Latency-Based and Psychophysiological Measures of Sexual Interest Show Convergent and Concurrent Validity.

PubMed

Ó Ciardha, Caoilte; Attard-Johnson, Janice; Bindemann, Markus

2018-04-01

Latency-based measures of sexual interest require additional evidence of validity, as do newer pupil dilation approaches. A total of 102 community men completed six latency-based measures of sexual interest. Pupillary responses were recorded during three of these tasks and in an additional task where no participant response was required. For adult stimuli, there was a high degree of intercorrelation between measures, suggesting that tasks may be measuring the same underlying construct (convergent validity). In addition to being correlated with one another, measures also predicted participants' self-reported sexual interest, demonstrating concurrent validity (i.e., the ability of a task to predict a more validated, simultaneously recorded, measure). Latency-based and pupillometric approaches also showed preliminary evidence of concurrent validity in predicting both self-reported interest in child molestation and viewing pornographic material containing children. Taken together, the study findings build on the evidence base for the validity of latency-based and pupillometric measures of sexual interest.

Factor Structure and Validation of a Set of Readiness Measures.

ERIC Educational Resources Information Center

Kaufman, Maurice; Lynch, Mervin

A study was undertaken to identify the factor structure of a battery of readiness measures and to demonstrate the concurrent and predictive validity of one instrument in that battery--the Pre-Reading Screening Procedures (PSP). Concurrent validity was determined by examining the correlation of the PSP with the Metropolitan Readiness Test (MRT),…

The Adaptation and Validation of the Emotion Matching Task for Preschool Children in Spain

ERIC Educational Resources Information Center

Alonso-Alberca, Natalia; Vergara, Ana I.; Fernandez-Berrocal, Pablo; Johnson, Stacy R.; Izard, Carroll E.

2012-01-01

The Emotion Matching Task (EMT; Izard, Haskins, Schultz, Trentacosta, & King, 2003) was developed to assess emotion knowledge in preschoolers and was demonstrated to show adequate convergent and predictive validity in an American sample (Morgan, Izard, & King, 2010). In light of the need for valid measures for assessing emotion…

Nursing students' confidence in medication calculations predicts math exam performance.

PubMed

Andrew, Sharon; Salamonson, Yenna; Halcomb, Elizabeth J

2009-02-01

The aim of this study was to examine the psychometric properties, including predictive validity, of the newly-developed nursing self-efficacy for mathematics (NSE-Math). The NSE-Math is a 12 item scale that comprises items related to mathematic and arithmetic concepts underpinning medication calculations. The NSE-Math instrument was administered to second year Bachelor of Nursing students enrolled in a nursing practice subject. Students' academic results for a compulsory medication calculation examination for this subject were collected. One-hundred and twelve students (73%) completed both the NSE-Math instrument and the drug calculation assessment task. The NSE-Math demonstrated two factors 'Confidence in application of mathematic concepts to nursing practice' and 'Confidence in arithmetic concepts' with 63.5% of variance explained. Cronbach alpha for the scale was 0.90. The NSE-Math demonstrated predictive validity with the medication calculation examination results (p=0.009). Psychometric testing suggests the NSE-Math is a valid measure of mathematics self-efficacy of second year nursing students.

Computationally efficient confidence intervals for cross-validated area under the ROC curve estimates.

PubMed

LeDell, Erin; Petersen, Maya; van der Laan, Mark

In binary classification problems, the area under the ROC curve (AUC) is commonly used to evaluate the performance of a prediction model. Often, it is combined with cross-validation in order to assess how the results will generalize to an independent data set. In order to evaluate the quality of an estimate for cross-validated AUC, we obtain an estimate of its variance. For massive data sets, the process of generating a single performance estimate can be computationally expensive. Additionally, when using a complex prediction method, the process of cross-validating a predictive model on even a relatively small data set can still require a large amount of computation time. Thus, in many practical settings, the bootstrap is a computationally intractable approach to variance estimation. As an alternative to the bootstrap, we demonstrate a computationally efficient influence curve based approach to obtaining a variance estimate for cross-validated AUC.

Computationally efficient confidence intervals for cross-validated area under the ROC curve estimates

PubMed Central

Petersen, Maya; van der Laan, Mark

2015-01-01

In binary classification problems, the area under the ROC curve (AUC) is commonly used to evaluate the performance of a prediction model. Often, it is combined with cross-validation in order to assess how the results will generalize to an independent data set. In order to evaluate the quality of an estimate for cross-validated AUC, we obtain an estimate of its variance. For massive data sets, the process of generating a single performance estimate can be computationally expensive. Additionally, when using a complex prediction method, the process of cross-validating a predictive model on even a relatively small data set can still require a large amount of computation time. Thus, in many practical settings, the bootstrap is a computationally intractable approach to variance estimation. As an alternative to the bootstrap, we demonstrate a computationally efficient influence curve based approach to obtaining a variance estimate for cross-validated AUC. PMID:26279737

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.

Prediction of Protein-Protein Interaction Sites with Machine-Learning-Based Data-Cleaning and Post-Filtering Procedures.

PubMed

Liu, Guang-Hui; Shen, Hong-Bin; Yu, Dong-Jun

2016-04-01

Accurately predicting protein-protein interaction sites (PPIs) is currently a hot topic because it has been demonstrated to be very useful for understanding disease mechanisms and designing drugs. Machine-learning-based computational approaches have been broadly utilized and demonstrated to be useful for PPI prediction. However, directly applying traditional machine learning algorithms, which often assume that samples in different classes are balanced, often leads to poor performance because of the severe class imbalance that exists in the PPI prediction problem. In this study, we propose a novel method for improving PPI prediction performance by relieving the severity of class imbalance using a data-cleaning procedure and reducing predicted false positives with a post-filtering procedure: First, a machine-learning-based data-cleaning procedure is applied to remove those marginal targets, which may potentially have a negative effect on training a model with a clear classification boundary, from the majority samples to relieve the severity of class imbalance in the original training dataset; then, a prediction model is trained on the cleaned dataset; finally, an effective post-filtering procedure is further used to reduce potential false positive predictions. Stringent cross-validation and independent validation tests on benchmark datasets demonstrated the efficacy of the proposed method, which exhibits highly competitive performance compared with existing state-of-the-art sequence-based PPIs predictors and should supplement existing PPI prediction methods.

Definition and Demonstration of a Methodology for Validating Aircraft Trajectory Predictors

NASA Technical Reports Server (NTRS)

Vivona, Robert A.; Paglione, Mike M.; Cate, Karen T.; Enea, Gabriele

2010-01-01

This paper presents a new methodology for validating an aircraft trajectory predictor, inspired by the lessons learned from a number of field trials, flight tests and simulation experiments for the development of trajectory-predictor-based automation. The methodology introduces new techniques and a new multi-staged approach to reduce the effort in identifying and resolving validation failures, avoiding the potentially large costs associated with failures during a single-stage, pass/fail approach. As a case study, the validation effort performed by the Federal Aviation Administration for its En Route Automation Modernization (ERAM) system is analyzed to illustrate the real-world applicability of this methodology. During this validation effort, ERAM initially failed to achieve six of its eight requirements associated with trajectory prediction and conflict probe. The ERAM validation issues have since been addressed, but to illustrate how the methodology could have benefited the FAA effort, additional techniques are presented that could have been used to resolve some of these issues. Using data from the ERAM validation effort, it is demonstrated that these new techniques could have identified trajectory prediction error sources that contributed to several of the unmet ERAM requirements.

New Perspectives on the Validity of the "GRE"® General Test for Predicting Graduate School Grades. ETS GRE® Board Research Report. ETS GRE®-14-03. ETS Research Report. RR-14-26

ERIC Educational Resources Information Center

Klieger, David M.; Cline, Frederick A.; Holtzman, Steven L.; Minsky, Jennifer L.; Lorenz, Florian

2014-01-01

Given the serious consequences of making ill-fated admissions and funding decisions for applicants to graduate and professional school, it is important to rely on sound evidence to optimize such judgments. Previous meta-analytic research has demonstrated the generalizable validity of the "GRE"® General Test for predicting academic…

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 .

Optimal test selection for prediction uncertainty reduction

DOE PAGES

Mullins, Joshua; Mahadevan, Sankaran; Urbina, Angel

2016-12-02

Economic factors and experimental limitations often lead to sparse and/or imprecise data used for the calibration and validation of computational models. This paper addresses resource allocation for calibration and validation experiments, in order to maximize their effectiveness within given resource constraints. When observation data are used for model calibration, the quality of the inferred parameter descriptions is directly affected by the quality and quantity of the data. This paper characterizes parameter uncertainty within a probabilistic framework, which enables the uncertainty to be systematically reduced with additional data. The validation assessment is also uncertain in the presence of sparse and imprecisemore » data; therefore, this paper proposes an approach for quantifying the resulting validation uncertainty. Since calibration and validation uncertainty affect the prediction of interest, the proposed framework explores the decision of cost versus importance of data in terms of the impact on the prediction uncertainty. Often, calibration and validation tests may be performed for different input scenarios, and this paper shows how the calibration and validation results from different conditions may be integrated into the prediction. Then, a constrained discrete optimization formulation that selects the number of tests of each type (calibration or validation at given input conditions) is proposed. Furthermore, the proposed test selection methodology is demonstrated on a microelectromechanical system (MEMS) example.« less

The teamwork in assertive community treatment (TACT) scale: development and validation.

PubMed

Wholey, Douglas R; Zhu, Xi; Knoke, David; Shah, Pri; Zellmer-Bruhn, Mary; Witheridge, Thomas F

2012-11-01

Team design is meticulously specified for assertive community treatment (ACT) teams, yet performance can vary across ACT teams, even those with high fidelity. By developing and validating the Teamwork in Assertive Community Treatment (TACT) scale, investigators examined the role of team processes in ACT performance. The TACT scale measuring ACT teamwork was developed from a conceptual model grounded in organizational research and adapted for the ACT and mental health context. TACT subscales were constructed after exploratory and confirmatory factor analyses. The reliability, discriminant validity, predictive validity, temporal stability, internal consistency, and within-team agreement were established with surveys from approximately 300 members of 26 Minnesota ACT teams who completed the questionnaire three times, at six-month intervals. Nine TACT subscales emerged from the analyses: exploration, exploitation of new and existing knowledge, psychological safety, goal agreement, conflict, constructive controversy, information accessibility, encounter preparedness, and consumer-centered care. These nine subscales demonstrated fit and temporal stability (confirmatory factor analysis), high internal consistency (Cronbach's alpha), and within-team agreement and between-team differences (rwg and intraclass correlations). Correlational analyses of the subscales revealed that they measure related yet distinctive aspects of ACT team processes, and regression analyses demonstrated predictive validity (encounter preparedness is related to staff outcomes). The TACT scale demonstrated high reliability and validity and can be included in research and evaluation of teamwork in ACT and mental health teams.

A Public-Private Partnership Develops and Externally Validates a 30-Day Hospital Readmission Risk Prediction Model

PubMed Central

Choudhry, Shahid A.; Li, Jing; Davis, Darcy; Erdmann, Cole; Sikka, Rishi; Sutariya, Bharat

2013-01-01

Introduction: Preventing the occurrence of hospital readmissions is needed to improve quality of care and foster population health across the care continuum. Hospitals are being held accountable for improving transitions of care to avert unnecessary readmissions. Advocate Health Care in Chicago and Cerner (ACC) collaborated to develop all-cause, 30-day hospital readmission risk prediction models to identify patients that need interventional resources. Ideally, prediction models should encompass several qualities: they should have high predictive ability; use reliable and clinically relevant data; use vigorous performance metrics to assess the models; be validated in populations where they are applied; and be scalable in heterogeneous populations. However, a systematic review of prediction models for hospital readmission risk determined that most performed poorly (average C-statistic of 0.66) and efforts to improve their performance are needed for widespread usage. Methods: The ACC team incorporated electronic health record data, utilized a mixed-method approach to evaluate risk factors, and externally validated their prediction models for generalizability. Inclusion and exclusion criteria were applied on the patient cohort and then split for derivation and internal validation. Stepwise logistic regression was performed to develop two predictive models: one for admission and one for discharge. The prediction models were assessed for discrimination ability, calibration, overall performance, and then externally validated. Results: The ACC Admission and Discharge Models demonstrated modest discrimination ability during derivation, internal and external validation post-recalibration (C-statistic of 0.76 and 0.78, respectively), and reasonable model fit during external validation for utility in heterogeneous populations. Conclusions: The ACC Admission and Discharge Models embody the design qualities of ideal prediction models. The ACC plans to continue its partnership to further improve and develop valuable clinical models. PMID:24224068

The Irvine, Beatties, and Bresnahan (IBB) Forelimb Recovery Scale: An Assessment of Reliability and Validity

PubMed Central

Irvine, Karen-Amanda; Ferguson, Adam R.; Mitchell, Kathleen D.; Beattie, Stephanie B.; Lin, Amity; Stuck, Ellen D.; Huie, J. Russell; Nielson, Jessica L.; Talbott, Jason F.; Inoue, Tomoo; Beattie, Michael S.; Bresnahan, Jacqueline C.

2014-01-01

The IBB scale is a recently developed forelimb scale for the assessment of fine control of the forelimb and digits after cervical spinal cord injury [SCI; (1)]. The present paper describes the assessment of inter-rater reliability and face, concurrent and construct validity of this scale following SCI. It demonstrates that the IBB is a reliable and valid scale that is sensitive to severity of SCI and to recovery over time. In addition, the IBB correlates with other outcome measures and is highly predictive of biological measures of tissue pathology. Multivariate analysis using principal component analysis (PCA) demonstrates that the IBB is highly predictive of the syndromic outcome after SCI (2), and is among the best predictors of bio-behavioral function, based on strong construct validity. Altogether, the data suggest that the IBB, especially in concert with other measures, is a reliable and valid tool for assessing neurological deficits in fine motor control of the distal forelimb, and represents a powerful addition to multivariate outcome batteries aimed at documenting recovery of function after cervical SCI in rats. PMID:25071704

Assessing psychological inflexibility: the psychometric properties of the Avoidance and Fusion Questionnaire for Youth in two adult samples.

PubMed

Fergus, Thomas A; Valentiner, David P; Gillen, Michael J; Hiraoka, Regina; Twohig, Michael P; Abramowitz, Jonathan S; McGrath, Patrick B

2012-06-01

The current study examined whether the Avoidance and Fusion Questionnaire for Youth (AFQ-Y; L. A. Greco, W. Lambert, & R. A. Baer, 2008), a self-report measure of psychological inflexibility for children and adolescents, might be useful for measuring psychological inflexibility for adults. The psychometric properties of the AFQ-Y were examined using data from a college student sample (N = 387) and a clinical sample of patients with anxiety disorders (N = 115). The AFQ-Y, but not the Acceptance and Action Questionnaire-II (AAQ-II; F. W. Bond et al., in press), demonstrated a reading level at or below the recommended 5th or 6th grade reading level. The AFQ-Y also demonstrated adequate reliability (internal consistency), factorial validity, convergent and discriminant validity, and concurrent validity predicting psychological symptoms. Moreover, the AFQ-Y showed incremental validity over the AAQ-II in predicting several psychological symptom domains. Implications for the assessment of psychological inflexibility are discussed. (c) 2012 APA, all rights reserved

Validity of Bioelectrical Impedance Analysis to Estimation Fat-Free Mass in the Army Cadets.

PubMed

Langer, Raquel D; Borges, Juliano H; Pascoa, Mauro A; Cirolini, Vagner X; Guerra-Júnior, Gil; Gonçalves, Ezequiel M

2016-03-11

Bioelectrical Impedance Analysis (BIA) is a fast, practical, non-invasive, and frequently used method for fat-free mass (FFM) estimation. The aims of this study were to validate predictive equations of BIA to FFM estimation in Army cadets and to develop and validate a specific BIA equation for this population. A total of 396 males, Brazilian Army cadets, aged 17-24 years were included. The study used eight published predictive BIA equations, a specific equation in FFM estimation, and dual-energy X-ray absorptiometry (DXA) as a reference method. Student's t-test (for paired sample), linear regression analysis, and Bland-Altman method were used to test the validity of the BIA equations. Predictive BIA equations showed significant differences in FFM compared to DXA (p < 0.05) and large limits of agreement by Bland-Altman. Predictive BIA equations explained 68% to 88% of FFM variance. Specific BIA equations showed no significant differences in FFM, compared to DXA values. Published BIA predictive equations showed poor accuracy in this sample. The specific BIA equations, developed in this study, demonstrated validity for this sample, although should be used with caution in samples with a large range of FFM.

Implicit Sex Guilt Predicts Sexual Behaviors: Evidence for the Validity of the Sex Guilt Implicit Association Test.

PubMed

Totonchi, Delaram A; Derlega, Valerian J; Janda, Louis H

2018-05-14

Self-report measures of sexuality may be influenced by people's conscious concerns about confidentiality and social desirability. Alternatively, non-conscious measures (e.g., implicit association tests; IATs) are designed to minimize these validity concerns. We constructed an IAT measure of sex guilt using 154 male and female university students. The sex guilt IAT demonstrated convergent validity as it correlated with various sexual behaviors and incremental validity as it improved the prediction of several sexual behaviors beyond that provided by the Mosher sex guilt scale. We conclude that a non-conscious measure of sex guilt may complement the use of self-reports in studying sexual behaviors.

Incremental Validity of Biographical Data in the Prediction of En Route Air Traffic Control Specialist Technical Skills

DOT National Transportation Integrated Search

2012-07-01

Previous research demonstrated that an empirically-keyed, response-option scored biographical data (biodata) : scale predicted supervisory ratings of air traffic control specialist (ATCS) job performance (Dean & Broach, : 2011). This research f...

Computational discovery and in vivo validation of hnf4 as a regulatory gene in planarian regeneration.

PubMed

Lobo, Daniel; Morokuma, Junji; Levin, Michael

2016-09-01

Automated computational methods can infer dynamic regulatory network models directly from temporal and spatial experimental data, such as genetic perturbations and their resultant morphologies. Recently, a computational method was able to reverse-engineer the first mechanistic model of planarian regeneration that can recapitulate the main anterior-posterior patterning experiments published in the literature. Validating this comprehensive regulatory model via novel experiments that had not yet been performed would add in our understanding of the remarkable regeneration capacity of planarian worms and demonstrate the power of this automated methodology. Using the Michigan Molecular Interactions and STRING databases and the MoCha software tool, we characterized as hnf4 an unknown regulatory gene predicted to exist by the reverse-engineered dynamic model of planarian regeneration. Then, we used the dynamic model to predict the morphological outcomes under different single and multiple knock-downs (RNA interference) of hnf4 and its predicted gene pathway interactors β-catenin and hh Interestingly, the model predicted that RNAi of hnf4 would rescue the abnormal regenerated phenotype (tailless) of RNAi of hh in amputated trunk fragments. Finally, we validated these predictions in vivo by performing the same surgical and genetic experiments with planarian worms, obtaining the same phenotypic outcomes predicted by the reverse-engineered model. These results suggest that hnf4 is a regulatory gene in planarian regeneration, validate the computational predictions of the reverse-engineered dynamic model, and demonstrate the automated methodology for the discovery of novel genes, pathways and experimental phenotypes. michael.levin@tufts.edu. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Translation and validation of the Canadian diabetes risk assessment questionnaire in China.

PubMed

Guo, Jia; Shi, Zhengkun; Chen, Jyu-Lin; Dixon, Jane K; Wiley, James; Parry, Monica

2018-01-01

To adapt the Canadian Diabetes Risk Assessment Questionnaire for the Chinese population and to evaluate its psychometric properties. A cross-sectional study was conducted with a convenience sample of 194 individuals aged 35-74 years from October 2014 to April 2015. The Canadian Diabetes Risk Assessment Questionnaire was adapted and translated for the Chinese population. Test-retest reliability was conducted to measure stability. Criterion and convergent validity of the adapted questionnaire were assessed using 2-hr 75 g oral glucose tolerance tests and the Finnish Diabetes Risk Scores, respectively. Sensitivity and specificity were evaluated to establish its predictive validity. The test-retest reliability was 0.988. Adequate validity of the adapted questionnaire was demonstrated by positive correlations found between the scores and 2-hr 75 g oral glucose tolerance tests (r = .343, p < .001) and with the Finnish Diabetes Risk Scores (r = .738, p < .001). The area under receiver operating characteristic curve was 0.705 (95% CI .632, .778), demonstrating moderate diagnostic value at a cutoff score of 30. The sensitivity was 73%, with a positive predictive value of 57% and negative predictive value of 78%. Our results provided evidence supporting the translation consistency, content validity, convergent validity, criterion validity, sensitivity, and specificity of the translated Canadian Diabetes Risk Assessment Questionnaire with minor modifications. This paper provides clinical, practical, and methodological information on how to adapt a diabetes risk calculator between cultures for public health nurses. © 2017 Wiley Periodicals, Inc.

A Cross-Modal Assessment of Reading Achievement in Children.

ERIC Educational Resources Information Center

Webb, Kathryn; And Others

1982-01-01

This study examined the ability of the Listen and Look (LL) test of cross-modal perception and the Metropolitan Readiness Test (MRT) to predict reading achievement. Data from 79 first-grade pupils were analyzed. Both the LL and MRT demonstrated predictive validity. (Author/BW)

Validation of Accelerometer Prediction Equations in Children with Chronic Disease.

PubMed

Stephens, Samantha; Takken, Tim; Esliger, Dale W; Pullenayegum, Eleanor; Beyene, Joseph; Tremblay, Mark; Schneiderman, Jane; Biggar, Doug; Longmuir, Pat; McCrindle, Brian; Abad, Audrey; Ignas, Dan; Van Der Net, Janjaap; Feldman, Brian

2016-02-01

The purpose of this study was to assess the criterion validity of existing accelerometer-based energy expenditure (EE) prediction equations among children with chronic conditions, and to develop new prediction equations. Children with congenital heart disease (CHD), cystic fibrosis (CF), dermatomyositis (JDM), juvenile arthritis (JA), inherited muscle disease (IMD), and hemophilia (HE) completed 7 tasks while EE was measured using indirect calorimetry with counts determined by accelerometer. Agreement between predicted EE and measured EE was assessed. Disease-specific equations and cut points were developed and cross-validated. In total, 196 subjects participated. One participant dropped out before testing due to time constraints, while 15 CHD, 32 CF, 31 JDM, 31 JA, 30 IMD, 28 HE, and 29 healthy controls completed the study. Agreement between predicted and measured EE varied across disease group and ranged from (ICC) .13-.46. Disease-specific prediction equations exhibited a range of results (ICC .62-.88) (SE 0.45-0.78). In conclusion, poor agreement was demonstrated using current prediction equations in children with chronic conditions. Disease-specific equations and cut points were developed.

LRSSLMDA: Laplacian Regularized Sparse Subspace Learning for MiRNA-Disease Association prediction

PubMed Central

Huang, Li

2017-01-01

Predicting novel microRNA (miRNA)-disease associations is clinically significant due to miRNAs’ potential roles of diagnostic biomarkers and therapeutic targets for various human diseases. Previous studies have demonstrated the viability of utilizing different types of biological data to computationally infer new disease-related miRNAs. Yet researchers face the challenge of how to effectively integrate diverse datasets and make reliable predictions. In this study, we presented a computational model named Laplacian Regularized Sparse Subspace Learning for MiRNA-Disease Association prediction (LRSSLMDA), which projected miRNAs/diseases’ statistical feature profile and graph theoretical feature profile to a common subspace. It used Laplacian regularization to preserve the local structures of the training data and a L1-norm constraint to select important miRNA/disease features for prediction. The strength of dimensionality reduction enabled the model to be easily extended to much higher dimensional datasets than those exploited in this study. Experimental results showed that LRSSLMDA outperformed ten previous models: the AUC of 0.9178 in global leave-one-out cross validation (LOOCV) and the AUC of 0.8418 in local LOOCV indicated the model’s superior prediction accuracy; and the average AUC of 0.9181+/-0.0004 in 5-fold cross validation justified its accuracy and stability. In addition, three types of case studies further demonstrated its predictive power. Potential miRNAs related to Colon Neoplasms, Lymphoma, Kidney Neoplasms, Esophageal Neoplasms and Breast Neoplasms were predicted by LRSSLMDA. Respectively, 98%, 88%, 96%, 98% and 98% out of the top 50 predictions were validated by experimental evidences. Therefore, we conclude that LRSSLMDA would be a valuable computational tool for miRNA-disease association prediction. PMID:29253885

Validation and Inter-comparison Against Observations of GODAE Ocean View Ocean Prediction Systems

NASA Astrophysics Data System (ADS)

Xu, J.; Davidson, F. J. M.; Smith, G. C.; Lu, Y.; Hernandez, F.; Regnier, C.; Drevillon, M.; Ryan, A.; Martin, M.; Spindler, T. D.; Brassington, G. B.; Oke, P. R.

2016-02-01

For weather forecasts, validation of forecast performance is done at the end user level as well as by the meteorological forecast centers. In the development of Ocean Prediction Capacity, the same level of care for ocean forecast performance and validation is needed. Herein we present results from a validation against observations of 6 Global Ocean Forecast Systems under the GODAE OceanView International Collaboration Network. These systems include the Global Ocean Ice Forecast System (GIOPS) developed by the Government of Canada, two systems PSY3 and PSY4 from the French Mercator-Ocean Ocean Forecasting Group, the FOAM system from UK met office, HYCOM-RTOFS from NOAA/NCEP/NWA of USA, and the Australian Bluelink-OceanMAPS system from the CSIRO, the Australian Meteorological Bureau and the Australian Navy.The observation data used in the comparison are sea surface temperature, sub-surface temperature, sub-surface salinity, sea level anomaly, and sea ice total concentration data. Results of the inter-comparison demonstrate forecast performance limits, strengths and weaknesses of each of the six systems. This work establishes validation protocols and routines by which all new prediction systems developed under the CONCEPTS Collaborative Network will be benchmarked prior to approval for operations. This includes anticipated delivery of CONCEPTS regional prediction systems over the next two years including a pan Canadian 1/12th degree resolution ice ocean prediction system and limited area 1/36th degree resolution prediction systems. The validation approach of comparing forecasts to observations at the time and location of the observation is called Class 4 metrics. It has been adopted by major international ocean prediction centers, and will be recommended to JCOMM-WMO as routine validation approach for operational oceanography worldwide.

Validation of catchment models for predicting land-use and climate change impacts. 2. Case study for a Mediterranean catchment

NASA Astrophysics Data System (ADS)

Parkin, G.; O'Donnell, G.; Ewen, J.; Bathurst, J. C.; O'Connell, P. E.; Lavabre, J.

1996-02-01

Validation methods commonly used to test catchment models are not capable of demonstrating a model's fitness for making predictions for catchments where the catchment response is not known (including hypothetical catchments, and future conditions of existing catchments which are subject to land-use or climate change). This paper describes the first use of a new method of validation (Ewen and Parkin, 1996. J. Hydrol., 175: 583-594) designed to address these types of application; the method involves making 'blind' predictions of selected hydrological responses which are considered important for a particular application. SHETRAN (a physically based, distributed catchment modelling system) is tested on a small Mediterranean catchment. The test involves quantification of the uncertainty in four predicted features of the catchment response (continuous hydrograph, peak discharge rates, monthly runoff, and total runoff), and comparison of observations with the predicted ranges for these features. The results of this test are considered encouraging.

Violence risk prediction. Clinical and actuarial measures and the role of the Psychopathy Checklist.

PubMed

Dolan, M; Doyle, M

2000-10-01

Violence risk prediction is a priority issue for clinicians working with mentally disordered offenders. To review the current status of violence risk prediction research. Literature search (Medline). Key words: violence, risk prediction, mental disorder. Systematic/structured risk assessment approaches may enhance the accuracy of clinical prediction of violent outcomes. Data on the predictive validity of available clinical risk assessment tools are based largely on American and North American studies and further validation is required in British samples. The Psychopathy Checklist appears to be a key predictor of violent recidivism in a variety of settings. Violence risk prediction is an inexact science and as such will continue to provoke debate. Clinicians clearly need to be able to demonstrate the rationale behind their decisions on violence risk and much can be learned from recent developments in research on violence risk prediction.

Validation of a prediction model that allows direct comparison of the Oxford Knee Score and American Knee Society clinical rating system.

PubMed

Maempel, J F; Clement, N D; Brenkel, I J; Walmsley, P J

2015-04-01

This study demonstrates a significant correlation between the American Knee Society (AKS) Clinical Rating System and the Oxford Knee Score (OKS) and provides a validated prediction tool to estimate score conversion. A total of 1022 patients were prospectively clinically assessed five years after TKR and completed AKS assessments and an OKS questionnaire. Multivariate regression analysis demonstrated significant correlations between OKS and the AKS knee and function scores but a stronger correlation (r = 0.68, p < 0.001) when using the sum of the AKS knee and function scores. Addition of body mass index and age (other statistically significant predictors of OKS) to the algorithm did not significantly increase the predictive value. The simple regression model was used to predict the OKS in a group of 236 patients who were clinically assessed nine to ten years after TKR using the AKS system. The predicted OKS was compared with actual OKS in the second group. Intra-class correlation demonstrated excellent reliability (r = 0.81, 95% confidence intervals 0.75 to 0.85) for the combined knee and function score when used to predict OKS. Our findings will facilitate comparison of outcome data from studies and registries using either the OKS or the AKS scores and may also be of value for those undertaking meta-analyses and systematic reviews. ©2015 The British Editorial Society of Bone & Joint Surgery.

The HIrisPlex-S system for eye, hair and skin colour prediction from DNA: Introduction and forensic developmental validation.

PubMed

Chaitanya, Lakshmi; Breslin, Krystal; Zuñiga, Sofia; Wirken, Laura; Pośpiech, Ewelina; Kukla-Bartoszek, Magdalena; Sijen, Titia; Knijff, Peter de; Liu, Fan; Branicki, Wojciech; Kayser, Manfred; Walsh, Susan

2018-07-01

Forensic DNA Phenotyping (FDP), i.e. the prediction of human externally visible traits from DNA, has become a fast growing subfield within forensic genetics due to the intelligence information it can provide from DNA traces. FDP outcomes can help focus police investigations in search of unknown perpetrators, who are generally unidentifiable with standard DNA profiling. Therefore, we previously developed and forensically validated the IrisPlex DNA test system for eye colour prediction and the HIrisPlex system for combined eye and hair colour prediction from DNA traces. Here we introduce and forensically validate the HIrisPlex-S DNA test system (S for skin) for the simultaneous prediction of eye, hair, and skin colour from trace DNA. This FDP system consists of two SNaPshot-based multiplex assays targeting a total of 41 SNPs via a novel multiplex assay for 17 skin colour predictive SNPs and the previous HIrisPlex assay for 24 eye and hair colour predictive SNPs, 19 of which also contribute to skin colour prediction. The HIrisPlex-S system further comprises three statistical prediction models, the previously developed IrisPlex model for eye colour prediction based on 6 SNPs, the previous HIrisPlex model for hair colour prediction based on 22 SNPs, and the recently introduced HIrisPlex-S model for skin colour prediction based on 36 SNPs. In the forensic developmental validation testing, the novel 17-plex assay performed in full agreement with the Scientific Working Group on DNA Analysis Methods (SWGDAM) guidelines, as previously shown for the 24-plex assay. Sensitivity testing of the 17-plex assay revealed complete SNP profiles from as little as 63 pg of input DNA, equalling the previously demonstrated sensitivity threshold of the 24-plex HIrisPlex assay. Testing of simulated forensic casework samples such as blood, semen, saliva stains, of inhibited DNA samples, of low quantity touch (trace) DNA samples, and of artificially degraded DNA samples as well as concordance testing, demonstrated the robustness, efficiency, and forensic suitability of the new 17-plex assay, as previously shown for the 24-plex assay. Finally, we provide an update to the publically available HIrisPlex website https://hirisplex.erasmusmc.nl/, now allowing the estimation of individual probabilities for 3 eye, 4 hair, and 5 skin colour categories from HIrisPlex-S input genotypes. The HIrisPlex-S DNA test represents the first forensically validated tool for skin colour prediction, and reflects the first forensically validated tool for simultaneous eye, hair and skin colour prediction from DNA. Copyright © 2018 Elsevier B.V. All rights reserved.

Development of a Wake Vortex Spacing System for Airport Capacity Enhancement and Delay Reduction

NASA Technical Reports Server (NTRS)

Hinton, David A.; OConnor, Cornelius J.

2000-01-01

The Terminal Area Productivity project has developed the technologies required (weather measurement, wake prediction, and wake measurement) to determine the aircraft spacing needed to prevent wake vortex encounters in various weather conditions. The system performs weather measurements, predicts bounds on wake vortex behavior in those conditions, derives safe wake spacing criteria, and validates the wake predictions with wake vortex measurements. System performance to date indicates that the potential runway arrival rate increase with Aircraft VOrtex Spacing System (AVOSS), considering common path effects and ATC delivery variance, is 5% to 12% depending on the ratio of large and heavy aircraft. The concept demonstration system, using early generation algorithms and minimal optimization, is performing the wake predictions with adequate robustness such that only 4 hard exceedances have been observed in 1235 wake validation cases. This performance demonstrates the feasibility of predicting wake behavior bounds with multiple uncertainties present, including the unknown aircraft weight and speed, weather persistence between the wake prediction and the observations, and the location of the weather sensors several kilometers from the approach location. A concept for the use of the AVOSS system for parallel runway operations has been suggested, and an initial study at the JFK International Airport suggests that a simplified AVOSS system can be successfully operated using only a single lidar as both the weather sensor and the wake validation instrument. Such a selfcontained AVOSS would be suitable for wake separation close to the airport, as is required for parallel approach concepts such as SOIA.

Understanding Interrater Reliability and Validity of Risk Assessment Tools Used to Predict Adverse Clinical Events.

PubMed

Siedlecki, Sandra L; Albert, Nancy M

This article will describe how to assess interrater reliability and validity of risk assessment tools, using easy-to-follow formulas, and to provide calculations that demonstrate principles discussed. Clinical nurse specialists should be able to identify risk assessment tools that provide high-quality interrater reliability and the highest validity for predicting true events of importance to clinical settings. Making best practice recommendations for assessment tool use is critical to high-quality patient care and safe practices that impact patient outcomes and nursing resources. Optimal risk assessment tool selection requires knowledge about interrater reliability and tool validity. The clinical nurse specialist will understand the reliability and validity issues associated with risk assessment tools, and be able to evaluate tools using basic calculations. Risk assessment tools are developed to objectively predict quality and safety events and ultimately reduce the risk of event occurrence through preventive interventions. To ensure high-quality tool use, clinical nurse specialists must critically assess tool properties. The better the tool's ability to predict adverse events, the more likely that event risk is mediated. Interrater reliability and validity assessment is relatively an easy skill to master and will result in better decisions when selecting or making recommendations for risk assessment tool use.

The Academic Diligence Task (ADT): Assessing Individual Differences in Effort on Tedious but Important Schoolwork

PubMed Central

Galla, Brian M.; Plummer, Benjamin D.; White, Rachel E.; Meketon, David; D’Mello, Sidney K.; Duckworth, Angela L.

2014-01-01

The current study reports on the development and validation of the Academic Diligence Task (ADT), designed to assess the tendency to expend effort on academic tasks which are tedious in the moment but valued in the long-term. In this novel online task, students allocate their time between solving simple math problems (framed as beneficial for problem solving skills) and, alternatively, playing Tetris or watching entertaining videos. Using a large sample of high school seniors (N = 921), the ADT demonstrated convergent validity with self-report ratings of Big Five conscientiousness and its facets, self-control and grit, as well as discriminant validity from theoretically unrelated constructs, such as Big Five extraversion, openness, and emotional stability, test anxiety, life satisfaction, and positive and negative affect. The ADT also demonstrated incremental predictive validity for objectively measured GPA, standardized math and reading achievement test scores, high school graduation, and college enrollment, over and beyond demographics and intelligence. Collectively, findings suggest the feasibility of online behavioral measures to assess noncognitive individual differences that predict academic outcomes. PMID:25258470

The Academic Diligence Task (ADT): Assessing Individual Differences in Effort on Tedious but Important Schoolwork.

PubMed

Galla, Brian M; Plummer, Benjamin D; White, Rachel E; Meketon, David; D'Mello, Sidney K; Duckworth, Angela L

2014-10-01

The current study reports on the development and validation of the Academic Diligence Task (ADT), designed to assess the tendency to expend effort on academic tasks which are tedious in the moment but valued in the long-term. In this novel online task, students allocate their time between solving simple math problems (framed as beneficial for problem solving skills) and, alternatively, playing Tetris or watching entertaining videos. Using a large sample of high school seniors ( N = 921), the ADT demonstrated convergent validity with self-report ratings of Big Five conscientiousness and its facets, self-control and grit, as well as discriminant validity from theoretically unrelated constructs, such as Big Five extraversion, openness, and emotional stability, test anxiety, life satisfaction, and positive and negative affect. The ADT also demonstrated incremental predictive validity for objectively measured GPA, standardized math and reading achievement test scores, high school graduation, and college enrollment, over and beyond demographics and intelligence. Collectively, findings suggest the feasibility of online behavioral measures to assess noncognitive individual differences that predict academic outcomes.

Predicting Third Grade Reading Success from Kindergarten Phonological Awareness

ERIC Educational Resources Information Center

Robinson, Stephanie J.

2013-01-01

Although phonological awareness (PA) is an essential preliteracy skill with well-established predictive validity for elementary school reading success, previous research indicates that PA intervention does not demonstrate long term effects on reading. The theory of automaticity was the underlying foundation used to understand the importance of…

Validation metrics for turbulent plasma transport

DOE PAGES

Holland, C.

2016-06-22

Developing accurate models of plasma dynamics is essential for confident predictive modeling of current and future fusion devices. In modern computer science and engineering, formal verification and validation processes are used to assess model accuracy and establish confidence in the predictive capabilities of a given model. This paper provides an overview of the key guiding principles and best practices for the development of validation metrics, illustrated using examples from investigations of turbulent transport in magnetically confined plasmas. Particular emphasis is given to the importance of uncertainty quantification and its inclusion within the metrics, and the need for utilizing synthetic diagnosticsmore » to enable quantitatively meaningful comparisons between simulation and experiment. As a starting point, the structure of commonly used global transport model metrics and their limitations is reviewed. An alternate approach is then presented, which focuses upon comparisons of predicted local fluxes, fluctuations, and equilibrium gradients against observation. Furthermore, the utility of metrics based upon these comparisons is demonstrated by applying them to gyrokinetic predictions of turbulent transport in a variety of discharges performed on the DIII-D tokamak, as part of a multi-year transport model validation activity.« less

One-year temporal stability and predictive and incremental validity of the body, eating, and exercise comparison orientation measure (BEECOM) among college women.

PubMed

Fitzsimmons-Craft, Ellen E; Bardone-Cone, Anna M

2014-01-01

This study examined the one-year temporal stability and the predictive and incremental validity of the Body, Eating, and Exercise Comparison Measure (BEECOM) in a sample of 237 college women who completed study measures at two time points about one year apart. One-year temporal stability was high for the BEECOM total and subscale (i.e., Body, Eating, and Exercise Comparison Orientation) scores. Additionally, the BEECOM exhibited predictive validity in that it accounted for variance in body dissatisfaction and eating disorder symptomatology one year later. These findings held even after controlling for body mass index and existing measures of social comparison orientation. However, results regarding the incremental validity of the BEECOM, or its ability to predict change in these constructs over time, were more mixed. Overall, this study demonstrated additional psychometric properties of the BEECOM among college women, further establishing the usefulness of this measure for more comprehensively assessing eating disorder-related social comparison. Copyright © 2013 Elsevier Ltd. All rights reserved.

Validation metrics for turbulent plasma transport

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

Holland, C.

Developing accurate models of plasma dynamics is essential for confident predictive modeling of current and future fusion devices. In modern computer science and engineering, formal verification and validation processes are used to assess model accuracy and establish confidence in the predictive capabilities of a given model. This paper provides an overview of the key guiding principles and best practices for the development of validation metrics, illustrated using examples from investigations of turbulent transport in magnetically confined plasmas. Particular emphasis is given to the importance of uncertainty quantification and its inclusion within the metrics, and the need for utilizing synthetic diagnosticsmore » to enable quantitatively meaningful comparisons between simulation and experiment. As a starting point, the structure of commonly used global transport model metrics and their limitations is reviewed. An alternate approach is then presented, which focuses upon comparisons of predicted local fluxes, fluctuations, and equilibrium gradients against observation. Furthermore, the utility of metrics based upon these comparisons is demonstrated by applying them to gyrokinetic predictions of turbulent transport in a variety of discharges performed on the DIII-D tokamak, as part of a multi-year transport model validation activity.« less

Finding Furfural Hydrogenation Catalysts via Predictive Modelling

PubMed Central

Strassberger, Zea; Mooijman, Maurice; Ruijter, Eelco; Alberts, Albert H; Maldonado, Ana G; Orru, Romano V A; Rothenberg, Gadi

2010-01-01

Abstract We combine multicomponent reactions, catalytic performance studies and predictive modelling to find transfer hydrogenation catalysts. An initial set of 18 ruthenium-carbene complexes were synthesized and screened in the transfer hydrogenation of furfural to furfurol with isopropyl alcohol complexes gave varied yields, from 62% up to >99.9%, with no obvious structure/activity correlations. Control experiments proved that the carbene ligand remains coordinated to the ruthenium centre throughout the reaction. Deuterium-labelling studies showed a secondary isotope effect (kH:kD=1.5). Further mechanistic studies showed that this transfer hydrogenation follows the so-called monohydride pathway. Using these data, we built a predictive model for 13 of the catalysts, based on 2D and 3D molecular descriptors. We tested and validated the model using the remaining five catalysts (cross-validation, R2=0.913). Then, with this model, the conversion and selectivity were predicted for four completely new ruthenium-carbene complexes. These four catalysts were then synthesized and tested. The results were within 3% of the model’s predictions, demonstrating the validity and value of predictive modelling in catalyst optimization. PMID:23193388

Translation, adaptation, and validation of the Sunderland Scale and the Cubbin & Jackson Revised Scale in Portuguese

PubMed Central

Sousa, Bruno

2013-01-01

Objective To translate into Portuguese and evaluate the measuring properties of the Sunderland Scale and the Cubbin & Jackson Revised Scale, which are instruments for evaluating the risk of developing pressure ulcers during intensive care. Methods This study included the process of translation and adaptation of the scales to the Portuguese language, as well as the validation of these tools. To assess the reliability, Cronbach alpha values of 0.702 to 0.708 were identified for the Sunderland Scale and the Cubbin & Jackson Revised Scale, respectively. The validation criteria (predictive) were performed comparatively with the Braden Scale (gold standard), and the main measurements evaluated were sensitivity, specificity, positive predictive value, negative predictive value, and area under the curve, which were calculated based on cutoff points. Results The Sunderland Scale exhibited 60% sensitivity, 86.7% specificity, 47.4% positive predictive value, 91.5% negative predictive value, and 0.86 for the area under the curve. The Cubbin & Jackson Revised Scale exhibited 73.3% sensitivity, 86.7% specificity, 52.4% positive predictive value, 94.2% negative predictive value, and 0.91 for the area under the curve. The Braden scale exhibited 100% sensitivity, 5.3% specificity, 17.4% positive predictive value, 100% negative predictive value, and 0.72 for the area under the curve. Conclusions Both tools demonstrated reliability and validity for this sample. The Cubbin & Jackson Revised Scale yielded better predictive values for the development of pressure ulcers during intensive care. PMID:23917975

Incorporating High-Frequency Physiologic Data Using Computational Dictionary Learning Improves Prediction of Delayed Cerebral Ischemia Compared to Existing Methods.

PubMed

Megjhani, Murad; Terilli, Kalijah; Frey, Hans-Peter; Velazquez, Angela G; Doyle, Kevin William; Connolly, Edward Sander; Roh, David Jinou; Agarwal, Sachin; Claassen, Jan; Elhadad, Noemie; Park, Soojin

2018-01-01

Accurate prediction of delayed cerebral ischemia (DCI) after subarachnoid hemorrhage (SAH) can be critical for planning interventions to prevent poor neurological outcome. This paper presents a model using convolution dictionary learning to extract features from physiological data available from bedside monitors. We develop and validate a prediction model for DCI after SAH, demonstrating improved precision over standard methods alone. 488 consecutive SAH admissions from 2006 to 2014 to a tertiary care hospital were included. Models were trained on 80%, while 20% were set aside for validation testing. Modified Fisher Scale was considered the standard grading scale in clinical use; baseline features also analyzed included age, sex, Hunt-Hess, and Glasgow Coma Scales. An unsupervised approach using convolution dictionary learning was used to extract features from physiological time series (systolic blood pressure and diastolic blood pressure, heart rate, respiratory rate, and oxygen saturation). Classifiers (partial least squares and linear and kernel support vector machines) were trained on feature subsets of the derivation dataset. Models were applied to the validation dataset. The performances of the best classifiers on the validation dataset are reported by feature subset. Standard grading scale (mFS): AUC 0.54. Combined demographics and grading scales (baseline features): AUC 0.63. Kernel derived physiologic features: AUC 0.66. Combined baseline and physiologic features with redundant feature reduction: AUC 0.71 on derivation dataset and 0.78 on validation dataset. Current DCI prediction tools rely on admission imaging and are advantageously simple to employ. However, using an agnostic and computationally inexpensive learning approach for high-frequency physiologic time series data, we demonstrated that we could incorporate individual physiologic data to achieve higher classification accuracy.

A Severe Sepsis Mortality Prediction Model and Score for Use with Administrative Data

PubMed Central

Ford, Dee W.; Goodwin, Andrew J.; Simpson, Annie N.; Johnson, Emily; Nadig, Nandita; Simpson, Kit N.

2016-01-01

Objective Administrative data is used for research, quality improvement, and health policy in severe sepsis. However, there is not a sepsis-specific tool applicable to administrative data with which to adjust for illness severity. Our objective was to develop, internally validate, and externally validate a severe sepsis mortality prediction model and associated mortality prediction score. Design Retrospective cohort study using 2012 administrative data from five US states. Three cohorts of patients with severe sepsis were created: 1) ICD-9-CM codes for severe sepsis/septic shock, 2) ‘Martin’ approach, and 3) ‘Angus’ approach. The model was developed and internally validated in ICD-9-CM cohort and externally validated in other cohorts. Integer point values for each predictor variable were generated to create a sepsis severity score. Setting Acute care, non-federal hospitals in NY, MD, FL, MI, and WA Subjects Patients in one of three severe sepsis cohorts: 1) explicitly coded (n=108,448), 2) Martin cohort (n=139,094), and 3) Angus cohort (n=523,637) Interventions None Measurements and Main Results Maximum likelihood estimation logistic regression to develop a predictive model for in-hospital mortality. Model calibration and discrimination assessed via Hosmer-Lemeshow goodness-of-fit (GOF) and C-statistics respectively. Primary cohort subset into risk deciles and observed versus predicted mortality plotted. GOF demonstrated p>0.05 for each cohort demonstrating sound calibration. C-statistic ranged from low of 0.709 (sepsis severity score) to high of 0.838 (Angus cohort) suggesting good to excellent model discrimination. Comparison of observed versus expected mortality was robust although accuracy decreased in highest risk decile. Conclusions Our sepsis severity model and score is a tool that provides reliable risk adjustment for administrative data. PMID:26496452

Predicting functional outcomes among college drinkers: reliability and predictive validity of the Young Adult Alcohol Consequences Questionnaire.

PubMed

Read, Jennifer P; Merrill, Jennifer E; Kahler, Christopher W; Strong, David R

2007-11-01

Heavy drinking and associated consequences are widespread among U.S. college students. Recently, Read et al. (Read, J. P., Kahler, C. W., Strong, D., & Colder, C. R. (2006). Development and preliminary validation of the Young Adult Alcohol Consequences Questionnaire. Journal of Studies on Alcohol, 67, 169-178) developed the Young Adult Alcohol Consequences Questionnaire (YAACQ) to assess the broad range of consequences that may result from heavy drinking in the college milieu. In the present study, we sought to add to the psychometric validation of this measure by employing a prospective design to examine the test-retest reliability, concurrent validity, and predictive validity of the YAACQ. We also sought to examine the utility of the YAACQ administered early in the semester in the prediction of functional outcomes later in the semester, including the persistence of heavy drinking, and academic functioning. Ninety-two college students (48 females) completed a self-report assessment battery during the first weeks of the Fall semester, and approximately one week later. Additionally, 64 subjects (37 females) participated at an optional third time point at the end of the semester. Overall, the YAACQ demonstrated strong internal consistency, test-retest reliability, and concurrent and predictive validity. YAACQ scores also were predictive of both drinking frequency, and "binge" drinking frequency. YAACQ total scores at baseline were an early indicator of academic performance later in the semester, with greater number of total consequences experienced being negatively associated with end-of-semester grade point average. Specific YAACQ subscale scores (Impaired Control, Dependence Symptoms, Blackout Drinking) showed unique prediction of persistent drinking and academic outcomes.

The development and validation of the Dieting Intentions Scale (DIS).

PubMed

Cruwys, Tegan; Platow, Michael J; Rieger, Elizabeth; Byrne, Don G

2013-03-01

This article presents information on the psychometric properties of the Dieting Intentions Scale (DIS), a new scale of dieting that predicts future behavioral efforts to lose weight. We begin by reviewing recent research indicating theoretical and empirical problems with traditional approaches to measuring dieting. The DIS addresses several of these problems by (a) focusing on naturalistic dieting behavior and (b) being future-oriented. Four validation studies are presented with a total of 741 participants. We demonstrate that the DIS has predictive utility for dieting behaviors and is positively correlated with other measures related to eating, weight, and shape. Furthermore, the DIS demonstrates discriminant validity by not being related to constructs such as self-esteem and social desirability. The DIS also has high internal consistency, with a 1-factor solution replicated with confirmatory factor analysis. The potential uses of the scale in both research and clinical settings are considered. PsycINFO Database Record (c) 2013 APA, all rights reserved.

Group-level self-definition and self-investment: a hierarchical (multicomponent) model of in-group identification.

PubMed

Leach, Colin Wayne; van Zomeren, Martijn; Zebel, Sven; Vliek, Michael L W; Pennekamp, Sjoerd F; Doosje, Bertjan; Ouwerkerk, Jaap W; Spears, Russell

2008-07-01

Recent research shows individuals' identification with in-groups to be psychologically important and socially consequential. However, there is little agreement about how identification should be conceptualized or measured. On the basis of previous work, the authors identified 5 specific components of in-group identification and offered a hierarchical 2-dimensional model within which these components are organized. Studies 1 and 2 used confirmatory factor analysis to validate the proposed model of self-definition (individual self-stereotyping, in-group homogeneity) and self-investment (solidarity, satisfaction, and centrality) dimensions, across 3 different group identities. Studies 3 and 4 demonstrated the construct validity of the 5 components by examining their (concurrent) correlations with established measures of in-group identification. Studies 5-7 demonstrated the predictive and discriminant validity of the 5 components by examining their (prospective) prediction of individuals' orientation to, and emotions about, real intergroup relations. Together, these studies illustrate the conceptual and empirical value of a hierarchical multicomponent model of in-group identification.

Examining the Predictive Validity of NIH Peer Review Scores

PubMed Central

Lindner, Mark D.; Nakamura, Richard K.

2015-01-01

The predictive validity of peer review at the National Institutes of Health (NIH) has not yet been demonstrated empirically. It might be assumed that the most efficient and expedient test of the predictive validity of NIH peer review would be an examination of the correlation between percentile scores from peer review and bibliometric indices of the publications produced from funded projects. The present study used a large dataset to examine the rationale for such a study, to determine if it would satisfy the requirements for a test of predictive validity. The results show significant restriction of range in the applications selected for funding. Furthermore, those few applications that are funded with slightly worse peer review scores are not selected at random or representative of other applications in the same range. The funding institutes also negotiate with applicants to address issues identified during peer review. Therefore, the peer review scores assigned to the submitted applications, especially for those few funded applications with slightly worse peer review scores, do not reflect the changed and improved projects that are eventually funded. In addition, citation metrics by themselves are not valid or appropriate measures of scientific impact. The use of bibliometric indices on their own to measure scientific impact would likely increase the inefficiencies and problems with replicability already largely attributed to the current over-emphasis on bibliometric indices. Therefore, retrospective analyses of the correlation between percentile scores from peer review and bibliometric indices of the publications resulting from funded grant applications are not valid tests of the predictive validity of peer review at the NIH. PMID:26039440

Assessing Risk for Sexual Offenders in New Zealand: Development and Validation of a Computer-Scored Risk Measure

ERIC Educational Resources Information Center

Skelton, Alexander; Riley, David; Wales, David; Vess, James

2006-01-01

A growing research base supports the predictive validity of actuarial methods of risk assessment with sexual offenders. These methods use clearly defined variables with demonstrated empirical association with re-offending. The advantages of actuarial measures for screening large numbers of offenders quickly and economically are further enhanced…

Predicting free-living energy expenditure using a miniaturized ear-worn sensor: an evaluation against doubly labeled water.

PubMed

Bouarfa, Loubna; Atallah, Louis; Kwasnicki, Richard Mark; Pettitt, Claire; Frost, Gary; Yang, Guang-Zhong

2014-02-01

Accurate estimation of daily total energy expenditure (EE)is a prerequisite for assisted weight management and assessing certain health conditions. The use of wearable sensors for predicting free-living EE is challenged by consistent sensor placement, user compliance, and estimation methods used. This paper examines whether a single ear-worn accelerometer can be used for EE estimation under free-living conditions.An EE prediction model as first derived and validated in a controlled setting using healthy subjects involving different physical activities. Ten different activities were assessed showing a tenfold cross validation error of 0.24. Furthermore, the EE prediction model shows a mean absolute deviation(MAD) below 1.2 metabolic equivalent of tasks. The same model was applied to a free-living setting with a different population for further validation. The results were compared against those derived from doubly labeled water. In free-living settings, the predicted daily EE has a correlation of 0.74, p 0.008, and a MAD of 272 kcal day. These results demonstrate that laboratory-derived prediction models can be used to predict EE under free-living conditions [corrected].

Validity of the stroke rehabilitation assessment of movement scale in acute rehabilitation: a comparison with the functional independence measure and stroke impact scale-16.

PubMed

Ward, Irene; Pivko, Susan; Brooks, Gary; Parkin, Kate

2011-11-01

To demonstrate sensitivity to change of the Stroke Rehabilitation Assessment of Movement (STREAM) as well as the concurrent and predictive validity of the STREAM in an acute rehabilitation setting. Prospective cohort study. Acute, in-patient rehabilitation department within a tertiary-care teaching hospital in the United States. Thirty adults with a newly diagnosed, first ischemic stroke. Clinical assessments were conducted on admission and then again on discharge from the rehabilitation hospital with the STREAM (total STREAM and upper extremity, lower extremity, and mobility subscales), Functional Independence Measure (FIM), and Stroke Impact Scale-16 (SIS-16). Sensitivity to change was determined with the Wilcoxon signed rank test and by the calculation of standardized response means. Spearman correlations were used to assess concurrent validity of the total STREAM and STREAM subscales with the FIM and SIS-16 on admission and discharge. We determined predictive validity for all instruments by correlating admission scores with actual and predicted length of stay and by testing associations between admission scores and discharge destination (home vs subacute facility). Not applicable. For all instruments, there was statistically significant improvement from admission to discharge. The standardized response means for the total STREAM and STREAM subscales were large. Spearman correlations between the total STREAM and STREAM subscales and the FIM and SIS-16 were moderate to excellent, both on admission and discharge. Among change scores, only the SIS-16 correlated with the total STREAM. All 3 instruments were significantly associated with discharge destination; however, the associations were strongest for the total STREAM and STREAM subscales. All instruments showed moderate-to-excellent correlations with predicted and actual length of stay. The STREAM is sensitive to change and demonstrates good concurrent and predictive validity as compared with the FIM and SIS-16 in the acute inpatient rehabilitation population. Copyright © 2011 American Academy of Physical Medicine and Rehabilitation. Published by Elsevier Inc. All rights reserved.

External validation of the diffuse intrinsic pontine glioma survival prediction model: a collaborative report from the International DIPG Registry and the SIOPE DIPG Registry.

PubMed

Veldhuijzen van Zanten, Sophie E M; Lane, Adam; Heymans, Martijn W; Baugh, Joshua; Chaney, Brooklyn; Hoffman, Lindsey M; Doughman, Renee; Jansen, Marc H A; Sanchez, Esther; Vandertop, William P; Kaspers, Gertjan J L; van Vuurden, Dannis G; Fouladi, Maryam; Jones, Blaise V; Leach, James

2017-08-01

We aimed to perform external validation of the recently developed survival prediction model for diffuse intrinsic pontine glioma (DIPG), and discuss its utility. The DIPG survival prediction model was developed in a cohort of patients from the Netherlands, United Kingdom and Germany, registered in the SIOPE DIPG Registry, and includes age <3 years, longer symptom duration and receipt of chemotherapy as favorable predictors, and presence of ring-enhancement on MRI as unfavorable predictor. Model performance was evaluated by analyzing the discrimination and calibration abilities. External validation was performed using an unselected cohort from the International DIPG Registry, including patients from United States, Canada, Australia and New Zealand. Basic comparison with the results of the original study was performed using descriptive statistics, and univariate- and multivariable regression analyses in the validation cohort. External validation was assessed following a variety of analyses described previously. Baseline patient characteristics and results from the regression analyses were largely comparable. Kaplan-Meier curves of the validation cohort reproduced separated groups of standard (n = 39), intermediate (n = 125), and high-risk (n = 78) patients. This discriminative ability was confirmed by similar values for the hazard ratios across these risk groups. The calibration curve in the validation cohort showed a symmetric underestimation of the predicted survival probabilities. In this external validation study, we demonstrate that the DIPG survival prediction model has acceptable cross-cohort calibration and is able to discriminate patients with short, average, and increased survival. We discuss how this clinico-radiological model may serve a useful role in current clinical practice.

Can Heterosexism Harm Organizations? Predicting the Perceived Organizational Citizenship Behaviors of Gay and Lesbian Employees

ERIC Educational Resources Information Center

Brenner, Bradley R.; Lyons, Heather Z.; Fassinger, Ruth E.

2010-01-01

An initial test and validation of a model predicting perceived organizational citizenship behaviors (OCBs) of lesbian and gay employees were conducted using structural equation modeling. The proposed structural model demonstrated acceptable goodness of ft and structural invariance across 2 samples (ns = 311 and 295), which suggested that…

(Very) Early technology assessment and translation of predictive biomarkers in breast cancer.

PubMed

Miquel-Cases, Anna; Schouten, Philip C; Steuten, Lotte M G; Retèl, Valesca P; Linn, Sabine C; van Harten, Wim H

2017-01-01

Predictive biomarkers can guide treatment decisions in breast cancer. Many studies are undertaken to discover and translate these biomarkers, yet few biomarkers make it to practice. Before use in clinical decision making, predictive biomarkers need to demonstrate analytical validity, clinical validity and clinical utility. While attaining analytical and clinical validity is relatively straightforward, by following methodological recommendations, the achievement of clinical utility is extremely challenging. It requires demonstrating three associations: the biomarker with the outcome (prognostic association), the effect of treatment independent of the biomarker, and the differential treatment effect between the prognostic and the predictive biomarker (predictive association). In addition, economical, ethical, regulatory, organizational and patient/doctor-related aspects are hampering the translational process. Traditionally, these aspects do not receive much attention until formal approval or reimbursement of a biomarker test (informed by Health Technology Assessment (HTA)) is at stake, at which point the clinical utility and sometimes price of the test can hardly be influenced anymore. When HTA analyses are performed earlier, during biomarker research and development, they may prevent further development of those biomarkers unlikely to ever provide sufficient added value to society, and rather facilitate translation of the promising ones. Early HTA is particularly relevant for the predictive biomarker field, as expensive medicines are under pressure and the need for biomarkers to guide their appropriate use is huge. Closer interaction between clinical researchers and HTA experts throughout the translational research process will ensure that available data and methodologies will be used most efficiently to facilitate biomarker translation. Copyright © 2016 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Reliability and Validity of the Work and Well-Being Inventory (WBI) for Employees.

PubMed

Vendrig, A A; Schaafsma, F G

2018-06-01

Purpose The purpose of this study is to measure the psychometric properties of the Work and Wellbeing Inventory (WBI) (in Dutch: VAR-2), a screening tool that is used within occupational health care and rehabilitation. Our research question focused on the reliability and validity of this inventory. Methods Over the years seven different samples of workers, patients and sick listed workers varying in size between 89 and 912 participants (total: 2514), were used to measure the test-retest reliability, the internal consistency, the construct and concurrent validity, and the criterion and predictive validity. Results The 13 scales displayed good internal consistency and test-retest reliability. The constructive validity of the WBI could clearly be demonstrated in both patients and healthy workers. Confirmative factor analyses revealed a CFI >.90 for all scales. The depression scale predicted future work absenteeism (>6 weeks) because of a common mental disorder in healthy workers. The job strain scale and the illness behavior scale predicted long term absenteeism (>3 months) in workers with short-term absenteeism. The illness behavior scale moderately predicted return to work in rehab patients attending an intensive multidisciplinary program. Conclusions The WBI is a valid and reliable tool for occupational health practitioners to screen for risk factors for prolonged or future sickness absence. With this tool they will have reliable indications for further advice and interventions to restore the work ability.

A collaborative environment for developing and validating predictive tools for protein biophysical characteristics

NASA Astrophysics Data System (ADS)

Johnston, Michael A.; Farrell, Damien; Nielsen, Jens Erik

2012-04-01

The exchange of information between experimentalists and theoreticians is crucial to improving the predictive ability of theoretical methods and hence our understanding of the related biology. However many barriers exist which prevent the flow of information between the two disciplines. Enabling effective collaboration requires that experimentalists can easily apply computational tools to their data, share their data with theoreticians, and that both the experimental data and computational results are accessible to the wider community. We present a prototype collaborative environment for developing and validating predictive tools for protein biophysical characteristics. The environment is built on two central components; a new python-based integration module which allows theoreticians to provide and manage remote access to their programs; and PEATDB, a program for storing and sharing experimental data from protein biophysical characterisation studies. We demonstrate our approach by integrating PEATSA, a web-based service for predicting changes in protein biophysical characteristics, into PEATDB. Furthermore, we illustrate how the resulting environment aids method development using the Potapov dataset of experimentally measured ΔΔGfold values, previously employed to validate and train protein stability prediction algorithms.

Adaptation of clinical prediction models for application in local settings.

PubMed

Kappen, Teus H; Vergouwe, Yvonne; van Klei, Wilton A; van Wolfswinkel, Leo; Kalkman, Cor J; Moons, Karel G M

2012-01-01

When planning to use a validated prediction model in new patients, adequate performance is not guaranteed. For example, changes in clinical practice over time or a different case mix than the original validation population may result in inaccurate risk predictions. To demonstrate how clinical information can direct updating a prediction model and development of a strategy for handling missing predictor values in clinical practice. A previously derived and validated prediction model for postoperative nausea and vomiting was updated using a data set of 1847 patients. The update consisted of 1) changing the definition of an existing predictor, 2) reestimating the regression coefficient of a predictor, and 3) adding a new predictor to the model. The updated model was then validated in a new series of 3822 patients. Furthermore, several imputation models were considered to handle real-time missing values, so that possible missing predictor values could be anticipated during actual model use. Differences in clinical practice between our local population and the original derivation population guided the update strategy of the prediction model. The predictive accuracy of the updated model was better (c statistic, 0.68; calibration slope, 1.0) than the original model (c statistic, 0.62; calibration slope, 0.57). Inclusion of logistical variables in the imputation models, besides observed patient characteristics, contributed to a strategy to deal with missing predictor values at the time of risk calculation. Extensive knowledge of local, clinical processes provides crucial information to guide the process of adapting a prediction model to new clinical practices.

Mean Flow and Noise Prediction for a Separate Flow Jet With Chevron Mixers

NASA Technical Reports Server (NTRS)

Koch, L. Danielle; Bridges, James; Khavaran, Abbas

2004-01-01

Experimental and numerical results are presented here for a separate flow nozzle employing chevrons arranged in an alternating pattern on the core nozzle. Comparisons of these results demonstrate that the combination of the WIND/MGBK suite of codes can predict the noise reduction trends measured between separate flow jets with and without chevrons on the core nozzle. Mean flow predictions were validated against Particle Image Velocimetry (PIV), pressure, and temperature data, and noise predictions were validated against acoustic measurements recorded in the NASA Glenn Aeroacoustic Propulsion Lab. Comparisons are also made to results from the CRAFT code. The work presented here is part of an on-going assessment of the WIND/MGBK suite for use in designing the next generation of quiet nozzles for turbofan engines.

Selection, calibration, and validation of models of tumor growth.

PubMed

Lima, E A B F; Oden, J T; Hormuth, D A; Yankeelov, T E; Almeida, R C

2016-11-01

This paper presents general approaches for addressing some of the most important issues in predictive computational oncology concerned with developing classes of predictive models of tumor growth. First, the process of developing mathematical models of vascular tumors evolving in the complex, heterogeneous, macroenvironment of living tissue; second, the selection of the most plausible models among these classes, given relevant observational data; third, the statistical calibration and validation of models in these classes, and finally, the prediction of key Quantities of Interest (QOIs) relevant to patient survival and the effect of various therapies. The most challenging aspects of this endeavor is that all of these issues often involve confounding uncertainties: in observational data, in model parameters, in model selection, and in the features targeted in the prediction. Our approach can be referred to as "model agnostic" in that no single model is advocated; rather, a general approach that explores powerful mixture-theory representations of tissue behavior while accounting for a range of relevant biological factors is presented, which leads to many potentially predictive models. Then representative classes are identified which provide a starting point for the implementation of OPAL, the Occam Plausibility Algorithm (OPAL) which enables the modeler to select the most plausible models (for given data) and to determine if the model is a valid tool for predicting tumor growth and morphology ( in vivo ). All of these approaches account for uncertainties in the model, the observational data, the model parameters, and the target QOI. We demonstrate these processes by comparing a list of models for tumor growth, including reaction-diffusion models, phase-fields models, and models with and without mechanical deformation effects, for glioma growth measured in murine experiments. Examples are provided that exhibit quite acceptable predictions of tumor growth in laboratory animals while demonstrating successful implementations of OPAL.

Validation of Clinical Testing for Warfarin Sensitivity

PubMed Central

Langley, Michael R.; Booker, Jessica K.; Evans, James P.; McLeod, Howard L.; Weck, Karen E.

2009-01-01

Responses to warfarin (Coumadin) anticoagulation therapy are affected by genetic variability in both the CYP2C9 and VKORC1 genes. Validation of pharmacogenetic testing for warfarin responses includes demonstration of analytical validity of testing platforms and of the clinical validity of testing. We compared four platforms for determining the relevant single nucleotide polymorphisms (SNPs) in both CYP2C9 and VKORC1 that are associated with warfarin sensitivity (Third Wave Invader Plus, ParagonDx/Cepheid Smart Cycler, Idaho Technology LightCycler, and AutoGenomics Infiniti). Each method was examined for accuracy, cost, and turnaround time. All genotyping methods demonstrated greater than 95% accuracy for identifying the relevant SNPs (CYP2C9 *2 and *3; VKORC1 −1639 or 1173). The ParagonDx and Idaho Technology assays had the shortest turnaround and hands-on times. The Third Wave assay was readily scalable to higher test volumes but had the longest hands-on time. The AutoGenomics assay interrogated the largest number of SNPs but had the longest turnaround time. Four published warfarin-dosing algorithms (Washington University, UCSF, Louisville, and Newcastle) were compared for accuracy for predicting warfarin dose in a retrospective analysis of a local patient population on long-term, stable warfarin therapy. The predicted doses from both the Washington University and UCSF algorithms demonstrated the best correlation with actual warfarin doses. PMID:19324988

Validation of clinical testing for warfarin sensitivity: comparison of CYP2C9-VKORC1 genotyping assays and warfarin-dosing algorithms.

PubMed

Langley, Michael R; Booker, Jessica K; Evans, James P; McLeod, Howard L; Weck, Karen E

2009-05-01

Responses to warfarin (Coumadin) anticoagulation therapy are affected by genetic variability in both the CYP2C9 and VKORC1 genes. Validation of pharmacogenetic testing for warfarin responses includes demonstration of analytical validity of testing platforms and of the clinical validity of testing. We compared four platforms for determining the relevant single nucleotide polymorphisms (SNPs) in both CYP2C9 and VKORC1 that are associated with warfarin sensitivity (Third Wave Invader Plus, ParagonDx/Cepheid Smart Cycler, Idaho Technology LightCycler, and AutoGenomics Infiniti). Each method was examined for accuracy, cost, and turnaround time. All genotyping methods demonstrated greater than 95% accuracy for identifying the relevant SNPs (CYP2C9 *2 and *3; VKORC1 -1639 or 1173). The ParagonDx and Idaho Technology assays had the shortest turnaround and hands-on times. The Third Wave assay was readily scalable to higher test volumes but had the longest hands-on time. The AutoGenomics assay interrogated the largest number of SNPs but had the longest turnaround time. Four published warfarin-dosing algorithms (Washington University, UCSF, Louisville, and Newcastle) were compared for accuracy for predicting warfarin dose in a retrospective analysis of a local patient population on long-term, stable warfarin therapy. The predicted doses from both the Washington University and UCSF algorithms demonstrated the best correlation with actual warfarin doses.

Multisite external validation of a risk prediction model for the diagnosis of blood stream infections in febrile pediatric oncology patients without severe neutropenia.

PubMed

Esbenshade, Adam J; Zhao, Zhiguo; Aftandilian, Catherine; Saab, Raya; Wattier, Rachel L; Beauchemin, Melissa; Miller, Tamara P; Wilkes, Jennifer J; Kelly, Michael J; Fernbach, Alison; Jeng, Michael; Schwartz, Cindy L; Dvorak, Christopher C; Shyr, Yu; Moons, Karl G M; Sulis, Maria-Luisa; Friedman, Debra L

2017-10-01

Pediatric oncology patients are at an increased risk of invasive bacterial infection due to immunosuppression. The risk of such infection in the absence of severe neutropenia (absolute neutrophil count ≥ 500/μL) is not well established and a validated prediction model for blood stream infection (BSI) risk offers clinical usefulness. A 6-site retrospective external validation was conducted using a previously published risk prediction model for BSI in febrile pediatric oncology patients without severe neutropenia: the Esbenshade/Vanderbilt (EsVan) model. A reduced model (EsVan2) excluding 2 less clinically reliable variables also was created using the initial EsVan model derivative cohort, and was validated using all 5 external validation cohorts. One data set was used only in sensitivity analyses due to missing some variables. From the 5 primary data sets, there were a total of 1197 febrile episodes and 76 episodes of bacteremia. The overall C statistic for predicting bacteremia was 0.695, with a calibration slope of 0.50 for the original model and a calibration slope of 1.0 when recalibration was applied to the model. The model performed better in predicting high-risk bacteremia (gram-negative or Staphylococcus aureus infection) versus BSI alone, with a C statistic of 0.801 and a calibration slope of 0.65. The EsVan2 model outperformed the EsVan model across data sets with a C statistic of 0.733 for predicting BSI and a C statistic of 0.841 for high-risk BSI. The results of this external validation demonstrated that the EsVan and EsVan2 models are able to predict BSI across multiple performance sites and, once validated and implemented prospectively, could assist in decision making in clinical practice. Cancer 2017;123:3781-3790. © 2017 American Cancer Society. © 2017 American Cancer Society.

Development of a Middle-Age and Geriatric Trauma Mortality Risk Score A Tool to Guide Palliative Care Consultations.

PubMed

Konda, Sanjit R; Seymour, Rachel; Manoli, Arthur; Gales, Jordan; Karunakar, Madhav A

2016-11-01

This study aimed to develop a tool to quantify risk of inpatient mortality among geriatric and middleaged trauma patients. This study sought to demonstrate the ability of the novel risk score in the early identification of high risk trauma patients for resource-sparing interventions, including referral to palliative medicine. This retrospective cohort study utilized data from a single level 1 trauma center. Regression analysis was used to create a novel risk of inpatient mortality score. A total of 2,387 low energy and 1,201 high-energy middle-aged (range: 55 to 64 years of age) and geriatric (65 years of age or odler) trauma patients comprised the study cohort. Model validation was performed using 37,474 lowenergy and 97,034 high-energy patients from the National Trauma Databank (NTDB). Potential hospital cost reduction was calculated for early referral of high risk trauma patients to palliative medicine services in comparison to no palliative medicine referral. Factors predictive of inpatient mortality among the study and validation patient cohorts included; age, Glasgow Coma Scale, and Abbreviated Injury Scale for the head and neck and chest. Within the validation cohort, the novel mortality risk score demonstrated greater predictive capacity than existing trauma scores [STTGMALE-AUROC: 0.83 vs. TRISS 0.80, (p < 0.01), STTGMAHE-AUROC: 0.86 vs. TRISS 0.85, (p < 0.01)]. Our model demonstrated early palliative medicine evaluation could produce $1,083,082 in net hospital savings per year. This novel risk score for older trauma patients has shown fidelity in prediction of inpatient mortality; in the study and validation cohorts. This tool may be used for early intervention in the care of patients at high risk of mortality and resource expenditure.

Further Validation of the Learning Alliance Inventory: The Roles of Working Alliance, Rapport, and Immediacy in Student Learning

ERIC Educational Resources Information Center

Rogers, Daniel T.

2015-01-01

This study further examined the reliability and validity of the Learning Alliance Inventory (LAI), a self-report measure designed to assess the working alliance between a student and a teacher. The LAI was found to have good internal consistency and test--retest reliability, and it demonstrated the predicted convergence with measures of immediacy…

Aeroacoustic Validation of Installed Low Noise Propulsion for NASA's N+2 Supersonic Airliner

NASA Technical Reports Server (NTRS)

Bridges, James

2018-01-01

An aeroacoustic test was conducted at NASA Glenn Research Center on an integrated propulsion system designed to meet noise regulations of ICAO Chapter 4 with 10EPNdB cumulative margin. The test had two objectives: to demonstrate that the aircraft design did meet the noise goal, and to validate the acoustic design tools used in the design. Variations in the propulsion system design and its installation were tested and the results compared against predictions. Far-field arrays of microphones measured the acoustic spectral directivity, which was transformed to full scale as noise certification levels. Phased array measurements confirmed that the shielding of the installation model adequately simulated the full aircraft and provided data for validating RANS-based noise prediction tools. Particle image velocimetry confirmed that the flow field around the nozzle on the jet rig mimicked that of the full aircraft and produced flow data to validate the RANS solutions used in the noise predictions. The far-field acoustic measurements confirmed the empirical predictions for the noise. Results provided here detail the steps taken to ensure accuracy of the measurements and give insights into the physics of exhaust noise from installed propulsion systems in future supersonic vehicles.

Validation metrics for turbulent plasma transport

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

Holland, C., E-mail: chholland@ucsd.edu

Developing accurate models of plasma dynamics is essential for confident predictive modeling of current and future fusion devices. In modern computer science and engineering, formal verification and validation processes are used to assess model accuracy and establish confidence in the predictive capabilities of a given model. This paper provides an overview of the key guiding principles and best practices for the development of validation metrics, illustrated using examples from investigations of turbulent transport in magnetically confined plasmas. Particular emphasis is given to the importance of uncertainty quantification and its inclusion within the metrics, and the need for utilizing synthetic diagnosticsmore » to enable quantitatively meaningful comparisons between simulation and experiment. As a starting point, the structure of commonly used global transport model metrics and their limitations is reviewed. An alternate approach is then presented, which focuses upon comparisons of predicted local fluxes, fluctuations, and equilibrium gradients against observation. The utility of metrics based upon these comparisons is demonstrated by applying them to gyrokinetic predictions of turbulent transport in a variety of discharges performed on the DIII-D tokamak [J. L. Luxon, Nucl. Fusion 42, 614 (2002)], as part of a multi-year transport model validation activity.« less

A Stochastic Framework for Evaluating Seizure Prediction Algorithms Using Hidden Markov Models

PubMed Central

Wong, Stephen; Gardner, Andrew B.; Krieger, Abba M.; Litt, Brian

2007-01-01

Responsive, implantable stimulation devices to treat epilepsy are now in clinical trials. New evidence suggests that these devices may be more effective when they deliver therapy before seizure onset. Despite years of effort, prospective seizure prediction, which could improve device performance, remains elusive. In large part, this is explained by lack of agreement on a statistical framework for modeling seizure generation and a method for validating algorithm performance. We present a novel stochastic framework based on a three-state hidden Markov model (HMM) (representing interictal, preictal, and seizure states) with the feature that periods of increased seizure probability can transition back to the interictal state. This notion reflects clinical experience and may enhance interpretation of published seizure prediction studies. Our model accommodates clipped EEG segments and formalizes intuitive notions regarding statistical validation. We derive equations for type I and type II errors as a function of the number of seizures, duration of interictal data, and prediction horizon length and we demonstrate the model’s utility with a novel seizure detection algorithm that appeared to predicted seizure onset. We propose this framework as a vital tool for designing and validating prediction algorithms and for facilitating collaborative research in this area. PMID:17021032

Thermo-mechanical simulations of early-age concrete cracking with durability predictions

NASA Astrophysics Data System (ADS)

Havlásek, Petr; Šmilauer, Vít; Hájková, Karolina; Baquerizo, Luis

2017-09-01

Concrete performance is strongly affected by mix design, thermal boundary conditions, its evolving mechanical properties, and internal/external restraints with consequences to possible cracking with impaired durability. Thermo-mechanical simulations are able to capture those relevant phenomena and boundary conditions for predicting temperature, strains, stresses or cracking in reinforced concrete structures. In this paper, we propose a weakly coupled thermo-mechanical model for early age concrete with an affinity-based hydration model for thermal part, taking into account concrete mix design, cement type and thermal boundary conditions. The mechanical part uses B3/B4 model for concrete creep and shrinkage with isotropic damage model for cracking, able to predict a crack width. All models have been implemented in an open-source OOFEM software package. Validations of thermo-mechanical simulations will be presented on several massive concrete structures, showing excellent temperature predictions. Likewise, strain validation demonstrates good predictions on a restrained reinforced concrete wall and concrete beam. Durability predictions stem from induction time of reinforcement corrosion, caused by carbonation and/or chloride ingress influenced by crack width. Reinforcement corrosion in concrete struts of a bridge will serve for validation.

Predicting Persuasion-Induced Behavior Change from the Brain

PubMed Central

Falk, Emily B.; Berkman, Elliot T.; Mann, Traci; Harrison, Brittany; Lieberman, Matthew D.

2011-01-01

Although persuasive messages often alter people’s self-reported attitudes and intentions to perform behaviors, these self-reports do not necessarily predict behavior change. We demonstrate that neural responses to persuasive messages can predict variability in behavior change in the subsequent week. Specifically, an a priori region of interest (ROI) in medial prefrontal cortex (MPFC) was reliably associated with behavior change (r = 0.49, p < 0.05). Additionally, an iterative cross-validation approach using activity in this MPFC ROI predicted an average 23% of the variance in behavior change beyond the variance predicted by self-reported attitudes and intentions. Thus, neural signals can predict behavioral changes that are not predicted from self-reported attitudes and intentions alone. Additionally, this is the first functional magnetic resonance imaging study to demonstrate that a neural signal can predict complex real world behavior days in advance. PMID:20573889

Action Prediction Allows Hypothesis Testing via Internal Forward Models at 6 Months of Age

PubMed Central

Gredebäck, Gustaf; Lindskog, Marcus; Juvrud, Joshua C.; Green, Dorota; Marciszko, Carin

2018-01-01

We propose that action prediction provides a cornerstone in a learning process known as internal forward models. According to this suggestion infants’ predictions (looking to the mouth of someone moving a spoon upward) will moments later be validated or proven false (spoon was in fact directed toward a bowl), information that is directly perceived as the distance between the predicted and actual goal. Using an individual difference approach we demonstrate that action prediction correlates with the tendency to react with surprise when social interactions are not acted out as expected (action evaluation). This association is demonstrated across tasks and in a large sample (n = 118) at 6 months of age. These results provide the first indication that infants might rely on internal forward models to structure their social world. Additional analysis, consistent with prior work and assumptions from embodied cognition, demonstrates that the latency of infants’ action predictions correlate with the infant’s own manual proficiency. PMID:29593600

Validation of biomarkers to predict response to immunotherapy in cancer: Volume I - pre-analytical and analytical validation.

