Sample records for accurately predict performance

  1. Do dual-route models accurately predict reading and spelling performance in individuals with acquired alexia and agraphia?

    PubMed

    Rapcsak, Steven Z; Henry, Maya L; Teague, Sommer L; Carnahan, Susan D; Beeson, Pélagie M

    2007-06-18

    Coltheart and co-workers [Castles, A., Bates, T. C., & Coltheart, M. (2006). John Marshall and the developmental dyslexias. Aphasiology, 20, 871-892; Coltheart, M., Rastle, K., Perry, C., Langdon, R., & Ziegler, J. (2001). DRC: A dual route cascaded model of visual word recognition and reading aloud. Psychological Review, 108, 204-256] have demonstrated that an equation derived from dual-route theory accurately predicts reading performance in young normal readers and in children with reading impairment due to developmental dyslexia or stroke. In this paper, we present evidence that the dual-route equation and a related multiple regression model also accurately predict both reading and spelling performance in adult neurological patients with acquired alexia and agraphia. These findings provide empirical support for dual-route theories of written language processing.

  2. A Weibull statistics-based lignocellulose saccharification model and a built-in parameter accurately predict lignocellulose hydrolysis performance.

    PubMed

    Wang, Mingyu; Han, Lijuan; Liu, Shasha; Zhao, Xuebing; Yang, Jinghua; Loh, Soh Kheang; Sun, Xiaomin; Zhang, Chenxi; Fang, Xu

    2015-09-01

    Renewable energy from lignocellulosic biomass has been deemed an alternative to depleting fossil fuels. In order to improve this technology, we aim to develop robust mathematical models for the enzymatic lignocellulose degradation process. By analyzing 96 groups of previously published and newly obtained lignocellulose saccharification results and fitting them to Weibull distribution, we discovered Weibull statistics can accurately predict lignocellulose saccharification data, regardless of the type of substrates, enzymes and saccharification conditions. A mathematical model for enzymatic lignocellulose degradation was subsequently constructed based on Weibull statistics. Further analysis of the mathematical structure of the model and experimental saccharification data showed the significance of the two parameters in this model. In particular, the λ value, defined the characteristic time, represents the overall performance of the saccharification system. This suggestion was further supported by statistical analysis of experimental saccharification data and analysis of the glucose production levels when λ and n values change. In conclusion, the constructed Weibull statistics-based model can accurately predict lignocellulose hydrolysis behavior and we can use the λ parameter to assess the overall performance of enzymatic lignocellulose degradation. Advantages and potential applications of the model and the λ value in saccharification performance assessment were discussed. Copyright © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  3. Rapid and accurate prediction of degradant formation rates in pharmaceutical formulations using high-performance liquid chromatography-mass spectrometry.

    PubMed

    Darrington, Richard T; Jiao, Jim

    2004-04-01

    Rapid and accurate stability prediction is essential to pharmaceutical formulation development. Commonly used stability prediction methods include monitoring parent drug loss at intended storage conditions or initial rate determination of degradants under accelerated conditions. Monitoring parent drug loss at the intended storage condition does not provide a rapid and accurate stability assessment because often <0.5% drug loss is all that can be observed in a realistic time frame, while the accelerated initial rate method in conjunction with extrapolation of rate constants using the Arrhenius or Eyring equations often introduces large errors in shelf-life prediction. In this study, the shelf life prediction of a model pharmaceutical preparation utilizing sensitive high-performance liquid chromatography-mass spectrometry (LC/MS) to directly quantitate degradant formation rates at the intended storage condition is proposed. This method was compared to traditional shelf life prediction approaches in terms of time required to predict shelf life and associated error in shelf life estimation. Results demonstrated that the proposed LC/MS method using initial rates analysis provided significantly improved confidence intervals for the predicted shelf life and required less overall time and effort to obtain the stability estimation compared to the other methods evaluated. Copyright 2004 Wiley-Liss, Inc. and the American Pharmacists Association.

  4. Mental models accurately predict emotion transitions.

    PubMed

    Thornton, Mark A; Tamir, Diana I

    2017-06-06

    Successful social interactions depend on people's ability to predict others' future actions and emotions. People possess many mechanisms for perceiving others' current emotional states, but how might they use this information to predict others' future states? We hypothesized that people might capitalize on an overlooked aspect of affective experience: current emotions predict future emotions. By attending to regularities in emotion transitions, perceivers might develop accurate mental models of others' emotional dynamics. People could then use these mental models of emotion transitions to predict others' future emotions from currently observable emotions. To test this hypothesis, studies 1-3 used data from three extant experience-sampling datasets to establish the actual rates of emotional transitions. We then collected three parallel datasets in which participants rated the transition likelihoods between the same set of emotions. Participants' ratings of emotion transitions predicted others' experienced transitional likelihoods with high accuracy. Study 4 demonstrated that four conceptual dimensions of mental state representation-valence, social impact, rationality, and human mind-inform participants' mental models. Study 5 used 2 million emotion reports on the Experience Project to replicate both of these findings: again people reported accurate models of emotion transitions, and these models were informed by the same four conceptual dimensions. Importantly, neither these conceptual dimensions nor holistic similarity could fully explain participants' accuracy, suggesting that their mental models contain accurate information about emotion dynamics above and beyond what might be predicted by static emotion knowledge alone.

  5. Mental models accurately predict emotion transitions

    PubMed Central

    Thornton, Mark A.; Tamir, Diana I.

    2017-01-01

    Successful social interactions depend on people’s ability to predict others’ future actions and emotions. People possess many mechanisms for perceiving others’ current emotional states, but how might they use this information to predict others’ future states? We hypothesized that people might capitalize on an overlooked aspect of affective experience: current emotions predict future emotions. By attending to regularities in emotion transitions, perceivers might develop accurate mental models of others’ emotional dynamics. People could then use these mental models of emotion transitions to predict others’ future emotions from currently observable emotions. To test this hypothesis, studies 1–3 used data from three extant experience-sampling datasets to establish the actual rates of emotional transitions. We then collected three parallel datasets in which participants rated the transition likelihoods between the same set of emotions. Participants’ ratings of emotion transitions predicted others’ experienced transitional likelihoods with high accuracy. Study 4 demonstrated that four conceptual dimensions of mental state representation—valence, social impact, rationality, and human mind—inform participants’ mental models. Study 5 used 2 million emotion reports on the Experience Project to replicate both of these findings: again people reported accurate models of emotion transitions, and these models were informed by the same four conceptual dimensions. Importantly, neither these conceptual dimensions nor holistic similarity could fully explain participants’ accuracy, suggesting that their mental models contain accurate information about emotion dynamics above and beyond what might be predicted by static emotion knowledge alone. PMID:28533373

  6. PredictSNP: Robust and Accurate Consensus Classifier for Prediction of Disease-Related Mutations

    PubMed Central

    Bendl, Jaroslav; Stourac, Jan; Salanda, Ondrej; Pavelka, Antonin; Wieben, Eric D.; Zendulka, Jaroslav; Brezovsky, Jan; Damborsky, Jiri

    2014-01-01

    Single nucleotide variants represent a prevalent form of genetic variation. Mutations in the coding regions are frequently associated with the development of various genetic diseases. Computational tools for the prediction of the effects of mutations on protein function are very important for analysis of single nucleotide variants and their prioritization for experimental characterization. Many computational tools are already widely employed for this purpose. Unfortunately, their comparison and further improvement is hindered by large overlaps between the training datasets and benchmark datasets, which lead to biased and overly optimistic reported performances. In this study, we have constructed three independent datasets by removing all duplicities, inconsistencies and mutations previously used in the training of evaluated tools. The benchmark dataset containing over 43,000 mutations was employed for the unbiased evaluation of eight established prediction tools: MAPP, nsSNPAnalyzer, PANTHER, PhD-SNP, PolyPhen-1, PolyPhen-2, SIFT and SNAP. The six best performing tools were combined into a consensus classifier PredictSNP, resulting into significantly improved prediction performance, and at the same time returned results for all mutations, confirming that consensus prediction represents an accurate and robust alternative to the predictions delivered by individual tools. A user-friendly web interface enables easy access to all eight prediction tools, the consensus classifier PredictSNP and annotations from the Protein Mutant Database and the UniProt database. The web server and the datasets are freely available to the academic community at http://loschmidt.chemi.muni.cz/predictsnp. PMID:24453961

  7. SCPRED: accurate prediction of protein structural class for sequences of twilight-zone similarity with predicting sequences.

    PubMed

    Kurgan, Lukasz; Cios, Krzysztof; Chen, Ke

    2008-05-01

    Protein structure prediction methods provide accurate results when a homologous protein is predicted, while poorer predictions are obtained in the absence of homologous templates. However, some protein chains that share twilight-zone pairwise identity can form similar folds and thus determining structural similarity without the sequence similarity would be desirable for the structure prediction. The folding type of a protein or its domain is defined as the structural class. Current structural class prediction methods that predict the four structural classes defined in SCOP provide up to 63% accuracy for the datasets in which sequence identity of any pair of sequences belongs to the twilight-zone. We propose SCPRED method that improves prediction accuracy for sequences that share twilight-zone pairwise similarity with sequences used for the prediction. SCPRED uses a support vector machine classifier that takes several custom-designed features as its input to predict the structural classes. Based on extensive design that considers over 2300 index-, composition- and physicochemical properties-based features along with features based on the predicted secondary structure and content, the classifier's input includes 8 features based on information extracted from the secondary structure predicted with PSI-PRED and one feature computed from the sequence. Tests performed with datasets of 1673 protein chains, in which any pair of sequences shares twilight-zone similarity, show that SCPRED obtains 80.3% accuracy when predicting the four SCOP-defined structural classes, which is superior when compared with over a dozen recent competing methods that are based on support vector machine, logistic regression, and ensemble of classifiers predictors. The SCPRED can accurately find similar structures for sequences that share low identity with sequence used for the prediction. The high predictive accuracy achieved by SCPRED is attributed to the design of the features, which are

  8. SCPRED: Accurate prediction of protein structural class for sequences of twilight-zone similarity with predicting sequences

    PubMed Central

    Kurgan, Lukasz; Cios, Krzysztof; Chen, Ke

    2008-01-01

    Background Protein structure prediction methods provide accurate results when a homologous protein is predicted, while poorer predictions are obtained in the absence of homologous templates. However, some protein chains that share twilight-zone pairwise identity can form similar folds and thus determining structural similarity without the sequence similarity would be desirable for the structure prediction. The folding type of a protein or its domain is defined as the structural class. Current structural class prediction methods that predict the four structural classes defined in SCOP provide up to 63% accuracy for the datasets in which sequence identity of any pair of sequences belongs to the twilight-zone. We propose SCPRED method that improves prediction accuracy for sequences that share twilight-zone pairwise similarity with sequences used for the prediction. Results SCPRED uses a support vector machine classifier that takes several custom-designed features as its input to predict the structural classes. Based on extensive design that considers over 2300 index-, composition- and physicochemical properties-based features along with features based on the predicted secondary structure and content, the classifier's input includes 8 features based on information extracted from the secondary structure predicted with PSI-PRED and one feature computed from the sequence. Tests performed with datasets of 1673 protein chains, in which any pair of sequences shares twilight-zone similarity, show that SCPRED obtains 80.3% accuracy when predicting the four SCOP-defined structural classes, which is superior when compared with over a dozen recent competing methods that are based on support vector machine, logistic regression, and ensemble of classifiers predictors. Conclusion The SCPRED can accurately find similar structures for sequences that share low identity with sequence used for the prediction. The high predictive accuracy achieved by SCPRED is attributed to the design of

  9. Simple Learned Weighted Sums of Inferior Temporal Neuronal Firing Rates Accurately Predict Human Core Object Recognition Performance

    PubMed Central

    Hong, Ha; Solomon, Ethan A.; DiCarlo, James J.

    2015-01-01

    database of images for evaluating object recognition performance. We used multielectrode arrays to characterize hundreds of neurons in the visual ventral stream of nonhuman primates and measured the object recognition performance of >100 human observers. Remarkably, we found that simple learned weighted sums of firing rates of neurons in monkey inferior temporal (IT) cortex accurately predicted human performance. Although previous work led us to expect that IT would outperform V4, we were surprised by the quantitative precision with which simple IT-based linking hypotheses accounted for human behavior. PMID:26424887

  10. Simple Learned Weighted Sums of Inferior Temporal Neuronal Firing Rates Accurately Predict Human Core Object Recognition Performance.

    PubMed

    Majaj, Najib J; Hong, Ha; Solomon, Ethan A; DiCarlo, James J

    2015-09-30

    database of images for evaluating object recognition performance. We used multielectrode arrays to characterize hundreds of neurons in the visual ventral stream of nonhuman primates and measured the object recognition performance of >100 human observers. Remarkably, we found that simple learned weighted sums of firing rates of neurons in monkey inferior temporal (IT) cortex accurately predicted human performance. Although previous work led us to expect that IT would outperform V4, we were surprised by the quantitative precision with which simple IT-based linking hypotheses accounted for human behavior. Copyright © 2015 the authors 0270-6474/15/3513402-17$15.00/0.

  11. Accurate prediction of energy expenditure using a shoe-based activity monitor.

    PubMed

    Sazonova, Nadezhda; Browning, Raymond C; Sazonov, Edward

    2011-07-01

    The aim of this study was to develop and validate a method for predicting energy expenditure (EE) using a footwear-based system with integrated accelerometer and pressure sensors. We developed a footwear-based device with an embedded accelerometer and insole pressure sensors for the prediction of EE. The data from the device can be used to perform accurate recognition of major postures and activities and to estimate EE using the acceleration, pressure, and posture/activity classification information in a branched algorithm without the need for individual calibration. We measured EE via indirect calorimetry as 16 adults (body mass index=19-39 kg·m) performed various low- to moderate-intensity activities and compared measured versus predicted EE using several models based on the acceleration and pressure signals. Inclusion of pressure data resulted in better accuracy of EE prediction during static postures such as sitting and standing. The activity-based branched model that included predictors from accelerometer and pressure sensors (BACC-PS) achieved the lowest error (e.g., root mean squared error (RMSE)=0.69 METs) compared with the accelerometer-only-based branched model BACC (RMSE=0.77 METs) and nonbranched model (RMSE=0.94-0.99 METs). Comparison of EE prediction models using data from both legs versus models using data from a single leg indicates that only one shoe needs to be equipped with sensors. These results suggest that foot acceleration combined with insole pressure measurement, when used in an activity-specific branched model, can accurately estimate the EE associated with common daily postures and activities. The accuracy and unobtrusiveness of a footwear-based device may make it an effective physical activity monitoring tool.

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

    PubMed Central

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

    2013-01-01

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

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

    PubMed

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

    2016-03-01

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

  14. Multi-fidelity machine learning models for accurate bandgap predictions of solids

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

    Pilania, Ghanshyam; Gubernatis, James E.; Lookman, Turab

    Here, we present a multi-fidelity co-kriging statistical learning framework that combines variable-fidelity quantum mechanical calculations of bandgaps to generate a machine-learned model that enables low-cost accurate predictions of the bandgaps at the highest fidelity level. Additionally, the adopted Gaussian process regression formulation allows us to predict the underlying uncertainties as a measure of our confidence in the predictions. In using a set of 600 elpasolite compounds as an example dataset and using semi-local and hybrid exchange correlation functionals within density functional theory as two levels of fidelities, we demonstrate the excellent learning performance of the method against actual high fidelitymore » quantum mechanical calculations of the bandgaps. The presented statistical learning method is not restricted to bandgaps or electronic structure methods and extends the utility of high throughput property predictions in a significant way.« less

  15. Multi-fidelity machine learning models for accurate bandgap predictions of solids

    DOE PAGES

    Pilania, Ghanshyam; Gubernatis, James E.; Lookman, Turab

    2016-12-28

    Here, we present a multi-fidelity co-kriging statistical learning framework that combines variable-fidelity quantum mechanical calculations of bandgaps to generate a machine-learned model that enables low-cost accurate predictions of the bandgaps at the highest fidelity level. Additionally, the adopted Gaussian process regression formulation allows us to predict the underlying uncertainties as a measure of our confidence in the predictions. In using a set of 600 elpasolite compounds as an example dataset and using semi-local and hybrid exchange correlation functionals within density functional theory as two levels of fidelities, we demonstrate the excellent learning performance of the method against actual high fidelitymore » quantum mechanical calculations of the bandgaps. The presented statistical learning method is not restricted to bandgaps or electronic structure methods and extends the utility of high throughput property predictions in a significant way.« less

  16. Heart rate during basketball game play and volleyball drills accurately predicts oxygen uptake and energy expenditure.

    PubMed

    Scribbans, T D; Berg, K; Narazaki, K; Janssen, I; Gurd, B J

    2015-09-01

    There is currently little information regarding the ability of metabolic prediction equations to accurately predict oxygen uptake and exercise intensity from heart rate (HR) during intermittent sport. The purpose of the present study was to develop and, cross-validate equations appropriate for accurately predicting oxygen cost (VO2) and energy expenditure from HR during intermittent sport participation. Eleven healthy adult males (19.9±1.1yrs) were recruited to establish the relationship between %VO2peak and %HRmax during low-intensity steady state endurance (END), moderate-intensity interval (MOD) and high intensity-interval exercise (HI), as performed on a cycle ergometer. Three equations (END, MOD, and HI) for predicting %VO2peak based on %HRmax were developed. HR and VO2 were directly measured during basketball games (6 male, 20.8±1.0 yrs; 6 female, 20.0±1.3yrs) and volleyball drills (12 female; 20.8±1.0yrs). Comparisons were made between measured and predicted VO2 and energy expenditure using the 3 equations developed and 2 previously published equations. The END and MOD equations accurately predicted VO2 and energy expenditure, while the HI equation underestimated, and the previously published equations systematically overestimated VO2 and energy expenditure. Intermittent sport VO2 and energy expenditure can be accurately predicted from heart rate data using either the END (%VO2peak=%HRmax x 1.008-17.17) or MOD (%VO2peak=%HRmax x 1.2-32) equations. These 2 simple equations provide an accessible and cost-effective method for accurate estimation of exercise intensity and energy expenditure during intermittent sport.

  17. Measuring the value of accurate link prediction for network seeding.

    PubMed

    Wei, Yijin; Spencer, Gwen

    2017-01-01

    The influence-maximization literature seeks small sets of individuals whose structural placement in the social network can drive large cascades of behavior. Optimization efforts to find the best seed set often assume perfect knowledge of the network topology. Unfortunately, social network links are rarely known in an exact way. When do seeding strategies based on less-than-accurate link prediction provide valuable insight? We introduce optimized-against-a-sample ([Formula: see text]) performance to measure the value of optimizing seeding based on a noisy observation of a network. Our computational study investigates [Formula: see text] under several threshold-spread models in synthetic and real-world networks. Our focus is on measuring the value of imprecise link information. The level of investment in link prediction that is strategic appears to depend closely on spread model: in some parameter ranges investments in improving link prediction can pay substantial premiums in cascade size. For other ranges, such investments would be wasted. Several trends were remarkably consistent across topologies.

