FLOCK cluster analysis of mast cell event clustering by high-sensitivity flow cytometry predicts systemic mastocytosis.

PubMed

Dorfman, David M; LaPlante, Charlotte D; Pozdnyakova, Olga; Li, Betty

2015-11-01

In our high-sensitivity flow cytometric approach for systemic mastocytosis (SM), we identified mast cell event clustering as a new diagnostic criterion for the disease. To objectively characterize mast cell gated event distributions, we performed cluster analysis using FLOCK, a computational approach to identify cell subsets in multidimensional flow cytometry data in an unbiased, automated fashion. FLOCK identified discrete mast cell populations in most cases of SM (56/75 [75%]) but only a minority of non-SM cases (17/124 [14%]). FLOCK-identified mast cell populations accounted for 2.46% of total cells on average in SM cases and 0.09% of total cells on average in non-SM cases (P < .0001) and were predictive of SM, with a sensitivity of 75%, a specificity of 86%, a positive predictive value of 76%, and a negative predictive value of 85%. FLOCK analysis provides useful diagnostic information for evaluating patients with suspected SM, and may be useful for the analysis of other hematopoietic neoplasms. Copyright© by the American Society for Clinical Pathology.

Analysis of sensitivity and uncertainty in an individual-based model of a threatened wildlife species

Treesearch

Bruce G. Marcot; Peter H. Singleton; Nathan H. Schumaker

2015-01-01

Sensitivity analysis—determination of how prediction variables affect response variables—of individual-based models (IBMs) are few but important to the interpretation of model output. We present sensitivity analysis of a spatially explicit IBM (HexSim) of a threatened species, the Northern Spotted Owl (NSO; Strix occidentalis caurina) in Washington...

Lyapunov exponents, covariant vectors and shadowing sensitivity analysis of 3D wakes: from laminar to chaotic regimes

NASA Astrophysics Data System (ADS)

Wang, Qiqi; Rigas, Georgios; Esclapez, Lucas; Magri, Luca; Blonigan, Patrick

2016-11-01

Bluff body flows are of fundamental importance to many engineering applications involving massive flow separation and in particular the transport industry. Coherent flow structures emanating in the wake of three-dimensional bluff bodies, such as cars, trucks and lorries, are directly linked to increased aerodynamic drag, noise and structural fatigue. For low Reynolds laminar and transitional regimes, hydrodynamic stability theory has aided the understanding and prediction of the unstable dynamics. In the same framework, sensitivity analysis provides the means for efficient and optimal control, provided the unstable modes can be accurately predicted. However, these methodologies are limited to laminar regimes where only a few unstable modes manifest. Here we extend the stability analysis to low-dimensional chaotic regimes by computing the Lyapunov covariant vectors and their associated Lyapunov exponents. We compare them to eigenvectors and eigenvalues computed in traditional hydrodynamic stability analysis. Computing Lyapunov covariant vectors and Lyapunov exponents also enables the extension of sensitivity analysis to chaotic flows via the shadowing method. We compare the computed shadowing sensitivities to traditional sensitivity analysis. These Lyapunov based methodologies do not rely on mean flow assumptions, and are mathematically rigorous for calculating sensitivities of fully unsteady flow simulations.

Data mining methods in the prediction of Dementia: A real-data comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees and random forests

PubMed Central

2011-01-01

Background Dementia and cognitive impairment associated with aging are a major medical and social concern. Neuropsychological testing is a key element in the diagnostic procedures of Mild Cognitive Impairment (MCI), but has presently a limited value in the prediction of progression to dementia. We advance the hypothesis that newer statistical classification methods derived from data mining and machine learning methods like Neural Networks, Support Vector Machines and Random Forests can improve accuracy, sensitivity and specificity of predictions obtained from neuropsychological testing. Seven non parametric classifiers derived from data mining methods (Multilayer Perceptrons Neural Networks, Radial Basis Function Neural Networks, Support Vector Machines, CART, CHAID and QUEST Classification Trees and Random Forests) were compared to three traditional classifiers (Linear Discriminant Analysis, Quadratic Discriminant Analysis and Logistic Regression) in terms of overall classification accuracy, specificity, sensitivity, Area under the ROC curve and Press'Q. Model predictors were 10 neuropsychological tests currently used in the diagnosis of dementia. Statistical distributions of classification parameters obtained from a 5-fold cross-validation were compared using the Friedman's nonparametric test. Results Press' Q test showed that all classifiers performed better than chance alone (p < 0.05). Support Vector Machines showed the larger overall classification accuracy (Median (Me) = 0.76) an area under the ROC (Me = 0.90). However this method showed high specificity (Me = 1.0) but low sensitivity (Me = 0.3). Random Forest ranked second in overall accuracy (Me = 0.73) with high area under the ROC (Me = 0.73) specificity (Me = 0.73) and sensitivity (Me = 0.64). Linear Discriminant Analysis also showed acceptable overall accuracy (Me = 0.66), with acceptable area under the ROC (Me = 0.72) specificity (Me = 0.66) and sensitivity (Me = 0.64). The remaining classifiers showed overall classification accuracy above a median value of 0.63, but for most sensitivity was around or even lower than a median value of 0.5. Conclusions When taking into account sensitivity, specificity and overall classification accuracy Random Forests and Linear Discriminant analysis rank first among all the classifiers tested in prediction of dementia using several neuropsychological tests. These methods may be used to improve accuracy, sensitivity and specificity of Dementia predictions from neuropsychological testing. PMID:21849043

Applying an intelligent model and sensitivity analysis to inspect mass transfer kinetics, shrinkage and crust color changes of deep-fat fried ostrich meat cubes.

PubMed

Amiryousefi, Mohammad Reza; Mohebbi, Mohebbat; Khodaiyan, Faramarz

2014-01-01

The objectives of this study were to use image analysis and artificial neural network (ANN) to predict mass transfer kinetics as well as color changes and shrinkage of deep-fat fried ostrich meat cubes. Two generalized feedforward networks were separately developed by using the operation conditions as inputs. Results based on the highest numerical quantities of the correlation coefficients between the experimental versus predicted values, showed proper fitting. Sensitivity analysis results of selected ANNs showed that among the input variables, frying temperature was the most sensitive to moisture content (MC) and fat content (FC) compared to other variables. Sensitivity analysis results of selected ANNs showed that MC and FC were the most sensitive to frying temperature compared to other input variables. Similarly, for the second ANN architecture, microwave power density was the most impressive variable having the maximum influence on both shrinkage percentage and color changes. Copyright © 2013 Elsevier Ltd. All rights reserved.

Meta-Analysis of Predictive Significance of the Black Hole Sign for Hematoma Expansion in Intracerebral Hemorrhage.

PubMed

Zheng, Jun; Yu, Zhiyuan; Guo, Rui; Li, Hao; You, Chao; Ma, Lu

2018-04-27

Hematoma expansion is related to unfavorable prognosis in intracerebral hemorrhage (ICH). The black hole sign is a novel marker on non-contrast computed tomography for predicting hematoma expansion. However, its predictive values are different in previous studies. Thus, this meta-analysis was conducted to evaluate the predictive significance of the black hole sign for hematoma expansion in ICH. A systematic literature search was performed. Original researches on the association between the black hole sign and hematoma expansion in ICH were included. Sensitivity and specificity were pooled to assess the predictive accuracy. Summary receiver operating characteristics curve (SROC) was developed. Deeks' funnel plot asymmetry test was used to assess the publication bias. Five studies with a total of 1495 patients were included in this study. The pooled sensitivity and specificity of the black hole sign for predicting hematoma expansion were 0.30 and 0.91, respectively. The area under the curve was 0.78 in SROC curve. There was no significant publication bias. This meta-analysis shows that the black hole sign is a helpful imaging marker for predicting hematoma expansion in ICH. Although the black hole sign has a relatively low sensitivity, its specificity is relatively high. Copyright © 2018 Elsevier Inc. All rights reserved.

Identification of biomarkers in human head and neck tumor cell lines that predict for in vitro sensitivity to gefitinib.

PubMed

Hickinson, D Mark; Marshall, Gayle B; Beran, Garry J; Varella-Garcia, Marileila; Mills, Elizabeth A; South, Marie C; Cassidy, Andrew M; Acheson, Kerry L; McWalter, Gael; McCormack, Rose M; Bunn, Paul A; French, Tim; Graham, Alex; Holloway, Brian R; Hirsch, Fred R; Speake, Georgina

2009-06-01

Potential biomarkers were identified for in vitro sensitivity to the epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor gefitinib in head and neck cancer. Gefitinib sensitivity was determined in cell lines, followed by transcript profiling coupled with a novel pathway analysis approach. Eleven cell lines were highly sensitive to gefitinib (inhibitor concentration required to give 50% growth inhibition [GI(50)] < 1 microM), three had intermediate sensitivity (GI(50) 1-7 microM), and six were resistant (GI(50) > 7 microM); an exploratory principal component analysis revealed a separation between the genomic profiles of sensitive and resistant cell lines. Subsequently, a hypothesis-driven analysis of Affymetrix data (Affymetrix, Inc., Santa Clara, CA, USA) revealed higher mRNA levels for E-cadherin (CDH1); transforming growth factor, alpha (TGF-alpha); amphiregulin (AREG); FLJ22662; EGFR; p21-activated kinase 6 (PAK6); glutathione S-transferase Pi (GSTP1); and ATP-binding cassette, subfamily C, member 5 (ABCC5) in sensitive versus resistant cell lines. A hypothesis-free analysis identified 46 gene transcripts that were strongly differentiated, seven of which had a known association with EGFR and head and neck cancer (human EGF receptor 3 [HER3], TGF-alpha, CDH1, EGFR, keratin 16 [KRT16], fibroblast growth factor 2 [FGF2], and cortactin [CTTN]). Polymerase chain reaction (PCR) and enzyme-linked immunoabsorbant assay analysis confirmed Affymetrix data, and EGFR gene mutation, amplification, and genomic gain correlated strongly with gefitinib sensitivity. We identified biomarkers that predict for in vitro responsiveness to gefitinib, seven of which have known association with EGFR and head and neck cancer. These in vitro predictive biomarkers may have potential utility in the clinic and warrant further investigation.

Mixing-model Sensitivity to Initial Conditions in Hydrodynamic Predictions

NASA Astrophysics Data System (ADS)

Bigelow, Josiah; Silva, Humberto; Truman, C. Randall; Vorobieff, Peter

2017-11-01

Amagat and Dalton mixing-models were studied to compare their thermodynamic prediction of shock states. Numerical simulations with the Sandia National Laboratories shock hydrodynamic code CTH modeled University of New Mexico (UNM) shock tube laboratory experiments shocking a 1:1 molar mixture of helium (He) and sulfur hexafluoride (SF6) . Five input parameters were varied for sensitivity analysis: driver section pressure, driver section density, test section pressure, test section density, and mixture ratio (mole fraction). We show via incremental Latin hypercube sampling (LHS) analysis that significant differences exist between Amagat and Dalton mixing-model predictions. The differences observed in predicted shock speeds, temperatures, and pressures grow more pronounced with higher shock speeds. Supported by NNSA Grant DE-0002913.

Combining 'Bottom-Up' and 'Top-Down' Methods to Assess Ethnic Difference in Clearance: Bitopertin as an Example.

PubMed

Feng, Sheng; Shi, Jun; Parrott, Neil; Hu, Pei; Weber, Cornelia; Martin-Facklam, Meret; Saito, Tomohisa; Peck, Richard

2016-07-01

We propose a strategy for studying ethnopharmacology by conducting sequential physiologically based pharmacokinetic (PBPK) prediction (a 'bottom-up' approach) and population pharmacokinetic (popPK) confirmation (a 'top-down' approach), or in reverse order, depending on whether the purpose is ethnic effect assessment for a new molecular entity under development or a tool for ethnic sensitivity prediction for a given pathway. The strategy is exemplified with bitopertin. A PBPK model was built using Simcyp(®) to simulate the pharmacokinetics of bitopertin and to predict the ethnic sensitivity in clearance, given pharmacokinetic data in just one ethnicity. Subsequently, a popPK model was built using NONMEM(®) to assess the effect of ethnicity on clearance, using human data from multiple ethnic groups. A comparison was made to confirm the PBPK-based ethnic sensitivity prediction, using the results of the popPK analysis. PBPK modelling predicted that the bitopertin geometric mean clearance values after 20 mg oral administration in Caucasians would be 1.32-fold and 1.27-fold higher than the values in Chinese and Japanese, respectively. The ratios of typical clearance in Caucasians to the values in Chinese and Japanese estimated by popPK analysis were 1.20 and 1.17, respectively. The popPK analysis results were similar to the PBPK modelling results. As a general framework, we propose that PBPK modelling should be considered to predict ethnic sensitivity of pharmacokinetics prior to any human data and/or with data in only one ethnicity. In some cases, this will be sufficient to guide initial dose selection in different ethnicities. After clinical trials in different ethnicities, popPK analysis can be used to confirm ethnic differences and to support dose justification and labelling. PBPK modelling prediction and popPK analysis confirmation can complement each other to assess ethnic differences in pharmacokinetics at different drug development stages.

Receiver operating characteristic analysis of prediction for gastric cancer development using serum pepsinogen and Helicobacter pylori antibody tests.

PubMed

Hamashima, Chisato; Sasazuki, Shizuka; Inoue, Manami; Tsugane, Shoichiro

2017-03-09

Chronic Helicobacter pylori infection plays a central role in the development of gastric cancer as shown by biological and epidemiological studies. The H. pylori antibody and serum pepsinogen (PG) tests have been anticipated to predict gastric cancer development. We determined the predictive sensitivity and specificity of gastric cancer development using these tests. Receiver operating characteristic analysis was performed, and areas under the curve were estimated. The predictive sensitivity and specificity of gastric cancer development were compared among single tests and combined methods using serum pepsinogen and H. pylori antibody tests. From a large-scale population-based cohort of over 100,000 subjects followed between 1990 and 2004, 497 gastric cancer subjects and 497 matched healthy controls were chosen. The predictive sensitivity and specificity were low in all single tests and combination methods. The highest predictive sensitivity and specificity were obtained for the serum PG I/II ratio. The optimal PG I/II cut-off values were 2.5 and 3.0. At a PG I/II cut-off value of 3.0, the sensitivity was 86.9% and the specificity was 39.8%. Even if three biomarkers were combined, the sensitivity was 97.2% and the specificity was 21.1% when the cut-off values were 3.0 for PG I/II, 70 ng/mL for PG I, and 10.0 U/mL for H. pylori antibody. The predictive accuracy of gastric cancer development was low with the serum pepsinogen and H. pylori antibody tests even if these tests were combined. To adopt these biomarkers for gastric cancer screening, a high specificity is required. When these tests are adopted for gastric cancer screening, they should be carefully interpreted with a clear understanding of their limitations.

Development of Flight Safety Prediction Methodology for U. S. Naval Safety Center. Revision 1

DTIC Science & Technology

1970-02-01

Safety Center. The methodology develoned encompassed functional analysis of the F-4J aircraft, assessment of the importance of safety- sensitive ... Sensitivity ... ....... . 4-8 V 4.5 Model Implementation ........ ......... . 4-10 4.5.1 Functional Analysis ..... ........... . 4-11 4. 5. 2 Major...Function Sensitivity Assignment ........ ... 4-13 i 4.5.3 Link Dependency Assignment ... ......... . 4-14 4.5.4 Computer Program for Sensitivity

Selection of optimal sensors for predicting performance of polymer electrolyte membrane fuel cell

NASA Astrophysics Data System (ADS)

Mao, Lei; Jackson, Lisa

2016-10-01

In this paper, sensor selection algorithms are investigated based on a sensitivity analysis, and the capability of optimal sensors in predicting PEM fuel cell performance is also studied using test data. The fuel cell model is developed for generating the sensitivity matrix relating sensor measurements and fuel cell health parameters. From the sensitivity matrix, two sensor selection approaches, including the largest gap method, and exhaustive brute force searching technique, are applied to find the optimal sensors providing reliable predictions. Based on the results, a sensor selection approach considering both sensor sensitivity and noise resistance is proposed to find the optimal sensor set with minimum size. Furthermore, the performance of the optimal sensor set is studied to predict fuel cell performance using test data from a PEM fuel cell system. Results demonstrate that with optimal sensors, the performance of PEM fuel cell can be predicted with good quality.

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.

Integrated Modeling Activities for the James Webb Space Telescope (JWST): Structural-Thermal-Optical Analysis

NASA Technical Reports Server (NTRS)

Johnston, John D.; Parrish, Keith; Howard, Joseph M.; Mosier, Gary E.; McGinnis, Mark; Bluth, Marcel; Kim, Kevin; Ha, Hong Q.

2004-01-01

This is a continuation of a series of papers on modeling activities for JWST. The structural-thermal- optical, often referred to as "STOP", analysis process is used to predict the effect of thermal distortion on optical performance. The benchmark STOP analysis for JWST assesses the effect of an observatory slew on wavefront error. The paper begins an overview of multi-disciplinary engineering analysis, or integrated modeling, which is a critical element of the JWST mission. The STOP analysis process is then described. This process consists of the following steps: thermal analysis, structural analysis, and optical analysis. Temperatures predicted using geometric and thermal math models are mapped to the structural finite element model in order to predict thermally-induced deformations. Motions and deformations at optical surfaces are input to optical models and optical performance is predicted using either an optical ray trace or WFE estimation techniques based on prior ray traces or first order optics. Following the discussion of the analysis process, results based on models representing the design at the time of the System Requirements Review. In addition to baseline performance predictions, sensitivity studies are performed to assess modeling uncertainties. Of particular interest is the sensitivity of optical performance to uncertainties in temperature predictions and variations in metal properties. The paper concludes with a discussion of modeling uncertainty as it pertains to STOP analysis.

Addressing issues associated with evaluating prediction models for survival endpoints based on the concordance statistic.

PubMed

Wang, Ming; Long, Qi

2016-09-01

Prediction models for disease risk and prognosis play an important role in biomedical research, and evaluating their predictive accuracy in the presence of censored data is of substantial interest. The standard concordance (c) statistic has been extended to provide a summary measure of predictive accuracy for survival models. Motivated by a prostate cancer study, we address several issues associated with evaluating survival prediction models based on c-statistic with a focus on estimators using the technique of inverse probability of censoring weighting (IPCW). Compared to the existing work, we provide complete results on the asymptotic properties of the IPCW estimators under the assumption of coarsening at random (CAR), and propose a sensitivity analysis under the mechanism of noncoarsening at random (NCAR). In addition, we extend the IPCW approach as well as the sensitivity analysis to high-dimensional settings. The predictive accuracy of prediction models for cancer recurrence after prostatectomy is assessed by applying the proposed approaches. We find that the estimated predictive accuracy for the models in consideration is sensitive to NCAR assumption, and thus identify the best predictive model. Finally, we further evaluate the performance of the proposed methods in both settings of low-dimensional and high-dimensional data under CAR and NCAR through simulations. © 2016, The International Biometric Society.

Development of a noise annoyance sensitivity scale

NASA Technical Reports Server (NTRS)

Bregman, H. L.; Pearson, R. G.

1972-01-01

Examining the problem of noise pollution from the psychological rather than the engineering view, a test of human sensitivity to noise was developed against the criterion of noise annoyance. Test development evolved from a previous study in which biographical, attitudinal, and personality data was collected on a sample of 166 subjects drawn from the adult community of Raleigh. Analysis revealed that only a small subset of the data collected was predictive of noise annoyance. Item analysis yielded 74 predictive items that composed the preliminary noise sensitivity test. This was administered to a sample of 80 adults who later rate the annoyance value of six sounds (equated in terms of peak sound pressure level) presented in a simulated home, living-room environment. A predictive model involving 20 test items was developed using multiple regression techniques, and an item weighting scheme was evaluated.

Development and optimization of SPECT gated blood pool cluster analysis for the prediction of CRT outcome.

PubMed

Lalonde, Michel; Wells, R Glenn; Birnie, David; Ruddy, Terrence D; Wassenaar, Richard

2014-07-01

Phase analysis of single photon emission computed tomography (SPECT) radionuclide angiography (RNA) has been investigated for its potential to predict the outcome of cardiac resynchronization therapy (CRT). However, phase analysis may be limited in its potential at predicting CRT outcome as valuable information may be lost by assuming that time-activity curves (TAC) follow a simple sinusoidal shape. A new method, cluster analysis, is proposed which directly evaluates the TACs and may lead to a better understanding of dyssynchrony patterns and CRT outcome. Cluster analysis algorithms were developed and optimized to maximize their ability to predict CRT response. About 49 patients (N = 27 ischemic etiology) received a SPECT RNA scan as well as positron emission tomography (PET) perfusion and viability scans prior to undergoing CRT. A semiautomated algorithm sampled the left ventricle wall to produce 568 TACs from SPECT RNA data. The TACs were then subjected to two different cluster analysis techniques, K-means, and normal average, where several input metrics were also varied to determine the optimal settings for the prediction of CRT outcome. Each TAC was assigned to a cluster group based on the comparison criteria and global and segmental cluster size and scores were used as measures of dyssynchrony and used to predict response to CRT. A repeated random twofold cross-validation technique was used to train and validate the cluster algorithm. Receiver operating characteristic (ROC) analysis was used to calculate the area under the curve (AUC) and compare results to those obtained for SPECT RNA phase analysis and PET scar size analysis methods. Using the normal average cluster analysis approach, the septal wall produced statistically significant results for predicting CRT results in the ischemic population (ROC AUC = 0.73;p < 0.05 vs. equal chance ROC AUC = 0.50) with an optimal operating point of 71% sensitivity and 60% specificity. Cluster analysis results were similar to SPECT RNA phase analysis (ROC AUC = 0.78, p = 0.73 vs cluster AUC; sensitivity/specificity = 59%/89%) and PET scar size analysis (ROC AUC = 0.73, p = 1.0 vs cluster AUC; sensitivity/specificity = 76%/67%). A SPECT RNA cluster analysis algorithm was developed for the prediction of CRT outcome. Cluster analysis results produced results equivalent to those obtained from Fourier and scar analysis.

Development and optimization of SPECT gated blood pool cluster analysis for the prediction of CRT outcome

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

Lalonde, Michel, E-mail: mlalonde15@rogers.com; Wassenaar, Richard; Wells, R. Glenn

2014-07-15

Purpose: Phase analysis of single photon emission computed tomography (SPECT) radionuclide angiography (RNA) has been investigated for its potential to predict the outcome of cardiac resynchronization therapy (CRT). However, phase analysis may be limited in its potential at predicting CRT outcome as valuable information may be lost by assuming that time-activity curves (TAC) follow a simple sinusoidal shape. A new method, cluster analysis, is proposed which directly evaluates the TACs and may lead to a better understanding of dyssynchrony patterns and CRT outcome. Cluster analysis algorithms were developed and optimized to maximize their ability to predict CRT response. Methods: Aboutmore » 49 patients (N = 27 ischemic etiology) received a SPECT RNA scan as well as positron emission tomography (PET) perfusion and viability scans prior to undergoing CRT. A semiautomated algorithm sampled the left ventricle wall to produce 568 TACs from SPECT RNA data. The TACs were then subjected to two different cluster analysis techniques, K-means, and normal average, where several input metrics were also varied to determine the optimal settings for the prediction of CRT outcome. Each TAC was assigned to a cluster group based on the comparison criteria and global and segmental cluster size and scores were used as measures of dyssynchrony and used to predict response to CRT. A repeated random twofold cross-validation technique was used to train and validate the cluster algorithm. Receiver operating characteristic (ROC) analysis was used to calculate the area under the curve (AUC) and compare results to those obtained for SPECT RNA phase analysis and PET scar size analysis methods. Results: Using the normal average cluster analysis approach, the septal wall produced statistically significant results for predicting CRT results in the ischemic population (ROC AUC = 0.73;p < 0.05 vs. equal chance ROC AUC = 0.50) with an optimal operating point of 71% sensitivity and 60% specificity. Cluster analysis results were similar to SPECT RNA phase analysis (ROC AUC = 0.78, p = 0.73 vs cluster AUC; sensitivity/specificity = 59%/89%) and PET scar size analysis (ROC AUC = 0.73, p = 1.0 vs cluster AUC; sensitivity/specificity = 76%/67%). Conclusions: A SPECT RNA cluster analysis algorithm was developed for the prediction of CRT outcome. Cluster analysis results produced results equivalent to those obtained from Fourier and scar analysis.« less

Prediction of coefficients of thermal expansion for unidirectional composites

NASA Technical Reports Server (NTRS)

Bowles, David E.; Tompkins, Stephen S.

1989-01-01

Several analyses for predicting the longitudinal, alpha(1), and transverse, alpha(2), coefficients of thermal expansion of unidirectional composites were compared with each other, and with experimental data on different graphite fiber reinforced resin, metal, and ceramic matrix composites. Analytical and numerical analyses that accurately accounted for Poisson restraining effects in the transverse direction were in consistently better agreement with experimental data for alpha(2), than the less rigorous analyses. All of the analyses predicted similar values of alpha(1), and were in good agreement with the experimental data. A sensitivity analysis was conducted to determine the relative influence of constituent properties on the predicted values of alpha(1), and alpha(2). As would be expected, the prediction of alpha(1) was most sensitive to longitudinal fiber properties and the prediction of alpha(2) was most sensitive to matrix properties.

Parameterization of the InVEST Crop Pollination Model to spatially predict abundance of wild blueberry (Vaccinium angustifolium Aiton) native bee pollinators in Maine, USA

USGS Publications Warehouse

Groff, Shannon C.; Loftin, Cynthia S.; Drummond, Frank; Bushmann, Sara; McGill, Brian J.

2016-01-01

Non-native honeybees historically have been managed for crop pollination, however, recent population declines draw attention to pollination services provided by native bees. We applied the InVEST Crop Pollination model, developed to predict native bee abundance from habitat resources, in Maine's wild blueberry crop landscape. We evaluated model performance with parameters informed by four approaches: 1) expert opinion; 2) sensitivity analysis; 3) sensitivity analysis informed model optimization; and, 4) simulated annealing (uninformed) model optimization. Uninformed optimization improved model performance by 29% compared to expert opinion-informed model, while sensitivity-analysis informed optimization improved model performance by 54%. This suggests that expert opinion may not result in the best parameter values for the InVEST model. The proportion of deciduous/mixed forest within 2000 m of a blueberry field also reliably predicted native bee abundance in blueberry fields, however, the InVEST model provides an efficient tool to estimate bee abundance beyond the field perimeter.

Preparation of the implementation plan of AASHTO Mechanistic-Empirical Pavement Design Guide (M-EPDG) in Connecticut : Phase II : expanded sensitivity analysis and validation with pavement management data.

DOT National Transportation Integrated Search

2017-02-08

The study re-evaluates distress prediction models using the Mechanistic-Empirical Pavement Design Guide (MEPDG) and expands the sensitivity analysis to a wide range of pavement structures and soils. In addition, an extensive validation analysis of th...

Analysis of Cyberbullying Sensitivity Levels of High School Students and Their Perceived Social Support Levels

ERIC Educational Resources Information Center

Akturk, Ahmet Oguz

2015-01-01

Purpose: The purpose of this paper is to determine the cyberbullying sensitivity levels of high school students and their perceived social supports levels, and analyze the variables that predict cyberbullying sensitivity. In addition, whether cyberbullying sensitivity levels and social support levels differed according to gender was also…

Optical coherence tomography in the diagnosis of dysplasia and adenocarcinoma in Barret's esophagus

NASA Astrophysics Data System (ADS)

Gladkova, N. D.; Zagaynova, E. V.; Zuccaro, G.; Kareta, M. V.; Feldchtein, F. I.; Balalaeva, I. V.; Balandina, E. B.

2007-02-01

Statistical analysis of endoscopic optical coherence tomography (EOCT) surveillance of 78 patients with Barrett's esophagus (BE) is presented in this study. The sensitivity of OCT device in retrospective open detection of early malignancy (including high grade dysplasia and intramucosal adenocarcinoma (IMAC)) was 75%, specificity 82%, diagnostic accuracy - 80%, positive predictive value- 60%, negative predictive value- 87%. In the open recognition of IMAC sensitivity was 81% and specificity were 85% each. Results of a blind recognition with the same material were similar: sensitivity - 77%, specificity 85%, diagnostic accuracy - 82%, positive predictive value- 70%, negative predictive value- 87%. As the endoscopic detection of early malignancy is problematic, OCT holds great promise in enhancing the diagnostic capability of clinical GI endoscopy.

GENOMIC PREDICTOR OF RESPONSE AND SURVIVAL FOLLOWING TAXANE-ANTHRACYCLINE CHEMOTHERAPY FOR INVASIVE BREAST CANCER

PubMed Central

Hatzis, Christos; Pusztai, Lajos; Valero, Vicente; Booser, Daniel J.; Esserman, Laura; Lluch, Ana; Vidaurre, Tatiana; Holmes, Frankie; Souchon, Eduardo; Martin, Miguel; Cotrina, José; Gomez, Henry; Hubbard, Rebekah; Chacón, J. Ignacio; Ferrer-Lozano, Jaime; Dyer, Richard; Buxton, Meredith; Gong, Yun; Wu, Yun; Ibrahim, Nuhad; Andreopoulou, Eleni; Ueno, Naoto T.; Hunt, Kelly; Yang, Wei; Nazario, Arlene; DeMichele, Angela; O’Shaughnessy, Joyce; Hortobagyi, Gabriel N.; Symmans, W. Fraser

2017-01-01

CONTEXT Accurate prediction of who will (or won’t) have high probability of survival benefit from standard treatments is fundamental for individualized cancer treatment strategies. OBJECTIVE To develop a predictor of response and survival from chemotherapy for newly diagnosed invasive breast cancer. DESIGN Development of different predictive signatures for resistance and response to neoadjuvant chemotherapy (stratified according to estrogen receptor (ER) status) from gene expression microarrays of newly diagnosed breast cancer (310 patients). Then prediction of breast cancer treatment-sensitivity using the combination of signatures for: 1) sensitivity to endocrine therapy, 2) chemo-resistance, and 3) chemo-sensitivity. Independent validation (198 patients) and comparison with other reported genomic predictors of chemotherapy response. SETTING Prospective multicenter study to develop and test genomic predictors for neoadjuvant chemotherapy. PATIENTS Newly diagnosed HER2-negative breast cancer treated with chemotherapy containing sequential taxane and anthracycline-based regimens then endocrine therapy (if hormone receptor-positive). MAIN OUTCOME MEASURES Distant relapse-free survival (DRFS) if predicted treatment-sensitive and absolute risk reduction (ARR, difference in DRFS of the two predicted groups) at median follow-up (3 years), and their 95% confidence intervals (CI). RESULTS Patients in the independent validation cohort (99% clinical Stage II–III) who were predicted to be treatment-sensitive (28% of total) had DRFS of 92% (CI 85–100) and survival benefit compared to others (absolute risk reduction (ARR) 18%; CI 6–28). Predictions were accurate if breast cancer was ER-positive (30% predicted sensitive, DRFS 97%, CI 91–100; ARR 11%, CI 0.1–21) or ER-negative (26% predicted sensitive, DRFS 83%, CI 68–100; ARR 26%, CI 4–28), and were significant in multivariate analysis after adjusting for relevant clinical-pathologic characteristics. Other genomic predictors showed paradoxically worse survival if predicted to be responsive to chemotherapy. CONCLUSION A genomic predictor combining ER status, predicted chemo-resistance, predicted chemo-sensitivity, and predicted endocrine sensitivity accurately identified patients with survival benefit following taxane-anthracycline chemotherapy. PMID:21558518

Sensitivity of predicted bioaerosol exposure from open windrow composting facilities to ADMS dispersion model parameters.

PubMed

Douglas, P; Tyrrel, S F; Kinnersley, R P; Whelan, M; Longhurst, P J; Walsh, K; Pollard, S J T; Drew, G H

2016-12-15

Bioaerosols are released in elevated quantities from composting facilities and are associated with negative health effects, although dose-response relationships are not well understood, and require improved exposure classification. Dispersion modelling has great potential to improve exposure classification, but has not yet been extensively used or validated in this context. We present a sensitivity analysis of the ADMS dispersion model specific to input parameter ranges relevant to bioaerosol emissions from open windrow composting. This analysis provides an aid for model calibration by prioritising parameter adjustment and targeting independent parameter estimation. Results showed that predicted exposure was most sensitive to the wet and dry deposition modules and the majority of parameters relating to emission source characteristics, including pollutant emission velocity, source geometry and source height. This research improves understanding of the accuracy of model input data required to provide more reliable exposure predictions. Copyright © 2016. Published by Elsevier Ltd.

Meta-analysis of diagnostic accuracy studies accounting for disease prevalence: alternative parameterizations and model selection.

PubMed

Chu, Haitao; Nie, Lei; Cole, Stephen R; Poole, Charles

2009-08-15

In a meta-analysis of diagnostic accuracy studies, the sensitivities and specificities of a diagnostic test may depend on the disease prevalence since the severity and definition of disease may differ from study to study due to the design and the population considered. In this paper, we extend the bivariate nonlinear random effects model on sensitivities and specificities to jointly model the disease prevalence, sensitivities and specificities using trivariate nonlinear random-effects models. Furthermore, as an alternative parameterization, we also propose jointly modeling the test prevalence and the predictive values, which reflect the clinical utility of a diagnostic test. These models allow investigators to study the complex relationship among the disease prevalence, sensitivities and specificities; or among test prevalence and the predictive values, which can reveal hidden information about test performance. We illustrate the proposed two approaches by reanalyzing the data from a meta-analysis of radiological evaluation of lymph node metastases in patients with cervical cancer and a simulation study. The latter illustrates the importance of carefully choosing an appropriate normality assumption for the disease prevalence, sensitivities and specificities, or the test prevalence and the predictive values. In practice, it is recommended to use model selection techniques to identify a best-fitting model for making statistical inference. In summary, the proposed trivariate random effects models are novel and can be very useful in practice for meta-analysis of diagnostic accuracy studies. Copyright 2009 John Wiley & Sons, Ltd.

Aerodynamic design optimization using sensitivity analysis and computational fluid dynamics

NASA Technical Reports Server (NTRS)

Baysal, Oktay; Eleshaky, Mohamed E.

1991-01-01

A new and efficient method is presented for aerodynamic design optimization, which is based on a computational fluid dynamics (CFD)-sensitivity analysis algorithm. The method is applied to design a scramjet-afterbody configuration for an optimized axial thrust. The Euler equations are solved for the inviscid analysis of the flow, which in turn provides the objective function and the constraints. The CFD analysis is then coupled with the optimization procedure that uses a constrained minimization method. The sensitivity coefficients, i.e. gradients of the objective function and the constraints, needed for the optimization are obtained using a quasi-analytical method rather than the traditional brute force method of finite difference approximations. During the one-dimensional search of the optimization procedure, an approximate flow analysis (predicted flow) based on a first-order Taylor series expansion is used to reduce the computational cost. Finally, the sensitivity of the optimum objective function to various design parameters, which are kept constant during the optimization, is computed to predict new optimum solutions. The flow analysis of the demonstrative example are compared with the experimental data. It is shown that the method is more efficient than the traditional methods.

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...

Assessment of HPV-mRNA test to predict recurrent disease in patients previously treated for CIN 2/3.

PubMed

Frega, Antonio; Sesti, Francesco; Lombardi, Danila; Votano, Sergio; Sopracordevole, Francesco; Catalano, Angelica; Milazzo, Giusi Natalia; Lombardo, Riccardo; Assorgi, Chiara; Olivola, Sara; Chiusuri, Valentina; Ricciardi, Enzo; French, Deborah; Moscarini, Massimo

2014-05-01

The use of HPV-mRNA test in the follow-up after LEEP is still matter of debate, with regard to its capacity of prediction relapse. The aim of the present study is to evaluate the reliability of HPV-mRNA test to predict the residual and recurrent disease, and its accuracy in the follow-up of patients treated for CIN 2/3. Multicenter prospective cohort study. Patients who underwent LEEP after a biopsy diagnosing CIN 2/3 were followed at 3, 6, 12, 24 and 36 months. Each check up included cytology, colposcopy, HPV-DNA test (LiPA) and HPV-mRNA test (PreTect HPV Proofer Kit NorChip). Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), of HPV-DNA test and HPV-mRNA test to predict relapse, recurrent and residual disease. Using multiple logistic regression, the statistical significant variables as assessed in univariate analysis were entered and investigated as predictors of relapse disease. The mRNA-test in predicting a residual disease had a sensitivity of 52% and a NPV of 91%, whereas DNA-test had 100% and 100%, respectively. On the contrary in the prediction of recurrent disease mRNA-test had a sensitivity and a NPV of 73.5% and 97%, whereas DNA-test had 44% and 93%. On the multivariate analysis, age, cytology, HPV DNA and mRNA test achieved the role of independent predictors of relapse. HPV-mRNA test has a higher sensitivity and a higher NPV in predicting recurrent disease, for this reason it should be used in the follow-up of patients treated with LEEP for CIN 2/3 in order to individualize the timing of check up. Copyright © 2014 Elsevier B.V. All rights reserved.

Uncertainty and Sensitivity Analysis of Afterbody Radiative Heating Predictions for Earth Entry

NASA Technical Reports Server (NTRS)

West, Thomas K., IV; Johnston, Christopher O.; Hosder, Serhat

2016-01-01

The objective of this work was to perform sensitivity analysis and uncertainty quantification for afterbody radiative heating predictions of Stardust capsule during Earth entry at peak afterbody radiation conditions. The radiation environment in the afterbody region poses significant challenges for accurate uncertainty quantification and sensitivity analysis due to the complexity of the flow physics, computational cost, and large number of un-certain variables. In this study, first a sparse collocation non-intrusive polynomial chaos approach along with global non-linear sensitivity analysis was used to identify the most significant uncertain variables and reduce the dimensions of the stochastic problem. Then, a total order stochastic expansion was constructed over only the important parameters for an efficient and accurate estimate of the uncertainty in radiation. Based on previous work, 388 uncertain parameters were considered in the radiation model, which came from the thermodynamics, flow field chemistry, and radiation modeling. The sensitivity analysis showed that only four of these variables contributed significantly to afterbody radiation uncertainty, accounting for almost 95% of the uncertainty. These included the electronic- impact excitation rate for N between level 2 and level 5 and rates of three chemical reactions in uencing N, N(+), O, and O(+) number densities in the flow field.

The Mesoscale Predictability of Terrain Induced Flows

DTIC Science & Technology

2009-09-30

simulations, we focus on assessing the predictability of winds, mountain waves and clear air turbulence ( CAT ) in the lee of the Sierra Nevada...complete description of the sensitivity of mountain waves, CAT and downslope to small variations in the initial conditions. WORK COMPLETED We...completed the analysis of the sensitivity of mountain waves, CAT and downslope winds to small perturbations in the upstream conditions. We also

Separating foliar physiology from morphology reveals the relative roles of vertically structured transpiration factors within red maple crowns and limitations of larger scale models

PubMed Central

Bauerle, William L.; Bowden, Joseph D.

2011-01-01

A spatially explicit mechanistic model, MAESTRA, was used to separate key parameters affecting transpiration to provide insights into the most influential parameters for accurate predictions of within-crown and within-canopy transpiration. Once validated among Acer rubrum L. genotypes, model responses to different parameterization scenarios were scaled up to stand transpiration (expressed per unit leaf area) to assess how transpiration might be affected by the spatial distribution of foliage properties. For example, when physiological differences were accounted for, differences in leaf width among A. rubrum L. genotypes resulted in a 25% difference in transpiration. An in silico within-canopy sensitivity analysis was conducted over the range of genotype parameter variation observed and under different climate forcing conditions. The analysis revealed that seven of 16 leaf traits had a ≥5% impact on transpiration predictions. Under sparse foliage conditions, comparisons of the present findings with previous studies were in agreement that parameters such as the maximum Rubisco-limited rate of photosynthesis can explain ∼20% of the variability in predicted transpiration. However, the spatial analysis shows how such parameters can decrease or change in importance below the uppermost canopy layer. Alternatively, model sensitivity to leaf width and minimum stomatal conductance was continuous along a vertical canopy depth profile. Foremost, transpiration sensitivity to an observed range of morphological and physiological parameters is examined and the spatial sensitivity of transpiration model predictions to vertical variations in microclimate and foliage density is identified to reduce the uncertainty of current transpiration predictions. PMID:21617246

Low maternal sensitivity at 6 months of age predicts higher BMI in 48 month old girls but not boys.

PubMed

Wendland, Barbara E; Atkinson, Leslie; Steiner, Meir; Fleming, Alison S; Pencharz, Paul; Moss, Ellen; Gaudreau, Hélène; Silveira, Patricia P; Arenovich, Tamara; Matthews, Stephen G; Meaney, Michael J; Levitan, Robert D

2014-11-01

Large population-based studies suggest that systematic measures of maternal sensitivity predict later risk for overweight and obesity. More work is needed to establish the developmental timing and potential moderators of this association. The current study examined the association between maternal sensitivity at 6 months of age and BMI z score measures at 48 months of age, and whether sex moderated this association. Longitudinal Canadian cohort of children from birth (the MAVAN project). This analysis was based on a dataset of 223 children (115 boys, 108 girls) who had structured assessments of maternal sensitivity at 6 months of age and 48-month BMI data available. Mother-child interactions were videotaped and systematically scored using the Maternal Behaviour Q-Sort (MBQS)-25 items, a standardized measure of maternal sensitivity. Linear mixed-effects models and logistic regression examined whether MBQS scores at 6 months predicted BMI at 48 months, controlling for other covariates. After controlling for weight-relevant covariates, there was a significant sex by MBQS interaction (P=0.015) in predicting 48 month BMI z. Further analysis revealed a strong negative association between MBQS scores and BMI in girls (P=0.01) but not boys (P=0.72). Logistic regression confirmed that in girls only, low maternal sensitivity was associated with the higher BMI categories as defined by the WHO (i.e. "at risk for overweight" or above). A significant association between low maternal sensitivity at 6 months of age and high body mass indices was found in girls but not boys at 48 months of age. These data suggest for the first time that the link between low maternal sensitivity and early BMI z may differ between boys and girls. Copyright © 2014 Elsevier Ltd. All rights reserved.

Epileptic Seizure Prediction Using Diffusion Distance and Bayesian Linear Discriminate Analysis on Intracranial EEG.

PubMed

Yuan, Shasha; Zhou, Weidong; Chen, Liyan

2018-02-01

Epilepsy is a chronic neurological disorder characterized by sudden and apparently unpredictable seizures. A system capable of forecasting the occurrence of seizures is crucial and could open new therapeutic possibilities for human health. This paper addresses an algorithm for seizure prediction using a novel feature - diffusion distance (DD) in intracranial Electroencephalograph (iEEG) recordings. Wavelet decomposition is conducted on segmented electroencephalograph (EEG) epochs and subband signals at scales 3, 4 and 5 are utilized to extract the diffusion distance. The features of all channels composing a feature vector are then fed into a Bayesian Linear Discriminant Analysis (BLDA) classifier. Finally, postprocessing procedure is applied to reduce false prediction alarms. The prediction method is evaluated on the public intracranial EEG dataset, which consists of 577.67[Formula: see text]h of intracranial EEG recordings from 21 patients with 87 seizures. We achieved a sensitivity of 85.11% for a seizure occurrence period of 30[Formula: see text]min and a sensitivity of 93.62% for a seizure occurrence period of 50[Formula: see text]min, both with the seizure prediction horizon of 10[Formula: see text]s. Our false prediction rate was 0.08/h. The proposed method yields a high sensitivity as well as a low false prediction rate, which demonstrates its potential for real-time prediction of seizures.

Spectrotemporal Modulation Sensitivity as a Predictor of Speech Intelligibility for Hearing-Impaired Listeners

PubMed Central

Bernstein, Joshua G.W.; Mehraei, Golbarg; Shamma, Shihab; Gallun, Frederick J.; Theodoroff, Sarah M.; Leek, Marjorie R.

2014-01-01

Background A model that can accurately predict speech intelligibility for a given hearing-impaired (HI) listener would be an important tool for hearing-aid fitting or hearing-aid algorithm development. Existing speech-intelligibility models do not incorporate variability in suprathreshold deficits that are not well predicted by classical audiometric measures. One possible approach to the incorporation of such deficits is to base intelligibility predictions on sensitivity to simultaneously spectrally and temporally modulated signals. Purpose The likelihood of success of this approach was evaluated by comparing estimates of spectrotemporal modulation (STM) sensitivity to speech intelligibility and to psychoacoustic estimates of frequency selectivity and temporal fine-structure (TFS) sensitivity across a group of HI listeners. Research Design The minimum modulation depth required to detect STM applied to an 86 dB SPL four-octave noise carrier was measured for combinations of temporal modulation rate (4, 12, or 32 Hz) and spectral modulation density (0.5, 1, 2, or 4 cycles/octave). STM sensitivity estimates for individual HI listeners were compared to estimates of frequency selectivity (measured using the notched-noise method at 500, 1000measured using the notched-noise method at 500, 2000, and 4000 Hz), TFS processing ability (2 Hz frequency-modulation detection thresholds for 500, 10002 Hz frequency-modulation detection thresholds for 500, 2000, and 4000 Hz carriers) and sentence intelligibility in noise (at a 0 dB signal-to-noise ratio) that were measured for the same listeners in a separate study. Study Sample Eight normal-hearing (NH) listeners and 12 listeners with a diagnosis of bilateral sensorineural hearing loss participated. Data Collection and Analysis STM sensitivity was compared between NH and HI listener groups using a repeated-measures analysis of variance. A stepwise regression analysis compared STM sensitivity for individual HI listeners to audiometric thresholds, age, and measures of frequency selectivity and TFS processing ability. A second stepwise regression analysis compared speech intelligibility to STM sensitivity and the audiogram-based Speech Intelligibility Index. Results STM detection thresholds were elevated for the HI listeners, but only for low rates and high densities. STM sensitivity for individual HI listeners was well predicted by a combination of estimates of frequency selectivity at 4000 Hz and TFS sensitivity at 500 Hz but was unrelated to audiometric thresholds. STM sensitivity accounted for an additional 40% of the variance in speech intelligibility beyond the 40% accounted for by the audibility-based Speech Intelligibility Index. Conclusions Impaired STM sensitivity likely results from a combination of a reduced ability to resolve spectral peaks and a reduced ability to use TFS information to follow spectral-peak movements. Combining STM sensitivity estimates with audiometric threshold measures for individual HI listeners provided a more accurate prediction of speech intelligibility than audiometric measures alone. These results suggest a significant likelihood of success for an STM-based model of speech intelligibility for HI listeners. PMID:23636210

Using demography and movement behavior to predict range expansion of the southern sea otter.

USGS Publications Warehouse

Tinker, M.T.; Doak, D.F.; Estes, J.A.

2008-01-01

In addition to forecasting population growth, basic demographic data combined with movement data provide a means for predicting rates of range expansion. Quantitative models of range expansion have rarely been applied to large vertebrates, although such tools could be useful for restoration and management of many threatened but recovering populations. Using the southern sea otter (Enhydra lutris nereis) as a case study, we utilized integro-difference equations in combination with a stage-structured projection matrix that incorporated spatial variation in dispersal and demography to make forecasts of population recovery and range recolonization. In addition to these basic predictions, we emphasize how to make these modeling predictions useful in a management context through the inclusion of parameter uncertainty and sensitivity analysis. Our models resulted in hind-cast (1989–2003) predictions of net population growth and range expansion that closely matched observed patterns. We next made projections of future range expansion and population growth, incorporating uncertainty in all model parameters, and explored the sensitivity of model predictions to variation in spatially explicit survival and dispersal rates. The predicted rate of southward range expansion (median = 5.2 km/yr) was sensitive to both dispersal and survival rates; elasticity analysis indicated that changes in adult survival would have the greatest potential effect on the rate of range expansion, while perturbation analysis showed that variation in subadult dispersal contributed most to variance in model predictions. Variation in survival and dispersal of females at the south end of the range contributed most of the variance in predicted southward range expansion. Our approach provides guidance for the acquisition of further data and a means of forecasting the consequence of specific management actions. Similar methods could aid in the management of other recovering populations.

Preparation for implementation of the mechanistic-empirical pavement design guide in Michigan : part 2 - evaluation of rehabilitation fixes (part 2).

DOT National Transportation Integrated Search

2013-08-01

The overall goal of Global Sensitivity Analysis (GSA) is to determine sensitivity of pavement performance prediction models to the variation in the design input values. The main difference between GSA and detailed sensitivity analyses is the way the ...

Lexical prosody beyond first-language boundary: Chinese lexical tone sensitivity predicts English reading comprehension.

PubMed

Choi, William; Tong, Xiuli; Cain, Kate

2016-08-01

This 1-year longitudinal study examined the role of Cantonese lexical tone sensitivity in predicting English reading comprehension and the pathways underlying their relation. Multiple measures of Cantonese lexical tone sensitivity, English lexical stress sensitivity, Cantonese segmental phonological awareness, general auditory sensitivity, English word reading, and English reading comprehension were administered to 133 Cantonese-English unbalanced bilingual second graders. Structural equation modeling analysis identified transfer of Cantonese lexical tone sensitivity to English reading comprehension. This transfer was realized through a direct pathway via English stress sensitivity and also an indirect pathway via English word reading. These results suggest that prosodic sensitivity is an important factor influencing English reading comprehension and that it needs to be incorporated into theoretical accounts of reading comprehension across languages. Copyright © 2016 Elsevier Inc. All rights reserved.

Attachment Insecurity Predicts Punishment Sensitivity in Anorexia Nervosa.

PubMed

Keating, Charlotte; Castle, David J; Newton, Richard; Huang, Chia; Rossell, Susan L

2016-10-01

Individuals with anorexia nervosa (AN) experience insecure attachment. We investigated whether insecure attachment is associated with punishment and reward sensitivity in women with AN. Women with AN (n = 24) and comparison women (n = 26) (CW) completed The Eating Disorder Examination Questionnaire, Depression Anxiety Stress Scale, The Attachment Style Questionnaire, and Sensitivity to Punishment/Sensitivity to Reward Questionnaire. Participants with AN returned higher ratings for insecure attachment (anxious and avoidant) experiences and greater sensitivity to punishment (p = 0.001) than CW. In AN, sensitivity to punishment was positively correlated with anxious attachment and negative emotionality but not eating disorder symptoms. Regression analysis revealed that anxious attachment independently predicted punishment sensitivity in AN. Anxious attachment experiences are related to punishment sensitivity in AN, independent of negative emotionality and eating disorder symptoms. Results support ongoing investigation of the contribution of attachment experiences in treatment and recovery.

Record of hospitalizations for ambulatory care sensitive conditions: validation of the hospital information system.

PubMed

Rehem, Tania Cristina Morais Santa Barbara; de Oliveira, Maria Regina Fernandes; Ciosak, Suely Itsuko; Egry, Emiko Yoshikawa

2013-01-01

To estimate the sensitivity, specificity and positive and negative predictive values of the Unified Health System's Hospital Information System for the appropriate recording of hospitalizations for ambulatory care-sensitive conditions. The hospital information system records for conditions which are sensitive to ambulatory care, and for those which are not, were considered for analysis, taking the medical records as the gold standard. Through simple random sampling, a sample of 816 medical records was defined and selected by means of a list of random numbers using the Statistical Package for Social Sciences. The sensitivity was 81.89%, specificity was 95.19%, the positive predictive value was 77.61% and the negative predictive value was 96.27%. In the study setting, the Hospital Information System (SIH) was more specific than sensitive, with nearly 20% of care sensitive conditions not detected. There are no validation studies in Brazil of the Hospital Information System records for the hospitalizations which are sensitive to primary health care. These results are relevant when one considers that this system is one of the bases for assessment of the effectiveness of primary health care.

CO2 Push-Pull Dual (Conjugate) Faults Injection Simulations

DOE Data Explorer

Oldenburg, Curtis (ORCID:0000000201326016); Lee, Kyung Jae; Doughty, Christine; Jung, Yoojin; Borgia, Andrea; Pan, Lehua; Zhang, Rui; Daley, Thomas M.; Altundas, Bilgin; Chugunov, Nikita

2017-07-20

This submission contains datasets and a final manuscript associated with a project simulating carbon dioxide push-pull into a conjugate fault system modeled after Dixie Valley- sensitivity analysis of significant parameters and uncertainty prediction by data-worth analysis. Datasets include: (1) Forward simulation runs of standard cases (push & pull phases), (2) Local sensitivity analyses (push & pull phases), and (3) Data-worth analysis (push & pull phases).

Effects of habitat map generalization in biodiversity assessment

NASA Technical Reports Server (NTRS)

Stoms, David M.

1992-01-01

Species richness is being mapped as part of an inventory of biological diversity in California (i.e., gap analysis). Species distributions are modeled with a GIS on the basis of maps of each species' preferred habitats. Species richness is then tallied in equal-area sampling units. A GIS sensitivity analysis examined the effects of the level of generalization of the habitat map on the predicted distribution of species richness in the southern Sierra Nevada. As the habitat map was generalized, the number of habitat types mapped within grid cells tended to decrease with a corresponding decline in numbers of species predicted. Further, the ranking of grid cells in order of predicted numbers of species changed dramatically between levels of generalization. Areas predicted to be of greatest conservation value on the basis of species richness may therefore be sensitive to GIS data resolution.

CUSUM-Logistic Regression analysis for the rapid detection of errors in clinical laboratory test results.

PubMed

Sampson, Maureen L; Gounden, Verena; van Deventer, Hendrik E; Remaley, Alan T

2016-02-01

The main drawback of the periodic analysis of quality control (QC) material is that test performance is not monitored in time periods between QC analyses, potentially leading to the reporting of faulty test results. The objective of this study was to develop a patient based QC procedure for the more timely detection of test errors. Results from a Chem-14 panel measured on the Beckman LX20 analyzer were used to develop the model. Each test result was predicted from the other 13 members of the panel by multiple regression, which resulted in correlation coefficients between the predicted and measured result of >0.7 for 8 of the 14 tests. A logistic regression model, which utilized the measured test result, the predicted test result, the day of the week and time of day, was then developed for predicting test errors. The output of the logistic regression was tallied by a daily CUSUM approach and used to predict test errors, with a fixed specificity of 90%. The mean average run length (ARL) before error detection by CUSUM-Logistic Regression (CSLR) was 20 with a mean sensitivity of 97%, which was considerably shorter than the mean ARL of 53 (sensitivity 87.5%) for a simple prediction model that only used the measured result for error detection. A CUSUM-Logistic Regression analysis of patient laboratory data can be an effective approach for the rapid and sensitive detection of clinical laboratory errors. Published by Elsevier Inc.

Generic Hypersonic Inlet Module Analysis

NASA Technical Reports Server (NTRS)

Cockrell, Chares E., Jr.; Huebner, Lawrence D.

2004-01-01

A computational study associated with an internal inlet drag analysis was performed for a generic hypersonic inlet module. The purpose of this study was to determine the feasibility of computing the internal drag force for a generic scramjet engine module using computational methods. The computational study consisted of obtaining two-dimensional (2D) and three-dimensional (3D) computational fluid dynamics (CFD) solutions using the Euler and parabolized Navier-Stokes (PNS) equations. The solution accuracy was assessed by comparisons with experimental pitot pressure data. The CFD analysis indicates that the 3D PNS solutions show the best agreement with experimental pitot pressure data. The internal inlet drag analysis consisted of obtaining drag force predictions based on experimental data and 3D CFD solutions. A comparative assessment of each of the drag prediction methods is made and the sensitivity of CFD drag values to computational procedures is documented. The analysis indicates that the CFD drag predictions are highly sensitive to the computational procedure used.

Rapid Debris Analysis Project Task 3 Final Report - Sensitivity of Fallout to Source Parameters, Near-Detonation Environment Material Properties, Topography, and Meteorology

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

Goldstein, Peter

2014-01-24

This report describes the sensitivity of predicted nuclear fallout to a variety of model input parameters, including yield, height of burst, particle and activity size distribution parameters, wind speed, wind direction, topography, and precipitation. We investigate sensitivity over a wide but plausible range of model input parameters. In addition, we investigate a specific example with a relatively narrow range to illustrate the potential for evaluating uncertainties in predictions when there are more precise constraints on model parameters.

Effective moisture penetration depth model for residential buildings: Sensitivity analysis and guidance on model inputs

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

Woods, Jason; Winkler, Jon

Moisture buffering of building materials has a significant impact on the building's indoor humidity, and building energy simulations need to model this buffering to accurately predict the humidity. Researchers requiring a simple moisture-buffering approach typically rely on the effective-capacitance model, which has been shown to be a poor predictor of actual indoor humidity. This paper describes an alternative two-layer effective moisture penetration depth (EMPD) model and its inputs. While this model has been used previously, there is a need to understand the sensitivity of this model to uncertain inputs. In this paper, we use the moisture-adsorbent materials exposed to themore » interior air: drywall, wood, and carpet. We use a global sensitivity analysis to determine which inputs are most influential and how the model's prediction capability degrades due to uncertainty in these inputs. We then compare the model's humidity prediction with measured data from five houses, which shows that this model, and a set of simple inputs, can give reasonable prediction of the indoor humidity.« less

Effective moisture penetration depth model for residential buildings: Sensitivity analysis and guidance on model inputs

DOE PAGES

Woods, Jason; Winkler, Jon

2018-01-31

Moisture buffering of building materials has a significant impact on the building's indoor humidity, and building energy simulations need to model this buffering to accurately predict the humidity. Researchers requiring a simple moisture-buffering approach typically rely on the effective-capacitance model, which has been shown to be a poor predictor of actual indoor humidity. This paper describes an alternative two-layer effective moisture penetration depth (EMPD) model and its inputs. While this model has been used previously, there is a need to understand the sensitivity of this model to uncertain inputs. In this paper, we use the moisture-adsorbent materials exposed to themore » interior air: drywall, wood, and carpet. We use a global sensitivity analysis to determine which inputs are most influential and how the model's prediction capability degrades due to uncertainty in these inputs. We then compare the model's humidity prediction with measured data from five houses, which shows that this model, and a set of simple inputs, can give reasonable prediction of the indoor humidity.« less

Review-of-systems questionnaire as a predictive tool for psychogenic nonepileptic seizures.

PubMed

Robles, Liliana; Chiang, Sharon; Haneef, Zulfi

2015-04-01

Patients with refractory epilepsy undergo video-electroencephalography for seizure characterization, among whom approximately 10-30% will be discharged with the diagnosis of psychogenic nonepileptic seizures (PNESs). Clinical PNES predictors have been described but in general are not sensitive or specific. We evaluated whether multiple complaints in a routine review-of-system (ROS) questionnaire could serve as a sensitive and specific marker of PNESs. We performed a retrospective analysis of a standardized ROS questionnaire completed by patients with definite PNESs and epileptic seizures (ESs) diagnosed in our adult epilepsy monitoring unit. A multivariate analysis of covariance (MANCOVA) was used to determine whether groups with PNES and ES differed with respect to the percentage of complaints in the ROS questionnaire. Tenfold cross-validation was used to evaluate the predictive error of a logistic regression classifier for PNES status based on the percentage of positive complaints in the ROS questionnaire. A total of 44 patients were included for analysis. Patients with PNESs had a significantly higher number of complaints in the ROS questionnaire compared to patients with epilepsy. A threshold of 17% positive complaints achieved a 78% specificity and 85% sensitivity for discriminating between PNESs and ESs. We conclude that the routine ROS questionnaire may be a sensitive and specific predictive tool for discriminating between PNESs and ESs. Published by Elsevier Inc.

An empirical comparison of a dynamic software testability metric to static cyclomatic complexity

NASA Technical Reports Server (NTRS)

Voas, Jeffrey M.; Miller, Keith W.; Payne, Jeffrey E.

1993-01-01

This paper compares the dynamic testability prediction technique termed 'sensitivity analysis' to the static testability technique termed cyclomatic complexity. The application that we chose in this empirical study is a CASE generated version of a B-737 autoland system. For the B-737 system we analyzed, we isolated those functions that we predict are more prone to hide errors during system/reliability testing. We also analyzed the code with several other well-known static metrics. This paper compares and contrasts the results of sensitivity analysis to the results of the static metrics.

Performance of blend sign in predicting hematoma expansion in intracerebral hemorrhage: A meta-analysis.

PubMed

Yu, Zhiyuan; Zheng, Jun; Guo, Rui; Ma, Lu; Li, Mou; Wang, Xiaoze; Lin, Sen; Li, Hao; You, Chao

2017-12-01

Hematoma expansion is independently associated with poor outcome in intracerebral hemorrhage (ICH). Blend sign is a simple predictor for hematoma expansion on non-contrast computed tomography. However, its accuracy for predicting hematoma expansion is inconsistent in previous studies. This meta-analysis is aimed to systematically assess the performance of blend sign in predicting hematoma expansion in ICH. A systematic literature search was conducted. Original studies about predictive accuracy of blend sign for hematoma expansion in ICH were included. Pooled sensitivity, specificity, positive and negative likelihood ratios were calculated. Summary receiver operating characteristics curve was constructed. Publication bias was assessed by Deeks' funnel plot asymmetry test. A total of 5 studies with 2248 patients were included in this meta-analysis. The pooled sensitivity, specificity, positive and negative likelihood ratios of blend sign for predicting hematoma expansion were 0.28, 0.92, 3.4 and 0.78, respectively. The area under the curve (AUC) was 0.85. No significant publication bias was found. This meta-analysis demonstrates that blend sign is a useful predictor with high specificity for hematoma expansion in ICH. Further studies with larger sample size are still necessary to verify the accuracy of blend sign for predicting hematoma expansion. Copyright © 2017 Elsevier B.V. All rights reserved.

Iodine concentration: a new, important characteristic of the spot sign that predicts haematoma expansion.

PubMed

Fu, Fan; Sun, Shengjun; Liu, Liping; Li, Jianying; Su, Yaping; Li, Yingying

2018-04-19

The computed tomography angiography (CTA) spot sign is a validated predictor of haematoma expansion (HE) in spontaneous intracerebral haemorrhage (SICH). We investigated whether defining the iodine concentration (IC) inside the spot sign and the haematoma on Gemstone spectral imaging (GSI) would improve its sensitivity and specificity for predicting HE. From 2014 to 2016, we prospectively enrolled 65 SICH patients who underwent single-phase spectral CTA within 6 h. Logistic regression was performed to assess the risk factors for HE. The predictive performance of individual spot sign characteristics was examined via receiver operating characteristic (ROC) analysis. The spot sign was detected in 46.1% (30/65) of patients. ROC analysis indicated that IC inside the spot sign had the greatest area under the ROC curve for HE (0.858; 95% confidence interval, 0.727-0.989; p = 0.003). Multivariate analysis found that spot sign with higher IC (i.e. IC > 7.82 100 μg/ml) was an independent predictor of HE (odds ratio = 34.27; 95% confidence interval, 5.608-209.41; p < 0.001) with sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of 0.81, 0.75, 0.90 and 0.60, respectively; while the spot sign showed sensitivity, specificity, PPV and NPV of 0.81, 0.79, 0.73 and 0.86. Logistic regression analysis indicated that the IC in haematomas was independently associated with HE (odds ratio = 1.525; 95% confidence interval, 1.041-2.235; p = 0.030). ICs in haematoma and in spot sign were all independently associated with HE. IC analysis in spectral imaging may help to identify SICH patients for targeted haemostatic therapy. • Iodine concentration in spot sign and haematoma can predict haematoma expansion • Spectral imaging could measure the IC inside the spot sign and haematoma • IC in spot sign improved the positive predictive value (PPV) cf. CTA.

IL-8 predicts pediatric oncology patients with febrile neutropenia at low risk for bacteremia.

PubMed

Cost, Carrye R; Stegner, Martha M; Leonard, David; Leavey, Patrick

2013-04-01

Despite a low bacteremia rate, pediatric oncology patients are frequently admitted for febrile neutropenia. A pediatric risk prediction model with high sensitivity to identify patients at low risk for bacteremia is not available. We performed a single-institution prospective cohort study of pediatric oncology patients with febrile neutropenia to create a risk prediction model using clinical factors, respiratory viral infection, and cytokine expression. Pediatric oncology patients with febrile neutropenia were enrolled between March 30, 2010 and April 1, 2011 and managed per institutional protocol. Blood samples for C-reactive protein and cytokine expression and nasopharyngeal swabs for respiratory viral testing were obtained. Medical records were reviewed for clinical data. Statistical analysis utilized mixed multiple logistic regression modeling. During the 12-month period, 195 febrile neutropenia episodes were enrolled. There were 24 (12%) episodes of bacteremia. Univariate analysis revealed several factors predictive for bacteremia, and interleukin (IL)-8 was the most predictive variable in the multivariate stepwise logistic regression. Low serum IL-8 predicted patients at low risk for bacteremia with a sensitivity of 0.9 and negative predictive value of 0.98. IL-8 is a highly sensitive predictor for patients at low risk for bacteremia. IL-8 should be utilized in a multi-institution prospective trial to assign risk stratification to pediatric patients admitted with febrile neutropenia.

Shape sensitivity analysis of flutter response of a laminated wing

NASA Technical Reports Server (NTRS)

Bergen, Fred D.; Kapania, Rakesh K.

1988-01-01

A method is presented for calculating the shape sensitivity of a wing aeroelastic response with respect to changes in geometric shape. Yates' modified strip method is used in conjunction with Giles' equivalent plate analysis to predict the flutter speed, frequency, and reduced frequency of the wing. Three methods are used to calculate the sensitivity of the eigenvalue. The first method is purely a finite difference calculation of the eigenvalue derivative directly from the solution of the flutter problem corresponding to the two different values of the shape parameters. The second method uses an analytic expression for the eigenvalue sensitivities of a general complex matrix, where the derivatives of the aerodynamic, mass, and stiffness matrices are computed using a finite difference approximation. The third method also uses an analytic expression for the eigenvalue sensitivities, but the aerodynamic matrix is computed analytically. All three methods are found to be in good agreement with each other. The sensitivities of the eigenvalues were used to predict the flutter speed, frequency, and reduced frequency. These approximations were found to be in good agreement with those obtained using a complete reanalysis.

Sensitivity of the reference evapotranspiration to key climatic variables during the growing season in the Ejina oasis northwest China.

PubMed

Hou, Lan-Gong; Zou, Song-Bing; Xiao, Hong-Lang; Yang, Yong-Gang

2013-01-01

The standardized FAO56 Penman-Monteith model, which has been the most reasonable method in both humid and arid climatic conditions, provides reference evapotranspiration (ETo) estimates for planning and efficient use of agricultural water resources. And sensitivity analysis is important in understanding the relative importance of climatic variables to the variation of reference evapotranspiration. In this study, a non-dimensional relative sensitivity coefficient was employed to predict responses of ETo to perturbations of four climatic variables in the Ejina oasis northwest China. A 20-year historical dataset of daily air temperature, wind speed, relative humidity and daily sunshine duration in the Ejina oasis was used in the analysis. Results have shown that daily sensitivity coefficients exhibited large fluctuations during the growing season, and shortwave radiation was the most sensitive variable in general for the Ejina oasis, followed by air temperature, wind speed and relative humidity. According to this study, the response of ETo can be preferably predicted under perturbation of air temperature, wind speed, relative humidity and shortwave radiation by their sensitivity coefficients.

Adjoint-based sensitivity analysis of low-order thermoacoustic networks using a wave-based approach

NASA Astrophysics Data System (ADS)

Aguilar, José G.; Magri, Luca; Juniper, Matthew P.

2017-07-01

Strict pollutant emission regulations are pushing gas turbine manufacturers to develop devices that operate in lean conditions, with the downside that combustion instabilities are more likely to occur. Methods to predict and control unstable modes inside combustion chambers have been developed in the last decades but, in some cases, they are computationally expensive. Sensitivity analysis aided by adjoint methods provides valuable sensitivity information at a low computational cost. This paper introduces adjoint methods and their application in wave-based low order network models, which are used as industrial tools, to predict and control thermoacoustic oscillations. Two thermoacoustic models of interest are analyzed. First, in the zero Mach number limit, a nonlinear eigenvalue problem is derived, and continuous and discrete adjoint methods are used to obtain the sensitivities of the system to small modifications. Sensitivities to base-state modification and feedback devices are presented. Second, a more general case with non-zero Mach number, a moving flame front and choked outlet, is presented. The influence of the entropy waves on the computed sensitivities is shown.

Sensitivity Analysis of Multidisciplinary Rotorcraft Simulations

NASA Technical Reports Server (NTRS)

Wang, Li; Diskin, Boris; Biedron, Robert T.; Nielsen, Eric J.; Bauchau, Olivier A.

2017-01-01

A multidisciplinary sensitivity analysis of rotorcraft simulations involving tightly coupled high-fidelity computational fluid dynamics and comprehensive analysis solvers is presented and evaluated. An unstructured sensitivity-enabled Navier-Stokes solver, FUN3D, and a nonlinear flexible multibody dynamics solver, DYMORE, are coupled to predict the aerodynamic loads and structural responses of helicopter rotor blades. A discretely-consistent adjoint-based sensitivity analysis available in FUN3D provides sensitivities arising from unsteady turbulent flows and unstructured dynamic overset meshes, while a complex-variable approach is used to compute DYMORE structural sensitivities with respect to aerodynamic loads. The multidisciplinary sensitivity analysis is conducted through integrating the sensitivity components from each discipline of the coupled system. Numerical results verify accuracy of the FUN3D/DYMORE system by conducting simulations for a benchmark rotorcraft test model and comparing solutions with established analyses and experimental data. Complex-variable implementation of sensitivity analysis of DYMORE and the coupled FUN3D/DYMORE system is verified by comparing with real-valued analysis and sensitivities. Correctness of adjoint formulations for FUN3D/DYMORE interfaces is verified by comparing adjoint-based and complex-variable sensitivities. Finally, sensitivities of the lift and drag functions obtained by complex-variable FUN3D/DYMORE simulations are compared with sensitivities computed by the multidisciplinary sensitivity analysis, which couples adjoint-based flow and grid sensitivities of FUN3D and FUN3D/DYMORE interfaces with complex-variable sensitivities of DYMORE structural responses.

Test and Analysis of a Buckling-Critical Large-Scale Sandwich Composite Cylinder

NASA Technical Reports Server (NTRS)

Schultz, Marc R.; Sleight, David W.; Gardner, Nathaniel W.; Rudd, Michelle T.; Hilburger, Mark W.; Palm, Tod E.; Oldfield, Nathan J.

2018-01-01

Structural stability is an important design consideration for launch-vehicle shell structures and it is well known that the buckling response of such shell structures can be very sensitive to small geometric imperfections. As part of an effort to develop new buckling design guidelines for sandwich composite cylindrical shells, an 8-ft-diameter honeycomb-core sandwich composite cylinder was tested under pure axial compression to failure. The results from this test are compared with finite-element-analysis predictions and overall agreement was very good. In particular, the predicted buckling load was within 1% of the test and the character of the response matched well. However, it was found that the agreement could be improved by including composite material nonlinearity in the analysis, and that the predicted buckling initiation site was sensitive to the addition of small bending loads to the primary axial load in analyses.

Prediction of the contact sensitizing potential of chemicals using analysis of gene expression changes in human THP-1 monocytes.

PubMed

Arkusz, Joanna; Stępnik, Maciej; Sobala, Wojciech; Dastych, Jarosław

2010-11-10

The aim of this study was to find differentially regulated genes in THP-1 monocytic cells exposed to sensitizers and nonsensitizers and to investigate if such genes could be reliable markers for an in vitro predictive method for the identification of skin sensitizing chemicals. Changes in expression of 35 genes in the THP-1 cell line following treatment with chemicals of different sensitizing potential (from nonsensitizers to extreme sensitizers) were assessed using real-time PCR. Verification of 13 candidate genes by testing a large number of chemicals (an additional 22 sensitizers and 8 nonsensitizers) revealed that prediction of contact sensitization potential was possible based on evaluation of changes in three genes: IL8, HMOX1 and PAIMP1. In total, changes in expression of these genes allowed correct detection of sensitization potential of 21 out of 27 (78%) test sensitizers. The gene expression levels inside potency groups varied and did not allow estimation of sensitization potency of test chemicals. Results of this study indicate that evaluation of changes in expression of proposed biomarkers in THP-1 cells could be a valuable model for preliminary screening of chemicals to discriminate an appreciable majority of sensitizers from nonsensitizers. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

Falls Risk Prediction for Older Inpatients in Acute Care Medical Wards: Is There an Interest to Combine an Early Nurse Assessment and the Artificial Neural Network Analysis?

PubMed

Beauchet, O; Noublanche, F; Simon, R; Sekhon, H; Chabot, J; Levinoff, E J; Kabeshova, A; Launay, C P

2018-01-01

Identification of the risk of falls is important among older inpatients. This study aims to examine performance criteria (i.e.; sensitivity, specificity, positive predictive value, negative predictive value and accuracy) for fall prediction resulting from a nurse assessment and an artificial neural networks (ANNs) analysis in older inpatients hospitalized in acute care medical wards. A total of 848 older inpatients (mean age, 83.0±7.2 years; 41.8% female) admitted to acute care medical wards in Angers University hospital (France) were included in this study using an observational prospective cohort design. Within 24 hours after admission of older inpatients, nurses performed a bedside clinical assessment. Participants were separated into non-fallers and fallers (i.e.; ≥1 fall during hospitalization stay). The analysis was conducted using three feed forward ANNs (multilayer perceptron [MLP], averaged neural network, and neuroevolution of augmenting topologies [NEAT]). Seventy-three (8.6%) participants fell at least once during their hospital stay. ANNs showed a high specificity, regardless of which ANN was used, and the highest value reported was with MLP (99.8%). In contrast, sensitivity was lower, with values ranging between 98.4 to 14.8%. MLP had the highest accuracy (99.7). Performance criteria for fall prediction resulting from a bedside nursing assessment and an ANNs analysis was associated with a high specificity but a low sensitivity, suggesting that this combined approach should be used more as a diagnostic test than a screening test when considering older inpatients in acute care medical ward.

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.

Linear bubble plume model for hypolimnetic oxygenation: Full-scale validation and sensitivity analysis

NASA Astrophysics Data System (ADS)

Singleton, V. L.; Gantzer, P.; Little, J. C.

2007-02-01

An existing linear bubble plume model was improved, and data collected from a full-scale diffuser installed in Spring Hollow Reservoir, Virginia, were used to validate the model. The depth of maximum plume rise was simulated well for two of the three diffuser tests. Temperature predictions deviated from measured profiles near the maximum plume rise height, but predicted dissolved oxygen profiles compared very well with observations. A sensitivity analysis was performed. The gas flow rate had the greatest effect on predicted plume rise height and induced water flow rate, both of which were directly proportional to gas flow rate. Oxygen transfer within the hypolimnion was independent of all parameters except initial bubble radius and was inversely proportional for radii greater than approximately 1 mm. The results of this work suggest that plume dynamics and oxygen transfer can successfully be predicted for linear bubble plumes using the discrete-bubble approach.

Computational Analysis of Epidermal Growth Factor Receptor Mutations Predicts Differential Drug Sensitivity Profiles toward Kinase Inhibitors.

PubMed

Akula, Sravani; Kamasani, Swapna; Sivan, Sree Kanth; Manga, Vijjulatha; Vudem, Dashavantha Reddy; Kancha, Rama Krishna

2018-05-01

A significant proportion of patients with lung cancer carry mutations in the EGFR kinase domain. The presence of a deletion mutation in exon 19 or L858R point mutation in the EGFR kinase domain has been shown to cause enhanced efficacy of inhibitor treatment in patients with NSCLC. Several less frequent (uncommon) mutations in the EGFR kinase domain with potential implications in treatment response have also been reported. The role of a limited number of uncommon mutations in drug sensitivity was experimentally verified. However, a huge number of these mutations remain uncharacterized for inhibitor sensitivity or resistance. A large-scale computational analysis of clinically reported 298 point mutants of EGFR kinase domain has been performed, and drug sensitivity profiles for each mutant toward seven kinase inhibitors has been determined by molecular docking. In addition, the relative inhibitor binding affinity toward each drug as compared with that of adenosine triphosphate was calculated for each mutant. The inhibitor sensitivity profiles predicted in this study for a set of previously characterized mutants correlated well with the published clinical, experimental, and computational data. Both the single and compound mutations displayed differential inhibitor sensitivity toward first- and next-generation kinase inhibitors. The present study provides predicted drug sensitivity profiles for a large panel of uncommon EGFR mutations toward multiple inhibitors, which may help clinicians in deciding mutant-specific treatment strategies. Copyright © 2018 International Association for the Study of Lung Cancer. Published by Elsevier Inc. All rights reserved.

Genotypic tropism testing by massively parallel sequencing: qualitative and quantitative analysis.

PubMed

Däumer, Martin; Kaiser, Rolf; Klein, Rolf; Lengauer, Thomas; Thiele, Bernhard; Thielen, Alexander

2011-05-13

Inferring viral tropism from genotype is a fast and inexpensive alternative to phenotypic testing. While being highly predictive when performed on clonal samples, sensitivity of predicting CXCR4-using (X4) variants drops substantially in clinical isolates. This is mainly attributed to minor variants not detected by standard bulk-sequencing. Massively parallel sequencing (MPS) detects single clones thereby being much more sensitive. Using this technology we wanted to improve genotypic prediction of coreceptor usage. Plasma samples from 55 antiretroviral-treated patients tested for coreceptor usage with the Monogram Trofile Assay were sequenced with standard population-based approaches. Fourteen of these samples were selected for further analysis with MPS. Tropism was predicted from each sequence with geno2pheno[coreceptor]. Prediction based on bulk-sequencing yielded 59.1% sensitivity and 90.9% specificity compared to the trofile assay. With MPS, 7600 reads were generated on average per isolate. Minorities of sequences with high confidence in CXCR4-usage were found in all samples, irrespective of phenotype. When using the default false-positive-rate of geno2pheno[coreceptor] (10%), and defining a minority cutoff of 5%, the results were concordant in all but one isolate. The combination of MPS and coreceptor usage prediction results in a fast and accurate alternative to phenotypic assays. The detection of X4-viruses in all isolates suggests that coreceptor usage as well as fitness of minorities is important for therapy outcome. The high sensitivity of this technology in combination with a quantitative description of the viral population may allow implementing meaningful cutoffs for predicting response to CCR5-antagonists in the presence of X4-minorities.

Heat-Energy Analysis for Solar Receivers

NASA Technical Reports Server (NTRS)

Lansing, F. L.

1982-01-01

Heat-energy analysis program (HEAP) solves general heat-transfer problems, with some specific features that are "custom made" for analyzing solar receivers. Can be utilized not only to predict receiver performance under varying solar flux, ambient temperature and local heat-transfer rates but also to detect locations of hotspots and metallurgical difficulties and to predict performance sensitivity of neighboring component parameters.

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

Application of neural networks and sensitivity analysis to improved prediction of trauma survival.

PubMed

Hunter, A; Kennedy, L; Henry, J; Ferguson, I

2000-05-01

The performance of trauma departments is widely audited by applying predictive models that assess probability of survival, and examining the rate of unexpected survivals and deaths. Although the TRISS methodology, a logistic regression modelling technique, is still the de facto standard, it is known that neural network models perform better. A key issue when applying neural network models is the selection of input variables. This paper proposes a novel form of sensitivity analysis, which is simpler to apply than existing techniques, and can be used for both numeric and nominal input variables. The technique is applied to the audit survival problem, and used to analyse the TRISS variables. The conclusions discuss the implications for the design of further improved scoring schemes and predictive models.

Utility and limitations of a peptide reactivity assay to predict fragrance allergens in vitro.

PubMed

Natsch, A; Gfeller, H; Rothaupt, M; Ellis, G

2007-10-01

A key step in the skin sensitization process is the formation of a covalent adduct between the skin sensitizer and endogenous proteins and/or peptides in the skin. A published peptide depletion assay was used to relate the in vitro reactivity of fragrance molecules to LLNA data. Using the classical assay, 22 of 28 tested moderate to strong sensitizers were positive. The prediction of weak sensitizers proved to be more difficult with only 50% of weak sensitizers giving a positive response, but for some compounds this could also be due to false-positive results from the LLNA. LC-MS analysis yielded the expected mass of the peptide adducts in several cases, whereas in other cases putative oxidation reactions led to adducts of unexpected molecular weight. Several moderately sensitizing aldehydes were correctly predicted by the depletion assay, but no adducts were found and the depletion appears to be due to an oxidation of the parent peptide catalyzed by the test compound. Finally, alternative test peptides derived from a physiological reactive protein with enhanced sensitivity for weak Michael acceptors were found, further increasing the sensitivity of the assay.

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

CADDIS Volume 4. Data Analysis: Advanced Analyses - Controlling for Natural Variability

EPA Pesticide Factsheets

Methods for controlling natural variability, predicting environmental conditions from biological observations method, biological trait data, species sensitivity distributions, propensity scores, Advanced Analyses of Data Analysis references.

Analysis of the economic impact of the national unified carbon trading market mechanism Hebei province, for example

NASA Astrophysics Data System (ADS)

Sun, Yuxing

2018-05-01

In this paper, a grey prediction model is used to predict the carbon emission in Hebei province, and the impact analysis model based on TermCo2 is established. At the same time, we read a lot about CGE and study on how to build the scene, the selection of key parameters, and sensitivity analysis of application scenarios do industry for reference.

Test battery with the human cell line activation test, direct peptide reactivity assay and DEREK based on a 139 chemical data set for predicting skin sensitizing potential and potency of chemicals.

PubMed

Takenouchi, Osamu; Fukui, Shiho; Okamoto, Kenji; Kurotani, Satoru; Imai, Noriyasu; Fujishiro, Miyuki; Kyotani, Daiki; Kato, Yoshinao; Kasahara, Toshihiko; Fujita, Masaharu; Toyoda, Akemi; Sekiya, Daisuke; Watanabe, Shinichi; Seto, Hirokazu; Hirota, Morihiko; Ashikaga, Takao; Miyazawa, Masaaki

2015-11-01

To develop a testing strategy incorporating the human cell line activation test (h-CLAT), direct peptide reactivity assay (DPRA) and DEREK, we created an expanded data set of 139 chemicals (102 sensitizers and 37 non-sensitizers) by combining the existing data set of 101 chemicals through the collaborative projects of Japan Cosmetic Industry Association. Of the additional 38 chemicals, 15 chemicals with relatively low water solubility (log Kow > 3.5) were selected to clarify the limitation of testing strategies regarding the lipophilic chemicals. Predictivities of the h-CLAT, DPRA and DEREK, and the combinations thereof were evaluated by comparison to results of the local lymph node assay. When evaluating 139 chemicals using combinations of three methods based on integrated testing strategy (ITS) concept (ITS-based test battery) and a sequential testing strategy (STS) weighing the predictive performance of the h-CLAT and DPRA, overall similar predictivities were found as before on the 101 chemical data set. An analysis of false negative chemicals suggested a major limitation of our strategies was the testing of low water-soluble chemicals. When excluded the negative results for chemicals with log Kow > 3.5, the sensitivity and accuracy of ITS improved to 97% (91 of 94 chemicals) and 89% (114 of 128). Likewise, the sensitivity and accuracy of STS to 98% (92 of 94) and 85% (111 of 129). Moreover, the ITS and STS also showed good correlation with local lymph node assay on three potency classifications, yielding accuracies of 74% (ITS) and 73% (STS). Thus, the inclusion of log Kow in analysis could give both strategies a higher predictive performance. Copyright © 2015 John Wiley & Sons, Ltd.