PubMed

Masucci, Giuseppe V; Cesano, Alessandra; Hawtin, Rachael; Janetzki, Sylvia; Zhang, Jenny; Kirsch, Ilan; Dobbin, Kevin K; Alvarez, John; Robbins, Paul B; Selvan, Senthamil R; Streicher, Howard Z; Butterfield, Lisa H; Thurin, Magdalena

2016-01-01

Immunotherapies have emerged as one of the most promising approaches to treat patients with cancer. Recently, there have been many clinical successes using checkpoint receptor blockade, including T cell inhibitory receptors such as cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4) and programmed cell death-1 (PD-1). Despite demonstrated successes in a variety of malignancies, responses only typically occur in a minority of patients in any given histology. Additionally, treatment is associated with inflammatory toxicity and high cost. Therefore, determining which patients would derive clinical benefit from immunotherapy is a compelling clinical question. Although numerous candidate biomarkers have been described, there are currently three FDA-approved assays based on PD-1 ligand expression (PD-L1) that have been clinically validated to identify patients who are more likely to benefit from a single-agent anti-PD-1/PD-L1 therapy. Because of the complexity of the immune response and tumor biology, it is unlikely that a single biomarker will be sufficient to predict clinical outcomes in response to immune-targeted therapy. Rather, the integration of multiple tumor and immune response parameters, such as protein expression, genomics, and transcriptomics, may be necessary for accurate prediction of clinical benefit. Before a candidate biomarker and/or new technology can be used in a clinical setting, several steps are necessary to demonstrate its clinical validity. Although regulatory guidelines provide general roadmaps for the validation process, their applicability to biomarkers in the cancer immunotherapy field is somewhat limited. Thus, Working Group 1 (WG1) of the Society for Immunotherapy of Cancer (SITC) Immune Biomarkers Task Force convened to address this need. In this two volume series, we discuss pre-analytical and analytical (Volume I) as well as clinical and regulatory (Volume II) aspects of the validation process as applied to predictive biomarkers for cancer immunotherapy. To illustrate the requirements for validation, we discuss examples of biomarker assays that have shown preliminary evidence of an association with clinical benefit from immunotherapeutic interventions. The scope includes only those assays and technologies that have established a certain level of validation for clinical use (fit-for-purpose). Recommendations to meet challenges and strategies to guide the choice of analytical and clinical validation design for specific assays are also provided.

Effective prediction of biodiversity in tidal flat habitats using an artificial neural network.

PubMed

Yoo, Jae-Won; Lee, Yong-Woo; Lee, Chang-Gun; Kim, Chang-Soo

2013-02-01

Accurate predictions of benthic macrofaunal biodiversity greatly benefit the efficient planning and management of habitat restoration efforts in tidal flat habitats. Artificial neural network (ANN) prediction models for such biodiversity were developed and tested based on 13 biophysical variables, collected from 50 sites of tidal flats along the coast of Korea during 1991-2006. The developed model showed high predictions during training, cross-validation and testing. Besides the training and testing procedures, an independent dataset from a different time period (2007-2010) was used to test the robustness and practical usage of the model. High prediction on the independent dataset (r = 0.84) validated the networks proper learning of predictive relationship and its generality. Key influential variables identified by follow-up sensitivity analyses were related with topographic dimension, environmental heterogeneity, and water column properties. Study demonstrates the successful application of ANN for the accurate prediction of benthic macrofaunal biodiversity and understanding of dynamics of candidate variables. Copyright © 2012 Elsevier Ltd. All rights reserved.

Reliability and validity of the Wolfram Unified Rating Scale (WURS)

PubMed Central

2012-01-01

Background Wolfram syndrome (WFS) is a rare, neurodegenerative disease that typically presents with childhood onset insulin dependent diabetes mellitus, followed by optic atrophy, diabetes insipidus, deafness, and neurological and psychiatric dysfunction. There is no cure for the disease, but recent advances in research have improved understanding of the disease course. Measuring disease severity and progression with reliable and validated tools is a prerequisite for clinical trials of any new intervention for neurodegenerative conditions. To this end, we developed the Wolfram Unified Rating Scale (WURS) to measure the severity and individual variability of WFS symptoms. The aim of this study is to develop and test the reliability and validity of the Wolfram Unified Rating Scale (WURS). Methods A rating scale of disease severity in WFS was developed by modifying a standardized assessment for another neurodegenerative condition (Batten disease). WFS experts scored the representativeness of WURS items for the disease. The WURS was administered to 13 individuals with WFS (6-25 years of age). Motor, balance, mood and quality of life were also evaluated with standard instruments. Inter-rater reliability, internal consistency reliability, concurrent, predictive and content validity of the WURS were calculated. Results The WURS had high inter-rater reliability (ICCs>.93), moderate to high internal consistency reliability (Cronbach’s α = 0.78-0.91) and demonstrated good concurrent and predictive validity. There were significant correlations between the WURS Physical Assessment and motor and balance tests (rs>.67, p<.03), between the WURS Behavioral Scale and reports of mood and behavior (rs>.76, p<.04) and between WURS Total scores and quality of life (rs=-.86, p=.001). The WURS demonstrated acceptable content validity (Scale-Content Validity Index=0.83). Conclusions These preliminary findings demonstrate that the WURS has acceptable reliability and validity and captures individual differences in disease severity in children and young adults with WFS. PMID:23148655

Prediction Interval Development for Wind-Tunnel Balance Check-Loading

NASA Technical Reports Server (NTRS)

Landman, Drew; Toro, Kenneth G.; Commo, Sean A.; Lynn, Keith C.

2014-01-01

Results from the Facility Analysis Verification and Operational Reliability project revealed a critical gap in capability in ground-based aeronautics research applications. Without a standardized process for check-loading the wind-tunnel balance or the model system, the quality of the aerodynamic force data collected varied significantly between facilities. A prediction interval is required in order to confirm a check-loading. The prediction interval provides an expected upper and lower bound on balance load prediction at a given confidence level. A method has been developed which accounts for sources of variability due to calibration and check-load application. The prediction interval method of calculation and a case study demonstrating its use is provided. Validation of the methods is demonstrated for the case study based on the probability of capture of confirmation points.

Environmental fate model for ultra-low-volume insecticide applications used for adult mosquito management

USGS Publications Warehouse

Schleier, Jerome J.; Peterson, Robert K.D.; Irvine, Kathryn M.; Marshall, Lucy M.; Weaver, David K.; Preftakes, Collin J.

2012-01-01

One of the more effective ways of managing high densities of adult mosquitoes that vector human and animal pathogens is ultra-low-volume (ULV) aerosol applications of insecticides. The U.S. Environmental Protection Agency uses models that are not validated for ULV insecticide applications and exposure assumptions to perform their human and ecological risk assessments. Currently, there is no validated model that can accurately predict deposition of insecticides applied using ULV technology for adult mosquito management. In addition, little is known about the deposition and drift of small droplets like those used under conditions encountered during ULV applications. The objective of this study was to perform field studies to measure environmental concentrations of insecticides and to develop a validated model to predict the deposition of ULV insecticides. The final regression model was selected by minimizing the Bayesian Information Criterion and its prediction performance was evaluated using k-fold cross validation. Density of the formulation and the density and CMD interaction coefficients were the largest in the model. The results showed that as density of the formulation decreases, deposition increases. The interaction of density and CMD showed that higher density formulations and larger droplets resulted in greater deposition. These results are supported by the aerosol physics literature. A k-fold cross validation demonstrated that the mean square error of the selected regression model is not biased, and the mean square error and mean square prediction error indicated good predictive ability.

Validation and clinical utility of the executive function performance test in persons with traumatic brain injury.

PubMed

Baum, C M; Wolf, T J; Wong, A W K; Chen, C H; Walker, K; Young, A C; Carlozzi, N E; Tulsky, D S; Heaton, R K; Heinemann, A W

2017-07-01

This study examined the relationships between the Executive Function Performance Test (EFPT), the NIH Toolbox Cognitive Function tests, and neuropsychological executive function measures in 182 persons with traumatic brain injury (TBI) and 46 controls to evaluate construct, discriminant, and predictive validity. Construct validity: There were moderate correlations between the EFPT and the NIH Toolbox Crystallized (r = -.479), Fluid Tests (r = -.420), and Total Composite Scores (r = -.496). Discriminant validity: Significant differences were found in the EFPT total and sequence scores across control, complicated mild/moderate, and severe TBI groups. We found differences in the organisation score between control and severe, and between mild and severe TBI groups. Both TBI groups had significantly lower scores in safety and judgement than controls. Compared to the controls, the severe TBI group demonstrated significantly lower performance on all instrumental activities of daily living (IADL) tasks. Compared to the mild TBI group, the controls performed better on the medication task, the severe TBI group performed worse in the cooking and telephone tasks. Predictive validity: The EFPT predicted the self-perception of independence measured by the TBI-QOL (beta = -0.49, p < .001) for the severe TBI group. Overall, these data support the validity of the EFPT for use in individuals with TBI.

Symbolic control of visual attention: semantic constraints on the spatial distribution of attention.

PubMed

Gibson, Bradley S; Scheutz, Matthias; Davis, Gregory J

2009-02-01

Humans routinely use spatial language to control the spatial distribution of attention. In so doing, spatial information may be communicated from one individual to another across opposing frames of reference, which in turn can lead to inconsistent mappings between symbols and directions (or locations). These inconsistencies may have important implications for the symbolic control of attention because they can be translated into differences in cue validity, a manipulation that is known to influence the focus of attention. This differential validity hypothesis was tested in Experiment 1 by comparing spatial word cues that were predicted to have high learned spatial validity ("above/below") and low learned spatial validity ("left/right"). Consistent with this prediction, when two measures of selective attention were used, the results indicated that attention was less focused in response to "left/right" cues than in response to "above/below" cues, even when the actual validity of each of the cues was equal. In addition, Experiment 2 predicted that spatial words such as "left/right" would have lower spatial validity than would other directional symbols that specify direction along the horizontal axis, such as "<--/-->" cues. The results were also consistent with this hypothesis. Altogether, the present findings demonstrate important semantic-based constraints on the spatial distribution of attention.

A mechanistic model for electricity consumption on dairy farms: definition, validation, and demonstration.

PubMed

Upton, J; Murphy, M; Shalloo, L; Groot Koerkamp, P W G; De Boer, I J M

2014-01-01

Our objective was to define and demonstrate a mechanistic model that enables dairy farmers to explore the impact of a technical or managerial innovation on electricity consumption, associated CO2 emissions, and electricity costs. We, therefore, (1) defined a model for electricity consumption on dairy farms (MECD) capable of simulating total electricity consumption along with related CO2 emissions and electricity costs on dairy farms on a monthly basis; (2) validated the MECD using empirical data of 1yr on commercial spring calving, grass-based dairy farms with 45, 88, and 195 milking cows; and (3) demonstrated the functionality of the model by applying 2 electricity tariffs to the electricity consumption data and examining the effect on total dairy farm electricity costs. The MECD was developed using a mechanistic modeling approach and required the key inputs of milk production, cow number, and details relating to the milk-cooling system, milking machine system, water-heating system, lighting systems, water pump systems, and the winter housing facilities as well as details relating to the management of the farm (e.g., season of calving). Model validation showed an overall relative prediction error (RPE) of less than 10% for total electricity consumption. More than 87% of the mean square prediction error of total electricity consumption was accounted for by random variation. The RPE values of the milk-cooling systems, water-heating systems, and milking machine systems were less than 20%. The RPE values for automatic scraper systems, lighting systems, and water pump systems varied from 18 to 113%, indicating a poor prediction for these metrics. However, automatic scrapers, lighting, and water pumps made up only 14% of total electricity consumption across all farms, reducing the overall impact of these poor predictions. Demonstration of the model showed that total farm electricity costs increased by between 29 and 38% by moving from a day and night tariff to a flat tariff. Copyright © 2014 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Multifactorial risk index for prediction of intraoperative blood transfusion in endovascular aneurysm repair.

PubMed

Mahmood, Eitezaz; Matyal, Robina; Mueller, Ariel; Mahmood, Feroze; Tung, Avery; Montealegre-Gallegos, Mario; Schermerhorn, Marc; Shahul, Sajid

2018-03-01

In some institutions, the current blood ordering practice does not discriminate minimally invasive endovascular aneurysm repair (EVAR) from open procedures, with consequent increasing costs and likelihood of blood product wastage for EVARs. This limitation in practice can possibly be addressed with the development of a reliable prediction model for transfusion risk in EVAR patients. We used the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) database to create a model for prediction of intraoperative blood transfusion occurrence in patients undergoing EVAR. Afterward, we tested our predictive model on the Vascular Study Group of New England (VSGNE) database. We used the ACS NSQIP database for patients who underwent EVAR from 2011 to 2013 (N = 4709) as our derivation set for identifying a risk index for predicting intraoperative blood transfusion. We then developed a clinical risk score and validated this model using patients who underwent EVAR from 2003 to 2014 in the VSGNE database (N = 4478). The transfusion rates were 8.4% and 6.1% for the ACS NSQIP (derivation set) and VSGNE (validation) databases, respectively. Hemoglobin concentration, American Society of Anesthesiologists class, age, and aneurysm diameter predicted blood transfusion in the derivation set. When it was applied on the validation set, our risk index demonstrated good discrimination in both the derivation and validation set (C statistic = 0.73 and 0.70, respectively) and calibration using the Hosmer-Lemeshow test (P = .27 and 0.31) for both data sets. We developed and validated a risk index for predicting the likelihood of intraoperative blood transfusion in EVAR patients. Implementation of this index may facilitate the blood management strategies specific for EVAR. Copyright © 2017 Society for Vascular Surgery. Published by Elsevier Inc. All rights reserved.

Variability in Predictions from Online Tools: A Demonstration Using Internet-Based Melanoma Predictors.

PubMed

Zabor, Emily C; Coit, Daniel; Gershenwald, Jeffrey E; McMasters, Kelly M; Michaelson, James S; Stromberg, Arnold J; Panageas, Katherine S

2018-02-22

Prognostic models are increasingly being made available online, where they can be publicly accessed by both patients and clinicians. These online tools are an important resource for patients to better understand their prognosis and for clinicians to make informed decisions about treatment and follow-up. The goal of this analysis was to highlight the possible variability in multiple online prognostic tools in a single disease. To demonstrate the variability in survival predictions across online prognostic tools, we applied a single validation dataset to three online melanoma prognostic tools. Data on melanoma patients treated at Memorial Sloan Kettering Cancer Center between 2000 and 2014 were retrospectively collected. Calibration was assessed using calibration plots and discrimination was assessed using the C-index. In this demonstration project, we found important differences across the three models that led to variability in individual patients' predicted survival across the tools, especially in the lower range of predictions. In a validation test using a single-institution data set, calibration and discrimination varied across the three models. This study underscores the potential variability both within and across online tools, and highlights the importance of using methodological rigor when developing a prognostic model that will be made publicly available online. The results also reinforce that careful development and thoughtful interpretation, including understanding a given tool's limitations, are required in order for online prognostic tools that provide survival predictions to be a useful resource for both patients and clinicians.

CASPer, an online pre-interview screen for personal/professional characteristics: prediction of national licensure scores.

PubMed

Dore, Kelly L; Reiter, Harold I; Kreuger, Sharyn; Norman, Geoffrey R

2017-05-01

Typically, only a minority of applicants to health professional training are invited to interview. However, pre-interview measures of cognitive skills predict for national licensure scores (Gauer et al. in Med Educ Online 21 2016) and subsequently licensure scores predict for performance in practice (Tamblyn et al. in JAMA 288(23): 3019-3026, 2002; Tamblyn et al. in JAMA 298(9):993-1001, 2007). Assessment of personal and professional characteristics, with the same psychometric rigour of measures of cognitive abilities, are needed upstream in the selection to health profession training programs. To fill that need, Computer-based Assessment for Sampling Personal characteristics (CASPer)-an on-line, video-based screening test-was created. In this paper, we examine the correlation between CASPer and Canadian national licensure examination outcomes in 109 doctors who took CASPer at the time of selection to medical school. Specifically, CASPer scores were correlated against performance on cognitive and 'non-cognitive' subsections of both the Medical Council of Canada Qualifying Examination (MCCQE) Parts I (end of medical school) and Part II (18 months into specialty training). Unlike most national licensure exams, MCCQE has specific subcomponents examining personal/professional qualities, providing a unique opportunity for comparison. The results demonstrated moderate predictive validity of CASPer to national licensure outcomes of personal/professional characteristics three to six years after admission to medical school. These types of disattenuated correlations (r = 0.3-0.5) are not otherwise predicted by traditional screening measures. These data support the ability of a computer-based strategy to screen applicants in a feasible, reliable test, which has now demonstrated predictive validity, lending evidence of its validation for medical school applicant selection.

Development and Validation of Decision Forest Model for Estrogen Receptor Binding Prediction of Chemicals Using Large Data Sets.

PubMed

Ng, Hui Wen; Doughty, Stephen W; Luo, Heng; Ye, Hao; Ge, Weigong; Tong, Weida; Hong, Huixiao

2015-12-21

Some chemicals in the environment possess the potential to interact with the endocrine system in the human body. Multiple receptors are involved in the endocrine system; estrogen receptor α (ERα) plays very important roles in endocrine activity and is the most studied receptor. Understanding and predicting estrogenic activity of chemicals facilitates the evaluation of their endocrine activity. Hence, we have developed a decision forest classification model to predict chemical binding to ERα using a large training data set of 3308 chemicals obtained from the U.S. Food and Drug Administration's Estrogenic Activity Database. We tested the model using cross validations and external data sets of 1641 chemicals obtained from the U.S. Environmental Protection Agency's ToxCast project. The model showed good performance in both internal (92% accuracy) and external validations (∼ 70-89% relative balanced accuracies), where the latter involved the validations of the model across different ER pathway-related assays in ToxCast. The important features that contribute to the prediction ability of the model were identified through informative descriptor analysis and were related to current knowledge of ER binding. Prediction confidence analysis revealed that the model had both high prediction confidence and accuracy for most predicted chemicals. The results demonstrated that the model constructed based on the large training data set is more accurate and robust for predicting ER binding of chemicals than the published models that have been developed using much smaller data sets. The model could be useful for the evaluation of ERα-mediated endocrine activity potential of environmental chemicals.

Development and Validation of a Measure of Quality of Life for the Young Elderly in Sri Lanka.

PubMed

de Silva, Sudirikku Hennadige Padmal; Jayasuriya, Anura Rohan; Rajapaksa, Lalini Chandika; de Silva, Ambepitiyawaduge Pubudu; Barraclough, Simon

2016-01-01

Sri Lanka has one of the fastest aging populations in the world. Measurement of quality of life (QoL) in the elderly needs instruments developed that encompass the sociocultural settings. An instrument was developed to measure QoL in the young elderly in Sri Lanka (QLI-YES), using accepted methods to generate and reduce items. The measure was validated using a community sample. Construct, criterion and predictive validity and reliability were tested. A first-order model of 24 items with 6 domains was found to have good fit indices (CMIN/df = 1.567, RMR = 0.05, CFI = 0.95, and RMSEA = 0.053). Both criterion and predictive validity were demonstrated. Good internal consistency reliability (Cronbach's α = 0.93) was shown. The development of the QLI-YES using a societal perspective relevant to the social and cultural beliefs has resulted in a robust and valid instrument to measure QoL for the young elderly in Sri Lanka. © 2015 APJPH.

The validity of the Health-Relevant Personality Inventory (HP5i) and the Junior Temperament and Character Inventory (JTCI) among adolescents referred for a substance misuse problem.

PubMed

Hemphälä, Malin; Gustavsson, J Petter; Tengström, Anders

2013-01-01

The aim was to study the validity of 2 personality instruments, the Health-Relevant Personality Inventory (HP5i) and the Junior Temperament and Character Inventory (JTCI), among adolescents with a substance use problem. Clinical interviews were completed with 180 adolescents and followed up after 12 months. Discriminant validity was demonstrated in the lack of correlation to intelligence in both instruments' scales. Two findings were in support of convergent validity: Negative affectivity (HP5i) and harm avoidance (JTCI) were correlated to internalizing symptoms, and impulsivity (HP5i) and novelty seeking (JTCI) were correlated to externalizing symptoms. The predictive validity of JTCI was partly supported. When psychiatric symptoms at baseline were controlled for, cooperativeness predicted conduct disorder after 12 months. Summarizing, both instruments can be used in adolescent clinical samples to tailor treatment efforts, although some scales need further investigation. It is important to include personality assessment when evaluating psychiatric problems in adolescents.

Development and Validation of a Measure of Quality of Life for the Young Elderly in Sri Lanka

PubMed Central

de Silva, Sudirikku Hennadige Padmal; Jayasuriya, Anura Rohan; Rajapaksa, Lalini Chandika; de Silva, Ambepitiyawaduge Pubudu; Barraclough, Simon

2016-01-01

Sri Lanka has one of the fastest aging populations in the world. Measurement of quality of life (QoL) in the elderly needs instruments developed that encompass the sociocultural settings. An instrument was developed to measure QoL in the young elderly in Sri Lanka (QLI-YES), using accepted methods to generate and reduce items. The measure was validated using a community sample. Construct, criterion and predictive validity and reliability were tested. A first-order model of 24 items with 6 domains was found to have good fit indices (CMIN/df = 1.567, RMR = 0.05, CFI = 0.95, and RMSEA = 0.053). Both criterion and predictive validity were demonstrated. Good internal consistency reliability (Cronbach’s α = 0.93) was shown. The development of the QLI-YES using a societal perspective relevant to the social and cultural beliefs has resulted in a robust and valid instrument to measure QoL for the young elderly in Sri Lanka. PMID:26712893

Rational selection of training and test sets for the development of validated QSAR models

NASA Astrophysics Data System (ADS)

Golbraikh, Alexander; Shen, Min; Xiao, Zhiyan; Xiao, Yun-De; Lee, Kuo-Hsiung; Tropsha, Alexander

2003-02-01

Quantitative Structure-Activity Relationship (QSAR) models are used increasingly to screen chemical databases and/or virtual chemical libraries for potentially bioactive molecules. These developments emphasize the importance of rigorous model validation to ensure that the models have acceptable predictive power. Using k nearest neighbors ( kNN) variable selection QSAR method for the analysis of several datasets, we have demonstrated recently that the widely accepted leave-one-out (LOO) cross-validated R2 (q2) is an inadequate characteristic to assess the predictive ability of the models [Golbraikh, A., Tropsha, A. Beware of q2! J. Mol. Graphics Mod. 20, 269-276, (2002)]. Herein, we provide additional evidence that there exists no correlation between the values of q 2 for the training set and accuracy of prediction ( R 2) for the test set and argue that this observation is a general property of any QSAR model developed with LOO cross-validation. We suggest that external validation using rationally selected training and test sets provides a means to establish a reliable QSAR model. We propose several approaches to the division of experimental datasets into training and test sets and apply them in QSAR studies of 48 functionalized amino acid anticonvulsants and a series of 157 epipodophyllotoxin derivatives with antitumor activity. We formulate a set of general criteria for the evaluation of predictive power of QSAR models.

Predictive validity of the Sødring Motor Evaluation of Stroke Patients (SMES).

PubMed

Wyller, T B; Sødring, K M; Sveen, U; Ljunggren, A E; Bautz-Holter, E

1996-12-01

The Sødring Motor Evaluation of Stroke Patients (SMES) has been developed as an instrument for the evaluation by physiotherapists of motor function and activities in stroke patients. The predictive validity of the instrument was studied in a consecutive sample of 93 acute stroke patients, assessed in the acute phase and after one year. The outcome measures were: survival, residence at home or in institution, the Barthel ADL index (dichotomized at 19/20), and the Frenchay Activities Index (FAI) (dichotomized at 9/10). The SMES, scored in the acute phase, demonstrated a marginally significant predictive power regarding survival, but was a highly significant predictor regarding the other outcomes. The adjusted odds ratio for a good versus a poor outcome for patients in the upper versus the lower tertile of the SMES arm subscore was 5.4 (95% confidence interval 0.9-59) for survival, 11.5 (2.1-88) for living at home, 86.3 (11-infinity) for a high Barthel score, and 31.4 (5.2-288) for a high FAI score. We conclude that SMES has high predictive validity.

Temporal and external validation of a prediction model for adverse outcomes among inpatients with diabetes.

PubMed

Adderley, N J; Mallett, S; Marshall, T; Ghosh, S; Rayman, G; Bellary, S; Coleman, J; Akiboye, F; Toulis, K A; Nirantharakumar, K

2018-06-01

To temporally and externally validate our previously developed prediction model, which used data from University Hospitals Birmingham to identify inpatients with diabetes at high risk of adverse outcome (mortality or excessive length of stay), in order to demonstrate its applicability to other hospital populations within the UK. Temporal validation was performed using data from University Hospitals Birmingham and external validation was performed using data from both the Heart of England NHS Foundation Trust and Ipswich Hospital. All adult inpatients with diabetes were included. Variables included in the model were age, gender, ethnicity, admission type, intensive therapy unit admission, insulin therapy, albumin, sodium, potassium, haemoglobin, C-reactive protein, estimated GFR and neutrophil count. Adverse outcome was defined as excessive length of stay or death. Model discrimination in the temporal and external validation datasets was good. In temporal validation using data from University Hospitals Birmingham, the area under the curve was 0.797 (95% CI 0.785-0.810), sensitivity was 70% (95% CI 67-72) and specificity was 75% (95% CI 74-76). In external validation using data from Heart of England NHS Foundation Trust, the area under the curve was 0.758 (95% CI 0.747-0.768), sensitivity was 73% (95% CI 71-74) and specificity was 66% (95% CI 65-67). In external validation using data from Ipswich, the area under the curve was 0.736 (95% CI 0.711-0.761), sensitivity was 63% (95% CI 59-68) and specificity was 69% (95% CI 67-72). These results were similar to those for the internally validated model derived from University Hospitals Birmingham. The prediction model to identify patients with diabetes at high risk of developing an adverse event while in hospital performed well in temporal and external validation. The externally validated prediction model is a novel tool that can be used to improve care pathways for inpatients with diabetes. Further research to assess clinical utility is needed. © 2018 Diabetes UK.

Recidivism in female offenders: PCL-R lifestyle factor and VRAG show predictive validity in a German sample.

PubMed

Eisenbarth, Hedwig; Osterheider, Michael; Nedopil, Norbert; Stadtland, Cornelis

2012-01-01

A clear and structured approach to evidence-based and gender-specific risk assessment of violence in female offenders is high on political and mental health agendas. However, most data on the factors involved in risk-assessment instruments are based on data of male offenders. The aim of the present study was to validate the use of the Psychopathy Checklist Revised (PCL-R), the HCR-20 and the Violence Risk Appraisal Guide (VRAG) for the prediction of recidivism in German female offenders. This study is part of the Munich Prognosis Project (MPP). It focuses on a subsample of female delinquents (n = 80) who had been referred for forensic-psychiatric evaluation prior to sentencing. The mean time at risk was 8 years (SD = 5 years; range: 1-18 years). During this time, 31% (n = 25) of the female offenders were reconvicted, 5% (n = 4) for violent and 26% (n = 21) for non-violent re-offenses. The predictive validity of the PCL-R for general recidivism was calculated. Analysis with receiver-operating characteristics revealed that the PCL-R total score, the PCL-R antisocial lifestyle factor, the PCL-R lifestyle factor and the PCL-R impulsive and irresponsible behavioral style factor had a moderate predictive validity for general recidivism (area under the curve, AUC = 0.66, p = 0.02). The VRAG has also demonstrated predictive validity (AUC = 0.72, p = 0.02), whereas the HCR-20 showed no predictive validity. These results appear to provide the first evidence that the PCL-R total score and the antisocial lifestyle factor are predictive for general female recidivism, as has been shown consistently for male recidivists. The implications of these findings for crime prevention, prognosis in women, and future research are discussed. Copyright © 2012 John Wiley & Sons, Ltd.

Measuring Work Engagement, Psychological Empowerment, and Organizational Citizenship Behavior Among Health Care Aides.

PubMed

Ginsburg, Liane; Berta, Whitney; Baumbusch, Jennifer; Rohit Dass, Adrian; Laporte, Audrey; Reid, R Colin; Squires, Janet; Taylor, Deanne

2016-04-01

Health care aides (HCAs) provide most direct care in long-term care (LTC) and home and community care (HCC) settings but are understudied. We validate three key work attitude measures to better understand HCAs' work experiences: work engagement (WEng), psychological empowerment (PE), and organizational citizenship behavior (OCB-O). Data were collected from 306 HCAs working in LTC and HCC, using survey items for WEng, PE, and OCB-O adapted for HCAs. Psychometric evaluation involved confirmatory factor analysis (CFA). Predictive validity (correlations with measures of job satisfaction and turnover intention) and internal consistency reliability were examined. CFA supported a one-factor model of WEng, a four-factor model of PE, and a one-factor model of OCB-O. HCC workers scored higher than LTC workers on Self-determination (PE) and lower on Impact, demonstrating concurrent validity. WEng and PE correlated with worker outcomes (job satisfaction, turnover intention, and OCB-O), demonstrating predictive validity. Reliability and validity analyses indicated sound psychometric properties overall. Study results support psychometric properties of measures of WEng, PE, and OCB-O for HCAs. Knowledge of HCAs' work attitudes and behaviors can inform recruitment programs, incentive systems, and retention/training strategies for this vital group of care providers. © The Author 2016. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Development and application of a predictive model of Aspergillus candidus growth as a tool to improve shelf life of bakery products.

PubMed

Huchet, V; Pavan, S; Lochardet, A; Divanac'h, M L; Postollec, F; Thuault, D

2013-12-01

Molds are responsible for spoilage of bakery products during storage. A modeling approach to predict the effect of water activity (aw) and temperature on the appearance time of Aspergillus candidus was developed and validated on cakes. The gamma concept of Zwietering was adapted to model fungal growth, taking into account the impact of temperature and aw. We hypothesized that the same model could be used to calculate the time for mycelium to become visible (tv), by substituting the matrix parameter by tv. Cardinal values of A. candidus were determined on potato dextrose agar, and predicted tv were further validated by challenge-tests run on 51 pastries. Taking into account the aw dynamics recorded in pastries during reasonable conditions of storage, high correlation was shown between predicted and observed tv when the aw at equilibrium (after 14 days of storage) was used for modeling (Af = 1.072, Bf = 0.979). Validation studies on industrial cakes confirmed the experimental results and demonstrated the suitability of the model to predict tv in food as a function of aw and temperature. Copyright © 2013 Elsevier Ltd. All rights reserved.

On the short circuit resilience of organic solar cells: prediction and validation.

PubMed

Oostra, A Jolt; Smits, Edsger C P; de Leeuw, Dago M; Blom, Paul W M; Michels, Jasper J

2015-09-07

The operational characteristics of organic solar cells manufactured with large area processing methods suffers from the occurrence of short-circuits due to defects in the photoactive thin film stack. In this work we study the effect of a shunt resistance on an organic solar cell and demonstrate that device performance is not affected negatively as long as the shunt resistance is higher than approximately 1000 Ohm. By studying charge transport across PSS-lithium fluoride/aluminum (LiF/Al) shunting junctions we show that this prerequisite is already met by applying a sufficiently thick (>1.5 nm) LiF layer. We demonstrate that this remarkable shunt-resilience stems from the formation of a significant charge transport barrier at the PSS-LiF/Al interface. We validate our predictions by fabricating devices with deliberately severed photoactive layers and find an excellent agreement between the calculated and experimental current-voltage characteristics.

A discrete event simulation tool to support and predict hospital and clinic staffing.

PubMed

DeRienzo, Christopher M; Shaw, Ryan J; Meanor, Phillip; Lada, Emily; Ferranti, Jeffrey; Tanaka, David

2017-06-01

We demonstrate how to develop a simulation tool to help healthcare managers and administrators predict and plan for staffing needs in a hospital neonatal intensive care unit using administrative data. We developed a discrete event simulation model of nursing staff needed in a neonatal intensive care unit and then validated the model against historical data. The process flow was translated into a discrete event simulation model. Results demonstrated that the model can be used to give a respectable estimate of annual admissions, transfers, and deaths based upon two different staffing levels. The discrete event simulation tool model can provide healthcare managers and administrators with (1) a valid method of modeling patient mix, patient acuity, staffing needs, and costs in the present state and (2) a forecast of how changes in a unit's staffing, referral patterns, or patient mix would affect a unit in a future state.

Acute Brain Dysfunction: Development and Validation of a Daily Prediction Model.

PubMed

Marra, Annachiara; Pandharipande, Pratik P; Shotwell, Matthew S; Chandrasekhar, Rameela; Girard, Timothy D; Shintani, Ayumi K; Peelen, Linda M; Moons, Karl G M; Dittus, Robert S; Ely, E Wesley; Vasilevskis, Eduard E

2018-03-24

The goal of this study was to develop and validate a dynamic risk model to predict daily changes in acute brain dysfunction (ie, delirium and coma), discharge, and mortality in ICU patients. Using data from a multicenter prospective ICU cohort, a daily acute brain dysfunction-prediction model (ABD-pm) was developed by using multinomial logistic regression that estimated 15 transition probabilities (from one of three brain function states [normal, delirious, or comatose] to one of five possible outcomes [normal, delirious, comatose, ICU discharge, or died]) using baseline and daily risk factors. Model discrimination was assessed by using predictive characteristics such as negative predictive value (NPV). Calibration was assessed by plotting empirical vs model-estimated probabilities. Internal validation was performed by using a bootstrap procedure. Data were analyzed from 810 patients (6,711 daily transitions). The ABD-pm included individual risk factors: mental status, age, preexisting cognitive impairment, baseline and daily severity of illness, and daily administration of sedatives. The model yielded very high NPVs for "next day" delirium (NPV: 0.823), coma (NPV: 0.892), normal cognitive state (NPV: 0.875), ICU discharge (NPV: 0.905), and mortality (NPV: 0.981). The model demonstrated outstanding calibration when predicting the total number of patients expected to be in any given state across predicted risk. We developed and internally validated a dynamic risk model that predicts the daily risk for one of three cognitive states, ICU discharge, or mortality. The ABD-pm may be useful for predicting the proportion of patients for each outcome state across entire ICU populations to guide quality, safety, and care delivery activities. Copyright © 2018 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved.

Demonstration of Hybrid DSMC-CFD Capability for Nonequilibrium Reacting Flow

DTIC Science & Technology

2018-02-09

Lens-XX facility. This flow was chosen since a recent blind-code validation exercise revealed differences in CFD predictions and experimental data... experimental data that could be due to rarefied flow effects. The CFD solutions (using the US3D code) were run with no-slip boundary conditions and with...excellent agreement with that predicted by CFD. This implies that the dif- ference between CFD predictions and experimental data is not due to rarefied

Predictors of early growth in academic achievement: the head-toes-knees-shoulders task

PubMed Central

McClelland, Megan M.; Cameron, Claire E.; Duncan, Robert; Bowles, Ryan P.; Acock, Alan C.; Miao, Alicia; Pratt, Megan E.

2014-01-01

Children's behavioral self-regulation and executive function (EF; including attentional or cognitive flexibility, working memory, and inhibitory control) are strong predictors of academic achievement. The present study examined the psychometric properties of a measure of behavioral self-regulation called the Head-Toes-Knees-Shoulders (HTKS) by assessing construct validity, including relations to EF measures, and predictive validity to academic achievement growth between prekindergarten and kindergarten. In the fall and spring of prekindergarten and kindergarten, 208 children (51% enrolled in Head Start) were assessed on the HTKS, measures of cognitive flexibility, working memory (WM), and inhibitory control, and measures of emergent literacy, mathematics, and vocabulary. For construct validity, the HTKS was significantly related to cognitive flexibility, working memory, and inhibitory control in prekindergarten and kindergarten. For predictive validity in prekindergarten, a random effects model indicated that the HTKS significantly predicted growth in mathematics, whereas a cognitive flexibility task significantly predicted growth in mathematics and vocabulary. In kindergarten, the HTKS was the only measure to significantly predict growth in all academic outcomes. An alternative conservative analytical approach, a fixed effects analysis (FEA) model, also indicated that growth in both the HTKS and measures of EF significantly predicted growth in mathematics over four time points between prekindergarten and kindergarten. Results demonstrate that the HTKS involves cognitive flexibility, working memory, and inhibitory control, and is substantively implicated in early achievement, with the strongest relations found for growth in achievement during kindergarten and associations with emergent mathematics. PMID:25071619

[Validation of the portuguese version of the Mini-Social Phobia Inventory (Mini-SPIN)].

PubMed

D'El Rey, Gustavo José Fonseca; Matos, Cláudia Wilmor

2009-01-01

Social phobia (also known as social anxiety disorder) is a severe mental disorder that brings distress and disability. The aim of this study was validate to the Portuguese language the Mini-Social Phobia Inventory (Mini-SPIN) in a populational sample. We performed a discriminative validity study of the Mini-SPIN in a sample of 644 subjects (Mini-SPIN positive group: n = 218 and control/negative group: n = 426) of a study of anxiety disorders' prevalence in the city of Santo André-SP. The Portuguese version of the Mini-SPIN (with score of 6 points, suggested in the original English version) demonstrated a sensitivity of 95.0%, specificity of 80.3%, positive predictive value of 52.8%, negative predictive value of 98.6% and incorrect classification rate of 16.9%. With score of 7 points, was observed an increase in the specificity and positive predictive value (88.6% and 62.7%), while the sensitivity and negative predictive value (84.8% and 96.2%) remained high. The Portuguese version of the Mini-SPIN showed satisfactory psychometric qualities in terms of discriminative validity. In this study, the cut-off of 7, was considered to be the most suitable to screening of the generalized social phobia.

FMRI Is a Valid Noninvasive Alternative to Wada Testing

PubMed Central

Binder, Jeffrey R.

2010-01-01

Partial removal of the anterior temporal lobe (ATL) is a highly effective surgical treatment for intractable temporal lobe epilepsy, yet roughly half of patients who undergo left ATL resection show decline in language or verbal memory function postoperatively. Two recent studies demonstrate that preoperative fMRI can predict postoperative naming and verbal memory changes in such patients. Most importantly, fMRI significantly improves the accuracy of prediction relative to other noninvasive measures used alone. Addition of language and memory lateralization data from the intracarotid amobarbital (Wada) test did not improve prediction accuracy in these studies. Thus, fMRI provides patients and practitioners with a safe, non-invasive, and well-validated tool for making better-informed decisions regarding elective surgery based on a quantitative assessment of cognitive risk. PMID:20850386

Conceptualizing and measuring illness self-concept: a comparison with self-esteem and optimism in predicting fibromyalgia adjustment.

PubMed

Morea, Jessica M; Friend, Ronald; Bennett, Robert M

2008-12-01

Illness self-concept (ISC), or the extent to which individuals are consumed by their illness, was theoretically described and evaluated with the Illness Self-Concept Scale (ISCS), a new 23-item scale, to predict adjustment in fibromyalgia. To establish convergent and discriminant validity, illness self-concept was compared to self-esteem and optimism in predicting health status, illness intrusiveness, depression, and life satisfaction. The ISCS demonstrated good reliability (alpha = .94; test-retest r = .80) and was a strong predictor of outcomes, even after controlling for optimism or self-esteem. The ISCS predicted unique variance in health-related outcomes; optimism and self-esteem did not, providing construct validation. Illness self-concept may play a significant role in coping with fibromyalgia and may prove useful in the evaluation of other chronic illnesses. (c) 2008 Wiley Periodicals, Inc.

Extending the validity of the Feeding Practices and Structure Questionnaire.

PubMed

Jansen, Elena; Mallan, Kimberley M; Daniels, Lynne A

2015-06-30

Feeding practices are commonly examined as potentially modifiable determinants of children's eating behaviours and weight status. Although a variety of questionnaires exist to assess different feeding aspects, many lack thorough reliability and validity testing. The Feeding Practices and Structure Questionnaire (FPSQ) is a tool designed to measure early feeding practices related to non-responsive feeding and structure of the meal environment. Face validity, factorial validity, internal reliability and cross-sectional correlations with children's eating behaviours have been established in mothers with 2-year-old children. The aim of the present study was to further extend the validity of the FPSQ by examining factorial, construct and predictive validity, and stability. Participants were from the NOURISH randomised controlled trial which evaluated an intervention with first-time mothers designed to promote protective feeding practices. Maternal feeding practices (FP) and child eating behaviours were assessed when children were aged 2 years and 3.7 years (n = 388). Confirmatory Factor analysis, group differences, predictive relationships, and stability were tested. The original 9-factor structure was confirmed when children were aged 3.7 ± 0.3 years. Cronbach's alpha was above the recommended 0.70 cut-off for all factors except Structured Meal Timing, Over Restriction and Distrust in Appetite which were 0.58, 0.67 and 0.66 respectively. Allocated group differences reflected behaviour consistent with intervention content and all feeding practices were stable across both time points (range of r = 0.45-0.70). There was some evidence for the predictive validity of factors with 2 FP showing expected relationships, 2 FP showing expected and unexpected relationships and 5 FP showing no relationship. Reliability and validity was demonstrated for most subscales of the FPSQ. Future validation is warranted with culturally diverse samples and with fathers and other caregivers. The use of additional outcomes to further explore predictive validity is recommended as well as testing test-retest reliability of the questionnaire.

Cross-validation pitfalls when selecting and assessing regression and classification models.

PubMed

Krstajic, Damjan; Buturovic, Ljubomir J; Leahy, David E; Thomas, Simon

2014-03-29

We address the problem of selecting and assessing classification and regression models using cross-validation. Current state-of-the-art methods can yield models with high variance, rendering them unsuitable for a number of practical applications including QSAR. In this paper we describe and evaluate best practices which improve reliability and increase confidence in selected models. A key operational component of the proposed methods is cloud computing which enables routine use of previously infeasible approaches. We describe in detail an algorithm for repeated grid-search V-fold cross-validation for parameter tuning in classification and regression, and we define a repeated nested cross-validation algorithm for model assessment. As regards variable selection and parameter tuning we define two algorithms (repeated grid-search cross-validation and double cross-validation), and provide arguments for using the repeated grid-search in the general case. We show results of our algorithms on seven QSAR datasets. The variation of the prediction performance, which is the result of choosing different splits of the dataset in V-fold cross-validation, needs to be taken into account when selecting and assessing classification and regression models. We demonstrate the importance of repeating cross-validation when selecting an optimal model, as well as the importance of repeating nested cross-validation when assessing a prediction error.

Subtyping attention-deficit/hyperactivity disorder using temperament dimensions: toward biologically based nosologic criteria

PubMed Central

Karalunas, Sarah L.; Fair, Damien; Musser, Erica D.; Aykes, Kamari; Iyer, Swathi P.; Nigg, Joel T.

2014-01-01

Importance Psychiatric nosology is limited by behavioral and biological heterogeneity within existing disorder categories. The imprecise nature of current nosological distinctions limits both mechanistic understanding and clinical prediction. Here, we demonstrate an approach consistent with the NIMH Research Domain Criteria (RDoC) initiative to identifying superior, neurobiologically-valid subgroups with better predictive capacity than existing psychiatric categories for childhood Attention-Deficit Hyperactivity Disorder (ADHD). Objective Refine subtyping of childhood ADHD by using biologically-based behavioral dimensions (i.e. temperament), novel classification algorithms, and multiple external validators. In doing so, we demonstrate how refined nosology is capable of improving on current predictive capacity of long-term outcomes relative to current DSM-based nosology. Design, Setting, Participants 437 clinically well-characterized, community-recruited children with and without ADHD participated in an on-going longitudinal study. Baseline data were used to classify children into subgroups based on temperament dimensions and to examine external validators including physiological and MRI measures. One-year longitudinal follow-up data are reported for a subgroup of the ADHD sample to address stability and clinical prediction. Main Outcome Measures Parent/guardian ratings of children on a measure of temperament were used as input features in novel community detection analyses to identify subgroups within the sample. Groups were validated using three widely-accepted external validators: peripheral physiology (cardiac measures of respiratory sinus arrhythmia and pre-ejection period), central nervous system functioning (via resting-state functional connectivity MRI), and clinical outcomes (at one-year longitudinal follow-up). Results The community detection algorithm suggested three novel types of ADHD, labeled as “Mild” (normative emotion regulation); “Surgent” (extreme levels of positive approach-motivation); and “Irritable” (extreme levels of negative emotionality, anger, and poor soothability). Types were independent of existing clinical demarcations, including DSM-5 presentations or symptom severity. These types showed stability over time and were distinguished by unique patterns of cardiac physiological response, resting-state functional brain connectivity, and clinical outcome one year later. Conclusions and Relevance Results suggest that a biologically-informed temperament-based typology, developed with a discovery-based community detection algorithm, provided a superior description of heterogeneity in the ADHD population than any current clinical nosology. This demonstration sets the stage for more aggressive attempts at a tractable, biologically-based nosology. PMID:25006969

Application of Multivariable Analysis and FTIR-ATR Spectroscopy to the Prediction of Properties in Campeche Honey

PubMed Central

Pat, Lucio; Ali, Bassam; Guerrero, Armando; Córdova, Atl V.; Garduza, José P.

2016-01-01

Attenuated total reflectance-Fourier transform infrared spectrometry and chemometrics model was used for determination of physicochemical properties (pH, redox potential, free acidity, electrical conductivity, moisture, total soluble solids (TSS), ash, and HMF) in honey samples. The reference values of 189 honey samples of different botanical origin were determined using Association Official Analytical Chemists, (AOAC), 1990; Codex Alimentarius, 2001, International Honey Commission, 2002, methods. Multivariate calibration models were built using partial least squares (PLS) for the measurands studied. The developed models were validated using cross-validation and external validation; several statistical parameters were obtained to determine the robustness of the calibration models: (PCs) optimum number of components principal, (SECV) standard error of cross-validation, (R 2 cal) coefficient of determination of cross-validation, (SEP) standard error of validation, and (R 2 val) coefficient of determination for external validation and coefficient of variation (CV). The prediction accuracy for pH, redox potential, electrical conductivity, moisture, TSS, and ash was good, while for free acidity and HMF it was poor. The results demonstrate that attenuated total reflectance-Fourier transform infrared spectrometry is a valuable, rapid, and nondestructive tool for the quantification of physicochemical properties of honey. PMID:28070445

Computational-experimental approach to drug-target interaction mapping: A case study on kinase inhibitors

PubMed Central

Ravikumar, Balaguru; Parri, Elina; Timonen, Sanna; Airola, Antti; Wennerberg, Krister

2017-01-01

Due to relatively high costs and labor required for experimental profiling of the full target space of chemical compounds, various machine learning models have been proposed as cost-effective means to advance this process in terms of predicting the most potent compound-target interactions for subsequent verification. However, most of the model predictions lack direct experimental validation in the laboratory, making their practical benefits for drug discovery or repurposing applications largely unknown. Here, we therefore introduce and carefully test a systematic computational-experimental framework for the prediction and pre-clinical verification of drug-target interactions using a well-established kernel-based regression algorithm as the prediction model. To evaluate its performance, we first predicted unmeasured binding affinities in a large-scale kinase inhibitor profiling study, and then experimentally tested 100 compound-kinase pairs. The relatively high correlation of 0.77 (p < 0.0001) between the predicted and measured bioactivities supports the potential of the model for filling the experimental gaps in existing compound-target interaction maps. Further, we subjected the model to a more challenging task of predicting target interactions for such a new candidate drug compound that lacks prior binding profile information. As a specific case study, we used tivozanib, an investigational VEGF receptor inhibitor with currently unknown off-target profile. Among 7 kinases with high predicted affinity, we experimentally validated 4 new off-targets of tivozanib, namely the Src-family kinases FRK and FYN A, the non-receptor tyrosine kinase ABL1, and the serine/threonine kinase SLK. Our sub-sequent experimental validation protocol effectively avoids any possible information leakage between the training and validation data, and therefore enables rigorous model validation for practical applications. These results demonstrate that the kernel-based modeling approach offers practical benefits for probing novel insights into the mode of action of investigational compounds, and for the identification of new target selectivities for drug repurposing applications. PMID:28787438

An Approach for Validating Actinide and Fission Product Burnup Credit Criticality Safety Analyses-Isotopic Composition Predictions

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

Radulescu, Georgeta; Gauld, Ian C; Ilas, Germina

2011-01-01

The expanded use of burnup credit in the United States (U.S.) for storage and transport casks, particularly in the acceptance of credit for fission products, has been constrained by the availability of experimental fission product data to support code validation. The U.S. Nuclear Regulatory Commission (NRC) staff has noted that the rationale for restricting the Interim Staff Guidance on burnup credit for storage and transportation casks (ISG-8) to actinide-only is based largely on the lack of clear, definitive experiments that can be used to estimate the bias and uncertainty for computational analyses associated with using burnup credit. To address themore » issues of burnup credit criticality validation, the NRC initiated a project with the Oak Ridge National Laboratory to (1) develop and establish a technically sound validation approach for commercial spent nuclear fuel (SNF) criticality safety evaluations based on best-available data and methods and (2) apply the approach for representative SNF storage and transport configurations/conditions to demonstrate its usage and applicability, as well as to provide reference bias results. The purpose of this paper is to describe the isotopic composition (depletion) validation approach and resulting observations and recommendations. Validation of the criticality calculations is addressed in a companion paper at this conference. For isotopic composition validation, the approach is to determine burnup-dependent bias and uncertainty in the effective neutron multiplication factor (keff) due to bias and uncertainty in isotopic predictions, via comparisons of isotopic composition predictions (calculated) and measured isotopic compositions from destructive radiochemical assay utilizing as much assay data as is available, and a best-estimate Monte Carlo based method. This paper (1) provides a detailed description of the burnup credit isotopic validation approach and its technical bases, (2) describes the application of the approach for representative pressurized water reactor and boiling water reactor safety analysis models to demonstrate its usage and applicability, (3) provides reference bias and uncertainty results based on a quality-assurance-controlled prerelease version of the Scale 6.1 code package and the ENDF/B-VII nuclear cross section data.« less

Validation in Support of Internationally Harmonised OECD Test Guidelines for Assessing the Safety of Chemicals.

PubMed

Gourmelon, Anne; Delrue, Nathalie

Ten years elapsed since the OECD published the Guidance document on the validation and international regulatory acceptance of test methods for hazard assessment. Much experience has been gained since then in validation centres, in countries and at the OECD on a variety of test methods that were subjected to validation studies. This chapter reviews validation principles and highlights common features that appear to be important for further regulatory acceptance across studies. Existing OECD-agreed validation principles will most likely generally remain relevant and applicable to address challenges associated with the validation of future test methods. Some adaptations may be needed to take into account the level of technique introduced in test systems, but demonstration of relevance and reliability will continue to play a central role as pre-requisite for the regulatory acceptance. Demonstration of relevance will become more challenging for test methods that form part of a set of predictive tools and methods, and that do not stand alone. OECD is keen on ensuring that while these concepts evolve, countries can continue to rely on valid methods and harmonised approaches for an efficient testing and assessment of chemicals.

Applying Mondrian Cross-Conformal Prediction To Estimate Prediction Confidence on Large Imbalanced Bioactivity Data Sets.

PubMed

Sun, Jiangming; Carlsson, Lars; Ahlberg, Ernst; Norinder, Ulf; Engkvist, Ola; Chen, Hongming

2017-07-24

Conformal prediction has been proposed as a more rigorous way to define prediction confidence compared to other application domain concepts that have earlier been used for QSAR modeling. One main advantage of such a method is that it provides a prediction region potentially with multiple predicted labels, which contrasts to the single valued (regression) or single label (classification) output predictions by standard QSAR modeling algorithms. Standard conformal prediction might not be suitable for imbalanced data sets. Therefore, Mondrian cross-conformal prediction (MCCP) which combines the Mondrian inductive conformal prediction with cross-fold calibration sets has been introduced. In this study, the MCCP method was applied to 18 publicly available data sets that have various imbalance levels varying from 1:10 to 1:1000 (ratio of active/inactive compounds). Our results show that MCCP in general performed well on bioactivity data sets with various imbalance levels. More importantly, the method not only provides confidence of prediction and prediction regions compared to standard machine learning methods but also produces valid predictions for the minority class. In addition, a compound similarity based nonconformity measure was investigated. Our results demonstrate that although it gives valid predictions, its efficiency is much worse than that of model dependent metrics.

A novel multi-target regression framework for time-series prediction of drug efficacy.

PubMed

Li, Haiqing; Zhang, Wei; Chen, Ying; Guo, Yumeng; Li, Guo-Zheng; Zhu, Xiaoxin

2017-01-18

Excavating from small samples is a challenging pharmacokinetic problem, where statistical methods can be applied. Pharmacokinetic data is special due to the small samples of high dimensionality, which makes it difficult to adopt conventional methods to predict the efficacy of traditional Chinese medicine (TCM) prescription. The main purpose of our study is to obtain some knowledge of the correlation in TCM prescription. Here, a novel method named Multi-target Regression Framework to deal with the problem of efficacy prediction is proposed. We employ the correlation between the values of different time sequences and add predictive targets of previous time as features to predict the value of current time. Several experiments are conducted to test the validity of our method and the results of leave-one-out cross-validation clearly manifest the competitiveness of our framework. Compared with linear regression, artificial neural networks, and partial least squares, support vector regression combined with our framework demonstrates the best performance, and appears to be more suitable for this task.

sNebula, a network-based algorithm to predict binding between human leukocyte antigens and peptides

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

Luo, Heng; Ye, Hao; Ng, Hui Wen

Understanding the binding between human leukocyte antigens (HLAs) and peptides is important to understand the functioning of the immune system. Since it is time-consuming and costly to measure the binding between large numbers of HLAs and peptides, computational methods including machine learning models and network approaches have been developed to predict HLA-peptide binding. However, there are several limitations for the existing methods. We developed a network-based algorithm called sNebula to address these limitations. We curated qualitative Class I HLA-peptide binding data and demonstrated the prediction performance of sNebula on this dataset using leave-one-out cross-validation and five-fold cross-validations. Furthermore, this algorithmmore » can predict not only peptides of different lengths and different types of HLAs, but also the peptides or HLAs that have no existing binding data. We believe sNebula is an effective method to predict HLA-peptide binding and thus improve our understanding of the immune system.« less

A novel multi-target regression framework for time-series prediction of drug efficacy

PubMed Central

Li, Haiqing; Zhang, Wei; Chen, Ying; Guo, Yumeng; Li, Guo-Zheng; Zhu, Xiaoxin

2017-01-01

Excavating from small samples is a challenging pharmacokinetic problem, where statistical methods can be applied. Pharmacokinetic data is special due to the small samples of high dimensionality, which makes it difficult to adopt conventional methods to predict the efficacy of traditional Chinese medicine (TCM) prescription. The main purpose of our study is to obtain some knowledge of the correlation in TCM prescription. Here, a novel method named Multi-target Regression Framework to deal with the problem of efficacy prediction is proposed. We employ the correlation between the values of different time sequences and add predictive targets of previous time as features to predict the value of current time. Several experiments are conducted to test the validity of our method and the results of leave-one-out cross-validation clearly manifest the competitiveness of our framework. Compared with linear regression, artificial neural networks, and partial least squares, support vector regression combined with our framework demonstrates the best performance, and appears to be more suitable for this task. PMID:28098186

sNebula, a network-based algorithm to predict binding between human leukocyte antigens and peptides

PubMed Central

Luo, Heng; Ye, Hao; Ng, Hui Wen; Sakkiah, Sugunadevi; Mendrick, Donna L.; Hong, Huixiao

2016-01-01

Understanding the binding between human leukocyte antigens (HLAs) and peptides is important to understand the functioning of the immune system. Since it is time-consuming and costly to measure the binding between large numbers of HLAs and peptides, computational methods including machine learning models and network approaches have been developed to predict HLA-peptide binding. However, there are several limitations for the existing methods. We developed a network-based algorithm called sNebula to address these limitations. We curated qualitative Class I HLA-peptide binding data and demonstrated the prediction performance of sNebula on this dataset using leave-one-out cross-validation and five-fold cross-validations. This algorithm can predict not only peptides of different lengths and different types of HLAs, but also the peptides or HLAs that have no existing binding data. We believe sNebula is an effective method to predict HLA-peptide binding and thus improve our understanding of the immune system. PMID:27558848

sNebula, a network-based algorithm to predict binding between human leukocyte antigens and peptides

DOE PAGES

Luo, Heng; Ye, Hao; Ng, Hui Wen; ...

2016-08-25

Understanding the binding between human leukocyte antigens (HLAs) and peptides is important to understand the functioning of the immune system. Since it is time-consuming and costly to measure the binding between large numbers of HLAs and peptides, computational methods including machine learning models and network approaches have been developed to predict HLA-peptide binding. However, there are several limitations for the existing methods. We developed a network-based algorithm called sNebula to address these limitations. We curated qualitative Class I HLA-peptide binding data and demonstrated the prediction performance of sNebula on this dataset using leave-one-out cross-validation and five-fold cross-validations. Furthermore, this algorithmmore » can predict not only peptides of different lengths and different types of HLAs, but also the peptides or HLAs that have no existing binding data. We believe sNebula is an effective method to predict HLA-peptide binding and thus improve our understanding of the immune system.« less

ERP evidence for selective drop in attentional costs in uncertain environments: challenging a purely premotor account of covert orienting of attention.

PubMed

Lasaponara, Stefano; Chica, Ana B; Lecce, Francesca; Lupianez, Juan; Doricchi, Fabrizio

2011-07-01

Several studies have proved that the reliability of endogenous spatial cues linearly modulates the reaction time advantage in the processing of targets at validly cued vs. invalidly cued locations, i.e. the "validity effect". This would imply that with non-predictive cues, no "validity effect" should be observed. However, contrary to this prediction, one could hypothesize that attentional benefits by valid cuing (i.e. the RT advantage for validly vs. neutrally cued targets) can still be maintained with non-predictive cues, if the brain were endowed with mechanisms allowing the selective reduction in costs of reorienting from invalidly cued locations (i.e. the reduction of the RT disadvantage for invalidly vs. neutrally cued targets). This separated modulation of attentional benefits and costs would be adaptive in uncertain contexts where cues predict at chance level the location of targets. Through the joint recording of manual reaction times and event-related cerebral potentials (ERPs), we have found that this is the case and that relying on non-predictive endogenous cues results in abatement of attentional costs and the difference in the amplitude of the P1 brain responses evoked by invalidly vs. neutrally cued targets. In contrast, the use of non-predictive cues leaves unaffected attentional benefits and the difference in the amplitude of the N1 responses evoked by validly vs. neutrally cued targets. At the individual level, the drop in costs with non-predictive cues was matched with equivalent lateral biases in RTs to neutrally and invalidly cued targets presented in the left and right visual field. During the cue period, the drop in costs with non-predictive cues was preceded by reduction of the Early Directing Attention Negativity (EDAN) on posterior occipital sites and by enhancement of the frontal Anterior Directing Attention Negativity (ADAN) correlated to preparatory voluntary orienting. These findings demonstrate, for the first time, that the segregation of mechanisms regulating attentional benefits and costs helps efficiency of orienting in "uncertain" visual spatial contexts characterized by poor probabilistic association between cues and targets. Copyright © 2011 Elsevier Ltd. All rights reserved.

Predictive Accuracy of the Liverpool Lung Project Risk Model for Stratifying Patients for Computed Tomography Screening for Lung Cancer

PubMed Central

Raji, Olaide Y.; Duffy, Stephen W.; Agbaje, Olorunshola F.; Baker, Stuart G.; Christiani, David C.; Cassidy, Adrian; Field, John K.

2013-01-01

Background External validation of existing lung cancer risk prediction models is limited. Using such models in clinical practice to guide the referral of patients for computed tomography (CT) screening for lung cancer depends on external validation and evidence of predicted clinical benefit. Objective To evaluate the discrimination of the Liverpool Lung Project (LLP) risk model and demonstrate its predicted benefit for stratifying patients for CT screening by using data from 3 independent studies from Europe and North America. Design Case–control and prospective cohort study. Setting Europe and North America. Patients Participants in the European Early Lung Cancer (EUELC) and Harvard case–control studies and the LLP population-based prospective cohort (LLPC) study. Measurements 5-year absolute risks for lung cancer predicted by the LLP model. Results The LLP risk model had good discrimination in both the Harvard (area under the receiver-operating characteristic curve [AUC], 0.76 [95% CI, 0.75 to 0.78]) and the LLPC (AUC, 0.82 [CI, 0.80 to 0.85]) studies and modest discrimination in the EUELC (AUC, 0.67 [CI, 0.64 to 0.69]) study. The decision utility analysis, which incorporates the harms and benefit of using a risk model to make clinical decisions, indicates that the LLP risk model performed better than smoking duration or family history alone in stratifying high-risk patients for lung cancer CT screening. Limitations The model cannot assess whether including other risk factors, such as lung function or genetic markers, would improve accuracy. Lack of information on asbestos exposure in the LLPC limited the ability to validate the complete LLP risk model. Conclusion Validation of the LLP risk model in 3 independent external data sets demonstrated good discrimination and evidence of predicted benefits for stratifying patients for lung cancer CT screening. Further studies are needed to prospectively evaluate model performance and evaluate the optimal population risk thresholds for initiating lung cancer screening. Primary Funding Source Roy Castle Lung Cancer Foundation. PMID:22910935

[ETAP: A smoking scale for Primary Health Care].

PubMed

González Romero, Pilar María; Cuevas Fernández, Francisco Javier; Marcelino Rodríguez, Itahisa; Rodríguez Pérez, María Del Cristo; Cabrera de León, Antonio; Aguirre-Jaime, Armando

2016-05-01

To obtain a scale of tobacco exposure to address smoking cessation. Follow-up of a cohort. Scale validation. Primary Care Research Unit. Tenerife. A total of 6729 participants from the "CDC de Canarias" cohort. A scale was constructed under the assumption that the time of exposure to tobacco is the key factor to express accumulated risk. Discriminant validity was tested on prevalent cases of acute myocardial infarction (AMI; n=171), and its best cut-off for preventive screening was obtained. Its predictive validity was tested with incident cases of AMI (n=46), comparing the predictive power with markers (age, sex) and classic risk factors of AMI (hypertension, diabetes, dyslipidaemia), including the pack-years index (PYI). The scale obtained was the sum of three times the years that they had smoked plus years exposed to smoking at home and at work. The frequency of AMI increased with the values of the scale, with the value 20 years of exposure being the most appropriate cut-off for preventive action, as it provided adequate predictive values for incident AMI. The scale surpassed PYI in predicting AMI, and competed with the known markers and risk factors. The proposed scale allows a valid measurement of exposure to smoking and provides a useful and simple approach that can help promote a willingness to change, as well as prevention. It still needs to demonstrate its validity, taking as reference other problems associated with smoking. Copyright © 2015 Elsevier España, S.L.U. All rights reserved.

Using Dynamic Risk and Protective Factors to Predict Inpatient Aggression: Reliability and Validity of START Assessments

PubMed Central

Desmarais, Sarah L.; Nicholls, Tonia L.; Wilson, Catherine M.; Brink, Johann

2012-01-01

The Short-Term Assessment of Risk and Treatability (START) is a relatively new structured professional judgment guide for the assessment and management of short-term risks associated with mental, substance use, and personality disorders. The scheme may be distinguished from other violence risk instruments because of its inclusion of 20 dynamic factors that are rated in terms of both vulnerability and strength. This study examined the reliability and validity of START assessments in predicting inpatient aggression. Research assistants completed START assessments for 120 male forensic psychiatric patients through review of hospital files. They additionally completed Historical-Clinical-Risk Management – 20 (HCR-20) and the Hare Psychopathy Checklist: Screening Version (PCL:SV) assessments. Outcome data was coded from hospital files for a 12-month follow-up period using the Overt Aggression Scale (OAS). START assessments evidenced excellent interrater reliability and demonstrated both predictive and incremental validity over the HCR-20 Historical subscale scores and PCL:SV total scores. Overall, results support the reliability and validity of START assessments, and use of the structured professional judgment approach more broadly, as well as the value of using dynamic risk and protective factors to assess violence risk. PMID:22250595

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

PubMed Central

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

2012-01-01

Background Prospective measures of high knee abduction moment (KAM) during landing identify female athletes at high risk for anterior cruciate ligament injury. Laboratory-based measurements demonstrate 90% accuracy in prediction of high KAM. Clinic-based prediction algorithms that employ correlates derived from laboratory-based measurements also demonstrate high accuracy for prediction of high KAM mechanics during landing. Hypotheses Clinic-based measures derived from highly predictive laboratory-based models are valid for the accurate prediction of high KAM status, and simultaneous measurements using laboratory-based and clinic-based techniques highly correlate. Study Design Cohort study (diagnosis); Level of evidence, 2. Methods One hundred female athletes (basketball, soccer, volleyball players) were tested using laboratory-based measures to confirm the validity of identified laboratory-based correlate variables to clinic-based measures included in a prediction algorithm to determine high KAM status. To analyze selected clinic-based surrogate predictors, another cohort of 20 female athletes was simultaneously tested with both clinic-based and laboratory-based measures. Results The prediction model (odds ratio: 95% confidence interval), derived from laboratory-based surrogates including (1) knee valgus motion (1.59: 1.17-2.16 cm), (2) knee flexion range of motion (0.94: 0.89°-1.00°), (3) body mass (0.98: 0.94-1.03 kg), (4) tibia length (1.55: 1.20-2.07 cm), and (5) quadriceps-to-hamstrings ratio (1.70: 0.48%-6.0%), predicted high KAM status with 84% sensitivity and 67% specificity (P < .001). Clinic-based techniques that used a calibrated physician’s scale, a standard measuring tape, standard camcorder, ImageJ software, and an isokinetic dynamometer showed high correlation (knee valgus motion, r = .87; knee flexion range of motion, r = .95; and tibia length, r = .98) to simultaneous laboratory-based measurements. Body mass and quadriceps-to-hamstrings ratio were included in both methodologies and therefore had r values of 1.0. Conclusion Clinically obtainable measures of increased knee valgus, knee flexion range of motion, body mass, tibia length, and quadriceps-to-hamstrings ratio predict high KAM status in female athletes with high sensitivity and specificity. Female athletes who demonstrate high KAM landing mechanics are at increased risk for anterior cruciate ligament injury and are more likely to benefit from neuromuscular training targeted to this risk factor. Use of the developed clinic-based assessment tool may facilitate high-risk athletes’ entry into appropriate interventions that will have greater potential to reduce their injury risk. PMID:20595554

Verification of an Analytical Method for Measuring Crystal Nucleation Rates in Glasses from DTA Data

NASA Technical Reports Server (NTRS)

Ranasinghe, K. S.; Wei, P. F.; Kelton, K. F.; Ray, C. S.; Day, D. E.

2004-01-01

A recently proposed analytical (DTA) method for estimating the nucleation rates in glasses has been evaluated by comparing experimental data with numerically computed nucleation rates for a model lithium disilicate glass. The time and temperature dependent nucleation rates were predicted using the model and compared with those values from an analysis of numerically calculated DTA curves. The validity of the numerical approach was demonstrated earlier by a comparison with experimental data. The excellent agreement between the nucleation rates from the model calculations and fiom the computer generated DTA data demonstrates the validity of the proposed analytical DTA method.

A design of experiments approach to validation sampling for logistic regression modeling with error-prone medical records.

PubMed

Ouyang, Liwen; Apley, Daniel W; Mehrotra, Sanjay

2016-04-01

Electronic medical record (EMR) databases offer significant potential for developing clinical hypotheses and identifying disease risk associations by fitting statistical models that capture the relationship between a binary response variable and a set of predictor variables that represent clinical, phenotypical, and demographic data for the patient. However, EMR response data may be error prone for a variety of reasons. Performing a manual chart review to validate data accuracy is time consuming, which limits the number of chart reviews in a large database. The authors' objective is to develop a new design-of-experiments-based systematic chart validation and review (DSCVR) approach that is more powerful than the random validation sampling used in existing approaches. The DSCVR approach judiciously and efficiently selects the cases to validate (i.e., validate whether the response values are correct for those cases) for maximum information content, based only on their predictor variable values. The final predictive model will be fit using only the validation sample, ignoring the remainder of the unvalidated and unreliable error-prone data. A Fisher information based D-optimality criterion is used, and an algorithm for optimizing it is developed. The authors' method is tested in a simulation comparison that is based on a sudden cardiac arrest case study with 23 041 patients' records. This DSCVR approach, using the Fisher information based D-optimality criterion, results in a fitted model with much better predictive performance, as measured by the receiver operating characteristic curve and the accuracy in predicting whether a patient will experience the event, than a model fitted using a random validation sample. The simulation comparisons demonstrate that this DSCVR approach can produce predictive models that are significantly better than those produced from random validation sampling, especially when the event rate is low. © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Manual unloading of the lumbar spine: can it identify immediate responders to mechanical traction in a low back pain population? A study of reliability and criterion referenced predictive validity

PubMed Central

Swanson, Brian T.; Riley, Sean P.; Cote, Mark P.; Leger, Robin R.; Moss, Isaac L.; Carlos,, John

2016-01-01

Background To date, no research has examined the reliability or predictive validity of manual unloading tests of the lumbar spine to identify potential responders to lumbar mechanical traction. Purpose To determine: (1) the intra and inter-rater reliability of a manual unloading test of the lumbar spine and (2) the criterion referenced predictive validity for the manual unloading test. Methods Ten volunteers with low back pain (LBP) underwent a manual unloading test to establish reliability. In a separate procedure, 30 consecutive patients with LBP (age 50·86±11·51) were assessed for pain in their most provocative standing position (visual analog scale (VAS) 49·53±25·52 mm). Patients were assessed with a manual unloading test in their most provocative position followed by a single application of intermittent mechanical traction. Post traction, pain in the provocative position was reassessed and utilized as the outcome criterion. Results The test of unloading demonstrated substantial intra and inter-rater reliability K = 1·00, P = 0·002, K = 0·737, P = 0·001, respectively. There were statistically significant within group differences for pain response following traction for patients with a positive manual unloading test (P<0·001), while patients with a negative manual unloading test did not demonstrate a statistically significant change (P>0·05). There were significant between group differences for proportion of responders to traction based on manual unloading response (P = 0·031), and manual unloading response demonstrated a moderate to strong relationship with traction response Phi = 0·443, P = 0·015. Discussion and conclusion The manual unloading test appears to be a reliable test and has a moderate to strong correlation with pain relief that exceeds minimal clinically important difference (MCID) following traction supporting the validity of this test. PMID:27559274

A novel artificial neural network method for biomedical prediction based on matrix pseudo-inversion.

PubMed

Cai, Binghuang; Jiang, Xia

2014-04-01

Biomedical prediction based on clinical and genome-wide data has become increasingly important in disease diagnosis and classification. To solve the prediction problem in an effective manner for the improvement of clinical care, we develop a novel Artificial Neural Network (ANN) method based on Matrix Pseudo-Inversion (MPI) for use in biomedical applications. The MPI-ANN is constructed as a three-layer (i.e., input, hidden, and output layers) feed-forward neural network, and the weights connecting the hidden and output layers are directly determined based on MPI without a lengthy learning iteration. The LASSO (Least Absolute Shrinkage and Selection Operator) method is also presented for comparative purposes. Single Nucleotide Polymorphism (SNP) simulated data and real breast cancer data are employed to validate the performance of the MPI-ANN method via 5-fold cross validation. Experimental results demonstrate the efficacy of the developed MPI-ANN for disease classification and prediction, in view of the significantly superior accuracy (i.e., the rate of correct predictions), as compared with LASSO. The results based on the real breast cancer data also show that the MPI-ANN has better performance than other machine learning methods (including support vector machine (SVM), logistic regression (LR), and an iterative ANN). In addition, experiments demonstrate that our MPI-ANN could be used for bio-marker selection as well. Copyright © 2013 Elsevier Inc. All rights reserved.

Utility of the response bias scale (RBS) and other MMPI-2 validity scales in predicting TOMM performance.

PubMed

Whitney, Kriscinda A; Davis, Jeremy J; Shepard, Polly H; Herman, Steven M

2008-01-01

The present study represents a replication and extension of the original Response Bias Scale (RBS) validation study. In addition to examining the relationship between the Test of Memory Malingering (TOMM), RBS, and several other well-researched Minnesota Multiphasic Personality Inventory 2 (MMPI-2) validity scales (i.e., F, Fb, Fp, and the Fake Bad Scale), the present study also included the recently developed Infrequency Post-Traumatic Stress Disorder Scale and the Henry-Heilbronner Index (HHI) of the MMPI-2. Findings from this retrospective data analysis (N=46) demonstrated the superiority of the RBS, and to a certain extent the HHI, over other MMPI-2 validity scales in predicting TOMM failure within the outpatient Veterans Affairs population. Results of the current study confirm the clinical utility of the RBS and suggest that, particularly if the MMPI-2 is an existing part of the neuropsychological assessment, examination of RBS scores is an efficient means of detecting negative response bias.

The Alcohol Relapse Situation Appraisal Questionnaire: Development and Validation

PubMed Central

Martin, Rosemarie A.; MacKinnon, Selene M.; Johnson, Jennifer E.; Myers, Mark G.; Cook, Travis A. R.; Rohsenow, Damaris J.

2011-01-01

Background The role of cognitive appraisal of the threat of alcohol relapse has received little attention. A previous instrument, the Relapse Situation Appraisal Questionnaire (RSAQ), was developed to assess cocaine users’ primary appraisal of the threat of situations posing a high risk for cocaine relapse. The purpose of the present study was to modify the RSAQ in order to measure primary appraisal in situations involving a high risk for alcohol relapse. Methods The development and psychometric properties of this instrument, the Alcohol Relapse Situation Appraisal Questionnaire (A-RSAQ), were examined with two samples of abstinent adults with alcohol abuse or dependence. Factor structure and validity were examined in Study 1 (N=104). Confirmation of the factor structure and predictive validity were assessed in Study 2 (N=161). Results Results demonstrated construct, discriminant and predictive validity and reliability of the A-RSAQ. Discussion Results support the important role of primary appraisal of degree of risk in alcohol relapse situations. PMID:21237586

Development of code evaluation criteria for assessing predictive capability and performance

NASA Technical Reports Server (NTRS)

Lin, Shyi-Jang; Barson, S. L.; Sindir, M. M.; Prueger, G. H.

1993-01-01

Computational Fluid Dynamics (CFD), because of its unique ability to predict complex three-dimensional flows, is being applied with increasing frequency in the aerospace industry. Currently, no consistent code validation procedure is applied within the industry. Such a procedure is needed to increase confidence in CFD and reduce risk in the use of these codes as a design and analysis tool. This final contract report defines classifications for three levels of code validation, directly relating the use of CFD codes to the engineering design cycle. Evaluation criteria by which codes are measured and classified are recommended and discussed. Criteria for selecting experimental data against which CFD results can be compared are outlined. A four phase CFD code validation procedure is described in detail. Finally, the code validation procedure is demonstrated through application of the REACT CFD code to a series of cases culminating in a code to data comparison on the Space Shuttle Main Engine High Pressure Fuel Turbopump Impeller.

Validation study of the Japanese version of the Obsessive-Compulsive Drinking Scale.

PubMed

Tatsuzawa, Yasutaka; Yoshimasu, Haruo; Moriyama, Yasushi; Furusawa, Teruyuki; Yoshino, Aihide

2002-02-01

The Obsessive-Compulsive Drinking Scale (OCDS) is a self-rating questionnaire that measures cognitive and behavioral aspects of craving for alcohol. The OCDS consists of two subscales: the obsessive thoughts of drinking subscale (OS) and the compulsive drinking subscale (CS). This study aims to validate the Japanese version of the OCDS. First, internal consistency and discriminant validity were evaluated. Second, a prospective longitudinal 3-month outcome study of 67 patients with alcohol dependence who participated in a relapse prevention program was designed to assess the concurrent and predictive validity of the OCDS. The OCDS demonstrated high internal consistency. The OS had high discriminant validity, while the CS did not. Twenty-three patients (34.3%) dropped out of treatment. These patients had significantly higher OS scores than those who completed the program. At 3 months, the relapse group had significantly higher OCDS scores than the no relapse group. Also, the OCDS score was higher in subjects who had early-onset alcohol dependence than late-onset dependence. The OCDS is useful for evaluating cognitive aspect of craving and predicts dropout and relapse.

An examination of three sets of MMPI-2 personality disorder scales.

PubMed

Jones, Alvin

2005-08-01

Three sets of personality disorder scales (PD scales) can be scored for the MMPI-2 (Butcher, Dahlstrom, Graham, Tellegen, & Kaemmer, 1989). Two sets (Levitt & Gotts, 1995; Morey, Waugh, & Blashfield, 1985) are derived from the MMPI (Hathaway & McKinley, 1983), and a third set (Somwaru & Ben-Porath, 1995) is based on the MMPI-2. There is no validity research for the Levitt and Gotts scale, and limited validity research is available for the Somwaru and Ben-Porath scales. There is a large body of research suggesting that the Morey et al. scales have good to excellent convergent validity when compared to a variety of other measures of personality disorders. Since the Morey et al. scales have established validity, there is a question if additional sets of PD scales are needed. The primary purpose of this research was to determine if the PD scales developed by Levitt and Gotts and those developed by Somwaru and Ben-Porath contribute incrementally to the scales developed by Morey et al. in predicting corresponding scales on the MCMI-II (Millon, 1987). In a sample of 494 individuals evaluated at an Army medical center, a hierarchical regression analysis demonstrated that the Somwaru and Ben-Porath Borderline, Antisocial, and Schizoid PD scales and the Levitt and Gotts Narcissistic and Histrionic scales contributed significantly and meaningfully to the Morey et al. scales in predicting the corresponding MCMI-II (Millon, 1987) scale. However, only the Somwaru and Ben-Porath scales demonstrated acceptable internal consistency and convergent validity.

Validation of serum progesterone <35nmol/L as a predictor of miscarriage among women with threatened miscarriage.

PubMed

Lek, Sze Min; Ku, Chee Wai; Allen, John C; Malhotra, Rahul; Tan, Nguan Soon; Østbye, Truls; Tan, Thiam Chye

2017-03-06

Our recent paper, based on a pilot cohort of 119 women, showed that serum progesterone <35 nmol/L was prognostic of spontaneous miscarriage by 16 weeks in women with threatened miscarriage in early pregnancy. Using a larger cohort of women from the same setting (validation cohort), we aim to assess the validity of serum progesterone <35 nmol/L with the outcome of spontaneous miscarriage by 16 weeks. In a prospective cohort study, 360 pregnant women presenting with threatened miscarriage between gestation weeks 6-10 at a tertiary hospital emergency unit for women in Singapore were recruited for this study. The main outcome measure measured is spontaneous miscarriage prior to week 16 of gestation. Area under the ROC curve (AUC) and test characteristics (sensitivity, specificity, positive and negative predictive value) at a serum progesterone cutpoint of <35 nmol/L for predicting high and low risk of spontaneous miscarriage by 16 weeks were compared between the Pilot and Validation cohorts. Test characteristics and AUC values using serum progesterone <35 nmol/L in the validation cohort were not significantly different from those in the Pilot cohort, demonstrating excellent accuracy and reproducibility of the proposed serum progesterone cut-off level. The cut-off value for serum progesterone (35 nmol/L) demonstrated clinical relevance and allow clinicians to stratify patients into high and low risk groups for spontaneous miscarriage.

