Surrogate Based Uni/Multi-Objective Optimization and Distribution Estimation Methods

NASA Astrophysics Data System (ADS)

Gong, W.; Duan, Q.; Huo, X.

2017-12-01

Parameter calibration has been demonstrated as an effective way to improve the performance of dynamic models, such as hydrological models, land surface models, weather and climate models etc. Traditional optimization algorithms usually cost a huge number of model evaluations, making dynamic model calibration very difficult, or even computationally prohibitive. With the help of a serious of recently developed adaptive surrogate-modelling based optimization methods: uni-objective optimization method ASMO, multi-objective optimization method MO-ASMO, and probability distribution estimation method ASMO-PODE, the number of model evaluations can be significantly reduced to several hundreds, making it possible to calibrate very expensive dynamic models, such as regional high resolution land surface models, weather forecast models such as WRF, and intermediate complexity earth system models such as LOVECLIM. This presentation provides a brief introduction to the common framework of adaptive surrogate-based optimization algorithms of ASMO, MO-ASMO and ASMO-PODE, a case study of Common Land Model (CoLM) calibration in Heihe river basin in Northwest China, and an outlook of the potential applications of the surrogate-based optimization methods.

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.

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. Copyright © 2015 IPEM. Published by Elsevier Ltd. All rights reserved.

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

Weighted Iterative Bayesian Compressive Sensing (WIBCS) for High Dimensional Polynomial Surrogate Construction

NASA Astrophysics Data System (ADS)

Sargsyan, K.; Ricciuto, D. M.; Safta, C.; Debusschere, B.; Najm, H. N.; Thornton, P. E.

2016-12-01

Surrogate construction has become a routine procedure when facing computationally intensive studies requiring multiple evaluations of complex models. In particular, surrogate models, otherwise called emulators or response surfaces, replace complex models in uncertainty quantification (UQ) studies, including uncertainty propagation (forward UQ) and parameter estimation (inverse UQ). Further, surrogates based on Polynomial Chaos (PC) expansions are especially convenient for forward UQ and global sensitivity analysis, also known as variance-based decomposition. However, the PC surrogate construction strongly suffers from the curse of dimensionality. With a large number of input parameters, the number of model simulations required for accurate surrogate construction is prohibitively large. Relatedly, non-adaptive PC expansions typically include infeasibly large number of basis terms far exceeding the number of available model evaluations. We develop Weighted Iterative Bayesian Compressive Sensing (WIBCS) algorithm for adaptive basis growth and PC surrogate construction leading to a sparse, high-dimensional PC surrogate with a very few model evaluations. The surrogate is then readily employed for global sensitivity analysis leading to further dimensionality reduction. Besides numerical tests, we demonstrate the construction on the example of Accelerated Climate Model for Energy (ACME) Land Model for several output QoIs at nearly 100 FLUXNET sites covering multiple plant functional types and climates, varying 65 input parameters over broad ranges of possible values. This work is supported by the U.S. Department of Energy, Office of Science, Biological and Environmental Research, Accelerated Climate Modeling for Energy (ACME) project. Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-AC04-94AL85000.

Incorporating approximation error in surrogate based Bayesian inversion

NASA Astrophysics Data System (ADS)

Zhang, J.; Zeng, L.; Li, W.; Wu, L.

2015-12-01

There are increasing interests in applying surrogates for inverse Bayesian modeling to reduce repetitive evaluations of original model. In this way, the computational cost is expected to be saved. However, the approximation error of surrogate model is usually overlooked. This is partly because that it is difficult to evaluate the approximation error for many surrogates. Previous studies have shown that, the direct combination of surrogates and Bayesian methods (e.g., Markov Chain Monte Carlo, MCMC) may lead to biased estimations when the surrogate cannot emulate the highly nonlinear original system. This problem can be alleviated by implementing MCMC in a two-stage manner. However, the computational cost is still high since a relatively large number of original model simulations are required. In this study, we illustrate the importance of incorporating approximation error in inverse Bayesian modeling. Gaussian process (GP) is chosen to construct the surrogate for its convenience in approximation error evaluation. Numerical cases of Bayesian experimental design and parameter estimation for contaminant source identification are used to illustrate this idea. It is shown that, once the surrogate approximation error is well incorporated into Bayesian framework, promising results can be obtained even when the surrogate is directly used, and no further original model simulations are required.

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.

Utilizing Adjoint-Based Error Estimates for Surrogate Models to Accurately Predict Probabilities of Events

DOE PAGES

Butler, Troy; Wildey, Timothy

2018-01-01

In thist study, we develop a procedure to utilize error estimates for samples of a surrogate model to compute robust upper and lower bounds on estimates of probabilities of events. We show that these error estimates can also be used in an adaptive algorithm to simultaneously reduce the computational cost and increase the accuracy in estimating probabilities of events using computationally expensive high-fidelity models. Specifically, we introduce the notion of reliability of a sample of a surrogate model, and we prove that utilizing the surrogate model for the reliable samples and the high-fidelity model for the unreliable samples gives preciselymore » the same estimate of the probability of the output event as would be obtained by evaluation of the original model for each sample. The adaptive algorithm uses the additional evaluations of the high-fidelity model for the unreliable samples to locally improve the surrogate model near the limit state, which significantly reduces the number of high-fidelity model evaluations as the limit state is resolved. Numerical results based on a recently developed adjoint-based approach for estimating the error in samples of a surrogate are provided to demonstrate (1) the robustness of the bounds on the probability of an event, and (2) that the adaptive enhancement algorithm provides a more accurate estimate of the probability of the QoI event than standard response surface approximation methods at a lower computational cost.« less

Utilizing Adjoint-Based Error Estimates for Surrogate Models to Accurately Predict Probabilities of Events

DOE Office of Scientific and Technical Information (OSTI.GOV)

Butler, Troy; Wildey, Timothy

In thist study, we develop a procedure to utilize error estimates for samples of a surrogate model to compute robust upper and lower bounds on estimates of probabilities of events. We show that these error estimates can also be used in an adaptive algorithm to simultaneously reduce the computational cost and increase the accuracy in estimating probabilities of events using computationally expensive high-fidelity models. Specifically, we introduce the notion of reliability of a sample of a surrogate model, and we prove that utilizing the surrogate model for the reliable samples and the high-fidelity model for the unreliable samples gives preciselymore » the same estimate of the probability of the output event as would be obtained by evaluation of the original model for each sample. The adaptive algorithm uses the additional evaluations of the high-fidelity model for the unreliable samples to locally improve the surrogate model near the limit state, which significantly reduces the number of high-fidelity model evaluations as the limit state is resolved. Numerical results based on a recently developed adjoint-based approach for estimating the error in samples of a surrogate are provided to demonstrate (1) the robustness of the bounds on the probability of an event, and (2) that the adaptive enhancement algorithm provides a more accurate estimate of the probability of the QoI event than standard response surface approximation methods at a lower computational cost.« less

Sobol‧ sensitivity analysis of NAPL-contaminated aquifer remediation process based on multiple surrogates

NASA Astrophysics Data System (ADS)

Luo, Jiannan; Lu, Wenxi

2014-06-01

Sobol‧ sensitivity analyses based on different surrogates were performed on a trichloroethylene (TCE)-contaminated aquifer to assess the sensitivity of the design variables of remediation duration, surfactant concentration and injection rates at four wells to remediation efficiency First, the surrogate models of a multi-phase flow simulation model were constructed by applying radial basis function artificial neural network (RBFANN) and Kriging methods, and the two models were then compared. Based on the developed surrogate models, the Sobol‧ method was used to calculate the sensitivity indices of the design variables which affect the remediation efficiency. The coefficient of determination (R2) and the mean square error (MSE) of these two surrogate models demonstrated that both models had acceptable approximation accuracy, furthermore, the approximation accuracy of the Kriging model was slightly better than that of the RBFANN model. Sobol‧ sensitivity analysis results demonstrated that the remediation duration was the most important variable influencing remediation efficiency, followed by rates of injection at wells 1 and 3, while rates of injection at wells 2 and 4 and the surfactant concentration had negligible influence on remediation efficiency. In addition, high-order sensitivity indices were all smaller than 0.01, which indicates that interaction effects of these six factors were practically insignificant. The proposed Sobol‧ sensitivity analysis based on surrogate is an effective tool for calculating sensitivity indices, because it shows the relative contribution of the design variables (individuals and interactions) to the output performance variability with a limited number of runs of a computationally expensive simulation model. The sensitivity analysis results lay a foundation for the optimal groundwater remediation process optimization.

Maintaining tumor targeting accuracy in real-time motion compensation systems for respiration-induced tumor motion.

PubMed

Malinowski, Kathleen; McAvoy, Thomas J; George, Rohini; Dieterich, Sonja; D'Souza, Warren D

2013-07-01

To determine how best to time respiratory surrogate-based tumor motion model updates by comparing a novel technique based on external measurements alone to three direct measurement methods. Concurrently measured tumor and respiratory surrogate positions from 166 treatment fractions for lung or pancreas lesions were analyzed. Partial-least-squares regression models of tumor position from marker motion were created from the first six measurements in each dataset. Successive tumor localizations were obtained at a rate of once per minute on average. Model updates were timed according to four methods: never, respiratory surrogate-based (when metrics based on respiratory surrogate measurements exceeded confidence limits), error-based (when localization error ≥ 3 mm), and always (approximately once per minute). Radial tumor displacement prediction errors (mean ± standard deviation) for the four schema described above were 2.4 ± 1.2, 1.9 ± 0.9, 1.9 ± 0.8, and 1.7 ± 0.8 mm, respectively. The never-update error was significantly larger than errors of the other methods. Mean update counts over 20 min were 0, 4, 9, and 24, respectively. The same improvement in tumor localization accuracy could be achieved through any of the three update methods, but significantly fewer updates were required when the respiratory surrogate method was utilized. This study establishes the feasibility of timing image acquisitions for updating respiratory surrogate models without direct tumor localization.

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. Copyright © 2015 John Wiley & Sons, Ltd.

Fast neural network surrogates for very high dimensional physics-based models in computational oceanography.

PubMed

van der Merwe, Rudolph; Leen, Todd K; Lu, Zhengdong; Frolov, Sergey; Baptista, Antonio M

2007-05-01

We present neural network surrogates that provide extremely fast and accurate emulation of a large-scale circulation model for the coupled Columbia River, its estuary and near ocean regions. The circulation model has O(10(7)) degrees of freedom, is highly nonlinear and is driven by ocean, atmospheric and river influences at its boundaries. The surrogates provide accurate emulation of the full circulation code and run over 1000 times faster. Such fast dynamic surrogates will enable significant advances in ensemble forecasts in oceanography and weather.

A multi-damages identification method for cantilever beam based on mode shape curvatures and Kriging surrogate model

NASA Astrophysics Data System (ADS)

Xie, Fengle; Jiang, Zhansi; Jiang, Hui

2018-05-01

This paper presents a multi-damages identification method for Cantilever Beam. First, the damage location is identified by using the mode shape curvatures. Second, samples of varying damage severities at the damage location and their corresponding natural frequencies are used to construct the initial Kriging surrogate model. Then a particle swarm optimization (PSO) algorithm is employed to identify the damage severities based on Kriging surrogate model. The simulation study of a double-damaged cantilever beam demonstrated that the proposed method is effective.

Model-assisted probability of detection of flaws in aluminum blocks using polynomial chaos expansions

NASA Astrophysics Data System (ADS)

Du, Xiaosong; Leifsson, Leifur; Grandin, Robert; Meeker, William; Roberts, Ronald; Song, Jiming

2018-04-01

Probability of detection (POD) is widely used for measuring reliability of nondestructive testing (NDT) systems. Typically, POD is determined experimentally, while it can be enhanced by utilizing physics-based computational models in combination with model-assisted POD (MAPOD) methods. With the development of advanced physics-based methods, such as ultrasonic NDT testing, the empirical information, needed for POD methods, can be reduced. However, performing accurate numerical simulations can be prohibitively time-consuming, especially as part of stochastic analysis. In this work, stochastic surrogate models for computational physics-based measurement simulations are developed for cost savings of MAPOD methods while simultaneously ensuring sufficient accuracy. The stochastic surrogate is used to propagate the random input variables through the physics-based simulation model to obtain the joint probability distribution of the output. The POD curves are then generated based on those results. Here, the stochastic surrogates are constructed using non-intrusive polynomial chaos (NIPC) expansions. In particular, the NIPC methods used are the quadrature, ordinary least-squares (OLS), and least-angle regression sparse (LARS) techniques. The proposed approach is demonstrated on the ultrasonic testing simulation of a flat bottom hole flaw in an aluminum block. The results show that the stochastic surrogates have at least two orders of magnitude faster convergence on the statistics than direct Monte Carlo sampling (MCS). Moreover, the evaluation of the stochastic surrogate models is over three orders of magnitude faster than the underlying simulation model for this case, which is the UTSim2 model.

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

DOE Office of Scientific and Technical Information (OSTI.GOV)

Malinowski, Kathleen T.; Fischell Department of Bioengineering, University of Maryland, College Park, MD; McAvoy, Thomas J.

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 precisionmore » 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.« less

Maintaining tumor targeting accuracy in real-time motion compensation systems for respiration-induced tumor motion

PubMed Central

Malinowski, Kathleen; McAvoy, Thomas J.; George, Rohini; Dieterich, Sonja; D’Souza, Warren D.

2013-01-01

Purpose: To determine how best to time respiratory surrogate-based tumor motion model updates by comparing a novel technique based on external measurements alone to three direct measurement methods. Methods: Concurrently measured tumor and respiratory surrogate positions from 166 treatment fractions for lung or pancreas lesions were analyzed. Partial-least-squares regression models of tumor position from marker motion were created from the first six measurements in each dataset. Successive tumor localizations were obtained at a rate of once per minute on average. Model updates were timed according to four methods: never, respiratory surrogate-based (when metrics based on respiratory surrogate measurements exceeded confidence limits), error-based (when localization error ≥3 mm), and always (approximately once per minute). Results: Radial tumor displacement prediction errors (mean ± standard deviation) for the four schema described above were 2.4 ± 1.2, 1.9 ± 0.9, 1.9 ± 0.8, and 1.7 ± 0.8 mm, respectively. The never-update error was significantly larger than errors of the other methods. Mean update counts over 20 min were 0, 4, 9, and 24, respectively. Conclusions: The same improvement in tumor localization accuracy could be achieved through any of the three update methods, but significantly fewer updates were required when the respiratory surrogate method was utilized. This study establishes the feasibility of timing image acquisitions for updating respiratory surrogate models without direct tumor localization. PMID:23822413

Reduced order surrogate modelling (ROSM) of high dimensional deterministic simulations

NASA Astrophysics Data System (ADS)

Mitry, Mina

Often, computationally expensive engineering simulations can prohibit the engineering design process. As a result, designers may turn to a less computationally demanding approximate, or surrogate, model to facilitate their design process. However, owing to the the curse of dimensionality, classical surrogate models become too computationally expensive for high dimensional data. To address this limitation of classical methods, we develop linear and non-linear Reduced Order Surrogate Modelling (ROSM) techniques. Two algorithms are presented, which are based on a combination of linear/kernel principal component analysis and radial basis functions. These algorithms are applied to subsonic and transonic aerodynamic data, as well as a model for a chemical spill in a channel. The results of this thesis show that ROSM can provide a significant computational benefit over classical surrogate modelling, sometimes at the expense of a minor loss in accuracy.

Identifying taxonomic and functional surrogates for spring biodiversity conservation.

PubMed

Jyväsjärvi, Jussi; Virtanen, Risto; Ilmonen, Jari; Paasivirta, Lauri; Muotka, Timo

2018-02-27

Surrogate approaches are widely used to estimate overall taxonomic diversity for conservation planning. Surrogate taxa are frequently selected based on rarity or charisma, whereas selection through statistical modeling has been applied rarely. We used boosted-regression-tree models (BRT) fitted to biological data from 165 springs to identify bryophyte and invertebrate surrogates for taxonomic and functional diversity of boreal springs. We focused on these 2 groups because they are well known and abundant in most boreal springs. The best indicators of taxonomic versus functional diversity differed. The bryophyte Bryum weigelii and the chironomid larva Paratrichocladius skirwithensis best indicated taxonomic diversity, whereas the isopod Asellus aquaticus and the chironomid Macropelopia spp. were the best surrogates of functional diversity. In a scoring algorithm for priority-site selection, taxonomic surrogates performed only slightly better than random selection for all spring-dwelling taxa, but they were very effective in representing spring specialists, providing a distinct improvement over random solutions. However, the surrogates for taxonomic diversity represented functional diversity poorly and vice versa. When combined with cross-taxon complementarity analyses, surrogate selection based on statistical modeling provides a promising approach for identifying groundwater-dependent ecosystems of special conservation value, a key requirement of the EU Water Framework Directive. © 2018 Society for Conservation Biology.

Runoff load estimation of particulate and dissolved nitrogen in Lake Inba watershed using continuous monitoring data on turbidity and electric conductivity.

PubMed

Kim, J; Nagano, Y; Furumai, H

2012-01-01

Easy-to-measure surrogate parameters for water quality indicators are needed for real time monitoring as well as for generating data for model calibration and validation. In this study, a novel linear regression model for estimating total nitrogen (TN) based on two surrogate parameters is proposed based on evaluation of pollutant loads flowing into a eutrophic lake. Based on their runoff characteristics during wet weather, electric conductivity (EC) and turbidity were selected as surrogates for particulate nitrogen (PN) and dissolved nitrogen (DN), respectively. Strong linear relationships were established between PN and turbidity and DN and EC, and both models subsequently combined for estimation of TN. This model was evaluated by comparison of estimated and observed TN runoff loads during rainfall events. This analysis showed that turbidity and EC are viable surrogates for PN and DN, respectively, and that the linear regression model for TN concentration was successful in estimating TN runoff loads during rainfall events and also under dry weather conditions.

Comparing biomarkers as principal surrogate endpoints.

PubMed

Huang, Ying; Gilbert, Peter B

2011-12-01

Recently a new definition of surrogate endpoint, the "principal surrogate," was proposed based on causal associations between treatment effects on the biomarker and on the clinical endpoint. Despite its appealing interpretation, limited research has been conducted to evaluate principal surrogates, and existing methods focus on risk models that consider a single biomarker. How to compare principal surrogate value of biomarkers or general risk models that consider multiple biomarkers remains an open research question. We propose to characterize a marker or risk model's principal surrogate value based on the distribution of risk difference between interventions. In addition, we propose a novel summary measure (the standardized total gain) that can be used to compare markers and to assess the incremental value of a new marker. We develop a semiparametric estimated-likelihood method to estimate the joint surrogate value of multiple biomarkers. This method accommodates two-phase sampling of biomarkers and is more widely applicable than existing nonparametric methods by incorporating continuous baseline covariates to predict the biomarker(s), and is more robust than existing parametric methods by leaving the error distribution of markers unspecified. The methodology is illustrated using a simulated example set and a real data set in the context of HIV vaccine trials. © 2011, The International Biometric Society.

Surrogate Model Application to the Identification of Optimal Groundwater Exploitation Scheme Based on Regression Kriging Method—A Case Study of Western Jilin Province

PubMed Central

An, Yongkai; Lu, Wenxi; Cheng, Weiguo

2015-01-01

This paper introduces a surrogate model to identify an optimal exploitation scheme, while the western Jilin province was selected as the study area. A numerical simulation model of groundwater flow was established first, and four exploitation wells were set in the Tongyu county and Qian Gorlos county respectively so as to supply water to Daan county. Second, the Latin Hypercube Sampling (LHS) method was used to collect data in the feasible region for input variables. A surrogate model of the numerical simulation model of groundwater flow was developed using the regression kriging method. An optimization model was established to search an optimal groundwater exploitation scheme using the minimum average drawdown of groundwater table and the minimum cost of groundwater exploitation as multi-objective functions. Finally, the surrogate model was invoked by the optimization model in the process of solving the optimization problem. Results show that the relative error and root mean square error of the groundwater table drawdown between the simulation model and the surrogate model for 10 validation samples are both lower than 5%, which is a high approximation accuracy. The contrast between the surrogate-based simulation optimization model and the conventional simulation optimization model for solving the same optimization problem, shows the former only needs 5.5 hours, and the latter needs 25 days. The above results indicate that the surrogate model developed in this study could not only considerably reduce the computational burden of the simulation optimization process, but also maintain high computational accuracy. This can thus provide an effective method for identifying an optimal groundwater exploitation scheme quickly and accurately. PMID:26264008

Calibration of an agricultural-hydrological model (RZWQM2) using surrogate global optimization

DOE PAGES

Xi, Maolong; Lu, Dan; Gui, Dongwei; ...

2016-11-27

Robust calibration of an agricultural-hydrological model is critical for simulating crop yield and water quality and making reasonable agricultural management. However, calibration of the agricultural-hydrological system models is challenging because of model complexity, the existence of strong parameter correlation, 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, which greatly restricts the successful application of the model. The goal of this study is to locate the optimal solution of the Root Zone Water Quality Model (RZWQM2) given a limited simulation time, so asmore » to improve the model simulation and help make rational and effective agricultural-hydrological decisions. To this end, we propose a computationally efficient global optimization procedure using sparse-grid based surrogates. We first used advanced sparse grid (SG) interpolation to construct a surrogate system of the actual RZWQM2, and then we calibrate the surrogate model using the global optimization algorithm, Quantum-behaved Particle Swarm Optimization (QPSO). As the surrogate model is a polynomial with fast evaluation, it can be efficiently evaluated with a sufficiently large number of times during the optimization, which facilitates the global search. We calibrate seven model parameters against five years of yield, drain flow, and NO 3-N loss data from a subsurface-drained corn-soybean field in Iowa. Results indicate that an accurate surrogate model can be created for the RZWQM2 with a relatively small number of SG points (i.e., RZWQM2 runs). Compared to the conventional QPSO algorithm, our surrogate-based optimization method can achieve a smaller objective function value and better calibration performance using a fewer number of expensive RZWQM2 executions, which greatly improves computational efficiency.« less

Calibration of an agricultural-hydrological model (RZWQM2) using surrogate global optimization

NASA Astrophysics Data System (ADS)

Xi, Maolong; Lu, Dan; Gui, Dongwei; Qi, Zhiming; Zhang, Guannan

2017-01-01

Robust calibration of an agricultural-hydrological model is critical for simulating crop yield and water quality and making reasonable agricultural management. However, calibration of the agricultural-hydrological system models is challenging because of model complexity, the existence of strong parameter correlation, 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, which greatly restricts the successful application of the model. The goal of this study is to locate the optimal solution of the Root Zone Water Quality Model (RZWQM2) given a limited simulation time, so as to improve the model simulation and help make rational and effective agricultural-hydrological decisions. To this end, we propose a computationally efficient global optimization procedure using sparse-grid based surrogates. We first used advanced sparse grid (SG) interpolation to construct a surrogate system of the actual RZWQM2, and then we calibrate the surrogate model using the global optimization algorithm, Quantum-behaved Particle Swarm Optimization (QPSO). As the surrogate model is a polynomial with fast evaluation, it can be efficiently evaluated with a sufficiently large number of times during the optimization, which facilitates the global search. We calibrate seven model parameters against five years of yield, drain flow, and NO3-N loss data from a subsurface-drained corn-soybean field in Iowa. Results indicate that an accurate surrogate model can be created for the RZWQM2 with a relatively small number of SG points (i.e., RZWQM2 runs). Compared to the conventional QPSO algorithm, our surrogate-based optimization method can achieve a smaller objective function value and better calibration performance using a fewer number of expensive RZWQM2 executions, which greatly improves computational efficiency.

Calibration of an agricultural-hydrological model (RZWQM2) using surrogate global optimization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Xi, Maolong; Lu, Dan; Gui, Dongwei

Robust calibration of an agricultural-hydrological model is critical for simulating crop yield and water quality and making reasonable agricultural management. However, calibration of the agricultural-hydrological system models is challenging because of model complexity, the existence of strong parameter correlation, 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, which greatly restricts the successful application of the model. The goal of this study is to locate the optimal solution of the Root Zone Water Quality Model (RZWQM2) given a limited simulation time, so asmore » to improve the model simulation and help make rational and effective agricultural-hydrological decisions. To this end, we propose a computationally efficient global optimization procedure using sparse-grid based surrogates. We first used advanced sparse grid (SG) interpolation to construct a surrogate system of the actual RZWQM2, and then we calibrate the surrogate model using the global optimization algorithm, Quantum-behaved Particle Swarm Optimization (QPSO). As the surrogate model is a polynomial with fast evaluation, it can be efficiently evaluated with a sufficiently large number of times during the optimization, which facilitates the global search. We calibrate seven model parameters against five years of yield, drain flow, and NO 3-N loss data from a subsurface-drained corn-soybean field in Iowa. Results indicate that an accurate surrogate model can be created for the RZWQM2 with a relatively small number of SG points (i.e., RZWQM2 runs). Compared to the conventional QPSO algorithm, our surrogate-based optimization method can achieve a smaller objective function value and better calibration performance using a fewer number of expensive RZWQM2 executions, which greatly improves computational efficiency.« less

Efficient SRAM yield optimization with mixture surrogate modeling

NASA Astrophysics Data System (ADS)

Zhongjian, Jiang; Zuochang, Ye; Yan, Wang

2016-12-01

Largely repeated cells such as SRAM cells usually require extremely low failure-rate to ensure a moderate chi yield. Though fast Monte Carlo methods such as importance sampling and its variants can be used for yield estimation, they are still very expensive if one needs to perform optimization based on such estimations. Typically the process of yield calculation requires a lot of SPICE simulation. The circuit SPICE simulation analysis accounted for the largest proportion of time in the process yield calculation. In the paper, a new method is proposed to address this issue. The key idea is to establish an efficient mixture surrogate model. The surrogate model is based on the design variables and process variables. This model construction method is based on the SPICE simulation to get a certain amount of sample points, these points are trained for mixture surrogate model by the lasso algorithm. Experimental results show that the proposed model is able to calculate accurate yield successfully and it brings significant speed ups to the calculation of failure rate. Based on the model, we made a further accelerated algorithm to further enhance the speed of the yield calculation. It is suitable for high-dimensional process variables and multi-performance applications.

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.

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.

An adaptive Gaussian process-based method for efficient Bayesian experimental design in groundwater contaminant source identification problems: ADAPTIVE GAUSSIAN PROCESS-BASED INVERSION

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Jiangjiang; Li, Weixuan; Zeng, Lingzao

Surrogate models are commonly used in Bayesian approaches such as Markov Chain Monte Carlo (MCMC) to avoid repetitive CPU-demanding model evaluations. However, the approximation error of a surrogate may lead to biased estimations of the posterior distribution. This bias can be corrected by constructing a very accurate surrogate or implementing MCMC in a two-stage manner. Since the two-stage MCMC requires extra original model evaluations, the computational cost is still high. If the information of measurement is incorporated, a locally accurate approximation of the original model can be adaptively constructed with low computational cost. Based on this idea, we propose amore » Gaussian process (GP) surrogate-based Bayesian experimental design and parameter estimation approach for groundwater contaminant source identification problems. A major advantage of the GP surrogate is that it provides a convenient estimation of the approximation error, which can be incorporated in the Bayesian formula to avoid over-confident estimation of the posterior distribution. The proposed approach is tested with a numerical case study. Without sacrificing the estimation accuracy, the new approach achieves about 200 times of speed-up compared to our previous work using two-stage MCMC.« less

Real-time characterization of partially observed epidemics using surrogate models.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Safta, Cosmin; Ray, Jaideep; Lefantzi, Sophia

We present a statistical method, predicated on the use of surrogate models, for the 'real-time' characterization of partially observed epidemics. Observations consist of counts of symptomatic patients, diagnosed with the disease, that may be available in the early epoch of an ongoing outbreak. Characterization, in this context, refers to estimation of epidemiological parameters that can be used to provide short-term forecasts of the ongoing epidemic, as well as to provide gross information on the dynamics of the etiologic agent in the affected population e.g., the time-dependent infection rate. The characterization problem is formulated as a Bayesian inverse problem, and epidemiologicalmore » parameters are estimated as distributions using a Markov chain Monte Carlo (MCMC) method, thus quantifying the uncertainty in the estimates. In some cases, the inverse problem can be computationally expensive, primarily due to the epidemic simulator used inside the inversion algorithm. We present a method, based on replacing the epidemiological model with computationally inexpensive surrogates, that can reduce the computational time to minutes, without a significant loss of accuracy. The surrogates are created by projecting the output of an epidemiological model on a set of polynomial chaos bases; thereafter, computations involving the surrogate model reduce to evaluations of a polynomial. We find that the epidemic characterizations obtained with the surrogate models is very close to that obtained with the original model. We also find that the number of projections required to construct a surrogate model is O(10)-O(10{sup 2}) less than the number of samples required by the MCMC to construct a stationary posterior distribution; thus, depending upon the epidemiological models in question, it may be possible to omit the offline creation and caching of surrogate models, prior to their use in an inverse problem. The technique is demonstrated on synthetic data as well as observations from the 1918 influenza pandemic collected at Camp Custer, Michigan.« less

A review on design of experiments and surrogate models in aircraft real-time and many-query aerodynamic analyses

NASA Astrophysics Data System (ADS)

Yondo, Raul; Andrés, Esther; Valero, Eusebio

2018-01-01

Full scale aerodynamic wind tunnel testing, numerical simulation of high dimensional (full-order) aerodynamic models or flight testing are some of the fundamental but complex steps in the various design phases of recent civil transport aircrafts. Current aircraft aerodynamic designs have increase in complexity (multidisciplinary, multi-objective or multi-fidelity) and need to address the challenges posed by the nonlinearity of the objective functions and constraints, uncertainty quantification in aerodynamic problems or the restrained computational budgets. With the aim to reduce the computational burden and generate low-cost but accurate models that mimic those full order models at different values of the design variables, Recent progresses have witnessed the introduction, in real-time and many-query analyses, of surrogate-based approaches as rapid and cheaper to simulate models. In this paper, a comprehensive and state-of-the art survey on common surrogate modeling techniques and surrogate-based optimization methods is given, with an emphasis on models selection and validation, dimensionality reduction, sensitivity analyses, constraints handling or infill and stopping criteria. Benefits, drawbacks and comparative discussions in applying those methods are described. Furthermore, the paper familiarizes the readers with surrogate models that have been successfully applied to the general field of fluid dynamics, but not yet in the aerospace industry. Additionally, the review revisits the most popular sampling strategies used in conducting physical and simulation-based experiments in aircraft aerodynamic design. Attractive or smart designs infrequently used in the field and discussions on advanced sampling methodologies are presented, to give a glance on the various efficient possibilities to a priori sample the parameter space. Closing remarks foster on future perspectives, challenges and shortcomings associated with the use of surrogate models by aircraft industrial aerodynamicists, despite their increased interest among the research communities.

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.

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.

Design Mining Interacting Wind Turbines.

PubMed

Preen, Richard J; Bull, Larry

2016-01-01

An initial study has recently been presented of surrogate-assisted evolutionary algorithms used to design vertical-axis wind turbines wherein candidate prototypes are evaluated under fan-generated wind conditions after being physically instantiated by a 3D printer. Unlike other approaches, such as computational fluid dynamics simulations, no mathematical formulations were used and no model assumptions were made. This paper extends that work by exploring alternative surrogate modelling and evolutionary techniques. The accuracy of various modelling algorithms used to estimate the fitness of evaluated individuals from the initial experiments is compared. The effect of temporally windowing surrogate model training samples is explored. A surrogate-assisted approach based on an enhanced local search is introduced; and alternative coevolution collaboration schemes are examined.

Convergence analysis of surrogate-based methods for Bayesian inverse problems

NASA Astrophysics Data System (ADS)

Yan, Liang; Zhang, Yuan-Xiang

2017-12-01

The major challenges in the Bayesian inverse problems arise from the need for repeated evaluations of the forward model, as required by Markov chain Monte Carlo (MCMC) methods for posterior sampling. Many attempts at accelerating Bayesian inference have relied on surrogates for the forward model, typically constructed through repeated forward simulations that are performed in an offline phase. Although such approaches can be quite effective at reducing computation cost, there has been little analysis of the approximation on posterior inference. In this work, we prove error bounds on the Kullback-Leibler (KL) distance between the true posterior distribution and the approximation based on surrogate models. Our rigorous error analysis show that if the forward model approximation converges at certain rate in the prior-weighted L 2 norm, then the posterior distribution generated by the approximation converges to the true posterior at least two times faster in the KL sense. The error bound on the Hellinger distance is also provided. To provide concrete examples focusing on the use of the surrogate model based methods, we present an efficient technique for constructing stochastic surrogate models to accelerate the Bayesian inference approach. The Christoffel least squares algorithms, based on generalized polynomial chaos, are used to construct a polynomial approximation of the forward solution over the support of the prior distribution. The numerical strategy and the predicted convergence rates are then demonstrated on the nonlinear inverse problems, involving the inference of parameters appearing in partial differential equations.

A Conceptual Model of the Role of Communication in Surrogate Decision Making for Hospitalized Adults

PubMed Central

Torke, Alexia M.; Petronio, Sandra; Sachs, Greg A.; Helft, Paul R.; Purnell, Christianna

2011-01-01

Objective To build a conceptual model of the role of communication in decision making, based on literature from medicine, communication studies and medical ethics. Methods We propose a model and describe each construct in detail. We review what is known about interpersonal and patient-physician communication, describe literature about surrogate-clinician communication, and discuss implications for our developing model. Results The communication literature proposes two major elements of interpersonal communication: information processing and relationship building. These elements are composed of constructs such as information disclosure and emotional support that are likely to be relevant to decision making. We propose these elements of communication impact decision making, which in turn affects outcomes for both patients and surrogates. Decision making quality may also mediate the relationship between communication and outcomes. Conclusion Although many elements of the model have been studied in relation to patient-clinician communication, there is limited data about surrogate decision making. There is evidence of high surrogate distress associated with decision making that may be alleviated by communication–focused interventions. More research is needed to test the relationships proposed in the model. Practice Implications Good communication with surrogates may improve both the quality of medical decisions and outcomes for the patient and surrogate. PMID:21889865

A conceptual model of the role of communication in surrogate decision making for hospitalized adults.

PubMed

Torke, Alexia M; Petronio, Sandra; Sachs, Greg A; Helft, Paul R; Purnell, Christianna

2012-04-01

To build a conceptual model of the role of communication in decision making, based on literature from medicine, communication studies and medical ethics. We proposed a model and described each construct in detail. We review what is known about interpersonal and patient-physician communication, described literature about surrogate-clinician communication, and discussed implications for our developing model. The communication literature proposes two major elements of interpersonal communication: information processing and relationship building. These elements are composed of constructs such as information disclosure and emotional support that are likely to be relevant to decision making. We propose these elements of communication impact decision making, which in turn affects outcomes for both patients and surrogates. Decision making quality may also mediate the relationship between communication and outcomes. Although many elements of the model have been studied in relation to patient-clinician communication, there is limited data about surrogate decision making. There is evidence of high surrogate distress associated with decision making that may be alleviated by communication-focused interventions. More research is needed to test the relationships proposed in the model. Good communication with surrogates may improve both the quality of medical decisions and outcomes for the patient and surrogate. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhao, T; Ruan, D

Purpose: The growing size and heterogeneity in training atlas necessitates sophisticated schemes to identify only the most relevant atlases for the specific multi-atlas-based image segmentation problem. This study aims to develop a model to infer the inaccessible oracle geometric relevance metric from surrogate image similarity metrics, and based on such model, provide guidance to atlas selection in multi-atlas-based image segmentation. Methods: We relate the oracle geometric relevance metric in label space to the surrogate metric in image space, by a monotonically non-decreasing function with additive random perturbations. Subsequently, a surrogate’s ability to prognosticate the oracle order for atlas subset selectionmore » is quantified probabilistically. Finally, important insights and guidance are provided for the design of fusion set size, balancing the competing demands to include the most relevant atlases and to exclude the most irrelevant ones. A systematic solution is derived based on an optimization framework. Model verification and performance assessment is performed based on clinical prostate MR images. Results: The proposed surrogate model was exemplified by a linear map with normally distributed perturbation, and verified with several commonly-used surrogates, including MSD, NCC and (N)MI. The derived behaviors of different surrogates in atlas selection and their corresponding performance in ultimate label estimate were validated. The performance of NCC and (N)MI was similarly superior to MSD, with a 10% higher atlas selection probability and a segmentation performance increase in DSC by 0.10 with the first and third quartiles of (0.83, 0.89), compared to (0.81, 0.89). The derived optimal fusion set size, valued at 7/8/8/7 for MSD/NCC/MI/NMI, agreed well with the appropriate range [4, 9] from empirical observation. Conclusion: This work has developed an efficacious probabilistic model to characterize the image-based surrogate metric on atlas selection. Analytical insights lead to valid guiding principles on fusion set size design.« less

Reduced cost mission design using surrogate models

NASA Astrophysics Data System (ADS)

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

2016-01-01

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

An efficient surrogate-based simulation-optimization method for calibrating a regional MODFLOW model

NASA Astrophysics Data System (ADS)

Chen, Mingjie; Izady, Azizallah; Abdalla, Osman A.

2017-01-01

Simulation-optimization method entails a large number of model simulations, which is computationally intensive or even prohibitive if the model simulation is extremely time-consuming. Statistical models have been examined as a surrogate of the high-fidelity physical model during simulation-optimization process to tackle this problem. Among them, Multivariate Adaptive Regression Splines (MARS), a non-parametric adaptive regression method, is superior in overcoming problems of high-dimensions and discontinuities of the data. Furthermore, the stability and accuracy of MARS model can be improved by bootstrap aggregating methods, namely, bagging. In this paper, Bagging MARS (BMARS) method is integrated to a surrogate-based simulation-optimization framework to calibrate a three-dimensional MODFLOW model, which is developed to simulate the groundwater flow in an arid hardrock-alluvium region in northwestern Oman. The physical MODFLOW model is surrogated by the statistical model developed using BMARS algorithm. The surrogate model, which is fitted and validated using training dataset generated by the physical model, can approximate solutions rapidly. An efficient Sobol' method is employed to calculate global sensitivities of head outputs to input parameters, which are used to analyze their importance for the model outputs spatiotemporally. Only sensitive parameters are included in the calibration process to further improve the computational efficiency. Normalized root mean square error (NRMSE) between measured and simulated heads at observation wells is used as the objective function to be minimized during optimization. The reasonable history match between the simulated and observed heads demonstrated feasibility of this high-efficient calibration framework.

Multi-Scale Modeling, Surrogate-Based Analysis, and Optimization of Lithium-Ion Batteries for Vehicle Applications

NASA Astrophysics Data System (ADS)

Du, Wenbo

A common attribute of electric-powered aerospace vehicles and systems such as unmanned aerial vehicles, hybrid- and fully-electric aircraft, and satellites is that their performance is usually limited by the energy density of their batteries. Although lithium-ion batteries offer distinct advantages such as high voltage and low weight over other battery technologies, they are a relatively new development, and thus significant gaps in the understanding of the physical phenomena that govern battery performance remain. As a result of this limited understanding, batteries must often undergo a cumbersome design process involving many manual iterations based on rules of thumb and ad-hoc design principles. A systematic study of the relationship between operational, geometric, morphological, and material-dependent properties and performance metrics such as energy and power density is non-trivial due to the multiphysics, multiphase, and multiscale nature of the battery system. To address these challenges, two numerical frameworks are established in this dissertation: a process for analyzing and optimizing several key design variables using surrogate modeling tools and gradient-based optimizers, and a multi-scale model that incorporates more detailed microstructural information into the computationally efficient but limited macro-homogeneous model. In the surrogate modeling process, multi-dimensional maps for the cell energy density with respect to design variables such as the particle size, ion diffusivity, and electron conductivity of the porous cathode material are created. A combined surrogate- and gradient-based approach is employed to identify optimal values for cathode thickness and porosity under various operating conditions, and quantify the uncertainty in the surrogate model. The performance of multiple cathode materials is also compared by defining dimensionless transport parameters. The multi-scale model makes use of detailed 3-D FEM simulations conducted at the particle-level. A monodisperse system of ellipsoidal particles is used to simulate the effective transport coefficients and interfacial reaction current density within the porous microstructure. Microscopic simulation results are shown to match well with experimental measurements, while differing significantly from homogenization approximations used in the macroscopic model. Global sensitivity analysis and surrogate modeling tools are applied to couple the two length scales and complete the multi-scale model.

Rapid Optimization of External Quantum Efficiency of Thin Film Solar Cells Using Surrogate Modeling of Absorptivity.

PubMed

Kaya, Mine; Hajimirza, Shima

2018-05-25

This paper uses surrogate modeling for very fast design of thin film solar cells with improved solar-to-electricity conversion efficiency. We demonstrate that the wavelength-specific optical absorptivity of a thin film multi-layered amorphous-silicon-based solar cell can be modeled accurately with Neural Networks and can be efficiently approximated as a function of cell geometry and wavelength. Consequently, the external quantum efficiency can be computed by averaging surrogate absorption and carrier recombination contributions over the entire irradiance spectrum in an efficient way. Using this framework, we optimize a multi-layer structure consisting of ITO front coating, metallic back-reflector and oxide layers for achieving maximum efficiency. Our required computation time for an entire model fitting and optimization is 5 to 20 times less than the best previous optimization results based on direct Finite Difference Time Domain (FDTD) simulations, therefore proving the value of surrogate modeling. The resulting optimization solution suggests at least 50% improvement in the external quantum efficiency compared to bare silicon, and 25% improvement compared to a random design.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Jiangjiang; Li, Weixuan; Lin, Guang

In decision-making for groundwater management and contamination remediation, it is important to accurately evaluate the probability of the occurrence of a failure event. For small failure probability analysis, a large number of model evaluations are needed in the Monte Carlo (MC) simulation, which is impractical for CPU-demanding models. One approach to alleviate the computational cost caused by the model evaluations is to construct a computationally inexpensive surrogate model instead. However, using a surrogate approximation can cause an extra error in the failure probability analysis. Moreover, constructing accurate surrogates is challenging for high-dimensional models, i.e., models containing many uncertain input parameters.more » To address these issues, we propose an efficient two-stage MC approach for small failure probability analysis in high-dimensional groundwater contaminant transport modeling. In the first stage, a low-dimensional representation of the original high-dimensional model is sought with Karhunen–Loève expansion and sliced inverse regression jointly, which allows for the easy construction of a surrogate with polynomial chaos expansion. Then a surrogate-based MC simulation is implemented. In the second stage, the small number of samples that are close to the failure boundary are re-evaluated with the original model, which corrects the bias introduced by the surrogate approximation. The proposed approach is tested with a numerical case study and is shown to be 100 times faster than the traditional MC approach in achieving the same level of estimation accuracy.« less

Optimization and Control of Agent-Based Models in Biology: A Perspective.

PubMed

An, G; Fitzpatrick, B G; Christley, S; Federico, P; Kanarek, A; Neilan, R Miller; Oremland, M; Salinas, R; Laubenbacher, R; Lenhart, S

2017-01-01

Agent-based models (ABMs) have become an increasingly important mode of inquiry for the life sciences. They are particularly valuable for systems that are not understood well enough to build an equation-based model. These advantages, however, are counterbalanced by the difficulty of analyzing and using ABMs, due to the lack of the type of mathematical tools available for more traditional models, which leaves simulation as the primary approach. As models become large, simulation becomes challenging. This paper proposes a novel approach to two mathematical aspects of ABMs, optimization and control, and it presents a few first steps outlining how one might carry out this approach. Rather than viewing the ABM as a model, it is to be viewed as a surrogate for the actual system. For a given optimization or control problem (which may change over time), the surrogate system is modeled instead, using data from the ABM and a modeling framework for which ready-made mathematical tools exist, such as differential equations, or for which control strategies can explored more easily. Once the optimization problem is solved for the model of the surrogate, it is then lifted to the surrogate and tested. The final step is to lift the optimization solution from the surrogate system to the actual system. This program is illustrated with published work, using two relatively simple ABMs as a demonstration, Sugarscape and a consumer-resource ABM. Specific techniques discussed include dimension reduction and approximation of an ABM by difference equations as well systems of PDEs, related to certain specific control objectives. This demonstration illustrates the very challenging mathematical problems that need to be solved before this approach can be realistically applied to complex and large ABMs, current and future. The paper outlines a research program to address them.

Greedy Sampling and Incremental Surrogate Model-Based Tailoring of Aeroservoelastic Model Database for Flexible Aircraft

NASA Technical Reports Server (NTRS)

Wang, Yi; Pant, Kapil; Brenner, Martin J.; Ouellette, Jeffrey A.

2018-01-01

This paper presents a data analysis and modeling framework to tailor and develop linear parameter-varying (LPV) aeroservoelastic (ASE) model database for flexible aircrafts in broad 2D flight parameter space. The Kriging surrogate model is constructed using ASE models at a fraction of grid points within the original model database, and then the ASE model at any flight condition can be obtained simply through surrogate model interpolation. The greedy sampling algorithm is developed to select the next sample point that carries the worst relative error between the surrogate model prediction and the benchmark model in the frequency domain among all input-output channels. The process is iterated to incrementally improve surrogate model accuracy till a pre-determined tolerance or iteration budget is met. The methodology is applied to the ASE model database of a flexible aircraft currently being tested at NASA/AFRC for flutter suppression and gust load alleviation. Our studies indicate that the proposed method can reduce the number of models in the original database by 67%. Even so the ASE models obtained through Kriging interpolation match the model in the original database constructed directly from the physics-based tool with the worst relative error far below 1%. The interpolated ASE model exhibits continuously-varying gains along a set of prescribed flight conditions. More importantly, the selected grid points are distributed non-uniformly in the parameter space, a) capturing the distinctly different dynamic behavior and its dependence on flight parameters, and b) reiterating the need and utility for adaptive space sampling techniques for ASE model database compaction. The present framework is directly extendible to high-dimensional flight parameter space, and can be used to guide the ASE model development, model order reduction, robust control synthesis and novel vehicle design of flexible aircraft.

Active Subspaces for Wind Plant Surrogate Modeling

DOE Office of Scientific and Technical Information (OSTI.GOV)

King, Ryan N; Quick, Julian; Dykes, Katherine L

Understanding the uncertainty in wind plant performance is crucial to their cost-effective design and operation. However, conventional approaches to uncertainty quantification (UQ), such as Monte Carlo techniques or surrogate modeling, are often computationally intractable for utility-scale wind plants because of poor congergence rates or the curse of dimensionality. In this paper we demonstrate that wind plant power uncertainty can be well represented with a low-dimensional active subspace, thereby achieving a significant reduction in the dimension of the surrogate modeling problem. We apply the active sub-spaces technique to UQ of plant power output with respect to uncertainty in turbine axial inductionmore » factors, and find a single active subspace direction dominates the sensitivity in power output. When this single active subspace direction is used to construct a quadratic surrogate model, the number of model unknowns can be reduced by up to 3 orders of magnitude without compromising performance on unseen test data. We conclude that the dimension reduction achieved with active subspaces makes surrogate-based UQ approaches tractable for utility-scale wind plants.« less

Effective use of integrated hydrological models in basin-scale water resources management: surrogate modeling approaches

NASA Astrophysics Data System (ADS)

Zheng, Y.; Wu, B.; Wu, X.

2015-12-01

Integrated hydrological models (IHMs) consider surface water and subsurface water as a unified system, and have been widely adopted in basin-scale water resources studies. However, due to IHMs' mathematical complexity and high computational cost, it is difficult to implement them in an iterative model evaluation process (e.g., Monte Carlo Simulation, simulation-optimization analysis, etc.), which diminishes their applicability for supporting decision-making in real-world situations. Our studies investigated how to effectively use complex IHMs to address real-world water issues via surrogate modeling. Three surrogate modeling approaches were considered, including 1) DYCORS (DYnamic COordinate search using Response Surface models), a well-established response surface-based optimization algorithm; 2) SOIM (Surrogate-based Optimization for Integrated surface water-groundwater Modeling), a response surface-based optimization algorithm that we developed specifically for IHMs; and 3) Probabilistic Collocation Method (PCM), a stochastic response surface approach. Our investigation was based on a modeling case study in the Heihe River Basin (HRB), China's second largest endorheic river basin. The GSFLOW (Coupled Ground-Water and Surface-Water Flow Model) model was employed. Two decision problems were discussed. One is to optimize, both in time and in space, the conjunctive use of surface water and groundwater for agricultural irrigation in the middle HRB region; and the other is to cost-effectively collect hydrological data based on a data-worth evaluation. Overall, our study results highlight the value of incorporating an IHM in making decisions of water resources management and hydrological data collection. An IHM like GSFLOW can provide great flexibility to formulating proper objective functions and constraints for various optimization problems. On the other hand, it has been demonstrated that surrogate modeling approaches can pave the path for such incorporation in real-world situations, since they can dramatically reduce the computational cost of using IHMs in an iterative model evaluation process. In addition, our studies generated insights into the human-nature water conflicts in the specific study area and suggested potential solutions to address them.

Surrogate-based optimization of hydraulic fracturing in pre-existing fracture networks

NASA Astrophysics Data System (ADS)

Chen, Mingjie; Sun, Yunwei; Fu, Pengcheng; Carrigan, Charles R.; Lu, Zhiming; Tong, Charles H.; Buscheck, Thomas A.

2013-08-01

Hydraulic fracturing has been used widely to stimulate production of oil, natural gas, and geothermal energy in formations with low natural permeability. Numerical optimization of fracture stimulation often requires a large number of evaluations of objective functions and constraints from forward hydraulic fracturing models, which are computationally expensive and even prohibitive in some situations. Moreover, there are a variety of uncertainties associated with the pre-existing fracture distributions and rock mechanical properties, which affect the optimized decisions for hydraulic fracturing. In this study, a surrogate-based approach is developed for efficient optimization of hydraulic fracturing well design in the presence of natural-system uncertainties. The fractal dimension is derived from the simulated fracturing network as the objective for maximizing energy recovery sweep efficiency. The surrogate model, which is constructed using training data from high-fidelity fracturing models for mapping the relationship between uncertain input parameters and the fractal dimension, provides fast approximation of the objective functions and constraints. A suite of surrogate models constructed using different fitting methods is evaluated and validated for fast predictions. Global sensitivity analysis is conducted to gain insights into the impact of the input variables on the output of interest, and further used for parameter screening. The high efficiency of the surrogate-based approach is demonstrated for three optimization scenarios with different and uncertain ambient conditions. Our results suggest the critical importance of considering uncertain pre-existing fracture networks in optimization studies of hydraulic fracturing.

Committee-Based Active Learning for Surrogate-Assisted Particle Swarm Optimization of Expensive Problems.

PubMed

Wang, Handing; Jin, Yaochu; Doherty, John

2017-09-01

Function evaluations (FEs) of many real-world optimization problems are time or resource consuming, posing a serious challenge to the application of evolutionary algorithms (EAs) to solve these problems. To address this challenge, the research on surrogate-assisted EAs has attracted increasing attention from both academia and industry over the past decades. However, most existing surrogate-assisted EAs (SAEAs) either still require thousands of expensive FEs to obtain acceptable solutions, or are only applied to very low-dimensional problems. In this paper, a novel surrogate-assisted particle swarm optimization (PSO) inspired from committee-based active learning (CAL) is proposed. In the proposed algorithm, a global model management strategy inspired from CAL is developed, which searches for the best and most uncertain solutions according to a surrogate ensemble using a PSO algorithm and evaluates these solutions using the expensive objective function. In addition, a local surrogate model is built around the best solution obtained so far. Then, a PSO algorithm searches on the local surrogate to find its optimum and evaluates it. The evolutionary search using the global model management strategy switches to the local search once no further improvement can be observed, and vice versa. This iterative search process continues until the computational budget is exhausted. Experimental results comparing the proposed algorithm with a few state-of-the-art SAEAs on both benchmark problems up to 30 decision variables as well as an airfoil design problem demonstrate that the proposed algorithm is able to achieve better or competitive solutions with a limited budget of hundreds of exact FEs.

Impact of competitor species composition on predicting diameter growth and survival rates of Douglas-fir trees in southwestern Oregon

USGS Publications Warehouse

Bravo, Felipe; Hann, D.W.; Maguire, Douglas A.

2001-01-01

Mixed conifer and hardwood stands in southwestern Oregon were studied to explore the hypothesis that competition effects on individual-tree growth and survival will differ according to the species comprising the competition measure. Likewise, it was hypothesized that competition measures should extrapolate best if crown-based surrogates are given preference over diameter-based (basal area based) surrogates. Diameter growth and probability of survival were modeled for individual Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) trees growing in pure stands. Alternative models expressing one-sided and two-sided competition as a function of either basal area or crown structure were then applied to other plots in which Douglas-fir was mixed with other conifers and (or) hardwood species. Crown-based variables outperformed basal area based variables as surrogates for one-sided competition in both diameter growth and survival probability, regardless of species composition. In contrast, two-sided competition was best represented by total basal area of competing trees. Surrogates reflecting differences in crown morphology among species relate more closely to the mechanics of competition for light and, hence, facilitate extrapolation to species combinations for which no observations are available.

Nonspinning numerical relativity waveform surrogates: assessing the model

NASA Astrophysics Data System (ADS)

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

2015-04-01

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

Surrogate assisted multidisciplinary design optimization for an all-electric GEO satellite

NASA Astrophysics Data System (ADS)

Shi, Renhe; Liu, Li; Long, Teng; Liu, Jian; Yuan, Bin

2017-09-01

State-of-the-art all-electric geostationary earth orbit (GEO) satellites use electric thrusters to execute all propulsive duties, which significantly differ from the traditional all-chemical ones in orbit-raising, station-keeping, radiation damage protection, and power budget, etc. Design optimization task of an all-electric GEO satellite is therefore a complex multidisciplinary design optimization (MDO) problem involving unique design considerations. However, solving the all-electric GEO satellite MDO problem faces big challenges in disciplinary modeling techniques and efficient optimization strategy. To address these challenges, we presents a surrogate assisted MDO framework consisting of several modules, i.e., MDO problem definition, multidisciplinary modeling, multidisciplinary analysis (MDA), and surrogate assisted optimizer. Based on the proposed framework, the all-electric GEO satellite MDO problem is formulated to minimize the total mass of the satellite system under a number of practical constraints. Then considerable efforts are spent on multidisciplinary modeling involving geosynchronous transfer, GEO station-keeping, power, thermal control, attitude control, and structure disciplines. Since orbit dynamics models and finite element structural model are computationally expensive, an adaptive response surface surrogate based optimizer is incorporated in the proposed framework to solve the satellite MDO problem with moderate computational cost, where a response surface surrogate is gradually refined to represent the computationally expensive MDA process. After optimization, the total mass of the studied GEO satellite is decreased by 185.3 kg (i.e., 7.3% of the total mass). Finally, the optimal design is further discussed to demonstrate the effectiveness of our proposed framework to cope with the all-electric GEO satellite system design optimization problems. This proposed surrogate assisted MDO framework can also provide valuable references for other all-electric spacecraft system design.

An entropy-based nonparametric test for the validation of surrogate endpoints.

PubMed

Miao, Xiaopeng; Wang, Yong-Cheng; Gangopadhyay, Ashis

2012-06-30

We present a nonparametric test to validate surrogate endpoints based on measure of divergence and random permutation. This test is a proposal to directly verify the Prentice statistical definition of surrogacy. The test does not impose distributional assumptions on the endpoints, and it is robust to model misspecification. Our simulation study shows that the proposed nonparametric test outperforms the practical test of the Prentice criterion in terms of both robustness of size and power. We also evaluate the performance of three leading methods that attempt to quantify the effect of surrogate endpoints. The proposed method is applied to validate magnetic resonance imaging lesions as the surrogate endpoint for clinical relapses in a multiple sclerosis trial. Copyright © 2012 John Wiley & Sons, Ltd.

An Efficient Variable Screening Method for Effective Surrogate Models for Reliability-Based Design Optimization

DTIC Science & Technology

2014-04-01

surrogate model generation is difficult for high -dimensional problems, due to the curse of dimensionality. Variable screening methods have been...a variable screening model was developed for the quasi-molecular treatment of ion-atom collision [16]. In engineering, a confidence interval of...for high -level radioactive waste [18]. Moreover, the design sensitivity method can be extended to the variable screening method because vital

Diesel surrogate fuels for engine testing and chemical-kinetic modeling: Compositions and properties

DOE PAGES

Mueller, Charles J.; Cannella, William J.; Bays, J. Timothy; ...

2016-01-07

The primary objectives of this work were to formulate, blend, and characterize a set of four ultralow-sulfur diesel surrogate fuels in quantities sufficient to enable their study in single-cylinder-engine and combustion-vessel experiments. The surrogate fuels feature increasing levels of compositional accuracy (i.e., increasing exactness in matching hydrocarbon structural characteristics) relative to the single target diesel fuel upon which the surrogate fuels are based. This approach was taken to assist in determining the minimum level of surrogate-fuel compositional accuracy that is required to adequately emulate the performance characteristics of the target fuel under different combustion modes. For each of the fourmore » surrogate fuels, an approximately 30 L batch was blended, and a number of the physical and chemical properties were measured. In conclusion, this work documents the surrogate-fuel creation process and the results of the property measurements.« less

Diesel Surrogate Fuels for Engine Testing and Chemical-Kinetic Modeling: Compositions and Properties

PubMed Central

Mueller, Charles J.; Cannella, William J.; Bays, J. Timothy; Bruno, Thomas J.; DeFabio, Kathy; Dettman, Heather D.; Gieleciak, Rafal M.; Huber, Marcia L.; Kweon, Chol-Bum; McConnell, Steven S.; Pitz, William J.; Ratcliff, Matthew A.

2016-01-01

The primary objectives of this work were to formulate, blend, and characterize a set of four ultralow-sulfur diesel surrogate fuels in quantities sufficient to enable their study in single-cylinder-engine and combustion-vessel experiments. The surrogate fuels feature increasing levels of compositional accuracy (i.e., increasing exactness in matching hydrocarbon structural characteristics) relative to the single target diesel fuel upon which the surrogate fuels are based. This approach was taken to assist in determining the minimum level of surrogate-fuel compositional accuracy that is required to adequately emulate the performance characteristics of the target fuel under different combustion modes. For each of the four surrogate fuels, an approximately 30 L batch was blended, and a number of the physical and chemical properties were measured. This work documents the surrogate-fuel creation process and the results of the property measurements. PMID:27330248

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

PubMed Central

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

2015-01-01

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. PMID:26271918

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

PubMed

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

2017-10-01

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.

Optimizing water resources management in large river basins with integrated surface water-groundwater modeling: A surrogate-based approach

NASA Astrophysics Data System (ADS)

Wu, Bin; Zheng, Yi; Wu, Xin; Tian, Yong; Han, Feng; Liu, Jie; Zheng, Chunmiao

2015-04-01

Integrated surface water-groundwater modeling can provide a comprehensive and coherent understanding on basin-scale water cycle, but its high computational cost has impeded its application in real-world management. This study developed a new surrogate-based approach, SOIM (Surrogate-based Optimization for Integrated surface water-groundwater Modeling), to incorporate the integrated modeling into water management optimization. Its applicability and advantages were evaluated and validated through an optimization research on the conjunctive use of surface water (SW) and groundwater (GW) for irrigation in a semiarid region in northwest China. GSFLOW, an integrated SW-GW model developed by USGS, was employed. The study results show that, due to the strong and complicated SW-GW interactions, basin-scale water saving could be achieved by spatially optimizing the ratios of groundwater use in different irrigation districts. The water-saving potential essentially stems from the reduction of nonbeneficial evapotranspiration from the aqueduct system and shallow groundwater, and its magnitude largely depends on both water management schemes and hydrological conditions. Important implications for water resources management in general include: first, environmental flow regulation needs to take into account interannual variation of hydrological conditions, as well as spatial complexity of SW-GW interactions; and second, to resolve water use conflicts between upper stream and lower stream, a system approach is highly desired to reflect ecological, economic, and social concerns in water management decisions. Overall, this study highlights that surrogate-based approaches like SOIM represent a promising solution to filling the gap between complex environmental modeling and real-world management decision-making.

Multi-fidelity Gaussian process regression for prediction of random fields

DOE Office of Scientific and Technical Information (OSTI.GOV)

Parussini, L.; Venturi, D., E-mail: venturi@ucsc.edu; Perdikaris, P.

We propose a new multi-fidelity Gaussian process regression (GPR) approach for prediction of random fields based on observations of surrogate models or hierarchies of surrogate models. Our method builds upon recent work on recursive Bayesian techniques, in particular recursive co-kriging, and extends it to vector-valued fields and various types of covariances, including separable and non-separable ones. The framework we propose is general and can be used to perform uncertainty propagation and quantification in model-based simulations, multi-fidelity data fusion, and surrogate-based optimization. We demonstrate the effectiveness of the proposed recursive GPR techniques through various examples. Specifically, we study the stochastic Burgersmore » equation and the stochastic Oberbeck–Boussinesq equations describing natural convection within a square enclosure. In both cases we find that the standard deviation of the Gaussian predictors as well as the absolute errors relative to benchmark stochastic solutions are very small, suggesting that the proposed multi-fidelity GPR approaches can yield highly accurate results.« less

Accurate quantification of PGE2 in the polyposis in rat colon (Pirc) model by surrogate analyte-based UPLC-MS/MS.

PubMed

Yun, Changhong; Dashwood, Wan-Mohaiza; Kwong, Lawrence N; Gao, Song; Yin, Taijun; Ling, Qinglan; Singh, Rashim; Dashwood, Roderick H; Hu, Ming

2018-01-30

An accurate and reliable UPLC-MS/MS method is reported for the quantification of endogenous Prostaglandin E2 (PGE 2 ) in rat colonic mucosa and polyps. This method adopted the "surrogate analyte plus authentic bio-matrix" approach, using two different stable isotopic labeled analogs - PGE 2 -d9 as the surrogate analyte and PGE 2 -d4 as the internal standard. A quantitative standard curve was constructed with the surrogate analyte in colonic mucosa homogenate, and the method was successfully validated with the authentic bio-matrix. Concentrations of endogenous PGE 2 in both normal and inflammatory tissue homogenates were back-calculated based on the regression equation. Because of no endogenous interference on the surrogate analyte determination, the specificity was particularly good. By using authentic bio-matrix for validation, the matrix effect and exaction recovery are identically same for the quantitative standard curve and actual samples - this notably increased the assay accuracy. The method is easy, fast, robust and reliable for colon PGE 2 determination. This "surrogate analyte" approach was applied to measure the Pirc (an Apc-mutant rat kindred that models human FAP) mucosa and polyps PGE 2 , one of the strong biomarkers of colorectal cancer. A similar concept could be applied to endogenous biomarkers in other tissues. Copyright © 2017 Elsevier B.V. All rights reserved.

SU-E-I-71: Quality Assessment of Surrogate Metrics in Multi-Atlas-Based Image Segmentation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhao, T; Ruan, D

Purpose: With the ever-growing data of heterogeneous quality, relevance assessment of atlases becomes increasingly critical for multi-atlas-based image segmentation. However, there is no universally recognized best relevance metric and even a standard to compare amongst candidates remains elusive. This study, for the first time, designs a quantification to assess relevance metrics’ quality, based on a novel perspective of the metric as surrogate for inferring the inaccessible oracle geometric agreement. Methods: We first develop an inference model to relate surrogate metrics in image space to the underlying oracle relevance metric in segmentation label space, with a monotonically non-decreasing function subject tomore » random perturbations. Subsequently, we investigate model parameters to reveal key contributing factors to surrogates’ ability in prognosticating the oracle relevance value, for the specific task of atlas selection. Finally, we design an effective contract-to-noise ratio (eCNR) to quantify surrogates’ quality based on insights from these analyses and empirical observations. Results: The inference model was specialized to a linear function with normally distributed perturbations, with surrogate metric exemplified by several widely-used image similarity metrics, i.e., MSD/NCC/(N)MI. Surrogates’ behaviors in selecting the most relevant atlases were assessed under varying eCNR, showing that surrogates with high eCNR dominated those with low eCNR in retaining the most relevant atlases. In an end-to-end validation, NCC/(N)MI with eCNR of 0.12 compared to MSD with eCNR of 0.10 resulted in statistically better segmentation with mean DSC of about 0.85 and the first and third quartiles of (0.83, 0.89), compared to MSD with mean DSC of 0.84 and the first and third quartiles of (0.81, 0.89). Conclusion: The designed eCNR is capable of characterizing surrogate metrics’ quality in prognosticating the oracle relevance value. It has been demonstrated to be correlated with the performance of relevant atlas selection and ultimate label fusion.« less

Sparse Polynomial Chaos Surrogate for ACME Land Model via Iterative Bayesian Compressive Sensing

NASA Astrophysics Data System (ADS)

Sargsyan, K.; Ricciuto, D. M.; Safta, C.; Debusschere, B.; Najm, H. N.; Thornton, P. E.

2015-12-01

For computationally expensive climate models, Monte-Carlo approaches of exploring the input parameter space are often prohibitive due to slow convergence with respect to ensemble size. To alleviate this, we build inexpensive surrogates using uncertainty quantification (UQ) methods employing Polynomial Chaos (PC) expansions that approximate the input-output relationships using as few model evaluations as possible. However, when many uncertain input parameters are present, such UQ studies suffer from the curse of dimensionality. In particular, for 50-100 input parameters non-adaptive PC representations have infeasible numbers of basis terms. To this end, we develop and employ Weighted Iterative Bayesian Compressive Sensing to learn the most important input parameter relationships for efficient, sparse PC surrogate construction with posterior uncertainty quantified due to insufficient data. Besides drastic dimensionality reduction, the uncertain surrogate can efficiently replace the model in computationally intensive studies such as forward uncertainty propagation and variance-based sensitivity analysis, as well as design optimization and parameter estimation using observational data. We applied the surrogate construction and variance-based uncertainty decomposition to Accelerated Climate Model for Energy (ACME) Land Model for several output QoIs at nearly 100 FLUXNET sites covering multiple plant functional types and climates, varying 65 input parameters over broad ranges of possible values. This work is supported by the U.S. Department of Energy, Office of Science, Biological and Environmental Research, Accelerated Climate Modeling for Energy (ACME) project. Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-AC04-94AL85000.

Uncertainty quantification of resonant ultrasound spectroscopy for material property and single crystal orientation estimation on a complex part

NASA Astrophysics Data System (ADS)

Aldrin, John C.; Mayes, Alexander; Jauriqui, Leanne; Biedermann, Eric; Heffernan, Julieanne; Livings, Richard; Goodlet, Brent; Mazdiyasni, Siamack

2018-04-01

A case study is presented evaluating uncertainty in Resonance Ultrasound Spectroscopy (RUS) inversion for a single crystal (SX) Ni-based superalloy Mar-M247 cylindrical dog-bone specimens. A number of surrogate models were developed with FEM model solutions, using different sampling schemes (regular grid, Monte Carlo sampling, Latin Hyper-cube sampling) and model approaches, N-dimensional cubic spline interpolation and Kriging. Repeated studies were used to quantify the well-posedness of the inversion problem, and the uncertainty was assessed in material property and crystallographic orientation estimates given typical geometric dimension variability in aerospace components. Surrogate model quality was found to be an important factor in inversion results when the model more closely represents the test data. One important discovery was when the model matches well with test data, a Kriging surrogate model using un-sorted Latin Hypercube sampled data performed as well as the best results from an N-dimensional interpolation model using sorted data. However, both surrogate model quality and mode sorting were found to be less critical when inverting properties from either experimental data or simulated test cases with uncontrolled geometric variation.

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 surrogate. As waveform generation is one of the dominant costs in parameter estimation algorithms and parameter space exploration, surrogate models offer a new and practical way to dramatically accelerate such studies without impacting accuracy. Surrogates built in this paper, as well as others, are available from GWSurrogate, a publicly available python package.

An Open-Source Toolbox for Surrogate Modeling of Joint Contact Mechanics

PubMed Central

Eskinazi, Ilan

2016-01-01

Goal Incorporation of elastic joint contact models into simulations of human movement could facilitate studying the interactions between muscles, ligaments, and bones. Unfortunately, elastic joint contact models are often too expensive computationally to be used within iterative simulation frameworks. This limitation can be overcome by using fast and accurate surrogate contact models that fit or interpolate input-output data sampled from existing elastic contact models. However, construction of surrogate contact models remains an arduous task. The aim of this paper is to introduce an open-source program called Surrogate Contact Modeling Toolbox (SCMT) that facilitates surrogate contact model creation, evaluation, and use. Methods SCMT interacts with the third party software FEBio to perform elastic contact analyses of finite element models and uses Matlab to train neural networks that fit the input-output contact data. SCMT features sample point generation for multiple domains, automated sampling, sample point filtering, and surrogate model training and testing. Results An overview of the software is presented along with two example applications. The first example demonstrates creation of surrogate contact models of artificial tibiofemoral and patellofemoral joints and evaluates their computational speed and accuracy, while the second demonstrates the use of surrogate contact models in a forward dynamic simulation of an open-chain leg extension-flexion motion. Conclusion SCMT facilitates the creation of computationally fast and accurate surrogate contact models. Additionally, it serves as a bridge between FEBio and OpenSim musculoskeletal modeling software. Significance Researchers may now create and deploy surrogate models of elastic joint contact with minimal effort. PMID:26186761

Fusion set selection with surrogate metric in multi-atlas based image segmentation

NASA Astrophysics Data System (ADS)

Zhao, Tingting; Ruan, Dan

2016-02-01

Multi-atlas based image segmentation sees unprecedented opportunities but also demanding challenges in the big data era. Relevant atlas selection before label fusion plays a crucial role in reducing potential performance loss from heterogeneous data quality and high computation cost from extensive data. This paper starts with investigating the image similarity metric (termed ‘surrogate’), an alternative to the inaccessible geometric agreement metric (termed ‘oracle’) in atlas relevance assessment, and probes into the problem of how to select the ‘most-relevant’ atlases and how many such atlases to incorporate. We propose an inference model to relate the surrogates and the oracle geometric agreement metrics. Based on this model, we quantify the behavior of the surrogates in mimicking oracle metrics for atlas relevance ordering. Finally, analytical insights on the choice of fusion set size are presented from a probabilistic perspective, with the integrated goal of including the most relevant atlases and excluding the irrelevant ones. Empirical evidence and performance assessment are provided based on prostate and corpus callosum segmentation.

Developing a particle tracking surrogate model to improve inversion of ground water - Surface water models

NASA Astrophysics Data System (ADS)

Cousquer, Yohann; Pryet, Alexandre; Atteia, Olivier; Ferré, Ty P. A.; Delbart, Célestine; Valois, Rémi; Dupuy, Alain

2018-03-01

The inverse problem of groundwater models is often ill-posed and model parameters are likely to be poorly constrained. Identifiability is improved if diverse data types are used for parameter estimation. However, some models, including detailed solute transport models, are further limited by prohibitive computation times. This often precludes the use of concentration data for parameter estimation, even if those data are available. In the case of surface water-groundwater (SW-GW) models, concentration data can provide SW-GW mixing ratios, which efficiently constrain the estimate of exchange flow, but are rarely used. We propose to reduce computational limits by simulating SW-GW exchange at a sink (well or drain) based on particle tracking under steady state flow conditions. Particle tracking is used to simulate advective transport. A comparison between the particle tracking surrogate model and an advective-dispersive model shows that dispersion can often be neglected when the mixing ratio is computed for a sink, allowing for use of the particle tracking surrogate model. The surrogate model was implemented to solve the inverse problem for a real SW-GW transport problem with heads and concentrations combined in a weighted hybrid objective function. The resulting inversion showed markedly reduced uncertainty in the transmissivity field compared to calibration on head data alone.

Model based Inverse Methods for Sizing Cracks of Varying Shape and Location in Bolt hole Eddy Current (BHEC) Inspections (Postprint)

DTIC Science & Technology

2016-02-10

using bolt hole eddy current (BHEC) techniques. Data was acquired for a wide range of crack sizes and shapes, including mid- bore , corner and through...to select the most appropriate VIC-3D surrogate model for subsequent crack sizing inversion step. Inversion results for select mid- bore , through and...the flaw. 15. SUBJECT TERMS Bolt hole eddy current (BHEC); mid- bore , corner and through-thickness crack types; VIC-3D generated surrogate models

A computational framework for simultaneous estimation of muscle and joint contact forces and body motion using optimization and surrogate modeling.