Probing 6D operators at future e - e + colliders

NASA Astrophysics Data System (ADS)

Chiu, Wen Han; Leung, Sze Ching; Liu, Tao; Lyu, Kun-Feng; Wang, Lian-Tao

2018-05-01

We explore the sensitivities at future e - e + colliders to probe a set of six-dimensional operators which can modify the SM predictions on Higgs physics and electroweak precision measurements. We consider the case in which the operators are turned on simultaneously. Such an analysis yields a "conservative" interpretation on the collider sensitivities, complementary to the "optimistic" scenario where the operators are individually probed. After a detail analysis at CEPC in both "conservative" and "optimistic" scenarios, we also considered the sensitivities for FCC-ee and ILC. As an illustration of the potential of constraining new physics models, we applied sensitivity analysis to two benchmarks: holographic composite Higgs model and littlest Higgs model.

Thalamic functional connectivity predicts seizure laterality in individual TLE patients: application of a biomarker development strategy.

PubMed

Barron, Daniel S; Fox, Peter T; Pardoe, Heath; Lancaster, Jack; Price, Larry R; Blackmon, Karen; Berry, Kristen; Cavazos, Jose E; Kuzniecky, Ruben; Devinsky, Orrin; Thesen, Thomas

2015-01-01

Noninvasive markers of brain function could yield biomarkers in many neurological disorders. Disease models constrained by coordinate-based meta-analysis are likely to increase this yield. Here, we evaluate a thalamic model of temporal lobe epilepsy that we proposed in a coordinate-based meta-analysis and extended in a diffusion tractography study of an independent patient population. Specifically, we evaluated whether thalamic functional connectivity (resting-state fMRI-BOLD) with temporal lobe areas can predict seizure onset laterality, as established with intracranial EEG. Twenty-four lesional and non-lesional temporal lobe epilepsy patients were studied. No significant differences in functional connection strength in patient and control groups were observed with Mann-Whitney Tests (corrected for multiple comparisons). Notwithstanding the lack of group differences, individual patient difference scores (from control mean connection strength) successfully predicted seizure onset zone as shown in ROC curves: discriminant analysis (two-dimensional) predicted seizure onset zone with 85% sensitivity and 91% specificity; logistic regression (four-dimensional) achieved 86% sensitivity and 100% specificity. The strongest markers in both analyses were left thalamo-hippocampal and right thalamo-entorhinal cortex functional connection strength. Thus, this study shows that thalamic functional connections are sensitive and specific markers of seizure onset laterality in individual temporal lobe epilepsy patients. This study also advances an overall strategy for the programmatic development of neuroimaging biomarkers in clinical and genetic populations: a disease model informed by coordinate-based meta-analysis was used to anatomically constrain individual patient analyses.

Predicting Vasovagal Syncope from Heart Rate and Blood Pressure: A Prospective Study in 140 Subjects.

PubMed

Virag, Nathalie; Erickson, Mark; Taraborrelli, Patricia; Vetter, Rolf; Lim, Phang Boon; Sutton, Richard

2018-04-28

We developed a vasovagal syncope (VVS) prediction algorithm for use during head-up tilt with simultaneous analysis of heart rate (HR) and systolic blood pressure (SBP). We previously tested this algorithm retrospectively in 1155 subjects, showing sensitivity 95%, specificity 93% and median prediction time of 59s. This study was prospective, single center, on 140 subjects to evaluate this VVS prediction algorithm and assess if retrospective results were reproduced and clinically relevant. Primary endpoint was VVS prediction: sensitivity and specificity >80%. In subjects, referred for 60° head-up tilt (Italian protocol), non-invasive HR and SBP were supplied to the VVS prediction algorithm: simultaneous analysis of RR intervals, SBP trends and their variability represented by low-frequency power generated cumulative risk which was compared with a predetermined VVS risk threshold. When cumulative risk exceeded threshold, an alert was generated. Prediction time was duration between first alert and syncope. Of 140 subjects enrolled, data was usable for 134. Of 83 tilt+ve (61.9%), 81 VVS events were correctly predicted and of 51 tilt-ve subjects (38.1%), 45 were correctly identified as negative by the algorithm. Resulting algorithm performance was sensitivity 97.6%, specificity 88.2%, meeting primary endpoint. Mean VVS prediction time was 2min 26s±3min16s with median 1min 25s. Using only HR and HR variability (without SBP) the mean prediction time reduced to 1min34s±1min45s with median 1min13s. The VVS prediction algorithm, is clinically-relevant tool and could offer applications including providing a patient alarm, shortening tilt-test time, or triggering pacing intervention in implantable devices. Copyright © 2018. Published by Elsevier Inc.

Variational Methods in Design Optimization and Sensitivity Analysis for Two-Dimensional Euler Equations

NASA Technical Reports Server (NTRS)

Ibrahim, A. H.; Tiwari, S. N.; Smith, R. E.

1997-01-01

Variational methods (VM) sensitivity analysis employed to derive the costate (adjoint) equations, the transversality conditions, and the functional sensitivity derivatives. In the derivation of the sensitivity equations, the variational methods use the generalized calculus of variations, in which the variable boundary is considered as the design function. The converged solution of the state equations together with the converged solution of the costate equations are integrated along the domain boundary to uniquely determine the functional sensitivity derivatives with respect to the design function. The application of the variational methods to aerodynamic shape optimization problems is demonstrated for internal flow problems at supersonic Mach number range. The study shows, that while maintaining the accuracy of the functional sensitivity derivatives within the reasonable range for engineering prediction purposes, the variational methods show a substantial gain in computational efficiency, i.e., computer time and memory, when compared with the finite difference sensitivity analysis.

Computing the sensitivity of drag and lift in flow past a circular cylinder: Time-stepping versus self-consistent analysis

NASA Astrophysics Data System (ADS)

Meliga, Philippe

2017-07-01

We provide in-depth scrutiny of two methods making use of adjoint-based gradients to compute the sensitivity of drag in the two-dimensional, periodic flow past a circular cylinder (Re≲189 ): first, the time-stepping analysis used in Meliga et al. [Phys. Fluids 26, 104101 (2014), 10.1063/1.4896941] that relies on classical Navier-Stokes modeling and determines the sensitivity to any generic control force from time-dependent adjoint equations marched backwards in time; and, second, a self-consistent approach building on the model of Mantič-Lugo et al. [Phys. Rev. Lett. 113, 084501 (2014), 10.1103/PhysRevLett.113.084501] to compute semilinear approximations of the sensitivity to the mean and fluctuating components of the force. Both approaches are applied to open-loop control by a small secondary cylinder and allow identifying the sensitive regions without knowledge of the controlled states. The theoretical predictions obtained by time-stepping analysis reproduce well the results obtained by direct numerical simulation of the two-cylinder system. So do the predictions obtained by self-consistent analysis, which corroborates the relevance of the approach as a guideline for efficient and systematic control design in the attempt to reduce drag, even though the Reynolds number is not close to the instability threshold and the oscillation amplitude is not small. This is because, unlike simpler approaches relying on linear stability analysis to predict the main features of the flow unsteadiness, the semilinear framework encompasses rigorously the effect of the control on the mean flow, as well as on the finite-amplitude fluctuation that feeds back nonlinearly onto the mean flow via the formation of Reynolds stresses. Such results are especially promising as the self-consistent approach determines the sensitivity from time-independent equations that can be solved iteratively, which makes it generally less computationally demanding. We ultimately discuss the extent to which relevant information can be gained from a hybrid modeling computing self-consistent sensitivities from the postprocessing of DNS data. Application to alternative control objectives such as increasing the lift and alleviating the fluctuating drag and lift is also discussed.

The effect of bathymetric filtering on nearshore process model results

USGS Publications Warehouse

Plant, N.G.; Edwards, K.L.; Kaihatu, J.M.; Veeramony, J.; Hsu, L.; Holland, K.T.

2009-01-01

Nearshore wave and flow model results are shown to exhibit a strong sensitivity to the resolution of the input bathymetry. In this analysis, bathymetric resolution was varied by applying smoothing filters to high-resolution survey data to produce a number of bathymetric grid surfaces. We demonstrate that the sensitivity of model-predicted wave height and flow to variations in bathymetric resolution had different characteristics. Wave height predictions were most sensitive to resolution of cross-shore variability associated with the structure of nearshore sandbars. Flow predictions were most sensitive to the resolution of intermediate scale alongshore variability associated with the prominent sandbar rhythmicity. Flow sensitivity increased in cases where a sandbar was closer to shore and shallower. Perhaps the most surprising implication of these results is that the interpolation and smoothing of bathymetric data could be optimized differently for the wave and flow models. We show that errors between observed and modeled flow and wave heights are well predicted by comparing model simulation results using progressively filtered bathymetry to results from the highest resolution simulation. The damage done by over smoothing or inadequate sampling can therefore be estimated using model simulations. We conclude that the ability to quantify prediction errors will be useful for supporting future data assimilation efforts that require this information.

FABRIC FILTER MODEL SENSITIVITY ANALYSIS

EPA Science Inventory

The report gives results of a series of sensitivity tests of a GCA fabric filter model, as a precursor to further laboratory and/or field tests. Preliminary tests had shown good agreement with field data. However, the apparent agreement between predicted and actual values was bas...

Correlation between experimental human and murine skin sensitization induction thresholds.

PubMed

Api, Anne Marie; Basketter, David; Lalko, Jon

2015-01-01

Quantitative risk assessment for skin sensitization is directed towards the determination of levels of exposure to known sensitizing substances that will avoid the induction of contact allergy in humans. A key component of this work is the predictive identification of relative skin sensitizing potency, achieved normally by the measurement of the threshold (the "EC3" value) in the local lymph node assay (LLNA). In an extended series of studies, the accuracy of this murine induction threshold as the predictor of the absence of a sensitizing effect has been verified by conduct of a human repeated insult patch test (HRIPT). Murine and human thresholds for a diverse set of 57 fragrance chemicals spanning approximately four orders of magnitude variation in potency have been compared. The results confirm that there is a useful correlation, with the LLNA EC3 value helping particularly to identify stronger sensitizers. Good correlation (with half an order of magnitude) was seen with three-quarters of the dataset. The analysis also helps to identify potential outlier types of (fragrance) chemistry, exemplified by hexyl and benzyl salicylates (an over-prediction) and trans-2-hexenal (an under-prediction).

[Validation of the abbreviated Zarit scales for measuring burden syndrome in the primary caregiver of an elderly patient].

PubMed

Vélez Lopera, Johana María; Berbesí Fernández, Dedsy; Cardona Arango, Doris; Segura Cardona, Angela; Ordóñez Molina, Jaime

2012-07-01

To determine which abbreviated Zarit Scale (ZS) better evaluates the burden of the caregiver of an elderly patient in Medellin, Colombia. Validation study. Primary Care setting in the city of Medellin. Primary caregiver of dependent elderly patients over 65 years old. Sensitivity, specificity, positive predictive value, and negative predictive value for the different abbreviated Zarit scales, plus performing a reliability analysis using the Cronbach Alpha coefficient. The abbreviated scales obtained a sensitivity of between 36.84 and 81.58%, specificity between 95.99 and 100%, positive predictive values between 71.05 and 100%, and negative predictive values of between 91.64 and 97.42%. The scale that better determined caregiver burden in Primary Care was the Bedard Screening scale, with a sensitivity of 81.58%, a specificity of 96.35% and positive and negative predictive values of 75.61% and 97.42%, respectively. Copyright © 2010 Elsevier España, S.L. All rights reserved.

Can histogram analysis of MR images predict aggressiveness in pancreatic neuroendocrine tumors?

PubMed

De Robertis, Riccardo; Maris, Bogdan; Cardobi, Nicolò; Tinazzi Martini, Paolo; Gobbo, Stefano; Capelli, Paola; Ortolani, Silvia; Cingarlini, Sara; Paiella, Salvatore; Landoni, Luca; Butturini, Giovanni; Regi, Paolo; Scarpa, Aldo; Tortora, Giampaolo; D'Onofrio, Mirko

2018-06-01

To evaluate MRI derived whole-tumour histogram analysis parameters in predicting pancreatic neuroendocrine neoplasm (panNEN) grade and aggressiveness. Pre-operative MR of 42 consecutive patients with panNEN >1 cm were retrospectively analysed. T1-/T2-weighted images and ADC maps were analysed. Histogram-derived parameters were compared to histopathological features using the Mann-Whitney U test. Diagnostic accuracy was assessed by ROC-AUC analysis; sensitivity and specificity were assessed for each histogram parameter. ADC entropy was significantly higher in G2-3 tumours with ROC-AUC 0.757; sensitivity and specificity were 83.3 % (95 % CI: 61.2-94.5) and 61.1 % (95 % CI: 36.1-81.7). ADC kurtosis was higher in panNENs with vascular involvement, nodal and hepatic metastases (p= .008, .021 and .008; ROC-AUC= 0.820, 0.709 and 0.820); sensitivity and specificity were: 85.7/74.3 % (95 % CI: 42-99.2 /56.4-86.9), 36.8/96.5 % (95 % CI: 17.2-61.4 /76-99.8) and 100/62.8 % (95 % CI: 56.1-100/44.9-78.1). No significant differences between groups were found for other histogram-derived parameters (p >.05). Whole-tumour histogram analysis of ADC maps may be helpful in predicting tumour grade, vascular involvement, nodal and liver metastases in panNENs. ADC entropy and ADC kurtosis are the most accurate parameters for identification of panNENs with malignant behaviour. • Whole-tumour ADC histogram analysis can predict aggressiveness in pancreatic neuroendocrine neoplasms. • ADC entropy and kurtosis are higher in aggressive tumours. • ADC histogram analysis can quantify tumour diffusion heterogeneity. • Non-invasive quantification of tumour heterogeneity can provide adjunctive information for prognostication.

Adjoint-Based Sensitivity and Uncertainty Analysis for Density and Composition: A User’s Guide

DOE PAGES

Favorite, Jeffrey A.; Perko, Zoltan; Kiedrowski, Brian C.; ...

2017-03-01

The ability to perform sensitivity analyses using adjoint-based first-order sensitivity theory has existed for decades. This paper provides guidance on how adjoint sensitivity methods can be used to predict the effect of material density and composition uncertainties in critical experiments, including when these uncertain parameters are correlated or constrained. Two widely used Monte Carlo codes, MCNP6 (Ref. 2) and SCALE 6.2 (Ref. 3), are both capable of computing isotopic density sensitivities in continuous energy and angle. Additionally, Perkó et al. have shown how individual isotope density sensitivities, easily computed using adjoint methods, can be combined to compute constrained first-order sensitivitiesmore » that may be used in the uncertainty analysis. This paper provides details on how the codes are used to compute first-order sensitivities and how the sensitivities are used in an uncertainty analysis. Constrained first-order sensitivities are computed in a simple example problem.« less

Predicting Risk Sensitivity in Humans and Lower Animals: Risk as Variance or Coefficient of Variation

ERIC Educational Resources Information Center

Weber, Elke U.; Shafir, Sharoni; Blais, Ann-Renee

2004-01-01

This article examines the statistical determinants of risk preference. In a meta-analysis of animal risk preference (foraging birds and insects), the coefficient of variation (CV), a measure of risk per unit of return, predicts choices far better than outcome variance, the risk measure of normative models. In a meta-analysis of human risk…

CADDIS Volume 4. Data Analysis: Advanced Analyses - Controlling for Natural Variability: SSD Plot Diagrams

EPA Pesticide Factsheets

Methods for controlling natural variability, predicting environmental conditions from biological observations method, biological trait data, species sensitivity distributions, propensity scores, Advanced Analyses of Data Analysis references.

Value of lower respiratory tract surveillance cultures to predict bacterial pathogens in ventilator-associated pneumonia: systematic review and diagnostic test accuracy meta-analysis.

PubMed

Brusselaers, Nele; Labeau, Sonia; Vogelaers, Dirk; Blot, Stijn

2013-03-01

In ventilator-associated pneumonia (VAP), early appropriate antimicrobial therapy may be hampered by involvement of multidrug-resistant (MDR) pathogens. A systematic review and diagnostic test accuracy meta-analysis were performed to analyse whether lower respiratory tract surveillance cultures accurately predict the causative pathogens of subsequent VAP in adult patients. Selection and assessment of eligibility were performed by three investigators by mutual consideration. Of the 525 studies retrieved, 14 were eligible for inclusion (all in English; published since 1994), accounting for 791 VAP episodes. The following data were collected: study and population characteristics; in- and exclusion criteria; diagnostic criteria for VAP; microbiological workup of surveillance and diagnostic VAP cultures. Sub-analyses were conducted for VAP caused by Staphylococcus aureus, Pseudomonas spp., and Acinetobacter spp., MDR microorganisms, frequency of sampling, and consideration of all versus the most recent surveillance cultures. The meta-analysis showed a high accuracy of surveillance cultures, with pooled sensitivities up to 0.75 and specificities up to 0.92 in culture-positive VAP. The area under the curve (AUC) of the hierarchical summary receiver-operating characteristic curve demonstrates moderate accuracy (AUC: 0.90) in predicting multidrug resistance. A sampling frequency of >2/week (sensitivity 0.79; specificity 0.96) and consideration of only the most recent surveillance culture (sensitivity 0.78; specificity 0.96) are associated with a higher accuracy of prediction. This study provides evidence for the benefit of surveillance cultures in predicting MDR bacterial pathogens in VAP. However, clinical and statistical heterogeneity, limited samples sizes, and bias remain important limitations of this meta-analysis.

[The role of endotracheal aspirate culture in the diagnosis of ventilator-associated pneumonia: a meta analysis].

PubMed

Wang, Fei; He, Bei

2013-01-01

To investigate the role of endotracheal aspirate (EA) culture in the diagnosis and antibiotic management in ventilator-associated pneumonia (VAP). We searched CNKI, Wanfang, PUBMED and EMBASE databases published from January 1990 to December 2011, to find relevant literatures on VAP microbiological diagnostic techniques including EA and bronchoalveolar lavage (BALF). The following key words were used: ventilator associated pneumonia, diagnosis and adult. Meta-analysis was performed and the sensitivity and specificity of EA on VAP diagnosis were calculated. Our literature search identified 1665 potential articles, 8 of which fulfilled our selection criteria including 561 patients with paired cultures. Using BALF quantitative culture as reference standard, the sensitivity and specificity of EA were 72% and 71%. When considering quantitative culture of EA only, the sensitivity and specificity improved to 90% and 65%, while the positive and the negative predictive values were 68% and 89% respectively. However, the sensitivity and specificity of semi-quantitative culture of EA were only 50% and 80%, with a positive predictive value of 77% and a negative predictive value of 58% respectively. EA culture had relatively poor sensitivity and specificity, although quantitative culture of EA only could improve the sensitivity. Initiating therapy on the basis of EA quantitative culture may still result in excessive antibiotic usage. Our data suggested that EA could provide some information for clinical decision but could not replace the role of BALF quantitative culture in VAP diagnosis.

Cell death, perfusion and electrical parameters are critical in models of hepatic radiofrequency ablation

PubMed Central

Hall, Sheldon K.; Ooi, Ean H.; Payne, Stephen J.

2015-01-01

Abstract Purpose: A sensitivity analysis has been performed on a mathematical model of radiofrequency ablation (RFA) in the liver. The purpose of this is to identify the most important parameters in the model, defined as those that produce the largest changes in the prediction. This is important in understanding the role of uncertainty and when comparing the model predictions to experimental data. Materials and methods: The Morris method was chosen to perform the sensitivity analysis because it is ideal for models with many parameters or that take a significant length of time to obtain solutions. A comprehensive literature review was performed to obtain ranges over which the model parameters are expected to vary, crucial input information. Results: The most important parameters in predicting the ablation zone size in our model of RFA are those representing the blood perfusion, electrical conductivity and the cell death model. The size of the 50 °C isotherm is sensitive to the electrical properties of tissue while the heat source is active, and to the thermal parameters during cooling. Conclusions: The parameter ranges chosen for the sensitivity analysis are believed to represent all that is currently known about their values in combination. The Morris method is able to compute global parameter sensitivities taking into account the interaction of all parameters, something that has not been done before. Research is needed to better understand the uncertainties in the cell death, electrical conductivity and perfusion models, but the other parameters are only of second order, providing a significant simplification. PMID:26000972

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

Longer-Term Investigation of the Value of 18F-FDG-PET and Magnetic Resonance Imaging for Predicting the Conversion of Mild Cognitive Impairment to Alzheimer's Disease: A Multicenter Study.

PubMed

Inui, Yoshitaka; Ito, Kengo; Kato, Takashi

2017-01-01

The value of fluorine-18-fluorodeoxyglucose positron emission tomography (18F-FDG-PET) and magnetic resonance imaging (MRI) for predicting conversion of mild cognitive impairment (MCI) to Alzheimer's disease (AD) in longer-term is unclear. To evaluate longer-term prediction of MCI to AD conversion using 18F-FDG-PET and MRI in a multicenter study. One-hundred and fourteen patients with MCI were followed for 5 years. They underwent clinical and neuropsychological examinations, 18F-FDG-PET, and MRI at baseline. PET images were visually classified into predefined dementia patterns. PET scores were calculated as a semi quantitative index. For structural MRI, z-scores in medial temporal area were calculated by automated volume-based morphometry (VBM). Overall, 72% patients with amnestic MCI progressed to AD during the 5-year follow-up. The diagnostic accuracy of PET scores over 5 years was 60% with 53% sensitivity and 84% specificity. Visual interpretation of PET images predicted conversion to AD with an overall 82% diagnostic accuracy, 94% sensitivity, and 53% specificity. The accuracy of VBM analysis presented little fluctuation through 5 years and it was highest (73%) at the 5-year follow-up, with 79% sensitivity and 63% specificity. The best performance (87.9% diagnostic accuracy, 89.8% sensitivity, and 82.4% specificity) was with a combination identified using multivariate logistic regression analysis that included PET visual interpretation, educational level, and neuropsychological tests as predictors. 18F-FDG-PET visual assessment showed high performance for predicting conversion to AD from MCI, particularly in combination with neuropsychological tests. PET scores showed high diagnostic specificity. Structural MRI focused on the medial temporal area showed stable predictive value throughout the 5-year course.

Acceleration and sensitivity analysis of lattice kinetic Monte Carlo simulations using parallel processing and rate constant rescaling

NASA Astrophysics Data System (ADS)

Núñez, M.; Robie, T.; Vlachos, D. G.

2017-10-01

Kinetic Monte Carlo (KMC) simulation provides insights into catalytic reactions unobtainable with either experiments or mean-field microkinetic models. Sensitivity analysis of KMC models assesses the robustness of the predictions to parametric perturbations and identifies rate determining steps in a chemical reaction network. Stiffness in the chemical reaction network, a ubiquitous feature, demands lengthy run times for KMC models and renders efficient sensitivity analysis based on the likelihood ratio method unusable. We address the challenge of efficiently conducting KMC simulations and performing accurate sensitivity analysis in systems with unknown time scales by employing two acceleration techniques: rate constant rescaling and parallel processing. We develop statistical criteria that ensure sufficient sampling of non-equilibrium steady state conditions. Our approach provides the twofold benefit of accelerating the simulation itself and enabling likelihood ratio sensitivity analysis, which provides further speedup relative to finite difference sensitivity analysis. As a result, the likelihood ratio method can be applied to real chemistry. We apply our methodology to the water-gas shift reaction on Pt(111).

Role of androgen ratios in the prediction of the metabolic phenotype in polycystic ovary syndrome.

PubMed

Minooee, Sonia; Ramezani Tehrani, Fahimeh; Tohidi, Maryam; Azizi, Fereidoun

2017-05-01

To identify the androgen ratio that best predicts insulin resistance and metabolic syndrome among women with polycystic ovary syndrome (PCOS). Data for 180 women with PCOS and 180 healthy controls were extracted from two previous studies in Iran (conducted during 2008-2010 and 2011-2013). The diagnosis of PCOS was based on the Rotterdam criteria. The serum concentration of different androgens was measured. Receiver operating characteristic curve analysis was used to assess the ability of various androgen ratios to predict insulin resistance and metabolic syndrome. Among women with PCOS, the testosterone-to-androstenedione ratio was the best predictor of insulin resistance (sensitivity 0.83, specificity 0.42) and metabolic syndrome (sensitivity 0.85, specificity 0.70). Among healthy controls, the ratio of free androgen index to testosterone was the best predictor of insulin resistance (sensitivity 0.84, specificity 0.33) and metabolic syndrome (sensitivity 0.91, specificity 0.17). The prediction of insulin resistance and metabolic syndrome among women with PCOS was best accomplished with the testosterone-to-androstenedione ratio. © 2017 International Federation of Gynecology and Obstetrics.

Diagnostic value and cost utility analysis for urine Gram stain and urine microscopic examination as screening tests for urinary tract infection.

PubMed

Wiwanitkit, Viroj; Udomsantisuk, Nibhond; Boonchalermvichian, Chaiyaporn

2005-06-01

The aim of this study was to evaluate the diagnostic properties of urine Gram stain and urine microscopic examination for screening for urinary tract infection (UTI), and to perform an additional cost utility analysis. This descriptive study was performed on 95 urine samples sent for urine culture to the Department of Microbiology, Faculty of Medicine, Chulalongkorn University. The first part of the study was to determine the diagnostic properties of two screening tests (urine Gram stain and urine microscopic examination). Urine culture was set as the gold standard and the results from both methods were compared to this. The second part of this study was to perform a cost utility analysis. The sensitivity of urine Gram stain was 96.2%, the specificity 93.0%, the positive predictive value 94.3% and the negative predictive value 95.2%. False positives occurred with a frequency of 7.0% and false negatives 3.8%. For the microscopic examination, the sensitivity was 65.4%, specificity 74.4%, positive predictive value 75.6% and negative predictive value 64.0%. False positives occurred with a frequency of 25.6% and false negatives 34.6%. Combining urine Gram stain and urine microscopic examination, the sensitivity was 98.1%, specificity 74.4%, positive predictive value 82.3% and negative predictive value 97.0%. False positives occurred with a frequency of 25.6% and false negatives 1.9%. However, the cost per utility of the combined method was higher than either urine microscopic examination or urine Gram stain alone. Urine Gram stain provided the lowest cost per utility. Economically, urine Gram stain is the proper screening tool for presumptive diagnosis of UTI.

Multi-scale Modeling of the Impact Response of a Strain Rate Sensitive High-Manganese Austenitic Steel

NASA Astrophysics Data System (ADS)

Önal, Orkun; Ozmenci, Cemre; Canadinc, Demircan

2014-09-01

A multi-scale modeling approach was applied to predict the impact response of a strain rate sensitive high-manganese austenitic steel. The roles of texture, geometry and strain rate sensitivity were successfully taken into account all at once by coupling crystal plasticity and finite element (FE) analysis. Specifically, crystal plasticity was utilized to obtain the multi-axial flow rule at different strain rates based on the experimental deformation response under uniaxial tensile loading. The equivalent stress - equivalent strain response was then incorporated into the FE model for the sake of a more representative hardening rule under impact loading. The current results demonstrate that reliable predictions can be obtained by proper coupling of crystal plasticity and FE analysis even if the experimental flow rule of the material is acquired under uniaxial loading and at moderate strain rates that are significantly slower than those attained during impact loading. Furthermore, the current findings also demonstrate the need for an experiment-based multi-scale modeling approach for the sake of reliable predictions of the impact response.

Blood DNA methylation biomarkers predict clinical reactivity in food-sensitized infants.

PubMed

Martino, David; Dang, Thanh; Sexton-Oates, Alexandra; Prescott, Susan; Tang, Mimi L K; Dharmage, Shyamali; Gurrin, Lyle; Koplin, Jennifer; Ponsonby, Anne-Louise; Allen, Katrina J; Saffery, Richard

2015-05-01

The diagnosis of food allergy (FA) can be challenging because approximately half of food-sensitized patients are asymptomatic. Current diagnostic tests are excellent makers of sensitization but poor predictors of clinical reactivity. Thus oral food challenges (OFCs) are required to determine a patient's risk of reactivity. We sought to discover genomic biomarkers of clinical FA with utility for predicting food challenge outcomes. Genome-wide DNA methylation (DNAm) profiling was performed on blood mononuclear cells from volunteers who had undergone objective OFCs, concurrent skin prick tests, and specific IgE tests. Fifty-eight food-sensitized patients (aged 11-15 months) were assessed, half of whom were clinically reactive. Thirteen nonallergic control subjects were also assessed. Reproducibility was assessed in an additional 48 samples by using methylation data from an independent population of patients with clinical FA. Using a supervised learning approach, we discovered a DNAm signature of 96 CpG sites that predict clinical outcomes. Diagnostic scores were derived from these 96 methylation sites, and cutoffs were determined in a sensitivity analysis. Methylation biomarkers outperformed allergen-specific IgE and skin prick tests for predicting OFC outcomes. FA status was correctly predicted in the replication cohort with an accuracy of 79.2%. DNAm biomarkers with clinical utility for predicting food challenge outcomes are readily detectable in blood. The development of this technology in detailed follow-up studies will yield highly innovative diagnostic assays. Copyright © 2015 American Academy of Allergy, Asthma & Immunology. Published by Elsevier Inc. All rights reserved.

IATA for skin sensitization potential – 1 out of 2 or 2 out of 3? ...

EPA Pesticide Factsheets

To meet EU regulatory requirements and to avoid or minimize animal testing, there is a need for non-animal methods to assess skin sensitization potential. Given the complexity of the skin sensitization endpoint, there is an expectation that integrated testing and assessment approaches (IATA) will need to be developed which rely on assays representing key events in the pathway. Three non-animal assays have been formally validated: the direct peptide reactivity assay (DPRA), the KeratinoSensTM assay and the h-CLAT assay. At the same time, there have been many efforts to develop IATA with the “2 out of 3” approach attracting much attention whereby a chemical is classified on the basis of the majority outcome. A set of 271 chemicals with mouse, human and non-animal sensitization test data was evaluated to compare the predictive performances of the 3 individual non-animal assays, their binary combinations and the ‘2 out of 3’ approach. The analysis revealed that the most predictive approach was to use both the DPRA and h-CLAT: 1. Perform DPRA – if positive, classify as a sensitizer; 2. If negative, perform h-CLAT – a positive outcome denotes a sensitizer, a negative, a non-sensitizer. With this approach, 83% (LLNA) and 93% (human) of the non-sensitizer predictions were correct, in contrast to the ‘2 out of 3’ approach which had 69% (LLNA) and 79% (human) of non-sensitizer predictions correct. The views expressed are those of the authors and do not ne

Automatic burst detection for the EEG of the preterm infant.

PubMed

Jennekens, Ward; Ruijs, Loes S; Lommen, Charlotte M L; Niemarkt, Hendrik J; Pasman, Jaco W; van Kranen-Mastenbroek, Vivianne H J M; Wijn, Pieter F F; van Pul, Carola; Andriessen, Peter

2011-10-01

To aid with prognosis and stratification of clinical treatment for preterm infants, a method for automated detection of bursts, interburst-intervals (IBIs) and continuous patterns in the electroencephalogram (EEG) is developed. Results are evaluated for preterm infants with normal neurological follow-up at 2 years. The detection algorithm (MATLAB®) for burst, IBI and continuous pattern is based on selection by amplitude, time span, number of channels and numbers of active electrodes. Annotations of two neurophysiologists were used to determine threshold values. The training set consisted of EEG recordings of four preterm infants with postmenstrual age (PMA, gestational age + postnatal age) of 29-34 weeks. Optimal threshold values were based on overall highest sensitivity. For evaluation, both observers verified detections in an independent dataset of four EEG recordings with comparable PMA. Algorithm performance was assessed by calculation of sensitivity and positive predictive value. The results of algorithm evaluation are as follows: sensitivity values of 90% ± 6%, 80% ± 9% and 97% ± 5% for burst, IBI and continuous patterns, respectively. Corresponding positive predictive values were 88% ± 8%, 96% ± 3% and 85% ± 15%, respectively. In conclusion, the algorithm showed high sensitivity and positive predictive values for bursts, IBIs and continuous patterns in preterm EEG. Computer-assisted analysis of EEG may allow objective and reproducible analysis for clinical treatment.

Post-buckling of a pressured biopolymer spherical shell with the mode interaction

NASA Astrophysics Data System (ADS)

Zhang, Lei; Ru, C. Q.

2018-03-01

Imperfection sensitivity is essential for mechanical behaviour of biopolymer shells characterized by high geometric heterogeneity. The present work studies initial post-buckling and imperfection sensitivity of a pressured biopolymer spherical shell based on non-axisymmetric buckling modes and associated mode interaction. Our results indicate that for biopolymer spherical shells with moderate radius-to-thickness ratio (say, less than 30) and smaller effective bending thickness (say, less than 0.2 times average shell thickness), the imperfection sensitivity predicted based on the axisymmetric mode without the mode interaction is close to the present results based on non-axisymmetric modes with the mode interaction with a small (typically, less than 10%) relative errors. However, for biopolymer spherical shells with larger effective bending thickness, the maximum load an imperfect shell can sustain predicted by the present non-axisymmetric analysis can be significantly (typically, around 30%) lower than those predicted based on the axisymmetric mode without the mode interaction. In such cases, a more accurate non-axisymmetric analysis with the mode interaction, as given in the present work, is required for imperfection sensitivity of pressured buckling of biopolymer spherical shells. Finally, the implications of the present study to two specific types of biopolymer spherical shells (viral capsids and ultrasound contrast agents) are discussed.

Quantitative contrast-enhanced ultrasound evaluation of pathological complete response in patients with locally advanced breast cancer receiving neoadjuvant chemotherapy.

PubMed

Wan, Cai-Feng; Liu, Xue-Song; Wang, Lin; Zhang, Jie; Lu, Jin-Song; Li, Feng-Hua

2018-06-01

To clarify whether the quantitative parameters of contrast-enhanced ultrasound (CEUS) can be used to predict pathological complete response (pCR) in patients with locally advanced breast cancer receiving neoadjuvant chemotherapy (NAC). Fifty-one patients with histologically proved locally advanced breast cancer scheduled for NAC were enrolled. The quantitative data for CEUS and the tumor diameter were collected at baseline and before surgery, and compared with the pathological response. Multiple logistic regression analysis was performed to examine quantitative parameters at CEUS and the tumor diameter to predict the pCR, and receiver operating characteristic (ROC) curve analysis was used as a summary statistic. Multiple logistic regression analysis revealed that PEAK (the maximum intensity of the time-intensity curve during bolus transit), PEAK%, TTP% (time to peak), and diameter% were significant independent predictors of pCR, and the area under the ROC curve was 0.932(Az 1 ), and the sensitivity and specificity to predict pCR were 93.7% and 80.0%. The area under the ROC curve for the quantitative parameters was 0.927(Az 2 ), and the sensitivity and specificity to predict pCR were 81.2% and 94.3%. For diameter%, the area under the ROC curve was 0.786 (Az 3 ), and the sensitivity and specificity to predict pCR were 93.8% and 54.3%. The values of Az 1 and Az 2 were significantly higher than that of Az 3 (P = 0.027 and P = 0.034, respectively). However, there was no significant difference between the values of Az 1 and Az 2 (P = 0.825). Quantitative analysis of tumor blood perfusion with CEUS is superior to diameter% to predict pCR, and can be used as a functional technique to evaluate tumor response to NAC. Copyright © 2018. Published by Elsevier B.V.

Usefulness and limitations of various guinea-pig test methods in detecting human skin sensitizers-validation of guinea-pig tests for skin hypersensitivity.

PubMed

Marzulli, F; Maguire, H C

1982-02-01

Several guinea-pig predictive test methods were evaluated by comparison of results with those obtained with human predictive tests, using ten compounds that have been used in cosmetics. The method involves the statistical analysis of the frequency with which guinea-pig tests agree with the findings of tests in humans. In addition, the frequencies of false positive and false negative predictive findings are considered and statistically analysed. The results clearly demonstrate the superiority of adjuvant tests (complete Freund's adjuvant) in determining skin sensitizers and the overall superiority of the guinea-pig maximization test in providing results similar to those obtained by human testing. A procedure is suggested for utilizing adjuvant and non-adjuvant test methods for characterizing compounds as of weak, moderate or strong sensitizing potential.

Plausibility and parameter sensitivity of micro-finite element-based joint load prediction at the proximal femur.

PubMed

Synek, Alexander; Pahr, Dieter H

2018-06-01

A micro-finite element-based method to estimate the bone loading history based on bone architecture was recently presented in the literature. However, a thorough investigation of the parameter sensitivity and plausibility of this method to predict joint loads is still missing. The goals of this study were (1) to analyse the parameter sensitivity of the joint load predictions at one proximal femur and (2) to assess the plausibility of the results by comparing load predictions of ten proximal femora to in vivo hip joint forces measured with instrumented prostheses (available from www.orthoload.com ). Joint loads were predicted by optimally scaling the magnitude of four unit loads (inclined [Formula: see text] to [Formula: see text] with respect to the vertical axis) applied to micro-finite element models created from high-resolution computed tomography scans ([Formula: see text]m voxel size). Parameter sensitivity analysis was performed by varying a total of nine parameters and showed that predictions of the peak load directions (range 10[Formula: see text]-[Formula: see text]) are more robust than the predicted peak load magnitudes (range 2344.8-4689.5 N). Comparing the results of all ten femora with the in vivo loading data of ten subjects showed that peak loads are plausible both in terms of the load direction (in vivo: [Formula: see text], predicted: [Formula: see text]) and magnitude (in vivo: [Formula: see text], predicted: [Formula: see text]). Overall, this study suggests that micro-finite element-based joint load predictions are both plausible and robust in terms of the predicted peak load direction, but predicted load magnitudes should be interpreted with caution.

Predicting the need for CT imaging in children with minor head injury using an ensemble of Naive Bayes classifiers.

PubMed

Klement, William; Wilk, Szymon; Michalowski, Wojtek; Farion, Ken J; Osmond, Martin H; Verter, Vedat

2012-03-01

Using an automatic data-driven approach, this paper develops a prediction model that achieves more balanced performance (in terms of sensitivity and specificity) than the Canadian Assessment of Tomography for Childhood Head Injury (CATCH) rule, when predicting the need for computed tomography (CT) imaging of children after a minor head injury. CT is widely considered an effective tool for evaluating patients with minor head trauma who have potentially suffered serious intracranial injury. However, its use poses possible harmful effects, particularly for children, due to exposure to radiation. Safety concerns, along with issues of cost and practice variability, have led to calls for the development of effective methods to decide when CT imaging is needed. Clinical decision rules represent such methods and are normally derived from the analysis of large prospectively collected patient data sets. The CATCH rule was created by a group of Canadian pediatric emergency physicians to support the decision of referring children with minor head injury to CT imaging. The goal of the CATCH rule was to maximize the sensitivity of predictions of potential intracranial lesion while keeping specificity at a reasonable level. After extensive analysis of the CATCH data set, characterized by severe class imbalance, and after a thorough evaluation of several data mining methods, we derived an ensemble of multiple Naive Bayes classifiers as the prediction model for CT imaging decisions. In the first phase of the experiment we compared the proposed ensemble model to other ensemble models employing rule-, tree- and instance-based member classifiers. Our prediction model demonstrated the best performance in terms of AUC, G-mean and sensitivity measures. In the second phase, using a bootstrapping experiment similar to that reported by the CATCH investigators, we showed that the proposed ensemble model achieved a more balanced predictive performance than the CATCH rule with an average sensitivity of 82.8% and an average specificity of 74.4% (vs. 98.1% and 50.0% for the CATCH rule respectively). Automatically derived prediction models cannot replace a physician's acumen. However, they help establish reference performance indicators for the purpose of developing clinical decision rules so the trade-off between prediction sensitivity and specificity is better understood. Copyright © 2011 Elsevier B.V. All rights reserved.

Testing alternative ground water models using cross-validation and other methods

USGS Publications Warehouse

Foglia, L.; Mehl, S.W.; Hill, M.C.; Perona, P.; Burlando, P.

2007-01-01

Many methods can be used to test alternative ground water models. Of concern in this work are methods able to (1) rank alternative models (also called model discrimination) and (2) identify observations important to parameter estimates and predictions (equivalent to the purpose served by some types of sensitivity analysis). Some of the measures investigated are computationally efficient; others are computationally demanding. The latter are generally needed to account for model nonlinearity. The efficient model discrimination methods investigated include the information criteria: the corrected Akaike information criterion, Bayesian information criterion, and generalized cross-validation. The efficient sensitivity analysis measures used are dimensionless scaled sensitivity (DSS), composite scaled sensitivity, and parameter correlation coefficient (PCC); the other statistics are DFBETAS, Cook's D, and observation-prediction statistic. Acronyms are explained in the introduction. Cross-validation (CV) is a computationally intensive nonlinear method that is used for both model discrimination and sensitivity analysis. The methods are tested using up to five alternative parsimoniously constructed models of the ground water system of the Maggia Valley in southern Switzerland. The alternative models differ in their representation of hydraulic conductivity. A new method for graphically representing CV and sensitivity analysis results for complex models is presented and used to evaluate the utility of the efficient statistics. The results indicate that for model selection, the information criteria produce similar results at much smaller computational cost than CV. For identifying important observations, the only obviously inferior linear measure is DSS; the poor performance was expected because DSS does not include the effects of parameter correlation and PCC reveals large parameter correlations. ?? 2007 National Ground Water Association.

Evaluation of prediction capability, robustness, and sensitivity in non-linear landslide susceptibility models, Guantánamo, Cuba

NASA Astrophysics Data System (ADS)

Melchiorre, C.; Castellanos Abella, E. A.; van Westen, C. J.; Matteucci, M.

2011-04-01

This paper describes a procedure for landslide susceptibility assessment based on artificial neural networks, and focuses on the estimation of the prediction capability, robustness, and sensitivity of susceptibility models. The study is carried out in the Guantanamo Province of Cuba, where 186 landslides were mapped using photo-interpretation. Twelve conditioning factors were mapped including geomorphology, geology, soils, landuse, slope angle, slope direction, internal relief, drainage density, distance from roads and faults, rainfall intensity, and ground peak acceleration. A methodology was used that subdivided the database in 3 subsets. A training set was used for updating the weights. A validation set was used to stop the training procedure when the network started losing generalization capability, and a test set was used to calculate the performance of the network. A 10-fold cross-validation was performed in order to show that the results are repeatable. The prediction capability, the robustness analysis, and the sensitivity analysis were tested on 10 mutually exclusive datasets. The results show that by means of artificial neural networks it is possible to obtain models with high prediction capability and high robustness, and that an exploration of the effect of the individual variables is possible, even if they are considered as a black-box model.

Heat and mass transport during microwave heating of mashed potato in domestic oven--model development, validation, and sensitivity analysis.

PubMed

Chen, Jiajia; Pitchai, Krishnamoorthy; Birla, Sohan; Negahban, Mehrdad; Jones, David; Subbiah, Jeyamkondan

2014-10-01

A 3-dimensional finite-element model coupling electromagnetics and heat and mass transfer was developed to understand the interactions between the microwaves and fresh mashed potato in a 500 mL tray. The model was validated by performing heating of mashed potato from 25 °C on a rotating turntable in a microwave oven, rated at 1200 W, for 3 min. The simulated spatial temperature profiles on the top and bottom layer of the mashed potato showed similar hot and cold spots when compared to the thermal images acquired by an infrared camera. Transient temperature profiles at 6 locations collected by fiber-optic sensors showed good agreement with predicted results, with the root mean square error ranging from 1.6 to 11.7 °C. The predicted total moisture loss matched well with the observed result. Several input parameters, such as the evaporation rate constant, the intrinsic permeability of water and gas, and the diffusion coefficient of water and gas, are not readily available for mashed potato, and they cannot be easily measured experimentally. Reported values for raw potato were used as baseline values. A sensitivity analysis of these input parameters on the temperature profiles and the total moisture loss was evaluated by changing the baseline values to their 10% and 1000%. The sensitivity analysis showed that the gas diffusion coefficient, intrinsic water permeability, and the evaporation rate constant greatly influenced the predicted temperature and total moisture loss, while the intrinsic gas permeability and the water diffusion coefficient had little influence. This model can be used by the food product developers to understand microwave heating of food products spatially and temporally. This tool will allow food product developers to design food package systems that would heat more uniformly in various microwave ovens. The sensitivity analysis of this study will help us determine the most significant parameters that need to be measured accurately for reliable model prediction. © 2014 Institute of Food Technologists®

Uncertainty and sensitivity analysis of control strategies using the benchmark simulation model No1 (BSM1).

PubMed

Flores-Alsina, Xavier; Rodriguez-Roda, Ignasi; Sin, Gürkan; Gernaey, Krist V

2009-01-01

The objective of this paper is to perform an uncertainty and sensitivity analysis of the predictions of the Benchmark Simulation Model (BSM) No. 1, when comparing four activated sludge control strategies. The Monte Carlo simulation technique is used to evaluate the uncertainty in the BSM1 predictions, considering the ASM1 bio-kinetic parameters and influent fractions as input uncertainties while the Effluent Quality Index (EQI) and the Operating Cost Index (OCI) are focused on as model outputs. The resulting Monte Carlo simulations are presented using descriptive statistics indicating the degree of uncertainty in the predicted EQI and OCI. Next, the Standard Regression Coefficients (SRC) method is used for sensitivity analysis to identify which input parameters influence the uncertainty in the EQI predictions the most. The results show that control strategies including an ammonium (S(NH)) controller reduce uncertainty in both overall pollution removal and effluent total Kjeldahl nitrogen. Also, control strategies with an external carbon source reduce the effluent nitrate (S(NO)) uncertainty increasing both their economical cost and variability as a trade-off. Finally, the maximum specific autotrophic growth rate (micro(A)) causes most of the variance in the effluent for all the evaluated control strategies. The influence of denitrification related parameters, e.g. eta(g) (anoxic growth rate correction factor) and eta(h) (anoxic hydrolysis rate correction factor), becomes less important when a S(NO) controller manipulating an external carbon source addition is implemented.

Vasospasm on transcranial Doppler is predictive of delayed cerebral ischemia in aneurysmal subarachnoid hemorrhage: a systematic review and meta-analysis.

PubMed

Kumar, Gyanendra; Shahripour, Reza Bavarsad; Harrigan, Mark R

2016-05-01

OBJECT The impact of transcranial Doppler (TCD) ultrasonography evidence of vasospasm on patient-centered clinical outcomes following aneurysmal subarachnoid hemorrhage (aSAH) is unknown. Vasospasm is known to lead to delayed cerebral ischemia (DCI) and poor outcomes. This systematic review and meta-analysis evaluates the predictive value of vasospasm on DCI, as diagnosed on TCD. METHODS MEDLINE, Scopus, the Cochrane trial register, and clinicaltrials.gov were searched through September 2014 using key words and the terms "subarachnoid hemorrhage," "aneurysm," "aneurysmal," "cerebral vasospasm," "vasospasm," "transcranial Doppler," and "TCD." Sensitivities, specificities, and positive and negative predictive values were pooled by a DerSimonian and Laird random-effects model. RESULTS Seventeen studies (n = 2870 patients) met inclusion criteria. The amount of variance attributable to heterogeneity was significant (I(2) > 50%) for all syntheses. No studies reported the impact of TCD evidence of vasospasm on functional outcome or mortality. TCD evidence of vasospasm was found to be highly predictive of DCI. Pooled estimates for TCD diagnosis of vasospasm (for DCI) were sensitivity 90% (95% confidence interval [CI] 77%-96%), specificity 71% (95% CI 51%-84%), positive predictive value 57% (95% CI 38%-71%), and negative predictive value 92% (95% CI 83%-96%). CONCLUSIONS TCD evidence of vasospasm is predictive of DCI with high accuracy. Although high sensitivity and negative predictive value make TCD an ideal monitoring device, it is not a mandated standard of care in aSAH due to the paucity of evidence on clinically relevant outcomes, despite recommendation by national guidelines. High-quality randomized trials evaluating the impact of TCD monitoring on patient-centered and physician-relevant outcomes are needed.

Ottawa Ankle Rules and Subjective Surgeon Perception to Evaluate Radiograph Necessity Following Foot and Ankle Sprain

PubMed Central

Pires, RES; Pereira, AA; Abreu-e-Silva, GM; Labronici, PJ; Figueiredo, LB; Godoy-Santos, AL; Kfuri, M

2014-01-01

Background: Foot and ankle injuries are frequent in emergency departments. Although only a few patients with foot and ankle sprain present fractures and the fracture patterns are almost always simple, lack of fracture diagnosis can lead to poor functional outcomes. Aim: The present study aims to evaluate the reliability of the Ottawa ankle rules and the orthopedic surgeon subjective perception to assess foot and ankle fractures after sprains. Subjects and Methods: A cross-sectional study was conducted from July 2012 to December 2012. Ethical approval was granted. Two hundred seventy-four adult patients admitted to the emergency department with foot and/or ankle sprain were evaluated by an orthopedic surgeon who completed a questionnaire prior to radiographic assessment. The Ottawa ankle rules and subjective perception of foot and/or ankle fractures were evaluated on the questionnaire. Results: Thirteen percent (36/274) patients presented fracture. Orthopedic surgeon subjective analysis showed 55.6% sensitivity, 90.1% specificity, 46.5% positive predictive value and 92.9% negative predictive value. The general orthopedic surgeon opinion accuracy was 85.4%. The Ottawa ankle rules presented 97.2% sensitivity, 7.8% specificity, 13.9% positive predictive value, 95% negative predictive value and 19.9% accuracy respectively. Weight-bearing inability was the Ottawa ankle rule item that presented the highest reliability, 69.4% sensitivity, 61.6% specificity, 63.1% accuracy, 21.9% positive predictive value and 93% negative predictive value respectively. Conclusion: The Ottawa ankle rules showed high reliability for deciding when to take radiographs in foot and/or ankle sprains. Weight-bearing inability was the most important isolated item to predict fracture presence. Orthopedic surgeon subjective analysis to predict fracture possibility showed a high specificity rate, representing a confident method to exclude unnecessary radiographic exams. PMID:24971221

Sensitivity, specificity and predictive values of anterior chamber tap in cases of bacterial endophthalmitis.

PubMed

Sjoholm-Gomez de Liano, Carl; Soberon-Ventura, Vidal F; Salcedo-Villanueva, Guillermo; Santos-Palacios, Abril; Guerrero-Naranjo, Jose Luis; Fromow-Guerra, Jans; García-Aguirre, Gerardo; Morales-Canton, Virgilio; Velez-Montoya, Raul

2017-01-01

To assess the sensitivity, specificity, positive predictive value and negative predictive value of anterior chamber tap for the diagnosis of bacterial endophthalmitis on a population with high prevalence. Retrospective, single centre, case series study. We reviewed all medical records with clinical diagnosis of bacterial endophthalmitis in our hospital from January 1st, 2000 to December 31st 2014. From each record, we documented general demographic data, best corrected visual acuity and vitreous and aqueous tap microbiological results. All cases were further divided according to the endophthalmitis aetiology to perform individual calculations of sensitivity, specificity, positive predictive value, negative predictive value, accuracy and prevalence. We used the results of the vitreous tap as the gold standard for diagnosis of bacterial endophthalmitis. We excluded those records in which the aqueous and vitreous samples were not taken simultaneously or had an incomplete microbiological report. Significance were assessed with chi squared statistics, with an alpha value of 0.05 for statistical significance. A total of 190 cases fulfilled the inclusion/exclusion criteria. Positive culture rate from vitreous samples was 64.74%. Positive culture rate from aqueous sample was 32.11%. Bacteria isolated from aqueous samples matched those isolated from vitreous samples 78.68% of the time. The overall sensitivity was 38.21%, specificity: 75.51%, positive predictive value: 79.66%, negative predictive value: 32.74% ( p  = 0.08). Subgroup analysis showed that anterior chamber taps in cases of post-surgical endophthalmitis had a moderate to low sensitivity (37.73%), high specificity (93%) and high positive predictive value (95%) ( p  < 0.04). The sensitivity and specificity of anterior chamber tap are low and should not be used for critical therapeutic decisions in patients with suspected bacterial endophthalmitis. In cases of post-surgical endophthalmitis, the result of an anterior chamber tap could be used for therapeutic guidance, but only in conjunction with clinical presentation and in the absence of a better method for diagnosis.

The use of atmospheric measurements to constrain model predictions of ozone change from chlorine perturbations

NASA Technical Reports Server (NTRS)

Douglass, Anne R.; Stolarski, Richard S.

1987-01-01

Atmospheric photochemistry models have been used to predict the sensitivity of the ozone layer to various perturbations. These same models also predict concentrations of chemical species in the present day atmosphere which can be compared to observations. Model results for both present day values and sensitivity to perturbation depend upon input data for reaction rates, photodissociation rates, and boundary conditions. A method of combining the results of a Monte Carlo uncertainty analysis with the existing set of present atmospheric species measurements is developed. The method is used to examine the range of values for the sensitivity of ozone to chlorine perturbations that is possible within the currently accepted ranges for input data. It is found that model runs which predict ozone column losses much greater than 10 percent as a result of present fluorocarbon fluxes produce concentrations and column amounts in the present atmosphere which are inconsistent with the measurements for ClO, HCl, NO, NO2, and HNO3.

Evaluation of Bioinformatic Programmes for the Analysis of Variants within Splice Site Consensus Regions

PubMed Central

Tang, Rongying; Prosser, Debra O.; Love, Donald R.

2016-01-01

The increasing diagnostic use of gene sequencing has led to an expanding dataset of novel variants that lie within consensus splice junctions. The challenge for diagnostic laboratories is the evaluation of these variants in order to determine if they affect splicing or are merely benign. A common evaluation strategy is to use in silico analysis, and it is here that a number of programmes are available online; however, currently, there are no consensus guidelines on the selection of programmes or protocols to interpret the prediction results. Using a collection of 222 pathogenic mutations and 50 benign polymorphisms, we evaluated the sensitivity and specificity of four in silico programmes in predicting the effect of each variant on splicing. The programmes comprised Human Splice Finder (HSF), Max Entropy Scan (MES), NNSplice, and ASSP. The MES and ASSP programmes gave the highest performance based on Receiver Operator Curve analysis, with an optimal cut-off of score reduction of 10%. The study also showed that the sensitivity of prediction is affected by the level of conservation of individual positions, with in silico predictions for variants at positions −4 and +7 within consensus splice sites being largely uninformative. PMID:27313609

SENSITIVITY ANALYSIS AND EVALUATION OF MICROFACPM: A MICROSCALE MOTOR VEHICLE EMISSION FACTOR MODEL FOR PARTICULATE MATTER EMISSIONS

EPA Science Inventory

A microscale emission factor model (MicroFacPM) for predicting real-time site-specific motor vehicle particulate matter emissions was presented in the companion paper entitled "Development of a Microscale Emission Factor Model for Particulate Matter (MicroFacPM) for Predicting Re...

Effects of soil moisture on the diurnal pattern of pesticide emission: Numerical simulation and sensitivity analysis

USDA-ARS?s Scientific Manuscript database

Accurate prediction of pesticide volatilization is important for the protection of human and environmental health. Due to the complexity of the volatilization process, sophisticated predictive models are needed, especially for dry soil conditions. A mathematical model was developed to allow simulati...

Combination Testing Using a Single MSH5 Variant alongside HLA Haplotypes Improves the Sensitivity of Predicting Coeliac Disease Risk in the Polish Population.

PubMed

Paziewska, Agnieszka; Cukrowska, Bozena; Dabrowska, Michalina; Goryca, Krzysztof; Piatkowska, Magdalena; Kluska, Anna; Mikula, Michal; Karczmarski, Jakub; Oralewska, Beata; Rybak, Anna; Socha, Jerzy; Balabas, Aneta; Zeber-Lubecka, Natalia; Ambrozkiewicz, Filip; Konopka, Ewa; Trojanowska, Ilona; Zagroba, Malgorzata; Szperl, Malgorzata; Ostrowski, Jerzy

2015-01-01

Assessment of non-HLA variants alongside standard HLA testing was previously shown to improve the identification of potential coeliac disease (CD) patients. We intended to identify new genetic variants associated with CD in the Polish population that would improve CD risk prediction when used alongside HLA haplotype analysis. DNA samples of 336 CD and 264 unrelated healthy controls were used to create DNA pools for a genome wide association study (GWAS). GWAS findings were validated with individual HLA tag single nucleotide polymorphism (SNP) typing of 473 patients and 714 healthy controls. Association analysis using four HLA-tagging SNPs showed that, as was found in other populations, positive predicting genotypes (HLA-DQ2.5/DQ2.5, HLA-DQ2.5/DQ2.2, and HLA-DQ2.5/DQ8) were found at higher frequencies in CD patients than in healthy control individuals in the Polish population. Both CD-associated SNPs discovered by GWAS were found in the CD susceptibility region, confirming the previously-determined association of the major histocompatibility (MHC) region with CD pathogenesis. The two most significant SNPs from the GWAS were rs9272346 (HLA-dependent; localized within 1 Kb of DQA1) and rs3130484 (HLA-independent; mapped to MSH5). Specificity of CD prediction using the four HLA-tagging SNPs achieved 92.9%, but sensitivity was only 45.5%. However, when a testing combination of the HLA-tagging SNPs and the MSH5 SNP was used, specificity decreased to 80%, and sensitivity increased to 74%. This study confirmed that improvement of CD risk prediction sensitivity could be achieved by including non-HLA SNPs alongside HLA SNPs in genetic testing.

Nervous System Sensitization as a Predictor of Outcome in the Treatment of Peripheral Musculoskeletal Conditions: A Systematic Review.

PubMed

O'Leary, Helen; Smart, Keith M; Moloney, Niamh A; Doody, Catherine M

2017-02-01

Research suggests that peripheral and central nervous system sensitization can contribute to the overall pain experience in peripheral musculoskeletal (MSK) conditions. It is unclear, however, whether sensitization of the nervous system results in poorer outcomes following the treatment. This systematic review investigated whether nervous system sensitization in peripheral MSK conditions predicts poorer clinical outcomes in response to a surgical or conservative intervention. Four electronic databases were searched to identify the relevant studies. Eligible studies had a prospective design, with a follow-up assessing the outcome in terms of pain or disability. Studies that used baseline indices of nervous system sensitization were included, such as quantitative sensory testing (QST) or questionnaires that measured centrally mediated symptoms. Thirteen studies met the inclusion criteria, of which six were at a high risk of bias. The peripheral MSK conditions investigated were knee and hip osteoarthritis, shoulder pain, and elbow tendinopathy. QST parameters indicative of sensitization (lower electrical pain thresholds, cold hyperalgesia, enhanced temporal summation, lower punctate sharpness thresholds) were associated with negative outcome (more pain or disability) in 5 small exploratory studies. Larger studies that accounted for multiple confounders in design and analysis did not support a predictive relationship between QST parameters and outcome. Two studies used self-report measures to capture comorbid centrally mediated symptoms, and found higher questionnaire scores were independently predictive of more persistent pain following a total joint arthroplasty. This systematic review found insufficient evidence to support an independent predictive relationship between QST measures of nervous system sensitization and treatment outcome. Self-report measures demonstrated better predictive ability. Further high-quality prognostic research is warranted. © 2016 World Institute of Pain.

Sensitivity analysis, calibration, and testing of a distributed hydrological model using error‐based weighting and one objective function

USGS Publications Warehouse

Foglia, L.; Hill, Mary C.; Mehl, Steffen W.; Burlando, P.

2009-01-01

We evaluate the utility of three interrelated means of using data to calibrate the fully distributed rainfall‐runoff model TOPKAPI as applied to the Maggia Valley drainage area in Switzerland. The use of error‐based weighting of observation and prior information data, local sensitivity analysis, and single‐objective function nonlinear regression provides quantitative evaluation of sensitivity of the 35 model parameters to the data, identification of data types most important to the calibration, and identification of correlations among parameters that contribute to nonuniqueness. Sensitivity analysis required only 71 model runs, and regression required about 50 model runs. The approach presented appears to be ideal for evaluation of models with long run times or as a preliminary step to more computationally demanding methods. The statistics used include composite scaled sensitivities, parameter correlation coefficients, leverage, Cook's D, and DFBETAS. Tests suggest predictive ability of the calibrated model typical of hydrologic models.

Tumor invasiveness defined by IASLC/ATS/ERS classification of ground-glass nodules can be predicted by quantitative CT parameters.

PubMed

Zhou, Qian-Jun; Zheng, Zhi-Chun; Zhu, Yong-Qiao; Lu, Pei-Ji; Huang, Jia; Ye, Jian-Ding; Zhang, Jie; Lu, Shun; Luo, Qing-Quan

2017-05-01

To investigate the potential value of CT parameters to differentiate ground-glass nodules between noninvasive adenocarcinoma and invasive pulmonary adenocarcinoma (IPA) as defined by IASLC/ATS/ERS classification. We retrospectively reviewed 211 patients with pathologically proved stage 0-IA lung adenocarcinoma which appeared as subsolid nodules, from January 2012 to January 2013 including 137 pure ground glass nodules (pGGNs) and 74 part-solid nodules (PSNs). Pathological data was classified under the 2011 IASLC/ATS/ERS classification. Both quantitative and qualitative CT parameters were used to determine the tumor invasiveness between noninvasive adenocarcinomas and IPAs. There were 154 noninvasive adenocarcinomas and 57 IPAs. In pGGNs, CT size and area, one-dimensional mean CT value and bubble lucency were significantly different between noninvasive adenocarcinomas and IPAs on univariate analysis. Multivariate regression and ROC analysis revealed that CT size and one-dimensional mean CT value were predictive of noninvasive adenocarcinomas compared to IPAs. Optimal cutoff value was 13.60 mm (sensitivity, 75.0%; specificity, 99.6%), and -583.60 HU (sensitivity, 68.8%; specificity, 66.9%). In PSNs, there were significant differences in CT size and area, solid component area, solid proportion, one-dimensional mean and maximum CT value, three-dimensional (3D) mean CT value between noninvasive adenocarcinomas and IPAs on univariate analysis. Multivariate and ROC analysis showed that CT size and 3D mean CT value were significantly differentiators. Optimal cutoff value was 19.64 mm (sensitivity, 53.7%; specificity, 93.9%), -571.63 HU (sensitivity, 85.4%; specificity, 75.8%). For pGGNs, CT size and one-dimensional mean CT value are determinants for tumor invasiveness. For PSNs, tumor invasiveness can be predicted by CT size and 3D mean CT value.

A contact-area model for rail-pads connections in 2-D simulations: sensitivity analysis of train-induced vibrations

NASA Astrophysics Data System (ADS)

Ferrara, R.; Leonardi, G.; Jourdan, F.

2013-09-01

A numerical model to predict train-induced vibrations is presented. The dynamic computation considers mutual interactions in vehicle/track coupled systems by means of a finite and discrete elements method. The rail defects and the case of out-of-round wheels are considered. The dynamic interaction between the wheel-sets and the rail is accomplished by using the non-linear Hertzian model with hysteresis damping. A sensitivity analysis is done to evaluate the variables affecting more the maintenance costs. The rail-sleeper contact is assumed extended to an area-defined contact zone, rather than a single-point assumption which fits better real case studies. Experimental validations show how prediction fits well experimental data.

Analysis of JPSS J1 VIIRS Polarization Sensitivity Using the NIST T-SIRCUS

NASA Technical Reports Server (NTRS)

McIntire, Jeffrey W.; Young, James B.; Moyer, David; Waluschka, Eugene; Oudrari, Hassan; Xiong, Xiaoxiong

2015-01-01

The polarization sensitivity of the Joint Polar Satellite System (JPSS) J1 Visible Infrared Imaging Radiometer Suite (VIIRS) measured pre-launch using a broadband source was observed to be larger than expected for many reflective bands. Ray trace modeling predicted that the observed polarization sensitivity was the result of larger diattenuation at the edges of the focal plane filter spectral bandpass. Additional ground measurements were performed using a monochromatic source (the NIST T-SIRCUS) to input linearly polarized light at a number of wavelengths across the bandpass of two VIIRS spectral bands and two scan angles. This work describes the data processing, analysis, and results derived from the T-SIRCUS measurements, comparing them with broadband measurements. Results have shown that the observed degree of linear polarization, when weighted by the sensor's spectral response function, is generally larger on the edges and smaller in the center of the spectral bandpass, as predicted. However, phase angle changes in the center of the bandpass differ between model and measurement. Integration of the monochromatic polarization sensitivity over wavelength produced results consistent with the broadband source measurements, for all cases considered.

LC-MS-based characterization of the peptide reactivity of chemicals to improve the in vitro prediction of the skin sensitization potential.

PubMed

Natsch, Andreas; Gfeller, Hans

2008-12-01

A key step in the skin sensitization process is the formation of a covalent adduct between skin sensitizers and endogenous proteins and/or peptides in the skin. Based on this mechanistic understanding, there is a renewed interest in in vitro assays to determine the reactivity of chemicals toward peptides in order to predict their sensitization potential. A standardized peptide reactivity assay yielded a promising predictivity. This published assay is based on high-performance liquid chromatography with ultraviolet detection to quantify peptide depletion after incubation with test chemicals. We had observed that peptide depletion may be due to either adduct formation or peptide oxidation. Here we report a modified assay based on both liquid chromatography-mass spectrometry (LC-MS) analysis and detection of free thiol groups. This approach allows simultaneous determination of (1) peptide depletion, (2) peptide oxidation (dimerization), (3) adduct formation, and (4) thiol reactivity and thus generates a more detailed characterization of the reactivity of a molecule. Highly reactive molecules are further discriminated with a kinetic measure. The assay was validated on 80 chemicals. Peptide depletion could accurately be quantified both with LC-MS detection and depletion of thiol groups. The majority of the moderate/strong/extreme sensitizers formed detectable peptide adducts, but many sensitizers were also able to catalyze peptide oxidation. Whereas adduct formation was only observed for sensitizers, this oxidation reaction was also observed for two nonsensitizing fragrance aldehydes, indicating that peptide depletion might not always be regarded as sufficient evidence for rating a chemical as a sensitizer. Thus, this modified assay gives a more informed view of the peptide reactivity of chemicals to better predict their sensitization potential.

Integrated Modeling Activities for the James Webb Space Telescope: Structural-Thermal-Optical Analysis

NASA Technical Reports Server (NTRS)

Johnston, John D.; Howard, Joseph M.; Mosier, Gary E.; Parrish, Keith A.; McGinnis, Mark A.; Bluth, Marcel; Kim, Kevin; Ha, Kong Q.

2004-01-01

The James Web Space Telescope (JWST) is a large, infrared-optimized space telescope scheduled for launch in 2011. This is a continuation of a series of papers on modeling activities for JWST. The structural-thermal-optical, often referred to as STOP, analysis process is used to predict the effect of thermal distortion on optical performance. The benchmark STOP analysis for JWST assesses the effect of an observatory slew on wavefront error. Temperatures predicted using geometric and thermal math models are mapped to a structural finite element model in order to predict thermally induced deformations. Motions and deformations at optical surfaces are then input to optical models, and optical performance is predicted using either an optical ray trace or a linear optical analysis tool. In addition to baseline performance predictions, a process for performing sensitivity studies to assess modeling uncertainties is described.

Co-acting gene networks predict TRAIL responsiveness of tumour cells with high accuracy.

PubMed

O'Reilly, Paul; Ortutay, Csaba; Gernon, Grainne; O'Connell, Enda; Seoighe, Cathal; Boyce, Susan; Serrano, Luis; Szegezdi, Eva

2014-12-19

Identification of differentially expressed genes from transcriptomic studies is one of the most common mechanisms to identify tumor biomarkers. This approach however is not well suited to identify interaction between genes whose protein products potentially influence each other, which limits its power to identify molecular wiring of tumour cells dictating response to a drug. Due to the fact that signal transduction pathways are not linear and highly interlinked, the biological response they drive may be better described by the relative amount of their components and their functional relationships than by their individual, absolute expression. Gene expression microarray data for 109 tumor cell lines with known sensitivity to the death ligand cytokine tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) was used to identify genes with potential functional relationships determining responsiveness to TRAIL-induced apoptosis. The machine learning technique Random Forest in the statistical environment "R" with backward elimination was used to identify the key predictors of TRAIL sensitivity and differentially expressed genes were identified using the software GeneSpring. Gene co-regulation and statistical interaction was assessed with q-order partial correlation analysis and non-rejection rate. Biological (functional) interactions amongst the co-acting genes were studied with Ingenuity network analysis. Prediction accuracy was assessed by calculating the area under the receiver operator curve using an independent dataset. We show that the gene panel identified could predict TRAIL-sensitivity with a very high degree of sensitivity and specificity (AUC=0·84). The genes in the panel are co-regulated and at least 40% of them functionally interact in signal transduction pathways that regulate cell death and cell survival, cellular differentiation and morphogenesis. Importantly, only 12% of the TRAIL-predictor genes were differentially expressed highlighting the importance of functional interactions in predicting the biological response. The advantage of co-acting gene clusters is that this analysis does not depend on differential expression and is able to incorporate direct- and indirect gene interactions as well as tissue- and cell-specific characteristics. This approach (1) identified a descriptor of TRAIL sensitivity which performs significantly better as a predictor of TRAIL sensitivity than any previously reported gene signatures, (2) identified potential novel regulators of TRAIL-responsiveness and (3) provided a systematic view highlighting fundamental differences between the molecular wiring of sensitive and resistant cell types.

Sensitivity of two dispersion models (AERMOD and ISCST3) to input parameters for a rural ground-level area source.

PubMed

Faulkner, William B; Shaw, Bryan W; Grosch, Tom

2008-10-01

As of December 2006, the American Meteorological Society/U.S. Environmental Protection Agency (EPA) Regulatory Model with Plume Rise Model Enhancements (AERMOD-PRIME; hereafter AERMOD) replaced the Industrial Source Complex Short Term Version 3 (ISCST3) as the EPA-preferred regulatory model. The change from ISCST3 to AERMOD will affect Prevention of Significant Deterioration (PSD) increment consumption as well as permit compliance in states where regulatory agencies limit property line concentrations using modeling analysis. Because of differences in model formulation and the treatment of terrain features, one cannot predict a priori whether ISCST3 or AERMOD will predict higher or lower pollutant concentrations downwind of a source. The objectives of this paper were to determine the sensitivity of AERMOD to various inputs and compare the highest downwind concentrations from a ground-level area source (GLAS) predicted by AERMOD to those predicted by ISCST3. Concentrations predicted using ISCST3 were sensitive to changes in wind speed, temperature, solar radiation (as it affects stability class), and mixing heights below 160 m. Surface roughness also affected downwind concentrations predicted by ISCST3. AERMOD was sensitive to changes in albedo, surface roughness, wind speed, temperature, and cloud cover. Bowen ratio did not affect the results from AERMOD. These results demonstrate AERMOD's sensitivity to small changes in wind speed and surface roughness. When AERMOD is used to determine property line concentrations, small changes in these variables may affect the distance within which concentration limits are exceeded by several hundred meters.

Analysis of the Sensitivity and Specificity of Noninvasive Imaging Tests for the Diagnosis of Renal Artery Stenosis

PubMed Central

Borelli, Flavio Antonio de Oliveira; Pinto, Ibraim M. F.; Amodeo, Celso; Smanio, Paola E. P.; Kambara, Antonio M.; Petisco, Ana Claudia G.; Moreira, Samuel M.; Paiva, Ricardo Calil; Lopes, Hugo Belotti; Sousa, Amanda G. M. R.

2013-01-01

Background Aging and atherosclerosis are related to renovascular hypertension in elderly individuals. Regardless of comorbidities, renal artery stenosis is itself an important cause of cardiovascular morbidity and mortality. Objective To define the sensitivity, specificity, positive predictive value, and negative predictive value of noninvasive imaging tests used in the diagnosis of renal artery stenosis. Methods In a group of 61 patients recruited, 122 arteries were analized, thus permitting the definition of sensitivity, specificity, and the relative contribution of each imaging study performed (Doppler, scintigraphy and computed tomographic angiography in comparison to renal arteriography). Results The mean age was 65.43 years (standard deviation: 8.7). Of the variables related to the study population that were compared to arteriography, two correlated with renal artery stenosis, renal dysfunction and triglycerides. The median glomerular filtration rate was 52.8 mL/min/m2. Doppler showed sensitivity of 82.90%, specificity of 70%, a positive predictive value of 85% and negative predictive value of 66.70%. For tomography, sensitivity was 66.70%, specificity 80%, positive predictive value 87.50% and negative predictive value 55.20%. With these findings, we could identify the imaging tests that best detected stenosis. Conclusion Tomography and Doppler showed good quality and efficacy in the diagnosis of renal artery stenosis, with Doppler having the advantage of not requiring the use of contrast medium for the assessment of a disease that is common in diabetics and is associated with renal dysfunction and severe left ventricular dysfunction. PMID:24061685

Role of the Egami score to predict immunoglobulin resistance in Kawasaki disease among a Western Mediterranean population.

PubMed

Sánchez-Manubens, Judith; Antón, Jordi; Bou, Rosa; Iglesias, Estíbaliz; Calzada-Hernandez, Joan; Borlan, Sergi; Gimenez-Roca, Clara; Rivera, Josefa

2016-07-01

Kawasaki disease is an acute self-limited systemic vasculitis common in childhood. Intravenous immunoglobulin (IVIG) is an effective treatment, and it reduces the incidence of cardiac complications. Egami score has been validated to identify IVIG non-responder patients in Japanese population, and it has shown high sensitivity and specificity to identify these non-responder patients. Although its effectiveness in Japan, Egami score has shown to be ineffective in non-Japanese populations. The aim of this study was to apply the Egami score in a Western Mediterranean population in Catalonia (Spain). Observational population-based study that includes patients from all Pediatric Units in 33 Catalan hospitals, both public and private management, between January 2004 and March 2014. Sensitivity and specificity for the Egami score was calculated, and a logistic regression analysis of predictors of overall response to IVIG was also developed. Predicting IVIG resistance with a cutoff for Egami score ≥3 obtained 26 % sensitivity and 82 % specificity. Negative predictive value was 85 % and positive predictive value 22 %. This low sensitivity implies that three out of four non-responders will not be identified by the Egami score. Besides, logistic regression models did not found significance for the use of the Egami score to predict IVIG resistance in Catalan population although having an area under the ROC curve of 0.618 (IC 95 % 0.538-0.698, p < 0.001). Although regression models found an area under the ROC curve >0.5 to predict IVIG resistance, the low sensitivity excludes the Egami score as a useful tool to predict IVIG resistance in Catalan population.

Global sensitivity analysis in stochastic simulators of uncertain reaction networks.

PubMed

Navarro Jimenez, M; Le Maître, O P; Knio, O M

2016-12-28

Stochastic models of chemical systems are often subjected to uncertainties in kinetic parameters in addition to the inherent random nature of their dynamics. Uncertainty quantification in such systems is generally achieved by means of sensitivity analyses in which one characterizes the variability with the uncertain kinetic parameters of the first statistical moments of model predictions. In this work, we propose an original global sensitivity analysis method where the parametric and inherent variability sources are both treated through Sobol's decomposition of the variance into contributions from arbitrary subset of uncertain parameters and stochastic reaction channels. The conceptual development only assumes that the inherent and parametric sources are independent, and considers the Poisson processes in the random-time-change representation of the state dynamics as the fundamental objects governing the inherent stochasticity. A sampling algorithm is proposed to perform the global sensitivity analysis, and to estimate the partial variances and sensitivity indices characterizing the importance of the various sources of variability and their interactions. The birth-death and Schlögl models are used to illustrate both the implementation of the algorithm and the richness of the proposed analysis method. The output of the proposed sensitivity analysis is also contrasted with a local derivative-based sensitivity analysis method classically used for this type of systems.

Global sensitivity analysis in stochastic simulators of uncertain reaction networks

DOE PAGES

Navarro Jimenez, M.; Le Maître, O. P.; Knio, O. M.

2016-12-23

Stochastic models of chemical systems are often subjected to uncertainties in kinetic parameters in addition to the inherent random nature of their dynamics. Uncertainty quantification in such systems is generally achieved by means of sensitivity analyses in which one characterizes the variability with the uncertain kinetic parameters of the first statistical moments of model predictions. In this work, we propose an original global sensitivity analysis method where the parametric and inherent variability sources are both treated through Sobol’s decomposition of the variance into contributions from arbitrary subset of uncertain parameters and stochastic reaction channels. The conceptual development only assumes thatmore » the inherent and parametric sources are independent, and considers the Poisson processes in the random-time-change representation of the state dynamics as the fundamental objects governing the inherent stochasticity. Here, a sampling algorithm is proposed to perform the global sensitivity analysis, and to estimate the partial variances and sensitivity indices characterizing the importance of the various sources of variability and their interactions. The birth-death and Schlögl models are used to illustrate both the implementation of the algorithm and the richness of the proposed analysis method. The output of the proposed sensitivity analysis is also contrasted with a local derivative-based sensitivity analysis method classically used for this type of systems.« less

Global sensitivity analysis in stochastic simulators of uncertain reaction networks

NASA Astrophysics Data System (ADS)

Navarro Jimenez, M.; Le Maître, O. P.; Knio, O. M.

2016-12-01

Stochastic models of chemical systems are often subjected to uncertainties in kinetic parameters in addition to the inherent random nature of their dynamics. Uncertainty quantification in such systems is generally achieved by means of sensitivity analyses in which one characterizes the variability with the uncertain kinetic parameters of the first statistical moments of model predictions. In this work, we propose an original global sensitivity analysis method where the parametric and inherent variability sources are both treated through Sobol's decomposition of the variance into contributions from arbitrary subset of uncertain parameters and stochastic reaction channels. The conceptual development only assumes that the inherent and parametric sources are independent, and considers the Poisson processes in the random-time-change representation of the state dynamics as the fundamental objects governing the inherent stochasticity. A sampling algorithm is proposed to perform the global sensitivity analysis, and to estimate the partial variances and sensitivity indices characterizing the importance of the various sources of variability and their interactions. The birth-death and Schlögl models are used to illustrate both the implementation of the algorithm and the richness of the proposed analysis method. The output of the proposed sensitivity analysis is also contrasted with a local derivative-based sensitivity analysis method classically used for this type of systems.

Advanced techniques for determining long term compatibility of materials with propellants

NASA Technical Reports Server (NTRS)

Green, R. L.; Stebbins, J. P.; Smith, A. W.; Pullen, K. E.

1973-01-01

A method for the prediction of propellant-material compatibility for periods of time up to ten years is presented. Advanced sensitive measurement techniques used in the prediction method are described. These include: neutron activation analysis, radioactive tracer technique, and atomic absorption spectroscopy with a graphite tube furnace sampler. The results of laboratory tests performed to verify the prediction method are presented.

Ability of King's College Criteria and Model for End-Stage Liver Disease Scores to Predict Mortality of Patients With Acute Liver Failure: A Meta-analysis.

PubMed

McPhail, Mark J W; Farne, Hugo; Senvar, Naz; Wendon, Julia A; Bernal, William

2016-04-01

Several prognostic factors are used to identify patients with acute liver failure (ALF) who require emergency liver transplantation. We performed a meta-analysis to determine the accuracy of King's College criteria (KCC) versus the model for end-stage liver disease (MELD) scores in predicting hospital mortality among patients with ALF. We performed a systematic search of the literature for articles published from 2001 through 2015 that compared the accuracy of the KCC with MELD scores in predicting hospital mortality in patients with ALF. We identified 23 studies (comprising 2153 patients) and assessed the quality of data, and then performed a meta-analysis of pooled sensitivity and specificity values, diagnostic odds ratios (DORs), and summary receiver operating characteristic curves. Subgroups analyzed included study quality, era, location (Europe vs non-Europe), and size; ALF etiology (acetaminophen-associated ALF [AALF] vs nonassociated [NAALF]); and whether or not the study included patients who underwent liver transplantation and if the study center was also a transplant center. The DOR for the KCC was 5.3 (95% confidence interval [CI], 3.7-7.6; 57% heterogeneity) and the DOR for MELD score was 7.0 (95% CI, 5.1-9.7; 48% heterogeneity), so the MELD score and KCC are comparable in overall accuracy. The summary area under the receiver operating characteristic curve values was 0.76 for the KCC and 0.78 for MELD scores. The KCC identified patients with AALF who died with 58% sensitivity (95% CI, 51%-65%) and 89% specificity (95% CI, 85%-93%), whereas MELD scores identified patients with AALF who died with 80% sensitivity (95% CI, 74%-86%) and 53% specificity (95% CI, 47%-59%). The KCC predicted hospital mortality in patients with NAALF with 58% sensitivity (95% CI, 54%-63%) and 74% specificity (95% CI, 69%-78%), whereas MELD scores predicted hospital mortality in patients with NAALF with 76% sensitivity (95% CI, 72%-80%) and 73% specificity (95% CI, 69%-78%). In patients with AALF, the KCC's DOR was 10.4 (95% CI, 4.9-22.1) and the MELD score's DOR was 6.6 (95% CI, 2.1-20.2). In patients with NAALF, the KCC's DOR was 4.16 (95% CI, 2.34-7.40) and the MELD score's DOR was 8.42 (95% CI, 5.98-11.88). Based on a meta-analysis of studies, the KCC more accurately predicts hospital mortality among patients with AALF, whereas MELD scores more accurately predict mortality among patients with NAALF. However, there is significant heterogeneity among studies and neither system is optimal for all patients. Given the importance of specificity in decision making for listing for emergency liver transplantation, MELD scores should not replace the KCC in predicting hospital mortality of patients with AALF, but could have a role for NAALF. Copyright © 2016 AGA Institute. Published by Elsevier Inc. All rights reserved.

Assessment of sexual orientation using the hemodynamic brain response to visual sexual stimuli.

PubMed

Ponseti, Jorge; Granert, Oliver; Jansen, Olav; Wolff, Stephan; Mehdorn, Hubertus; Bosinski, Hartmut; Siebner, Hartwig

2009-06-01

The assessment of sexual orientation is of importance to the diagnosis and treatment of sex offenders and paraphilic disorders. Phallometry is considered gold standard in objectifying sexual orientation, yet this measurement has been criticized because of its intrusiveness and limited reliability. To evaluate whether the spatial response pattern to sexual stimuli as revealed by a change in blood oxygen level-dependent (BOLD) signal can be used for individual classification of sexual orientation. We used a preexisting functional MRI (fMRI) data set that had been acquired in a nonclinical sample of 12 heterosexual men and 14 homosexual men. During fMRI, participants were briefly exposed to pictures of same-sex and opposite-sex genitals. Data analysis involved four steps: (i) differences in the BOLD response to female and male sexual stimuli were calculated for each subject; (ii) these contrast images were entered into a group analysis to calculate whole-brain difference maps between homosexual and heterosexual participants; (iii) a single expression value was computed for each subject expressing its correspondence to the group result; and (iv) based on these expression values, Fisher's linear discriminant analysis and the kappa-nearest neighbor classification method were used to predict the sexual orientation of each subject. Sensitivity and specificity of the two classification methods in predicting individual sexual orientation. Both classification methods performed well in predicting individual sexual orientation with a mean accuracy of >85% (Fisher's linear discriminant analysis: 92% sensitivity, 85% specificity; kappa-nearest neighbor classification: 88% sensitivity, 92% specificity). Despite the small sample size, the functional response patterns of the brain to sexual stimuli contained sufficient information to predict individual sexual orientation with high accuracy. These results suggest that fMRI-based classification methods hold promise for the diagnosis of paraphilic disorders (e.g., pedophilia).

