%; SCT 29%; II 7%; OLT 30%. Neither CS-SRM nor stage explained significant variance. In the cross theory analysis, habit (OLT), timeline acute (CS-SRM), and outcome expectancy (SCT) entered the equation, together explaining 38% of the variance. Behavioural intention - theory level variance explained was: TPB 30%; SCT 24%; OLT 58%, CS-SRM 27%. GDPs in the action stage had significantly higher intention to place fissure sealants. In the cross theory analysis, habit (OLT) and attitude (TPB) entered the equation, together explaining 68% of the variance in intention. 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 may predict clinical behaviour

  15. Surrogates for herbicide removal in stormwater biofilters.

    PubMed

    Zhang, Kefeng; Deletic, Ana; Page, Declan; McCarthy, David T

    2015-09-15

    Real time monitoring of suitable surrogate parameters are critical to the validation of any water treatment processes, and is of particularly high importance for validation of natural stormwater treatment systems. In this study, potential surrogates for herbicide removal in stormwater biofilters (also known as stormwater bio-retention or rain-gardens) were assessed using field challenge tests and matched laboratory column experiments. Differential UV absorbance at 254mn (ΔUVA254), total phosphorus (ΔTP), dissolved phosphorus (ΔDP), total nitrogen (ΔTN), ammonia (ΔNH3), nitrate and nitrite (ΔNO3+NO2), dissolved organic carbon (ΔDOC) and total suspended solids (ΔTSS) were compared with glyphosate, atrazine, simazine and prometryn removal rates. The influence of different challenge conditions on the performance of each surrogate was studied. Differential TP was significantly and linearly related to glyphosate reduction (R(2) = 0.75-0.98, P < 0.01), while ΔTP and ΔUVA254 were linearly correlated (R(2) = 0.44-0.84, P < 0.05) to the reduction of triazines (atrazine, simazine and prometryn) in both field and laboratory tests. The performance of ΔTP and ΔUVA254 as surrogates for herbicides were reliable under normal and challenge dry conditions, but weaker correlations were observed under challenge wet conditions. Of those tested, ΔTP is the most promising surrogate for glyphosate removal and ΔUVA254 is a suitable surrogate for triazines removal in stormwater biofilters. Copyright © 2015 Elsevier Ltd. All rights reserved.

  16. Significance of Including a Surrogate Arousal for Sleep Apnea-Hypopnea Syndrome Diagnosis by Respiratory Polygraphy

    PubMed Central

    Masa, Juan F.; Corral, Jaime; Gomez de Terreros, Javier; Duran-Cantolla, Joaquin; Cabello, Marta; Hernández-Blasco, Luis; Monasterio, Carmen; Alonso, Alberto; Chiner, Eusebi; Aizpuru, Felipe; Zamorano, Jose; Cano, Ricardo; Montserrat, Jose M.; Garcia-Ledesma, Estefania; Pereira, Ricardo; Cancelo, Laura; Martinez, Angeles; Sacristan, Lirios; Salord, Neus; Carrera, Miguel; Sancho-Chust, José N.; Embid, Cristina

    2013-01-01

    Rationale: Respiratory polygraphy is an accepted alternative to polysomnography (PSG) for sleep apnea/hypopnea syndrome (SAHS) diagnosis, although it underestimates the apnea-hypopnea index (AHI) because respiratory polygraphy cannot identify arousals. Objectives: We performed a multicentric, randomized, blinded crossover study to determine the agreement between home respiratory polygraphy (HRP) and PSG, and between simultaneous respiratory polygraphy (respiratory polygraphy with PSG) (SimultRP) and PSG by means of 2 AHI scoring protocols with or without hyperventilation following flow reduction considered as a surrogate arousal. Methods: We included suspected SAHS patients from 8 hospitals. They were assigned to home and hospital protocols at random. We determined the agreement between respiratory polygraphy AHI and PSG AHI scorings using Bland and Altman plots and diagnostic agreement using receiver operating characteristic (ROC) curves. The agreement in therapeutic decisions (continuous positive airway pressure treatment or not) between HRP and PSG scorings was done with likelihood ratios and post-test probability calculations. Results: Of 366 randomized patients, 342 completed the protocol. AHI from HRP scorings (with and without surrogate arousal) had similar agreement with PSG. AHI from SimultRP with surrogate arousal scoring had better agreement with PSG than AHI from SimultRP without surrogate arousal. HRP with surrogate arousal scoring had slightly worse ROC curves than HRP without surrogate arousal, and the opposite was true for SimultRP scorings. HRP with surrogate arousal showed slightly better agreement with PSG in therapeutic decisions than for HRP without surrogate arousal. Conclusion: Incorporating a surrogate arousal measure into HRP did not substantially increase its agreement with PSG when compared with the usual procedure (HRP without surrogate arousal). Citation: Masa JF; Corral J; Gomez de Terreros J; Duran-Cantolla J; Cabello M; Hern

  17. Conserved residues in RF-NH₂ receptor models identify predicted contact sites in ligand-receptor binding.

    PubMed

    Bass, C; Katanski, C; Maynard, B; Zurro, I; Mariane, E; Matta, M; Loi, M; Melis, V; Capponi, V; Muroni, P; Setzu, M; Nichols, R

    2014-03-01

    Peptides in the RF-NH2 family are grouped together based on an amidated dipeptide C terminus and signal through G-protein coupled receptors (GPCRs) to influence diverse physiological functions. By determining the mechanisms underlying RF-NH2 signaling targets can be identified to modulate physiological activity; yet, how RF-NH2 peptides interact with GPCRs is relatively unexplored. We predicted conserved residues played a role in Drosophila melanogaster RF-NH2 ligand-receptor interactions. In this study D. melanogaster rhodopsin-like family A peptide GPCRs alignments identified eight conserved residues unique to RF-NH2 receptors. Three of these residues were in extra-cellular loops of modeled RF-NH2 receptors and four in transmembrane helices oriented into a ligand binding pocket to allow contact with a peptide. The eighth residue was unavailable for interaction; yet its conservation suggested it played another role. A novel hydrophobic region representative of RF-NH2 receptors was also discovered. The presence of rhodopsin-like family A GPCR structural motifs including a toggle switch indicated RF-NH2s signal classically; however, some features of the DMS receptors were distinct from other RF-NH2 GPCRs. Additionally, differences in RF-NH2 receptor structures which bind the same peptide explained ligand specificity. Our novel results predicted conserved residues as RF-NH2 ligand-receptor contact sites and identified unique and classic structural features. These discoveries will aid antagonist design to modulate RF-NH2 signaling.

  18. De novo sequencing of circulating miRNAs identifies novel markers predicting clinical outcome of locally advanced breast cancer

    PubMed Central

    2012-01-01

    Background MicroRNAs (miRNAs) have been recently detected in the circulation of cancer patients, where they are associated with clinical parameters. Discovery profiling of circulating small RNAs has not been reported in breast cancer (BC), and was carried out in this study to identify blood-based small RNA markers of BC clinical outcome. Methods The pre-treatment sera of 42 stage II-III locally advanced and inflammatory BC patients who received neoadjuvant chemotherapy (NCT) followed by surgical tumor resection were analyzed for marker identification by deep sequencing all circulating small RNAs. An independent validation cohort of 26 stage II-III BC patients was used to assess the power of identified miRNA markers. Results More than 800 miRNA species were detected in the circulation, and observed patterns showed association with histopathological profiles of BC. Groups of circulating miRNAs differentially associated with ER/PR/HER2 status and inflammatory BC were identified. The relative levels of selected miRNAs measured by PCR showed consistency with their abundance determined by deep sequencing. Two circulating miRNAs, miR-375 and miR-122, exhibited strong correlations with clinical outcomes, including NCT response and relapse with metastatic disease. In the validation cohort, higher levels of circulating miR-122 specifically predicted metastatic recurrence in stage II-III BC patients. Conclusions Our study indicates that certain miRNAs can serve as potential blood-based biomarkers for NCT response, and that miR-122 prevalence in the circulation predicts BC metastasis in early-stage patients. These results may allow optimized chemotherapy treatments and preventive anti-metastasis interventions in future clinical applications. PMID:22400902