  18. Does resident ranking during recruitment accurately predict subsequent performance as a surgical resident?

    PubMed

    Fryer, Jonathan P; Corcoran, Noreen; George, Brian; Wang, Ed; Darosa, Debra

    2012-01-01

    While the primary goal of ranking applicants for surgical residency training positions is to identify the candidates who will subsequently perform best as surgical residents, the effectiveness of the ranking process has not been adequately studied. We evaluated our general surgery resident recruitment process between 2001 and 2011 inclusive, to determine if our recruitment ranking parameters effectively predicted subsequent resident performance. We identified 3 candidate ranking parameters (United States Medical Licensing Examination [USMLE] Step 1 score, unadjusted ranking score [URS], and final adjusted ranking [FAR]), and 4 resident performance parameters (American Board of Surgery In-Training Examination [ABSITE] score, PGY1 resident evaluation grade [REG], overall REG, and independent faculty rating ranking [IFRR]), and assessed whether the former were predictive of the latter. Analyses utilized Spearman correlation coefficient. We found that the URS, which is based on objective and criterion based parameters, was a better predictor of subsequent performance than the FAR, which is a modification of the URS based on subsequent determinations of the resident selection committee. USMLE score was a reliable predictor of ABSITE scores only. However, when we compared our worst residence performances with the performances of the other residents in this evaluation, the data did not produce convincing evidence that poor resident performances could be reliably predicted by any of the recruitment ranking parameters. Finally, stratifying candidates based on their rank range did not effectively define a ranking cut-off beyond which resident performance would drop off. Based on these findings, we recommend surgery programs may be better served by utilizing a more structured resident ranking process and that subsequent adjustments to the rank list generated by this process should be undertaken with caution. Copyright © 2012 Association of Program Directors in Surgery

  19. An accurate model for predicting high frequency noise of nanoscale NMOS SOI transistors

    NASA Astrophysics Data System (ADS)

    Shen, Yanfei; Cui, Jie; Mohammadi, Saeed

    2017-05-01

    A nonlinear and scalable model suitable for predicting high frequency noise of N-type Metal Oxide Semiconductor (NMOS) transistors is presented. The model is developed for a commercial 45 nm CMOS SOI technology and its accuracy is validated through comparison with measured performance of a microwave low noise amplifier. The model employs the virtual source nonlinear core and adds parasitic elements to accurately simulate the RF behavior of multi-finger NMOS transistors up to 40 GHz. For the first time, the traditional long-channel thermal noise model is supplemented with an injection noise model to accurately represent the noise behavior of these short-channel transistors up to 26 GHz. The developed model is simple and easy to extract, yet very accurate.

  20. EOID Model Validation and Performance Prediction

    DTIC Science & Technology

    2002-09-30

    Our long-term goal is to accurately predict the capability of the current generation of laser-based underwater imaging sensors to perform Electro ... Optic Identification (EOID) against relevant targets in a variety of realistic environmental conditions. The two most prominent technologies in this area

  1. Accurate Identification of Fear Facial Expressions Predicts Prosocial Behavior

    PubMed Central

    Marsh, Abigail A.; Kozak, Megan N.; Ambady, Nalini

    2009-01-01

    The fear facial expression is a distress cue that is associated with the provision of help and prosocial behavior. Prior psychiatric studies have found deficits in the recognition of this expression by individuals with antisocial tendencies. However, no prior study has shown accuracy for recognition of fear to predict actual prosocial or antisocial behavior in an experimental setting. In 3 studies, the authors tested the prediction that individuals who recognize fear more accurately will behave more prosocially. In Study 1, participants who identified fear more accurately also donated more money and time to a victim in a classic altruism paradigm. In Studies 2 and 3, participants’ ability to identify the fear expression predicted prosocial behavior in a novel task designed to control for confounding variables. In Study 3, accuracy for recognizing fear proved a better predictor of prosocial behavior than gender, mood, or scores on an empathy scale. PMID:17516803

  2. Accurate identification of fear facial expressions predicts prosocial behavior.

    PubMed

    Marsh, Abigail A; Kozak, Megan N; Ambady, Nalini

    2007-05-01

    The fear facial expression is a distress cue that is associated with the provision of help and prosocial behavior. Prior psychiatric studies have found deficits in the recognition of this expression by individuals with antisocial tendencies. However, no prior study has shown accuracy for recognition of fear to predict actual prosocial or antisocial behavior in an experimental setting. In 3 studies, the authors tested the prediction that individuals who recognize fear more accurately will behave more prosocially. In Study 1, participants who identified fear more accurately also donated more money and time to a victim in a classic altruism paradigm. In Studies 2 and 3, participants' ability to identify the fear expression predicted prosocial behavior in a novel task designed to control for confounding variables. In Study 3, accuracy for recognizing fear proved a better predictor of prosocial behavior than gender, mood, or scores on an empathy scale.

  3. 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.

  4. Can phenological models predict tree phenology accurately under climate change conditions?

    NASA Astrophysics Data System (ADS)

    Chuine, Isabelle; Bonhomme, Marc; Legave, Jean Michel; García de Cortázar-Atauri, Inaki; Charrier, Guillaume; Lacointe, André; Améglio, Thierry

    2014-05-01

    The onset of the growing season of trees has been globally earlier by 2.3 days/decade during the last 50 years because of global warming and this trend is predicted to continue according to climate forecast. The effect of temperature on plant phenology is however not linear because temperature has a dual effect on bud development. On one hand, low temperatures are necessary to break bud dormancy, and on the other hand higher temperatures are necessary to promote bud cells growth afterwards. Increasing phenological changes in temperate woody species have strong impacts on forest trees distribution and productivity, as well as crops cultivation areas. Accurate predictions of trees phenology are therefore a prerequisite to understand and foresee the impacts of climate change on forests and agrosystems. Different process-based models have been developed in the last two decades to predict the date of budburst or flowering of woody species. They are two main families: (1) one-phase models which consider only the ecodormancy phase and make the assumption that endodormancy is always broken before adequate climatic conditions for cell growth occur; and (2) two-phase models which consider both the endodormancy and ecodormancy phases and predict a date of dormancy break which varies from year to year. So far, one-phase models have been able to predict accurately tree bud break and flowering under historical climate. However, because they do not consider what happens prior to ecodormancy, and especially the possible negative effect of winter temperature warming on dormancy break, it seems unlikely that they can provide accurate predictions in future climate conditions. It is indeed well known that a lack of low temperature results in abnormal pattern of bud break and development in temperate fruit trees. An accurate modelling of the dormancy break date has thus become a major issue in phenology modelling. Two-phases phenological models predict that global warming should delay

  5. Accurate Prediction of Contact Numbers for Multi-Spanning Helical Membrane Proteins

    PubMed Central

    Li, Bian; Mendenhall, Jeffrey; Nguyen, Elizabeth Dong; Weiner, Brian E.; Fischer, Axel W.; Meiler, Jens

    2017-01-01

    Prediction of the three-dimensional (3D) structures of proteins by computational methods is acknowledged as an unsolved problem. Accurate prediction of important structural characteristics such as contact number is expected to accelerate the otherwise slow progress being made in the prediction of 3D structure of proteins. Here, we present a dropout neural network-based method, TMH-Expo, for predicting the contact number of transmembrane helix (TMH) residues from sequence. Neuronal dropout is a strategy where certain neurons of the network are excluded from back-propagation to prevent co-adaptation of hidden-layer neurons. By using neuronal dropout, overfitting was significantly reduced and performance was noticeably improved. For multi-spanning helical membrane proteins, TMH-Expo achieved a remarkable Pearson correlation coefficient of 0.69 between predicted and experimental values and a mean absolute error of only 1.68. In addition, among those membrane protein–membrane protein interface residues, 76.8% were correctly predicted. Mapping of predicted contact numbers onto structures indicates that contact numbers predicted by TMH-Expo reflect the exposure patterns of TMHs and reveal membrane protein–membrane protein interfaces, reinforcing the potential of predicted contact numbers to be used as restraints for 3D structure prediction and protein–protein docking. TMH-Expo can be accessed via a Web server at www.meilerlab.org. PMID:26804342

  6. Searching for an Accurate Marker-Based Prediction of an Individual Quantitative Trait in Molecular Plant Breeding

    PubMed Central

    Fu, Yong-Bi; Yang, Mo-Hua; Zeng, Fangqin; Biligetu, Bill

    2017-01-01

    Molecular plant breeding with the aid of molecular markers has played an important role in modern plant breeding over the last two decades. Many marker-based predictions for quantitative traits have been made to enhance parental selection, but the trait prediction accuracy remains generally low, even with the aid of dense, genome-wide SNP markers. To search for more accurate trait-specific prediction with informative SNP markers, we conducted a literature review on the prediction issues in molecular plant breeding and on the applicability of an RNA-Seq technique for developing function-associated specific trait (FAST) SNP markers. To understand whether and how FAST SNP markers could enhance trait prediction, we also performed a theoretical reasoning on the effectiveness of these markers in a trait-specific prediction, and verified the reasoning through computer simulation. To the end, the search yielded an alternative to regular genomic selection with FAST SNP markers that could be explored to achieve more accurate trait-specific prediction. Continuous search for better alternatives is encouraged to enhance marker-based predictions for an individual quantitative trait in molecular plant breeding. PMID:28729875

  7. ASTRAL, DRAGON and SEDAN scores predict stroke outcome more accurately than physicians.

    PubMed

    Ntaios, G; Gioulekas, F; Papavasileiou, V; Strbian, D; Michel, P

    2016-11-01

    ASTRAL, SEDAN and DRAGON scores are three well-validated scores for stroke outcome prediction. Whether these scores predict stroke outcome more accurately compared with physicians interested in stroke was investigated. Physicians interested in stroke were invited to an online anonymous survey to provide outcome estimates in randomly allocated structured scenarios of recent real-life stroke patients. Their estimates were compared to scores' predictions in the same scenarios. An estimate was considered accurate if it was within 95% confidence intervals of actual outcome. In all, 244 participants from 32 different countries responded assessing 720 real scenarios and 2636 outcomes. The majority of physicians' estimates were inaccurate (1422/2636, 53.9%). 400 (56.8%) of physicians' estimates about the percentage probability of 3-month modified Rankin score (mRS) > 2 were accurate compared with 609 (86.5%) of ASTRAL score estimates (P < 0.0001). 394 (61.2%) of physicians' estimates about the percentage probability of post-thrombolysis symptomatic intracranial haemorrhage were accurate compared with 583 (90.5%) of SEDAN score estimates (P < 0.0001). 160 (24.8%) of physicians' estimates about post-thrombolysis 3-month percentage probability of mRS 0-2 were accurate compared with 240 (37.3%) DRAGON score estimates (P < 0.0001). 260 (40.4%) of physicians' estimates about the percentage probability of post-thrombolysis mRS 5-6 were accurate compared with 518 (80.4%) DRAGON score estimates (P < 0.0001). ASTRAL, DRAGON and SEDAN scores predict outcome of acute ischaemic stroke patients with higher accuracy compared to physicians interested in stroke. © 2016 EAN.

  8. Simple prediction scores predict good and devastating outcomes after stroke more accurately than physicians.

    PubMed

    Reid, John Michael; Dai, Dingwei; Delmonte, Susanna; Counsell, Carl; Phillips, Stephen J; MacLeod, Mary Joan

    2017-05-01

    physicians are often asked to prognosticate soon after a patient presents with stroke. This study aimed to compare two outcome prediction scores (Five Simple Variables [FSV] score and the PLAN [Preadmission comorbidities, Level of consciousness, Age, and focal Neurologic deficit]) with informal prediction by physicians. demographic and clinical variables were prospectively collected from consecutive patients hospitalised with acute ischaemic or haemorrhagic stroke (2012-13). In-person or telephone follow-up at 6 months established vital and functional status (modified Rankin score [mRS]). Area under the receiver operating curves (AUC) was used to establish prediction score performance. five hundred and seventy-five patients were included; 46% female, median age 76 years, 88% ischaemic stroke. Six months after stroke, 47% of patients had a good outcome (alive and independent, mRS 0-2) and 26% a devastating outcome (dead or severely dependent, mRS 5-6). The FSV and PLAN scores were superior to physician prediction (AUCs of 0.823-0.863 versus 0.773-0.805, P < 0.0001) for good and devastating outcomes. The FSV score was superior to the PLAN score for predicting good outcomes and vice versa for devastating outcomes (P < 0.001). Outcome prediction was more accurate for those with later presentations (>24 hours from onset). the FSV and PLAN scores are validated in this population for outcome prediction after both ischaemic and haemorrhagic stroke. The FSV score is the least complex of all developed scores and can assist outcome prediction by physicians. © The Author 2016. Published by Oxford University Press on behalf of the British Geriatrics Society. All rights reserved. For permissions, please email: journals.permissions@oup.com

  9. Integrating metabolic performance, thermal tolerance, and plasticity enables for more accurate predictions on species vulnerability to acute and chronic effects of global warming.

    PubMed

    Magozzi, Sarah; Calosi, Piero

    2015-01-01

    Predicting species vulnerability to global warming requires a comprehensive, mechanistic understanding of sublethal and lethal thermal tolerances. To date, however, most studies investigating species physiological responses to increasing temperature have focused on the underlying physiological traits of either acute or chronic tolerance in isolation. Here we propose an integrative, synthetic approach including the investigation of multiple physiological traits (metabolic performance and thermal tolerance), and their plasticity, to provide more accurate and balanced predictions on species and assemblage vulnerability to both acute and chronic effects of global warming. We applied this approach to more accurately elucidate relative species vulnerability to warming within an assemblage of six caridean prawns occurring in the same geographic, hence macroclimatic, region, but living in different thermal habitats. Prawns were exposed to four incubation temperatures (10, 15, 20 and 25 °C) for 7 days, their metabolic rates and upper thermal limits were measured, and plasticity was calculated according to the concept of Reaction Norms, as well as Q10 for metabolism. Compared to species occupying narrower/more stable thermal niches, species inhabiting broader/more variable thermal environments (including the invasive Palaemon macrodactylus) are likely to be less vulnerable to extreme acute thermal events as a result of their higher upper thermal limits. Nevertheless, they may be at greater risk from chronic exposure to warming due to the greater metabolic costs they incur. Indeed, a trade-off between acute and chronic tolerance was apparent in the assemblage investigated. However, the invasive species P. macrodactylus represents an exception to this pattern, showing elevated thermal limits and plasticity of these limits, as well as a high metabolic control. In general, integrating multiple proxies for species physiological acute and chronic responses to increasing

  10. Predicting Microbial Fuel Cell Biofilm Communities and Bioreactor Performance using Artificial Neural Networks.

    PubMed

    Lesnik, Keaton Larson; Liu, Hong

    2017-09-19

    The complex interactions that occur in mixed-species bioelectrochemical reactors, like microbial fuel cells (MFCs), make accurate predictions of performance outcomes under untested conditions difficult. While direct correlations between any individual waste stream characteristic or microbial community structure and reactor performance have not been able to be directly established, the increase in sequencing data and readily available computational power enables the development of alternate approaches. In the current study, 33 MFCs were evaluated under a range of conditions including eight separate substrates and three different wastewaters. Artificial Neural Networks (ANNs) were used to establish mathematical relationships between wastewater/solution characteristics, biofilm communities, and reactor performance. ANN models that incorporated biotic interactions predicted reactor performance outcomes more accurately than those that did not. The average percent error of power density predictions was 16.01 ± 4.35%, while the average percent error of Coulombic efficiency and COD removal rate predictions were 1.77 ± 0.57% and 4.07 ± 1.06%, respectively. Predictions of power density improved to within 5.76 ± 3.16% percent error through classifying taxonomic data at the family versus class level. Results suggest that the microbial communities and performance of bioelectrochemical systems can be accurately predicted using data-mining, machine-learning techniques.

  11. Feedback about More Accurate versus Less Accurate Trials: Differential Effects on Self-Confidence and Activation

    ERIC Educational Resources Information Center

    Badami, Rokhsareh; VaezMousavi, Mohammad; Wulf, Gabriele; Namazizadeh, Mahdi

    2012-01-01

    One purpose of the present study was to examine whether self-confidence or anxiety would be differentially affected by feedback from more accurate rather than less accurate trials. The second purpose was to determine whether arousal variations (activation) would predict performance. On Day 1, participants performed a golf putting task under one of…

  12. Accurate and dynamic predictive model for better prediction in medicine and healthcare.

    PubMed

    Alanazi, H O; Abdullah, A H; Qureshi, K N; Ismail, A S

    2018-05-01

    Information and communication technologies (ICTs) have changed the trend into new integrated operations and methods in all fields of life. The health sector has also adopted new technologies to improve the systems and provide better services to customers. Predictive models in health care are also influenced from new technologies to predict the different disease outcomes. However, still, existing predictive models have suffered from some limitations in terms of predictive outcomes performance. In order to improve predictive model performance, this paper proposed a predictive model by classifying the disease predictions into different categories. To achieve this model performance, this paper uses traumatic brain injury (TBI) datasets. TBI is one of the serious diseases worldwide and needs more attention due to its seriousness and serious impacts on human life. The proposed predictive model improves the predictive performance of TBI. The TBI data set is developed and approved by neurologists to set its features. The experiment results show that the proposed model has achieved significant results including accuracy, sensitivity, and specificity.

  13. Accurate approximation method for prediction of class I MHC affinities for peptides of length 8, 10 and 11 using prediction tools trained on 9mers.

    PubMed

    Lundegaard, Claus; Lund, Ole; Nielsen, Morten

    2008-06-01

    Several accurate prediction systems have been developed for prediction of class I major histocompatibility complex (MHC):peptide binding. Most of these are trained on binding affinity data of primarily 9mer peptides. Here, we show how prediction methods trained on 9mer data can be used for accurate binding affinity prediction of peptides of length 8, 10 and 11. The method gives the opportunity to predict peptides with a different length than nine for MHC alleles where no such peptides have been measured. As validation, the performance of this approach is compared to predictors trained on peptides of the peptide length in question. In this validation, the approximation method has an accuracy that is comparable to or better than methods trained on a peptide length identical to the predicted peptides. The algorithm has been implemented in the web-accessible servers NetMHC-3.0: http://www.cbs.dtu.dk/services/NetMHC-3.0, and NetMHCpan-1.1: http://www.cbs.dtu.dk/services/NetMHCpan-1.1

  14. Improving medical decisions for incapacitated persons: does focusing on "accurate predictions" lead to an inaccurate picture?

    PubMed

    Kim, Scott Y H

    2014-04-01

    The Patient Preference Predictor (PPP) proposal places a high priority on the accuracy of predicting patients' preferences and finds the performance of surrogates inadequate. However, the quest to develop a highly accurate, individualized statistical model has significant obstacles. First, it will be impossible to validate the PPP beyond the limit imposed by 60%-80% reliability of people's preferences for future medical decisions--a figure no better than the known average accuracy of surrogates. Second, evidence supports the view that a sizable minority of persons may not even have preferences to predict. Third, many, perhaps most, people express their autonomy just as much by entrusting their loved ones to exercise their judgment than by desiring to specifically control future decisions. Surrogate decision making faces none of these issues and, in fact, it may be more efficient, accurate, and authoritative than is commonly assumed.