Bronchial and non-bronchial systemic arteries: value of multidetector CT angiography in diagnosis and angiographic embolisation feasibility analysis.

PubMed

Lin, Yuning; Chen, Ziqian; Yang, Xizhang; Zhong, Qun; Zhang, Hongwen; Yang, Li; Xu, Shangwen; Li, Hui

2013-12-01

The aim of this study is to evaluate the diagnostic performance of multidetector CT angiography (CTA) in depicting bronchial and non-bronchial systemic arteries in patients with haemoptysis and to assess whether this modality helps determine the feasibility of angiographic embolisation. Fifty-two patients with haemoptysis between January 2010 and July 2011 underwent both preoperative multidetector CTA and digital subtraction angiography (DSA) imaging. Diagnostic performance of CTA in depicting arteries causing haemoptysis was assessed on a per-patient and a per-artery basis. The feasibility of the endovascular treatment evaluated by CTA was analysed. Sensitivity, specificity, and positive and negative predictive values for those analyses were determined. Fifty patients were included in the artery-presence-number analysis. In the per-patient analysis, neither CTA (P = 0.25) nor DSA (P = 1.00) showed statistical difference in the detection of arteries causing haemoptysis. The sensitivity, specificity, and positive and negative predictive values were 94%, 100%, 100%, and 40%, respectively, for the presence of pathologic arteries evaluated by CTA, and 98%, 100%, 100%, and 67%, respectively, for DSA. On the per-artery basis, CTA correctly identified 97% (107/110). Fifty-two patients were included in the feasibility analysis. The performance of CTA in predicting the feasibility of angiographic embolisation was not statistically different from the treatment performed (P = 1.00). The sensitivity, specificity, and positive and negative predictive values were 96%, 80%, 98% and 67%, respectively, for CTA. Multidetector CTA is an accurate imaging method in depicting the presence and number of arteries causing haemoptysis. This modality is also useful for determining the feasibility of angiographic embolisation for haemoptysis. © 2013 The Authors. Journal of Medical Imaging and Radiation Oncology © 2013 The Royal Australian and New Zealand College of Radiologists.

From web search to healthcare utilization: privacy-sensitive studies from mobile data.

PubMed

White, Ryen; Horvitz, Eric

2013-01-01

We explore relationships between health information seeking activities and engagement with healthcare professionals via a privacy-sensitive analysis of geo-tagged data from mobile devices. We analyze logs of mobile interaction data stripped of individually identifiable information and location data. The data analyzed consist of time-stamped search queries and distances to medical care centers. We examine search activity that precedes the observation of salient evidence of healthcare utilization (EHU) (ie, data suggesting that the searcher is using healthcare resources), in our case taken as queries occurring at or near medical facilities. We show that the time between symptom searches and observation of salient evidence of seeking healthcare utilization depends on the acuity of symptoms. We construct statistical models that make predictions of forthcoming EHU based on observations about the current search session, prior medical search activities, and prior EHU. The predictive accuracy of the models varies (65%-90%) depending on the features used and the timeframe of the analysis, which we explore via a sensitivity analysis. We provide a privacy-sensitive analysis that can be used to generate insights about the pursuit of health information and healthcare. The findings demonstrate how large-scale studies of mobile devices can provide insights on how concerns about symptomatology lead to the pursuit of professional care. We present new methods for the analysis of mobile logs and describe a study that provides evidence about how people transition from mobile searches on symptoms and diseases to the pursuit of healthcare in the world.

Sensitivity Analysis of the Sheet Metal Stamping Processes Based on Inverse Finite Element Modeling and Monte Carlo Simulation

NASA Astrophysics Data System (ADS)

Yu, Maolin; Du, R.

2005-08-01

Sheet metal stamping is one of the most commonly used manufacturing processes, and hence, much research has been carried for economic gain. Searching through the literatures, however, it is found that there are still a lots of problems unsolved. For example, it is well known that for a same press, same workpiece material, and same set of die, the product quality may vary owing to a number of factors, such as the inhomogeneous of the workpice material, the loading error, the lubrication, and etc. Presently, few seem able to predict the quality variation, not to mention what contribute to the quality variation. As a result, trial-and-error is still needed in the shop floor, causing additional cost and time delay. This paper introduces a new approach to predict the product quality variation and identify the sensitive design / process parameters. The new approach is based on a combination of inverse Finite Element Modeling (FEM) and Monte Carlo Simulation (more specifically, the Latin Hypercube Sampling (LHS) approach). With an acceptable accuracy, the inverse FEM (also called one-step FEM) requires much less computation load than that of the usual incremental FEM and hence, can be used to predict the quality variations under various conditions. LHS is a statistical method, through which the sensitivity analysis can be carried out. The result of the sensitivity analysis has clear physical meaning and can be used to optimize the die design and / or the process design. Two simulation examples are presented including drawing a rectangular box and drawing a two-step rectangular box.

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

Nelson, Stacy; English, Shawn; Briggs, Timothy

Fiber-reinforced composite materials offer light-weight solutions to many structural challenges. In the development of high-performance composite structures, a thorough understanding is required of the composite materials themselves as well as methods for the analysis and failure prediction of the relevant composite structures. However, the mechanical properties required for the complete constitutive definition of a composite material can be difficult to determine through experimentation. Therefore, efficient methods are necessary that can be used to determine which properties are relevant to the analysis of a specific structure and to establish a structure's response to a material parameter that can only be definedmore » through estimation. The objectives of this paper deal with demonstrating the potential value of sensitivity and uncertainty quantification techniques during the failure analysis of loaded composite structures; and the proposed methods are applied to the simulation of the four-point flexural characterization of a carbon fiber composite material. Utilizing a recently implemented, phenomenological orthotropic material model that is capable of predicting progressive composite damage and failure, a sensitivity analysis is completed to establish which material parameters are truly relevant to a simulation's outcome. Then, a parameter study is completed to determine the effect of the relevant material properties' expected variations on the simulated four-point flexural behavior as well as to determine the value of an unknown material property. This process demonstrates the ability to formulate accurate predictions in the absence of a rigorous material characterization effort. Finally, the presented results indicate that a sensitivity analysis and parameter study can be used to streamline the material definition process as the described flexural characterization was used for model validation.« less

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).

Application of Anaerobic Digestion Model No. 1 for simulating anaerobic mesophilic sludge digestion

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

Mendes, Carlos, E-mail: carllosmendez@gmail.com; Esquerre, Karla, E-mail: karlaesquerre@ufba.br; Matos Queiroz, Luciano, E-mail: lmqueiroz@ufba.br

2015-01-15

Highlights: • The behavior of a anaerobic reactor was evaluated through modeling. • Parametric sensitivity analysis was used to select most sensitive of the ADM1. • The results indicate that the ADM1 was able to predict the experimental results. • Organic load rate above of 35 kg/m{sup 3} day affects the performance of the process. - Abstract: Improving anaerobic digestion of sewage sludge by monitoring common indicators such as volatile fatty acids (VFAs), gas composition and pH is a suitable solution for better sludge management. Modeling is an important tool to assess and to predict process performance. The present studymore » focuses on the application of the Anaerobic Digestion Model No. 1 (ADM1) to simulate the dynamic behavior of a reactor fed with sewage sludge under mesophilic conditions. Parametric sensitivity analysis is used to select the most sensitive ADM1 parameters for estimation using a numerical procedure while other parameters are applied without any modification to the original values presented in the ADM1 report. The results indicate that the ADM1 model after parameter estimation was able to predict the experimental results of effluent acetate, propionate, composites and biogas flows and pH with reasonable accuracy. The simulation of the effect of organic shock loading clearly showed that an organic shock loading rate above of 35 kg/m{sup 3} day affects the performance of the reactor. The results demonstrate that simulations can be helpful to support decisions on predicting the anaerobic digestion process of sewage sludge.« less

Evaluation of quantitative image analysis criteria for the high-resolution microendoscopic detection of neoplasia in Barrett's esophagus

NASA Astrophysics Data System (ADS)

Muldoon, Timothy J.; Thekkek, Nadhi; Roblyer, Darren; Maru, Dipen; Harpaz, Noam; Potack, Jonathan; Anandasabapathy, Sharmila; Richards-Kortum, Rebecca

2010-03-01

Early detection of neoplasia in patients with Barrett's esophagus is essential to improve outcomes. The aim of this ex vivo study was to evaluate the ability of high-resolution microendoscopic imaging and quantitative image analysis to identify neoplastic lesions in patients with Barrett's esophagus. Nine patients with pathologically confirmed Barrett's esophagus underwent endoscopic examination with biopsies or endoscopic mucosal resection. Resected fresh tissue was imaged with fiber bundle microendoscopy; images were analyzed by visual interpretation or by quantitative image analysis to predict whether the imaged sites were non-neoplastic or neoplastic. The best performing pair of quantitative features were chosen based on their ability to correctly classify the data into the two groups. Predictions were compared to the gold standard of histopathology. Subjective analysis of the images by expert clinicians achieved average sensitivity and specificity of 87% and 61%, respectively. The best performing quantitative classification algorithm relied on two image textural features and achieved a sensitivity and specificity of 87% and 85%, respectively. This ex vivo pilot trial demonstrates that quantitative analysis of images obtained with a simple microendoscope system can distinguish neoplasia in Barrett's esophagus with good sensitivity and specificity when compared to histopathology and to subjective image interpretation.

Urinary Squamous Epithelial Cells Do Not Accurately Predict Urine Culture Contamination, but May Predict Urinalysis Performance in Predicting Bacteriuria.

PubMed

Mohr, Nicholas M; Harland, Karisa K; Crabb, Victoria; Mutnick, Rachel; Baumgartner, David; Spinosi, Stephanie; Haarstad, Michael; Ahmed, Azeemuddin; Schweizer, Marin; Faine, Brett

2016-03-01

The presence of squamous epithelial cells (SECs) has been advocated to identify urinary contamination despite a paucity of evidence supporting this practice. We sought to determine the value of using quantitative SECs as a predictor of urinalysis contamination. Retrospective cross-sectional study of adults (≥18 years old) presenting to a tertiary academic medical center who had urinalysis with microscopy and urine culture performed. Patients with missing or implausible demographic data were excluded (2.5% of total sample). The primary analysis aimed to determine an SEC threshold that predicted urine culture contamination using receiver operating characteristics (ROC) curve analysis. The a priori secondary analysis explored how demographic variables (age, sex, body mass index) may modify the SEC test performance and whether SECs impacted traditional urinalysis indicators of bacteriuria. A total of 19,328 records were included. ROC curve analysis demonstrated that SEC count was a poor predictor of urine culture contamination (area under the ROC curve = 0.680, 95% confidence interval [CI] = 0.671 to 0.689). In secondary analysis, the positive likelihood ratio (LR+) of predicting bacteriuria via urinalysis among noncontaminated specimens was 4.98 (95% CI = 4.59 to 5.40) in the absence of SECs, but the LR+ fell to 2.35 (95% CI = 2.17 to 2.54) for samples with more than 8 SECs/low-powered field (lpf). In an independent validation cohort, urinalysis samples with fewer than 8 SECs/lpf predicted bacteriuria better (sensitivity = 75%, specificity = 84%) than samples with more than 8 SECs/lpf (sensitivity = 86%, specificity = 70%; diagnostic odds ratio = 17.5 [14.9 to 20.7] vs. 8.7 [7.3 to 10.5]). Squamous epithelial cells are a poor predictor of urine culture contamination, but may predict poor predictive performance of traditional urinalysis measures. © 2016 by the Society for Academic Emergency Medicine.

In vivo serial MRI-based models and statistical methods to quantify sensitivity and specificity of mechanical predictors for carotid plaque rupture: location and beyond.

PubMed

Wu, Zheyang; Yang, Chun; Tang, Dalin

2011-06-01

It has been hypothesized that mechanical risk factors may be used to predict future atherosclerotic plaque rupture. Truly predictive methods for plaque rupture and methods to identify the best predictor(s) from all the candidates are lacking in the literature. A novel combination of computational and statistical models based on serial magnetic resonance imaging (MRI) was introduced to quantify sensitivity and specificity of mechanical predictors to identify the best candidate for plaque rupture site prediction. Serial in vivo MRI data of carotid plaque from one patient was acquired with follow-up scan showing ulceration. 3D computational fluid-structure interaction (FSI) models using both baseline and follow-up data were constructed and plaque wall stress (PWS) and strain (PWSn) and flow maximum shear stress (FSS) were extracted from all 600 matched nodal points (100 points per matched slice, baseline matching follow-up) on the lumen surface for analysis. Each of the 600 points was marked "ulcer" or "nonulcer" using follow-up scan. Predictive statistical models for each of the seven combinations of PWS, PWSn, and FSS were trained using the follow-up data and applied to the baseline data to assess their sensitivity and specificity using the 600 data points for ulcer predictions. Sensitivity of prediction is defined as the proportion of the true positive outcomes that are predicted to be positive. Specificity of prediction is defined as the proportion of the true negative outcomes that are correctly predicted to be negative. Using probability 0.3 as a threshold to infer ulcer occurrence at the prediction stage, the combination of PWS and PWSn provided the best predictive accuracy with (sensitivity, specificity) = (0.97, 0.958). Sensitivity and specificity given by PWS, PWSn, and FSS individually were (0.788, 0.968), (0.515, 0.968), and (0.758, 0.928), respectively. The proposed computational-statistical process provides a novel method and a framework to assess the sensitivity and specificity of various risk indicators and offers the potential to identify the optimized predictor for plaque rupture using serial MRI with follow-up scan showing ulceration as the gold standard for method validation. While serial MRI data with actual rupture are hard to acquire, this single-case study suggests that combination of multiple predictors may provide potential improvement to existing plaque assessment schemes. With large-scale patient studies, this predictive modeling process may provide more solid ground for rupture predictor selection strategies and methods for image-based plaque vulnerability assessment.

Prostate Specific Membrane Antigen Positron Emission Tomography May Improve the Diagnostic Accuracy of Multiparametric Magnetic Resonance Imaging in Localized Prostate Cancer.

PubMed

Rhee, H; Thomas, P; Shepherd, B; Gustafson, S; Vela, I; Russell, P J; Nelson, C; Chung, E; Wood, G; Malone, G; Wood, S; Heathcote, P

2016-10-01

Positron emission tomography using ligands targeting prostate specific membrane antigen has recently been introduced. Positron emission tomography imaging with (68)Ga-PSMA-HBED-CC has been shown to detect metastatic prostate cancer lesions at a high rate. In this study we compare multiparametric magnetic resonance imaging and prostate specific membrane antigen positron emission tomography of the prostate with whole mount ex vivo prostate histopathology to determine the true sensitivity and specificity of these imaging modalities for detecting and locating tumor foci within the prostate. In a prospective clinical trial setting 20 patients with localized prostate cancer and a planned radical prostatectomy were recruited. All patients underwent multiparametric magnetic resonance imaging and positron emission tomography before surgery, and whole mount histopathology slides were directly compared to the images. European Society of Urogenital Radiology guidelines for reporting magnetic resonance imaging were used as a template for regional units of analysis. The uropathologist and radiologists were blinded to individual components of the study, and the final correlation was performed by visual and deformable registration analysis. A total of 50 clinically significant lesions were identified from the whole mount histopathological analysis. Based on regional analysis the sensitivity, specificity, positive predictive value and negative predictive value for multiparametric magnetic resonance imaging were 44%, 94%, 81% and 76%, respectively. With prostate specific membrane antigen positron emission tomography the sensitivity, specificity, positive predictive value and negative predictive value were 49%, 95%, 85% and 88%, respectively. Prostate specific membrane antigen positron emission tomography yielded a higher specificity and positive predictive value. A significant proportion of cancers are potentially missed and underestimated by both imaging modalities. Prostate specific membrane antigen positron emission tomography may be used in addition to multiparametric magnetic resonance imaging to help improve local staging in those patients undergoing retropubic radical prostatectomy. Copyright © 2016 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.

A comprehensive prediction and evaluation method of pilot workload

PubMed Central

Feng, Chuanyan; Wanyan, Xiaoru; Yang, Kun; Zhuang, Damin; Wu, Xu

2018-01-01

BACKGROUND: The prediction and evaluation of pilot workload is a key problem in human factor airworthiness of cockpit. OBJECTIVE: A pilot traffic pattern task was designed in a flight simulation environment in order to carry out the pilot workload prediction and improve the evaluation method. METHODS: The prediction of typical flight subtasks and dynamic workloads (cruise, approach, and landing) were built up based on multiple resource theory, and a favorable validity was achieved by the correlation analysis verification between sensitive physiological data and the predicted value. RESULTS: Statistical analysis indicated that eye movement indices (fixation frequency, mean fixation time, saccade frequency, mean saccade time, and mean pupil diameter), Electrocardiogram indices (mean normal-to-normal interval and the ratio between low frequency and sum of low frequency and high frequency), and Electrodermal Activity indices (mean tonic and mean phasic) were all sensitive to typical workloads of subjects. CONCLUSION: A multinominal logistic regression model based on combination of physiological indices (fixation frequency, mean normal-to-normal interval, the ratio between low frequency and sum of low frequency and high frequency, and mean tonic) was constructed, and the discriminate accuracy was comparatively ideal with a rate of 84.85%. PMID:29710742

A comprehensive prediction and evaluation method of pilot workload.

PubMed

Feng, Chuanyan; Wanyan, Xiaoru; Yang, Kun; Zhuang, Damin; Wu, Xu

2018-01-01

The prediction and evaluation of pilot workload is a key problem in human factor airworthiness of cockpit. A pilot traffic pattern task was designed in a flight simulation environment in order to carry out the pilot workload prediction and improve the evaluation method. The prediction of typical flight subtasks and dynamic workloads (cruise, approach, and landing) were built up based on multiple resource theory, and a favorable validity was achieved by the correlation analysis verification between sensitive physiological data and the predicted value. Statistical analysis indicated that eye movement indices (fixation frequency, mean fixation time, saccade frequency, mean saccade time, and mean pupil diameter), Electrocardiogram indices (mean normal-to-normal interval and the ratio between low frequency and sum of low frequency and high frequency), and Electrodermal Activity indices (mean tonic and mean phasic) were all sensitive to typical workloads of subjects. A multinominal logistic regression model based on combination of physiological indices (fixation frequency, mean normal-to-normal interval, the ratio between low frequency and sum of low frequency and high frequency, and mean tonic) was constructed, and the discriminate accuracy was comparatively ideal with a rate of 84.85%.

The Sensitivity Analysis for the Flow Past Obstacles Problem with Respect to the Reynolds Number

PubMed Central

Ito, Kazufumi; Li, Zhilin; Qiao, Zhonghua

2013-01-01

In this paper, numerical sensitivity analysis with respect to the Reynolds number for the flow past obstacle problem is presented. To carry out such analysis, at each time step, we need to solve the incompressible Navier-Stokes equations on irregular domains twice, one for the primary variables; the other is for the sensitivity variables with homogeneous boundary conditions. The Navier-Stokes solver is the augmented immersed interface method for Navier-Stokes equations on irregular domains. One of the most important contribution of this paper is that our analysis can predict the critical Reynolds number at which the vortex shading begins to develop in the wake of the obstacle. Some interesting experiments are shown to illustrate how the critical Reynolds number varies with different geometric settings. PMID:24910780

The Sensitivity Analysis for the Flow Past Obstacles Problem with Respect to the Reynolds Number.

PubMed

Ito, Kazufumi; Li, Zhilin; Qiao, Zhonghua

2012-02-01

In this paper, numerical sensitivity analysis with respect to the Reynolds number for the flow past obstacle problem is presented. To carry out such analysis, at each time step, we need to solve the incompressible Navier-Stokes equations on irregular domains twice, one for the primary variables; the other is for the sensitivity variables with homogeneous boundary conditions. The Navier-Stokes solver is the augmented immersed interface method for Navier-Stokes equations on irregular domains. One of the most important contribution of this paper is that our analysis can predict the critical Reynolds number at which the vortex shading begins to develop in the wake of the obstacle. Some interesting experiments are shown to illustrate how the critical Reynolds number varies with different geometric settings.

Can currently available non-animal methods detect pre and pro-haptens relevant for skin sensitization?

PubMed

Patlewicz, Grace; Casati, Silvia; Basketter, David A; Asturiol, David; Roberts, David W; Lepoittevin, Jean-Pierre; Worth, Andrew P; Aschberger, Karin

2016-12-01

Predictive testing to characterize substances for their skin sensitization potential has historically been based on animal tests such as the Local Lymph Node Assay (LLNA). In recent years, regulations in the cosmetics and chemicals sectors have provided strong impetus to develop non-animal alternatives. Three test methods have undergone OECD validation: the direct peptide reactivity assay (DPRA), the KeratinoSens™ and the human Cell Line Activation Test (h-CLAT). Whilst these methods perform relatively well in predicting LLNA results, a concern raised is their ability to predict chemicals that need activation to be sensitizing (pre- or pro-haptens). This current study reviewed an EURL ECVAM dataset of 127 substances for which information was available in the LLNA and three non-animal test methods. Twenty eight of the sensitizers needed to be activated, with the majority being pre-haptens. These were correctly identified by 1 or more of the test methods. Six substances were categorized exclusively as pro-haptens, but were correctly identified by at least one of the cell-based assays. The analysis here showed that skin metabolism was not likely to be a major consideration for assessing sensitization potential and that sensitizers requiring activation could be identified correctly using one or more of the current non-animal methods. Published by Elsevier Inc.

PRospective Imaging of CErvical cancer and neoadjuvant treatment (PRICE) study: role of ultrasound to predict partial response in locally advanced cervical cancer patients undergoing chemoradiation and radical surgery.

PubMed

Testa, A C; Ferrandina, G; Moro, F; Pasciuto, T; Moruzzi, M C; De Blasis, I; Mascilini, F; Foti, E; Autorino, R; Collarino, A; Gui, B; Zannoni, G F; Gambacorta, M A; Valentini, A L; Rufini, V; Scambia, G

2018-05-01

Chemoradiation-based neoadjuvant treatment followed by radical surgery is an alternative therapeutic strategy for locally advanced cervical cancer (LACC), but ultrasound variables used to predict partial response to neoadjuvant treatment are not well defined. Our goal was to analyze prospectively the potential role of transvaginal ultrasound in early prediction of partial pathological response, assessed in terms of residual disease at histology, in a large, single-institution series of LACC patients triaged to neoadjuvant treatment followed by radical surgery. Between October 2010 and June 2014, we screened 108 women with histologically documented LACC Stage IB2-IVA, of whom 88 were included in the final analysis. Tumor volume, three-dimensional (3D) power Doppler indices and contrast parameters were obtained before (baseline examination) and after 2 weeks of treatment. The pathological response was defined as complete (absence of any residual tumor after treatment) or partial (microscopic and/or macroscopic residual tumor at pathological examination). Complete-response and partial-response groups were compared and receiver-operating characteristics (ROC) curves were generated for ultrasound variables that were statistically significant on univariate analysis to evaluate their diagnostic ability to predict partial pathological response. There was a complete pathological response to neoadjuvant therapy in 40 (45.5%) patients and a partial response in 48 (54.5%). At baseline examination, tumor volume did not differ between the two groups. However, after 2 weeks of neoadjuvant treatment, the tumor volume was significantly greater in patients with partial response than it was in those with complete response (P = 0.019). Among the 3D vascular indices, the vascularization index (VI) was significantly lower in the partial-response compared with the complete-response group, both before and after 2 weeks of treatment (P = 0.037 and P = 0.024, respectively). At baseline examination in the contrast analysis, women with partial response had lower tumor peak enhancement (PE) as well as lower tumor wash-in rate (WiR) and longer tumor rise time (RT) compared with complete responders (P = 0.006, P = 0.003, P = 0.038, respectively). There was no difference in terms of contrast parameters after 2 weeks of treatment. ROC-curve analysis of baseline parameters showed that the best cut-offs for predicting partial pathological response were 41.5% for VI (sensitivity, 63.6%; specificity, 66.7%); 16123.5 auxiliary units for tumor PE (sensitivity, 47.9%; specificity, 84.2%); 7.8 s for tumor RT (sensitivity, 68.8%; specificity, 57.9%); and 4902 for tumor WiR (sensitivity, 77.1%; specificity, 60.5%). ROC curves of parameters after 2 weeks of treatment showed that the best cut-off for predicting partial pathological response was 18.1 cm 3 for tumor volume (sensitivity, 70.8%; specificity 60.0%) and 39.5% for VI (sensitivity; 62.5%; specificity, 73.5%). Ultrasound and contrast parameters differ between LACC patients with complete response and those with partial response before and after 2 weeks of neoadjuvant treatment. However, neither ultrasound parameters before treatment nor those after 2 weeks of treatment had cut-off values with acceptable sensitivity and specificity for predicting partial pathological response to neoadjuvant therapy. Copyright © 2017 ISUOG. Published by John Wiley & Sons Ltd. Copyright © 2017 ISUOG. Published by John Wiley & Sons Ltd.

Quantitation of cytokine mRNA expression as an endpoint for prediction and diagnosis of xenobiotic-induced hypersensitivity reactions.

PubMed

Gaspard, I; Kerdine, S; Pallardy, M; Lebrec, H

1999-09-01

Xenobiotic-induced hypersensitivity reactions are immune-mediated effects that involve specific antibodies and/or effector and regulatory T lymphocytes. Cytokines are key mediators of such responses and must be considered as possible endpoints for predicting sensitizing potency of drugs and chemicals, as well as for helping diagnosis of allergy. Detecting cytokine production at the protein level has been shown to not be always sensitive enough. This paper describes three examples of the utilization of semiquantitative or competitive reverse transcription polymerase chain reaction analysis of interleukin-4, interferon gamma, and interleukin-1beta mRNAs as endpoints for assessing T-cell or dendritic cell responses to sensitizing drugs (beta-lactam antibiotics) or chemicals (dinitrochlorobenzene). Copyright 1999 Academic Press.

Familial aggregation of MATRICS Consensus Cognitive Battery scores in a large sample of outpatients with schizophrenia and their unaffected relatives.

PubMed

Mucci, A; Galderisi, S; Green, M F; Nuechterlein, K; Rucci, P; Gibertoni, D; Rossi, A; Rocca, P; Bertolino, A; Bucci, P; Hellemann, G; Spisto, M; Palumbo, D; Aguglia, E; Amodeo, G; Amore, M; Bellomo, A; Brugnoli, R; Carpiniello, B; Dell'Osso, L; Di Fabio, F; di Giannantonio, M; Di Lorenzo, G; Marchesi, C; Monteleone, P; Montemagni, C; Oldani, L; Romano, R; Roncone, R; Stratta, P; Tenconi, E; Vita, A; Zeppegno, P; Maj, M

2018-06-01

The increased use of the MATRICS Consensus Cognitive Battery (MCCB) to investigate cognitive dysfunctions in schizophrenia fostered interest in its sensitivity in the context of family studies. As various measures of the same cognitive domains may have different power to distinguish between unaffected relatives of patients and controls, the relative sensitivity of MCCB tests for relative-control differences has to be established. We compared MCCB scores of 852 outpatients with schizophrenia (SCZ) with those of 342 unaffected relatives (REL) and a normative Italian sample of 774 healthy subjects (HCS). We examined familial aggregation of cognitive impairment by investigating within-family prediction of MCCB scores based on probands' scores. Multivariate analysis of variance was used to analyze group differences in adjusted MCCB scores. Weighted least-squares analysis was used to investigate whether probands' MCCB scores predicted REL neurocognitive performance. SCZ were significantly impaired on all MCCB domains. REL had intermediate scores between SCZ and HCS, showing a similar pattern of impairment, except for social cognition. Proband's scores significantly predicted REL MCCB scores on all domains except for visual learning. In a large sample of stable patients with schizophrenia, living in the community, and in their unaffected relatives, MCCB demonstrated sensitivity to cognitive deficits in both groups. Our findings of significant within-family prediction of MCCB scores might reflect disease-related genetic or environmental factors.

Validation of automatic wheeze detection in patients with obstructed airways and in healthy subjects.

PubMed

Guntupalli, Kalpalatha K; Alapat, Philip M; Bandi, Venkata D; Kushnir, Igal

2008-12-01

Computerized lung-sound analysis is a sensitive and quantitative method to identify wheezing by its typical pattern on spectral analysis. We evaluated the accuracy of the VRI, a multi-sensor, computer-based device with an automated technique of wheeze detection. The method was validated in 100 sound files from seven subjects with asthma or chronic obstructive pulmonary disease and seven healthy subjects by comparison of auscultation findings, examination of audio files, and computer detection of wheezes. Three blinded physicians identified 40 sound files with wheezes and 60 sound files without wheezes. Sensitivity and specificity were 83% and 85%, respectively. Negative predictive value and positive predictive value were 89% and 79%, respectively. Overall inter-rater agreement was 84%. False positive cases were found to contain sounds that simulate wheezes, such as background noises with high frequencies or strong noises from the throat that could be heard and identified without a stethoscope. The present findings demonstrate that the wheeze detection algorithm has good accuracy, sensitivity, specificity, negative predictive value and positive predictive value for wheeze detection in regional analyses with a single sensor and multiple sensors. Results are similar to those reported in the literature. The device is user-friendly, requires minimal patient effort, and, distinct from other devices, it provides a dynamic image of breath sound distribution with wheeze detection output in less than 1 minute.

Composite laminate failure parameter optimization through four-point flexure experimentation and analysis

DOE PAGES

Nelson, Stacy; English, Shawn; Briggs, Timothy

2016-05-06

Fiber-reinforced composite materials offer light-weight solutions to many structural challenges. In the development of high-performance composite structures, a thorough understanding is required of the composite materials themselves as well as methods for the analysis and failure prediction of the relevant composite structures. However, the mechanical properties required for the complete constitutive definition of a composite material can be difficult to determine through experimentation. Therefore, efficient methods are necessary that can be used to determine which properties are relevant to the analysis of a specific structure and to establish a structure's response to a material parameter that can only be definedmore » through estimation. The objectives of this paper deal with demonstrating the potential value of sensitivity and uncertainty quantification techniques during the failure analysis of loaded composite structures; and the proposed methods are applied to the simulation of the four-point flexural characterization of a carbon fiber composite material. Utilizing a recently implemented, phenomenological orthotropic material model that is capable of predicting progressive composite damage and failure, a sensitivity analysis is completed to establish which material parameters are truly relevant to a simulation's outcome. Then, a parameter study is completed to determine the effect of the relevant material properties' expected variations on the simulated four-point flexural behavior as well as to determine the value of an unknown material property. This process demonstrates the ability to formulate accurate predictions in the absence of a rigorous material characterization effort. Finally, the presented results indicate that a sensitivity analysis and parameter study can be used to streamline the material definition process as the described flexural characterization was used for model validation.« less

Dynamic sensitivity analysis of long running landslide models through basis set expansion and meta-modelling

NASA Astrophysics Data System (ADS)

Rohmer, Jeremy

2016-04-01

Predicting the temporal evolution of landslides is typically supported by numerical modelling. Dynamic sensitivity analysis aims at assessing the influence of the landslide properties on the time-dependent predictions (e.g., time series of landslide displacements). Yet two major difficulties arise: 1. Global sensitivity analysis require running the landslide model a high number of times (> 1000), which may become impracticable when the landslide model has a high computation time cost (> several hours); 2. Landslide model outputs are not scalar, but function of time, i.e. they are n-dimensional vectors with n usually ranging from 100 to 1000. In this article, I explore the use of a basis set expansion, such as principal component analysis, to reduce the output dimensionality to a few components, each of them being interpreted as a dominant mode of variation in the overall structure of the temporal evolution. The computationally intensive calculation of the Sobol' indices for each of these components are then achieved through meta-modelling, i.e. by replacing the landslide model by a "costless-to-evaluate" approximation (e.g., a projection pursuit regression model). The methodology combining "basis set expansion - meta-model - Sobol' indices" is then applied to the La Frasse landslide to investigate the dynamic sensitivity analysis of the surface horizontal displacements to the slip surface properties during the pore pressure changes. I show how to extract information on the sensitivity of each main modes of temporal behaviour using a limited number (a few tens) of long running simulations. In particular, I identify the parameters, which trigger the occurrence of a turning point marking a shift between a regime of low values of landslide displacements and one of high values.

Diagnostic accuracy of the aspartate aminotransferase-to-platelet ratio index for the prediction of hepatitis B-related fibrosis: a leading meta-analysis

PubMed Central

2012-01-01

Background The aspartate aminotransferase-to-platelet ratio index (APRI), a tool with limited expense and widespread availability, is a promising noninvasive alternative to liver biopsy for detecting hepatic fibrosis. The objective of this study was to systematically review the performance of the APRI in predicting significant fibrosis and cirrhosis in hepatitis B-related fibrosis. Methods Areas under summary receiver operating characteristic curves (AUROC), sensitivity and specificity were used to examine the accuracy of the APRI for the diagnosis of hepatitis B-related significant fibrosis and cirrhosis. Heterogeneity was explored using meta-regression. Results Nine studies were included in this meta-analysis (n = 1,798). Prevalence of significant fibrosis and cirrhosis were 53.1% and 13.5%, respectively. The summary AUCs of the APRI for significant fibrosis and cirrhosis were 0.79 and 0.75, respectively. For significant fibrosis, an APRI threshold of 0.5 was 84% sensitive and 41% specific. At the cutoff of 1.5, the summary sensitivity and specificity were 49% and 84%, respectively. For cirrhosis, an APRI threshold of 1.0-1.5 was 54% sensitive and 78% specific. At the cutoff of 2.0, the summary sensitivity and specificity were 28% and 87%, respectively. Meta-regression analysis indicated that the APRI accuracy for both significant fibrosis and cirrhosis was affected by histological classification systems, but not influenced by the interval between Biopsy & APRI or blind biopsy. Conclusion Our meta-analysis suggests that APRI show limited value in identifying hepatitis B-related significant fibrosis and cirrhosis. PMID:22333407

Ataque de nervios: relationship to anxiety sensitivity and dissociation predisposition.

PubMed

Hinton, Devon E; Chong, Roberto; Pollack, Mark H; Barlow, David H; McNally, Richard J

2008-01-01

We investigated the relative importance of "fear of arousal symptoms" (i.e., anxiety sensitivity) and "dissociation tendency" in generating ataque de nervios. Puerto Rican patients attending an outpatient psychiatric clinic were assessed for ataque de nervios frequency in the previous month, and they completed the Anxiety Sensitivity Index (ASI) and the Dissociation Experiences Scale (DES). ASI scores were especially high in the ataque-positive group (M=41.6, SD=12.8) as compared with the ataque-negative group (M=27.2, SD=11.7), t(2, 68)=4.6, P<.001. Among the whole sample (N=70), in a logistic regression analysis, the ASI significantly predicted (odds ratio=2.6) the presence of ataque de nervios, but the DES did not. In a linear regression analysis, ataque severity was significantly predicted by both the ASI (beta=.46) and the DES (beta=.29). The theoretical and clinical implications of the strong relationship of the ASI to ataque severity are discussed.

The influence of weather on migraine – are migraine attacks predictable?

PubMed Central

Hoffmann, Jan; Schirra, Tonio; Lo, Hendra; Neeb, Lars; Reuter, Uwe; Martus, Peter

2015-01-01

Objective The study aimed at elucidating a potential correlation between specific meteorological variables and the prevalence and intensity of migraine attacks as well as exploring a potential individual predictability of a migraine attack based on meteorological variables and their changes. Methods Attack prevalence and intensity of 100 migraineurs were correlated with atmospheric pressure, relative air humidity, and ambient temperature in 4-h intervals over 12 consecutive months. For each correlation, meteorological parameters at the time of the migraine attack as well as their variation within the preceding 24 h were analyzed. For migraineurs showing a positive correlation, logistic regression analysis was used to assess the predictability of a migraine attack based on meteorological information. Results In a subgroup of migraineurs, a significant weather sensitivity could be observed. In contrast, pooled analysis of all patients did not reveal a significant association. An individual prediction of a migraine attack based on meteorological data was not possible, mainly as a result of the small prevalence of attacks. Interpretation The results suggest that only a subgroup of migraineurs is sensitive to specific weather conditions. Our findings may provide an explanation as to why previous studies, which commonly rely on a pooled analysis, show inconclusive results. The lack of individual attack predictability indicates that the use of preventive measures based on meteorological conditions is not feasible. PMID:25642431

Sensitivity Analysis of Fatigue Crack Growth Model for API Steels in Gaseous Hydrogen.

PubMed

Amaro, Robert L; Rustagi, Neha; Drexler, Elizabeth S; Slifka, Andrew J

2014-01-01

A model to predict fatigue crack growth of API pipeline steels in high pressure gaseous hydrogen has been developed and is presented elsewhere. The model currently has several parameters that must be calibrated for each pipeline steel of interest. This work provides a sensitivity analysis of the model parameters in order to provide (a) insight to the underlying mathematical and mechanistic aspects of the model, and (b) guidance for model calibration of other API steels.

An analysis, sensitivity and prediction of winter fog events using FASP model over Indo-Gangetic plains, India

NASA Astrophysics Data System (ADS)

Srivastava, S. K., Sr.; Sharma, D. A.; Sachdeva, K.

2017-12-01

Indo-Gangetic plains of India experience severe fog conditions during the peak winter months of December and January every year. In this paper an attempt has been to analyze the spatial and temporal variability of winter fog over Indo-Gangetic plains. Further, an attempt has also been made to configure an efficient meso-scale numerical weather prediction model using different parameterization schemes and develop a forecasting tool for prediction of fog during winter months over Indo-Gangetic plains. The study revealed that an alarming increasing positive trend of fog frequency prevails over many locations of IGP. Hot spot and cluster analysis were conducted to identify the high fog prone zones using GIS and inferential statistical tools respectively. Hot spots on an average experiences fog on 68.27% days, it is followed by moderate and cold spots with 48.03% and 21.79% respectively. The study proposes a new FASP (Fog Analysis, sensitivity and prediction) Model for overall analysis and prediction of fog at a particular location and period over IGP. In the first phase of this model long term climatological fog data of a location is analyzed to determine its characteristics and prevailing trend using various advanced statistical techniques. During a second phase a sensitivity test is conducted with different combination of parameterization schemes to determine the most suitable combination for fog simulation over a particular location and period and in the third and final phase, first ARIMA model is used to predict the number of fog days in future . Thereafter, Numerical model is used to predict the various meteorological parameters favourable for fog forecast. Finally, Hybrid model is used for fog forecast over the study location. The results of the FASP model are validated with actual ground based fog data using statistical tools. Forecast Fog-gram generated using hybrid model during Jan 2017 shows highly encouraging results for fog occurrence/Non occurrence between 25 hrs to 72 hours forecast. The model predicted the fog occurrences/Non occurrence with more than 85 % accuracy over most of the locations across the study area. The minimum visibility departure is within 500 m on 90% occasions over the central IGP and within 1000m on more than 80 % occasions over most of the locations across Indo-Gangetic plains.

A multi-model assessment of terrestrial biosphere model data needs

NASA Astrophysics Data System (ADS)

Gardella, A.; Cowdery, E.; De Kauwe, M. G.; Desai, A. R.; Duveneck, M.; Fer, I.; Fisher, R.; Knox, R. G.; Kooper, R.; LeBauer, D.; McCabe, T.; Minunno, F.; Raiho, A.; Serbin, S.; Shiklomanov, A. N.; Thomas, A.; Walker, A.; Dietze, M.

2017-12-01

Terrestrial biosphere models provide us with the means to simulate the impacts of climate change and their uncertainties. Going beyond direct observation and experimentation, models synthesize our current understanding of ecosystem processes and can give us insight on data needed to constrain model parameters. In previous work, we leveraged the Predictive Ecosystem Analyzer (PEcAn) to assess the contribution of different parameters to the uncertainty of the Ecosystem Demography model v2 (ED) model outputs across various North American biomes (Dietze et al., JGR-G, 2014). While this analysis identified key research priorities, the extent to which these priorities were model- and/or biome-specific was unclear. Furthermore, because the analysis only studied one model, we were unable to comment on the effect of variability in model structure to overall predictive uncertainty. Here, we expand this analysis to all biomes globally and a wide sample of models that vary in complexity: BioCro, CABLE, CLM, DALEC, ED2, FATES, G'DAY, JULES, LANDIS, LINKAGES, LPJ-GUESS, MAESPA, PRELES, SDGVM, SIPNET, and TEM. Prior to performing uncertainty analyses, model parameter uncertainties were assessed by assimilating all available trait data from the combination of the BETYdb and TRY trait databases, using an updated multivariate version of PEcAn's Hierarchical Bayesian meta-analysis. Next, sensitivity analyses were performed for all models across a range of sites globally to assess sensitivities for a range of different outputs (GPP, ET, SH, Ra, NPP, Rh, NEE, LAI) at multiple time scales from the sub-annual to the decadal. Finally, parameter uncertainties and model sensitivities were combined to evaluate the fractional contribution of each parameter to the predictive uncertainty for a specific variable at a specific site and timescale. Facilitated by PEcAn's automated workflows, this analysis represents the broadest assessment of the sensitivities and uncertainties in terrestrial models to date, and provides a comprehensive roadmap for constraining model uncertainties through model development and data collection.

Clinical usefulness of the clock drawing test applying rasch analysis in predicting of cognitive impairment.

PubMed

Yoo, Doo Han; Lee, Jae Shin

2016-07-01

[Purpose] This study examined the clinical usefulness of the clock drawing test applying Rasch analysis for predicting the level of cognitive impairment. [Subjects and Methods] A total of 187 stroke patients with cognitive impairment were enrolled in this study. The 187 patients were evaluated by the clock drawing test developed through Rasch analysis along with the mini-mental state examination of cognitive evaluation tool. An analysis of the variance was performed to examine the significance of the mini-mental state examination and the clock drawing test according to the general characteristics of the subjects. Receiver operating characteristic analysis was performed to determine the cutoff point for cognitive impairment and to calculate the sensitivity and specificity values. [Results] The results of comparison of the clock drawing test with the mini-mental state showed significant differences in according to gender, age, education, and affected side. A total CDT of 10.5, which was selected as the cutoff point to identify cognitive impairement, showed a sensitivity, specificity, Youden index, positive predictive, and negative predicive values of 86.4%, 91.5%, 0.8, 95%, and 88.2%. [Conclusion] The clock drawing test is believed to be useful in assessments and interventions based on its excellent ability to identify cognitive disorders.

[Clinical research on clearance of leukemic cell during induction of remission therapy in children with precursor B cell acute lymphoblastic leukemia].

PubMed

Yi, Zhi-gang; Cui, Lei; Gao, Chao; Jin, Mei; Zhang, Rui-dong; Li, Zhi-gang; Wu, Min-yuan

2011-03-01

To investigate the clinical value of clearance of leukemic cell during induction of remission therapy in children with precursor B cell acute lymphoblastic leukemia (BCP-ALL), and to assess the applicative value of different indexes. From April 2005 to April 2008, 206 children with de novo BCP-ALL were admitted. We firstly analyzed the effect of clearance of leukemic cells during induction of remission therapy on relapse-free survival (RFS). Four indexes were used to assess the clearance of leukemic cells including prednisone response on day 8 (d8-PR), percentage of lymphoblast in bone marrow on day 22 (d22-BM) and day 33 (d33-BM), and bone marrow (BM) minimal residual disease (MRD) detection on day 33 (d33-MRD). Then the sensitivity, specificity, positive predictive value and negative predictive value of the four indexes to assess their ability to predict relapse were analyzed. Finally, the consistency between two of the four indexes to explore the relationships among them were analyzed. There were significant differences between RFS of the sub-groups divided according to d8-PR, d22-BM, d33-BM, d33-MRD (P < 0.01); Cox proportional hazard model analysis showed that d33-MRD ≥ 10(-3) and positive BCR/ABL fusion gene were the independent prognostic factors. Sensitivity of d33-MRD was higher than that of morphology detection (d22-BM, d33-BM and d8-PR) in prediction of relapse, and positive predictive value of morphology detection was higher than that of d33-MRD. Sensitivity could be greatly increased by combination with clinical and biological characteristics. Consistency could not be found between d8-PR and d22-BM, d33-BM, d33-MRD, as well as between d22-BM, d33-BM, and d33-MRD. However, all cases of d22-BM, d33-BM M2/M3 were d33-MRD ≥ 10(-3), while the same phenomenon could not be found for patients with poor d8-PR. Clearance of leukemic cell during induction of remission therapy in children with BCP-ALL had important clinical value. Sensitivity of MRD detection after induction of remission therapy was higher than that of morphological analysis to predict relapse. Morphological analysis could only identify a few patients with very high risk of relapse and the sensitivity could be increased by combination with clinical biological characteristics. The simple prednisone response may contain some prognostic information that could not be covered by analysis of BM cells. It may be the best way to assess the clearance of leukemic cells to combine the prednisone response with MRD detection after induction of remission therapy.

Predicting paclitaxel-induced neutropenia using the DMET platform.

PubMed

Nieuweboer, Annemieke J M; Smid, Marcel; de Graan, Anne-Joy M; Elbouazzaoui, Samira; de Bruijn, Peter; Martens, John W; Mathijssen, Ron H J; van Schaik, Ron H N

2015-01-01

The use of paclitaxel in cancer treatment is limited by paclitaxel-induced neutropenia. We investigated the ability of genetic variation in drug-metabolizing enzymes and transporters to predict hematological toxicity. Using a discovery and validation approach, we identified a pharmacogenetic predictive model for neutropenia. For this, a drug-metabolizing enzymes and transporters plus DNA chip was used, which contains 1936 SNPs in 225 metabolic enzyme and drug-transporter genes. Our 10-SNP model in 279 paclitaxel-dosed patients reached 43% sensitivity in the validation cohort. Analysis in 3-weekly treated patients only resulted in improved sensitivity of 79%, with a specificity of 33%. None of our models reached statistical significance. Our drug-metabolizing enzymes and transporters-based SNP-models are currently of limited value for predicting paclitaxel-induced neutropenia in clinical practice. Original submitted 9 March 2015; Revision submitted 20 May 2015.

Increased sensitivity of prolonged P-wave during exercise stress test in detection of angiographically documented coronary artery disease.

PubMed

Wsol, Agnieszka; Wydra, Wioletta; Chmielewski, Marek; Swiatowiec, Andrzej; Kuch, Marek

2017-01-01

A retrospective study was designed to investigate P-wave duration changes in exercise stress test (EST) for the prediction of angiographically documented substantial coronary artery disease (CAD). We analyzed 265 cases of patients, who underwent EST and subsequently coronary angiography. Analysis of P-wave duration was performed in leads II, V5 at rest, and in the recovery period. The sensitivity and specificity for the isolated ST-segment depression were only 31% and 76%, respectively. The combination of ST-depression with other exercise-induced clinical and electrocardio-graphic abnormalities (chest pain, ventricular arrhythmia, hypotension, left bundle branch block) was characterized by 41% sensitivity and 69% specificity. The combination of abnormal recovery P-wave duration (≥ 120 ms) with ST-depression and other exercise-induced abnormalities had 83% sensitivity but only 20% specificity. Combined analysis of increased delta P-wave duration, ST-depression and other exercise-induced abnormalities had 69% sensitivity and 42% specificity. Sensitivity and specificity of the increase in delta P-wave duration for left CAD was 69% and 47%, respectively, and for 3-vessel CAD 70% and 50%, respectively. The presence of arterial hypertension negatively influenced the prog-nostic value of P-wave changes in the stress test. The results of the study show that an addition of P-wave duration changes assessment to ST-depression analysis and other exercise-induced abnormalities increase sensitivity of EST, especially for left CAD and 3-vessel coronary disease. We have also provided evidence for the negative influence of the presence of arterial hypertension on the predictive value of P-wave changes in the stress test. (Cardiol J 2017; 24, 2: 159-166).

Meta-analysis of the relative sensitivity of semi-natural vegetation species to ozone.

PubMed

Hayes, F; Jones, M L M; Mills, G; Ashmore, M

2007-04-01

This study identified 83 species from existing publications suitable for inclusion in a database of sensitivity of species to ozone (OZOVEG database). An index, the relative sensitivity to ozone, was calculated for each species based on changes in biomass in order to test for species traits associated with ozone sensitivity. Meta-analysis of the ozone sensitivity data showed a wide inter-specific range in response to ozone. Some relationships in comparison to plant physiological and ecological characteristics were identified. Plants of the therophyte lifeform were particularly sensitive to ozone. Species with higher mature leaf N concentration were more sensitive to ozone than those with lower leaf N concentration. Some relationships between relative sensitivity to ozone and Ellenberg habitat requirements were also identified. In contrast, no relationships between relative sensitivity to ozone and mature leaf P concentration, Grime's CSR strategy, leaf longevity, flowering season, stomatal density and maximum altitude were found. The relative sensitivity of species and relationships with plant characteristics identified in this study could be used to predict sensitivity to ozone of untested species and communities.

Extracting falsifiable predictions from sloppy models.

PubMed

Gutenkunst, Ryan N; Casey, Fergal P; Waterfall, Joshua J; Myers, Christopher R; Sethna, James P

2007-12-01

Successful predictions are among the most compelling validations of any model. Extracting falsifiable predictions from nonlinear multiparameter models is complicated by the fact that such models are commonly sloppy, possessing sensitivities to different parameter combinations that range over many decades. Here we discuss how sloppiness affects the sorts of data that best constrain model predictions, makes linear uncertainty approximations dangerous, and introduces computational difficulties in Monte-Carlo uncertainty analysis. We also present a useful test problem and suggest refinements to the standards by which models are communicated.

Design and analysis of forward and reverse models for predicting defect accumulation, defect energetics, and irradiation conditions

DOE PAGES

Stewart, James A.; Kohnert, Aaron A.; Capolungo, Laurent; ...

2018-03-06

The complexity of radiation effects in a material’s microstructure makes developing predictive models a difficult task. In principle, a complete list of all possible reactions between defect species being considered can be used to elucidate damage evolution mechanisms and its associated impact on microstructure evolution. However, a central limitation is that many models use a limited and incomplete catalog of defect energetics and associated reactions. Even for a given model, estimating its input parameters remains a challenge, especially for complex material systems. Here, we present a computational analysis to identify the extent to which defect accumulation, energetics, and irradiation conditionsmore » can be determined via forward and reverse regression models constructed and trained from large data sets produced by cluster dynamics simulations. A global sensitivity analysis, via Sobol’ indices, concisely characterizes parameter sensitivity and demonstrates how this can be connected to variability in defect evolution. Based on this analysis and depending on the definition of what constitutes the input and output spaces, forward and reverse regression models are constructed and allow for the direct calculation of defect accumulation, defect energetics, and irradiation conditions. Here, this computational analysis, exercised on a simplified cluster dynamics model, demonstrates the ability to design predictive surrogate and reduced-order models, and provides guidelines for improving model predictions within the context of forward and reverse engineering of mathematical models for radiation effects in a materials’ microstructure.« less

Design and analysis of forward and reverse models for predicting defect accumulation, defect energetics, and irradiation conditions

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

Stewart, James A.; Kohnert, Aaron A.; Capolungo, Laurent

The complexity of radiation effects in a material’s microstructure makes developing predictive models a difficult task. In principle, a complete list of all possible reactions between defect species being considered can be used to elucidate damage evolution mechanisms and its associated impact on microstructure evolution. However, a central limitation is that many models use a limited and incomplete catalog of defect energetics and associated reactions. Even for a given model, estimating its input parameters remains a challenge, especially for complex material systems. Here, we present a computational analysis to identify the extent to which defect accumulation, energetics, and irradiation conditionsmore » can be determined via forward and reverse regression models constructed and trained from large data sets produced by cluster dynamics simulations. A global sensitivity analysis, via Sobol’ indices, concisely characterizes parameter sensitivity and demonstrates how this can be connected to variability in defect evolution. Based on this analysis and depending on the definition of what constitutes the input and output spaces, forward and reverse regression models are constructed and allow for the direct calculation of defect accumulation, defect energetics, and irradiation conditions. Here, this computational analysis, exercised on a simplified cluster dynamics model, demonstrates the ability to design predictive surrogate and reduced-order models, and provides guidelines for improving model predictions within the context of forward and reverse engineering of mathematical models for radiation effects in a materials’ microstructure.« less

The Validity of Conscientiousness Is Overestimated in the Prediction of Job Performance.

PubMed

Kepes, Sven; McDaniel, Michael A

2015-01-01

Sensitivity analyses refer to investigations of the degree to which the results of a meta-analysis remain stable when conditions of the data or the analysis change. To the extent that results remain stable, one can refer to them as robust. Sensitivity analyses are rarely conducted in the organizational science literature. Despite conscientiousness being a valued predictor in employment selection, sensitivity analyses have not been conducted with respect to meta-analytic estimates of the correlation (i.e., validity) between conscientiousness and job performance. To address this deficiency, we reanalyzed the largest collection of conscientiousness validity data in the personnel selection literature and conducted a variety of sensitivity analyses. Publication bias analyses demonstrated that the validity of conscientiousness is moderately overestimated (by around 30%; a correlation difference of about .06). The misestimation of the validity appears to be due primarily to suppression of small effects sizes in the journal literature. These inflated validity estimates result in an overestimate of the dollar utility of personnel selection by millions of dollars and should be of considerable concern for organizations. The fields of management and applied psychology seldom conduct sensitivity analyses. Through the use of sensitivity analyses, this paper documents that the existing literature overestimates the validity of conscientiousness in the prediction of job performance. Our data show that effect sizes from journal articles are largely responsible for this overestimation.

The Validity of Conscientiousness Is Overestimated in the Prediction of Job Performance

PubMed Central

2015-01-01

Introduction Sensitivity analyses refer to investigations of the degree to which the results of a meta-analysis remain stable when conditions of the data or the analysis change. To the extent that results remain stable, one can refer to them as robust. Sensitivity analyses are rarely conducted in the organizational science literature. Despite conscientiousness being a valued predictor in employment selection, sensitivity analyses have not been conducted with respect to meta-analytic estimates of the correlation (i.e., validity) between conscientiousness and job performance. Methods To address this deficiency, we reanalyzed the largest collection of conscientiousness validity data in the personnel selection literature and conducted a variety of sensitivity analyses. Results Publication bias analyses demonstrated that the validity of conscientiousness is moderately overestimated (by around 30%; a correlation difference of about .06). The misestimation of the validity appears to be due primarily to suppression of small effects sizes in the journal literature. These inflated validity estimates result in an overestimate of the dollar utility of personnel selection by millions of dollars and should be of considerable concern for organizations. Conclusion The fields of management and applied psychology seldom conduct sensitivity analyses. Through the use of sensitivity analyses, this paper documents that the existing literature overestimates the validity of conscientiousness in the prediction of job performance. Our data show that effect sizes from journal articles are largely responsible for this overestimation. PMID:26517553

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.

Immune Profiles to Predict Response to Desensitization Therapy in Highly HLA-Sensitized Kidney Transplant Candidates

PubMed Central

Yabu, Julie M.; Siebert, Janet C.; Maecker, Holden T.

2016-01-01

Background Kidney transplantation is the most effective treatment for end-stage kidney disease. Sensitization, the formation of human leukocyte antigen (HLA) antibodies, remains a major barrier to successful kidney transplantation. Despite the implementation of desensitization strategies, many candidates fail to respond. Current progress is hindered by the lack of biomarkers to predict response and to guide therapy. Our objective was to determine whether differences in immune and gene profiles may help identify which candidates will respond to desensitization therapy. Methods and Findings Single-cell mass cytometry by time-of-flight (CyTOF) phenotyping, gene arrays, and phosphoepitope flow cytometry were performed in a study of 20 highly sensitized kidney transplant candidates undergoing desensitization therapy. Responders to desensitization therapy were defined as 5% or greater decrease in cumulative calculated panel reactive antibody (cPRA) levels, and non-responders had 0% decrease in cPRA. Using a decision tree analysis, we found that a combination of transitional B cell and regulatory T cell (Treg) frequencies at baseline before initiation of desensitization therapy could distinguish responders from non-responders. Using a support vector machine (SVM) and longitudinal data, TRAF3IP3 transcripts and HLA-DR-CD38+CD4+ T cells could also distinguish responders from non-responders. Combining all assays in a multivariate analysis and elastic net regression model with 72 analytes, we identified seven that were highly interrelated and eleven that predicted response to desensitization therapy. Conclusions Measuring baseline and longitudinal immune and gene profiles could provide a useful strategy to distinguish responders from non-responders to desensitization therapy. This study presents the integration of novel translational studies including CyTOF immunophenotyping in a multivariate analysis model that has potential applications to predict response to desensitization, select candidates, and personalize medicine to ultimately improve overall outcomes in highly sensitized kidney transplant candidates. PMID:27078882

Immune Profiles to Predict Response to Desensitization Therapy in Highly HLA-Sensitized Kidney Transplant Candidates.

PubMed

Yabu, Julie M; Siebert, Janet C; Maecker, Holden T

2016-01-01

Kidney transplantation is the most effective treatment for end-stage kidney disease. Sensitization, the formation of human leukocyte antigen (HLA) antibodies, remains a major barrier to successful kidney transplantation. Despite the implementation of desensitization strategies, many candidates fail to respond. Current progress is hindered by the lack of biomarkers to predict response and to guide therapy. Our objective was to determine whether differences in immune and gene profiles may help identify which candidates will respond to desensitization therapy. Single-cell mass cytometry by time-of-flight (CyTOF) phenotyping, gene arrays, and phosphoepitope flow cytometry were performed in a study of 20 highly sensitized kidney transplant candidates undergoing desensitization therapy. Responders to desensitization therapy were defined as 5% or greater decrease in cumulative calculated panel reactive antibody (cPRA) levels, and non-responders had 0% decrease in cPRA. Using a decision tree analysis, we found that a combination of transitional B cell and regulatory T cell (Treg) frequencies at baseline before initiation of desensitization therapy could distinguish responders from non-responders. Using a support vector machine (SVM) and longitudinal data, TRAF3IP3 transcripts and HLA-DR-CD38+CD4+ T cells could also distinguish responders from non-responders. Combining all assays in a multivariate analysis and elastic net regression model with 72 analytes, we identified seven that were highly interrelated and eleven that predicted response to desensitization therapy. Measuring baseline and longitudinal immune and gene profiles could provide a useful strategy to distinguish responders from non-responders to desensitization therapy. This study presents the integration of novel translational studies including CyTOF immunophenotyping in a multivariate analysis model that has potential applications to predict response to desensitization, select candidates, and personalize medicine to ultimately improve overall outcomes in highly sensitized kidney transplant candidates.

Vibration analysis of the SA349/2 helicopter

NASA Technical Reports Server (NTRS)

Heffernan, Ruth; Precetti, Dominique; Johnson, Wayne

1991-01-01

Helicopter airframe vibration is examined using calculations and measurements for the SA349/2 research helicopter. The hub loads, which transmit excitations to the fuselage, are predicted using a comprehensive rotorcraft analysis and correlated with measuring hub loads. The predicted and measured hub loads are then coupled with finite element models representing the SA349/2 fuselage. The resulting vertical acceleration at the pilot seat is examined. Adjustments are made to the airframe structural models to examine the sensitivity of predicted vertical acceleration to the model. Changes of a few percent to the damping and frequency of specific models lead to large reductions in predicted vibration, and to major improvements in the correlations with measured pilot-seat vertical acceleration.

Relationships among depression, anxiety, anxiety sensitivity, and perceived social support in adolescents with conversion disorder.

PubMed

Yılmaz, Savaş; Bilgiç, Ayhan; Akça, Ömer Faruk; Türkoğlu, Serhat; Hergüner, Sabri

2016-01-01

This study aimed to assess the relationships of depression, anxiety, anxiety sensitivity, and perceived social support with conversion symptoms in adolescents with conversion disorder (CD). Fifty outpatients, aged 8-18 years, who had been diagnosed with CD and members of a control group were assessed using the psychological questionnaires. Compared with controls, adolescents with CD scored higher on the Child Depression Inventory (CDI), Screen for Child Anxiety-related Emotional Disorders (SCARED), Childhood Anxiety Sensitivity Index (CASI) total, CASI physical and cognitive subscales, and Multidimensional Scale of Perceived Social Support family subscale. Multiple regression analysis showed that CDI, CASI total, and CASI cognitive scores predicted the Somatoform Dissociation Questionnaire (SDQ) scores and that CDI and CASI total scores predicted the Children's Somatization Inventory (CSI) scores of subjects. This study suggest that adolescents with CD had poor psychosocial well-being, and depression, global anxiety sensitivity and anxiety sensitivity cognitive concerns are related to conversion symptoms.

Predictive modeling of infrared radiative heating in tomato dry-peeling process: Part II. Model validation and sensitivity analysis

USDA-ARS?s Scientific Manuscript database

A predictive mathematical model was developed to simulate heat transfer in a tomato undergoing double sided infrared (IR) heating in a dry-peeling process. The aims of this study were to validate the developed model using experimental data and to investigate different engineering parameters that mos...

Getting the Best Return on Your Screening Investment: An Analysis of the Suicidal Ideation Questionnaire and Reynolds Adolescent Depression Scale

ERIC Educational Resources Information Center

Gutierrez, Peter M.; Osman, Augustine

2009-01-01

Data from 64 adolescent inpatients admitted for serious suicidal ideation, 50 adolescent inpatients admitted following a suicide attempt, and 56 randomly selected high school control participants were used to evaluate the sensitivity, specificity, positive predictive value, and negative predictive value of the Suicidal Ideation Questionnaire (SIQ)…

PREVALENCE OF METABOLIC SYNDROME IN YOUNG MEXICANS: A SENSITIVITY ANALYSIS ON ITS COMPONENTS.

PubMed

Murguía-Romero, Miguel; Jiménez-Flores, J Rafael; Sigrist-Flores, Santiago C; Tapia-Pancardo, Diana C; Jiménez-Ramos, Arnulfo; Méndez-Cruz, A René; Villalobos-Molina, Rafael

2015-07-28

obesity is a worldwide epidemic, and the high prevalence of diabetes type II (DM2) and cardiovascular disease (CVD) is in great part a consequence of that epidemic. Metabolic syndrome is a useful tool to estimate the risk of a young population to evolve to DM2 and CVD. to estimate the MetS prevalence in young Mexicans, and to evaluate each parameter as an independent indicator through a sensitivity analysis. the prevalence of MetS was estimated in 6 063 young of the México City metropolitan area. A sensitivity analysis was conducted to estimate the performance of each one of the components of MetS, as an indicator of the presence of MetS itself. Five statistical of the sensitivity analysis were calculated for each MetS component and the other parameters included: sensitivity, specificity, positive predictive value or precision, negative predictive value, and accuracy. the prevalence of MetS in Mexican young population was estimated to be 13.4%. Waist circumference presented the highest sensitivity (96.8% women; 90.0% men), blood pressure presented the highest specificity for women (97.7%) and glucose for men (91.0%). When all the five statistical are considered triglycerides is the component with the highest values, showing a value of 75% or more in four of them. Differences by sex are detected for averages of all components of MetS in young without alterations. Mexican young are highly prone to acquire MetS: 71% have at least one and up to five MetS parameters altered, and 13.4% of them have MetS. From all the five components of MetS, waist circumference presented the highest sensitivity as a predictor of MetS, and triglycerides is the best parameter if a single factor is to be taken as sole predictor of MetS in Mexican young population, triglycerides is also the parameter with the highest accuracy. Copyright AULA MEDICA EDICIONES 2014. Published by AULA MEDICA. All rights reserved.

Artificial neural network models for prediction of cardiovascular autonomic dysfunction in general Chinese population

PubMed Central

2013-01-01

Background The present study aimed to develop an artificial neural network (ANN) based prediction model for cardiovascular autonomic (CA) dysfunction in the general population. Methods We analyzed a previous dataset based on a population sample consisted of 2,092 individuals aged 30–80 years. The prediction models were derived from an exploratory set using ANN analysis. Performances of these prediction models were evaluated in the validation set. Results Univariate analysis indicated that 14 risk factors showed statistically significant association with CA dysfunction (P < 0.05). The mean area under the receiver-operating curve was 0.762 (95% CI 0.732–0.793) for prediction model developed using ANN analysis. The mean sensitivity, specificity, positive and negative predictive values were similar in the prediction models was 0.751, 0.665, 0.330 and 0.924, respectively. All HL statistics were less than 15.0. Conclusion ANN is an effective tool for developing prediction models with high value for predicting CA dysfunction among the general population. PMID:23902963

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.

Selecting focal species as surrogates for imperiled species using relative sensitivities derived from occupancy analysis

USGS Publications Warehouse

Silvano, Amy; Guyer, Craig; Steury, Todd; Grand, James B.

2017-01-01

Most imperiled species are rare or elusive and difficult to detect, which makes gathering data to estimate their response to habitat restoration a challenge. We used a repeatable, systematic method for selecting focal species using relative sensitivities derived from occupancy analysis. Our objective was to select suites of focal species that would be useful as surrogates when predicting effects of restoration of habitat characteristics preferred by imperiled species. We developed 27 habitat profiles that represent general habitat relationships for 118 imperiled species. We identified 23 regularly encountered species that were sensitive to important aspects of those profiles. We validated our approach by examining the correlation between estimated probabilities of occupancy for species of concern and focal species selected using our method. Occupancy rates of focal species were more related to occupancy rates of imperiled species when they were sensitive to more of the parameters appearing in profiles of imperiled species. We suggest that this approach can be an effective means of predicting responses by imperiled species to proposed management actions. However, adequate monitoring will be required to determine the effectiveness of using focal species to guide management actions.

Expanding the occupational health methodology: A concatenated artificial neural network approach to model the burnout process in Chinese nurses.

PubMed

Ladstätter, Felix; Garrosa, Eva; Moreno-Jiménez, Bernardo; Ponsoda, Vicente; Reales Aviles, José Manuel; Dai, Junming

2016-01-01

Artificial neural networks are sophisticated modelling and prediction tools capable of extracting complex, non-linear relationships between predictor (input) and predicted (output) variables. This study explores this capacity by modelling non-linearities in the hardiness-modulated burnout process with a neural network. Specifically, two multi-layer feed-forward artificial neural networks are concatenated in an attempt to model the composite non-linear burnout process. Sensitivity analysis, a Monte Carlo-based global simulation technique, is then utilised to examine the first-order effects of the predictor variables on the burnout sub-dimensions and consequences. Results show that (1) this concatenated artificial neural network approach is feasible to model the burnout process, (2) sensitivity analysis is a prolific method to study the relative importance of predictor variables and (3) the relationships among variables involved in the development of burnout and its consequences are to different degrees non-linear. Many relationships among variables (e.g., stressors and strains) are not linear, yet researchers use linear methods such as Pearson correlation or linear regression to analyse these relationships. Artificial neural network analysis is an innovative method to analyse non-linear relationships and in combination with sensitivity analysis superior to linear methods.

From web search to healthcare utilization: privacy-sensitive studies from mobile data

PubMed Central

Horvitz, Eric

2013-01-01

Objective We explore relationships between health information seeking activities and engagement with healthcare professionals via a privacy-sensitive analysis of geo-tagged data from mobile devices. Materials and methods We analyze logs of mobile interaction data stripped of individually identifiable information and location data. The data analyzed consist of time-stamped search queries and distances to medical care centers. We examine search activity that precedes the observation of salient evidence of healthcare utilization (EHU) (ie, data suggesting that the searcher is using healthcare resources), in our case taken as queries occurring at or near medical facilities. Results We show that the time between symptom searches and observation of salient evidence of seeking healthcare utilization depends on the acuity of symptoms. We construct statistical models that make predictions of forthcoming EHU based on observations about the current search session, prior medical search activities, and prior EHU. The predictive accuracy of the models varies (65%–90%) depending on the features used and the timeframe of the analysis, which we explore via a sensitivity analysis. Discussion We provide a privacy-sensitive analysis that can be used to generate insights about the pursuit of health information and healthcare. The findings demonstrate how large-scale studies of mobile devices can provide insights on how concerns about symptomatology lead to the pursuit of professional care. Conclusion We present new methods for the analysis of mobile logs and describe a study that provides evidence about how people transition from mobile searches on symptoms and diseases to the pursuit of healthcare in the world. PMID:22661560

Prediction of the severity of acute pancreatitis on admission by urinary trypsinogen activation peptide: A meta-analysis

PubMed Central

Huang, Wei; Altaf, Kiran; Jin, Tao; Xiong, Jun-Jie; Wen, Li; Javed, Muhammad A; Johnstone, Marianne; Xue, Ping; Halloran, Christopher M; Xia, Qing

2013-01-01

AIM: To undertake a meta-analysis on the value of urinary trypsinogen activation peptide (uTAP) in predicting severity of acute pancreatitis on admission. METHODS: Major databases including Medline, Embase, Science Citation Index Expanded and the Cochrane Central Register of Controlled Trials in the Cochrane Library were searched to identify all relevant studies from January 1990 to January 2013. Pooled sensitivity, specificity and the diagnostic odds ratios (DORs) with 95%CI were calculated for each study and were compared to other systems/biomarkers if mentioned within the same study. Summary receiver-operating curves were conducted and the area under the curve (AUC) was evaluated. RESULTS: In total, six studies of uTAP with a cut-off value of 35 nmol/L were included in this meta-analysis. Overall, the pooled sensitivity and specificity of uTAP for predicting severity of acute pancreatitis, at time of admission, was 71% and 75%, respectively (AUC = 0.83, DOR = 8.67, 95%CI: 3.70-20.33). When uTAP was compared with plasma C-reactive protein, the pooled sensitivity, specificity, AUC and DOR were 0.64 vs 0.67, 0.77 vs 0.75, 0.82 vs 0.79 and 6.27 vs 6.32, respectively. Similarly, the pooled sensitivity, specificity, AUC and DOR of uTAP vs Acute Physiology and Chronic Health Evaluation II within the first 48 h of admission were found to be 0.64 vs 0.69, 0.77 vs 0.61, 0.82 vs 0.73 and 6.27 vs 4.61, respectively. CONCLUSION: uTAP has the potential to act as a stratification marker on admission for differentiating disease severity of acute pancreatitis. PMID:23901239

Summarising and validating test accuracy results across multiple studies for use in clinical practice.

PubMed

Riley, Richard D; Ahmed, Ikhlaaq; Debray, Thomas P A; Willis, Brian H; Noordzij, J Pieter; Higgins, Julian P T; Deeks, Jonathan J

2015-06-15

Following a meta-analysis of test accuracy studies, the translation of summary results into clinical practice is potentially problematic. The sensitivity, specificity and positive (PPV) and negative (NPV) predictive values of a test may differ substantially from the average meta-analysis findings, because of heterogeneity. Clinicians thus need more guidance: given the meta-analysis, is a test likely to be useful in new populations, and if so, how should test results inform the probability of existing disease (for a diagnostic test) or future adverse outcome (for a prognostic test)? We propose ways to address this. Firstly, following a meta-analysis, we suggest deriving prediction intervals and probability statements about the potential accuracy of a test in a new population. Secondly, we suggest strategies on how clinicians should derive post-test probabilities (PPV and NPV) in a new population based on existing meta-analysis results and propose a cross-validation approach for examining and comparing their calibration performance. Application is made to two clinical examples. In the first example, the joint probability that both sensitivity and specificity will be >80% in a new population is just 0.19, because of a low sensitivity. However, the summary PPV of 0.97 is high and calibrates well in new populations, with a probability of 0.78 that the true PPV will be at least 0.95. In the second example, post-test probabilities calibrate better when tailored to the prevalence in the new population, with cross-validation revealing a probability of 0.97 that the observed NPV will be within 10% of the predicted NPV. © 2015 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

A Predictive Model to Estimate Cost Savings of a Novel Diagnostic Blood Panel for Diagnosis of Diarrhea-predominant Irritable Bowel Syndrome.

PubMed

Pimentel, Mark; Purdy, Chris; Magar, Raf; Rezaie, Ali

2016-07-01

A high incidence of irritable bowel syndrome (IBS) is associated with significant medical costs. Diarrhea-predominant IBS (IBS-D) is diagnosed on the basis of clinical presentation and diagnostic test results and procedures that exclude other conditions. This study was conducted to estimate the potential cost savings of a novel IBS diagnostic blood panel that tests for the presence of antibodies to cytolethal distending toxin B and anti-vinculin associated with IBS-D. A cost-minimization (CM) decision tree model was used to compare the costs of a novel IBS diagnostic blood panel pathway versus an exclusionary diagnostic pathway (ie, standard of care). The probability that patients proceed to treatment was modeled as a function of sensitivity, specificity, and likelihood ratios of the individual biomarker tests. One-way sensitivity analyses were performed for key variables, and a break-even analysis was performed for the pretest probability of IBS-D. Budget impact analysis of the CM model was extrapolated to a health plan with 1 million covered lives. The CM model (base-case) predicted $509 cost savings for the novel IBS diagnostic blood panel versus the exclusionary diagnostic pathway because of the avoidance of downstream testing (eg, colonoscopy, computed tomography scans). Sensitivity analysis indicated that an increase in both positive likelihood ratios modestly increased cost savings. Break-even analysis estimated that the pretest probability of disease would be 0.451 to attain cost neutrality. The budget impact analysis predicted a cost savings of $3,634,006 ($0.30 per member per month). The novel IBS diagnostic blood panel may yield significant cost savings by allowing patients to proceed to treatment earlier, thereby avoiding unnecessary testing. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Prediction error and somatosensory insula activation in women recovered from anorexia nervosa

PubMed Central

Frank, Guido K.W.; Collier, Shaleise; Shott, Megan E.; O’Reilly, Randall C.

2016-01-01

Background Previous research in patients with anorexia nervosa showed heightened brain response during a taste reward conditioning task and heightened sensitivity to rewarding and punishing stimuli. Here we tested the hypothesis that individuals recovered from anorexia nervosa would also experience greater brain activation during this task as well as higher sensitivity to salient stimuli than controls. Methods Women recovered from restricting-type anorexia nervosa and healthy control women underwent fMRI during application of a prediction error taste reward learning paradigm. Results Twenty-four women recovered from anorexia nervosa (mean age 30.3 ± 8.1 yr) and 24 control women (mean age 27.4 ± 6.3 yr) took part in this study. The recovered anorexia nervosa group showed greater left posterior insula activation for the prediction error model analysis than the control group (family-wise error– and small volume–corrected p < 0.05). A group × condition analysis found greater posterior insula response in women recovered from anorexia nervosa than controls for unexpected stimulus omission, but not for unexpected receipt. Sensitivity to punishment was elevated in women recovered from anorexia nervosa. Limitations This was a cross-sectional study, and the sample size was modest. Conclusion Anorexia nervosa after recovery is associated with heightened prediction error–related brain response in the posterior insula as well as greater response to unexpected reward stimulus omission. This finding, together with behaviourally increased sensitivity to punishment, could indicate that individuals recovered from anorexia nervosa are particularly responsive to punishment. The posterior insula processes somatosensory stimuli, including unexpected bodily states, and greater response could indicate altered perception or integration of unexpected or maybe unwanted bodily feelings. Whether those findings develop during the ill state or whether they are biological traits requires further study. PMID:26836623

Prediction error and somatosensory insula activation in women recovered from anorexia nervosa.

PubMed

Frank, Guido K W; Collier, Shaleise; Shott, Megan E; O'Reilly, Randall C

2016-08-01

Previous research in patients with anorexia nervosa showed heightened brain response during a taste reward conditioning task and heightened sensitivity to rewarding and punishing stimuli. Here we tested the hypothesis that individuals recovered from anorexia nervosa would also experience greater brain activation during this task as well as higher sensitivity to salient stimuli than controls. Women recovered from restricting-type anorexia nervosa and healthy control women underwent fMRI during application of a prediction error taste reward learning paradigm. Twenty-four women recovered from anorexia nervosa (mean age 30.3 ± 8.1 yr) and 24 control women (mean age 27.4 ± 6.3 yr) took part in this study. The recovered anorexia nervosa group showed greater left posterior insula activation for the prediction error model analysis than the control group (family-wise error- and small volume-corrected p < 0.05). A group × condition analysis found greater posterior insula response in women recovered from anorexia nervosa than controls for unexpected stimulus omission, but not for unexpected receipt. Sensitivity to punishment was elevated in women recovered from anorexia nervosa. This was a cross-sectional study, and the sample size was modest. Anorexia nervosa after recovery is associated with heightened prediction error-related brain response in the posterior insula as well as greater response to unexpected reward stimulus omission. This finding, together with behaviourally increased sensitivity to punishment, could indicate that individuals recovered from anorexia nervosa are particularly responsive to punishment. The posterior insula processes somatosensory stimuli, including unexpected bodily states, and greater response could indicate altered perception or integration of unexpected or maybe unwanted bodily feelings. Whether those findings develop during the ill state or whether they are biological traits requires further study.

Acute toxicity value extrapolation with fish and aquatic invertebrates

USGS Publications Warehouse

Buckler, Denny R.; Mayer, Foster L.; Ellersieck, Mark R.; Asfaw, Amha

2005-01-01

Assessment of risk posed by an environmental contaminant to an aquatic community requires estimation of both its magnitude of occurrence (exposure) and its ability to cause harm (effects). Our ability to estimate effects is often hindered by limited toxicological information. As a result, resource managers and environmental regulators are often faced with the need to extrapolate across taxonomic groups in order to protect the more sensitive members of the aquatic community. The goals of this effort were to 1) compile and organize an extensive body of acute toxicity data, 2) characterize the distribution of toxicant sensitivity across taxa and species, and 3) evaluate the utility of toxicity extrapolation methods based upon sensitivity relations among species and chemicals. Although the analysis encompassed a wide range of toxicants and species, pesticides and freshwater fish and invertebrates were emphasized as a reflection of available data. Although it is obviously desirable to have high-quality acute toxicity values for as many species as possible, the results of this effort allow for better use of available information for predicting the sensitivity of untested species to environmental contaminants. A software program entitled “Ecological Risk Analysis” (ERA) was developed that predicts toxicity values for sensitive members of the aquatic community using species sensitivity distributions. Of several methods evaluated, the ERA program used with minimum data sets comprising acute toxicity values for rainbow trout, bluegill, daphnia, and mysids provided the most satisfactory predictions with the least amount of data. However, if predictions must be made using data for a single species, the most satisfactory results were obtained with extrapolation factors developed for rainbow trout (0.412), bluegill (0.331), or scud (0.041). Although many specific exceptions occur, our results also support the conventional wisdom that invertebrates are generally more sensitive to contaminants than fish are.

Kinase Pathway Dependence in Primary Human Leukemias Determined by Rapid Inhibitor Screening

PubMed Central

Tyner, Jeffrey W.; Yang, Wayne F.; Bankhead, Armand; Fan, Guang; Fletcher, Luke B.; Bryant, Jade; Glover, Jason M.; Chang, Bill H.; Spurgeon, Stephen E.; Fleming, William H.; Kovacsovics, Tibor; Gotlib, Jason R.; Oh, Stephen T.; Deininger, Michael W.; Zwaan, C. Michel; Den Boer, Monique L.; van den Heuvel-Eibrink, Marry M.; O’Hare, Thomas; Druker, Brian J.; Loriaux, Marc M.

2012-01-01

Kinases are dysregulated in most cancer but the frequency of specific kinase mutations is low, indicating a complex etiology in kinase dysregulation. Here we report a strategy to rapidly identify functionally important kinase targets, irrespective of the etiology of kinase pathway dysregulation, ultimately enabling a correlation of patient genetic profiles to clinically effective kinase inhibitors. Our methodology assessed the sensitivity of primary leukemia patient samples to a panel of 66 small-molecule kinase inhibitors over 3 days. Screening of 151 leukemia patient samples revealed a wide diversity of drug sensitivities, with 70% of the clinical specimens exhibiting hypersensitivity to one or more drugs. From this data set, we developed an algorithm to predict kinase pathway dependence based on analysis of inhibitor sensitivity patterns. Applying this algorithm correctly identified pathway dependence in proof-of-principle specimens with known oncogenes, including a rare FLT3 mutation outside regions covered by standard molecular diagnostic tests. Interrogation of all 151 patient specimens with this algorithm identified a diversity of gene targets and signaling pathways that could aid prioritization of deep sequencing data sets, permitting a cumulative analysis to understand kinase pathway dependence within leukemia subsets. In a proof-of-principle case, we showed that in vitro drug sensitivity could predict both a clinical response and the development of drug resistance. Taken together, our results suggested that drug target scores derived from a comprehensive kinase inhibitor panel could predict pathway dependence in cancer cells while simultaneously identifying potential therapeutic options. PMID:23087056

Flows of dioxins and furans in coastal food webs: inverse modeling, sensitivity analysis, and applications of linear system theory.

PubMed

Saloranta, Tuomo M; Andersen, Tom; Naes, Kristoffer

2006-01-01

Rate constant bioaccumulation models are applied to simulate the flow of polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) in the coastal marine food web of Frierfjorden, a contaminated fjord in southern Norway. We apply two different ways to parameterize the rate constants in the model, global sensitivity analysis of the models using Extended Fourier Amplitude Sensitivity Test (Extended FAST) method, as well as results from general linear system theory, in order to obtain a more thorough insight to the system's behavior and to the flow pathways of the PCDD/Fs. We calibrate our models against observed body concentrations of PCDD/Fs in the food web of Frierfjorden. Differences between the predictions from the two models (using the same forcing and parameter values) are of the same magnitude as their individual deviations from observations, and the models can be said to perform about equally well in our case. Sensitivity analysis indicates that the success or failure of the models in predicting the PCDD/F concentrations in the food web organisms highly depends on the adequate estimation of the truly dissolved concentrations in water and sediment pore water. We discuss the pros and cons of such models in understanding and estimating the present and future concentrations and bioaccumulation of persistent organic pollutants in aquatic food webs.

Validation of Vehicle Panel/Equipment Response from Diffuse Acoustic Field Excitation Using Spatially Correlated Transfer Function Approach

NASA Technical Reports Server (NTRS)

Smith, Andrew; LaVerde, Bruce; Fulcher, Clay; Hunt, Ron

2012-01-01

An approach for predicting the vibration, strain, and force responses of a flight-like vehicle panel assembly to acoustic pressures is presented. Important validation for the approach is provided by comparison to ground test measurements in a reverberant chamber. The test article and the corresponding analytical model were assembled in several configurations to demonstrate the suitability of the approach for response predictions when the vehicle panel is integrated with equipment. Critical choices in the analysis necessary for convergence of the predicted and measured responses are illustrated through sensitivity studies. The methodology includes representation of spatial correlation of the pressure field over the panel surface. Therefore, it is possible to demonstrate the effects of hydrodynamic coincidence in the response. The sensitivity to pressure patch density clearly illustrates the onset of coincidence effects on the panel response predictions.

