Sensitivity-Based Guided Model Calibration

NASA Astrophysics Data System (ADS)

Semnani, M.; Asadzadeh, M.

2017-12-01

A common practice in automatic calibration of hydrologic models is applying the sensitivity analysis prior to the global optimization to reduce the number of decision variables (DVs) by identifying the most sensitive ones. This two-stage process aims to improve the optimization efficiency. However, Parameter sensitivity information can be used to enhance the ability of the optimization algorithms to find good quality solutions in a fewer number of solution evaluations. This improvement can be achieved by increasing the focus of optimization on sampling from the most sensitive parameters in each iteration. In this study, the selection process of the dynamically dimensioned search (DDS) optimization algorithm is enhanced by utilizing a sensitivity analysis method to put more emphasis on the most sensitive decision variables for perturbation. The performance of DDS with the sensitivity information is compared to the original version of DDS for different mathematical test functions and a model calibration case study. Overall, the results show that DDS with sensitivity information finds nearly the same solutions as original DDS, however, in a significantly fewer number of solution evaluations.

Fast computation of derivative based sensitivities of PSHA models via algorithmic differentiation

NASA Astrophysics Data System (ADS)

Leövey, Hernan; Molkenthin, Christian; Scherbaum, Frank; Griewank, Andreas; Kuehn, Nicolas; Stafford, Peter

2015-04-01

Probabilistic seismic hazard analysis (PSHA) is the preferred tool for estimation of potential ground-shaking hazard due to future earthquakes at a site of interest. A modern PSHA represents a complex framework which combines different models with possible many inputs. Sensitivity analysis is a valuable tool for quantifying changes of a model output as inputs are perturbed, identifying critical input parameters and obtaining insight in the model behavior. Differential sensitivity analysis relies on calculating first-order partial derivatives of the model output with respect to its inputs. Moreover, derivative based global sensitivity measures (Sobol' & Kucherenko '09) can be practically used to detect non-essential inputs of the models, thus restricting the focus of attention to a possible much smaller set of inputs. Nevertheless, obtaining first-order partial derivatives of complex models with traditional approaches can be very challenging, and usually increases the computation complexity linearly with the number of inputs appearing in the models. In this study we show how Algorithmic Differentiation (AD) tools can be used in a complex framework such as PSHA to successfully estimate derivative based sensitivities, as is the case in various other domains such as meteorology or aerodynamics, without no significant increase in the computation complexity required for the original computations. First we demonstrate the feasibility of the AD methodology by comparing AD derived sensitivities to analytically derived sensitivities for a basic case of PSHA using a simple ground-motion prediction equation. In a second step, we derive sensitivities via AD for a more complex PSHA study using a ground motion attenuation relation based on a stochastic method to simulate strong motion. The presented approach is general enough to accommodate more advanced PSHA studies of higher complexity.

Model of urban water management towards water sensitive city: a literature review

NASA Astrophysics Data System (ADS)

Maftuhah, D. I.; Anityasari, M.; Sholihah, M.

2018-04-01

Nowadays, many cities are facing with complex issues such as climate change, social, economic, culture, and environmental problems, especially urban water. In other words, the city has to struggle with the challenge to make sure its sustainability in all aspects. This research focuses on how to ensure the city sustainability and resilience on urban water management. Many research were not only conducted in urban water management, but also in sustainability itself. Moreover, water sustainability shifts from urban water management into water sensitive city. This transition needs comprehensive aspects such as social, institutional dynamics, technical innovation, and local contents. Some literatures about model of urban water management and the transition towards water sensitivity had been reviewed in this study. This study proposed discussion about model of urban water management and the transition towards water sensitive city. Research findings suggest that there are many different models developed in urban water management, but they are not comprehensive yet and only few studies discuss about the transition towards water sensitive and resilience city. The drawbacks of previous research can identify and fulfill the gap of this study. Therefore, the paper contributes a general framework for the urban water management modelling studies.

Global Sensitivity of Simulated Water Balance Indicators Under Future Climate Change in the Colorado Basin

DOE PAGES

Bennett, Katrina Eleanor; Urrego Blanco, Jorge Rolando; Jonko, Alexandra; ...

2017-11-20

The Colorado River basin is a fundamentally important river for society, ecology and energy in the United States. Streamflow estimates are often provided using modeling tools which rely on uncertain parameters; sensitivity analysis can help determine which parameters impact model results. Despite the fact that simulated flows respond to changing climate and vegetation in the basin, parameter sensitivity of the simulations under climate change has rarely been considered. In this study, we conduct a global sensitivity analysis to relate changes in runoff, evapotranspiration, snow water equivalent and soil moisture to model parameters in the Variable Infiltration Capacity (VIC) hydrologic model.more » Here, we combine global sensitivity analysis with a space-filling Latin Hypercube sampling of the model parameter space and statistical emulation of the VIC model to examine sensitivities to uncertainties in 46 model parameters following a variance-based approach.« less

Analysis of the sensitivity properties of a model of vector-borne bubonic plague.

PubMed

Buzby, Megan; Neckels, David; Antolin, Michael F; Estep, Donald

2008-09-06

Model sensitivity is a key to evaluation of mathematical models in ecology and evolution, especially in complex models with numerous parameters. In this paper, we use some recently developed methods for sensitivity analysis to study the parameter sensitivity of a model of vector-borne bubonic plague in a rodent population proposed by Keeling & Gilligan. The new sensitivity tools are based on a variational analysis involving the adjoint equation. The new approach provides a relatively inexpensive way to obtain derivative information about model output with respect to parameters. We use this approach to determine the sensitivity of a quantity of interest (the force of infection from rats and their fleas to humans) to various model parameters, determine a region over which linearization at a specific parameter reference point is valid, develop a global picture of the output surface, and search for maxima and minima in a given region in the parameter space.

Global Sensitivity of Simulated Water Balance Indicators Under Future Climate Change in the Colorado Basin

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

Bennett, Katrina Eleanor; Urrego Blanco, Jorge Rolando; Jonko, Alexandra

The Colorado River basin is a fundamentally important river for society, ecology and energy in the United States. Streamflow estimates are often provided using modeling tools which rely on uncertain parameters; sensitivity analysis can help determine which parameters impact model results. Despite the fact that simulated flows respond to changing climate and vegetation in the basin, parameter sensitivity of the simulations under climate change has rarely been considered. In this study, we conduct a global sensitivity analysis to relate changes in runoff, evapotranspiration, snow water equivalent and soil moisture to model parameters in the Variable Infiltration Capacity (VIC) hydrologic model.more » Here, we combine global sensitivity analysis with a space-filling Latin Hypercube sampling of the model parameter space and statistical emulation of the VIC model to examine sensitivities to uncertainties in 46 model parameters following a variance-based approach.« less

4D-Fingerprint Categorical QSAR Models for Skin Sensitization Based on Classification Local Lymph Node Assay Measures

PubMed Central

Li, Yi; Tseng, Yufeng J.; Pan, Dahua; Liu, Jianzhong; Kern, Petra S.; Gerberick, G. Frank; Hopfinger, Anton J.

2008-01-01

Currently, the only validated methods to identify skin sensitization effects are in vivo models, such as the Local Lymph Node Assay (LLNA) and guinea pig studies. There is a tremendous need, in particular due to novel legislation, to develop animal alternatives, eg. Quantitative Structure-Activity Relationship (QSAR) models. Here, QSAR models for skin sensitization using LLNA data have been constructed. The descriptors used to generate these models are derived from the 4D-molecular similarity paradigm and are referred to as universal 4D-fingerprints. A training set of 132 structurally diverse compounds and a test set of 15 structurally diverse compounds were used in this study. The statistical methodologies used to build the models are logistic regression (LR), and partial least square coupled logistic regression (PLS-LR), which prove to be effective tools for studying skin sensitization measures expressed in the two categorical terms of sensitizer and non-sensitizer. QSAR models with low values of the Hosmer-Lemeshow goodness-of-fit statistic, χHL2, are significant and predictive. For the training set, the cross-validated prediction accuracy of the logistic regression models ranges from 77.3% to 78.0%, while that of PLS-logistic regression models ranges from 87.1% to 89.4%. For the test set, the prediction accuracy of logistic regression models ranges from 80.0%-86.7%, while that of PLS-logistic regression models ranges from 73.3%-80.0%. The QSAR models are made up of 4D-fingerprints related to aromatic atoms, hydrogen bond acceptors and negatively partially charged atoms. PMID:17226934

Comparison of two propeller source models for aircraft interior noise studies

NASA Technical Reports Server (NTRS)

Mahan, J. R.; Fuller, C. R.

1986-01-01

The sensitivity of the predicted synchrophasing (SP) effectiveness trends to the propeller source model issued is investigated with reference to the development of advanced turboprop engines for transport aircraft. SP effectiveness is shown to be sensitive to the type of source model used. For the virtually rotating dipole source model, the SP effectiveness is sensitive to the direction of rotation at some frequencies but not at others. The SP effectiveness obtained from the virtually rotating dipole model is not very sensitive to the radial location of the source distribution within reasonable limits. Finally, the predicted SP effectiveness is shown to be more sensitive to the details of the source model used for the case of corotation than for the case of counterrotation.

Study and comparison of different sensitivity models for a two-plane Compton camera.

PubMed

Muñoz, Enrique; Barrio, John; Bernabéu, José; Etxebeste, Ane; Lacasta, Carlos; Llosá, Gabriela; Ros, Ana; Roser, Jorge; Oliver, Josep F

2018-06-25

Given the strong variations in the sensitivity of Compton cameras for the detection of events originating from different points in the field of view (FoV), sensitivity correction is often necessary in Compton image reconstruction. Several approaches for the calculation of the sensitivity matrix have been proposed in the literature. While most of these models are easily implemented and can be useful in many cases, they usually assume high angular coverage over the scattered photon, which is not the case for our prototype. In this work, we have derived an analytical model that allows us to calculate a detailed sensitivity matrix, which has been compared to other sensitivity models in the literature. Specifically, the proposed model describes the probability of measuring a useful event in a two-plane Compton camera, including the most relevant physical processes involved. The model has been used to obtain an expression for the system and sensitivity matrices for iterative image reconstruction. These matrices have been validated taking Monte Carlo simulations as a reference. In order to study the impact of the sensitivity, images reconstructed with our sensitivity model and with other models have been compared. Images have been reconstructed from several simulated sources, including point-like sources and extended distributions of activity, and also from experimental data measured with 22 Na sources. Results show that our sensitivity model is the best suited for our prototype. Although other models in the literature perform successfully in many scenarios, they are not applicable in all the geometrical configurations of interest for our system. In general, our model allows to effectively recover the intensity of point-like sources at different positions in the FoV and to reconstruct regions of homogeneous activity with minimal variance. Moreover, it can be employed for all Compton camera configurations, including those with low angular coverage over the scatterer.

Univariate and bivariate likelihood-based meta-analysis methods performed comparably when marginal sensitivity and specificity were the targets of inference.

PubMed

Dahabreh, Issa J; Trikalinos, Thomas A; Lau, Joseph; Schmid, Christopher H

2017-03-01

To compare statistical methods for meta-analysis of sensitivity and specificity of medical tests (e.g., diagnostic or screening tests). We constructed a database of PubMed-indexed meta-analyses of test performance from which 2 × 2 tables for each included study could be extracted. We reanalyzed the data using univariate and bivariate random effects models fit with inverse variance and maximum likelihood methods. Analyses were performed using both normal and binomial likelihoods to describe within-study variability. The bivariate model using the binomial likelihood was also fit using a fully Bayesian approach. We use two worked examples-thoracic computerized tomography to detect aortic injury and rapid prescreening of Papanicolaou smears to detect cytological abnormalities-to highlight that different meta-analysis approaches can produce different results. We also present results from reanalysis of 308 meta-analyses of sensitivity and specificity. Models using the normal approximation produced sensitivity and specificity estimates closer to 50% and smaller standard errors compared to models using the binomial likelihood; absolute differences of 5% or greater were observed in 12% and 5% of meta-analyses for sensitivity and specificity, respectively. Results from univariate and bivariate random effects models were similar, regardless of estimation method. Maximum likelihood and Bayesian methods produced almost identical summary estimates under the bivariate model; however, Bayesian analyses indicated greater uncertainty around those estimates. Bivariate models produced imprecise estimates of the between-study correlation of sensitivity and specificity. Differences between methods were larger with increasing proportion of studies that were small or required a continuity correction. The binomial likelihood should be used to model within-study variability. Univariate and bivariate models give similar estimates of the marginal distributions for sensitivity and specificity. Bayesian methods fully quantify uncertainty and their ability to incorporate external evidence may be useful for imprecisely estimated parameters. Copyright © 2017 Elsevier Inc. All rights reserved.

DEVELOPMENT OF MESOSCALE AIR QUALITY SIMULATION MODELS. VOLUME 1. COMPARATIVE SENSITIVITY STUDIES OF PUFF, PLUME, AND GRID MODELS FOR LONG DISTANCE DISPERSION

EPA Science Inventory

This report provides detailed comparisons and sensitivity analyses of three candidate models, MESOPLUME, MESOPUFF, and MESOGRID. This was not a validation study; there was no suitable regional air quality data base for the Four Corners area. Rather, the models have been evaluated...

Predicting chemically-induced skin reactions. Part II: QSAR models of skin permeability and the relationships between skin permeability and skin sensitization

PubMed Central

Alves, Vinicius M.; Muratov, Eugene; Fourches, Denis; Strickland, Judy; Kleinstreuer, Nicole; Andrade, Carolina H.; Tropsha, Alexander

2015-01-01

Skin permeability is widely considered to be mechanistically implicated in chemically-induced skin sensitization. Although many chemicals have been identified as skin sensitizers, there have been very few reports analyzing the relationships between molecular structure and skin permeability of sensitizers and non-sensitizers. The goals of this study were to: (i) compile, curate, and integrate the largest publicly available dataset of chemicals studied for their skin permeability; (ii) develop and rigorously validate QSAR models to predict skin permeability; and (iii) explore the complex relationships between skin sensitization and skin permeability. Based on the largest publicly available dataset compiled in this study, we found no overall correlation between skin permeability and skin sensitization. In addition, cross-species correlation coefficient between human and rodent permeability data was found to be as low as R2=0.44. Human skin permeability models based on the random forest method have been developed and validated using OECD-compliant QSAR modeling workflow. Their external accuracy was high (Q2ext = 0.73 for 63% of external compounds inside the applicability domain). The extended analysis using both experimentally-measured and QSAR-imputed data still confirmed the absence of any overall concordance between skin permeability and skin sensitization. This observation suggests that chemical modifications that affect skin permeability should not be presumed a priori to modulate the sensitization potential of chemicals. The models reported herein as well as those developed in the companion paper on skin sensitization suggest that it may be possible to rationally design compounds with the desired high skin permeability but low sensitization potential. PMID:25560673

A sensitivity analysis of regional and small watershed hydrologic models

NASA Technical Reports Server (NTRS)

Ambaruch, R.; Salomonson, V. V.; Simmons, J. W.

1975-01-01

Continuous simulation models of the hydrologic behavior of watersheds are important tools in several practical applications such as hydroelectric power planning, navigation, and flood control. Several recent studies have addressed the feasibility of using remote earth observations as sources of input data for hydrologic models. The objective of the study reported here was to determine how accurately remotely sensed measurements must be to provide inputs to hydrologic models of watersheds, within the tolerances needed for acceptably accurate synthesis of streamflow by the models. The study objective was achieved by performing a series of sensitivity analyses using continuous simulation models of three watersheds. The sensitivity analysis showed quantitatively how variations in each of 46 model inputs and parameters affect simulation accuracy with respect to five different performance indices.

Predicting chemically-induced skin reactions. Part I: QSAR models of skin sensitization and their application to identify potentially hazardous compounds

PubMed Central

Alves, Vinicius M.; Muratov, Eugene; Fourches, Denis; Strickland, Judy; Kleinstreuer, Nicole; Andrade, Carolina H.; Tropsha, Alexander

2015-01-01

Repetitive exposure to a chemical agent can induce an immune reaction in inherently susceptible individuals that leads to skin sensitization. Although many chemicals have been reported as skin sensitizers, there have been very few rigorously validated QSAR models with defined applicability domains (AD) that were developed using a large group of chemically diverse compounds. In this study, we have aimed to compile, curate, and integrate the largest publicly available dataset related to chemically-induced skin sensitization, use this data to generate rigorously validated and QSAR models for skin sensitization, and employ these models as a virtual screening tool for identifying putative sensitizers among environmental chemicals. We followed best practices for model building and validation implemented with our predictive QSAR workflow using random forest modeling technique in combination with SiRMS and Dragon descriptors. The Correct Classification Rate (CCR) for QSAR models discriminating sensitizers from non-sensitizers were 71–88% when evaluated on several external validation sets, within a broad AD, with positive (for sensitizers) and negative (for non-sensitizers) predicted rates of 85% and 79% respectively. When compared to the skin sensitization module included in the OECD QSAR toolbox as well as to the skin sensitization model in publicly available VEGA software, our models showed a significantly higher prediction accuracy for the same sets of external compounds as evaluated by Positive Predicted Rate, Negative Predicted Rate, and CCR. These models were applied to identify putative chemical hazards in the ScoreCard database of possible skin or sense organ toxicants as primary candidates for experimental validation. PMID:25560674

Predicting chemically-induced skin reactions. Part I: QSAR models of skin sensitization and their application to identify potentially hazardous compounds

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

Alves, Vinicius M.; Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599; Muratov, Eugene

Repetitive exposure to a chemical agent can induce an immune reaction in inherently susceptible individuals that leads to skin sensitization. Although many chemicals have been reported as skin sensitizers, there have been very few rigorously validated QSAR models with defined applicability domains (AD) that were developed using a large group of chemically diverse compounds. In this study, we have aimed to compile, curate, and integrate the largest publicly available dataset related to chemically-induced skin sensitization, use this data to generate rigorously validated and QSAR models for skin sensitization, and employ these models as a virtual screening tool for identifying putativemore » sensitizers among environmental chemicals. We followed best practices for model building and validation implemented with our predictive QSAR workflow using Random Forest modeling technique in combination with SiRMS and Dragon descriptors. The Correct Classification Rate (CCR) for QSAR models discriminating sensitizers from non-sensitizers was 71–88% when evaluated on several external validation sets, within a broad AD, with positive (for sensitizers) and negative (for non-sensitizers) predicted rates of 85% and 79% respectively. When compared to the skin sensitization module included in the OECD QSAR Toolbox as well as to the skin sensitization model in publicly available VEGA software, our models showed a significantly higher prediction accuracy for the same sets of external compounds as evaluated by Positive Predicted Rate, Negative Predicted Rate, and CCR. These models were applied to identify putative chemical hazards in the Scorecard database of possible skin or sense organ toxicants as primary candidates for experimental validation. - Highlights: • It was compiled the largest publicly-available skin sensitization dataset. • Predictive QSAR models were developed for skin sensitization. • Developed models have higher prediction accuracy than OECD QSAR Toolbox. • Putative chemical hazards in the Scorecard database were found using our models.« less

Grid sensitivity for aerodynamic optimization and flow analysis

NASA Technical Reports Server (NTRS)

Sadrehaghighi, I.; Tiwari, S. N.

1993-01-01

After reviewing relevant literature, it is apparent that one aspect of aerodynamic sensitivity analysis, namely grid sensitivity, has not been investigated extensively. The grid sensitivity algorithms in most of these studies are based on structural design models. Such models, although sufficient for preliminary or conceptional design, are not acceptable for detailed design analysis. Careless grid sensitivity evaluations, would introduce gradient errors within the sensitivity module, therefore, infecting the overall optimization process. Development of an efficient and reliable grid sensitivity module with special emphasis on aerodynamic applications appear essential. The organization of this study is as follows. The physical and geometric representations of a typical model are derived in chapter 2. The grid generation algorithm and boundary grid distribution are developed in chapter 3. Chapter 4 discusses the theoretical formulation and aerodynamic sensitivity equation. The method of solution is provided in chapter 5. The results are presented and discussed in chapter 6. Finally, some concluding remarks are provided in chapter 7.

Standard Information Models for Representing Adverse Sensitivity Information in Clinical Documents.

PubMed

Topaz, M; Seger, D L; Goss, F; Lai, K; Slight, S P; Lau, J J; Nandigam, H; Zhou, L

2016-01-01

Adverse sensitivity (e.g., allergy and intolerance) information is a critical component of any electronic health record system. While several standards exist for structured entry of adverse sensitivity information, many clinicians record this data as free text. This study aimed to 1) identify and compare the existing common adverse sensitivity information models, and 2) to evaluate the coverage of the adverse sensitivity information models for representing allergy information on a subset of inpatient and outpatient adverse sensitivity clinical notes. We compared four common adverse sensitivity information models: Health Level 7 Allergy and Intolerance Domain Analysis Model, HL7-DAM; the Fast Healthcare Interoperability Resources, FHIR; the Consolidated Continuity of Care Document, C-CDA; and OpenEHR, and evaluated their coverage on a corpus of inpatient and outpatient notes (n = 120). We found that allergy specialists' notes had the highest frequency of adverse sensitivity attributes per note, whereas emergency department notes had the fewest attributes. Overall, the models had many similarities in the central attributes which covered between 75% and 95% of adverse sensitivity information contained within the notes. However, representations of some attributes (especially the value-sets) were not well aligned between the models, which is likely to present an obstacle for achieving data interoperability. Also, adverse sensitivity exceptions were not well represented among the information models. Although we found that common adverse sensitivity models cover a significant portion of relevant information in the clinical notes, our results highlight areas needed to be reconciled between the standards for data interoperability.

Efficient stochastic approaches for sensitivity studies of an Eulerian large-scale air pollution model

NASA Astrophysics Data System (ADS)

Dimov, I.; Georgieva, R.; Todorov, V.; Ostromsky, Tz.

2017-10-01

Reliability of large-scale mathematical models is an important issue when such models are used to support decision makers. Sensitivity analysis of model outputs to variation or natural uncertainties of model inputs is crucial for improving the reliability of mathematical models. A comprehensive experimental study of Monte Carlo algorithms based on Sobol sequences for multidimensional numerical integration has been done. A comparison with Latin hypercube sampling and a particular quasi-Monte Carlo lattice rule based on generalized Fibonacci numbers has been presented. The algorithms have been successfully applied to compute global Sobol sensitivity measures corresponding to the influence of several input parameters (six chemical reactions rates and four different groups of pollutants) on the concentrations of important air pollutants. The concentration values have been generated by the Unified Danish Eulerian Model. The sensitivity study has been done for the areas of several European cities with different geographical locations. The numerical tests show that the stochastic algorithms under consideration are efficient for multidimensional integration and especially for computing small by value sensitivity indices. It is a crucial element since even small indices may be important to be estimated in order to achieve a more accurate distribution of inputs influence and a more reliable interpretation of the mathematical model results.

Towards simplification of hydrologic modeling: Identification of dominant processes

USGS Publications Warehouse

Markstrom, Steven; Hay, Lauren E.; Clark, Martyn P.

2016-01-01

The Precipitation–Runoff Modeling System (PRMS), a distributed-parameter hydrologic model, has been applied to the conterminous US (CONUS). Parameter sensitivity analysis was used to identify: (1) the sensitive input parameters and (2) particular model output variables that could be associated with the dominant hydrologic process(es). Sensitivity values of 35 PRMS calibration parameters were computed using the Fourier amplitude sensitivity test procedure on 110 000 independent hydrologically based spatial modeling units covering the CONUS and then summarized to process (snowmelt, surface runoff, infiltration, soil moisture, evapotranspiration, interflow, baseflow, and runoff) and model performance statistic (mean, coefficient of variation, and autoregressive lag 1). Identified parameters and processes provide insight into model performance at the location of each unit and allow the modeler to identify the most dominant process on the basis of which processes are associated with the most sensitive parameters. The results of this study indicate that: (1) the choice of performance statistic and output variables has a strong influence on parameter sensitivity, (2) the apparent model complexity to the modeler can be reduced by focusing on those processes that are associated with sensitive parameters and disregarding those that are not, (3) different processes require different numbers of parameters for simulation, and (4) some sensitive parameters influence only one hydrologic process, while others may influence many

Exploring Intercultural Sensitivity in Early Adolescence: A Mixed Methods Study

ERIC Educational Resources Information Center

Mellizo, Jennifer M.

2017-01-01

The purpose of this mixed methods study was to explore levels of intercultural sensitivity in a sample of fourth to eighth grade students in the United States (n = 162). "Intercultural sensitivity" was conceptualised through Bennett's Developmental Model of Sensitivity, and assessed through the Adapted Intercultural Sensitivity Index.…

Uncertainty quantification and global sensitivity analysis of the Los Alamos sea ice model

NASA Astrophysics Data System (ADS)

Urrego-Blanco, Jorge R.; Urban, Nathan M.; Hunke, Elizabeth C.; Turner, Adrian K.; Jeffery, Nicole

2016-04-01

Changes in the high-latitude climate system have the potential to affect global climate through feedbacks with the atmosphere and connections with midlatitudes. Sea ice and climate models used to understand these changes have uncertainties that need to be characterized and quantified. We present a quantitative way to assess uncertainty in complex computer models, which is a new approach in the analysis of sea ice models. We characterize parametric uncertainty in the Los Alamos sea ice model (CICE) in a standalone configuration and quantify the sensitivity of sea ice area, extent, and volume with respect to uncertainty in 39 individual model parameters. Unlike common sensitivity analyses conducted in previous studies where parameters are varied one at a time, this study uses a global variance-based approach in which Sobol' sequences are used to efficiently sample the full 39-dimensional parameter space. We implement a fast emulator of the sea ice model whose predictions of sea ice extent, area, and volume are used to compute the Sobol' sensitivity indices of the 39 parameters. Main effects and interactions among the most influential parameters are also estimated by a nonparametric regression technique based on generalized additive models. A ranking based on the sensitivity indices indicates that model predictions are most sensitive to snow parameters such as snow conductivity and grain size, and the drainage of melt ponds. It is recommended that research be prioritized toward more accurately determining these most influential parameter values by observational studies or by improving parameterizations in the sea ice model.

A global sensitivity analysis approach for morphogenesis models.

PubMed

Boas, Sonja E M; Navarro Jimenez, Maria I; Merks, Roeland M H; Blom, Joke G

2015-11-21

Morphogenesis is a developmental process in which cells organize into shapes and patterns. Complex, non-linear and multi-factorial models with images as output are commonly used to study morphogenesis. It is difficult to understand the relation between the uncertainty in the input and the output of such 'black-box' models, giving rise to the need for sensitivity analysis tools. In this paper, we introduce a workflow for a global sensitivity analysis approach to study the impact of single parameters and the interactions between them on the output of morphogenesis models. To demonstrate the workflow, we used a published, well-studied model of vascular morphogenesis. The parameters of this cellular Potts model (CPM) represent cell properties and behaviors that drive the mechanisms of angiogenic sprouting. The global sensitivity analysis correctly identified the dominant parameters in the model, consistent with previous studies. Additionally, the analysis provided information on the relative impact of single parameters and of interactions between them. This is very relevant because interactions of parameters impede the experimental verification of the predicted effect of single parameters. The parameter interactions, although of low impact, provided also new insights in the mechanisms of in silico sprouting. Finally, the analysis indicated that the model could be reduced by one parameter. We propose global sensitivity analysis as an alternative approach to study the mechanisms of morphogenesis. Comparison of the ranking of the impact of the model parameters to knowledge derived from experimental data and from manipulation experiments can help to falsify models and to find the operand mechanisms in morphogenesis. The workflow is applicable to all 'black-box' models, including high-throughput in vitro models in which output measures are affected by a set of experimental perturbations.

Sensitivity of the Boundary Plasma to the Plasma-Material Interface

DOE PAGES

Canik, John M.; Tang, X. -Z.

2017-01-01

While the sensitivity of the scrape-off layer and divertor plasma to the highly uncertain cross-field transport assumptions is widely recognized, the plasma is also sensitive to the details of the plasma-material interface (PMI) models used as part of comprehensive predictive simulations. Here in this paper, these PMI sensitivities are studied by varying the relevant sub-models within the SOLPS plasma transport code. Two aspects are explored: the sheath model used as a boundary condition in SOLPS, and fast particle reflection rates for ions impinging on a material surface. Both of these have been the study of recent high-fidelity simulation efforts aimedmore » at improving the understanding and prediction of these phenomena. It is found that in both cases quantitative changes to the plasma solution result from modification of the PMI model, with a larger impact in the case of the reflection coefficient variation. Finally, this indicates the necessity to better quantify the uncertainties within the PMI models themselves, and perform thorough sensitivity analysis to propagate these throughout the boundary model; this is especially important for validation against experiment, where the error in the simulation is a critical and less-studied piece of the code-experiment comparison.« less

Comparison of the sensitivity of landscape-fire-succession models to variation in terrain, fuel pattern, climate and weather

Treesearch

Geoffrey J. Cary; Robert E. Keane; Robert H. Gardner; Sandra Lavorel; Michael D. Flannigan; Ian D. Davies; Chao Li; James M. Lenihan; T. Scott Rupp; Florent Mouillot

2006-01-01

The purpose of this study was to compare the sensitivity of modelled area burned to environmental factors across a range of independently-developed landscape-fire-succession models. The sensitivity of area burned to variation in four factors, namely terrain (flat, undulating and mountainous), fuel pattern (finely and coarsely clumped), climate (observed, warmer &...

Uncertainty Quantification and Sensitivity Analysis in the CICE v5.1 Sea Ice Model

NASA Astrophysics Data System (ADS)

Urrego-Blanco, J. R.; Urban, N. M.

2015-12-01

Changes in the high latitude climate system have the potential to affect global climate through feedbacks with the atmosphere and connections with mid latitudes. Sea ice and climate models used to understand these changes have uncertainties that need to be characterized and quantified. In this work we characterize parametric uncertainty in Los Alamos Sea Ice model (CICE) and quantify the sensitivity of sea ice area, extent and volume with respect to uncertainty in about 40 individual model parameters. Unlike common sensitivity analyses conducted in previous studies where parameters are varied one-at-a-time, this study uses a global variance-based approach in which Sobol sequences are used to efficiently sample the full 40-dimensional parameter space. This approach requires a very large number of model evaluations, which are expensive to run. A more computationally efficient approach is implemented by training and cross-validating a surrogate (emulator) of the sea ice model with model output from 400 model runs. The emulator is used to make predictions of sea ice extent, area, and volume at several model configurations, which are then used to compute the Sobol sensitivity indices of the 40 parameters. A ranking based on the sensitivity indices indicates that model output is most sensitive to snow parameters such as conductivity and grain size, and the drainage of melt ponds. The main effects and interactions among the most influential parameters are also estimated by a non-parametric regression technique based on generalized additive models. It is recommended research to be prioritized towards more accurately determining these most influential parameters values by observational studies or by improving existing parameterizations in the sea ice model.

[Sensitization and oral challenge with ovoalbumin in an animal model of food allergy].

PubMed

Vinuesa, Miguel Ngel; Bassan, Norberto David; Chaparro, Soledad; Martìnez, Adriel; Batle, Rocìo; Giacomozzi, Florencia; Torres, Valentìn

2012-01-01

Gut-associated lymphoid tissue (GALT) is mainly formed by the gut mucosa and associated lymphatic structures that under normal conditions induces hyporesponsiveness, a phenomenon termed oral tolerance. However, the potential brakeup of oral tolerance could otherwise lead to disorders such as food allergy. The aim of the study is to characterise the histopathological and immunohistochemical modifications in intestinal gut mucosa in an animal model of food allergy. New Zealand rabbits were subcutaneously sensitized twice with ovalbumin (OVA), on day 30 after first sensitization, animals were oral challenged with the same antigen. Lymphatic cell population and accessory cells from gut mucosa were studied by conventional histology, histochemistry and immunohistochemistry. An important increase in number of eosinophils were observed in sensitized and challenged group as well as CD25+cells increase in sensitized animals without challenge. Data obtained demonstrated that subcutaneous sensitization and challenge with OVA induced generation of specific IgE antibodies and an anaphylactic inflammatory response. This pattern induced quantitative modifications in studied cells and structural changes in mucosa like oedema at intestinal villi in sensitized and challenged rabbits in this animal model of food allergy.

Mars approach for global sensitivity analysis of differential equation models with applications to dynamics of influenza infection.

PubMed

Lee, Yeonok; Wu, Hulin

2012-01-01

Differential equation models are widely used for the study of natural phenomena in many fields. The study usually involves unknown factors such as initial conditions and/or parameters. It is important to investigate the impact of unknown factors (parameters and initial conditions) on model outputs in order to better understand the system the model represents. Apportioning the uncertainty (variation) of output variables of a model according to the input factors is referred to as sensitivity analysis. In this paper, we focus on the global sensitivity analysis of ordinary differential equation (ODE) models over a time period using the multivariate adaptive regression spline (MARS) as a meta model based on the concept of the variance of conditional expectation (VCE). We suggest to evaluate the VCE analytically using the MARS model structure of univariate tensor-product functions which is more computationally efficient. Our simulation studies show that the MARS model approach performs very well and helps to significantly reduce the computational cost. We present an application example of sensitivity analysis of ODE models for influenza infection to further illustrate the usefulness of the proposed method.

[Comparison of simple pooling and bivariate model used in meta-analyses of diagnostic test accuracy published in Chinese journals].

PubMed

Huang, Yuan-sheng; Yang, Zhi-rong; Zhan, Si-yan

2015-06-18

To investigate the use of simple pooling and bivariate model in meta-analyses of diagnostic test accuracy (DTA) published in Chinese journals (January to November, 2014), compare the differences of results from these two models, and explore the impact of between-study variability of sensitivity and specificity on the differences. DTA meta-analyses were searched through Chinese Biomedical Literature Database (January to November, 2014). Details in models and data for fourfold table were extracted. Descriptive analysis was conducted to investigate the prevalence of the use of simple pooling method and bivariate model in the included literature. Data were re-analyzed with the two models respectively. Differences in the results were examined by Wilcoxon signed rank test. How the results differences were affected by between-study variability of sensitivity and specificity, expressed by I2, was explored. The 55 systematic reviews, containing 58 DTA meta-analyses, were included and 25 DTA meta-analyses were eligible for re-analysis. Simple pooling was used in 50 (90.9%) systematic reviews and bivariate model in 1 (1.8%). The remaining 4 (7.3%) articles used other models pooling sensitivity and specificity or pooled neither of them. Of the reviews simply pooling sensitivity and specificity, 41(82.0%) were at the risk of wrongly using Meta-disc software. The differences in medians of sensitivity and specificity between two models were both 0.011 (P<0.001, P=0.031 respectively). Greater differences could be found as I2 of sensitivity or specificity became larger, especially when I2>75%. Most DTA meta-analyses published in Chinese journals(January to November, 2014) combine the sensitivity and specificity by simple pooling. Meta-disc software can pool the sensitivity and specificity only through fixed-effect model, but a high proportion of authors think it can implement random-effect model. Simple pooling tends to underestimate the results compared with bivariate model. The greater the between-study variance is, the more likely the simple pooling has larger deviation. It is necessary to increase the knowledge level of statistical methods and software for meta-analyses of DTA data.

Uncertainty quantification and global sensitivity analysis of the Los Alamos sea ice model

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

Urrego-Blanco, Jorge Rolando; Urban, Nathan Mark; Hunke, Elizabeth Clare

Changes in the high-latitude climate system have the potential to affect global climate through feedbacks with the atmosphere and connections with midlatitudes. Sea ice and climate models used to understand these changes have uncertainties that need to be characterized and quantified. We present a quantitative way to assess uncertainty in complex computer models, which is a new approach in the analysis of sea ice models. We characterize parametric uncertainty in the Los Alamos sea ice model (CICE) in a standalone configuration and quantify the sensitivity of sea ice area, extent, and volume with respect to uncertainty in 39 individual modelmore » parameters. Unlike common sensitivity analyses conducted in previous studies where parameters are varied one at a time, this study uses a global variance-based approach in which Sobol' sequences are used to efficiently sample the full 39-dimensional parameter space. We implement a fast emulator of the sea ice model whose predictions of sea ice extent, area, and volume are used to compute the Sobol' sensitivity indices of the 39 parameters. Main effects and interactions among the most influential parameters are also estimated by a nonparametric regression technique based on generalized additive models. A ranking based on the sensitivity indices indicates that model predictions are most sensitive to snow parameters such as snow conductivity and grain size, and the drainage of melt ponds. Lastly, it is recommended that research be prioritized toward more accurately determining these most influential parameter values by observational studies or by improving parameterizations in the sea ice model.« less

Uncertainty quantification and global sensitivity analysis of the Los Alamos sea ice model

DOE PAGES

Urrego-Blanco, Jorge Rolando; Urban, Nathan Mark; Hunke, Elizabeth Clare; ...

2016-04-01

Changes in the high-latitude climate system have the potential to affect global climate through feedbacks with the atmosphere and connections with midlatitudes. Sea ice and climate models used to understand these changes have uncertainties that need to be characterized and quantified. We present a quantitative way to assess uncertainty in complex computer models, which is a new approach in the analysis of sea ice models. We characterize parametric uncertainty in the Los Alamos sea ice model (CICE) in a standalone configuration and quantify the sensitivity of sea ice area, extent, and volume with respect to uncertainty in 39 individual modelmore » parameters. Unlike common sensitivity analyses conducted in previous studies where parameters are varied one at a time, this study uses a global variance-based approach in which Sobol' sequences are used to efficiently sample the full 39-dimensional parameter space. We implement a fast emulator of the sea ice model whose predictions of sea ice extent, area, and volume are used to compute the Sobol' sensitivity indices of the 39 parameters. Main effects and interactions among the most influential parameters are also estimated by a nonparametric regression technique based on generalized additive models. A ranking based on the sensitivity indices indicates that model predictions are most sensitive to snow parameters such as snow conductivity and grain size, and the drainage of melt ponds. Lastly, it is recommended that research be prioritized toward more accurately determining these most influential parameter values by observational studies or by improving parameterizations in the sea ice model.« less

Morphine sensitization as a model of mania: comparative study of the effects of repeated lithium or carbamazepine administration.

PubMed

Grappi, Silvia; Marchese, Giovanna; Secci, Maria Elena; De Montis, Maria Graziella; Gambarana, Carla; Scheggi, Simona

2011-10-01

Repeated unavoidable stress induces in rats decreased reactivity to avoidable stressors and an anhedonia-like condition that are reverted by long-term antidepressant treatments and regarded as models of core symptoms of depression. Morphine-sensitized rats present resilience to stress-induced behavioral deficits and, if hyporeactivity to stress models a depressive symptom, stress resistance can be regarded as a manic symptom. This hypothesis is supported by the observation that long-term lithium administration reinstates sensitivity to stress in sensitized rats. The first aim of the study was to examine the effects of carbamazepine, a standard antimanic treatment, on the stress resilience of sensitized rats, to further characterize morphine sensitization as a model of manic symptom. Carbamazepine administration abolished stress resilience but did not interfere with the expression of sensitization. The second aim of the study was to assess whether repeated carbamazepine treatment affected the dopaminergic and behavioral responses to a natural reward, a palatable food (vanilla sugar, VS), in non food-deprived sensitized and control rats and compare these possible effects with those of repeated lithium administration. Control and sensitized rats showed increased extraneuronal dopamine levels in the nucleus accumbens shell after VS consumption and competence to acquire an instrumental VS-sustained appetitive behavior (VAB). Repeated carbamazepine treatment abolished the dopaminergic response to VS consumption and disrupted the competence to acquire VAB in control rats. Lithium-treated rats showed a dopaminergic response to VS and easily acquired the appetitive behavior. In sensitized rats, neither carbamazepine nor lithium administration interfered with the dopaminergic response to VS and the acquisition of VAB. In summary, the effect of carbamazepine on the stress resilience of sensitized rats further supported the hypothesis that morphine sensitization might model some symptoms of mania. Moreover, in control rats carbamazepine treatment elicited an anhedonia-like condition that clearly distinguished the effects of this drug from those of lithium. Copyright © 2011 Elsevier Inc. All rights reserved.

SCIENTIFIC UNCERTAINTIES IN ATMOSPHERIC MERCURY MODELS II: SENSITIVITY ANALYSIS IN THE CONUS DOMAIN

EPA Science Inventory

In this study, we present the response of model results to different scientific treatments in an effort to quantify the uncertainties caused by the incomplete understanding of mercury science and by model assumptions in atmospheric mercury models. Two sets of sensitivity simulati...

Regional modeling of wind erosion in the North West and South West of Iran

NASA Astrophysics Data System (ADS)

Mirmousavi, S. H.

2016-08-01

About two-thirds of the Iran's area is located in the arid and semiarid region. Lack of soil moisture and vegetation is poor in most areas can lead to soil erosion caused by wind. So that the annual suffered severe damage to large areas of rich soils. Modeling studies of wind erosion in Iran is very low and incomplete. Therefore, this study aimed to wind erosion modeling, taking into three factors: wind speed, vegetation and soil types have been done. Wind erosion sensitivity was modeled using the key factors of soil sensitivity, vegetation cover and wind erodibility as proxies. These factors were first estimated separately by factor sensitivity maps and later combined by fuzzy logic into a regional-scale wind erosion sensitivity map. Large areas were evaluated by using publicly available datasets of remotely sensed vegetation information, soil maps and meteorological data on wind speed. The resulting estimates were verified by field studies and examining the economic losses from wind erosion as compensated by the state insurance company. The spatial resolution of the resulting sensitivity map is suitable for regional applications, as identifying sensitive areas is the foundation for diverse land development control measures and implementing management activities.

Effect of ice-albedo feedback on global sensitivity in a one-dimensional radiative-convective climate model

NASA Technical Reports Server (NTRS)

Wang, W.-C.; Stone, P. H.

1980-01-01

The feedback between the ice albedo and temperature is included in a one-dimensional radiative-convective climate model. The effect of this feedback on global sensitivity to changes in solar constant is studied for the current climate conditions. This ice-albedo feedback amplifies global sensitivity by 26 and 39%, respectively, for assumptions of fixed cloud altitude and fixed cloud temperature. The global sensitivity is not affected significantly if the latitudinal variations of mean solar zenith angle and cloud cover are included in the global model. The differences in global sensitivity between one-dimensional radiative-convective models and energy balance models are examined. It is shown that the models are in close agreement when the same feedback mechanisms are included. The one-dimensional radiative-convective model with ice-albedo feedback included is used to compute the equilibrium ice line as a function of solar constant.

An Initial Study of the Sensitivity of Aircraft Vortex Spacing System (AVOSS) Spacing Sensitivity to Weather and Configuration Input Parameters

NASA Technical Reports Server (NTRS)

Riddick, Stephen E.; Hinton, David A.

2000-01-01

A study has been performed on a computer code modeling an aircraft wake vortex spacing system during final approach. This code represents an initial engineering model of a system to calculate reduced approach separation criteria needed to increase airport productivity. This report evaluates model sensitivity toward various weather conditions (crosswind, crosswind variance, turbulent kinetic energy, and thermal gradient), code configurations (approach corridor option, and wake demise definition), and post-processing techniques (rounding of provided spacing values, and controller time variance).

Examining Equity Sensitivity: An Investigation Using the Big Five and HEXACO Models of Personality.

PubMed

Woodley, Hayden J R; Bourdage, Joshua S; Ogunfowora, Babatunde; Nguyen, Brenda

2015-01-01

The construct of equity sensitivity describes an individual's preference about his/her desired input to outcome ratio. Individuals high on equity sensitivity tend to be more input oriented, and are often called "Benevolents." Individuals low on equity sensitivity are more outcome oriented, and are described as "Entitleds." Given that equity sensitivity has often been described as a trait, the purpose of the present study was to examine major personality correlates of equity sensitivity, so as to inform both the nature of equity sensitivity, and the potential processes through which certain broad personality traits may relate to outcomes. We examined the personality correlates of equity sensitivity across three studies (total N = 1170), two personality models (i.e., the Big Five and HEXACO), the two most common measures of equity sensitivity (i.e., the Equity Preference Questionnaire and Equity Sensitivity Inventory), and using both self and peer reports of personality (in Study 3). Although results varied somewhat across samples, the personality variables of Conscientiousness and Honesty-Humility, followed by Agreeableness, were the most robust predictors of equity sensitivity. Individuals higher on these traits were more likely to be Benevolents, whereas those lower on these traits were more likely to be Entitleds. Although some associations between Extraversion, Openness, and Neuroticism and equity sensitivity were observed, these were generally not robust. Overall, it appears that there are several prominent personality variables underlying equity sensitivity, and that the addition of the HEXACO model's dimension of Honesty-Humility substantially contributes to our understanding of equity sensitivity.

Examining Equity Sensitivity: An Investigation Using the Big Five and HEXACO Models of Personality

PubMed Central

Woodley, Hayden J. R.; Bourdage, Joshua S.; Ogunfowora, Babatunde; Nguyen, Brenda

2016-01-01

The construct of equity sensitivity describes an individual's preference about his/her desired input to outcome ratio. Individuals high on equity sensitivity tend to be more input oriented, and are often called “Benevolents.” Individuals low on equity sensitivity are more outcome oriented, and are described as “Entitleds.” Given that equity sensitivity has often been described as a trait, the purpose of the present study was to examine major personality correlates of equity sensitivity, so as to inform both the nature of equity sensitivity, and the potential processes through which certain broad personality traits may relate to outcomes. We examined the personality correlates of equity sensitivity across three studies (total N = 1170), two personality models (i.e., the Big Five and HEXACO), the two most common measures of equity sensitivity (i.e., the Equity Preference Questionnaire and Equity Sensitivity Inventory), and using both self and peer reports of personality (in Study 3). Although results varied somewhat across samples, the personality variables of Conscientiousness and Honesty-Humility, followed by Agreeableness, were the most robust predictors of equity sensitivity. Individuals higher on these traits were more likely to be Benevolents, whereas those lower on these traits were more likely to be Entitleds. Although some associations between Extraversion, Openness, and Neuroticism and equity sensitivity were observed, these were generally not robust. Overall, it appears that there are several prominent personality variables underlying equity sensitivity, and that the addition of the HEXACO model's dimension of Honesty-Humility substantially contributes to our understanding of equity sensitivity. PMID:26779102

A two-step sensitivity analysis for hydrological signatures in Jinhua River Basin, East China

NASA Astrophysics Data System (ADS)

Pan, S.; Fu, G.; Chiang, Y. M.; Xu, Y. P.

2016-12-01

Owing to model complexity and large number of parameters, calibration and sensitivity analysis are difficult processes for distributed hydrological models. In this study, a two-step sensitivity analysis approach is proposed for analyzing the hydrological signatures in Jinhua River Basin, East China, using the Distributed Hydrology-Soil-Vegetation Model (DHSVM). A rough sensitivity analysis is firstly conducted to obtain preliminary influential parameters via Analysis of Variance. The number of parameters was greatly reduced from eighteen-three to sixteen. Afterwards, the sixteen parameters are further analyzed based on a variance-based global sensitivity analysis, i.e., Sobol's sensitivity analysis method, to achieve robust sensitivity rankings and parameter contributions. Parallel-Computing is applied to reduce computational burden in variance-based sensitivity analysis. The results reveal that only a few number of model parameters are significantly sensitive, including rain LAI multiplier, lateral conductivity, porosity, field capacity, wilting point of clay loam, understory monthly LAI, understory minimum resistance and root zone depths of croplands. Finally several hydrological signatures are used for investigating the performance of DHSVM. Results show that high value of efficiency criteria didn't indicate excellent performance of hydrological signatures. For most samples from Sobol's sensitivity analysis, water yield was simulated very well. However, lowest and maximum annual daily runoffs were underestimated. Most of seven-day minimum runoffs were overestimated. Nevertheless, good performances of the three signatures above still exist in a number of samples. Analysis of peak flow shows that small and medium floods are simulated perfectly while slight underestimations happen to large floods. The work in this study helps to further multi-objective calibration of DHSVM model and indicates where to improve the reliability and credibility of model simulation.

An evaluation of selected in silico models for the assessment ...

EPA Pesticide Factsheets

Skin sensitization remains an important endpoint for consumers, manufacturers and regulators. Although the development of alternative approaches to assess skin sensitization potential has been extremely active over many years, the implication of regulations such as REACH and the Cosmetics Directive in EU has provided a much stronger impetus to actualize this research into practical tools for decision making. Thus there has been considerable focus on the development, evaluation, and integration of alternative approaches for skin sensitization hazard and risk assessment. This includes in silico approaches such as (Q)SARs and expert systems. This study aimed to evaluate the predictive performance of a selection of in silico models and then to explore whether combining those models led to an improvement in accuracy. A dataset of 473 substances that had been tested in the local lymph node assay (LLNA) was compiled. This comprised 295 sensitizers and 178 non-sensitizers. Four freely available models were identified - 2 statistical models VEGA and MultiCASE model A33 for skin sensitization (MCASE A33) from the Danish National Food Institute and two mechanistic models Toxtree’s Skin sensitization Reaction domains (Toxtree SS Rxn domains) and the OASIS v1.3 protein binding alerts for skin sensitization from the OECD Toolbox (OASIS). VEGA and MCASE A33 aim to predict sensitization as a binary score whereas the mechanistic models identified reaction domains or structura

Sensitivity Analysis in Sequential Decision Models.

PubMed

Chen, Qiushi; Ayer, Turgay; Chhatwal, Jagpreet

2017-02-01

Sequential decision problems are frequently encountered in medical decision making, which are commonly solved using Markov decision processes (MDPs). Modeling guidelines recommend conducting sensitivity analyses in decision-analytic models to assess the robustness of the model results against the uncertainty in model parameters. However, standard methods of conducting sensitivity analyses cannot be directly applied to sequential decision problems because this would require evaluating all possible decision sequences, typically in the order of trillions, which is not practically feasible. As a result, most MDP-based modeling studies do not examine confidence in their recommended policies. In this study, we provide an approach to estimate uncertainty and confidence in the results of sequential decision models. First, we provide a probabilistic univariate method to identify the most sensitive parameters in MDPs. Second, we present a probabilistic multivariate approach to estimate the overall confidence in the recommended optimal policy considering joint uncertainty in the model parameters. We provide a graphical representation, which we call a policy acceptability curve, to summarize the confidence in the optimal policy by incorporating stakeholders' willingness to accept the base case policy. For a cost-effectiveness analysis, we provide an approach to construct a cost-effectiveness acceptability frontier, which shows the most cost-effective policy as well as the confidence in that for a given willingness to pay threshold. We demonstrate our approach using a simple MDP case study. We developed a method to conduct sensitivity analysis in sequential decision models, which could increase the credibility of these models among stakeholders.

Sensitivity analysis of conservative and reactive stream transient storage models applied to field data from multiple-reach experiments

USGS Publications Warehouse

Gooseff, M.N.; Bencala, K.E.; Scott, D.T.; Runkel, R.L.; McKnight, Diane M.

2005-01-01

The transient storage model (TSM) has been widely used in studies of stream solute transport and fate, with an increasing emphasis on reactive solute transport. In this study we perform sensitivity analyses of a conservative TSM and two different reactive solute transport models (RSTM), one that includes first-order decay in the stream and the storage zone, and a second that considers sorption of a reactive solute on streambed sediments. Two previously analyzed data sets are examined with a focus on the reliability of these RSTMs in characterizing stream and storage zone solute reactions. Sensitivities of simulations to parameters within and among reaches, parameter coefficients of variation, and correlation coefficients are computed and analyzed. Our results indicate that (1) simulated values have the greatest sensitivity to parameters within the same reach, (2) simulated values are also sensitive to parameters in reaches immediately upstream and downstream (inter-reach sensitivity), (3) simulated values have decreasing sensitivity to parameters in reaches farther downstream, and (4) in-stream reactive solute data provide adequate data to resolve effective storage zone reaction parameters, given the model formulations. Simulations of reactive solutes are shown to be equally sensitive to transport parameters and effective reaction parameters of the model, evidence of the control of physical transport on reactive solute dynamics. Similar to conservative transport analysis, reactive solute simulations appear to be most sensitive to data collected during the rising and falling limb of the concentration breakthrough curve. ?? 2005 Elsevier Ltd. All rights reserved.

A three-dimensional cohesive sediment transport model with data assimilation: Model development, sensitivity analysis and parameter estimation

NASA Astrophysics Data System (ADS)

Wang, Daosheng; Cao, Anzhou; Zhang, Jicai; Fan, Daidu; Liu, Yongzhi; Zhang, Yue

2018-06-01

Based on the theory of inverse problems, a three-dimensional sigma-coordinate cohesive sediment transport model with the adjoint data assimilation is developed. In this model, the physical processes of cohesive sediment transport, including deposition, erosion and advection-diffusion, are parameterized by corresponding model parameters. These parameters are usually poorly known and have traditionally been assigned empirically. By assimilating observations into the model, the model parameters can be estimated using the adjoint method; meanwhile, the data misfit between model results and observations can be decreased. The model developed in this work contains numerous parameters; therefore, it is necessary to investigate the parameter sensitivity of the model, which is assessed by calculating a relative sensitivity function and the gradient of the cost function with respect to each parameter. The results of parameter sensitivity analysis indicate that the model is sensitive to the initial conditions, inflow open boundary conditions, suspended sediment settling velocity and resuspension rate, while the model is insensitive to horizontal and vertical diffusivity coefficients. A detailed explanation of the pattern of sensitivity analysis is also given. In ideal twin experiments, constant parameters are estimated by assimilating 'pseudo' observations. The results show that the sensitive parameters are estimated more easily than the insensitive parameters. The conclusions of this work can provide guidance for the practical applications of this model to simulate sediment transport in the study area.

An approach to measure parameter sensitivity in watershed ...

EPA Pesticide Factsheets

Hydrologic responses vary spatially and temporally according to watershed characteristics. In this study, the hydrologic models that we developed earlier for the Little Miami River (LMR) and Las Vegas Wash (LVW) watersheds were used for detail sensitivity analyses. To compare the relative sensitivities of the hydrologic parameters of these two models, we used Normalized Root Mean Square Error (NRMSE). By combining the NRMSE index with the flow duration curve analysis, we derived an approach to measure parameter sensitivities under different flow regimes. Results show that the parameters related to groundwater are highly sensitive in the LMR watershed, whereas the LVW watershed is primarily sensitive to near surface and impervious parameters. The high and medium flows are more impacted by most of the parameters. Low flow regime was highly sensitive to groundwater related parameters. Moreover, our approach is found to be useful in facilitating model development and calibration. This journal article describes hydrological modeling of climate change and land use changes on stream hydrology, and elucidates the importance of hydrological model construction in generating valid modeling results.

Identification of key outcome measures when using the instrumented timed up and go and/or posturography for fall screening.

PubMed

Sample, Renee Beach; Kinney, Allison L; Jackson, Kurt; Diestelkamp, Wiebke; Bigelow, Kimberly Edginton

2017-09-01

The Timed Up and Go (TUG) has been commonly used for fall risk assessment. The instrumented Timed Up and Go (iTUG) adds wearable sensors to capture sub-movements and may be more sensitive. Posturography assessments have also been used for determining fall risk. This study used stepwise logistic regression models to identify key outcome measures for the iTUG and posturography protocols. The effectiveness of the models containing these measures in differentiating fallers from non-fallers were then compared for each: iTUG total time duration only, iTUG, posturography, and combined iTUG and posturography assessments. One hundred and fifty older adults participated in this study. The iTUG measures were calculated utilizing APDM Inc.'s Mobility Lab software. Traditional and non-linear posturography measures were calculated from center of pressure during quiet-standing. The key outcome measures incorporated in the iTUG assessment model (sit-to-stand lean angle and height) resulted in a model sensitivity of 48.1% and max re-scaled R 2 value of 0.19. This was a higher sensitivity, indicating better differentiation, compared to the model only including total time duration (outcome of the traditional TUG), which had a sensitivity of 18.2%. When the key outcome measures of the iTUG and the posturography assessments were combined into a single model, the sensitivity was approximately the same as the iTUG model alone. Overall the findings of this study support that the iTUG demonstrates greater sensitivity than the total time duration, but that carrying out both iTUG and posturography does not greatly improve sensitivity when used as a fall risk screening tool. Copyright © 2017 Elsevier B.V. All rights reserved.

[Sensitivity analysis of AnnAGNPS model's hydrology and water quality parameters based on the perturbation analysis method].

PubMed

Xi, Qing; Li, Zhao-Fu; Luo, Chuan

2014-05-01

Sensitivity analysis of hydrology and water quality parameters has a great significance for integrated model's construction and application. Based on AnnAGNPS model's mechanism, terrain, hydrology and meteorology, field management, soil and other four major categories of 31 parameters were selected for the sensitivity analysis in Zhongtian river watershed which is a typical small watershed of hilly region in the Taihu Lake, and then used the perturbation method to evaluate the sensitivity of the parameters to the model's simulation results. The results showed that: in the 11 terrain parameters, LS was sensitive to all the model results, RMN, RS and RVC were generally sensitive and less sensitive to the output of sediment but insensitive to the remaining results. For hydrometeorological parameters, CN was more sensitive to runoff and sediment and relatively sensitive for the rest results. In field management, fertilizer and vegetation parameters, CCC, CRM and RR were less sensitive to sediment and particulate pollutants, the six fertilizer parameters (FR, FD, FID, FOD, FIP, FOP) were particularly sensitive for nitrogen and phosphorus nutrients. For soil parameters, K is quite sensitive to all the results except the runoff, the four parameters of the soil's nitrogen and phosphorus ratio (SONR, SINR, SOPR, SIPR) were less sensitive to the corresponding results. The simulation and verification results of runoff in Zhongtian watershed show a good accuracy with the deviation less than 10% during 2005- 2010. Research results have a direct reference value on AnnAGNPS model's parameter selection and calibration adjustment. The runoff simulation results of the study area also proved that the sensitivity analysis was practicable to the parameter's adjustment and showed the adaptability to the hydrology simulation in the Taihu Lake basin's hilly region and provide reference for the model's promotion in China.

Sensitivity Analysis of the Land Surface Model NOAH-MP for Different Model Fluxes

NASA Astrophysics Data System (ADS)

Mai, Juliane; Thober, Stephan; Samaniego, Luis; Branch, Oliver; Wulfmeyer, Volker; Clark, Martyn; Attinger, Sabine; Kumar, Rohini; Cuntz, Matthias

2015-04-01

Land Surface Models (LSMs) use a plenitude of process descriptions to represent the carbon, energy and water cycles. They are highly complex and computationally expensive. Practitioners, however, are often only interested in specific outputs of the model such as latent heat or surface runoff. In model applications like parameter estimation, the most important parameters are then chosen by experience or expert knowledge. Hydrologists interested in surface runoff therefore chose mostly soil parameters while biogeochemists interested in carbon fluxes focus on vegetation parameters. However, this might lead to the omission of parameters that are important, for example, through strong interactions with the parameters chosen. It also happens during model development that some process descriptions contain fixed values, which are supposedly unimportant parameters. However, these hidden parameters remain normally undetected although they might be highly relevant during model calibration. Sensitivity analyses are used to identify informative model parameters for a specific model output. Standard methods for sensitivity analysis such as Sobol indexes require large amounts of model evaluations, specifically in case of many model parameters. We hence propose to first use a recently developed inexpensive sequential screening method based on Elementary Effects that has proven to identify the relevant informative parameters. This reduces the number parameters and therefore model evaluations for subsequent analyses such as sensitivity analysis or model calibration. In this study, we quantify parametric sensitivities of the land surface model NOAH-MP that is a state-of-the-art LSM and used at regional scale as the land surface scheme of the atmospheric Weather Research and Forecasting Model (WRF). NOAH-MP contains multiple process parameterizations yielding a considerable amount of parameters (˜ 100). Sensitivities for the three model outputs (a) surface runoff, (b) soil drainage and (c) latent heat are calculated on twelve Model Parameter Estimation Experiment (MOPEX) catchments ranging in size from 1020 to 4421 km2. This allows investigation of parametric sensitivities for distinct hydro-climatic characteristics, emphasizing different land-surface processes. The sequential screening identifies the most informative parameters of NOAH-MP for different model output variables. The number of parameters is reduced substantially for all of the three model outputs to approximately 25. The subsequent Sobol method quantifies the sensitivities of these informative parameters. The study demonstrates the existence of sensitive, important parameters in almost all parts of the model irrespective of the considered output. Soil parameters, e.g., are informative for all three output variables whereas plant parameters are not only informative for latent heat but also for soil drainage because soil drainage is strongly coupled to transpiration through the soil water balance. These results contrast to the choice of only soil parameters in hydrological studies and only plant parameters in biogeochemical ones. The sequential screening identified several important hidden parameters that carry large sensitivities and have hence to be included during model calibration.

The impact of standard and hard-coded parameters on the hydrologic fluxes in the Noah-MP land surface model

NASA Astrophysics Data System (ADS)

Thober, S.; Cuntz, M.; Mai, J.; Samaniego, L. E.; Clark, M. P.; Branch, O.; Wulfmeyer, V.; Attinger, S.

2016-12-01

Land surface models incorporate a large number of processes, described by physical, chemical and empirical equations. The agility of the models to react to different meteorological conditions is artificially constrained by having hard-coded parameters in their equations. Here we searched for hard-coded parameters in the computer code of the land surface model Noah with multiple process options (Noah-MP) to assess the model's agility during parameter estimation. We found 139 hard-coded values in all Noah-MP process options in addition to the 71 standard parameters. We performed a Sobol' global sensitivity analysis to variations of the standard and hard-coded parameters. The sensitivities of the hydrologic output fluxes latent heat and total runoff, their component fluxes, as well as photosynthesis and sensible heat were evaluated at twelve catchments of the Eastern United States with very different hydro-meteorological regimes. Noah-MP's output fluxes are sensitive to two thirds of its standard parameters. The most sensitive parameter is, however, a hard-coded value in the formulation of soil surface resistance for evaporation, which proved to be oversensitive in other land surface models as well. Latent heat and total runoff show very similar sensitivities towards standard and hard-coded parameters. They are sensitive to both soil and plant parameters, which means that model calibrations of hydrologic or land surface models should take both soil and plant parameters into account. Sensible and latent heat exhibit almost the same sensitivities so that calibration or sensitivity analysis can be performed with either of the two. Photosynthesis has almost the same sensitivities as transpiration, which are different from the sensitivities of latent heat. Including photosynthesis and latent heat in model calibration might therefore be beneficial. Surface runoff is sensitive to almost all hard-coded snow parameters. These sensitivities get, however, diminished in total runoff. It is thus recommended to include the most sensitive hard-coded model parameters that were exposed in this study when calibrating Noah-MP.

[Lethal anaphylactic shock model induced by human mixed serum in guinea pigs].

PubMed

Ren, Guang-Mu; Bai, Ji-Wei; Gao, Cai-Rong

2005-08-01

To establish an anaphylactic shock model induced by human mixed serum in guinea pigs. Eighteen guinea pigs were divided into two groups: sensitized and control, The sensitized group were immunized intracutaneously with human mixed serum and then induced by endocardiac injection after 3 weeks. Symptoms of anaphylactic shock appeared in the sensitized group. The level of serum IgE were increased in the sensitized group significantly. An animal model of anaphylactic shock wer established successfully. It provide a tool for both forensic study and anaphylactic shock therapy.

[Parameter sensitivity of simulating net primary productivity of Larix olgensis forest based on BIOME-BGC model].

PubMed

He, Li-hong; Wang, Hai-yan; Lei, Xiang-dong

2016-02-01

Model based on vegetation ecophysiological process contains many parameters, and reasonable parameter values will greatly improve simulation ability. Sensitivity analysis, as an important method to screen out the sensitive parameters, can comprehensively analyze how model parameters affect the simulation results. In this paper, we conducted parameter sensitivity analysis of BIOME-BGC model with a case study of simulating net primary productivity (NPP) of Larix olgensis forest in Wangqing, Jilin Province. First, with the contrastive analysis between field measurement data and the simulation results, we tested the BIOME-BGC model' s capability of simulating the NPP of L. olgensis forest. Then, Morris and EFAST sensitivity methods were used to screen the sensitive parameters that had strong influence on NPP. On this basis, we also quantitatively estimated the sensitivity of the screened parameters, and calculated the global, the first-order and the second-order sensitivity indices. The results showed that the BIOME-BGC model could well simulate the NPP of L. olgensis forest in the sample plot. The Morris sensitivity method provided a reliable parameter sensitivity analysis result under the condition of a relatively small sample size. The EFAST sensitivity method could quantitatively measure the impact of simulation result of a single parameter as well as the interaction between the parameters in BIOME-BGC model. The influential sensitive parameters for L. olgensis forest NPP were new stem carbon to new leaf carbon allocation and leaf carbon to nitrogen ratio, the effect of their interaction was significantly greater than the other parameter' teraction effect.

Categorical QSAR models for skin sensitization based on local lymph node assay measures and both ground and excited state 4D-fingerprint descriptors

NASA Astrophysics Data System (ADS)

Liu, Jianzhong; Kern, Petra S.; Gerberick, G. Frank; Santos-Filho, Osvaldo A.; Esposito, Emilio X.; Hopfinger, Anton J.; Tseng, Yufeng J.

2008-06-01

In previous studies we have developed categorical QSAR models for predicting skin-sensitization potency based on 4D-fingerprint (4D-FP) descriptors and in vivo murine local lymph node assay (LLNA) measures. Only 4D-FP derived from the ground state (GMAX) structures of the molecules were used to build the QSAR models. In this study we have generated 4D-FP descriptors from the first excited state (EMAX) structures of the molecules. The GMAX, EMAX and the combined ground and excited state 4D-FP descriptors (GEMAX) were employed in building categorical QSAR models. Logistic regression (LR) and partial least square coupled logistic regression (PLS-CLR), found to be effective model building for the LLNA skin-sensitization measures in our previous studies, were used again in this study. This also permitted comparison of the prior ground state models to those involving first excited state 4D-FP descriptors. Three types of categorical QSAR models were constructed for each of the GMAX, EMAX and GEMAX datasets: a binary model (2-state), an ordinal model (3-state) and a binary-binary model (two-2-state). No significant differences exist among the LR 2-state model constructed for each of the three datasets. However, the PLS-CLR 3-state and 2-state models based on the EMAX and GEMAX datasets have higher predictivity than those constructed using only the GMAX dataset. These EMAX and GMAX categorical models are also more significant and predictive than corresponding models built in our previous QSAR studies of LLNA skin-sensitization measures.

Two-step sensitivity testing of parametrized and regionalized life cycle assessments: methodology and case study.

PubMed

Mutel, Christopher L; de Baan, Laura; Hellweg, Stefanie

2013-06-04

Comprehensive sensitivity analysis is a significant tool to interpret and improve life cycle assessment (LCA) models, but is rarely performed. Sensitivity analysis will increase in importance as inventory databases become regionalized, increasing the number of system parameters, and parametrized, adding complexity through variables and nonlinear formulas. We propose and implement a new two-step approach to sensitivity analysis. First, we identify parameters with high global sensitivities for further examination and analysis with a screening step, the method of elementary effects. Second, the more computationally intensive contribution to variance test is used to quantify the relative importance of these parameters. The two-step sensitivity test is illustrated on a regionalized, nonlinear case study of the biodiversity impacts from land use of cocoa production, including a worldwide cocoa products trade model. Our simplified trade model can be used for transformable commodities where one is assessing market shares that vary over time. In the case study, the highly uncertain characterization factors for the Ivory Coast and Ghana contributed more than 50% of variance for almost all countries and years examined. The two-step sensitivity test allows for the interpretation, understanding, and improvement of large, complex, and nonlinear LCA systems.

How does the sensitivity of climate affect stratospheric solar radiation management?

NASA Astrophysics Data System (ADS)

Ricke, K.; Rowlands, D. J.; Ingram, W.; Keith, D.; Morgan, M. G.

2011-12-01

If implementation of proposals to engineer the climate through solar radiation management (SRM) ever occurs, it is likely to be contingent upon climate sensitivity. Despite this, no modeling studies have examined how the effectiveness of SRM forcings differs between the typical Atmosphere-Ocean General Circulation Models (AOGCMs) with climate sensitivities close to the Coupled Model Intercomparison Project (CMIP) mean and ones with high climate sensitivities. Here, we use a perturbed physics ensemble modeling experiment to examine variations in the response of climate to SRM under different climate sensitivities. When SRM is used as a substitute for mitigation its ability to maintain the current climate state gets worse with increased climate sensitivity and with increased concentrations of greenhouse gases. However, our results also demonstrate that the potential of SRM to slow climate change, even at the regional level, grows with climate sensitivity. On average, SRM reduces regional rates of temperature change by more than 90 percent and rates of precipitation change by more than 50 percent in these higher sensitivity model configurations. To investigate how SRM might behave in models with high climate sensitivity that are also consistent with recent observed climate change we perform a "perturbed physics" ensemble (PPE) modelling experiment with the climateprediction.net (cpdn) version of the HadCM3L AOGCM. Like other perturbed physics climate modelling experiments, we simulate past and future climate scenarios using a wide range of model parameter combinations that both reproduce past climate within a specified level of accuracy and simulate future climates with a wide range of climate sensitivities. We chose 43 members ("model versions") from a subset of the 1,550 from the British Broadcasting Corporation (BBC) climateprediction.net project that have data that allow restarts. We use our results to explore how much assessments of SRM that use best-estimate models, and so near-median climate sensitivity, may be ignoring important contingencies associated with implementing SRM in reality. A primary motivation for studying SRM via the injection of aerosols in the stratosphere is to evaluate its potential effectiveness as "insurance" in the case of higher-than-expected climate response to global warming. We find that this is precisely when SRM appears to be least effective in returning regional climates to their baseline states and reducing regional rates of precipitation change. On the other hand, given the very high regional temperature anomalies associated with rising greenhouse gas concentrations in high sensitivity models, it is also where SRM is most effective in reducing rates of change relative to a no SRM alternative.

Examining the Latent Structure of Anxiety Sensitivity in Adolescents using Factor Mixture Modeling

PubMed Central

Allan, Nicholas P.; MacPherson, Laura; Young, Kevin C.; Lejuez, Carl W.; Schmidt, Norman B.

2014-01-01

Anxiety sensitivity has been implicated as an important risk factor, generalizable to most anxiety disorders. In adults, factor mixture modeling has been used to demonstrate that anxiety sensitivity is best conceptualized as categorical between individuals. That is, whereas most adults appear to possess normative levels of anxiety sensitivity, a small subset of the population appears to possess abnormally high levels of anxiety sensitivity. Further, those in the high anxiety sensitivity group are at increased risk of having high levels of anxiety and of having an anxiety disorder. This study was designed to determine whether these findings extend to adolescents. Factor mixture modeling was used to examine the best fitting model of anxiety sensitivity in a sample of 277 adolescents (M age = 11.0, SD = .81). Consistent with research in adults, the best fitting model consisted of two classes, one containing adolescents with high levels of anxiety sensitivity (n = 25), and another containing adolescents with normative levels of anxiety sensitivity (n = 252). Examination of anxiety sensitivity subscales revealed that the social concerns subscale was not important for classification of individuals. Convergent and discriminant validity of anxiety sensitivity classes were found in that membership in the high anxiety sensitivity class was associated with higher mean levels of anxiety symptoms, controlling for depression and externalizing problems, and was not associated with higher mean levels of depression or externalizing symptoms controlling for anxiety problems. PMID:24749756

MOVES sensitivity study

DOT National Transportation Integrated Search

2012-01-01

Purpose: : To determine ranking of important parameters and the overall sensitivity to values of variables in MOVES : To allow a greater understanding of the MOVES modeling process for users : Continued support by FHWA to transportation modeling comm...

Birth weight, current anthropometric markers, and high sensitivity C-reactive protein in Brazilian school children.

PubMed

Boscaini, Camile; Pellanda, Lucia Campos

2015-01-01

Studies have shown associations of birth weight with increased concentrations of high sensitivity C-reactive protein. This study assessed the relationship between birth weight, anthropometric and metabolic parameters during childhood, and high sensitivity C-reactive protein. A total of 612 Brazilian school children aged 5-13 years were included in the study. High sensitivity C-reactive protein was measured by particle-enhanced immunonephelometry. Nutritional status was assessed by body mass index, waist circumference, and skinfolds. Total cholesterol and fractions, triglycerides, and glucose were measured by enzymatic methods. Insulin sensitivity was determined by the homeostasis model assessment method. Statistical analysis included chi-square test, General Linear Model, and General Linear Model for Gamma Distribution. Body mass index, waist circumference, and skinfolds were directly associated with birth weight (P < 0.001, P = 0.001, and P = 0.015, resp.). Large for gestational age children showed higher high sensitivity C-reactive protein levels (P < 0.001) than small for gestational age. High birth weight is associated with higher levels of high sensitivity C-reactive protein, body mass index, waist circumference, and skinfolds. Large for gestational age altered high sensitivity C-reactive protein and promoted additional risk factor for atherosclerosis in these school children, independent of current nutritional status.

Simulations of the HDO and H2O-18 atmospheric cycles using the NASA GISS general circulation model - Sensitivity experiments for present-day conditions

NASA Technical Reports Server (NTRS)

Jouzel, Jean; Koster, R. D.; Suozzo, R. J.; Russell, G. L.; White, J. W. C.

1991-01-01

Incorporating the full geochemical cycles of stable water isotopes (HDO and H2O-18) into an atmospheric general circulation model (GCM) allows an improved understanding of global delta-D and delta-O-18 distributions and might even allow an analysis of the GCM's hydrological cycle. A detailed sensitivity analysis using the NASA/Goddard Institute for Space Studies (GISS) model II GCM is presented that examines the nature of isotope modeling. The tests indicate that delta-D and delta-O-18 values in nonpolar regions are not strongly sensitive to details in the model precipitation parameterizations. This result, while implying that isotope modeling has limited potential use in the calibration of GCM convection schemes, also suggests that certain necessarily arbitrary aspects of these schemes are adequate for many isotope studies. Deuterium excess, a second-order variable, does show some sensitivity to precipitation parameterization and thus may be more useful for GCM calibration.

Identifying key sources of uncertainty in the modelling of greenhouse gas emissions from wastewater treatment.

PubMed

Sweetapple, Christine; Fu, Guangtao; Butler, David

2013-09-01

This study investigates sources of uncertainty in the modelling of greenhouse gas emissions from wastewater treatment, through the use of local and global sensitivity analysis tools, and contributes to an in-depth understanding of wastewater treatment modelling by revealing critical parameters and parameter interactions. One-factor-at-a-time sensitivity analysis is used to screen model parameters and identify those with significant individual effects on three performance indicators: total greenhouse gas emissions, effluent quality and operational cost. Sobol's method enables identification of parameters with significant higher order effects and of particular parameter pairs to which model outputs are sensitive. Use of a variance-based global sensitivity analysis tool to investigate parameter interactions enables identification of important parameters not revealed in one-factor-at-a-time sensitivity analysis. These interaction effects have not been considered in previous studies and thus provide a better understanding wastewater treatment plant model characterisation. It was found that uncertainty in modelled nitrous oxide emissions is the primary contributor to uncertainty in total greenhouse gas emissions, due largely to the interaction effects of three nitrogen conversion modelling parameters. The higher order effects of these parameters are also shown to be a key source of uncertainty in effluent quality. Copyright © 2013 Elsevier Ltd. All rights reserved.

Time variation of effective climate sensitivity in GCMs

NASA Astrophysics Data System (ADS)

Williams, K. D.; Ingram, W. J.; Gregory, J. M.

2009-04-01

Effective climate sensitivity is often assumed to be constant (if uncertain), but some previous studies of General Circulation Model (GCM) simulations have found it varying as the simulation progresses. This complicates the fitting of simple models to such simulations, as well as having implications for the estimation of climate sensitivity from observations. This study examines the evolution of the feedbacks determining the climate sensitivity in GCMs submitted to the Coupled Model Intercomparison Project. Apparent centennial-timescale variations of effective climate sensitivity during stabilisation to a forcing can be considered an artefact of using conventional forcings which only allow for instantaneous effects and stratospheric adjustment. If the forcing is adjusted for processes occurring on timescales which are short compared to the climate stabilisation timescale then there is little centennial timescale evolution of effective climate sensitivity in any of the GCMs. We suggest that much of the apparent variation in effective climate sensitivity identified in previous studies is actually due to the comparatively fast forcing adjustment. Persistent differences are found in the strength of the feedbacks between the coupled atmosphere - ocean (AO) versions and their atmosphere - mixed-layer ocean (AML) counterparts, (the latter are often assumed to give the equilibrium climate sensitivity of the AOGCM). The AML model can typically only estimate the equilibrium climate sensitivity of the parallel AO version to within about 0.5K. The adjustment to the forcing to account for comparatively fast processes varies in magnitude and sign between GCMs, as well as differing between AO and AML versions of the same model. There is evidence from one AOGCM that the forcing adjustment may take a couple of decades, with implications for observationally based estimates of equilibrium climate sensitivity. We suggest that at least some of the spread in 21st century global temperature predictions between GCMs is due to differing adjustment processes, hence work to understand these differences should be a priority.

Importance analysis for Hudson River PCB transport and fate model parameters using robust sensitivity studies

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

Zhang, S.; Toll, J.; Cothern, K.

1995-12-31

The authors have performed robust sensitivity studies of the physico-chemical Hudson River PCB model PCHEPM to identify the parameters and process uncertainties contributing the most to uncertainty in predictions of water column and sediment PCB concentrations, over the time period 1977--1991 in one segment of the lower Hudson River. The term ``robust sensitivity studies`` refers to the use of several sensitivity analysis techniques to obtain a more accurate depiction of the relative importance of different sources of uncertainty. Local sensitivity analysis provided data on the sensitivity of PCB concentration estimates to small perturbations in nominal parameter values. Range sensitivity analysismore » provided information about the magnitude of prediction uncertainty associated with each input uncertainty. Rank correlation analysis indicated which parameters had the most dominant influence on model predictions. Factorial analysis identified important interactions among model parameters. Finally, term analysis looked at the aggregate influence of combinations of parameters representing physico-chemical processes. The authors scored the results of the local and range sensitivity and rank correlation analyses. The authors considered parameters that scored high on two of the three analyses to be important contributors to PCB concentration prediction uncertainty, and treated them probabilistically in simulations. They also treated probabilistically parameters identified in the factorial analysis as interacting with important parameters. The authors used the term analysis to better understand how uncertain parameters were influencing the PCB concentration predictions. The importance analysis allowed us to reduce the number of parameters to be modeled probabilistically from 16 to 5. This reduced the computational complexity of Monte Carlo simulations, and more importantly, provided a more lucid depiction of prediction uncertainty and its causes.« less

Pain sensitivity mediates the relationship between stress and headache intensity in chronic tension-type headache.

PubMed

Cathcart, Stuart; Bhullar, Navjot; Immink, Maarten; Della Vedova, Chris; Hayball, John

2012-01-01

A central model for chronic tension-type headache (CTH) posits that stress contributes to headache, in part, by aggravating existing hyperalgesia in CTH sufferers. The prediction from this model that pain sensitivity mediates the relationship between stress and headache activity has not yet been examined. To determine whether pain sensitivity mediates the relationship between stress and prospective headache activity in CTH sufferers. Self-reported stress, pain sensitivity and prospective headache activity were measured in 53 CTH sufferers recruited from the general population. Pain sensitivity was modelled as a mediator between stress and headache activity, and tested using a nonparametric bootstrap analysis. Pain sensitivity significantly mediated the relationship between stress and headache intensity. The results of the present study support the central model for CTH, which posits that stress contributes to headache, in part, by aggravating existing hyperalgesia in CTH sufferers. Implications for the mechanisms and treatment of CTH are discussed.

Non-randomized response model for sensitive survey with noncompliance.