  15. Ensemble predictive model for more accurate soil organic carbon spectroscopic estimation

    NASA Astrophysics Data System (ADS)

    Vašát, Radim; Kodešová, Radka; Borůvka, Luboš

    2017-07-01

    A myriad of signal pre-processing strategies and multivariate calibration techniques has been explored in attempt to improve the spectroscopic prediction of soil organic carbon (SOC) over the last few decades. Therefore, to come up with a novel, more powerful, and accurate predictive approach to beat the rank becomes a challenging task. However, there may be a way, so that combine several individual predictions into a single final one (according to ensemble learning theory). As this approach performs best when combining in nature different predictive algorithms that are calibrated with structurally different predictor variables, we tested predictors of two different kinds: 1) reflectance values (or transforms) at each wavelength and 2) absorption feature parameters. Consequently we applied four different calibration techniques, two per each type of predictors: a) partial least squares regression and support vector machines for type 1, and b) multiple linear regression and random forest for type 2. The weights to be assigned to individual predictions within the ensemble model (constructed as a weighted average) were determined by an automated procedure that ensured the best solution among all possible was selected. The approach was tested at soil samples taken from surface horizon of four sites differing in the prevailing soil units. By employing the ensemble predictive model the prediction accuracy of SOC improved at all four sites. The coefficient of determination in cross-validation (R2cv) increased from 0.849, 0.611, 0.811 and 0.644 (the best individual predictions) to 0.864, 0.650, 0.824 and 0.698 for Site 1, 2, 3 and 4, respectively. Generally, the ensemble model affected the final prediction so that the maximal deviations of predicted vs. observed values of the individual predictions were reduced, and thus the correlation cloud became thinner as desired.

  16. Predicting Visual Distraction Using Driving Performance Data

    PubMed Central

    Kircher, Katja; Ahlstrom, Christer

    2010-01-01

    Behavioral variables are often used as performance indicators (PIs) of visual or internal distraction induced by secondary tasks. The objective of this study is to investigate whether visual distraction can be predicted by driving performance PIs in a naturalistic setting. Visual distraction is here defined by a gaze based real-time distraction detection algorithm called AttenD. Seven drivers used an instrumented vehicle for one month each in a small scale field operational test. For each of the visual distraction events detected by AttenD, seven PIs such as steering wheel reversal rate and throttle hold were calculated. Corresponding data were also calculated for time periods during which the drivers were classified as attentive. For each PI, means between distracted and attentive states were calculated using t-tests for different time-window sizes (2 – 40 s), and the window width with the smallest resulting p-value was selected as optimal. Based on the optimized PIs, logistic regression was used to predict whether the drivers were attentive or distracted. The logistic regression resulted in predictions which were 76 % correct (sensitivity = 77 % and specificity = 76 %). The conclusion is that there is a relationship between behavioral variables and visual distraction, but the relationship is not strong enough to accurately predict visual driver distraction. Instead, behavioral PIs are probably best suited as complementary to eye tracking based algorithms in order to make them more accurate and robust. PMID:21050615

  17. Towards First Principles-Based Prediction of Highly Accurate Electrochemical Pourbaix Diagrams

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

    Zeng, Zhenhua; Chan, Maria K. Y.; Zhao, Zhi-Jian

    2015-08-13

    Electrochemical potential/pH (Pourbaix) diagrams underpin many aqueous electrochemical processes and are central to the identification of stable phases of metals for processes ranging from electrocatalysis to corrosion. Even though standard DFT calculations are potentially powerful tools for the prediction of such diagrams, inherent errors in the description of transition metal (hydroxy)oxides, together with neglect of van der Waals interactions, have limited the reliability of such predictions for even the simplest pure metal bulk compounds, and corresponding predictions for more complex alloy or surface structures are even more challenging. In the present work, through synergistic use of a Hubbard U correction,more » a state-of-the-art dispersion correction, and a water-based bulk reference state for the calculations, these errors are systematically corrected. The approach describes the weak binding that occurs between hydroxyl-containing functional groups in certain compounds in Pourbaix diagrams, corrects for self-interaction errors in transition metal compounds, and reduces residual errors on oxygen atoms by preserving a consistent oxidation state between the reference state, water, and the relevant bulk phases. The strong performance is illustrated on a series of bulk transition metal (Mn, Fe, Co and Ni) hydroxides, oxyhydroxides, binary, and ternary oxides, where the corresponding thermodynamics of redox and (de)hydration are described with standard errors of 0.04 eV per (reaction) formula unit. The approach further preserves accurate descriptions of the overall thermodynamics of electrochemically-relevant bulk reactions, such as water formation, which is an essential condition for facilitating accurate analysis of reaction energies for electrochemical processes on surfaces. The overall generality and transferability of the scheme suggests that it may find useful application in the construction of a broad array of electrochemical phase diagrams, including

  18. Accurate Prediction of Motor Failures by Application of Multi CBM Tools: A Case Study

    NASA Astrophysics Data System (ADS)

    Dutta, Rana; Singh, Veerendra Pratap; Dwivedi, Jai Prakash

    2018-02-01

    Motor failures are very difficult to predict accurately with a single condition-monitoring tool as both electrical and the mechanical systems are closely related. Electrical problem, like phase unbalance, stator winding insulation failures can, at times, lead to vibration problem and at the same time mechanical failures like bearing failure, leads to rotor eccentricity. In this case study of a 550 kW blower motor it has been shown that a rotor bar crack was detected by current signature analysis and vibration monitoring confirmed the same. In later months in a similar motor vibration monitoring predicted bearing failure and current signature analysis confirmed the same. In both the cases, after dismantling the motor, the predictions were found to be accurate. In this paper we will be discussing the accurate predictions of motor failures through use of multi condition monitoring tools with two case studies.

  19. ChIP-seq Accurately Predicts Tissue-Specific Activity of Enhancers

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

    Visel, Axel; Blow, Matthew J.; Li, Zirong

    2009-02-01

    A major yet unresolved quest in decoding the human genome is the identification of the regulatory sequences that control the spatial and temporal expression of genes. Distant-acting transcriptional enhancers are particularly challenging to uncover since they are scattered amongst the vast non-coding portion of the genome. Evolutionary sequence constraint can facilitate the discovery of enhancers, but fails to predict when and where they are active in vivo. Here, we performed chromatin immunoprecipitation with the enhancer-associated protein p300, followed by massively-parallel sequencing, to map several thousand in vivo binding sites of p300 in mouse embryonic forebrain, midbrain, and limb tissue. Wemore » tested 86 of these sequences in a transgenic mouse assay, which in nearly all cases revealed reproducible enhancer activity in those tissues predicted by p300 binding. Our results indicate that in vivo mapping of p300 binding is a highly accurate means for identifying enhancers and their associated activities and suggest that such datasets will be useful to study the role of tissue-specific enhancers in human biology and disease on a genome-wide scale.« less

  20. Radiomics biomarkers for accurate tumor progression prediction of oropharyngeal cancer

    NASA Astrophysics Data System (ADS)

    Hadjiiski, Lubomir; Chan, Heang-Ping; Cha, Kenny H.; Srinivasan, Ashok; Wei, Jun; Zhou, Chuan; Prince, Mark; Papagerakis, Silvana

    2017-03-01

    Accurate tumor progression prediction for oropharyngeal cancers is crucial for identifying patients who would best be treated with optimized treatment and therefore minimize the risk of under- or over-treatment. An objective decision support system that can merge the available radiomics, histopathologic and molecular biomarkers in a predictive model based on statistical outcomes of previous cases and machine learning may assist clinicians in making more accurate assessment of oropharyngeal tumor progression. In this study, we evaluated the feasibility of developing individual and combined predictive models based on quantitative image analysis from radiomics, histopathology and molecular biomarkers for oropharyngeal tumor progression prediction. With IRB approval, 31, 84, and 127 patients with head and neck CT (CT-HN), tumor tissue microarrays (TMAs) and molecular biomarker expressions, respectively, were collected. For 8 of the patients all 3 types of biomarkers were available and they were sequestered in a test set. The CT-HN lesions were automatically segmented using our level sets based method. Morphological, texture and molecular based features were extracted from CT-HN and TMA images, and selected features were merged by a neural network. The classification accuracy was quantified using the area under the ROC curve (AUC). Test AUCs of 0.87, 0.74, and 0.71 were obtained with the individual predictive models based on radiomics, histopathologic, and molecular features, respectively. Combining the radiomics and molecular models increased the test AUC to 0.90. Combining all 3 models increased the test AUC further to 0.94. This preliminary study demonstrates that the individual domains of biomarkers are useful and the integrated multi-domain approach is most promising for tumor progression prediction.

  1. User's Self-Prediction of Performance in Motor Imagery Brain-Computer Interface.

    PubMed

    Ahn, Minkyu; Cho, Hohyun; Ahn, Sangtae; Jun, Sung C

    2018-01-01

    Performance variation is a critical issue in motor imagery brain-computer interface (MI-BCI), and various neurophysiological, psychological, and anatomical correlates have been reported in the literature. Although the main aim of such studies is to predict MI-BCI performance for the prescreening of poor performers, studies which focus on the user's sense of the motor imagery process and directly estimate MI-BCI performance through the user's self-prediction are lacking. In this study, we first test each user's self-prediction idea regarding motor imagery experimental datasets. Fifty-two subjects participated in a classical, two-class motor imagery experiment and were asked to evaluate their easiness with motor imagery and to predict their own MI-BCI performance. During the motor imagery experiment, an electroencephalogram (EEG) was recorded; however, no feedback on motor imagery was given to subjects. From EEG recordings, the offline classification accuracy was estimated and compared with several questionnaire scores of subjects, as well as with each subject's self-prediction of MI-BCI performance. The subjects' performance predictions during motor imagery task showed a high positive correlation ( r = 0.64, p < 0.01). Interestingly, it was observed that the self-prediction became more accurate as the subjects conducted more motor imagery tasks in the Correlation coefficient (pre-task to 2nd run: r = 0.02 to r = 0.54, p < 0.01) and root mean square error (pre-task to 3rd run: 17.7% to 10%, p < 0.01). We demonstrated that subjects may accurately predict their MI-BCI performance even without feedback information. This implies that the human brain is an active learning system and, by self-experiencing the endogenous motor imagery process, it can sense and adopt the quality of the process. Thus, it is believed that users may be able to predict MI-BCI performance and results may contribute to a better understanding of low performance and advancing BCI.

  2. Measured and predicted rotor performance for the SERI advanced wind turbine blades

    NASA Astrophysics Data System (ADS)

    Tangler, J.; Smith, B.; Kelley, N.; Jager, D.

    1992-02-01

    Measured and predicted rotor performance for the Solar Energy Research Institute (SERI) advanced wind turbine blades were compared to assess the accuracy of predictions and to identify the sources of error affecting both predictions and measurements. An awareness of these sources of error contributes to improved prediction and measurement methods that will ultimately benefit future rotor design efforts. Propeller/vane anemometers were found to underestimate the wind speed in turbulent environments such as the San Gorgonio Pass wind farm area. Using sonic or cup anemometers, good agreement was achieved between predicted and measured power output for wind speeds up to 8 m/sec. At higher wind speeds an optimistic predicted power output and the occurrence of peak power at wind speeds lower than measurements resulted from the omission of turbulence and yaw error. In addition, accurate two-dimensional (2-D) airfoil data prior to stall and a post stall airfoil data synthesization method that reflects three-dimensional (3-D) effects were found to be essential for accurate performance prediction.

  3. How accurate are resting energy expenditure prediction equations in obese trauma and burn patients?

    PubMed

    Stucky, Chee-Chee H; Moncure, Michael; Hise, Mary; Gossage, Clint M; Northrop, David

    2008-01-01

    While the prevalence of obesity continues to increase in our society, outdated resting energy expenditure (REE) prediction equations may overpredict energy requirements in obese patients. Accurate feeding is essential since overfeeding has been demonstrated to adversely affect outcomes. The first objective was to compare REE calculated by prediction equations to the measured REE in obese trauma and burn patients. Our hypothesis was that an equation using fat-free mass would give a more accurate prediction. The second objective was to consider the effect of a commonly used injury factor on the predicted REE. A retrospective chart review was performed on 28 patients. REE was measured using indirect calorimetry and compared with the Harris-Benedict and Cunningham equations, and an equation using type II diabetes as a factor. Statistical analyses used were paired t test, +/-95% confidence interval, and the Bland-Altman method. Measured average REE in trauma and burn patients was 21.37 +/- 5.26 and 21.81 +/- 3.35 kcal/kg/d, respectively. Harris-Benedict underpredicted REE in trauma and burn patients to the least extent, while the Cunningham equation underpredicted REE in both populations to the greatest extent. Using an injury factor of 1.2, Cunningham continued to underestimate REE in both populations, while the Harris-Benedict and Diabetic equations overpredicted REE in both populations. The measured average REE is significantly less than current guidelines. This finding suggests that a hypocaloric regimen is worth considering for ICU patients. Also, if an injury factor of 1.2 is incorporated in certain equations, patients may be given too many calories.

  4. Modeling methodology for the accurate and prompt prediction of symptomatic events in chronic diseases.

    PubMed

    Pagán, Josué; Risco-Martín, José L; Moya, José M; Ayala, José L

    2016-08-01

    Prediction of symptomatic crises in chronic diseases allows to take decisions before the symptoms occur, such as the intake of drugs to avoid the symptoms or the activation of medical alarms. The prediction horizon is in this case an important parameter in order to fulfill the pharmacokinetics of medications, or the time response of medical services. This paper presents a study about the prediction limits of a chronic disease with symptomatic crises: the migraine. For that purpose, this work develops a methodology to build predictive migraine models and to improve these predictions beyond the limits of the initial models. The maximum prediction horizon is analyzed, and its dependency on the selected features is studied. A strategy for model selection is proposed to tackle the trade off between conservative but robust predictive models, with respect to less accurate predictions with higher horizons. The obtained results show a prediction horizon close to 40min, which is in the time range of the drug pharmacokinetics. Experiments have been performed in a realistic scenario where input data have been acquired in an ambulatory clinical study by the deployment of a non-intrusive Wireless Body Sensor Network. Our results provide an effective methodology for the selection of the future horizon in the development of prediction algorithms for diseases experiencing symptomatic crises. Copyright © 2016 Elsevier Inc. All rights reserved.

  5. Proactive Supply Chain Performance Management with Predictive Analytics

    PubMed Central

    Stefanovic, Nenad

    2014-01-01

    Today's business climate requires supply chains to be proactive rather than reactive, which demands a new approach that incorporates data mining predictive analytics. This paper introduces a predictive supply chain performance management model which combines process modelling, performance measurement, data mining models, and web portal technologies into a unique model. It presents the supply chain modelling approach based on the specialized metamodel which allows modelling of any supply chain configuration and at different level of details. The paper also presents the supply chain semantic business intelligence (BI) model which encapsulates data sources and business rules and includes the data warehouse model with specific supply chain dimensions, measures, and KPIs (key performance indicators). Next, the paper describes two generic approaches for designing the KPI predictive data mining models based on the BI semantic model. KPI predictive models were trained and tested with a real-world data set. Finally, a specialized analytical web portal which offers collaborative performance monitoring and decision making is presented. The results show that these models give very accurate KPI projections and provide valuable insights into newly emerging trends, opportunities, and problems. This should lead to more intelligent, predictive, and responsive supply chains capable of adapting to future business environment. PMID:25386605

  6. Proactive supply chain performance management with predictive analytics.

    PubMed

    Stefanovic, Nenad

    2014-01-01

    Today's business climate requires supply chains to be proactive rather than reactive, which demands a new approach that incorporates data mining predictive analytics. This paper introduces a predictive supply chain performance management model which combines process modelling, performance measurement, data mining models, and web portal technologies into a unique model. It presents the supply chain modelling approach based on the specialized metamodel which allows modelling of any supply chain configuration and at different level of details. The paper also presents the supply chain semantic business intelligence (BI) model which encapsulates data sources and business rules and includes the data warehouse model with specific supply chain dimensions, measures, and KPIs (key performance indicators). Next, the paper describes two generic approaches for designing the KPI predictive data mining models based on the BI semantic model. KPI predictive models were trained and tested with a real-world data set. Finally, a specialized analytical web portal which offers collaborative performance monitoring and decision making is presented. The results show that these models give very accurate KPI projections and provide valuable insights into newly emerging trends, opportunities, and problems. This should lead to more intelligent, predictive, and responsive supply chains capable of adapting to future business environment.

  7. 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.

  8. Accurate and robust genomic prediction of celiac disease using statistical learning.

    PubMed

    Abraham, Gad; Tye-Din, Jason A; Bhalala, Oneil G; Kowalczyk, Adam; Zobel, Justin; Inouye, Michael

    2014-02-01

    Practical application of genomic-based risk stratification to clinical diagnosis is appealing yet performance varies widely depending on the disease and genomic risk score (GRS) method. Celiac disease (CD), a common immune-mediated illness, is strongly genetically determined and requires specific HLA haplotypes. HLA testing can exclude diagnosis but has low specificity, providing little information suitable for clinical risk stratification. Using six European cohorts, we provide a proof-of-concept that statistical learning approaches which simultaneously model all SNPs can generate robust and highly accurate predictive models of CD based on genome-wide SNP profiles. The high predictive capacity replicated both in cross-validation within each cohort (AUC of 0.87-0.89) and in independent replication across cohorts (AUC of 0.86-0.9), despite differences in ethnicity. The models explained 30-35% of disease variance and up to ∼43% of heritability. The GRS's utility was assessed in different clinically relevant settings. Comparable to HLA typing, the GRS can be used to identify individuals without CD with ≥99.6% negative predictive value however, unlike HLA typing, fine-scale stratification of individuals into categories of higher-risk for CD can identify those that would benefit from more invasive and costly definitive testing. The GRS is flexible and its performance can be adapted to the clinical situation by adjusting the threshold cut-off. Despite explaining a minority of disease heritability, our findings indicate a genomic risk score provides clinically relevant information to improve upon current diagnostic pathways for CD and support further studies evaluating the clinical utility of this approach in CD and other complex diseases.

  9. A Unified Model of Performance: Validation of its Predictions across Different Sleep/Wake Schedules.

    PubMed

    Ramakrishnan, Sridhar; Wesensten, Nancy J; Balkin, Thomas J; Reifman, Jaques

    2016-01-01

    Historically, mathematical models of human neurobehavioral performance developed on data from one sleep study were limited to predicting performance in similar studies, restricting their practical utility. We recently developed a unified model of performance (UMP) to predict the effects of the continuum of sleep loss-from chronic sleep restriction (CSR) to total sleep deprivation (TSD) challenges-and validated it using data from two studies of one laboratory. Here, we significantly extended this effort by validating the UMP predictions across a wide range of sleep/wake schedules from different studies and laboratories. We developed the UMP on psychomotor vigilance task (PVT) lapse data from one study encompassing four different CSR conditions (7 d of 3, 5, 7, and 9 h of sleep/night), and predicted performance in five other studies (from four laboratories), including different combinations of TSD (40 to 88 h), CSR (2 to 6 h of sleep/night), control (8 to 10 h of sleep/night), and nap (nocturnal and diurnal) schedules. The UMP accurately predicted PVT performance trends across 14 different sleep/wake conditions, yielding average prediction errors between 7% and 36%, with the predictions lying within 2 standard errors of the measured data 87% of the time. In addition, the UMP accurately predicted performance impairment (average error of 15%) for schedules (TSD and naps) not used in model development. The unified model of performance can be used as a tool to help design sleep/wake schedules to optimize the extent and duration of neurobehavioral performance and to accelerate recovery after sleep loss. © 2016 Associated Professional Sleep Societies, LLC.