  19. Myocardial Gene Expression Profiling to Predict and Identify Cardiac Allograft Acute Cellular Rejection: The GET-Study

    PubMed Central

    Bodez, Diane; Hocini, Hakim; Tchitchek, Nicolas; Tisserand, Pascaline; Benhaiem, Nicole; Barau, Caroline; Kharoubi, Mounira; Guellich, Aziz; Guendouz, Soulef; Radu, Costin; Couetil, Jean-Paul; Ghaleh, Bijan; Dubois-Randé, Jean-Luc; Teiger, Emmanuel; Hittinger, Luc

    2016-01-01

    Aims Serial invasive endomyocardial biopsies (EMB) remain the gold standard for acute cellular rejection (ACR) diagnosis. However histological grading has several limitations. We aimed to explore the value of myocardial Gene Expression Profiling (GEP) for diagnosing and identifying predictive biomarkers of ACR. Methods A case-control study nested within a retrospective heart transplant patients cohort included 126 patients with median (IQR) age 50 (41–57) years and 111 (88%) males. Among 1157 EMB performed, 467 were eligible (i.e, corresponding to either ISHLT grade 0 or ≥3A), among which 36 were selected for GEP according to the grading: 0 (CISHLT, n = 13); rejection ≥3A (RISHLT, n = 13); 0 one month before ACR (BRISHLT, n = 10). Results We found 294 genes differentially expressed between CISHLT and RISHLT, mainly involved in immune activation, and inflammation. Hierarchical clustering showed a clear segregation of CISHLT and RISHLT groups and heterogeneity of GEP within RISHLT. All EMB presented immune activation, but some RISHLT EMB were strongly subject to inflammation, whereas others, closer to CISHLT, were characterized by structural modifications with lower inflammation level. We identified 15 probes significantly different between BRISHLT and CISHLT, including the gene of the muscular protein TTN. This result suggests that structural alterations precede inflammation in ACR. Linear Discriminant Analysis based on these 15 probes was able to identify the histological status of every 36 samples. Conclusion Myocardial GEP is a helpful method to accurately diagnose ACR, and predicts rejection one month before its histological occurrence. These results should be considered in cardiac allograft recipients’ care. PMID:27898719

  20. A Western diet ecological module identified from the 'humanized' mouse microbiota predicts diet in adults and formula feeding in children.

    PubMed

    Siddharth, Jay; Holway, Nicholas; Parkinson, Scott J

    2013-01-01

    The interplay between diet and the microbiota has been implicated in the growing frequency of chronic diseases associated with the Western lifestyle. However, the complexity and variability of microbial ecology in humans and preclinical models has hampered identification of the molecular mechanisms underlying the association of the microbiota in this context. We sought to address two key questions. Can the microbial ecology of preclinical models predict human populations? And can we identify underlying principles that surpass the plasticity of microbial ecology in humans? To do this, we focused our study on diet; perhaps the most influential factor determining the composition of the gut microbiota. Beginning with a study in 'humanized' mice we identified an interactive module of 9 genera allied with Western diet intake. This module was applied to a controlled dietary study in humans. The abundance of the Western ecological module correctly predicted the dietary intake of 19/21 top and 21/21 of the bottom quartile samples inclusive of all 5 Western and 'low-fat' diet subjects, respectively. In 98 volunteers the abundance of the Western module correlated appropriately with dietary intake of saturated fatty acids, fat-soluble vitamins and fiber. Furthermore, it correlated with the geographical location and dietary habits of healthy adults from the Western, developing and third world. The module was also coupled to dietary intake in children (and piglets) correlating with formula (vs breast) feeding and associated with a precipitous development of the ecological module in young children. Our study provides a conceptual platform to translate microbial ecology from preclinical models to humans and identifies an ecological network module underlying the association of the gut microbiota with Western dietary habits.

  1. A machine learning approach for identifying amino acid signatures in the HIV env gene predictive of dementia.

    PubMed

    Holman, Alexander G; Gabuzda, Dana

    2012-01-01

    The identification of nucleotide sequence variations in viral pathogens linked to disease and clinical outcomes is important for developing vaccines and therapies. However, identifying these genetic variations in rapidly evolving pathogens adapting to selection pressures unique to each host presents several challenges. Machine learning tools provide new opportunities to address these challenges. In HIV infection, virus replicating within the brain causes HIV-associated dementia (HAD) and milder forms of neurocognitive impairment in 20-30% of patients with unsuppressed viremia. HIV neurotropism is primarily determined by the viral envelope (env) gene. To identify amino acid signatures in the HIV env gene predictive of HAD, we developed a machine learning pipeline using the PART rule-learning algorithm and C4.5 decision tree inducer to train a classifier on a meta-dataset (n = 860 env sequences from 78 patients: 40 HAD, 38 non-HAD). To increase the flexibility and biological relevance of our analysis, we included 4 numeric factors describing amino acid hydrophobicity, polarity, bulkiness, and charge, in addition to amino acid identities. The classifier had 75% predictive accuracy in leave-one-out cross-validation, and identified 5 signatures associated with HAD diagnosis (p<0.05, Fisher's exact test). These HAD signatures were found in the majority of brain sequences from 8 of 10 HAD patients from an independent cohort. Additionally, 2 HAD signatures were validated against env sequences from CSF of a second independent cohort. This analysis provides insight into viral genetic determinants associated with HAD, and develops novel methods for applying machine learning tools to analyze the genetics of rapidly evolving pathogens.

  2. The impact of climate change on indigenous Arabica coffee (Coffea arabica): predicting future trends and identifying priorities.

    PubMed

    Davis, Aaron P; Gole, Tadesse Woldemariam; Baena, Susana; Moat, Justin

    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 plantations

  3. A Machine Learning Approach for Identifying Amino Acid Signatures in the HIV Env Gene Predictive of Dementia

    PubMed Central

    Holman, Alexander G.; Gabuzda, Dana

    2012-01-01

    The identification of nucleotide sequence variations in viral pathogens linked to disease and clinical outcomes is important for developing vaccines and therapies. However, identifying these genetic variations in rapidly evolving pathogens adapting to selection pressures unique to each host presents several challenges. Machine learning tools provide new opportunities to address these challenges. In HIV infection, virus replicating within the brain causes HIV-associated dementia (HAD) and milder forms of neurocognitive impairment in 20–30% of patients with unsuppressed viremia. HIV neurotropism is primarily determined by the viral envelope (env) gene. To identify amino acid signatures in the HIV env gene predictive of HAD, we developed a machine learning pipeline using the PART rule-learning algorithm and C4.5 decision tree inducer to train a classifier on a meta-dataset (n = 860 env sequences from 78 patients: 40 HAD, 38 non-HAD). To increase the flexibility and biological relevance of our analysis, we included 4 numeric factors describing amino acid hydrophobicity, polarity, bulkiness, and charge, in addition to amino acid identities. The classifier had 75% predictive accuracy in leave-one-out cross-validation, and identified 5 signatures associated with HAD diagnosis (p<0.05, Fisher’s exact test). These HAD signatures were found in the majority of brain sequences from 8 of 10 HAD patients from an independent cohort. Additionally, 2 HAD signatures were validated against env sequences from CSF of a second independent cohort. This analysis provides insight into viral genetic determinants associated with HAD, and develops novel methods for applying machine learning tools to analyze the genetics of rapidly evolving pathogens. PMID:23166702

  4. 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

  5. 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

  6. Carbonylations of alkenes with CO surrogates.

    PubMed

    Wu, Lipeng; Liu, Qiang; Jackstell, Ralf; Beller, Matthias

    2014-06-16

    Alkene carbonylation reactions are important for the production of value-added bulk and fine chemicals. Nowadays, all industrial carbonylation processes make use of highly toxic and flammable carbon monoxide. In fact, these properties impede the wider use of carbonylation reactions in industry and academia. Hence, performing carbonylations without the use of CO is highly desired and will contribute to the further advancement of sustainable chemistry. Although the use of carbon monoxide surrogates in alkene carbonylation reactions has been reported intermittently in the last 30 years, only recently has this area attracted significant interest. This Minireview summarizes carbonylation reactions of alkenes using different carbon monoxide surrogates.