The predictive validity of the BioMedical Admissions Test for pre-clinical examination performance.

PubMed

Emery, Joanne L; Bell, John F

2009-06-01

Some medical courses in the UK have many more applicants than places and almost all applicants have the highest possible previous and predicted examination grades. The BioMedical Admissions Test (BMAT) was designed to assist in the student selection process specifically for a number of 'traditional' medical courses with clear pre-clinical and clinical phases and a strong focus on science teaching in the early years. It is intended to supplement the information provided by examination results, interviews and personal statements. This paper reports on the predictive validity of the BMAT and its predecessor, the Medical and Veterinary Admissions Test. Results from the earliest 4 years of the test (2000-2003) were matched to the pre-clinical examination results of those accepted onto the medical course at the University of Cambridge. Correlation and logistic regression analyses were performed for each cohort. Section 2 of the test ('Scientific Knowledge') correlated more strongly with examination marks than did Section 1 ('Aptitude and Skills'). It also had a stronger relationship with the probability of achieving the highest examination class. The BMAT and its predecessor demonstrate predictive validity for the pre-clinical years of the medical course at the University of Cambridge. The test identifies important differences in skills and knowledge between candidates, not shown by their previous attainment, which predict their examination performance. It is thus a valid source of additional admissions information for medical courses with a strong scientific emphasis when previous attainment is very high.

The development and initial validation of the Decent Work Scale.

PubMed

Duffy, Ryan D; Allan, Blake A; England, Jessica W; Blustein, David L; Autin, Kelsey L; Douglass, Richard P; Ferreira, Joaquim; Santos, Eduardo J R

2017-03-01

Decent work is positioned as the centerpiece of the recently developed Psychology of Working Theory (PWT; Duffy, Blustein, Diemer, & Autin, 2016). However, to date, no instrument exists which assesses all 5 components of decent work from a psychological perspective. In the current study, we developed the Decent Work Scale (DWS) and demonstrated several aspects of validity with 2 samples of working adults. In Study 1 (N = 275), a large pool of items were developed and exploratory factor analysis was conducted resulting in a final 15-item scale with 5 factors/subscales corresponding to the 5 components of decent work: (a) physically and interpersonally safe working conditions, (b) access to health care, (c) adequate compensation, (d) hours that allow for free time and rest, and (e) organizational values that complement family and social values. In Study 2 (N = 589), confirmatory factor analysis demonstrated that a 5-factor, bifactor model offered the strongest and most parsimonious fit to the data. Configural, metric, and scalar invariance models were tested demonstrating that the structure of the instrument did not differ across gender, income, social class, and majority/minority racial/ethnic groups. Finally, the overall scale score and 5 subscale scores correlated in the expected directions with similar constructs supporting convergent and discriminant evidence of validity, and subscale scores evidenced predictive validity in the prediction of job satisfaction, work meaning, and withdrawal intentions. The development of this scale provides a useful tool for researchers and practitioners seeking to assess the attainment of decent work among employed adults. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Polymer Brushes under High Load

PubMed Central

Balko, Suzanne M.; Kreer, Torsten; Costanzo, Philip J.; Patten, Tim E.; Johner, Albert; Kuhl, Tonya L.; Marques, Carlos M.

2013-01-01

Polymer coatings are frequently used to provide repulsive forces between surfaces in solution. After 25 years of design and study, a quantitative model to explain and predict repulsion under strong compression is still lacking. Here, we combine experiments, simulations, and theory to study polymer coatings under high loads and demonstrate a validated model for the repulsive forces, proposing that this universal behavior can be predicted from the polymer solution properties. PMID:23516470

Prediction of the effect of formulation on the toxicity of chemicals.

PubMed

Mistry, Pritesh; Neagu, Daniel; Sanchez-Ruiz, Antonio; Trundle, Paul R; Vessey, Jonathan D; Gosling, John Paul

2017-01-01

Two approaches for the prediction of which of two vehicles will result in lower toxicity for anticancer agents are presented. Machine-learning models are developed using decision tree, random forest and partial least squares methodologies and statistical evidence is presented to demonstrate that they represent valid models. Separately, a clustering method is presented that allows the ordering of vehicles by the toxicity they show for chemically-related compounds.

Wavelet Filtering to Reduce Conservatism in Aeroservoelastic Robust Stability Margins

NASA Technical Reports Server (NTRS)

Brenner, Marty; Lind, Rick

1998-01-01

Wavelet analysis for filtering and system identification was used to improve the estimation of aeroservoelastic stability margins. The conservatism of the robust stability margins was reduced with parametric and nonparametric time-frequency analysis of flight data in the model validation process. Nonparametric wavelet processing of data was used to reduce the effects of external desirableness and unmodeled dynamics. Parametric estimates of modal stability were also extracted using the wavelet transform. Computation of robust stability margins for stability boundary prediction depends on uncertainty descriptions derived from the data for model validation. F-18 high Alpha Research Vehicle aeroservoelastic flight test data demonstrated improved robust stability prediction by extension of the stability boundary beyond the flight regime.

An Automated, Adaptive Framework for Optimizing Preprocessing Pipelines in Task-Based Functional MRI

PubMed Central

Churchill, Nathan W.; Spring, Robyn; Afshin-Pour, Babak; Dong, Fan; Strother, Stephen C.

2015-01-01

BOLD fMRI is sensitive to blood-oxygenation changes correlated with brain function; however, it is limited by relatively weak signal and significant noise confounds. Many preprocessing algorithms have been developed to control noise and improve signal detection in fMRI. Although the chosen set of preprocessing and analysis steps (the “pipeline”) significantly affects signal detection, pipelines are rarely quantitatively validated in the neuroimaging literature, due to complex preprocessing interactions. This paper outlines and validates an adaptive resampling framework for evaluating and optimizing preprocessing choices by optimizing data-driven metrics of task prediction and spatial reproducibility. Compared to standard “fixed” preprocessing pipelines, this optimization approach significantly improves independent validation measures of within-subject test-retest, and between-subject activation overlap, and behavioural prediction accuracy. We demonstrate that preprocessing choices function as implicit model regularizers, and that improvements due to pipeline optimization generalize across a range of simple to complex experimental tasks and analysis models. Results are shown for brief scanning sessions (<3 minutes each), demonstrating that with pipeline optimization, it is possible to obtain reliable results and brain-behaviour correlations in relatively small datasets. PMID:26161667

Predictive Variables of Half-Marathon Performance for Male Runners

PubMed Central

Gómez-Molina, Josué; Ogueta-Alday, Ana; Camara, Jesus; Stickley, Christoper; Rodríguez-Marroyo, José A.; García-López, Juan

2017-01-01

The aims of this study were to establish and validate various predictive equations of half-marathon performance. Seventy-eight half-marathon male runners participated in two different phases. Phase 1 (n = 48) was used to establish the equations for estimating half-marathon performance, and Phase 2 (n = 30) to validate these equations. Apart from half-marathon performance, training-related and anthropometric variables were recorded, and an incremental test on a treadmill was performed, in which physiological (VO2max, speed at the anaerobic threshold, peak speed) and biomechanical variables (contact and flight times, step length and step rate) were registered. In Phase 1, half-marathon performance could be predicted to 90.3% by variables related to training and anthropometry (Equation 1), 94.9% by physiological variables (Equation 2), 93.7% by biomechanical parameters (Equation 3) and 96.2% by a general equation (Equation 4). Using these equations, in Phase 2 the predicted time was significantly correlated with performance (r = 0.78, 0.92, 0.90 and 0.95, respectively). The proposed equations and their validation showed a high prediction of half-marathon performance in long distance male runners, considered from different approaches. Furthermore, they improved the prediction performance of previous studies, which makes them a highly practical application in the field of training and performance. Key points The present study obtained four equations involving anthropometric, training, physiological and biomechanical variables to estimate half-marathon performance. These equations were validated in a different population, demonstrating narrows ranges of prediction than previous studies and also their consistency. As a novelty, some biomechanical variables (i.e. step length and step rate at RCT, and maximal step length) have been related to half-marathon performance. PMID:28630571

Pyrotechnic Shock Analysis Using Statistical Energy Analysis

DTIC Science & Technology

2015-10-23

SEA subsystems. A couple of validation examples are provided to demonstrate the new approach. KEY WORDS : Peak Ratio, phase perturbation...Ballistic Shock Prediction Models and Techniques for Use in the Crusader Combat Vehicle Program,” 11th Annual US Army Ground Vehicle Survivability

Assessing Students' Spiritual and Religious Qualities

ERIC Educational Resources Information Center

Astin, Alexander W.; Astin, Helen S.; Lindholm, Jennifer A.

2011-01-01

This paper describes a comprehensive set of 12 new measures for studying undergraduate students' spiritual and religious development. The three measures of spirituality, four measures of "spiritually related" qualities, and five measures of religiousness demonstrate satisfactory reliability, robustness, and both concurrent and predictive validity.…

Real-Time Prognostics of a Rotary Valve Actuator

NASA Technical Reports Server (NTRS)

Daigle, Matthew

2015-01-01

Valves are used in many domains and often have system-critical functions. As such, it is important to monitor the health of valves and their actuators and predict remaining useful life. In this work, we develop a model-based prognostics approach for a rotary valve actuator. Due to limited observability of the component with multiple failure modes, a lumped damage approach is proposed for estimation and prediction of damage progression. In order to support the goal of real-time prognostics, an approach to prediction is developed that does not require online simulation to compute remaining life, rather, a function mapping the damage state to remaining useful life is found offline so that predictions can be made quickly online with a single function evaluation. Simulation results demonstrate the overall methodology, validating the lumped damage approach and demonstrating real-time prognostics.

Validation of the Spanish Version of the Mammography-Specific Self-Efficacy Scale.

PubMed

Jerome-D'Emilia, Bonnie; Suplee, Patricia; Akincigil, Ayse

2015-05-01

To consider psychometric estimates of the validity and reliability of the Spanish translation of a mammography-specific self-efficacy scale. A cross-sectional study. Three primarily Hispanic churches and a Hispanic community center in a low-income urban area of New Jersey. 153 low-income Hispanic women aged 40-85 years. The translated scale was administered to participants during a six-month period. Internal consistency, reliability, and construct and predictive validity were assessed. Demographic variables included income and insurance status. Outcome variables included total mammography-specific self-efficacy and having had a mammogram within the past two years. Preliminary evidence of reliability and validity were found, and predictive validity was demonstrated. The health needs of specific populations can be addressed only when research instruments have been appropriately validated and all relevant factors are considered. Diverse groups of low-income women face similar challenges and barriers in their efforts to get screened. Nurses are in an ideal position to help women with preventive care decision making (e.g., screening for breast cancer). Understanding how a woman's level of self-efficacy affects her decision making should be considered when counseling a client.

Application of advanced sampling and analysis methods to predict the structure of adsorbed protein on a material surface

PubMed Central

Abramyan, Tigran M.; Hyde-Volpe, David L.; Stuart, Steven J.; Latour, Robert A.

2017-01-01

The use of standard molecular dynamics simulation methods to predict the interactions of a protein with a material surface have the inherent limitations of lacking the ability to determine the most likely conformations and orientations of the adsorbed protein on the surface and to determine the level of convergence attained by the simulation. In addition, standard mixing rules are typically applied to combine the nonbonded force field parameters of the solution and solid phases the system to represent interfacial behavior without validation. As a means to circumvent these problems, the authors demonstrate the application of an efficient advanced sampling method (TIGER2A) for the simulation of the adsorption of hen egg-white lysozyme on a crystalline (110) high-density polyethylene surface plane. Simulations are conducted to generate a Boltzmann-weighted ensemble of sampled states using force field parameters that were validated to represent interfacial behavior for this system. The resulting ensembles of sampled states were then analyzed using an in-house-developed cluster analysis method to predict the most probable orientations and conformations of the protein on the surface based on the amount of sampling performed, from which free energy differences between the adsorbed states were able to be calculated. In addition, by conducting two independent sets of TIGER2A simulations combined with cluster analyses, the authors demonstrate a method to estimate the degree of convergence achieved for a given amount of sampling. The results from these simulations demonstrate that these methods enable the most probable orientations and conformations of an adsorbed protein to be predicted and that the use of our validated interfacial force field parameter set provides closer agreement to available experimental results compared to using standard CHARMM force field parameterization to represent molecular behavior at the interface. PMID:28514864

miRTar2GO: a novel rule-based model learning method for cell line specific microRNA target prediction that integrates Ago2 CLIP-Seq and validated microRNA-target interaction data.

PubMed

Ahadi, Alireza; Sablok, Gaurav; Hutvagner, Gyorgy

2017-04-07

MicroRNAs (miRNAs) are ∼19-22 nucleotides (nt) long regulatory RNAs that regulate gene expression by recognizing and binding to complementary sequences on mRNAs. The key step in revealing the function of a miRNA, is the identification of miRNA target genes. Recent biochemical advances including PAR-CLIP and HITS-CLIP allow for improved miRNA target predictions and are widely used to validate miRNA targets. Here, we present miRTar2GO, which is a model, trained on the common rules of miRNA-target interactions, Argonaute (Ago) CLIP-Seq data and experimentally validated miRNA target interactions. miRTar2GO is designed to predict miRNA target sites using more relaxed miRNA-target binding characteristics. More importantly, miRTar2GO allows for the prediction of cell-type specific miRNA targets. We have evaluated miRTar2GO against other widely used miRNA target prediction algorithms and demonstrated that miRTar2GO produced significantly higher F1 and G scores. Target predictions, binding specifications, results of the pathway analysis and gene ontology enrichment of miRNA targets are freely available at http://www.mirtar2go.org. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

Experimental demonstration of Klyshko's advanced-wave picture using a coincidence-count based, camera-enabled imaging system

NASA Astrophysics Data System (ADS)

Aspden, Reuben S.; Tasca, Daniel S.; Forbes, Andrew; Boyd, Robert W.; Padgett, Miles J.

2014-04-01

The Klyshko advanced-wave picture is a well-known tool useful in the conceptualisation of parametric down-conversion (SPDC) experiments. Despite being well-known and understood, there have been few experimental demonstrations illustrating its validity. Here, we present an experimental demonstration of this picture using a time-gated camera in an image-based coincidence measurement. We show an excellent agreement between the spatial distributions as predicted by the Klyshko picture and those obtained using the SPDC photon pairs. An interesting speckle feature is present in the Klyshko predictive images due to the spatial coherence of the back-propagated beam in the multi-mode fibre. This effect can be removed by mechanically twisting the fibre, thus degrading the spatial coherence of the beam and time-averaging the speckle pattern, giving an accurate correspondence between the predictive and SPDC images.

Material Characterization for Ductile Fracture Prediction

NASA Technical Reports Server (NTRS)

Hill, Michael R.

2000-01-01

The research summarized in this document provides valuable information for structural health evaluation of NASA infrastructure. Specifically, material properties are reported which will enable calibration of ductile fracture prediction methods for three high-toughness metallic materials and one aluminum alloy which can be found in various NASA facilities. The task of investigating these materials has also served to validate an overall methodology for ductile fracture prediction is currently being employed at NASA. In facilitating the ability to incorporate various materials into the prediction scheme, we have provided data to enable demonstration of the overall generality of the approach.

Derivation, Validation and Application of a Pragmatic Risk Prediction Index for Benchmarking of Surgical Outcomes.

PubMed

Spence, Richard T; Chang, David C; Kaafarani, Haytham M A; Panieri, Eugenio; Anderson, Geoffrey A; Hutter, Matthew M

2018-02-01

Despite the existence of multiple validated risk assessment and quality benchmarking tools in surgery, their utility outside of high-income countries is limited. We sought to derive, validate and apply a scoring system that is both (1) feasible, and (2) reliably predicts mortality in a middle-income country (MIC) context. A 5-step methodology was used: (1) development of a de novo surgical outcomes database modeled around the American College of Surgeons' National Surgical Quality Improvement Program (ACS-NSQIP) in South Africa (SA dataset), (2) use of the resultant data to identify all predictors of in-hospital death with more than 90% capture indicating feasibility of collection, (3) use these predictors to derive and validate an integer-based score that reliably predicts in-hospital death in the 2012 ACS-NSQIP, (4) apply the score in the original SA dataset and demonstrate its performance, (5) identify threshold cutoffs of the score to prompt action and drive quality improvement. Following step one-three above, the 13 point Codman's score was derived and validated on 211,737 and 109,079 patients, respectively, and includes: age 65 (1), partially or completely dependent functional status (1), preoperative transfusions ≥4 units (1), emergency operation (2), sepsis or septic shock (2) American Society of Anesthesia score ≥3 (3) and operative procedure (1-3). Application of the score to 373 patients in the SA dataset showed good discrimination and calibration to predict an in-hospital death. A Codman Score of 8 is an optimal cutoff point for defining expected and unexpected deaths. We have designed a novel risk prediction score specific for a MIC context. The Codman Score can prove useful for both (1) preoperative decision-making and (2) benchmarking the quality of surgical care in MIC's.

Dynamic Forces in Spur Gears - Measurement, Prediction, and Code Validation

NASA Technical Reports Server (NTRS)

Oswald, Fred B.; Townsend, Dennis P.; Rebbechi, Brian; Lin, Hsiang Hsi

1996-01-01

Measured and computed values for dynamic loads in spur gears were compared to validate a new version of the NASA gear dynamics code DANST-PC. Strain gage data from six gear sets with different tooth profiles were processed to determine the dynamic forces acting between the gear teeth. Results demonstrate that the analysis code successfully simulates the dynamic behavior of the gears. Differences between analysis and experiment were less than 10 percent under most conditions.

Testing Math or Testing Language? The Construct Validity of the KeyMath-Revised for Children With Intellectual Disability and Language Difficulties.

PubMed

Rhodes, Katherine T; Branum-Martin, Lee; Morris, Robin D; Romski, MaryAnn; Sevcik, Rose A

2015-11-01

Although it is often assumed that mathematics ability alone predicts mathematics test performance, linguistic demands may also predict achievement. This study examined the role of language in mathematics assessment performance for children with intellectual disability (ID) at less severe levels, on the KeyMath-Revised Inventory (KM-R) with a sample of 264 children, in grades 2-5. Using confirmatory factor analysis, the hypothesis that the KM-R would demonstrate discriminant validity with measures of language abilities in a two-factor model was compared to two plausible alternative models. Results indicated that KM-R did not have discriminant validity with measures of children's language abilities and was a multidimensional test of both mathematics and language abilities for this population of test users. Implications are considered for test development, interpretation, and intervention.

Predicting risk behaviors: development and validation of a diagnostic scale.

PubMed

Witte, K; Cameron, K A; McKeon, J K; Berkowitz, J M

1996-01-01

The goal of this study was to develop and validate the Risk Behavior Diagnosis (RBD) Scale for use by health care providers and practitioners interested in promoting healthy behaviors. Theoretically guided by the Extended Parallel Process Model (EPPM; a fear appeal theory), the RBD scale was designed to work in conjunction with an easy-to-use formula to determine which types of health risk messages would be most appropriate for a given individual or audience. Because some health risk messages promote behavior change and others backfire, this type of scale offers guidance to practitioners on how to develop the best persuasive message possible to motivate healthy behaviors. The results of the study demonstrate the RBD scale to have a high degree of content, construct, and predictive validity. Specific examples and practical suggestions are offered to facilitate use of the scale for health practitioners.

Reliability and validity of a treatment adherence measure for child psychiatric rehabilitation.

PubMed

Williams, Nathaniel J; Green, Philip

2012-09-01

Treatment adherence, defined as the degree to which practitioners implemented prescribed program principles and activities and avoided proscribed activities, has been an area of growing interest in mental health services for children with severe emotional and behavioral disorders. This study evaluated the reliability and validity of a treatment adherence measure for child psychiatric rehabilitation (CPSR). Parents of children receiving CPSR (n = 79) or psychotherapy (n = 27) completed the Children's Psychosocial Rehabilitation Treatment Adherence Measure (CTAM) and a measure of 2-week session impact. Psychiatric rehabilitation (PSR) supervisors identified PSR practitioners with reputations for high or low adherence to the model. The CTAM's discriminant validity was assessed by using known-groups procedures and predictive validity by examining its relationship to 2-week session impact. The CTAM demonstrated excellent internal consistency (α = .92), discriminant validity (p = .002, d = .72; p = .021, d = .59), and predictive validity (B = 2.24, SE = .31, p < .001), accounting for 28% of the child-level variance in 2-week session impact. Findings suggest the CTAM is a reliable and valid measure of treatment adherence for CPSR programs with a skill-teaching focus. Providers and agencies should take steps to enhance treatment adherence because it may be an important predictor of children's short-term response to CPSR. (PsycINFO Database Record (c) 2012 APA, all rights reserved).

Acquaintance Rape: Applying Crime Scene Analysis to the Prediction of Sexual Recidivism.

PubMed

Lehmann, Robert J B; Goodwill, Alasdair M; Hanson, R Karl; Dahle, Klaus-Peter

2016-10-01

The aim of the current study was to enhance the assessment and predictive accuracy of risk assessments for sexual offenders by utilizing detailed crime scene analysis (CSA). CSA was conducted on a sample of 247 male acquaintance rapists from Berlin (Germany) using a nonmetric, multidimensional scaling (MDS) Behavioral Thematic Analysis (BTA) approach. The age of the offenders at the time of the index offense ranged from 14 to 64 years (M = 32.3; SD = 11.4). The BTA procedure revealed three behavioral themes of hostility, criminality, and pseudo-intimacy, consistent with previous CSA research on stranger rape. The construct validity of the three themes was demonstrated through correlational analyses with known sexual offending measures and criminal histories. The themes of hostility and pseudo-intimacy were significant predictors of sexual recidivism. In addition, the pseudo-intimacy theme led to a significant increase in the incremental validity of the Static-99 actuarial risk assessment instrument for the prediction of sexual recidivism. The results indicate the potential utility and validity of crime scene behaviors in the applied risk assessment of sexual offenders. © The Author(s) 2015.

Design of Quiet Rotorcraft Approach Trajectories

NASA Technical Reports Server (NTRS)

Padula, Sharon L.; Burley, Casey L.; Boyd, D. Douglas, Jr.; Marcolini, Michael A.

2009-01-01

A optimization procedure for identifying quiet rotorcraft approach trajectories is proposed and demonstrated. The procedure employs a multi-objective genetic algorithm in order to reduce noise and create approach paths that will be acceptable to pilots and passengers. The concept is demonstrated by application to two different helicopters. The optimized paths are compared with one another and to a standard 6-deg approach path. The two demonstration cases validate the optimization procedure but highlight the need for improved noise prediction techniques and for additional rotorcraft acoustic data sets.

Development and Validation of Computational Fluid Dynamics Models for Prediction of Heat Transfer and Thermal Microenvironments of Corals

PubMed Central

Ong, Robert H.; King, Andrew J. C.; Mullins, Benjamin J.; Cooper, Timothy F.; Caley, M. Julian

2012-01-01

We present Computational Fluid Dynamics (CFD) models of the coupled dynamics of water flow, heat transfer and irradiance in and around corals to predict temperatures experienced by corals. These models were validated against controlled laboratory experiments, under constant and transient irradiance, for hemispherical and branching corals. Our CFD models agree very well with experimental studies. A linear relationship between irradiance and coral surface warming was evident in both the simulation and experimental result agreeing with heat transfer theory. However, CFD models for the steady state simulation produced a better fit to the linear relationship than the experimental data, likely due to experimental error in the empirical measurements. The consistency of our modelling results with experimental observations demonstrates the applicability of CFD simulations, such as the models developed here, to coral bleaching studies. A study of the influence of coral skeletal porosity and skeletal bulk density on surface warming was also undertaken, demonstrating boundary layer behaviour, and interstitial flow magnitude and temperature profiles in coral cross sections. Our models compliment recent studies showing systematic changes in these parameters in some coral colonies and have utility in the prediction of coral bleaching. PMID:22701582

Intra-/inter-laboratory validation study on reactive oxygen species assay for chemical photosafety evaluation using two different solar simulators.

PubMed

Onoue, Satomi; Hosoi, Kazuhiro; Toda, Tsuguto; Takagi, Hironori; Osaki, Naoto; Matsumoto, Yasuhiro; Kawakami, Satoru; Wakuri, Shinobu; Iwase, Yumiko; Yamamoto, Toshinobu; Nakamura, Kazuichi; Ohno, Yasuo; Kojima, Hajime

2014-06-01

A previous multi-center validation study demonstrated high transferability and reliability of reactive oxygen species (ROS) assay for photosafety evaluation. The present validation study was undertaken to verify further the applicability of different solar simulators and assay performance. In 7 participating laboratories, 2 standards and 42 coded chemicals, including 23 phototoxins and 19 non-phototoxic drugs/chemicals, were assessed by the ROS assay using two different solar simulators (Atlas Suntest CPS series, 3 labs; and Seric SXL-2500V2, 4 labs). Irradiation conditions could be optimized using quinine and sulisobenzone as positive and negative standards to offer consistent assay outcomes. In both solar simulators, the intra- and inter-day precisions (coefficient of variation; CV) for quinine were found to be below 10%. The inter-laboratory CV for quinine averaged 15.4% (Atlas Suntest CPS) and 13.2% (Seric SXL-2500V2) for singlet oxygen and 17.0% (Atlas Suntest CPS) and 7.1% (Seric SXL-2500V2) for superoxide, suggesting high inter-laboratory reproducibility even though different solar simulators were employed for the ROS assay. In the ROS assay on 42 coded chemicals, some chemicals (ca. 19-29%) were unevaluable because of limited solubility and spectral interference. Although several false positives appeared with positive predictivity of ca. 76-92% (Atlas Suntest CPS) and ca. 75-84% (Seric SXL-2500V2), there were no false negative predictions in both solar simulators. A multi-center validation study on the ROS assay demonstrated satisfactory transferability, accuracy, precision, and predictivity, as well as the availability of other solar simulators. Copyright © 2013 Elsevier Ltd. All rights reserved.

Applicability Analysis of Validation Evidence for Biomedical Computational Models

DOE PAGES

Pathmanathan, Pras; Gray, Richard A.; Romero, Vicente J.; ...

2017-09-07

Computational modeling has the potential to revolutionize medicine the way it transformed engineering. However, despite decades of work, there has only been limited progress to successfully translate modeling research to patient care. One major difficulty which often occurs with biomedical computational models is an inability to perform validation in a setting that closely resembles how the model will be used. For example, for a biomedical model that makes in vivo clinically relevant predictions, direct validation of predictions may be impossible for ethical, technological, or financial reasons. Unavoidable limitations inherent to the validation process lead to challenges in evaluating the credibilitymore » of biomedical model predictions. Therefore, when evaluating biomedical models, it is critical to rigorously assess applicability, that is, the relevance of the computational model, and its validation evidence to the proposed context of use (COU). However, there are no well-established methods for assessing applicability. In this paper, we present a novel framework for performing applicability analysis and demonstrate its use with a medical device computational model. The framework provides a systematic, step-by-step method for breaking down the broad question of applicability into a series of focused questions, which may be addressed using supporting evidence and subject matter expertise. The framework can be used for model justification, model assessment, and validation planning. While motivated by biomedical models, it is relevant to a broad range of disciplines and underlying physics. Finally, the proposed applicability framework could help overcome some of the barriers inherent to validation of, and aid clinical implementation of, biomedical models.« less

Applicability Analysis of Validation Evidence for Biomedical Computational Models

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

Pathmanathan, Pras; Gray, Richard A.; Romero, Vicente J.

Computational modeling has the potential to revolutionize medicine the way it transformed engineering. However, despite decades of work, there has only been limited progress to successfully translate modeling research to patient care. One major difficulty which often occurs with biomedical computational models is an inability to perform validation in a setting that closely resembles how the model will be used. For example, for a biomedical model that makes in vivo clinically relevant predictions, direct validation of predictions may be impossible for ethical, technological, or financial reasons. Unavoidable limitations inherent to the validation process lead to challenges in evaluating the credibilitymore » of biomedical model predictions. Therefore, when evaluating biomedical models, it is critical to rigorously assess applicability, that is, the relevance of the computational model, and its validation evidence to the proposed context of use (COU). However, there are no well-established methods for assessing applicability. In this paper, we present a novel framework for performing applicability analysis and demonstrate its use with a medical device computational model. The framework provides a systematic, step-by-step method for breaking down the broad question of applicability into a series of focused questions, which may be addressed using supporting evidence and subject matter expertise. The framework can be used for model justification, model assessment, and validation planning. While motivated by biomedical models, it is relevant to a broad range of disciplines and underlying physics. Finally, the proposed applicability framework could help overcome some of the barriers inherent to validation of, and aid clinical implementation of, biomedical models.« less

Optical observables in stars with non-stationary atmospheres. [fireballs and cepheid models

NASA Technical Reports Server (NTRS)

Hillendahl, R. W.

1980-01-01

Experience gained by use of Cepheid modeling codes to predict the dimensional and photometric behavior of nuclear fireballs is used as a means of validating various computational techniques used in the Cepheid codes. Predicted results from Cepheid models are compared with observations of the continuum and lines in an effort to demonstrate that the atmospheric phenomena in Cepheids are quite complex but that they can be quantitatively modeled.

The development and validation of the Physical Appearance Comparison Scale-Revised (PACS-R).

PubMed

Schaefer, Lauren M; Thompson, J Kevin

2014-04-01

The Physical Appearance Comparison Scale (PACS; Thompson, Heinberg, & Tantleff, 1991) was revised to assess appearance comparisons relevant to women and men in a wide variety of contexts. The revised scale (Physical Appearance Comparison Scale-Revised, PACS-R) was administered to 1176 college females. In Study 1, exploratory factor analysis and parallel analysis using one half of the sample suggested a single factor structure for the PACS-R. Study 2 utilized the remaining half of the sample to conduct confirmatory factor analysis, item analysis, and to examine the convergent validity of the scale. These analyses resulted in an 11-item measure that demonstrated excellent internal consistency and convergent validity with measures of body satisfaction, eating pathology, sociocultural influences on appearance, and self-esteem. Regression analyses demonstrated the utility of the PACS-R in predicting body satisfaction and eating pathology. Overall, results indicate that the PACS-R is a reliable and valid tool for assessing appearance comparison tendencies in women. Copyright © 2014. Published by Elsevier Ltd.

Validation of the thermal challenge problem using Bayesian Belief Networks.

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

McFarland, John; Swiler, Laura Painton

The thermal challenge problem has been developed at Sandia National Laboratories as a testbed for demonstrating various types of validation approaches and prediction methods. This report discusses one particular methodology to assess the validity of a computational model given experimental data. This methodology is based on Bayesian Belief Networks (BBNs) and can incorporate uncertainty in experimental measurements, in physical quantities, and model uncertainties. The approach uses the prior and posterior distributions of model output to compute a validation metric based on Bayesian hypothesis testing (a Bayes' factor). This report discusses various aspects of the BBN, specifically in the context ofmore » the thermal challenge problem. A BBN is developed for a given set of experimental data in a particular experimental configuration. The development of the BBN and the method for ''solving'' the BBN to develop the posterior distribution of model output through Monte Carlo Markov Chain sampling is discussed in detail. The use of the BBN to compute a Bayes' factor is demonstrated.« less

Recognition of Atypical Symptoms of Acute Myocardial Infarction: Development and Validation of a Risk Scoring System.

PubMed

Li, Polly W C; Yu, Doris S F

Atypical symptom presentation in patients with acute myocardial infarction (AMI) is associated with longer delay in care seeking and poorer prognosis. Symptom recognition in these patients is a challenging task. Our purpose in this risk prediction model development study was to develop and validate a risk scoring system for estimating cumulative risk for atypical AMI presentation. A consecutive sample was recruited for the developmental (n = 300) and validation (n = 97) cohorts. Symptom experience was measured with the validated Chinese version of the Symptoms of Acute Coronary Syndromes Inventory. Potential predictors were identified from the literature. Multivariable logistic regression was performed to identify significant predictors. A risk scoring system was then constructed by assigning weights to each significant predictor according to their b coefficients. Five independent predictors for atypical symptom presentation were older age (≥75 years), female gender, diabetes mellitus, history of AMI, and absence of hyperlipidemia. The Hosmer and Lemeshow test (χ6 = 4.47, P = .62) indicated that this predictive model was adequate to predict the outcome. Acceptable discrimination was demonstrated, with area under the receiver operating characteristic curve as 0.74 (95% confidence interval, 0.67-0.82) (P < .001). The predictive power of this risk scoring system was confirmed in the validation cohort. Atypical AMI presentation is common. A simple risk scoring system developed on the basis of the 5 identified predictors can raise awareness of atypical AMI presentation and promote symptom recognition by estimating the cumulative risk for an individual to present with atypical AMI symptoms.

Comparison of machine-learning algorithms to build a predictive model for detecting undiagnosed diabetes - ELSA-Brasil: accuracy study.

PubMed

Olivera, André Rodrigues; Roesler, Valter; Iochpe, Cirano; Schmidt, Maria Inês; Vigo, Álvaro; Barreto, Sandhi Maria; Duncan, Bruce Bartholow

2017-01-01

Type 2 diabetes is a chronic disease associated with a wide range of serious health complications that have a major impact on overall health. The aims here were to develop and validate predictive models for detecting undiagnosed diabetes using data from the Longitudinal Study of Adult Health (ELSA-Brasil) and to compare the performance of different machine-learning algorithms in this task. Comparison of machine-learning algorithms to develop predictive models using data from ELSA-Brasil. After selecting a subset of 27 candidate variables from the literature, models were built and validated in four sequential steps: (i) parameter tuning with tenfold cross-validation, repeated three times; (ii) automatic variable selection using forward selection, a wrapper strategy with four different machine-learning algorithms and tenfold cross-validation (repeated three times), to evaluate each subset of variables; (iii) error estimation of model parameters with tenfold cross-validation, repeated ten times; and (iv) generalization testing on an independent dataset. The models were created with the following machine-learning algorithms: logistic regression, artificial neural network, naïve Bayes, K-nearest neighbor and random forest. The best models were created using artificial neural networks and logistic regression. -These achieved mean areas under the curve of, respectively, 75.24% and 74.98% in the error estimation step and 74.17% and 74.41% in the generalization testing step. Most of the predictive models produced similar results, and demonstrated the feasibility of identifying individuals with highest probability of having undiagnosed diabetes, through easily-obtained clinical data.

Uncertainty aggregation and reduction in structure-material performance prediction

NASA Astrophysics Data System (ADS)

Hu, Zhen; Mahadevan, Sankaran; Ao, Dan

2018-02-01

An uncertainty aggregation and reduction framework is presented for structure-material performance prediction. Different types of uncertainty sources, structural analysis model, and material performance prediction model are connected through a Bayesian network for systematic uncertainty aggregation analysis. To reduce the uncertainty in the computational structure-material performance prediction model, Bayesian updating using experimental observation data is investigated based on the Bayesian network. It is observed that the Bayesian updating results will have large error if the model cannot accurately represent the actual physics, and that this error will be propagated to the predicted performance distribution. To address this issue, this paper proposes a novel uncertainty reduction method by integrating Bayesian calibration with model validation adaptively. The observation domain of the quantity of interest is first discretized into multiple segments. An adaptive algorithm is then developed to perform model validation and Bayesian updating over these observation segments sequentially. Only information from observation segments where the model prediction is highly reliable is used for Bayesian updating; this is found to increase the effectiveness and efficiency of uncertainty reduction. A composite rotorcraft hub component fatigue life prediction model, which combines a finite element structural analysis model and a material damage model, is used to demonstrate the proposed method.

Choice Defines Value: A Predictive Modeling Competition in Health Preference Research.

PubMed

Jakubczyk, Michał; Craig, Benjamin M; Barra, Mathias; Groothuis-Oudshoorn, Catharina G M; Hartman, John D; Huynh, Elisabeth; Ramos-Goñi, Juan M; Stolk, Elly A; Rand, Kim

2018-02-01

To identify which specifications and approaches to model selection better predict health preferences, the International Academy of Health Preference Research (IAHPR) hosted a predictive modeling competition including 18 teams from around the world. In April 2016, an exploratory survey was fielded: 4074 US respondents completed 20 out of 1560 paired comparisons by choosing between two health descriptions (e.g., longer life span vs. better health). The exploratory data were distributed to all teams. By July, eight teams had submitted their predictions for 1600 additional pairs and described their analytical approach. After these predictions had been posted online, a confirmatory survey was fielded (4148 additional respondents). The victorious team, "Discreetly Charming Econometricians," led by Michał Jakubczyk, achieved the smallest χ 2 , 4391.54 (a predefined criterion). Its primary scientific findings were that different models performed better with different pairs, that the value of life span is not constant proportional, and that logit models have poor predictive validity in health valuation. The results demonstrated the diversity and potential of new analytical approaches in health preference research and highlighted the importance of predictive validity in health valuation. Copyright © 2018 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

Differences in SEM-AVS and ERM-ERL predictions of sediment impacts from metals in two US Virgin Islands marinas.

PubMed

Hinkey, Lynne M; Zaidi, Baqar R

2007-02-01

Two US Virgin Islands marinas were examined for potential metal impacts by comparing sediment chemistry data with two sediment quality guideline (SQG) values: the ratio of simultaneously extractable metals to acid volatile sulfides (SEM-AVS), and effects range-low and -mean (ERL-ERM) values. ERL-ERMs predicted the marina/boatyard complex (IBY: 2118 microg/g dry weight total metals, two exceeded ERMs) would have greater impacts than the marina with no boatyard (CBM: 231 microg/g dry weight total metals, no ERMs exceeded). The AVS-SEM method predicted IBY would have fewer effects due to high AVS-forming metal sulfide complexes, reducing trace metal bioavailability. These contradictory predictions demonstrate the importance of validating the results of either of these methods with other toxicity measures before making any management or regulatory decisions regarding boating and marina impacts. This is especially important in non-temperate areas where sediment quality guidelines have not been validated.

Derivation and validation of simple anthropometric equations to predict adipose tissue mass and total fat mass with MRI as the reference method

PubMed Central

Al-Gindan, Yasmin Y.; Hankey, Catherine R.; Govan, Lindsay; Gallagher, Dympna; Heymsfield, Steven B.; Lean, Michael E. J.

2017-01-01

The reference organ-level body composition measurement method is MRI. Practical estimations of total adipose tissue mass (TATM), total adipose tissue fat mass (TATFM) and total body fat are valuable for epidemiology, but validated prediction equations based on MRI are not currently available. We aimed to derive and validate new anthropometric equations to estimate MRI-measured TATM/TATFM/total body fat and compare them with existing prediction equations using older methods. The derivation sample included 416 participants (222 women), aged between 18 and 88 years with BMI between 15·9 and 40·8 (kg/m2). The validation sample included 204 participants (110 women), aged between 18 and 86 years with BMI between 15·7 and 36·4 (kg/m2). Both samples included mixed ethnic/racial groups. All the participants underwent whole-body MRI to quantify TATM (dependent variable) and anthropometry (independent variables). Prediction equations developed using stepwise multiple regression were further investigated for agreement and bias before validation in separate data sets. Simplest equations with optimal R2 and Bland–Altman plots demonstrated good agreement without bias in the validation analyses: men: TATM (kg) = 0·198 weight (kg) + 0·478 waist (cm) − 0·147 height (cm) − 12·8 (validation: R2 0·79, CV = 20 %, standard error of the estimate (SEE)=3·8 kg) and women: TATM (kg)=0·789 weight (kg) + 0·0786 age (years) − 0·342 height (cm) + 24·5 (validation: R2 0·84, CV = 13 %, SEE = 3·0 kg). Published anthropometric prediction equations, based on MRI and computed tomographic scans, correlated strongly with MRI-measured TATM: (R2 0·70 – 0·82). Estimated TATFM correlated well with published prediction equations for total body fat based on underwater weighing (R2 0·70–0·80), with mean bias of 2·5–4·9 kg, correctable with log-transformation in most equations. In conclusion, new equations, using simple anthropometric measurements, estimated MRI-measured TATM with correlations and agreements suitable for use in groups and populations across a wide range of fatness. PMID:26435103

Measuring Work Functioning: Validity of a Weighted Composite Work Functioning Approach.

PubMed

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

2015-09-01

To examine the construct validity of a weighted composite work functioning measurement approach. Workers (health-impaired/healthy) (n = 117) completed a composite measure survey that recorded four central work functioning aspects with existing scales: capacity to work, quality of work performance, quantity of work, and recovery from work. Previous derived weights reflecting the relative importance of these aspects of work functioning were used to calculate the composite weighted work functioning score of the workers. Work role functioning, productivity, and quality of life were used for validation. Correlations were calculated and norms applied to examine convergent and divergent construct validity. A t test was conducted and a norm applied to examine discriminative construct validity. Overall the weighted composite work functioning measure demonstrated construct validity. As predicted, the weighted composite score correlated (p < .001) strongly (r > .60) with work role functioning and productivity (convergent construct validity), and moderately (.30 < r < .60) with physical quality of life and less strongly than work role functioning and productivity with mental quality of life (divergent validity). Further, the weighted composite measure detected that health-impaired workers show with a large effect size (Cohen's d > .80) significantly worse work functioning than healthy workers (discriminative validity). The weighted composite work functioning measurement approach takes into account the relative importance of the different work functioning aspects and demonstrated good convergent, fair divergent, and good discriminative construct validity.

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

Evidence for the Continuous Latent Structure of Mania in the Epidemiologic Catchment Area from Multiple Latent Structure and Construct Validation Methodologies

PubMed Central

Prisciandaro, James J.; Roberts, John E.

2011-01-01

Background Although psychiatric diagnostic systems have conceptualized mania as a discrete phenomenon, appropriate latent structure investigations testing this conceptualization are lacking. In contrast to these diagnostic systems, several influential theories of mania have suggested a continuous conceptualization. The present study examined whether mania has a continuous or discrete latent structure using a comprehensive approach including taxometric, information-theoretic latent distribution modeling (ITLDM), and predictive validity methodologies in the Epidemiologic Catchment Area (ECA) study. Methods Eight dichotomous manic symptom items were submitted to a variety of latent structural analyses; including factor analyses, taxometric procedures, and ITLDM; in 10,105 ECA community participants. Additionally, a variety of continuous and discrete models of mania were compared in terms of their relative abilities to predict outcomes (i.e., health service utilization, internalizing and externalizing disorders, and suicidal behavior). Results Taxometric and ITLDM analyses consistently supported a continuous conceptualization of mania. In ITLDM analyses, a continuous model of mania demonstrated 6:52:1 odds over the best fitting latent class model of mania. Factor analyses suggested that the continuous structure of mania was best represented by a single latent factor. Predictive validity analyses demonstrated a consistent superior ability of continuous models of mania relative to discrete models. Conclusions The present study provided three independent lines of support for a continuous conceptualization of mania. The implications of a continuous model of mania are discussed. PMID:20507671

Initial Validation of a Comprehensive Assessment Instrument for Bereavement-Related Grief Symptoms and Risk of Complications: The Indicator of Bereavement Adaptation—Cruse Scotland (IBACS)

PubMed Central

Schut, Henk; Stroebe, Margaret S.; Wilson, Stewart; Birrell, John

2016-01-01

Objective This study assessed the validity of the Indicator of Bereavement Adaptation Cruse Scotland (IBACS). Designed for use in clinical and non-clinical settings, the IBACS measures severity of grief symptoms and risk of developing complications. Method N = 196 (44 male, 152 female) help-seeking, bereaved Scottish adults participated at two timepoints: T1 (baseline) and T2 (after 18 months). Four validated assessment instruments were administered: CORE-R, ICG-R, IES-R, SCL-90-R. Discriminative ability was assessed using ROC curve analysis. Concurrent validity was tested through correlation analysis at T1. Predictive validity was assessed using correlation analyses and ROC curve analysis. Optimal IBACS cutoff values were obtained by calculating a maximal Youden index J in ROC curve analysis. Clinical implications were compared across instruments. Results ROC curve analysis results (AUC = .84, p < .01, 95% CI between .77 and .90) indicated the IBACS is a good diagnostic instrument for assessing complicated grief. Positive correlations (p < .01, 2-tailed) with all four instruments at T1 demonstrated the IBACS' concurrent validity, strongest with complicated grief measures (r = .82). Predictive validity was shown to be fair in T2 ROC curve analysis results (n = 67, AUC = .78, 95% CI between .65 and .92; p < .01). Predictive validity was also supported by stable positive correlations between IBACS and other instruments at T2. Clinical indications were found not to differ across instruments. Conclusions The IBACS offers effective grief symptom and risk assessment for use by non-clinicians. Indications are sufficient to support intake assessment for a stepped model of bereavement intervention. PMID:27741246

Validation of a new measure of availability and accommodation of health care that is valid for rural and urban contexts.

PubMed

Haggerty, Jeannie L; Levesque, Jean-Frédéric

2017-04-01

Patients are the most valid source for evaluating the accessibility of services, but a previous study observed differential psychometric performance of instruments in rural and urban respondents. To validate a measure of organizational accessibility free of differential rural-urban performance that predicts consequences of difficult access for patient-initiated care. Sequential qualitative-quantitative study. Qualitative findings used to adapt or develop evaluative and reporting items. Quantitative validation study. Primary data by telephone from 750 urban, rural and remote respondents in Quebec, Canada; follow-up mailed questionnaire to a subset of 316. Items were developed for barriers along the care trajectory. We used common factor and confirmatory factor analysis to identify constructs and compare models. We used item response theory analysis to test for differential rural-urban performance; examine individual item performance; adjust response options; and exclude redundant or non-discriminatory items. We used logistic regression to examine predictive validity of the subscale on access difficulty (outcome). Initial factor resolution suggested geographic and organizational dimensions, plus consequences of access difficulty. After second administration, organizational accommodation and geographic indicators were integrated into a 6-item subscale of Effective Availability and Accommodation, which demonstrates good variability and internal consistency (α = 0.84) and no differential functioning by geographic area. Each unit increase predicts decreased likelihood of consequences of access difficulties (unmet need and problem aggravation). The new subscale is a practical, valid and reliable measure for patients to evaluate first-contact health services accessibility, yielding valid comparisons between urban and rural contexts. © 2016 The Authors. Health Expectations published by John Wiley & Sons Ltd.

Development and validation of a cancer-specific swallowing assessment tool: MASA-C.

PubMed

Carnaby, Giselle D; Crary, Michael A

2014-03-01

We present data from a sample of patients receiving radiotherapy for head/neck cancer to define and measure the validity of a new clinical assessment measure for swallowing. Fifty-eight patients undergoing radiotherapy (±chemotherapy) for head/neck cancer (HNC) supported the development of a physiology-based assessment tool of swallowing (Mann Assessment of Swallowing Ability--Cancer: MASA-C) administered at two time points (baseline and following radiotherapy treatment). The new exam was evaluated for internal consistency of items using Cronbach's alpha. Reliability of measurement was evaluated with intraclass correlation (ICC) and the Kappa statistic between two independent raters. Concurrent validity was established through comparison with the original MASA examination and against the referent standard videofluoroscopic swallowing examination (VFE). Sensitivity, specificity, and likelihood ratios along with 95 % confidence intervals (CIs) were derived for comparison of the two evaluation forms (MASA vs. MASA-C). Accuracy of diagnostic precision was displayed using receiver operator characteristic curves. The new MASA-C tool demonstrated superior validity to the original MASA examination applied to a HNC population. In comparison to the VFE referent exam, the MASA-C revealed strong sensitivity and specificity (Se 83, Sp 96), predictive values (positive predictive value (PPV) 0.95, negative predictive value (NPV) 0.86), and likelihood ratios (21.6). In addition, it demonstrated good reliability (ICC = 0.96) between speech-language pathology raters. The MASA-C is a reliable and valid scale that is sensitive to differences in swallowing performance in HNC patients with and without dysphagia. Future longitudinal evaluation of this tool in larger samples is suggested. The development and refinement of this swallowing assessment tool for use in multidisciplinary HNC teams will facilitate earlier identification of patients with swallowing difficulties and enable more efficient allocation of resources to the management of dysphagia in this population. The MASA-C may also prove useful in future clinical HNC rehabilitation trials with this population.

Observed Emotional and Behavioral Indicators of Motivation Predict School Readiness in Head Start Graduates

PubMed Central

Berhenke, Amanda; Miller, Alison L.; Brown, Eleanor; Seifer, Ronald; Dickstein, Susan

2011-01-01

Emotions and behaviors observed during challenging tasks are hypothesized to be valuable indicators of young children's motivation, the assessment of which may be particularly important for children at risk for school failure. The current study demonstrated reliability and concurrent validity of a new observational assessment of motivation in young children. Head Start graduates completed challenging puzzle and trivia tasks during their kindergarten year. Children's emotion expression and task engagement were assessed based on their observed facial and verbal expressions and behavioral cues. Hierarchical regression analyses revealed that observed persistence and shame predicted teacher ratings of children's academic achievement, whereas interest, anxiety, pride, shame, and persistence predicted children's social skills and learning-related behaviors. Children's emotional and behavioral responses to challenge thus appeared to be important indicators of school success. Observation of such responses may be a useful and valid alternative to self-report measures of motivation at this age. PMID:21949599

Comparing measures of attachment: "To whom one turns in times of stress", parental warmth, and partner satisfaction.

PubMed

Lindberg, Marc A; Fugett, April; Thomas, Stuart W

2012-01-01

The Attachment and Clinical Issues Questionnaire (ACIQ; M. A. Lindberg & S. W. Thomas, 2011), was developed over an 18-year period containing 29 scales. The purpose of the present study was to test (a) the validity of the attachment scales in terms of how they predict to whom one turns in times of stress and for affective sharing, and (b) how the attachment scales compared with the Experiences in Close Relationship Questionnaire (ECR) in terms of concurrent, convergent, and discriminant evidence. The relevant secure scales of the ACIQ predicted to whom one turned in study 1, and study 2 demonstrated good convergent evidence with the ECR, but superior concurrent evidence in predicting partner satisfaction, and superior discriminant evidence in differentially correlating with mother and father warmth. Thus, the ACIQ passed essential validity and psychometric tests and was a more robust measure than the ECR with these defining characteristics of attachment.

Noise and diffusion of a vibrated self-propelled granular particle

NASA Astrophysics Data System (ADS)

Walsh, Lee; Wagner, Caleb G.; Schlossberg, Sarah; Olson, Christopher; Baskaran, Aparna; Menon, Narayanan

Granular materials are an important physical realization of active matter. In vibration-fluidized granular matter, both diffusion and self-propulsion derive from the same collisional forcing, unlike many other active systems where there is a clean separation between the origin of single-particle mobility and the coupling to noise. Here we present experimental studies of single-particle motion in a vibrated granular monolayer, along with theoretical analysis that compares grain motion at short and long time scales to the assumptions and predictions, respectively, of the active Brownian particle (ABP) model. The results demonstrate that despite the unique relation between noise and propulsion, granular media do show the generic features predicted by the ABP model and indicate that this is a valid framework to predict collective phenomena. Additionally, our scheme of analysis for validating the inputs and outputs of the model can be applied to other granular and non-granular systems.

Tandem internal models execute motor learning in the cerebellum.

PubMed

Honda, Takeru; Nagao, Soichi; Hashimoto, Yuji; Ishikawa, Kinya; Yokota, Takanori; Mizusawa, Hidehiro; Ito, Masao

2018-06-25

In performing skillful movement, humans use predictions from internal models formed by repetition learning. However, the computational organization of internal models in the brain remains unknown. Here, we demonstrate that a computational architecture employing a tandem configuration of forward and inverse internal models enables efficient motor learning in the cerebellum. The model predicted learning adaptations observed in hand-reaching experiments in humans wearing a prism lens and explained the kinetic components of these behavioral adaptations. The tandem system also predicted a form of subliminal motor learning that was experimentally validated after training intentional misses of hand targets. Patients with cerebellar degeneration disease showed behavioral impairments consistent with tandemly arranged internal models. These findings validate computational tandemization of internal models in motor control and its potential uses in more complex forms of learning and cognition. Copyright © 2018 the Author(s). Published by PNAS.

Psychometric properties and the predictive validity of the insomnia daytime worry scale: a pilot study.

PubMed

Kallestad, Håvard; Hansen, Bjarne; Langsrud, Knut; Hjemdal, Odin; Stiles, Tore C

2010-01-01

The relationship between presleep worry and insomnia has been investigated in previous studies, but less attention has been given to the role of daytime worry and symptoms of insomnia. The aims of the current study were (a) to assess the psychometric properties of a novel scale measuring insomnia-specific worry during daytime and (b) to examine whether levels of daytime worry predict severity of insomnia symptoms. Participants (N = 353) completed the Insomnia Daytime Worry Scale (IDWS) and the Insomnia Severity Index. An explorative principal-axis factor analysis extracted two factors from the IDWS, accounting for 70.5% of the variance. The IDWS demonstrated good reliability. The total score of IDWS and both factors predicted levels of insomnia severity in two separate hierarchical regression analyses. This preliminary evidence suggests that the IDWS is a valid and reliable scale to measure daytime worry in insomnia.

Multi-gene genetic programming based predictive models for municipal solid waste gasification in a fluidized bed gasifier.

PubMed

Pandey, Daya Shankar; Pan, Indranil; Das, Saptarshi; Leahy, James J; Kwapinski, Witold

2015-03-01

A multi-gene genetic programming technique is proposed as a new method to predict syngas yield production and the lower heating value for municipal solid waste gasification in a fluidized bed gasifier. The study shows that the predicted outputs of the municipal solid waste gasification process are in good agreement with the experimental dataset and also generalise well to validation (untrained) data. Published experimental datasets are used for model training and validation purposes. The results show the effectiveness of the genetic programming technique for solving complex nonlinear regression problems. The multi-gene genetic programming are also compared with a single-gene genetic programming model to show the relative merits and demerits of the technique. This study demonstrates that the genetic programming based data-driven modelling strategy can be a good candidate for developing models for other types of fuels as well. Copyright © 2014 Elsevier Ltd. All rights reserved.

Observed Emotional and Behavioral Indicators of Motivation Predict School Readiness in Head Start Graduates.

PubMed

Berhenke, Amanda; Miller, Alison L; Brown, Eleanor; Seifer, Ronald; Dickstein, Susan

2011-01-01

Emotions and behaviors observed during challenging tasks are hypothesized to be valuable indicators of young children's motivation, the assessment of which may be particularly important for children at risk for school failure. The current study demonstrated reliability and concurrent validity of a new observational assessment of motivation in young children. Head Start graduates completed challenging puzzle and trivia tasks during their kindergarten year. Children's emotion expression and task engagement were assessed based on their observed facial and verbal expressions and behavioral cues. Hierarchical regression analyses revealed that observed persistence and shame predicted teacher ratings of children's academic achievement, whereas interest, anxiety, pride, shame, and persistence predicted children's social skills and learning-related behaviors. Children's emotional and behavioral responses to challenge thus appeared to be important indicators of school success. Observation of such responses may be a useful and valid alternative to self-report measures of motivation at this age.

On-Line Robust Modal Stability Prediction using Wavelet Processing

NASA Technical Reports Server (NTRS)

Brenner, Martin J.; Lind, Rick

1998-01-01

Wavelet analysis for filtering and system identification has been used to improve the estimation of aeroservoelastic stability margins. The conservatism of the robust stability margins is reduced with parametric and nonparametric time- frequency analysis of flight data in the model validation process. Nonparametric wavelet processing of data is used to reduce the effects of external disturbances and unmodeled dynamics. Parametric estimates of modal stability are also extracted using the wavelet transform. Computation of robust stability margins for stability boundary prediction depends on uncertainty descriptions derived from the data for model validation. The F-18 High Alpha Research Vehicle aeroservoelastic flight test data demonstrates improved robust stability prediction by extension of the stability boundary beyond the flight regime. Guidelines and computation times are presented to show the efficiency and practical aspects of these procedures for on-line implementation. Feasibility of the method is shown for processing flight data from time- varying nonstationary test points.

Measurement of Family Affective Structure.

ERIC Educational Resources Information Center

Lowman, Joseph

1980-01-01

Three studies demonstrate that the Inventory of Family Feelings, a measure of family affective structure, has high reliability and construct and concurrent validity. It is appropriate for affective comparisons by age, sex, and ordinal position of children and for measuring change after family or marital therapy, or after predictable stress…

Validity of one-repetition maximum predictive equations in men with spinal cord injury.

PubMed

Ribeiro Neto, F; Guanais, P; Dornelas, E; Coutinho, A C B; Costa, R R G

2017-10-01

Cross-sectional study. The study aimed (a) to test the cross-validation of current one-repetition maximum (1RM) predictive equations in men with spinal cord injury (SCI); (b) to compare the current 1RM predictive equations to a newly developed equation based on the 4- to 12-repetition maximum test (4-12RM). SARAH Rehabilitation Hospital Network, Brasilia, Brazil. Forty-five men aged 28.0 years with SCI between C6 and L2 causing complete motor impairment were enrolled in the study. Volunteers were tested, in a random order, in 1RM test or 4-12RM with 2-3 interval days. Multiple regression analysis was used to generate an equation for predicting 1RM. There were no significant differences between 1RM test and the current predictive equations. ICC values were significant and were classified as excellent for all current predictive equations. The predictive equation of Lombardi presented the best Bland-Altman results (0.5 kg and 12.8 kg for mean difference and interval range around the differences, respectively). The two created equation models for 1RM demonstrated the same and a high adjusted R 2 (0.971, P<0.01), but different SEE of measured 1RM (2.88 kg or 5.4% and 2.90 kg or 5.5%). All 1RM predictive equations are accurate to assess individuals with SCI at the bench press exercise. However, the predictive equation of Lombardi presented the best associated cross-validity results. A specific 1RM prediction equation was also elaborated for individuals with SCI. The created equation should be tested in order to verify whether it presents better accuracy than the current ones.

Systematic bias of correlation coefficient may explain negative accuracy of genomic prediction.

PubMed

Zhou, Yao; Vales, M Isabel; Wang, Aoxue; Zhang, Zhiwu

2017-09-01

Accuracy of genomic prediction is commonly calculated as the Pearson correlation coefficient between the predicted and observed phenotypes in the inference population by using cross-validation analysis. More frequently than expected, significant negative accuracies of genomic prediction have been reported in genomic selection studies. These negative values are surprising, given that the minimum value for prediction accuracy should hover around zero when randomly permuted data sets are analyzed. We reviewed the two common approaches for calculating the Pearson correlation and hypothesized that these negative accuracy values reflect potential bias owing to artifacts caused by the mathematical formulas used to calculate prediction accuracy. The first approach, Instant accuracy, calculates correlations for each fold and reports prediction accuracy as the mean of correlations across fold. The other approach, Hold accuracy, predicts all phenotypes in all fold and calculates correlation between the observed and predicted phenotypes at the end of the cross-validation process. Using simulated and real data, we demonstrated that our hypothesis is true. Both approaches are biased downward under certain conditions. The biases become larger when more fold are employed and when the expected accuracy is low. The bias of Instant accuracy can be corrected using a modified formula. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

An empirical study of the predictive validity of number grades in medical school using 3 decades of longitudinal data: implications for a grading system.

PubMed

Gonnella, Joseph S; Erdmann, James B; Hojat, Mohammadreza

2004-04-01

Context It is important to establish the predictive validity of medical school grades. The strength of predictive validity and the ability to identify at-risk students in medical schools depends upon assessment systems such as number grades, pass/fail (P/F) or honours/pass/fail (H/P/F) systems. Objective This study was designed to examine the predictive validity of number grades in medical school, and to determine whether any important information is lost in a shift from number to P/F and H/P/F grading systems. Subjects The participants in this prospective, longitudinal study were 6656 medical students who studied at Jefferson Medical College over 3 decades. They were grouped into 10 deciles based on their number grades in Year 1 of medical school. Methods Participants were compared on academic accomplishments in Years 2 and 3 of medical school, medical school class rank, delayed graduation and attrition, performance on medical licensing examinations and clinical competence ratings in the first postgraduate year. Results Results supported the short- and longterm predictive validity of the number grades. Ratings of clinical competence beyond medical school were predicted by number grades in medical school. We demonstrated that small differences in number grades are statistically meaningful, and that important information for identifying students in need of remedial education is lost when students who narrowly meet faculty's expectations are included with the rest of the class in a broad 'pass' category. Conclusions The findings refute the argument that knowledge of sciences basic to medicine is not critical to subsequent performance in medical school and beyond if an appropriate evaluation system is used. Furthermore, the results of this study raise questions about abandoning number grades in favour of a pass/fail system. Consideration of these findings in policy decisions regarding assessment systems of medical students is recommended.

Finite Element Model of the Knee for Investigation of Injury Mechanisms: Development and Validation

PubMed Central

Kiapour, Ali; Kiapour, Ata M.; Kaul, Vikas; Quatman, Carmen E.; Wordeman, Samuel C.; Hewett, Timothy E.; Demetropoulos, Constantine K.; Goel, Vijay K.

2014-01-01

Multiple computational models have been developed to study knee biomechanics. However, the majority of these models are mainly validated against a limited range of loading conditions and/or do not include sufficient details of the critical anatomical structures within the joint. Due to the multifactorial dynamic nature of knee injuries, anatomic finite element (FE) models validated against multiple factors under a broad range of loading conditions are necessary. This study presents a validated FE model of the lower extremity with an anatomically accurate representation of the knee joint. The model was validated against tibiofemoral kinematics, ligaments strain/force, and articular cartilage pressure data measured directly from static, quasi-static, and dynamic cadaveric experiments. Strong correlations were observed between model predictions and experimental data (r > 0.8 and p < 0.0005 for all comparisons). FE predictions showed low deviations (root-mean-square (RMS) error) from average experimental data under all modes of static and quasi-static loading, falling within 2.5 deg of tibiofemoral rotation, 1% of anterior cruciate ligament (ACL) and medial collateral ligament (MCL) strains, 17 N of ACL load, and 1 mm of tibiofemoral center of pressure. Similarly, the FE model was able to accurately predict tibiofemoral kinematics and ACL and MCL strains during simulated bipedal landings (dynamic loading). In addition to minimal deviation from direct cadaveric measurements, all model predictions fell within 95% confidence intervals of the average experimental data. Agreement between model predictions and experimental data demonstrates the ability of the developed model to predict the kinematics of the human knee joint as well as the complex, nonuniform stress and strain fields that occur in biological soft tissue. Such a model will facilitate the in-depth understanding of a multitude of potential knee injury mechanisms with special emphasis on ACL injury. PMID:24763546

Fatigue Failure of Space Shuttle Main Engine Turbine Blades

NASA Technical Reports Server (NTRS)

Swanson, Gregrory R.; Arakere, Nagaraj K.

2000-01-01

Experimental validation of finite element modeling of single crystal turbine blades is presented. Experimental results from uniaxial high cycle fatigue (HCF) test specimens and full scale Space Shuttle Main Engine test firings with the High Pressure Fuel Turbopump Alternate Turbopump (HPFTP/AT) provide the data used for the validation. The conclusions show the significant contribution of the crystal orientation within the blade on the resulting life of the component, that the analysis can predict this variation, and that experimental testing demonstrates it.

Experimental verification of bremsstrahlung production and dosimetry predictions for 15.5 MeV electrons

NASA Astrophysics Data System (ADS)

Sanford, T. W. L.; Beutler, D. E.; Halbleib, J. A.; Knott, D. P.

1991-12-01

The radiation produced by a 15.5-MeV monoenergetic electron beam incident on optimized and nonoptimized bremsstrahlung targets is characterized using the ITS Monte Carlo code and measurements with equilibrated and nonequilibrated TLD dosimetry. Comparisons between calculations and measurements verify the calculations and demonstrate that the code can be used to predict both bremsstrahlung production and TLD response for radiation fields that are characteristic of those produced by pulsed simulators of gamma rays. The comparisons provide independent confirmation of the validity of the TLD calibration for photon fields characteristic of gamma-ray simulators. The empirical Martin equation, which is often used to calculate radiation dose from optimized bremsstrahlung targets, is examined, and its range of validity is established.

Comparison of Analysis with Test for Static Loading of Two Hypersonic Inflatable Aerodynamic Decelerator Concepts

NASA Technical Reports Server (NTRS)

Lyle, Karen H.

2015-01-01

Acceptance of new spacecraft structural architectures and concepts requires validated design methods to minimize the expense involved with technology demonstration via flight-testing. Hypersonic Inflatable Aerodynamic Decelerator (HIAD) architectures are attractive for spacecraft deceleration because they are lightweight, store compactly, and utilize the atmosphere to decelerate a spacecraft during entry. However, designers are hesitant to include these inflatable approaches for large payloads or spacecraft because of the lack of flight validation. This publication summarizes results comparing analytical results with test data for two concepts subjected to representative entry, static loading. The level of agreement and ability to predict the load distribution is considered sufficient to enable analytical predictions to be used in the design process.

Don't panic: interpretation bias is predictive of new onsets of panic disorder.

PubMed

Woud, Marcella L; Zhang, Xiao Chi; Becker, Eni S; McNally, Richard J; Margraf, Jürgen

2014-01-01

Psychological models of panic disorder postulate that interpretation of ambiguous material as threatening is an important maintaining factor for the disorder. However, demonstrations of whether such a bias predicts onset of panic disorder are missing. In the present study, we used data from the Dresden Prediction Study, in which a epidemiologic sample of young German women was tested at two time points approximately 17 months apart, allowing the study of biased interpretation as a potential risk factor. At time point one, participants completed an Interpretation Questionnaire including two types of ambiguous scenarios: panic-related and general threat-related. Analyses revealed that a panic-related interpretation bias predicted onset of panic disorder, even after controlling for two established risk factors: anxiety sensitivity and fear of bodily sensations. This is the first prospective study demonstrating the incremental validity of interpretation bias as a predictor of panic disorder onset. Copyright © 2013 Elsevier Ltd. All rights reserved.

Cancer-related Concerns of Spouses of Women with Breast Cancer

PubMed Central

Fletcher, Kristin A.; Lewis, Frances Marcus; Haberman, Mel R.

2009-01-01

Objective To describe spouses' reported cancer-related demands attributed to their wife's breast cancer and to test the construct and predictive validity of a brief standardized measure of these demands. Methods Cross-sectional and longitudinal data were obtained from 151 spouses of women newly diagnosed with non-metastatic breast cancer. Descriptive statistics were computed to describe spouses' dominant cancer-related demands and multivariate regression analyses tested the construct and predictive validity of the standardized measure. Results Five categories of spouses' cancer-related demands were identified, such as concerns about: spouses' own functioning; wife's well being and response to treatment; couples' sexual activities; the family's and children's well-being; and the spouses' role in supporting their wives. A 33-item short version of the standardized measure of cancer demands demonstrated construct and predictive validity that was comparable to a 123-item version of the same questionnaire. Greater numbers of illness demands occurred when spouses were more depressed and had less confidence in their ability to manage the impact of the cancer (F=18.08 (3, 103), p<.001). Predictive validity was established by the short form's ability to significantly predict the quality of marital communication and spouses' self-efficacy at a two-month interval. Conclusion The short-version of the standardized measure of cancer-related demands shows promise for future application in clinic settings. Additional testing of the questionnaire is warranted. Spouses' breast cancer-related demands deserve attention by providers. In the absence of assisting them, spouses' illness pressures have deleterious consequences for the quality of marital communication and spouses' self-confidence. PMID:20014184

Validation of risk stratification for children with febrile neutropenia in a pediatric oncology unit in India.

PubMed

Das, Anirban; Trehan, Amita; Oberoi, Sapna; Bansal, Deepak

2017-06-01

The study aims to validate a score predicting risk of complications in pediatric patients with chemotherapy-related febrile neutropenia (FN) and evaluate the performance of previously published models for risk stratification. Children diagnosed with cancer and presenting with FN were evaluated in a prospective single-center study. A score predicting the risk of complications, previously derived in the unit, was validated on a prospective cohort. Performance of six predictive models published from geographically distinct settings was assessed on the same cohort. Complications were observed in 109 (26.3%) of 414 episodes of FN over 15 months. A risk score based on undernutrition (two points), time from last chemotherapy (<7 days = two points), presence of a nonupper respiratory focus of infection (two points), C-reactive protein (>60 mg/l = five points), and absolute neutrophil count (<100 per μl = two points) was used to stratify patients into "low risk" (score <7, n = 208) and assessed using the following parameters: overall performance (Nagelkerke R 2 = 34.4%), calibration (calibration slope = 0.39; P = 0.25 in Hosmer-Lemeshow test), discrimination (c-statistic = 0.81), overall sensitivity (86%), negative predictive value (93%), and clinical net benefit (0.43). Six previously published rules demonstrated inferior performance in this cohort. An indigenous decision rule using five simple predefined variables was successful in identifying children at risk for complications. Prediction models derived in developed nations may not be appropriate for low-middle-income settings and need to be validated before use. © 2016 Wiley Periodicals, Inc.

The validity of DSM-5 severity specifiers for anorexia nervosa, bulimia nervosa, and binge-eating disorder.

PubMed

Smith, Kathryn E; Ellison, Jo M; Crosby, Ross D; Engel, Scott G; Mitchell, James E; Crow, Scott J; Peterson, Carol B; Le Grange, Daniel; Wonderlich, Stephen A

2017-09-01

The DSM-5 includes severity specifiers (i.e., mild, moderate, severe, extreme) for anorexia nervosa (AN), bulimia nervosa (BN), and binge-eating disorder (BED), which are determined by weight status (AN) and frequencies of binge-eating episodes (BED) or inappropriate compensatory behaviors (BN). Given limited data regarding the validity of eating disorder (ED) severity specifiers, this study examined the concurrent and predictive validity of severity specifiers in AN, BN, and BED. Adults with AN (n = 109), BN (n = 76), and BED (n = 216) were identified from previous datasets. Concurrent validity was assessed by measures of ED psychopathology, depression, anxiety, quality of life, and physical health. Predictive validity was assessed by ED symptoms at the end of the treatment in BN and BED. Severity categories did not differ in baseline validators, though the mild AN group evidenced greater ED symptoms compared to the severe group. In BN, greater severity was related to greater end of treatment binge-eating and compensatory behaviors, and lower likelihood of abstinence; however, in BED, greater severity was related to lower ED symptoms at the end of the treatment. Results demonstrated limited support for the validity of DSM-5 severity specifiers. Future research is warranted to explore additional validators and possible alternative indicators of severity in EDs. © 2017 Wiley Periodicals, Inc.

Machine Learning Techniques for Prediction of Early Childhood Obesity.

PubMed

Dugan, T M; Mukhopadhyay, S; Carroll, A; Downs, S

2015-01-01

This paper aims to predict childhood obesity after age two, using only data collected prior to the second birthday by a clinical decision support system called CHICA. Analyses of six different machine learning methods: RandomTree, RandomForest, J48, ID3, Naïve Bayes, and Bayes trained on CHICA data show that an accurate, sensitive model can be created. Of the methods analyzed, the ID3 model trained on the CHICA dataset proved the best overall performance with accuracy of 85% and sensitivity of 89%. Additionally, the ID3 model had a positive predictive value of 84% and a negative predictive value of 88%. The structure of the tree also gives insight into the strongest predictors of future obesity in children. Many of the strongest predictors seen in the ID3 modeling of the CHICA dataset have been independently validated in the literature as correlated with obesity, thereby supporting the validity of the model. This study demonstrated that data from a production clinical decision support system can be used to build an accurate machine learning model to predict obesity in children after age two.

Factor structure, validity and reliability of the Cambridge Worry Scale in a pregnant population.

PubMed

Green, Josephine M; Kafetsios, Konstantinos; Statham, Helen E; Snowdon, Claire M

2003-11-01

This article presents the Cambridge Worry Scale (CWS), a content-based measure for assessing worries, and discusses its psychometric properties based on a longitudinal study of 1,207 pregnant women. Principal components analysis revealed a four-factor structure of women's concerns during pregnancy: socio-medical, own health, socio-economic and relational. The measure demonstrated good reliability and validity. Total CWS scores were strongly associated with state and trait anxiety (convergent validity) but also had significant and unique predictive value for mood outcomes (discriminant validity). The CWS discriminated better between women with different reproductive histories than measures of state and trait anxiety. We conclude that the CWS is a reliable and valid tool for assessing the extent and content of worries in specific situations.

Development and internal validation of a side-specific, multiparametric magnetic resonance imaging-based nomogram for the prediction of extracapsular extension of prostate cancer.

PubMed

Martini, Alberto; Gupta, Akriti; Lewis, Sara C; Cumarasamy, Shivaram; Haines, Kenneth G; Briganti, Alberto; Montorsi, Francesco; Tewari, Ashutosh K

2018-04-19

To develop a nomogram for predicting side-specific extracapsular extension (ECE) for planning nerve-sparing radical prostatectomy. We retrospectively analysed data from 561 patients who underwent robot-assisted radical prostatectomy between February 2014 and October 2015. To develop a side-specific predictive model, we considered the prostatic lobes separately. Four variables were included: prostate-specific antigen; highest ipsilateral biopsy Gleason grade; highest ipsilateral percentage core involvement; and ECE on multiparametric magnetic resonance imaging (mpMRI). A multivariable logistic regression analysis was fitted to predict side-specific ECE. A nomogram was built based on the coefficients of the logit function. Internal validation was performed using 'leave-one-out' cross-validation. Calibration was graphically investigated. The decision curve analysis was used to evaluate the net clinical benefit. The study population consisted of 829 side-specific cases, after excluding negative biopsy observations (n = 293). ECE was reported on mpMRI and final pathology in 115 (14%) and 142 (17.1%) cases, respectively. Among these, mpMRI was able to predict ECE correctly in 57 (40.1%) cases. All variables in the model except highest percentage core involvement were predictors of ECE (all P ≤ 0.006). All variables were considered for inclusion in the nomogram. After internal validation, the area under the curve was 82.11%. The model demonstrated excellent calibration and improved clinical risk prediction, especially when compared with relying on mpMRI prediction of ECE alone. When retrospectively applying the nomogram-derived probability, using a 20% threshold for performing nerve-sparing, nine out of 14 positive surgical margins (PSMs) at the site of ECE resulted above the threshold. We developed an easy-to-use model for the prediction of side-specific ECE, and hope it serves as a tool for planning nerve-sparing radical prostatectomy and in the reduction of PSM in future series. © 2018 The Authors BJU International © 2018 BJU International Published by John Wiley & Sons Ltd.

Analytic Validation of Immunohistochemical Assays: A Comparison of Laboratory Practices Before and After Introduction of an Evidence-Based Guideline.

PubMed

Fitzgibbons, Patrick L; Goldsmith, Jeffrey D; Souers, Rhona J; Fatheree, Lisa A; Volmar, Keith E; Stuart, Lauren N; Nowak, Jan A; Astles, J Rex; Nakhleh, Raouf E

2017-09-01

- Laboratories must demonstrate analytic validity before any test can be used clinically, but studies have shown inconsistent practices in immunohistochemical assay validation. - To assess changes in immunohistochemistry analytic validation practices after publication of an evidence-based laboratory practice guideline. - A survey on current immunohistochemistry assay validation practices and on the awareness and adoption of a recently published guideline was sent to subscribers enrolled in one of 3 relevant College of American Pathologists proficiency testing programs and to additional nonsubscribing laboratories that perform immunohistochemical testing. The results were compared with an earlier survey of validation practices. - Analysis was based on responses from 1085 laboratories that perform immunohistochemical staining. Of 1057 responses, 65.4% (691) were aware of the guideline recommendations before this survey was sent and 79.9% (550 of 688) of those have already adopted some or all of the recommendations. Compared with the 2010 survey, a significant number of laboratories now have written validation procedures for both predictive and nonpredictive marker assays and specifications for the minimum numbers of cases needed for validation. There was also significant improvement in compliance with validation requirements, with 99% (100 of 102) having validated their most recently introduced predictive marker assay, compared with 74.9% (326 of 435) in 2010. The difficulty in finding validation cases for rare antigens and resource limitations were cited as the biggest challenges in implementing the guideline. - Dissemination of the 2014 evidence-based guideline validation practices had a positive impact on laboratory performance; some or all of the recommendations have been adopted by nearly 80% of respondents.

The ABC’s of Suicide Risk Assessment: Applying a Tripartite Approach to Individual Evaluations

PubMed Central

Harris, Keith M.; Syu, Jia-Jia; Lello, Owen D.; Chew, Y. L. Eileen; Willcox, Christopher H.; Ho, Roger H. M.

2015-01-01

There is considerable need for accurate suicide risk assessment for clinical, screening, and research purposes. This study applied the tripartite affect-behavior-cognition theory, the suicidal barometer model, classical test theory, and item response theory (IRT), to develop a brief self-report measure of suicide risk that is theoretically-grounded, reliable and valid. An initial survey (n = 359) employed an iterative process to an item pool, resulting in the six-item Suicidal Affect-Behavior-Cognition Scale (SABCS). Three additional studies tested the SABCS and a highly endorsed comparison measure. Studies included two online surveys (Ns = 1007, and 713), and one prospective clinical survey (n = 72; Time 2, n = 54). Factor analyses demonstrated SABCS construct validity through unidimensionality. Internal reliability was high (α = .86-.93, split-half = .90-.94)). The scale was predictive of future suicidal behaviors and suicidality (r = .68, .73, respectively), showed convergent validity, and the SABCS-4 demonstrated clinically relevant sensitivity to change. IRT analyses revealed the SABCS captured more information than the comparison measure, and better defined participants at low, moderate, and high risk. The SABCS is the first suicide risk measure to demonstrate no differential item functioning by sex, age, or ethnicity. In all comparisons, the SABCS showed incremental improvements over a highly endorsed scale through stronger predictive ability, reliability, and other properties. The SABCS is in the public domain, with this publication, and is suitable for clinical evaluations, public screening, and research. PMID:26030590

Measuring attitudes towards suicide: Preliminary evaluation of an attitude towards suicide scale.

PubMed

Cwik, Jan Christopher; Till, Benedikt; Bieda, Angela; Blackwell, Simon E; Walter, Carolin; Teismann, Tobias

2017-01-01

Our study aimed to validate a previously published scale assessing attitudes towards suicide. Factor structure, convergent and discriminant validity, and predictive validity were investigated. Adult German participants (N=503; mean age=24.74years; age range=18-67years) anonymously completed a set of questionnaires. An exploratory factor analysis was conducted, and incongruous items were deleted. Subsequently, scale properties of the reduced scale and its construct validity were analyzed. A confirmatory factor analysis was then conducted in an independent sample (N=266; mean age=28.77years; age range=18-88years) to further confirm the factor structure of the questionnaire. Parallel analysis indicated a three-factor solution, which was also supported by confirmatory factor analysis: right to commit suicide, interpersonal gesture and resilience. The subscales demonstrated acceptable construct and discriminant validity. Cronbach's α for the subscales ranged from 0.67 to 0.83, explaining 49.70% of the total variance. Positive attitudes towards suicide proved to be predictive of suicide risk status, providing preliminary evidence for the utility of the scale. Future studies aiming to reproduce the factor structure in a more heterogeneous sample are warranted. Copyright © 2016 Elsevier Inc. All rights reserved.

Aeroservoelastic Model Validation and Test Data Analysis of the F/A-18 Active Aeroelastic Wing

NASA Technical Reports Server (NTRS)

Brenner, Martin J.; Prazenica, Richard J.

2003-01-01

Model validation and flight test data analysis require careful consideration of the effects of uncertainty, noise, and nonlinearity. Uncertainty prevails in the data analysis techniques and results in a composite model uncertainty from unmodeled dynamics, assumptions and mechanics of the estimation procedures, noise, and nonlinearity. A fundamental requirement for reliable and robust model development is an attempt to account for each of these sources of error, in particular, for model validation, robust stability prediction, and flight control system development. This paper is concerned with data processing procedures for uncertainty reduction in model validation for stability estimation and nonlinear identification. F/A-18 Active Aeroelastic Wing (AAW) aircraft data is used to demonstrate signal representation effects on uncertain model development, stability estimation, and nonlinear identification. Data is decomposed using adaptive orthonormal best-basis and wavelet-basis signal decompositions for signal denoising into linear and nonlinear identification algorithms. Nonlinear identification from a wavelet-based Volterra kernel procedure is used to extract nonlinear dynamics from aeroelastic responses, and to assist model development and uncertainty reduction for model validation and stability prediction by removing a class of nonlinearity from the uncertainty.