PubMed

Eskinazi, Ilan; Fregly, Benjamin J

2018-04-01

Concurrent estimation of muscle activations, joint contact forces, and joint kinematics by means of gradient-based optimization of musculoskeletal models is hindered by computationally expensive and non-smooth joint contact and muscle wrapping algorithms. We present a framework that simultaneously speeds up computation and removes sources of non-smoothness from muscle force optimizations using a combination of parallelization and surrogate modeling, with special emphasis on a novel method for modeling joint contact as a surrogate model of a static analysis. The approach allows one to efficiently introduce elastic joint contact models within static and dynamic optimizations of human motion. We demonstrate the approach by performing two optimizations, one static and one dynamic, using a pelvis-leg musculoskeletal model undergoing a gait cycle. We observed convergence on the order of seconds for a static optimization time frame and on the order of minutes for an entire dynamic optimization. The presented framework may facilitate model-based efforts to predict how planned surgical or rehabilitation interventions will affect post-treatment joint and muscle function. Copyright © 2018 IPEM. Published by Elsevier Ltd. All rights reserved.

A data-driven dynamics simulation framework for railway vehicles

NASA Astrophysics Data System (ADS)

Nie, Yinyu; Tang, Zhao; Liu, Fengjia; Chang, Jian; Zhang, Jianjun

2018-03-01

The finite element (FE) method is essential for simulating vehicle dynamics with fine details, especially for train crash simulations. However, factors such as the complexity of meshes and the distortion involved in a large deformation would undermine its calculation efficiency. An alternative method, the multi-body (MB) dynamics simulation provides satisfying time efficiency but limited accuracy when highly nonlinear dynamic process is involved. To maintain the advantages of both methods, this paper proposes a data-driven simulation framework for dynamics simulation of railway vehicles. This framework uses machine learning techniques to extract nonlinear features from training data generated by FE simulations so that specific mesh structures can be formulated by a surrogate element (or surrogate elements) to replace the original mechanical elements, and the dynamics simulation can be implemented by co-simulation with the surrogate element(s) embedded into a MB model. This framework consists of a series of techniques including data collection, feature extraction, training data sampling, surrogate element building, and model evaluation and selection. To verify the feasibility of this framework, we present two case studies, a vertical dynamics simulation and a longitudinal dynamics simulation, based on co-simulation with MATLAB/Simulink and Simpack, and a further comparison with a popular data-driven model (the Kriging model) is provided. The simulation result shows that using the legendre polynomial regression model in building surrogate elements can largely cut down the simulation time without sacrifice in accuracy.

Parametric geometric model and hydrodynamic shape optimization of a flying-wing structure underwater glider

NASA Astrophysics Data System (ADS)

Wang, Zhen-yu; Yu, Jian-cheng; Zhang, Ai-qun; Wang, Ya-xing; Zhao, Wen-tao

2017-12-01

Combining high precision numerical analysis methods with optimization algorithms to make a systematic exploration of a design space has become an important topic in the modern design methods. During the design process of an underwater glider's flying-wing structure, a surrogate model is introduced to decrease the computation time for a high precision analysis. By these means, the contradiction between precision and efficiency is solved effectively. Based on the parametric geometry modeling, mesh generation and computational fluid dynamics analysis, a surrogate model is constructed by adopting the design of experiment (DOE) theory to solve the multi-objects design optimization problem of the underwater glider. The procedure of a surrogate model construction is presented, and the Gaussian kernel function is specifically discussed. The Particle Swarm Optimization (PSO) algorithm is applied to hydrodynamic design optimization. The hydrodynamic performance of the optimized flying-wing structure underwater glider increases by 9.1%.

Real-time tumor motion estimation using respiratory surrogate via memory-based learning

NASA Astrophysics Data System (ADS)

Li, Ruijiang; Lewis, John H.; Berbeco, Ross I.; Xing, Lei

2012-08-01

Respiratory tumor motion is a major challenge in radiation therapy for thoracic and abdominal cancers. Effective motion management requires an accurate knowledge of the real-time tumor motion. External respiration monitoring devices (optical, etc) provide a noninvasive, non-ionizing, low-cost and practical approach to obtain the respiratory signal. Due to the highly complex and nonlinear relations between tumor and surrogate motion, its ultimate success hinges on the ability to accurately infer the tumor motion from respiratory surrogates. Given their widespread use in the clinic, such a method is critically needed. We propose to use a powerful memory-based learning method to find the complex relations between tumor motion and respiratory surrogates. The method first stores the training data in memory and then finds relevant data to answer a particular query. Nearby data points are assigned high relevance (or weights) and conversely distant data are assigned low relevance. By fitting relatively simple models to local patches instead of fitting one single global model, it is able to capture highly nonlinear and complex relations between the internal tumor motion and external surrogates accurately. Due to the local nature of weighting functions, the method is inherently robust to outliers in the training data. Moreover, both training and adapting to new data are performed almost instantaneously with memory-based learning, making it suitable for dynamically following variable internal/external relations. We evaluated the method using respiratory motion data from 11 patients. The data set consists of simultaneous measurement of 3D tumor motion and 1D abdominal surface (used as the surrogate signal in this study). There are a total of 171 respiratory traces, with an average peak-to-peak amplitude of ∼15 mm and average duration of ∼115 s per trace. Given only 5 s (roughly one breath) pretreatment training data, the method achieved an average 3D error of 1.5 mm and 95th percentile error of 3.4 mm on unseen test data. The average 3D error was further reduced to 1.4 mm when the model was tuned to its optimal setting for each respiratory trace. In one trace where a few outliers are present in the training data, the proposed method achieved an error reduction of as much as ∼50% compared with the best linear model (1.0 mm versus 2.1 mm). The memory-based learning technique is able to accurately capture the highly complex and nonlinear relations between tumor and surrogate motion in an efficient manner (a few milliseconds per estimate). Furthermore, the algorithm is particularly suitable to handle situations where the training data are contaminated by large errors or outliers. These desirable properties make it an ideal candidate for accurate and robust tumor gating/tracking using respiratory surrogates.

Hamiltonian Monte Carlo acceleration using surrogate functions with random bases.

PubMed

Zhang, Cheng; Shahbaba, Babak; Zhao, Hongkai

2017-11-01

For big data analysis, high computational cost for Bayesian methods often limits their applications in practice. In recent years, there have been many attempts to improve computational efficiency of Bayesian inference. Here we propose an efficient and scalable computational technique for a state-of-the-art Markov chain Monte Carlo methods, namely, Hamiltonian Monte Carlo. The key idea is to explore and exploit the structure and regularity in parameter space for the underlying probabilistic model to construct an effective approximation of its geometric properties. To this end, we build a surrogate function to approximate the target distribution using properly chosen random bases and an efficient optimization process. The resulting method provides a flexible, scalable, and efficient sampling algorithm, which converges to the correct target distribution. We show that by choosing the basis functions and optimization process differently, our method can be related to other approaches for the construction of surrogate functions such as generalized additive models or Gaussian process models. Experiments based on simulated and real data show that our approach leads to substantially more efficient sampling algorithms compared to existing state-of-the-art methods.

WE-AB-303-11: Verification of a Deformable 4DCT Motion Model for Lung Tumor Tracking Using Different Driving Surrogates

DOE Office of Scientific and Technical Information (OSTI.GOV)

Woelfelschneider, J; Friedrich-Alexander-University Erlangen-Nuremberg, Erlangen, DE; Seregni, M

2015-06-15

Purpose: Tumor tracking is an advanced technique to treat intra-fractionally moving tumors. The aim of this study is to validate a surrogate-driven model based on four-dimensional computed tomography (4DCT) that is able to predict CT volumes corresponding to arbitrary respiratory states. Further, the comparison of three different driving surrogates is evaluated. Methods: This study is based on multiple 4DCTs of two patients treated for bronchial carcinoma and metastasis. Analyses for 18 additional patients are currently ongoing. The motion model was estimated from the planning 4DCT through deformable image registration. To predict a certain phase of a follow-up 4DCT, the modelmore » considers for inter-fractional variations (baseline correction) and intra-fractional respiratory parameters (amplitude and phase) derived from surrogates. In this evaluation, three different approaches were used to extract the motion surrogate: for each 4DCT phase, the 3D thoraco-abdominal surface motion, the body volume and the anterior-posterior motion of a virtual single external marker defined on the sternum were investigated. The estimated volumes resulting from the model were compared to the ground-truth clinical 4DCTs using absolute HU differences in the lung volume and landmarks localized using the Scale Invariant Feature Transform (SIFT). Results: The results show absolute HU differences between estimated and ground-truth images with median values limited to 55 HU and inter-quartile ranges (IQR) lower than 100 HU. Median 3D distances between about 1500 matching landmarks are below 2 mm for 3D surface motion and body volume methods. The single marker surrogates Result in increased median distances up to 0.6 mm. Analyses for the extended database incl. 20 patients are currently in progress. Conclusion: The results depend mainly on the image quality of the initial 4DCTs and the deformable image registration. All investigated surrogates can be used to estimate follow-up 4DCT phases, however uncertainties decrease for three-dimensional approaches. This work was funded in parts by the German Research Council (DFG) - KFO 214/2.« less

Modeling methods for merging computational and experimental aerodynamic pressure data

NASA Astrophysics Data System (ADS)

Haderlie, Jacob C.

This research describes a process to model surface pressure data sets as a function of wing geometry from computational and wind tunnel sources and then merge them into a single predicted value. The described merging process will enable engineers to integrate these data sets with the goal of utilizing the advantages of each data source while overcoming the limitations of both; this provides a single, combined data set to support analysis and design. The main challenge with this process is accurately representing each data source everywhere on the wing. Additionally, this effort demonstrates methods to model wind tunnel pressure data as a function of angle of attack as an initial step towards a merging process that uses both location on the wing and flow conditions (e.g., angle of attack, flow velocity or Reynold's number) as independent variables. This surrogate model of pressure as a function of angle of attack can be useful for engineers that need to predict the location of zero-order discontinuities, e.g., flow separation or normal shocks. Because, to the author's best knowledge, there is no published, well-established merging method for aerodynamic pressure data (here, the coefficient of pressure Cp), this work identifies promising modeling and merging methods, and then makes a critical comparison of these methods. Surrogate models represent the pressure data for both data sets. Cubic B-spline surrogate models represent the computational simulation results. Machine learning and multi-fidelity surrogate models represent the experimental data. This research compares three surrogates for the experimental data (sequential--a.k.a. online--Gaussian processes, batch Gaussian processes, and multi-fidelity additive corrector) on the merits of accuracy and computational cost. The Gaussian process (GP) methods employ cubic B-spline CFD surrogates as a model basis function to build a surrogate model of the WT data, and this usage of the CFD surrogate in building the WT data could serve as a "merging" because the resulting WT pressure prediction uses information from both sources. In the GP approach, this model basis function concept seems to place more "weight" on the Cp values from the wind tunnel (WT) because the GP surrogate uses the CFD to approximate the WT data values. Conversely, the computationally inexpensive additive corrector method uses the CFD B-spline surrogate to define the shape of the spanwise distribution of the Cp while minimizing prediction error at all spanwise locations for a given arc length position; this, too, combines information from both sources to make a prediction of the 2-D WT-based Cp distribution, but the additive corrector approach gives more weight to the CFD prediction than to the WT data. Three surrogate models of the experimental data as a function of angle of attack are also compared for accuracy and computational cost. These surrogates are a single Gaussian process model (a single "expert"), product of experts, and generalized product of experts. The merging approach provides a single pressure distribution that combines experimental and computational data. The batch Gaussian process method provides a relatively accurate surrogate that is computationally acceptable, and can receive wind tunnel data from port locations that are not necessarily parallel to a variable direction. On the other hand, the sequential Gaussian process and additive corrector methods must receive a sufficient number of data points aligned with one direction, e.g., from pressure port bands (tap rows) aligned with the freestream. The generalized product of experts best represents wind tunnel pressure as a function of angle of attack, but at higher computational cost than the single expert approach. The format of the application data from computational and experimental sources in this work precluded the merging process from including flow condition variables (e.g., angle of attack) in the independent variables, so the merging process is only conducted in the wing geometry variables of arc length and span. The merging process of Cp data allows a more "hands-off" approach to aircraft design and analysis, (i.e., not as many engineers needed to debate the Cp distribution shape) and generates Cp predictions at any location on the wing. However, the cost with these benefits are engineer time (learning how to build surrogates), computational time in constructing the surrogates, and surrogate accuracy (surrogates introduce error into data predictions). This dissertation effort used the Trap Wing / First AIAA CFD High-Lift Prediction Workshop as a relevant transonic wing with a multi-element high-lift system, and this work identified that the batch GP model for the WT data and the B-spline surrogate for the CFD might best be combined using expert belief weights to describe Cp as a function of location on the wing element surface. (Abstract shortened by ProQuest.).

Active Learning to Understand Infectious Disease Models and Improve Policy Making

PubMed Central

Vladislavleva, Ekaterina; Broeckhove, Jan; Beutels, Philippe; Hens, Niel

2014-01-01

Modeling plays a major role in policy making, especially for infectious disease interventions but such models can be complex and computationally intensive. A more systematic exploration is needed to gain a thorough systems understanding. We present an active learning approach based on machine learning techniques as iterative surrogate modeling and model-guided experimentation to systematically analyze both common and edge manifestations of complex model runs. Symbolic regression is used for nonlinear response surface modeling with automatic feature selection. First, we illustrate our approach using an individual-based model for influenza vaccination. After optimizing the parameter space, we observe an inverse relationship between vaccination coverage and cumulative attack rate reinforced by herd immunity. Second, we demonstrate the use of surrogate modeling techniques on input-response data from a deterministic dynamic model, which was designed to explore the cost-effectiveness of varicella-zoster virus vaccination. We use symbolic regression to handle high dimensionality and correlated inputs and to identify the most influential variables. Provided insight is used to focus research, reduce dimensionality and decrease decision uncertainty. We conclude that active learning is needed to fully understand complex systems behavior. Surrogate models can be readily explored at no computational expense, and can also be used as emulator to improve rapid policy making in various settings. PMID:24743387

Active learning to understand infectious disease models and improve policy making.

PubMed

Willem, Lander; Stijven, Sean; Vladislavleva, Ekaterina; Broeckhove, Jan; Beutels, Philippe; Hens, Niel

2014-04-01

Modeling plays a major role in policy making, especially for infectious disease interventions but such models can be complex and computationally intensive. A more systematic exploration is needed to gain a thorough systems understanding. We present an active learning approach based on machine learning techniques as iterative surrogate modeling and model-guided experimentation to systematically analyze both common and edge manifestations of complex model runs. Symbolic regression is used for nonlinear response surface modeling with automatic feature selection. First, we illustrate our approach using an individual-based model for influenza vaccination. After optimizing the parameter space, we observe an inverse relationship between vaccination coverage and cumulative attack rate reinforced by herd immunity. Second, we demonstrate the use of surrogate modeling techniques on input-response data from a deterministic dynamic model, which was designed to explore the cost-effectiveness of varicella-zoster virus vaccination. We use symbolic regression to handle high dimensionality and correlated inputs and to identify the most influential variables. Provided insight is used to focus research, reduce dimensionality and decrease decision uncertainty. We conclude that active learning is needed to fully understand complex systems behavior. Surrogate models can be readily explored at no computational expense, and can also be used as emulator to improve rapid policy making in various settings.

Gaussian process-based surrogate modeling framework for process planning in laser powder-bed fusion additive manufacturing of 316L stainless steel

DOE PAGES

Tapia, Gustavo; Khairallah, Saad A.; Matthews, Manyalibo J.; ...

2017-09-22

Here, Laser Powder-Bed Fusion (L-PBF) metal-based additive manufacturing (AM) is complex and not fully understood. Successful processing for one material, might not necessarily apply to a different material. This paper describes a workflow process that aims at creating a material data sheet standard that describes regimes where the process can be expected to be robust. The procedure consists of building a Gaussian process-based surrogate model of the L-PBF process that predicts melt pool depth in single-track experiments given a laser power, scan speed, and laser beam size combination. The predictions are then mapped onto a power versus scan speed diagrammore » delimiting the conduction from the keyhole melting controlled regimes. This statistical framework is shown to be robust even for cases where experimental training data might be suboptimal in quality, if appropriate physics-based filters are applied. Additionally, it is demonstrated that a high-fidelity simulation model of L-PBF can equally be successfully used for building a surrogate model, which is beneficial since simulations are getting more efficient and are more practical to study the response of different materials, than to re-tool an AM machine for new material powder.« less

Gaussian process-based surrogate modeling framework for process planning in laser powder-bed fusion additive manufacturing of 316L stainless steel

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tapia, Gustavo; Khairallah, Saad A.; Matthews, Manyalibo J.

Here, Laser Powder-Bed Fusion (L-PBF) metal-based additive manufacturing (AM) is complex and not fully understood. Successful processing for one material, might not necessarily apply to a different material. This paper describes a workflow process that aims at creating a material data sheet standard that describes regimes where the process can be expected to be robust. The procedure consists of building a Gaussian process-based surrogate model of the L-PBF process that predicts melt pool depth in single-track experiments given a laser power, scan speed, and laser beam size combination. The predictions are then mapped onto a power versus scan speed diagrammore » delimiting the conduction from the keyhole melting controlled regimes. This statistical framework is shown to be robust even for cases where experimental training data might be suboptimal in quality, if appropriate physics-based filters are applied. Additionally, it is demonstrated that a high-fidelity simulation model of L-PBF can equally be successfully used for building a surrogate model, which is beneficial since simulations are getting more efficient and are more practical to study the response of different materials, than to re-tool an AM machine for new material powder.« less

Gaussian process regression of chirplet decomposed ultrasonic B-scans of a simulated design case

NASA Astrophysics Data System (ADS)

Wertz, John; Homa, Laura; Welter, John; Sparkman, Daniel; Aldrin, John

2018-04-01

The US Air Force seeks to implement damage tolerant lifecycle management of composite structures. Nondestructive characterization of damage is a key input to this framework. One approach to characterization is model-based inversion of the ultrasonic response from damage features; however, the computational expense of modeling the ultrasonic waves within composites is a major hurdle to implementation. A surrogate forward model with sufficient accuracy and greater computational efficiency is therefore critical to enabling model-based inversion and damage characterization. In this work, a surrogate model is developed on the simulated ultrasonic response from delamination-like structures placed at different locations within a representative composite layup. The resulting B-scans are decomposed via the chirplet transform, and a Gaussian process model is trained on the chirplet parameters. The quality of the surrogate is tested by comparing the B-scan for a delamination configuration not represented within the training data set. The estimated B-scan has a maximum error of ˜15% for an estimated reduction in computational runtime of ˜95% for 200 function calls. This considerable reduction in computational expense makes full 3D characterization of impact damage tractable.

An efficient adaptive sampling strategy for global surrogate modeling with applications in multiphase flow simulation

NASA Astrophysics Data System (ADS)

Mo, S.; Lu, D.; Shi, X.; Zhang, G.; Ye, M.; Wu, J.

2016-12-01

Surrogate models have shown remarkable computational efficiency in hydrological simulations involving design space exploration, sensitivity analysis, uncertainty quantification, etc. The central task of constructing a global surrogate models is to achieve a prescribed approximation accuracy with as few original model executions as possible, which requires a good design strategy to optimize the distribution of data points in the parameter domains and an effective stopping criterion to automatically terminate the design process when desired approximation accuracy is achieved. This study proposes a novel adaptive sampling strategy, which starts from a small number of initial samples and adaptively selects additional samples by balancing the collection in unexplored regions and refinement in interesting areas. We define an efficient and effective evaluation metric basing on Taylor expansion to select the most promising potential samples from candidate points, and propose a robust stopping criterion basing on the approximation accuracy at new points to guarantee the achievement of desired accuracy. The numerical results of several benchmark analytical functions indicate that the proposed approach is more computationally efficient and robust than the widely used maximin distance design and two other well-known adaptive sampling strategies. The application to two complicated multiphase flow problems further demonstrates the efficiency and effectiveness of our method in constructing global surrogate models for high-dimensional and highly nonlinear problems. Acknowledgements: This work was financially supported by the National Nature Science Foundation of China grants No. 41030746 and 41172206.

An adaptive sparse-grid high-order stochastic collocation method for Bayesian inference in groundwater reactive transport modeling

NASA Astrophysics Data System (ADS)

Zhang, Guannan; Lu, Dan; Ye, Ming; Gunzburger, Max; Webster, Clayton

2013-10-01

Bayesian analysis has become vital to uncertainty quantification in groundwater modeling, but its application has been hindered by the computational cost associated with numerous model executions required by exploring the posterior probability density function (PPDF) of model parameters. This is particularly the case when the PPDF is estimated using Markov Chain Monte Carlo (MCMC) sampling. In this study, a new approach is developed to improve the computational efficiency of Bayesian inference by constructing a surrogate of the PPDF, using an adaptive sparse-grid high-order stochastic collocation (aSG-hSC) method. Unlike previous works using first-order hierarchical basis, this paper utilizes a compactly supported higher-order hierarchical basis to construct the surrogate system, resulting in a significant reduction in the number of required model executions. In addition, using the hierarchical surplus as an error indicator allows locally adaptive refinement of sparse grids in the parameter space, which further improves computational efficiency. To efficiently build the surrogate system for the PPDF with multiple significant modes, optimization techniques are used to identify the modes, for which high-probability regions are defined and components of the aSG-hSC approximation are constructed. After the surrogate is determined, the PPDF can be evaluated by sampling the surrogate system directly without model execution, resulting in improved efficiency of the surrogate-based MCMC compared with conventional MCMC. The developed method is evaluated using two synthetic groundwater reactive transport models. The first example involves coupled linear reactions and demonstrates the accuracy of our high-order hierarchical basis approach in approximating high-dimensional posteriori distribution. The second example is highly nonlinear because of the reactions of uranium surface complexation, and demonstrates how the iterative aSG-hSC method is able to capture multimodal and non-Gaussian features of PPDF caused by model nonlinearity. Both experiments show that aSG-hSC is an effective and efficient tool for Bayesian inference.

A Bayesian approach to modelling the impact of hydrodynamic shear stress on biofilm deformation

PubMed Central

Wilkinson, Darren J.; Jayathilake, Pahala Gedara; Rushton, Steve P.; Bridgens, Ben; Li, Bowen; Zuliani, Paolo

2018-01-01

We investigate the feasibility of using a surrogate-based method to emulate the deformation and detachment behaviour of a biofilm in response to hydrodynamic shear stress. The influence of shear force, growth rate and viscoelastic parameters on the patterns of growth, structure and resulting shape of microbial biofilms was examined. We develop a statistical modelling approach to this problem, using combination of Bayesian Poisson regression and dynamic linear models for the emulation. We observe that the hydrodynamic shear force affects biofilm deformation in line with some literature. Sensitivity results also showed that the expected number of shear events, shear flow, yield coefficient for heterotrophic bacteria and extracellular polymeric substance (EPS) stiffness per unit EPS mass are the four principal mechanisms governing the bacteria detachment in this study. The sensitivity of the model parameters is temporally dynamic, emphasising the significance of conducting the sensitivity analysis across multiple time points. The surrogate models are shown to perform well, and produced ≈ 480 fold increase in computational efficiency. We conclude that a surrogate-based approach is effective, and resulting biofilm structure is determined primarily by a balance between bacteria growth, viscoelastic parameters and applied shear stress. PMID:29649240

Wavelet-based surrogate time series for multiscale simulation of heterogeneous catalysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Savara, Aditya Ashi; Daw, C. Stuart; Xiong, Qingang

We propose a wavelet-based scheme that encodes the essential dynamics of discrete microscale surface reactions in a form that can be coupled with continuum macroscale flow simulations with high computational efficiency. This makes it possible to simulate the dynamic behavior of reactor-scale heterogeneous catalysis without requiring detailed concurrent simulations at both the surface and continuum scales using different models. Our scheme is based on the application of wavelet-based surrogate time series that encodes the essential temporal and/or spatial fine-scale dynamics at the catalyst surface. The encoded dynamics are then used to generate statistically equivalent, randomized surrogate time series, which canmore » be linked to the continuum scale simulation. As a result, we illustrate an application of this approach using two different kinetic Monte Carlo simulations with different characteristic behaviors typical for heterogeneous chemical reactions.« less

Wavelet-based surrogate time series for multiscale simulation of heterogeneous catalysis

DOE PAGES

Savara, Aditya Ashi; Daw, C. Stuart; Xiong, Qingang; ...

2016-01-28

We propose a wavelet-based scheme that encodes the essential dynamics of discrete microscale surface reactions in a form that can be coupled with continuum macroscale flow simulations with high computational efficiency. This makes it possible to simulate the dynamic behavior of reactor-scale heterogeneous catalysis without requiring detailed concurrent simulations at both the surface and continuum scales using different models. Our scheme is based on the application of wavelet-based surrogate time series that encodes the essential temporal and/or spatial fine-scale dynamics at the catalyst surface. The encoded dynamics are then used to generate statistically equivalent, randomized surrogate time series, which canmore » be linked to the continuum scale simulation. As a result, we illustrate an application of this approach using two different kinetic Monte Carlo simulations with different characteristic behaviors typical for heterogeneous chemical reactions.« less

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rabiti, Cristian; Alfonsi, Andrea; Huang, Dongli

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.

Uncertainty quantification for accident management using ACE surrogates

DOE Office of Scientific and Technical Information (OSTI.GOV)

Varuttamaseni, A.; Lee, J. C.; Youngblood, R. W.

The alternating conditional expectation (ACE) regression method is used to generate RELAP5 surrogates which are then used to determine the distribution of the peak clad temperature (PCT) during the loss of feedwater accident coupled with a subsequent initiation of the feed and bleed (F and B) operation in the Zion-1 nuclear power plant. The construction of the surrogates assumes conditional independence relations among key reactor parameters. The choice of parameters to model is based on the macroscopic balance statements governing the behavior of the reactor. The peak clad temperature is calculated based on the independent variables that are known tomore » be important in determining the success of the F and B operation. The relationship between these independent variables and the plant parameters such as coolant pressure and temperature is represented by surrogates that are constructed based on 45 RELAP5 cases. The time-dependent PCT for different values of F and B parameters is calculated by sampling the independent variables from their probability distributions and propagating the information through two layers of surrogates. The results of our analysis show that the ACE surrogates are able to satisfactorily reproduce the behavior of the plant parameters even though a quasi-static assumption is primarily used in their construction. The PCT is found to be lower in cases where the F and B operation is initiated, compared to the case without F and B, regardless of the F and B parameters used. (authors)« less

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sen, Oishik, E-mail: oishik-sen@uiowa.edu; Gaul, Nicholas J., E-mail: nicholas-gaul@ramdosolutions.com; Choi, K.K., E-mail: kyung-choi@uiowa.edu

Macro-scale computations of shocked particulate flows require closure laws that model the exchange of momentum/energy between the fluid and particle phases. Closure laws are constructed in this work in the form of surrogate models derived from highly resolved mesoscale computations of shock-particle interactions. The mesoscale computations are performed to calculate the drag force on a cluster of particles for different values of Mach Number and particle volume fraction. Two Kriging-based methods, viz. the Dynamic Kriging Method (DKG) and the Modified Bayesian Kriging Method (MBKG) are evaluated for their ability to construct surrogate models with sparse data; i.e. using the leastmore » number of mesoscale simulations. It is shown that if the input data is noise-free, the DKG method converges monotonically; convergence is less robust in the presence of noise. The MBKG method converges monotonically even with noisy input data and is therefore more suitable for surrogate model construction from numerical experiments. This work is the first step towards a full multiscale modeling of interaction of shocked particle laden flows.« less

An integrated simulation and optimization approach for managing human health risks of atmospheric pollutants by coal-fired power plants.

PubMed

Dai, C; Cai, X H; Cai, Y P; Guo, H C; Sun, W; Tan, Q; Huang, G H

2014-06-01

This research developed a simulation-aided nonlinear programming model (SNPM). This model incorporated the consideration of pollutant dispersion modeling, and the management of coal blending and the related human health risks within a general modeling framework In SNPM, the simulation effort (i.e., California puff [CALPUFF]) was used to forecast the fate of air pollutants for quantifying the health risk under various conditions, while the optimization studies were to identify the optimal coal blending strategies from a number of alternatives. To solve the model, a surrogate-based indirect search approach was proposed, where the support vector regression (SVR) was used to create a set of easy-to-use and rapid-response surrogates for identifying the function relationships between coal-blending operating conditions and health risks. Through replacing the CALPUFF and the corresponding hazard quotient equation with the surrogates, the computation efficiency could be improved. The developed SNPM was applied to minimize the human health risk associated with air pollutants discharged from Gaojing and Shijingshan power plants in the west of Beijing. Solution results indicated that it could be used for reducing the health risk of the public in the vicinity of the two power plants, identifying desired coal blending strategies for decision makers, and considering a proper balance between coal purchase cost and human health risk. A simulation-aided nonlinear programming model (SNPM) is developed. It integrates the advantages of CALPUFF and nonlinear programming model. To solve the model, a surrogate-based indirect search approach based on the combination of support vector regression and genetic algorithm is proposed. SNPM is applied to reduce the health risk caused by air pollutants discharged from Gaojing and Shijingshan power plants in the west of Beijing. Solution results indicate that it is useful for generating coal blending schemes, reducing the health risk of the public, reflecting the trade-offbetween coal purchase cost and health risk.

Reliability based design including future tests and multiagent approaches

NASA Astrophysics Data System (ADS)

Villanueva, Diane

The initial stages of reliability-based design optimization involve the formulation of objective functions and constraints, and building a model to estimate the reliability of the design with quantified uncertainties. However, even experienced hands often overlook important objective functions and constraints that affect the design. In addition, uncertainty reduction measures, such as tests and redesign, are often not considered in reliability calculations during the initial stages. This research considers two areas that concern the design of engineering systems: 1) the trade-off of the effect of a test and post-test redesign on reliability and cost and 2) the search for multiple candidate designs as insurance against unforeseen faults in some designs. In this research, a methodology was developed to estimate the effect of a single future test and post-test redesign on reliability and cost. The methodology uses assumed distributions of computational and experimental errors with re-design rules to simulate alternative future test and redesign outcomes to form a probabilistic estimate of the reliability and cost for a given design. Further, it was explored how modeling a future test and redesign provides a company an opportunity to balance development costs versus performance by simultaneously designing the design and the post-test redesign rules during the initial design stage. The second area of this research considers the use of dynamic local surrogates, or surrogate-based agents, to locate multiple candidate designs. Surrogate-based global optimization algorithms often require search in multiple candidate regions of design space, expending most of the computation needed to define multiple alternate designs. Thus, focusing on solely locating the best design may be wasteful. We extended adaptive sampling surrogate techniques to locate multiple optima by building local surrogates in sub-regions of the design space to identify optima. The efficiency of this method was studied, and the method was compared to other surrogate-based optimization methods that aim to locate the global optimum using two two-dimensional test functions, a six-dimensional test function, and a five-dimensional engineering example.

Atomic Radius and Charge Parameter Uncertainty in Biomolecular Solvation Energy Calculations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yang, Xiu; Lei, Huan; Gao, Peiyuan

Atomic radii and charges are two major parameters used in implicit solvent electrostatics and energy calculations. The optimization problem for charges and radii is under-determined, leading to uncertainty in the values of these parameters and in the results of solvation energy calculations using these parameters. This paper presents a method for quantifying this uncertainty in solvation energies using surrogate models based on generalized polynomial chaos (gPC) expansions. There are relatively few atom types used to specify radii parameters in implicit solvation calculations; therefore, surrogate models for these low-dimensional spaces could be constructed using least-squares fitting. However, there are many moremore » types of atomic charges; therefore, construction of surrogate models for the charge parameter space required compressed sensing combined with an iterative rotation method to enhance problem sparsity. We present results for the uncertainty in small molecule solvation energies based on these approaches. Additionally, we explore the correlation between uncertainties due to radii and charges which motivates the need for future work in uncertainty quantification methods for high-dimensional parameter spaces.« less

Scalability of surrogate-assisted multi-objective optimization of antenna structures exploiting variable-fidelity electromagnetic simulation models

NASA Astrophysics Data System (ADS)

Koziel, Slawomir; Bekasiewicz, Adrian

2016-10-01

Multi-objective optimization of antenna structures is a challenging task owing to the high computational cost of evaluating the design objectives as well as the large number of adjustable parameters. Design speed-up can be achieved by means of surrogate-based optimization techniques. In particular, a combination of variable-fidelity electromagnetic (EM) simulations, design space reduction techniques, response surface approximation models and design refinement methods permits identification of the Pareto-optimal set of designs within a reasonable timeframe. Here, a study concerning the scalability of surrogate-assisted multi-objective antenna design is carried out based on a set of benchmark problems, with the dimensionality of the design space ranging from six to 24 and a CPU cost of the EM antenna model from 10 to 20 min per simulation. Numerical results indicate that the computational overhead of the design process increases more or less quadratically with the number of adjustable geometric parameters of the antenna structure at hand, which is a promising result from the point of view of handling even more complex problems.

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 error analysis capability has been developed which provides probabilistic error estimates on the true performance for a design randomly selected near the surrogate-predicted optimal design.

Improved design of prodromal Alzheimer's disease trials through cohort enrichment and surrogate endpoints.

PubMed

Macklin, Eric A; Blacker, Deborah; Hyman, Bradley T; Betensky, Rebecca A

2013-01-01

Alzheimer's disease (AD) trials initiated during or before the prodrome are costly and lengthy because patients are enrolled long before clinical symptoms are apparent, when disease progression is slow. We hypothesized that design of such trials could be improved by: 1) selecting individuals at moderate near-term risk of progression to AD dementia (the current clinical standard) and 2) by using short-term surrogate endpoints that predict progression to AD dementia. We used a longitudinal cohort of older, initially non-demented, community-dwelling participants (n = 358) to derive selection criteria and surrogate endpoints and tested them in an independent national data set (n = 6,243). To identify a "mid-risk" subgroup, we applied conditional tree-based survival models to Clinical Dementia Rating (CDR) scale scores and common neuropsychological tests. In the validation cohort, a time-to-AD dementia trial applying these mid-risk selection criteria to a pool of all non-demented individuals could achieve equivalent power with 47% fewer participants than enrolling at random from that pool. We evaluated surrogate endpoints measureable over two years of follow-up based on cross-validated concordance between predictions from Cox models and observed time to AD dementia. The best performing surrogate, rate of change in CDR sum-of-boxes, did not reduce the trial duration required for equivalent power using estimates from the validation cohort, but alternative surrogates with better ability to predict time to AD dementia should be able to do so. The approach tested here might improve efficiency of prodromal AD trials using other potential measures and could be generalized to other diseases with long prodromal phases.

Improved design of prodromal Alzheimer’s disease trials through cohort enrichment and surrogate endpoints

PubMed Central

Macklin, Eric A.; Blacker, Deborah; Hyman, Bradley T.; Betensky, Rebecca A.

2013-01-01

Summary Alzheimer’s disease (AD) trials initiated during or before the prodrome are costly and lengthy because patients are enrolled long before clinical symptoms are apparent, when disease progression is slow. We hypothesized that design of such trials could be improved by: (1) selecting individuals at moderate near-term risk of progression to AD dementia (the current clinical standard) and (2) by using short-term surrogate endpoints that predict progression to AD dementia. We used a longitudinal cohort of older, initially non-demented, community-dwelling participants (n=358) to derive selection criteria and surrogate endpoints and tested them in an independent national data set (n=6,243). To identify a “mid-risk” subgroup, we applied conditional tree-based survival models to Clinical Dementia Rating (CDR) scale scores and common neuropsychological tests. In the validation cohort, a time-to-AD dementia trial applying these mid-risk selection criteria to a pool of all non-demented individuals could achieve equivalent power with 47% fewer participants than enrolling at random from that pool. We evaluated surrogate endpoints measureable over two years of follow-up based on cross-validated concordance between predictions from Cox models and observed time to AD dementia. The best performing surrogate, rate of change in CDR sum-of-boxes, did not reduce the trial duration required for equivalent power using estimates from the validation cohort, but alternative surrogates with better ability to predict time to AD dementia should be able to do so. The approach tested here might improve efficiency of prodromal AD trials using other potential measures and could be generalized to other diseases with long prodromal phases. PMID:23629586

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.

Mass diffusion coefficient measurement for vitreous humor using FEM and MRI

NASA Astrophysics Data System (ADS)

Rattanakijsuntorn, Komsan; Penkova, Anita; Sadha, Satwindar S.

2018-01-01

In early studies, the ‘contour method’ for determining the diffusion coefficient of the vitreous humor was developed. This technique relied on careful injection of an MRI contrast agent (surrogate drug) into the vitreous humor of fresh bovine eyes, and tracking the contours of the contrast agent in time. In addition, an analytical solution was developed for the theoretical contours built on point source model for the injected surrogate drug. The match between theoretical and experimental contours as a least square fit, while floating the diffusion coefficient, led to the value of the diffusion coefficient. This method had its limitation that the initial injection of the surrogate had to be spherical or ellipsoidal because of the analytical result based on the point-source model. With a new finite element model for the analysis in this study, the technique is much less restrictive and handles irregular shapes of the initial bolus. The fresh bovine eyes were used for drug diffusion study in the vitreous and three contrast agents of different molecular masses: gadolinium-diethylenetriaminepentaacetic acid (Gd-DTPA, 938 Da), non-ionic gadoteridol (Prohance, 559 Da), and bovine albumin conjugated with gadolinium (Galbumin, 74 kDa) were used as drug surrogates to visualize the diffusion process by MRI. The 3D finite element model was developed to determine the diffusion coefficients of these surrogates with the images from MRI. This method can be used for other types of bioporous media provided the concentration profile can be visualized (by methods such as MRI or fluorescence).

Development of surrogate models for the prediction of the flow around an aircraft propeller

NASA Astrophysics Data System (ADS)

Salpigidou, Christina; Misirlis, Dimitris; Vlahostergios, Zinon; Yakinthos, Kyros

2018-05-01

In the present work, the derivation of two surrogate models (SMs) for modelling the flow around a propeller for small aircrafts is presented. Both methodologies use derived functions based on computations with the detailed propeller geometry. The computations were performed using k-ω shear stress transport for modelling turbulence. In the SMs, the modelling of the propeller was performed in a computational domain of disk-like geometry, where source terms were introduced in the momentum equations. In the first SM, the source terms were polynomial functions of swirl and thrust, mainly related to the propeller radius. In the second SM, regression analysis was used to correlate the source terms with the velocity distribution through the propeller. The proposed SMs achieved faster convergence, in relation to the detail model, by providing also results closer to the available operational data. The regression-based model was the most accurate and required less computational time for convergence.

The effectiveness of surrogate taxa to conserve freshwater biodiversity

USGS Publications Warehouse

Stewart, David R.; Underwood, Zachary E.; Rahel, Frank J.; Walters, Annika W.

2018-01-01

Establishing protected areas has long been an effective conservation strategy, and is often based on more readily surveyed species. The potential of any freshwater taxa to be a surrogate of other aquatic groups has not been fully explored. We compiled occurrence data on 72 species of freshwater fish, amphibians, mussels, and aquatic reptiles for the Great Plains, Wyoming. We used hierarchical Bayesian multi-species mixture models and MaxEnt models to describe species distributions, and program Zonation to identify conservation priority areas for each aquatic group. The landscape-scale factors that best characterized aquatic species distributions differed among groups. There was low agreement and congruence among taxa-specific conservation priorities (<20%), meaning that no surrogate priority areas would include or protect the best habitats of other aquatic taxa. We found that common, wide-ranging aquatic species were included in taxa-specific priority areas, but rare freshwater species were not included. Thus, the development of conservation priorities based on a single freshwater aquatic group would not protect all species in the other aquatic groups.

Surrogate waveform models

NASA Astrophysics Data System (ADS)

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

2015-04-01

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

A refined 2010-based VOC emission inventory and its improvement on modeling regional ozone in the Pearl River Delta Region, China.

PubMed

Yin, Shasha; Zheng, Junyu; Lu, Qing; Yuan, Zibing; Huang, Zhijiong; Zhong, Liuju; Lin, Hui

2015-05-01

Accurate and gridded VOC emission inventories are important for improving regional air quality model performance. In this study, a four-level VOC emission source categorization system was proposed. A 2010-based gridded Pearl River Delta (PRD) regional VOC emission inventory was developed with more comprehensive source coverage, latest emission factors, and updated activity data. The total anthropogenic VOC emission was estimated to be about 117.4 × 10(4)t, in which on-road mobile source shared the largest contribution, followed by industrial solvent use and industrial processes sources. Among the industrial solvent use source, furniture manufacturing and shoemaking were major VOC emission contributors. The spatial surrogates of VOC emission were updated for major VOC sources such as industrial sectors and gas stations. Subsector-based temporal characteristics were investigated and their temporal variations were characterized. The impacts of updated VOC emission estimates and spatial surrogates were evaluated by modeling O₃ concentration in the PRD region in the July and October of 2010, respectively. The results indicated that both updated emission estimates and spatial allocations can effectively reduce model bias on O₃ simulation. Further efforts should be made on the refinement of source classification, comprehensive collection of activity data, and spatial-temporal surrogates in order to reduce uncertainty in emission inventory and improve model performance. Copyright © 2015 Elsevier B.V. All rights reserved.

Evaluation of the validity of treatment decisions based on surrogate country models before introduction of the Polish FRAX and recommendations in comparison to current practice

PubMed Central

Narloch, Jerzy; Glinkowska, Bożena; Bandura, Małgorzata

2016-01-01

Introduction Patients diagnosed before the Polish FRAX was introduced may require re-evaluation and treatment changes if the diagnosis was established according to a surrogate country FRAX score. The aim of the study was to evaluate the validity of treatment decisions based on the surrogate country model before introduction of the Polish FRAX and to provide recommendations based on the current practice. Material and methods We evaluated a group of 142 postmenopausal women (70.7 ±8.9 years) who underwent bone mineral density measurements. We used 22 country-specific FRAX models and compared these to the Polish model. Results The mean risk values for hip and major osteoporotic fractures within 10 years were 4.575 (from 0.82 to 8.46) and 12.47% (from 2.18 to 21.65), respectively. In the case of a major fracture, 94.4% of women would receive lifestyle advice, and 5.6% would receive treatment according to the Polish FRAX using the guidelines of the National Osteoporosis Foundation (NOF). Polish treatment thresholds would implement pharmacotherapy in 32.4% of the study group. In the case of hip fractures, 45% of women according to the NOF would require pharmacotherapy but only 9.8% of women would qualify according to Polish guidelines. Nearly all surrogate FRAX calculator scores proved significantly different form Polish (p > 0.05). Conclusions More patients might have received antiresorptive medication before the Polish FRAX. This study recommends re-evaluation of patients who received medical therapy before the Polish FRAX was introduced and a review of the recommendations, considering the side effects of antiresorptive medication. PMID:29593808

Evaluation of the validity of treatment decisions based on surrogate country models before introduction of the Polish FRAX and recommendations in comparison to current practice.

PubMed

Glinkowski, Wojciech M; Narloch, Jerzy; Glinkowska, Bożena; Bandura, Małgorzata

2018-03-01

Patients diagnosed before the Polish FRAX was introduced may require re-evaluation and treatment changes if the diagnosis was established according to a surrogate country FRAX score. The aim of the study was to evaluate the validity of treatment decisions based on the surrogate country model before introduction of the Polish FRAX and to provide recommendations based on the current practice. We evaluated a group of 142 postmenopausal women (70.7 ±8.9 years) who underwent bone mineral density measurements. We used 22 country-specific FRAX models and compared these to the Polish model. The mean risk values for hip and major osteoporotic fractures within 10 years were 4.575 (from 0.82 to 8.46) and 12.47% (from 2.18 to 21.65), respectively. In the case of a major fracture, 94.4% of women would receive lifestyle advice, and 5.6% would receive treatment according to the Polish FRAX using the guidelines of the National Osteoporosis Foundation (NOF). Polish treatment thresholds would implement pharmacotherapy in 32.4% of the study group. In the case of hip fractures, 45% of women according to the NOF would require pharmacotherapy but only 9.8% of women would qualify according to Polish guidelines. Nearly all surrogate FRAX calculator scores proved significantly different form Polish ( p > 0.05). More patients might have received antiresorptive medication before the Polish FRAX. This study recommends re-evaluation of patients who received medical therapy before the Polish FRAX was introduced and a review of the recommendations, considering the side effects of antiresorptive medication.

An Integrated Optimization Design Method Based on Surrogate Modeling Applied to Diverging Duct Design

NASA Astrophysics Data System (ADS)

Hanan, Lu; Qiushi, Li; Shaobin, Li

2016-12-01

This paper presents an integrated optimization design method in which uniform design, response surface methodology and genetic algorithm are used in combination. In detail, uniform design is used to select the experimental sampling points in the experimental domain and the system performance is evaluated by means of computational fluid dynamics to construct a database. After that, response surface methodology is employed to generate a surrogate mathematical model relating the optimization objective and the design variables. Subsequently, genetic algorithm is adopted and applied to the surrogate model to acquire the optimal solution in the case of satisfying some constraints. The method has been applied to the optimization design of an axisymmetric diverging duct, dealing with three design variables including one qualitative variable and two quantitative variables. The method of modeling and optimization design performs well in improving the duct aerodynamic performance and can be also applied to wider fields of mechanical design and seen as a useful tool for engineering designers, by reducing the design time and computation consumption.

Magnetic Resonance Imaging–Guided versus Surrogate-Based Motion Tracking in Liver Radiation Therapy: A Prospective Comparative Study

DOE Office of Scientific and Technical Information (OSTI.GOV)

Paganelli, Chiara, E-mail: chiara.paganelli@polimi.it; Seregni, Matteo; Fattori, Giovanni

Purpose: This study applied automatic feature detection on cine–magnetic resonance imaging (MRI) liver images in order to provide a prospective comparison between MRI-guided and surrogate-based tracking methods for motion-compensated liver radiation therapy. Methods and Materials: In a population of 30 subjects (5 volunteers plus 25 patients), 2 oblique sagittal slices were acquired across the liver at high temporal resolution. An algorithm based on scale invariant feature transform (SIFT) was used to extract and track multiple features throughout the image sequence. The position of abdominal markers was also measured directly from the image series, and the internal motion of each featuremore » was quantified through multiparametric analysis. Surrogate-based tumor tracking with a state-of-the-art external/internal correlation model was simulated. The geometrical tracking error was measured, and its correlation with external motion parameters was also investigated. Finally, the potential gain in tracking accuracy relying on MRI guidance was quantified as a function of the maximum allowed tracking error. Results: An average of 45 features was extracted for each subject across the whole liver. The multi-parametric motion analysis reported relevant inter- and intrasubject variability, highlighting the value of patient-specific and spatially-distributed measurements. Surrogate-based tracking errors (relative to the motion amplitude) were were in the range 7% to 23% (1.02-3.57mm) and were significantly influenced by external motion parameters. The gain of MRI guidance compared to surrogate-based motion tracking was larger than 30% in 50% of the subjects when considering a 1.5-mm tracking error tolerance. Conclusions: Automatic feature detection applied to cine-MRI allows detailed liver motion description to be obtained. Such information was used to quantify the performance of surrogate-based tracking methods and to provide a prospective comparison with respect to MRI-guided radiation therapy, which could support the definition of patient-specific optimal treatment strategies.« less

The Secondary Organic Aerosol Processor (SOAP v1.0) model: a unified model with different ranges of complexity based on the molecular surrogate approach

NASA Astrophysics Data System (ADS)

Couvidat, F.; Sartelet, K.

2014-01-01

The Secondary Organic Aerosol Processor (SOAP v1.0) model is presented. This model is designed to be modular with different user options depending on the computing time and the complexity required by the user. This model is based on the molecular surrogate approach, in which each surrogate compound is associated with a molecular structure to estimate some properties and parameters (hygroscopicity, absorption on the aqueous phase of particles, activity coefficients, phase separation). Each surrogate can be hydrophilic (condenses only on the aqueous phase of particles), hydrophobic (condenses only on the organic phase of particles) or both (condenses on both the aqueous and the organic phases of particles). Activity coefficients are computed with the UNIFAC thermodynamic model for short-range interactions and with the AIOMFAC parameterization for medium and long-range interactions between electrolytes and organic compounds. Phase separation is determined by Gibbs energy minimization. The user can choose between an equilibrium and a dynamic representation of the organic aerosol. In the equilibrium representation, compounds in the particle phase are assumed to be at equilibrium with the gas phase. However, recent studies show that the organic aerosol (OA) is not at equilibrium with the gas phase because the organic phase could be semi-solid (very viscous liquid phase). The condensation or evaporation of organic compounds could then be limited by the diffusion in the organic phase due to the high viscosity. A dynamic representation of secondary organic aerosols (SOA) is used with OA divided into layers, the first layer at the center of the particle (slowly reaches equilibrium) and the final layer near the interface with the gas phase (quickly reaches equilibrium).

Ecological risk of anthropogenic pollutants to reptiles: Evaluating assumptions of sensitivity and exposure.

PubMed

Weir, Scott M; Suski, Jamie G; Salice, Christopher J

2010-12-01

A large data gap for reptile ecotoxicology still persists; therefore, ecological risk assessments of reptiles usually incorporate the use of surrogate species. This necessitates that (1) the surrogate is at least as sensitive as the target taxon and/or (2) exposures to the surrogate are greater than that of the target taxon. We evaluated these assumptions for the use of birds as surrogates for reptiles. Based on a survey of the literature, birds were more sensitive than reptiles in less than 1/4 of the chemicals investigated. Dietary and dermal exposure modeling indicated that exposure to reptiles was relatively high, particularly when the dermal route was considered. We conclude that caution is warranted in the use of avian receptors as surrogates for reptiles in ecological risk assessment and emphasize the need to better understand the magnitude and mechanism of contaminant exposure in reptiles to improve exposure and risk estimation. Copyright © 2010 Elsevier Ltd. All rights reserved.

Signal decomposition for surrogate modeling of a constrained ultrasonic design space

NASA Astrophysics Data System (ADS)

Homa, Laura; Sparkman, Daniel; Wertz, John; Welter, John; Aldrin, John C.

2018-04-01

The U.S. Air Force seeks to improve the methods and measures by which the lifecycle of composite structures are managed. Nondestructive evaluation of damage - particularly internal damage resulting from impact - represents a significant input to that improvement. Conventional ultrasound can detect this damage; however, full 3D characterization has not been demonstrated. A proposed approach for robust characterization uses model-based inversion through fitting of simulated results to experimental data. One challenge with this approach is the high computational expense of the forward model to simulate the ultrasonic B-scans for each damage scenario. A potential solution is to construct a surrogate model using a subset of simulated ultrasonic scans built using a highly accurate, computationally expensive forward model. However, the dimensionality of these simulated B-scans makes interpolating between them a difficult and potentially infeasible problem. Thus, we propose using the chirplet decomposition to reduce the dimensionality of the data, and allow for interpolation in the chirplet parameter space. By applying the chirplet decomposition, we are able to extract the salient features in the data and construct a surrogate forward model.

Inverse finite-size scaling for high-dimensional significance analysis

NASA Astrophysics Data System (ADS)

Xu, Yingying; Puranen, Santeri; Corander, Jukka; Kabashima, Yoshiyuki

2018-06-01

We propose an efficient procedure for significance determination in high-dimensional dependence learning based on surrogate data testing, termed inverse finite-size scaling (IFSS). The IFSS method is based on our discovery of a universal scaling property of random matrices which enables inference about signal behavior from much smaller scale surrogate data than the dimensionality of the original data. As a motivating example, we demonstrate the procedure for ultra-high-dimensional Potts models with order of 1010 parameters. IFSS reduces the computational effort of the data-testing procedure by several orders of magnitude, making it very efficient for practical purposes. This approach thus holds considerable potential for generalization to other types of complex models.

21 CFR 314.510 - Approval based on a surrogate endpoint or on an effect on a clinical endpoint other than survival...

Code of Federal Regulations, 2011 CFR

2011-04-01

... 21 Food and Drugs 5 2011-04-01 2011-04-01 false Approval based on a surrogate endpoint or on an... Serious or Life-Threatening Illnesses § 314.510 Approval based on a surrogate endpoint or on an effect on... the drug product has an effect on a surrogate endpoint that is reasonably likely, based on...

21 CFR 314.510 - Approval based on a surrogate endpoint or on an effect on a clinical endpoint other than survival...

Code of Federal Regulations, 2014 CFR

2014-04-01

... 21 Food and Drugs 5 2014-04-01 2014-04-01 false Approval based on a surrogate endpoint or on an... Serious or Life-Threatening Illnesses § 314.510 Approval based on a surrogate endpoint or on an effect on... the drug product has an effect on a surrogate endpoint that is reasonably likely, based on...

21 CFR 314.510 - Approval based on a surrogate endpoint or on an effect on a clinical endpoint other than survival...

Code of Federal Regulations, 2013 CFR

2013-04-01

... 21 Food and Drugs 5 2013-04-01 2013-04-01 false Approval based on a surrogate endpoint or on an... Serious or Life-Threatening Illnesses § 314.510 Approval based on a surrogate endpoint or on an effect on... the drug product has an effect on a surrogate endpoint that is reasonably likely, based on...

21 CFR 314.510 - Approval based on a surrogate endpoint or on an effect on a clinical endpoint other than survival...

Code of Federal Regulations, 2012 CFR

2012-04-01

... 21 Food and Drugs 5 2012-04-01 2012-04-01 false Approval based on a surrogate endpoint or on an... Serious or Life-Threatening Illnesses § 314.510 Approval based on a surrogate endpoint or on an effect on... the drug product has an effect on a surrogate endpoint that is reasonably likely, based on...

Comparative study of surrogate models for groundwater contamination source identification at DNAPL-contaminated sites

NASA Astrophysics Data System (ADS)

Hou, Zeyu; Lu, Wenxi

2018-05-01

Knowledge of groundwater contamination sources is critical for effectively protecting groundwater resources, estimating risks, mitigating disaster, and designing remediation strategies. Many methods for groundwater contamination source identification (GCSI) have been developed in recent years, including the simulation-optimization technique. This study proposes utilizing a support vector regression (SVR) model and a kernel extreme learning machine (KELM) model to enrich the content of the surrogate model. The surrogate model was itself key in replacing the simulation model, reducing the huge computational burden of iterations in the simulation-optimization technique to solve GCSI problems, especially in GCSI problems of aquifers contaminated by dense nonaqueous phase liquids (DNAPLs). A comparative study between the Kriging, SVR, and KELM models is reported. Additionally, there is analysis of the influence of parameter optimization and the structure of the training sample dataset on the approximation accuracy of the surrogate model. It was found that the KELM model was the most accurate surrogate model, and its performance was significantly improved after parameter optimization. The approximation accuracy of the surrogate model to the simulation model did not always improve with increasing numbers of training samples. Using the appropriate number of training samples was critical for improving the performance of the surrogate model and avoiding unnecessary computational workload. It was concluded that the KELM model developed in this work could reasonably predict system responses in given operation conditions. Replacing the simulation model with a KELM model considerably reduced the computational burden of the simulation-optimization process and also maintained high computation accuracy.

Low-rank separated representation surrogates of high-dimensional stochastic functions: Application in Bayesian inference

NASA Astrophysics Data System (ADS)

Validi, AbdoulAhad

2014-03-01

This study introduces a non-intrusive approach in the context of low-rank separated representation to construct a surrogate of high-dimensional stochastic functions, e.g., PDEs/ODEs, in order to decrease the computational cost of Markov Chain Monte Carlo simulations in Bayesian inference. The surrogate model is constructed via a regularized alternative least-square regression with Tikhonov regularization using a roughening matrix computing the gradient of the solution, in conjunction with a perturbation-based error indicator to detect optimal model complexities. The model approximates a vector of a continuous solution at discrete values of a physical variable. The required number of random realizations to achieve a successful approximation linearly depends on the function dimensionality. The computational cost of the model construction is quadratic in the number of random inputs, which potentially tackles the curse of dimensionality in high-dimensional stochastic functions. Furthermore, this vector-valued separated representation-based model, in comparison to the available scalar-valued case, leads to a significant reduction in the cost of approximation by an order of magnitude equal to the vector size. The performance of the method is studied through its application to three numerical examples including a 41-dimensional elliptic PDE and a 21-dimensional cavity flow.

Inverse Modeling Using Markov Chain Monte Carlo Aided by Adaptive Stochastic Collocation Method with Transformation

NASA Astrophysics Data System (ADS)

Zhang, D.; Liao, Q.

2016-12-01

The Bayesian inference provides a convenient framework to solve statistical inverse problems. In this method, the parameters to be identified are treated as random variables. The prior knowledge, the system nonlinearity, and the measurement errors can be directly incorporated in the posterior probability density function (PDF) of the parameters. The Markov chain Monte Carlo (MCMC) method is a powerful tool to generate samples from the posterior PDF. However, since the MCMC usually requires thousands or even millions of forward simulations, it can be a computationally intensive endeavor, particularly when faced with large-scale flow and transport models. To address this issue, we construct a surrogate system for the model responses in the form of polynomials by the stochastic collocation method. In addition, we employ interpolation based on the nested sparse grids and takes into account the different importance of the parameters, under the condition of high random dimensions in the stochastic space. Furthermore, in case of low regularity such as discontinuous or unsmooth relation between the input parameters and the output responses, we introduce an additional transform process to improve the accuracy of the surrogate model. Once we build the surrogate system, we may evaluate the likelihood with very little computational cost. We analyzed the convergence rate of the forward solution and the surrogate posterior by Kullback-Leibler divergence, which quantifies the difference between probability distributions. The fast convergence of the forward solution implies fast convergence of the surrogate posterior to the true posterior. We also tested the proposed algorithm on water-flooding two-phase flow reservoir examples. The posterior PDF calculated from a very long chain with direct forward simulation is assumed to be accurate. The posterior PDF calculated using the surrogate model is in reasonable agreement with the reference, revealing a great improvement in terms of computational efficiency.

Surrogate Plant Data Base : Volume 1. Introduction, Appendix A : The Development of Surrogate Plant Data ; Appendix B : Application of the Surrogate .

DOT National Transportation Integrated Search

1983-03-01

This four volume report consists of a data base describing "surrogate" automobile and truck manufacturing plants developed as part of a methodology for evaluating capital investment requirements in new manufacturing facilities to build new fleets of ...

Statistical Surrogate Modeling of Atmospheric Dispersion Events Using Bayesian Adaptive Splines

NASA Astrophysics Data System (ADS)

Francom, D.; Sansó, B.; Bulaevskaya, V.; Lucas, D. D.

2016-12-01

Uncertainty in the inputs of complex computer models, including atmospheric dispersion and transport codes, is often assessed via statistical surrogate models. Surrogate models are computationally efficient statistical approximations of expensive computer models that enable uncertainty analysis. We introduce Bayesian adaptive spline methods for producing surrogate models that capture the major spatiotemporal patterns of the parent model, while satisfying all the necessities of flexibility, accuracy and computational feasibility. We present novel methodological and computational approaches motivated by a controlled atmospheric tracer release experiment conducted at the Diablo Canyon nuclear power plant in California. Traditional methods for building statistical surrogate models often do not scale well to experiments with large amounts of data. Our approach is well suited to experiments involving large numbers of model inputs, large numbers of simulations, and functional output for each simulation. Our approach allows us to perform global sensitivity analysis with ease. We also present an approach to calibration of simulators using field data.

Surrogacy, Compensation, and Legal Parentage: Against the Adoption Model.

PubMed

van Zyl, Liezl; Walker, Ruth

2015-09-01

Surrogate motherhood is treated as a form of adoption in many countries: the birth mother and her partner are presumed to be the parents of the child, while the intended parents have to adopt the baby once it is born. Other than compensation for expenses related to the pregnancy, payment to surrogates is not permitted. We believe that the failure to compensate surrogate mothers for their labour as well as the significant risks they undertake is both unfair and exploitative. We accept that introducing payment for surrogates would create a significant tension in the adoption model. However, we recommend rejecting the adoption model altogether rather than continuing to prohibit compensation to surrogates.

21 CFR 601.41 - Approval based on a surrogate endpoint or on an effect on a clinical endpoint other than survival...

Code of Federal Regulations, 2014 CFR

2014-04-01

... 21 Food and Drugs 7 2014-04-01 2014-04-01 false Approval based on a surrogate endpoint or on an... Approval based on a surrogate endpoint or on an effect on a clinical endpoint other than survival or... uncertainty as to the relation of the surrogate endpoint to clinical benefit, or of the observed clinical...

21 CFR 601.41 - Approval based on a surrogate endpoint or on an effect on a clinical endpoint other than survival...

Code of Federal Regulations, 2013 CFR

2013-04-01

... 21 Food and Drugs 7 2013-04-01 2013-04-01 false Approval based on a surrogate endpoint or on an... Approval based on a surrogate endpoint or on an effect on a clinical endpoint other than survival or... uncertainty as to the relation of the surrogate endpoint to clinical benefit, or of the observed clinical...

21 CFR 601.41 - Approval based on a surrogate endpoint or on an effect on a clinical endpoint other than survival...

Code of Federal Regulations, 2012 CFR

2012-04-01

... 21 Food and Drugs 7 2012-04-01 2012-04-01 false Approval based on a surrogate endpoint or on an... Approval based on a surrogate endpoint or on an effect on a clinical endpoint other than survival or... uncertainty as to the relation of the surrogate endpoint to clinical benefit, or of the observed clinical...

21 CFR 601.41 - Approval based on a surrogate endpoint or on an effect on a clinical endpoint other than survival...

Code of Federal Regulations, 2011 CFR

2011-04-01

... 21 Food and Drugs 7 2011-04-01 2010-04-01 true Approval based on a surrogate endpoint or on an... Approval based on a surrogate endpoint or on an effect on a clinical endpoint other than survival or... uncertainty as to the relation of the surrogate endpoint to clinical benefit, or of the observed clinical...

21 CFR 601.41 - Approval based on a surrogate endpoint or on an effect on a clinical endpoint other than survival...

Code of Federal Regulations, 2010 CFR

2010-04-01

... 21 Food and Drugs 7 2010-04-01 2010-04-01 false Approval based on a surrogate endpoint or on an... Approval based on a surrogate endpoint or on an effect on a clinical endpoint other than survival or... uncertainty as to the relation of the surrogate endpoint to clinical benefit, or of the observed clinical...

The use of surrogates for an optimal management of coupled groundwater-agriculture hydrosystems

NASA Astrophysics Data System (ADS)

Grundmann, J.; Schütze, N.; Brettschneider, M.; Schmitz, G. H.; Lennartz, F.

2012-04-01

For ensuring an optimal sustainable water resources management in arid coastal environments, we develop a new simulation based integrated water management system. It aims at achieving best possible solutions for groundwater withdrawals for agricultural and municipal water use including saline water management together with a substantial increase of the water use efficiency in irrigated agriculture. To achieve a robust and fast operation of the management system regarding water quality and water quantity we develop appropriate surrogate models by combining physically based process modelling with methods of artificial intelligence. Thereby we use an artificial neural network for modelling the aquifer response, inclusive the seawater interface, which was trained on a scenario database generated by a numerical density depended groundwater flow model. For simulating the behaviour of high productive agricultural farms crop water production functions are generated by means of soil-vegetation-atmosphere-transport (SVAT)-models, adapted to the regional climate conditions, and a novel evolutionary optimisation algorithm for optimal irrigation scheduling and control. We apply both surrogates exemplarily within a simulation based optimisation environment using the characteristics of the south Batinah region in the Sultanate of Oman which is affected by saltwater intrusion into the coastal aquifer due to excessive groundwater withdrawal for irrigated agriculture. We demonstrate the effectiveness of our methodology for the evaluation and optimisation of different irrigation practices, cropping pattern and resulting abstraction scenarios. Due to contradicting objectives like profit-oriented agriculture vs. aquifer sustainability a multi-criterial optimisation is performed.

A polynomial chaos expansion based molecular dynamics study for probabilistic strength analysis of nano-twinned copper

NASA Astrophysics Data System (ADS)

Mahata, Avik; Mukhopadhyay, Tanmoy; Adhikari, Sondipon

2016-03-01

Nano-twinned structures are mechanically stronger, ductile and stable than its non-twinned form. We have investigated the effect of varying twin spacing and twin boundary width (TBW) on the yield strength of the nano-twinned copper in a probabilistic framework. An efficient surrogate modelling approach based on polynomial chaos expansion has been proposed for the analysis. Effectively utilising 15 sets of expensive molecular dynamics simulations, thousands of outputs have been obtained corresponding to different sets of twin spacing and twin width using virtual experiments based on the surrogates. One of the major outcomes of this work is that there exists an optimal combination of twin boundary spacing and twin width until which the strength can be increased and after that critical point the nanowires weaken. This study also reveals that the yield strength of nano-twinned copper is more sensitive to TBW than twin spacing. Such robust inferences have been possible to be drawn only because of applying the surrogate modelling approach, which makes it feasible to obtain results corresponding to 40 000 combinations of different twin boundary spacing and twin width in a computationally efficient framework.

Sequential experimental design based generalised ANOVA

NASA Astrophysics Data System (ADS)

Chakraborty, Souvik; Chowdhury, Rajib

2016-07-01

Over the last decade, surrogate modelling technique has gained wide popularity in the field of uncertainty quantification, optimization, model exploration and sensitivity analysis. This approach relies on experimental design to generate training points and regression/interpolation for generating the surrogate. In this work, it is argued that conventional experimental design may render a surrogate model inefficient. In order to address this issue, this paper presents a novel distribution adaptive sequential experimental design (DA-SED). The proposed DA-SED has been coupled with a variant of generalised analysis of variance (G-ANOVA), developed by representing the component function using the generalised polynomial chaos expansion. Moreover, generalised analytical expressions for calculating the first two statistical moments of the response, which are utilized in predicting the probability of failure, have also been developed. The proposed approach has been utilized in predicting probability of failure of three structural mechanics problems. It is observed that the proposed approach yields accurate and computationally efficient estimate of the failure probability.

Sequential experimental design based generalised ANOVA

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chakraborty, Souvik, E-mail: csouvik41@gmail.com; Chowdhury, Rajib, E-mail: rajibfce@iitr.ac.in

Over the last decade, surrogate modelling technique has gained wide popularity in the field of uncertainty quantification, optimization, model exploration and sensitivity analysis. This approach relies on experimental design to generate training points and regression/interpolation for generating the surrogate. In this work, it is argued that conventional experimental design may render a surrogate model inefficient. In order to address this issue, this paper presents a novel distribution adaptive sequential experimental design (DA-SED). The proposed DA-SED has been coupled with a variant of generalised analysis of variance (G-ANOVA), developed by representing the component function using the generalised polynomial chaos expansion. Moreover,more » generalised analytical expressions for calculating the first two statistical moments of the response, which are utilized in predicting the probability of failure, have also been developed. The proposed approach has been utilized in predicting probability of failure of three structural mechanics problems. It is observed that the proposed approach yields accurate and computationally efficient estimate of the failure probability.« less

To address surface reaction network complexity using scaling relations machine learning and DFT calculations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ulissi, Zachary W.; Medford, Andrew J.; Bligaard, Thomas

Surface reaction networks involving hydrocarbons exhibit enormous complexity with thousands of species and reactions for all but the very simplest of chemistries. We present a framework for optimization under uncertainty for heterogeneous catalysis reaction networks using surrogate models that are trained on the fly. The surrogate model is constructed by teaching a Gaussian process adsorption energies based on group additivity fingerprints, combined with transition-state scaling relations and a simple classifier for determining the rate-limiting step. The surrogate model is iteratively used to predict the most important reaction step to be calculated explicitly with computationally demanding electronic structure theory. Applying thesemore » methods to the reaction of syngas on rhodium(111), we identify the most likely reaction mechanism. Lastly, propagating uncertainty throughout this process yields the likelihood that the final mechanism is complete given measurements on only a subset of the entire network and uncertainty in the underlying density functional theory calculations.« less

To address surface reaction network complexity using scaling relations machine learning and DFT calculations

DOE PAGES

Ulissi, Zachary W.; Medford, Andrew J.; Bligaard, Thomas; ...

2017-03-06

Surface reaction networks involving hydrocarbons exhibit enormous complexity with thousands of species and reactions for all but the very simplest of chemistries. We present a framework for optimization under uncertainty for heterogeneous catalysis reaction networks using surrogate models that are trained on the fly. The surrogate model is constructed by teaching a Gaussian process adsorption energies based on group additivity fingerprints, combined with transition-state scaling relations and a simple classifier for determining the rate-limiting step. The surrogate model is iteratively used to predict the most important reaction step to be calculated explicitly with computationally demanding electronic structure theory. Applying thesemore » methods to the reaction of syngas on rhodium(111), we identify the most likely reaction mechanism. Lastly, propagating uncertainty throughout this process yields the likelihood that the final mechanism is complete given measurements on only a subset of the entire network and uncertainty in the underlying density functional theory calculations.« less

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

NASA Astrophysics Data System (ADS)

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

2015-09-01

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

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).

The effectiveness of surrogate taxa to conserve freshwater biodiversity.

PubMed

Stewart, David R; Underwood, Zachary E; Rahel, Frank J; Walters, Annika W

2018-02-01

Establishing protected areas has long been an effective conservation strategy and is often based on readily surveyed species. The potential of any freshwater taxa to be a surrogate for other aquatic groups has not been explored fully. We compiled occurrence data on 72 species of freshwater fishes, amphibians, mussels, and aquatic reptiles for the Great Plains, Wyoming (U.S.A.). We used hierarchical Bayesian multispecies mixture models and MaxEnt models to describe species' distributions and the program Zonation to identify areas of conservation priority for each aquatic group. The landscape-scale factors that best characterized aquatic species' distributions differed among groups. There was low agreement and congruence among taxa-specific conservation priorities (<20%), meaning no surrogate priority areas would include or protect the best habitats of other aquatic taxa. Common, wideranging aquatic species were included in taxa-specific priority areas, but rare freshwater species were not included. Thus, the development of conservation priorities based on a single freshwater aquatic group would not protect all species in the other aquatic groups. © 2017 Society for Conservation Biology.

An information-theoretic approach to surrogate-marker evaluation with failure time endpoints.

PubMed

Pryseley, Assam; Tilahun, Abel; Alonso, Ariel; Molenberghs, Geert

2011-04-01

Over the last decades, the evaluation of potential surrogate endpoints in clinical trials has steadily been growing in importance, not only thanks to the availability of ever more potential markers and surrogate endpoints, also because more methodological development has become available. While early work has been devoted, to a large extent, to Gaussian, binary, and longitudinal endpoints, the case of time-to-event endpoints is in need of careful scrutiny as well, owing to the strong presence of such endpoints in oncology and beyond. While work had been done in the past, it was often cumbersome to use such tools in practice, because of the need for fitting copula or frailty models that were further embedded in a hierarchical or two-stage modeling approach. In this paper, we present a methodologically elegant and easy-to-use approach based on information theory. We resolve essential issues, including the quantification of "surrogacy" based on such an approach. Our results are put to the test in a simulation study and are applied to data from clinical trials in oncology. The methodology has been implemented in R.

Narrative Interest Standard: A Novel Approach to Surrogate Decision-Making for People With Dementia.

PubMed

Wilkins, James M

2017-06-17

Dementia is a common neurodegenerative process that can significantly impair decision-making capacity as the disease progresses. When a person is found to lack capacity to make a decision, a surrogate decision-maker is generally sought to aid in decision-making. Typical bases for surrogate decision-making include the substituted judgment standard and the best interest standard. Given the heterogeneous and progressive course of dementia, however, these standards for surrogate decision-making are often insufficient in providing guidance for the decision-making for a person with dementia, escalating the likelihood of conflict in these decisions. In this article, the narrative interest standard is presented as a novel and more appropriate approach to surrogate decision-making for people with dementia. Through case presentation and ethical analysis, the standard mechanisms for surrogate decision-making for people with dementia are reviewed and critiqued. The narrative interest standard is then introduced and discussed as a dementia-specific model for surrogate decision-making. Through incorporation of elements of a best interest standard in focusing on the current benefit-burden ratio and elements of narrative to provide context, history, and flexibility for values and preferences that may change over time, the narrative interest standard allows for elaboration of an enriched context for surrogate decision-making for people with dementia. More importantly, however, a narrative approach encourages the direct contribution from people with dementia in authoring the story of what matters to them in their lives.