  10. Accurate prediction of bacterial type IV secreted effectors using amino acid composition and PSSM profiles.

    PubMed

    Zou, Lingyun; Nan, Chonghan; Hu, Fuquan

    2013-12-15

    Various human pathogens secret effector proteins into hosts cells via the type IV secretion system (T4SS). These proteins play important roles in the interaction between bacteria and hosts. Computational methods for T4SS effector prediction have been developed for screening experimental targets in several isolated bacterial species; however, widely applicable prediction approaches are still unavailable In this work, four types of distinctive features, namely, amino acid composition, dipeptide composition, .position-specific scoring matrix composition and auto covariance transformation of position-specific scoring matrix, were calculated from primary sequences. A classifier, T4EffPred, was developed using the support vector machine with these features and their different combinations for effector prediction. Various theoretical tests were performed in a newly established dataset, and the results were measured with four indexes. We demonstrated that T4EffPred can discriminate IVA and IVB effectors in benchmark datasets with positive rates of 76.7% and 89.7%, respectively. The overall accuracy of 95.9% shows that the present method is accurate for distinguishing the T4SS effector in unidentified sequences. A classifier ensemble was designed to synthesize all single classifiers. Notable performance improvement was observed using this ensemble system in benchmark tests. To demonstrate the model's application, a genome-scale prediction of effectors was performed in Bartonella henselae, an important zoonotic pathogen. A number of putative candidates were distinguished. A web server implementing the prediction method and the source code are both available at http://bioinfo.tmmu.edu.cn/T4EffPred.

  11. 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

  12. Accurate prediction of protein–protein interactions from sequence alignments using a Bayesian method

    PubMed Central

    Burger, Lukas; van Nimwegen, Erik

    2008-01-01

    Accurate and large-scale prediction of protein–protein interactions directly from amino-acid sequences is one of the great challenges in computational biology. Here we present a new Bayesian network method that predicts interaction partners using only multiple alignments of amino-acid sequences of interacting protein domains, without tunable parameters, and without the need for any training examples. We first apply the method to bacterial two-component systems and comprehensively reconstruct two-component signaling networks across all sequenced bacteria. Comparisons of our predictions with known interactions show that our method infers interaction partners genome-wide with high accuracy. To demonstrate the general applicability of our method we show that it also accurately predicts interaction partners in a recent dataset of polyketide synthases. Analysis of the predicted genome-wide two-component signaling networks shows that cognates (interacting kinase/regulator pairs, which lie adjacent on the genome) and orphans (which lie isolated) form two relatively independent components of the signaling network in each genome. In addition, while most genes are predicted to have only a small number of interaction partners, we find that 10% of orphans form a separate class of ‘hub' nodes that distribute and integrate signals to and from up to tens of different interaction partners. PMID:18277381

  13. A Novel Method for Accurate Operon Predictions in All SequencedProkaryotes

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

    Price, Morgan N.; Huang, Katherine H.; Alm, Eric J.

    2004-12-01

    We combine comparative genomic measures and the distance separating adjacent genes to predict operons in 124 completely sequenced prokaryotic genomes. Our method automatically tailors itself to each genome using sequence information alone, and thus can be applied to any prokaryote. For Escherichia coli K12 and Bacillus subtilis, our method is 85 and 83% accurate, respectively, which is similar to the accuracy of methods that use the same features but are trained on experimentally characterized transcripts. In Halobacterium NRC-1 and in Helicobacterpylori, our method correctly infers that genes in operons are separated by shorter distances than they are in E.coli, andmore » its predictions using distance alone are more accurate than distance-only predictions trained on a database of E.coli transcripts. We use microarray data from sixphylogenetically diverse prokaryotes to show that combining intergenic distance with comparative genomic measures further improves accuracy and that our method is broadly effective. Finally, we survey operon structure across 124 genomes, and find several surprises: H.pylori has many operons, contrary to previous reports; Bacillus anthracis has an unusual number of pseudogenes within conserved operons; and Synechocystis PCC6803 has many operons even though it has unusually wide spacings between conserved adjacent genes.« less

  14. Predicting High-Power Performance in Professional Cyclists.

    PubMed

    Sanders, Dajo; Heijboer, Mathieu; Akubat, Ibrahim; Meijer, Kenneth; Hesselink, Matthijs K

    2017-03-01

    To assess if short-duration (5 to ~300 s) high-power performance can accurately be predicted using the anaerobic power reserve (APR) model in professional cyclists. Data from 4 professional cyclists from a World Tour cycling team were used. Using the maximal aerobic power, sprint peak power output, and an exponential constant describing the decrement in power over time, a power-duration relationship was established for each participant. To test the predictive accuracy of the model, several all-out field trials of different durations were performed by each cyclist. The power output achieved during the all-out trials was compared with the predicted power output by the APR model. The power output predicted by the model showed very large to nearly perfect correlations to the actual power output obtained during the all-out trials for each cyclist (r = .88 ± .21, .92 ± .17, .95 ± .13, and .97 ± .09). Power output during the all-out trials remained within an average of 6.6% (53 W) of the predicted power output by the model. This preliminary pilot study presents 4 case studies on the applicability of the APR model in professional cyclists using a field-based approach. The decrement in all-out performance during high-intensity exercise seems to conform to a general relationship with a single exponential-decay model describing the decrement in power vs increasing duration. These results are in line with previous studies using the APR model to predict performance during brief all-out trials. Future research should evaluate the APR model with a larger sample size of elite cyclists.

  15. Numerical simulation of turbulence flow in a Kaplan turbine -Evaluation on turbine performance prediction accuracy-

    NASA Astrophysics Data System (ADS)

    Ko, P.; Kurosawa, S.

    2014-03-01

    The understanding and accurate prediction of the flow behaviour related to cavitation and pressure fluctuation in a Kaplan turbine are important to the design work enhancing the turbine performance including the elongation of the operation life span and the improvement of turbine efficiency. In this paper, high accuracy turbine and cavitation performance prediction method based on entire flow passage for a Kaplan turbine is presented and evaluated. Two-phase flow field is predicted by solving Reynolds-Averaged Navier-Stokes equations expressed by volume of fluid method tracking the free surface and combined with Reynolds Stress model. The growth and collapse of cavitation bubbles are modelled by the modified Rayleigh-Plesset equation. The prediction accuracy is evaluated by comparing with the model test results of Ns 400 Kaplan model turbine. As a result that the experimentally measured data including turbine efficiency, cavitation performance, and pressure fluctuation are accurately predicted. Furthermore, the cavitation occurrence on the runner blade surface and the influence to the hydraulic loss of the flow passage are discussed. Evaluated prediction method for the turbine flow and performance is introduced to facilitate the future design and research works on Kaplan type turbine.

  16. A Unified Model of Performance: Validation of its Predictions across Different Sleep/Wake Schedules

    PubMed Central

    Ramakrishnan, Sridhar; Wesensten, Nancy J.; Balkin, Thomas J.; Reifman, Jaques

    2016-01-01

    Study Objectives: Historically, mathematical models of human neurobehavioral performance developed on data from one sleep study were limited to predicting performance in similar studies, restricting their practical utility. We recently developed a unified model of performance (UMP) to predict the effects of the continuum of sleep loss—from chronic sleep restriction (CSR) to total sleep deprivation (TSD) challenges—and validated it using data from two studies of one laboratory. Here, we significantly extended this effort by validating the UMP predictions across a wide range of sleep/wake schedules from different studies and laboratories. Methods: We developed the UMP on psychomotor vigilance task (PVT) lapse data from one study encompassing four different CSR conditions (7 d of 3, 5, 7, and 9 h of sleep/night), and predicted performance in five other studies (from four laboratories), including different combinations of TSD (40 to 88 h), CSR (2 to 6 h of sleep/night), control (8 to 10 h of sleep/night), and nap (nocturnal and diurnal) schedules. Results: The UMP accurately predicted PVT performance trends across 14 different sleep/wake conditions, yielding average prediction errors between 7% and 36%, with the predictions lying within 2 standard errors of the measured data 87% of the time. In addition, the UMP accurately predicted performance impairment (average error of 15%) for schedules (TSD and naps) not used in model development. Conclusions: The unified model of performance can be used as a tool to help design sleep/wake schedules to optimize the extent and duration of neurobehavioral performance and to accelerate recovery after sleep loss. Citation: Ramakrishnan S, Wesensten NJ, Balkin TJ, Reifman J. A unified model of performance: validation of its predictions across different sleep/wake schedules. SLEEP 2016;39(1):249–262. PMID:26518594

  17. Are EMS call volume predictions based on demand pattern analysis accurate?

    PubMed

    Brown, Lawrence H; Lerner, E Brooke; Larmon, Baxter; LeGassick, Todd; Taigman, Michael

    2007-01-01

    Most EMS systems determine the number of crews they will deploy in their communities and when those crews will be scheduled based on anticipated call volumes. Many systems use historical data to calculate their anticipated call volumes, a method of prediction known as demand pattern analysis. To evaluate the accuracy of call volume predictions calculated using demand pattern analysis. Seven EMS systems provided 73 consecutive weeks of hourly call volume data. The first 20 weeks of data were used to calculate three common demand pattern analysis constructs for call volume prediction: average peak demand (AP), smoothed average peak demand (SAP), and 90th percentile rank (90%R). The 21st week served as a buffer. Actual call volumes in the last 52 weeks were then compared to the predicted call volumes by using descriptive statistics. There were 61,152 hourly observations in the test period. All three constructs accurately predicted peaks and troughs in call volume but not exact call volume. Predictions were accurate (+/-1 call) 13% of the time using AP, 10% using SAP, and 19% using 90%R. Call volumes were overestimated 83% of the time using AP, 86% using SAP, and 74% using 90%R. When call volumes were overestimated, predictions exceeded actual call volume by a median (Interquartile range) of 4 (2-6) calls for AP, 4 (2-6) for SAP, and 3 (2-5) for 90%R. Call volumes were underestimated 4% of time using AP, 4% using SAP, and 7% using 90%R predictions. When call volumes were underestimated, call volumes exceeded predictions by a median (Interquartile range; maximum under estimation) of 1 (1-2; 18) call for AP, 1 (1-2; 18) for SAP, and 2 (1-3; 20) for 90%R. Results did not vary between systems. Generally, demand pattern analysis estimated or overestimated call volume, making it a reasonable predictor for ambulance staffing patterns. However, it did underestimate call volume between 4% and 7% of the time. Communities need to determine if these rates of over

  18. Rapid and accurate prediction and scoring of water molecules in protein binding sites.

    PubMed

    Ross, Gregory A; Morris, Garrett M; Biggin, Philip C

    2012-01-01

    Water plays a critical role in ligand-protein interactions. However, it is still challenging to predict accurately not only where water molecules prefer to bind, but also which of those water molecules might be displaceable. The latter is often seen as a route to optimizing affinity of potential drug candidates. Using a protocol we call WaterDock, we show that the freely available AutoDock Vina tool can be used to predict accurately the binding sites of water molecules. WaterDock was validated using data from X-ray crystallography, neutron diffraction and molecular dynamics simulations and correctly predicted 97% of the water molecules in the test set. In addition, we combined data-mining, heuristic and machine learning techniques to develop probabilistic water molecule classifiers. When applied to WaterDock predictions in the Astex Diverse Set of protein ligand complexes, we could identify whether a water molecule was conserved or displaced to an accuracy of 75%. A second model predicted whether water molecules were displaced by polar groups or by non-polar groups to an accuracy of 80%. These results should prove useful for anyone wishing to undertake rational design of new compounds where the displacement of water molecules is being considered as a route to improved affinity.

  19. A Unified Model of Performance for Predicting the Effects of Sleep and Caffeine.

    PubMed

    Ramakrishnan, Sridhar; Wesensten, Nancy J; Kamimori, Gary H; Moon, James E; Balkin, Thomas J; Reifman, Jaques

    2016-10-01

    Existing mathematical models of neurobehavioral performance cannot predict the beneficial effects of caffeine across the spectrum of sleep loss conditions, limiting their practical utility. Here, we closed this research gap by integrating a model of caffeine effects with the recently validated unified model of performance (UMP) into a single, unified modeling framework. We then assessed the accuracy of this new UMP in predicting performance across multiple studies. We hypothesized that the pharmacodynamics of caffeine vary similarly during both wakefulness and sleep, and that caffeine has a multiplicative effect on performance. Accordingly, to represent the effects of caffeine in the UMP, we multiplied a dose-dependent caffeine factor (which accounts for the pharmacokinetics and pharmacodynamics of caffeine) to the performance estimated in the absence of caffeine. We assessed the UMP predictions in 14 distinct laboratory- and field-study conditions, including 7 different sleep-loss schedules (from 5 h of sleep per night to continuous sleep loss for 85 h) and 6 different caffeine doses (from placebo to repeated 200 mg doses to a single dose of 600 mg). The UMP accurately predicted group-average psychomotor vigilance task performance data across the different sleep loss and caffeine conditions (6% < error < 27%), yielding greater accuracy for mild and moderate sleep loss conditions than for more severe cases. Overall, accounting for the effects of caffeine resulted in improved predictions (after caffeine consumption) by up to 70%. The UMP provides the first comprehensive tool for accurate selection of combinations of sleep schedules and caffeine countermeasure strategies to optimize neurobehavioral performance. © 2016 Associated Professional Sleep Societies, LLC.

  20. Predicting Team Performance through Human Behavioral Sensing and Quantitative Workflow Instrumentation

    DTIC Science & Technology

    2016-07-27

    make risk-informed decisions during serious games . Statistical models of intra- game performance were developed to determine whether behaviors in...specific facets of the gameplay workflow were predictive of analytical performance and games outcomes. A study of over seventy instrumented teams revealed...more accurate game decisions. 2 Keywords: Humatics · Serious Games · Human-System Interaction · Instrumentation · Teamwork · Communication Analysis

  1. Blinded by Beauty: Attractiveness Bias and Accurate Perceptions of Academic Performance.

    PubMed

    Talamas, Sean N; Mavor, Kenneth I; Perrett, David I

    2016-01-01

    Despite the old adage not to 'judge a book by its cover', facial cues often guide first impressions and these first impressions guide our decisions. Literature suggests there are valid facial cues that assist us in assessing someone's health or intelligence, but such cues are overshadowed by an 'attractiveness halo' whereby desirable attributions are preferentially ascribed to attractive people. The impact of the attractiveness halo effect on perceptions of academic performance in the classroom is concerning as this has shown to influence students' future performance. We investigated the limiting effects of the attractiveness halo on perceptions of actual academic performance in faces of 100 university students. Given the ambiguity and various perspectives on the definition of intelligence and the growing consensus on the importance of conscientiousness over intelligence in predicting actual academic performance, we also investigated whether perceived conscientiousness was a more accurate predictor of academic performance than perceived intelligence. Perceived conscientiousness was found to be a better predictor of actual academic performance when compared to perceived intelligence and perceived academic performance, and accuracy was improved when controlling for the influence of attractiveness on judgments. These findings emphasize the misleading effect of attractiveness on the accuracy of first impressions of competence, which can have serious consequences in areas such as education and hiring. The findings also have implications for future research investigating impression accuracy based on facial stimuli.

  2. A Unified Model of Performance for Predicting the Effects of Sleep and Caffeine

    PubMed Central

    Ramakrishnan, Sridhar; Wesensten, Nancy J.; Kamimori, Gary H.; Moon, James E.; Balkin, Thomas J.; Reifman, Jaques

    2016-01-01

    Study Objectives: Existing mathematical models of neurobehavioral performance cannot predict the beneficial effects of caffeine across the spectrum of sleep loss conditions, limiting their practical utility. Here, we closed this research gap by integrating a model of caffeine effects with the recently validated unified model of performance (UMP) into a single, unified modeling framework. We then assessed the accuracy of this new UMP in predicting performance across multiple studies. Methods: We hypothesized that the pharmacodynamics of caffeine vary similarly during both wakefulness and sleep, and that caffeine has a multiplicative effect on performance. Accordingly, to represent the effects of caffeine in the UMP, we multiplied a dose-dependent caffeine factor (which accounts for the pharmacokinetics and pharmacodynamics of caffeine) to the performance estimated in the absence of caffeine. We assessed the UMP predictions in 14 distinct laboratory- and field-study conditions, including 7 different sleep-loss schedules (from 5 h of sleep per night to continuous sleep loss for 85 h) and 6 different caffeine doses (from placebo to repeated 200 mg doses to a single dose of 600 mg). Results: The UMP accurately predicted group-average psychomotor vigilance task performance data across the different sleep loss and caffeine conditions (6% < error < 27%), yielding greater accuracy for mild and moderate sleep loss conditions than for more severe cases. Overall, accounting for the effects of caffeine resulted in improved predictions (after caffeine consumption) by up to 70%. Conclusions: The UMP provides the first comprehensive tool for accurate selection of combinations of sleep schedules and caffeine countermeasure strategies to optimize neurobehavioral performance. Citation: Ramakrishnan S, Wesensten NJ, Kamimori GH, Moon JE, Balkin TJ, Reifman J. A unified model of performance for predicting the effects of sleep and caffeine. SLEEP 2016;39(10):1827–1841. PMID:27397562

  3. Accurate prediction of secondary metabolite gene clusters in filamentous fungi.

    PubMed

    Andersen, Mikael R; Nielsen, Jakob B; Klitgaard, Andreas; Petersen, Lene M; Zachariasen, Mia; Hansen, Tilde J; Blicher, Lene H; Gotfredsen, Charlotte H; Larsen, Thomas O; Nielsen, Kristian F; Mortensen, Uffe H

    2013-01-02

    Biosynthetic pathways of secondary metabolites from fungi are currently subject to an intense effort to elucidate the genetic basis for these compounds due to their large potential within pharmaceutics and synthetic biochemistry. The preferred method is methodical gene deletions to identify supporting enzymes for key synthases one cluster at a time. In this study, we design and apply a DNA expression array for Aspergillus nidulans in combination with legacy data to form a comprehensive gene expression compendium. We apply a guilt-by-association-based analysis to predict the extent of the biosynthetic clusters for the 58 synthases active in our set of experimental conditions. A comparison with legacy data shows the method to be accurate in 13 of 16 known clusters and nearly accurate for the remaining 3 clusters. Furthermore, we apply a data clustering approach, which identifies cross-chemistry between physically separate gene clusters (superclusters), and validate this both with legacy data and experimentally by prediction and verification of a supercluster consisting of the synthase AN1242 and the prenyltransferase AN11080, as well as identification of the product compound nidulanin A. We have used A. nidulans for our method development and validation due to the wealth of available biochemical data, but the method can be applied to any fungus with a sequenced and assembled genome, thus supporting further secondary metabolite pathway elucidation in the fungal kingdom.

  4. Basophile: Accurate Fragment Charge State Prediction Improves Peptide Identification Rates

    DOE PAGES

    Wang, Dong; Dasari, Surendra; Chambers, Matthew C.; ...