Measurement of COPD Severity Using a Survey-Based Score

PubMed Central

Omachi, Theodore A.; Katz, Patricia P.; Yelin, Edward H.; Iribarren, Carlos; Blanc, Paul D.

2010-01-01

Background: A comprehensive survey-based COPD severity score has usefulness for epidemiologic and health outcomes research. We previously developed and validated the survey-based COPD Severity Score without using lung function or other physiologic measurements. In this study, we aimed to further validate the severity score in a different COPD cohort and using a combination of patient-reported and objective physiologic measurements. Methods: Using data from the Function, Living, Outcomes, and Work cohort study of COPD, we evaluated the concurrent and predictive validity of the COPD Severity Score among 1,202 subjects. The survey instrument is a 35-point score based on symptoms, medication and oxygen use, and prior hospitalization or intubation for COPD. Subjects were systemically assessed using structured telephone survey, spirometry, and 6-min walk testing. Results: We found evidence to support concurrent validity of the score. Higher COPD Severity Score values were associated with poorer FEV1 (r = −0.38), FEV1% predicted (r = −0.40), Body mass, Obstruction, Dyspnea, Exercise Index (r = 0.57), and distance walked in 6 min (r = −0.43) (P < .0001 in all cases). Greater COPD severity was also related to poorer generic physical health status (r = −0.49) and disease-specific health-related quality of life (r = 0.57) (P < .0001). The score also demonstrated predictive validity. It was also associated with a greater prospective risk of acute exacerbation of COPD defined as ED visits (hazard ratio [HR], 1.31; 95% CI, 1.24-1.39), hospitalizations (HR, 1.59; 95% CI, 1.44-1.75), and either measure of hospital-based care for COPD (HR, 1.34; 95% CI, 1.26-1.41) (P < .0001 in all cases). Conclusion: The COPD Severity Score is a valid survey-based measure of disease-specific severity, both in terms of concurrent and predictive validity. The score is a psychometrically sound instrument for use in epidemiologic and outcomes research in COPD. PMID:20040611

A preliminary correlation of the orbiter stability and control aerodynamics from the first two Space Shuttle flights /STS-1 & 2/ with preflight predictions

NASA Technical Reports Server (NTRS)

Underwood, J. M.; Cooke, D. R.

1982-01-01

A correlation of the stability and control derivatives from flight (STS-1 & 2) with preflight predictions is presented across the Mach range from 0.9 to 25. Flight data obtained from specially designed flight test maneuvers as well as from conventional bank maneuvers generally indicate good agreement with predicted data. However, the vehicle appears to be lateral-directionally more stable than predicted in the transonic regime. Aerodynamic 'reasonableness tests' are employed to test for validity of flight data. The importance of testing multiple models in multiple wind tunnels at the same test conditions is demonstrated.

Systematically evaluating read-across prediction and performance using a local validity approach characterized by chemical structure and bioactivity information

EPA Science Inventory

Read-across is a popular data gap filling technique within category and analogue approaches for regulatory purposes. Acceptance of read-across remains an ongoing challenge with several efforts underway for identifying and addressing uncertainties. Here we demonstrate an algorithm...

USE OF PHARMACOKINETIC MODELING TO DESIGN STUDIES FOR PATHWAY-SPECIFIC EXPOSURE MODEL EVALUATION

EPA Science Inventory

Validating an exposure pathway model is difficult because the biomarker, which is often used to evaluate the model prediction, is an integrated measure for exposures from all the exposure routes/pathways. The purpose of this paper is to demonstrate a method to use pharmacokeneti...

The Clinical Utility of Personality Subtypes in Patients with Anorexia Nervosa

ERIC Educational Resources Information Center

Wildes, Jennifer E.; Marcus, Marsha D.; Crosby, Ross D.; Ringham, Rebecca M.; Dapelo, Marcela Marin; Gaskill, Jill A.; Forbush, Kelsie T.

2011-01-01

Objective: Elucidation of clinically relevant subtypes has been proposed as a means of advancing treatment research, but classifying anorexia nervosa (AN) patients into restricting and binge-eating/purging types has demonstrated limited predictive validity. This study aimed to evaluate whether an approach to classifying eating disorder patients on…

Differential Lung Toxicity of Biomass Smoke from Smoldering and Flaming Phases Following Acute Inhalation Exposure

EPA Science Inventory

We previously demonstrated that, on a mass basis, lung toxicity associated with particulate matter (PM) from flaming smoke aspirated into mouse lungs is greater than smoldering PM. This finding however has to be validated in inhalation studies to better predict real-world exposu...

A predictive bone drilling force model for haptic rendering with experimental validation using fresh cadaveric bone.

PubMed

Lin, Yanping; Chen, Huajiang; Yu, Dedong; Zhang, Ying; Yuan, Wen

2017-01-01

Bone drilling simulators with virtual and haptic feedback provide a safe, cost-effective and repeatable alternative to traditional surgical training methods. To develop such a simulator, accurate haptic rendering based on a force model is required to feedback bone drilling forces based on user input. Current predictive bone drilling force models based on bovine bones with various drilling conditions and parameters are not representative of the bone drilling process in bone surgery. The objective of this study was to provide a bone drilling force model for haptic rendering based on calibration and validation experiments in fresh cadaveric bones with different bone densities. Using a commonly used drill bit geometry (2 mm diameter), feed rates (20-60 mm/min) and spindle speeds (4000-6000 rpm) in orthognathic surgeries, the bone drilling forces of specimens from two groups were measured and the calibration coefficients of the specific normal and frictional pressures were determined. The comparison of the predicted forces and the measured forces from validation experiments with a large range of feed rates and spindle speeds demonstrates that the proposed bone drilling forces can predict the trends and average forces well. The presented bone drilling force model can be used for haptic rendering in surgical simulators.

Developing and Validating a Survival Prediction Model for NSCLC Patients Through Distributed Learning Across 3 Countries.

PubMed

Jochems, Arthur; Deist, Timo M; El Naqa, Issam; Kessler, Marc; Mayo, Chuck; Reeves, Jackson; Jolly, Shruti; Matuszak, Martha; Ten Haken, Randall; van Soest, Johan; Oberije, Cary; Faivre-Finn, Corinne; Price, Gareth; de Ruysscher, Dirk; Lambin, Philippe; Dekker, Andre

2017-10-01

Tools for survival prediction for non-small cell lung cancer (NSCLC) patients treated with chemoradiation or radiation therapy are of limited quality. In this work, we developed a predictive model of survival at 2 years. The model is based on a large volume of historical patient data and serves as a proof of concept to demonstrate the distributed learning approach. Clinical data from 698 lung cancer patients, treated with curative intent with chemoradiation or radiation therapy alone, were collected and stored at 2 different cancer institutes (559 patients at Maastro clinic (Netherlands) and 139 at Michigan university [United States]). The model was further validated on 196 patients originating from The Christie (United Kingdon). A Bayesian network model was adapted for distributed learning (the animation can be viewed at https://www.youtube.com/watch?v=ZDJFOxpwqEA). Two-year posttreatment survival was chosen as the endpoint. The Maastro clinic cohort data are publicly available at https://www.cancerdata.org/publication/developing-and-validating-survival-prediction-model-nsclc-patients-through-distributed, and the developed models can be found at www.predictcancer.org. Variables included in the final model were T and N category, age, performance status, and total tumor dose. The model has an area under the curve (AUC) of 0.66 on the external validation set and an AUC of 0.62 on a 5-fold cross validation. A model based on the T and N category performed with an AUC of 0.47 on the validation set, significantly worse than our model (P<.001). Learning the model in a centralized or distributed fashion yields a minor difference on the probabilities of the conditional probability tables (0.6%); the discriminative performance of the models on the validation set is similar (P=.26). Distributed learning from federated databases allows learning of predictive models on data originating from multiple institutions while avoiding many of the data-sharing barriers. We believe that distributed learning is the future of sharing data in health care. Copyright © 2017 The Author(s). Published by Elsevier Inc. All rights reserved.

Critical validation studies of neurofeedback.

PubMed

Gruzelier, John; Egner, Tobias

2005-01-01

The field of neurofeedback training has proceeded largely without validation. In this article the authors review studies directed at validating sensory motor rhythm, beta and alpha-theta protocols for improving attention, memory, and music performance in healthy participants. Importantly, benefits were demonstrable with cognitive and neurophysiologic measures that were predicted on the basis of regression models of learning to enhance sensory motor rhythm and beta activity. The first evidence of operant control over the alpha-theta ratio is provided, together with remarkable improvements in artistic aspects of music performance equivalent to two class grades in conservatory students. These are initial steps in providing a much needed scientific basis to neurofeedback.

A contextual approach to social skills assessment in the peer group: who is the best judge?

PubMed

Kwon, Kyongboon; Kim, Elizabeth Moorman; Sheridan, Susan M

2012-09-01

Using a contextual approach to social skills assessment in the peer group, this study examined the criterion-related validity of contextually relevant social skills and the incremental validity of peers and teachers as judges of children's social skills. Study participants included 342 (180 male and 162 female) students and their classroom teachers (N = 22) from rural communities. As expected, contextually relevant social skills were significantly related to a variety of social status indicators (i.e., likability, peer- and teacher-assessed popularity, reciprocated friendships, clique centrality) and positive school functioning (i.e., school liking and academic competence). Peer-assessed social skills, not teacher-assessed social skills, demonstrated consistent incremental validity in predicting various indicators of social status outcomes; peer- and teacher-assessed social skills alike showed incremental validity in predicting positive school functioning. The relation between contextually relevant social skills and study outcomes did not vary by child gender. Findings are discussed in terms of the significance of peers in the assessment of children's social skills in the peer group as well as the usefulness of a contextual approach to social skills assessment.

The preliminary analysis of the reliability and validity of the Chinese Edition of the CSBS DP.

PubMed

Lin, Chu-Sui; Chang, Shu-Hui; Cheng, Shu-Fen; Chao, Pen-Chiang; Chiu, Chun-Hao

2015-03-01

This study marked a preliminary attempt to standardize the Chinese Edition of the Communication and Symbolic Behavior Scales Developmental Profile (Wetherby & Prizant, 2002; CSBS DP) to assist in the early identification of young children with special needs in Taiwan. The study was conducted among 171 infants and toddlers aged 1-2. It also included a follow-up study one year after the initial test. Three domestically developed standardized child development inventories were used to measure the concurrent validity and predictive validity. The Chinese Edition of the CSBS DP demonstrated overall good test-retest and inter-rater reliability. It also showed good concurrent and predictive validity. The current study yields preliminary evidence that the Chinese Edition of the CSBS DP could be a valuable assessment tool worthy of wider distribution. Future research should employ random sampling to establish a true national norm. Additionally, the follow-up study needs to include atypical groups and to expand to children aged 6-12 months to strengthen the applicability of the instrument in Taiwan. Copyright © 2014 Elsevier Ltd. All rights reserved.

Validity of a parent vocabulary checklist for young Spanish speaking children of Mexican immigrants.

PubMed

Guiberson, Mark

2008-01-01

The primary objective of the current investigation was to examine the concurrent and predictive validity of a parent vocabulary checklist with young Spanish speaking children of Mexican immigrants. This study implemented a longitudinal approach. Nineteen families participated when children were 15-16 months of age, and then again at 30-32 months of age. The Spanish version of the MacArthur Communicative Development Inventory (Inventarios del Desarrollo de Habilidades Communicativas, INV) and spontaneous language samples collected during naturalistic play were used to examine the relationship between observed and reported vocabulary. Vocabulary reported through the INV-II and vocabulary observed at 30-32 months were significantly correlated, suggesting that the INV-II captures a valid representation of vocabulary at this age. Comparatively, vocabulary reported on the INV-I, was not correlated with observed vocabulary at 15-16 months of age or reported or observed vocabulary at 30-32 months of age. These results suggest that the INV-I, when used with 14-16-month-olds, demonstrates limited concurrent and predictive validity. Implications for the clinical use of the INV-I and INV-II are presented.

Cross-validation of bioelectrical impedance analysis of body composition in children and adolescents.

PubMed

Wu, Y T; Nielsen, D H; Cassady, S L; Cook, J S; Janz, K F; Hansen, J R

1993-05-01

The reliability and validity of measurements obtained with two bioelectrical impedance analyzers (BIAs), an RJL Systems model BIA-103 and a Berkeley Medical Research BMR-2000, were investigated using the manufacturers' prediction equations for the assessment of fat-free mass (FFM) (in kilograms) in children and adolescents. Forty-seven healthy children and adolescents (23 male, 24 female), ranging in age from 8 to 20 years (mean = 12.1, SD = 2.3), participated. In the context of a repeated-measures design, the data were analyzed according to gender and maturation (Tanner staging). Hydrostatic weighing (HYDRO) and Lohman's Siri age-adjusted body density prediction equation served as the criteria for validating the BIA-obtained measurements. High intraclass correlation coefficients (ICC > or = .987) demonstrated good test-retest (between-week) measurement reliability for HYDRO and both BIA methods. Between-method (HYDRO versus BIA) correlation coefficients were high for both boys and girls (r > or = .97). The standard errors of estimate (SEEs) for FFM were slightly larger for boys than for girls and were consistently smaller for the RJL system than for the BMR system (RJL SEE = 1.8 kg for boys, 1.3 kg for girls; BMR SEE = 2.4 kg for boys, 1.9 kg for girls). The coefficients of determination were high for both BIA methods (r2 > or = .929). Total prediction errors (TEs) for FFM showed similar between-method trends (RJL TE = 2.1 kg for boys, 1.5 kg for girls; BMR TE = 4.4 kg for boys, 1.9 kg for girls). This study demonstrated that the RJL BIA with the manufacturer's prediction equations can be used to reliably and accurately assess FFM in 8- to 20-year-old children and adolescents. The prediction of FFM by the BMR system was acceptable for girls, but significant overprediction of FFM for boys was noted.

Objective validation of central sensitization in the rat UVB and heat rekindling model

PubMed Central

Weerasinghe, NS; Lumb, BM; Apps, R; Koutsikou, S; Murrell, JC

2014-01-01

Background The UVB and heat rekindling (UVB/HR) model shows potential as a translatable inflammatory pain model. However, the occurrence of central sensitization in this model, a fundamental mechanism underlying chronic pain, has been debated. Face, construct and predictive validity are key requisites of animal models; electromyogram (EMG) recordings were utilized to objectively demonstrate validity of the rat UVB/HR model. Methods The UVB/HR model was induced on the heel of the hind paw under anaesthesia. Mechanical withdrawal thresholds (MWTs) were obtained from biceps femoris EMG responses to a gradually increasing pinch at the mid hind paw region under alfaxalone anaesthesia, 96 h after UVB irradiation. MWT was compared between UVB/HR and SHAM-treated rats (anaesthetic only). Underlying central mechanisms in the model were pharmacologically validated by MWT measurement following intrathecal N-methyl-d-aspartate (NMDA) receptor antagonist, MK-801, or saline. Results Secondary hyperalgesia was confirmed by a significantly lower pre-drug MWT {mean [±standard error of the mean (SEM)]} in UVB/HR [56.3 (±2.1) g/mm2, n = 15] compared with SHAM-treated rats [69.3 (±2.9) g/mm2, n = 8], confirming face validity of the model. Predictive validity was demonstrated by the attenuation of secondary hyperalgesia by MK-801, where mean (±SEM) MWT was significantly higher [77.2 (±5.9) g/mm2 n = 7] in comparison with pre-drug [57.8 (±3.5) g/mm2 n = 7] and saline [57.0 (±3.2) g/mm2 n = 8] at peak drug effect. The occurrence of central sensitization confirmed construct validity of the UVB/HR model. Conclusions This study used objective outcome measures of secondary hyperalgesia to validate the rat UVB/HR model as a translational model of inflammatory pain. What's already known about this topic? Most current animal chronic pain models lack translatability to human subjects. Primary hyperalgesia is an established feature of the UVB/heat rekindling inflammatory pain model in rodents and humans, but the presence of secondary hyperalgesia, a hallmark feature of central sensitization and thus chronic pain, is contentious. What does this study add? Secondary hyperalgesia was demonstrated in the rat UVB/heat rekindling model using an objective outcome measure (electromyogram), overcoming the subjective limitations of previous behavioural studies. PMID:24590815

Development and validation of a prediction model for functional decline in older medical inpatients.

PubMed

Takada, Toshihiko; Fukuma, Shingo; Yamamoto, Yosuke; Tsugihashi, Yukio; Nagano, Hiroyuki; Hayashi, Michio; Miyashita, Jun; Azuma, Teruhisa; Fukuhara, Shunichi

2018-05-17

To prevent functional decline in older inpatients, identification of high-risk patients is crucial. The aim of this study was to develop and validate a prediction model to assess the risk of functional decline in older medical inpatients. In this retrospective cohort study, patients ≥65 years admitted acutely to medical wards were included. The healthcare database of 246 acute care hospitals (n = 229,913) was used for derivation, and two acute care hospitals (n = 1767 and 5443, respectively) were used for validation. Data were collected using a national administrative claims and discharge database. Functional decline was defined as a decline of the Katz score at discharge compared with on admission. About 6% of patients in the derivation cohort and 9% and 2% in each validation cohort developed functional decline. A model with 7 items, age, body mass index, living in a nursing home, ambulance use, need for assistance in walking, dementia, and bedsore, was developed. On internal validation, it demonstrated a c-statistic of 0.77 (95% confidence interval (CI) = 0.767-0.771) and good fit on the calibration plot. On external validation, the c-statistics were 0.79 (95% CI = 0.77-0.81) and 0.75 (95% CI = 0.73-0.77) for each cohort, respectively. Calibration plots showed good fit in one cohort and overestimation in the other one. A prediction model for functional decline in older medical inpatients was derived and validated. It is expected that use of the model would lead to early identification of high-risk patients and introducing early intervention. Copyright © 2018 Elsevier B.V. All rights reserved.

On Nomological Validity and Auxiliary Assumptions: The Importance of Simultaneously Testing Effects in Social Cognitive Theories Applied to Health Behavior and Some Guidelines

PubMed Central

Hagger, Martin S.; Gucciardi, Daniel F.; Chatzisarantis, Nikos L. D.

2017-01-01

Tests of social cognitive theories provide informative data on the factors that relate to health behavior, and the processes and mechanisms involved. In the present article, we contend that tests of social cognitive theories should adhere to the principles of nomological validity, defined as the degree to which predictions in a formal theoretical network are confirmed. We highlight the importance of nomological validity tests to ensure theory predictions can be disconfirmed through observation. We argue that researchers should be explicit on the conditions that lead to theory disconfirmation, and identify any auxiliary assumptions on which theory effects may be conditional. We contend that few researchers formally test the nomological validity of theories, or outline conditions that lead to model rejection and the auxiliary assumptions that may explain findings that run counter to hypotheses, raising potential for ‘falsification evasion.’ We present a brief analysis of studies (k = 122) testing four key social cognitive theories in health behavior to illustrate deficiencies in reporting theory tests and evaluations of nomological validity. Our analysis revealed that few articles report explicit statements suggesting that their findings support or reject the hypotheses of the theories tested, even when findings point to rejection. We illustrate the importance of explicit a priori specification of fundamental theory hypotheses and associated auxiliary assumptions, and identification of the conditions which would lead to rejection of theory predictions. We also demonstrate the value of confirmatory analytic techniques, meta-analytic structural equation modeling, and Bayesian analyses in providing robust converging evidence for nomological validity. We provide a set of guidelines for researchers on how to adopt and apply the nomological validity approach to testing health behavior models. PMID:29163307

Blood autoantibody and cytokine profiles predict response to anti-tumor necrosis factor therapy in rheumatoid arthritis

PubMed Central

Hueber, Wolfgang; Tomooka, Beren H; Batliwalla, Franak; Li, Wentian; Monach, Paul A; Tibshirani, Robert J; Van Vollenhoven, Ronald F; Lampa, Jon; Saito, Kazuyoshi; Tanaka, Yoshiya; Genovese, Mark C; Klareskog, Lars; Gregersen, Peter K; Robinson, William H

2009-01-01

Introduction Anti-TNF therapies have revolutionized the treatment of rheumatoid arthritis (RA), a common systemic autoimmune disease involving destruction of the synovial joints. However, in the practice of rheumatology approximately one-third of patients demonstrate no clinical improvement in response to treatment with anti-TNF therapies, while another third demonstrate a partial response, and one-third an excellent and sustained response. Since no clinical or laboratory tests are available to predict response to anti-TNF therapies, great need exists for predictive biomarkers. Methods Here we present a multi-step proteomics approach using arthritis antigen arrays, a multiplex cytokine assay, and conventional ELISA, with the objective to identify a biomarker signature in three ethnically diverse cohorts of RA patients treated with the anti-TNF therapy etanercept. Results We identified a 24-biomarker signature that enabled prediction of a positive clinical response to etanercept in all three cohorts (positive predictive values 58 to 72%; negative predictive values 63 to 78%). Conclusions We identified a multi-parameter protein biomarker that enables pretreatment classification and prediction of etanercept responders, and tested this biomarker using three independent cohorts of RA patients. Although further validation in prospective and larger cohorts is needed, our observations demonstrate that multiplex characterization of autoantibodies and cytokines provides clinical utility for predicting response to the anti-TNF therapy etanercept in RA patients. PMID:19460157

Brazilian validation of the Alberta Infant Motor Scale.

PubMed

Valentini, Nadia Cristina; Saccani, Raquel

2012-03-01

The Alberta Infant Motor Scale (AIMS) is a well-known motor assessment tool used to identify potential delays in infants' motor development. Although Brazilian researchers and practitioners have used the AIMS in laboratories and clinical settings, its translation to Portuguese and validation for the Brazilian population is yet to be investigated. This study aimed to translate and validate all AIMS items with respect to internal consistency and content, criterion, and construct validity. A cross-sectional and longitudinal design was used. A cross-cultural translation was used to generate a Brazilian-Portuguese version of the AIMS. In addition, a validation process was conducted involving 22 professionals and 766 Brazilian infants (aged 0-18 months). The results demonstrated language clarity and internal consistency for the motor criteria (motor development score, α=.90; prone, α=.85; supine, α=.92; sitting, α=.84; and standing, α=.86). The analysis also revealed high discriminative power to identify typical and atypical development (motor development score, P<.001; percentile, P=.04; classification criterion, χ(2)=6.03; P=.05). Temporal stability (P=.07) (rho=.85, P<.001) was observed, and predictive power (P<.001) was limited to the group of infants aged from 3 months to 9 months. Limited predictive validity was observed, which may have been due to the restricted time that the groups were followed longitudinally. In sum, the translated version of AIMS presented adequate validity and reliability.

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

Electric train energy consumption modeling

DOE PAGES

Wang, Jinghui; Rakha, Hesham A.

2017-05-01

For this paper we develop an electric train energy consumption modeling framework considering instantaneous regenerative braking efficiency in support of a rail simulation system. The model is calibrated with data from Portland, Oregon using an unconstrained non-linear optimization procedure, and validated using data from Chicago, Illinois by comparing model predictions against the National Transit Database (NTD) estimates. The results demonstrate that regenerative braking efficiency varies as an exponential function of the deceleration level, rather than an average constant as assumed in previous studies. The model predictions are demonstrated to be consistent with the NTD estimates, producing a predicted error ofmore » 1.87% and -2.31%. The paper demonstrates that energy recovery reduces the overall power consumption by 20% for the tested Chicago route. Furthermore, the paper demonstrates that the proposed modeling approach is able to capture energy consumption differences associated with train, route and operational parameters, and thus is applicable for project-level analysis. The model can be easily implemented in traffic simulation software, used in smartphone applications and eco-transit programs given its fast execution time and easy integration in complex frameworks.« less

Electric train energy consumption modeling

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

Wang, Jinghui; Rakha, Hesham A.

For this paper we develop an electric train energy consumption modeling framework considering instantaneous regenerative braking efficiency in support of a rail simulation system. The model is calibrated with data from Portland, Oregon using an unconstrained non-linear optimization procedure, and validated using data from Chicago, Illinois by comparing model predictions against the National Transit Database (NTD) estimates. The results demonstrate that regenerative braking efficiency varies as an exponential function of the deceleration level, rather than an average constant as assumed in previous studies. The model predictions are demonstrated to be consistent with the NTD estimates, producing a predicted error ofmore » 1.87% and -2.31%. The paper demonstrates that energy recovery reduces the overall power consumption by 20% for the tested Chicago route. Furthermore, the paper demonstrates that the proposed modeling approach is able to capture energy consumption differences associated with train, route and operational parameters, and thus is applicable for project-level analysis. The model can be easily implemented in traffic simulation software, used in smartphone applications and eco-transit programs given its fast execution time and easy integration in complex frameworks.« less

Real-Time Onboard Global Nonlinear Aerodynamic Modeling from Flight Data

NASA Technical Reports Server (NTRS)

Brandon, Jay M.; Morelli, Eugene A.

2014-01-01

Flight test and modeling techniques were developed to accurately identify global nonlinear aerodynamic models onboard an aircraft. The techniques were developed and demonstrated during piloted flight testing of an Aermacchi MB-326M Impala jet aircraft. Advanced piloting techniques and nonlinear modeling techniques based on fuzzy logic and multivariate orthogonal function methods were implemented with efficient onboard calculations and flight operations to achieve real-time maneuver monitoring and analysis, and near-real-time global nonlinear aerodynamic modeling and prediction validation testing in flight. Results demonstrated that global nonlinear aerodynamic models for a large portion of the flight envelope were identified rapidly and accurately using piloted flight test maneuvers during a single flight, with the final identified and validated models available before the aircraft landed.

Artificial neural networks predict the incidence of portosplenomesenteric venous thrombosis in patients with acute pancreatitis.

PubMed

Fei, Y; Hu, J; Li, W-Q; Wang, W; Zong, G-Q

2017-03-01

Essentials Predicting the occurrence of portosplenomesenteric vein thrombosis (PSMVT) is difficult. We studied 72 patients with acute pancreatitis. Artificial neural networks modeling was more accurate than logistic regression in predicting PSMVT. Additional predictive factors may be incorporated into artificial neural networks. Objective To construct and validate artificial neural networks (ANNs) for predicting the occurrence of portosplenomesenteric venous thrombosis (PSMVT) and compare the predictive ability of the ANNs with that of logistic regression. Methods The ANNs and logistic regression modeling were constructed using simple clinical and laboratory data of 72 acute pancreatitis (AP) patients. The ANNs and logistic modeling were first trained on 48 randomly chosen patients and validated on the remaining 24 patients. The accuracy and the performance characteristics were compared between these two approaches by SPSS17.0 software. Results The training set and validation set did not differ on any of the 11 variables. After training, the back propagation network training error converged to 1 × 10 -20 , and it retained excellent pattern recognition ability. When the ANNs model was applied to the validation set, it revealed a sensitivity of 80%, specificity of 85.7%, a positive predictive value of 77.6% and negative predictive value of 90.7%. The accuracy was 83.3%. Differences could be found between ANNs modeling and logistic regression modeling in these parameters (10.0% [95% CI, -14.3 to 34.3%], 14.3% [95% CI, -8.6 to 37.2%], 15.7% [95% CI, -9.9 to 41.3%], 11.8% [95% CI, -8.2 to 31.8%], 22.6% [95% CI, -1.9 to 47.1%], respectively). When ANNs modeling was used to identify PSMVT, the area under receiver operating characteristic curve was 0.849 (95% CI, 0.807-0.901), which demonstrated better overall properties than logistic regression modeling (AUC = 0.716) (95% CI, 0.679-0.761). Conclusions ANNs modeling was a more accurate tool than logistic regression in predicting the occurrence of PSMVT following AP. More clinical factors or biomarkers may be incorporated into ANNs modeling to improve its predictive ability. © 2016 International Society on Thrombosis and Haemostasis.

External validation of a Cox prognostic model: principles and methods

PubMed Central

2013-01-01

Background A prognostic model should not enter clinical practice unless it has been demonstrated that it performs a useful role. External validation denotes evaluation of model performance in a sample independent of that used to develop the model. Unlike for logistic regression models, external validation of Cox models is sparsely treated in the literature. Successful validation of a model means achieving satisfactory discrimination and calibration (prediction accuracy) in the validation sample. Validating Cox models is not straightforward because event probabilities are estimated relative to an unspecified baseline function. Methods We describe statistical approaches to external validation of a published Cox model according to the level of published information, specifically (1) the prognostic index only, (2) the prognostic index together with Kaplan-Meier curves for risk groups, and (3) the first two plus the baseline survival curve (the estimated survival function at the mean prognostic index across the sample). The most challenging task, requiring level 3 information, is assessing calibration, for which we suggest a method of approximating the baseline survival function. Results We apply the methods to two comparable datasets in primary breast cancer, treating one as derivation and the other as validation sample. Results are presented for discrimination and calibration. We demonstrate plots of survival probabilities that can assist model evaluation. Conclusions Our validation methods are applicable to a wide range of prognostic studies and provide researchers with a toolkit for external validation of a published Cox model. PMID:23496923

Validation of Model-Based Prognostics for Pneumatic Valves in a Demonstration Testbed

DTIC Science & Technology

2014-10-02

predict end of life ( EOL ) and remaining useful life (RUL). The approach still follows the general estimation-prediction framework devel- oped in the...atmosphere, with linearly increasing leak area. kA2leak = Cleak (16) We define valve end of life ( EOL ) through open/close time limits of the valves, as in...represents end of life ( EOL ), and ∆kE represents remaining useful life (RUL). For valves, timing requirements are provided that de- fine the maximum

Development and validation of a measure of display rule knowledge: the display rule assessment inventory.

PubMed

Matsumoto, David; Yoo, Seung Hee; Hirayama, Satoko; Petrova, Galina

2005-03-01

As one component of emotion regulation, display rules, which reflect the regulation of expressive behavior, have been the topic of many studies. Despite their theoretical and empirical importance, however, to date there is no measure of display rules that assesses a full range of behavioral responses that are theoretically possible when emotion is elicited. This article reports the development of a new measure of display rules that surveys 5 expressive modes: expression, deamplification, amplification, qualification, and masking. Two studies provide evidence for its internal and temporal reliability and for its content, convergent, discriminant, external, and concurrent predictive validity. Additionally, Study 1, involving American, Russian, and Japanese participants, demonstrated predictable cultural differences on each of the expressive modes. Copyright 2005 APA, all rights reserved.

A Finite Element Model to Predict the Effect of Porosity on Elastic Modulus in Low-Porosity Materials

NASA Astrophysics Data System (ADS)

Morrissey, Liam S.; Nakhla, Sam

2018-07-01

The effect of porosity on elastic modulus in low-porosity materials is investigated. First, several models used to predict the reduction in elastic modulus due to porosity are compared with a compilation of experimental data to determine their ranges of validity and accuracy. The overlapping solid spheres model is found to be most accurate with the experimental data and valid between 3 and 10 pct porosity. Next, a FEM is developed with the objective of demonstrating that a macroscale plate with a center hole can be used to model the effect of microscale porosity on elastic modulus. The FEM agrees best with the overlapping solid spheres model and shows higher accuracy with experimental data than the overlapping solid spheres model.

A novel computer algorithm improves antibody epitope prediction using affinity-selected mimotopes: a case study using monoclonal antibodies against the West Nile virus E protein.

PubMed

Denisova, Galina F; Denisov, Dimitri A; Yeung, Jeffrey; Loeb, Mark B; Diamond, Michael S; Bramson, Jonathan L

2008-11-01

Understanding antibody function is often enhanced by knowledge of the specific binding epitope. Here, we describe a computer algorithm that permits epitope prediction based on a collection of random peptide epitopes (mimotopes) isolated by antibody affinity purification. We applied this methodology to the prediction of epitopes for five monoclonal antibodies against the West Nile virus (WNV) E protein, two of which exhibit therapeutic activity in vivo. This strategy was validated by comparison of our results with existing F(ab)-E protein crystal structures and mutational analysis by yeast surface display. We demonstrate that by combining the results of the mimotope method with our data from mutational analysis, epitopes could be predicted with greater certainty. The two methods displayed great complementarity as the mutational analysis facilitated epitope prediction when the results with the mimotope method were equivocal and the mimotope method revealed a broader number of residues within the epitope than the mutational analysis. Our results demonstrate that the combination of these two prediction strategies provides a robust platform for epitope characterization.

Psychometric properties of the Stroke Impairment Assessment Set (SIAS).

PubMed

Liu, Meigen; Chino, Naoichi; Tuji, Testuya; Masakado, Yoshihisa; Hase, Kimitaka; Kimura, Akio

2002-12-01

To review the psychometric properties of the Stroke Impairment Assessment Set (SAS), which was developed in 1990 as a comprehensive instrument to assess stroke impairment. Articles related to the SIAS were retrieved from the MEDLINE and the Folia Centro Japonica. Thirty-five articles were retrieved and analyzed. 1) Scale quality: Rasch analysis demonstrated the unidimensionality of the SIAS. Factor analysis produced factors corresponding to the 6 SIAS subscales. 2) Interrater reliability: The weighted kappas were high except for the unaffected side quadriceps item for which the score distribution was skewed. 3) Concurrent validity: Significant correlations were found between a) SIAS motor items and the Motricity Index or the Brunnstrom stage, b) SIAS lower extremity scores and the Functional Independence Measure (FIMSM) locomotion scores, c) trunk scores and abdominal manual muscle testing, d) visuospatial scores and line bisection and copying task scores, and e) speech scores and the FIMSM communication scores. 4) Predictive validity: Three studies attempting to predict discharge functional status demonstrated that adding the SIAS as one of the predictors enhanced the predictive power 5) Responsiveness: The SIAS was more responsive to changes than the Motricity Index, the Brunnstrom stage, or the National Institutes of Health Stroke Scale. The SIAS is a useful measure of stroke impairment with well-established psychometric properties.

Prediction of ethanol in bottled Chinese rice wine by NIR spectroscopy

NASA Astrophysics Data System (ADS)

Ying, Yibin; Yu, Haiyan; Pan, Xingxiang; Lin, Tao

2006-10-01

To evaluate the applicability of non-invasive visible and near infrared (VIS-NIR) spectroscopy for determining ethanol concentration of Chinese rice wine in square brown glass bottle, transmission spectra of 100 bottled Chinese rice wine samples were collected in the spectral range of 350-1200 nm. Statistical equations were established between the reference data and VIS-NIR spectra by partial least squares (PLS) regression method. Performance of three kinds of mathematical treatment of spectra (original spectra, first derivative spectra and second derivative spectra) were also discussed. The PLS models of original spectra turned out better results, with higher correlation coefficient in calibration (R cal) of 0.89, lower root mean standard error of calibration (RMSEC) of 0.165, and lower root mean standard error of cross validation (RMSECV) of 0.179. Using original spectra, PLS models for ethanol concentration prediction were developed. The R cal and the correlation coefficient in validation (R val) were 0.928 and 0.875, respectively; and the RMSEC and the root mean standard error of validation (RMSEP) were 0.135 (%, v v -1) and 0.177 (%, v v -1), respectively. The results demonstrated that VIS-NIR spectroscopy could be used to predict ethanol concentration in bottled Chinese rice wine.

Neural Network Prediction of ICU Length of Stay Following Cardiac Surgery Based on Pre-Incision Variables

PubMed Central

Pothula, Venu M.; Yuan, Stanley C.; Maerz, David A.; Montes, Lucresia; Oleszkiewicz, Stephen M.; Yusupov, Albert; Perline, Richard

2015-01-01

Background Advanced predictive analytical techniques are being increasingly applied to clinical risk assessment. This study compared a neural network model to several other models in predicting the length of stay (LOS) in the cardiac surgical intensive care unit (ICU) based on pre-incision patient characteristics. Methods Thirty six variables collected from 185 cardiac surgical patients were analyzed for contribution to ICU LOS. The Automatic Linear Modeling (ALM) module of IBM-SPSS software identified 8 factors with statistically significant associations with ICU LOS; these factors were also analyzed with the Artificial Neural Network (ANN) module of the same software. The weighted contributions of each factor (“trained” data) were then applied to data for a “new” patient to predict ICU LOS for that individual. Results Factors identified in the ALM model were: use of an intra-aortic balloon pump; O2 delivery index; age; use of positive cardiac inotropic agents; hematocrit; serum creatinine ≥ 1.3 mg/deciliter; gender; arterial pCO2. The r2 value for ALM prediction of ICU LOS in the initial (training) model was 0.356, p <0.0001. Cross validation in prediction of a “new” patient yielded r2 = 0.200, p <0.0001. The same 8 factors analyzed with ANN yielded a training prediction r2 of 0.535 (p <0.0001) and a cross validation prediction r2 of 0.410, p <0.0001. Two additional predictive algorithms were studied, but they had lower prediction accuracies. Our validated neural network model identified the upper quartile of ICU LOS with an odds ratio of 9.8(p <0.0001). Conclusions ANN demonstrated a 2-fold greater accuracy than ALM in prediction of observed ICU LOS. This greater accuracy would be presumed to result from the capacity of ANN to capture nonlinear effects and higher order interactions. Predictive modeling may be of value in early anticipation of risks of post-operative morbidity and utilization of ICU facilities. PMID:26710254

Neural correlates of the spatial and expectancy components of endogenous and stimulus-driven orienting of attention in the Posner task.

PubMed

Doricchi, Fabrizio; Macci, Enrica; Silvetti, Massimo; Macaluso, Emiliano

2010-07-01

Voluntary orienting of visual attention is conventionally measured in tasks with predictive central cues followed by frequent valid targets at the cued location and by infrequent invalid targets at the uncued location. This implies that invalid targets entail both spatial reorienting of attention and breaching of the expected spatial congruency between cues and targets. Here, we used event-related functional magnetic resonance imaging (fMRI) to separate the neural correlates of the spatial and expectancy components of both endogenous orienting and stimulus-driven reorienting of attention. We found that during endogenous orienting with predictive cues, there was a significant deactivation of the right Temporal-Parietal Junction (TPJ). We also discovered that the lack of an equivalent deactivation with nonpredictive cues was matched to drop in attentional costs and preservation of attentional benefits. The right TPJ showed equivalent responses to invalid targets following predictive and nonpredictive cues. On the contrary, infrequent-unexpected invalid targets following predictive cues specifically activated the right Middle and Inferior Frontal Gyrus (MFG-IFG). Additional comparisons with spatially neutral trials demonstrated that, independently of cue predictiveness, valid targets activate the left TPJ, whereas invalid targets activate both the left and right TPJs. These findings show that the selective right TPJ activation that is found in the comparison between invalid and valid trials results from the reciprocal cancelling of the different activations that in the left TPJ are related to the processing of valid and invalid targets. We propose that left and right TPJs provide "matching and mismatching to attentional template" signals. These signals enable reorienting of attention and play a crucial role in the updating of the statistical contingency between cues and targets.

Multimethod Investigation of Interpersonal Functioning in Borderline Personality Disorder

PubMed Central

Stepp, Stephanie D.; Hallquist, Michael N.; Morse, Jennifer Q.; Pilkonis, Paul A.

2011-01-01

Even though interpersonal functioning is of great clinical importance for patients with borderline personality disorder (BPD), the comparative validity of different assessment methods for interpersonal dysfunction has not yet been tested. This study examined multiple methods of assessing interpersonal functioning, including self- and other-reports, clinical ratings, electronic diaries, and social cognitions in three groups of psychiatric patients (N=138): patients with (1) BPD, (2) another personality disorder, and (3) Axis I psychopathology only. Using dominance analysis, we examined the predictive validity of each method in detecting changes in symptom distress and social functioning six months later. Across multiple methods, the BPD group often reported higher interpersonal dysfunction scores compared to other groups. Predictive validity results demonstrated that self-report and electronic diary ratings were the most important predictors of distress and social functioning. Our findings suggest that self-report scores and electronic diary ratings have high clinical utility, as these methods appear most sensitive to change. PMID:21808661

CFD Validation with Experiment and Verification with Physics of a Propellant Damping Device

NASA Technical Reports Server (NTRS)

Yang, H. Q.; Peugeot, John

2011-01-01

This paper will document our effort in validating a coupled fluid-structure interaction CFD tool in predicting a damping device performance in the laboratory condition. Consistently good comparisons of "blind" CFD predictions against experimental data under various operation conditions, design parameters, and cryogenic environment will be presented. The power of the coupled CFD-structures interaction code in explaining some unexpected phenomena of the device observed during the technology development will be illustrated. The evolution of the damper device design inside the LOX tank will be used to demonstrate the contribution of the tool in understanding, optimization and implementation of LOX damper in Ares I vehicle. It is due to the present validation effort, the LOX damper technology has matured to TRL 5. The present effort has also contributed to the transition of the technology from an early conceptual observation to the baseline design of thrust oscillation mitigation for the Ares I within a 10 month period.

Short-Term Forecasts Using NU-WRF for the Winter Olympics 2018

NASA Technical Reports Server (NTRS)

Srikishen, Jayanthi; Case, Jonathan L.; Petersen, Walter A.; Iguchi, Takamichi; Tao, Wei-Kuo; Zavodsky, Bradley T.; Molthan, Andrew

2017-01-01

The NASA Unified-Weather Research and Forecasting model (NU-WRF) will be included for testing and evaluation in the forecast demonstration project (FDP) of the International Collaborative Experiment -PyeongChang 2018 Olympic and Paralympic (ICE-POP) Winter Games. An international array of radar and supporting ground based observations together with various forecast and now-cast models will be operational during ICE-POP. In conjunction with personnel from NASA's Goddard Space Flight Center, the NASA Short-term Prediction Research and Transition (SPoRT) Center is developing benchmark simulations for a real-time NU-WRF configuration to run during the FDP. ICE-POP observational datasets will be used to validate model simulations and investigate improved model physics and performance for prediction of snow events during the research phase (RDP) of the project The NU-WRF model simulations will also support NASA Global Precipitation Measurement (GPM) Mission ground-validation physical and direct validation activities in relation to verifying, testing and improving satellite-based snowfall retrieval algorithms over complex terrain.

Acquiring and Producing Sentences: Whether Learners Use Verb-Specific or Verb-General Information Depends on Cue Validity

PubMed Central

Thothathiri, Malathi; Rattinger, Michelle G.

2016-01-01

Learning to produce sentences involves learning patterns that enable the generation of new utterances. Language contains both verb-specific and verb-general regularities that are relevant to this capacity. Previous research has focused on whether one source is more important than the other. We tested whether the production system can flexibly learn to use either source, depending on the predictive validity of different cues in the input. Participants learned new sentence structures in a miniature language paradigm. In three experiments, we manipulated whether individual verbs or verb-general mappings better predicted the structures heard during learning. Evaluation of participants’ subsequent production revealed that they could use either the structural preferences of individual verbs or abstract meaning-to-form mappings to construct new sentences. Further, this choice varied according to cue validity. These results demonstrate flexibility within the production architecture and the importance of considering how language was learned when discussing how language is used. PMID:27047428

Validation of 131I ecological transfer models and thyroid dose assessments using Chernobyl fallout data from the Plavsk district, Russia

PubMed Central

Zvonova, I.; Krajewski, P.; Berkovsky, V.; Ammann, M.; Duffa, C.; Filistovic, V.; Homma, T.; Kanyar, B.; Nedveckaite, T.; Simon, S.L.; Vlasov, O.; Webbe-Wood, D.

2009-01-01

Within the project “Environmental Modelling for Radiation Safety” (EMRAS) organized by the IAEA in 2003 experimental data of 131I measurements following the Chernobyl accident in the Plavsk district of Tula region, Russia were used to validate the calculations of some radioecological transfer models. Nine models participated in the inter-comparison. Levels of 137Cs soil contamination in all the settlements and 131I/137Cs isotopic ratios in the depositions in some locations were used as the main input information. 370 measurements of 131I content in thyroid of townspeople and villagers, and 90 measurements of 131I concentration in milk were used for validation of the model predictions. A remarkable improvement in models performance comparing with previous inter-comparison exercise was demonstrated. Predictions of the various models were within a factor of three relative to the observations, discrepancies between the estimates of average doses to thyroid produced by most participant not exceeded a factor of ten. PMID:19783331

Further validation and definition of the psychometric properties of the Asthma Impact Survey.

PubMed

Schatz, Michael; Zeiger, Robert S; Yang, Su-Jau; Chen, Wansu; Kosinski, Mark

2011-07-01

The Asthma Impact Survey (AIS-6) is a brief disease-specific quality-of-life instrument with limited published validation data. To obtain additional validation data and psychometric properties of the AIS-6. In November, 2007, patients with persistent asthma were mailed a survey that included the AIS-6, the mini-Asthma Quality of Life Questionnaire (mAQLQ), and the Asthma Control Test (ACT). Follow-up surveys were sent in April, July, and October 2008. Year 2008 exacerbations and short-acting β-agonist (SABA) dispensings were captured from administrative data. A total of 2680 patients had complete baseline survey data. Criterion validity was demonstrated by the strong correlations of the AIS-6 with the mAQLQ (r = -0.84 to -0.86); construct validity by significant relationships (P < .0001) of the AIS-6 with mAQLQ domain scores, ACT score, and history of exacerbations; and predictive validity by significant relationships (P < .0001) between AIS-6 scores at the end of 2007 and year 2008 exacerbations and high SABA dispensings. Responsiveness was demonstrated by significant (P < .0001) correlations (r = -0.39 to -0.58) between changes in AIS-6 scores and changes in mAQLQ and ACT scores over time. A preliminary minimally important difference (MID) in AIS-6 was estimated to be 4 by using the mAQLQ MID as an anchor. Excellent internal consistency (α = 0.94) and test-retest reliability (intraclass correlation coefficient = 0.86-0.91) were also demonstrated. The AIS-6 demonstrated good psychometric properties in a large independent sample and could be used to assess asthma-specific quality of life in clinical practice and clinical research. Copyright © 2011 American Academy of Allergy, Asthma & Immunology. Published by Mosby, Inc. All rights reserved.

Prediction of Metastasis Using Second Harmonic Generation

DTIC Science & Technology

2016-07-01

extracellular matrix through which metastasizing cells must travel. We and others have demonstrated that tumor collagen structure, as measured with the...algorithm using separate training and validation sets, etc. Keywords: metastasis, overtreatment, extracellular matrix , collagen , second harmonic...optical process called second harmonic generation (SHG), influences tumor metastasis. This suggests that collagen structure may provide prognostic

Use of Neuropsychological Tests to Identify High School Students with Epilepsy Who Later Demonstrate Inadequate Performances in Life.

ERIC Educational Resources Information Center

Dodrill, Carl B.; Clemmons, David

1984-01-01

Examined the validity of intellectual, neuropsychological, and emotional adjustment measures administered in high school in predicting vocational adjustment of 39 young adults with epilepsy. Results showed neuropsychological tests were the best predictors of later adjustment. Abilities were more related to final adjustment than variables…

Beyond Static and Dynamic Risk Factors: The Incremental Validity of Release Planning for Predicting Sex Offender Recidivism

ERIC Educational Resources Information Center

Scoones, Carwyn D.; Willis, Gwenda M.; Grace, Randolph C.

2012-01-01

Both desistance research and strengths-based approaches to offender rehabilitation suggest that attempts to reduce sex offender recidivism should attend to an offender's release environment. Recent research has demonstrated that better quality release planning is associated with reduced recidivism; however, whether release planning contributes…

Life Ownership Orientation and Attitudes toward Abortion, Suicide, Doctor-Assisted Suicide, and Capital Punishment.

ERIC Educational Resources Information Center

Ross, Lisa Thomson; Kaplan, Kalman J.

1994-01-01

Examined life ownership orientation (extent to which one believes that God, individual, or society has power over one's life) among 117 college students who completed Life Ownership Orientation Questionnaire (LOOQ). Found LOOQ scores demonstrated higher predictive validity with regard to attitudes toward abortion, suicide, doctor-assisted suicide,…

Observed Emotional and Behavioral Indicators of Motivation Predict School Readiness in Head Start Graduates

ERIC Educational Resources Information Center

Berhenke, Amanda; Miller, Alison L.; Brown, Eleanor; Seifer, Ronald; Dickstein, Susan

2011-01-01

Emotions and behaviors observed during challenging tasks are hypothesized to be valuable indicators of young children's motivation, the assessment of which may be particularly important for children at risk for school failure. The current study demonstrated reliability and concurrent validity of a new observational assessment of motivation in…

Generation and Validation of the iKp1289 Metabolic Model for Klebsiella pneumoniae KPPR1

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

Henry, Christopher S.; Rotman, Ella; Lathem, Wyndham W.

Klebsiella pneumoniae has a reputation for causing a wide range of infectious conditions, with numerous highly virulent and antibiotic-resistant strains. Metabolic models have the potential to provide insights into the growth behavior, nutrient requirements, essential genes, and candidate drug targets in these strains. Here we develop a metabolic model for KPPR1, a highly virulent strain of K. pneumoniae. We apply a combination of Biolog phenotype data and fitness data to validate and refine our KPPR1 model. The final model displays a predictive accuracy of 75% in identifying potential carbon and nitrogen sources for K. pneumoniae and of 99% in predictingmore » nonessential genes in rich media. We demonstrate how this model is useful in studying the differences in the metabolic capabilities of the low-virulence MGH 78578 strain and the highly virulent KPPR1 strain. For example, we demonstrate that these strains differ in carbohydrate metabolism, including the ability to metabolize dulcitol as a primary carbon source. Our model makes numerous other predictions for follow-up verification and analysis.« less

WORMHOLE: Novel Least Diverged Ortholog Prediction through Machine Learning

PubMed Central

Sutphin, George L.; Mahoney, J. Matthew; Sheppard, Keith; Walton, David O.; Korstanje, Ron

2016-01-01

The rapid advancement of technology in genomics and targeted genetic manipulation has made comparative biology an increasingly prominent strategy to model human disease processes. Predicting orthology relationships between species is a vital component of comparative biology. Dozens of strategies for predicting orthologs have been developed using combinations of gene and protein sequence, phylogenetic history, and functional interaction with progressively increasing accuracy. A relatively new class of orthology prediction strategies combines aspects of multiple methods into meta-tools, resulting in improved prediction performance. Here we present WORMHOLE, a novel ortholog prediction meta-tool that applies machine learning to integrate 17 distinct ortholog prediction algorithms to identify novel least diverged orthologs (LDOs) between 6 eukaryotic species—humans, mice, zebrafish, fruit flies, nematodes, and budding yeast. Machine learning allows WORMHOLE to intelligently incorporate predictions from a wide-spectrum of strategies in order to form aggregate predictions of LDOs with high confidence. In this study we demonstrate the performance of WORMHOLE across each combination of query and target species. We show that WORMHOLE is particularly adept at improving LDO prediction performance between distantly related species, expanding the pool of LDOs while maintaining low evolutionary distance and a high level of functional relatedness between genes in LDO pairs. We present extensive validation, including cross-validated prediction of PANTHER LDOs and evaluation of evolutionary divergence and functional similarity, and discuss future applications of machine learning in ortholog prediction. A WORMHOLE web tool has been developed and is available at http://wormhole.jax.org/. PMID:27812085

WORMHOLE: Novel Least Diverged Ortholog Prediction through Machine Learning.

PubMed

Sutphin, George L; Mahoney, J Matthew; Sheppard, Keith; Walton, David O; Korstanje, Ron

2016-11-01

The rapid advancement of technology in genomics and targeted genetic manipulation has made comparative biology an increasingly prominent strategy to model human disease processes. Predicting orthology relationships between species is a vital component of comparative biology. Dozens of strategies for predicting orthologs have been developed using combinations of gene and protein sequence, phylogenetic history, and functional interaction with progressively increasing accuracy. A relatively new class of orthology prediction strategies combines aspects of multiple methods into meta-tools, resulting in improved prediction performance. Here we present WORMHOLE, a novel ortholog prediction meta-tool that applies machine learning to integrate 17 distinct ortholog prediction algorithms to identify novel least diverged orthologs (LDOs) between 6 eukaryotic species-humans, mice, zebrafish, fruit flies, nematodes, and budding yeast. Machine learning allows WORMHOLE to intelligently incorporate predictions from a wide-spectrum of strategies in order to form aggregate predictions of LDOs with high confidence. In this study we demonstrate the performance of WORMHOLE across each combination of query and target species. We show that WORMHOLE is particularly adept at improving LDO prediction performance between distantly related species, expanding the pool of LDOs while maintaining low evolutionary distance and a high level of functional relatedness between genes in LDO pairs. We present extensive validation, including cross-validated prediction of PANTHER LDOs and evaluation of evolutionary divergence and functional similarity, and discuss future applications of machine learning in ortholog prediction. A WORMHOLE web tool has been developed and is available at http://wormhole.jax.org/.

Towards personalized therapy for multiple sclerosis: prediction of individual treatment response.

PubMed

Kalincik, Tomas; Manouchehrinia, Ali; Sobisek, Lukas; Jokubaitis, Vilija; Spelman, Tim; Horakova, Dana; Havrdova, Eva; Trojano, Maria; Izquierdo, Guillermo; Lugaresi, Alessandra; Girard, Marc; Prat, Alexandre; Duquette, Pierre; Grammond, Pierre; Sola, Patrizia; Hupperts, Raymond; Grand'Maison, Francois; Pucci, Eugenio; Boz, Cavit; Alroughani, Raed; Van Pesch, Vincent; Lechner-Scott, Jeannette; Terzi, Murat; Bergamaschi, Roberto; Iuliano, Gerardo; Granella, Franco; Spitaleri, Daniele; Shaygannejad, Vahid; Oreja-Guevara, Celia; Slee, Mark; Ampapa, Radek; Verheul, Freek; McCombe, Pamela; Olascoaga, Javier; Amato, Maria Pia; Vucic, Steve; Hodgkinson, Suzanne; Ramo-Tello, Cristina; Flechter, Shlomo; Cristiano, Edgardo; Rozsa, Csilla; Moore, Fraser; Luis Sanchez-Menoyo, Jose; Laura Saladino, Maria; Barnett, Michael; Hillert, Jan; Butzkueven, Helmut

2017-09-01

Timely initiation of effective therapy is crucial for preventing disability in multiple sclerosis; however, treatment response varies greatly among patients. Comprehensive predictive models of individual treatment response are lacking. Our aims were: (i) to develop predictive algorithms for individual treatment response using demographic, clinical and paraclinical predictors in patients with multiple sclerosis; and (ii) to evaluate accuracy, and internal and external validity of these algorithms. This study evaluated 27 demographic, clinical and paraclinical predictors of individual response to seven disease-modifying therapies in MSBase, a large global cohort study. Treatment response was analysed separately for disability progression, disability regression, relapse frequency, conversion to secondary progressive disease, change in the cumulative disease burden, and the probability of treatment discontinuation. Multivariable survival and generalized linear models were used, together with the principal component analysis to reduce model dimensionality and prevent overparameterization. Accuracy of the individual prediction was tested and its internal validity was evaluated in a separate, non-overlapping cohort. External validity was evaluated in a geographically distinct cohort, the Swedish Multiple Sclerosis Registry. In the training cohort (n = 8513), the most prominent modifiers of treatment response comprised age, disease duration, disease course, previous relapse activity, disability, predominant relapse phenotype and previous therapy. Importantly, the magnitude and direction of the associations varied among therapies and disease outcomes. Higher probability of disability progression during treatment with injectable therapies was predominantly associated with a greater disability at treatment start and the previous therapy. For fingolimod, natalizumab or mitoxantrone, it was mainly associated with lower pretreatment relapse activity. The probability of disability regression was predominantly associated with pre-baseline disability, therapy and relapse activity. Relapse incidence was associated with pretreatment relapse activity, age and relapsing disease course, with the strength of these associations varying among therapies. Accuracy and internal validity (n = 1196) of the resulting predictive models was high (>80%) for relapse incidence during the first year and for disability outcomes, moderate for relapse incidence in Years 2-4 and for the change in the cumulative disease burden, and low for conversion to secondary progressive disease and treatment discontinuation. External validation showed similar results, demonstrating high external validity for disability and relapse outcomes, moderate external validity for cumulative disease burden and low external validity for conversion to secondary progressive disease and treatment discontinuation. We conclude that demographic, clinical and paraclinical information helps predict individual response to disease-modifying therapies at the time of their commencement. © The Author (2017). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

The Social Interaction Phobia Scale: Continued support for the psychometric validity of the SIPS using clinical and non-clinical samples.

PubMed

Menatti, Alison R; Weeks, Justin W; Carleton, R Nicholas; Morrison, Amanda S; Heimberg, Richard G; Hope, Debra A; Blanco, Carlos; Schneier, Franklin R; Liebowitz, Michael R

2015-05-01

The present study sought to extend findings supporting the psychometric validity of a promising measure of social anxiety (SA) symptoms, the Social Interaction Phobia Scale (SIPS; Carleton et al., 2009). Analyses were conducted using three samples: social anxiety disorder (SAD) patients, generalized anxiety disorder (GAD) patients, and healthy controls. SIPS scores of SAD patients demonstrated internal consistency and construct validity, and the previously demonstrated three-factor structure of the SIPS was replicated. Further, the SIPS total score uniquely predicted SA symptoms, and SIPS scores were significantly higher for SAD patients than GAD patients or controls. Two cut-off scores that discriminated SAD patients from GAD patients and from healthy controls were identified. The current study is the first to replicate the SIPS three-factor model in a large, treatment-seeking sample of SAD patients and establish a cut-off score discriminating SAD from GAD patients. Findings support the SIPS as a valid, SAD-specific assessment instrument. Copyright © 2015 Elsevier Ltd. All rights reserved.

A computable phenotype for asthma case identification in adult and pediatric patients: External validation in the Chicago Area Patient-Outcomes Research Network (CAPriCORN).

PubMed

Afshar, Majid; Press, Valerie G; Robison, Rachel G; Kho, Abel N; Bandi, Sindhura; Biswas, Ashvini; Avila, Pedro C; Kumar, Harsha Vardhan Madan; Yu, Byung; Naureckas, Edward T; Nyenhuis, Sharmilee M; Codispoti, Christopher D

2017-10-13

Comprehensive, rapid, and accurate identification of patients with asthma for clinical care and engagement in research efforts is needed. The original development and validation of a computable phenotype for asthma case identification occurred at a single institution in Chicago and demonstrated excellent test characteristics. However, its application in a diverse payer mix, across different health systems and multiple electronic health record vendors, and in both children and adults was not examined. The objective of this study is to externally validate the computable phenotype across diverse Chicago institutions to accurately identify pediatric and adult patients with asthma. A cohort of 900 asthma and control patients was identified from the electronic health record between January 1, 2012 and November 30, 2014. Two physicians at each site independently reviewed the patient chart to annotate cases. The inter-observer reliability between the physician reviewers had a κ-coefficient of 0.95 (95% CI 0.93-0.97). The accuracy, sensitivity, specificity, negative predictive value, and positive predictive value of the computable phenotype were all above 94% in the full cohort. The excellent positive and negative predictive values in this multi-center external validation study establish a useful tool to identify asthma cases in in the electronic health record for research and care. This computable phenotype could be used in large-scale comparative-effectiveness trials.

AIRS Retrieval Validation During the EAQUATE

NASA Technical Reports Server (NTRS)

Zhou, Daniel K.; Smith, William L.; Cuomo, Vincenzo; Taylor, Jonathan P.; Barnet, Christopher D.; DiGirolamo, Paolo; Pappalardo, Gelsomina; Larar, Allen M.; Liu, Xu; Newman, Stuart M.

2006-01-01

Atmospheric and surface thermodynamic parameters retrieved with advanced hyperspectral remote sensors of Earth observing satellites are critical for weather prediction and scientific research. The retrieval algorithms and retrieved parameters from satellite sounders must be validated to demonstrate the capability and accuracy of both observation and data processing systems. The European AQUA Thermodynamic Experiment (EAQUATE) was conducted mainly for validation of the Atmospheric InfraRed Sounder (AIRS) on the AQUA satellite, but also for assessment of validation systems of both ground-based and aircraft-based instruments which will be used for other satellite systems such as the Infrared Atmospheric Sounding Interferometer (IASI) on the European MetOp satellite, the Cross-track Infrared Sounder (CrIS) from the NPOESS Preparatory Project and the following NPOESS series of satellites. Detailed inter-comparisons were conducted and presented using different retrieval methodologies: measurements from airborne ultraspectral Fourier transform spectrometers, aircraft in-situ instruments, dedicated dropsondes and radiosondes, and ground based Raman Lidar, as well as from the European Center for Medium range Weather Forecasting (ECMWF) modeled thermal structures. The results of this study not only illustrate the quality of the measurements and retrieval products but also demonstrate the capability of these validation systems which are put in place to validate current and future hyperspectral sounding instruments and their scientific products.

Evaluation of the ability of three physical activity monitors to predict weight change and estimate energy expenditure.

PubMed

Correa, John B; Apolzan, John W; Shepard, Desti N; Heil, Daniel P; Rood, Jennifer C; Martin, Corby K

2016-07-01

Activity monitors such as the Actical accelerometer, the Sensewear armband, and the Intelligent Device for Energy Expenditure and Activity (IDEEA) are commonly validated against gold standards (e.g., doubly labeled water, or DLW) to determine whether they accurately measure total daily energy expenditure (TEE) or activity energy expenditure (AEE). However, little research has assessed whether these parameters or others (e.g., posture allocation) predict body weight change over time. The aims of this study were to (i) test whether estimated energy expenditure or posture allocation from the devices was associated with weight change during and following a low-calorie diet (LCD) and (ii) compare free-living TEE and AEE predictions from the devices against DLW before weight change. Eighty-seven participants from 2 clinical trials wore 2 of the 3 devices simultaneously for 1 week of a 2-week DLW period. Participants then completed an 8-week LCD and were weighed at the start and end of the LCD and 6 and 12 months after the LCD. More time spent walking at baseline, measured by the IDEEA, significantly predicted greater weight loss during the 8-week LCD. Measures of posture allocation demonstrated medium effect sizes in their relationships with weight change. Bland-Altman analyses indicated that the Sensewear and the IDEEA accurately estimated TEE, and the IDEEA accurately measured AEE. The results suggest that the ability of energy expenditure and posture allocation to predict weight change is limited, and the accuracy of TEE and AEE measurements varies across activity monitoring devices, with multi-sensor monitors demonstrating stronger validity.

Evaluation of the ability of three physical activity monitors to predict weight change and estimate energy expenditure

PubMed Central

Correa, John B.; Apolzan, John W.; Shepard, Desti N.; Heil, Daniel P.; Rood, Jennifer C.; Martin, Corby K.

2016-01-01

Activity monitors such as the Actical accelerometer, the Sensewear armband, and the Intelligent Device for Energy Expenditure and Activity (IDEEA) are commonly validated against gold standards (e.g., doubly labeled water, or DLW) to determine whether they accurately measure total daily energy expenditure (TEE) or activity energy expenditure (AEE). However, little research has assessed whether these parameters or others (e.g., posture allocation) predict body weight change over time. The aims of this study were to (i) test whether estimated energy expenditure or posture allocation from the devices was associated with weight change during and following a low-calorie diet (LCD) and (ii) compare free-living TEE and AEE predictions from the devices against DLW before weight change. Eighty-seven participants from 2 clinical trials wore 2 of the 3 devices simultaneously for 1 week of a 2-week DLW period. Participants then completed an 8-week LCD and were weighed at the start and end of the LCD and 6 and 12 months after the LCD. More time spent walking at baseline, measured by the IDEEA, significantly predicted greater weight loss during the 8-week LCD. Measures of posture allocation demonstrated medium effect sizes in their relationships with weight change. Bland–Altman analyses indicated that the Sensewear and the IDEEA accurately estimated TEE, and the IDEEA accurately measured AEE. The results suggest that the ability of energy expenditure and posture allocation to predict weight change is limited, and the accuracy of TEE and AEE measurements varies across activity monitoring devices, with multi-sensor monitors demonstrating stronger validity. PMID:27270210

Overview of Heat Addition and Efficiency Predictions for an Advanced Stirling Convertor

NASA Technical Reports Server (NTRS)

Wilson, Scott D.; Reid, Terry; Schifer, Nicholas; Briggs, Maxwell

2011-01-01

Past methods of predicting net heat input needed to be validated. Validation effort pursued with several paths including improving model inputs, using test hardware to provide validation data, and validating high fidelity models. Validation test hardware provided direct measurement of net heat input for comparison to predicted values. Predicted value of net heat input was 1.7 percent less than measured value and initial calculations of measurement uncertainty were 2.1 percent (under review). Lessons learned during validation effort were incorporated into convertor modeling approach which improved predictions of convertor efficiency.

Validating the BISON fuel performance code to integral LWR experiments

DOE PAGES

Williamson, R. L.; Gamble, K. A.; Perez, D. M.; ...

2016-03-24

BISON is a modern finite element-based nuclear fuel performance code that has been under development at the Idaho National Laboratory (INL) since 2009. The code is applicable to both steady and transient fuel behavior and has been used to analyze a variety of fuel forms in 1D spherical, 2D axisymmetric, or 3D geometries. Code validation is underway and is the subject of this study. A brief overview of BISON’s computational framework, governing equations, and general material and behavioral models is provided. BISON code and solution verification procedures are described, followed by a summary of the experimental data used to datemore » for validation of Light Water Reactor (LWR) fuel. Validation comparisons focus on fuel centerline temperature, fission gas release, and rod diameter both before and following fuel-clad mechanical contact. Comparisons for 35 LWR rods are consolidated to provide an overall view of how the code is predicting physical behavior, with a few select validation cases discussed in greater detail. Our results demonstrate that 1) fuel centerline temperature comparisons through all phases of fuel life are very reasonable with deviations between predictions and experimental data within ±10% for early life through high burnup fuel and only slightly out of these bounds for power ramp experiments, 2) accuracy in predicting fission gas release appears to be consistent with state-of-the-art modeling and with the involved uncertainties and 3) comparison of rod diameter results indicates a tendency to overpredict clad diameter reduction early in life, when clad creepdown dominates, and more significantly overpredict the diameter increase late in life, when fuel expansion controls the mechanical response. In the initial rod diameter comparisons they were unsatisfactory and have lead to consideration of additional separate effects experiments to better understand and predict clad and fuel mechanical behavior. Results from this study are being used to define priorities for ongoing code development and validation activities.« less

Development of a QSAR Model for Thyroperoxidase Inhbition ...

EPA Pesticide Factsheets

hyroid hormones (THs) are involved in multiple biological processes and are critical modulators of fetal development. Even moderate changes in maternal or fetal TH levels can produce irreversible neurological deficits in children, such as lower IQ. The enzyme thyroperoxidase (TPO) plays a key role in the synthesis of THs, and inhibition of TPO by xenobiotics results in decreased TH synthesis. Recently, a high-throughput screening assay for TPO inhibition (AUR-TPO) was developed and used to test the ToxCast Phase I and II chemicals. In the present study, we used the results from AUR-TPO to develop a Quantitative Structure-Activity Relationship (QSAR) model for TPO inhibition. The training set consisted of 898 discrete organic chemicals: 134 inhibitors and 764 non-inhibitors. A five times two-fold cross-validation of the model was performed, yielding a balanced accuracy of 78.7%. More recently, an additional ~800 chemicals were tested in the AUR-TPO assay. These data were used for a blinded external validation of the QSAR model, demonstrating a balanced accuracy of 85.7%. Overall, the cross- and external validation indicate a robust model with high predictive performance. Next, we used the QSAR model to predict 72,526 REACH pre-registered substances. The model could predict 49.5% (35,925) of the substances in its applicability domain and of these, 8,863 (24.7%) were predicted to be TPO inhibitors. Predictions from this screening can be used in a tiered approach to

Systematically Differentiating Functions for Alternatively Spliced Isoforms through Integrating RNA-seq Data

PubMed Central

Menon, Rajasree; Wen, Yuchen; Omenn, Gilbert S.; Kretzler, Matthias; Guan, Yuanfang

2013-01-01

Integrating large-scale functional genomic data has significantly accelerated our understanding of gene functions. However, no algorithm has been developed to differentiate functions for isoforms of the same gene using high-throughput genomic data. This is because standard supervised learning requires ‘ground-truth’ functional annotations, which are lacking at the isoform level. To address this challenge, we developed a generic framework that interrogates public RNA-seq data at the transcript level to differentiate functions for alternatively spliced isoforms. For a specific function, our algorithm identifies the ‘responsible’ isoform(s) of a gene and generates classifying models at the isoform level instead of at the gene level. Through cross-validation, we demonstrated that our algorithm is effective in assigning functions to genes, especially the ones with multiple isoforms, and robust to gene expression levels and removal of homologous gene pairs. We identified genes in the mouse whose isoforms are predicted to have disparate functionalities and experimentally validated the ‘responsible’ isoforms using data from mammary tissue. With protein structure modeling and experimental evidence, we further validated the predicted isoform functional differences for the genes Cdkn2a and Anxa6. Our generic framework is the first to predict and differentiate functions for alternatively spliced isoforms, instead of genes, using genomic data. It is extendable to any base machine learner and other species with alternatively spliced isoforms, and shifts the current gene-centered function prediction to isoform-level predictions. PMID:24244129

Validity and reliability testing of the Prenatal Psychosocial Profile.

PubMed

Curry, M A; Campbell, R A; Christian, M

1994-04-01

Two studies of low-income pregnant women (N = 179) were done to examine the validity and reliability of the Prenatal Psychosocial Profile (PPP). The PPP, a composite of the Rosenberg Self-Esteem Scale, the Support Behaviors Inventory, and a newly developed measure of stress, is a brief, comprehensive clinical assessment of psychosocial risk during pregnancy. Construct validity of the stress scale was supported by theoretically predicted negative correlations with self-esteem, partner support, and support from others (N = 91). Convergent validity of the stress scale was demonstrated by a correlation of .71 with the Difficult Life Circumstances Scale. Adequate levels of internal consistency were found. Interrelationships between the four subscales were consistent with the underlying conceptualization, and there was beginning evidence of the factorial independence of the subscales.

Prediction of high-risk areas for visceral leishmaniasis using socioeconomic indicators and remote sensing data

PubMed Central

2014-01-01

Spatial heterogeneity in the incidence of visceral leishmaniasis (VL) is an important aspect to be considered in planning control actions for the disease. The objective of this study was to predict areas at high risk for visceral leishmaniasis (VL) based on socioeconomic indicators and remote sensing data. We applied classification and regression trees to develop and validate prediction models. Performance of the models was assessed by means of sensitivity, specificity and area under the ROC curve. The model developed was able to discriminate 15 subsets of census tracts (CT) with different probabilities of containing CT with high risk of VL occurrence. The model presented, respectively, in the validation and learning samples, sensitivity of 79% and 52%, specificity of 75% and 66%, and area under the ROC curve of 83% and 66%. Considering the complex network of factors involved in the occurrence of VL in urban areas, the results of this study showed that the development of a predictive model for VL might be feasible and useful for guiding interventions against the disease, but it is still a challenge as demonstrated by the unsatisfactory predictive performance of the model developed. PMID:24885128

Hypoalbuminemia, Low Base Excess Values, and Tachypnea Predict 28-Day Mortality in Severe Sepsis and Septic Shock Patients in the Emergency Department.

PubMed

Seo, Min Ho; Choa, Minhong; You, Je Sung; Lee, Hye Sun; Hong, Jung Hwa; Park, Yoo Seok; Chung, Sung Phil; Park, Incheol

2016-11-01

The objective of this study was to develop a new nomogram that can predict 28-day mortality in severe sepsis and/or septic shock patients using a combination of several biomarkers that are inexpensive and readily available in most emergency departments, with and without scoring systems. We enrolled 561 patients who were admitted to an emergency department (ED) and received early goal-directed therapy for severe sepsis or septic shock. We collected demographic data, initial vital signs, and laboratory data sampled at the time of ED admission. Patients were randomly assigned to a training set or validation set. For the training set, we generated models using independent variables associated with 28-day mortality by multivariate analysis, and developed a new nomogram for the prediction of 28-day mortality. Thereafter, the diagnostic accuracy of the nomogram was tested using the validation set. The prediction model that included albumin, base excess, and respiratory rate demonstrated the largest area under the receiver operating characteristic curve (AUC) value of 0.8173 [95% confidence interval (CI), 0.7605-0.8741]. The logistic analysis revealed that a conventional scoring system was not associated with 28-day mortality. In the validation set, the discrimination of a newly developed nomogram was also good, with an AUC value of 0.7537 (95% CI, 0.6563-0.8512). Our new nomogram is valuable in predicting the 28-day mortality of patients with severe sepsis and/or septic shock in the emergency department. Moreover, our readily available nomogram is superior to conventional scoring systems in predicting mortality.

Bridging the gap between computation and clinical biology: validation of cable theory in humans

PubMed Central

Finlay, Malcolm C.; Xu, Lei; Taggart, Peter; Hanson, Ben; Lambiase, Pier D.

2013-01-01

Introduction: Computerized simulations of cardiac activity have significantly contributed to our understanding of cardiac electrophysiology, but techniques of simulations based on patient-acquired data remain in their infancy. We sought to integrate data acquired from human electrophysiological studies into patient-specific models, and validated this approach by testing whether electrophysiological responses to sequential premature stimuli could be predicted in a quantitatively accurate manner. Methods: Eleven patients with structurally normal hearts underwent electrophysiological studies. Semi-automated analysis was used to reconstruct activation and repolarization dynamics for each electrode. This S2 extrastimuli data was used to inform individualized models of cardiac conduction, including a novel derivation of conduction velocity restitution. Activation dynamics of multiple premature extrastimuli were then predicted from this model and compared against measured patient data as well as data derived from the ten-Tusscher cell-ionic model. Results: Activation dynamics following a premature S3 were significantly different from those after an S2. Patient specific models demonstrated accurate prediction of the S3 activation wave, (Pearson's R2 = 0.90, median error 4%). Examination of the modeled conduction dynamics allowed inferences into the spatial dispersion of activation delay. Further validation was performed against data from the ten-Tusscher cell-ionic model, with our model accurately recapitulating predictions of repolarization times (R2 = 0.99). Conclusions: Simulations based on clinically acquired data can be used to successfully predict complex activation patterns following sequential extrastimuli. Such modeling techniques may be useful as a method of incorporation of clinical data into predictive models. PMID:24027527

A risk score for in-hospital death in patients admitted with ischemic or hemorrhagic stroke.

PubMed

Smith, Eric E; Shobha, Nandavar; Dai, David; Olson, DaiWai M; Reeves, Mathew J; Saver, Jeffrey L; Hernandez, Adrian F; Peterson, Eric D; Fonarow, Gregg C; Schwamm, Lee H

2013-01-28

We aimed to derive and validate a single risk score for predicting death from ischemic stroke (IS), intracerebral hemorrhage (ICH), and subarachnoid hemorrhage (SAH). Data from 333 865 stroke patients (IS, 82.4%; ICH, 11.2%; SAH, 2.6%; uncertain type, 3.8%) in the Get With The Guidelines-Stroke database were used. In-hospital mortality varied greatly according to stroke type (IS, 5.5%; ICH, 27.2%; SAH, 25.1%; unknown type, 6.0%; P<0.001). The patients were randomly divided into derivation (60%) and validation (40%) samples. Logistic regression was used to determine the independent predictors of mortality and to assign point scores for a prediction model in the overall population and in the subset with the National Institutes of Health Stroke Scale (NIHSS) recorded (37.1%). The c statistic, a measure of how well the models discriminate the risk of death, was 0.78 in the overall validation sample and 0.86 in the model including NIHSS. The model with NIHSS performed nearly as well in each stroke type as in the overall model including all types (c statistics for IS alone, 0.85; for ICH alone, 0.83; for SAH alone, 0.83; uncertain type alone, 0.86). The calibration of the model was excellent, as demonstrated by plots of observed versus predicted mortality. A single prediction score for all stroke types can be used to predict risk of in-hospital death following stroke admission. Incorporation of NIHSS information substantially improves this predictive accuracy.

Memory Binding Test Predicts Incident Amnestic Mild Cognitive Impairment.

PubMed

Mowrey, Wenzhu B; Lipton, Richard B; Katz, Mindy J; Ramratan, Wendy S; Loewenstein, David A; Zimmerman, Molly E; Buschke, Herman

2016-07-14

The Memory Binding Test (MBT), previously known as Memory Capacity Test, has demonstrated discriminative validity for distinguishing persons with amnestic mild cognitive impairment (aMCI) and dementia from cognitively normal elderly. We aimed to assess the predictive validity of the MBT for incident aMCI. In a longitudinal, community-based study of adults aged 70+, we administered the MBT to 246 cognitively normal elderly adults at baseline and followed them annually. Based on previous work, a subtle reduction in memory binding at baseline was defined by a Total Items in the Paired (TIP) condition score of ≤22 on the MBT. Cox proportional hazards models were used to assess the predictive validity of the MBT for incident aMCI accounting for the effects of covariates. The hazard ratio of incident aMCI was also assessed for different prediction time windows ranging from 4 to 7 years of follow-up, separately. Among 246 controls who were cognitively normal at baseline, 48 developed incident aMCI during follow-up. A baseline MBT reduction was associated with an increased risk for developing incident aMCI (hazard ratio (HR) = 2.44, 95% confidence interval: 1.30-4.56, p = 0.005). When varying the prediction window from 4-7 years, the MBT reduction remained significant for predicting incident aMCI (HR range: 2.33-3.12, p: 0.0007-0.04). Persons with poor performance on the MBT are at significantly greater risk for developing incident aMCI. High hazard ratios up to seven years of follow-up suggest that the MBT is sensitive to early disease.

High-fidelity Simulation of Jet Noise from Rectangular Nozzles . [Large Eddy Simulation (LES) Model for Noise Reduction in Advanced Jet Engines and Automobiles

NASA Technical Reports Server (NTRS)

Sinha, Neeraj

2014-01-01

This Phase II project validated a state-of-the-art LES model, coupled with a Ffowcs Williams-Hawkings (FW-H) far-field acoustic solver, to support the development of advanced engine concepts. These concepts include innovative flow control strategies to attenuate jet noise emissions. The end-to-end LES/ FW-H noise prediction model was demonstrated and validated by applying it to rectangular nozzle designs with a high aspect ratio. The model also was validated against acoustic and flow-field data from a realistic jet-pylon experiment, thereby significantly advancing the state of the art for LES.

Advanced Subsonic Technology (AST) 22-Inch Low Noise Research Fan Rig Preliminary Design of ADP-Type Fan 3

NASA Technical Reports Server (NTRS)

Jeracki, Robert J. (Technical Monitor); Topol, David A.; Ingram, Clint L.; Larkin, Michael J.; Roche, Charles H.; Thulin, Robert D.

2004-01-01

This report presents results of the work completed on the preliminary design of Fan 3 of NASA s 22-inch Fan Low Noise Research project. Fan 3 was intended to build on the experience gained from Fans 1 and 2 by demonstrating noise reduction technology that surpasses 1992 levels by 6 dB. The work was performed as part of NASA s Advanced Subsonic Technology (AST) program. Work on this task was conducted in the areas of CFD code validation, acoustic prediction and validation, rotor parametric studies, and fan exit guide vane (FEGV) studies up to the time when a NASA decision was made to cancel the design, fabrication and testing phases of the work. The scope of the program changed accordingly to concentrate on two subtasks: (1) Rig data analysis and CFD code validation and (2) Fan and FEGV optimization studies. The results of the CFD code validation work showed that this tool predicts 3D flowfield features well from the blade trailing edge to about a chord downstream. The CFD tool loses accuracy as the distance from the trailing edge increases beyond a blade chord. The comparisons of noise predictions to rig test data showed that both the tone noise tool and the broadband noise tool demonstrated reasonable agreement with the data to the degree that these tools can reliably be used for design work. The section on rig airflow and inlet separation analysis describes the method used to determine total fan airflow, shows the good agreement of predicted boundary layer profiles to measured profiles, and shows separation angles of attack ranging from 29.5 to 27deg for the range of airflows tested. The results of the rotor parametric studies were significant in leading to the decision not to pursue a new rotor design for Fan 3 and resulted in recommendations to concentrate efforts on FEGV stator designs. The ensuing parametric study on FEGV designs showed the potential for 8 to 10 EPNdB noise reduction relative to the baseline.