Testing Pairwise Association between Spatially Autocorrelated Variables: A New Approach Using Surrogate Lattice Data

PubMed Central

Deblauwe, Vincent; Kennel, Pol; Couteron, Pierre

2012-01-01

Background Independence between observations is a standard prerequisite of traditional statistical tests of association. This condition is, however, violated when autocorrelation is present within the data. In the case of variables that are regularly sampled in space (i.e. lattice data or images), such as those provided by remote-sensing or geographical databases, this problem is particularly acute. Because analytic derivation of the null probability distribution of the test statistic (e.g. Pearson's r) is not always possible when autocorrelation is present, we propose instead the use of a Monte Carlo simulation with surrogate data. Methodology/Principal Findings The null hypothesis that two observed mapped variables are the result of independent pattern generating processes is tested here by generating sets of random image data while preserving the autocorrelation function of the original images. Surrogates are generated by matching the dual-tree complex wavelet spectra (and hence the autocorrelation functions) of white noise images with the spectra of the original images. The generated images can then be used to build the probability distribution function of any statistic of association under the null hypothesis. We demonstrate the validity of a statistical test of association based on these surrogates with both actual and synthetic data and compare it with a corrected parametric test and three existing methods that generate surrogates (randomization, random rotations and shifts, and iterative amplitude adjusted Fourier transform). Type I error control was excellent, even with strong and long-range autocorrelation, which is not the case for alternative methods. Conclusions/Significance The wavelet-based surrogates are particularly appropriate in cases where autocorrelation appears at all scales or is direction-dependent (anisotropy). We explore the potential of the method for association tests involving a lattice of binary data and discuss its potential for validation of species distribution models. An implementation of the method in Java for the generation of wavelet-based surrogates is available online as supporting material. PMID:23144961

Comparing and combining biomarkers as principle surrogates for time-to-event clinical endpoints.

PubMed

Gabriel, Erin E; Sachs, Michael C; Gilbert, Peter B

2015-02-10

Principal surrogate endpoints are useful as targets for phase I and II trials. In many recent trials, multiple post-randomization biomarkers are measured. However, few statistical methods exist for comparison of or combination of biomarkers as principal surrogates, and none of these methods to our knowledge utilize time-to-event clinical endpoint information. We propose a Weibull model extension of the semi-parametric estimated maximum likelihood method that allows for the inclusion of multiple biomarkers in the same risk model as multivariate candidate principal surrogates. We propose several methods for comparing candidate principal surrogates and evaluating multivariate principal surrogates. These include the time-dependent and surrogate-dependent true and false positive fraction, the time-dependent and the integrated standardized total gain, and the cumulative distribution function of the risk difference. We illustrate the operating characteristics of our proposed methods in simulations and outline how these statistics can be used to evaluate and compare candidate principal surrogates. We use these methods to investigate candidate surrogates in the Diabetes Control and Complications Trial. Copyright © 2014 John Wiley & Sons, Ltd.

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. © 2015 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

Surrogate modeling of joint flood risk across coastal watersheds

NASA Astrophysics Data System (ADS)

Bass, Benjamin; Bedient, Philip

2018-03-01

This study discusses the development and performance of a rapid prediction system capable of representing the joint rainfall-runoff and storm surge flood response of tropical cyclones (TCs) for probabilistic risk analysis. Due to the computational demand required for accurately representing storm surge with the high-fidelity ADvanced CIRCulation (ADCIRC) hydrodynamic model and its coupling with additional numerical models to represent rainfall-runoff, a surrogate or statistical model was trained to represent the relationship between hurricane wind- and pressure-field characteristics and their peak joint flood response typically determined from physics based numerical models. This builds upon past studies that have only evaluated surrogate models for predicting peak surge, and provides the first system capable of probabilistically representing joint flood levels from TCs. The utility of this joint flood prediction system is then demonstrated by improving upon probabilistic TC flood risk products, which currently account for storm surge but do not take into account TC associated rainfall-runoff. Results demonstrate the source apportionment of rainfall-runoff versus storm surge and highlight that slight increases in flood risk levels may occur due to the interaction between rainfall-runoff and storm surge as compared to the Federal Emergency Management Association's (FEMAs) current practices.

The Use of Surrogate Data in Demographic Population Viability Analysis: A Case Study of California Sea Lions

PubMed Central

2015-01-01

Reliable data necessary to parameterize population models are seldom available for imperiled species. As an alternative, data from populations of the same species or from ecologically similar species have been used to construct models. In this study, we evaluated the use of demographic data collected at one California sea lion colony (Los Islotes) to predict the population dynamics of the same species from two other colonies (San Jorge and Granito) in the Gulf of California, Mexico, for which demographic data are lacking. To do so, we developed a stochastic demographic age-structured matrix model and conducted a population viability analysis for each colony. For the Los Islotes colony we used site-specific pup, juvenile, and adult survival probabilities, as well as birth rates for older females. For the other colonies, we used site-specific pup and juvenile survival probabilities, but used surrogate data from Los Islotes for adult survival probabilities and birth rates. We assessed these models by comparing simulated retrospective population trajectories to observed population trends based on count data. The projected population trajectories approximated the observed trends when surrogate data were used for one colony but failed to match for a second colony. Our results indicate that species-specific and even region-specific surrogate data may lead to erroneous conservation decisions. These results highlight the importance of using population-specific demographic data in assessing extinction risk. When vital rates are not available and immediate management actions must be taken, in particular for imperiled species, we recommend the use of surrogate data only when the populations appear to have similar population trends. PMID:26413746

External Heat Transfer Coefficient Measurements on a Surrogate Indirect Inertial Confinement Fusion Target

DOE PAGES

Miles, Robin; Havstad, Mark; LeBlanc, Mary; ...

2015-09-15

External heat transfer coefficients were measured around a surrogate Indirect inertial confinement fusion (ICF) based on the Laser Inertial Fusion Energy (LIFE) design target to validate thermal models of the LIFE target during flight through a fusion chamber. Results indicate that heat transfer coefficients for this target 25-50 W/m 2∙K are consistent with theoretically derived heat transfer coefficients and valid for use in calculation of target heating during flight through a fusion chamber.

"Development of Model-Based Air Pollution Exposure Metrics for use in Epidemiologic Studies"

EPA Science Inventory

Population-based epidemiological studies of air pollution have traditionally relied upon imperfect surrogates of personal exposures, such as area-wide ambient air pollution levels based on readily available concentrations from central monitoring sites. U.S. EPA in collaboration w...

DEVELOPMENT OF MODEL-BASED AIR POLLUTION EXPOSURE METRICS FOR USE IN EPIDEMIOLOGIC STUDIES

EPA Science Inventory

Population-based epidemiological studies of air pollution have traditionally relied upon imperfect surrogates of personal exposures, such as area-wide ambient air pollution levels based on readily available concentrations from central monitoring sites. U.S. EPA in collaboration w...

PROJECT SUMMARY: DEVELOPMENT OF THE VIRTUAL BEACH MODEL, PHASE I: AN EMPIRICAL MODEL

EPA Science Inventory

Mathematical models based on water-quality and other environmental surrogates may help to provide water quality assessment within a few hours and potentially provide one to three day forecasts, providing beach managers and public-health officials a tool for developing beach-speci...

Parameter estimation of a nonlinear Burger's model using nanoindentation and finite element-based inverse analysis

NASA Astrophysics Data System (ADS)

Hamim, Salah Uddin Ahmed

Nanoindentation involves probing a hard diamond tip into a material, where the load and the displacement experienced by the tip is recorded continuously. This load-displacement data is a direct function of material's innate stress-strain behavior. Thus, theoretically it is possible to extract mechanical properties of a material through nanoindentation. However, due to various nonlinearities associated with nanoindentation the process of interpreting load-displacement data into material properties is difficult. Although, simple elastic behavior can be characterized easily, a method to characterize complicated material behavior such as nonlinear viscoelasticity is still lacking. In this study, a nanoindentation-based material characterization technique is developed to characterize soft materials exhibiting nonlinear viscoelasticity. Nanoindentation experiment was modeled in finite element analysis software (ABAQUS), where a nonlinear viscoelastic behavior was incorporated using user-defined subroutine (UMAT). The model parameters were calibrated using a process called inverse analysis. In this study, a surrogate model-based approach was used for the inverse analysis. The different factors affecting the surrogate model performance are analyzed in order to optimize the performance with respect to the computational cost.

Hybrid surrogate-model-based multi-fidelity efficient global optimization applied to helicopter blade design

NASA Astrophysics Data System (ADS)

Ariyarit, Atthaphon; Sugiura, Masahiko; Tanabe, Yasutada; Kanazaki, Masahiro

2018-06-01

A multi-fidelity optimization technique by an efficient global optimization process using a hybrid surrogate model is investigated for solving real-world design problems. The model constructs the local deviation using the kriging method and the global model using a radial basis function. The expected improvement is computed to decide additional samples that can improve the model. The approach was first investigated by solving mathematical test problems. The results were compared with optimization results from an ordinary kriging method and a co-kriging method, and the proposed method produced the best solution. The proposed method was also applied to aerodynamic design optimization of helicopter blades to obtain the maximum blade efficiency. The optimal shape obtained by the proposed method achieved performance almost equivalent to that obtained using the high-fidelity, evaluation-based single-fidelity optimization. Comparing all three methods, the proposed method required the lowest total number of high-fidelity evaluation runs to obtain a converged solution.

Surrogate marker analysis in cancer clinical trials through time-to-event mediation techniques.

PubMed

Vandenberghe, Sjouke; Duchateau, Luc; Slaets, Leen; Bogaerts, Jan; Vansteelandt, Stijn

2017-01-01

The meta-analytic approach is the gold standard for validation of surrogate markers, but has the drawback of requiring data from several trials. We refine modern mediation analysis techniques for time-to-event endpoints and apply them to investigate whether pathological complete response can be used as a surrogate marker for disease-free survival in the EORTC 10994/BIG 1-00 randomised phase 3 trial in which locally advanced breast cancer patients were randomised to either taxane or anthracycline based neoadjuvant chemotherapy. In the mediation analysis, the treatment effect is decomposed into an indirect effect via pathological complete response and the remaining direct effect. It shows that only 4.2% of the treatment effect on disease-free survival after five years is mediated by the treatment effect on pathological complete response. There is thus no evidence from our analysis that pathological complete response is a valuable surrogate marker to evaluate the effect of taxane versus anthracycline based chemotherapies on progression free survival of locally advanced breast cancer patients. The proposed analysis strategy is broadly applicable to mediation analyses of time-to-event endpoints, is easy to apply and outperforms existing strategies in terms of precision as well as robustness against model misspecification.

Surrogate models for efficient stability analysis of brake systems

NASA Astrophysics Data System (ADS)

Nechak, Lyes; Gillot, Frédéric; Besset, Sébastien; Sinou, Jean-Jacques

2015-07-01

This study assesses capacities of the global sensitivity analysis combined together with the kriging formalism to be useful in the robust stability analysis of brake systems, which is too costly when performed with the classical complex eigenvalues analysis (CEA) based on finite element models (FEMs). By considering a simplified brake system, the global sensitivity analysis is first shown very helpful for understanding the effects of design parameters on the brake system's stability. This is allowed by the so-called Sobol indices which discriminate design parameters with respect to their influence on the stability. Consequently, only uncertainty of influent parameters is taken into account in the following step, namely, the surrogate modelling based on kriging. The latter is then demonstrated to be an interesting alternative to FEMs since it allowed, with a lower cost, an accurate estimation of the system's proportions of instability corresponding to the influent parameters.

Surrogates for numerical simulations; optimization of eddy-promoter heat exchangers

NASA Technical Reports Server (NTRS)

Patera, Anthony T.; Patera, Anthony

1993-01-01

Although the advent of fast and inexpensive parallel computers has rendered numerous previously intractable calculations feasible, many numerical simulations remain too resource-intensive to be directly inserted in engineering optimization efforts. An attractive alternative to direct insertion considers models for computational systems: the expensive simulation is evoked only to construct and validate a simplified, input-output model; this simplified input-output model then serves as a simulation surrogate in subsequent engineering optimization studies. A simple 'Bayesian-validated' statistical framework for the construction, validation, and purposive application of static computer simulation surrogates is presented. As an example, dissipation-transport optimization of laminar-flow eddy-promoter heat exchangers are considered: parallel spectral element Navier-Stokes calculations serve to construct and validate surrogates for the flowrate and Nusselt number; these surrogates then represent the originating Navier-Stokes equations in the ensuing design process.

A perfect correlate does not a surrogate make

PubMed Central

Baker, Stuart G; Kramer, Barnett S

2003-01-01

Background There is common belief among some medical researchers that if a potential surrogate endpoint is highly correlated with a true endpoint, then a positive (or negative) difference in potential surrogate endpoints between randomization groups would imply a positive (or negative) difference in unobserved true endpoints between randomization groups. We investigate this belief when the potential surrogate and unobserved true endpoints are perfectly correlated within each randomization group. Methods We use a graphical approach. The vertical axis is the unobserved true endpoint and the horizontal axis is the potential surrogate endpoint. Perfect correlation within each randomization group implies that, for each randomization group, potential surrogate and true endpoints are related by a straight line. In this scenario the investigator does not know the slopes or intercepts. We consider a plausible example where the slope of the line is higher for the experimental group than for the control group. Results In our example with unknown lines, a decrease in mean potential surrogate endpoints from control to experimental groups corresponds to an increase in mean true endpoint from control to experimental groups. Thus the potential surrogate endpoints give the wrong inference. Similar results hold for binary potential surrogate and true outcomes (although the notion of correlation does not apply). The potential surrogate endpointwould give the correct inference if either (i) the unknown lines for the two group coincided, which means that the distribution of true endpoint conditional on potential surrogate endpoint does not depend on treatment group, which is called the Prentice Criterion or (ii) if one could accurately predict the lines based on data from prior studies. Conclusion Perfect correlation between potential surrogate and unobserved true outcomes within randomized groups does not guarantee correct inference based on a potential surrogate endpoint. Even in early phase trials, investigators should not base conclusions on potential surrogate endpoints in which the only validation is high correlation with the true endpoint within a group. PMID:12962545

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

Application of Design of Experiments and Surrogate Modeling within the NASA Advanced Concepts Office, Earth-to-Orbit Design Process

NASA Technical Reports Server (NTRS)

Zwack, Mathew R.; Dees, Patrick D.; Holt, James B.

2016-01-01

Decisions made during early conceptual design have a large impact upon the expected life-cycle cost (LCC) of a new program. It is widely accepted that up to 80% of such cost is committed during these early design phases. Therefore, to help minimize LCC, decisions made during conceptual design must be based upon as much information as possible. To aid in the decision making for new launch vehicle programs, the Advanced Concepts Office (ACO) at NASA Marshall Space Flight Center (MSFC) provides rapid turnaround pre-phase A and phase A concept definition studies. The ACO team utilizes a proven set of tools to provide customers with a full vehicle mass breakdown to tertiary subsystems, preliminary structural sizing based upon worst-case flight loads, and trajectory optimization to quantify integrated vehicle performance for a given mission. Although the team provides rapid turnaround for single vehicle concepts, the scope of the trade space can be limited due to analyst availability and the manpower requirements for manual execution of the analysis tools. In order to enable exploration of a broader design space, the ACO team has implemented an advanced design methods (ADM) based approach. This approach applies the concepts of design of experiments (DOE) and surrogate modeling to more exhaustively explore the trade space and provide the customer with additional design information to inform decision making. This paper will first discuss the automation of the ACO tool set, which represents a majority of the development effort. In order to fit a surrogate model within tolerable error bounds a number of DOE cases are needed. This number will scale with the number of variable parameters desired and the complexity of the system's response to those variables. For all but the smallest design spaces, the number of cases required cannot be produced within an acceptable timeframe using a manual process. Therefore, automation of the tools was a key enabler for the successful application of an ADM approach to an ACO design study. Following the overview of the tool set automation, an example problem will be given to illustrate the implementation of the ADM approach. The example problem will first cover the inclusion of ground rules and assumptions (GR&A) for a study. The GR&A are very important to the study as they determine the constraints within which a trade study can be conducted. These trades must ultimately reconcile with the customer's desired output and any anticipated "what if" questions. The example problem will then illustrate the setup and execution of a DOE through the automated ACO tools. This process is accomplished more efficiently in this work by splitting the tools into two separate environments. The first environment encompasses the structural optimization and mass estimation tools, while the second is focused on trajectory optimization. Surrogate models are fit to the outputs of each environment and are "integrated" via connection of the surrogate equations. Throughout this process, checks are implemented to compare the output of the surrogates to the output of manually run cases to ensure that the error of the final surrogates is at an acceptable level. The conclusion of the example problem demonstrates the utility of the ADM based approach. Using surrogate models gives the ACO team the ability to visualize vehicle sensitivities to various design parameters and identify regions of interest within the design space. The ADM approach can thus be used to inform concept down selection and isolate promising vehicle configurations to be explored in more detail through the manual design process. In addition it provides the customer with an almost instantaneous turnaround on any ''what if" questions that may arise within the bounds of the surrogate model. This approach ultimately expands the ability of the ACO team to provide its customer with broad and rapid turnaround trade studies for launch vehicle conceptual design. The ability to identify a selection of designs which can meet the customer requirements will help ensure lower LCC of launch vehicle designs originating from ACO.

Application of Design of Experiments and Surrogate Modeling within the NASA Advanced Concepts Office, Earth-to-Orbit Design Process

NASA Technical Reports Server (NTRS)

Zwack, Mathew R.; Dees, Patrick D.; Holt, James B.

2016-01-01

Decisions made during early conceptual design have a large impact upon the expected life-cycle cost (LCC) of a new program. It is widely accepted that up to 80% of such cost is committed during these early design phases.1 Therefore, to help minimize LCC, decisions made during conceptual design must be based upon as much information as possible. To aid in the decision making for new launch vehicle programs, the Advanced Concepts Office (ACO) at NASA Marshall Space Flight Center (MSFC) provides rapid turnaround pre-phase A and phase A concept definition studies. The ACO team utilizes a proven set of tools to provide customers with a full vehicle mass breakdown to tertiary subsystems, preliminary structural sizing based upon worst-case flight loads, and trajectory optimization to quantify integrated vehicle performance for a given mission.2 Although the team provides rapid turnaround for single vehicle concepts, the scope of the trade space can be limited due to analyst availability and the manpower requirements for manual execution of the analysis tools. In order to enable exploration of a broader design space, the ACO team has implemented an Advanced Design Methods (ADM) based approach. This approach applies the concepts of Design of Experiments (DOE) and surrogate modeling to more exhaustively explore the trade space and provide the customer with additional design information to inform decision making. This paper will first discuss the automation of the ACO tool set, which represents a majority of the development e ort. In order to t a surrogate model within tolerable error bounds a number of DOE cases are needed. This number will scale with the number of variable parameters desired and the complexity of the system's response to those variables. For all but the smallest design spaces, the number of cases required cannot be produced within an acceptable timeframe using a manual process. Therefore, automation of the tools was a key enabler for the successful application of an ADM approach to an ACO design study. Following the overview of the tool set automation, an example problem will be given to illustrate the implementation of the ADM approach. The example problem will first cover the inclusion of Ground Rules and Assumptions (GR&A) for a study. The GR&A are very important to the study as they determine the constraints within which a trade study can be conducted. These trades must ultimately reconcile with the customer's desired output and any anticipated \\what if" questions. The example problem will then illustrate the setup and execution of a DOE through the automated ACO tools. This process is accomplished more efficiently in this work by splitting the tools into two separate environments. The first environment encompasses the structural optimization and mass estimation tools, while the second is focused on trajectory optimization. Surrogate models are t to the outputs of each environment and are integrated via connection of the surrogate equations. Throughout this process, checks are implemented to compare the output of the surrogates to the output of manually run cases to ensure that the error of the final surrogates is at an acceptable level. The conclusion of the example problem demonstrates the utility of the ADM based approach. Using surrogate models gives the ACO team the ability to visualize vehicle sensitivities to various design parameters and identify regions of interest within the design space. The ADM approach can thus be used to inform concept down selection and isolate promising vehicle configurations to be explored in more detail through the manual design process. In addition it provides the customer with an almost instantaneous turnaround on any \\what if" questions that may arise within the bounds of the surrogate model. This approach ultimately expands the ability of the ACO team to provide its customer with broad and rapid turnaround trade studies for launch vehicle conceptual design. The ability to identify a selection of designs which can meet the customer requirements will have the potential to lower LCC of launch vehicle designs originating from ACO.

Beyond multi-fractals: surrogate time series and fields

NASA Astrophysics Data System (ADS)

Venema, V.; Simmer, C.

2007-12-01

Most natural complex are characterised by variability on a large range of temporal and spatial scales. The two main methodologies to generate such structures are Fourier/FARIMA based algorithms and multifractal methods. The former is restricted to Gaussian data, whereas the latter requires the structure to be self-similar. This work will present so-called surrogate data as an alternative that works with any (empirical) distribution and power spectrum. The best-known surrogate algorithm is the iterative amplitude adjusted Fourier transform (IAAFT) algorithm. We have studied six different geophysical time series (two clouds, runoff of a small and a large river, temperature and rain) and their surrogates. The power spectra and consequently the 2nd order structure functions were replicated accurately. Even the fourth order structure function was more accurately reproduced by the surrogates as would be possible by a fractal method, because the measured structure deviated too strong from fractal scaling. Only in case of the daily rain sums a fractal method could have been more accurate. Just as Fourier and multifractal methods, the current surrogates are not able to model the asymmetric increment distributions observed for runoff, i.e., they cannot reproduce nonlinear dynamical processes that are asymmetric in time. Furthermore, we have found differences for the structure functions on small scales. Surrogate methods are especially valuable for empirical studies, because the time series and fields that are generated are able to mimic measured variables accurately. Our main application is radiative transfer through structured clouds. Like many geophysical fields, clouds can only be sampled sparsely, e.g. with in-situ airborne instruments. However, for radiative transfer calculations we need full 3-dimensional cloud fields. A first study relating the measured properties of the cloud droplets and the radiative properties of the cloud field by generating surrogate cloud fields yielded good results within the measurement error. A further test of the suitability of the surrogate clouds for radiative transfer is evaluated by comparing the radiative properties of model cloud fields of sparse cumulus and stratocumulus with their surrogate fields. The bias and root mean square error in various radiative properties is small and the deviations in the radiances and irradiances are not statistically significant, i.e. these deviations can be attributed to the Monte Carlo noise of the radiative transfer calculations. We compared these results with optical properties of synthetic clouds that have either the correct distribution (but no spatial correlations) or the correct power spectrum (but a Gaussian distribution). These clouds did show statistical significant deviations. For more information see: http://www.meteo.uni-bonn.de/venema/themes/surrogates/

Dependence of subject-specific parameters for a fast helical CT respiratory motion model on breathing rate: an animal study

NASA Astrophysics Data System (ADS)

O'Connell, Dylan; Thomas, David H.; Lamb, James M.; Lewis, John H.; Dou, Tai; Sieren, Jered P.; Saylor, Melissa; Hofmann, Christian; Hoffman, Eric A.; Lee, Percy P.; Low, Daniel A.

2018-02-01

To determine if the parameters relating lung tissue displacement to a breathing surrogate signal in a previously published respiratory motion model vary with the rate of breathing during image acquisition. An anesthetized pig was imaged using multiple fast helical scans to sample the breathing cycle with simultaneous surrogate monitoring. Three datasets were collected while the animal was mechanically ventilated with different respiratory rates: 12 bpm (breaths per minute), 17 bpm, and 24 bpm. Three sets of motion model parameters describing the correspondences between surrogate signals and tissue displacements were determined. The model error was calculated individually for each dataset, as well asfor pairs of parameters and surrogate signals from different experiments. The values of one model parameter, a vector field denoted α which related tissue displacement to surrogate amplitude, determined for each experiment were compared. The mean model error of the three datasets was 1.00  ±  0.36 mm with a 95th percentile value of 1.69 mm. The mean error computed from all combinations of parameters and surrogate signals from different datasets was 1.14  ±  0.42 mm with a 95th percentile of 1.95 mm. The mean difference in α over all pairs of experiments was 4.7%  ±  5.4%, and the 95th percentile was 16.8%. The mean angle between pairs of α was 5.0  ±  4.0 degrees, with a 95th percentile of 13.2 mm. The motion model parameters were largely unaffected by changes in the breathing rate during image acquisition. The mean error associated with mismatched sets of parameters and surrogate signals was 0.14 mm greater than the error achieved when using parameters and surrogate signals acquired with the same breathing rate, while maximum respiratory motion was 23.23 mm on average.

An information-theoretic approach for the evaluation of surrogate endpoints based on causal inference.

PubMed

Alonso, Ariel; Van der Elst, Wim; Molenberghs, Geert; Buyse, Marc; Burzykowski, Tomasz

2016-09-01

In this work a new metric of surrogacy, the so-called individual causal association (ICA), is introduced using information-theoretic concepts and a causal inference model for a binary surrogate and true endpoint. The ICA has a simple and appealing interpretation in terms of uncertainty reduction and, in some scenarios, it seems to provide a more coherent assessment of the validity of a surrogate than existing measures. The 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 proposed to study the behavior of the ICA on the previous region. The method is illustrated using data from the Collaborative Initial Glaucoma Treatment Study. A newly developed and user-friendly R package Surrogate is provided to carry out the evaluation exercise. © 2016, The International Biometric Society.

An adaptive Gaussian process-based iterative ensemble smoother for data assimilation

NASA Astrophysics Data System (ADS)

Ju, Lei; Zhang, Jiangjiang; Meng, Long; Wu, Laosheng; Zeng, Lingzao

2018-05-01

Accurate characterization of subsurface hydraulic conductivity is vital for modeling of subsurface flow and transport. The iterative ensemble smoother (IES) has been proposed to estimate the heterogeneous parameter field. As a Monte Carlo-based method, IES requires a relatively large ensemble size to guarantee its performance. To improve the computational efficiency, we propose an adaptive Gaussian process (GP)-based iterative ensemble smoother (GPIES) in this study. At each iteration, the GP surrogate is adaptively refined by adding a few new base points chosen from the updated parameter realizations. Then the sensitivity information between model parameters and measurements is calculated from a large number of realizations generated by the GP surrogate with virtually no computational cost. Since the original model evaluations are only required for base points, whose number is much smaller than the ensemble size, the computational cost is significantly reduced. The applicability of GPIES in estimating heterogeneous conductivity is evaluated by the saturated and unsaturated flow problems, respectively. Without sacrificing estimation accuracy, GPIES achieves about an order of magnitude of speed-up compared with the standard IES. Although subsurface flow problems are considered in this study, the proposed method can be equally applied to other hydrological models.

Fecal indicator organism modeling and microbial source tracking in environmental waters: Chapter 3.4.6

USGS Publications Warehouse

Nevers, Meredith; Byappanahalli, Muruleedhara; Phanikumar, Mantha S.; Whitman, Richard L.

2016-01-01

Mathematical models have been widely applied to surface waters to estimate rates of settling, resuspension, flow, dispersion, and advection in order to calculate movement of particles that influence water quality. Of particular interest are the movement, survival, and persistence of microbial pathogens or their surrogates, which may contaminate recreational water, drinking water, or shellfish. Most models devoted to microbial water quality have been focused on fecal indicator organisms (FIO), which act as a surrogate for pathogens and viruses. Process-based modeling and statistical modeling have been used to track contamination events to source and to predict future events. The use of these two types of models require different levels of expertise and input; process-based models rely on theoretical physical constructs to explain present conditions and biological distribution while data-based, statistical models use extant paired data to do the same. The selection of the appropriate model and interpretation of results is critical to proper use of these tools in microbial source tracking. Integration of the modeling approaches could provide insight for tracking and predicting contamination events in real time. A review of modeling efforts reveals that process-based modeling has great promise for microbial source tracking efforts; further, combining the understanding of physical processes influencing FIO contamination developed with process-based models and molecular characterization of the population by gene-based (i.e., biological) or chemical markers may be an effective approach for locating sources and remediating contamination in order to protect human health better.

IVGTT-based simple assessment of glucose tolerance in the Zucker fatty rat: Validation against minimal models.

PubMed

Morettini, Micaela; Faelli, Emanuela; Perasso, Luisa; Fioretti, Sandro; Burattini, Laura; Ruggeri, Piero; Di Nardo, Francesco

2017-01-01

For the assessment of glucose tolerance from IVGTT data in Zucker rat, minimal model methodology is reliable but time- and money-consuming. This study aimed to validate for the first time in Zucker rat, simple surrogate indexes of insulin sensitivity and secretion against the glucose-minimal-model insulin sensitivity index (SI) and against first- (Φ1) and second-phase (Φ2) β-cell responsiveness indexes provided by C-peptide minimal model. Validation of the surrogate insulin sensitivity index (ISI) and of two sets of coupled insulin-based indexes for insulin secretion, differing from the cut-off point between phases (FPIR3-SPIR3, t = 3 min and FPIR5-SPIR5, t = 5 min), was carried out in a population of ten Zucker fatty rats (ZFR) and ten Zucker lean rats (ZLR). Considering the whole rat population (ZLR+ZFR), ISI showed a significant strong correlation with SI (Spearman's correlation coefficient, r = 0.88; P<0.001). Both FPIR3 and FPIR5 showed a significant (P<0.001) strong correlation with Φ1 (r = 0.76 and r = 0.75, respectively). Both SPIR3 and SPIR5 showed a significant (P<0.001) strong correlation with Φ2 (r = 0.85 and r = 0.83, respectively). ISI is able to detect (P<0.001) the well-recognized reduction in insulin sensitivity in ZFRs, compared to ZLRs. The insulin-based indexes of insulin secretion are able to detect in ZFRs (P<0.001) the compensatory increase of first- and second-phase secretion, associated to the insulin-resistant state. The ability of the surrogate indexes in describing glucose tolerance in the ZFRs was confirmed by the Disposition Index analysis. The model-based validation performed in the present study supports the utilization of low-cost, insulin-based indexes for the assessment of glucose tolerance in Zucker rat, reliable animal model of human metabolic syndrome.

On a sparse pressure-flow rate condensation of rigid circulation models

PubMed Central

Schiavazzi, D. E.; Hsia, T. Y.; Marsden, A. L.

2015-01-01

Cardiovascular simulation has shown potential value in clinical decision-making, providing a framework to assess changes in hemodynamics produced by physiological and surgical alterations. State-of-the-art predictions are provided by deterministic multiscale numerical approaches coupling 3D finite element Navier Stokes simulations to lumped parameter circulation models governed by ODEs. Development of next-generation stochastic multiscale models whose parameters can be learned from available clinical data under uncertainty constitutes a research challenge made more difficult by the high computational cost typically associated with the solution of these models. We present a methodology for constructing reduced representations that condense the behavior of 3D anatomical models using outlet pressure-flow polynomial surrogates, based on multiscale model solutions spanning several heart cycles. Relevance vector machine regression is compared with maximum likelihood estimation, showing that sparse pressure/flow rate approximations offer superior performance in producing working surrogate models to be included in lumped circulation networks. Sensitivities of outlets flow rates are also quantified through a Sobol’ decomposition of their total variance encoded in the orthogonal polynomial expansion. Finally, we show that augmented lumped parameter models including the proposed surrogates accurately reproduce the response of multiscale models they were derived from. In particular, results are presented for models of the coronary circulation with closed loop boundary conditions and the abdominal aorta with open loop boundary conditions. PMID:26671219

Genetic Algorithm Phase Retrieval for the Systematic Image-Based Optical Alignment Testbed

NASA Technical Reports Server (NTRS)

Rakoczy, John; Steincamp, James; Taylor, Jaime

2003-01-01

A reduced surrogate, one point crossover genetic algorithm with random rank-based selection was used successfully to estimate the multiple phases of a segmented optical system modeled on the seven-mirror Systematic Image-Based Optical Alignment testbed located at NASA's Marshall Space Flight Center.

Airfoil Shape Optimization based on Surrogate Model

NASA Astrophysics Data System (ADS)

Mukesh, R.; Lingadurai, K.; Selvakumar, U.

2018-02-01

Engineering design problems always require enormous amount of real-time experiments and computational simulations in order to assess and ensure the design objectives of the problems subject to various constraints. In most of the cases, the computational resources and time required per simulation are large. In certain cases like sensitivity analysis, design optimisation etc where thousands and millions of simulations have to be carried out, it leads to have a life time of difficulty for designers. Nowadays approximation models, otherwise called as surrogate models (SM), are more widely employed in order to reduce the requirement of computational resources and time in analysing various engineering systems. Various approaches such as Kriging, neural networks, polynomials, Gaussian processes etc are used to construct the approximation models. The primary intention of this work is to employ the k-fold cross validation approach to study and evaluate the influence of various theoretical variogram models on the accuracy of the surrogate model construction. Ordinary Kriging and design of experiments (DOE) approaches are used to construct the SMs by approximating panel and viscous solution algorithms which are primarily used to solve the flow around airfoils and aircraft wings. The method of coupling the SMs with a suitable optimisation scheme to carryout an aerodynamic design optimisation process for airfoil shapes is also discussed.

Local-metrics error-based Shepard interpolation as surrogate for highly non-linear material models in high dimensions

NASA Astrophysics Data System (ADS)

Lorenzi, Juan M.; Stecher, Thomas; Reuter, Karsten; Matera, Sebastian

2017-10-01

Many problems in computational materials science and chemistry require the evaluation of expensive functions with locally rapid changes, such as the turn-over frequency of first principles kinetic Monte Carlo models for heterogeneous catalysis. Because of the high computational cost, it is often desirable to replace the original with a surrogate model, e.g., for use in coupled multiscale simulations. The construction of surrogates becomes particularly challenging in high-dimensions. Here, we present a novel version of the modified Shepard interpolation method which can overcome the curse of dimensionality for such functions to give faithful reconstructions even from very modest numbers of function evaluations. The introduction of local metrics allows us to take advantage of the fact that, on a local scale, rapid variation often occurs only across a small number of directions. Furthermore, we use local error estimates to weigh different local approximations, which helps avoid artificial oscillations. Finally, we test our approach on a number of challenging analytic functions as well as a realistic kinetic Monte Carlo model. Our method not only outperforms existing isotropic metric Shepard methods but also state-of-the-art Gaussian process regression.

Local-metrics error-based Shepard interpolation as surrogate for highly non-linear material models in high dimensions.

PubMed

Lorenzi, Juan M; Stecher, Thomas; Reuter, Karsten; Matera, Sebastian

2017-10-28

Many problems in computational materials science and chemistry require the evaluation of expensive functions with locally rapid changes, such as the turn-over frequency of first principles kinetic Monte Carlo models for heterogeneous catalysis. Because of the high computational cost, it is often desirable to replace the original with a surrogate model, e.g., for use in coupled multiscale simulations. The construction of surrogates becomes particularly challenging in high-dimensions. Here, we present a novel version of the modified Shepard interpolation method which can overcome the curse of dimensionality for such functions to give faithful reconstructions even from very modest numbers of function evaluations. The introduction of local metrics allows us to take advantage of the fact that, on a local scale, rapid variation often occurs only across a small number of directions. Furthermore, we use local error estimates to weigh different local approximations, which helps avoid artificial oscillations. Finally, we test our approach on a number of challenging analytic functions as well as a realistic kinetic Monte Carlo model. Our method not only outperforms existing isotropic metric Shepard methods but also state-of-the-art Gaussian process regression.

Advanced topics in evidence-based urologic oncology: surrogate endpoints.

PubMed

Lavallée, Luke T; Montori, Victor M; Canfield, Stephen E; Breau, Rodney H

2011-01-01

Clinical trials often report surrogate endpoint data. A surrogate endpoint is a biological marker or clinical sign that can be substituted for a patient-important outcome. Using surrogate endpoints correctly may facilitate and expedite clinical trials and may improve medical decisions. However, rigorous criteria must be met for an endpoint to be considered a valid surrogate. The purpose of this article is to review the topic of surrogate endpoints in the context of a urologic encounter. Copyright © 2011 Elsevier Inc. All rights reserved.

redNumerical modelling of a peripheral arterial stenosis using dimensionally reduced models and kernel methods.

PubMed

Köppl, Tobias; Santin, Gabriele; Haasdonk, Bernard; Helmig, Rainer

2018-05-06

In this work, we consider two kinds of model reduction techniques to simulate blood flow through the largest systemic arteries, where a stenosis is located in a peripheral artery i.e. in an artery that is located far away from the heart. For our simulations we place the stenosis in one of the tibial arteries belonging to the right lower leg (right post tibial artery). The model reduction techniques that are used are on the one hand dimensionally reduced models (1-D and 0-D models, the so-called mixed-dimension model) and on the other hand surrogate models produced by kernel methods. Both methods are combined in such a way that the mixed-dimension models yield training data for the surrogate model, where the surrogate model is parametrised by the degree of narrowing of the peripheral stenosis. By means of a well-trained surrogate model, we show that simulation data can be reproduced with a satisfactory accuracy and that parameter optimisation or state estimation problems can be solved in a very efficient way. Furthermore it is demonstrated that a surrogate model enables us to present after a very short simulation time the impact of a varying degree of stenosis on blood flow, obtaining a speedup of several orders over the full model. This article is protected by copyright. All rights reserved.

A surrogate-based sensitivity quantification and Bayesian inversion of a regional groundwater flow model

NASA Astrophysics Data System (ADS)

Chen, Mingjie; Izady, Azizallah; Abdalla, Osman A.; Amerjeed, Mansoor

2018-02-01

Bayesian inference using Markov Chain Monte Carlo (MCMC) provides an explicit framework for stochastic calibration of hydrogeologic models accounting for uncertainties; however, the MCMC sampling entails a large number of model calls, and could easily become computationally unwieldy if the high-fidelity hydrogeologic model simulation is time consuming. This study proposes a surrogate-based Bayesian framework to address this notorious issue, and illustrates the methodology by inverse modeling a regional MODFLOW model. The high-fidelity groundwater model is approximated by a fast statistical model using Bagging Multivariate Adaptive Regression Spline (BMARS) algorithm, and hence the MCMC sampling can be efficiently performed. In this study, the MODFLOW model is developed to simulate the groundwater flow in an arid region of Oman consisting of mountain-coast aquifers, and used to run representative simulations to generate training dataset for BMARS model construction. A BMARS-based Sobol' method is also employed to efficiently calculate input parameter sensitivities, which are used to evaluate and rank their importance for the groundwater flow model system. According to sensitivity analysis, insensitive parameters are screened out of Bayesian inversion of the MODFLOW model, further saving computing efforts. The posterior probability distribution of input parameters is efficiently inferred from the prescribed prior distribution using observed head data, demonstrating that the presented BMARS-based Bayesian framework is an efficient tool to reduce parameter uncertainties of a groundwater system.

MRI-guided tumor tracking in lung cancer radiotherapy

NASA Astrophysics Data System (ADS)

Cerviño, Laura I.; Du, Jiang; Jiang, Steve B.

2011-07-01

Precise tracking of lung tumor motion during treatment delivery still represents a challenge in radiation therapy. Prototypes of MRI-linac hybrid systems are being created which have the potential of ionization-free real-time imaging of the tumor. This study evaluates the performance of lung tumor tracking algorithms in cine-MRI sagittal images from five healthy volunteers. Visible vascular structures were used as targets. Volunteers performed several series of regular and irregular breathing. Two tracking algorithms were implemented and evaluated: a template matching (TM) algorithm in combination with surrogate tracking using the diaphragm (surrogate was used when the maximum correlation between the template and the image in the search window was less than specified), and an artificial neural network (ANN) model based on the principal components of a region of interest that encompasses the target motion. The mean tracking error ē and the error at 95% confidence level e95 were evaluated for each model. The ANN model led to ē = 1.5 mm and e95 = 4.2 mm, while TM led to ē = 0.6 mm and e95 = 1.0 mm. An extra series was considered separately to evaluate the benefit of using surrogate tracking in combination with TM when target out-of-plane motion occurs. For this series, the mean error was 7.2 mm using only TM and 1.7 mm when the surrogate was used in combination with TM. Results show that, as opposed to tracking with other imaging modalities, ANN does not perform well in MR-guided tracking. TM, however, leads to highly accurate tracking. Out-of-plane motion could be addressed by surrogate tracking using the diaphragm, which can be easily identified in the images.

Investigation of Navier-Stokes Code Verification and Design Optimization

NASA Technical Reports Server (NTRS)

Vaidyanathan, Rajkumar

2004-01-01

With rapid progress made in employing computational techniques for various complex Navier-Stokes fluid flow problems, design optimization problems traditionally based on empirical formulations and experiments are now being addressed with the aid of computational fluid dynamics (CFD). To be able to carry out an effective CFD-based optimization study, it is essential that the uncertainty and appropriate confidence limits of the CFD solutions be quantified over the chosen design space. The present dissertation investigates the issues related to code verification, surrogate model-based optimization and sensitivity evaluation. For Navier-Stokes (NS) CFD code verification a least square extrapolation (LSE) method is assessed. This method projects numerically computed NS solutions from multiple, coarser base grids onto a freer grid and improves solution accuracy by minimizing the residual of the discretized NS equations over the projected grid. In this dissertation, the finite volume (FV) formulation is focused on. The interplay between the xi concepts and the outcome of LSE, and the effects of solution gradients and singularities, nonlinear physics, and coupling of flow variables on the effectiveness of LSE are investigated. A CFD-based design optimization of a single element liquid rocket injector is conducted with surrogate models developed using response surface methodology (RSM) based on CFD solutions. The computational model consists of the NS equations, finite rate chemistry, and the k-6 turbulence closure. With the aid of these surrogate models, sensitivity and trade-off analyses are carried out for the injector design whose geometry (hydrogen flow angle, hydrogen and oxygen flow areas and oxygen post tip thickness) is optimized to attain desirable goals in performance (combustion length) and life/survivability (the maximum temperatures on the oxidizer post tip and injector face and a combustion chamber wall temperature). A preliminary multi-objective optimization study is carried out using a geometric mean approach. Following this, sensitivity analyses with the aid of variance-based non-parametric approach and partial correlation coefficients are conducted using data available from surrogate models of the objectives and the multi-objective optima to identify the contribution of the design variables to the objective variability and to analyze the variability of the design variables and the objectives. In summary the present dissertation offers insight into an improved coarse to fine grid extrapolation technique for Navier-Stokes computations and also suggests tools for a designer to conduct design optimization study and related sensitivity analyses for a given design problem.

ASME V\\&V challenge problem: Surrogate-based V&V

DOE Office of Scientific and Technical Information (OSTI.GOV)

Beghini, Lauren L.; Hough, Patricia D.

2015-12-18

The process of verification and validation can be resource intensive. From the computational model perspective, the resource demand typically arises from long simulation run times on multiple cores coupled with the need to characterize and propagate uncertainties. In addition, predictive computations performed for safety and reliability analyses have similar resource requirements. For this reason, there is a tradeoff between the time required to complete the requisite studies and the fidelity or accuracy of the results that can be obtained. At a high level, our approach is cast within a validation hierarchy that provides a framework in which we perform sensitivitymore » analysis, model calibration, model validation, and prediction. The evidence gathered as part of these activities is mapped into the Predictive Capability Maturity Model to assess credibility of the model used for the reliability predictions. With regard to specific technical aspects of our analysis, we employ surrogate-based methods, primarily based on polynomial chaos expansions and Gaussian processes, for model calibration, sensitivity analysis, and uncertainty quantification in order to reduce the number of simulations that must be done. The goal is to tip the tradeoff balance to improving accuracy without increasing the computational demands.« less

The intermediate endpoint effect in logistic and probit regression

PubMed Central

MacKinnon, DP; Lockwood, CM; Brown, CH; Wang, W; Hoffman, JM

2010-01-01

Background An intermediate endpoint is hypothesized to be in the middle of the causal sequence relating an independent variable to a dependent variable. The intermediate variable is also called a surrogate or mediating variable and the corresponding effect is called the mediated, surrogate endpoint, or intermediate endpoint effect. Clinical studies are often designed to change an intermediate or surrogate endpoint and through this intermediate change influence the ultimate endpoint. In many intermediate endpoint clinical studies the dependent variable is binary, and logistic or probit regression is used. Purpose The purpose of this study is to describe a limitation of a widely used approach to assessing intermediate endpoint effects and to propose an alternative method, based on products of coefficients, that yields more accurate results. Methods The intermediate endpoint model for a binary outcome is described for a true binary outcome and for a dichotomization of a latent continuous outcome. Plots of true values and a simulation study are used to evaluate the different methods. Results Distorted estimates of the intermediate endpoint effect and incorrect conclusions can result from the application of widely used methods to assess the intermediate endpoint effect. The same problem occurs for the proportion of an effect explained by an intermediate endpoint, which has been suggested as a useful measure for identifying intermediate endpoints. A solution to this problem is given based on the relationship between latent variable modeling and logistic or probit regression. Limitations More complicated intermediate variable models are not addressed in the study, although the methods described in the article can be extended to these more complicated models. Conclusions Researchers are encouraged to use an intermediate endpoint method based on the product of regression coefficients. A common method based on difference in coefficient methods can lead to distorted conclusions regarding the intermediate effect. PMID:17942466

Comparison of treatment effect sizes from pivotal and postapproval trials of novel therapeutics approved by the FDA based on surrogate markers of disease: a meta-epidemiological study.

PubMed

Wallach, Joshua D; Ciani, Oriana; Pease, Alison M; Gonsalves, Gregg S; Krumholz, Harlan M; Taylor, Rod S; Ross, Joseph S

2018-03-21

The U.S. Food and Drug Administration (FDA) often approves new drugs based on trials that use surrogate markers for endpoints, which involve certain trade-offs and may risk making erroneous inferences about the medical product's actual clinical effect. This study aims to compare the treatment effects among pivotal trials supporting FDA approval of novel therapeutics based on surrogate markers of disease with those observed among postapproval trials for the same indication. We searched Drugs@FDA and PubMed to identify published randomized superiority design pivotal trials for all novel drugs initially approved by the FDA between 2005 and 2012 based on surrogate markers as primary endpoints and published postapproval trials using the same surrogate markers or patient-relevant outcomes as endpoints. Summary ratio of odds ratios (RORs) and difference between standardized mean differences (dSMDs) were used to quantify the average difference in treatment effects between pivotal and matched postapproval trials. Between 2005 and 2012, the FDA approved 88 novel drugs for 90 indications based on one or multiple pivotal trials using surrogate markers of disease. Of these, 27 novel drugs for 27 indications were approved based on pivotal trials using surrogate markers as primary endpoints that could be matched to at least one postapproval trial, for a total of 43 matches. For nine (75.0%) of the 12 matches using the same non-continuous surrogate markers as trial endpoints, pivotal trials had larger treatment effects than postapproval trials. On average, treatment effects were 50% higher (more beneficial) in the pivotal than the postapproval trials (ROR 1.5; 95% confidence interval CI 1.01-2.23). For 17 (54.8%) of the 31 matches using the same continuous surrogate markers as trial endpoints, pivotal trials had larger treatment effects than the postapproval trials. On average, there was no difference in treatment effects between pivotal and postapproval trials (dSMDs 0.01; 95% CI -0.15-0.16). Many postapproval drug trials are not directly comparable to previously published pivotal trials, particularly with respect to endpoint selection. Although treatment effects from pivotal trials supporting FDA approval of novel therapeutics based on non-continuous surrogate markers of disease are often larger than those observed among postapproval trials using surrogate markers as trial endpoints, there is no evidence of difference between pivotal and postapproval trials using continuous surrogate markers.

Evaluation of lung tumor motion management in radiation therapy with dynamic MRI

NASA Astrophysics Data System (ADS)

Park, Seyoun; Farah, Rana; Shea, Steven M.; Tryggestad, Erik; Hales, Russell; Lee, Junghoon

2017-03-01

Surrogate-based tumor motion estimation and tracing methods are commonly used in radiotherapy despite the lack of continuous real time 3D tumor and surrogate data. In this study, we propose a method to simultaneously track the tumor and external surrogates with dynamic MRI, which allows us to evaluate their reproducible correlation. Four MRIcompatible fiducials are placed on the patient's chest and upper abdomen, and multi-slice 2D cine MRIs are acquired to capture the lung and whole tumor, followed by two-slice 2D cine MRIs to simultaneously track the tumor and fiducials, all in sagittal orientation. A phase-binned 4D-MRI is first reconstructed from multi-slice MR images using body area as a respiratory surrogate and group-wise registration. The 4D-MRI provides 3D template volumes for different breathing phases. 3D tumor position is calculated by 3D-2D template matching in which 3D tumor templates in 4D-MRI reconstruction and the 2D cine MRIs from the two-slice tracking dataset are registered. 3D trajectories of the external surrogates are derived via matching a 3D geometrical model to the fiducial segmentations on the 2D cine MRIs. We tested our method on five lung cancer patients. Internal target volume from 4D-CT showed average sensitivity of 86.5% compared to the actual tumor motion for 5 min. 3D tumor motion correlated with the external surrogate signal, but showed a noticeable phase mismatch. The 3D tumor trajectory showed significant cycle-to-cycle variation, while the external surrogate was not sensitive enough to capture such variations. Additionally, there was significant phase mismatch between surrogate signals obtained from fiducials at different locations.

Design of Aminobenzothiazole Inhibitors of Rho Kinases 1 and 2 by Using Protein Kinase A as a Structure Surrogate.

PubMed

Judge, Russell A; Vasudevan, Anil; Scott, Victoria E; Simler, Gricelda H; Pratt, Steve D; Namovic, Marian T; Putman, C Brent; Aguirre, Ana; Stoll, Vincent S; Mamo, Mulugeta; Swann, Steven I; Cassar, Steven C; Faltynek, Connie R; Kage, Karen L; Boyce-Rustay, Janel M; Hobson, Adrian D

2018-03-16

We describe the design, synthesis, and structure-activity relationships (SARs) of a series of 2-aminobenzothiazole inhibitors of Rho kinases (ROCKs) 1 and 2, which were optimized to low nanomolar potencies by use of protein kinase A (PKA) as a structure surrogate to guide compound design. A subset of these molecules also showed robust activity in a cell-based myosin phosphatase assay and in a mechanical hyperalgesia in vivo pain model. © 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

In situ densimetric measurements as a surrogate for suspended-sediment concentrations in the Rio Puerco, New Mexico

USGS Publications Warehouse

Brown, Jeb E.; Gray, John R.; Hornewer, Nancy J.

2015-01-01

Surrogate measurements of suspended-sediment concentration (SSC) are increasingly used to provide continuous, high-resolution, and demonstrably accurate data at a reasonable cost. Densimetric data, calculated from the difference between two in situ pressure measurements, exploit variations in real-time streamflow densities to infer SSCs. Unlike other suspendedsediment surrogate technologies based on bulk or digital optics, laser, or hydroacoustics, the accuracy of SSC data estimated using the pressure-difference (also referred to as densimetric) surrogate technology theoretically improves with increasing SCCs. Coupled with streamflow data, continuous suspended-sediment discharges can be calculated using SSC data estimated in real-time using the densimetric technology. The densimetric technology was evaluated at the Rio Puerco in New Mexico, a stream where SSC values regularly range from 10,000-200,000 milligrams per liter (mg/L) and have exceeded 500,000 mg/L. The constant-flow dual-orifice bubbler measures pressure using two precision pressure-transducer sensors at vertically aligned fixed locations in a water column. Water density is calculated from the temperature-compensated differential pressure and SSCs are inferred from the density data. A linear regression model comparing density values to field-measured SSC values yielded an R² of 0.74. Although the application of the densimetric surrogate is likely limited to fluvial systems with SSCs larger than about 10,000 mg/L, based on this and previous studies, the densimetric technology fills a void for monitoring streams with high SSCs.

A rank test for bivariate time-to-event outcomes when one event is a surrogate

PubMed Central

Shaw, Pamela A.; Fay, Michael P.

2016-01-01

In many clinical settings, improving patient survival is of interest but a practical surrogate, such as time to disease progression, is instead used as a clinical trial’s primary endpoint. A time-to-first endpoint (e.g. death or disease progression) is commonly analyzed but may not be adequate to summarize patient outcomes if a subsequent event contains important additional information. We consider a surrogate outcome very generally, as one correlated with the true endpoint of interest. Settings of interest include those where the surrogate indicates a beneficial outcome so that the usual time-to-first endpoint of death or surrogate event is nonsensical. We present a new two-sample test for bivariate, interval-censored time-to-event data, where one endpoint is a surrogate for the second, less frequently observed endpoint of true interest. This test examines whether patient groups have equal clinical severity. If the true endpoint rarely occurs, the proposed test acts like a weighted logrank test on the surrogate; if it occurs for most individuals, then our test acts like a weighted logrank test on the true endpoint. If the surrogate is a useful statistical surrogate, our test can have better power than tests based on the surrogate that naively handle the true endpoint. In settings where the surrogate is not valid (treatment affects the surrogate but not the true endpoint), our test incorporates the information regarding the lack of treatment effect from the observed true endpoints and hence is expected to have a dampened treatment effect compared to tests based on the surrogate alone. PMID:27059817

Statistical Surrogate Models for Estimating Probability of High-Consequence Climate Change

NASA Astrophysics Data System (ADS)

Field, R.; Constantine, P.; Boslough, M.

2011-12-01

We have posed the climate change problem in a framework similar to that used in safety engineering, by acknowledging that probabilistic risk assessments focused on low-probability, high-consequence climate events are perhaps more appropriate than studies focused simply on best estimates. 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 have developed specialized statistical surrogate models (SSMs) that can be used to make predictions about the tails of the associated probability distributions. A SSM is different than a deterministic surrogate model in that it represents each climate variable of interest as a space/time random field, that is, a random variable for every fixed location in the atmosphere at all times. The SSM can be calibrated to available spatial and temporal data from existing climate databases, or to a collection of outputs from general circulation models. 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 was also developed to provide quantitative measures of confidence, via Bayesian credible intervals, to assess these risks. To illustrate the use of the SSM, we considered two collections of NCAR CCSM 3.0 output data. The first collection corresponds to average December surface temperature for years 1990-1999 based on a collection of 8 different model runs obtained from the Program for Climate Model Diagnosis and Intercomparison (PCMDI). We calibrated the surrogate model to the available model data and make various point predictions. We also analyzed average precipitation rate in June, July, and August over a 54-year period assuming a cyclic Y2K ocean model. We applied the calibrated surrogate model to study the probability that the precipitation rate falls below certain thresholds and utilized the Bayesian approach to quantify our confidence in these predictions. Sandia is a multiprogram laboratory operated by Sandia Corporation, a Lockheed Martin Company, for the United States Department of Energy's National Nuclear Security Administration under Contract DE-AC04-94AL85000.

Using an external surrogate for predictor model training in real-time motion management of lung tumors.

PubMed

Rottmann, Joerg; Berbeco, Ross

2014-12-01

Precise prediction of respiratory motion is a prerequisite for real-time motion compensation techniques such as beam, dynamic couch, or dynamic multileaf collimator tracking. Collection of tumor motion data to train the prediction model is required for most algorithms. To avoid exposure of patients to additional dose from imaging during this procedure, the feasibility of training a linear respiratory motion prediction model with an external surrogate signal is investigated and its performance benchmarked against training the model with tumor positions directly. The authors implement a lung tumor motion prediction algorithm based on linear ridge regression that is suitable to overcome system latencies up to about 300 ms. Its performance is investigated on a data set of 91 patient breathing trajectories recorded from fiducial marker tracking during radiotherapy delivery to the lung of ten patients. The expected 3D geometric error is quantified as a function of predictor lookahead time, signal sampling frequency and history vector length. Additionally, adaptive model retraining is evaluated, i.e., repeatedly updating the prediction model after initial training. Training length for this is gradually increased with incoming (internal) data availability. To assess practical feasibility model calculation times as well as various minimum data lengths for retraining are evaluated. Relative performance of model training with external surrogate motion data versus tumor motion data is evaluated. However, an internal-external motion correlation model is not utilized, i.e., prediction is solely driven by internal motion in both cases. Similar prediction performance was achieved for training the model with external surrogate data versus internal (tumor motion) data. Adaptive model retraining can substantially boost performance in the case of external surrogate training while it has little impact for training with internal motion data. A minimum adaptive retraining data length of 8 s and history vector length of 3 s achieve maximal performance. Sampling frequency appears to have little impact on performance confirming previously published work. By using the linear predictor, a relative geometric 3D error reduction of about 50% was achieved (using adaptive retraining, a history vector length of 3 s and with results averaged over all investigated lookahead times and signal sampling frequencies). The absolute mean error could be reduced from (2.0 ± 1.6) mm when using no prediction at all to (0.9 ± 0.8) mm and (1.0 ± 0.9) mm when using the predictor trained with internal tumor motion training data and external surrogate motion training data, respectively (for a typical lookahead time of 250 ms and sampling frequency of 15 Hz). A linear prediction model can reduce latency induced tracking errors by an average of about 50% in real-time image guided radiotherapy systems with system latencies of up to 300 ms. Training a linear model for lung tumor motion prediction with an external surrogate signal alone is feasible and results in similar performance as training with (internal) tumor motion. Particularly for scenarios where motion data are extracted from fluoroscopic imaging with ionizing radiation, this may alleviate the need for additional imaging dose during the collection of model training data.

Surrogacy assessment using principal stratification with multivariate normal and Gaussian copula models.

PubMed

Taylor, Jeremy M G; Conlon, Anna S C; Elliott, Michael R

2015-08-01

The validation of intermediate markers as surrogate markers (S) for the true outcome of interest (T) in clinical trials offers the possibility for trials to be run more quickly and cheaply by using the surrogate endpoint in place of the true endpoint. Working within a principal stratification framework, we propose causal quantities to evaluate surrogacy using a Gaussian copula model for an ordinal surrogate and time-to-event final outcome. The methods are applied to data from four colorectal cancer clinical trials, where S is tumor response and T is overall survival. For the Gaussian copula model, a Bayesian estimation strategy is used and, as some parameters are not identifiable from the data, we explore the use of informative priors that are consistent with reasonable assumptions in the surrogate marker setting to aid in estimation. While there is some bias in the estimation of the surrogacy quantities of interest, the estimation procedure does reasonably well at distinguishing between poor and good surrogate markers. Some of the parameters of the proposed model are not identifiable from the data, and therefore, assumptions must be made in order to aid in their estimation. The proposed quantities can be used in combination to provide evidence about the validity of S as a surrogate marker for T. © The Author(s) 2014.

Controlled experiments for dense gas diffusion: Experimental design and execution, model comparison

DOE Office of Scientific and Technical Information (OSTI.GOV)

Egami, R.; Bowen, J.; Coulombe, W.

1995-07-01

An experimental baseline CO2 release experiment at the DOE Spill Test Facility on the Nevada Test Site in Southern Nevada is described. This experiment was unique in its use of CO2 as a surrogate gas representative of a variety of specific chemicals. Introductory discussion places the experiment in historical perspective. CO2 was selected as a surrogate gas to provide a data base suitable for evaluation of model scenarios involving a variety of specific dense gases. The experiment design and setup are described, including design rationale and quality assurance methods employed. Resulting experimental data are summarized. Data usefulness is examined throughmore » a preliminary comparison of experimental results with simulations performed using the SLAV and DEGADIS dense gas models.« less

Surrogate safety measures from traffic simulation models

DOT National Transportation Integrated Search

2003-01-01

This project investigates the potential for deriving surrogate measures of safety from existing microscopic traffic simulation models for intersections. The process of computing the measures in the simulation, extracting the required data, and summar...

Integrating surrogate models into subsurface simulation framework allows computation of complex reactive transport scenarios

NASA Astrophysics Data System (ADS)

De Lucia, Marco; Kempka, Thomas; Jatnieks, Janis; Kühn, Michael

2017-04-01

Reactive transport simulations - where geochemical reactions are coupled with hydrodynamic transport of reactants - are extremely time consuming and suffer from significant numerical issues. Given the high uncertainties inherently associated with the geochemical models, which also constitute the major computational bottleneck, such requirements may seem inappropriate and probably constitute the main limitation for their wide application. A promising way to ease and speed-up such coupled simulations is achievable employing statistical surrogates instead of "full-physics" geochemical models [1]. Data-driven surrogates are reduced models obtained on a set of pre-calculated "full physics" simulations, capturing their principal features while being extremely fast to compute. Model reduction of course comes at price of a precision loss; however, this appears justified in presence of large uncertainties regarding the parametrization of geochemical processes. This contribution illustrates the integration of surrogates into the flexible simulation framework currently being developed by the authors' research group [2]. The high level language of choice for obtaining and dealing with surrogate models is R, which profits from state-of-the-art methods for statistical analysis of large simulations ensembles. A stand-alone advective mass transport module was furthermore developed in order to add such capability to any multiphase finite volume hydrodynamic simulator within the simulation framework. We present 2D and 3D case studies benchmarking the performance of surrogates and "full physics" chemistry in scenarios pertaining the assessment of geological subsurface utilization. [1] Jatnieks, J., De Lucia, M., Dransch, D., Sips, M.: "Data-driven surrogate model approach for improving the performance of reactive transport simulations.", Energy Procedia 97, 2016, p. 447-453. [2] Kempka, T., Nakaten, B., De Lucia, M., Nakaten, N., Otto, C., Pohl, M., Chabab [Tillner], E., Kühn, M.: "Flexible Simulation Framework to Couple Processes in Complex 3D Models for Subsurface Utilization Assessment.", Energy Procedia, 97, 2016 p. 494-501.

21 CFR 314.510 - Approval based on a surrogate endpoint or on an effect on a clinical endpoint other than survival...

Code of Federal Regulations, 2010 CFR

2010-04-01

... 21 Food and Drugs 5 2010-04-01 2010-04-01 false Approval based on a surrogate endpoint or on an effect on a clinical endpoint other than survival or irreversible morbidity. 314.510 Section 314.510 Food... Serious or Life-Threatening Illnesses § 314.510 Approval based on a surrogate endpoint or on an effect on...

Generation of fluoroscopic 3D images with a respiratory motion model based on an external surrogate signal

NASA Astrophysics Data System (ADS)

Hurwitz, Martina; Williams, Christopher L.; Mishra, Pankaj; Rottmann, Joerg; Dhou, Salam; Wagar, Matthew; Mannarino, Edward G.; Mak, Raymond H.; Lewis, John H.

2015-01-01

Respiratory motion during radiotherapy can cause uncertainties in definition of the target volume and in estimation of the dose delivered to the target and healthy tissue. In this paper, we generate volumetric images of the internal patient anatomy during treatment using only the motion of a surrogate signal. Pre-treatment four-dimensional CT imaging is used to create a patient-specific model correlating internal respiratory motion with the trajectory of an external surrogate placed on the chest. The performance of this model is assessed with digital and physical phantoms reproducing measured irregular patient breathing patterns. Ten patient breathing patterns are incorporated in a digital phantom. For each patient breathing pattern, the model is used to generate images over the course of thirty seconds. The tumor position predicted by the model is compared to ground truth information from the digital phantom. Over the ten patient breathing patterns, the average absolute error in the tumor centroid position predicted by the motion model is 1.4 mm. The corresponding error for one patient breathing pattern implemented in an anthropomorphic physical phantom was 0.6 mm. The global voxel intensity error was used to compare the full image to the ground truth and demonstrates good agreement between predicted and true images. The model also generates accurate predictions for breathing patterns with irregular phases or amplitudes.

Development of Algal Interspecies Correlation Estimation Models for Chemical Hazard Assessment

EPA Science Inventory

Web-based Interspecies Correlation Estimation (ICE) is an application developed to predict the acute toxicity of a chemical from 1 species to another taxon. Web-ICE models use the acute toxicity value for a surrogate species to predict effect values for other species, thus potent...

Use of surrogate technologies to estimate suspended sediment in the Clearwater River, Idaho, and Snake River, Washington, 2008-10

USGS Publications Warehouse

Wood, Molly S.; Teasdale, Gregg N.

2013-01-01

Elevated levels of fluvial sediment can reduce the biological productivity of aquatic systems, impair freshwater quality, decrease reservoir storage capacity, and decrease the capacity of hydraulic structures. The need to measure fluvial sediment has led to the development of sediment surrogate technologies, particularly in locations where streamflow alone is not a good estimator of sediment load because of regulated flow, load hysteresis, episodic sediment sources, and non-equilibrium sediment transport. An effective surrogate technology is low maintenance and sturdy over a range of hydrologic conditions, and measured variables can be modeled to estimate suspended-sediment concentration (SSC), load, and duration of elevated levels on a real-time basis. Among the most promising techniques is the measurement of acoustic backscatter strength using acoustic Doppler velocity meters (ADVMs) deployed in rivers. The U.S. Geological Survey, in cooperation with the U.S. Army Corps of Engineers, Walla Walla District, evaluated the use of acoustic backscatter, turbidity, laser diffraction, and streamflow as surrogates for estimating real-time SSC and loads in the Clearwater and Snake Rivers, which adjoin in Lewiston, Idaho, and flow into Lower Granite Reservoir. The study was conducted from May 2008 to September 2010 and is part of the U.S. Army Corps of Engineers Lower Snake River Programmatic Sediment Management Plan to identify and manage sediment sources in basins draining into lower Snake River reservoirs. Commercially available acoustic instruments have shown great promise in sediment surrogate studies because they require little maintenance and measure profiles of the surrogate parameter across a sampling volume rather than at a single point. The strength of acoustic backscatter theoretically increases as more particles are suspended in the water to reflect the acoustic pulse emitted by the ADVM. ADVMs of different frequencies (0.5, 1.5, and 3 Megahertz) were tested to target various sediment grain sizes. Laser diffraction and turbidity also were tested as surrogate technologies. Models between SSC and surrogate variables were developed using ordinary least-squares regression. Acoustic backscatter using the high frequency ADVM at each site was the best predictor of sediment, explaining 93 and 92 percent of the variability in SSC and matching sediment sample data within +8.6 and +10 percent, on average, at the Clearwater River and Snake River study sites, respectively. Additional surrogate models were developed to estimate sand and fines fractions of suspended sediment based on acoustic backscatter. Acoustic backscatter generally appears to be a better estimator of suspended sediment concentration and load over short (storm event and monthly) and long (annual) time scales than transport curves derived solely from the regression of conventional sediment measurements and streamflow. Changing grain sizes, the presence of organic matter, and aggregation of sediments in the river likely introduce some variability in the model between acoustic backscatter and SSC.

Physiology-Based Modeling May Predict Surgical Treatment Outcome for Obstructive Sleep Apnea

PubMed Central

Li, Yanru; Ye, Jingying; Han, Demin; Cao, Xin; Ding, Xiu; Zhang, Yuhuan; Xu, Wen; Orr, Jeremy; Jen, Rachel; Sands, Scott; Malhotra, Atul; Owens, Robert

2017-01-01

Study Objectives: To test whether the integration of both anatomical and nonanatomical parameters (ventilatory control, arousal threshold, muscle responsiveness) in a physiology-based model will improve the ability to predict outcomes after upper airway surgery for obstructive sleep apnea (OSA). Methods: In 31 patients who underwent upper airway surgery for OSA, loop gain and arousal threshold were calculated from preoperative polysomnography (PSG). Three models were compared: (1) a multiple regression based on an extensive list of PSG parameters alone; (2) a multivariate regression using PSG parameters plus PSG-derived estimates of loop gain, arousal threshold, and other trait surrogates; (3) a physiological model incorporating selected variables as surrogates of anatomical and nonanatomical traits important for OSA pathogenesis. Results: Although preoperative loop gain was positively correlated with postoperative apnea-hypopnea index (AHI) (P = .008) and arousal threshold was negatively correlated (P = .011), in both model 1 and 2, the only significant variable was preoperative AHI, which explained 42% of the variance in postoperative AHI. In contrast, the physiological model (model 3), which included AHIREM (anatomy term), fraction of events that were hypopnea (arousal term), the ratio of AHIREM and AHINREM (muscle responsiveness term), loop gain, and central/mixed apnea index (control of breathing terms), was able to explain 61% of the variance in postoperative AHI. Conclusions: Although loop gain and arousal threshold are associated with residual AHI after surgery, only preoperative AHI was predictive using multivariate regression modeling. Instead, incorporating selected surrogates of physiological traits on the basis of OSA pathophysiology created a model that has more association with actual residual AHI. Commentary: A commentary on this article appears in this issue on page 1023. Clinical Trial Registration: ClinicalTrials.Gov; Title: The Impact of Sleep Apnea Treatment on Physiology Traits in Chinese Patients With Obstructive Sleep Apnea; Identifier: NCT02696629; URL: https://clinicaltrials.gov/show/NCT02696629 Citation: Li Y, Ye J, Han D, Cao X, Ding X, Zhang Y, Xu W, Orr J, Jen R, Sands S, Malhotra A, Owens R. Physiology-based modeling may predict surgical treatment outcome for obstructive sleep apnea. J Clin Sleep Med. 2017;13(9):1029–1037. PMID:28818154

An integrated, multi-sensing approach to describe the dynamic relations between turbulence, fluid-forces, and reconfiguration of a submerged plant model in steady flows.

NASA Astrophysics Data System (ADS)

Henry, Pierre-Yves; Aberle, Jochen; Dijkstra, Jasper; Myrhaug, Dag

2016-04-01

Aquatic vegetation plays a vital role in ecohydrological systems regulating many physical, chemical, and biological processes across a wide range of spatial and temporal scales. As a consequence, plant-flow interactions are of particular interest to a wide range of disciplines. While early studies of the interactions between vegetation and flowing water employed simplified and non-flexible structures such as rigid cylinders, recent studies have included flexible plants to identify the main characteristics of the hydrodynamics of vegetated flows. However, the description of plant reconfiguration has often been based on a static approach, i.e. considering the plant's deformation under a static load and neglecting turbulent fluctuations. Correlations between drag fluctuations, plant movements, and upstream turbulence were recently established showing that shear layer turbulence at the surface of the different plant elements (such as blades or stems) can contribute significantly to the dynamic behaviour of the plant. However, the relations between plant movement and force fluctuations might change under varying flow velocities, and although this point is crucial for mixing processes and plant dislodgement by fatigue, these aspects of fluid-structure interactions applied to aquatic vegetation remain largely unexplored. Using an innovative combination of sensing techniques in one set of experiments, this study investigates the relations between turbulence, fluctuating fluid forces and movements of a flexible cylindrical plant surrogate. A silicone-based flexible cylinder was attached at the bottom of a 1m wide flume in fully-developed uniform flow. The lower 22 cm of the plant surrogate were made of plain flexible silicone, while the higher 13cm included a casted rigid sensor, measuring accelerations at the tip of the surrogate. Forces were sampled at high frequencies at the surrogate's base by a 6-degrees-of-freedom force/torque sensor measuring down to the gram-force. Point measurements of turbulence were realized by two ADVs which were located upstream and downstream of the surrogate. Detailed motions of the surrogate were recorded by two cameras above and next to the flume. Image processing allowed for the characterization of the mean deformation and the different modes of horizontal and vertical 'vibration' of the surrogate. The experimental results were compared to numerical simulations obtained from an updated version of the Dynveg code developed by Deltares. The results showed a clear correlation between the cylinder's movements and the (drag) force fluctuations. Due to the swaying motion of the surrogate, the turbulence spectrum is significantly affected when the flow passes the plant model. The succession of several motion modes are observed as the velocity increases, affecting the dominant frequencies in the drag force spectrum and the overall drag. These preliminary results emphasise the importance of the dynamics of the plant flow interactions, and provide an example of the use of new methodologies to provide deeper insights into the physics of complex flows.

Machine Learning Techniques for Global Sensitivity Analysis in Climate Models

NASA Astrophysics Data System (ADS)

Safta, C.; Sargsyan, K.; Ricciuto, D. M.

2017-12-01

Climate models studies are not only challenged by the compute intensive nature of these models but also by the high-dimensionality of the input parameter space. In our previous work with the land model components (Sargsyan et al., 2014) we identified subsets of 10 to 20 parameters relevant for each QoI via Bayesian compressive sensing and variance-based decomposition. Nevertheless the algorithms were challenged by the nonlinear input-output dependencies for some of the relevant QoIs. In this work we will explore a combination of techniques to extract relevant parameters for each QoI and subsequently construct surrogate models with quantified uncertainty necessary to future developments, e.g. model calibration and prediction studies. In the first step, we will compare the skill of machine-learning models (e.g. neural networks, support vector machine) to identify the optimal number of classes in selected QoIs and construct robust multi-class classifiers that will partition the parameter space in regions with smooth input-output dependencies. These classifiers will be coupled with techniques aimed at building sparse and/or low-rank surrogate models tailored to each class. Specifically we will explore and compare sparse learning techniques with low-rank tensor decompositions. These models will be used to identify parameters that are important for each QoI. Surrogate accuracy requirements are higher for subsequent model calibration studies and we will ascertain the performance of this workflow for multi-site ALM simulation ensembles.