  7. Magnetic resonance imaging as a surrogate outcome for multiple sclerosis relapses

    PubMed Central

    Petkau, J; Reingold, SC; Held, U; Cutter, GR; Fleming, TR; Hughes, MD; Miller, DH; McFarland, HF; Wolinsky, JS

    2009-01-01

    Background Magnetic resonance imaging (MRI) of lesions in the brain may be the best current candidate for a surrogate biological marker of clinical outcomes in relapsing remitting multiple sclerosis (MS), based on its role as an objective indicator of disease pathology. No biological surrogate marker has yet been validated for MS clinical outcomes. Objective The objective of this study was to use a multi-phased study to determine if a valid surrogate relationship could be demonstrated between counts of contrast enhancing lesions (CELs) and occurrence of relapses in MS. Methods We examined correlations for the concurrent and predictive relationship between CELs over 6 months and MS relapses over the same 6 months and an additional 6 months (total: 12 months), using available data on untreated patients from a large clinical trial and natural history database. Results Concurrent and predictive correlations were inadequate to justify continuation of this study to the planned additional phases required to demonstrate a surrogate relationship between CELs and MS relapses. Conclusions Confidence intervals for correlations between CELs and MS relapses exclude the possibility that CELs can be a good surrogate for relapses over the time scales we investigated. Further exploration of surrogacy between MRI measures and MS clinical outcomes may require improved datasets, the development of MRI techniques that couple better to clinical disease, and the ability to test a wide range of imaging- and clinically-based hypotheses for surrogacy. PMID:18535021

  8. Which one is a valuable surrogate for predicting survival between Tomita and Tokuhashi scores in patients with spinal metastases? A meta-analysis for diagnostic test accuracy and individual participant data analysis.

    PubMed

    Lee, Chang-Hyun; Chung, Chun Kee; Jahng, Tae-Ahn; Kim, Ki-jeong; Kim, Chi Heon; Hyun, Seung-Jae; Kim, Hyun-Jib; Jeon, Sang Ryong; Chang, Ung-Kyu; Lee, Sun-Ho; Moon, Seong-Hwan; Majeed, Haroon; Zhang, Dan; Gravis, Gwenaelle; Wibmer, Christine; Kumar, Naresh; Moon, Kyung Yun; Park, Jin Hoon; Tabouret, Emeline; Fuentes, Stephane

    2015-06-01

    This study is to estimate the diagnostic accuracy of Tokuhashi and Tomita scores that assures 6-month predicting survival regarded as a standard of surgical treatment. We searched PubMed, EMBASE, European PubMed central, and the Cochrane library for papers about the sensitivities and specificities of the Tokuhashi and/or Tomita scores to estimate predicting survival. Studies with cut-off values of ≥9 for Tokuhashi and ≤7 for Tomita scores based on prior studies were enrolled. Sensitivity, specificity, diagnostic odds ratio (DOR), area under the curve (AUC), and the best cut-off value were calculated via meta-analysis and individual participant data analysis. Finally, 22 studies were enrolled in the meta-analysis, and 1095 patients from 8 studies were included in the individual data analysis. In the meta-analysis, the pooled sensitivity/specificity/DOR for 6-month survival were 57.7 %/76.6 %/4.70 for the Tokuhashi score and 81.8 %/47.8 %/4.93 for Tomita score. The AUC of summary receiver operating characteristic plots was 0.748 for the Tokuhashi score and 0.714 for the Tomita score. Although Tokuhashi score was more accurate than Tomita score slightly, both showed low accuracy to predict 6 months residual survival. Moreover, the best cut-off values of Tokuhashi and Tomita scores were 8 and 6, not 9 and 7, for predicting 6-month survival, respectively. Estimation of 6-month predicting survival to decide surgery in patients with spinal metastasis is quite limited by using Tokuhashi and Tomita scores alone. Tokuhashi and Tomita scores could be incorporated as part of a multidisciplinary approach or perhaps interpreted in the context of a multidisciplinary approach.

  9. Molecular dissection of colorectal cancer in pre-clinical models identifies biomarkers predicting sensitivity to EGFR inhibitors

    PubMed Central

    Schütte, Moritz; Risch, Thomas; Abdavi-Azar, Nilofar; Boehnke, Karsten; Schumacher, Dirk; Keil, Marlen; Yildiriman, Reha; Jandrasits, Christine; Borodina, Tatiana; Amstislavskiy, Vyacheslav; Worth, Catherine L.; Schweiger, Caroline; Liebs, Sandra; Lange, Martin; Warnatz, Hans- Jörg; Butcher, Lee M.; Barrett, James E.; Sultan, Marc; Wierling, Christoph; Golob-Schwarzl, Nicole; Lax, Sigurd; Uranitsch, Stefan; Becker, Michael; Welte, Yvonne; Regan, Joseph Lewis; Silvestrov, Maxine; Kehler, Inge; Fusi, Alberto; Kessler, Thomas; Herwig, Ralf; Landegren, Ulf; Wienke, Dirk; Nilsson, Mats; Velasco, Juan A.; Garin-Chesa, Pilar; Reinhard, Christoph; Beck, Stephan; Schäfer, Reinhold; Regenbrecht, Christian R. A.; Henderson, David; Lange, Bodo; Haybaeck, Johannes; Keilholz, Ulrich; Hoffmann, Jens; Lehrach, Hans; Yaspo, Marie-Laure

    2017-01-01

    Colorectal carcinoma represents a heterogeneous entity, with only a fraction of the tumours responding to available therapies, requiring a better molecular understanding of the disease in precision oncology. To address this challenge, the OncoTrack consortium recruited 106 CRC patients (stages I–IV) and developed a pre-clinical platform generating a compendium of drug sensitivity data totalling >4,000 assays testing 16 clinical drugs on patient-derived in vivo and in vitro models. This large biobank of 106 tumours, 35 organoids and 59 xenografts, with extensive omics data comparing donor tumours and derived models provides a resource for advancing our understanding of CRC. Models recapitulate many of the genetic and transcriptomic features of the donors, but defined less complex molecular sub-groups because of the loss of human stroma. Linking molecular profiles with drug sensitivity patterns identifies novel biomarkers, including a signature outperforming RAS/RAF mutations in predicting sensitivity to the EGFR inhibitor cetuximab. PMID:28186126

  10. Integration of Traditional and Metabolomics Biomarkers Identifies Prognostic Metabolites for Predicting Responsiveness to Nutritional Intervention against Oxidative Stress and Inflammation

    PubMed Central

    Kim, You Jin; Huh, Iksoo; Kim, Ji Yeon; Park, Saejong; Ryu, Sung Ha; Kim, Kyu-Bong; Kim, Suhkmann; Park, Taesung; Kwon, Oran

    2017-01-01

    Various statistical approaches can be applied to integrate traditional and omics biomarkers, allowing the discovery of prognostic markers to classify subjects into poor and good prognosis groups in terms of responses to nutritional interventions. Here, we performed a prototype study to identify metabolites that predict responses to an intervention against oxidative stress and inflammation, using a data set from a randomized controlled trial evaluating Korean black raspberry (KBR) in sedentary overweight/obese subjects. First, a linear mixed-effects model analysis with multiple testing correction showed that four-week consumption of KBR significantly changed oxidized glutathione (GSSG, q = 0.027) level, the ratio of reduced glutathione (GSH) to GSSG (q = 0.039) in erythrocytes, malondialdehyde (MDA, q = 0.006) and interleukin-6 (q = 0.006) levels in plasma, and seventeen NMR metabolites in urine compared with those in the placebo group. A subsequent generalized linear mixed model analysis showed linear correlations between baseline urinary glycine and N-phenylacetylglycine (PAG) and changes in the GSH:GSSG ratio (p = 0.008 and 0.004) as well as between baseline urinary adenine and changes in MDA (p = 0.018). Then, receiver operating characteristic analysis revealed that a two-metabolite set (glycine and PAG) had the strongest prognostic relevance for future interventions against oxidative stress (the area under the curve (AUC) = 0.778). Leave-one-out cross-validation confirmed the accuracy of prediction (AUC = 0.683). The current findings suggest that a higher level of this two-metabolite set at baseline is useful for predicting responders to dietary interventions in subjects with oxidative stress and inflammation, contributing to the emergence of personalized nutrition. PMID:28273855

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

    PubMed

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

    2016-03-18

    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.