A measure of smoking abstinence-related motivational engagement: Development and initial validation

PubMed Central

Heckman, Bryan W.; Ditre, Joseph W.; Brandon, Thomas H.

2010-01-01

Introduction: Although a great deal of research has focused on measuring motivation and readiness to quit smoking, little research has assessed gross motivational changes after a smoker has made an attempt to quit smoking. Unlike previous single-item global measures of motivation to remain abstinent, we developed the abstinence-related motivational engagement (ARME) scale to evaluate the degree to which abstinence motivation is reflected by an ex-smoker’s daily experience in areas that include cognitive effort, priority, vigilance, and excitement. The aim of this study was to collect reliability and initial construct validity data on this new measure. Methods: Participants were 199 ex-smokers recruited from the community and smoking cessation Web sites. Participants completed online measures including a global motivation measure, the ARME scale, demographic questionnaire, and a measure of cessation self-efficacy. Results: The 16-item ARME questionnaire demonstrated high internal consistency reliability (α = .89). Analyses provided support for convergent, discriminant, and construct validity of the scale. ARME demonstrated the predicted correlation with a traditional measure of global cessation motivation, yet, also as predicted, only the ARME was negatively associated with length of abstinence. Moreover, as hypothesized, ex-smokers engaged in the quitting process via ongoing smoking Web site participation showed higher ARME scores than a comparison community sample. A five-item short form demonstrated similar psychometric properties. Discussion: This study provided initial support for the ARME construct and offers two versions of a reliable instrument for assessing this construct. Future research will examine the ARME as a predictor of cessation outcome and a potential target for relapse prevention. PMID:20190004

A measure of smoking abstinence-related motivational engagement: development and initial validation.

PubMed

Simmons, Vani N; Heckman, Bryan W; Ditre, Joseph W; Brandon, Thomas H

2010-04-01

Although a great deal of research has focused on measuring motivation and readiness to quit smoking, little research has assessed gross motivational changes after a smoker has made an attempt to quit smoking. Unlike previous single-item global measures of motivation to remain abstinent, we developed the abstinence-related motivational engagement (ARME) scale to evaluate the degree to which abstinence motivation is reflected by an ex-smoker's daily experience in areas that include cognitive effort, priority, vigilance, and excitement. The aim of this study was to collect reliability and initial construct validity data on this new measure. Participants were 199 ex-smokers recruited from the community and smoking cessation Web sites. Participants completed online measures including a global motivation measure, the ARME scale, demographic questionnaire, and a measure of cessation self-efficacy. The 16-item ARME questionnaire demonstrated high internal consistency reliability (alpha = .89). Analyses provided support for convergent, discriminant, and construct validity of the scale. ARME demonstrated the predicted correlation with a traditional measure of global cessation motivation, yet, also as predicted, only the ARME was negatively associated with length of abstinence. Moreover, as hypothesized, ex-smokers engaged in the quitting process via ongoing smoking Web site participation showed higher ARME scores than a comparison community sample. A five-item short form demonstrated similar psychometric properties. This study provided initial support for the ARME construct and offers two versions of a reliable instrument for assessing this construct. Future research will examine the ARME as a predictor of cessation outcome and a potential target for relapse prevention.

Predicting Relapse among Young Adults: Psychometric Validation of the Advanced Warning of Relapse (AWARE) Scale

PubMed Central

Kelly, John F.; Hoeppner, Bettina B.; Urbanoski, Karen A.; Slaymaker, Valerie

2011-01-01

Objective Failure to maintain abstinence despite incurring severe harm is perhaps the key defining feature of addiction. Relapse prevention strategies have been developed to attenuate this propensity to relapse, but predicting who will, and who will not, relapse has stymied attempts to more efficiently tailor treatments according to relapse risk profile. Here we examine the psychometric properties of a promising relapse risk measure - the Advance WArning of RElapse scale (AWARE) scale (Miller and Harris, 2000) in an understudied but clinically important sample of young adults. Method Inpatient youth (N=303; Age 18-24; 26% female) completed the AWARE scale and the Brief Symptom Inventory-18 (BSI) at the end of residential treatment, and at 1-, 3-, and 6-months following discharge. Internal and convergent validity was tested for each of these four timepoints using confirmatory factor analysis and correlations (with BSI scores). Predictive validity was tested for relapse 1, 3, and 6 months following discharge, as was incremental utility, where AWARE scores were used as predictors of any substance use while controlling for treatment entry substance use severity and having spent time in a controlled environment following treatment. Results Confirmatory factor analysis revealed a single, internally consistent, 25-item factor that demonstrated convergent validity and predicted subsequent relapse alone and when controlling for other important relapse risk predictors. Conclusions The AWARE scale may be a useful and efficient clinical tool for assessing short-term relapse risk among young people and, thus, could serve to enhance the effectiveness of relapse prevention efforts. PMID:21700396

Predicting relapse among young adults: psychometric validation of the Advanced WArning of RElapse (AWARE) scale.

PubMed

Kelly, John F; Hoeppner, Bettina B; Urbanoski, Karen A; Slaymaker, Valerie

2011-10-01

Failure to maintain abstinence despite incurring severe harm is perhaps the key defining feature of addiction. Relapse prevention strategies have been developed to attenuate this propensity to relapse, but predicting who will, and who will not, relapse has stymied attempts to more efficiently tailor treatments according to relapse risk profile. Here we examine the psychometric properties of a promising relapse risk measure-the Advance WArning of RElapse (AWARE) scale (Miller & Harris, 2000) in an understudied but clinically important sample of young adults. Inpatient youth (N=303; Ages 18-24; 26% female) completed the AWARE scale and the Brief Symptom Inventory-18 (BSI) at the end of residential treatment, and at 1-, 3-, and 6-months following discharge. Internal and convergent validity was tested for each of these four timepoints using confirmatory factor analysis and correlations (with BSI scores). Predictive validity was tested for relapse 1, 3, and 6 months following discharge, as was incremental utility, where AWARE scores were used as predictors of any substance use while controlling for treatment entry substance use severity and having spent time in a controlled environment following treatment. Confirmatory factor analysis revealed a single, internally consistent, 25-item factor that demonstrated convergent validity and predicted subsequent relapse alone and when controlling for other important relapse risk predictors. The AWARE scale may be a useful and efficient clinical tool for assessing short-term relapse risk among young people and, thus, could serve to enhance the effectiveness of relapse prevention efforts. Copyright © 2011 Elsevier Ltd. All rights reserved.

Validation of the Hospital Anxiety and Depression Scale in patients with epilepsy.

PubMed

Wiglusz, Mariusz S; Landowski, Jerzy; Michalak, Lidia; Cubała, Wiesław J

2016-05-01

Despite the fact that depressive disorders are the most common comorbidities among patients with epilepsy (PWEs), they often go unrecognized and untreated. The availability of validated screening instruments to detect depression in PWEs is limited. The aim of the present study was to validate the Hospital Anxiety and Depression Scale (HADS) in adult PWEs. A consecutive group of 118 outpatient PWEs was invited to participate in the study. Ninety-six patients met inclusion criteria, completed HADS, and were examined by a trained psychiatrist using Structured Clinical Interview (SCID-I) for DSM-IV-TR. Receiver operating characteristic (ROC) curves were used to determine the optimal threshold scores for the HADS depression subscale (HADS-D). Receiver operating characteristic analyses showed areas under the curve at approximately 84%. For diagnoses of MDD, the HADS-D demonstrated the best psychometric properties for a cutoff score ≥7 with sensitivity of 90.5%, specificity of 70.7%, positive predictive value of 46.3%, and negative predictive value of 96.4%. In the case of the group with 'any depressive disorder', the HADS-D optimum cutoff score was ≥6 with sensitivity of 82.5%, specificity of 73.2%, positive predictive value of 68.8%, and negative predictive value of 85.4%. The HADS-D proved to be a valid and reliable psychometric instrument in terms of screening for depressive disorders in PWEs. In the epilepsy setting, HADS-D maintains adequate sensitivity, acceptable specificity, and high NPV but low PPV for diagnosing MDD with an optimum cutoff score ≥7. Copyright © 2016 Elsevier Inc. All rights reserved.

Design Characteristics Influence Performance of Clinical Prediction Rules in Validation: A Meta-Epidemiological Study

PubMed Central

Ban, Jong-Wook; Emparanza, José Ignacio; Urreta, Iratxe; Burls, Amanda

2016-01-01

Background Many new clinical prediction rules are derived and validated. But the design and reporting quality of clinical prediction research has been less than optimal. We aimed to assess whether design characteristics of validation studies were associated with the overestimation of clinical prediction rules’ performance. We also aimed to evaluate whether validation studies clearly reported important methodological characteristics. Methods Electronic databases were searched for systematic reviews of clinical prediction rule studies published between 2006 and 2010. Data were extracted from the eligible validation studies included in the systematic reviews. A meta-analytic meta-epidemiological approach was used to assess the influence of design characteristics on predictive performance. From each validation study, it was assessed whether 7 design and 7 reporting characteristics were properly described. Results A total of 287 validation studies of clinical prediction rule were collected from 15 systematic reviews (31 meta-analyses). Validation studies using case-control design produced a summary diagnostic odds ratio (DOR) 2.2 times (95% CI: 1.2–4.3) larger than validation studies using cohort design and unclear design. When differential verification was used, the summary DOR was overestimated by twofold (95% CI: 1.2 -3.1) compared to complete, partial and unclear verification. The summary RDOR of validation studies with inadequate sample size was 1.9 (95% CI: 1.2 -3.1) compared to studies with adequate sample size. Study site, reliability, and clinical prediction rule was adequately described in 10.1%, 9.4%, and 7.0% of validation studies respectively. Conclusion Validation studies with design shortcomings may overestimate the performance of clinical prediction rules. The quality of reporting among studies validating clinical prediction rules needs to be improved. PMID:26730980

Design Characteristics Influence Performance of Clinical Prediction Rules in Validation: A Meta-Epidemiological Study.

PubMed

Ban, Jong-Wook; Emparanza, José Ignacio; Urreta, Iratxe; Burls, Amanda

2016-01-01

Many new clinical prediction rules are derived and validated. But the design and reporting quality of clinical prediction research has been less than optimal. We aimed to assess whether design characteristics of validation studies were associated with the overestimation of clinical prediction rules' performance. We also aimed to evaluate whether validation studies clearly reported important methodological characteristics. Electronic databases were searched for systematic reviews of clinical prediction rule studies published between 2006 and 2010. Data were extracted from the eligible validation studies included in the systematic reviews. A meta-analytic meta-epidemiological approach was used to assess the influence of design characteristics on predictive performance. From each validation study, it was assessed whether 7 design and 7 reporting characteristics were properly described. A total of 287 validation studies of clinical prediction rule were collected from 15 systematic reviews (31 meta-analyses). Validation studies using case-control design produced a summary diagnostic odds ratio (DOR) 2.2 times (95% CI: 1.2-4.3) larger than validation studies using cohort design and unclear design. When differential verification was used, the summary DOR was overestimated by twofold (95% CI: 1.2 -3.1) compared to complete, partial and unclear verification. The summary RDOR of validation studies with inadequate sample size was 1.9 (95% CI: 1.2 -3.1) compared to studies with adequate sample size. Study site, reliability, and clinical prediction rule was adequately described in 10.1%, 9.4%, and 7.0% of validation studies respectively. Validation studies with design shortcomings may overestimate the performance of clinical prediction rules. The quality of reporting among studies validating clinical prediction rules needs to be improved.

Meta-analytic estimates predict the effectiveness of emotion regulation strategies in the "real world": reply to Augustine and Hemenover (2013).

PubMed

Miles, Eleanor; Sheeran, Paschal; Webb, Thomas L

2013-05-01

Augustine and Hemenover (2013) were right to state that meta-analyses should be accurate and generalizable. However, we disagree that our meta-analysis of emotion regulation strategies (Webb, Miles, & Sheeran, 2012) fell short in these respects. Augustine and Hemenover's concerns appear to have accrued from misunderstandings of our inclusion criteria or from disagreements with methodological decisions that are crucial to the validity of meta-analysis. This response clarifies the bases of these decisions and discusses implications for the accuracy and validity of meta-analyses. Furthermore, we show that our findings are consistent with theoretical predictions and previous reviews, and we present new evidence that the effect sizes that we obtained are generalizable. In particular, we demonstrate that our estimates of the effectiveness of emotion regulation strategies reveal how well these strategies predict important emotional outcomes over 1 year. PsycINFO Database Record (c) 2013 APA, all rights reserved.

Development and validation of an electronic phenotyping algorithm for chronic kidney disease

PubMed Central

Nadkarni, Girish N; Gottesman, Omri; Linneman, James G; Chase, Herbert; Berg, Richard L; Farouk, Samira; Nadukuru, Rajiv; Lotay, Vaneet; Ellis, Steve; Hripcsak, George; Peissig, Peggy; Weng, Chunhua; Bottinger, Erwin P

2014-01-01

Twenty-six million Americans are estimated to have chronic kidney disease (CKD) with increased risk for cardiovascular disease and end stage renal disease. CKD is frequently undiagnosed and patients are unaware, hampering intervention. A tool for accurate and timely identification of CKD from electronic medical records (EMR) could improve healthcare quality and identify patients for research. As members of eMERGE (electronic medical records and genomics) Network, we developed an automated phenotyping algorithm that can be deployed to identify rapidly diabetic and/or hypertensive CKD cases and controls in health systems with EMRs It uses diagnostic codes, laboratory results, medication and blood pressure records, and textual information culled from notes. Validation statistics demonstrated positive predictive values of 96% and negative predictive values of 93.3. Similar results were obtained on implementation by two independent eMERGE member institutions. The algorithm dramatically outperformed identification by ICD-9-CM codes with 63% positive and 54% negative predictive values, respectively. PMID:25954398

Body shape indices are predictors for estimating fat-free mass in male athletes

PubMed Central

Aoki, Toru; Komori, Daisuke; Oyamada, Kazuyuki; Murata, Kensuke; Fujita, Eiji; Akamine, Takuya; Urita, Yoshihisa; Yamamoto, Masayoshi

2018-01-01

It is unknown whether body size and body shape parameters can be predictors for estimating whole body fat-free mass (FFM) in male athletes. This study aimed to investigate whether body size and shape variables can be predictors for FFM in male athletes. Using a whole-body dual-energy X-ray absorptiometry scanner, whole body fat mass (FM) and FFM were determined in 132 male athletes and 14 sedentary males. The sample was divided into two groups: validation (N = 98) and cross-validation (N = 48) groups. Body height (BH), body mass (BM), and waist circumference at immediately above the iliac crest (W) were measured. BM-to-W and W-to-BH ratios were calculated as indices of body shapes. Stepwise multiple regression analysis revealed that BM/W and W/BH were selected as explainable variables for predicting FFM. The equation developed in the validation group was FFM (kg) = 0.883 × BM/W (kg/m) + 43.674 × W/BH (cm/cm)– 41.480 [R2 = 0.900, SEE (%SEE) = 2.3 kg (3.8%)], which was validated in the cross-validation group. Thus, the current results demonstrate that an equation using BM/W and W/BH as independent variables is applicable for predicting FFM in male athletes. PMID:29346452

Development and Psychometric Evaluation of the HPV Clinical Trial Survey for Parents (CTSP-HPV) Using Traditional Survey Development Methods and Community Engagement Principles.

PubMed

Cunningham, Jennifer; Wallston, Kenneth A; Wilkins, Consuelo H; Hull, Pamela C; Miller, Stephania T

2015-12-01

This study describes the development and psychometric evaluation of HPV Clinical Trial Survey for Parents with Children Aged 9 to 15 (CTSP-HPV) using traditional instrument development methods and community engagement principles. An expert panel and parental input informed survey content and parents recommended study design changes (e.g., flyer wording). A convenience sample of 256 parents completed the final survey measuring parental willingness to consent to HPV clinical trial (CT) participation and other factors hypothesized to influence willingness (e.g., HPV vaccine benefits). Cronbach's a, Spearman correlations, and multiple linear regression were used to estimate internal consistency, convergent and discriminant validity, and predictively validity, respectively. Internal reliability was confirmed for all scales (a ≥ 0.70.). Parental willingness was positively associated (p < 0.05) with trust in medical researchers, adolescent CT knowledge, HPV vaccine benefits, advantages of adolescent CTs (r range 0.33-0.42), supporting convergent validity. Moderate discriminant construct validity was also demonstrated. Regression results indicate reasonable predictive validity with the six scales accounting for 31% of the variance in parents' willingness. This instrument can inform interventions based on factors that influence parental willingness, which may lead to the eventual increase in trial participation. Further psychometric testing is warranted. © 2015 Wiley Periodicals, Inc.

Predictive Validation of an Influenza Spread Model

PubMed Central

Hyder, Ayaz; Buckeridge, David L.; Leung, Brian

2013-01-01

Background Modeling plays a critical role in mitigating impacts of seasonal influenza epidemics. Complex simulation models are currently at the forefront of evaluating optimal mitigation strategies at multiple scales and levels of organization. Given their evaluative role, these models remain limited in their ability to predict and forecast future epidemics leading some researchers and public-health practitioners to question their usefulness. The objective of this study is to evaluate the predictive ability of an existing complex simulation model of influenza spread. Methods and Findings We used extensive data on past epidemics to demonstrate the process of predictive validation. This involved generalizing an individual-based model for influenza spread and fitting it to laboratory-confirmed influenza infection data from a single observed epidemic (1998–1999). Next, we used the fitted model and modified two of its parameters based on data on real-world perturbations (vaccination coverage by age group and strain type). Simulating epidemics under these changes allowed us to estimate the deviation/error between the expected epidemic curve under perturbation and observed epidemics taking place from 1999 to 2006. Our model was able to forecast absolute intensity and epidemic peak week several weeks earlier with reasonable reliability and depended on the method of forecasting-static or dynamic. Conclusions Good predictive ability of influenza epidemics is critical for implementing mitigation strategies in an effective and timely manner. Through the process of predictive validation applied to a current complex simulation model of influenza spread, we provided users of the model (e.g. public-health officials and policy-makers) with quantitative metrics and practical recommendations on mitigating impacts of seasonal influenza epidemics. This methodology may be applied to other models of communicable infectious diseases to test and potentially improve their predictive ability. PMID:23755236

Using the Johns Hopkins' Aggregated Diagnosis Groups (ADGs) to predict 1-year mortality in population-based cohorts of patients with diabetes in Ontario, Canada.

PubMed

Austin, P C; Shah, B R; Newman, A; Anderson, G M

2012-09-01

There are limited validated methods to ascertain comorbidities for risk adjustment in ambulatory populations of patients with diabetes using administrative health-care databases. The objective was to examine the ability of the Johns Hopkins' Aggregated Diagnosis Groups to predict mortality in population-based ambulatory samples of both incident and prevalent subjects with diabetes. Retrospective cohorts constructed using population-based administrative data. The incident cohort consisted of all 346,297 subjects diagnosed with diabetes between 1 April 2004 and 31 March 2008. The prevalent cohort consisted of all 879,849 subjects with pre-existing diabetes on 1 January, 2007. The outcome was death within 1 year of the subject's index date. A logistic regression model consisting of age, sex and indicator variables for 22 of the 32 Johns Hopkins' Aggregated Diagnosis Group categories had excellent discrimination for predicting mortality in incident diabetes patients: the c-statistic was 0.87 in an independent validation sample. A similar model had excellent discrimination for predicting mortality in prevalent diabetes patients: the c-statistic was 0.84 in an independent validation sample. Both models demonstrated very good calibration, denoting good agreement between observed and predicted mortality across the range of predicted mortality in which the large majority of subjects lay. For comparative purposes, regression models incorporating the Charlson comorbidity index, age and sex, age and sex, and age alone had poorer discrimination than the model that incorporated the Johns Hopkins' Aggregated Diagnosis Groups. Logistical regression models using age, sex and the John Hopkins' Aggregated Diagnosis Groups were able to accurately predict 1-year mortality in population-based samples of patients with diabetes. © 2011 The Authors. Diabetic Medicine © 2011 Diabetes UK.

A quantitative property-property relationship for the internal diffusion coefficients of organic compounds in solid materials.

PubMed

Huang, L; Fantke, P; Ernstoff, A; Jolliet, O

2017-11-01

Indoor releases of organic chemicals encapsulated in solid materials are major contributors to human exposures and are directly related to the internal diffusion coefficient in solid materials. Existing correlations to estimate the diffusion coefficient are only valid for a limited number of chemical-material combinations. This paper develops and evaluates a quantitative property-property relationship (QPPR) to predict diffusion coefficients for a wide range of organic chemicals and materials. We first compiled a training dataset of 1103 measured diffusion coefficients for 158 chemicals in 32 consolidated material types. Following a detailed analysis of the temperature influence, we developed a multiple linear regression model to predict diffusion coefficients as a function of chemical molecular weight (MW), temperature, and material type (adjusted R 2 of .93). The internal validations showed the model to be robust, stable and not a result of chance correlation. The external validation against two separate prediction datasets demonstrated the model has good predicting ability within its applicability domain (Rext2>.8), namely MW between 30 and 1178 g/mol and temperature between 4 and 180°C. By covering a much wider range of organic chemicals and materials, this QPPR facilitates high-throughput estimates of human exposures for chemicals encapsulated in solid materials. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Predicting prolonged intensive care unit length of stay in patients undergoing coronary artery bypass surgery--development of an entirely preoperative scorecard.

PubMed

Herman, Christine; Karolak, Wojtek; Yip, Alexandra M; Buth, Karen J; Hassan, Ansar; Légaré, Jean-Francois

2009-10-01

We sought to develop a predictive model based exclusively on preoperative factors to identify patients at risk for PrlICULOS following coronary artery bypass grafting (CABG). Retrospective analysis was performed on patients undergoing isolated CABG at a single center between June 1998 and December 2002. PrlICULOS was defined as initial admission to ICU exceeding 72 h. A parsimonious risk-predictive model was constructed on the basis of preoperative factors, with subsequent internal validation. Of 3483 patients undergoing isolated CABG between June 1998 and December 2002, 411 (11.8%) experienced PrlICULOS. Overall in-hospital mortality was higher among these patients (14.4% vs. 1.2%, P

Limb-Enhancer Genie: An accessible resource of accurate enhancer predictions in the developing limb

DOE PAGES

Monti, Remo; Barozzi, Iros; Osterwalder, Marco; ...

2017-08-21

Epigenomic mapping of enhancer-associated chromatin modifications facilitates the genome-wide discovery of tissue-specific enhancers in vivo. However, reliance on single chromatin marks leads to high rates of false-positive predictions. More sophisticated, integrative methods have been described, but commonly suffer from limited accessibility to the resulting predictions and reduced biological interpretability. Here we present the Limb-Enhancer Genie (LEG), a collection of highly accurate, genome-wide predictions of enhancers in the developing limb, available through a user-friendly online interface. We predict limb enhancers using a combination of > 50 published limb-specific datasets and clusters of evolutionarily conserved transcription factor binding sites, taking advantage ofmore » the patterns observed at previously in vivo validated elements. By combining different statistical models, our approach outperforms current state-of-the-art methods and provides interpretable measures of feature importance. Our results indicate that including a previously unappreciated score that quantifies tissue-specific nuclease accessibility significantly improves prediction performance. We demonstrate the utility of our approach through in vivo validation of newly predicted elements. Moreover, we describe general features that can guide the type of datasets to include when predicting tissue-specific enhancers genome-wide, while providing an accessible resource to the general biological community and facilitating the functional interpretation of genetic studies of limb malformations.« less

Validation Metrics for Improving Our Understanding of Turbulent Transport - Moving Beyond Proof by Pretty Picture and Loud Assertion

NASA Astrophysics Data System (ADS)

Holland, C.

2013-10-01

Developing validated models of plasma dynamics is essential for confident predictive modeling of current and future fusion devices. This tutorial will present an overview of the key guiding principles and practices for state-of-the-art validation studies, illustrated using examples from investigations of turbulent transport in magnetically confined plasmas. The primary focus of the talk will be the development of quantiatve validation metrics, which are essential for moving beyond qualitative and subjective assessments of model performance and fidelity. Particular emphasis and discussion is given to (i) the need for utilizing synthetic diagnostics to enable quantitatively meaningful comparisons between simulation and experiment, and (ii) the importance of robust uncertainty quantification and its inclusion within the metrics. To illustrate these concepts, we first review the structure and key insights gained from commonly used ``global'' transport model metrics (e.g. predictions of incremental stored energy or radially-averaged temperature), as well as their limitations. Building upon these results, a new form of turbulent transport metrics is then proposed, which focuses upon comparisons of predicted local gradients and fluctuation characteristics against observation. We demonstrate the utility of these metrics by applying them to simulations and modeling of a newly developed ``validation database'' derived from the results of a systematic, multi-year turbulent transport validation campaign on the DIII-D tokamak, in which comprehensive profile and fluctuation measurements have been obtained from a wide variety of heating and confinement scenarios. Finally, we discuss extensions of these metrics and their underlying design concepts to other areas of plasma confinement research, including both magnetohydrodynamic stability and integrated scenario modeling. Supported by the US DOE under DE-FG02-07ER54917 and DE-FC02-08ER54977.

Development and validation of the Dimensional Anhedonia Rating Scale (DARS) in a community sample and individuals with major depression.

PubMed

Rizvi, Sakina J; Quilty, Lena C; Sproule, Beth A; Cyriac, Anna; Michael Bagby, R; Kennedy, Sidney H

2015-09-30

Anhedonia, a core symptom of Major Depressive Disorder (MDD), is predictive of antidepressant non-response. In contrast to the definition of anhedonia as a "loss of pleasure", neuropsychological studies provide evidence for multiple facets of hedonic function. The aim of the current study was to develop and validate the Dimensional Anhedonia Rating Scale (DARS), a dynamic scale that measures desire, motivation, effort and consummatory pleasure across hedonic domains. Following item selection procedures and reliability testing using data from community participants (N=229) (Study 1), the 17-item scale was validated in an online study with community participants (N=150) (Study 2). The DARS was also validated in unipolar or bipolar depressed patients (n=52) and controls (n=50) (Study 3). Principal components analysis of the 17-item DARS revealed a 4-component structure mapping onto the domains of anhedonia: hobbies, food/drink, social activities, and sensory experience. Reliability of the DARS subscales was high across studies (Cronbach's α=0.75-0.92). The DARS also demonstrated good convergent and divergent validity. Hierarchical regression analysis revealed the DARS showed additional utility over the Snaith-Hamilton Pleasure Scale (SHAPS) in predicting reward function and distinguishing MDD subgroups. These studies provide support for the reliability and validity of the DARS. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

The Reliability and Validity of the Thoracolumbar Injury Classification System in Pediatric Spine Trauma.

PubMed

Savage, Jason W; Moore, Timothy A; Arnold, Paul M; Thakur, Nikhil; Hsu, Wellington K; Patel, Alpesh A; McCarthy, Kathryn; Schroeder, Gregory D; Vaccaro, Alexander R; Dimar, John R; Anderson, Paul A

2015-09-15

The thoracolumbar injury classification system (TLICS) was evaluated in 20 consecutive pediatric spine trauma cases. The purpose of this study was to determine the reliability and validity of the TLICS in pediatric spine trauma. The TLICS was developed to improve the categorization and management of thoracolumbar trauma. TLICS has been shown to have good reliability and validity in the adult population. The clinical and radiographical findings of 20 pediatric thoracolumbar fractures were prospectively presented to 20 surgeons with disparate levels of training and experience with spinal trauma. These injuries were consecutively scored using the TLICS. Cohen unweighted κ coefficients and Spearman rank order correlation values were calculated for the key parameters (injury morphology, status of posterior ligamentous complex, neurological status, TLICS total score, and proposed management) to assess the inter-rater reliabilities. Five surgeons scored the same cases 3 months later to assess the intra-rater reliability. The actual management of each case was then compared with the treatment recommended by the TLICS algorithm to assess validity. The inter-rater κ statistics of all subgroups (injury morphology, status of the posterior ligamentous complex, neurological status, TLICS total score, and proposed treatment) were within the range of moderate to substantial reproducibility (0.524-0.958). All subgroups had excellent intra-rater reliability (0.748-1.000). The various indices for validity were calculated (80.3% correct, 0.836 sensitivity, 0.785 specificity, 0.676 positive predictive value, 0.899 negative predictive value). Overall, TLICS demonstrated good validity. The TLICS has good reliability and validity when used in the pediatric population. The inter-rater reliability of predicting management and indices for validity are lower than those in adults with thoracolumbar fractures, which is likely due to differences in the way children are treated for certain types of injuries. TLICS can be used to reliably categorize thoracolumbar injuries in the pediatric population; however, modifications may be needed to better guide treatment in this specific patient population. 4.

Development of Decision Support Formulas for the Prediction of Bladder Outlet Obstruction and Prostatic Surgery in Patients With Lower Urinary Tract Symptom/Benign Prostatic Hyperplasia: Part I, Development of the Formula and its Internal Validation.

PubMed

Choo, Min Soo; Yoo, Changwon; Cho, Sung Yong; Jeong, Seong Jin; Jeong, Chang Wook; Ku, Ja Hyeon; Oh, Seung-June

2017-04-01

As the elderly population increases, a growing number of patients have lower urinary tract symptom (LUTS)/benign prostatic hyperplasia (BPH). The aim of this study was to develop decision support formulas and nomograms for the prediction of bladder outlet obstruction (BOO) and for BOO-related surgical decision-making, and to validate them in patients with LUTS/BPH. Patient with LUTS/BPH between October 2004 and May 2014 were enrolled as a development cohort. The available variables included age, International Prostate Symptom Score, free uroflowmetry, postvoid residual volume, total prostate volume, and the results of a pressure-flow study. A causal Bayesian network analysis was used to identify relevant parameters. Using multivariate logistic regression analysis, formulas were developed to calculate the probabilities of having BOO and requiring prostatic surgery. Patients between June 2014 and December 2015 were prospectively enrolled for internal validation. Receiver operating characteristic curve analysis, calibration plots, and decision curve analysis were performed. A total of 1,179 male patients with LUTS/BPH, with a mean age of 66.1 years, were included as a development cohort. Another 253 patients were enrolled as an internal validation cohort. Using multivariate logistic regression analysis, 2 and 4 formulas were established to estimate the probabilities of having BOO and requiring prostatic surgery, respectively. Our analysis of the predictive accuracy of the model revealed area under the curve values of 0.82 for BOO and 0.87 for prostatic surgery. The sensitivity and specificity were 53.6% and 87.0% for BOO, and 91.6% and 50.0% for prostatic surgery, respectively. The calibration plot indicated that these prediction models showed a good correspondence. In addition, the decision curve analysis showed a high net benefit across the entire spectrum of probability thresholds. We established nomograms for the prediction of BOO and BOO-related prostatic surgery in patients with LUTS/BPH. Internal validation of the nomograms demonstrated that they predicted both having BOO and requiring prostatic surgery very well.

The Validity of the Montgomery-Asberg Depression Rating Scale in an Inpatient Sample with Alcohol Dependence

PubMed Central

Hobden, Breanne; Schwandt, Melanie L.; Carey, Mariko; Lee, Mary R.; Farokhnia, Mehdi; Bouhlal, Sofia; Oldmeadow, Christopher; Leggio, Lorenzo

2017-01-01

Background The Montgomery-Asberg Depression Rating Scale (MADRS) is commonly used to examine depressive symptoms in clinical settings, including facilities treating patients for alcohol addiction. No studies have examined the validity of the MADRS compared to an established clinical diagnostic tool of depression in this population. This study aimed to examine: 1) the validity of the MADRS compared to a clinical diagnosis of a depressive disorder (using the Structured Clinical Interview for DSM-IV (SCID)) in patients seeking treatment for alcohol dependence (AD); 2) whether the validity of the MADRS differs by type of SCID-based diagnosis of depression; and 3) which items contribute to the optimal predictive model of the MADRS compared to a SCID diagnosis of a depressive disorder. Methods Individuals seeking treatment for AD and admitted to an inpatient unit were administered the MADRS at day 2 of their detoxification program. Clinical diagnoses of AD and depression were made via the Structured Clinical Interview for the Diagnostic and Statistical Manual of Mental Disorders-IV at the beginning of treatment. Results In total, 803 participants were included in the study. The MADRS demonstrated low overall accuracy relative to the clinical diagnosis of depression with an area under the curve of 0.68. The optimal threshold for balancing sensitivity and specificity identified by the Euclidean distance was >14. This cut-point demonstrated a sensitivity of 66%, a specificity of 60%, a positive predictive value of 50% and a negative predictive value of 75%. The MADRS performed slightly better for major depressive disorders compared to alcohol-induced depression. Items related to lassitude, concentration and appetite slightly decreased the accuracy of the MADRS. Conclusion The MADRS does not appear to be an appropriate substitute for a diagnostic tool among alcohol-dependent patients. The MADRS may, however, still be a useful screening tool assuming careful consideration of cut-off scores. PMID:28421616

The Validity of the Montgomery-Asberg Depression Rating Scale in an Inpatient Sample with Alcohol Dependence.

PubMed

Hobden, Breanne; Schwandt, Melanie L; Carey, Mariko; Lee, Mary R; Farokhnia, Mehdi; Bouhlal, Sofia; Oldmeadow, Christopher; Leggio, Lorenzo

2017-06-01

The Montgomery-Asberg Depression Rating Scale (MADRS) is commonly used to examine depressive symptoms in clinical settings, including facilities treating patients for alcohol addiction. No studies have examined the validity of the MADRS compared to an established clinical diagnostic tool of depression in this population. This study aimed to examine the following: (i) the validity of the MADRS compared to a clinical diagnosis of a depressive disorder (using the Structured Clinical Interview for DSM-IV-TR [SCID-IV-TR]) in patients seeking treatment for alcohol dependence (AD); (ii) whether the validity of the MADRS differs by type of SCID-IV-TR-based diagnosis of depression; and (iii) which items contribute to the optimal predictive model of the MADRS compared to a SCID-IV-TR diagnosis of a depressive disorder. Individuals seeking treatment for AD and admitted to an inpatient unit were administered the MADRS at day 2 of their detoxification program. Clinical diagnoses of AD and depression were made via the SCID-IV-TR at the beginning of treatment. In total, 803 participants were included in the study. The MADRS demonstrated low overall accuracy relative to the clinical diagnosis of depression with an area under the receiver operating characteristic curve of 0.68. The optimal threshold for balancing sensitivity and specificity identified by the Euclidean distance was >14. This cut-point demonstrated a sensitivity of 66%, a specificity of 60%, a positive predictive value of 50%, and a negative predictive value of 75%. The MADRS performed slightly better for major depressive disorders compared to alcohol-induced depression. Items related to lassitude, concentration, and appetite slightly decreased the accuracy of the MADRS. The MADRS does not appear to be an appropriate substitute for a diagnostic tool among alcohol-dependent patients. The MADRS may, however, still be a useful screening tool assuming careful consideration of cut-points. Copyright © 2017 by the Research Society on Alcoholism.

Modeling the spatiotemporal dynamics of light and heat propagation for in vivo optogenetics

PubMed Central

Stujenske, Joseph M.; Spellman, Timothy; Gordon, Joshua A.

2015-01-01

Summary Despite the increasing use of optogenetics in vivo, the effects of direct light exposure to brain tissue are understudied. Of particular concern is the potential for heat induced by prolonged optical stimulation. We demonstrate that high intensity light, delivered through an optical fiber, is capable of elevating firing rate locally, even in the absence of opsin expression. Predicting the severity and spatial extent of any temperature increase during optogenetic stimulation is therefore of considerable importance. Here we describe a realistic model that simulates light and heat propagation during optogenetic experiments. We validated the model by comparing predicted and measured temperature changes in vivo. We further demonstrate the utility of this model by comparing predictions for various wavelengths of light and fiber sizes, as well as testing methods for reducing heat effects on neural targets in vivo. PMID:26166563

High Fidelity Ion Beam Simulation of High Dose Neutron Irradiation

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

Was, Gary; Wirth, Brian; Motta, Athur

The objective of this proposal is to demonstrate the capability to predict the evolution of microstructure and properties of structural materials in-reactor and at high doses, using ion irradiation as a surrogate for reactor irradiations. “Properties” includes both physical properties (irradiated microstructure) and the mechanical properties of the material. Demonstration of the capability to predict properties has two components. One is ion irradiation of a set of alloys to yield an irradiated microstructure and corresponding mechanical behavior that are substantially the same as results from neutron exposure in the appropriate reactor environment. Second is the capability to predict the irradiatedmore » microstructure and corresponding mechanical behavior on the basis of improved models, validated against both ion and reactor irradiations and verified against ion irradiations. Taken together, achievement of these objectives will yield an enhanced capability for simulating the behavior of materials in reactor irradiations.« less

Ethical leadership: meta-analytic evidence of criterion-related and incremental validity.

PubMed

Ng, Thomas W H; Feldman, Daniel C

2015-05-01

This study examines the criterion-related and incremental validity of ethical leadership (EL) with meta-analytic data. Across 101 samples published over the last 15 years (N = 29,620), we observed that EL demonstrated acceptable criterion-related validity with variables that tap followers' job attitudes, job performance, and evaluations of their leaders. Further, followers' trust in the leader mediated the relationships of EL with job attitudes and performance. In terms of incremental validity, we found that EL significantly, albeit weakly in some cases, predicted task performance, citizenship behavior, and counterproductive work behavior-even after controlling for the effects of such variables as transformational leadership, use of contingent rewards, management by exception, interactional fairness, and destructive leadership. The article concludes with a discussion of ways to strengthen the incremental validity of EL. (PsycINFO Database Record (c) 2015 APA, all rights reserved).

Assessing attitude toward same-sex marriage: scale development and validation.

PubMed

Lannutti, Pamela J; Lachlan, Kenneth A

2007-01-01

This paper reports the results of three studies conducted to develop, refine, and validate a scale which assessed heterosexual adults' attitudes toward same-sex marriage, the Attitude Toward Same-Sex Marriage Scale (ASSMS). The need for such a scale is evidenced in the increasing importance of same-sex marriage in the political arena of the United States and other nations, as well as the growing body of empirical research examining same-sex marriage and related issues (e.g., Lannutti, 2005; Solomon, Rothblum, & Balsam, 2004). The results demonstrate strong reliability, convergent validity, and predictive validity for the ASSMS and suggest that the ASSMS may be adapted to measure attitudes toward civil unions and other forms of relational recognition for same-sex couples. Gender comparisons using the validated scale showed that in college and non-college samples, women had a significantly more positive attitude toward same-sex marriage than did men.

Development and validation of the coping with terror scale.

PubMed

Stein, Nathan R; Schorr, Yonit; Litz, Brett T; King, Lynda A; King, Daniel W; Solomon, Zahava; Horesh, Danny

2013-10-01

Terrorism creates lingering anxiety about future attacks. In prior terror research, the conceptualization and measurement of coping behaviors were constrained by the use of existing coping scales that index reactions to daily hassles and demands. The authors created and validated the Coping with Terror Scale to fill the measurement gap. The authors emphasized content validity, leveraging the knowledge of terror experts and groups of Israelis. A multistep approach involved construct definition and item generation, trimming and refining the measure, exploring the factor structure underlying item responses, and garnering evidence for reliability and validity. The final scale comprised six factors that were generally consistent with the authors' original construct specifications. Scores on items linked to these factors demonstrate good reliability and validity. Future studies using the Coping with Terror Scale with other populations facing terrorist threats are needed to test its ability to predict resilience, functional impairment, and psychological distress.

The use of video clips in teleconsultation for preschool children with movement disorders.

PubMed

Gorter, Hetty; Lucas, Cees; Groothuis-Oudshoorn, Karin; Maathuis, Carel; van Wijlen-Hempel, Rietje; Elvers, Hans

2013-01-01

To investigate the reliability and validity of video clips in assessing movement disorders in preschool children. The study group included 27 children with neuromotor concerns. The explorative validity group included children with motor problems (n = 21) or with typical development (n = 9). Hempel screening was used for live observation of the child, full recording, and short video clips. The explorative study tested the validity of the clinical classifications "typical" or "suspect." Agreement between live observation and the full recording was almost perfect; Agreement for the clinical classification "typical" or "suspect" was substantial. Agreement between the full recording and short video clips was substantial to moderate. The explorative validity study, based on short video clips and the presence of a neuromotor developmental disorder, showed substantial agreement. Hempel screening enables reliable and valid observation of video clips, but further research is necessary to demonstrate the predictive value.

Nomogram Prediction of Overall Survival After Curative Irradiation for Uterine Cervical Cancer

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

Seo, YoungSeok; Yoo, Seong Yul; Kim, Mi-Sook

Purpose: The purpose of this study was to develop a nomogram capable of predicting the probability of 5-year survival after radical radiotherapy (RT) without chemotherapy for uterine cervical cancer. Methods and Materials: We retrospectively analyzed 549 patients that underwent radical RT for uterine cervical cancer between March 1994 and April 2002 at our institution. Multivariate analysis using Cox proportional hazards regression was performed and this Cox model was used as the basis for the devised nomogram. The model was internally validated for discrimination and calibration by bootstrap resampling. Results: By multivariate regression analysis, the model showed that age, hemoglobin levelmore » before RT, Federation Internationale de Gynecologie Obstetrique (FIGO) stage, maximal tumor diameter, lymph node status, and RT dose at Point A significantly predicted overall survival. The survival prediction model demonstrated good calibration and discrimination. The bootstrap-corrected concordance index was 0.67. The predictive ability of the nomogram proved to be superior to FIGO stage (p = 0.01). Conclusions: The devised nomogram offers a significantly better level of discrimination than the FIGO staging system. In particular, it improves predictions of survival probability and could be useful for counseling patients, choosing treatment modalities and schedules, and designing clinical trials. However, before this nomogram is used clinically, it should be externally validated.« less

Updated systematic review and meta-analysis of the performance of risk prediction rules in children and young people with febrile neutropenia.

PubMed

Phillips, Robert S; Lehrnbecher, Thomas; Alexander, Sarah; Sung, Lillian

2012-01-01

Febrile neutropenia is a common and potentially life-threatening complication of treatment for childhood cancer, which has increasingly been subject to targeted treatment based on clinical risk stratification. Our previous meta-analysis demonstrated 16 rules had been described and 2 of them subject to validation in more than one study. We aimed to advance our knowledge of evidence on the discriminatory ability and predictive accuracy of such risk stratification clinical decision rules (CDR) for children and young people with cancer by updating our systematic review. The review was conducted in accordance with Centre for Reviews and Dissemination methods, searching multiple electronic databases, using two independent reviewers, formal critical appraisal with QUADAS and meta-analysis with random effects models where appropriate. It was registered with PROSPERO: CRD42011001685. We found 9 new publications describing a further 7 new CDR, and validations of 7 rules. Six CDR have now been subject to testing across more than two data sets. Most validations demonstrated the rule to be less efficient than when initially proposed; geographical differences appeared to be one explanation for this. The use of clinical decision rules will require local validation before widespread use. Considerable uncertainty remains over the most effective rule to use in each population, and an ongoing individual-patient-data meta-analysis should develop and test a more reliable CDR to improve stratification and optimise therapy. Despite current challenges, we believe it will be possible to define an internationally effective CDR to harmonise the treatment of children with febrile neutropenia.

Updated Systematic Review and Meta-Analysis of the Performance of Risk Prediction Rules in Children and Young People with Febrile Neutropenia

PubMed Central

Phillips, Robert S.; Lehrnbecher, Thomas; Alexander, Sarah; Sung, Lillian

2012-01-01

Introduction Febrile neutropenia is a common and potentially life-threatening complication of treatment for childhood cancer, which has increasingly been subject to targeted treatment based on clinical risk stratification. Our previous meta-analysis demonstrated 16 rules had been described and 2 of them subject to validation in more than one study. We aimed to advance our knowledge of evidence on the discriminatory ability and predictive accuracy of such risk stratification clinical decision rules (CDR) for children and young people with cancer by updating our systematic review. Methods The review was conducted in accordance with Centre for Reviews and Dissemination methods, searching multiple electronic databases, using two independent reviewers, formal critical appraisal with QUADAS and meta-analysis with random effects models where appropriate. It was registered with PROSPERO: CRD42011001685. Results We found 9 new publications describing a further 7 new CDR, and validations of 7 rules. Six CDR have now been subject to testing across more than two data sets. Most validations demonstrated the rule to be less efficient than when initially proposed; geographical differences appeared to be one explanation for this. Conclusion The use of clinical decision rules will require local validation before widespread use. Considerable uncertainty remains over the most effective rule to use in each population, and an ongoing individual-patient-data meta-analysis should develop and test a more reliable CDR to improve stratification and optimise therapy. Despite current challenges, we believe it will be possible to define an internationally effective CDR to harmonise the treatment of children with febrile neutropenia. PMID:22693615

Development and validation of an instrument for rapidly assessing symptoms: the general symptom distress scale.

PubMed

Badger, Terry A; Segrin, Chris; Meek, Paula

2011-03-01

Symptom assessment has increasingly focused on the evaluation of total symptom distress or burden rather than assessing only individual symptoms. The challenge for clinicians and researchers alike is to assess symptoms, and to determine the symptom distress associated with the symptoms and the patient's ability for symptom management without a lengthy and burdensome assessment process. The objective of this article was to discuss the psychometric evaluation of a brief general symptom distress scale (GSDS) developed to assess specific symptoms and how they rank in relation to each other, the overall symptom distress associated with the symptom schema, and provide an assessment of how well or poorly that symptom schema is managed. Results from a pilot study about the initial development of the GSDS with 76 hospitalized patients are presented, followed by a more complete psychometric evaluation of the GSDS using three samples of cancer patients (n=190) and their social network members, called partners in these studies (n=94). Descriptive statistics were used to describe the GSDS symptoms, symptom distress, and symptom management. Point biserial correlations indexed the associations between dichotomous symptoms and continuous measures, and conditional probabilities were used to illustrate the substantial comorbidities of this sample. Internal consistency was examined using the KR-20 coefficient, and test-retest reliability was examined. Construct validity and predictive validity also were examined. The GSDS demonstrated satisfactory internal consistency and test-retest reliability, and good construct validity and predictive validity. The total score on the GSDS, symptom distress, and symptom management correlated significantly with related constructs of depression, positive and negative affect, and general health. The GSDS was able to demonstrate its ability to distinguish between those with or without chronic illness, and was able to significantly predict scores on criterion measures such as depression. Collectively, these results suggest that the GSDS is a straightforward and useful instrument for rapidly assessing symptoms that can disrupt health-related quality of life. Copyright © 2011 U.S. Cancer Pain Relief Committee. Published by Elsevier Inc. All rights reserved.

Subtyping attention-deficit/hyperactivity disorder using temperament dimensions: toward biologically based nosologic criteria.

PubMed

Karalunas, Sarah L; Fair, Damien; Musser, Erica D; Aykes, Kamari; Iyer, Swathi P; Nigg, Joel T

2014-09-01

Psychiatric nosology is limited by behavioral and biological heterogeneity within existing disorder categories. The imprecise nature of current nosologic distinctions limits both mechanistic understanding and clinical prediction. We demonstrate an approach consistent with the National Institute of Mental Health Research Domain Criteria initiative to identify superior, neurobiologically valid subgroups with better predictive capacity than existing psychiatric categories for childhood attention-deficit/hyperactivity disorder (ADHD). To refine subtyping of childhood ADHD by using biologically based behavioral dimensions (i.e., temperament), novel classification algorithms, and multiple external validators. A total of 437 clinically well-characterized, community-recruited children, with and without ADHD, participated in an ongoing longitudinal study. Baseline data were used to classify children into subgroups based on temperament dimensions and examine external validators including physiological and magnetic resonance imaging measures. One-year longitudinal follow-up data are reported for a subgroup of the ADHD sample to address stability and clinical prediction. Parent/guardian ratings of children on a measure of temperament were used as input features in novel community detection analyses to identify subgroups within the sample. Groups were validated using 3 widely accepted external validators: peripheral physiological characteristics (cardiac measures of respiratory sinus arrhythmia and pre-ejection period), central nervous system functioning (via resting-state functional connectivity magnetic resonance imaging), and clinical outcomes (at 1-year longitudinal follow-up). The community detection algorithm suggested 3 novel types of ADHD, labeled as mild (normative emotion regulation), surgent (extreme levels of positive approach-motivation), and irritable (extreme levels of negative emotionality, anger, and poor soothability). Types were independent of existing clinical demarcations including DSM-5 presentations or symptom severity. These types showed stability over time and were distinguished by unique patterns of cardiac physiological response, resting-state functional brain connectivity, and clinical outcomes 1 year later. Results suggest that a biologically informed temperament-based typology, developed with a discovery-based community detection algorithm, provides a superior description of heterogeneity in the ADHD population than does any current clinical nosologic criteria. This demonstration sets the stage for more aggressive attempts at a tractable, biologically based nosology.

Validating genetic markers of response to recombinant human growth hormone in children with growth hormone deficiency and Turner syndrome: the PREDICT validation study

PubMed Central

Stevens, Adam; Murray, Philip; Wojcik, Jerome; Raelson, John; Koledova, Ekaterina; Chatelain, Pierre

2016-01-01

Objective Single-nucleotide polymorphisms (SNPs) associated with the response to recombinant human growth hormone (r-hGH) have previously been identified in growth hormone deficiency (GHD) and Turner syndrome (TS) children in the PREDICT long-term follow-up (LTFU) study (Nbib699855). Here, we describe the PREDICT validation (VAL) study (Nbib1419249), which aimed to confirm these genetic associations. Design and methods Children with GHD (n = 293) or TS (n = 132) were recruited retrospectively from 29 sites in nine countries. All children had completed 1 year of r-hGH therapy. 48 SNPs previously identified as associated with first year growth response to r-hGH were genotyped. Regression analysis was used to assess the association between genotype and growth response using clinical/auxological variables as covariates. Further analysis was undertaken using random forest classification. Results The children were younger, and the growth response was higher in VAL study. Direct genotype analysis did not replicate what was found in the LTFU study. However, using exploratory regression models with covariates, a consistent relationship with growth response in both VAL and LTFU was shown for four genes – SOS1 and INPPL1 in GHD and ESR1 and PTPN1 in TS. The random forest analysis demonstrated that only clinical covariates were important in the prediction of growth response in mild GHD (>4 to <10 μg/L on GH stimulation test), however, in severe GHD (≤4 μg/L) several SNPs contributed (in IGF2, GRB10, FOS, IGFBP3 and GHRHR). Conclusions The PREDICT validation study supports, in an independent cohort, the association of four of 48 genetic markers with growth response to r-hGH treatment in both pre-pubertal GHD and TS children after controlling for clinical/auxological covariates. However, the contribution of these SNPs in a prediction model of first-year response is not sufficient for routine clinical use. PMID:27651465

Validating genetic markers of response to recombinant human growth hormone in children with growth hormone deficiency and Turner syndrome: the PREDICT validation study.

PubMed

Stevens, Adam; Murray, Philip; Wojcik, Jerome; Raelson, John; Koledova, Ekaterina; Chatelain, Pierre; Clayton, Peter

2016-12-01

Single-nucleotide polymorphisms (SNPs) associated with the response to recombinant human growth hormone (r-hGH) have previously been identified in growth hormone deficiency (GHD) and Turner syndrome (TS) children in the PREDICT long-term follow-up (LTFU) study (Nbib699855). Here, we describe the PREDICT validation (VAL) study (Nbib1419249), which aimed to confirm these genetic associations. Children with GHD (n = 293) or TS (n = 132) were recruited retrospectively from 29 sites in nine countries. All children had completed 1 year of r-hGH therapy. 48 SNPs previously identified as associated with first year growth response to r-hGH were genotyped. Regression analysis was used to assess the association between genotype and growth response using clinical/auxological variables as covariates. Further analysis was undertaken using random forest classification. The children were younger, and the growth response was higher in VAL study. Direct genotype analysis did not replicate what was found in the LTFU study. However, using exploratory regression models with covariates, a consistent relationship with growth response in both VAL and LTFU was shown for four genes - SOS1 and INPPL1 in GHD and ESR1 and PTPN1 in TS. The random forest analysis demonstrated that only clinical covariates were important in the prediction of growth response in mild GHD (>4 to <10 μg/L on GH stimulation test), however, in severe GHD (≤4 μg/L) several SNPs contributed (in IGF2, GRB10, FOS, IGFBP3 and GHRHR). The PREDICT validation study supports, in an independent cohort, the association of four of 48 genetic markers with growth response to r-hGH treatment in both pre-pubertal GHD and TS children after controlling for clinical/auxological covariates. However, the contribution of these SNPs in a prediction model of first-year response is not sufficient for routine clinical use. © 2016 European Society of Endocrinology.

High-throughput prediction of eucalypt lignin syringyl/guaiacyl content using multivariate analysis: a comparison between mid-infrared, near-infrared, and Raman spectroscopies for model development

PubMed Central

2014-01-01

Background In order to rapidly and efficiently screen potential biofuel feedstock candidates for quintessential traits, robust high-throughput analytical techniques must be developed and honed. The traditional methods of measuring lignin syringyl/guaiacyl (S/G) ratio can be laborious, involve hazardous reagents, and/or be destructive. Vibrational spectroscopy can furnish high-throughput instrumentation without the limitations of the traditional techniques. Spectral data from mid-infrared, near-infrared, and Raman spectroscopies was combined with S/G ratios, obtained using pyrolysis molecular beam mass spectrometry, from 245 different eucalypt and Acacia trees across 17 species. Iterations of spectral processing allowed the assembly of robust predictive models using partial least squares (PLS). Results The PLS models were rigorously evaluated using three different randomly generated calibration and validation sets for each spectral processing approach. Root mean standard errors of prediction for validation sets were lowest for models comprised of Raman (0.13 to 0.16) and mid-infrared (0.13 to 0.15) spectral data, while near-infrared spectroscopy led to more erroneous predictions (0.18 to 0.21). Correlation coefficients (r) for the validation sets followed a similar pattern: Raman (0.89 to 0.91), mid-infrared (0.87 to 0.91), and near-infrared (0.79 to 0.82). These statistics signify that Raman and mid-infrared spectroscopy led to the most accurate predictions of S/G ratio in a diverse consortium of feedstocks. Conclusion Eucalypts present an attractive option for biofuel and biochemical production. Given the assortment of over 900 different species of Eucalyptus and Corymbia, in addition to various species of Acacia, it is necessary to isolate those possessing ideal biofuel traits. This research has demonstrated the validity of vibrational spectroscopy to efficiently partition different potential biofuel feedstocks according to lignin S/G ratio, significantly reducing experiment and analysis time and expense while providing non-destructive, accurate, global, predictive models encompassing a diverse array of feedstocks. PMID:24955114

Evaluation of CASL boiling model for DNB performance in full scale 5x5 fuel bundle with spacer grids

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

Kim, Seung Jun

As one of main tasks for FY17 CASL-THM activity, Evaluation study on applicability of the CASL baseline boiling model for 5x5 DNB application is conducted and the predictive capability of the DNB analysis is reported here. While the baseline CASL-boiling model (GEN- 1A) approach has been successfully implemented and validated with a single pipe application in the previous year’s task, the extended DNB validation for realistic sub-channels with detailed spacer grid configurations are tasked in FY17. The focus area of the current study is to demonstrate the robustness and feasibility of the CASL baseline boiling model for DNB performance inmore » a full 5x5 fuel bundle application. A quantitative evaluation of the DNB predictive capability is performed by comparing with corresponding experimental measurements (i.e. reference for the model validation). The reference data are provided from the Westinghouse Electricity Company (WEC). Two different grid configurations tested here include Non-Mixing Vane Grid (NMVG), and Mixing Vane Grid (MVG). Thorough validation studies with two sub-channel configurations are performed at a wide range of realistic PWR operational conditions.« less

Assessing Workplace Emotional Intelligence: Development and Validation of an Ability-based Measure.

PubMed

Krishnakumar, Sukumarakurup; Hopkins, Kay; Szmerekovsky, Joseph G; Robinson, Michael D

2016-01-01

Existing measures of Emotional Intelligence (EI), defined as the ability to perceive, understand, and manage emotions for productive purposes, have displayed limitations in predicting workplace outcomes, likely in part because they do not target this context. Such considerations led to the development of an ability EI measure with work-related scenarios in which respondents infer the likely emotions (perception) and combinations of emotion (understanding) that would occur to protagonists while rating the effectiveness of ways of responding (management). Study 1 (n = 290 undergraduates) used item-total correlations to select scenarios from a larger pool and Study 2 (n = 578) reduced the measure-termed the NEAT-to 30 scenarios on the basis of structural equation modeling. Study 3 (n = 96) then showed that the NEAT had expected correlations with personality and cognitive ability and Study 4 (n = 85) demonstrated convergent validity with other ability EI measures. Last, study 5 (n = 91) established that the NEAT had predictive validity with respect to job satisfaction, job stress, and job performance. The findings affirm the importance of EI in the workplace in the context of a valid new instrument for assessing relevant skills.

A Clinical Tool for the Prediction of Venous Thromboembolism in Pediatric Trauma Patients.

PubMed

Connelly, Christopher R; Laird, Amy; Barton, Jeffrey S; Fischer, Peter E; Krishnaswami, Sanjay; Schreiber, Martin A; Zonies, David H; Watters, Jennifer M

2016-01-01

Although rare, the incidence of venous thromboembolism (VTE) in pediatric trauma patients is increasing, and the consequences of VTE in children are significant. Studies have demonstrated increasing VTE risk in older pediatric trauma patients and improved VTE rates with institutional interventions. While national evidence-based guidelines for VTE screening and prevention are in place for adults, none exist for pediatric patients, to our knowledge. To develop a risk prediction calculator for VTE in children admitted to the hospital after traumatic injury to assist efforts in developing screening and prophylaxis guidelines for this population. Retrospective review of 536,423 pediatric patients 0 to 17 years old using the National Trauma Data Bank from January 1, 2007, to December 31, 2012. Five mixed-effects logistic regression models of varying complexity were fit on a training data set. Model validity was determined by comparison of the area under the receiver operating characteristic curve (AUROC) for the training and validation data sets from the original model fit. A clinical tool to predict the risk of VTE based on individual patient clinical characteristics was developed from the optimal model. Diagnosis of VTE during hospital admission. Venous thromboembolism was diagnosed in 1141 of 536,423 children (overall rate, 0.2%). The AUROCs in the training data set were high (range, 0.873-0.946) for each model, with minimal AUROC attenuation in the validation data set. A prediction tool was developed from a model that achieved a balance of high performance (AUROCs, 0.945 and 0.932 in the training and validation data sets, respectively; P = .048) and parsimony. Points are assigned to each variable considered (Glasgow Coma Scale score, age, sex, intensive care unit admission, intubation, transfusion of blood products, central venous catheter placement, presence of pelvic or lower extremity fractures, and major surgery), and the points total is converted to a VTE risk score. The predicted risk of VTE ranged from 0.0% to 14.4%. We developed a simple clinical tool to predict the risk of developing VTE in pediatric trauma patients. It is based on a model created using a large national database and was internally validated. The clinical tool requires external validation but provides an initial step toward the development of the specific VTE protocols for pediatric trauma patients.

An Approach for Validating Actinide and Fission Product Burnup Credit Criticality Safety Analyses: Criticality (k eff) Predictions

DOE PAGES

Scaglione, John M.; Mueller, Don E.; Wagner, John C.

2014-12-01

One of the most important remaining challenges associated with expanded implementation of burnup credit in the United States is the validation of depletion and criticality calculations used in the safety evaluation—in particular, the availability and use of applicable measured data to support validation, especially for fission products (FPs). Applicants and regulatory reviewers have been constrained by both a scarcity of data and a lack of clear technical basis or approach for use of the data. In this study, this paper describes a validation approach for commercial spent nuclear fuel (SNF) criticality safety (k eff) evaluations based on best-available data andmore » methods and applies the approach for representative SNF storage and transport configurations/conditions to demonstrate its usage and applicability, as well as to provide reference bias results. The criticality validation approach utilizes not only available laboratory critical experiment (LCE) data from the International Handbook of Evaluated Criticality Safety Benchmark Experiments and the French Haut Taux de Combustion program to support validation of the principal actinides but also calculated sensitivities, nuclear data uncertainties, and limited available FP LCE data to predict and verify individual biases for relevant minor actinides and FPs. The results demonstrate that (a) sufficient critical experiment data exist to adequately validate k eff calculations via conventional validation approaches for the primary actinides, (b) sensitivity-based critical experiment selection is more appropriate for generating accurate application model bias and uncertainty, and (c) calculated sensitivities and nuclear data uncertainties can be used for generating conservative estimates of bias for minor actinides and FPs. Results based on the SCALE 6.1 and the ENDF/B-VII.0 cross-section libraries indicate that a conservative estimate of the bias for the minor actinides and FPs is 1.5% of their worth within the application model. Finally, this paper provides a detailed description of the approach and its technical bases, describes the application of the approach for representative pressurized water reactor and boiling water reactor safety analysis models, and provides reference bias results based on the prerelease SCALE 6.1 code package and ENDF/B-VII nuclear cross-section data.« less

Predictive validity of a selection centre testing non-technical skills for recruitment to training in anaesthesia.

PubMed

Gale, T C E; Roberts, M J; Sice, P J; Langton, J A; Patterson, F C; Carr, A S; Anderson, I R; Lam, W H; Davies, P R F

2010-11-01

Assessment centres are an accepted method of recruitment in industry and are gaining popularity within medicine. We describe the development and validation of a selection centre for recruitment to speciality training in anaesthesia based on an assessment centre model incorporating the rating of candidate's non-technical skills. Expert consensus identified non-technical skills suitable for assessment at the point of selection. Four stations-structured interview, portfolio review, presentation, and simulation-were developed, the latter two being realistic scenarios of work-related tasks. Evaluation of the selection centre focused on applicant and assessor feedback ratings, inter-rater agreement, and internal consistency reliability coefficients. Predictive validity was sought via correlations of selection centre scores with subsequent workplace-based ratings of appointed trainees. Two hundred and twenty-four candidates were assessed over two consecutive annual recruitment rounds; 68 were appointed and followed up during training. Candidates and assessors demonstrated strong approval of the selection centre with more than 70% of ratings 'good' or 'excellent'. Mean inter-rater agreement coefficients ranged from 0.62 to 0.77 and internal consistency reliability of the selection centre score was high (Cronbach's α=0.88-0.91). The overall selection centre score was a good predictor of workplace performance during the first year of appointment. An assessment centre model based on the rating of non-technical skills can produce a reliable and valid selection tool for recruitment to speciality training in anaesthesia. Early results on predictive validity are encouraging and justify further development and evaluation.

Feasibility and validity of the structured attention module among economically disadvantaged preschool-age children.

PubMed

Bush, Hillary H; Eisenhower, Abbey; Briggs-Gowan, Margaret; Carter, Alice S

2015-01-01

Rooted in the theory of attention put forth by Mirsky, Anthony, Duncan, Ahearn, and Kellam (1991), the Structured Attention Module (SAM) is a developmentally sensitive, computer-based performance task designed specifically to assess sustained selective attention among 3- to 6-year-old children. The current study addressed the feasibility and validity of the SAM among 64 economically disadvantaged preschool-age children (mean age = 58 months; 55% female); a population known to be at risk for attention problems and adverse math performance outcomes. Feasibility was demonstrated by high completion rates and strong associations between SAM performance and age. Principal Factor Analysis with rotation produced robust support for a three-factor model (Accuracy, Speed, and Endurance) of SAM performance, which largely corresponded with existing theorized models of selective and sustained attention. Construct validity was evidenced by positive correlations between SAM Composite scores and all three SAM factors and IQ, and between SAM Accuracy and sequential memory. Value-added predictive validity was not confirmed through main effects of SAM on math performance above and beyond age and IQ; however, significant interactions by child sex were observed: Accuracy and Endurance both interacted with child sex to predict math performance. In both cases, the SAM factors predicted math performance more strongly for girls than for boys. There were no overall sex differences in SAM performance. In sum, the current findings suggest that interindividual variation in sustained selective attention, and potentially other aspects of attention and executive function, among young, high-risk children can be captured validly with developmentally sensitive measures.

Can nutrient status of four woody plant species be predicted using field spectrometry?

NASA Astrophysics Data System (ADS)

Ferwerda, Jelle G.; Skidmore, Andrew K.

This paper demonstrates the potential of hyperspectral remote sensing to predict the chemical composition (i.e., nitrogen, phosphorous, calcium, potassium, sodium, and magnesium) of three tree species (i.e., willow, mopane and olive) and one shrub species (i.e., heather). Reflectance spectra, derivative spectra and continuum-removed spectra were compared in terms of predictive power. Results showed that the best predictions for nitrogen, phosphorous, and magnesium occur when using derivative spectra, and the best predictions for sodium, potassium, and calcium occur when using continuum-removed data. To test whether a general model for multiple species is also valid for individual species, a bootstrapping routine was applied. Prediction accuracies for the individual species were lower then prediction accuracies obtained for the combined dataset for all except one element/species combination, indicating that indices with high prediction accuracies at the landscape scale are less appropriate to detect the chemical content of individual species.

Correspondence between ion-cluster and bulk thermodynamics: on the validity of the cluster pair approximation

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

Vlcek, Lukas; Chialvo, Ariel; Simonson, J Michael

2013-01-01

Molecular models and experimental estimates based on the cluster pair approximation (CPA) provide inconsistent predictions of absolute single-ion hydration properties. To understand the origin of this discrepancy we used molecular simulations to study the transition between hydration of alkali metal and halide ions in small aqueous clusters and bulk water. The results demonstrate that the assumptions underlying the CPA are not generally valid as a result of a significant shift in the ion hydration free energies (~15 kJ/mol) and enthalpies (~47 kJ/mol) in the intermediate range of cluster sizes. When this effect is accounted for, the systematic differences between modelsmore » and experimental predictions disappear, and the value of absolute proton hydration enthalpy based on the CPA gets in closer agreement with other estimates.« less

NASA's Evolutionary Xenon Thruster (NEXT) Project Qualification Propellant Throughput Milestone: Performance, Erosion, and Thruster Service Life Prediction After 450 kg

NASA Technical Reports Server (NTRS)

Herman, Daniel A.

2010-01-01

The NASA s Evolutionary Xenon Thruster (NEXT) program is tasked with significantly improving and extending the capabilities of current state-of-the-art NSTAR thruster. The service life capability of the NEXT ion thruster is being assessed by thruster wear test and life-modeling of critical thruster components, such as the ion optics and cathodes. The NEXT Long-Duration Test (LDT) was initiated to validate and qualify the NEXT thruster propellant throughput capability. The NEXT thruster completed the primary goal of the LDT; namely to demonstrate the project qualification throughput of 450 kg by the end of calendar year 2009. The NEXT LDT has demonstrated 28,500 hr of operation and processed 466 kg of xenon throughput--more than double the throughput demonstrated by the NSTAR flight-spare. Thruster performance changes have been consistent with a priori predictions. Thruster erosion has been minimal and consistent with the thruster service life assessment, which predicts the first failure mode at greater than 750 kg throughput. The life-limiting failure mode for NEXT is predicted to be loss of structural integrity of the accelerator grid due to erosion by charge-exchange ions.

Building Scientific Confidence in the Development and Evaluation of Read-Across - GenRA: Evaluating local validity for read-across prediction using chemical and biological information (SOT/QSAR conference)

EPA Science Inventory

Read-across remains a popular data gap filling technique within category and analogue approaches for regulatory purposes. Acceptance of read-across is an ongoing challenge with several efforts underway for identifying and addressing uncertainties. Here we demonstrate an algorithm...

EL_PSSM-RT: DNA-binding residue prediction by integrating ensemble learning with PSSM Relation Transformation.

PubMed

Zhou, Jiyun; Lu, Qin; Xu, Ruifeng; He, Yulan; Wang, Hongpeng

2017-08-29

Prediction of DNA-binding residue is important for understanding the protein-DNA recognition mechanism. Many computational methods have been proposed for the prediction, but most of them do not consider the relationships of evolutionary information between residues. In this paper, we first propose a novel residue encoding method, referred to as the Position Specific Score Matrix (PSSM) Relation Transformation (PSSM-RT), to encode residues by utilizing the relationships of evolutionary information between residues. PDNA-62 and PDNA-224 are used to evaluate PSSM-RT and two existing PSSM encoding methods by five-fold cross-validation. Performance evaluations indicate that PSSM-RT is more effective than previous methods. This validates the point that the relationship of evolutionary information between residues is indeed useful in DNA-binding residue prediction. An ensemble learning classifier (EL_PSSM-RT) is also proposed by combining ensemble learning model and PSSM-RT to better handle the imbalance between binding and non-binding residues in datasets. EL_PSSM-RT is evaluated by five-fold cross-validation using PDNA-62 and PDNA-224 as well as two independent datasets TS-72 and TS-61. Performance comparisons with existing predictors on the four datasets demonstrate that EL_PSSM-RT is the best-performing method among all the predicting methods with improvement between 0.02-0.07 for MCC, 4.18-21.47% for ST and 0.013-0.131 for AUC. Furthermore, we analyze the importance of the pair-relationships extracted by PSSM-RT and the results validates the usefulness of PSSM-RT for encoding DNA-binding residues. We propose a novel prediction method for the prediction of DNA-binding residue with the inclusion of relationship of evolutionary information and ensemble learning. Performance evaluation shows that the relationship of evolutionary information between residues is indeed useful in DNA-binding residue prediction and ensemble learning can be used to address the data imbalance issue between binding and non-binding residues. A web service of EL_PSSM-RT ( http://hlt.hitsz.edu.cn:8080/PSSM-RT_SVM/ ) is provided for free access to the biological research community.

Development and Psychometric Evaluation of the Child Neglect Questionnaire.

PubMed

Stewart, Chris; Kirisci, Levent; Long, Abigail L; Giancola, Peter R

2015-11-01

Neglect poses a significant risk for children throughout their development and is often linked with serious consequences that reach into adulthood. The Child Neglect Questionnaire (CNQ) fills existing gaps by incorporating multiple perspectives from both parents and the child, as well as measuring the complex phenomenon of neglect multidimensionally. Furthermore, this measure addresses the need for an instrument specifically developed for late childhood (ages 10-12), as much of the extant evidence and corresponding measures focus on young children and their mothers. A panel of three psychologists, using Cicchetti's model of child neglect as a theoretical guide, began by selecting items from an existing database. Results of exploratory and confirmatory factor analyses and item response theory demonstrated the unidimensionality of physical, emotional, educational, and supervision neglect as well as a second-order construct of child neglect. Analyses controlling for risk status due to father's substance use disorder, socioeconomic status, and child's ethnicity demonstrated that father's and mother's (parental) neglect, particularly in the child's versions, had sound concurrent and predictive validity. Concurrently, at age 10-12, the child's version of both parents' neglect correlated with their parenting behaviors evaluated by other available measures. Prospectively, from 10-12 years of age to 11-13 years of age, parental neglect predicted child's drug use frequency with coexisting psychological dysregulation, psychiatric symptoms, antisocial behavior, non-normative sexual behavior, involvement with deviant peers and leisure activities thus demonstrating sound predictive validity. Also, internal consistency and inter-rater reliability were excellent. The CNQ, particularly the child's version, may thus be useful for detecting children at high risk for parental neglect. © The Author(s) 2014.

Artificial neural network prediction of aircraft aeroelastic behavior

NASA Astrophysics Data System (ADS)

Pesonen, Urpo Juhani

An Artificial Neural Network that predicts aeroelastic behavior of aircraft is presented. The neural net was designed to predict the shape of a flexible wing in static flight conditions using results from a structural analysis and an aerodynamic analysis performed with traditional computational tools. To generate reliable training and testing data for the network, an aeroelastic analysis code using these tools as components was designed and validated. To demonstrate the advantages and reliability of Artificial Neural Networks, a network was also designed and trained to predict airfoil maximum lift at low Reynolds numbers where wind tunnel data was used for the training. Finally, a neural net was designed and trained to predict the static aeroelastic behavior of a wing without the need to iterate between the structural and aerodynamic solvers.

End-of-Discharge and End-of-Life Prediction in Lithium-Ion Batteries with Electrochemistry-Based Aging Models

NASA Technical Reports Server (NTRS)

Daigle, Matthew; Kulkarni, Chetan S.

2016-01-01

As batteries become increasingly prevalent in complex systems such as aircraft and electric cars, monitoring and predicting battery state of charge and state of health becomes critical. In order to accurately predict the remaining battery power to support system operations for informed operational decision-making, age-dependent changes in dynamics must be accounted for. Using an electrochemistry-based model, we investigate how key parameters of the battery change as aging occurs, and develop models to describe aging through these key parameters. Using these models, we demonstrate how we can (i) accurately predict end-of-discharge for aged batteries, and (ii) predict the end-of-life of a battery as a function of anticipated usage. The approach is validated through an experimental set of randomized discharge profiles.

Disentangling the Predictive Validity of High School Grades for Academic Success in University

ERIC Educational Resources Information Center

Vulperhorst, Jonne; Lutz, Christel; de Kleijn, Renske; van Tartwijk, Jan

2018-01-01

To refine selective admission models, we investigate which measure of prior achievement has the best predictive validity for academic success in university. We compare the predictive validity of three core high school subjects to the predictive validity of high school grade point average (GPA) for academic achievement in a liberal arts university…

A comparison of modified versions of the Static-99 and the Sex Offender Risk Appraisal Guide.

PubMed

Nunes, Kevin L; Firestone, Philip; Bradford, John M; Greenberg, David M; Broom, Ian

2002-07-01

The predictive validity of 2 risk assessment instruments for sex offenders, modified versions of the Static-99 and the Sex Offender Risk Appraisal Guide, was examined and compared in a sample of 258 adult male sex offenders. In addition, the independent contributions to the prediction of recidivism made by each instrument and by various phallometric indices were explored. Both instruments demonstrated moderate levels of predictive accuracy for sexual and violent (including sexual) recidivism. They were not significantly different in terms of their predictive accuracy for sexual or violent recidivism, nor did they contribute independently to the prediction of sexual or violent recidivism. Of the phallometric indices examined, only the pedophile index added significantly to the prediction of sexual recidivism, but not violent recidivism, above the Static-99 alone.

COMPI Fertility Problem Stress Scales is a brief, valid and reliable tool for assessing stress in patients seeking treatment.

PubMed

Sobral, Maria P; Costa, Maria E; Schmidt, Lone; Martins, Mariana V

2017-02-01

Are the Copenhagen Multi-Centre Psychosocial Infertility research program Fertility Problem Stress Scales (COMPI-FPSS) a reliable and valid measure across gender and culture? The COMPI-FPSS is a valid and reliable measure, presenting excellent or good fit in the majority of the analyzed countries, and demonstrating full invariance across genders and partial invariance across cultures. Cross-cultural and gender validation is needed to consider a measure as standard care within fertility. The present study is the first attempting to establish comparability of fertility-related stress across genders and countries. Cross-sectional study. First, we tested the structure of the COMPI-FPSS. Then, reliability and validity (convergent and discriminant) were examined for the final model. Finally, measurement invariance both across genders and cultures was tested. Our final sample had 3923 fertility patients (1691 men and 2232 women) recruited in clinical settings from seven different countries: Denmark, China, Croatia, Germany, Greece, Hungary and Sweden. Participants had a mean age of 34 years and the majority (84%) were childless. Findings confirmed the original three-factor structure of the COMPI-FPSS, although suggesting a shortened measurement model using less items that fitted the data better than the full version model. While data from the Chinese and Croatian subsamples did not fit, all other counties presented good fit (χ 2 /df ≤ 5.4; comparative fit index ≥ 0.94; root-mean-square error of approximation ≤ 0.07; modified expected cross-validation index ≤ 0.77). In general, reliability, convergent validity, and discriminant validity were observed in all subscales from each country (composite reliability ≥ 0.63; average variance extracted ≥ 0.38; squared correlation ≥ 0.13). Full invariance was established across genders, and partial invariance was demonstrated across countries. Generalizability regarding the validation of the COMPI-FPSS cannot be made regarding infertile individuals not seeking treatment, or non-European patients. This study did not investigate predictive validity, and hence the capability of this instrument in detecting changes in fertility-specific adjustment over time and predicting the psychological impact needs to be established in future research. Besides extending knowledge on the psychometric properties of one of the most used fertility stress questionnaire, this study demonstrates both research and clinical usefulness of the COMPI-FPSS. This study was supported by European Union Funds (FEDER/COMPETE-Operational Competitiveness Program, and by national funds (FCT-Portuguese Foundation for Science and Technology) under the projects PTDC/MHC-PSC/4195/2012 and SFRH/BPD/85789/2012). There are no conflicts of interest to declare. N/A. © The Author 2016. Published by Oxford University Press on behalf of the European Society of Human Reproduction and Embryology. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Validation of Alternative In Vitro Methods to Animal Testing: Concepts, Challenges, Processes and Tools.

PubMed

Griesinger, Claudius; Desprez, Bertrand; Coecke, Sandra; Casey, Warren; Zuang, Valérie

This chapter explores the concepts, processes, tools and challenges relating to the validation of alternative methods for toxicity and safety testing. In general terms, validation is the process of assessing the appropriateness and usefulness of a tool for its intended purpose. Validation is routinely used in various contexts in science, technology, the manufacturing and services sectors. It serves to assess the fitness-for-purpose of devices, systems, software up to entire methodologies. In the area of toxicity testing, validation plays an indispensable role: "alternative approaches" are increasingly replacing animal models as predictive tools and it needs to be demonstrated that these novel methods are fit for purpose. Alternative approaches include in vitro test methods, non-testing approaches such as predictive computer models up to entire testing and assessment strategies composed of method suites, data sources and decision-aiding tools. Data generated with alternative approaches are ultimately used for decision-making on public health and the protection of the environment. It is therefore essential that the underlying methods and methodologies are thoroughly characterised, assessed and transparently documented through validation studies involving impartial actors. Importantly, validation serves as a filter to ensure that only test methods able to produce data that help to address legislative requirements (e.g. EU's REACH legislation) are accepted as official testing tools and, owing to the globalisation of markets, recognised on international level (e.g. through inclusion in OECD test guidelines). Since validation creates a credible and transparent evidence base on test methods, it provides a quality stamp, supporting companies developing and marketing alternative methods and creating considerable business opportunities. Validation of alternative methods is conducted through scientific studies assessing two key hypotheses, reliability and relevance of the test method for a given purpose. Relevance encapsulates the scientific basis of the test method, its capacity to predict adverse effects in the "target system" (i.e. human health or the environment) as well as its applicability for the intended purpose. In this chapter we focus on the validation of non-animal in vitro alternative testing methods and review the concepts, challenges, processes and tools fundamental to the validation of in vitro methods intended for hazard testing of chemicals. We explore major challenges and peculiarities of validation in this area. Based on the notion that validation per se is a scientific endeavour that needs to adhere to key scientific principles, namely objectivity and appropriate choice of methodology, we examine basic aspects of study design and management, and provide illustrations of statistical approaches to describe predictive performance of validated test methods as well as their reliability.

Status of the NASA's Evolutionary Xenon Thruster (NEXT) Long-Duration Test After 30,352 Hours of Operation

NASA Technical Reports Server (NTRS)

Herman, Daniel A.