Comprehensive mollusk acute toxicity database improves the use of Interspecies Correlation Estimation (ICE) models to predict toxicity of untested freshwater and endangered mussel species

EPA Science Inventory

Interspecies correlation estimation (ICE) models extrapolate acute toxicity data from surrogate test species to untested taxa. A suite of ICE models developed from a comprehensive database is available on the US Environmental Protection Agency’s web-based application, Web-I...

Developing and applying metamodels of high resolution process-based simulations for high throughput exposure assessment of organic chemicals in riverine ecosystems

EPA Science Inventory

As defined by Wikipedia (https://en.wikipedia.org/wiki/Metamodeling), “(a) metamodel or surrogate model is a model of a model, and metamodeling is the process of generating such metamodels.” The goals of metamodeling include, but are not limited to (1) developing func...

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. © The Author(s) 2015.

Dynamic response due to behind helmet blunt trauma measured with a human head surrogate.

PubMed

Freitas, Christopher J; Mathis, James T; Scott, Nikki; Bigger, Rory P; Mackiewicz, James

2014-01-01

A Human Head Surrogate has been developed for use in behind helmet blunt trauma experiments. This human head surrogate fills the void between Post-Mortem Human Subject testing (with biofidelity but handling restrictions) and commercial ballistic head forms (with no biofidelity but ease of use). This unique human head surrogate is based on refreshed human craniums and surrogate materials representing human head soft tissues such as the skin, dura, and brain. A methodology for refreshing the craniums is developed and verified through material testing. A test methodology utilizing these unique human head surrogates is also developed and then demonstrated in a series of experiments in which non-perforating ballistic impact of combat helmets is performed with and without supplemental ceramic appliques for protecting against larger caliber threats. Sensors embedded in the human head surrogates allow for direct measurement of intracranial pressure, cranial strain, and head and helmet acceleration. Over seventy (70) fully instrumented experiments have been executed using this unique surrogate. Examples of the data collected are presented. Based on these series of tests, the Southwest Research Institute (SwRI) Human Head Surrogate has demonstrated great potential for providing insights in to injury mechanics resulting from non-perforating ballistic impact on combat helmets, and directly supports behind helmet blunt trauma studies.

Dynamic Response Due to Behind Helmet Blunt Trauma Measured with a Human Head Surrogate

PubMed Central

Freitas, Christopher J.; Mathis, James T.; Scott, Nikki; Bigger, Rory P.; MacKiewicz, James

2014-01-01

A Human Head Surrogate has been developed for use in behind helmet blunt trauma experiments. This human head surrogate fills the void between Post-Mortem Human Subject testing (with biofidelity but handling restrictions) and commercial ballistic head forms (with no biofidelity but ease of use). This unique human head surrogate is based on refreshed human craniums and surrogate materials representing human head soft tissues such as the skin, dura, and brain. A methodology for refreshing the craniums is developed and verified through material testing. A test methodology utilizing these unique human head surrogates is also developed and then demonstrated in a series of experiments in which non-perforating ballistic impact of combat helmets is performed with and without supplemental ceramic appliques for protecting against larger caliber threats. Sensors embedded in the human head surrogates allow for direct measurement of intracranial pressure, cranial strain, and head and helmet acceleration. Over seventy (70) fully instrumented experiments have been executed using this unique surrogate. Examples of the data collected are presented. Based on these series of tests, the Southwest Research Institute (SwRI) Human Head Surrogate has demonstrated great potential for providing insights in to injury mechanics resulting from non-perforating ballistic impact on combat helmets, and directly supports behind helmet blunt trauma studies. PMID:24688303

EVALUATING HABITAT AS A SURROGATE FOR POPULATION VIABILITY USING A SPATIALLY EXPLICIT POPULATION MODEL

EPA Science Inventory

Because data for conservation planning are always limited, surrogates are often substituted for intractable measurements such as species richness or population viability. We examined the ability of habitat quality to act as a surrogate for population performance for both Red-sho...

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dasari, Paul K. R.; Shazeeb, Mohammed Salman; Könik, Arda

Purpose: Binning list-mode acquisitions as a function of a surrogate signal related to respiration has been employed to reduce the impact of respiratory motion on image quality in cardiac emission tomography (SPECT and PET). Inherent in amplitude binning is the assumption that there is a monotonic relationship between the amplitude of the surrogate signal and respiratory motion of the heart. This assumption is not valid in the presence of hysteresis when heart motion exhibits a different relationship with the surrogate during inspiration and expiration. The purpose of this study was to investigate the novel approach of using the Bouc–Wen (BW)more » model to provide a signal accounting for hysteresis when binning list-mode data with the goal of thereby improving motion correction. The study is based on the authors’ previous observations that hysteresis between chest and abdomen markers was indicative of hysteresis between abdomen markers and the internal motion of the heart. Methods: In 19 healthy volunteers, they determined the internal motion of the heart and diaphragm in the superior–inferior direction during free breathing using MRI navigators. A visual tracking system (VTS) synchronized with MRI acquisition tracked the anterior–posterior motions of external markers placed on the chest and abdomen. These data were employed to develop and test the Bouc–Wen model by inputting the VTS derived chest and abdomen motions into it and using the resulting output signals as surrogates for cardiac motion. The data of the volunteers were divided into training and testing sets. The training set was used to obtain initial values for the model parameters for all of the volunteers in the set, and for set members based on whether they were or were not classified as exhibiting hysteresis using a metric derived from the markers. These initial parameters were then employed with the testing set to estimate output signals. Pearson’s linear correlation coefficient between the abdomen, chest, average of chest and abdomen markers, and Bouc–Wen derived signals versus the true internal motion of the heart from MRI was used to judge the signals match to the heart motion. Results: The results show that the Bouc–Wen model generated signals demonstrated strong correlation with the heart motion. This correlation was slightly larger on average than that of the external surrogate signals derived from the abdomen marker, and average of the abdomen and chest markers, but was not statistically significantly different from them. Conclusions: The results suggest that the proposed model has the potential to be a unified framework for modeling hysteresis in respiratory motion in cardiac perfusion studies and beyond.« less

Fiber orientation interpolation for the multiscale analysis of short fiber reinforced composite parts

NASA Astrophysics Data System (ADS)

Köbler, Jonathan; Schneider, Matti; Ospald, Felix; Andrä, Heiko; Müller, Ralf

2018-06-01

For short fiber reinforced plastic parts the local fiber orientation has a strong influence on the mechanical properties. To enable multiscale computations using surrogate models we advocate a two-step identification strategy. Firstly, for a number of sample orientations an effective model is derived by numerical methods available in the literature. Secondly, to cover a general orientation state, these effective models are interpolated. In this article we develop a novel and effective strategy to carry out this interpolation. Firstly, taking into account symmetry arguments, we reduce the fiber orientation phase space to a triangle in R^2 . For an associated triangulation of this triangle we furnish each node with an surrogate model. Then, we use linear interpolation on the fiber orientation triangle to equip each fiber orientation state with an effective stress. The proposed approach is quite general, and works for any physically nonlinear constitutive law on the micro-scale, as long as surrogate models for single fiber orientation states can be extracted. To demonstrate the capabilities of our scheme we study the viscoelastic creep behavior of short glass fiber reinforced PA66, and use Schapery's collocation method together with FFT-based computational homogenization to derive single orientation state effective models. We discuss the efficient implementation of our method, and present results of a component scale computation on a benchmark component by using ABAQUS ®.

Fiber orientation interpolation for the multiscale analysis of short fiber reinforced composite parts

NASA Astrophysics Data System (ADS)

Köbler, Jonathan; Schneider, Matti; Ospald, Felix; Andrä, Heiko; Müller, Ralf

2018-04-01

For short fiber reinforced plastic parts the local fiber orientation has a strong influence on the mechanical properties. To enable multiscale computations using surrogate models we advocate a two-step identification strategy. Firstly, for a number of sample orientations an effective model is derived by numerical methods available in the literature. Secondly, to cover a general orientation state, these effective models are interpolated. In this article we develop a novel and effective strategy to carry out this interpolation. Firstly, taking into account symmetry arguments, we reduce the fiber orientation phase space to a triangle in R^2 . For an associated triangulation of this triangle we furnish each node with an surrogate model. Then, we use linear interpolation on the fiber orientation triangle to equip each fiber orientation state with an effective stress. The proposed approach is quite general, and works for any physically nonlinear constitutive law on the micro-scale, as long as surrogate models for single fiber orientation states can be extracted. To demonstrate the capabilities of our scheme we study the viscoelastic creep behavior of short glass fiber reinforced PA66, and use Schapery's collocation method together with FFT-based computational homogenization to derive single orientation state effective models. We discuss the efficient implementation of our method, and present results of a component scale computation on a benchmark component by using ABAQUS ®.

Surrogate Safety Assessment Model (SSAM)--software user manual

DOT National Transportation Integrated Search

2008-05-01

This document presents guidelines for the installation and use of the Surrogate Safety Assessment Model (SSAM) software. For more information regarding the SSAM application, including discussion of theoretical background and the results of a series o...

Surrogate model approach for improving the performance of reactive transport simulations

NASA Astrophysics Data System (ADS)

Jatnieks, Janis; De Lucia, Marco; Sips, Mike; Dransch, Doris

2016-04-01

Reactive transport models can serve a large number of important geoscientific applications involving underground resources in industry and scientific research. It is common for simulation of reactive transport to consist of at least two coupled simulation models. First is a hydrodynamics simulator that is responsible for simulating the flow of groundwaters and transport of solutes. Hydrodynamics simulators are well established technology and can be very efficient. When hydrodynamics simulations are performed without coupled geochemistry, their spatial geometries can span millions of elements even when running on desktop workstations. Second is a geochemical simulation model that is coupled to the hydrodynamics simulator. Geochemical simulation models are much more computationally costly. This is a problem that makes reactive transport simulations spanning millions of spatial elements very difficult to achieve. To address this problem we propose to replace the coupled geochemical simulation model with a surrogate model. A surrogate is a statistical model created to include only the necessary subset of simulator complexity for a particular scenario. To demonstrate the viability of such an approach we tested it on a popular reactive transport benchmark problem that involves 1D Calcite transport. This is a published benchmark problem (Kolditz, 2012) for simulation models and for this reason we use it to test the surrogate model approach. To do this we tried a number of statistical models available through the caret and DiceEval packages for R, to be used as surrogate models. These were trained on randomly sampled subset of the input-output data from the geochemical simulation model used in the original reactive transport simulation. For validation we use the surrogate model to predict the simulator output using the part of sampled input data that was not used for training the statistical model. For this scenario we find that the multivariate adaptive regression splines (MARS) method provides the best trade-off between speed and accuracy. This proof-of-concept forms an essential step towards building an interactive visual analytics system to enable user-driven systematic creation of geochemical surrogate models. Such a system shall enable reactive transport simulations with unprecedented spatial and temporal detail to become possible. References: Kolditz, O., Görke, U.J., Shao, H. and Wang, W., 2012. Thermo-hydro-mechanical-chemical processes in porous media: benchmarks and examples (Vol. 86). Springer Science & Business Media.

Evaluating Expected Costs and Benefits of Granting Access to New Treatments on the Basis of Progression-Free Survival in Non-Small-Cell Lung Cancer.

PubMed

Lakdawalla, Darius N; Chou, Jacquelyn W; Linthicum, Mark T; MacEwan, Joanna P; Zhang, Jie; Goldman, Dana P

2015-05-01

Surrogate end points may be used as proxy for more robust clinical end points. One prominent example is the use of progression-free survival (PFS) as a surrogate for overall survival (OS) in trials for oncologic treatments. Decisions based on surrogate end points may expedite regulatory approval but may not accurately reflect drug efficacy. Payers and clinicians must balance the potential benefits of earlier treatment access based on surrogate end points against the risks of clinical uncertainty. To present a framework for evaluating the expected net benefit or cost of providing early access to new treatments on the basis of evidence of PFS benefits before OS results are available, using non-small-cell lung cancer (NSCLC) as an example. A probabilistic decision model was used to estimate expected incremental social value of the decision to grant access to a new treatment on the basis of PFS evidence. The model analyzed a hypothetical population of patients with NSCLC who could be treated during the period between PFS and OS evidence publication. Estimates for delay in publication of OS evidence following publication of PFS evidence, expected OS benefit given PFS benefit, incremental cost of new treatment, and other parameters were drawn from the literature on treatment of NSCLC. Incremental social value of early access for each additional patient per month (in 2014 US dollars). For "medium-value" model parameters, early reimbursement of drugs with any PFS benefit yields an incremental social cost of more than $170,000 per newly treated patient per month. In contrast, granting early access on the basis of PFS benefit between 1 and 3.5 months produces more than $73,000 in incremental social value. Across the full range of model parameter values, granting access for drugs with PFS benefit between 3 and 3.5 months is robustly beneficial, generating incremental social value ranging from $38,000 to more than $1 million per newly treated patient per month, whereas access for all drugs with any PFS benefit is usually not beneficial. The value of providing access to new treatments on the basis of surrogate end points, and PFS in particular, likely varies considerably. Payers and clinicians should carefully consider how to use PFS data in balancing potential benefits against costs in each particular disease.

Surrogate outcomes: experiences at the Common Drug Review

PubMed Central

2013-01-01

Background Surrogate outcomes are a significant challenge in drug evaluation for health technology assessment (HTA) agencies. The research objectives were to: identify factors associated with surrogate use and acceptability in Canada’s Common Drug Review (CDR) recommendations, and compare the CDR with other HTA or regulatory agencies regarding surrogate concerns. Methods Final recommendations were identified from CDR inception (September 2003) to December 31, 2010. Recommendations were classified by type of outcome (surrogate, final, other) and acceptability of surrogates (determined by the presence/absence of statements of concern regarding surrogates). Descriptive and statistical analyses examined factors related to surrogate use and acceptability. For thirteen surrogate-based submissions, recommendations from international HTA and regulatory agencies were reviewed for statements about surrogate acceptability. Results Of 156 final recommendations, 68 (44%) involved surrogates. The overall ‘do not list’ (DNL) rate was 48%; the DNL rate for surrogates was 41% (p = 0.175). The DNL rate was 64% for non-accepted surrogates (n = 28) versus 25% for accepted surrogates (odds ratio 5.4, p = 0.002). Clinical uncertainty, use of economic evidence over price alone, and a premium price were significantly associated with non-accepted surrogates. Surrogates were used most commonly for HIV, diabetes, rare diseases, cardiovascular disease and cancer. For the subset of drugs studied, other HTA agencies did not express concerns for most recommendations, while regulatory agencies frequently stated surrogate acceptance. Conclusions The majority of surrogates were accepted at the CDR. Non-accepted surrogates were significantly associated with clinical uncertainty and a DNL recommendation. There was inconsistency of surrogate acceptability across several international agencies. Stakeholders should consider collaboratively establishing guidelines on the use, validation, and acceptability of surrogates. PMID:24341379

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

PubMed

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

2011-06-01

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

Near Real-Time Probabilistic Damage Diagnosis Using Surrogate Modeling and High Performance Computing

NASA Technical Reports Server (NTRS)

Warner, James E.; Zubair, Mohammad; Ranjan, Desh

2017-01-01

This work investigates novel approaches to probabilistic damage diagnosis that utilize surrogate modeling and high performance computing (HPC) to achieve substantial computational speedup. Motivated by Digital Twin, a structural health management (SHM) paradigm that integrates vehicle-specific characteristics with continual in-situ damage diagnosis and prognosis, the methods studied herein yield near real-time damage assessments that could enable monitoring of a vehicle's health while it is operating (i.e. online SHM). High-fidelity modeling and uncertainty quantification (UQ), both critical to Digital Twin, are incorporated using finite element method simulations and Bayesian inference, respectively. The crux of the proposed Bayesian diagnosis methods, however, is the reformulation of the numerical sampling algorithms (e.g. Markov chain Monte Carlo) used to generate the resulting probabilistic damage estimates. To this end, three distinct methods are demonstrated for rapid sampling that utilize surrogate modeling and exploit various degrees of parallelism for leveraging HPC. The accuracy and computational efficiency of the methods are compared on the problem of strain-based crack identification in thin plates. While each approach has inherent problem-specific strengths and weaknesses, all approaches are shown to provide accurate probabilistic damage diagnoses and several orders of magnitude computational speedup relative to a baseline Bayesian diagnosis implementation.

Experimental Validation and Combustion Modeling of a JP-8 Surrogate in a Single Cylinder Diesel Engine

DTIC Science & Technology

2014-04-15

SINGLE CYLINDER DIESEL ENGINE Amit Shrestha, Umashankar Joshi, Ziliang Zheng, Tamer Badawy, Naeim A. Henein, Wayne State University, Detroit, MI, USA...13-03-2014 4. TITLE AND SUBTITLE EXPERIMENTAL VALIDATION AND COMBUSTION MODELING OF A JP-8 SURROGATE IN A SINGLE CYLINDER DIESEL ENGINE 5a...INTERNATIONAL UNCLASSIFIED • Validate a two-component JP-8 surrogate in a single cylinder diesel engine. Validation parameters include – Ignition delay

A review of selection-based tests of abiotic surrogates for species representation.

PubMed

Beier, Paul; Sutcliffe, Patricia; Hjort, Jan; Faith, Daniel P; Pressey, Robert L; Albuquerque, Fabio

2015-06-01

Because conservation planners typically lack data on where species occur, environmental surrogates--including geophysical settings and climate types--have been used to prioritize sites within a planning area. We reviewed 622 evaluations of the effectiveness of abiotic surrogates in representing species in 19 study areas. Sites selected using abiotic surrogates represented more species than an equal number of randomly selected sites in 43% of tests (55% for plants) and on average improved on random selection of sites by about 8% (21% for plants). Environmental diversity (ED) (42% median improvement on random selection) and biotically informed clusters showed promising results and merit additional testing. We suggest 4 ways to improve performance of abiotic surrogates. First, analysts should consider a broad spectrum of candidate variables to define surrogates, including rarely used variables related to geographic separation, distance from coast, hydrology, and within-site abiotic diversity. Second, abiotic surrogates should be defined at fine thematic resolution. Third, sites (the landscape units prioritized within a planning area) should be small enough to ensure that surrogates reflect species' environments and to produce prioritizations that match the spatial resolution of conservation decisions. Fourth, if species inventories are available for some planning units, planners should define surrogates based on the abiotic variables that most influence species turnover in the planning area. Although species inventories increase the cost of using abiotic surrogates, a modest number of inventories could provide the data needed to select variables and evaluate surrogates. Additional tests of nonclimate abiotic surrogates are needed to evaluate the utility of conserving nature's stage as a strategy for conservation planning in the face of climate change. © 2015 Society for Conservation Biology.

A Poisson approach to the validation of failure time surrogate endpoints in individual patient data meta-analyses.

PubMed

Rotolo, Federico; Paoletti, Xavier; Burzykowski, Tomasz; Buyse, Marc; Michiels, Stefan

2017-01-01

Surrogate endpoints are often used in clinical trials instead of well-established hard endpoints for practical convenience. The meta-analytic approach relies on two measures of surrogacy: one at the individual level and one at the trial level. In the survival data setting, a two-step model based on copulas is commonly used. We present a new approach which employs a bivariate survival model with an individual random effect shared between the two endpoints and correlated treatment-by-trial interactions. We fit this model using auxiliary mixed Poisson models. We study via simulations the operating characteristics of this mixed Poisson approach as compared to the two-step copula approach. We illustrate the application of the methods on two individual patient data meta-analyses in gastric cancer, in the advanced setting (4069 patients from 20 randomized trials) and in the adjuvant setting (3288 patients from 14 randomized trials).

Surrogate modelling for the prediction of spatial fields based on simultaneous dimensionality reduction of high-dimensional input/output spaces.

PubMed

Crevillén-García, D

2018-04-01

Time-consuming numerical simulators for solving groundwater flow and dissolution models of physico-chemical processes in deep aquifers normally require some of the model inputs to be defined in high-dimensional spaces in order to return realistic results. Sometimes, the outputs of interest are spatial fields leading to high-dimensional output spaces. Although Gaussian process emulation has been satisfactorily used for computing faithful and inexpensive approximations of complex simulators, these have been mostly applied to problems defined in low-dimensional input spaces. In this paper, we propose a method for simultaneously reducing the dimensionality of very high-dimensional input and output spaces in Gaussian process emulators for stochastic partial differential equation models while retaining the qualitative features of the original models. This allows us to build a surrogate model for the prediction of spatial fields in such time-consuming simulators. We apply the methodology to a model of convection and dissolution processes occurring during carbon capture and storage.

MODELING POPULATION EXPOSURES TO OUTDOOR SOURCES OF HAZARDOUS AIR POLLUTANTS

EPA Science Inventory

Accurate assessment of human exposures is an important part of environmental health effects research. However, most air pollution epidemiology studies rely upon imperfect surrogates of personal exposures, such as information based on available central-site outdoor concentration ...

Assessing the validity of surrogate endpoints in the context of a controversy about the measurement of effectiveness of hepatitis C virus treatment.

PubMed

Dobler, Claudia C; Morgan, Rebecca L; Falck-Ytter, Yngve; Montori, Victor M; Murad, M Hassan

2018-04-01

Surrogate endpoints are often used in clinical trials, as they allow for indirect measures of outcomes (eg, shorter trials with less participants). Improvements in surrogate endpoints (eg, reduction in low density lipoprotein cholesterol, normalisation of glycated haemoglobin) achieved with an intervention are, however, not always associated with improvements in patient-important outcomes. The common tendency in evidence-based medicine is to view results based on surrogate endpoints as less certain than results based on long term, final patient-important outcomes and rate them as 'lower quality evidence'. However, careful appraisal of the validity of a surrogate endpoint as a measure of the final, patient-important outcome is more useful than an automatic judgement. In this guide, we use a contemporary and currently highly debated example of the surrogate endpoint 'sustained viral response' (ie, viral eradication considered to represent successful treatment) in patients treated for chronic hepatitis C virus. We demonstrate how the validity of a surrogate endpoint can be critically appraised to assess the quality of the evidence (ie, the certainty in estimates) and the implications for decision-making. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

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

DOE Office of Scientific and Technical Information (OSTI.GOV)

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.more » 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.« less

The Limits of Surrogates' Moral Authority and Physician Professionalism: Can the Paradigm of Palliative Sedation Be Instructive?

PubMed

Berger, Jeffrey T

2017-01-01

With narrow exception, physicians' treatment of incapacitated patients requires the consent of health surrogates. Although the decision-making authority of surrogates is appropriately broad, their moral authority is not without limits. Discerning these bounds is particularly germane to ethically complex treatments and has important implications for the welfare of patients, for the professional integrity of clinicians, and, in fact, for the welfare of surrogates. Palliative sedation is one such complex treatment; as such, it provides a valuable model for analyzing the scope of surrogates' moral authority. Guidelines for palliative sedation that present it as a "last-resort" treatment for severe and intractable suffering yet require surrogate consent in order to offer it are ethically untenable, precisely because the moral limits of surrogate authority have not been considered. © 2017 The Hastings Center.

Surrogate outcomes are associated with low methodological quality of studies of rheumatoid arthritis treated with antitumour necrosis factor agents: a systematic review.

PubMed

Nobre, Moacyr Roberto Cuce; da Costa, Frnanda Marques

2012-02-01

Surrogate endpoints may be used as substitutes for, but often do not predict clinically relevant events. Objective To assess the methodological quality of articles that present their conclusions based on clinically relevant or surrogate outcomes in a systematic review of randomised trials and cohort studies of patients with rheumatoid arthritis treated with antitumour necrosis factor (TNF) agents. PubMed, Embase and Cochrane databases were searched. The Jadad score, the percentage of Consolidated Standards Of Reporting Trials (CONSORT) statement items adequately reported and levels-of-evidence (Center for Evidence-based Medicine, Oxford) were used in a descriptive synthesis. Among 88 articles appraised, 27 had surrogate endpoints, mainly radiographic, and 44 were duplicate publications; 74% of articles with surrogate and 39% of articles with clinical endpoints (p=0.006). Fewer articles with surrogate endpoints represented a high level of evidence (Level 1b, 33% vs 62%, p=0.037) and the mean percentage of CONSORT statement items met was also lower for articles with surrogate endpoints (62.5 vs 70.7, p=0.026). Although fewer articles with surrogate endpoints were randomised trials (63% vs 74%, p=0.307) and articles with surrogate endpoints had lower Jadad scores (3.0 vs 3.2, p=0.538), these differences were not statistically significant. Studies of anti-TNF agents that report surrogate outcomes are of lesser methodological quality. As such, inclusion of such studies in evidence syntheses may bias results.

Role of Volatility in the Development of JP-8 Surrogates for Diesel Engine Application

DTIC Science & Technology

2014-01-01

distillation curves of the surrogate fuels were calculated using the Aspen HYSYS [41] software package, and the Peng- Robinson model was chosen to...distillation curves for the surrogate fuels developed in this investigation, the accuracy of Aspen HYSYS software predictions were compared with...and SF3. The distillation curves calculated using Aspen HYSYS software for the five surrogate fuels of Table 1 are shown in Figure 7, along with the

Reduced-order surrogate models for Green's functions in black hole spacetimes

NASA Astrophysics Data System (ADS)

Galley, Chad; Wardell, Barry

2016-03-01

The fundamental nature of linear wave propagation in curved spacetime is encoded in the retarded Green's function (or propagator). Green's functions are useful tools because almost any field quantity of interest can be computed via convolution integrals with a source. In addition, perturbation theories involving nonlinear wave propagation can be expressed in terms of multiple convolutions of the Green's function. Recently, numerical solutions for propagators in black hole spacetimes have been found that are globally valid and accurate for computing physical quantities. However, the data generated is too large for practical use because the propagator depends on two spacetime points that must be sampled finely to yield accurate convolutions. I describe how to build a reduced-order model that can be evaluated as a substitute, or surrogate, for solutions of the curved spacetime Green's function equation. The resulting surrogate accurately and quickly models the original and out-of-sample data. I discuss applications of the surrogate, including self-consistent evolutions and waveforms of extreme mass ratio binaries. Green's function surrogate models provide a new and practical way to handle many old problems involving wave propagation and motion in curved spacetimes.

In Vitro Engineering of Vascularized Tissue Surrogates

PubMed Central

Sakaguchi, Katsuhisa; Shimizu, Tatsuya; Horaguchi, Shigeto; Sekine, Hidekazu; Yamato, Masayuki; Umezu, Mitsuo; Okano, Teruo

2013-01-01

In vitro scaling up of bioengineered tissues is known to be limited by diffusion issues, specifically a lack of vasculature. Here, we report a new strategy for preserving cell viability in three-dimensional tissues using cell sheet technology and a perfusion bioreactor having collagen-based microchannels. When triple-layer cardiac cell sheets are incubated within this bioreactor, endothelial cells in the cell sheets migrate to vascularize in the collagen gel, and finally connect with the microchannels. Medium readily flows into the cell sheets through the microchannels and the newly developed capillaries, while the cardiac construct shows simultaneous beating. When additional triple-layer cell sheets are repeatedly layered, new multi-layer construct spontaneously integrates and the resulting construct becomes a vascularized thick tissue. These results confirmed our method to fabricate in vitro vascularized tissue surrogates that overcomes engineered-tissue thickness limitations. The surrogates promise new therapies for damaged organs as well as new in vitro tissue models. PMID:23419835

A Comparison of Turbidity-Based and Streamflow-Based Estimates of Suspended-Sediment Concentrations in Three Chesapeake Bay Tributaries

USGS Publications Warehouse

Jastram, John D.; Moyer, Douglas; Hyer, Kenneth

2009-01-01

Fluvial transport of sediment into the Chesapeake Bay estuary is a persistent water-quality issue with major implications for the overall health of the bay ecosystem. Accurately and precisely estimating the suspended-sediment concentrations (SSC) and loads that are delivered to the bay, however, remains challenging. Although manual sampling of SSC produces an accurate series of point-in-time measurements, robust extrapolation to unmeasured periods (especially highflow periods) has proven to be difficult. Sediment concentrations typically have been estimated using regression relations between individual SSC values and associated streamflow values; however, suspended-sediment transport during storm events is extremely variable, and it is often difficult to relate a unique SSC to a given streamflow. With this limitation for estimating SSC, innovative approaches for generating detailed records of suspended-sediment transport are needed. One effective method for improved suspended-sediment determination involves the continuous monitoring of turbidity as a surrogate for SSC. Turbidity measurements are theoretically well correlated to SSC because turbidity represents a measure of water clarity that is directly influenced by suspended sediments; thus, turbidity-based estimation models typically are effective tools for generating SSC data. The U.S. Geological Survey, in cooperation with the U.S. Environmental Protection Agency Chesapeake Bay Program and Virginia Department of Environmental Quality, initiated continuous turbidity monitoring on three major tributaries of the bay - the James, Rappahannock, and North Fork Shenandoah Rivers - to evaluate the use of turbidity as a sediment surrogate in rivers that deliver sediment to the bay. Results of this surrogate approach were compared to the traditionally applied streamflow-based approach for estimating SSC. Additionally, evaluation and comparison of these two approaches were conducted for nutrient estimations. Results demonstrate that the application of turbidity-based estimation models provides an improved method for generating a continuous record of SSC, relative to the classical approach that uses streamflow as a surrogate for SSC. Turbidity-based estimates of SSC were found to be more accurate and precise than SSC estimates from streamflow-based approaches. The turbidity-based SSC estimation models explained 92 to 98 percent of the variability in SSC, while streamflow-based models explained 74 to 88 percent of the variability in SSC. Furthermore, the mean absolute error of turbidity-based SSC estimates was 50 to 87 percent less than the corresponding values from the streamflow-based models. Statistically significant differences were detected between the distributions of residual errors and estimates from the two approaches, indicating that the turbidity-based approach yields estimates of SSC with greater precision than the streamflow-based approach. Similar improvements were identified for turbidity-based estimates of total phosphorus, which is strongly related to turbidity because total phosphorus occurs predominantly in particulate form. Total nitrogen estimation models based on turbidity and streamflow generated estimates of similar quality, with the turbidity-based models providing slight improvements in the quality of estimations. This result is attributed to the understanding that nitrogen transport is dominated by dissolved forms that relate less directly to streamflow and turbidity. Improvements in concentration estimation resulted in improved estimates of load. Turbidity-based suspended-sediment loads estimated for the James River at Cartersville, VA, monitoring station exhibited tighter confidence interval bounds and a coefficient of variation of 12 percent, compared with a coefficient of variation of 38 percent for the streamflow-based load.

Formulation of an RP-1 Pyrolysis Surrogate from Shock Tube Measurements of Fuel and Ethylene Time Histories

DTIC Science & Technology

2012-04-01

They also provide modelers (in both kinetics and computational fluid dynamics) with a method of representing, during simulation, a fuel that may have...1 and RP-2 from [Huber 2009a] Composition, mole fraction Fluid RP-1 surrogate RP-2 surrogate -methyldecalin 0.354 0.354 5-methylnonane 0.150...modeling and experimental results. Experimental Thermal and Fluid Science, 28(7):701–708, 2004. L. F. Albright, B. L. Crynes, and W. H. Corcoran

Developing and applying metamodels of high resolution process-based simulations for high throughput exposure assessment of organic chemicals in riverine ecosystems

EPA Science Inventory

As defined by Wikipedia (https://en.wikipedia.org/wiki/Metamodeling), “(a) metamodel or surrogate model is a model of a model, and metamodeling is the process of generating such metamodels.” The goals of metamodeling include, but are not limited to (1) developing functional or st...

Inactivation of Tulane virus, a novel surrogate for human norovirus

USDA-ARS?s Scientific Manuscript database

Human noroviruses (HuNoVs) are the major cause of non-bacterial epidemics of gastroenteritis. Due to the inability to cultivate HuNoVs and the lack of an efficient small animal model, surrogates are used to study HuNoV biology. Two such surrogates, the feline calicivirus (FCV) and the murine norovir...

Using a gel/plastic surrogate to study the biomechanical response of the head under air shock loading: a combined experimental and numerical investigation.

PubMed

Zhu, Feng; Wagner, Christina; Dal Cengio Leonardi, Alessandra; Jin, Xin; Vandevord, Pamela; Chou, Clifford; Yang, King H; King, Albert I

2012-03-01

A combined experimental and numerical study was conducted to determine a method to elucidate the biomechanical response of a head surrogate physical model under air shock loading. In the physical experiments, a gel-filled egg-shaped skull/brain surrogate was exposed to blast overpressure in a shock tube environment, and static pressures within the shock tube and the surrogate were recorded throughout the event. A numerical model of the shock tube was developed using the Eulerian approach and validated against experimental data. An arbitrary Lagrangian-Eulerian (ALE) fluid-structure coupling algorithm was then utilized to simulate the interaction of the shock wave and the head surrogate. After model validation, a comprehensive series of parametric studies was carried out on the egg-shaped surrogate FE model to assess the effect of several key factors, such as the elastic modulus of the shell, bulk modulus of the core, head orientation, and internal sensor location, on pressure and strain responses. Results indicate that increasing the elastic modulus of the shell within the range simulated in this study led to considerable rise of the overpressures. Varying the bulk modulus of the core from 0.5 to 2.0 GPa, the overpressure had an increase of 7.2%. The curvature of the surface facing the shock wave significantly affected both the peak positive and negative pressures. Simulations of the head surrogate with the blunt end facing the advancing shock front had a higher pressure compared to the simulations with the pointed end facing the shock front. The influence of an opening (possibly mimicking anatomical apertures) on the peak pressures was evaluated using a surrogate head with a hole on the shell of the blunt end. It was revealed that the presence of the opening had little influence on the positive pressures but could affect the negative pressure evidently.

GENERATING SOPHISTICATED SPATIAL SURROGATES USING THE MIMS SPATIAL ALLOCATOR

EPA Science Inventory

The Multimedia Integrated Modeling System (MIMS) Spatial Allocator is open-source software for generating spatial surrogates for emissions modeling, changing the map projection of Shapefiles, and performing other types of spatial allocation that does not require the use of a comm...

Love as a regulative ideal in surrogate decision making.

PubMed

Stonestreet, Erica Lucast

2014-10-01

This discussion aims to give a normative theoretical basis for a "best judgment" model of surrogate decision making rooted in a regulative ideal of love. Currently, there are two basic models of surrogate decision making for incompetent patients: the "substituted judgment" model and the "best interests" model. The former draws on the value of autonomy and responds with respect; the latter draws on the value of welfare and responds with beneficence. It can be difficult to determine which of these two models is more appropriate for a given patient, and both approaches may seem inadequate for a surrogate who loves the patient. The proposed "best judgment" model effectively draws on the values incorporated in each of the traditional standards, but does so because these values are important to someone who loves a patient, since love responds to the patient as the specific person she is. © The Author 2014. Published by Oxford University Press, on behalf of the Journal of Medicine and Philosophy Inc. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Experimental and computational study of methane counterflow diffusion flames perturbed by trace amounts of either jet fuel or a 6-component surrogate under non-sooting conditions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bufferand, H.; Tosatto, L.; La Mantia, B.

2009-08-15

The chemical structure of a methane counterflow diffusion flame and of the same flame doped with 1000 ppm (molar) of either jet fuel or a 6-component jet fuel surrogate was analyzed experimentally, by gas sampling via quartz microprobes and subsequent GC/MS analysis, and computationally using a semi-detailed kinetic mechanism for the surrogate blend. Conditions were chosen to ensure that all three flames were non-sooting, with identical temperature profiles and stoichiometric mixture fraction, through a judicious selection of feed stream composition and strain rate. The experimental dataset provides a glimpse of the pyrolysis and oxidation behavior of jet fuel in amore » diffusion flame. The jet fuel initial oxidation is consistent with anticipated chemical kinetic behavior, based on thermal decomposition of large alkanes to smaller and smaller fragments and the survival of ring-stabilized aromatics at higher temperatures. The 6-component surrogate captures the same trend correctly, but the agreement is not quantitative with respect to some of the aromatics such as benzene and toluene. Various alkanes, alkenes and aromatics among the jet fuel components are either only qualitatively characterized or could not be identified, because of the presence of many isomers and overlapping spectra in the chromatogram, leaving 80% of the carbon from the jet fuel unaccounted for in the early pyrolysis history of the parent fuel. Computationally, the one-dimensional code adopted a semi-detailed kinetic mechanism for the surrogate blend that is based on an existing hierarchically constructed kinetic model for alkanes and simple aromatics, extended to account for the presence of tetralin and methylcyclohexane as reference fuels. The computational results are in reasonably good agreement with the experimental ones for the surrogate behavior, with the greatest discrepancy in the concentrations of aromatics and ethylene. (author)« less

Blended shared control utilizing online identification : Regulating grasping forces of a surrogate surgical grasper.

PubMed

Stephens, Trevor K; Kong, Nathan J; Dockter, Rodney L; O'Neill, John J; Sweet, Robert M; Kowalewski, Timothy M

2018-06-01

Surgical robots are increasingly common, yet routine tasks such as tissue grasping remain potentially harmful with high occurrences of tissue crush injury due to the lack of force feedback from the grasper. This work aims to investigate whether a blended shared control framework which utilizes real-time identification of the object being grasped as part of the feedback may help address the prevalence of tissue crush injury in robotic surgeries. This work tests the proposed shared control framework and tissue identification algorithm on a custom surrogate surgical robotic grasping setup. This scheme utilizes identification of the object being grasped as part of the feedback to regulate to a desired force. The blended shared control is arbitrated between human and an implicit force controller based on a computed confidence in the identification of the grasped object. The online identification is performed using least squares based on a nonlinear tissue model. Testing was performed on five silicone tissue surrogates. Twenty grasps were conducted, with half of the grasps performed under manual control and half of the grasps performed with the proposed blended shared control, to test the efficacy of the control scheme. The identification method resulted in an average of 95% accuracy across all time samples of all tissue grasps using a full leave-grasp-out cross-validation. There was an average convergence time of [Formula: see text] ms across all training grasps for all tissue surrogates. Additionally, there was a reduction in peak forces induced during grasping for all tissue surrogates when applying blended shared control online. The blended shared control using online identification more successfully regulated grasping forces to the desired target force when compared with manual control. The preliminary work on this surrogate setup for surgical grasping merits further investigation on real surgical tools and with real human tissues.

Shared Decision Making in Intensive Care Units: An American College of Critical Care Medicine and American Thoracic Society Policy Statement

PubMed Central

Kon, Alexander A.; Davidson, Judy E.; Morrison, Wynne; Danis, Marion; White, Douglas B.

2015-01-01

Objectives Shared decision-making (SDM) is endorsed by critical care organizations, however there remains confusion about what SDM is, when it should be used, and approaches to promote partnerships in treatment decisions. The purpose of this statement is to define SDM, recommend when SDM should be used, identify the range of ethically acceptable decision-making models, and present important communication skills. Methods The American College of Critical Care Medicine (ACCM) and American Thoracic Society (ATS) Ethics Committees reviewed empirical research and normative analyses published in peer-reviewed journals to generate recommendations. Recommendations approved by consensus of the full Ethics Committees of ACCM and ATS were included in the statement. Main Results Six recommendations were endorsed: 1) Definition: Shared decision-making is a collaborative process that allows patients, or their surrogates, and clinicians to make health care decisions together, taking into account the best scientific evidence available, as well as the patient’s values, goals, and preferences. 2) Clinicians should engage in a SDM process to define overall goals of care (including decisions regarding limiting or withdrawing life-prolonging interventions) and when making major treatment decisions that may be affected by personal values, goals, and preferences. 3) Clinicians should use as their “default” approach a SDM process that includes three main elements: information exchange, deliberation, and making a treatment decision. 4) A wide range of decision-making approaches are ethically supportable including patient- or surrogate-directed and clinician-directed models. Clinicians should tailor the decision-making process based on the preferences of the patient or surrogate. 5) Clinicians should be trained in communication skills. 6) Research is needed to evaluate decision-making strategies. Conclusions Patient and surrogate preferences for decision-making roles regarding value-laden choices range from preferring to exercise significant authority to ceding such authority to providers. Clinicians should adapt the decision-making model to the needs and preferences of the patient or surrogate. PMID:26509317

Shared Decision Making in ICUs: An American College of Critical Care Medicine and American Thoracic Society Policy Statement.

PubMed

Kon, Alexander A; Davidson, Judy E; Morrison, Wynne; Danis, Marion; White, Douglas B

2016-01-01

Shared decision making is endorsed by critical care organizations; however, there remains confusion about what shared decision making is, when it should be used, and approaches to promote partnerships in treatment decisions. The purpose of this statement is to define shared decision making, recommend when shared decision making should be used, identify the range of ethically acceptable decision-making models, and present important communication skills. The American College of Critical Care Medicine and American Thoracic Society Ethics Committees reviewed empirical research and normative analyses published in peer-reviewed journals to generate recommendations. Recommendations approved by consensus of the full Ethics Committees of American College of Critical Care Medicine and American Thoracic Society were included in the statement. Six recommendations were endorsed: 1) DEFINITION: Shared decision making is a collaborative process that allows patients, or their surrogates, and clinicians to make healthcare decisions together, taking into account the best scientific evidence available, as well as the patient's values, goals, and preferences. 2) Clinicians should engage in a shared decision making process to define overall goals of care (including decisions regarding limiting or withdrawing life-prolonging interventions) and when making major treatment decisions that may be affected by personal values, goals, and preferences. 3) Clinicians should use as their "default" approach a shared decision making process that includes three main elements: information exchange, deliberation, and making a treatment decision. 4) A wide range of decision-making approaches are ethically supportable, including patient- or surrogate-directed and clinician-directed models. Clinicians should tailor the decision-making process based on the preferences of the patient or surrogate. 5) Clinicians should be trained in communication skills. 6) Research is needed to evaluate decision-making strategies. Patient and surrogate preferences for decision-making roles regarding value-laden choices range from preferring to exercise significant authority to ceding such authority to providers. Clinicians should adapt the decision-making model to the needs and preferences of the patient or surrogate.

Estimation of k-ε parameters using surrogate models and jet-in-crossflow data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lefantzi, Sophia; Ray, Jaideep; Arunajatesan, Srinivasan

2014-11-01

We demonstrate a Bayesian method that can be used to calibrate computationally expensive 3D RANS (Reynolds Av- eraged Navier Stokes) models with complex response surfaces. Such calibrations, conditioned on experimental data, can yield turbulence model parameters as probability density functions (PDF), concisely capturing the uncertainty in the parameter estimates. Methods such as Markov chain Monte Carlo (MCMC) estimate the PDF by sampling, with each sample requiring a run of the RANS model. Consequently a quick-running surrogate is used instead to the RANS simulator. The surrogate can be very difficult to design if the model's response i.e., the dependence of themore » calibration variable (the observable) on the parameter being estimated is complex. We show how the training data used to construct the surrogate can be employed to isolate a promising and physically realistic part of the parameter space, within which the response is well-behaved and easily modeled. We design a classifier, based on treed linear models, to model the "well-behaved region". This classifier serves as a prior in a Bayesian calibration study aimed at estimating 3 k - ε parameters ( C μ, C ε2 , C ε1 ) from experimental data of a transonic jet-in-crossflow interaction. The robustness of the calibration is investigated by checking its predictions of variables not included in the cal- ibration data. We also check the limit of applicability of the calibration by testing at off-calibration flow regimes. We find that calibration yield turbulence model parameters which predict the flowfield far better than when the nomi- nal values of the parameters are used. Substantial improvements are still obtained when we use the calibrated RANS model to predict jet-in-crossflow at Mach numbers and jet strengths quite different from those used to generate the ex- perimental (calibration) data. Thus the primary reason for poor predictive skill of RANS, when using nominal values of the turbulence model parameters, was parametric uncertainty, which was rectified by calibration. Post-calibration, the dominant contribution to model inaccuraries are due to the structural errors in RANS.« less

TOXICITY OF PENTACHLOROPHENOL TO ENDANGERED AND SURROGATE FISH SPECIES

EPA Science Inventory

Water quality criteria (WQC) generally are based on the responses of easily cultured and tested surrogate species. Little is known about the relative sensitivity of surrogate and endangered species. The objective of this study was to compare acute and chronic (early life-stage) ...

Optimization and uncertainty assessment of strongly nonlinear groundwater models with high parameter dimensionality

NASA Astrophysics Data System (ADS)

Keating, Elizabeth H.; Doherty, John; Vrugt, Jasper A.; Kang, Qinjun

2010-10-01

Highly parameterized and CPU-intensive groundwater models are increasingly being used to understand and predict flow and transport through aquifers. Despite their frequent use, these models pose significant challenges for parameter estimation and predictive uncertainty analysis algorithms, particularly global methods which usually require very large numbers of forward runs. Here we present a general methodology for parameter estimation and uncertainty analysis that can be utilized in these situations. Our proposed method includes extraction of a surrogate model that mimics key characteristics of a full process model, followed by testing and implementation of a pragmatic uncertainty analysis technique, called null-space Monte Carlo (NSMC), that merges the strengths of gradient-based search and parameter dimensionality reduction. As part of the surrogate model analysis, the results of NSMC are compared with a formal Bayesian approach using the DiffeRential Evolution Adaptive Metropolis (DREAM) algorithm. Such a comparison has never been accomplished before, especially in the context of high parameter dimensionality. Despite the highly nonlinear nature of the inverse problem, the existence of multiple local minima, and the relatively large parameter dimensionality, both methods performed well and results compare favorably with each other. Experiences gained from the surrogate model analysis are then transferred to calibrate the full highly parameterized and CPU intensive groundwater model and to explore predictive uncertainty of predictions made by that model. The methodology presented here is generally applicable to any highly parameterized and CPU-intensive environmental model, where efficient methods such as NSMC provide the only practical means for conducting predictive uncertainty analysis.

Program to Optimize Simulated Trajectories II (POST2) Surrogate Models for Mars Ascent Vehicle (MAV) Performance Assessment

NASA Technical Reports Server (NTRS)

Zwack, M. R.; Dees, P. D.; Thomas, H. D.; Polsgrove, T. P.; Holt, J. B.

2017-01-01

The primary purpose of the multiPOST tool is to enable the execution of much larger sets of vehicle cases to allow for broader trade space exploration. However, this exploration is not achieved solely with the increased case throughput. The multiPOST tool is applied to carry out a Design of Experiments (DOE), which is a set of cases that have been structured to capture a maximum amount of information about the design space with minimal computational effort. The results of the DOE are then used to fit a surrogate model, ultimately enabling parametric design space exploration. The approach used for the MAV study includes both DOE and surrogate modeling. First, the primary design considerations for the vehicle were used to develop the variables and ranges for the multiPOST DOE. The final set of DOE variables were carefully selected in order to capture the desired vehicle trades and take into account any special considerations for surrogate modeling. Next, the DOE sets were executed through multiPOST. Following successful completion of the DOE cases, a manual verification trial was performed. The trial involved randomly selecting cases from the DOE set and running them by hand. The results from the human analyst's run and multiPOST were then compared to ensure that the automated runs were being executed properly. Completion of the verification trials was then followed by surrogate model fitting. After fits to the multiPOST data were successfully created, the surrogate models were used as a stand-in for POST2 to carry out the desired MAV trades. Using the surrogate models in lieu of POST2 allowed for visualization of vehicle sensitivities to the input variables as well as rapid evaluation of vehicle performance. Although the models introduce some error into the output of the trade study, they were very effective at identifying areas of interest within the trade space for further refinement by human analysts. The next section will cover all of the ground rules and assumptions associated with DOE setup and multiPOST execution. Section 3.1 gives the final DOE variables and ranges, while section 3.2 addresses the POST2 specific assumptions. The results of the verification trials are given in section 4. Section 5 gives the surrogate model fitting results, including the goodness-of-fit metrics for each fit. Finally, the MAV specific results are discussed in section 6.

Characterization of exposure in epidemiological studies on air pollution from biodegradable wastes: Misclassification and comparison of exposure assessment strategies.

PubMed

Cantuaria, Manuella Lech; Suh, Helen; Løfstrøm, Per; Blanes-Vidal, Victoria

2016-11-01

The assignment of exposure is one of the main challenges faced by environmental epidemiologists. However, misclassification of exposures has not been explored in population epidemiological studies on air pollution from biodegradable wastes. The objective of this study was to investigate the use of different approaches for assessing exposure to air pollution from biodegradable wastes by analyzing (1) the misclassification of exposure that is committed by using these surrogates, (2) the existence of differential misclassification (3) the effects that misclassification may have on health effect estimates and the interpretation of epidemiological results, and (4) the ability of the exposure measures to predict health outcomes using 10-fold cross validation. Four different exposure assessment approaches were studied: ammonia concentrations at the residence (Metric I), distance to the closest source (Metric II), number of sources within certain distances from the residence (Metric IIIa,b) and location in a specific region (Metric IV). Exposure-response models based on Metric I provided the highest predictive ability (72.3%) and goodness-of-fit, followed by IV, III and II. When compared to Metric I, Metric IV yielded the best results for exposure misclassification analysis and interpretation of health effect estimates, followed by Metric IIIb, IIIa and II. The study showed that modelled NH 3 concentrations provide more accurate estimations of true exposure than distances-based surrogates, and that distance-based surrogates (especially those based on distance to the closest point source) are imprecise methods to identify exposed populations, although they may be useful for initial studies. Copyright © 2016 Elsevier GmbH. All rights reserved.

A machine learning approach as a surrogate of finite element analysis-based inverse method to estimate the zero-pressure geometry of human thoracic aorta.

PubMed

Liang, Liang; Liu, Minliang; Martin, Caitlin; Sun, Wei

2018-05-09

Advances in structural finite element analysis (FEA) and medical imaging have made it possible to investigate the in vivo biomechanics of human organs such as blood vessels, for which organ geometries at the zero-pressure level need to be recovered. Although FEA-based inverse methods are available for zero-pressure geometry estimation, these methods typically require iterative computation, which are time-consuming and may be not suitable for time-sensitive clinical applications. In this study, by using machine learning (ML) techniques, we developed an ML model to estimate the zero-pressure geometry of human thoracic aorta given 2 pressurized geometries of the same patient at 2 different blood pressure levels. For the ML model development, a FEA-based method was used to generate a dataset of aorta geometries of 3125 virtual patients. The ML model, which was trained and tested on the dataset, is capable of recovering zero-pressure geometries consistent with those generated by the FEA-based method. Thus, this study demonstrates the feasibility and great potential of using ML techniques as a fast surrogate of FEA-based inverse methods to recover zero-pressure geometries of human organs. Copyright © 2018 John Wiley & Sons, Ltd.

3.4.1: Numerical Methods for Propagating Uncertainty Across Scales and for Hybrid Stochastic-Deterministic Systems

DTIC Science & Technology

2017-10-04

Fisher’s equation, as well as a two‐dimensional Allen‐ Cahn equation. We  observe  good performance of the  method  for nonlinear problems as well as...rates, discrepancies between  the  two  methods   are  observed ,  hence  revealing  strong  additional  coupling  between  different  fluctuating variables...of random fields based on  observations  of surrogate models or hierarchies of surrogate models.  Our  method  builds upon recent work on recursive

The Secondary Organic Aerosol Processor (SOAP v1.0) model: a unified model with different ranges of complexity based on the molecular surrogate approach

NASA Astrophysics Data System (ADS)

Couvidat, F.; Sartelet, K.

2015-04-01

In this paper the Secondary Organic Aerosol Processor (SOAP v1.0) model is presented. This model determines the partitioning of organic compounds between the gas and particle phases. It is designed to be modular with different user options depending on the computation time and the complexity required by the user. This model is based on the molecular surrogate approach, in which each surrogate compound is associated with a molecular structure to estimate some properties and parameters (hygroscopicity, absorption into the aqueous phase of particles, activity coefficients and phase separation). Each surrogate can be hydrophilic (condenses only into the aqueous phase of particles), hydrophobic (condenses only into the organic phases of particles) or both (condenses into both the aqueous and the organic phases of particles). Activity coefficients are computed with the UNIFAC (UNIversal Functional group Activity Coefficient; Fredenslund et al., 1975) thermodynamic model for short-range interactions and with the Aerosol Inorganic-Organic Mixtures Functional groups Activity Coefficients (AIOMFAC) parameterization for medium- and long-range interactions between electrolytes and organic compounds. Phase separation is determined by Gibbs energy minimization. The user can choose between an equilibrium representation and a dynamic representation of organic aerosols (OAs). In the equilibrium representation, compounds in the particle phase are assumed to be at equilibrium with the gas phase. However, recent studies show that the organic aerosol is not at equilibrium with the gas phase because the organic phases could be semi-solid (very viscous liquid phase). The condensation-evaporation of organic compounds could then be limited by the diffusion in the organic phases due to the high viscosity. An implicit dynamic representation of secondary organic aerosols (SOAs) is available in SOAP with OAs divided into layers, the first layer being at the center of the particle (slowly reaches equilibrium) and the final layer being near the interface with the gas phase (quickly reaches equilibrium). Although this dynamic implicit representation is a simplified approach to model condensation-evaporation with a low number of layers and short CPU (central processing unit) time, it shows good agreements with an explicit representation of condensation-evaporation (no significant differences after a few hours of condensation).

Model Updating of Complex Structures Using the Combination of Component Mode Synthesis and Kriging Predictor

PubMed Central

Li, Yan; Wang, Dejun; Zhang, Shaoyi

2014-01-01

Updating the structural model of complex structures is time-consuming due to the large size of the finite element model (FEM). Using conventional methods for these cases is computationally expensive or even impossible. A two-level method, which combined the Kriging predictor and the component mode synthesis (CMS) technique, was proposed to ensure the successful implementing of FEM updating of large-scale structures. In the first level, the CMS was applied to build a reasonable condensed FEM of complex structures. In the second level, the Kriging predictor that was deemed as a surrogate FEM in structural dynamics was generated based on the condensed FEM. Some key issues of the application of the metamodel (surrogate FEM) to FEM updating were also discussed. Finally, the effectiveness of the proposed method was demonstrated by updating the FEM of a real arch bridge with the measured modal parameters. PMID:24634612

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

PubMed

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

2015-11-27

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. 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. 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. 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. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

A Method of Surrogate Model Construction which Leverages Lower-Fidelity Information using Space Mapping Techniques

DTIC Science & Technology

2014-03-27

fidelity. This pairing is accomplished through the use of a space mapping technique, which is a process where the design space of a lower fidelity model...is aligned a higher fidelity model. The intent of applying space mapping techniques to the field of surrogate construction is to leverage the

Proposed ethical guidelines and legislative framework for permitting gestational surrogacy in Singapore.

PubMed

Heng, Boon Chin

2007-09-01

Gestational surrogacy is currently banned in Singapore but is much debated. Some ethical guidelines and legislation for permitting gestational surrogacy in Singapore are proposed and discussed including: (i) review and approval of gestational surrogacy by the Ministry of Health on a case-by-case basis; (ii) stringent guidelines for gonadotrophin stimulation, IVF and ICSI procedures in 'traditional' surrogacy; (iii) restriction of gestational surrogates to parous married women with stable family relationships; (iv) exclusion of foreign women from acting as gestational surrogates, except for close relatives of the recipient couple; (v) reimbursement and/or compensation of gestational surrogates based on the direct expenses model; (vi) exclusion of medical professionals from surrogate recruitment and reimbursement; (vii) the surrogacy contract must make it legally binding for the prospective recipient couple to accept the child, even if it is born with congenital deformities; (viii) stringent guidelines for combining surrogacy with egg donation from a third woman, who is neither the social nor gestational mother. Policymakers in Singapore should conduct a public referendum on the legalization of gestational surrogacy and actively consult the views of healthcare professionals, religious and community leaders, as well as the general public, before reaching any decision.

Is farm-related job title an adequate surrogate for pesticide exposure in occupational cancer epidemiology?

PubMed

MacFarlane, E; Glass, D; Fritschi, L

2009-08-01

Accurate assessment of exposure is a key factor in occupational epidemiology but can be problematic, particularly where exposures of interest may be many decades removed from relevant health outcomes. Studies have traditionally relied on crude surrogates of exposure based on job title only, for instance farm-related job title as a surrogate for pesticide exposure. This analysis was based on data collected in Western Australia in 2000-2001. Using a multivariate regression model, we compared expert-assessed likelihood of pesticide exposure based on detailed, individual-specific questionnaire and job specific module interview information with reported farm-related job titles as a surrogate for pesticide exposure. Most (68.8%) jobs with likely pesticide exposure were farm jobs, but 78.3% of farm jobs were assessed as having no likelihood of pesticide exposure. Likely pesticide exposure was more frequent among jobs on crop farms than on livestock farms. Likely pesticide exposure was also more frequent among jobs commenced in more recent decades and jobs of longer duration. Our results suggest that very little misclassification would have resulted from the inverse assumption that all non-farming jobs are not pesticide exposed since only a very small fraction of non-agricultural jobs were likely to have had pesticide exposure. Classification of all farm jobs as pesticide exposed is likely to substantially over-estimate the number of individuals exposed. Our results also suggest that researchers should pay special attention to farm type, length of service and historical period of employment when assessing the likelihood of pesticide exposure in farming jobs.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Myronakis, M; Cai, W; Dhou, S

Purpose: To determine if 4DCT-based motion modeling and external surrogate motion measured during treatment simulation can enhance prediction of residual tumor motion and duty cycle during treatment delivery. Methods: This experiment was conducted using simultaneously recorded tumor and external surrogate motion acquired over multiple fractions of lung cancer radiotherapy. These breathing traces were combined with the XCAT phantom to simulate CT images. Data from the first day was used to estimate the residual tumor motion and duty cycle both directly from the 4DCT (the current clinical standard), and from external-surrogate based motion modeling. The accuracy of these estimated residual tumormore » motions and duty cycles are evaluated by comparing to the measured internal/external motions from other treatment days. Results: All calculations were done for 25% and 50% duty cycles. The results indicated that duty cycle derived from 4DCT information alone is not enough to accurately predict duty cycles during treatment. Residual tumor motion was determined from the recorded data and compared with the estimated residual tumor motion from 4DCT. Relative differences in residual tumor motion varied from −30% to 55%, suggesting that more information is required to properly predict residual tumor motion. Compared to estimations made from 4DCT, in three out of four patients examined, the 30 seconds of motion modeling data was able to predict the duty cycle with better accuracy than 4DCT. No improvement was observed in prediction of residual tumor motion for this dataset. Conclusion: Motion modeling during simulation has the potential to enhance 4DCT and provide more information about target motion, duty cycles, and delivered dose. Based on these four patients, 30 seconds of motion modeling data produced improve duty cycle estimations but showed no measurable improvement in residual tumor motion prediction. More patient data is needed to verify this Result. I would like to acknowledge funding from MRA, VARIAN Medical Systems, Inc.« less

SU-E-J-163: A Biomechanical Lung Model for Respiratory Motion Study

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, X; Belcher, AH; Grelewicz, Z

2015-06-15

Purpose: This work presents a biomechanical model to investigate the complex respiratory motion for the lung tumor tracking in radiosurgery by computer simulation. Methods: The models include networked massspring-dampers to describe the tumor motion, different types of surrogate signals, and the force generated by the diaphragm. Each mass-springdamper has the same mechanical structure and each model can have different numbers of mass-spring-dampers. Both linear and nonlinear stiffness parameters were considered, and the damping ratio was tuned in a range so that the tumor motion was over-damped (no natural tumor oscillation occurs without force from the diaphragm). The simulation was runmore » by using ODE45 (ordinary differential equations by Runge-Kutta method) in MATLAB, and all time courses of motions and inputs (force) were generated and compared. Results: The curvature of the motion time courses around their peaks was sensitive to the damping ratio. Therefore, the damping ratio can be determined based on the clinical data of a high sampling rate. The peak values of different signals and the time the peaks occurred were compared, and it was found that the diaphragm force had a time lead over the tumor motion, and the lead time (0.1–0.4 seconds) depended on the distance between the tumor and the diaphragm. Conclusion: We reported a model based analysis approach for the spatial and temporal relation between the motion of the lung tumor and the surrogate signals. Due to the phase lead of the diaphragm in comparing with the lung tumor motion, the measurement of diaphragm motion (or its electromyography signal) can be used as a beam gating signal in radiosurgery, and it can also be an additional surrogate signal for better tumor motion tracking. The research is funded by the American Cancer Society (ACS) grant. The grant name is: Frameless SRS Based on Robotic Head Motion Cancellation. The grant number is: RSG-13-313-01-CCE.« less

An Artificial Turf-Based Surrogate Surface Collector for the Direct Measurement of Atmospheric Mercury Dry Deposition

EPA Science Inventory

This paper describes the development of a new artificial turf surrogate surface (ATSS) sampler for use in the measurement of mercury (Hg) dry deposition. In contrast to many existing surrogate surface designs, the ATSS utilizes a three-dimensional deposition surface that may more...

Effectiveness of Spray-Based Decontamination Methods for ...

EPA Pesticide Factsheets

Report The objective of this project was to assess the effectiveness of spray-based common decontamination methods for inactivating Bacillus (B.) atrophaeus (surrogate for B. anthracis) spores and bacteriophage MS2 (surrogate for foot and mouth disease virus [FMDV]) on selected test surfaces (with or without a model agricultural soil load). Relocation of viable viruses or spores from the contaminated coupon surfaces into aerosol or liquid fractions during the decontamination methods was investigated. This project was conducted to support jointly held missions of the U.S. Department of Homeland Security (DHS) and the U.S. Environmental Protection Agency (EPA). Within the EPA, the project supports the mission of EPA’s Homeland Security Research Program (HSRP) by providing relevant information pertinent to the decontamination of contaminated areas resulting from a biological incident.

Constructing Surrogate Models of Complex Systems with Enhanced Sparsity: Quantifying the Influence of Conformational Uncertainty in Biomolecular Solvation

DOE PAGES

Lei, Huan; Yang, Xiu; Zheng, Bin; ...

2015-11-05

Biomolecules exhibit conformational fluctuations near equilibrium states, inducing uncertainty in various biological properties in a dynamic way. We have developed a general method to quantify the uncertainty of target properties induced by conformational fluctuations. Using a generalized polynomial chaos (gPC) expansion, we construct a surrogate model of the target property with respect to varying conformational states. We also propose a method to increase the sparsity of the gPC expansion by defining a set of conformational “active space” random variables. With the increased sparsity, we employ the compressive sensing method to accurately construct the surrogate model. We demonstrate the performance ofmore » the surrogate model by evaluating fluctuation-induced uncertainty in solvent-accessible surface area for the bovine trypsin inhibitor protein system and show that the new approach offers more accurate statistical information than standard Monte Carlo approaches. Further more, the constructed surrogate model also enables us to directly evaluate the target property under various conformational states, yielding a more accurate response surface than standard sparse grid collocation methods. In particular, the new method provides higher accuracy in high-dimensional systems, such as biomolecules, where sparse grid performance is limited by the accuracy of the computed quantity of interest. Finally, our new framework is generalizable and can be used to investigate the uncertainty of a wide variety of target properties in biomolecular systems.« less

Sediment transport and evaluation of sediment surrogate ratings in the Kootenai River near Bonners Ferry, Idaho, Water Years 2011–14

USGS Publications Warehouse

Wood, Molly S.; Fosness, Ryan L.; Etheridge, Alexandra B.

2015-12-14

Acoustic surrogate ratings were developed between backscatter data collected using acoustic Doppler velocity meters (ADVMs) and results of suspended-sediment samples. Ratings were successfully fit to various sediment size classes (total, fines, and sands) using ADVMs of different frequencies (1.5 and 3 megahertz). Surrogate ratings also were developed using variations of streamflow and seasonal explanatory variables. The streamflow surrogate ratings produced average annual sediment load estimates that were 8–32 percent higher, depending on site and sediment type, than estimates produced using the acoustic surrogate ratings. The streamflow surrogate ratings tended to overestimate suspended-sediment concentrations and loads during periods of elevated releases from Libby Dam as well as on the falling limb of the streamflow hydrograph. Estimates from the acoustic surrogate ratings more closely matched suspended-sediment sample results than did estimates from the streamflow surrogate ratings during these periods as well as for rating validation samples collected in water year 2014. Acoustic surrogate technologies are an effective means to obtain continuous, accurate estimates of suspended-sediment concentrations and loads for general monitoring and sediment-transport modeling. In the Kootenai River, continued operation of the acoustic surrogate sites and use of the acoustic surrogate ratings to calculate continuous suspended-sediment concentrations and loads will allow for tracking changes in sediment transport over time.

Measurement, modeling, and analysis of nonmethane hydrocarbons and ozone in the southeast United States national parks

NASA Astrophysics Data System (ADS)

Kang, Daiwen

In this research, the sources, distributions, transport, ozone formation potential, and biogenic emissions of VOCs are investigated focusing on three Southeast United States National Parks: Shenandoah National Park, Big Meadows site (SHEN), Great Smoky Mountains National Park at Cove Mountain (GRSM) and Mammoth Cave National Park (MACA). A detailed modeling analysis is conducted using the Multiscale Air Quality SImulation Platform (MAQSIP) focusing on nonmethane hydrocarbons and ozone characterized by high O3 surface concentrations. Nine emissions perturbation using the Multiscale Air Quality SImulation Platform (MAQSIP) focusing on nonmethane hydrocarbons and ozone characterized by high O 3 surface concentrations. In the observation-based analysis, source classification techniques based on correlation coefficient, chemical reactivity, and certain ratios were developed and applied to the data set. Anthropogenic VOCs from automobile exhaust dominate at Mammoth Cave National Park, and at Cove Mountain, Great Smoky Mountains National Park, while at Big Meadows, Shenandoah National Park, the source composition is complex and changed from 1995 to 1996. The dependence of isoprene concentrations on ambient temperatures is investigated, and similar regressional relationships are obtained for all three monitoring locations. Propylene-equivalent concentrations are calculated to account for differences in reaction rates between the OH and individual hydrocarbons, and to thereby estimate their relative contributions to ozone formation. Isoprene fluxes were also estimated for all these rural areas. Model predictions (base scenario) tend to give lower daily maximum O 3 concentrations than observations by 10 to 30%. Model predicted concentrations of lumped paraffin compounds are of the same order of magnitude as the observed values, while the observed concentrations for other species (isoprene, ethene, surrogate olefin, surrogate toluene, and surrogate xylene) are usually an order of magnitude higher than the predictions. Detailed sensitivity and process analyses in terms of ozone and VOC scenarios including the base scenario are designed and utilized in the model simulations. Model predictions are compared with the observed values at the three locations for the same time period. Detailed sensitivity and process analyses in terms of ozone and VOC budgets, and relative importance of various VOCs species are provided. (Abstract shortened by UMI.)

Reduced Order Model Implementation in the Risk-Informed Safety Margin Characterization Toolkit

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mandelli, Diego; Smith, Curtis L.; Alfonsi, Andrea

2015-09-01

The RISMC project aims to develop new advanced simulation-based tools to perform Probabilistic Risk Analysis (PRA) for the existing fleet of U.S. nuclear power plants (NPPs). These tools numerically model not only the thermo-hydraulic behavior of the reactor primary and secondary systems but also external events temporal evolution and components/system ageing. Thus, this is not only a multi-physics problem but also a multi-scale problem (both spatial, µm-mm-m, and temporal, ms-s-minutes-years). As part of the RISMC PRA approach, a large amount of computationally expensive simulation runs are required. An important aspect is that even though computational power is regularly growing, themore » overall computational cost of a RISMC analysis may be not viable for certain cases. A solution that is being evaluated is the use of reduce order modeling techniques. During the FY2015, we investigated and applied reduced order modeling techniques to decrease the RICM analysis computational cost by decreasing the number of simulations runs to perform and employ surrogate models instead of the actual simulation codes. This report focuses on the use of reduced order modeling techniques that can be applied to any RISMC analysis to generate, analyze and visualize data. In particular, we focus on surrogate models that approximate the simulation results but in a much faster time (µs instead of hours/days). We apply reduced order and surrogate modeling techniques to several RISMC types of analyses using RAVEN and RELAP-7 and show the advantages that can be gained.« less

Internet health information seeking is a team sport: analysis of the Pew Internet Survey.

PubMed

Sadasivam, Rajani S; Kinney, Rebecca L; Lemon, Stephenie C; Shimada, Stephanie L; Allison, Jeroan J; Houston, Thomas K

2013-03-01

Previous studies examining characteristics of Internet health information seekers do not distinguish between those who only seek for themselves, and surrogate seekers who look for health information for family or friends. Identifying the unique characteristics of surrogate seekers would help in developing Internet interventions that better support these information seekers. To assess differences between self seekers versus those that act also as surrogate seekers. We analyzed data from the cross-sectional Pew Internet and American Life Project November/December 2008 health survey. Our dependent variable was self-report of type of health information seeking (surrogate versus self seeking). Independent variables included demographics, health status, and caregiving. After bivariate comparisons, we then developed multivariable models using logistic regression to assess characteristics associated with surrogate seeking. Out of 1250 respondents who reported seeking health information online, 56% (N=705) reported being surrogate seekers. In multivariable models, compared with those who sought information for themselves only, surrogate seekers were more likely both married and a parent (OR=1.57, CI=1.08, 2.28), having good (OR=2.05, CI=1.34, 3.12) or excellent (OR=2.72, CI=1.70, 4.33) health status, being caregiver of an adult relative (OR=1.76, CI=1.34, 2.30), having someone close with a serious medical condition (OR=1.62, CI=1.21, 2.17) and having someone close to them facing a chronic illness (OR=1.55, CI=1.17, 2.04). Our findings provide evidence that information needs of surrogate seekers are not being met, specifically of caregivers. Additional research is needed to develop new functions that support surrogate seekers. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

A Computationally-Efficient Inverse Approach to Probabilistic Strain-Based Damage Diagnosis

NASA Technical Reports Server (NTRS)

Warner, James E.; Hochhalter, Jacob D.; Leser, William P.; Leser, Patrick E.; Newman, John A

2016-01-01

This work presents a computationally-efficient inverse approach to probabilistic damage diagnosis. Given strain data at a limited number of measurement locations, Bayesian inference and Markov Chain Monte Carlo (MCMC) sampling are used to estimate probability distributions of the unknown location, size, and orientation of damage. Substantial computational speedup is obtained by replacing a three-dimensional finite element (FE) model with an efficient surrogate model. The approach is experimentally validated on cracked test specimens where full field strains are determined using digital image correlation (DIC). Access to full field DIC data allows for testing of different hypothetical sensor arrangements, facilitating the study of strain-based diagnosis effectiveness as the distance between damage and measurement locations increases. The ability of the framework to effectively perform both probabilistic damage localization and characterization in cracked plates is demonstrated and the impact of measurement location on uncertainty in the predictions is shown. Furthermore, the analysis time to produce these predictions is orders of magnitude less than a baseline Bayesian approach with the FE method by utilizing surrogate modeling and effective numerical sampling approaches.

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...

Estimation of Model's Marginal likelihood Using Adaptive Sparse Grid Surrogates in Bayesian Model Averaging

NASA Astrophysics Data System (ADS)

Zeng, X.

2015-12-01

A large number of model executions are required to obtain alternative conceptual models' predictions and their posterior probabilities in Bayesian model averaging (BMA). The posterior model probability is estimated through models' marginal likelihood and prior probability. The heavy computation burden hinders the implementation of BMA prediction, especially for the elaborated marginal likelihood estimator. For overcoming the computation burden of BMA, an adaptive sparse grid (SG) stochastic collocation method is used to build surrogates for alternative conceptual models through the numerical experiment of a synthetical groundwater model. BMA predictions depend on model posterior weights (or marginal likelihoods), and this study also evaluated four marginal likelihood estimators, including arithmetic mean estimator (AME), harmonic mean estimator (HME), stabilized harmonic mean estimator (SHME), and thermodynamic integration estimator (TIE). The results demonstrate that TIE is accurate in estimating conceptual models' marginal likelihoods. The BMA-TIE has better predictive performance than other BMA predictions. TIE has high stability for estimating conceptual model's marginal likelihood. The repeated estimated conceptual model's marginal likelihoods by TIE have significant less variability than that estimated by other estimators. In addition, the SG surrogates are efficient to facilitate BMA predictions, especially for BMA-TIE. The number of model executions needed for building surrogates is 4.13%, 6.89%, 3.44%, and 0.43% of the required model executions of BMA-AME, BMA-HME, BMA-SHME, and BMA-TIE, respectively.

Computer-Simulation Surrogates for Optimization: Application to Trapezoidal Ducts and Axisymmetric Bodies

NASA Technical Reports Server (NTRS)

Otto, John C.; Paraschivoiu, Marius; Yesilyurt, Serhat; Patera, Anthony T.