PubMed

Wu, Qin; Tang, Man-Lai

2016-12-01

Collecting representative data on sensitive issues has long been problematic and challenging in public health prevalence investigation (e.g. non-suicidal self-injury), medical research (e.g. drug habits), social issue studies (e.g. history of child abuse), and their interdisciplinary studies (e.g. premarital sexual intercourse). Alternative data collection techniques that can be adopted to study sensitive questions validly become more important and necessary. As an alternative to the famous Warner randomized response model, non-randomized response triangular model has recently been developed to encourage participants to provide truthful responses in surveys involving sensitive questions. Unfortunately, both randomized and non-randomized response models could underestimate the proportion of subjects with the sensitive characteristic as some respondents do not believe that these techniques can protect their anonymity. As a result, some authors hypothesized that lack of trust and noncompliance should be highest among those who have the most to lose and the least to use for the anonymity provided by using these techniques. Some researchers noticed the existence of noncompliance and proposed new models to measure noncompliance in order to get reliable information. However, all proposed methods were based on randomized response models which require randomizing devices, restrict the survey to only face-to-face interview and are lack of reproductivity. Taking the noncompliance into consideration, we introduce new non-randomized response techniques in which no covariate is required. Asymptotic properties of the proposed estimates for sensitive characteristic as well as noncompliance probabilities are developed. Our proposed techniques are empirically shown to yield accurate estimates for both sensitive and noncompliance probabilities. A real example about premarital sex among university students is used to demonstrate our methodologies. © The Author(s) 2014.

Global Sensitivity Analysis of Environmental Models: Convergence, Robustness and Validation

NASA Astrophysics Data System (ADS)

Sarrazin, Fanny; Pianosi, Francesca; Khorashadi Zadeh, Farkhondeh; Van Griensven, Ann; Wagener, Thorsten

2015-04-01

Global Sensitivity Analysis aims to characterize the impact that variations in model input factors (e.g. the parameters) have on the model output (e.g. simulated streamflow). In sampling-based Global Sensitivity Analysis, the sample size has to be chosen carefully in order to obtain reliable sensitivity estimates while spending computational resources efficiently. Furthermore, insensitive parameters are typically identified through the definition of a screening threshold: the theoretical value of their sensitivity index is zero but in a sampling-base framework they regularly take non-zero values. There is little guidance available for these two steps in environmental modelling though. The objective of the present study is to support modellers in making appropriate choices, regarding both sample size and screening threshold, so that a robust sensitivity analysis can be implemented. We performed sensitivity analysis for the parameters of three hydrological models with increasing level of complexity (Hymod, HBV and SWAT), and tested three widely used sensitivity analysis methods (Elementary Effect Test or method of Morris, Regional Sensitivity Analysis, and Variance-Based Sensitivity Analysis). We defined criteria based on a bootstrap approach to assess three different types of convergence: the convergence of the value of the sensitivity indices, of the ranking (the ordering among the parameters) and of the screening (the identification of the insensitive parameters). We investigated the screening threshold through the definition of a validation procedure. The results showed that full convergence of the value of the sensitivity indices is not necessarily needed to rank or to screen the model input factors. Furthermore, typical values of the sample sizes that are reported in the literature can be well below the sample sizes that actually ensure convergence of ranking and screening.

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

Monte Carlo sensitivity analysis of land surface parameters using the Variable Infiltration Capacity model

NASA Astrophysics Data System (ADS)

Demaria, Eleonora M.; Nijssen, Bart; Wagener, Thorsten

2007-06-01

Current land surface models use increasingly complex descriptions of the processes that they represent. Increase in complexity is accompanied by an increase in the number of model parameters, many of which cannot be measured directly at large spatial scales. A Monte Carlo framework was used to evaluate the sensitivity and identifiability of ten parameters controlling surface and subsurface runoff generation in the Variable Infiltration Capacity model (VIC). Using the Monte Carlo Analysis Toolbox (MCAT), parameter sensitivities were studied for four U.S. watersheds along a hydroclimatic gradient, based on a 20-year data set developed for the Model Parameter Estimation Experiment (MOPEX). Results showed that simulated streamflows are sensitive to three parameters when evaluated with different objective functions. Sensitivity of the infiltration parameter (b) and the drainage parameter (exp) were strongly related to the hydroclimatic gradient. The placement of vegetation roots played an important role in the sensitivity of model simulations to the thickness of the second soil layer (thick2). Overparameterization was found in the base flow formulation indicating that a simplified version could be implemented. Parameter sensitivity was more strongly dictated by climatic gradients than by changes in soil properties. Results showed how a complex model can be reduced to a more parsimonious form, leading to a more identifiable model with an increased chance of successful regionalization to ungauged basins. Although parameter sensitivities are strictly valid for VIC, this model is representative of a wider class of macroscale hydrological models. Consequently, the results and methodology will have applicability to other hydrological models.

Study of Nonclassical Fields in Phase-Sensitive Reservoirs

NASA Technical Reports Server (NTRS)

Kim, Myung Shik; Imoto, Nobuyuki

1996-01-01

We show that the reservoir influence can be modeled by an infinite array of beam splitters. The superposition of the input fields in the beam splitter is discussed with the convolution laws for their quasiprobabilities. We derive the Fokker-Planck equation for the cavity field coupled with a phase-sensitive reservoir using the convolution law. We also analyze the amplification in the phase-sensitive reservoir with use of the modified beam splitter model. We show the similarities and differences between the dissipation and amplification models. We show that a super-Poissonian input field cannot become sub-Poissonian by the phase-sensitive amplification.

Behavioral metabolomics analysis identifies novel neurochemical signatures in methamphetamine sensitization

PubMed Central

Adkins, Daniel E.; McClay, Joseph L.; Vunck, Sarah A.; Batman, Angela M.; Vann, Robert E.; Clark, Shaunna L.; Souza, Renan P.; Crowley, James J.; Sullivan, Patrick F.; van den Oord, Edwin J.C.G.; Beardsley, Patrick M.

2014-01-01

Behavioral sensitization has been widely studied in animal models and is theorized to reflect neural modifications associated with human psychostimulant addiction. While the mesolimbic dopaminergic pathway is known to play a role, the neurochemical mechanisms underlying behavioral sensitization remain incompletely understood. In the present study, we conducted the first metabolomics analysis to globally characterize neurochemical differences associated with behavioral sensitization. Methamphetamine-induced sensitization measures were generated by statistically modeling longitudinal activity data for eight inbred strains of mice. Subsequent to behavioral testing, nontargeted liquid and gas chromatography-mass spectrometry profiling was performed on 48 brain samples, yielding 301 metabolite levels per sample after quality control. Association testing between metabolite levels and three primary dimensions of behavioral sensitization (total distance, stereotypy and margin time) showed four robust, significant associations at a stringent metabolome-wide significance threshold (false discovery rate < 0.05). Results implicated homocarnosine, a dipeptide of GABA and histidine, in total distance sensitization, GABA metabolite 4-guanidinobutanoate and pantothenate in stereotypy sensitization, and myo-inositol in margin time sensitization. Secondary analyses indicated that these associations were independent of concurrent methamphetamine levels and, with the exception of the myo-inositol association, suggest a mechanism whereby strain-based genetic variation produces specific baseline neurochemical differences that substantially influence the magnitude of MA-induced sensitization. These findings demonstrate the utility of mouse metabolomics for identifying novel biomarkers, and developing more comprehensive neurochemical models, of psychostimulant sensitization. PMID:24034544

Sensitivity of Fit Indices to Misspecification in Growth Curve Models

ERIC Educational Resources Information Center

Wu, Wei; West, Stephen G.

2010-01-01

This study investigated the sensitivity of fit indices to model misspecification in within-individual covariance structure, between-individual covariance structure, and marginal mean structure in growth curve models. Five commonly used fit indices were examined, including the likelihood ratio test statistic, root mean square error of…

Acute Stress Decreases but Chronic Stress Increases Myocardial Sensitivity to Ischemic Injury in Rodents

PubMed Central

Eisenmann, Eric D.; Rorabaugh, Boyd R.; Zoladz, Phillip R.

2016-01-01

Cardiovascular disease (CVD) is the largest cause of mortality worldwide, and stress is a significant contributor to the development of CVD. The relationship between acute and chronic stress and CVD is well evidenced. Acute stress can lead to arrhythmias and ischemic injury. However, recent evidence in rodent models suggests that acute stress can decrease sensitivity to myocardial ischemia–reperfusion injury (IRI). Conversely, chronic stress is arrhythmogenic and increases sensitivity to myocardial IRI. Few studies have examined the impact of validated animal models of stress-related psychological disorders on the ischemic heart. This review examines the work that has been completed using rat models to study the effects of stress on myocardial sensitivity to ischemic injury. Utilization of animal models of stress-related psychological disorders is critical in the prevention and treatment of cardiovascular disorders in patients experiencing stress-related psychiatric conditions. PMID:27199778

Acute Stress Decreases but Chronic Stress Increases Myocardial Sensitivity to Ischemic Injury in Rodents.

PubMed

Eisenmann, Eric D; Rorabaugh, Boyd R; Zoladz, Phillip R

2016-01-01

Cardiovascular disease (CVD) is the largest cause of mortality worldwide, and stress is a significant contributor to the development of CVD. The relationship between acute and chronic stress and CVD is well evidenced. Acute stress can lead to arrhythmias and ischemic injury. However, recent evidence in rodent models suggests that acute stress can decrease sensitivity to myocardial ischemia-reperfusion injury (IRI). Conversely, chronic stress is arrhythmogenic and increases sensitivity to myocardial IRI. Few studies have examined the impact of validated animal models of stress-related psychological disorders on the ischemic heart. This review examines the work that has been completed using rat models to study the effects of stress on myocardial sensitivity to ischemic injury. Utilization of animal models of stress-related psychological disorders is critical in the prevention and treatment of cardiovascular disorders in patients experiencing stress-related psychiatric conditions.

An analysis of sensitivity of CLIMEX parameters in mapping species potential distribution and the broad-scale changes observed with minor variations in parameters values: an investigation using open-field Solanum lycopersicum and Neoleucinodes elegantalis as an example

NASA Astrophysics Data System (ADS)

da Silva, Ricardo Siqueira; Kumar, Lalit; Shabani, Farzin; Picanço, Marcelo Coutinho

2018-04-01

A sensitivity analysis can categorize levels of parameter influence on a model's output. Identifying parameters having the most influence facilitates establishing the best values for parameters of models, providing useful implications in species modelling of crops and associated insect pests. The aim of this study was to quantify the response of species models through a CLIMEX sensitivity analysis. Using open-field Solanum lycopersicum and Neoleucinodes elegantalis distribution records, and 17 fitting parameters, including growth and stress parameters, comparisons were made in model performance by altering one parameter value at a time, in comparison to the best-fit parameter values. Parameters that were found to have a greater effect on the model results are termed "sensitive". Through the use of two species, we show that even when the Ecoclimatic Index has a major change through upward or downward parameter value alterations, the effect on the species is dependent on the selection of suitability categories and regions of modelling. Two parameters were shown to have the greatest sensitivity, dependent on the suitability categories of each species in the study. Results enhance user understanding of which climatic factors had a greater impact on both species distributions in our model, in terms of suitability categories and areas, when parameter values were perturbed by higher or lower values, compared to the best-fit parameter values. Thus, the sensitivity analyses have the potential to provide additional information for end users, in terms of improving management, by identifying the climatic variables that are most sensitive.

A contrast-sensitive channelized-Hotelling observer to predict human performance in a detection task using lumpy backgrounds and Gaussian signals

NASA Astrophysics Data System (ADS)

Park, Subok; Badano, Aldo; Gallas, Brandon D.; Myers, Kyle J.

2007-03-01

Previously, a non-prewhitening matched filter (NPWMF) incorporating a model for the contrast sensitivity of the human visual system was introduced for modeling human performance in detection tasks with different viewing angles and white-noise backgrounds by Badano et al. But NPWMF observers do not perform well detection tasks involving complex backgrounds since they do not account for random backgrounds. A channelized-Hotelling observer (CHO) using difference-of-Gaussians (DOG) channels has been shown to track human performance well in detection tasks using lumpy backgrounds. In this work, a CHO with DOG channels, incorporating the model of the human contrast sensitivity, was developed similarly. We call this new observer a contrast-sensitive CHO (CS-CHO). The Barten model was the basis of our human contrast sensitivity model. A scalar was multiplied to the Barten model and varied to control the thresholding effect of the contrast sensitivity on luminance-valued images and hence the performance-prediction ability of the CS-CHO. The performance of the CS-CHO was compared to the average human performance from the psychophysical study by Park et al., where the task was to detect a known Gaussian signal in non-Gaussian distributed lumpy backgrounds. Six different signal-intensity values were used in this study. We chose the free parameter of our model to match the mean human performance in the detection experiment at the strongest signal intensity. Then we compared the model to the human at five different signal-intensity values in order to see if the performance of the CS-CHO matched human performance. Our results indicate that the CS-CHO with the chosen scalar for the contrast sensitivity predicts human performance closely as a function of signal intensity.

Sensitivity analysis of a sound absorption model with correlated inputs

NASA Astrophysics Data System (ADS)

Chai, W.; Christen, J.-L.; Zine, A.-M.; Ichchou, M.

2017-04-01

Sound absorption in porous media is a complex phenomenon, which is usually addressed with homogenized models, depending on macroscopic parameters. Since these parameters emerge from the structure at microscopic scale, they may be correlated. This paper deals with sensitivity analysis methods of a sound absorption model with correlated inputs. Specifically, the Johnson-Champoux-Allard model (JCA) is chosen as the objective model with correlation effects generated by a secondary micro-macro semi-empirical model. To deal with this case, a relatively new sensitivity analysis method Fourier Amplitude Sensitivity Test with Correlation design (FASTC), based on Iman's transform, is taken into application. This method requires a priori information such as variables' marginal distribution functions and their correlation matrix. The results are compared to the Correlation Ratio Method (CRM) for reference and validation. The distribution of the macroscopic variables arising from the microstructure, as well as their correlation matrix are studied. Finally the results of tests shows that the correlation has a very important impact on the results of sensitivity analysis. Assessment of correlation strength among input variables on the sensitivity analysis is also achieved.

Revisiting the radionuclide atmospheric dispersion event of the Chernobyl disaster - modelling sensitivity and data assimilation

NASA Astrophysics Data System (ADS)

Roustan, Yelva; Duhanyan, Nora; Bocquet, Marc; Winiarek, Victor

2013-04-01

A sensitivity study of the numerical model, as well as, an inverse modelling approach applied to the atmospheric dispersion issues after the Chernobyl disaster are both presented in this paper. On the one hand, the robustness of the source term reconstruction through advanced data assimilation techniques was tested. On the other hand, the classical approaches for sensitivity analysis were enhanced by the use of an optimised forcing field which otherwise is known to be strongly uncertain. The POLYPHEMUS air quality system was used to perform the simulations of radionuclide dispersion. Activity concentrations in air and deposited to the ground of iodine-131, caesium-137 and caesium-134 were considered. The impact of the implemented parameterizations of the physical processes (dry and wet depositions, vertical turbulent diffusion), of the forcing fields (meteorology and source terms) and of the numerical configuration (horizontal resolution) were investigated for the sensitivity study of the model. A four dimensional variational scheme (4D-Var) based on the approximate adjoint of the chemistry transport model was used to invert the source term. The data assimilation is performed with measurements of activity concentrations in air extracted from the Radioactivity Environmental Monitoring (REM) database. For most of the investigated configurations (sensitivity study), the statistics to compare the model results to the field measurements as regards the concentrations in air are clearly improved while using a reconstructed source term. As regards the ground deposited concentrations, an improvement can only be seen in case of satisfactorily modelled episode. Through these studies, the source term and the meteorological fields are proved to have a major impact on the activity concentrations in air. These studies also reinforce the use of reconstructed source term instead of the usual estimated one. A more detailed parameterization of the deposition process seems also to be able to improve the simulation results. For deposited activities the results are more complex probably due to a strong sensitivity to some of the meteorological fields which remain quite uncertain.

Inferring Instantaneous, Multivariate and Nonlinear Sensitivities for the Analysis of Feedback Processes in a Dynamical System: Lorenz Model Case Study

NASA Technical Reports Server (NTRS)

Aires, Filipe; Rossow, William B.; Hansen, James E. (Technical Monitor)

2001-01-01

A new approach is presented for the analysis of feedback processes in a nonlinear dynamical system by observing its variations. The new methodology consists of statistical estimates of the sensitivities between all pairs of variables in the system based on a neural network modeling of the dynamical system. The model can then be used to estimate the instantaneous, multivariate and nonlinear sensitivities, which are shown to be essential for the analysis of the feedbacks processes involved in the dynamical system. The method is described and tested on synthetic data from the low-order Lorenz circulation model where the correct sensitivities can be evaluated analytically.

Scalable Online Network Modeling and Simulation

DTIC Science & Technology

2005-08-01

ONLINE NETWORK MODELING AND SIMULATION 6. AUTHOR(S) Boleslaw Szymanski , Shivkumar Kalyanaraman, Biplab Sikdar and Christopher Carothers 5...performance for a wide range of parameter values (parameter sensitivity), understanding of protocol stability and dynamics, and studying feature ...a wide range of parameter values (parameter sensitivity), understanding of protocol stability and dynamics, and studying feature interactions

Parameter optimization, sensitivity, and uncertainty analysis of an ecosystem model at a forest flux tower site in the United States

USGS Publications Warehouse

Wu, Yiping; Liu, Shuguang; Huang, Zhihong; Yan, Wende

2014-01-01

Ecosystem models are useful tools for understanding ecological processes and for sustainable management of resources. In biogeochemical field, numerical models have been widely used for investigating carbon dynamics under global changes from site to regional and global scales. However, it is still challenging to optimize parameters and estimate parameterization uncertainty for complex process-based models such as the Erosion Deposition Carbon Model (EDCM), a modified version of CENTURY, that consider carbon, water, and nutrient cycles of ecosystems. This study was designed to conduct the parameter identifiability, optimization, sensitivity, and uncertainty analysis of EDCM using our developed EDCM-Auto, which incorporated a comprehensive R package—Flexible Modeling Framework (FME) and the Shuffled Complex Evolution (SCE) algorithm. Using a forest flux tower site as a case study, we implemented a comprehensive modeling analysis involving nine parameters and four target variables (carbon and water fluxes) with their corresponding measurements based on the eddy covariance technique. The local sensitivity analysis shows that the plant production-related parameters (e.g., PPDF1 and PRDX) are most sensitive to the model cost function. Both SCE and FME are comparable and performed well in deriving the optimal parameter set with satisfactory simulations of target variables. Global sensitivity and uncertainty analysis indicate that the parameter uncertainty and the resulting output uncertainty can be quantified, and that the magnitude of parameter-uncertainty effects depends on variables and seasons. This study also demonstrates that using the cutting-edge R functions such as FME can be feasible and attractive for conducting comprehensive parameter analysis for ecosystem modeling.

Examining the latent structure of anxiety sensitivity in adolescents using factor mixture modeling.

PubMed

Allan, Nicholas P; MacPherson, Laura; Young, Kevin C; Lejuez, Carl W; Schmidt, Norman B

2014-09-01

Anxiety sensitivity has been implicated as an important risk factor, generalizable to most anxiety disorders. In adults, factor mixture modeling has been used to demonstrate that anxiety sensitivity is best conceptualized as categorical between individuals. That is, whereas most adults appear to possess normative levels of anxiety sensitivity, a small subset of the population appears to possess abnormally high levels of anxiety sensitivity. Further, those in the high anxiety sensitivity group are at increased risk of having high levels of anxiety and of having an anxiety disorder. This study was designed to determine whether these findings extend to adolescents. Factor mixture modeling was used to examine the best fitting model of anxiety sensitivity in a sample of 277 adolescents (M age = 11.0 years, SD = 0.81). Consistent with research in adults, the best fitting model consisted of 2 classes, 1 containing adolescents with high levels of anxiety sensitivity (n = 25) and another containing adolescents with normative levels of anxiety sensitivity (n = 252). Examination of anxiety sensitivity subscales revealed that the social concerns subscale was not important for classification of individuals. Convergent and discriminant validity of anxiety sensitivity classes were found in that membership in the high anxiety sensitivity class was associated with higher mean levels of anxiety symptoms, controlling for depression and externalizing problems, and was not associated with higher mean levels of depression or externalizing symptoms controlling for anxiety problems. PsycINFO Database Record (c) 2014 APA, all rights reserved.

Optimizing spectral wave estimates with adjoint-based sensitivity maps

NASA Astrophysics Data System (ADS)

Orzech, Mark; Veeramony, Jay; Flampouris, Stylianos

2014-04-01

A discrete numerical adjoint has recently been developed for the stochastic wave model SWAN. In the present study, this adjoint code is used to construct spectral sensitivity maps for two nearshore domains. The maps display the correlations of spectral energy levels throughout the domain with the observed energy levels at a selected location or region of interest (LOI/ROI), providing a full spectrum of values at all locations in the domain. We investigate the effectiveness of sensitivity maps based on significant wave height ( H s ) in determining alternate offshore instrument deployment sites when a chosen nearshore location or region is inaccessible. Wave and bathymetry datasets are employed from one shallower, small-scale domain (Duck, NC) and one deeper, larger-scale domain (San Diego, CA). The effects of seasonal changes in wave climate, errors in bathymetry, and multiple assimilation points on sensitivity map shapes and model performance are investigated. Model accuracy is evaluated by comparing spectral statistics as well as with an RMS skill score, which estimates a mean model-data error across all spectral bins. Results indicate that data assimilation from identified high-sensitivity alternate locations consistently improves model performance at nearshore LOIs, while assimilation from low-sensitivity locations results in lesser or no improvement. Use of sub-sampled or alongshore-averaged bathymetry has a domain-specific effect on model performance when assimilating from a high-sensitivity alternate location. When multiple alternate assimilation locations are used from areas of lower sensitivity, model performance may be worse than with a single, high-sensitivity assimilation point.

Global Sensitivity Analysis for Process Identification under Model Uncertainty

NASA Astrophysics Data System (ADS)

Ye, M.; Dai, H.; Walker, A. P.; Shi, L.; Yang, J.

2015-12-01

The environmental system consists of various physical, chemical, and biological processes, and environmental models are always built to simulate these processes and their interactions. For model building, improvement, and validation, it is necessary to identify important processes so that limited resources can be used to better characterize the processes. While global sensitivity analysis has been widely used to identify important processes, the process identification is always based on deterministic process conceptualization that uses a single model for representing a process. However, environmental systems are complex, and it happens often that a single process may be simulated by multiple alternative models. Ignoring the model uncertainty in process identification may lead to biased identification in that identified important processes may not be so in the real world. This study addresses this problem by developing a new method of global sensitivity analysis for process identification. The new method is based on the concept of Sobol sensitivity analysis and model averaging. Similar to the Sobol sensitivity analysis to identify important parameters, our new method evaluates variance change when a process is fixed at its different conceptualizations. The variance considers both parametric and model uncertainty using the method of model averaging. The method is demonstrated using a synthetic study of groundwater modeling that considers recharge process and parameterization process. Each process has two alternative models. Important processes of groundwater flow and transport are evaluated using our new method. The method is mathematically general, and can be applied to a wide range of environmental problems.

Comparison of the sensitivity of landscape-fire-succession models to variation in terrain, fuel pattern, climate and weather.

Treesearch

Geoffrey J. Cary; Robert E. Keane; Robert H. Gardner; Sandra Lavorel; Mike D. Flannigan; Ian D. Davies; Chao Li; James M. Lenihan; T. Scott Rupp; Florent Mouillot

2006-01-01

The purpose of this study was to compare the sensitivity of nlodelled area burned to environmental factors across a range of independently-developed landscape-fire-succession models. The sensitivity of area burned to variation in four factors, namely terrain (flat, undulating and mountainous), fuel pattern (finely and coarsely clumped), climate (observed, warmer &...

Sensitivity of Lumped Constraints Using the Adjoint Method

NASA Technical Reports Server (NTRS)

Akgun, Mehmet A.; Haftka, Raphael T.; Wu, K. Chauncey; Walsh, Joanne L.

1999-01-01

Adjoint sensitivity calculation of stress, buckling and displacement constraints may be much less expensive than direct sensitivity calculation when the number of load cases is large. Adjoint stress and displacement sensitivities are available in the literature. Expressions for local buckling sensitivity of isotropic plate elements are derived in this study. Computational efficiency of the adjoint method is sensitive to the number of constraints and, therefore, the method benefits from constraint lumping. A continuum version of the Kreisselmeier-Steinhauser (KS) function is chosen to lump constraints. The adjoint and direct methods are compared for three examples: a truss structure, a simple HSCT wing model, and a large HSCT model. These sensitivity derivatives are then used in optimization.

Classification of hydrological parameter sensitivity and evaluation of parameter transferability across 431 US MOPEX basins

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

Ren, Huiying; Hou, Zhangshuan; Huang, Maoyi

The Community Land Model (CLM) represents physical, chemical, and biological processes of the terrestrial ecosystems that interact with climate across a range of spatial and temporal scales. As CLM includes numerous sub-models and associated parameters, the high-dimensional parameter space presents a formidable challenge for quantifying uncertainty and improving Earth system predictions needed to assess environmental changes and risks. This study aims to evaluate the potential of transferring hydrologic model parameters in CLM through sensitivity analyses and classification across watersheds from the Model Parameter Estimation Experiment (MOPEX) in the United States. The sensitivity of CLM-simulated water and energy fluxes to hydrologicalmore » parameters across 431 MOPEX basins are first examined using an efficient stochastic sampling-based sensitivity analysis approach. Linear, interaction, and high-order nonlinear impacts are all identified via statistical tests and stepwise backward removal parameter screening. The basins are then classified accordingly to their parameter sensitivity patterns (internal attributes), as well as their hydrologic indices/attributes (external hydrologic factors) separately, using a Principal component analyses (PCA) and expectation-maximization (EM) –based clustering approach. Similarities and differences among the parameter sensitivity-based classification system (S-Class), the hydrologic indices-based classification (H-Class), and the Koppen climate classification systems (K-Class) are discussed. Within each S-class with similar parameter sensitivity characteristics, similar inversion modeling setups can be used for parameter calibration, and the parameters and their contribution or significance to water and energy cycling may also be more transferrable. This classification study provides guidance on identifiable parameters, and on parameterization and inverse model design for CLM but the methodology is applicable to other models. Inverting parameters at representative sites belonging to the same class can significantly reduce parameter calibration efforts.« less

Impact of the time scale of model sensitivity response on coupled model parameter estimation

NASA Astrophysics Data System (ADS)

Liu, Chang; Zhang, Shaoqing; Li, Shan; Liu, Zhengyu

2017-11-01

That a model has sensitivity responses to parameter uncertainties is a key concept in implementing model parameter estimation using filtering theory and methodology. Depending on the nature of associated physics and characteristic variability of the fluid in a coupled system, the response time scales of a model to parameters can be different, from hourly to decadal. Unlike state estimation, where the update frequency is usually linked with observational frequency, the update frequency for parameter estimation must be associated with the time scale of the model sensitivity response to the parameter being estimated. Here, with a simple coupled model, the impact of model sensitivity response time scales on coupled model parameter estimation is studied. The model includes characteristic synoptic to decadal scales by coupling a long-term varying deep ocean with a slow-varying upper ocean forced by a chaotic atmosphere. Results show that, using the update frequency determined by the model sensitivity response time scale, both the reliability and quality of parameter estimation can be improved significantly, and thus the estimated parameters make the model more consistent with the observation. These simple model results provide a guideline for when real observations are used to optimize the parameters in a coupled general circulation model for improving climate analysis and prediction initialization.

Modeling and sensitivity analysis on the transport of aluminum oxide nanoparticles in saturated sand: effects of ionic strength, flow rate, and nanoparticle concentration.

PubMed

Rahman, Tanzina; Millwater, Harry; Shipley, Heather J

2014-11-15

Aluminum oxide nanoparticles have been widely used in various consumer products and there are growing concerns regarding their exposure in the environment. This study deals with the modeling, sensitivity analysis and uncertainty quantification of one-dimensional transport of nano-sized (~82 nm) aluminum oxide particles in saturated sand. The transport of aluminum oxide nanoparticles was modeled using a two-kinetic-site model with a blocking function. The modeling was done at different ionic strengths, flow rates, and nanoparticle concentrations. The two sites representing fast and slow attachments along with a blocking term yielded good agreement with the experimental results from the column studies of aluminum oxide nanoparticles. The same model was used to simulate breakthrough curves under different conditions using experimental data and calculated 95% confidence bounds of the generated breakthroughs. The sensitivity analysis results showed that slow attachment was the most sensitive parameter for high influent concentrations (e.g. 150 mg/L Al2O3) and the maximum solid phase retention capacity (related to blocking function) was the most sensitive parameter for low concentrations (e.g. 50 mg/L Al2O3). Copyright © 2014 Elsevier B.V. All rights reserved.

Assessing the sensitivity of bovine tuberculosis surveillance in Canada's cattle population, 2009-2013.

PubMed

El Allaki, Farouk; Harrington, Noel; Howden, Krista

2016-11-01

The objectives of this study were (1) to estimate the annual sensitivity of Canada's bTB surveillance system and its three system components (slaughter surveillance, export testing and disease investigation) using a scenario tree modelling approach, and (2) to identify key model parameters that influence the estimates of the surveillance system sensitivity (SSSe). To achieve these objectives, we designed stochastic scenario tree models for three surveillance system components included in the analysis. Demographic data, slaughter data, export testing data, and disease investigation data from 2009 to 2013 were extracted for input into the scenario trees. Sensitivity analysis was conducted to identify key influential parameters on SSSe estimates. The median annual SSSe estimates generated from the study were very high, ranging from 0.95 (95% probability interval [PI]: 0.88-0.98) to 0.97 (95% PI: 0.93-0.99). Median annual sensitivity estimates for the slaughter surveillance component ranged from 0.95 (95% PI: 0.88-0.98) to 0.97 (95% PI: 0.93-0.99). This shows that slaughter surveillance to be the major contributor to overall surveillance system sensitivity with a high probability to detect M. bovis infection if present at a prevalence of 0.00028% or greater during the study period. The export testing and disease investigation components had extremely low component sensitivity estimates-the maximum median sensitivity estimates were 0.02 (95% PI: 0.014-0.023) and 0.0061 (95% PI: 0.0056-0.0066) respectively. The three most influential input parameters on the model's output (SSSe) were the probability of a granuloma being detected at slaughter inspection, the probability of a granuloma being present in older animals (≥12 months of age), and the probability of a granuloma sample being submitted to the laboratory. Additional studies are required to reduce the levels of uncertainty and variability associated with these three parameters influencing the surveillance system sensitivity. Crown Copyright © 2016. Published by Elsevier B.V. All rights reserved.

[Application of Fourier amplitude sensitivity test in Chinese healthy volunteer population pharmacokinetic model of tacrolimus].

PubMed

Guan, Zheng; Zhang, Guan-min; Ma, Ping; Liu, Li-hong; Zhou, Tian-yan; Lu, Wei

2010-07-01

In this study, we evaluated the influence of different variance from each of the parameters on the output of tacrolimus population pharmacokinetic (PopPK) model in Chinese healthy volunteers, using Fourier amplitude sensitivity test (FAST). Besides, we estimated the index of sensitivity within whole course of blood sampling, designed different sampling times, and evaluated the quality of parameters' and the efficiency of prediction. It was observed that besides CL1/F, the index of sensitivity for all of the other four parameters (V1/F, V2/F, CL2/F and k(a)) in tacrolimus PopPK model showed relatively high level and changed fast with the time passing. With the increase of the variance of k(a), its indices of sensitivity increased obviously, associated with significant decrease in sensitivity index for the other parameters, and obvious change in peak time as well. According to the simulation of NONMEM and the comparison among different fitting results, we found that the sampling time points designed according to FAST surpassed the other time points. It suggests that FAST can access the sensitivities of model parameters effectively, and assist the design of clinical sampling times and the construction of PopPK model.

Quantifying Key Climate Parameter Uncertainties Using an Earth System Model with a Dynamic 3D Ocean

NASA Astrophysics Data System (ADS)

Olson, R.; Sriver, R. L.; Goes, M. P.; Urban, N.; Matthews, D.; Haran, M.; Keller, K.

2011-12-01

Climate projections hinge critically on uncertain climate model parameters such as climate sensitivity, vertical ocean diffusivity and anthropogenic sulfate aerosol forcings. Climate sensitivity is defined as the equilibrium global mean temperature response to a doubling of atmospheric CO2 concentrations. Vertical ocean diffusivity parameterizes sub-grid scale ocean vertical mixing processes. These parameters are typically estimated using Intermediate Complexity Earth System Models (EMICs) that lack a full 3D representation of the oceans, thereby neglecting the effects of mixing on ocean dynamics and meridional overturning. We improve on these studies by employing an EMIC with a dynamic 3D ocean model to estimate these parameters. We carry out historical climate simulations with the University of Victoria Earth System Climate Model (UVic ESCM) varying parameters that affect climate sensitivity, vertical ocean mixing, and effects of anthropogenic sulfate aerosols. We use a Bayesian approach whereby the likelihood of each parameter combination depends on how well the model simulates surface air temperature and upper ocean heat content. We use a Gaussian process emulator to interpolate the model output to an arbitrary parameter setting. We use Markov Chain Monte Carlo method to estimate the posterior probability distribution function (pdf) of these parameters. We explore the sensitivity of the results to prior assumptions about the parameters. In addition, we estimate the relative skill of different observations to constrain the parameters. We quantify the uncertainty in parameter estimates stemming from climate variability, model and observational errors. We explore the sensitivity of key decision-relevant climate projections to these parameters. We find that climate sensitivity and vertical ocean diffusivity estimates are consistent with previously published results. The climate sensitivity pdf is strongly affected by the prior assumptions, and by the scaling parameter for the aerosols. The estimation method is computationally fast and can be used with more complex models where climate sensitivity is diagnosed rather than prescribed. The parameter estimates can be used to create probabilistic climate projections using the UVic ESCM model in future studies.

[Temporal and spatial heterogeneity analysis of optimal value of sensitive parameters in ecological process model: The BIOME-BGC model as an example.

PubMed

Li, Yi Zhe; Zhang, Ting Long; Liu, Qiu Yu; Li, Ying

2018-01-01

The ecological process models are powerful tools for studying terrestrial ecosystem water and carbon cycle at present. However, there are many parameters for these models, and weather the reasonable values of these parameters were taken, have important impact on the models simulation results. In the past, the sensitivity and the optimization of model parameters were analyzed and discussed in many researches. But the temporal and spatial heterogeneity of the optimal parameters is less concerned. In this paper, the BIOME-BGC model was used as an example. In the evergreen broad-leaved forest, deciduous broad-leaved forest and C3 grassland, the sensitive parameters of the model were selected by constructing the sensitivity judgment index with two experimental sites selected under each vegetation type. The objective function was constructed by using the simulated annealing algorithm combined with the flux data to obtain the monthly optimal values of the sensitive parameters at each site. Then we constructed the temporal heterogeneity judgment index, the spatial heterogeneity judgment index and the temporal and spatial heterogeneity judgment index to quantitatively analyze the temporal and spatial heterogeneity of the optimal values of the model sensitive parameters. The results showed that the sensitivity of BIOME-BGC model parameters was different under different vegetation types, but the selected sensitive parameters were mostly consistent. The optimal values of the sensitive parameters of BIOME-BGC model mostly presented time-space heterogeneity to different degrees which varied with vegetation types. The sensitive parameters related to vegetation physiology and ecology had relatively little temporal and spatial heterogeneity while those related to environment and phenology had generally larger temporal and spatial heterogeneity. In addition, the temporal heterogeneity of the optimal values of the model sensitive parameters showed a significant linear correlation with the spatial heterogeneity under the three vegetation types. According to the temporal and spatial heterogeneity of the optimal values, the parameters of the BIOME-BGC model could be classified in order to adopt different parameter strategies in practical application. The conclusion could help to deeply understand the parameters and the optimal values of the ecological process models, and provide a way or reference for obtaining the reasonable values of parameters in models application.

Pain sensitivity mediates the relationship between stress and headache intensity in chronic tension-type headache

PubMed Central

Cathcart, Stuart; Bhullar, Navjot; Immink, Maarten; Della Vedova, Chris; Hayball, John

2012-01-01

BACKGROUND: A central model for chronic tension-type headache (CTH) posits that stress contributes to headache, in part, by aggravating existing hyperalgesia in CTH sufferers. The prediction from this model that pain sensitivity mediates the relationship between stress and headache activity has not yet been examined. OBJECTIVE: To determine whether pain sensitivity mediates the relationship between stress and prospective headache activity in CTH sufferers. METHOD: Self-reported stress, pain sensitivity and prospective headache activity were measured in 53 CTH sufferers recruited from the general population. Pain sensitivity was modelled as a mediator between stress and headache activity, and tested using a nonparametric bootstrap analysis. RESULTS: Pain sensitivity significantly mediated the relationship between stress and headache intensity. CONCLUSIONS: The results of the present study support the central model for CTH, which posits that stress contributes to headache, in part, by aggravating existing hyperalgesia in CTH sufferers. Implications for the mechanisms and treatment of CTH are discussed. PMID:23248808

Material and morphology parameter sensitivity analysis in particulate composite materials

NASA Astrophysics Data System (ADS)

Zhang, Xiaoyu; Oskay, Caglar

2017-12-01

This manuscript presents a novel parameter sensitivity analysis framework for damage and failure modeling of particulate composite materials subjected to dynamic loading. The proposed framework employs global sensitivity analysis to study the variance in the failure response as a function of model parameters. In view of the computational complexity of performing thousands of detailed microstructural simulations to characterize sensitivities, Gaussian process (GP) surrogate modeling is incorporated into the framework. In order to capture the discontinuity in response surfaces, the GP models are integrated with a support vector machine classification algorithm that identifies the discontinuities within response surfaces. The proposed framework is employed to quantify variability and sensitivities in the failure response of polymer bonded particulate energetic materials under dynamic loads to material properties and morphological parameters that define the material microstructure. Particular emphasis is placed on the identification of sensitivity to interfaces between the polymer binder and the energetic particles. The proposed framework has been demonstrated to identify the most consequential material and morphological parameters under vibrational and impact loads.

Sensitivity analysis of machine-learning models of hydrologic time series

NASA Astrophysics Data System (ADS)

O'Reilly, A. M.

2017-12-01

Sensitivity analysis traditionally has been applied to assessing model response to perturbations in model parameters, where the parameters are those model input variables adjusted during calibration. Unlike physics-based models where parameters represent real phenomena, the equivalent of parameters for machine-learning models are simply mathematical "knobs" that are automatically adjusted during training/testing/verification procedures. Thus the challenge of extracting knowledge of hydrologic system functionality from machine-learning models lies in their very nature, leading to the label "black box." Sensitivity analysis of the forcing-response behavior of machine-learning models, however, can provide understanding of how the physical phenomena represented by model inputs affect the physical phenomena represented by model outputs.As part of a previous study, hybrid spectral-decomposition artificial neural network (ANN) models were developed to simulate the observed behavior of hydrologic response contained in multidecadal datasets of lake water level, groundwater level, and spring flow. Model inputs used moving window averages (MWA) to represent various frequencies and frequency-band components of time series of rainfall and groundwater use. Using these forcing time series, the MWA-ANN models were trained to predict time series of lake water level, groundwater level, and spring flow at 51 sites in central Florida, USA. A time series of sensitivities for each MWA-ANN model was produced by perturbing forcing time-series and computing the change in response time-series per unit change in perturbation. Variations in forcing-response sensitivities are evident between types (lake, groundwater level, or spring), spatially (among sites of the same type), and temporally. Two generally common characteristics among sites are more uniform sensitivities to rainfall over time and notable increases in sensitivities to groundwater usage during significant drought periods.

An approach to measure parameter sensitivity in watershed hydrological modelling

EPA Science Inventory

Hydrologic responses vary spatially and temporally according to watershed characteristics. In this study, the hydrologic models that we developed earlier for the Little Miami River (LMR) and Las Vegas Wash (LVW) watersheds were used for detail sensitivity analyses. To compare the...

Sensitivity Analysis of the Integrated Medical Model for ISS Programs

NASA Technical Reports Server (NTRS)

Goodenow, D. A.; Myers, J. G.; Arellano, J.; Boley, L.; Garcia, Y.; Saile, L.; Walton, M.; Kerstman, E.; Reyes, D.; Young, M.

2016-01-01

Sensitivity analysis estimates the relative contribution of the uncertainty in input values to the uncertainty of model outputs. Partial Rank Correlation Coefficient (PRCC) and Standardized Rank Regression Coefficient (SRRC) are methods of conducting sensitivity analysis on nonlinear simulation models like the Integrated Medical Model (IMM). The PRCC method estimates the sensitivity using partial correlation of the ranks of the generated input values to each generated output value. The partial part is so named because adjustments are made for the linear effects of all the other input values in the calculation of correlation between a particular input and each output. In SRRC, standardized regression-based coefficients measure the sensitivity of each input, adjusted for all the other inputs, on each output. Because the relative ranking of each of the inputs and outputs is used, as opposed to the values themselves, both methods accommodate the nonlinear relationship of the underlying model. As part of the IMM v4.0 validation study, simulations are available that predict 33 person-missions on ISS and 111 person-missions on STS. These simulated data predictions feed the sensitivity analysis procedures. The inputs to the sensitivity procedures include the number occurrences of each of the one hundred IMM medical conditions generated over the simulations and the associated IMM outputs: total quality time lost (QTL), number of evacuations (EVAC), and number of loss of crew lives (LOCL). The IMM team will report the results of using PRCC and SRRC on IMM v4.0 predictions of the ISS and STS missions created as part of the external validation study. Tornado plots will assist in the visualization of the condition-related input sensitivities to each of the main outcomes. The outcomes of this sensitivity analysis will drive review focus by identifying conditions where changes in uncertainty could drive changes in overall model output uncertainty. These efforts are an integral part of the overall verification, validation, and credibility review of IMM v4.0.

From Lexical Tone to Lexical Stress: A Cross-Language Mediation Model for Cantonese Children Learning English as a Second Language

PubMed Central

Choi, William; Tong, Xiuli; Singh, Leher

2017-01-01

This study investigated how Cantonese lexical tone sensitivity contributed to English lexical stress sensitivity among Cantonese children who learned English as a second language (ESL). Five-hundred-and-sixteen second-to-third grade Cantonese ESL children were tested on their Cantonese lexical tone sensitivity, English lexical stress sensitivity, general auditory sensitivity, and working memory. Structural equation modeling revealed that Cantonese lexical tone sensitivity contributed to English lexical stress sensitivity both directly, and indirectly through the mediation of general auditory sensitivity, in which the direct pathway had a larger relative contribution to English lexical stress sensitivity than the indirect pathway. These results suggest that the tone-stress association might be accounted for by joint phonological and acoustic processes that underlie lexical tone and lexical stress perception. PMID:28408898

Assessment of phototoxicity, skin irritation, and sensitization potential of polystyrene and TiO2 nanoparticles

NASA Astrophysics Data System (ADS)

Park, Yoon-Hee; Jeong, Sang Hoon; Yi, Sang Min; Hyeok Choi, Byeong; Kim, Yu-Ri; Kim, In-Kyoung; Kim, Meyoung-Kon; Son, Sang Wook

2011-07-01

The human skin equivalent model (HSEM) is well known as an attractive alternative model for evaluation of dermal toxicity. However, only limited data are available on the usefulness of an HSEM for nanotoxicity testing. This study was designed to investigate cutaneous toxicity of polystyrene and TiO2 nanoparticles using cultured keratinocytes, an HSEM, and an animal model. In addition, we also evaluated the skin sensitization potential of nanoparticles using a local lymph node assay with incorporation of BrdU. Findings from the present study indicate that polystyrene and TiO2 nanoparticles do not induce phototoxicity, acute cutaneous irritation, or skin sensitization. Results from evaluation of the HSEMs correspond well with those from animal models. Our findings suggest that the HSEM might be a useful alternative model for evaluation of dermal nanotoxicity.

Quantitative analysis of the thermal requirements for stepwise physical dormancy-break in seeds of the winter annual Geranium carolinianum (Geraniaceae)

PubMed Central

Gama-Arachchige, N. S.; Baskin, J. M.; Geneve, R. L.; Baskin, C. C.

2013-01-01

Background and Aims Physical dormancy (PY)-break in some annual plant species is a two-step process controlled by two different temperature and/or moisture regimes. The thermal time model has been used to quantify PY-break in several species of Fabaceae, but not to describe stepwise PY-break. The primary aims of this study were to quantify the thermal requirement for sensitivity induction by developing a thermal time model and to propose a mechanism for stepwise PY-breaking in the winter annual Geranium carolinianum. Methods Seeds of G. carolinianum were stored under dry conditions at different constant and alternating temperatures to induce sensitivity (step I). Sensitivity induction was analysed based on the thermal time approach using the Gompertz function. The effect of temperature on step II was studied by incubating sensitive seeds at low temperatures. Scanning electron microscopy, penetrometer techniques, and different humidity levels and temperatures were used to explain the mechanism of stepwise PY-break. Key Results The base temperature (Tb) for sensitivity induction was 17·2 °C and constant for all seed fractions of the population. Thermal time for sensitivity induction during step I in the PY-breaking process agreed with the three-parameter Gompertz model. Step II (PY-break) did not agree with the thermal time concept. Q10 values for the rate of sensitivity induction and PY-break were between 2·0 and 3·5 and between 0·02 and 0·1, respectively. The force required to separate the water gap palisade layer from the sub-palisade layer was significantly reduced after sensitivity induction. Conclusions Step I and step II in PY-breaking of G. carolinianum are controlled by chemical and physical processes, respectively. This study indicates the feasibility of applying the developed thermal time model to predict or manipulate sensitivity induction in seeds with two-step PY-breaking processes. The model is the first and most detailed one yet developed for sensitivity induction in PY-break. PMID:23456728

Strategies to Improve the Accuracy of Mars-GRAM Sensitivity Studies at Large Optical Depths

NASA Technical Reports Server (NTRS)

Justh, Hilary L.; Justus, Carl G.; Badger, Andrew M.

2010-01-01

The poster provides an overview of techniques to improve the Mars Global Reference Atmospheric Model (Mars-GRAM) sensitivity. It has been discovered during the Mars Science Laboratory (MSL) site selection process that the Mars Global Reference Atmospheric Model (Mars-GRAM) when used for sensitivity studies for TES MapYear = 0 and large optical depth values such as tau = 3 is less than realistic. A preliminary fix has been made to Mars-GRAM by adding a density factor value that was determined for tau = 0.3, 1 and 3.

Modeling Nitrogen Dynamics in a Waste Stabilization Pond System Using Flexible Modeling Environment with MCMC.

PubMed

Mukhtar, Hussnain; Lin, Yu-Pin; Shipin, Oleg V; Petway, Joy R

2017-07-12

This study presents an approach for obtaining realization sets of parameters for nitrogen removal in a pilot-scale waste stabilization pond (WSP) system. The proposed approach was designed for optimal parameterization, local sensitivity analysis, and global uncertainty analysis of a dynamic simulation model for the WSP by using the R software package Flexible Modeling Environment (R-FME) with the Markov chain Monte Carlo (MCMC) method. Additionally, generalized likelihood uncertainty estimation (GLUE) was integrated into the FME to evaluate the major parameters that affect the simulation outputs in the study WSP. Comprehensive modeling analysis was used to simulate and assess nine parameters and concentrations of ON-N, NH₃-N and NO₃-N. Results indicate that the integrated FME-GLUE-based model, with good Nash-Sutcliffe coefficients (0.53-0.69) and correlation coefficients (0.76-0.83), successfully simulates the concentrations of ON-N, NH₃-N and NO₃-N. Moreover, the Arrhenius constant was the only parameter sensitive to model performances of ON-N and NH₃-N simulations. However, Nitrosomonas growth rate, the denitrification constant, and the maximum growth rate at 20 °C were sensitive to ON-N and NO₃-N simulation, which was measured using global sensitivity.

Musculoskeletal sensitization and sleep: chronic muscle pain fragments sleep of mice without altering its duration.

PubMed

Sutton, Blair C; Opp, Mark R

2014-03-01

Musculoskeletal pain in humans is often associated with poor sleep quality. We used a model in which mechanical hypersensitivity was induced by injection of acidified saline into muscle to study the impact of musculoskeletal sensitization on sleep of mice. A one month pre-clinical study was designed to determine the impact of musculoskeletal sensitization on sleep of C57BL/6J mice. We instrumented mice with telemeters to record the electroencephalogram (EEG) and body temperature. We used an established model of musculoskeletal sensitization in which mechanical hypersensitivity was induced using two unilateral injections of acidified saline (pH 4.0). The injections were given into the gastrocnemius muscle and spaced five days apart. EEG and body temperature recordings started prior to injections (baseline) and continued for three weeks after musculoskeletal sensitization was induced by the second injection. Mechanical hypersensitivity was assessed using von Frey filaments at baseline (before any injections) and on days 1, 3, 7, 14, and 21 after the second injection. Mice injected with acidified saline developed bilateral mechanical hypersensitivity at the hind paws as measured by von Frey testing and as compared to control mice and baseline data. Sleep during the light period was fragmented in experimental mice injected with acidified saline, and EEG spectra altered. Musculoskeletal sensitization did not alter the duration of time spent in wakefulness, non-rapid eye movement sleep, or rapid eye movement sleep. Musculoskeletal sensitization in this model results in a distinct sleep phenotype in which sleep is fragmented during the light period, but the overall duration of sleep is not changed. This study suggests the consequences of musculoskeletal pain include sleep disruption, an observation that has been made in the clinical literature but has yet to be studied using preclinical models.

Finite element study of human pelvis model in side impact for Chinese adult occupants.

PubMed

Ma, Zhengwei; Lan, Fengchong; Chen, Jiqing; Liu, Weiguo

2015-01-01

The occupant's pelvis is very vulnerable to side collision in road accidents. Finite element (FE) studies on pelvic injury help to design occupant protection devices to improve vehicle safety. This study was aimed to develop a highly biofidelic pelvis model of Chinese adults and assess its sensitivity to variations in pelvis cortical bone thickness, bone material properties, and loading conditions. In this study, 4 different FE models of the pelvis were developed from the computed tomography (CT) data of a volunteer representing the 50th percentile Chinese male. Two of them were meshed using entirely hexahedral elements with variable and constant cortical thickness distribution (the V-Hex and C-Hex models), and the others were modeled with hexahedral elements for cancellous bone and variable or constant thickness shell elements for cortical bone (the V-HS and C-HS models). In model developments, the semi-automatic multiblock meshing approach was employed to maintain the pelvis geometric curvature and generate a high-quality hexahedral mesh. Then, several simulations with postmortem human subjects (PMHS) tests were performed to obtain the most accurate model in predicting pelvic injury. Based on the most accurate model, sensitivity studies were conducted to analyze the effects of the cortex thickness, Young's modulus of the cortical and cancellous bone, impactor velocity, and impactor with or without padding on the biomechanical responses and injuries of pelvis. The results indicate that the models with variable cortical bone thickness can give more accurate predictions than those with constant cortical thickness. Both the V-Hex and V-HS models are favorable for simulating pelvic response and injury, but the simulation results of the V-Hex model agree with the tests better. The sensitivity study shows that pelvic response is more sensitive to alterations in the Young's modulus of cortical bone than cancellous bone. Compared to failure displacement, peak force is more sensitive to the cortical bone thickness. However, displacement is more sensitive to the Young's modulus of cancellous bone than peak force. The padding attached on the impactor plays a significant role in absorbing the impact energy and alleviating pelvic injury. The all-hex meshing method with variable cortical bone thickness has the highest accuracy but is time-consuming. The cortical bone plays a determining role in resisting pelvic fracture. Peak impact force appears to be a reasonable injury predictor for pelvic injury assessment. Some appropriate energy absorbers installed in the car door can significantly reduce pelvic injury and will be beneficial for occupant protection.

Finnish Secondary School Students' Interreligious Sensitivity

ERIC Educational Resources Information Center

Holm, Kristiina; Nokelainen, Petri; Tirri, Kirsi

2014-01-01

The aim of this study was to assess the self-evaluations of Finnish secondary school students' (N?=?549) interreligious sensitivity. The data were collected from 12-16-year-old young people with a 15-item Interreligious Sensitivity Scale Questionnaire (IRRSSQ). The IRRSSQ is based on Abu-Nimer's Developmental Model of Interreligious Sensitivity,…

THP-1 monocytes but not macrophages as a potential alternative for CD34{sup +} dendritic cells to identify chemical skin sensitizers

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

Lambrechts, Nathalie; Verstraelen, Sandra; Lodewyckx, Hanne

2009-04-15

Early detection of the sensitizing potential of chemicals is an emerging issue for chemical, pharmaceutical and cosmetic industries. In our institute, an in vitro classification model for prediction of chemical-induced skin sensitization based on gene expression signatures in human CD34{sup +} progenitor-derived dendritic cells (DC) has been developed. This primary cell model is able to closely mimic the induction phase of sensitization by Langerhans cells in the skin, but it has drawbacks, such as the availability of cord blood. The aim of this study was to investigate whether human in vitro cultured THP-1 monocytes or macrophages display a similar expressionmore » profile for 13 predictive gene markers previously identified in DC and whether they also possess a discriminating capacity towards skin sensitizers and non-sensitizers based on these marker genes. To this end, the cell models were exposed to 5 skin sensitizers (ammonium hexachloroplatinate IV, 1-chloro-2,4-dinitrobenzene, eugenol, para-phenylenediamine, and tetramethylthiuram disulfide) and 5 non-sensitizers (L-glutamic acid, methyl salicylate, sodium dodecyl sulfate, tributyltin chloride, and zinc sulfate) for 6, 10, and 24 h, and mRNA expression of the 13 genes was analyzed using real-time RT-PCR. The transcriptional response of 7 out of 13 genes in THP-1 monocytes was significantly correlated with DC, whereas only 2 out of 13 genes in THP-1 macrophages. After a cross-validation of a discriminant analysis of the gene expression profiles in the THP-1 monocytes, this cell model demonstrated to also have a capacity to distinguish skin sensitizers from non-sensitizers. However, the DC model was superior to the monocyte model for discrimination of (non-)sensitizing chemicals.« less

Energy-balance climate models

NASA Technical Reports Server (NTRS)

North, G. R.; Cahalan, R. F.; Coakley, J. A., Jr.

1980-01-01

An introductory survey of the global energy balance climate models is presented with an emphasis on analytical results. A sequence of increasingly complicated models involving ice cap and radiative feedback processes are solved and the solutions and parameter sensitivities are studied. The model parameterizations are examined critically in light of many current uncertainties. A simple seasonal model is used to study the effects of changes in orbital elements on the temperature field. A linear stability theorem and a complete nonlinear stability analysis for the models are developed. Analytical solutions are also obtained for the linearized models driven by stochastic forcing elements. In this context the relation between natural fluctuation statistics and climate sensitivity is stressed.

Energy balance climate models

NASA Technical Reports Server (NTRS)

North, G. R.; Cahalan, R. F.; Coakley, J. A., Jr.

1981-01-01

An introductory survey of the global energy balance climate models is presented with an emphasis on analytical results. A sequence of increasingly complicated models involving ice cap and radiative feedback processes are solved, and the solutions and parameter sensitivities are studied. The model parameterizations are examined critically in light of many current uncertainties. A simple seasonal model is used to study the effects of changes in orbital elements on the temperature field. A linear stability theorem and a complete nonlinear stability analysis for the models are developed. Analytical solutions are also obtained for the linearized models driven by stochastic forcing elements. In this context the relation between natural fluctuation statistics and climate sensitivity is stressed.

Dissociation of thirst and sodium appetite in the furo/cap model of extracellular dehydration and a role for N-methyl-D-aspartate receptors in the sensitization of sodium appetite

PubMed Central

Hurley, Seth. W.; Johnson, Alan Kim

2015-01-01

Depletion of extracellular fluids motivates many animals to seek out and ingest water and sodium. Animals with a history of extracellular dehydration display enhanced sodium appetite and in some cases thirst. The progressive increase in sodium intake induced by repeated sodium depletions is known as sensitization of sodium appetite. Administration of the diuretic and natriuretic drug, furosemide, along with a low dose of captopril (furo/cap), elicits thirst and a rapid onset of sodium appetite. In the present studies the furo/cap model was used to explore the physiological mechanisms of sensitization of sodium appetite. However, when thirst and sodium appetite were measured concurrently in the furo/cap model, individual rats exhibited sensitization of either thirst or sodium appetite. In subsequent studies, thirst and sodium appetite were dissociated by offering either water prior to sodium or sodium before water. When water and sodium intake were dissociated in time, the furo/cap model reliably produced sensitization of sodium appetite. It is likely that neuroplasticity mediates this sensitization. Glutamatergic N-methyl-d-aspartate receptor (NMDA-R) activation is critical for the development of most forms of neuroplasticity. Therefore, we hypothesized that integrity of NMDA-R function is necessary for the sensitization of sodium appetite. Pharmacological blockade of NMDA-Rs with systemic administration of MK-801 (0.15mg/kg) prevented the sensitization of fluid intake in general when water and sodium were offered concurrently, and prevented sensitization of sodium intake specifically when water and sodium intake were dissociated. The involvement of NMDA-Rs provides support for the possibility that sensitization of sodium appetite is mediated by neuroplasticity. PMID:24341713

Genome-Wide Association Study of the Modified Stumvoll Insulin Sensitivity Index Identifies BCL2 and FAM19A2 as Novel Insulin Sensitivity Loci

PubMed Central

Gustafsson, Stefan; Rybin, Denis; Stančáková, Alena; Chen, Han; Liu, Ching-Ti; Hong, Jaeyoung; Jensen, Richard A.; Rice, Ken; Morris, Andrew P.; Mägi, Reedik; Tönjes, Anke; Prokopenko, Inga; Kleber, Marcus E.; Delgado, Graciela; Silbernagel, Günther; Jackson, Anne U.; Appel, Emil V.; Grarup, Niels; Lewis, Joshua P.; Montasser, May E.; Landenvall, Claes; Staiger, Harald; Luan, Jian’an; Frayling, Timothy M.; Weedon, Michael N.; Xie, Weijia; Morcillo, Sonsoles; Martínez-Larrad, María Teresa; Biggs, Mary L.; Chen, Yii-Der Ida; Corbaton-Anchuelo, Arturo; Færch, Kristine; Gómez-Zumaquero, Juan Miguel; Goodarzi, Mark O.; Kizer, Jorge R.; Koistinen, Heikki A.; Leong, Aaron; Lind, Lars; Lindgren, Cecilia; Machicao, Fausto; Manning, Alisa K.; Martín-Núñez, Gracia María; Rojo-Martínez, Gemma; Rotter, Jerome I.; Siscovick, David S.; Zmuda, Joseph M.; Zhang, Zhongyang; Serrano-Rios, Manuel; Smith, Ulf; Soriguer, Federico; Hansen, Torben; Jørgensen, Torben J.; Linnenberg, Allan; Pedersen, Oluf; Walker, Mark; Langenberg, Claudia; Scott, Robert A.; Wareham, Nicholas J.; Fritsche, Andreas; Häring, Hans-Ulrich; Stefan, Norbert; Groop, Leif; O’Connell, Jeff R.; Boehnke, Michael; Bergman, Richard N.; Collins, Francis S.; Mohlke, Karen L.; Tuomilehto, Jaakko; März, Winfried; Kovacs, Peter; Stumvoll, Michael; Psaty, Bruce M.; Kuusisto, Johanna; Laakso, Markku; Meigs, James B.; Dupuis, Josée; Ingelsson, Erik; Florez, Jose C.

2016-01-01

Genome-wide association studies (GWAS) have found few common variants that influence fasting measures of insulin sensitivity. We hypothesized that a GWAS of an integrated assessment of fasting and dynamic measures of insulin sensitivity would detect novel common variants. We performed a GWAS of the modified Stumvoll Insulin Sensitivity Index (ISI) within the Meta-Analyses of Glucose and Insulin-Related Traits Consortium. Discovery for genetic association was performed in 16,753 individuals, and replication was attempted for the 23 most significant novel loci in 13,354 independent individuals. Association with ISI was tested in models adjusted for age, sex, and BMI and in a model analyzing the combined influence of the genotype effect adjusted for BMI and the interaction effect between the genotype and BMI on ISI (model 3). In model 3, three variants reached genome-wide significance: rs13422522 (NYAP2; P = 8.87 × 10−11), rs12454712 (BCL2; P = 2.7 × 10−8), and rs10506418 (FAM19A2; P = 1.9 × 10−8). The association at NYAP2 was eliminated by conditioning on the known IRS1 insulin sensitivity locus; the BCL2 and FAM19A2 associations were independent of known cardiometabolic loci. In conclusion, we identified two novel loci and replicated known variants associated with insulin sensitivity. Further studies are needed to clarify the causal variant and function at the BCL2 and FAM19A2 loci. PMID:27416945

Three-Dimensional Sensitivity Kernels of Z/H Amplitude Ratios of Surface and Body Waves

NASA Astrophysics Data System (ADS)

Bao, X.; Shen, Y.

2017-12-01

The ellipticity of Rayleigh wave particle motion, or Z/H amplitude ratio, has received increasing attention in inversion for shallow Earth structures. Previous studies of the Z/H ratio assumed one-dimensional (1D) velocity structures beneath the receiver, ignoring the effects of three-dimensional (3D) heterogeneities on wave amplitudes. This simplification may introduce bias in the resulting models. Here we present 3D sensitivity kernels of the Z/H ratio to Vs, Vp, and density perturbations, based on finite-difference modeling of wave propagation in 3D structures and the scattering-integral method. Our full-wave approach overcomes two main issues in previous studies of Rayleigh wave ellipticity: (1) the finite-frequency effects of wave propagation in 3D Earth structures, and (2) isolation of the fundamental mode Rayleigh waves from Rayleigh wave overtones and converted Love waves. In contrast to the 1D depth sensitivity kernels in previous studies, our 3D sensitivity kernels exhibit patterns that vary with azimuths and distances to the receiver. The laterally-summed 3D sensitivity kernels and 1D depth sensitivity kernels, based on the same homogeneous reference model, are nearly identical with small differences that are attributable to the single period of the 1D kernels and a finite period range of the 3D kernels. We further verify the 3D sensitivity kernels by comparing the predictions from the kernels with the measurements from numerical simulations of wave propagation for models with various small-scale perturbations. We also calculate and verify the amplitude kernels for P waves. This study shows that both Rayleigh and body wave Z/H ratios provide vertical and lateral constraints on the structure near the receiver. With seismic arrays, the 3D kernels afford a powerful tool to use the Z/H ratios to obtain accurate and high-resolution Earth models.

Large-Scale Features of Pliocene Climate: Results from the Pliocene Model Intercomparison Project

NASA Technical Reports Server (NTRS)

Haywood, A. M.; Hill, D.J.; Dolan, A. M.; Otto-Bliesner, B. L.; Bragg, F.; Chan, W.-L.; Chandler, M. A.; Contoux, C.; Dowsett, H. J.; Jost, A.;

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

Roberts, Jesse D.; Chang, Grace; Magalen, Jason

A modified version of an indust ry standard wave modeling tool was evaluated, optimized, and utilized to investigate model sensitivity to input parameters a nd wave energy converter ( WEC ) array deployment scenarios. Wave propagation was investigated d ownstream of the WECs to evaluate overall near - and far - field effects of WEC arrays. The sensitivity study illustrate d that wave direction and WEC device type we r e most sensitive to the variation in the model parameters examined in this study . Generally, the changes in wave height we re the primary alteration caused by the presencemore » of a WEC array. Specifically, W EC device type and subsequently their size directly re sult ed in wave height variations; however, it is important to utilize ongoing laboratory studies and future field tests to determine the most appropriate power matrix values for a particular WEC device and configuration in order to improve modeling results .« less

Mixture models in diagnostic meta-analyses--clustering summary receiver operating characteristic curves accounted for heterogeneity and correlation.

PubMed

Schlattmann, Peter; Verba, Maryna; Dewey, Marc; Walther, Mario

2015-01-01

Bivariate linear and generalized linear random effects are frequently used to perform a diagnostic meta-analysis. The objective of this article was to apply a finite mixture model of bivariate normal distributions that can be used for the construction of componentwise summary receiver operating characteristic (sROC) curves. Bivariate linear random effects and a bivariate finite mixture model are used. The latter model is developed as an extension of a univariate finite mixture model. Two examples, computed tomography (CT) angiography for ruling out coronary artery disease and procalcitonin as a diagnostic marker for sepsis, are used to estimate mean sensitivity and mean specificity and to construct sROC curves. The suggested approach of a bivariate finite mixture model identifies two latent classes of diagnostic accuracy for the CT angiography example. Both classes show high sensitivity but mainly two different levels of specificity. For the procalcitonin example, this approach identifies three latent classes of diagnostic accuracy. Here, sensitivities and specificities are quite different as such that sensitivity increases with decreasing specificity. Additionally, the model is used to construct componentwise sROC curves and to classify individual studies. The proposed method offers an alternative approach to model between-study heterogeneity in a diagnostic meta-analysis. Furthermore, it is possible to construct sROC curves even if a positive correlation between sensitivity and specificity is present. Copyright © 2015 Elsevier Inc. All rights reserved.

Sensitivity of snowpack storage to precipitation and temperature using spatial and temporal analog models

NASA Astrophysics Data System (ADS)

Luce, Charles H.; Lopez-Burgos, Viviana; Holden, Zachary

2014-12-01

Empirical sensitivity analyses are important for evaluation of the effects of a changing climate on water resources and ecosystems. Although mechanistic models are commonly applied for evaluation of climate effects for snowmelt, empirical relationships provide a first-order validation of the various postulates required for their implementation. Previous studies of empirical sensitivity for April 1 snow water equivalent (SWE) in the western United States were developed by regressing interannual variations in SWE to winter precipitation and temperature. This offers a temporal analog for climate change, positing that a warmer future looks like warmer years. Spatial analogs are used to hypothesize that a warmer future may look like warmer places, and are frequently applied alternatives for complex processes, or states/metrics that show little interannual variability (e.g., forest cover). We contrast spatial and temporal analogs for sensitivity of April 1 SWE and the mean residence time of snow (SRT) using data from 524 Snowpack Telemetry (SNOTEL) stations across the western U.S. We built relatively strong models using spatial analogs to relate temperature and precipitation climatology to snowpack climatology (April 1 SWE, R2=0.87, and SRT, R2=0.81). Although the poorest temporal analog relationships were in areas showing the highest sensitivity to warming, spatial analog models showed consistent performance throughout the range of temperature and precipitation. Generally, slopes from the spatial relationships showed greater thermal sensitivity than the temporal analogs, and high elevation stations showed greater vulnerability using a spatial analog than shown in previous modeling and sensitivity studies. The spatial analog models provide a simple perspective to evaluate potential futures and may be useful in further evaluation of snowpack with warming.

Dual sensitivity of inferior colliculus neurons to ITD in the envelopes of high-frequency sounds: experimental and modeling study

PubMed Central

Wang, Le; Devore, Sasha; Delgutte, Bertrand

2013-01-01

Human listeners are sensitive to interaural time differences (ITDs) in the envelopes of sounds, which can serve as a cue for sound localization. Many high-frequency neurons in the mammalian inferior colliculus (IC) are sensitive to envelope-ITDs of sinusoidally amplitude-modulated (SAM) sounds. Typically, envelope-ITD-sensitive IC neurons exhibit either peak-type sensitivity, discharging maximally at the same delay across frequencies, or trough-type sensitivity, discharging minimally at the same delay across frequencies, consistent with responses observed at the primary site of binaural interaction in the medial and lateral superior olives (MSO and LSO), respectively. However, some high-frequency IC neurons exhibit dual types of envelope-ITD sensitivity in their responses to SAM tones, that is, they exhibit peak-type sensitivity at some modulation frequencies and trough-type sensitivity at other frequencies. Here we show that high-frequency IC neurons in the unanesthetized rabbit can also exhibit dual types of envelope-ITD sensitivity in their responses to SAM noise. Such complex responses to SAM stimuli could be achieved by convergent inputs from MSO and LSO onto single IC neurons. We test this hypothesis by implementing a physiologically explicit, computational model of the binaural pathway. Specifically, we examined envelope-ITD sensitivity of a simple model IC neuron that receives convergent inputs from MSO and LSO model neurons. We show that dual envelope-ITD sensitivity emerges in the IC when convergent MSO and LSO inputs are differentially tuned for modulation frequency. PMID:24155013

The Impact of Spring Subsurface Soil Temperature Anomaly in the Western U.S. on North American Summer Precipitation: A Case Study Using Regional Climate Model Downscaling

DTIC Science & Technology

2012-06-02

regional climate model downscaling , J. Geophys. Res., 117, D11103, doi:10.1029/2012JD017692. 1. Introduction [2] Modeling studies and data analyses...based on ground and satellite data have demonstrated that the land surface state variables, such as soil moisture, snow, vegetation, and soil temperature... downscaling rather than simply applying reanal- ysis data as LBC for both Eta control and sensitivity experiments as done in many RCM sensitivity studies

Remote sensing of mineral dust aerosol using AERI during the UAE2: A modeling and sensitivity study

NASA Astrophysics Data System (ADS)

Hansell, R. A.; Liou, K. N.; Ou, S. C.; Tsay, S. C.; Ji, Q.; Reid, J. S.

2008-09-01

Numerical simulations and sensitivity studies have been performed to assess the potential for using brightness temperature spectra from a ground-based Atmospheric Emitted Radiance Interferometer (AERI) during the United Arab Emirates Unified Aerosol Experiment (UAE2) for detecting/retrieving mineral dust aerosol. A methodology for separating dust from clouds and retrieving the dust IR optical depths was developed by exploiting differences between their spectral absorptive powers in prescribed thermal IR window subbands. Dust microphysical models were constructed using in situ data from the UAE2 and prior field studies while composition was modeled using refractive index data sets for minerals commonly observed around the UAE region including quartz, kaolinite, and calcium carbonate. The T-matrix, finite difference time domain (FDTD), and Lorenz-Mie light scattering programs were employed to calculate the single scattering properties for three dust shapes: oblate spheroids, hexagonal plates, and spheres. We used the Code for High-resolution Accelerated Radiative Transfer with Scattering (CHARTS) radiative transfer program to investigate sensitivity of the modeled AERI spectra to key dust and atmospheric parameters. Sensitivity studies show that characterization of the thermodynamic boundary layer is crucial for accurate AERI dust detection/retrieval. Furthermore, AERI sensitivity to dust optical depth is manifested in the strong subband slope dependence of the window region. Two daytime UAE2 cases were examined to demonstrate the present detection/retrieval technique, and we show that the results compare reasonably well to collocated AERONET Sun photometer/MPLNET micropulse lidar measurements. Finally, sensitivity of the developed methodology to the AERI's estimated MgCdTe detector nonlinearity was evaluated.

Calibration of a complex activated sludge model for the full-scale wastewater treatment plant.

PubMed

Liwarska-Bizukojc, Ewa; Olejnik, Dorota; Biernacki, Rafal; Ledakowicz, Stanislaw

2011-08-01

In this study, the results of the calibration of the complex activated sludge model implemented in BioWin software for the full-scale wastewater treatment plant are presented. Within the calibration of the model, sensitivity analysis of its parameters and the fractions of carbonaceous substrate were performed. In the steady-state and dynamic calibrations, a successful agreement between the measured and simulated values of the output variables was achieved. Sensitivity analysis revealed that upon the calculations of normalized sensitivity coefficient (S(i,j)) 17 (steady-state) or 19 (dynamic conditions) kinetic and stoichiometric parameters are sensitive. Most of them are associated with growth and decay of ordinary heterotrophic organisms and phosphorus accumulating organisms. The rankings of ten most sensitive parameters established on the basis of the calculations of the mean square sensitivity measure (δ(msqr)j) indicate that irrespective of the fact, whether the steady-state or dynamic calibration was performed, there is an agreement in the sensitivity of parameters.

Depth estimation of multi-layered impact damage in PMC using lateral thermography

NASA Astrophysics Data System (ADS)

Whitlow, Travis; Kramb, Victoria; Reibel, Rick; Dierken, Josiah

2018-04-01

Characterization of impact damage in polymer matrix composites (PMCs) continues to be a challenge due to the complex internal structure of the material. Nondestructive characterization approaches such as normal incident immersion ultrasound and flash thermography are sensitive to delamination damage, but do not provide information regarding damage obscured by the delaminations. Characterization of material state below a delamination requires a technique which is sensitive to in-plane damage modes such as matrix cracking and fiber breakage. Previous studies of the lateral heat flow through a composite laminate showed that the diffusion time was sensitive to the depth of the simulated damage zone. The current study will further evaluate the lateral diffusion model to provide sensitivity limits for the modeled flaw dimensions. Comparisons between the model simulations and experimental data obtained using a concentrated heat source and machined targets will also be presented.

Probability density function shape sensitivity in the statistical modeling of turbulent particle dispersion

NASA Technical Reports Server (NTRS)

Litchford, Ron J.; Jeng, San-Mou

1992-01-01

The performance of a recently introduced statistical transport model for turbulent particle dispersion is studied here for rigid particles injected into a round turbulent jet. Both uniform and isosceles triangle pdfs are used. The statistical sensitivity to parcel pdf shape is demonstrated.

Sensitivity of subject-specific models to Hill muscle-tendon model parameters in simulations of gait.

PubMed

Carbone, V; van der Krogt, M M; Koopman, H F J M; Verdonschot, N

2016-06-14

Subject-specific musculoskeletal (MS) models of the lower extremity are essential for applications such as predicting the effects of orthopedic surgery. We performed an extensive sensitivity analysis to assess the effects of potential errors in Hill muscle-tendon (MT) model parameters for each of the 56 MT parts contained in a state-of-the-art MS model. We used two metrics, namely a Local Sensitivity Index (LSI) and an Overall Sensitivity Index (OSI), to distinguish the effect of the perturbation on the predicted force produced by the perturbed MT parts and by all the remaining MT parts, respectively, during a simulated gait cycle. Results indicated that sensitivity of the model depended on the specific role of each MT part during gait, and not merely on its size and length. Tendon slack length was the most sensitive parameter, followed by maximal isometric muscle force and optimal muscle fiber length, while nominal pennation angle showed very low sensitivity. The highest sensitivity values were found for the MT parts that act as prime movers of gait (Soleus: average OSI=5.27%, Rectus Femoris: average OSI=4.47%, Gastrocnemius: average OSI=3.77%, Vastus Lateralis: average OSI=1.36%, Biceps Femoris Caput Longum: average OSI=1.06%) and hip stabilizers (Gluteus Medius: average OSI=3.10%, Obturator Internus: average OSI=1.96%, Gluteus Minimus: average OSI=1.40%, Piriformis: average OSI=0.98%), followed by the Peroneal muscles (average OSI=2.20%) and Tibialis Anterior (average OSI=1.78%) some of which were not included in previous sensitivity studies. Finally, the proposed priority list provides quantitative information to indicate which MT parts and which MT parameters should be estimated most accurately to create detailed and reliable subject-specific MS models. Copyright © 2016 Elsevier Ltd. All rights reserved.

An Animal Model of Trichloroethylene-Induced Skin Sensitization in BALB/c Mice.

PubMed

Wang, Hui; Zhang, Jia-xiang; Li, Shu-long; Wang, Feng; Zha, Wan-sheng; Shen, Tong; Wu, Changhao; Zhu, Qi-xing

2015-01-01

Trichloroethylene (TCE) is a major occupational hazard and environmental contaminant that can cause multisystem disorders in the form of occupational medicamentosa-like dermatitis. Development of dermatitis involves several proinflammatory cytokines, but their role in TCE-mediated dermatitis has not been examined in a well-defined experimental model. In addition, few animal models of TCE sensitization are available, and the current guinea pig model has apparent limitations. This study aimed to establish a model of TCE-induced skin sensitization in BALB/c mice and to examine the role of several key inflammatory cytokines on TCE sensitization. The sensitization rate of dorsal painted group was 38.3%. Skin edema and erythema occurred in TCE-sensitized groups, as seen in 2,4-dinitrochlorobenzene (DNCB) positive control. Trichloroethylene sensitization-positive (dermatitis [+]) group exhibited increased thickness of epidermis, inflammatory cell infiltration, swelling, and necrosis in dermis and around hair follicle, but ear painted group did not show these histological changes. The concentrations of serum proinflammatory cytokines including tumor necrosis factor (TNF)-α, interferon (IFN)-γ, and interleukin (IL)-2 were significantly increased in 24, 48, and 72 hours dermatitis [+] groups treated with TCE and peaked at 72 hours. Deposition of TNF-α, IFN-γ, and IL-2 into the skin tissue was also revealed by immunohistochemistry. We have established a new animal model of skin sensitization induced by repeated TCE stimulations, and we provide the first evidence that key proinflammatory cytokines including TNF-α, IFN-γ, and IL-2 play an important role in the process of TCE sensitization. © The Author(s) 2015.

How can we reduce phosphorus export from lowland polders? Implications from a sensitivity analysis of a coupled model.

PubMed

Huang, Jiacong; Gao, Junfeng; Yan, Renhua

2016-08-15

Phosphorus (P) export from lowland polders has caused severe water pollution. Numerical models are an important resource that help water managers control P export. This study coupled three models, i.e., Phosphorus Dynamic model for Polders (PDP), Integrated Catchments model of Phosphorus dynamics (INCA-P) and Universal Soil Loss Equation (USLE), to describe the P dynamics in polders. Based on the coupled models and a dataset collected from Polder Jian in China, sensitivity analysis were carried out to analyze the cause-effect relationships between environmental factors and P export from Polder Jian. The sensitivity analysis results showed that P export from Polder Jian were strongly affected by air temperature, precipitation and fertilization. Proper fertilization management should be a strategic priority for reducing P export from Polder Jian. This study demonstrated the success of model coupling, and its application in investigating potential strategies to support pollution control in polder systems. Copyright © 2016. Published by Elsevier B.V.

A model for hormonal control of the menstrual cycle: structural consistency but sensitivity with regard to data.

PubMed

Selgrade, J F; Harris, L A; Pasteur, R D

2009-10-21

This study presents a 13-dimensional system of delayed differential equations which predicts serum concentrations of five hormones important for regulation of the menstrual cycle. Parameters for the system are fit to two different data sets for normally cycling women. For these best fit parameter sets, model simulations agree well with the two different data sets but one model also has an abnormal stable periodic solution, which may represent polycystic ovarian syndrome. This abnormal cycle occurs for the model in which the normal cycle has estradiol levels at the high end of the normal range. Differences in model behavior are explained by studying hysteresis curves in bifurcation diagrams with respect to sensitive model parameters. For instance, one sensitive parameter is indicative of the estradiol concentration that promotes pituitary synthesis of a large amount of luteinizing hormone, which is required for ovulation. Also, it is observed that models with greater early follicular growth rates may have a greater risk of cycling abnormally.

Relationship of Gender and Academic Achievement to Finnish Students' Intercultural Sensitivity

ERIC Educational Resources Information Center

Holm, Kristiina; Nokelainen, Petri; Tirri, Kirsi

2009-01-01

This study examined the intercultural sensitivity of Finnish 12-16-year-old secondary school students (N=549) with a 23-item Intercultural Sensitivity Scale Questionnaire (ICSSQ). The ICSSQ is based on Bennett's (1993) Developmental Model of Intercultural Sensitivity (DMIS), which is a conceptual tool to situate certain reactions towards cultural…

Structure of Preschool Phonological Sensitivity: Overlapping Sensitivity to Rhyme, Words, Syllables, and Phonemes.

ERIC Educational Resources Information Center

Anthony, Jason L.; Lonigan, Christopher J.; Burgess, Stephen R.; Driscoll, Kimberly; Phillips, Beth M.; Cantor, Brenlee G.

2002-01-01

This study examined relations among sensitivity to words, syllables, rhymes, and phonemes in older and younger preschoolers. Confirmatory factor analyses found that a one-factor model best explained the date from both groups of children. Only variance common to all phonological sensitivity skills was related to print knowledge and rudimentary…

Comparison of the sensitivity of landscape-fire-succession models to variation in terrain, fuel pattern, climate and weather

Treesearch

Geoffrey J. Cary; Robert E. Keane; Robert H. Gardner; Sandra Lavorel; Mike D. Flannigan; Ian D. Davies; Chao Li; James M. Lenihan; T. Scott Rupp; Florent Mouillot

2006-01-01

The relative importance of variables in determining area burned is an important management consideration although gaining insights from existing empirical data has proven difficult. The purpose of this study was to compare the sensitivity of modeled area burned to environmental factors across a range of independently-developed landscape-fire-succession models. The...