    2013-03-07

    In shotgun proteomics, database search algorithms rely on fragmentation models to predict fragment ions that should be observed for a given peptide sequence. The most widely used strategy (Naive model) is oversimplified, cleaving all peptide bonds with equal probability to produce fragments of all charges below that of the precursor ion. More accurate models, based on fragmentation simulation, are too computationally intensive for on-the-fly use in database search algorithms. We have created an ordinal-regression-based model called Basophile that takes fragment size and basic residue distribution into account when determining the charge retention during CID/higher-energy collision induced dissociation (HCD) of chargedmore » peptides. This model improves the accuracy of predictions by reducing the number of unnecessary fragments that are routinely predicted for highly-charged precursors. Basophile increased the identification rates by 26% (on average) over the Naive model, when analyzing triply-charged precursors from ion trap data. Basophile achieves simplicity and speed by solving the prediction problem with an ordinal regression equation, which can be incorporated into any database search software for shotgun proteomic identification.« less

  5. Blinded by Beauty: Attractiveness Bias and Accurate Perceptions of Academic Performance

    PubMed Central

    Talamas, Sean N.; Mavor, Kenneth I.; Perrett, David I.

    2016-01-01

    Despite the old adage not to ‘judge a book by its cover’, facial cues often guide first impressions and these first impressions guide our decisions. Literature suggests there are valid facial cues that assist us in assessing someone’s health or intelligence, but such cues are overshadowed by an ‘attractiveness halo’ whereby desirable attributions are preferentially ascribed to attractive people. The impact of the attractiveness halo effect on perceptions of academic performance in the classroom is concerning as this has shown to influence students’ future performance. We investigated the limiting effects of the attractiveness halo on perceptions of actual academic performance in faces of 100 university students. Given the ambiguity and various perspectives on the definition of intelligence and the growing consensus on the importance of conscientiousness over intelligence in predicting actual academic performance, we also investigated whether perceived conscientiousness was a more accurate predictor of academic performance than perceived intelligence. Perceived conscientiousness was found to be a better predictor of actual academic performance when compared to perceived intelligence and perceived academic performance, and accuracy was improved when controlling for the influence of attractiveness on judgments. These findings emphasize the misleading effect of attractiveness on the accuracy of first impressions of competence, which can have serious consequences in areas such as education and hiring. The findings also have implications for future research investigating impression accuracy based on facial stimuli. PMID:26885976

  6. Early Prediction of Student Dropout and Performance in MOOCSs Using Higher Granularity Temporal Information

    ERIC Educational Resources Information Center

    Ye, Cheng; Biswas, Gautam

    2014-01-01

    Our project is motivated by the early dropout and low completion rate problem in MOOCs. We have extended traditional features for MOOC analysis with richer and higher granularity information to make more accurate predictions of dropout and performance. The results show that finer-grained temporal information increases the predictive power in the…

  7. Fast and Accurate Prediction of Stratified Steel Temperature During Holding Period of Ladle

    NASA Astrophysics Data System (ADS)

    Deodhar, Anirudh; Singh, Umesh; Shukla, Rishabh; Gautham, B. P.; Singh, Amarendra K.

    2017-04-01

    Thermal stratification of liquid steel in a ladle during the holding period and the teeming operation has a direct bearing on the superheat available at the caster and hence on the caster set points such as casting speed and cooling rates. The changes in the caster set points are typically carried out based on temperature measurements at the end of tundish outlet. Thermal prediction models provide advance knowledge of the influence of process and design parameters on the steel temperature at various stages. Therefore, they can be used in making accurate decisions about the caster set points in real time. However, this requires both fast and accurate thermal prediction models. In this work, we develop a surrogate model for the prediction of thermal stratification using data extracted from a set of computational fluid dynamics (CFD) simulations, pre-determined using design of experiments technique. Regression method is used for training the predictor. The model predicts the stratified temperature profile instantaneously, for a given set of process parameters such as initial steel temperature, refractory heat content, slag thickness, and holding time. More than 96 pct of the predicted values are within an error range of ±5 K (±5 °C), when compared against corresponding CFD results. Considering its accuracy and computational efficiency, the model can be extended for thermal control of casting operations. This work also sets a benchmark for developing similar thermal models for downstream processes such as tundish and caster.

  8. Combining transcription factor binding affinities with open-chromatin data for accurate gene expression prediction

    PubMed Central

    Schmidt, Florian; Gasparoni, Nina; Gasparoni, Gilles; Gianmoena, Kathrin; Cadenas, Cristina; Polansky, Julia K.; Ebert, Peter; Nordström, Karl; Barann, Matthias; Sinha, Anupam; Fröhler, Sebastian; Xiong, Jieyi; Dehghani Amirabad, Azim; Behjati Ardakani, Fatemeh; Hutter, Barbara; Zipprich, Gideon; Felder, Bärbel; Eils, Jürgen; Brors, Benedikt; Chen, Wei; Hengstler, Jan G.; Hamann, Alf; Lengauer, Thomas; Rosenstiel, Philip; Walter, Jörn; Schulz, Marcel H.

    2017-01-01

    The binding and contribution of transcription factors (TF) to cell specific gene expression is often deduced from open-chromatin measurements to avoid costly TF ChIP-seq assays. Thus, it is important to develop computational methods for accurate TF binding prediction in open-chromatin regions (OCRs). Here, we report a novel segmentation-based method, TEPIC, to predict TF binding by combining sets of OCRs with position weight matrices. TEPIC can be applied to various open-chromatin data, e.g. DNaseI-seq and NOMe-seq. Additionally, Histone-Marks (HMs) can be used to identify candidate TF binding sites. TEPIC computes TF affinities and uses open-chromatin/HM signal intensity as quantitative measures of TF binding strength. Using machine learning, we find low affinity binding sites to improve our ability to explain gene expression variability compared to the standard presence/absence classification of binding sites. Further, we show that both footprints and peaks capture essential TF binding events and lead to a good prediction performance. In our application, gene-based scores computed by TEPIC with one open-chromatin assay nearly reach the quality of several TF ChIP-seq data sets. Finally, these scores correctly predict known transcriptional regulators as illustrated by the application to novel DNaseI-seq and NOMe-seq data for primary human hepatocytes and CD4+ T-cells, respectively. PMID:27899623

  9. Competitive Abilities in Experimental Microcosms Are Accurately Predicted by a Demographic Index for R*

    PubMed Central

    Murrell, Ebony G.; Juliano, Steven A.

    2012-01-01

    Resource competition theory predicts that R*, the equilibrium resource amount yielding zero growth of a consumer population, should predict species' competitive abilities for that resource. This concept has been supported for unicellular organisms, but has not been well-tested for metazoans, probably due to the difficulty of raising experimental populations to equilibrium and measuring population growth rates for species with long or complex life cycles. We developed an index (Rindex) of R* based on demography of one insect cohort, growing from egg to adult in a non-equilibrium setting, and tested whether Rindex yielded accurate predictions of competitive abilities using mosquitoes as a model system. We estimated finite rate of increase (λ′) from demographic data for cohorts of three mosquito species raised with different detritus amounts, and estimated each species' Rindex using nonlinear regressions of λ′ vs. initial detritus amount. All three species' Rindex differed significantly, and accurately predicted competitive hierarchy of the species determined in simultaneous pairwise competition experiments. Our Rindex could provide estimates and rigorous statistical comparisons of competitive ability for organisms for which typical chemostat methods and equilibrium population conditions are impractical. PMID:22970128

  10. SIFTER search: a web server for accurate phylogeny-based protein function prediction

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

    Sahraeian, Sayed M.; Luo, Kevin R.; Brenner, Steven E.

    We are awash in proteins discovered through high-throughput sequencing projects. As only a minuscule fraction of these have been experimentally characterized, computational methods are widely used for automated annotation. Here, we introduce a user-friendly web interface for accurate protein function prediction using the SIFTER algorithm. SIFTER is a state-of-the-art sequence-based gene molecular function prediction algorithm that uses a statistical model of function evolution to incorporate annotations throughout the phylogenetic tree. Due to the resources needed by the SIFTER algorithm, running SIFTER locally is not trivial for most users, especially for large-scale problems. The SIFTER web server thus provides access tomore » precomputed predictions on 16 863 537 proteins from 232 403 species. Users can explore SIFTER predictions with queries for proteins, species, functions, and homologs of sequences not in the precomputed prediction set. Lastly, the SIFTER web server is accessible at http://sifter.berkeley.edu/ and the source code can be downloaded.« less

  11. SIFTER search: a web server for accurate phylogeny-based protein function prediction

    DOE PAGES

    Sahraeian, Sayed M.; Luo, Kevin R.; Brenner, Steven E.

    2015-05-15

    We are awash in proteins discovered through high-throughput sequencing projects. As only a minuscule fraction of these have been experimentally characterized, computational methods are widely used for automated annotation. Here, we introduce a user-friendly web interface for accurate protein function prediction using the SIFTER algorithm. SIFTER is a state-of-the-art sequence-based gene molecular function prediction algorithm that uses a statistical model of function evolution to incorporate annotations throughout the phylogenetic tree. Due to the resources needed by the SIFTER algorithm, running SIFTER locally is not trivial for most users, especially for large-scale problems. The SIFTER web server thus provides access tomore » precomputed predictions on 16 863 537 proteins from 232 403 species. Users can explore SIFTER predictions with queries for proteins, species, functions, and homologs of sequences not in the precomputed prediction set. Lastly, the SIFTER web server is accessible at http://sifter.berkeley.edu/ and the source code can be downloaded.« less

  12. XenoSite: accurately predicting CYP-mediated sites of metabolism with neural networks.

    PubMed

    Zaretzki, Jed; Matlock, Matthew; Swamidass, S Joshua

    2013-12-23

    Understanding how xenobiotic molecules are metabolized is important because it influences the safety, efficacy, and dose of medicines and how they can be modified to improve these properties. The cytochrome P450s (CYPs) are proteins responsible for metabolizing 90% of drugs on the market, and many computational methods can predict which atomic sites of a molecule--sites of metabolism (SOMs)--are modified during CYP-mediated metabolism. This study improves on prior methods of predicting CYP-mediated SOMs by using new descriptors and machine learning based on neural networks. The new method, XenoSite, is faster to train and more accurate by as much as 4% or 5% for some isozymes. Furthermore, some "incorrect" predictions made by XenoSite were subsequently validated as correct predictions by revaluation of the source literature. Moreover, XenoSite output is interpretable as a probability, which reflects both the confidence of the model that a particular atom is metabolized and the statistical likelihood that its prediction for that atom is correct.

  13. Tank System Integrated Model: A Cryogenic Tank Performance Prediction Program

    NASA Technical Reports Server (NTRS)

    Bolshinskiy, L. G.; Hedayat, A.; Hastings, L. J.; Sutherlin, S. G.; Schnell, A. R.; Moder, J. P.

    2017-01-01

    Accurate predictions of the thermodynamic state of the cryogenic propellants, pressurization rate, and performance of pressure control techniques in cryogenic tanks are required for development of cryogenic fluid long-duration storage technology and planning for future space exploration missions. This Technical Memorandum (TM) presents the analytical tool, Tank System Integrated Model (TankSIM), which can be used for modeling pressure control and predicting the behavior of cryogenic propellant for long-term storage for future space missions. Utilizing TankSIM, the following processes can be modeled: tank self-pressurization, boiloff, ullage venting, mixing, and condensation on the tank wall. This TM also includes comparisons of TankSIM program predictions with the test data andexamples of multiphase mission calculations.

  14. Quokka: a comprehensive tool for rapid and accurate prediction of kinase family-specific phosphorylation sites in the human proteome.

    PubMed

    Li, Fuyi; Li, Chen; Marquez-Lago, Tatiana T; Leier, André; Akutsu, Tatsuya; Purcell, Anthony W; Smith, A Ian; Lithgow, Trevor; Daly, Roger J; Song, Jiangning; Chou, Kuo-Chen

    2018-06-27

    Kinase-regulated phosphorylation is a ubiquitous type of post-translational modification (PTM) in both eukaryotic and prokaryotic cells. Phosphorylation plays fundamental roles in many signalling pathways and biological processes, such as protein degradation and protein-protein interactions. Experimental studies have revealed that signalling defects caused by aberrant phosphorylation are highly associated with a variety of human diseases, especially cancers. In light of this, a number of computational methods aiming to accurately predict protein kinase family-specific or kinase-specific phosphorylation sites have been established, thereby facilitating phosphoproteomic data analysis. In this work, we present Quokka, a novel bioinformatics tool that allows users to rapidly and accurately identify human kinase family-regulated phosphorylation sites. Quokka was developed by using a variety of sequence scoring functions combined with an optimized logistic regression algorithm. We evaluated Quokka based on well-prepared up-to-date benchmark and independent test datasets, curated from the Phospho.ELM and UniProt databases, respectively. The independent test demonstrates that Quokka improves the prediction performance compared with state-of-the-art computational tools for phosphorylation prediction. In summary, our tool provides users with high-quality predicted human phosphorylation sites for hypothesis generation and biological validation. The Quokka webserver and datasets are freely available at http://quokka.erc.monash.edu/. Supplementary data are available at Bioinformatics online.

  15. New PDS will predict performance of pallets made with used parts

    Treesearch

    John W. Clarke; Marshall S. White; Philip A. Araman

    2001-01-01

    The Pallet Design System (PDS) is a computer design program developed by Virginia Tech, the National Wooden Pallet & Container Association, and the U.S. Forest Service to quickly and accurately predict the performance of new wood pallets. PDS has been upgraded annually since its original version in 1984. All of the previous upgrades, however, have continued to...

  16. The Impact of Trajectory Prediction Uncertainty on Air Traffic Controller Performance and Acceptability

    NASA Technical Reports Server (NTRS)

    Mercer, Joey S.; Bienert, Nancy; Gomez, Ashley; Hunt, Sarah; Kraut, Joshua; Martin, Lynne; Morey, Susan; Green, Steven M.; Prevot, Thomas; Wu, Minghong G.

    2013-01-01

    A Human-In-The-Loop air traffic control simulation investigated the impact of uncertainties in trajectory predictions on NextGen Trajectory-Based Operations concepts, seeking to understand when the automation would become unacceptable to controllers or when performance targets could no longer be met. Retired air traffic controllers staffed two en route transition sectors, delivering arrival traffic to the northwest corner-post of Atlanta approach control under time-based metering operations. Using trajectory-based decision-support tools, the participants worked the traffic under varying levels of wind forecast error and aircraft performance model error, impacting the ground automations ability to make accurate predictions. Results suggest that the controllers were able to maintain high levels of performance, despite even the highest levels of trajectory prediction errors.

  17. Development and Preliminary Performance of a Risk Factor Screen to Predict Posttraumatic Psychological Disorder After Trauma Exposure

    PubMed Central

    Carlson, Eve B.; Palmieri, Patrick A.; Spain, David A.

    2017-01-01

    Objective We examined data from a prospective study of risk factors that increase vulnerability or resilience, exacerbate distress, or foster recovery to determine whether risk factors accurately predict which individuals will later have high posttraumatic (PT) symptom levels and whether brief measures of risk factors also accurately predict later symptom elevations. Method Using data from 129 adults exposed to traumatic injury of self or a loved one, we conducted receiver operating characteristic (ROC) analyses of 14 risk factors assessed by full-length measures, determined optimal cutoff scores and calculated predictive performance for the nine that were most predictive. For five risk factors, we identified sets of items that accounted for 90% of variance in total scores and calculated predictive performance for sets of brief risk measures. Results A set of nine risk factors assessed by full measures identified 89% of those who later had elevated PT symptoms (sensitivity) and 78% of those who did not (specificity). A set of four brief risk factor measures assessed soon after injury identified 86% of those who later had elevated PT symptoms and 72% of those who did not. Conclusions Use of sets of brief risk factor measures shows promise of accurate prediction of PT psychological disorder and probable PTSD or depression. Replication of predictive accuracy is needed in a new and larger sample. PMID:28622811

  18. Accurate prediction of personalized olfactory perception from large-scale chemoinformatic features.

    PubMed

    Li, Hongyang; Panwar, Bharat; Omenn, Gilbert S; Guan, Yuanfang

    2018-02-01

    The olfactory stimulus-percept problem has been studied for more than a century, yet it is still hard to precisely predict the odor given the large-scale chemoinformatic features of an odorant molecule. A major challenge is that the perceived qualities vary greatly among individuals due to different genetic and cultural backgrounds. Moreover, the combinatorial interactions between multiple odorant receptors and diverse molecules significantly complicate the olfaction prediction. Many attempts have been made to establish structure-odor relationships for intensity and pleasantness, but no models are available to predict the personalized multi-odor attributes of molecules. In this study, we describe our winning algorithm for predicting individual and population perceptual responses to various odorants in the DREAM Olfaction Prediction Challenge. We find that random forest model consisting of multiple decision trees is well suited to this prediction problem, given the large feature spaces and high variability of perceptual ratings among individuals. Integrating both population and individual perceptions into our model effectively reduces the influence of noise and outliers. By analyzing the importance of each chemical feature, we find that a small set of low- and nondegenerative features is sufficient for accurate prediction. Our random forest model successfully predicts personalized odor attributes of structurally diverse molecules. This model together with the top discriminative features has the potential to extend our understanding of olfactory perception mechanisms and provide an alternative for rational odorant design.

  19. A Critical Review for Developing Accurate and Dynamic Predictive Models Using Machine Learning Methods in Medicine and Health Care.

    PubMed

    Alanazi, Hamdan O; Abdullah, Abdul Hanan; Qureshi, Kashif Naseer

    2017-04-01

    Recently, Artificial Intelligence (AI) has been used widely in medicine and health care sector. In machine learning, the classification or prediction is a major field of AI. Today, the study of existing predictive models based on machine learning methods is extremely active. Doctors need accurate predictions for the outcomes of their patients' diseases. In addition, for accurate predictions, timing is another significant factor that influences treatment decisions. In this paper, existing predictive models in medicine and health care have critically reviewed. Furthermore, the most famous machine learning methods have explained, and the confusion between a statistical approach and machine learning has clarified. A review of related literature reveals that the predictions of existing predictive models differ even when the same dataset is used. Therefore, existing predictive models are essential, and current methods must be improved.