1995-01-01

Engineering design and optimization efforts using computational systems rapidly become resource intensive. The goal of the surrogate-based approach is to perform a complete optimization with limited resources. In this paper we present a Bayesian-validated approach that informs the designer as to how well the surrogate performs; in particular, our surrogate framework provides precise (albeit probabilistic) bounds on the errors incurred in the surrogate-for-simulation substitution. The theory and algorithms of our computer{simulation surrogate framework are first described. The utility of the framework is then demonstrated through two illustrative examples: maximization of the flowrate of fully developed ow in trapezoidal ducts; and design of an axisymmetric body that achieves a target Stokes drag.

Structural reliability analysis under evidence theory using the active learning kriging model

NASA Astrophysics Data System (ADS)

Yang, Xufeng; Liu, Yongshou; Ma, Panke

2017-11-01

Structural reliability analysis under evidence theory is investigated. It is rigorously proved that a surrogate model providing only correct sign prediction of the performance function can meet the accuracy requirement of evidence-theory-based reliability analysis. Accordingly, a method based on the active learning kriging model which only correctly predicts the sign of the performance function is proposed. Interval Monte Carlo simulation and a modified optimization method based on Karush-Kuhn-Tucker conditions are introduced to make the method more efficient in estimating the bounds of failure probability based on the kriging model. Four examples are investigated to demonstrate the efficiency and accuracy of the proposed method.

The role of imperfect surrogate endpoint information in drug approval and reimbursement decisions.

PubMed

Bognar, Katalin; Romley, John A; Bae, Jay P; Murray, James; Chou, Jacquelyn W; Lakdawalla, Darius N

2017-01-01

Approval of new drugs is increasingly reliant on "surrogate endpoints," which correlate with but imperfectly predict clinical benefits. Proponents argue surrogate endpoints allow for faster approval, but critics charge they provide inadequate evidence. We develop an economic framework that addresses the value of improvement in the predictive power, or "quality," of surrogate endpoints, and clarifies how quality can influence decisions by regulators, payers, and manufacturers. For example, the framework shows how lower-quality surrogates lead to greater misalignment of incentives between payers and regulators, resulting in more drugs that are approved for use but not covered by payers. Efficient price-negotiation in the marketplace can help align payer incentives for granting access based on surrogates. Higher-quality surrogates increase manufacturer profits and social surplus from early access to new drugs. Since the return on better quality is shared between manufacturers and payers, private incentives to invest in higher-quality surrogates are inefficiently low. Copyright © 2016 The Author(s). Published by Elsevier B.V. All rights reserved.

Gaseous Surrogate Hydrocarbons for a Hifire Scramjet that Mimic Opposed Jet Extinction Limits for Cracked JP Fuels

NASA Technical Reports Server (NTRS)

Pellett, Gerald L.; Vaden, Sarah N.; Wilson, Lloyd G.

2008-01-01

This paper describes, first, the top-down methodology used to define simple gaseous surrogate hydrocarbon (HC) fuel mixtures for a hypersonic scramjet combustion subtask of the HiFIRE program. It then presents new and updated Opposed Jet Burner (OJB) extinction-limit Flame Strength (FS) data obtained from laminar non-premixed HC vs. air counterflow diffusion flames at 1-atm, which follow from earlier investigations. FS represents a strain-induced extinction limit based on cross-section-average air jet velocity, U(sub air), that sustains combustion of a counter jet of gaseous fuel just before extinction. FS uniquely characterizes a kinetically limited fuel combustion rate. More generally, Applied Stress Rates (ASRs) at extinction (U(sub air) normalized by nozzle or tube diameter, D(sub n or t) can directly be compared with extinction limits determined numerically using either a 1-D or (preferably) a 2-D Navier Stokes simulation with detailed transport and finite rate chemistry. The FS results help to characterize and define three candidate surrogate HC fuel mixtures that exhibit a common FS 70% greater than for vaporized JP-7 fuel. These include a binary fuel mixture of 64% ethylene + 36% methane, which is our primary recommendation. It is intended to mimic the critical flameholding limit of a thermally- or catalytically-cracked JP-7 like fuel in HiFIRE scramjet combustion tests. Our supporting experimental results include: (1) An idealized kinetically-limited ASR reactivity scale, which represents maximum strength non-premixed flames for several gaseous and vaporized liquid HCs; (2) FS characterizations of Colket and Spadaccini s suggested ternary surrogate, of 60% ethylene + 30% methane + 10% n-heptane, which matches the ignition delay of a typical cracked JP fuel; (3) Data showing how our recommended binary surrogate, of 64% ethylene + 36% methane, has an identical FS; (4) Data that characterize an alternate surrogate of 44% ethylene + 56% ethane with identical FS and nearly equal molecular weights; this could be useful when systematically varying the fuel composition. However, the mixture liquefies at much lower pressure, which limits on-board storage of gaseous fuel; (5) Dynamic Flame Weakening results that show how oscillations in OJB input flow (and composition) can weaken (extinguish) surrogate flames up to 200 Hz, but the weakening is 2.5x smaller compared to pure methane; and finally, (6) FS limits at 1-atm that compare with three published 1-D numerical OJB extinction results using four chemical kinetic models. The methane kinetics generally agree closely at 1-atm, whereas, the various ethylene models predict extinction limits that average 45% high, which represents a significant problem for numerical simulation of surrogate-based flameholding in a scramjet cavity. Finally, we continue advocating the FS approach as more direct and fundamental for assessing idealized scramjet flameholding potentials than measurements of "unstrained" premixed laminar burning velocity or blowout in a Perfectly Stirred Reactor.

Investigating conflict in ICUs - Is the clinicians’ perspective enough?

PubMed Central

Schuster, Rachel A.; Hong, Seo Yeon; Arnold, Robert M.; White, Douglas B.

2013-01-01

Objective Most studies have assessed conflict between clinicians and surrogate decision makers in ICUs from only clinicians’ perspectives. It is unknown if surrogates’ perceptions differ from clinicians’. We sought to determine the degree of agreement between physicians and surrogates about conflict, and to identify predictors of physician-surrogate conflict. Design Prospective cohort study. Setting Four ICUs of two hospitals in San Francisco, California. Patients 230 surrogate decision makers and 100 physicians of 175 critically ill patients. Measurements Questionnaires addressing participants’ perceptions of whether there was physician-surrogate conflict, as well as attitudes and preferences about clinician-surrogate communication; kappa scores to quantify physician-surrogate concordance about the presence of conflict; and hierarchical multivariate modeling to determine predictors of conflict. Main Results Either the physician or surrogate identified conflict in 63% of cases. Physicians were less likely to perceive conflict than surrogates (27.8% vs 42.3%; p=0.007). Agreement between physicians and surrogates about conflict was poor (kappa = 0.14). Multivariable analysis with surrogate-assessed conflict as the outcome revealed that higher levels of surrogates’ satisfaction with physicians’ bedside manner were associated with lower odds of conflict (OR: 0.75 per 1 point increase in satisfaction, 95% CI 0.59–0.96). Multivariable analysis with physician-assessed conflict as the outcome revealed that the surrogate having felt discriminated against in the healthcare setting was associated with higher odds of conflict (OR 17.5, 95% CI 1.6–190.1) while surrogates’ satisfaction with physicians’ bedside manner was associated with lower odds of conflict (0–10 scale, OR 0.76 per 1 point increase, 95% CI 0.58–0.99). Conclusions Conflict between physicians and surrogates is common in ICUs. There is little agreement between physicians and surrogates about whether physician-surrogate conflict has occurred. Further work is needed to develop reliable and valid methods to assess conflict. In the interim, future studies should assess conflict from the perspective of both clinicians and surrogates. PMID:24434440

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rottmann, Joerg; Berbeco, Ross

Purpose: Precise prediction of respiratory motion is a prerequisite for real-time motion compensation techniques such as beam, dynamic couch, or dynamic multileaf collimator tracking. Collection of tumor motion data to train the prediction model is required for most algorithms. To avoid exposure of patients to additional dose from imaging during this procedure, the feasibility of training a linear respiratory motion prediction model with an external surrogate signal is investigated and its performance benchmarked against training the model with tumor positions directly. Methods: The authors implement a lung tumor motion prediction algorithm based on linear ridge regression that is suitable tomore » overcome system latencies up to about 300 ms. Its performance is investigated on a data set of 91 patient breathing trajectories recorded from fiducial marker tracking during radiotherapy delivery to the lung of ten patients. The expected 3D geometric error is quantified as a function of predictor lookahead time, signal sampling frequency and history vector length. Additionally, adaptive model retraining is evaluated, i.e., repeatedly updating the prediction model after initial training. Training length for this is gradually increased with incoming (internal) data availability. To assess practical feasibility model calculation times as well as various minimum data lengths for retraining are evaluated. Relative performance of model training with external surrogate motion data versus tumor motion data is evaluated. However, an internal–external motion correlation model is not utilized, i.e., prediction is solely driven by internal motion in both cases. Results: Similar prediction performance was achieved for training the model with external surrogate data versus internal (tumor motion) data. Adaptive model retraining can substantially boost performance in the case of external surrogate training while it has little impact for training with internal motion data. A minimum adaptive retraining data length of 8 s and history vector length of 3 s achieve maximal performance. Sampling frequency appears to have little impact on performance confirming previously published work. By using the linear predictor, a relative geometric 3D error reduction of about 50% was achieved (using adaptive retraining, a history vector length of 3 s and with results averaged over all investigated lookahead times and signal sampling frequencies). The absolute mean error could be reduced from (2.0 ± 1.6) mm when using no prediction at all to (0.9 ± 0.8) mm and (1.0 ± 0.9) mm when using the predictor trained with internal tumor motion training data and external surrogate motion training data, respectively (for a typical lookahead time of 250 ms and sampling frequency of 15 Hz). Conclusions: A linear prediction model can reduce latency induced tracking errors by an average of about 50% in real-time image guided radiotherapy systems with system latencies of up to 300 ms. Training a linear model for lung tumor motion prediction with an external surrogate signal alone is feasible and results in similar performance as training with (internal) tumor motion. Particularly for scenarios where motion data are extracted from fluoroscopic imaging with ionizing radiation, this may alleviate the need for additional imaging dose during the collection of model training data.« less

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kadoura, Ahmad, E-mail: ahmad.kadoura@kaust.edu.sa, E-mail: adil.siripatana@kaust.edu.sa, E-mail: shuyu.sun@kaust.edu.sa, E-mail: omar.knio@kaust.edu.sa; Sun, Shuyu, E-mail: ahmad.kadoura@kaust.edu.sa, E-mail: adil.siripatana@kaust.edu.sa, E-mail: shuyu.sun@kaust.edu.sa, E-mail: omar.knio@kaust.edu.sa; Siripatana, Adil, E-mail: ahmad.kadoura@kaust.edu.sa, E-mail: adil.siripatana@kaust.edu.sa, E-mail: shuyu.sun@kaust.edu.sa, E-mail: omar.knio@kaust.edu.sa

In this work, two Polynomial Chaos (PC) surrogates were generated to reproduce Monte Carlo (MC) molecular simulation results of the canonical (single-phase) and the NVT-Gibbs (two-phase) ensembles for a system of normalized structureless Lennard-Jones (LJ) particles. The main advantage of such surrogates, once generated, is the capability of accurately computing the needed thermodynamic quantities in a few seconds, thus efficiently replacing the computationally expensive MC molecular simulations. Benefiting from the tremendous computational time reduction, the PC surrogates were used to conduct large-scale optimization in order to propose single-site LJ models for several simple molecules. Experimental data, a set of supercriticalmore » isotherms, and part of the two-phase envelope, of several pure components were used for tuning the LJ parameters (ε, σ). Based on the conducted optimization, excellent fit was obtained for different noble gases (Ar, Kr, and Xe) and other small molecules (CH{sub 4}, N{sub 2}, and CO). On the other hand, due to the simplicity of the LJ model used, dramatic deviations between simulation and experimental data were observed, especially in the two-phase region, for more complex molecules such as CO{sub 2} and C{sub 2} H{sub 6}.« less

LANL* V1.0: a radiation belt drift shell model suitable for real-time and reanalysis applications

DOE Office of Scientific and Technical Information (OSTI.GOV)

Koller, Josep; Reeves, Geoffrey D; Friedel, Reiner H W

2008-01-01

Space weather modeling, forecasts, and predictions, especially for the radiation belts in the inner magnetosphere, require detailed information about the Earth's magnetic field. Results depend on the magnetic field model and the L* (pron. L-star) values which are used to describe particle drift shells. Space wather models require integrating particle motions along trajectories that encircle the Earth. Numerical integration typically takes on the order of 10{sup 5} calls to a magnetic field model which makes the L* calculations very slow, in particular when using a dynamic and more accurate magnetic field model. Researchers currently tend to pick simplistic models overmore » more accurate ones but also risking large inaccuracies and even wrong conclusions. For example, magnetic field models affect the calculation of electron phase space density by applying adiabatic invariants including the drift shell value L*. We present here a new method using a surrogate model based on a neural network technique to replace the time consuming L* calculations made with modern magnetic field models. The advantage of surrogate models (or meta-models) is that they can compute the same output in a fraction of the time while adding only a marginal error. Our drift shell model LANL* (Los Alamos National Lab L-star) is based on L* calculation using the TSK03 model. The surrogate model has currently been tested and validated only for geosynchronous regions but the method is generally applicable to any satellite orbit. Computations with the new model are several million times faster compared to the standard integration method while adding less than 1% error. Currently, real-time applications for forecasting and even nowcasting inner magnetospheric space weather is limited partly due to the long computing time of accurate L* values. Without them, real-time applications are limited in accuracy. Reanalysis application of past conditions in the inner magnetosphere are used to understand physical processes and their effect. Without sufficiently accurate L* values, the interpretation of reanalysis results becomes difficult and uncertain. However, with a method that can calculate accurate L* values orders of magnitude faster, analyzing whole solar cycles worth of data suddenly becomes feasible.« less

An image-based skeletal dosimetry model for the ICRP reference newborn—internal electron sources

NASA Astrophysics Data System (ADS)

Pafundi, Deanna; Rajon, Didier; Jokisch, Derek; Lee, Choonsik; Bolch, Wesley

2010-04-01

In this study, a comprehensive electron dosimetry model of newborn skeletal tissues is presented. The model is constructed using the University of Florida newborn hybrid phantom of Lee et al (2007 Phys. Med. Biol. 52 3309-33), the newborn skeletal tissue model of Pafundi et al (2009 Phys. Med. Biol. 54 4497-531) and the EGSnrc-based Paired Image Radiation Transport code of Shah et al (2005 J. Nucl. Med. 46 344-53). Target tissues include the active bone marrow (surrogate tissue for hematopoietic stem cells), shallow marrow (surrogate tissue for osteoprogenitor cells) and unossified cartilage (surrogate tissue for chondrocytes). Monoenergetic electron emissions are considered over the energy range 1 keV to 10 MeV for the following source tissues: active marrow, trabecular bone (surfaces and volumes), cortical bone (surfaces and volumes) and cartilage. Transport results are reported as specific absorbed fractions according to the MIRD schema and are given as skeletal-averaged values in the paper with bone-specific values reported in both tabular and graphic format as electronic annexes (supplementary data). The method utilized in this work uniquely includes (1) explicit accounting for the finite size and shape of newborn ossification centers (spongiosa regions), (2) explicit accounting for active and shallow marrow dose from electron emissions in cortical bone as well as sites of unossified cartilage, (3) proper accounting of the distribution of trabecular and cortical volumes and surfaces in the newborn skeleton when considering mineral bone sources and (4) explicit consideration of the marrow cellularity changes for active marrow self-irradiation as applicable to radionuclide therapy of diseased marrow in the newborn child.

A comparative framework to infer landscape effects on population genetic structure: Are habitat suitability models effective in explaining gene flow?

Treesearch

Maria C. Mateo-Sanchez; Niko Balkenhol; Samuel Cushman; Trinidad Perez; Ana Dominguez; Santiago Saura

2015-01-01

Most current methods to assess connectivity begin with landscape resistance maps. The prevailing resistance models are commonly based on expert opinion and, more recently, on a direct transformation of habitat suitability. However, habitat associations are not necessarily accurate indicators of dispersal, and thus may fail as a surrogate of resistance to...

Optimization of the Alkyl Linker of TO Base Surrogate in Triplex-Forming PNA for Enhanced Binding to Double-Stranded RNA.

PubMed

Sato, Takaya; Sato, Yusuke; Nishizawa, Seiichi

2017-03-23

A series of triplex-forming peptide nucleic acid (TFP) probes carrying a thiazole orange (TO) base surrogate through an alkyl linker was synthesized, and the interactions between these so-called tFIT probes and purine-rich sequences within double-stranded RNA (dsRNA) were examined. We found that the TO base surrogate linker significantly affected both the binding affinity and the fluorescence response upon triplex formation with the target dsRNA. Among the probes examined, the TO base surrogate connected through the propyl linker in the tFIT probes increased the binding affinity by a factor of ten while maintaining its function as the fluorescent universal base. Isothermal titration calorimetry experiments revealed that the increased binding affinity resulted from the gain in the binding enthalpy, which could be explained by the enhanced π-stacking interaction between the TO base surrogate and the dsRNA part of the triplex. We expect that these results will provide a molecular basis for designing strong binding tFIT probes for fluorescence sensing of various kinds of purine-rich dsRNAs sequences including those carrying a pyrimidine-purine inversion. The obtained data also offers a new insight into further development of the universal bases incorporated in TFP. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

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.

Probabilistic Prognosis of Non-Planar Fatigue Crack Growth

NASA Technical Reports Server (NTRS)

Leser, Patrick E.; Newman, John A.; Warner, James E.; Leser, William P.; Hochhalter, Jacob D.; Yuan, Fuh-Gwo

2016-01-01

Quantifying the uncertainty in model parameters for the purpose of damage prognosis can be accomplished utilizing Bayesian inference and damage diagnosis data from sources such as non-destructive evaluation or structural health monitoring. The number of samples required to solve the Bayesian inverse problem through common sampling techniques (e.g., Markov chain Monte Carlo) renders high-fidelity finite element-based damage growth models unusable due to prohibitive computation times. However, these types of models are often the only option when attempting to model complex damage growth in real-world structures. Here, a recently developed high-fidelity crack growth model is used which, when compared to finite element-based modeling, has demonstrated reductions in computation times of three orders of magnitude through the use of surrogate models and machine learning. The model is flexible in that only the expensive computation of the crack driving forces is replaced by the surrogate models, leaving the remaining parameters accessible for uncertainty quantification. A probabilistic prognosis framework incorporating this model is developed and demonstrated for non-planar crack growth in a modified, edge-notched, aluminum tensile specimen. Predictions of remaining useful life are made over time for five updates of the damage diagnosis data, and prognostic metrics are utilized to evaluate the performance of the prognostic framework. Challenges specific to the probabilistic prognosis of non-planar fatigue crack growth are highlighted and discussed in the context of the experimental results.

Review of meta-analyses evaluating surrogate endpoints for overall survival in oncology.

PubMed

Sherrill, Beth; Kaye, James A; Sandin, Rickard; Cappelleri, Joseph C; Chen, Connie

2012-01-01

Overall survival (OS) is the gold standard in measuring the treatment effect of new drug therapies for cancer. However, practical factors may preclude the collection of unconfounded OS data, and surrogate endpoints are often used instead. Meta-analyses have been widely used for the validation of surrogate endpoints, specifically in oncology. This research reviewed published meta-analyses on the types of surrogate measures used in oncology studies and examined the extent of correlation between surrogate endpoints and OS for different cancer types. A search was conducted in October 2010 to compile available published evidence in the English language for the validation of disease progression-related endpoints as surrogates of OS, based on meta-analyses. We summarize published meta-analyses that quantified the correlation between progression-based endpoints and OS for multiple advanced solid-tumor types. We also discuss issues that affect the interpretation of these findings. Progression-free survival is the most commonly used surrogate measure in studies of advanced solid tumors, and correlation with OS is reported for a limited number of cancer types. Given the increased use of crossover in trials and the availability of second-/third-line treatment options available to patients after progression, it will become increasingly more difficult to establish correlation between effects on progression-free survival and OS in additional tumor types.

Web-based Interspecies Correlation Estimation (Web-ICE) for Acute Toxicity: User Manual 3.2

EPA Science Inventory

The Web-ICE Endangered Species module simultaneously estimates toxicity to taxa representing threatened or endangered species using up to 25 surrogates. This module batch processes toxicity values for endangered species from all species, genus, and family level models available f...

Surrogate Plant Data Base : Volume 2. Appendix C : Facilities Planning Baseline Data

DOT National Transportation Integrated Search

1983-05-01

This four volume report consists of a data base describing "surrogate" automobile and truck manufacturing plants developed as part of a methodology for evaluating capital investment requirements in new manufacturing facilities to build new fleets of ...

Surrogate Plant Data Base : Volume 4. Appendix E : Medium and Heavy Truck Manufacturing

DOT National Transportation Integrated Search

1983-05-01

This four volume report consists of a data base describing "surrogate" automobile and truck manufacturing plants developed as part of a methodology for evaluating capital investment requirements in new manufacturing facilities to build new fleets of ...

Analysis of safety impacts of access management alternatives using the surrogate safety assessment model : final report.

DOT National Transportation Integrated Search

2017-06-01

The purpose of this study was to evaluate if the Surrogate Safety Assessment Model (SSAM) could be used to assess the safety of a highway segment or an intersection in terms of the number and type of conflicts and to compare the safety effects of mul...

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mueller, Juliane

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.

Used fuel rail shock and vibration testing options analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ross, Steven B.; Best, Ralph E.; Klymyshyn, Nicholas A.

2014-09-25

The objective of the rail shock and vibration tests is to complete the framework needed to quantify loads of fuel assembly components that are necessary to guide materials research and establish a technical basis for review organizations such as the U.S. Nuclear Regulatory Commission (NRC). A significant body of experimental and numerical modeling data exists to quantify loads and failure limits applicable to normal conditions of transport (NCT) rail transport, but the data are based on assumptions that can only be verified through experimental testing. The test options presented in this report represent possible paths for acquiring the data thatmore » are needed to confirm the assumptions of previous work, validate modeling methods that will be needed for evaluating transported fuel on a case-by-case basis, and inform material test campaigns on the anticipated range of fuel loading. The ultimate goal of this testing is to close all of the existing knowledge gaps related to the loading of used fuel under NCT conditions and inform the experiments and analysis program on specific endpoints for their research. The options include tests that would use an actual railcar, surrogate assemblies, and real or simulated rail transportation casks. The railcar carrying the cradle, cask, and surrogate fuel assembly payload would be moved in a train operating over rail track modified or selected to impart shock and vibration forces that occur during normal rail transportation. Computer modeling would be used to help design surrogates that may be needed for a rail cask, a cask’s internal basket, and a transport cradle. The objective of the design of surrogate components would be to provide a test platform that effectively simulates responses to rail shock and vibration loads that would be exhibited by state-of-the-art rail cask, basket, and/or cradle structures. The computer models would also be used to help determine the placement of instrumentation (accelerometers and strain gauges) on the surrogate fuel assemblies, cask and cradle structures, and the railcar so that forces and deflections that would result in the greatest potential for damage to high burnup and long-cooled UNF can be determined. For purposes of this report we consider testing on controlled track when we have control of the track and speed to facilitate modeling.« less

Simultaneous tumor and surrogate motion tracking with dynamic MRI for radiation therapy planning

NASA Astrophysics Data System (ADS)

Park, Seyoun; Farah, Rana; Shea, Steven M.; Tryggestad, Erik; Hales, Russell; Lee, Junghoon

2018-01-01

Respiration-induced tumor motion is a major obstacle for achieving high-precision radiotherapy of cancers in the thoracic and abdominal regions. Surrogate-based estimation and tracking methods are commonly used in radiotherapy, but with limited understanding of quantified correlation to tumor motion. In this study, we propose a method to simultaneously track the lung tumor and external surrogates to evaluate their spatial correlation in a quantitative way using dynamic MRI, which allows real-time acquisition without ionizing radiation exposure. To capture the lung and whole tumor, four MRI-compatible fiducials are placed on the patient’s chest and upper abdomen. Two different types of acquisitions are performed in the sagittal orientation including multi-slice 2D cine MRIs to reconstruct 4D-MRI and two-slice 2D cine MRIs to simultaneously track the tumor and fiducials. A phase-binned 4D-MRI is first reconstructed from multi-slice MR images using body area as a respiratory surrogate and groupwise registration. The 4D-MRI provides 3D template volumes for different breathing phases. 3D tumor position is calculated by 3D-2D template matching in which 3D tumor templates in the 4D-MRI reconstruction and the 2D cine MRIs from the two-slice tracking dataset are registered. 3D trajectories of the external surrogates are derived via matching a 3D geometrical model of the fiducials to their segmentations on the 2D cine MRIs. We tested our method on ten lung cancer patients. Using a correlation analysis, the 3D tumor trajectory demonstrates a noticeable phase mismatch and significant cycle-to-cycle motion variation, while the external surrogate was not sensitive enough to capture such variations. Additionally, there was significant phase mismatch between surrogate signals obtained from the fiducials at different locations.

Predicting trace organic compound attenuation by ozone oxidation: Development of indicator and surrogate models.

PubMed

Park, Minkyu; Anumol, Tarun; Daniels, Kevin D; Wu, Shimin; Ziska, Austin D; Snyder, Shane A

2017-08-01

Ozone oxidation has been demonstrated to be an effective treatment process for the attenuation of trace organic compounds (TOrCs); however, predicting TOrC attenuation by ozone processes is challenging in wastewaters. Since ozone is rapidly consumed, determining the exposure times of ozone and hydroxyl radical proves to be difficult. As direct potable reuse schemes continue to gain traction, there is an increasing need for the development of real-time monitoring strategies for TOrC abatement in ozone oxidation processes. Hence, this study is primarily aimed at developing indicator and surrogate models for the prediction of TOrC attenuation by ozone oxidation. To this end, the second-order kinetic equations with a second-phase R ct value (ratio of hydroxyl radical exposure to molecular ozone exposure) were used to calculate comparative kinetics of TOrC attenuation and the reduction of indicator and spectroscopic surrogate parameters, including UV absorbance at 254 nm (UVA 254 ) and total fluorescence (TF). The developed indicator model using meprobamate as an indicator compound and the surrogate models with UVA 254 and TF exhibited good predictive power for the attenuation of 13 kinetically distinct TOrCs in five filtered and unfiltered wastewater effluents (R 2 values > 0.8). This study is intended to help provide a guideline for the implementation of indicator/surrogate models for real-time monitoring of TOrC abatement with ozone processes and integrate them into a regulatory framework in water reuse. Copyright © 2017 Elsevier Ltd. All rights reserved.

Surrogate matrix and surrogate analyte approaches for definitive quantitation of endogenous biomolecules.

PubMed

Jones, Barry R; Schultz, Gary A; Eckstein, James A; Ackermann, Bradley L

2012-10-01

Quantitation of biomarkers by LC-MS/MS is complicated by the presence of endogenous analytes. This challenge is most commonly overcome by calibration using an authentic standard spiked into a surrogate matrix devoid of the target analyte. A second approach involves use of a stable-isotope-labeled standard as a surrogate analyte to allow calibration in the actual biological matrix. For both methods, parallelism between calibration standards and the target analyte in biological matrix must be demonstrated in order to ensure accurate quantitation. In this communication, the surrogate matrix and surrogate analyte approaches are compared for the analysis of five amino acids in human plasma: alanine, valine, methionine, leucine and isoleucine. In addition, methodology based on standard addition is introduced, which enables a robust examination of parallelism in both surrogate analyte and surrogate matrix methods prior to formal validation. Results from additional assays are presented to introduce the standard-addition methodology and to highlight the strengths and weaknesses of each approach. For the analysis of amino acids in human plasma, comparable precision and accuracy were obtained by the surrogate matrix and surrogate analyte methods. Both assays were well within tolerances prescribed by regulatory guidance for validation of xenobiotic assays. When stable-isotope-labeled standards are readily available, the surrogate analyte approach allows for facile method development. By comparison, the surrogate matrix method requires greater up-front method development; however, this deficit is offset by the long-term advantage of simplified sample analysis.

surrkick: Black-hole kicks from numerical-relativity surrogate models

NASA Astrophysics Data System (ADS)

Gerosa, Davide; Hébert, François; Stein, Leo C.

2018-04-01

surrkick quickly and reliably extract recoils imparted to generic, precessing, black hole binaries. It uses a numerical-relativity surrogate model to obtain the gravitational waveform given a set of binary parameters, and from this waveform directly integrates the gravitational-wave linear momentum flux. This entirely bypasses the need of fitting formulae which are typically used to model black-hole recoils in astrophysical contexts.

Surrogate Parents and Children with Disabilities: State-Level Approaches. inForum

ERIC Educational Resources Information Center

Muller, Eve

2009-01-01

Based on a survey of states, this document summarizes state-level approaches to using surrogate parents in order to meet the needs of children with disabilities. Most respondents noted that their states had issued policy or formal guidance pertaining to surrogate parents and children with disabilities, and most also described efforts to ensure…

Surrogate outcomes in health technology assessment: an international comparison.

PubMed

Velasco Garrido, Marcial; Mangiapane, Sandra

2009-07-01

Our aim was to review the recommendations given by health technology assessment (HTA) institutions in their methodological guidelines concerning the use of surrogate outcomes in their assessments. In a second step, we aimed at quantifying the role surrogate parameters take in assessment reports. We analyzed methodological papers and guidelines from HTA agencies with International Network of Agencies for Health Technology Assessment membership as well as from institutions related to pharmaceutical regulation (i.e., reimbursement, pricing). We analyzed the use of surrogate outcomes in a sample of HTA reports randomly drawn from the HTA database. We checked methods, results (including evidence tables), and conclusions sections and extracted the outcomes reported. We report descriptive statistics on the presence of surrogate outcomes in the reports. We identified thirty-four methodological guidelines, twenty of them addressing the issue of outcome parameter choice and the problematic of surrogate outcomes. Overall HTA agencies call on caution regarding the reliance on surrogate outcomes. None of the agencies has provided a list or catalog of acceptable and validated surrogate outcomes. We extracted the outcome parameter of 140 HTA reports. Only around half of the reports determined the outcomes for the assessment prospectively. Surrogate outcomes had been used in 62 percent of the reports. However, only 3.6 percent were based upon surrogate outcomes exclusively. All of them assessed diagnostic or screening technologies and the surrogate outcomes were predominantly test characteristics. HTA institutions seem to agree on a cautious approach to the use of surrogate outcomes in technology assessment. Thorough assessment of health technologies should not rely exclusively on surrogate outcomes.

Improving Mixed Variable Optimization of Computational and Model Parameters Using Multiple Surrogate Functions

DTIC Science & Technology

2008-03-01

multiplicative corrections as well as space mapping transformations for models defined over a lower dimensional space. A corrected surrogate model for the...correction functions used in [72]. If the low fidelity model g(x̃) is defined over a lower dimensional space then a space mapping transformation is...required. As defined in [21, 72], space mapping is a method of mapping between models of different dimensionality or fidelity. Let P denote the space

Evaluation of Enterococcus faecium NRRL B-2354 as a Surrogate for Salmonella During Extrusion of Low-Moisture Food.

PubMed

Verma, Tushar; Wei, Xinyao; Lau, Soon Kiat; Bianchini, Andreia; Eskridge, Kent M; Subbiah, Jeyamkondan

2018-04-01

Salmonella in low-moisture foods is an emerging challenge due to numerous food product recalls and foodborne illness outbreaks. Identification of suitable surrogate is critical for process validation at industry level due to implementation of new Food Safety Modernization Act of 2011. The objective of this study was to evaluate Enterococcus faecium NRRL B-2354 as a surrogate for Salmonella during the extrusion of low-moisture food. Oat flour, a low-moisture food, was adjusted to different moisture (14% to 26% wet basis) and fat (5% to 15% w/w) contents and was inoculated with E. faecium NRRL B-2354. Inoculated material was then extruded in a lab-scale single-screw extruder running at different screw speeds (75 to 225 rpm) and different temperatures (75, 85, and 95 °C). A split-plot central composite 2nd order response surface design was used, with the central point replicated six times. The data from the selective media (m-Enterococcus agar) was used to build the response surface model for inactivation of E. faecium NRRL B-2354. Results indicated that E. faecium NRRL B-2354 always had higher heat resistance compared to Salmonella at all conditions evaluated in this study. However, the patterns of contour plots showing the effect of various product and process parameters on inactivation of E. faecium NRRL B-2354 was different from that of Salmonella. Although E. faecium NRRL B-2354 may be an acceptable surrogate for extrusion of low-moisture products due to higher resistance than Salmonella, another surrogate with similar inactivation behavior may be preferred and needs to be identified. Food Safety Modernization Act requires the food industry to validate processing interventions. This study validated extrusion processing and demonstrated that E. faecium NRRL B-2354 is an acceptable surrogate for extrusion of low-moisture products. The developed response surface model allows the industry to identify process conditions to achieve a desired lethality for their products based on composition. © 2018 Institute of Food Technologists®.

Modeling the Test-Retest Statistics of a Localization Experiment in the Full Horizontal Plane.

PubMed

Morsnowski, André; Maune, Steffen

2016-10-01

Two approaches to model the test-retest statistics of a localization experiment basing on Gaussian distribution and on surrogate data are introduced. Their efficiency is investigated using different measures describing directional hearing ability. A localization experiment in the full horizontal plane is a challenging task for hearing impaired patients. In clinical routine, we use this experiment to evaluate the progress of our cochlear implant (CI) recipients. Listening and time effort limit the reproducibility. The localization experiment consists of a 12 loudspeaker circle, placed in an anechoic room, a "camera silens". In darkness, HSM sentences are presented at 65 dB pseudo-erratically from all 12 directions with five repetitions. This experiment is modeled by a set of Gaussian distributions with different standard deviations added to a perfect estimator, as well as by surrogate data. Five repetitions per direction are used to produce surrogate data distributions for the sensation directions. To investigate the statistics, we retrospectively use the data of 33 CI patients with 92 pairs of test-retest-measurements from the same day. The first model does not take inversions into account, (i.e., permutations of the direction from back to front and vice versa are not considered), although they are common for hearing impaired persons particularly in the rear hemisphere. The second model considers these inversions but does not work with all measures. The introduced models successfully describe test-retest statistics of directional hearing. However, since their applications on the investigated measures perform differently no general recommendation can be provided. The presented test-retest statistics enable pair test comparisons for localization experiments.

Technical Note: Radiological properties of tissue surrogates used in a multimodality deformable pelvic phantom for MR-guided radiotherapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Niebuhr, Nina I., E-mail: n.niebuhr@dkfz.de; Johnen, Wibke; Güldaglar, Timur

Purpose: Phantom surrogates were developed to allow multimodal [computed tomography (CT), magnetic resonance imaging (MRI), and teletherapy] and anthropomorphic tissue simulation as well as materials and methods to construct deformable organ shapes and anthropomorphic bone models. Methods: Agarose gels of variable concentrations and loadings were investigated to simulate various soft tissue types. Oils, fats, and Vaseline were investigated as surrogates for adipose tissue and bone marrow. Anthropomorphic shapes of bone and organs were realized using 3D-printing techniques based on segmentations of patient CT-scans. All materials were characterized in dual energy CT and MRI to adapt CT numbers, electron density, effectivemore » atomic number, as well as T1- and T2-relaxation times to patient and literature values. Results: Soft tissue simulation could be achieved with agarose gels in combination with a gadolinium-based contrast agent and NaF to simulate muscle, prostate, and tumor tissues. Vegetable oils were shown to be a good representation for adipose tissue in all modalities. Inner bone was realized using a mixture of Vaseline and K{sub 2}HPO{sub 4}, resulting in both a fatty bone marrow signal in MRI and inhomogeneous areas of low and high attenuation in CT. The high attenuation of outer bone was additionally adapted by applying gypsum bandages to the 3D-printed hollow bone case with values up to 1200 HU. Deformable hollow organs were manufactured using silicone. Signal loss in the MR images based on the conductivity of the gels needs to be further investigated. Conclusions: The presented surrogates and techniques allow the customized construction of multimodality, anthropomorphic, and deformable phantoms as exemplarily shown for a pelvic phantom, which is intended to study adaptive treatment scenarios in MR-guided radiation therapy.« less

Technical Note: Radiological properties of tissue surrogates used in a multimodality deformable pelvic phantom for MR-guided radiotherapy.

PubMed

Niebuhr, Nina I; Johnen, Wibke; Güldaglar, Timur; Runz, Armin; Echner, Gernot; Mann, Philipp; Möhler, Christian; Pfaffenberger, Asja; Jäkel, Oliver; Greilich, Steffen

2016-02-01

Phantom surrogates were developed to allow multimodal [computed tomography (CT), magnetic resonance imaging (MRI), and teletherapy] and anthropomorphic tissue simulation as well as materials and methods to construct deformable organ shapes and anthropomorphic bone models. Agarose gels of variable concentrations and loadings were investigated to simulate various soft tissue types. Oils, fats, and Vaseline were investigated as surrogates for adipose tissue and bone marrow. Anthropomorphic shapes of bone and organs were realized using 3D-printing techniques based on segmentations of patient CT-scans. All materials were characterized in dual energy CT and MRI to adapt CT numbers, electron density, effective atomic number, as well as T1- and T2-relaxation times to patient and literature values. Soft tissue simulation could be achieved with agarose gels in combination with a gadolinium-based contrast agent and NaF to simulate muscle, prostate, and tumor tissues. Vegetable oils were shown to be a good representation for adipose tissue in all modalities. Inner bone was realized using a mixture of Vaseline and K2HPO4, resulting in both a fatty bone marrow signal in MRI and inhomogeneous areas of low and high attenuation in CT. The high attenuation of outer bone was additionally adapted by applying gypsum bandages to the 3D-printed hollow bone case with values up to 1200 HU. Deformable hollow organs were manufactured using silicone. Signal loss in the MR images based on the conductivity of the gels needs to be further investigated. The presented surrogates and techniques allow the customized construction of multimodality, anthropomorphic, and deformable phantoms as exemplarily shown for a pelvic phantom, which is intended to study adaptive treatment scenarios in MR-guided radiation therapy.

Surrogacy assessment using principal stratification when surrogate and outcome measures are multivariate normal.

PubMed

Conlon, Anna S C; Taylor, Jeremy M G; Elliott, Michael R

2014-04-01

In clinical trials, a surrogate outcome variable (S) can be measured before the outcome of interest (T) and may provide early information regarding the treatment (Z) effect on T. Using the principal surrogacy framework introduced by Frangakis and Rubin (2002. Principal stratification in causal inference. Biometrics 58, 21-29), we consider an approach that has a causal interpretation and develop a Bayesian estimation strategy for surrogate validation when the joint distribution of potential surrogate and outcome measures is multivariate normal. From the joint conditional distribution of the potential outcomes of T, given the potential outcomes of S, we propose surrogacy validation measures from this model. As the model is not fully identifiable from the data, we propose some reasonable prior distributions and assumptions that can be placed on weakly identified parameters to aid in estimation. We explore the relationship between our surrogacy measures and the surrogacy measures proposed by Prentice (1989. Surrogate endpoints in clinical trials: definition and operational criteria. Statistics in Medicine 8, 431-440). The method is applied to data from a macular degeneration study and an ovarian cancer study.

Surrogacy assessment using principal stratification when surrogate and outcome measures are multivariate normal

PubMed Central

Conlon, Anna S. C.; Taylor, Jeremy M. G.; Elliott, Michael R.

2014-01-01

In clinical trials, a surrogate outcome variable (S) can be measured before the outcome of interest (T) and may provide early information regarding the treatment (Z) effect on T. Using the principal surrogacy framework introduced by Frangakis and Rubin (2002. Principal stratification in causal inference. Biometrics 58, 21–29), we consider an approach that has a causal interpretation and develop a Bayesian estimation strategy for surrogate validation when the joint distribution of potential surrogate and outcome measures is multivariate normal. From the joint conditional distribution of the potential outcomes of T, given the potential outcomes of S, we propose surrogacy validation measures from this model. As the model is not fully identifiable from the data, we propose some reasonable prior distributions and assumptions that can be placed on weakly identified parameters to aid in estimation. We explore the relationship between our surrogacy measures and the surrogacy measures proposed by Prentice (1989. Surrogate endpoints in clinical trials: definition and operational criteria. Statistics in Medicine 8, 431–440). The method is applied to data from a macular degeneration study and an ovarian cancer study. PMID:24285772

Uncertainty propagation through an aeroelastic wind turbine model using polynomial surrogates

DOE Office of Scientific and Technical Information (OSTI.GOV)

Murcia, Juan Pablo; Réthoré, Pierre-Elouan; Dimitrov, Nikolay

Polynomial surrogates are used to characterize the energy production and lifetime equivalent fatigue loads for different components of the DTU 10 MW reference wind turbine under realistic atmospheric conditions. The variability caused by different turbulent inflow fields are captured by creating independent surrogates for the mean and standard deviation of each output with respect to the inflow realizations. A global sensitivity analysis shows that the turbulent inflow realization has a bigger impact on the total distribution of equivalent fatigue loads than the shear coefficient or yaw miss-alignment. The methodology presented extends the deterministic power and thrust coefficient curves to uncertaintymore » models and adds new variables like damage equivalent fatigue loads in different components of the turbine. These surrogate models can then be implemented inside other work-flows such as: estimation of the uncertainty in annual energy production due to wind resource variability and/or robust wind power plant layout optimization. It can be concluded that it is possible to capture the global behavior of a modern wind turbine and its uncertainty under realistic inflow conditions using polynomial response surfaces. In conclusion, the surrogates are a way to obtain power and load estimation under site specific characteristics without sharing the proprietary aeroelastic design.« less

Uncertainty propagation through an aeroelastic wind turbine model using polynomial surrogates

DOE PAGES

Murcia, Juan Pablo; Réthoré, Pierre-Elouan; Dimitrov, Nikolay; ...

2017-07-17

Polynomial surrogates are used to characterize the energy production and lifetime equivalent fatigue loads for different components of the DTU 10 MW reference wind turbine under realistic atmospheric conditions. The variability caused by different turbulent inflow fields are captured by creating independent surrogates for the mean and standard deviation of each output with respect to the inflow realizations. A global sensitivity analysis shows that the turbulent inflow realization has a bigger impact on the total distribution of equivalent fatigue loads than the shear coefficient or yaw miss-alignment. The methodology presented extends the deterministic power and thrust coefficient curves to uncertaintymore » models and adds new variables like damage equivalent fatigue loads in different components of the turbine. These surrogate models can then be implemented inside other work-flows such as: estimation of the uncertainty in annual energy production due to wind resource variability and/or robust wind power plant layout optimization. It can be concluded that it is possible to capture the global behavior of a modern wind turbine and its uncertainty under realistic inflow conditions using polynomial response surfaces. In conclusion, the surrogates are a way to obtain power and load estimation under site specific characteristics without sharing the proprietary aeroelastic design.« less

The Biomarker-Surrogacy Evaluation Schema: a review of the biomarker-surrogate literature and a proposal for a criterion-based, quantitative, multidimensional hierarchical levels of evidence schema for evaluating the status of biomarkers as surrogate endpoints.

PubMed

Lassere, Marissa N

2008-06-01

There are clear advantages to using biomarkers and surrogate endpoints, but concerns about clinical and statistical validity and systematic methods to evaluate these aspects hinder their efficient application. Section 2 is a systematic, historical review of the biomarker-surrogate endpoint literature with special reference to the nomenclature, the systems of classification and statistical methods developed for their evaluation. In Section 3 an explicit, criterion-based, quantitative, multidimensional hierarchical levels of evidence schema - Biomarker-Surrogacy Evaluation Schema - is proposed to evaluate and co-ordinate the multiple dimensions (biological, epidemiological, statistical, clinical trial and risk-benefit evidence) of the biomarker clinical endpoint relationships. The schema systematically evaluates and ranks the surrogacy status of biomarkers and surrogate endpoints using defined levels of evidence. The schema incorporates the three independent domains: Study Design, Target Outcome and Statistical Evaluation. Each domain has items ranked from zero to five. An additional category called Penalties incorporates additional considerations of biological plausibility, risk-benefit and generalizability. The total score (0-15) determines the level of evidence, with Level 1 the strongest and Level 5 the weakest. The term ;surrogate' is restricted to markers attaining Levels 1 or 2 only. Surrogacy status of markers can then be directly compared within and across different areas of medicine to guide individual, trial-based or drug-development decisions. This schema would facilitate communication between clinical, researcher, regulatory, industry and consumer participants necessary for evaluation of the biomarker-surrogate-clinical endpoint relationship in their different settings.

Development of a Post-Intensive Care Unit Storytelling Intervention for Surrogates Involved in Decisions to Limit Life-Sustaining Treatment

PubMed Central

Schenker, Yael; Dew, Mary Amanda; Reynolds, Charles F.; Arnold, Robert M.; Tiver, Greer A.; Barnato, Amber E.

2014-01-01

Objectives Surrogates involved in decisions to limit life-sustaining treatment for a loved one in the intensive care unit (ICU) are at increased risk for adverse psychological outcomes lasting months to years after the ICU experience. Post-ICU interventions to reduce surrogate distress have not been developed. We sought to 1) describe a conceptual framework underlying the beneficial mental health effects of storytelling and 2) present formative work developing a storytelling intervention to reduce distress for recently bereaved surrogates. Methods An interdisciplinary team conceived the idea for a storytelling intervention based upon evidence from narrative theory that storytelling reduces distress from traumatic events through emotional disclosure, cognitive processing, and social connections. We developed an initial storytelling guide based upon this theory and the clinical perspectives of team members. We then conducted a case series with recently bereaved surrogates to iteratively test and modify the guide. Results The storytelling guide covered three key domains of the surrogate's experience of the patient's illness and death: antecedents, ICU experience, and aftermath. The facilitator focused on parts of the story that appeared to generate strong emotions and used non-judgmental statements to attend to these emotions. Between September 2012 and May 2013 we identified 28 eligible surrogates from 1 medical ICU and consented 20 for medical record review and recontact; 10 became eligible of whom 6 consented and completed the storytelling intervention. The single-session storytelling intervention lasted 40-92 minutes. All storytelling participants endorsed the intervention as acceptable, and 5 of 6 reported that it was helpful. Significance of Results Surrogate storytelling is an innovative and acceptable post-ICU intervention for recently bereaved surrogates and should be evaluated further. PMID:24524736

Development of a post-intensive care unit storytelling intervention for surrogates involved in decisions to limit life-sustaining treatment.

PubMed

Schenker, Yael; Dew, Mary Amanda; Reynolds, Charles F; Arnold, Robert M; Tiver, Greer A; Barnato, Amber E

2015-06-01

Surrogates involved in decisions to limit life-sustaining treatment for a loved one in the intensive care unit (ICU) are at increased risk for adverse psychological outcomes that can last for months to years after the ICU experience. Post-ICU interventions to reduce surrogate distress have not yet been developed. We sought to (1) describe a conceptual framework underlying the beneficial mental health effects of storytelling, and (2) present formative work developing a storytelling intervention to reduce distress for recently bereaved surrogates. An interdisciplinary team conceived the idea for a storytelling intervention based on evidence from narrative theory that storytelling reduces distress from traumatic events through emotional disclosure, cognitive processing, and social connection. We developed an initial storytelling guide based on this theory and the clinical perspectives of team members. We then conducted a case series with recently bereaved surrogates to iteratively test and modify the guide. The storytelling guide covered three key domains of the surrogate's experience of the patient's illness and death: antecedents, ICU experience, and aftermath. The facilitator focused on the parts of a story that appeared to generate strong emotions and used nonjudgmental statements to attend to these emotions. Between September 2012 and May 2013, we identified 28 eligible surrogates from a medical ICU and consented 20 for medical record review and recontact; 10 became eligible, of whom 6 consented and completed the storytelling intervention. The single-session storytelling intervention lasted from 40 to 92 minutes. All storytelling participants endorsed the intervention as acceptable, and five of six reported it as helpful. Surrogate storytelling is an innovative and acceptable post-ICU intervention for recently bereaved surrogates and should be evaluated further.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mueller, Charles J.; Cannella, William J.; Bays, J. Timothy

The primary objectives of this work were to formulate, blend, and characterize a set of four ultralow-sulfur diesel surrogate fuels in quantities sufficient to enable their study in single-cylinder-engine and combustion-vessel experiments. The surrogate fuels feature increasing levels of compositional accuracy (i.e., increasing exactness in matching hydrocarbon structural characteristics) relative to the single target diesel fuel upon which the surrogate fuels are based. This approach was taken to assist in determining the minimum level of surrogate-fuel compositional accuracy that is required to adequately emulate the performance characteristics of the target fuel under different combustion modes. For each of the fourmore » surrogate fuels, an approximately 30 L batch was blended, and a number of the physical and chemical properties were measured. In conclusion, this work documents the surrogate-fuel creation process and the results of the property measurements.« less

A Priori Analysis of Flamelet-Based Modeling for a Dual-Mode Scramjet Combustor

NASA Technical Reports Server (NTRS)

Quinlan, Jesse R.; McDaniel, James C.; Drozda, Tomasz G.; Lacaze, Guilhem; Oefelein, Joseph

2014-01-01

An a priori investigation of the applicability of flamelet-based combustion models to dual-mode scramjet combustion was performed utilizing Reynolds-averaged simulations (RAS). For this purpose, the HIFiRE Direct Connect Rig (HDCR) flowpath, fueled with a JP-7 fuel surrogate and operating in dual- and scram-mode was considered. The chemistry of the JP-7 fuel surrogate was modeled using a 22 species, 18-step chemical reaction mechanism. Simulation results were compared to experimentally-obtained, time-averaged, wall pressure measurements to validate the RAS solutions. The analysis of the dual-mode operation of this flowpath showed regions of predominately non-premixed, high-Damkohler number, combustion. Regions of premixed combustion were also present but associated with only a small fraction of the total heat-release in the flow. This is in contrast to the scram-mode operation, where a comparable amount of heat is released from non-premixed and premixed combustion modes. Representative flamelet boundary conditions were estimated by analyzing probability density functions for temperature and pressure for pure fuel and oxidizer conditions. The results of the present study reveal the potential for a flamelet model to accurately model the combustion processes in the HDCR and likely other high-speed flowpaths of engineering interest.

Development of an RF-EMF Exposure Surrogate for Epidemiologic Research.

PubMed

Roser, Katharina; Schoeni, Anna; Bürgi, Alfred; Röösli, Martin

2015-05-22

Exposure assessment is a crucial part in studying potential effects of RF-EMF. Using data from the HERMES study on adolescents, we developed an integrative exposure surrogate combining near-field and far-field RF-EMF exposure in a single brain and whole-body exposure measure. Contributions from far-field sources were modelled by propagation modelling and multivariable regression modelling using personal measurements. Contributions from near-field sources were assessed from both, questionnaires and mobile phone operator records. Mean cumulative brain and whole-body doses were 1559.7 mJ/kg and 339.9 mJ/kg per day, respectively. 98.4% of the brain dose originated from near-field sources, mainly from GSM mobile phone calls (93.1%) and from DECT phone calls (4.8%). Main contributors to the whole-body dose were GSM mobile phone calls (69.0%), use of computer, laptop and tablet connected to WLAN (12.2%) and data traffic on the mobile phone via WLAN (6.5%). The exposure from mobile phone base stations contributed 1.8% to the whole-body dose, while uplink exposure from other people's mobile phones contributed 3.6%. In conclusion, the proposed approach is considered useful to combine near-field and far-field exposure to an integrative exposure surrogate for exposure assessment in epidemiologic studies. However, substantial uncertainties remain about exposure contributions from various near-field and far-field sources.

Development of an RF-EMF Exposure Surrogate for Epidemiologic Research

PubMed Central

Roser, Katharina; Schoeni, Anna; Bürgi, Alfred; Röösli, Martin

2015-01-01

Exposure assessment is a crucial part in studying potential effects of RF-EMF. Using data from the HERMES study on adolescents, we developed an integrative exposure surrogate combining near-field and far-field RF-EMF exposure in a single brain and whole-body exposure measure. Contributions from far-field sources were modelled by propagation modelling and multivariable regression modelling using personal measurements. Contributions from near-field sources were assessed from both, questionnaires and mobile phone operator records. Mean cumulative brain and whole-body doses were 1559.7 mJ/kg and 339.9 mJ/kg per day, respectively. 98.4% of the brain dose originated from near-field sources, mainly from GSM mobile phone calls (93.1%) and from DECT phone calls (4.8%). Main contributors to the whole-body dose were GSM mobile phone calls (69.0%), use of computer, laptop and tablet connected to WLAN (12.2%) and data traffic on the mobile phone via WLAN (6.5%). The exposure from mobile phone base stations contributed 1.8% to the whole-body dose, while uplink exposure from other people’s mobile phones contributed 3.6%. In conclusion, the proposed approach is considered useful to combine near-field and far-field exposure to an integrative exposure surrogate for exposure assessment in epidemiologic studies. However, substantial uncertainties remain about exposure contributions from various near-field and far-field sources. PMID:26006132

Patient Preferences and Surrogate Decision Making in Neuroscience Intensive Care Units

PubMed Central

Cai, Xuemei; Robinson, Jennifer; Muehlschlegel, Susanne; White, Douglas B.; Holloway, Robert G.; Sheth, Kevin N.; Fraenkel, Liana; Hwang, David Y.

2016-01-01

In the neuroscience intensive care unit (NICU), most patients lack the capacity to make their own preferences known. This fact leads to situations where surrogate decision makers must fill the role of the patient in terms of making preference-based treatment decisions, oftentimes in challenging situations where prognosis is uncertain. The neurointensivist has a large responsibility and role to play in this shared decision making process. This review covers how NICU patient preferences are determined through existing advance care documentation or surrogate decision makers and how the optimum roles of the physician and surrogate decision maker are addressed. We outline the process of reaching a shared decision between family and care team and describe a practice for conducting optimum family meetings based on studies of ICU families in crisis. We review challenges in the decision making process between surrogate decision makers and medical teams in neurocritical care settings, as well as methods to ameliorate conflicts. Ultimately, the goal of shared decision making is to increase knowledge amongst surrogates and care providers, decrease decisional conflict, promote realistic expectations and preference-centered treatment strategies, and lift the emotional burden on families of neurocritical care patients. PMID:25990137

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

Beauty and the beast: Some perspectives on efficient model analysis, surrogate models, and the future of modeling

NASA Astrophysics Data System (ADS)

Hill, M. C.; Jakeman, J.; Razavi, S.; Tolson, B.

2015-12-01

For many environmental systems model runtimes have remained very long as more capable computers have been used to add more processes and more time and space discretization. Scientists have also added more parameters and kinds of observations, and many model runs are needed to explore the models. Computational demand equals run time multiplied by number of model runs divided by parallelization opportunities. Model exploration is conducted using sensitivity analysis, optimization, and uncertainty quantification. Sensitivity analysis is used to reveal consequences of what may be very complex simulated relations, optimization is used to identify parameter values that fit the data best, or at least better, and uncertainty quantification is used to evaluate the precision of simulated results. The long execution times make such analyses a challenge. Methods for addressing this challenges include computationally frugal analysis of the demanding original model and a number of ingenious surrogate modeling methods. Both commonly use about 50-100 runs of the demanding original model. In this talk we consider the tradeoffs between (1) original model development decisions, (2) computationally frugal analysis of the original model, and (3) using many model runs of the fast surrogate model. Some questions of interest are as follows. If the added processes and discretization invested in (1) are compared with the restrictions and approximations in model analysis produced by long model execution times, is there a net benefit related of the goals of the model? Are there changes to the numerical methods that could reduce the computational demands while giving up less fidelity than is compromised by using computationally frugal methods or surrogate models for model analysis? Both the computationally frugal methods and surrogate models require that the solution of interest be a smooth function of the parameters or interest. How does the information obtained from the local methods typical of (2) and the global averaged methods typical of (3) compare for typical systems? The discussion will use examples of response of the Greenland glacier to global warming and surface and groundwater modeling.

Review of meta-analyses evaluating surrogate endpoints for overall survival in oncology

PubMed Central

Sherrill, Beth; Kaye, James A; Sandin, Rickard; Cappelleri, Joseph C; Chen, Connie

2012-01-01

Overall survival (OS) is the gold standard in measuring the treatment effect of new drug therapies for cancer. However, practical factors may preclude the collection of unconfounded OS data, and surrogate endpoints are often used instead. Meta-analyses have been widely used for the validation of surrogate endpoints, specifically in oncology. This research reviewed published meta-analyses on the types of surrogate measures used in oncology studies and examined the extent of correlation between surrogate endpoints and OS for different cancer types. A search was conducted in October 2010 to compile available published evidence in the English language for the validation of disease progression-related endpoints as surrogates of OS, based on meta-analyses. We summarize published meta-analyses that quantified the correlation between progression-based endpoints and OS for multiple advanced solid-tumor types. We also discuss issues that affect the interpretation of these findings. Progression-free survival is the most commonly used surrogate measure in studies of advanced solid tumors, and correlation with OS is reported for a limited number of cancer types. Given the increased use of crossover in trials and the availability of second-/third-line treatment options available to patients after progression, it will become increasingly more difficult to establish correlation between effects on progression-free survival and OS in additional tumor types. PMID:23109809

Surrogate Plant Data Base : Volume 3. Appendix D : Facilities Planning Data ; Operating Manpower, Manufacturing Budgets and Pre-Production Launch ...

DOT National Transportation Integrated Search

1983-05-01

This four volume report consists of a data base describing "surrogate" automobile and truck manufacturing plants developed as part of a methodology for evaluating capital investment requirements in new manufacturing facilities to build new fleets of ...

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 ...

surrosurv: An R package for the evaluation of failure time surrogate endpoints in individual patient data meta-analyses of randomized clinical trials.

PubMed

Rotolo, Federico; Paoletti, Xavier; Michiels, Stefan

2018-03-01

Surrogate endpoints are attractive for use in clinical trials instead of well-established endpoints because of practical convenience. To validate a surrogate endpoint, two important measures can be estimated in a meta-analytic context when individual patient data are available: the R indiv 2 or the Kendall's τ at the individual level, and the R trial 2 at the trial level. We aimed at providing an R implementation of classical and well-established as well as more recent statistical methods for surrogacy assessment with failure time endpoints. We also intended incorporating utilities for model checking and visualization and data generating methods described in the literature to date. In the case of failure time endpoints, the classical approach is based on two steps. First, a Kendall's τ is estimated as measure of individual level surrogacy using a copula model. Then, the R trial 2 is computed via a linear regression of the estimated treatment effects; at this second step, the estimation uncertainty can be accounted for via measurement-error model or via weights. In addition to the classical approach, we recently developed an approach based on bivariate auxiliary Poisson models with individual random effects to measure the Kendall's τ and treatment-by-trial interactions to measure the R trial 2 . The most common data simulation models described in the literature are based on: copula models, mixed proportional hazard models, and mixture of half-normal and exponential random variables. The R package surrosurv implements the classical two-step method with Clayton, Plackett, and Hougaard copulas. It also allows to optionally adjusting the second-step linear regression for measurement-error. The mixed Poisson approach is implemented with different reduced models in addition to the full model. We present the package functions for estimating the surrogacy models, for checking their convergence, for performing leave-one-trial-out cross-validation, and for plotting the results. We illustrate their use in practice on individual patient data from a meta-analysis of 4069 patients with advanced gastric cancer from 20 trials of chemotherapy. The surrosurv package provides an R implementation of classical and recent statistical methods for surrogacy assessment of failure time endpoints. Flexible simulation functions are available to generate data according to the methods described in the literature. Copyright © 2017 Elsevier B.V. All rights reserved.

Thermophysics Characterization of Kerosene Combustion

NASA Technical Reports Server (NTRS)

Wang, Ten-See

2000-01-01

A one-formula surrogate fuel formulation and its quasi-global combustion kinetics model are developed to support the design of injectors and thrust chambers of kerosene-fueled rocket engines. This surrogate fuel model depicts a fuel blend that properly represents the general physical and chemical properties of kerosene. The accompanying gaseous-phase thermodynamics of the surrogate fuel is anchored with the heat of formation of kerosene and verified by comparing a series of one-dimensional rocket thrust chamber calculations. The quasi-global combustion kinetics model consists of several global steps for parent fuel decomposition, soot formation, and soot oxidation, and a detailed wet-CO mechanism. The final thermophysics formulations are incorporated with a computational fluid dynamics model for prediction of the combustor efficiency of an uni-element, tri-propellant combustor and the radiation of a kerosene-fueled thruster plume. The model predictions agreed reasonably well with those of the tests.

Thermophysics Characterization of Kerosene Combustion

NASA Technical Reports Server (NTRS)

Wang, Ten-See

2001-01-01

A one-formula surrogate fuel formulation and its quasi-global combustion kinetics model are developed to support the design of injectors and thrust chambers of kerosene-fueled rocket engines. This surrogate fuel model depicts a fuel blend that properly represents the general physical and chemical properties of kerosene. The accompanying gaseous-phase thermodynamics of the surrogate fuel is anchored with the heat of formation of kerosene and verified by comparing a series of one-dimensional rocket thrust chamber calculations. The quasi-global combustion kinetics model consists of several global steps for parent fuel decomposition, soot formation, and soot oxidation and a detailed wet-CO mechanism to complete the combustion process. The final thermophysics formulations are incorporated with a computational fluid dynamics model for prediction of the combustion efficiency of an unielement, tripropellant combustor and the radiation of a kerosene-fueled thruster plume. The model predictions agreed reasonably well with those of the tests.

Exposure assessment of mobile phone base station radiation in an outdoor environment using sequential surrogate modeling.

PubMed

Aerts, Sam; Deschrijver, Dirk; Joseph, Wout; Verloock, Leen; Goeminne, Francis; Martens, Luc; Dhaene, Tom

2013-05-01

Human exposure to background radiofrequency electromagnetic fields (RF-EMF) has been increasing with the introduction of new technologies. There is a definite need for the quantification of RF-EMF exposure but a robust exposure assessment is not yet possible, mainly due to the lack of a fast and efficient measurement procedure. In this article, a new procedure is proposed for accurately mapping the exposure to base station radiation in an outdoor environment based on surrogate modeling and sequential design, an entirely new approach in the domain of dosimetry for human RF exposure. We tested our procedure in an urban area of about 0.04 km(2) for Global System for Mobile Communications (GSM) technology at 900 MHz (GSM900) using a personal exposimeter. Fifty measurement locations were sufficient to obtain a coarse street exposure map, locating regions of high and low exposure; 70 measurement locations were sufficient to characterize the electric field distribution in the area and build an accurate predictive interpolation model. Hence, accurate GSM900 downlink outdoor exposure maps (for use in, e.g., governmental risk communication and epidemiological studies) are developed by combining the proven efficiency of sequential design with the speed of exposimeter measurements and their ease of handling. Copyright © 2013 Wiley Periodicals, Inc.

Comparison between Surrogate Indexes of Insulin Sensitivity/Resistance and Hyperinsulinemic Euglycemic Glucose Clamps in Rhesus Monkeys

PubMed Central

Lee, Ho-Won; Muniyappa, Ranganath; Yan, Xu; Yue, Lilly Q.; Linden, Ellen H.; Chen, Hui; Hansen, Barbara C.

2011-01-01

The euglycemic glucose clamp is the reference method for assessing insulin sensitivity in humans and animals. However, clamps are ill-suited for large studies because of extensive requirements for cost, time, labor, and technical expertise. Simple surrogate indexes of insulin sensitivity/resistance including quantitative insulin-sensitivity check index (QUICKI) and homeostasis model assessment (HOMA) have been developed and validated in humans. However, validation studies of QUICKI and HOMA in both rats and mice suggest that differences in metabolic physiology between rodents and humans limit their value in rodents. Rhesus monkeys are a species more similar to humans than rodents. Therefore, in the present study, we evaluated data from 199 glucose clamp studies obtained from a large cohort of 86 monkeys with a broad range of insulin sensitivity. Data were used to evaluate simple surrogate indexes of insulin sensitivity/resistance (QUICKI, HOMA, Log HOMA, 1/HOMA, and 1/Fasting insulin) with respect to linear regression, predictive accuracy using a calibration model, and diagnostic performance using receiver operating characteristic. Most surrogates had modest linear correlations with SIClamp (r ≈ 0.4–0.64) with comparable correlation coefficients. Predictive accuracy determined by calibration model analysis demonstrated better predictive accuracy of QUICKI than HOMA and Log HOMA. Receiver operating characteristic analysis showed equivalent sensitivity and specificity of most surrogate indexes to detect insulin resistance. Thus, unlike in rodents but similar to humans, surrogate indexes of insulin sensitivity/resistance including QUICKI and log HOMA may be reasonable to use in large studies of rhesus monkeys where it may be impractical to conduct glucose clamp studies. PMID:21209021

Synthetic thrombus model for in vitro studies of laser thrombolysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hermes, R.E.; Trajkovska, K.

1998-07-01

Laser thrombolysis is the controlled ablation of a thrombus (blood clot) blockage in a living arterial system. Theoretical modeling of the interaction of laser light with thrombi relies on the ability to perform in vitro experiments with well characterized surrogate materials. A synthetic thrombus formulation may offer more accurate results when compared to in vivo clinical experiments. The authors describe the development of new surrogate materials based on formulations incorporating chick egg, guar gum, modified food starch, and a laser light absorbing dye. The sound speed and physical consistency of the materials were very close to porcine (arterial) and humanmore » (venous) thrombi. Photographic and videotape recordings of pulsed dye laser ablation experiments under various experimental conditions were used to evaluate the new material as compared to in vitro tests with human (venous) thrombus. The characteristics of ablation and mass removal were similar to that of real thrombi, and therefore provide a more realistic model for in vitro laser thrombolysis when compared to gelatin.« less

Probabilistic safety analysis for urgent situations following the accidental release of a pollutant in the atmosphere

NASA Astrophysics Data System (ADS)

Armand, P.; Brocheton, F.; Poulet, D.; Vendel, F.; Dubourg, V.; Yalamas, T.

2014-10-01

This paper is an original contribution to uncertainty quantification in atmospheric transport & dispersion (AT&D) at the local scale (1-10 km). It is proposed to account for the imprecise knowledge of the meteorological and release conditions in the case of an accidental hazardous atmospheric emission. The aim is to produce probabilistic risk maps instead of a deterministic toxic load map in order to help the stakeholders making their decisions. Due to the urge attached to such situations, the proposed methodology is able to produce such maps in a limited amount of time. It resorts to a Lagrangian particle dispersion model (LPDM) using wind fields interpolated from a pre-established database that collects the results from a computational fluid dynamics (CFD) model. This enables a decoupling of the CFD simulations from the dispersion analysis, thus a considerable saving of computational time. In order to make the Monte-Carlo-sampling-based estimation of the probability field even faster, it is also proposed to recourse to the use of a vector Gaussian process surrogate model together with high performance computing (HPC) resources. The Gaussian process (GP) surrogate modelling technique is coupled with a probabilistic principal component analysis (PCA) for reducing the number of GP predictors to fit, store and predict. The design of experiments (DOE) from which the surrogate model is built, is run over a cluster of PCs for making the total production time as short as possible. The use of GP predictors is validated by comparing the results produced by this technique with those obtained by crude Monte Carlo sampling.

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. |

Responding to surrogate requests that seem inconsistent with a patient's living will.

PubMed

Vig, Elizabeth K; Sudore, Rebecca L; Berg, Karina M; Fromme, Erik K; Arnold, Robert M

2011-11-01

Clinicians may feel conflicted when a patient's legal decision maker is making decisions that seem inconsistent with a patient's living will. We provide evidence-based information to help clinicians consider whether a surrogate's inconsistent decisions are ethically appropriate. Surrogates are not flawless translators of their loved one's preferences; they are influenced by their own hopes and the current clinical context. Patients may be aware of this, are often concerned about burdening their loved ones, and often grant their surrogates leeway in interpreting their wishes. When appropriate, clinicians should respect surrogates' interpretations of patient values and take steps to decrease surrogate stress during the decision-making process. Finally, if clinicians are cognizant of their own values and preferences, they may recognize how these may affect their responses to certain clinical cases. Copyright © 2011 U.S. Cancer Pain Relief Committee. All rights reserved.

The Surgeon Volume-outcome Relationship: Not Yet Ready for Policy.

PubMed

Modrall, J Gregory; Minter, Rebecca M; Minhajuddin, Abu; Eslava-Schmalbach, Javier; Joshi, Girish P; Patel, Shivani; Rosero, Eric B

2018-05-01

Increasing surgeon volume may improve outcomes for index operations. We hypothesized that there may be surrogate operative experiences that yield similar outcomes for surgeons with a low-volume experience with a specific index operation, such as esophagectomy. The relationship between surgeon volume and outcomes has potential implications for credentialing of surgeons. Restrictions of privileges based on surgeon volume are only reasonable if there is no substitute for direct experience with the index operation. This study was aimed at determining whether there are valid surrogates for direct experience with a sample index operation-open esophagectomy. The Nationwide Inpatient Sample (2003-2009) was utilized. Surgeons were stratified into low and high-volume groups based on annual volume of esophagectomy. Surrogate volume was defined as the aggregate annual volume per surgeon of upper gastrointestinal operations including excision of esophageal diverticulum, gastrectomy, gastroduodenectomy, and repair of diaphragmatic hernia. In all, 26,795 esophagectomies were performed nationwide (2003-2009), with a crude inhospital mortality rate of 5.2%. Inhospital mortality decreased with increasing volume of esophagectomies performed annually: 7.7% and 3.8% for low and high-volume surgeons, respectively (P < 0.0001). Among surgeons with a low-volume esophagectomy experience, increasing volume of surrogate operations improved the outcomes observed for esophagectomy: 9.7%, 7.1%, and 4.3% for low, medium, and high-surrogate-volume surgeons, respectively (P = 0.016). Both operation-specific volume and surrogate volume are significant predictors of inhospital mortality for esophagectomy. Based on these observations, it would be premature to limit hospital privileges based solely on operation-specific surgeon volume criteria.

EVALUATION OF MURINE NOROVIRUS, FELINE CALICIVIRUS, POLIOVIRUS, AND MS2 AS SURROGATES FOR HUMAN NOROVIRUS IN a Model of Viral Persistence in SURFACE Water AND GROUNDWATER

EPA Science Inventory

Human noroviruses (NoV) are a significant cause of non bacterial gastroenteritis worldwide with contaminated drinking water a potential transmission route. The absence of a cell culture infectivity model for NoV necessitates the use of molecular methods and/or viral surrogate mod...

Comparison of task-based exposure metrics for an epidemiologic study of isocyanate inhalation exposures among autobody shop workers.

PubMed

Woskie, Susan R; Bello, Dhimiter; Gore, Rebecca J; Stowe, Meredith H; Eisen, Ellen A; Liu, Youcheng; Sparer, Judy A; Redlich, Carrie A; Cullen, Mark R

2008-09-01

Because many occupational epidemiologic studies use exposure surrogates rather than quantitative exposure metrics, the UMass Lowell and Yale study of autobody shop workers provided an opportunity to evaluate the relative utility of surrogates and quantitative exposure metrics in an exposure response analysis of cross-week change in respiratory function. A task-based exposure assessment was used to develop several metrics of inhalation exposure to isocyanates. The metrics included the surrogates, job title, counts of spray painting events during the day, counts of spray and bystander exposure events, and a quantitative exposure metric that incorporated exposure determinant models based on task sampling and a personal workplace protection factor for respirator use, combined with a daily task checklist. The result of the quantitative exposure algorithm was an estimate of the daily time-weighted average respirator-corrected total NCO exposure (microg/m(3)). In general, these four metrics were found to be variable in agreement using measures such as weighted kappa and Spearman correlation. A logistic model for 10% drop in FEV(1) from Monday morning to Thursday morning was used to evaluate the utility of each exposure metric. The quantitative exposure metric was the most favorable, producing the best model fit, as well as the greatest strength and magnitude of association. This finding supports the reports of others that reducing exposure misclassification can improve risk estimates that otherwise would be biased toward the null. Although detailed and quantitative exposure assessment can be more time consuming and costly, it can improve exposure-disease evaluations and is more useful for risk assessment purposes. The task-based exposure modeling method successfully produced estimates of daily time-weighted average exposures in the complex and changing autobody shop work environment. The ambient TWA exposures of all of the office workers and technicians and 57% of the painters were found to be below the current U.K. Health and Safety Executive occupational exposure limit (OEL) for total NCO of 20 microg/m(3). When respirator use was incorporated, all personal daily exposures were below the U.K. OEL.

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 modification analysis, and is closely connected to the treatment marker selection problem. The results are illustrated with application to a vaccine efficacy trial, where ACN and ACS for an antibody marker are found to be consistent with the data and hence support the Prentice definition and consistency.