What Constitutes a "Good" Sensitivity Analysis? Elements and Tools for a Robust Sensitivity Analysis with Reduced Computational Cost

NASA Astrophysics Data System (ADS)

Razavi, Saman; Gupta, Hoshin; Haghnegahdar, Amin

2016-04-01

Global sensitivity analysis (GSA) is a systems theoretic approach to characterizing the overall (average) sensitivity of one or more model responses across the factor space, by attributing the variability of those responses to different controlling (but uncertain) factors (e.g., model parameters, forcings, and boundary and initial conditions). GSA can be very helpful to improve the credibility and utility of Earth and Environmental System Models (EESMs), as these models are continually growing in complexity and dimensionality with continuous advances in understanding and computing power. However, conventional approaches to GSA suffer from (1) an ambiguous characterization of sensitivity, and (2) poor computational efficiency, particularly as the problem dimension grows. Here, we identify several important sensitivity-related characteristics of response surfaces that must be considered when investigating and interpreting the ''global sensitivity'' of a model response (e.g., a metric of model performance) to its parameters/factors. Accordingly, we present a new and general sensitivity and uncertainty analysis framework, Variogram Analysis of Response Surfaces (VARS), based on an analogy to 'variogram analysis', that characterizes a comprehensive spectrum of information on sensitivity. We prove, theoretically, that Morris (derivative-based) and Sobol (variance-based) methods and their extensions are special cases of VARS, and that their SA indices are contained within the VARS framework. We also present a practical strategy for the application of VARS to real-world problems, called STAR-VARS, including a new sampling strategy, called "star-based sampling". Our results across several case studies show the STAR-VARS approach to provide reliable and stable assessments of "global" sensitivity, while being at least 1-2 orders of magnitude more efficient than the benchmark Morris and Sobol approaches.

Radiation Sensitization in Cancer Therapy.

ERIC Educational Resources Information Center

Greenstock, Clive L.

1981-01-01

Discusses various aspects of radiation damage to biological material, including free radical mechanisms, radiation sensitization and protection, tumor hypoxia, mechanism of hypoxic cell radiosensitization, redox model for radiation modification, sensitizer probes of cellular radiation targets, pulse radiolysis studies of free radical kinetics,…

Differences in sensitivity to parenting depending on child temperament: A meta-analysis.

PubMed

Slagt, Meike; Dubas, Judith Semon; Deković, Maja; van Aken, Marcel A G

2016-10-01

Several models of individual differences in environmental sensitivity postulate increased sensitivity of some individuals to either stressful (diathesis-stress), supportive (vantage sensitivity), or both environments (differential susceptibility). In this meta-analysis we examine whether children vary in sensitivity to parenting depending on their temperament, and if so, which model can best be used to describe this sensitivity pattern. We tested whether associations between negative parenting and negative or positive child adjustment as well as between positive parenting and positive or negative child adjustment would be stronger among children higher on putative sensitivity markers (difficult temperament, negative emotionality, surgency, and effortful control). Longitudinal studies with children up to 18 years (k = 105 samples from 84 studies, Nmean = 6,153) that reported on a parenting-by-temperament interaction predicting child adjustment were included. We found 235 independent effect sizes for associations between parenting and child adjustment. Results showed that children with a more difficult temperament (compared with those with a more easy temperament) were more vulnerable to negative parenting, but also profited more from positive parenting, supporting the differential susceptibility model. Differences in susceptibility were expressed in externalizing and internalizing problems and in social and cognitive competence. Support for differential susceptibility for negative emotionality was, however, only present when this trait was assessed during infancy. Surgency and effortful control did not consistently moderate associations between parenting and child adjustment, providing little support for differential susceptibility, diathesis-stress, or vantage sensitivity models. Finally, parenting-by-temperament interactions were more pronounced when parenting was assessed using observations compared to questionnaires. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Longitudinal Effects of Embryonic Exposure to Cocaine on Morphology, Cardiovascular Physiology, and Behavior in Zebrafish.

PubMed

Mersereau, Eric J; Boyle, Cody A; Poitra, Shelby; Espinoza, Ana; Seiler, Joclyn; Longie, Robert; Delvo, Lisa; Szarkowski, Megan; Maliske, Joshua; Chalmers, Sarah; Darland, Diane C; Darland, Tristan

2016-05-31

A sizeable portion of the societal drain from cocaine abuse results from the complications of in utero drug exposure. Because of challenges in using humans and mammalian model organisms as test subjects, much debate remains about the impact of in utero cocaine exposure. Zebrafish offer a number of advantages as a model in longitudinal toxicology studies and are quite sensitive physiologically and behaviorally to cocaine. In this study, we have used zebrafish to model the effects of embryonic pre-exposure to cocaine on development and on subsequent cardiovascular physiology and cocaine-induced conditioned place preference (CPP) in longitudinal adults. Larval fish showed a progressive decrease in telencephalic size with increased doses of cocaine. These treated larvae also showed a dose dependent response in heart rate that persisted 24 h after drug cessation. Embryonic cocaine exposure had little effect on overall health of longitudinal adults, but subtle changes in cardiovascular physiology were seen including decreased sensitivity to isoproterenol and increased sensitivity to cocaine. These longitudinal adult fish also showed an embryonic dose-dependent change in CPP behavior, suggesting an increased sensitivity. These studies clearly show that pre-exposure during embryonic development affects subsequent cocaine sensitivity in longitudinal adults.

The impact of standard and hard-coded parameters on the hydrologic fluxes in the Noah-MP land surface model

NASA Astrophysics Data System (ADS)

Cuntz, Matthias; Mai, Juliane; Samaniego, Luis; Clark, Martyn; Wulfmeyer, Volker; Branch, Oliver; Attinger, Sabine; Thober, Stephan

2016-09-01

Land surface models incorporate a large number of process descriptions, containing a multitude of parameters. These parameters are typically read from tabulated input files. Some of these parameters might be fixed numbers in the computer code though, which hinder model agility during calibration. Here we identified 139 hard-coded parameters in the model code of the Noah land surface model with multiple process options (Noah-MP). We performed a Sobol' global sensitivity analysis of Noah-MP for a specific set of process options, which includes 42 out of the 71 standard parameters and 75 out of the 139 hard-coded parameters. The sensitivities of the hydrologic output fluxes latent heat and total runoff as well as their component fluxes were evaluated at 12 catchments within the United States with very different hydrometeorological regimes. Noah-MP's hydrologic output fluxes are sensitive to two thirds of its applicable standard parameters (i.e., Sobol' indexes above 1%). The most sensitive parameter is, however, a hard-coded value in the formulation of soil surface resistance for direct evaporation, which proved to be oversensitive in other land surface models as well. Surface runoff is sensitive to almost all hard-coded parameters of the snow processes and the meteorological inputs. These parameter sensitivities diminish in total runoff. Assessing these parameters in model calibration would require detailed snow observations or the calculation of hydrologic signatures of the runoff data. Latent heat and total runoff exhibit very similar sensitivities because of their tight coupling via the water balance. A calibration of Noah-MP against either of these fluxes should therefore give comparable results. Moreover, these fluxes are sensitive to both plant and soil parameters. Calibrating, for example, only soil parameters hence limit the ability to derive realistic model parameters. It is thus recommended to include the most sensitive hard-coded model parameters that were exposed in this study when calibrating Noah-MP.

Sensitivity of WRF Regional Climate Simulations to Choice of Land Use Dataset

EPA Science Inventory

The goal of this study is to assess the sensitivity of regional climate simulations run with the Weather Research and Forecasting (WRF) model to the choice of datasets representing land use and land cover (LULC). Within a regional climate modeling application, an accurate repres...

Tone matters for Cantonese-English bilingual children's English word reading development: A unified model of phonological transfer.

PubMed

Tong, Xiuli; He, Xinjie; Deacon, S Hélène

2017-02-01

Languages differ considerably in how they use prosodic features, or variations in pitch, duration, and intensity, to distinguish one word from another. Prosodic features include lexical tone in Chinese and lexical stress in English. Recent cross-sectional studies show a surprising result that Mandarin Chinese tone sensitivity is related to Mandarin-English bilingual children's English word reading. This study explores the mechanism underlying this relation by testing two explanations of these effects: the prosodic hypothesis and segmental phonological awareness transfer. We administered multiple measures of Cantonese tone sensitivity, English stress sensitivity, segmental phonological awareness in Cantonese and English, nonverbal ability, and English word reading to 123 Cantonese-English bilingual children ages 7 and 8 years. Structural equation modeling revealed a longitudinal prediction of Cantonese tone sensitivity to English word reading between 8 and 9 years of age. This relation was realized through two parallel routes. In one, Cantonese tone sensitivity predicted English stress sensitivity, and English stress sensitivity, in turn, significantly predicted English word reading, as postulated by the prosodic hypothesis. In the second, Cantonese tone sensitivity predicted English word reading through the transfer of segmental phonological awareness between Cantonese and English, as predicted by segmental phonological transfer. These results support a unified model of phonological transfer, emphasizing the role of tone in English word reading for Cantonese-English bilingual children.

Study of positive and negative feedback sensitivity in psychosis using the Wisconsin Card Sorting Test.

PubMed

Farreny, Aida; Del Rey-Mejías, Ángel; Escartin, Gemma; Usall, Judith; Tous, Núria; Haro, Josep Maria; Ochoa, Susana

2016-07-01

Schizophrenia involves marked motivational and learning deficits that may reflect abnormalities in reward processing. The purpose of this study was to examine positive and negative feedback sensitivity in schizophrenia using computational modeling derived from the Wisconsin Card Sorting Test (WCST). We also aimed to explore feedback sensitivity in a sample with bipolar disorder. Eighty-three individuals with schizophrenia and 27 with bipolar disorder were included. Demographic, clinical and cognitive outcomes, together with the WCST, were considered in both samples. Computational modeling was performed using the R syntax to calculate 3 parameters based on trial-by-trial execution on the WCST: reward sensitivity (R), punishment sensitivity (P), and choice consistency (D). The associations between outcome variables and the parameters were investigated. Positive and negative sensitivity showed deficits, but P parameter was clearly diminished in schizophrenia. Cognitive variables, age, and symptoms were associated with R, P, and D parameters in schizophrenia. The sample with bipolar disorder would show cognitive deficits and feedback abnormalities to a lesser extent than individuals with schizophrenia. Negative feedback sensitivity demonstrated greater deficit in both samples. Idiosyncratic cognitive requirements in the WCST might introduce confusion when supposing model-free reinforcement learning. Negative symptoms of schizophrenia were related to lower feedback sensitivity and less goal-directed patterns of choice. Copyright © 2016 Elsevier Inc. All rights reserved.

Addressing Curse of Dimensionality in Sensitivity Analysis: How Can We Handle High-Dimensional Problems?

NASA Astrophysics Data System (ADS)

Safaei, S.; Haghnegahdar, A.; Razavi, S.

2016-12-01

Complex environmental models are now the primary tool to inform decision makers for the current or future management of environmental resources under the climate and environmental changes. These complex models often contain a large number of parameters that need to be determined by a computationally intensive calibration procedure. Sensitivity analysis (SA) is a very useful tool that not only allows for understanding the model behavior, but also helps in reducing the number of calibration parameters by identifying unimportant ones. The issue is that most global sensitivity techniques are highly computationally demanding themselves for generating robust and stable sensitivity metrics over the entire model response surface. Recently, a novel global sensitivity analysis method, Variogram Analysis of Response Surfaces (VARS), is introduced that can efficiently provide a comprehensive assessment of global sensitivity using the Variogram concept. In this work, we aim to evaluate the effectiveness of this highly efficient GSA method in saving computational burden, when applied to systems with extra-large number of input factors ( 100). We use a test function and a hydrological modelling case study to demonstrate the capability of VARS method in reducing problem dimensionality by identifying important vs unimportant input factors.

Gut Microbiota in a Rat Oral Sensitization Model: Effect of a Cocoa-Enriched Diet

PubMed Central

Camps-Bossacoma, Mariona; Pérez-Cano, Francisco J.; Franch, Àngels

2017-01-01

Increasing evidence is emerging suggesting a relation between dietary compounds, microbiota, and the susceptibility to allergic diseases, particularly food allergy. Cocoa, a source of antioxidant polyphenols, has shown effects on gut microbiota and the ability to promote tolerance in an oral sensitization model. Taking these facts into consideration, the aim of the present study was to establish the influence of an oral sensitization model, both alone and together with a cocoa-enriched diet, on gut microbiota. Lewis rats were orally sensitized and fed with either a standard or 10% cocoa diet. Faecal microbiota was analysed through metagenomics study. Intestinal IgA concentration was also determined. Oral sensitization produced few changes in intestinal microbiota, but in those rats fed a cocoa diet significant modifications appeared. Decreased bacteria from the Firmicutes and Proteobacteria phyla and a higher percentage of bacteria belonging to the Tenericutes and Cyanobacteria phyla were observed. In conclusion, a cocoa diet is able to modify the microbiota bacterial pattern in orally sensitized animals. As cocoa inhibits the synthesis of specific antibodies and also intestinal IgA, those changes in microbiota pattern, particularly those of the Proteobacteria phylum, might be partially responsible for the tolerogenic effect of cocoa. PMID:28239436

Gut Microbiota in a Rat Oral Sensitization Model: Effect of a Cocoa-Enriched Diet.

PubMed

Camps-Bossacoma, Mariona; Pérez-Cano, Francisco J; Franch, Àngels; Castell, Margarida

2017-01-01

Increasing evidence is emerging suggesting a relation between dietary compounds, microbiota, and the susceptibility to allergic diseases, particularly food allergy. Cocoa, a source of antioxidant polyphenols, has shown effects on gut microbiota and the ability to promote tolerance in an oral sensitization model. Taking these facts into consideration, the aim of the present study was to establish the influence of an oral sensitization model, both alone and together with a cocoa-enriched diet, on gut microbiota. Lewis rats were orally sensitized and fed with either a standard or 10% cocoa diet. Faecal microbiota was analysed through metagenomics study. Intestinal IgA concentration was also determined. Oral sensitization produced few changes in intestinal microbiota, but in those rats fed a cocoa diet significant modifications appeared. Decreased bacteria from the Firmicutes and Proteobacteria phyla and a higher percentage of bacteria belonging to the Tenericutes and Cyanobacteria phyla were observed. In conclusion, a cocoa diet is able to modify the microbiota bacterial pattern in orally sensitized animals. As cocoa inhibits the synthesis of specific antibodies and also intestinal IgA, those changes in microbiota pattern, particularly those of the Proteobacteria phylum, might be partially responsible for the tolerogenic effect of cocoa.

A diagnostic model for the detection of sensitization to wheat allergens was developed and validated in bakery workers.

PubMed

Suarthana, Eva; Vergouwe, Yvonne; Moons, Karel G; de Monchy, Jan; Grobbee, Diederick; Heederik, Dick; Meijer, Evert

2010-09-01

To develop and validate a prediction model to detect sensitization to wheat allergens in bakery workers. The prediction model was developed in 867 Dutch bakery workers (development set, prevalence of sensitization 13%) and included questionnaire items (candidate predictors). First, principal component analysis was used to reduce the number of candidate predictors. Then, multivariable logistic regression analysis was used to develop the model. Internal validation and extent of optimism was assessed with bootstrapping. External validation was studied in 390 independent Dutch bakery workers (validation set, prevalence of sensitization 20%). The prediction model contained the predictors nasoconjunctival symptoms, asthma symptoms, shortness of breath and wheeze, work-related upper and lower respiratory symptoms, and traditional bakery. The model showed good discrimination with an area under the receiver operating characteristic (ROC) curve area of 0.76 (and 0.75 after internal validation). Application of the model in the validation set gave a reasonable discrimination (ROC area=0.69) and good calibration after a small adjustment of the model intercept. A simple model with questionnaire items only can be used to stratify bakers according to their risk of sensitization to wheat allergens. Its use may increase the cost-effectiveness of (subsequent) medical surveillance.

Analysis of sensitivity of simulated recharge to selected parameters for seven watersheds modeled using the precipitation-runoff modeling system

USGS Publications Warehouse

Ely, D. Matthew

2006-01-01

Recharge is a vital component of the ground-water budget and methods for estimating it range from extremely complex to relatively simple. The most commonly used techniques, however, are limited by the scale of application. One method that can be used to estimate ground-water recharge includes process-based models that compute distributed water budgets on a watershed scale. These models should be evaluated to determine which model parameters are the dominant controls in determining ground-water recharge. Seven existing watershed models from different humid regions of the United States were chosen to analyze the sensitivity of simulated recharge to model parameters. Parameter sensitivities were determined using a nonlinear regression computer program to generate a suite of diagnostic statistics. The statistics identify model parameters that have the greatest effect on simulated ground-water recharge and that compare and contrast the hydrologic system responses to those parameters. Simulated recharge in the Lost River and Big Creek watersheds in Washington State was sensitive to small changes in air temperature. The Hamden watershed model in west-central Minnesota was developed to investigate the relations that wetlands and other landscape features have with runoff processes. Excess soil moisture in the Hamden watershed simulation was preferentially routed to wetlands, instead of to the ground-water system, resulting in little sensitivity of any parameters to recharge. Simulated recharge in the North Fork Pheasant Branch watershed, Wisconsin, demonstrated the greatest sensitivity to parameters related to evapotranspiration. Three watersheds were simulated as part of the Model Parameter Estimation Experiment (MOPEX). Parameter sensitivities for the MOPEX watersheds, Amite River, Louisiana and Mississippi, English River, Iowa, and South Branch Potomac River, West Virginia, were similar and most sensitive to small changes in air temperature and a user-defined flow routing parameter. Although the primary objective of this study was to identify, by geographic region, the importance of the parameter value to the simulation of ground-water recharge, the secondary objectives proved valuable for future modeling efforts. The value of a rigorous sensitivity analysis can (1) make the calibration process more efficient, (2) guide additional data collection, (3) identify model limitations, and (4) explain simulated results.

An in vitro method for detecting chemical sensitization using human reconstructed skin models and its applicability to cosmetic, pharmaceutical, and medical device safety testing.

PubMed

McKim, James M; Keller, Donald J; Gorski, Joel R

2012-12-01

Chemical sensitization is a serious condition caused by small reactive molecules and is characterized by a delayed type hypersensitivity known as allergic contact dermatitis (ACD). Contact with these molecules via dermal exposure represent a significant concern for chemical manufacturers. Recent legislation in the EU has created the need to develop non-animal alternative methods for many routine safety studies including sensitization. Although most of the alternative research has focused on pure chemicals that possess reasonable solubility properties, it is important for any successful in vitro method to have the ability to test compounds with low aqueous solubility. This is especially true for the medical device industry where device extracts must be prepared in both polar and non-polar vehicles in order to evaluate chemical sensitization. The aim of this research was to demonstrate the functionality and applicability of the human reconstituted skin models (MatTek Epiderm(®) and SkinEthic RHE) as a test system for the evaluation of chemical sensitization and its potential use for medical device testing. In addition, the development of the human 3D skin model should allow the in vitro sensitization assay to be used for finished product testing in the personal care, cosmetics, and pharmaceutical industries. This approach combines solubility, chemical reactivity, cytotoxicity, and activation of the Nrf2/ARE expression pathway to identify and categorize chemical sensitizers. Known chemical sensitizers representing extreme/strong-, moderate-, weak-, and non-sensitizing potency categories were first evaluated in the skin models at six exposure concentrations ranging from 0.1 to 2500 µM for 24 h. The expression of eight Nrf2/ARE, one AhR/XRE and two Nrf1/MRE controlled gene were measured by qRT-PCR. The fold-induction at each exposure concentration was combined with reactivity and cytotoxicity data to determine the sensitization potential. The results demonstrated that both the MatTek and SkinEthic models performed in a manner consistent with data previously reported with the human keratinocyte (HaCaT) cell line. The system was tested further by evaluating chemicals known to be associated with the manufacture of medical devices. In all cases, the human skin models performed as well or better than the HaCaT cell model previously evaluated. In addition, this study identifies a clear unifying trigger that controls both the Nrf2/ARE pathway and essential biochemical events required for the development of ACD. Finally, this study has demonstrated that by utilizing human reconstructed skin models, it is possible to evaluate non-polar extracts from medical devices and low solubility finished products.

Modeling Nitrogen Dynamics in a Waste Stabilization Pond System Using Flexible Modeling Environment with MCMC

PubMed Central

Mukhtar, Hussnain; Lin, Yu-Pin; Shipin, Oleg V.; Petway, Joy R.

2017-01-01

This study presents an approach for obtaining realization sets of parameters for nitrogen removal in a pilot-scale waste stabilization pond (WSP) system. The proposed approach was designed for optimal parameterization, local sensitivity analysis, and global uncertainty analysis of a dynamic simulation model for the WSP by using the R software package Flexible Modeling Environment (R-FME) with the Markov chain Monte Carlo (MCMC) method. Additionally, generalized likelihood uncertainty estimation (GLUE) was integrated into the FME to evaluate the major parameters that affect the simulation outputs in the study WSP. Comprehensive modeling analysis was used to simulate and assess nine parameters and concentrations of ON-N, NH3-N and NO3-N. Results indicate that the integrated FME-GLUE-based model, with good Nash–Sutcliffe coefficients (0.53–0.69) and correlation coefficients (0.76–0.83), successfully simulates the concentrations of ON-N, NH3-N and NO3-N. Moreover, the Arrhenius constant was the only parameter sensitive to model performances of ON-N and NH3-N simulations. However, Nitrosomonas growth rate, the denitrification constant, and the maximum growth rate at 20 °C were sensitive to ON-N and NO3-N simulation, which was measured using global sensitivity. PMID:28704958

Sensitivity of summer ensembles of fledgling superparameterized U.S. mesoscale convective systems to cloud resolving model microphysics and grid configuration

DOE PAGES

Elliott, Elizabeth J.; Yu, Sungduk; Kooperman, Gabriel J.; ...

2016-05-01

The sensitivities of simulated mesoscale convective systems (MCSs) in the central U.S. to microphysics and grid configuration are evaluated here in a global climate model (GCM) that also permits global-scale feedbacks and variability. Since conventional GCMs do not simulate MCSs, studying their sensitivities in a global framework useful for climate change simulations has not previously been possible. To date, MCS sensitivity experiments have relied on controlled cloud resolving model (CRM) studies with limited domains, which avoid internal variability and neglect feedbacks between local convection and larger-scale dynamics. However, recent work with superparameterized (SP) GCMs has shown that eastward propagating MCS-likemore » events are captured when embedded CRMs replace convective parameterizations. This study uses a SP version of the Community Atmosphere Model version 5 (SP-CAM5) to evaluate MCS sensitivities, applying an objective empirical orthogonal function algorithm to identify MCS-like events, and harmonizing composite storms to account for seasonal and spatial heterogeneity. A five-summer control simulation is used to assess the magnitude of internal and interannual variability relative to 10 sensitivity experiments with varied CRM parameters, including ice fall speed, one-moment and two-moment microphysics, and grid spacing. MCS sensitivities were found to be subtle with respect to internal variability, and indicate that ensembles of over 100 storms may be necessary to detect robust differences in SP-GCMs. Furthermore, these results emphasize that the properties of MCSs can vary widely across individual events, and improving their representation in global simulations with significant internal variability may require comparison to long (multidecadal) time series of observed events rather than single season field campaigns.« less

Quantitative methods to direct exploration based on hydrogeologic information

USGS Publications Warehouse

Graettinger, A.J.; Lee, J.; Reeves, H.W.; Dethan, D.

2006-01-01

Quantitatively Directed Exploration (QDE) approaches based on information such as model sensitivity, input data covariance and model output covariance are presented. Seven approaches for directing exploration are developed, applied, and evaluated on a synthetic hydrogeologic site. The QDE approaches evaluate input information uncertainty, subsurface model sensitivity and, most importantly, output covariance to identify the next location to sample. Spatial input parameter values and covariances are calculated with the multivariate conditional probability calculation from a limited number of samples. A variogram structure is used during data extrapolation to describe the spatial continuity, or correlation, of subsurface information. Model sensitivity can be determined by perturbing input data and evaluating output response or, as in this work, sensitivities can be programmed directly into an analysis model. Output covariance is calculated by the First-Order Second Moment (FOSM) method, which combines the covariance of input information with model sensitivity. A groundwater flow example, modeled in MODFLOW-2000, is chosen to demonstrate the seven QDE approaches. MODFLOW-2000 is used to obtain the piezometric head and the model sensitivity simultaneously. The seven QDE approaches are evaluated based on the accuracy of the modeled piezometric head after information from a QDE sample is added. For the synthetic site used in this study, the QDE approach that identifies the location of hydraulic conductivity that contributes the most to the overall piezometric head variance proved to be the best method to quantitatively direct exploration. ?? IWA Publishing 2006.

Sensitivity Analysis to Turbulent Combustion Models for Combustor-Turbine Interactions

NASA Astrophysics Data System (ADS)

Miki, Kenji; Moder, Jeff; Liou, Meng-Sing

2017-11-01

The recently-updated Open National CombustionCode (Open NCC) equipped with alarge-eddy simulation (LES) is applied to model the flow field inside the Energy Efficient Engine (EEE) in conjunction with sensitivity analysis to turbulent combustion models. In this study, we consider three different turbulence-combustion interaction models, the Eddy-Breakup model (EBU), the Linear-Eddy Model (LEM) and the Probability Density Function (PDF)model as well as the laminar chemistry model. Acomprehensive comparison of the flow field and the flame structure will be provided. One of our main interests isto understand how a different model predicts thermal variation on the surface of the first stage vane. Considering that these models are often used in combustor/turbine communities, this study should provide some guidelines on numerical modeling of combustor-turbine interactions.

Basal plasma insulin and homeostasis model assessment (HOMA) are indicators of insulin sensitivity in cats.

PubMed

Appleton, D J; Rand, J S; Sunvold, G D

2005-06-01

The objective of this study was to compare simpler indices of insulin sensitivity with the minimal model-derived insulin sensitivity index to identify a simple and reliable alternative method for assessing insulin sensitivity in cats. In addition, we aimed to determine whether this simpler measure or measures showed consistency of association across differing body weights and glucose tolerance levels. Data from glucose tolerance and insulin sensitivity tests performed in 32 cats with varying body weights (underweight to obese), including seven cats with impaired glucose tolerance, were used to assess the relationship between Bergman's minimal model-derived insulin sensitivity index (S(I)), and various simpler measures of insulin sensitivity. The most useful overall predictors of insulin sensitivity were basal plasma insulin concentrations and the homeostasis model assessment (HOMA), which is the product of basal glucose and insulin concentrations divided by 22.5. It is concluded that measurement of plasma insulin concentrations in cats with food withheld for 24 h, in conjunction with HOMA, could be used in clinical research projects and by practicing veterinarians to screen for reduced insulin sensitivity in cats. Such cats may be at increased risk of developing impaired glucose tolerance and type 2 diabetes mellitus. Early detection of these cats would enable preventative intervention programs such as weight reduction, increased physical activity and dietary modifications to be instigated.

Variability in results from negative binomial models for Lyme disease measured at different spatial scales.

PubMed

Tran, Phoebe; Waller, Lance

2015-01-01

Lyme disease has been the subject of many studies due to increasing incidence rates year after year and the severe complications that can arise in later stages of the disease. Negative binomial models have been used to model Lyme disease in the past with some success. However, there has been little focus on the reliability and consistency of these models when they are used to study Lyme disease at multiple spatial scales. This study seeks to explore how sensitive/consistent negative binomial models are when they are used to study Lyme disease at different spatial scales (at the regional and sub-regional levels). The study area includes the thirteen states in the Northeastern United States with the highest Lyme disease incidence during the 2002-2006 period. Lyme disease incidence at county level for the period of 2002-2006 was linked with several previously identified key landscape and climatic variables in a negative binomial regression model for the Northeastern region and two smaller sub-regions (the New England sub-region and the Mid-Atlantic sub-region). This study found that negative binomial models, indeed, were sensitive/inconsistent when used at different spatial scales. We discuss various plausible explanations for such behavior of negative binomial models. Further investigation of the inconsistency and sensitivity of negative binomial models when used at different spatial scales is important for not only future Lyme disease studies and Lyme disease risk assessment/management but any study that requires use of this model type in a spatial context. Copyright © 2014 Elsevier Inc. All rights reserved.

Bayesian Sensitivity Analysis of Statistical Models with Missing Data

PubMed Central

ZHU, HONGTU; IBRAHIM, JOSEPH G.; TANG, NIANSHENG

2013-01-01

Methods for handling missing data depend strongly on the mechanism that generated the missing values, such as missing completely at random (MCAR) or missing at random (MAR), as well as other distributional and modeling assumptions at various stages. It is well known that the resulting estimates and tests may be sensitive to these assumptions as well as to outlying observations. In this paper, we introduce various perturbations to modeling assumptions and individual observations, and then develop a formal sensitivity analysis to assess these perturbations in the Bayesian analysis of statistical models with missing data. We develop a geometric framework, called the Bayesian perturbation manifold, to characterize the intrinsic structure of these perturbations. We propose several intrinsic influence measures to perform sensitivity analysis and quantify the effect of various perturbations to statistical models. We use the proposed sensitivity analysis procedure to systematically investigate the tenability of the non-ignorable missing at random (NMAR) assumption. Simulation studies are conducted to evaluate our methods, and a dataset is analyzed to illustrate the use of our diagnostic measures. PMID:24753718

Occupancy Modeling for Improved Accuracy and Understanding of Pathogen Prevalence and Dynamics

PubMed Central

Colvin, Michael E.; Peterson, James T.; Kent, Michael L.; Schreck, Carl B.

2015-01-01

Most pathogen detection tests are imperfect, with a sensitivity < 100%, thereby resulting in the potential for a false negative, where a pathogen is present but not detected. False negatives in a sample inflate the number of non-detections, negatively biasing estimates of pathogen prevalence. Histological examination of tissues as a diagnostic test can be advantageous as multiple pathogens can be examined and providing important information on associated pathological changes to the host. However, it is usually less sensitive than molecular or microbiological tests for specific pathogens. Our study objectives were to 1) develop a hierarchical occupancy model to examine pathogen prevalence in spring Chinook salmon Oncorhynchus tshawytscha and their distribution among host tissues 2) use the model to estimate pathogen-specific test sensitivities and infection rates, and 3) illustrate the effect of using replicate within host sampling on sample sizes required to detect a pathogen. We examined histological sections of replicate tissue samples from spring Chinook salmon O. tshawytscha collected after spawning for common pathogens seen in this population: Apophallus/echinostome metacercariae, Parvicapsula minibicornis, Nanophyetus salmincola/ metacercariae, and Renibacterium salmoninarum. A hierarchical occupancy model was developed to estimate pathogen and tissue-specific test sensitivities and unbiased estimation of host- and organ-level infection rates. Model estimated sensitivities and host- and organ-level infections rates varied among pathogens and model estimated infection rate was higher than prevalence unadjusted for test sensitivity, confirming that prevalence unadjusted for test sensitivity was negatively biased. The modeling approach provided an analytical approach for using hierarchically structured pathogen detection data from lower sensitivity diagnostic tests, such as histology, to obtain unbiased pathogen prevalence estimates with associated uncertainties. Accounting for test sensitivity using within host replicate samples also required fewer individual fish to be sampled. This approach is useful for evaluating pathogen or microbe community dynamics when test sensitivity is <100%. PMID:25738709

Occupancy modeling for improved accuracy and understanding of pathogen prevalence and dynamics

USGS Publications Warehouse

Colvin, Michael E.; Peterson, James T.; Kent, Michael L.; Schreck, Carl B.

2015-01-01

Most pathogen detection tests are imperfect, with a sensitivity < 100%, thereby resulting in the potential for a false negative, where a pathogen is present but not detected. False negatives in a sample inflate the number of non-detections, negatively biasing estimates of pathogen prevalence. Histological examination of tissues as a diagnostic test can be advantageous as multiple pathogens can be examined and providing important information on associated pathological changes to the host. However, it is usually less sensitive than molecular or microbiological tests for specific pathogens. Our study objectives were to 1) develop a hierarchical occupancy model to examine pathogen prevalence in spring Chinook salmonOncorhynchus tshawytscha and their distribution among host tissues 2) use the model to estimate pathogen-specific test sensitivities and infection rates, and 3) illustrate the effect of using replicate within host sampling on sample sizes required to detect a pathogen. We examined histological sections of replicate tissue samples from spring Chinook salmon O. tshawytscha collected after spawning for common pathogens seen in this population:Apophallus/echinostome metacercariae, Parvicapsula minibicornis, Nanophyetus salmincola/metacercariae, and Renibacterium salmoninarum. A hierarchical occupancy model was developed to estimate pathogen and tissue-specific test sensitivities and unbiased estimation of host- and organ-level infection rates. Model estimated sensitivities and host- and organ-level infections rates varied among pathogens and model estimated infection rate was higher than prevalence unadjusted for test sensitivity, confirming that prevalence unadjusted for test sensitivity was negatively biased. The modeling approach provided an analytical approach for using hierarchically structured pathogen detection data from lower sensitivity diagnostic tests, such as histology, to obtain unbiased pathogen prevalence estimates with associated uncertainties. Accounting for test sensitivity using within host replicate samples also required fewer individual fish to be sampled. This approach is useful for evaluating pathogen or microbe community dynamics when test sensitivity is <100%.

[Study on the automatic parameters identification of water pipe network model].

PubMed

Jia, Hai-Feng; Zhao, Qi-Feng

2010-01-01

Based on the problems analysis on development and application of water pipe network model, the model parameters automatic identification is regarded as a kernel bottleneck of model's application in water supply enterprise. The methodology of water pipe network model parameters automatic identification based on GIS and SCADA database is proposed. Then the kernel algorithm of model parameters automatic identification is studied, RSA (Regionalized Sensitivity Analysis) is used for automatic recognition of sensitive parameters, and MCS (Monte-Carlo Sampling) is used for automatic identification of parameters, the detail technical route based on RSA and MCS is presented. The module of water pipe network model parameters automatic identification is developed. At last, selected a typical water pipe network as a case, the case study on water pipe network model parameters automatic identification is conducted and the satisfied results are achieved.

Sensitivity Analysis of Launch Vehicle Debris Risk Model

NASA Technical Reports Server (NTRS)

Gee, Ken; Lawrence, Scott L.

2010-01-01

As part of an analysis of the loss of crew risk associated with an ascent abort system for a manned launch vehicle, a model was developed to predict the impact risk of the debris resulting from an explosion of the launch vehicle on the crew module. The model consisted of a debris catalog describing the number, size and imparted velocity of each piece of debris, a method to compute the trajectories of the debris and a method to calculate the impact risk given the abort trajectory of the crew module. The model provided a point estimate of the strike probability as a function of the debris catalog, the time of abort and the delay time between the abort and destruction of the launch vehicle. A study was conducted to determine the sensitivity of the strike probability to the various model input parameters and to develop a response surface model for use in the sensitivity analysis of the overall ascent abort risk model. The results of the sensitivity analysis and the response surface model are presented in this paper.

Sensitivity analysis of the near-road dispersion model RLINE - An evaluation at Detroit, Michigan

NASA Astrophysics Data System (ADS)

Milando, Chad W.; Batterman, Stuart A.

2018-05-01

The development of accurate and appropriate exposure metrics for health effect studies of traffic-related air pollutants (TRAPs) remains challenging and important given that traffic has become the dominant urban exposure source and that exposure estimates can affect estimates of associated health risk. Exposure estimates obtained using dispersion models can overcome many of the limitations of monitoring data, and such estimates have been used in several recent health studies. This study examines the sensitivity of exposure estimates produced by dispersion models to meteorological, emission and traffic allocation inputs, focusing on applications to health studies examining near-road exposures to TRAP. Daily average concentrations of CO and NOx predicted using the Research Line source model (RLINE) and a spatially and temporally resolved mobile source emissions inventory are compared to ambient measurements at near-road monitoring sites in Detroit, MI, and are used to assess the potential for exposure measurement error in cohort and population-based studies. Sensitivity of exposure estimates is assessed by comparing nominal and alternative model inputs using statistical performance evaluation metrics and three sets of receptors. The analysis shows considerable sensitivity to meteorological inputs; generally the best performance was obtained using data specific to each monitoring site. An updated emission factor database provided some improvement, particularly at near-road sites, while the use of site-specific diurnal traffic allocations did not improve performance compared to simpler default profiles. Overall, this study highlights the need for appropriate inputs, especially meteorological inputs, to dispersion models aimed at estimating near-road concentrations of TRAPs. It also highlights the potential for systematic biases that might affect analyses that use concentration predictions as exposure measures in health studies.

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.

Direct and indirect effects of contextual factors, caregiver depression, and parenting on attachment security in preschoolers.

PubMed

Hopkins, Joyce; Gouze, Karen R; Lavigne, John V

2013-01-01

The aim of this study was to develop a multiple-level-of-analysis model of preschool attachment security and to determine the processes (direct and indirect) whereby factors from different domains (e.g., stress and parenting) are related to attachment during this period. This study examined the direct and indirect effects of stress, family conflict, caregiver depression symptoms, and parenting on attachment security in a large (N = 796) and diverse sample of 4-year-olds. This study used the 3-Boxes Task to assess aspects of parenting critical to sensitivity in the preschool period, labeling this construct sensitivity/scaffolding. Parent-report questionnaires were used to assess stress, conflict, caregiver depressive symptoms, parent support/engagement, and parent hostility/coercion. Direct observation (3-Boxes Task) was used to assess sensitivity/scaffolding and attachment (Attachment Q-Sort) based on a 2½-3 hour home visit. Results of structural equation modeling indicated a good overall fit for the model. Among the parenting variables, sensitivity/scaffolding had the strongest effect on attachment. Depressive symptoms had both direct and indirect effects (mediated by parenting). The effects of stress and family conflict were mediated by caregiver depression symptoms and parenting. These data show that a developmentally appropriate measure of sensitivity plays a significant role in attachment security in preschoolers. Thus, strategies designed to enhance sensitivity/scaffolding may increase child resilience by enhancing attachment security.

Contextual and behavioral control of antipsychotic sensitization induced by haloperidol and olanzapine.

PubMed

Zhang, Chen; Li, Ming

2012-02-01

Repeated administration of haloperidol (HAL) and olanzapine (OLZ) causes a progressively enhanced disruption of the conditioned avoidance response (CAR) and a progressively enhanced inhibition of phencyclidine (PCP)-induced hyperlocomotion in rats (termed antipsychotic sensitization). Both actions are thought to reflect intrinsic antipsychotic activity. The present study examined the extent to which antipsychotic-induced sensitization in one model (e.g. CAR) can be transferred or maintained in another (e.g. PCP hyperlocomotion) as a means of investigating the contextual and behavioral controls of antipsychotic sensitization. Well-trained male Sprague-Dawley rats were first repeatedly tested in the CAR or the PCP (3.2 mg/kg, subcutaneously) hyperlocomotion model under HAL or OLZ for 5 consecutive days. Then they were switched to the other model and tested for the expression of sensitization. Finally, all rats were switched back to the original model and retested for the expression of sensitization. Repeated HAL or OLZ treatment progressively disrupted avoidance responding and decreased PCP-induced hyperlocomotion, indicating a robust sensitization. When tested in a different model, rats previously treated with HAL or OLZ did not show a stronger inhibition of CAR-induced or PCP-induced hyperlocomotion than those treated with these drugs for the first time; however, they did show such an effect when tested in the original model in which they received repeated antipsychotic treatment. These findings suggest that the expression of antipsychotic sensitization is strongly influenced by the testing environment and/or selected behavioral response under certain experimental conditions. Distinct contextual cues and behavioral responses may develop an association with unconditional drug effects through a Pavlovian conditioning process. They may also serve as occasion setters to modulate the expression of sensitized responses. As antipsychotic sensitization mimics the clinical effects of antipsychotic treatment, understanding the neurobiological mechanisms of antipsychotic sensitization and its contextual control would greatly enhance our understanding of the psychological and neurochemical nature of antipsychotic treatment in the clinic.

Contextual and behavioral control of antipsychotic sensitization induced by haloperidol and olanzapine

PubMed Central

Zhang, Chen; Li, Ming

2011-01-01

Repeated administration of haloperidol and olanzapine causes a progressively enhanced disruption of conditioned avoidance response (CAR) and a progressively enhanced inhibition of phencyclidine (PCP)-induced hyperlocomotion in rats (termed antipsychotic sensitization). Both actions are thought to reflect intrinsic antipsychotic activity. The present study examined to the extent to which antipsychotic-induced sensitization in one model (e.g. CAR) can be transferred or maintained in another (e.g. PCP hyperlocomotion) as a means of investigating the contextual and behavioral controls of antipsychotic sensitization. Well-trained male Sprague-Dawley rats were first repeatedly tested in the CAR or PCP (3.2 mg/kg, sc) hyperlocomotion model under haloperidol or olanzapine for five consecutive days. Then they were switched to the other model and tested for the expression of sensitization. Finally, all rats were switched back to the original model and retested for the expression of sensitization. Repeated haloperidol or olanzapine treatment progressively disrupted avoidance responding and decreased PCP-induced hyperlocomotion, indicating a robust sensitization. When tested in a different model, rats previously treated with haloperidol or olanzapine did not show a stronger inhibition of CAR or PCP-induced hyperlocomotion than those treated with these drugs for the first time; however, they did show such an effect when tested in the original model in which they received repeated antipsychotic treatment. These findings suggest that the expression of antipsychotic sensitization is strongly influenced by the testing environment and/or selected behavioral response under certain experimental conditions. Distinct contextual cues and behavioral responses may enter an association with unconditional drug effects via a Pavlovian conditioning process. They may also serve as occasion-setters to modulate the expression of sensitized responses. Because antipsychotic sensitization mimics clinical effects of antipsychotic treatment, understanding the neurobiological mechanisms of antipsychotic sensitization and its contextual control would greatly enhance our understanding of the psychological and neurochemical nature of antipsychotic treatment in the clinic. PMID:22157143

Bill Wilkins as a Model for Sensitivity Training.

ERIC Educational Resources Information Center

Smith, Henry C.

This case study is presented as a model for a sensitivity training program planned at Michigan State University. The goals, procedures, and criteria for conducting a program are illustrated. Based on the assumption that empathy is the mainspring of impression formation, and that empathy and evaluation interact, the goal is accurate evaluation.…

Identifying sensitive ranges in global warming precipitation change dependence on convective parameters

DOE PAGES

Bernstein, Diana N.; Neelin, J. David

2016-04-28

A branch-run perturbed-physics ensemble in the Community Earth System Model estimates impacts of parameters in the deep convection scheme on current hydroclimate and on end-of-century precipitation change projections under global warming. Regional precipitation change patterns prove highly sensitive to these parameters, especially in the tropics with local changes exceeding 3mm/d, comparable to the magnitude of the predicted change and to differences in global warming predictions among the Coupled Model Intercomparison Project phase 5 models. This sensitivity is distributed nonlinearly across the feasible parameter range, notably in the low-entrainment range of the parameter for turbulent entrainment in the deep convection scheme.more » This suggests that a useful target for parameter sensitivity studies is to identify such disproportionately sensitive dangerous ranges. Here, the low-entrainment range is used to illustrate the reduction in global warming regional precipitation sensitivity that could occur if this dangerous range can be excluded based on evidence from current climate.« less

Identifying sensitive ranges in global warming precipitation change dependence on convective parameters

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

Bernstein, Diana N.; Neelin, J. David

A branch-run perturbed-physics ensemble in the Community Earth System Model estimates impacts of parameters in the deep convection scheme on current hydroclimate and on end-of-century precipitation change projections under global warming. Regional precipitation change patterns prove highly sensitive to these parameters, especially in the tropics with local changes exceeding 3mm/d, comparable to the magnitude of the predicted change and to differences in global warming predictions among the Coupled Model Intercomparison Project phase 5 models. This sensitivity is distributed nonlinearly across the feasible parameter range, notably in the low-entrainment range of the parameter for turbulent entrainment in the deep convection scheme.more » This suggests that a useful target for parameter sensitivity studies is to identify such disproportionately sensitive dangerous ranges. Here, the low-entrainment range is used to illustrate the reduction in global warming regional precipitation sensitivity that could occur if this dangerous range can be excluded based on evidence from current climate.« less

A geostatistics-informed hierarchical sensitivity analysis method for complex groundwater flow and transport modeling

NASA Astrophysics Data System (ADS)

Dai, Heng; Chen, Xingyuan; Ye, Ming; Song, Xuehang; Zachara, John M.

2017-05-01

Sensitivity analysis is an important tool for development and improvement of mathematical models, especially for complex systems with a high dimension of spatially correlated parameters. Variance-based global sensitivity analysis has gained popularity because it can quantify the relative contribution of uncertainty from different sources. However, its computational cost increases dramatically with the complexity of the considered model and the dimension of model parameters. In this study, we developed a new sensitivity analysis method that integrates the concept of variance-based method with a hierarchical uncertainty quantification framework. Different uncertain inputs are grouped and organized into a multilayer framework based on their characteristics and dependency relationships to reduce the dimensionality of the sensitivity analysis. A set of new sensitivity indices are defined for the grouped inputs using the variance decomposition method. Using this methodology, we identified the most important uncertainty source for a dynamic groundwater flow and solute transport model at the Department of Energy (DOE) Hanford site. The results indicate that boundary conditions and permeability field contribute the most uncertainty to the simulated head field and tracer plume, respectively. The relative contribution from each source varied spatially and temporally. By using a geostatistical approach to reduce the number of realizations needed for the sensitivity analysis, the computational cost of implementing the developed method was reduced to a practically manageable level. The developed sensitivity analysis method is generally applicable to a wide range of hydrologic and environmental problems that deal with high-dimensional spatially distributed input variables.

A Geostatistics-Informed Hierarchical Sensitivity Analysis Method for Complex Groundwater Flow and Transport Modeling

NASA Astrophysics Data System (ADS)

Dai, H.; Chen, X.; Ye, M.; Song, X.; Zachara, J. M.

2017-12-01

Sensitivity analysis is an important tool for development and improvement of mathematical models, especially for complex systems with a high dimension of spatially correlated parameters. Variance-based global sensitivity analysis has gained popularity because it can quantify the relative contribution of uncertainty from different sources. However, its computational cost increases dramatically with the complexity of the considered model and the dimension of model parameters. In this study we developed a new sensitivity analysis method that integrates the concept of variance-based method with a hierarchical uncertainty quantification framework. Different uncertain inputs are grouped and organized into a multi-layer framework based on their characteristics and dependency relationships to reduce the dimensionality of the sensitivity analysis. A set of new sensitivity indices are defined for the grouped inputs using the variance decomposition method. Using this methodology, we identified the most important uncertainty source for a dynamic groundwater flow and solute transport model at the Department of Energy (DOE) Hanford site. The results indicate that boundary conditions and permeability field contribute the most uncertainty to the simulated head field and tracer plume, respectively. The relative contribution from each source varied spatially and temporally. By using a geostatistical approach to reduce the number of realizations needed for the sensitivity analysis, the computational cost of implementing the developed method was reduced to a practically manageable level. The developed sensitivity analysis method is generally applicable to a wide range of hydrologic and environmental problems that deal with high-dimensional spatially-distributed input variables.

Active Brownian agents with concentration-dependent chemotactic sensitivity.

PubMed

Meyer, Marcel; Schimansky-Geier, Lutz; Romanczuk, Pawel

2014-02-01

We study a biologically motivated model of overdamped, autochemotactic Brownian agents with concentration-dependent chemotactic sensitivity. The agents in our model move stochastically and produce a chemical ligand at their current position. The ligand concentration obeys a reaction-diffusion equation and acts as a chemoattractant for the agents, which bias their motion towards higher concentrations of the dynamically altered chemical field. We explore the impact of concentration-dependent response to chemoattractant gradients on large-scale pattern formation, by deriving a coarse-grained macroscopic description of the individual-based model, and compare the conditions for emergence of inhomogeneous solutions for different variants of the chemotactic sensitivity. We focus primarily on the so-called receptor-law sensitivity, which models a nonlinear decrease of chemotactic sensitivity with increasing ligand concentration. Our results reveal qualitative differences between the receptor law, the constant chemotactic response, and the so-called log law, with respect to stability of the homogeneous solution, as well as the emergence of different patterns (labyrinthine structures, clusters, and bubbles) via spinodal decomposition or nucleation. We discuss two limiting cases, where the model can be reduced to the dynamics of single species: (I) the agent density governed by a density-dependent effective diffusion coefficient and (II) the ligand field with an effective bistable, time-dependent reaction rate. In the end, we turn to single clusters of agents, studying domain growth and determining mean characteristics of the stationary inhomogeneous state. Analytical results are confirmed and extended by large-scale GPU simulations of the individual based model.

Kindling of life stress in bipolar disorder: comparison of sensitization and autonomy models.

PubMed

Weiss, Rachel B; Stange, Jonathan P; Boland, Elaine M; Black, Shimrit K; LaBelle, Denise R; Abramson, Lyn Y; Alloy, Lauren B

2015-02-01

Research on life stress in bipolar disorder largely fails to account for the possibility of a dynamic relationship between psychosocial stress and episode initiation. The kindling hypothesis (Post, 1992) states that over the course of recurrent affective disorders, there is a weakening temporal relationship between major life stress and episode initiation that could reflect either a progressive sensitization or progressive autonomy to life stress. The present study involved a comprehensive and precise examination of the kindling hypothesis in 102 participants with bipolar II disorder that allowed for a direct comparison of sensitization and autonomy models. Polarity-specific tests were conducted across the continuum of event severity with respect to impact and frequency of life events. Hypotheses were polarity- and event-valence specific and were based on the stress sensitization model. Results were only partially consistent with the sensitization model: Individuals with more prior mood episodes had an increased frequency of minor negative events before depression and of minor positive events before hypomania. However, the number of past episodes did not moderate relationships between life events and time until prospective onset of mood episodes. These results are more consistent with a sensitization than an autonomy model, but several predictions of the sensitization model were not supported. Methodological strengths, limitations, and implications are discussed regarding putative changes in stress reactivity that may occur with repeated exposure to mood episodes in bipolar II disorder. (PsycINFO Database Record (c) 2015 APA, all rights reserved).

Understanding Socio-Hydrology System in the Kissimmee River Basin

NASA Astrophysics Data System (ADS)

Chen, X.; Wang, D.; Tian, F.; Sivapalan, M.

2014-12-01

This study is to develop a conceptual socio-hydrology model for the Kissimmee River Basin. The Kissimmee River located in Florida was channelized in mid-20 century for flood protection. However, the environmental issues caused by channelization led Floridians to conduct a restoration project recently, focusing on wetland recovery. As a complex coupled human-water system, Kissimmee River Basin shows the typical socio-hydrology interactions. Hypothetically, the major reason to drive the system from channelization to restoration is that the community sensitivity towards the environment has changed from controlling to restoring. The model developed in this study includes 5 components: water balance, flood risk, wetland area, crop land area, and community sensitivity. Furthermore, urban population and rural population in the basin have different community sensitivities towards the hydrologic system. The urban population, who live further away from the river are more sensitive to wetland restoration; while the rural population, who live closer to the river are more sensitive to flood protection. The power dynamics between the two groups and its impact on management decision making is described in the model. The model is calibrated based on the observed watershed outflow, wetland area and crop land area. The results show that the overall focus of community sensitivity has changed from flood protection to wetland restoration in the past 60 years in Kissimmee River Basin, which confirms the study hypothesis. There are two main reasons for the community sensitivity change. Firstly, people's flood memory is fading because of the effective flood protection, while the continuously shrinking wetland and the decreasing bird and fish population draw more and more attention. Secondly, in the last 60 years, the urban population in Florida drastically increased compared with a much slower increase of rural population. As a result, the community sensitivity of urban population towards wetland restoration has more weight than the rural population's towards flood protection.

A Sensitivity Analysis Method to Study the Behavior of Complex Process-based Models

NASA Astrophysics Data System (ADS)

Brugnach, M.; Neilson, R.; Bolte, J.

2001-12-01

The use of process-based models as a tool for scientific inquiry is becoming increasingly relevant in ecosystem studies. Process-based models are artificial constructs that simulate the system by mechanistically mimicking the functioning of its component processes. Structurally, a process-based model can be characterized, in terms of its processes and the relationships established among them. Each process comprises a set of functional relationships among several model components (e.g., state variables, parameters and input data). While not encoded explicitly, the dynamics of the model emerge from this set of components and interactions organized in terms of processes. It is the task of the modeler to guarantee that the dynamics generated are appropriate and semantically equivalent to the phenomena being modeled. Despite the availability of techniques to characterize and understand model behavior, they do not suffice to completely and easily understand how a complex process-based model operates. For example, sensitivity analysis studies model behavior by determining the rate of change in model output as parameters or input data are varied. One of the problems with this approach is that it considers the model as a "black box", and it focuses on explaining model behavior by analyzing the relationship input-output. Since, these models have a high degree of non-linearity, understanding how the input affects an output can be an extremely difficult task. Operationally, the application of this technique may constitute a challenging task because complex process-based models are generally characterized by a large parameter space. In order to overcome some of these difficulties, we propose a method of sensitivity analysis to be applicable to complex process-based models. This method focuses sensitivity analysis at the process level, and it aims to determine how sensitive the model output is to variations in the processes. Once the processes that exert the major influence in the output are identified, the causes of its variability can be found. Some of the advantages of this approach are that it reduces the dimensionality of the search space, it facilitates the interpretation of the results and it provides information that allows exploration of uncertainty at the process level, and how it might affect model output. We present an example using the vegetation model BIOME-BGC.

A hybrid Bayesian hierarchical model combining cohort and case-control studies for meta-analysis of diagnostic tests: Accounting for partial verification bias.

PubMed

Ma, Xiaoye; Chen, Yong; Cole, Stephen R; Chu, Haitao

2016-12-01

To account for between-study heterogeneity in meta-analysis of diagnostic accuracy studies, bivariate random effects models have been recommended to jointly model the sensitivities and specificities. As study design and population vary, the definition of disease status or severity could differ across studies. Consequently, sensitivity and specificity may be correlated with disease prevalence. To account for this dependence, a trivariate random effects model had been proposed. However, the proposed approach can only include cohort studies with information estimating study-specific disease prevalence. In addition, some diagnostic accuracy studies only select a subset of samples to be verified by the reference test. It is known that ignoring unverified subjects may lead to partial verification bias in the estimation of prevalence, sensitivities, and specificities in a single study. However, the impact of this bias on a meta-analysis has not been investigated. In this paper, we propose a novel hybrid Bayesian hierarchical model combining cohort and case-control studies and correcting partial verification bias at the same time. We investigate the performance of the proposed methods through a set of simulation studies. Two case studies on assessing the diagnostic accuracy of gadolinium-enhanced magnetic resonance imaging in detecting lymph node metastases and of adrenal fluorine-18 fluorodeoxyglucose positron emission tomography in characterizing adrenal masses are presented. © The Author(s) 2014.

A Hybrid Bayesian Hierarchical Model Combining Cohort and Case-control Studies for Meta-analysis of Diagnostic Tests: Accounting for Partial Verification Bias

PubMed Central

Ma, Xiaoye; Chen, Yong; Cole, Stephen R.; Chu, Haitao

2014-01-01

To account for between-study heterogeneity in meta-analysis of diagnostic accuracy studies, bivariate random effects models have been recommended to jointly model the sensitivities and specificities. As study design and population vary, the definition of disease status or severity could differ across studies. Consequently, sensitivity and specificity may be correlated with disease prevalence. To account for this dependence, a trivariate random effects model had been proposed. However, the proposed approach can only include cohort studies with information estimating study-specific disease prevalence. In addition, some diagnostic accuracy studies only select a subset of samples to be verified by the reference test. It is known that ignoring unverified subjects may lead to partial verification bias in the estimation of prevalence, sensitivities and specificities in a single study. However, the impact of this bias on a meta-analysis has not been investigated. In this paper, we propose a novel hybrid Bayesian hierarchical model combining cohort and case-control studies and correcting partial verification bias at the same time. We investigate the performance of the proposed methods through a set of simulation studies. Two case studies on assessing the diagnostic accuracy of gadolinium-enhanced magnetic resonance imaging in detecting lymph node metastases and of adrenal fluorine-18 fluorodeoxyglucose positron emission tomography in characterizing adrenal masses are presented. PMID:24862512

Sensitivity of Rooftop PV Projections in the SunShot Vision Study to Market Assumptions

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

Drury, E.; Denholm, P.; Margolis, R.

2013-01-01

The SunShot Vision Study explored the potential growth of solar markets if solar prices decreased by about 75% from 2010 to 2020. The SolarDS model was used to simulate rooftop PV demand for this study, based on several PV market assumptions--future electricity rates, customer access to financing, and others--in addition to the SunShot PV price projections. This paper finds that modeled PV demand is highly sensitive to several non-price market assumptions, particularly PV financing parameters.

Estimation of genetic variance for macro- and micro-environmental sensitivity using double hierarchical generalized linear models.

PubMed

Mulder, Han A; Rönnegård, Lars; Fikse, W Freddy; Veerkamp, Roel F; Strandberg, Erling

2013-07-04

Genetic variation for environmental sensitivity indicates that animals are genetically different in their response to environmental factors. Environmental factors are either identifiable (e.g. temperature) and called macro-environmental or unknown and called micro-environmental. The objectives of this study were to develop a statistical method to estimate genetic parameters for macro- and micro-environmental sensitivities simultaneously, to investigate bias and precision of resulting estimates of genetic parameters and to develop and evaluate use of Akaike's information criterion using h-likelihood to select the best fitting model. We assumed that genetic variation in macro- and micro-environmental sensitivities is expressed as genetic variance in the slope of a linear reaction norm and environmental variance, respectively. A reaction norm model to estimate genetic variance for macro-environmental sensitivity was combined with a structural model for residual variance to estimate genetic variance for micro-environmental sensitivity using a double hierarchical generalized linear model in ASReml. Akaike's information criterion was constructed as model selection criterion using approximated h-likelihood. Populations of sires with large half-sib offspring groups were simulated to investigate bias and precision of estimated genetic parameters. Designs with 100 sires, each with at least 100 offspring, are required to have standard deviations of estimated variances lower than 50% of the true value. When the number of offspring increased, standard deviations of estimates across replicates decreased substantially, especially for genetic variances of macro- and micro-environmental sensitivities. Standard deviations of estimated genetic correlations across replicates were quite large (between 0.1 and 0.4), especially when sires had few offspring. Practically, no bias was observed for estimates of any of the parameters. Using Akaike's information criterion the true genetic model was selected as the best statistical model in at least 90% of 100 replicates when the number of offspring per sire was 100. Application of the model to lactation milk yield in dairy cattle showed that genetic variance for micro- and macro-environmental sensitivities existed. The algorithm and model selection criterion presented here can contribute to better understand genetic control of macro- and micro-environmental sensitivities. Designs or datasets should have at least 100 sires each with 100 offspring.

Applying under-sampling techniques and cost-sensitive learning methods on risk assessment of breast cancer.

PubMed

Hsu, Jia-Lien; Hung, Ping-Cheng; Lin, Hung-Yen; Hsieh, Chung-Ho

2015-04-01

Breast cancer is one of the most common cause of cancer mortality. Early detection through mammography screening could significantly reduce mortality from breast cancer. However, most of screening methods may consume large amount of resources. We propose a computational model, which is solely based on personal health information, for breast cancer risk assessment. Our model can be served as a pre-screening program in the low-cost setting. In our study, the data set, consisting of 3976 records, is collected from Taipei City Hospital starting from 2008.1.1 to 2008.12.31. Based on the dataset, we first apply the sampling techniques and dimension reduction method to preprocess the testing data. Then, we construct various kinds of classifiers (including basic classifiers, ensemble methods, and cost-sensitive methods) to predict the risk. The cost-sensitive method with random forest classifier is able to achieve recall (or sensitivity) as 100 %. At the recall of 100 %, the precision (positive predictive value, PPV), and specificity of cost-sensitive method with random forest classifier was 2.9 % and 14.87 %, respectively. In our study, we build a breast cancer risk assessment model by using the data mining techniques. Our model has the potential to be served as an assisting tool in the breast cancer screening.

Global sensitivity analysis of a filtration model for submerged anaerobic membrane bioreactors (AnMBR).

PubMed

Robles, A; Ruano, M V; Ribes, J; Seco, A; Ferrer, J

2014-04-01

The results of a global sensitivity analysis of a filtration model for submerged anaerobic MBRs (AnMBRs) are assessed in this paper. This study aimed to (1) identify the less- (or non-) influential factors of the model in order to facilitate model calibration and (2) validate the modelling approach (i.e. to determine the need for each of the proposed factors to be included in the model). The sensitivity analysis was conducted using a revised version of the Morris screening method. The dynamic simulations were conducted using long-term data obtained from an AnMBR plant fitted with industrial-scale hollow-fibre membranes. Of the 14 factors in the model, six were identified as influential, i.e. those calibrated using off-line protocols. A dynamic calibration (based on optimisation algorithms) of these influential factors was conducted. The resulting estimated model factors accurately predicted membrane performance. Copyright © 2014 Elsevier Ltd. All rights reserved.

Sleep Fragmentation Exacerbates Mechanical Hypersensitivity and Alters Subsequent Sleep-Wake Behavior in a Mouse Model of Musculoskeletal Sensitization

PubMed Central

Sutton, Blair C.; Opp, Mark R.

2014-01-01

Study Objectives: Sleep deprivation, or sleep disruption, enhances pain in human subjects. Chronic musculoskeletal pain is prevalent in our society, and constitutes a tremendous public health burden. Although preclinical models of neuropathic and inflammatory pain demonstrate effects on sleep, few studies focus on musculoskeletal pain. We reported elsewhere in this issue of SLEEP that musculoskeletal sensitization alters sleep of mice. In this study we hypothesize that sleep fragmentation during the development of musculoskeletal sensitization will exacerbate subsequent pain responses and alter sleep-wake behavior of mice. Design: This is a preclinical study using C57BL/6J mice to determine the effect on behavioral outcomes of sleep fragmentation combined with musculoskeletal sensitization. Methods: Musculoskeletal sensitization, a model of chronic muscle pain, was induced using two unilateral injections of acidified saline (pH 4.0) into the gastrocnemius muscle, spaced 5 days apart. Musculoskeletal sensitization manifests as mechanical hypersensitivity determined by von Frey filament testing at the hindpaws. Sleep fragmentation took place during the consecutive 12-h light periods of the 5 days between intramuscular injections. Electroencephalogram (EEG) and body temperature were recorded from some mice at baseline and for 3 weeks after musculoskeletal sensitization. Mechanical hypersensitivity was determined at preinjection baseline and on days 1, 3, 7, 14, and 21 after sensitization. Two additional experiments were conducted to determine the independent effects of sleep fragmentation or musculoskeletal sensitization on mechanical hypersensitivity. Results: Five days of sleep fragmentation alone did not induce mechanical hypersensitivity, whereas sleep fragmentation combined with musculoskeletal sensitization resulted in prolonged and exacerbated mechanical hypersensitivity. Sleep fragmentation combined with musculoskeletal sensitization had an effect on subsequent sleep of mice as demonstrated by increased numbers of sleep-wake state transitions during the light and dark periods; changes in nonrapid eye movement (NREM) sleep, rapid eye movement sleep, and wakefulness; and altered delta power during NREM sleep. These effects persisted for at least 3 weeks postsensitization. Conclusions: Our data demonstrate that sleep fragmentation combined with musculoskeletal sensitization exacerbates the physiological and behavioral responses of mice to musculoskeletal sensitization, including mechanical hypersensitivity and sleep-wake behavior. These data contribute to increasing literature demonstrating bidirectional relationships between sleep and pain. The prevalence and incidence of insufficient sleep and pathologies characterized by chronic musculoskeletal pain are increasing in the United States. These demographic data underscore the need for research focused on insufficient sleep and chronic pain so that the quality of life for the millions of individuals with these conditions may be improved. Citation: Sutton BC; Opp MR. Sleep fragmentation exacerbates mechanical hypersensitivity and alters subsequent sleep-wake behavior in a mouse model of musculoskeletal sensitization. SLEEP 2014;37(3):515-524. PMID:24587574

Sensitivity of medial and lateral knee contact force predictions to frontal plane alignment and contact locations.

PubMed

Saliba, Christopher M; Brandon, Scott C E; Deluzio, Kevin J

2017-05-24

Musculoskeletal models are increasingly used to estimate medial and lateral knee contact forces, which are difficult to measure in vivo. The sensitivity of contact force predictions to modeling parameters is important to the interpretation and implication of results generated by the model. The purpose of this study was to quantify the sensitivity of knee contact force predictions to simultaneous errors in frontal plane knee alignment and contact locations under different dynamic conditions. We scaled a generic musculoskeletal model for N=23 subjects' stature and radiographic knee alignment, then perturbed frontal plane alignment and mediolateral contact locations within experimentally-possible ranges of 10° to -10° and 10 to -10mm, respectively. The sensitivity of first peak, second peak, and mean medial and lateral knee contact forces to knee adduction angle and contact locations was modeled using linear regression. Medial loads increased, and lateral loads decreased, by between 3% and 6% bodyweight for each degree of varus perturbation. Shifting the medial contact point medially increased medial loads and decreased lateral loads by between 1% and 4% bodyweight per millimeter. This study demonstrates that realistic measurement errors of 5mm (contact distance) or 5° (frontal plane alignment) could result in a combined 50% BW error in subject specific contact force estimates. We also show that model sensitivity varies between subjects as a result of differences in gait dynamics. These results demonstrate that predicted knee joint contact forces should be considered as a range of possible values determined by model uncertainty. Copyright © 2017 Elsevier Ltd. All rights reserved.

Model-based POD study of manual ultrasound inspection and sensitivity analysis using metamodel

NASA Astrophysics Data System (ADS)

Ribay, Guillemette; Artusi, Xavier; Jenson, Frédéric; Reece, Christopher; Lhuillier, Pierre-Emile

2016-02-01

The reliability of NDE can be quantified by using the Probability of Detection (POD) approach. Former studies have shown the potential of the model-assisted POD (MAPOD) approach to replace expensive experimental determination of POD curves. In this paper, we make use of CIVA software to determine POD curves for a manual ultrasonic inspection of a heavy component, for which a whole experimental POD campaign was not available. The influential parameters were determined by expert analysis. The semi-analytical models used in CIVA for wave propagation and beam-defect interaction have been validated in the range of variation of the influential parameters by comparison with finite element modelling (Athena). The POD curves are computed for « hit/miss » and « â versus a » analysis. The verification of Berens hypothesis is evaluated by statistical tools. A sensitivity study is performed to measure the relative influence of parameters on the defect response amplitude variance, using the Sobol sensitivity index. A meta-model is also built to reduce computing cost and enhance the precision of estimated index.

Sleep duration and sleep quality are associated differently with alterations of glucose homeostasis.

PubMed

Byberg, S; Hansen, A-L S; Christensen, D L; Vistisen, D; Aadahl, M; Linneberg, A; Witte, D R

2012-09-01

Studies suggest that inadequate sleep duration and poor sleep quality increase the risk of impaired glucose regulation and diabetes. However, associations with specific markers of glucose homeostasis are less well explained. The objective of this study was to explore possible associations of sleep duration and sleep quality with markers of glucose homeostasis and glucose tolerance status in a healthy population-based study sample. The study comprised 771 participants from the Danish, population-based cross-sectional 'Health2008' study. Sleep duration and sleep quality were measured by self-report. Markers of glucose homeostasis were derived from a 3-point oral glucose tolerance test and included fasting plasma glucose, 2-h plasma glucose, HbA(1c), two measures of insulin sensitivity (the insulin sensitivity index(0,120) and homeostasis model assessment of insulin sensitivity), the homeostasis model assessment of β-cell function and glucose tolerance status. Associations of sleep duration and sleep quality with markers of glucose homeostasis and tolerance were analysed by multiple linear and logistic regression. A 1-h increment in sleep duration was associated with a 0.3 mmol/mol (0.3%) decrement in HbA(1c) and a 25% reduction in the risk of having impaired glucose regulation. Further, a 1-point increment in sleep quality was associated with a 2% increase in both the insulin sensitivity index(0,120) and homeostasis model assessment of insulin sensitivity, as well as a 1% decrease in homeostasis model assessment of β-cell function. In the present study, shorter sleep duration was mainly associated with later alterations in glucose homeostasis, whereas poorer sleep quality was mainly associated with earlier alterations in glucose homeostasis. Thus, adopting healthy sleep habits may benefit glucose metabolism in healthy populations. © 2012 The Authors. Diabetic Medicine © 2012 Diabetes UK.

Meta-analysis of diagnostic accuracy studies in mental health

PubMed Central

Takwoingi, Yemisi; Riley, Richard D; Deeks, Jonathan J

2015-01-01

Objectives To explain methods for data synthesis of evidence from diagnostic test accuracy (DTA) studies, and to illustrate different types of analyses that may be performed in a DTA systematic review. Methods We described properties of meta-analytic methods for quantitative synthesis of evidence. We used a DTA review comparing the accuracy of three screening questionnaires for bipolar disorder to illustrate application of the methods for each type of analysis. Results The discriminatory ability of a test is commonly expressed in terms of sensitivity (proportion of those with the condition who test positive) and specificity (proportion of those without the condition who test negative). There is a trade-off between sensitivity and specificity, as an increasing threshold for defining test positivity will decrease sensitivity and increase specificity. Methods recommended for meta-analysis of DTA studies --such as the bivariate or hierarchical summary receiver operating characteristic (HSROC) model --jointly summarise sensitivity and specificity while taking into account this threshold effect, as well as allowing for between study differences in test performance beyond what would be expected by chance. The bivariate model focuses on estimation of a summary sensitivity and specificity at a common threshold while the HSROC model focuses on the estimation of a summary curve from studies that have used different thresholds. Conclusions Meta-analyses of diagnostic accuracy studies can provide answers to important clinical questions. We hope this article will provide clinicians with sufficient understanding of the terminology and methods to aid interpretation of systematic reviews and facilitate better patient care. PMID:26446042

Simulating smoke transport from wildland fires with a regional-scale air quality model: sensitivity to spatiotemporal allocation of fire emissions.

PubMed

Garcia-Menendez, Fernando; Hu, Yongtao; Odman, Mehmet T

2014-09-15

Air quality forecasts generated with chemical transport models can provide valuable information about the potential impacts of fires on pollutant levels. However, significant uncertainties are associated with fire-related emission estimates as well as their distribution on gridded modeling domains. In this study, we explore the sensitivity of fine particulate matter concentrations predicted by a regional-scale air quality model to the spatial and temporal allocation of fire emissions. The assessment was completed by simulating a fire-related smoke episode in which air quality throughout the Atlanta metropolitan area was affected on February 28, 2007. Sensitivity analyses were carried out to evaluate the significance of emission distribution among the model's vertical layers, along the horizontal plane, and into hourly inputs. Predicted PM2.5 concentrations were highly sensitive to emission injection altitude relative to planetary boundary layer height. Simulations were also responsive to the horizontal allocation of fire emissions and their distribution into single or multiple grid cells. Additionally, modeled concentrations were greatly sensitive to the temporal distribution of fire-related emissions. The analyses demonstrate that, in addition to adequate estimates of emitted mass, successfully modeling the impacts of fires on air quality depends on an accurate spatiotemporal allocation of emissions. Copyright © 2014 Elsevier B.V. All rights reserved.

Predictive models of lameness in dairy cows achieve high sensitivity and specificity with force measurements in three dimensions.

PubMed

Dunthorn, Jason; Dyer, Robert M; Neerchal, Nagaraj K; McHenry, Jonathan S; Rajkondawar, Parimal G; Steingraber, Gary; Tasch, Uri

2015-11-01

Lameness remains a significant cause of production losses, a growing welfare concern and may be a greater economic burden than clinical mastitis . A growing need for accurate, continuous automated detection systems continues because US prevalence of lameness is 12.5% while individual herds may experience prevalence's of 27.8-50.8%. To that end the first force-plate system restricted to the vertical dimension identified lame cows with 85% specificity and 52% sensitivity. These results lead to the hypothesis that addition of transverse and longitudinal dimensions could improve sensitivity of lameness detection. To address the hypothesis we upgraded the original force plate system to measure ground reaction forces (GRFs) across three directions. GRFs and locomotion scores were generated from randomly selected cows and logistic regression was used to develop a model that characterised relationships of locomotion scores to the GRFs. This preliminary study showed 76 variables across 3 dimensions produced a model with greater than 90% sensitivity, specificity, and area under the receiver operating curve (AUC). The result was a marked improvement on the 52% sensitivity, and 85% specificity previously observed with the 1 dimensional model or the 45% sensitivities reported with visual observations. Validation of model accuracy continues with the goal to finalise accurate automated methods of lameness detection.

Adult attachment status predicts the developmental trajectory of maternal sensitivity in new motherhood among Chinese mothers.

PubMed

Liang, X; Wang, Z-Y; Liu, H-Y; Lin, Q; Wang, Z; Liu, Y

2015-01-01

to investigate adult attachment status in first-time mothers, and stability and/or changes in maternal sensitivity during infancy. longitudinal study using quantitative and qualitative methods, and statistical modelling. Three home visits were undertaken when the infant was approximately six, nine and 14 months old. The Adult-to-Parental Attachment Experience Survey was used, and scores for three dimensions were obtained: secure-autonomous, preoccupied and dismissive. Maternal sensitivity was assessed at each time point using the Maternal Behaviour Q-Sort by observing interaction between the mother and infant at home. homes and community settings in greater metropolitan Beijing, North China. 83 mothers and infants born in 2010 enrolled in this study. Data were missing for one or more time points in 20 cases. the mean score for maternal sensitivity tended to increase from six to 14 months. Post-hoc analyses of one-way repeated-measures analysis of variance revealed that maternal sensitivity was significantly higher at 14 months than at six or nine months. An unconditional latent growth model (LGM) of maternal sensitivity, estimated using the Bayesian approach, provided a good fit for the data. Using three attachment-related variables as predictors in the conditional LGM, the model fitting indices were found to be sufficient, and the results suggested that the secure score positively predicted the intercept of the growth model, and the dismissive score negatively predicted both the intercept and slope of the growth model. maternal sensitivity increased over time during infancy. Furthermore, individual differences existed in the developmental trajectory, which was influenced by maternal attachment status. knowledge about attachment-related differences in the trajectory of first-time mothers' sensitivity to infants may help midwives and doctors to provide individualised information and support, with special attention given to mothers with a dismissive attachment status. Copyright © 2014 Elsevier Ltd. All rights reserved.

A New Framework for Effective and Efficient Global Sensitivity Analysis of Earth and Environmental Systems Models

NASA Astrophysics Data System (ADS)

Razavi, Saman; Gupta, Hoshin

2015-04-01

Earth and Environmental Systems (EES) models are essential components of research, development, and decision-making in science and engineering disciplines. With continuous advances in understanding and computing power, such models are becoming more complex with increasingly more factors to be specified (model parameters, forcings, boundary conditions, etc.). To facilitate better understanding of the role and importance of different factors in producing the model responses, the procedure known as 'Sensitivity Analysis' (SA) can be very helpful. Despite the availability of a large body of literature on the development and application of various SA approaches, two issues continue to pose major challenges: (1) Ambiguous Definition of Sensitivity - Different SA methods are based in different philosophies and theoretical definitions of sensitivity, and can result in different, even conflicting, assessments of the underlying sensitivities for a given problem, (2) Computational Cost - The cost of carrying out SA can be large, even excessive, for high-dimensional problems and/or computationally intensive models. In this presentation, we propose a new approach to sensitivity analysis that addresses the dual aspects of 'effectiveness' and 'efficiency'. By effective, we mean achieving an assessment that is both meaningful and clearly reflective of the objective of the analysis (the first challenge above), while by efficiency we mean achieving statistically robust results with minimal computational cost (the second challenge above). Based on this approach, we develop a 'global' sensitivity analysis framework that efficiently generates a newly-defined set of sensitivity indices that characterize a range of important properties of metric 'response surfaces' encountered when performing SA on EES models. Further, we show how this framework embraces, and is consistent with, a spectrum of different concepts regarding 'sensitivity', and that commonly-used SA approaches (e.g., Sobol, Morris, etc.) are actually limiting cases of our approach under specific conditions. Multiple case studies are used to demonstrate the value of the new framework. The results show that the new framework provides a fundamental understanding of the underlying sensitivities for any given problem, while requiring orders of magnitude fewer model runs.

Colorado River basin sensitivity to disturbance impacts

NASA Astrophysics Data System (ADS)

Bennett, K. E.; Urrego-Blanco, J. R.; Jonko, A. K.; Vano, J. A.; Newman, A. J.; Bohn, T. J.; Middleton, R. S.

2017-12-01

The Colorado River basin is an important river for the food-energy-water nexus in the United States and is projected to change under future scenarios of increased CO2emissions and warming. Streamflow estimates to consider climate impacts occurring as a result of this warming are often provided using modeling tools which rely on uncertain inputs—to fully understand impacts on streamflow sensitivity analysis can help determine how models respond under changing disturbances such as climate and vegetation. In this study, we conduct a global sensitivity analysis with a space-filling Latin Hypercube sampling of the model parameter space and statistical emulation of the Variable Infiltration Capacity (VIC) hydrologic model to relate changes in runoff, evapotranspiration, snow water equivalent and soil moisture to model parameters in VIC. Additionally, we examine sensitivities of basin-wide model simulations using an approach that incorporates changes in temperature, precipitation and vegetation to consider impact responses for snow-dominated headwater catchments, low elevation arid basins, and for the upper and lower river basins. We find that for the Colorado River basin, snow-dominated regions are more sensitive to uncertainties. New parameter sensitivities identified include runoff/evapotranspiration sensitivity to albedo, while changes in snow water equivalent are sensitive to canopy fraction and Leaf Area Index (LAI). Basin-wide streamflow sensitivities to precipitation, temperature and vegetation are variable seasonally and also between sub-basins; with the largest sensitivities for smaller, snow-driven headwater systems where forests are dense. For a major headwater basin, a 1ºC of warming equaled a 30% loss of forest cover, while a 10% precipitation loss equaled a 90% forest cover decline. Scenarios utilizing multiple disturbances led to unexpected results where changes could either magnify or diminish extremes, such as low and peak flows and streamflow timing, dependent on the strength and direction of the forcing. These results indicate the importance of understanding model sensitivities under disturbance impacts to manage these shifts; plan for future water resource changes and determine how the impacts will affect the sustainability and adaptability of food-energy-water systems.

Optimization of Parameter Ranges for Composite Tape Winding Process Based on Sensitivity Analysis

NASA Astrophysics Data System (ADS)

Yu, Tao; Shi, Yaoyao; He, Xiaodong; Kang, Chao; Deng, Bo; Song, Shibo

2017-08-01

This study is focus on the parameters sensitivity of winding process for composite prepreg tape. The methods of multi-parameter relative sensitivity analysis and single-parameter sensitivity analysis are proposed. The polynomial empirical model of interlaminar shear strength is established by response surface experimental method. Using this model, the relative sensitivity of key process parameters including temperature, tension, pressure and velocity is calculated, while the single-parameter sensitivity curves are obtained. According to the analysis of sensitivity curves, the stability and instability range of each parameter are recognized. Finally, the optimization method of winding process parameters is developed. The analysis results show that the optimized ranges of the process parameters for interlaminar shear strength are: temperature within [100 °C, 150 °C], tension within [275 N, 387 N], pressure within [800 N, 1500 N], and velocity within [0.2 m/s, 0.4 m/s], respectively.

Evaluating a Skin Sensitization Model and Examining Common Assumptions of Skin Sensitizers (QSAR conference)

EPA Science Inventory

Skin sensitization is an adverse outcome that has been well studied over many decades. Knowledge of the mechanism of action was recently summarized using the Adverse Outcome Pathway (AOP) framework as part of the OECD work programme (OECD, 2012). Currently there is a strong focus...

The Evaluation of Bivariate Mixed Models in Meta-analyses of Diagnostic Accuracy Studies with SAS, Stata and R.