2010-01-01

The NASA s Evolutionary Xenon Thruster (NEXT) program is tasked with significantly improving and extending the capabilities of current state-of-the-art NSTAR thruster. The service life capability of the NEXT ion thruster is being assessed by thruster wear test and life-modeling of critical thruster components, such as the ion optics and cathodes. The NEXT Long-Duration Test (LDT) was initiated to validate and qualify the NEXT thruster propellant throughput capability. The NEXT thruster completed the primary goal of the LDT; namely to demonstrate the project qualification throughput of 450 kg by the end of calendar year 2009. The NEXT LDT has demonstrated 30,352 hr of operation and processed 490 kg of xenon throughput--surpassing the NSTAR Extended Life Test hours demonstrated and more than double the throughput demonstrated by the NSTAR flight-spare. Thruster performance changes have been consistent with a priori predictions. Thruster erosion has been minimal and consistent with the thruster service life assessment, which predicts the first failure mode at greater than 750 kg throughput. The life-limiting failure mode for NEXT is predicted to be loss of structural integrity of the accelerator grid due to erosion by charge-exchange ions.

Measuring Community Connectedness among Diverse Sexual Minority Populations

PubMed Central

Frost, David M.; Meyer, Ilan H.

2011-01-01

Theory and research agree that connectedness to the lesbian, gay, bisexual, and transgender (LGBT) community is an important construct to account for in understanding issues related to health and well-being among gay and bisexual men. However, the measurement of this construct among lesbian and bisexual women or racial/ethnic minority individuals has not yet been adequately investigated. This study examined the reliability and validity of an existing measure of Connectedness to the LGBT Community among a diverse group of sexual minority individuals in New York City and whether differences in connectedness existed across gender and race/ethnicity. Scores on the measure demonstrated both internal consistency and construct stability across subgroups defined by gender and race/ethnicity. The subgroups did not differ in their mean levels of connectedness and scores on the measure demonstrated factorial, convergent, and discriminate validity both generally and within each of the subgroups. Inconsistencies were observed with regard to which scores on the measure demonstrated predictive validity in their associations with indicators of mental health and well-being. The scale is a useful tool for researchers and practitioners interested in understanding the role of community connectedness in the lives of diverse populations of sexual minority individuals. PMID:21512945

Methods for evaluating the predictive accuracy of structural dynamic models

NASA Technical Reports Server (NTRS)

Hasselman, Timothy K.; Chrostowski, Jon D.

1991-01-01

Modeling uncertainty is defined in terms of the difference between predicted and measured eigenvalues and eigenvectors. Data compiled from 22 sets of analysis/test results was used to create statistical databases for large truss-type space structures and both pretest and posttest models of conventional satellite-type space structures. Modeling uncertainty is propagated through the model to produce intervals of uncertainty on frequency response functions, both amplitude and phase. This methodology was used successfully to evaluate the predictive accuracy of several structures, including the NASA CSI Evolutionary Structure tested at Langley Research Center. Test measurements for this structure were within + one-sigma intervals of predicted accuracy for the most part, demonstrating the validity of the methodology and computer code.

Experimental investigation of an RNA sequence space

NASA Technical Reports Server (NTRS)

Lee, Youn-Hyung; Dsouza, Lisa; Fox, George E.

1993-01-01

Modern rRNAs are the historic consequence of an ongoing evolutionary exploration of a sequence space. These extant sequences belong to a special subset of the sequence space that is comprised only of those primary sequences that can validly perform the biological function(s) required of the particular RNA. If it were possible to readily identify all such valid sequences, stochastic predictions could be made about the relative likelihood of various evolutionary pathways available to an RNA. Herein an experimental system which can assess whether a particular sequence is likely to have validity as a eubacterial 5S rRNA is described. A total of ten naturally occurring, and hence known to be valid, sequences and two point mutants of unknown validity were used to test the usefulness of the approach. Nine of the ten valid sequences tested positive whereas both mutants tested as clearly defective. The tenth valid sequence gave results that would be interpreted as reflecting a borderline status were the answer not known. These results demonstrate that it is possible to experimentally determine which sequences in local regions of the sequence space are potentially valid 5S rRNAs.

Validation of the Seattle angina questionnaire in women with ischemic heart disease.

PubMed

Patel, Krishna K; Arnold, Suzanne V; Chan, Paul S; Tang, Yuanyuan; Jones, Philip G; Guo, Jianping; Buchanan, Donna M; Qintar, Mohammed; Decker, Carole; Morrow, David A; Spertus, John A

2018-07-01

Although the Seattle Angina Questionnaire (SAQ) has been widely used to assess disease-specific health status in patients with ischemic heart disease, it was originally developed in a predominantly male population and its validity in women has been questioned. Using data from 8892 men and 4013 women across 2 multicenter trials and 5 registries, we assessed the construct validity, test-retest reliability, responsiveness to clinical change, and predictive validity of the SAQ Summary Score (SS) and its 5 subdomains (Physical Limitation (PL), Anginal Stability (AS), Angina Frequency (AF), Treatment Satisfaction (TS), and Quality of Life (QoL)) separately in men and women. Comparable correlations of the SAQ SS with Canadian Cardiovascular Society class was demonstrated in both men and women (-0.48 for men, -0.46 for women). Similar correlations between the SAQ PL scale with treadmill exercise duration and Short Form-12 (SF-12) Physical Component Summary were observed in women and men (0.34-0.63 and 0.40-0.63, respectively). SAQ AS scores were significantly lower for both men and women with acute syndromes compared with 1 month later. The SAQ AF scale was strongly correlated with daily angina diaries (0.62 for men and 0.66 for women). The SAQ QoL scores were moderately correlated with the EQ5D visual analog scale and SF-12 general health question in men (0.43-0.50) and women (0.33-0.39). All SAQ scales demonstrated excellent reliability (intraclass correlation ≥0.78) in both men and women with stable CAD and were very sensitive to change after percutaneous coronary intervention (≥15-point difference in scores, standardized response mean ≥ 0.67). The SAQ SS was similarly predictive of 1-year mortality and cardiac re-hospitalizations for both men and women. The SAQ demonstrates similar psychometric properties in men and women with CAD. These findings provide evidence for validity of the SAQ in assessing women with IHD. Copyright © 2018 Elsevier Inc. All rights reserved.

Validation of the Australian Propensity for Angry Driving Scale (Aus-PADS).

PubMed

Leal, Nerida L; Pachana, Nancy A

2009-09-01

The present study used a university sample to assess the test-retest reliability and validity of the Australian Propensity for Angry Driving Scale (Aus-PADS). The scale has stability over time, and convergent validity was established, as Aus-PADS scores correlated significantly with established anger and impulsivity measures. Discriminant validity was also established, as Aus-PADS scores did not correlate with Venturesomeness scores. The Aus-PADS has demonstrated criterion validity, as scores were correlated with behavioural measures, such as yelling at other drivers, gesturing at other drivers, and feeling angry but not doing anything. Aus-PADS scores reliably predicted the frequency of these behaviours over and above other study variables. No significant relationship between aggressive driving and crash involvement was observed. It was concluded that the Aus-PADS is a reliable and valid tool appropriate for use in Australian research, and that the potential relationship between aggressive driving and crash involvement warrants further investigation with a more representative (and diverse) driver sample.

Carbon-carbon primary structure for SSTO vehicles

NASA Astrophysics Data System (ADS)

Croop, Harold C.; Lowndes, Holland B.

1997-01-01

A hot structures development program is nearing completion to validate use of carbon-carbon composite structure for primary load carrying members in a single-stage-to-orbit, or SSTO, vehicle. A four phase program was pursued which involved design development and fabrication of a full-scale wing torque box demonstration component. The design development included vehicle and component selection, design criteria and approach, design data development, demonstration component design and analysis, test fixture design and analysis, demonstration component test planning, and high temperature test instrumentation development. The fabrication effort encompassed fabrication of structural elements for mechanical property verification as well as fabrication of the demonstration component itself and associated test fixturing. The demonstration component features 3D woven graphite preforms, integral spars, oxidation inhibited matrix, chemical vapor deposited (CVD) SiC oxidation protection coating, and ceramic matrix composite fasteners. The demonstration component has been delivered to the United States Air Force (USAF) for testing in the Wright Laboratory Structural Test Facility, WPAFB, OH. Multiple thermal-mechanical load cycles will be applied simulating two atmospheric cruise missions and one orbital mission. This paper discusses the overall approach to validation testing of the wing box component and presents some preliminary analytical test predictions.

Predicting risk and outcomes for frail older adults: an umbrella review of frailty screening tools

PubMed Central

Apóstolo, João; Cooke, Richard; Bobrowicz-Campos, Elzbieta; Santana, Silvina; Marcucci, Maura; Cano, Antonio; Vollenbroek-Hutten, Miriam; Germini, Federico; Holland, Carol

2017-01-01

EXECUTIVE SUMMARY Background A scoping search identified systematic reviews on diagnostic accuracy and predictive ability of frailty measures in older adults. In most cases, research was confined to specific assessment measures related to a specific clinical model. Objectives To summarize the best available evidence from systematic reviews in relation to reliability, validity, diagnostic accuracy and predictive ability of frailty measures in older adults. Inclusion criteria Population Older adults aged 60 years or older recruited from community, primary care, long-term residential care and hospitals. Index test Available frailty measures in older adults. Reference test Cardiovascular Health Study phenotype model, the Canadian Study of Health and Aging cumulative deficit model, Comprehensive Geriatric Assessment or other reference tests. Diagnosis of interest Frailty defined as an age-related state of decreased physiological reserves characterized by an increased risk of poor clinical outcomes. Types of studies Quantitative systematic reviews. Search strategy A three-step search strategy was utilized to find systematic reviews, available in English, published between January 2001 and October 2015. Methodological quality Assessed by two independent reviewers using the Joanna Briggs Institute critical appraisal checklist for systematic reviews and research synthesis. Data extraction Two independent reviewers extracted data using the standardized data extraction tool designed for umbrella reviews. Data synthesis Data were only presented in a narrative form due to the heterogeneity of included reviews. Results Five reviews with a total of 227,381 participants were included in this umbrella review. Two reviews focused on reliability, validity and diagnostic accuracy; two examined predictive ability for adverse health outcomes; and one investigated validity, diagnostic accuracy and predictive ability. In total, 26 questionnaires and brief assessments and eight frailty indicators were analyzed, most of which were applied to community-dwelling older people. The Frailty Index was examined in almost all these dimensions, with the exception of reliability, and its diagnostic and predictive characteristics were shown to be satisfactory. Gait speed showed high sensitivity, but only moderate specificity, and excellent predictive ability for future disability in activities of daily living. The Tilburg Frailty Indicator was shown to be a reliable and valid measure for frailty screening, but its diagnostic accuracy was not evaluated. Screening Letter, Timed-up-and-go test and PRISMA 7 (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) demonstrated high sensitivity and moderate specificity for identifying frailty. In general, low physical activity, variously measured, was one of the most powerful predictors of future decline in activities of daily living. Conclusion Only a few frailty measures seem to be demonstrably valid, reliable and diagnostically accurate, and have good predictive ability. Among them, the Frailty Index and gait speed emerged as the most useful in routine care and community settings. However, none of the included systematic reviews provided responses that met all of our research questions on their own and there is a need for studies that could fill this gap, covering all these issues within the same study. Nevertheless, it was clear that no suitable tool for assessing frailty appropriately in emergency departments was identified. PMID:28398987

Enhanced NMR Discrimination of Pharmaceutically Relevant Molecular Crystal Forms through Fragment-Based Ab Initio Chemical Shift Predictions.

PubMed

Hartman, Joshua D; Day, Graeme M; Beran, Gregory J O

2016-11-02

Chemical shift prediction plays an important role in the determination or validation of crystal structures with solid-state nuclear magnetic resonance (NMR) spectroscopy. One of the fundamental theoretical challenges lies in discriminating variations in chemical shifts resulting from different crystallographic environments. Fragment-based electronic structure methods provide an alternative to the widely used plane wave gauge-including projector augmented wave (GIPAW) density functional technique for chemical shift prediction. Fragment methods allow hybrid density functionals to be employed routinely in chemical shift prediction, and we have recently demonstrated appreciable improvements in the accuracy of the predicted shifts when using the hybrid PBE0 functional instead of generalized gradient approximation (GGA) functionals like PBE. Here, we investigate the solid-state 13 C and 15 N NMR spectra for multiple crystal forms of acetaminophen, phenobarbital, and testosterone. We demonstrate that the use of the hybrid density functional instead of a GGA provides both higher accuracy in the chemical shifts and increased discrimination among the different crystallographic environments. Finally, these results also provide compelling evidence for the transferability of the linear regression parameters mapping predicted chemical shieldings to chemical shifts that were derived in an earlier study.

Enhanced NMR Discrimination of Pharmaceutically Relevant Molecular Crystal Forms through Fragment-Based Ab Initio Chemical Shift Predictions

PubMed Central

2016-01-01

Chemical shift prediction plays an important role in the determination or validation of crystal structures with solid-state nuclear magnetic resonance (NMR) spectroscopy. One of the fundamental theoretical challenges lies in discriminating variations in chemical shifts resulting from different crystallographic environments. Fragment-based electronic structure methods provide an alternative to the widely used plane wave gauge-including projector augmented wave (GIPAW) density functional technique for chemical shift prediction. Fragment methods allow hybrid density functionals to be employed routinely in chemical shift prediction, and we have recently demonstrated appreciable improvements in the accuracy of the predicted shifts when using the hybrid PBE0 functional instead of generalized gradient approximation (GGA) functionals like PBE. Here, we investigate the solid-state 13C and 15N NMR spectra for multiple crystal forms of acetaminophen, phenobarbital, and testosterone. We demonstrate that the use of the hybrid density functional instead of a GGA provides both higher accuracy in the chemical shifts and increased discrimination among the different crystallographic environments. Finally, these results also provide compelling evidence for the transferability of the linear regression parameters mapping predicted chemical shieldings to chemical shifts that were derived in an earlier study. PMID:27829821

Predicting protein-binding regions in RNA using nucleotide profiles and compositions.

PubMed

Choi, Daesik; Park, Byungkyu; Chae, Hanju; Lee, Wook; Han, Kyungsook

2017-03-14

Motivated by the increased amount of data on protein-RNA interactions and the availability of complete genome sequences of several organisms, many computational methods have been proposed to predict binding sites in protein-RNA interactions. However, most computational methods are limited to finding RNA-binding sites in proteins instead of protein-binding sites in RNAs. Predicting protein-binding sites in RNA is more challenging than predicting RNA-binding sites in proteins. Recent computational methods for finding protein-binding sites in RNAs have several drawbacks for practical use. We developed a new support vector machine (SVM) model for predicting protein-binding regions in mRNA sequences. The model uses sequence profiles constructed from log-odds scores of mono- and di-nucleotides and nucleotide compositions. The model was evaluated by standard 10-fold cross validation, leave-one-protein-out (LOPO) cross validation and independent testing. Since actual mRNA sequences have more non-binding regions than protein-binding regions, we tested the model on several datasets with different ratios of protein-binding regions to non-binding regions. The best performance of the model was obtained in a balanced dataset of positive and negative instances. 10-fold cross validation with a balanced dataset achieved a sensitivity of 91.6%, a specificity of 92.4%, an accuracy of 92.0%, a positive predictive value (PPV) of 91.7%, a negative predictive value (NPV) of 92.3% and a Matthews correlation coefficient (MCC) of 0.840. LOPO cross validation showed a lower performance than the 10-fold cross validation, but the performance remains high (87.6% accuracy and 0.752 MCC). In testing the model on independent datasets, it achieved an accuracy of 82.2% and an MCC of 0.656. Testing of our model and other state-of-the-art methods on a same dataset showed that our model is better than the others. Sequence profiles of log-odds scores of mono- and di-nucleotides were much more powerful features than nucleotide compositions in finding protein-binding regions in RNA sequences. But, a slight performance gain was obtained when using the sequence profiles along with nucleotide compositions. These are preliminary results of ongoing research, but demonstrate the potential of our approach as a powerful predictor of protein-binding regions in RNA. The program and supporting data are available at http://bclab.inha.ac.kr/RBPbinding .

Prediction of MHC class II binding affinity using SMM-align, a novel stabilization matrix alignment method

PubMed Central

Nielsen, Morten; Lundegaard, Claus; Lund, Ole

2007-01-01

Background Antigen presenting cells (APCs) sample the extra cellular space and present peptides from here to T helper cells, which can be activated if the peptides are of foreign origin. The peptides are presented on the surface of the cells in complex with major histocompatibility class II (MHC II) molecules. Identification of peptides that bind MHC II molecules is thus a key step in rational vaccine design and developing methods for accurate prediction of the peptide:MHC interactions play a central role in epitope discovery. The MHC class II binding groove is open at both ends making the correct alignment of a peptide in the binding groove a crucial part of identifying the core of an MHC class II binding motif. Here, we present a novel stabilization matrix alignment method, SMM-align, that allows for direct prediction of peptide:MHC binding affinities. The predictive performance of the method is validated on a large MHC class II benchmark data set covering 14 HLA-DR (human MHC) and three mouse H2-IA alleles. Results The predictive performance of the SMM-align method was demonstrated to be superior to that of the Gibbs sampler, TEPITOPE, SVRMHC, and MHCpred methods. Cross validation between peptide data set obtained from different sources demonstrated that direct incorporation of peptide length potentially results in over-fitting of the binding prediction method. Focusing on amino terminal peptide flanking residues (PFR), we demonstrate a consistent gain in predictive performance by favoring binding registers with a minimum PFR length of two amino acids. Visualizing the binding motif as obtained by the SMM-align and TEPITOPE methods highlights a series of fundamental discrepancies between the two predicted motifs. For the DRB1*1302 allele for instance, the TEPITOPE method favors basic amino acids at most anchor positions, whereas the SMM-align method identifies a preference for hydrophobic or neutral amino acids at the anchors. Conclusion The SMM-align method was shown to outperform other state of the art MHC class II prediction methods. The method predicts quantitative peptide:MHC binding affinity values, making it ideally suited for rational epitope discovery. The method has been trained and evaluated on the, to our knowledge, largest benchmark data set publicly available and covers the nine HLA-DR supertypes suggested as well as three mouse H2-IA allele. Both the peptide benchmark data set, and SMM-align prediction method (NetMHCII) are made publicly available. PMID:17608956

Prediction of MHC class II binding affinity using SMM-align, a novel stabilization matrix alignment method.

PubMed

Nielsen, Morten; Lundegaard, Claus; Lund, Ole

2007-07-04

Antigen presenting cells (APCs) sample the extra cellular space and present peptides from here to T helper cells, which can be activated if the peptides are of foreign origin. The peptides are presented on the surface of the cells in complex with major histocompatibility class II (MHC II) molecules. Identification of peptides that bind MHC II molecules is thus a key step in rational vaccine design and developing methods for accurate prediction of the peptide:MHC interactions play a central role in epitope discovery. The MHC class II binding groove is open at both ends making the correct alignment of a peptide in the binding groove a crucial part of identifying the core of an MHC class II binding motif. Here, we present a novel stabilization matrix alignment method, SMM-align, that allows for direct prediction of peptide:MHC binding affinities. The predictive performance of the method is validated on a large MHC class II benchmark data set covering 14 HLA-DR (human MHC) and three mouse H2-IA alleles. The predictive performance of the SMM-align method was demonstrated to be superior to that of the Gibbs sampler, TEPITOPE, SVRMHC, and MHCpred methods. Cross validation between peptide data set obtained from different sources demonstrated that direct incorporation of peptide length potentially results in over-fitting of the binding prediction method. Focusing on amino terminal peptide flanking residues (PFR), we demonstrate a consistent gain in predictive performance by favoring binding registers with a minimum PFR length of two amino acids. Visualizing the binding motif as obtained by the SMM-align and TEPITOPE methods highlights a series of fundamental discrepancies between the two predicted motifs. For the DRB1*1302 allele for instance, the TEPITOPE method favors basic amino acids at most anchor positions, whereas the SMM-align method identifies a preference for hydrophobic or neutral amino acids at the anchors. The SMM-align method was shown to outperform other state of the art MHC class II prediction methods. The method predicts quantitative peptide:MHC binding affinity values, making it ideally suited for rational epitope discovery. The method has been trained and evaluated on the, to our knowledge, largest benchmark data set publicly available and covers the nine HLA-DR supertypes suggested as well as three mouse H2-IA allele. Both the peptide benchmark data set, and SMM-align prediction method (NetMHCII) are made publicly available.

Validation of the post-delivery perceived stress inventory.

PubMed

Razurel, Chantal; Kaiser, Barbara; Dupuis, Marc; Antonietti, Jean-Philippe; Sellenet, Catherine; Epiney, Manuela

2014-01-01

This article presents the post-delivery perceived stress inventory (PDPSI) and its psychometric properties. This inventory is unique in that it links the measurement of perceived stress to events experienced during and after delivery. A total of 235 French-speaking, primiparous mothers completed the PDPSI two days after their delivery. To evaluate the predictive validity of the PDPSI on anxiety and depression, participants also completed the EPDS and the STAI two days and six weeks postpartum. The exploratory analysis revealed a 16-item structure divided into five factors: F1: relationship with the child; F2: delivery; F3: fatigue after delivery; F4: breastfeeding; and F5: relationship with the caregivers. The PDPSI demonstrated good internal consistency. Moreover, confirmatory factor analysis produced excellent indices, indicating that the complexity of the PDPSI was taken into account and its fit to the sample. The discriminant analysis showed that the PDPSI was not sensitive to specific changes in the sample making the inventory generalizable to other populations. Predictive validity showed that the scale significantly predicted depression and anxiety in the early postpartum period as well as anxiety six weeks postpartum. Overall, the PDPSI showed excellent psychometric qualities, making it a useful tool for future research-evaluating interventions related to perceived stress during the postpartum period.

Fractional viscoelasticity of soft elastomers and auxetic foams

NASA Astrophysics Data System (ADS)

Solheim, Hannah; Stanisauskis, Eugenia; Miles, Paul; Oates, William

2018-03-01

Dielectric elastomers are commonly implemented in adaptive structures due to their unique capabilities for real time control of a structure's shape, stiffness, and damping. These active polymers are often used in applications where actuator control or dynamic tunability are important, making an accurate understanding of the viscoelastic behavior critical. This challenge is complicated as these elastomers often operate over a broad range of deformation rates. Whereas research has demonstrated success in applying a nonlinear viscoelastic constitutive model to characterize the behavior of Very High Bond (VHB) 4910, robust predictions of the viscoelastic response over the entire range of time scales is still a significant challenge. An alternative formulation for viscoelastic modeling using fractional order calculus has shown significant improvement in predictive capabilities. While fractional calculus has been explored theoretically in the field of linear viscoelasticity, limited experimental validation and statistical evaluation of the underlying phenomena have been considered. In the present study, predictions across several orders of magnitude in deformation rates are validated against data using a single set of model parameters. Moreover, we illustrate the fractional order is material dependent by running complementary experiments and parameter estimation on the elastomer VHB 4949 as well as an auxetic foam. All results are statistically validated using Bayesian uncertainty methods to obtain posterior densities for the fractional order as well as the hyperelastic parameters.

Analysis of the Mean Absolute Error (MAE) and the Root Mean Square Error (RMSE) in Assessing Rounding Model

NASA Astrophysics Data System (ADS)

Wang, Weijie; Lu, Yanmin

2018-03-01

Most existing Collaborative Filtering (CF) algorithms predict a rating as the preference of an active user toward a given item, which is always a decimal fraction. Meanwhile, the actual ratings in most data sets are integers. In this paper, we discuss and demonstrate why rounding can bring different influences to these two metrics; prove that rounding is necessary in post-processing of the predicted ratings, eliminate of model prediction bias, improving the accuracy of the prediction. In addition, we also propose two new rounding approaches based on the predicted rating probability distribution, which can be used to round the predicted rating to an optimal integer rating, and get better prediction accuracy compared to the Basic Rounding approach. Extensive experiments on different data sets validate the correctness of our analysis and the effectiveness of our proposed rounding approaches.

ACCESS - A Science and Engineering Assessment of Space Coronagraph Concepts for the Direct Imaging and Spectroscopy of Exoplanetary Systems

NASA Technical Reports Server (NTRS)

Trauger, John

2008-01-01

Topics include and overview, science objectives, study objectives, coronagraph types, metrics, ACCESS observatory, laboratory validations, and summary. Individual slides examine ACCESS engineering approach, ACCESS gamut of coronagraph types, coronagraph metrics, ACCESS Discovery Space, coronagraph optical layout, wavefront control on the "level playing field", deformable mirror development for HCIT, laboratory testbed demonstrations, high contract imaging with the HCIT, laboratory coronagraph contrast and stability, model validation and performance predictions, HCIT coronagraph optical layout, Lyot coronagraph on the HCIT, pupil mapping (PIAA), shaped pupils, and vortex phase mask experiments on the HCIT.

A Surrogate Approach to the Experimental Optimization of Multielement Airfoils

NASA Technical Reports Server (NTRS)

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

1996-01-01

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

Individual differences in situation awareness: Validation of the Situationism Scale

PubMed Central

Roberts, Megan E.; Gibbons, Frederick X.; Gerrard, Meg; Klein, William M. P.

2015-01-01

This paper concerns the construct of lay situationism—an individual’s belief in the importance of a behavior’s context. Study 1 identified a 13-item Situationism Scale, which demonstrated good reliability and validity. In particular, higher situationism was associated with greater situation-control (strategies to manipulate the environment in order to avoid temptation). Subsequent laboratory studies indicated that people higher on the situationism subscales used greater situation-control by sitting farther from junk food (Study 2) and choosing to drink non-alcoholic beverages before a cognitive task (Study 3). Overall, findings provide preliminary support for the psychometric validity and predictive utility of the Situationism Scale and offer this individual difference construct as a means to expand self-regulation theory. PMID:25329242

Validation of the revised Mystical Experience Questionnaire in experimental sessions with psilocybin.

PubMed

Barrett, Frederick S; Johnson, Matthew W; Griffiths, Roland R

2015-11-01

The 30-item revised Mystical Experience Questionnaire (MEQ30) was previously developed within an online survey of mystical-type experiences occasioned by psilocybin-containing mushrooms. The rated experiences occurred on average eight years before completion of the questionnaire. The current paper validates the MEQ30 using data from experimental studies with controlled doses of psilocybin. Data were pooled and analyzed from five laboratory experiments in which participants (n=184) received a moderate to high oral dose of psilocybin (at least 20 mg/70 kg). Results of confirmatory factor analysis demonstrate the reliability and internal validity of the MEQ30. Structural equation models demonstrate the external and convergent validity of the MEQ30 by showing that latent variable scores on the MEQ30 positively predict persisting change in attitudes, behavior, and well-being attributed to experiences with psilocybin while controlling for the contribution of the participant-rated intensity of drug effects. These findings support the use of the MEQ30 as an efficient measure of individual mystical experiences. A method to score a "complete mystical experience" that was used in previous versions of the mystical experience questionnaire is validated in the MEQ30, and a stand-alone version of the MEQ30 is provided for use in future research. © The Author(s) 2015.

Validation of the revised Mystical Experience Questionnaire in experimental sessions with psilocybin

PubMed Central

Barrett, Frederick S; Johnson, Matthew W; Griffiths, Roland R

2016-01-01

The 30-item revised Mystical Experience Questionnaire (MEQ30) was previously developed within an online survey of mystical-type experiences occasioned by psilocybin-containing mushrooms. The rated experiences occurred on average eight years before completion of the questionnaire. The current paper validates the MEQ30 using data from experimental studies with controlled doses of psilocybin. Data were pooled and analyzed from five laboratory experiments in which participants (n=184) received a moderate to high oral dose of psilocybin (at least 20 mg/70 kg). Results of confirmatory factor analysis demonstrate the reliability and internal validity of the MEQ30. Structural equation models demonstrate the external and convergent validity of the MEQ30 by showing that latent variable scores on the MEQ30 positively predict persisting change in attitudes, behavior, and well-being attributed to experiences with psilocybin while controlling for the contribution of the participant-rated intensity of drug effects. These findings support the use of the MEQ30 as an efficient measure of individual mystical experiences. A method to score a “complete mystical experience” that was used in previous versions of the mystical experience questionnaire is validated in the MEQ30, and a stand-alone version of the MEQ30 is provided for use in future research. PMID:26442957

Predicting Response to Histone Deacetylase Inhibitors Using High-Throughput Genomics.

PubMed

Geeleher, Paul; Loboda, Andrey; Lenkala, Divya; Wang, Fan; LaCroix, Bonnie; Karovic, Sanja; Wang, Jacqueline; Nebozhyn, Michael; Chisamore, Michael; Hardwick, James; Maitland, Michael L; Huang, R Stephanie

2015-11-01

Many disparate biomarkers have been proposed as predictors of response to histone deacetylase inhibitors (HDI); however, all have failed when applied clinically. Rather than this being entirely an issue of reproducibility, response to the HDI vorinostat may be determined by the additive effect of multiple molecular factors, many of which have previously been demonstrated. We conducted a large-scale gene expression analysis using the Cancer Genome Project for discovery and generated another large independent cancer cell line dataset across different cancers for validation. We compared different approaches in terms of how accurately vorinostat response can be predicted on an independent out-of-batch set of samples and applied the polygenic marker prediction principles in a clinical trial. Using machine learning, the small effects that aggregate, resulting in sensitivity or resistance, can be recovered from gene expression data in a large panel of cancer cell lines.This approach can predict vorinostat response accurately, whereas single gene or pathway markers cannot. Our analyses recapitulated and contextualized many previous findings and suggest an important role for processes such as chromatin remodeling, autophagy, and apoptosis. As a proof of concept, we also discovered a novel causative role for CHD4, a helicase involved in the histone deacetylase complex that is associated with poor clinical outcome. As a clinical validation, we demonstrated that a common dose-limiting toxicity of vorinostat, thrombocytopenia, can be predicted (r = 0.55, P = .004) several days before it is detected clinically. Our work suggests a paradigm shift from single-gene/pathway evaluation to simultaneously evaluating multiple independent high-throughput gene expression datasets, which can be easily extended to other investigational compounds where similar issues are hampering clinical adoption. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

A Bayesian antedependence model for whole genome prediction.

PubMed

Yang, Wenzhao; Tempelman, Robert J

2012-04-01

Hierarchical mixed effects models have been demonstrated to be powerful for predicting genomic merit of livestock and plants, on the basis of high-density single-nucleotide polymorphism (SNP) marker panels, and their use is being increasingly advocated for genomic predictions in human health. Two particularly popular approaches, labeled BayesA and BayesB, are based on specifying all SNP-associated effects to be independent of each other. BayesB extends BayesA by allowing a large proportion of SNP markers to be associated with null effects. We further extend these two models to specify SNP effects as being spatially correlated due to the chromosomally proximal effects of causal variants. These two models, that we respectively dub as ante-BayesA and ante-BayesB, are based on a first-order nonstationary antedependence specification between SNP effects. In a simulation study involving 20 replicate data sets, each analyzed at six different SNP marker densities with average LD levels ranging from r(2) = 0.15 to 0.31, the antedependence methods had significantly (P < 0.01) higher accuracies than their corresponding classical counterparts at higher LD levels (r(2) > 0. 24) with differences exceeding 3%. A cross-validation study was also conducted on the heterogeneous stock mice data resource (http://mus.well.ox.ac.uk/mouse/HS/) using 6-week body weights as the phenotype. The antedependence methods increased cross-validation prediction accuracies by up to 3.6% compared to their classical counterparts (P < 0.001). Finally, we applied our method to other benchmark data sets and demonstrated that the antedependence methods were more accurate than their classical counterparts for genomic predictions, even for individuals several generations beyond the training data.

Predicting fitness-to-drive following stroke using the Occupational Therapy - Driver Off Road Assessment Battery.

PubMed

Unsworth, Carolyn A; Baker, Anne; Lannin, Natasha; Harries, Priscilla; Strahan, Janene; Browne, Matthew

2018-02-28

It is difficult to determine if, or when, individuals with stroke are ready to undergo on-road fitness-to-drive assessment. The Occupational Therapy - Driver Off Road Assessment Battery was developed to determine client suitability to resume driving. The predictive validity of the Battery needs to be verified for people with stroke. Examine the predictive validity of the Occupational Therapy - Driver Off Road Assessment Battery for on-road performance among people with stroke. Off-road data were collected from 148 people post stroke on the Battery and the outcome of their on-road assessment was recorded as: fit-to-drive or not fit-to-drive. The majority of participants (76%) were able to resume driving. A classification and regression tree (CART) analysis using four subtests (three cognitive and one physical) from the Battery demonstrated an area under the curve (AUC) of 0.8311. Using a threshold of 0.5, the model correctly predicted 98/112 fit-to-drive (87.5%) and 26/36 people not fit-to-drive (72.2%). The three cognitive subtests from the Occupational Therapy - Driver Off Road Assessment Battery and potentially one of the physical tests have good predictive validity for client fitness-to-drive. These tests can be used to screen client suitability for proceeding to an on-road test following stroke. Implications for Rehabilitation: Following stroke, drivers should be counseled (including consideration of local legislation) concerning return to driving. The Occupational Therapy - Driver Off Road Assessment Battery can be used in the clinic to screen people for suitability to undertake on road assessment. Scores on four of the Occupational Therapy - Driver Off Road Assessment Battery subtests are predictive of resumption of driving following stroke.

Assessing the stability of human locomotion: a review of current measures

PubMed Central

Bruijn, S. M.; Meijer, O. G.; Beek, P. J.; van Dieën, J. H.

2013-01-01

Falling poses a major threat to the steadily growing population of the elderly in modern-day society. A major challenge in the prevention of falls is the identification of individuals who are at risk of falling owing to an unstable gait. At present, several methods are available for estimating gait stability, each with its own advantages and disadvantages. In this paper, we review the currently available measures: the maximum Lyapunov exponent (λS and λL), the maximum Floquet multiplier, variability measures, long-range correlations, extrapolated centre of mass, stabilizing and destabilizing forces, foot placement estimator, gait sensitivity norm and maximum allowable perturbation. We explain what these measures represent and how they are calculated, and we assess their validity, divided up into construct validity, predictive validity in simple models, convergent validity in experimental studies, and predictive validity in observational studies. We conclude that (i) the validity of variability measures and λS is best supported across all levels, (ii) the maximum Floquet multiplier and λL have good construct validity, but negative predictive validity in models, negative convergent validity and (for λL) negative predictive validity in observational studies, (iii) long-range correlations lack construct validity and predictive validity in models and have negative convergent validity, and (iv) measures derived from perturbation experiments have good construct validity, but data are lacking on convergent validity in experimental studies and predictive validity in observational studies. In closing, directions for future research on dynamic gait stability are discussed. PMID:23516062

Developing and Testing a Model to Predict Outcomes of Organizational Change

PubMed Central

Gustafson, David H; Sainfort, François; Eichler, Mary; Adams, Laura; Bisognano, Maureen; Steudel, Harold

2003-01-01

Objective To test the effectiveness of a Bayesian model employing subjective probability estimates for predicting success and failure of health care improvement projects. Data Sources Experts' subjective assessment data for model development and independent retrospective data on 221 healthcare improvement projects in the United States, Canada, and the Netherlands collected between 1996 and 2000 for validation. Methods A panel of theoretical and practical experts and literature in organizational change were used to identify factors predicting the outcome of improvement efforts. A Bayesian model was developed to estimate probability of successful change using subjective estimates of likelihood ratios and prior odds elicited from the panel of experts. A subsequent retrospective empirical analysis of change efforts in 198 health care organizations was performed to validate the model. Logistic regression and ROC analysis were used to evaluate the model's performance using three alternative definitions of success. Data Collection For the model development, experts' subjective assessments were elicited using an integrative group process. For the validation study, a staff person intimately involved in each improvement project responded to a written survey asking questions about model factors and project outcomes. Results Logistic regression chi-square statistics and areas under the ROC curve demonstrated a high level of model performance in predicting success. Chi-square statistics were significant at the 0.001 level and areas under the ROC curve were greater than 0.84. Conclusions A subjective Bayesian model was effective in predicting the outcome of actual improvement projects. Additional prospective evaluations as well as testing the impact of this model as an intervention are warranted. PMID:12785571

Application of a validated prediction model for in vitro fertilization: comparison of live birth rates and multiple birth rates with 1 embryo transferred over 2 cycles vs 2 embryos in 1 cycle.

PubMed

Luke, Barbara; Brown, Morton B; Wantman, Ethan; Stern, Judy E; Baker, Valerie L; Widra, Eric; Coddington, Charles C; Gibbons, William E; Van Voorhis, Bradley J; Ball, G David

2015-05-01

The purpose of this study was to use a validated prediction model to examine whether single embryo transfer (SET) over 2 cycles results in live birth rates (LBR) comparable with 2 embryos transferred (DET) in 1 cycle and reduces the probability of a multiple birth (ie, multiple birth rate [MBR]). Prediction models of LBR and MBR for a woman considering assisted reproductive technology developed from linked cycles from the Society for Assisted Reproductive Technology Clinic Outcome Reporting System for 2006-2012 were used to compare SET over 2 cycles with DET in 1 cycle. The prediction model was based on a woman's age, body mass index (BMI), gravidity, previous full-term births, infertility diagnoses, embryo state, number of embryos transferred, and number of cycles. To demonstrate the effect of the number of embryos transferred (1 or 2), the LBRs and MBRs were estimated for women with a single infertility diagnosis (male factor, ovulation disorders, diminished ovarian reserve, and unexplained); nulligravid; BMI of 20, 25, 30, and 35 kg/m2; and ages 25, 35, and 40 years old by cycle (first or second). The cumulative LBR over 2 cycles with SET was similar to or better than the LBR with DET in a single cycle (for example, for women with the diagnosis of ovulation disorders: 35 years old; BMI, 30 kg/m2; 54.4% vs 46.5%; and for women who are 40 years old: BMI, 30 kg/m(2); 31.3% vs 28.9%). The MBR with DET in 1 cycle was 32.8% for women 35 years old and 20.9% for women 40 years old; with SET, the cumulative MBR was 2.7% and 1.6%, respectively. The application of this validated predictive model demonstrated that the cumulative LBR is as good as or better with SET over 2 cycles than with DET in 1 cycle, while greatly reducing the probability of a multiple birth. Copyright © 2015 Elsevier Inc. All rights reserved.

Development and validation of a clinical prediction rule to identify suspected breast cancer: a prospective cohort study.

PubMed

Galvin, Rose; Joyce, Doireann; Downey, Eithne; Boland, Fiona; Fahey, Tom; Hill, Arnold K

2014-10-03

The number of primary care referrals of women with breast symptoms to symptomatic breast units (SBUs) has increased exponentially in the past decade in Ireland. The aim of this study is to develop and validate a clinical prediction rule (CPR) to identify women with breast cancer so that a more evidence based approach to referral from primary care to these SBUs can be developed. We analysed routine data from a prospective cohort of consecutive women reviewed at a SBU with breast symptoms. The dataset was split into a derivation and validation cohort. Regression analysis was used to derive a CPR from the patient's history and clinical findings. Validation of the CPR consisted of estimating the number of breast cancers predicted to occur compared with the actual number of observed breast cancers across deciles of risk. A total of 6,590 patients were included in the derivation study and 4.9% were diagnosed with breast cancer. Independent clinical predictors for breast cancer were: increasing age by year (adjusted odds ratio 1.08, 95% CI 1.07-1.09); presence of a lump (5.63, 95% CI 4.2-7.56); nipple change (2.77, 95% CI 1.68-4.58) and nipple discharge (2.09, 95% CI 1.1-3.97). Validation of the rule (n = 911) demonstrated that the probability of breast cancer was higher with an increasing number of these independent variables. The Hosmer-Lemeshow goodness of fit showed no overall significant difference between the expected and the observed numbers of breast cancer (χ(2)HL: 6.74, p-value: 0.56). This study derived and validated a CPR for breast cancer in women attending an Irish national SBU. We found that increasing age, presence of a lump, nipple discharge and nipple change are all associated with increased risk of breast cancer. Further validation of the rule is necessary as well as an assessment of its impact on referral practice.

T2* Mapping Provides Information That Is Statistically Comparable to an Arthroscopic Evaluation of Acetabular Cartilage.

PubMed

Morgan, Patrick; Nissi, Mikko J; Hughes, John; Mortazavi, Shabnam; Ellerman, Jutta

2017-07-01

Objectives The purpose of this study was to validate T2* mapping as an objective, noninvasive method for the prediction of acetabular cartilage damage. Methods This is the second step in the validation of T2*. In a previous study, we established a quantitative predictive model for identifying and grading acetabular cartilage damage. In this study, the model was applied to a second cohort of 27 consecutive hips to validate the model. A clinical 3.0-T imaging protocol with T2* mapping was used. Acetabular regions of interest (ROI) were identified on magnetic resonance and graded using the previously established model. Each ROI was then graded in a blinded fashion by arthroscopy. Accurate surgical location of ROIs was facilitated with a 2-dimensional map projection of the acetabulum. A total of 459 ROIs were studied. Results When T2* mapping and arthroscopic assessment were compared, 82% of ROIs were within 1 Beck group (of a total 6 possible) and 32% of ROIs were classified identically. Disease prediction based on receiver operating characteristic curve analysis demonstrated a sensitivity of 0.713 and a specificity of 0.804. Model stability evaluation required no significant changes to the predictive model produced in the initial study. Conclusions These results validate that T2* mapping provides statistically comparable information regarding acetabular cartilage when compared to arthroscopy. In contrast to arthroscopy, T2* mapping is quantitative, noninvasive, and can be used in follow-up. Unlike research quantitative magnetic resonance protocols, T2* takes little time and does not require a contrast agent. This may facilitate its use in the clinical sphere.

General inattentiveness is a long-term reliable trait independently predictive of psychological health: Danish validation studies of the Mindful Attention Awareness Scale.

PubMed

Jensen, Christian Gaden; Niclasen, Janni; Vangkilde, Signe Allerup; Petersen, Anders; Hasselbalch, Steen Gregers

2016-05-01

The Mindful Attention Awareness Scale (MAAS) measures perceived degree of inattentiveness in different contexts and is often used as a reversed indicator of mindfulness. MAAS is hypothesized to reflect a psychological trait or disposition when used outside attentional training contexts, but the long-term test-retest reliability of MAAS scores is virtually untested. It is unknown whether MAAS predicts psychological health after controlling for standardized socioeconomic status classifications. First, MAAS translated to Danish was validated psychometrically within a randomly invited healthy adult community sample (N = 490). Factor analysis confirmed that MAAS scores quantified a unifactorial construct of excellent composite reliability and consistent convergent validity. Structural equation modeling revealed that MAAS scores contributed independently to predicting psychological distress and mental health, after controlling for age, gender, income, socioeconomic occupational class, stressful life events, and social desirability (β = 0.32-.42, ps < .001). Second, MAAS scores showed satisfactory short-term test-retest reliability in 100 retested healthy university students. Finally, MAAS sample mean scores as well as individuals' scores demonstrated satisfactory test-retest reliability across a 6 months interval in the adult community (retested N = 407), intraclass correlations ≥ .74. MAAS scores displayed significantly stronger long-term test-retest reliability than scores measuring psychological distress (z = 2.78, p = .005). Test-retest reliability estimates did not differ within demographic and socioeconomic strata. Scores on the Danish MAAS were psychometrically validated in healthy adults. MAAS's inattentiveness scores reflected a unidimensional construct, long-term reliable disposition, and a factor of independent significance for predicting psychological health. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Validation of asthma recording in electronic health records: a systematic review

PubMed Central

Nissen, Francis; Quint, Jennifer K; Wilkinson, Samantha; Mullerova, Hana; Smeeth, Liam; Douglas, Ian J

2017-01-01

Objective To describe the methods used to validate asthma diagnoses in electronic health records and summarize the results of the validation studies. Background Electronic health records are increasingly being used for research on asthma to inform health services and health policy. Validation of the recording of asthma diagnoses in electronic health records is essential to use these databases for credible epidemiological asthma research. Methods We searched EMBASE and MEDLINE databases for studies that validated asthma diagnoses detected in electronic health records up to October 2016. Two reviewers independently assessed the full text against the predetermined inclusion criteria. Key data including author, year, data source, case definitions, reference standard, and validation statistics (including sensitivity, specificity, positive predictive value [PPV], and negative predictive value [NPV]) were summarized in two tables. Results Thirteen studies met the inclusion criteria. Most studies demonstrated a high validity using at least one case definition (PPV >80%). Ten studies used a manual validation as the reference standard; each had at least one case definition with a PPV of at least 63%, up to 100%. We also found two studies using a second independent database to validate asthma diagnoses. The PPVs of the best performing case definitions ranged from 46% to 58%. We found one study which used a questionnaire as the reference standard to validate a database case definition; the PPV of the case definition algorithm in this study was 89%. Conclusion Attaining high PPVs (>80%) is possible using each of the discussed validation methods. Identifying asthma cases in electronic health records is possible with high sensitivity, specificity or PPV, by combining multiple data sources, or by focusing on specific test measures. Studies testing a range of case definitions show wide variation in the validity of each definition, suggesting this may be important for obtaining asthma definitions with optimal validity. PMID:29238227

Independent external validation of predictive models for urinary dysfunction following external beam radiotherapy of the prostate: Issues in model development and reporting.

PubMed

Yahya, Noorazrul; Ebert, Martin A; Bulsara, Max; Kennedy, Angel; Joseph, David J; Denham, James W

2016-08-01

Most predictive models are not sufficiently validated for prospective use. We performed independent external validation of published predictive models for urinary dysfunctions following radiotherapy of the prostate. Multivariable models developed to predict atomised and generalised urinary symptoms, both acute and late, were considered for validation using a dataset representing 754 participants from the TROG 03.04-RADAR trial. Endpoints and features were harmonised to match the predictive models. The overall performance, calibration and discrimination were assessed. 14 models from four publications were validated. The discrimination of the predictive models in an independent external validation cohort, measured using the area under the receiver operating characteristic (ROC) curve, ranged from 0.473 to 0.695, generally lower than in internal validation. 4 models had ROC >0.6. Shrinkage was required for all predictive models' coefficients ranging from -0.309 (prediction probability was inverse to observed proportion) to 0.823. Predictive models which include baseline symptoms as a feature produced the highest discrimination. Two models produced a predicted probability of 0 and 1 for all patients. Predictive models vary in performance and transferability illustrating the need for improvements in model development and reporting. Several models showed reasonable potential but efforts should be increased to improve performance. Baseline symptoms should always be considered as potential features for predictive models. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Endoscopic third ventriculostomy in the treatment of childhood hydrocephalus.

PubMed

Kulkarni, Abhaya V; Drake, James M; Mallucci, Conor L; Sgouros, Spyros; Roth, Jonathan; Constantini, Shlomi

2009-08-01

To develop a model to predict the probability of endoscopic third ventriculostomy (ETV) success in the treatment for hydrocephalus on the basis of a child's individual characteristics. We analyzed 618 ETVs performed consecutively on children at 12 international institutions to identify predictors of ETV success at 6 months. A multivariable logistic regression model was developed on 70% of the dataset (training set) and validated on 30% of the dataset (validation set). In the training set, 305/455 ETVs (67.0%) were successful. The regression model (containing patient age, cause of hydrocephalus, and previous cerebrospinal fluid shunt) demonstrated good fit (Hosmer-Lemeshow, P = .78) and discrimination (C statistic = 0.70). In the validation set, 105/163 ETVs (64.4%) were successful and the model maintained good fit (Hosmer-Lemeshow, P = .45), discrimination (C statistic = 0.68), and calibration (calibration slope = 0.88). A simplified ETV Success Score was devised that closely approximates the predicted probability of ETV success. Children most likely to succeed with ETV can now be accurately identified and spared the long-term complications of CSF shunting.

Knowledge discovery by accuracy maximization

PubMed Central

Cacciatore, Stefano; Luchinat, Claudio; Tenori, Leonardo

2014-01-01

Here we describe KODAMA (knowledge discovery by accuracy maximization), an unsupervised and semisupervised learning algorithm that performs feature extraction from noisy and high-dimensional data. Unlike other data mining methods, the peculiarity of KODAMA is that it is driven by an integrated procedure of cross-validation of the results. The discovery of a local manifold’s topology is led by a classifier through a Monte Carlo procedure of maximization of cross-validated predictive accuracy. Briefly, our approach differs from previous methods in that it has an integrated procedure of validation of the results. In this way, the method ensures the highest robustness of the obtained solution. This robustness is demonstrated on experimental datasets of gene expression and metabolomics, where KODAMA compares favorably with other existing feature extraction methods. KODAMA is then applied to an astronomical dataset, revealing unexpected features. Interesting and not easily predictable features are also found in the analysis of the State of the Union speeches by American presidents: KODAMA reveals an abrupt linguistic transition sharply separating all post-Reagan from all pre-Reagan speeches. The transition occurs during Reagan’s presidency and not from its beginning. PMID:24706821

Geographical origin discrimination of lentils (Lens culinaris Medik.) using 1H NMR fingerprinting and multivariate statistical analyses.

PubMed

Longobardi, Francesco; Innamorato, Valentina; Di Gioia, Annalisa; Ventrella, Andrea; Lippolis, Vincenzo; Logrieco, Antonio F; Catucci, Lucia; Agostiano, Angela

2017-12-15

Lentil samples coming from two different countries, i.e. Italy and Canada, were analysed using untargeted 1 H NMR fingerprinting in combination with chemometrics in order to build models able to classify them according to their geographical origin. For such aim, Soft Independent Modelling of Class Analogy (SIMCA), k-Nearest Neighbor (k-NN), Principal Component Analysis followed by Linear Discriminant Analysis (PCA-LDA) and Partial Least Squares-Discriminant Analysis (PLS-DA) were applied to the NMR data and the results were compared. The best combination of average recognition (100%) and cross-validation prediction abilities (96.7%) was obtained for the PCA-LDA. All the statistical models were validated both by using a test set and by carrying out a Monte Carlo Cross Validation: the obtained performances were found to be satisfying for all the models, with prediction abilities higher than 95% demonstrating the suitability of the developed methods. Finally, the metabolites that mostly contributed to the lentil discrimination were indicated. Copyright © 2017 Elsevier Ltd. All rights reserved.

Assessment of MARMOT Grain Growth Model

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

Fromm, B.; Zhang, Y.; Schwen, D.

2015-12-01

This report assesses the MARMOT grain growth model by comparing modeling predictions with experimental results from thermal annealing. The purpose here is threefold: (1) to demonstrate the validation approach of using thermal annealing experiments with non-destructive characterization, (2) to test the reconstruction capability and computation efficiency in MOOSE, and (3) to validate the grain growth model and the associated parameters that are implemented in MARMOT for UO 2. To assure a rigorous comparison, the 2D and 3D initial experimental microstructures of UO 2 samples were characterized using non-destructive Synchrotron x-ray. The same samples were then annealed at 2273K for grainmore » growth, and their initial microstructures were used as initial conditions for simulated annealing at the same temperature using MARMOT. After annealing, the final experimental microstructures were characterized again to compare with the results from simulations. So far, comparison between modeling and experiments has been done for 2D microstructures, and 3D comparison is underway. The preliminary results demonstrated the usefulness of the non-destructive characterization method for MARMOT grain growth model validation. A detailed analysis of the 3D microstructures is in progress to fully validate the current model in MARMOT.« less

Direct Validation of Differential Prediction.

ERIC Educational Resources Information Center

Lunneborg, Clifford E.

Using academic achievement data for 655 University students, direct validation of differential predictions based on a battery of aptitude/achievement measures selected for their differential prediction efficiency was attempted. In the cross-validation of the prediction of actual differences among five academic area GPA's, this set of differential…

The design of a joined wing flight demonstrator aircraft

NASA Technical Reports Server (NTRS)

Smith, S. C.; Cliff, S. E.; Kroo, I. M.

1987-01-01

A joined-wing flight demonstrator aircraft has been developed at the NASA Ames Research Center in collaboration with ACA Industries. The aircraft is designed to utilize the fuselage, engines, and undercarriage of the existing NASA AD-1 flight demonstrator aircraft. The design objectives, methods, constraints, and the resulting aircraft design, called the JW-1, are presented. A wind-tunnel model of the JW-1 was tested in the NASA Ames 12-foot wind tunnel. The test results indicate that the JW-1 has satisfactory flying qualities for a flight demonstrator aircraft. Good agreement of test results with design predictions confirmed the validity of the design methods used for application to joined-wing configurations.

Development and Validation of the Food Liking Questionnaire in a French-Canadian Population

PubMed Central

Carbonneau, Elise; Bradette-Laplante, Maude; Lamarche, Benoît; Provencher, Véronique; Bégin, Catherine; Robitaille, Julie; Desroches, Sophie; Corneau, Louise; Lemieux, Simone

2017-01-01

The purpose of this study was to develop and validate a questionnaire assessing food liking in a French-Canadian population. A questionnaire was developed, in which participants were asked to rate their degree of liking of 50 food items. An expert panel evaluated the content validity. For the validation study, 150 men and women completed the questionnaire twice. An Exploratory Factor Analysis (EFA) was performed to assess the number of subscales of the questionnaire. Internal consistency and test-retest reliability of the subscales were evaluated. Concurrent validity was assessed through correlations between liking scores and self-reported frequencies of consumption. Comments from the experts led to changes in the list of foods included in the questionnaire. The EFA revealed a two-factor structure for the questionnaire (i.e., savory and sweet foods) and led to the removal of nine items, resulting in a 32-item questionnaire. The two subscales revealed good internal consistency (Cronbach alphas: 0.85 and 0.89) and test-retest reliability (p = 0.84 and 0.86). The questionnaire demonstrated adequate concurrent validity, with moderate correlations between food liking and self-reported frequency of consumption (r = 0.19–0.39, ps < 0.05). This new Food Liking Questionnaire assessing liking of a variety of savory and sweet foods demonstrated good psychometric properties in every validation step. This questionnaire will be useful to explore the role of food liking and its interactions with other factors in predicting eating behaviors and energy intake. PMID:29292754

Development and Validation of the Food Liking Questionnaire in a French-Canadian Population.

PubMed

Carbonneau, Elise; Bradette-Laplante, Maude; Lamarche, Benoît; Provencher, Véronique; Bégin, Catherine; Robitaille, Julie; Desroches, Sophie; Vohl, Marie-Claude; Corneau, Louise; Lemieux, Simone

2017-12-08

The purpose of this study was to develop and validate a questionnaire assessing food liking in a French-Canadian population. A questionnaire was developed, in which participants were asked to rate their degree of liking of 50 food items. An expert panel evaluated the content validity. For the validation study, 150 men and women completed the questionnaire twice. An Exploratory Factor Analysis (EFA) was performed to assess the number of subscales of the questionnaire. Internal consistency and test-retest reliability of the subscales were evaluated. Concurrent validity was assessed through correlations between liking scores and self-reported frequencies of consumption. Comments from the experts led to changes in the list of foods included in the questionnaire. The EFA revealed a two-factor structure for the questionnaire (i.e., savory and sweet foods) and led to the removal of nine items, resulting in a 32-item questionnaire. The two subscales revealed good internal consistency (Cronbach alphas: 0.85 and 0.89) and test-retest reliability ( p = 0.84 and 0.86). The questionnaire demonstrated adequate concurrent validity, with moderate correlations between food liking and self-reported frequency of consumption ( r = 0.19-0.39, p s < 0.05). This new Food Liking Questionnaire assessing liking of a variety of savory and sweet foods demonstrated good psychometric properties in every validation step. This questionnaire will be useful to explore the role of food liking and its interactions with other factors in predicting eating behaviors and energy intake.

The application of a clinical prediction rule for patients with neck pain likely to benefit from cervical traction: A case report.

PubMed

Bernstetter, Andrew

2016-10-01

Cervical traction is a commonly utilized intervention in the treatment of patients with neck pain. In 2009, a clinical prediction rule (CPR) was developed as a way to assist clinicians in determining the patient population most likely to respond to cervical traction, though this CPR has yet to be validated. The purpose of this case report is to demonstrate the application of that CPR. The patient was a 46-year-old female with a four-week history of right-sided neck and shoulder pain, with numbness and tingling of her thumb and index finger. Treatment consisted of five sessions provided over 3 weeks. The plan of care included home mechanical cervical traction, exercise, and manual therapy. The patient achieved pain-free cervical range of motion. Neck disability index scores decreased from 28% to 6%, and the Patient-Specific Functional Scale average score improved from 5.5 to 10 out of 10. This case report demonstrates the application of a CPR to assist in deciding if cervical traction is an appropriate intervention. Further research is needed to validate the CPR and to establish the optimal mode of delivery for traction.

A frequency-domain approach to improve ANNs generalization quality via proper initialization.

PubMed

Chaari, Majdi; Fekih, Afef; Seibi, Abdennour C; Hmida, Jalel Ben

2018-08-01

The ability to train a network without memorizing the input/output data, thereby allowing a good predictive performance when applied to unseen data, is paramount in ANN applications. In this paper, we propose a frequency-domain approach to evaluate the network initialization in terms of quality of training, i.e., generalization capabilities. As an alternative to the conventional time-domain methods, the proposed approach eliminates the approximate nature of network validation using an excess of unseen data. The benefits of the proposed approach are demonstrated using two numerical examples, where two trained networks performed similarly on the training and the validation data sets, yet they revealed a significant difference in prediction accuracy when tested using a different data set. This observation is of utmost importance in modeling applications requiring a high degree of accuracy. The efficiency of the proposed approach is further demonstrated on a real-world problem, where unlike other initialization methods, a more conclusive assessment of generalization is achieved. On the practical front, subtle methodological and implementational facets are addressed to ensure reproducibility and pinpoint the limitations of the proposed approach. Copyright © 2018 Elsevier Ltd. All rights reserved.

Clinical Translation and Validation of a Predictive Biomarker for Patritumab, an Anti-human Epidermal Growth Factor Receptor 3 (HER3) Monoclonal Antibody, in Patients With Advanced Non-small Cell Lung Cancer

PubMed Central

Mendell, Jeanne; Freeman, Daniel J.; Feng, Wenqin; Hettmann, Thore; Schneider, Matthias; Blum, Sabine; Ruhe, Jens; Bange, Johannes; Nakamaru, Kenji; Chen, Shuquan; Tsuchihashi, Zenta; von Pawel, Joachim; Copigneaux, Catherine; Beckman, Robert A.

2015-01-01

Background During early clinical development, prospective identification of a predictive biomarker and validation of an assay method may not always be feasible. Dichotomizing a continuous biomarker measure to classify responders also leads to challenges. We present a case study of a prospective–retrospective approach for a continuous biomarker identified after patient enrollment but defined prospectively before the unblinding of data. An analysis of the strengths and weaknesses of this approach and the challenges encountered in its practical application are also provided. Methods HERALD (NCT02134015) was a double-blind, phase 2 study in patients with non-small cell lung cancer (NSCLC) randomized to erlotinib with placebo or with high or low doses of patritumab, a monoclonal antibody targeted against human epidermal growth factor receptor 3 (HER3). While the primary objective was to assess safety and progression-free survival (PFS), a secondary objective was to determine a single predictive biomarker hypothesis to identify subjects most likely to benefit from the addition of patritumab. Although not identified as the primary biomarker in the study protocol, on the basis of preclinical results from 2 independent laboratories, expression levels of the HER3 ligand heregulin (HRG) were prospectively declared the predictive biomarker before data unblinding but after subject enrollment. An assay to measure HRG mRNA was developed and validated. Other biomarkers, such as epidermal growth factor receptor (EGFR) mutation status, were also evaluated in an exploratory fashion. The cutoff value for high vs. low HRG mRNA levels was set at the median delta threshold cycle. A maximum likelihood analysis was performed to evaluate the provisional cutoff. The relationship of HRG values to PFS hazard ratios (HRs) was assessed as a measure of internal validation. Additional NSCLC samples were analyzed to characterize HRG mRNA distribution. Results The subgroup of patients with high HRG mRNA levels (“HRG-high”) demonstrated clinical benefit from patritumab treatment with HRs of 0.37 (P = 0.0283) and 0.29 (P = 0.0027) in the high- and low-dose patritumab arms, respectively. However, only 102 of the 215 randomized patients (47.4%) had sufficient tumor samples for HRG mRNA measurement. Maximum likelihood analysis showed that the provisional cutoff was within the optimal range. In the placebo arm, the HRG-high subgroup demonstrated worse prognosis compared with HRG-low. A continuous relationship was observed between increased HRG mRNA levels and lower HR. Additional NSCLC samples (N = 300) demonstrated a similar unimodal distribution to that observed in this study, suggesting that the defined cutoff may be applicable to future NSCLC studies. Conclusions The prospective–retrospective approach was successful in clinically validating a probable predictive biomarker. Post hoc in vitro studies and statistical analyses permitted further testing of the underlying assumptions. However, limitations of this analysis include the incomplete collection of adequate tumor tissue and a lack of stratification. In a phase 3 study, findings are being confirmed, and the HRG cutoff value is being further refined. ClinicalTrials.gov Number NCT02134015. PMID:26137564

Development and Validation of a Novel Scoring System for Predicting Technical Success of Chronic Total Occlusion Percutaneous Coronary Interventions: The PROGRESS CTO (Prospective Global Registry for the Study of Chronic Total Occlusion Intervention) Score.

PubMed

Christopoulos, Georgios; Kandzari, David E; Yeh, Robert W; Jaffer, Farouc A; Karmpaliotis, Dimitri; Wyman, Michael R; Alaswad, Khaldoon; Lombardi, William; Grantham, J Aaron; Moses, Jeffrey; Christakopoulos, Georgios; Tarar, Muhammad Nauman J; Rangan, Bavana V; Lembo, Nicholas; Garcia, Santiago; Cipher, Daisha; Thompson, Craig A; Banerjee, Subhash; Brilakis, Emmanouil S

2016-01-11

This study sought to develop a novel parsimonious score for predicting technical success of chronic total occlusion (CTO) percutaneous coronary intervention (PCI) performed using the hybrid approach. Predicting technical success of CTO PCI can facilitate clinical decision making and procedural planning. We analyzed clinical and angiographic parameters from 781 CTO PCIs included in PROGRESS CTO (Prospective Global Registry for the Study of Chronic Total Occlusion Intervention) using a derivation and validation cohort (2:1 sampling ratio). Variables with strong association with technical success in multivariable analysis were assigned 1 point, and a 4-point score was developed from summing all points. The PROGRESS CTO score was subsequently compared with the J-CTO (Multicenter Chronic Total Occlusion Registry in Japan) score in the validation cohort. Technical success was 92.9%. On multivariable analysis, factors associated with technical success included proximal cap ambiguity (beta coefficient [b] = 0.88), moderate/severe tortuosity (b = 1.18), circumflex artery CTO (b = 0.99), and absence of "interventional" collaterals (b = 0.88). The resulting score demonstrated good calibration and discriminatory capacity in the derivation (Hosmer-Lemeshow chi-square = 2.633; p = 0.268, and receiver-operator characteristic [ROC] area = 0.778) and validation (Hosmer-Lemeshow chi-square = 5.333; p = 0.070, and ROC area = 0.720) subset. In the validation cohort, the PROGRESS CTO and J-CTO scores performed similarly in predicting technical success (ROC area 0.720 vs. 0.746, area under the curve difference = 0.026, 95% confidence interval = -0.093 to 0.144). The PROGRESS CTO score is a novel useful tool for estimating technical success in CTO PCI performed using the hybrid approach. Copyright © 2016 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.

Development and Validation of a Predictive Model for Functional Outcome After Stroke Rehabilitation: The Maugeri Model.

PubMed

Scrutinio, Domenico; Lanzillo, Bernardo; Guida, Pietro; Mastropasqua, Filippo; Monitillo, Vincenzo; Pusineri, Monica; Formica, Roberto; Russo, Giovanna; Guarnaschelli, Caterina; Ferretti, Chiara; Calabrese, Gianluigi

2017-12-01

Prediction of outcome after stroke rehabilitation may help clinicians in decision-making and planning rehabilitation care. We developed and validated a predictive tool to estimate the probability of achieving improvement in physical functioning (model 1) and a level of independence requiring no more than supervision (model 2) after stroke rehabilitation. The models were derived from 717 patients admitted for stroke rehabilitation. We used multivariable logistic regression analysis to build each model. Then, each model was prospectively validated in 875 patients. Model 1 included age, time from stroke occurrence to rehabilitation admission, admission motor and cognitive Functional Independence Measure scores, and neglect. Model 2 included age, male gender, time since stroke onset, and admission motor and cognitive Functional Independence Measure score. Both models demonstrated excellent discrimination. In the derivation cohort, the area under the curve was 0.883 (95% confidence intervals, 0.858-0.910) for model 1 and 0.913 (95% confidence intervals, 0.884-0.942) for model 2. The Hosmer-Lemeshow χ 2 was 4.12 ( P =0.249) and 1.20 ( P =0.754), respectively. In the validation cohort, the area under the curve was 0.866 (95% confidence intervals, 0.840-0.892) for model 1 and 0.850 (95% confidence intervals, 0.815-0.885) for model 2. The Hosmer-Lemeshow χ 2 was 8.86 ( P =0.115) and 34.50 ( P =0.001), respectively. Both improvement in physical functioning (hazard ratios, 0.43; 0.25-0.71; P =0.001) and a level of independence requiring no more than supervision (hazard ratios, 0.32; 0.14-0.68; P =0.004) were independently associated with improved 4-year survival. A calculator is freely available for download at https://goo.gl/fEAp81. This study provides researchers and clinicians with an easy-to-use, accurate, and validated predictive tool for potential application in rehabilitation research and stroke management. © 2017 American Heart Association, Inc.

A simulation of dementia epidemiology and resource use in Australia.

PubMed

Standfield, Lachlan B; Comans, Tracy; Scuffham, Paul

2018-06-01

The number of people in the developed world who have dementia is predicted to rise markedly. This study presents a validated predictive model to assist decision-makers to determine this population's future resource requirements and target scarce health and welfare resources appropriately. A novel individual patient discrete event simulation was developed to estimate the future prevalence of dementia and related health and welfare resource use in Australia. When compared to other published results, the simulation generated valid estimates of dementia prevalence and resource use. The analysis predicted 298,000, 387,000 and 928,000 persons in Australia will have dementia in 2011, 2020 and 2050, respectively. Health and welfare resource use increased markedly over the simulated time-horizon and was affected by capacity constraints. This simulation provides useful estimates of future demands on dementia-related services allowing the exploration of the effects of capacity constraints. Implications for public health: The model demonstrates that under-resourcing of residential aged care may lead to inappropriate and inefficient use of hospital resources. To avoid these capacity constraints it is predicted that the number of aged care beds for persons with dementia will need to increase more than threefold from 2011 to 2050. © 2017 The Authors.

Predictive Modeling and Concentration of the Risk of Suicide: Implications for Preventive Interventions in the US Department of Veterans Affairs

PubMed Central

McCarthy, John F.; Katz, Ira R.; Thompson, Caitlin; Kemp, Janet; Hannemann, Claire M.; Nielson, Christopher; Schoenbaum, Michael

2015-01-01

Objectives. The Veterans Health Administration (VHA) evaluated the use of predictive modeling to identify patients at risk for suicide and to supplement ongoing care with risk-stratified interventions. Methods. Suicide data came from the National Death Index. Predictors were measures from VHA clinical records incorporating patient-months from October 1, 2008, to September 30, 2011, for all suicide decedents and 1% of living patients, divided randomly into development and validation samples. We used data on all patients alive on September 30, 2010, to evaluate predictions of suicide risk over 1 year. Results. Modeling demonstrated that suicide rates were 82 and 60 times greater than the rate in the overall sample in the highest 0.01% stratum for calculated risk for the development and validation samples, respectively; 39 and 30 times greater in the highest 0.10%; 14 and 12 times greater in the highest 1.00%; and 6.3 and 5.7 times greater in the highest 5.00%. Conclusions. Predictive modeling can identify high-risk patients who were not identified on clinical grounds. VHA is developing modeling to enhance clinical care and to guide the delivery of preventive interventions. PMID:26066914

Predictive Modeling and Concentration of the Risk of Suicide: Implications for Preventive Interventions in the US Department of Veterans Affairs.

PubMed

McCarthy, John F; Bossarte, Robert M; Katz, Ira R; Thompson, Caitlin; Kemp, Janet; Hannemann, Claire M; Nielson, Christopher; Schoenbaum, Michael

2015-09-01

The Veterans Health Administration (VHA) evaluated the use of predictive modeling to identify patients at risk for suicide and to supplement ongoing care with risk-stratified interventions. Suicide data came from the National Death Index. Predictors were measures from VHA clinical records incorporating patient-months from October 1, 2008, to September 30, 2011, for all suicide decedents and 1% of living patients, divided randomly into development and validation samples. We used data on all patients alive on September 30, 2010, to evaluate predictions of suicide risk over 1 year. Modeling demonstrated that suicide rates were 82 and 60 times greater than the rate in the overall sample in the highest 0.01% stratum for calculated risk for the development and validation samples, respectively; 39 and 30 times greater in the highest 0.10%; 14 and 12 times greater in the highest 1.00%; and 6.3 and 5.7 times greater in the highest 5.00%. Predictive modeling can identify high-risk patients who were not identified on clinical grounds. VHA is developing modeling to enhance clinical care and to guide the delivery of preventive interventions.

Validity of Teacher-Based Vision Screening and Factors Associated with the Accuracy of Vision Screening in Vietnamese Children.

PubMed

Paudel, Prakash; Kovai, Vilas; Naduvilath, Thomas; Phuong, Ha Thanh; Ho, Suit May; Giap, Nguyen Viet

2016-01-01

To assess validity of teacher-based vision screening and elicit factors associated with accuracy of vision screening in Vietnam. After brief training, teachers independently measured visual acuity (VA) in 555 children aged 12-15 years in Ba Ria - Vung Tau Province. Teacher VA measurements were compared to those of refractionists. Sensitivity, specificity, positive predictive value and negative predictive value were calculated for uncorrected VA (UVA) and presenting VA (PVA) 20/40 or worse in either eye. Chi-square, Fisher's exact test and multivariate logistic regression were used to assess factors associated with accuracy of vision screening. Level of significance was set at 5%. Trained teachers in Vietnam demonstrated 86.7% sensitivity, 95.7% specificity, 86.7% positive predictive value and 95.7% negative predictive value in identifying children with visual impairment using the UVA measurement. PVA measurement revealed low accuracy for teachers, which was significantly associated with child's age, sex, spectacle wear and myopic status, but UVA measurement showed no such associations. Better accuracy was achieved in measurement of VA and identification of children with visual impairment using UVA measurement compared to PVA. UVA measurement is recommended for teacher-based vision screening programs.

Predicting Transmembrane Helix Packing Arrangements using Residue Contacts and a Force-Directed Algorithm

PubMed Central

Nugent, Timothy; Jones, David T.

2010-01-01

Alpha-helical transmembrane proteins constitute roughly 30% of a typical genome and are involved in a wide variety of important biological processes including cell signalling, transport of membrane-impermeable molecules and cell recognition. Despite significant efforts to predict transmembrane protein topology, comparatively little attention has been directed toward developing a method to pack the helices together. Here, we present a novel approach to predict lipid exposure, residue contacts, helix-helix interactions and finally the optimal helical packing arrangement of transmembrane proteins. Using molecular dynamics data, we have trained and cross-validated a support vector machine (SVM) classifier to predict per residue lipid exposure with 69% accuracy. This information is combined with additional features to train a second SVM to predict residue contacts which are then used to determine helix-helix interaction with up to 65% accuracy under stringent cross-validation on a non-redundant test set. Our method is also able to discriminate native from decoy helical packing arrangements with up to 70% accuracy. Finally, we employ a force-directed algorithm to construct the optimal helical packing arrangement which demonstrates success for proteins containing up to 13 transmembrane helices. This software is freely available as source code from http://bioinf.cs.ucl.ac.uk/memsat/mempack/. PMID:20333233

A neural network - based algorithm for predicting stone -free status after ESWL therapy

PubMed Central

Seckiner, Ilker; Seckiner, Serap; Sen, Haluk; Bayrak, Omer; Dogan, Kazım; Erturhan, Sakip

2017-01-01

ABSTRACT Objective: The prototype artificial neural network (ANN) model was developed using data from patients with renal stone, in order to predict stone-free status and to help in planning treatment with Extracorporeal Shock Wave Lithotripsy (ESWL) for kidney stones. Materials and Methods: Data were collected from the 203 patients including gender, single or multiple nature of the stone, location of the stone, infundibulopelvic angle primary or secondary nature of the stone, status of hydronephrosis, stone size after ESWL, age, size, skin to stone distance, stone density and creatinine, for eleven variables. Regression analysis and the ANN method were applied to predict treatment success using the same series of data. Results: Subsequently, patients were divided into three groups by neural network software, in order to implement the ANN: training group (n=139), validation group (n=32), and the test group (n=32). ANN analysis demonstrated that the prediction accuracy of the stone-free rate was 99.25% in the training group, 85.48% in the validation group, and 88.70% in the test group. Conclusions: Successful results were obtained to predict the stone-free rate, with the help of the ANN model designed by using a series of data collected from real patients in whom ESWL was implemented to help in planning treatment for kidney stones. PMID:28727384

Peridynamics for failure and residual strength prediction of fiber-reinforced composites

NASA Astrophysics Data System (ADS)

Colavito, Kyle

Peridynamics is a reformulation of classical continuum mechanics that utilizes integral equations in place of partial differential equations to remove the difficulty in handling discontinuities, such as cracks or interfaces, within a body. Damage is included within the constitutive model; initiation and propagation can occur without resorting to special crack growth criteria necessary in other commonly utilized approaches. Predicting damage and residual strengths of composite materials involves capturing complex, distinct and progressive failure modes. The peridynamic laminate theory correctly predicts the load redistribution in general laminate layups in the presence of complex failure modes through the use of multiple interaction types. This study presents two approaches to obtain the critical peridynamic failure parameters necessary to capture the residual strength of a composite structure. The validity of both approaches is first demonstrated by considering the residual strength of isotropic materials. The peridynamic theory is used to predict the crack growth and final failure load in both a diagonally loaded square plate with a center crack, as well as a four-point shear specimen subjected to asymmetric loading. This study also establishes the validity of each approach by considering composite laminate specimens in which each failure mode is isolated. Finally, the failure loads and final failure modes are predicted in a laminate with various hole diameters subjected to tensile and compressive loads.

Traumatic Brain Injury in Children: Role of CDRs-PECARN as a Clinical Predictive Resource for Evaluation of Intracranical Lesions and Neuropsychiatric Outcomes.

PubMed

Ferrara, Pietro; Basile, Maria Cristina; Dell'Aquila, Livia; Vena, Flaminia; Coppo, Elena; Chiaretti, Antonio; Verrotti, Alberto; Paolini, Fabrizio; Caldarelli, Massimo

2016-01-01

Cranial computed tomography (CT) is considered the gold standard for the diagnosis of traumatic brain injury (TBI). The aim of this study was to evaluate if the clinical decision rules proposed by the Pediatric Emergency Care Applied Research Network (CDRs-PECARN) are really able to identify the patients who do not need cranial CT. This study investigates the neuropsychiatric outcome after TBI according to a pediatric version of the Glasgow Outcome Scale-Extended (GOS-E Peds). We calculated the sensitivity, specificity, negative predictive value (NPV) and positive predictive value of the CDRs-PECARN in 2 age groups. Sensitivity was very high in both groups, and the NPV was very useful for predicting which subjects, of those who presented without CDRs- PECARN, would have a negative cranial CT. We also evaluated the correlations between the GOS-E Peds and Glasgow Coma Scale and between the GOS-E Peds and cranial CT scan. Our study confirms the validation of the PECARN TBI prediction rules as a clinical instrument which can play a significant role in CT decision-making for children with TBI. It also demonstrates that the GOS-E Peds is a valid pediatric outcome scale for children with TBI, despite some important limitations. © 2016 S. Karger AG, Basel.

Development and Validation of a Near-Infrared Spectroscopy Method for the Prediction of Acrylamide Content in French-Fried Potato.

PubMed

Adedipe, Oluwatosin E; Johanningsmeier, Suzanne D; Truong, Van-Den; Yencho, G Craig

2016-03-02

This study investigated the ability of near-infrared spectroscopy (NIRS) to predict acrylamide content in French-fried potato. Potato flour spiked with acrylamide (50-8000 μg/kg) was used to determine if acrylamide could be accurately predicted in a potato matrix. French fries produced with various pretreatments and cook times (n = 84) and obtained from quick-service restaurants (n = 64) were used for model development and validation. Acrylamide was quantified using gas chromatography-mass spectrometry, and reflectance spectra (400-2500 nm) of each freeze-dried sample were captured on a Foss XDS Rapid Content Analyzer-NIR spectrometer. Partial least-squares (PLS) discriminant analysis and PLS regression modeling demonstrated that NIRS could accurately detect acrylamide content as low as 50 μg/kg in the model potato matrix. Prediction errors of 135 μg/kg (R(2) = 0.98) and 255 μg/kg (R(2) = 0.93) were achieved with the best PLS models for acrylamide prediction in Russet Norkotah French-fried potato and multiple samples of unknown varieties, respectively. The findings indicate that NIRS can be used as a screening tool in potato breeding and potato processing research to reduce acrylamide in the food supply.

Identifying patients with undetected colorectal cancer: an independent validation of QCancer (Colorectal).

PubMed

Collins, G S; Altman, D G

2012-07-10

Early identification of colorectal cancer is an unresolved challenge and the predictive value of single symptoms is limited. We evaluated the performance of QCancer (Colorectal) prediction model for predicting the absolute risk of colorectal cancer in an independent UK cohort of patients from general practice records. A total of 2.1 million patients registered with a general practice surgery between 01 January 2000 and 30 June 2008, aged 30-84 years (3.7 million person-years) with 3712 colorectal cancer cases were included in the analysis. Colorectal cancer was defined as incident diagnosis of colorectal cancer during the 2 years after study entry. The results from this independent and external validation of QCancer (Colorectal) prediction model demonstrated good performance data on a large cohort of general practice patients. QCancer (Colorectal) had very good discrimination with an area under the ROC curve of 0.92 (women) and 0.91 (men), and explained 68% (women) and 66% (men) of the variation. QCancer (Colorectal) was well calibrated across all tenths of risk and over all age ranges with predicted risks closely matching observed risks. QCancer (Colorectal) appears to be a useful tool for identifying undetected cases of undiagnosed colorectal cancer in primary care in the United Kingdom.

A Systematic Approach to Predicting Spring Force for Sagittal Craniosynostosis Surgery.

PubMed

Zhang, Guangming; Tan, Hua; Qian, Xiaohua; Zhang, Jian; Li, King; David, Lisa R; Zhou, Xiaobo

2016-05-01

Spring-assisted surgery (SAS) can effectively treat scaphocephaly by reshaping crania with the appropriate spring force. However, it is difficult to accurately estimate spring force without considering biomechanical properties of tissues. This study presents and validates a reliable system to accurately predict the spring force for sagittal craniosynostosis surgery. The authors randomly chose 23 patients who underwent SAS and had been followed for at least 2 years. An elastic model was designed to characterize the biomechanical behavior of calvarial bone tissue for each individual. After simulating the contact force on accurate position of the skull strip with the springs, the finite element method was applied to calculating the stress of each tissue node based on the elastic model. A support vector regression approach was then used to model the relationships between biomechanical properties generated from spring force, bone thickness, and the change of cephalic index after surgery. Therefore, for a new patient, the optimal spring force can be predicted based on the learned model with virtual spring simulation and dynamic programming approach prior to SAS. Leave-one-out cross-validation was implemented to assess the accuracy of our prediction. As a result, the mean prediction accuracy of this model was 93.35%, demonstrating the great potential of this model as a useful adjunct for preoperative planning tool.

The Wisconsin Predicting Patients' Relapse questionnaire

PubMed Central

Bolt, Daniel M.; McCarthy, Danielle E.; Japuntich, Sandra J.; Fiore, Michael C.; Smith, Stevens S.; Baker, Timothy B.