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 effect modification analysis, and is closely connected to the treatment marker selection problem. The results are illustrated with application to a vaccine efficacy trial, where ACN and ACS for an antibody marker are found to be consistent with the data and hence support the Prentice definition and consistency. PMID:26722639

Surrogate-driven deformable motion model for organ motion tracking in particle radiation therapy

NASA Astrophysics Data System (ADS)

Fassi, Aurora; Seregni, Matteo; Riboldi, Marco; Cerveri, Pietro; Sarrut, David; Battista Ivaldi, Giovanni; Tabarelli de Fatis, Paola; Liotta, Marco; Baroni, Guido

2015-02-01

The aim of this study is the development and experimental testing of a tumor tracking method for particle radiation therapy, providing the daily respiratory dynamics of the patient’s thoraco-abdominal anatomy as a function of an external surface surrogate combined with an a priori motion model. The proposed tracking approach is based on a patient-specific breathing motion model, estimated from the four-dimensional (4D) planning computed tomography (CT) through deformable image registration. The model is adapted to the interfraction baseline variations in the patient’s anatomical configuration. The driving amplitude and phase parameters are obtained intrafractionally from a respiratory surrogate signal derived from the external surface displacement. The developed technique was assessed on a dataset of seven lung cancer patients, who underwent two repeated 4D CT scans. The first 4D CT was used to build the respiratory motion model, which was tested on the second scan. The geometric accuracy in localizing lung lesions, mediated over all breathing phases, ranged between 0.6 and 1.7 mm across all patients. Errors in tracking the surrounding organs at risk, such as lungs, trachea and esophagus, were lower than 1.3 mm on average. The median absolute variation in water equivalent path length (WEL) within the target volume did not exceed 1.9 mm-WEL for simulated particle beams. A significant improvement was achieved compared with error compensation based on standard rigid alignment. The present work can be regarded as a feasibility study for the potential extension of tumor tracking techniques in particle treatments. Differently from current tracking methods applied in conventional radiotherapy, the proposed approach allows for the dynamic localization of all anatomical structures scanned in the planning CT, thus providing complete information on density and WEL variations required for particle beam range adaptation.

Topology of correlation-based minimal spanning trees in real and model markets

NASA Astrophysics Data System (ADS)

Bonanno, Giovanni; Caldarelli, Guido; Lillo, Fabrizio; Mantegna, Rosario N.

2003-10-01

We compare the topological properties of the minimal spanning tree obtained from a large group of stocks traded at the New York Stock Exchange during a 12-year trading period with the one obtained from surrogated data simulated by using simple market models. We find that the empirical tree has features of a complex network that cannot be reproduced, even as a first approximation, by a random market model and by the widespread one-factor model.

A methodology to enable rapid evaluation of aviation environmental impacts and aircraft technologies

NASA Astrophysics Data System (ADS)

Becker, Keith Frederick

Commercial aviation has become an integral part of modern society and enables unprecedented global connectivity by increasing rapid business, cultural, and personal connectivity. In the decades following World War II, passenger travel through commercial aviation quickly grew at a rate of roughly 8% per year globally. The FAA's most recent Terminal Area Forecast predicts growth to continue at a rate of 2.5% domestically, and the market outlooks produced by Airbus and Boeing generally predict growth to continue at a rate of 5% per year globally over the next several decades, which translates into a need for up to 30,000 new aircraft produced by 2025. With such large numbers of new aircraft potentially entering service, any negative consequences of commercial aviation must undergo examination and mitigation by governing bodies so that growth may still be achieved. Options to simultaneously grow while reducing environmental impact include evolution of the commercial fleet through changes in operations, aircraft mix, and technology adoption. Methods to rapidly evaluate fleet environmental metrics are needed to enable decision makers to quickly compare the impact of different scenarios and weigh the impact of multiple policy options. As the fleet evolves, interdependencies may emerge in the form of tradeoffs between improvements in different environmental metrics as new technologies are brought into service. In order to include the impacts of these interdependencies on fleet evolution, physics-based modeling is required at the appropriate level of fidelity. Evaluation of environmental metrics in a physics-based manner can be done at the individual aircraft level, but will then not capture aggregate fleet metrics. Contrastingly, evaluation of environmental metrics at the fleet level is already being done for aircraft in the commercial fleet, but current tools and approaches require enhancement because they currently capture technology implementation through post-processing, which does not capture physical interdependencies that may arise at the aircraft-level. The goal of the work that has been conducted here was the development of a methodology to develop surrogate fleet approaches that leverage the capability of physics-based aircraft models and the development of connectivity to fleet-level analysis tools to enable rapid evaluation of fuel burn and emissions metrics. Instead of requiring development of an individual physics-based model for each vehicle in the fleet, the surrogate fleet approaches seek to reduce the number of such models needed while still accurately capturing performance of the fleet. By reducing the number of models, both development time and execution time to generate fleet-level results may also be reduced. The initial steps leading to surrogate fleet formulation were a characterization of the commercial fleet into groups based on capability followed by the selection of a reference vehicle model and a reference set of operations for each group. Next, three potential surrogate fleet approaches were formulated. These approaches include the parametric correction factor approach, in which the results of a reference vehicle model are corrected to match the aggregate results of each group; the average replacement approach, in which a new vehicle model is developed to generate aggregate results of each group, and the best-in-class replacement approach, in which results for a reference vehicle are simply substituted for the entire group. Once candidate surrogate fleet approaches were developed, they were each applied to and evaluated over the set of reference operations. Then each approach was evaluated for their ability to model variations in operations. Finally, the ability of each surrogate fleet approach to capture implementation of different technology suites along with corresponding interdependencies between fuel burn and emissions was evaluated using the concept of a virtual fleet to simulate the technology response of multiple aircraft families. The results of experimentation led to a down selection to the best approach to use to rapidly characterize the performance of the commercial fleet for accurately in the context of acceptability of current fleet evaluation methods. The parametric correction factor and average replacement approaches were shown to be successful in capturing reference fleet results as well as fleet performance with variations in operations. The best-in-class replacement approach was shown to be unacceptable as a model for the larger fleet in each of the scenarios tested. Finally, the average replacement approach was the only one that was successful in capturing the impact of technologies on a larger fleet. These results are meaningful because they show that it is possible to calculate the fuel burn and emissions of a larger fleet with a reduced number of physics-based models within acceptable bounds of accuracy. At the same time, the physics-based modeling also provides the ability to evaluate the impact of technologies on fleet-level fuel burn and emissions metrics. The value of such a capability is that multiple future fleet scenarios involving changes in both aircraft operations and technology levels may now be rapidly evaluated to inform and equip policy makers of the implications of impacts of changes on fleet-level metrics.

Sequential Designs Based on Bayesian Uncertainty Quantification in Sparse Representation Surrogate Modeling

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chen, Ray -Bing; Wang, Weichung; Jeff Wu, C. F.

A numerical method, called OBSM, was recently proposed which employs overcomplete basis functions to achieve sparse representations. While the method can handle non-stationary response without the need of inverting large covariance matrices, it lacks the capability to quantify uncertainty in predictions. We address this issue by proposing a Bayesian approach which first imposes a normal prior on the large space of linear coefficients, then applies the MCMC algorithm to generate posterior samples for predictions. From these samples, Bayesian credible intervals can then be obtained to assess prediction uncertainty. A key application for the proposed method is the efficient construction ofmore » sequential designs. Several sequential design procedures with different infill criteria are proposed based on the generated posterior samples. As a result, numerical studies show that the proposed schemes are capable of solving problems of positive point identification, optimization, and surrogate fitting.« less

Sequential Designs Based on Bayesian Uncertainty Quantification in Sparse Representation Surrogate Modeling

DOE PAGES

Chen, Ray -Bing; Wang, Weichung; Jeff Wu, C. F.

2017-04-12

A numerical method, called OBSM, was recently proposed which employs overcomplete basis functions to achieve sparse representations. While the method can handle non-stationary response without the need of inverting large covariance matrices, it lacks the capability to quantify uncertainty in predictions. We address this issue by proposing a Bayesian approach which first imposes a normal prior on the large space of linear coefficients, then applies the MCMC algorithm to generate posterior samples for predictions. From these samples, Bayesian credible intervals can then be obtained to assess prediction uncertainty. A key application for the proposed method is the efficient construction ofmore » sequential designs. Several sequential design procedures with different infill criteria are proposed based on the generated posterior samples. As a result, numerical studies show that the proposed schemes are capable of solving problems of positive point identification, optimization, and surrogate fitting.« less

Surrogate endpoints in oncology: when are they acceptable for regulatory and clinical decisions, and are they currently overused?

PubMed

Kemp, Robert; Prasad, Vinay

2017-07-21

Surrogate outcomes are not intrinsically beneficial to patients, but are designed to be easier and faster to measure than clinically meaningful outcomes. The use of surrogates as an endpoint in clinical trials and basis for regulatory approval is common, and frequently exceeds the guidance given by regulatory bodies. In this article, we demonstrate that the use of surrogates in oncology is widespread and increasing. At the same time, the strength of association between the surrogates used and clinically meaningful outcomes is often unknown or weak. Attempts to validate surrogates are rarely undertaken. When this is done, validation relies on only a fraction of available data, and often concludes that the surrogate is poor. Post-marketing studies, designed to ensure drugs have meaningful benefits, are often not performed. Alternatively, if a drug fails to improve quality of life or overall survival, market authorization is rarely revoked. We suggest this reliance on surrogates, and the imprecision surrounding their acceptable use, means that numerous drugs are now approved based on small yet statistically significant increases in surrogates of questionable reliability. In turn, this means the benefits of many approved drugs are uncertain. This is an unacceptable situation for patients and professionals, as prior experience has shown that such uncertainty can be associated with significant harm. The use of surrogate outcomes should be limited to situations where a surrogate has demonstrated robust ability to predict meaningful benefits, or where cases are dire, rare or with few treatment options. In both cases, surrogates must be used only when continuing studies examining hard endpoints have been fully recruited.

New geometric design consistency model based on operating speed profiles for road safety evaluation.

PubMed

Camacho-Torregrosa, Francisco J; Pérez-Zuriaga, Ana M; Campoy-Ungría, J Manuel; García-García, Alfredo

2013-12-01

To assist in the on-going effort to reduce road fatalities as much as possible, this paper presents a new methodology to evaluate road safety in both the design and redesign stages of two-lane rural highways. This methodology is based on the analysis of road geometric design consistency, a value which will be a surrogate measure of the safety level of the two-lane rural road segment. The consistency model presented in this paper is based on the consideration of continuous operating speed profiles. The models used for their construction were obtained by using an innovative GPS-data collection method that is based on continuous operating speed profiles recorded from individual drivers. This new methodology allowed the researchers to observe the actual behavior of drivers and to develop more accurate operating speed models than was previously possible with spot-speed data collection, thereby enabling a more accurate approximation to the real phenomenon and thus a better consistency measurement. Operating speed profiles were built for 33 Spanish two-lane rural road segments, and several consistency measurements based on the global and local operating speed were checked. The final consistency model takes into account not only the global dispersion of the operating speed, but also some indexes that consider both local speed decelerations and speeds over posted speeds as well. For the development of the consistency model, the crash frequency for each study site was considered, which allowed estimating the number of crashes on a road segment by means of the calculation of its geometric design consistency. Consequently, the presented consistency evaluation method is a promising innovative tool that can be used as a surrogate measure to estimate the safety of a road segment. Copyright © 2012 Elsevier Ltd. All rights reserved.

Surrogate gas prediction model as a proxy for Δ14C-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 CO 2 (and its associated derived Δ 14 C value) is an ideal tracer for determination of the fossil fuel derived CO 2 enhancement contributing to any atmospheric CO 2 measurement ( C ff ). Given enough such measurements, independent top-down estimation of US fossil fuel-CO 2 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 C ff 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, SF 6 , 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 potentially increase the number of C ff estimates by approximately a factor of three. Provided that these estimates are of comparable quality to Δ 14 C-based estimates, we expect an improved determination of fossil fuel-CO 2 emissions.

Incidence of Changes in Respiration-Induced Tumor Motion and Its Relationship With Respiratory Surrogates During Individual Treatment Fractions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Malinowski, Kathleen; Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, MD; McAvoy, Thomas J.

2012-04-01

Purpose: To determine how frequently (1) tumor motion and (2) the spatial relationship between tumor and respiratory surrogate markers change during a treatment fraction in lung and pancreas cancer patients. Methods and Materials: A Cyberknife Synchrony system radiographically localized the tumor and simultaneously tracked three respiratory surrogate markers fixed to a form-fitting vest. Data in 55 lung and 29 pancreas fractions were divided into successive 10-min blocks. Mean tumor positions and tumor position distributions were compared across 10-min blocks of data. Treatment margins were calculated from both 10 and 30 min of data. Partial least squares (PLS) regression models ofmore » tumor positions as a function of external surrogate marker positions were created from the first 10 min of data in each fraction; the incidence of significant PLS model degradation was used to assess changes in the spatial relationship between tumors and surrogate markers. Results: The absolute change in mean tumor position from first to third 10-min blocks was >5 mm in 13% and 7% of lung and pancreas cases, respectively. Superior-inferior and medial-lateral differences in mean tumor position were significantly associated with the lobe of lung. In 61% and 54% of lung and pancreas fractions, respectively, margins calculated from 30 min of data were larger than margins calculated from 10 min of data. The change in treatment margin magnitude for superior-inferior motion was >1 mm in 42% of lung and 45% of pancreas fractions. Significantly increasing tumor position prediction model error (mean {+-} standard deviation rates of change of 1.6 {+-} 2.5 mm per 10 min) over 30 min indicated tumor-surrogate relationship changes in 63% of fractions. Conclusions: Both tumor motion and the relationship between tumor and respiratory surrogate displacements change in most treatment fractions for patient in-room time of 30 min.« less

A model for emergency department end-of-life communications after acute devastating events--part I: decision-making capacity, surrogates, and advance directives.

PubMed

Limehouse, Walter E; Feeser, V Ramana; Bookman, Kelly J; Derse, Arthur

2012-09-01

Making decisions for a patient affected by sudden devastating illness or injury traumatizes a patient's family and loved ones. Even in the absence of an emergency, surrogates making end-of-life treatment decisions may experience negative emotional effects. Helping surrogates with these end-of-life decisions under emergent conditions requires the emergency physician (EP) to be clear, making medical recommendations with sensitivity. This model for emergency department (ED) end-of-life communications after acute devastating events comprises the following steps: 1) determine the patient's decision-making capacity; 2) identify the legal surrogate; 3) elicit patient values as expressed in completed advance directives; 4) determine patient/surrogate understanding of the life-limiting event and expectant treatment goals; 5) convey physician understanding of the event, including prognosis, treatment options, and recommendation; 6) share decisions regarding withdrawing or withholding of resuscitative efforts, using available resources and considering options for organ donation; and 7) revise treatment goals as needed. Emergency physicians should break bad news compassionately, yet sufficiently, so that surrogate and family understand both the gravity of the situation and the lack of long-term benefit of continued life-sustaining interventions. EPs should also help the surrogate and family understand that palliative care addresses comfort needs of the patient including adequate treatment for pain, dyspnea, or anxiety. Part I of this communications model reviews determination of decision-making capacity, surrogacy laws, and advance directives, including legal definitions and application of these steps; Part II (which will appear in a future issue of AEM) covers communication moving from resuscitative to end-of-life and palliative treatment. EPs should recognize acute devastating illness or injuries, when appropriate, as opportunities to initiate end-of-life discussions and to implement shared decisions. © 2012 by the Society for Academic Emergency Medicine.

Incidence of changes in respiration-induced tumor motion and its relationship with respiratory surrogates during individual treatment fractions.

PubMed

Malinowski, Kathleen; McAvoy, Thomas J; George, Rohini; Dietrich, Sonja; D'Souza, Warren D

2012-04-01

To determine how frequently (1) tumor motion and (2) the spatial relationship between tumor and respiratory surrogate markers change during a treatment fraction in lung and pancreas cancer patients. A Cyberknife Synchrony system radiographically localized the tumor and simultaneously tracked three respiratory surrogate markers fixed to a form-fitting vest. Data in 55 lung and 29 pancreas fractions were divided into successive 10-min blocks. Mean tumor positions and tumor position distributions were compared across 10-min blocks of data. Treatment margins were calculated from both 10 and 30 min of data. Partial least squares (PLS) regression models of tumor positions as a function of external surrogate marker positions were created from the first 10 min of data in each fraction; the incidence of significant PLS model degradation was used to assess changes in the spatial relationship between tumors and surrogate markers. The absolute change in mean tumor position from first to third 10-min blocks was >5 mm in 13% and 7% of lung and pancreas cases, respectively. Superior-inferior and medial-lateral differences in mean tumor position were significantly associated with the lobe of lung. In 61% and 54% of lung and pancreas fractions, respectively, margins calculated from 30 min of data were larger than margins calculated from 10 min of data. The change in treatment margin magnitude for superior-inferior motion was >1 mm in 42% of lung and 45% of pancreas fractions. Significantly increasing tumor position prediction model error (mean ± standard deviation rates of change of 1.6 ± 2.5 mm per 10 min) over 30 min indicated tumor-surrogate relationship changes in 63% of fractions. Both tumor motion and the relationship between tumor and respiratory surrogate displacements change in most treatment fractions for patient in-room time of 30 min. Copyright © 2012. Published by Elsevier Inc.

Bayesian Adaptive Trial Design for a Newly Validated Surrogate Endpoint

PubMed Central

Renfro, Lindsay A.; Carlin, Bradley P.; Sargent, Daniel J.

2011-01-01

Summary The evaluation of surrogate endpoints for primary use in future clinical trials is an increasingly important research area, due to demands for more efficient trials coupled with recent regulatory acceptance of some surrogates as ‘valid.’ However, little consideration has been given to how a trial which utilizes a newly-validated surrogate endpoint as its primary endpoint might be appropriately designed. We propose a novel Bayesian adaptive trial design that allows the new surrogate endpoint to play a dominant role in assessing the effect of an intervention, while remaining realistically cautious about its use. By incorporating multi-trial historical information on the validated relationship between the surrogate and clinical endpoints, then subsequently evaluating accumulating data against this relationship as the new trial progresses, we adaptively guard against an erroneous assessment of treatment based upon a truly invalid surrogate. When the joint outcomes in the new trial seem plausible given similar historical trials, we proceed with the surrogate endpoint as the primary endpoint, and do so adaptively–perhaps stopping the trial for early success or inferiority of the experimental treatment, or for futility. Otherwise, we discard the surrogate and switch adaptive determinations to the original primary endpoint. We use simulation to test the operating characteristics of this new design compared to a standard O’Brien-Fleming approach, as well as the ability of our design to discriminate trustworthy from untrustworthy surrogates in hypothetical future trials. Furthermore, we investigate possible benefits using patient-level data from 18 adjuvant therapy trials in colon cancer, where disease-free survival is considered a newly-validated surrogate endpoint for overall survival. PMID:21838811

Validation of a Functional Pyelocalyceal Renal Model for the Evaluation of Renal Calculi Passage While Riding a Roller Coaster.

PubMed

Mitchell, Marc A; Wartinger, David D

2016-10-01

The identification and evaluation of activities capable of dislodging calyceal renal calculi require a patient surrogate or validated functional pyelocalyceal renal model. To evaluate roller coaster facilitation of calyceal renal calculi passage using a functional pyelocalyceal renal model. A previously described adult ureteroscopy and renoscopy simulator (Ideal Anatomic) was modified and remolded to function as a patient surrogate. Three renal calculi of different sizes from the patient who provided the original computed tomographic urograph on which the simulator was based were used. The renal calculi were suspended in urine in the model and taken for 20 rides on the Big Thunder Mountain Railroad roller coaster at Walt Disney World in Orlando, Florida. The roller coaster rides were analyzed using variables of renal calculi volume, calyceal location, model position on the roller coaster, and renal calculi passage. Sixty renal calculi rides were analyzed. Independent of renal calculi volume and calyceal location, front seating on the roller coaster resulted in a passage rate of 4 of 24. Independent of renal calculi volume and calyceal location, rear seating on the roller coaster resulted in a passage rate of 23 of 36. Independent of renal calculi volume in rear seating, calyceal location differed in passage rates, with an upper calyceal calculi passage rate of 100%; a middle calyceal passage rate of 55.6%; and a lower calyceal passage rate of 40.0%. The functional pyelocalyceal renal model serves as a functional patient surrogate to evaluate activities that facilitate calyceal renal calculi passage. The rear seating position on the roller coaster led to the most renal calculi passages.

Biomarkers and Surrogate Markers: An FDA Perspective

PubMed Central

Katz, Russell

2004-01-01

Summary: Interest is increasing rapidly in the use of surrogate markers as primary measures of the effectiveness of investigational drugs in definitive drug trials. Many such surrogate markers have been proposed as potential candidates for use in definitive effectiveness trials of agents to treat neurologic or psychiatric disease, but as of this date, there are no such markers that have been adequately “validated,” that is, shown to predict the effect of the treatment on the clinical outcome of interest. While the current law and regulations permit the United States Food and Drug Administration to base the approval of a drug product on a determination the effect of the drug on an unvalidated surrogate marker (that is, one for which it is not known that an effect on the surrogate actually predicts the desired clinical benefit), there are a number of difficulties in interpreting trials that use surrogate markers as primary measures of drug effect. In this article, the relevant regulatory context will be discussed, as well as the epistemological problems related to the interpretation of clinical trials in which unvalidated surrogate markers are used as primary outcomes. PMID:15717019

The Different Moral Bases of Patient and Surrogate Decision-Making.

PubMed

Brudney, Daniel

2018-01-01

My topic is a problem with our practice of surrogate decision-making in health care, namely, the problem of the surrogate who is not doing her job-the surrogate who cannot be reached or the surrogate who seems to refuse to understand or to be unable to understand the clinical situation. The analysis raises a question about the surrogate who simply disagrees with the medical team. One might think that such a surrogate is doing her job-the team just doesn't like how she is doing it. My analysis raises the question of whether (or perhaps when) she should be overridden. In approaching this problem, I focus not on the range of difficulties in practice but on the underlying moral conceptual issue. My concern will be to show that the moral values that underpin patient decision-making are fundamentally different from those that underpin surrogate decision-making. Identifying the distinctions will set parameters for any successful solution to the "Who should decide?" A patient has a specific kind of moral right to make her own medical decisions. A surrogate has no analogous moral right to decide for someone else. We want the surrogate to make the decision because we believe that she has a relevant epistemological advantage over anyone else on the scene. If and when she has no such advantage or if she refuses or is unable to use it, then there might not be sufficient reason to let her be the decision-maker. © 2018 The Hastings Center.

The Use of Surrogate Endpoints in Regulating Medicines for Cardio-Renal Disease: Opinions of Stakeholders

PubMed Central

Schievink, Bauke; Lambers Heerspink, Hiddo; Leufkens, Hubert; De Zeeuw, Dick; Hoekman, Jarno

2014-01-01

Aim There is discussion whether medicines can be authorized on the market based on evidence from surrogate endpoints. We assessed opinions of different stakeholders on this topic. Methods We conducted an online questionnaire that targeted various stakeholder groups (regulatory agencies, pharmaceutical industry, academia, relevant public sector organisations) and medical specialties (cardiology or nephrology vs. other). Participants were enrolled through purposeful sampling. We inquired for conditions under which surrogate endpoints can be used, the validity of various cardio-renal biomarkers and new approaches for biomarker use. Results Participants agreed that surrogate endpoints can be used when the surrogate is scientifically valid (5-point Likert response format, mean score: 4.3, SD: 0.9) or when there is an unmet clinical need (mean score: 3.8, SD: 1.2). Industry participants agreed to a greater extent than regulators and academics. However, out of four proposed surrogates (blood pressure (BP), HbA1c, albuminuria, CRP) for cardiovascular outcomes or end-stage renal disease, only use of BP for cardiovascular outcomes was deemed moderately accurate (mean: 3.6, SD: 1.1). Specialists in cardiology or nephrology tended to be more positive about the use of surrogate endpoints. Conclusion Stakeholders in drug development do not oppose to the use of surrogate endpoints in drug marketing authorization, but most surrogates are not considered valid. To solve this impasse, increased efforts are required to validate surrogate endpoints and to explore alternative ways to use them. PMID:25268242

The use of surrogate endpoints in regulating medicines for cardio-renal disease: opinions of stakeholders.

PubMed

Schievink, Bauke; Lambers Heerspink, Hiddo; Leufkens, Hubert; De Zeeuw, Dick; Hoekman, Jarno

2014-01-01

There is discussion whether medicines can be authorized on the market based on evidence from surrogate endpoints. We assessed opinions of different stakeholders on this topic. We conducted an online questionnaire that targeted various stakeholder groups (regulatory agencies, pharmaceutical industry, academia, relevant public sector organisations) and medical specialties (cardiology or nephrology vs. other). Participants were enrolled through purposeful sampling. We inquired for conditions under which surrogate endpoints can be used, the validity of various cardio-renal biomarkers and new approaches for biomarker use. Participants agreed that surrogate endpoints can be used when the surrogate is scientifically valid (5-point Likert response format, mean score: 4.3, SD: 0.9) or when there is an unmet clinical need (mean score: 3.8, SD: 1.2). Industry participants agreed to a greater extent than regulators and academics. However, out of four proposed surrogates (blood pressure (BP), HbA1c, albuminuria, CRP) for cardiovascular outcomes or end-stage renal disease, only use of BP for cardiovascular outcomes was deemed moderately accurate (mean: 3.6, SD: 1.1). Specialists in cardiology or nephrology tended to be more positive about the use of surrogate endpoints. Stakeholders in drug development do not oppose to the use of surrogate endpoints in drug marketing authorization, but most surrogates are not considered valid. To solve this impasse, increased efforts are required to validate surrogate endpoints and to explore alternative ways to use them.

76 FR 1620 - Trials to Verify and Describe Clinical Benefit of Midodrine Hydrochloride; Establishment of...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-01-11

... a surrogate endpoint that is reasonably likely to predict clinical benefit or based on a clinical endpoint other than survival or irreversible morbidity. Approval of PROAMATINE was based on trials... surrogate endpoints are ``subject to the requirement that the applicant study the drug further to verify and...

Air pollution exposure modeling of individuals

EPA Science Inventory

Air pollution epidemiology studies of ambient fine particulate matter (PM2.5) often use outdoor concentrations as exposure surrogates. These surrogates can induce exposure error since they do not account for (1) time spent indoors with ambient PM2.5 levels attenuated from outdoor...

Chance-constrained multi-objective optimization of groundwater remediation design at DNAPLs-contaminated sites using a multi-algorithm genetically adaptive method

NASA Astrophysics Data System (ADS)

Ouyang, Qi; Lu, Wenxi; Hou, Zeyu; Zhang, Yu; Li, Shuai; Luo, Jiannan

2017-05-01

In this paper, a multi-algorithm genetically adaptive multi-objective (AMALGAM) method is proposed as a multi-objective optimization solver. It was implemented in the multi-objective optimization of a groundwater remediation design at sites contaminated by dense non-aqueous phase liquids. In this study, there were two objectives: minimization of the total remediation cost, and minimization of the remediation time. A non-dominated sorting genetic algorithm II (NSGA-II) was adopted to compare with the proposed method. For efficiency, the time-consuming surfactant-enhanced aquifer remediation simulation model was replaced by a surrogate model constructed by a multi-gene genetic programming (MGGP) technique. Similarly, two other surrogate modeling methods-support vector regression (SVR) and Kriging (KRG)-were employed to make comparisons with MGGP. In addition, the surrogate-modeling uncertainty was incorporated in the optimization model by chance-constrained programming (CCP). The results showed that, for the problem considered in this study, (1) the solutions obtained by AMALGAM incurred less remediation cost and required less time than those of NSGA-II, indicating that AMALGAM outperformed NSGA-II. It was additionally shown that (2) the MGGP surrogate model was more accurate than SVR and KRG; and (3) the remediation cost and time increased with the confidence level, which can enable decision makers to make a suitable choice by considering the given budget, remediation time, and reliability.

A Multi-Fidelity Surrogate Model for the Equation of State for Mixtures of Real Gases

NASA Astrophysics Data System (ADS)

Ouellet, Frederick; Park, Chanyoung; Koneru, Rahul; Balachandar, S.; Rollin, Bertrand

2017-11-01

The explosive dispersal of particles is a complex multiphase and multi-species fluid flow problem. In these flows, the products of detonated explosives must be treated as real gases while the ideal gas equation of state is used for the ambient air. As the products expand outward, they mix with the air and create a region where both state equations must be satisfied. One of the most accurate, yet expensive, methods to handle this problem is an algorithm that iterates between both state equations until both pressure and thermal equilibrium are achieved inside of each computational cell. This work creates a multi-fidelity surrogate model to replace this process. This is achieved by using a Kriging model to produce a curve fit which interpolates selected data from the iterative algorithm. The surrogate is optimized for computing speed and model accuracy by varying the number of sampling points chosen to construct the model. The performance of the surrogate with respect to the iterative method is tested in simulations using a finite volume code. The model's computational speed and accuracy are analyzed to show the benefits of this novel approach. This work was supported by the U.S. Department of Energy, National Nuclear Security Administration, Advanced Simulation and Computing Program, as a Cooperative Agreement under the Predictive Science Academic Alliance Program, under Contract No. DE-NA00023.

Gradient-based optimization with B-splines on sparse grids for solving forward-dynamics simulations of three-dimensional, continuum-mechanical musculoskeletal system models.

PubMed

Valentin, J; Sprenger, M; Pflüger, D; Röhrle, O

2018-05-01

Investigating the interplay between muscular activity and motion is the basis to improve our understanding of healthy or diseased musculoskeletal systems. To be able to analyze the musculoskeletal systems, computational models are used. Albeit some severe modeling assumptions, almost all existing musculoskeletal system simulations appeal to multibody simulation frameworks. Although continuum-mechanical musculoskeletal system models can compensate for some of these limitations, they are essentially not considered because of their computational complexity and cost. The proposed framework is the first activation-driven musculoskeletal system model, in which the exerted skeletal muscle forces are computed using 3-dimensional, continuum-mechanical skeletal muscle models and in which muscle activations are determined based on a constraint optimization problem. Numerical feasibility is achieved by computing sparse grid surrogates with hierarchical B-splines, and adaptive sparse grid refinement further reduces the computational effort. The choice of B-splines allows the use of all existing gradient-based optimization techniques without further numerical approximation. This paper demonstrates that the resulting surrogates have low relative errors (less than 0.76%) and can be used within forward simulations that are subject to constraint optimization. To demonstrate this, we set up several different test scenarios in which an upper limb model consisting of the elbow joint, the biceps and triceps brachii, and an external load is subjected to different optimization criteria. Even though this novel method has only been demonstrated for a 2-muscle system, it can easily be extended to musculoskeletal systems with 3 or more muscles. Copyright © 2018 John Wiley & Sons, Ltd.

Improvements to surrogate data methods for nonstationary time series.

PubMed

Lucio, J H; Valdés, R; Rodríguez, L R

2012-05-01

The method of surrogate data has been extensively applied to hypothesis testing of system linearity, when only one realization of the system, a time series, is known. Normally, surrogate data should preserve the linear stochastic structure and the amplitude distribution of the original series. Classical surrogate data methods (such as random permutation, amplitude adjusted Fourier transform, or iterative amplitude adjusted Fourier transform) are successful at preserving one or both of these features in stationary cases. However, they always produce stationary surrogates, hence existing nonstationarity could be interpreted as dynamic nonlinearity. Certain modifications have been proposed that additionally preserve some nonstationarity, at the expense of reproducing a great deal of nonlinearity. However, even those methods generally fail to preserve the trend (i.e., global nonstationarity in the mean) of the original series. This is the case of time series with unit roots in their autoregressive structure. Additionally, those methods, based on Fourier transform, either need first and last values in the original series to match, or they need to select a piece of the original series with matching ends. These conditions are often inapplicable and the resulting surrogates are adversely affected by the well-known artefact problem. In this study, we propose a simple technique that, applied within existing Fourier-transform-based methods, generates surrogate data that jointly preserve the aforementioned characteristics of the original series, including (even strong) trends. Moreover, our technique avoids the negative effects of end mismatch. Several artificial and real, stationary and nonstationary, linear and nonlinear time series are examined, in order to demonstrate the advantages of the methods. Corresponding surrogate data are produced with the classical and with the proposed methods, and the results are compared.

The effect of emotion and physician communication behaviors on surrogates' life-sustaining treatment decisions: a randomized simulation experiment.

PubMed

Barnato, Amber E; Arnold, Robert M

2013-07-01

Surrogate decision makers for critically ill patients experience strong negative emotional states. Emotions influence risk perception, risk preferences, and decision making. We sought to explore the effect of emotional state and physician communication behaviors on surrogates' life-sustaining treatment decisions. 5 × 2 between-subject randomized factorial experiment. Web-based simulated interactive video meeting with an intensivist to discuss code status. Community-based participants 35 and older who self-identified as the surrogate for a parent or spouse recruited from eight U.S. cities through public advertisements. Block random assignment to emotion arousal manipulation and each of the four physician communication behaviors. Surrogate's code status decision (cardiopulmonary resuscitation vs do not resuscitate/allow natural death). Two hundred fifty-six of 373 respondents (69%) logged-in and were randomized: average age was 50; 70% were surrogates for a parent; 63.5% were women; 76% were white, 11% black, and 9% Asian; and 81% were college educated. When asked about code status, 56% chose cardiopulmonary resuscitation. The emotion arousal manipulation increased the score on depression-dejection scale (β = 1.76 [0.58 - 2.94]) but did not influence cardiopulmonary resuscitation choice. Physician attending to emotion and framing the decision as the patient's rather than the surrogate's did not influence cardiopulmonary resuscitation choice. Framing no cardiopulmonary resuscitation as the norm rather than cardiopulmonary resuscitation resulted in fewer surrogates choosing cardiopulmonary resuscitation (48% vs 64%, odds ratio, 0.52 [95% CI, 0.32-0.87]), as did framing the alternative to cardiopulmonary resuscitation as "allow natural death" rather than do not resuscitate (49% vs 61%, odds ratio, 0.58 [95% CI, 0.35-0.96]). Experimentally induced emotional state did not influence code status decisions, although small changes in physician communication behaviors substantially influenced this decision.

A unified framework for the evaluation of surrogate endpoints in mental-health clinical trials.

PubMed

Molenberghs, Geert; Burzykowski, Tomasz; Alonso, Ariel; Assam, Pryseley; Tilahun, Abel; Buyse, Marc

2010-06-01

For a number of reasons, surrogate endpoints are considered instead of the so-called true endpoint in clinical studies, especially when such endpoints can be measured earlier, and/or with less burden for patient and experimenter. Surrogate endpoints may occur more frequently than their standard counterparts. For these reasons, it is not surprising that the use of surrogate endpoints in clinical practice is increasing. Building on the seminal work of Prentice(1) and Freedman et al.,(2) Buyse et al. (3) framed the evaluation exercise within a meta-analytic setting, in an effort to overcome difficulties that necessarily surround evaluation efforts based on a single trial. In this article, we review the meta-analytic approach for continuous outcomes, discuss extensions to non-normal and longitudinal settings, as well as proposals to unify the somewhat disparate collection of validation measures currently on the market. Implications for design and for predicting the effect of treatment in a new trial, based on the surrogate, are discussed. A case study in schizophrenia is analysed.

Biomarkers and Surrogate Endpoints in Drug Development: A European Regulatory View.

PubMed

Wickström, Kerstin; Moseley, Jane

2017-05-01

To give a European regulatory overview of the requirements on and the use of biomarkers or surrogate endpoints in the development of drugs for ocular disease. Definitions, methods to validate new markers, and circumstances where surrogate endpoints can be appropriate are summarized. The key endpoints that have been used in registration studies so far are based on visual acuity, signs, and symptoms, or on surrogate endpoints. In some ocular conditions, established outcome measures such as those based on visual acuity or visual field are not feasible (as with slowly progressing diseases), or lack relevance (e.g., when central visual acuity may be preserved even though the patient is legally blind owing to a severely restricted visual field, or vice versa). There are several ocular conditions for which there is an unmet medical need. In some of these conditions, surrogate endpoints as well as new clinical endpoints are needed to help speed up patient access to new medicines. Interaction with European regulators through the pathway specific for the development of biomarkers or novel methods is encouraged.

Structural habitat predicts functional dispersal habitat of a large carnivore: how leopards change spots.

PubMed

Fattebert, Julien; Robinson, Hugh S; Balme, Guy; Slotow, Rob; Hunter, Luke

2015-10-01

Natal dispersal promotes inter-population linkage, and is key to spatial distribution of populations. Degradation of suitable landscape structures beyond the specific threshold of an individual's ability to disperse can therefore lead to disruption of functional landscape connectivity and impact metapopulation function. Because it ignores behavioral responses of individuals, structural connectivity is easier to assess than functional connectivity and is often used as a surrogate for landscape connectivity modeling. However using structural resource selection models as surrogate for modeling functional connectivity through dispersal could be erroneous. We tested how well a second-order resource selection function (RSF) models (structural connectivity), based on GPS telemetry data from resident adult leopard (Panthera pardus L.), could predict subadult habitat use during dispersal (functional connectivity). We created eight non-exclusive subsets of the subadult data based on differing definitions of dispersal to assess the predictive ability of our adult-based RSF model extrapolated over a broader landscape. Dispersing leopards used habitats in accordance with adult selection patterns, regardless of the definition of dispersal considered. We demonstrate that, for a wide-ranging apex carnivore, functional connectivity through natal dispersal corresponds to structural connectivity as modeled by a second-order RSF. Mapping of the adult-based habitat classes provides direct visualization of the potential linkages between populations, without the need to model paths between a priori starting and destination points. The use of such landscape scale RSFs may provide insight into predicting suitable dispersal habitat peninsulas in human-dominated landscapes where mitigation of human-wildlife conflict should be focused. We recommend the use of second-order RSFs for landscape conservation planning and propose a similar approach to the conservation of other wide-ranging large carnivore species where landscape-scale resource selection data already exist.

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 outcome. PMID:19706785

A unified procedure for meta-analytic evaluation of surrogate end points in randomized clinical trials

PubMed Central

Dai, James Y.; Hughes, James P.

2012-01-01

The meta-analytic approach to evaluating surrogate end points assesses the predictiveness of treatment effect on the surrogate toward treatment effect on the clinical end point based on multiple clinical trials. Definition and estimation of the correlation of treatment effects were developed in linear mixed models and later extended to binary or failure time outcomes on a case-by-case basis. In a general regression setting that covers nonnormal outcomes, we discuss in this paper several metrics that are useful in the meta-analytic evaluation of surrogacy. We propose a unified 3-step procedure to assess these metrics in settings with binary end points, time-to-event outcomes, or repeated measures. First, the joint distribution of estimated treatment effects is ascertained by an estimating equation approach; second, the restricted maximum likelihood method is used to estimate the means and the variance components of the random treatment effects; finally, confidence intervals are constructed by a parametric bootstrap procedure. The proposed method is evaluated by simulations and applications to 2 clinical trials. PMID:22394448

Validation of surrogate endpoints in cancer clinical trials via principal stratification with an application to a prostate cancer trial.

PubMed

Tanaka, Shiro; Matsuyama, Yutaka; Ohashi, Yasuo

2017-08-30

Increasing attention has been focused on the use and validation of surrogate endpoints in cancer clinical trials. Previous literature on validation of surrogate endpoints are classified into four approaches: the proportion explained approach; the indirect effects approach; the meta-analytic approach; and the principal stratification approach. The mainstream in cancer research has seen the application of a meta-analytic approach. However, VanderWeele (2013) showed that all four of these approaches potentially suffer from the surrogate paradox. It was also shown that, if a principal surrogate satisfies additional criteria called one-sided average causal sufficiency, the surrogate cannot exhibit a surrogate paradox. Here, we propose a method for estimating principal effects under a monotonicity assumption. Specifically, we consider cancer clinical trials which compare a binary surrogate endpoint and a time-to-event clinical endpoint under two naturally ordered treatments (e.g. combined therapy vs. monotherapy). Estimation based on a mean score estimating equation will be implemented by the expectation-maximization algorithm. We will also apply the proposed method as well as other surrogacy criteria to evaluate the surrogacy of prostate-specific antigen using data from a phase III advanced prostate cancer trial, clarifying the complementary roles of both the principal stratification and meta-analytic approaches in the evaluation of surrogate endpoints in cancer. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

Inter-fraction variations in respiratory motion models

NASA Astrophysics Data System (ADS)

McClelland, J. R.; Hughes, S.; Modat, M.; Qureshi, A.; Ahmad, S.; Landau, D. B.; Ourselin, S.; Hawkes, D. J.

2011-01-01

Respiratory motion can vary dramatically between the planning stage and the different fractions of radiotherapy treatment. Motion predictions used when constructing the radiotherapy plan may be unsuitable for later fractions of treatment. This paper presents a methodology for constructing patient-specific respiratory motion models and uses these models to evaluate and analyse the inter-fraction variations in the respiratory motion. The internal respiratory motion is determined from the deformable registration of Cine CT data and related to a respiratory surrogate signal derived from 3D skin surface data. Three different models for relating the internal motion to the surrogate signal have been investigated in this work. Data were acquired from six lung cancer patients. Two full datasets were acquired for each patient, one before the course of radiotherapy treatment and one at the end (approximately 6 weeks later). Separate models were built for each dataset. All models could accurately predict the respiratory motion in the same dataset, but had large errors when predicting the motion in the other dataset. Analysis of the inter-fraction variations revealed that most variations were spatially varying base-line shifts, but changes to the anatomy and the motion trajectories were also observed.

A Fast Surrogate-facilitated Data-driven Bayesian Approach to Uncertainty Quantification of a Regional Groundwater Flow Model with Structural Error

NASA Astrophysics Data System (ADS)

Xu, T.; Valocchi, A. J.; Ye, M.; Liang, F.

2016-12-01

Due to simplification and/or misrepresentation of the real aquifer system, numerical groundwater flow and solute transport models are usually subject to model structural error. During model calibration, the hydrogeological parameters may be overly adjusted to compensate for unknown structural error. This may result in biased predictions when models are used to forecast aquifer response to new forcing. In this study, we extend a fully Bayesian method [Xu and Valocchi, 2015] to calibrate a real-world, regional groundwater flow model. The method uses a data-driven error model to describe model structural error and jointly infers model parameters and structural error. In this study, Bayesian inference is facilitated using high performance computing and fast surrogate models. The surrogate models are constructed using machine learning techniques to emulate the response simulated by the computationally expensive groundwater model. We demonstrate in the real-world case study that explicitly accounting for model structural error yields parameter posterior distributions that are substantially different from those derived by the classical Bayesian calibration that does not account for model structural error. In addition, the Bayesian with error model method gives significantly more accurate prediction along with reasonable credible intervals.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fogg, P; Aland, T; West, M

Purpose: To investigate the effects of external surrogate and tumour motion by observing the reconstructed phases and AveCT in an Amplitude and Time based 4DCT. Methods: Based on patient motion studies, Cos6 and sinusoidal motions were simulated as external surrogate and tumour motions in a motion phantom. The diaphragm and tumour motions may or may not display the same waveform therefore the same and different waveforms were programmed into the phantom, scanned and reconstructed based on Amplitude and Time. The AveCT and phases were investigated with these different scenarios. The AveCT phantom images were also compared with CBCT phantom imagesmore » programmed with the same motions. Results: For the same surrogate and tumour sin motions, the phases (Amplitude and Time) and AveCT indicated similar motions based on the position of the BB at the slice and displayed contrast values respectively. For cos6 motions, due to the varied time the tumour spends at each position, the Amplitude and Time based phases differed. The AveCT images represented the actual tumour motions and the Time and Amplitude based phases were represented by the surrogate with varied times. Conclusion: Different external surrogate and tumour motions may result in different displayed image motions when observing the AveCT and reconstructed phases. During the 4DCT, the surrogate motion is readily available for observation of the amplitude and time of the diaphragm position. Following image reconstruction, the user may need to observe the AveCT in addition to the reconstructed phases to comprehend the time weightings of the tumour motion during the scan. This may also apply to 3D CBCT images where the displayed tumour position in the images is influenced by the long duration of the CBCT. Knowledge of the tumour motion represented by the greyscale of the AveCT may also assist in CBCT treatment beam verification matching.« less

Global Sensitivity Analysis of Environmental Systems via Multiple Indices based on Statistical Moments of Model Outputs

NASA Astrophysics Data System (ADS)

Guadagnini, A.; Riva, M.; Dell'Oca, A.

2017-12-01

We propose to ground sensitivity of uncertain parameters of environmental models on a set of indices based on the main (statistical) moments, i.e., mean, variance, skewness and kurtosis, of the probability density function (pdf) of a target model output. This enables us to perform Global Sensitivity Analysis (GSA) of a model in terms of multiple statistical moments and yields a quantification of the impact of model parameters on features driving the shape of the pdf of model output. Our GSA approach includes the possibility of being coupled with the construction of a reduced complexity model that allows approximating the full model response at a reduced computational cost. We demonstrate our approach through a variety of test cases. These include a commonly used analytical benchmark, a simplified model representing pumping in a coastal aquifer, a laboratory-scale tracer experiment, and the migration of fracturing fluid through a naturally fractured reservoir (source) to reach an overlying formation (target). Our strategy allows discriminating the relative importance of model parameters to the four statistical moments considered. We also provide an appraisal of the error associated with the evaluation of our sensitivity metrics by replacing the original system model through the selected surrogate model. Our results suggest that one might need to construct a surrogate model with increasing level of accuracy depending on the statistical moment considered in the GSA. The methodological framework we propose can assist the development of analysis techniques targeted to model calibration, design of experiment, uncertainty quantification and risk assessment.

Developing the role of the social worker as coordinator of services at the surrogate parenting center.

PubMed

Gagin, Roni; Cohen, Miri; Greenblatt, Lee; Solomon, Hanah; Itskovitz-Eldor, Joseph

2004-01-01

A law permitting couples to conceive biological children through surrogacy was legislated in Israel in March 1996. The Rambam Medical Center has established the only nonprofit Surrogate Parenting Center at a public hospital in Israel. The multidisciplinary teamwork at the Center is case managed by a social worker. An important role of the social work intervention is consultation and support for the couple and the surrogate at all stages of the process. The case study presented in the article illustrates the need for sensitive and professional intervention due to the complexity of the surrogacy process and the crisis it involves for both the surrogate and the couple. In light of the growing parenting surrogacy cases in the United States, Europe, and Israel, a structured social work intervention model is described, which may be implemented at public or private surrogate parenting centers.

Selecting surrogate endpoints for estimating pesticide effects on avian reproductive success.

PubMed

Bennett, Richard S; Etterson, Matthew A

2013-10-01

A Markov chain nest productivity model (MCnest) has been developed for projecting the effects of a specific pesticide-use scenario on the annual reproductive success of avian species of concern. A critical element in MCnest is the use of surrogate endpoints, defined as measured endpoints from avian toxicity tests that represent specific types of effects possible in field populations at specific phases of a nesting attempt. In this article, we discuss the attributes of surrogate endpoints and provide guidance for selecting surrogates from existing avian laboratory tests as well as other possible sources. We also discuss some of the assumptions and uncertainties related to using surrogate endpoints to represent field effects. The process of explicitly considering how toxicity test results can be used to assess effects in the field helps identify uncertainties and data gaps that could be targeted in higher-tier risk assessments. © 2013 SETAC.

Simulation and optimization of pressure swing adsorption systmes using reduced-order modeling

DOE Office of Scientific and Technical Information (OSTI.GOV)

Agarwal, A.; Biegler, L.; Zitney, S.

2009-01-01

Over the past three decades, pressure swing adsorption (PSA) processes have been widely used as energyefficient gas separation techniques, especially for high purity hydrogen purification from refinery gases. Models for PSA processes are multiple instances of partial differential equations (PDEs) in time and space with periodic boundary conditions that link the processing steps together. The solution of this coupled stiff PDE system is governed by steep fronts moving with time. As a result, the optimization of such systems represents a significant computational challenge to current differential algebraic equation (DAE) optimization techniques and nonlinear programming algorithms. Model reduction is one approachmore » to generate cost-efficient low-order models which can be used as surrogate models in the optimization problems. This study develops a reducedorder model (ROM) based on proper orthogonal decomposition (POD), which is a low-dimensional approximation to a dynamic PDE-based model. The proposed method leads to a DAE system of significantly lower order, thus replacing the one obtained from spatial discretization and making the optimization problem computationally efficient. The method has been applied to the dynamic coupled PDE-based model of a twobed four-step PSA process for separation of hydrogen from methane. Separate ROMs have been developed for each operating step with different POD modes for each of them. A significant reduction in the order of the number of states has been achieved. The reduced-order model has been successfully used to maximize hydrogen recovery by manipulating operating pressures, step times and feed and regeneration velocities, while meeting product purity and tight bounds on these parameters. Current results indicate the proposed ROM methodology as a promising surrogate modeling technique for cost-effective optimization purposes.« less

Predicting treatment effect from surrogate endpoints and historical trials: an extrapolation involving probabilities of a binary outcome or survival to a specific time

PubMed Central

Sargent, Daniel J.; Buyse, Marc; Burzykowski, Tomasz

2011-01-01

SUMMARY Using multiple historical trials with surrogate and true endpoints, we consider various models to predict the effect of treatment on a true endpoint in a target trial in which only a surrogate endpoint is observed. This predicted result is computed using (1) a prediction model (mixture, linear, or principal stratification) estimated from historical trials and the surrogate endpoint of the target trial and (2) a random extrapolation error estimated from successively leaving out each trial among the historical trials. The method applies to either binary outcomes or survival to a particular time that is computed from censored survival data. We compute a 95% confidence interval for the predicted result and validate its coverage using simulation. To summarize the additional uncertainty from using a predicted instead of true result for the estimated treatment effect, we compute its multiplier of standard error. Software is available for download. PMID:21838732

Using abiotic variables to predict importance of sites for species representation.

PubMed

Albuquerque, Fabio; Beier, Paul

2015-10-01

In systematic conservation planning, species distribution data for all sites in a planning area are used to prioritize each site in terms of the site's importance toward meeting the goal of species representation. But comprehensive species data are not available in most planning areas and would be expensive to acquire. As a shortcut, ecologists use surrogates, such as occurrences of birds or another well-surveyed taxon, or land types defined from remotely sensed data, in the hope that sites that represent the surrogates also represent biodiversity. Unfortunately, surrogates have not performed reliably. We propose a new type of surrogate, predicted importance, that can be developed from species data for a q% subset of sites. With species data from this subset of sites, importance can be modeled as a function of abiotic variables available at no charge for all terrestrial areas on Earth. Predicted importance can then be used as a surrogate to prioritize all sites. We tested this surrogate with 8 sets of species data. For each data set, we used a q% subset of sites to model importance as a function of abiotic variables, used the resulting function to predict importance for all sites, and evaluated the number of species in the sites with highest predicted importance. Sites with the highest predicted importance represented species efficiently for all data sets when q = 25% and for 7 of 8 data sets when q = 20%. Predicted importance requires less survey effort than direct selection for species representation and meets representation goals well compared with other surrogates currently in use. This less expensive surrogate may be useful in those areas of the world that need it most, namely tropical regions with the highest biodiversity, greatest biodiversity loss, most severe lack of inventory data, and poorly developed protected area networks. © 2015 Society for Conservation Biology.

On the control of riverbed incision induced by run-of-river power plant

NASA Astrophysics Data System (ADS)

Bizzi, Simone; Dinh, Quang; Bernardi, Dario; Denaro, Simona; Schippa, Leonardo; Soncini-Sessa, Rodolfo

2015-07-01

Water resource management (WRM) through dams or reservoirs is worldwide necessary to support key human-related activities, ranging from hydropower production to water allocation and flood risk mitigation. Designing of reservoir operations aims primarily to fulfill the main purpose (or purposes) for which the structure has been built. However, it is well known that reservoirs strongly influence river geomorphic processes, causing sediment deficits downstream, altering water, and sediment fluxes, leading to riverbed incision and causing infrastructure instability and ecological degradation. We propose a framework that, by combining physically based modeling, surrogate modeling techniques, and multiobjective (MO) optimization, allows to include fluvial geomorphology into MO optimization whose main objectives are the maximization of hydropower revenue and the minimization of riverbed degradation. The case study is a run-of-the-river power plant on the River Po (Italy). A 1-D mobile-bed hydro-morphological model simulated the riverbed evolution over a 10 year horizon for alternatives operation rules of the power plant. The knowledge provided by such a physically based model is integrated into a MO optimization routine via surrogate modeling using the response surface methodology. Hence, this framework overcomes the high computational costs that so far hindered the integration of river geomorphology into WRM. We provided numerical proof that river morphologic processes and hydropower production are indeed in conflict but that the conflict may be mitigated with appropriate control strategies.

Impaired associative learning in schizophrenia: behavioral and computational studies

PubMed Central

Diwadkar, Vaibhav A.; Flaugher, Brad; Jones, Trevor; Zalányi, László; Ujfalussy, Balázs; Keshavan, Matcheri S.

2008-01-01

Associative learning is a central building block of human cognition and in large part depends on mechanisms of synaptic plasticity, memory capacity and fronto–hippocampal interactions. A disorder like schizophrenia is thought to be characterized by altered plasticity, and impaired frontal and hippocampal function. Understanding the expression of this dysfunction through appropriate experimental studies, and understanding the processes that may give rise to impaired behavior through biologically plausible computational models will help clarify the nature of these deficits. We present a preliminary computational model designed to capture learning dynamics in healthy control and schizophrenia subjects. Experimental data was collected on a spatial-object paired-associate learning task. The task evinces classic patterns of negatively accelerated learning in both healthy control subjects and patients, with patients demonstrating lower rates of learning than controls. Our rudimentary computational model of the task was based on biologically plausible assumptions, including the separation of dorsal/spatial and ventral/object visual streams, implementation of rules of learning, the explicit parameterization of learning rates (a plausible surrogate for synaptic plasticity), and learning capacity (a plausible surrogate for memory capacity). Reductions in learning dynamics in schizophrenia were well-modeled by reductions in learning rate and learning capacity. The synergy between experimental research and a detailed computational model of performance provides a framework within which to infer plausible biological bases of impaired learning dynamics in schizophrenia. PMID:19003486

Composite Sampling of a Bacillus anthracis Surrogate with ...

EPA Pesticide Factsheets

Journal Article A series of experiments were conducted to explore the utility of composite-based collection of surface samples for the detection of a Bacillus anthracis surrogate using cellulose sponge samplers on a stainless steel surface.

A New Approach to Ballistic Missile Defense for Countering Antiaccess/Area-Denial Threats from Precision-Guided Weapons

DTIC Science & Technology

2013-04-01

performance esti- mates. Four notional developments of the PAA surrogate were mod- eled , with burnout velocities of 5 km/second and 6 km/second (40...his weapon on the intended target. Interceptor Models The notional baseline surface-launch interceptor was modeled with 3.5 km/second burnout ...agility. The AWL upper-tier interceptor was modeled, based on employment from an F-35A.11 In general the upper-tier interceptor has a burnout

Licensing Surrogate Decision-Makers.

PubMed

Rosoff, Philip M

2017-06-01

As medical technology continues to improve, more people will live longer lives with multiple chronic illnesses with increasing cumulative debilitation, including cognitive dysfunction. Combined with the aging of society in most developed countries, an ever-growing number of patients will require surrogate decision-makers. While advance care planning by patients still capable of expressing their preferences about medical interventions and end-of-life care can improve the quality and accuracy of surrogate decisions, this is often not the case, not infrequently leading to demands for ineffective, inappropriate and prolonged interventions. In 1980 LaFollette called for the licensing of prospective parents, basing his argument on the harm they can do to vulnerable people (children). In this paper, I apply his arguments to surrogate decision-makers for cognitively incapacitated patients, rhetorically suggesting that we require potential surrogates to qualify for this position by demonstrating their ability to make reasonable and rational decisions for others. I employ this theoretical approach to argue that the loose criteria by which we authorize surrogates' generally unchallenged power should be reconsidered.

Limited predictive ability of surrogate indices of insulin sensitivity/resistance in Asian-Indian men.

PubMed

Muniyappa, Ranganath; Irving, Brian A; Unni, Uma S; Briggs, William M; Nair, K Sreekumaran; Quon, Michael J; Kurpad, Anura V

2010-12-01

Insulin resistance is highly prevalent in Asian Indians and contributes to worldwide public health problems, including diabetes and related disorders. Surrogate measurements of insulin sensitivity/resistance are used frequently to study Asian Indians, but these are not formally validated in this population. In this study, we compared the ability of simple surrogate indices to accurately predict insulin sensitivity as determined by the reference glucose clamp method. In this cross-sectional study of Asian-Indian men (n = 70), we used a calibration model to assess the ability of simple surrogate indices for insulin sensitivity [quantitative insulin sensitivity check index (QUICKI), homeostasis model assessment (HOMA2-IR), fasting insulin-to-glucose ratio (FIGR), and fasting insulin (FI)] to predict an insulin sensitivity index derived from the reference glucose clamp method (SI(Clamp)). Predictive accuracy was assessed by both root mean squared error (RMSE) of prediction as well as leave-one-out cross-validation-type RMSE of prediction (CVPE). QUICKI, FIGR, and FI, but not HOMA2-IR, had modest linear correlations with SI(Clamp) (QUICKI: r = 0.36; FIGR: r = -0.36; FI: r = -0.27; P < 0.05). No significant differences were noted among CVPE or RMSE from any of the surrogate indices when compared with QUICKI. Surrogate measurements of insulin sensitivity/resistance such as QUICKI, FIGR, and FI are easily obtainable in large clinical studies, but these may only be useful as secondary outcome measurements in assessing insulin sensitivity/resistance in clinical studies of Asian Indians.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lei, Huan; Yang, Xiu; Zheng, Bin

Biomolecules exhibit conformational fluctuations near equilibrium states, inducing uncertainty in various biological properties in a dynamic way. We have developed a general method to quantify the uncertainty of target properties induced by conformational fluctuations. Using a generalized polynomial chaos (gPC) expansion, we construct a surrogate model of the target property with respect to varying conformational states. We also propose a method to increase the sparsity of the gPC expansion by defining a set of conformational “active space” random variables. With the increased sparsity, we employ the compressive sensing method to accurately construct the surrogate model. We demonstrate the performance ofmore » the surrogate model by evaluating fluctuation-induced uncertainty in solvent-accessible surface area for the bovine trypsin inhibitor protein system and show that the new approach offers more accurate statistical information than standard Monte Carlo approaches. Further more, the constructed surrogate model also enables us to directly evaluate the target property under various conformational states, yielding a more accurate response surface than standard sparse grid collocation methods. In particular, the new method provides higher accuracy in high-dimensional systems, such as biomolecules, where sparse grid performance is limited by the accuracy of the computed quantity of interest. Finally, our new framework is generalizable and can be used to investigate the uncertainty of a wide variety of target properties in biomolecular systems.« less

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lei, Huan; Yang, Xiu; Zheng, Bin

Biomolecules exhibit conformational fluctuations near equilibrium states, inducing uncertainty in various biological properties in a dynamic way. We have developed a general method to quantify the uncertainty of target properties induced by conformational fluctuations. Using a generalized polynomial chaos (gPC) expansion, we construct a surrogate model of the target property with respect to varying conformational states. We also propose a method to increase the sparsity of the gPC expansion by defining a set of conformational “active space” random variables. With the increased sparsity, we employ the compressive sensing method to accurately construct the surrogate model. We demonstrate the performance ofmore » the surrogate model by evaluating fluctuation-induced uncertainty in solvent-accessible surface area for the bovine trypsin inhibitor protein system and show that the new approach offers more accurate statistical information than standard Monte Carlo approaches. Further more, the constructed surrogate model also enables us to directly evaluate the target property under various conformational states, yielding a more accurate response surface than standard sparse grid collocation methods. In particular, the new method provides higher accuracy in high-dimensional systems, such as biomolecules, where sparse grid performance is limited by the accuracy of the computed quantity of interest. Our new framework is generalizable and can be used to investigate the uncertainty of a wide variety of target properties in biomolecular systems.« less

Oncotype DX breast cancer recurrence score can be predicted with a novel nomogram using clinicopathologic data.

PubMed

Orucevic, Amila; Bell, John L; McNabb, Alison P; Heidel, Robert E

2017-05-01

Oncotype DX (ODX) recurrence score (RS) breast cancer (BC) assay is costly, and performed in only ~1/3 of estrogen receptor (ER)-positive BC patients in the USA. We have now developed a user-friendly nomogram surrogate prediction model for ODX based on a large dataset from the National Cancer Data Base (NCDB) to assist in selecting patients for which further ODX testing may not be necessary and as a surrogate for patients for which ODX testing is not affordable or available. Six clinicopathologic variables of 27,719 ODX-tested ER+/HER2-/lymph node-negative patients with 6-50 mm tumor size captured by the NCDB from 2010 to 2012 were assessed with logistic regression to predict high-risk or low-risk ODXRS test results with TAILORx-trial and commercial cut-off values; 12,763 ODX-tested patients in 2013 were used for external validation. The predictive accuracy of the regression model was yielded using a Receiver Operator Characteristic analysis. Model fit was analyzed by plotting the predicted probabilities against the actual probabilities. A user-friendly calculator version of nomograms is available online at the University of Tennessee Medical Center website (Knoxville, TN). Grade and progesterone receptor status were the highest predictors of both low-risk and high-risk ODXRS, followed by age, tumor size, histologic tumor type and lymph-vascular invasion (C-indexes-.0.85 vs. 0.88 for TAILORx-trial vs. commercial cut-off values, respectively). This is the first study of this scale showing confidently that clinicopathologic variables can be used for prediction of low-risk or high-risk ODXRS using our nomogram models. These novel nomograms will be useful tools to help physicians and patients decide whether further ODX testing is necessary and are excellent surrogates for patients for which ODX testing is not affordable or available.

Phenotypic and genomic comparison of Mycobacterium aurum and surrogate model species to Mycobacterium tuberculosis: implications for drug discovery.

PubMed

Namouchi, Amine; Cimino, Mena; Favre-Rochex, Sandrine; Charles, Patricia; Gicquel, Brigitte

2017-07-13

Tuberculosis (TB) is caused by Mycobacterium tuberculosis and represents one of the major challenges facing drug discovery initiatives worldwide. The considerable rise in bacterial drug resistance in recent years has led to the need of new drugs and drug regimens. Model systems are regularly used to speed-up the drug discovery process and circumvent biosafety issues associated with manipulating M. tuberculosis. These include the use of strains such as Mycobacterium smegmatis and Mycobacterium marinum that can be handled in biosafety level 2 facilities, making high-throughput screening feasible. However, each of these model species have their own limitations. We report and describe the first complete genome sequence of Mycobacterium aurum ATCC23366, an environmental mycobacterium that can also grow in the gut of humans and animals as part of the microbiota. This species shows a comparable resistance profile to that of M. tuberculosis for several anti-TB drugs. The aims of this study were to (i) determine the drug resistance profile of a recently proposed model species, Mycobacterium aurum, strain ATCC23366, for anti-TB drug discovery as well as Mycobacterium smegmatis and Mycobacterium marinum (ii) sequence and annotate the complete genome sequence of this species obtained using Pacific Bioscience technology (iii) perform comparative genomics analyses of the various surrogate strains with M. tuberculosis (iv) discuss how the choice of the surrogate model used for drug screening can affect the drug discovery process. We describe the complete genome sequence of M. aurum, a surrogate model for anti-tuberculosis drug discovery. Most of the genes already reported to be associated with drug resistance are shared between all the surrogate strains and M. tuberculosis. We consider that M. aurum might be used in high-throughput screening for tuberculosis drug discovery. We also highly recommend the use of different model species during the drug discovery screening process.

Robust optimization of supersonic ORC nozzle guide vanes

NASA Astrophysics Data System (ADS)

Bufi, Elio A.; Cinnella, Paola

2017-03-01

An efficient Robust Optimization (RO) strategy is developed for the design of 2D supersonic Organic Rankine Cycle turbine expanders. The dense gas effects are not-negligible for this application and they are taken into account describing the thermodynamics by means of the Peng-Robinson-Stryjek-Vera equation of state. The design methodology combines an Uncertainty Quantification (UQ) loop based on a Bayesian kriging model of the system response to the uncertain parameters, used to approximate statistics (mean and variance) of the uncertain system output, a CFD solver, and a multi-objective non-dominated sorting algorithm (NSGA), also based on a Kriging surrogate of the multi-objective fitness function, along with an adaptive infill strategy for surrogate enrichment at each generation of the NSGA. The objective functions are the average and variance of the isentropic efficiency. The blade shape is parametrized by means of a Free Form Deformation (FFD) approach. The robust optimal blades are compared to the baseline design (based on the Method of Characteristics) and to a blade obtained by means of a deterministic CFD-based optimization.

Adaptive surrogate modeling by ANOVA and sparse polynomial dimensional decomposition for global sensitivity analysis in fluid simulation

NASA Astrophysics Data System (ADS)

Tang, Kunkun; Congedo, Pietro M.; Abgrall, Rémi

2016-06-01

The Polynomial Dimensional Decomposition (PDD) is employed in this work for the global sensitivity analysis and uncertainty quantification (UQ) of stochastic systems subject to a moderate to large number of input random variables. Due to the intimate connection between the PDD and the Analysis of Variance (ANOVA) approaches, PDD is able to provide a simpler and more direct evaluation of the Sobol' sensitivity indices, when compared to the Polynomial Chaos expansion (PC). Unfortunately, the number of PDD terms grows exponentially with respect to the size of the input random vector, which makes the computational cost of standard methods unaffordable for real engineering applications. In order to address the problem of the curse of dimensionality, this work proposes essentially variance-based adaptive strategies aiming to build a cheap meta-model (i.e. surrogate model) by employing the sparse PDD approach with its coefficients computed by regression. Three levels of adaptivity are carried out in this paper: 1) the truncated dimensionality for ANOVA component functions, 2) the active dimension technique especially for second- and higher-order parameter interactions, and 3) the stepwise regression approach designed to retain only the most influential polynomials in the PDD expansion. During this adaptive procedure featuring stepwise regressions, the surrogate model representation keeps containing few terms, so that the cost to resolve repeatedly the linear systems of the least-squares regression problem is negligible. The size of the finally obtained sparse PDD representation is much smaller than the one of the full expansion, since only significant terms are eventually retained. Consequently, a much smaller number of calls to the deterministic model is required to compute the final PDD coefficients.

Air Pollution Exposure Modeling for Epidemiology Studies and Public Health

EPA Science Inventory

Air pollution epidemiology studies of ambient fine particulate matter (PM2.5) often use outdoor concentrations as exposure surrogates. These surrogates can induce exposure error since they do not account for (1) time spent indoors with ambient PM2.5 levels attenuated from outdoor...

Selecting surrogate endpoints for estimating pesticide effects on avian reproductive success

EPA Science Inventory

A Markov chain nest productivity model (MCnest) has been developed for projecting the effects of a specific pesticide-use scenario on the annual reproductive success of avian species of concern. A critical element in MCnest is the use of surrogate endpoints, defined as measured ...

Correlation between external and internal respiratory motion: a validation study.

PubMed

Ernst, Floris; Bruder, Ralf; Schlaefer, Alexander; Schweikard, Achim

2012-05-01

In motion-compensated image-guided radiotherapy, accurate tracking of the target region is required. This tracking process includes building a correlation model between external surrogate motion and the motion of the target region. A novel correlation method is presented and compared with the commonly used polynomial model. The CyberKnife system (Accuray, Inc., Sunnyvale/CA) uses a polynomial correlation model to relate externally measured surrogate data (optical fibres on the patient's chest emitting red light) to infrequently acquired internal measurements (X-ray data). A new correlation algorithm based on ɛ -Support Vector Regression (SVR) was developed. Validation and comparison testing were done with human volunteers using live 3D ultrasound and externally measured infrared light-emitting diodes (IR LEDs). Seven data sets (5:03-6:27 min long) were recorded from six volunteers. Polynomial correlation algorithms were compared to the SVR-based algorithm demonstrating an average increase in root mean square (RMS) accuracy of 21.3% (0.4 mm). For three signals, the increase was more than 29% and for one signal as much as 45.6% (corresponding to more than 1.5 mm RMS). Further analysis showed the improvement to be statistically significant. The new SVR-based correlation method outperforms traditional polynomial correlation methods for motion tracking. This method is suitable for clinical implementation and may improve the overall accuracy of targeted radiotherapy.

Probabilistic Fatigue Damage Prognosis Using a Surrogate Model Trained Via 3D Finite Element Analysis

NASA Technical Reports Server (NTRS)

Leser, Patrick E.; Hochhalter, Jacob D.; Newman, John A.; Leser, William P.; Warner, James E.; Wawrzynek, Paul A.; Yuan, Fuh-Gwo

2015-01-01

Utilizing inverse uncertainty quantification techniques, structural health monitoring can be integrated with damage progression models to form probabilistic predictions of a structure's remaining useful life. However, damage evolution in realistic structures is physically complex. Accurately representing this behavior requires high-fidelity models which are typically computationally prohibitive. In the present work, a high-fidelity finite element model is represented by a surrogate model, reducing computation times. The new approach is used with damage diagnosis data to form a probabilistic prediction of remaining useful life for a test specimen under mixed-mode conditions.

An MR-based Model for Cardio-Respiratory Motion Compensation of Overlays in X-Ray Fluoroscopy

PubMed Central

Fischer, Peter; Faranesh, Anthony; Pohl, Thomas; Maier, Andreas; Rogers, Toby; Ratnayaka, Kanishka; Lederman, Robert; Hornegger, Joachim

2017-01-01

In X-ray fluoroscopy, static overlays are used to visualize soft tissue. We propose a system for cardiac and respiratory motion compensation of these overlays. It consists of a 3-D motion model created from real-time MR imaging. Multiple sagittal slices are acquired and retrospectively stacked to consistent 3-D volumes. Slice stacking considers cardiac information derived from the ECG and respiratory information extracted from the images. Additionally, temporal smoothness of the stacking is enhanced. Motion is estimated from the MR volumes using deformable 3-D/3-D registration. The motion model itself is a linear direct correspondence model using the same surrogate signals as slice stacking. In X-ray fluoroscopy, only the surrogate signals need to be extracted to apply the motion model and animate the overlay in real time. For evaluation, points are manually annotated in oblique MR slices and in contrast-enhanced X-ray images. The 2-D Euclidean distance of these points is reduced from 3.85 mm to 2.75 mm in MR and from 3.0 mm to 1.8 mm in X-ray compared to the static baseline. Furthermore, the motion-compensated overlays are shown qualitatively as images and videos. PMID:28692969

Applications of New Surrogate Global Optimization Algorithms including Efficient Synchronous and Asynchronous Parallelism for Calibration of Expensive Nonlinear Geophysical Simulation Models.

NASA Astrophysics Data System (ADS)

Shoemaker, C. A.; Pang, M.; Akhtar, T.; Bindel, D.

2016-12-01

New parallel surrogate global optimization algorithms are developed and applied to objective functions that are expensive simulations (possibly with multiple local minima). The algorithms can be applied to most geophysical simulations, including those with nonlinear partial differential equations. The optimization does not require simulations be parallelized. Asynchronous (and synchronous) parallel execution is available in the optimization toolbox "pySOT". The parallel algorithms are modified from serial to eliminate fine grained parallelism. The optimization is computed with open source software pySOT, a Surrogate Global Optimization Toolbox that allows user to pick the type of surrogate (or ensembles), the search procedure on surrogate, and the type of parallelism (synchronous or asynchronous). pySOT also allows the user to develop new algorithms by modifying parts of the code. In the applications here, the objective function takes up to 30 minutes for one simulation, and serial optimization can take over 200 hours. Results from Yellowstone (NSF) and NCSS (Singapore) supercomputers are given for groundwater contaminant hydrology simulations with applications to model parameter estimation and decontamination management. All results are compared with alternatives. The first results are for optimization of pumping at many wells to reduce cost for decontamination of groundwater at a superfund site. The optimization runs with up to 128 processors. Superlinear speed up is obtained for up to 16 processors, and efficiency with 64 processors is over 80%. Each evaluation of the objective function requires the solution of nonlinear partial differential equations to describe the impact of spatially distributed pumping and model parameters on model predictions for the spatial and temporal distribution of groundwater contaminants. The second application uses an asynchronous parallel global optimization for groundwater quality model calibration. The time for a single objective function evaluation varies unpredictably, so efficiency is improved with asynchronous parallel calculations to improve load balancing. The third application (done at NCSS) incorporates new global surrogate multi-objective parallel search algorithms into pySOT and applies it to a large watershed calibration problem.