PubMed

Vogelgesang, Felicitas; Schlattmann, Peter; Dewey, Marc

2018-05-01

Meta-analyses require a thoroughly planned procedure to obtain unbiased overall estimates. From a statistical point of view not only model selection but also model implementation in the software affects the results. The present simulation study investigates the accuracy of different implementations of general and generalized bivariate mixed models in SAS (using proc mixed, proc glimmix and proc nlmixed), Stata (using gllamm, xtmelogit and midas) and R (using reitsma from package mada and glmer from package lme4). Both models incorporate the relationship between sensitivity and specificity - the two outcomes of interest in meta-analyses of diagnostic accuracy studies - utilizing random effects. Model performance is compared in nine meta-analytic scenarios reflecting the combination of three sizes for meta-analyses (89, 30 and 10 studies) with three pairs of sensitivity/specificity values (97%/87%; 85%/75%; 90%/93%). The evaluation of accuracy in terms of bias, standard error and mean squared error reveals that all implementations of the generalized bivariate model calculate sensitivity and specificity estimates with deviations less than two percentage points. proc mixed which together with reitsma implements the general bivariate mixed model proposed by Reitsma rather shows convergence problems. The random effect parameters are in general underestimated. This study shows that flexibility and simplicity of model specification together with convergence robustness should influence implementation recommendations, as the accuracy in terms of bias was acceptable in all implementations using the generalized approach. Schattauer GmbH.

Study of clustering structures through breakup reactions

NASA Astrophysics Data System (ADS)

Capel, Pierre

2014-12-01

Models for the description of breakup reactions used to study the structure of exotic cluster structures like halos are reviewed. The sensitivity of these models to the projectile description is presented. Calculations are sensitive to the projectile ground state mostly through its asymptotic normalisation coefficient (ANC). They also probe the continuum of the projectile. This enables studying not only resonant states of the projectile but also its non-resonant continuum both resonant and non-resonant. This opens the possibility to study correlations between both halo neutrons in two-neutron halo nuclei.

The γ-aminobutyric acid type B (GABAB) receptor agonist baclofen inhibits morphine sensitization by decreasing the dopamine level in rat nucleus accumbens.

PubMed

Fu, Zhenyu; Yang, Hongfa; Xiao, Yuqiang; Zhao, Gang; Huang, Haiyan

2012-07-10

Repeated morphine exposure can induce behavioral sensitization. There are evidences have shown that central gamma-aminobutyric acid (GABA) system is involved in morphine dependence. However, the effect of a GABAB receptor agonist baclofen on morphine-induced behavioral sensitization in rats is unclear. We used morphine-induced behavioral sensitization model in rat to investigate the effects of baclofen on behavioral sensitization. Moreover, dopamine release in the shell of the nucleus accumbens was evaluated using microdialysis assay in vivo. The present study demonstrated that morphine challenge (3 mg/kg, s.c.) obviously enhanced the locomotor activity following 4-day consecutive morphine administration and 3-day withdrawal period, which indicated the expression of morphine sensitization. In addition, chronic treatment with baclofen (2.5, 5 mg/kg) significantly inhibited the development of morphine sensitization. It was also found that morphine challenge 3 days after repeated morphine administration produced a significant increase of extracellular dopamine release in nucleus accumbens. Furthermore, chronic treatment with baclofen decreased the dopamine release induced by morphine challenge. Our results indicated that gamma-aminobutyric acid system plays an important role in the morphine sensitization in rat and suggested that behavioral sensitization is a promising model to study the mechanism underlying drug abuse.

Lamotrigine blocks repeated high-dose methamphetamine-induced behavioral sensitization to dizocilpine (MK-801), but not methamphetamine in rats.

PubMed

Nakato, Yasuya; Abekawa, Tomohiro; Inoue, Takeshi; Ito, Koki; Koyama, Tsukasa

2011-10-24

We recently proposed a new psychostimulant animal model of the progressive pathophysiological changes of schizophrenia. Studies using that model produced a treatment strategy for preventing progression. Lamotrigine (LTG) blocks repeated high-dosage methamphetamine (METH)-induced initiation and expression of prepulse inhibition deficit and development of apoptosis in the medial prefrontal cortex (mPFC). Moreover, it inhibits METH-induced increases in extracellular glutamate levels in the mPFC (Nakato et al., 2011, Neurosci. Lett.). Abnormal behavior induced by METH or NMDA receptor antagonists is regarded as an animal model of schizophrenia. This study examined the effects of LTG on the development of behavioral sensitization to METH and cross-sensitization to dizocilpine (MK-801) by repeated administration of high-dose METH (2.5mg/kg, 10 times s.c.). Rats were injected repeatedly with LTG (30mg/kg) after 120min METH administration (2.5mg/kg). Repeated co-administration of LTG blocked the development of behavioral cross-sensitization to MK-801 (0.15mg/kg), but it did not prevent behavioral sensitization to METH (0.2mg/kg). The LTG-induced prevention of increased glutamate by high-dose METH might be related to the former finding. Combined results of our previous studies and this study suggest that LTG is useful to treat schizophrenia, especially at a critical point in its progression. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

On the Influence of Material Parameters in a Complex Material Model for Powder Compaction

NASA Astrophysics Data System (ADS)

Staf, Hjalmar; Lindskog, Per; Andersson, Daniel C.; Larsson, Per-Lennart

2016-10-01

Parameters in a complex material model for powder compaction, based on a continuum mechanics approach, are evaluated using real insert geometries. The parameter sensitivity with respect to density and stress after compaction, pertinent to a wide range of geometries, is studied in order to investigate completeness and limitations of the material model. Finite element simulations with varied material parameters are used to build surrogate models for the sensitivity study. The conclusion from this analysis is that a simplification of the material model is relevant, especially for simple insert geometries. Parameters linked to anisotropy and the plastic strain evolution angle have a small impact on the final result.

Sensitivity and Uncertainty Analysis for Streamflow Prediction Using Different Objective Functions and Optimization Algorithms: San Joaquin California

NASA Astrophysics Data System (ADS)

Paul, M.; Negahban-Azar, M.

2017-12-01

The hydrologic models usually need to be calibrated against observed streamflow at the outlet of a particular drainage area through a careful model calibration. However, a large number of parameters are required to fit in the model due to their unavailability of the field measurement. Therefore, it is difficult to calibrate the model for a large number of potential uncertain model parameters. This even becomes more challenging if the model is for a large watershed with multiple land uses and various geophysical characteristics. Sensitivity analysis (SA) can be used as a tool to identify most sensitive model parameters which affect the calibrated model performance. There are many different calibration and uncertainty analysis algorithms which can be performed with different objective functions. By incorporating sensitive parameters in streamflow simulation, effects of the suitable algorithm in improving model performance can be demonstrated by the Soil and Water Assessment Tool (SWAT) modeling. In this study, the SWAT was applied in the San Joaquin Watershed in California covering 19704 km2 to calibrate the daily streamflow. Recently, sever water stress escalating due to intensified climate variability, prolonged drought and depleting groundwater for agricultural irrigation in this watershed. Therefore it is important to perform a proper uncertainty analysis given the uncertainties inherent in hydrologic modeling to predict the spatial and temporal variation of the hydrologic process to evaluate the impacts of different hydrologic variables. The purpose of this study was to evaluate the sensitivity and uncertainty of the calibrated parameters for predicting streamflow. To evaluate the sensitivity of the calibrated parameters three different optimization algorithms (Sequential Uncertainty Fitting- SUFI-2, Generalized Likelihood Uncertainty Estimation- GLUE and Parameter Solution- ParaSol) were used with four different objective functions (coefficient of determination- r2, Nash-Sutcliffe efficiency- NSE, percent bias- PBIAS, and Kling-Gupta efficiency- KGE). The preliminary results showed that using the SUFI-2 algorithm with the objective function NSE and KGE has improved significantly the calibration (e.g. R2 and NSE is found 0.52 and 0.47 respectively for daily streamflow calibration).

Sensitivity study of a dynamic thermodynamic sea ice model

NASA Astrophysics Data System (ADS)

Holland, David M.; Mysak, Lawrence A.; Manak, Davinder K.; Oberhuber, Josef M.

1993-02-01

A numerical simulation of the seasonal sea ice cover in the Arctic Ocean and the Greenland, Iceland, and Norwegian seas is presented. The sea ice model is extracted from Oberhuber's (1990) coupled sea ice-mixed layer-isopycnal general circulation model and is written in spherical coordinates. The advantage of such a model over previous sea ice models is that it can be easily coupled to either global atmospheric or ocean general circulation models written in spherical coordinates. In this model, the thermodynamics are a modification of that of Parkinson and Washington (1979), while the dynamics use the full Hibler (1979) viscous-plastic rheology. Monthly thermodynamic and dynamic forcing fields for the atmosphere and ocean are specified. The simulations of the seasonal cycle of ice thickness, compactness, and velocity, for a control set of parameters, compare favorably with the known seasonal characteristics of these fields. A sensitivity study of the control simulation of the seasonal sea ice cover is presented. The sensitivity runs are carried out under three different themes, namely, numerical conditions, parameter values, and physical processes. This last theme refers to experiments in which physical processes are either newly added or completely removed from the model. Approximately 80 sensitivity runs have been performed in which a change from the control run environment has been implemented. Comparisons have been made between the control run and a particular sensitivity run based on time series of the seasonal cycle of the domain-averaged ice thickness, compactness, areal coverage, and kinetic energy. In addition, spatially varying fields of ice thickness, compactness, velocity, and surface temperature for each season are presented for selected experiments. A brief description and discussion of the more interesting experiments are presented. The simulation of the seasonal cycle of Arctic sea ice cover is shown to be robust.

Sensitivity Analysis of Biome-Bgc Model for Dry Tropical Forests of Vindhyan Highlands, India

NASA Astrophysics Data System (ADS)

Kumar, M.; Raghubanshi, A. S.

2011-08-01

A process-based model BIOME-BGC was run for sensitivity analysis to see the effect of ecophysiological parameters on net primary production (NPP) of dry tropical forest of India. The sensitivity test reveals that the forest NPP was highly sensitive to the following ecophysiological parameters: Canopy light extinction coefficient (k), Canopy average specific leaf area (SLA), New stem C : New leaf C (SC:LC), Maximum stomatal conductance (gs,max), C:N of fine roots (C:Nfr), All-sided to projected leaf area ratio and Canopy water interception coefficient (Wint). Therefore, these parameters need more precision and attention during estimation and observation in the field studies.

Variation of solar cell sensitivity and solar radiation on tilted surfaces

NASA Technical Reports Server (NTRS)

Klucher, T. M.

1978-01-01

The validity is studied that one of various insolation models used to compute solar radiation incident on tilted surfaces from global data measured on horizontal surfaces. The variation of solar cell sensitivity to solar radiation is determined over a wide range of atmospheric condition. A new model was formulated that reduced the deviations between measured and predicted insolation to less than 3 percent. Evaluation of solar cell sensitivity data indicates small change (2-3 percent) in sensitivity from winter to summer for tilted cells. The feasibility of using such global data as a means for calibrating terrestrial solar cells is discussed.

Relating retinal nerve fiber thickness to behavioral sensitivity in patients with glaucoma: application of a linear model.

PubMed

Hood, Donald C

2007-05-01

Glaucoma causes damage to the retinal ganglion cells and their axons, and this damage can be detected with both structural and functional tests. The purpose of this study was to better understand the relationship between a structural measure of retinal nerve fiber layer (RNFL) and the most common functional test, behavioral sensitivity with static automated perimetry (SAP). First, a linear model, previously shown to describe the relationship between local visual evoked potentials and SAP sensitivity, was modified to predict the change in RNFL as measured by optical coherence tomography. Second, previous work by others was shown to be consistent with this model.

(abstract) Using TOPEX/Poseidon Sea Level Observations to Test the Sensitivity of an Ocean Model to Wind Forcing

NASA Technical Reports Server (NTRS)

Fu, Lee-Lueng; Chao, Yi

1996-01-01

It has been demonstrated that current-generation global ocean general circulation models (OGCM) are able to simulate large-scale sea level variations fairly well. In this study, a GFDL/MOM-based OGCM was used to investigate its sensitivity to different wind forcing. Simulations of global sea level using wind forcing from the ERS-1 Scatterometer and the NMC operational analysis were compared to the observations made by the TOPEX/Poseidon (T/P) radar altimeter for a two-year period. The result of the study has demonstrated the sensitivity of the OGCM to the quality of wind forcing, as well as the synergistic use of two spaceborne sensors in advancing the study of wind-driven ocean dynamics.

APPROACHES TO ECOSYSTEM AND HUMAN EXPOSURE TO MERCURY FOR SENSITIVE POPULATIONS

EPA Science Inventory

Both human and ecosystem exposure studies evaluate exposure of sensitive and vulnerable populations. We will discuss how ecosystem exposure modeling studies completed for input into the US Clean Air Mercury Rule (CAMR) to evaluate the response of aquatic ecosystems to changes in ...

Ozone Response to Aircraft Emissions: Sensitivity Studies with Two-dimensional Models

NASA Technical Reports Server (NTRS)

Ko, Malcolm K. W.; Weisenstein, Debra; Jackman, Charles H.; Douglass, Anne R.; Bureske, K.; Weubbles, Donald J.; Kinnison, Douglas E.; Brasseur, G.; Pyle, J.; Jones, Anna

1992-01-01

Our first intercomparison/assessment of the effects of a proposed high-speed civil transport (HSCT) fleet on the stratosphere is presented. These model calculations should be considered more as sensitivity studies, primarily designed to serve the following purposes: (1) to allow for intercomparison of model predictions; (2) to focus on the range of fleet operations and engine specifications giving minimal environmental impact; and (3) to provide the basis for future assessment studies. The basic scenarios were chosen to be as realistic as possible, using the information available on anticipated developments in technology. They are not to be interpreted as a commitment or goal for environmental acceptability.

Sensitivity analysis of dynamic biological systems with time-delays.

PubMed

Wu, Wu Hsiung; Wang, Feng Sheng; Chang, Maw Shang

2010-10-15

Mathematical modeling has been applied to the study and analysis of complex biological systems for a long time. Some processes in biological systems, such as the gene expression and feedback control in signal transduction networks, involve a time delay. These systems are represented as delay differential equation (DDE) models. Numerical sensitivity analysis of a DDE model by the direct method requires the solutions of model and sensitivity equations with time-delays. The major effort is the computation of Jacobian matrix when computing the solution of sensitivity equations. The computation of partial derivatives of complex equations either by the analytic method or by symbolic manipulation is time consuming, inconvenient, and prone to introduce human errors. To address this problem, an automatic approach to obtain the derivatives of complex functions efficiently and accurately is necessary. We have proposed an efficient algorithm with an adaptive step size control to compute the solution and dynamic sensitivities of biological systems described by ordinal differential equations (ODEs). The adaptive direct-decoupled algorithm is extended to solve the solution and dynamic sensitivities of time-delay systems describing by DDEs. To save the human effort and avoid the human errors in the computation of partial derivatives, an automatic differentiation technique is embedded in the extended algorithm to evaluate the Jacobian matrix. The extended algorithm is implemented and applied to two realistic models with time-delays: the cardiovascular control system and the TNF-α signal transduction network. The results show that the extended algorithm is a good tool for dynamic sensitivity analysis on DDE models with less user intervention. By comparing with direct-coupled methods in theory, the extended algorithm is efficient, accurate, and easy to use for end users without programming background to do dynamic sensitivity analysis on complex biological systems with time-delays.

Ground water flow modeling with sensitivity analyses to guide field data collection in a mountain watershed

USGS Publications Warehouse

Johnson, Raymond H.

2007-01-01

In mountain watersheds, the increased demand for clean water resources has led to an increased need for an understanding of ground water flow in alpine settings. In Prospect Gulch, located in southwestern Colorado, understanding the ground water flow system is an important first step in addressing metal loads from acid-mine drainage and acid-rock drainage in an area with historical mining. Ground water flow modeling with sensitivity analyses are presented as a general tool to guide future field data collection, which is applicable to any ground water study, including mountain watersheds. For a series of conceptual models, the observation and sensitivity capabilities of MODFLOW-2000 are used to determine composite scaled sensitivities, dimensionless scaled sensitivities, and 1% scaled sensitivity maps of hydraulic head. These sensitivities determine the most important input parameter(s) along with the location of observation data that are most useful for future model calibration. The results are generally independent of the conceptual model and indicate recharge in a high-elevation recharge zone as the most important parameter, followed by the hydraulic conductivities in all layers and recharge in the next lower-elevation zone. The most important observation data in determining these parameters are hydraulic heads at high elevations, with a depth of less than 100 m being adequate. Evaluation of a possible geologic structure with a different hydraulic conductivity than the surrounding bedrock indicates that ground water discharge to individual stream reaches has the potential to identify some of these structures. Results of these sensitivity analyses can be used to prioritize data collection in an effort to reduce time and money spend by collecting the most relevant model calibration data.

Sensitivity of selected landscape pattern metrics to land-cover misclassification and differences in land-cover composition

Treesearch

James D. Wickham; Robert V. O' Neill; Kurt H. Riitters; Timothy G. Wade; K. Bruce Jones

1997-01-01

Calculation of landscape metrics from land-cover data is becoming increasingly common. Some studies have shown that these measurements are sensitive to differences in land-cover composition, but none are known to have tested also their a sensitivity to land-cover misclassification. An error simulation model was written to test the sensitivity of selected land-scape...

Modelling ecological and human exposure to POPs in Venice lagoon - Part II: Quantitative uncertainty and sensitivity analysis in coupled exposure models.

PubMed

Radomyski, Artur; Giubilato, Elisa; Ciffroy, Philippe; Critto, Andrea; Brochot, Céline; Marcomini, Antonio

2016-11-01

The study is focused on applying uncertainty and sensitivity analysis to support the application and evaluation of large exposure models where a significant number of parameters and complex exposure scenarios might be involved. The recently developed MERLIN-Expo exposure modelling tool was applied to probabilistically assess the ecological and human exposure to PCB 126 and 2,3,7,8-TCDD in the Venice lagoon (Italy). The 'Phytoplankton', 'Aquatic Invertebrate', 'Fish', 'Human intake' and PBPK models available in MERLIN-Expo library were integrated to create a specific food web to dynamically simulate bioaccumulation in various aquatic species and in the human body over individual lifetimes from 1932 until 1998. MERLIN-Expo is a high tier exposure modelling tool allowing propagation of uncertainty on the model predictions through Monte Carlo simulation. Uncertainty in model output can be further apportioned between parameters by applying built-in sensitivity analysis tools. In this study, uncertainty has been extensively addressed in the distribution functions to describe the data input and the effect on model results by applying sensitivity analysis techniques (screening Morris method, regression analysis, and variance-based method EFAST). In the exposure scenario developed for the Lagoon of Venice, the concentrations of 2,3,7,8-TCDD and PCB 126 in human blood turned out to be mainly influenced by a combination of parameters (half-lives of the chemicals, body weight variability, lipid fraction, food assimilation efficiency), physiological processes (uptake/elimination rates), environmental exposure concentrations (sediment, water, food) and eating behaviours (amount of food eaten). In conclusion, this case study demonstrated feasibility of MERLIN-Expo to be successfully employed in integrated, high tier exposure assessment. Copyright © 2016 Elsevier B.V. All rights reserved.

GHI calculation sensitivity on microphysics, land- and cumulus parameterization in WRF over the Reunion Island

NASA Astrophysics Data System (ADS)

De Meij, A.; Vinuesa, J.-F.; Maupas, V.

2018-05-01

The sensitivity of different microphysics and dynamics schemes on calculated global horizontal irradiation (GHI) values in the Weather Research Forecasting (WRF) model is studied. 13 sensitivity simulations were performed for which the microphysics, cumulus parameterization schemes and land surface models were changed. Firstly we evaluated the model's performance by comparing calculated GHI values for the Base Case with observations for the Reunion Island for 2014. In general, the model calculates the largest bias during the austral summer. This indicates that the model is less accurate in timing the formation and dissipation of clouds during the summer, when higher water vapor quantities are present in the atmosphere than during the austral winter. Secondly, the model sensitivity on changing the microphysics, cumulus parameterization and land surface models on calculated GHI values is evaluated. The sensitivity simulations showed that changing the microphysics from the Thompson scheme (or Single-Moment 6-class scheme) to the Morrison double-moment scheme, the relative bias improves from 45% to 10%. The underlying reason for this improvement is that the Morrison double-moment scheme predicts the mass and number concentrations of five hydrometeors, which help to improve the calculation of the densities, size and lifetime of the cloud droplets. While the single moment schemes only predicts the mass for less hydrometeors. Changing the cumulus parameterization schemes and land surface models does not have a large impact on GHI calculations.

Subject-specific finite element modelling of the human foot complex during walking: sensitivity analysis of material properties, boundary and loading conditions.

PubMed

Akrami, Mohammad; Qian, Zhihui; Zou, Zhemin; Howard, David; Nester, Chris J; Ren, Lei

2018-04-01

The objective of this study was to develop and validate a subject-specific framework for modelling the human foot. This was achieved by integrating medical image-based finite element modelling, individualised multi-body musculoskeletal modelling and 3D gait measurements. A 3D ankle-foot finite element model comprising all major foot structures was constructed based on MRI of one individual. A multi-body musculoskeletal model and 3D gait measurements for the same subject were used to define loading and boundary conditions. Sensitivity analyses were used to investigate the effects of key modelling parameters on model predictions. Prediction errors of average and peak plantar pressures were below 10% in all ten plantar regions at five key gait events with only one exception (lateral heel, in early stance, error of 14.44%). The sensitivity analyses results suggest that predictions of peak plantar pressures are moderately sensitive to material properties, ground reaction forces and muscle forces, and significantly sensitive to foot orientation. The maximum region-specific percentage change ratios (peak stress percentage change over parameter percentage change) were 1.935-2.258 for ground reaction forces, 1.528-2.727 for plantar flexor muscles and 4.84-11.37 for foot orientations. This strongly suggests that loading and boundary conditions need to be very carefully defined based on personalised measurement data.

Evaluation of a prognostic scoring system for dogs managed with hemodialysis.

PubMed

Perondi, Francesca; Lippi, Ilaria; Ceccherini, Gianila; Marchetti, Veronica; Bernicchi, Lucrezia; Guidi, Grazia

2018-06-24

To investigate prognostic models in a cohort of dogs with acute kidney injury (AKI) and acute on chronic kidney disease (AKI/CKD) managed by hemodialysis. Retrospective study from July 2011 to November 2014. University Veterinary Teaching Hospital. Forty dogs with historical, clinical, imaging, and laboratory findings consistent with AKI or AKI/CKD managed with intermittent hemodialysis were included. Scoring system models previously established by Segev et al for outcome prediction in dogs with AKI were applied to all dogs. Models A, B, and C correctly classified outcomes in 68%, 83%, and 85% of cases, respectively. In our cohort Model A showed sensitivity of 58% and specificity of 86%, Model B showed sensitivity of 79% and specificity of 87%, Model C showed sensitivity of 86% and specificity of 84%. The presence of anuria (P < 0.0002), respiratory complications (P < 0.0001), disseminated intravascular coagulation (DIC) (P = 0.0004), grade of AKI (P = 0.0023), pancreatitis (P = 0.0001), and systemic inflammatory response syndrome (SIRS) (P = 0.0001) was significantly higher in nonsurvivors compared with survivors. In our cohort of patients, Segev's model C showed the best sensitivity and specificity for predicting prognosis, while model A had lower sensitivity. In our cohort of dialysis patients, the presence of respiratory complications, DIC, SIRS, and pancreatitis at hospitalization, were correlated with a poor prognosis. © Veterinary Emergency and Critical Care Society 2018.

The Triandis model for the study of health and illness behavior: a social behavior theory with sensitivity to diversity.

PubMed

Facione, N C

1993-03-01

The Triandis model of social behavior offers exceptional promise to nurse researchers whose goal is to achieve cultural sensitivity in their research investigations. The model includes six components: consequential beliefs, affect, social influences, previous behavioral habits, physiologic arousal, and facilitating environmental resources. A directed methodology to include culture-relevant items in the measurement of each of these model components allows researchers to capture the diverse explanations of health and illness behavior that might pertain in diverse populations. Researchers utilizing the model can achieve theory-based explanations of differences they observe by gender, race/ethnicity, social class, and sexual orientation. The Triandis model can provide studies to target variables for future intervention studies, as well as highlight areas for needed political action to equalize access to and delivery of nursing care.

Tinnitus sensitization: Sensory and psychophysiological aspects of a new pathway of acquired centralization of chronic tinnitus.

PubMed

Zenner, Hans P; Pfister, Markus; Birbaumer, Niels

2006-12-01

Acquired centralized tinnitus (ACT) is the most frequent form of chronic tinnitus. The proposed ACT sensitization (ACTS) assumes a peripheral initiation of tinnitus whereby sensitizing signals from the auditory system establish new neuronal connections in the brain. Consequently, permanent neurophysiological malfunction within the information-processing modules results. Successful treatment has to target these malfunctioning information processing. We present in this study the neurophysiological and psychophysiological aspects of a recently suggested neurophysiological model, which may explain the symptoms caused by central cognitive tinnitus sensitization. Although conditioned reflexes, as a causal agent of chronic tinnitus, respond to extinction procedures, sensitization may initiate a vicious circle of overexcitation of the auditory system, resisting extinction and habituation. We used the literature database as indicated under "References" covering English and German works. For the ACTS model we extracted neurophysiological hypotheses of the auditory stimulus processing and the neuronal connections of the central auditory system with other brain regions to explain the malfunctions of auditory information processing. The model does not assume information-processing changes specific for tinnitus but treats the processing of tinnitus signals comparable with the processing of other external stimuli. The model uses the extensive knowledge available on sensitization of perception and memory processes and highlights the similarities of tinnitus with central neuropathic pain. Quality, validity, and comparability of the extracted data were evaluated by peer reviewing. Statistical techniques were not used. According to the tinnitus sensitization model, a tinnitus signal originates (as a type I-IV tinnitus) in the cochlea. In the brain, concerned with perception and cognition, the 1) conditioned associations, as postulated by the tinnitus model of Jastreboff, and the 2) unconditioned sensitized stimulus responses, as postulated in the present ACTS model, are actively connected with and attributed to the tinnitus signal. Attention to the tinnitus constitutes a typical undesired sensitized response. Some of the tinnitus-associated attributes may be called essential, unconditioned sensitization attributes. By a process called facilitation, the tinnitus' essential attributes are suggested to activate the tinnitus response. The result is an undesired increase in responsivity, such as an increase in attentional focus to the eliciting tinnitus stimulus. The mechanisms underlying sensitization are known as a specific nonassociative learning process producing a structural fixation of long-term facilitation at the synaptic level. This sensitization model may be important for the development of a sensitization-specific treatment if extinction procedures alone do not lead to satisfactory outcome. Inasmuch as this model considers sensitization as a nonassociative learning process based on cortical plasticity, it is reasonable to assume that this learning process can be altered by counteracting learning procedures. These counteracting learning procedures may consist of tinnitus-specific cognitive and behavioral procedures.

Computational studies of transthoracic and transvenous defibrillation in a detailed 3-D human thorax model.

PubMed

Jorgenson, D B; Haynor, D R; Bardy, G H; Kim, Y

1995-02-01

A method for constructing and solving detailed patient-specific 3-D finite element models of the human thorax is presented for use in defibrillation studies. The method utilizes the patient's own X-ray CT scan and a simplified meshing scheme to quickly and efficiently generate a model typically composed of approximately 400,000 elements. A parameter sensitivity study on one human thorax model to examine the effects of variation in assigned tissue resistivity values, level of anatomical detail included in the model, and number of CT slices used to produce the model is presented. Of the seven tissue types examined, the average left ventricular (LV) myocardial voltage gradient was most sensitive to the values of myocardial and blood resistivity. Incorrectly simplifying the model, for example modeling the heart as a homogeneous structure by ignoring the blood in the chambers, caused the average LV myocardial voltage gradient to increase by 12%. The sensitivity of the model to variations in electrode size and position was also examined. Small changes (< 2.0 cm) in electrode position caused average LV myocardial voltage gradient values to increase by up to 12%. We conclude that patient-specific 3-D finite element modeling of human thoracic electric fields is feasible and may reduce the empiric approach to insertion of implantable defibrillators and improve transthoracic defibrillation techniques.

Sensitivity and uncertainty analysis for the annual phosphorus loss estimator model

USDA-ARS?s Scientific Manuscript database

Models are often used to predict phosphorus (P) loss from agricultural fields. While it is commonly recognized that there are inherent uncertainties with model predictions, limited studies have addressed model prediction uncertainty. In this study we assess the effect of model input error on predict...

A geostatistics-informed hierarchical sensitivity analysis method for complex groundwater flow and transport modeling: GEOSTATISTICAL SENSITIVITY ANALYSIS

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

Dai, Heng; Chen, Xingyuan; Ye, Ming

Sensitivity analysis is an important tool for quantifying uncertainty in the outputs of mathematical models, especially for complex systems with a high dimension of spatially correlated parameters. Variance-based global sensitivity analysis has gained popularity because it can quantify the relative contribution of uncertainty from different sources. However, its computational cost increases dramatically with the complexity of the considered model and the dimension of model parameters. In this study we developed a hierarchical sensitivity analysis method that (1) constructs an uncertainty hierarchy by analyzing the input uncertainty sources, and (2) accounts for the spatial correlation among parameters at each level ofmore » the hierarchy using geostatistical tools. The contribution of uncertainty source at each hierarchy level is measured by sensitivity indices calculated using the variance decomposition method. Using this methodology, we identified the most important uncertainty source for a dynamic groundwater flow and solute transport in model at the Department of Energy (DOE) Hanford site. The results indicate that boundary conditions and permeability field contribute the most uncertainty to the simulated head field and tracer plume, respectively. The relative contribution from each source varied spatially and temporally as driven by the dynamic interaction between groundwater and river water at the site. By using a geostatistical approach to reduce the number of realizations needed for the sensitivity analysis, the computational cost of implementing the developed method was reduced to a practically manageable level. The developed sensitivity analysis method is generally applicable to a wide range of hydrologic and environmental problems that deal with high-dimensional spatially-distributed parameters.« less

Sediment delivery modeling in practice: Comparing the effects of watershed characteristics and data resolution across hydroclimatic regions.

PubMed

Hamel, Perrine; Falinski, Kim; Sharp, Richard; Auerbach, Daniel A; Sánchez-Canales, María; Dennedy-Frank, P James

2017-02-15

Geospatial models are commonly used to quantify sediment contributions at the watershed scale. However, the sensitivity of these models to variation in hydrological and geomorphological features, in particular to land use and topography data, remains uncertain. Here, we assessed the performance of one such model, the InVEST sediment delivery model, for six sites comprising a total of 28 watersheds varying in area (6-13,500km 2 ), climate (tropical, subtropical, mediterranean), topography, and land use/land cover. For each site, we compared uncalibrated and calibrated model predictions with observations and alternative models. We then performed correlation analyses between model outputs and watershed characteristics, followed by sensitivity analyses on the digital elevation model (DEM) resolution. Model performance varied across sites (overall r 2 =0.47), but estimates of the magnitude of specific sediment export were as or more accurate than global models. We found significant correlations between metrics of sediment delivery and watershed characteristics, including erosivity, suggesting that empirical relationships may ultimately be developed for ungauged watersheds. Model sensitivity to DEM resolution varied across and within sites, but did not correlate with other observed watershed variables. These results were corroborated by sensitivity analyses performed on synthetic watersheds ranging in mean slope and DEM resolution. Our study provides modelers using InVEST or similar geospatial sediment models with practical insights into model behavior and structural uncertainty: first, comparison of model predictions across regions is possible when environmental conditions differ significantly; second, local knowledge on the sediment budget is needed for calibration; and third, model outputs often show significant sensitivity to DEM resolution. Copyright © 2016 Elsevier B.V. All rights reserved.

First status report on regional ground-water flow modeling for the Paradox Basin, Utah

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

Andrews, R.W.

1984-05-01

Regional ground-water flow within the principal hydrogeologic units of the Paradox Basin is evaluated by developing a conceptual model of the flow regime in the shallow aquifers and the deep-basin brine aquifers and testing these models using a three-dimensional, finite-difference flow code. Semiquantitative sensitivity analysis (a limited parametric study) is conducted to define the system response to changes in hydrologic properties or boundary conditions. A direct method for sensitivity analysis using an adjoint form of the flow equation is applied to the conceptualized flow regime in the Leadville limestone aquifer. All steps leading to the final results and conclusions aremore » incorporated in this report. The available data utilized in this study is summarized. The specific conceptual models, defining the areal and vertical averaging of litho-logic units, aquifer properties, fluid properties, and hydrologic boundary conditions, are described in detail. Two models were evaluated in this study: a regional model encompassing the hydrogeologic units above and below the Paradox Formation/Hermosa Group and a refined scale model which incorporated only the post Paradox strata. The results are delineated by the simulated potentiometric surfaces and tables summarizing areal and vertical boundary fluxes, Darcy velocities at specific points, and ground-water travel paths. Results from the adjoint sensitivity analysis include importance functions and sensitivity coefficients, using heads or the average Darcy velocities to represent system response. The reported work is the first stage of an ongoing evaluation of the Gibson Dome area within the Paradox Basin as a potential repository for high-level radioactive wastes.« less

Systematic parameter estimation and sensitivity analysis using a multidimensional PEMFC model coupled with DAKOTA.

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

Wang, Chao Yang; Luo, Gang; Jiang, Fangming

2010-05-01

Current computational models for proton exchange membrane fuel cells (PEMFCs) include a large number of parameters such as boundary conditions, material properties, and numerous parameters used in sub-models for membrane transport, two-phase flow and electrochemistry. In order to successfully use a computational PEMFC model in design and optimization, it is important to identify critical parameters under a wide variety of operating conditions, such as relative humidity, current load, temperature, etc. Moreover, when experimental data is available in the form of polarization curves or local distribution of current and reactant/product species (e.g., O2, H2O concentrations), critical parameters can be estimated inmore » order to enable the model to better fit the data. Sensitivity analysis and parameter estimation are typically performed using manual adjustment of parameters, which is also common in parameter studies. We present work to demonstrate a systematic approach based on using a widely available toolkit developed at Sandia called DAKOTA that supports many kinds of design studies, such as sensitivity analysis as well as optimization and uncertainty quantification. In the present work, we couple a multidimensional PEMFC model (which is being developed, tested and later validated in a joint effort by a team from Penn State Univ. and Sandia National Laboratories) with DAKOTA through the mapping of model parameters to system responses. Using this interface, we demonstrate the efficiency of performing simple parameter studies as well as identifying critical parameters using sensitivity analysis. Finally, we show examples of optimization and parameter estimation using the automated capability in DAKOTA.« less

Tinnitus sensitization: a neurophysiological pathway of chronic complex tinnitus.

PubMed

Zenner, Hans P

2006-01-01

A novel neuro- and psychophysiological pathway for central cognition of tinnitus, i.e. tinnitus sensitization, is presented here. As a complement to the neurophysiological pathway for the conditioned reflex according to Jastreboff, which permits therapeutic procedures to bring about an extinction of the tinnitus (e.g. by the acoustic tinnitus retraining therapy), sensitization can be treated with procedures that act at the cognitive level. Since on the one hand therapeutic extinction procedures (e.g. the therapeutic application of sound) are still to be proven effective in controlled studies, while on the other cognitive interventions such as cognitive behavioral therapies have in fact acquired evidence level IIa in prospective studies, it is indeed appropriate to discuss whether the earlier neurophysiological model of a conditioned reflex is sufficient on its own, and whether in fact it needs to be complemented with the sensitization model.

Accidental falls in hospital inpatients: evaluation of sensitivity and specificity of two risk assessment tools.

PubMed

Lovallo, Carmela; Rolandi, Stefano; Rossetti, Anna Maria; Lusignani, Maura

2010-03-01

This paper is a report of a study comparing the effectiveness of two falls risk assessment tools (Conley Scale and Hendrich Risk Model) by using them simultaneously with the same sample of hospital inpatients. Different risk assessment tools are available in literature. However, neither recent critical reviews nor international guidelines on fall prevention have identified tools that can be generalized to all categories of hospitalized patients. A prospective observational study was carried out in acute medical, surgical wards and rehabilitation units. From October 2007 to January 2008, 1148 patients were assessed with both instruments, subsequently noting the occurrence of falls. The sensitivity, specificity, positive and negative predictive values, and Receiver Operating Characteristics curves were calculated. The number of patients correctly identified with the Conley Scale (n = 41) was higher than with the Hendrich Model (n = 27). The Conley Scale gave sensitivity and specificity values of 69.49% and 61% respectively. The Hendrich Model gave a sensitivity value of 45.76% and a specificity value of 71%. Positive and negative predictive values were comparable. The Conley Scale is indicated for use in the medical sector, on the strength of its high sensitivity. However, since its specificity is very low, it is deemed useful to submit individual patients giving positive results to more in-depth clinical evaluation in order to decide whether preventive measures need to be taken. In surgical sectors, the low sensitivity values given by both scales suggest that further studies are warranted.

Hearing characteristics of cephalopods: modeling and environmental impact study.

PubMed

Zhang, Yunfeng; Shi, Feng; Song, Jiakun; Zhang, Xugang; Yu, Shiliang

2015-01-01

Cephalopods (octopus, squid and cuttlefish) are some of the most intriguing molluscs, and they represent economically important commercial marine species for fisheries. Previous studies have shown that cephalopods are sensitive to underwater particle motion, especially at low frequencies in the order of 10 Hz. The present paper deals with quantitative modeling of the statocyst system in three cephalopod species: Octopus vulgaris, Sepia officinalis and Loligo vulgaris. The octopus's macula/statolith organ was modeled as a 2nd-order dynamic oscillator using parameter values estimated from scanning electron micrograph images. The modeling results agree reasonably well with experimental data (acceleration threshold) in the three cephalopod species. Insights made from quantitative modeling and simulating the particle motion sensing mechanism of cephalopods elucidated their underwater particle motion detection capabilities. Sensitivity to emerging environmental issues, such as low frequency noise caused by near-shore wind farms and increasing levels of carbon dioxide in the ocean, and sensitivity to sounds produced by impending landslides were investigated in octopus using the model. © 2014 International Society of Zoological Sciences, Institute of Zoology/Chinese Academy of Sciences and Wiley Publishing Asia Pty Ltd.

Theoretical Study on Stress Sensitivity of Fractal Porous Media with Irreducible Water

NASA Astrophysics Data System (ADS)

Lei, Gang; Dong, Zhenzhen; Li, Weirong; Wen, Qingzhi; Wang, Cai

The couple flow deformation behavior in porous media has drawn tremendous attention in various scientific and engineering fields. However, though the coupled flow deformation mechanism has been intensively investigated in the last decades, the essential controls on stress sensitivity are not determined. It is of practical significance to use analytic methods to study stress sensitivity of porous media. Unfortunately, because of the disordered and extremely complicated microstructures of porous media, the theoretical model for stress sensitivity is scarce. The goal of this work is to establish a novel and reasonable quantitative model to determine the essential controls on stress sensitivity. The predictions of the theoretical model, derived from the Hertzian contact theory and fractal geometry, agree well with the available experimental data. Compared with the previous models, our model takes into account more factors, including the influence of the water saturation and the microstructural parameters of the pore space. The proposed models can reveal more mechanisms that affect the coupled flow deformation behavior in fractal porous media. The results show that the irreducible water saturation increases with the increase of effective stress, and decreases with the increased rock elastic modulus (or increased power law index) at a given effective stress. The effect of stress variation on porosity is smaller than that on permeability. Under a given effective stress, the normalized permeability (or the normalized porosity) becomes smaller with the decrease of rock elastic modulus (or the decrease of power law index). And a lower capillary pressure will correspond to an increased rock elastic modulus (or an increased power law index) under a given water saturation.

A Culture-Sensitive Agent in Kirman's Ant Model

NASA Astrophysics Data System (ADS)

Chen, Shu-Heng; Liou, Wen-Ching; Chen, Ting-Yu

The global financial crisis brought a serious collapse involving a "systemic" meltdown. Internet technology and globalization have increased the chances for interaction between countries and people. The global economy has become more complex than ever before. Mark Buchanan [12] indicated that agent-based computer models will prevent another financial crisis and has been particularly influential in contributing insights. There are two reasons why culture-sensitive agent on the financial market has become so important. Therefore, the aim of this article is to establish a culture-sensitive agent and forecast the process of change regarding herding behavior in the financial market. We based our study on the Kirman's Ant Model[4,5] and Hofstede's Natational Culture[11] to establish our culture-sensitive agent based model. Kirman's Ant Model is quite famous and describes financial market herding behavior from the expectations of the future of financial investors. Hofstede's cultural consequence used the staff of IBM in 72 different countries to understand the cultural difference. As a result, this paper focuses on one of the five dimensions of culture from Hofstede: individualism versus collectivism and creates a culture-sensitive agent and predicts the process of change regarding herding behavior in the financial market. To conclude, this study will be of importance in explaining the herding behavior with cultural factors, as well as in providing researchers with a clearer understanding of how herding beliefs of people about different cultures relate to their finance market strategies.

Evaluating a Skin Sensitization Model and Examining Common Assumptions of Skin Sensitizers (ASCCT meeting)

EPA Science Inventory

Skin sensitization is an adverse outcome that has been well studied over many decades. It was summarized using the adverse outcome pathway (AOP) framework as part of the OECD work programme (OECD, 2012). Currently there is a strong focus on how AOPs can be applied for different r...

Development of animal models and sandwich-ELISA tests to detect the allergenicity and antigenicity of fining agent residues in wines.

PubMed

Lifrani, Awatif; Dos Santos, Jacinthe; Dubarry, Michel; Rautureau, Michelle; Blachier, Francois; Tome, Daniel

2009-01-28

Food allergy can cause food-related anaphylaxis. Food allergen labeling is the principal means of protecting sensitized individuals. This motivated European Directive 2003/89 on the labeling of ingredients or additives that could trigger adverse reactions, which has been in effect since 2005. During this study, we developed animal models with allergy to ovalbumin, caseinate, and isinglass in order to be able to detect fining agent residues that could induce anaphylactic reactions in sensitized mice. The second aim of the study was to design sandwich ELISA tests specific to each fining agent in order to detect their residue antigenicity, both during wine processing and in commercially available bottled wines. Sensitized mice and sandwich ELISA methods were established to test a vast panel of wines. The results showed that although they were positive to our highly sensitive sandwich-ELISA tests, some commercially available wines are not allergenic in sensitized mice. Commercially available bottled wines made using standardized processes, fining, maturation, and filtration, do not therefore represent any risk of anaphylactic reactions in sensitized mice.

Sensitivity Analysis Tailored to Constrain 21st Century Terrestrial Carbon-Uptake

NASA Astrophysics Data System (ADS)

Muller, S. J.; Gerber, S.

2013-12-01

The long-term fate of terrestrial carbon (C) in response to climate change remains a dominant source of uncertainty in Earth-system model projections. Increasing atmospheric CO2 could be mitigated by long-term net uptake of C, through processes such as increased plant productivity due to "CO2-fertilization". Conversely, atmospheric conditions could be exacerbated by long-term net release of C, through processes such as increased decomposition due to higher temperatures. This balance is an important area of study, and a major source of uncertainty in long-term (>year 2050) projections of planetary response to climate change. We present results from an innovative application of sensitivity analysis to LM3V, a dynamic global vegetation model (DGVM), intended to identify observed/observable variables that are useful for constraining long-term projections of C-uptake. We analyzed the sensitivity of cumulative C-uptake by 2100, as modeled by LM3V in response to IPCC AR4 scenario climate data (1860-2100), to perturbations in over 50 model parameters. We concurrently analyzed the sensitivity of over 100 observable model variables, during the extant record period (1970-2010), to the same parameter changes. By correlating the sensitivities of observable variables with the sensitivity of long-term C-uptake we identified model calibration variables that would also constrain long-term C-uptake projections. LM3V employs a coupled carbon-nitrogen cycle to account for N-limitation, and we find that N-related variables have an important role to play in constraining long-term C-uptake. This work has implications for prioritizing field campaigns to collect global data that can help reduce uncertainties in the long-term land-atmosphere C-balance. Though results of this study are specific to LM3V, the processes that characterize this model are not completely divorced from other DGVMs (or reality), and our approach provides valuable insights into how data can be leveraged to be better constrain projections for the land carbon sink.

A hybrid model for combining case-control and cohort studies in systematic reviews of diagnostic tests

PubMed Central

Chen, Yong; Liu, Yulun; Ning, Jing; Cormier, Janice; Chu, Haitao

2014-01-01

Systematic reviews of diagnostic tests often involve a mixture of case-control and cohort studies. The standard methods for evaluating diagnostic accuracy only focus on sensitivity and specificity and ignore the information on disease prevalence contained in cohort studies. Consequently, such methods cannot provide estimates of measures related to disease prevalence, such as population averaged or overall positive and negative predictive values, which reflect the clinical utility of a diagnostic test. In this paper, we propose a hybrid approach that jointly models the disease prevalence along with the diagnostic test sensitivity and specificity in cohort studies, and the sensitivity and specificity in case-control studies. In order to overcome the potential computational difficulties in the standard full likelihood inference of the proposed hybrid model, we propose an alternative inference procedure based on the composite likelihood. Such composite likelihood based inference does not suffer computational problems and maintains high relative efficiency. In addition, it is more robust to model mis-specifications compared to the standard full likelihood inference. We apply our approach to a review of the performance of contemporary diagnostic imaging modalities for detecting metastases in patients with melanoma. PMID:25897179

A theoretical approach to study the optical sensitivity of a MESFET

NASA Astrophysics Data System (ADS)

Dutta, Sutanu

2018-05-01

A theoretical model to study the optical sensitivity of a metal-semiconductor field effect transistor has been proposed for a relatively high drain field. An analytical expression of drain current of the device has been derived for a MESFET under optical illumination considering field dependent mobility of electrons across the channel. The variation of drain current with and without optical illumination has been studied with drain and gate voltages. The optical sensitivity of the drain current has been studied for different biasing conditions and gate lengths. In addition, the shift in threshold voltage of a MESFET under optical illumination is determined and optical sensitivity of the device in terms of its threshold voltage has been studied.

Evaluation of Offline Models Used to Simulate Components of the Permafrost Carbon Feedback: Experience from the Permafrost Carbon Network Model Integration Group

NASA Astrophysics Data System (ADS)

McGuire, A. D.

2016-12-01

The Model Integration Group of the Permafrost Carbon Network (see http://www.permafrostcarbon.org/) has conducted studies to evaluate the sensitivity of offline terrestrial permafrost and carbon models to both historical and projected climate change. These studies indicate that there is a wide range of (1) initial states permafrost extend and carbon stocks simulated by these models and (2) responses of permafrost extent and carbon stocks to both historical and projected climate change. In this study, we synthesize what has been learned about the variability in initial states among models and the driving factors that contribute to variability in the sensitivity of responses. We conclude the talk with a discussion of efforts needed by (1) the modeling community to standardize structural representation of permafrost and carbon dynamics among models that are used to evaluate the permafrost carbon feedback and (2) the modeling and observational communities to jointly develop data sets and methodologies to more effectively benchmark models.

Numerical parametric studies of spray combustion instability

NASA Technical Reports Server (NTRS)

Pindera, M. Z.

1993-01-01

A coupled numerical algorithm has been developed for studies of combustion instabilities in spray-driven liquid rocket engines. The model couples gas and liquid phase physics using the method of fractional steps. Also introduced is a novel, efficient methodology for accounting for spray formation through direct solution of liquid phase equations. Preliminary parametric studies show marked sensitivity of spray penetration and geometry to droplet diameter, considerations of liquid core, and acoustic interactions. Less sensitivity was shown to the combustion model type although more rigorous (multi-step) formulations may be needed for the differences to become apparent.

The influence of model resolution on ozone in industrial volatile organic compound plumes.

PubMed

Henderson, Barron H; Jeffries, Harvey E; Kim, Byeong-Uk; Vizuete, William G

2010-09-01

Regions with concentrated petrochemical industrial activity (e.g., Houston or Baton Rouge) frequently experience large, localized releases of volatile organic compounds (VOCs). Aircraft measurements suggest these released VOCs create plumes with ozone (O3) production rates 2-5 times higher than typical urban conditions. Modeling studies found that simulating high O3 productions requires superfine (1-km) horizontal grid cell size. Compared with fine modeling (4-kmin), the superfine resolution increases the peak O3 concentration by as much as 46%. To understand this drastic O3 change, this study quantifies model processes for O3 and "odd oxygen" (Ox) in both resolutions. For the entire plume, the superfine resolution increases the maximum O3 concentration 3% but only decreases the maximum Ox concentration 0.2%. The two grid sizes produce approximately equal Ox mass but by different reaction pathways. Derived sensitivity to oxides of nitrogen (NOx) and VOC emissions suggests resolution-specific sensitivity to NOx and VOC emissions. Different sensitivity to emissions will result in different O3 responses to subsequently encountered emissions (within the city or downwind). Sensitivity of O3 to emission changes also results in different simulated O3 responses to the same control strategies. Sensitivity of O3 to NOx and VOC emission changes is attributed to finer resolved Eulerian grid and finer resolved NOx emissions. Urban NOx concentration gradients are often caused by roadway mobile sources that would not typically be addressed with Plume-in-Grid models. This study shows that grid cell size (an artifact of modeling) influences simulated control strategies and could bias regulatory decisions. Understanding the dynamics of VOC plume dependence on grid size is the first step toward providing more detailed guidance for resolution. These results underscore VOC and NOx resolution interdependencies best addressed by finer resolution. On the basis of these results, the authors suggest a need for quantitative metrics for horizontal grid resolution in future model guidance.

Parameter Estimation and Sensitivity Analysis of an Urban Surface Energy Balance Parameterization at a Tropical Suburban Site

NASA Astrophysics Data System (ADS)

Harshan, S.; Roth, M.; Velasco, E.

2014-12-01

Forecasting of the urban weather and climate is of great importance as our cities become more populated and considering the combined effects of global warming and local land use changes which make urban inhabitants more vulnerable to e.g. heat waves and flash floods. In meso/global scale models, urban parameterization schemes are used to represent the urban effects. However, these schemes require a large set of input parameters related to urban morphological and thermal properties. Obtaining all these parameters through direct measurements are usually not feasible. A number of studies have reported on parameter estimation and sensitivity analysis to adjust and determine the most influential parameters for land surface schemes in non-urban areas. Similar work for urban areas is scarce, in particular studies on urban parameterization schemes in tropical cities have so far not been reported. In order to address above issues, the town energy balance (TEB) urban parameterization scheme (part of the SURFEX land surface modeling system) was subjected to a sensitivity and optimization/parameter estimation experiment at a suburban site in, tropical Singapore. The sensitivity analysis was carried out as a screening test to identify the most sensitive or influential parameters. Thereafter, an optimization/parameter estimation experiment was performed to calibrate the input parameter. The sensitivity experiment was based on the "improved Sobol's global variance decomposition method" . The analysis showed that parameters related to road, roof and soil moisture have significant influence on the performance of the model. The optimization/parameter estimation experiment was performed using the AMALGM (a multi-algorithm genetically adaptive multi-objective method) evolutionary algorithm. The experiment showed a remarkable improvement compared to the simulations using the default parameter set. The calibrated parameters from this optimization experiment can be used for further model validation studies to identify inherent deficiencies in model physics.

Evaluation of habitat suitability index models by global sensitivity and uncertainty analyses: a case study for submerged aquatic vegetation

USGS Publications Warehouse

Zajac, Zuzanna; Stith, Bradley M.; Bowling, Andrea C.; Langtimm, Catherine A.; Swain, Eric D.

2015-01-01

Habitat suitability index (HSI) models are commonly used to predict habitat quality and species distributions and are used to develop biological surveys, assess reserve and management priorities, and anticipate possible change under different management or climate change scenarios. Important management decisions may be based on model results, often without a clear understanding of the level of uncertainty associated with model outputs. We present an integrated methodology to assess the propagation of uncertainty from both inputs and structure of the HSI models on model outputs (uncertainty analysis: UA) and relative importance of uncertain model inputs and their interactions on the model output uncertainty (global sensitivity analysis: GSA). We illustrate the GSA/UA framework using simulated hydrology input data from a hydrodynamic model representing sea level changes and HSI models for two species of submerged aquatic vegetation (SAV) in southwest Everglades National Park: Vallisneria americana (tape grass) and Halodule wrightii (shoal grass). We found considerable spatial variation in uncertainty for both species, but distributions of HSI scores still allowed discrimination of sites with good versus poor conditions. Ranking of input parameter sensitivities also varied spatially for both species, with high habitat quality sites showing higher sensitivity to different parameters than low-quality sites. HSI models may be especially useful when species distribution data are unavailable, providing means of exploiting widely available environmental datasets to model past, current, and future habitat conditions. The GSA/UA approach provides a general method for better understanding HSI model dynamics, the spatial and temporal variation in uncertainties, and the parameters that contribute most to model uncertainty. Including an uncertainty and sensitivity analysis in modeling efforts as part of the decision-making framework will result in better-informed, more robust decisions.

[Psychosocial stressors and pain sensitivity in chronic pain disorder with somatic and psychological factors (F45.41)].

PubMed

Studer, M; Stewart, J; Egloff, N; Zürcher, E; von Känel, R; Brodbeck, J; Grosse Holtforth, M

2017-02-01

Increased pain sensitivity is characteristic for patients with chronic pain disorder with somatic and psychological factors (F45.41). Persistent stress can induce, sustain, and intensify pain sensitivity, thereby modulating pain perception. In this context, it would be favorable to investigate which psychosocial stressors are empirically linked to pain sensitivity. The aim of this study was to examine the relationship between psychosocial stressors and pain sensitivity in a naturalistic sample of patients with chronic pain disorder with somatic and psychological factors (F45.41). We assessed 166 patients with chronic pain disorder with somatic and psychological factors (F45.41) at entry into an inpatient pain clinic. Pain sensitivity was measured with a pain provocation test (Algopeg) at the middle finger and earlobe. Stressors assessed were exposure to war experiences, adverse childhood experiences, illness-related inability to work, relationship problems, and potentially life-threatening accidents. Correlation analyses and structural equation modeling were used to examine which stressors showed the strongest prediction of pain sensitivity. Patients exhibited generally heightened pain sensitivity. Both exposure to war and illness-related inability to work showed significant bivariate correlations with pain sensitivity. In addition to age, they also predicted a further increase in pain sensitivity in the structural equation model. Bearing in mind the limitations of this cross-sectional study, these findings may contribute to a better understanding of the link between psychosocial stressors and pain sensitivity.

Methamphetamine-sensitized rats show augmented dopamine release to methylphenidate stimulation: a positron emission tomography using [18F]fallypride.

PubMed

Ota, Miho; Ogawa, Shintaro; Kato, Koichi; Wakabayashi, Chisato; Kunugi, Hiroshi

2015-04-30

Previous studies demonstrated that patients with schizophrenia show greater sensitivity to psychostimulants than healthy subjects. Sensitization to psychostimulants and resultant alteration of dopaminergic neurotransmission in rodents have been suggested as a useful model of schizophrenia. This study was aimed to examine the use of methylphenidate as a psychostimulant to induce dopamine release and that of [18F]fallypride as a radioligand to estimate the release in a rat model of schizophrenia. Six rats were scanned by positron emission tomography (PET) twice before and after methylphenidate challenge to evaluate dopamine release. After the scans, these rats were sensitized by using repeated methamphetamine (MAP) administration. Then, they were re-scanned twice again before and after methylphenidate challenge to evaluate whether MAP-sensitized rats show greater sensitivity to methylphenidate. We revealed a main effect of MAP-pretreatment and that of metylphenidate challenge. We found that % change of distribution volume ratio after repeated administration of MAP was greater than that before sensitization. These results suggest that methylphenidate-induced striatal dopamine release increased after sensitization to MAP. PET scan using [18F]fallypride at methylphenidate-challenge may provide a biological marker for schizophrenia and be useful to diagnose schizophrenia. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

The challenge of modelling nitrogen management at the field scale: simulation and sensitivity analysis of N2O fluxes across nine experimental sites using DailyDayCent

NASA Astrophysics Data System (ADS)

Fitton, N.; Datta, A.; Hastings, A.; Kuhnert, M.; Topp, C. F. E.; Cloy, J. M.; Rees, R. M.; Cardenas, L. M.; Williams, J. R.; Smith, K.; Chadwick, D.; Smith, P.

2014-09-01

The United Kingdom currently reports nitrous oxide emissions from agriculture using the IPCC default Tier 1 methodology. However Tier 1 estimates have a large degree of uncertainty as they do not account for spatial variations in emissions. Therefore biogeochemical models such as DailyDayCent (DDC) are increasingly being used to provide a spatially disaggregated assessment of annual emissions. Prior to use, an assessment of the ability of the model to predict annual emissions should be undertaken, coupled with an analysis of how model inputs influence model outputs, and whether the modelled estimates are more robust that those derived from the Tier 1 methodology. The aims of the study were (a) to evaluate if the DailyDayCent model can accurately estimate annual N2O emissions across nine different experimental sites, (b) to examine its sensitivity to different soil and climate inputs across a number of experimental sites and (c) to examine the influence of uncertainty in the measured inputs on modelled N2O emissions. DailyDayCent performed well across the range of cropland and grassland sites, particularly for fertilized fields indicating that it is robust for UK conditions. The sensitivity of the model varied across the sites and also between fertilizer/manure treatments. Overall our results showed that there was a stronger correlation between the sensitivity of N2O emissions to changes in soil pH and clay content than the remaining input parameters used in this study. The lower the initial site values for soil pH and clay content, the more sensitive DDC was to changes from their initial value. When we compared modelled estimates with Tier 1 estimates for each site, we found that DailyDayCent provided a more accurate representation of the rate of annual emissions.

Towards a Better Understanding of Water Stores and Fluxes: Model Observation Synthesis in a Snowmelt Dominated Research Watershed

NASA Astrophysics Data System (ADS)

Ryken, A.; Gochis, D.; Carroll, R. W. H.; Bearup, L. A.; Williams, K. H.; Maxwell, R. M.

2017-12-01

The hydrology of high-elevation, mountainous regions is poorly represented in Earth Systems Models (ESMs). In addition to regulating downstream water delivery, these ecosystems play an important role in the storage and land-atmosphere exchange of carbon and water. Water balances are sensitive to the amount of water stored in the snowpack (SWE) and the amount of water leaving the system in the form of evapotranspiration—two pieces of the hydrologic cycle that are difficult to observe and model in heterogeneous mountainous regions due to spatially variant weather patterns. In an effort to resolve this hydrologic gap in ESMs, this study seeks to better understand the interactions between groundwater, carbon flux, and the lower atmosphere in these high-altitude environments through integration of field observations and model simulations. We compare model simulations to field observations to elucidate process performance combined with a sensitivity analysis to better understand parameter uncertainty. Observations from a meteorological station in the East River Basin are used to force an integrated single-column hydrologic model, ParFlow-CLM. This met station is co-located with an eddy covariance tower, which, along with snow surveys, is used to better constrain the water, carbon, and energy fluxes in the coupled land-atmosphere model to increase our understanding of high-altitude headwaters. Preliminary results suggest the model compares well to the eddy covariance tower and field observations, shown through both correct magnitude and timing of peak SWE along with similar magnitudes and diurnal patterns of heat and water fluxes. Initial sensitivity analysis results show that an increase in temperature leads to a decrease in peak SWE as well as an increase in latent heat revealing a sensitivity of the model to air temperature. Further sensitivity analysis will help us understand more parameter uncertainty. Through obtaining more accurate and higher resolution meteorological data and applying it to a coupled hydrologic model, this study can lead to better representation of mountainous environments in all ESMs.

A comparison of bivariate, multivariate random-effects, and Poisson correlated gamma-frailty models to meta-analyze individual patient data of ordinal scale diagnostic tests.

PubMed

Simoneau, Gabrielle; Levis, Brooke; Cuijpers, Pim; Ioannidis, John P A; Patten, Scott B; Shrier, Ian; Bombardier, Charles H; de Lima Osório, Flavia; Fann, Jesse R; Gjerdingen, Dwenda; Lamers, Femke; Lotrakul, Manote; Löwe, Bernd; Shaaban, Juwita; Stafford, Lesley; van Weert, Henk C P M; Whooley, Mary A; Wittkampf, Karin A; Yeung, Albert S; Thombs, Brett D; Benedetti, Andrea

2017-11-01

Individual patient data (IPD) meta-analyses are increasingly common in the literature. In the context of estimating the diagnostic accuracy of ordinal or semi-continuous scale tests, sensitivity and specificity are often reported for a given threshold or a small set of thresholds, and a meta-analysis is conducted via a bivariate approach to account for their correlation. When IPD are available, sensitivity and specificity can be pooled for every possible threshold. Our objective was to compare the bivariate approach, which can be applied separately at every threshold, to two multivariate methods: the ordinal multivariate random-effects model and the Poisson correlated gamma-frailty model. Our comparison was empirical, using IPD from 13 studies that evaluated the diagnostic accuracy of the 9-item Patient Health Questionnaire depression screening tool, and included simulations. The empirical comparison showed that the implementation of the two multivariate methods is more laborious in terms of computational time and sensitivity to user-supplied values compared to the bivariate approach. Simulations showed that ignoring the within-study correlation of sensitivity and specificity across thresholds did not worsen inferences with the bivariate approach compared to the Poisson model. The ordinal approach was not suitable for simulations because the model was highly sensitive to user-supplied starting values. We tentatively recommend the bivariate approach rather than more complex multivariate methods for IPD diagnostic accuracy meta-analyses of ordinal scale tests, although the limited type of diagnostic data considered in the simulation study restricts the generalization of our findings. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Optimizing signal recycling for detecting a stochastic gravitational-wave background

NASA Astrophysics Data System (ADS)

Tao, Duo; Christensen, Nelson

2018-06-01

Signal recycling is applied in laser interferometers such as the Advanced Laser Interferometer Gravitational-Wave Observatory (aLIGO) to increase their sensitivity to gravitational waves. In this study, signal recycling configurations for detecting a stochastic gravitational wave background are optimized based on aLIGO parameters. Optimal transmission of the signal recycling mirror (SRM) and detuning phase of the signal recycling cavity under a fixed laser power and low-frequency cutoff are calculated. Based on the optimal configurations, the compatibility with a binary neutron star (BNS) search is discussed. Then, different laser powers and low-frequency cutoffs are considered. Two models for the dimensionless energy density of gravitational waves , the flat model and the model, are studied. For a stochastic background search, it is found that an interferometer using signal recycling has a better sensitivity than an interferometer not using it. The optimal stochastic search configurations are typically found when both the SRM transmission and the signal recycling detuning phase are low. In this region, the BNS range mostly lies between 160 and 180 Mpc. When a lower laser power is used the optimal signal recycling detuning phase increases, the optimal SRM transmission increases and the optimal sensitivity improves. A reduced low-frequency cutoff gives a better sensitivity limit. For both models of , a typical optimal sensitivity limit on the order of 10‑10 is achieved at a reference frequency of Hz.

A prediction model based on an artificial intelligence system for moderate to severe obstructive sleep apnea.

PubMed

Sun, Lei Ming; Chiu, Hung-Wen; Chuang, Chih Yuan; Liu, Li

2011-09-01

Obstructive sleep apnea (OSA) is a major concern in modern medicine; however, it is difficult to diagnose. Screening questionnaires such as the Berlin questionnaire, Rome questionnaire, and BASH'IM score are used to identify patients with OSA. However, the sensitivity and specificity of these tools are not satisfactory. We aim to introduce an artificial intelligence method to screen moderate to severe OSA patients (apnea-hypopnea index ≧15). One hundred twenty patients were asked to complete a newly developed questionnaire before undergoing an overnight polysomnography (PSG) study. One hundred ten validated questionnaires were enrolled in this study. Genetic algorithm (GA) was used to build the five best models based on these questionnaires. The same data were analyzed with logistic regression (LR) for comparison. The sensitivity of the GA models varied from 81.8% to 88.0%, with a specificity of 95% to 97%. On the other hand, the sensitivity and specificity of the LR model were 55.6% and 57.9%, respectively. GA provides a good solution to build models for screening moderate to severe OSA patients, who require PSG evaluation and medical intervention. The questionnaire did not require any special biochemistry data and was easily self-administered. The sensitivity and specificity of the GA models are satisfactory and may improve when more patients are recruited.

Using a numerical model to understand the connection between the ocean and acoustic travel-time measurements.

PubMed

Powell, Brian S; Kerry, Colette G; Cornuelle, Bruce D

2013-10-01

Measurements of acoustic ray travel-times in the ocean provide synoptic integrals of the ocean state between source and receiver. It is known that the ray travel-time is sensitive to variations in the ocean at the transmission time, but the sensitivity of the travel-time to spatial variations in the ocean prior to the acoustic transmission have not been quantified. This study examines the sensitivity of ray travel-time to the temporally and spatially evolving ocean state in the Philippine Sea using the adjoint of a numerical model. A one year series of five day backward integrations of the adjoint model quantify the sensitivity of travel-times to varying dynamics that can alter the travel-time of a 611 km ray by 200 ms. The early evolution of the sensitivities reveals high-mode internal waves that dissipate quickly, leaving the lowest three modes, providing a connection to variations in the internal tide generation prior to the sample time. They are also strongly sensitive to advective effects that alter density along the ray path. These sensitivities reveal how travel-time measurements are affected by both nearby and distant waters. Temporal nonlinearity of the sensitivities suggests that prior knowledge of the ocean state is necessary to exploit the travel-time observations.

Improved Upper Ocean/Sea Ice Modeling in the GISS GCM for Investigating Climate Change

NASA Technical Reports Server (NTRS)

1997-01-01

This project built on our previous results in which we highlighted the importance of sea ice in overall climate sensitivity by determining that for both warming and cooling climates, when sea ice was not allowed to change, climate sensitivity was reduced by 35-40%. We also modified the Goddard Institute for Space Studies (GISS) 8 deg x lO deg atmospheric General Circulation Model (GCM) to include an upper-ocean/sea-ice model involving the Semtner three-layer ice/snow thermodynamic model, the Price et al. (1986) ocean mixed layer model and a general upper ocean vertical advection/diffusion scheme for maintaining and fluxing properties across the pycnocline. This effort, in addition to improving the sea ice representation in the AGCM, revealed a number of sensitive components of the sea ice/ocean system. For example, the ability to flux heat through the ice/snow properly is critical in order to resolve the surface temperature properly, since small errors in this lead to unrestrained climate drift. The present project, summarized in this report, had as its objectives: (1) introducing a series of sea ice and ocean improvements aimed at overcoming remaining weaknesses in the GCM sea ice/ocean representation, and (2) performing a series of sensitivity experiments designed to evaluate the climate sensitivity of the revised model to both Antarctic and Arctic sea ice, determine the sensitivity of the climate response to initial ice distribution, and investigate the transient response to doubling CO2.

Progress on Reconstructed Human Skin Models for Allergy Research and Identifying Contact Sensitizers.

PubMed

Rodrigues Neves, Charlotte; Gibbs, Susan

2018-06-23

Contact with the skin is inevitable or desirable for daily life products such as cosmetics, hair dyes, perfumes, drugs, household products, and industrial and agricultural products. Whereas the majority of these products are harmless, a number can become metabolized and/or activate the immunological defense via innate and adaptive mechanisms resulting in sensitization and allergic contact dermatitis upon following exposures to the same substance. Therefore, strict safety (hazard) assessment of actives and ingredients in products and drugs applied to the skin is essential to determine I) whether the chemical is a potential sensitizer and if so II) what is the safe concentration for human exposure to prevent sensitization from occurring. Ex vivo skin is a valuable model for skin penetration studies but due to logistical and viability limitations the development of in vitro alternatives is required. The aim of this review is to give a clear overview of the organotypic in vitro skin models (reconstructed human epidermis, reconstructed human skin, immune competent skin models incorporating Langerhans Cells and T-cells, skin-on-chip) that are currently commercially available or which are being used in a laboratory research setting for hazard assessment of potential sensitizers and for investigating the mechanisms (sensitization key events 1-4) related to allergic contact dermatitis. The limitations of the models, their current applications, and their future potential in replacing animals in allergy-related science are discussed.

Acute sensitivity of freshwater mollusks and commonly tested invertebrates to select chemicals with different toxic models of action

EPA Science Inventory

Previous studies indicate that freshwater mollusks are more sensitive than commonly tested organisms to some chemicals, such as copper and ammonia. Nevertheless, mollusks are generally under-represented in toxicity databases. Studies are needed to generate data with which to comp...

Benchmark On Sensitivity Calculation (Phase III)

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

Ivanova, Tatiana; Laville, Cedric; Dyrda, James

2012-01-01

The sensitivities of the keff eigenvalue to neutron cross sections have become commonly used in similarity studies and as part of the validation algorithm for criticality safety assessments. To test calculations of the sensitivity coefficients, a benchmark study (Phase III) has been established by the OECD-NEA/WPNCS/EG UACSA (Expert Group on Uncertainty Analysis for Criticality Safety Assessment). This paper presents some sensitivity results generated by the benchmark participants using various computational tools based upon different computational methods: SCALE/TSUNAMI-3D and -1D, MONK, APOLLO2-MORET 5, DRAGON-SUSD3D and MMKKENO. The study demonstrates the performance of the tools. It also illustrates how model simplifications impactmore » the sensitivity results and demonstrates the importance of 'implicit' (self-shielding) sensitivities. This work has been a useful step towards verification of the existing and developed sensitivity analysis methods.« less

Verification, Validation and Sensitivity Studies in Computational Biomechanics

PubMed Central

Anderson, Andrew E.; Ellis, Benjamin J.; Weiss, Jeffrey A.

2012-01-01

Computational techniques and software for the analysis of problems in mechanics have naturally moved from their origins in the traditional engineering disciplines to the study of cell, tissue and organ biomechanics. Increasingly complex models have been developed to describe and predict the mechanical behavior of such biological systems. While the availability of advanced computational tools has led to exciting research advances in the field, the utility of these models is often the subject of criticism due to inadequate model verification and validation. The objective of this review is to present the concepts of verification, validation and sensitivity studies with regard to the construction, analysis and interpretation of models in computational biomechanics. Specific examples from the field are discussed. It is hoped that this review will serve as a guide to the use of verification and validation principles in the field of computational biomechanics, thereby improving the peer acceptance of studies that use computational modeling techniques. PMID:17558646

Contributions to Future Stratospheric Climate Change: An Idealized Chemistry-Climate Model Sensitivity Study

NASA Technical Reports Server (NTRS)

Hurwitz, M. M.; Braesicke, P.; Pyle, J. A.

2010-01-01

Within the framework of an idealized model sensitivity study, three of the main contributors to future stratospheric climate change are evaluated: increases in greenhouse gas concentrations, ozone recovery, and changing sea surface temperatures (SSTs). These three contributors are explored in combination and separately, to test the interactions between ozone and climate; the linearity of their contributions to stratospheric climate change is also assessed. In a simplified chemistry-climate model, stratospheric global mean temperature is most sensitive to CO2 doubling, followed by ozone depletion, then by increased SSTs. At polar latitudes, the Northern Hemisphere (NH) stratosphere is more sensitive to changes in CO2, SSTs and O3 than is the Southern Hemisphere (SH); the opposing responses to ozone depletion under low or high background CO2 concentrations, as seen with present-day SSTs, are much weaker and are not statistically significant under enhanced SSTs. Consistent with previous studies, the strength of the Brewer-Dobson circulation is found to increase in an idealized future climate; SSTs contribute most to this increase in the upper troposphere/lower stratosphere (UT/LS) region, while CO2 and ozone changes contribute most in the stratosphere and mesosphere.

Cancer biomarker discovery is improved by accounting for variability in general levels of drug sensitivity in pre-clinical models.

PubMed

Geeleher, Paul; Cox, Nancy J; Huang, R Stephanie

2016-09-21

We show that variability in general levels of drug sensitivity in pre-clinical cancer models confounds biomarker discovery. However, using a very large panel of cell lines, each treated with many drugs, we could estimate a general level of sensitivity to all drugs in each cell line. By conditioning on this variable, biomarkers were identified that were more likely to be effective in clinical trials than those identified using a conventional uncorrected approach. We find that differences in general levels of drug sensitivity are driven by biologically relevant processes. We developed a gene expression based method that can be used to correct for this confounder in future studies.

Estimation of genetic variance for macro- and micro-environmental sensitivity using double hierarchical generalized linear models

PubMed Central

2013-01-01

Background Genetic variation for environmental sensitivity indicates that animals are genetically different in their response to environmental factors. Environmental factors are either identifiable (e.g. temperature) and called macro-environmental or unknown and called micro-environmental. The objectives of this study were to develop a statistical method to estimate genetic parameters for macro- and micro-environmental sensitivities simultaneously, to investigate bias and precision of resulting estimates of genetic parameters and to develop and evaluate use of Akaike’s information criterion using h-likelihood to select the best fitting model. Methods We assumed that genetic variation in macro- and micro-environmental sensitivities is expressed as genetic variance in the slope of a linear reaction norm and environmental variance, respectively. A reaction norm model to estimate genetic variance for macro-environmental sensitivity was combined with a structural model for residual variance to estimate genetic variance for micro-environmental sensitivity using a double hierarchical generalized linear model in ASReml. Akaike’s information criterion was constructed as model selection criterion using approximated h-likelihood. Populations of sires with large half-sib offspring groups were simulated to investigate bias and precision of estimated genetic parameters. Results Designs with 100 sires, each with at least 100 offspring, are required to have standard deviations of estimated variances lower than 50% of the true value. When the number of offspring increased, standard deviations of estimates across replicates decreased substantially, especially for genetic variances of macro- and micro-environmental sensitivities. Standard deviations of estimated genetic correlations across replicates were quite large (between 0.1 and 0.4), especially when sires had few offspring. Practically, no bias was observed for estimates of any of the parameters. Using Akaike’s information criterion the true genetic model was selected as the best statistical model in at least 90% of 100 replicates when the number of offspring per sire was 100. Application of the model to lactation milk yield in dairy cattle showed that genetic variance for micro- and macro-environmental sensitivities existed. Conclusion The algorithm and model selection criterion presented here can contribute to better understand genetic control of macro- and micro-environmental sensitivities. Designs or datasets should have at least 100 sires each with 100 offspring. PMID:23827014

Open pit mining profit maximization considering selling stage and waste rehabilitation cost

NASA Astrophysics Data System (ADS)

Muttaqin, B. I. A.; Rosyidi, C. N.

2017-11-01

In open pit mining activities, determination of the cut-off grade becomes crucial for the company since the cut-off grade affects how much profit will be earned for the mining company. In this study, we developed a cut-off grade determination mode for the open pit mining industry considering the cost of mining, waste removal (rehabilitation) cost, processing cost, fixed cost, and selling stage cost. The main goal of this study is to develop a model of cut-off grade determination to get the maximum total profit. Secondly, this study is also developed to observe the model of sensitivity based on changes in the cost components. The optimization results show that the models can help mining company managers to determine the optimal cut-off grade and also estimate how much profit that can be earned by the mining company. To illustrate the application of the models, a numerical example and a set of sensitivity analysis are presented. From the results of sensitivity analysis, we conclude that the changes in the sales price greatly affects the optimal cut-off value and the total profit.

Parametric study and global sensitivity analysis for co-pyrolysis of rape straw and waste tire via variance-based decomposition.

PubMed

Xu, Li; Jiang, Yong; Qiu, Rong

2018-01-01

In present study, co-pyrolysis behavior of rape straw, waste tire and their various blends were investigated. TG-FTIR indicated that co-pyrolysis was characterized by a four-step reaction, and H 2 O, CH, OH, CO 2 and CO groups were the main products evolved during the process. Additionally, using BBD-based experimental results, best-fit multiple regression models with high R 2 -pred values (94.10% for mass loss and 95.37% for reaction heat), which correlated explanatory variables with the responses, were presented. The derived models were analyzed by ANOVA at 95% confidence interval, F-test, lack-of-fit test and residues normal probability plots implied the models described well the experimental data. Finally, the model uncertainties as well as the interactive effect of these parameters were studied, the total-, first- and second-order sensitivity indices of operating factors were proposed using Sobol' variance decomposition. To the authors' knowledge, this is the first time global parameter sensitivity analysis has been performed in (co-)pyrolysis literature. Copyright © 2017 Elsevier Ltd. All rights reserved.

Inversed estimation of critical factors for controlling over-prediction of summertime tropospheric O3 over East Asia based of the combination of DDM sensitivity analysis and modeled Green's function method

NASA Astrophysics Data System (ADS)

Itahashi, S.; Yumimoto, K.; Uno, I.; Kim, S.

2012-12-01

Air quality studies based on the chemical transport model have been provided many important results for promoting our knowledge of air pollution phenomena, however, discrepancies between modeling results and observation data are still important issue to overcome. One of the concerning issue would be an over-prediction of summertime tropospheric ozone in remote area of Japan. This problem has been pointed out in the model comparison study of both regional scale (e.g., MICS-Asia) and global scale model (e.g., TH-FTAP). Several reasons for this issue can be listed as, (i) the modeled reproducibility on the penetration of clean oceanic air mass, (ii) correct estimation of the anthropogenic NOx / VOC emissions over East Asia, (iii) the chemical reaction scheme used in model simulation. In this study, we attempt to inverse estimation of some important chemical reactions based on the combining system of DDM (decoupled direct method) sensitivity analysis and modeled Green's function approach. The decoupled direct method (DDM) is an efficient and accurate way of performing sensitivity analysis to model inputs, calculates sensitivity coefficients representing the responsiveness of atmospheric chemical concentrations to perturbations in a model input or parameter. The inverse solutions with the Green's functions are given by a linear, least-squares method but are still robust against nonlinearities, To construct the response matrix (i.e., Green's functions), we can directly use the results of DDM sensitivity analysis. The solution of chemical reaction constants which have relatively large uncertainties are determined with constraints of observed ozone concentration data over the remote area in Japan. Our inversed estimation demonstrated that the underestimation of reaction constant to produce HNO3 (NO2 + OH + M → HNO3 + M) in SAPRC99 chemical scheme, and the inversed results indicated the +29.0 % increment to this reaction. This estimation has good agreement when compared with the CB4 and CB5, and also to the SAPRC07 estimation. For the NO2 photolysis rates, 49.4 % reduction was pronounced. This result indicates the importance of heavy aerosol effect for the change of photolysis rate must be incorporated in the numerical study.

Improved numerical solutions for chaotic-cancer-model

NASA Astrophysics Data System (ADS)

Yasir, Muhammad; Ahmad, Salman; Ahmed, Faizan; Aqeel, Muhammad; Akbar, Muhammad Zubair

2017-01-01

In biological sciences, dynamical system of cancer model is well known due to its sensitivity and chaoticity. Present work provides detailed computational study of cancer model by counterbalancing its sensitive dependency on initial conditions and parameter values. Cancer chaotic model is discretized into a system of nonlinear equations that are solved using the well-known Successive-Over-Relaxation (SOR) method with a proven convergence. This technique enables to solve large systems and provides more accurate approximation which is illustrated through tables, time history maps and phase portraits with detailed analysis.

Sensitivity of tsunami evacuation modeling to direction and land cover assumptions

USGS Publications Warehouse

Schmidtlein, Mathew C.; Wood, Nathan J.

2015-01-01

Although anisotropic least-cost-distance (LCD) modeling is becoming a common tool for estimating pedestrian-evacuation travel times out of tsunami hazard zones, there has been insufficient attention paid to understanding model sensitivity behind the estimates. To support tsunami risk-reduction planning, we explore two aspects of LCD modeling as it applies to pedestrian evacuations and use the coastal community of Seward, Alaska, as our case study. First, we explore the sensitivity of modeling to the direction of movement by comparing standard safety-to-hazard evacuation times to hazard-to-safety evacuation times for a sample of 3985 points in Seward's tsunami-hazard zone. Safety-to-hazard evacuation times slightly overestimated hazard-to-safety evacuation times but the strong relationship to the hazard-to-safety evacuation times, slightly conservative bias, and shorter processing times of the safety-to-hazard approach make it the preferred approach. Second, we explore how variations in land cover speed conservation values (SCVs) influence model performance using a Monte Carlo approach with one thousand sets of land cover SCVs. The LCD model was relatively robust to changes in land cover SCVs with the magnitude of local model sensitivity greatest in areas with higher evacuation times or with wetland or shore land cover types, where model results may slightly underestimate travel times. This study demonstrates that emergency managers should be concerned not only with populations in locations with evacuation times greater than wave arrival times, but also with populations with evacuation times lower than but close to expected wave arrival times, particularly if they are required to cross wetlands or beaches.