  20. An Interpretable Machine Learning Model for Accurate Prediction of Sepsis in the ICU.

    PubMed

    Nemati, Shamim; Holder, Andre; Razmi, Fereshteh; Stanley, Matthew D; Clifford, Gari D; Buchman, Timothy G

    2018-04-01

    Sepsis is among the leading causes of morbidity, mortality, and cost overruns in critically ill patients. Early intervention with antibiotics improves survival in septic patients. However, no clinically validated system exists for real-time prediction of sepsis onset. We aimed to develop and validate an Artificial Intelligence Sepsis Expert algorithm for early prediction of sepsis. Observational cohort study. Academic medical center from January 2013 to December 2015. Over 31,000 admissions to the ICUs at two Emory University hospitals (development cohort), in addition to over 52,000 ICU patients from the publicly available Medical Information Mart for Intensive Care-III ICU database (validation cohort). Patients who met the Third International Consensus Definitions for Sepsis (Sepsis-3) prior to or within 4 hours of their ICU admission were excluded, resulting in roughly 27,000 and 42,000 patients within our development and validation cohorts, respectively. None. High-resolution vital signs time series and electronic medical record data were extracted. A set of 65 features (variables) were calculated on hourly basis and passed to the Artificial Intelligence Sepsis Expert algorithm to predict onset of sepsis in the proceeding T hours (where T = 12, 8, 6, or 4). Artificial Intelligence Sepsis Expert was used to predict onset of sepsis in the proceeding T hours and to produce a list of the most significant contributing factors. For the 12-, 8-, 6-, and 4-hour ahead prediction of sepsis, Artificial Intelligence Sepsis Expert achieved area under the receiver operating characteristic in the range of 0.83-0.85. Performance of the Artificial Intelligence Sepsis Expert on the development and validation cohorts was indistinguishable. Using data available in the ICU in real-time, Artificial Intelligence Sepsis Expert can accurately predict the onset of sepsis in an ICU patient 4-12 hours prior to clinical recognition. A prospective study is necessary to determine the

  1. Combining transcription factor binding affinities with open-chromatin data for accurate gene expression prediction.

    PubMed

    Schmidt, Florian; Gasparoni, Nina; Gasparoni, Gilles; Gianmoena, Kathrin; Cadenas, Cristina; Polansky, Julia K; Ebert, Peter; Nordström, Karl; Barann, Matthias; Sinha, Anupam; Fröhler, Sebastian; Xiong, Jieyi; Dehghani Amirabad, Azim; Behjati Ardakani, Fatemeh; Hutter, Barbara; Zipprich, Gideon; Felder, Bärbel; Eils, Jürgen; Brors, Benedikt; Chen, Wei; Hengstler, Jan G; Hamann, Alf; Lengauer, Thomas; Rosenstiel, Philip; Walter, Jörn; Schulz, Marcel H

    2017-01-09

    The binding and contribution of transcription factors (TF) to cell specific gene expression is often deduced from open-chromatin measurements to avoid costly TF ChIP-seq assays. Thus, it is important to develop computational methods for accurate TF binding prediction in open-chromatin regions (OCRs). Here, we report a novel segmentation-based method, TEPIC, to predict TF binding by combining sets of OCRs with position weight matrices. TEPIC can be applied to various open-chromatin data, e.g. DNaseI-seq and NOMe-seq. Additionally, Histone-Marks (HMs) can be used to identify candidate TF binding sites. TEPIC computes TF affinities and uses open-chromatin/HM signal intensity as quantitative measures of TF binding strength. Using machine learning, we find low affinity binding sites to improve our ability to explain gene expression variability compared to the standard presence/absence classification of binding sites. Further, we show that both footprints and peaks capture essential TF binding events and lead to a good prediction performance. In our application, gene-based scores computed by TEPIC with one open-chromatin assay nearly reach the quality of several TF ChIP-seq data sets. Finally, these scores correctly predict known transcriptional regulators as illustrated by the application to novel DNaseI-seq and NOMe-seq data for primary human hepatocytes and CD4+ T-cells, respectively. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

  2. Predictive performance of universal termination of resuscitation rules in an Asian community: are they accurate enough?

    PubMed

    Chiang, Wen-Chu; Ko, Patrick Chow-In; Chang, Anna Marie; Liu, Sot Shih-Hung; Wang, Hui-Chih; Yang, Chih-Wei; Hsieh, Ming-Ju; Chen, Shey-Ying; Lai, Mei-Shu; Ma, Matthew Huei-Ming

    2015-04-01

    Prehospital termination of resuscitation (TOR) rules have not been widely validated outside of Western countries. This study evaluated the performance of TOR rules in an Asian metropolitan with a mixed-tier emergency medical service (EMS). We analysed the Utstein registry of adult, non-traumatic out-of-hospital cardiac arrests (OHCAs) in Taipei to test the performance of TOR rules for advanced life support (ALS) or basic life support (BLS) providers. ALS and BLS-TOR rules were tested in OHCAs among three subgroups: (1) resuscitated by ALS, (2) by BLS and (3) by mixed ALS and BLS. Outcome definition was in-hospital death. Sensitivity, specificity, positive predictive value (PPV), negative predictive value and decreased transport rate (DTR) among various provider combinations were calculated. Of the 3489 OHCAs included, 240 were resuscitated by ALS, 1727 by BLS and 1522 by ALS and BLS. Overall survival to hospital discharge was 197 patients (5.6%). Specificity and PPV of ALS-TOR and BLS-TOR for identifying death ranged from 70.7% to 81.8% and 95.1% to 98.1%, respectively. Applying the TOR rules would have a DTR of 34.2-63.9%. BLS rules had better predictive accuracy and DTR than ALS rules among all subgroups. Application of the ALS and BLS TOR rules would have decreased OHCA transported to the hospital, and BLS rules are reasonable as the universal criteria in a mixed-tier EMS. However, 1.9-4.9% of those who survived would be misclassified as non-survivors, raising concern of compromising patient safety for the implementation of the rules. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

  3. An Extrapolation of a Radical Equation More Accurately Predicts Shelf Life of Frozen Biological Matrices.

    PubMed

    De Vore, Karl W; Fatahi, Nadia M; Sass, John E

    2016-08-01

    Arrhenius modeling of analyte recovery at increased temperatures to predict long-term colder storage stability of biological raw materials, reagents, calibrators, and controls is standard practice in the diagnostics industry. Predicting subzero temperature stability using the same practice is frequently criticized but nevertheless heavily relied upon. We compared the ability to predict analyte recovery during frozen storage using 3 separate strategies: traditional accelerated studies with Arrhenius modeling, and extrapolation of recovery at 20% of shelf life using either ordinary least squares or a radical equation y = B1x(0.5) + B0. Computer simulations were performed to establish equivalence of statistical power to discern the expected changes during frozen storage or accelerated stress. This was followed by actual predictive and follow-up confirmatory testing of 12 chemistry and immunoassay analytes. Linear extrapolations tended to be the most conservative in the predicted percent recovery, reducing customer and patient risk. However, the majority of analytes followed a rate of change that slowed over time, which was fit best to a radical equation of the form y = B1x(0.5) + B0. Other evidence strongly suggested that the slowing of the rate was not due to higher-order kinetics, but to changes in the matrix during storage. Predicting shelf life of frozen products through extrapolation of early initial real-time storage analyte recovery should be considered the most accurate method. Although in this study the time required for a prediction was longer than a typical accelerated testing protocol, there are less potential sources of error, reduced costs, and a lower expenditure of resources. © 2016 American Association for Clinical Chemistry.

  4. Highly accurate prediction of emotions surrounding the attacks of September 11, 2001 over 1-, 2-, and 7-year prediction intervals.

    PubMed

    Doré, Bruce P; Meksin, Robert; Mather, Mara; Hirst, William; Ochsner, Kevin N

    2016-06-01

    In the aftermath of a national tragedy, important decisions are predicated on judgments of the emotional significance of the tragedy in the present and future. Research in affective forecasting has largely focused on ways in which people fail to make accurate predictions about the nature and duration of feelings experienced in the aftermath of an event. Here we ask a related but understudied question: can people forecast how they will feel in the future about a tragic event that has already occurred? We found that people were strikingly accurate when predicting how they would feel about the September 11 attacks over 1-, 2-, and 7-year prediction intervals. Although people slightly under- or overestimated their future feelings at times, they nonetheless showed high accuracy in forecasting (a) the overall intensity of their future negative emotion, and (b) the relative degree of different types of negative emotion (i.e., sadness, fear, or anger). Using a path model, we found that the relationship between forecasted and actual future emotion was partially mediated by current emotion and remembered emotion. These results extend theories of affective forecasting by showing that emotional responses to an event of ongoing national significance can be predicted with high accuracy, and by identifying current and remembered feelings as independent sources of this accuracy. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

  5. Highly accurate prediction of emotions surrounding the attacks of September 11, 2001 over 1-, 2-, and 7-year prediction intervals

    PubMed Central

    Doré, B.P.; Meksin, R.; Mather, M.; Hirst, W.; Ochsner, K.N

    2016-01-01

    In the aftermath of a national tragedy, important decisions are predicated on judgments of the emotional significance of the tragedy in the present and future. Research in affective forecasting has largely focused on ways in which people fail to make accurate predictions about the nature and duration of feelings experienced in the aftermath of an event. Here we ask a related but understudied question: can people forecast how they will feel in the future about a tragic event that has already occurred? We found that people were strikingly accurate when predicting how they would feel about the September 11 attacks over 1-, 2-, and 7-year prediction intervals. Although people slightly under- or overestimated their future feelings at times, they nonetheless showed high accuracy in forecasting 1) the overall intensity of their future negative emotion, and 2) the relative degree of different types of negative emotion (i.e., sadness, fear, or anger). Using a path model, we found that the relationship between forecasted and actual future emotion was partially mediated by current emotion and remembered emotion. These results extend theories of affective forecasting by showing that emotional responses to an event of ongoing national significance can be predicted with high accuracy, and by identifying current and remembered feelings as independent sources of this accuracy. PMID:27100309

  6. Improved Helicopter Rotor Performance Prediction through Loose and Tight CFD/CSD Coupling

    NASA Astrophysics Data System (ADS)

    Ickes, Jacob C.

    Helicopters and other Vertical Take-Off or Landing (VTOL) vehicles exhibit an interesting combination of structural dynamic and aerodynamic phenomena which together drive the rotor performance. The combination of factors involved make simulating the rotor a challenging and multidisciplinary effort, and one which is still an active area of interest in the industry because of the money and time it could save during design. Modern tools allow the prediction of rotorcraft physics from first principles. Analysis of the rotor system with this level of accuracy provides the understanding necessary to improve its performance. There has historically been a divide between the comprehensive codes which perform aeroelastic rotor simulations using simplified aerodynamic models, and the very computationally intensive Navier-Stokes Computational Fluid Dynamics (CFD) solvers. As computer resources become more available, efforts have been made to replace the simplified aerodynamics of the comprehensive codes with the more accurate results from a CFD code. The objective of this work is to perform aeroelastic rotorcraft analysis using first-principles simulations for both fluids and structural predictions using tools available at the University of Toledo. Two separate codes are coupled together in both loose coupling (data exchange on a periodic interval) and tight coupling (data exchange each time step) schemes. To allow the coupling to be carried out in a reliable and efficient way, a Fluid-Structure Interaction code was developed which automatically performs primary functions of loose and tight coupling procedures. Flow phenomena such as transonics, dynamic stall, locally reversed flow on a blade, and Blade-Vortex Interaction (BVI) were simulated in this work. Results of the analysis show aerodynamic load improvement due to the inclusion of the CFD-based airloads in the structural dynamics analysis of the Computational Structural Dynamics (CSD) code. Improvements came in the form

  7. High Order Schemes in Bats-R-US for Faster and More Accurate Predictions

    NASA Astrophysics Data System (ADS)

    Chen, Y.; Toth, G.; Gombosi, T. I.

    2014-12-01

    BATS-R-US is a widely used global magnetohydrodynamics model that originally employed second order accurate TVD schemes combined with block based Adaptive Mesh Refinement (AMR) to achieve high resolution in the regions of interest. In the last years we have implemented fifth order accurate finite difference schemes CWENO5 and MP5 for uniform Cartesian grids. Now the high order schemes have been extended to generalized coordinates, including spherical grids and also to the non-uniform AMR grids including dynamic regridding. We present numerical tests that verify the preservation of free-stream solution and high-order accuracy as well as robust oscillation-free behavior near discontinuities. We apply the new high order accurate schemes to both heliospheric and magnetospheric simulations and show that it is robust and can achieve the same accuracy as the second order scheme with much less computational resources. This is especially important for space weather prediction that requires faster than real time code execution.

  8. Perceived Physician-informed Weight Status Predicts Accurate Weight Self-Perception and Weight Self-Regulation in Low-income, African American Women.

    PubMed

    Harris, Charlie L; Strayhorn, Gregory; Moore, Sandra; Goldman, Brian; Martin, Michelle Y

    2016-01-01

    Obese African American women under-appraise their body mass index (BMI) classification and report fewer weight loss attempts than women who accurately appraise their weight status. This cross-sectional study examined whether physician-informed weight status could predict weight self-perception and weight self-regulation strategies in obese women. A convenience sample of 118 low-income women completed a survey assessing demographic characteristics, comorbidities, weight self-perception, and weight self-regulation strategies. BMI was calculated during nurse triage. Binary logistic regression models were performed to test hypotheses. The odds of obese accurate appraisers having been informed about their weight status were six times greater than those of under-appraisers. The odds of those using an "approach" self-regulation strategy having been physician-informed were four times greater compared with those using an "avoidance" strategy. Physicians are uniquely positioned to influence accurate weight self-perception and adaptive weight self-regulation strategies in underserved women, reducing their risk for obesity-related morbidity.

  9. Adult age differences in predicting memory performance: the effects of normative information and task experience.

    PubMed

    McDonald-Miszczak, L; Hunter, M A; Hultsch, D F

    1994-03-01

    Two experiments addressed the effects of task information and experience on younger and older adults' ability to predict their memory for words. The first study examined the effects of normative task information on subjects' predictions for 30-word lists across three trials. The second study looked at the effects of making predictions and recalling either an easy (15) or a difficult (45) word list prior to making predictions and recalling a moderately difficult (30) word list. The results from both studies showed that task information and experience affected subjects' predictions and that elderly adults predicted their performance more accurately than younger adults.

  10. Accurate high-throughput structure mapping and prediction with transition metal ion FRET

    PubMed Central

    Yu, Xiaozhen; Wu, Xiongwu; Bermejo, Guillermo A.; Brooks, Bernard R.; Taraska, Justin W.

    2013-01-01

    Mapping the landscape of a protein’s conformational space is essential to understanding its functions and regulation. The limitations of many structural methods have made this process challenging for most proteins. Here, we report that transition metal ion FRET (tmFRET) can be used in a rapid, highly parallel screen, to determine distances from multiple locations within a protein at extremely low concentrations. The distances generated through this screen for the protein Maltose Binding Protein (MBP) match distances from the crystal structure to within a few angstroms. Furthermore, energy transfer accurately detects structural changes during ligand binding. Finally, fluorescence-derived distances can be used to guide molecular simulations to find low energy states. Our results open the door to rapid, accurate mapping and prediction of protein structures at low concentrations, in large complex systems, and in living cells. PMID:23273426

  11. Evaluation of Turbulence-Model Performance as Applied to Jet-Noise Prediction

    NASA Technical Reports Server (NTRS)

    Woodruff, S. L.; Seiner, J. M.; Hussaini, M. Y.; Erlebacher, G.

    1998-01-01

    The accurate prediction of jet noise is possible only if the jet flow field can be predicted accurately. Predictions for the mean velocity and turbulence quantities in the jet flowfield are typically the product of a Reynolds-averaged Navier-Stokes solver coupled with a turbulence model. To evaluate the effectiveness of solvers and turbulence models in predicting those quantities most important to jet noise prediction, two CFD codes and several turbulence models were applied to a jet configuration over a range of jet temperatures for which experimental data is available.

  12. Does the emergency surgery score accurately predict outcomes in emergent laparotomies?

    PubMed

    Peponis, Thomas; Bohnen, Jordan D; Sangji, Naveen F; Nandan, Anirudh R; Han, Kelsey; Lee, Jarone; Yeh, D Dante; de Moya, Marc A; Velmahos, George C; Chang, David C; Kaafarani, Haytham M A

    2017-08-01

    The emergency surgery score is a mortality-risk calculator for emergency general operation patients. We sought to examine whether the emergency surgery score predicts 30-day morbidity and mortality in a high-risk group of patients undergoing emergent laparotomy. Using the 2011-2012 American College of Surgeons National Surgical Quality Improvement Program database, we identified all patients who underwent emergent laparotomy using (1) the American College of Surgeons National Surgical Quality Improvement Program definition of "emergent," and (2) all Current Procedural Terminology codes denoting a laparotomy, excluding aortic aneurysm rupture. Multivariable logistic regression analyses were performed to measure the correlation (c-statistic) between the emergency surgery score and (1) 30-day mortality, and (2) 30-day morbidity after emergent laparotomy. As sensitivity analyses, the correlation between the emergency surgery score and 30-day mortality was also evaluated in prespecified subgroups based on Current Procedural Terminology codes. A total of 26,410 emergent laparotomy patients were included. Thirty-day mortality and morbidity were 10.2% and 43.8%, respectively. The emergency surgery score correlated well with mortality (c-statistic = 0.84); scores of 1, 11, and 22 correlated with mortalities of 0.4%, 39%, and 100%, respectively. Similarly, the emergency surgery score correlated well with morbidity (c-statistic = 0.74); scores of 0, 7, and 11 correlated with complication rates of 13%, 58%, and 79%, respectively. The morbidity rates plateaued for scores higher than 11. Sensitivity analyses demonstrated that the emergency surgery score effectively predicts mortality in patients undergoing emergent (1) splenic, (2) gastroduodenal, (3) intestinal, (4) hepatobiliary, or (5) incarcerated ventral hernia operation. The emergency surgery score accurately predicts outcomes in all types of emergent laparotomy patients and may prove valuable as a bedside decision

  13. Lightweight ZERODUR: Validation of Mirror Performance and Mirror Modeling Predictions

    NASA Technical Reports Server (NTRS)

    Hull, Tony; Stahl, H. Philip; Westerhoff, Thomas; Valente, Martin; Brooks, Thomas; Eng, Ron

    2017-01-01

    Upcoming spaceborne missions, both moderate and large in scale, require extreme dimensional stability while relying both upon established lightweight mirror materials, and also upon accurate modeling methods to predict performance under varying boundary conditions. We describe tests, recently performed at NASA's XRCF chambers and laboratories in Huntsville Alabama, during which a 1.2 m diameter, f/1.2988% lightweighted SCHOTT lightweighted ZERODUR(TradeMark) mirror was tested for thermal stability under static loads in steps down to 230K. Test results are compared to model predictions, based upon recently published data on ZERODUR(TradeMark). In addition to monitoring the mirror surface for thermal perturbations in XRCF Thermal Vacuum tests, static load gravity deformations have been measured and compared to model predictions. Also the Modal Response(dynamic disturbance) was measured and compared to model. We will discuss the fabrication approach and optomechanical design of the ZERODUR(TradeMark) mirror substrate by SCHOTT, its optical preparation for test by Arizona Optical Systems (AOS). Summarize the outcome of NASA's XRCF tests and model validations

  14. Lightweight ZERODUR®: Validation of mirror performance and mirror modeling predictions

    NASA Astrophysics Data System (ADS)

    Hull, Anthony B.; Stahl, H. Philip; Westerhoff, Thomas; Valente, Martin; Brooks, Thomas; Eng, Ron

    2017-01-01

    Upcoming spaceborne missions, both moderate and large in scale, require extreme dimensional stability while relying both upon established lightweight mirror materials, and also upon accurate modeling methods to predict performance under varying boundary conditions. We describe tests, recently performed at NASA’s XRCF chambers and laboratories in Huntsville Alabama, during which a 1.2m diameter, f/1.29 88% lightweighted SCHOTT lightweighted ZERODUR® mirror was tested for thermal stability under static loads in steps down to 230K. Test results are compared to model predictions, based upon recently published data on ZERODUR®. In addition to monitoring the mirror surface for thermal perturbations in XRCF Thermal Vacuum tests, static load gravity deformations have been measured and compared to model predictions. Also the Modal Response (dynamic disturbance) was measured and compared to model. We will discuss the fabrication approach and optomechanical design of the ZERODUR® mirror substrate by SCHOTT, its optical preparation for test by Arizona Optical Systems (AOS), and summarize the outcome of NASA’s XRCF tests and model validations.