MODFLOW-2000, the U.S. Geological Survey modular ground-water model -- Documentation of MOD-PREDICT for predictions, prediction sensitivity analysis, and evaluation of uncertainty

USGS Publications Warehouse

Tonkin, M.J.; Hill, Mary C.; Doherty, John

2003-01-01

This document describes the MOD-PREDICT program, which helps evaluate userdefined sets of observations, prior information, and predictions, using the ground-water model MODFLOW-2000. MOD-PREDICT takes advantage of the existing Observation and Sensitivity Processes (Hill and others, 2000) by initiating runs of MODFLOW-2000 and using the output files produced. The names and formats of the MODFLOW-2000 input files are unchanged, such that full backward compatibility is maintained. A new name file and input files are required for MOD-PREDICT. The performance of MOD-PREDICT has been tested in a variety of applications. Future applications, however, might reveal errors that were not detected in the test simulations. Users are requested to notify the U.S. Geological Survey of any errors found in this document or the computer program using the email address available at the web address below. Updates might occasionally be made to this document, to the MOD-PREDICT program, and to MODFLOW- 2000. Users can check for updates on the Internet at URL http://water.usgs.gov/software/ground water.html/.

Empirical Observations on the Sensitivity of Hot Cathode Ionization Type Vacuum Gages

NASA Technical Reports Server (NTRS)

Summers, R. L.

1969-01-01

A study of empirical methods of predicting tile relative sensitivities of hot cathode ionization gages is presented. Using previously published gage sensitivities, several rules for predicting relative sensitivity are tested. The relative sensitivity to different gases is shown to be invariant with gage type, in the linear range of gage operation. The total ionization cross section, molecular and molar polarizability, and refractive index are demonstrated to be useful parameters for predicting relative gage sensitivity. Using data from the literature, the probable error of predictions of relative gage sensitivity based on these molecular properties is found to be about 10 percent. A comprehensive table of predicted relative sensitivities, based on empirical methods, is presented.

Anxiety sensitivity as a predictor of broad dimensions of psychopathology after cognitive behavioral therapy for panic disorder.

PubMed

Ino, Keiko; Ogawa, Sei; Kondo, Masaki; Imai, Risa; Ii, Toshitaka; Furukawa, Toshi A; Akechi, Tatsuo

2017-01-01

Panic disorder (PD) is a common disease and presents with broad dimensions of psychopathology. Cognitive behavioral therapy (CBT) is known to improve these broad dimensions of psychopathology in addition to PD symptoms. However, little is known about the predictors of treatment response in comorbid psychiatric symptoms after CBT for PD. Recent studies suggest that anxiety sensitivity (AS) may be a key vulnerability for PD. This study aimed to examine AS as a predictor of broad dimensions of psychopathology after CBT for PD. In total, 118 patients with PD were treated with manualized group CBT. We used multiple regression analysis to examine the associations between 3 Anxiety Sensitivity Index (ASI) factors (physical concerns, mental incapacitation concerns, and social concerns) at baseline and the subscales of the Symptom Checklist-90 Revised (SCL-90-R) at endpoint. Low levels of social concerns at baseline predicted low levels on 5 SCL-90-R subscales after CBT: interpersonal sensitivity, depression, hostility, paranoid ideation, and psychosis. High levels of mental incapacitation concerns significantly predicted low levels on 3 SCL-90-R subscales after treatment: interpersonal sensitivity, hostility, and paranoid ideation. Physical concerns at baseline did not predict broad dimensions of psychopathology. This study suggested that the social concerns and mental incapacitation concerns subscales of the ASI at baseline predicted several dimensions of psychopathology after CBT for PD. To improve comorbid psychopathology, it may be useful to direct more attention to these ASI subscales.

The Montreal Cognitive Assessment and the mini-mental state examination as screening instruments for cognitive impairment: item analyses and threshold scores.

PubMed

Damian, Anne M; Jacobson, Sandra A; Hentz, Joseph G; Belden, Christine M; Shill, Holly A; Sabbagh, Marwan N; Caviness, John N; Adler, Charles H

2011-01-01

To perform an item analysis of the Montreal Cognitive Assessment (MoCA) versus the Mini-Mental State Examination (MMSE) in the prediction of cognitive impairment, and to examine the characteristics of different MoCA threshold scores. 135 subjects enrolled in a longitudinal clinicopathologic study were administered the MoCA by a single physician and the MMSE by a trained research assistant. Subjects were classified as cognitively impaired or cognitively normal based on independent neuropsychological testing. 89 subjects were found to be cognitively normal, and 46 cognitively impaired (20 with dementia, 26 with mild cognitive impairment). The MoCA was superior in both sensitivity and specificity to the MMSE, although not all MoCA tasks were of equal predictive value. A MoCA threshold score of 26 had a sensitivity of 98% and a specificity of 52% in this population. In a population with a 20% prevalence of cognitive impairment, a threshold of 24 was optimal (negative predictive value 96%, positive predictive value 47%). This analysis suggests the potential for creating an abbreviated MoCA. For screening in primary care, the MoCA threshold of 26 appears optimal. For testing in a memory disorders clinic, a lower threshold has better predictive value. Copyright © 2011 S. Karger AG, Basel.

Comparison of linear–stochastic and nonlinear–deterministic algorithms in the analysis of 15-minute clinical ECGs to predict risk of arrhythmic death

PubMed Central

Skinner, James E; Meyer, Michael; Nester, Brian A; Geary, Una; Taggart, Pamela; Mangione, Antoinette; Ramalanjaona, George; Terregino, Carol; Dalsey, William C

2009-01-01

Objective: Comparative algorithmic evaluation of heartbeat series in low-to-high risk cardiac patients for the prospective prediction of risk of arrhythmic death (AD). Background: Heartbeat variation reflects cardiac autonomic function and risk of AD. Indices based on linear stochastic models are independent risk factors for AD in post-myocardial infarction (post-MI) cohorts. Indices based on nonlinear deterministic models have superior predictability in retrospective data. Methods: Patients were enrolled (N = 397) in three emergency departments upon presenting with chest pain and were determined to be at low-to-high risk of acute MI (>7%). Brief ECGs were recorded (15 min) and R-R intervals assessed by three nonlinear algorithms (PD2i, DFA, and ApEn) and four conventional linear-stochastic measures (SDNN, MNN, 1/f-Slope, LF/HF). Out-of-hospital AD was determined by modified Hinkle–Thaler criteria. Results: All-cause mortality at one-year follow-up was 10.3%, with 7.7% adjudicated to be AD. The sensitivity and relative risk for predicting AD was highest at all time-points for the nonlinear PD2i algorithm (p ≤0.001). The sensitivity at 30 days was 100%, specificity 58%, and relative risk >100 (p ≤0.001); sensitivity at 360 days was 95%, specificity 58%, and relative risk >11.4 (p ≤0.001). Conclusions: Heartbeat analysis by the time-dependent nonlinear PD2i algorithm is comparatively the superior test. PMID:19707283

Molecular Evolution of the Tissue-nonspecific Alkaline Phosphatase Allows Prediction and Validation of Missense Mutations Responsible for Hypophosphatasia*

PubMed Central

Silvent, Jérémie; Gasse, Barbara; Mornet, Etienne; Sire, Jean-Yves

2014-01-01

ALPL encodes the tissue nonspecific alkaline phosphatase (TNSALP), which removes phosphate groups from various substrates. Its function is essential for bone and tooth mineralization. In humans, ALPL mutations lead to hypophosphatasia, a genetic disorder characterized by defective bone and/or tooth mineralization. To date, 275 ALPL mutations have been reported to cause hypophosphatasia, of which 204 were simple missense mutations. Molecular evolutionary analysis has proved to be an efficient method to highlight residues important for the protein function and to predict or validate sensitive positions for genetic disease. Here we analyzed 58 mammalian TNSALP to identify amino acids unchanged, or only substituted by residues sharing similar properties, through 220 millions years of mammalian evolution. We found 469 sensitive positions of the 524 residues of human TNSALP, which indicates a highly constrained protein. Any substitution occurring at one of these positions is predicted to lead to hypophosphatasia. We tested the 204 missense mutations resulting in hypophosphatasia against our predictive chart, and validated 99% of them. Most sensitive positions were located in functionally important regions of TNSALP (active site, homodimeric interface, crown domain, calcium site, …). However, some important positions are located in regions, the structure and/or biological function of which are still unknown. Our chart of sensitive positions in human TNSALP (i) enables to validate or invalidate at low cost any ALPL mutation, which would be suspected to be responsible for hypophosphatasia, by contrast with time consuming and expensive functional tests, and (ii) displays higher predictive power than in silico models of prediction. PMID:25023282

Predictive and concurrent validity of the Braden scale in long-term care: a meta-analysis.

PubMed

Wilchesky, Machelle; Lungu, Ovidiu

2015-01-01

Pressure ulcer prevention is an important long-term care (LTC) quality indicator. While the Braden Scale is a recommended risk assessment tool, there is a paucity of information specifically pertaining to its validity within the LTC setting. We, therefore, undertook a systematic review and meta-analysis comparing Braden Scale predictive and concurrent validity within this context. We searched the Medline, EMBASE, PsychINFO and PubMed databases from 1985-2014 for studies containing the requisite information to analyze tool validity. Our initial search yielded 3,773 articles. Eleven datasets emanating from nine published studies describing 40,361 residents met all meta-analysis inclusion criteria and were analyzed using random effects models. Pooled sensitivity, specificity, positive predictive value (PPV), and negative predictive values were 86%, 38%, 28%, and 93%, respectively. Specificity was poorer in concurrent samples as compared with predictive samples (38% vs. 72%), while PPV was low in both sample types (25 and 37%). Though random effects model results showed that the Scale had good overall predictive ability [RR, 4.33; 95% CI, 3.28-5.72], none of the concurrent samples were found to have "optimal" sensitivity and specificity. In conclusion, the appropriateness of the Braden Scale in LTC is questionable given its low specificity and PPV, in particular in concurrent validity studies. Future studies should further explore the extent to which the apparent low validity of the Scale in LTC is due to the choice of cutoff point and/or preventive strategies implemented by LTC staff as a matter of course. © 2015 by the Wound Healing Society.

Prediction of rodent carcinogenic potential of naturally occurring chemicals in the human diet using high-throughput QSAR predictive modeling

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

Valerio, Luis G.; Arvidson, Kirk B.; Chanderbhan, Ronald F.

2007-07-01

Consistent with the U.S. Food and Drug Administration (FDA) Critical Path Initiative, predictive toxicology software programs employing quantitative structure-activity relationship (QSAR) models are currently under evaluation for regulatory risk assessment and scientific decision support for highly sensitive endpoints such as carcinogenicity, mutagenicity and reproductive toxicity. At the FDA's Center for Food Safety and Applied Nutrition's Office of Food Additive Safety and the Center for Drug Evaluation and Research's Informatics and Computational Safety Analysis Staff (ICSAS), the use of computational SAR tools for both qualitative and quantitative risk assessment applications are being developed and evaluated. One tool of current interest ismore » MDL-QSAR predictive discriminant analysis modeling of rodent carcinogenicity, which has been previously evaluated for pharmaceutical applications by the FDA ICSAS. The study described in this paper aims to evaluate the utility of this software to estimate the carcinogenic potential of small, organic, naturally occurring chemicals found in the human diet. In addition, a group of 19 known synthetic dietary constituents that were positive in rodent carcinogenicity studies served as a control group. In the test group of naturally occurring chemicals, 101 were found to be suitable for predictive modeling using this software's discriminant analysis modeling approach. Predictions performed on these compounds were compared to published experimental evidence of each compound's carcinogenic potential. Experimental evidence included relevant toxicological studies such as rodent cancer bioassays, rodent anti-carcinogenicity studies, genotoxic studies, and the presence of chemical structural alerts. Statistical indices of predictive performance were calculated to assess the utility of the predictive modeling method. Results revealed good predictive performance using this software's rodent carcinogenicity module of over 1200 chemicals, comprised primarily of pharmaceutical, industrial and some natural products developed under an FDA-MDL cooperative research and development agreement (CRADA). The predictive performance for this group of dietary natural products and the control group was 97% sensitivity and 80% concordance. Specificity was marginal at 53%. This study finds that the in silico QSAR analysis employing this software's rodent carcinogenicity database is capable of identifying the rodent carcinogenic potential of naturally occurring organic molecules found in the human diet with a high degree of sensitivity. It is the first study to demonstrate successful QSAR predictive modeling of naturally occurring carcinogens found in the human diet using an external validation test. Further test validation of this software and expansion of the training data set for dietary chemicals will help to support the future use of such QSAR methods for screening and prioritizing the risk of dietary chemicals when actual animal data are inadequate, equivocal, or absent.« less

Accuracy of ultrasound for the prediction of placenta accreta.

PubMed

Bowman, Zachary S; Eller, Alexandra G; Kennedy, Anne M; Richards, Douglas S; Winter, Thomas C; Woodward, Paula J; Silver, Robert M

2014-08-01

Ultrasound has been reported to be greater than 90% sensitive for the diagnosis of accreta. Prior studies may be subject to bias because of single expert observers, suspicion for accreta, and knowledge of risk factors. We aimed to assess the accuracy of ultrasound for the prediction of accreta. Patients with accreta at a single academic center were matched to patients with placenta previa, but no accreta, by year of delivery. Ultrasound studies with views of the placenta were collected, deidentified, blinded to clinical history, and placed in random sequence. Six investigators prospectively interpreted each study for the presence of accreta and findings reported to be associated with its diagnosis. Sensitivity, specificity, positive predictive, negative predictive value, and accuracy were calculated. Characteristics of accurate findings were compared using univariate and multivariate analyses. Six investigators examined 229 ultrasound studies from 55 patients with accreta and 56 controls for 1374 independent observations. 1205/1374 (87.7% overall, 90% controls, 84.9% cases) studies were given a diagnosis. There were 371 (27.0%) true positives; 81 (5.9%) false positives; 533 (38.8%) true negatives, 220 (16.0%) false negatives, and 169 (12.3%) with uncertain diagnosis. Sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 53.5%, 88.0%, 82.1%, 64.8%, and 64.8%, respectively. In multivariate analysis, true positives were more likely to have placental lacunae (odds ratio [OR], 1.5; 95% confidence interval [CI], 1.4-1.6), loss of retroplacental clear space (OR, 2.4; 95% CI, 1.1-4.9), or abnormalities on color Doppler (OR, 2.1; 95% CI, 1.8-2.4). Ultrasound for the prediction of placenta accreta may not be as sensitive as previously described. Copyright © 2014 Mosby, Inc. All rights reserved.

Two-layer convective heating prediction procedures and sensitivities for blunt body reentry vehicles

NASA Technical Reports Server (NTRS)

Bouslog, Stanley A.; An, Michael Y.; Wang, K. C.; Tam, Luen T.; Caram, Jose M.

1993-01-01

This paper provides a description of procedures typically used to predict convective heating rates to hypersonic reentry vehicles using the two-layer method. These procedures were used to compute the pitch-plane heating distributions to the Apollo geometry for a wind tunnel test case and for three flight cases. Both simple engineering methods and coupled inviscid/boundary layer solutions were used to predict the heating rates. The sensitivity of the heating results in the choice of metrics, pressure distributions, boundary layer edge conditions, and wall catalycity used in the heating analysis were evaluated. Streamline metrics, pressure distributions, and boundary layer edge properties were defined from perfect gas (wind tunnel case) and chemical equilibrium and nonequilibrium (flight cases) inviscid flow-field solutions. The results of this study indicated that the use of CFD-derived metrics and pressures provided better predictions of heating when compared to wind tunnel test data. The study also showed that modeling entropy layer swallowing and ionization had little effect on the heating predictions.

Simplifiying global biogeochemistry models to evaluate methane emissions

NASA Astrophysics Data System (ADS)

Gerber, S.; Alonso-Contes, C.

2017-12-01

Process-based models are important tools to quantify wetland methane emissions, particularly also under climate change scenarios, evaluating these models is often cumbersome as they are embedded in larger land-surface models where fluctuating water table and the carbon cycle (including new readily decomposable plant material) are predicted variables. Here, we build on these large scale models but instead of modeling water table and plant productivity we provide values as boundary conditions. In contrast, aerobic and anaerobic decomposition, as well as soil column transport of oxygen and methane are predicted by the model. Because of these simplifications, the model has the potential to be more readily adaptable to the analysis of field-scale data. Here we determine the sensitivity of the model to specific setups, parameter choices, and to boundary conditions in order to determine set-up needs and inform what critical auxiliary variables need to be measured in order to better predict field-scale methane emissions from wetland soils. To that end we performed a global sensitivity analysis that also considers non-linear interactions between processes. The global sensitivity analysis revealed, not surprisingly, that water table dynamics (both mean level and amplitude of fluctuations), and the rate of the carbon cycle (i.e. net primary productivity) are critical determinants of methane emissions. The depth-scale where most of the potential decomposition occurs also affects methane emissions. Different transport mechanisms are compensating each other to some degree: If plant conduits are constrained, methane emissions by diffusive flux and ebullition compensate to some degree, however annual emissions are higher when plants help to bypass methanotrophs in temporally unsaturated upper layers. Finally, while oxygen consumption by plant roots help creating anoxic conditions it has little effect on overall methane emission. Our initial sensitivity analysis helps guiding further model development and improvement. However, an important goal for our model is to use it in field settings as a tool to deconvolve the different processes that contribute to the net transfer of methane from soils to atmosphere.

The link between hypomania risk and creativity: The role of heightened behavioral activation system (BAS) sensitivity.

PubMed

Kim, Bin-Na; Kwon, Seok-Man

2017-06-01

The relationship between bipolar disorder (BD) and creativity is well-known; however, relatively little is known about its potential mechanism. We investigated whether heightened behavioral activation system (BAS) sensitivity may mediate such relationship. Korean young adults (N=543) completed self-report questionnaires that included the Hypomanic Personality Scale (HPS), the Behavioral Activation System(BAS) Scale, the Everyday Creativity Scale (ECS), the Positive Affect and Negative Affect Schedule (PANAS), and the Altman Self-Rating Mania Scale (ASRM). Correlational, hierarchical regression and mediation analyses using bootstrap confidence intervals were conducted. As predicted, BAS sensitivity was associated with self-reported creativity as well as hypomania risk and symptoms. Even when positive affect was controlled, BAS sensitivity predicted incrementally significant variance in explaining creativity. In mediation analysis, BAS sensitivity partially mediated the relation between hypomania risk and creativity. Reliance on self-report measures in assessing creativity and usage of non-clinical sample. BAS sensitivity was related not only to mood pathology but also to creativity. As a basic affective temperament, BAS sensitivity may help explain incompatible sides of adaptation associated with BD. Copyright © 2017 Elsevier B.V. All rights reserved.

A comparison of pre ICU admission SIRS, EWS and q SOFA scores for predicting mortality and length of stay in ICU.

PubMed

Siddiqui, Shahla; Chua, Maureen; Kumaresh, Venkatesan; Choo, Robin

2017-10-01

The 2015 sepsis definitions suggest using the quick SOFA score for risk stratification of sepsis patients among other changes in sepsis definition. Our aim was to validate the q sofa score for diagnosing sepsis and comparing it to traditional scores of pre ICU admission sepsis outcome prediction such as EWS and SIRS in our setting in order to predict mortality and length of stay. This was a retrospective cohort study. We retrospectively calculated the q sofa, SIRS and EWS scores of all ICU patients admitted with the diagnosis of sepsis at our center in 2015. This was analysed using STATA 12. Logistic regression and ROC curves were used for analysis in addition to descriptive analysis. 58 patients were included in the study. Based on our one year results we have shown that although q SOFA is more sensitive in predicting LOS in ICU of sepsis patients, the EWS score is more sensitive and specific in predicting mortality in the ICU of such patients when compared to q SOFA and SIRS scores. In conclusion, we find that in our setting, EWS is better than SIRS and q SOFA for predicting mortality and perhaps length of stay as well. The q Sofa score remains validated for diagnosis of sepsis. Copyright © 2017 Elsevier Inc. All rights reserved.

Atrazine transport within a coastal zone in Southeastern Puerto Rico: a sensitivity analysis of an agricultural field model and riparian zone management model

USDA-ARS?s Scientific Manuscript database

Water quality models are used to predict effects of conservation practices to mitigate the transport of herbicides to water bodies. We used two models - the Agricultural Policy/Environmental eXtender (APEX) and the Riparian Ecosystem Management Model (REMM) to predict the movement of atrazine from ...

Evaluating remedial alternatives for an acid mine drainage stream: A model post audit

USGS Publications Warehouse

Runkel, Robert L.; Kimball, Briant A.; Walton-Day, Katherine; Verplanck, Philip L.; Broshears, Robert E.

2012-01-01

A post audit for a reactive transport model used to evaluate acid mine drainage treatment systems is presented herein. The post audit is based on a paired synoptic approach in which hydrogeochemical data are collected at low (existing conditions) and elevated (following treatment) pH. Data obtained under existing, low-pH conditions are used for calibration, and the resultant model is used to predict metal concentrations observed following treatment. Predictions for Al, As, Fe, H+, and Pb accurately reproduce the observed reduction in dissolved concentrations afforded by the treatment system, and the information provided in regard to standard attainment is also accurate (predictions correctly indicate attainment or nonattainment of water quality standards for 19 of 25 cases). Errors associated with Cd, Cu, and Zn are attributed to misspecification of sorbent mass (precipitated Fe). In addition to these specific results, the post audit provides insight in regard to calibration and sensitivity analysis that is contrary to conventional wisdom. Steps taken during the calibration process to improve simulations of As sorption were ultimately detrimental to the predictive results, for example, and the sensitivity analysis failed to bracket observed metal concentrations.

Evaluating remedial alternatives for an acid mine drainage stream: a model post audit.

PubMed

Runkel, Robert L; Kimball, Briant A; Walton-Day, Katherine; Verplanck, Philip L; Broshears, Robert E

2012-01-03

A post audit for a reactive transport model used to evaluate acid mine drainage treatment systems is presented herein. The post audit is based on a paired synoptic approach in which hydrogeochemical data are collected at low (existing conditions) and elevated (following treatment) pH. Data obtained under existing, low-pH conditions are used for calibration, and the resultant model is used to predict metal concentrations observed following treatment. Predictions for Al, As, Fe, H(+), and Pb accurately reproduce the observed reduction in dissolved concentrations afforded by the treatment system, and the information provided in regard to standard attainment is also accurate (predictions correctly indicate attainment or nonattainment of water quality standards for 19 of 25 cases). Errors associated with Cd, Cu, and Zn are attributed to misspecification of sorbent mass (precipitated Fe). In addition to these specific results, the post audit provides insight in regard to calibration and sensitivity analysis that is contrary to conventional wisdom. Steps taken during the calibration process to improve simulations of As sorption were ultimately detrimental to the predictive results, for example, and the sensitivity analysis failed to bracket observed metal concentrations.

Multidimensional severity assessment in bronchiectasis: an analysis of seven European cohorts

PubMed Central

McDonnell, M J; Aliberti, S; Goeminne, P C; Dimakou, K; Zucchetti, S C; Davidson, J; Ward, C; Laffey, J G; Finch, S; Pesci, A; Dupont, L J; Fardon, T C; Skrbic, D; Obradovic, D; Cowman, S; Loebinger, M R; Rutherford, R M; De Soyza, A; Chalmers, J D

2016-01-01

Introduction Bronchiectasis is a multidimensional disease associated with substantial morbidity and mortality. Two disease-specific clinical prediction tools have been developed, the Bronchiectasis Severity Index (BSI) and the FACED score, both of which stratify patients into severity risk categories to predict the probability of mortality. Methods We aimed to compare the predictive utility of BSI and FACED in assessing clinically relevant disease outcomes across seven European cohorts independent of their original validation studies. Results The combined cohorts totalled 1612. Pooled analysis showed that both scores had a good discriminatory predictive value for mortality (pooled area under the curve (AUC) 0.76, 95% CI 0.74 to 0.78 for both scores) with the BSI demonstrating a higher sensitivity (65% vs 28%) but lower specificity (70% vs 93%) compared with the FACED score. Calibration analysis suggested that the BSI performed consistently well across all cohorts, while FACED consistently overestimated mortality in ‘severe’ patients (pooled OR 0.33 (0.23 to 0.48), p<0.0001). The BSI accurately predicted hospitalisations (pooled AUC 0.82, 95% CI 0.78 to 0.84), exacerbations, quality of life (QoL) and respiratory symptoms across all risk categories. FACED had poor discrimination for hospital admissions (pooled AUC 0.65, 95% CI 0.63 to 0.67) with low sensitivity at 16% and did not consistently predict future risk of exacerbations, QoL or respiratory symptoms. No association was observed with FACED and 6 min walk distance (6MWD) or lung function decline. Conclusion The BSI accurately predicts mortality, hospital admissions, exacerbations, QoL, respiratory symptoms, 6MWD and lung function decline in bronchiectasis, providing a clinically relevant evaluation of disease severity. PMID:27516225

A comparison of computer-assisted detection (CAD) programs for the identification of colorectal polyps: performance and sensitivity analysis, current limitations and practical tips for radiologists.

PubMed

Bell, L T O; Gandhi, S

2018-06-01

To directly compare the accuracy and speed of analysis of two commercially available computer-assisted detection (CAD) programs in detecting colorectal polyps. In this retrospective single-centre study, patients who had colorectal polyps identified on computed tomography colonography (CTC) and subsequent lower gastrointestinal endoscopy, were analysed using two commercially available CAD programs (CAD1 and CAD2). Results were compared against endoscopy to ascertain sensitivity and positive predictive value (PPV) for colorectal polyps. Time taken for CAD analysis was also calculated. CAD1 demonstrated a sensitivity of 89.8%, PPV of 17.6% and mean analysis time of 125.8 seconds. CAD2 demonstrated a sensitivity of 75.5%, PPV of 44.0% and mean analysis time of 84.6 seconds. The sensitivity and PPV for colorectal polyps and CAD analysis times can vary widely between current commercially available CAD programs. There is still room for improvement. Generally, there is a trade-off between sensitivity and PPV, and so further developments should aim to optimise both. Information on these factors should be made routinely available, so that an informed choice on their use can be made. This information could also potentially influence the radiologist's use of CAD results. Copyright © 2018 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

Ancient numerical daemons of conceptual hydrological modeling: 2. Impact of time stepping schemes on model analysis and prediction

NASA Astrophysics Data System (ADS)

Kavetski, Dmitri; Clark, Martyn P.

2010-10-01

Despite the widespread use of conceptual hydrological models in environmental research and operations, they remain frequently implemented using numerically unreliable methods. This paper considers the impact of the time stepping scheme on model analysis (sensitivity analysis, parameter optimization, and Markov chain Monte Carlo-based uncertainty estimation) and prediction. It builds on the companion paper (Clark and Kavetski, 2010), which focused on numerical accuracy, fidelity, and computational efficiency. Empirical and theoretical analysis of eight distinct time stepping schemes for six different hydrological models in 13 diverse basins demonstrates several critical conclusions. (1) Unreliable time stepping schemes, in particular, fixed-step explicit methods, suffer from troublesome numerical artifacts that severely deform the objective function of the model. These deformations are not rare isolated instances but can arise in any model structure, in any catchment, and under common hydroclimatic conditions. (2) Sensitivity analysis can be severely contaminated by numerical errors, often to the extent that it becomes dominated by the sensitivity of truncation errors rather than the model equations. (3) Robust time stepping schemes generally produce "better behaved" objective functions, free of spurious local optima, and with sufficient numerical continuity to permit parameter optimization using efficient quasi Newton methods. When implemented within a multistart framework, modern Newton-type optimizers are robust even when started far from the optima and provide valuable diagnostic insights not directly available from evolutionary global optimizers. (4) Unreliable time stepping schemes lead to inconsistent and biased inferences of the model parameters and internal states. (5) Even when interactions between hydrological parameters and numerical errors provide "the right result for the wrong reason" and the calibrated model performance appears adequate, unreliable time stepping schemes make the model unnecessarily fragile in predictive mode, undermining validation assessments and operational use. Erroneous or misleading conclusions of model analysis and prediction arising from numerical artifacts in hydrological models are intolerable, especially given that robust numerics are accepted as mainstream in other areas of science and engineering. We hope that the vivid empirical findings will encourage the conceptual hydrological community to close its Pandora's box of numerical problems, paving the way for more meaningful model application and interpretation.

[Serum PTH levels as a predictive factor of hypocalcaemia after total thyroidectomy].

PubMed

Díez Alonso, Manuel; Sánchez López, José Daniel; Sánchez-Seco Peña, María Isabel; Ratia Jiménez, Tomás; Arribas Gómez, Ignacio; Rodríguez Pascual, Angel; Martín-Duce, Antonio; Guadalix Hidalgo, Gregorio; Hernández Domínguez, Sara; Granell Vicent, Javier

2009-02-01

Postoperative parathyroid hormone (PTH) levels as a predictor of hypocalcaemia in patients subjected to total thyroidectomy is analyzed. Prospective study involving 67 patients who underwent total thyroidectomy due to a benign disease. Serum PTH and ionised calcium were measured 20 h after surgery. Sensitivity, specificity and predictive values of PTH and ionised calcium levels were calculated to predict clinical and analytical hypocalcaemia. A total of 42 (62.7%) patients developed hypocalcaemia (ionised calcium<0.95 mmol/l), but only 20 (29.9%) presented with symptoms. PTH concentration the day after surgery was significantly lower in the group that developed symptomatic hypocalcaemia (5.57+/-6.4 pg/ml) than in the asymptomatic (21.5+/-15.3 pg/ml) or normocalcaemic (26.8+/-24.9 pg/ml) groups (p=0.001). Taking the value of 13 pg/ml as a cut-off point of PTH levels, sensitivity, specificity, positive predictive value and negative predictive value were 54%, 72%, 76% and 48%, respectively. On the other hand, sensitivity for predicting symptomatic hypocalcaemia was 95% and specificity was 76%. The test showed a high incidence of false positives (11/30, 36%). Negative predictive value was 97% and positive predictive value was 65%. In multivariate analysis, PTH and ionised calcium were the only perioperative factors that showed an independent predictive value as risk indicators of symptomatic hypocalcaemia. Normal PTH levels 20 h after surgery practically rule out the subsequent appearance of hypocalcaemia symptoms. On the other hand, low PTH levels are not necessarily associated to symptomatic hypocalcaemia due to the high number of false positives.

Urinary neutrophil gelatinase-associated lipocalin for diagnosis and estimating activity in lupus nephritis: a meta-analysis.

PubMed

Fang, Y G; Chen, N N; Cheng, Y B; Sun, S J; Li, H X; Sun, F; Xiang, Y

2015-12-01

Urinary neutrophil gelatinase-associated lipocalin (uNGAL) is relatively specific in lupus nephritis (LN) patients. However, its diagnostic value has not been evaluated. The aim of this review was to determine the value of uNGAL for diagnosis and estimating activity in LN. A comprehensive search was performed on PubMed, EMBASE, Web of Knowledge, Cochrane electronic databases through December 2014. Meta-analysis of sensitivity and specificity was performed with a random-effects model. Additionally, summary receiver operating characteristic (SROC) curves and area under the curve (AUC) values were calculated. Fourteen studies were selected for this review. With respect to diagnosing LN, the pooled sensitivity and specificity were 73.6% (95% confidence interval (CI), 61.9-83.3) and 78.1% (95% CI, 69.0-85.6), respectively. The SROC-AUC value was 0.8632. Regarding estimating LN activity, the pooled sensitivity and specificity were 66.2% (95% CI, 60.4-71.7) and 62.1% (95% CI, 57.9-66.3), respectively. The SROC-AUC value was 0.7583. In predicting renal flares, the pooled sensitivity and specificity were 77.5% (95% CI, 68.1-85.1) and 65.3% (95% CI, 60.0-70.3), respectively. The SROC-AUC value was 0.7756. In conclusion, this meta-analysis indicates that uNGAL has relatively fair sensitivity and specificity in diagnosing LN, estimating LN activity and predicting renal flares, suggesting that uNGAL is a potential biomarker in diagnosing LN and monitoring LN activity. © The Author(s) 2015.

Prediction of miscarriage in women with viable intrauterine pregnancy-A systematic review and diagnostic accuracy meta-analysis.

PubMed

Pillai, Rekha N; Konje, Justin C; Richardson, Matthew; Tincello, Douglas G; Potdar, Neelam

2018-01-01

Both ultrasound and biochemical markers either alone or in combination have been described in the literature for the prediction of miscarriage. We performed this systematic review and meta-analysis to determine the best combination of biochemical, ultrasound and demographic markers to predict miscarriage in women with viable intrauterine pregnancy. The electronic database search included Medline (1946-June 2017), Embase (1980-June 2017), CINAHL (1981-June 2017) and Cochrane library. Key MESH and Boolean terms were used for the search. Data extraction and collection was performed based on the eligibility criteria by two authors independently. Quality assessment of the individual studies was done using QUADAS 2 (Quality Assessment for Diagnostic Accuracy Studies-2: A Revised Tool) and statistical analysis performed using the Cochrane systematic review manager 5.3 and STATA vs.13.0. Due to the diversity of the combinations used for prediction in the included papers it was not possible to perform a meta-analysis on combination markers. Therefore, we proceeded to perform a meta-analysis on ultrasound markers alone to determine the best marker that can help to improve the diagnostic accuracy of predicting miscarriage in women with viable intrauterine pregnancy. The systematic review identified 18 eligible studies for the quantitative meta-analysis with a total of 5584 women. Among the ultrasound scan markers, fetal bradycardia (n=10 studies, n=1762 women) on hierarchical summary receiver operating characteristic showed sensitivity of 68.41%, specificity of 97.84%, positive likelihood ratio of 31.73 (indicating a large effect on increasing the probability of predicting miscarriage) and negative likelihood ratio of 0.32. In studies for women with threatened miscarriage (n=5 studies, n=771 women) fetal bradycardia showed further increase in sensitivity (84.18%) for miscarriage prediction. Although there is gestational age dependent variation in the fetal heart rate, a plot of fetal heart rate cut off level versus log diagnostic odds ratio showed that at ≤110 beat per minutes the diagnostic power to predict miscarriage is higher. Other markers of intra uterine hematoma, crown rump length and yolk sac had significantly decreased predictive value. Therefore in women with threatened miscarriage and presence of fetal bradycardia on ultrasound scan, there is a role for offering repeat ultrasound scan in a week to ten days interval. Crown Copyright © 2017. Published by Elsevier B.V. All rights reserved.

Magnetic resonance spectroscopic determination of a neuronal and axonal marker in white matter predicts reversibility of deficits in secondary normal pressure hydrocephalus

PubMed Central

Shiino, A; Nishida, Y; Yasuda, H; Suzuki, M; Matsuda, M; Inubushi, T

2004-01-01

Background: Normal pressure hydrocephalus (NPH) is considered to be a treatable form of dementia, because cerebrospinal fluid (CSF) shunting can lessen symptoms. However, neuroimaging has failed to predict when shunting will be effective. Objective: To investigate whether 1H (proton) magnetic resonance (MR) spectroscopy could predict functional outcome in patients after shunting. Methods: Neurological state including Hasegawa's dementia scale, gait, continence, and the modified Rankin scale were evaluated in 21 patients with secondary NPH who underwent ventriculo-peritoneal shunting. Outcomes were measured postoperatively at one and 12 months and were classified as excellent, fair, or poor. MR spectra were obtained from left hemispheric white matter. Results: Significant preoperative differences in N-acetyl aspartate (NAA)/creatine (Cr) and NAA/choline (Cho) were noted between patients with excellent and poor outcome at one month (p = 0.0014 and 0.0036, respectively). Multiple regression analysis linked higher preoperative NAA/Cr ratio, gait score, and modified Rankin scale to better one month outcome. Predictive value, sensitivity, and specificity for excellent outcome following shunting were 95.2%, 100%, and 87.5%. Multiple regression analysis indicated that NAA/Cho had the best predictive value for one year outcome (p = 0.0032); predictive value, sensitivity, and specificity were 89.5%, 90.0%, and 88.9%. Conclusions: MR spectroscopy predicted long term post-shunting outcomes in patients with secondary NPH, and it would be a useful assessment tool before lumbar drainage. PMID:15258216

Fecal immunochemical test for predicting mucosal healing in ulcerative colitis patients: A systematic review and meta-analysis.

PubMed

Dai, Cong; Jiang, Min; Sun, Ming-Jun; Cao, Qin

2018-05-01

Fecal immunochemical test (FIT) is a promising marker for assessment of inflammatory bowel disease activity. However, the utility of FIT for predicting mucosal healing (MH) of ulcerative colitis (UC) patients has yet to be clearly demonstrated. The objective of our study was to perform a diagnostic test accuracy test meta-analysis evaluating the diagnostic accuracy of FIT in predicting MH of UC patients. We systematically searched the databases from inception to November 2017 that evaluated MH in UC. The methodological quality of each study was assessed according to the Quality Assessment of Diagnostic Accuracy Studies checklist. The extracted data were pooled using a summary receiver operating characteristic curve model. Random-effects model was used to summarize the diagnostic odds ratio, sensitivity, specificity, positive likelihood ratio, and negative likelihood ratio. Six studies comprising 625 UC patients were included in the meta-analysis. The pooled sensitivity and specificity values for predicting MH in UC were 0.77 (95% confidence interval [CI], 0.72-0.81) and 0.81 (95% CI, 0.76-0.85), respectively. The FIT level had a high rule-in value (positive likelihood ratio, 3.79; 95% CI, 2.85-5.03) and a moderate rule-out value (negative likelihood ratio, 0.26; 95% CI, 0.16-0.43) for predicting MH in UC. The results of the receiver operating characteristic curve analysis (area under the curve, 0.88; standard error of the mean, 0.02) and diagnostic odds ratio (18.08; 95% CI, 9.57-34.13) also revealed improved discrimination for identifying MH in UC with FIT concentration. Our meta-analysis has found that FIT is a simple, reliable non-invasive marker for predicting MH in UC patients. © 2018 Journal of Gastroenterology and Hepatology Foundation and John Wiley & Sons Australia, Ltd.

Evaluation of the AnnAGNPS Model for Predicting Runoff and Nutrient Export in a Typical Small Watershed in the Hilly Region of Taihu Lake

PubMed Central

Luo, Chuan; Li, Zhaofu; Li, Hengpeng; Chen, Xiaomin

2015-01-01

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. PMID:26364642

Use of a Process Analysis Tool for Diagnostic Study on Fine Particulate Matter Predictions in the U.S.-Part II: Analysis and Sensitivity Simulations

EPA Science Inventory

Following the Part I paper that described an application of the U.S. EPA Models-3/Community Multiscale Air Quality (CMAQ) modeling system to the 1999 Southern Oxidants Study episode, this paper presents results from process analysis (PA) using the PA tool embedded in CMAQ and s...

Comparison of Prediction Model for Cardiovascular Autonomic Dysfunction Using Artificial Neural Network and Logistic Regression Analysis

PubMed Central

Zeng, Fangfang; Li, Zhongtao; Yu, Xiaoling; Zhou, Linuo

2013-01-01

Background This study aimed to develop the artificial neural network (ANN) and multivariable logistic regression (LR) analyses for prediction modeling of cardiovascular autonomic (CA) dysfunction in the general population, and compare the prediction models using the two approaches. Methods and Materials We analyzed a previous dataset based on a Chinese population sample consisting of 2,092 individuals aged 30–80 years. The prediction models were derived from an exploratory set using ANN and LR analysis, and were tested in the validation set. Performances of these prediction models were then compared. Results Univariate analysis indicated that 14 risk factors showed statistically significant association with the prevalence of CA dysfunction (P<0.05). The mean area under the receiver-operating curve was 0.758 (95% CI 0.724–0.793) for LR and 0.762 (95% CI 0.732–0.793) for ANN analysis, but noninferiority result was found (P<0.001). The similar results were found in comparisons of sensitivity, specificity, and predictive values in the prediction models between the LR and ANN analyses. Conclusion The prediction models for CA dysfunction were developed using ANN and LR. ANN and LR are two effective tools for developing prediction models based on our dataset. PMID:23940593

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

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

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, wemore » 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 R{sup 2} = 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 (Q{sup 2}{sub ext} = 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. - Highlights: • It was compiled the largest publicly-available skin permeability dataset. • Predictive QSAR models were developed for skin permeability. • No concordance between skin sensitization and skin permeability has been found. • Structural rules for optimizing sensitization and penetration were established.« less

Predictive value of high-sensitivity troponin-I for future adverse cardiovascular outcome in stable patients with type 2 diabetes mellitus

PubMed Central

2014-01-01

Introduction High-sensitivity cardiac troponin I(hs-TnI) and T levels(hs-TnT) are sensitive biomarkers of cardiomyocyte turnover or necrosis. Prior studies of the predictive role of hs-TnT in type 2 diabetes mellitus(T2DM) patients have yielded conflicting results. This study aimed to determine whether hs-TnI, which is detectable in a higher proportion of normal subjects than hsTnT, is associated with a major adverse cardiovascular event(MACE) in T2DM patients. Methods and results We compared hs-TnI level in stored serum samples from 276 consecutive patients (mean age 65 ± 10 years; 57% male) with T2DM with that of 115 age-and sex-matched controls. All T2DM patients were prospectively followed up for at least 4 years for incidence of MACE including heart failure(HF), myocardial infarction(MI) and cardiovascular mortality. At baseline, 274(99%) patients with T2DM had detectable hs-TnI, and 57(21%) had elevated hs-TnI (male: 8.5 ng/L, female: 7.6 ng/L, above the 99th percentile in healthy controls). A total of 43 MACE occurred: HF(n = 18), MI(n = 11) and cardiovascular mortality(n = 14). Kaplan-Meier analysis showed that an elevated hs-TnI was associated with MACE, HF, MI and cardiovascular mortality. Although multivariate analysis revealed that an elevated hs-TnI independently predicted MACE, it had limited sensitivity(62.7%) and positive predictive value(38.5%). Contrary to this, a normal hs-TnI level had an excellent negative predictive value(92.2%) for future MACE in patients with T2DM. Conclusion The present study demonstrates that elevated hs-TnI in patients with T2DM is associated with increased MACE, HF, MI and cardiovascular mortality. Importantly, a normal hs-TnI level has an excellent negative predictive value for future adverse cardiovascular events during long-term follow-up. PMID:24661773

Sensitivity and specificity of dosing alerts for dosing errors among hospitalized pediatric patients

PubMed Central

Stultz, Jeremy S; Porter, Kyle; Nahata, Milap C

2014-01-01

Objectives To determine the sensitivity and specificity of a dosing alert system for dosing errors and to compare the sensitivity of a proprietary system with and without institutional customization at a pediatric hospital. Methods A retrospective analysis of medication orders, orders causing dosing alerts, reported adverse drug events, and dosing errors during July, 2011 was conducted. Dosing errors with and without alerts were identified and the sensitivity of the system with and without customization was compared. Results There were 47 181 inpatient pediatric orders during the studied period; 257 dosing errors were identified (0.54%). The sensitivity of the system for identifying dosing errors was 54.1% (95% CI 47.8% to 60.3%) if customization had not occurred and increased to 60.3% (CI 54.0% to 66.3%) with customization (p=0.02). The sensitivity of the system for underdoses was 49.6% without customization and 60.3% with customization (p=0.01). Specificity of the customized system for dosing errors was 96.2% (CI 96.0% to 96.3%) with a positive predictive value of 8.0% (CI 6.8% to 9.3). All dosing errors had an alert over-ridden by the prescriber and 40.6% of dosing errors with alerts were administered to the patient. The lack of indication-specific dose ranges was the most common reason why an alert did not occur for a dosing error. Discussion Advances in dosing alert systems should aim to improve the sensitivity and positive predictive value of the system for dosing errors. Conclusions The dosing alert system had a low sensitivity and positive predictive value for dosing errors, but might have prevented dosing errors from reaching patients. Customization increased the sensitivity of the system for dosing errors. PMID:24496386

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.

Coupled rotor/airframe vibration analysis

NASA Technical Reports Server (NTRS)

Sopher, R.; Studwell, R. E.; Cassarino, S.; Kottapalli, S. B. R.

1982-01-01

A coupled rotor/airframe vibration analysis developed as a design tool for predicting helicopter vibrations and a research tool to quantify the effects of structural properties, aerodynamic interactions, and vibration reduction devices on vehicle vibration levels is described. The analysis consists of a base program utilizing an impedance matching technique to represent the coupled rotor/airframe dynamics of the system supported by inputs from several external programs supplying sophisticated rotor and airframe aerodynamic and structural dynamic representation. The theoretical background, computer program capabilities and limited correlation results are presented in this report. Correlation results using scale model wind tunnel results show that the analysis can adequately predict trends of vibration variations with airspeed and higher harmonic control effects. Predictions of absolute values of vibration levels were found to be very sensitive to modal characteristics and results were not representative of measured values.

Atmospheric model development in support of SEASAT. Volume 1: Summary of findings

NASA Technical Reports Server (NTRS)

Kesel, P. G.

1977-01-01

Atmospheric analysis and prediction models of varying (grid) resolution were developed. The models were tested using real observational data for the purpose of assessing the impact of grid resolution on short range numerical weather prediction. The discretionary model procedures were examined so that the computational viability of SEASAT data might be enhanced during the conduct of (future) sensitivity tests. The analysis effort covers: (1) examining the procedures for allowing data to influence the analysis; (2) examining the effects of varying the weights in the analysis procedure; (3) testing and implementing procedures for solving the minimization equation in an optimal way; (4) describing the impact of grid resolution on analysis; and (5) devising and implementing numerous practical solutions to analysis problems, generally.

Optimization of Paclitaxel Containing pH-Sensitive Liposomes By 3 Factor, 3 Level Box-Behnken Design.

PubMed

Rane, Smita; Prabhakar, Bala

2013-07-01

The aim of this study was to investigate the combined influence of 3 independent variables in the preparation of paclitaxel containing pH-sensitive liposomes. A 3 factor, 3 levels Box-Behnken design was used to derive a second order polynomial equation and construct contour plots to predict responses. The independent variables selected were molar ratio phosphatidylcholine:diolylphosphatidylethanolamine (X1), molar concentration of cholesterylhemisuccinate (X2), and amount of drug (X3). Fifteen batches were prepared by thin film hydration method and evaluated for percent drug entrapment, vesicle size, and pH sensitivity. The transformed values of the independent variables and the percent drug entrapment were subjected to multiple regression to establish full model second order polynomial equation. F was calculated to confirm the omission of insignificant terms from the full model equation to derive a reduced model polynomial equation to predict the dependent variables. Contour plots were constructed to show the effects of X1, X2, and X3 on the percent drug entrapment. A model was validated for accurate prediction of the percent drug entrapment by performing checkpoint analysis. The computer optimization process and contour plots predicted the levels of independent variables X1, X2, and X3 (0.99, -0.06, 0, respectively), for maximized response of percent drug entrapment with constraints on vesicle size and pH sensitivity.

The Sensitivity of a Global Ocean Model to Wind Forcing: A Test Using Sea Level and Wind Observations from Satellites and Operational Analysis

NASA Technical Reports Server (NTRS)

Fu, L. L.; Chao, Y.

1997-01-01

Investigated in this study is the response of a global ocean general circulation model to forcing provided by two wind products: operational analysis from the National Center for Environmental Prediction (NCEP); observations made by the ERS-1 radar scatterometer.

Integrated Application of Random Forest and Artificial Neural Network Algorithms to Predict Viral Contamination in Coastal Waters

NASA Astrophysics Data System (ADS)

Shamkhali Chenar, S.; Deng, Z.

2017-12-01

Pathogenic viruses pose a significant public health threat and economic losses to shellfish industry in the coastal environment. Norovirus is a contagious virus and the leading cause of epidemic gastroenteritis following consumption of oysters harvested from sewage-contaminated waters. While it is challenging to detect noroviruses in coastal waters due to the lack of sensitive and routine diagnostic methods, machine learning techniques are allowing us to prevent or at least reduce the risks by developing effective predictive models. This study attempts to develop an algorithm between historical norovirus outbreak reports and environmental parameters including water temperature, solar radiation, water level, salinity, precipitation, and wind. For this purpose, the Random Forests statistical technique was utilized to select relevant environmental parameters and their various combinations with different time lags controlling the virus distribution in oyster harvesting areas along the Louisiana Coast. An Artificial Neural Networks (ANN) approach was then presented to predict the outbreaks using a final set of input variables. Finally, a sensitivity analysis was conducted to evaluate relative importance and contribution of the input variables to the model output. Findings demonstrated that the developed model was capable of reproducing historical oyster norovirus outbreaks along the Louisiana Coast with the overall accuracy of than 99.83%, demonstrating the efficacy of the model. Moreover, the increase in water temperature, solar radiation, water level, and salinity, and the decrease in wind and rainfall are associated with the reduction in the model-predicted risk of norovirus outbreak according to sensitivity analysis results. In conclusion, the presented machine learning approach provided reliable tools for predicting potential norovirus outbreaks and could be used for early detection of possible outbreaks and reduce the risk of norovirus to public health and the seafood industry.

Metabolomic analysis of insulin resistance across different mouse strains and diets.

PubMed

Stöckli, Jacqueline; Fisher-Wellman, Kelsey H; Chaudhuri, Rima; Zeng, Xiao-Yi; Fazakerley, Daniel J; Meoli, Christopher C; Thomas, Kristen C; Hoffman, Nolan J; Mangiafico, Salvatore P; Xirouchaki, Chrysovalantou E; Yang, Chieh-Hsin; Ilkayeva, Olga; Wong, Kari; Cooney, Gregory J; Andrikopoulos, Sofianos; Muoio, Deborah M; James, David E

2017-11-24

Insulin resistance is a major risk factor for many diseases. However, its underlying mechanism remains unclear in part because it is triggered by a complex relationship between multiple factors, including genes and the environment. Here, we used metabolomics combined with computational methods to identify factors that classified insulin resistance across individual mice derived from three different mouse strains fed two different diets. Three inbred ILSXISS strains were fed high-fat or chow diets and subjected to metabolic phenotyping and metabolomics analysis of skeletal muscle. There was significant metabolic heterogeneity between strains, diets, and individual animals. Distinct metabolites were changed with insulin resistance, diet, and between strains. Computational analysis revealed 113 metabolites that were correlated with metabolic phenotypes. Using these 113 metabolites, combined with machine learning to segregate mice based on insulin sensitivity, we identified C22:1-CoA, C2-carnitine, and C16-ceramide as the best classifiers. Strikingly, when these three metabolites were combined into one signature, they classified mice based on insulin sensitivity more accurately than each metabolite on its own or other published metabolic signatures. Furthermore, C22:1-CoA was 2.3-fold higher in insulin-resistant mice and correlated significantly with insulin resistance. We have identified a metabolomic signature composed of three functionally unrelated metabolites that accurately predicts whole-body insulin sensitivity across three mouse strains. These data indicate the power of simultaneous analysis of individual, genetic, and environmental variance in mice for identifying novel factors that accurately predict metabolic phenotypes like whole-body insulin sensitivity. © 2017 by The American Society for Biochemistry and Molecular Biology, Inc.

Variational Methods in Sensitivity Analysis and Optimization for Aerodynamic Applications

NASA Technical Reports Server (NTRS)

Ibrahim, A. H.; Hou, G. J.-W.; Tiwari, S. N. (Principal Investigator)

1996-01-01

Variational methods (VM) sensitivity analysis, which is the continuous alternative to the discrete sensitivity analysis, is employed to derive the costate (adjoint) equations, the transversality conditions, and the functional sensitivity derivatives. In the derivation of the sensitivity equations, the variational methods use the generalized calculus of variations, in which the variable boundary is considered as the design function. The converged solution of the state equations together with the converged solution of the costate equations are integrated along the domain boundary to uniquely determine the functional sensitivity derivatives with respect to the design function. The determination of the sensitivity derivatives of the performance index or functional entails the coupled solutions of the state and costate equations. As the stable and converged numerical solution of the costate equations with their boundary conditions are a priori unknown, numerical stability analysis is performed on both the state and costate equations. Thereafter, based on the amplification factors obtained by solving the generalized eigenvalue equations, the stability behavior of the costate equations is discussed and compared with the state (Euler) equations. The stability analysis of the costate equations suggests that the converged and stable solution of the costate equation is possible only if the computational domain of the costate equations is transformed to take into account the reverse flow nature of the costate equations. The application of the variational methods to aerodynamic shape optimization problems is demonstrated for internal flow problems at supersonic Mach number range. The study shows, that while maintaining the accuracy of the functional sensitivity derivatives within the reasonable range for engineering prediction purposes, the variational methods show a substantial gain in computational efficiency, i.e., computer time and memory, when compared with the finite difference sensitivity analysis.

Development and Sensitivity Analysis of a Frost Risk model based primarily on freely distributed Earth Observation data

NASA Astrophysics Data System (ADS)

Louka, Panagiota; Petropoulos, George; Papanikolaou, Ioannis

2015-04-01

The ability to map the spatiotemporal distribution of extreme climatic conditions, such as frost, is a significant tool in successful agricultural management and decision making. Nowadays, with the development of Earth Observation (EO) technology, it is possible to obtain accurately, timely and in a cost-effective way information on the spatiotemporal distribution of frost conditions, particularly over large and otherwise inaccessible areas. The present study aimed at developing and evaluating a frost risk prediction model, exploiting primarily EO data from MODIS and ASTER sensors and ancillary ground observation data. For the evaluation of our model, a region in north-western Greece was selected as test site and a detailed sensitivity analysis was implemented. The agreement between the model predictions and the observed (remotely sensed) frost frequency obtained by MODIS sensor was evaluated thoroughly. Also, detailed comparisons of the model predictions were performed against reference frost ground observations acquired from the Greek Agricultural Insurance Organization (ELGA) over a period of 10-years (2000-2010). Overall, results evidenced the ability of the model to produce reasonably well the frost conditions, following largely explainable patterns in respect to the study site and local weather conditions characteristics. Implementation of our proposed frost risk model is based primarily on satellite imagery analysis provided nowadays globally at no cost. It is also straightforward and computationally inexpensive, requiring much less effort in comparison for example to field surveying. Finally, the method is adjustable to be potentially integrated with other high resolution data available from both commercial and non-commercial vendors. Keywords: Sensitivity analysis, frost risk mapping, GIS, remote sensing, MODIS, Greece

Is there still a role for computed tomography and bone scintigraphy in prostate cancer staging? An analysis from the EUREKA-1 database.

PubMed

Gabriele, D; Collura, D; Oderda, M; Stura, I; Fiorito, C; Porpiglia, F; Terrone, C; Zacchero, M; Guiot, C; Gabriele, P

2016-04-01

According to the current guidelines, computed tomography (CT) and bone scintigraphy (BS) are optional in intermediate-risk and recommended in high-risk prostate cancer (PCa). We wonder whether it is time for these examinations to be dismissed, evaluating their staging accuracy in a large cohort of radical prostatectomy (RP) patients. To evaluate the ability of CT to predict lymph node involvement (LNI), we included 1091 patients treated with RP and pelvic lymph node dissection, previously staged with abdomino-pelvic CT. As for bone metastases, we included 1145 PCa patients deemed fit for surgery, previously staged with Tc-99m methylene diphosphonate planar BS. CT scan showed a sensitivity and specificity in predicting LNI of 8.8 and 98 %; subgroup analysis disclosed a significant association only for the high-risk subgroup of 334 patients (P 0.009) with a sensitivity of 11.8 % and positive predictive value (PPV) of 44.4 %. However, logistic multivariate regression analysis including preoperative risk factors excluded any additional predictive ability of CT even in the high-risk group (P 0.40). These data are confirmed by ROC curve analysis, showing a low AUC of 54 % for CT, compared with 69 % for Partin tables and 80 % for Briganti nomogram. BS showed some positivity in 74 cases, only four of whom progressed, while 49 patients with negative BS progressed during their follow-up, six of them immediately after surgery. According to our opinion, the role of CT and BS should be restricted to selected high-risk patients, while clinical predictive nomograms should be adopted for the surgical planning.

Combined panel of serum human tissue kallikreins and CA-125 for the detection of epithelial ovarian cancer.

PubMed

Koh, Stephen Chee Liang; Huak, Chan Yiong; Lutan, Delfi; Marpuang, Johny; Ketut, Suwiyoga; Budiana, Nyoma Gede; Saleh, Agustria Zainu; Aziz, Mohamad Farid; Winarto, Hariyono; Pradjatmo, Heru; Hoan, Nguyen Khac Han; Thanh, Pham Viet; Choolani, Mahesh

2012-07-01

To determine the predictive accuracy of the combined panels of serum human tissue kallikreins (hKs) and CA-125 for the detection of epithelial ovarian cancer. Serum specimens collected from 5 Indonesian centers and 1 Vietnamese center were analyzed for CA-125, hK6, and hK10 levels. A total of 375 specimens from patients presenting with ovarian tumors, which include 156 benign cysts, 172 epithelial ovarian cancers (stage I/II, n=72; stage III/IV, n=100), 36 germ cell tumors and 11 borderline tumors, were included in the study analysis. Receiver operating characteristic analysis were performed to determine the cutoffs for age, CA-125, hK6, and hK10. Sensitivity, specificity, negative, and positive predictive values were determined for various combinations of the biomarkers. The levels of hK6 and hK10 were significantly elevated in ovarian cancer cases compared to benign cysts. Combination of 3 markers, age/CA-125/hk6 or CA-125/hk6/hk10, showed improved specificity (100%) and positive predictive value (100%) for prediction of ovarian cancer, when compared to the performance of single markers having 80-92% specificity and 74-87% positive predictive value. Four-marker combination, age/CA-125/hK6/hK10 also showed 100% specificity and 100% positive predictive value, although it demonstrated low sensitivity (11.9%) and negative predictive value (52.8%). The combination of human tissue kallikreins and CA-125 showed potential for improving prediction of epithelial ovarian cancer in patients presenting with ovarian tumors.

A novel clinical index for the assessment of RVD in acute pulmonary embolism: Blood pressure index.

PubMed

Ates, Hale; Ates, Ihsan; Kundi, Harun; Arikan, Mehmet Fettah; Yilmaz, Fatma Meric

2017-10-01

This study aims to investigate the role of the blood pressure index (BPI), which is a new index that we developed, in detection of right ventricular dysfunction (RVD) in acute pulmonary embolism (APE). A total of 539 patients, (253 males and 286 females), diagnosed with APE using computer tomography pulmonary angiography were included in the study. The BPI was obtained by dividing systolic blood pressure (SBP) by diastolic blood pressure (DBP). Mean DBP (75±11mmHg vs 63±15mmHg; p<0.001, respectively) was found to be higher in RVD patients compared to those without RVD, whereas BPI (1.5±0.1 vs 1.9±0.2; p<0.001, respectively) was lower. Examining the performance of BPI in prediction of RVD using receiver operating characteristic curve analysis (area under curve±SE=0.975±0.006; p<0.001), it was found that BPI could predict RVD with very high sensitivity (92.8%) and specificity (100%) and had a positive predictive value of 100% and a negative predictive value of 42.1%. According to the analysis, the highest youden index for the optimal prediction value was found to be 0.478 and the BPI≤1.4 was found to predict mortality 68.6% sensitivity and 80.8% specificity (Area under curve±SE=0.777±0.051; p<0.001). We found that BPI was an index with high positive predictive value and low negative predictive value in detection of RVD. Copyright © 2017 Elsevier Inc. All rights reserved.

Validity, accuracy, and predictive value of urinary tract infection signs and symptoms in individuals with spinal cord injury on intermittent catheterization.

PubMed

Massa, Luiz M; Hoffman, Jeanne M; Cardenas, Diana D

2009-01-01

To determine the validity, accuracy, and predictive value of the signs and symptoms of urinary tract infection (UTI) for individuals with spinal cord injury (SCI) using intermittent catheterization (IC) and the accuracy of individuals with SCI on IC at predicting their own UTI. Prospective cohort based on data from the first 3 months of a 1-year randomized controlled trial to evaluate UTI prevention effectiveness of hydrophilic and standard catheters. Fifty-six community-based individuals on IC. Presence of UTI as defined as bacteriuria with a colony count of at least 10(5) colony-forming units/mL and at least 1 sign or symptom of UTI. Analysis of monthly urine culture and urinalysis data combined with analysis of monthly data collected using a questionnaire that asked subjects to self-report on UTI signs and symptoms and whether or not they felt they had a UTI. Overall, "cloudy urine" had the highest accuracy (83.1%), and "leukocytes in the urine" had the highest sensitivity (82.8%). The highest specificity was for "fever" (99.0%); however, it had a very low sensitivity (6.9%). Subjects were able to predict their own UTI with an accuracy of 66.2%, and the negative predictive value (82.8%) was substantially higher than the positive predictive value (32.6%). The UTI signs and symptoms can predict a UTI more accurately than individual subjects can by using subjective impressions of their own signs and symptoms. Subjects were better at predicting when they did not have a UTI than when they did have a UTI.

IUS solid rocket motor contamination prediction methods

NASA Technical Reports Server (NTRS)

Mullen, C. R.; Kearnes, J. H.

1980-01-01

A series of computer codes were developed to predict solid rocket motor produced contamination to spacecraft sensitive surfaces. Subscale and flight test data have confirmed some of the analytical results. Application of the analysis tools to a typical spacecraft has provided early identification of potential spacecraft contamination problems and provided insight into their solution; e.g., flight plan modifications, plume or outgassing shields and/or contamination covers.

Inventory-based sensitivity analysis of the Large Tree Diameter Growth Submodel of the Southern Variant of the FVS

Treesearch

Giorgio Vacchiano; John D. Shaw; R. Justin DeRose; James N. Long

2008-01-01

Diameter increment is an important variable in modeling tree growth. Most facets of predicted tree development are dependent in part on diameter or diameter increment, the most commonly measured stand variable. The behavior of the Forest Vegetation Simulator (FVS) largely relies on the performance of the diameter increment model and the subsequent use of predicted dbh...

Finite Element Model Calibration Approach for Area I-X

NASA Technical Reports Server (NTRS)

Horta, Lucas G.; Reaves, Mercedes C.; Buehrle, Ralph D.; Templeton, Justin D.; Gaspar, James L.; Lazor, Daniel R.; Parks, Russell A.; Bartolotta, Paul A.

2010-01-01

Ares I-X is a pathfinder vehicle concept under development by NASA to demonstrate a new class of launch vehicles. Although this vehicle is essentially a shell of what the Ares I vehicle will be, efforts are underway to model and calibrate the analytical models before its maiden flight. Work reported in this document will summarize the model calibration approach used including uncertainty quantification of vehicle responses and the use of non-conventional boundary conditions during component testing. Since finite element modeling is the primary modeling tool, the calibration process uses these models, often developed by different groups, to assess model deficiencies and to update parameters to reconcile test with predictions. Data for two major component tests and the flight vehicle are presented along with the calibration results. For calibration, sensitivity analysis is conducted using Analysis of Variance (ANOVA). To reduce the computational burden associated with ANOVA calculations, response surface models are used in lieu of computationally intensive finite element solutions. From the sensitivity studies, parameter importance is assessed as a function of frequency. In addition, the work presents an approach to evaluate the probability that a parameter set exists to reconcile test with analysis. Comparisons of pretest predictions of frequency response uncertainty bounds with measured data, results from the variance-based sensitivity analysis, and results from component test models with calibrated boundary stiffness models are all presented.

Finite Element Model Calibration Approach for Ares I-X

NASA Technical Reports Server (NTRS)

Horta, Lucas G.; Reaves, Mercedes C.; Buehrle, Ralph D.; Templeton, Justin D.; Lazor, Daniel R.; Gaspar, James L.; Parks, Russel A.; Bartolotta, Paul A.

2010-01-01

Ares I-X is a pathfinder vehicle concept under development by NASA to demonstrate a new class of launch vehicles. Although this vehicle is essentially a shell of what the Ares I vehicle will be, efforts are underway to model and calibrate the analytical models before its maiden flight. Work reported in this document will summarize the model calibration approach used including uncertainty quantification of vehicle responses and the use of nonconventional boundary conditions during component testing. Since finite element modeling is the primary modeling tool, the calibration process uses these models, often developed by different groups, to assess model deficiencies and to update parameters to reconcile test with predictions. Data for two major component tests and the flight vehicle are presented along with the calibration results. For calibration, sensitivity analysis is conducted using Analysis of Variance (ANOVA). To reduce the computational burden associated with ANOVA calculations, response surface models are used in lieu of computationally intensive finite element solutions. From the sensitivity studies, parameter importance is assessed as a function of frequency. In addition, the work presents an approach to evaluate the probability that a parameter set exists to reconcile test with analysis. Comparisons of pre-test predictions of frequency response uncertainty bounds with measured data, results from the variance-based sensitivity analysis, and results from component test models with calibrated boundary stiffness models are all presented.

General methods for sensitivity analysis of equilibrium dynamics in patch occupancy models

USGS Publications Warehouse

Miller, David A.W.

2012-01-01

Sensitivity analysis is a useful tool for the study of ecological models that has many potential applications for patch occupancy modeling. Drawing from the rich foundation of existing methods for Markov chain models, I demonstrate new methods for sensitivity analysis of the equilibrium state dynamics of occupancy models. Estimates from three previous studies are used to illustrate the utility of the sensitivity calculations: a joint occupancy model for a prey species, its predators, and habitat used by both; occurrence dynamics from a well-known metapopulation study of three butterfly species; and Golden Eagle occupancy and reproductive dynamics. I show how to deal efficiently with multistate models and how to calculate sensitivities involving derived state variables and lower-level parameters. In addition, I extend methods to incorporate environmental variation by allowing for spatial and temporal variability in transition probabilities. The approach used here is concise and general and can fully account for environmental variability in transition parameters. The methods can be used to improve inferences in occupancy studies by quantifying the effects of underlying parameters, aiding prediction of future system states, and identifying priorities for sampling effort.

Quantifying the importance of spatial resolution and other factors through global sensitivity analysis of a flood inundation model

NASA Astrophysics Data System (ADS)

Thomas Steven Savage, James; Pianosi, Francesca; Bates, Paul; Freer, Jim; Wagener, Thorsten

2016-11-01

Where high-resolution topographic data are available, modelers are faced with the decision of whether it is better to spend computational resource on resolving topography at finer resolutions or on running more simulations to account for various uncertain input factors (e.g., model parameters). In this paper we apply global sensitivity analysis to explore how influential the choice of spatial resolution is when compared to uncertainties in the Manning's friction coefficient parameters, the inflow hydrograph, and those stemming from the coarsening of topographic data used to produce Digital Elevation Models (DEMs). We apply the hydraulic model LISFLOOD-FP to produce several temporally and spatially variable model outputs that represent different aspects of flood inundation processes, including flood extent, water depth, and time of inundation. We find that the most influential input factor for flood extent predictions changes during the flood event, starting with the inflow hydrograph during the rising limb before switching to the channel friction parameter during peak flood inundation, and finally to the floodplain friction parameter during the drying phase of the flood event. Spatial resolution and uncertainty introduced by resampling topographic data to coarser resolutions are much more important for water depth predictions, which are also sensitive to different input factors spatially and temporally. Our findings indicate that the sensitivity of LISFLOOD-FP predictions is more complex than previously thought. Consequently, the input factors that modelers should prioritize will differ depending on the model output assessed, and the location and time of when and where this output is most relevant.

A cost effective FBG-based security fence with fire alarm function

NASA Astrophysics Data System (ADS)

Wu, H. J.; Li, S. S.; Lu, X. L.; Wu, Y.; Rao, Y. J.

2012-02-01

Fiber Bragg Grating (FBG) is sensitive to the temperature as well when it is measuring the strain change, which is always avoided in most measurement applications. However, in this paper strain/temperature dual sensitivity is utilized to construct a special security fence with a second function of fire threat prediction. In an FBG-based fiber fence configuration, only by characteristics analysis and identification method, it can intelligently distinguish the different effects of personal threats and fires from their different trends of the wavelength drifts. Thus without any additional temperature sensing fittings or other fire alarm systems integrated, a normal perimeter security system can possess a second function of fire prediction, which can not only monitor the intrusion induced by personal actions but also predict fire threats in advance. The experimental results show the effectiveness of the method.

Neurological Outcome Scale for Traumatic Brain Injury: III. Criterion-Related Validity and Sensitivity to Change in the NABIS Hypothermia-II Clinical Trial

PubMed Central

Wilde, Elisabeth A.; Moretti, Paolo; MacLeod, Marianne C.; Pedroza, Claudia; Drever, Pamala; Fourwinds, Sierra; Frisby, Melisa L.; Beers, Sue R.; Scott, James N.; Hunter, Jill V.; Traipe, Elfrides; Valadka, Alex B.; Okonkwo, David O.; Zygun, David A.; Puccio, Ava M.; Clifton, Guy L.

2013-01-01

Abstract The Neurological Outcome Scale for Traumatic Brain Injury (NOS-TBI) is a measure assessing neurological functioning in patients with TBI. We hypothesized that the NOS-TBI would exhibit adequate concurrent and predictive validity and demonstrate more sensitivity to change, compared with other well-established outcome measures. We analyzed data from the National Acute Brain Injury Study: Hypothermia-II clinical trial. Participants were 16–45 years of age with severe TBI assessed at 1, 3, 6, and 12 months postinjury. For analysis of criterion-related validity (concurrent and predictive), Spearman's rank-order correlations were calculated between the NOS-TBI and the Glasgow Outcome Scale (GOS), GOS-Extended (GOS-E), Disability Rating Scale (DRS), and Neurobehavioral Rating Scale-Revised (NRS-R). Concurrent validity was demonstrated through significant correlations between the NOS-TBI and GOS, GOS-E, DRS, and NRS-R measured contemporaneously at 3, 6, and 12 months postinjury (all p<0.0013). For prediction analyses, the multiplicity-adjusted p value using the false discovery rate was <0.015. The 1-month NOS-TBI score was a significant predictor of outcome in the GOS, GOS-E, and DRS at 3 and 6 months postinjury (all p<0.015). The 3-month NOS-TBI significantly predicted GOS, GOS-E, DRS, and NRS-R outcomes at 6 and 12 months postinjury (all p<0.0015). Sensitivity to change was analyzed using Wilcoxon's signed rank-sum test of subsamples demonstrating no change in the GOS or GOS-E between 3 and 6 months. The NOS-TBI demonstrated higher sensitivity to change, compared with the GOS (p<0.038) and GOS-E (p<0.016). In summary, the NOS-TBI demonstrated adequate concurrent and predictive validity as well as sensitivity to change, compared with gold-standard outcome measures. The NOS-TBI may enhance prediction of outcome in clinical practice and measurement of outcome in TBI research. PMID:23617608

Error sensitivity analysis in 10-30-day extended range forecasting by using a nonlinear cross-prediction error model

NASA Astrophysics Data System (ADS)

Xia, Zhiye; Xu, Lisheng; Chen, Hongbin; Wang, Yongqian; Liu, Jinbao; Feng, Wenlan

2017-06-01

Extended range forecasting of 10-30 days, which lies between medium-term and climate prediction in terms of timescale, plays a significant role in decision-making processes for the prevention and mitigation of disastrous meteorological events. The sensitivity of initial error, model parameter error, and random error in a nonlinear crossprediction error (NCPE) model, and their stability in the prediction validity period in 10-30-day extended range forecasting, are analyzed quantitatively. The associated sensitivity of precipitable water, temperature, and geopotential height during cases of heavy rain and hurricane is also discussed. The results are summarized as follows. First, the initial error and random error interact. When the ratio of random error to initial error is small (10-6-10-2), minor variation in random error cannot significantly change the dynamic features of a chaotic system, and therefore random error has minimal effect on the prediction. When the ratio is in the range of 10-1-2 (i.e., random error dominates), attention should be paid to the random error instead of only the initial error. When the ratio is around 10-2-10-1, both influences must be considered. Their mutual effects may bring considerable uncertainty to extended range forecasting, and de-noising is therefore necessary. Second, in terms of model parameter error, the embedding dimension m should be determined by the factual nonlinear time series. The dynamic features of a chaotic system cannot be depicted because of the incomplete structure of the attractor when m is small. When m is large, prediction indicators can vanish because of the scarcity of phase points in phase space. A method for overcoming the cut-off effect ( m > 4) is proposed. Third, for heavy rains, precipitable water is more sensitive to the prediction validity period than temperature or geopotential height; however, for hurricanes, geopotential height is most sensitive, followed by precipitable water.

Predicting lower mantle heterogeneity from 4-D Earth models

NASA Astrophysics Data System (ADS)

Flament, Nicolas; Williams, Simon; Müller, Dietmar; Gurnis, Michael; Bower, Dan J.

2016-04-01

The Earth's lower mantle is characterized by two large-low-shear velocity provinces (LLSVPs), approximately ˜15000 km in diameter and 500-1000 km high, located under Africa and the Pacific Ocean. The spatial stability and chemical nature of these LLSVPs are debated. Here, we compare the lower mantle structure predicted by forward global mantle flow models constrained by tectonic reconstructions (Bower et al., 2015) to an analysis of five global tomography models. In the dynamic models, spanning 230 million years, slabs subducting deep into the mantle deform an initially uniform basal layer containing 2% of the volume of the mantle. Basal density, convective vigour (Rayleigh number Ra), mantle viscosity, absolute plate motions, and relative plate motions are varied in a series of model cases. We use cluster analysis to classify a set of equally-spaced points (average separation ˜0.45°) on the Earth's surface into two groups of points with similar variations in present-day temperature between 1000-2800 km depth, for each model case. Below ˜2400 km depth, this procedure reveals a high-temperature cluster in which mantle temperature is significantly larger than ambient and a low-temperature cluster in which mantle temperature is lower than ambient. The spatial extent of the high-temperature cluster is in first-order agreement with the outlines of the African and Pacific LLSVPs revealed by a similar cluster analysis of five tomography models (Lekic et al., 2012). Model success is quantified by computing the accuracy and sensitivity of the predicted temperature clusters in predicting the low-velocity cluster obtained from tomography (Lekic et al., 2012). In these cases, the accuracy varies between 0.61-0.80, where a value of 0.5 represents the random case, and the sensitivity ranges between 0.18-0.83. The largest accuracies and sensitivities are obtained for models with Ra ≈ 5 x 107, no asthenosphere (or an asthenosphere restricted to the oceanic domain), and a basal layer ˜ 4% denser than ambient mantle. Increasing convective vigour (Ra ≈ 5 x 108) or decreasing the density of the basal layer decreases both the accuracy and sensitivity of the predicted lower mantle structure. References: D. J. Bower, M. Gurnis, N. Flament, Assimilating lithosphere and slab history in 4-D Earth models. Phys. Earth Planet. Inter. 238, 8-22 (2015). V. Lekic, S. Cottaar, A. Dziewonski, B. Romanowicz, Cluster analysis of global lower mantle tomography: A new class of structure and implications for chemical heterogeneity. Earth Planet. Sci. Lett. 357, 68-77 (2012).

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.

ProTSAV: A protein tertiary structure analysis and validation server.

PubMed

Singh, Ankita; Kaushik, Rahul; Mishra, Avinash; Shanker, Asheesh; Jayaram, B

2016-01-01

Quality assessment of predicted model structures of proteins is as important as the protein tertiary structure prediction. A highly efficient quality assessment of predicted model structures directs further research on function. Here we present a new server ProTSAV, capable of evaluating predicted model structures based on some popular online servers and standalone tools. ProTSAV furnishes the user with a single quality score in case of individual protein structure along with a graphical representation and ranking in case of multiple protein structure assessment. The server is validated on ~64,446 protein structures including experimental structures from RCSB and predicted model structures for CASP targets and from public decoy sets. ProTSAV succeeds in predicting quality of protein structures with a specificity of 100% and a sensitivity of 98% on experimentally solved structures and achieves a specificity of 88%and a sensitivity of 91% on predicted protein structures of CASP11 targets under 2Å.The server overcomes the limitations of any single server/method and is seen to be robust in helping in quality assessment. ProTSAV is freely available at http://www.scfbio-iitd.res.in/software/proteomics/protsav.jsp. Copyright © 2015 Elsevier B.V. All rights reserved.

A novel QSAR model of Salmonella mutagenicity and its application in the safety assessment of drug impurities

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

Valencia, Antoni; Prous, Josep; Mora, Oscar

As indicated in ICH M7 draft guidance, in silico predictive tools including statistically-based QSARs and expert analysis may be used as a computational assessment for bacterial mutagenicity for the qualification of impurities in pharmaceuticals. To address this need, we developed and validated a QSAR model to predict Salmonella t. mutagenicity (Ames assay outcome) of pharmaceutical impurities using Prous Institute's Symmetry℠, a new in silico solution for drug discovery and toxicity screening, and the Mold2 molecular descriptor package (FDA/NCTR). Data was sourced from public benchmark databases with known Ames assay mutagenicity outcomes for 7300 chemicals (57% mutagens). Of these data, 90%more » was used to train the model and the remaining 10% was set aside as a holdout set for validation. The model's applicability to drug impurities was tested using a FDA/CDER database of 951 structures, of which 94% were found within the model's applicability domain. The predictive performance of the model is acceptable for supporting regulatory decision-making with 84 ± 1% sensitivity, 81 ± 1% specificity, 83 ± 1% concordance and 79 ± 1% negative predictivity based on internal cross-validation, while the holdout dataset yielded 83% sensitivity, 77% specificity, 80% concordance and 78% negative predictivity. Given the importance of having confidence in negative predictions, an additional external validation of the model was also carried out, using marketed drugs known to be Ames-negative, and obtained 98% coverage and 81% specificity. Additionally, Ames mutagenicity data from FDA/CFSAN was used to create another data set of 1535 chemicals for external validation of the model, yielding 98% coverage, 73% sensitivity, 86% specificity, 81% concordance and 84% negative predictivity. - Highlights: • A new in silico QSAR model to predict Ames mutagenicity is described. • The model is extensively validated with chemicals from the FDA and the public domain. • Validation tests show desirable high sensitivity and high negative predictivity. • The model predicted 14 reportedly difficult to predict drug impurities with accuracy. • The model is suitable to support risk evaluation of potentially mutagenic compounds.« less

Checking the predictive accuracy of basic symptoms against ultra high-risk criteria and testing of a multivariable prediction model: Evidence from a prospective three-year observational study of persons at clinical high-risk for psychosis.

PubMed

Hengartner, M P; Heekeren, K; Dvorsky, D; Walitza, S; Rössler, W; Theodoridou, A

2017-09-01

The aim of this study was to critically examine the prognostic validity of various clinical high-risk (CHR) criteria alone and in combination with additional clinical characteristics. A total of 188 CHR positive persons from the region of Zurich, Switzerland (mean age 20.5 years; 60.2% male), meeting ultra high-risk (UHR) and/or basic symptoms (BS) criteria, were followed over three years. The test battery included the Structured Interview for Prodromal Syndromes (SIPS), verbal IQ and many other screening tools. Conversion to psychosis was defined according to ICD-10 criteria for schizophrenia (F20) or brief psychotic disorder (F23). Altogether n=24 persons developed manifest psychosis within three years and according to Kaplan-Meier survival analysis, the projected conversion rate was 17.5%. The predictive accuracy of UHR was statistically significant but poor (area under the curve [AUC]=0.65, P<.05), whereas BS did not predict psychosis beyond mere chance (AUC=0.52, P=.730). Sensitivity and specificity were 0.83 and 0.47 for UHR, and 0.96 and 0.09 for BS. UHR plus BS achieved an AUC=0.66, with sensitivity and specificity of 0.75 and 0.56. In comparison, baseline antipsychotic medication yielded a predictive accuracy of AUC=0.62 (sensitivity=0.42; specificity=0.82). A multivariable prediction model comprising continuous measures of positive symptoms and verbal IQ achieved a substantially improved prognostic accuracy (AUC=0.85; sensitivity=0.86; specificity=0.85; positive predictive value=0.54; negative predictive value=0.97). We showed that BS have no predictive accuracy beyond chance, while UHR criteria poorly predict conversion to psychosis. Combining BS with UHR criteria did not improve the predictive accuracy of UHR alone. In contrast, dimensional measures of both positive symptoms and verbal IQ showed excellent prognostic validity. A critical re-thinking of binary at-risk criteria is necessary in order to improve the prognosis of psychotic disorders. Copyright © 2017 Elsevier Masson SAS. All rights reserved.

Application of data mining techniques to explore predictors of HCC in Egyptian patients with HCV-related chronic liver disease.

PubMed

Omran, Dalia Abd El Hamid; Awad, AbuBakr Hussein; Mabrouk, Mahasen Abd El Rahman; Soliman, Ahmad Fouad; Aziz, Ashraf Omar Abdel

2015-01-01

Hepatocellular carcinoma (HCC) is the second most common malignancy in Egypt. Data mining is a method of predictive analysis which can explore tremendous volumes of information to discover hidden patterns and relationships. Our aim here was to develop a non-invasive algorithm for prediction of HCC. Such an algorithm should be economical, reliable, easy to apply and acceptable by domain experts. This cross-sectional study enrolled 315 patients with hepatitis C virus (HCV) related chronic liver disease (CLD); 135 HCC, 116 cirrhotic patients without HCC and 64 patients with chronic hepatitis C. Using data mining analysis, we constructed a decision tree learning algorithm to predict HCC. The decision tree algorithm was able to predict HCC with recall (sensitivity) of 83.5% and precession (specificity) of 83.3% using only routine data. The correctly classified instances were 259 (82.2%), and the incorrectly classified instances were 56 (17.8%). Out of 29 attributes, serum alpha fetoprotein (AFP), with an optimal cutoff value of ≥50.3 ng/ml was selected as the best predictor of HCC. To a lesser extent, male sex, presence of cirrhosis, AST>64U/L, and ascites were variables associated with HCC. Data mining analysis allows discovery of hidden patterns and enables the development of models to predict HCC, utilizing routine data as an alternative to CT and liver biopsy. This study has highlighted a new cutoff for AFP (≥50.3 ng/ml). Presence of a score of >2 risk variables (out of 5) can successfully predict HCC with a sensitivity of 96% and specificity of 82%.

Predicting uncertainty in future marine ice sheet volume using Bayesian statistical methods

NASA Astrophysics Data System (ADS)

Davis, A. D.

2015-12-01

The marine ice instability can trigger rapid retreat of marine ice streams. Recent observations suggest that marine ice systems in West Antarctica have begun retreating. However, unknown ice dynamics, computationally intensive mathematical models, and uncertain parameters in these models make predicting retreat rate and ice volume difficult. In this work, we fuse current observational data with ice stream/shelf models to develop probabilistic predictions of future grounded ice sheet volume. Given observational data (e.g., thickness, surface elevation, and velocity) and a forward model that relates uncertain parameters (e.g., basal friction and basal topography) to these observations, we use a Bayesian framework to define a posterior distribution over the parameters. A stochastic predictive model then propagates uncertainties in these parameters to uncertainty in a particular quantity of interest (QoI)---here, the volume of grounded ice at a specified future time. While the Bayesian approach can in principle characterize the posterior predictive distribution of the QoI, the computational cost of both the forward and predictive models makes this effort prohibitively expensive. To tackle this challenge, we introduce a new Markov chain Monte Carlo method that constructs convergent approximations of the QoI target density in an online fashion, yielding accurate characterizations of future ice sheet volume at significantly reduced computational cost.Our second goal is to attribute uncertainty in these Bayesian predictions to uncertainties in particular parameters. Doing so can help target data collection, for the purpose of constraining the parameters that contribute most strongly to uncertainty in the future volume of grounded ice. For instance, smaller uncertainties in parameters to which the QoI is highly sensitive may account for more variability in the prediction than larger uncertainties in parameters to which the QoI is less sensitive. We use global sensitivity analysis to help answer this question, and make the computation of sensitivity indices computationally tractable using a combination of polynomial chaos and Monte Carlo techniques.