The gamma-aminobutyric acid type B (GABAB) receptor agonist baclofen inhibits morphine sensitization by decreasing the dopamine level in rat nucleus accumbens

PubMed Central

2012-01-01

Background Repeated morphine exposure can induce behavioral sensitization. There are evidences have shown that central gamma-aminobutyric acid (GABA) system is involved in morphine dependence. However, the effect of a GABAB receptor agonist baclofen on morphine-induced behavioral sensitization in rats is unclear. Methods We used morphine-induced behavioral sensitization model in rat to investigate the effects of baclofen on behavioral sensitization. Moreover, dopamine release in the shell of the nucleus accumbens was evaluated using microdialysis assay in vivo. Results The present study demonstrated that morphine challenge (3 mg/kg, s.c.) obviously enhanced the locomotor activity following 4-day consecutive morphine administration and 3-day withdrawal period, which indicated the expression of morphine sensitization. In addition, chronic treatment with baclofen (2.5, 5 mg/kg) significantly inhibited the development of morphine sensitization. It was also found that morphine challenge 3 days after repeated morphine administration produced a significant increase of extracellular dopamine release in nucleus accumbens. Furthermore, chronic treatment with baclofen decreased the dopamine release induced by morphine challenge. Conclusions Our results indicated that gamma-aminobutyric acid system plays an important role in the morphine sensitization in rat and suggested that behavioral sensitization is a promising model to study the mechanism underlying drug abuse. PMID:22559224

An Incidence Loss Model for Wave Rotors with Axially Aligned Passages

NASA Technical Reports Server (NTRS)

Paxson, Daniel E.

1998-01-01

A simple mathematical model is described to account for the losses incurred when the flow in the duct (port) of a wave rotor is not aligned with the passages. The model, specifically for wave rotors with axially aligned passages, describes a loss mechanism which is sensitive to incident flow angle and Mach number. Implementation of the model in a one-dimensional CFD based wave rotor simulation is presented. Comparisons with limited experimental results are consistent with the model. Sensitivity studies are presented which highlight the significance of the incidence loss relative to other loss mechanisms in the wave rotor.

Parametric sensitivity analysis of leachate transport simulations at landfills.

PubMed

Bou-Zeid, E; El-Fadel, M

2004-01-01

This paper presents a case study in simulating leachate generation and transport at a 2000 ton/day landfill facility and assesses leachate migration away from the landfill in order to control associated environmental impacts, particularly on groundwater wells down gradient of the site. The site offers unique characteristics in that it is a former quarry converted to a landfill and is planned to have refuse depths that could reach 100 m, making it one of the deepest in the world. Leachate quantity and potential percolation into the subsurface are estimated using the Hydrologic Evaluation of Landfill Performance (HELP) model. A three-dimensional subsurface model (PORFLOW) was adopted to simulate ground water flow and contaminant transport away from the site. A comprehensive sensitivity analysis to leachate transport control parameters was also conducted. Sensitivity analysis suggests that changes in partition coefficient, source strength, aquifer hydraulic conductivity, and dispersivity have the most significant impact on model output indicating that these parameters should be carefully selected when similar modeling studies are performed. Copyright 2004 Elsevier Ltd.

Sensitivity studies with a coupled ice-ocean model of the marginal ice zone

NASA Technical Reports Server (NTRS)

Roed, L. P.

1983-01-01

An analytical coupled ice-ocean model is considered which is forced by a specified wind stress acting on the open ocean as well as the ice. The analysis supports the conjecture that the upwelling dynamics at ice edges can be understood by means of a simple analytical model. In similarity with coastal problems it is shown that the ice edge upwelling is determined by the net mass flux at the boundaries of the considered region. The model is used to study the sensitivity of the upwelling dynamics in the marginal ice zone to variation in the controlling parameters. These parameters consist of combinations of the drag coefficients used in the parameterization of the stresses on the three interfaces atmosphere-ice, atmosphere-ocean, and ice-ocean. The response is shown to be sensitive to variations in these parameters in that one set of parameters may give upwelling while a slightly different set of parameters may give downwelling.

Virtual Patients and Sensitivity Analysis of the Guyton Model of Blood Pressure Regulation: Towards Individualized Models of Whole-Body Physiology

PubMed Central

Moss, Robert; Grosse, Thibault; Marchant, Ivanny; Lassau, Nathalie; Gueyffier, François; Thomas, S. Randall

2012-01-01

Mathematical models that integrate multi-scale physiological data can offer insight into physiological and pathophysiological function, and may eventually assist in individualized predictive medicine. We present a methodology for performing systematic analyses of multi-parameter interactions in such complex, multi-scale models. Human physiology models are often based on or inspired by Arthur Guyton's whole-body circulatory regulation model. Despite the significance of this model, it has not been the subject of a systematic and comprehensive sensitivity study. Therefore, we use this model as a case study for our methodology. Our analysis of the Guyton model reveals how the multitude of model parameters combine to affect the model dynamics, and how interesting combinations of parameters may be identified. It also includes a “virtual population” from which “virtual individuals” can be chosen, on the basis of exhibiting conditions similar to those of a real-world patient. This lays the groundwork for using the Guyton model for in silico exploration of pathophysiological states and treatment strategies. The results presented here illustrate several potential uses for the entire dataset of sensitivity results and the “virtual individuals” that we have generated, which are included in the supplementary material. More generally, the presented methodology is applicable to modern, more complex multi-scale physiological models. PMID:22761561

Actinic Flux Calculations: A Model Sensitivity Study

NASA Technical Reports Server (NTRS)

Krotkov, Nickolay A.; Flittner, D.; Ahmad, Z.; Herman, J. R.; Einaudi, Franco (Technical Monitor)

2000-01-01

calculate direct and diffuse surface irradiance and actinic flux (downwelling (2p) and total (4p)) for the reference model. Sensitivity analysis has shown that the accuracy of the radiative transfer flux calculations for a unit ETS (i.e. atmospheric transmittance) together with a numerical interpolation technique for the constituents' vertical profiles is better than 1% for SZA less than 70(sub o) and wavelengths longer than 310 nm. The differences increase for shorter wavelengths and larger SZA, due to the differences in pseudo-spherical correction techniques and vertical discretetization among the codes. Our sensitivity study includes variation of ozone cross-sections, ETS spectra and the effects of wavelength shifts between vacuum and air scales. We also investigate the effects of aerosols on the spectral flux components in the UV and visible spectral regions. The "aerosol correction factors" (ACFs) were calculated at discrete wavelengths and different SZAs for each flux component (direct, diffuse, reflected) and prescribed IPMMI aerosol parameters. Finally, the sensitivity study was extended to calculation of selected photolysis rates coefficients.

Taxometric and Factor Analytic Models of Anxiety Sensitivity among Youth: Exploring the Latent Structure of Anxiety Psychopathology Vulnerability

ERIC Educational Resources Information Center

Bernstein, Amit; Zvolensky, Michael J.; Stewart, Sherry; Comeau, Nancy

2007-01-01

This study represents an effort to better understand the latent structure of anxiety sensitivity (AS), a well-established affect-sensitivity individual difference factor, among youth by employing taxometric and factor analytic approaches in an integrative manner. Taxometric analyses indicated that AS, as indexed by the Child Anxiety Sensitivity…

Variation of solar cell sensitivity and solar radiation on tilted surfaces

NASA Technical Reports Server (NTRS)

Klucher, T. M.

1978-01-01

An empirical study was performed (1) to evaluate the validity of various insolation models used to compute solar radiation incident on tilted surfaces from global data measured on horizontal surfaces and (2) to determine the variation of solar cell sensitivity to solar radiation over a wide range of atmospheric condition. Evaluation of the insolation data indicates that the isotropic sky model of Liu and Jordan underestimates the amount of solar radiation falling on tilted surfaces by as much as 10%. An anisotropic-clear-sky model proposed by Temps and Coulson was also evaluated and found to be deficient under cloudy conditions. A new model, formulated herein, reduced the deviations between measured and predicted insolation to less than 3%. Evaluation of solar cell sensitivity data indicates small change (2-3%) in sensitivity from winter to summer for tilted cells. The feasibility of using such global data as a means for calibrating terrestrial solar cells as done by Treble is discussed.

Testing the role of reward and punishment sensitivity in avoidance behavior: a computational modeling approach

PubMed Central

Sheynin, Jony; Moustafa, Ahmed A.; Beck, Kevin D.; Servatius, Richard J.; Myers, Catherine E.

2015-01-01

Exaggerated avoidance behavior is a predominant symptom in all anxiety disorders and its degree often parallels the development and persistence of these conditions. Both human and non-human animal studies suggest that individual differences as well as various contextual cues may impact avoidance behavior. Specifically, we have recently shown that female sex and inhibited temperament, two anxiety vulnerability factors, are associated with greater duration and rate of the avoidance behavior, as demonstrated on a computer-based task closely related to common rodent avoidance paradigms. We have also demonstrated that avoidance is attenuated by the administration of explicit visual signals during “non-threat” periods (i.e., safety signals). Here, we use a reinforcement-learning network model to investigate the underlying mechanisms of these empirical findings, with a special focus on distinct reward and punishment sensitivities. Model simulations suggest that sex and inhibited temperament are associated with specific aspects of these sensitivities. Specifically, differences in relative sensitivity to reward and punishment might underlie the longer avoidance duration demonstrated by females, whereas higher sensitivity to punishment might underlie the higher avoidance rate demonstrated by inhibited individuals. Simulations also suggest that safety signals attenuate avoidance behavior by strengthening the competing approach response. Lastly, several predictions generated by the model suggest that extinction-based cognitive-behavioral therapies might benefit from the use of safety signals, especially if given to individuals with high reward sensitivity and during longer safe periods. Overall, this study is the first to suggest cognitive mechanisms underlying the greater avoidance behavior observed in healthy individuals with different anxiety vulnerabilities. PMID:25639540

A comprehensive approach to identify dominant controls of the behavior of a land surface-hydrology model across various hydroclimatic conditions

NASA Astrophysics Data System (ADS)

Haghnegahdar, Amin; Elshamy, Mohamed; Yassin, Fuad; Razavi, Saman; Wheater, Howard; Pietroniro, Al

2017-04-01

Complex physically-based environmental models are being increasingly used as the primary tool for watershed planning and management due to advances in computation power and data acquisition. Model sensitivity analysis plays a crucial role in understanding the behavior of these complex models and improving their performance. Due to the non-linearity and interactions within these complex models, Global sensitivity analysis (GSA) techniques should be adopted to provide a comprehensive understanding of model behavior and identify its dominant controls. In this study we adopt a multi-basin multi-criteria GSA approach to systematically assess the behavior of the Modélisation Environmentale-Surface et Hydrologie (MESH) across various hydroclimatic conditions in Canada including areas in the Great Lakes Basin, Mackenzie River Basin, and South Saskatchewan River Basin. MESH is a semi-distributed physically-based coupled land surface-hydrology modelling system developed by Environment and Climate Change Canada (ECCC) for various water resources management purposes in Canada. We use a novel method, called Variogram Analysis of Response Surfaces (VARS), to perform sensitivity analysis. VARS is a variogram-based GSA technique that can efficiently provide a spectrum of sensitivity information across a range of scales within the parameter space. We use multiple metrics to identify dominant controls of model response (e.g. streamflow) to model parameters under various conditions such as high flows, low flows, and flow volume. We also investigate the influence of initial conditions on model behavior as part of this study. Our preliminary results suggest that this type of GSA can significantly help with estimating model parameters, decreasing calibration computational burden, and reducing prediction uncertainty.

Evaluation of seasonal and spatial variations of lumped water balance model sensitivity to precipitation data errors

NASA Astrophysics Data System (ADS)

Xu, Chong-yu; Tunemar, Liselotte; Chen, Yongqin David; Singh, V. P.

2006-06-01

Sensitivity of hydrological models to input data errors have been reported in the literature for particular models on a single or a few catchments. A more important issue, i.e. how model's response to input data error changes as the catchment conditions change has not been addressed previously. This study investigates the seasonal and spatial effects of precipitation data errors on the performance of conceptual hydrological models. For this study, a monthly conceptual water balance model, NOPEX-6, was applied to 26 catchments in the Mälaren basin in Central Sweden. Both systematic and random errors were considered. For the systematic errors, 5-15% of mean monthly precipitation values were added to the original precipitation to form the corrupted input scenarios. Random values were generated by Monte Carlo simulation and were assumed to be (1) independent between months, and (2) distributed according to a Gaussian law of zero mean and constant standard deviation that were taken as 5, 10, 15, 20, and 25% of the mean monthly standard deviation of precipitation. The results show that the response of the model parameters and model performance depends, among others, on the type of the error, the magnitude of the error, physical characteristics of the catchment, and the season of the year. In particular, the model appears less sensitive to the random error than to the systematic error. The catchments with smaller values of runoff coefficients were more influenced by input data errors than were the catchments with higher values. Dry months were more sensitive to precipitation errors than were wet months. Recalibration of the model with erroneous data compensated in part for the data errors by altering the model parameters.

MOVES2010a regional level sensitivity analysis

DOT National Transportation Integrated Search

2012-12-10

This document discusses the sensitivity of various input parameter effects on emission rates using the US Environmental Protection Agencys (EPAs) MOVES2010a model at the regional level. Pollutants included in the study are carbon monoxide (CO),...

Shot noise-limited Cramér-Rao bound and algorithmic sensitivity for wavelength shifting interferometry

NASA Astrophysics Data System (ADS)

Chen, Shichao; Zhu, Yizheng

2017-02-01

Sensitivity is a critical index to measure the temporal fluctuation of the retrieved optical pathlength in quantitative phase imaging system. However, an accurate and comprehensive analysis for sensitivity evaluation is still lacking in current literature. In particular, previous theoretical studies for fundamental sensitivity based on Gaussian noise models are not applicable to modern cameras and detectors, which are dominated by shot noise. In this paper, we derive two shot noiselimited theoretical sensitivities, Cramér-Rao bound and algorithmic sensitivity for wavelength shifting interferometry, which is a major category of on-axis interferometry techniques in quantitative phase imaging. Based on the derivations, we show that the shot noise-limited model permits accurate estimation of theoretical sensitivities directly from measured data. These results can provide important insights into fundamental constraints in system performance and can be used to guide system design and optimization. The same concepts can be generalized to other quantitative phase imaging techniques as well.

Effect of noisy stimulation on neurobiological sensitization systems and its role for normal and pathological physiology

NASA Astrophysics Data System (ADS)

Huber, Martin; Braun, Hans; Krieg, J.\\:Urgen-Christian

2004-03-01

Sensitization is discussed as an important phenomenon playing a role in normal physiology but also with respect to the initiation and progression of a variety of neuropsychiatric disorders such as epilepsia, substance-related disorders or recurrent affective disorders. The relevance to understand the dynamics of sensitization phenomena is emphasized by recent findings that even single stimulations can induce longlasting changes in biological systems. To address specific questions associated with the sensitization dynamics, we use a computational approach and develop simple but physiologically-plausible models. In the present study we examine the effect of noisy stimulation on sensitization development in the model. We consider sub- and suprathresold stimulations with varying noise intensities and determine as response measures the (i) absolute number of stimulus-induced sensitzations and (ii) the temporal relsation of stimulus-sensitization coupling. The findings indicate that stochastic effects including stochastic resonance might well contribute to the physiology of sensitization mechanisms under both nomal and pathological conditions.

Input and Age-Dependent Variation in Second Language Learning: A Connectionist Account.

PubMed

Janciauskas, Marius; Chang, Franklin

2017-07-26

Language learning requires linguistic input, but several studies have found that knowledge of second language (L2) rules does not seem to improve with more language exposure (e.g., Johnson & Newport, 1989). One reason for this is that previous studies did not factor out variation due to the different rules tested. To examine this issue, we reanalyzed grammaticality judgment scores in Flege, Yeni-Komshian, and Liu's (1999) study of L2 learners using rule-related predictors and found that, in addition to the overall drop in performance due to a sensitive period, L2 knowledge increased with years of input. Knowledge of different grammar rules was negatively associated with input frequency of those rules. To better understand these effects, we modeled the results using a connectionist model that was trained using Korean as a first language (L1) and then English as an L2. To explain the sensitive period in L2 learning, the model's learning rate was reduced in an age-related manner. By assigning different learning rates for syntax and lexical learning, we were able to model the difference between early and late L2 learners in input sensitivity. The model's learning mechanism allowed transfer between the L1 and L2, and this helped to explain the differences between different rules in the grammaticality judgment task. This work demonstrates that an L1 model of learning and processing can be adapted to provide an explicit account of how the input and the sensitive period interact in L2 learning. © 2017 The Authors. Cognitive Science - A Multidisciplinary Journal published by Wiley Periodicals, Inc.

"A Bayesian sensitivity analysis to evaluate the impact of unmeasured confounding with external data: a real world comparative effectiveness study in osteoporosis".

PubMed

Zhang, Xiang; Faries, Douglas E; Boytsov, Natalie; Stamey, James D; Seaman, John W

2016-09-01

Observational studies are frequently used to assess the effectiveness of medical interventions in routine clinical practice. However, the use of observational data for comparative effectiveness is challenged by selection bias and the potential of unmeasured confounding. This is especially problematic for analyses using a health care administrative database, in which key clinical measures are often not available. This paper provides an approach to conducting a sensitivity analyses to investigate the impact of unmeasured confounding in observational studies. In a real world osteoporosis comparative effectiveness study, the bone mineral density (BMD) score, an important predictor of fracture risk and a factor in the selection of osteoporosis treatments, is unavailable in the data base and lack of baseline BMD could potentially lead to significant selection bias. We implemented Bayesian twin-regression models, which simultaneously model both the observed outcome and the unobserved unmeasured confounder, using information from external sources. A sensitivity analysis was also conducted to assess the robustness of our conclusions to changes in such external data. The use of Bayesian modeling in this study suggests that the lack of baseline BMD did have a strong impact on the analysis, reversing the direction of the estimated effect (odds ratio of fracture incidence at 24 months: 0.40 vs. 1.36, with/without adjusting for unmeasured baseline BMD). The Bayesian twin-regression models provide a flexible sensitivity analysis tool to quantitatively assess the impact of unmeasured confounding in observational studies. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Comparative study of Korean White Ginseng and Korean Red Ginseng on efficacies of OVA-induced asthma model in mice.

PubMed

Lim, Chi-Yeon; Moon, Jeong-Min; Kim, Bu-Yeo; Lim, Se-Hyun; Lee, Guem-San; Yu, Hak-Sun; Cho, Su-In

2015-01-01

Korean ginseng is a well-known medicinal herb that has been widely used in traditional medicine to treat various diseases, including asthma. Ginseng can be classified as white ginseng (WG) or red ginseng (RG), according to processing conditions. In this study, the authors compared the efficacies of these two ginseng types in a mouse model of acute asthma. To produce the acute asthma model, BALB/c mice were sensitized with ovalbumin (OVA) and aluminum hydroxide, and then challenged with OVA. WG and RG extracts were administered to mice orally. The influences of WG and RG on airway hyperresponsiveness (AHR), immune cell distributions in bronchoalveolar lavage fluid (BALF), and OVA-specific immunoglobulin E (IgE), IgG1, and IgG2a in serum were investigated. Cytokine production by lymphocytes isolated from peribronchial lymph nodes and histopathological changes was also examined. In OVA-sensitized mice, both WG and RG reduced AHR and suppressed immune cell infiltration in bronchoalveolar regions. BALF OVA-specific IgE levels were significantly lower in RG-treated OVA-sensitized mice than in the OVA-sensitized control group. WG and RG also suppressed inflammatory cytokine production by peribronchial lymphocytes. Histopathological findings showed reduced inflammatory cell infiltration and airway remodeling (e.g., epithelial hyperplasia) in WG- and RG-treated OVA mice compared with OVA controls. In this study, WG and RG showed antiasthmatic effects in an OVA-sensitized mouse model, and the efficacies of RG were found to be better than those of WG.

Impact of the hard-coded parameters on the hydrologic fluxes of the land surface model Noah-MP

NASA Astrophysics Data System (ADS)

Cuntz, Matthias; Mai, Juliane; Samaniego, Luis; Clark, Martyn; Wulfmeyer, Volker; Attinger, Sabine; Thober, Stephan

2016-04-01

Land surface models incorporate a large number of processes, described by physical, chemical and empirical equations. The process descriptions contain a number of parameters that can be soil or plant type dependent and are typically read from tabulated input files. Land surface models may have, however, process descriptions that contain fixed, hard-coded numbers in the computer code, which are not identified as model parameters. Here we searched for hard-coded parameters in the computer code of the land surface model Noah with multiple process options (Noah-MP) to assess the importance of the fixed values on restricting the model's agility during parameter estimation. We found 139 hard-coded values in all Noah-MP process options, which are mostly spatially constant values. This is in addition to the 71 standard parameters of Noah-MP, which mostly get distributed spatially by given vegetation and soil input maps. We performed a Sobol' global sensitivity analysis of Noah-MP to variations of the standard and hard-coded parameters for a specific set of process options. 42 standard parameters and 75 hard-coded parameters were active with the chosen process options. The sensitivities of the hydrologic output fluxes latent heat and total runoff as well as their component fluxes were evaluated. These sensitivities were evaluated at twelve catchments of the Eastern United States with very different hydro-meteorological regimes. Noah-MP's hydrologic output fluxes are sensitive to two thirds of its standard parameters. The most sensitive parameter is, however, a hard-coded value in the formulation of soil surface resistance for evaporation, which proved to be oversensitive in other land surface models as well. Surface runoff is sensitive to almost all hard-coded parameters of the snow processes and the meteorological inputs. These parameter sensitivities diminish in total runoff. Assessing these parameters in model calibration would require detailed snow observations or the calculation of hydrologic signatures of the runoff data. Latent heat and total runoff exhibit very similar sensitivities towards standard and hard-coded parameters in Noah-MP because of their tight coupling via the water balance. It should therefore be comparable to calibrate Noah-MP either against latent heat observations or against river runoff data. Latent heat and total runoff are sensitive to both, plant and soil parameters. Calibrating only a parameter sub-set of only soil parameters, for example, thus limits the ability to derive realistic model parameters. It is thus recommended to include the most sensitive hard-coded model parameters that were exposed in this study when calibrating Noah-MP.

Advances in global sensitivity analyses of demographic-based species distribution models to address uncertainties in dynamic landscapes.

PubMed

Naujokaitis-Lewis, Ilona; Curtis, Janelle M R

2016-01-01

Developing a rigorous understanding of multiple global threats to species persistence requires the use of integrated modeling methods that capture processes which influence species distributions. Species distribution models (SDMs) coupled with population dynamics models can incorporate relationships between changing environments and demographics and are increasingly used to quantify relative extinction risks associated with climate and land-use changes. Despite their appeal, uncertainties associated with complex models can undermine their usefulness for advancing predictive ecology and informing conservation management decisions. We developed a computationally-efficient and freely available tool (GRIP 2.0) that implements and automates a global sensitivity analysis of coupled SDM-population dynamics models for comparing the relative influence of demographic parameters and habitat attributes on predicted extinction risk. Advances over previous global sensitivity analyses include the ability to vary habitat suitability across gradients, as well as habitat amount and configuration of spatially-explicit suitability maps of real and simulated landscapes. Using GRIP 2.0, we carried out a multi-model global sensitivity analysis of a coupled SDM-population dynamics model of whitebark pine (Pinus albicaulis) in Mount Rainier National Park as a case study and quantified the relative influence of input parameters and their interactions on model predictions. Our results differed from the one-at-time analyses used in the original study, and we found that the most influential parameters included the total amount of suitable habitat within the landscape, survival rates, and effects of a prevalent disease, white pine blister rust. Strong interactions between habitat amount and survival rates of older trees suggests the importance of habitat in mediating the negative influences of white pine blister rust. Our results underscore the importance of considering habitat attributes along with demographic parameters in sensitivity routines. GRIP 2.0 is an important decision-support tool that can be used to prioritize research, identify habitat-based thresholds and management intervention points to improve probability of species persistence, and evaluate trade-offs of alternative management options.

Advances in global sensitivity analyses of demographic-based species distribution models to address uncertainties in dynamic landscapes

PubMed Central

Curtis, Janelle M.R.

2016-01-01

Developing a rigorous understanding of multiple global threats to species persistence requires the use of integrated modeling methods that capture processes which influence species distributions. Species distribution models (SDMs) coupled with population dynamics models can incorporate relationships between changing environments and demographics and are increasingly used to quantify relative extinction risks associated with climate and land-use changes. Despite their appeal, uncertainties associated with complex models can undermine their usefulness for advancing predictive ecology and informing conservation management decisions. We developed a computationally-efficient and freely available tool (GRIP 2.0) that implements and automates a global sensitivity analysis of coupled SDM-population dynamics models for comparing the relative influence of demographic parameters and habitat attributes on predicted extinction risk. Advances over previous global sensitivity analyses include the ability to vary habitat suitability across gradients, as well as habitat amount and configuration of spatially-explicit suitability maps of real and simulated landscapes. Using GRIP 2.0, we carried out a multi-model global sensitivity analysis of a coupled SDM-population dynamics model of whitebark pine (Pinus albicaulis) in Mount Rainier National Park as a case study and quantified the relative influence of input parameters and their interactions on model predictions. Our results differed from the one-at-time analyses used in the original study, and we found that the most influential parameters included the total amount of suitable habitat within the landscape, survival rates, and effects of a prevalent disease, white pine blister rust. Strong interactions between habitat amount and survival rates of older trees suggests the importance of habitat in mediating the negative influences of white pine blister rust. Our results underscore the importance of considering habitat attributes along with demographic parameters in sensitivity routines. GRIP 2.0 is an important decision-support tool that can be used to prioritize research, identify habitat-based thresholds and management intervention points to improve probability of species persistence, and evaluate trade-offs of alternative management options. PMID:27547529

Short ensembles: An Efficient Method for Discerning Climate-relevant Sensitivities in Atmospheric General Circulation Models

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

Wan, Hui; Rasch, Philip J.; Zhang, Kai

2014-09-08

This paper explores the feasibility of an experimentation strategy for investigating sensitivities in fast components of atmospheric general circulation models. The basic idea is to replace the traditional serial-in-time long-term climate integrations by representative ensembles of shorter simulations. The key advantage of the proposed method lies in its efficiency: since fewer days of simulation are needed, the computational cost is less, and because individual realizations are independent and can be integrated simultaneously, the new dimension of parallelism can dramatically reduce the turnaround time in benchmark tests, sensitivities studies, and model tuning exercises. The strategy is not appropriate for exploring sensitivitymore » of all model features, but it is very effective in many situations. Two examples are presented using the Community Atmosphere Model version 5. The first example demonstrates that the method is capable of characterizing the model cloud and precipitation sensitivity to time step length. A nudging technique is also applied to an additional set of simulations to help understand the contribution of physics-dynamics interaction to the detected time step sensitivity. In the second example, multiple empirical parameters related to cloud microphysics and aerosol lifecycle are perturbed simultaneously in order to explore which parameters have the largest impact on the simulated global mean top-of-atmosphere radiation balance. Results show that in both examples, short ensembles are able to correctly reproduce the main signals of model sensitivities revealed by traditional long-term climate simulations for fast processes in the climate system. The efficiency of the ensemble method makes it particularly useful for the development of high-resolution, costly and complex climate models.« less

Mesoscale Assimilation of TMI Rainfall Data with 4DVAR: Sensitivity Studies

NASA Technical Reports Server (NTRS)

Tao, Wei-Kuo; Pu, Zhaoxia

2003-01-01

Sensitivity studies are performed on the assimilation of TRMM (Tropical Rainfall Measurement Mission) Microwave Imager (TMI) derived rainfall data into a mesoscale model using a four-dimensional variational data assimilation (4DVAR) technique. A series of numerical experiments is conducted to evaluate the impact of TMI rainfall data on the numerical simulation of Hurricane Bonnie (1998). The results indicate that rainfall data assimilation is sensitive to the error characteristics of the data and the inclusion of physics in the adjoint and forward models. In addition, assimilating the rainfall data alone is helpful for producing a more realistic eye and rain bands in the hurricane but does not ensure improvements in hurricane intensity forecasts. Further study indicated that it is necessary to incorporate TMI rainfall data together with other types of data such as wind data into the model, in which case the inclusion of the rainfall data further improves the intensity forecast of the hurricane. This implies that proper constraints may be needed for rainfall assimilation.

Acidification and Prognosis for Future Recovery of Acid-Sensitive Streams in the Southern Blue Ridge Province

Treesearch

Timothy Sullivan; Bernard Cosby; William Jackson; Kai Snyder; Alan Herlihy

2011-01-01

This study applied the Model of Acidification of Groundwater in Catchments (MAGIC) to estimate the sensitivity of 66 watersheds in the Southern Blue Ridge Province of the Southern Appalachian Mountains, United States, to changes in atmospheric sulfur (S) deposition. MAGIC predicted that stream acid neutralizing capacity (ANC) values were above 20 μeq/L in all modeled...

Mean and turbulent flow downstream of a low-intensity fire: influence of canopy and background atmospheric conditions

Treesearch

Michael T. Kiefer; Warren E. Heilman; Shiyuan Zhong; Joseph J. Charney; Xindi Bian

2015-01-01

This study examines the sensitivity of mean and turbulent flow in the planetary boundary layer and roughness sublayer to a low-intensity fire and evaluates whether the sensitivity is dependent on canopy and background atmospheric properties. The ARPS-CANOPY model, a modified version of the Advanced Regional Prediction System (ARPS) model with a canopy parameterization...

A sensitivity analysis of low salinity habitats simulated by a hydrodynamic model in the Manatee River estuary in Florida, USA

NASA Astrophysics Data System (ADS)

Chen, XinJian

2012-06-01

This paper presents a sensitivity study of simulated availability of low salinity habitats by a hydrodynamic model for the Manatee River estuary located in the southwest portion of the Florida peninsula. The purpose of the modeling study was to establish a regulatory minimum freshwater flow rate required to prevent the estuarine ecosystem from significant harm. The model used in the study was a multi-block model that dynamically couples a three-dimensional (3D) hydrodynamic model with a laterally averaged (2DV) hydrodynamic model. The model was calibrated and verified against measured real-time data of surface elevation and salinity at five stations during March 2005-July 2006. The calibrated model was then used to conduct a series of scenario runs to investigate effects of the flow reduction on salinity distributions in the Manatee River estuary. Based on simulated salinity distribution in the estuary, water volumes, bottom areas and shoreline lengths for salinity less than certain predefined values were calculated and analyzed to help establish the minimum freshwater flow rate for the estuarine system. The sensitivity analysis conducted during the modeling study for the Manatee River estuary examined effects of the bottom roughness, ambient vertical eddy viscosity/diffusivity, horizontal eddy viscosity/diffusivity, and ungauged flow on the model results and identified the relative importance of these model parameters (input data) to the outcome of the availability of low salinity habitats. It is found that the ambient vertical eddy viscosity/diffusivity is the most influential factor controlling the model outcome, while the horizontal eddy viscosity/diffusivity is the least influential one.

Environmental and Hydroclimatic Sensitivities of Greenhouse Gas (GHG) Fluxes from Coastal Wetlands

NASA Astrophysics Data System (ADS)

Abdul-Aziz, O. I.; Ishtiaq, K. S.

2016-12-01

We computed the reference environmental and hydroclimatic sensitivities of the greenhouse gas (GHG) fluxes (CO2 and CH4) from coastal salt marshes. Non-linear partial least squares regression models of CO2 (net uptake) and CH4 (net emissions) fluxes were developed with a bootstrap resampling approach using the photosynthetically active radiation (PAR), air and soil temperatures, water height, soil moisture, porewater salinity, and pH as predictors. Analytical sensitivity coefficients of different predictors were then analytically derived from the estimated models. The numerical sensitivities of the dominant drivers were determined by perturbing the variables individually and simultaneously to compute their individual and combined (respectively) effects on the GHG fluxes. Four tidal wetlands of Waquoit Bay, MA — incorporating a gradient in land-use, salinity and hydrology — were considered as the case study sites. The wetlands were dominated by native Spartina Alterniflora, and characterized by high salinity and frequent flooding. Results indicated a high sensitivity of CO2 fluxes to temperature and PAR, a moderate sensitivity to soil salinity and water height, and a weak sensitivity to pH and soil moisture. In contrast, the CH4 fluxes were more sensitive to temperature and salinity, compared to that of PAR, pH, and hydrologic variables. The estimated sensitivities and mechanistic insights can aid the management of coastal carbon under a changing climate and environment. The sensitivity coefficients also indicated the most dominant drivers of GHG fluxes for the development of a parsimonious predictive model.

Regionalising MUSLE factors for application to a data-scarce catchment

NASA Astrophysics Data System (ADS)

Gwapedza, David; Slaughter, Andrew; Hughes, Denis; Mantel, Sukhmani

2018-04-01

The estimation of soil loss and sediment transport is important for effective management of catchments. A model for semi-arid catchments in southern Africa has been developed; however, simplification of the model parameters and further testing are required. Soil loss is calculated through the Modified Universal Soil Loss Equation (MUSLE). The aims of the current study were to: (1) regionalise the MUSLE erodibility factors and; (2) perform a sensitivity analysis and validate the soil loss outputs against independently-estimated measures. The regionalisation was developed using Geographic Information Systems (GIS) coverages. The model was applied to a high erosion semi-arid region in the Eastern Cape, South Africa. Sensitivity analysis indicated model outputs to be more sensitive to the vegetation cover factor. The simulated soil loss estimates of 40 t ha-1 yr-1 were within the range of estimates by previous studies. The outcome of the present research is a framework for parameter estimation for the MUSLE through regionalisation. This is part of the ongoing development of a model which can estimate soil loss and sediment delivery at broad spatial and temporal scales.

Greenhouse gas scenario sensitivity and uncertainties in precipitation projections for central Belgium

NASA Astrophysics Data System (ADS)

Van Uytven, E.; Willems, P.

2018-03-01

Climate change impact assessment on meteorological variables involves large uncertainties as a result of incomplete knowledge on the future greenhouse gas concentrations and climate model physics, next to the inherent internal variability of the climate system. Given that the alteration in greenhouse gas concentrations is the driver for the change, one expects the impacts to be highly dependent on the considered greenhouse gas scenario (GHS). In this study, we denote this behavior as GHS sensitivity. Due to the climate model related uncertainties, this sensitivity is, at local scale, not always that strong as expected. This paper aims to study the GHS sensitivity and its contributing role to climate scenarios for a case study in Belgium. An ensemble of 160 CMIP5 climate model runs is considered and climate change signals are studied for precipitation accumulation, daily precipitation intensities and wet day frequencies. This was done for the different seasons of the year and the scenario periods 2011-2040, 2031-2060, 2051-2081 and 2071-2100. By means of variance decomposition, the total variance in the climate change signals was separated in the contribution of the differences in GHSs and the other model-related uncertainty sources. These contributions were found dependent on the variable and season. Following the time of emergence concept, the GHS uncertainty contribution is found dependent on the time horizon and increases over time. For the most distinct time horizon (2071-2100), the climate model uncertainty accounts for the largest uncertainty contribution. The GHS differences explain up to 18% of the total variance in the climate change signals. The results point further at the importance of the climate model ensemble design, specifically the ensemble size and the combination of climate models, whereupon climate scenarios are based. The numerical noise, introduced at scales smaller than the skillful scale, e.g. at local scale, was not considered in this study.

Diagnosis and Management of Deployed Adults with Chest Pain.

DTIC Science & Technology

1983-01-31

sequential Bayesian model using the likelihood ratios that he supplied to the United States Navy. Because the results of his study were not provided...THE EKG A. All BWH Patients ( n = 899 ) MODEL MI NO MI MI 179 20 199 TRUTH NO MI 248 452 700 427 472 899 SENSITIVITY = 79 = .90199 SPECIFICITY...WITH THE EKG C. BWH Males, < 60 years old, ( n = 250) MODEL MI NO MI MI 49 4 53 TRUTH NO MI 59 138 197 108 142 250 SENSITIVITY = - = .92 53 SPECIFICITY

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.

Determination of Highly Sensitive Biological Cell Model Systems to Screen BPA-Related Health Hazards Using Pathway Studio.

PubMed

Ryu, Do-Yeal; Rahman, Md Saidur; Pang, Myung-Geol

2017-09-06

Bisphenol-A (BPA) is a ubiquitous endocrine-disrupting chemical. Recently, many issues have arisen surrounding the disease pathogenesis of BPA. Therefore, several studies have been conducted to investigate the proteomic biomarkers of BPA that are associated with disease processes. However, studies on identifying highly sensitive biological cell model systems in determining BPA health risk are lacking. Here, we determined suitable cell model systems and potential biomarkers for predicting BPA-mediated disease using the bioinformatics tool Pathway Studio. We compiled known BPA-mediated diseases in humans, which were categorized into five major types. Subsequently, we investigated the differentially expressed proteins following BPA exposure in several cell types, and analyzed the efficacy of altered proteins to investigate their associations with BPA-mediated diseases. Our results demonstrated that colon cancer cells (SW480), mammary gland, and Sertoli cells were highly sensitive biological model systems, because of the efficacy of predicting the majority of BPA-mediated diseases. We selected glucose-6-phosphate dehydrogenase (G6PD), cytochrome b-c1 complex subunit 1 (UQCRC1), and voltage-dependent anion-selective channel protein 2 (VDAC2) as highly sensitive biomarkers to predict BPA-mediated diseases. Furthermore, we summarized proteomic studies in spermatozoa following BPA exposure, which have recently been considered as another suitable cell type for predicting BPA-mediated diseases.

Determination of Highly Sensitive Biological Cell Model Systems to Screen BPA-Related Health Hazards Using Pathway Studio

PubMed Central

Ryu, Do-Yeal

2017-01-01

Bisphenol-A (BPA) is a ubiquitous endocrine-disrupting chemical. Recently, many issues have arisen surrounding the disease pathogenesis of BPA. Therefore, several studies have been conducted to investigate the proteomic biomarkers of BPA that are associated with disease processes. However, studies on identifying highly sensitive biological cell model systems in determining BPA health risk are lacking. Here, we determined suitable cell model systems and potential biomarkers for predicting BPA-mediated disease using the bioinformatics tool Pathway Studio. We compiled known BPA-mediated diseases in humans, which were categorized into five major types. Subsequently, we investigated the differentially expressed proteins following BPA exposure in several cell types, and analyzed the efficacy of altered proteins to investigate their associations with BPA-mediated diseases. Our results demonstrated that colon cancer cells (SW480), mammary gland, and Sertoli cells were highly sensitive biological model systems, because of the efficacy of predicting the majority of BPA-mediated diseases. We selected glucose-6-phosphate dehydrogenase (G6PD), cytochrome b-c1 complex subunit 1 (UQCRC1), and voltage-dependent anion-selective channel protein 2 (VDAC2) as highly sensitive biomarkers to predict BPA-mediated diseases. Furthermore, we summarized proteomic studies in spermatozoa following BPA exposure, which have recently been considered as another suitable cell type for predicting BPA-mediated diseases. PMID:28878155

Analyses of a heterogeneous lattice hydrodynamic model with low and high-sensitivity vehicles

NASA Astrophysics Data System (ADS)

Kaur, Ramanpreet; Sharma, Sapna

2018-06-01

Basic lattice model is extended to study the heterogeneous traffic by considering the optimal current difference effect on a unidirectional single lane highway. Heterogeneous traffic consisting of low- and high-sensitivity vehicles is modeled and their impact on stability of mixed traffic flow has been examined through linear stability analysis. The stability of flow is investigated in five distinct regions of the neutral stability diagram corresponding to the amount of higher sensitivity vehicles present on road. In order to investigate the propagating behavior of density waves non linear analysis is performed and near the critical point, the kink antikink soliton is obtained by driving mKdV equation. The effect of fraction parameter corresponding to high sensitivity vehicles is investigated and the results indicates that the stability rise up due to the fraction parameter. The theoretical findings are verified via direct numerical simulation.

Equilibrium and Effective Climate Sensitivity

NASA Astrophysics Data System (ADS)

Rugenstein, M.; Bloch-Johnson, J.

2016-12-01

Atmosphere-ocean general circulation models, as well as the real world, take thousands of years to equilibrate to CO2 induced radiative perturbations. Equilibrium climate sensitivity - a fully equilibrated 2xCO2 perturbation - has been used for decades as a benchmark in model intercomparisons, as a test of our understanding of the climate system and paleo proxies, and to predict or project future climate change. Computational costs and limited time lead to the widespread practice of extrapolating equilibrium conditions from just a few decades of coupled simulations. The most common workaround is the "effective climate sensitivity" - defined through an extrapolation of a 150 year abrupt2xCO2 simulation, including the assumption of linear climate feedbacks. The definitions of effective and equilibrium climate sensitivity are often mixed up and used equivalently, and it is argued that "transient climate sensitivity" is the more relevant measure for predicting the next decades. We present an ongoing model intercomparison, the "LongRunMIP", to study century and millennia time scales of AOGCM equilibration and the linearity assumptions around feedback analysis. As a true ensemble of opportunity, there is no protocol and the only condition to participate is a coupled model simulation of any stabilizing scenario simulating more than 1000 years. Many of the submitted simulations took several years to conduct. As of July 2016 the contribution comprises 27 scenario simulations of 13 different models originating from 7 modeling centers, each between 1000 and 6000 years. To contribute, please contact the authors as soon as possible We present preliminary results, discussing differences between effective and equilibrium climate sensitivity, the usefulness of transient climate sensitivity, extrapolation methods, and the state of the coupled climate system close to equilibrium. Caption for the Figure below: Evolution of temperature anomaly and radiative imbalance of 22 simulations with 12 models (color indicates the model). 20 year moving average.

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

Elliott, Elizabeth J.; Yu, Sungduk; Kooperman, Gabriel J.

The sensitivities of simulated mesoscale convective systems (MCSs) in the central U.S. to microphysics and grid configuration are evaluated here in a global climate model (GCM) that also permits global-scale feedbacks and variability. Since conventional GCMs do not simulate MCSs, studying their sensitivities in a global framework useful for climate change simulations has not previously been possible. To date, MCS sensitivity experiments have relied on controlled cloud resolving model (CRM) studies with limited domains, which avoid internal variability and neglect feedbacks between local convection and larger-scale dynamics. However, recent work with superparameterized (SP) GCMs has shown that eastward propagating MCS-likemore » events are captured when embedded CRMs replace convective parameterizations. This study uses a SP version of the Community Atmosphere Model version 5 (SP-CAM5) to evaluate MCS sensitivities, applying an objective empirical orthogonal function algorithm to identify MCS-like events, and harmonizing composite storms to account for seasonal and spatial heterogeneity. A five-summer control simulation is used to assess the magnitude of internal and interannual variability relative to 10 sensitivity experiments with varied CRM parameters, including ice fall speed, one-moment and two-moment microphysics, and grid spacing. MCS sensitivities were found to be subtle with respect to internal variability, and indicate that ensembles of over 100 storms may be necessary to detect robust differences in SP-GCMs. Furthermore, these results emphasize that the properties of MCSs can vary widely across individual events, and improving their representation in global simulations with significant internal variability may require comparison to long (multidecadal) time series of observed events rather than single season field campaigns.« less

Variation of the NMVOC speciation in the solvent sector and the sensitivity of modelled tropospheric ozone

NASA Astrophysics Data System (ADS)

von Schneidemesser, E.; Coates, J.; Denier van der Gon, H. A. C.; Visschedijk, A. J. H.; Butler, T. M.

2016-06-01

Non-methane volatile organic compounds (NMVOCs) are detrimental to human health owing to the toxicity of many of the NMVOC species, as well as their role in the formation of secondary air pollutants such as tropospheric ozone (O3) and secondary organic aerosol. The speciation and amount of NMVOCs emitted into the troposphere are represented in emission inventories (EIs) for input to chemical transport models that predict air pollutant levels. Much of the information in EIs pertaining to speciation of NMVOCs is likely outdated, but before taking on the task of providing an up-to-date and highly speciated EI, a better understanding of the sensitivity of models to the change in NMVOC input would be highly beneficial. According to the EIs, the solvent sector is the most important sector for NMVOC emissions. Here, the sensitivity of modelled tropospheric O3 to NMVOC emission inventory speciation was investigated by comparing the maximum potential difference in O3 produced using a variety of reported solvent sector EI speciations in an idealized study using a box model. The sensitivity was tested using three chemical mechanisms that describe O3 production chemistry, typically employed for different types of modelling scales - point (MCM v3.2), regional (RADM2), and global (MOZART-4). In the box model simulations, a maximum difference of 15 ppbv (ca. 22% of the mean O3 mixing ratio of 69 ppbv) between the different EI speciations of the solvent sector was calculated. In comparison, for the same EI speciation, but comparing the three different mechanisms, a maximum difference of 6.7 ppbv was observed. Relationships were found between the relative contribution of NMVOC compound classes (alkanes and oxygenated species) in the speciations to the amount of Ox produced in the box model. These results indicate that modelled tropospheric O3 is sensitive to the speciation of NMVOCs as specified by emission inventories, suggesting that detailed updates to the EI speciation information would be warranted. Furthermore, modelled tropospheric O3 was also sensitive to the choice of chemical mechanism and further evaluation of both of these sensitivities in more realistic chemical-transport models is needed.

Modeling the Sensitivity of Field Surveys for Detection of Environmental DNA (eDNA)

PubMed Central

Schultz, Martin T.; Lance, Richard F.

2015-01-01

The environmental DNA (eDNA) method is the practice of collecting environmental samples and analyzing them for the presence of a genetic marker specific to a target species. Little is known about the sensitivity of the eDNA method. Sensitivity is the probability that the target marker will be detected if it is present in the water body. Methods and tools are needed to assess the sensitivity of sampling protocols, design eDNA surveys, and interpret survey results. In this study, the sensitivity of the eDNA method is modeled as a function of ambient target marker concentration. The model accounts for five steps of sample collection and analysis, including: 1) collection of a filtered water sample from the source; 2) extraction of DNA from the filter and isolation in a purified elution; 3) removal of aliquots from the elution for use in the polymerase chain reaction (PCR) assay; 4) PCR; and 5) genetic sequencing. The model is applicable to any target species. For demonstration purposes, the model is parameterized for bighead carp (Hypophthalmichthys nobilis) and silver carp (H. molitrix) assuming sampling protocols used in the Chicago Area Waterway System (CAWS). Simulation results show that eDNA surveys have a high false negative rate at low concentrations of the genetic marker. This is attributed to processing of water samples and division of the extraction elution in preparation for the PCR assay. Increases in field survey sensitivity can be achieved by increasing sample volume, sample number, and PCR replicates. Increasing sample volume yields the greatest increase in sensitivity. It is recommended that investigators estimate and communicate the sensitivity of eDNA surveys to help facilitate interpretation of eDNA survey results. In the absence of such information, it is difficult to evaluate the results of surveys in which no water samples test positive for the target marker. It is also recommended that invasive species managers articulate concentration-based sensitivity objectives for eDNA surveys. In the absence of such information, it is difficult to design appropriate sampling protocols. The model provides insights into how sampling protocols can be designed or modified to achieve these sensitivity objectives. PMID:26509674

Modeling the Sensitivity of Field Surveys for Detection of Environmental DNA (eDNA).

PubMed

Schultz, Martin T; Lance, Richard F

2015-01-01

The environmental DNA (eDNA) method is the practice of collecting environmental samples and analyzing them for the presence of a genetic marker specific to a target species. Little is known about the sensitivity of the eDNA method. Sensitivity is the probability that the target marker will be detected if it is present in the water body. Methods and tools are needed to assess the sensitivity of sampling protocols, design eDNA surveys, and interpret survey results. In this study, the sensitivity of the eDNA method is modeled as a function of ambient target marker concentration. The model accounts for five steps of sample collection and analysis, including: 1) collection of a filtered water sample from the source; 2) extraction of DNA from the filter and isolation in a purified elution; 3) removal of aliquots from the elution for use in the polymerase chain reaction (PCR) assay; 4) PCR; and 5) genetic sequencing. The model is applicable to any target species. For demonstration purposes, the model is parameterized for bighead carp (Hypophthalmichthys nobilis) and silver carp (H. molitrix) assuming sampling protocols used in the Chicago Area Waterway System (CAWS). Simulation results show that eDNA surveys have a high false negative rate at low concentrations of the genetic marker. This is attributed to processing of water samples and division of the extraction elution in preparation for the PCR assay. Increases in field survey sensitivity can be achieved by increasing sample volume, sample number, and PCR replicates. Increasing sample volume yields the greatest increase in sensitivity. It is recommended that investigators estimate and communicate the sensitivity of eDNA surveys to help facilitate interpretation of eDNA survey results. In the absence of such information, it is difficult to evaluate the results of surveys in which no water samples test positive for the target marker. It is also recommended that invasive species managers articulate concentration-based sensitivity objectives for eDNA surveys. In the absence of such information, it is difficult to design appropriate sampling protocols. The model provides insights into how sampling protocols can be designed or modified to achieve these sensitivity objectives.

How can sensitivity analysis improve the robustness of mathematical models utilized by the re/insurance industry?

NASA Astrophysics Data System (ADS)

Noacco, V.; Wagener, T.; Pianosi, F.; Philp, T.

2017-12-01

Insurance companies provide insurance against a wide range of threats, such as natural catastrophes, nuclear incidents and terrorism. To quantify risk and support investment decisions, mathematical models are used, for example to set the premiums charged to clients that protect from financial loss, should deleterious events occur. While these models are essential tools for adequately assessing the risk attached to an insurer's portfolio, their development is costly and their value for decision-making may be limited by an incomplete understanding of uncertainty and sensitivity. Aside from the business need to understand risk and uncertainty, the insurance sector also faces regulation which requires them to test their models in such a way that uncertainties are appropriately captured and that plans are in place to assess the risks and their mitigation. The building and testing of models constitutes a high cost for insurance companies, and it is a time intensive activity. This study uses an established global sensitivity analysis toolbox (SAFE) to more efficiently capture the uncertainties and sensitivities embedded in models used by a leading re/insurance firm, with structured approaches to validate these models and test the impact of assumptions on the model predictions. It is hoped that this in turn will lead to better-informed and more robust business decisions.

Using Simulation to Model and Validate Invasive Breast Cancer Progression in Women in the Study and Control Groups of the Canadian National Breast Screening Studies I and II.

PubMed

Taghipour, Sharareh; Caudrelier, Laurent N; Miller, Anthony B; Harvey, Bart

2017-02-01

Modeling breast cancer progression and the effect of various risk is helpful in deciding when a woman should start and end screening, and how often the screening should be undertaken. We modeled the natural progression of breast cancer using a hidden Markov process, and incorporated the effects of covariates. Patients are women aged 50-59 (older) and 40-49 (younger) years from the Canadian National Breast Screening Studies. We included prevalent cancers, estimated the screening sensitivities and rates of over-diagnosis, and validated the models using simulation. We found that older women have a higher rate of transition from a healthy to preclinical state and other causes of death but a lower rate of transition from preclinical to clinical state. Reciprocally, younger women have a lower rate of transition from a healthy to preclinical state and other causes of death but a higher rate of transition from a preclinical to clinical state. Different risk factors were significant for the age groups. The mean sojourn times for older and younger women were 2.53 and 2.96 years, respectively. In the study group, the sensitivities of the initial physical examination and mammography for older and younger women were 0.87 and 0.81, respectively, and the sensitivity of the subsequent screens were 0.78 and 0.53, respectively. In the control groups, the sensitivities of the initial physical examination for older and younger women were 0.769 and 0.671, respectively, and the sensitivity of the subsequent physical examinations for the control group aged 50-59 years was 0.37. The upper-bounds for over-diagnosis in older and younger women were 25% and 27%, respectively. The present work offers a basis for the better modeling of cancer incidence for a population with the inclusion of prevalent cancers.

An automatic and effective parameter optimization method for model tuning

NASA Astrophysics Data System (ADS)

Zhang, T.; Li, L.; Lin, Y.; Xue, W.; Xie, F.; Xu, H.; Huang, X.

2015-05-01

Physical parameterizations in General Circulation Models (GCMs), having various uncertain parameters, greatly impact model performance and model climate sensitivity. Traditional manual and empirical tuning of these parameters is time consuming and ineffective. In this study, a "three-step" methodology is proposed to automatically and effectively obtain the optimum combination of some key parameters in cloud and convective parameterizations according to a comprehensive objective evaluation metrics. Different from the traditional optimization methods, two extra steps, one determines parameter sensitivity and the other chooses the optimum initial value of sensitive parameters, are introduced before the downhill simplex method to reduce the computational cost and improve the tuning performance. Atmospheric GCM simulation results show that the optimum combination of these parameters determined using this method is able to improve the model's overall performance by 9%. The proposed methodology and software framework can be easily applied to other GCMs to speed up the model development process, especially regarding unavoidable comprehensive parameters tuning during the model development stage.

Overflow Simulations using MPAS-Ocean in Idealized and Realistic Domains

NASA Astrophysics Data System (ADS)

Reckinger, S.; Petersen, M. R.; Reckinger, S. J.

2016-02-01

MPAS-Ocean is used to simulate an idealized, density-driven overflow using the dynamics of overflow mixing and entrainment (DOME) setup. Numerical simulations are benchmarked against other models, including the MITgcm's z-coordinate model and HIM's isopycnal coordinate model. A full parameter study is presented that looks at how sensitive overflow simulations are to vertical grid type, resolution, and viscosity. Horizontal resolutions with 50 km grid cells are under-resolved and produce poor results, regardless of other parameter settings. Vertical grids ranging in thickness from 15 m to 120 m were tested. A horizontal resolution of 10 km and a vertical resolution of 60 m are sufficient to resolve the mesoscale dynamics of the DOME configuration, which mimics real-world overflow parameters. Mixing and final buoyancy are least sensitive to horizontal viscosity, but strongly sensitive to vertical viscosity. This suggests that vertical viscosity could be adjusted in overflow water formation regions to influence mixing and product water characteristics. Also, the study shows that sigma coordinates produce much less mixing than z-type coordinates, resulting in heavier plumes that go further down slope. Sigma coordinates are less sensitive to changes in resolution but as sensitive to vertical viscosity compared to z-coordinates. Additionally, preliminary measurements of overflow diagnostics on global simulations using a realistic oceanic domain are presented.

Toxicity of tributyltin (TBT) to the freshwater planarian Schmidtea mediterranea.

PubMed

Ofoegbu, Pearl U; Simão, Fátima C P; Cruz, Andreia; Mendo, Sónia; Soares, Amadeu M V M; Pestana, João L T

2016-04-01

The freshwater planarian Schmidtea mediterranea, one of the best characterized animal models for regeneration research and developmental biology, is being recognised as a useful species for ecotoxicological studies. Sensitive endpoints related to planarians' behaviour and regeneration can be easily evaluated after exposure to environmental stressors. In this work the sensitivity of S. mediterranea to a gradient of environmentally relevant concentrations of TBT was studied using multiple endpoints like survival, locomotion, head regeneration and DNA damage. In addition, a feeding assay based on planarian's predatory behaviour was performed. Results indicated that TBT is toxic to planarians with LC50's of 1.87 μg L(-1) Sn and 1.31 μg L(-1) Sn at 48 h and 96 h of exposure respectively. Sub-lethal exposures to TBT significantly reduced locomotion and feeding, delayed head regeneration and caused DNA damage in planarians. The behavioural endpoints (feeding and locomotion) and head regeneration were the most sensitive parameters followed by DNA damage. Similar to other aquatic model organisms, S. mediterranea showed high sensitivity towards TBT exposure. Based on our results, and though further research is required concerning their sensitivity to other pollutants, the use of freshwater planarians as a model species in ecotoxicology is discussed. Copyright © 2016. Published by Elsevier Ltd.

The GFDL global atmosphere and land model AM4.0/LM4.0: 2. Model description, sensitivity studies, and tuning strategies

USGS Publications Warehouse

Zhao, M.; Golaz, J.-C.; Held, I. M.; Guo, H.; Balaji, V.; Benson, R.; Chen, J.-H.; Chen, X.; Donner, L. J.; Dunne, J. P.; Dunne, Krista A.; Durachta, J.; Fan, S.-M.; Freidenreich, S. M.; Garner, S. T.; Ginoux, P.; Harris, L. M.; Horowitz, L. W.; Krasting, J. P.; Langenhorst, A. R.; Liang, Z.; Lin, P.; Lin, S.-J.; Malyshev, S. L.; Mason, E.; Milly, Paul C.D.; Ming, Y.; Naik, V.; Paulot, F.; Paynter, D.; Phillipps, P.; Radhakrishnan, A.; Ramaswamy, V.; Robinson, T.; Schwarzkopf, D.; Seman, C. J.; Shevliakova, E.; Shen, Z.; Shin, H.; Silvers, L.; Wilson, J. R.; Winton, M.; Wittenberg, A. T.; Wyman, B.; Xiang, B.

2018-01-01

In Part 2 of this two‐part paper, documentation is provided of key aspects of a version of the AM4.0/LM4.0 atmosphere/land model that will serve as a base for a new set of climate and Earth system models (CM4 and ESM4) under development at NOAA's Geophysical Fluid Dynamics Laboratory (GFDL). The quality of the simulation in AMIP (Atmospheric Model Intercomparison Project) mode has been provided in Part 1. Part 2 provides documentation of key components and some sensitivities to choices of model formulation and values of parameters, highlighting the convection parameterization and orographic gravity wave drag. The approach taken to tune the model's clouds to observations is a particular focal point. Care is taken to describe the extent to which aerosol effective forcing and Cess sensitivity have been tuned through the model development process, both of which are relevant to the ability of the model to simulate the evolution of temperatures over the last century when coupled to an ocean model.

The GFDL Global Atmosphere and Land Model AM4.0/LM4.0: 2. Model Description, Sensitivity Studies, and Tuning Strategies

DOE PAGES

Zhao, Ming; Golaz, J. -C.; Held, I. M.; ...

2018-02-19

Here, in Part 2 of this two–part paper, documentation is provided of key aspects of a version of the AM4.0/LM4.0 atmosphere/land model that will serve as a base for a new set of climate and Earth system models (CM4 and ESM4) under development at NOAA's Geophysical Fluid Dynamics Laboratory (GFDL). The quality of the simulation in AMIP (Atmospheric Model Intercomparison Project) mode has been provided in Part 1. Part 2 provides documentation of key components and some sensitivities to choices of model formulation and values of parameters, highlighting the convection parameterization and orographic gravity wave drag. The approach taken tomore » tune the model's clouds to observations is a particular focal point. Care is taken to describe the extent to which aerosol effective forcing and Cess sensitivity have been tuned through the model development process, both of which are relevant to the ability of the model to simulate the evolution of temperatures over the last century when coupled to an ocean model.« less

The GFDL Global Atmosphere and Land Model AM4.0/LM4.0: 2. Model Description, Sensitivity Studies, and Tuning Strategies

NASA Astrophysics Data System (ADS)

Zhao, M.; Golaz, J.-C.; Held, I. M.; Guo, H.; Balaji, V.; Benson, R.; Chen, J.-H.; Chen, X.; Donner, L. J.; Dunne, J. P.; Dunne, K.; Durachta, J.; Fan, S.-M.; Freidenreich, S. M.; Garner, S. T.; Ginoux, P.; Harris, L. M.; Horowitz, L. W.; Krasting, J. P.; Langenhorst, A. R.; Liang, Z.; Lin, P.; Lin, S.-J.; Malyshev, S. L.; Mason, E.; Milly, P. C. D.; Ming, Y.; Naik, V.; Paulot, F.; Paynter, D.; Phillipps, P.; Radhakrishnan, A.; Ramaswamy, V.; Robinson, T.; Schwarzkopf, D.; Seman, C. J.; Shevliakova, E.; Shen, Z.; Shin, H.; Silvers, L. G.; Wilson, J. R.; Winton, M.; Wittenberg, A. T.; Wyman, B.; Xiang, B.

2018-03-01

In Part 2 of this two-part paper, documentation is provided of key aspects of a version of the AM4.0/LM4.0 atmosphere/land model that will serve as a base for a new set of climate and Earth system models (CM4 and ESM4) under development at NOAA's Geophysical Fluid Dynamics Laboratory (GFDL). The quality of the simulation in AMIP (Atmospheric Model Intercomparison Project) mode has been provided in Part 1. Part 2 provides documentation of key components and some sensitivities to choices of model formulation and values of parameters, highlighting the convection parameterization and orographic gravity wave drag. The approach taken to tune the model's clouds to observations is a particular focal point. Care is taken to describe the extent to which aerosol effective forcing and Cess sensitivity have been tuned through the model development process, both of which are relevant to the ability of the model to simulate the evolution of temperatures over the last century when coupled to an ocean model.

The GFDL Global Atmosphere and Land Model AM4.0/LM4.0: 2. Model Description, Sensitivity Studies, and Tuning Strategies

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

Zhao, Ming; Golaz, J. -C.; Held, I. M.

Here, in Part 2 of this two–part paper, documentation is provided of key aspects of a version of the AM4.0/LM4.0 atmosphere/land model that will serve as a base for a new set of climate and Earth system models (CM4 and ESM4) under development at NOAA's Geophysical Fluid Dynamics Laboratory (GFDL). The quality of the simulation in AMIP (Atmospheric Model Intercomparison Project) mode has been provided in Part 1. Part 2 provides documentation of key components and some sensitivities to choices of model formulation and values of parameters, highlighting the convection parameterization and orographic gravity wave drag. The approach taken tomore » tune the model's clouds to observations is a particular focal point. Care is taken to describe the extent to which aerosol effective forcing and Cess sensitivity have been tuned through the model development process, both of which are relevant to the ability of the model to simulate the evolution of temperatures over the last century when coupled to an ocean model.« less

GOCE gravity gradient data for lithospheric modeling - From well surveyed to frontier areas

NASA Astrophysics Data System (ADS)

Bouman, J.; Ebbing, J.; Gradmann, S.; Fuchs, M.; Fattah, R. Abdul; Meekes, S.; Schmidt, M.; Lieb, V.; Haagmans, R.

2012-04-01

We explore how GOCE gravity gradient data can improve modeling of the Earth's lithosphere and thereby contribute to a better understanding of the Earth's dynamic processes. The idea is to invert satellite gravity gradients and terrestrial gravity data in the well explored and understood North-East Atlantic Margin and to compare the results of this inversion, providing improved information about the lithosphere and upper mantle, with results obtained by means of models based upon other sources like seismics and magnetic field information. Transfer of the obtained knowledge to the less explored Rub' al Khali desert is foreseen. We present a case study for the North-East Atlantic margin, where we analyze the use of satellite gravity gradients by comparison with a well-constrained 3D density model that provides a detailed picture from the upper mantle to the top basement (base of sediments). The latter horizon is well resolved from gravity and especially magnetic data, whereas sedimentary layers are mainly constrained from seismic studies, but do in general not show a prominent effect in the gravity and magnetic field. We analyze how gravity gradients can increase confidence in the modeled structures by calculating a sensitivity matrix for the existing 3D model. This sensitivity matrix describes the relation between calculated gravity gradient data and geological structures with respect to their depth, extent and relative density contrast. As the sensitivity of the modeled bodies varies for different tensor components, we can use this matrix for a weighted inversion of gradient data to optimize the model. This sensitivity analysis will be used as input to study the Rub' al Khali desert in Saudi Arabia. In terms of modeling and data availability this is a frontier area. Here gravity gradient data will be used to better identify the extent of anomalous structures within the basin, with the goal to improve the modeling for hydrocarbon exploration purposes.

Soil maps as data input for soil erosion models: errors related to map scales

NASA Astrophysics Data System (ADS)

van Dijk, Paul; Sauter, Joëlle; Hofstetter, Elodie

2010-05-01

Soil erosion rates depend in many ways on soil and soil surface characteristics which vary in space and in time. To account for spatial variations of soil features, most distributed soil erosion models require data input derived from soil maps. Ideally, the level of spatial detail contained in the applied soil map should correspond to the objective of the modelling study. However, often the model user has only one soil map available which is then applied without questioning its suitability. The present study seeks to determine in how far soil map scale can be a source of error in erosion model output. The study was conducted on two different spatial scales, with for each of them a convenient soil erosion model: a) the catchment scale using the physically-based Limbourg Soil Erosion Model (LISEM), and b) the regional scale using the decision-tree expert model MESALES. The suitability of the applied soil map was evaluated with respect to an imaginary though realistic study objective for both models: the definition of erosion control measures at strategic locations at the catchment scale; the identification of target areas for the definition of control measures strategies at the regional scale. Two catchments were selected to test the sensitivity of LISEM to the spatial detail contained in soil maps: one catchment with relatively little contrast in soil texture, dominated by loess-derived soil (south of the Alsace), and one catchment with strongly contrasted soils at the limit between the Alsatian piedmont and the loess-covered hills of the Kochersberg. LISEM was run for both catchments using different soil maps ranging in scale from 1/25 000 to 1/100 000 to derive soil related input parameters. The comparison of the output differences was used to quantify the map scale impact on the quality of the model output. The sensitivity of MESALES was tested on the Haut-Rhin county for which two soil maps are available for comparison: 1/50 000 and 1/100 000. The order of resulting target areas (communes) was compared to evaluate the error induced by using the coarser soil data at 1/100 000. Results shows that both models are sensitive to the soil map scale used for model data input. A low sensitivity was found for the catchment with relatively homogeneous soil textures and the use of 1/100 000 soil maps seems allowed. The results for the catchment with strong soil texture variations showed significant differences depending on soil map scale on 75% of the catchment area. Here, the use of 1/100 000 soil map will indeed lead to wrong erosion diagnostics and will hamper the definition of a sound erosion control strategy. The regional scale model MESALES proved to be very sensitive to soil information. The two soil related model parameters (crusting sensitivity, and soil erodibility) reacted very often in the same direction therewith amplifying the change in the final erosion hazard class. The 1/100 000 soil map yielded different results on 40% of the sloping area compared to the 1/50 000 map. Significant differences in the order of target areas were found as well. The present study shows that the degree of sensitivity of the model output to soil map scale is rather variable and depends partly on the spatial variability of soil texture within the study area. Soil (textural) diversity needs to be accounted for to assure a fruitful use of soil erosion models. In some situations this might imply that additional soil data need to be collected in the field to refine the available soil map.

The sensitivity of ecosystem service models to choices of input data and spatial resolution

Treesearch

Kenneth J. Bagstad; Erika Cohen; Zachary H. Ancona; Steven. G. McNulty; Ge   Sun

2018-01-01

Although ecosystem service (ES) modeling has progressed rapidly in the last 10–15 years, comparative studies on data and model selection effects have become more common only recently. Such studies have drawn mixed conclusions about whether different data and model choices yield divergent results. In this study, we compared the results of different models to address...

Minimizing effects of methodological decisions on interpretation and prediction in species distribution studies: An example with background selection

USGS Publications Warehouse

Jarnevich, Catherine S.; Talbert, Marian; Morisette, Jeffrey T.; Aldridge, Cameron L.; Brown, Cynthia; Kumar, Sunil; Manier, Daniel; Talbert, Colin; Holcombe, Tracy R.

2017-01-01

Evaluating the conditions where a species can persist is an important question in ecology both to understand tolerances of organisms and to predict distributions across landscapes. Presence data combined with background or pseudo-absence locations are commonly used with species distribution modeling to develop these relationships. However, there is not a standard method to generate background or pseudo-absence locations, and method choice affects model outcomes. We evaluated combinations of both model algorithms (simple and complex generalized linear models, multivariate adaptive regression splines, Maxent, boosted regression trees, and random forest) and background methods (random, minimum convex polygon, and continuous and binary kernel density estimator (KDE)) to assess the sensitivity of model outcomes to choices made. We evaluated six questions related to model results, including five beyond the common comparison of model accuracy assessment metrics (biological interpretability of response curves, cross-validation robustness, independent data accuracy and robustness, and prediction consistency). For our case study with cheatgrass in the western US, random forest was least sensitive to background choice and the binary KDE method was least sensitive to model algorithm choice. While this outcome may not hold for other locations or species, the methods we used can be implemented to help determine appropriate methodologies for particular research questions.

Global sensitivity analysis, probabilistic calibration, and predictive assessment for the data assimilation linked ecosystem carbon model

DOE PAGES

Safta, C.; Ricciuto, Daniel M.; Sargsyan, Khachik; ...

2015-07-01

In this paper we propose a probabilistic framework for an uncertainty quantification (UQ) study of a carbon cycle model and focus on the comparison between steady-state and transient simulation setups. A global sensitivity analysis (GSA) study indicates the parameters and parameter couplings that are important at different times of the year for quantities of interest (QoIs) obtained with the data assimilation linked ecosystem carbon (DALEC) model. We then employ a Bayesian approach and a statistical model error term to calibrate the parameters of DALEC using net ecosystem exchange (NEE) observations at the Harvard Forest site. The calibration results are employedmore » in the second part of the paper to assess the predictive skill of the model via posterior predictive checks.« less

Thermodynamic modeling of transcription: sensitivity analysis differentiates biological mechanism from mathematical model-induced effects.

PubMed

Dresch, Jacqueline M; Liu, Xiaozhou; Arnosti, David N; Ay, Ahmet

2010-10-24

Quantitative models of gene expression generate parameter values that can shed light on biological features such as transcription factor activity, cooperativity, and local effects of repressors. An important element in such investigations is sensitivity analysis, which determines how strongly a model's output reacts to variations in parameter values. Parameters of low sensitivity may not be accurately estimated, leading to unwarranted conclusions. Low sensitivity may reflect the nature of the biological data, or it may be a result of the model structure. Here, we focus on the analysis of thermodynamic models, which have been used extensively to analyze gene transcription. Extracted parameter values have been interpreted biologically, but until now little attention has been given to parameter sensitivity in this context. We apply local and global sensitivity analyses to two recent transcriptional models to determine the sensitivity of individual parameters. We show that in one case, values for repressor efficiencies are very sensitive, while values for protein cooperativities are not, and provide insights on why these differential sensitivities stem from both biological effects and the structure of the applied models. In a second case, we demonstrate that parameters that were thought to prove the system's dependence on activator-activator cooperativity are relatively insensitive. We show that there are numerous parameter sets that do not satisfy the relationships proferred as the optimal solutions, indicating that structural differences between the two types of transcriptional enhancers analyzed may not be as simple as altered activator cooperativity. Our results emphasize the need for sensitivity analysis to examine model construction and forms of biological data used for modeling transcriptional processes, in order to determine the significance of estimated parameter values for thermodynamic models. Knowledge of parameter sensitivities can provide the necessary context to determine how modeling results should be interpreted in biological systems.

Meta-analysis for the comparison of two diagnostic tests to a common gold standard: A generalized linear mixed model approach.

PubMed

Hoyer, Annika; Kuss, Oliver

2018-05-01

Meta-analysis of diagnostic studies is still a rapidly developing area of biostatistical research. Especially, there is an increasing interest in methods to compare different diagnostic tests to a common gold standard. Restricting to the case of two diagnostic tests, in these meta-analyses the parameters of interest are the differences of sensitivities and specificities (with their corresponding confidence intervals) between the two diagnostic tests while accounting for the various associations across single studies and between the two tests. We propose statistical models with a quadrivariate response (where sensitivity of test 1, specificity of test 1, sensitivity of test 2, and specificity of test 2 are the four responses) as a sensible approach to this task. Using a quadrivariate generalized linear mixed model naturally generalizes the common standard bivariate model of meta-analysis for a single diagnostic test. If information on several thresholds of the tests is available, the quadrivariate model can be further generalized to yield a comparison of full receiver operating characteristic (ROC) curves. We illustrate our model by an example where two screening methods for the diagnosis of type 2 diabetes are compared.

Sensitivity of Glacier Mass Balance Estimates to the Selection of WRF Cloud Microphysics Parameterization in the Indus River Watershed

NASA Astrophysics Data System (ADS)

Johnson, E. S.; Rupper, S.; Steenburgh, W. J.; Strong, C.; Kochanski, A.

2017-12-01

Climate model outputs are often used as inputs to glacier energy and mass balance models, which are essential glaciological tools for testing glacier sensitivity, providing mass balance estimates in regions with little glaciological data, and providing a means to model future changes. Climate model outputs, however, are sensitive to the choice of physical parameterizations, such as those for cloud microphysics, land-surface schemes, surface layer options, etc. Furthermore, glacier mass balance (MB) estimates that use these climate model outputs as inputs are likely sensitive to the specific parameterization schemes, but this sensitivity has not been carefully assessed. Here we evaluate the sensitivity of glacier MB estimates across the Indus Basin to the selection of cloud microphysics parameterizations in the Weather Research and Forecasting Model (WRF). Cloud microphysics parameterizations differ in how they specify the size distributions of hydrometeors, the rate of graupel and snow production, their fall speed assumptions, the rates at which they convert from one hydrometeor type to the other, etc. While glacier MB estimates are likely sensitive to other parameterizations in WRF, our preliminary results suggest that glacier MB is highly sensitive to the timing, frequency, and amount of snowfall, which is influenced by the cloud microphysics parameterization. To this end, the Indus Basin is an ideal study site, as it has both westerly (winter) and monsoonal (summer) precipitation influences, is a data-sparse region (so models are critical), and still has lingering questions as to glacier importance for local and regional resources. WRF is run at a 4 km grid scale using two commonly used parameterizations: the Thompson scheme and the Goddard scheme. On average, these parameterizations result in minimal differences in annual precipitation. However, localized regions exhibit differences in precipitation of up to 3 m w.e. a-1. The different schemes also impact the radiative budgets over the glacierized areas. Our results show that glacier MB estimates can differ by up to 45% depending on the chosen cloud microphysics scheme. These findings highlight the need to better account for uncertainties in meteorological inputs into glacier energy and mass balance models.

Correspondence of electroencephalography and near-infrared spectroscopy sensitivities to the cerebral cortex using a high-density layout

PubMed Central

Giacometti, Paolo; Diamond, Solomon G.

2014-01-01

Abstract. This study investigates the correspondence of the cortical sensitivity of electroencephalography (EEG) and near-infrared spectroscopy (NIRS). EEG forward model sensitivity to the cerebral cortex was calculated for 329 EEG electrodes following the 10-5 EEG positioning system using a segmented structural magnetic resonance imaging scan of a human subject. NIRS forward model sensitivity was calculated for the same subject using 156 NIRS source-detector pairs selected from 32 source and 32 detector optodes positioned on the scalp using a subset of the 10-5 EEG positioning system. Sensitivity correlations between colocalized NIRS source-detector pair groups and EEG channels yielded R=0.46±0.08. Groups of NIRS source-detector pairs with maximum correlations to EEG electrode sensitivities are tabulated. The mean correlation between the point spread functions for EEG and NIRS regions of interest (ROI) was R=0.43±0.07. Spherical ROIs with radii of 26 mm yielded the maximum correlation between EEG and NIRS averaged across all cortical mesh nodes. These sensitivity correlations between EEG and NIRS should be taken into account when designing multimodal studies of neurovascular coupling and when using NIRS as a statistical prior for EEG source localization. PMID:25558462

Taxometric and Factor Analytic Models of Anxiety Sensitivity: Integrating Approaches to Latent Structural Research

ERIC Educational Resources Information Center

Bernstein, Amit; Zvolensky, Michael J.; Norton, Peter J.; Schmidt, Norman B.; Taylor, Steven; Forsyth, John P.; Lewis, Sarah F.; Feldner, Matthew T.; Leen-Feldner, Ellen W.; Stewart, Sherry H.; Cox, Brian

2007-01-01

This study represents an effort to better understand the latent structure of anxiety sensitivity (AS), as indexed by the 16-item Anxiety Sensitivity Index (ASI; S. Reiss, R. A. Peterson, M. Gursky, & R. J. McNally, 1986), by using taxometric and factor-analytic approaches in an integrative manner. Taxometric analyses indicated that AS has a…

The Effects of Recycling and Response Sensitivity on the Acquisition of Social Studies Concepts.

ERIC Educational Resources Information Center

Ford, Mary Jane; McKinney, C. Warren

1986-01-01

Two studies are reported which investigate the concept learning of 116 sixth graders (study 1) and 107 second graders (study 2) depending on the model of concept presentation. Results showed no difference between the structured Merrill and Tennyson model and adaptations of the model which were responsive to student's questions or recycled missed…

Sensitivity of Regulated Flow Regimes to Climate Change in the Western United States

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

Zhou, Tian; Voisin, Nathalie; Leng, Guoyong

Water management activities or flow regulations modify water fluxes at the land surface and affect water resources in space and time. We hypothesize that flow regulations change the sensitivity of river flow to climate change with respect to unmanaged water resources. Quantifying these changes in sensitivity could help elucidate the impacts of water management at different spatiotemporal scales and inform climate adaptation decisions. In this study, we compared the emergence of significant changes in natural and regulated river flow regimes across the Western United States from simulations driven by multiple climate models and scenarios. We find that significant climate change-inducedmore » alterations in natural flow do not cascade linearly through water management activities. At the annual time scale, 50% of the Hydrologic Unit Code 4 (HUC4) sub-basins over the Western U.S. regions tend to have regulated flow regime more sensitive to the climate change than natural flow regime. Seasonality analyses show that the sensitivity varies remarkably across the seasons. We also find that the sensitivity is related to the level of water management. For 35% of the HUC4 sub-basins with the highest level of water management, the summer and winter flows tend to show a heightened sensitivity to climate change due to the complexity of joint reservoir operations. We further demonstrate that the impacts of considering water management in models are comparable to those that arises from uncertainties across climate models and emission scenarios. This prompts further climate adaptation studies research about nonlinearity effects of climate change through water management activities.« less

Laser ablation under different electron heat conduction models in inertial confinement fusion

NASA Astrophysics Data System (ADS)

Li, Shuanggui; Ren, Guoli; Huo, Wen Yi

2018-06-01

In this paper, we study the influence of three different electron heat conduction models on the laser ablation of gold plane target. Different from previous studies, we concentrate on the plasma conditions, the conversion efficiency from laser into soft x rays and the scaling relation of mass ablation, which are relevant to hohlraum physics study in indirect drive inertial confinement fusion. We find that the simulated electron temperature in corona region is sensitive to the electron heat conduction models. For different electron heat conduction models, there are obvious differences in magnitude and spatial profile of electron temperature. For the flux limit model, the calculated conversion efficiency is sensitive to flux limiters. In the laser ablation of gold, most of the laser energies are converted into x rays. So the scaling relation of mass ablation rate is quite different from that of low Z materials.

Whole season compared to growth-stage resolved temperature trends: implications for US maize yield

NASA Astrophysics Data System (ADS)

Butler, E. E.; Mueller, N. D.; Huybers, P. J.

2014-12-01

The effect of temperature on maize yield has generally been considered using a single value for the entire growing season. We compare the effect of temperature trends on yield between two distinct models: a single temperature sensitivity for the whole season and a variable sensitivity across four distinct agronomic development stages. The more resolved variable-sensitivity model indicates roughly a factor of two greater influence of temperature on yield than that implied by the single-sensitivity model. The largest discrepancies occur in silking, which is demonstrated to be the most sensitive stage in the variable-sensitivity model. For instance, whereas median yields are observed to be only 53% of typical values during the hottest 1% of silking-stage temperatures, the single-sensitivity model over predicts median yields of 68% whereas the variable-sensitivity model more correctly predicts median yields of 61%. That the variable sensitivity model is also not capable of capturing the full extent of yield losses suggests that further refinement to represent the non-linear response would be useful. Results from the variable sensitivity model also indicate that management decisions regarding planting times, which have generally shifted toward earlier dates, have led to greater yield benefit than that implied by the single-sensitivity model. Together, the variation of both temperature trends and yield variability within growing stages calls for closer attention to how changes in management interact with changes in climate to ultimately affect yields.