  15. Navier-Stokes and Comprehensive Analysis Performance Predictions of the NREL Phase VI Experiment

    NASA Technical Reports Server (NTRS)

    Duque, Earl P. N.; Burklund, Michael D.; Johnson, Wayne

    2003-01-01

    A vortex lattice code, CAMRAD II, and a Reynolds-Averaged Navier-Stoke code, OVERFLOW-D2, were used to predict the aerodynamic performance of a two-bladed horizontal axis wind turbine. All computations were compared with experimental data that was collected at the NASA Ames Research Center 80- by 120-Foot Wind Tunnel. Computations were performed for both axial as well as yawed operating conditions. Various stall delay models and dynamics stall models were used by the CAMRAD II code. Comparisons between the experimental data and computed aerodynamic loads show that the OVERFLOW-D2 code can accurately predict the power and spanwise loading of a wind turbine rotor.

  16. A Free Wake Numerical Simulation for Darrieus Vertical Axis Wind Turbine Performance Prediction

    NASA Astrophysics Data System (ADS)

    Belu, Radian

    2010-11-01

    In the last four decades, several aerodynamic prediction models have been formulated for the Darrieus wind turbine performances and characteristics. We can identified two families: stream-tube and vortex. The paper presents a simplified numerical techniques for simulating vertical axis wind turbine flow, based on the lifting line theory and a free vortex wake model, including dynamic stall effects for predicting the performances of a 3-D vertical axis wind turbine. A vortex model is used in which the wake is composed of trailing stream-wise and shedding span-wise vortices, whose strengths are equal to the change in the bound vortex strength as required by the Helmholz and Kelvin theorems. Performance parameters are computed by application of the Biot-Savart law along with the Kutta-Jukowski theorem and a semi-empirical stall model. We tested the developed model with an adaptation of the earlier multiple stream-tube performance prediction model for the Darrieus turbines. Predictions by using our method are shown to compare favorably with existing experimental data and the outputs of other numerical models. The method can predict accurately the local and global performances of a vertical axis wind turbine, and can be used in the design and optimization of wind turbines for built environment applications.

  17. Can phenological models predict tree phenology accurately in the future? The unrevealed hurdle of endodormancy break.

    PubMed

    Chuine, Isabelle; Bonhomme, Marc; Legave, Jean-Michel; García de Cortázar-Atauri, Iñaki; Charrier, Guillaume; Lacointe, André; Améglio, Thierry

    2016-10-01

    The onset of the growing season of trees has been earlier by 2.3 days per decade during the last 40 years in temperate Europe because of global warming. The effect of temperature on plant phenology is, however, not linear because temperature has a dual effect on bud development. On one hand, low temperatures are necessary to break bud endodormancy, and, on the other hand, higher temperatures are necessary to promote bud cell growth afterward. Different process-based models have been developed in the last decades to predict the date of budbreak of woody species. They predict that global warming should delay or compromise endodormancy break at the species equatorward range limits leading to a delay or even impossibility to flower or set new leaves. These models are classically parameterized with flowering or budbreak dates only, with no information on the endodormancy break date because this information is very scarce. Here, we evaluated the efficiency of a set of phenological models to accurately predict the endodormancy break dates of three fruit trees. Our results show that models calibrated solely with budbreak dates usually do not accurately predict the endodormancy break date. Providing endodormancy break date for the model parameterization results in much more accurate prediction of this latter, with, however, a higher error than that on budbreak dates. Most importantly, we show that models not calibrated with endodormancy break dates can generate large discrepancies in forecasted budbreak dates when using climate scenarios as compared to models calibrated with endodormancy break dates. This discrepancy increases with mean annual temperature and is therefore the strongest after 2050 in the southernmost regions. Our results claim for the urgent need of massive measurements of endodormancy break dates in forest and fruit trees to yield more robust projections of phenological changes in a near future. © 2016 John Wiley & Sons Ltd.

  18. How accurate is our clinical prediction of "minimal prostate cancer"?

    PubMed

    Leibovici, Dan; Shikanov, Sergey; Gofrit, Ofer N; Zagaja, Gregory P; Shilo, Yaniv; Shalhav, Arieh L

    2013-07-01

    Recommendations for active surveillance versus immediate treatment for low risk prostate cancer are based on biopsy and clinical data, assuming that a low volume of well-differentiated carcinoma will be associated with a low progression risk. However, the accuracy of clinical prediction of minimal prostate cancer (MPC) is unclear. To define preoperative predictors for MPC in prostatectomy specimens and to examine the accuracy of such prediction. Data collected on 1526 consecutive radical prostatectomy patients operated in a single center between 2003 and 2008 included: age, body mass index, preoperative prostate-specific antigen level, biopsy Gleason score, clinical stage, percentage of positive biopsy cores, and maximal core length (MCL) involvement. MPC was defined as < 5% of prostate volume involvement with organ-confined Gleason score < or = 6. Univariate and multivariate logistic regression analyses were used to define independent predictors of minimal disease. Classification and Regression Tree (CART) analysis was used to define cutoff values for the predictors and measure the accuracy of prediction. MPC was found in 241 patients (15.8%). Clinical stage, biopsy Gleason's score, percent of positive biopsy cores, and maximal involved core length were associated with minimal disease (OR 0.42, 0.1, 0.92, and 0.9, respectively). Independent predictors of MPC included: biopsy Gleason score, percent of positive cores and MCL (OR 0.21, 095 and 0.95, respectively). CART showed that when the MCL exceeded 11.5%, the likelihood of MPC was 3.8%. Conversely, when applying the most favorable preoperative conditions (Gleason < or = 6, < 20% positive cores, MCL < or = 11.5%) the chance of minimal disease was 41%. Biopsy Gleason score, the percent of positive cores and MCL are independently associated with MPC. While preoperative prediction of significant prostate cancer was accurate, clinical prediction of MPC was incorrect 59% of the time. Caution is necessary when

  19. Predicting Performance on Academic and Non-Academic Tasks: A Comparison of Adolescents with and without Learning Disabilities

    ERIC Educational Resources Information Center

    Job, Jenelle M.; Klassen, Robert M.

    2012-01-01

    Previous research suggests that adolescents with learning disabilities (LD) are less accurate in predicting academic performance than normally achieving (NA) adolescents and display a tendency to overestimate their level of performance (e.g., Klassen, 2007). However, no studies have been conducted investigating whether this overestimation is…

  20. Accurate electrical prediction of memory array through SEM-based edge-contour extraction using SPICE simulation

    NASA Astrophysics Data System (ADS)

    Shauly, Eitan; Rotstein, Israel; Peltinov, Ram; Latinski, Sergei; Adan, Ofer; Levi, Shimon; Menadeva, Ovadya

    2009-03-01

    The continues transistors scaling efforts, for smaller devices, similar (or larger) drive current/um and faster devices, increase the challenge to predict and to control the transistor off-state current. Typically, electrical simulators like SPICE, are using the design intent (as-drawn GDS data). At more sophisticated cases, the simulators are fed with the pattern after lithography and etch process simulations. As the importance of electrical simulation accuracy is increasing and leakage is becoming more dominant, there is a need to feed these simulators, with more accurate information extracted from physical on-silicon transistors. Our methodology to predict changes in device performances due to systematic lithography and etch effects was used in this paper. In general, the methodology consists on using the OPCCmaxTM for systematic Edge-Contour-Extraction (ECE) from transistors, taking along the manufacturing and includes any image distortions like line-end shortening, corner rounding and line-edge roughness. These measurements are used for SPICE modeling. Possible application of this new metrology is to provide a-head of time, physical and electrical statistical data improving time to market. In this work, we applied our methodology to analyze a small and large array's of 2.14um2 6T-SRAM, manufactured using Tower Standard Logic for General Purposes Platform. 4 out of the 6 transistors used "U-Shape AA", known to have higher variability. The predicted electrical performances of the transistors drive current and leakage current, in terms of nominal values and variability are presented. We also used the methodology to analyze an entire SRAM Block array. Study of an isolation leakage and variability are presented.

  1. Machine learning predictions of molecular properties: Accurate many-body potentials and nonlocality in chemical space

    DOE PAGES

    Hansen, Katja; Biegler, Franziska; Ramakrishnan, Raghunathan; ...

    2015-06-04

    Simultaneously accurate and efficient prediction of molecular properties throughout chemical compound space is a critical ingredient toward rational compound design in chemical and pharmaceutical industries. Aiming toward this goal, we develop and apply a systematic hierarchy of efficient empirical methods to estimate atomization and total energies of molecules. These methods range from a simple sum over atoms, to addition of bond energies, to pairwise interatomic force fields, reaching to the more sophisticated machine learning approaches that are capable of describing collective interactions between many atoms or bonds. In the case of equilibrium molecular geometries, even simple pairwise force fields demonstratemore » prediction accuracy comparable to benchmark energies calculated using density functional theory with hybrid exchange-correlation functionals; however, accounting for the collective many-body interactions proves to be essential for approaching the “holy grail” of chemical accuracy of 1 kcal/mol for both equilibrium and out-of-equilibrium geometries. This remarkable accuracy is achieved by a vectorized representation of molecules (so-called Bag of Bonds model) that exhibits strong nonlocality in chemical space. The same representation allows us to predict accurate electronic properties of molecules, such as their polarizability and molecular frontier orbital energies.« less

  2. Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space

    PubMed Central

    2015-01-01

    Simultaneously accurate and efficient prediction of molecular properties throughout chemical compound space is a critical ingredient toward rational compound design in chemical and pharmaceutical industries. Aiming toward this goal, we develop and apply a systematic hierarchy of efficient empirical methods to estimate atomization and total energies of molecules. These methods range from a simple sum over atoms, to addition of bond energies, to pairwise interatomic force fields, reaching to the more sophisticated machine learning approaches that are capable of describing collective interactions between many atoms or bonds. In the case of equilibrium molecular geometries, even simple pairwise force fields demonstrate prediction accuracy comparable to benchmark energies calculated using density functional theory with hybrid exchange-correlation functionals; however, accounting for the collective many-body interactions proves to be essential for approaching the “holy grail” of chemical accuracy of 1 kcal/mol for both equilibrium and out-of-equilibrium geometries. This remarkable accuracy is achieved by a vectorized representation of molecules (so-called Bag of Bonds model) that exhibits strong nonlocality in chemical space. In addition, the same representation allows us to predict accurate electronic properties of molecules, such as their polarizability and molecular frontier orbital energies. PMID:26113956

  3. Long-Term Post-CABG Survival: Performance of Clinical Risk Models Versus Actuarial Predictions.

    PubMed

    Carr, Brendan M; Romeiser, Jamie; Ruan, Joyce; Gupta, Sandeep; Seifert, Frank C; Zhu, Wei; Shroyer, A Laurie

    2016-01-01

    Clinical risk models are commonly used to predict short-term coronary artery bypass grafting (CABG) mortality but are less commonly used to predict long-term mortality. The added value of long-term mortality clinical risk models over traditional actuarial models has not been evaluated. To address this, the predictive performance of a long-term clinical risk model was compared with that of an actuarial model to identify the clinical variable(s) most responsible for any differences observed. Long-term mortality for 1028 CABG patients was estimated using the Hannan New York State clinical risk model and an actuarial model (based on age, gender, and race/ethnicity). Vital status was assessed using the Social Security Death Index. Observed/expected (O/E) ratios were calculated, and the models' predictive performances were compared using a nested c-index approach. Linear regression analyses identified the subgroup of risk factors driving the differences observed. Mortality rates were 3%, 9%, and 17% at one-, three-, and five years, respectively (median follow-up: five years). The clinical risk model provided more accurate predictions. Greater divergence between model estimates occurred with increasing long-term mortality risk, with baseline renal dysfunction identified as a particularly important driver of these differences. Long-term mortality clinical risk models provide enhanced predictive power compared to actuarial models. Using the Hannan risk model, a patient's long-term mortality risk can be accurately assessed and subgroups of higher-risk patients can be identified for enhanced follow-up care. More research appears warranted to refine long-term CABG clinical risk models. © 2015 The Authors. Journal of Cardiac Surgery Published by Wiley Periodicals, Inc.

  4. Long‐Term Post‐CABG Survival: Performance of Clinical Risk Models Versus Actuarial Predictions

    PubMed Central

    Carr, Brendan M.; Romeiser, Jamie; Ruan, Joyce; Gupta, Sandeep; Seifert, Frank C.; Zhu, Wei

    2015-01-01

    Abstract Background/aim Clinical risk models are commonly used to predict short‐term coronary artery bypass grafting (CABG) mortality but are less commonly used to predict long‐term mortality. The added value of long‐term mortality clinical risk models over traditional actuarial models has not been evaluated. To address this, the predictive performance of a long‐term clinical risk model was compared with that of an actuarial model to identify the clinical variable(s) most responsible for any differences observed. Methods Long‐term mortality for 1028 CABG patients was estimated using the Hannan New York State clinical risk model and an actuarial model (based on age, gender, and race/ethnicity). Vital status was assessed using the Social Security Death Index. Observed/expected (O/E) ratios were calculated, and the models' predictive performances were compared using a nested c‐index approach. Linear regression analyses identified the subgroup of risk factors driving the differences observed. Results Mortality rates were 3%, 9%, and 17% at one‐, three‐, and five years, respectively (median follow‐up: five years). The clinical risk model provided more accurate predictions. Greater divergence between model estimates occurred with increasing long‐term mortality risk, with baseline renal dysfunction identified as a particularly important driver of these differences. Conclusions Long‐term mortality clinical risk models provide enhanced predictive power compared to actuarial models. Using the Hannan risk model, a patient's long‐term mortality risk can be accurately assessed and subgroups of higher‐risk patients can be identified for enhanced follow‐up care. More research appears warranted to refine long‐term CABG clinical risk models. doi: 10.1111/jocs.12665 (J Card Surg 2016;31:23–30) PMID:26543019

  5. An Accurate Link Correlation Estimator for Improving Wireless Protocol Performance

    PubMed Central

    Zhao, Zhiwei; Xu, Xianghua; Dong, Wei; Bu, Jiajun

    2015-01-01

    Wireless link correlation has shown significant impact on the performance of various sensor network protocols. Many works have been devoted to exploiting link correlation for protocol improvements. However, the effectiveness of these designs heavily relies on the accuracy of link correlation measurement. In this paper, we investigate state-of-the-art link correlation measurement and analyze the limitations of existing works. We then propose a novel lightweight and accurate link correlation estimation (LACE) approach based on the reasoning of link correlation formation. LACE combines both long-term and short-term link behaviors for link correlation estimation. We implement LACE as a stand-alone interface in TinyOS and incorporate it into both routing and flooding protocols. Simulation and testbed results show that LACE: (1) achieves more accurate and lightweight link correlation measurements than the state-of-the-art work; and (2) greatly improves the performance of protocols exploiting link correlation. PMID:25686314

  6. MiRduplexSVM: A High-Performing MiRNA-Duplex Prediction and Evaluation Methodology

    PubMed Central

    Karathanasis, Nestoras; Tsamardinos, Ioannis; Poirazi, Panayiota

    2015-01-01

    We address the problem of predicting the position of a miRNA duplex on a microRNA hairpin via the development and application of a novel SVM-based methodology. Our method combines a unique problem representation and an unbiased optimization protocol to learn from mirBase19.0 an accurate predictive model, termed MiRduplexSVM. This is the first model that provides precise information about all four ends of the miRNA duplex. We show that (a) our method outperforms four state-of-the-art tools, namely MaturePred, MiRPara, MatureBayes, MiRdup as well as a Simple Geometric Locator when applied on the same training datasets employed for each tool and evaluated on a common blind test set. (b) In all comparisons, MiRduplexSVM shows superior performance, achieving up to a 60% increase in prediction accuracy for mammalian hairpins and can generalize very well on plant hairpins, without any special optimization. (c) The tool has a number of important applications such as the ability to accurately predict the miRNA or the miRNA*, given the opposite strand of a duplex. Its performance on this task is superior to the 2nts overhang rule commonly used in computational studies and similar to that of a comparative genomic approach, without the need for prior knowledge or the complexity of performing multiple alignments. Finally, it is able to evaluate novel, potential miRNAs found either computationally or experimentally. In relation with recent confidence evaluation methods used in miRBase, MiRduplexSVM was successful in identifying high confidence potential miRNAs. PMID:25961860

  7. An Accurate GPS-IMU/DR Data Fusion Method for Driverless Car Based on a Set of Predictive Models and Grid Constraints

    PubMed Central

    Wang, Shiyao; Deng, Zhidong; Yin, Gang

    2016-01-01

    A high-performance differential global positioning system (GPS)  receiver with real time kinematics provides absolute localization for driverless cars. However, it is not only susceptible to multipath effect but also unable to effectively fulfill precise error correction in a wide range of driving areas. This paper proposes an accurate GPS–inertial measurement unit (IMU)/dead reckoning (DR) data fusion method based on a set of predictive models and occupancy grid constraints. First, we employ a set of autoregressive and moving average (ARMA) equations that have different structural parameters to build maximum likelihood models of raw navigation. Second, both grid constraints and spatial consensus checks on all predictive results and current measurements are required to have removal of outliers. Navigation data that satisfy stationary stochastic process are further fused to achieve accurate localization results. Third, the standard deviation of multimodal data fusion can be pre-specified by grid size. Finally, we perform a lot of field tests on a diversity of real urban scenarios. The experimental results demonstrate that the method can significantly smooth small jumps in bias and considerably reduce accumulated position errors due to DR. With low computational complexity, the position accuracy of our method surpasses existing state-of-the-arts on the same dataset and the new data fusion method is practically applied in our driverless car. PMID:26927108

  8. An Accurate GPS-IMU/DR Data Fusion Method for Driverless Car Based on a Set of Predictive Models and Grid Constraints.

    PubMed

    Wang, Shiyao; Deng, Zhidong; Yin, Gang

    2016-02-24

    A high-performance differential global positioning system (GPS)  receiver with real time kinematics provides absolute localization for driverless cars. However, it is not only susceptible to multipath effect but also unable to effectively fulfill precise error correction in a wide range of driving areas. This paper proposes an accurate GPS-inertial measurement unit (IMU)/dead reckoning (DR) data fusion method based on a set of predictive models and occupancy grid constraints. First, we employ a set of autoregressive and moving average (ARMA) equations that have different structural parameters to build maximum likelihood models of raw navigation. Second, both grid constraints and spatial consensus checks on all predictive results and current measurements are required to have removal of outliers. Navigation data that satisfy stationary stochastic process are further fused to achieve accurate localization results. Third, the standard deviation of multimodal data fusion can be pre-specified by grid size. Finally, we perform a lot of field tests on a diversity of real urban scenarios. The experimental results demonstrate that the method can significantly smooth small jumps in bias and considerably reduce accumulated position errors due to DR. With low computational complexity, the position accuracy of our method surpasses existing state-of-the-arts on the same dataset and the new data fusion method is practically applied in our driverless car.