2009-01-01

Introduction: Relapse is the most common smoking cessation outcome. Accurate prediction of relapse likelihood could be an important clinical tool used to influence treatment selection or duration. The aim of this research was to develop a brief clinical relapse proneness questionnaire to be used with smokers interested in quitting in a clinical setting where time is at a premium. Methods: Diverse items assessing constructs shown in previous research to be related to relapse risk, such as nicotine dependence and self-efficacy, were evaluated to determine their independent contributions to relapse prediction. In an exploratory dataset, candidate items were assessed among smokers motivated to quit smoking who enrolled in one of three randomized controlled smoking cessation trials. A cross-validation dataset was used to compare the relative predictive power of the new instrument against the Fagerström Test for Nicotine Dependence (FTND) at 1-week, 8-week, and 6-month postquit assessments. Results: We selected seven items with relatively nonoverlapping content for the Wisconsin Predicting Patient's Relapse (WI-PREPARE) measure, a brief, seven-item questionnaire that taps physical dependence, environmental factors, and individual difference characteristics. Cross-validation analyses suggested that the WI-PREPARE demonstrated a stronger prediction of relapse at 1-week and 8-week postquit assessments than the FTND and comparable prediction to the FTND at a 6-month postquit assessment. Discussion: The WI-PREPARE is easy to score, suggests the nature of a patient's relapse risk, and predicts short- and medium-term relapse better than the FTND. PMID:19372573

Evaluating measurements of carbon dioxide emissions using a precision source--A natural gas burner.

PubMed

Bryant, Rodney; Bundy, Matthew; Zong, Ruowen

2015-07-01

A natural gas burner has been used as a precise and accurate source for generating large quantities of carbon dioxide (CO2) to evaluate emissions measurements at near-industrial scale. Two methods for determining carbon dioxide emissions from stationary sources are considered here: predicting emissions based on fuel consumption measurements-predicted emissions measurements, and direct measurement of emissions quantities in the flue gas-direct emissions measurements. Uncertainty for the predicted emissions measurement was estimated at less than 1%. Uncertainty estimates for the direct emissions measurement of carbon dioxide were on the order of ±4%. The relative difference between the direct emissions measurements and the predicted emissions measurements was within the range of the measurement uncertainty, therefore demonstrating good agreement. The study demonstrates how independent methods are used to validate source emissions measurements, while also demonstrating how a fire research facility can be used as a precision test-bed to evaluate and improve carbon dioxide emissions measurements from stationary sources. Fossil-fuel-consuming stationary sources such as electric power plants and industrial facilities account for more than half of the CO2 emissions in the United States. Therefore, accurate emissions measurements from these sources are critical for evaluating efforts to reduce greenhouse gas emissions. This study demonstrates how a surrogate for a stationary source, a fire research facility, can be used to evaluate the accuracy of measurements of CO2 emissions.

The utility of the Edmonton Symptom Assessment System in screening for anxiety and depression.

PubMed

Bagha, S M; Macedo, A; Jacks, L M; Lo, C; Zimmermann, C; Rodin, G; Li, M

2013-01-01

The Edmonton Symptom Assessment System (ESAS) is a common screening tool in cancer, although its validity for distress screening is unproven. Here, screening performance of the ESAS anxiety (ESAS-A) and depression (ESAS-D) items were validated against the anxiety [Generalised Anxiety Disorder-7 (GAD-7)] and depression [Patient Health Questionnaire-9 (PHQ-9)] subscales of the PHQ. A total of 1215 cancer patients completed the Distress Assessment and Response Tool (DART), a computerised distress screening instrument. Spearman's rank correlation coefficients and receiver operating characteristic curve analyses were used to evaluate the ability of ESAS-A and ESAS-D to identify moderate distress (GAD-7/PHQ-9 ≥ 10). Spearman's rank correlation coefficients comparing ESAS-A and ESAS-D with GAD-7 and PHQ-9 were 0.74 and 0.72 respectively. Areas under the receiver operating characteristic curves were 0.89 and 0.88 for anxiety and depression respectively. A cut-off of ≥3 on ESAS-A demonstrated a sensitivity of 0.91, specificity of 0.68, positive predictive value of 0.34 and negative predictive value of 0.97. A cut-off of ≥2 on the ESAS-D demonstrated a sensitivity of 0.86, specificity of 0.72, positive predictive value of 0.46 and negative predictive value of 0.95. High sensitivities of ESAS-A and ESAS-D at certain cut-offs suggest they have use in ruling-out distress. However, their low specificities indicate secondary screening is needed to rule-in anxiety or depression for case-finding. © 2012 Blackwell Publishing Ltd.

An Approach for Validating Actinide and Fission Product Burnup Credit Criticality Safety Analyses--Criticality (keff) Predictions

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

Scaglione, John M; Mueller, Don; Wagner, John C

2011-01-01

One of the most significant remaining challenges associated with expanded implementation of burnup credit in the United States is the validation of depletion and criticality calculations used in the safety evaluation - in particular, the availability and use of applicable measured data to support validation, especially for fission products. Applicants and regulatory reviewers have been constrained by both a scarcity of data and a lack of clear technical basis or approach for use of the data. U.S. Nuclear Regulatory Commission (NRC) staff have noted that the rationale for restricting their Interim Staff Guidance on burnup credit (ISG-8) to actinide-only ismore » based largely on the lack of clear, definitive experiments that can be used to estimate the bias and uncertainty for computational analyses associated with using burnup credit. To address the issue of validation, the NRC initiated a project with the Oak Ridge National Laboratory to (1) develop and establish a technically sound validation approach (both depletion and criticality) for commercial spent nuclear fuel (SNF) criticality safety evaluations based on best-available data and methods and (2) apply the approach for representative SNF storage and transport configurations/conditions to demonstrate its usage and applicability, as well as to provide reference bias results. The purpose of this paper is to describe the criticality (k{sub eff}) validation approach, and resulting observations and recommendations. Validation of the isotopic composition (depletion) calculations is addressed in a companion paper at this conference. For criticality validation, the approach is to utilize (1) available laboratory critical experiment (LCE) data from the International Handbook of Evaluated Criticality Safety Benchmark Experiments and the French Haut Taux de Combustion (HTC) program to support validation of the principal actinides and (2) calculated sensitivities, nuclear data uncertainties, and the limited available fission product LCE data to predict and verify individual biases for relevant minor actinides and fission products. This paper (1) provides a detailed description of the approach and its technical bases, (2) describes the application of the approach for representative pressurized water reactor and boiling water reactor safety analysis models to demonstrate its usage and applicability, (3) provides reference bias results based on the prerelease SCALE 6.1 code package and ENDF/B-VII nuclear cross-section data, and (4) provides recommendations for application of the results and methods to other code and data packages.« less

The Social Meaning in Life Events Scale (SMILES): A preliminary psychometric evaluation in a bereaved sample.

PubMed

Bellet, Benjamin W; Holland, Jason M; Neimeyer, Robert A

2018-06-05

A mourner's success in making meaning of a loss has proven key in predicting a wide array of bereavement outcomes. However, much of this meaning-making process takes place in an interpersonal framework that is hypothesized to either aid or obstruct this process. To date, a psychometrically validated measure of the degree to which a mourner successfully makes meaning of a loss in a social context has yet to be developed. The present study examines the factor structure, reliability, and validity of a new measure called the Social Meaning in Life Events Scale (SMILES) in a sample of bereaved college students (N = 590). The SMILES displayed a two-factor structure, with one factor assessing the extent to which a mourner's efforts at making meaning were invalidated (Social Invalidation subscale), and the other assessing the extent to which a mourner's meaning-making process was validated (Social Validation subscale). The subscales displayed good reliability and construct validity in reference to several outcome variables of interest (complicated grief, general health, and post-loss growth), as well as related but different variables (social support and meaning made). The subscales also demonstrated group differences according to two demographic variables associated with complications in the mourning process (age and mode of loss), as well as incremental validity in predicting adverse bereavement outcomes over and above general social support. Clinical and research implications involving the use of this new measure are discussed.

Rapid hybridization of nucleic acids using isotachophoresis

PubMed Central

Bercovici, Moran; Han, Crystal M.; Liao, Joseph C.; Santiago, Juan G.

2012-01-01

We use isotachophoresis (ITP) to control and increase the rate of nucleic acid hybridization reactions in free solution. We present a new physical model, validation experiments, and demonstrations of this assay. We studied the coupled physicochemical processes of preconcentration, mixing, and chemical reaction kinetics under ITP. Our experimentally validated model enables a closed form solution for ITP-aided reaction kinetics, and reveals a new characteristic time scale which correctly predicts order 10,000-fold speed-up of chemical reaction rate for order 100 pM reactants, and greater enhancement at lower concentrations. At 500 pM concentration, we measured a reaction time which is 14,000-fold lower than that predicted for standard second-order hybridization. The model and method are generally applicable to acceleration of reactions involving nucleic acids, and may be applicable to a wide range of reactions involving ionic reactants. PMID:22733732

Quantitative determination of additive Chlorantraniliprole in Abamectin preparation: Investigation of bootstrapping soft shrinkage approach by mid-infrared spectroscopy

NASA Astrophysics Data System (ADS)

Yan, Hong; Song, Xiangzhong; Tian, Kuangda; Chen, Yilin; Xiong, Yanmei; Min, Shungeng

2018-02-01

A novel method, mid-infrared (MIR) spectroscopy, which enables the determination of Chlorantraniliprole in Abamectin within minutes, is proposed. We further evaluate the prediction ability of four wavelength selection methods, including bootstrapping soft shrinkage approach (BOSS), Monte Carlo uninformative variable elimination (MCUVE), genetic algorithm partial least squares (GA-PLS) and competitive adaptive reweighted sampling (CARS) respectively. The results showed that BOSS method obtained the lowest root mean squared error of cross validation (RMSECV) (0.0245) and root mean squared error of prediction (RMSEP) (0.0271), as well as the highest coefficient of determination of cross-validation (Qcv2) (0.9998) and the coefficient of determination of test set (Q2test) (0.9989), which demonstrated that the mid infrared spectroscopy can be used to detect Chlorantraniliprole in Abamectin conveniently. Meanwhile, a suitable wavelength selection method (BOSS) is essential to conducting a component spectral analysis.

A kinetic model of municipal sludge degradation during non-catalytic wet oxidation.

PubMed

Prince-Pike, Arrian; Wilson, David I; Baroutian, Saeid; Andrews, John; Gapes, Daniel J

2015-12-15

Wet oxidation is a successful process for the treatment of municipal sludge. In addition, the resulting effluent from wet oxidation is a useful carbon source for subsequent biological nutrient removal processes in wastewater treatment. Owing to limitations with current kinetic models, this study produced a kinetic model which predicts the concentrations of key intermediate components during wet oxidation. The model was regressed from lab-scale experiments and then subsequently validated using data from a wet oxidation pilot plant. The model was shown to be accurate in predicting the concentrations of each component, and produced good results when applied to a plant 500 times larger in size. A statistical study was undertaken to investigate the validity of the regressed model parameters. Finally the usefulness of the model was demonstrated by suggesting optimum operating conditions such that volatile fatty acids were maximised. Copyright © 2015 Elsevier Ltd. All rights reserved.

XV-15 Low-Noise Terminal Area Operations Testing

NASA Technical Reports Server (NTRS)

Edwards, B. D.

1998-01-01

Test procedures related to XV-15 noise tests conducted by NASA-Langley and Bell Helicopter Textron, Inc. are discussed. The tests. which took place during October and November 1995, near Waxahachie, Texas, documented the noise signature of the XV-15 tilt-rotor aircraft at a wide variety of flight conditions. The stated objectives were to: -provide a comprehensive acoustic database for NASA and U.S. Industry -validate noise prediction methodologies, and -develop and demonstrate low-noise flight profiles. The test consisted of two distinct phases. Phase 1 provided an acoustic database for validating analytical noise prediction techniques; Phase 2 directly measured noise contour information at a broad range of operating profiles, with emphasis on minimizing 'approach' noise. This report is limited to a documentation of the test procedures, flight conditions, microphone locations, meteorological conditions, and test personnel used in the test. The acoustic results are not included.

Performance Evaluation and Modeling of Erosion Resistant Turbine Engine Thermal Barrier Coatings

NASA Technical Reports Server (NTRS)

Miller, Robert A.; Zhu, Dongming; Kuczmarski, Maria

2008-01-01

The erosion resistant turbine thermal barrier coating system is critical to the rotorcraft engine performance and durability. The objective of this work was to determine erosion resistance of advanced thermal barrier coating systems under simulated engine erosion and thermal gradient environments, thus validating a new thermal barrier coating turbine blade technology for future rotorcraft applications. A high velocity burner rig based erosion test approach was established and a new series of rare earth oxide- and TiO2/Ta2O5- alloyed, ZrO2-based low conductivity thermal barrier coatings were designed and processed. The low conductivity thermal barrier coating systems demonstrated significant improvements in the erosion resistance. A comprehensive model based on accumulated strain damage low cycle fatigue is formulated for blade erosion life prediction. The work is currently aiming at the simulated engine erosion testing of advanced thermal barrier coated turbine blades to establish and validate the coating life prediction models.

Measurement and prediction of the thermomechanical response of shape memory alloy hybrid composite beams

NASA Astrophysics Data System (ADS)

Davis, Brian; Turner, Travis L.; Seelecke, Stefan

2005-05-01

Previous work at NASA Langley Research Center (LaRC) involved fabrication and testing of composite beams with embedded, pre-strained shape memory alloy (SMA) ribbons within the beam structures. That study also provided comparison of experimental results with numerical predictions from a research code making use of a new thermoelastic model for shape memory alloy hybrid composite (SMAHC) structures. The previous work showed qualitative validation of the numerical model. However, deficiencies in the experimental-numerical correlation were noted and hypotheses for the discrepancies were given for further investigation. The goal of this work is to refine the experimental measurement and numerical modeling approaches in order to better understand the discrepancies, improve the correlation between prediction and measurement, and provide rigorous quantitative validation of the numerical analysis/design tool. The experimental investigation is refined by a more thorough test procedure and incorporation of higher fidelity measurements such as infrared thermography and projection moire interferometry. The numerical results are produced by a recently commercialized version of the constitutive model as implemented in ABAQUS and are refined by incorporation of additional measured parameters such as geometric imperfection. Thermal buckling, post-buckling, and random responses to thermal and inertial (base acceleration) loads are studied. The results demonstrate the effectiveness of SMAHC structures in controlling static and dynamic responses by adaptive stiffening. Excellent agreement is achieved between the predicted and measured results of the static and dynamic thermomechanical response, thereby providing quantitative validation of the numerical tool.

Measurement and Prediction of the Thermomechanical Response of Shape Memory Alloy Hybrid Composite Beams

NASA Technical Reports Server (NTRS)

Davis, Brian; Turner, Travis L.; Seelecke, Stefan

2005-01-01

Previous work at NASA Langley Research Center (LaRC) involved fabrication and testing of composite beams with embedded, pre-strained shape memory alloy (SMA) ribbons within the beam structures. That study also provided comparison of experimental results with numerical predictions from a research code making use of a new thermoelastic model for shape memory alloy hybrid composite (SMAHC) structures. The previous work showed qualitative validation of the numerical model. However, deficiencies in the experimental-numerical correlation were noted and hypotheses for the discrepancies were given for further investigation. The goal of this work is to refine the experimental measurement and numerical modeling approaches in order to better understand the discrepancies, improve the correlation between prediction and measurement, and provide rigorous quantitative validation of the numerical analysis/design tool. The experimental investigation is refined by a more thorough test procedure and incorporation of higher fidelity measurements such as infrared thermography and projection moire interferometry. The numerical results are produced by a recently commercialized version of the constitutive model as implemented in ABAQUS and are refined by incorporation of additional measured parameters such as geometric imperfection. Thermal buckling, post-buckling, and random responses to thermal and inertial (base acceleration) loads are studied. The results demonstrate the effectiveness of SMAHC structures in controlling static and dynamic responses by adaptive stiffening. Excellent agreement is achieved between the predicted and measured results of the static and dynamic thermomechanical response, thereby providing quantitative validation of the numerical tool.

Development and Psychometric Evaluation of the HPV Clinical Trial Survey for Parents (CTSP‐HPV) Using Traditional Survey Development Methods and Community Engagement Principles

PubMed Central

Wallston, Kenneth A.; Wilkins, Consuelo H.; Hull, Pamela C.; Miller, Stephania T.

2015-01-01

Abstract Objective This study describes the development and psychometric evaluation of HPV Clinical Trial Survey for Parents with Children Aged 9 to 15 (CTSP‐HPV) using traditional instrument development methods and community engagement principles. Methods An expert panel and parental input informed survey content and parents recommended study design changes (e.g., flyer wording). A convenience sample of 256 parents completed the final survey measuring parental willingness to consent to HPV clinical trial (CT) participation and other factors hypothesized to influence willingness (e.g., HPV vaccine benefits). Cronbach's a, Spearman correlations, and multiple linear regression were used to estimate internal consistency, convergent and discriminant validity, and predictively validity, respectively. Results Internal reliability was confirmed for all scales (a ≥ 0.70.). Parental willingness was positively associated (p < 0.05) with trust in medical researchers, adolescent CT knowledge, HPV vaccine benefits, advantages of adolescent CTs (r range 0.33–0.42), supporting convergent validity. Moderate discriminant construct validity was also demonstrated. Regression results indicate reasonable predictive validity with the six scales accounting for 31% of the variance in parents’ willingness. Conclusions This instrument can inform interventions based on factors that influence parental willingness, which may lead to the eventual increase in trial participation. Further psychometric testing is warranted. PMID:26530324

Effect of topological patterning on self-rolling of nanomembranes.

PubMed

Chen, Cheng; Song, Pengfei; Meng, Fanchao; Ou, Pengfei; Liu, Xinyu; Song, Jun

2018-08-24

The effects of topological patterning (i.e., grating and rectangular patterns) on the self-rolling behaviors of heteroepitaxial strained nanomembranes have been systematically studied. An analytical modeling framework, validated through finite-element simulations, has been formulated to predict the resultant curvature of the patterned nanomembrane as the pattern thickness and density vary. The effectiveness of the grating pattern in regulating the rolling direction of the nanomembrane has been demonstrated and quantitatively assessed. Further to the rolling of nanomembranes, a route to achieve predictive design of helical structures has been proposed and showcased. The present study provides new knowledge and mechanistic guidance towards predictive control and tuning of roll-up nanostructures via topological patterning.

Adaptive vibration control of structures under earthquakes

NASA Astrophysics Data System (ADS)

Lew, Jiann-Shiun; Juang, Jer-Nan; Loh, Chin-Hsiung

2017-04-01

techniques, for structural vibration suppression under earthquakes. Various control strategies have been developed to protect structures from natural hazards and improve the comfort of occupants in buildings. However, there has been little development of adaptive building control with the integration of real-time system identification and control design. Generalized predictive control, which combines the process of real-time system identification and the process of predictive control design, has received widespread acceptance and has been successfully applied to various test-beds. This paper presents a formulation of the predictive control scheme for adaptive vibration control of structures under earthquakes. Comprehensive simulations are performed to demonstrate and validate the proposed adaptive control technique for earthquake-induced vibration of a building.

A network of molecular switches controls the activation of the two-component response regulator NtrC

NASA Astrophysics Data System (ADS)

Vanatta, Dan K.; Shukla, Diwakar; Lawrenz, Morgan; Pande, Vijay S.

2015-06-01

Recent successes in simulating protein structure and folding dynamics have demonstrated the power of molecular dynamics to predict the long timescale behaviour of proteins. Here, we extend and improve these methods to predict molecular switches that characterize conformational change pathways between the active and inactive state of nitrogen regulatory protein C (NtrC). By employing unbiased Markov state model-based molecular dynamics simulations, we construct a dynamic picture of the activation pathways of this key bacterial signalling protein that is consistent with experimental observations and predicts new mutants that could be used for validation of the mechanism. Moreover, these results suggest a novel mechanistic paradigm for conformational switching.

Auralization Architectures for NASA?s Next Generation Aircraft Noise Prediction Program

NASA Technical Reports Server (NTRS)

Rizzi, Stephen A.; Lopes, Leonard V.; Burley, Casey L.; Aumann, Aric R.

2013-01-01

Aircraft community noise is a significant concern due to continued growth in air traffic, increasingly stringent environmental goals, and operational limitations imposed by airport authorities. The assessment of human response to noise from future aircraft can only be afforded through laboratory testing using simulated flyover noise. Recent work by the authors demonstrated the ability to auralize predicted flyover noise for a state-of-the-art reference aircraft and a future hybrid wing body aircraft concept. This auralization used source noise predictions from NASA's Aircraft NOise Prediction Program (ANOPP) as input. The results from this process demonstrated that auralization based upon system noise predictions is consistent with, and complementary to, system noise predictions alone. To further develop and validate the auralization process, improvements to the interfaces between the synthesis capability and the system noise tools are required. This paper describes the key elements required for accurate noise synthesis and introduces auralization architectures for use with the next-generation ANOPP (ANOPP2). The architectures are built around a new auralization library and its associated Application Programming Interface (API) that utilize ANOPP2 APIs to access data required for auralization. The architectures are designed to make the process of auralizing flyover noise a common element of system noise prediction.

Validation of a Plasma-Based Comprehensive Cancer Genotyping Assay Utilizing Orthogonal Tissue- and Plasma-Based Methodologies.

PubMed

Odegaard, Justin I; Vincent, John J; Mortimer, Stefanie; Vowles, James V; Ulrich, Bryan C; Banks, Kimberly C; Fairclough, Stephen R; Zill, Oliver A; Sikora, Marcin; Mokhtari, Reza; Abdueva, Diana; Nagy, Rebecca J; Lee, Christine E; Kiedrowski, Lesli A; Paweletz, Cloud P; Eltoukhy, Helmy; Lanman, Richard B; Chudova, Darya I; Talasaz, AmirAli

2018-04-24

Purpose: To analytically and clinically validate a circulating cell-free tumor DNA sequencing test for comprehensive tumor genotyping and demonstrate its clinical feasibility. Experimental Design: Analytic validation was conducted according to established principles and guidelines. Blood-to-blood clinical validation comprised blinded external comparison with clinical droplet digital PCR across 222 consecutive biomarker-positive clinical samples. Blood-to-tissue clinical validation comprised comparison of digital sequencing calls to those documented in the medical record of 543 consecutive lung cancer patients. Clinical experience was reported from 10,593 consecutive clinical samples. Results: Digital sequencing technology enabled variant detection down to 0.02% to 0.04% allelic fraction/2.12 copies with ≤0.3%/2.24-2.76 copies 95% limits of detection while maintaining high specificity [prevalence-adjusted positive predictive values (PPV) >98%]. Clinical validation using orthogonal plasma- and tissue-based clinical genotyping across >750 patients demonstrated high accuracy and specificity [positive percent agreement (PPAs) and negative percent agreement (NPAs) >99% and PPVs 92%-100%]. Clinical use in 10,593 advanced adult solid tumor patients demonstrated high feasibility (>99.6% technical success rate) and clinical sensitivity (85.9%), with high potential actionability (16.7% with FDA-approved on-label treatment options; 72.0% with treatment or trial recommendations), particularly in non-small cell lung cancer, where 34.5% of patient samples comprised a directly targetable standard-of-care biomarker. Conclusions: High concordance with orthogonal clinical plasma- and tissue-based genotyping methods supports the clinical accuracy of digital sequencing across all four types of targetable genomic alterations. Digital sequencing's clinical applicability is further supported by high rates of technical success and biomarker target discovery. Clin Cancer Res; 1-11. ©2018 AACR. ©2018 American Association for Cancer Research.

Incremental validity of the MMPI-2-RF over-reporting scales and RBS in assessing the veracity of memory complaints.

PubMed

Gervais, Roger O; Ben-Porath, Yossef S; Wygant, Dustin B; Sellbom, Martin

2010-06-01

The Response Bias Scale (RBS) has been found to be a better predictor of over-reported memory complaints than Minnesota Multiphasic Personality Inventory-2 (MMPI-2) F, Back Infrequency (Fb), Infrequency-Psychopathology (Fp), and FBS scales. The MMPI-2-Restructured Form (RF) validity scales were designed to meet or exceed the sensitivity of their MMPI-2 counterparts to symptom over-reporting. This study examined the incremental validity of MMPI-2-RF validity scales and RBS in assessing memory complaints. The MMPI-2-RF over-reporting validity scales were more strongly associated with mean Memory Complaints Inventory scores than their MMPI-2 counterparts (d = 0.22 to 0.49). RBS showed the strongest relationship with memory complaints. Regression analyses demonstrated the incremental validity of the MMPI-2-RF Infrequent Responses, Infrequent Psychopathology Responses, Infrequent Somatic Responses, and FBS-r scales relative to MMPI-2 F, Fp, and FBS in predicting memory complaints. This is consistent with the development objectives of the MMPI-2-RF validity scales as more efficient and sensitive measures of symptom over-reporting.

Development and validation of a prognostic nomogram for colorectal cancer after radical resection based on individual patient data from three large-scale phase III trials

PubMed Central

Akiyoshi, Takashi; Maeda, Hiromichi; Kashiwabara, Kosuke; Kanda, Mitsuro; Mayanagi, Shuhei; Aoyama, Toru; Hamada, Chikuma; Sadahiro, Sotaro; Fukunaga, Yosuke; Ueno, Masashi; Sakamoto, Junichi; Saji, Shigetoyo; Yoshikawa, Takaki

2017-01-01

Background Few prediction models have so far been developed and assessed for the prognosis of patients who undergo curative resection for colorectal cancer (CRC). Materials and Methods We prepared a clinical dataset including 5,530 patients who participated in three major randomized controlled trials as a training dataset and 2,263 consecutive patients who were treated at a cancer-specialized hospital as a validation dataset. All subjects underwent radical resection for CRC which was histologically diagnosed to be adenocarcinoma. The main outcomes that were predicted were the overall survival (OS) and disease free survival (DFS). The identification of the variables in this nomogram was based on a Cox regression analysis and the model performance was evaluated by Harrell's c-index. The calibration plot and its slope were also studied. For the external validation assessment, risk group stratification was employed. Results The multivariate Cox model identified variables; sex, age, pathological T and N factor, tumor location, size, lymphnode dissection, postoperative complications and adjuvant chemotherapy. The c-index was 0.72 (95% confidence interval [CI] 0.66-0.77) for the OS and 0.74 (95% CI 0.69-0.78) for the DFS. The proposed stratification in the risk groups demonstrated a significant distinction between the Kaplan–Meier curves for OS and DFS in the external validation dataset. Conclusions We established a clinically reliable nomogram to predict the OS and DFS in patients with CRC using large scale and reliable independent patient data from phase III randomized controlled trials. The external validity was also confirmed on the practical dataset. PMID:29228760

Spatio-temporal modeling of chronic PM 10 exposure for the Nurses' Health Study

NASA Astrophysics Data System (ADS)

Yanosky, Jeff D.; Paciorek, Christopher J.; Schwartz, Joel; Laden, Francine; Puett, Robin; Suh, Helen H.

2008-06-01

Chronic epidemiological studies of airborne particulate matter (PM) have typically characterized the chronic PM exposures of their study populations using city- or county-wide ambient concentrations, which limit the studies to areas where nearby monitoring data are available and which ignore within-city spatial gradients in ambient PM concentrations. To provide more spatially refined and precise chronic exposure measures, we used a Geographic Information System (GIS)-based spatial smoothing model to predict monthly outdoor PM10 concentrations in the northeastern and midwestern United States. This model included monthly smooth spatial terms and smooth regression terms of GIS-derived and meteorological predictors. Using cross-validation and other pre-specified selection criteria, terms for distance to road by road class, urban land use, block group and county population density, point- and area-source PM10 emissions, elevation, wind speed, and precipitation were found to be important determinants of PM10 concentrations and were included in the final model. Final model performance was strong (cross-validation R2=0.62), with little bias (-0.4 μg m-3) and high precision (6.4 μg m-3). The final model (with monthly spatial terms) performed better than a model with seasonal spatial terms (cross-validation R2=0.54). The addition of GIS-derived and meteorological predictors improved predictive performance over spatial smoothing (cross-validation R2=0.51) or inverse distance weighted interpolation (cross-validation R2=0.29) methods alone and increased the spatial resolution of predictions. The model performed well in both rural and urban areas, across seasons, and across the entire time period. The strong model performance demonstrates its suitability as a means to estimate individual-specific chronic PM10 exposures for large populations.

Bridging the Technology Readiness "Valley of Death" Utilizing Nanosats

NASA Technical Reports Server (NTRS)

Bauer, Robert A.; Millar, Pamela S.; Norton, Charles D.

2015-01-01

Incorporating new technology is a hallmark of space missions. Missions demand ever-improving tools and techniques to allow them to meet the mission science requirements. In Earth Science, these technologies are normally expressed in new instrument capabilities that can enable new measurement concepts, extended capabilities of existing measurement techniques, or totally new detection capabilities, and also, information systems technologies that can enhance data analysis or enable new data analyses to advance modeling and prediction capabilities. Incorporating new technologies has never been easy. There is a large development step beyond demonstration in a laboratory or on an airborne platform to the eventual space environment that is sometimes referred to as the "technology valley of death." Studies have shown that non-validated technology is a primary cause of NASA and DoD mission delays and cost overruns. With the demise of the New Millennium Program within NASA, opportunities for demonstrating technologies in space have been rare. Many technologies are suitable for a flight project after only ground testing. However, some require validation in a relevant or a space flight environment, which cannot be fully tested on the ground or in airborne systems. NASA's Earth Science Technology Program has initiated a nimble program to provide a fairly rapid turn-around of space validated technologies, and thereby reducing future mission risk in incorporating new technologies. The program, called In-Space Validation of Earth Science Technology (InVEST), now has five tasks in development. Each are 3U CubeSats and they are targeted for launch opportunities in the 2016 time period. Prior to formalizing an InVEST program, the technology program office was asked to demonstrate how the program would work and what sort of technologies could benefit from space validation. Three projects were developed and launched, and have demonstrated the technologies that they set out to validate. This paper will provide a brief status of the pre-InVEST CubeSats, and discuss the development and status of the InVEST program. Figure

The Children, Intimate Relationships, and Conflictual Life Events (CIRCLE) interview for simultaneous measurement of intimate partner and parent to child aggression.

PubMed

Marshall, Amy D; Feinberg, Mark E; Jones, Damon E; Chote, Daniel R

2017-08-01

Despite substantial rates of parent to child aggression (PCA) and intimate partner aggression (IPA) co-occurrence within families, the co-occurrence of PCA and IPA within incidents of aggression has not previously been examined. To do so, we developed the Children, Intimate Relationships, and Conflictual Life Events (CIRCLE) interview to simultaneously measure incidents of psychological and physical PCA and IPA. The CIRCLE interview was administered quarterly for approximately 1 year to 109 women and 94 men from 111 couples with a first born child approximately 32 months of age at study initiation. Demonstrating the CIRCLE interview's ability to yield new knowledge about the nature of family aggression, we describe the frequency of aggressive incidents, the average number of aggressive behaviors within incidents, the daily occurrence of multiple aggressive incidents, and rates of within-incident PCA and IPA co-occurrence. With the exception of men's physical IPA, aggression scores derived from the CIRCLE interview exhibited a relatively high degree of interpartner reporting concordance, as well as structural validity and convergent validity with common aggression measures. Aggression reports via repeated testing were not influenced by social desirability or attempts to avoid aggression. Participants who perceived enhanced memory for aggression as a function of study participation reported increasing PCA and IPA frequencies over time. In the prediction of child conduct and emotional problems, the CIRCLE interview demonstrated predictive validity and incremental validity over traditional aggression measures. For the first time, within-incident co-occurrence of PCA and IPA was documented and shown to uniquely impact child outcomes. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

A mathematical prediction model incorporating molecular subtype for risk of non-sentinel lymph node metastasis in sentinel lymph node-positive breast cancer patients: a retrospective analysis and nomogram development.

PubMed

Wang, Na-Na; Yang, Zheng-Jun; Wang, Xue; Chen, Li-Xuan; Zhao, Hong-Meng; Cao, Wen-Feng; Zhang, Bin

2018-04-25

Molecular subtype of breast cancer is associated with sentinel lymph node status. We sought to establish a mathematical prediction model that included breast cancer molecular subtype for risk of positive non-sentinel lymph nodes in breast cancer patients with sentinel lymph node metastasis and further validate the model in a separate validation cohort. We reviewed the clinicopathologic data of breast cancer patients with sentinel lymph node metastasis who underwent axillary lymph node dissection between June 16, 2014 and November 16, 2017 at our hospital. Sentinel lymph node biopsy was performed and patients with pathologically proven sentinel lymph node metastasis underwent axillary lymph node dissection. Independent risks for non-sentinel lymph node metastasis were assessed in a training cohort by multivariate analysis and incorporated into a mathematical prediction model. The model was further validated in a separate validation cohort, and a nomogram was developed and evaluated for diagnostic performance in predicting the risk of non-sentinel lymph node metastasis. Moreover, we assessed the performance of five different models in predicting non-sentinel lymph node metastasis in training cohort. Totally, 495 cases were eligible for the study, including 291 patients in the training cohort and 204 in the validation cohort. Non-sentinel lymph node metastasis was observed in 33.3% (97/291) patients in the training cohort. The AUC of MSKCC, Tenon, MDA, Ljubljana, and Louisville models in training cohort were 0.7613, 0.7142, 0.7076, 0.7483, and 0.671, respectively. Multivariate regression analysis indicated that tumor size (OR = 1.439; 95% CI 1.025-2.021; P = 0.036), sentinel lymph node macro-metastasis versus micro-metastasis (OR = 5.063; 95% CI 1.111-23.074; P = 0.036), the number of positive sentinel lymph nodes (OR = 2.583, 95% CI 1.714-3.892; P < 0.001), and the number of negative sentinel lymph nodes (OR = 0.686, 95% CI 0.575-0.817; P < 0.001) were independent statistically significant predictors of non-sentinel lymph node metastasis. Furthermore, luminal B (OR = 3.311, 95% CI 1.593-6.884; P = 0.001) and HER2 overexpression (OR = 4.308, 95% CI 1.097-16.912; P = 0.036) were independent and statistically significant predictor of non-sentinel lymph node metastasis versus luminal A. A regression model based on the results of multivariate analysis was established to predict the risk of non-sentinel lymph node metastasis, which had an AUC of 0.8188. The model was validated in the validation cohort and showed excellent diagnostic performance. The mathematical prediction model that incorporates five variables including breast cancer molecular subtype demonstrates excellent diagnostic performance in assessing the risk of non-sentinel lymph node metastasis in sentinel lymph node-positive patients. The prediction model could be of help surgeons in evaluating the risk of non-sentinel lymph node involvement for breast cancer patients; however, the model requires further validation in prospective studies.

Artificial neural network predictions of lengths of stay on a post-coronary care unit.

PubMed

Mobley, B A; Leasure, R; Davidson, L

1995-01-01

To create and validate a model that predicts length of hospital unit stay. Ex post facto. Seventy-four independent admission variables in 15 general categories were utilized to predict possible stays of 1 to 20 days. Laboratory. Records of patients discharged from a post-coronary care unit in early 1993. An artificial neural network was trained on 629 records and tested on an additional 127 records of patients. The absolute disparity between the actual lengths of stays in the test records and the predictions of the network averaged 1.4 days per record, and the actual length of stay was predicted within 1 day 72% of the time. The artificial neural network demonstrated the capacity to utilize common patient admission characteristics to predict lengths of stay. This technology shows promise in aiding timely initiation of treatment and effective resource planning and cost control.

Statistical prediction of dynamic distortion of inlet flow using minimum dynamic measurement. An application to the Melick statistical method and inlet flow dynamic distortion prediction without RMS measurements

NASA Technical Reports Server (NTRS)

Schweikhard, W. G.; Chen, Y. S.

1986-01-01

The Melick method of inlet flow dynamic distortion prediction by statistical means is outlined. A hypothetic vortex model is used as the basis for the mathematical formulations. The main variables are identified by matching the theoretical total pressure rms ratio with the measured total pressure rms ratio. Data comparisons, using the HiMAT inlet test data set, indicate satisfactory prediction of the dynamic peak distortion for cases with boundary layer control device vortex generators. A method for the dynamic probe selection was developed. Validity of the probe selection criteria is demonstrated by comparing the reduced-probe predictions with the 40-probe predictions. It is indicated that the the number of dynamic probes can be reduced to as few as two and still retain good accuracy.

Pediatric Heart Donor Assessment Tool (PH-DAT): A novel donor risk scoring system to predict 1-year mortality in pediatric heart transplantation.

PubMed

Zafar, Farhan; Jaquiss, Robert D; Almond, Christopher S; Lorts, Angela; Chin, Clifford; Rizwan, Raheel; Bryant, Roosevelt; Tweddell, James S; Morales, David L S

2018-03-01

In this study we sought to quantify hazards associated with various donor factors into a cumulative risk scoring system (the Pediatric Heart Donor Assessment Tool, or PH-DAT) to predict 1-year mortality after pediatric heart transplantation (PHT). PHT data with complete donor information (5,732) were randomly divided into a derivation cohort and a validation cohort (3:1). From the derivation cohort, donor-specific variables associated with 1-year mortality (exploratory p-value < 0.2) were incorporated into a multivariate logistic regression model. Scores were assigned to independent predictors (p < 0.05) based on relative odds ratios (ORs). The final model had an acceptable predictive value (c-statistic = 0.62). The significant 5 variables (ischemic time, stroke as the cause of death, donor-to-recipient height ratio, donor left ventricular ejection fraction, glomerular filtration rate) were used for the scoring system. The validation cohort demonstrated a strong correlation between the observed and expected rates of 1-year mortality (r = 0.87). The risk of 1-year mortality increases by 11% (OR 1.11 [1.08 to 1.14]; p < 0.001) in the derivation cohort and 9% (OR 1.09 [1.04 to 1.14]; p = 0.001) in the validation cohort with an increase of 1-point in score. Mortality risk increased 5 times from the lowest to the highest donor score in this cohort. Based on this model, a donor score range of 10 to 28 predicted 1-year recipient mortality of 11% to 31%. This novel pediatric-specific, donor risk scoring system appears capable of predicting post-transplant mortality. Although the PH-DAT may benefit organ allocation and assessment of recipient risk while controlling for donor risk, prospective validation of this model is warranted. Copyright © 2018 International Society for the Heart and Lung Transplantation. Published by Elsevier Inc. All rights reserved.

Accurate Binding Free Energy Predictions in Fragment Optimization.

PubMed

Steinbrecher, Thomas B; Dahlgren, Markus; Cappel, Daniel; Lin, Teng; Wang, Lingle; Krilov, Goran; Abel, Robert; Friesner, Richard; Sherman, Woody

2015-11-23

Predicting protein-ligand binding free energies is a central aim of computational structure-based drug design (SBDD)--improved accuracy in binding free energy predictions could significantly reduce costs and accelerate project timelines in lead discovery and optimization. The recent development and validation of advanced free energy calculation methods represents a major step toward this goal. Accurately predicting the relative binding free energy changes of modifications to ligands is especially valuable in the field of fragment-based drug design, since fragment screens tend to deliver initial hits of low binding affinity that require multiple rounds of synthesis to gain the requisite potency for a project. In this study, we show that a free energy perturbation protocol, FEP+, which was previously validated on drug-like lead compounds, is suitable for the calculation of relative binding strengths of fragment-sized compounds as well. We study several pharmaceutically relevant targets with a total of more than 90 fragments and find that the FEP+ methodology, which uses explicit solvent molecular dynamics and physics-based scoring with no parameters adjusted, can accurately predict relative fragment binding affinities. The calculations afford R(2)-values on average greater than 0.5 compared to experimental data and RMS errors of ca. 1.1 kcal/mol overall, demonstrating significant improvements over the docking and MM-GBSA methods tested in this work and indicating that FEP+ has the requisite predictive power to impact fragment-based affinity optimization projects.

Statistical optimization of the phytoremediation of arsenic by Ludwigia octovalvis- in a pilot reed bed using response surface methodology (RSM) versus an artificial neural network (ANN).

PubMed

Titah, Harmin Sulistiyaning; Halmi, Mohd Izuan Effendi Bin; Abdullah, Siti Rozaimah Sheikh; Hasan, Hassimi Abu; Idris, Mushrifah; Anuar, Nurina

2018-06-07

In this study, the removal of arsenic (As) by plant, Ludwigia octovalvis, in a pilot reed bed was optimized. A Box-Behnken design was employed including a comparative analysis of both Response Surface Methodology (RSM) and an Artificial Neural Network (ANN) for the prediction of maximum arsenic removal. The predicted optimum condition using the desirability function of both models was 39 mg kg -1 for the arsenic concentration in soil, an elapsed time of 42 days (the sampling day) and an aeration rate of 0.22 L/min, with the predicted values of arsenic removal by RSM and ANN being 72.6% and 71.4%, respectively. The validation of the predicted optimum point showed an actual arsenic removal of 70.6%. This was achieved with the deviation between the validation value and the predicted values being within 3.49% (RSM) and 1.87% (ANN). The performance evaluation of the RSM and ANN models showed that ANN performs better than RSM with a higher R 2 (0.97) close to 1.0 and very small Average Absolute Deviation (AAD) (0.02) and Root Mean Square Error (RMSE) (0.004) values close to zero. Both models were appropriate for the optimization of arsenic removal with ANN demonstrating significantly higher predictive and fitting ability than RSM.

Developmental validation of the HIrisPlex system: DNA-based eye and hair colour prediction for forensic and anthropological usage.

PubMed

Walsh, Susan; Chaitanya, Lakshmi; Clarisse, Lindy; Wirken, Laura; Draus-Barini, Jolanta; Kovatsi, Leda; Maeda, Hitoshi; Ishikawa, Takaki; Sijen, Titia; de Knijff, Peter; Branicki, Wojciech; Liu, Fan; Kayser, Manfred

2014-03-01

Forensic DNA Phenotyping or 'DNA intelligence' tools are expected to aid police investigations and find unknown individuals by providing information on externally visible characteristics of unknown suspects, perpetrators and missing persons from biological samples. This is especially useful in cases where conventional DNA profiling or other means remain non-informative. Recently, we introduced the HIrisPlex system, capable of predicting both eye and hair colour from DNA. In the present developmental validation study, we demonstrate that the HIrisPlex assay performs in full agreement with the Scientific Working Group on DNA Analysis Methods (SWGDAM) guidelines providing an essential prerequisite for future HIrisPlex applications to forensic casework. The HIrisPlex assay produces complete profiles down to only 63 pg of DNA. Species testing revealed human specificity for a complete HIrisPlex profile, while only non-human primates showed the closest full profile at 20 out of the 24 DNA markers, in all animals tested. Rigorous testing of simulated forensic casework samples such as blood, semen, saliva stains, hairs with roots as well as extremely low quantity touch (trace) DNA samples, produced complete profiles in 88% of cases. Concordance testing performed between five independent forensic laboratories displayed consistent reproducible results on varying types of DNA samples. Due to its design, the assay caters for degraded samples, underlined here by results from artificially degraded DNA and from simulated casework samples of degraded DNA. This aspect was also demonstrated previously on DNA samples from human remains up to several hundreds of years old. With this paper, we also introduce enhanced eye and hair colour prediction models based on enlarged underlying databases of HIrisPlex genotypes and eye/hair colour phenotypes (eye colour: N = 9188 and hair colour: N = 1601). Furthermore, we present an online web-based system for individual eye and hair colour prediction from full and partial HIrisPlex DNA profiles. By demonstrating that the HIrisPlex assay is fully compatible with the SWGDAM guidelines, we provide the first forensically validated DNA test system for parallel eye and hair colour prediction now available to forensic laboratories for immediate casework application, including missing person cases. Given the robustness and sensitivity described here and in previous work, the HIrisPlex system is also suitable for analysing old and ancient DNA in anthropological and evolutionary studies. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Development and validation of the Acculturative Stress Scale for Chinese College Students in the United States (ASSCS).

PubMed

Bai, Jieru

2016-04-01

Chinese students are the biggest ethnic group of international students in the United States. This study aims to develop a reliable and valid scale to accurately measure their acculturative stress. A 72-item pool was sent online to Chinese students and a five-factor scale of 32 items was generated by exploratory factor analysis. The five factors included language insufficiency, social isolation, perceived discrimination, academic pressure, and guilt toward family. The Acculturative Stress Scale for Chinese Students demonstrated high reliability and initial validity by predicting depression and life satisfaction. It was the first Chinese scale of acculturative stress developed and validated among a Chinese student sample in the United States. In the future, the scale can be used as a diagnostic tool by mental health professionals and a self-assessment tool by Chinese students. (c) 2016 APA, all rights reserved.

Why don't you exercise? Development of the Amotivation Toward Exercise Scale among older inactive individuals.

PubMed

Vlachopoulos, Symeon P; Gigoudi, Maria A

2008-07-01

This article reports on the development and initial validation of the Amotivation Toward Exercise Scale (ATES), which reflects a taxonomy of older adults' reasons to refrain from exercise. Drawing on work by Pelletier, Dion, Tuson, and Green-Demers (1999) and Legault, Green-Demers, and Pelletier (2006), these dimensions were the outcome beliefs, capacity beliefs, effort beliefs, and value amotivation beliefs toward exercise. The results supported a 4-factor correlated model that fit the data better than either a unidimensional model or a 4-factor uncorrelated model or a hierarchical model with strong internal reliability for all the subscales. Evidence also emerged for the discriminant validity of the subscale scores. Furthermore, the predictive validity of the subscale scores was supported, and satisfactory measurement invariance was demonstrated across the calibration and validation samples, supporting the generalizability of the scale's measurement properties.

The Model Human Processor and the Older Adult: Parameter Estimation and Validation Within a Mobile Phone Task

PubMed Central

Jastrzembski, Tiffany S.; Charness, Neil

2009-01-01

The authors estimate weighted mean values for nine information processing parameters for older adults using the Card, Moran, and Newell (1983) Model Human Processor model. The authors validate a subset of these parameters by modeling two mobile phone tasks using two different phones and comparing model predictions to a sample of younger (N = 20; Mage = 20) and older (N = 20; Mage = 69) adults. Older adult models fit keystroke-level performance at the aggregate grain of analysis extremely well (R = 0.99) and produced equivalent fits to previously validated younger adult models. Critical path analyses highlighted points of poor design as a function of cognitive workload, hardware/software design, and user characteristics. The findings demonstrate that estimated older adult information processing parameters are valid for modeling purposes, can help designers understand age-related performance using existing interfaces, and may support the development of age-sensitive technologies. PMID:18194048

The Model Human Processor and the older adult: parameter estimation and validation within a mobile phone task.

PubMed

Jastrzembski, Tiffany S; Charness, Neil

2007-12-01

The authors estimate weighted mean values for nine information processing parameters for older adults using the Card, Moran, and Newell (1983) Model Human Processor model. The authors validate a subset of these parameters by modeling two mobile phone tasks using two different phones and comparing model predictions to a sample of younger (N = 20; M-sub(age) = 20) and older (N = 20; M-sub(age) = 69) adults. Older adult models fit keystroke-level performance at the aggregate grain of analysis extremely well (R = 0.99) and produced equivalent fits to previously validated younger adult models. Critical path analyses highlighted points of poor design as a function of cognitive workload, hardware/software design, and user characteristics. The findings demonstrate that estimated older adult information processing parameters are valid for modeling purposes, can help designers understand age-related performance using existing interfaces, and may support the development of age-sensitive technologies.

A five-factor measure of obsessive-compulsive personality traits.

PubMed

Samuel, Douglas B; Riddell, Ashley D B; Lynam, Donald R; Miller, Joshua D; Widiger, Thomas A

2012-01-01

This study provides convergent, discriminant, and incremental validity data for the Five-Factor Obsessive-Compulsive Inventory (FFOCI), a newly developed measure of traits relevant to obsessive-compulsive personality disorder (OCPD) from the perspective of the Five-factor model (FFM). Twelve scales were constructed as maladaptive variants of specific FFM facets (e.g., Perfectionism as a maladaptive variant of FFM competence). On the basis of data from 407 undergraduates (oversampled for OCPD symptoms) these 12 scales demonstrated convergent correlations with established measures of OCPD and the FFM. Further, they obtained strong discriminant validity with respect to facets from other FFM domains. Most important, the individual scales and total score of the FFOCI obtained incremental validity beyond existing measures of the FFM and OCPD for predicting a composite measure of obsessive-compulsive symptomatology. The findings support the validity of the FFOCI as a measure of obsessive-compulsive personality traits, as well as of maladaptive variants of the FFM.

The influence of work personality on job satisfaction: incremental validity and mediation effects.

PubMed

Heller, Daniel; Ferris, D Lance; Brown, Douglas; Watson, David

2009-08-01

Drawing from recent developments regarding the contextual nature of personality (e.g., D. Wood & B. W. Roberts, 2006), we conducted 2 studies (1 cross-sectional and 1 longitudinal over 1 year) to examine the validity of work personality in predicting job satisfaction and its mediation of the effect of global personality on job satisfaction. Study 1 showed that (a) individuals vary systematically in their personality between roles- they were significantly more conscientious and open to experience and less extraverted at work compared to at home; (b) work personality was a better predictor of job satisfaction than both global personality and home personality; and (c) work personality demonstrated incremental validity above and beyond the other two personality measures. Study 2 further showed that each of the work personality dimensions fully mediated the association between its corresponding global personality trait and job satisfaction. Evidence for the discriminant validity of the findings is also presented.

An integrated approach utilising chemometrics and GC/MS for classification of chamomile flowers, essential oils and commercial products.

PubMed

Wang, Mei; Avula, Bharathi; Wang, Yan-Hong; Zhao, Jianping; Avonto, Cristina; Parcher, Jon F; Raman, Vijayasankar; Zweigenbaum, Jerry A; Wylie, Philip L; Khan, Ikhlas A

2014-01-01

As part of an ongoing research program on authentication, safety and biological evaluation of phytochemicals and dietary supplements, an in-depth chemical investigation of different types of chamomile was performed. A collection of chamomile samples including authenticated plants, commercial products and essential oils was analysed by GC/MS. Twenty-seven authenticated plant samples representing three types of chamomile, viz. German chamomile, Roman chamomile and Juhua were analysed. This set of data was employed to construct a sample class prediction (SCP) model based on stepwise reduction of data dimensionality followed by principle component analysis (PCA) and partial least squares discriminant analysis (PLS-DA). The model was cross-validated with samples including authenticated plants and commercial products. The model demonstrated 100.0% accuracy for both recognition and prediction abilities. In addition, 35 commercial products and 11 essential oils purported to contain chamomile were subsequently predicted by the validated PLS-DA model. Furthermore, tentative identification of the marker compounds correlated with different types of chamomile was explored. Copyright © 2013 Elsevier Ltd. All rights reserved.

Near-infrared reflectance spectroscopy predicts protein, starch, and seed weight in intact seeds of common bean ( Phaseolus vulgaris L.).

PubMed

Hacisalihoglu, Gokhan; Larbi, Bismark; Settles, A Mark

2010-01-27

The objective of this study was to explore the potential of near-infrared reflectance (NIR) spectroscopy to determine individual seed composition in common bean ( Phaseolus vulgaris L.). NIR spectra and analytical measurements of seed weight, protein, and starch were collected from 267 individual bean seeds representing 91 diverse genotypes. Partial least-squares (PLS) regression models were developed with 61 bean accessions randomly assigned to a calibration data set and 30 accessions assigned to an external validation set. Protein gave the most accurate PLS regression, with the external validation set having a standard error of prediction (SEP) = 1.6%. PLS regressions for seed weight and starch had sufficient accuracy for seed sorting applications, with SEP = 41.2 mg and 4.9%, respectively. Seed color had a clear effect on the NIR spectra, with black beans having a distinct spectral type. Seed coat color did not impact the accuracy of PLS predictions. This research demonstrates that NIR is a promising technique for simultaneous sorting of multiple seed traits in single bean seeds with no sample preparation.

Cross-cultural Adaptation and Validation of the Medication Regimen Complexity Index Adapted to Spanish.

PubMed

Saez de la Fuente, Javier; Such Diaz, Ana; Cañamares-Orbis, Irene; Ramila, Estela; Izquierdo-Garcia, Elsa; Esteban, Concepcion; Escobar-Rodríguez, Ismael

2016-11-01

The most widely used validated instrument to assess the complexity of medication regimens is the Medication Regimen Complexity Index (MRCI). This study aimed to translate, adapt, and validate a reliable version of the MRCI adapted to Spanish (MRCI-E). The cross-cultural adaptation process consisted of an independent translation by 3 clinical pharmacists and a backtranslation by 2 native English speakers. A reliability analysis was conducted on 20 elderly randomly selected patients. Two clinical pharmacists calculated the MRCI-E from discharge treatments and 2 months later. For the validity analysis, the sample was augmented to 60 patients. Convergent validity was assessed by analyzing the correlation between the number of medications; discriminant validity was stratified by gender; and predictive validity was determined by analyzing the ability to predict readmission and mortality at 3 and 6 months. The MRCI-E retained the original structure of 3 sections. The reliability analysis demonstrated an excellent internal consistency (Cronbach's α=0.83), and the intraclass correlation coefficient exceeded 0.9 in all cases. The correlation coefficient with the number of medications was 0.883 ( P<0.001). No significant differences were found when stratified by gender (3.6; 95%CI=-2.9 to 10.2; P=0.27). Patients who were readmitted at 3 months had a higher MRCI-E score (10.7; 95%CI=4.4 to 17.2; P=0.001). The differences remained significant in patients readmitted at 6 months, but differences in mortality were not detected. The MRCI-E retains the reliability and validity of the original index and provides a suitable tool to assess the complexity of medication regimens in Spanish.

Hierarchical calibration and validation of computational fluid dynamics models for solid sorbent-based carbon capture

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

Lai, Canhai; Xu, Zhijie; Pan, Wenxiao

2016-01-01

To quantify the predictive confidence of a solid sorbent-based carbon capture design, a hierarchical validation methodology—consisting of basic unit problems with increasing physical complexity coupled with filtered model-based geometric upscaling has been developed and implemented. This paper describes the computational fluid dynamics (CFD) multi-phase reactive flow simulations and the associated data flows among different unit problems performed within the said hierarchical validation approach. The bench-top experiments used in this calibration and validation effort were carefully designed to follow the desired simple-to-complex unit problem hierarchy, with corresponding data acquisition to support model parameters calibrations at each unit problem level. A Bayesianmore » calibration procedure is employed and the posterior model parameter distributions obtained at one unit-problem level are used as prior distributions for the same parameters in the next-tier simulations. Overall, the results have demonstrated that the multiphase reactive flow models within MFIX can be used to capture the bed pressure, temperature, CO2 capture capacity, and kinetics with quantitative accuracy. The CFD modeling methodology and associated uncertainty quantification techniques presented herein offer a solid framework for estimating the predictive confidence in the virtual scale up of a larger carbon capture device.« less

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

Delmau, L.H.; Haverlock, T.J.; Sloop, F.V., Jr.

This report presents the work that followed the CSSX model development completed in FY2002. The developed cesium and potassium extraction model was based on extraction data obtained from simple aqueous media. It was tested to ensure the validity of the prediction for the cesium extraction from actual waste. Compositions of the actual tank waste were obtained from the Savannah River Site personnel and were used to prepare defined simulants and to predict cesium distribution ratios using the model. It was therefore possible to compare the cesium distribution ratios obtained from the actual waste, the simulant, and the predicted values. Itmore » was determined that the predicted values agree with the measured values for the simulants. Predicted values also agreed, with three exceptions, with measured values for the tank wastes. Discrepancies were attributed in part to the uncertainty in the cation/anion balance in the actual waste composition, but likely more so to the uncertainty in the potassium concentration in the waste, given the demonstrated large competing effect of this metal on cesium extraction. It was demonstrated that the upper limit for the potassium concentration in the feed ought to not exceed 0.05 M in order to maintain suitable cesium distribution ratios.« less

Attitudes Toward Transgender Men and Women: Development and Validation of a New Measure

PubMed Central

Billard, Thomas J

2018-01-01

A series of three studies were conducted to generate, develop, and validate the Attitudes toward Transgender Men and Women (ATTMW) scale. In Study 1, 120 American adults responded to an open-ended questionnaire probing various dimensions of their perceptions of transgender individuals and identity. Qualitative thematic analysis generated 200 items based on their responses. In Study 2, 238 American adults completed a questionnaire consisting of the generated items. Exploratory factor analysis (EFA) revealed two non-identical 12-item subscales (ATTM and ATTW) of the full 24-item scale. In Study 3, 150 undergraduate students completed a survey containing the ATTMW and a number of validity-testing variables. Confirmatory factor analysis (CFA) verified the single-factor structures of the ATTM and ATTW subscales, and the convergent, discriminant, predictive, and concurrent validities of the ATTMW were also established. Together, our results demonstrate that the ATTMW is a reliable and valid measure of attitudes toward transgender individuals. PMID:29666595

Development and Validation of Triarchic Construct Scales from the Psychopathic Personality Inventory

PubMed Central

Hall, Jason R.; Drislane, Laura E.; Patrick, Christopher J.; Morano, Mario; Lilienfeld, Scott O.; Poythress, Norman G.

2014-01-01

The Triarchic model of psychopathy describes this complex condition in terms of distinct phenotypic components of boldness, meanness, and disinhibition. Brief self-report scales designed specifically to index these psychopathy facets have thus far demonstrated promising construct validity. The present study sought to develop and validate scales for assessing facets of the Triarchic model using items from a well-validated existing measure of psychopathy—the Psychopathic Personality Inventory (PPI). A consensus rating approach was used to identify PPI items relevant to each Triarchic facet, and the convergent and discriminant validity of the resulting PPI-based Triarchic scales were evaluated in relation to multiple criterion variables (i.e., other psychopathy inventories, antisocial personality disorder features, personality traits, psychosocial functioning) in offender and non-offender samples. The PPI-based Triarchic scales showed good internal consistency and related to criterion variables in ways consistent with predictions based on the Triarchic model. Findings are discussed in terms of implications for conceptualization and assessment of psychopathy. PMID:24447280

Development and validation of Triarchic construct scales from the psychopathic personality inventory.

PubMed

Hall, Jason R; Drislane, Laura E; Patrick, Christopher J; Morano, Mario; Lilienfeld, Scott O; Poythress, Norman G

2014-06-01

The Triarchic model of psychopathy describes this complex condition in terms of distinct phenotypic components of boldness, meanness, and disinhibition. Brief self-report scales designed specifically to index these psychopathy facets have thus far demonstrated promising construct validity. The present study sought to develop and validate scales for assessing facets of the Triarchic model using items from a well-validated existing measure of psychopathy-the Psychopathic Personality Inventory (PPI). A consensus-rating approach was used to identify PPI items relevant to each Triarchic facet, and the convergent and discriminant validity of the resulting PPI-based Triarchic scales were evaluated in relation to multiple criterion variables (i.e., other psychopathy inventories, antisocial personality disorder features, personality traits, psychosocial functioning) in offender and nonoffender samples. The PPI-based Triarchic scales showed good internal consistency and related to criterion variables in ways consistent with predictions based on the Triarchic model. Findings are discussed in terms of implications for conceptualization and assessment of psychopathy.

Improving Photometric Redshifts for Hyper Suprime-Cam

NASA Astrophysics Data System (ADS)

Speagle, Josh S.; Leauthaud, Alexie; Eisenstein, Daniel; Bundy, Kevin; Capak, Peter L.; Leistedt, Boris; Masters, Daniel C.; Mortlock, Daniel; Peiris, Hiranya; HSC Photo-z Team; HSC Weak Lensing Team

2017-01-01

Deriving accurate photometric redshift (photo-z) probability distribution functions (PDFs) are crucial science components for current and upcoming large-scale surveys. We outline how rigorous Bayesian inference and machine learning can be combined to quickly derive joint photo-z PDFs to individual galaxies and their parent populations. Using the first 170 deg^2 of data from the ongoing Hyper Suprime-Cam survey, we demonstrate our method is able to generate accurate predictions and reliable credible intervals over ~370k high-quality redshifts. We then use galaxy-galaxy lensing to empirically validate our predicted photo-z's over ~14M objects, finding a robust signal.

Validity of the MCAT in Predicting Performance in the First Two Years of Medical School.

ERIC Educational Resources Information Center

Jones, Robert F.; Thomae-Forgues, Maria

1984-01-01

The first systematic summary of predictive validity research on the new Medical College Admission Test (MCAT) is presented. The results show that MCAT scores have significant predictive validity with respect to first- and second-year medical school course grades. Further directions for MCAT validity research are described. (Author/MLW)

TargetNet: a web service for predicting potential drug-target interaction profiling via multi-target SAR models.

PubMed

Yao, Zhi-Jiang; Dong, Jie; Che, Yu-Jing; Zhu, Min-Feng; Wen, Ming; Wang, Ning-Ning; Wang, Shan; Lu, Ai-Ping; Cao, Dong-Sheng

2016-05-01

Drug-target interactions (DTIs) are central to current drug discovery processes and public health fields. Analyzing the DTI profiling of the drugs helps to infer drug indications, adverse drug reactions, drug-drug interactions, and drug mode of actions. Therefore, it is of high importance to reliably and fast predict DTI profiling of the drugs on a genome-scale level. Here, we develop the TargetNet server, which can make real-time DTI predictions based only on molecular structures, following the spirit of multi-target SAR methodology. Naïve Bayes models together with various molecular fingerprints were employed to construct prediction models. Ensemble learning from these fingerprints was also provided to improve the prediction ability. When the user submits a molecule, the server will predict the activity of the user's molecule across 623 human proteins by the established high quality SAR model, thus generating a DTI profiling that can be used as a feature vector of chemicals for wide applications. The 623 SAR models related to 623 human proteins were strictly evaluated and validated by several model validation strategies, resulting in the AUC scores of 75-100 %. We applied the generated DTI profiling to successfully predict potential targets, toxicity classification, drug-drug interactions, and drug mode of action, which sufficiently demonstrated the wide application value of the potential DTI profiling. The TargetNet webserver is designed based on the Django framework in Python, and is freely accessible at http://targetnet.scbdd.com .

TargetNet: a web service for predicting potential drug-target interaction profiling via multi-target SAR models

NASA Astrophysics Data System (ADS)

Yao, Zhi-Jiang; Dong, Jie; Che, Yu-Jing; Zhu, Min-Feng; Wen, Ming; Wang, Ning-Ning; Wang, Shan; Lu, Ai-Ping; Cao, Dong-Sheng

2016-05-01

Drug-target interactions (DTIs) are central to current drug discovery processes and public health fields. Analyzing the DTI profiling of the drugs helps to infer drug indications, adverse drug reactions, drug-drug interactions, and drug mode of actions. Therefore, it is of high importance to reliably and fast predict DTI profiling of the drugs on a genome-scale level. Here, we develop the TargetNet server, which can make real-time DTI predictions based only on molecular structures, following the spirit of multi-target SAR methodology. Naïve Bayes models together with various molecular fingerprints were employed to construct prediction models. Ensemble learning from these fingerprints was also provided to improve the prediction ability. When the user submits a molecule, the server will predict the activity of the user's molecule across 623 human proteins by the established high quality SAR model, thus generating a DTI profiling that can be used as a feature vector of chemicals for wide applications. The 623 SAR models related to 623 human proteins were strictly evaluated and validated by several model validation strategies, resulting in the AUC scores of 75-100 %. We applied the generated DTI profiling to successfully predict potential targets, toxicity classification, drug-drug interactions, and drug mode of action, which sufficiently demonstrated the wide application value of the potential DTI profiling. The TargetNet webserver is designed based on the Django framework in Python, and is freely accessible at http://targetnet.scbdd.com.

Pneumococcal vaccine targeting strategy for older adults: customized risk profiling.

PubMed

Balicer, Ran D; Cohen, Chandra J; Leibowitz, Morton; Feldman, Becca S; Brufman, Ilan; Roberts, Craig; Hoshen, Moshe

2014-02-12

Current pneumococcal vaccine campaigns take a broad, primarily age-based approach to immunization targeting, overlooking many clinical and administrative considerations necessary in disease prevention and resource planning for specific patient populations. We aim to demonstrate the utility of a population-specific predictive model for hospital-treated pneumonia to direct effective vaccine targeting. Data was extracted for 1,053,435 members of an Israeli HMO, age 50 and older, during the study period 2008-2010. We developed and validated a logistic regression model to predict hospital-treated pneumonia using training and test samples, including a set of standard and population-specific risk factors. The model's predictive value was tested for prospectively identifying cases of pneumonia and invasive pneumococcal disease (IPD), and was compared to the existing international paradigm for patient immunization targeting. In a multivariate regression, age, co-morbidity burden and previous pneumonia events were most strongly positively associated with hospital-treated pneumonia. The model predicting hospital-treated pneumonia yielded a c-statistic of 0.80. Utilizing the predictive model, the top 17% highest-risk within the study validation population were targeted to detect 54% of those members who were subsequently treated for hospitalized pneumonia in the follow up period. The high-risk population identified through this model included 46% of the follow-up year's IPD cases, and 27% of community-treated pneumonia cases. These outcomes were compared with international guidelines for risk for pneumococcal diseases that accurately identified only 35% of hospitalized pneumonia, 41% of IPD cases and 21% of community-treated pneumonia. We demonstrate that a customized model for vaccine targeting performs better than international guidelines, and therefore, risk modeling may allow for more precise vaccine targeting and resource allocation than current national and international guidelines. Health care managers and policy-makers may consider the strategic potential of utilizing clinical and administrative databases for creating population-specific risk prediction models to inform vaccination campaigns. Copyright © 2013 Elsevier Ltd. All rights reserved.

Concurrent and predictive evaluation of malnutrition diagnostic measures in hip fracture inpatients: a diagnostic accuracy study.

PubMed

Bell, J J; Bauer, J D; Capra, S; Pulle, R C

2014-03-01

Differences in malnutrition diagnostic measures impact malnutrition prevalence and outcomes data in hip fracture. This study investigated the concurrent and predictive validity of commonly reported malnutrition diagnostic measures in patients admitted to a metropolitan hospital acute hip fracture unit. A prospective, consecutive level II diagnostic accuracy study (n=142; 8 exclusions) including the International Classification of Disease, 10th Revision, Australian Modification (ICD10-AM) protein-energy malnutrition criteria, a body mass index (BMI) <18.5 kg/m(2), the Mini-Nutrition Assessment Short-Form (MNA-SF), pre-operative albumin and geriatrician individualised assessment. Patients were predominantly elderly (median age 83.5, range 50-100 years), female (68%), multimorbid (median five comorbidities), with 15% 4-month mortality. Malnutrition prevalence was lowest when assessed by BMI (13%), followed by MNA-SF (27%), ICD10-AM (48%), albumin (53%) and geriatrician assessment (55%). Agreement between measures was highest between ICD10-AM and geriatrician assessment (κ=0.61) followed by ICD10-AM and MNA-SF measures (κ=0.34). ICD10-AM diagnosed malnutrition was the only measure associated with 48-h mobilisation (35.0 vs 55.3%; P=0.018). Reduced likelihood of home discharge was predicted by ICD-10-AM (20.6 vs 57.1%; P=0.001) and MNA-SF (18.8 vs 47.8%; P=0.035). Bivariate analysis demonstrated ICD10-AM (relative risk (RR)1.2; 1.05-1.42) and MNA-SF (RR1.2; 1.0-1.5) predicted 4-month mortality. When adjusted for age, usual place of residency, comorbidities and time to surgery only ICD-10AM criteria predicted mortality (odds ratio 3.59; 1.10-11.77). Albumin, BMI and geriatrician assessment demonstrated limited concurrent and predictive validity. Malnutrition prevalence in hip fracture varies substantially depending on the diagnostic measure applied. ICD-10AM criteria or the MNA-SF should be considered for the diagnosis of protein-energy malnutrition in frail, multi-morbid hip fracture inpatients.

Systematic feature selection improves accuracy of methylation-based forensic age estimation in Han Chinese males.

PubMed

Feng, Lei; Peng, Fuduan; Li, Shanfei; Jiang, Li; Sun, Hui; Ji, Anquan; Zeng, Changqing; Li, Caixia; Liu, Fan

2018-03-23

Estimating individual age from biomarkers may provide key information facilitating forensic investigations. Recent progress has shown DNA methylation at age-associated CpG sites as the most informative biomarkers for estimating the individual age of an unknown donor. Optimal feature selection plays a critical role in determining the performance of the final prediction model. In this study we investigate methylation levels at 153 age-associated CpG sites from 21 previously reported genomic regions using the EpiTYPER system for their predictive power on individual age in 390 Han Chinese males ranging from 15 to 75 years of age. We conducted a systematic feature selection using a stepwise backward multiple linear regression analysis as well as an exhaustive searching algorithm. Both approaches identified the same subset of 9 CpG sites, which in linear combination provided the optimal model fitting with mean absolute deviation (MAD) of 2.89 years of age and explainable variance (R 2 ) of 0.92. The final model was validated in two independent Han Chinese male samples (validation set 1, N = 65, MAD = 2.49, R 2  = 0.95, and validation set 2, N = 62, MAD = 3.36, R 2  = 0.89). Other competing models such as support vector machine and artificial neural network did not outperform the linear model to any noticeable degree. The validation set 1 was additionally analyzed using Pyrosequencing technology for cross-platform validation and was termed as validation set 3. Directly applying our model, in which the methylation levels were detected by the EpiTYPER system, to the data from pyrosequencing technology showed, however, less accurate results in terms of MAD (validation set 3, N = 65 Han Chinese males, MAD = 4.20, R 2  = 0.93), suggesting the presence of a batch effect between different data generation platforms. This batch effect could be partially overcome by a z-score transformation (MAD = 2.76, R 2  = 0.93). Overall, our systematic feature selection identified 9 CpG sites as the optimal subset for forensic age estimation and the prediction model consisting of these 9 markers demonstrated high potential in forensic practice. An age estimator implementing our prediction model allowing missing markers is freely available at http://liufan.big.ac.cn/AgePrediction. Copyright © 2018 Elsevier B.V. All rights reserved.

Reliability, validity and administrative burden of the community reintegration of injured service members computer adaptive test (CRIS-CAT)".

PubMed

Resnik, Linda; Borgia, Matthew; Ni, Pensheng; Pirraglia, Paul A; Jette, Alan

2012-09-17

The Computer Adaptive Test version of the Community Reintegration of Injured Service Members measure (CRIS-CAT) consists of three scales measuring Extent of, Perceived Limitations in, and Satisfaction with community integration. The CRIS-CAT was developed using item response theory methods. The purposes of this study were to assess the reliability, concurrent, known group and predictive validity and respondent burden of the CRIS-CAT.The CRIS-CAT was developed using item response theory methods. The purposes of this study were to assess the reliability, concurrent, known group and predictive validity and respondent burden of the CRIS-CAT. This was a three-part study that included a 1) a cross-sectional field study of 517 homeless, employed, and Operation Enduring Freedom/Operation Iraqi Freedom (OEF/OIF) Veterans; who completed all items in the CRIS item set, 2) a cohort study with one year follow-up study of 135 OEF/OIF Veterans, and 3) a 50-person study of CRIS-CAT administration. Conditional reliability of simulated CAT scores was calculated from the field study data, and concurrent validity and known group validity were examined using Pearson product correlations and ANOVAs. Data from the cohort were used to examine the ability of the CRIS-CAT to predict key one year outcomes. Data from the CRIS-CAT administration study were used to calculate ICC (2,1) minimum detectable change (MDC), and average number of items used during CAT administration. Reliability scores for all scales were above 0.75, but decreased at both ends of the score continuum. CRIS-CAT scores were correlated with concurrent validity indicators and differed significantly between the three Veteran groups (P < .001). The odds of having any Emergency Room visits were reduced for Veterans with better CRIS-CAT scores (Extent, Perceived Satisfaction respectively: OR = 0.94, 0.93, 0.95; P < .05). CRIS-CAT scores were predictive of SF-12 physical and mental health related quality of life scores at the 1 year follow-up. Scales had ICCs >0.9. MDCs were 5.9, 6.2, and 3.6, respectively for Extent, Perceived and Satisfaction subscales. Number of items (mn, SD) administered at Visit 1 were 14.6 (3.8) 10.9 (2.7) and 10.4 (1.7) respectively for Extent, Perceived and Satisfaction subscales. The CRIS-CAT demonstrated sound measurement properties including reliability, construct, known group and predictive validity, and it was administered with minimal respondent burden. These findings support the use of this measure in assessing community reintegration.

Support for HIV-1 Intervention Therapy

DTIC Science & Technology

1993-10-01

I. Kiselev, and E. S. Severin. 1990. Amplification of DNA 46 sequences of Epstein - Barr and human immunodeficiency viruses using DNA-polymerase from... develop and validate assays that predict or demonstrate disease progression for use in interventional trials with an emphasis on molecular biologic...to stay on the leading edge of technology development . A potential problem in obtaining quality sequence information is the occurrence of template

Reasoning, Problem Solving, and Intelligence.

DTIC Science & Technology

1980-04-01

designed to test the validity of their model of response choice in analogical reason- ing. In the first experiment, they set out to demonstrate that...second experiment were somewhat consistent with the prediction. The third experiment used a concept-formation design in which subjects were required to... designed to show interrelationships between various forms of inductive reasoning. Their model fits were highly comparable to those of Rumelhart and

General relationships between ultrasonic attenuation and dispersion

NASA Technical Reports Server (NTRS)

Odonnell, M.; Jaynes, E. T.; Miller, J. G.

1978-01-01

General relationships between the ultrasonic attenuation and dispersion are presented. The validity of these nonlocal relationships hinges only on the properties of causality and linearity, and does not depend upon details of the mechanism responsible for the attenuation and dispersion. Approximate, nearly local relationships are presented and are demonstrated to predict accurately the ultrasonic dispersion in solutions of hemoglobin from the results of attenuation measurements.

Long-term predictions using natural analogues

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

Ewing, R.C.

1995-09-01

One of the unique and scientifically most challenging aspects of nuclear waste isolation is the extrapolation of short-term laboratory data (hours to years) to the long time periods (10{sup 3}-10{sup 5} years) required by regulatory agencies for performance assessment. The direct validation of these extrapolations is not possible, but methods must be developed to demonstrate compliance with government regulations and to satisfy the lay public that there is a demonstrable and reasonable basis for accepting the long-term extrapolations. Natural systems (e.g., {open_quotes}natural analogues{close_quotes}) provide perhaps the only means of partial {open_quotes}validation,{close_quotes} as well as data that may be used directlymore » in the models that are used in the extrapolation. Natural systems provide data on very large spatial (nm to km) and temporal (10{sup 3}-10{sup 8} years) scales and in highly complex terranes in which unknown synergisms may affect radionuclide migration. This paper reviews the application (and most importantly, the limitations) of data from natural analogue systems to the {open_quotes}validation{close_quotes} of performance assessments.« less

Validation of SCIAMACHY and TOMS UV Radiances Using Ground and Space Observations

NASA Technical Reports Server (NTRS)

Hilsenrath, E.; Bhartia, P. K.; Bojkov, B. R.; Kowalewski, M.; Labow, G.; Ahmad, Z.

2004-01-01

Verification of a stratospheric ozone recovery remains a high priority for environmental research and policy definition. Models predict an ozone recovery at a much lower rate than the measured depletion rate observed to date. Therefore improved precision of the satellite and ground ozone observing systems are required over the long term to verify its recovery. We show that validation of satellite radiances from space and from the ground can be a very effective means for correcting long term drifts of backscatter type satellite measurements and can be used to cross calibrate all B W instruments in orbit (TOMS, SBW/2, GOME, SCIAMACHY, OM, GOME-2, OMPS). This method bypasses the retrieval algorithms used for both satellite and ground based measurements that are normally used to validate and correct the satellite data. Radiance comparisons employ forward models and are inherently more accurate than inverse (retrieval) algorithms. This approach however requires well calibrated instruments and an accurate radiative transfer model that accounts for aerosols. TOMS and SCIAMACHY calibrations are checked to demonstrate this method and to demonstrate applicability for long term trends.

3D tumor microtissues as an in vitro testing platform for microenvironmentally-triggered drug delivery systems.

PubMed

Brancato, Virginia; Gioiella, Filomena; Profeta, Martina; Imparato, Giorgia; Guarnieri, Daniela; Urciuolo, Francesco; Melone, Pietro; Netti, Paolo A

2017-07-15

Therapeutic approaches based on nanomedicine have garnered great attention in cancer research. In vitro biological models that better mimic in vivo conditions are crucial tools to more accurately predict their therapeutic efficacy in vivo. In this work, a new 3D breast cancer microtissue has been developed to recapitulate the complexity of the tumor microenvironment and to test its efficacy as screening platform for drug delivery systems. The proposed 3D cancer model presents human breast adenocarcinoma cells and cancer-associated fibroblasts embedded in their own ECM, thus showing several features of an in vivo tumor, such as overexpression of metallo-proteinases (MMPs). After demonstrating at molecular and protein level the MMP2 overexpression in such tumor microtissues, we used them to test a recently validated formulation of endogenous MMP2-responsive nanoparticles (NP). The presence of the MMP2-sensitive linker allows doxorubicin release from NP only upon specific enzymatic cleavage of the peptide. The same NP without the MMP-sensitive linker and healthy breast microtissues were also produced to demonstrate NP specificity and selectivity. Cell viability after NP treatment confirmed that controlled drug delivery is achieved only in 3D tumor microtissues suggesting that the validation of therapeutic strategies in such 3D tumor model could predict human response. A major issue of modern cancer research is the development of accurate and predictive experimental models of human tumors consistent with tumor microenvironment and applicable as screening platforms for novel therapeutic strategies. In this work, we developed and validated a new 3D microtissue model of human breast tumor as a testing platform of anti-cancer drug delivery systems. To this aim, biodegradable nanoparticles responsive to physiological changes specifically occurring in tumor microenvironment were used. Our findings clearly demonstrate that the breast tumor microtissue well recapitulates in vivo physiological features of tumor tissue and elicits a specific response to microenvironmentally-responsive nanoparticles compared to healthy tissue. We believe this study is of particular interest for cancer research and paves the way to exploit tumor microtissues for several testing purposes. Copyright © 2017 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Genomic prediction of piglet response to infection with one of two porcine reproductive and respiratory syndrome virus isolates.

PubMed

Waide, Emily H; Tuggle, Christopher K; Serão, Nick V L; Schroyen, Martine; Hess, Andrew; Rowland, Raymond R R; Lunney, Joan K; Plastow, Graham; Dekkers, Jack C M

2018-02-01

Genomic prediction of the pig's response to the porcine reproductive and respiratory syndrome (PRRS) virus (PRRSV) would be a useful tool in the swine industry. This study investigated the accuracy of genomic prediction based on porcine SNP60 Beadchip data using training and validation datasets from populations with different genetic backgrounds that were challenged with different PRRSV isolates. Genomic prediction accuracy averaged 0.34 for viral load (VL) and 0.23 for weight gain (WG) following experimental PRRSV challenge, which demonstrates that genomic selection could be used to improve response to PRRSV infection. Training on WG data during infection with a less virulent PRRSV, KS06, resulted in poor accuracy of prediction for WG during infection with a more virulent PRRSV, NVSL. Inclusion of single nucleotide polymorphisms (SNPs) that are in linkage disequilibrium with a major quantitative trait locus (QTL) on chromosome 4 was vital for accurate prediction of VL. Overall, SNPs that were significantly associated with either trait in single SNP genome-wide association analysis were unable to predict the phenotypes with an accuracy as high as that obtained by using all genotyped SNPs across the genome. Inclusion of data from close relatives into the training population increased whole genome prediction accuracy by 33% for VL and by 37% for WG but did not affect the accuracy of prediction when using only SNPs in the major QTL region. Results show that genomic prediction of response to PRRSV infection is moderately accurate and, when using all SNPs on the porcine SNP60 Beadchip, is not very sensitive to differences in virulence of the PRRSV in training and validation populations. Including close relatives in the training population increased prediction accuracy when using the whole genome or SNPs other than those near a major QTL.