Sensitivity Analysis of an ENteric Immunity SImulator (ENISI)-Based Model of Immune Responses to Helicobacter pylori Infection

PubMed Central

Alam, Maksudul; Deng, Xinwei; Philipson, Casandra; Bassaganya-Riera, Josep; Bisset, Keith; Carbo, Adria; Eubank, Stephen; Hontecillas, Raquel; Hoops, Stefan; Mei, Yongguo; Abedi, Vida; Marathe, Madhav

2015-01-01

Agent-based models (ABM) are widely used to study immune systems, providing a procedural and interactive view of the underlying system. The interaction of components and the behavior of individual objects is described procedurally as a function of the internal states and the local interactions, which are often stochastic in nature. Such models typically have complex structures and consist of a large number of modeling parameters. Determining the key modeling parameters which govern the outcomes of the system is very challenging. Sensitivity analysis plays a vital role in quantifying the impact of modeling parameters in massively interacting systems, including large complex ABM. The high computational cost of executing simulations impedes running experiments with exhaustive parameter settings. Existing techniques of analyzing such a complex system typically focus on local sensitivity analysis, i.e. one parameter at a time, or a close “neighborhood” of particular parameter settings. However, such methods are not adequate to measure the uncertainty and sensitivity of parameters accurately because they overlook the global impacts of parameters on the system. In this article, we develop novel experimental design and analysis techniques to perform both global and local sensitivity analysis of large-scale ABMs. The proposed method can efficiently identify the most significant parameters and quantify their contributions to outcomes of the system. We demonstrate the proposed methodology for ENteric Immune SImulator (ENISI), a large-scale ABM environment, using a computational model of immune responses to Helicobacter pylori colonization of the gastric mucosa. PMID:26327290

Sensitivity Analysis of an ENteric Immunity SImulator (ENISI)-Based Model of Immune Responses to Helicobacter pylori Infection.

PubMed

Alam, Maksudul; Deng, Xinwei; Philipson, Casandra; Bassaganya-Riera, Josep; Bisset, Keith; Carbo, Adria; Eubank, Stephen; Hontecillas, Raquel; Hoops, Stefan; Mei, Yongguo; Abedi, Vida; Marathe, Madhav

2015-01-01

Agent-based models (ABM) are widely used to study immune systems, providing a procedural and interactive view of the underlying system. The interaction of components and the behavior of individual objects is described procedurally as a function of the internal states and the local interactions, which are often stochastic in nature. Such models typically have complex structures and consist of a large number of modeling parameters. Determining the key modeling parameters which govern the outcomes of the system is very challenging. Sensitivity analysis plays a vital role in quantifying the impact of modeling parameters in massively interacting systems, including large complex ABM. The high computational cost of executing simulations impedes running experiments with exhaustive parameter settings. Existing techniques of analyzing such a complex system typically focus on local sensitivity analysis, i.e. one parameter at a time, or a close "neighborhood" of particular parameter settings. However, such methods are not adequate to measure the uncertainty and sensitivity of parameters accurately because they overlook the global impacts of parameters on the system. In this article, we develop novel experimental design and analysis techniques to perform both global and local sensitivity analysis of large-scale ABMs. The proposed method can efficiently identify the most significant parameters and quantify their contributions to outcomes of the system. We demonstrate the proposed methodology for ENteric Immune SImulator (ENISI), a large-scale ABM environment, using a computational model of immune responses to Helicobacter pylori colonization of the gastric mucosa.

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

Roberts, Jesse D.; Grace Chang; Jason Magalen

A n indust ry standard wave modeling tool was utilized to investigate model sensitivity to input parameters and wave energy converter ( WEC ) array deploym ent scenarios. Wave propagation was investigated d ownstream of the WECs to evaluate overall near - and far - field effects of WEC arrays. The sensitivity study illustrate d that b oth wave height and near - bottom orbital velocity we re subject to the largest pote ntial variations, each decreas ed in sensitivity as transmission coefficient increase d , as number and spacing of WEC devices decrease d , and as the deploymentmore » location move d offshore. Wave direction wa s affected consistently for all parameters and wave perio d was not affected (or negligibly affected) by varying model parameters or WEC configuration .« less

Sensors and Clinical Mastitis—The Quest for the Perfect Alert

PubMed Central

Hogeveen, Henk; Kamphuis, Claudia; Steeneveld, Wilma; Mollenhorst, Herman

2010-01-01

When cows on dairy farms are milked with an automatic milking system or in high capacity milking parlors, clinical mastitis (CM) cannot be adequately detected without sensors. The objective of this paper is to describe the performance demands of sensor systems to detect CM and evaluats the current performance of these sensor systems. Several detection models based on different sensors were studied in the past. When evaluating these models, three factors are important: performance (in terms of sensitivity and specificity), the time window and the similarity of the study data with real farm data. A CM detection system should offer at least a sensitivity of 80% and a specificity of 99%. The time window should not be longer than 48 hours and study circumstances should be as similar to practical farm circumstances as possible. The study design should comprise more than one farm for data collection. Since 1992, 16 peer-reviewed papers have been published with a description and evaluation of CM detection models. There is a large variation in the use of sensors and algorithms. All this makes these results not very comparable. There is a also large difference in performance between the detection models and also a large variation in time windows used and little similarity between study data. Therefore, it is difficult to compare the overall performance of the different CM detection models. The sensitivity and specificity found in the different studies could, for a large part, be explained in differences in the used time window. None of the described studies satisfied the demands for CM detection models. PMID:22163637

Sensors and clinical mastitis--the quest for the perfect alert.

PubMed

Hogeveen, Henk; Kamphuis, Claudia; Steeneveld, Wilma; Mollenhorst, Herman

2010-01-01

When cows on dairy farms are milked with an automatic milking system or in high capacity milking parlors, clinical mastitis (CM) cannot be adequately detected without sensors. The objective of this paper is to describe the performance demands of sensor systems to detect CM and evaluats the current performance of these sensor systems. Several detection models based on different sensors were studied in the past. When evaluating these models, three factors are important: performance (in terms of sensitivity and specificity), the time window and the similarity of the study data with real farm data. A CM detection system should offer at least a sensitivity of 80% and a specificity of 99%. The time window should not be longer than 48 hours and study circumstances should be as similar to practical farm circumstances as possible. The study design should comprise more than one farm for data collection. Since 1992, 16 peer-reviewed papers have been published with a description and evaluation of CM detection models. There is a large variation in the use of sensors and algorithms. All this makes these results not very comparable. There is a also large difference in performance between the detection models and also a large variation in time windows used and little similarity between study data. Therefore, it is difficult to compare the overall performance of the different CM detection models. The sensitivity and specificity found in the different studies could, for a large part, be explained in differences in the used time window. None of the described studies satisfied the demands for CM detection models.

Meta-analysis of diagnostic tests accounting for disease prevalence: a new model using trivariate copulas.

PubMed

Hoyer, A; Kuss, O

2015-05-20

In real life and somewhat contrary to biostatistical textbook knowledge, sensitivity and specificity (and not only predictive values) of diagnostic tests can vary with the underlying prevalence of disease. In meta-analysis of diagnostic studies, accounting for this fact naturally leads to a trivariate expansion of the traditional bivariate logistic regression model with random study effects. In this paper, a new model is proposed using trivariate copulas and beta-binomial marginal distributions for sensitivity, specificity, and prevalence as an expansion of the bivariate model. Two different copulas are used, the trivariate Gaussian copula and a trivariate vine copula based on the bivariate Plackett copula. This model has a closed-form likelihood, so standard software (e.g., SAS PROC NLMIXED) can be used. The results of a simulation study have shown that the copula models perform at least as good but frequently better than the standard model. The methods are illustrated by two examples. Copyright © 2015 John Wiley & Sons, Ltd.

Sensitivity studies and a simple ozone perturbation experiment with a truncated two-dimensional model of the stratosphere

NASA Technical Reports Server (NTRS)

Stordal, Frode; Garcia, Rolando R.

1987-01-01

The 1-1/2-D model of Holton (1986), which is actually a highly truncated two-dimensional model, describes latitudinal variations of tracer mixing ratios in terms of their projections onto second-order Legendre polynomials. The present study extends the work of Holton by including tracers with photochemical production in the stratosphere (O3 and NOy). It also includes latitudinal variations in the photochemical sources and sinks, improving slightly the calculated global mean profiles for the long-lived tracers studied by Holton and improving substantially the latitudinal behavior of ozone. Sensitivity tests of the dynamical parameters in the model are performed, showing that the response of the model to changes in vertical residual meridional winds and horizontal diffusion coefficients is similar to that of a full two-dimensional model. A simple ozone perturbation experiment shows the model's ability to reproduce large-scale latitudinal variations in total ozone column depletions as well as ozone changes in the chemically controlled upper stratosphere.

Development of a system emulating the global carbon cycle in Earth system models

NASA Astrophysics Data System (ADS)

Tachiiri, K.; Hargreaves, J. C.; Annan, J. D.; Oka, A.; Abe-Ouchi, A.; Kawamiya, M.

2010-08-01

Recent studies have indicated that the uncertainty in the global carbon cycle may have a significant impact on the climate. Since state of the art models are too computationally expensive for it to be possible to explore their parametric uncertainty in anything approaching a comprehensive fashion, we have developed a simplified system for investigating this problem. By combining the strong points of general circulation models (GCMs), which contain detailed and complex processes, and Earth system models of intermediate complexity (EMICs), which are quick and capable of large ensembles, we have developed a loosely coupled model (LCM) which can represent the outputs of a GCM-based Earth system model, using much smaller computational resources. We address the problem of relatively poor representation of precipitation within our EMIC, which prevents us from directly coupling it to a vegetation model, by coupling it to a precomputed transient simulation using a full GCM. The LCM consists of three components: an EMIC (MIROC-lite) which consists of a 2-D energy balance atmosphere coupled to a low resolution 3-D GCM ocean (COCO) including an ocean carbon cycle (an NPZD-type marine ecosystem model); a state of the art vegetation model (Sim-CYCLE); and a database of daily temperature, precipitation, and other necessary climatic fields to drive Sim-CYCLE from a precomputed transient simulation from a state of the art AOGCM. The transient warming of the climate system is calculated from MIROC-lite, with the global temperature anomaly used to select the most appropriate annual climatic field from the pre-computed AOGCM simulation which, in this case, is a 1% pa increasing CO2 concentration scenario. By adjusting the effective climate sensitivity (equivalent to the equilibrium climate sensitivity for an energy balance model) of MIROC-lite, the transient warming of the LCM could be adjusted to closely follow the low sensitivity (with an equilibrium climate sensitivity of 4.0 K) version of MIROC3.2. By tuning of the physical and biogeochemical parameters it was possible to reasonably reproduce the bulk physical and biogeochemical properties of previously published CO2 stabilisation scenarios for that model. As an example of an application of the LCM, the behavior of the high sensitivity version of MIROC3.2 (with a 6.3 K equilibrium climate sensitivity) is also demonstrated. Given the highly adjustable nature of the model, we believe that the LCM should be a very useful tool for studying uncertainty in global climate change, and we have named the model, JUMP-LCM, after the name of our research group (Japan Uncertainty Modelling Project).

Modeling rate sensitivity of exercise transient responses to limb motion.

PubMed

Yamashiro, Stanley M; Kato, Takahide

2014-10-01

Transient responses of ventilation (V̇e) to limb motion can exhibit predictive characteristics. In response to a change in limb motion, a rapid change in V̇e is commonly observed with characteristics different than during a change in workload. This rapid change has been attributed to a feed-forward or adaptive response. Rate sensitivity was explored as a specific hypothesis to explain predictive V̇e responses to limb motion. A simple model assuming an additive feed-forward summation of V̇e proportional to the rate of change of limb motion was studied. This model was able to successfully account for the adaptive phase correction observed during human sinusoidal changes in limb motion. Adaptation of rate sensitivity might also explain the reduction of the fast component of V̇e responses previously reported following sudden exercise termination. Adaptation of the fast component of V̇e response could occur by reduction of rate sensitivity. Rate sensitivity of limb motion was predicted by the model to reduce the phase delay between limb motion and V̇e response without changing the steady-state response to exercise load. In this way, V̇e can respond more quickly to an exercise change without interfering with overall feedback control. The asymmetry between responses to an incremental and decremental ramp change in exercise can also be accounted for by the proposed model. Rate sensitivity leads to predicted behavior, which resembles responses observed in exercise tied to expiratory reserve volume. Copyright © 2014 the American Physiological Society.

Local influence for generalized linear models with missing covariates.

PubMed

Shi, Xiaoyan; Zhu, Hongtu; Ibrahim, Joseph G

2009-12-01

In the analysis of missing data, sensitivity analyses are commonly used to check the sensitivity of the parameters of interest with respect to the missing data mechanism and other distributional and modeling assumptions. In this article, we formally develop a general local influence method to carry out sensitivity analyses of minor perturbations to generalized linear models in the presence of missing covariate data. We examine two types of perturbation schemes (the single-case and global perturbation schemes) for perturbing various assumptions in this setting. We show that the metric tensor of a perturbation manifold provides useful information for selecting an appropriate perturbation. We also develop several local influence measures to identify influential points and test model misspecification. Simulation studies are conducted to evaluate our methods, and real datasets are analyzed to illustrate the use of our local influence measures.

Bayesian model selection techniques as decision support for shaping a statistical analysis plan of a clinical trial: An example from a vertigo phase III study with longitudinal count data as primary endpoint

PubMed Central

2012-01-01

Background A statistical analysis plan (SAP) is a critical link between how a clinical trial is conducted and the clinical study report. To secure objective study results, regulatory bodies expect that the SAP will meet requirements in pre-specifying inferential analyses and other important statistical techniques. To write a good SAP for model-based sensitivity and ancillary analyses involves non-trivial decisions on and justification of many aspects of the chosen setting. In particular, trials with longitudinal count data as primary endpoints pose challenges for model choice and model validation. In the random effects setting, frequentist strategies for model assessment and model diagnosis are complex and not easily implemented and have several limitations. Therefore, it is of interest to explore Bayesian alternatives which provide the needed decision support to finalize a SAP. Methods We focus on generalized linear mixed models (GLMMs) for the analysis of longitudinal count data. A series of distributions with over- and under-dispersion is considered. Additionally, the structure of the variance components is modified. We perform a simulation study to investigate the discriminatory power of Bayesian tools for model criticism in different scenarios derived from the model setting. We apply the findings to the data from an open clinical trial on vertigo attacks. These data are seen as pilot data for an ongoing phase III trial. To fit GLMMs we use a novel Bayesian computational approach based on integrated nested Laplace approximations (INLAs). The INLA methodology enables the direct computation of leave-one-out predictive distributions. These distributions are crucial for Bayesian model assessment. We evaluate competing GLMMs for longitudinal count data according to the deviance information criterion (DIC) or probability integral transform (PIT), and by using proper scoring rules (e.g. the logarithmic score). Results The instruments under study provide excellent tools for preparing decisions within the SAP in a transparent way when structuring the primary analysis, sensitivity or ancillary analyses, and specific analyses for secondary endpoints. The mean logarithmic score and DIC discriminate well between different model scenarios. It becomes obvious that the naive choice of a conventional random effects Poisson model is often inappropriate for real-life count data. The findings are used to specify an appropriate mixed model employed in the sensitivity analyses of an ongoing phase III trial. Conclusions The proposed Bayesian methods are not only appealing for inference but notably provide a sophisticated insight into different aspects of model performance, such as forecast verification or calibration checks, and can be applied within the model selection process. The mean of the logarithmic score is a robust tool for model ranking and is not sensitive to sample size. Therefore, these Bayesian model selection techniques offer helpful decision support for shaping sensitivity and ancillary analyses in a statistical analysis plan of a clinical trial with longitudinal count data as the primary endpoint. PMID:22962944

Bayesian model selection techniques as decision support for shaping a statistical analysis plan of a clinical trial: an example from a vertigo phase III study with longitudinal count data as primary endpoint.

PubMed

Adrion, Christine; Mansmann, Ulrich

2012-09-10

A statistical analysis plan (SAP) is a critical link between how a clinical trial is conducted and the clinical study report. To secure objective study results, regulatory bodies expect that the SAP will meet requirements in pre-specifying inferential analyses and other important statistical techniques. To write a good SAP for model-based sensitivity and ancillary analyses involves non-trivial decisions on and justification of many aspects of the chosen setting. In particular, trials with longitudinal count data as primary endpoints pose challenges for model choice and model validation. In the random effects setting, frequentist strategies for model assessment and model diagnosis are complex and not easily implemented and have several limitations. Therefore, it is of interest to explore Bayesian alternatives which provide the needed decision support to finalize a SAP. We focus on generalized linear mixed models (GLMMs) for the analysis of longitudinal count data. A series of distributions with over- and under-dispersion is considered. Additionally, the structure of the variance components is modified. We perform a simulation study to investigate the discriminatory power of Bayesian tools for model criticism in different scenarios derived from the model setting. We apply the findings to the data from an open clinical trial on vertigo attacks. These data are seen as pilot data for an ongoing phase III trial. To fit GLMMs we use a novel Bayesian computational approach based on integrated nested Laplace approximations (INLAs). The INLA methodology enables the direct computation of leave-one-out predictive distributions. These distributions are crucial for Bayesian model assessment. We evaluate competing GLMMs for longitudinal count data according to the deviance information criterion (DIC) or probability integral transform (PIT), and by using proper scoring rules (e.g. the logarithmic score). The instruments under study provide excellent tools for preparing decisions within the SAP in a transparent way when structuring the primary analysis, sensitivity or ancillary analyses, and specific analyses for secondary endpoints. The mean logarithmic score and DIC discriminate well between different model scenarios. It becomes obvious that the naive choice of a conventional random effects Poisson model is often inappropriate for real-life count data. The findings are used to specify an appropriate mixed model employed in the sensitivity analyses of an ongoing phase III trial. The proposed Bayesian methods are not only appealing for inference but notably provide a sophisticated insight into different aspects of model performance, such as forecast verification or calibration checks, and can be applied within the model selection process. The mean of the logarithmic score is a robust tool for model ranking and is not sensitive to sample size. Therefore, these Bayesian model selection techniques offer helpful decision support for shaping sensitivity and ancillary analyses in a statistical analysis plan of a clinical trial with longitudinal count data as the primary endpoint.

Free to be me: The relationship between the true self, rejection sensitivity, and use of online dating sites.

PubMed

Hance, Margaret A; Blackhart, Ginette; Dew, Megan

2018-01-01

Prior research (Blackhart et al., 2014) found that rejection-sensitive individuals are more likely to use online dating sites. The purpose of the current research was to explain the relationship between rejection sensitivity and online dating site usage. Study 1 examined whether true self mediated the relation between rejection sensitivity and online dating. Study 2 sought to replicate the findings of Study 1 and to examine whether self-disclosure moderated the relationship between true self and online dating in the mediation model. Results replicated those found by Blackhart et al. and also found that true self mediated the relationship between rejection sensitivity and online dating site usage. These findings suggest that rejection-sensitive individuals feel they can more easily represent their "true" selves in online environments, such as online dating sites, which partially explains why they are more likely to engage in online dating.

Sensitivity analysis of bi-layered ceramic dental restorations.

PubMed

Zhang, Zhongpu; Zhou, Shiwei; Li, Qing; Li, Wei; Swain, Michael V

2012-02-01

The reliability and longevity of ceramic prostheses have become a major concern. The existing studies have focused on some critical issues from clinical perspectives, but more researches are needed to address fundamental sciences and fabrication issues to ensure the longevity and durability of ceramic prostheses. The aim of this paper was to explore how "sensitive" the thermal and mechanical responses, in terms of changes in temperature and thermal residual stress of the bi-layered ceramic systems and crown models will be with respect to the perturbation of the design variables chosen (e.g. layer thickness and heat transfer coefficient) in a quantitative way. In this study, three bi-layered ceramic models with different geometries are considered: (i) a simple bi-layered plate, (ii) a simple bi-layer triangle, and (iii) an axisymmetric bi-layered crown. The layer thickness and convective heat transfer coefficient (or cooling rate) seem to be more sensitive for the porcelain fused on zirconia substrate models. The resultant sensitivities indicate a critical importance of the heat transfer coefficient and thickness ratio of core to veneer on the temperature distributions and residual stresses in each model. The findings provide a quantitative basis for assessing the effects of fabrication uncertainties and optimizing the design of ceramic prostheses. Copyright © 2011 Academy of Dental Materials. Published by Elsevier Ltd. All rights reserved.

Prediction of brittleness based on anisotropic rock physics model for kerogen-rich shale

NASA Astrophysics Data System (ADS)

Qian, Ke-Ran; He, Zhi-Liang; Chen, Ye-Quan; Liu, Xi-Wu; Li, Xiang-Yang

2017-12-01

The construction of a shale rock physics model and the selection of an appropriate brittleness index ( BI) are two significant steps that can influence the accuracy of brittleness prediction. On one hand, the existing models of kerogen-rich shale are controversial, so a reasonable rock physics model needs to be built. On the other hand, several types of equations already exist for predicting the BI whose feasibility needs to be carefully considered. This study constructed a kerogen-rich rock physics model by performing the selfconsistent approximation and the differential effective medium theory to model intercoupled clay and kerogen mixtures. The feasibility of our model was confirmed by comparison with classical models, showing better accuracy. Templates were constructed based on our model to link physical properties and the BI. Different equations for the BI had different sensitivities, making them suitable for different types of formations. Equations based on Young's Modulus were sensitive to variations in lithology, while those using Lame's Coefficients were sensitive to porosity and pore fluids. Physical information must be considered to improve brittleness prediction.

Fermentation of Saccharomyces cerevisiae - Combining kinetic modeling and optimization techniques points out avenues to effective process design.

PubMed

Scheiblauer, Johannes; Scheiner, Stefan; Joksch, Martin; Kavsek, Barbara

2018-09-14

A combined experimental/theoretical approach is presented, for improving the predictability of Saccharomyces cerevisiae fermentations. In particular, a mathematical model was developed explicitly taking into account the main mechanisms of the fermentation process, allowing for continuous computation of key process variables, including the biomass concentration and the respiratory quotient (RQ). For model calibration and experimental validation, batch and fed-batch fermentations were carried out. Comparison of the model-predicted biomass concentrations and RQ developments with the corresponding experimentally recorded values shows a remarkably good agreement for both batch and fed-batch processes, confirming the adequacy of the model. Furthermore, sensitivity studies were performed, in order to identify model parameters whose variations have significant effects on the model predictions: our model responds with significant sensitivity to the variations of only six parameters. These studies provide a valuable basis for model reduction, as also demonstrated in this paper. Finally, optimization-based parametric studies demonstrate how our model can be utilized for improving the efficiency of Saccharomyces cerevisiae fermentations. Copyright © 2018 Elsevier Ltd. All rights reserved.

Simplification and its consequences in biological modelling: conclusions from a study of calcium oscillations in hepatocytes.

PubMed

Hetherington, James P J; Warner, Anne; Seymour, Robert M

2006-04-22

Systems Biology requires that biological modelling is scaled up from small components to system level. This can produce exceedingly complex models, which obscure understanding rather than facilitate it. The successful use of highly simplified models would resolve many of the current problems faced in Systems Biology. This paper questions whether the conclusions of simple mathematical models of biological systems are trustworthy. The simplification of a specific model of calcium oscillations in hepatocytes is examined in detail, and the conclusions drawn from this scrutiny generalized. We formalize our choice of simplification approach through the use of functional 'building blocks'. A collection of models is constructed, each a progressively more simplified version of a well-understood model. The limiting model is a piecewise linear model that can be solved analytically. We find that, as expected, in many cases the simpler models produce incorrect results. However, when we make a sensitivity analysis, examining which aspects of the behaviour of the system are controlled by which parameters, the conclusions of the simple model often agree with those of the richer model. The hypothesis that the simplified model retains no information about the real sensitivities of the unsimplified model can be very strongly ruled out by treating the simplification process as a pseudo-random perturbation on the true sensitivity data. We conclude that sensitivity analysis is, therefore, of great importance to the analysis of simple mathematical models in biology. Our comparisons reveal which results of the sensitivity analysis regarding calcium oscillations in hepatocytes are robust to the simplifications necessarily involved in mathematical modelling. For example, we find that if a treatment is observed to strongly decrease the period of the oscillations while increasing the proportion of the cycle during which cellular calcium concentrations are rising, without affecting the inter-spike or maximum calcium concentrations, then it is likely that the treatment is acting on the plasma membrane calcium pump.

Working covariance model selection for generalized estimating equations.

PubMed

Carey, Vincent J; Wang, You-Gan

2011-11-20

We investigate methods for data-based selection of working covariance models in the analysis of correlated data with generalized estimating equations. We study two selection criteria: Gaussian pseudolikelihood and a geodesic distance based on discrepancy between model-sensitive and model-robust regression parameter covariance estimators. The Gaussian pseudolikelihood is found in simulation to be reasonably sensitive for several response distributions and noncanonical mean-variance relations for longitudinal data. Application is also made to a clinical dataset. Assessment of adequacy of both correlation and variance models for longitudinal data should be routine in applications, and we describe open-source software supporting this practice. Copyright © 2011 John Wiley & Sons, Ltd.

Modification and Validation of the Triglyceride-to-HDL Cholesterol Ratio as a Surrogate of Insulin Sensitivity in White Juveniles and Adults without Diabetes Mellitus: The Single Point Insulin Sensitivity Estimator (SPISE).

PubMed

Paulmichl, Katharina; Hatunic, Mensud; Højlund, Kurt; Jotic, Aleksandra; Krebs, Michael; Mitrakou, Asimina; Porcellati, Francesca; Tura, Andrea; Bergsten, Peter; Forslund, Anders; Manell, Hannes; Widhalm, Kurt; Weghuber, Daniel; Anderwald, Christian-Heinz

2016-09-01

The triglyceride-to-HDL cholesterol (TG/HDL-C) ratio was introduced as a tool to estimate insulin resistance, because circulating lipid measurements are available in routine settings. Insulin, C-peptide, and free fatty acids are components of other insulin-sensitivity indices but their measurement is expensive. Easier and more affordable tools are of interest for both pediatric and adult patients. Study participants from the Relationship Between Insulin Sensitivity and Cardiovascular Disease [43.9 (8.3) years, n = 1260] as well as the Beta-Cell Function in Juvenile Diabetes and Obesity study cohorts [15 (1.9) years, n = 29] underwent oral-glucose-tolerance tests and euglycemic clamp tests for estimation of whole-body insulin sensitivity and calculation of insulin sensitivity indices. To refine the TG/HDL ratio, mathematical modeling was applied including body mass index (BMI), fasting TG, and HDL cholesterol and compared to the clamp-derived M-value as an estimate of insulin sensitivity. Each modeling result was scored by identifying insulin resistance and correlation coefficient. The Single Point Insulin Sensitivity Estimator (SPISE) was compared to traditional insulin sensitivity indices using area under the ROC curve (aROC) analysis and χ(2) test. The novel formula for SPISE was computed as follows: SPISE = 600 × HDL-C(0.185)/(TG(0.2) × BMI(1.338)), with fasting HDL-C (mg/dL), fasting TG concentrations (mg/dL), and BMI (kg/m(2)). A cutoff value of 6.61 corresponds to an M-value smaller than 4.7 mg · kg(-1) · min(-1) (aROC, M:0.797). SPISE showed a significantly better aROC than the TG/HDL-C ratio. SPISE aROC was comparable to the Matsuda ISI (insulin sensitivity index) and equal to the QUICKI (quantitative insulin sensitivity check index) and HOMA-IR (homeostasis model assessment-insulin resistance) when calculated with M-values. The SPISE seems well suited to surrogate whole-body insulin sensitivity from inexpensive fasting single-point blood draw and BMI in white adolescents and adults. © 2016 American Association for Clinical Chemistry.

Measurement Uncertainty Budget of the PMV Thermal Comfort Equation

NASA Astrophysics Data System (ADS)

Ekici, Can

2016-05-01

Fanger's predicted mean vote (PMV) equation is the result of the combined quantitative effects of the air temperature, mean radiant temperature, air velocity, humidity activity level and clothing thermal resistance. PMV is a mathematical model of thermal comfort which was developed by Fanger. The uncertainty budget of the PMV equation was developed according to GUM in this study. An example is given for the uncertainty model of PMV in the exemplification section of the study. Sensitivity coefficients were derived from the PMV equation. Uncertainty budgets can be seen in the tables. A mathematical model of the sensitivity coefficients of Ta, hc, T_{mrt}, T_{cl}, and Pa is given in this study. And the uncertainty budgets for hc, T_{cl}, and Pa are given in this study.

Does the Superior Colliculus Control Perceptual Sensitivity or Choice Bias during Attention? Evidence from a Multialternative Decision Framework

PubMed Central

Steinmetz, Nicholas A.; Moore, Tirin; Knudsen, Eric I.

2017-01-01

Distinct networks in the forebrain and the midbrain coordinate to control spatial attention. The critical involvement of the superior colliculus (SC)—the central structure in the midbrain network—in visuospatial attention has been shown by four seminal, published studies in monkeys (Macaca mulatta) performing multialternative tasks. However, due to the lack of a mechanistic framework for interpreting behavioral data in such tasks, the nature of the SC's contribution to attention remains unclear. Here we present and validate a novel decision framework for analyzing behavioral data in multialternative attention tasks. We apply this framework to re-examine the behavioral evidence from these published studies. Our model is a multidimensional extension to signal detection theory that distinguishes between two major classes of attentional mechanisms: those that alter the quality of sensory information or “sensitivity,” and those that alter the selective gating of sensory information or “choice bias.” Model-based simulations and model-based analyses of data from these published studies revealed a converging pattern of results that indicated that choice-bias changes, rather than sensitivity changes, were the primary outcome of SC manipulation. Our results suggest that the SC contributes to attentional performance predominantly by generating a spatial choice bias for stimuli at a selected location, and that this bias operates downstream of forebrain mechanisms that enhance sensitivity. The findings lead to a testable mechanistic framework of how the midbrain and forebrain networks interact to control spatial attention. SIGNIFICANCE STATEMENT Attention involves the selection of the most relevant information for differential sensory processing and decision making. While the mechanisms by which attention alters sensory encoding (sensitivity control) are well studied, the mechanisms by which attention alters decisional weighting of sensory evidence (choice-bias control) are poorly understood. Here, we introduce a model of multialternative decision making that distinguishes bias from sensitivity effects in attention tasks. With our model, we simulate experimental data from four seminal studies that microstimulated or inactivated a key attention-related midbrain structure, the superior colliculus (SC). We demonstrate that the experimental effects of SC manipulation are entirely consistent with the SC controlling attention by changing choice bias, thereby shedding new light on how the brain mediates attention. PMID:28100734

Does the Superior Colliculus Control Perceptual Sensitivity or Choice Bias during Attention? Evidence from a Multialternative Decision Framework.

PubMed

Sridharan, Devarajan; Steinmetz, Nicholas A; Moore, Tirin; Knudsen, Eric I

2017-01-18

Distinct networks in the forebrain and the midbrain coordinate to control spatial attention. The critical involvement of the superior colliculus (SC)-the central structure in the midbrain network-in visuospatial attention has been shown by four seminal, published studies in monkeys (Macaca mulatta) performing multialternative tasks. However, due to the lack of a mechanistic framework for interpreting behavioral data in such tasks, the nature of the SC's contribution to attention remains unclear. Here we present and validate a novel decision framework for analyzing behavioral data in multialternative attention tasks. We apply this framework to re-examine the behavioral evidence from these published studies. Our model is a multidimensional extension to signal detection theory that distinguishes between two major classes of attentional mechanisms: those that alter the quality of sensory information or "sensitivity," and those that alter the selective gating of sensory information or "choice bias." Model-based simulations and model-based analyses of data from these published studies revealed a converging pattern of results that indicated that choice-bias changes, rather than sensitivity changes, were the primary outcome of SC manipulation. Our results suggest that the SC contributes to attentional performance predominantly by generating a spatial choice bias for stimuli at a selected location, and that this bias operates downstream of forebrain mechanisms that enhance sensitivity. The findings lead to a testable mechanistic framework of how the midbrain and forebrain networks interact to control spatial attention. Attention involves the selection of the most relevant information for differential sensory processing and decision making. While the mechanisms by which attention alters sensory encoding (sensitivity control) are well studied, the mechanisms by which attention alters decisional weighting of sensory evidence (choice-bias control) are poorly understood. Here, we introduce a model of multialternative decision making that distinguishes bias from sensitivity effects in attention tasks. With our model, we simulate experimental data from four seminal studies that microstimulated or inactivated a key attention-related midbrain structure, the superior colliculus (SC). We demonstrate that the experimental effects of SC manipulation are entirely consistent with the SC controlling attention by changing choice bias, thereby shedding new light on how the brain mediates attention. Copyright © 2017 the authors 0270-6474/17/370480-32$15.00/0.

Examining the mediational role of psychological flexibility, pain catastrophizing, and visceral sensitivity in the relationship between psychological distress, irritable bowel symptom frequency, and quality of life.

PubMed

Cassar, G E; Knowles, S; Youssef, G J; Moulding, R; Uiterwijk, D; Waters, L; Austin, D W

2018-06-08

The aim of the current study was to use Structural Equation Modelling (SEM) to examine whether psychological flexibility (i.e. mindfulness, acceptance, valued-living) mediates the relationship between distress, irritable bowel syndrome (IBS) symptom frequency, and quality of life (QoL). Ninety-two individuals participated in the study (12 male, 80 female, M age  = 36.24) by completing an online survey including measures of visceral sensitivity, distress, IBS-related QoL, mindfulness, bowel symptoms, pain catastrophizing, acceptance, and valued-living. A final model with excellent fit was identified. Psychological distress significantly and directly predicted pain catastrophizing, valued-living, and IBS symptom frequency. Pain catastrophizing directly predicted visceral sensitivity and acceptance, while visceral sensitivity significantly and directly predicted IBS symptom frequency and QoL. Symptom frequency also had a direct and significant relationship with QoL. The current findings suggest that interventions designed to address unhelpful cognitive processes related to visceral sensitivity, pain catastrophizing, and psychological distress may be of most benefit to IBS-related QoL.

Striatal and extrastriatal dopamine release in the common marmoset brain measured by positron emission tomography and [(18)F]fallypride.

PubMed

Ota, Miho; Ogawa, Shintaro; Kato, Koichi; Masuda, Chiaki; Kunugi, Hiroshi

2015-12-01

Previous studies have demonstrated that patients with schizophrenia show greater sensitivity to psychostimulants than healthy subjects. Sensitization to psychostimulants and resultant alteration of dopaminergic neurotransmission in rodents has been suggested as a useful model of schizophrenia. This study sought to examine the use of methylphenidate as a psychostimulant to induce dopamine release and that of [(18)F]fallypride as a radioligand to quantify the release in a primate model of schizophrenia. Four common marmosets were scanned by positron emission tomography twice, before and after methylphenidate challenge, to evaluate dopamine release. Four other marmosets were sensitized by repeated methamphetamine (MAP) administration. Then, they were scanned twice, before and after methylphenidate challenge, to evaluate whether MAP-sensitization induced greater sensitivity to methylphenidate. We revealed a main effect of the methylphenidate challenge but not the MAP pretreatment on the striatal binding potential. These results suggest that methylphenidate-induced striatal dopamine release in the common marmoset could be evaluated by [(18)F]fallypride. Copyright © 2015 Elsevier Ireland Ltd and the Japan Neuroscience Society. All rights reserved.

Sensitivity Analysis for some Water Pollution Problem

NASA Astrophysics Data System (ADS)

Le Dimet, François-Xavier; Tran Thu, Ha; Hussaini, Yousuff

2014-05-01

Sensitivity Analysis for Some Water Pollution Problems Francois-Xavier Le Dimet1 & Tran Thu Ha2 & M. Yousuff Hussaini3 1Université de Grenoble, France, 2Vietnamese Academy of Sciences, 3 Florida State University Sensitivity analysis employs some response function and the variable with respect to which its sensitivity is evaluated. If the state of the system is retrieved through a variational data assimilation process, then the observation appears only in the Optimality System (OS). In many cases, observations have errors and it is important to estimate their impact. Therefore, sensitivity analysis has to be carried out on the OS, and in that sense sensitivity analysis is a second order property. The OS can be considered as a generalized model because it contains all the available information. This presentation proposes a method to carry out sensitivity analysis in general. The method is demonstrated with an application to water pollution problem. The model involves shallow waters equations and an equation for the pollutant concentration. These equations are discretized using a finite volume method. The response function depends on the pollutant source, and its sensitivity with respect to the source term of the pollutant is studied. Specifically, we consider: • Identification of unknown parameters, and • Identification of sources of pollution and sensitivity with respect to the sources. We also use a Singular Evolutive Interpolated Kalman Filter to study this problem. The presentation includes a comparison of the results from these two methods. .

Sensitivities of simulated satellite views of clouds to subgrid-scale overlap and condensate heterogeneity

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

Hillman, Benjamin R.; Marchand, Roger T.; Ackerman, Thomas P.

Satellite simulators are often used to account for limitations in satellite retrievals of cloud properties in comparisons between models and satellite observations. The purpose of the simulator framework is to enable more robust evaluation of model cloud properties, so that di erences between models and observations can more con dently be attributed to model errors. However, these simulators are subject to uncertainties themselves. A fundamental uncertainty exists in connecting the spatial scales at which cloud properties are retrieved with those at which clouds are simulated in global models. In this study, we create a series of sensitivity tests using 4more » km global model output from the Multiscale Modeling Framework to evaluate the sensitivity of simulated satellite retrievals when applied to climate models whose grid spacing is many tens to hundreds of kilometers. In particular, we examine the impact of cloud and precipitation overlap and of condensate spatial variability. We find the simulated retrievals are sensitive to these assumptions. Specifically, using maximum-random overlap with homogeneous cloud and precipitation condensate, which is often used in global climate models, leads to large errors in MISR and ISCCP-simulated cloud cover and in CloudSat-simulated radar reflectivity. To correct for these errors, an improved treatment of unresolved clouds and precipitation is implemented for use with the simulator framework and is shown to substantially reduce the identified errors.« less

The impact of missing trauma data on predicting massive transfusion

PubMed Central

Trickey, Amber W.; Fox, Erin E.; del Junco, Deborah J.; Ning, Jing; Holcomb, John B.; Brasel, Karen J.; Cohen, Mitchell J.; Schreiber, Martin A.; Bulger, Eileen M.; Phelan, Herb A.; Alarcon, Louis H.; Myers, John G.; Muskat, Peter; Cotton, Bryan A.; Wade, Charles E.; Rahbar, Mohammad H.

2013-01-01

INTRODUCTION Missing data are inherent in clinical research and may be especially problematic for trauma studies. This study describes a sensitivity analysis to evaluate the impact of missing data on clinical risk prediction algorithms. Three blood transfusion prediction models were evaluated utilizing an observational trauma dataset with valid missing data. METHODS The PRospective Observational Multi-center Major Trauma Transfusion (PROMMTT) study included patients requiring ≥ 1 unit of red blood cells (RBC) at 10 participating U.S. Level I trauma centers from July 2009 – October 2010. Physiologic, laboratory, and treatment data were collected prospectively up to 24h after hospital admission. Subjects who received ≥ 10 RBC units within 24h of admission were classified as massive transfusion (MT) patients. Correct classification percentages for three MT prediction models were evaluated using complete case analysis and multiple imputation. A sensitivity analysis for missing data was conducted to determine the upper and lower bounds for correct classification percentages. RESULTS PROMMTT enrolled 1,245 subjects. MT was received by 297 patients (24%). Missing percentage ranged from 2.2% (heart rate) to 45% (respiratory rate). Proportions of complete cases utilized in the MT prediction models ranged from 41% to 88%. All models demonstrated similar correct classification percentages using complete case analysis and multiple imputation. In the sensitivity analysis, correct classification upper-lower bound ranges per model were 4%, 10%, and 12%. Predictive accuracy for all models using PROMMTT data was lower than reported in the original datasets. CONCLUSIONS Evaluating the accuracy clinical prediction models with missing data can be misleading, especially with many predictor variables and moderate levels of missingness per variable. The proposed sensitivity analysis describes the influence of missing data on risk prediction algorithms. Reporting upper/lower bounds for percent correct classification may be more informative than multiple imputation, which provided similar results to complete case analysis in this study. PMID:23778514

Comprehensive Monte-Carlo simulator for optimization of imaging parameters for high sensitivity detection of skin cancer at the THz

NASA Astrophysics Data System (ADS)

Ney, Michael; Abdulhalim, Ibrahim

2016-03-01

Skin cancer detection at its early stages has been the focus of a large number of experimental and theoretical studies during the past decades. Among these studies two prominent approaches presenting high potential are reflectometric sensing at the THz wavelengths region and polarimetric imaging techniques in the visible wavelengths. While THz radiation contrast agent and source of sensitivity to cancer related tissue alterations was considered to be mainly the elevated water content in the cancerous tissue, the polarimetric approach has been verified to enable cancerous tissue differentiation based on cancer induced structural alterations to the tissue. Combining THz with the polarimetric approach, which is considered in this study, is examined in order to enable higher detection sensitivity than previously pure reflectometric THz measurements. For this, a comprehensive MC simulation of radiative transfer in a complex skin tissue model fitted for the THz domain that considers the skin`s stratified structure, tissue material optical dispersion modeling, surface roughness, scatterers, and substructure organelles has been developed. Additionally, a narrow beam Mueller matrix differential analysis technique is suggested for assessing skin cancer induced changes in the polarimetric image, enabling the tissue model and MC simulation to be utilized for determining the imaging parameters resulting in maximal detection sensitivity.

Critique and sensitivity analysis of the compensation function used in the LMS Hudson River striped bass models. Environmental Sciences Division publication No. 944

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

Van Winkle, W.; Christensen, S.W.; Kauffman, G.

1976-12-01

The description and justification for the compensation function developed and used by Lawler, Matusky and Skelly Engineers (LMS) (under contract to Consolidated Edison Company of New York) in their Hudson River striped bass models are presented. A sensitivity analysis of this compensation function is reported, based on computer runs with a modified version of the LMS completely mixed (spatially homogeneous) model. Two types of sensitivity analysis were performed: a parametric study involving at least five levels for each of the three parameters in the compensation function, and a study of the form of the compensation function itself, involving comparison ofmore » the LMS function with functions having no compensation at standing crops either less than or greater than the equilibrium standing crops. For the range of parameter values used in this study, estimates of percent reduction are least sensitive to changes in YS, the equilibrium standing crop, and most sensitive to changes in KXO, the minimum mortality rate coefficient. Eliminating compensation at standing crops either less than or greater than the equilibrium standing crops results in higher estimates of percent reduction. For all values of KXO and for values of YS and KX at and above the baseline values, eliminating compensation at standing crops less than the equilibrium standing crops results in a greater increase in percent reduction than eliminating compensation at standing crops greater than the equilibrium standing crops.« less

A study of overflow simulations using MPAS-Ocean: Vertical grids, resolution, and viscosity

NASA Astrophysics Data System (ADS)

Reckinger, Shanon M.; Petersen, Mark R.; Reckinger, Scott J.

2015-12-01

MPAS-Ocean is used to simulate an idealized, density-driven overflow using the dynamics of overflow mixing and entrainment (DOME) setup. Numerical simulations are carried out using three of the vertical coordinate types available in MPAS-Ocean, including z-star with partial bottom cells, z-star with full cells, and sigma coordinates. The results are first benchmarked against other models, including the MITgcm's z-coordinate model and HIM's isopycnal coordinate model, which are used to set the base case used for this work. A full parameter study is presented that looks at how sensitive overflow simulations are to vertical grid type, resolution, and viscosity. Horizontal resolutions with 50 km grid cells are under-resolved and produce poor results, regardless of other parameter settings. Vertical grids ranging in thickness from 15 m to 120 m were tested. A horizontal resolution of 10 km and a vertical resolution of 60 m are sufficient to resolve the mesoscale dynamics of the DOME configuration, which mimics real-world overflow parameters. Mixing and final buoyancy are least sensitive to horizontal viscosity, but strongly sensitive to vertical viscosity. This suggests that vertical viscosity could be adjusted in overflow water formation regions to influence mixing and product water characteristics. Lastly, the study shows that sigma coordinates produce much less mixing than z-type coordinates, resulting in heavier plumes that go further down slope. Sigma coordinates are less sensitive to changes in resolution but as sensitive to vertical viscosity compared to z-coordinates.

OECD/NEA expert group on uncertainty analysis for criticality safety assessment: Results of benchmark on sensitivity calculation (phase III)

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

Ivanova, T.; Laville, C.; Dyrda, J.

2012-07-01

The sensitivities of the k{sub eff} eigenvalue to neutron cross sections have become commonly used in similarity studies and as part of the validation algorithm for criticality safety assessments. To test calculations of the sensitivity coefficients, a benchmark study (Phase III) has been established by the OECD-NEA/WPNCS/EG UACSA (Expert Group on Uncertainty Analysis for Criticality Safety Assessment). This paper presents some sensitivity results generated by the benchmark participants using various computational tools based upon different computational methods: SCALE/TSUNAMI-3D and -1D, MONK, APOLLO2-MORET 5, DRAGON-SUSD3D and MMKKENO. The study demonstrates the performance of the tools. It also illustrates how model simplificationsmore » impact the sensitivity results and demonstrates the importance of 'implicit' (self-shielding) sensitivities. This work has been a useful step towards verification of the existing and developed sensitivity analysis methods. (authors)« less

Real versus Artificial Variation in the Thermal Sensitivity of Biological Traits.

PubMed

Pawar, Samraat; Dell, Anthony I; Savage, Van M; Knies, Jennifer L

2016-02-01

Whether the thermal sensitivity of an organism's traits follows the simple Boltzmann-Arrhenius model remains a contentious issue that centers around consideration of its operational temperature range and whether the sensitivity corresponds to one or a few underlying rate-limiting enzymes. Resolving this issue is crucial, because mechanistic models for temperature dependence of traits are required to predict the biological effects of climate change. Here, by combining theory with data on 1,085 thermal responses from a wide range of traits and organisms, we show that substantial variation in thermal sensitivity (activation energy) estimates can arise simply because of variation in the range of measured temperatures. Furthermore, when thermal responses deviate systematically from the Boltzmann-Arrhenius model, variation in measured temperature ranges across studies can bias estimated activation energy distributions toward higher mean, median, variance, and skewness. Remarkably, this bias alone can yield activation energies that encompass the range expected from biochemical reactions (from ~0.2 to 1.2 eV), making it difficult to establish whether a single activation energy appropriately captures thermal sensitivity. We provide guidelines and a simple equation for partially correcting for such artifacts. Our results have important implications for understanding the mechanistic basis of thermal responses of biological traits and for accurately modeling effects of variation in thermal sensitivity on responses of individuals, populations, and ecological communities to changing climatic temperatures.

The Dependencies of Ecosystem Pattern, Structure, and Dynamics on Climate, Climate Variability, and Climate Change

NASA Astrophysics Data System (ADS)

Flanagan, S.; Hurtt, G. C.; Fisk, J. P.; Rourke, O.

2012-12-01

A robust understanding of the sensitivity of the pattern, structure, and dynamics of ecosystems to climate, climate variability, and climate change is needed to predict ecosystem responses to current and projected climate change. We present results of a study designed to first quantify the sensitivity of ecosystems to climate through the use of climate and ecosystem data, and then use the results to test the sensitivity of the climate data in a state-of the art ecosystem model. A database of available ecosystem characteristics such as mean canopy height, above ground biomass, and basal area was constructed from sources like the National Biomass and Carbon Dataset (NBCD). The ecosystem characteristics were then paired by latitude and longitude with the corresponding climate characteristics temperature, precipitation, photosynthetically active radiation (PAR) and dew point that were retrieved from the North American Regional Reanalysis (NARR). The average yearly and seasonal means of the climate data, and their associated maximum and minimum values, over the 1979-2010 time frame provided by NARR were constructed and paired with the ecosystem data. The compiled results provide natural patterns of vegetation structure and distribution with regard to climate data. An advanced ecosystem model, the Ecosystem Demography model (ED), was then modified to allow yearly alterations to its mechanistic climate lookup table and used to predict the sensitivities of ecosystem pattern, structure, and dynamics to climate data. The combined ecosystem structure and climate data results were compared to ED's output to check the validity of the model. After verification, climate change scenarios such as those used in the last IPCC were run and future forest structure changes due to climate sensitivities were identified. The results of this study can be used to both quantify and test key relationships for next generation models. The sensitivity of ecosystem characteristics to climate data shown in the database construction and by the model reinforces the need for high-resolution datasets and stresses the importance of understanding and incorporating climate change scenarios into earth system models.

Ethical Sensitivity in Nursing Ethical Leadership: A Content Analysis of Iranian Nurses Experiences

PubMed Central

Esmaelzadeh, Fatemeh; Abbaszadeh, Abbas; Borhani, Fariba; Peyrovi, Hamid

2017-01-01

Background: Considering that many nursing actions affect other people’s health and life, sensitivity to ethics in nursing practice is highly important to ethical leaders as a role model. Objective: The study aims to explore ethical sensitivity in ethical nursing leaders in Iran. Method: This was a qualitative study based on the conventional content analysis in 2015. Data were collected using deep and semi-structured interviews with 20 Iranian nurses. The participants were chosen using purposive sampling. Data were analyzed using conventional content analysis. In order to increase the accuracy and integrity of the data, Lincoln and Guba's criteria were considered. Results: Fourteen sub-categories and five main categories emerged. Main categories consisted of sensitivity to care, sensitivity to errors, sensitivity to communication, sensitivity in decision making and sensitivity to ethical practice. Conclusion: Ethical sensitivity appears to be a valuable attribute for ethical nurse leaders, having an important effect on various aspects of professional practice and help the development of ethics in nursing practice. PMID:28584564

Aggressive Behavior between Siblings and the Development of Externalizing Problems: Evidence from a Genetically Sensitive Study

ERIC Educational Resources Information Center

Natsuaki, Misaki N.; Ge, Xiaojia; Reiss, David; Neiderhiser, Jenae M.

2009-01-01

This study investigated the prospective links between sibling aggression and the development of externalizing problems using a multilevel modeling approach with a genetically sensitive design. The sample consisted of 780 adolescents (390 sibling pairs) who participated in 2 waves of the Nonshared Environment in Adolescent Development project.…

A comparison between the minimal model and the glucose clamp in the assessment of insulin sensitivity across the spectrum of glucose tolerance. Insulin Resistance Atherosclerosis Study.

PubMed

Saad, M F; Anderson, R L; Laws, A; Watanabe, R M; Kades, W W; Chen, Y D; Sands, R E; Pei, D; Savage, P J; Bergman, R N

1994-09-01

An insulin-modified frequently sampled intravenous glucose tolerance test (FSIGTT) with minimal model analysis was compared with the glucose clamp in 11 subjects with normal glucose tolerance (NGT), 20 with impaired glucose tolerance (IGT), and 24 with non-insulin-dependent diabetes mellitus (NIDDM). The insulin sensitivity index (SI) was calculated from FSIGTT using 22- and 12-sample protocols (SI(22) and SI(12), respectively). Insulin sensitivity from the clamp was expressed as SI(clamp) and SIP(clamp). Minimal model parameters were similar when calculated with SI(22) and SI(12). SI could not be distinguished from 0 in approximately 50% of diabetic patients with either protocol. SI(22) correlated significantly with SI(clamp) in the whole group (r = 0.62), and in the NGT (r = 0.53), IGT (r = 0.48), and NIDDM (r = 0.41) groups (P < 0.05 for each). SI(12) correlated significantly with SI(clamp) in the whole group (r = 0.55, P < 0.001) and in the NGT (r = 0.53, P = 0.046) and IGT (r = 0.58, P = 0.008) but not NIDDM (r = 0.30, P = 0.085) groups. When SI(22), SI(clamp), and SIP(clamp) were expressed in the same units, SI(22) was 66 +/- 5% (mean +/- SE) and 50 +/- 8% lower than SI(clamp) and SIP(clamp), respectively. Thus, minimal model analysis of the insulin-modified FSIGTT provides estimates of insulin sensitivity that correlate significantly with those from the glucose clamp. The correlation was weaker, however, in NIDDM. The insulin-modified FSIGTT can be used as a simple test for assessment of insulin sensitivity in population studies involving nondiabetic subjects. Additional studies are needed before using this test routinely in patients with NIDDM.

Sensitivity Analysis of Dispersion Model Results in the NEXUS Health Study Due to Uncertainties in Traffic-Related Emissions Inputs

EPA Science Inventory

Dispersion modeling tools have traditionally provided critical information for air quality management decisions, but have been used recently to provide exposure estimates to support health studies. However, these models can be challenging to implement, particularly in near-road s...

Evaluation of a Mysis bioenergetics model

USGS Publications Warehouse

Chipps, S.R.; Bennett, D.H.

2002-01-01

Direct approaches for estimating the feeding rate of the opossum shrimp Mysis relicta can be hampered by variable gut residence time (evacuation rate models) and non-linear functional responses (clearance rate models). Bioenergetics modeling provides an alternative method, but the reliability of this approach needs to be evaluated using independent measures of growth and food consumption. In this study, we measured growth and food consumption for M. relicta and compared experimental results with those predicted from a Mysis bioenergetics model. For Mysis reared at 10??C, model predictions were not significantly different from observed values. Moreover, decomposition of mean square error indicated that 70% of the variation between model predictions and observed values was attributable to random error. On average, model predictions were within 12% of observed values. A sensitivity analysis revealed that Mysis respiration and prey energy density were the most sensitive parameters affecting model output. By accounting for uncertainty (95% CLs) in Mysis respiration, we observed a significant improvement in the accuracy of model output (within 5% of observed values), illustrating the importance of sensitive input parameters for model performance. These findings help corroborate the Mysis bioenergetics model and demonstrate the usefulness of this approach for estimating Mysis feeding rate.

Modeling the effect of terraces on land degradation in tropical upland agricultural area

NASA Astrophysics Data System (ADS)

Christanto, N.; Shrestha, D. P.; Jetten, V. G.; Setiawan, A.

2012-04-01

Java, the most populated Island in Indonesia, in the pas view decades suffer land degradation do to extreme weather, population pressure and landuse/cover change. The study area, Serayu sub-catchment, as part of Serayu catchment is one of the representative example of Indonesia region facing land use change and land degradation problem. The study attempted to simulate the effect of terraces on land degradation (Soil erosion and landslide hazard) in Serayu sub-catchment using deterministic modeling by means of PCRaster® simulation. The effect of the terraces on tropical upland agricultural area is less studied. This paper will discuss about the effect of terraces on land degradation assessment. Detail Dem is extremely difficult to obtain in developing country like Indonesia. Therefore, an artificial DEM which give an impression of the terraces was built. Topographical maps, Ikonos Image and average of height distribution based on field measurement were used to build the artificial DEM. The result is used in STARWARS model as an input. In combine with Erosion model and PROBSTAB, soil erosion and landslide hazard were quantified. The models were run in two different environment based on the: 1) normal DEM 2.) Artificial DEM (with terraces impression). The result is compared. The result shows that the models run in an artificial DEM give a significant increase on the probability of failure by 20.5%. In the other hand, the erosion rate has fall by 11.32% as compared to the normal DEM. The result of hydrological sensitivity analysis shows that soil depth was the most sensitive parameter. For the slope stability modeling, the most sensitive parameter was slope followed by friction angle and cohesion. The erosion modeling, the model was sensitive to the vegetation cover, soil erodibility followed by BD and KSat. Model validations were applied to assess the accuracy of the models. However, the results of dynamic modeling are ideal for land degradation assessment. Dynamic modeling software such as PC Raster® which is open source and free are reliable alternative to other commercial software

Evaluation of CALGRID using two different ozone episodes and comparison to UAM results

NASA Astrophysics Data System (ADS)

Kumar, Naresh; Russell, Armistead G.; Tesche, Thomas W.; McNally, Dennis E.

Air quality models serve as the foundation for policy decisions regarding programs designed to improve air quality. The California Air Resources Board Airshed Model (CALGRID) is one of the latest photochemical air quality models developed for assessing ozone control strategies. CALGRID was modified to include the lates CBIV chemical mechanism in place of the original SAPRC mechanism. After modification, a detailed evaluation of CALGRID was carried out using two different ozone episodes. The first evaluation used data obtained during the Southern California Air Quality Study (SCAQS). The second evaluation used data obtained for the September, 1984 SCCCAMP episodes in the South Central Coast Air Basin (SCCAB). Model results were compared against observations of O 3, NO, NO 2, and different organic compounds. For the SCCAB episode, the results were also compared with those obtained from the Urban Airshed Model (UAM). Similar to other studies, the ozone predictions from the SCAQS application were biased low, as were various ROG components. The reason for this can be linked to the under-representation of ROG and CO in the emissions inventory. For the SCCAB episode, both the UAM and CALGRID models significantly underestimated NO and NO 2 concentrations. The two models slightly underestimated ozone concentrations above approximately 9 pphm on the third and last day of the simulation. Sensitivity experiments were performed for both the studies. It was found that both CALGRID and UAM are strongly sensitive to the boundary conditions and moderately sensitive to the emissions for the episodes modeled.

Evaluation of NOx Emissions and Modeling

NASA Astrophysics Data System (ADS)

Henderson, B. H.; Simon, H. A.; Timin, B.; Dolwick, P. D.; Owen, R. C.; Eyth, A.; Foley, K.; Toro, C.; Baker, K. R.

2017-12-01

Studies focusing on ambient measurements of NOy have concluded that NOx emissions are overestimated and some have attributed the error to the onroad mobile sector. We investigate this conclusion to identify the cause of observed bias. First, we compare DISCOVER-AQ Baltimore ambient measurements to fine-scale modeling with NOy tagged by sector. Sector-based relationships with bias are present, but these are sensitive to simulated vertical mixing. This is evident both in sensitivity to mixing parameterization and the seasonal patterns of bias. We also evaluate observation-based indicators, like CO:NOy ratios, that are commonly used to diagnose emissions inventories. Second, we examine the sensitivity of predicted NOx and NOy to temporal allocation of emissions. We investigate alternative temporal allocations for EGUs without CEMS, on-road mobile, and several non-road categories. These results show some location-specific sensitivity and will lead to some improved temporal allocations. Third, near-road studies have inherently fewer confounding variables, and have been examined for more direct evaluation of emissions and dispersion models. From 2008-2011, the EPA and FHWA conducted near-road studies in Las Vegas and Detroit. These measurements are used to more directly evaluate the emissions and dispersion using site-specific traffic data. In addition, the site-specific emissions are being compared to the emissions used in larger-scale photochemical modeling to identify key discrepancies. These efforts are part of a larger coordinated effort by EPA scientist to ensure the highest quality in emissions and model processes. We look forward to sharing the state of these analyses and expected updates.

Model-based methods for case definitions from administrative health data: application to rheumatoid arthritis

PubMed Central

Kroeker, Kristine; Widdifield, Jessica; Muthukumarana, Saman; Jiang, Depeng; Lix, Lisa M

2017-01-01

Objective This research proposes a model-based method to facilitate the selection of disease case definitions from validation studies for administrative health data. The method is demonstrated for a rheumatoid arthritis (RA) validation study. Study design and setting Data were from 148 definitions to ascertain cases of RA in hospital, physician and prescription medication administrative data. We considered: (A) separate univariate models for sensitivity and specificity, (B) univariate model for Youden’s summary index and (C) bivariate (ie, joint) mixed-effects model for sensitivity and specificity. Model covariates included the number of diagnoses in physician, hospital and emergency department records, physician diagnosis observation time, duration of time between physician diagnoses and number of RA-related prescription medication records. Results The most common case definition attributes were: 1+ hospital diagnosis (65%), 2+ physician diagnoses (43%), 1+ specialist physician diagnosis (51%) and 2+ years of physician diagnosis observation time (27%). Statistically significant improvements in sensitivity and/or specificity for separate univariate models were associated with (all p values <0.01): 2+ and 3+ physician diagnoses, unlimited physician diagnosis observation time, 1+ specialist physician diagnosis and 1+ RA-related prescription medication records (65+ years only). The bivariate model produced similar results. Youden’s index was associated with these same case definition criteria, except for the length of the physician diagnosis observation time. Conclusion A model-based method provides valuable empirical evidence to aid in selecting a definition(s) for ascertaining diagnosed disease cases from administrative health data. The choice between univariate and bivariate models depends on the goals of the validation study and number of case definitions. PMID:28645978

Water quality modeling for urban reach of Yamuna river, India (1999-2009), using QUAL2Kw

NASA Astrophysics Data System (ADS)

Sharma, Deepshikha; Kansal, Arun; Pelletier, Greg

2017-06-01

The study was to characterize and understand the water quality of the river Yamuna in Delhi (India) prior to an efficient restoration plan. A combination of collection of monitored data, mathematical modeling, sensitivity, and uncertainty analysis has been done using the QUAL2Kw, a river quality model. The model was applied to simulate DO, BOD, total coliform, and total nitrogen at four monitoring stations, namely Palla, Old Delhi Railway Bridge, Nizamuddin, and Okhla for 10 years (October 1999-June 2009) excluding the monsoon seasons (July-September). The study period was divided into two parts: monthly average data from October 1999-June 2004 (45 months) were used to calibrate the model and monthly average data from October 2005-June 2009 (45 months) were used to validate the model. The R2 for CBODf and TN lies within the range of 0.53-0.75 and 0.68-0.83, respectively. This shows that the model has given satisfactory results in terms of R2 for CBODf, TN, and TC. Sensitivity analysis showed that DO, CBODf, TN, and TC predictions are highly sensitive toward headwater flow and point source flow and quality. Uncertainty analysis using Monte Carlo showed that the input data have been simulated in accordance with the prevalent river conditions.

IMPORTANCE OF MOVEMENT VARIES IN STATIC AND DYNAMIC LANDSCAPES

EPA Science Inventory

The relative sensitivity of spatially explicit population models (SEPMs) to movement parameters is a topic of ongoing debate among theoretical ecologists. In this study, we add additional realism to this debate by examining a SEPM's sensitivity to dispersal ability in static vs....

Sensitivity of an Antarctic Ice Sheet Model to Sub-Ice-Shelf Melting

NASA Astrophysics Data System (ADS)

Lipscomb, W. H.; Leguy, G.; Urban, N. M.; Berdahl, M.

2017-12-01

Theory and observations suggest that marine-based sectors of the Antarctic ice sheet could retreat rapidly under ocean warming and increased melting beneath ice shelves. Numerical models of marine ice sheets vary widely in sensitivity, depending on grid resolution and the parameterization of key processes (e.g., calving and hydrofracture). Here we study the sensitivity of the Antarctic ice sheet to ocean warming and sub-shelf melting in standalone simulations of the Community Ice Sheet Model (CISM). Melt rates either are prescribed based on observations and high-resolution ocean model output, or are derived from a plume model forced by idealized ocean temperature profiles. In CISM, we vary the model resolution (between 1 and 8 km), Stokes approximation (shallow-shelf, depth-integrated higher-order, or 3D higher-order) and calving scheme to create an ensemble of plausible responses to sub-shelf melting. This work supports a broader goal of building statistical and reduced models that can translate large-scale Earth-system model projections to changes in Antarctic ocean temperatures and ice sheet discharge, thus better quantifying uncertainty in Antarctic-sourced sea-level rise.

Sensitivity analysis of Repast computational ecology models with R/Repast.

PubMed

Prestes García, Antonio; Rodríguez-Patón, Alfonso

2016-12-01

Computational ecology is an emerging interdisciplinary discipline founded mainly on modeling and simulation methods for studying ecological systems. Among the existing modeling formalisms, the individual-based modeling is particularly well suited for capturing the complex temporal and spatial dynamics as well as the nonlinearities arising in ecosystems, communities, or populations due to individual variability. In addition, being a bottom-up approach, it is useful for providing new insights on the local mechanisms which are generating some observed global dynamics. Of course, no conclusions about model results could be taken seriously if they are based on a single model execution and they are not analyzed carefully. Therefore, a sound methodology should always be used for underpinning the interpretation of model results. The sensitivity analysis is a methodology for quantitatively assessing the effect of input uncertainty in the simulation output which should be incorporated compulsorily to every work based on in-silico experimental setup. In this article, we present R/Repast a GNU R package for running and analyzing Repast Simphony models accompanied by two worked examples on how to perform global sensitivity analysis and how to interpret the results.

An automatic and effective parameter optimization method for model tuning

NASA Astrophysics Data System (ADS)

Zhang, T.; Li, L.; Lin, Y.; Xue, W.; Xie, F.; Xu, H.; Huang, X.

2015-11-01

Physical parameterizations in general circulation models (GCMs), having various uncertain parameters, greatly impact model performance and model climate sensitivity. Traditional manual and empirical tuning of these parameters is time-consuming and ineffective. In this study, a "three-step" methodology is proposed to automatically and effectively obtain the optimum combination of some key parameters in cloud and convective parameterizations according to a comprehensive objective evaluation metrics. Different from the traditional optimization methods, two extra steps, one determining the model's sensitivity to the parameters and the other choosing the optimum initial value for those sensitive parameters, are introduced before the downhill simplex method. This new method reduces the number of parameters to be tuned and accelerates the convergence of the downhill simplex method. Atmospheric GCM simulation results show that the optimum combination of these parameters determined using this method is able to improve the model's overall performance by 9 %. The proposed methodology and software framework can be easily applied to other GCMs to speed up the model development process, especially regarding unavoidable comprehensive parameter tuning during the model development stage.

Polymorphisms of three genes (ACE, AGT and CYP11B2) in the renin-angiotensin-aldosterone system are not associated with blood pressure salt sensitivity: A systematic meta-analysis.

PubMed

Sun, Jiahong; Zhao, Min; Miao, Song; Xi, Bo

2016-01-01

Many studies have suggested that polymorphisms of three key genes (ACE, AGT and CYP11B2) in the renin-angiotensin-aldosterone system (RAAS) play important roles in the development of blood pressure (BP) salt sensitivity, but they have revealed inconsistent results. Thus, we performed a meta-analysis to clarify the association. PubMed and Embase databases were searched for eligible published articles. Fixed- or random-effect models were used to pool odds ratios and 95% confidence intervals based on whether there was significant heterogeneity between studies. In total, seven studies [237 salt-sensitive (SS) cases and 251 salt-resistant (SR) controls] for ACE gene I/D polymorphism, three studies (130 SS cases and 221 SR controls) for AGT gene M235T polymorphism and three studies (113 SS cases and 218 SR controls) for CYP11B2 gene C344T polymorphism were included in this meta-analysis. The results showed that there was no significant association between polymorphisms of these three polymorphisms in the RAAS and BP salt sensitivity under three genetic models (all p > 0.05). The meta-analysis suggested that three polymorphisms (ACE gene I/D, AGT gene M235T, CYP11B2 gene C344T) in the RAAS have no significant effect on BP salt sensitivity.

Automating calibration, sensitivity and uncertainty analysis of complex models using the R package Flexible Modeling Environment (FME): SWAT as an example

USGS Publications Warehouse

Wu, Y.; Liu, S.

2012-01-01

Parameter optimization and uncertainty issues are a great challenge for the application of large environmental models like the Soil and Water Assessment Tool (SWAT), which is a physically-based hydrological model for simulating water and nutrient cycles at the watershed scale. In this study, we present a comprehensive modeling environment for SWAT, including automated calibration, and sensitivity and uncertainty analysis capabilities through integration with the R package Flexible Modeling Environment (FME). To address challenges (e.g., calling the model in R and transferring variables between Fortran and R) in developing such a two-language coupling framework, 1) we converted the Fortran-based SWAT model to an R function (R-SWAT) using the RFortran platform, and alternatively 2) we compiled SWAT as a Dynamic Link Library (DLL). We then wrapped SWAT (via R-SWAT) with FME to perform complex applications including parameter identifiability, inverse modeling, and sensitivity and uncertainty analysis in the R environment. The final R-SWAT-FME framework has the following key functionalities: automatic initialization of R, running Fortran-based SWAT and R commands in parallel, transferring parameters and model output between SWAT and R, and inverse modeling with visualization. To examine this framework and demonstrate how it works, a case study simulating streamflow in the Cedar River Basin in Iowa in the United Sates was used, and we compared it with the built-in auto-calibration tool of SWAT in parameter optimization. Results indicate that both methods performed well and similarly in searching a set of optimal parameters. Nonetheless, the R-SWAT-FME is more attractive due to its instant visualization, and potential to take advantage of other R packages (e.g., inverse modeling and statistical graphics). The methods presented in the paper are readily adaptable to other model applications that require capability for automated calibration, and sensitivity and uncertainty analysis.

VARS-TOOL: A Comprehensive, Efficient, and Robust Sensitivity Analysis Toolbox

NASA Astrophysics Data System (ADS)

Razavi, S.; Sheikholeslami, R.; Haghnegahdar, A.; Esfahbod, B.

2016-12-01

VARS-TOOL is an advanced sensitivity and uncertainty analysis toolbox, applicable to the full range of computer simulation models, including Earth and Environmental Systems Models (EESMs). The toolbox was developed originally around VARS (Variogram Analysis of Response Surfaces), which is a general framework for Global Sensitivity Analysis (GSA) that utilizes the variogram/covariogram concept to characterize the full spectrum of sensitivity-related information, thereby providing a comprehensive set of "global" sensitivity metrics with minimal computational cost. VARS-TOOL is unique in that, with a single sample set (set of simulation model runs), it generates simultaneously three philosophically different families of global sensitivity metrics, including (1) variogram-based metrics called IVARS (Integrated Variogram Across a Range of Scales - VARS approach), (2) variance-based total-order effects (Sobol approach), and (3) derivative-based elementary effects (Morris approach). VARS-TOOL is also enabled with two novel features; the first one being a sequential sampling algorithm, called Progressive Latin Hypercube Sampling (PLHS), which allows progressively increasing the sample size for GSA while maintaining the required sample distributional properties. The second feature is a "grouping strategy" that adaptively groups the model parameters based on their sensitivity or functioning to maximize the reliability of GSA results. These features in conjunction with bootstrapping enable the user to monitor the stability, robustness, and convergence of GSA with the increase in sample size for any given case study. VARS-TOOL has been shown to achieve robust and stable results within 1-2 orders of magnitude smaller sample sizes (fewer model runs) than alternative tools. VARS-TOOL, available in MATLAB and Python, is under continuous development and new capabilities and features are forthcoming.

Comment on "Simulation of Surface Ozone Pollution in the Central Gulf Coast Region Using WRF/Chem Model: Sensitivity to PBL and Land Surface Physics"

EPA Science Inventory

A recently published meteorology and air quality modeling study has several serious deficiencies deserving comment. The study uses the weather research and forecasting/chemistry (WRF/Chem) model to compare and evaluate boundary layer and land surface modeling options. The most se...

How Victim Sensitivity leads to Uncooperative Behavior via Expectancies of Injustice

PubMed Central

Maltese, Simona; Baumert, Anna; Schmitt, Manfred J.; MacLeod, Colin

2016-01-01

According to the Sensitivity-to-mean-intentions model, dispositional victim sensitivity involves a suspicious mindset that is activated by situational cues and guides subsequent information processing and behavior like a schema. Study 1 tested whether victim-sensitive persons are more prone to form expectancies of injustice in ambiguous situations and whether these expectancies mediate the relationship between victim sensitivity and cooperation behavior in a trust game. Results show an indirect effect of victim sensitivity on cooperation after unfair treatment (vs. control condition), mediated by expectancies of injustice. In Study 2 we directly manipulated the tendency to form expectancies of injustice in ambiguous situations to test for causality. Results confirmed that the readiness to expect unjust outcomes led to lower cooperation, compared to a control condition. These findings provide direct evidence that expectancy tendencies are implicated in elevated victim sensitivity and are of theoretical and practical relevance. PMID:26793163

What do we mean by sensitivity analysis? The need for comprehensive characterization of "global" sensitivity in Earth and Environmental systems models

NASA Astrophysics Data System (ADS)

Razavi, Saman; Gupta, Hoshin V.

2015-05-01

Sensitivity analysis is an essential paradigm in Earth and Environmental Systems modeling. However, the term "sensitivity" has a clear definition, based in partial derivatives, only when specified locally around a particular point (e.g., optimal solution) in the problem space. Accordingly, no unique definition exists for "global sensitivity" across the problem space, when considering one or more model responses to different factors such as model parameters or forcings. A variety of approaches have been proposed for global sensitivity analysis, based on different philosophies and theories, and each of these formally characterizes a different "intuitive" understanding of sensitivity. These approaches focus on different properties of the model response at a fundamental level and may therefore lead to different (even conflicting) conclusions about the underlying sensitivities. Here we revisit the theoretical basis for sensitivity analysis, summarize and critically evaluate existing approaches in the literature, and demonstrate their flaws and shortcomings through conceptual examples. We also demonstrate the difficulty involved in interpreting "global" interaction effects, which may undermine the value of existing interpretive approaches. With this background, we identify several important properties of response surfaces that are associated with the understanding and interpretation of sensitivities in the context of Earth and Environmental System models. Finally, we highlight the need for a new, comprehensive framework for sensitivity analysis that effectively characterizes all of the important sensitivity-related properties of model response surfaces.

Comparative Sensitivity Analysis of Muscle Activation Dynamics

PubMed Central

Günther, Michael; Götz, Thomas

2015-01-01

We mathematically compared two models of mammalian striated muscle activation dynamics proposed by Hatze and Zajac. Both models are representative for a broad variety of biomechanical models formulated as ordinary differential equations (ODEs). These models incorporate parameters that directly represent known physiological properties. Other parameters have been introduced to reproduce empirical observations. We used sensitivity analysis to investigate the influence of model parameters on the ODE solutions. In addition, we expanded an existing approach to treating initial conditions as parameters and to calculating second-order sensitivities. Furthermore, we used a global sensitivity analysis approach to include finite ranges of parameter values. Hence, a theoretician striving for model reduction could use the method for identifying particularly low sensitivities to detect superfluous parameters. An experimenter could use it for identifying particularly high sensitivities to improve parameter estimation. Hatze's nonlinear model incorporates some parameters to which activation dynamics is clearly more sensitive than to any parameter in Zajac's linear model. Other than Zajac's model, Hatze's model can, however, reproduce measured shifts in optimal muscle length with varied muscle activity. Accordingly we extracted a specific parameter set for Hatze's model that combines best with a particular muscle force-length relation. PMID:26417379

An approach for estimating item sensitivity to within-person change over time: An illustration using the Alzheimer's Disease Assessment Scale-Cognitive subscale (ADAS-Cog).

PubMed

Dowling, N Maritza; Bolt, Daniel M; Deng, Sien

2016-12-01

When assessments are primarily used to measure change over time, it is important to evaluate items according to their sensitivity to change, specifically. Items that demonstrate good sensitivity to between-person differences at baseline may not show good sensitivity to change over time, and vice versa. In this study, we applied a longitudinal factor model of change to a widely used cognitive test designed to assess global cognitive status in dementia, and contrasted the relative sensitivity of items to change. Statistically nested models were estimated introducing distinct latent factors related to initial status differences between test-takers and within-person latent change across successive time points of measurement. Models were estimated using all available longitudinal item-level data from the Alzheimer's Disease Assessment Scale-Cognitive subscale, including participants representing the full-spectrum of disease status who were enrolled in the multisite Alzheimer's Disease Neuroimaging Initiative. Five of the 13 Alzheimer's Disease Assessment Scale-Cognitive items demonstrated noticeably higher loadings with respect to sensitivity to change. Attending to performance change on only these 5 items yielded a clearer picture of cognitive decline more consistent with theoretical expectations in comparison to the full 13-item scale. Items that show good psychometric properties in cross-sectional studies are not necessarily the best items at measuring change over time, such as cognitive decline. Applications of the methodological approach described and illustrated in this study can advance our understanding regarding the types of items that best detect fine-grained early pathological changes in cognition. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Animal models of asthma: utility and limitations.

PubMed

Aun, Marcelo Vivolo; Bonamichi-Santos, Rafael; Arantes-Costa, Fernanda Magalhães; Kalil, Jorge; Giavina-Bianchi, Pedro

2017-01-01

Clinical studies in asthma are not able to clear up all aspects of disease pathophysiology. Animal models have been developed to better understand these mechanisms and to evaluate both safety and efficacy of therapies before starting clinical trials. Several species of animals have been used in experimental models of asthma, such as Drosophila , rats, guinea pigs, cats, dogs, pigs, primates and equines. However, the most common species studied in the last two decades is mice, particularly BALB/c. Animal models of asthma try to mimic the pathophysiology of human disease. They classically include two phases: sensitization and challenge. Sensitization is traditionally performed by intraperitoneal and subcutaneous routes, but intranasal instillation of allergens has been increasingly used because human asthma is induced by inhalation of allergens. Challenges with allergens are performed through aerosol, intranasal or intratracheal instillation. However, few studies have compared different routes of sensitization and challenge. The causative allergen is another important issue in developing a good animal model. Despite being more traditional and leading to intense inflammation, ovalbumin has been replaced by aeroallergens, such as house dust mites, to use the allergens that cause human disease. Finally, researchers should define outcomes to be evaluated, such as serum-specific antibodies, airway hyperresponsiveness, inflammation and remodeling. The present review analyzes the animal models of asthma, assessing differences between species, allergens and routes of allergen administration.

Proteins associated with EGFR-TKIs resistance in patients with non-small cell lung cancer revealed by mass spectrometry.

PubMed

Yang, Shaoyu; Chen, Xueqin; Pan, Yuelong; Yu, Jiekai; Li, Xin; Ma, Shenglin

2016-11-01

The present study aimed to identify potential serum biomarkers for predicting the clinical outcomes of patients with advanced non-small cell lung cancer (NSCLC) treated with epidermal growth factor receptor tyrosine kinase inhibitors (EGFR‑TKIs). A total of 61 samples were collected and analyzed using the integrated approach of magnetic bead‑based weak cation exchange chromatography and matrix‑assisted laser desorption/ionization‑time of flight‑mass spectrometry. The Zhejiang University Protein Chip Data Analysis system was used to identify the protein spectra of patients that are resistant and sensitive to EGFR‑TKIs. Furthermore, a support vector machine was used to construct a predictive model with high accuracy. The model was trained using 46 samples and tested with the remaining 15 samples. In addition, the ExPASy Bioinformatics Resource Portal was used to search potential candidate proteins for peaks in the predictive model. Seven mass/charge (m/z) peaks at 3,264, 9,156, 9,172, 3,964, 9,451, 4,295 and 3,983 Da, were identified as significantly different peaks between the EGFR‑TKIs sensitive and resistant groups. A predictive model was generated with three protein peaks at 3,264, 9,451 and 4,295 Da (m/z). This three‑peak model was capable of distinguishing EGFR‑TKIs resistant patients from sensitive patients with a specificity of 80% and a sensitivity of 80.77%. Furthermore, in a blind test, this model exhibited a high specificity (80%) and a high sensitivity (90%). Apelin, TYRO protein tyrosine kinase‑binding protein and big endothelin‑1 may be potential candidates for the proteins identified with an m/z of 3,264, 9,451 and 4,295 Da, respectively. The predictive model used in the present study may provide an improved understanding of the pathogenesis of NSCLC, and may provide insights for the development of TKI treatment plans tailored to specific patients.

Sleep fragmentation exacerbates mechanical hypersensitivity and alters subsequent sleep-wake behavior in a mouse model of musculoskeletal sensitization.

PubMed

Sutton, Blair C; Opp, Mark R

2014-03-01

Sleep deprivation, or sleep disruption, enhances pain in human subjects. Chronic musculoskeletal pain is prevalent in our society, and constitutes a tremendous public health burden. Although preclinical models of neuropathic and inflammatory pain demonstrate effects on sleep, few studies focus on musculoskeletal pain. We reported elsewhere in this issue of SLEEP that musculoskeletal sensitization alters sleep of mice. In this study we hypothesize that sleep fragmentation during the development of musculoskeletal sensitization will exacerbate subsequent pain responses and alter sleep-wake behavior of mice. This is a preclinical study using C57BL/6J mice to determine the effect on behavioral outcomes of sleep fragmentation combined with musculoskeletal sensitization. Musculoskeletal sensitization, a model of chronic muscle pain, was induced using two unilateral injections of acidified saline (pH 4.0) into the gastrocnemius muscle, spaced 5 days apart. Musculoskeletal sensitization manifests as mechanical hypersensitivity determined by von Frey filament testing at the hindpaws. Sleep fragmentation took place during the consecutive 12-h light periods of the 5 days between intramuscular injections. Electroencephalogram (EEG) and body temperature were recorded from some mice at baseline and for 3 weeks after musculoskeletal sensitization. Mechanical hypersensitivity was determined at preinjection baseline and on days 1, 3, 7, 14, and 21 after sensitization. Two additional experiments were conducted to determine the independent effects of sleep fragmentation or musculoskeletal sensitization on mechanical hypersensitivity. Five days of sleep fragmentation alone did not induce mechanical hypersensitivity, whereas sleep fragmentation combined with musculoskeletal sensitization resulted in prolonged and exacerbated mechanical hypersensitivity. Sleep fragmentation combined with musculoskeletal sensitization had an effect on subsequent sleep of mice as demonstrated by increased numbers of sleep-wake state transitions during the light and dark periods; changes in nonrapid eye movement (NREM) sleep, rapid eye movement sleep, and wakefulness; and altered delta power during NREM sleep. These effects persisted for at least 3 weeks postsensitization. Our data demonstrate that sleep fragmentation combined with musculoskeletal sensitization exacerbates the physiological and behavioral responses of mice to musculoskeletal sensitization, including mechanical hypersensitivity and sleep-wake behavior. These data contribute to increasing literature demonstrating bidirectional relationships between sleep and pain. The prevalence and incidence of insufficient sleep and pathologies characterized by chronic musculoskeletal pain are increasing in the United States. These demographic data underscore the need for research focused on insufficient sleep and chronic pain so that the quality of life for the millions of individuals with these conditions may be improved.