Temporal Expression-based Analysis of Metabolism

PubMed Central

Segrè, Daniel

2012-01-01

Metabolic flux is frequently rerouted through cellular metabolism in response to dynamic changes in the intra- and extra-cellular environment. Capturing the mechanisms underlying these metabolic transitions in quantitative and predictive models is a prominent challenge in systems biology. Progress in this regard has been made by integrating high-throughput gene expression data into genome-scale stoichiometric models of metabolism. Here, we extend previous approaches to perform a Temporal Expression-based Analysis of Metabolism (TEAM). We apply TEAM to understanding the complex metabolic dynamics of the respiratorily versatile bacterium Shewanella oneidensis grown under aerobic, lactate-limited conditions. TEAM predicts temporal metabolic flux distributions using time-series gene expression data. Increased predictive power is achieved by supplementing these data with a large reference compendium of gene expression, which allows us to take into account the unique character of the distribution of expression of each individual gene. We further propose a straightforward method for studying the sensitivity of TEAM to changes in its fundamental free threshold parameter θ, and reveal that discrete zones of distinct metabolic behavior arise as this parameter is changed. By comparing the qualitative characteristics of these zones to additional experimental data, we are able to constrain the range of θ to a small, well-defined interval. In parallel, the sensitivity analysis reveals the inherently difficult nature of dynamic metabolic flux modeling: small errors early in the simulation propagate to relatively large changes later in the simulation. We expect that handling such “history-dependent” sensitivities will be a major challenge in the future development of dynamic metabolic-modeling techniques. PMID:23209390

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.

First Trimester Urine and Serum Metabolomics for Prediction of Preeclampsia and Gestational Hypertension: A Prospective Screening Study.

PubMed

Austdal, Marie; Tangerås, Line H; Skråstad, Ragnhild B; Salvesen, Kjell; Austgulen, Rigmor; Iversen, Ann-Charlotte; Bathen, Tone F

2015-09-08

Hypertensive disorders of pregnancy, including preeclampsia, are major contributors to maternal morbidity. The goal of this study was to evaluate the potential of metabolomics to predict preeclampsia and gestational hypertension from urine and serum samples in early pregnancy, and elucidate the metabolic changes related to the diseases. Metabolic profiles were obtained by nuclear magnetic resonance spectroscopy of serum and urine samples from 599 women at medium to high risk of preeclampsia (nulliparous or previous preeclampsia/gestational hypertension). Preeclampsia developed in 26 (4.3%) and gestational hypertension in 21 (3.5%) women. Multivariate analyses of the metabolic profiles were performed to establish prediction models for the hypertensive disorders individually and combined. Urinary metabolomic profiles predicted preeclampsia and gestational hypertension at 51.3% and 40% sensitivity, respectively, at 10% false positive rate, with hippurate as the most important metabolite for the prediction. Serum metabolomic profiles predicted preeclampsia and gestational hypertension at 15% and 33% sensitivity, respectively, with increased lipid levels and an atherogenic lipid profile as most important for the prediction. Combining maternal characteristics with the urinary hippurate/creatinine level improved the prediction rates of preeclampsia in a logistic regression model. The study indicates a potential future role of clinical importance for metabolomic analysis of urine in prediction of preeclampsia.

Risk stratification for arrhythmic death in an emergency department cohort: a new method of nonlinear PD2i analysis of the ECG

PubMed Central

Skinner, James E; Meyer, Michael; Dalsey, William C; Nester, Brian A; Ramalanjaona, George; O’Neil, Brian J; Mangione, Antoinette; Terregino, Carol; Moreyra, Abel; Weiss, Daniel N; Anchin, Jerry M; Geary, Una; Taggart, Pamela

2008-01-01

Heart rate variability (HRV) reflects both cardiac autonomic function and risk of sudden arrhythmic death (AD). Indices of HRV based on linear stochastic models are independent risk factors for AD in postmyocardial infarction (MI) cohorts. Indices based on nonlinear deterministic models have a higher sensitivity and specificity for predicting AD in retrospective data. A new nonlinear deterministic model, the automated Point Correlation Dimension (PD2i), was prospectively evaluated for prediction of AD. Patients were enrolled (N = 918) in 6 emergency departments (EDs) upon presentation with chest pain and being determined to be at risk of acute MI (AMI) >7%. Brief digital ECGs (>1000 heartbeats, ∼15 min) were recorded and automated PD2i results obtained. Out-of-hospital AD was determined by modified Hinkle-Thaler criteria. All-cause mortality at 1 year was 6.2%, with 3.5% being ADs. Of the AD fatalities, 34% were without previous history of MI or diagnosis of AMI. The PD2i prediction of AD had sensitivity = 96%, specificity = 85%, negative predictive value = 99%, and relative risk >24.2 (p ≤ 0.001). HRV analysis by the time-dependent nonlinear PD2i algorithm can accurately predict risk of AD in an ED cohort and may have both life-saving and resource-saving implications for individual risk assessment. PMID:19209249

Diagnostic accuracy of EEG changes during carotid endarterectomy in predicting perioperative strokes.

PubMed

Thirumala, Parthasarathy D; Thiagarajan, Karthy; Gedela, Satyanarayana; Crammond, Donald J; Balzer, Jeffrey R

2016-03-01

The 30 day stroke rate following carotid endarterectomy (CEA) ranges between 2-6%. Such periprocedural strokes are associated with a three-fold increased risk of mortality. Our primary aim was to determine the diagnostic accuracy of electroencephalogram (EEG) in predicting perioperative strokes through meta-analysis of existing literature. An extensive search for relevant literature was undertaken using PubMed and Web of Science databases. Studies were included after screening using predetermined criteria. Data was extracted and analyzed. Summary sensitivity, specificity and diagnostic odds ratio were obtained. Subgroup analysis of studies using eight or more EEG channels was done. Perioperative stroke rate for the cohort of 8765 patients was 1.75%. Pooled sensitivity and specificity of EEG changes in predicting these strokes were 52% (95% confidence interval [CI], 43-61%) and 84% (95% CI, 81-86%) respectively. Summary estimates of the subgroup were similar. The diagnostic odds ratio was 5.85 (95% CI, 3.71-9.22). For the observed stroke rate, the positive likelihood ratio was 3.25 while the negative predictive value was 98.99%. According to these results, patients with perioperative strokes have six times greater odds of experiencing an intraoperative change in EEG during CEA. EEG monitoring was found to be highly specific in predicting perioperative strokes after CEA. Copyright © 2015 Elsevier Ltd. All rights reserved.

Improvement of Advanced Storm-scale Analysis and Prediction System (ASAPS) on Seoul Metropolitan Area, Korea

NASA Astrophysics Data System (ADS)

Park, Jeong-Gyun; Jee, Joon-Bum

2017-04-01

Dangerous weather such as severe rain, heavy snow, drought and heat wave caused by climate change make more damage in the urban area that dense populated and industry areas. Urban areas, unlike the rural area, have big population and transportation, dense the buildings and fuel consumption. Anthropogenic factors such as road energy balance, the flow of air in the urban is unique meteorological phenomena. However several researches are in process about prediction of urban meteorology. ASAPS (Advanced Storm-scale Analysis and Prediction System) predicts a severe weather with very short range (prediction with 6 hour) and high resolution (every hour with time and 1 km with space) on Seoul metropolitan area based on KLAPS (Korea Local Analysis and Prediction System) from KMA (Korea Meteorological Administration). This system configured three parts that make a background field (SUF5), analysis field (SU01) with observation and forecast field with high resolution (SUF1). In this study, we improve a high-resolution ASAPS model and perform a sensitivity test for the rainfall case. The improvement of ASAPS include model domain configuration, high resolution topographic data and data assimilation with WISE observation data.

The Advantages of Hybrid 4DEnVar in the Context of the Forecast Sensitivity to Initial Conditions

NASA Astrophysics Data System (ADS)

Song, Hyo-Jong; Shin, Seoleun; Ha, Ji-Hyun; Lim, Sujeong

2017-11-01

Hybrid four-dimensional ensemble variational data assimilation (hybrid 4DEnVar) is a prospective successor to three-dimensional variational data assimilation (3DVar) in operational weather prediction centers currently developing a new weather prediction model and those that do not operate adjoint models. In experiments using real observations, hybrid 4DEnVar improved Northern Hemisphere (NH; 20°N-90°N) 500 hPa geopotential height forecasts up to 5 days in a NH summer month compared to 3DVar, with statistical significance. This result is verified against ERA-Interim through a Monte Carlo test. By a regression analysis, the sensitivity of 5 day forecast is associated with the quality of the initial condition. The increased analysis skill for midtropospheric midlatitude temperature and subtropical moisture has the most apparent effect on forecast skill in the NH including a typhoon prediction case. Through attributing the analysis improvements by hybrid 4DEnVar separately to the ensemble background error covariance (BEC), its four-dimensional (4-D) extension, and climatological BEC, it is revealed that the ensemble BEC contributes to the subtropical moisture analysis, whereas the 4-D extension does to the midtropospheric midlatitude temperature. This result implies that hourly wind-mass correlation in 6 h analysis window is required to extract the potential of hybrid 4DEnVar for the midlatitude temperature analysis to the maximum. However, the temporal ensemble correlation, in hourly time scale, between moisture and another variable is invalid so that it could not work for improving the hybrid 4DEnVar analysis.

A Comparison of Various Stress Rupture Life Models for Orbiter Composite Pressure Vessels and Confidence Intervals

NASA Technical Reports Server (NTRS)

Grimes-Ledesma, Lorie; Murthy, Pappu L. N.; Phoenix, S. Leigh; Glaser, Ronald

2007-01-01

In conjunction with a recent NASA Engineering and Safety Center (NESC) investigation of flight worthiness of Kevlar Overwrapped Composite Pressure Vessels (COPVs) on board the Orbiter, two stress rupture life prediction models were proposed independently by Phoenix and by Glaser. In this paper, the use of these models to determine the system reliability of 24 COPVs currently in service on board the Orbiter is discussed. The models are briefly described, compared to each other, and model parameters and parameter uncertainties are also reviewed to understand confidence in reliability estimation as well as the sensitivities of these parameters in influencing overall predicted reliability levels. Differences and similarities in the various models will be compared via stress rupture reliability curves (stress ratio vs. lifetime plots). Also outlined will be the differences in the underlying model premises, and predictive outcomes. Sources of error and sensitivities in the models will be examined and discussed based on sensitivity analysis and confidence interval determination. Confidence interval results and their implications will be discussed for the models by Phoenix and Glaser.

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

PubMed Central

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

2015-01-01

We investigate the effect of the choice of parameterisation of meta-analytic models and related uncertainty on the validation of surrogate endpoints. Different meta-analytical approaches take into account different levels of uncertainty which may impact on the accuracy of the predictions of treatment effect on the target outcome from the treatment effect on a surrogate endpoint obtained from these models. A range of Bayesian as well as frequentist meta-analytical methods are implemented using illustrative examples in relapsing–remitting multiple sclerosis, where the treatment effect on disability worsening is the primary outcome of interest in healthcare evaluation, while the effect on relapse rate is considered as a potential surrogate to the effect on disability progression, and in gastric cancer, where the disease-free survival has been shown to be a good surrogate endpoint to the overall survival. Sensitivity analysis was carried out to assess the impact of distributional assumptions on the predictions. Also, sensitivity to modelling assumptions and performance of the models were investigated by simulation. Although different methods can predict mean true outcome almost equally well, inclusion of uncertainty around all relevant parameters of the model may lead to less certain and hence more conservative predictions. When investigating endpoints as candidate surrogate outcomes, a careful choice of the meta-analytical approach has to be made. Models underestimating the uncertainty of available evidence may lead to overoptimistic predictions which can then have an effect on decisions made based on such predictions. PMID:26271918

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

PubMed

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

2017-10-01

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

Novel immunological and nutritional-based prognostic index for gastric cancer.

PubMed

Sun, Kai-Yu; Xu, Jian-Bo; Chen, Shu-Ling; Yuan, Yu-Jie; Wu, Hui; Peng, Jian-Jun; Chen, Chuang-Qi; Guo, Pi; Hao, Yuan-Tao; He, Yu-Long

2015-05-21

To assess the prognostic significance of immunological and nutritional-based indices, including the prognostic nutritional index (PNI), neutrophil-lymphocyte ratio (NLR), and platelet-lymphocyte ratio in gastric cancer. We retrospectively reviewed 632 gastric cancer patients who underwent gastrectomy between 1998 and 2008. Areas under the receiver operating characteristic curve were calculated to compare the predictive ability of the indices, together with estimating the sensitivity, specificity and agreement rate. Univariate and multivariate analyses were performed to identify risk factors for overall survival (OS). Propensity score analysis was performed to adjust variables to control for selection bias. Each index could predict OS in gastric cancer patients in univariate analysis, but only PNI had independent prognostic significance in multivariate analysis before and after adjustment with propensity scoring (hazard ratio, 1.668; 95% confidence interval: 1.368-2.035). In subgroup analysis, a low PNI predicted a significantly shorter OS in patients with stage II-III disease (P = 0.019, P < 0.001), T3-T4 tumors (P < 0.001), or lymph node metastasis (P < 0.001). Canton score, a combination of PNI, NLR, and platelet, was a better indicator for OS than PNI, with the largest area under the curve for 12-, 36-, 60-mo OS and overall OS (P = 0.022, P = 0.030, P < 0.001, and P = 0.024, respectively). The maximum sensitivity, specificity, and agreement rate of Canton score for predicting prognosis were 84.6%, 34.9%, and 70.1%, respectively. PNI is an independent prognostic factor for OS in gastric cancer. Canton score can be a novel preoperative prognostic index in gastric cancer.

Locally advanced rectal cancer: post-chemoradiotherapy ADC histogram analysis for predicting a complete response.

PubMed

Cho, Seung Hyun; Kim, Gab Chul; Jang, Yun-Jin; Ryeom, Hunkyu; Kim, Hye Jung; Shin, Kyung-Min; Park, Jun Seok; Choi, Gyu-Seog; Kim, See Hyung

2015-09-01

The value of diffusion-weighted imaging (DWI) for reliable differentiation between pathologic complete response (pCR) and residual tumor is still unclear. Recently, a few studies reported that histogram analysis can be helpful to monitor the therapeutic response in various cancer research. To investigate whether post-chemoradiotherapy (CRT) apparent diffusion coefficient (ADC) histogram analysis can be helpful to predict a pCR in locally advanced rectal cancer (LARC). Fifty patients who underwent preoperative CRT followed by surgery were enrolled in this retrospective study, non-pCR (n = 41) and pCR (n = 9), respectively. ADC histogram analysis encompassing the whole tumor was performed on two post-CRT ADC600 and ADC1000 (b factors 0, 600 vs. 0, 1000 s/mm(2)) maps. Mean, minimum, maximum, SD, mode, 10th, 25th, 50th, 75th, 90th percentile ADCs, skewness, and kurtosis were derived. Diagnostic performance for predicting pCR was evaluated and compared. On both maps, 10th and 25th ADCs showed better diagnostic performance than that using mean ADC. Tenth percentile ADCs revealed the best diagnostic performance on both ADC600 (AZ 0.841, sensitivity 100%, specificity 70.7%) and ADC1000 (AZ 0.821, sensitivity 77.8%, specificity 87.8%) maps. In comparison between 10th percentile and mean ADC, the specificity was significantly improved on both ADC600 (70.7% vs. 53.7%; P = 0.031) and ADC1000 (87.8% vs. 73.2%; P = 0.039) maps. Post-CRT ADC histogram analysis is helpful for predicting pCR in LARC, especially, in improving the specificity, compared with mean ADC. © The Foundation Acta Radiologica 2014.

The predictive accuracy of the black hole sign and the spot sign for hematoma expansion in patients with spontaneous intracerebral hemorrhage.

PubMed

Yu, Zhiyuan; Zheng, Jun; Ma, Lu; Guo, Rui; Li, Mou; Wang, Xiaoze; Lin, Sen; Li, Hao; You, Chao

2017-09-01

In patients with spontaneous intracerebral hemorrhage (sICH), hematoma expansion (HE) is associated with poor outcome. Spot sign and black hole sign are neuroimaging predictors for HE. This study was aimed to compare the predictive value of two signs for HE. Within 6 h after onset of sICH, patients were screened for the computed tomography angiography spot sign and the non-contrast computed tomography black hole sign. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of two signs for HE prediction were calculated. The accuracy of two signs in predicting HE was analyzed by receiver-operator analysis. A total of 129 patients were included in this study. Spot sign was identified in 30 (23.3%) patients and black hole sign in 29 (22.5%) patients, respectively. Of 32 patients with HE, spot sign was observed in 19 (59.4%) and black hole sign was found in 14 (43.8%). The occurrence of black hole sign was significantly associated with spot sign (P < 0.001). The sensitivity, specificity, PPV, and NPV of spot sign for predicting HE were 59.38, 88.66, 63.33, and 86.87% respectively. In contrast, the sensitivity, specificity, PPV, and NPV of black hole sign for predicting HE were 43.75, 84.54, 48.28, and 82.00%, respectively. The area under the curve was 0.740 for spot sign and 0.641 for black hole sign. (P = 0.228) Both spot sign and black hole sign appeared to have good predictive value for HE, and spot sign seemed to be a better predictor.

The use of copula functions for predictive analysis of correlations between extreme storm tides

NASA Astrophysics Data System (ADS)

Domino, Krzysztof; Błachowicz, Tomasz; Ciupak, Maurycy

2014-11-01

In this paper we present a method used in quantitative description of weakly predictable hydrological, extreme events at inland sea. Investigations for correlations between variations of individual measuring points, employing combined statistical methods, were carried out. As a main tool for this analysis we used a two-dimensional copula function sensitive for correlated extreme effects. Additionally, a new proposed methodology, based on Detrended Fluctuations Analysis (DFA) and Anomalous Diffusion (AD), was used for the prediction of negative and positive auto-correlations and associated optimum choice of copula functions. As a practical example we analysed maximum storm tides data recorded at five spatially separated places at the Baltic Sea. For the analysis we used Gumbel, Clayton, and Frank copula functions and introduced the reversed Clayton copula. The application of our research model is associated with modelling the risk of high storm tides and possible storm flooding.

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

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.

Application of a stochastic snowmelt model for probabilistic decisionmaking

NASA Technical Reports Server (NTRS)

Mccuen, R. H.

1983-01-01

A stochastic form of the snowmelt runoff model that can be used for probabilistic decision-making was developed. The use of probabilistic streamflow predictions instead of single valued deterministic predictions leads to greater accuracy in decisions. While the accuracy of the output function is important in decisionmaking, it is also important to understand the relative importance of the coefficients. Therefore, a sensitivity analysis was made for each of the coefficients.

Measuring diagnostic and predictive accuracy in disease management: an introduction to receiver operating characteristic (ROC) analysis.

PubMed

Linden, Ariel

2006-04-01

Diagnostic or predictive accuracy concerns are common in all phases of a disease management (DM) programme, and ultimately play an influential role in the assessment of programme effectiveness. Areas, such as the identification of diseased patients, predictive modelling of future health status and costs and risk stratification, are just a few of the domains in which assessment of accuracy is beneficial, if not critical. The most commonly used analytical model for this purpose is the standard 2 x 2 table method in which sensitivity and specificity are calculated. However, there are several limitations to this approach, including the reliance on a single defined criterion or cut-off for determining a true-positive result, use of non-standardized measurement instruments and sensitivity to outcome prevalence. This paper introduces the receiver operator characteristic (ROC) analysis as a more appropriate and useful technique for assessing diagnostic and predictive accuracy in DM. Its advantages include; testing accuracy across the entire range of scores and thereby not requiring a predetermined cut-off point, easily examined visual and statistical comparisons across tests or scores, and independence from outcome prevalence. Therefore the implementation of ROC as an evaluation tool should be strongly considered in the various phases of a DM programme.

Childhood peer reputation as a predictor of competence and symptoms 10 years later.

PubMed

Gest, Scott D; Sesma, Arturo; Masten, Ann S; Tellegen, Auke

2006-08-01

This study examined the differential developmental significance of multiple domains of peer reputation in childhood for current and future competence and symptoms. Participants were 205 children from a normative school cohort who completed assessments in grades 3-6 and then again 10 years later. Through re-analysis of original data from the Revised Class Play (RCP; N=612), new narrow-band subscales were examined as distinct correlates and predictors of competence in age-relevant developmental tasks and psychological well being as indexed by internalizing symptoms. Findings support the differentiation of peer exclusion, withdrawal, and sadness within the broad sensitive-isolated domain of reputation, as well as the distinctive meaning of reputations for Popularity-Leadership and Prosocial Behavior within the broad Sociable-Leader domain. When the Sensitive-Isolated predictors were considered, academic and job competence at the 10-year follow-up were predicted uniquely and negatively by peer exclusion, problems in the social and romantic domains were predicted distinctively by withdrawal from peers, and internalizing symptoms were uniquely predicted by childhood reputation as Sad-Sensitive. When the Sociable-Leader predictors were considered, academic and (for ethnic minority youth) job success was predicted by a Prosocial reputation, social success was forecasted by Popularity-Leadership, and romantic competence was predicted positively by Popularity-Leadership and negatively by Prosocial reputation. Negative academic and job outcomes were also predicted by a childhood reputation as Aggressive-Disruptive. Results are discussed in relation to conceptualizing and measuring peer social competence and its relation to later adaptation.

BIPS: BIANA Interolog Prediction Server. A tool for protein-protein interaction inference.

PubMed

Garcia-Garcia, Javier; Schleker, Sylvia; Klein-Seetharaman, Judith; Oliva, Baldo

2012-07-01

Protein-protein interactions (PPIs) play a crucial role in biology, and high-throughput experiments have greatly increased the coverage of known interactions. Still, identification of complete inter- and intraspecies interactomes is far from being complete. Experimental data can be complemented by the prediction of PPIs within an organism or between two organisms based on the known interactions of the orthologous genes of other organisms (interologs). Here, we present the BIANA (Biologic Interactions and Network Analysis) Interolog Prediction Server (BIPS), which offers a web-based interface to facilitate PPI predictions based on interolog information. BIPS benefits from the capabilities of the framework BIANA to integrate the several PPI-related databases. Additional metadata can be used to improve the reliability of the predicted interactions. Sensitivity and specificity of the server have been calculated using known PPIs from different interactomes using a leave-one-out approach. The specificity is between 72 and 98%, whereas sensitivity varies between 1 and 59%, depending on the sequence identity cut-off used to calculate similarities between sequences. BIPS is freely accessible at http://sbi.imim.es/BIPS.php.

Assessment of the predictive accuracy of five in silico prediction tools, alone or in combination, and two metaservers to classify long QT syndrome gene mutations.

PubMed

Leong, Ivone U S; Stuckey, Alexander; Lai, Daniel; Skinner, Jonathan R; Love, Donald R

2015-05-13

Long QT syndrome (LQTS) is an autosomal dominant condition predisposing to sudden death from malignant arrhythmia. Genetic testing identifies many missense single nucleotide variants of uncertain pathogenicity. Establishing genetic pathogenicity is an essential prerequisite to family cascade screening. Many laboratories use in silico prediction tools, either alone or in combination, or metaservers, in order to predict pathogenicity; however, their accuracy in the context of LQTS is unknown. We evaluated the accuracy of five in silico programs and two metaservers in the analysis of LQTS 1-3 gene variants. The in silico tools SIFT, PolyPhen-2, PROVEAN, SNPs&GO and SNAP, either alone or in all possible combinations, and the metaservers Meta-SNP and PredictSNP, were tested on 312 KCNQ1, KCNH2 and SCN5A gene variants that have previously been characterised by either in vitro or co-segregation studies as either "pathogenic" (283) or "benign" (29). The accuracy, sensitivity, specificity and Matthews Correlation Coefficient (MCC) were calculated to determine the best combination of in silico tools for each LQTS gene, and when all genes are combined. The best combination of in silico tools for KCNQ1 is PROVEAN, SNPs&GO and SIFT (accuracy 92.7%, sensitivity 93.1%, specificity 100% and MCC 0.70). The best combination of in silico tools for KCNH2 is SIFT and PROVEAN or PROVEAN, SNPs&GO and SIFT. Both combinations have the same scores for accuracy (91.1%), sensitivity (91.5%), specificity (87.5%) and MCC (0.62). In the case of SCN5A, SNAP and PROVEAN provided the best combination (accuracy 81.4%, sensitivity 86.9%, specificity 50.0%, and MCC 0.32). When all three LQT genes are combined, SIFT, PROVEAN and SNAP is the combination with the best performance (accuracy 82.7%, sensitivity 83.0%, specificity 80.0%, and MCC 0.44). Both metaservers performed better than the single in silico tools; however, they did not perform better than the best performing combination of in silico tools. The combination of in silico tools with the best performance is gene-dependent. The in silico tools reported here may have some value in assessing variants in the KCNQ1 and KCNH2 genes, but caution should be taken when the analysis is applied to SCN5A gene variants.

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.

Rotator cuff crepitus: could Codman really feel a cuff tear?

PubMed

Ponce, Brent A; Kundukulam, Joseph A; Sheppard, Evan D; Determann, Jason R; McGwin, Gerald; Narducci, Carl A; Crowther, Marshall J

2014-07-01

The objective of this study was to assess the accuracy of palpating crepitus to diagnose rotator cuff tears. Seventy consecutive consenting patients who presented with shoulder pain and no previous imaging or surgery on the affected shoulder were prospectively enrolled during a 10-month period. A standardized patient history and examination, including the crepitus test, were recorded in addition to obtaining standard radiographs. Additional imaging after initial evaluation was performed with magnetic resonance imaging and interpreted by a musculoskeletal radiologist blinded to the examination findings. Statistical analysis was used to determine sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the crepitus test in the clinical diagnosis of a rotator cuff tear. Sixty-three patients had histories, examinations, and imaging studies available for analysis. The crepitus test had a sensitivity of 67%, specificity of 80%, PPV of 91%, and NPV of 43% for all types of rotator cuff tears. The sensitivity and specificity for full-thickness or high-grade partial tears was 82% and 73%, respectively; the PPV and NPV were 77% and 79%. Increasing age improved accuracy as the presence of crepitus in patients older than 55 years had a sensitivity of 76%, specificity of 100%, PPV of 100%, and NPV of 38%. The crepitus test has a favorable sensitivity, specificity, PPV, and NPV to assess the integrity of the rotator cuff and may be a useful examination in the clinical diagnosis of a rotator cuff tear. Published by Mosby, Inc.

Pandemic preparedness in Hawaii: a multicenter verification of real-time RT-PCR for the direct detection of influenza virus types A and B.

PubMed

Whelen, A Christian; Bankowski, Matthew J; Furuya, Glenn; Honda, Stacey; Ueki, Robert; Chan, Amelia; Higa, Karen; Kumashiro, Diane; Moore, Nathaniel; Lee, Roland; Koyamatsu, Terrie; Effler, Paul V

2010-01-01

We integrated multicenter, real-time (RTi) reverse transcription polymerase chain reaction (RT-PCR) screening into a statewide laboratory algorithm for influenza surveillance and response. Each of three sites developed its own testing strategy and was challenged with one randomized and blinded panel of 50 specimens previously tested for respiratory viruses. Following testing, each participating laboratory reported its results to the Hawaii State Department of Health, State Laboratories Division for evaluation and possible discrepant analysis. Two of three laboratories reported a 100% sensitivity and specificity, resulting in a 100% positive predictive value and a 100% negative predictive value (NPV) for influenza type A. The third laboratory showed a 71% sensitivity for influenza type A (83% NPV) with 100% specificity. All three laboratories were 100% sensitive and specific for the detection of influenza type B. Discrepant analysis indicated that the lack of sensitivity experienced by the third laboratory may have been due to the analyte-specific reagent probe used by that laboratory. Use of a newer version of the product with a secondary panel of 20 specimens resulted in a sensitivity and specificity of 100%. All three laboratories successfully verified their ability to conduct clinical testing for influenza using diverse nucleic acid extraction and RTi RT-PCR platforms. Successful completion of the verification by all collaborating laboratories paved the way for the integration of those facilities into a statewide laboratory algorithm for influenza surveillance and response.

Photovoltaic System Modeling. Uncertainty and Sensitivity Analyses

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

Hansen, Clifford W.; Martin, Curtis E.

2015-08-01

We report an uncertainty and sensitivity analysis for modeling AC energy from ph otovoltaic systems . Output from a PV system is predicted by a sequence of models. We quantify u ncertainty i n the output of each model using empirical distribution s of each model's residuals. We propagate uncertainty through the sequence of models by sampli ng these distributions to obtain a n empirical distribution of a PV system's output. We consider models that: (1) translate measured global horizontal, direct and global diffuse irradiance to plane - of - array irradiance; (2) estimate effective irradiance; (3) predict cell temperature;more » (4) estimate DC voltage, current and power ; (5) reduce DC power for losses due to inefficient maximum power point tracking or mismatch among modules; and (6) convert DC to AC power . O ur analysis consider s a notional PV system com prising an array of FirstSolar FS - 387 modules and a 250 kW AC inverter ; we use measured irradiance and weather at Albuquerque, NM. We found the uncertainty in PV syste m output to be relatively small, on the order of 1% for daily energy. We found that unce rtainty in the models for POA irradiance and effective irradiance to be the dominant contributors to uncertainty in predicted daily energy. Our analysis indicates that efforts to reduce the uncertainty in PV system output predictions may yield the greatest improvements by focusing on the POA and effective irradiance models.« less

Predictive value and construct validity of the work functioning screener-healthcare (WFS-H).

PubMed

Boezeman, Edwin J; Nieuwenhuijsen, Karen; Sluiter, Judith K

2016-05-25

To test the predictive value and convergent construct validity of a 6-item work functioning screener (WFS-H). Healthcare workers (249 nurses) completed a questionnaire containing the work functioning screener (WFS-H) and a work functioning instrument (NWFQ) measuring the following: cognitive aspects of task execution and general incidents, avoidance behavior, conflicts and irritation with colleagues, impaired contact with patients and their family, and level of energy and motivation. Productivity and mental health were also measured. Negative and positive predictive values, AUC values, and sensitivity and specificity were calculated to examine the predictive value of the screener. Correlation analysis was used to examine the construct validity. The screener had good predictive value, since the results showed that a negative screener score is a strong indicator of work functioning not hindered by mental health problems (negative predictive values: 94%-98%; positive predictive values: 21%-36%; AUC:.64-.82; sensitivity: 42%-76%; and specificity 85%-87%). The screener has good construct validity due to moderate, but significant (p<.001), associations with productivity (r=.51), mental health (r=.48), and distress (r=.47). The screener (WFS-H) had good predictive value and good construct validity. Its score offers occupational health professionals a helpful preliminary insight into the work functioning of healthcare workers.

Asymptomatic bacteriuria in pregnant women attending Boo-Ali Hospital Tehran Iran: Urine analysis vs. urine culture.

PubMed

Etminan-Bakhsh, Mina; Tadi, Sima; Darabi, Roksana

2017-11-01

Asymptomatic bacteriuria is one of the common problems in pregnancy. Asymptomatic bacteriuria is associated with pyelonephritis, preterm labor and low birth weight infants. The physiological and anatomical changes in pregnancy facilitate urinary tract infection (UTI) during pregnancy. Several tests are available for diagnosis of asymptomatic bacteriuria. The urine culture is a gold standard diagnostic test for asymptomatic bacteriuria but it is expensive and time-consuming. Screening methods may be useful in detecting high-risk pregnant women for asymptomatic bacteriuria. The aim of the present study was to compare urine analysis as a rapid screening test to urine culture in diagnosis of asymptomatic bacteriuria. A total of 123 pregnant women attending the obstetrics clinic of Boo-Ali hospital in Tehran, Iran from March 2013 to September 2014 were included in the present diagnostic cross-sectional study. One hundred twenty three mid-stream urine samples were inoculated into cultures and were processed by dipstick (nitrite test and leucocyte esterase test) and microscopic pus cell count. The sensitivity, specificity, positive predictive value and negative predictive value of nitrite test, leucocyte esterase test and microscopic pus cell count were compared with urine culture in diagnosis of asymptomatic bacteriuria by using SPSS version 19. Of 123 urine samples, significant asymptomatic bacteriuria (≥10 4 cfu/Ml) was detected in 8 (6.5%) subjects. The sensitivity and specificity of nitrite test were 37% and 100% respectively. The sensitivity of pus cell count alone and leucocyte esterase test alone were 100% but the specificity of them were 64% and 65% respectively. We found high negative predictive value by Pus cell count and the leucocyte esterase test (100%) and low positive predictive value by them (16% and 17% respectively). Urine culture is the most useful test for diagnosis of asymptomatic bacteriuria. None of our screening tests had a sensitivity and specificity of 100%, whereas we can only refer the pregnant women with positive leucocyte esterase test and significant pyuria to the urine culture.

Predicting Fluid Responsiveness by Passive Leg Raising: A Systematic Review and Meta-Analysis of 23 Clinical Trials.

PubMed

Cherpanath, Thomas G V; Hirsch, Alexander; Geerts, Bart F; Lagrand, Wim K; Leeflang, Mariska M; Schultz, Marcus J; Groeneveld, A B Johan

2016-05-01

Passive leg raising creates a reversible increase in venous return allowing for the prediction of fluid responsiveness. However, the amount of venous return may vary in various clinical settings potentially affecting the diagnostic performance of passive leg raising. Therefore we performed a systematic meta-analysis determining the diagnostic performance of passive leg raising in different clinical settings with exploration of patient characteristics, measurement techniques, and outcome variables. PubMed, EMBASE, the Cochrane Database of Systematic Reviews, and citation tracking of relevant articles. Clinical trials were selected when passive leg raising was performed in combination with a fluid challenge as gold standard to define fluid responders and non-responders. Trials were included if data were reported allowing the extraction of sensitivity, specificity, and area under the receiver operating characteristic curve. Twenty-three studies with a total of 1,013 patients and 1,034 fluid challenges were included. The analysis demonstrated a pooled sensitivity of 86% (95% CI, 79-92), pooled specificity of 92% (95% CI, 88-96), and a summary area under the receiver operating characteristic curve of 0.95 (95% CI, 0.92-0.98). Mode of ventilation, type of fluid used, passive leg raising starting position, and measurement technique did not affect the diagnostic performance of passive leg raising. The use of changes in pulse pressure on passive leg raising showed a lower diagnostic performance when compared with passive leg raising-induced changes in flow variables, such as cardiac output or its direct derivatives (sensitivity of 58% [95% CI, 44-70] and specificity of 83% [95% CI, 68-92] vs sensitivity of 85% [95% CI, 78-90] and specificity of 92% [95% CI, 87-94], respectively; p < 0.001). Passive leg raising retains a high diagnostic performance in various clinical settings and patient groups. The predictive value of a change in pulse pressure on passive leg raising is inferior to a passive leg raising-induced change in a flow variable.

Breastfeeding and maternal sensitivity predict early infant temperament.

PubMed

Jonas, Wibke; Atkinson, Leslie; Steiner, Meir; Meaney, Michael J; Wazana, Ashley; Fleming, Alison S

2015-07-01

Research findings are inconclusive when it comes to whether breastfeeding is associated with the mother-infant relationship or infant temperament. We examined the association between breastfeeding at three months postpartum and infant temperament at 18 months postpartum and whether this link was affected by the mothers' anxiety and mediated by her sensitivity. We assessed 170 mothers for breastfeeding and anxiety using the Spielberger State-Trait Anxiety Inventory (STAI) at three months postpartum, maternal sensitivity using the Ainsworth Sensitivity Scale at six months postpartum and infant temperament using the Early Childhood Behaviour Questionnaire at 18 months postpartum. Mothers who breastfed at three months postpartum were more sensitive in their interactions with their infants at six months postpartum, and elevated sensitivity, in turn, predicted reduced levels of negative affectivity in infant temperament at 18 months postpartum. This indirect mediation persisted after controlling for confounders (effect ab = -0.0312 [0.0208], 95% CI = -0.0884 to -0.0031). A subsequent analysis showed that the mediation through sensitivity only occurred in women experiencing higher anxiety, with a STAI score ≥33.56 at three months (ab = -0.0250 [0.0179], 95% CI = -0.0759 to -0.0013). Our results suggest that breastfeeding and maternal sensitivity may have a positive impact on the early development of infant temperament. ©2015 Foundation Acta Paediatrica. Published by John Wiley & Sons Ltd.

Adding insult to injury

NASA Astrophysics Data System (ADS)

Friebele, Elaine

New predictions and observations suggest that global warming will exact the highest costs on developing countries. A recent economic analysis of global climate change indicates that developed countries, the primary emitters of carbon dioxide, would benefit by $82 billion per year from a 2°C increase in global mean temperature, while underdeveloped countries would lose $40 billion per year.For the economic analysis, global climate predictions were combined with economic data (for agriculture, forestry, coastal resources, energy, and tourism), but natural climate variability, including frosts, droughts, or severe thunderstorms, was not included. Countries predicted to suffer the greatest economic losses from global warming are island nations, said Michael Schlesinger, a University of Illinois atmospheric scientist who performed the economic analysis with colleagues from Yale University and Middlebury College. “These countries have long coast lines, sensitive tourism industries, and small, undeveloped economies.”

Development and validation of a numerical acoustic analysis program for aircraft interior noise prediction

NASA Astrophysics Data System (ADS)

Garcea, Ralph; Leigh, Barry; Wong, R. L. M.

Reduction of interior noise in propeller-driven aircraft, to levels comparable with those obtained in jet transports, has become a leading factor in the early design stages of the new generation turboprops- and may be essential if these new designs are to succeed. The need for an analytical capability to predict interior noise is accepted throughout the turboprop aircraft industry. To this end, an analytical noise prediction program, which incorporates the SYSNOISE numerical acoustic analysis software, is under development at de Havilland. The discussion contained herein looks at the development program and how it was used in a design sensitivity analysis to optimize the structural design of the aircraft cabin for the purpose of reducing interior noise levels. This report also summarizes the validation of the SYSNOISE package using numerous classical cases from the literature.

Lack of Early Improvement Predicts Poor Outcome Following Acute Intracerebral Hemorrhage.

PubMed

Yogendrakumar, Vignan; Smith, Eric E; Demchuk, Andrew M; Aviv, Richard I; Rodriguez-Luna, David; Molina, Carlos A; Silva Blas, Yolanda; Dzialowski, Imanuel; Kobayashi, Adam; Boulanger, Jean-Martin; Lum, Cheemun; Gubitz, Gord; Padma, Vasantha; Roy, Jayanta; Kase, Carlos S; Bhatia, Rohit; Ali, Myzoon; Lyden, Patrick; Hill, Michael D; Dowlatshahi, Dar

2018-04-01

There are limited data as to what degree of early neurologic change best relates to outcome in acute intracerebral hemorrhage. We aimed to derive and validate a threshold for early postintracerebral hemorrhage change that best predicts 90-day outcomes. Derivation: retrospective analysis of collated clinical stroke trial data (Virtual International Stroke Trials Archive). retrospective analysis of a prospective multicenter cohort study (Prediction of haematoma growth and outcome in patients with intracerebral haemorrhage using the CT-angiography spot sign [PREDICT]). Neurocritical and ICUs. Patients with acute intracerebral hemorrhage presenting less than 6 hours. Derivation: 552 patients; validation: 275 patients. None. We generated a receiver operating characteristic curve for the association between 24-hour National Institutes of Health Stroke Scale change and clinical outcome. The primary outcome was a modified Rankin Scale score of 4-6 at 90 days; secondary outcomes were other modified Rankin Scale score ranges (modified Rankin Scale, 2-6, 3-6, 5-6, 6). We employed Youden's J Index to select optimal cut points and calculated sensitivity, specificity, and predictive values. We determined independent predictors via multivariable logistic regression. The derived definitions were validated in the PREDICT cohort. Twenty-four-hour National Institutes of Health Stroke Scale change was strongly associated with 90-day outcome with an area under the receiver operating characteristic curve of 0.75. Youden's method showed an optimum cut point at -0.5, corresponding to National Institutes of Health Stroke Scale change of greater than or equal to 0 (a lack of clinical improvement), which was seen in 46%. Early neurologic change accurately predicted poor outcome when defined as greater than or equal to 0 (sensitivity, 65%; specificity, 73%; positive predictive value, 70%; adjusted odds ratio, 5.05 [CI, 3.25-7.85]) or greater than or equal to 4 (sensitivity, 19%; specificity, 98%; positive predictive value, 91%; adjusted odds ratio, 12.24 [CI, 4.08-36.66]). All definitions reproduced well in the validation cohort. Lack of clinical improvement at 24 hours robustly predicted poor outcome and showed good discrimination for individual patients who would do poorly. These findings are useful for prognostication and may also present as a potential early surrogate outcome for future intracerebral hemorrhage treatment trials.

The Accuracy of the Spot Sign and the Blend Sign for Predicting Hematoma Expansion in Patients with Spontaneous Intracerebral Hemorrhage.

PubMed

Zheng, Jun; Yu, Zhiyuan; Xu, Zhao; Li, Mou; Wang, Xiaoze; Lin, Sen; Li, Hao; You, Chao

2017-05-12

BACKGROUND Hematoma expansion is associated with poor outcome in intracerebral hemorrhage (ICH) patients. The spot sign and the blend sign are reliable tools for predicting hematoma expansion in ICH patients. The aim of this study was to compare the accuracy of the two signs in the prediction of hematoma expansion. MATERIAL AND METHODS Patients with spontaneous ICH were screened for the presence of the computed tomography angiography (CTA) spot sign and the non-contrast CT (NCCT) blend sign within 6 hours after onset of symptoms. The sensitivity, specificity, and positive and negative predictive values of the spot sign and the blend sign in predicting hematoma expansion were calculated. The accuracy of the spot sign and the blend sign in predicting hematoma expansion was analyzed by receiver-operator analysis. RESULTS A total of 115 patients were enrolled in this study. The spot sign was observed in 25 (21.74%) patients, whereas the blend sign was observed in 22 (19.13%) patients. Of the 28 patients with hematoma expansion, the CTA spot sign was found on admission CT scans in 16 (57.14%) and the NCCT blend sign in 12 (42.86%), respectively. The sensitivity, specificity, positive predictive value, and negative predictive value of the spot sign for predicting hematoma expansion were 57.14%, 89.66%, 64.00%, and 86.67%, respectively. In contrast, the sensitivity, specificity, positive predictive value, and negative predictive value of the blend sign were 42.86%, 88.51%, 54.55%, and 82.80%, respectively. The area under the curve (AUC) of the spot sign was 0.734, which was higher than that of the blend sign (0.657). CONCLUSIONS Both the spot sign and the blend sign seemed to be good predictors for hematoma expansion, and the spot sign appeared to have better predictive accuracy.

The Accuracy of the Spot Sign and the Blend Sign for Predicting Hematoma Expansion in Patients with Spontaneous Intracerebral Hemorrhage

PubMed Central

Zheng, Jun; Yu, Zhiyuan; Xu, Zhao; Li, Mou; Wang, Xiaoze; Lin, Sen; Li, Hao; You, Chao

2017-01-01

Background Hematoma expansion is associated with poor outcome in intracerebral hemorrhage (ICH) patients. The spot sign and the blend sign are reliable tools for predicting hematoma expansion in ICH patients. The aim of this study was to compare the accuracy of the two signs in the prediction of hematoma expansion. Material/Methods Patients with spontaneous ICH were screened for the presence of the computed tomography angiography (CTA) spot sign and the non-contrast CT (NCCT) blend sign within 6 hours after onset of symptoms. The sensitivity, specificity, and positive and negative predictive values of the spot sign and the blend sign in predicting hematoma expansion were calculated. The accuracy of the spot sign and the blend sign in predicting hematoma expansion was analyzed by receiver-operator analysis. Results A total of 115 patients were enrolled in this study. The spot sign was observed in 25 (21.74%) patients, whereas the blend sign was observed in 22 (19.13%) patients. Of the 28 patients with hematoma expansion, the CTA spot sign was found on admission CT scans in 16 (57.14%) and the NCCT blend sign in 12 (42.86%), respectively. The sensitivity, specificity, positive predictive value, and negative predictive value of the spot sign for predicting hematoma expansion were 57.14%, 89.66%, 64.00%, and 86.67%, respectively. In contrast, the sensitivity, specificity, positive predictive value, and negative predictive value of the blend sign were 42.86%, 88.51%, 54.55%, and 82.80%, respectively. The area under the curve (AUC) of the spot sign was 0.734, which was higher than that of the blend sign (0.657). Conclusions Both the spot sign and the blend sign seemed to be good predictors for hematoma expansion, and the spot sign appeared to have better predictive accuracy. PMID:28498827

Analysis of a Shock-Associated Noise Prediction Model Using Measured Jet Far-Field Noise Data

NASA Technical Reports Server (NTRS)

Dahl, Milo D.; Sharpe, Jacob A.

2014-01-01

A code for predicting supersonic jet broadband shock-associated noise was assessed using a database containing noise measurements of a jet issuing from a convergent nozzle. The jet was operated at 24 conditions covering six fully expanded Mach numbers with four total temperature ratios. To enable comparisons of the predicted shock-associated noise component spectra with data, the measured total jet noise spectra were separated into mixing noise and shock-associated noise component spectra. Comparisons between predicted and measured shock-associated noise component spectra were used to identify deficiencies in the prediction model. Proposed revisions to the model, based on a study of the overall sound pressure levels for the shock-associated noise component of the measured data, a sensitivity analysis of the model parameters with emphasis on the definition of the convection velocity parameter, and a least-squares fit of the predicted to the measured shock-associated noise component spectra, resulted in a new definition for the source strength spectrum in the model. An error analysis showed that the average error in the predicted spectra was reduced by as much as 3.5 dB for the revised model relative to the average error for the original model.

Ultrasonographic fetal head position to predict mode of delivery: a systematic review and bivariate meta-analysis.

PubMed

Verhoeven, C J M; Rückert, M E P F; Opmeer, B C; Pajkrt, E; Mol, B W J

2012-07-01

We performed a systematic review to determine whether sonographic assessment of occipital position of the fetal head can contribute to the prediction of the mode of delivery. We performed a systematic literature search of electronic databases from inception to May 2011. Two reviewers independently extracted data from the included studies. We used a bivariate model to estimate point estimates for sensitivity and specificity curves for the outcome Cesarean delivery. Eligible studies were cohort studies or cross-sectional studies that reported on both the position of the fetal head, as assessed by ultrasound, before or at the beginning of active labor as well as the outcome of labor in women at term. We included 11 primary articles reporting on 5053 women, of whom 898 had a Cesarean section. All studies indicated disappointing values for sensitivity and specificity in the prediction of Cesarean section. Summary point estimates of sensitivity and specificity were 0.39 (95% CI, 0.32-0.48) and 0.71 (95% CI, 0.67-0.74), respectively. Sonographic assessment of occipital position of the fetal head before delivery should not be used in the prediction of mode of delivery. Copyright © 2012 ISUOG. Published by John Wiley & Sons, Ltd.

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

Cohesive detachment of an elastic pillar from a dissimilar substrate

NASA Astrophysics Data System (ADS)

Fleck, N. A.; Khaderi, S. N.; McMeeking, R. M.; Arzt, E.

The adhesion of micron-scale surfaces due to intermolecular interactions is a subject of intense interest spanning electronics, biomechanics and the application of soft materials to engineering devices. The degree of adhesion is sensitive to the diameter of micro-pillars in addition to the degree of elastic mismatch between pillar and substrate. Adhesion-strength-controlled detachment of an elastic circular cylinder from a dissimilar substrate is predicted using a Dugdale-type of analysis, with a cohesive zone of uniform tensile strength emanating from the interface corner. Detachment initiates when the opening of the cohesive zone attains a critical value, giving way to crack formation. When the cohesive zone size at crack initiation is small compared to the pillar diameter, the initiation of detachment can be expressed in terms of a critical value Hc of the corner stress intensity. The estimated pull-off force is somewhat sensitive to the choice of stick/slip boundary condition used on the cohesive zone, especially when the substrate material is much stiffer than the pillar material. The analysis can be used to predict the sensitivity of detachment force to the size of pillar and to the degree of elastic mismatch between pillar and substrate.

Novel and Practical Scoring Systems for the Diagnosis of Thyroid Nodules

PubMed Central

Wei, Ying; Zhou, Xinrong; Liu, Siyue; Wang, Hong; Liu, Limin; Liu, Renze; Kang, Jinsong; Hong, Kai; Wang, Daowen; Yuan, Gang

2016-01-01

Objective The clinical management of patients with thyroid nodules that are biopsied by fine-needle aspiration cytology and yield indeterminate results remains unsettled. The BRAF V600E mutation has dubious diagnostic value due to its low sensitivity. Novel strategies are urgently needed to distinguish thyroid malignancies from thyroid nodules. Design This prospective study included 504 thyroid nodules diagnosed by ultrasonography from 468 patients, and fine-needle aspiration cytology was performed under ultrasound guidance. Cytology and molecular analysis, including BRAF V600E, RET/PTC1 and RET/PTC3, were conducted simultaneously. The cytology, ultrasonography results, and mutational status were gathered and analyzed together. Predictive scoring systems were designed using a combination of diagnostic parameters for ultrasonography, cytology and genetic analysis. The utility of the scoring systems was analyzed and compared to detection using the individual methods alone or combined. Result The sensitivity of scoring systema (ultrasonography, cytology, BRAF V600E, RET/PTC) was nearly identical to that of scoring systemb (ultrasonography, cytology, BRAF V600E); these were 91.0% and 90.2%, respectively. These sensitivities were significantly higher than those obtained using FNAC, genetic analysis and US alone or combined; their sensitivities were 63.9%, 70.7% and 87.2%, respectively. Scoring systemc (ultrasonography, cytology) was slightly inferior to the former two scoring systems but still had relatively high sensitivity and specificity (80.5% and 95.1%, respectively), which were significantly superior to those of single cytology, ultrasonography or genetic analysis. In nodules with uncertainty cytology, scoring systema, scoring systemb and scoring systemc could elevate the malignancy detection rates to 69.7%, 69.7% and 63.6%, respectively. Conclusion These three scoring systems were quick for clinicians to master and could provide quantified information to predict the probability of malignant nodules. Scoring systemb is recommended for improving the detection rate among nodules of uncertain cytology. PMID:27654865

Modeling Effects of Annealing on Coal Char Reactivity to O 2 and CO 2 , Based on Preparation Conditions

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

Holland, Troy; Bhat, Sham; Marcy, Peter

Oxy-fired coal combustion is a promising potential carbon capture technology. Predictive computational fluid dynamics (CFD) simulations are valuable tools in evaluating and deploying oxyfuel and other carbon capture technologies, either as retrofit technologies or for new construction. However, accurate predictive combustor simulations require physically realistic submodels with low computational requirements. A recent sensitivity analysis of a detailed char conversion model (Char Conversion Kinetics (CCK)) found thermal annealing to be an extremely sensitive submodel. In the present work, further analysis of the previous annealing model revealed significant disagreement with numerous datasets from experiments performed after that annealing model was developed. Themore » annealing model was accordingly extended to reflect experimentally observed reactivity loss, because of the thermal annealing of a variety of coals under diverse char preparation conditions. The model extension was informed by a Bayesian calibration analysis. In addition, since oxyfuel conditions include extraordinarily high levels of CO 2, the development of a first-ever CO 2 reactivity loss model due to annealing is presented.« less

Modeling Effects of Annealing on Coal Char Reactivity to O 2 and CO 2 , Based on Preparation Conditions

DOE PAGES

Holland, Troy; Bhat, Sham; Marcy, Peter; ...

2017-08-25

Oxy-fired coal combustion is a promising potential carbon capture technology. Predictive computational fluid dynamics (CFD) simulations are valuable tools in evaluating and deploying oxyfuel and other carbon capture technologies, either as retrofit technologies or for new construction. However, accurate predictive combustor simulations require physically realistic submodels with low computational requirements. A recent sensitivity analysis of a detailed char conversion model (Char Conversion Kinetics (CCK)) found thermal annealing to be an extremely sensitive submodel. In the present work, further analysis of the previous annealing model revealed significant disagreement with numerous datasets from experiments performed after that annealing model was developed. Themore » annealing model was accordingly extended to reflect experimentally observed reactivity loss, because of the thermal annealing of a variety of coals under diverse char preparation conditions. The model extension was informed by a Bayesian calibration analysis. In addition, since oxyfuel conditions include extraordinarily high levels of CO 2, the development of a first-ever CO 2 reactivity loss model due to annealing is presented.« less

Comparison of Swirl Sign and Black Hole Sign in Predicting Early Hematoma Growth in Patients with Spontaneous Intracerebral Hemorrhage.

PubMed

Xiong, Xin; Li, Qi; Yang, Wen-Song; Wei, Xiao; Hu, Xi; Wang, Xing-Chen; Zhu, Dan; Li, Rui; Cao, Du; Xie, Peng

2018-01-29

BACKGROUND Early hematoma growth is associated with poor outcome in patients with spontaneous intracerebral hemorrhage (ICH). The swirl sign (SS) and the black hole sign (BHS) are imaging markers in ICH patients. The aim of this study was to compare the predictive value of these 2 signs for early hematoma growth. MATERIAL AND METHODS ICH patients were screened for the appearance of the 2 signs within 6 h after onset of symptoms. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the 2 signs in predicting early hematoma growth were assessed. The accuracy of the 2 signs in predicting early hematoma growth was analyzed by receiver-operator analysis. RESULTS A total of 200 patients were enrolled in this study. BHS was found in 30 (15%) patients, and SS was found in 70 (35%) patients. Of the 71 patients with early hematoma growth, BHS was found on initial computed tomography scans in 24 (33.8%) and SS in 33 (46.5%). The sensitivity, specificity, PPV, and NPV of BHS for predicting early hematoma growth were 33.8%, 95.3%, 80.0%, and 72.0%, respectively. The sensitivity, specificity, PPV, and NPV of SS were 46.5%, 71.3%, 47.0%, and 71.0%, respectively. The area under the curve was 0.646 for BHS and 0.589 for SS (P=0.08). Multivariate logistic regression showed that presence of BHS is an independent predictor of early hematoma growth. CONCLUSIONS The Black hole sign seems to be good predictor for hematoma growth. The presence of swirl sign on admission CT does not independently predict hematoma growth in patients with ICH.

Outcome Prediction in Pneumonia Induced ALI/ARDS by Clinical Features and Peptide Patterns of BALF Determined by Mass Spectrometry

PubMed Central

Frenzel, Jochen; Gessner, Christian; Sandvoss, Torsten; Hammerschmidt, Stefan; Schellenberger, Wolfgang; Sack, Ulrich; Eschrich, Klaus; Wirtz, Hubert

2011-01-01

Background Peptide patterns of bronchoalveolar lavage fluid (BALF) were assumed to reflect the complex pathology of acute lung injury (ALI)/acute respiratory distress syndrome (ARDS) better than clinical and inflammatory parameters and may be superior for outcome prediction. Methodology/Principal Findings A training group of patients suffering from ALI/ARDS was compiled from equal numbers of survivors and nonsurvivors. Clinical history, ventilation parameters, Murray's lung injury severity score (Murray's LISS) and interleukins in BALF were gathered. In addition, samples of bronchoalveolar lavage fluid were analyzed by means of hydrophobic chromatography and MALDI-ToF mass spectrometry (MALDI-ToF MS). Receiver operating characteristic (ROC) analysis for each clinical and cytokine parameter revealed interleukin-6>interleukin-8>diabetes mellitus>Murray's LISS as the best outcome predictors. Outcome predicted on the basis of BALF levels of interleukin-6 resulted in 79.4% accuracy, 82.7% sensitivity and 76.1% specificity (area under the ROC curve, AUC, 0.853). Both clinical parameters and cytokines as well as peptide patterns determined by MALDI-ToF MS were analyzed by classification and regression tree (CART) analysis and support vector machine (SVM) algorithms. CART analysis including Murray's LISS, interleukin-6 and interleukin-8 in combination was correct in 78.0%. MALDI-ToF MS of BALF peptides did not reveal a single identifiable biomarker for ARDS. However, classification of patients was successfully achieved based on the entire peptide pattern analyzed using SVM. This method resulted in 90% accuracy, 93.3% sensitivity and 86.7% specificity following a 10-fold cross validation (AUC = 0.953). Subsequent validation of the optimized SVM algorithm with a test group of patients with unknown prognosis yielded 87.5% accuracy, 83.3% sensitivity and 90.0% specificity. Conclusions/Significance MALDI-ToF MS peptide patterns of BALF, evaluated by appropriate mathematical methods can be of value in predicting outcome in pneumonia induced ALI/ARDS. PMID:21991318

Outcome prediction in pneumonia induced ALI/ARDS by clinical features and peptide patterns of BALF determined by mass spectrometry.

PubMed

Frenzel, Jochen; Gessner, Christian; Sandvoss, Torsten; Hammerschmidt, Stefan; Schellenberger, Wolfgang; Sack, Ulrich; Eschrich, Klaus; Wirtz, Hubert

2011-01-01

Peptide patterns of bronchoalveolar lavage fluid (BALF) were assumed to reflect the complex pathology of acute lung injury (ALI)/acute respiratory distress syndrome (ARDS) better than clinical and inflammatory parameters and may be superior for outcome prediction. A training group of patients suffering from ALI/ARDS was compiled from equal numbers of survivors and nonsurvivors. Clinical history, ventilation parameters, Murray's lung injury severity score (Murray's LISS) and interleukins in BALF were gathered. In addition, samples of bronchoalveolar lavage fluid were analyzed by means of hydrophobic chromatography and MALDI-ToF mass spectrometry (MALDI-ToF MS). Receiver operating characteristic (ROC) analysis for each clinical and cytokine parameter revealed interleukin-6>interleukin-8>diabetes mellitus>Murray's LISS as the best outcome predictors. Outcome predicted on the basis of BALF levels of interleukin-6 resulted in 79.4% accuracy, 82.7% sensitivity and 76.1% specificity (area under the ROC curve, AUC, 0.853). Both clinical parameters and cytokines as well as peptide patterns determined by MALDI-ToF MS were analyzed by classification and regression tree (CART) analysis and support vector machine (SVM) algorithms. CART analysis including Murray's LISS, interleukin-6 and interleukin-8 in combination was correct in 78.0%. MALDI-ToF MS of BALF peptides did not reveal a single identifiable biomarker for ARDS. However, classification of patients was successfully achieved based on the entire peptide pattern analyzed using SVM. This method resulted in 90% accuracy, 93.3% sensitivity and 86.7% specificity following a 10-fold cross validation (AUC = 0.953). Subsequent validation of the optimized SVM algorithm with a test group of patients with unknown prognosis yielded 87.5% accuracy, 83.3% sensitivity and 90.0% specificity. MALDI-ToF MS peptide patterns of BALF, evaluated by appropriate mathematical methods can be of value in predicting outcome in pneumonia induced ALI/ARDS.

Modeling and sensitivity analysis of mass transfer in active multilayer polymeric film for food applications

NASA Astrophysics Data System (ADS)

Bedane, T.; Di Maio, L.; Scarfato, P.; Incarnato, L.; Marra, F.

2015-12-01

The barrier performance of multilayer polymeric films for food applications has been significantly improved by incorporating oxygen scavenging materials. The scavenging activity depends on parameters such as diffusion coefficient, solubility, concentration of scavenger loaded and the number of available reactive sites. These parameters influence the barrier performance of the film in different ways. Virtualization of the process is useful to characterize, design and optimize the barrier performance based on physical configuration of the films. Also, the knowledge of values of parameters is important to predict the performances. Inverse modeling and sensitivity analysis are sole way to find reasonable values of poorly defined, unmeasured parameters and to analyze the most influencing parameters. Thus, the objective of this work was to develop a model to predict barrier properties of multilayer film incorporated with reactive layers and to analyze and characterize their performances. Polymeric film based on three layers of Polyethylene terephthalate (PET), with a core reactive layer, at different thickness configurations was considered in the model. A one dimensional diffusion equation with reaction was solved numerically to predict the concentration of oxygen diffused into the polymer taking into account the reactive ability of the core layer. The model was solved using commercial software for different film layer configurations and sensitivity analysis based on inverse modeling was carried out to understand the effect of physical parameters. The results have shown that the use of sensitivity analysis can provide physical understanding of the parameters which highly affect the gas permeation into the film. Solubility and the number of available reactive sites were the factors mainly influencing the barrier performance of three layered polymeric film. Multilayer films slightly modified the steady transport properties in comparison to net PET, giving a small reduction in the permeability and oxygen transfer rate values. Scavenging capacity of the multilayer film increased linearly with the increase of the reactive layer thickness and the oxygen absorption reaction at short times decreased proportionally with the thickness of the external PET layer.

Modeling and sensitivity analysis of mass transfer in active multilayer polymeric film for food applications

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

Bedane, T.; Di Maio, L.; Scarfato, P.

The barrier performance of multilayer polymeric films for food applications has been significantly improved by incorporating oxygen scavenging materials. The scavenging activity depends on parameters such as diffusion coefficient, solubility, concentration of scavenger loaded and the number of available reactive sites. These parameters influence the barrier performance of the film in different ways. Virtualization of the process is useful to characterize, design and optimize the barrier performance based on physical configuration of the films. Also, the knowledge of values of parameters is important to predict the performances. Inverse modeling and sensitivity analysis are sole way to find reasonable values ofmore » poorly defined, unmeasured parameters and to analyze the most influencing parameters. Thus, the objective of this work was to develop a model to predict barrier properties of multilayer film incorporated with reactive layers and to analyze and characterize their performances. Polymeric film based on three layers of Polyethylene terephthalate (PET), with a core reactive layer, at different thickness configurations was considered in the model. A one dimensional diffusion equation with reaction was solved numerically to predict the concentration of oxygen diffused into the polymer taking into account the reactive ability of the core layer. The model was solved using commercial software for different film layer configurations and sensitivity analysis based on inverse modeling was carried out to understand the effect of physical parameters. The results have shown that the use of sensitivity analysis can provide physical understanding of the parameters which highly affect the gas permeation into the film. Solubility and the number of available reactive sites were the factors mainly influencing the barrier performance of three layered polymeric film. Multilayer films slightly modified the steady transport properties in comparison to net PET, giving a small reduction in the permeability and oxygen transfer rate values. Scavenging capacity of the multilayer film increased linearly with the increase of the reactive layer thickness and the oxygen absorption reaction at short times decreased proportionally with the thickness of the external PET layer.« less

Gene expression analysis in zebrafish embryos: a potential approach to predict effect concentrations in the fish early life stage test.

PubMed

Weil, Mirco; Scholz, Stefan; Zimmer, Michaela; Sacher, Frank; Duis, Karen

2009-09-01

Based on the hypothesis that analysis of gene expression could be used to predict chronic fish toxicity, the zebrafish (Danio rerio) embryo test (DarT), developed as a replacement method for the acute fish test, was expanded to a gene expression D. rerio embryo test (Gene-DarT). The effects of 14 substances on lethal and sublethal endpoints of the DarT and on expression of potential marker genes were investigated: the aryl hydrocarbon receptor 2, cytochrome P450 1A (cypla), heat shock protein 70, fizzy-related protein 1, the transcription factors v-maf musculoaponeurotic fibrosarcoma oncogene family protein g (avian) 1 and NF-E2-p45-related factor, and heme oxygenase 1 (hmox1). After exposure of zebrafish embryos for 48 h, differential gene expression was evaluated using reverse transcriptase-polymerase chain reaction, gel electrophoresis, and densitometric analysis of the gels. All tested compounds significantly affected the expression of at least one potential marker gene, with cyp1a and hmox1 being most sensitive. Lowest-observed-effect concentrations (LOECs) for gene expression were below concentrations resulting in 10% lethal effects in the DarT. For 10 (3,4- and 3,5-dichloroaniline, 1,4-dichlorobenzene, 2,4-dinitrophenol, atrazine, parathion-ethyl, chlorotoluron, genistein, 4-nitroquinoline-1-oxide, and cadmium) out of the 14 tested substances, LOEC values derived with the Gene-DarT differ by a factor of less than 10 from LOEC values of fish early life stage tests with zebrafish. For pentachloroaniline and pentachlorobenzene, the Gene-DarT showed a 23- and 153-fold higher sensitivity, respectively, while for lindane, it showed a 13-fold lower sensitivity. For ivermectin, the Gene-DarT was by a factor of more than 1,000 less sensitive than the acute fish test. The results of the present study indicate that gene expression analysis in zebrafish embryos could principally be used to predict effect concentrations in the fish early life stage test.

Liver fat, visceral adiposity, and sleep disturbances contribute to the development of insulin resistance and glucose intolerance in nondiabetic dialysis patients.

PubMed

Sakkas, Giorgos K; Karatzaferi, Christina; Zintzaras, Elias; Giannaki, Christoforos D; Liakopoulos, Vassilios; Lavdas, Eleftherios; Damani, Eleni; Liakos, Nikos; Fezoulidis, Ioannis; Koutedakis, Yiannis; Stefanidis, Ioannis

2008-12-01

Hemodialysis patients exhibit insulin resistance (IR) in target organs such as liver, muscles, and adipose tissue. The aim of this study was to identify contributors to IR and to develop a model for predicting glucose intolerance in nondiabetic hemodialysis patients. After a 2-h, 75-g oral glucose tolerance test (OGTT), 34 hemodialysis patients were divided into groups with normal (NGT) and impaired glucose tolerance (IGT). Indices of insulin sensitivity were derived from OGTT data. Measurements included liver and muscle fat infiltration and central adiposity by computed tomography scans, body composition by dual energy X-ray absorptiometer, sleep quality by full polysomnography, and functional capacity and quality of life (QoL) by a battery of exercise tests and questionnaires. Cut-off points, as well as sensitivity and specificity calculations were based on IR (insulin sensitivity index by Matsuda) using a receiver operator characteristics (ROC) curve analysis. Fifteen patients were assigned to the IGT, and 19 subjects to the NGT group. Intrahepatic fat content and visceral adiposity were significantly higher in the IGT group. IR indices strongly correlated with sleep disturbances, visceral adiposity, functional capacity, and QoL. Visceral adiposity, O2 desaturation during sleep, intrahepatic fat content, and QoL score fitted into the model for predicting glucose intolerance. A ROC curve analysis identified an intrahepatic fat content of > 3.97% (sensitivity, 100; specificity, 35.7) as the best cutoff point for predicting IR. Visceral and intrahepatic fat content, as well as QoL and sleep seemed to be involved at some point in the development of glucose intolerance in hemodialysis patients. Means of reducing fat depots in the liver and splachnic area might prove promising in combating IR and cardiovascular risk in hemodialysis patients.

Inhibiting food reward: delay discounting, food reward sensitivity, and palatable food intake in overweight and obese women

PubMed Central

Appelhans, Bradley M.; Woolf, Kathleen; Pagoto, Sherry L.; Schneider, Kristin L.; Whited, Matthew C.; Liebman, Rebecca

2012-01-01

Overeating is believed to result when the appetitive motivation to consume palatable food exceeds an individual’s capacity for inhibitory control of eating. This hypothesis was supported in recent studies involving predominantly normal weight women, but has not been tested in obese populations. The current study tested the interaction between food reward sensitivity and inhibitory control in predicting palatable food intake among energy-replete overweight and obese women (N=62). Sensitivity to palatable food reward was measured with the Power of Food Scale. Inhibitory control was assessed with a computerized choice task that captures the tendency to discount large delayed rewards relative to smaller immediate rewards. Participants completed an eating in the absence of hunger protocol in which homeostatic energy needs were eliminated with a bland preload of plain oatmeal, followed by a bogus laboratory taste test of palatable and bland snacks. The interaction between food reward sensitivity and inhibitory control was a significant predictor of palatable food intake in regression analyses controlling for body mass index and the amount of preload consumed. Probing this interaction indicated that higher food reward sensitivity predicted greater palatable food intake at low levels of inhibitory control, but was not associated with intake at high levels of inhibitory control. As expected, no associations were found in a similar regression analysis predicting intake of bland foods. Findings support a neurobehavioral model of eating behavior in which sensitivity to palatable food reward drives overeating only when accompanied by insufficient inhibitory control. Strengthening inhibitory control could enhance weight management programs. PMID:21475139

Serum Protein Electrophoresis in the Evaluation of Lytic Bone Lesions

PubMed Central

Nystrom, Lukas M.; Buckwalter, Joseph A.; Syrbu, Sergei; Miller, Benjamin J.

2013-01-01

Serum protein electrophoresis (SPEP) is often obtained at the initial evaluation of a radiolucent bone lesion of unknown etiology. The results are considered convincing evidence of the presence or absence of a plasma cell neoplasm. The sensitivity and specificity of the SPEP have not been reported in this clinical scenario. Our purpose is to assess the diagnostic value of the SPEP in the initial work-up of the radiolucent bone lesion. We identified 182 patients undergoing evaluation of a radiolucent bone lesion that included tissue biopsy and an SPEP value. We then calculated the sen-sitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of SPEP as a diagnostic test for a plasma cell neo-plasm in this clinical scenario. Forty-six of 182 (25.3%) patients in our series were diagnosed with a plasma cell neo-plasm by histopathologic analysis. The sensitivity of SPEP was 71% and the specificity was 83%. PPV was 47% and NPV was 94%. When analyzing only those presenting with multiple lesions, the percentage of patients diag-nosed with multiple myeloma increased to 44.7% (34 of 76 patients). The SPEP, however, did not have a substantially increased diagnostic accuracy with sensitivity of 71%, specificity 79%, PPV 40% and NPV 93%. SPEP lacks sensitivity and positive predictive value to provide a definitive diagnosis of myeloma in radiolucent bone lesions, but has a high negative predictive value which may make it useful in ruling out the disease. We recommend that this test either be performed in conjunction with urine electrophoresis, immunofixation electro-phoresis and free light chain assay, or after biopsy confirming the diagnosis of myeloma. PMID:24027470

Development of an artificial neural network model for risk assessment of skin sensitization using human cell line activation test, direct peptide reactivity assay, KeratinoSens™ and in silico structure alert parameter.

PubMed

Hirota, Morihiko; Ashikaga, Takao; Kouzuki, Hirokazu

2018-04-01

It is important to predict the potential of cosmetic ingredients to cause skin sensitization, and in accordance with the European Union cosmetic directive for the replacement of animal tests, several in vitro tests based on the adverse outcome pathway have been developed for hazard identification, such as the direct peptide reactivity assay, KeratinoSens™ and the human cell line activation test. Here, we describe the development of an artificial neural network (ANN) prediction model for skin sensitization risk assessment based on the integrated testing strategy concept, using direct peptide reactivity assay, KeratinoSens™, human cell line activation test and an in silico or structure alert parameter. We first investigated the relationship between published murine local lymph node assay EC3 values, which represent skin sensitization potency, and in vitro test results using a panel of about 134 chemicals for which all the required data were available. Predictions based on ANN analysis using combinations of parameters from all three in vitro tests showed a good correlation with local lymph node assay EC3 values. However, when the ANN model was applied to a testing set of 28 chemicals that had not been included in the training set, predicted EC3s were overestimated for some chemicals. Incorporation of an additional in silico or structure alert descriptor (obtained with TIMES-M or Toxtree software) in the ANN model improved the results. Our findings suggest that the ANN model based on the integrated testing strategy concept could be useful for evaluating the skin sensitization potential. Copyright © 2017 John Wiley & Sons, Ltd.

BreathDx - molecular analysis of exhaled breath as a diagnostic test for ventilator-associated pneumonia: protocol for a European multicentre observational study.

PubMed

van Oort, Pouline M P; Nijsen, Tamara; Weda, Hans; Knobel, Hugo; Dark, Paul; Felton, Timothy; Rattray, Nicholas J W; Lawal, Oluwasola; Ahmed, Waqar; Portsmouth, Craig; Sterk, Peter J; Schultz, Marcus J; Zakharkina, Tetyana; Artigas, Antonio; Povoa, Pedro; Martin-Loeches, Ignacio; Fowler, Stephen J; Bos, Lieuwe D J

2017-01-03

The diagnosis of ventilator-associated pneumonia (VAP) remains time-consuming and costly, the clinical tools lack specificity and a bedside test to exclude infection in suspected patients is unavailable. Breath contains hundreds to thousands of volatile organic compounds (VOCs) that result from host and microbial metabolism as well as the environment. The present study aims to use breath VOC analysis to develop a model that can discriminate between patients who have positive cultures and who have negative cultures with a high sensitivity. The Molecular Analysis of Exhaled Breath as Diagnostic Test for Ventilator-Associated Pneumonia (BreathDx) study is a multicentre observational study. Breath and bronchial lavage samples will be collected from 100 and 53 intubated and ventilated patients suspected of VAP. Breath will be analysed using Thermal Desorption - Gas Chromatography - Mass Spectrometry (TD-GC-MS). The primary endpoint is the accuracy of cross-validated prediction for positive respiratory cultures in patients that are suspected of VAP, with a sensitivity of at least 99% (high negative predictive value). To our knowledge, BreathDx is the first study powered to investigate whether molecular analysis of breath can be used to classify suspected VAP patients with and without positive microbiological cultures with 99% sensitivity. UKCRN ID number 19086, registered May 2015; as well as registration at www.trialregister.nl under the acronym 'BreathDx' with trial ID number NTR 6114 (retrospectively registered on 28 October 2016).

Sensitivity analysis for simulating pesticide impacts on honey bee colonies

EPA Science Inventory

Background/Question/Methods Regulatory agencies assess risks to honey bees from pesticides through a tiered process that includes predictive modeling with empirical toxicity and chemical data of pesticides as a line of evidence. We evaluate the Varroapop colony model, proposed by...

Highly sensitive detection of individual HEAT and ARM repeats with HHpred and COACH.

PubMed

Kippert, Fred; Gerloff, Dietlind L

2009-09-24

HEAT and ARM repeats occur in a large number of eukaryotic proteins. As these repeats are often highly diverged, the prediction of HEAT or ARM domains can be challenging. Except for the most clear-cut cases, identification at the individual repeat level is indispensable, in particular for determining domain boundaries. However, methods using single sequence queries do not have the sensitivity required to deal with more divergent repeats and, when applied to proteins with known structures, in some cases failed to detect a single repeat. Testing algorithms which use multiple sequence alignments as queries, we found two of them, HHpred and COACH, to detect HEAT and ARM repeats with greatly enhanced sensitivity. Calibration against experimentally determined structures suggests the use of three score classes with increasing confidence in the prediction, and prediction thresholds for each method. When we applied a new protocol using both HHpred and COACH to these structures, it detected 82% of HEAT repeats and 90% of ARM repeats, with the minimum for a given protein of 57% for HEAT repeats and 60% for ARM repeats. Application to bona fide HEAT and ARM proteins or domains indicated that similar numbers can be expected for the full complement of HEAT/ARM proteins. A systematic screen of the Protein Data Bank for false positive hits revealed their number to be low, in particular for ARM repeats. Double false positive hits for a given protein were rare for HEAT and not at all observed for ARM repeats. In combination with fold prediction and consistency checking (multiple sequence alignments, secondary structure prediction, and position analysis), repeat prediction with the new HHpred/COACH protocol dramatically improves prediction in the twilight zone of fold prediction methods, as well as the delineation of HEAT/ARM domain boundaries. A protocol is presented for the identification of individual HEAT or ARM repeats which is straightforward to implement. It provides high sensitivity at a low false positive rate and will therefore greatly enhance the accuracy of predictions of HEAT and ARM domains.

Highly Sensitive Detection of Individual HEAT and ARM Repeats with HHpred and COACH

PubMed Central

Kippert, Fred; Gerloff, Dietlind L.

2009-01-01

Background HEAT and ARM repeats occur in a large number of eukaryotic proteins. As these repeats are often highly diverged, the prediction of HEAT or ARM domains can be challenging. Except for the most clear-cut cases, identification at the individual repeat level is indispensable, in particular for determining domain boundaries. However, methods using single sequence queries do not have the sensitivity required to deal with more divergent repeats and, when applied to proteins with known structures, in some cases failed to detect a single repeat. Methodology and Principal Findings Testing algorithms which use multiple sequence alignments as queries, we found two of them, HHpred and COACH, to detect HEAT and ARM repeats with greatly enhanced sensitivity. Calibration against experimentally determined structures suggests the use of three score classes with increasing confidence in the prediction, and prediction thresholds for each method. When we applied a new protocol using both HHpred and COACH to these structures, it detected 82% of HEAT repeats and 90% of ARM repeats, with the minimum for a given protein of 57% for HEAT repeats and 60% for ARM repeats. Application to bona fide HEAT and ARM proteins or domains indicated that similar numbers can be expected for the full complement of HEAT/ARM proteins. A systematic screen of the Protein Data Bank for false positive hits revealed their number to be low, in particular for ARM repeats. Double false positive hits for a given protein were rare for HEAT and not at all observed for ARM repeats. In combination with fold prediction and consistency checking (multiple sequence alignments, secondary structure prediction, and position analysis), repeat prediction with the new HHpred/COACH protocol dramatically improves prediction in the twilight zone of fold prediction methods, as well as the delineation of HEAT/ARM domain boundaries. Significance A protocol is presented for the identification of individual HEAT or ARM repeats which is straightforward to implement. It provides high sensitivity at a low false positive rate and will therefore greatly enhance the accuracy of predictions of HEAT and ARM domains. PMID:19777061

Discrete analysis of spatial-sensitivity models

NASA Technical Reports Server (NTRS)

Nielsen, Kenneth R. K.; Wandell, Brian A.

1988-01-01

Procedures for reducing the computational burden of current models of spatial vision are described, the simplifications being consistent with the prediction of the complete model. A method for using pattern-sensitivity measurements to estimate the initial linear transformation is also proposed which is based on the assumption that detection performance is monotonic with the vector length of the sensor responses. It is shown how contrast-threshold data can be used to estimate the linear transformation needed to characterize threshold performance.

Clinical value of preoperative serum CA 19-9 and CA 125 levels in predicting the resectability of hilar cholangiocarcinoma.

PubMed

Hu, Hai-Jie; Mao, Hui; Tan, Yong-Qiong; Shrestha, Anuj; Ma, Wen-Jie; Yang, Qin; Wang, Jun-Ke; Cheng, Nan-Sheng; Li, Fu-Yu

2016-01-01

To examine the predictive value of tumor markers for evaluating tumor resectability in patients with hilar cholangiocarcinoma and to explore the prognostic effect of various preoperative factors on resectability in patients with potentially resectable tumors. Patients with potentially resectable tumors judged by radiologic examination were included. The receiver operating characteristic (ROC) analysis was conducted to evaluate serum carbohydrate antigenic determinant 19-9 (CA 19-9), carbohydrate antigen 125 (CA 125) and carcino embryonie antigen levels on tumor resectability. Univariate and multivariate logistic regression models were also conducted to analysis the correlation of preoperative factors with resectability. In patients with normal bilirubin levels, ROC curve analysis calculated the ideal CA 19-9 cut-off value of 203.96 U/ml in prediction of resectability, with a sensitivity of 83.7 %, specificity of 80 %, positive predictive value of 91.1 % and negative predictive value of 66.7 %. Meanwhile, the optimal cut-off value for CA 125 to predict resectability was 25.905 U/ml (sensitivity, 78.6 %; specificity, 67.5 %). In a multivariate logistic regression model, tumor size ≤3 cm (OR 4.149, 95 % CI 1.326-12.981, P = 0.015), preoperative CA 19-9 level ≤200 U/ml (OR 20.324, 95 % CI 6.509-63.467, P < 0.001), preoperative CA 125 levels ≤26 U/ml (OR 8.209, 95 % CI 2.624-25.677, P < 0.001) were independent determinants of resectability in patients diagnosed as hilar cholangiocarcinoma. Preoperative CA 19-9 and CA 125 levels predict resectability in patients with radiological resectable hilar cholangiocarcinoma. Increased preoperative CA 19-9 levels and CA 125 levels are associated with poor resectability rate.

Multidimensional severity assessment in bronchiectasis: an analysis of seven European cohorts.

PubMed

McDonnell, M J; Aliberti, S; Goeminne, P C; Dimakou, K; Zucchetti, S C; Davidson, J; Ward, C; Laffey, J G; Finch, S; Pesci, A; Dupont, L J; Fardon, T C; Skrbic, D; Obradovic, D; Cowman, S; Loebinger, M R; Rutherford, R M; De Soyza, A; Chalmers, J D

2016-12-01

Bronchiectasis is a multidimensional disease associated with substantial morbidity and mortality. Two disease-specific clinical prediction tools have been developed, the Bronchiectasis Severity Index (BSI) and the FACED score, both of which stratify patients into severity risk categories to predict the probability of mortality. We aimed to compare the predictive utility of BSI and FACED in assessing clinically relevant disease outcomes across seven European cohorts independent of their original validation studies. The combined cohorts totalled 1612. Pooled analysis showed that both scores had a good discriminatory predictive value for mortality (pooled area under the curve (AUC) 0.76, 95% CI 0.74 to 0.78 for both scores) with the BSI demonstrating a higher sensitivity (65% vs 28%) but lower specificity (70% vs 93%) compared with the FACED score. Calibration analysis suggested that the BSI performed consistently well across all cohorts, while FACED consistently overestimated mortality in 'severe' patients (pooled OR 0.33 (0.23 to 0.48), p<0.0001). The BSI accurately predicted hospitalisations (pooled AUC 0.82, 95% CI 0.78 to 0.84), exacerbations, quality of life (QoL) and respiratory symptoms across all risk categories. FACED had poor discrimination for hospital admissions (pooled AUC 0.65, 95% CI 0.63 to 0.67) with low sensitivity at 16% and did not consistently predict future risk of exacerbations, QoL or respiratory symptoms. No association was observed with FACED and 6 min walk distance (6MWD) or lung function decline. The BSI accurately predicts mortality, hospital admissions, exacerbations, QoL, respiratory symptoms, 6MWD and lung function decline in bronchiectasis, providing a clinically relevant evaluation of disease severity. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

Predicting Return to Work in a Heterogeneous Sample of Recently Injured Workers Using the Brief ÖMPSQ-SF.

PubMed

Nicholas, M K; Costa, D S J; Linton, S J; Main, C J; Shaw, W S; Pearce, R; Gleeson, M; Pinto, R Z; Blyth, F M; McCauley, J H; Maher, C G; Smeets, R J E M; McGarity, A

2018-05-23

Purpose (1) to examine the ability of the Örebro Musculoskeletal Pain Screening Questionnaire-short version (ÖMPSQ-SF) to predict time to return to pre-injury work duties (PID) following a work-related soft tissue injury (regardless of body location); and (2) to examine the appropriateness of 50/100 as a suitable cut-off score for case identification. Methods Injured workers (IW) from six public hospitals in Sydney, Australia, who had taken medically-sanctioned time off work due to their injury, were recruited by insurance case managers within 5-15 days of their injury. Eligible participants (N = 213 in total) were administered the ÖMPSQ-SF over the telephone by the case manager. For objective (1) Cox proportional hazards regression analysis was used to predict days to return to PID using the ÖMPSQ-SF. For objective (2) receiver operator characteristic (ROC) analysis was used to determine the ÖMPSQ-SF total score that optimises sensitivity and specificity in detecting whether or not participants had returned to PID within 2-7 weeks. Results The total ÖMPSQ-SF score significantly predicted number of days to return to PID, such that for every 1-point increase in the total ÖMPSQ-SF score the predicted chance of returning to work reduced by 4% (i.e., hazard ratio = 0.96), p < 0.001. Sensitivity and specificity for the ROC analysis comparing ÖMPSQ-SF total score to return to PID within 2-7 weeks suggested 48 as the optimal cut off (sensitivity = 0.65, specificity = 0.79). Conclusion The results provide strong support for the use of the ÖMPSQ-SF in an applied setting for identifying those IW likely to have delayed RTW when administered within 15 days of the injury. While a score of 48/100 was the optimal cut point for sensitivity and specificity, pragmatically, 50/100 should be acceptable as a cut-off in future studies of this type.