  12. 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.

  13. 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

  14. Experimental and kinetic modeling study of combustion of JP-8, its surrogates and components in laminar premixed flows

    NASA Astrophysics Data System (ADS)

    Seshadri, Kalyanasundaram; Frassoldati, Alessio; Cuoci, Alberto; Faravelli, Tiziano; Niemann, Ulrich; Weydert, Patrick; Ranzi, Eliseo

    2011-08-01

    Experimental and kinetic modeling studies are carried out to characterize premixed combustion of jet fuels, their surrogates, and reference components in laminar nonuniform flows. In previous studies, it was established that the Aachen surrogate made up of 80 % n-decane and 20 % trimethylbenzene by weight, and surrogate C made up of 57 % n-dodecane, 21 % methylcyclohexane and 22 % o-xylene by weight, reproduce key aspects of combustion of jet fuels in laminar nonpremixed flows. Here, these surrogates and a jet fuel are tested in premixed, nonuniform flows. The counterflow configuration is employed, and critical conditions of extinction are measured. In addition, the reference components tested are n-heptane, n-decane, n-dodecane, methylcyclohexane, trimethylbenzene, and o-xylene. Measured critical conditions of extinction of the Aachen surrogate and surrogate C are compared with those for the jet fuel. In general the alkanes n-heptane, n-decane, and n-dodecane, and methylcyclohexane are found to be more reactive than the aromatics o-xylene and trimethylbenzene. Flame structure and critical conditions of extinction are predicted for the reference components and the surrogates using a semi-detailed kinetic model. The predicted values are compared with experimental data. Sensitivity analysis shows that the lower reactivity of the aromatic species arises from the formation of resonantly stabilized radicals. These radicals are found to have a scavenging effect. The present study on premixed flows together with previous studies on nonpremixed flows show that the Aachen surrogate and surrogate C reproduce many aspects of premixed and nonpremixed combustion of jet fuels.

  15. RELIABILITY OF SURROGATES FOR DETERMINING CRYPTOSPORIDIUM REMOVAL

    EPA Science Inventory

    Testing of field-scale bag filtration systems yielded results indicating that 4-6-um polystyrene microspheres can be used as a reliable surrogate for determining Cryptosporidium oocyst removal in bag filtration process. A nearly perfect linear correlation was observed between log...

  16. Surrogate Motherhood I: Responses to Infertility.

    ERIC Educational Resources Information Center

    Schwartz, Lita Linzer

    1987-01-01

    Surrogate motherhood is a path to parenthood filled with legal "potholes" and psychological "rocks." Mental health specialists, especially marital and family therapists, may well be called upon to provide their professional services to people attempting to negotiate it. Introduces a number of potential hazards, presenting the…

  17. Surrogate Motherhood I: Responses to Infertility.

    ERIC Educational Resources Information Center

    Schwartz, Lita Linzer

    1987-01-01

    Surrogate motherhood is a path to parenthood filled with legal "potholes" and psychological "rocks." Mental health specialists, especially marital and family therapists, may well be called upon to provide their professional services to people attempting to negotiate it. Introduces a number of potential hazards, presenting the…

  18. Derivation and Validation of a Novel Prediction Model to Identify Low-Risk Patients With Acute Pulmonary Embolism.

    PubMed

    Subramanian, Muthiah; Gopalan, Sowmya; Ramadurai, Srinivasan; Arthur, Preetam; Prabhu, Mukund A; Thachathodiyl, Rajesh; Natarajan, Kumaraswamy

    2017-08-15

    Accurate identification of low-risk patients with acute pulmonary embolism (PE) who may be eligible for outpatient treatment or early discharge can have substantial cost-saving benefit. The purpose of this study was to derive and validate a prediction model to effectively identify patients with PE at low risk of short-term mortality, right ventricular dysfunction, and other nonfatal outcomes. This study analyzed data from 400 consecutive patients with acute PE. We derived and internally validated our prediction rule based on clinically significant variables that are routinely available at initial examination and that were categorized and weighted using coefficients in the multivariate logistic regression. The model was externally validated in an independent cohort of 82 patients. The final model (HOPPE score) consisted of 5 categorized patient variables (1, 2, or 3 points, respectively): systolic blood pressure (>120, 100 to 119, <99 mm Hg), diastolic blood pressure (>80, 65 to 79, <64 mm Hg), heart rate (<80, 81 to 100, >101 beats/min), arterial partial pressure of oxygen (>80, 60 to 79, <59 mm Hg), and modified electrocardiographic score (<2, 2 to 4, >4). The 30-day mortality rates were 0% in low risk (0 to 6 points), 7.5% to 8.5% in intermediate risk (7 to 10), and 18.2% to 18.8% in high-risk patients (≥11) across the derivation and validation cohorts. In comparison with the previously validated PESI score, the HOPPE score had a higher discriminatory power (area under the curve 0.74 vs 0.85, p = 0.033) and significantly improved both the discrimination (integrated discrimination improvement, p = 0.002) and reclassification (net reclassification improvement, p = 0.003) of the model for short-term mortality. In conclusion, the HOPPE score accurately identifies acute patients with PE at low risk of short-term mortality, right ventricular dysfunction, and other nonfatal outcomes. Prospective validation of the prediction model is necessary before implementation

  19. Predictors of Self and Surrogate Online Health Information Seeking in Family Caregivers to Cancer Survivors.

    PubMed

    Oh, Young Sam

    2015-01-01

    The purpose of this research is to investigate various factors predicting online health information seeking for themselves (self OHIS) and online health information seeking for others (surrogate OHIS) in family caregivers to cancer survivors. To address this purpose, this study applies the comprehensive model of information seeking as a theoretical framework for explaining the relationships between various predictors and two types of OHIS. The data used in this study were taken from the Health Information National Trends Survey 4. A total of 1,113 family caregivers were included in this study. Logistic regression analyses were conducted to examine the effects of predictors on Internet use for health information seeking. Caregivers' self and surrogate OHIS were commonly predicted by their self-rated health and attention to the Internet. However, age, race, and education were significantly associated with self OHIS only, while gender and marital status were significantly associated with surrogate OHIS only. These results suggest that family caregivers' self and surrogate OHIS are predicted by common factors, as well as predicted by different specific factors.

  20. 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

  1. Can We Rely on Computational Predictions To Correctly Identify Ligand Binding Sites on Novel Protein Drug Targets? Assessment of Binding Site Prediction Methods and a Protocol for Validation of Predicted Binding Sites.