Incorporation of surface albedo-temperature feedback in a one-dimensional radiative-connective climate model

NASA Technical Reports Server (NTRS)

Wang, W. C.; Stone, P. H.

1979-01-01

The feedback between ice snow albedo and temperature is included in a one dimensional radiative convective climate model. The effect of this feedback on sensitivity to changes in solar constant is studied for the current values of the solar constant and cloud characteristics. The ice snow albedo feedback amplifies global climate sensitivity by 33% and 50%, respectively, for assumptions of constant cloud altitude and constant cloud temperature.

On the coalescence-dispersion modeling of turbulent molecular mixing

NASA Technical Reports Server (NTRS)

Givi, Peyman; Kosaly, George

1987-01-01

The general coalescence-dispersion (C/D) closure provides phenomenological modeling of turbulent molecular mixing. The models of Curl and Dopazo and O'Brien appear as two limiting C/D models that bracket the range of results one can obtain by various models. This finding is used to investigate the sensitivtiy of the results to the choice of the model. Inert scalar mixing is found to be less model-sensitive than mixing accompanied by chemical reaction. Infinitely fast chemistry approximation is used to relate the C/D approach to Toor's earlier results. Pure mixing and infinite rate chemistry calculations are compared to study further a recent result of Hsieh and O'Brien who found that higher concentration moments are not sensitive to chemistry.

Is Freedom Contagious? A Self-Regulatory Model of Reactance and Sensitivity to Deviant Peers.

PubMed

Leander, N Pontus; vanDellen, Michelle R; Rachl-Willberger, Judith; Shah, James Y; Fitzsimons, Gavan J; Chartrand, Tanya L

2016-12-01

Psychological reactance is typically assumed to motivate resistance to controlling peer influences and societal prohibitions. However, some peer influences encourage behaviors prohibited by society. We consider whether reactant individuals are sensitive to such opportunities to enhance their autonomy. We specifically propose a self-regulatory perspective on reactance, wherein freedom/autonomy is the superordinate goal, and thus highly reactant individuals will be sensitive to peer influences that could enhance their behavioral freedoms. In two studies, we find that reactant individuals can be cooperative in response to autonomy-supportive peer influences. Participants read a scenario in which a peer's intentions to engage in substance use were manipulated to imply freedom of choice or not. Results indicated that highly reactant participants were sensitive to deviant peers whose own behavior towards alcohol (Study 1, N = 160) or marijuana (Study 2, N = 124) appeared to be motivated by autonomy and thus afforded free choice. Altogether, the results support a self-regulatory model of reactance, wherein deviant peer influence can be a means to pursue autonomy.

Sensitivity of decomposition rates of soil organic matter with respect to simultaneous changes in temperature and moisture

NASA Astrophysics Data System (ADS)

Sierra, Carlos A.; Trumbore, Susan E.; Davidson, Eric A.; Vicca, Sara; Janssens, I.

2015-03-01

The sensitivity of soil organic matter decomposition to global environmental change is a topic of prominent relevance for the global carbon cycle. Decomposition depends on multiple factors that are being altered simultaneously as a result of global environmental change; therefore, it is important to study the sensitivity of the rates of soil organic matter decomposition with respect to multiple and interacting drivers. In this manuscript, we present an analysis of the potential response of decomposition rates to simultaneous changes in temperature and moisture. To address this problem, we first present a theoretical framework to study the sensitivity of soil organic matter decomposition when multiple driving factors change simultaneously. We then apply this framework to models and data at different levels of abstraction: (1) to a mechanistic model that addresses the limitation of enzyme activity by simultaneous effects of temperature and soil water content, the latter controlling substrate supply and oxygen concentration for microbial activity; (2) to different mathematical functions used to represent temperature and moisture effects on decomposition in biogeochemical models. To contrast model predictions at these two levels of organization, we compiled different data sets of observed responses in field and laboratory studies. Then we applied our conceptual framework to: (3) observations of heterotrophic respiration at the ecosystem level; (4) laboratory experiments looking at the response of heterotrophic respiration to independent changes in moisture and temperature; and (5) ecosystem-level experiments manipulating soil temperature and water content simultaneously.

Experimental Validation of Model Updating and Damage Detection via Eigenvalue Sensitivity Methods with Artificial Boundary Conditions

DTIC Science & Technology

2017-09-01

VALIDATION OF MODEL UPDATING AND DAMAGE DETECTION VIA EIGENVALUE SENSITIVITY METHODS WITH ARTIFICIAL BOUNDARY CONDITIONS by Matthew D. Bouwense...VALIDATION OF MODEL UPDATING AND DAMAGE DETECTION VIA EIGENVALUE SENSITIVITY METHODS WITH ARTIFICIAL BOUNDARY CONDITIONS 5. FUNDING NUMBERS 6. AUTHOR...unlimited. EXPERIMENTAL VALIDATION OF MODEL UPDATING AND DAMAGE DETECTION VIA EIGENVALUE SENSITIVITY METHODS WITH ARTIFICIAL BOUNDARY

A cascaded QSAR model for efficient prediction of overall power conversion efficiency of all-organic dye-sensitized solar cells.

PubMed

Li, Hongzhi; Zhong, Ziyan; Li, Lin; Gao, Rui; Cui, Jingxia; Gao, Ting; Hu, Li Hong; Lu, Yinghua; Su, Zhong-Min; Li, Hui

2015-05-30

A cascaded model is proposed to establish the quantitative structure-activity relationship (QSAR) between the overall power conversion efficiency (PCE) and quantum chemical molecular descriptors of all-organic dye sensitizers. The cascaded model is a two-level network in which the outputs of the first level (JSC, VOC, and FF) are the inputs of the second level, and the ultimate end-point is the overall PCE of dye-sensitized solar cells (DSSCs). The model combines quantum chemical methods and machine learning methods, further including quantum chemical calculations, data division, feature selection, regression, and validation steps. To improve the efficiency of the model and reduce the redundancy and noise of the molecular descriptors, six feature selection methods (multiple linear regression, genetic algorithms, mean impact value, forward selection, backward elimination, and +n-m algorithm) are used with the support vector machine. The best established cascaded model predicts the PCE values of DSSCs with a MAE of 0.57 (%), which is about 10% of the mean value PCE (5.62%). The validation parameters according to the OECD principles are R(2) (0.75), Q(2) (0.77), and Qcv2 (0.76), which demonstrate the great goodness-of-fit, predictivity, and robustness of the model. Additionally, the applicability domain of the cascaded QSAR model is defined for further application. This study demonstrates that the established cascaded model is able to effectively predict the PCE for organic dye sensitizers with very low cost and relatively high accuracy, providing a useful tool for the design of dye sensitizers with high PCE. © 2015 Wiley Periodicals, Inc.

Validation of simple indexes to assess insulin sensitivity during pregnancy in Wistar and Sprague-Dawley rats.

PubMed

Cacho, J; Sevillano, J; de Castro, J; Herrera, E; Ramos, M P

2008-11-01

Insulin resistance plays a role in the pathogenesis of diabetes, including gestational diabetes. The glucose clamp is considered the gold standard for determining in vivo insulin sensitivity, both in human and in animal models. However, the clamp is laborious, time consuming and, in animals, requires anesthesia and collection of multiple blood samples. In human studies, a number of simple indexes, derived from fasting glucose and insulin levels, have been obtained and validated against the glucose clamp. However, these indexes have not been validated in rats and their accuracy in predicting altered insulin sensitivity remains to be established. In the present study, we have evaluated whether indirect estimates based on fasting glucose and insulin levels are valid predictors of insulin sensitivity in nonpregnant and 20-day-pregnant Wistar and Sprague-Dawley rats. We have analyzed the homeostasis model assessment of insulin resistance (HOMA-IR), the quantitative insulin sensitivity check index (QUICKI), and the fasting glucose-to-insulin ratio (FGIR) by comparing them with the insulin sensitivity (SI(Clamp)) values obtained during the hyperinsulinemic-isoglycemic clamp. We have performed a calibration analysis to evaluate the ability of these indexes to accurately predict insulin sensitivity as determined by the reference glucose clamp. Finally, to assess the reliability of these indexes for the identification of animals with impaired insulin sensitivity, performance of the indexes was analyzed by receiver operating characteristic (ROC) curves in Wistar and Sprague-Dawley rats. We found that HOMA-IR, QUICKI, and FGIR correlated significantly with SI(Clamp), exhibited good sensitivity and specificity, accurately predicted SI(Clamp), and yielded lower insulin sensitivity in pregnant than in nonpregnant rats. Together, our data demonstrate that these indexes provide an easy and accurate measure of insulin sensitivity during pregnancy in the rat.

Sensitivity of the ocean overturning circulation to wind and mixing: theoretical scalings and global ocean models

NASA Astrophysics Data System (ADS)

Nikurashin, Maxim; Gunn, Andrew

2017-04-01

The meridional overturning circulation (MOC) is a planetary-scale oceanic flow which is of direct importance to the climate system: it transports heat meridionally and regulates the exchange of CO2 with the atmosphere. The MOC is forced by wind and heat and freshwater fluxes at the surface and turbulent mixing in the ocean interior. A number of conceptual theories for the sensitivity of the MOC to changes in forcing have recently been developed and tested with idealized numerical models. However, the skill of the simple conceptual theories to describe the MOC simulated with higher complexity global models remains largely unknown. In this study, we present a systematic comparison of theoretical and modelled sensitivity of the MOC and associated deep ocean stratification to vertical mixing and southern hemisphere westerlies. The results show that theories that simplify the ocean into a single-basin, zonally-symmetric box are generally in a good agreement with a realistic, global ocean circulation model. Some disagreement occurs in the abyssal ocean, where complex bottom topography is not taken into account by simple theories. Distinct regimes, where the MOC has a different sensitivity to wind or mixing, as predicted by simple theories, are also clearly shown by the global ocean model. The sensitivity of the Indo-Pacific, Atlantic, and global basins is analysed separately to validate the conceptual understanding of the upper and lower overturning cells in the theory.

A discourse on sensitivity analysis for discretely-modeled structures

NASA Technical Reports Server (NTRS)

Adelman, Howard M.; Haftka, Raphael T.

1991-01-01

A descriptive review is presented of the most recent methods for performing sensitivity analysis of the structural behavior of discretely-modeled systems. The methods are generally but not exclusively aimed at finite element modeled structures. Topics included are: selections of finite difference step sizes; special consideration for finite difference sensitivity of iteratively-solved response problems; first and second derivatives of static structural response; sensitivity of stresses; nonlinear static response sensitivity; eigenvalue and eigenvector sensitivities for both distinct and repeated eigenvalues; and sensitivity of transient response for both linear and nonlinear structural response.

A Chain of Modeling Tools For Gas and Aqueous Phase Chemstry

NASA Astrophysics Data System (ADS)

Audiffren, N.; Djouad, R.; Sportisse, B.

Atmospheric chemistry is characterized by the use of large set of chemical species and reactions. Handling with the set of data required for the definition of the model is a quite difficult task. We prsent in this short article a preprocessor for diphasic models (gas phase and aqueous phase in cloud droplets) named SPACK. The main interest of SPACK is the automatic generation of lumped species related to fast equilibria. We also developped a linear tangent model using the automatic differentiation tool named ODYSSEE in order to perform a sensitivity analysis of an atmospheric multi- phase mechanism based on RADM2 kinetic scheme.Local sensitivity coefficients are computed for two different scenarii. We focus in this study on the sensitivity of the ozone,NOx,HOx, system with respect to some aqueous phase reactions and we inves- tigate the influence of the reduction in the photolysis rates in the area below the cloud region.

Panic and phobic anxiety: associations among neuroticism, physiological hyperarousal, anxiety sensitivity, and three phobias.

PubMed

Longley, Susan L; Watson, David; Noyes, Russell; Yoder, Kevin

2006-01-01

A dimensional and psychometrically informed taxonomy of anxiety is emerging, but the specific and nonspecific dimensions of panic and phobic anxiety require greater clarification. In this study, confirmatory factor analyses of data from a sample of 438 college students were used to validate a model of panic and phobic anxiety with six content factors; multiple scales from self-report measures were indicators of each model component. The model included a nonspecific component of (1) neuroticism and two specific components of panic attack, (2) physiological hyperarousal, and (3) anxiety sensitivity. The model also included three phobia components of (4) classically defined agoraphobia, (5) social phobia, and (6) blood-injection phobia. In these data, agoraphobia correlated more strongly with both the social phobia and blood phobia components than with either the physiological hyperarousal or the anxiety sensitivity components. These findings suggest that the association between panic attacks and agoraphobia warrants greater attention.

Using sensitivity analysis in model calibration efforts

USGS Publications Warehouse

Tiedeman, Claire; Hill, Mary C.

2003-01-01

In models of natural and engineered systems, sensitivity analysis can be used to assess relations among system state observations, model parameters, and model predictions. The model itself links these three entities, and model sensitivities can be used to quantify the links. Sensitivities are defined as the derivatives of simulated quantities (such as simulated equivalents of observations, or model predictions) with respect to model parameters. We present four measures calculated from model sensitivities that quantify the observation-parameter-prediction links and that are especially useful during the calibration and prediction phases of modeling. These four measures are composite scaled sensitivities (CSS), prediction scaled sensitivities (PSS), the value of improved information (VOII) statistic, and the observation prediction (OPR) statistic. These measures can be used to help guide initial calibration of models, collection of field data beneficial to model predictions, and recalibration of models updated with new field information. Once model sensitivities have been calculated, each of the four measures requires minimal computational effort. We apply the four measures to a three-layer MODFLOW-2000 (Harbaugh et al., 2000; Hill et al., 2000) model of the Death Valley regional ground-water flow system (DVRFS), located in southern Nevada and California. D’Agnese et al. (1997, 1999) developed and calibrated the model using nonlinear regression methods. Figure 1 shows some of the observations, parameters, and predictions for the DVRFS model. Observed quantities include hydraulic heads and spring flows. The 23 defined model parameters include hydraulic conductivities, vertical anisotropies, recharge rates, evapotranspiration rates, and pumpage. Predictions of interest for this regional-scale model are advective transport paths from potential contamination sites underlying the Nevada Test Site and Yucca Mountain.

A Practical Model of Quartz Crystal Microbalance in Actual Applications.

PubMed

Huang, Xianhe; Bai, Qingsong; Hu, Jianguo; Hou, Dong

2017-08-03

A practical model of quartz crystal microbalance (QCM) is presented, which considers both the Gaussian distribution characteristic of mass sensitivity and the influence of electrodes on the mass sensitivity. The equivalent mass sensitivity of 5 MHz and 10 MHz AT-cut QCMs with different sized electrodes were calculated according to this practical model. The equivalent mass sensitivity of this practical model is different from the Sauerbrey's mass sensitivity, and the error between them increases sharply as the electrode radius decreases. A series of experiments which plate rigid gold film onto QCMs were carried out and the experimental results proved this practical model is more valid and correct rather than the classical Sauerbrey equation. The practical model based on the equivalent mass sensitivity is convenient and accurate in actual measurements.

A practical approach to the sensitivity analysis for kinetic Monte Carlo simulation of heterogeneous catalysis

DOE PAGES

Hoffmann, Max J.; Engelmann, Felix; Matera, Sebastian

2017-01-31

Lattice kinetic Monte Carlo simulations have become a vital tool for predictive quality atomistic understanding of complex surface chemical reaction kinetics over a wide range of reaction conditions. In order to expand their practical value in terms of giving guidelines for atomic level design of catalytic systems, it is very desirable to readily evaluate a sensitivity analysis for a given model. The result of such a sensitivity analysis quantitatively expresses the dependency of the turnover frequency, being the main output variable, on the rate constants entering the model. In the past the application of sensitivity analysis, such as Degree ofmore » Rate Control, has been hampered by its exuberant computational effort required to accurately sample numerical derivatives of a property that is obtained from a stochastic simulation method. Here in this study we present an efficient and robust three stage approach that is capable of reliably evaluating the sensitivity measures for stiff microkinetic models as we demonstrate using CO oxidation on RuO 2(110) as a prototypical reaction. In a first step, we utilize the Fisher Information Matrix for filtering out elementary processes which only yield negligible sensitivity. Then we employ an estimator based on linear response theory for calculating the sensitivity measure for non-critical conditions which covers the majority of cases. Finally we adopt a method for sampling coupled finite differences for evaluating the sensitivity measure of lattice based models. This allows efficient evaluation even in critical regions near a second order phase transition that are hitherto difficult to control. The combined approach leads to significant computational savings over straightforward numerical derivatives and should aid in accelerating the nano scale design of heterogeneous catalysts.« less

A practical approach to the sensitivity analysis for kinetic Monte Carlo simulation of heterogeneous catalysis.

PubMed

Hoffmann, Max J; Engelmann, Felix; Matera, Sebastian

2017-01-28

Lattice kinetic Monte Carlo simulations have become a vital tool for predictive quality atomistic understanding of complex surface chemical reaction kinetics over a wide range of reaction conditions. In order to expand their practical value in terms of giving guidelines for the atomic level design of catalytic systems, it is very desirable to readily evaluate a sensitivity analysis for a given model. The result of such a sensitivity analysis quantitatively expresses the dependency of the turnover frequency, being the main output variable, on the rate constants entering the model. In the past, the application of sensitivity analysis, such as degree of rate control, has been hampered by its exuberant computational effort required to accurately sample numerical derivatives of a property that is obtained from a stochastic simulation method. In this study, we present an efficient and robust three-stage approach that is capable of reliably evaluating the sensitivity measures for stiff microkinetic models as we demonstrate using the CO oxidation on RuO 2 (110) as a prototypical reaction. In the first step, we utilize the Fisher information matrix for filtering out elementary processes which only yield negligible sensitivity. Then we employ an estimator based on the linear response theory for calculating the sensitivity measure for non-critical conditions which covers the majority of cases. Finally, we adapt a method for sampling coupled finite differences for evaluating the sensitivity measure for lattice based models. This allows for an efficient evaluation even in critical regions near a second order phase transition that are hitherto difficult to control. The combined approach leads to significant computational savings over straightforward numerical derivatives and should aid in accelerating the nano-scale design of heterogeneous catalysts.

A practical approach to the sensitivity analysis for kinetic Monte Carlo simulation of heterogeneous catalysis

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

Hoffmann, Max J.; Engelmann, Felix; Matera, Sebastian

Lattice kinetic Monte Carlo simulations have become a vital tool for predictive quality atomistic understanding of complex surface chemical reaction kinetics over a wide range of reaction conditions. In order to expand their practical value in terms of giving guidelines for atomic level design of catalytic systems, it is very desirable to readily evaluate a sensitivity analysis for a given model. The result of such a sensitivity analysis quantitatively expresses the dependency of the turnover frequency, being the main output variable, on the rate constants entering the model. In the past the application of sensitivity analysis, such as Degree ofmore » Rate Control, has been hampered by its exuberant computational effort required to accurately sample numerical derivatives of a property that is obtained from a stochastic simulation method. Here in this study we present an efficient and robust three stage approach that is capable of reliably evaluating the sensitivity measures for stiff microkinetic models as we demonstrate using CO oxidation on RuO 2(110) as a prototypical reaction. In a first step, we utilize the Fisher Information Matrix for filtering out elementary processes which only yield negligible sensitivity. Then we employ an estimator based on linear response theory for calculating the sensitivity measure for non-critical conditions which covers the majority of cases. Finally we adopt a method for sampling coupled finite differences for evaluating the sensitivity measure of lattice based models. This allows efficient evaluation even in critical regions near a second order phase transition that are hitherto difficult to control. The combined approach leads to significant computational savings over straightforward numerical derivatives and should aid in accelerating the nano scale design of heterogeneous catalysts.« less

A practical approach to the sensitivity analysis for kinetic Monte Carlo simulation of heterogeneous catalysis

NASA Astrophysics Data System (ADS)

Hoffmann, Max J.; Engelmann, Felix; Matera, Sebastian

2017-01-01

Lattice kinetic Monte Carlo simulations have become a vital tool for predictive quality atomistic understanding of complex surface chemical reaction kinetics over a wide range of reaction conditions. In order to expand their practical value in terms of giving guidelines for the atomic level design of catalytic systems, it is very desirable to readily evaluate a sensitivity analysis for a given model. The result of such a sensitivity analysis quantitatively expresses the dependency of the turnover frequency, being the main output variable, on the rate constants entering the model. In the past, the application of sensitivity analysis, such as degree of rate control, has been hampered by its exuberant computational effort required to accurately sample numerical derivatives of a property that is obtained from a stochastic simulation method. In this study, we present an efficient and robust three-stage approach that is capable of reliably evaluating the sensitivity measures for stiff microkinetic models as we demonstrate using the CO oxidation on RuO2(110) as a prototypical reaction. In the first step, we utilize the Fisher information matrix for filtering out elementary processes which only yield negligible sensitivity. Then we employ an estimator based on the linear response theory for calculating the sensitivity measure for non-critical conditions which covers the majority of cases. Finally, we adapt a method for sampling coupled finite differences for evaluating the sensitivity measure for lattice based models. This allows for an efficient evaluation even in critical regions near a second order phase transition that are hitherto difficult to control. The combined approach leads to significant computational savings over straightforward numerical derivatives and should aid in accelerating the nano-scale design of heterogeneous catalysts.

Multi-Toxic Endpoints of the Foodborne Mycotoxins in Nematode Caenorhabditis elegans

PubMed Central

Yang, Zhendong; Xue, Kathy S.; Sun, Xiulan; Tang, Lili; Wang, Jia-Sheng

2015-01-01

Aflatoxins B1 (AFB1), deoxynivalenol (DON), fumonisin B1 (FB1), T-2 toxin (T-2), and zearalenone (ZEA) are the major foodborne mycotoxins of public health concerns. In the present study, the multiple toxic endpoints of these naturally-occurring mycotoxins were evaluated in Caenorhabditis elegans model for their lethality, toxic effects on growth and reproduction, as well as influence on lifespan. We found that the lethality endpoint was more sensitive for T-2 toxicity with the EC50 at 1.38 mg/L, the growth endpoint was relatively sensitive for AFB1 toxic effects, and the reproduction endpoint was more sensitive for toxicities of AFB1, FB1, and ZEA. Moreover, the lifespan endpoint was sensitive to toxic effects of all five tested mycotoxins. Data obtained from this study may serve as an important contribution to knowledge on assessment of mycotoxin toxic effects, especially for assessing developmental and reproductive toxic effects, using the C. elegans model. PMID:26633509

Monitoring Tumor Response to Carbogen Breathing by Oxygen-Sensitive Magnetic Resonance Parameters to Predict the Outcome of Radiation Therapy: A Preclinical Study

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

Cao-Pham, Thanh-Trang; Tran, Ly-Binh-An; Colliez, Florence

Purpose: In an effort to develop noninvasive in vivo methods for mapping tumor oxygenation, magnetic resonance (MR)-derived parameters are being considered, including global R{sub 1}, water R{sub 1}, lipids R{sub 1}, and R{sub 2}*. R{sub 1} is sensitive to dissolved molecular oxygen, whereas R{sub 2}* is sensitive to blood oxygenation, detecting changes in dHb. This work compares global R{sub 1}, water R{sub 1}, lipids R{sub 1}, and R{sub 2}* with pO{sub 2} assessed by electron paramagnetic resonance (EPR) oximetry, as potential markers of the outcome of radiation therapy (RT). Methods and Materials: R{sub 1}, R{sub 2}*, and EPR were performed onmore » rhabdomyosarcoma and 9L-glioma tumor models, under air and carbogen breathing conditions (95% O{sub 2}, 5% CO{sub 2}). Because the models demonstrated different radiosensitivity properties toward carbogen, a growth delay (GD) assay was performed on the rhabdomyosarcoma model and a tumor control dose 50% (TCD50) was performed on the 9L-glioma model. Results: Magnetic resonance imaging oxygen-sensitive parameters detected the positive changes in oxygenation induced by carbogen within tumors. No consistent correlation was seen throughout the study between MR parameters and pO{sub 2}. Global and lipids R{sub 1} were found to be correlated to pO{sub 2} in the rhabdomyosarcoma model, whereas R{sub 2}* was found to be inversely correlated to pO{sub 2} in the 9L-glioma model (P=.05 and .03). Carbogen increased the TCD50 of 9L-glioma but did not increase the GD of rhabdomyosarcoma. Only R{sub 2}* was predictive (P<.05) for the curability of 9L-glioma at 40 Gy, a dose that showed a difference in response to RT between carbogen and air-breathing groups. {sup 18}F-FAZA positron emission tomography imaging has been shown to be a predictive marker under the same conditions. Conclusion: This work illustrates the sensitivity of oxygen-sensitive R{sub 1} and R{sub 2}* parameters to changes in tumor oxygenation. However, R{sub 1} parameters showed limitations in terms of predicting the outcome of RT in the tumor models studied, whereas R{sub 2}* was found to be correlated with the outcome in the responsive model.« less

Sensitivity analysis of infectious disease models: methods, advances and their application

PubMed Central

Wu, Jianyong; Dhingra, Radhika; Gambhir, Manoj; Remais, Justin V.

2013-01-01

Sensitivity analysis (SA) can aid in identifying influential model parameters and optimizing model structure, yet infectious disease modelling has yet to adopt advanced SA techniques that are capable of providing considerable insights over traditional methods. We investigate five global SA methods—scatter plots, the Morris and Sobol’ methods, Latin hypercube sampling-partial rank correlation coefficient and the sensitivity heat map method—and detail their relative merits and pitfalls when applied to a microparasite (cholera) and macroparasite (schistosomaisis) transmission model. The methods investigated yielded similar results with respect to identifying influential parameters, but offered specific insights that vary by method. The classical methods differed in their ability to provide information on the quantitative relationship between parameters and model output, particularly over time. The heat map approach provides information about the group sensitivity of all model state variables, and the parameter sensitivity spectrum obtained using this method reveals the sensitivity of all state variables to each parameter over the course of the simulation period, especially valuable for expressing the dynamic sensitivity of a microparasite epidemic model to its parameters. A summary comparison is presented to aid infectious disease modellers in selecting appropriate methods, with the goal of improving model performance and design. PMID:23864497

An adjuvant free mouse model of oral allergenic sensitization to rice seeds protein

PubMed Central

2011-01-01

Background Rice is commonly known as a staple crop consumed worldwide, though with several rice proteins being reported for allergic properties in clinical studies. Thus, there is a growing need for the development of an animal model to better understand the allergenicity of rice proteins and the immunological and pathophysiological mechanisms underlying the development of food allergy. Methods Groups of BALB/c mice were sensitized daily with freshly homogenized rice flour (30 mg or 80 mg) without adjuvant by intragastric gavage. In addition, the mice were challenged with extracted rice flour proteins at several time points intragastrically. Hypersensitivity symptoms in mice were evaluated according to a scoring system. Vascular leakage, ELISA of rice protein-specific IgE, histopathology of small intestine, and passive cutaneous anaphylaxis were conducted on challenged mice. Results An adjuvant free mouse model of rice allergy was established with sensitized mice showing increased scratching behaviors and increased vascular permeability. Rice protein-specific IgE was detected after eighteen days of sensitization and from the fifth challenge onwards. Inflammatory damage to the epithelium in the small intestine of mice was observed beyond one month of sensitization. Passive cutaneous anaphylaxis results confirmed the positive rice allergy in the mouse model. Conclusions We introduced a BALB/c mouse model of rice allergy with simple oral sensitization without the use of adjuvant. This model would serve as a useful tool for further analysis on the immunopathogenic mechanisms of the various rice allergens, for the evaluation of the hypersensitivity of rice or other cereal grains, and to serve as a platform for the development of immunotherapies against rice allergens. PMID:21605393

Assessing Sexual Dicromatism: The Importance of Proper Parameterization in Tetrachromatic Visual Models

PubMed Central

Janisse, Kevyn; Doucet, Stéphanie M.

2017-01-01

Perceptual models of animal vision have greatly contributed to our understanding of animal-animal and plant-animal communication. The receptor-noise model of color contrasts has been central to this research as it quantifies the difference between two colors for any visual system of interest. However, if the properties of the visual system are unknown, assumptions regarding parameter values must be made, generally with unknown consequences. In this study, we conduct a sensitivity analysis of the receptor-noise model using avian visual system parameters to systematically investigate the influence of variation in light environment, photoreceptor sensitivities, photoreceptor densities, and light transmission properties of the ocular media and the oil droplets. We calculated the chromatic contrast of 15 plumage patches to quantify a dichromatism score for 70 species of Galliformes, a group of birds that display a wide range of sexual dimorphism. We found that the photoreceptor densities and the wavelength of maximum sensitivity of the short-wavelength-sensitive photoreceptor 1 (SWS1) can change dichromatism scores by 50% to 100%. In contrast, the light environment, transmission properties of the oil droplets, transmission properties of the ocular media, and the peak sensitivities of the cone photoreceptors had a smaller impact on the scores. By investigating the effect of varying two or more parameters simultaneously, we further demonstrate that improper parameterization could lead to differences between calculated and actual contrasts of more than 650%. Our findings demonstrate that improper parameterization of tetrachromatic visual models can have very large effects on measures of dichromatism scores, potentially leading to erroneous inferences. We urge more complete characterization of avian retinal properties and recommend that researchers either determine whether their species of interest possess an ultraviolet or near-ultraviolet sensitive SWS1 photoreceptor, or present models for both. PMID:28076391

Stochastic sensitivity analysis of the variability of dynamics and transition to chaos in the business cycles model

NASA Astrophysics Data System (ADS)

Bashkirtseva, Irina; Ryashko, Lev; Ryazanova, Tatyana

2018-01-01

A problem of mathematical modeling of complex stochastic processes in macroeconomics is discussed. For the description of dynamics of income and capital stock, the well-known Kaldor model of business cycles is used as a basic example. The aim of the paper is to give an overview of the variety of stochastic phenomena which occur in Kaldor model forced by additive and parametric random noise. We study a generation of small- and large-amplitude stochastic oscillations, and their mixed-mode intermittency. To analyze these phenomena, we suggest a constructive approach combining the study of the peculiarities of deterministic phase portrait, and stochastic sensitivity of attractors. We show how parametric noise can stabilize the unstable equilibrium and transform dynamics of Kaldor system from order to chaos.

Evaluation of the AnnAGNPS Model for Predicting Runoff and Nutrient Export in a Typical Small Watershed in the Hilly Region of Taihu Lake.

PubMed

Luo, Chuan; Li, Zhaofu; Li, Hengpeng; Chen, Xiaomin

2015-09-02

The application of hydrological and water quality models is an efficient approach to better understand the processes of environmental deterioration. This study evaluated the ability of the Annualized Agricultural Non-Point Source (AnnAGNPS) model to predict runoff, total nitrogen (TN) and total phosphorus (TP) loading in a typical small watershed of a hilly region near Taihu Lake, China. Runoff was calibrated and validated at both an annual and monthly scale, and parameter sensitivity analysis was performed for TN and TP before the two water quality components were calibrated. The results showed that the model satisfactorily simulated runoff at annual and monthly scales, both during calibration and validation processes. Additionally, results of parameter sensitivity analysis showed that the parameters Fertilizer rate, Fertilizer organic, Canopy cover and Fertilizer inorganic were more sensitive to TN output. In terms of TP, the parameters Residue mass ratio, Fertilizer rate, Fertilizer inorganic and Canopy cover were the most sensitive. Based on these sensitive parameters, calibration was performed. TN loading produced satisfactory results for both the calibration and validation processes, whereas the performance of TP loading was slightly poor. The simulation results showed that AnnAGNPS has the potential to be used as a valuable tool for the planning and management of watersheds.

Inflammation-induced pain sensitization in men and women: does sex matter in experimental endotoxemia?

PubMed Central

Wegner, Alexander; Elsenbruch, Sigrid; Rebernik, Laura; Roderigo, Till; Engelbrecht, Elisa; Jäger, Marcus; Engler, Harald; Schedlowski, Manfred; Benson, Sven

2015-01-01

Abstract A role of the innate immune system is increasingly recognized as a mechanism contributing to pain sensitization. Experimental administration of the bacterial endotoxin lipopolysaccharide (LPS) constitutes a model to study inflammation-induced pain sensitization, but all existing human evidence comes from male participants. We assessed visceral and musculoskeletal pain sensitivity after low-dose LPS administration in healthy men and women to test the hypothesis that women show greater LPS-induced hyperalgesia compared with men. In this randomized, double-blind, placebo-controlled crossover study, healthy men (n = 20) and healthy women using oral contraceptives (n = 20) received an intravenous injection of 0.4 ng/kg body weight LPS or placebo. Pain sensitivity was assessed with established visceral and musculoskeletal pain models (ie, rectal pain thresholds; pressure pain thresholds for different muscle groups), together with a heartbeat perception (interoceptive accuracy) task. Plasma cytokines (tumor necrosis factor-α and interleukin-6) were measured along with state anxiety at baseline and up to 6-hour postinjection. Lipopolysaccharide application led to significant increases in plasma cytokines and state anxiety and decreased interoceptive awareness in men and women (P < 0.001, condition effects), with more pronounced LPS-induced cytokine increases in women (P < 0.05, interaction effects). Although both rectal and pressure pain thresholds were significantly decreased in the LPS condition (all P < 0.05, condition effect), no sex differences in endotoxin-induced sensitization were observed. In summary, LPS-induced systemic immune activation leads to visceral and musculoskeletal hyperalgesia, irrespective of biological sex. These findings support the broad applicability of experimental endotoxin administration as a translational preclinical model of inflammation-induced pain sensitization in both sexes. PMID:26058036

Prediction of skin sensitization potency using machine learning approaches.

PubMed

Zang, Qingda; Paris, Michael; Lehmann, David M; Bell, Shannon; Kleinstreuer, Nicole; Allen, David; Matheson, Joanna; Jacobs, Abigail; Casey, Warren; Strickland, Judy

2017-07-01

The replacement of animal use in testing for regulatory classification of skin sensitizers is a priority for US federal agencies that use data from such testing. Machine learning models that classify substances as sensitizers or non-sensitizers without using animal data have been developed and evaluated. Because some regulatory agencies require that sensitizers be further classified into potency categories, we developed statistical models to predict skin sensitization potency for murine local lymph node assay (LLNA) and human outcomes. Input variables for our models included six physicochemical properties and data from three non-animal test methods: direct peptide reactivity assay; human cell line activation test; and KeratinoSens™ assay. Models were built to predict three potency categories using four machine learning approaches and were validated using external test sets and leave-one-out cross-validation. A one-tiered strategy modeled all three categories of response together while a two-tiered strategy modeled sensitizer/non-sensitizer responses and then classified the sensitizers as strong or weak sensitizers. The two-tiered model using the support vector machine with all assay and physicochemical data inputs provided the best performance, yielding accuracy of 88% for prediction of LLNA outcomes (120 substances) and 81% for prediction of human test outcomes (87 substances). The best one-tiered model predicted LLNA outcomes with 78% accuracy and human outcomes with 75% accuracy. By comparison, the LLNA predicts human potency categories with 69% accuracy (60 of 87 substances correctly categorized). These results suggest that computational models using non-animal methods may provide valuable information for assessing skin sensitization potency. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

Sensitivity of subject-specific models to errors in musculo-skeletal geometry.

PubMed

Carbone, V; van der Krogt, M M; Koopman, H F J M; Verdonschot, N

2012-09-21

Subject-specific musculo-skeletal models of the lower extremity are an important tool for investigating various biomechanical problems, for instance the results of surgery such as joint replacements and tendon transfers. The aim of this study was to assess the potential effects of errors in musculo-skeletal geometry on subject-specific model results. We performed an extensive sensitivity analysis to quantify the effect of the perturbation of origin, insertion and via points of each of the 56 musculo-tendon parts contained in the model. We used two metrics, namely a Local Sensitivity Index (LSI) and an Overall Sensitivity Index (OSI), to distinguish the effect of the perturbation on the predicted force produced by only the perturbed musculo-tendon parts and by all the remaining musculo-tendon parts, respectively, during a simulated gait cycle. Results indicated that, for each musculo-tendon part, only two points show a significant sensitivity: its origin, or pseudo-origin, point and its insertion, or pseudo-insertion, point. The most sensitive points belong to those musculo-tendon parts that act as prime movers in the walking movement (insertion point of the Achilles Tendon: LSI=15.56%, OSI=7.17%; origin points of the Rectus Femoris: LSI=13.89%, OSI=2.44%) and as hip stabilizers (insertion points of the Gluteus Medius Anterior: LSI=17.92%, OSI=2.79%; insertion point of the Gluteus Minimus: LSI=21.71%, OSI=2.41%). The proposed priority list provides quantitative information to improve the predictive accuracy of subject-specific musculo-skeletal models. Copyright © 2012 Elsevier Ltd. All rights reserved.

Performance-based workload assessment: Allocation strategy and added task sensitivity

NASA Technical Reports Server (NTRS)

Vidulich, Michael A.

1990-01-01

The preliminary results of a research program investigating the use of added tasks to evaluate mental workload are reviewed. The focus of the first studies was a reappraisal of the traditional secondary task logic that encouraged the use of low-priority instructions for the added task. It was believed that such low-priority tasks would encourage subjects to split their available resources among the two tasks. The primary task would be assigned all the resources it needed, and any remaining reserve capacity would be assigned to the secondary task. If the model were correct, this approach was expected to combine sensitivity to primary task difficulty with unintrusiveness to primary task performance. The first studies of the current project demonstrated that a high-priority added task, although intrusive, could be more sensitive than the traditional low-priority secondary task. These results suggested that a more appropriate model of the attentional effects associated with added task performance might be based on capacity switching, rather than the traditional optimal allocation model.

A preliminary cost-effectiveness analysis of hepatitis E vaccination among pregnant women in epidemic regions.

PubMed

Zhao, Yueyuan; Zhang, Xuefeng; Zhu, Fengcai; Jin, Hui; Wang, Bei

2016-08-02

Objective To estimate the cost-effectiveness of hepatitis E vaccination among pregnant women in epidemic regions. Methods A decision tree model was constructed to evaluate the cost-effectiveness of 3 hepatitis E virus vaccination strategies from societal perspectives. The model parameters were estimated on the basis of published studies and experts' experience. Sensitivity analysis was used to evaluate the uncertainties of the model. Results Vaccination was more economically effective on the basis of the incremental cost-effectiveness ratio (ICER< 3 times China's per capital gross domestic product/quality-adjusted life years); moreover, screening and vaccination had higher QALYs and lower costs compared with universal vaccination. No parameters significantly impacted ICER in one-way sensitivity analysis, and probabilistic sensitivity analysis also showed screening and vaccination to be the dominant strategy. Conclusion Screening and vaccination is the most economical strategy for pregnant women in epidemic regions; however, further studies are necessary to confirm the efficacy and safety of the hepatitis E vaccines.

Mouse models to unravel the role of inhaled pollutants on allergic sensitization and airway inflammation

PubMed Central

2010-01-01

Air pollutant exposure has been linked to a rise in wheezing illnesses. Clinical data highlight that exposure to mainstream tobacco smoke (MS) and environmental tobacco smoke (ETS) as well as exposure to diesel exhaust particles (DEP) could promote allergic sensitization or aggravate symptoms of asthma, suggesting a role for these inhaled pollutants in the pathogenesis of asthma. Mouse models are a valuable tool to study the potential effects of these pollutants in the pathogenesis of asthma, with the opportunity to investigate their impact during processes leading to sensitization, acute inflammation and chronic disease. Mice allow us to perform mechanistic studies and to evaluate the importance of specific cell types in asthma pathogenesis. In this review, the major clinical effects of tobacco smoke and diesel exhaust exposure regarding to asthma development and progression are described. Clinical data are compared with findings from murine models of asthma and inhalable pollutant exposure. Moreover, the potential mechanisms by which both pollutants could aggravate asthma are discussed. PMID:20092634

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

Lopez, Jesse E.; Baptista, António M.

A sediment model coupled to the hydrodynamic model SELFE is validated against a benchmark combining a set of idealized tests and an application to a field-data rich energetic estuary. After sensitivity studies, model results for the idealized tests largely agree with previously reported results from other models in addition to analytical, semi-analytical, or laboratory results. Results of suspended sediment in an open channel test with fixed bottom are sensitive to turbulence closure and treatment for hydrodynamic bottom boundary. Results for the migration of a trench are very sensitive to critical stress and erosion rate, but largely insensitive to turbulence closure.more » The model is able to qualitatively represent sediment dynamics associated with estuarine turbidity maxima in an idealized estuary. Applied to the Columbia River estuary, the model qualitatively captures sediment dynamics observed by fixed stations and shipborne profiles. Representation of the vertical structure of suspended sediment degrades when stratification is underpredicted. Across all tests, skill metrics of suspended sediments lag those of hydrodynamics even when qualitatively representing dynamics. The benchmark is fully documented in an openly available repository to encourage unambiguous comparisons against other models.« less

Pleistocene tropical Pacific temperature sensitivity to radiative greenhouse gas forcing

NASA Astrophysics Data System (ADS)

Dyck, K. A.; Ravelo, A. C.

2011-12-01

How high will Earth's global average surface temperature ultimately rise as greenhouse gas concentrations increase in the future? One way to tackle this question is to compare contemporaneous temperature and greenhouse gas concentration data from paleoclimate records, while considering that other radiative forcing mechanisms (e.g. changes in the amount and distribution of incoming solar radiation associated with changes in the Earth's orbital configuration) also contribute to surface temperature change. Since the sensitivity of surface temperature varies with location and latitude, here we choose a central location representative of the west Pacific warm pool, far from upwelling regions or surface temperature gradients in order to minimize climate feedbacks associated with high-latitude regions or oceanic dynamics. The 'steady-state' or long-term temperature change associated with greenhouse gas radiative forcing is often labeled as equilibrium (or 'Earth system') climate sensitivity to the doubling of atmospheric greenhouse gas concentration. Climate models suggest that Earth system sensitivity does not change dramatically over times when CO2 was lower or higher than the modern atmospheric value. Thus, in our investigation of the changes in tropical SST, from the glacial to interglacial states when greenhouse gas forcing nearly doubled, we use Late Pleistocene paleoclimate records to constrain earth system sensitivity for the tropics. Here we use Mg/Ca-paleothermometry using the foraminifera G. ruber from ODP Site 871 from the past 500 kyr in the western Pacific warm pool to estimate tropical Pacific equilibrium climate sensitivity to a doubling of greenhouse gas concentrations to be ~4°C. This tropical SST sensitivity to greenhouse gas forcing is ~1-2°C higher than that predicted by climate models of past glacial periods or future warming for the tropical Pacific. Equatorial Pacific SST sensitivity may be higher than predicted by models for a number of reasons. First, models may not be adequately representing long-term deep ocean feedbacks. Second, models may incorrectly parameterize tropical cloud (or other short-term) feedback processes. Lastly, either paleo-temperature or radiative forcing may have been incorrectly estimated (e.g. through calibration of paleoclimate evidence for temperature change). Since theory suggests that surface temperature in the high latitudes is more sensitive to radiative forcing changes than surface temperature in the tropics, the results of this study also imply that globally averaged Earth system sensitivity to greenhouse gas concentrations may be higher than most climate models predict.

Sensitivity analysis of a short distance atmospheric dispersion model applied to the Fukushima disaster

NASA Astrophysics Data System (ADS)

Périllat, Raphaël; Girard, Sylvain; Korsakissok, Irène; Mallet, Vinien

2015-04-01

In a previous study, the sensitivity of a long distance model was analyzed on the Fukushima Daiichi disaster case with the Morris screening method. It showed that a few variables, such as horizontal diffusion coefficient or clouds thickness, have a weak influence on most of the chosen outputs. The purpose of the present study is to apply a similar methodology on the IRSN's operational short distance atmospheric dispersion model, called pX. Atmospheric dispersion models are very useful in case of accidental releases of pollutant to minimize the population exposure during the accident and to obtain an accurate assessment of short and long term environmental and sanitary impact. Long range models are mostly used for consequences assessment while short range models are more adapted to the early phases of the crisis and are used to make prognosis. The Morris screening method was used to estimate the sensitivity of a set of outputs and to rank the inputs by their influences. The input ranking is highly dependent on the considered output, but a few variables seem to have a weak influence on most of them. This first step revealed that interactions and non-linearity are much more pronounced with the short range model than with the long range one. Afterward, the Sobol screening method was used to obtain more quantitative results on the same set of outputs. Using this method was possible for the short range model because it is far less computationally demanding than the long range model. The study also confronts two parameterizations, Doury's and Pasquill's models, to contrast their behavior. The Doury's model seems to excessively inflate the influence of some inputs compared to the Pasquill's model, such as the altitude of emission and the air stability which do not have the same role in the two models. The outputs of the long range model were dominated by only a few inputs. On the contrary, in this study the influence is shared more evenly between the inputs.

Antigen-Specific Gut Inflammation and Systemic Immune Responses Induced by Prolonging Wheat Gluten Sensitization in BALB/c Murine Model.

PubMed

Vijaykrishnaraj, M; Mohan Kumar, B V; Muthukumar, S P; Kurrey, Nawneet K; Prabhasankar, P

2017-10-06

Gluten-related diseases such as wheat allergy, celiac disease, and gluten intolerance are widespread around the globe to genetically predisposed individuals. The present study aims to develop a wheat-gluten induced BALB/c murine model for addressing wheat-gluten related disorders by sensitizing the wheat gluten through the route of intraperitoneal and oral challenge in prolonged days. During the sensitization, the sera were collected for specific antigliadin antibodies response and proinflammatory markers quantification. Ex vivo primary cells and organs were collected for subsequent analysis of inflammatory profile. Prolonging sensitization of gluten can moderate the antigen-specific inflammatory markers such as IL-1β, IL-4, IL-15, IL-6, IFN-γ and TNF-α levels in mice sera. However, ex vivo primary cells of splenocytes (SPLs) and intestinal epithelial lymphocytes (IELs) significantly increased the IL-6, IL-15, IL-1β, and IL-4 levels in G+ (gliadin and gluten) treated cells. Histopathology staining of jejunum sections indicates enterocyte degeneration in the apical part of villi and damage of tight junctions in G+ (gliadin and gluten) sensitized murine model. Immunohistochemistry of embedded jejunum sections showed significant expression of positive cells of IL-15, tTG and IL-4 in G+ sensitized murine model. In contrast, all markers of gluten-related disorders are expressed exclusively such as tTG, ZO-1, IL-15, IL-6, IL-4, and intestinal inflammation was mediated by iNOS, COX-2, TLR-4 and NF- k Bp50 signaling mechanism in G+ sensitized mice.

Adaptation of an urban land surface model to a tropical suburban area: Offline evaluation, sensitivity analysis, and optimization of TEB/ISBA (SURFEX)

NASA Astrophysics Data System (ADS)

Harshan, Suraj

The main objective of the present thesis is the improvement of the TEB/ISBA (SURFEX) urban land surface model (ULSM) through comprehensive evaluation, sensitivity analysis, and optimization experiments using energy balance and radiative and air temperature data observed during 11 months at a tropical sub-urban site in Singapore. Overall the performance of the model is satisfactory, with a small underestimation of net radiation and an overestimation of sensible heat flux. Weaknesses in predicting the latent heat flux are apparent with smaller model values during daytime and the model also significantly underpredicts both the daytime peak and nighttime storage heat. Surface temperatures of all facets are generally overpredicted. Significant variation exists in the model behaviour between dry and wet seasons. The vegetation parametrization used in the model is inadequate to represent the moisture dynamics, producing unrealistically low latent heat fluxes during a particularly dry period. The comprehensive evaluation of the USLM shows the need for accurate estimation of input parameter values for present site. Since obtaining many of these parameters through empirical methods is not feasible, the present study employed a two step approach aimed at providing information about the most sensitive parameters and an optimized parameter set from model calibration. Two well established sensitivity analysis methods (global: Sobol and local: Morris) and a state-of-the-art multiobjective evolutionary algorithm (Borg) were employed for sensitivity analysis and parameter estimation. Experiments were carried out for three different weather periods. The analysis indicates that roof related parameters are the most important ones in controlling the behaviour of the sensible heat flux and net radiation flux, with roof and road albedo as the most influential parameters. Soil moisture initialization parameters are important in controlling the latent heat flux. The built (town) fraction has a significant influence on all fluxes considered. Comparison between the Sobol and Morris methods shows similar sensitivities, indicating the robustness of the present analysis and that the Morris method can be employed as a computationally cheaper alternative of Sobol's method. Optimization as well as the sensitivity experiments for the three periods (dry, wet and mixed), show a noticeable difference in parameter sensitivity and parameter convergence, indicating inadequacies in model formulation. Existence of a significant proportion of less sensitive parameters might be indicating an over-parametrized model. Borg MOEA showed great promise in optimizing the input parameters set. The optimized model modified using the site specific values for thermal roughness length parametrization shows an improvement in the performances of outgoing longwave radiation flux, overall surface temperature, heat storage flux and sensible heat flux.

The Enduring Predictive Significance of Early Maternal Sensitivity: Social and Academic Competence through Age 32 Years

ERIC Educational Resources Information Center

Raby, K. Lee; Roisman, Glenn I.; Fraley, R. Chris; Simpson, Jeffry A.

2015-01-01

This study leveraged data from the Minnesota Longitudinal Study of Risk and Adaptation (N = 243) to investigate the predictive significance of maternal sensitivity during the first 3 years of life for social and academic competence through age 32 years. Structural model comparisons replicated previous findings that early maternal sensitivity…

Sensory-motor responses to mechanical stimulation of the esophagus after sensitization with acid.

PubMed

Drewes, Asbjørn-Mohr; Reddy, Hariprasad; Staahl, Camilla; Pedersen, Jan; Funch-Jensen, Peter; Arendt-Nielsen, Lars; Gregersen, Hans

2005-07-28

Sensitization most likely plays an important role in chronic pain disorders, and such sensitization can be mimicked by experimental acid perfusion of the esophagus. The current study systematically investigated the sensory and motor responses of the esophagus to controlled mechanical stimuli before and after sensitization. Thirty healthy subjects were included. Distension of the distal esophagus with a balloon was performed before and after perfusion with 0.1 mol/L hydrochloric acid for 30 min. An impedance planimetry system was used to measure cross-sectional area, volume, pressure, and tension during the distensions. A new model allowed evaluation of the phasic contractions by the tension during contractions as a function of the initial muscle length before the contraction (comparable to the Frank-Starling law for the heart). Length-tension diagrams were used to evaluate the muscle tone before and after relaxation of the smooth muscle with butylscopolamine. The sensitization resulted in allodynia and hyperalgesia to the distension volumes, and the degree of sensitization was related to the infused volume of acid. Furthermore, a nearly 50% increase in the evoked referred pain was seen after sensitization. The mechanical analysis demonstrated hyper-reactivity of the esophagus following acid perfusion, with an increased number and force of the phasic contractions, but the muscle tone did not change. Acid perfusion of the esophagus sensitizes the sensory pathways and facilitates secondary contractions. The new model can be used to study abnormal sensory-motor mechanisms in visceral organs.

Sensory-motor responses to mechanical stimulation of the esophagus after sensitization with acid

PubMed Central

Drewes, Asbjorn Mohr; Reddy, Hariprasad; Staahl, Camilla; Pedersen, Jan; Funch-Jensen, Peter; Arendt-Nielsen, Lars; Gregersen, Hans

2005-01-01

AIM: Sensitization most likely plays an important role in chronic pain disorders, and such sensitization can be mimicked by experimental acid perfusion of the esophagus. The current study systematically investigated the sensory and motor responses of the esophagus to controlled mechanical stimuli before and after sensitization. METHODS: Thirty healthy subjects were included. Distension of the distal esophagus with a balloon was performed before and after perfusion with 0.1 mol/L hydrochloric acid for 30 min. An impedance planimetry system was used to measure cross-sectional area, volume, pressure, and tension during the distensions. A new model allowed evaluation of the phasic contractions by the tension during contractions as a function of the initial muscle length before the contraction (comparable to the Frank-Starling law for the heart). Length-tension diagrams were used to evaluate the muscle tone before and after relaxation of the smooth muscle with butylscopolamine. RESULTS: The sensitization resulted in allodynia and hyperalgesia to the distension volumes, and the degree of sensitization was related to the infused volume of acid. Furthermore, a nearly 50% increase in the evoked referred pain was seen after sensitization. The mechanical analysis demonstrated hyper-reactivity of the esophagus following acid perfusion, with an increased number and force of the phasic contractions, but the muscle tone did not change. CONCLUSION: Acid perfusion of the esophagus sensitizes the sensory pathways and facilitates secondary contractions. The new model can be used to study abnormal sensory-motor mechanisms in visceral organs. PMID:16038036

Sensitivity Analysis and Parameter Estimation for a Reactive Transport Model of Uranium Bioremediation

NASA Astrophysics Data System (ADS)

Meyer, P. D.; Yabusaki, S.; Curtis, G. P.; Ye, M.; Fang, Y.

2011-12-01

A three-dimensional, variably-saturated flow and multicomponent biogeochemical reactive transport model of uranium bioremediation was used to generate synthetic data . The 3-D model was based on a field experiment at the U.S. Dept. of Energy Rifle Integrated Field Research Challenge site that used acetate biostimulation of indigenous metal reducing bacteria to catalyze the conversion of aqueous uranium in the +6 oxidation state to immobile solid-associated uranium in the +4 oxidation state. A key assumption in past modeling studies at this site was that a comprehensive reaction network could be developed largely through one-dimensional modeling. Sensitivity analyses and parameter estimation were completed for a 1-D reactive transport model abstracted from the 3-D model to test this assumption, to identify parameters with the greatest potential to contribute to model predictive uncertainty, and to evaluate model structure and data limitations. Results showed that sensitivities of key biogeochemical concentrations varied in space and time, that model nonlinearities and/or parameter interactions have a significant impact on calculated sensitivities, and that the complexity of the model's representation of processes affecting Fe(II) in the system may make it difficult to correctly attribute observed Fe(II) behavior to modeled processes. Non-uniformity of the 3-D simulated groundwater flux and averaging of the 3-D synthetic data for use as calibration targets in the 1-D modeling resulted in systematic errors in the 1-D model parameter estimates and outputs. This occurred despite using the same reaction network for 1-D modeling as used in the data-generating 3-D model. Predictive uncertainty of the 1-D model appeared to be significantly underestimated by linear parameter uncertainty estimates.

Model Sensitivity and Use of the Comparative Finite Element Method in Mammalian Jaw Mechanics: Mandible Performance in the Gray Wolf

PubMed Central

Tseng, Zhijie Jack; Mcnitt-Gray, Jill L.; Flashner, Henryk; Wang, Xiaoming; Enciso, Reyes

2011-01-01

Finite Element Analysis (FEA) is a powerful tool gaining use in studies of biological form and function. This method is particularly conducive to studies of extinct and fossilized organisms, as models can be assigned properties that approximate living tissues. In disciplines where model validation is difficult or impossible, the choice of model parameters and their effects on the results become increasingly important, especially in comparing outputs to infer function. To evaluate the extent to which performance measures are affected by initial model input, we tested the sensitivity of bite force, strain energy, and stress to changes in seven parameters that are required in testing craniodental function with FEA. Simulations were performed on FE models of a Gray Wolf (Canis lupus) mandible. Results showed that unilateral bite force outputs are least affected by the relative ratios of the balancing and working muscles, but only ratios above 0.5 provided balancing-working side joint reaction force relationships that are consistent with experimental data. The constraints modeled at the bite point had the greatest effect on bite force output, but the most appropriate constraint may depend on the study question. Strain energy is least affected by variation in bite point constraint, but larger variations in strain energy values are observed in models with different number of tetrahedral elements, masticatory muscle ratios and muscle subgroups present, and number of material properties. These findings indicate that performance measures are differentially affected by variation in initial model parameters. In the absence of validated input values, FE models can nevertheless provide robust comparisons if these parameters are standardized within a given study to minimize variation that arise during the model-building process. Sensitivity tests incorporated into the study design not only aid in the interpretation of simulation results, but can also provide additional insights on form and function. PMID:21559475

Current estimates of the cure fraction: a feasibility study of statistical cure for breast and colorectal cancer.

PubMed

Stedman, Margaret R; Feuer, Eric J; Mariotto, Angela B

2014-11-01

The probability of cure is a long-term prognostic measure of cancer survival. Estimates of the cure fraction, the proportion of patients "cured" of the disease, are based on extrapolating survival models beyond the range of data. The objective of this work is to evaluate the sensitivity of cure fraction estimates to model choice and study design. Data were obtained from the Surveillance, Epidemiology, and End Results (SEER)-9 registries to construct a cohort of breast and colorectal cancer patients diagnosed from 1975 to 1985. In a sensitivity analysis, cure fraction estimates are compared from different study designs with short- and long-term follow-up. Methods tested include: cause-specific and relative survival, parametric mixture, and flexible models. In a separate analysis, estimates are projected for 2008 diagnoses using study designs including the full cohort (1975-2008 diagnoses) and restricted to recent diagnoses (1998-2008) with follow-up to 2009. We show that flexible models often provide higher estimates of the cure fraction compared to parametric mixture models. Log normal models generate lower estimates than Weibull parametric models. In general, 12 years is enough follow-up time to estimate the cure fraction for regional and distant stage colorectal cancer but not for breast cancer. 2008 colorectal cure projections show a 15% increase in the cure fraction since 1985. Estimates of the cure fraction are model and study design dependent. It is best to compare results from multiple models and examine model fit to determine the reliability of the estimate. Early-stage cancers are sensitive to survival type and follow-up time because of their longer survival. More flexible models are susceptible to slight fluctuations in the shape of the survival curve which can influence the stability of the estimate; however, stability may be improved by lengthening follow-up and restricting the cohort to reduce heterogeneity in the data. Published by Oxford University Press 2014.

The development of a murine model for Forcipomyia taiwana (biting midge) allergy.

PubMed

Lee, Mey-Fann; Yang, Kai-Jei; Wang, Nancy M; Chiu, Yung-Tsung; Chen, Pei-Chih; Chen, Yi-Hsing

2014-01-01

Forcipomyia taiwana (biting midge) allergy is the most prevalent biting insect allergy in Taiwan. An animal model corresponding to the human immuno-pathologic features of midge allergy is needed for investigating the mechanisms and therapies. This study successfully developed a murine model of Forcipomyia taiwana allergy. BALB/c mice were sensitized intra-peritoneally with midge extract on days 0, 7, 14, 21 then intra-dermally on days 28, 31 and 35. Serum midge-specific IgE, IgG1, and IgG2a were measured every 14 days by indirect ELISA. The mice were challenged intradermally with midge extract at day 40 and then sacrificed. Proliferation and cytokine production of splenocytes after stimulation with midge extract were determined by MTT assay and ELISA, respectively. The cytokine mRNA expression in response to midge stimulation was analyzed by RT-PCR. Serum IgE, total IgG, and IgG1 antibody levels against midge extract were significantly higher in the midge-sensitized mice than in the control mice. After the two-step sensitization, all mice in the midge-sensitized group displayed immediate itch and plasma extravasation reactions in response to challenge with midge extract. Skin histology from midge-sensitized mice showed marked eosinophil and lymphocyte infiltrations similar to that observed in humans. Stimulation of murine splenocytes with midge extract elicited significant proliferation, IL-4, IL-10, IL-13 and IFN-γ protein production, and up-regulation of mRNA in a dose-dependent manner in the midge-sensitized group, but not in the control group. A murine model of midge bite allergy has been successfully developed using a two-step sensitization protocol. The sensitized mice have very similar clinical and immunologic reactions to challenge with midge proteins as the reactions of human to midge bites. This murine model may be a useful platform for future research and the development of treatment strategies for insect bite allergy.

Analytical modeling of intumescent coating thermal protection system in a JP-5 fuel fire environment

NASA Technical Reports Server (NTRS)

Clark, K. J.; Shimizu, A. B.; Suchsland, K. E.; Moyer, C. B.

1974-01-01

The thermochemical response of Coating 313 when exposed to a fuel fire environment was studied to provide a tool for predicting the reaction time. The existing Aerotherm Charring Material Thermal Response and Ablation (CMA) computer program was modified to treat swelling materials. The modified code is now designated Aerotherm Transient Response of Intumescing Materials (TRIM) code. In addition, thermophysical property data for Coating 313 were analyzed and reduced for use in the TRIM code. An input data sensitivity study was performed, and performance tests of Coating 313/steel substrate models were carried out. The end product is a reliable computational model, the TRIM code, which was thoroughly validated for Coating 313. The tasks reported include: generation of input data, development of swell model and implementation in TRIM code, sensitivity study, acquisition of experimental data, comparisons of predictions with data, and predictions with intermediate insulation.

Parametric Study of Synthetic-Jet-Based Flow Control on a Vertical Tail Model

NASA Astrophysics Data System (ADS)

Monastero, Marianne; Lindstrom, Annika; Beyar, Michael; Amitay, Michael

2015-11-01

Separation control over the rudder of the vertical tail of a commercial airplane using synthetic-jet-based flow control can lead to a reduction in tail size, with an associated decrease in drag and increase in fuel savings. A parametric, experimental study was undertaken using an array of finite span synthetic jets to investigate the sensitivity of the enhanced vertical tail side force to jet parameters, such as jet spanwise spacing and jet momentum coefficient. A generic wind tunnel model was designed and fabricated to fundamentally study the effects of the jet parameters at varying rudder deflection and model sideslip angles. Wind tunnel results obtained from pressure measurements and tuft flow visualization in the Rensselaer Polytechnic Subsonic Wind Tunnel show a decrease in separation severity and increase in model performance in comparison to the baseline, non-actuated case. The sensitivity to various parameters will be presented.

Converging Climate Sensitivities of European Forests Between Observed Radial Tree Growth and Vegetation Models

NASA Technical Reports Server (NTRS)

Zhang, Zhen; Babst, Flurin; Bellassen, Valentin; Frank, David; Launois, Thomas; Tan, Kun; Ciais, Philippe; Poulter, Benjamin

2017-01-01

The impacts of climate variability and trends on European forests are unevenly distributed across different bioclimatic zones and species. Extreme climate events are also becoming more frequent and it is unknown how they will affect feed backs of CO2 between forest ecosystems and the atmosphere. An improved understanding of species differences at the regional scale of the response of forest productivity to climate variation and extremes is thus important for forecasting forest dynamics. In this study, we evaluate the climate sensitivity of above ground net primary production (NPP) simulated by two dynamic global vegetation models (DGVM; ORCHIDEE and LPJ-wsl) against tree ring width (TRW) observations from about1000 sites distributed across Europe. In both the model simulations and the TRW observations, forests in northern Europe and the Alps respond positively to warmer spring and summer temperature, and their overall temperature sensitivity is larger than that of the soil-moisture-limited forests in central Europe and Mediterranean regions. Compared with TRW observations, simulated NPP from ORCHIDEE and LPJ-wsl appear to be overly sensitive to climatic factors. Our results indicate that the models lack biological processes that control time lags, such as carbohydrate storage and remobilization, that delay the effects of radial growth dynamics to climate. Our study highlights the need for re-evaluating the physiological controls on the climate sensitivity of NPP simulated by DGVMs. In particular, DGVMs could be further enhanced by a more detailed representation of carbon reserves and allocation that control year-to year variation in plant growth.

A stochastic model to determine the economic value of changing diagnostic test characteristics for identification of cattle for treatment of bovine respiratory disease.

PubMed

Theurer, M E; White, B J; Larson, R L; Schroeder, T C

2015-03-01

Bovine respiratory disease is an economically important syndrome in the beef industry, and diagnostic accuracy is important for optimal disease management. The objective of this study was to determine whether improving diagnostic sensitivity or specificity was of greater economic value at varied levels of respiratory disease prevalence by using Monte Carlo simulation. Existing literature was used to populate model distributions of published sensitivity, specificity, and performance (ADG, carcass weight, yield grade, quality grade, and mortality risk) differences among calves based on clinical respiratory disease status. Data from multiple cattle feeding operations were used to generate true ranges of respiratory disease prevalence and associated mortality. Input variables were combined into a single model that calculated estimated net returns for animals by diagnostic category (true positive, false positive, false negative, and true negative) based on the prevalence, sensitivity, and specificity for each iteration. Net returns for each diagnostic category were multiplied by the proportion of animals in each diagnostic category to determine group profitability. Apparent prevalence was categorized into low (<15%) and high (≥15%) groups. For both apparent prevalence categories, increasing specificity created more rapid, positive change in net returns than increasing sensitivity. Improvement of diagnostic specificity, perhaps through a confirmatory test interpreted in series or pen-level diagnostics, can increase diagnostic value more than improving sensitivity. Mortality risk was the primary driver for net returns. The results from this study are important for determining future research priorities to analyze diagnostic techniques for bovine respiratory disease and provide a novel way for modeling diagnostic tests.

Sensitivity analysis of a model of CO2 exchange in tundra ecosystems by the adjoint method

NASA Technical Reports Server (NTRS)

Waelbroek, C.; Louis, J.-F.

1995-01-01

A model of net primary production (NPP), decomposition, and nitrogen cycling in tundra ecosystems has been developed. The adjoint technique is used to study the sensitivity of the computed annual net CO2 flux to perturbation in initial conditions, climatic inputs, and model's main parameters describing current seasonal CO2 exchange in wet sedge tundra at Barrow, Alaska. The results show that net CO2 flux is most sensitive to parameters characterizing litter chemical composition and more sensitive to decomposition parameters than to NPP parameters. This underlines the fact that in nutrient-limited ecosystems, decomposition drives net CO2 exchange by controlling mineralization of main nutrients. The results also indicate that the short-term (1 year) response of wet sedge tundra to CO2-induced warming is a significant increase in CO2 emission, creating a positive feedback to atmosphreic CO2 accumulation. However, a cloudiness increase during the same year can severely alter this response and lead to either a slight decrease or a strong increase in emitted CO2, depending on its exact timing. These results demonstrate that the adjoint method is well suited to study systems encountering regime changes, as a single run of the adjoint model provides sensitivities of the net CO2 flux to perturbations in all parameters and variables at any time of the year. Moreover, it is shown that large errors due to the presence of thresholds can be avoided by first delimiting the range of applicability of the adjoint results.

The application of sensitivity analysis to models of large scale physiological systems

NASA Technical Reports Server (NTRS)

Leonard, J. I.

1974-01-01

A survey of the literature of sensitivity analysis as it applies to biological systems is reported as well as a brief development of sensitivity theory. A simple population model and a more complex thermoregulatory model illustrate the investigatory techniques and interpretation of parameter sensitivity analysis. The role of sensitivity analysis in validating and verifying models, and in identifying relative parameter influence in estimating errors in model behavior due to uncertainty in input data is presented. This analysis is valuable to the simulationist and the experimentalist in allocating resources for data collection. A method for reducing highly complex, nonlinear models to simple linear algebraic models that could be useful for making rapid, first order calculations of system behavior is presented.

Simulation-based sensitivity analysis for non-ignorably missing data.

PubMed

Yin, Peng; Shi, Jian Q

2017-01-01

Sensitivity analysis is popular in dealing with missing data problems particularly for non-ignorable missingness, where full-likelihood method cannot be adopted. It analyses how sensitively the conclusions (output) may depend on assumptions or parameters (input) about missing data, i.e. missing data mechanism. We call models with the problem of uncertainty sensitivity models. To make conventional sensitivity analysis more useful in practice we need to define some simple and interpretable statistical quantities to assess the sensitivity models and make evidence based analysis. We propose a novel approach in this paper on attempting to investigate the possibility of each missing data mechanism model assumption, by comparing the simulated datasets from various MNAR models with the observed data non-parametrically, using the K-nearest-neighbour distances. Some asymptotic theory has also been provided. A key step of this method is to plug in a plausibility evaluation system towards each sensitivity parameter, to select plausible values and reject unlikely values, instead of considering all proposed values of sensitivity parameters as in the conventional sensitivity analysis method. The method is generic and has been applied successfully to several specific models in this paper including meta-analysis model with publication bias, analysis of incomplete longitudinal data and mean estimation with non-ignorable missing data.

Personality traits as vulnerability factors in body dysmorphic disorder.

PubMed

Schieber, Katharina; Kollei, Ines; de Zwaan, Martina; Müller, Astrid; Martin, Alexandra

2013-11-30

Cognitive behavioural models consider certain personality traits to be risk factors for the development of Body Dysmorphic Disorder (BDD). Research on personality traits in BDD is scarce, therefore this study examined perfectionism, aesthetic sensitivity and the behavioural inhibition system (BIS) in BDD. Furthermore, the association between these personality traits and the extent of dysmorphic concerns was investigated. Individuals with BDD (n=58) and a population based control sample (n=2071), selected from a representative German population survey, completed self-report questionnaires assessing DSM-5 criteria of BDD, dysmorphic concerns, perfectionism, aesthetic sensitivity and BIS-reactivity. Individuals with BDD reported significantly higher degrees of perfectionism as well as of BIS-reactivity compared to the population based control sample, whereas the groups did not differ significantly regarding aesthetic sensitivity. However, for the total sample, each of the personality traits was related dimensionally to dysmorphic concerns. Current BDD models consider perfectionism and aesthetic sensitivity to be vulnerability factors. In addition to these concepts, the present study suggests that BIS-reactivity is related to BDD. Self-reported aesthetic sensitivity was not found to be specifically pronounced in BDD, but along with perfectionism and BIS-reactivity aesthetic sensitivity was generally associated with dysmorphic concerns. © 2013 Elsevier Ireland Ltd. All rights reserved.

Application of ADM1 for modeling of biogas production from anaerobic digestion of Hydrilla verticillata.

PubMed

Chen, Xiaojuan; Chen, Zhihua; Wang, Xun; Huo, Chan; Hu, Zhiquan; Xiao, Bo; Hu, Mian

2016-07-01

The present study focused on the application of anaerobic digestion model no. 1 (ADM1) to simulate biogas production from Hydrilla verticillata. Model simulation was carried out by implementing ADM1 in AQUASIM 2.0 software. Sensitivity analysis was used to select the most sensitive parameters for estimation using the absolute-relative sensitivity function. Among all the kinetic parameters, disintegration constant (kdis), hydrolysis constant of protein (khyd_pr), Monod maximum specific substrate uptake rate (km_aa, km_ac, km_h2) and half-saturation constants (Ks_aa, Ks_ac) affect biogas production significantly, which were optimized by fitting of the model equations to the data obtained from batch experiments. The ADM1 model after parameter estimation was able to well predict the experimental results of daily biogas production and biogas composition. The simulation results of evolution of organic acids, bacteria concentrations and inhibition effects also helped to get insight into the reaction mechanisms. Copyright © 2016. Published by Elsevier Ltd.

What Do We Mean By Sensitivity Analysis? The Need For A Comprehensive Characterization Of Sensitivity In Earth System Models

NASA Astrophysics Data System (ADS)

Razavi, S.; Gupta, H. V.

2014-12-01

Sensitivity analysis (SA) is an important paradigm in the context of Earth System model development and application, and provides a powerful tool that serves several essential functions in modelling practice, including 1) Uncertainty Apportionment - attribution of total uncertainty to different uncertainty sources, 2) Assessment of Similarity - diagnostic testing and evaluation of similarities between the functioning of the model and the real system, 3) Factor and Model Reduction - identification of non-influential factors and/or insensitive components of model structure, and 4) Factor Interdependence - investigation of the nature and strength of interactions between the factors, and the degree to which factors intensify, cancel, or compensate for the effects of each other. A variety of sensitivity analysis approaches have been proposed, each of which formally characterizes a different "intuitive" understanding of what is meant by the "sensitivity" of one or more model responses to its dependent factors (such as model parameters or forcings). These approaches are based on different philosophies and theoretical definitions of sensitivity, and range from simple local derivatives and one-factor-at-a-time procedures to rigorous variance-based (Sobol-type) approaches. In general, each approach focuses on, and identifies, different features and properties of the model response and may therefore lead to different (even conflicting) conclusions about the underlying sensitivity. This presentation revisits the theoretical basis for sensitivity analysis, and critically evaluates existing approaches so as to demonstrate their flaws and shortcomings. With this background, we discuss several important properties of response surfaces that are associated with the understanding and interpretation of sensitivity. Finally, a new approach towards global sensitivity assessment is developed that is consistent with important properties of Earth System model response surfaces.

Watershed scale response to climate change--Yampa River Basin, Colorado

USGS Publications Warehouse

Hay, Lauren E.; Battaglin, William A.; Markstrom, Steven L.

2012-01-01

General Circulation Model simulations of future climate through 2099 project a wide range of possible scenarios. To determine the sensitivity and potential effect of long-term climate change on the freshwater resources of the United States, the U.S. Geological Survey Global Change study, "An integrated watershed scale response to global change in selected basins across the United States" was started in 2008. The long-term goal of this national study is to provide the foundation for hydrologically based climate change studies across the nation. Fourteen basins for which the Precipitation Runoff Modeling System has been calibrated and evaluated were selected as study sites. Precipitation Runoff Modeling System is a deterministic, distributed parameter watershed model developed to evaluate the effects of various combinations of precipitation, temperature, and land use on streamflow and general basin hydrology. Output from five General Circulation Model simulations and four emission scenarios were used to develop an ensemble of climate-change scenarios for each basin. These ensembles were simulated with the corresponding Precipitation Runoff Modeling System model. This fact sheet summarizes the hydrologic effect and sensitivity of the Precipitation Runoff Modeling System simulations to climate change for the Yampa River Basin at Steamboat Springs, Colorado.

Distributed modeling of ablation (1996–2011) and climate sensitivity on the glaciers of Taylor Valley, Antarctica

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

Hoffman, Matthew J.; Fountain, Andrew G.; Liston, Glen E.

Here, the McMurdo Dry Valleys of Antarctica host the coldest and driest ecosystem on Earth, which is acutely sensitive to the availability of water coming from glacial runoff. We modeled the spatial variability in ablation and assessed climate sensitivity of the glacier ablation zones using 16 years of meteorological and surface mass-balance observations collected in Taylor Valley. Sublimation was the primary form of mass loss over much of the ablation zones, except for near the termini where melt, primarily below the surface, dominated. Microclimates in ~10 m scale topographic basins generated melt rates up to ten times higher than overmore » smooth glacier surfaces. In contrast, the vertical terminal cliffs on the glaciers can have higher or lower melt rates than the horizontal surfaces due to differences in incoming solar radiation. The model systematically underpredicted ablation for the final 5 years studied, possibly due to an increase of windblown sediment. Surface mass-balance sensitivity to temperature was ~–0.02 m w.e. K –1, which is among the smallest magnitudes observed globally. We also identified a high sensitivity to ice albedo, with a decrease of 0.02 having similar effects as a 1 K increase in temperature, and a complex sensitivity to wind speed.« less

Distributed modeling of ablation (1996–2011) and climate sensitivity on the glaciers of Taylor Valley, Antarctica

DOE PAGES

Hoffman, Matthew J.; Fountain, Andrew G.; Liston, Glen E.

2016-02-24

Here, the McMurdo Dry Valleys of Antarctica host the coldest and driest ecosystem on Earth, which is acutely sensitive to the availability of water coming from glacial runoff. We modeled the spatial variability in ablation and assessed climate sensitivity of the glacier ablation zones using 16 years of meteorological and surface mass-balance observations collected in Taylor Valley. Sublimation was the primary form of mass loss over much of the ablation zones, except for near the termini where melt, primarily below the surface, dominated. Microclimates in ~10 m scale topographic basins generated melt rates up to ten times higher than overmore » smooth glacier surfaces. In contrast, the vertical terminal cliffs on the glaciers can have higher or lower melt rates than the horizontal surfaces due to differences in incoming solar radiation. The model systematically underpredicted ablation for the final 5 years studied, possibly due to an increase of windblown sediment. Surface mass-balance sensitivity to temperature was ~–0.02 m w.e. K –1, which is among the smallest magnitudes observed globally. We also identified a high sensitivity to ice albedo, with a decrease of 0.02 having similar effects as a 1 K increase in temperature, and a complex sensitivity to wind speed.« less

Turbulence model sensitivity and scour gap effect of unsteady flow around pipe: a CFD study.

PubMed

Ali, Abbod; Sharma, R K; Ganesan, P; Akib, Shatirah

2014-01-01

A numerical investigation of incompressible and transient flow around circular pipe has been carried out at different five gap phases. Flow equations such as Navier-Stokes and continuity equations have been solved using finite volume method. Unsteady horizontal velocity and kinetic energy square root profiles are plotted using different turbulence models and their sensitivity is checked against published experimental results. Flow parameters such as horizontal velocity under pipe, pressure coefficient, wall shear stress, drag coefficient, and lift coefficient are studied and presented graphically to investigate the flow behavior around an immovable pipe and scoured bed.

Modelling of resonant MEMS magnetic field sensor with electromagnetic induction sensing

NASA Astrophysics Data System (ADS)

Liu, Song; Xu, Huaying; Xu, Dehui; Xiong, Bin

2017-06-01

This paper presents an analytical model of resonant MEMS magnetic field sensor with electromagnetic induction sensing. The resonant structure vibrates in square extensional (SE) mode. By analyzing the vibration amplitude and quality factor of the resonant structure, the magnetic field sensitivity as a function of device structure parameters and encapsulation pressure is established. The developed analytical model has been verified by comparing calculated results with experiment results and the deviation between them is only 10.25%, which shows the feasibility of the proposed device model. The model can provide theoretical guidance for further design optimization of the sensor. Moreover, a quantitative study of the magnetic field sensitivity is conducted with respect to the structure parameters and encapsulation pressure based on the proposed model.

Analysis of Publically Available Skin Sensitization Data from REACH Registrations 2008–2014

PubMed Central

Luechtefeld, Thomas; Maertens, Alexandra; Russo, Daniel P.; Rovida, Costanza; Zhu, Hao; Hartung, Thomas

2017-01-01

Summary The public data on skin sensitization from REACH registrations already included 19,111 studies on skin sensitization in December 2014, making it the largest repository of such data so far (1,470 substances with mouse LLNA, 2,787 with GPMT, 762 with both in vivo and in vitro and 139 with only in vitro data). 21% were classified as sensitizers. The extracted skin sensitization data was analyzed to identify relationships in skin sensitization guidelines, visualize structural relationships of sensitizers, and build models to predict sensitization. A chemical with molecular weight > 500 Da is generally considered non-sensitizing owing to low bioavailability, but 49 sensitizing chemicals with a molecular weight > 500 Da were found. A chemical similarity map was produced using PubChem’s 2D Tanimoto similarity metric and Gephi force layout visualization. Nine clusters of chemicals were identified by Blondel’s module recognition algorithm revealing wide module-dependent variation. Approximately 31% of mapped chemicals are Michael’s acceptors but alone this does not imply skin sensitization. A simple sensitization model using molecular weight and five ToxTree structural alerts showed a balanced accuracy of 65.8% (specificity 80.4%, sensitivity 51.4%), demonstrating that structural alerts have information value. A simple variant of k-nearest neighbors outperformed the ToxTree approach even at 75% similarity threshold (82% balanced accuracy at 0.95 threshold). At higher thresholds, the balanced accuracy increased. Lower similarity thresholds decrease sensitivity faster than specificity. This analysis scopes the landscape of chemical skin sensitization, demonstrating the value of large public datasets for health hazard prediction. PMID:26863411

Three Dimensional Distribution of Sensitive Field and Stress Field Inversion of Force Sensitive Materials under Constant Current Excitation.