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

A Quantitative Study of Oxygen as a Metabolic Regulator

NASA Technical Reports Server (NTRS)

Radhakrishnan, Krishnan; LaManna, Joseph C.; Cabrera, Marco E.

1999-01-01

An acute reduction in oxygen (O2) delivery to a tissue is generally associated with a decrease in phosphocreatine, increases in ADP, NADH/NAD, and inorganic phosphate, increased rates of glycolysis and lactate production, and reduced rates of pyruvate and fatty acid oxidation. However, given the complexity of the human bioenergetic system and its components, it is difficult to determine quantitatively how cellular metabolic processes interact to maintain ATP homeostasis during stress (e.g., hypoxia, ischemia, and exercise). Of special interest is the determination of mechanisms relating tissue oxygenation to observed metabolic responses at the tissue, organ, and whole body levels and the quantification of how changes in tissue O2 availability affect the pathways of ATP synthesis and the metabolites that control these pathways. In this study, we extend a previously developed mathematical model of human bioenergetics to provide a physicochemical framework that permits quantitative understanding of O2 as a metabolic regulator. Specifically, the enhancement permits studying the effects of variations in tissue oxygenation and in parameters controlling the rate of cellular respiration on glycolysis, lactate production, and pyruvate oxidation. The whole body is described as a bioenergetic system consisting of metabolically distinct tissue/organ subsystems that exchange materials with the blood. In order to study the dynamic response of each subsystem to stimuli, we solve the ordinary differential equations describing the temporal evolution of metabolite levels, given the initial concentrations. The solver used in the present study is the packaged code LSODE, as implemented in the NASA Lewis kinetics and sensitivity analysis code, LSENS. A major advantage of LSENS is the efficient procedures supporting systematic sensitivity analysis, which provides the basic methods for studying parameter sensitivities (i.e., changes in model behavior due to parameter variation). Sensitivity analysis establishes relationships between model predictions and problem parameters (i.e., initial concentrations, rate coefficients, etc). It helps determine the effects of uncertainties or changes in these input parameters on the predictions, which ultimately are compared with experimental observations in order to validate the model. Sensitivity analysis can identify parameters that must be determined accurately because of their large effect on the model predictions and parameters that need not be known with great precision because they have little or no effect on the solution. This capability may prove to be important in optimizing the design of experiments, thereby reducing the use of animals. This approach can be applied to study the metabolic effects of reduced oxygen delivery to cardiac muscle due to local myocardial ischemia and the effects of acute hypoxia on brain metabolism. Other important applications of sensitivity analysis include identification of quantitatively relevant pathways and biochemical species within an overall mechanism, when examining the effects of a genetic anomaly or pathological state on energetic system components and whole system behavior.

Prediction of redox-sensitive cysteines using sequential distance and other sequence-based features.

PubMed

Sun, Ming-An; Zhang, Qing; Wang, Yejun; Ge, Wei; Guo, Dianjing

2016-08-24

Reactive oxygen species can modify the structure and function of proteins and may also act as important signaling molecules in various cellular processes. Cysteine thiol groups of proteins are particularly susceptible to oxidation. Meanwhile, their reversible oxidation is of critical roles for redox regulation and signaling. Recently, several computational tools have been developed for predicting redox-sensitive cysteines; however, those methods either only focus on catalytic redox-sensitive cysteines in thiol oxidoreductases, or heavily depend on protein structural data, thus cannot be widely used. In this study, we analyzed various sequence-based features potentially related to cysteine redox-sensitivity, and identified three types of features for efficient computational prediction of redox-sensitive cysteines. These features are: sequential distance to the nearby cysteines, PSSM profile and predicted secondary structure of flanking residues. After further feature selection using SVM-RFE, we developed Redox-Sensitive Cysteine Predictor (RSCP), a SVM based classifier for redox-sensitive cysteine prediction using primary sequence only. Using 10-fold cross-validation on RSC758 dataset, the accuracy, sensitivity, specificity, MCC and AUC were estimated as 0.679, 0.602, 0.756, 0.362 and 0.727, respectively. When evaluated using 10-fold cross-validation with BALOSCTdb dataset which has structure information, the model achieved performance comparable to current structure-based method. Further validation using an independent dataset indicates it is robust and of relatively better accuracy for predicting redox-sensitive cysteines from non-enzyme proteins. In this study, we developed a sequence-based classifier for predicting redox-sensitive cysteines. The major advantage of this method is that it does not rely on protein structure data, which ensures more extensive application compared to other current implementations. Accurate prediction of redox-sensitive cysteines not only enhances our understanding about the redox sensitivity of cysteine, it may also complement the proteomics approach and facilitate further experimental investigation of important redox-sensitive cysteines.

Mapping anhedonia onto reinforcement learning: a behavioural meta-analysis

PubMed Central

2013-01-01

Background Depression is characterised partly by blunted reactions to reward. However, tasks probing this deficiency have not distinguished insensitivity to reward from insensitivity to the prediction errors for reward that determine learning and are putatively reported by the phasic activity of dopamine neurons. We attempted to disentangle these factors with respect to anhedonia in the context of stress, Major Depressive Disorder (MDD), Bipolar Disorder (BPD) and a dopaminergic challenge. Methods Six behavioural datasets involving 392 experimental sessions were subjected to a model-based, Bayesian meta-analysis. Participants across all six studies performed a probabilistic reward task that used an asymmetric reinforcement schedule to assess reward learning. Healthy controls were tested under baseline conditions, stress or after receiving the dopamine D2 agonist pramipexole. In addition, participants with current or past MDD or BPD were evaluated. Reinforcement learning models isolated the contributions of variation in reward sensitivity and learning rate. Results MDD and anhedonia reduced reward sensitivity more than they affected the learning rate, while a low dose of the dopamine D2 agonist pramipexole showed the opposite pattern. Stress led to a pattern consistent with a mixed effect on reward sensitivity and learning rate. Conclusion Reward-related learning reflected at least two partially separable contributions. The first related to phasic prediction error signalling, and was preferentially modulated by a low dose of the dopamine agonist pramipexole. The second related directly to reward sensitivity, and was preferentially reduced in MDD and anhedonia. Stress altered both components. Collectively, these findings highlight the contribution of model-based reinforcement learning meta-analysis for dissecting anhedonic behavior. PMID:23782813

Sensitivity analysis of tall buildings in Semarang, Indonesia due to fault earthquakes with maximum 7 Mw

NASA Astrophysics Data System (ADS)

Partono, Windu; Pardoyo, Bambang; Atmanto, Indrastono Dwi; Azizah, Lisa; Chintami, Rouli Dian

2017-11-01

Fault is one of the dangerous earthquake sources that can cause building failure. A lot of buildings were collapsed caused by Yogyakarta (2006) and Pidie (2016) fault source earthquakes with maximum magnitude 6.4 Mw. Following the research conducted by Team for Revision of Seismic Hazard Maps of Indonesia 2010 and 2016, Lasem, Demak and Semarang faults are three closest earthquake sources surrounding Semarang. The ground motion from those three earthquake sources should be taken into account for structural design and evaluation. Most of tall buildings, with minimum 40 meter high, in Semarang were designed and constructed following the 2002 and 2012 Indonesian Seismic Code. This paper presents the result of sensitivity analysis research with emphasis on the prediction of deformation and inter-story drift of existing tall building within the city against fault earthquakes. The analysis was performed by conducting dynamic structural analysis of 8 (eight) tall buildings using modified acceleration time histories. The modified acceleration time histories were calculated for three fault earthquakes with magnitude from 6 Mw to 7 Mw. The modified acceleration time histories were implemented due to inadequate time histories data caused by those three fault earthquakes. Sensitivity analysis of building against earthquake can be predicted by evaluating surface response spectra calculated using seismic code and surface response spectra calculated from acceleration time histories from a specific earthquake event. If surface response spectra calculated using seismic code is greater than surface response spectra calculated from acceleration time histories the structure will stable enough to resist the earthquake force.

Machine-learning-based calving prediction from activity, lying, and ruminating behaviors in dairy cattle.

PubMed

Borchers, M R; Chang, Y M; Proudfoot, K L; Wadsworth, B A; Stone, A E; Bewley, J M

2017-07-01

The objective of this study was to use automated activity, lying, and rumination monitors to characterize prepartum behavior and predict calving in dairy cattle. Data were collected from 20 primiparous and 33 multiparous Holstein dairy cattle from September 2011 to May 2013 at the University of Kentucky Coldstream Dairy. The HR Tag (SCR Engineers Ltd., Netanya, Israel) automatically collected neck activity and rumination data in 2-h increments. The IceQube (IceRobotics Ltd., South Queensferry, United Kingdom) automatically collected number of steps, lying time, standing time, number of transitions from standing to lying (lying bouts), and total motion, summed in 15-min increments. IceQube data were summed in 2-h increments to match HR Tag data. All behavioral data were collected for 14 d before the predicted calving date. Retrospective data analysis was performed using mixed linear models to examine behavioral changes by day in the 14 d before calving. Bihourly behavioral differences from baseline values over the 14 d before calving were also evaluated using mixed linear models. Changes in daily rumination time, total motion, lying time, and lying bouts occurred in the 14 d before calving. In the bihourly analysis, extreme values for all behaviors occurred in the final 24 h, indicating that the monitored behaviors may be useful in calving prediction. To determine whether technologies were useful at predicting calving, random forest, linear discriminant analysis, and neural network machine-learning techniques were constructed and implemented using R version 3.1.0 (R Foundation for Statistical Computing, Vienna, Austria). These methods were used on variables from each technology and all combined variables from both technologies. A neural network analysis that combined variables from both technologies at the daily level yielded 100.0% sensitivity and 86.8% specificity. A neural network analysis that combined variables from both technologies in bihourly increments was used to identify 2-h periods in the 8 h before calving with 82.8% sensitivity and 80.4% specificity. Changes in behavior and machine-learning alerts indicate that commercially marketed behavioral monitors may have calving prediction potential. Copyright © 2017 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

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

Fine mapping and identification of candidate genes for the sy-2 locus in a temperature-sensitive chili pepper (Capsicum chinense).

PubMed

Liu, Li; Venkatesh, Jelli; Jo, Yeong Deuk; Koeda, Sota; Hosokawa, Munetaka; Kang, Jin-Ho; Goritschnig, Sandra; Kang, Byoung-Cheorl

2016-08-01

The sy - 2 temperature-sensitive gene from Capsicum chinense was fine mapped to a 138.8-kb region at the distal portion of pepper chromosome 1. Based on expression analyses, two putative F-box genes were identified as sy - 2 candidate genes. Seychelles-2 ('sy-2') is a temperature-sensitive natural mutant of Capsicum chinense, which exhibits an abnormal leaf phenotype when grown at temperatures below 24 °C. We previously showed that the sy-2 phenotype is controlled by a single recessive gene, sy-2, located on pepper chromosome 1. In this study, a high-resolution genetic and physical map for the sy-2 locus was constructed using two individual F2 mapping populations derived from a cross between C. chinense mutant 'sy-2' and wild-type 'No. 3341'. The sy-2 gene was fine mapped to a 138.8-kb region between markers SNP 5-5 and SNP 3-8 at the distal portion of chromosome 1, based on comparative genomic analysis and genomic information from pepper. The sy-2 target region was predicted to contain 27 genes. Expression analysis of these predicted genes showed a differential expression pattern for ORF10 and ORF20 between mutant and wild-type plants; with both having significantly lower expression in 'sy-2' than in wild-type plants. In addition, the coding sequences of both ORF10 and ORF20 contained single nucleotide polymorphisms (SNPs) causing amino acid changes, which may have important functional consequences. ORF10 and ORF20 are predicted to encode F-box proteins, which are components of the SCF complex. Based on the differential expression pattern and the presence of nonsynonymous SNPs, we suggest that these two putative F-box genes are most likely responsible for the temperature-sensitive phenotypes in pepper. Further investigation of these genes may enable a better understanding of the molecular mechanisms of low temperature sensitivity in plants.

Combined use of late phase dimercapto-succinic acid renal scintigraphy and ultrasound as first line screening after urinary tract infection in children.

PubMed

Quirino, Isabel G; Silva, Jose Maria P; Diniz, Jose S; Lima, Eleonora M; Rocha, Ana Cristina S; Simões e Silva, Ana Cristina; Oliveira, Eduardo A

2011-01-01

The aim of this study was to evaluate the diagnostic accuracy of dimercapto-succinic acid renal scintigraphy and renal ultrasound in identifying high grade vesicoureteral reflux in children after a first episode of urinary tract infection. A total of 533 children following a first urinary tract infection were included in the analysis. Patients were assessed by 3 diagnostic imaging studies, renal ultrasound, dimercapto-succinic acid scan and voiding cystourethrography. The main event of interest was the presence of high grade (III to V) vesicoureteral reflux. The combined and separate diagnostic accuracy of screening methods was assessed by calculation of diagnostic OR, sensitivity, specificity, positive predictive value, negative predictive value and likelihood ratio. A total of 246 patients had reflux, of whom 144 (27%) had high grade (III to V) disease. Sensitivity, negative predictive value and diagnostic OR of ultrasound for high grade reflux were 83.3%, 90.8% and 7.9, respectively. Dimercapto-succinic acid scan had the same sensitivity as ultrasound but a higher negative predictive value (91.7%) and diagnostic OR (10.9). If both tests were analyzed in parallel by using the OR rule, ie a negative diagnosis was established only when both test results were normal, sensitivity increased to 97%, negative predictive value to 97% and diagnostic OR to 25.3. Only 9 children (6.3%) with dilating reflux had an absence of alterations in both tests. Our findings support the idea that ultrasound and dimercapto-succinic acid scan used in combination are reliable predictors of dilating vesicoureteral reflux. Copyright © 2011 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.

Multi-dimensional scores to predict mortality in patients with idiopathic pulmonary fibrosis undergoing lung transplantation assessment.

PubMed

Fisher, Jolene H; Al-Hejaili, Faris; Kandel, Sonja; Hirji, Alim; Shapera, Shane; Mura, Marco

2017-04-01

The heterogeneous progression of idiopathic pulmonary fibrosis (IPF) makes prognostication difficult and contributes to high mortality on the waitlist for lung transplantation (LTx). Multi-dimensional scores (Composite Physiologic index [CPI], [Gender-Age-Physiology [GAP]; RIsk Stratification scorE [RISE]) demonstrated enhanced predictive power towards outcome in IPF. The lung allocation score (LAS) is a multi-dimensional tool commonly used to stratify patients assessed for LTx. We sought to investigate whether IPF-specific multi-dimensional scores predict mortality in patients with IPF assessed for LTx. The study included 302 patients with IPF who underwent a LTx assessment (2003-2014). Multi-dimensional scores were calculated. The primary outcome was 12-month mortality after assessment. LTx was considered as competing event in all analyses. At the end of the observation period, there were 134 transplants, 63 deaths, and 105 patients were alive without LTx. Multi-dimensional scores predicted mortality with accuracy similar to LAS, and superior to that of individual variables: area under the curve (AUC) for LAS was 0.78 (sensitivity 71%, specificity 86%); CPI 0.75 (sensitivity 67%, specificity 82%); GAP 0.67 (sensitivity 59%, specificity 74%); RISE 0.78 (sensitivity 71%, specificity 84%). A separate analysis conducted only in patients actively listed for LTx (n = 247; 50 deaths) yielded similar results. In patients with IPF assessed for LTx as well as in those actually listed, multi-dimensional scores predict mortality better than individual variables, and with accuracy similar to the LAS. If validated, multi-dimensional scores may serve as inexpensive tools to guide decisions on the timing of referral and listing for LTx. Copyright © 2017 Elsevier Ltd. All rights reserved.

Preoperative Magnetic Resonance Volumetry in Predicting Myometrial Invasion, Lymphovascular Space Invasion, and Tumor Grade: Is It Valuable in International Federation of Gynecology and Obstetrics Stage I Endometrial Cancer?

PubMed

Sahin, Hilal; Sarioglu, Fatma Ceren; Bagci, Mustafa; Karadeniz, Tugba; Uluer, Hatice; Sanci, Muzaffer

2018-05-01

The aim of this retrospective single-center study was to evaluate the relationship between maximum tumor size, tumor volume, tumor volume ratio (TVR) based on preoperative magnetic resonance (MR) volumetry, and negative histological prognostic parameters (deep myometrial invasion [MI], lymphovascular space invasion, tumor histological grade, and subtype) in International Federation of Gynecology and Obstetrics stage I endometrial cancer. Preoperative pelvic MR imaging studies of 68 women with surgical-pathologic diagnosis of International Federation of Gynecology and Obstetrics stage I endometrial cancer were reviewed for assessment of MR volumetry and qualitative assessment of MI. Volume of the tumor and uterus was measured with manual tracing of each section on sagittal T2-weighted images. Tumor volume ratio was calculated according to the following formula: TVR = (total tumor volume/total uterine volume) × 100. Receiver operating characteristics curve was performed to investigate a threshold for TVR associated with MI. The Mann-Whitney U test, Kruskal-Wallis test, and linear regression analysis were applied to evaluate possible differences between tumor size, tumor volume, TVR, and negative prognostic parameters. Receiver operating characteristics curve analysis of TVR for prediction of deep MI was statistically significant (P = 0.013). An optimal TVR threshold of 7.3% predicted deep myometrial invasion with 85.7% sensitivity, 46.8% specificity, 41.9% positive predictive value, and 88.0% negative predictive value. Receiver operating characteristics curve analyses of TVR, tumor size, and tumor volume for prediction of tumor histological grade or lymphovascular space invasion were not significant. The concordance between radiologic and pathologic assessment for MI was almost excellent (κ value, 0.799; P < 0.001). Addition of TVR to standard radiologic assessment of deep MI increased the sensitivity from 90.5% to 95.2%. Tumor volume ratio, based on preoperative MR volumetry, seems to predict deep MI independently in stage I endometrial cancer with insufficient sensitivity and specificity. Its value in clinical practice for risk stratification models in endometrial cancer has to be studied in larger cohort of patients.

Weather Research and Forecasting Model Wind Sensitivity Study at Edwards Air Force Base, CA

NASA Technical Reports Server (NTRS)

Watson, Leela R.; Bauman, William H., III; Hoeth, Brian

2009-01-01

This abstract describes work that will be done by the Applied Meteorology Unit (AMU) in assessing the success of different model configurations in predicting "wind cycling" cases at Edwards Air Force Base, CA (EAFB), in which the wind speeds and directions oscillate among towers near the EAFB runway. The Weather Research and Forecasting (WRF) model allows users to choose among two dynamical cores - the Advanced Research WRF (ARW) and the Non-hydrostatic Mesoscale Model (NMM). There are also data assimilation analysis packages available for the initialization of the WRF model - the Local Analysis and Prediction System (LAPS) and the Advanced Regional Prediction System (ARPS) Data Analysis System (ADAS). Having a series of initialization options and WRF cores, as well as many options within each core, creates challenges for local forecasters, such as determining which configuration options are best to address specific forecast concerns. The goal of this project is to assess the different configurations available and determine which configuration will best predict surface wind speed and direction at EAFB.

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.

Molecular Nodal Staging Using miRNA Expression in Lung Cancer Patients by Endobronchial Ultrasound-Guided Transbronchial Needle Aspiration.

PubMed

Inage, Terunaga; Nakajima, Takahiro; Itoga, Sakae; Ishige, Takayuki; Fujiwara, Taiki; Sakairi, Yuichi; Wada, Hironobu; Suzuki, Hidemi; Iwata, Takekazu; Chiyo, Masako; Yoshida, Shigetoshi; Matsushita, Kazuyuki; Yasufuku, Kazuhiro; Yoshino, Ichiro

2018-06-13

The limited negative predictive value of endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) has often been discussed. The aim of this study was to identify a highly sensitive molecular biomarker for lymph node staging by EBUS-TBNA. Five microRNAs (miRNAs) (miR-200a, miR-200b, miR-200c, miR-141, and let-7e) were selected as biomarker candidates for the detection of nodal metastasis in a miRNA expression analysis. After having established a cutoff level of expression for each marker to differentiate malignant from benign lymph nodes among surgically dissected lymph nodes, the cutoff level was applied to snap-frozen EBUS-TBNA samples. Archived formalin-fixed paraffin- embedded (FFPE) samples rebiopsied by EBUS-TBNA after induction chemoradiotherapy were also analyzed. The expression of all candidate miRNAs was significantly higher in metastatic lymph nodes than in benign ones (p < 0.05) among the surgical samples. miR-200c showed the highest diagnostic yield, with a sensitivity of 95.4% and a specificity of 100%. When the cutoff value for miR-200c was applied to the snap-frozen EBUS-TBNA samples, the sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy were 97.4, 81.8, 95.0, 90.0, and 94.0%, respectively. For restaging FFPE EBUS- TBNA samples, the sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy were 100, 60.0, 80.0, 100, and 84.6%, respectively. Among the restaged samples, 4 malignant lymph nodes were false negative by EBUS-TBNA, but they were accurately identified by miR-200c. miR-200c can be used as a highly sensitive molecular staging biomarker that will enhance nodal staging of lung cancer. © 2018 S. Karger AG, Basel.

Can the Ni classification of vessels predict neoplasia? A systematic review and meta-analysis.

PubMed

Mehlum, Camilla S; Rosenberg, Tine; Dyrvig, Anne-Kirstine; Groentved, Aagot Moeller; Kjaergaard, Thomas; Godballe, Christian

2018-01-01

The Ni classification of vascular change from 2011 is well documented for evaluating pharyngeal and laryngeal lesions, primarily focusing on cancer. In the planning of surgery it may be more relevant to differentiate neoplasia from non-neoplasia. We aimed to evaluate the ability of the Ni classification to predict laryngeal or hypopharyngeal neoplasia and to investigate if a changed cutoff value would support the recent European Laryngological Society (ELS) proposal of perpendicular vascular changes as indicative of neoplasia. PubMed, Embase, Cochrane, and Scopus databases. A systematic review and meta-analysis was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis statement. We systematically searched for publications from 2011 until 2016. All retrieved studies were reviewed and qualitatively assessed. The pooled sensitivity and specificity of the Ni classification with two different cutoffs were calculated, and bubble and summary receiver operating characteristics plots were created. The combined sensitivity of five studies (n = 687) with Ni type IV-V defined as test-positive was 0.89 (95% confidence interval [CI]: 0.76-0.95), and specificity was 0.82 (95% CI: 0.72-0.89). The equivalent combined sensitivity of four studies (n = 624) with Ni type V defined as test-positive was 0.82 (95% CI: 0.75-0.87), and specificity was 0.93 (95% CI: 0.82-0.97). The diagnostic accuracy of the Ni classification in predicting neoplasia was high, without significant difference between the two analyzed cutoff values. Implementation of the proposed ELS classification of vascular changes seems reasonable from a clinical perspective, with comparable accuracy. Attention must be drawn to the accompanying risk of exposing patients to unnecessary surgery. Laryngoscope, 128:168-176, 2018. © 2017 The American Laryngological, Rhinological and Otological Society, Inc.

Uncertainty and sensitivity analysis for photovoltaic system modeling.

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

Hansen, Clifford W.; Pohl, Andrew Phillip; Jordan, Dirk

2013-12-01

We report an uncertainty and sensitivity analysis for modeling DC energy from photovoltaic systems. We consider two systems, each comprised of a single module using either crystalline silicon or CdTe cells, and located either at Albuquerque, NM, or Golden, CO. Output from a PV system is predicted by a sequence of models. Uncertainty in the output of each model is quantified by empirical distributions of each model's residuals. We sample these distributions to propagate uncertainty through the sequence of models to obtain an empirical distribution for each PV system's output. We considered models that: (1) translate measured global horizontal, directmore » and global diffuse irradiance to plane-of-array irradiance; (2) estimate effective irradiance from plane-of-array irradiance; (3) predict cell temperature; and (4) estimate DC voltage, current and power. We found that the uncertainty in PV system output to be relatively small, on the order of 1% for daily energy. Four alternative models were considered for the POA irradiance modeling step; we did not find the choice of one of these models to be of great significance. However, we observed that the POA irradiance model introduced a bias of upwards of 5% of daily energy which translates directly to a systematic difference in predicted energy. Sensitivity analyses relate uncertainty in the PV system output to uncertainty arising from each model. We found that the residuals arising from the POA irradiance and the effective irradiance models to be the dominant contributors to residuals for daily energy, for either technology or location considered. This analysis indicates that efforts to reduce the uncertainty in PV system output should focus on improvements to the POA and effective irradiance models.« less

A perceptual space of local image statistics.

PubMed

Victor, Jonathan D; Thengone, Daniel J; Rizvi, Syed M; Conte, Mary M

2015-12-01

Local image statistics are important for visual analysis of textures, surfaces, and form. There are many kinds of local statistics, including those that capture luminance distributions, spatial contrast, oriented segments, and corners. While sensitivity to each of these kinds of statistics have been well-studied, much less is known about visual processing when multiple kinds of statistics are relevant, in large part because the dimensionality of the problem is high and different kinds of statistics interact. To approach this problem, we focused on binary images on a square lattice - a reduced set of stimuli which nevertheless taps many kinds of local statistics. In this 10-parameter space, we determined psychophysical thresholds to each kind of statistic (16 observers) and all of their pairwise combinations (4 observers). Sensitivities and isodiscrimination contours were consistent across observers. Isodiscrimination contours were elliptical, implying a quadratic interaction rule, which in turn determined ellipsoidal isodiscrimination surfaces in the full 10-dimensional space, and made predictions for sensitivities to complex combinations of statistics. These predictions, including the prediction of a combination of statistics that was metameric to random, were verified experimentally. Finally, check size had only a mild effect on sensitivities over the range from 2.8 to 14min, but sensitivities to second- and higher-order statistics was substantially lower at 1.4min. In sum, local image statistics form a perceptual space that is highly stereotyped across observers, in which different kinds of statistics interact according to simple rules. Copyright © 2015 Elsevier Ltd. All rights reserved.

A perceptual space of local image statistics

PubMed Central

Victor, Jonathan D.; Thengone, Daniel J.; Rizvi, Syed M.; Conte, Mary M.

2015-01-01

Local image statistics are important for visual analysis of textures, surfaces, and form. There are many kinds of local statistics, including those that capture luminance distributions, spatial contrast, oriented segments, and corners. While sensitivity to each of these kinds of statistics have been well-studied, much less is known about visual processing when multiple kinds of statistics are relevant, in large part because the dimensionality of the problem is high and different kinds of statistics interact. To approach this problem, we focused on binary images on a square lattice – a reduced set of stimuli which nevertheless taps many kinds of local statistics. In this 10-parameter space, we determined psychophysical thresholds to each kind of statistic (16 observers) and all of their pairwise combinations (4 observers). Sensitivities and isodiscrimination contours were consistent across observers. Isodiscrimination contours were elliptical, implying a quadratic interaction rule, which in turn determined ellipsoidal isodiscrimination surfaces in the full 10-dimensional space, and made predictions for sensitivities to complex combinations of statistics. These predictions, including the prediction of a combination of statistics that was metameric to random, were verified experimentally. Finally, check size had only a mild effect on sensitivities over the range from 2.8 to 14 min, but sensitivities to second- and higher-order statistics was substantially lower at 1.4 min. In sum, local image statistics forms a perceptual space that is highly stereotyped across observers, in which different kinds of statistics interact according to simple rules. PMID:26130606

C-Reactive Protein and Procalcitonin as Early Markers of Septic Complications after Laparoscopic Sleeve Gastrectomy in Morbidly Obese Patients Within an Enhanced Recovery After Surgery Program.

PubMed

Muñoz, José Luis; Ruiz-Tovar, Jaime; Miranda, Elena; Berrio, Diana Lorena; Moya, Pedro; Gutiérrez, Manuel; Flores, Raquel; Picó, Carlos; Pérez, Ana

2016-05-01

The performance of most bariatric procedures within an Enhanced Recovery After Surgery (ERAS) programs has resulted in considerable advantages, including a reduction in the length of hospital stay to 2 to 3 days. However, some postoperative complications can appear after the patient has been discharged. The aim of this study was to investigate the efficacy of various acute-phase parameters determined 24 and 48 hours after laparoscopic sleeve gastrectomy (LSG) as bariatric procedure, for predicting septic complications, such a surgical site infection (SSI), in the postoperative course. A prospective study of 115 morbidly obese patients who underwent LSG within an ERAS program between 2012 and 2015 was conducted. Blood analysis was performed 24 and 48 hours after surgery. Acute-phase parameters (C-reactive protein [CRP], procalcitonin, and fibrinogen) and WBC count were investigated. Septic complications were observed in 13 patients (11.3%). Using receiver operating characteristic analysis at 24 hours postoperatively, a cutoff level of CRP at 70 mg/L achieved 85% sensitivity and 90% specificity for predicting SSI, and a cutoff level of procalcitonin at 0.2 ng/mL achieved 70% sensitivity and 90% specificity. At 48 hours postoperatively, a cutoff level of CRP at 150 mg/L and procalcitonin at 0.95 ng/mL achieved 100% sensitivity and 100% specificity for predicting SSI. The use of CRP and procalcitonin in the first day and especially in the second day postoperative can predict septic complications after LSG. This is most useful for patients within an ERAS program who will be discharged early. Copyright © 2016 American College of Surgeons. Published by Elsevier Inc. All rights reserved.

Rejection sensitivity prospectively predicts increased rumination.

PubMed

Pearson, Katherine A; Watkins, Edward R; Mullan, Eugene G

2011-10-01

Converging research findings indicate that rumination is correlated with a specific maladaptive interpersonal style encapsulating submissive (overly-accommodating, non-assertive and self-sacrificing) behaviours, and an attachment orientation characterised by rejection sensitivity. This study examined the prospective longitudinal relationship between rumination, the submissive interpersonal style, and rejection sensitivity by comparing two alternative hypotheses: (a) the submissive interpersonal style and rejection sensitivity prospectively predict increased rumination; (b) rumination prospectively predicts the submissive interpersonal style and rejection sensitivity. Currently depressed (n = 22), previously depressed (n = 42) and never depressed (n = 28) individuals completed self-report measures assessing depressive rumination and key psychosocial measures of interpersonal style and behaviours, at baseline and again six months later. Baseline rejection sensitivity prospectively predicted increased rumination six months later, after statistically controlling for baseline rumination, gender and depression. Baseline rumination did not predict the submissive interpersonal style or rejection sensitivity. The results provide a first step towards delineating a potential casual relationship between rejection sensitivity and rumination, and suggest the potential value of clinical assessment and intervention for both rejection sensitivity and rumination in individuals who present with either difficulty. Copyright © 2011 Elsevier Ltd. All rights reserved.

A sensitivity analysis for a thermomechanical model of the Antarctic ice sheet and ice shelves

NASA Astrophysics Data System (ADS)

Baratelli, F.; Castellani, G.; Vassena, C.; Giudici, M.

2012-04-01

The outcomes of an ice sheet model depend on a number of parameters and physical quantities which are often estimated with large uncertainty, because of lack of sufficient experimental measurements in such remote environments. Therefore, the efforts to improve the accuracy of the predictions of ice sheet models by including more physical processes and interactions with atmosphere, hydrosphere and lithosphere can be affected by the inaccuracy of the fundamental input data. A sensitivity analysis can help to understand which are the input data that most affect the different predictions of the model. In this context, a finite difference thermomechanical ice sheet model based on the Shallow-Ice Approximation (SIA) and on the Shallow-Shelf Approximation (SSA) has been developed and applied for the simulation of the evolution of the Antarctic ice sheet and ice shelves for the last 200 000 years. The sensitivity analysis of the model outcomes (e.g., the volume of the ice sheet and of the ice shelves, the basal melt rate of the ice sheet, the mean velocity of the Ross and Ronne-Filchner ice shelves, the wet area at the base of the ice sheet) with respect to the model parameters (e.g., the basal sliding coefficient, the geothermal heat flux, the present-day surface accumulation and temperature, the mean ice shelves viscosity, the melt rate at the base of the ice shelves) has been performed by computing three synthetic numerical indices: two local sensitivity indices and a global sensitivity index. Local sensitivity indices imply a linearization of the model and neglect both non-linear and joint effects of the parameters. The global variance-based sensitivity index, instead, takes into account the complete variability of the input parameters but is usually conducted with a Monte Carlo approach which is computationally very demanding for non-linear complex models. Therefore, the global sensitivity index has been computed using a development of the model outputs in a neighborhood of the reference parameter values with a second-order approximation. The comparison of the three sensitivity indices proved that the approximation of the non-linear model with a second-order expansion is sufficient to show some differences between the local and the global indices. As a general result, the sensitivity analysis showed that most of the model outcomes are mainly sensitive to the present-day surface temperature and accumulation, which, in principle, can be measured more easily (e.g., with remote sensing techniques) than the other input parameters considered. On the other hand, the parameters to which the model resulted less sensitive are the basal sliding coefficient and the mean ice shelves viscosity.

CFD and Aeroelastic Analysis of the MEXICO Wind Turbine

NASA Astrophysics Data System (ADS)

Carrión, M.; Woodgate, M.; Steijl, R.; Barakos, G.; Gómez-Iradi, S.; Munduate, X.

2014-12-01

This paper presents an aerodynamic and aeroelastic analysis of the MEXICO wind turbine, using the compressible HMB solver of Liverpool. The aeroelasticity of the blade, as well as the effect of a low-Mach scheme were studied for the zero-yaw 15m/s wind case and steady- state computations. The wake developed behind the rotor was also extracted and compared with the experimental data, using the compressible solver and a low-Mach scheme. It was found that the loads were not sensitive to the Mach number effects, although the low-Mach scheme improved the wake predictions. The sensitivity of the results to the blade structural properties was also highlighted.

The meaning of diagnostic test results: a spreadsheet for swift data analysis.

PubMed

Maceneaney, P M; Malone, D E

2000-03-01

To design a spreadsheet program to: (a) analyse rapidly diagnostic test result data produced in local research or reported in the literature; (b) correct reported predictive values for disease prevalence in any population; (c) estimate the post-test probability of disease in individual patients. Microsoft Excel(TM)was used. Section A: a contingency (2 x 2) table was incorporated into the spreadsheet. Formulae for standard calculations [sample size, disease prevalence, sensitivity and specificity with 95% confidence intervals, predictive values and likelihood ratios (LRs)] were linked to this table. The results change automatically when the data in the true or false negative and positive cells are changed. Section B: this estimates predictive values in any population, compensating for altered disease prevalence. Sections C-F: Bayes' theorem was incorporated to generate individual post-test probabilities. The spreadsheet generates 95% confidence intervals, LRs and a table and graph of conditional probabilities once the sensitivity and specificity of the test are entered. The latter shows the expected post-test probability of disease for any pre-test probability when a test of known sensitivity and specificity is positive or negative. This spreadsheet can be used on desktop and palmtop computers. The MS Excel(TM)version can be downloaded via the Internet from the URL ftp://radiography.com/pub/Rad-data99.xls A spreadsheet is useful for contingency table data analysis and assessment of the clinical meaning of diagnostic test results. Copyright 2000 The Royal College of Radiologists.

Assessment of tinnitus-related impairments and disabilities using the German THI-12: sensitivity and stability of the scale over time.

PubMed

Görtelmeyer, Roman; Schmidt, Jürgen; Suckfüll, Markus; Jastreboff, Pawel; Gebauer, Alexander; Krüger, Hagen; Wittmann, Werner

2011-08-01

To evaluate the reliability, dimensionality, predictive validity, construct validity, and sensitivity to change of the THI-12 total and sub-scales as diagnostic aids to describe and quantify tinnitus-evoked reactions and evaluate treatment efficacy. Explorative analysis of the German tinnitus handicap inventory (THI-12) to assess potential sensitivity to tinnitus therapy in placebo-controlled randomized studies. Correlation analysis, including Cronbach's coefficient α and explorative common factor analysis (EFA), was conducted within and between assessments to demonstrate the construct validity, dimensionality, and factorial structure of the THI-12. N = 618 patients suffering from subjective tinnitus who were to be screened to participate in a randomized, placebo-controlled, 16-week, longitudinal study. The THI-12 can reliably diagnose tinnitus-related impairments and disabilities and assess changes over time. The test-retest coefficient for neighboured visits was r > 0.69, the internal consistency of the THI-12 total score was α ≤ 0.79 and α ≤ 0.89 at subsequent visits. Predictability of THI-12 total score and overall variance increased with successive measurements. The three-factorial structure allowed for evaluation of factors that affect aspects of patients' health-related quality of life. The THI-12, with its three-factorial structure, is a simple, reliable, and valid instrument for the diagnosis and assessment of tinnitus and associated impairment over time.

Evaluation of a Decision Support System for Obstructive Sleep Apnea with Nonlinear Analysis of Respiratory Signals.

PubMed

Kaimakamis, Evangelos; Tsara, Venetia; Bratsas, Charalambos; Sichletidis, Lazaros; Karvounis, Charalambos; Maglaveras, Nikolaos

2016-01-01

Obstructive Sleep Apnea (OSA) is a common sleep disorder requiring the time/money consuming polysomnography for diagnosis. Alternative methods for initial evaluation are sought. Our aim was the prediction of Apnea-Hypopnea Index (AHI) in patients potentially suffering from OSA based on nonlinear analysis of respiratory biosignals during sleep, a method that is related to the pathophysiology of the disorder. Patients referred to a Sleep Unit (135) underwent full polysomnography. Three nonlinear indices (Largest Lyapunov Exponent, Detrended Fluctuation Analysis and Approximate Entropy) extracted from two biosignals (airflow from a nasal cannula, thoracic movement) and one linear derived from Oxygen saturation provided input to a data mining application with contemporary classification algorithms for the creation of predictive models for AHI. A linear regression model presented a correlation coefficient of 0.77 in predicting AHI. With a cutoff value of AHI = 8, the sensitivity and specificity were 93% and 71.4% in discrimination between patients and normal subjects. The decision tree for the discrimination between patients and normal had sensitivity and specificity of 91% and 60%, respectively. Certain obtained nonlinear values correlated significantly with commonly accepted physiological parameters of people suffering from OSA. We developed a predictive model for the presence/severity of OSA using a simple linear equation and additional decision trees with nonlinear features extracted from 3 respiratory recordings. The accuracy of the methodology is high and the findings provide insight to the underlying pathophysiology of the syndrome. Reliable predictions of OSA are possible using linear and nonlinear indices from only 3 respiratory signals during sleep. The proposed models could lead to a better study of the pathophysiology of OSA and facilitate initial evaluation/follow up of suspected patients OSA utilizing a practical low cost methodology. ClinicalTrials.gov NCT01161381.

Pretransplantation soluble CD30 level as a predictor of acute rejection in kidney transplantation: a meta-analysis.

PubMed

Chen, Yile; Tai, Qiang; Hong, Shaodong; Kong, Yuan; Shang, Yushu; Liang, Wenhua; Guo, Zhiyong; He, Xiaoshun

2012-11-15

The question of whether high pretransplantation soluble CD30 (sCD30) level can be a predictor of kidney transplant acute rejection (AR) is under debate. Herein, we performed a meta-analysis on the predictive efficacy of sCD30 for AR in renal transplantation. PubMed (1966-2012), EMBASE (1988-2012), and Web of Science (1986-2012) databases were searched for studies concerning the predictive efficacy of sCD30 for AR after kidney transplantation. After a careful review of eligible studies, sensitivity, specificity, and other measures of the accuracy of sCD30 were pooled. A summary receiver operating characteristic curve was used to represent the overall test performance. Twelve studies enrolling 2507 patients met the inclusion criteria. The pooled estimates for pretransplantation sCD30 in prediction of allograft rejection risk were poor, with a sensitivity of 0.70 (95% confidence interval (CI), 0.66-0.74), a specificity of 0.48 (95% CI, 0.46-0.50), a positive likelihood ratio of 1.35 (95% CI, 1.20-1.53), a negative likelihood ratio of 0.68 (95% CI, 0.55-0.84), and a diagnostic odds ratio of 2.07 (95% CI, 1.54-2.80). The area under curve of the summary receiver operating characteristic curve was 0.60, indicating poor overall accuracy of the serum sCD30 level in the prediction of patients at risk for AR. The results of the meta-analysis show that the accuracy of pretransplantation sCD30 for predicting posttransplantation AR was poor. Prospective studies are needed to clarify the usefulness of this test for identifying risks of AR in transplant recipients.

Weather Research and Forecasting Model Sensitivity Comparisons for Warm Season Convective Initiation

NASA Technical Reports Server (NTRS)

Watson, Leela R.

2007-01-01

This report describes the work done by the Applied Meteorology Unit (AMU) in assessing the success of different model configurations in predicting warm season convection over East-Central Florida. The Weather Research and Forecasting Environmental Modeling System (WRF EMS) software allows users to choose among two dynamical cores - the Advanced Research WRF (ARW) and the Non-hydrostatic Mesoscale Model (NMM). There are also data assimilation analysis packages available for the initialization of the WRF model - the Local Analysis and Prediction System (LAPS) and the Advanced Regional Prediction System (ARPS) Data Analysis System (ADAS). Besides model core and initialization options, the WRF model can be run with one- or two-way nesting. Having a series of initialization options and WRF cores, as well as many options within each core, creates challenges for local forecasters, such as determining which configuration options are best to address specific forecast concerns. This project assessed three different model intializations available to determine which configuration best predicts warm season convective initiation in East-Central Florida. The project also examined the use of one- and two-way nesting in predicting warm season convection.

[Development and Application of a Performance Prediction Model for Home Care Nursing Based on a Balanced Scorecard using the Bayesian Belief Network].

PubMed

Noh, Wonjung; Seomun, Gyeongae

2015-06-01

This study was conducted to develop key performance indicators (KPIs) for home care nursing (HCN) based on a balanced scorecard, and to construct a performance prediction model of strategic objectives using the Bayesian Belief Network (BBN). This methodological study included four steps: establishment of KPIs, performance prediction modeling, development of a performance prediction model using BBN, and simulation of a suggested nursing management strategy. An HCN expert group and a staff group participated. The content validity index was analyzed using STATA 13.0, and BBN was analyzed using HUGIN 8.0. We generated a list of KPIs composed of 4 perspectives, 10 strategic objectives, and 31 KPIs. In the validity test of the performance prediction model, the factor with the greatest variance for increasing profit was maximum cost reduction of HCN services. The factor with the smallest variance for increasing profit was a minimum image improvement for HCN. During sensitivity analysis, the probability of the expert group did not affect the sensitivity. Furthermore, simulation of a 10% image improvement predicted the most effective way to increase profit. KPIs of HCN can estimate financial and non-financial performance. The performance prediction model for HCN will be useful to improve performance.

A global analysis of traits predicting species sensitivity to habitat fragmentation

USGS Publications Warehouse

Keinath, Douglas; Doak, Daniel F.; Hodges, Karen E.; Prugh, Laura R.; Fagan, William F.; Sekercioglu, Cagan H.; Buchart, Stuart H. M.; Kauffman, Matthew J.

2017-01-01

AimElucidating patterns in species responses to habitat fragmentation is an important focus of ecology and conservation, but studies are often geographically restricted, taxonomically narrow or use indirect measures of species vulnerability. We investigated predictors of species presence after fragmentation using data from studies around the world that included all four terrestrial vertebrate classes, thus allowing direct inter-taxonomic comparison.LocationWorld-wide.MethodsWe used generalized linear mixed-effect models in an information theoretic framework to assess the factors that explained species presence in remnant habitat patches (3342 patches; 1559 species, mostly birds; and 65,695 records of patch-specific presence–absence). We developed a novel metric of fragmentation sensitivity, defined as the maximum rate of change in probability of presence with changing patch size (‘Peak Change’), to distinguish between general rarity on the landscape and sensitivity to fragmentation per se.ResultsSize of remnant habitat patches was the most important driver of species presence. Across all classes, habitat specialists, carnivores and larger species had a lower probability of presence, and those effects were substantially modified by interactions. Sensitivity to fragmentation (measured by Peak Change) was influenced primarily by habitat type and specialization, but also by fecundity, life span and body mass. Reptiles were more sensitive than other classes. Grassland species had a lower probability of presence, though sample size was relatively small, but forest and shrubland species were more sensitive.Main conclusionsHabitat relationships were more important than life-history characteristics in predicting the effects of fragmentation. Habitat specialization increased sensitivity to fragmentation and interacted with class and habitat type; forest specialists and habitat-specific reptiles were particularly sensitive to fragmentation. Our results suggest that when conservationists are faced with disturbances that could fragment habitat they should pay particular attention to specialists, particularly reptiles. Further, our results highlight that the probability of presence in fragmented landscapes and true sensitivity to fragmentation are predicted by different factors.

Diagnostic Value of Cerebrospinal Fluid T-SPOT.TB for Tuberculousis Meningitis in China.

PubMed

Li, Xue Lian; Xie, Na; Wang, Song Wang; Wu, Qian Hong; Ma, Yan; Shu, Wei; Chen, Hong Mei; Zhang, Li Qun; Wu, Xiao Guang; Ma, Li Ping; Che, Nan Ying; Gao, Meng Qiu

2017-09-01

The aim of this study was to evaluate the diagnostic value of the cerebrospinal fluid (CSF) T-SPOT.TB test for the diagnosis of TB meningitis (TBM). A retrospective analysis of 96 patients with manifested meningitis was conducted; T-SPOT.TB test was performed for diagnosing TBM to determine the diagnostic sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). A receiver operating characteristic (ROC) curve was also drawn to assess the diagnostic accuracy. The sensitivity, specificity, PPV, and NPV of CSF T-SPOT.TB test were 97.8%, 78.0%, 80.3%, and 97.5%, respectively, for 52 patients (54.2%) of the 96 enrolled patients. The area under the curve (AUC) was 0.910, and the sensitivities of CSF T-SPOT.TB for patients with stages I, II, and III of TBM were 96.7%, 97.2%, and 98.9%, respectively. CSF T-SPOT.TB test is a rapid and accurate diagnostic method with higher sensitivity and specificity for diagnosing TBM. Copyright © 2017 The Editorial Board of Biomedical and Environmental Sciences. Published by China CDC. All rights reserved.

Developing a clinical utility framework to evaluate prediction models in radiogenomics

NASA Astrophysics Data System (ADS)

Wu, Yirong; Liu, Jie; Munoz del Rio, Alejandro; Page, David C.; Alagoz, Oguzhan; Peissig, Peggy; Onitilo, Adedayo A.; Burnside, Elizabeth S.

2015-03-01

Combining imaging and genetic information to predict disease presence and behavior is being codified into an emerging discipline called "radiogenomics." Optimal evaluation methodologies for radiogenomics techniques have not been established. We aim to develop a clinical decision framework based on utility analysis to assess prediction models for breast cancer. Our data comes from a retrospective case-control study, collecting Gail model risk factors, genetic variants (single nucleotide polymorphisms-SNPs), and mammographic features in Breast Imaging Reporting and Data System (BI-RADS) lexicon. We first constructed three logistic regression models built on different sets of predictive features: (1) Gail, (2) Gail+SNP, and (3) Gail+SNP+BI-RADS. Then, we generated ROC curves for three models. After we assigned utility values for each category of findings (true negative, false positive, false negative and true positive), we pursued optimal operating points on ROC curves to achieve maximum expected utility (MEU) of breast cancer diagnosis. We used McNemar's test to compare the predictive performance of the three models. We found that SNPs and BI-RADS features augmented the baseline Gail model in terms of the area under ROC curve (AUC) and MEU. SNPs improved sensitivity of the Gail model (0.276 vs. 0.147) and reduced specificity (0.855 vs. 0.912). When additional mammographic features were added, sensitivity increased to 0.457 and specificity to 0.872. SNPs and mammographic features played a significant role in breast cancer risk estimation (p-value < 0.001). Our decision framework comprising utility analysis and McNemar's test provides a novel framework to evaluate prediction models in the realm of radiogenomics.

Validation of the Children's Hospital of Philadelphia Retinopathy of Prematurity (CHOP ROP) Model.

PubMed

Binenbaum, Gil; Ying, Gui-Shuang; Tomlinson, Lauren A

2017-08-01

The Children's Hospital of Philadelphia Retinopathy of Prematurity (CHOP ROP) model uses birth weight (BW), gestational age at birth (GA), and weight gain rate to predict the risk of severe retinopathy of prematurity (ROP). In a model development study, it predicted all infants requiring treatment, while greatly reducing the number of examinations compared with current screening guidelines. To validate the CHOP ROP model in a multicenter cohort that is large enough to obtain a precise estimate of the model's sensitivity for treatment-requiring ROP. This investigation was a secondary analysis of data from the Postnatal Growth and Retinopathy of Prematurity (G-ROP) Study. The setting was 30 hospitals in the United States and Canada between January 1, 2006, and June 30, 2012. The dates of analysis were September 28 to October 5, 2015. Participants were premature infants at risk for ROP with a known ROP outcome. Sensitivity for Early Treatment of Retinopathy of Prematurity type 1 ROP and potential reduction in the number of infants requiring examinations. In the primary analysis, the CHOP ROP model was applied weekly to predict the risk of ROP. If the risk was above a cut-point level (high risk), examinations were indicated, while low-risk infants received no examinations. In a secondary analysis, low-risk infants received fewer examinations rather than no examinations. Participants included 7483 premature infants at risk for ROP with a known ROP outcome. Their median BW was 1070 g (range, 310-3000 g), and their median GA was 28 weeks (range, 22-35 weeks). Among them, 3575 (47.8%) were female, and their race/ethnicity was 3615 white (48.3%), 2310 black (30.9%), 233 Asian (3.1%), 93 Pacific Islander (1.2%), and 40 American Indian/Alaskan native (0.5%). The original CHOP ROP model correctly predicted 452 of 459 infants who developed type 1 ROP (sensitivity, 98.5%; 95% CI, 96.9%-99.3%), reducing the number of infants requiring examinations by 34.3% if only high-risk infants received examinations. Lowering the cut point to capture all type 1 ROP cases (sensitivity, 100%; 95% CI, 99.2%-100%) resulted in only 6.8% of infants not requiring examinations. However, if low-risk infants were examined at 37 weeks' postmenstrual age and followed up only if ROP was present at that examination, all type 1 ROP cases would be captured, and the number of examinations performed among infants with GA exceeding 27 weeks would be reduced by 28.4%. The CHOP ROP model demonstrated high but not 100% sensitivity and may be better used to reduce examination frequency. The model might be used reliably to guide a modified ROP screening schedule and decrease the number of examinations performed.

Efficiency of the Bethesda System for Thyroid Cytopathology.

PubMed

Mora-Guzmán, Ismael; Muñoz de Nova, José Luis; Marín-Campos, Cristina; Jiménez-Heffernan, José Antonio; Cuesta Pérez, Juan Julián; Lahera Vargas, Marcos; Torres Mínguez, Emma; Martín-Pérez, Elena

2018-03-28

Fine-needle aspiration biopsies are a key tool for preoperative assessment of thyroid nodules, and the Bethesda system is the preferred method to report cytological analysis. The purpose of this study is to assess the efficiency of the Bethesda system to identify the malignancy risk of thyroid nodules. Patients who underwent thyroid surgery between June 2010 and June 2017 were included. Samples were classified into 6categories according to rates of malignancy associated with each diagnostic category. In order to investigate the correlation between categories, a statistical analysis compared the categories with pathology reports. Diagnostic indicators were calculated as a screening test (categories IV, V, VI as true-positive) and as a method to identify malignancy (V, VI as true-positive). In a series of 522 patients, we found 184 (35.2%) malignant tumours, papillary carcinoma being the most prevalent with 155 cases (84.2%). Malignant rates for diagnostic categories were: I, 0%; II, 1.5%; III, 6.4%; IV, 31%; V, 86.5%; VI, 100%. A robust correlation was identified between categories on statistical analysis. For the «screening test» analysis, sensitivity was 98.9%, specificity 84.4%, positive predictive value 69.6%, negative predictive value 99.5%, and diagnostic accuracy 88.2%. Analysing the accuracy to detect malignancy, values were: sensitivity 98.6%, specificity 97.6%, positive predictive value 93.5%, negative predictive value 99.5%, diagnostic accuracy 97.9%. The Bethesda system is a clear and reliable approach to report thyroid cytology and therefore is an effective tool to identify malignancy risk and guide clinical management. Copyright © 2018 AEC. Publicado por Elsevier España, S.L.U. All rights reserved.

New Substrate-Guided Method of Predicting Slow Conducting Isthmuses of Ventricular Tachycardia: Preliminary Analysis to the Combined Use of Voltage Limit Adjustment and Fast-Fourier Transform Analysis.

PubMed

Kuroki, Kenji; Nogami, Akihiko; Igarashi, Miyako; Masuda, Keita; Kowase, Shinya; Kurosaki, Kenji; Komatsu, Yuki; Naruse, Yoshihisa; Machino, Takeshi; Yamasaki, Hiro; Xu, Dongzhu; Murakoshi, Nobuyuki; Sekiguchi, Yukio; Aonuma, Kazutaka

2018-04-01

Several conducting channels of ventricular tachycardia (VT) can be identified using voltage limit adjustment (VLA) of substrate mapping. However, the sensitivity or specificity to predict a VT isthmus is not high by using VLA alone. This study aimed to evaluate the efficacy of the combined use of VLA and fast-Fourier transform analysis to predict VT isthmuses. VLA and fast-Fourier transform analyses of local ventricular bipolar electrograms during sinus rhythm were performed in 9 postinfarction patients who underwent catheter ablation for a total of 13 monomorphic VTs. Relatively higher voltage areas on an electroanatomical map were defined as high voltage channels (HVCs), and relatively higher fast-Fourier transform areas were defined as high-frequency channels (HFCs). HVCs were classified into full or partial HVCs (the entire or >30% of HVC can be detectable, respectively). Twelve full HVCs were identified in 7 of 9 patients. HFCs were located on 7 of 12 full HVCs. Five VT isthmuses (71%) were included in the 7 full HVC+/HFC+ sites, whereas no VT isthmus was found in the 5 full HVC+/HFC- sites. HFCs were identical to 9 of 16 partial HVCs. Eight VT isthmuses (89%) were included in the 9 partial HVC+/HFC+ sites, whereas no VT isthmus was found in the 7 partial HVC+/HFC- sites. All HVC+/HFC+ sites predicted VT isthmus with a sensitivity of 100% and a specificity of 80%. Combined use of VLA and fast-Fourier transform analysis may be a useful method to detect VT isthmuses. © 2018 American Heart Association, Inc.

Application of biodynamic imaging for personalized chemotherapy in canine lymphoma

NASA Astrophysics Data System (ADS)

Custead, Michelle R.

Biodynamic imaging (BDI) is a novel phenotypic cancer profiling technology which characterizes changes in cellular and subcellular motion in living tumor tissue samples following in vitro or ex vivo treatment with chemotherapeutics. The ability of BDI to predict clinical response to single-agent doxorubicin chemotherapy was tested in ten dogs with naturally-occurring non-Hodgkin's lymphomas (NHL). Pre-treatment tumor biopsy samples were obtained from all dogs and treated with doxorubicin (10 muM) ex vivo. BDI captured cellular and subcellular motility measures on all biopsy samples at baseline and at regular intervals for 9 hours following drug application. All dogs subsequently received treatment with a standard single-agent doxorubicin protocol. Objective response (OR) to doxorubicin and progression-free survival time (PFST) following chemotherapy were recorded for all dogs. The dynamic biomarkers measured by BDI were entered into a multivariate logistic model to determine the extent to which BDI predicted OR and PFST following doxorubicin therapy. The model showed that the sensitivity, specificity, and accuracy of BDI for predicting treatment outcome were 95%, 91%, and 93%, respectively. To account for possible over-fitting of data to the predictive model, cross-validation with a one-left-out analysis was performed, and the adjusted sensitivity, specificity, and accuracy following this analysis were 93%, 87%, and 91%, respectively. These findings suggest that BDI can predict, with high accuracy, treatment outcome following single-agent doxorubicin chemotherapy in a relevant spontaneous canine cancer model, and is a promising novel technology for advancing personalized cancer medicine.

Serum Adiponectin, Vitamin D, and Alpha-Fetoprotein in Children with Chronic Hepatitis C: Can They Predict Treatment Response?

PubMed Central

Khedr, Mohamed Ahmed; Sira, Ahmad Mohamed; Saber, Magdy Anwar; Raia, Gamal Yousef

2015-01-01

Background & Aims. The currently available treatment for chronic hepatitis C (CHC) in children is costly and with much toxicity. So, predicting the likelihood of response before starting therapy is important. Methods. Serum adiponectin, vitamin D, and alpha-fetoprotein (AFP) were measured before starting pegylated-interferon/ribavirin therapy for 50 children with CHC. Another 21 healthy children were recruited as controls. Results. Serum adiponectin, vitamin D, and AFP were higher in the CHC group than healthy controls (p < 0.0001, p = 0.071, and p = 0.87, resp.). In univariate analysis, serum adiponectin was significantly higher in responders than nonresponders (p < 0.0001) and at a cutoff value ≥8.04 ng/mL it can predict treatment response by 77.8% sensitivity and 92.9% specificity, while both AFP and viremia were significantly lower in responders than nonresponders, p < 0.0001 and p = 0.0003, respectively, and at cutoff values ≤3.265 ng/mL and ≤235,384 IU/mL, respectively, they can predict treatment response with a sensitivity of 83.3% for both and specificity of 85.7% and 78.6%, respectively. In multivariate analysis, adiponectin was found to be the only independent predictor of treatment response (p = 0.044). Conclusions. The pretreatment serum level of adiponectin can predict the likelihood of treatment response, thus avoiding toxicities for those unlikely to respond to therapy. PMID:26640716

Serum Adiponectin, Vitamin D, and Alpha-Fetoprotein in Children with Chronic Hepatitis C: Can They Predict Treatment Response?

PubMed

Khedr, Mohamed Ahmed; Sira, Ahmad Mohamed; Saber, Magdy Anwar; Raia, Gamal Yousef

2015-01-01

Background & Aims. The currently available treatment for chronic hepatitis C (CHC) in children is costly and with much toxicity. So, predicting the likelihood of response before starting therapy is important. Methods. Serum adiponectin, vitamin D, and alpha-fetoprotein (AFP) were measured before starting pegylated-interferon/ribavirin therapy for 50 children with CHC. Another 21 healthy children were recruited as controls. Results. Serum adiponectin, vitamin D, and AFP were higher in the CHC group than healthy controls (p < 0.0001, p = 0.071, and p = 0.87, resp.). In univariate analysis, serum adiponectin was significantly higher in responders than nonresponders (p < 0.0001) and at a cutoff value ≥8.04 ng/mL it can predict treatment response by 77.8% sensitivity and 92.9% specificity, while both AFP and viremia were significantly lower in responders than nonresponders, p < 0.0001 and p = 0.0003, respectively, and at cutoff values ≤3.265 ng/mL and ≤235,384 IU/mL, respectively, they can predict treatment response with a sensitivity of 83.3% for both and specificity of 85.7% and 78.6%, respectively. In multivariate analysis, adiponectin was found to be the only independent predictor of treatment response (p = 0.044). Conclusions. The pretreatment serum level of adiponectin can predict the likelihood of treatment response, thus avoiding toxicities for those unlikely to respond to therapy.

Validation of a new radiographic measurement of acetabular version: the transverse axis distance (TAD).

PubMed

Nitschke, Ashley; Lambert, Jeffery R; Glueck, Deborah H; Jesse, Mary Kristen; Mei-Dan, Omer; Strickland, Colin; Petersen, Brian

2015-11-01

This study has three aims: (1) validate a new radiographic measure of acetabular version, the transverse axis distance (TAD) by showing equivalent TAD accuracy in predicting CT equatorial acetabular version when compared to a previously validated, but more cumbersome, radiographic measure, the p/a ratio; (2) establish predictive equations of CT acetabular version from TAD; (3) calculate a sensitive and specific cut point for predicting excessive CT acetabular anteversion using TAD. A 14-month retrospective review was performed of patients who had undergone a dedicated MSK CT pelvis study and who also had a technically adequate AP pelvis radiograph. Two trained observers measured the radiographic p/a ratio, TAD, and CT acetabular equatorial version for 110 hips on a PACS workstation. Mixed model analysis was used to find prediction equations, and ROC analysis was used to evaluate the diagnostic accuracy of p/a ratio and TAD. CT equatorial acetabular version can accurately be predicted from either p/a ratio (p < 0.001) or TAD (p < 0.001). The diagnostic accuracies of p/a ratio and TAD are comparable (p =0.46). Patients whose TAD is higher than 17 mm may have excessive acetabular anteversion. For that cutpoint, the sensitivity of TAD is 0.73, with specificity of 0.82. TAD is an accurate radiographic predictor of CT acetabular anteversion and provides an easy-to-use and intuitive point-of-care assessment of acetabular version in patients with hip pain.

Influence of ECG sampling rate in fetal heart rate variability analysis.

PubMed

De Jonckheere, J; Garabedian, C; Charlier, P; Champion, C; Servan-Schreiber, E; Storme, L; Debarge, V; Jeanne, M; Logier, R

2017-07-01

Fetal hypoxia results in a fetal blood acidosis (pH<;7.10). In such a situation, the fetus develops several adaptation mechanisms regulated by the autonomic nervous system. Many studies demonstrated significant changes in heart rate variability in hypoxic fetuses. So, fetal heart rate variability analysis could be of precious help for fetal hypoxia prediction. Commonly used fetal heart rate variability analysis methods have been shown to be sensitive to the ECG signal sampling rate. Indeed, a low sampling rate could induce variability in the heart beat detection which will alter the heart rate variability estimation. In this paper, we introduce an original fetal heart rate variability analysis method. We hypothesize that this method will be less sensitive to ECG sampling frequency changes than common heart rate variability analysis methods. We then compared the results of this new heart rate variability analysis method with two different sampling frequencies (250-1000 Hz).

Decision making for pancreatic resection in patients with intraductal papillary mucinous neoplasms.

PubMed

Xu, Bin; Ding, Wei-Xing; Jin, Da-Yong; Wang, Dan-Song; Lou, Wen-Hui

2013-03-07

To identify a practical approach for preoperative decision-making in patients with intraductal papillary mucinous neoplasms (IPMNs) of the pancreas. Between March 1999 and November 2006, the clinical characteristics, pathological data and computed tomography/magnetic resonance imaging (CT/MRI) of 54 IPMNs cases were retrieved and analyzed. The relationships between the above data and decision-making for pancreatic resection were analyzed using SPSS 13.0 software. Univariate analysis of risk factors for malignant or invasive IPMNs was performed with regard to the following variables: carcinoembryonic antigen, carbohydrate antigen 19-9 (CA19-9) and the characteristics from CT/MRI images. Receiver operating characteristic (ROC) curve analysis for pancreatic resection was performed using significant factors from the univariate analysis. CT/MRI images, including main and mixed duct IPMNs, tumor size > 30 mm or a solid component appearance in the lesion, and preoperative serum CA19-9 > 37 U/mL had good predictive value for determining pancreatic resection (P < 0.05), but with limitations. Combining the above factors (CT/MRI images and CA19-9) improved the accuracy and sensitivity for determining pancreatic resection in IPMNs. Using ROC analysis, the area under the curve reached 0.893 (P < 0.01, 95%CI: 0.763-1.023), with a sensitivity, specificity, positive predictive value and negative predictive value of 95.2%, 83.3%, 95.2% and 83.3%, respectively. Combining preoperative CT/MRI images and CA19-9 level may provide useful information for surgical decision-making in IPMNs.

Rainfall-induced fecal indicator organisms transport from manured fields: Model sensitivity analysis

EPA Science Inventory

Microbial quality of surface waters attracts attention due to food- and waterborne disease outbreaks. Fecal indicator organisms (FIOs) are commonly used for the microbial pollution level evaluation. Models predicting the fate and transport of FIOs are required to design and evalu...

A genomic biomarker signature can predict skin sensitizers using a cell-based in vitro alternative to animal tests

PubMed Central

2011-01-01

Background Allergic contact dermatitis is an inflammatory skin disease that affects a significant proportion of the population. This disease is caused by an adverse immune response towards chemical haptens, and leads to a substantial economic burden for society. Current test of sensitizing chemicals rely on animal experimentation. New legislations on the registration and use of chemicals within pharmaceutical and cosmetic industries have stimulated significant research efforts to develop alternative, human cell-based assays for the prediction of sensitization. The aim is to replace animal experiments with in vitro tests displaying a higher predictive power. Results We have developed a novel cell-based assay for the prediction of sensitizing chemicals. By analyzing the transcriptome of the human cell line MUTZ-3 after 24 h stimulation, using 20 different sensitizing chemicals, 20 non-sensitizing chemicals and vehicle controls, we have identified a biomarker signature of 200 genes with potent discriminatory ability. Using a Support Vector Machine for supervised classification, the prediction performance of the assay revealed an area under the ROC curve of 0.98. In addition, categorizing the chemicals according to the LLNA assay, this gene signature could also predict sensitizing potency. The identified markers are involved in biological pathways with immunological relevant functions, which can shed light on the process of human sensitization. Conclusions A gene signature predicting sensitization, using a human cell line in vitro, has been identified. This simple and robust cell-based assay has the potential to completely replace or drastically reduce the utilization of test systems based on experimental animals. Being based on human biology, the assay is proposed to be more accurate for predicting sensitization in humans, than the traditional animal-based tests. PMID:21824406

Can paediatric early warning scores (PEWS) be used to guide the need for hospital admission and predict significant illness in children presenting to the emergency department? An assessment of PEWS diagnostic accuracy using sensitivity and specificity.

PubMed

Lillitos, Peter J; Hadley, Graeme; Maconochie, Ian

2016-05-01

Designed to detect early deterioration of the hospitalised child, paediatric early warning scores (PEWS) validity in the emergency department (ED) is less validated. We aimed to evaluate sensitivity and specificity of two commonly used PEWS (Brighton and COAST) in predicting hospital admission and, for the first time, significant illness. Retrospective analysis of PEWS data for paediatric ED attendances at St Mary's Hospital, London, UK, in November 2012. Patients with missing data were excluded. Diagnoses were grouped: medical and surgical. To classify diagnoses as significant, established guidelines were used and, where not available, common agreement between three acute paediatricians. 1921 patients were analysed. There were 211 admissions (11%). 1630 attendances were medical (86%) and 273 (14%) surgical. Brighton and COAST PEWS performed similarly. hospital admission: PEWS of ≥3 was specific (93%) but poorly sensitive (32%). The area under the receiver operating curve (AUC) was low at 0.690. Significant illness: for medical illness, PEWS ≥3 was highly specific (96%) but poorly sensitive (44%). The AUC was 0.754 and 0.755 for Brighton and COAST PEWS, respectively. Both scores performed poorly for predicting significant surgical illness (AUC 0.642). PEWS ≥3 performed well in predicting significant respiratory illness: sensitivity 75%, specificity 91%. Both Brighton and COAST PEWS scores performed similarly. A score of ≥3 has good specificity but poor sensitivity for predicting hospital admission and significant illness. Therefore, a high PEWS should be taken seriously but a low score is poor at ruling out the requirement for admission or serious underlying illness. PEWS was better at detecting significant medical illness compared with detecting the need for admission. PEWS performed poorly in detecting significant surgical illness. PEWS may be particularly useful in evaluating respiratory illness in a paediatric ED. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Evaluation and Improvement of Liquid Propellant Rocket Chugging Analysis Techniques. Part 1: A One-Dimensional Analysis of Low Frequency Combustion Instability in the Fuel Preburner of the Space Shuttle Main Engine. Final Report M.S. Thesis - Aug. 1986

NASA Technical Reports Server (NTRS)

Lim, Kair Chuan

1986-01-01

Low frequency combustion instability, known as chugging, is consistently experienced during shutdown in the fuel and oxidizer preburners of the Space Shuttle Main Engines. Such problems always occur during the helium purge of the residual oxidizer from the preburner manifolds during the shutdown sequence. Possible causes and triggering mechanisms are analyzed and details in modeling the fuel preburner chug are presented. A linearized chugging model, based on the foundation of previous models, capable of predicting the chug occurrence is discussed and the predicted results are presented and compared to experimental work performed by NASA. Sensitivity parameters such as chamber pressure, fuel and oxidizer temperatures, and the effective bulk modulus of the liquid oxidizer are considered in analyzing the fuel preburner chug. The computer program CHUGTEST is utilized to generate the stability boundary for each sensitivity study and the region for stable operation is identified.

A GIS based spatially-explicit sensitivity and uncertainty analysis approach for multi-criteria decision analysis.

PubMed

Feizizadeh, Bakhtiar; Jankowski, Piotr; Blaschke, Thomas

2014-03-01

GIS multicriteria decision analysis (MCDA) techniques are increasingly used in landslide susceptibility mapping for the prediction of future hazards, land use planning, as well as for hazard preparedness. However, the uncertainties associated with MCDA techniques are inevitable and model outcomes are open to multiple types of uncertainty. In this paper, we present a systematic approach to uncertainty and sensitivity analysis. We access the uncertainty of landslide susceptibility maps produced with GIS-MCDA techniques. A new spatially-explicit approach and Dempster-Shafer Theory (DST) are employed to assess the uncertainties associated with two MCDA techniques, namely Analytical Hierarchical Process (AHP) and Ordered Weighted Averaging (OWA) implemented in GIS. The methodology is composed of three different phases. First, weights are computed to express the relative importance of factors (criteria) for landslide susceptibility. Next, the uncertainty and sensitivity of landslide susceptibility is analyzed as a function of weights using Monte Carlo Simulation and Global Sensitivity Analysis. Finally, the results are validated using a landslide inventory database and by applying DST. The comparisons of the obtained landslide susceptibility maps of both MCDA techniques with known landslides show that the AHP outperforms OWA. However, the OWA-generated landslide susceptibility map shows lower uncertainty than the AHP-generated map. The results demonstrate that further improvement in the accuracy of GIS-based MCDA can be achieved by employing an integrated uncertainty-sensitivity analysis approach, in which the uncertainty of landslide susceptibility model is decomposed and attributed to model's criteria weights.

A GIS based spatially-explicit sensitivity and uncertainty analysis approach for multi-criteria decision analysis

NASA Astrophysics Data System (ADS)

Feizizadeh, Bakhtiar; Jankowski, Piotr; Blaschke, Thomas

2014-03-01

GIS multicriteria decision analysis (MCDA) techniques are increasingly used in landslide susceptibility mapping for the prediction of future hazards, land use planning, as well as for hazard preparedness. However, the uncertainties associated with MCDA techniques are inevitable and model outcomes are open to multiple types of uncertainty. In this paper, we present a systematic approach to uncertainty and sensitivity analysis. We access the uncertainty of landslide susceptibility maps produced with GIS-MCDA techniques. A new spatially-explicit approach and Dempster-Shafer Theory (DST) are employed to assess the uncertainties associated with two MCDA techniques, namely Analytical Hierarchical Process (AHP) and Ordered Weighted Averaging (OWA) implemented in GIS. The methodology is composed of three different phases. First, weights are computed to express the relative importance of factors (criteria) for landslide susceptibility. Next, the uncertainty and sensitivity of landslide susceptibility is analyzed as a function of weights using Monte Carlo Simulation and Global Sensitivity Analysis. Finally, the results are validated using a landslide inventory database and by applying DST. The comparisons of the obtained landslide susceptibility maps of both MCDA techniques with known landslides show that the AHP outperforms OWA. However, the OWA-generated landslide susceptibility map shows lower uncertainty than the AHP-generated map. The results demonstrate that further improvement in the accuracy of GIS-based MCDA can be achieved by employing an integrated uncertainty-sensitivity analysis approach, in which the uncertainty of landslide susceptibility model is decomposed and attributed to model's criteria weights.

The inclusion of N-terminal pro-brain natriuretic peptide in a sensitive screening strategy for systemic sclerosis-related pulmonary arterial hypertension: a cohort study

PubMed Central

2013-01-01

Introduction Pulmonary arterial hypertension (PAH) is a major cause of mortality in systemic sclerosis (SSc). Screening guidelines for PAH recommend multiple investigations, including annual echocardiography, which together have low specificity and may not be cost-effective. We sought to evaluate the predictive accuracy of serum N-terminal pro-brain natriuretic peptide (NT-proBNP) in combination with pulmonary function tests (PFT) (‘proposed’ algorithm) in a screening algorithm for SSc-PAH. Methods We evaluated our proposed algorithm (PFT with NT-proBNP) on 49 consecutive SSc patients with suspected pulmonary hypertension undergoing right heart catherisation (RHC). The predictive accuracy of the proposed algorithm was compared with existing screening recommendations, and is presented as sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). Results Overall, 27 patients were found to have pulmonary hypertension (PH) at RHC, while 22 had no PH. The sensitivity, specificity, PPV and NPV of the proposed algorithm for PAH was 94.1%, 54.5%, 61.5% and 92.3%, respectively; current European Society of Cardiology (ESC)/European Respiratory Society (ERS) guidelines achieved a sensitivity, specificity, PPV and NPV of 94.1%, 31.8%, 51.6% and 87.5%, respectively. In an alternate case scenario analysis, estimating a PAH prevalence of 10%, the proposed algorithm achieved a sensitivity, specificity, PPV and NPV for PAH of 94.1%, 54.5%, 18.7% and 98.8%, respectively. Conclusions The combination of NT-proBNP with PFT is a sensitive, yet simple and non-invasive, screening strategy for SSc-PAH. Patients with a positive screening result can be referred for echocardiography, and further confirmatory testing for PAH. In this way, it may be possible to shift the burden of routine screening away from echocardiography. The findings of this study should be confirmed in larger studies. PMID:24246100

Determination of the optimal training principle and input variables in artificial neural network model for the biweekly chlorophyll-a prediction: a case study of the Yuqiao Reservoir, China.

PubMed

Liu, Yu; Xi, Du-Gang; Li, Zhao-Liang

2015-01-01

Predicting the levels of chlorophyll-a (Chl-a) is a vital component of water quality management, which ensures that urban drinking water is safe from harmful algal blooms. This study developed a model to predict Chl-a levels in the Yuqiao Reservoir (Tianjin, China) biweekly using water quality and meteorological data from 1999-2012. First, six artificial neural networks (ANNs) and two non-ANN methods (principal component analysis and the support vector regression model) were compared to determine the appropriate training principle. Subsequently, three predictors with different input variables were developed to examine the feasibility of incorporating meteorological factors into Chl-a prediction, which usually only uses water quality data. Finally, a sensitivity analysis was performed to examine how the Chl-a predictor reacts to changes in input variables. The results were as follows: first, ANN is a powerful predictive alternative to the traditional modeling techniques used for Chl-a prediction. The back program (BP) model yields slightly better results than all other ANNs, with the normalized mean square error (NMSE), the correlation coefficient (Corr), and the Nash-Sutcliffe coefficient of efficiency (NSE) at 0.003 mg/l, 0.880 and 0.754, respectively, in the testing period. Second, the incorporation of meteorological data greatly improved Chl-a prediction compared to models solely using water quality factors or meteorological data; the correlation coefficient increased from 0.574-0.686 to 0.880 when meteorological data were included. Finally, the Chl-a predictor is more sensitive to air pressure and pH compared to other water quality and meteorological variables.

Sensitivity and Nonlinearity of Thermoacoustic Oscillations

NASA Astrophysics Data System (ADS)

Juniper, Matthew P.; Sujith, R. I.

2018-01-01

Nine decades of rocket engine and gas turbine development have shown that thermoacoustic oscillations are difficult to predict but can usually be eliminated with relatively small ad hoc design changes. These changes can, however, be ruinously expensive to devise. This review explains why linear and nonlinear thermoacoustic behavior is so sensitive to parameters such as operating point, fuel composition, and injector geometry. It shows how nonperiodic behavior arises in experiments and simulations and discusses how fluctuations in thermoacoustic systems with turbulent reacting flow, which are usually filtered or averaged out as noise, can reveal useful information. Finally, it proposes tools to exploit this sensitivity in the future: adjoint-based sensitivity analysis to optimize passive control designs and complex systems theory to warn of impending thermoacoustic oscillations and to identify the most sensitive elements of a thermoacoustic system.

Cognitive domains that predict time to diagnosis in prodromal Huntington disease.

PubMed

Harrington, Deborah Lynn; Smith, Megan M; Zhang, Ying; Carlozzi, Noelle E; Paulsen, Jane S

2012-06-01

Prodromal Huntington's disease (prHD) is associated with a myriad of cognitive changes but the domains that best predict time to clinical diagnosis have not been studied. This is a notable gap because some domains may be more sensitive to cognitive decline, which would inform clinical trials. The present study sought to characterise cognitive domains underlying a large test battery and for the first time, evaluate their ability to predict time to diagnosis. Participants included gene negative and gene positive prHD participants who were enrolled in the PREDICT-HD study. The CAG-age product (CAP) score was the measure of an individual's genetic signature. A factor analysis of 18 tests was performed to identify sets of measures or latent factors that elucidated core constructs of tests. Factor scores were then fit to a survival model to evaluate their ability to predict time to diagnosis. Six factors were identified: (1) speed/inhibition, (2) verbal working memory, (3) motor planning/speed, (4) attention-information integration, (5) sensory-perceptual processing and (6) verbal learning/memory. Factor scores were sensitive to worsening of cognitive functioning in prHD, typically more so than performances on individual tests comprising the factors. Only the motor planning/speed and sensory-perceptual processing factors predicted time to diagnosis, after controlling for CAP scores and motor symptoms. Conclusions The results suggest that motor planning/speed and sensory-perceptual processing are important markers of disease prognosis. The findings also have implications for using composite indices of cognition in preventive Huntington's disease trials where they may be more sensitive than individual tests.

Detecting non-orthology in the COGs database and other approaches grouping orthologs using genome-specific best hits.

PubMed

Dessimoz, Christophe; Boeckmann, Brigitte; Roth, Alexander C J; Gonnet, Gaston H

2006-01-01

Correct orthology assignment is a critical prerequisite of numerous comparative genomics procedures, such as function prediction, construction of phylogenetic species trees and genome rearrangement analysis. We present an algorithm for the detection of non-orthologs that arise by mistake in current orthology classification methods based on genome-specific best hits, such as the COGs database. The algorithm works with pairwise distance estimates, rather than computationally expensive and error-prone tree-building methods. The accuracy of the algorithm is evaluated through verification of the distribution of predicted cases, case-by-case phylogenetic analysis and comparisons with predictions from other projects using independent methods. Our results show that a very significant fraction of the COG groups include non-orthologs: using conservative parameters, the algorithm detects non-orthology in a third of all COG groups. Consequently, sequence analysis sensitive to correct orthology assignments will greatly benefit from these findings.

Global proteomics profiling improves drug sensitivity prediction: results from a multi-omics, pan-cancer modeling approach.

PubMed

Ali, Mehreen; Khan, Suleiman A; Wennerberg, Krister; Aittokallio, Tero

2018-04-15

Proteomics profiling is increasingly being used for molecular stratification of cancer patients and cell-line panels. However, systematic assessment of the predictive power of large-scale proteomic technologies across various drug classes and cancer types is currently lacking. To that end, we carried out the first pan-cancer, multi-omics comparative analysis of the relative performance of two proteomic technologies, targeted reverse phase protein array (RPPA) and global mass spectrometry (MS), in terms of their accuracy for predicting the sensitivity of cancer cells to both cytotoxic chemotherapeutics and molecularly targeted anticancer compounds. Our results in two cell-line panels demonstrate how MS profiling improves drug response predictions beyond that of the RPPA or the other omics profiles when used alone. However, frequent missing MS data values complicate its use in predictive modeling and required additional filtering, such as focusing on completely measured or known oncoproteins, to obtain maximal predictive performance. Rather strikingly, the two proteomics profiles provided complementary predictive signal both for the cytotoxic and targeted compounds. Further, information about the cellular-abundance of primary target proteins was found critical for predicting the response of targeted compounds, although the non-target features also contributed significantly to the predictive power. The clinical relevance of the selected protein markers was confirmed in cancer patient data. These results provide novel insights into the relative performance and optimal use of the widely applied proteomic technologies, MS and RPPA, which should prove useful in translational applications, such as defining the best combination of omics technologies and marker panels for understanding and predicting drug sensitivities in cancer patients. Processed datasets, R as well as Matlab implementations of the methods are available at https://github.com/mehr-een/bemkl-rbps. mehreen.ali@helsinki.fi or tero.aittokallio@fimm.fi. Supplementary data are available at Bioinformatics online.

A computational model that predicts behavioral sensitivity to intracortical microstimulation

PubMed Central

Kim, Sungshin; Callier, Thierri; Bensmaia, Sliman J.

2016-01-01

Objective Intracortical microstimulation (ICMS) is a powerful tool to investigate the neural mechanisms of perception and can be used to restore sensation for patients who have lost it. While sensitivity to ICMS has previously been characterized, no systematic framework has been developed to summarize the detectability of individual ICMS pulse trains or the discriminability of pairs of pulse trains. Approach We develop a simple simulation that describes the responses of a population of neurons to a train of electrical pulses delivered through a microelectrode. We then perform an ideal observer analysis on the simulated population responses to predict the behavioral performance of non-human primates in ICMS detection and discrimination tasks. Main results Our computational model can predict behavioral performance across a wide range of stimulation conditions with high accuracy (R2 = 0.97) and generalizes to novel ICMS pulse trains that were not used to fit its parameters. Furthermore, the model provides a theoretical basis for the finding that amplitude discrimination based on ICMS violates Weber's law. Significance The model can be used to characterize the sensitivity to ICMS across the range of perceptible and safe stimulation regimes. As such, it will be a useful tool for both neuroscience and neuroprosthetics. PMID:27977419

A computational model that predicts behavioral sensitivity to intracortical microstimulation.

PubMed

Kim, Sungshin; Callier, Thierri; Bensmaia, Sliman J

2017-02-01

Intracortical microstimulation (ICMS) is a powerful tool to investigate the neural mechanisms of perception and can be used to restore sensation for patients who have lost it. While sensitivity to ICMS has previously been characterized, no systematic framework has been developed to summarize the detectability of individual ICMS pulse trains or the discriminability of pairs of pulse trains. We develop a simple simulation that describes the responses of a population of neurons to a train of electrical pulses delivered through a microelectrode. We then perform an ideal observer analysis on the simulated population responses to predict the behavioral performance of non-human primates in ICMS detection and discrimination tasks. Our computational model can predict behavioral performance across a wide range of stimulation conditions with high accuracy (R 2  = 0.97) and generalizes to novel ICMS pulse trains that were not used to fit its parameters. Furthermore, the model provides a theoretical basis for the finding that amplitude discrimination based on ICMS violates Weber's law. The model can be used to characterize the sensitivity to ICMS across the range of perceptible and safe stimulation regimes. As such, it will be a useful tool for both neuroscience and neuroprosthetics.