    PubMed

    Broomhead, Neal K; Soliman, Mahmoud E

    2017-03-01

    In the field of medicinal chemistry there is increasing focus on identifying key proteins whose biochemical functions can firmly be linked to serious diseases. Such proteins become targets for drug or inhibitor molecules that could treat or halt the disease through therapeutic action or by blocking the protein function respectively. The protein must be targeted at the relevant biologically active site for drug or inhibitor binding to be effective. As insufficient experimental data is available to confirm the biologically active binding site for novel protein targets, researchers often rely on computational prediction methods to identify binding sites. Presented herein is a short review on structure-based computational methods that (i) predict putative binding sites and (ii) assess the druggability of predicted binding sites on protein targets. This review briefly covers the principles upon which these methods are based, where they can be accessed and their reliability in identifying the correct binding site on a protein target. Based on this review, we believe that these methods are useful in predicting putative binding sites, but as they do not account for the dynamic nature of protein-ligand binding interactions, they cannot definitively identify the correct site from a ranked list of putative sites. To overcome this shortcoming, we strongly recommend using molecular docking to predict the most likely protein-ligand binding site(s) and mode(s), followed by molecular dynamics simulations and binding thermodynamics calculations to validate the docking results. This protocol provides a valuable platform for experimental and computational efforts to design novel drugs and inhibitors that target disease-related proteins.

  2. Validation of a predictive model that identifies patients at high risk of developing febrile neutropaenia following chemotherapy for breast cancer.

    PubMed

    Jenkins, P; Scaife, J; Freeman, S

    2012-07-01

    We have previously developed a predictive model that identifies patients at increased risk of febrile neutropaenia (FN) following chemotherapy, based on pretreatment haematological indices. This study was designed to validate our earlier findings in a separate cohort of patients undergoing more myelosuppressive chemotherapy supported by growth factors. We conducted a retrospective analysis of 263 patients who had been treated with adjuvant docetaxel, adriamycin and cyclophosphamide (TAC) chemotherapy for breast cancer. All patients received prophylactic pegfilgrastim and the majority also received prophylactic antibiotics. Thirty-one patients (12%) developed FN. Using our previous model, patients in the highest risk group (pretreatment absolute neutrophil count≤3.1 10(9)/l and absolute lymphocyte count≤1.5 10(9)/l) comprised 8% of the total population and had a 33% risk of developing FN. Compared with the rest of the cohort, this group had a 3.4-fold increased risk of developing FN (P=0.001) and a 5.2-fold increased risk of cycle 1 FN (P<0.001). A simple model based on pretreatment differential white blood cell count can be applied to pegfilgrastim-supported patients to identify those who are at higher risk of FN.

  3. Identifying prognostic factors predicting outcome in patients with chronic neck pain after multimodal treatment: A retrospective study.

    PubMed

    De Pauw, R; Kregel, J; De Blaiser, C; Van Akeleyen, J; Logghe, T; Danneels, L; Cagnie, B

    2015-08-01

    This study was conducted to identify possible prognostic factors to predict drop-out and favorable outcome in patients following a multimodal treatment program at an outpatient rehabilitation clinic. A retrospective cohort study was conducted on 437 patients with chronic neck pain involved in an exercise-based rehabilitation program of an outpatient rehabilitation center between January 2008 and November 2011. Prognostic factors were analyzed through a univariate and a multivariate logistic regression analysis. Multivariate logistic regression revealed that a higher age (OR=0.960), presence of headache (OR=0.436) or low back pain (OR=0.525), and having low levels of depression (OR=1.044) increase the odds to complete the multimodal treatment program. A high NDI-score (OR=0.945), a high NRS-score for pain in the upper extremities (OR=0.862), a low NRS score for pain in the neck (OR=1.372), and a trauma in the patient's history (OR=0.411) decrease the odds of having a favorable outcome after the given treatment program. It is important to assess these prognostic factors as they may help therapists to identify patients with a good prognosis or patients at risk. For those at risk, this would allow the treatment approach to be redirected to address their specific needs. Copyright © 2015 Elsevier Ltd. All rights reserved.

  4. A simple and predictive phenotypic High Content Imaging assay for Plasmodium falciparum mature gametocytes to identify malaria transmission blocking compounds

    PubMed Central

    Lucantoni, Leonardo; Silvestrini, Francesco; Signore, Michele; Siciliano, Giulia; Eldering, Maarten; Dechering, Koen J.; Avery, Vicky M.; Alano, Pietro

    2015-01-01

    Plasmodium falciparum gametocytes, specifically the mature stages, are the only malaria parasite stage in humans transmissible to the mosquito vector. Anti-malarial drugs capable of killing these forms are considered essential for the eradication of malaria and tools allowing the screening of large compound libraries with high predictive power are needed to identify new candidates. As gametocytes are not a replicative stage it is difficult to apply the same drug screening methods used for asexual stages. Here we propose an assay, based on high content imaging, combining “classic” gametocyte viability readout based on gametocyte counts with a functional viability readout, based on gametocyte activation and the discrimination of the typical gamete spherical morphology. This simple and rapid assay has been miniaturized to a 384-well format using acridine orange staining of wild type P. falciparum 3D7A sexual forms, and was validated by screening reference antimalarial drugs and the MMV Malaria Box. The assay demonstrated excellent robustness and ability to identify quality hits with high likelihood of confirmation of transmission reducing activity in subsequent mosquito membrane feeding assays. PMID:26553647

  5. Uncertainty quantification of squeal instability via surrogate modelling

    NASA Astrophysics Data System (ADS)

    Nobari, Amir; Ouyang, Huajiang; Bannister, Paul

    2015-08-01

    One of the major issues that car manufacturers are facing is the noise and vibration of brake systems. Of the different sorts of noise and vibration, which a brake system may generate, squeal as an irritating high-frequency noise costs the manufacturers significantly. Despite considerable research that has been conducted on brake squeal, the root cause of squeal is still not fully understood. The most common assumption, however, is mode-coupling. Complex eigenvalue analysis is the most widely used approach to the analysis of brake squeal problems. One of the major drawbacks of this technique, nevertheless, is that the effects of variability and uncertainty are not included in the results. Apparently, uncertainty and variability are two inseparable parts of any brake system. Uncertainty is mainly caused by friction, contact, wear and thermal effects while variability mostly stems from the manufacturing process, material properties and component geometries. Evaluating the effects of uncertainty and variability in the complex eigenvalue analysis improves the predictability of noise propensity and helps produce a more robust design. The biggest hurdle in the uncertainty analysis of brake systems is the computational cost and time. Most uncertainty analysis techniques rely on the results of many deterministic analyses. A full finite element model of a brake system typically consists of millions of degrees-of-freedom and many load cases. Running time of such models is so long that automotive industry is reluctant to do many deterministic analyses. This paper, instead, proposes an efficient method of uncertainty propagation via surrogate modelling. A surrogate model of a brake system is constructed in order to reproduce the outputs of the large-scale finite element model and overcome the issue of computational workloads. The probability distribution of the real part of an unstable mode can then be obtained by using the surrogate model with a massive saving of

  6. Value-Driven Design and Sensitivity Analysis of Hybrid Energy Systems using Surrogate Modeling

    SciTech Connect

    Wenbo Du; Humberto E. Garcia; William R. Binder; Christiaan J. J. Paredis

    2001-10-01

    A surrogate modeling and analysis methodology is applied to study dynamic hybrid energy systems (HES). The effect of battery size on the smoothing of variability in renewable energy generation is investigated. Global sensitivity indices calculated using surrogate models show the relative sensitivity of system variability to dynamic properties of key components. A value maximization approach is used to consider the tradeoff between system variability and required battery size. Results are found to be highly sensitive to the renewable power profile considered, demonstrating the importance of accurate renewable resource modeling and prediction. The documented computational framework and preliminary results represent an important step towards a comprehensive methodology for HES evaluation, design, and optimization.

  7. Using data to improve surrogate consent for clinical research with incapacitated adults.

    PubMed

    Abdoler, Emily; Wendler, David

    2012-04-01

    Current practice relies on surrogates to enroll incapacitated adults in research. Yet, it is unclear to what extent this practice protects adults who have lost the ability to consent for themselves. To address this question, we conducted two literature searches to identify articles which report empirical data on three issues central to protecting adults who have lost the ability to consent: (1) adults' willingness to participate in research should they lose the ability to consent; (2) adults' willingness to allow a surrogate to make research decisions for them; and (3) the extent to which surrogates' enrollment decisions are consistent with their charges' preferences and values. These searches identified 21 articles, representing 20 distinct datasets. The data indicate that many adults are willing to participate in research should they lose the ability to consent, and many are willing to allow their family members to make research decisions for them if they become incapacitated. The data also raise concern that surrogates may be making research enrollment decisions that, in some cases, are inconsistent with their charges' preferences and values. These findings suggest that modifications to current practice should be considered to better protect adults who have lost the ability to consent. One option would be to require, in addition to surrogate permission and subject assent, sufficient evidence that enrollment is consistent with the individual's preferences and values.