PubMed

Zhao, Shuanfeng; Liu, Min; Guo, Wei; Zhang, Chuanwei

2018-02-28

Force sensitive conductive composite materials are functional materials which can be used as the sensitive material of force sensors. However, the existing sensors only use one-dimensional electrical properties of force sensitive conductive materials. Even in tactile sensors, the measurement of contact pressure is achieved by large-scale arrays and the units of a large-scale array are also based on the one-dimensional electrical properties of force sensitive materials. The main contribution of this work is to study the three-dimensional electrical properties and the inversion method of three-dimensional stress field of a force sensitive material (conductive rubber), which pushes the application of force sensitive material from one dimensional to three-dimensional. First, the mathematical model of the conductive rubber current field distribution under a constant force is established by the effective medium theory, and the current field distribution model of conductive rubber with different geometry, conductive rubber content and conductive rubber relaxation parameters is deduced. Secondly, the inversion method of the three-dimensional stress field of conductive rubber is established, which provides a theoretical basis for the design of a new tactile sensor, three-dimensional stress field and space force based on force sensitive materials.

Mechanisms of change in cognitive behavioral therapy for panic disorder: The unique effects of self-efficacy and anxiety sensitivity

PubMed Central

Gallagher, Matthew W.; Payne, Laura A.; White, Kamila S.; Shear, Katherine M.; Woods, Scott W.; Gorman, Jack M.; Barlow, David H.

2013-01-01

The present study examined temporal dependencies of change of panic symptoms and two promising mechanisms of change (self-efficacy and anxiety sensitivity) during an 11-session course of cognitive-behavior therapy (CBT) for Panic Disorder (PD). 361 individuals with a principal diagnosis of PD completed measures of self-efficacy, anxiety sensitivity, and PD symptoms at each session during treatment. Effect size analyses indicated that the greatest changes in anxiety sensitivity occurred early in treatment, whereas the greatest changes in self-efficacy occurred later in treatment. Results of parallel process latent growth curve models indicated that changes in self-efficacy and anxiety sensitivity across treatment uniquely predicted changes in PD symptoms. Bivariate and multivariate latent difference score models indicated, as expected, that changes in anxiety sensitivity and self-efficacy temporally preceded changes in panic symptoms, and that intraindividual changes in anxiety sensitivity and self-efficacy independently predicted subsequent intraindividual changes in panic symptoms. These results provide strong evidence that changes in self-efficacy and anxiety sensitivity during CBT influence subsequent changes in panic symptoms, and that self-efficacy and anxiety sensitivity may therefore be two distinct mechanisms of change of CBT for PD that have their greatest impact at different stages of treatment. PMID:24095901

Exploring the Relationship between Reward and Punishment Sensitivity and Gambling Disorder in a Clinical Sample: A Path Modeling Analysis.

PubMed

Jiménez-Murcia, Susana; Fernández-Aranda, Fernando; Mestre-Bach, Gemma; Granero, Roser; Tárrega, Salomé; Torrubia, Rafael; Aymamí, Neus; Gómez-Peña, Mónica; Soriano-Mas, Carles; Steward, Trevor; Moragas, Laura; Baño, Marta; Del Pino-Gutiérrez, Amparo; Menchón, José M

2017-06-01

Most individuals will gamble during their lifetime, yet only a select few will develop gambling disorder. Gray's Reinforcement Sensitivity Theory holds promise for providing insight into gambling disorder etiology and symptomatology as it ascertains that neurobiological differences in reward and punishment sensitivity play a crucial role in determining an individual's affect and motives. The aim of the study was to assess a mediational pathway, which included patients' sex, personality traits, reward and punishment sensitivity, and gambling-severity variables. The Sensitivity to Punishment and Sensitivity to Reward Questionnaire, the South Oaks Gambling Screen, the Symptom Checklist-Revised, and the Temperament and Character Inventory-Revised were administered to a sample of gambling disorder outpatients (N = 831), diagnosed according to DSM-5 criteria, attending a specialized outpatient unit. Sociodemographic variables were also recorded. A structural equation model found that both reward and punishment sensitivity were positively and directly associated with increased gambling severity, sociodemographic variables, and certain personality traits while also revealing a complex mediational role for these dimensions. To this end, our findings suggest that the Sensitivity to Punishment and Sensitivity to Reward Questionnaire could be a useful tool for gaining a better understanding of different gambling disorder phenotypes and developing tailored interventions.

A shorter and more specific oral sensitization-based experimental model of food allergy in mice.

PubMed

Bailón, Elvira; Cueto-Sola, Margarita; Utrilla, Pilar; Rodríguez-Ruiz, Judith; Garrido-Mesa, Natividad; Zarzuelo, Antonio; Xaus, Jordi; Gálvez, Julio; Comalada, Mònica

2012-07-31

Cow's milk protein allergy (CMPA) is one of the most prevalent human food-borne allergies, particularly in children. Experimental animal models have become critical tools with which to perform research on new therapeutic approaches and on the molecular mechanisms involved. However, oral food allergen sensitization in mice requires several weeks and is usually associated with unspecific immune responses. To overcome these inconveniences, we have developed a new food allergy model that takes only two weeks while retaining the main characters of allergic response to food antigens. The new model is characterized by oral sensitization of weaned Balb/c mice with 5 doses of purified cow's milk protein (CMP) plus cholera toxin (CT) for only two weeks and posterior challenge with an intraperitoneal administration of the allergen at the end of the sensitization period. In parallel, we studied a conventional protocol that lasts for seven weeks, and also the non-specific effects exerted by CT in both protocols. The shorter protocol achieves a similar clinical score as the original food allergy model without macroscopically affecting gut morphology or physiology. Moreover, the shorter protocol caused an increased IL-4 production and a more selective antigen-specific IgG1 response. Finally, the extended CT administration during the sensitization period of the conventional protocol is responsible for the exacerbated immune response observed in that model. Therefore, the new model presented here allows a reduction not only in experimental time but also in the number of animals required per experiment while maintaining the features of conventional allergy models. We propose that the new protocol reported will contribute to advancing allergy research. Copyright © 2012 Elsevier B.V. All rights reserved.

Models of the electrically stimulated binaural system: A review.

PubMed

Dietz, Mathias

2016-01-01

In an increasing number of countries, the standard treatment for deaf individuals is moving toward the implantation of two cochlear implants. Today's device technology and fitting procedure, however, appears as if the two implants would serve two independent ears and brains. Many experimental studies have demonstrated that after careful matching and balancing of left and right stimulation in controlled laboratory studies most patients have almost normal sensitivity to interaural level differences and some sensitivity to interaural time differences (ITDs). Mechanisms underlying the limited ITD sensitivity are still poorly understood and many different aspects may contribute. Recent pioneering computational approaches identified some of the functional implications the electric input imposes on the neural brainstem circuits. Simultaneously these studies have raised new questions and certainly demonstrated that further refinement of the model stages is necessary. They join the experimental study's conclusions that binaural device technology, binaural fitting, specific speech coding strategies, and binaural signal processing algorithms are obviously missing components to maximize the benefit of bilateral implantation. Within this review, the existing models of the electrically stimulated binaural system are explained, compared, and discussed from a viewpoint of a "CI device with auditory system" and from that of neurophysiological research.

Examining the intersection of sex and stress in modelling neuropsychiatric disorders.

PubMed

Goel, N; Bale, T L

2009-03-01

Sex-biased neuropsychiatric disorders, including major depressive disorder and schizophrenia, are the major cause of disability in the developed world. Elevated stress sensitivity has been proposed as a key underlying factor in disease onset. Sex differences in stress sensitivity are associated with corticotrophin-releasing factor (CRF) and serotonin neurotransmission, which are important central regulators of mood and coping responses. To elucidate the underlying neurobiology of stress-related disease predisposition, it is critical to develop appropriate animal models of stress pathway dysregulation. Furthermore, the inclusion of sex difference comparisons in stress responsive behaviours, physiology and central stress pathway maturation in these models is essential. Recent studies by our laboratory and others have begun to investigate the intersection of stress and sex where the development of mouse models of stress pathway dysregulation via prenatal stress experience or early-life manipulations has provided insight into points of developmental vulnerability. In addition, examination of the maturation of these pathways, including the functional importance of the organisational and activational effects of gonadal hormones on stress responsivity, is essential for determination of when sex differences in stress sensitivity may begin. In such studies, we have detected distinct sex differences in stress coping strategies where activational effects of testosterone produced females that displayed male-like strategies in tests of passive coping, but were similar to females in tests of active coping. In a second model of elevated stress sensitivity, male mice experiencing prenatal stress early in gestation showed feminised physiological and behavioural stress responses, and were highly sensitive to a low dose of selective serotonin reuptake inhibitors. Analyses of expression and epigenetic patterns revealed changes in CRF and glucocorticoid receptor genes in these mice. Mechanistically, stress early in pregnancy produced a significant sex-dependent effect on placental gene expression that was supportive of altered foetal transport of key growth factors and nutrients. These mouse models examining alterations and hormonal effects on development of stress pathways provide necessary insight into how specific stress responses can be reprogrammed early in development resulting in sex differences in stress sensitivity and neuropsychiatric disease vulnerability.

Examining the intersection of sex and stress in modeling neuropsychiatric disorders

PubMed Central

Goel, Nirupa; Bale, Tracy L.

2009-01-01

Sex-biased neuropsychiatric disorders, including major depressive disorder and schizophrenia, are the major cause of disability in the developed world. Elevated stress sensitivity has been proposed as a key underlying factor in disease onset. Sex differences in stress sensitivity are associated with CRF and serotonin neurotransmission, important central regulators of mood and coping responses. To elucidate the underlying neurobiology of stress-related disease predisposition, it is critical to develop appropriate animal models of stress pathway dysregulation. Further, the inclusion of sex difference comparisons in stress responsive behaviors, physiology, and central stress pathway maturation in these models is essential. Recent studies by our lab and others have begun to investigate the intersection of stress and sex where the development of mouse models of stress pathway dysregulation via prenatal stress experience or early life manipulations has provided insight into points of developmental vulnerability. In addition, examination of the maturation of these pathways including the functional importance of the organizational and activational effects of gonadal hormones on stress responsivity is essential for determination of when sex differences in stress sensitivity may begin. In such studies, we have detected distinct sex differences in stress coping strategies where activational effects of testosterone produced females that displayed male-like strategies in tests of passive coping, but were similar to females in tests of active coping. In a second model of elevated stress sensitivity, male mice experiencing prenatal stress early in gestation showed feminized physiological and behavioral stress responses, and were highly sensitive to a low dose of SSRI. Analyses of expression and epigenetic patterns revealed changes in CRF and glucocorticoid receptor genes in these mice. Mechanistically, stress early in pregnancy produced a significant sex-dependent effect on placental gene expression supportive of altered fetal transport of key growth factors and nutrients. These mouse models examining alterations and hormonal effects on development of stress pathways provide necessary insight into how specific stress responses can be reprogrammed early in development resulting in sex differences in stress sensitivity and neuropsychiatric disease vulnerability. PMID:19187468

Investigation of uncertainty in CO 2 reservoir models: A sensitivity analysis of relative permeability parameter values

DOE PAGES

Yoshida, Nozomu; Levine, Jonathan S.; Stauffer, Philip H.

2016-03-22

Numerical reservoir models of CO 2 injection in saline formations rely on parameterization of laboratory-measured pore-scale processes. Here, we have performed a parameter sensitivity study and Monte Carlo simulations to determine the normalized change in total CO 2 injected using the finite element heat and mass-transfer code (FEHM) numerical reservoir simulator. Experimentally measured relative permeability parameter values were used to generate distribution functions for parameter sampling. The parameter sensitivity study analyzed five different levels for each of the relative permeability model parameters. All but one of the parameters changed the CO 2 injectivity by <10%, less than the geostatistical uncertainty that applies to all large subsurface systems due to natural geophysical variability and inherently small sample sizes. The exception was the end-point CO 2 relative permeability, kmore » $$0\\atop{r}$$ CO2, the maximum attainable effective CO 2 permeability during CO 2 invasion, which changed CO2 injectivity by as much as 80%. Similarly, Monte Carlo simulation using 1000 realizations of relative permeability parameters showed no relationship between CO 2 injectivity and any of the parameters but k$$0\\atop{r}$$ CO2, which had a very strong (R 2 = 0.9685) power law relationship with total CO 2 injected. Model sensitivity to k$$0\\atop{r}$$ CO2 points to the importance of accurate core flood and wettability measurements.« less

Investigation of uncertainty in CO 2 reservoir models: A sensitivity analysis of relative permeability parameter values

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

Yoshida, Nozomu; Levine, Jonathan S.; Stauffer, Philip H.

Numerical reservoir models of CO 2 injection in saline formations rely on parameterization of laboratory-measured pore-scale processes. Here, we have performed a parameter sensitivity study and Monte Carlo simulations to determine the normalized change in total CO 2 injected using the finite element heat and mass-transfer code (FEHM) numerical reservoir simulator. Experimentally measured relative permeability parameter values were used to generate distribution functions for parameter sampling. The parameter sensitivity study analyzed five different levels for each of the relative permeability model parameters. All but one of the parameters changed the CO 2 injectivity by <10%, less than the geostatistical uncertainty that applies to all large subsurface systems due to natural geophysical variability and inherently small sample sizes. The exception was the end-point CO 2 relative permeability, kmore » $$0\\atop{r}$$ CO2, the maximum attainable effective CO 2 permeability during CO 2 invasion, which changed CO2 injectivity by as much as 80%. Similarly, Monte Carlo simulation using 1000 realizations of relative permeability parameters showed no relationship between CO 2 injectivity and any of the parameters but k$$0\\atop{r}$$ CO2, which had a very strong (R 2 = 0.9685) power law relationship with total CO 2 injected. Model sensitivity to k$$0\\atop{r}$$ CO2 points to the importance of accurate core flood and wettability measurements.« less

Impact of intercontinental pollution transport on North American ozone air pollution: an HTAP phase 2 multi-model study

NASA Astrophysics Data System (ADS)

Huang, Min; Carmichael, Gregory R.; Pierce, R. Bradley; Jo, Duseong S.; Park, Rokjin J.; Flemming, Johannes; Emmons, Louisa K.; Bowman, Kevin W.; Henze, Daven K.; Davila, Yanko; Sudo, Kengo; Eiof Jonson, Jan; Tronstad Lund, Marianne; Janssens-Maenhout, Greet; Dentener, Frank J.; Keating, Terry J.; Oetjen, Hilke; Payne, Vivienne H.

2017-05-01

The recent update on the US National Ambient Air Quality Standards (NAAQS) of the ground-level ozone (O3) can benefit from a better understanding of its source contributions in different US regions during recent years. In the Hemispheric Transport of Air Pollution experiment phase 1 (HTAP1), various global models were used to determine the O3 source-receptor (SR) relationships among three continents in the Northern Hemisphere in 2001. In support of the HTAP phase 2 (HTAP2) experiment that studies more recent years and involves higher-resolution global models and regional models' participation, we conduct a number of regional-scale Sulfur Transport and dEposition Model (STEM) air quality base and sensitivity simulations over North America during May-June 2010. STEM's top and lateral chemical boundary conditions were downscaled from three global chemical transport models' (i.e., GEOS-Chem, RAQMS, and ECMWF C-IFS) base and sensitivity simulations in which the East Asian (EAS) anthropogenic emissions were reduced by 20 %. The mean differences between STEM surface O3 sensitivities to the emission changes and its corresponding boundary condition model's are smaller than those among its boundary condition models, in terms of the regional/period-mean (< 10 %) and the spatial distributions. An additional STEM simulation was performed in which the boundary conditions were downscaled from a RAQMS (Realtime Air Quality Modeling System) simulation without EAS anthropogenic emissions. The scalability of O3 sensitivities to the size of the emission perturbation is spatially varying, and the full (i.e., based on a 100 % emission reduction) source contribution obtained from linearly scaling the North American mean O3 sensitivities to a 20 % reduction in the EAS anthropogenic emissions may be underestimated by at least 10 %. The three boundary condition models' mean O3 sensitivities to the 20 % EAS emission perturbations are ˜ 8 % (May-June 2010)/˜ 11 % (2010 annual) lower than those estimated by eight global models, and the multi-model ensemble estimates are higher than the HTAP1 reported 2001 conditions. GEOS-Chem sensitivities indicate that the EAS anthropogenic NOx emissions matter more than the other EAS O3 precursors to the North American O3, qualitatively consistent with previous adjoint sensitivity calculations. In addition to the analyses on large spatial-temporal scales relative to the HTAP1, we also show results on subcontinental and event scales that are more relevant to the US air quality management. The EAS pollution impacts are weaker during observed O3 exceedances than on all days in most US regions except over some high-terrain western US rural/remote areas. Satellite O3 (TES, JPL-IASI, and AIRS) and carbon monoxide (TES and AIRS) products, along with surface measurements and model calculations, show that during certain episodes stratospheric O3 intrusions and the transported EAS pollution influenced O3 in the western and the eastern US differently. Free-running (i.e., without chemical data assimilation) global models underpredicted the transported background O3 during these episodes, posing difficulties for STEM to accurately simulate the surface O3 and its source contribution. Although we effectively improved the modeled O3 by incorporating satellite O3 (OMI and MLS) and evaluated the quality of the HTAP2 emission inventory with the Royal Netherlands Meteorological Institute-Ozone Monitoring Instrument (KNMI-OMI) nitrogen dioxide, using observations to evaluate and improve O3 source attribution still remains to be further explored.

Impact of intercontinental pollution transport on North American ozone air pollution: an HTAP phase 2 multi-model study.

PubMed

Huang, Min; Carmichael, Gregory R; Pierce, R Bradley; Jo, Duseong S; Park, Rokjin J; Flemming, Johannes; Emmons, Louisa K; Bowman, Kevin W; Henze, Daven K; Davila, Yanko; Sudo, Kengo; Jonson, Jan Eiof; Lund, Marianne Tronstad; Janssens-Maenhout, Greet; Dentener, Frank J; Keating, Terry J; Oetjen, Hilke; Payne, Vivienne H

2017-05-08

The recent update on the US National Ambient Air Quality Standards (NAAQS) of the ground-level ozone (O 3 / can benefit from a better understanding of its source contributions in different US regions during recent years. In the Hemispheric Transport of Air Pollution experiment phase 1 (HTAP1), various global models were used to determine the O 3 source-receptor (SR) relationships among three continents in the Northern Hemisphere in 2001. In support of the HTAP phase 2 (HTAP2) experiment that studies more recent years and involves higher-resolution global models and regional models' participation, we conduct a number of regional-scale Sulfur Transport and dEposition Model (STEM) air quality base and sensitivity simulations over North America during May-June 2010. STEM's top and lateral chemical boundary conditions were downscaled from three global chemical transport models' (i.e., GEOS-Chem, RAQMS, and ECMWF C-IFS) base and sensitivity simulations in which the East Asian (EAS) anthropogenic emissions were reduced by 20 %. The mean differences between STEM surface O 3 sensitivities to the emission changes and its corresponding boundary condition model's are smaller than those among its boundary condition models, in terms of the regional/period-mean (<10 %) and the spatial distributions. An additional STEM simulation was performed in which the boundary conditions were downscaled from a RAQMS (Realtime Air Quality Modeling System) simulation without EAS anthropogenic emissions. The scalability of O 3 sensitivities to the size of the emission perturbation is spatially varying, and the full (i.e., based on a 100% emission reduction) source contribution obtained from linearly scaling the North American mean O 3 sensitivities to a 20% reduction in the EAS anthropogenic emissions may be underestimated by at least 10 %. The three boundary condition models' mean O 3 sensitivities to the 20% EAS emission perturbations are ~8% (May-June 2010)/~11% (2010 annual) lower than those estimated by eight global models, and the multi-model ensemble estimates are higher than the HTAP1 reported 2001 conditions. GEOS-Chem sensitivities indicate that the EAS anthropogenic NO x emissions matter more than the other EAS O 3 precursors to the North American O 3 , qualitatively consistent with previous adjoint sensitivity calculations. In addition to the analyses on large spatial-temporal scales relative to the HTAP1, we also show results on subcontinental and event scales that are more relevant to the US air quality management. The EAS pollution impacts are weaker during observed O 3 exceedances than on all days in most US regions except over some high-terrain western US rural/remote areas. Satellite O 3 (TES, JPL-IASI, and AIRS) and carbon monoxide (TES and AIRS) products, along with surface measurements and model calculations, show that during certain episodes stratospheric O 3 intrusions and the transported EAS pollution influenced O 3 in the western and the eastern US differently. Free-running (i.e., without chemical data assimilation) global models underpredicted the transported background O 3 during these episodes, posing difficulties for STEM to accurately simulate the surface O 3 and its source contribution. Although we effectively improved the modeled O 3 by incorporating satellite O 3 (OMI and MLS) and evaluated the quality of the HTAP2 emission inventory with the Royal Netherlands Meteorological Institute-Ozone Monitoring Instrument (KNMI-OMI) nitrogen dioxide, using observations to evaluate and improve O 3 source attribution still remains to be further explored.

Proof-of-Concept Study for Uncertainty Quantification and Sensitivity Analysis using the BRL Shaped-Charge Example

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

Hughes, Justin Matthew

These are the slides for a graduate presentation at Mississippi State University. It covers the following: the BRL Shaped-Charge Geometry in PAGOSA, mesh refinement study, surrogate modeling using a radial basis function network (RBFN), ruling out parameters using sensitivity analysis (equation of state study), uncertainty quantification (UQ) methodology, and sensitivity analysis (SA) methodology. In summary, a mesh convergence study was used to ensure that solutions were numerically stable by comparing PDV data between simulations. A Design of Experiments (DOE) method was used to reduce the simulation space to study the effects of the Jones-Wilkins-Lee (JWL) Parameters for the Composition Bmore » main charge. Uncertainty was quantified by computing the 95% data range about the median of simulation output using a brute force Monte Carlo (MC) random sampling method. Parameter sensitivities were quantified using the Fourier Amplitude Sensitivity Test (FAST) spectral analysis method where it was determined that detonation velocity, initial density, C1, and B1 controlled jet tip velocity.« less

Numerical modeling of the effects of wave energy converter characteristics on nearshore wave conditions

DOE PAGES

Chang, G.; Ruehl, K.; Jones, C. A.; ...

2015-12-24

Modeled nearshore wave propagation was investigated downstream of simulated wave energy converters (WECs) to evaluate overall near- and far-field effects of WEC arrays. Model sensitivity to WEC characteristics and WEC array deployment scenarios was evaluated using a modified version of an industry standard wave model, Simulating WAves Nearshore (SWAN), which allows the incorporation of device-specific WEC characteristics to specify obstacle transmission. The sensitivity study illustrated that WEC device type and subsequently its size directly resulted in wave height variations in the lee of the WEC array. Wave heights decreased up to 30% between modeled scenarios with and without WECs formore » large arrays (100 devices) of relatively sizable devices (26 m in diameter) with peak power generation near to the modeled incident wave height. Other WEC types resulted in less than 15% differences in modeled wave height with and without WECs, with lesser influence for WECs less than 10 m in diameter. Wave directions and periods were largely insensitive to changes in parameters. Furthermore, additional model parameterization and analysis are required to fully explore the model sensitivity of peak wave period and mean wave direction to the varying of the parameters.« less

Investigating the Sensitivity of Streamflow and Water Quality to Climate Change and Urbanization in 20 U.S. Watersheds

NASA Astrophysics Data System (ADS)

Johnson, T. E.; Weaver, C. P.; Butcher, J.; Parker, A.

2011-12-01

Watershed modeling was conducted in 20 large (15,000-60,000 km2), U.S. watersheds to address gaps in our knowledge of the sensitivity of U.S. streamflow, nutrient (N and P) and sediment loading to potential future climate change, and methodological challenges associated with integrating existing tools (e.g., climate models, watershed models) and datasets to address these questions. Climate change scenarios are based on dynamically downscaled (50x50 km2) output from four of the GCMs used in the Intergovernmental Panel on Climate Change (IPCC) 4th Assessment Report for the period 2041-2070 archived by the North American Regional Climate Change Assessment Program (NARCCAP). To explore the potential interaction of climate change and urbanization, model simulations also include urban and residential development scenarios for each of the 20 study watersheds. Urban and residential development scenarios were acquired from EPA's national-scale Integrated Climate and Land Use Scenarios (ICLUS) project. Watershed modeling was conducted using the Hydrologic Simulation Program-FORTRAN (HSPF) and Soil and Water Assessment Tool (SWAT) models. Here we present a summary of results for 5 of the study watersheds; the Minnesota River, the Susquehanna River, the Apalachicola-Chattahoochee-Flint, the Salt/Verde/San Pedro, and the Willamette River Basins. This set of results provide an overview of the response to climate change in different regions of the U.S., the different sensitivities of different streamflow and water quality endpoints, and illustrate a number of methodological issues including the sensitivities and uncertainties associated with use of different watershed models, approaches for downscaling climate change projections, and interaction between climate change and other forcing factors, specifically urbanization and changes in atmospheric CO2 concentration.

Enhanced Sensitivity to Rapid Input Fluctuations by Nonlinear Threshold Dynamics in Neocortical Pyramidal Neurons.

PubMed

Mensi, Skander; Hagens, Olivier; Gerstner, Wulfram; Pozzorini, Christian

2016-02-01

The way in which single neurons transform input into output spike trains has fundamental consequences for network coding. Theories and modeling studies based on standard Integrate-and-Fire models implicitly assume that, in response to increasingly strong inputs, neurons modify their coding strategy by progressively reducing their selective sensitivity to rapid input fluctuations. Combining mathematical modeling with in vitro experiments, we demonstrate that, in L5 pyramidal neurons, the firing threshold dynamics adaptively adjust the effective timescale of somatic integration in order to preserve sensitivity to rapid signals over a broad range of input statistics. For that, a new Generalized Integrate-and-Fire model featuring nonlinear firing threshold dynamics and conductance-based adaptation is introduced that outperforms state-of-the-art neuron models in predicting the spiking activity of neurons responding to a variety of in vivo-like fluctuating currents. Our model allows for efficient parameter extraction and can be analytically mapped to a Generalized Linear Model in which both the input filter--describing somatic integration--and the spike-history filter--accounting for spike-frequency adaptation--dynamically adapt to the input statistics, as experimentally observed. Overall, our results provide new insights on the computational role of different biophysical processes known to underlie adaptive coding in single neurons and support previous theoretical findings indicating that the nonlinear dynamics of the firing threshold due to Na+-channel inactivation regulate the sensitivity to rapid input fluctuations.

A Numerical Study of Hypersonic Forebody/Inlet Integration Problem

NASA Technical Reports Server (NTRS)

Kumar, Ajay

1991-01-01

A numerical study of hypersonic forebody/inlet integration problem is presented in the form of the view-graphs. The following topics are covered: physical/chemical modeling; solution procedure; flow conditions; mass flow rate at inlet face; heating and skin friction loads; 3-D forebogy/inlet integration model; and sensitivity studies.

Parametric Sensitivity Analysis of Oscillatory Delay Systems with an Application to Gene Regulation.

PubMed

Ingalls, Brian; Mincheva, Maya; Roussel, Marc R

2017-07-01

A parametric sensitivity analysis for periodic solutions of delay-differential equations is developed. Because phase shifts cause the sensitivity coefficients of a periodic orbit to diverge, we focus on sensitivities of the extrema, from which amplitude sensitivities are computed, and of the period. Delay-differential equations are often used to model gene expression networks. In these models, the parametric sensitivities of a particular genotype define the local geometry of the evolutionary landscape. Thus, sensitivities can be used to investigate directions of gradual evolutionary change. An oscillatory protein synthesis model whose properties are modulated by RNA interference is used as an example. This model consists of a set of coupled delay-differential equations involving three delays. Sensitivity analyses are carried out at several operating points. Comments on the evolutionary implications of the results are offered.

Estimating the sensitivity of passive surveillance for HPAI H5N1 in Bayelsa state, Nigeria.

PubMed

Ojimelukwe, Agatha E; Prakarnkamanant, Apisit; Rushton, Jonathan

2016-07-01

This study identified characteristics of poultry farming with a focus on practices that affect the detection of HPAI; and estimated the system sensitivity of passive surveillance for HPAI H5N1 in commercial and backyard chicken farms in Bayelsa-State, Nigeria. Field studies were carried out in Yenegoa and Ogbia local government areas in Bayelsa state. Willingness to report HPAI was highest in commercial poultry farms (13/13) than in Backyard farms (8/13). Poor means of dead bird disposal was common to both commercial and backyard farms. Administering some form of treatment to sick birds without prior consultation with a professional was higher in backyard farms (8/13) than in commercial farms (4/13). Consumption of sick birds was reported in 4/13 backyard farms and sale of dead birds was recorded in one commercial farm. The sensitivity of passive surveillance for HPAI was assessed using scenario tree modelling. A scenario tree model was developed and applied to estimate the sensitivity, i.e. the probability of detecting one or more infected chicken farms in Bayelsa state at different levels of disease prevalence. The model showed a median sensitivity of 100%, 67% and 23% for detecting HPAI by passive surveillance at a disease prevalence of 0.1%, a minimum of 10 and 3 infected poultry farms respectively. Passive surveillance system sensitivity at a design prevalence of 10 infected farms is increasable up to 86% when the disease detection in backyard chicken farms is enhanced. Copyright © 2016 Elsevier B.V. All rights reserved.

Variogram Analysis of Response surfaces (VARS): A New Framework for Global Sensitivity Analysis of Earth and Environmental Systems Models

NASA Astrophysics Data System (ADS)

Razavi, S.; Gupta, H. V.

2015-12-01

Earth and environmental systems models (EESMs) are continually growing in complexity and dimensionality with continuous advances in understanding and computing power. Complexity and dimensionality are manifested by introducing many different factors in EESMs (i.e., model parameters, forcings, boundary conditions, etc.) to be identified. Sensitivity Analysis (SA) provides an essential means for characterizing the role and importance of such factors in producing the model responses. However, conventional approaches to SA suffer from (1) an ambiguous characterization of sensitivity, and (2) poor computational efficiency, particularly as the problem dimension grows. Here, we present a new and general sensitivity analysis framework (called VARS), based on an analogy to 'variogram analysis', that provides an intuitive and comprehensive characterization of sensitivity across the full spectrum of scales in the factor space. We prove, theoretically, that Morris (derivative-based) and Sobol (variance-based) methods and their extensions are limiting cases of VARS, and that their SA indices can be computed as by-products of the VARS framework. We also present a practical strategy for the application of VARS to real-world problems, called STAR-VARS, including a new sampling strategy, called "star-based sampling". Our results across several case studies show the STAR-VARS approach to provide reliable and stable assessments of "global" sensitivity across the full range of scales in the factor space, while being at least 1-2 orders of magnitude more efficient than the benchmark Morris and Sobol approaches.

A Sensitivity Analysis of fMRI Balloon Model.

PubMed

Zayane, Chadia; Laleg-Kirati, Taous Meriem

2015-01-01

Functional magnetic resonance imaging (fMRI) allows the mapping of the brain activation through measurements of the Blood Oxygenation Level Dependent (BOLD) contrast. The characterization of the pathway from the input stimulus to the output BOLD signal requires the selection of an adequate hemodynamic model and the satisfaction of some specific conditions while conducting the experiment and calibrating the model. This paper, focuses on the identifiability of the Balloon hemodynamic model. By identifiability, we mean the ability to estimate accurately the model parameters given the input and the output measurement. Previous studies of the Balloon model have somehow added knowledge either by choosing prior distributions for the parameters, freezing some of them, or looking for the solution as a projection on a natural basis of some vector space. In these studies, the identification was generally assessed using event-related paradigms. This paper justifies the reasons behind the need of adding knowledge, choosing certain paradigms, and completing the few existing identifiability studies through a global sensitivity analysis of the Balloon model in the case of blocked design experiment.

Neuroadaptation in Nicotine Addiction: Update on the Sensitization-Homeostasis Model

PubMed Central

DiFranza, Joseph R.; Huang, Wei; King, Jean

2012-01-01

The role of neuronal plasticity in supporting the addictive state has generated much research and some conceptual theories. One such theory, the sensitization-homeostasis (SH) model, postulates that nicotine suppresses craving circuits, and this triggers the development of homeostatic adaptations that autonomously support craving. Based on clinical studies, the SH model predicts the existence of three distinct forms of neuroplasticity that are responsible for withdrawal, tolerance and the resolution of withdrawal. Over the past decade, many controversial aspects of the SH model have become well established by the literature, while some details have been disproven. Here we update the model based on new studies showing that nicotine dependence develops through a set sequence of symptoms in all smokers, and that the latency to withdrawal, the time it takes for withdrawal symptoms to appear during abstinence, is initially very long but shortens by several orders of magnitude over time. We conclude by outlining directions for future research based on the updated model, and commenting on how new experimental studies can gain from the framework put forth in the SH model. PMID:24961259

Are quantitative sensitivity analysis methods always reliable?

NASA Astrophysics Data System (ADS)

Huang, X.

2016-12-01

Physical parameterizations developed to represent subgrid-scale physical processes include various uncertain parameters, leading to large uncertainties in today's Earth System Models (ESMs). Sensitivity Analysis (SA) is an efficient approach to quantitatively determine how the uncertainty of the evaluation metric can be apportioned to each parameter. Also, SA can identify the most influential parameters, as a result to reduce the high dimensional parametric space. In previous studies, some SA-based approaches, such as Sobol' and Fourier amplitude sensitivity testing (FAST), divide the parameters into sensitive and insensitive groups respectively. The first one is reserved but the other is eliminated for certain scientific study. However, these approaches ignore the disappearance of the interactive effects between the reserved parameters and the eliminated ones, which are also part of the total sensitive indices. Therefore, the wrong sensitive parameters might be identified by these traditional SA approaches and tools. In this study, we propose a dynamic global sensitivity analysis method (DGSAM), which iteratively removes the least important parameter until there are only two parameters left. We use the CLM-CASA, a global terrestrial model, as an example to verify our findings with different sample sizes ranging from 7000 to 280000. The result shows DGSAM has abilities to identify more influential parameters, which is confirmed by parameter calibration experiments using four popular optimization methods. For example, optimization using Top3 parameters filtered by DGSAM could achieve substantial improvement against Sobol' by 10%. Furthermore, the current computational cost for calibration has been reduced to 1/6 of the original one. In future, it is necessary to explore alternative SA methods emphasizing parameter interactions.

New Uses for Sensitivity Analysis: How Different Movement Tasks Effect Limb Model Parameter Sensitivity

NASA Technical Reports Server (NTRS)

Winters, J. M.; Stark, L.

1984-01-01

Original results for a newly developed eight-order nonlinear limb antagonistic muscle model of elbow flexion and extension are presented. A wider variety of sensitivity analysis techniques are used and a systematic protocol is established that shows how the different methods can be used efficiently to complement one another for maximum insight into model sensitivity. It is explicitly shown how the sensitivity of output behaviors to model parameters is a function of the controller input sequence, i.e., of the movement task. When the task is changed (for instance, from an input sequence that results in the usual fast movement task to a slower movement that may also involve external loading, etc.) the set of parameters with high sensitivity will in general also change. Such task-specific use of sensitivity analysis techniques identifies the set of parameters most important for a given task, and even suggests task-specific model reduction possibilities.

Reservoir Performance Under Future Climate For Basins With Different Hydrologic Sensitivities

NASA Astrophysics Data System (ADS)

Mateus, M. C.; Tullos, D. D.

2013-12-01

In addition to long-standing uncertainties related to variable inflows and market price of power, reservoir operators face a number of new uncertainties related to hydrologic nonstationarity, changing environmental regulations, and rapidly growing water and energy demands. This study investigates the impact, sensitivity, and uncertainty of changing hydrology on hydrosystem performance across different hydrogeologic settings. We evaluate the performance of reservoirs in the Santiam River basin, including a case study in the North Santiam Basin, with high permeability and extensive groundwater storage, and the South Santiam Basin, with low permeability, little groundwater storage and rapid runoff response. The modeling objective is to address the following study questions: (1) for the two hydrologic regimes, how does the flood management, water supply, and environmental performance of current reservoir operations change under future 2.5, 50 and 97.5 percentile streamflow projections; and (2) how much change in inflow is required to initiate a failure to meet downstream minimum or maximum flows in the future. We couple global climate model results with a rainfall-runoff model and a formal Bayesian uncertainty analysis to simulate future inflow hydrographs as inputs to a reservoir operations model. To evaluate reservoir performance under a changing climate, we calculate reservoir refill reliability, changes in flood frequency, and reservoir time and volumetric reliability of meeting minimum spring and summer flow target. Reservoir performance under future hydrology appears to vary with hydrogeology. We find higher sensitivity to floods for the North Santiam Basin and higher sensitivity to minimum flow targets for the South Santiam Basin. Higher uncertainty is related with basins with a more complex hydrologeology. Results from model simulations contribute to understanding of the reliability and vulnerability of reservoirs to a changing climate.

Objective validation of central sensitization in the rat UVB and heat rekindling model

PubMed Central

Weerasinghe, NS; Lumb, BM; Apps, R; Koutsikou, S; Murrell, JC

2014-01-01

Background The UVB and heat rekindling (UVB/HR) model shows potential as a translatable inflammatory pain model. However, the occurrence of central sensitization in this model, a fundamental mechanism underlying chronic pain, has been debated. Face, construct and predictive validity are key requisites of animal models; electromyogram (EMG) recordings were utilized to objectively demonstrate validity of the rat UVB/HR model. Methods The UVB/HR model was induced on the heel of the hind paw under anaesthesia. Mechanical withdrawal thresholds (MWTs) were obtained from biceps femoris EMG responses to a gradually increasing pinch at the mid hind paw region under alfaxalone anaesthesia, 96 h after UVB irradiation. MWT was compared between UVB/HR and SHAM-treated rats (anaesthetic only). Underlying central mechanisms in the model were pharmacologically validated by MWT measurement following intrathecal N-methyl-d-aspartate (NMDA) receptor antagonist, MK-801, or saline. Results Secondary hyperalgesia was confirmed by a significantly lower pre-drug MWT {mean [±standard error of the mean (SEM)]} in UVB/HR [56.3 (±2.1) g/mm2, n = 15] compared with SHAM-treated rats [69.3 (±2.9) g/mm2, n = 8], confirming face validity of the model. Predictive validity was demonstrated by the attenuation of secondary hyperalgesia by MK-801, where mean (±SEM) MWT was significantly higher [77.2 (±5.9) g/mm2 n = 7] in comparison with pre-drug [57.8 (±3.5) g/mm2 n = 7] and saline [57.0 (±3.2) g/mm2 n = 8] at peak drug effect. The occurrence of central sensitization confirmed construct validity of the UVB/HR model. Conclusions This study used objective outcome measures of secondary hyperalgesia to validate the rat UVB/HR model as a translational model of inflammatory pain. What's already known about this topic? Most current animal chronic pain models lack translatability to human subjects. Primary hyperalgesia is an established feature of the UVB/heat rekindling inflammatory pain model in rodents and humans, but the presence of secondary hyperalgesia, a hallmark feature of central sensitization and thus chronic pain, is contentious. What does this study add? Secondary hyperalgesia was demonstrated in the rat UVB/heat rekindling model using an objective outcome measure (electromyogram), overcoming the subjective limitations of previous behavioural studies. PMID:24590815

Global Sensitivity Analysis for Identifying Important Parameters of Nitrogen Nitrification and Denitrification under Model and Scenario Uncertainties

NASA Astrophysics Data System (ADS)

Ye, M.; Chen, Z.; Shi, L.; Zhu, Y.; Yang, J.

2017-12-01

Nitrogen reactive transport modeling is subject to uncertainty in model parameters, structures, and scenarios. While global sensitivity analysis is a vital tool for identifying the parameters important to nitrogen reactive transport, conventional global sensitivity analysis only considers parametric uncertainty. This may result in inaccurate selection of important parameters, because parameter importance may vary under different models and modeling scenarios. By using a recently developed variance-based global sensitivity analysis method, this paper identifies important parameters with simultaneous consideration of parametric uncertainty, model uncertainty, and scenario uncertainty. In a numerical example of nitrogen reactive transport modeling, a combination of three scenarios of soil temperature and two scenarios of soil moisture leads to a total of six scenarios. Four alternative models are used to evaluate reduction functions used for calculating actual rates of nitrification and denitrification. The model uncertainty is tangled with scenario uncertainty, as the reduction functions depend on soil temperature and moisture content. The results of sensitivity analysis show that parameter importance varies substantially between different models and modeling scenarios, which may lead to inaccurate selection of important parameters if model and scenario uncertainties are not considered. This problem is avoided by using the new method of sensitivity analysis in the context of model averaging and scenario averaging. The new method of sensitivity analysis can be applied to other problems of contaminant transport modeling when model uncertainty and/or scenario uncertainty are present.

Sensitivity analysis of radionuclides atmospheric dispersion following the Fukushima accident

NASA Astrophysics Data System (ADS)

Girard, Sylvain; Korsakissok, Irène; Mallet, Vivien

2014-05-01

Atmospheric dispersion models are used in response to accidental releases with two purposes: - minimising the population exposure during the accident; - complementing field measurements for the assessment of short and long term environmental and sanitary impacts. The predictions of these models are subject to considerable uncertainties of various origins. Notably, input data, such as meteorological fields or estimations of emitted quantities as function of time, are highly uncertain. The case studied here is the atmospheric release of radionuclides following the Fukushima Daiichi disaster. The model used in this study is Polyphemus/Polair3D, from which derives IRSN's operational long distance atmospheric dispersion model ldX. A sensitivity analysis was conducted in order to estimate the relative importance of a set of identified uncertainty sources. The complexity of this task was increased by four characteristics shared by most environmental models: - high dimensional inputs; - correlated inputs or inputs with complex structures; - high dimensional output; - multiplicity of purposes that require sophisticated and non-systematic post-processing of the output. The sensitivities of a set of outputs were estimated with the Morris screening method. The input ranking was highly dependent on the considered output. Yet, a few variables, such as horizontal diffusion coefficient or clouds thickness, were found to have a weak influence on most of them and could be discarded from further studies. The sensitivity analysis procedure was also applied to indicators of the model performance computed on a set of gamma dose rates observations. This original approach is of particular interest since observations could be used later to calibrate the input variables probability distributions. Indeed, only the variables that are influential on performance scores are likely to allow for calibration. An indicator based on emission peaks time matching was elaborated in order to complement classical statistical scores which were dominated by deposit dose rates and almost insensitive to lower atmosphere dose rates. The substantial sensitivity of these performance indicators is auspicious for future calibration attempts and indicates that the simple perturbations used here may be sufficient to represent an essential part of the overall uncertainty.

The 2006 Cape Canaveral Air Force Station Range Reference Atmosphere Model Validation Study and Sensitivity Analysis to the National Aeronautics and Space Administration's Space Shuttle

NASA Technical Reports Server (NTRS)

Burns, Lee; Merry, Carl; Decker, Ryan; Harrington, Brian

2008-01-01

The 2006 Cape Canaveral Air Force Station (CCAFS) Range Reference Atmosphere (RRA) is a statistical model summarizing the wind and thermodynamic atmospheric variability from surface to 70 kin. Launches of the National Aeronautics and Space Administration's (NASA) Space Shuttle from Kennedy Space Center utilize CCAFS RRA data to evaluate environmental constraints on various aspects of the vehicle during ascent. An update to the CCAFS RRA was recently completed. As part of the update, a validation study on the 2006 version was conducted as well as a comparison analysis of the 2006 version to the existing CCAFS RRA database version 1983. Assessments to the Space Shuttle vehicle ascent profile characteristics were performed to determine impacts of the updated model to the vehicle performance. Details on the model updates and the vehicle sensitivity analyses with the update model are presented.

The Impact of Rainfall on Soil Moisture Dynamics in a Foggy Desert.

PubMed

Li, Bonan; Wang, Lixin; Kaseke, Kudzai F; Li, Lin; Seely, Mary K

2016-01-01

Soil moisture is a key variable in dryland ecosystems since it determines the occurrence and duration of vegetation water stress and affects the development of weather patterns including rainfall. However, the lack of ground observations of soil moisture and rainfall dynamics in many drylands has long been a major obstacle in understanding ecohydrological processes in these ecosystems. It is also uncertain to what extent rainfall controls soil moisture dynamics in fog dominated dryland systems. To this end, in this study, twelve to nineteen months' continuous daily records of rainfall and soil moisture (from January 2014 to August 2015) obtained from three sites (one sand dune site and two gravel plain sites) in the Namib Desert are reported. A process-based model simulating the stochastic soil moisture dynamics in water-limited systems was used to study the relationships between soil moisture and rainfall dynamics. Model sensitivity in response to different soil and vegetation parameters under diverse soil textures was also investigated. Our field observations showed that surface soil moisture dynamics generally follow rainfall patterns at the two gravel plain sites, whereas soil moisture dynamics in the sand dune site did not show a significant relationship with rainfall pattern. The modeling results suggested that most of the soil moisture dynamics can be simulated except the daily fluctuations, which may require a modification of the model structure to include non-rainfall components. Sensitivity analyses suggested that soil hygroscopic point (sh) and field capacity (sfc) were two main parameters controlling soil moisture output, though permanent wilting point (sw) was also very sensitive under the parameter setting of sand dune (Gobabeb) and gravel plain (Kleinberg). Overall, the modeling results were not sensitive to the parameters in non-bounded group (e.g., soil hydraulic conductivity (Ks) and soil porosity (n)). Field observations, stochastic modeling results as well as sensitivity analyses provide soil moisture baseline information for future monitoring and the prediction of soil moisture patterns in the Namib Desert.

The Impact of Rainfall on Soil Moisture Dynamics in a Foggy Desert

PubMed Central

Li, Bonan; Wang, Lixin; Kaseke, Kudzai F.; Li, Lin; Seely, Mary K.

2016-01-01

Soil moisture is a key variable in dryland ecosystems since it determines the occurrence and duration of vegetation water stress and affects the development of weather patterns including rainfall. However, the lack of ground observations of soil moisture and rainfall dynamics in many drylands has long been a major obstacle in understanding ecohydrological processes in these ecosystems. It is also uncertain to what extent rainfall controls soil moisture dynamics in fog dominated dryland systems. To this end, in this study, twelve to nineteen months’ continuous daily records of rainfall and soil moisture (from January 2014 to August 2015) obtained from three sites (one sand dune site and two gravel plain sites) in the Namib Desert are reported. A process-based model simulating the stochastic soil moisture dynamics in water-limited systems was used to study the relationships between soil moisture and rainfall dynamics. Model sensitivity in response to different soil and vegetation parameters under diverse soil textures was also investigated. Our field observations showed that surface soil moisture dynamics generally follow rainfall patterns at the two gravel plain sites, whereas soil moisture dynamics in the sand dune site did not show a significant relationship with rainfall pattern. The modeling results suggested that most of the soil moisture dynamics can be simulated except the daily fluctuations, which may require a modification of the model structure to include non-rainfall components. Sensitivity analyses suggested that soil hygroscopic point (sh) and field capacity (sfc) were two main parameters controlling soil moisture output, though permanent wilting point (sw) was also very sensitive under the parameter setting of sand dune (Gobabeb) and gravel plain (Kleinberg). Overall, the modeling results were not sensitive to the parameters in non-bounded group (e.g., soil hydraulic conductivity (Ks) and soil porosity (n)). Field observations, stochastic modeling results as well as sensitivity analyses provide soil moisture baseline information for future monitoring and the prediction of soil moisture patterns in the Namib Desert. PMID:27764203

A new framework for climate sensitivity and prediction: a modelling perspective

NASA Astrophysics Data System (ADS)

Ragone, Francesco; Lucarini, Valerio; Lunkeit, Frank

2016-03-01

The sensitivity of climate models to increasing CO2 concentration and the climate response at decadal time-scales are still major factors of uncertainty for the assessment of the long and short term effects of anthropogenic climate change. While the relative slow progress on these issues is partly due to the inherent inaccuracies of numerical climate models, this also hints at the need for stronger theoretical foundations to the problem of studying climate sensitivity and performing climate change predictions with numerical models. Here we demonstrate that it is possible to use Ruelle's response theory to predict the impact of an arbitrary CO2 forcing scenario on the global surface temperature of a general circulation model. Response theory puts the concept of climate sensitivity on firm theoretical grounds, and addresses rigorously the problem of predictability at different time-scales. Conceptually, these results show that performing climate change experiments with general circulation models is a well defined problem from a physical and mathematical point of view. Practically, these results show that considering one single CO2 forcing scenario is enough to construct operators able to predict the response of climatic observables to any other CO2 forcing scenario, without the need to perform additional numerical simulations. We also introduce a general relationship between climate sensitivity and climate response at different time scales, thus providing an explicit definition of the inertia of the system at different time scales. This technique allows also for studying systematically, for a large variety of forcing scenarios, the time horizon at which the climate change signal (in an ensemble sense) becomes statistically significant. While what we report here refers to the linear response, the general theory allows for treating nonlinear effects as well. These results pave the way for redesigning and interpreting climate change experiments from a radically new perspective.

The Impact of Microphysics and Planetary Boundary Layer Physics on Model Simulation of U.S. Deep South Summer Convection

NASA Technical Reports Server (NTRS)

McCaul, Eugene W., Jr.; Case, Jonathan L.; Zavodsky, Bradley T.; Srikishen, Jayanthi; Medlin, Jeffrey M.; Wood, Lance

2014-01-01

Inspection of output from various configurations of high-resolution, explicit convection forecast models such as the Weather Research and Forecasting (WRF) model indicates significant sensitivity to the choices of model physics pararneterizations employed. Some of the largest apparent sensitivities are related to the specifications of the cloud microphysics and planetary boundary layer physics packages. In addition, these sensitivities appear to be especially pronounced for the weakly-sheared, multicell modes of deep convection characteristic of the Deep South of the United States during the boreal summer. Possible ocean-land sensitivities also argue for further examination of the impacts of using unique ocean-land surface initialization datasets provided by the NASA Short-term Prediction Research and Transition (SPoRn Center to select NOAAlNWS weather forecast offices. To obtain better quantitative understanding of these sensitivities and also to determine the utility of the ocean-land initialization data, we have executed matrices of regional WRF forecasts for selected convective events near Mobile, AL (MOB), and Houston, TX (HGX). The matrices consist of identically initialized WRF 24-h forecasts using any of eight microphysics choices and any of three planetary boWldary layer choices. The resulting 24 simulations performed for each event within either the MOB or HGX regions are then compared to identify the sensitivities of various convective storm metrics to the physics choices. Particular emphasis is placed on sensitivities of precipitation timing, intensity, and coverage, as well as amount and coverage oflightuing activity diagnosed from storm kinematics and graupel in the mixed phase layer. The results confirm impressions gleaned from study of the behavior of variously configured WRF runs contained in the ensembles produced each spring at the Center for the Analysis and Prediction of Storms, but with the benefit of more straightforward control of the physics package choices. The design of the experiments thus allows for more direct interpretation of the sensitivities to each possible physics combination. The results should assist forecasters in their efforts to anticipate and correct for possible biases in simulated WRF convection patterns, and help the modeling community refine their model parameterizations.

The Impacts of Microphysics and Planetary Boundary Layer Physics on Model Simulations of U. S. Deep South Summer Convection

NASA Technical Reports Server (NTRS)

McCaul, E. W., Jr.; Case, J. L.; Zavodsky, B. T.; Srikishen, J.; Medlin, J. M.; Wood, L.

2014-01-01

Inspection of output from various configurations of high-resolution, explicit convection forecast models such as the Weather Research and Forecasting (WRF) model indicates significant sensitivity to the choices of model physics parameterizations employed. Some of the largest apparent sensitivities are related to the specifications of the cloud microphysics and planetary boundary layer physics packages. In addition, these sensitivities appear to be especially pronounced for the weakly-sheared, multicell modes of deep convection characteristic of the Deep South of the United States during the boreal summer. Possible ocean-land sensitivities also argue for further examination of the impacts of using unique ocean-land surface initialization datasets provided by the NASA Short-term Prediction Research and Transition (SPoRT Center to select NOAA/NWS weather forecast offices. To obtain better quantitative understanding of these sensitivities and also to determine the utility of the ocean-land initialization data, we have executed matrices of regional WRF forecasts for selected convective events near Mobile, AL (MOB), and Houston, TX (HGX). The matrices consist of identically initialized WRF 24-h forecasts using any of eight microphysics choices and any of three planetary boundary layer choices. The resulting 24 simulations performed for each event within either the MOB or HGX regions are then compared to identify the sensitivities of various convective storm metrics to the physics choices. Particular emphasis is placed on sensitivities of precipitation timing, intensity, and coverage, as well as amount and coverage of lightning activity diagnosed from storm kinematics and graupel in the mixed phase layer. The results confirm impressions gleaned from study of the behavior of variously configured WRF runs contained in the ensembles produced each spring at the Center for the Analysis and Prediction of Storms, but with the benefit of more straightforward control of the physics package choices. The design of the experiments thus allows for more direct interpretation of the sensitivities to each possible physics combination. The results should assist forecasters in their efforts to anticipate and correct for possible biases in simulated WRF convection patterns, and help the modeling community refine their model parameterizations.

Design sensitivity analysis of rotorcraft airframe structures for vibration reduction

NASA Technical Reports Server (NTRS)

Murthy, T. Sreekanta

1987-01-01

Optimization of rotorcraft structures for vibration reduction was studied. The objective of this study is to develop practical computational procedures for structural optimization of airframes subject to steady-state vibration response constraints. One of the key elements of any such computational procedure is design sensitivity analysis. A method for design sensitivity analysis of airframes under vibration response constraints is presented. The mathematical formulation of the method and its implementation as a new solution sequence in MSC/NASTRAN are described. The results of the application of the method to a simple finite element stick model of the AH-1G helicopter airframe are presented and discussed. Selection of design variables that are most likely to bring about changes in the response at specified locations in the airframe is based on consideration of forced response strain energy. Sensitivity coefficients are determined for the selected design variable set. Constraints on the natural frequencies are also included in addition to the constraints on the steady-state response. Sensitivity coefficients for these constraints are determined. Results of the analysis and insights gained in applying the method to the airframe model are discussed. The general nature of future work to be conducted is described.

Ganglion Cell Loss and Age-Related Visual Loss: A Cortical Pooling Analysis

PubMed Central

SCHMIDT, LAURA A.; LY-SCHROEDER, EMILY; SWANSON, WILLIAM H.

2006-01-01

Purpose To evaluate the ability of the cortical pooling model to predict the effects of random, mild ganglion cell loss, we compared the predictions of the model with the age-related loss and variability in achromatic and chromatic contrast sensitivity. Methods The relative sensitivity to small (0.5°) and large (3.0°) stimuli was compared in older (mean = 67 years, n = 27) and younger (mean = 23 years, n = 32) adults. Contrast sensitivity for modulations along the luminance, equiluminant L-cone, and equiluminant S-cone axes was assessed at the fovea and at four peripheral locations (12°). Results When the stimuli were large, threshold measurements obtained from all participants were reliable and well within the range of modulations along the chromatic axes that could be produced by the phosphors of the CRT. For the large stimuli, neither long- nor short-term variability increased as a function of age. Increasing the size of the stimulus did not decrease the magnitude of the age-related losses when the stimulus was chromatic, and visual losses observed with large chromatic stimuli were not different from those obtained with small achromatic stimuli. Moreover, chromatic contrast sensitivity assessments identified significant visual losses in four individuals who were not identified by achromatic contrast sensitivity assessments and only missed identifying one individual with significant losses in achromatic contrast sensitivity. Conclusions The declines in achromatic and chromatic sensitivity as a function of age (0.4 – 0.7 dB per decade) were similar to those obtained in previous studies of achromatic and chromatic perimetry and are consistent with the loss of retinal ganglion cells reported in histologic studies. The results of this study are consistent with the predictions the cortical pooling model makes for both variability and contrast sensitivity. These findings emphasize that selective visual impairments do not necessarily reflect preferential damage to a single ganglion cell class and that it is important to include the influence of higher cortical processing when quantifying the relation between ganglion cells and visual function. PMID:16840870

Emotional and Cardiovascular Sensitization to Daily Stress Following Childhood Parental Loss

ERIC Educational Resources Information Center

Luecken, Linda J.; Kraft, Amy; Appelhans, Bradley M.; Enders, Craig

2009-01-01

Adverse childhood events can influence the development of emotional and physiological self-regulatory abilities, with significant consequences for vulnerability to psychological and physical illness. This study evaluated stress sensitization and inoculation models of the impact of early parental death on stress exposure and reactivity in late…

IMPROVING PARTICULATE MATTER SOURCE APPORTIONMENT FOR HEALTH STUDIES: A TRAINED RECEPTOR MODELING APPROACH WITH SENSITIVITY, UNCERTAINTY AND SPATIAL ANALYSES

EPA Science Inventory

An approach for conducting PM source apportionment will be developed, tested, and applied that directly addresses limitations in current SA methods, in particular variability, biases, and intensive resource requirements. Uncertainties in SA results and sensitivities to SA inpu...

Sensitivity of Ambient Atmospheric Formaldehyde and Ozone to Precursor Species and Source Types Across the United States

EPA Science Inventory

Formaldehyde (HCHO) is an important air pollutant from both an atmospheric chemistry and human health standpoint. This study uses an instrumented photochemical Air Quality Model, CMAQ-DDM, to identify the sensitivity of HCHO concentrations across the United States (U.S.) to major...

Social Causes and Consequences of Rejection Sensitivity

ERIC Educational Resources Information Center

London, Bonita; Downey, Geraldine; Bonica, Cheryl; Paltin, Iris

2007-01-01

Predictions from the Rejection Sensitivity (RS) model concerning the social causes and consequences of RS were examined in a longitudinal study of 150 middle school students. Peer nominations of rejection, self-report measures of anxious and angry rejection expectations, and social anxiety, social withdrawal, and loneliness were assessed at two…

Characterization of a developmental toxicity dose-response model.

PubMed Central

Faustman, E M; Wellington, D G; Smith, W P; Kimmel, C A

1989-01-01

The Rai and Van Ryzin dose-response model proposed for teratology experiments has been characterized for its appropriateness and applicability in modeling the dichotomous response data from developmental toxicity studies. Modifications were made in the initial probability statements to reflect more accurately biological events underlying developmental toxicity. Data sets used for the evaluation were obtained from the National Toxicology Program and U.S. EPA laboratories. The studies included developmental evaluations of ethylene glycol, diethylhexyl phthalate, di- and triethylene glycol dimethyl ethers, and nitrofen in rats, mice, or rabbits. Graphic examination and statistical evaluation demonstrate that this model is sensitive to the data when compared to directly measured experimental outcomes. The model was used to interpolate to low-risk dose levels, and comparisons were made between the values obtained and the no-observed-adverse-effect levels (NOAELs) divided by an uncertainty factor. Our investigation suggests that the Rai and Van Ryzin model is sensitive to the developmental toxicity end points, prenatal deaths, and malformations, and appears to model closely their relationship to dose. PMID:2707204

Uncertainty and sensitivity analysis of fission gas behavior in engineering-scale fuel modeling

DOE PAGES

Pastore, Giovanni; Swiler, L. P.; Hales, Jason D.; ...

2014-10-12

The role of uncertainties in fission gas behavior calculations as part of engineering-scale nuclear fuel modeling is investigated using the BISON fuel performance code and a recently implemented physics-based model for the coupled fission gas release and swelling. Through the integration of BISON with the DAKOTA software, a sensitivity analysis of the results to selected model parameters is carried out based on UO2 single-pellet simulations covering different power regimes. The parameters are varied within ranges representative of the relative uncertainties and consistent with the information from the open literature. The study leads to an initial quantitative assessment of the uncertaintymore » in fission gas behavior modeling with the parameter characterization presently available. Also, the relative importance of the single parameters is evaluated. Moreover, a sensitivity analysis is carried out based on simulations of a fuel rod irradiation experiment, pointing out a significant impact of the considered uncertainties on the calculated fission gas release and cladding diametral strain. The results of the study indicate that the commonly accepted deviation between calculated and measured fission gas release by a factor of 2 approximately corresponds to the inherent modeling uncertainty at high fission gas release. Nevertheless, higher deviations may be expected for values around 10% and lower. Implications are discussed in terms of directions of research for the improved modeling of fission gas behavior for engineering purposes.« less

Voxel-based measurement sensitivity of spatially resolved near-infrared spectroscopy in layered tissues

NASA Astrophysics Data System (ADS)

Niwayama, Masatsugu

2018-03-01

We quantitatively investigated the measurement sensitivity of spatially resolved spectroscopy (SRS) across six tissue models: cerebral tissue, a small animal brain, the forehead of a fetus, an adult brain, forearm muscle, and thigh muscle. The optical path length in the voxel of the model was analyzed using Monte Carlo simulations. It was found that the measurement sensitivity can be represented as the product of the change in the absorption coefficient and the difference in optical path length in two states with different source-detector distances. The results clarified the sensitivity ratio between the surface layer and the deep layer at each source-detector distance for each model and identified changes in the deep measurement area when one of the detectors was close to the light source. A comparison was made with the results from continuous-wave spectroscopy. The study also identified measurement challenges that arise when the surface layer is inhomogeneous. Findings on the measurement sensitivity of SRS at each voxel and in each layer can support the correct interpretation of measured values when near-infrared oximetry or functional near-infrared spectroscopy is used to investigate different tissue structures.

A new framework for comprehensive, robust, and efficient global sensitivity analysis: 1. Theory

NASA Astrophysics Data System (ADS)

Razavi, Saman; Gupta, Hoshin V.

2016-01-01

Computer simulation models are continually growing in complexity with increasingly more factors to be identified. Sensitivity Analysis (SA) provides an essential means for understanding the role and importance of these factors in producing model responses. However, conventional approaches to SA suffer from (1) an ambiguous characterization of sensitivity, and (2) poor computational efficiency, particularly as the problem dimension grows. Here, we present a new and general sensitivity analysis framework (called VARS), based on an analogy to "variogram analysis," that provides an intuitive and comprehensive characterization of sensitivity across the full spectrum of scales in the factor space. We prove, theoretically, that Morris (derivative-based) and Sobol (variance-based) methods and their extensions are special cases of VARS, and that their SA indices can be computed as by-products of the VARS framework. Synthetic functions that resemble actual model response surfaces are used to illustrate the concepts, and show VARS to be as much as two orders of magnitude more computationally efficient than the state-of-the-art Sobol approach. In a companion paper, we propose a practical implementation strategy, and demonstrate the effectiveness, efficiency, and reliability (robustness) of the VARS framework on real-data case studies.

Modeling motivated misreports to sensitive survey questions.

PubMed

Böckenholt, Ulf

2014-07-01

Asking sensitive or personal questions in surveys or experimental studies can both lower response rates and increase item non-response and misreports. Although non-response is easily diagnosed, misreports are not. However, misreports cannot be ignored because they give rise to systematic bias. The purpose of this paper is to present a modeling approach that identifies misreports and corrects for them. Misreports are conceptualized as a motivated process under which respondents edit their answers before they report them. For example, systematic bias introduced by overreports of socially desirable behaviors or underreports of less socially desirable ones can be modeled, leading to more-valid inferences. The proposed approach is applied to a large-scale experimental study and shows that respondents who feel powerful tend to overclaim their knowledge.

Designing novel cellulase systems through agent-based modeling and global sensitivity analysis.

PubMed

Apte, Advait A; Senger, Ryan S; Fong, Stephen S

2014-01-01

Experimental techniques allow engineering of biological systems to modify functionality; however, there still remains a need to develop tools to prioritize targets for modification. In this study, agent-based modeling (ABM) was used to build stochastic models of complexed and non-complexed cellulose hydrolysis, including enzymatic mechanisms for endoglucanase, exoglucanase, and β-glucosidase activity. Modeling results were consistent with experimental observations of higher efficiency in complexed systems than non-complexed systems and established relationships between specific cellulolytic mechanisms and overall efficiency. Global sensitivity analysis (GSA) of model results identified key parameters for improving overall cellulose hydrolysis efficiency including: (1) the cellulase half-life, (2) the exoglucanase activity, and (3) the cellulase composition. Overall, the following parameters were found to significantly influence cellulose consumption in a consolidated bioprocess (CBP): (1) the glucose uptake rate of the culture, (2) the bacterial cell concentration, and (3) the nature of the cellulase enzyme system (complexed or non-complexed). Broadly, these results demonstrate the utility of combining modeling and sensitivity analysis to identify key parameters and/or targets for experimental improvement.

Designing novel cellulase systems through agent-based modeling and global sensitivity analysis

PubMed Central

Apte, Advait A; Senger, Ryan S; Fong, Stephen S

2014-01-01

Experimental techniques allow engineering of biological systems to modify functionality; however, there still remains a need to develop tools to prioritize targets for modification. In this study, agent-based modeling (ABM) was used to build stochastic models of complexed and non-complexed cellulose hydrolysis, including enzymatic mechanisms for endoglucanase, exoglucanase, and β-glucosidase activity. Modeling results were consistent with experimental observations of higher efficiency in complexed systems than non-complexed systems and established relationships between specific cellulolytic mechanisms and overall efficiency. Global sensitivity analysis (GSA) of model results identified key parameters for improving overall cellulose hydrolysis efficiency including: (1) the cellulase half-life, (2) the exoglucanase activity, and (3) the cellulase composition. Overall, the following parameters were found to significantly influence cellulose consumption in a consolidated bioprocess (CBP): (1) the glucose uptake rate of the culture, (2) the bacterial cell concentration, and (3) the nature of the cellulase enzyme system (complexed or non-complexed). Broadly, these results demonstrate the utility of combining modeling and sensitivity analysis to identify key parameters and/or targets for experimental improvement. PMID:24830736

Emulation and Sobol' sensitivity analysis of an atmospheric dispersion model applied to the Fukushima nuclear accident

NASA Astrophysics Data System (ADS)

Girard, Sylvain; Mallet, Vivien; Korsakissok, Irène; Mathieu, Anne

2016-04-01

Simulations of the atmospheric dispersion of radionuclides involve large uncertainties originating from the limited knowledge of meteorological input data, composition, amount and timing of emissions, and some model parameters. The estimation of these uncertainties is an essential complement to modeling for decision making in case of an accidental release. We have studied the relative influence of a set of uncertain inputs on several outputs from the Eulerian model Polyphemus/Polair3D on the Fukushima case. We chose to use the variance-based sensitivity analysis method of Sobol'. This method requires a large number of model evaluations which was not achievable directly due to the high computational cost of Polyphemus/Polair3D. To circumvent this issue, we built a mathematical approximation of the model using Gaussian process emulation. We observed that aggregated outputs are mainly driven by the amount of emitted radionuclides, while local outputs are mostly sensitive to wind perturbations. The release height is notably influential, but only in the vicinity of the source. Finally, averaging either spatially or temporally tends to cancel out interactions between uncertain inputs.

Basin-scale geothermal model calibration: experience from the Perth Basin, Australia

NASA Astrophysics Data System (ADS)

Wellmann, Florian; Reid, Lynn

2014-05-01

The calibration of large-scale geothermal models for entire sedimentary basins is challenging as direct measurements of rock properties and subsurface temperatures are commonly scarce and the basal boundary conditions poorly constrained. Instead of the often applied "trial-and-error" manual model calibration, we examine here if we can gain additional insight into parameter sensitivities and model uncertainty with a model analysis and calibration study. Our geothermal model is based on a high-resolution full 3-D geological model, covering an area of more than 100,000 square kilometers and extending to a depth of 55 kilometers. The model contains all major faults (>80 ) and geological units (13) for the entire basin. This geological model is discretised into a rectilinear mesh with a lateral resolution of 500 x 500 m, and a variable resolution at depth. The highest resolution of 25 m is applied to a depth range of 1000-3000 m where most temperature measurements are available. The entire discretised model consists of approximately 50 million cells. The top thermal boundary condition is derived from surface temperature measurements on land and ocean floor. The base of the model extents below the Moho, and we apply the heat flux over the Moho as a basal heat flux boundary condition. Rock properties (thermal conductivity, porosity, and heat production) have been compiled from several existing data sets. The conductive geothermal forward simulation is performed with SHEMAT, and we then use the stand-alone capabilities of iTOUGH2 for sensitivity analysis and model calibration. Simulated temperatures are compared to 130 quality weighted bottom hole temperature measurements. The sensitivity analysis provided a clear insight into the most sensitive parameters and parameter correlations. This proved to be of value as strong correlations, for example between basal heat flux and heat production in deep geological units, can significantly influence the model calibration procedure. The calibration resulted in a better determination of subsurface temperatures, and, in addition, provided an insight into model quality. Furthermore, a detailed analysis of the measurements used for calibration highlighted potential outliers, and limitations with the model assumptions. Extending the previously existing large-scale geothermal simulation with iTOUGH2 provided us with a valuable insight into the sensitive parameters and data in the model, which would clearly not be possible with a simple trial-and-error calibration method. Using the gained knowledge, future work will include more detailed studies on the influence of advection and convection.

Using Pressure- and Temperature-Sensitive Paint for Global Surface Pressure and Temperature Measurements on the Aft-Body of a Capsule Reentry Vehicle

NASA Technical Reports Server (NTRS)

Watkins, A. Neal; Buck, Gregory M.; Leighty, Bradley D.; Lipford, William E.; Oglesby, Donald M.

2008-01-01

Pressure Sensitive Paint (PSP) and Temperature Sensitive Paint (TSP) were used to visualize and quantify the surface interactions of reaction control system (RCS) jets on the aft body of capsule reentry vehicle shapes. The first model tested was an Apollo-like configuration and was used to focus primarily on the effects of the forward facing roll and yaw jets. The second model tested was an early Orion Crew Module configuration blowing only out of its forward-most yaw jet, which was expected to have the most intense aerodynamic heating augmentation on the model surface. This paper will present the results from the experiments, which show that with proper system design, both PSP and TSP are effective tools for studying these types of interaction in hypersonic testing environments.

Thermophysical property sensitivity effects in steel solidification

NASA Technical Reports Server (NTRS)

Overfelt, Tony

1993-01-01

The simulation of advanced solidification processes via digital computer techniques has gained widespread acceptance during the last decade or so. Models today can predict transient temperature fields, fluid flow fields, important microstructural parameters, and potential defects in castings. However, the lack of accurate thermophysical property data on important industrial alloys threatens to limit the ability of manufacturers to fully capitalize on the technology's benefits. A study of the sensitivity of one such numerical model of a steel plate casting to imposed variations in the data utilized for the thermal conductivity, specific heat, density, and heat of fusion is described. The sensitivity of the data's variability is characterized by its effects on the net solidification time of various points along the centerline of the plate casting. Recommendations for property measurements are given and the implications of data uncertainty for modelers are discussed.

Dynamic Modeling of Cell-Free Biochemical Networks Using Effective Kinetic Models

DTIC Science & Technology

2015-03-16

sensitivity value was the maximum uncertainty in that value estimated by the Sobol method. 2.4. Global Sensitivity Analysis of the Reduced Order Coagulation...sensitivity analysis, using the variance-based method of Sobol , to estimate which parameters controlled the performance of the reduced order model [69]. We...Environment. Comput. Sci. Eng. 2007, 9, 90–95. 69. Sobol , I. Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates

Using Landsat, MODIS, and a Biophysical Model to Evaluate LST in Urban Centers

NASA Technical Reports Server (NTRS)

Al-Hamdan, Mohammad Z.; Quattrochi, Dale A.; Bounoua, Lahouari; Lachir, Asia; Zhang, Ping

2016-01-01

In this paper, we assessed and compared land surface temperature (LST) in urban centers using data from Landsat, MODIS, and the Simple Biosphere model (SiB2). We also evaluated the sensitivity of the models LST to different land cover types, fractions (percentages), and emissivities compared to reference points derived from Landsat thermal data. This was demonstrated in three climatologically- and morphologically-different cities of Atlanta, GA, New York, NY, and Washington, DC. Our results showed that in these cities SiB2 was sensitive to both the emissivity and the land cover type and fraction, but much more sensitive to the latter. The practical implications of these results are rather significant since they imply that the SiB2 model can be used to run different scenarios for evaluating urban heat island (UHI) mitigation strategies. This study also showed that using detailed emissivities per land cover type and fractions from Landsat-derived data caused a convergence of the model results towards the Landsat-derived LST for most of the studied cases. This study also showed that SiB2 LSTs are closer in magnitude to Landsat-derived LSTs than MODIS-derived LSTs. It is important, however, to emphasize that both Landsat and MODIS LSTs are not direct observations and, as such, do not represent a ground truth. More studies will be needed to compare these results to in situ LST data and provide further validation.

Evolution of Geometric Sensitivity Derivatives from Computer Aided Design Models

NASA Technical Reports Server (NTRS)

Jones, William T.; Lazzara, David; Haimes, Robert

2010-01-01

The generation of design parameter sensitivity derivatives is required for gradient-based optimization. Such sensitivity derivatives are elusive at best when working with geometry defined within the solid modeling context of Computer-Aided Design (CAD) systems. Solid modeling CAD systems are often proprietary and always complex, thereby necessitating ad hoc procedures to infer parameter sensitivity. A new perspective is presented that makes direct use of the hierarchical associativity of CAD features to trace their evolution and thereby track design parameter sensitivity. In contrast to ad hoc methods, this method provides a more concise procedure following the model design intent and determining the sensitivity of CAD geometry directly to its respective defining parameters.

A Comparison between Decision Tree and Random Forest in Determining the Risk Factors Associated with Type 2 Diabetes.

PubMed

Esmaily, Habibollah; Tayefi, Maryam; Doosti, Hassan; Ghayour-Mobarhan, Majid; Nezami, Hossein; Amirabadizadeh, Alireza

2018-04-24

We aimed to identify the associated risk factors of type 2 diabetes mellitus (T2DM) using data mining approach, decision tree and random forest techniques using the Mashhad Stroke and Heart Atherosclerotic Disorders (MASHAD) Study program. A cross-sectional study. The MASHAD study started in 2010 and will continue until 2020. Two data mining tools, namely decision trees, and random forests, are used for predicting T2DM when some other characteristics are observed on 9528 subjects recruited from MASHAD database. This paper makes a comparison between these two models in terms of accuracy, sensitivity, specificity and the area under ROC curve. The prevalence rate of T2DM was 14% among these subjects. The decision tree model has 64.9% accuracy, 64.5% sensitivity, 66.8% specificity, and area under the ROC curve measuring 68.6%, while the random forest model has 71.1% accuracy, 71.3% sensitivity, 69.9% specificity, and area under the ROC curve measuring 77.3% respectively. The random forest model, when used with demographic, clinical, and anthropometric and biochemical measurements, can provide a simple tool to identify associated risk factors for type 2 diabetes. Such identification can substantially use for managing the health policy to reduce the number of subjects with T2DM .

Sensitivity of Caligus rogercresseyi (Boxshall and Bravo 2000) to pyrethroids and azamethiphos measured using bioassay tests-A large scale spatial study.

PubMed

Marín, S L; Ibarra, R; Medina, M H; Jansen, P A

2015-11-01

The variety of antiparasitics that can be used against caligid copepods is limited and efforts are needed to maintain their efficacies. The objective of this study was to monitor the sensitivity of Caligus rogercresseyi, populations towards antiparasitics based on deltamethrin, cypermethrin and azamethiphos within and across geographic regions. The bioassay design consisted of exposing parasites collected from 23 farms to the different chemotherapeutants at the concentration and exposure times recommended for field treatment, under laboratory conditions, and evaluating the number of dead and live parasites 48h after exposure. Parasites were collected from 23 farms distributed in four macrozones in the Los Lagos region and three macrozones in the Aysén region. Parasite sensitivity was evaluated using a Generalized Linear Mixed Model of the Binomial family (Logit) fit by the maximum likelihood, using the lme4 package in R. Parasite gender, macrozone, and antiparasitics were used as fixed factors and farm was the random factor. The model including all the factors proved to be a useful tool for predicting parasite sensitivity. This approach identified (i) those macrozones with a greater likelihood of finding parasite populations which are more or less sensitive to the three antiparasitics, (ii) cases in which parasite sensitivity to the different antiparasitics varied within a given macrozone, (iii) differences in sensitivity between females and males and (iv) an important random effect associated with farm. The results indicate a spatial variability of parasite sensitivity to antiparasitics which, added to the continuous treatments applied on farms, suggest it is necessary to regularly update the sensitivity status in the macrozones. This would allow managers to improve their decision making processes regarding the type of antiparasitic to be used in a given situation. The one-concentration type bioassay performed in this study allowed us to perform a large spatial study with replicated tests of the sensitivity of C. rogercresseyi to pyrethroids and azamethiphos. Further studies should focus on the farm effects, the relationship between the sensitivity of parasites and field efficacy, as well as parasite population structure and connectivity with regard to parasite transmission between farms. Copyright © 2015 Elsevier B.V. All rights reserved.

Physics of Canopy Boundary Layer Resistance for Better Quantification of Sensitivity of Deforestation Scenarios

NASA Astrophysics Data System (ADS)

Ragi, K. B.; Patel, R.

2015-12-01

A great deal of studies focused on deforestation scenarios in the tropical rainforests. Though all these efforts are useful in the understanding of its response to climate, the systematic understanding of uncertainties in representation of physical processes related to vegetation through sensitivity studies is imperative antecedently to understand the real role of vegetation in changing the climate. It is understood that the dense vegetation fluxes energy and moisture to the atmosphere. But, how much a specific process/a group of processes in the surface conditions of a specific area helps flux energy, moisture and tracers is unknown due to lack of process sensitivity studies and uncertain due to malfunctioning of processes. In this presentation, we have found a faulty parameterization, through process sensitivity studies, that would abet in energy and moisture fluxes to the atmosphere. The model we have employed is the Common Land Model2014. The area we have chosen is the Congolese rainforest. We have discovered the flaw in the leaf boundary layer resistance (LBLR), through sensitivity studies in the LSMs, especially in the dense forest regions. This LBLR is over-parameterized with constant heat transfer coefficient and characteristic dimension of leaves; and friction velocity. However, it is too scant because of overlooking of significant complex physics of turbulence and canopy roughness boundary layer to function it realistically. Our sensitivity results show the deficiency of this process and we have formulated canopy boundary layer resistance, instead of LBLR, with depending variables such as LAI, roughness length, vegetation temperature using appropriate thermo-fluid dynamical principles. We are running the sensitivity experiments with new formulations for setting the parameter values for the data not available so far. This effort would lead to better physics for the land-use change studies and demand for the retrieval of new parameters from satellite/field experiments such as leaf mass per area and specific heat capacity of vegetation.

Benchmarking an unstructured grid sediment model in an energetic estuary

DOE PAGES

Lopez, Jesse E.; Baptista, António M.

2016-12-14

A sediment model coupled to the hydrodynamic model SELFE is validated against a benchmark combining a set of idealized tests and an application to a field-data rich energetic estuary. After sensitivity studies, model results for the idealized tests largely agree with previously reported results from other models in addition to analytical, semi-analytical, or laboratory results. Results of suspended sediment in an open channel test with fixed bottom are sensitive to turbulence closure and treatment for hydrodynamic bottom boundary. Results for the migration of a trench are very sensitive to critical stress and erosion rate, but largely insensitive to turbulence closure.more » The model is able to qualitatively represent sediment dynamics associated with estuarine turbidity maxima in an idealized estuary. Applied to the Columbia River estuary, the model qualitatively captures sediment dynamics observed by fixed stations and shipborne profiles. Representation of the vertical structure of suspended sediment degrades when stratification is underpredicted. Across all tests, skill metrics of suspended sediments lag those of hydrodynamics even when qualitatively representing dynamics. The benchmark is fully documented in an openly available repository to encourage unambiguous comparisons against other models.« less

Sensitivity of resource selection and connectivity models to landscape definition

Treesearch

Katherine A. Zeller; Kevin McGarigal; Samuel A. Cushman; Paul Beier; T. Winston Vickers; Walter M. Boyce

2017-01-01

Context: The definition of the geospatial landscape is the underlying basis for species-habitat models, yet sensitivity of habitat use inference, predicted probability surfaces, and connectivity models to landscape definition has received little attention. Objectives: We evaluated the sensitivity of resource selection and connectivity models to four landscape...