A computational model that predicts behavioral sensitivity to intracortical microstimulation

NASA Astrophysics Data System (ADS)

Kim, Sungshin; Callier, Thierri; Bensmaia, Sliman J.

2017-02-01

Objective. Intracortical microstimulation (ICMS) is a powerful tool to investigate the neural mechanisms of perception and can be used to restore sensation for patients who have lost it. While sensitivity to ICMS has previously been characterized, no systematic framework has been developed to summarize the detectability of individual ICMS pulse trains or the discriminability of pairs of pulse trains. Approach. We develop a simple simulation that describes the responses of a population of neurons to a train of electrical pulses delivered through a microelectrode. We then perform an ideal observer analysis on the simulated population responses to predict the behavioral performance of non-human primates in ICMS detection and discrimination tasks. Main results. Our computational model can predict behavioral performance across a wide range of stimulation conditions with high accuracy (R 2 = 0.97) and generalizes to novel ICMS pulse trains that were not used to fit its parameters. Furthermore, the model provides a theoretical basis for the finding that amplitude discrimination based on ICMS violates Weber’s law. Significance. The model can be used to characterize the sensitivity to ICMS across the range of perceptible and safe stimulation regimes. As such, it will be a useful tool for both neuroscience and neuroprosthetics.

Validation and sensitivity of the FINE Bayesian network for forecasting aquatic exposure to nano-silver.

PubMed

Money, Eric S; Barton, Lauren E; Dawson, Joseph; Reckhow, Kenneth H; Wiesner, Mark R

2014-03-01

The adaptive nature of the Forecasting the Impacts of Nanomaterials in the Environment (FINE) Bayesian network is explored. We create an updated FINE model (FINEAgNP-2) for predicting aquatic exposure concentrations of silver nanoparticles (AgNP) by combining the expert-based parameters from the baseline model established in previous work with literature data related to particle behavior, exposure, and nano-ecotoxicology via parameter learning. We validate the AgNP forecast from the updated model using mesocosm-scale field data and determine the sensitivity of several key variables to changes in environmental conditions, particle characteristics, and particle fate. Results show that the prediction accuracy of the FINEAgNP-2 model increased approximately 70% over the baseline model, with an error rate of only 20%, suggesting that FINE is a reliable tool to predict aquatic concentrations of nano-silver. Sensitivity analysis suggests that fractal dimension, particle diameter, conductivity, time, and particle fate have the most influence on aquatic exposure given the current knowledge; however, numerous knowledge gaps can be identified to suggest further research efforts that will reduce the uncertainty in subsequent exposure and risk forecasts. Copyright © 2013 Elsevier B.V. All rights reserved.

Differentiating the origin of outflow tract ventricular arrhythmia using a simple, novel approach.

PubMed

Efimova, Elena; Dinov, Borislav; Acou, Willem-Jan; Schirripa, Valentina; Kornej, Jelena; Kosiuk, Jedrzej; Rolf, Sascha; Sommer, Philipp; Richter, Sergio; Bollmann, Andreas; Hindricks, Gerhard; Arya, Arash

2015-07-01

Numerous electrocardiographic (ECG) criteria have been proposed to identify localization of outflow tract ventricular arrhythmias (OT-VAs); however, in some cases, it is difficult to accurately localize the origin of OT-VA using the surface ECG. The purpose of this study was to assess a simple criterion for localization of OT-VAs during electrophysiology study. We measured the interval from the onset of the earliest QRS complex of premature ventricular contractions (PVCs) to the distal right ventricular apical signal (the QRS-RVA interval) in 66 patients (31 men aged 53.3 ± 14.0 years; right ventricular outflow tract [RVOT] origin in 37) referred for ablation of symptomatic outflow tract PVCs. We prospectively validated this criterion in 39 patients (22 men aged 52 ± 15 years; RVOT origin in 19). Compared with patients with RVOT PVCs, the QRS-RVA interval was significantly longer in patients with left ventricular outflow tract (LVOT) PVCs (70 ± 14 vs 33.4±10 ms, P < .001). Receiver operating characteristic analysis showed that a QRS-RVA interval ≥49 ms had sensitivity, specificity, and positive and negative predictive values of 100%, 94.6%, 93.5%, and 100%, respectively, for prediction of an LVOT origin. The same analysis in the validation cohort showed sensitivity, specificity, and positive and negative predictive values of 94.7%, 95%, 95%, and 94.7%, respectively. When these data were combined, a QRS-RVA interval ≥49 ms had sensitivity, specificity, and positive and negative predictive values of 98%, 94.6%, 94.1%, and 98.1%, respectively, for prediction of an LVOT origin. A QRS-RVA interval ≥49 ms suggests an LVOT origin. The QRS-RVA interval is a simple and accurate criterion for differentiating the origin of outflow tract arrhythmia during electrophysiology study; however, the accuracy of this criterion in identifying OT-VA from the right coronary cusp is limited. Copyright © 2015 Heart Rhythm Society. Published by Elsevier Inc. All rights reserved.

Identifying Important Attributes for Prognostic Prediction in Traumatic Brain Injury Patients. A Hybrid Method of Decision Tree and Neural Network.

PubMed

Pourahmad, Saeedeh; Hafizi-Rastani, Iman; Khalili, Hosseinali; Paydar, Shahram

2016-10-17

Generally, traumatic brain injury (TBI) patients do not have a stable condition, particularly after the first week of TBI. Hence, indicating the attributes in prognosis through a prediction model is of utmost importance since it helps caregivers with treatment-decision options, or prepares the relatives for the most-likely outcome. This study attempted to determine and order the attributes in prognostic prediction in TBI patients, based on early clinical findings. A hybrid method was employed, which combines a decision tree (DT) and an artificial neural network (ANN) in order to improve the modeling process. The DT approach was applied as the initial analysis of the network architecture to increase accuracy in prediction. Afterwards, the ANN structure was mapped from the initial DT based on a part of the data. Subsequently, the designed network was trained and validated by the remaining data. 5-fold cross-validation method was applied to train the network. The area under the receiver operating characteristic (ROC) curve, sensitivity, specificity, and accuracy rate were utilized as performance measures. The important attributes were then determined from the trained network using two methods: change of mean squared error (MSE), and sensitivity analysis (SA). The hybrid method offered better results compared to the DT method. The accuracy rate of 86.3 % vs. 82.2 %, sensitivity value of 55.1 % vs. 47.6 %, specificity value of 93.6 % vs. 91.1 %, and the area under the ROC curve of 0.705 vs. 0.695 were achieved for the hybrid method and DT, respectively. However, the attributes' order by DT method was more consistent with the clinical literature. The combination of different modeling methods can enhance their performance. However, it may create some complexities in computations and interpretations. The outcome of the present study could deliver some useful hints in prognostic prediction on the basis of early clinical findings for TBI patients.

A whole blood gene expression-based signature for smoking status

PubMed Central

2012-01-01

Background Smoking is the leading cause of preventable death worldwide and has been shown to increase the risk of multiple diseases including coronary artery disease (CAD). We sought to identify genes whose levels of expression in whole blood correlate with self-reported smoking status. Methods Microarrays were used to identify gene expression changes in whole blood which correlated with self-reported smoking status; a set of significant genes from the microarray analysis were validated by qRT-PCR in an independent set of subjects. Stepwise forward logistic regression was performed using the qRT-PCR data to create a predictive model whose performance was validated in an independent set of subjects and compared to cotinine, a nicotine metabolite. Results Microarray analysis of whole blood RNA from 209 PREDICT subjects (41 current smokers, 4 quit ≤ 2 months, 64 quit > 2 months, 100 never smoked; NCT00500617) identified 4214 genes significantly correlated with self-reported smoking status. qRT-PCR was performed on 1,071 PREDICT subjects across 256 microarray genes significantly correlated with smoking or CAD. A five gene (CLDND1, LRRN3, MUC1, GOPC, LEF1) predictive model, derived from the qRT-PCR data using stepwise forward logistic regression, had a cross-validated mean AUC of 0.93 (sensitivity=0.78; specificity=0.95), and was validated using 180 independent PREDICT subjects (AUC=0.82, CI 0.69-0.94; sensitivity=0.63; specificity=0.94). Plasma from the 180 validation subjects was used to assess levels of cotinine; a model using a threshold of 10 ng/ml cotinine resulted in an AUC of 0.89 (CI 0.81-0.97; sensitivity=0.81; specificity=0.97; kappa with expression model = 0.53). Conclusion We have constructed and validated a whole blood gene expression score for the evaluation of smoking status, demonstrating that clinical and environmental factors contributing to cardiovascular disease risk can be assessed by gene expression. PMID:23210427

Prediction of Transcriptional Terminators in Bacillus subtilis and Related Species

PubMed Central

de Hoon, Michiel J. L.; Makita, Yuko; Nakai, Kenta; Miyano, Satoru

2005-01-01

In prokaryotes, genes belonging to the same operon are transcribed in a single mRNA molecule. Transcription starts as the RNA polymerase binds to the promoter and continues until it reaches a transcriptional terminator. Some terminators rely on the presence of the Rho protein, whereas others function independently of Rho. Such Rho-independent terminators consist of an inverted repeat followed by a stretch of thymine residues, allowing us to predict their presence directly from the DNA sequence. Unlike in Escherichia coli, the Rho protein is dispensable in Bacillus subtilis, suggesting a limited role for Rho-dependent termination in this organism and possibly in other Firmicutes. We analyzed 463 experimentally known terminating sequences in B. subtilis and found a decision rule to distinguish Rho-independent transcriptional terminators from non-terminating sequences. The decision rule allowed us to find the boundaries of operons in B. subtilis with a sensitivity and specificity of about 94%. Using the same decision rule, we found an average sensitivity of 94% for 57 bacteria belonging to the Firmicutes phylum, and a considerably lower sensitivity for other bacteria. Our analysis shows that Rho-independent termination is dominant for Firmicutes in general, and that the properties of the transcriptional terminators are conserved. Terminator prediction can be used to reliably predict the operon structure in these organisms, even in the absence of experimentally known operons. Genome-wide predictions of Rho-independent terminators for the 57 Firmicutes are available in the Supporting Information section. PMID:16110342

Early prediction of olanzapine-induced weight gain for schizophrenia patients.

PubMed

Lin, Ching-Hua; Lin, Shih-Chi; Huang, Yu-Hui; Wang, Fu-Chiang; Huang, Chun-Jen

2018-05-01

The aim of this study was to determine whether weight changes at week 2 or other factors predicted weight gain at week 6 for schizophrenia patients receiving olanzapine. This study was the secondary analysis of a six-week trial for 94 patients receiving olanzapine (5 mg/d) plus trifluoperazine (5 mg/d), or olanzapine (10 mg/d) alone. Patients were included in analysis only if they had completed the 6-week trial (per protocol analysis). Weight gain was defined as a 7% or greater increase of the patient's baseline weight. The receiver operating characteristic curve was employed to determine the optimal cutoff points of statistically significant predictors. Eleven of the 67 patients completing the 6-week trial were classified as weight gainers. Weight change at week 2 was the statistically significant predictor for ultimate weight gain at week 6. A weight change of 1.0 kg at week 2 appeared to be the optimal cutoff point, with a sensitivity of 0.92, a specificity of 0.75, and an AUC of 0.85. Using weight change at week 2 to predict weight gain at week 6 is favorable in terms of both specificity and sensitivity. Weight change of 1.0 kg or more at 2 weeks is a reliable predictor. Copyright © 2018 Elsevier B.V. All rights reserved.

Intraoperative clinical assessment and pressure measurements of sentinel lymph nodes in breast cancer.

PubMed

Nathanson, S David; Shah, Rupen; Chitale, Dhananjay A; Mahan, Meredith

2014-01-01

Clinicians have long regarded firm enlarged axillary nodes as suspicious for metastasis, and this has been confirmed to represent increased pressure in sentinel lymph nodes (SLN) in vivo in breast cancer. We hypothesized that measuring intranodal pressure (INP) in the operating room would correlate with metastasis size and be more sensitive than clinical observation. Intranodal pressure mmHg was measured in SLNs #1 and #2 (N = 134 and 32) in 122 patients with T1/2 cN0 and 6 controls (T0) (8 bilateral). Clinical "Level of Suspicion" (LOS) was: 0 = benign; 1 = slightly suspicious; 2 = obvious metastasis. Statistical analysis was performed to compare INP, LOS, and SLN metastasis size mm. Sentinel lymph nodes met size correlated with INP (r = 0.65; p < 0.001). INP was 22.0 ± 1.3 mmHg in 35 SLNs with metastases compared with 9.3 ± 0.7 mmHg in 132 without (p < 0.001). Six groups created by combining LOS 0, 1, and 2 with INP >17 or ≤17 mmHg showed a significant (p < 0.001) correlation with SLN histology; sensitivity and specificity for LOS = 2/INP >17 mmHg = 100 % at predicting metastases; LOS = 0/INP ≤17 mmHg most often correct at predicting negative nodes (sensitivity 50 %, specificity 92.9 %, positive predictive value 55 %, negative predictive value 90.7 %). INP was better than LOS at predicting positive nodes in eight patients where INP was >17 mmHg. INP and LOS correlated significantly (p < 0.001). Clinical suspicion of metastasis correlated well with INP particularly at predicting macrometastases. INP was slightly better at predicting micrometastases. Measurement of INP may be valuable adjunct when performing SLN biopsy when further axillary surgery is contemplated.

Predicting suicide attempts with the SAD PERSONS scale: a longitudinal analysis.

PubMed

Bolton, James M; Spiwak, Rae; Sareen, Jitender

2012-06-01

The SAD PERSONS scale is a widely used risk assessment tool for suicidal behavior despite a paucity of supporting data. The objective of this study was to examine the ability of the scale in predicting suicide attempts. Participants consisted of consecutive referrals (N=4,019) over 2 years (January 1, 2009 to December 31, 2010) to psychiatric services in the emergency departments of the 2 largest tertiary care hospitals in the province of Manitoba, Canada. SAD PERSONS and Modified SAD PERSONS (MSPS) scale scores were recorded for individuals at their index and all subsequent presentations. The 2 main outcome measures in the study included current suicide attempts (at index presentation) and future suicide attempts (within the next 6 months). The ability of the scales to predict suicide attempts was evaluated with logistic regression, sensitivity and specificity analyses, and receiver operating characteristic curves. 566 people presented with suicide attempts (14.1% of the sample). Both SAD PERSONS and MSPS showed poor predictive ability for future suicide attempts. Compared to low risk scores, high risk baseline scores had low sensitivity (19.6% and 40.0%, respectively) and low positive predictive value (5.3% and 7.4%, respectively). SAD PERSONS did not predict suicide attempts better than chance (area under the curve =0.572; 95% confidence interval [CI], 0.51-0.64; P value nonsignificant). Stepwise regression identified 5 original scale items that accounted for the greatest proportion of future suicide attempt variance. High risk scores using this model had high sensitivity (93.5%) and were associated with a 5-fold higher likelihood of future suicide attempt presentation (odds ratio =5.58; 95% CI, 2.24-13.86; P<.001). In their current form, SAD PERSONS and MSPS do not accurately predict future suicide attempts. © Copyright 2012 Physicians Postgraduate Press, Inc.

Evaluation of differential representative values between Chinese hamster cells and human lymphocytes in mitomycin C-induced cytogenetic assays and caspase-3 activity.

PubMed

Liao, Pei-Hu; Lin, Ruey-Hseng; Yang, Ming-Ling; Li, Yi-Ching; Kuan, Yu-Hsiang

2012-03-01

Chinese hamster ovary (CHO) cells, its lung fibroblasts (V79), and human lymphocytes are routinely used in in vitro cytogenetic assays, which include micronuclei (MN), sister chromatid exchange (SCE), and chromosome aberration (CA) assays. Mitomycin C (MMC), a DNA cross-link alkylating agent, is both an anticancer medicine and a carcinogen. To study the differential representative values of cell types in MMC-treated cytogenetic assays and its upstream factor, cysteine aspartic acid-specific protease (caspase)-3. Among the chosen cell types, lymphocytes expressed the highest sensitivity in all three MMC-induced assays, whereas CHO and V79 showed varied sensitivity in different assays. In MN assay, the sensitivity of CHO is higher than or equal to V79; in SCE assay, the sensitivity of CHO is the same as V79; and in CA assay, the sensitivity of CHO is higher than V79. In-depth analysis of CA revealed that in chromatid breaks and dicentrics formation, lymphocyte was the most sensitive of all and CHO was more sensitive than V79; and in acentrics and interchanges formation, lymphocyte was much more sensitive than the others. Furthermore, we found caspase-3 activity plays an important role in MMC-induced cytogenetic assays, with MMC-induced caspase-3 activity resulting in more sensitivity in lymphocytes than in CHO and V79. Based on these findings, lymphocyte will make a suitable predictive or representative control reference in cytogenetic assays and caspase-3 activity with its high specificity, positive predictive value, and sensitivity.

Correlation of SA349/2 helicopter flight-test data with a comprehensive rotorcraft model

NASA Technical Reports Server (NTRS)

Yamauchi, Gloria K.; Heffernan, Ruth M.; Gaubert, Michel

1986-01-01

A comprehensive rotorcraft analysis model was used to predict blade aerodynamic and structural loads for comparison with flight test data. The data were obtained from an SA349/2 helicopter with an advanced geometry rotor. Sensitivity of the correlation to wake geometry, blade dynamics, and blade aerodynamic effects was investigated. Blade chordwise pressure coefficients were predicted for the blade transonic regimes using the model coupled with two finite-difference codes.

Extracting sensitive spectrum bands of rapeseed using multiscale multifractal detrended fluctuation analysis

NASA Astrophysics Data System (ADS)

Jiang, Shan; Wang, Fang; Shen, Luming; Liao, Guiping; Wang, Lin

2017-03-01

Spectrum technology has been widely used in crop non-destructive testing diagnosis for crop information acquisition. Since spectrum covers a wide range of bands, it is of critical importance to extract the sensitive bands. In this paper, we propose a methodology to extract the sensitive spectrum bands of rapeseed using multiscale multifractal detrended fluctuation analysis. Our obtained sensitive bands are relatively robust in the range of 534 nm-574 nm. Further, by using the multifractal parameter (Hurst exponent) of the extracted sensitive bands, we propose a prediction model to forecast the Soil and plant analyzer development values ((SPAD), often used as a parameter to indicate the chlorophyll content) and an identification model to distinguish the different planting patterns. Three vegetation indices (VIs) based on previous work are used for comparison. Three evaluation indicators, namely, the root mean square error, the correlation coefficient, and the relative error employed in the SPAD values prediction model all demonstrate that our Hurst exponent has the best performance. Four rapeseed compound planting factors, namely, seeding method, planting density, fertilizer type, and weed control method are considered in the identification model. The Youden indices calculated by the random decision forest method and the K-nearest neighbor method show that our Hurst exponent is superior to other three Vis, and their combination for the factor of seeding method. In addition, there is no significant difference among the five features for other three planting factors. This interesting finding suggests that the transplanting and the direct seeding would make a big difference in the growth of rapeseed.

FUNCTIONAL SUBCLONE PROFILING FOR PREDICTION OF TREATMENT-INDUCED INTRA-TUMOR POPULATION SHIFTS AND DISCOVERY OF RATIONAL DRUG COMBINATIONS IN HUMAN GLIOBLASTOMA

PubMed Central

Reinartz, Roman; Wang, Shanshan; Kebir, Sied; Silver, Daniel J.; Wieland, Anja; Zheng, Tong; Küpper, Marius; Rauschenbach, Laurèl; Fimmers, Rolf; Shepherd, Timothy M.; Trageser, Daniel; Till, Andreas; Schäfer, Niklas; Glas, Martin; Hillmer, Axel M.; Cichon, Sven; Smith, Amy A.; Pietsch, Torsten; Liu, Ying; Reynolds, Brent A.; Yachnis, Anthony; Pincus, David W.; Simon, Matthias; Brüstle, Oliver; Steindler, Dennis A.; Scheffler, Björn

2016-01-01

Purpose Investigation of clonal heterogeneity may be key to understanding mechanisms of therapeutic failure in human cancer. However, little is known on the consequences of therapeutic intervention on the clonal composition of solid tumors. Experimental Design Here, we used 33 single cell-derived subclones generated from five clinical glioblastoma specimens for exploring intra- and inter-individual spectra of drug resistance profiles in vitro. In a personalized setting, we explored whether differences in pharmacological sensitivity among subclones could be employed to predict drug-dependent changes to the clonal composition of tumors. Results Subclones from individual tumors exhibited a remarkable heterogeneity of drug resistance to a library of potential anti-glioblastoma compounds. A more comprehensive intra-tumoral analysis revealed that stable genetic and phenotypic characteristics of co-existing subclones could be correlated with distinct drug sensitivity profiles. The data obtained from differential drug response analysis could be employed to predict clonal population shifts within the naïve parental tumor in vitro and in orthotopic xenografts. Furthermore, the value of pharmacological profiles could be shown for establishing rational strategies for individualized secondary lines of treatment. Conclusions Our data provide a previously unrecognized strategy for revealing functional consequences of intra-tumor heterogeneity by enabling predictive modeling of treatment-related subclone dynamics in human glioblastoma. PMID:27521447

Prediction of zeolite-cement-sand unconfined compressive strength using polynomial neural network

NASA Astrophysics Data System (ADS)

MolaAbasi, H.; Shooshpasha, I.

2016-04-01

The improvement of local soils with cement and zeolite can provide great benefits, including strengthening slopes in slope stability problems, stabilizing problematic soils and preventing soil liquefaction. Recently, dosage methodologies are being developed for improved soils based on a rational criterion as it exists in concrete technology. There are numerous earlier studies showing the possibility of relating Unconfined Compressive Strength (UCS) and Cemented sand (CS) parameters (voids/cement ratio) as a power function fits. Taking into account the fact that the existing equations are incapable of estimating UCS for zeolite cemented sand mixture (ZCS) well, artificial intelligence methods are used for forecasting them. Polynomial-type neural network is applied to estimate the UCS from more simply determined index properties such as zeolite and cement content, porosity as well as curing time. In order to assess the merits of the proposed approach, a total number of 216 unconfined compressive tests have been done. A comparison is carried out between the experimentally measured UCS with the predictions in order to evaluate the performance of the current method. The results demonstrate that generalized polynomial-type neural network has a great ability for prediction of the UCS. At the end sensitivity analysis of the polynomial model is applied to study the influence of input parameters on model output. The sensitivity analysis reveals that cement and zeolite content have significant influence on predicting UCS.

Systematic review and meta-analysis of the diagnostic and therapeutic role of water-soluble contrast agent in adhesive small bowel obstruction.

PubMed

Branco, B C; Barmparas, G; Schnüriger, B; Inaba, K; Chan, L S; Demetriades, D

2010-04-01

This meta-analysis assessed the diagnostic and therapeutic role of water-soluble contrast agent (WSCA) in adhesive small bowel obstruction (SBO). PubMed, Embase and Cochrane databases were searched systematically. The primary outcome in the diagnostic role of WSCA was its ability to predict the need for surgery. In the therapeutic role, the following were evaluated: resolution of SBO without surgery, time from admission to resolution, duration of hospital stay, complications and mortality. To assess the diagnostic role of WSCA, pooled estimates of sensitivity, specificity, positive and negative predictive values, and likelihood ratios were derived. For the therapeutic role of WSCA, weighted odds ratio (OR) and weighted mean difference (WMD) were obtained. Fourteen prospective studies were included. The appearance of contrast in the colon within 4-24 h after administration had a sensitivity of 96 per cent and specificity of 98 per cent in predicting resolution of SBO. WSCA administration was effective in reducing the need for surgery (OR 0.62; P = 0.007) and shortening hospital stay (WMD -1.87 days; P < 0.001) compared with conventional treatment. Water-soluble contrast was effective in predicting the need for surgery in patients with adhesive SBO. In addition, it reduced the need for operation and shortened hospital stay. Copyright (c) 2010 British Journal of Surgery Society Ltd. Published by John Wiley & Sons, Ltd.

Hamiltonian Markov Chain Monte Carlo Methods for the CUORE Neutrinoless Double Beta Decay Sensitivity

NASA Astrophysics Data System (ADS)

Graham, Eleanor; Cuore Collaboration

2017-09-01

The CUORE experiment is a large-scale bolometric detector seeking to observe the never-before-seen process of neutrinoless double beta decay. Predictions for CUORE's sensitivity to neutrinoless double beta decay allow for an understanding of the half-life ranges that the detector can probe, and also to evaluate the relative importance of different detector parameters. Currently, CUORE uses a Bayesian analysis based in BAT, which uses Metropolis-Hastings Markov Chain Monte Carlo, for its sensitivity studies. My work evaluates the viability and potential improvements of switching the Bayesian analysis to Hamiltonian Monte Carlo, realized through the program Stan and its Morpho interface. I demonstrate that the BAT study can be successfully recreated in Stan, and perform a detailed comparison between the results and computation times of the two methods.

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.

Real-data comparison of data mining methods in prediction of diabetes in iran.

PubMed

Tapak, Lily; Mahjub, Hossein; Hamidi, Omid; Poorolajal, Jalal

2013-09-01

Diabetes is one of the most common non-communicable diseases in developing countries. Early screening and diagnosis play an important role in effective prevention strategies. This study compared two traditional classification methods (logistic regression and Fisher linear discriminant analysis) and four machine-learning classifiers (neural networks, support vector machines, fuzzy c-mean, and random forests) to classify persons with and without diabetes. The data set used in this study included 6,500 subjects from the Iranian national non-communicable diseases risk factors surveillance obtained through a cross-sectional survey. The obtained sample was based on cluster sampling of the Iran population which was conducted in 2005-2009 to assess the prevalence of major non-communicable disease risk factors. Ten risk factors that are commonly associated with diabetes were selected to compare the performance of six classifiers in terms of sensitivity, specificity, total accuracy, and area under the receiver operating characteristic (ROC) curve criteria. Support vector machines showed the highest total accuracy (0.986) as well as area under the ROC (0.979). Also, this method showed high specificity (1.000) and sensitivity (0.820). All other methods produced total accuracy of more than 85%, but for all methods, the sensitivity values were very low (less than 0.350). The results of this study indicate that, in terms of sensitivity, specificity, and overall classification accuracy, the support vector machine model ranks first among all the classifiers tested in the prediction of diabetes. Therefore, this approach is a promising classifier for predicting diabetes, and it should be further investigated for the prediction of other diseases.

[The prediction of atrial fibrillation recurrence after electrical cardioversion with the chemoreflex sensitivity].

PubMed

Budeus, M; Hennersdorf, M; Perings, C; Strauer, B E

2004-04-01

Patients with paroxysmal atrial fibrillation have a lower chemoreflex sensitivity (CHRS) which is characterized as an autonomic dysfunction. Because of this observation we examined the theory of an autonomic dysfunction as the reason for the reccurrence of atrial fibrilation after electrical cardioversion. We measured the CHRS among 43 patients 24 h after successful electrical cardioversion and the patients were controlled for at least 6 months. During the six months of follow-up a recurrence was observed in 18 patients with a mean of 8.3 days. There was no difference in organic heart disease or in the use of drugs. Left atrial diameter was not significantly larger in patients with a recurrence. Patients with a recurrence have a significantly lower CHRS than patients with sinus rhythm (2.41 +/- 1.82 vs 5.62 +/- 3.02 ms/mmHg, p < 0.04). The diagnostic value of a CHRS below 3.0 ms/mmHg achieved a specificity of 68%, a sensitivity of 67%, a positive and negative predictive value of 60% and 74%. An analysis of CHRS seems to be an appropriate method to predict a recurrence of atrial fibrillation. The predictive power of the method has to be examined by prospective investigations of a larger patient population and a longer follow-up. Patients with paroxysmal atrial fibrillation have a lower chemoreflex sensitivity (CHRS) which is characterized as an autonomic dysfunction. Because of this observation we examined the theory of an autonomic dysfunction as the reason for the recurrence of atrial fibrillation after electrical cardioversion.

Comparison of diagnostic methods in the evaluation of onychomycosis.

PubMed

Haghani, Iman; Shokohi, Tahereh; Hajheidari, Zohreh; Khalilian, Alireza; Aghili, Seyed Reza

2013-04-01

Onychomycosis is a common nail problem, accounting for up to half of all nail diseases. Several nail disorders may mimic the onychomycosis clinically. Therefore, a sensitive, quick, and inexpensive test is essential for screening nail specimens for the administration of the proper drug. The aim of this study was to compare 4 different diagnostic methods in the evaluation of onychomycosis and to determine their sensitivity, specificity, positive predictive value, and negative predictive value. In a cross-sectional study, nail specimens were collected from 101 patients suspected to have onychomycosis during a 14-month period. The nail specimens were examined using potassium hydroxide (KOH) 20 %, KOH-treated nail clipping stained with periodic acid-Schiff (KONCPA), and calcofluor white (CFW) stain, and grew a fungal culture. The culture was chosen as the gold standard for statistical analysis using the McNemar and chi-square tests. Out of 101 patients, 100 (99 %) patients had at least 1 of the 4 diagnostic methods positive for the presence of organisms. The positive rates for the fungal culture, KOH preparation, CFW, and KONCPA were 74.2, 85.1, 91.09, and 99.01 %, respectively. The sensitivity and negative predictive value of KONCPA was 100 %. KONCPA was the most sensitive among the tests and was also superior to other methods in its negative predictive value. KONCPA was easy to perform, rapid, and gave significantly higher rates of detection of onychomycosis compared to the standard methods of KOH preparation and fungal culture. Therefore, KONCPA should be the single method of choice for the evaluation of onychomycosis.

A Transient Dopamine Signal Represents Avoidance Value and Causally Influences the Demand to Avoid

PubMed Central

Pultorak, Katherine J.; Schelp, Scott A.; Isaacs, Dominic P.; Krzystyniak, Gregory

2018-01-01

Abstract While an extensive literature supports the notion that mesocorticolimbic dopamine plays a role in negative reinforcement, recent evidence suggests that dopamine exclusively encodes the value of positive reinforcement. In the present study, we employed a behavioral economics approach to investigate whether dopamine plays a role in the valuation of negative reinforcement. Using rats as subjects, we first applied fast-scan cyclic voltammetry (FSCV) to determine that dopamine concentration decreases with the number of lever presses required to avoid electrical footshock (i.e., the economic price of avoidance). Analysis of the rate of decay of avoidance demand curves, which depict an inverse relationship between avoidance and increasing price, allows for inference of the worth an animal places on avoidance outcomes. Rapidly decaying demand curves indicate increased price sensitivity, or low worth placed on avoidance outcomes, while slow rates of decay indicate reduced price sensitivity, or greater worth placed on avoidance outcomes. We therefore used optogenetics to assess how inducing dopamine release causally modifies the demand to avoid electrical footshock in an economic setting. Increasing release at an avoidance predictive cue made animals more sensitive to price, consistent with a negative reward prediction error (i.e., the animal perceives they received a worse outcome than expected). Increasing release at avoidance made animals less sensitive to price, consistent with a positive reward prediction error (i.e., the animal perceives they received a better outcome than expected). These data demonstrate that transient dopamine release events represent the value of avoidance outcomes and can predictably modify the demand to avoid. PMID:29766047

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

A statistical approach to nuclear fuel design and performance

NASA Astrophysics Data System (ADS)

Cunning, Travis Andrew

As CANDU fuel failures can have significant economic and operational consequences on the Canadian nuclear power industry, it is essential that factors impacting fuel performance are adequately understood. Current industrial practice relies on deterministic safety analysis and the highly conservative "limit of operating envelope" approach, where all parameters are assumed to be at their limits simultaneously. This results in a conservative prediction of event consequences with little consideration given to the high quality and precision of current manufacturing processes. This study employs a novel approach to the prediction of CANDU fuel reliability. Probability distributions are fitted to actual fuel manufacturing datasets provided by Cameco Fuel Manufacturing, Inc. They are used to form input for two industry-standard fuel performance codes: ELESTRES for the steady-state case and ELOCA for the transient case---a hypothesized 80% reactor outlet header break loss of coolant accident. Using a Monte Carlo technique for input generation, 105 independent trials are conducted and probability distributions are fitted to key model output quantities. Comparing model output against recognized industrial acceptance criteria, no fuel failures are predicted for either case. Output distributions are well removed from failure limit values, implying that margin exists in current fuel manufacturing and design. To validate the results and attempt to reduce the simulation burden of the methodology, two dimensional reduction methods are assessed. Using just 36 trials, both methods are able to produce output distributions that agree strongly with those obtained via the brute-force Monte Carlo method, often to a relative discrepancy of less than 0.3% when predicting the first statistical moment, and a relative discrepancy of less than 5% when predicting the second statistical moment. In terms of global sensitivity, pellet density proves to have the greatest impact on fuel performance, with an average sensitivity index of 48.93% on key output quantities. Pellet grain size and dish depth are also significant contributors, at 31.53% and 13.46%, respectively. A traditional limit of operating envelope case is also evaluated. This case produces output values that exceed the maximum values observed during the 105 Monte Carlo trials for all output quantities of interest. In many cases the difference between the predictions of the two methods is very prominent, and the highly conservative nature of the deterministic approach is demonstrated. A reliability analysis of CANDU fuel manufacturing parametric data, specifically pertaining to the quantification of fuel performance margins, has not been conducted previously. Key Words: CANDU, nuclear fuel, Cameco, fuel manufacturing, fuel modelling, fuel performance, fuel reliability, ELESTRES, ELOCA, dimensional reduction methods, global sensitivity analysis, deterministic safety analysis, probabilistic safety analysis.

Determination of IgE antibodies to the benzylpenicilloyl determinant: a comparison of the sensitivity and specificity of three radio allergo sorbent test methods.

PubMed

Garcia, J J; Blanca, M; Moreno, F; Vega, J M; Mayorga, C; Fernandez, J; Juarez, C; Romano, A; de Ramon, E

1997-01-01

The quantitation of in vitro IgE antibodies to the benzylpenicilloyl determinant (BPO) is a useful tool for evaluating suspected penicillin allergic subjects. Although many different methods have been employed, few studies have compared their diagnostic specificity and sensitivity. In this study, the sensitivity and specificity of three different radio allergo sorbent test (RAST) methods for quantitating specific IgE antibodies to the BPO determinant were compared. Thirty positive control sera (serum samples from penicillin allergic subjects with a positive clinical history and a positive penicillin skin test) and 30 negative control sera (sera from subjects with no history of penicillin allergy and negative skin tests) were tested for BPO-specific IgE antibodies by RAST using three different conjugates coupled to the solid phase: benzylpenicillin conjugated to polylysine (BPO-PLL), benzylpenicillin conjugated to human serum albumin (BPO-HSA), and benzylpenicillin conjugated to an aminospacer (BPO-SP). Receiver operator control curves (ROC analysis) were carried out by determining different cut-off points between positive and negative values. Contingence tables were constructed and sensitivity, specificity, negative predictive values (PV-), and positive predictive values (PV+) were calculated. Pearson correlation coefficients (r) and intraclass correlation coefficients (ICC) were determined and the differences between methods were compared by chi 2 analysis. Analysis of the areas defined by the ROC curves showed statistical differences among the three methods. When cut-off points for optimal sensitivity and specificity were chosen, the BPO-HSA assay was less sensitive and less specific and had a lower PV- and PV+ than the BPO-PLL and BPO-SP assays. Assessment of r and ICC indicated that the correlation was very high, but the concordance between the PLL and SP methods was higher than between the PLL and HSA or SP and HSA methods. We conclude that for quantitating IgE antibodies by RAST to the BPO determinant, BPO-SP or BPO-PLL conjugates offer advantages in sensitivity and specificity compared with BPO-HSA. These results support and extend previous in vitro studies by our group and highlight the importance of the carrier for RAST assays.

Prediction of Chemical Respiratory and Contact Sensitizers by OX40L Expression in Dendritic Cells Using a Novel 3D Coculture System.

PubMed

Mizoguchi, Izuru; Ohashi, Mio; Chiba, Yukino; Hasegawa, Hideaki; Xu, Mingli; Owaki, Toshiyuki; Yoshimoto, Takayuki

2017-01-01

The use of animal models in chemical safety testing will be significantly limited due to the recent introduction of the 3Rs principle of animal experimentation in research. Although several in vitro assays to predict the sensitizing potential of chemicals have been developed, these methods cannot distinguish chemical respiratory sensitizers and skin sensitizers. In the present study, we describe a novel in vitro assay that can discriminate respiratory sensitizers from chemical skin sensitizers by taking advantage of the fundamental difference between their modes of action, namely the development of the T helper 2 immune response, which is critically important for respiratory sensitization. First, we established a novel three-dimensional (3D) coculture system of human upper airway epithelium using a commercially available scaffold. It consists of human airway epithelial cell line BEAS-2B, immature dendritic cells (DCs) derived from human peripheral blood CD14 + monocytes, and human lung fibroblast cell line MRC-5. Respective cells were first cultured in individual scaffolds and subsequently assembled into a 3D multi-cell tissue model to more closely mimic the in vivo situation. Then, three typical chemicals that are known respiratory sensitizers (ortho-phthaldialdehyde, hexamethylene diisocyanate, and trimellitic anhydride) and skin sensitizers (oxazolone, formaldehyde, and dinitrochlorobenzene) were added individually to the 3D coculture system. Immunohistochemical analysis revealed that DCs do not migrate into other scaffolds under the experimental conditions. Therefore, the 3D structure was disassembled and real-time reverse transcriptase-PCR analysis was performed in individual scaffolds to analyze the expression levels of molecules critical for Th2 differentiation such as OX40 ligand (OX40L), interleukin (IL)-4, IL-10, IL-33, and thymic stromal lymphopoietin. Both sensitizers showed similarly augmented expression of DC maturation markers (e.g., CD86), but among these molecules, OX40L expression in DCs was most consistently and significantly enhanced by respiratory sensitizers as compared to that by skin sensitizers. Thus, we have established a 3D coculture system mimicking the airway upper epithelium that may be successfully applied to discriminate chemical respiratory sensitizers from skin sensitizers by measuring the critical molecule for Th2 differentiation, OX40L, in DCs.

A proteinuria cut-off level of 0.7 g/day after 12 months of treatment best predicts long-term renal outcome in lupus nephritis: data from the MAINTAIN Nephritis Trial

PubMed Central

Tamirou, Farah; Lauwerys, Bernard R; Dall'Era, Maria; Mackay, Meggan; Rovin, Brad; Cervera, Ricard; Houssiau, Frédéric A

2015-01-01

Background Although an early decrease in proteinuria has been correlated with good long-term renal outcome in lupus nephritis (LN), studies aimed at defining a cut-off proteinuria value are missing, except a recent analysis performed on patients randomised in the Euro-Lupus Nephritis Trial, demonstrating that a target value of 0.8 g/day at month 12 optimised sensitivity and specificity for the prediction of good renal outcome. The objective of the current work is to validate this target in another LN study, namely the MAINTAIN Nephritis Trial (MNT). Methods Long-term (at least 7 years) renal function data were available for 90 patients randomised in the MNT. Receiver operating characteristic curves were built to test the performance of proteinuria measured within the 1st year as short-term predictor of long-term renal outcome. We calculated the positive and negative predictive values (PPV, NPV). Results After 12 months of treatment, achievement of a proteinuria <0.7 g/day best predicted good renal outcome, with a sensitivity and a specificity of 71% and 75%, respectively. The PPV was high (94%) but the NPV low (29%). Addition of the requirement of urine red blood cells ≤5/hpf as response criteria at month 12 reduced sensitivity from 71% to 41%. Conclusions In this cohort of mainly Caucasian patients suffering from a first episode of LN in most cases, achievement of a proteinuria <0.7 g/day at month 12 best predicts good outcome at 7 years and inclusion of haematuria in the set of criteria at month 12 undermines the sensitivity of early proteinuria decrease for the prediction of good outcome. The robustness of these conclusions stems from the very similar results obtained in two distinct LN cohorts. Trial registration number: NCT00204022. PMID:26629352

Diagnosis of 25 genotypes of human papillomaviruses for their physical statuses in cervical precancerous/cancerous lesions: a comparison of E2/E6E7 ratio-based vs. multiple E1-L1/E6E7 ratio-based detection techniques.

PubMed

Zhang, Rong; He, Yi-feng; Chen, Mo; Chen, Chun-mei; Zhu, Qiu-jing; Lu, Huan; Wei, Zhen-hong; Li, Fang; Zhang, Xiao-xin; Xu, Cong-jian; Yu, Long

2014-10-02

Cervical lesions caused by integrated human papillomavirus (HPV) infection are highly dangerous because they can quickly develop into invasive cancers. However, clinicians are currently hampered by the lack of a quick, convenient and precise technique to detect integrated/mixed infections of various genotypes of HPVs in the cervix. This study aimed to develop a practical tool to determine the physical status of different HPVs and evaluate its clinical significance. The target population comprised 1162 women with an HPV infection history of > six months and an abnormal cervical cytological finding. The multiple E1-L1/E6E7 ratio analysis, a novel technique, was developed based on determining the ratios of E1/E6E7, E2/E6E7, E4E5/E6E7, L2/E6E7 and L1/E6E7 within the viral genome. Any imbalanced ratios indicate integration. Its diagnostic and predictive performances were compared with those of E2/E6E7 ratio analysis. The detection accuracy of both techniques was evaluated using the gold-standard technique "detection of integrated papillomavirus sequences" (DIPS). To realize a multigenotypic detection goal, a primer and probe library was established. The integration rate of a particular genotype of HPV was correlated with its tumorigenic potential and women with higher lesion grades often carried lower viral loads. The E1-L1/E6E7 ratio analysis achieved 92.7% sensitivity and 99.0% specificity in detecting HPV integration, while the E2/E6E7 ratio analysis showed a much lower sensitivity (75.6%) and a similar specificity (99.3%). Interference due to episomal copies was observed in both techniques, leading to false-negative results. However, some positive results of E1-L1/E6E7 ratio analysis were missed by DIPS due to its stochastic detection nature. The E1-L1/E6E7 ratio analysis is more efficient than E2/E6E7 ratio analysis and DIPS in predicting precancerous/cancerous lesions, in which both positive predictive values (36.7%-82.3%) and negative predictive values (75.9%-100%) were highest (based on the results of three rounds of biopsies). The multiple E1-L1/E6E7 ratio analysis is more sensitive and predictive than E2/E6E7 ratio analysis as a triage test for detecting HPV integration. It can effectively narrow the range of candidates for colposcopic examination and cervical biopsy, thereby lowering the expense of cervical cancer prevention.

Influence of Injury Risk Thresholds on the Performance of an Algorithm to Predict Crashes with Serious Injuries

PubMed Central

Bahouth, George; Digges, Kennerly; Schulman, Carl

2012-01-01

This paper presents methods to estimate crash injury risk based on crash characteristics captured by some passenger vehicles equipped with Advanced Automatic Crash Notification technology. The resulting injury risk estimates could be used within an algorithm to optimize rescue care. Regression analysis was applied to the National Automotive Sampling System / Crashworthiness Data System (NASS/CDS) to determine how variations in a specific injury risk threshold would influence the accuracy of predicting crashes with serious injuries. The recommended thresholds for classifying crashes with severe injuries are 0.10 for frontal crashes and 0.05 for side crashes. The regression analysis of NASS/CDS indicates that these thresholds will provide sensitivity above 0.67 while maintaining a positive predictive value in the range of 0.20. PMID:23169132

Analysis of Mannitol, as Tracer of Bacterial Infections in Cane and Beet Sugar Factories

USDA-ARS?s Scientific Manuscript database

Mannitol, formed mainly by Leuconostoc mesenteroides bacteria, is a sensitive marker of sugarcane and sugarbeet deterioration that can predict multiple processing problems. The delivery of consignments of deteriorated sugarcane or sugar beets to factories can detrimentally affect multiple process u...

Analysis of Mannitol, as Tracer of Bacterial Infections in Cane and Beet Sugar Factories

USDA-ARS?s Scientific Manuscript database

Mannitol, formed mainly by Leuconostoc mesenteroides bacteria, is a sensitive marker of sugarcane and sugarbeet deterioration that can predict multiple processing problems. The delivery of consignments of deteriorated sugarcane or sugar beets to factories can detrimentally affect multiple process un...

Sensitivity, specificity, and predictive values of pediatric metabolic syndrome components in relation to adult metabolic syndrome: the Princeton LRC follow-up study.

PubMed

Huang, Terry T-K; Nansel, Tonja R; Belsheim, Allen R; Morrison, John A

2008-02-01

To estimate the sensitivity, specificity, and predictive values of pediatric metabolic syndrome (MetS) components (obesity, fasting glucose, triglycerides, high-density lipoprotein, and blood pressure) at various cutoff points in relation to adult MetS. Data from the National Heart, Lung, and Blood Institute Lipid Research Clinics Princeton Prevalence Study (1973-1976) and the Princeton Follow-up Study (2000-2004) were used to calculate sensitivity, specificity, and positive and negative predictive values for each component at a given cutoff point and for aggregates of components. Individual pediatric components alone showed low to moderate sensitivity, high specificity, and moderate predictive values in relation to adult MetS. When all 5 pediatric MetS components were considered, the presence of at least 1 abnormality had higher sensitivity for adult MetS than individual components alone. When multiple abnormalities were mandatory for MetS, positive predictive value was high and sensitivity was low. Childhood body mass alone showed neither high sensitivity nor high positive predictive value for adult MetS. Considering multiple metabolic variables in childhood can improve the predictive usefulness for adult MetS, compared with each component or body mass alone. MetS variables may be useful for identifying some children who are at risk for prevention interventions.

Performance of the dipstick screening test as a predictor of negative urine culture.

PubMed

Marques, Alexandre Gimenes; Doi, André Mario; Pasternak, Jacyr; Damascena, Márcio Dos Santos; França, Carolina Nunes; Martino, Marinês Dalla Valle

2017-01-01

To investigate whether the urine dipstick screening test can be used to predict urine culture results. A retrospective study conducted between January and December 2014 based on data from 8,587 patients with a medical order for urine dipstick test, urine sediment analysis and urine culture. Sensitivity, specificity, positive and negative predictive values were determined and ROC curve analysis was performed. The percentage of positive cultures was 17.5%. Nitrite had 28% sensitivity and 99% specificity, with positive and negative predictive values of 89% and 87%, respectively. Leukocyte esterase had 79% sensitivity and 84% specificity, with positive and negative predictive values of 51% and 95%, respectively. The combination of positive nitrite or positive leukocyte esterase tests had 85% sensitivity and 84% specificity, with positive and negative predictive values of 53% and 96%, respectively. Positive urinary sediment (more than ten leukocytes per microliter) had 92% sensitivity and 71% specificity, with positive and negative predictive values of 40% and 98%, respectively. The combination of nitrite positive test and positive urinary sediment had 82% sensitivity and 99% specificity, with positive and negative predictive values of 91% and 98%, respectively. The combination of nitrite or leukocyte esterase positive tests and positive urinary sediment had the highest sensitivity (94%) and specificity (84%), with positive and negative predictive values of 58% and 99%, respectively. Based on ROC curve analysis, the best indicator of positive urine culture was the combination of positives leukocyte esterase or nitrite tests and positive urinary sediment, followed by positives leukocyte and nitrite tests, positive urinary sediment alone, positive leukocyte esterase test alone, positive nitrite test alone and finally association of positives nitrite and urinary sediment (AUC: 0.845, 0.844, 0.817, 0.814, 0.635 and 0.626, respectively). A negative urine culture can be predicted by negative dipstick test results. Therefore, this test may be a reliable predictor of negative urine culture. Verificar se a triagem de urina por fitas reativas é capaz de predizer a cultura de urina. Métodos Estudo retrospectivo realizado entre janeiro e dezembro de 2014 com 8.587 pacientes, com solicitação médica de triagem de urina (fita), sedimento urinário e cultura de urina. sensibilidade, especificidade, valor preditivo positivo, valor preditivo negativo e curva ROC. Foram positivas 17,5% das culturas. O nitrito apresentou sensibilidade de 28% e especificidade de 99%. O valor preditivo positivo foi de 89% e o valor preditivo negativo de 87%. Esterase apresentou sensibilidade de 79% e especificidade de 84%. Valor preditivo positivo e valor preditivo negativo foram de 51% e 95%, respectivamente. A combinação de nitrito ou esterase positivos apresentou sensibilidade de 85% e especificidade de 84%. Valor preditivo positivo e valor preditivo negativo foram, respectivamente, 53% e 96%. O sedimento positivo (mais de dez leucócitos por microlitro) apresentou sensibilidade de 92% e especificidade de 71%. O valor preditivo positivo foi 40% e o negativo, 98%. A combinação de nitrito e sedimento urinário positivos apresentou sensibilidade de 82% e especificidade de 99%. Os valores preditivos positivo e negativo foram 91% e 98%, respectivamente. Para o nitrito ou esterase positivos mais os leucócitos positivos, a sensibilidade foi de 94% e a especificidade de 84%. O valor preditivo positivo foi de 58% e o negativo foi de 99%. Com base na curva ROC, o melhor indicador de urocultura positiva foi a associação entre a esterase ou nitrito positivos na fita mais os leucócitos positivos no sedimento, seguido por nitrito e esterase positivos, sedimento urinário positivo isolado, esterase positiva isolada, nitrito positivo isolado e, finalmente, pela associação entre nitrito e sedimento urinário positivos (AUC: 0,845, 0,844, 0,817, 0,814, 0,635 e 0,626, respectivamente). Uma urocultura negativa pode ser prevista com resultados negativos na fita. Portanto, este teste pode ser um preditor confiável de urocultura negativa.

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.

Analysis of a Shock-Associated Noise Prediction Model Using Measured Jet Far-Field Noise Data

NASA Technical Reports Server (NTRS)

Dahl, Milo D.; Sharpe, Jacob A.

2014-01-01

A code for predicting supersonic jet broadband shock-associated noise was assessed us- ing a database containing noise measurements of a jet issuing from a convergent nozzle. The jet was operated at 24 conditions covering six fully expanded Mach numbers with four total temperature ratios. To enable comparisons of the predicted shock-associated noise component spectra with data, the measured total jet noise spectra were separated into mixing noise and shock-associated noise component spectra. Comparisons between predicted and measured shock-associated noise component spectra were used to identify de ciencies in the prediction model. Proposed revisions to the model, based on a study of the overall sound pressure levels for the shock-associated noise component of the mea- sured data, a sensitivity analysis of the model parameters with emphasis on the de nition of the convection velocity parameter, and a least-squares t of the predicted to the mea- sured shock-associated noise component spectra, resulted in a new de nition for the source strength spectrum in the model. An error analysis showed that the average error in the predicted spectra was reduced by as much as 3.5 dB for the revised model relative to the average error for the original model.

A statistical analysis of RNA folding algorithms through thermodynamic parameter perturbation.

PubMed

Layton, D M; Bundschuh, R

2005-01-01

Computational RNA secondary structure prediction is rather well established. However, such prediction algorithms always depend on a large number of experimentally measured parameters. Here, we study how sensitive structure prediction algorithms are to changes in these parameters. We found already that for changes corresponding to the actual experimental error to which these parameters have been determined, 30% of the structure are falsely predicted whereas the ground state structure is preserved under parameter perturbation in only 5% of all the cases. We establish that base-pairing probabilities calculated in a thermal ensemble are viable although not a perfect measure for the reliability of the prediction of individual structure elements. Here, a new measure of stability using parameter perturbation is proposed, and its limitations are discussed.

The cut-off values of anthropometric variables for predicting mild cognitive impairment in Malaysian older adults: a large population based cross-sectional study.

PubMed

Won, Huiloo; Abdul, Manaf Zahara; Mat Ludin, Arimi Fitri; Omar, Mohd Azahadi; Razali, Rosdinom; Shahar, Suzana

2017-01-01

Older adults are at risk of mild cognitive impairment (MCI), and simple anthropometric measurements can be used to screen for this condition. Thus, the aim of this study was to explore the cut-off values of body mass index (BMI) and waist circumference (WC) for predicting the risk of MCI in older Malaysian adults. A total of 2,240 Malaysian older adults aged ≥60 years were recruited using multistage random sampling in a population based cross-sectional study. Receiver operating characteristic (ROC) curve was used to determine the cut-off values of BMI and WC with optimum sensitivity and specificity for the detection of MCI. Age, gender, years of education, smoking habit, alcohol consumption, depression, and medical conditions were used as confounding factors in this analysis. A BMI cut-off value of 26 kg/m 2 (area under the receiver operating characteristic curve [AUC] 0.725; sensitivity 90.5%; specificity 38.8%) was appropriate in identifying the risk of getting MCI in both men and women. The optimum WC cut-offs for likelihood of MCI were 90 cm (AUC 0.745; sensitivity 78.0%; specificity 59.8%) for men and 82 cm (AUC 0.714; sensitivity 84.3%; specificity 49.7%) for women. The optimum calf circumference (CC) cut-off values for identifying MCI were 29 cm (AUC 0.731; sensitivity 72.6%; specificity 61.1%) for men and 26 cm (AUC 0.598; sensitivity 79.1%; specificity 45.3%) for women. The cut-off values could be advocated and used as part of the screening of MCI among older Malaysian adults. There is a need to further determine the predictive values of these cut-off points on outcomes through longitudinal study design.

Integrated Patient-Derived Models Delineate Individualized Therapeutic Vulnerabilities of Pancreatic Cancer.

PubMed

Witkiewicz, Agnieszka K; Balaji, Uthra; Eslinger, Cody; McMillan, Elizabeth; Conway, William; Posner, Bruce; Mills, Gordon B; O'Reilly, Eileen M; Knudsen, Erik S

2016-08-16

Pancreatic ductal adenocarcinoma (PDAC) harbors the worst prognosis of any common solid tumor, and multiple failed clinical trials indicate therapeutic recalcitrance. Here, we use exome sequencing of patient tumors and find multiple conserved genetic alterations. However, the majority of tumors exhibit no clearly defined therapeutic target. High-throughput drug screens using patient-derived cell lines found rare examples of sensitivity to monotherapy, with most models requiring combination therapy. Using PDX models, we confirmed the effectiveness and selectivity of the identified treatment responses. Out of more than 500 single and combination drug regimens tested, no single treatment was effective for the majority of PDAC tumors, and each case had unique sensitivity profiles that could not be predicted using genetic analyses. These data indicate a shortcoming of reliance on genetic analysis to predict efficacy of currently available agents against PDAC and suggest that sensitivity profiling of patient-derived models could inform personalized therapy design for PDAC. Copyright © 2016 The Author(s). Published by Elsevier Inc. All rights reserved.

Application of Sal classification to parotid gland fine-needle aspiration cytology: 10-year retrospective analysis of 312 patients.

PubMed

Kilavuz, Ahmet Erdem; Songu, Murat; İmre, Abdulkadir; Arslanoğlu, Secil; Özkul, Yilmaz; Pinar, Ercan; Ateş, Düzgün

2018-05-01

The accuracy of fine-needle aspiration biopsy (FNAB) is controversial in parotid tumors. We aimed to compare FNAB results with the final histopathological diagnosis and to apply the "Sal classification" to our data and discuss its results and its place in parotid gland cytology. The FNAB cytological findings and final histological diagnosis were assessed retrospectively in 2 different scenarios based on the distribution of nondefinitive cytology, and we applied the Sal classification and determined malignancy rate, sensitivity, and specificity for each category. In 2 different scenarios FNAB sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were found to be 81%, 87%, 54.7%, and 96.1%; and 65.3%, 100%, 100%, and 96.1%, respectively. The malignancy rates and sensitivity and specificity were also calculated and discussed for each Sal category. We believe that the Sal classification has a great potential to be a useful tool in classification of parotid gland cytology. © 2018 Wiley Periodicals, Inc.

Analysis of bacterial migration. 2: Studies with multiple attractant gradients

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

Strauss, I.; Frymier, P.D.; Hahn, C.M.

1995-02-01

Many motile bacteria exhibit chemotaxis, the ability to bias their random motion toward or away from increasing concentrations of chemical substances which benefit or inhibit their survival, respectively. Since bacteria encounter numerous chemical concentration gradients simultaneously in natural surroundings, it is necessary to know quantitatively how a bacterial population responds in the presence of more than one chemical stimulus to develop predictive mathematical models describing bacterial migration in natural systems. This work evaluates three hypothetical models describing the integration of chemical signals from multiple stimuli: high sensitivity, maximum signal, and simple additivity. An expression for the tumbling probability for individualmore » stimuli is modified according to the proposed models and incorporated into the cell balance equation for a 1-D attractant gradient. Random motility and chemotactic sensitivity coefficients, required input parameters for the model, are measured for single stimulus responses. Theoretical predictions with the three signal integration models are compared to the net chemotactic response of Escherichia coli to co- and antidirectional gradients of D-fucose and [alpha]-methylaspartate in the stopped-flow diffusion chamber assay. Results eliminate the high-sensitivity model and favor the simple additivity over the maximum signal. None of the simple models, however, accurately predict the observed behavior, suggesting a more complex model with more steps in the signal processing mechanism is required to predict responses to multiple stimuli.« less

Does mesenteric venous imaging assessment accurately predict pathologic invasion in localized pancreatic ductal adenocarcinoma?

PubMed

Clanton, Jesse; Oh, Stephen; Kaplan, Stephen J; Johnson, Emily; Ross, Andrew; Kozarek, Richard; Alseidi, Adnan; Biehl, Thomas; Picozzi, Vincent J; Helton, William S; Coy, David; Dorer, Russell; Rocha, Flavio G

2018-05-09

Accurate prediction of mesenteric venous involvement in pancreatic ductal adenocarcinoma (PDAC) is necessary for adequate staging and treatment. A retrospective cohort study was conducted in PDAC patients at a single institution. All patients with resected PDAC and staging CT and EUS between 2003 and 2014 were included and sub-divided into "upfront resected" and "neoadjuvant chemotherapy (NAC)" groups. Independent imaging re-review was correlated to venous resection and venous invasion. Sensitivity, specificity, positive and negative predictive values were then calculated. A total of 109 patients underwent analysis, 60 received upfront resection, and 49 NAC. Venous resection (30%) and vein invasion (13%) was less common in patients resected upfront than those who received NAC (53% and 16%, respectively). Both CT and EUS had poor sensitivity (14-44%) but high specificity (75-95%) for detecting venous resection and vein invasion in patients resected upfront, whereas sensitivity was high (84-100%) and specificity was low (27-44%) after NAC. Preoperative CT and EUS in PDAC have similar efficacy but different predictive capacity in assessing mesenteric venous involvement depending on whether patients are resected upfront or received NAC. Both modalities appear to significantly overestimate true vascular involvement and should be interpreted in the appropriate clinical context. Copyright © 2018 International Hepato-Pancreato-Biliary Association Inc. Published by Elsevier Ltd. All rights reserved.

Perturbation analysis for patch occupancy dynamics

USGS Publications Warehouse

Martin, Julien; Nichols, James D.; McIntyre, Carol L.; Ferraz, Goncalo; Hines, James E.

2009-01-01

Perturbation analysis is a powerful tool to study population and community dynamics. This article describes expressions for sensitivity metrics reflecting changes in equilibrium occupancy resulting from small changes in the vital rates of patch occupancy dynamics (i.e., probabilities of local patch colonization and extinction). We illustrate our approach with a case study of occupancy dynamics of Golden Eagle (Aquila chrysaetos) nesting territories. Examination of the hypothesis of system equilibrium suggests that the system satisfies equilibrium conditions. Estimates of vital rates obtained using patch occupancy models are used to estimate equilibrium patch occupancy of eagles. We then compute estimates of sensitivity metrics and discuss their implications for eagle population ecology and management. Finally, we discuss the intuition underlying our sensitivity metrics and then provide examples of ecological questions that can be addressed using perturbation analyses. For instance, the sensitivity metrics lead to predictions about the relative importance of local colonization and local extinction probabilities in influencing equilibrium occupancy for rare and common species.

Attempted validation of the NUn score and inflammatory markers as predictors of esophageal anastomotic leak and major complications.

PubMed

Findlay, J M; Tilson, R C; Harikrishnan, A; Sgromo, B; Marshall, R E K; Maynard, N D; Gillies, R S; Middleton, M R

2015-10-01

The ability to predict complications following esophagectomy/extended total gastrectomy would be of great clinical value. A recent study demonstrated significant correlations between anastomotic leak (AL) and numerical values of C-reactive protein (CRP), white cell count (WCC) and albumin measured on postoperative day (POD) 4. A predictive model comprising all three (NUn score >10) was found to be highly sensitive and discriminant in predicting AL and complications. We attempted a retrospective validation in our center. Data were collected on all resections performed during a 5-year period (April 2008-2013) using prospectively maintained databases. Our biochemistry laboratory uses a maximum CRP value (156 mg/L), unlike that of the original study; otherwise all variables and outcome measures were comparable. Analysis was performed for all patients with complete blood results on POD4. Three hundred twenty-six patients underwent resection, of which 248 had POD4 bloods. There were 21 AL overall (6.44%); 16 among those with complete POD4 blood results (6.45%). There were 8 (2.45%) in-hospital deaths; 7 (2.82%) in those with POD4 results. No parameters were associated with AL or complication severity on univariate analysis. WCC was associated with AL in multivariate binary logistic regression with albumin and CRP (OR 1.23 [95% CI 1.03-1.47]; P = 0.021). When a binary variable of CRP ≥ 156 mg/L was used rather than an absolute value, no factors were significant. Mean NUn was 8.30 for AL, compared with 8.40 for non-AL (P = 0.710 independent t-test). NUn > 10 predicted 0 of 16 leaks (sensitivity 0.00%, specificity 94.4%, receiver operator curve [ROC] area under the curve [AUC] 0.485; P = 0.843). NUn > 7.65 was 93% sensitive and 21.6% specific. ROC for WCC alone was comparable with NUn (AUC 0.641 [0.504-0.779]; P = 0.059; WCC > 6.89 93.8% sensitive, 20.7% specific; WCC > 15 6.3% sensitive and 97% specific). There were no associations between any parameters and other complications. In a comparable cohort with the original study, we demonstrated a similar multivariate association between WCC alone on POD4 and subsequent demonstration of AL, but not albumin or CRP (measured up to 156 mg/L). The NUn score overall (calculated with this caveat) and a threshold of 10 was not found to have clinical utility in predicting AL or complications. © 2014 International Society for Diseases of the Esophagus.

Dynamic sensitivity analysis of biological systems

PubMed Central

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

2008-01-01

Background A mathematical model to understand, predict, control, or even design a real biological system is a central theme in systems biology. A dynamic biological system is always modeled as a nonlinear ordinary differential equation (ODE) system. How to simulate the dynamic behavior and dynamic parameter sensitivities of systems described by ODEs efficiently and accurately is a critical job. In many practical applications, e.g., the fed-batch fermentation systems, the system admissible input (corresponding to independent variables of the system) can be time-dependent. The main difficulty for investigating the dynamic log gains of these systems is the infinite dimension due to the time-dependent input. The classical dynamic sensitivity analysis does not take into account this case for the dynamic log gains. Results We present an algorithm with an adaptive step size control that can be used for computing the solution and dynamic sensitivities of an autonomous ODE system simultaneously. Although our algorithm is one of the decouple direct methods in computing dynamic sensitivities of an ODE system, the step size determined by model equations can be used on the computations of the time profile and dynamic sensitivities with moderate accuracy even when sensitivity equations are more stiff than model equations. To show this algorithm can perform the dynamic sensitivity analysis on very stiff ODE systems with moderate accuracy, it is implemented and applied to two sets of chemical reactions: pyrolysis of ethane and oxidation of formaldehyde. The accuracy of this algorithm is demonstrated by comparing the dynamic parameter sensitivities obtained from this new algorithm and from the direct method with Rosenbrock stiff integrator based on the indirect method. The same dynamic sensitivity analysis was performed on an ethanol fed-batch fermentation system with a time-varying feed rate to evaluate the applicability of the algorithm to realistic models with time-dependent admissible input. Conclusion By combining the accuracy we show with the efficiency of being a decouple direct method, our algorithm is an excellent method for computing dynamic parameter sensitivities in stiff problems. We extend the scope of classical dynamic sensitivity analysis to the investigation of dynamic log gains of models with time-dependent admissible input. PMID:19091016

Accuracy of Prediction Instruments for Diagnosing Large Vessel Occlusion in Individuals With Suspected Stroke: A Systematic Review for the 2018 Guidelines for the Early Management of Patients With Acute Ischemic Stroke.

PubMed

Smith, Eric E; Kent, David M; Bulsara, Ketan R; Leung, Lester Y; Lichtman, Judith H; Reeves, Mathew J; Towfighi, Amytis; Whiteley, William N; Zahuranec, Darin B

2018-03-01

Endovascular thrombectomy is a highly efficacious treatment for large vessel occlusion (LVO). LVO prediction instruments, based on stroke signs and symptoms, have been proposed to identify stroke patients with LVO for rapid transport to endovascular thrombectomy-capable hospitals. This evidence review committee was commissioned by the American Heart Association/American Stroke Association to systematically review evidence for the accuracy of LVO prediction instruments. Medline, Embase, and Cochrane databases were searched on October 27, 2016. Study quality was assessed with the Quality Assessment of Diagnostic Accuracy-2 tool. Thirty-six relevant studies were identified. Most studies (21 of 36) recruited patients with ischemic stroke, with few studies in the prehospital setting (4 of 36) and in populations that included hemorrhagic stroke or stroke mimics (12 of 36). The most frequently studied prediction instrument was the National Institutes of Health Stroke Scale. Most studies had either some risk of bias or unclear risk of bias. Reported discrimination of LVO mostly ranged from 0.70 to 0.85, as measured by the C statistic. In meta-analysis, sensitivity was as high as 87% and specificity was as high as 90%, but no threshold on any instruments predicted LVO with both high sensitivity and specificity. With a positive LVO prediction test, the probability of LVO could be 50% to 60% (depending on the LVO prevalence in the population), but the probability of LVO with a negative test could still be ≥10%. No scale predicted LVO with both high sensitivity and high specificity. Systems that use LVO prediction instruments for triage will miss some patients with LVO and milder stroke. More prospective studies are needed to assess the accuracy of LVO prediction instruments in the prehospital setting in all patients with suspected stroke, including patients with hemorrhagic stroke and stroke mimics. © 2018 American Heart Association, Inc.

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

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 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.

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.

Parameterization, sensitivity analysis, and inversion: an investigation using groundwater modeling of the surface-mined Tivoli-Guidonia basin (Metropolitan City of Rome, Italy)

NASA Astrophysics Data System (ADS)

La Vigna, Francesco; Hill, Mary C.; Rossetto, Rudy; Mazza, Roberto

2016-09-01

With respect to model parameterization and sensitivity analysis, this work uses a practical example to suggest that methods that start with simple models and use computationally frugal model analysis methods remain valuable in any toolbox of model development methods. In this work, groundwater model calibration starts with a simple parameterization that evolves into a moderately complex model. The model is developed for a water management study of the Tivoli-Guidonia basin (Rome, Italy) where surface mining has been conducted in conjunction with substantial dewatering. The approach to model development used in this work employs repeated analysis using sensitivity and inverse methods, including use of a new observation-stacked parameter importance graph. The methods are highly parallelizable and require few model runs, which make the repeated analyses and attendant insights possible. The success of a model development design can be measured by insights attained and demonstrated model accuracy relevant to predictions. Example insights were obtained: (1) A long-held belief that, except for a few distinct fractures, the travertine is homogeneous was found to be inadequate, and (2) The dewatering pumping rate is more critical to model accuracy than expected. The latter insight motivated additional data collection and improved pumpage estimates. Validation tests using three other recharge and pumpage conditions suggest good accuracy for the predictions considered. The model was used to evaluate management scenarios and showed that similar dewatering results could be achieved using 20 % less pumped water, but would require installing newly positioned wells and cooperation between mine owners.

Use of the polymerase chain reaction to directly detect malaria parasites in blood samples from the Venezuelan Amazon.

PubMed

Laserson, K F; Petralanda, I; Hamlin, D M; Almera, R; Fuentes, M; Carrasquel, A; Barker, R H

1994-02-01

We have examined the reproducibility, sensitivity, and specificity of detecting Plasmodium falciparum using the polymerase chain reaction (PCR) and the species-specific probe pPF14 under field conditions in the Venezuelan Amazon. Up to eight samples were field collected from each of 48 consenting Amerindians presenting with symptoms of malaria. Sample processing and analysis was performed at the Centro Amazonico para la Investigacion y Control de Enfermedades Tropicales Simon Bolivar. A total of 229 samples from 48 patients were analyzed by PCR methods using four different P. falciparum-specific probes. One P. vivax-specific probe and by conventional microscopy. Samples in which results from PCR and microscopy differed were reanalyzed at a higher sensitivity by microscopy. Results suggest that microscopy-negative, PCR-positive samples are true positives, and that microscopy-positive and PCR-negative samples are true negatives. The sensitivity of the DNA probe/PCR method was 78% and its specificity was 97%. The positive predictive value of the PCR method was 88%, and the negative predictive value was 95%. Through the analysis of multiple blood samples from each individual, the DNA probe/PCR methodology was found to have an inherent reproducibility that was highly statistically significant.

CFD Sensitivity Analysis of a Modern Civil Transport Near Buffet-Onset Conditions

NASA Technical Reports Server (NTRS)

Rumsey, Christopher L.; Allison, Dennis O.; Biedron, Robert T.; Buning, Pieter G.; Gainer, Thomas G.; Morrison, Joseph H.; Rivers, S. Melissa; Mysko, Stephen J.; Witkowski, David P.

2001-01-01

A computational fluid dynamics (CFD) sensitivity analysis is conducted for a modern civil transport at several conditions ranging from mostly attached flow to flow with substantial separation. Two different Navier-Stokes computer codes and four different turbulence models are utilized, and results are compared both to wind tunnel data at flight Reynolds number and flight data. In-depth CFD sensitivities to grid, code, spatial differencing method, aeroelastic shape, and turbulence model are described for conditions near buffet onset (a condition at which significant separation exists). In summary, given a grid of sufficient density for a given aeroelastic wing shape, the combined approximate error band in CFD at conditions near buffet onset due to code, spatial differencing method, and turbulence model is: 6% in lift, 7% in drag, and 16% in moment. The biggest two contributers to this uncertainty are turbulence model and code. Computed results agree well with wind tunnel surface pressure measurements both for an overspeed 'cruise' case as well as a case with small trailing edge separation. At and beyond buffet onset, computed results agree well over the inner half of the wing, but shock location is predicted too far aft at some of the outboard stations. Lift, drag, and moment curves are predicted in good agreement with experimental results from the wind tunnel.

A GIS based spatially-explicit sensitivity and uncertainty analysis approach for multi-criteria decision analysis☆

PubMed Central

Feizizadeh, Bakhtiar; Jankowski, Piotr; Blaschke, Thomas

2014-01-01

GIS multicriteria decision analysis (MCDA) techniques are increasingly used in landslide susceptibility mapping for the prediction of future hazards, land use planning, as well as for hazard preparedness. However, the uncertainties associated with MCDA techniques are inevitable and model outcomes are open to multiple types of uncertainty. In this paper, we present a systematic approach to uncertainty and sensitivity analysis. We access the uncertainty of landslide susceptibility maps produced with GIS-MCDA techniques. A new spatially-explicit approach and Dempster–Shafer Theory (DST) are employed to assess the uncertainties associated with two MCDA techniques, namely Analytical Hierarchical Process (AHP) and Ordered Weighted Averaging (OWA) implemented in GIS. The methodology is composed of three different phases. First, weights are computed to express the relative importance of factors (criteria) for landslide susceptibility. Next, the uncertainty and sensitivity of landslide susceptibility is analyzed as a function of weights using Monte Carlo Simulation and Global Sensitivity Analysis. Finally, the results are validated using a landslide inventory database and by applying DST. The comparisons of the obtained landslide susceptibility maps of both MCDA techniques with known landslides show that the AHP outperforms OWA. However, the OWA-generated landslide susceptibility map shows lower uncertainty than the AHP-generated map. The results demonstrate that further improvement in the accuracy of GIS-based MCDA can be achieved by employing an integrated uncertainty–sensitivity analysis approach, in which the uncertainty of landslide susceptibility model is decomposed and attributed to model's criteria weights. PMID:25843987

Spatial segregation of adaptation and predictive sensitization in retinal ganglion cells

PubMed Central

Kastner, David B.; Baccus, Stephen A.

2014-01-01

Sensory systems change their sensitivity based upon recent stimuli to adjust their response range to the range of inputs, and to predict future sensory input. Here we report the presence of retinal ganglion cells that have antagonistic plasticity, showing central adaptation and peripheral sensitization. Ganglion cell responses were captured by a spatiotemporal model with independently adapting excitatory and inhibitory subunits, and sensitization requires GABAergic inhibition. Using a simple theory of signal detection we show that the sensitizing surround conforms to an optimal inference model that continually updates the prior signal probability. This indicates that small receptive field regions have dual functionality—to adapt to the local range of signals, but sensitize based upon the probability of the presence of that signal. Within this framework, we show that sensitization predicts the location of a nearby object, revealing prediction as a new functional role for adapting inhibition in the nervous system. PMID:23932000

Global Sensitivity Analysis and Parameter Calibration for an Ecosystem Carbon Model

NASA Astrophysics Data System (ADS)

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

2013-12-01

We present uncertainty quantification results for a process-based ecosystem carbon model. The model employs 18 parameters and is driven by meteorological data corresponding to years 1992-2006 at the Harvard Forest site. Daily Net Ecosystem Exchange (NEE) observations were available to calibrate the model parameters and test the performance of the model. Posterior distributions show good predictive capabilities for the calibrated model. A global sensitivity analysis was first performed to determine the important model parameters based on their contribution to the variance of NEE. We then proceed to calibrate the model parameters in a Bayesian framework. The daily discrepancies between measured and predicted NEE values were modeled as independent and identically distributed Gaussians with prescribed daily variance according to the recorded instrument error. All model parameters were assumed to have uninformative priors with bounds set according to expert opinion. The global sensitivity results show that the rate of leaf fall (LEAFALL) is responsible for approximately 25% of the total variance in the average NEE for 1992-2005. A set of 4 other parameters, Nitrogen use efficiency (NUE), base rate for maintenance respiration (BR_MR), growth respiration fraction (RG_FRAC), and allocation to plant stem pool (ASTEM) contribute between 5% and 12% to the variance in average NEE, while the rest of the parameters have smaller contributions. The posterior distributions, sampled with a Markov Chain Monte Carlo algorithm, exhibit significant correlations between model parameters. However LEAFALL, the most important parameter for the average NEE, is not informed by the observational data, while less important parameters show significant updates between their prior and posterior densities. The Fisher information matrix values, indicating which parameters are most informed by the experimental observations, are examined to augment the comparison between the calibration and global sensitivity analysis results.

ASPASIA: A toolkit for evaluating the effects of biological interventions on SBML model behaviour.

PubMed

Evans, Stephanie; Alden, Kieran; Cucurull-Sanchez, Lourdes; Larminie, Christopher; Coles, Mark C; Kullberg, Marika C; Timmis, Jon

2017-02-01

A calibrated computational model reflects behaviours that are expected or observed in a complex system, providing a baseline upon which sensitivity analysis techniques can be used to analyse pathways that may impact model responses. However, calibration of a model where a behaviour depends on an intervention introduced after a defined time point is difficult, as model responses may be dependent on the conditions at the time the intervention is applied. We present ASPASIA (Automated Simulation Parameter Alteration and SensItivity Analysis), a cross-platform, open-source Java toolkit that addresses a key deficiency in software tools for understanding the impact an intervention has on system behaviour for models specified in Systems Biology Markup Language (SBML). ASPASIA can generate and modify models using SBML solver output as an initial parameter set, allowing interventions to be applied once a steady state has been reached. Additionally, multiple SBML models can be generated where a subset of parameter values are perturbed using local and global sensitivity analysis techniques, revealing the model's sensitivity to the intervention. To illustrate the capabilities of ASPASIA, we demonstrate how this tool has generated novel hypotheses regarding the mechanisms by which Th17-cell plasticity may be controlled in vivo. By using ASPASIA in conjunction with an SBML model of Th17-cell polarisation, we predict that promotion of the Th1-associated transcription factor T-bet, rather than inhibition of the Th17-associated transcription factor RORγt, is sufficient to drive switching of Th17 cells towards an IFN-γ-producing phenotype. Our approach can be applied to all SBML-encoded models to predict the effect that intervention strategies have on system behaviour. ASPASIA, released under the Artistic License (2.0), can be downloaded from http://www.york.ac.uk/ycil/software.

The reliability and validity of video analysis for the assessment of the clinical signs of concussion in Australian football.

PubMed

Makdissi, Michael; Davis, Gavin

2016-10-01

The objective of this study was to determine the reliability and validity of identifying clinical signs of concussion using video analysis in Australian football. Prospective cohort study. All impacts and collisions potentially resulting in a concussion were identified during 2012 and 2013 Australian Football League seasons. Consensus definitions were developed for clinical signs associated with concussion. For intra- and inter-rater reliability analysis, two experienced clinicians independently assessed 102 randomly selected videos on two occasions. Sensitivity, specificity, positive and negative predictive values were calculated based on the diagnosis provided by team medical staff. 212 incidents resulting in possible concussion were identified in 414 Australian Football League games. The intra-rater reliability of the video-based identification of signs associated with concussion was good to excellent. Inter-rater reliability was good to excellent for impact seizure, slow to get up, motor incoordination, ragdoll appearance (2 of 4 analyses), clutching at head and facial injury. Inter-rater reliability for loss of responsiveness and blank and vacant look was only fair and did not reach statistical significance. The feature with the highest sensitivity was slow to get up (87%), but this sign had a low specificity (19%). Other video signs had a high specificity but low sensitivity. Blank and vacant look (100%) and motor incoordination (81%) had the highest positive predictive value. Video analysis may be a useful adjunct to the side-line assessment of a possible concussion. Video analysis however should not replace the need for a thorough multimodal clinical assessment. Copyright © 2016 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.

Field Evaluation of the Pedostructure-Based Model (Kamel®)

USDA-ARS?s Scientific Manuscript database

This study involves a field evaluation of the pedostructure-based model Kamel and comparisons between Kamel and the Hydrus-1D model for predicting profile soil moisture. This paper also presents a sensitivity analysis of Kamel with an evaluation field site used as the base scenario. The field site u...

Extraction and identification of mixed pesticides’ Raman signal and establishment of their prediction models

USDA-ARS?s Scientific Manuscript database

A nondestructive and sensitive method was developed to detect the presence of mixed pesticides of acetamiprid, chlorpyrifos and carbendazim on apples by surface-enhanced Raman spectroscopy (SERS). Self-modeling mixture analysis (SMA) was used to extract and identify the Raman spectra of individual p...

Iron Status in Toddlerhood Predicts Sensitivity to Psychostimulants in Children

ERIC Educational Resources Information Center

Turner, Catharyn A.; Xie, Diqiong; Zimmerman, Bridget M.; Calarge, Chadi A.

2012-01-01

Objective: Iron deficiency is associated with impaired dopaminergic signaling and externalizing behavior. The authors examine, whether iron stores in toddlerhood influence later response to psychostimulants. Method: Youth participating in a study monitoring the long-term safety of risperidone were included in this analysis if they had received…

DIAGNOSTIC STUDY ON FINE PARTICULATE MATTER PREDICTIONS OF CMAQ IN THE SOUTHEASTERN U.S.

EPA Science Inventory

In this study, the authors use the process analysis tool embedded in CMAQ to examine major processes that govern the fate of key pollutants, identify the most influential processes that contribute to model errors, and guide the diagnostic and sensitivity studies aimed at improvin...

Elevated amniotic fluid lactate predicts labor disorders and cesarean delivery in nulliparous women at term.

PubMed

Murphy, Martina; Butler, Michelle; Coughlan, Barbara; Brennan, Donal; O'Herlihy, Colm; Robson, Michael

2015-11-01

We sought to assess amniotic fluid lactate (AFL) at diagnosis of spontaneous labor at term (≥37 weeks) as a predictor of labor disorders (dystocia) and cesarean delivery (CD). This was a single-institution, prospective cohort study of 905 singleton, cephalic, term (≥37 weeks) nulliparous women in spontaneous labor. A standard management of labor (active management of labor) including a standard oxytocin regimen up to a maximum dose of 30 mU/min was applied. AFL was measured using a point-of-care device (LMU061; ObsteCare, Stockholm, Sweden). Labor arrest in the first stage of labor was defined as the need for oxytocin when cervical dilatation was <1 cm/h over 2 hours and in the second stage of labor by poor descent and rotation over 1 hour. Standard statistical analysis included analysis of variance, Pearson correlations, and binary logistic regression. Unsupervised decision tree analysis with 10-fold cross-validation was used to identify AFL thresholds. AFL was normally distributed and did not correlate with age, body mass index, or gestation. Unsupervised decision tree analysis demonstrated that AFL could be divided into 3 groups: 0-4.9 mmol/L (n = 118), 5.0-9.9 mmol/L (n = 707), and ≥10.0 mmol/L (n = 80). Increasing AFL was associated with higher total oxytocin dose (P = .001), labor disorders (P = .005), and CD (P ≤ .001). Multivariable regression analysis demonstrated that women with AFL ≥5.0-9.9 mmol/L (odds ratio [OR], 1.6; 95% confidence interval [CI], 1.06-2.39) and AFL ≥10.0 mmol/L (OR, 1.72; 95% CI, 1.01-2.93) were independent predictors of a labor disorder. AFL ≥5.0-9.9 mmol/L did not predict CD but multivariable analysis confirmed that AFL ≥10.0 mmol/L was an independent predictor of CD (OR, 3.35; 95% CI, 1.73-6.46). AFL ≥5.0-9.9 mmol/L had a sensitivity of 89% in predicting a labor disorder and a sensitivity of 93% in predicting CD with a 97% negative predictive value. AFL ≥10.0 mmol/L was highly specific but lacked sensitivity for CD. There was no difference in birthweight of infants according to labor disorder and delivery method. AFL at diagnosis of labor in spontaneously laboring single cephalic nulliparous term women is an independent predictor of a labor disorder and CD. These data suggest that women with AFL between 5.0-9.9 mmol/L with a labor disorder may be amenable to correction using the active management of labor protocol. Copyright © 2015 Elsevier Inc. All rights reserved.

Six rapid tests for direct detection of Clostridium difficile and its toxins in fecal samples compared with the fibroblast cytotoxicity assay.