  8. System Reliability Analysis Capability and Surrogate Model Application in RAVEN

    SciTech Connect

    Rabiti, Cristian; Alfonsi, Andrea; Huang, Dongli; Gleicher, Frederick; Wang, Bei; Adbel-Khalik, Hany S.; Pascucci, Valerio; Smith, Curtis L.

    2015-11-01

    This report collect the effort performed to improve the reliability analysis capabilities of the RAVEN code and explore new opportunity in the usage of surrogate model by extending the current RAVEN capabilities to multi physics surrogate models and construction of surrogate models for high dimensionality fields.

  9. 26 CFR 1.7874-2 - Surrogate foreign corporation.

    Code of Federal Regulations, 2013 CFR

    2013-04-01

    ... 26 Internal Revenue 13 2013-04-01 2013-04-01 false Surrogate foreign corporation. 1.7874-2 Section... TAX (CONTINUED) INCOME TAXES (CONTINUED) General Actuarial Valuations § 1.7874-2 Surrogate foreign... as a surrogate foreign corporation under section 7874(a)(2)(B). Paragraph (b) of this...

  10. 26 CFR 1.7874-2 - Surrogate foreign corporation.

    Code of Federal Regulations, 2014 CFR

    2014-04-01

    ... 26 Internal Revenue 13 2014-04-01 2014-04-01 false Surrogate foreign corporation. 1.7874-2 Section... TAX (CONTINUED) INCOME TAXES (CONTINUED) General Actuarial Valuations § 1.7874-2 Surrogate foreign... as a surrogate foreign corporation under section 7874(a)(2)(B). Paragraph (b) of this...

  11. Development of a multi-objective optimization algorithm using surrogate models for coastal aquifer management

    NASA Astrophysics Data System (ADS)

    Kourakos, George; Mantoglou, Aristotelis

    2013-02-01

    SummaryThe demand for fresh water in coastal areas and islands can be very high due to increased local needs and tourism. A multi-objective optimization methodology is developed, involving minimization of economic and environmental costs while satisfying water demand. The methodology considers desalinization of pumped water and injection of treated water into the aquifer. Variable density aquifer models are computationally intractable when integrated in optimization algorithms. In order to alleviate this problem, a multi-objective optimization algorithm is developed combining surrogate models based on Modular Neural Networks [MOSA(MNNs)]. The surrogate models are trained adaptively during optimization based on a genetic algorithm. In the crossover step, each pair of parents generates a pool of offspring which are evaluated using the fast surrogate model. Then, the most promising offspring are evaluated using the exact numerical model. This procedure eliminates errors in Pareto solution due to imprecise predictions of the surrogate model. The method has important advancements compared to previous methods such as precise evaluation of the Pareto set and alleviation of propagation of errors due to surrogate model approximations. The method is applied to an aquifer in the Greek island of Santorini. The results show that the new MOSA(MNN) algorithm offers significant reduction in computational time compared to previous methods (in the case study it requires only 5% of the time required by other methods). Further, the Pareto solution is better than the solution obtained by alternative algorithms.

  12. Global Parameter Optimization of CLM4.5 Using Sparse-Grid Based Surrogates

    NASA Astrophysics Data System (ADS)

    Lu, D.; Ricciuto, D. M.; Gu, L.

    2016-12-01

    Calibration of the Community Land Model (CLM) is challenging because of its model complexity, large parameter sets, and significant computational requirements. Therefore, only a limited number of simulations can be allowed in any attempt to find a near-optimal solution within an affordable time. The goal of this study is to calibrate some of the CLM parameters in order to improve model projection of carbon fluxes. To this end, we propose a computationally efficient global optimization procedure using sparse-grid based surrogates. We first use advanced sparse grid (SG) interpolation to construct a surrogate system of the actual CLM model, and then we calibrate the surrogate model in the optimization process. As the surrogate model is a polynomial whose evaluation is fast, it can be efficiently evaluated with sufficiently large number of times in the optimization, which facilitates the global search. We calibrate five parameters against 12 months of GPP, NEP, and TLAI data from the U.S. Missouri Ozark (US-MOz) tower. The results indicate that an accurate surrogate model can be created for the CLM4.5 with a relatively small number of SG points (i.e., CLM4.5 simulations), and the application of the optimized parameters leads to a higher predictive capacity than the default parameter values in the CLM4.5 for the US-MOz site.

  13. Kinetic Modeling of Gasoline Surrogate Components and Mixtures under Engine Conditions

    SciTech Connect

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

    2010-01-11

    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, an improved 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 multicomponent 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 (3-50 atm, 650-1200K, stoichiometric fuel/air mixtures). Simulation results are discussed focusing attention on the mixing effects of the fuel components.

  14. 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.

  15. Use of Neuroanatomical Pattern Classification to Identify Subjects in At-Risk Mental States of Psychosis and Predict Disease Transition

    PubMed Central

    Koutsouleris, Nikolaos; Meisenzahl, Eva M.; Davatzikos, Christos; Bottlender, Ronald; Frodl, Thomas; Scheuerecker, Johanna; Schmitt, Gisela; Zetzsche, Thomas; Decker, Petra; Reiser, Maximilian; Möller, Hans-Jürgen; Gaser, Christian

    2014-01-01

    Context Identification of individuals at high risk of developing psychosis has relied on prodromal symptomatology. Recently, machine learning algorithms have been successfully used for magnetic resonance imaging–based diagnostic classification of neuropsychiatric patient populations. Objective To determine whether multivariate neuroanatomical pattern classification facilitates identification of individuals in different at-risk mental states (ARMS) of psychosis and enables the prediction of disease transition at the individual level. Design Multivariate neuroanatomical pattern classification was performed on the structural magnetic resonance imaging data of individuals in early or late ARMS vs healthy controls (HCs). The predictive power of the method was then evaluated by categorizing the baseline imaging data of individuals with transition to psychosis vs those without transition vs HCs after 4 years of clinical follow-up. Classification generalizability was estimated by cross-validation and by categorizing an independent cohort of 45 new HCs. Setting Departments of Psychiatry and Psychotherapy, Ludwig-Maximilians-University, Munich, Germany. Participants The first classification analysis included 20 early and 25 late at-risk individuals and 25 matched HCs. The second analysis consisted of 15 individuals with transition, 18 without transition, and 17 matched HCs. Main Outcome Measures Specificity, sensitivity, and accuracy of classification. Results The 3-group, cross-validated classification accuracies of the first analysis were 86% (HCs vs the rest), 91% (early at-risk individuals vs the rest), and 86% (late at-risk individuals vs the rest). The accuracies in the second analysis were 90% (HCs vs the rest), 88% (individuals with transition vs the rest), and 86% (individuals without transition vs the rest). Independent HCs were correctly classified in 96% (first analysis) and 93% (second analysis) of cases. Conclusions Different ARMSs and their clinical outcomes

  16. Mitigating Errors in External Respiratory Surrogate-Based Models of Tumor Position

    SciTech Connect

    Malinowski, Kathleen T.; McAvoy, Thomas J.; George, Rohini; Dieterich, Sonja; D'Souza, Warren D.