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

Statistical characteristics of surrogate data based on geophysical measurements

NASA Astrophysics Data System (ADS)

Venema, V.; Bachner, S.; Rust, H. W.; Simmer, C.

2006-09-01

In this study, the statistical properties of a range of measurements are compared with those of their surrogate time series. Seven different records are studied, amongst others, historical time series of mean daily temperature, daily rain sums and runoff from two rivers, and cloud measurements. Seven different algorithms are used to generate the surrogate time series. The best-known method is the iterative amplitude adjusted Fourier transform (IAAFT) algorithm, which is able to reproduce the measured distribution as well as the power spectrum. Using this setup, the measurements and their surrogates are compared with respect to their power spectrum, increment distribution, structure functions, annual percentiles and return values. It is found that the surrogates that reproduce the power spectrum and the distribution of the measurements are able to closely match the increment distributions and the structure functions of the measurements, but this often does not hold for surrogates that only mimic the power spectrum of the measurement. However, even the best performing surrogates do not have asymmetric increment distributions, i.e., they cannot reproduce nonlinear dynamical processes that are asymmetric in time. Furthermore, we have found deviations of the structure functions on small scales.

An adaptive least-squares global sensitivity method and application to a plasma-coupled combustion prediction with parametric correlation

NASA Astrophysics Data System (ADS)

Tang, Kunkun; Massa, Luca; Wang, Jonathan; Freund, Jonathan B.

2018-05-01

We introduce an efficient non-intrusive surrogate-based methodology for global sensitivity analysis and uncertainty quantification. Modified covariance-based sensitivity indices (mCov-SI) are defined for outputs that reflect correlated effects. The overall approach is applied to simulations of a complex plasma-coupled combustion system with disparate uncertain parameters in sub-models for chemical kinetics and a laser-induced breakdown ignition seed. The surrogate is based on an Analysis of Variance (ANOVA) expansion, such as widely used in statistics, with orthogonal polynomials representing the ANOVA subspaces and a polynomial dimensional decomposition (PDD) representing its multi-dimensional components. The coefficients of the PDD expansion are obtained using a least-squares regression, which both avoids the direct computation of high-dimensional integrals and affords an attractive flexibility in choosing sampling points. This facilitates importance sampling using a Bayesian calibrated posterior distribution, which is fast and thus particularly advantageous in common practical cases, such as our large-scale demonstration, for which the asymptotic convergence properties of polynomial expansions cannot be realized due to computation expense. Effort, instead, is focused on efficient finite-resolution sampling. Standard covariance-based sensitivity indices (Cov-SI) are employed to account for correlation of the uncertain parameters. Magnitude of Cov-SI is unfortunately unbounded, which can produce extremely large indices that limit their utility. Alternatively, mCov-SI are then proposed in order to bound this magnitude ∈ [ 0 , 1 ]. The polynomial expansion is coupled with an adaptive ANOVA strategy to provide an accurate surrogate as the union of several low-dimensional spaces, avoiding the typical computational cost of a high-dimensional expansion. It is also adaptively simplified according to the relative contribution of the different polynomials to the total variance. The approach is demonstrated for a laser-induced turbulent combustion simulation model, which includes parameters with correlated effects.

Best Practicable Aggregation of Species: a step forward for species surrogacy in environmental assessment and monitoring

PubMed Central

Bevilacqua, Stanislao; Claudet, Joachim; Terlizzi, Antonio

2013-01-01

The available taxonomic expertise and knowledge of species is still inadequate to cope with the urgent need for cost-effective methods to quantifying community response to natural and anthropogenic drivers of change. So far, the mainstream approach to overcome these impediments has focused on using higher taxa as surrogates for species. However, the use of such taxonomic surrogates often limits inferences about the causality of community patterns, which in turn is essential for effective environmental management strategies. Here, we propose an alternative approach to species surrogacy, the “Best Practicable Aggregation of Species” (BestAgg), in which surrogates exulate from fixed taxonomic schemes. The approach uses null models from random aggregations of species to minimizing the number of surrogates without causing significant losses of information on community patterns. Surrogate types are then selected in order to maximize ecological information. We applied the approach to real case studies on natural and human-driven gradients from marine benthic communities. Outcomes from BestAgg were also compared with those obtained using classic taxonomic surrogates. Results showed that BestAgg surrogates are effective in detecting community changes. In contrast to classic taxonomic surrogates, BestAgg surrogates allow retaining significantly higher information on species-level community patterns than what is expected to occur by chance and a potential time saving during sample processing up to 25% higher. Our findings showed that BestAgg surrogates from a pilot study could be used successfully in similar environmental investigations in the same area, or for subsequent long-term monitoring programs. BestAgg is virtually applicable to any environmental context, allowing exploiting multiple surrogacy schemes beyond stagnant perspectives strictly relying on taxonomic relatedness among species. This prerogative is crucial to extend the concept of species surrogacy to ecological traits of species, thus leading to ecologically meaningful surrogates that, while cost effective in reflecting community patterns, may also contribute to unveil underlying processes. A specific R code for BestAgg is provided. PMID:24198939

Proposing an Evidence-Based Strategy for Software Requirements Engineering.

PubMed

Lindoerfer, Doris; Mansmann, Ulrich

2016-01-01

This paper discusses an evidence-based approach to software requirements engineering. The approach is called evidence-based, since it uses publications on the specific problem as a surrogate for stakeholder interests, to formulate risks and testing experiences. This complements the idea that agile software development models are more relevant, in which requirements and solutions evolve through collaboration between self-organizing cross-functional teams. The strategy is exemplified and applied to the development of a Software Requirements list used to develop software systems for patient registries.

Development and Validation of Methods for Applying Pharmacokinetic Data in Risk Assessment. Volume 3. Tetrachloroethylene

DTIC Science & Technology

1990-12-01

Armstrong Aerospace Medical Research Laboratory, Wright Paterson Air Force Base, and Drs. Melvin Andersen and Michael Cargas , formerly with the Harry G...based on the arterial blood concentration surrogate were more III-1-10 similar to those de ’!ved in the traditional manner than were the estimates based on...pharmacokinetic modeling. Prepared by Office of Risk Analysis, Oak Ridge National L-ioratory, Oak Pidge, Tenn.zsee. Prepared under Contract No. DE -ACO5-84

A traits-based approach for prioritizing species for monitoring and surrogacy selection

DOE PAGES

Pracheil, Brenda M.; McManamay, Ryan A.; Bevelhimer, Mark S.; ...

2016-11-28

The bar for justifying the use of vertebrate animals for study is being increasingly raised, thus requiring increased rigor for species selection and study design. Although we have power analyses to provide quantitative backing for the numbers of organisms used, quantitative backing for selection of study species is not frequently employed. This can be especially important when measuring the impacts of ecosystem alteration, when study species must be chosen that are both sensitive to the alteration and of sufficient abundance for study. Just as important is providing justification for designation of surrogate species for study, especially when the species ofmore » interest is rare or of conservation concern and selection of an appropriate surrogate can have legal implications. In this study, we use a combination of GIS, a fish traits database and multivariate statistical analyses to quantitatively prioritize species for study and to determine potential study surrogate species. We provide two case studies to illustrate our quantitative, traits-based approach for designating study species and surrogate species. In the first case study, we select broadly representative fish species to understand the effects of turbine passage on adult fishes based on traits that suggest sensitivity to turbine passage. In our second case study, we present a framework for selecting a surrogate species for an endangered species. Lastly, we suggest that our traits-based framework can provide quantitative backing and added justification to selection of study species while expanding the inference space of study results.« less

A traits-based approach for prioritizing species for monitoring and surrogacy selection

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pracheil, Brenda M.; McManamay, Ryan A.; Bevelhimer, Mark S.

The bar for justifying the use of vertebrate animals for study is being increasingly raised, thus requiring increased rigor for species selection and study design. Although we have power analyses to provide quantitative backing for the numbers of organisms used, quantitative backing for selection of study species is not frequently employed. This can be especially important when measuring the impacts of ecosystem alteration, when study species must be chosen that are both sensitive to the alteration and of sufficient abundance for study. Just as important is providing justification for designation of surrogate species for study, especially when the species ofmore » interest is rare or of conservation concern and selection of an appropriate surrogate can have legal implications. In this study, we use a combination of GIS, a fish traits database and multivariate statistical analyses to quantitatively prioritize species for study and to determine potential study surrogate species. We provide two case studies to illustrate our quantitative, traits-based approach for designating study species and surrogate species. In the first case study, we select broadly representative fish species to understand the effects of turbine passage on adult fishes based on traits that suggest sensitivity to turbine passage. In our second case study, we present a framework for selecting a surrogate species for an endangered species. Lastly, we suggest that our traits-based framework can provide quantitative backing and added justification to selection of study species while expanding the inference space of study results.« less

Evaluation of countermeasures for red light running by traffic simulator-based surrogate safety measures.

PubMed

Lee, Changju; So, Jaehyun Jason; Ma, Jiaqi

2018-01-02

The conflicts among motorists entering a signalized intersection with the red light indication have become a national safety issue. Because of its sensitivity, efforts have been made to investigate the possible causes and effectiveness of countermeasures using comparison sites and/or before-and-after studies. Nevertheless, these approaches are ineffective when comparison sites cannot be found, or crash data sets are not readily available or not reliable for statistical analysis. Considering the random nature of red light running (RLR) crashes, an inventive approach regardless of data availability is necessary to evaluate the effectiveness of each countermeasure face to face. The aims of this research are to (1) review erstwhile literature related to red light running and traffic safety models; (2) propose a practical methodology for evaluation of RLR countermeasures with a microscopic traffic simulation model and surrogate safety assessment model (SSAM); (3) apply the proposed methodology to actual signalized intersection in Virginia, with the most prevalent scenarios-increasing the yellow signal interval duration, installing an advance warning sign, and an RLR camera; and (4) analyze the relative effectiveness by RLR frequency and the number of conflicts (rear-end and crossing). All scenarios show a reduction in RLR frequency (-7.8, -45.5, and -52.4%, respectively), but only increasing the yellow signal interval duration results in a reduced total number of conflicts (-11.3%; a surrogate safety measure of possible RLR-related crashes). An RLR camera makes the greatest reduction (-60.9%) in crossing conflicts (a surrogate safety measure of possible angle crashes), whereas increasing the yellow signal interval duration results in only a 12.8% reduction of rear-end conflicts (a surrogate safety measure of possible rear-end crash). Although increasing the yellow signal interval duration is advantageous because this reduces the total conflicts (a possibility of total RLR-related crashes), each countermeasure shows different effects by RLR-related conflict types that can be referred to when making a decision. Given that each intersection has different RLR crash issues, evaluated countermeasures are directly applicable to enhance the cost and time effectiveness, according to the situation of the target intersection. In addition, the proposed methodology is replicable at any site that has a dearth of crash data and/or comparison sites in order to test any other countermeasures (both engineering and enforcement countermeasures) for RLR crashes.

Chance-constrained multi-objective optimization of groundwater remediation design at DNAPLs-contaminated sites using a multi-algorithm genetically adaptive method.

PubMed

Ouyang, Qi; Lu, Wenxi; Hou, Zeyu; Zhang, Yu; Li, Shuai; Luo, Jiannan

2017-05-01

In this paper, a multi-algorithm genetically adaptive multi-objective (AMALGAM) method is proposed as a multi-objective optimization solver. It was implemented in the multi-objective optimization of a groundwater remediation design at sites contaminated by dense non-aqueous phase liquids. In this study, there were two objectives: minimization of the total remediation cost, and minimization of the remediation time. A non-dominated sorting genetic algorithm II (NSGA-II) was adopted to compare with the proposed method. For efficiency, the time-consuming surfactant-enhanced aquifer remediation simulation model was replaced by a surrogate model constructed by a multi-gene genetic programming (MGGP) technique. Similarly, two other surrogate modeling methods-support vector regression (SVR) and Kriging (KRG)-were employed to make comparisons with MGGP. In addition, the surrogate-modeling uncertainty was incorporated in the optimization model by chance-constrained programming (CCP). The results showed that, for the problem considered in this study, (1) the solutions obtained by AMALGAM incurred less remediation cost and required less time than those of NSGA-II, indicating that AMALGAM outperformed NSGA-II. It was additionally shown that (2) the MGGP surrogate model was more accurate than SVR and KRG; and (3) the remediation cost and time increased with the confidence level, which can enable decision makers to make a suitable choice by considering the given budget, remediation time, and reliability. Copyright © 2017 Elsevier B.V. All rights reserved.

Dual door entry to exciplex emission in a chimeric DNA duplex containing non-nucleoside-nucleoside pair.

PubMed

Bag, Subhendu Sekhar; Talukdar, Sangita; Kundu, Rajen; Saito, Isao; Jana, Subhashis

2014-01-25

Dual door entry to exciplex formation was established in a chimeric DNA duplex wherein a fluorescent non-nucleosidic base surrogate () is paired against a fluorescent nucleosidic base surrogate (). Packing of the nucleobases via intercalative stacking interactions led to an exciplex emission either via FRET from the donor or direct excitation of the FRET acceptor .

From Surrogacy to Contested Adoption: What Went Wrong?

ERIC Educational Resources Information Center

Greenfield, Joanna; Jennings, Seamus

1995-01-01

Notes the complex and emotive nature of surrogate motherhood. Describes a surrogacy arrangement that was apparently based initially on a clear agreement and partnership, but developed into a disputed adoption application when the surrogate mother became dissatisfied with contact arrangements. (HTH)

[Selection of "surrogate" and "endpoints" evaluation of the efficacy of medical interventions].

PubMed

Lazebnik, L B; Gusein-Zade, M G; Efremov, L I

2011-01-01

With the advent of new medical technologies and medicines, as well as due to changes in disease patterns and demographic problems rises the need for continued increases in health spending. Increased costs can be totally inadequate, if it has been done without studying the effectiveness of medical interventions, based on the results of evidence-based medicine and economic of their feasibility. To evaluate the clinical effectiveness of medical interventions have been recently used specific criteria, so called points of clinical efficacy (surrogate and endpoints), that allow to conclude feasibility or harmfulness of the introduction or application of the intervention in clinical practice. The endpoint is reliable indicator the effectiveness of medical intervention. Surrogate point--is a biomarker that is intended to replace the endpoint and is a predictor of the effectiveness of medical intervention. The use of surrogate points has several advantages such as simple in identification and measurement, as well as more higher in compare with endpoints the vents frequency, that can significantly reduce the size of the selection and duration and cost of clinical trials, respectively. Finally, the surrogate points allow to evaluate treatment effect in situations where the use of endpoints is difficult or is unethical.

Bayesian calibration of the Community Land Model using surrogates

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ray, Jaideep; Hou, Zhangshuan; Huang, Maoyi

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 errormore » 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.« less

Determination of oral mucosal Poisson's ratio and coefficient of friction from in-vivo contact pressure measurements.

PubMed

Chen, Junning; Suenaga, Hanako; Hogg, Michael; Li, Wei; Swain, Michael; Li, Qing

2016-01-01

Despite their considerable importance to biomechanics, there are no existing methods available to directly measure apparent Poisson's ratio and friction coefficient of oral mucosa. This study aimed to develop an inverse procedure to determine these two biomechanical parameters by utilizing in vivo experiment of contact pressure between partial denture and beneath mucosa through nonlinear finite element (FE) analysis and surrogate response surface (RS) modelling technique. First, the in vivo denture-mucosa contact pressure was measured by a tactile electronic sensing sheet. Second, a 3D FE model was constructed based on the patient CT images. Third, a range of apparent Poisson's ratios and the coefficients of friction from literature was considered as the design variables in a series of FE runs for constructing a RS surrogate model. Finally, the discrepancy between computed in silico and measured in vivo results was minimized to identify the best matching Poisson's ratio and coefficient of friction. The established non-invasive methodology was demonstrated effective to identify such biomechanical parameters of oral mucosa and can be potentially used for determining the biomaterial properties of other soft biological tissues.

Optimal Reference Strain Structure for Studying Dynamic Responses of Flexible Rockets

NASA Technical Reports Server (NTRS)

Tsushima, Natsuki; Su, Weihua; Wolf, Michael G.; Griffin, Edwin D.; Dumoulin, Marie P.

2017-01-01

In the proposed paper, the optimal design of reference strain structures (RSS) will be performed targeting for the accurate observation of the dynamic bending and torsion deformation of a flexible rocket. It will provide the detailed description of the finite-element (FE) model of a notional flexible rocket created in MSC.Patran. The RSS will be attached longitudinally along the side of the rocket and to track the deformation of the thin-walled structure under external loads. An integrated surrogate-based multi-objective optimization approach will be developed to find the optimal design of the RSS using the FE model. The Kriging method will be used to construct the surrogate model. For the data sampling and the performance evaluation, static/transient analyses will be performed with MSC.Natran/Patran. The multi-objective optimization will be solved with NSGA-II to minimize the difference between the strains of the launch vehicle and RSS. Finally, the performance of the optimal RSS will be evaluated by checking its strain-tracking capability in different numerical simulations of the flexible rocket.

Key stakeholders' perceptions of the acceptability and usefulness of a tablet-based tool to improve communication and shared decision making in ICUs.

PubMed

Ernecoff, Natalie C; Witteman, Holly O; Chon, Kristen; Chen, Yanquan Iris; Buddadhumaruk, Praewpannarai; Chiarchiaro, Jared; Shotsberger, Kaitlin J; Shields, Anne-Marie; Myers, Brad A; Hough, Catherine L; Carson, Shannon S; Lo, Bernard; Matthay, Michael A; Anderson, Wendy G; Peterson, Michael W; Steingrub, Jay S; Arnold, Robert M; White, Douglas B

2016-06-01

Although barriers to shared decision making in intensive care units are well documented, there are currently no easily scaled interventions to overcome these problems. We sought to assess stakeholders' perceptions of the acceptability, usefulness, and design suggestions for a tablet-based tool to support communication and shared decision making in ICUs. We conducted in-depth semi-structured interviews with 58 key stakeholders (30 surrogates and 28 ICU care providers). Interviews explored stakeholders' perceptions about the acceptability of a tablet-based tool to support communication and shared decision making, including the usefulness of modules focused on orienting families to the ICU, educating them about the surrogate's role, completing a question prompt list, eliciting patient values, educating about treatment options, eliciting perceptions about prognosis, and providing psychosocial support resources. The interviewer also elicited stakeholders' design suggestions for such a tool. We used constant comparative methods to identify key themes that arose during the interviews. Overall, 95% (55/58) of participants perceived the proposed tool to be acceptable, with 98% (57/58) of interviewees finding six or more of the seven content domains acceptable. Stakeholders identified several potential benefits of the tool including that it would help families prepare for the surrogate role and for family meetings as well as give surrogates time and a framework to think about the patient's values and treatment options. Key design suggestions included: conceptualize the tool as a supplement to rather than a substitute for surrogate-clinician communication; make the tool flexible with respect to how, where, and when surrogates can access the tool; incorporate interactive exercises; use video and narration to minimize the cognitive load of the intervention; and build an extremely simple user interface to maximize usefulness for individuals with low computer literacy. There is broad support among stakeholders for the use of a tablet-based tool to improve communication and shared decision making in ICUs. Eliciting the perspectives of key stakeholders early in the design process yielded important insights to create a tool tailored to the needs of surrogates and care providers in ICUs. Copyright © 2016 Elsevier Inc. All rights reserved.

A toxin-free enzyme-linked immunosorbent assay for the analysis of aflatoxins based on a VHH surrogate standard.

PubMed

Wang, Yanru; Li, Peiwu; Zhang, Qi; Hu, Xiaofeng; Zhang, Wen

2016-09-01

A toxin-free enzyme-linked immunosorbent assay (ELISA) for aflatoxins was developed using an anti-idiotype nanobody VHH 2-5 as surrogate standard. Anti-idiotype nanobody VHH 2-5 was generated by immunizing an alpaca with anti-aflatoxin monoclonal antibody 1C11. This assay was used to detect aflatoxins in agro-products after a simple extraction with 75 % methanol/H2O. Aflatoxin concentration was calculated by a two-step approach: the concentration of VHH 2-5 was first obtained by a four-parameter logistic regression from the detected absorbance value at 450 nm, and then converted to aflatoxin concentration by a linear equation. The assay exhibits a limit of detection (LOD) of 0.015 ng mL(-1), which is better than or comparable with conventional immunoassays. The performance of our VHH surrogate-based ELISA was further validated with a high-performance liquid chromatography (HPLC) method for total aflatoxins determination in 20 naturally contaminated peanut samples, displaying a good correlation (R (2) = 0.988). In conclusion, the proposed assay represents a first example applying an anti-idiotype VHH antibody as a standard surrogate in ELISA. With the advantages of high stability and ease of production, the VHH antibody-based standard surrogate can be extended in the future to immunoassays for other highly toxic compounds. Graphical Abstract ᅟ.

Validation of the Family Inpatient Communication Survey.

PubMed

Torke, Alexia M; Monahan, Patrick; Callahan, Christopher M; Helft, Paul R; Sachs, Greg A; Wocial, Lucia D; Slaven, James E; Montz, Kianna; Inger, Lev; Burke, Emily S

2017-01-01

Although many family members who make surrogate decisions report problems with communication, there is no validated instrument to accurately measure surrogate/clinician communication for older adults in the acute hospital setting. The objective of this study was to validate a survey of surrogate-rated communication quality in the hospital that would be useful to clinicians, researchers, and health systems. After expert review and cognitive interviewing (n = 10 surrogates), we enrolled 350 surrogates (250 development sample and 100 validation sample) of hospitalized adults aged 65 years and older from three hospitals in one metropolitan area. The communication survey and a measure of decision quality were administered within hospital days 3 and 10. Mental health and satisfaction measures were administered six to eight weeks later. Factor analysis showed support for both one-factor (Total Communication) and two-factor models (Information and Emotional Support). Item reduction led to a final 30-item scale. For the validation sample, internal reliability (Cronbach's alpha) was 0.96 (total), 0.94 (Information), and 0.90 (Emotional Support). Confirmatory factor analysis fit statistics were adequate (one-factor model, comparative fit index = 0.981, root mean square error of approximation = 0.62, weighted root mean square residual = 1.011; two-factor model comparative fit index = 0.984, root mean square error of approximation = 0.055, weighted root mean square residual = 0.930). Total score and subscales showed significant associations with the Decision Conflict Scale (Pearson correlation -0.43, P < 0.001 for total score). Emotional Support was associated with improved mental health outcomes at six to eight weeks, such as anxiety (-0.19 P < 0.001), and Information was associated with satisfaction with the hospital stay (0.49, P < 0.001). The survey shows high reliability and validity in measuring communication experiences for hospital surrogates. The scale has promise for measurement of communication quality and is predictive of important outcomes, such as surrogate satisfaction and well-being. Copyright © 2016 American Academy of Hospice and Palliative Medicine. Published by Elsevier Inc. All rights reserved.

Tumor response and progression-free survival as potential surrogate endpoints for overall survival in extensive stage small-cell lung cancer: findings on the basis of North Central Cancer Treatment Group trials.

PubMed

Foster, Nathan R; Qi, Yingwei; Shi, Qian; Krook, James E; Kugler, John W; Jett, James R; Molina, Julian R; Schild, Steven E; Adjei, Alex A; Mandrekar, Sumithra J

2011-03-15

The authors investigated the putative surrogate endpoints of best response, complete response (CR), confirmed response, and progression-free survival (PFS) for associations with overall survival (OS), and as possible surrogate endpoints for OS. Individual patient data from 870 untreated extensive stage small-cell lung cancer patients participating in 6 single-arm (274 patients) and 3 randomized trials (596 patients) were pooled. Patient-level associations between putative surrogate endpoints and OS were assessed by Cox models using landmark analyses. Trial-level surrogacy of putative surrogate endpoints were assessed by the association of treatment effects on OS and individual putative surrogate endpoints. Trial-level surrogacy measures included: R(2) from weighted least squares regression model, Spearman correlation coefficient, and R(2) from bivariate survival model (Copula R(2) ). Median OS and PFS were 9.6 (95% confidence interval [CI], 9.1-10.0) and 5.5 (95% CI, 5.2-5.9) months, respectively; best response, CR, and confirmed response rates were 44%, 22%, and 34%, respectively. Patient-level associations showed that PFS status at 4 months was a strong predictor of subsequent survival (hazard ratio [HR], 0.42; 95% CI, 0.35-0.51; concordance index 0.63; P < .01), with 6-month PFS being the strongest (HR, 0.41; 95% CI, 0.35-0.49; concordance index, 0.66, P < .01). At the trial level, PFS showed the highest level of surrogacy for OS (weighted least squares R(2) = 0.79; Copula R(2) = 0.80), explaining 79% of the variance in OS. Tumor response endpoints showed lower surrogacy levels (weighted least squares R(2) ≤0.48). PFS was strongly associated with OS at both the patient and trial levels. PFS also shows promise as a potential surrogate for OS, but further validation is needed using data from a larger number of randomized phase 3 trials. Copyright © 2010 American Cancer Society.

Total N-nitrosamine Precursor Adsorption with Carbon Nanotubes: Elucidating Controlling Physiochemical Properties and Developing a Size-Resolved Precursor Surrogate

NASA Astrophysics Data System (ADS)

Needham, Erin Michelle

As drinking water sources become increasingly impaired with nutrients and wastewater treatment plant (WWTP) effluent, formation of disinfection byproducts (DBPs)--such as trihalomethanes (THMs), dihaloacetonitriles (DHANs), and N-nitrosamines--during water treatment may also increase. N-nitrosamines may comprise the bulk of the chronic toxicity in treated drinking waters despite forming at low ng/L levels. This research seeks to elucidate physicochemical properties of carbon nanotubes (CNTs) for removal of DBP precursors, with an emphasis on total N-nitrosamines (TONO). Batch experiments with CNTs were completed to assess adsorption of THM, DHAN, and TONO precursors; physiochemical properties of CNTs were quantified through gas adsorption isotherms and x-ray photoelectron spectroscopy. Numerical modeling was used to elucidate characteristics of CNTs controlling DBP precursor adsorption. Multivariate models developed with unmodified CNTs revealed that surface carboxyl groups and, for TONO precursors, cumulative pore volume (CPV), controlled DBP precursor adsorption. Models developed with modified CNTs revealed that specific surface area controlled adsorption of THM and DHAN precursors while CPV and surface oxygen content were significant for adsorption of TONO precursors. While surrogates of THM and DHAN precursors leverage metrics from UV absorbance and fluorescence spectroscopy, a TONO precursor surrogate has proved elusive. This is important as measurements of TONO formation potential (TONOFP) require large sample volumes and long processing times, which impairs development of treatment processes. TONO precursor surrogates were developed using samples that had undergone oxidative or sorption treatments. Precursors were analyzed with asymmetric flow field-flow fractionation (AF4) with inline fluorescence detection (FLD) and whole water fluorescence excitation-emission matrices (EEMs). TONO precursor surrogates were discovered, capable of predicting changes in TONOFP in WWTP samples that have undergone oxidation (R2 = 0.996) and sorption (R2 = 0.576). Importantly, both surrogates only require just 2 mL of sample volume to measure and take only 1 hour. Application of the sorption precursor surrogate revealed that DBP precursor adsorption was feasible with freeform CNT microstructures with various dimensions and surface chemistries, establishing a framework for development of this novel CNT application for drinking water treatment.

Bioaccessibility of metals in alloys: Evaluation of three surrogate biofluids

PubMed Central

Hillwalker, Wendy E.; Anderson, Kim A.

2014-01-01

Bioaccessibility in vitro tests measure the solubility of materials in surrogate biofluids. However, the lack of uniform methods and the effects of variable test parameters on material solubility limit interpretation. One aim of this study was to measure and compare bioaccessibility of selected economically important alloys and metals in surrogate physiologically based biofluids representing oral, inhalation and dermal exposures. A second aim was to experimentally test different biofluid formulations and residence times in vitro. A third aim was evaluation of dissolution behavior of alloys with in vitro lung and dermal biofluid surrogates. This study evaluated the bioaccessibility of sixteen elements in six alloys and 3 elemental/metal powders. We found that the alloys/metals, the chemical properties of the surrogate fluid, and residence time all had major impacts on metal solubility. The large variability of bioaccessibility indicates the relevancy of assessing alloys as toxicologically distinct relative to individual metals. PMID:24212234

The virucidal effects against murine norovirus and feline calicivirus F4 as surrogates for human norovirus by the different additive concentrations of ethanol-based sanitizers.

PubMed

Akasaka, Tempei; Shimizu-Onda, Yuko; Hayakawa, Satoshi; Ushijima, Hiroshi

2016-03-01

Since human norovirus is non-cultivable, murine norovirus and feline calicivirus have been used as surrogates. In this study, the virucidal effects of ethanol-based sanitizers with different concentrations of additives (malic acid/sodium malate, glycerin-fatty acid ester) against murine norovirus and feline calicivirus F4 were examined. The ethanol-based sanitizers at pH 7 showed sufficient virucidal effects, but glycerin-fatty acid ester included in ethanol-based sanitizers at pH 4 or 6 reduced the virucidal effects against murine norovirus. The ethanol-based sanitizers containing malic acid/sodium malate inactivated feline calicivirus F4 in shorter time, but there is no difference between ethanol-based sanitizers with and without glycerin-fatty acid ester. Traditionally, feline calicivirus has been used for long time as a surrogate virus for human norovirus. However, this study suggested that murine norovirus and feline calicivirus F4 had different sensitivity with the additive components of ethanol-based sanitizers. Therefore, using feline calicivirus alone as a surrogate for human norovirus may not be sufficient to evaluate the virucidal effect of sanitizers on food-borne infections caused by human norovirus. Sanitizers having virucidal effects against at least both murine norovirus and feline calicivirus may be more suitable to inactivate human norovirus. Copyright © 2015. Published by Elsevier Ltd.

Uncertainty Quantification in CO 2 Sequestration Using Surrogate Models from Polynomial Chaos Expansion

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Yan; Sahinidis, Nikolaos V.

2013-03-06

In this paper, surrogate models are iteratively built using polynomial chaos expansion (PCE) and detailed numerical simulations of a carbon sequestration system. Output variables from a numerical simulator are approximated as polynomial functions of uncertain parameters. Once generated, PCE representations can be used in place of the numerical simulator and often decrease simulation times by several orders of magnitude. However, PCE models are expensive to derive unless the number of terms in the expansion is moderate, which requires a relatively small number of uncertain variables and a low degree of expansion. To cope with this limitation, instead of using amore » classical full expansion at each step of an iterative PCE construction method, we introduce a mixed-integer programming (MIP) formulation to identify the best subset of basis terms in the expansion. This approach makes it possible to keep the number of terms small in the expansion. Monte Carlo (MC) simulation is then performed by substituting the values of the uncertain parameters into the closed-form polynomial functions. Based on the results of MC simulation, the uncertainties of injecting CO{sub 2} underground are quantified for a saline aquifer. Moreover, based on the PCE model, we formulate an optimization problem to determine the optimal CO{sub 2} injection rate so as to maximize the gas saturation (residual trapping) during injection, and thereby minimize the chance of leakage.« less

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

PubMed Central

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

2014-01-01

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

A new algorithm combining geostatistics with the surrogate data approach to increase the accuracy of comparisons of point radiation measurements with cloud measurements

NASA Astrophysics Data System (ADS)

Venema, V. K. C.; Lindau, R.; Varnai, T.; Simmer, C.

2009-04-01

Two main groups of statistical methods used in the Earth sciences are geostatistics and stochastic modelling. Geostatistical methods, such as various kriging algorithms, aim at estimating the mean value for every point as well as possible. In case of sparse measurements, such fields have less variability at small scales and a narrower distribution as the true field. This can lead to biases if a nonlinear process is simulated on such a kriged field. Stochastic modelling aims at reproducing the structure of the data. One of the stochastic modelling methods, the so-called surrogate data approach, replicates the value distribution and power spectrum of a certain data set. However, while stochastic methods reproduce the statistical properties of the data, the location of the measurement is not considered. Because radiative transfer through clouds is a highly nonlinear process it is essential to model the distribution (e.g. of optical depth, extinction, liquid water content or liquid water path) accurately as well as the correlations in the cloud field because of horizontal photon transport. This explains the success of surrogate cloud fields for use in 3D radiative transfer studies. However, up to now we could only achieve good results for the radiative properties averaged over the field, but not for a radiation measurement located at a certain position. Therefore we have developed a new algorithm that combines the accuracy of stochastic (surrogate) modelling with the positioning capabilities of kriging. In this way, we can automatically profit from the large geostatistical literature and software. The algorithm is tested on cloud fields from large eddy simulations (LES). On these clouds a measurement is simulated. From the pseudo-measurement we estimated the distribution and power spectrum. Furthermore, the pseudo-measurement is kriged to a field the size of the final surrogate cloud. The distribution, spectrum and the kriged field are the inputs to the algorithm. This algorithm is similar to the standard iterative amplitude adjusted Fourier transform (IAAFT) algorithm, but has an additional iterative step in which the surrogate field is nudged towards the kriged field. The nudging strength is gradually reduced to zero. We work with four types of pseudo-measurements: one zenith pointing measurement (which together with the wind produces a line measurement), five zenith pointing measurements, a slow and a fast azimuth scan (which together with the wind produce spirals). Because we work with LES clouds and the truth is known, we can validate the algorithm by performing 3D radiative transfer calculations on the original LES clouds and on the new surrogate clouds. For comparison also the radiative properties of the kriged fields and standard surrogate fields are computed. Preliminary results already show that these new surrogate clouds reproduce the structure of the original clouds very well and the minima and maxima are located where the pseudo-measurements sees them. The main limitation seems to be the amount of data, which is especially very limited in case of just one zenith pointing measurement.

Surrogate decision making and intellectual virtue.

PubMed

Bock, Gregory L

2014-01-01

Patients can be harmed by a religiously motivated surrogate decision maker whose decisions are contrary to the standard of care; therefore, surrogate decision making should be held to a high standard. Stewart Eskew and Christopher Meyers proposed a two-part rule for deciding which religiously based decisions to honor: (1) a secular reason condition and (2) a rationality condition. The second condition is based on a coherence theory of rationality, which they claim is accessible, generous, and culturally sensitive. In this article, I will propose strengthening the rationality condition by grounding it in a theory of intellectual virtue, which is both rigorous and culturally sensitive. Copyright 2014 The Journal of Clinical Ethics. All rights reserved.

Testing for nonlinearity in non-stationary physiological time series.

PubMed

Guarín, Diego; Delgado, Edilson; Orozco, Álvaro

2011-01-01

Testing for nonlinearity is one of the most important preprocessing steps in nonlinear time series analysis. Typically, this is done by means of the linear surrogate data methods. But it is a known fact that the validity of the results heavily depends on the stationarity of the time series. Since most physiological signals are non-stationary, it is easy to falsely detect nonlinearity using the linear surrogate data methods. In this document, we propose a methodology to extend the procedure for generating constrained surrogate time series in order to assess nonlinearity in non-stationary data. The method is based on the band-phase-randomized surrogates, which consists (contrary to the linear surrogate data methods) in randomizing only a portion of the Fourier phases in the high frequency domain. Analysis of simulated time series showed that in comparison to the linear surrogate data method, our method is able to discriminate between linear stationarity, linear non-stationary and nonlinear time series. Applying our methodology to heart rate variability (HRV) records of five healthy patients, we encountered that nonlinear correlations are present in this non-stationary physiological signals.

Production of cloned NIBS (Nippon Institute for Biological Science) and α-1, 3-galactosyltransferase knockout MGH miniature pigs by somatic cell nuclear transfer using the NIBS breed as surrogates.

PubMed

Shimatsu, Yoshiki; Yamada, Kazuhiko; Horii, Wataru; Hirakata, Atsushi; Sakamoto, Yuji; Waki, Shiori; Sano, Junichi; Saitoh, Toshiki; Sahara, Hisashi; Shimizu, Akira; Yazawa, Hajime; Sachs, David H; Nunoya, Tetsuo

2013-01-01

Nuclear transfer (NT) technologies offer a means for producing the genetically modified pigs necessary to develop swine models for mechanistic studies of disease processes as well as to serve as organ donors for xenotransplantation. Most previous studies have used commercial pigs as surrogates. In this study, we established a cloning technique for miniature pigs by somatic cell nuclear transfer (SCNT) using Nippon Institute for Biological Science (NIBS) miniature pigs as surrogates. Moreover, utilizing this technique, we have successfully produced an α-1, 3-galactosyltransferase knockout (GalT-KO) miniature swine. Fibroblasts procured from a NIBS miniature pig fetus were injected into 1312 enucleated oocytes. The cloned embryos were transferred to 11 surrogates of which five successfully delivered 13 cloned offspring; the production efficiency was 1.0% (13/1312). In a second experiment, lung fibroblasts obtained from neonatal GalT-KO MGH miniature swine were used as donor cells and 1953 cloned embryos were transferred to 12 surrogates. Six cloned offspring were born from five surrogates, a production efficiency of 0.3% (6/1953). These results demonstrate successful establishment of a miniature pig cloning technique by SCNT using NIBS miniature pigs as surrogates. To our knowledge, this is the first demonstration of successful production of GalT-KO miniature swine using miniature swine surrogates. This technique could help to ensure a stable supply of the cloned pigs through the use of miniature pig surrogates and could expand production in countries with limited space or in facilities with special regulations such as specific pathogen-free or good laboratory practice. © 2013 John Wiley & Sons A/S.

Production of cloned NIBS (Nippon Institute for Biological Science) and α-1, 3-galactosyltransferase knockout MGH miniature pigs by somatic cell nuclear transfer using the NIBS breed as surrogates

PubMed Central

Shimatsu, Yoshiki; Yamada, Kazuhiko; Horii, Wataru; Hirakata, Atsushi; Sakamoto, Yuji; Waki, Shiori; Sano, Junichi; Saitoh, Toshiki; Sahara, Hisashi; Shimizu, Akira; Yazawa, Hajime; Sachs, David H.; Nunoya, Tetsuo

2013-01-01

Background Nuclear transfer (NT) technologies offer a means for producing the genetically modified pigs necessary to develop swine models for mechanistic studies of disease processes as well as to serve as organ donors for xenotransplantation. Most previous studies have used commercial pigs as surrogates. Method and Results In this study, we established a cloning technique for miniature pigs by somatic cell nuclear transfer (SCNT) using Nippon Institute for Biological Science (NIBS) miniature pigs as surrogates. Moreover, utilizing this technique, we have successfully produced an α-1, 3-galactosyltransferase knockout (GalT-KO) miniature swine. Fibroblasts procured from a NIBS miniature pig fetus were injected into 1312 enucleated oocytes. The cloned embryos were transferred to 11 surrogates of which five successfully delivered 13 cloned offspring; the production efficiency was 1.0% (13/1312). In a second experiment, lung fibroblasts obtained from neonatal GalT-KO MGH miniature swine were used as donor cells and 1953 cloned embryos were transferred to 12 surrogates. Six cloned offspring were born from five surrogates, a production efficiency of 0.3% (6/1953). Conclusions These results demonstrate successful establishment of a miniature pig cloning technique by SCNT using NIBS miniature pigs as surrogates. To our knowledge, this is the first demonstration of successful production of GalT-KO miniature swine using miniature swine surrogates. This technique could help to ensure a stable supply of the cloned pigs through the use of miniature pig surrogates and could expand production in countries with limited space or in facilities with special regulations such as specific pathogen-free or good laboratory practice. PMID:23581451

Perinatal outcomes after natural conception versus in vitro fertilization (IVF) in gestational surrogates: a model to evaluate IVF treatment versus maternal effects.

PubMed

Woo, Irene; Hindoyan, Rita; Landay, Melanie; Ho, Jacqueline; Ingles, Sue Ann; McGinnis, Lynda K; Paulson, Richard J; Chung, Karine

2017-12-01

To study the perinatal outcomes between singleton live births achieved with the use of commissioned versus spontaneously conceived embryos carried by the same gestational surrogate. Retrospective cohort study. Academic in vitro fertilization center. Gestational surrogate. None. Pregnancy outcome, gestational age at birth, birth weight, perinatal complications. We identified 124 gestational surrogates who achieved a total of 494 pregnancies. Pregnancy outcomes for surrogate and spontaneous pregnancies were significantly different (P<.001), with surrogate pregnancies more likely to result in twin pregnancies: 33% vs. 1%. Miscarriage and ectopic rates were similar. Of these pregnancies, there were 352 singleton live births: 103 achieved from commissioned embryos and 249 conceived spontaneously. Surrogate births had lower mean gestational age at delivery (38.8 ± 2.1 vs. 39.7 ± 1.4), higher rates of preterm birth (10.7% vs. 3.1%), and higher rates of low birth weight (7.8% vs. 2.4%). Neonates from surrogacy had birth weights that were, on average, 105 g lower. Surrogate births had significantly higher obstetrical complications, including gestational diabetes, hypertension, use of amniocentesis, placenta previa, antibiotic requirement during labor, and cesarean section. Neonates born from commissioned embryos and carried by gestational surrogates have increased adverse perinatal outcomes, including preterm birth, low birth weight, hypertension, maternal gestational diabetes, and placenta previa, compared with singletons conceived spontaneously and carried by the same woman. Our data suggest that assisted reproductive procedures may potentially affect embryo quality and that its negative impact can not be overcome even with a proven healthy uterine environment. Copyright © 2017 American Society for Reproductive Medicine. Published by Elsevier Inc. All rights reserved.

Statistical Validation of Surrogate Endpoints: Another Look at the Prentice Criterion and Other Criteria.

PubMed

Saraf, Sanatan; Mathew, Thomas; Roy, Anindya

2015-01-01

For the statistical validation of surrogate endpoints, an alternative formulation is proposed for testing Prentice's fourth criterion, under a bivariate normal model. In such a setup, the criterion involves inference concerning an appropriate regression parameter, and the criterion holds if the regression parameter is zero. Testing such a null hypothesis has been criticized in the literature since it can only be used to reject a poor surrogate, and not to validate a good surrogate. In order to circumvent this, an equivalence hypothesis is formulated for the regression parameter, namely the hypothesis that the parameter is equivalent to zero. Such an equivalence hypothesis is formulated as an alternative hypothesis, so that the surrogate endpoint is statistically validated when the null hypothesis is rejected. Confidence intervals for the regression parameter and tests for the equivalence hypothesis are proposed using bootstrap methods and small sample asymptotics, and their performances are numerically evaluated and recommendations are made. The choice of the equivalence margin is a regulatory issue that needs to be addressed. The proposed equivalence testing formulation is also adopted for other parameters that have been proposed in the literature on surrogate endpoint validation, namely, the relative effect and proportion explained.

Partnership for Edge Physics (EPSI), University of Texas Final Report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Moser, Robert; Carey, Varis; Michoski, Craig

Simulations of tokamak plasmas require a number of inputs whose values are uncertain. The effects of these input uncertainties on the reliability of model predictions is of great importance when validating predictions by comparison to experimental observations, and when using the predictions for design and operation of devices. However, high fidelity simulation of tokamak plasmas, particular those aimed at characterization of the edge plasma physics, are computationally expensive, so lower cost surrogates are required to enable practical uncertainty estimates. Two surrogate modeling techniques have been explored in the context of tokamak plasma simulations using the XGC family of plasma simulationmore » codes. The first is a response surface surrogate, and the second is an augmented surrogate relying on scenario extrapolation. In addition, to reduce the costs of the XGC simulations, a particle resampling algorithm was developed, which allows marker particle distributions to be adjusted to maintain optimal importance sampling. This means that the total number of particles in and therefore the cost of a simulation can be reduced while maintaining the same accuracy.« less

Deliberative assessment of surrogate consent in dementia research.

PubMed

Kim, Scott Y H; Uhlmann, Rebecca A; Appelbaum, Paul S; Knopman, David S; Kim, H Myra; Damschroder, Laura; Beattie, Elizabeth; Struble, Laura; De Vries, Raymond

2010-07-01

Research involving incapacitated persons with dementia entails complex scientific, legal, and ethical issues, making traditional surveys of layperson views on the ethics of such research challenging. We therefore assessed the impact of democratic deliberation (DD), involving balanced, detailed education and peer deliberation, on the views of those responsible for persons with dementia. One hundred and seventy-eight community-recruited caregivers or primary decision-makers for persons with dementia were randomly assigned to either an all-day DD session group or a control group. Educational materials used for the DD session were vetted for balance and accuracy by an interdisciplinary advisory panel. We assessed the acceptability of family-surrogate consent for dementia research ("surrogate-based research") from a societal policy perspective as well as from the more personal perspectives of deciding for a loved one or for oneself (surrogate and self-perspectives), assessed at baseline, immediately post-DD session, and 1 month after DD date, for four research scenarios of varying risk-benefit profiles. At baseline, a majority in both the DD and control groups supported a policy of family consent for dementia research in all research scenarios. The support for a policy of family consent for surrogate-based research increased in the DD group, but not in the control group. The change in the DD group was maintained 1 month later. In the DD group, there were transient changes in attitudes from surrogate or self-perspectives. In the control group, there were no changes from baseline in attitude toward surrogate consent from any perspective. Intensive, balanced, and accurate education, along with peer deliberation provided by democratic deliberation, led to a sustained increase in support for a societal policy of family consent in dementia research among those responsible for dementia patients. Copyright 2010 The Alzheimer

Standardized Procedures for Use of Nucleic Acid-Based Tools

DTIC Science & Technology

2008-08-01

Dhc size or cell wall characteristics including Brevundimonas diminuta (small), Micrococcus sp. (small coccoid) and Halobacterium sp. (Dhc-like cell...Halobacterium species as a Dhc surrogate. Another potential surrogate based on size and shape is Micrococcus species including Micrococcus luteus... Micrococcus luteus (ATCC-4442) are relatively small (1,000 nm) spherical bacteria (Madigan et al., 2006). Due to the fact that introduction and

Optimizing Parameters of Axial Pressure-Compounded Ultra-Low Power Impulse Turbines at Preliminary Design

NASA Astrophysics Data System (ADS)

Kalabukhov, D. S.; Radko, V. M.; Grigoriev, V. A.

2018-01-01

Ultra-low power turbine drives are used as energy sources in auxiliary power systems, energy units, terrestrial, marine, air and space transport within the confines of shaft power N td = 0.01…10 kW. In this paper we propose a new approach to the development of surrogate models for evaluating the integrated efficiency of multistage ultra-low power impulse turbine with pressure stages. This method is based on the use of existing mathematical models of ultra-low power turbine stage efficiency and mass. It has been used in a method for selecting the rational parameters of two-stage axial ultra-low power turbine. The article describes the basic features of an algorithm for two-stage turbine parameters optimization and for efficiency criteria evaluating. Pledged mathematical models are intended for use at the preliminary design of turbine drive. The optimization method was tested at preliminary design of an air starter turbine. Validation was carried out by comparing the results of optimization calculations and numerical gas-dynamic simulation in the Ansys CFX package. The results indicate a sufficient accuracy of used surrogate models for axial two-stage turbine parameters selection

Numerical modeling anti-personnel blast mines coupled to a deformable leg structure

NASA Astrophysics Data System (ADS)

Cronin, Duane; Worswick, Mike; Williams, Kevin; Bourget, Daniel; Pageau, Gilles

2001-06-01

The development of improved landmine protective footwear requires an understanding of the physics and damage mechanisms associated with a close proximity blast event. Numerical models have been developed to model surrogate mines buried in soil using the Arbitrary Lagrangian Eulerian (ALE) technique to model the explosive and surrounding air, while the soil is modeled as a deformable Lagrangian solid. The advantage of the ALE model is the ability to model large deformations, such as the expanding gases of a high explosive. This model has been validated using the available experimental data [1]. The effect of varying depth of burial and soil conditions has been investigated with these numerical models and compares favorably to data in the literature. The surrogate landmine model has been coupled to a numerical model of a Simplified Lower Leg (SLL), which is designed to mimic the response and failure mechanisms of a human leg. The SLL consists of a bone and tissue simulant arranged as concentric cylinders. A new strain-rate dependant hyperelastic material model for the tissue simulant, ballistic gelatin, has been developed to model the tissue simulant response. The polymeric bone simulant material has been characterized and implemented as a strain-rate dependent material in the numerical model. The numerical model results agree with the measured response of the SLL during experimental blast tests [2]. The numerical model results are used to explain the experimental data. These models predict that, for a surface or sub-surface buried anti-personnel mine, the coupling between the mine and SLL is an important effect. In addition, the soil properties have a significant effect on the load transmitted to the leg. [1] Bergeron, D., Walker, R. and Coffey, C., 1998, “Detonation of 100-Gram Anti-Personnel Mine Surrogate Charges in Sand”, Report number SR 668, Defence Research Establishment Suffield, Canada. [2] Bourget, D., Williams, K., Pageau, G., and Cronin, D., “AP Mine Blast Effects on Surrogate Lower Leg”, Military Aspects of Ballistics and Shock, MABS 16, 2000.

Surrogate health information seeking in Europe: Influence of source type and social network variables.

PubMed

Reifegerste, Doreen; Bachl, Marko; Baumann, Eva

2017-07-01

Health information seeking on behalf of others is an important form of social support by which laypeople provide important sources of information for patients. Based on social network theory, we analyze whether this phenomenon also occurs in offline sources. We also seek to learn more about the type of relationships between information seekers and patients, as research to date indicates that surrogate seeking mostly occurs in close relationships between the seeker and the patient. Using a large-scale representative survey from the 28 member states of the European Union (N=26,566), our data comprise all respondents who reported seeking health information online or offline (n=18,750; 70.6%). Within the past year, 61.0% of the online health information seekers and 61.1% of the offline health information seekers had searched on behalf of someone else. Independent of the information channel, surrogate seekers primarily searched for health information for family members (online: 89.8%; offline: 92.8%); they were significantly less likely to search for information on behalf of someone with whom they had weaker ties, such as colleagues (online: 25.1%; offline: 24.4%). In a multilevel generalized linear model, living together with someone was by far the most relevant determinant for surrogate seeking, with differences between countries or Internet activity being less important. These results support the assumptions of social network theory. Implications are discussed, especially with regard to the provision of adequate health information. Copyright © 2017 Elsevier B.V. All rights reserved.

Magnitude and sign of long-range correlated time series: Decomposition and surrogate signal generation.

PubMed

Gómez-Extremera, Manuel; Carpena, Pedro; Ivanov, Plamen Ch; Bernaola-Galván, Pedro A

2016-04-01

We systematically study the scaling properties of the magnitude and sign of the fluctuations in correlated time series, which is a simple and useful approach to distinguish between systems with different dynamical properties but the same linear correlations. First, we decompose artificial long-range power-law linearly correlated time series into magnitude and sign series derived from the consecutive increments in the original series, and we study their correlation properties. We find analytical expressions for the correlation exponent of the sign series as a function of the exponent of the original series. Such expressions are necessary for modeling surrogate time series with desired scaling properties. Next, we study linear and nonlinear correlation properties of series composed as products of independent magnitude and sign series. These surrogate series can be considered as a zero-order approximation to the analysis of the coupling of magnitude and sign in real data, a problem still open in many fields. We find analytical results for the scaling behavior of the composed series as a function of the correlation exponents of the magnitude and sign series used in the composition, and we determine the ranges of magnitude and sign correlation exponents leading to either single scaling or to crossover behaviors. Finally, we obtain how the linear and nonlinear properties of the composed series depend on the correlation exponents of their magnitude and sign series. Based on this information we propose a method to generate surrogate series with controlled correlation exponent and multifractal spectrum.

Assessing rear-end collision risk of cars and heavy vehicles on freeways using a surrogate safety measure.

PubMed

Zhao, Peibo; Lee, Chris

2018-04-01

This study analyzes rear-end collision risk of cars and heavy vehicles on freeways using a surrogate safety measure. The crash potential index (CPI) was modified to reflect driver's reaction time and estimated by types of lead and following vehicles (car or heavy vehicle). CPIs were estimated using the individual vehicle trajectory data from a segment of the US-101 freeway in Los Angeles, U.S.A. It was found that the CPI was generally higher for the following heavy vehicle than the following car due to heavy vehicle's lower braking capability. This study also validates the CPI using the simulated traffic data which replicate the observed traffic conditions a few minutes before the crash time upstream and downstream of the crash locations. The observed data were obtained from crash records and loop detectors on a section of the Gardiner Expressway in Toronto, Canada. The result shows that the values of CPI were consistently higher during the traffic conditions immediately before the crash time (crash case) than the normal traffic conditions (non-crash case). This demonstrates that the CPI can be used to capture rear-end collision risk during car-following maneuver on freeways. The result also shows that rear-end collision risk is lower for heavy vehicles than cars in the crash case due to their shorter reaction time and lower speed when spacing is shorter. Thus, it is important to reflect the differences in driver behavior and vehicle performance characteristics between cars and heavy vehicles in estimating surrogate safety measures. Lastly, it was found that the CPI-based crash prediction model can correctly identify the crash and non-crash cases at higher accuracy than the other crash prediction models based on detectors. Copyright © 2018 Elsevier Ltd. All rights reserved.

Effects of Exposure Measurement Error in the Analysis of Health Effects from Traffic-Related Air Pollution

EPA Science Inventory

In large epidemiological studies, many researchers use surrogates of air pollution exposure such as geographic information system (GIS)-based characterizations of traffic or simple housing characteristics. It is important to validate these surrogates against measured pollutant co...

VOLATILE ORGANIC COMPOUND DETERMINATIONS USING SURROGATE-BASED CORRECTION FOR METHOD AND MATRIX EFFECTS

EPA Science Inventory

The principal properties related to analyte recovery in a vacuum distillate are boiling point and relative volatility. The basis for selecting compounds to measure the relationship between these properties and recovery for a vacuum distillation is presented. Surrogates are incorp...

Comparison of organs' shapes with geometric and Zernike 3D moments.

PubMed

Broggio, D; Moignier, A; Ben Brahim, K; Gardumi, A; Grandgirard, N; Pierrat, N; Chea, M; Derreumaux, S; Desbrée, A; Boisserie, G; Aubert, B; Mazeron, J-J; Franck, D

2013-09-01

The morphological similarity of organs is studied with feature vectors based on geometric and Zernike 3D moments. It is particularly investigated if outliers and average models can be identified. For this purpose, the relative proximity to the mean feature vector is defined, principal coordinate and clustering analyses are also performed. To study the consistency and usefulness of this approach, 17 livers and 76 hearts voxel models from several sources are considered. In the liver case, models with similar morphological feature are identified. For the limited amount of studied cases, the liver of the ICRP male voxel model is identified as a better surrogate than the female one. For hearts, the clustering analysis shows that three heart shapes represent about 80% of the morphological variations. The relative proximity and clustering analysis rather consistently identify outliers and average models. For the two cases, identification of outliers and surrogate of average models is rather robust. However, deeper classification of morphological feature is subject to caution and can only be performed after cross analysis of at least two kinds of feature vectors. Finally, the Zernike moments contain all the information needed to re-construct the studied objects and thus appear as a promising tool to derive statistical organ shapes. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Overcoming complexities: Damage detection using dictionary learning framework

NASA Astrophysics Data System (ADS)

Alguri, K. Supreet; Melville, Joseph; Deemer, Chris; Harley, Joel B.

2018-04-01

For in situ damage detection, guided wave structural health monitoring systems have been widely researched due to their ability to evaluate large areas and their ability detect many types of damage. These systems often evaluate structural health by recording initial baseline measurements from a pristine (i.e., undamaged) test structure and then comparing later measurements with that baseline. Yet, it is not always feasible to have a pristine baseline. As an alternative, substituting the baseline with data from a surrogate (nearly identical and pristine) structure is a logical option. While effective in some circumstance, surrogate data is often still a poor substitute for pristine baseline measurements due to minor differences between the structures. To overcome this challenge, we present a dictionary learning framework to adapt surrogate baseline data to better represent an undamaged test structure. We compare the performance of our framework with two other surrogate-based damage detection strategies: (1) using raw surrogate data for comparison and (2) using sparse wavenumber analysis, a precursor to our framework for improving the surrogate data. We apply our framework to guided wave data from two 108 mm by 108 mm aluminum plates. With 20 measurements, we show that our dictionary learning framework achieves a 98% accuracy, raw surrogate data achieves a 92% accuracy, and sparse wavenumber analysis achieves a 57% accuracy.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ebeida, Mohamed S.; Mitchell, Scott A.; Swiler, Laura P.

We introduce a novel technique, POF-Darts, to estimate the Probability Of Failure based on random disk-packing in the uncertain parameter space. POF-Darts uses hyperplane sampling to explore the unexplored part of the uncertain space. We use the function evaluation at a sample point to determine whether it belongs to failure or non-failure regions, and surround it with a protection sphere region to avoid clustering. We decompose the domain into Voronoi cells around the function evaluations as seeds and choose the radius of the protection sphere depending on the local Lipschitz continuity. As sampling proceeds, regions uncovered with spheres will shrink,more » improving the estimation accuracy. After exhausting the function evaluation budget, we build a surrogate model using the function evaluations associated with the sample points and estimate the probability of failure by exhaustive sampling of that surrogate. In comparison to other similar methods, our algorithm has the advantages of decoupling the sampling step from the surrogate construction one, the ability to reach target POF values with fewer samples, and the capability of estimating the number and locations of disconnected failure regions, not just the POF value. Furthermore, we present various examples to demonstrate the efficiency of our novel approach.« less

The Impact of Parametric Uncertainties on Biogeochemistry in the E3SM Land Model

NASA Astrophysics Data System (ADS)

Ricciuto, Daniel; Sargsyan, Khachik; Thornton, Peter

2018-02-01

We conduct a global sensitivity analysis (GSA) of the Energy Exascale Earth System Model (E3SM), land model (ELM) to calculate the sensitivity of five key carbon cycle outputs to 68 model parameters. This GSA is conducted by first constructing a Polynomial Chaos (PC) surrogate via new Weighted Iterative Bayesian Compressive Sensing (WIBCS) algorithm for adaptive basis growth leading to a sparse, high-dimensional PC surrogate with 3,000 model evaluations. The PC surrogate allows efficient extraction of GSA information leading to further dimensionality reduction. The GSA is performed at 96 FLUXNET sites covering multiple plant functional types (PFTs) and climate conditions. About 20 of the model parameters are identified as sensitive with the rest being relatively insensitive across all outputs and PFTs. These sensitivities are dependent on PFT, and are relatively consistent among sites within the same PFT. The five model outputs have a majority of their highly sensitive parameters in common. A common subset of sensitive parameters is also shared among PFTs, but some parameters are specific to certain types (e.g., deciduous phenology). The relative importance of these parameters shifts significantly among PFTs and with climatic variables such as mean annual temperature.

Surrogate models for sheet metal stamping problem based on the combination of proper orthogonal decomposition and radial basis function

NASA Astrophysics Data System (ADS)

Dang, Van Tuan; Lafon, Pascal; Labergere, Carl

2017-10-01

In this work, a combination of Proper Orthogonal Decomposition (POD) and Radial Basis Function (RBF) is proposed to build a surrogate model based on the Benchmark Springback 3D bending from the Numisheet2011 congress. The influence of the two design parameters, the geometrical parameter of the die radius and the process parameter of the blank holder force, on the springback of the sheet after a stamping operation is analyzed. The classical Design of Experience (DoE) uses Full Factorial to design the parameter space with sample points as input data for finite element method (FEM) numerical simulation of the sheet metal stamping process. The basic idea is to consider the design parameters as additional dimensions for the solution of the displacement fields. The order of the resultant high-fidelity model is reduced through the use of POD method which performs model space reduction and results in the basis functions of the low order model. Specifically, the snapshot method is used in our work, in which the basis functions is derived from snapshot deviation of the matrix of the final displacements fields of the FEM numerical simulation. The obtained basis functions are then used to determine the POD coefficients and RBF is used for the interpolation of these POD coefficients over the parameter space. Finally, the presented POD-RBF approach which is used for shape optimization can be performed with high accuracy.

On the applicability of surrogate-based Markov chain Monte Carlo-Bayesian inversion to the Community Land Model: Case studies at flux tower sites: SURROGATE-BASED MCMC FOR CLM

DOE Office of Scientific and Technical Information (OSTI.GOV)

Huang, Maoyi; Ray, Jaideep; Hou, Zhangshuan

2016-07-04

The Community Land Model (CLM) has been widely used in climate and Earth system modeling. Accurate estimation of model parameters is needed for reliable model simulations and predictions under current and future conditions, respectively. In our previous work, a subset of hydrological parameters has been identified to have significant impact on surface energy fluxes at selected flux tower sites based on parameter screening and sensitivity analysis, which indicate that the parameters could potentially be estimated from surface flux observations at the towers. To date, such estimates do not exist. In this paper, we assess the feasibility of applying a Bayesianmore » model calibration technique to estimate CLM parameters at selected flux tower sites under various site conditions. The parameters are estimated as a joint probability density function (PDF) that provides estimates of uncertainty of the parameters being inverted, conditional on climatologically-average latent heat fluxes derived from observations. We find that the simulated mean latent heat fluxes from CLM using the calibrated parameters are generally improved at all sites when compared to those obtained with CLM simulations using default parameter sets. Further, our calibration method also results in credibility bounds around the simulated mean fluxes which bracket the measured data. The modes (or maximum a posteriori values) and 95% credibility intervals of the site-specific posterior PDFs are tabulated as suggested parameter values for each site. Analysis of relationships between the posterior PDFs and site conditions suggests that the parameter values are likely correlated with the plant functional type, which needs to be confirmed in future studies by extending the approach to more sites.« less

Dynamics of intracranial electroencephalographic recordings from epilepsy patients using univariate and bivariate recurrence networks.

PubMed

Subramaniyam, Narayan Puthanmadam; Hyttinen, Jari

2015-02-01

Recently Andrezejak et al. combined the randomness and nonlinear independence test with iterative amplitude adjusted Fourier transform (iAAFT) surrogates to distinguish between the dynamics of seizure-free intracranial electroencephalographic (EEG) signals recorded from epileptogenic (focal) and nonepileptogenic (nonfocal) brain areas of epileptic patients. However, stationarity is a part of the null hypothesis for iAAFT surrogates and thus nonstationarity can violate the null hypothesis. In this work we first propose the application of the randomness and nonlinear independence test based on recurrence network measures to distinguish between the dynamics of focal and nonfocal EEG signals. Furthermore, we combine these tests with both iAAFT and truncated Fourier transform (TFT) surrogate methods, which also preserves the nonstationarity of the original data in the surrogates along with its linear structure. Our results indicate that focal EEG signals exhibit an increased degree of structural complexity and interdependency compared to nonfocal EEG signals. In general, we find higher rejections for randomness and nonlinear independence tests for focal EEG signals compared to nonfocal EEG signals. In particular, the univariate recurrence network measures, the average clustering coefficient C and assortativity R, and the bivariate recurrence network measure, the average cross-clustering coefficient C(cross), can successfully distinguish between the focal and nonfocal EEG signals, even when the analysis is restricted to nonstationary signals, irrespective of the type of surrogates used. On the other hand, we find that the univariate recurrence network measures, the average path length L, and the average betweenness centrality BC fail to distinguish between the focal and nonfocal EEG signals when iAAFT surrogates are used. However, these two measures can distinguish between focal and nonfocal EEG signals when TFT surrogates are used for nonstationary signals. We also report an improvement in the performance of nonlinear prediction error N and nonlinear interdependence measure L used by Andrezejak et al., when TFT surrogates are used for nonstationary EEG signals. We also find that the outcome of the nonlinear independence test based on the average cross-clustering coefficient C(cross) is independent of the outcome of the randomness test based on the average clustering coefficient C. Thus, the univariate and bivariate recurrence network measures provide independent information regarding the dynamics of the focal and nonfocal EEG signals. In conclusion, recurrence network analysis combined with nonstationary surrogates can be applied to derive reliable biomarkers to distinguish between epileptogenic and nonepileptogenic brain areas using EEG signals.

Dynamics of intracranial electroencephalographic recordings from epilepsy patients using univariate and bivariate recurrence networks

NASA Astrophysics Data System (ADS)

Subramaniyam, Narayan Puthanmadam; Hyttinen, Jari

2015-02-01

Recently Andrezejak et al. combined the randomness and nonlinear independence test with iterative amplitude adjusted Fourier transform (iAAFT) surrogates to distinguish between the dynamics of seizure-free intracranial electroencephalographic (EEG) signals recorded from epileptogenic (focal) and nonepileptogenic (nonfocal) brain areas of epileptic patients. However, stationarity is a part of the null hypothesis for iAAFT surrogates and thus nonstationarity can violate the null hypothesis. In this work we first propose the application of the randomness and nonlinear independence test based on recurrence network measures to distinguish between the dynamics of focal and nonfocal EEG signals. Furthermore, we combine these tests with both iAAFT and truncated Fourier transform (TFT) surrogate methods, which also preserves the nonstationarity of the original data in the surrogates along with its linear structure. Our results indicate that focal EEG signals exhibit an increased degree of structural complexity and interdependency compared to nonfocal EEG signals. In general, we find higher rejections for randomness and nonlinear independence tests for focal EEG signals compared to nonfocal EEG signals. In particular, the univariate recurrence network measures, the average clustering coefficient C and assortativity R , and the bivariate recurrence network measure, the average cross-clustering coefficient Ccross, can successfully distinguish between the focal and nonfocal EEG signals, even when the analysis is restricted to nonstationary signals, irrespective of the type of surrogates used. On the other hand, we find that the univariate recurrence network measures, the average path length L , and the average betweenness centrality BC fail to distinguish between the focal and nonfocal EEG signals when iAAFT surrogates are used. However, these two measures can distinguish between focal and nonfocal EEG signals when TFT surrogates are used for nonstationary signals. We also report an improvement in the performance of nonlinear prediction error N and nonlinear interdependence measure L used by Andrezejak et al., when TFT surrogates are used for nonstationary EEG signals. We also find that the outcome of the nonlinear independence test based on the average cross-clustering coefficient Ccross is independent of the outcome of the randomness test based on the average clustering coefficient C . Thus, the univariate and bivariate recurrence network measures provide independent information regarding the dynamics of the focal and nonfocal EEG signals. In conclusion, recurrence network analysis combined with nonstationary surrogates can be applied to derive reliable biomarkers to distinguish between epileptogenic and nonepileptogenic brain areas using EEG signals.

Methodology for Formulating Diesel Surrogate Fuels with Accurate Compositional, Ignition-Quality, and Volatility Characteristics

DOE PAGES

Mueller, Charles J.; Cannella, William J.; Bruno, Thomas J.; ...

2012-05-22

In this study, a novel approach was developed to formulate surrogate fuels having characteristics that are representative of diesel fuels produced from real-world refinery streams. Because diesel fuels typically consist of hundreds of compounds, it is difficult to conclusively determine the effects of fuel composition on combustion properties. Surrogate fuels, being simpler representations of these practical fuels, are of interest because they can provide a better understanding of fundamental fuel-composition and property effects on combustion and emissions-formation processes in internal-combustion engines. In addition, the application of surrogate fuels in numerical simulations with accurate vaporization, mixing, and combustion models could revolutionizemore » future engine designs by enabling computational optimization for evolving real fuels. Dependable computational design would not only improve engine function, it would do so at significant cost savings relative to current optimization strategies that rely on physical testing of hardware prototypes. The approach in this study utilized the state-of-the-art techniques of 13C and 1H nuclear magnetic resonance spectroscopy and the advanced distillation curve to characterize fuel composition and volatility, respectively. The ignition quality was quantified by the derived cetane number. Two well-characterized, ultra-low-sulfur #2 diesel reference fuels produced from refinery streams were used as target fuels: a 2007 emissions certification fuel and a Coordinating Research Council (CRC) Fuels for Advanced Combustion Engines (FACE) diesel fuel. A surrogate was created for each target fuel by blending eight pure compounds. The known carbon bond types within the pure compounds, as well as models for the ignition qualities and volatilities of their mixtures, were used in a multiproperty regression algorithm to determine optimal surrogate formulations. The predicted and measured surrogate-fuel properties were quantitatively compared to the measured target-fuel properties, and good agreement was found.« less

ANALYSES OF FISH TISSUE BY VACUUM DISTILLATION/GAS CHROMATOGRAPHY/MASS SPECTROMETRY

EPA Science Inventory

The analyses of fish tissue using VD/GC/MS with surrogate-based matrix corrections is described. Techniques for equilibrating surrogate and analyte spikes with a tissue matrix are presented, and equilibrated spiked samples are used to document method performance. The removal of a...

Laser dimpling process parameters selection and optimization using surrogate-driven process capability space

NASA Astrophysics Data System (ADS)

Ozkat, Erkan Caner; Franciosa, Pasquale; Ceglarek, Dariusz

2017-08-01

Remote laser welding technology offers opportunities for high production throughput at a competitive cost. However, the remote laser welding process of zinc-coated sheet metal parts in lap joint configuration poses a challenge due to the difference between the melting temperature of the steel (∼1500 °C) and the vapourizing temperature of the zinc (∼907 °C). In fact, the zinc layer at the faying surface is vapourized and the vapour might be trapped within the melting pool leading to weld defects. Various solutions have been proposed to overcome this problem over the years. Among them, laser dimpling has been adopted by manufacturers because of its flexibility and effectiveness along with its cost advantages. In essence, the dimple works as a spacer between the two sheets in lap joint and allows the zinc vapour escape during welding process, thereby preventing weld defects. However, there is a lack of comprehensive characterization of dimpling process for effective implementation in real manufacturing system taking into consideration inherent changes in variability of process parameters. This paper introduces a methodology to develop (i) surrogate model for dimpling process characterization considering multiple-inputs (i.e. key control characteristics) and multiple-outputs (i.e. key performance indicators) system by conducting physical experimentation and using multivariate adaptive regression splines; (ii) process capability space (Cp-Space) based on the developed surrogate model that allows the estimation of a desired process fallout rate in the case of violation of process requirements in the presence of stochastic variation; and, (iii) selection and optimization of the process parameters based on the process capability space. The proposed methodology provides a unique capability to: (i) simulate the effect of process variation as generated by manufacturing process; (ii) model quality requirements with multiple and coupled quality requirements; and (iii) optimize process parameters under competing quality requirements such as maximizing the dimple height while minimizing the dimple lower surface area.

Surrogate endpoints for overall survival in advanced colorectal cancer: a clinician's perspective.

PubMed

Piedbois, Pascal; Miller Croswell, Jennifer

2008-10-01

Surrogate endpoints in oncology research and practice have garnered increasing attention over the past two decades. This activity has largely been driven by the promise surrogate endpoints appear to hold: the potential to get new therapies to seriously ill patients more rapidly. However, uncertainties abound. Even agreeing upon a definition of a "valid" surrogate endpoint has not been a straightforward exercise; this article begins by highlighting differences in how this term has been previously captured and applied, as well as laying out the basic criteria essential for its application in advanced colorectal cancer. Ideally, these elements include (but are not limited to) ease of measurement, rapid indication of treatment effect, and, most importantly, reliable and consistent prediction of the true impact of a treatment on the ultimate outcome of interest: overall survival. The strengths and weaknesses of current potential surrogate endpoints in advanced colorectal cancer, including performance status, carcinoembryonic antigen plasma level, overall response rate, time to progression, and disease-free survival, are each considered in turn. Finally, limitations of surrogate endpoints in the clinical setting, including challenges in extrapolation to new therapies, and the incomplete provision of information about potential adverse effects, are discussed. Work remains to be done between physicians and statisticians to bridge the gap between that which is statistically demonstrable and that which will be clinically useful.The term ;surrogate endpoint' was virtually unknown by most oncologists 15 years ago. A search in PubMed [http://www.ncbi.nlm.nih.gov] based on the words ;surrogate and cancer' shows that more than 2000 papers were published in medical journals in the last 20 years, with a dramatic increase of interest in the last five years. Interestingly, the same trend is observed when the words ;surrogate and heart' are entered into PubMed, suggesting that the issue of surrogate endpoints goes beyond the field of oncology, although the frequency of discussion varies (Figure 1; note different y-axis scales for oncology and cardiology).The goal of the present paper is to discuss the main issues surrounding surrogate endpoints from a clinician's point of view, using as an example surrogate endpoints of overall survival (OS) in advanced colorectal cancer (ACC).

Bridging Scales: A Model-Based Assessment of the Technical Tidal-Stream Energy Resource off Massachusetts, USA

NASA Astrophysics Data System (ADS)

Cowles, G. W.; Hakim, A.; Churchill, J. H.