PubMed

Turgeon, David K; Novicki, Thomas J; Quick, John; Carlson, LaDonna; Miller, Pat; Ulness, Bruce; Cent, Anne; Ashley, Rhoda; Larson, Ann; Coyle, Marie; Limaye, Ajit P; Cookson, Brad T; Fritsche, Thomas R

2003-02-01

Clostridium difficile is one of the most frequent causes of nosocomial gastrointestinal disease. Risk factors include prior antibiotic therapy, bowel surgery, and the immunocompromised state. Direct fecal analysis for C. difficile toxin B by tissue culture cytotoxin B assay (CBA), while only 60 to 85% sensitive overall, is a common laboratory method. We have used 1,003 consecutive, nonduplicate fecal samples to compare six commercially available immunoassays (IA) for C. difficile detection with CBA: Prima System Clostridium difficile Tox A and VIDAS Clostridium difficile Tox A II, which detect C. difficile toxin A; Premier Cytoclone A/B and Techlab Clostridium difficile Tox A/B, which detect toxins A and B; and ImmunoCard Clostridium difficile and Triage Micro C. difficile panels, which detect toxin A and a species-specific antigen. For all tests, Triage antigen was most sensitive (89.1%; negative predictive value [NPV] = 98.7%) while ImmunoCard was most specific (99.7%; positive predictive value [PPV] = 95.0%). For toxin tests only, Prima System had the highest sensitivity (82.2%; NPV = 98.0%) while ImmunoCard had the highest specificity (99.7%; PPV = 95.0%). Hematopoietic stem cell transplant (HSCT) patients contributed 44.7% of all samples tested, and no significant differences in sensitivity or specificity were noted between HSCT and non-HSCT patients. IAs, while not as sensitive as direct fecal CBA, produce reasonable predictive values, especially when both antigen and toxin are detected. They also offer significant advantages over CBA in terms of turnaround time and ease of use.

[Evaluation of performance of five bioinformatics software for the prediction of missense mutations].

PubMed

Chen, Qianting; Dai, Congling; Zhang, Qianjun; Du, Juan; Li, Wen

2016-10-01

To study the prediction performance evaluation with five kinds of bioinformatics software (SIFT, PolyPhen2, MutationTaster, Provean, MutationAssessor). From own database for genetic mutations collected over the past five years, Chinese literature database, Human Gene Mutation Database, and dbSNP, 121 missense mutations confirmed by functional studies, and 121 missense mutations suspected to be pathogenic by pedigree analysis were used as positive gold standard, while 242 missense mutations with minor allele frequency (MAF)>5% in dominant hereditary diseases were used as negative gold standard. The selected mutations were predicted with the five software. Based on the results, the performance of the five software was evaluated for their sensitivity, specificity, positive predict value, false positive rate, negative predict value, false negative rate, false discovery rate, accuracy, and receiver operating characteristic curve (ROC). In terms of sensitivity, negative predictive value and false negative rate, the rank was MutationTaster, PolyPhen2, Provean, SIFT, and MutationAssessor. For specificity and false positive rate, the rank was MutationTaster, Provean, MutationAssessor, SIFT, and PolyPhen2. For positive predict value and false discovery rate, the rank was MutationTaster, Provean, MutationAssessor, PolyPhen2, and SIFT. For area under the ROC curve (AUC) and accuracy, the rank was MutationTaster, Provean, PolyPhen2, MutationAssessor, and SIFT. The prediction performance of software may be different when using different parameters. Among the five software, MutationTaster has the best prediction performance.

An empirical investigation on the forecasting ability of mallows model averaging in a macro economic environment

NASA Astrophysics Data System (ADS)

Yin, Yip Chee; Hock-Eam, Lim

2012-09-01

This paper investigates the forecasting ability of Mallows Model Averaging (MMA) by conducting an empirical analysis of five Asia countries, Malaysia, Thailand, Philippines, Indonesia and China's GDP growth rate. Results reveal that MMA has no noticeable differences in predictive ability compared to the general autoregressive fractional integrated moving average model (ARFIMA) and its predictive ability is sensitive to the effect of financial crisis. MMA could be an alternative forecasting method for samples without recent outliers such as financial crisis.

EUS elastography (strain ratio) and fractal-based quantitative analysis for the diagnosis of solid pancreatic lesions.

PubMed

Carrara, Silvia; Di Leo, Milena; Grizzi, Fabio; Correale, Loredana; Rahal, Daoud; Anderloni, Andrea; Auriemma, Francesco; Fugazza, Alessandro; Preatoni, Paoletta; Maselli, Roberta; Hassan, Cesare; Finati, Elena; Mangiavillano, Benedetto; Repici, Alessandro

2018-06-01

EUS elastography is useful in characterizing solid pancreatic lesions (SPLs), and fractal analysis-based technology has been used to evaluate geometric complexity in oncology. The aim of this study was to evaluate EUS elastography (strain ratio) and fractal analysis for the characterization of SPLs. Consecutive patients with SPLs were prospectively enrolled between December 2015 and February 2017. Elastographic evaluation included parenchymal strain ratio (pSR) and wall strain ratio (wSR) and was performed with a new compact US processor. Elastographic images were analyzed using a computer program to determine the 3-dimensional histogram fractal dimension. A composite cytology/histology/clinical reference standard was used to assess sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiver operating curve. Overall, 102 SPLs from 100 patients were studied. At final diagnosis, 69 (68%) were malignant and 33 benign. At elastography, both pSR and wSR appeared to be significantly higher in malignant as compared with benign SPLs (pSR, 24.5 vs 6.4 [P < .001]; wSR, 56.6 vs 15.3 [P < .001]). When the best cut-off levels of pSR and wSR at 9.10 and 16.2, respectively, were used, sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiver operating curve were 88.4%, 78.8%, 89.7%, 76.9%, and 86.7% and 91.3%, 69.7%, 86.5%, 80%, and 85.7%, respectively. Fractal analysis showed a significant statistical difference (P = .0087) between the mean surface fractal dimension of malignant lesions (D = 2.66 ± .01) versus neuroendocrine tumor (D = 2.73 ± .03) and a statistical difference for all 3 channels red, green, and blue (P < .0001). EUS elastography with pSR and fractal-based analysis are useful in characterizing SPLs. (Clinical trial registration number: NCT02855151.). Copyright © 2018 American Society for Gastrointestinal Endoscopy. Published by Elsevier Inc. All rights reserved.

A Sensitivity Study of the Aircraft Vortex Spacing System (AVOSS) Wake Predictor Algorithm to the Resolution of Input Meteorological Profiles

NASA Technical Reports Server (NTRS)

Rutishauser, David K.; Butler, Patrick; Riggins, Jamie

2004-01-01

The AVOSS project demonstrated the feasibility of applying aircraft wake vortex sensing and prediction technologies to safe aircraft spacing for single runway arrivals. On average, AVOSS provided spacing recommendations that were less than the current FAA prescribed spacing rules, resulting in a potential airport efficiency gain. Subsequent efforts have included quantifying the operational specifications for future Wake Vortex Advisory Systems (WakeVAS). In support of these efforts, each of the candidate subsystems for a WakeVAS must be specified. The specifications represent a consensus between the high-level requirements and the capabilities of the candidate technologies. This report documents the beginnings of an effort to quantify the capabilities of the AVOSS Prediction Algorithm (APA). Specifically, the APA horizontal position and circulation strength output sensitivity to the resolution of its wind and turbulence inputs is examined. The results of this analysis have implications for the requirements of the meteorological sensing and prediction systems comprising a WakeVAS implementation.

In-situ biogas upgrading process: Modeling and simulations aspects.

PubMed

Lovato, Giovanna; Alvarado-Morales, Merlin; Kovalovszki, Adam; Peprah, Maria; Kougias, Panagiotis G; Rodrigues, José Alberto Domingues; Angelidaki, Irini

2017-12-01

Biogas upgrading processes by in-situ hydrogen (H 2 ) injection are still challenging and could benefit from a mathematical model to predict system performance. Therefore, a previous model on anaerobic digestion was updated and expanded to include the effect of H 2 injection into the liquid phase of a fermenter with the aim of modeling and simulating these processes. This was done by including hydrogenotrophic methanogen kinetics for H 2 consumption and inhibition effect on the acetogenic steps. Special attention was paid to gas to liquid transfer of H 2 . The final model was successfully validated considering a set of Case Studies. Biogas composition and H 2 utilization were correctly predicted, with overall deviation below 10% compared to experimental measurements. Parameter sensitivity analysis revealed that the model is highly sensitive to the H 2 injection rate and mass transfer coefficient. The model developed is an effective tool for predicting process performance in scenarios with biogas upgrading. Copyright © 2017 Elsevier Ltd. All rights reserved.

Evaluating aquatic invertebrate vulnerability to insecticides based on intrinsic sensitivity, biological traits, and toxic mode of action.

PubMed

Rico, Andreu; Van den Brink, Paul J

2015-08-01

In the present study, the authors evaluated the vulnerability of aquatic invertebrates to insecticides based on their intrinsic sensitivity and their population-level recovery potential. The relative sensitivity of invertebrates to 5 different classes of insecticides was calculated at the genus, family, and order levels using the acute toxicity data available in the US Environmental Protection Agency ECOTOX database. Biological trait information was linked to the calculated relative sensitivity to evaluate correlations between traits and sensitivity and to calculate a vulnerability index, which combines intrinsic sensitivity and traits describing the recovery potential of populations partially exposed to insecticides (e.g., voltinism, flying strength, occurrence in drift). The analysis shows that the relative sensitivity of arthropods depends on the insecticide mode of action. Traits such as degree of sclerotization, size, and respiration type showed good correlation to sensitivity and can be used to make predictions for invertebrate taxa without a priori sensitivity knowledge. The vulnerability analysis revealed that some of the Ephemeroptera, Plecoptera, and Trichoptera taxa were vulnerable to all insecticide classes and indicated that particular gastropod and bivalve species were potentially vulnerable. Microcrustaceans (e.g., daphnids, copepods) showed low potential vulnerability, particularly in lentic ecosystems. The methods described in the present study can be used for the selection of focal species to be included as part of ecological scenarios and higher tier risk assessments. © 2015 SETAC.

Sensitivity analysis of periodic errors in heterodyne interferometry

NASA Astrophysics Data System (ADS)

Ganguly, Vasishta; Kim, Nam Ho; Kim, Hyo Soo; Schmitz, Tony

2011-03-01

Periodic errors in heterodyne displacement measuring interferometry occur due to frequency mixing in the interferometer. These nonlinearities are typically characterized as first- and second-order periodic errors which cause a cyclical (non-cumulative) variation in the reported displacement about the true value. This study implements an existing analytical periodic error model in order to identify sensitivities of the first- and second-order periodic errors to the input parameters, including rotational misalignments of the polarizing beam splitter and mixing polarizer, non-orthogonality of the two laser frequencies, ellipticity in the polarizations of the two laser beams, and different transmission coefficients in the polarizing beam splitter. A local sensitivity analysis is first conducted to examine the sensitivities of the periodic errors with respect to each input parameter about the nominal input values. Next, a variance-based approach is used to study the global sensitivities of the periodic errors by calculating the Sobol' sensitivity indices using Monte Carlo simulation. The effect of variation in the input uncertainty on the computed sensitivity indices is examined. It is seen that the first-order periodic error is highly sensitive to non-orthogonality of the two linearly polarized laser frequencies, while the second-order error is most sensitive to the rotational misalignment between the laser beams and the polarizing beam splitter. A particle swarm optimization technique is finally used to predict the possible setup imperfections based on experimentally generated values for periodic errors.

Constitutive BAK activation as a determinant of drug sensitivity in malignant lymphohematopoietic cells

PubMed Central

Dai, Haiming; Ding, Husheng; Meng, X. Wei; Peterson, Kevin L.; Schneider, Paula A.; Karp, Judith E.; Kaufmann, Scott H.

2015-01-01

Mitochondrial outer membrane permeabilization (MOMP), a key step in the intrinsic apoptotic pathway, is incompletely understood. Current models emphasize the role of BH3-only BCL2 family members in BAX and BAK activation. Here we demonstrate concentration-dependent BAK autoactivation under cell-free conditions and provide evidence that this autoactivation plays a key role in regulating the intrinsic apoptotic pathway in intact cells. In particular, we show that up to 80% of BAK (but not BAX) in lymphohematopoietic cell lines is oligomerized and bound to anti-apoptotic BCL2 family members in the absence of exogenous death stimuli. The extent of this constitutive BAK oligomerization is diminished by BAK knockdown and unaffected by BIM or PUMA down-regulation. Further analysis indicates that sensitivity of cells to BH3 mimetics reflects the identity of the anti-apoptotic proteins to which BAK is constitutively bound, with extensive BCLXL•BAK complexes predicting navitoclax sensitivity, and extensive MCL1•BAK complexes predicting A1210477 sensitivity. Moreover, high BAK expression correlates with sensitivity of clinical acute myelogenous leukemia to chemotherapy, whereas low BAK levels correlate with resistance and relapse. Collectively, these results inform current understanding of MOMP and provide new insight into the ability of BH3 mimetics to induce apoptosis without directly activating BAX or BAK. PMID:26494789

Extinction, survival or recovery of large predatory fishes

PubMed Central

Myers, Ransom A.; Worm, Boris

2005-01-01

Large predatory fishes have long played an important role in marine ecosystems and fisheries. Overexploitation, however, is gradually diminishing this role. Recent estimates indicate that exploitation has depleted large predatory fish communities worldwide by at least 90% over the past 50–100 years. We demonstrate that these declines are general, independent of methodology, and even higher for sensitive species such as sharks. We also attempt to predict the future prospects of large predatory fishes. (i) An analysis of maximum reproductive rates predicts the collapse and extinction of sensitive species under current levels of fishing mortality. Sensitive species occur in marine habitats worldwide and have to be considered in most management situations. (ii) We show that to ensure the survival of sensitive species in the northwest Atlantic fishing mortality has to be reduced by 40–80%. (iii) We show that rapid recovery of community biomass and diversity usually occurs when fishing mortality is reduced. However, recovery is more variable for single species, often because of the influence of species interactions. We conclude that management of multi-species fisheries needs to be tailored to the most sensitive, rather than the more robust species. This requires reductions in fishing effort, reduction in bycatch mortality and protection of key areas to initiate recovery of severely depleted communities. PMID:15713586

Extinction, survival or recovery of large predatory fishes.

PubMed

Myers, Ransom A; Worm, Boris

2005-01-29

Large predatory fishes have long played an important role in marine ecosystems and fisheries. Overexploitation, however, is gradually diminishing this role. Recent estimates indicate that exploitation has depleted large predatory fish communities worldwide by at least 90% over the past 50-100 years. We demonstrate that these declines are general, independent of methodology, and even higher for sensitive species such as sharks. We also attempt to predict the future prospects of large predatory fishes. (i) An analysis of maximum reproductive rates predicts the collapse and extinction of sensitive species under current levels of fishing mortality. Sensitive species occur in marine habitats worldwide and have to be considered in most management situations. (ii) We show that to ensure the survival of sensitive species in the northwest Atlantic fishing mortality has to be reduced by 40-80%. (iii) We show that rapid recovery of community biomass and diversity usually occurs when fishing mortality is reduced. However, recovery is more variable for single species, often because of the influence of species interactions. We conclude that management of multi-species fisheries needs to be tailored to the most sensitive, rather than the more robust species. This requires reductions in fishing effort, reduction in bycatch mortality and protection of key areas to initiate recovery of severely depleted communities.

Dynamic analysis of I cross beam section dissimilar plate joined by TIG welding

NASA Astrophysics Data System (ADS)

Sani, M. S. M.; Nazri, N. A.; Rani, M. N. Abdul; Yunus, M. A.

2018-04-01

In this paper, finite element (FE) joint modelling technique for prediction of dynamic properties of sheet metal jointed by tungsten inert gas (TTG) will be presented. I cross section dissimilar flat plate with different series of aluminium alloy; AA7075 and AA6061 joined by TTG are used. In order to find the most optimum set of TTG welding dissimilar plate, the finite element model with three types of joint modelling were engaged in this study; bar element (CBAR), beam element and spot weld element connector (CWELD). Experimental modal analysis (EMA) was carried out by impact hammer excitation on the dissimilar plates that welding by TTG method. Modal properties of FE model with joints were compared and validated with model testing. CWELD element was chosen to represent weld model for TTG joints due to its accurate prediction of mode shapes and contains an updating parameter for weld modelling compare to other weld modelling. Model updating was performed to improve correlation between EMA and FEA and before proceeds to updating, sensitivity analysis was done to select the most sensitive updating parameter. After perform model updating, average percentage of error of the natural frequencies for CWELD model is improved significantly.

Uncertainties propagation and global sensitivity analysis of the frequency response function of piezoelectric energy harvesters

NASA Astrophysics Data System (ADS)

Ruiz, Rafael O.; Meruane, Viviana

2017-06-01

The goal of this work is to describe a framework to propagate uncertainties in piezoelectric energy harvesters (PEHs). These uncertainties are related to the incomplete knowledge of the model parameters. The framework presented could be employed to conduct prior robust stochastic predictions. The prior analysis assumes a known probability density function for the uncertain variables and propagates the uncertainties to the output voltage. The framework is particularized to evaluate the behavior of the frequency response functions (FRFs) in PEHs, while its implementation is illustrated by the use of different unimorph and bimorph PEHs subjected to different scenarios: free of uncertainties, common uncertainties, and uncertainties as a product of imperfect clamping. The common variability associated with the PEH parameters are tabulated and reported. A global sensitivity analysis is conducted to identify the Sobol indices. Results indicate that the elastic modulus, density, and thickness of the piezoelectric layer are the most relevant parameters of the output variability. The importance of including the model parameter uncertainties in the estimation of the FRFs is revealed. In this sense, the present framework constitutes a powerful tool in the robust design and prediction of PEH performance.

Sensitivity analysis of urban flood flows to hydraulic controls

NASA Astrophysics Data System (ADS)

Chen, Shangzhi; Garambois, Pierre-André; Finaud-Guyot, Pascal; Dellinger, Guilhem; Terfous, Abdelali; Ghenaim, Abdallah

2017-04-01

Flooding represents one of the most significant natural hazards on each continent and particularly in highly populated areas. Improving the accuracy and robustness of prediction systems has become a priority. However, in situ measurements of floods remain difficult while a better understanding of flood flow spatiotemporal dynamics along with dataset for model validations appear essential. The present contribution is based on a unique experimental device at the scale 1/200, able to produce urban flooding with flood flows corresponding to frequent to rare return periods. The influence of 1D Saint Venant and 2D Shallow water model input parameters on simulated flows is assessed using global sensitivity analysis (GSA). The tested parameters are: global and local boundary conditions (water heights and discharge), spatially uniform or distributed friction coefficient and or porosity respectively tested in various ranges centered around their nominal values - calibrated thanks to accurate experimental data and related uncertainties. For various experimental configurations a variance decomposition method (ANOVA) is used to calculate spatially distributed Sobol' sensitivity indices (Si's). The sensitivity of water depth to input parameters on two main streets of the experimental device is presented here. Results show that the closer from the downstream boundary condition on water height, the higher the Sobol' index as predicted by hydraulic theory for subcritical flow, while interestingly the sensitivity to friction decreases. The sensitivity indices of all lateral inflows, representing crossroads in 1D, are also quantified in this study along with their asymptotic trends along flow distance. The relationship between lateral discharge magnitude and resulting sensitivity index of water depth is investigated. Concerning simulations with distributed friction coefficients, crossroad friction is shown to have much higher influence on upstream water depth profile than street friction coefficients. This methodology could be applied to any urban flood configuration in order to better understand flow dynamics and repartition but also guide model calibration in the light of flow controls.

Detection of Organophosphorus Pesticides with Colorimetry and Computer Image Analysis.

PubMed

Li, Yanjie; Hou, Changjun; Lei, Jincan; Deng, Bo; Huang, Jing; Yang, Mei

2016-01-01

Organophosphorus pesticides (OPs) represent a very important class of pesticides that are widely used in agriculture because of their relatively high-performance and moderate environmental persistence, hence the sensitive and specific detection of OPs is highly significant. Based on the inhibitory effect of acetylcholinesterase (AChE) induced by inhibitors, including OPs and carbamates, a colorimetric analysis was used for detection of OPs with computer image analysis of color density in CMYK (cyan, magenta, yellow and black) color space and non-linear modeling. The results showed that there was a gradually weakened trend of yellow intensity with the increase of the concentration of dichlorvos. The quantitative analysis of dichlorvos was achieved by Artificial Neural Network (ANN) modeling, and the results showed that the established model had a good predictive ability between training sets and predictive sets. Real cabbage samples containing dichlorvos were detected by colorimetry and gas chromatography (GC), respectively. The results showed that there was no significant difference between colorimetry and GC (P > 0.05). The experiments of accuracy, precision and repeatability revealed good performance for detection of OPs. AChE can also be inhibited by carbamates, and therefore this method has potential applications in real samples for OPs and carbamates because of high selectivity and sensitivity.

A retrospective analysis to identify the factors affecting infection in patients undergoing chemotherapy.

PubMed

Park, Ji Hyun; Kim, Hyeon-Young; Lee, Hanna; Yun, Eun Kyoung

2015-12-01

This study compares the performance of the logistic regression and decision tree analysis methods for assessing the risk factors for infection in cancer patients undergoing chemotherapy. The subjects were 732 cancer patients who were receiving chemotherapy at K university hospital in Seoul, Korea. The data were collected between March 2011 and February 2013 and were processed for descriptive analysis, logistic regression and decision tree analysis using the IBM SPSS Statistics 19 and Modeler 15.1 programs. The most common risk factors for infection in cancer patients receiving chemotherapy were identified as alkylating agents, vinca alkaloid and underlying diabetes mellitus. The logistic regression explained 66.7% of the variation in the data in terms of sensitivity and 88.9% in terms of specificity. The decision tree analysis accounted for 55.0% of the variation in the data in terms of sensitivity and 89.0% in terms of specificity. As for the overall classification accuracy, the logistic regression explained 88.0% and the decision tree analysis explained 87.2%. The logistic regression analysis showed a higher degree of sensitivity and classification accuracy. Therefore, logistic regression analysis is concluded to be the more effective and useful method for establishing an infection prediction model for patients undergoing chemotherapy. Copyright © 2015 Elsevier Ltd. All rights reserved.

Prognosis of Pregnant Women with One Abnormal Value on 75g OGTT.

PubMed

Kozuma, Yutaka; Inoue, Shigeru; Horinouchi, Takashi; Shinagawa, Takaaki; Nakayama, Hitomi; Kawaguchi, Atsushi; Hori, Daizo; Kamura, Toshiharu; Yamada, Kentaro; Ushijima, Kimio

2015-01-01

The aim of this study was to identify risk factors to allow us to detect patients at high risk of requiring insulin therapy, among Japanese pregnant women with one abnormal value (OAV) on a 75-g oral glucose tolerance test (75-g OGTT). A total of 118 pregnant women with OAV on a previous 75-g OGTT between 1997 and 2010 were studied. We identified the factors which can predict patients at high risk of requiring insulin therapy among Japanese pregnant women with OAV, by comparing severe abnormal glucose tolerance (insulin treatment; n=17) with mild glucose tolerance patients (diet only; n=101). The following factors were examined; plasma level of glucose (PG) and immunoreactive insulin (IRI) at fasting, 0.5, 1 and 2 hours after loading glucose, insulinogenic index, homeostasis model assessment insulin resistance (HOMA-IR), insulin sensitivity index-composite (ISI composite), and HbA1c at the time of the 75-g OGTT. Univariate analysis showed a positive correlation between insulin therapy and 2-h PG value, 0.5-h and 1-h IRI values, AUC-IRI and insulinogenic index (p<0.05). Multivariate analysis showed that the PG 2-h value and insulinogenic index were independent predictive factors of insulin therapy. A 2-h PG ≥153 mg / dl and an insulinogenic index of <0.42 had a sensitivity of 81.8%, a specificity of 83.8%, a positive predictive value of 60.0% and a negative predictive value of 93.9% for the prediction of patients who required insulin therapy among pregnant women with OAV. These results suggest that a level of 2-h PG ≥153 mg/dl and an insulinogenic index of <0.42 on 75-g OGTT are predictive factors for insulin therapy in Japanese pregnant women with OAV.

The CURB65 pneumonia severity score outperforms generic sepsis and early warning scores in predicting mortality in community‐acquired pneumonia

PubMed Central

Barlow, Gavin; Nathwani, Dilip; Davey, Peter

2007-01-01

Background The performance of CURB65 in predicting mortality in community‐acquired pneumonia (CAP) has been tested in two large observational studies. However, it has not been tested against generic sepsis and early warning scores, which are increasingly being advocated for identification of high‐risk patients in acute medical wards. Method A retrospective analysis was performed of data prospectively collected for a CAP quality improvement study. The ability to stratify mortality and performance characteristics (sensitivity, specificity, positive predictive value, negative predictive value and area under the receiver operating curve) were calculated for stratifications of CURB65, CRB65, the systemic inflammatory response syndrome (SIRS) criteria and the standardised early warning score (SEWS). Results 419 patients were included in the main analysis with a median age of 74 years (men = 47%). CURB65 and CRB65 stratified mortality in a more clinically useful way and had more favourable operating characteristics than SIRS or SEWS; for example, mortality in low‐risk patients was 2% when defined by CURB65, but 9% when defined by SEWS and 11–17% when defined by variations of the SIRS criteria. The sensitivity, specificity, positive predictive value and negative predictive value of CURB65 was 71%, 69%, 35% and 91%, respectively, compared with 62%, 73%, 35% and 89% for the best performing version of SIRS and 52%, 67%, 27% and 86% for SEWS. CURB65 had the greatest area under the receiver operating curve (0.78 v 0.73 for CRB65, 0.68 for SIRS and 0.64 for SEWS). Conclusions CURB65 should not be supplanted by SIRS or SEWS for initial prognostic assessment in CAP. Further research to identify better generic prognostic tools is required. PMID:16928720

Knowledge-guided gene prioritization reveals new insights into the mechanisms of chemoresistance.

PubMed

Emad, Amin; Cairns, Junmei; Kalari, Krishna R; Wang, Liewei; Sinha, Saurabh

2017-08-11

Identification of genes whose basal mRNA expression predicts the sensitivity of tumor cells to cytotoxic treatments can play an important role in individualized cancer medicine. It enables detailed characterization of the mechanism of action of drugs. Furthermore, screening the expression of these genes in the tumor tissue may suggest the best course of chemotherapy or a combination of drugs to overcome drug resistance. We developed a computational method called ProGENI to identify genes most associated with the variation of drug response across different individuals, based on gene expression data. In contrast to existing methods, ProGENI also utilizes prior knowledge of protein-protein and genetic interactions, using random walk techniques. Analysis of two relatively new and large datasets including gene expression data on hundreds of cell lines and their cytotoxic responses to a large compendium of drugs reveals a significant improvement in prediction of drug sensitivity using genes identified by ProGENI compared to other methods. Our siRNA knockdown experiments on ProGENI-identified genes confirmed the role of many new genes in sensitivity to three chemotherapy drugs: cisplatin, docetaxel, and doxorubicin. Based on such experiments and extensive literature survey, we demonstrate that about 73% of our top predicted genes modulate drug response in selected cancer cell lines. In addition, global analysis of genes associated with groups of drugs uncovered pathways of cytotoxic response shared by each group. Our results suggest that knowledge-guided prioritization of genes using ProGENI gives new insight into mechanisms of drug resistance and identifies genes that may be targeted to overcome this phenomenon.

Diagnostic performance of procalcitonin for hospitalised children with acute pyelonephritis presenting to the paediatric emergency department.

PubMed

Chen, Shan-Ming; Chang, Hung-Ming; Hung, Tung-Wei; Chao, Yu-Hua; Tsai, Jeng-Dau; Lue, Ko-Huang; Sheu, Ji-Nan

2013-05-01

Urinary tract infection (UTI) is a common bacterial infection in children that can result in permanent renal damage. This study prospectively assessed the diagnostic performance of procalcitonin (PCT) for predicting acute pyelonephritis (APN) among children with febrile UTI presenting to the paediatric emergency department (ED). Children aged ≤10 years with febrile UTI admitted to hospital from the paediatric ED were prospectively studied. Blood PCT, C reactive protein (CRP) and white blood cell (WBC) count were measured in the ED. Sensitivity, specificity, predictive values, multilevel likelihood ratios, receiver operating characteristic (ROC) curve analysis and multivariate logistic regression were used to assess quantitative variables for diagnosing APN. The 136 enrolled patients (56 boys and 80 girls; age range 1 month to 10 years) were divided into APN (n=87) and lower UTI (n=49) groups according to (99m)Tc-dimercaptosuccinic acid scan results. The cut-off value for maximum diagnostic performance of PCT was 1.3 ng/ml (sensitivity 86.2%, specificity 89.8%). By multivariate regression analysis, only PCT and CRP were retained as significant predictors of APN. Comparing ROC curves, PCT had a significantly greater area under the curve than CRP, WBC count and fever for differentiating between APN and lower UTI. PCT has better sensitivity and specificity than CRP and WBC count for distinguishing between APN and lower UTI. PCT is a valuable marker for predicting APN in children with febrile UTI. It may be considered in the initial investigation and therapeutic strategies for children presenting to the ED.

Cost-effectiveness of digital subtraction angiography in the setting of computed tomographic angiography negative subarachnoid hemorrhage.

PubMed

Jethwa, Pinakin R; Punia, Vineet; Patel, Tapan D; Duffis, E Jesus; Gandhi, Chirag D; Prestigiacomo, Charles J

2013-04-01

Recent studies have documented the high sensitivity of computed tomography angiography (CTA) in detecting a ruptured aneurysm in the presence of acute subarachnoid hemorrhage (SAH). The practice of digital subtraction angiography (DSA) when CTA does not reveal an aneurysm has thus been called into question. We examined this dilemma from a cost-effectiveness perspective by using current decision analysis techniques. A decision tree was created with the use of TreeAge Pro Suite 2012; in 1 arm, a CTA-negative SAH was followed up with DSA; in the other arm, patients were observed without further imaging. Based on literature review, costs and utilities were assigned to each potential outcome. Base-case and sensitivity analyses were performed to determine the cost-effectiveness of each strategy. A Monte Carlo simulation was then conducted by sampling each variable over a plausible distribution to evaluate the robustness of the model. With the use of a negative predictive value of 95.7% for CTA, observation was found to be the most cost-effective strategy ($6737/Quality Adjusted Life Year [QALY] vs $8460/QALY) in the base-case analysis. One-way sensitivity analysis demonstrated that DSA became the more cost-effective option if the negative predictive value of CTA fell below 93.72%. The Monte Carlo simulation produced an incremental cost-effectiveness ratio of $83 083/QALY. At the conventional willingness-to-pay threshold of $50 000/QALY, observation was the more cost-effective strategy in 83.6% of simulations. The decision to perform a DSA in CTA-negative SAH depends strongly on the sensitivity of CTA, and therefore must be evaluated at each center treating these types of patients. Given the high sensitivity of CTA reported in the current literature, performing DSA on all patients with CTA negative SAH may not be cost-effective at every institution.

High-fidelity modeling and impact footprint prediction for vehicle breakup analysis

NASA Astrophysics Data System (ADS)

Ling, Lisa

For decades, vehicle breakup analysis had been performed for space missions that used nuclear heater or power units in order to assess aerospace nuclear safety for potential launch failures leading to inadvertent atmospheric reentry. Such pre-launch risk analysis is imperative to assess possible environmental impacts, obtain launch approval, and for launch contingency planning. In order to accurately perform a vehicle breakup analysis, the analysis tool should include a trajectory propagation algorithm coupled with thermal and structural analyses and influences. Since such a software tool was not available commercially or in the public domain, a basic analysis tool was developed by Dr. Angus McRonald prior to this study. This legacy software consisted of low-fidelity modeling and had the capability to predict vehicle breakup, but did not predict the surface impact point of the nuclear component. Thus the main thrust of this study was to develop and verify the additional dynamics modeling and capabilities for the analysis tool with the objectives to (1) have the capability to predict impact point and footprint, (2) increase the fidelity in the prediction of vehicle breakup, and (3) reduce the effort and time required to complete an analysis. The new functions developed for predicting the impact point and footprint included 3-degrees-of-freedom trajectory propagation, the generation of non-arbitrary entry conditions, sensitivity analysis, and the calculation of impact footprint. The functions to increase the fidelity in the prediction of vehicle breakup included a panel code to calculate the hypersonic aerodynamic coefficients for an arbitrary-shaped body and the modeling of local winds. The function to reduce the effort and time required to complete an analysis included the calculation of node failure criteria. The derivation and development of these new functions are presented in this dissertation, and examples are given to demonstrate the new capabilities and the improvements made, with comparisons between the results obtained from the upgraded analysis tool and the legacy software wherever applicable.

Sensitivity, Specificity, and Predictive Values of Pediatric Metabolic Syndrome Components in Relation to Adult Metabolic Syndrome: The Princeton LRC Follow-up Study

PubMed Central

Huang, Terry T-K; Nansel, Tonja R.; Belsheim, Allen R.; Morrison, John A.

2008-01-01

Objective To estimate the sensitivity, specificity, and predictive values of pediatric metabolic syndrome (MetS) components (obesity, fasting glucose, triglycerides, high-density lipoprotein, and blood pressure) at various cutoffs in relation to adult MetS. Study design Data from the NHLBI Lipid Research Clinics (LRC) Princeton Prevalence Study (1973–76) and the Princeton Follow-up Study (PFS, 2000-4) were used to calculate sensitivity, specificity, and positive and negative predictive values for each component at a given cutoff, as well as for aggregates of components. Results Individual pediatric components alone showed low to moderate sensitivity, high specificity, and moderate predictive values in relation to adult MetS. When all five pediatric MetS components were considered, the presence of at least one abnormality had higher sensitivity for adult MetS than individual components alone. When multiple abnormalities were mandatory for MetS, positive predictive value was high and sensitivity was low. Childhood body mass alone showed neither high sensitivity nor high positive predictive value for adult MetS. Conclusions Considering multiple metabolic variables in childhood can improve the predictive utility for adult MetS, compared to each component or body mass alone. MetS variables may be useful for identifying some at risk children for prevention interventions. PMID:18206687

Practical limits for reverse engineering of dynamical systems: a statistical analysis of sensitivity and parameter inferability in systems biology models.

PubMed

Erguler, Kamil; Stumpf, Michael P H

2011-05-01

The size and complexity of cellular systems make building predictive models an extremely difficult task. In principle dynamical time-course data can be used to elucidate the structure of the underlying molecular mechanisms, but a central and recurring problem is that many and very different models can be fitted to experimental data, especially when the latter are limited and subject to noise. Even given a model, estimating its parameters remains challenging in real-world systems. Here we present a comprehensive analysis of 180 systems biology models, which allows us to classify the parameters with respect to their contribution to the overall dynamical behaviour of the different systems. Our results reveal candidate elements of control in biochemical pathways that differentially contribute to dynamics. We introduce sensitivity profiles that concisely characterize parameter sensitivity and demonstrate how this can be connected to variability in data. Systematically linking data and model sloppiness allows us to extract features of dynamical systems that determine how well parameters can be estimated from time-course measurements, and associates the extent of data required for parameter inference with the model structure, and also with the global dynamical state of the system. The comprehensive analysis of so many systems biology models reaffirms the inability to estimate precisely most model or kinetic parameters as a generic feature of dynamical systems, and provides safe guidelines for performing better inferences and model predictions in the context of reverse engineering of mathematical models for biological systems.

Modified obstetric early warning scoring systems (MOEWS): validating the diagnostic performance for severe sepsis in women with chorioamnionitis.

PubMed

Edwards, Sian E; Grobman, William A; Lappen, Justin R; Winter, Cathy; Fox, Robert; Lenguerrand, Erik; Draycott, Timothy

2015-04-01

We sought to compare the predictive power of published modified obstetric early warning scoring systems (MOEWS) for the development of severe sepsis in women with chorioamnionitis. This was a retrospective cohort study using prospectively collected clinical observations at a single tertiary unit (Chicago, IL). Hospital databases and patient records were searched to identify and verify cases with clinically diagnosed chorioamnionitis during the study period (June 2006 through November 2007). Vital sign data (heart rate, respiratory rate, blood pressure, temperature, mental state) for these cases were extracted from an electronic database and the single worst composite recording was identified for analysis. Global literature databases were searched (2014) to identify examples of MOEWS. Scores for each identified MOEWS were derived from each set of vital sign recordings during the presentation with chorioamnionitis. The performance of these MOEWS (the primary outcome) was then analyzed and compared using their sensitivity, specificity, positive and negative predictive values, and receiver-operating characteristic curve for severe sepsis. Six MOEWS were identified. There was wide variation in design and pathophysiological thresholds used for clinical alerts. In all, 913 women with chorioamnionitis were identified from the clinical database. In all, 364 cases with complete data for all physiological indicators were included in analysis. Five women developed severe sepsis, including 1 woman who died. The sensitivities of the MOEWS in predicting the severe deterioration ranged from 40-100% and the specificities varied even more ranging from 4-97%. The positive predictive values were low for all MOEWS ranging from <2-15%. The MOEWS with simpler designs tended to be more sensitive, whereas the more complex MOEWS were more specific, but failed to identify some of the women who developed severe sepsis. Currently used MOEWS vary widely in terms of alert thresholds, format, and accuracy. Most MOEWS have not been validated. The MOEWS generally performed poorly in predicting severe sepsis in obstetric patients; in general severe sepsis was overdetected. Simple MOEWS with high sensitivity followed with more specific secondary testing is likely to be the best way forward. Further research is required to develop early warning systems for use in this setting. Copyright © 2015 Elsevier Inc. All rights reserved.

Influence of Finite Element Size in Residual Strength Prediction of Composite Structures

NASA Technical Reports Server (NTRS)

Satyanarayana, Arunkumar; Bogert, Philip B.; Karayev, Kazbek Z.; Nordman, Paul S.; Razi, Hamid

2012-01-01

The sensitivity of failure load to the element size used in a progressive failure analysis (PFA) of carbon composite center notched laminates is evaluated. The sensitivity study employs a PFA methodology previously developed by the authors consisting of Hashin-Rotem intra-laminar fiber and matrix failure criteria and a complete stress degradation scheme for damage simulation. The approach is implemented with a user defined subroutine in the ABAQUS/Explicit finite element package. The effect of element size near the notch tips on residual strength predictions was assessed for a brittle failure mode with a parametric study that included three laminates of varying material system, thickness and stacking sequence. The study resulted in the selection of an element size of 0.09 in. X 0.09 in., which was later used for predicting crack paths and failure loads in sandwich panels and monolithic laminated panels. Comparison of predicted crack paths and failure loads for these panels agreed well with experimental observations. Additionally, the element size vs. normalized failure load relationship, determined in the parametric study, was used to evaluate strength-scaling factors for three different element sizes. The failure loads predicted with all three element sizes provided converged failure loads with respect to that corresponding with the 0.09 in. X 0.09 in. element size. Though preliminary in nature, the strength-scaling concept has the potential to greatly reduce the computational time required for PFA and can enable the analysis of large scale structural components where failure is dominated by fiber failure in tension.

Asymptomatic bacteriuria in pregnant women attending Boo-Ali Hospital Tehran Iran: Urine analysis vs. urine culture

PubMed Central

Etminan-Bakhsh, Mina; Tadi, Sima; Darabi, Roksana

2017-01-01

Background Asymptomatic bacteriuria is one of the common problems in pregnancy. Asymptomatic bacteriuria is associated with pyelonephritis, preterm labor and low birth weight infants. The physiological and anatomical changes in pregnancy facilitate urinary tract infection (UTI) during pregnancy. Several tests are available for diagnosis of asymptomatic bacteriuria. The urine culture is a gold standard diagnostic test for asymptomatic bacteriuria but it is expensive and time-consuming. Screening methods may be useful in detecting high-risk pregnant women for asymptomatic bacteriuria. Objective The aim of the present study was to compare urine analysis as a rapid screening test to urine culture in diagnosis of asymptomatic bacteriuria. Methods A total of 123 pregnant women attending the obstetrics clinic of Boo-Ali hospital in Tehran, Iran from March 2013 to September 2014 were included in the present diagnostic cross-sectional study. One hundred twenty three mid-stream urine samples were inoculated into cultures and were processed by dipstick (nitrite test and leucocyte esterase test) and microscopic pus cell count. The sensitivity, specificity, positive predictive value and negative predictive value of nitrite test, leucocyte esterase test and microscopic pus cell count were compared with urine culture in diagnosis of asymptomatic bacteriuria by using SPSS version 19. Results Of 123 urine samples, significant asymptomatic bacteriuria (≥104 cfu/Ml) was detected in 8 (6.5%) subjects. The sensitivity and specificity of nitrite test were 37% and 100% respectively. The sensitivity of pus cell count alone and leucocyte esterase test alone were 100% but the specificity of them were 64% and 65% respectively. We found high negative predictive value by Pus cell count and the leucocyte esterase test (100%) and low positive predictive value by them (16% and 17% respectively). Conclusion Urine culture is the most useful test for diagnosis of asymptomatic bacteriuria. None of our screening tests had a sensitivity and specificity of 100%, whereas we can only refer the pregnant women with positive leucocyte esterase test and significant pyuria to the urine culture. PMID:29403616

Comparison of FIB-4 index, NAFLD fibrosis score and BARD score for prediction of advanced fibrosis in adult patients with non-alcoholic fatty liver disease: A meta-analysis study.

PubMed

Sun, Wenjing; Cui, Hongli; Li, Ning; Wei, Yanling; Lai, Shujie; Yang, Yang; Yin, Xinru; Chen, Dong-Feng

2016-08-01

Non-alcoholic fatty liver disease (NAFLD)-related advanced hepatic fibrosis is associated with liver and cardiovascular morbidity and mortality. This study aims to compare the FIB-4 index, NAFLD fibrosis score (NFS) and BARD score for prediction of advanced liver fibrosis. Pooled sensitivity, specificity, diagnostic odds ratio (DOR), summary receiver-operator curves (SROC) and Spearman's rank correlation coefficient were used to examine the accuracy of each non-invasive scoring system for predicting NAFLD-related advanced fibrosis. Four studies with 1038 adult patients were included in this meta-analysis. A total of 135 patients (13.0%) had advanced fibrosis. In the FIB-4 index group, pooled sensitivity and specificity with 95% confidence interval (CI), and the area under the ROC (AUROC) were 0.844 (0.772-0.901), 0.685 (0.654-0.716) and 0.8496 ± 0.0680, respectively, at a cut-off of 1.30. At a threshold of 3.25, the same parameters were 0.38 (0.30-0.47), 0.96 (0.95-0.98) and 0.8445 ± 0.0981. At a cut-off of -1.455, values were 0.77 (0.69-0.84), 0.70 (0.67-0.73) and 0.8355 ± 0.0667, respectively. At a 0.676 cut-off, pooled sensitivity and specificity with 95% CI were 0.27 (0.19-0.35) and 0.98 (0.96-0.98), respectively; and the AUROC was 0.647 ± 0.2208. In the BARD score group, pooled sensitivity and specificity with 95% CI were 0.74 (0.66-0.81) and 0.66 (0.63-0.69), respectively; and the AUROC was 0.7625 ± 0.0285. FIB-4 index with a 1.30 cut-off has better diagnostic accuracy than the FIB-4 index with a 3.25 cut-off, NFS and BARD score, despite showing its limited value for predicting NAFLD-related advanced fibrosis. © 2016 The Japan Society of Hepatology.

The Nature and Variability of Ensemble Sensitivity Fields that Diagnose Severe Convection

NASA Astrophysics Data System (ADS)

Ancell, B. C.

2017-12-01

Ensemble sensitivity analysis (ESA) is a statistical technique that uses information from an ensemble of forecasts to reveal relationships between chosen forecast metrics and the larger atmospheric state at various forecast times. A number of studies have employed ESA from the perspectives of dynamical interpretation, observation targeting, and ensemble subsetting toward improved probabilistic prediction of high-impact events, mostly at synoptic scales. We tested ESA using convective forecast metrics at the 2016 HWT Spring Forecast Experiment to understand the utility of convective ensemble sensitivity fields in improving forecasts of severe convection and its individual hazards. The main purpose of this evaluation was to understand the temporal coherence and general characteristics of convective sensitivity fields toward future use in improving ensemble predictability within an operational framework.The magnitude and coverage of simulated reflectivity, updraft helicity, and surface wind speed were used as response functions, and the sensitivity of these functions to winds, temperatures, geopotential heights, and dew points at different atmospheric levels and at different forecast times were evaluated on a daily basis throughout the HWT Spring Forecast experiment. These sensitivities were calculated within the Texas Tech real-time ensemble system, which possesses 42 members that run twice daily to 48-hr forecast time. Here we summarize both the findings regarding the nature of the sensitivity fields and the evaluation of the participants that reflects their opinions of the utility of operational ESA. The future direction of ESA for operational use will also be discussed.

Comparison of the sensitivity of the UKCAT and A Levels to sociodemographic characteristics: a national study

PubMed Central

2014-01-01

Background The UK Clinical Aptitude Test (UKCAT) was introduced to facilitate widening participation in medical and dental education in the UK by providing universities with a continuous variable to aid selection; one that might be less sensitive to the sociodemographic background of candidates compared to traditional measures of educational attainment. Initial research suggested that males, candidates from more advantaged socioeconomic backgrounds and those who attended independent or grammar schools performed better on the test. The introduction of the A* grade at A level permits more detailed analysis of the relationship between UKCAT scores, secondary educational attainment and sociodemographic variables. Thus, our aim was to further assess whether the UKCAT is likely to add incremental value over A level (predicted or actual) attainment in the selection process. Methods Data relating to UKCAT and A level performance from 8,180 candidates applying to medicine in 2009 who had complete information relating to six key sociodemographic variables were analysed. A series of regression analyses were conducted in order to evaluate the ability of sociodemographic status to predict performance on two outcome measures: A level ‘best of three’ tariff score; and the UKCAT scores. Results In this sample A level attainment was independently and positively predicted by four sociodemographic variables (independent/grammar schooling, White ethnicity, age and professional social class background). These variables also independently and positively predicted UKCAT scores. There was a suggestion that UKCAT scores were less sensitive to educational background compared to A level attainment. In contrast to A level attainment, UKCAT score was independently and positively predicted by having English as a first language and male sex. Conclusions Our findings are consistent with a previous report; most of the sociodemographic factors that predict A level attainment also predict UKCAT performance. However, compared to A levels, males and those speaking English as a first language perform better on UKCAT. Our findings suggest that UKCAT scores may be more influenced by sex and less sensitive to school type compared to A levels. These factors must be considered by institutions utilising the UKCAT as a component of the medical and dental school selection process. PMID:24400861

Predictability of lesion durability for AF ablation using phased radiofrequency: Power, temperature, and duration impact creation of transmural lesions.

PubMed

Hocini, Mélèze; Condie, Cathy; Stewart, Mark T; Kirchhof, Nicole; Foell, Jason D

2016-07-01

Long-term clinical outcomes for atrial fibrillation ablation depend on the creation of durable transmural lesions during pulmonary vein isolation and on substrate modification. Focal conventional radiofrequency (RF) ablation studies have demonstrated that tissue temperature and power are important factors for lesion formation. However, the impact and predictability of temperature and power on contiguous, transmural lesion formation with a phased RF system has not been described. The purpose of this study was to determine the sensitivity, specificity, and predictability of power and temperature to create contiguous, transmural lesions with the temperature-controlled, multielectrode phased RF PVAC GOLD catheter. Single ablations with the PVAC GOLD catheter were performed in the superior vena cava of 22 pigs. Ablations from 198 PVAC GOLD electrodes were evaluated by gross examination and histopathology for lesion transmurality and contiguity. Lesions were compared to temperature and power data from the phased RF GENius generator. Effective contact was defined as electrodes with a temperature of ≥50°C and a power of ≥3 W. Eighty-five percent (168 of 198) of the lesions were transmural and 79% (106 of 134) were contiguous. Electrode analysis showed that >30 seconds of effective contact identified transmural lesions with 85% sensitivity (95% confidence interval [CI] 78%-89%), 93% specificity (95% CI 76%-99%), and 99% positive predictive value (95% CI 94%-100%). Sensitivity for lesion contiguity was 95% (95% CI 89%-98%), with 62% specificity (95% CI 42%-78%) and 90% positive predictive value (95% CI 83%-95%). No char or coagulum was observed on the catheter or tissue. PVAC GOLD safely, effectively, and predictably creates transmural and contiguous lesions. Copyright © 2016 Heart Rhythm Society. Published by Elsevier Inc. All rights reserved.

DeSigN: connecting gene expression with therapeutics for drug repurposing and development.

PubMed

Lee, Bernard Kok Bang; Tiong, Kai Hung; Chang, Jit Kang; Liew, Chee Sun; Abdul Rahman, Zainal Ariff; Tan, Aik Choon; Khang, Tsung Fei; Cheong, Sok Ching

2017-01-25

The drug discovery and development pipeline is a long and arduous process that inevitably hampers rapid drug development. Therefore, strategies to improve the efficiency of drug development are urgently needed to enable effective drugs to enter the clinic. Precision medicine has demonstrated that genetic features of cancer cells can be used for predicting drug response, and emerging evidence suggest that gene-drug connections could be predicted more accurately by exploring the cumulative effects of many genes simultaneously. We developed DeSigN, a web-based tool for predicting drug efficacy against cancer cell lines using gene expression patterns. The algorithm correlates phenotype-specific gene signatures derived from differentially expressed genes with pre-defined gene expression profiles associated with drug response data (IC 50 ) from 140 drugs. DeSigN successfully predicted the right drug sensitivity outcome in four published GEO studies. Additionally, it predicted bosutinib, a Src/Abl kinase inhibitor, as a sensitive inhibitor for oral squamous cell carcinoma (OSCC) cell lines. In vitro validation of bosutinib in OSCC cell lines demonstrated that indeed, these cell lines were sensitive to bosutinib with IC 50 of 0.8-1.2 μM. As further confirmation, we demonstrated experimentally that bosutinib has anti-proliferative activity in OSCC cell lines, demonstrating that DeSigN was able to robustly predict drug that could be beneficial for tumour control. DeSigN is a robust method that is useful for the identification of candidate drugs using an input gene signature obtained from gene expression analysis. This user-friendly platform could be used to identify drugs with unanticipated efficacy against cancer cell lines of interest, and therefore could be used for the repurposing of drugs, thus improving the efficiency of drug development.

Health economic comparison of SLIT allergen and SCIT allergoid immunotherapy in patients with seasonal grass-allergic rhinoconjunctivitis in Germany.

PubMed

Verheggen, Bram G; Westerhout, Kirsten Y; Schreder, Carl H; Augustin, Matthias

2015-01-01

Allergoids are chemically modified allergen extracts administered to reduce allergenicity and to maintain immunogenicity. Oralair® (the 5-grass tablet) is a sublingual native grass allergen tablet for pre- and co-seasonal treatment. Based on a literature review, meta-analysis, and cost-effectiveness analysis the relative effects and costs of the 5-grass tablet versus a mix of subcutaneous allergoid compounds for grass pollen allergic rhinoconjunctivitis were assessed. A Markov model with a time horizon of nine years was used to assess the costs and effects of three-year immunotherapy treatment. Relative efficacy expressed as standardized mean differences was estimated using an indirect comparison on symptom scores extracted from available clinical trials. The Rhinitis Symptom Utility Index (RSUI) was applied as a proxy to estimate utility values for symptom scores. Drug acquisition and other medical costs were derived from published sources as well as estimates for resource use, immunotherapy persistence, and occurrence of asthma. The analysis was executed from the German payer's perspective, which includes payments of the Statutory Health Insurance (SHI) and additional payments by insurants. Comprehensive deterministic and probabilistic sensitivity analyses and different scenarios were performed to test the uncertainty concerning the incremental model outcomes. The applied model predicted a cost-utility ratio of the 5-grass tablet versus a market mix of injectable allergoid products of € 12,593 per QALY in the base case analysis. Predicted incremental costs and QALYs were € 458 (95% confidence interval, CI: € 220; € 739) and 0.036 (95% CI: 0.002; 0.078), respectively. Compared to the allergoid mix the probability of the 5-grass tablet being the most cost-effective treatment option was predicted to be 76% at a willingness-to-pay threshold of € 20,000. The results were most sensitive to changes in efficacy estimates, duration of the pollen season, and immunotherapy persistence rates. This analysis suggests the sublingual native 5-grass tablet to be cost-effective relative to a mix of subcutaneous allergoid compounds. The robustness of these statements has been confirmed in extensive sensitivity and scenario analyses.

Distinguishing between learning and motivation in behavioral tests of the reinforcement sensitivity theory of personality.

PubMed

Smillie, Luke D; Dalgleish, Len I; Jackson, Chris J

2007-04-01

According to Gray's (1973) Reinforcement Sensitivity Theory (RST), a Behavioral Inhibition System (BIS) and a Behavioral Activation System (BAS) mediate effects of goal conflict and reward on behavior. BIS functioning has been linked with individual differences in trait anxiety and BAS functioning with individual differences in trait impulsivity. In this article, it is argued that behavioral outputs of the BIS and BAS can be distinguished in terms of learning and motivation processes and that these can be operationalized using the Signal Detection Theory measures of response-sensitivity and response-bias. In Experiment 1, two measures of BIS-reactivity predicted increased response-sensitivity under goal conflict, whereas one measure of BAS-reactivity predicted increased response-sensitivity under reward. In Experiment 2, two measures of BIS-reactivity predicted response-bias under goal conflict, whereas a measure of BAS-reactivity predicted motivation response-bias under reward. In both experiments, impulsivity measures did not predict criteria for BAS-reactivity as traditionally predicted by RST.

[Performance of Thallium 201 rest-redistribution spect to predict viability in recent myocardial infarction].

PubMed

Coll, Claudia; González, Patricio; Massardo, Teresa; Sierralta, Paulina; Humeres, Pamela; Jofré, Josefina; Yovanovich, Jorge; Aramburú, Ivonne; Brugère, Solange; Chamorro, Hernán; Ramírez, Alfredo; Kunstmann, Sonia; López, Héctor

2002-03-01

The detection of viability after acute myocardial infarction is primordial to select the most appropriate therapy, to decrease cardiac events and abnormal remodeling. Thallium201 SPECT is one of the radionuclide techniques used to detect viability. To evaluate the use of Thallium201 rest-redistribution SPECT to detect myocardial viability in reperfused patients after a recent myocardial infarction. Forty one patients with up to of 24 days of evolution of a myocardial infarction were studied. All had angiographically demonstrated coronary artery disease and were subjected to a successful thrombolysis, angioplasty or bypass grafting. SPECT Thallium201 images were acquired at rest and after 4 h of redistribution. These results were compared with variations in wall motion score, studied at baseline and after 3 or 4 months with echocardiography. The sensitivity of rest-redistribution Thallium201 SPECT, to predict recovery of wall motion was 91% when patient analysis was performed and 79% when segmental analysis was done in the culprit region. The figures for specificity were 56 and 73% respectively. Rest-distribution Thallium201 SPECT has an excellent sensitivity to predict myocardial viability in recent myocardial infarction. The data obtained in this study is similar to that reported for chronic coronary artery disease.

High Resolution Melting Analysis for JAK2 Exon 14 and Exon 12 Mutations

PubMed Central

Rapado, Inmaculada; Grande, Silvia; Albizua, Enriqueta; Ayala, Rosa; Hernández, José-Angel; Gallardo, Miguel; Gilsanz, Florinda; Martinez-Lopez, Joaquin

2009-01-01

JAK2 mutations are important criteria for the diagnosis of Philadelphia chromosome-negative myeloproliferative neoplasms. We aimed to assess JAK2 exon 14 and exon 12 mutations by high-resolution melting (HRM) analysis, which allows variation screening. The exon 14 analysis included 163 patients with polycythemia vera, secondary erythrocytoses, essential thrombocythemia, or secondary thrombocytoses, and 126 healthy subjects. The study of exon 12 included 40 JAK2 V617F-negative patients (nine of which had polycythemia vera, and 31 with splanchnic vein thrombosis) and 30 healthy subjects. HRM analyses of JAK2 exons 14 and 12 gave analytical sensitivities near 1% and both intra- and interday coefficients of variation of less than 1%. For HRM analysis of JAK2 exon 14 in polycythemia vera and essential thrombocythemia, clinical sensitivities were 93.5% and 67.9%, clinical specificities were 98.8% and 97.0%, positive predictive values were 93.5% and 79.2%, and negative predictive values were 98.8% and 94.6, respectively. Correlations were observed between the results from HRM and three commonly used analytical methods. The JAK2 exon 12 HRM results agreed completely with those from sequencing analysis, and the three mutations in exon 12 were detected by both methods. Hence, HRM analysis of exons 14 and 12 in JAK2 shows better diagnostic values than three other routinely used methods against which it was compared. In addition, HRM analysis has the advantage of detecting unknown mutations. PMID:19225136

Diagnostic value of C-reactive protein to rule out infectious complications after major abdominal surgery: a systematic review and meta-analysis.

PubMed

Gans, Sarah L; Atema, Jasper J; van Dieren, Susan; Groot Koerkamp, Bas; Boermeester, Marja A

2015-07-01

Infectious complications occur frequently after major abdominal surgery and have a major influence on patient outcome and hospital costs. A marker that can rule out postoperative infectious complications (PICs) could aid patient selection for safe and early hospital discharge. C-reactive protein (CRP) is a widely available, fast, and cheap marker that might be of value in detecting PIC. Present meta-analysis evaluates the diagnostic value of CRP to rule out PIC following major abdominal surgery, aiding patient selection for early discharge. A systematic literature search of Medline, PubMed, and Cochrane was performed identifying all prospective studies evaluating the diagnostic value of CRP after abdominal surgery. Meta-analysis was performed according to the PRISMA statement. Twenty-two studies were included for qualitative analysis of which 16 studies were eligible for meta-analysis, representing 2215 patients. Most studies analyzed the value of CRP in colorectal surgery (eight studies). The pooled negative predictive value (NPV) improved each day after surgery up to 90% at postoperative day (POD) 3 for a pooled CRP cutoff of 159 mg/L (range 92-200). Maximum predictive values for PICs were reached on POD 5 for a pooled CRP cutoff of 114 mg/L (range 48-150): a pooled sensitivity of 86% (95% confidence interval (CI) 79-91%), specificity of 86% (95% CI 75-92%), and a positive predictive value of 64% (95% CI 49-77%). The pooled sensitivity and specificity were significantly higher on POD 5 than on other PODs (p < 0.001). Infectious complications after major abdominal surgery are very unlikely in patients with a CRP below 159 mg/L on POD 3. This can aid patient selection for safe and early hospital discharge and prevent overuse of imaging.

Anatomic features of the neck as predictive markers of difficult direct laryngoscopy in men and women: A prospective study

PubMed Central

Chara, Liaskou; Eleftherios, Vouzounerakis; Maria, Moirasgenti; Anastasia, Trikoupi; Chryssoula, Staikou

2014-01-01

Background and Aims: Difficult airway assessment is based on various anatomic parameters of upper airway, much of it being concentrated on oral cavity and the pharyngeal structures. The diagnostic value of tests based on neck anatomy in predicting difficult laryngoscopy was assessed in this prospective, open cohort study. Methods: We studied 341 adult patients scheduled to receive general anaesthesia. Thyromental distance (TMD), sternomental distance (STMD), ratio of height to thyromental distance (RHTMD) and neck circumference (NC) were measured pre-operatively. The laryngoscopic view was classified according to the Cormack–Lehane Grade (1-4). Difficult laryngoscopy was defined as Cormack–Lehane Grade 3 or 4. The optimal cut-off points for each variable were identified by using receiver operating characteristic analysis. Sensitivity, specificity and positive predictive value and negative predictive value (NPV) were calculated for each test. Multivariate analysis with logistic regression, including all variables, was used to create a predictive model. Comparisons between genders were also performed. Results: Laryngoscopy was difficult in 12.6% of the patients. The cut-off values were: TMD ≤7 cm, STMD ≤15 cm, RHTMD >18.4 and NC >37.5 cm. The RHTMD had the highest sensitivity (88.4%) and NPV (95.2%), while TMD had the highest specificity (83.9%). The area under curve (AUC) for the TMD, STMD, RHTMD and NC was 0.63, 0.64, 0.62 and 0.54, respectively. The predictive model exhibited a higher and statistically significant diagnostic accuracy (AUC: 0.68, P < 0.001). Gender-specific cut-off points improved the predictive accuracy of NC in women (AUC: 0.65). Conclusions: The TMD, STMD, RHTMD and NC were found to be poor single predictors of difficult laryngoscopy, while a model including all four variables had a significant predictive accuracy. Among the studied tests, gender-specific cut-off points should be used for NC. PMID:24963183

Anatomic features of the neck as predictive markers of difficult direct laryngoscopy in men and women: A prospective study.

PubMed

Liaskou, Chara; Chara, Liaskou; Vouzounerakis, Eleftherios; Eleftherios, Vouzounerakis; Moirasgenti, Maria; Maria, Moirasgenti; Trikoupi, Anastasia; Anastasia, Trikoupi; Staikou, Chryssoula; Chryssoula, Staikou

2014-03-01

Difficult airway assessment is based on various anatomic parameters of upper airway, much of it being concentrated on oral cavity and the pharyngeal structures. The diagnostic value of tests based on neck anatomy in predicting difficult laryngoscopy was assessed in this prospective, open cohort study. We studied 341 adult patients scheduled to receive general anaesthesia. Thyromental distance (TMD), sternomental distance (STMD), ratio of height to thyromental distance (RHTMD) and neck circumference (NC) were measured pre-operatively. The laryngoscopic view was classified according to the Cormack-Lehane Grade (1-4). Difficult laryngoscopy was defined as Cormack-Lehane Grade 3 or 4. The optimal cut-off points for each variable were identified by using receiver operating characteristic analysis. Sensitivity, specificity and positive predictive value and negative predictive value (NPV) were calculated for each test. Multivariate analysis with logistic regression, including all variables, was used to create a predictive model. Comparisons between genders were also performed. Laryngoscopy was difficult in 12.6% of the patients. The cut-off values were: TMD ≤7 cm, STMD ≤15 cm, RHTMD >18.4 and NC >37.5 cm. The RHTMD had the highest sensitivity (88.4%) and NPV (95.2%), while TMD had the highest specificity (83.9%). The area under curve (AUC) for the TMD, STMD, RHTMD and NC was 0.63, 0.64, 0.62 and 0.54, respectively. The predictive model exhibited a higher and statistically significant diagnostic accuracy (AUC: 0.68, P < 0.001). Gender-specific cut-off points improved the predictive accuracy of NC in women (AUC: 0.65). The TMD, STMD, RHTMD and NC were found to be poor single predictors of difficult laryngoscopy, while a model including all four variables had a significant predictive accuracy. Among the studied tests, gender-specific cut-off points should be used for NC.

Planning for subacute care: predicting demand using acute activity data.

PubMed

Green, Janette P; McNamee, Jennifer P; Kobel, Conrad; Seraji, Md Habibur R; Lawrence, Suanne J

2016-01-01

Objective The aim of the present study was to develop a robust model that uses the concept of 'rehabilitation-sensitive' Diagnosis Related Groups (DRGs) in predicting demand for rehabilitation and geriatric evaluation and management (GEM) care following acute in-patient episodes provided in Australian hospitals. Methods The model was developed using statistical analyses of national datasets, informed by a panel of expert clinicians and jurisdictional advice. Logistic regression analysis was undertaken using acute in-patient data, published national hospital statistics and data from the Australasian Rehabilitation Outcomes Centre. Results The predictive model comprises tables of probabilities that patients will require rehabilitation or GEM care after an acute episode, with columns defined by age group and rows defined by grouped Australian Refined (AR)-DRGs. Conclusions The existing concept of rehabilitation-sensitive DRGs was revised and extended. When applied to national data, the model provided a conservative estimate of 83% of the activity actually provided. An example demonstrates the application of the model for service planning. What is known about the topic? Health service planning is core business for jurisdictions and local areas. With populations ageing and an acknowledgement of the underservicing of subacute care, it is timely to find improved methods of estimating demand for this type of care. Traditionally, age-sex standardised utilisation rates for individual DRGs have been applied to Australian Bureau of Statistics (ABS) population projections to predict the future need for subacute services. Improved predictions became possible when some AR-DRGs were designated 'rehabilitation-sensitive'. This improved methodology has been used in several Australian jurisdictions. What does this paper add? This paper presents a new tool, or model, to predict demand for rehabilitation and GEM services based on in-patient acute activity. In this model, the methodology based on rehabilitation-sensitive AR-DRGs has been extended by updating them to AR-DRG Version 7.0, quantifying the level of 'sensitivity' and incorporating the patient's age to improve the prediction of demand for subacute services. What are the implications for practitioners? The predictive model takes the form of tables of probabilities that patients will require rehabilitation or GEM care after an acute episode and can be applied to acute in-patient administrative datasets in any Australian jurisdiction or local area. The use of patient-level characteristics will enable service planners to improve their forecasting of demand for these services. Clinicians and jurisdictional representatives consulted during the project regarded the model favourably and believed that it was an improvement on currently available methods.

Resolution of VTI anisotropy with elastic full-waveform inversion: theory and basic numerical examples

NASA Astrophysics Data System (ADS)

Podgornova, O.; Leaney, S.; Liang, L.

2018-07-01

Extracting medium properties from seismic data faces some limitations due to the finite frequency content of the data and restricted spatial positions of the sources and receivers. Some distributions of the medium properties make low impact on the data (including none). If these properties are used as the inversion parameters, then the inverse problem becomes overparametrized, leading to ambiguous results. We present an analysis of multiparameter resolution for the linearized inverse problem in the framework of elastic full-waveform inversion. We show that the spatial and multiparameter sensitivities are intertwined and non-sensitive properties are spatial distributions of some non-trivial combinations of the conventional elastic parameters. The analysis accounts for the Hessian information and frequency content of the data; it is semi-analytical (in some scenarios analytical), easy to interpret and enhances results of the widely used radiation pattern analysis. Single-type scattering is shown to have limited sensitivity, even for full-aperture data. Finite-frequency data lose multiparameter sensitivity at smooth and fine spatial scales. Also, we establish ways to quantify a spatial-multiparameter coupling and demonstrate that the theoretical predictions agree well with the numerical results.

A link prediction approach to cancer drug sensitivity prediction.

PubMed

Turki, Turki; Wei, Zhi

2017-10-03

Predicting the response to a drug for cancer disease patients based on genomic information is an important problem in modern clinical oncology. This problem occurs in part because many available drug sensitivity prediction algorithms do not consider better quality cancer cell lines and the adoption of new feature representations; both lead to the accurate prediction of drug responses. By predicting accurate drug responses to cancer, oncologists gain a more complete understanding of the effective treatments for each patient, which is a core goal in precision medicine. In this paper, we model cancer drug sensitivity as a link prediction, which is shown to be an effective technique. We evaluate our proposed link prediction algorithms and compare them with an existing drug sensitivity prediction approach based on clinical trial data. The experimental results based on the clinical trial data show the stability of our link prediction algorithms, which yield the highest area under the ROC curve (AUC) and are statistically significant. We propose a link prediction approach to obtain new feature representation. Compared with an existing approach, the results show that incorporating the new feature representation to the link prediction algorithms has significantly improved the performance.

Sensitivity and specificity of subacute computerized neurocognitive testing and symptom evaluation in predicting outcomes after sports-related concussion.

PubMed

Lau, Brian C; Collins, Michael W; Lovell, Mark R

2011-06-01

Concussions affect an estimated 136 000 high school athletes yearly. Computerized neurocognitive testing has been shown to be appropriately sensitive and specific in diagnosing concussions, but no studies have assessed its utility to predict length of recovery. Determining prognosis during subacute recovery after sports concussion will help clinicians more confidently address return-to-play and academic decisions. To quantify the prognostic ability of computerized neurocognitive testing in combination with symptoms during the subacute recovery phase from sports-related concussion. Cohort study (prognosis); Level of evidence, 2. In sum, 108 male high school football athletes completed a computer-based neurocognitive test battery within 2.23 days of injury and were followed until returned to play as set by international guidelines. Athletes were grouped into protracted recovery (>14 days; n = 50) or short-recovery (≤14 days; n = 58). Separate discriminant function analyses were performed using total symptom score on Post-Concussion Symptom Scale, symptom clusters (migraine, cognitive, sleep, neuropsychiatric), and Immediate Postconcussion Assessment and Cognitive Testing neurocognitive scores (verbal memory, visual memory, reaction time, processing speed). Multiple discriminant function analyses revealed that the combination of 4 symptom clusters and 4 neurocognitive composite scores had the highest sensitivity (65.22%), specificity (80.36%), positive predictive value (73.17%), and negative predictive value (73.80%) in predicting protracted recovery. Discriminant function analyses of total symptoms on the Post-Concussion Symptom Scale alone had a sensitivity of 40.81%; specificity, 79.31%; positive predictive value, 62.50%; and negative predictive value, 61.33%. The 4 symptom clusters alone discriminant function analyses had a sensitivity of 46.94%; specificity, 77.20%; positive predictive value, 63.90%; and negative predictive value, 62.86%. Discriminant function analyses of the 4 computerized neurocognitive scores alone had a sensitivity of 53.20%; specificity, 75.44%; positive predictive value, 64.10%; and negative predictive value, 66.15%. The use of computerized neurocognitive testing in conjunction with symptom clusters results improves sensitivity, specificity, positive predictive value, and negative predictive value of predicting protracted recovery compared with each used alone. There is also a net increase in sensitivity of 24.41% when using neurocognitive testing and symptom clusters together compared with using total symptoms on Post-Concussion Symptom Scale alone.

Genome-Wide Prediction and Analysis of 3D-Domain Swapped Proteins in the Human Genome from Sequence Information.

PubMed

Upadhyay, Atul Kumar; Sowdhamini, Ramanathan

2016-01-01

3D-domain swapping is one of the mechanisms of protein oligomerization and the proteins exhibiting this phenomenon have many biological functions. These proteins, which undergo domain swapping, have acquired much attention owing to their involvement in human diseases, such as conformational diseases, amyloidosis, serpinopathies, proteionopathies etc. Early realisation of proteins in the whole human genome that retain tendency to domain swap will enable many aspects of disease control management. Predictive models were developed by using machine learning approaches with an average accuracy of 78% (85.6% of sensitivity, 87.5% of specificity and an MCC value of 0.72) to predict putative domain swapping in protein sequences. These models were applied to many complete genomes with special emphasis on the human genome. Nearly 44% of the protein sequences in the human genome were predicted positive for domain swapping. Enrichment analysis was performed on the positively predicted sequences from human genome for their domain distribution, disease association and functional importance based on Gene Ontology (GO). Enrichment analysis was also performed to infer a better understanding of the functional importance of these sequences. Finally, we developed hinge region prediction, in the given putative domain swapped sequence, by using important physicochemical properties of amino acids.

Accuracy of stroke volume variation in predicting fluid responsiveness: a systematic review and meta-analysis.

PubMed

Zhang, Zhongheng; Lu, Baolong; Sheng, Xiaoyan; Jin, Ni

2011-12-01

Stroke volume variation (SVV) appears to be a good predictor of fluid responsiveness in critically ill patients. However, a wide range of its predictive values has been reported in recent years. We therefore undertook a systematic review and meta-analysis of clinical trials that investigated the diagnostic value of SVV in predicting fluid responsiveness. Clinical investigations were identified from several sources, including MEDLINE, EMBASE, WANFANG, and CENTRAL. Original articles investigating the diagnostic value of SVV in predicting fluid responsiveness were considered to be eligible. Participants included critically ill patients in the intensive care unit (ICU) or operating room (OR) who require hemodynamic monitoring. A total of 568 patients from 23 studies were included in our final analysis. Baseline SVV was correlated to fluid responsiveness with a pooled correlation coefficient of 0.718. Across all settings, we found a diagnostic odds ratio of 18.4 for SVV to predict fluid responsiveness at a sensitivity of 0.81 and specificity of 0.80. The SVV was of diagnostic value for fluid responsiveness in OR or ICU patients monitored with the PiCCO or the FloTrac/Vigileo system, and in patients ventilated with tidal volume greater than 8 ml/kg. SVV is of diagnostic value in predicting fluid responsiveness in various settings.

Mid-L/D Lifting Body Entry Demise Analysis

NASA Technical Reports Server (NTRS)

Ling, Lisa

2017-01-01

The mid-lift-to-drag ratio (mid-L/D) lifting body is a fully autonomous spacecraft under design at NASA for enabling a rapid return of scientific payloads from the International Space Station (ISS). For contingency planning and risk assessment for the Earth-return trajectory, an entry demise analysis was performed to examine three potential failure scenarios: (1) nominal entry interface conditions with loss of control, (2) controlled entry at maximum flight path angle, and (3) controlled entry at minimum flight path angle. The objectives of the analysis were to predict the spacecraft breakup sequence and timeline, determine debris survival, and calculate the debris dispersion footprint. Sensitivity analysis was also performed to determine the effect of the initial pitch rate on the spacecraft stability and breakup during the entry. This report describes the mid-L/D lifting body and presents the results of the entry demise and sensitivity analyses.

Switch of Sensitivity Dynamics Revealed with DyGloSA Toolbox for Dynamical Global Sensitivity Analysis as an Early Warning for System's Critical Transition

PubMed Central

Baumuratova, Tatiana; Dobre, Simona; Bastogne, Thierry; Sauter, Thomas

2013-01-01

Systems with bifurcations may experience abrupt irreversible and often unwanted shifts in their performance, called critical transitions. For many systems like climate, economy, ecosystems it is highly desirable to identify indicators serving as early warnings of such regime shifts. Several statistical measures were recently proposed as early warnings of critical transitions including increased variance, autocorrelation and skewness of experimental or model-generated data. The lack of automatized tool for model-based prediction of critical transitions led to designing DyGloSA – a MATLAB toolbox for dynamical global parameter sensitivity analysis (GPSA) of ordinary differential equations models. We suggest that the switch in dynamics of parameter sensitivities revealed by our toolbox is an early warning that a system is approaching a critical transition. We illustrate the efficiency of our toolbox by analyzing several models with bifurcations and predicting the time periods when systems can still avoid going to a critical transition by manipulating certain parameter values, which is not detectable with the existing SA techniques. DyGloSA is based on the SBToolbox2 and contains functions, which compute dynamically the global sensitivity indices of the system by applying four main GPSA methods: eFAST, Sobol's ANOVA, PRCC and WALS. It includes parallelized versions of the functions enabling significant reduction of the computational time (up to 12 times). DyGloSA is freely available as a set of MATLAB scripts at http://bio.uni.lu/systems_biology/software/dyglosa. It requires installation of MATLAB (versions R2008b or later) and the Systems Biology Toolbox2 available at www.sbtoolbox2.org. DyGloSA can be run on Windows and Linux systems, -32 and -64 bits. PMID:24367574

Switch of sensitivity dynamics revealed with DyGloSA toolbox for dynamical global sensitivity analysis as an early warning for system's critical transition.

PubMed

Baumuratova, Tatiana; Dobre, Simona; Bastogne, Thierry; Sauter, Thomas

2013-01-01

Systems with bifurcations may experience abrupt irreversible and often unwanted shifts in their performance, called critical transitions. For many systems like climate, economy, ecosystems it is highly desirable to identify indicators serving as early warnings of such regime shifts. Several statistical measures were recently proposed as early warnings of critical transitions including increased variance, autocorrelation and skewness of experimental or model-generated data. The lack of automatized tool for model-based prediction of critical transitions led to designing DyGloSA - a MATLAB toolbox for dynamical global parameter sensitivity analysis (GPSA) of ordinary differential equations models. We suggest that the switch in dynamics of parameter sensitivities revealed by our toolbox is an early warning that a system is approaching a critical transition. We illustrate the efficiency of our toolbox by analyzing several models with bifurcations and predicting the time periods when systems can still avoid going to a critical transition by manipulating certain parameter values, which is not detectable with the existing SA techniques. DyGloSA is based on the SBToolbox2 and contains functions, which compute dynamically the global sensitivity indices of the system by applying four main GPSA methods: eFAST, Sobol's ANOVA, PRCC and WALS. It includes parallelized versions of the functions enabling significant reduction of the computational time (up to 12 times). DyGloSA is freely available as a set of MATLAB scripts at http://bio.uni.lu/systems_biology/software/dyglosa. It requires installation of MATLAB (versions R2008b or later) and the Systems Biology Toolbox2 available at www.sbtoolbox2.org. DyGloSA can be run on Windows and Linux systems, -32 and -64 bits.

Development of estrogen receptor beta binding prediction model using large sets of chemicals.

PubMed

Sakkiah, Sugunadevi; Selvaraj, Chandrabose; Gong, Ping; Zhang, Chaoyang; Tong, Weida; Hong, Huixiao

2017-11-03

We developed an ER β binding prediction model to facilitate identification of chemicals specifically bind ER β or ER α together with our previously developed ER α binding model. Decision Forest was used to train ER β binding prediction model based on a large set of compounds obtained from EADB. Model performance was estimated through 1000 iterations of 5-fold cross validations. Prediction confidence was analyzed using predictions from the cross validations. Informative chemical features for ER β binding were identified through analysis of the frequency data of chemical descriptors used in the models in the 5-fold cross validations. 1000 permutations were conducted to assess the chance correlation. The average accuracy of 5-fold cross validations was 93.14% with a standard deviation of 0.64%. Prediction confidence analysis indicated that the higher the prediction confidence the more accurate the predictions. Permutation testing results revealed that the prediction model is unlikely generated by chance. Eighteen informative descriptors were identified to be important to ER β binding prediction. Application of the prediction model to the data from ToxCast project yielded very high sensitivity of 90-92%. Our results demonstrated ER β binding of chemicals could be accurately predicted using the developed model. Coupling with our previously developed ER α prediction model, this model could be expected to facilitate drug development through identification of chemicals that specifically bind ER β or ER α .

Prediction of clinical response to drugs in ovarian cancer using the chemotherapy resistance test (CTR-test).

PubMed

Kischkel, Frank Christian; Meyer, Carina; Eich, Julia; Nassir, Mani; Mentze, Monika; Braicu, Ioana; Kopp-Schneider, Annette; Sehouli, Jalid

2017-10-27

In order to validate if the test result of the Chemotherapy Resistance Test (CTR-Test) is able to predict the resistances or sensitivities of tumors in ovarian cancer patients to drugs, the CTR-Test result and the corresponding clinical response of individual patients were correlated retrospectively. Results were compared to previous recorded correlations. The CTR-Test was performed on tumor samples from 52 ovarian cancer patients for specific chemotherapeutic drugs. Patients were treated with monotherapies or drug combinations. Resistances were classified as extreme (ER), medium (MR) or slight (SR) resistance in the CTR-Test. Combination treatment resistances were transformed by a scoring system into these classifications. Accurate sensitivity prediction was accomplished in 79% of the cases and accurate prediction of resistance in 100% of the cases in the total data set. The data set of single agent treatment and drug combination treatment were analyzed individually. Single agent treatment lead to an accurate sensitivity in 44% of the cases and the drug combination to 95% accuracy. The detection of resistances was in both cases to 100% correct. ROC curve analysis indicates that the CTR-Test result correlates with the clinical response, at least for the combination chemotherapy. Those values are similar or better than the values from a publication from 1990. Chemotherapy resistance testing in vitro via the CTR-Test is able to accurately detect resistances in ovarian cancer patients. These numbers confirm and even exceed results published in 1990. Better sensitivity detection might be caused by a higher percentage of drug combinations tested in 2012 compared to 1990. Our study confirms the functionality of the CTR-Test to plan an efficient chemotherapeutic treatment for ovarian cancer patients.

A randomized and blinded comparison of qPCR and NGS-based detection of aneuploidy in a cell line mixture model of blastocyst biopsy mosaicism.

PubMed

Goodrich, David; Tao, Xin; Bohrer, Chelsea; Lonczak, Agnieszka; Xing, Tongji; Zimmerman, Rebekah; Zhan, Yiping; Scott, Richard T; Treff, Nathan R

2016-11-01

A subset of preimplantation stage embryos may possess mosaicism of chromosomal constitution, representing a possible limitation to the clinical predictive value of comprehensive chromosome screening (CCS) from a single biopsy. However, contemporary methods of CCS may be capable of predicting mosaicism in the blastocyst by detecting intermediate levels of aneuploidy within a trophectoderm biopsy. This study evaluates the sensitivity and specificity of aneuploidy detection by two CCS platforms using a cell line mixture model of a mosaic trophectoderm biopsy. Four cell lines with known karyotypes were obtained and mixed together at specific ratios of six total cells (0:6, 1:5, 2:4, 3:3, 4:2, 5:1, and 6:0). A female euploid and a male trisomy 18 cell line were used for one set, and a male trisomy 13 and a male trisomy 15 cell line were used for another. Replicates of each mixture were prepared, randomized, and blinded for analysis by one of two CCS platforms (quantitative polymerase chain reaction (qPCR) or VeriSeq next-generation sequencing (NGS)). Sensitivity and specificity of aneuploidy detection at each level of mosaicism was determined and compared between platforms. With the default settings for each platform, the sensitivity of qPCR and NGS were not statistically different, and 100 % specificity was observed (no false positives) at all levels of mosaicism. However, the use of previously published custom criteria for NGS increased sensitivity but also significantly decreased specificity (33 % false-positive prediction of aneuploidy). By demonstrating increased false-positive diagnoses when reducing the stringency of predicting an abnormality, these data illustrate the importance of preclinical evaluation of new testing paradigms before clinical implementation.

Integration of Attributes from Non-Linear Characterization of Cardiovascular Time-Series for Prediction of Defibrillation Outcomes

PubMed Central

Shandilya, Sharad; Kurz, Michael C.; Ward, Kevin R.; Najarian, Kayvan

2016-01-01

Objective The timing of defibrillation is mostly at arbitrary intervals during cardio-pulmonary resuscitation (CPR), rather than during intervals when the out-of-hospital cardiac arrest (OOH-CA) patient is physiologically primed for successful countershock. Interruptions to CPR may negatively impact defibrillation success. Multiple defibrillations can be associated with decreased post-resuscitation myocardial function. We hypothesize that a more complete picture of the cardiovascular system can be gained through non-linear dynamics and integration of multiple physiologic measures from biomedical signals. Materials and Methods Retrospective analysis of 153 anonymized OOH-CA patients who received at least one defibrillation for ventricular fibrillation (VF) was undertaken. A machine learning model, termed Multiple Domain Integrative (MDI) model, was developed to predict defibrillation success. We explore the rationale for non-linear dynamics and statistically validate heuristics involved in feature extraction for model development. Performance of MDI is then compared to the amplitude spectrum area (AMSA) technique. Results 358 defibrillations were evaluated (218 unsuccessful and 140 successful). Non-linear properties (Lyapunov exponent > 0) of the ECG signals indicate a chaotic nature and validate the use of novel non-linear dynamic methods for feature extraction. Classification using MDI yielded ROC-AUC of 83.2% and accuracy of 78.8%, for the model built with ECG data only. Utilizing 10-fold cross-validation, at 80% specificity level, MDI (74% sensitivity) outperformed AMSA (53.6% sensitivity). At 90% specificity level, MDI had 68.4% sensitivity while AMSA had 43.3% sensitivity. Integrating available end-tidal carbon dioxide features into MDI, for the available 48 defibrillations, boosted ROC-AUC to 93.8% and accuracy to 83.3% at 80% sensitivity. Conclusion At clinically relevant sensitivity thresholds, the MDI provides improved performance as compared to AMSA, yielding fewer unsuccessful defibrillations. Addition of partial end-tidal carbon dioxide (PetCO2) signal improves accuracy and sensitivity of the MDI prediction model. PMID:26741805

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.