    2012-04-01

    Purpose: To investigate the effect of tumor site, measurement precision, tumor-surrogate correlation, training data selection, model design, and interpatient and interfraction variations on the accuracy of external marker-based models of tumor position. Methods and Materials: Cyberknife Synchrony system log files comprising synchronously acquired positions of external markers and the tumor from 167 treatment fractions were analyzed. The accuracy of Synchrony, ordinary-least-squares regression, and partial-least-squares regression models for predicting the tumor position from the external markers was evaluated. The quantity and timing of the data used to build the predictive model were varied. The effects of tumor-surrogate correlation and the precision in both the tumor and the external surrogate position measurements were explored by adding noise to the data. Results: The tumor position prediction errors increased during the duration of a fraction. Increasing the training data quantities did not always lead to more accurate models. Adding uncorrelated noise to the external marker-based inputs degraded the tumor-surrogate correlation models by 16% for partial-least-squares and 57% for ordinary-least-squares. External marker and tumor position measurement errors led to tumor position prediction changes 0.3-3.6 times the magnitude of the measurement errors, varying widely with model algorithm. The tumor position prediction errors were significantly associated with the patient index but not with the fraction index or tumor site. Partial-least-squares was as accurate as Synchrony and more accurate than ordinary-least-squares. Conclusions: The accuracy of surrogate-based inferential models of tumor position was affected by all the investigated factors, except for the tumor site and fraction index.

  17. The effectiveness of surrogate taxa for the representation of biodiversity.

    PubMed

    Lewandowski, Adam S; Noss, Reed F; Parsons, David R

    2010-10-01

    Biodiversity is too complex to measure directly, so conservation planning must rely on surrogates to estimate the biodiversity of sites. The species richness of selected taxa is often used as a surrogate for the richness of other taxa. Surrogacy values of taxa have been evaluated in diverse contexts, yet broad trends in their effectiveness remain unclear. We reviewed published studies testing the ability of species richness of surrogate taxa to capture the richness of other (target) taxa. We stratified studies into two groups based on whether a complementarity approach (surrogates used to select a combination of sites that together maximize total species richness for the taxon) or a richness-hotspot approach (surrogates used to select sites containing the highest species richness for the taxon) was used. For each comparison of one surrogate taxon with one target, we used the following predictor variables: biome, spatial extent of study area, surrogate taxon, and target taxon. We developed a binary response variable based on whether the surrogate taxon provided better than random representation of the target taxon. For studies that used an evaluation approach that was not based on better than random representation of target taxa, we based the response variable on the interpretation of results in the original study. We performed a categorical regression to elucidate trends in the effectiveness of surrogate taxa with regard to each of the predictor variables. A surrogate was 25% more likely to be effective with a complementarity approach than with a hotspot approach. For hotspot-based approaches, biome, extent of study, surrogate taxon, and target taxon significantly influenced effectiveness of the surrogate. For complementarity-based approaches, biome, extent, and surrogate taxon significantly influenced effectiveness of the surrogate. For all surrogate evaluations, biome explained the greatest amount of variation in surrogate effectiveness. From most to least

  18. Insights Into Atmospheric Aqueous Organic Chemistry Through Controlled Experiments with Cloud Water Surrogates

    NASA Astrophysics Data System (ADS)

    Turpin, B. J.; Ramos, A.; Kirkland, J. R.; Lim, Y. B.; Seitzinger, S.

    2011-12-01

    There is considerable laboratory and field-based evidence that chemical processing in clouds and wet aerosols alters organic composition and contributes to the formation of secondary organic aerosol (SOA). Single-compound laboratory experiments have played an important role in developing aqueous-phase chemical mechanisms that aid prediction of SOA formation through multiphase chemistry. In this work we conduct similar experiments with cloud/fog water surrogates, to 1) evaluate to what extent the previously studied chemistry is observed in these more realistic atmospheric waters, and 2) to identify additional atmospherically-relevant precursors and products that require further study. We used filtered Camden and Pinelands, NJ rainwater as a surrogate for cloud water. OH radical (~10-12 M) was formed by photolysis of hydrogen peroxide and samples were analyzed in real-time by electrospray ionization mass spectroscopy (ESI-MS). Discrete samples were also analyzed by ion chromatography (IC) and ESI-MS after IC separation. All experiments were performed in duplicate. Standards of glyoxal, methylglyoxal and glycolaldehyde and their major aqueous oxidation products were also analyzed, and control experiments performed. Decreases in the ion abundance of many positive mode compounds and increases in the ion abundance of many negative mode compounds (e.g., organic acids) suggest that precursors are predominantly aldehydes, organic peroxides and/or alcohols. Real-time ESI mass spectra were consistent with the expected loss of methylglyoxal and subsequent formation of pyruvate, glyoxylate, and oxalate. New insights regarding other potential precursors and products will be provided.

  19. Biomarkers to identify ILD and predict lung function decline in scleroderma lung disease or idiopathic pulmonary fibrosis.

    PubMed

    Kennedy, Barry; Branagan, Peter; Moloney, Fiachra; Haroon, Muhammad; O'Connell, Oisin J; O'Connor, Terence M; O'Regan, Kevin; Harney, Sinead; Henry, Michael T

    2015-09-14

    SSc-ILD and IPF demonstrate significant morbidity and mortality. Predicting disease progression is challenging in both diseases. We sought a serum biomarker that could identify patients with SSc-ILD or IPF and prospectively predict short-term decline in lung function in these patients. 10 healthy controls, 5 SSc w/o ILD, 6 SSc-ILD and 13 IPF patients underwent venesection. An array of cytokines including KL-6, SP-D and MMP7 were measured. PFTs were obtained at baseline and six months. Cytokine measurements were correlated with PFTs. KL-6 in IPF patients (633 ng/ml, IQR 492-1675) was significantly elevated compared to controls (198 ng/ml, IQR 52-360, p<0.01) and SSc w/o ILD patients (192 ng/ml, IQR 0-524, p<0.05); KL-6 in SSc-ILD patients (836 ng/ml, IQR 431-1303) was significantly higher than in controls (p<0.05). SP-D was significantly higher in IPF patients (542 ng/ml, IQR 305-577) compared to controls (137 ng/ml, IQR 97-284, p<0.01) or to SSc w/o ILD patients (169 ng/ml, IQR 137-219, p<0.05). In comparison with controls (0.0 ng/ml, IQR 0.0-0.6), MMP7 was significantly higher in both IPF patients (2.85 ng/ml, IQR 1.5-3.6, p<0.05) and SSc-ILD patients (5.41 ng/ml, IQR 2.6-7.2, p<0.001). Using a cut-off level of 459ng/ml for KL-6 and of 1.28 ng/ml for MMP7, 18 out of 19 patients with ILD had a serum value of either KL-6 or MMP7 above these thresholds. For all ILD patients, baseline serum SP-D correlated with ΔFVC %pred over six months (r=-0.63, p=0.005, 95% CI -0.85 to -0.24). Combining KL-6 with MMP7 may be a useful screening tool for patients at risk of ILD. SP-D may predict short-term decline in lung function.

  20. Robust estimation of the proportion of treatment effect explained by surrogate marker information.

    PubMed

    Parast, Layla; McDermott, Mary M; Tian, Lu

    2016-05-10

    In randomized treatment studies where the primary outcome requires long follow-up of patients and/or expensive or invasive obtainment procedures, the availability of a surrogate marker that could be used to estimate the treatment effect and could potentially be observed earlier than the primary outcome would allow researchers to make conclusions regarding the treatment effect with less required follow-up time and resources. The Prentice criterion for a valid surrogate marker requires that a test for treatment effect on the surrogate marker also be a valid test for treatment effect on the primary outcome of interest. Based on this criterion, methods have been developed to define and estimate the proportion of treatment effect on the primary outcome that is explained by the treatment effect on the surrogate marker. These methods aim to identify useful statistical surrogates that capture a large proportion of the treatment effect. However, current methods to estimate this proportion usually require restrictive model assumptions that may not hold in practice and thus may lead to biased estimates of this quantity. In this paper, we propose a nonparametric procedure to estimate the proportion of treatment effect on the primary outcome that is explained by the treatment effect on a potential surrogate marker and extend this procedure to a setting with multiple surrogate markers. We compare our approach with previously proposed model-based approaches and propose a variance estimation procedure based on a perturbation-resampling method. Simulation studies demonstrate that the procedure performs well in finite samples and outperforms model-based procedures when the specified models are not correct. We illustrate our proposed procedure using a data set from a randomized study investigating a group-mediated cognitive behavioral intervention for peripheral artery disease participants.