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.

SU-D-BRB-01: A Predictive Planning Tool for Stereotactic Radiosurgery

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

Palefsky, S; Roper, J; Elder, E

Purpose: To demonstrate the feasibility of a predictive planning tool which provides SRS planning guidance based on simple patient anatomical properties: PTV size, PTV shape and distance from critical structures. Methods: Ten framed SRS cases treated at Winship Cancer Institute of Emory University were analyzed to extract data on PTV size, sphericity (shape), and distance from critical structures such as the brainstem and optic chiasm. The cases consisted of five pairs. Each pair consisted of two cases with a similar diagnosis (such as pituitary adenoma or arteriovenous malformation) that were treated with different techniques: DCA, or IMRS. A Naive Bayesmore » Classifier was trained on this data to establish the conditions under which each treatment modality was used. This model was validated by classifying ten other randomly-selected cases into DCA or IMRS classes, calculating the probability of each technique, and comparing results to the treated technique. Results: Of the ten cases used to validate the model, nine had their technique predicted correctly. The three cases treated with IMRS were all identified as such. Their probabilities of being treated with IMRS ranged between 59% and 100%. Six of the seven cases treated with DCA were correctly classified. These probabilities ranged between 51% and 95%. One case treated with DCA was incorrectly predicted to be an IMRS plan. The model’s confidence in this case was 91%. Conclusion: These findings indicate that a predictive planning tool based on simple patient anatomical properties can predict the SRS technique used for treatment. The algorithm operated with 90% accuracy. With further validation on larger patient populations, this tool may be used clinically to guide planners in choosing an appropriate treatment technique. The prediction algorithm could also be adapted to guide selection of treatment parameters such as treatment modality and number of fields for radiotherapy across anatomical sites.« less

Testing and validating environmental models

USGS Publications Warehouse

Kirchner, J.W.; Hooper, R.P.; Kendall, C.; Neal, C.; Leavesley, G.

1996-01-01

Generally accepted standards for testing and validating ecosystem models would benefit both modellers and model users. Universally applicable test procedures are difficult to prescribe, given the diversity of modelling approaches and the many uses for models. However, the generally accepted scientific principles of documentation and disclosure provide a useful framework for devising general standards for model evaluation. Adequately documenting model tests requires explicit performance criteria, and explicit benchmarks against which model performance is compared. A model's validity, reliability, and accuracy can be most meaningfully judged by explicit comparison against the available alternatives. In contrast, current practice is often characterized by vague, subjective claims that model predictions show 'acceptable' agreement with data; such claims provide little basis for choosing among alternative models. Strict model tests (those that invalid models are unlikely to pass) are the only ones capable of convincing rational skeptics that a model is probably valid. However, 'false positive' rates as low as 10% can substantially erode the power of validation tests, making them insufficiently strict to convince rational skeptics. Validation tests are often undermined by excessive parameter calibration and overuse of ad hoc model features. Tests are often also divorced from the conditions under which a model will be used, particularly when it is designed to forecast beyond the range of historical experience. In such situations, data from laboratory and field manipulation experiments can provide particularly effective tests, because one can create experimental conditions quite different from historical data, and because experimental data can provide a more precisely defined 'target' for the model to hit. We present a simple demonstration showing that the two most common methods for comparing model predictions to environmental time series (plotting model time series against data time series, and plotting predicted versus observed values) have little diagnostic power. We propose that it may be more useful to statistically extract the relationships of primary interest from the time series, and test the model directly against them.

Predicting the need for institutional care shortly after admission to rehabilitation: Rasch analysis and predictive validity of the BRASS Index.

PubMed

Panella, L; La Porta, F; Caselli, S; Marchisio, S; Tennant, A

2012-09-01

Effective discharge planning is increasingly recognised as a critical component of hospital-based Rehabilitation. The BRASS index is a risk screening tool for identification, shortly after hospital admission, of patients who are at risk of post-discharge problems. To evaluate the internal construct validity and reliability of the Blaylock Risk Assessment Screening Score (BRASS) within the rehabilitation setting. Observational prospective study. Rehabilitation ward of an Italian district hospital. One hundred and four consecutively admitted patients. Using classical psychometric methods and Rasch analysis (RA), the internal construct validity and reliability of the BRASS were examined. Also, external and predictive validity of the Rasch-modified BRASS (RMB) score were determined. Reliability of the original BRASS was low (Cronbach's alpha=0.595) and factor analyses showed that it was clearly multidimensional. A RA, based on a reduced 7-BRASS item set (RMB), satisfied model's expectations. Reliability was 0.777. The RMB scores strongly correlated with the original BRASS (rho=0.952; P<0.000) and with FIM™ admission scores (rho=-0.853; P<0.000). A RMB score of 12 was associated with an increased risk of nursing home admission (RR=2.1, 95%CI=1.7-2.5), whereas a score of 17 was associated to a higher risk of length of stay >28 days (RR=7.6, 95%CI=1.8-31.9). This study demonstrated that the original BRASS was multidimensional and unreliable. However, the RMB holds adequate internal construct validity and is sufficiently reliable as a predictor of discharge problems for group, but not individual use. The application of tools and methods (such as the BRASS Index) developed under the biomedical paradigm in a Physical and Rehabilitation Medicine setting may have limitations. Further research is needed to develop, within the rehabilitation setting, a valid measuring tool of risk of post-discharge problems at the individual level.

A predictive scoring instrument for tuberculosis lost to follow-up outcome

PubMed Central

2012-01-01

Background Adherence to tuberculosis (TB) treatment is troublesome, due to long therapy duration, quick therapeutic response which allows the patient to disregard about the rest of their treatment and the lack of motivation on behalf of the patient for improved. The objective of this study was to develop and validate a scoring system to predict the probability of lost to follow-up outcome in TB patients as a way to identify patients suitable for directly observed treatments (DOT) and other interventions to improve adherence. Methods Two prospective cohorts, were used to develop and validate a logistic regression model. A scoring system was constructed, based on the coefficients of factors associated with a lost to follow-up outcome. The probability of lost to follow-up outcome associated with each score was calculated. Predictions in both cohorts were tested using receiver operating characteristic curves (ROC). Results The best model to predict lost to follow-up outcome included the following characteristics: immigration (1 point value), living alone (1 point) or in an institution (2 points), previous anti-TB treatment (2 points), poor patient understanding (2 points), intravenous drugs use (IDU) (4 points) or unknown IDU status (1 point). Scores of 0, 1, 2, 3, 4 and 5 points were associated with a lost to follow-up probability of 2,2% 5,4% 9,9%, 16,4%, 15%, and 28%, respectively. The ROC curve for the validation group demonstrated a good fit (AUC: 0,67 [95% CI; 0,65-0,70]). Conclusion This model has a good capacity to predict a lost to follow-up outcome. Its use could help TB Programs to determine which patients are good candidates for DOT and other strategies to improve TB treatment adherence. PMID:22938040

Comparative validity of brief to medium-length Big Five and Big Six Personality Questionnaires.

PubMed

Thalmayer, Amber Gayle; Saucier, Gerard; Eigenhuis, Annemarie

2011-12-01

A general consensus on the Big Five model of personality attributes has been highly generative for the field of personality psychology. Many important psychological and life outcome correlates with Big Five trait dimensions have been established. But researchers must choose between multiple Big Five inventories when conducting a study and are faced with a variety of options as to inventory length. Furthermore, a 6-factor model has been proposed to extend and update the Big Five model, in part by adding a dimension of Honesty/Humility or Honesty/Propriety. In this study, 3 popular brief to medium-length Big Five measures (NEO Five Factor Inventory, Big Five Inventory [BFI], and International Personality Item Pool), and 3 six-factor measures (HEXACO Personality Inventory, Questionnaire Big Six Scales, and a 6-factor version of the BFI) were placed in competition to best predict important student life outcomes. The effect of test length was investigated by comparing brief versions of most measures (subsets of items) with original versions. Personality questionnaires were administered to undergraduate students (N = 227). Participants' college transcripts and student conduct records were obtained 6-9 months after data was collected. Six-factor inventories demonstrated better predictive ability for life outcomes than did some Big Five inventories. Additional behavioral observations made on participants, including their Facebook profiles and cell-phone text usage, were predicted similarly by Big Five and 6-factor measures. A brief version of the BFI performed surprisingly well; across inventory platforms, increasing test length had little effect on predictive validity. Comparative validity of the models and measures in terms of outcome prediction and parsimony is discussed.

External Validation and Recalibration of Risk Prediction Models for Acute Traumatic Brain Injury among Critically Ill Adult Patients in the United Kingdom

PubMed Central

Griggs, Kathryn A.; Prabhu, Gita; Gomes, Manuel; Lecky, Fiona E.; Hutchinson, Peter J. A.; Menon, David K.; Rowan, Kathryn M.

2015-01-01

Abstract This study validates risk prediction models for acute traumatic brain injury (TBI) in critical care units in the United Kingdom and recalibrates the models to this population. The Risk Adjustment In Neurocritical care (RAIN) Study was a prospective, observational cohort study in 67 adult critical care units. Adult patients admitted to critical care following acute TBI with a last pre-sedation Glasgow Coma Scale score of less than 15 were recruited. The primary outcomes were mortality and unfavorable outcome (death or severe disability, assessed using the Extended Glasgow Outcome Scale) at six months following TBI. Of 3626 critical care unit admissions, 2975 were analyzed. Following imputation of missing outcomes, mortality at six months was 25.7% and unfavorable outcome 57.4%. Ten risk prediction models were validated from Hukkelhoven and colleagues, the Medical Research Council (MRC) Corticosteroid Randomisation After Significant Head Injury (CRASH) Trial Collaborators, and the International Mission for Prognosis and Analysis of Clinical Trials in TBI (IMPACT) group. The model with the best discrimination was the IMPACT “Lab” model (C index, 0.779 for mortality and 0.713 for unfavorable outcome). This model was well calibrated for mortality at six months but substantially under-predicted the risk of unfavorable outcome. Recalibration of the models resulted in small improvements in discrimination and excellent calibration for all models. The risk prediction models demonstrated sufficient statistical performance to support their use in research and audit but fell below the level required to guide individual patient decision-making. The published models for unfavorable outcome at six months had poor calibration in the UK critical care setting and the models recalibrated to this setting should be used in future research. PMID:25898072

External Validation and Recalibration of Risk Prediction Models for Acute Traumatic Brain Injury among Critically Ill Adult Patients in the United Kingdom.

PubMed

Harrison, David A; Griggs, Kathryn A; Prabhu, Gita; Gomes, Manuel; Lecky, Fiona E; Hutchinson, Peter J A; Menon, David K; Rowan, Kathryn M

2015-10-01

This study validates risk prediction models for acute traumatic brain injury (TBI) in critical care units in the United Kingdom and recalibrates the models to this population. The Risk Adjustment In Neurocritical care (RAIN) Study was a prospective, observational cohort study in 67 adult critical care units. Adult patients admitted to critical care following acute TBI with a last pre-sedation Glasgow Coma Scale score of less than 15 were recruited. The primary outcomes were mortality and unfavorable outcome (death or severe disability, assessed using the Extended Glasgow Outcome Scale) at six months following TBI. Of 3626 critical care unit admissions, 2975 were analyzed. Following imputation of missing outcomes, mortality at six months was 25.7% and unfavorable outcome 57.4%. Ten risk prediction models were validated from Hukkelhoven and colleagues, the Medical Research Council (MRC) Corticosteroid Randomisation After Significant Head Injury (CRASH) Trial Collaborators, and the International Mission for Prognosis and Analysis of Clinical Trials in TBI (IMPACT) group. The model with the best discrimination was the IMPACT "Lab" model (C index, 0.779 for mortality and 0.713 for unfavorable outcome). This model was well calibrated for mortality at six months but substantially under-predicted the risk of unfavorable outcome. Recalibration of the models resulted in small improvements in discrimination and excellent calibration for all models. The risk prediction models demonstrated sufficient statistical performance to support their use in research and audit but fell below the level required to guide individual patient decision-making. The published models for unfavorable outcome at six months had poor calibration in the UK critical care setting and the models recalibrated to this setting should be used in future research.

Improving the detection and prediction of suicidal behavior among military personnel by measuring suicidal beliefs: an evaluation of the Suicide Cognitions Scale.

PubMed

Bryan, Craig J; David Rudd, M; Wertenberger, Evelyn; Etienne, Neysa; Ray-Sannerud, Bobbie N; Morrow, Chad E; Peterson, Alan L; Young-McCaughon, Stacey

2014-04-01

Newer approaches for understanding suicidal behavior suggest the assessment of suicide-specific beliefs and cognitions may improve the detection and prediction of suicidal thoughts and behaviors. The Suicide Cognitions Scale (SCS) was developed to measure suicide-specific beliefs, but it has not been tested in a military setting. Data were analyzed from two separate studies conducted at three military mental health clinics (one U.S. Army, two U.S. Air Force). Participants included 175 active duty Army personnel with acute suicidal ideation and/or a recent suicide attempt referred for a treatment study (Sample 1) and 151 active duty Air Force personnel receiving routine outpatient mental health care (Sample 2). In both samples, participants completed self-report measures and clinician-administered interviews. Follow-up suicide attempts were assessed via clinician-administered interview for Sample 1. Statistical analyses included confirmatory factor analysis, between-group comparisons by history of suicidality, and generalized regression modeling. Two latent factors were confirmed for the SCS: Unloveability and Unbearability. Each demonstrated good internal consistency, convergent validity, and divergent validity. Both scales significantly predicted current suicidal ideation (βs >0.316, ps <0.002) and significantly differentiated suicide attempts from nonsuicidal self-injury and control groups (F(6, 286)=9.801, p<0.001). Both scales significantly predicted future suicide attempts (AORs>1.07, ps <0.050) better than other risk factors. Self-report methodology, small sample sizes, predominantly male samples. The SCS is a reliable and valid measure that predicts suicidal ideation and suicide attempts among military personnel better than other well-established risk factors. Copyright © 2014 Elsevier B.V. All rights reserved.

Drug Repositioning by Kernel-Based Integration of Molecular Structure, Molecular Activity, and Phenotype Data

PubMed Central

Wang, Yongcui; Chen, Shilong; Deng, Naiyang; Wang, Yong

2013-01-01

Computational inference of novel therapeutic values for existing drugs, i.e., drug repositioning, offers the great prospect for faster and low-risk drug development. Previous researches have indicated that chemical structures, target proteins, and side-effects could provide rich information in drug similarity assessment and further disease similarity. However, each single data source is important in its own way and data integration holds the great promise to reposition drug more accurately. Here, we propose a new method for drug repositioning, PreDR (Predict Drug Repositioning), to integrate molecular structure, molecular activity, and phenotype data. Specifically, we characterize drug by profiling in chemical structure, target protein, and side-effects space, and define a kernel function to correlate drugs with diseases. Then we train a support vector machine (SVM) to computationally predict novel drug-disease interactions. PreDR is validated on a well-established drug-disease network with 1,933 interactions among 593 drugs and 313 diseases. By cross-validation, we find that chemical structure, drug target, and side-effects information are all predictive for drug-disease relationships. More experimentally observed drug-disease interactions can be revealed by integrating these three data sources. Comparison with existing methods demonstrates that PreDR is competitive both in accuracy and coverage. Follow-up database search and pathway analysis indicate that our new predictions are worthy of further experimental validation. Particularly several novel predictions are supported by clinical trials databases and this shows the significant prospects of PreDR in future drug treatment. In conclusion, our new method, PreDR, can serve as a useful tool in drug discovery to efficiently identify novel drug-disease interactions. In addition, our heterogeneous data integration framework can be applied to other problems. PMID:24244318

Experimental validation of a numerical model for subway induced vibrations

NASA Astrophysics Data System (ADS)

Gupta, S.; Degrande, G.; Lombaert, G.

2009-04-01

This paper presents the experimental validation of a coupled periodic finite element-boundary element model for the prediction of subway induced vibrations. The model fully accounts for the dynamic interaction between the train, the track, the tunnel and the soil. The periodicity or invariance of the tunnel and the soil in the longitudinal direction is exploited using the Floquet transformation, which allows for an efficient formulation in the frequency-wavenumber domain. A general analytical formulation is used to compute the response of three-dimensional invariant or periodic media that are excited by moving loads. The numerical model is validated by means of several experiments that have been performed at a site in Regent's Park on the Bakerloo line of London Underground. Vibration measurements have been performed on the axle boxes of the train, on the rail, the tunnel invert and the tunnel wall, and in the free field, both at the surface and at a depth of 15 m. Prior to these vibration measurements, the dynamic soil characteristics and the track characteristics have been determined. The Bakerloo line tunnel of London Underground has been modelled using the coupled periodic finite element-boundary element approach and free field vibrations due to the passage of a train at different speeds have been predicted and compared to the measurements. The correspondence between the predicted and measured response in the tunnel is reasonably good, although some differences are observed in the free field. The discrepancies are explained on the basis of various uncertainties involved in the problem. The variation in the response with train speed is similar for the measurements as well as the predictions. This study demonstrates the applicability of the coupled periodic finite element-boundary element model to make realistic predictions of the vibrations from underground railways.

Memory Binding Test Predicts Incident Dementia: Results from the Einstein Aging Study.

PubMed

Mowrey, Wenzhu B; Lipton, Richard B; Katz, Mindy J; Ramratan, Wendy S; Loewenstein, David A; Zimmerman, Molly E; Buschke, Herman

2018-01-01

The Memory Binding Test (MBT) demonstrated good cross-sectional discriminative validity and predicted incident aMCI. To assess whether the MBT predicts incident dementia better than a conventional list learning test in a longitudinal community-based study. As a sub-study in the Einstein Aging Study, 309 participants age≥70 initially free of dementia were administered the MBT and followed annually for incident dementia for up to 13 years. Based on previous work, poor memory binding was defined using an optimal empirical cut-score of≤17 on the binding measure of the MBT, Total Items in the Paired condition (TIP). Cox proportional hazards models were used to assess predictive validity adjusting for covariates. We compared the predictive validity of MBT TIP to that of the free and cued selective reminding test free recall score (FCSRT-FR; cut-score:≤24) and the single list recall measure of the MBT, Cued Recalled from List 1 (CR-L1; cut-score:≤12). Thirty-five of 309 participants developed incident dementia. When assessing each test alone, the hazard ratio (HR) for dementia was significant for MBT TIP (HR = 8.58, 95% CI: (3.58, 20.58), p < 0.0001), FCSRT-FR (HR = 4.19, 95% CI: (1.94, 9.04), p = 0.0003) and MBT CR-L1 (HR = 2.91, 95% CI: (1.37, 6.18), p = 0.006). MBT TIP remained a significant predictor of dementia (p = 0.0002) when adjusting for FCSRT-FR or CR-L1. Older adults with poor memory binding as measured by the MBT TIP were at increased risk for incident dementia. This measure outperforms conventional episodic memory measures of free and cued recall, supporting the memory binding hypothesis.

Predictive Validity of ICD-11 PTSD as Measured by the Impact of Event Scale-Revised: A 15-Year Prospective Study of Political Prisoners.

PubMed

Hyland, Philip; Brewin, Chris R; Maercker, Andreas

2017-04-01

The 11 th edition of the International Classification of Diseases (ICD-11; World Health Organization, 2017) proposes a model of posttraumatic stress disorder (PTSD) that includes 6 symptoms. This study assessed the ability of a classification-independent measure of posttraumatic stress symptoms, the Impact of Event Scale-Revised (Weiss & Marmar, 1996), to capture the ICD-11 model of PTSD. The current study also provided the first assessment of the predictive validity of ICD-11 PTSD. Former East German political prisoners were assessed in 1994 (N = 144) and in 2008-2009 (N = 88) on numerous psychological variables using self-report measures. Of the participants, 48.2% and 36.8% met probable diagnosis for ICD-11 PTSD at the first and second assessments, respectively. Confirmatory factor analysis supported the factorial validity of the 3-factor ICD-11 model of PTSD, as represented by items selected from the Impact of Event Scale-Revised. Hierarchical multiple regression analysis demonstrated that, controlling for sex, the symptom clusters of ICD-11 PTSD (reexperiencing, avoidance, and sense of threat) significantly contributed to the explanation of depression (R 2 = .17), quality of life (R 2 = .21), internalized anger (R 2 = .10), externalized anger (R 2 = .12), hatred of perpetrators (R 2 = .15), dysfunctional disclosure (R 2 = .27), and social acknowledgment as a victim (R 2 = .12) across the 15-year study period. Current findings add support for the factorial and predictive validity of ICD-11 PTSD within a unique cohort of political prisoners. Copyright © 2017 International Society for Traumatic Stress Studies.

Using the NANA toolkit at home to predict older adults' future depression.

PubMed

Andrews, J A; Harrison, R F; Brown, L J E; MacLean, L M; Hwang, F; Smith, T; Williams, E A; Timon, C; Adlam, T; Khadra, H; Astell, A J

2017-04-15

Depression is currently underdiagnosed among older adults. As part of the Novel Assessment of Nutrition and Aging (NANA) validation study, 40 older adults self-reported their mood using a touchscreen computer over three, one-week periods. Here, we demonstrate the potential of these data to predict future depression status. We analysed data from the NANA validation study using a machine learning approach. We applied the least absolute shrinkage and selection operator with a logistic model to averages of six measures of mood, with depression status according to the Geriatric Depression Scale 10 weeks later as the outcome variable. We tested multiple values of the selection parameter in order to produce a model with low deviance. We used a cross-validation framework to avoid overspecialisation, and receiver operating characteristic (ROC) curve analysis to determine the quality of the fitted model. The model we report contained coefficients for two variables: sadness and tiredness, as well as a constant. The cross-validated area under the ROC curve for this model was 0.88 (CI: 0.69-0.97). While results are based on a small sample, the methodology for the selection of variables appears suitable for the problem at hand, suggesting promise for a wider study and ultimate deployment with older adults at increased risk of depression. We have identified self-reported scales of sadness and tiredness as sensitive measures which have the potential to predict future depression status in older adults, partially addressing the problem of underdiagnosis. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

miREE: miRNA recognition elements ensemble

PubMed Central

2011-01-01

Background Computational methods for microRNA target prediction are a fundamental step to understand the miRNA role in gene regulation, a key process in molecular biology. In this paper we present miREE, a novel microRNA target prediction tool. miREE is an ensemble of two parts entailing complementary but integrated roles in the prediction. The Ab-Initio module leverages upon a genetic algorithmic approach to generate a set of candidate sites on the basis of their microRNA-mRNA duplex stability properties. Then, a Support Vector Machine (SVM) learning module evaluates the impact of microRNA recognition elements on the target gene. As a result the prediction takes into account information regarding both miRNA-target structural stability and accessibility. Results The proposed method significantly improves the state-of-the-art prediction tools in terms of accuracy with a better balance between specificity and sensitivity, as demonstrated by the experiments conducted on several large datasets across different species. miREE achieves this result by tackling two of the main challenges of current prediction tools: (1) The reduced number of false positives for the Ab-Initio part thanks to the integration of a machine learning module (2) the specificity of the machine learning part, obtained through an innovative technique for rich and representative negative records generation. The validation was conducted on experimental datasets where the miRNA:mRNA interactions had been obtained through (1) direct validation where even the binding site is provided, or through (2) indirect validation, based on gene expression variations obtained from high-throughput experiments where the specific interaction is not validated in detail and consequently the specific binding site is not provided. Conclusions The coupling of two parts: a sensitive Ab-Initio module and a selective machine learning part capable of recognizing the false positives, leads to an improved balance between sensitivity and specificity. miREE obtains a reasonable trade-off between filtering false positives and identifying targets. miREE tool is available online at http://didattica-online.polito.it/eda/miREE/ PMID:22115078

Extension of HCDstruct for Transonic Aeroservoelastic Analysis of Unconventional Aircraft Concepts

NASA Technical Reports Server (NTRS)

Quinlan, Jesse R.; Gern, Frank H.

2017-01-01

A substantial effort has been made to implement an enhanced aerodynamic modeling capability in the Higher-fidelity Conceptual Design and structural optimization tool. This additional capability is needed for a rapid, physics-based method of modeling advanced aircraft concepts at risk of structural failure due to dynamic aeroelastic instabilities. To adequately predict these instabilities, in particular for transonic applications, a generalized aerodynamic matching algorithm was implemented to correct the doublet-lattice model available in Nastran using solution data from a priori computational fluid dynamics anal- ysis. This new capability is demonstrated for two tube-and-wing aircraft configurations, including a Boeing 737-200 for implementation validation and the NASA D8 as a first use case. Results validate the current implementation of the aerodynamic matching utility and demonstrate the importance of using such a method for aircraft configurations featuring fuselage-wing aerodynamic interaction.

Assessing teachers' positive psychological functioning at work: Development and validation of the Teacher Subjective Wellbeing Questionnaire.

PubMed

Renshaw, Tyler L; Long, Anna C J; Cook, Clayton R

2015-06-01

This study reports on the initial development and validation of the Teacher Subjective Wellbeing Questionnaire (TSWQ) with 2 samples of educators-a general sample of 185 elementary and middle school teachers, and a target sample of 21 elementary school teachers experiencing classroom management challenges. The TSWQ is an 8-item self-report instrument for assessing teachers' subjective wellbeing, which is operationalized via subscales measuring school connectedness and teaching efficacy. The conceptualization and development processes underlying the TSWQ are described, and results from a series of preliminary psychometric and exploratory analyses are reported to establish initial construct validity. Findings indicated that the TSWQ was characterized by 2 conceptually sound latent factors, that both subscales and the composite scale demonstrated strong internal consistency, and that all scales demonstrated convergent validity with self-reported school supports and divergent validity with self-reported stress and emotional burnout. Furthermore, results indicated that TSWQ scores did not differ according to teachers' school level (i.e., elementary vs. middle), but that they did differ according to unique school environment (e.g., 1 middle school vs. another middle school) and teacher stressors (i.e., general teachers vs. teachers experiencing classroom management challenges). Results also indicated that, for teachers experiencing classroom challenges, the TSWQ had strong short-term predictive validity for psychological distress, accounting for approximately half of the variance in teacher stress and emotional burnout. Implications for theory, research, and the practice of school psychology are discussed. (c) 2015 APA, all rights reserved).

Drug-Target Interaction Prediction through Label Propagation with Linear Neighborhood Information.

PubMed

Zhang, Wen; Chen, Yanlin; Li, Dingfang

2017-11-25

Interactions between drugs and target proteins provide important information for the drug discovery. Currently, experiments identified only a small number of drug-target interactions. Therefore, the development of computational methods for drug-target interaction prediction is an urgent task of theoretical interest and practical significance. In this paper, we propose a label propagation method with linear neighborhood information (LPLNI) for predicting unobserved drug-target interactions. Firstly, we calculate drug-drug linear neighborhood similarity in the feature spaces, by considering how to reconstruct data points from neighbors. Then, we take similarities as the manifold of drugs, and assume the manifold unchanged in the interaction space. At last, we predict unobserved interactions between known drugs and targets by using drug-drug linear neighborhood similarity and known drug-target interactions. The experiments show that LPLNI can utilize only known drug-target interactions to make high-accuracy predictions on four benchmark datasets. Furthermore, we consider incorporating chemical structures into LPLNI models. Experimental results demonstrate that the model with integrated information (LPLNI-II) can produce improved performances, better than other state-of-the-art methods. The known drug-target interactions are an important information source for computational predictions. The usefulness of the proposed method is demonstrated by cross validation and the case study.

Using Computer-extracted Image Phenotypes from Tumors on Breast MRI to Predict Breast Cancer Pathologic Stage

PubMed Central

Burnside, Elizabeth S.; Drukker, Karen; Li, Hui; Bonaccio, Ermelinda; Zuley, Margarita; Ganott, Marie; Net, Jose M.; Sutton, Elizabeth; Brandt, Kathleen R.; Whitman, Gary; Conzen, Suzanne; Lan, Li; Ji, Yuan; Zhu, Yitan; Jaffe, Carl; Huang, Erich; Freymann, John; Kirby, Justin; Morris, Elizabeth; Giger, Maryellen

2015-01-01

Background To demonstrate that computer-extracted image phenotypes (CEIPs) of biopsy-proven breast cancer on MRI can accurately predict pathologic stage. Methods We used a dataset of de-identified breast MRIs organized by the National Cancer Institute in The Cancer Imaging Archive. We analyzed 91 biopsy-proven breast cancer cases with pathologic stage (stage I = 22; stage II = 58; stage III = 11) and surgically proven nodal status (negative nodes = 46, ≥ 1 positive node = 44, no nodes examined = 1). We characterized tumors by (a) radiologist measured size, and (b) CEIP. We built models combining two CEIPs to predict tumor pathologic stage and lymph node involvement, evaluated them in leave-one-out cross-validation with area under the ROC curve (AUC) as figure of merit. Results Tumor size was the most powerful predictor of pathologic stage but CEIPs capturing biologic behavior also emerged as predictive (e.g. stage I+II vs. III demonstrated AUC = 0.83). No size measure was successful in the prediction of positive lymph nodes but adding a CEIP describing tumor “homogeneity,” significantly improved this discrimination (AUC = 0.62, p=.003) over chance. Conclusions Our results indicate that MRI phenotypes show promise for predicting breast cancer pathologic stage and lymph node status. PMID:26619259

Determinants of Scanpath Regularity in Reading.

PubMed

von der Malsburg, Titus; Kliegl, Reinhold; Vasishth, Shravan

2015-09-01

Scanpaths have played an important role in classic research on reading behavior. Nevertheless, they have largely been neglected in later research perhaps due to a lack of suitable analytical tools. Recently, von der Malsburg and Vasishth (2011) proposed a new measure for quantifying differences between scanpaths and demonstrated that this measure can recover effects that were missed with the traditional eyetracking measures. However, the sentences used in that study were difficult to process and scanpath effects accordingly strong. The purpose of the present study was to test the validity, sensitivity, and scope of applicability of the scanpath measure, using simple sentences that are typically read from left to right. We derived predictions for the regularity of scanpaths from the literature on oculomotor control, sentence processing, and cognitive aging and tested these predictions using the scanpath measure and a large database of eye movements. All predictions were confirmed: Sentences with short words and syntactically more difficult sentences elicited more irregular scanpaths. Also, older readers produced more irregular scanpaths than younger readers. In addition, we found an effect that was not reported earlier: Syntax had a smaller influence on the eye movements of older readers than on those of young readers. We discuss this interaction of syntactic parsing cost with age in terms of shifts in processing strategies and a decline of executive control as readers age. Overall, our results demonstrate the validity and sensitivity of the scanpath measure and thus establish it as a productive and versatile tool for reading research. Copyright © 2014 Cognitive Science Society, Inc.

Measurement of Physical Activity and Energy Expenditure in Wheelchair Users: Methods, Considerations and Future Directions.

PubMed

Nightingale, Tom E; Rouse, Peter C; Thompson, Dylan; Bilzon, James L J

2017-12-01

Accurately measuring physical activity and energy expenditure in persons with chronic physical disabilities who use wheelchairs is a considerable and ongoing challenge. Quantifying various free-living lifestyle behaviours in this group is at present restricted by our understanding of appropriate measurement tools and analytical techniques. This review provides a detailed evaluation of the currently available measurement tools used to predict physical activity and energy expenditure in persons who use wheelchairs. It also outlines numerous considerations specific to this population and suggests suitable future directions for the field. Of the existing three self-report methods utilised in this population, the 3-day Physical Activity Recall Assessment for People with Spinal Cord Injury (PARA-SCI) telephone interview demonstrates the best reliability and validity. However, the complexity of interview administration and potential for recall bias are notable limitations. Objective measurement tools, which overcome such considerations, have been validated using controlled laboratory protocols. These have consistently demonstrated the arm or wrist as the most suitable anatomical location to wear accelerometers. Yet, more complex data analysis methodologies may be necessary to further improve energy expenditure prediction for more intricate movements or behaviours. Multi-sensor devices that incorporate physiological signals and acceleration have recently been adapted for persons who use wheelchairs. Population specific algorithms offer considerable improvements in energy expenditure prediction accuracy. This review highlights the progress in the field and aims to encourage the wider scientific community to develop innovative solutions to accurately quantify physical activity in this population.

SMART empirical approaches for predicting field performance of PV modules from results of reliability tests

NASA Astrophysics Data System (ADS)

Hardikar, Kedar Y.; Liu, Bill J. J.; Bheemreddy, Venkata

2016-09-01

Gaining an understanding of degradation mechanisms and their characterization are critical in developing relevant accelerated tests to ensure PV module performance warranty over a typical lifetime of 25 years. As newer technologies are adapted for PV, including new PV cell technologies, new packaging materials, and newer product designs, the availability of field data over extended periods of time for product performance assessment cannot be expected within the typical timeframe for business decisions. In this work, to enable product design decisions and product performance assessment for PV modules utilizing newer technologies, Simulation and Mechanism based Accelerated Reliability Testing (SMART) methodology and empirical approaches to predict field performance from accelerated test results are presented. The method is demonstrated for field life assessment of flexible PV modules based on degradation mechanisms observed in two accelerated tests, namely, Damp Heat and Thermal Cycling. The method is based on design of accelerated testing scheme with the intent to develop relevant acceleration factor models. The acceleration factor model is validated by extensive reliability testing under different conditions going beyond the established certification standards. Once the acceleration factor model is validated for the test matrix a modeling scheme is developed to predict field performance from results of accelerated testing for particular failure modes of interest. Further refinement of the model can continue as more field data becomes available. While the demonstration of the method in this work is for thin film flexible PV modules, the framework and methodology can be adapted to other PV products.

The incremental value of troponin biomarkers in risk stratification of acute coronary syndromes: is the relationship multiplicative?

PubMed

Amin, Amit P; Nathan, Sandeep; Vassallo, Patricia; Calvin, James E

2009-05-20

To emphasize the importance of troponin in the context of a new score for risk stratifying acute coronary syndromes (ACS) patients. Although troponins have powerful prognostic value, current ACS scores do not fully capitalize this prognostic ability. Here, we weigh troponin status in a multiplicative manner to develop the TRACS score from previously published Rush score risk factors (RRF). 2,866 ACS patients (46.7% troponin positive) from 9 centers comprising the TRACS registry, were randomly split into derivation (n=1,422) and validation (n=1,444) cohorts. In the derivation sample, RRF sum was multiplied by 3 if troponins were positive to yield the TRACS score, which was grouped into five categories of 0-2, 3-5, 6-8, 9-11, 12-15 (multiples of 3). Predictive performance of this score to predict hospital death was ascertained in the validation sample. The TRACS score had ROC AUC of 0.71 in the validation cohort. Logistic regression, Kaplan-Meier analysis, likelihood-ratio and Bayesian Information Criterion (BIC) test indicated that weighing troponin status with 3 in the TRACS score improved the prediction of mortality. Hosmer-Lemeshow test indicated sound model fit. We demonstrate that weighing troponin as a multiple of 3 yields robust prognostication of hospital mortality in ACS patients, when used in the context of the TRACS score.

The Incremental Value of Troponin Biomarkers in Risk Stratification of Acute Coronary Syndromes: Is the Relationship Multiplicative?

PubMed Central

Amin, Amit P; Nathan, Sandeep; Vassallo, Patricia; Calvin, James E

2009-01-01

Structured Abstract Objective: To emphasize the importance of troponin in the context of a new score for risk stratifying acute coronary syndromes (ACS) patients. Although troponins have powerful prognostic value, current ACS scores do not fully capitalize this prognostic ability. Here, we weigh troponin status in a multiplicative manner to develop the TRACS score from previously published Rush score risk factors (RRF). Methods: 2,866 ACS patients (46.7% troponin positive) from 9 centers comprising the TRACS registry, were randomly split into derivation (n=1,422) and validation (n=1,444) cohorts. In the derivation sample, RRF sum was multiplied by 3 if troponins were positive to yield the TRACS score, which was grouped into five categories of 0-2, 3-5, 6-8, 9-11, 12-15 (multiples of 3). Predictive performance of this score to predict hospital death was ascertained in the validation sample. Results: The TRACS score had ROC AUC of 0.71 in the validation cohort. Logistic regression, Kaplan-Meier analysis, likelihood-ratio and Bayesian Information Criterion (BIC) test indicated that weighing troponin status with 3 in the TRACS score improved the prediction of mortality. Hosmer-Lemeshow test indicated sound model fit. Conclusions: We demonstrate that weighing troponin as a multiple of 3 yields robust prognostication of hospital mortality in ACS patients, when used in the context of the TRACS score. PMID:19557150

Insights on in vitro models for safety and toxicity assessment of cosmetic ingredients.

PubMed

Almeida, Andreia; Sarmento, Bruno; Rodrigues, Francisca

2017-03-15

According to the current European legislation, the safety assessment of each individual cosmetic ingredient of any formulation is the basis for the safety evaluation of a cosmetic product. Also, animal testing in the European Union is prohibited for cosmetic ingredients and products since 2004 and 2009, respectively. Additionally, the commercialization of any cosmetic products containing ingredients tested on animal models was forbidden in 2009. In consequence of these boundaries, the European Centre for the Validation of Alternative Methods (ECVAM) proposes a list of validated cell-based in vitro models for predicting the safety and toxicity of cosmetic ingredients. These models have been demonstrated as valuable and effective tools to overcome the limitations of animal in vivo studies. Although the use of in vitro cell-based models for the evaluation of absorption and permeability of cosmetic ingredients is widespread, a detailed study on the properties of these platforms and the in vitro-in vivo correlation compared with human data are required. Moreover, additional efforts must be taken to develop in vitro models to predict carcinogenicity, repeat dose toxicity and reproductive toxicity, for which no alternative in vitro methods are currently available. This review paper summarizes and characterizes the most relevant in vitro models validated by ECVAM employed to predict the safety and toxicology of cosmetic ingredients. Copyright © 2017 Elsevier B.V. All rights reserved.

Identifying and classifying hyperostosis frontalis interna via computerized tomography.

PubMed

May, Hila; Peled, Nathan; Dar, Gali; Hay, Ori; Abbas, Janan; Masharawi, Youssef; Hershkovitz, Israel

2010-12-01

The aim of this study was to recognize the radiological characteristics of hyperostosis frontalis interna (HFI) and to establish a valid and reliable method for its identification and classification. A reliability test was carried out on 27 individuals who had undergone a head computerized tomography (CT) scan. Intra-observer reliability was obtained by examining the images three times, by the same researcher, with a 2-week interval between each sample ranking. The inter-observer test was performed by three independent researchers. A validity test was carried out using two methods for identifying and classifying HFI: 46 cadaver skullcaps were ranked twice via computerized tomography scans and then by direct observation. Reliability and validity were calculated using Kappa test (SPSS 15.0). Reliability tests of ranking HFI via CT scans demonstrated good results (K > 0.7). As for validity, a very good consensus was obtained between the CT and direct observation, when moderate and advanced types of HFI were present (K = 0.82). The suggested classification method for HFI, using CT, demonstrated a sensitivity of 84%, specificity of 90.5%, and positive predictive value of 91.3%. In conclusion, volume rendering is a reliable and valid tool for identifying HFI. The suggested three-scale classification is most suitable for radiological diagnosis of the phenomena. Considering the increasing awareness of HFI as an early indicator of a developing malady, this study may assist radiologists in identifying and classifying the phenomena.

The Dutch motor skills assessment as tool for talent development in table tennis: a reproducibility and validity study.

PubMed

Faber, Irene R; Nijhuis-Van Der Sanden, Maria W G; Elferink-Gemser, Marije T; Oosterveld, Frits G J

2015-01-01

A motor skills assessment could be helpful in talent development by estimating essential perceptuo-motor skills of young players, which are considered requisite to develop excellent technical and tactical qualities. The Netherlands Table Tennis Association uses a motor skills assessment in their talent development programme consisting of eight items measuring perceptuo-motor skills specific to table tennis under varying conditions. This study aimed to investigate this assessment regarding its reproducibility, internal consistency, underlying dimensions and concurrent validity in 113 young table tennis players (6-10 years). Intraclass correlation coefficients of six test items met the criteria of 0.7 with coefficients of variation between 3% and 8%. Cronbach's alpha valued 0.853 for internal consistency. The principal components analysis distinguished two conceptually meaningful factors: "ball control" and "gross motor function." Concurrent validity analyses demonstrated moderate associations between the motor skills assessment's results and national ranking; boys r = -0.53 (P < 0.001) and girls r = -0.45 (P = 0.015). In conclusion, this evaluation demonstrated six test items with acceptable reproducibility, good internal consistency and good prospects for validity. Two test items need revision to upgrade reproducibility. Since the motor skills assessment seems to be a reproducible, objective part of a talent development programme, more longitudinal studies are required to investigate its predictive validity.

A points-based algorithm for prognosticating clinical outcome of Chiari malformation Type I with syringomyelia: results from a predictive model analysis of 82 surgically managed adult patients.

PubMed

Thakar, Sumit; Sivaraju, Laxminadh; Jacob, Kuruthukulangara S; Arun, Aditya Atal; Aryan, Saritha; Mohan, Dilip; Sai Kiran, Narayanam Anantha; Hegde, Alangar S

2018-01-01

OBJECTIVE Although various predictors of postoperative outcome have been previously identified in patients with Chiari malformation Type I (CMI) with syringomyelia, there is no known algorithm for predicting a multifactorial outcome measure in this widely studied disorder. Using one of the largest preoperative variable arrays used so far in CMI research, the authors attempted to generate a formula for predicting postoperative outcome. METHODS Data from the clinical records of 82 symptomatic adult patients with CMI and altered hindbrain CSF flow who were managed with foramen magnum decompression, C-1 laminectomy, and duraplasty over an 8-year period were collected and analyzed. Various preoperative clinical and radiological variables in the 57 patients who formed the study cohort were assessed in a bivariate analysis to determine their ability to predict clinical outcome (as measured on the Chicago Chiari Outcome Scale [CCOS]) and the resolution of syrinx at the last follow-up. The variables that were significant in the bivariate analysis were further analyzed in a multiple linear regression analysis. Different regression models were tested, and the model with the best prediction of CCOS was identified and internally validated in a subcohort of 25 patients. RESULTS There was no correlation between CCOS score and syrinx resolution (p = 0.24) at a mean ± SD follow-up of 40.29 ± 10.36 months. Multiple linear regression analysis revealed that the presence of gait instability, obex position, and the M-line-fourth ventricle vertex (FVV) distance correlated with CCOS score, while the presence of motor deficits was associated with poor syrinx resolution (p ≤ 0.05). The algorithm generated from the regression model demonstrated good diagnostic accuracy (area under curve 0.81), with a score of more than 128 points demonstrating 100% specificity for clinical improvement (CCOS score of 11 or greater). The model had excellent reliability (κ = 0.85) and was validated with fair accuracy in the validation cohort (area under the curve 0.75). CONCLUSIONS The presence of gait imbalance and motor deficits independently predict worse clinical and radiological outcomes, respectively, after decompressive surgery for CMI with altered hindbrain CSF flow. Caudal displacement of the obex and a shorter M-line-FVV distance correlated with good CCOS scores, indicating that patients with a greater degree of hindbrain pathology respond better to surgery. The proposed points-based algorithm has good predictive value for postoperative multifactorial outcome in these patients.

Thin-slice vision: inference of confidence measure from perceptual video quality

NASA Astrophysics Data System (ADS)

Hameed, Abdul; Balas, Benjamin; Dai, Rui

2016-11-01

There has been considerable research on thin-slice judgments, but no study has demonstrated the predictive validity of confidence measures when assessors watch videos acquired from communication systems, in which the perceptual quality of videos could be degraded by limited bandwidth and unreliable network conditions. This paper studies the relationship between high-level thin-slice judgments of human behavior and factors that contribute to perceptual video quality. Based on a large number of subjective test results, it has been found that the confidence of a single individual present in all the videos, called speaker's confidence (SC), could be predicted by a list of features that contribute to perceptual video quality. Two prediction models, one based on artificial neural network and the other based on a decision tree, were built to predict SC. Experimental results have shown that both prediction models can result in high correlation measures.

Customer demand prediction of service-oriented manufacturing using the least square support vector machine optimized by particle swarm optimization algorithm

NASA Astrophysics Data System (ADS)

Cao, Jin; Jiang, Zhibin; Wang, Kangzhou

2017-07-01

Many nonlinear customer satisfaction-related factors significantly influence the future customer demand for service-oriented manufacturing (SOM). To address this issue and enhance the prediction accuracy, this article develops a novel customer demand prediction approach for SOM. The approach combines the phase space reconstruction (PSR) technique with the optimized least square support vector machine (LSSVM). First, the prediction sample space is reconstructed by the PSR to enrich the time-series dynamics of the limited data sample. Then, the generalization and learning ability of the LSSVM are improved by the hybrid polynomial and radial basis function kernel. Finally, the key parameters of the LSSVM are optimized by the particle swarm optimization algorithm. In a real case study, the customer demand prediction of an air conditioner compressor is implemented. Furthermore, the effectiveness and validity of the proposed approach are demonstrated by comparison with other classical predication approaches.

Ab initio NMR Confirmed Evolutionary Structure Prediction for Organic Molecular Crystals

NASA Astrophysics Data System (ADS)

Pham, Cong-Huy; Kucukbenli, Emine; de Gironcoli, Stefano

2015-03-01

Ab initio crystal structure prediction of even small organic compounds is extremely challenging due to polymorphism, molecular flexibility and difficulties in addressing the dispersion interaction from first principles. We recently implemented vdW-aware density functionals and demonstrated their success in energy ordering of aminoacid crystals. In this work we combine this development with the evolutionary structure prediction method to study cholesterol polymorphs. Cholesterol crystals have paramount importance in various diseases, from cancer to atherosclerosis. The structure of some polymorphs (e.g. ChM, ChAl, ChAh) have already been resolved while some others, which display distinct NMR spectra and are involved in disease formation, are yet to be determined. Here we thoroughly assess the applicability of evolutionary structure prediction to address such real world problems. We validate the newly predicted structures with ab initio NMR chemical shift data using secondary referencing for an improved comparison with experiments.

The development of a probabilistic approach to forecast coastal change

USGS Publications Warehouse

Lentz, Erika E.; Hapke, Cheryl J.; Rosati, Julie D.; Wang, Ping; Roberts, Tiffany M.

2011-01-01

This study demonstrates the applicability of a Bayesian probabilistic model as an effective tool in predicting post-storm beach changes along sandy coastlines. Volume change and net shoreline movement are modeled for two study sites at Fire Island, New York in response to two extratropical storms in 2007 and 2009. Both study areas include modified areas adjacent to unmodified areas in morphologically different segments of coast. Predicted outcomes are evaluated against observed changes to test model accuracy and uncertainty along 163 cross-shore transects. Results show strong agreement in the cross validation of predictions vs. observations, with 70-82% accuracies reported. Although no consistent spatial pattern in inaccurate predictions could be determined, the highest prediction uncertainties appeared in locations that had been recently replenished. Further testing and model refinement are needed; however, these initial results show that Bayesian networks have the potential to serve as important decision-support tools in forecasting coastal change.

A new framework to enhance the interpretation of external validation studies of clinical prediction models.

PubMed

Debray, Thomas P A; Vergouwe, Yvonne; Koffijberg, Hendrik; Nieboer, Daan; Steyerberg, Ewout W; Moons, Karel G M

2015-03-01

It is widely acknowledged that the performance of diagnostic and prognostic prediction models should be assessed in external validation studies with independent data from "different but related" samples as compared with that of the development sample. We developed a framework of methodological steps and statistical methods for analyzing and enhancing the interpretation of results from external validation studies of prediction models. We propose to quantify the degree of relatedness between development and validation samples on a scale ranging from reproducibility to transportability by evaluating their corresponding case-mix differences. We subsequently assess the models' performance in the validation sample and interpret the performance in view of the case-mix differences. Finally, we may adjust the model to the validation setting. We illustrate this three-step framework with a prediction model for diagnosing deep venous thrombosis using three validation samples with varying case mix. While one external validation sample merely assessed the model's reproducibility, two other samples rather assessed model transportability. The performance in all validation samples was adequate, and the model did not require extensive updating to correct for miscalibration or poor fit to the validation settings. The proposed framework enhances the interpretation of findings at external validation of prediction models. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

Association of Adjuvant Chemotherapy With Survival in Patients With Stage II or III Gastric Cancer

PubMed Central

Jiang, Yuming; Li, Tuanjie; Liang, Xiaoling; Hu, Yanfeng; Huang, Lei; Liao, Zhenchen; Zhao, Liying; Han, Zhen; Zhu, Shuguang; Wang, Menglan; Xu, Yangwei; Qi, Xiaolong; Liu, Hao; Yang, Yang; Yu, Jiang; Liu, Wei; Cai, Shirong

2017-01-01

Importance The current staging system of gastric cancer is not adequate for defining a prognosis and predicting the patients most likely to benefit from chemotherapy. Objective To construct a survival prediction model based on specific tumor and patient characteristics that enables individualized predictions of the net survival benefit of adjuvant chemotherapy for patients with stage II or stage III gastric cancer. Design, Setting, and Participants In this multicenter retrospective analysis, a survival prediction model was constructed using data from a training cohort of 746 patients with stage II or stage III gastric cancer who satisfied the study’s inclusion criteria and underwent surgery between January 1, 2004, and December 31, 2012, at Nanfang Hospital in Guangzhou, China. Patient and tumor characteristics were included as covariates, and their association with overall survival and disease-free survival with and without adjuvant chemotherapy was assessed. The model was internally validated for discrimination and calibration using bootstrap resampling. To externally validate the model, data were included from a validation cohort of 973 patients with stage II or stage III gastric cancer who met the inclusion criteria and underwent surgery at First Affiliated Hospital in Guangzhou, China, and at West China Hospital of Sichuan Hospital in Chendu, China, between January 1, 2000, and June 30, 2009. Data were analyzed from July 10, 2016, to September 1, 2016. Main Outcomes and Measures Concordance index and decision curve analysis for each measure associated with postoperative overall survival and disease-free survival. Results Of the 1719 patients analyzed, 1183 (68.8%) were men and 536 (31.2%) were women and the median (interquartile range) age was 57 (49-66) years. Age, location, differentiation, carcinoembryonic antigen, cancer antigen 19-9, depth of invasion, lymph node metastasis, and adjuvant chemotherapy were significantly associated with overall survival and disease-free survival, with P < .05. The survival prediction model demonstrated good calibration and discrimination, with relatively high bootstrap-corrected concordance indexes in the training and validation cohorts. In the validation cohort, the concordance index for overall survival was 0.693 (95% CI, 0.671-0.715) and for disease-free survival was 0.704 (95% CI, 0.681-0.728). Two nomograms and a calculating tool were built on the basis of specific input variables to estimate an individual’s net survival gain attributable to adjuvant chemotherapy. Conclusions and Relevance The survival prediction model can be used to make individualized predictions of the expected survival benefit from the addition of adjuvant chemotherapy for patients with stage II or stage III gastric cancer. PMID:28538950

Technical reference for the use of the slow crack growth test for modeling and predicting the long-term performance of polyethylene gas pipes. Final report, March 1987-May 1992. Volume 2

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

Kanninen, M.F.; O'Donoghue, P.E.; Popelar, C.F.

1993-02-01

The project was undertaken for the purposes of quantifying the Battelle slow crack growth (SCG) test for predicting long-term performance of polyethylene gas distribution pipes, and of demonstrating the applicability of the methodology for use by the gas industry for accelerated characterization testing, thereby bringing the SCG test development effort to a closure. The work has revealed that the Battelle SCG test, and the linear fracture mechanics interpretation that it currently utilizes, is valid for a class of PE materials. The long-term performance of these materials in various operating conditions can therefore be effectively predicted.

Validity and validation of expert (Q)SAR systems.

PubMed

Hulzebos, E; Sijm, D; Traas, T; Posthumus, R; Maslankiewicz, L

2005-08-01

At a recent workshop in Setubal (Portugal) principles were drafted to assess the suitability of (quantitative) structure-activity relationships ((Q)SARs) for assessing the hazards and risks of chemicals. In the present study we applied some of the Setubal principles to test the validity of three (Q)SAR expert systems and validate the results. These principles include a mechanistic basis, the availability of a training set and validation. ECOSAR, BIOWIN and DEREK for Windows have a mechanistic or empirical basis. ECOSAR has a training set for each QSAR. For half of the structural fragments the number of chemicals in the training set is >4. Based on structural fragments and log Kow, ECOSAR uses linear regression to predict ecotoxicity. Validating ECOSAR for three 'valid' classes results in predictivity of > or = 64%. BIOWIN uses (non-)linear regressions to predict the probability of biodegradability based on fragments and molecular weight. It has a large training set and predicts non-ready biodegradability well. DEREK for Windows predictions are supported by a mechanistic rationale and literature references. The structural alerts in this program have been developed with a training set of positive and negative toxicity data. However, to support the prediction only a limited number of chemicals in the training set is presented to the user. DEREK for Windows predicts effects by 'if-then' reasoning. The program predicts best for mutagenicity and carcinogenicity. Each structural fragment in ECOSAR and DEREK for Windows needs to be evaluated and validated separately.

A cascaded QSAR model for efficient prediction of overall power conversion efficiency of all-organic dye-sensitized solar cells.

PubMed

Li, Hongzhi; Zhong, Ziyan; Li, Lin; Gao, Rui; Cui, Jingxia; Gao, Ting; Hu, Li Hong; Lu, Yinghua; Su, Zhong-Min; Li, Hui

2015-05-30

A cascaded model is proposed to establish the quantitative structure-activity relationship (QSAR) between the overall power conversion efficiency (PCE) and quantum chemical molecular descriptors of all-organic dye sensitizers. The cascaded model is a two-level network in which the outputs of the first level (JSC, VOC, and FF) are the inputs of the second level, and the ultimate end-point is the overall PCE of dye-sensitized solar cells (DSSCs). The model combines quantum chemical methods and machine learning methods, further including quantum chemical calculations, data division, feature selection, regression, and validation steps. To improve the efficiency of the model and reduce the redundancy and noise of the molecular descriptors, six feature selection methods (multiple linear regression, genetic algorithms, mean impact value, forward selection, backward elimination, and +n-m algorithm) are used with the support vector machine. The best established cascaded model predicts the PCE values of DSSCs with a MAE of 0.57 (%), which is about 10% of the mean value PCE (5.62%). The validation parameters according to the OECD principles are R(2) (0.75), Q(2) (0.77), and Qcv2 (0.76), which demonstrate the great goodness-of-fit, predictivity, and robustness of the model. Additionally, the applicability domain of the cascaded QSAR model is defined for further application. This study demonstrates that the established cascaded model is able to effectively predict the PCE for organic dye sensitizers with very low cost and relatively high accuracy, providing a useful tool for the design of dye sensitizers with high PCE. © 2015 Wiley Periodicals, Inc.

Prospective validation of pathologic complete response models in rectal cancer: Transferability and reproducibility.

PubMed

van Soest, Johan; Meldolesi, Elisa; van Stiphout, Ruud; Gatta, Roberto; Damiani, Andrea; Valentini, Vincenzo; Lambin, Philippe; Dekker, Andre

2017-09-01

Multiple models have been developed to predict pathologic complete response (pCR) in locally advanced rectal cancer patients. Unfortunately, validation of these models normally omit the implications of cohort differences on prediction model performance. In this work, we will perform a prospective validation of three pCR models, including information whether this validation will target transferability or reproducibility (cohort differences) of the given models. We applied a novel methodology, the cohort differences model, to predict whether a patient belongs to the training or to the validation cohort. If the cohort differences model performs well, it would suggest a large difference in cohort characteristics meaning we would validate the transferability of the model rather than reproducibility. We tested our method in a prospective validation of three existing models for pCR prediction in 154 patients. Our results showed a large difference between training and validation cohort for one of the three tested models [Area under the Receiver Operating Curve (AUC) cohort differences model: 0.85], signaling the validation leans towards transferability. Two out of three models had a lower AUC for validation (0.66 and 0.58), one model showed a higher AUC in the validation cohort (0.70). We have successfully applied a new methodology in the validation of three prediction models, which allows us to indicate if a validation targeted transferability (large differences between training/validation cohort) or reproducibility (small cohort differences). © 2017 American Association of Physicists in Medicine.

The development and validation of the client expectations of massage scale.

PubMed

Boulanger, Karen T; Campo, Shelly; Glanville, Jennifer L; Lowe, John B; Yang, Jingzhen

2012-01-01

Although there is evidence that client expectations influence client outcomes, a valid and reliable scale for measuring the range of client expectations for both massage therapy and the behaviors of their massage therapists does not exist. Understanding how client expectations influence client outcomes would provide insight into how massage achieves its reported effects. To develop and validate the Client Expectations of Massage Scale (CEMS), a measure of clients' clinical, educational, interpersonal, and outcome expectations. Offices of licensed massage therapists in Iowa. A practice-based research methodology was used to collect data from two samples of massage therapy clients. For Sample 1, 21 volunteer massage therapists collected data from their clients before the massage. Factor analysis was conducted to test construct validity and coefficient alpha was used to assess reliability. Correlational analyses with the CEMS, previous measures of client expectations, and the Life Orientation Test-Revised were examined to test the convergent and discriminant validity of the CEMS. For Sample 2, 24 massage therapists distributed study materials for clients to complete before and after a massage therapy session. Structural equation modeling was used to assess the construct, discriminant, and predictive validity of the CEMS. Sample 1 involved 320 and Sample 2 involved 321 adult massage clients. Standard care provided by licensed massage therapists. Numeric Rating Scale for pain and Positive and Negative Affect Schedule-Revised (including the Serenity subscale). The CEMS demonstrated good construct, convergent, discriminant and predictive validity, and adequate reliability. Client expectations were generally positive toward massage and their massage therapists. Positive outcome expectations had a positive effect on clients' changes in pain and serenity. High interpersonal expectations had a negative effect on clients' changes in serenity. Client expectations contribute to the nonspecific effects of massage therapy.

NetMHCpan, a Method for Quantitative Predictions of Peptide Binding to Any HLA-A and -B Locus Protein of Known Sequence

PubMed Central

Nielsen, Morten; Lundegaard, Claus; Blicher, Thomas; Lamberth, Kasper; Harndahl, Mikkel; Justesen, Sune; Røder, Gustav; Peters, Bjoern; Sette, Alessandro; Lund, Ole; Buus, Søren

2007-01-01

Background Binding of peptides to Major Histocompatibility Complex (MHC) molecules is the single most selective step in the recognition of pathogens by the cellular immune system. The human MHC class I system (HLA-I) is extremely polymorphic. The number of registered HLA-I molecules has now surpassed 1500. Characterizing the specificity of each separately would be a major undertaking. Principal Findings Here, we have drawn on a large database of known peptide-HLA-I interactions to develop a bioinformatics method, which takes both peptide and HLA sequence information into account, and generates quantitative predictions of the affinity of any peptide-HLA-I interaction. Prospective experimental validation of peptides predicted to bind to previously untested HLA-I molecules, cross-validation, and retrospective prediction of known HIV immune epitopes and endogenous presented peptides, all successfully validate this method. We further demonstrate that the method can be applied to perform a clustering analysis of MHC specificities and suggest using this clustering to select particularly informative novel MHC molecules for future biochemical and functional analysis. Conclusions Encompassing all HLA molecules, this high-throughput computational method lends itself to epitope searches that are not only genome- and pathogen-wide, but also HLA-wide. Thus, it offers a truly global analysis of immune responses supporting rational development of vaccines and immunotherapy. It also promises to provide new basic insights into HLA structure-function relationships. The method is available at http://www.cbs.dtu.dk/services/NetMHCpan. PMID:17726526

Structural vascular disease in Africans: Performance of ethnic-specific waist circumference cut points using logistic regression and neural network analyses: The SABPA study.

PubMed

Botha, J; de Ridder, J H; Potgieter, J C; Steyn, H S; Malan, L

2013-10-01

A recently proposed model for waist circumference cut points (RPWC), driven by increased blood pressure, was demonstrated in an African population. We therefore aimed to validate the RPWC by comparing the RPWC and the Joint Statement Consensus (JSC) models via Logistic Regression (LR) and Neural Networks (NN) analyses. Urban African gender groups (N=171) were stratified according to the JSC and RPWC cut point models. Ultrasound carotid intima media thickness (CIMT), blood pressure (BP) and fasting bloods (glucose, high density lipoprotein (HDL) and triglycerides) were obtained in a well-controlled setting. The RPWC male model (LR ROC AUC: 0.71, NN ROC AUC: 0.71) was practically equal to the JSC model (LR ROC AUC: 0.71, NN ROC AUC: 0.69) to predict structural vascular -disease. Similarly, the female RPWC model (LR ROC AUC: 0.84, NN ROC AUC: 0.82) and JSC model (LR ROC AUC: 0.82, NN ROC AUC: 0.81) equally predicted CIMT as surrogate marker for structural vascular disease. Odds ratios supported validity where prediction of CIMT revealed -clinical -significance, well over 1, for both the JSC and RPWC models in African males and females (OR 3.75-13.98). In conclusion, the proposed RPWC model was substantially validated utilizing linear and non-linear analyses. We therefore propose ethnic-specific WC cut points (African males, ≥90 cm; -females, ≥98 cm) to predict a surrogate marker for structural vascular disease. © J. A. Barth Verlag in Georg Thieme Verlag KG Stuttgart · New York.

External Validation of the HERNIAscore: An Observational Study.

PubMed

Cherla, Deepa V; Moses, Maya L; Mueck, Krislynn M; Hannon, Craig; Ko, Tien C; Kao, Lillian S; Liang, Mike K

2017-09-01

The HERNIAscore is a ventral incisional hernia (VIH) risk assessment tool that uses only preoperative variables and predictable intraoperative variables. The aim of this study was to validate and modify, if needed, the HERNIAscore in an external dataset. This was a retrospective observational study of all patients undergoing resection for gastrointestinal malignancy from 2011 through 2015 at a safety-net hospital. The primary end point was clinical postoperative VIH. Patients were stratified into low-risk, medium-risk, and high-risk groups based on HERNIAscore. A revised HERNIAscore was calculated with the addition of earlier abdominal operation as a categorical variable. Cox regression of incisional hernia with stratification by risk class was performed. Incidence rates of clinical VIH formation within each risk class were also calculated. Two hundred and forty-seven patents were enrolled. On Cox regression, in addition to the 3 variables of the HERNIAscore (BMI, COPD, and incision length), earlier abdominal operation was also predictive of VIH. The revised HERNIAscore demonstrated improved predictive accuracy for clinical VIH. Although the original HERNIAscore effectively stratified the risk of an incisional radiographic VIH developing, the revised HERNIAscore provided a statistically significant stratification for both clinical and radiographic VIHs in this patient cohort. We have externally validated and improved the HERNIAscore. The revised HERNIAscore uses BMI, incision length, COPD, and earlier abdominal operation to predict risk of postoperative incisional hernia. Future research should assess methods to prevent incisional hernias in moderate-to-high risk patients. Copyright © 2017 American College of Surgeons. Published by Elsevier Inc. All rights reserved.

Predicting the need for muscle flap salvage after open groin vascular procedures: a clinical assessment tool.

PubMed

Fischer, John P; Nelson, Jonas A; Shang, Eric K; Wink, Jason D; Wingate, Nicholas A; Woo, Edward Y; Jackson, Benjamin M; Kovach, Stephen J; Kanchwala, Suhail

2014-12-01

Groin wound complications after open vascular surgery procedures are common, morbid, and costly. The purpose of this study was to generate a simple, validated, clinically usable risk assessment tool for predicting groin wound morbidity after infra-inguinal vascular surgery. A retrospective review of consecutive patients undergoing groin cutdowns for femoral access between 2005-2011 was performed. Patients necessitating salvage flaps were compared to those who did not, and a stepwise logistic regression was performed and validated using a bootstrap technique. Utilising this analysis, a simplified risk score was developed to predict the risk of developing a wound which would necessitate salvage. A total of 925 patients were included in the study. The salvage flap rate was 11.2% (n = 104). Predictors determined by logistic regression included prior groin surgery (OR = 4.0, p < 0.001), prosthetic graft (OR = 2.7, p < 0.001), coronary artery disease (OR = 1.8, p = 0.019), peripheral arterial disease (OR = 5.0, p < 0.001), and obesity (OR = 1.7, p = 0.039). Based upon the respective logistic coefficients, a simplified scoring system was developed to enable the preoperative risk stratification regarding the likelihood of a significant complication which would require a salvage muscle flap. The c-statistic for the regression demonstrated excellent discrimination at 0.89. This study presents a simple, internally validated risk assessment tool that accurately predicts wound morbidity requiring flap salvage in open groin vascular surgery patients. The preoperatively high-risk patient can be identified and selectively targeted as a candidate for a prophylactic muscle flap.

Validating emotional attention regulation as a component of emotional intelligence: A Stroop approach to individual differences in tuning in to and out of nonverbal cues.

PubMed

Elfenbein, Hillary Anger; Jang, Daisung; Sharma, Sudeep; Sanchez-Burks, Jeffrey

2017-03-01

Emotional intelligence (EI) has captivated researchers and the public alike, but it has been challenging to establish its components as objective abilities. Self-report scales lack divergent validity from personality traits, and few ability tests have objectively correct answers. We adapt the Stroop task to introduce a new facet of EI called emotional attention regulation (EAR), which involves focusing emotion-related attention for the sake of information processing rather than for the sake of regulating one's own internal state. EAR includes 2 distinct components. First, tuning in to nonverbal cues involves identifying nonverbal cues while ignoring alternate content, that is, emotion recognition under conditions of distraction by competing stimuli. Second, tuning out of nonverbal cues involves ignoring nonverbal cues while identifying alternate content, that is, the ability to interrupt emotion recognition when needed to focus attention elsewhere. An auditory test of valence included positive and negative words spoken in positive and negative vocal tones. A visual test of approach-avoidance included green- and red-colored facial expressions depicting happiness and anger. The error rates for incongruent trials met the key criteria for establishing the validity of an EI test, in that the measure demonstrated test-retest reliability, convergent validity with other EI measures, divergent validity from factors such as general processing speed and mostly personality, and predictive validity in this case for well-being. By demonstrating that facets of EI can be validly theorized and empirically assessed, results also speak to the validity of EI more generally. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Deep learning-based features of breast MRI for prediction of occult invasive disease following a diagnosis of ductal carcinoma in situ: preliminary data

NASA Astrophysics Data System (ADS)

Zhu, Zhe; Harowicz, Michael; Zhang, Jun; Saha, Ashirbani; Grimm, Lars J.; Hwang, Shelley; Mazurowski, Maciej A.

2018-02-01

Approximately 25% of patients with ductal carcinoma in situ (DCIS) diagnosed from core needle biopsy are subsequently upstaged to invasive cancer at surgical excision. Identifying patients with occult invasive disease is important as it changes treatment and precludes enrollment in active surveillance for DCIS. In this study, we investigated upstaging of DCIS to invasive disease using deep features. While deep neural networks require large amounts of training data, the available data to predict DCIS upstaging is sparse and thus directly training a neural network is unlikely to be successful. In this work, a pre-trained neural network is used as a feature extractor and a support vector machine (SVM) is trained on the extracted features. We used the dynamic contrast-enhanced (DCE) MRIs of patients at our institution from January 1, 2000, through March 23, 2014 who underwent MRI following a diagnosis of DCIS. Among the 131 DCIS patients, there were 35 patients who were upstaged to invasive cancer. Area under the ROC curve within the 10-fold cross-validation scheme was used for validation of our predictive model. The use of deep features was able to achieve an AUC of 0.68 (95% CI: 0.56-0.78) to predict occult invasive disease. This preliminary work demonstrates the promise of deep features to predict surgical upstaging following a diagnosis of DCIS.

Evaluation of the phototoxicity of unsubstituted and alkylated polycyclic aromatic hydrocarbons to mysid shrimp (Americamysis bahia): Validation of predictive models.

PubMed

Finch, Bryson E; Marzooghi, Solmaz; Di Toro, Dominic M; Stubblefield, William A

2017-08-01

Crude oils are composed of an assortment of hydrocarbons, some of which are polycyclic aromatic hydrocarbons (PAHs). Polycyclic aromatic hydrocarbons are of particular interest due to their narcotic and potential phototoxic effects. Several studies have examined the phototoxicity of individual PAHs and fresh and weathered crude oils, and several models have been developed to predict PAH toxicity. Fingerprint analyses of oils have shown that PAHs in crude oils are predominantly alkylated. However, current models for estimating PAH phototoxicity assume toxic equivalence between unsubstituted (i.e., parent) and alkyl-substituted compounds. This approach may be incorrect if substantial differences in toxic potency exist between unsubstituted and substituted PAHs. The objective of the present study was to examine the narcotic and photo-enhanced toxicity of commercially available unsubstituted and alkylated PAHs to mysid shrimp (Americamysis bahia). Data were used to validate predictive models of phototoxicity based on the highest occupied molecular orbital-lowest unoccupied molecular orbital (HOMO-LUMO) gap approach and to develop relative effect potencies. Results demonstrated that photo-enhanced toxicity increased with increasing methylation and that phototoxic PAH potencies vary significantly among unsubstituted compounds. Overall, predictive models based on the HOMO-LUMO gap were relatively accurate in predicting phototoxicity for unsubstituted PAHs but are limited to qualitative assessments. Environ Toxicol Chem 2017;36:2043-2049. © 2017 SETAC. © 2017 SETAC.

External Validation of the Updated Partin Tables in a Cohort of French and Italian Men

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

Bhojani, Naeem; Department of Urology, University of Montreal, Montreal, PQ; Salomon, Laurent

2009-02-01

Purpose: To test the discrimination and calibration properties of the newly developed 2007 Partin Tables in two European cohorts with localized prostate cancer. Methods: Data on clinical and pathologic characteristics were obtained for 1,064 men treated with radical prostatectomy at the Creteil University Health Center in France (n = 839) and at the Milan University Vita-Salute in Italy (n = 225). Overall discrimination was assessed with receiver operating characteristic curve analysis, which quantified the accuracy of stage predictions for each center. Calibration plots graphically explored the relationship between predicted and observed rates of extracapsular extension (ECE), seminal vesicle invasion (SVI)more » and lymph node invasion (LNI). Results: The rates of ECE, SVI, and LNI were 28%, 14%, and 2% in the Creteil cohort vs. 11%, 5%, and 5% in the Milan cohort. In the Creteil cohort, the accuracy of ECE, SVI, and LNI prediction was 61%, 71%, and 82% vs. 66%, 92% and 75% for the Milan cohort. Important departures were recorded between Partin Tables' predicted and observed rates of ECE, SVI, and LNI within both cohorts. Conclusions: The 2007 Partin Tables demonstrated worse performance in European men than they originally did in North American men. This indicates that predictive models need to be externally validated before their implementation into clinical practice.« less

Does early childhood callous-unemotional behavior uniquely predict behavior problems or callous-unemotional behavior in late childhood?

PubMed Central

Waller, Rebecca; Dishion, Thomas J.; Shaw, Daniel S.; Gardner, Frances; Wilson, Melvin N.; Hyde, Luke W.

2016-01-01

Callous unemotional (CU) behavior has been linked to behavior problems in children and adolescents. However, few studies have examined whether CU behavior in early childhood predicts behavior problems or CU behavior in late childhood. This study examined whether indicators of CU behavior at ages 2–4 predicted aggression, rule-breaking, and CU behavior across informants at age 9.5. To test the unique predictive and convergent validity of CU behavior in early childhood, we accounted for stability in behavior problems and method effects to rule out the possibility that rater biases inflated the magnitude of any associations found. Cross-informant data were collected from a multi-ethnic, high-risk sample (N = 731; female = 49%) at ages 2–4 and again at age 9.5. From age 3, CU behavior uniquely predicted aggression and rule-breaking across informants. There were also unique associations between CU behavior assessed at ages 3 and 4 and CU behavior assessed at age 9.5. Findings demonstrate that early-childhood indicators of CU behavior account for unique variance in later childhood behavior problems and CU behavior, taking into account stability in behavior problems over time and method effects. Convergence with a traditional measure of CU behavior in late childhood provides support for the construct validity of a brief early childhood measure of CU behavior. PMID:27598253

The Mt. Hood challenge: cross-testing two diabetes simulation models.

PubMed

Brown, J B; Palmer, A J; Bisgaard, P; Chan, W; Pedula, K; Russell, A

2000-11-01

Starting from identical patients with type 2 diabetes, we compared the 20-year predictions of two computer simulation models, a 1998 version of the IMIB model and version 2.17 of the Global Diabetes Model (GDM). Primary measures of outcome were 20-year cumulative rates of: survival, first (incident) acute myocardial infarction (AMI), first stroke, proliferative diabetic retinopathy (PDR), macro-albuminuria (gross proteinuria, or GPR), and amputation. Standardized test patients were newly diagnosed males aged 45 or 75, with high and low levels of glycated hemoglobin (HbA(1c)), systolic blood pressure (SBP), and serum lipids. Both models generated realistic results and appropriate responses to changes in risk factors. Compared with the GDM, the IMIB model predicted much higher rates of mortality and AMI, and fewer strokes. These differences can be explained by differences in model architecture (Markov vs. microsimulation), different evidence bases for cardiovascular prediction (Framingham Heart Study cohort vs. Kaiser Permanente patients), and isolated versus interdependent prediction of cardiovascular events. Compared with IMIB, GDM predicted much higher lifetime costs, because of lower mortality and the use of a different costing method. It is feasible to cross-validate and explicate dissimilar diabetes simulation models using standardized patients. The wide differences in the model results that we observed demonstrate the need for cross-validation. We propose to hold a second 'Mt Hood Challenge' in 2001 and invite all diabetes modelers to attend.

Empirically Examining the Risk of Intimate Partner Violence: The Revised Domestic Violence Screening Instrument (DVSI-R)

PubMed Central

Grant, Stephen R

2006-01-01

SYNOPSIS Objective This study extends recent research on assessing the risk of intimate partner violence by determining the concurrent and predictive validity of a revised version of the Domestic Violence Screening Instrument (DVSI-R) and whether evidence of such validity is sustained independent of perpetrator demographic characteristics and forms of intimate violence. The analyses highlight violent incidents involving multiple victims as an indicator of “severe” violence. Previous research did not address these issues. Methods Data were analyzed on 14,970 assessments conducted in the State of Connecticut from September 1, 2004 through May 2, 2005. Hierarchical regression and receiver operating characteristic analyses were used to address the objectives of this research. Results The empirical findings support the concurrent and predictive validity of the DVSI-R and show that it is robust in its applicability. The findings further show that incidents involving multiple victims are highly associated with DVSI-R risk scores and recidivistic violence. Conclusion Validating and demonstrating the robustness of a risk assessment instrument is only a first step in preventing violence involving intimate partners or others in family or family-like relationships. The challenge is to train professionals responsible for addressing the problem of such violence to link valid risk assessments to well-crafted strategies of supervision and treatment so that the victimized or other potential victims are protected and perpetrators are held accountable for their actions. PMID:16827441

First-harmonic nonlinearities can predict unseen third-harmonics in medium-amplitude oscillatory shear (MAOS)

NASA Astrophysics Data System (ADS)

Carey-De La Torre, Olivia; Ewoldt, Randy H.

2018-02-01

We use first-harmonic MAOS nonlinearities from G 1' and G 1″ to test a predictive structure-rheology model for a transient polymer network. Using experiments with PVA-Borax (polyvinyl alcohol cross-linked by sodium tetraborate (borax)) at 11 different compositions, the model is calibrated to first-harmonic MAOS data on a torque-controlled rheometer at a fixed frequency, and used to predict third-harmonic MAOS on a displacement controlled rheometer at a different frequency three times larger. The prediction matches experiments for decomposed MAOS measures [ e 3] and [ v 3] with median disagreement of 13% and 25%, respectively, across all 11 compositions tested. This supports the validity of this model, and demonstrates the value of using all four MAOS signatures to understand and test structure-rheology relations for complex fluids.

Incremental value of the CT coronary calcium score for the prediction of coronary artery disease

PubMed Central

Genders, Tessa S. S.; Pugliese, Francesca; Mollet, Nico R.; Meijboom, W. Bob; Weustink, Annick C.; van Mieghem, Carlos A. G.; de Feyter, Pim J.

2010-01-01

Objectives: To validate published prediction models for the presence of obstructive coronary artery disease (CAD) in patients with new onset stable typical or atypical angina pectoris and to assess the incremental value of the CT coronary calcium score (CTCS). Methods: We searched the literature for clinical prediction rules for the diagnosis of obstructive CAD, defined as ≥50% stenosis in at least one vessel on conventional coronary angiography. Significant variables were re-analysed in our dataset of 254 patients with logistic regression. CTCS was subsequently included in the models. The area under the receiver operating characteristic curve (AUC) was calculated to assess diagnostic performance. Results: Re-analysing the variables used by Diamond & Forrester yielded an AUC of 0.798, which increased to 0.890 by adding CTCS. For Pryor, Morise 1994, Morise 1997 and Shaw the AUC increased from 0.838 to 0.901, 0.831 to 0.899, 0.840 to 0.898 and 0.833 to 0.899. CTCS significantly improved model performance in each model. Conclusions: Validation demonstrated good diagnostic performance across all models. CTCS improves the prediction of the presence of obstructive CAD, independent of clinical predictors, and should be considered in its diagnostic work-up. PMID:20559838

BiRen: predicting enhancers with a deep-learning-based model using the DNA sequence alone.

PubMed

Yang, Bite; Liu, Feng; Ren, Chao; Ouyang, Zhangyi; Xie, Ziwei; Bo, Xiaochen; Shu, Wenjie

2017-07-01

Enhancer elements are noncoding stretches of DNA that play key roles in controlling gene expression programmes. Despite major efforts to develop accurate enhancer prediction methods, identifying enhancer sequences continues to be a challenge in the annotation of mammalian genomes. One of the major issues is the lack of large, sufficiently comprehensive and experimentally validated enhancers for humans or other species. Thus, the development of computational methods based on limited experimentally validated enhancers and deciphering the transcriptional regulatory code encoded in the enhancer sequences is urgent. We present a deep-learning-based hybrid architecture, BiRen, which predicts enhancers using the DNA sequence alone. Our results demonstrate that BiRen can learn common enhancer patterns directly from the DNA sequence and exhibits superior accuracy, robustness and generalizability in enhancer prediction relative to other state-of-the-art enhancer predictors based on sequence characteristics. Our BiRen will enable researchers to acquire a deeper understanding of the regulatory code of enhancer sequences. Our BiRen method can be freely accessed at https://github.com/wenjiegroup/BiRen . shuwj@bmi.ac.cn or boxc@bmi.ac.cn. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

A simple method for HPLC retention time prediction: linear calibration using two reference substances.

PubMed

Sun, Lei; Jin, Hong-Yu; Tian, Run-Tao; Wang, Ming-Juan; Liu, Li-Na; Ye, Liu-Ping; Zuo, Tian-Tian; Ma, Shuang-Cheng

2017-01-01

Analysis of related substances in pharmaceutical chemicals and multi-components in traditional Chinese medicines needs bulk of reference substances to identify the chromatographic peaks accurately. But the reference substances are costly. Thus, the relative retention (RR) method has been widely adopted in pharmacopoeias and literatures for characterizing HPLC behaviors of those reference substances unavailable. The problem is it is difficult to reproduce the RR on different columns due to the error between measured retention time (t R ) and predicted t R in some cases. Therefore, it is useful to develop an alternative and simple method for prediction of t R accurately. In the present study, based on the thermodynamic theory of HPLC, a method named linear calibration using two reference substances (LCTRS) was proposed. The method includes three steps, procedure of two points prediction, procedure of validation by multiple points regression and sequential matching. The t R of compounds on a HPLC column can be calculated by standard retention time and linear relationship. The method was validated in two medicines on 30 columns. It was demonstrated that, LCTRS method is simple, but more accurate and more robust on different HPLC columns than RR method. Hence quality standards using LCTRS method are easy to reproduce in different laboratories with lower cost of reference substances.