2016-02-01

Tidal in-stream energy conversion (TISEC) facilities provide a highly predictable and dependable source of energy. Given the economic and social incentives to migrate towards renewable energy sources there has been tremendous interest in the technology. Key challenges to the design process stem from the wide range of problem scales extending from device to array. In the present approach we apply a multi-model approach to bridge the scales of interest and select optimal device geometries to estimate the technical resource for several realistic sites in the coastal waters of Massachusetts, USA. The approach links two computational models. To establish flow conditions at site scales ( 10m), a barotropic setup of the unstructured grid ocean model FVCOM is employed. The model is validated using shipboard and fixed ADCP as well as pressure data. For device scale, the structured multiblock flow solver SUmb is selected. A large ensemble of simulations of 2D cross-flow tidal turbines is used to construct a surrogate design model. The surrogate model is then queried using velocity profiles extracted from the tidal model to determine the optimal geometry for the conditions at each site. After device selection, the annual technical yield of the array is evaluated with FVCOM using a linear momentum actuator disk approach to model the turbines. Results for several key Massachusetts sites including comparison with theoretical approaches will be presented.

System Reliability-Based Design Optimization Under Input and Model Uncertainties

DTIC Science & Technology

2014-02-02

Standard Form 298 (Rev 8/98) Prescribed by ANSI Std. Z39.18 W911NF-09- 1 -0250 319-335-5684 Final Report 56025-NS.40 a. REPORT 14. ABSTRACT 16...resource. In such cases the inaccuracy and uncertainty of the surrogate model needs to 1 . REPORT DATE (DD-MM-YYYY) 4. TITLE AND SUBTITLE 13...estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the

Hybrid optimal scheduling for intermittent androgen suppression of prostate cancer

NASA Astrophysics Data System (ADS)

Hirata, Yoshito; di Bernardo, Mario; Bruchovsky, Nicholas; Aihara, Kazuyuki

2010-12-01

We propose a method for achieving an optimal protocol of intermittent androgen suppression for the treatment of prostate cancer. Since the model that reproduces the dynamical behavior of the surrogate tumor marker, prostate specific antigen, is piecewise linear, we can obtain an analytical solution for the model. Based on this, we derive conditions for either stopping or delaying recurrent disease. The solution also provides a design principle for the most favorable schedule of treatment that minimizes the rate of expansion of the malignant cell population.

Uncertainty quantification for environmental models

USGS Publications Warehouse

Hill, Mary C.; Lu, Dan; Kavetski, Dmitri; Clark, Martyn P.; Ye, Ming

2012-01-01

Environmental models are used to evaluate the fate of fertilizers in agricultural settings (including soil denitrification), the degradation of hydrocarbons at spill sites, and water supply for people and ecosystems in small to large basins and cities—to mention but a few applications of these models. They also play a role in understanding and diagnosing potential environmental impacts of global climate change. The models are typically mildly to extremely nonlinear. The persistent demand for enhanced dynamics and resolution to improve model realism [17] means that lengthy individual model execution times will remain common, notwithstanding continued enhancements in computer power. In addition, high-dimensional parameter spaces are often defined, which increases the number of model runs required to quantify uncertainty [2]. Some environmental modeling projects have access to extensive funding and computational resources; many do not. The many recent studies of uncertainty quantification in environmental model predictions have focused on uncertainties related to data error and sparsity of data, expert judgment expressed mathematically through prior information, poorly known parameter values, and model structure (see, for example, [1,7,9,10,13,18]). Approaches for quantifying uncertainty include frequentist (potentially with prior information [7,9]), Bayesian [13,18,19], and likelihood-based. A few of the numerous methods, including some sensitivity and inverse methods with consequences for understanding and quantifying uncertainty, are as follows: Bayesian hierarchical modeling and Bayesian model averaging; single-objective optimization with error-based weighting [7] and multi-objective optimization [3]; methods based on local derivatives [2,7,10]; screening methods like OAT (one at a time) and the method of Morris [14]; FAST (Fourier amplitude sensitivity testing) [14]; the Sobol' method [14]; randomized maximum likelihood [10]; Markov chain Monte Carlo (MCMC) [10]. There are also bootstrapping and cross-validation approaches.Sometimes analyses are conducted using surrogate models [12]. The availability of so many options can be confusing. Categorizing methods based on fundamental questions assists in communicating the essential results of uncertainty analyses to stakeholders. Such questions can focus on model adequacy (e.g., How well does the model reproduce observed system characteristics and dynamics?) and sensitivity analysis (e.g., What parameters can be estimated with available data? What observations are important to parameters and predictions? What parameters are important to predictions?), as well as on the uncertainty quantification (e.g., How accurate and precise are the predictions?). The methods can also be classified by the number of model runs required: few (10s to 1000s) or many (10,000s to 1,000,000s). Of the methods listed above, the most computationally frugal are generally those based on local derivatives; MCMC methods tend to be among the most computationally demanding. Surrogate models (emulators)do not necessarily produce computational frugality because many runs of the full model are generally needed to create a meaningful surrogate model. With this categorization, we can, in general, address all the fundamental questions mentioned above using either computationally frugal or demanding methods. Model development and analysis can thus be conducted consistently using either computation-ally frugal or demanding methods; alternatively, different fundamental questions can be addressed using methods that require different levels of effort. Based on this perspective, we pose the question: Can computationally frugal methods be useful companions to computationally demanding meth-ods? The reliability of computationally frugal methods generally depends on the model being reasonably linear, which usually means smooth nonlin-earities and the assumption of Gaussian errors; both tend to be more valid with more linear

A prospective gating method to acquire a diverse set of free-breathing CT images for model-based 4DCT

NASA Astrophysics Data System (ADS)

O'Connell, D.; Ruan, D.; Thomas, D. H.; Dou, T. H.; Lewis, J. H.; Santhanam, A.; Lee, P.; Low, D. A.

2018-02-01

Breathing motion modeling requires observation of tissues at sufficiently distinct respiratory states for proper 4D characterization. This work proposes a method to improve sampling of the breathing cycle with limited imaging dose. We designed and tested a prospective free-breathing acquisition protocol with a simulation using datasets from five patients imaged with a model-based 4DCT technique. Each dataset contained 25 free-breathing fast helical CT scans with simultaneous breathing surrogate measurements. Tissue displacements were measured using deformable image registration. A correspondence model related tissue displacement to the surrogate. Model residual was computed by comparing predicted displacements to image registration results. To determine a stopping criteria for the prospective protocol, i.e. when the breathing cycle had been sufficiently sampled, subsets of N scans where 5  ⩽  N  ⩽  9 were used to fit reduced models for each patient. A previously published metric was employed to describe the phase coverage, or ‘spread’, of the respiratory trajectories of each subset. Minimum phase coverage necessary to achieve mean model residual within 0.5 mm of the full 25-scan model was determined and used as the stopping criteria. Using the patient breathing traces, a prospective acquisition protocol was simulated. In all patients, phase coverage greater than the threshold necessary for model accuracy within 0.5 mm of the 25 scan model was achieved in six or fewer scans. The prospectively selected respiratory trajectories ranked in the (97.5  ±  4.2)th percentile among subsets of the originally sampled scans on average. Simulation results suggest that the proposed prospective method provides an effective means to sample the breathing cycle with limited free-breathing scans. One application of the method is to reduce the imaging dose of a previously published model-based 4DCT protocol to 25% of its original value while achieving mean model residual within 0.5 mm.

Testing Surrogacy Assumptions: Can Threatened and Endangered Plants Be Grouped by Biological Similarity and Abundances?

PubMed Central

Che-Castaldo, Judy P.; Neel, Maile C.

2012-01-01

There is renewed interest in implementing surrogate species approaches in conservation planning due to the large number of species in need of management but limited resources and data. One type of surrogate approach involves selection of one or a few species to represent a larger group of species requiring similar management actions, so that protection and persistence of the selected species would result in conservation of the group of species. However, among the criticisms of surrogate approaches is the need to test underlying assumptions, which remain rarely examined. In this study, we tested one of the fundamental assumptions underlying use of surrogate species in recovery planning: that there exist groups of threatened and endangered species that are sufficiently similar to warrant similar management or recovery criteria. Using a comprehensive database of all plant species listed under the U.S. Endangered Species Act and tree-based random forest analysis, we found no evidence of species groups based on a set of distributional and biological traits or by abundances and patterns of decline. Our results suggested that application of surrogate approaches for endangered species recovery would be unjustified. Thus, conservation planning focused on individual species and their patterns of decline will likely be required to recover listed species. PMID:23240051

Testing surrogacy assumptions: can threatened and endangered plants be grouped by biological similarity and abundances?

PubMed

Che-Castaldo, Judy P; Neel, Maile C

2012-01-01

There is renewed interest in implementing surrogate species approaches in conservation planning due to the large number of species in need of management but limited resources and data. One type of surrogate approach involves selection of one or a few species to represent a larger group of species requiring similar management actions, so that protection and persistence of the selected species would result in conservation of the group of species. However, among the criticisms of surrogate approaches is the need to test underlying assumptions, which remain rarely examined. In this study, we tested one of the fundamental assumptions underlying use of surrogate species in recovery planning: that there exist groups of threatened and endangered species that are sufficiently similar to warrant similar management or recovery criteria. Using a comprehensive database of all plant species listed under the U.S. Endangered Species Act and tree-based random forest analysis, we found no evidence of species groups based on a set of distributional and biological traits or by abundances and patterns of decline. Our results suggested that application of surrogate approaches for endangered species recovery would be unjustified. Thus, conservation planning focused on individual species and their patterns of decline will likely be required to recover listed species.

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 based upon each patients measured bleaching needs to be attempted. In the case of ALA, lack of PpIX is more likely an indicator that alternative PpIX production methods must be implemented. Parsimonious dosimetry, using surrogate measurements that are clinically acceptable, might strategically help to advance PDT in a medical world that is increasingly cost and time sensitive. Careful attention to methodologies that can identify and advance the most critical dosimetric measurements, either direct or surrogate, are needed to ensure successful incorporation of PDT into niche clinical procedures.

Evaluation of Mass Filtered, Time Dilated, Time-of-Flight Mass Spectrometry

DTIC Science & Technology

2010-01-01

Figure 4.4: Mass resolution dependence on field for selected actinides and surrogates...45 Figure 4.7: Mass resolution dependence on field for selected actinides and actinide surrogates, modeled with no initial...system. A somewhat better mass resolution would need to be achieved in order to separate hydride molecules in the actinide region. However, the

Knowledge Representation and Ontologies

NASA Astrophysics Data System (ADS)

Grimm, Stephan

Knowledge representation and reasoning aims at designing computer systems that reason about a machine-interpretable representation of the world. Knowledge-based systems have a computational model of some domain of interest in which symbols serve as surrogates for real world domain artefacts, such as physical objects, events, relationships, etc. [1]. The domain of interest can cover any part of the real world or any hypothetical system about which one desires to represent knowledge for com-putational purposes. A knowledge-based system maintains a knowledge base, which stores the symbols of the computational model in the form of statements about the domain, and it performs reasoning by manipulating these symbols. Applications can base their decisions on answers to domain-relevant questions posed to a knowledge base.

CT contrast predicts pancreatic cancer treatment response to verteporfin-based photodynamic therapy

NASA Astrophysics Data System (ADS)

Jermyn, Michael; Davis, Scott C.; Dehghani, Hamid; Huggett, Matthew T.; Hasan, Tayyaba; Pereira, Stephen P.; Bown, Stephen G.; Pogue, Brian W.

2014-04-01

The goal of this study was to determine dominant factors affecting treatment response in pancreatic cancer photodynamic therapy (PDT), based on clinically available information in the VERTPAC-01 trial. This trial investigated the safety and efficacy of verteporfin PDT in 15 patients with locally advanced pancreatic adenocarcinoma. CT scans before and after contrast enhancement from the 15 patients in the VERTPAC-01 trial were used to determine venous-phase blood contrast enhancement and this was correlated with necrotic volume determined from post-treatment CT scans, along with estimation of optical absorption in the pancreas for use in light modeling of the PDT treatment. Energy threshold contours yielded estimates for necrotic volume based on this light modeling. Both contrast-derived venous blood content and necrotic volume from light modeling yielded strong correlations with observed necrotic volume (R2 = 0.85 and 0.91, respectively). These correlations were much stronger than those obtained by correlating energy delivered versus necrotic volume in the VERTPAC-01 study and in retrospective analysis from a prior clinical study. This demonstrates that contrast CT can provide key surrogate dosimetry information to assess treatment response. It also implies that light attenuation is likely the dominant factor in the VERTPAC treatment response, as opposed to other factors such as drug distribution. This study is the first to show that contrast CT provides needed surrogate dosimetry information to predict treatment response in a manner which uses standard-of-care clinical images, rather than invasive dosimetry methods.

Predictive uncertainty analysis of plume distribution for geological carbon sequestration using sparse-grid Bayesian method

NASA Astrophysics Data System (ADS)

Shi, X.; Zhang, G.

2013-12-01

Because of the extensive computational burden, parametric uncertainty analyses are rarely conducted for geological carbon sequestration (GCS) process based multi-phase models. The difficulty of predictive uncertainty analysis for the CO2 plume migration in realistic GCS models is not only due to the spatial distribution of the caprock and reservoir (i.e. heterogeneous model parameters), but also because the GCS optimization estimation problem has multiple local minima due to the complex nonlinear multi-phase (gas and aqueous), and multi-component (water, CO2, salt) transport equations. The geological model built by Doughty and Pruess (2004) for the Frio pilot site (Texas) was selected and assumed to represent the 'true' system, which was composed of seven different facies (geological units) distributed among 10 layers. We chose to calibrate the permeabilities of these facies. Pressure and gas saturation values from this true model were then extracted and used as observations for subsequent model calibration. Random noise was added to the observations to approximate realistic field conditions. Each simulation of the model lasts about 2 hours. In this study, we develop a new approach that improves computational efficiency of Bayesian inference by constructing a surrogate system based on an adaptive sparse-grid stochastic collocation method. This surrogate response surface global optimization algorithm is firstly used to calibrate the model parameters, then prediction uncertainty of the CO2 plume position is quantified due to the propagation from parametric uncertainty in the numerical experiments, which is also compared to the actual plume from the 'true' model. Results prove that the approach is computationally efficient for multi-modal optimization and prediction uncertainty quantification for computationally expensive simulation models. Both our inverse methodology and findings can be broadly applicable to GCS in heterogeneous storage formations.

A Systematic Review of Cardiovascular Outcomes-Based Cost-Effectiveness Analyses of Lipid-Lowering Therapies.

PubMed

Wei, Ching-Yun; Quek, Ruben G W; Villa, Guillermo; Gandra, Shravanthi R; Forbes, Carol A; Ryder, Steve; Armstrong, Nigel; Deshpande, Sohan; Duffy, Steven; Kleijnen, Jos; Lindgren, Peter

2017-03-01

Previous reviews have evaluated economic analyses of lipid-lowering therapies using lipid levels as surrogate markers for cardiovascular disease. However, drug approval and health technology assessment agencies have stressed that surrogates should only be used in the absence of clinical endpoints. The aim of this systematic review was to identify and summarise the methodologies, weaknesses and strengths of economic models based on atherosclerotic cardiovascular disease event rates. Cost-effectiveness evaluations of lipid-lowering therapies using cardiovascular event rates in adults with hyperlipidaemia were sought in Medline, Embase, Medline In-Process, PubMed and NHS EED and conference proceedings. Search results were independently screened, extracted and quality checked by two reviewers. Searches until February 2016 retrieved 3443 records, from which 26 studies (29 publications) were selected. Twenty-two studies evaluated secondary prevention (four also assessed primary prevention), two considered only primary prevention and two included mixed primary and secondary prevention populations. Most studies (18) based treatment-effect estimates on single trials, although more recent evaluations deployed meta-analyses (5/10 over the last 10 years). Markov models (14 studies) were most commonly used and only one study employed discrete event simulation. Models varied particularly in terms of health states and treatment-effect duration. No studies used a systematic review to obtain utilities. Most studies took a healthcare perspective (21/26) and sourced resource use from key trials instead of local data. Overall, reporting quality was suboptimal. This review reveals methodological changes over time, but reporting weaknesses remain, particularly with respect to transparency of model reporting.

Skin models for the testing of transdermal drugs

PubMed Central

Abd, Eman; Yousef, Shereen A; Pastore, Michael N; Telaprolu, Krishna; Mohammed, Yousuf H; Namjoshi, Sarika; Grice, Jeffrey E; Roberts, Michael S

2016-01-01

The assessment of percutaneous permeation of molecules is a key step in the evaluation of dermal or transdermal delivery systems. If the drugs are intended for delivery to humans, the most appropriate setting in which to do the assessment is the in vivo human. However, this may not be possible for ethical, practical, or economic reasons, particularly in the early phases of development. It is thus necessary to find alternative methods using accessible and reproducible surrogates for in vivo human skin. A range of models has been developed, including ex vivo human skin, usually obtained from cadavers or plastic surgery patients, ex vivo animal skin, and artificial or reconstructed skin models. Increasingly, largely driven by regulatory authorities and industry, there is a focus on developing standardized techniques and protocols. With this comes the need to demonstrate that the surrogate models produce results that correlate with those from in vivo human studies and that they can be used to show bioequivalence of different topical products. This review discusses the alternative skin models that have been developed as surrogates for normal and diseased skin and examines the concepts of using model systems for in vitro–in vivo correlation and the demonstration of bioequivalence. PMID:27799831

A simple analytical procedure to replace HPLC for monitoring treatment concentrations of chloramine-T on fish culture facilities

USGS Publications Warehouse

Dawson, Verdel K.; Meinertz, Jeffery R.; Schmidt, Larry J.; Gingerich, William H.

2003-01-01

Concentrations of chloramine-T must be monitored during experimental treatments of fish when studying the effectiveness of the drug for controlling bacterial gill disease. A surrogate analytical method for analysis of chloramine-T to replace the existing high-performance liquid chromatography (HPLC) method is described. A surrogate method was needed because the existing HPLC method is expensive, requires a specialist to use, and is not generally available at fish hatcheries. Criteria for selection of a replacement method included ease of use, analysis time, cost, safety, sensitivity, accuracy, and precision. The most promising approach was to use the determination of chlorine concentrations as an indicator of chloramine-T. Of the currently available methods for analysis of chlorine, the DPD (N,N-diethyl-p-phenylenediamine) colorimetric method best fit the established criteria. The surrogate method was evaluated under a variety of water quality conditions. Regression analysis of all DPD colorimetric analyses with the HPLC values produced a linear model (Y=0.9602 X+0.1259) with an r2 value of 0.9960. The average accuracy (percent recovery) of the DPD method relative to the HPLC method for the combined set of water quality data was 101.5%. The surrogate method was also evaluated with chloramine-T solutions that contained various concentrations of fish feed or selected densities of rainbow trout. When samples were analyzed within 2 h, the results of the surrogate method were consistent with those of the HPLC method. When samples with high concentrations of organic material were allowed to age more than 2 h before being analyzed, the DPD method seemed to be susceptible to interference, possibly from the development of other chloramine compounds. However, even after aging samples 6 h, the accuracy of the surrogate DPD method relative to the HPLC method was within the range of 80–120%. Based on the data comparing the two methods, the U.S. Food and Drug Administration has concluded that the DPD colorimetric method is appropriate to use to measure chloramine-T in water during pivotal efficacy trials designed to support the approval of chloramine-T for use in fish culture.

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 advanced discussions with the patient about treatment preferences were at higher risk to report less confidence in carrying out the surrogate role. Better-quality clinician-family communication was associated with both more confidence among family members to act as surrogates and a shorter duration of use of life support among patients who died.

SU-E-J-234: Application of a Breathing Motion Model to ViewRay Cine MR Images

DOE Office of Scientific and Technical Information (OSTI.GOV)

O’Connell, D. P.; Thomas, D. H.; Dou, T. H.

2015-06-15

Purpose: A respiratory motion model previously used to generate breathing-gated CT images was used with cine MR images. Accuracy and predictive ability of the in-plane models were evaluated. Methods: Sagittalplane cine MR images of a patient undergoing treatment on a ViewRay MRI/radiotherapy system were acquired before and during treatment. Images were acquired at 4 frames/second with 3.5 × 3.5 mm resolution and a slice thickness of 5 mm. The first cine frame was deformably registered to following frames. Superior/inferior component of the tumor centroid position was used as a breathing surrogate. Deformation vectors and surrogate measurements were used to determinemore » motion model parameters. Model error was evaluated and subsequent treatment cines were predicted from breathing surrogate data. A simulated CT cine was created by generating breathing-gated volumetric images at 0.25 second intervals along the measured breathing trace, selecting a sagittal slice and downsampling to the resolution of the MR cines. A motion model was built using the first half of the simulated cine data. Model accuracy and error in predicting the remaining frames of the cine were evaluated. Results: Mean difference between model predicted and deformably registered lung tissue positions for the 28 second preview MR cine acquired before treatment was 0.81 +/− 0.30 mm. The model was used to predict two minutes of the subsequent treatment cine with a mean accuracy of 1.59 +/− 0.63 mm. Conclusion: Inplane motion models were built using MR cine images and evaluated for accuracy and ability to predict future respiratory motion from breathing surrogate measurements. Examination of long term predictive ability is ongoing. The technique was applied to simulated CT cines for further validation, and the authors are currently investigating use of in-plane models to update pre-existing volumetric motion models used for generation of breathing-gated CT planning images.« less

Forensic issues in medical evaluation: competency and end-of-life issues.

PubMed

Soliman, Sherif; Hall, Ryan C W

2015-01-01

Decision-making capacity is a common reason for psychiatric consultation that is likely to become more common as the population ages. Capacity assessments are frequently compromised by misconceptions, such as the belief that incapacity is permanent or that patients with dementia categorically lack capacity. This chapter will review the conceptual framework of decision-making capacity and discuss its application to medical decision-making. We will review selected developments in capacity assessment and recommend an approach to assessing decision-making capacity. We will discuss the unique challenges posed by end-of-life care, including determining capacity, identifying surrogate decision-makers, and working with surrogate decision-makers. We will discuss clinical and legal approaches to incapacity, including advance directives, surrogate decision-makers, and guardians. We will discuss the legal standards based on which surrogates make medical decisions and outline options for resolving disagreements between clinical staff and surrogate decision-makers. We will offer recommendations for approaching decision-making capacity assessments. © 2015 S. Karger AG, Basel.

Deterministic and reliability based optimization of integrated thermal protection system composite panel using adaptive sampling techniques

NASA Astrophysics Data System (ADS)

Ravishankar, Bharani

Conventional space vehicles have thermal protection systems (TPS) that provide protection to an underlying structure that carries the flight loads. In an attempt to save weight, there is interest in an integrated TPS (ITPS) that combines the structural function and the TPS function. This has weight saving potential, but complicates the design of the ITPS that now has both thermal and structural failure modes. The main objectives of this dissertation was to optimally design the ITPS subjected to thermal and mechanical loads through deterministic and reliability based optimization. The optimization of the ITPS structure requires computationally expensive finite element analyses of 3D ITPS (solid) model. To reduce the computational expenses involved in the structural analysis, finite element based homogenization method was employed, homogenizing the 3D ITPS model to a 2D orthotropic plate. However it was found that homogenization was applicable only for panels that are much larger than the characteristic dimensions of the repeating unit cell in the ITPS panel. Hence a single unit cell was used for the optimization process to reduce the computational cost. Deterministic and probabilistic optimization of the ITPS panel required evaluation of failure constraints at various design points. This further demands computationally expensive finite element analyses which was replaced by efficient, low fidelity surrogate models. In an optimization process, it is important to represent the constraints accurately to find the optimum design. Instead of building global surrogate models using large number of designs, the computational resources were directed towards target regions near constraint boundaries for accurate representation of constraints using adaptive sampling strategies. Efficient Global Reliability Analyses (EGRA) facilitates sequentially sampling of design points around the region of interest in the design space. EGRA was applied to the response surface construction of the failure constraints in the deterministic and reliability based optimization of the ITPS panel. It was shown that using adaptive sampling, the number of designs required to find the optimum were reduced drastically, while improving the accuracy. System reliability of ITPS was estimated using Monte Carlo Simulation (MCS) based method. Separable Monte Carlo method was employed that allowed separable sampling of the random variables to predict the probability of failure accurately. The reliability analysis considered uncertainties in the geometry, material properties, loading conditions of the panel and error in finite element modeling. These uncertainties further increased the computational cost of MCS techniques which was also reduced by employing surrogate models. In order to estimate the error in the probability of failure estimate, bootstrapping method was applied. This research work thus demonstrates optimization of the ITPS composite panel with multiple failure modes and large number of uncertainties using adaptive sampling techniques.

Do-not-resuscitate orders in an extended-care study group.

PubMed

Meyers, R M; Lurie, N; Breitenbucher, R B; Waring, C J

1990-09-01

We examined the charts of 911 nursing home patients in Hennepin County, Minnesota, to determine the prevalence of written do-not-resuscitate (DNR) orders. Information regarding demographic characteristics, and whether a surrogate decisionmaker was available and participated in the decision, was also collected. Twenty-seven percent of patients had DNR orders. Ninety percent of all patients had potentially available surrogate decisionmakers. However, for 31% of patients with DNR orders, there was no documentation of patient or surrogate participation in the DNR decision. Univariate analysis identified female sex; increased age, level of care (skilled versus intermediate), presence of a potential surrogate decisionmaker, and increasing length of time since nursing home admission as factors associated with presence of DNR orders. When a logistic regression model was used, increased age, increased length of time since nursing home admission, skilled versus intermediate level of care, and presence of a surrogate decisionmaker were independently associated with presence of DNR status. Several variables are independently associated with written DNR orders; their relationship to the factors physicians use in decision making requires further study.

Fast prediction and evaluation of eccentric inspirals using reduced-order models

NASA Astrophysics Data System (ADS)

Barta, Dániel; Vasúth, Mátyás

2018-06-01

A large number of theoretically predicted waveforms are required by matched-filtering searches for the gravitational-wave signals produced by compact binary coalescence. In order to substantially alleviate the computational burden in gravitational-wave searches and parameter estimation without degrading the signal detectability, we propose a novel reduced-order-model (ROM) approach with applications to adiabatic 3PN-accurate inspiral waveforms of nonspinning sources that evolve on either highly or slightly eccentric orbits. We provide a singular-value decomposition-based reduced-basis method in the frequency domain to generate reduced-order approximations of any gravitational waves with acceptable accuracy and precision within the parameter range of the model. We construct efficient reduced bases comprised of a relatively small number of the most relevant waveforms over three-dimensional parameter-space covered by the template bank (total mass 2.15 M⊙≤M ≤215 M⊙ , mass ratio 0.01 ≤q ≤1 , and initial orbital eccentricity 0 ≤e0≤0.95 ). The ROM is designed to predict signals in the frequency band from 10 Hz to 2 kHz for aLIGO and aVirgo design sensitivity. Beside moderating the data reduction, finer sampling of fiducial templates improves the accuracy of surrogates. Considerable increase in the speedup from several hundreds to thousands can be achieved by evaluating surrogates for low-mass systems especially when combined with high-eccentricity.

Effect of Abdominal Loading Location on Liver Motion: Experimental Assessment using Ultrafast Ultrasound Imaging and Simulation with a Human Body Model.

PubMed

Le Ruyet, Anicet; Berthet, Fabien; Rongiéras, Frédéric; Beillas, Philippe

2016-11-01

A protocol based on ultrafast ultrasound imaging was applied to study the in situ motion of the liver while the abdomen was subjected to compressive loading at 3 m/s by a hemispherical impactor or a seatbelt. The loading was applied to various locations between the lower abdomen and the mid thorax while feature points inside the liver were followed on the ultrasound movie (2000 frames per second). Based on tests performed on five post mortem human surrogates (including four tested in the current study), trends were found between the loading location and feature point trajectory parameters such as the initial angle of motion or the peak displacement in the direction of impact. The impactor tests were then simulated using the GHBMC M50 human body model that was globally scaled to the dimensions of each surrogate. Some of the experimental trends observed could be reproduced in the simulations (e.g. initial angle) while others differed more widely (e.g. final caudal motion). The causes for the discrepancies need to be further investigated. The liver strain energy density predicted by the model was also widely affected by the impact location. Experimental and simulation results both highlight the importance of the liver position with respect to the impactor when studying its response in situ.

Meta-analyses evaluating surrogate endpoints for overall survival in cancer randomized trials: A critical review.

PubMed

Savina, Marion; Gourgou, Sophie; Italiano, Antoine; Dinart, Derek; Rondeau, Virginie; Penel, Nicolas; Mathoulin-Pelissier, Simone; Bellera, Carine

2018-03-01

In cancer randomized controlled trials (RCT), alternative endpoints are increasingly being used in place of overall survival (OS) to reduce sample size, duration and cost of trials. It is necessary to ensure that these endpoints are valid surrogates for OS. Our aim was to identify meta-analyses that evaluated surrogate endpoints for OS and assess the strength of evidence for each meta-analysis (MA). We performed a systematic review to identify MA of cancer RCTs assessing surrogate endpoints for OS. We evaluated the strength of the association between the endpoints based on (i) the German Institute of Quality and Efficiency in Health Care guidelines and (ii) the Biomarker-Surrogate Evaluation Schema. Fifty-three publications reported on 164 MA, with heterogeneous statistical methods Disease-free survival (DFS) and progression-free survival (PFS) showed good surrogacy properties for OS in colorectal, lung and head and neck cancers. DFS was highly correlated to OS in gastric cancer. The statistical methodology used to evaluate surrogate endpoints requires consistency in order to facilitate the accurate interpretation of the results. Despite the limited number of clinical settings with validated surrogate endpoints for OS, there is evidence of good surrogacy for DFS and PFS in tumor types that account for a large proportion of cancer cases. Copyright © 2017 Elsevier B.V. All rights reserved.

Anthropometric surrogates for screening of low birth weight newborns: a community-based study.

PubMed

Rustagi, Neeti; Prasuna, J G; Taneja, D K

2012-03-01

In developing countries, where about 75% of births occur at home or in the community, logistic problems prevent the weighing of every newborn child. This study compares various anthropometric surrogates for identification of low birth weight neonates. A longitudinal community based study was done in an urban resettlement colony and 283 singleton neonates within 7 days of birth were examined for the anthropometric measurements such as head, chest, mid upper arm circumference and foot length as a screening tool for low birth weight. Chest circumference measured within 7 days of birth appeared to be the most appropriate surrogate of low birth weight with highest sensitivity (75.4%), specificity (78.4%), and positive predictive value (48.9%) as compared with other anthropometric parameters. Low birth weight neonates in absence of weighing scales can be early identified by using simple anthropometric measurements for enhanced home-based care and timely referral.

Effects of Fuel Composition on Combustion Stability and NO X Emissions for Traditional and Alternative Jet Fuels

NASA Astrophysics Data System (ADS)

Vijlee, Shazib Z.

Synthetic jet fuels are studied to help understand their viability as alternatives to traditionally derived jet fuel. Two combustion parameters -- flame stability and NOX emissions -- are used to compare these fuels through experiments and models. At its core, this is a fuels study comparing how chemical makeup and behavior relate. Six 'real', complex fuels are studied in this work -- four are synthetic from alternative sources and two are traditional from petroleum sources. Two of the synthetic fuels are derived from natural gas and coal via the Fischer Tropsch catalytic process. The other two are derived from Camelina oil and tallow via hydroprocessing. The traditional military jet fuel, JP8, is used as a baseline as it is derived from petroleum. The sixth fuel is derived from petroleum and is used to study the effects of aromatic content on the synthetic fuels. The synthetic fuels lack aromatic compounds, which are an important class of hydrocarbons necessary for fuel handling systems to function properly. Several single-component fuels are studied (through models and/or experiments) to facilitate interpretation and understanding. The flame stability study first compares all the 'real', complex fuels for blowout. A toroidal stirred reactor is used to try and isolate temperature and chemical effects. The modeling study of blowout in the toroidal reactor is the key to understanding any fuel-based differences in blowout behavior. A detailed, reacting CFD model of methane is used to understand how the reactor stabilizes the flame and how that changes as the reactor approaches blowout. A 22 species reduced form of GRI 3.0 is used to model methane chemistry. The knowledge of the radical species role is utilized to investigate the differences between a highly aliphatic fuel (surrogated by iso-octane) and a highly aromatic fuel (surrogated by toluene). A perfectly stirred reactor model is used to study the chemical kinetic pathways for these fuels near blowout. The differences in flame stabilization can be attributed to the rate at which these fuels are attacked and destroyed by radical species. The slow disintegration of the aromatic rings reduces the radical pool available for chain-initiating and chain-branching, which ultimately leads to an earlier blowout. The NOX study compares JP8, the aromatic additive, the synthetic fuels with and without an aromatic additive, and an aromatic surrogate (1,3,5-trimethylbenzene). A jet stirred reactor is used to try and isolate temperature and chemical effects. The reactor has a volume of 15.8 mL and a residence time of approximately 2.5 ms. The fuel flow rate (hence equivalence ratio) is adjusted to achieve nominally consistent temperatures of 1800, 1850, and 1900K. Small oscillations in fuel flow rate cause the data to appear in bands, which facilitated Arrhenius-type NOX-temperature correlations for direct comparison between fuels. The fuel comparisons are somewhat inconsistent, especially when the aromatic fuel is blended into the synthetic fuels. In general, the aromatic surrogate (1,3,5-trimethylbenzene) produces the most NOX, followed by JP8. The synthetic fuels (without aromatic additive) are always in the same ranking order for NOX production (HP Camelina > FT Coal > FT Natural Gas > HP Tallow). The aromatic additive ranks differently based on the temperature, which appears to indicate that some of the differences in NOX formation are due to the Zeldovich NOX formation pathway. The aromatic additive increases NOX for the HP Tallow and decreases NOX for the FT Coal. The aromatic additive causes increased NOX at low temperatures but decreases NOX at high temperatures for the HP Camelina and FT Natural Gas. A single perfectly stirred reactor model is used with several chemical kinetic mechanisms to study the effects of fuel (and fuel class) on NO X formation. The 27 unique NOX formation reactions from GRI 3.0 are added to published mechanisms for jet fuel surrogates. The investigation first looked at iso-octane and toluene and found that toluene produces more NOX because of a larger pool of O radical. The O radical concentration was lower for iso-octane because of an increased concentration of methyl (CH 3) radical that consumes O radical readily. Several surrogate fuels (iso-octane, toluene, propylcyclohexane, n-octane, and 1,3,5-trimethylbenzene) are modeled to look for differences in NOX production. The trend (increased CH3 → decreased O → decreased NOX) is consistently true for all surrogate fuels with multiple kinetic mechanisms. It appears that the manner in which the fuel disintegrates and creates methyl radical is an extremely important aspect of how much NOX a fuel will produce. (Abstract shortened by UMI.).

Evaluation of invertebrate infection models for pathogenic corynebacteria.

PubMed

Ott, Lisa; McKenzie, Ashleigh; Baltazar, Maria Teresa; Britting, Sabine; Bischof, Andrea; Burkovski, Andreas; Hoskisson, Paul A

2012-08-01

For several pathogenic bacteria, model systems for host-pathogen interactions were developed, which provide the possibility of quick and cost-effective high throughput screening of mutant bacteria for genes involved in pathogenesis. A number of different model systems, including amoeba, nematodes, insects, and fish, have been introduced, and it was observed that different bacteria respond in different ways to putative surrogate hosts, and distinct model systems might be more or less suitable for a certain pathogen. The aim of this study was to develop a suitable invertebrate model for the human and animal pathogens Corynebacterium diphtheriae, Corynebacterium pseudotuberculosis, and Corynebacterium ulcerans. The results obtained in this study indicate that Acanthamoeba polyphaga is not optimal as surrogate host, while both Caenorhabtitis elegans and Galleria larvae seem to offer tractable models for rapid assessment of virulence between strains. Caenorhabtitis elegans gives more differentiated results and might be the best model system for pathogenic corynebacteria, given the tractability of bacteria and the range of mutant nematodes available to investigate the host response in combination with bacterial virulence. Nevertheless, Galleria will also be useful in respect to innate immune responses to pathogens because insects offer a more complex cell-based innate immune system compared with the simple innate immune system of C. elegans. © 2012 Federation of European Microbiological Societies. Published by Blackwell Publishing Ltd. All rights reserved.

Large Eddy Simulation of Turbulent Combustion

DTIC Science & Technology

2005-10-01

a new method to automatically generate skeletal kinetic mechanisms for surrogate fuels, using the directed relation graph method with error...propagation, was developed. These mechanisms are guaranteed to match results obtained using detailed chemistry within a user- defined accuracy for any...specified target. They can be combined together to produce adequate chemical models for surrogate fuels. A library containing skeletal mechanisms of various

Dynamic role and importance of surrogate species for assessing potential adverse environmental impacts of genetically engineered insect-resistant plants on non-target organisms.

PubMed

Wach, Michael; Hellmich, Richard L; Layton, Raymond; Romeis, Jörg; Gadaleta, Patricia G

2016-08-01

Surrogate species have a long history of use in research and regulatory settings to understand the potentially harmful effects of toxic substances including pesticides. More recently, surrogate species have been used to evaluate the potential effects of proteins contained in genetically engineered insect resistant (GEIR) crops. Species commonly used in GEIR crop testing include beneficial organisms such as honeybees, arthropod predators, and parasitoids. The choice of appropriate surrogates is influenced by scientific factors such as the knowledge of the mode of action and the spectrum of activity as well as societal factors such as protection goals that assign value to certain ecosystem services such as pollination or pest control. The primary reasons for using surrogates include the inability to test all possible organisms, the restrictions on using certain organisms in testing (e.g., rare, threatened, or endangered species), and the ability to achieve greater sensitivity and statistical power by using laboratory testing of certain species. The acceptance of surrogate species data can allow results from one region to be applied or "transported" for use in another region. On the basis of over a decade of using surrogate species to evaluate potential effects of GEIR crops, it appears that the current surrogates have worked well to predict effects of GEIR crops that have been developed (Carstens et al. GM Crops Food 5:1-5, 2014), and it is expected that they should work well to predict effects of future GEIR crops based on similar technologies.

Time resolved aerosol monitoring in the urban centre of Soweto

NASA Astrophysics Data System (ADS)

Formenti, P.; Annegarn, H. J.; Piketh, S. J.

1998-03-01

A programme of aerosol sampling was conducted from 1982 to 1984 in the urban area of Soweto, Johannesburg, South Africa. The particulate matter (aerodynamic diameter <15 μm) was collected using a two hours time resolution single stage streaker sampler and elemental concentrations were resolved via Particle Induced X-ray Emission (PIXE) analysis. Samples have been selected for analysis from an aerosol sample archive to establish base-line atmospheric conditions that existed in Soweto prior to large scale electrification, and to establish source apportionment of crustal elements between coal smoke and traffic induced road dust, based on chemical elemental measurements. A novel technique is demonstrated for processing PIXE-derived time sequence elemental concentration vectors. Slowly varying background components have been extracted from sulphur and crustal aerosol components, using alternatively two digital filters: a moving minimum, and a moving average. The residuals of the crustal elements, assigned to locally generated aerosol components, were modelled using surrogate tracers: sulphur as a surrogate for coal smoke; and Pb as a surrogate for traffic activity. Results from this source apportionment revealed coal emissions contributed between 40% and 50% of the aerosol mineral matter, while 18-22% originated from road dust. Background aerosol, characteristic of the regional winter aerosol burden over the South African Highveld, was between 12% and 21%. Minor contributors identified included a manganese smelter, located 30 km from the sampling site, and informal trash burning, as the source of intermittent heavy metals (Cu, Zn). Elemental source profiles derived for these various sources are presented.

Assessment of the 3He pressure inside the CABRI transient rods - Development of a surrogate model based on measurements and complementary CFD calculations

NASA Astrophysics Data System (ADS)

Clamens, Olivier; Lecerf, Johann; Hudelot, Jean-Pascal; Duc, Bertrand; Cadiou, Thierry; Blaise, Patrick; Biard, Bruno

2018-01-01

CABRI is an experimental pulse reactor, funded by the French Nuclear Safety and Radioprotection Institute (IRSN) and operated by CEA at the Cadarache research center. It is designed to study fuel behavior under RIA conditions. In order to produce the power transients, reactivity is injected by depressurization of a neutron absorber (3He) situated in transient rods inside the reactor core. The shapes of power transients depend on the total amount of reactivity injected and on the injection speed. The injected reactivity can be calculated by conversion of the 3He gas density into units of reactivity. So, it is of upmost importance to properly master gas density evolution in transient rods during a power transient. The 3He depressurization was studied by CFD calculations and completed with measurements using pressure transducers. The CFD calculations show that the density evolution is slower than the pressure drop. Surrogate models were built based on CFD calculations and validated against preliminary tests in the CABRI transient system. Studies also show that it is harder to predict the depressurization during the power transients because of neutron/3He capture reactions that induce a gas heating. This phenomenon can be studied by a multiphysics approach based on reaction rate calculation thanks to Monte Carlo code and study the resulting heating effect with the validated CFD simulation.

From in vitro to in vivo: Integration of the virtual cell based assay with physiologically based kinetic modelling.

PubMed

Paini, Alicia; Sala Benito, Jose Vicente; Bessems, Jos; Worth, Andrew P

2017-12-01

Physiologically based kinetic (PBK) models and the virtual cell based assay can be linked to form so called physiologically based dynamic (PBD) models. This study illustrates the development and application of a PBK model for prediction of estragole-induced DNA adduct formation and hepatotoxicity in humans. To address the hepatotoxicity, HepaRG cells were used as a surrogate for liver cells, with cell viability being used as the in vitro toxicological endpoint. Information on DNA adduct formation was taken from the literature. Since estragole induced cell damage is not directly caused by the parent compound, but by a reactive metabolite, information on the metabolic pathway was incorporated into the model. In addition, a user-friendly tool was developed by implementing the PBK/D model into a KNIME workflow. This workflow can be used to perform in vitro to in vivo extrapolation and forward as backward dosimetry in support of chemical risk assessment. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

Isotope Inversion Experiment evaluating the suitability of calibration in surrogate matrix for quantification via LC-MS/MS-Exemplary application for a steroid multi-method.

PubMed

Suhr, Anna Catharina; Vogeser, Michael; Grimm, Stefanie H

2016-05-30

For quotable quantitative analysis of endogenous analytes in complex biological samples by isotope dilution LC-MS/MS, the creation of appropriate calibrators is a challenge, since analyte-free authentic material is in general not available. Thus, surrogate matrices are often used to prepare calibrators and controls. However, currently employed validation protocols do not include specific experiments to verify the suitability of a surrogate matrix calibration for quantification of authentic matrix samples. The aim of the study was the development of a novel validation experiment to test whether surrogate matrix based calibrators enable correct quantification of authentic matrix samples. The key element of the novel validation experiment is the inversion of nonlabelled analytes and their stable isotope labelled (SIL) counterparts in respect to their functions, i.e. SIL compound is the analyte and nonlabelled substance is employed as internal standard. As a consequence, both surrogate and authentic matrix are analyte-free regarding SIL analytes, which allows a comparison of both matrices. We called this approach Isotope Inversion Experiment. As figure of merit we defined the accuracy of inverse quality controls in authentic matrix quantified by means of a surrogate matrix calibration curve. As a proof-of-concept application a LC-MS/MS assay addressing six corticosteroids (cortisol, cortisone, corticosterone, 11-deoxycortisol, 11-deoxycorticosterone, and 17-OH-progesterone) was chosen. The integration of the Isotope Inversion Experiment in the validation protocol for the steroid assay was successfully realized. The accuracy results of the inverse quality controls were all in all very satisfying. As a consequence the suitability of a surrogate matrix calibration for quantification of the targeted steroids in human serum as authentic matrix could be successfully demonstrated. The Isotope Inversion Experiment fills a gap in the validation process for LC-MS/MS assays quantifying endogenous analytes. We consider it a valuable and convenient tool to evaluate the correct quantification of authentic matrix samples based on a calibration curve in surrogate matrix. Copyright © 2016 Elsevier B.V. All rights reserved.

Effect of pH on the Electrophoretic Mobility of Spores of Bacillus anthracis and Its Surrogates in Aqueous Solutions

EPA Science Inventory

Electrophoretic mobility (EPM) of endospores of Bacillus anthracis and surrogates were measured in aqueous solution across a broad pH range and several ionic strengths. EPM values trended around phylogenetic clustering based on the 16S rRNA gene. Measurements reported here prov...

Relational autonomy: moving beyond the limits of isolated individualism.

PubMed

Walter, Jennifer K; Ross, Lainie Friedman

2014-02-01

Although clinicians may value respecting a patient's or surrogate's autonomy in decision-making, it is not always clear how to proceed in clinical practice. The confusion results, in part, from which conception of autonomy is used to guide ethical practice. Reliance on an individualistic conception such as the "in-control agent" model prioritizes self-sufficiency in decision-making and highlights a decision-maker's capacity to have reason transcend one's emotional experience. An alternative model of autonomy, relational autonomy, highlights the social context within which all individuals exist and acknowledges the emotional and embodied aspects of decision-makers. These 2 conceptions of autonomy lead to different interpretations of several aspects of ethical decision-making. The in-control agent model believes patients or surrogates should avoid both the influence of others and emotional persuasion in decision-making. As a result, providers have a limited role to play and are expected to provide medical expertise but not interfere with the individual's decision-making process. In contrast, a relational autonomy approach acknowledges the central role of others in decision-making, including clinicians, who have a responsibility to engage patients' and surrogates' emotional experiences and offer clear guidance when patients are confronting serious illness. In the pediatric setting, in which decision-making is complicated by having a surrogate decision-maker in addition to a patient, these conceptions of autonomy also may influence expectations about the role that adolescents can play in decision-making.

An architecture for efficient gravitational wave parameter estimation with multimodal linear surrogate models

NASA Astrophysics Data System (ADS)

O'Shaughnessy, Richard; Blackman, Jonathan; Field, Scott E.

2017-07-01

The recent direct observation of gravitational waves has further emphasized the desire for fast, low-cost, and accurate methods to infer the parameters of gravitational wave sources. Due to expense in waveform generation and data handling, the cost of evaluating the likelihood function limits the computational performance of these calculations. Building on recently developed surrogate models and a novel parameter estimation pipeline, we show how to quickly generate the likelihood function as an analytic, closed-form expression. Using a straightforward variant of a production-scale parameter estimation code, we demonstrate our method using surrogate models of effective-one-body and numerical relativity waveforms. Our study is the first time these models have been used for parameter estimation and one of the first ever parameter estimation calculations with multi-modal numerical relativity waveforms, which include all \\ell ≤slant 4 modes. Our grid-free method enables rapid parameter estimation for any waveform with a suitable reduced-order model. The methods described in this paper may also find use in other data analysis studies, such as vetting coincident events or the computation of the coalescing-compact-binary detection statistic.

The Impact of Parametric Uncertainties on Biogeochemistry in the E3SM Land Model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ricciuto, Daniel; Sargsyan, Khachik; Thornton, Peter

We conduct a global sensitivity analysis (GSA) of the Energy Exascale Earth System Model (E3SM), land model (ELM) to calculate the sensitivity of five key carbon cycle outputs to 68 model parameters. This GSA is conducted by first constructing a Polynomial Chaos (PC) surrogate via new Weighted Iterative Bayesian Compressive Sensing (WIBCS) algorithm for adaptive basis growth leading to a sparse, high-dimensional PC surrogate with 3,000 model evaluations. The PC surrogate allows efficient extraction of GSA information leading to further dimensionality reduction. The GSA is performed at 96 FLUXNET sites covering multiple plant functional types (PFTs) and climate conditions. Aboutmore » 20 of the model parameters are identified as sensitive with the rest being relatively insensitive across all outputs and PFTs. These sensitivities are dependent on PFT, and are relatively consistent among sites within the same PFT. The five model outputs have a majority of their highly sensitive parameters in common. A common subset of sensitive parameters is also shared among PFTs, but some parameters are specific to certain types (e.g., deciduous phenology). In conclusion, the relative importance of these parameters shifts significantly among PFTs and with climatic variables such as mean annual temperature.« less

The Impact of Parametric Uncertainties on Biogeochemistry in the E3SM Land Model

DOE PAGES

Ricciuto, Daniel; Sargsyan, Khachik; Thornton, Peter

2018-02-27

We conduct a global sensitivity analysis (GSA) of the Energy Exascale Earth System Model (E3SM), land model (ELM) to calculate the sensitivity of five key carbon cycle outputs to 68 model parameters. This GSA is conducted by first constructing a Polynomial Chaos (PC) surrogate via new Weighted Iterative Bayesian Compressive Sensing (WIBCS) algorithm for adaptive basis growth leading to a sparse, high-dimensional PC surrogate with 3,000 model evaluations. The PC surrogate allows efficient extraction of GSA information leading to further dimensionality reduction. The GSA is performed at 96 FLUXNET sites covering multiple plant functional types (PFTs) and climate conditions. Aboutmore » 20 of the model parameters are identified as sensitive with the rest being relatively insensitive across all outputs and PFTs. These sensitivities are dependent on PFT, and are relatively consistent among sites within the same PFT. The five model outputs have a majority of their highly sensitive parameters in common. A common subset of sensitive parameters is also shared among PFTs, but some parameters are specific to certain types (e.g., deciduous phenology). In conclusion, the relative importance of these parameters shifts significantly among PFTs and with climatic variables such as mean annual temperature.« less

Heart-Rate Variability—More than Heart Beats?

PubMed Central

Ernst, Gernot

2017-01-01

Heart-rate variability (HRV) is frequently introduced as mirroring imbalances within the autonomous nerve system. Many investigations are based on the paradigm that increased sympathetic tone is associated with decreased parasympathetic tone and vice versa. But HRV is probably more than an indicator for probable disturbances in the autonomous system. Some perturbations trigger not reciprocal, but parallel changes of vagal and sympathetic nerve activity. HRV has also been considered as a surrogate parameter of the complex interaction between brain and cardiovascular system. Systems biology is an inter-disciplinary field of study focusing on complex interactions within biological systems like the cardiovascular system, with the help of computational models and time series analysis, beyond others. Time series are considered surrogates of the particular system, reflecting robustness or fragility. Increased variability is usually seen as associated with a good health condition, whereas lowered variability might signify pathological changes. This might explain why lower HRV parameters were related to decreased life expectancy in several studies. Newer integrating theories have been proposed. According to them, HRV reflects as much the state of the heart as the state of the brain. The polyvagal theory suggests that the physiological state dictates the range of behavior and psychological experience. Stressful events perpetuate the rhythms of autonomic states, and subsequently, behaviors. Reduced variability will according to this theory not only be a surrogate but represent a fundamental homeostasis mechanism in a pathological state. The neurovisceral integration model proposes that cardiac vagal tone, described in HRV beyond others as HF-index, can mirror the functional balance of the neural networks implicated in emotion–cognition interactions. Both recent models represent a more holistic approach to understanding the significance of HRV. PMID:28955705

Heart-Rate Variability-More than Heart Beats?

PubMed

Ernst, Gernot

2017-01-01

Heart-rate variability (HRV) is frequently introduced as mirroring imbalances within the autonomous nerve system. Many investigations are based on the paradigm that increased sympathetic tone is associated with decreased parasympathetic tone and vice versa . But HRV is probably more than an indicator for probable disturbances in the autonomous system. Some perturbations trigger not reciprocal, but parallel changes of vagal and sympathetic nerve activity. HRV has also been considered as a surrogate parameter of the complex interaction between brain and cardiovascular system. Systems biology is an inter-disciplinary field of study focusing on complex interactions within biological systems like the cardiovascular system, with the help of computational models and time series analysis, beyond others. Time series are considered surrogates of the particular system, reflecting robustness or fragility. Increased variability is usually seen as associated with a good health condition, whereas lowered variability might signify pathological changes. This might explain why lower HRV parameters were related to decreased life expectancy in several studies. Newer integrating theories have been proposed. According to them, HRV reflects as much the state of the heart as the state of the brain. The polyvagal theory suggests that the physiological state dictates the range of behavior and psychological experience. Stressful events perpetuate the rhythms of autonomic states, and subsequently, behaviors. Reduced variability will according to this theory not only be a surrogate but represent a fundamental homeostasis mechanism in a pathological state. The neurovisceral integration model proposes that cardiac vagal tone, described in HRV beyond others as HF-index, can mirror the functional balance of the neural networks implicated in emotion-cognition interactions. Both recent models represent a more holistic approach to understanding the significance of HRV.

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

NASA Astrophysics Data System (ADS)

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

2014-05-01

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

Testing for intracycle determinism in pseudoperiodic time series.

PubMed

Coelho, Mara C S; Mendes, Eduardo M A M; Aguirre, Luis A

2008-06-01

A determinism test is proposed based on the well-known method of the surrogate data. Assuming predictability to be a signature of determinism, the proposed method checks for intracycle (e.g., short-term) determinism in the pseudoperiodic time series for which standard methods of surrogate analysis do not apply. The approach presented is composed of two steps. First, the data are preprocessed to reduce the effects of seasonal and trend components. Second, standard tests of surrogate analysis can then be used. The determinism test is applied to simulated and experimental pseudoperiodic time series and the results show the applicability of the proposed test.

Evaluation of safety effect of turbo-roundabout lane dividers using floating car data and video observation.

PubMed

Kieć, Mariusz; Ambros, Jiří; Bąk, Radosław; Gogolín, Ondřej

2018-06-01

Roundabouts are one of the safest types of intersections. However, the needs to meet the requirements of operation, capacity, traffic organization and surrounding development lead to a variety of design solutions. One of such alternatives are turbo-roundabouts, which simplify drivers' decision making, limit lane changing in the roundabout, and induce low driving speed thanks to raised lane dividers. However, in spite of their generally positive reception, the safety impact of turbo-roundabouts has not been sufficiently studied. Given the low number of existing turbo-roundabouts and the statistical rarity of accident occurrence, the prevalent previously conducted studies applied only simple before-after designs or relied on traffic conflicts in micro-simulations. Nevertheless, the presence of raised lane dividers is acknowledged as an important feature of well performing and safe turbo-roundabouts. Following the previous Polish studies, the primary objective of the present study was assessment of influence of presence of lane dividers on road safety and developing a reliable and valid surrogate safety measure based on field data, which will circumvent the limitations of accident data or micro-simulations. The secondary objective was using the developed surrogate safety measure to assess and compare the safety levels of Polish turbo-roundabout samples with and without raised lane dividers. The surrogate safety measure was based on speed and lane behaviour. Speed was obtained from video observations and floating car data, which enabled the construction of representative speed profiles. Lane behaviour data was gathered from video observations. The collection of the data allowed for a relative validation of the method by comparing the safety performance of turbo-roundabouts with and without raised lane dividers. In the end, the surrogate measure was applied for evaluation of safety levels and enhancement of the existing safety performance functions, which combine traffic volumes, and speeds as a function of radii). The final models may help quantify the safety impact of different turbo-roundabout solutions. Copyright © 2018 Elsevier Ltd. All rights reserved.

A computer-based education intervention to enhance surrogates' informed consent for genomics research.

PubMed

Shelton, Ann K; Freeman, Bradley D; Fish, Anne F; Bachman, Jean A; Richardson, Lloyd I

2015-03-01

Many research studies conducted today in critical care have a genomics component. Patients' surrogates asked to authorize participation in genomics research for a loved one in the intensive care unit may not be prepared to make informed decisions about a patient's participation in the research. To examine the effectiveness of a new, computer-based education module on surrogates' understanding of the process of informed consent for genomics research. A pilot study was conducted with visitors in the waiting rooms of 2 intensive care units in a Midwestern tertiary care medical center. Visitors were randomly assigned to the experimental (education module plus a sample genomics consent form; n = 65) or the control (sample genomics consent form only; n = 69) group. Participants later completed a test on informed genomics consent. Understanding the process of informed consent was greater (P = .001) in the experimental group than in the control group. Specifically, compared with the control group, the experimental group had a greater understanding of 8 of 13 elements of informed consent: intended benefits of research (P = .02), definition of surrogate consenter (P= .001), withdrawal from the study (P = .001), explanation of risk (P = .002), purpose of the institutional review board (P = .001), definition of substituted judgment (P = .03), compensation for harm (P = .001), and alternative treatments (P = .004). Computer-based education modules may be an important addition to conventional approaches for obtaining informed consent in the intensive care unit. Preparing patients' family members who may consider serving as surrogate consenters is critical to facilitating genomics research in critical care. ©2015 American Association of Critical-Care Nurses.

An anthropomorphic multimodality (CT/MRI) head phantom prototype for end-to-end tests in ion radiotherapy.

PubMed

Gallas, Raya R; Hünemohr, Nora; Runz, Armin; Niebuhr, Nina I; Jäkel, Oliver; Greilich, Steffen

2015-12-01

With the increasing complexity of external beam therapy "end-to-end" tests are intended to cover every step from therapy planning through to follow-up in order to fulfill the higher demands on quality assurance. As magnetic resonance imaging (MRI) has become an important part of the treatment process, established phantoms such as the Alderson head cannot fully be used for those tests and novel phantoms have to be developed. Here, we present a feasibility study of a customizable multimodality head phantom. It is initially intended for ion radiotherapy but may also be used in photon therapy. As basis for the anthropomorphic head shape we have used a set of patient computed tomography (CT) images. The phantom recipient consisting of epoxy resin was produced by using a 3D printer. It includes a nasal air cavity, a cranial bone surrogate (based on dipotassium phosphate), a brain surrogate (based on agarose gel), and a surrogate for cerebrospinal fluid (based on distilled water). Furthermore, a volume filled with normoxic dosimetric gel mimicked a tumor. The entire workflow of a proton therapy could be successfully applied to the phantom. CT measurements revealed CT numbers agreeing with reference values for all surrogates in the range from 2 HU to 978 HU (120 kV). MRI showed the desired contrasts between the different phantom materials especially in T2-weighted images (except for the bone surrogate). T2-weighted readout of the polymerization gel dosimeter allowed approximate range verification. Copyright © 2015. Published by Elsevier GmbH.

Quantitation of a PEGylated protein in monkey serum by UHPLC-HRMS using a surrogate disulfide-containing peptide: A new approach to bioanalysis and in vivo stability evaluation of disulfide-rich protein therapeutics.

PubMed

Zheng, Naiyu; Zeng, Jianing; Manney, Amy; Williams, Lakenya; Aubry, Anne-Françoise; Voronin, Kimberly; Buzescu, Adela; Zhang, Yan J; Allentoff, Alban; Xu, Carrie; Shen, Hongwu; Warner, William; Arnold, Mark E

2016-04-15

To quantify a therapeutic PEGylated protein in monkey serum as well as to monitor its potential in vivo instability and methionine oxidation, a novel ultra high performance liquid chromatography-high resolution mass spectrometric (UHPLC-HRMS) assay was developed using a surrogate disulfide-containing peptide, DCP(SS), and a confirmatory peptide, CP, a disulfide-free peptide. DCP(SS) was obtained by eliminating the step of reduction/alkylation before trypsin digestion. It contains an intact disulfide linkage between two peptide sequences that are essential for drug function but susceptible to potential in vivo cleavages. HRMS-based single ion monitoring (SIM) on a Q Exactive™ mass spectrometer was employed to improve assay specificity and sensitivity for DCP(SS) due to its poor fragmentation and low sensitivity with SRM detection. The assay has been validated for the protein drug in monkey serum using both surrogate peptides with excellent accuracy (within ±4.4%Dev) and precision (within 7.5%CV) with a lower limit of quantitation (LLOQ) at 10 ng mL(-1). The protein concentrations in monkey serum obtained from the DCP(SS)-based assay not only provided important pharmacokinetic parameters, but also confirmed in vivo stability of the peptide regions of interest by comparing drug concentrations with those obtained from the CP-based assay or from a ligand-binding assay (LBA). Furthermore, UHPLC-HRMS allowed simultaneous monitoring of the oxidized forms of both surrogate peptides to evaluate potential ex vivo/in vivo oxidation of one methionine present in each of both surrogate peptides. To the best of our knowledge, this is the first report of using a surrogate disulfide-containing peptide for LC-MS bioanalysis of a therapeutic protein. Copyright © 2016 Elsevier B.V. All rights reserved.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zawisza, I; Yan, H; Yin, F

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 surrogatemore » 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 algorithm is effective in estimating surrogate motion multiple-steps in advance. Relative-weighting method shows better prediction accuracy than equal-weighting method. More parameters of this algorithm are under investigation.« less

Using a Gaussian Process Emulator for Data-driven Surrogate Modelling of a Complex Urban Drainage Simulator

NASA Astrophysics Data System (ADS)

Bellos, V.; Mahmoodian, M.; Leopold, U.; Torres-Matallana, J. A.; Schutz, G.; Clemens, F.

2017-12-01

Surrogate models help to decrease the run-time of computationally expensive, detailed models. Recent studies show that Gaussian Process Emulators (GPE) are promising techniques in the field of urban drainage modelling. However, this study focusses on developing a GPE-based surrogate model for later application in Real Time Control (RTC) using input and output time series of a complex simulator. The case study is an urban drainage catchment in Luxembourg. A detailed simulator, implemented in InfoWorks ICM, is used to generate 120 input-output ensembles, from which, 100 are used for training the emulator and 20 for validation of the results. An ensemble of historical rainfall events with 2 hours duration and 10 minutes time steps are considered as the input data. Two example outputs, are selected as wastewater volume and total COD concentration in a storage tank in the network. The results of the emulator are tested with unseen random rainfall events from the ensemble dataset. The emulator is approximately 1000 times faster than the original simulator for this small case study. Whereas the overall patterns of the simulator are matched by the emulator, in some cases the emulator deviates from the simulator. To quantify the accuracy of the emulator in comparison with the original simulator, Nash-Sutcliffe efficiency (NSE) between the emulator and simulator is calculated for unseen rainfall scenarios. The range of NSE for the case of tank volume is from 0.88 to 0.99 with a mean value of 0.95, whereas for COD is from 0.71 to 0.99 with a mean value of 0.92. The emulator is able to predict the tank volume with higher accuracy as the relationship between rainfall intensity and tank volume is linear. For COD, which has a non-linear behaviour, the predictions are less accurate and more uncertain, in particular when rainfall intensity increases. This predictions were improved by including a larger amount of training data for the higher rainfall intensities. It was observed that, the accuracy of the emulator predictions depends on the ensemble training dataset design and the amount of data fed. Finally, more investigation is required to test the possibility of applying this type of fast emulators for model-based RTC applications in which limited number of inputs and outputs are considered in a short prediction horizon.

Verification of satellite radar remote sensing based estimates of boreal and subalpine growing seasons using an ecosystem process model and surface biophysical measurement network information

NASA Technical Reports Server (NTRS)

McDonald, K. C.; Kimball, J. S.; Zimmerman, R.

2002-01-01

We employ daily surface Radar backscatter data from the SeaWinds Ku-band Scatterometer onboard Quikscat to estimate landscape freeze-thaw state and associated length of the seasonal non-frozen period as a surrogate for determining the annual growing season across boreal and subalpine regions of North America for 2000 and 2001.

A review of selected inorganic surface water quality-monitoring practices: are we really measuring what we think, and if so, are we doing it right?

USGS Publications Warehouse

Horowitz, Arthur J.

2013-01-01

Successful environmental/water quality-monitoring programs usually require a balance between analytical capabilities, the collection and preservation of representative samples, and available financial/personnel resources. Due to current economic conditions, monitoring programs are under increasing pressure to do more with less. Hence, a review of current sampling and analytical methodologies, and some of the underlying assumptions that form the bases for these programs seems appropriate, to see if they are achieving their intended objectives within acceptable error limits and/or measurement uncertainty, in a cost-effective manner. That evaluation appears to indicate that several common sampling/processing/analytical procedures (e.g., dip (point) samples/measurements, nitrogen determinations, total recoverable analytical procedures) are generating biased or nonrepresentative data, and that some of the underlying assumptions relative to current programs, such as calendar-based sampling and stationarity are no longer defensible. The extensive use of statistical models as well as surrogates (e.g., turbidity) also needs to be re-examined because the hydrologic interrelationships that support their use tend to be dynamic rather than static. As a result, a number of monitoring programs may need redesigning, some sampling and analytical procedures may need to be updated, and model/surrogate interrelationships may require recalibration.

Surrogate endpoints for cancer screening trials: general principles and an illustration using the UK Flexible Sigmoidoscopy Screening Trial.

PubMed

Cuzick, Jack; Cafferty, Fay H; Edwards, Robert; Møller, Henrik; Duffy, Stephen W

2007-01-01

Cancer screening is aimed primarily at reducing deaths. Thus, site-specific cancer mortality is the appropriate endpoint for evaluating screening interventions. However, it is also the most demanding endpoint, requiring follow-up and a large numbers of patients order to have adequate power. Therefore, it is highly desirable to have surrogate endpoints that can reliably predict mortality reductions many years earlier. We here review a range of surrogate markers in terms of their potential advantages and pitfalls, and argue that a measure which weights incident cancers according to their predicted mortality has many advantages over other measures and should be used more routinely. Application to the UK Flexible Sigmoidoscopy Screening Trial data suggests that predicted colorectal cancer mortality, based on stage-specific incidence, is a more powerful endpoint than actual mortality and could advance the analysis time by about three years. Total colorectal cancer incidence as a surrogate endpoint provides little advance in the analysis time over actual mortality. The approach requires reliable prognostic data, (e.g. stage), for both the study cohort and a representative sample of the whole population. The routine collection of such data should be a priority for cancer registries. Surrogate endpoints should not replace a long-term analysis based directly on mortality, but can provide reliable early indicators which can be useful both for monitoring ongoing screening programmes and for making policy decisions.

Sequence of pathogenic events in cynomolgus macaques infected with aerosolized monkeypox virus.

PubMed

Tree, J A; Hall, G; Pearson, G; Rayner, E; Graham, V A; Steeds, K; Bewley, K R; Hatch, G J; Dennis, M; Taylor, I; Roberts, A D; Funnell, S G P; Vipond, J

2015-04-01

To evaluate new vaccines when human efficacy studies are not possible, the FDA's "Animal Rule" requires well-characterized models of infection. Thus, in the present study, the early pathogenic events of monkeypox infection in nonhuman primates, a surrogate for variola virus infection, were characterized. Cynomolgus macaques were exposed to aerosolized monkeypox virus (10(5) PFU). Clinical observations, viral loads, immune responses, and pathological changes were examined on days 2, 4, 6, 8, 10, and 12 postchallenge. Viral DNA (vDNA) was detected in the lungs on day 2 postchallenge, and viral antigen was detected, by immunostaining, in the epithelium of bronchi, bronchioles, and alveolar walls. Lesions comprised rare foci of dysplastic and sloughed cells in respiratory bronchioles. By day 4, vDNA was detected in the throat, tonsil, and spleen, and monkeypox antigen was detected in the lung, hilar and submandibular lymph nodes, spleen, and colon. Lung lesions comprised focal epithelial necrosis and inflammation. Body temperature peaked on day 6, pox lesions appeared on the skin, and lesions, with positive immunostaining, were present in the lung, tonsil, spleen, lymph nodes, and colon. By day 8, vDNA was present in 9/13 tissues. Blood concentrations of interleukin 1ra (IL-1ra), IL-6, and gamma interferon (IFN-γ) increased markedly. By day 10, circulating IgG antibody concentrations increased, and on day 12, animals showed early signs of recovery. These results define early events occurring in an inhalational macaque monkeypox infection model, supporting its use as a surrogate model for human smallpox. Bioterrorism poses a major threat to public health, as the deliberate release of infectious agents, such smallpox or a related virus, monkeypox, would have catastrophic consequences. The development and testing of new medical countermeasures, e.g., vaccines, are thus priorities; however, tests for efficacy in humans cannot be performed because it would be unethical and field trials are not feasible. To overcome this, the FDA may grant marketing approval of a new product based upon the "Animal Rule," in which interventions are tested for efficacy in well-characterized animal models. Monkeypox virus infection of nonhuman primates (NHPs) presents a potential surrogate disease model for smallpox. Previously, the later stages of monkeypox infection were defined, but the early course of infection remains unstudied. Here, the early pathogenic events of inhalational monkeypox infection in NHPs were characterized, and the results support the use of this surrogate model for testing human smallpox interventions. Copyright © 2015, American Society for Microbiology. All Rights Reserved.

Sequence of Pathogenic Events in Cynomolgus Macaques Infected with Aerosolized Monkeypox Virus

PubMed Central

Hall, G.; Pearson, G.; Rayner, E.; Graham, V. A.; Steeds, K.; Bewley, K. R.; Hatch, G. J.; Dennis, M.; Taylor, I.; Roberts, A. D.; Funnell, S. G. P.; Vipond, J.

2015-01-01

ABSTRACT To evaluate new vaccines when human efficacy studies are not possible, the FDA's “Animal Rule” requires well-characterized models of infection. Thus, in the present study, the early pathogenic events of monkeypox infection in nonhuman primates, a surrogate for variola virus infection, were characterized. Cynomolgus macaques were exposed to aerosolized monkeypox virus (105 PFU). Clinical observations, viral loads, immune responses, and pathological changes were examined on days 2, 4, 6, 8, 10, and 12 postchallenge. Viral DNA (vDNA) was detected in the lungs on day 2 postchallenge, and viral antigen was detected, by immunostaining, in the epithelium of bronchi, bronchioles, and alveolar walls. Lesions comprised rare foci of dysplastic and sloughed cells in respiratory bronchioles. By day 4, vDNA was detected in the throat, tonsil, and spleen, and monkeypox antigen was detected in the lung, hilar and submandibular lymph nodes, spleen, and colon. Lung lesions comprised focal epithelial necrosis and inflammation. Body temperature peaked on day 6, pox lesions appeared on the skin, and lesions, with positive immunostaining, were present in the lung, tonsil, spleen, lymph nodes, and colon. By day 8, vDNA was present in 9/13 tissues. Blood concentrations of interleukin 1ra (IL-1ra), IL-6, and gamma interferon (IFN-γ) increased markedly. By day 10, circulating IgG antibody concentrations increased, and on day 12, animals showed early signs of recovery. These results define early events occurring in an inhalational macaque monkeypox infection model, supporting its use as a surrogate model for human smallpox. IMPORTANCE Bioterrorism poses a major threat to public health, as the deliberate release of infectious agents, such smallpox or a related virus, monkeypox, would have catastrophic consequences. The development and testing of new medical countermeasures, e.g., vaccines, are thus priorities; however, tests for efficacy in humans cannot be performed because it would be unethical and field trials are not feasible. To overcome this, the FDA may grant marketing approval of a new product based upon the “Animal Rule,” in which interventions are tested for efficacy in well-characterized animal models. Monkeypox virus infection of nonhuman primates (NHPs) presents a potential surrogate disease model for smallpox. Previously, the later stages of monkeypox infection were defined, but the early course of infection remains unstudied. Here, the early pathogenic events of inhalational monkeypox infection in NHPs were characterized, and the results support the use of this surrogate model for testing human smallpox interventions. PMID:25653439

Recapturing Graphite-Based Fuel Element Technology for Nuclear Thermal Propulsion

DOE Office of Scientific and Technical Information (OSTI.GOV)

Trammell, Michael P; Jolly, Brian C; Miller, James Henry

ORNL is currently recapturing graphite based fuel forms for Nuclear Thermal Propulsion (NTP). This effort involves research and development on materials selection, extrusion, and coating processes to produce fuel elements representative of historical ROVER and NERVA fuel. Initially, lab scale specimens were fabricated using surrogate oxides to develop processing parameters that could be applied to full length NTP fuel elements. Progress toward understanding the effect of these processing parameters on surrogate fuel microstructure is presented.

Emulator-assisted data assimilation in complex models

NASA Astrophysics Data System (ADS)

Margvelashvili, Nugzar Yu; Herzfeld, Mike; Rizwi, Farhan; Mongin, Mathieu; Baird, Mark E.; Jones, Emlyn; Schaffelke, Britta; King, Edward; Schroeder, Thomas

2016-09-01

Emulators are surrogates of complex models that run orders of magnitude faster than the original model. The utility of emulators for the data assimilation into ocean models is still not well understood. High complexity of ocean models translates into high uncertainty of the corresponding emulators which may undermine the quality of the assimilation schemes based on such emulators. Numerical experiments with a chaotic Lorenz-95 model are conducted to illustrate this point and suggest a strategy to alleviate this problem through the localization of the emulation and data assimilation procedures. Insights gained through these experiments are used to design and implement data assimilation scenario for a 3D fine-resolution sediment transport model of the Great Barrier Reef (GBR), Australia.