  1. Surrogate analysis and index developer (SAID) tool and real-time data dissemination utilities

    USGS Publications Warehouse

    Domanski, Marian M.; Straub, Timothy D.; Wood, Molly S.; Landers, Mark N.; Wall, Gary R.; Brady, Steven J.

    2015-01-01

    The use of acoustic and other parameters as surrogates for suspended-sediment concentrations (SSC) in rivers has been successful in multiple applications across the Nation. Critical to advancing the operational use of surrogates are tools to process and evaluate the data along with the subsequent development of regression models from which real-time sediment concentrations can be made available to the public. Recent developments in both areas are having an immediate impact on surrogate research, and on surrogate monitoring sites currently in operation. The Surrogate Analysis and Index Developer (SAID) standalone tool, under development by the U.S. Geological Survey (USGS), assists in the creation of regression models that relate response and explanatory variables by providing visual and quantitative diagnostics to the user. SAID also processes acoustic parameters to be used as explanatory variables for suspended-sediment concentrations. The sediment acoustic method utilizes acoustic parameters from fixed-mount stationary equipment. The background theory and method used by the tool have been described in recent publications, and the tool also serves to support sediment-acoustic-index methods being drafted by the multi-agency Sediment Acoustic Leadership Team (SALT), and other surrogate guidelines like USGS Techniques and Methods 3-C4 for turbidity and SSC. The regression models in SAID can be used in utilities that have been developed to work with the USGS National Water Information System (NWIS) and for the USGS National Real-Time Water Quality (NRTWQ) Web site. The real-time dissemination of predicted SSC and prediction intervals for each time step has substantial potential to improve understanding of sediment-related water-quality and associated engineering and ecological management decisions.

  2. Reversible Bending Fatigue Testing on Zry-4 Surrogate Rods

    SciTech Connect

    Wang, Jy-An John; Wang, Hong; Bevard, Bruce Balkcom; Howard, Rob L

    2014-01-01

    Testing high-burnup spent nuclear fuel (SNF) presents many challenges in areas such as specimen preparation, specimen installation, mechanical loading, load control, measurements, data acquisition, and specimen disposal because these tasks are complicated by the radioactivity of the test specimens. Research and comparison studies conducted at Oak Ridge National Laboratory (ORNL) resulted in a new concept in 2010 for a U-frame testing setup on which to perform hot-cell reversible bending fatigue testing. Subsequently, the three-dimensional finite element analysis and the engineering design of components were completed. In 2013 the ORNL team finalized the upgrade of the U-frame testing setup and the integration of the U-frame setup into a Bose dual linear motor test bench to develop a cyclic integrated reversible-bending fatigue tester (CIRFT). A final check was conducted on the CIRFT test system in August 2013, and the CIRFT was installed in the hot cell in September 2013 to evaluate both the static and dynamic mechanical response of SNF rods under simulated loads. The fatigue responses of Zircaloy-4 (Zry-4) cladding and the role of pellet pellet and pellet clad interactions are critical to SNF vibration integrity, but such data are not available due to the unavailability of an effective testing system. While the deployment of the developed CIRFT test system in a hot cell will provide the opportunity to generate the data, the use of a surrogate rod has proven quite effective in identifying the underlying deformation mechanism of an SNF composite rod under an equivalent loading condition. This paper presents the experimental results of using surrogate rods under CIRFT reversible cyclic loading. Specifically, monotonic and cyclic bending tests were conducted on surrogate rods made of a Zry-4 tube and alumina pellet inserts, both with and without an epoxy bond.

  3. A predictive enrichment procedure to identify potential responders to a new therapy for randomized, comparative controlled clinical studies.

    PubMed

    Li, Junlong; Zhao, Lihui; Tian, Lu; Cai, Tianxi; Claggett, Brian; Callegaro, Andrea; Dizier, Benjamin; Spiessens, Bart; Ulloa-Montoya, Fernando; Wei, Lee-Jen

    2016-09-01

    To evaluate a new therapy versus a control via a randomized, comparative clinical study or a series of trials, due to heterogeneity of the study patient population, a pre-specified, predictive enrichment procedure may be implemented to identify an "enrichable" subpopulation. For patients in this subpopulation, the therapy is expected to have a desirable overall risk-benefit profile. To develop and validate such a "therapy-diagnostic co-development" strategy, a three-step procedure may be conducted with three independent data sets from a series of similar studies or a single trial. At the first stage, we create various candidate scoring systems based on the baseline information of the patients via, for example, parametric models using the first data set. Each individual score reflects an anticipated average treatment difference for future patients who share similar baseline profiles. A large score indicates that these patients tend to benefit from the new therapy. At the second step, a potentially promising, enrichable subgroup is identified using the totality of evidence from these scoring systems. At the final stage, we validate such a selection via two-sample inference procedures for assessing the treatment effectiveness statistically and clinically with the third data set, the so-called holdout sample. When the study size is not large, one may combine the first two steps using a "cross-training-evaluation" process. Comprehensive numerical studies are conducted to investigate the operational characteristics of the proposed method. The entire enrichment procedure is illustrated with the data from a cardiovascular trial to evaluate a beta-blocker versus a placebo for treating chronic heart failure patients. © 2015, The International Biometric Society.

  4. GppFst: genomic posterior predictive simulations of FST and dXY for identifying outlier loci from population genomic data.

    PubMed

    Adams, Richard H; Schield, Drew R; Card, Daren C; Blackmon, Heath; Castoe, Todd A

    2016-12-23

    We introduce GppFst, an open source R package that generates posterior predictive distributions of FST and dx under a neutral coalescent model to identify putative targets of selection from genomic data.

  5. 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.

  6. A Statistical Test for Identifying the Number of Creep Regimes When Using the Wilshire Equations for Creep Property Predictions

    NASA Astrophysics Data System (ADS)

    Evans, Mark

    2016-12-01

    A new parametric approach, termed the Wilshire equations, offers the realistic potential of being able to accurately lift materials operating at in-service conditions from accelerated test results lasting no more than 5000 hours. The success of this approach can be attributed to a well-defined linear relationship that appears to exist between various creep properties and a log transformation of the normalized stress. However, these linear trends are subject to discontinuities, the number of which appears to differ from material to material. These discontinuities have until now been (1) treated as abrupt in nature and (2) identified by eye from an inspection of simple graphical plots of the data. This article puts forward a statistical test for determining the correct number of discontinuities present within a creep data set and a method for allowing these discontinuities to occur more gradually, so that the methodology is more in line with the accepted view as to how creep mechanisms evolve with changing test conditions. These two developments are fully illustrated using creep data sets on two steel alloys. When these new procedures are applied to these steel alloys, not only do they produce more accurate and realistic looking long-term predictions of the minimum creep rate, but they also lead to different conclusions about the mechanisms determining the rates of creep from those originally put forward by Wilshire.

  7. Identifying the participant characteristics that predict recruitment and retention of participants to randomised controlled trials involving children: a systematic review.

    PubMed

    Robinson, Louise; Adair, Pauline; Coffey, Margaret; Harris, Rebecca; Burnside, Girvan

    2016-06-22

    internal consistency of results. However, few studies discussed the external validity of the results or provided recommendations for future research. Parent characteristics may predict participation of children and their families to RCTs; however, there was a lack of consensus. Whilst sociodemographic variables may be useful in identifying which groups are least likely to participate they do not provide insight into the processes and barriers to participation for children and families. Further studies that explore variables that can be influenced are warranted. Reporting of studies in this field need greater clarity as well as agree