  9. A novel fibrosis index comprising a non-cholesterol sterol accurately predicts HCV-related liver cirrhosis.

    PubMed

    Ydreborg, Magdalena; Lisovskaja, Vera; Lagging, Martin; Brehm Christensen, Peer; Langeland, Nina; Buhl, Mads Rauning; Pedersen, Court; Mørch, Kristine; Wejstål, Rune; Norkrans, Gunnar; Lindh, Magnus; Färkkilä, Martti; Westin, Johan

    2014-01-01

    Diagnosis of liver cirrhosis is essential in the management of chronic hepatitis C virus (HCV) infection. Liver biopsy is invasive and thus entails a risk of complications as well as a potential risk of sampling error. Therefore, non-invasive diagnostic tools are preferential. The aim of the present study was to create a model for accurate prediction of liver cirrhosis based on patient characteristics and biomarkers of liver fibrosis, including a panel of non-cholesterol sterols reflecting cholesterol synthesis and absorption and secretion. We evaluated variables with potential predictive significance for liver fibrosis in 278 patients originally included in a multicenter phase III treatment trial for chronic HCV infection. A stepwise multivariate logistic model selection was performed with liver cirrhosis, defined as Ishak fibrosis stage 5-6, as the outcome variable. A new index, referred to as Nordic Liver Index (NoLI) in the paper, was based on the model: Log-odds (predicting cirrhosis) = -12.17+ (age × 0.11) + (BMI (kg/m(2)) × 0.23) + (D7-lathosterol (μg/100 mg cholesterol)×(-0.013)) + (Platelet count (x10(9)/L) × (-0.018)) + (Prothrombin-INR × 3.69). The area under the ROC curve (AUROC) for prediction of cirrhosis was 0.91 (95% CI 0.86-0.96). The index was validated in a separate cohort of 83 patients and the AUROC for this cohort was similar (0.90; 95% CI: 0.82-0.98). In conclusion, the new index may complement other methods in diagnosing cirrhosis in patients with chronic HCV infection.

  10. Improved Ecosystem Predictions of the California Current System via Accurate Light Calculations

    DTIC Science & Technology

    2011-09-30

    System via Accurate Light Calculations Curtis D. Mobley Sequoia Scientific, Inc. 2700 Richards Road, Suite 107 Bellevue, WA 98005 phone: 425...7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) Sequoia Scientific, Inc,2700 Richards Road, Suite 107,Bellevue,WA,98005 8. PERFORMING...EcoLight-S 1.0 Users’ Guide and Technical Documentation. Sequoia Scientific, Inc., Bellevue, WA, 38 pages. Mobley, C. D., 2011. Fast light calculations

  11. Fast and accurate predictions of covalent bonds in chemical space.

    PubMed

    Chang, K Y Samuel; Fias, Stijn; Ramakrishnan, Raghunathan; von Lilienfeld, O Anatole

    2016-05-07

    We assess the predictive accuracy of perturbation theory based estimates of changes in covalent bonding due to linear alchemical interpolations among molecules. We have investigated σ bonding to hydrogen, as well as σ and π bonding between main-group elements, occurring in small sets of iso-valence-electronic molecules with elements drawn from second to fourth rows in the p-block of the periodic table. Numerical evidence suggests that first order Taylor expansions of covalent bonding potentials can achieve high accuracy if (i) the alchemical interpolation is vertical (fixed geometry), (ii) it involves elements from the third and fourth rows of the periodic table, and (iii) an optimal reference geometry is used. This leads to near linear changes in the bonding potential, resulting in analytical predictions with chemical accuracy (∼1 kcal/mol). Second order estimates deteriorate the prediction. If initial and final molecules differ not only in composition but also in geometry, all estimates become substantially worse, with second order being slightly more accurate than first order. The independent particle approximation based second order perturbation theory performs poorly when compared to the coupled perturbed or finite difference approach. Taylor series expansions up to fourth order of the potential energy curve of highly symmetric systems indicate a finite radius of convergence, as illustrated for the alchemical stretching of H2 (+). Results are presented for (i) covalent bonds to hydrogen in 12 molecules with 8 valence electrons (CH4, NH3, H2O, HF, SiH4, PH3, H2S, HCl, GeH4, AsH3, H2Se, HBr); (ii) main-group single bonds in 9 molecules with 14 valence electrons (CH3F, CH3Cl, CH3Br, SiH3F, SiH3Cl, SiH3Br, GeH3F, GeH3Cl, GeH3Br); (iii) main-group double bonds in 9 molecules with 12 valence electrons (CH2O, CH2S, CH2Se, SiH2O, SiH2S, SiH2Se, GeH2O, GeH2S, GeH2Se); (iv) main-group triple bonds in 9 molecules with 10 valence electrons (HCN, HCP, HCAs, HSiN, HSi

  12. Simple Mathematical Models Do Not Accurately Predict Early SIV Dynamics

    PubMed Central

    Noecker, Cecilia; Schaefer, Krista; Zaccheo, Kelly; Yang, Yiding; Day, Judy; Ganusov, Vitaly V.

    2015-01-01

    Upon infection of a new host, human immunodeficiency virus (HIV) replicates in the mucosal tissues and is generally undetectable in circulation for 1–2 weeks post-infection. Several interventions against HIV including vaccines and antiretroviral prophylaxis target virus replication at this earliest stage of infection. Mathematical models have been used to understand how HIV spreads from mucosal tissues systemically and what impact vaccination and/or antiretroviral prophylaxis has on viral eradication. Because predictions of such models have been rarely compared to experimental data, it remains unclear which processes included in these models are critical for predicting early HIV dynamics. Here we modified the “standard” mathematical model of HIV infection to include two populations of infected cells: cells that are actively producing the virus and cells that are transitioning into virus production mode. We evaluated the effects of several poorly known parameters on infection outcomes in this model and compared model predictions to experimental data on infection of non-human primates with variable doses of simian immunodifficiency virus (SIV). First, we found that the mode of virus production by infected cells (budding vs. bursting) has a minimal impact on the early virus dynamics for a wide range of model parameters, as long as the parameters are constrained to provide the observed rate of SIV load increase in the blood of infected animals. Interestingly and in contrast with previous results, we found that the bursting mode of virus production generally results in a higher probability of viral extinction than the budding mode of virus production. Second, this mathematical model was not able to accurately describe the change in experimentally determined probability of host infection with increasing viral doses. Third and finally, the model was also unable to accurately explain the decline in the time to virus detection with increasing viral dose. These results

  13. Influence of accurate and inaccurate 'split-time' feedback upon 10-mile time trial cycling performance.

    PubMed

    Wilson, Mathew G; Lane, Andy M; Beedie, Chris J; Farooq, Abdulaziz

    2012-01-01

    The objective of the study is to examine the impact of accurate and inaccurate 'split-time' feedback upon a 10-mile time trial (TT) performance and to quantify power output into a practically meaningful unit of variation. Seven well-trained cyclists completed four randomised bouts of a 10-mile TT on a SRM™ cycle ergometer. TTs were performed with (1) accurate performance feedback, (2) without performance feedback, (3) and (4) false negative and false positive 'split-time' feedback showing performance 5% slower or 5% faster than actual performance. There were no significant differences in completion time, average power output, heart rate or blood lactate between the four feedback conditions. There were significantly lower (p < 0.001) average [Formula: see text] (ml min(-1)) and [Formula: see text] (l min(-1)) scores in the false positive (3,485 ± 596; 119 ± 33) and accurate (3,471 ± 513; 117 ± 22) feedback conditions compared to the false negative (3,753 ± 410; 127 ± 27) and blind (3,772 ± 378; 124 ± 21) feedback conditions. Cyclists spent a greater amount of time in a '20 watt zone' 10 W either side of average power in the negative feedback condition (fastest) than the accurate feedback (slowest) condition (39.3 vs. 32.2%, p < 0.05). There were no significant differences in the 10-mile TT performance time between accurate and inaccurate feedback conditions, despite significantly lower average [Formula: see text] and [Formula: see text] scores in the false positive and accurate feedback conditions. Additionally, cycling with a small variation in power output (10 W either side of average power) produced the fastest TT. Further psycho-physiological research should examine the mechanism(s) why lower [Formula: see text] and [Formula: see text] scores are observed when cycling in a false positive or accurate feedback condition compared to a false negative or blind feedback condition.

  14. Comparison of Taxi Time Prediction Performance Using Different Taxi Speed Decision Trees

    NASA Technical Reports Server (NTRS)

    Lee, Hanbong

    2017-01-01

    In the STBO modeler and tactical surface scheduler for ATD-2 project, taxi speed decision trees are used to calculate the unimpeded taxi times of flights taxiing on the airport surface. The initial taxi speed values in these decision trees did not show good prediction accuracy of taxi times. Using the more recent, reliable surveillance data, new taxi speed values in ramp area and movement area were computed. Before integrating these values into the STBO system, we performed test runs using live data from Charlotte airport, with different taxi speed settings: 1) initial taxi speed values and 2) new ones. Taxi time prediction performance was evaluated by comparing various metrics. The results show that the new taxi speed decision trees can calculate the unimpeded taxi-out times more accurately.

  15. Accurate prediction of pregnancy viability by means of a simple scoring system.

    PubMed

    Bottomley, Cecilia; Van Belle, Vanya; Kirk, Emma; Van Huffel, Sabine; Timmerman, Dirk; Bourne, Tom

    2013-01-01

    What is the performance of a simple scoring system to predict whether women will have an ongoing viable intrauterine pregnancy beyond the first trimester? A simple scoring system using demographic and initial ultrasound variables accurately predicts pregnancy viability beyond the first trimester with an area under the curve (AUC) in a receiver operating characteristic curve of 0.924 [95% confidence interval (CI) 0.900-0.947] on an independent test set. Individual demographic and ultrasound factors, such as maternal age, vaginal bleeding and gestational sac size, are strong predictors of miscarriage. Previous mathematical models have combined individual risk factors with reasonable performance. A simple scoring system derived from a mathematical model that can be easily implemented in clinical practice has not previously been described for the prediction of ongoing viability. This was a prospective observational study in a single early pregnancy assessment centre during a 9-month period. A cohort of 1881 consecutive women undergoing transvaginal ultrasound scan at a gestational age <84 days were included. Women were excluded if the first trimester outcome was not known. Demographic features, symptoms and ultrasound variables were tested for their influence on ongoing viability. Logistic regression was used to determine the influence on first trimester viability from demographics and symptoms alone, ultrasound findings alone and then from all the variables combined. Each model was developed on a training data set, and a simple scoring system was derived from this. This scoring system was tested on an independent test data set. The final outcome based on a total of 1435 participants was an ongoing viable pregnancy in 885 (61.7%) and early pregnancy loss in 550 (38.3%) women. The scoring system using significant demographic variables alone (maternal age and amount of bleeding) to predict ongoing viability gave an AUC of 0.724 (95% CI = 0.692-0.756) in the training set

  16. Predicting Energy Performance of a Net-Zero Energy Building: A Statistical Approach

    PubMed Central

    Kneifel, Joshua; Webb, David

    2016-01-01

    Performance-based building requirements have become more prevalent because it gives freedom in building design while still maintaining or exceeding the energy performance required by prescriptive-based requirements. In order to determine if building designs reach target energy efficiency improvements, it is necessary to estimate the energy performance of a building using predictive models and different weather conditions. Physics-based whole building energy simulation modeling is the most common approach. However, these physics-based models include underlying assumptions and require significant amounts of information in order to specify the input parameter values. An alternative approach to test the performance of a building is to develop a statistically derived predictive regression model using post-occupancy data that can accurately predict energy consumption and production based on a few common weather-based factors, thus requiring less information than simulation models. A regression model based on measured data should be able to predict energy performance of a building for a given day as long as the weather conditions are similar to those during the data collection time frame. This article uses data from the National Institute of Standards and Technology (NIST) Net-Zero Energy Residential Test Facility (NZERTF) to develop and validate a regression model to predict the energy performance of the NZERTF using two weather variables aggregated to the daily level, applies the model to estimate the energy performance of hypothetical NZERTFs located in different cities in the Mixed-Humid climate zone, and compares these estimates to the results from already existing EnergyPlus whole building energy simulations. This regression model exhibits agreement with EnergyPlus predictive trends in energy production and net consumption, but differs greatly in energy consumption. The model can be used as a framework for alternative and more complex models based on the

  17. Predicting Energy Performance of a Net-Zero Energy Building: A Statistical Approach.

    PubMed

    Kneifel, Joshua; Webb, David

    2016-09-01

    Performance-based building requirements have become more prevalent because it gives freedom in building design while still maintaining or exceeding the energy performance required by prescriptive-based requirements. In order to determine if building designs reach target energy efficiency improvements, it is necessary to estimate the energy performance of a building using predictive models and different weather conditions. Physics-based whole building energy simulation modeling is the most common approach. However, these physics-based models include underlying assumptions and require significant amounts of information in order to specify the input parameter values. An alternative approach to test the performance of a building is to develop a statistically derived predictive regression model using post-occupancy data that can accurately predict energy consumption and production based on a few common weather-based factors, thus requiring less information than simulation models. A regression model based on measured data should be able to predict energy performance of a building for a given day as long as the weather conditions are similar to those during the data collection time frame. This article uses data from the National Institute of Standards and Technology (NIST) Net-Zero Energy Residential Test Facility (NZERTF) to develop and validate a regression model to predict the energy performance of the NZERTF using two weather variables aggregated to the daily level, applies the model to estimate the energy performance of hypothetical NZERTFs located in different cities in the Mixed-Humid climate zone, and compares these estimates to the results from already existing EnergyPlus whole building energy simulations. This regression model exhibits agreement with EnergyPlus predictive trends in energy production and net consumption, but differs greatly in energy consumption. The model can be used as a framework for alternative and more complex models based on the

  18. Kinetic approach to degradation mechanisms in polymer solar cells and their accurate lifetime predictions

    NASA Astrophysics Data System (ADS)

    Arshad, Muhammad Azeem; Maaroufi, AbdelKrim

    2018-07-01

    A beginning has been made in the present study regarding the accurate lifetime predictions of polymer solar cells. Certain reservations about the conventionally employed temperature accelerated lifetime measurements test for its unworthiness of predicting reliable lifetimes of polymer solar cells are brought into light. Critical issues concerning the accelerated lifetime testing include, assuming reaction mechanism instead of determining it, and relying solely on the temperature acceleration of a single property of material. An advanced approach comprising a set of theoretical models to estimate the accurate lifetimes of polymer solar cells is therefore suggested in order to suitably alternate the accelerated lifetime testing. This approach takes into account systematic kinetic modeling of various possible polymer degradation mechanisms under natural weathering conditions. The proposed kinetic approach is substantiated by its applications on experimental aging data-sets of polymer solar materials/solar cells including, P3HT polymer film, bulk heterojunction (MDMO-PPV:PCBM) and dye-sensitized solar cells. Based on the suggested approach, an efficacious lifetime determination formula for polymer solar cells is derived and tested on dye-sensitized solar cells. Some important merits of the proposed method are also pointed out and its prospective applications are discussed.

  19. Development of a New Model for Accurate Prediction of Cloud Water Deposition on Vegetation

    NASA Astrophysics Data System (ADS)

    Katata, G.; Nagai, H.; Wrzesinsky, T.; Klemm, O.; Eugster, W.; Burkard, R.

    2006-12-01

    Scarcity of water resources in arid and semi-arid areas is of great concern in the light of population growth and food shortages. Several experiments focusing on cloud (fog) water deposition on the land surface suggest that cloud water plays an important role in water resource in such regions. A one-dimensional vegetation model including the process of cloud water deposition on vegetation has been developed to better predict cloud water deposition on the vegetation. New schemes to calculate capture efficiency of leaf, cloud droplet size distribution, and gravitational flux of cloud water were incorporated in the model. Model calculations were compared with the data acquired at the Norway spruce forest at the Waldstein site, Germany. High performance of the model was confirmed by comparisons of calculated net radiation, sensible and latent heat, and cloud water fluxes over the forest with measurements. The present model provided a better prediction of measured turbulent and gravitational fluxes of cloud water over the canopy than the Lovett model, which is a commonly used cloud water deposition model. Detailed calculations of evapotranspiration and of turbulent exchange of heat and water vapor within the canopy and the modifications are necessary for accurate prediction of cloud water deposition. Numerical experiments to examine the dependence of cloud water deposition on the vegetation species (coniferous and broad-leaved trees, flat and cylindrical grasses) and structures (Leaf Area Index (LAI) and canopy height) are performed using the presented model. The results indicate that the differences of leaf shape and size have a large impact on cloud water deposition. Cloud water deposition also varies with the growth of vegetation and seasonal change of LAI. We found that the coniferous trees whose height and LAI are 24 m and 2.0 m2m-2, respectively, produce the largest amount of cloud water deposition in all combinations of vegetation species and structures in the

  20. Accurate prediction of interfacial residues in two-domain proteins using evolutionary information: implications for three-dimensional modeling.

    PubMed

    Bhaskara, Ramachandra M; Padhi, Amrita; Srinivasan, Narayanaswamy

    2014-07-01

    With the preponderance of multidomain proteins in eukaryotic genomes, it is essential to recognize the constituent domains and their functions. Often function involves communications across the domain interfaces, and the knowledge of the interacting sites is essential to our understanding of the structure-function relationship. Using evolutionary information extracted from homologous domains in at least two diverse domain architectures (single and multidomain), we predict the interface residues corresponding to domains from the two-domain proteins. We also use information from the three-dimensional structures of individual domains of two-domain proteins to train naïve Bayes classifier model to predict the interfacial residues. Our predictions are highly accurate (∼85%) and specific (∼95%) to the domain-domain interfaces. This method is specific to multidomain proteins which contain domains in at least more than one protein architectural context. Using predicted residues to constrain domain-domain interaction, rigid-body docking was able to provide us with accurate full-length protein structures with correct orientation of domains. We believe that these results can be of considerable interest toward rational protein and interaction design, apart from providing us with valuable information on the nature of interactions. © 2013 Wiley Periodicals, Inc.

  1. Towards more accurate and reliable predictions for nuclear applications

    NASA Astrophysics Data System (ADS)

    Goriely, Stephane; Hilaire, Stephane; Dubray, Noel; Lemaître, Jean-François

    2017-09-01

    The need for nuclear data far from the valley of stability, for applications such as nuclear astrophysics or future nuclear facilities, challenges the robustness as well as the predictive power of present nuclear models. Most of the nuclear data evaluation and prediction are still performed on the basis of phenomenological nuclear models. For the last decades, important progress has been achieved in fundamental nuclear physics, making it now feasible to use more reliable, but also more complex microscopic or semi-microscopic models in the evaluation and prediction of nuclear data for practical applications. Nowadays mean-field models can be tuned at the same level of accuracy as the phenomenological models, renormalized on experimental data if needed, and therefore can replace the phenomenological inputs in the evaluation of nuclear data. The latest achievements to determine nuclear masses within the non-relativistic HFB approach, including the related uncertainties in the model predictions, are discussed. Similarly, recent efforts to determine fission observables within the mean-field approach are described and compared with more traditional existing models.

  2. Intermolecular potentials and the accurate prediction of the thermodynamic properties of water

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

    Shvab, I.; Sadus, Richard J., E-mail: rsadus@swin.edu.au

    2013-11-21

    The ability of intermolecular potentials to correctly predict the thermodynamic properties of liquid water at a density of 0.998 g/cm{sup 3} for a wide range of temperatures (298–650 K) and pressures (0.1–700 MPa) is investigated. Molecular dynamics simulations are reported for the pressure, thermal pressure coefficient, thermal expansion coefficient, isothermal and adiabatic compressibilities, isobaric and isochoric heat capacities, and Joule-Thomson coefficient of liquid water using the non-polarizable SPC/E and TIP4P/2005 potentials. The results are compared with both experiment data and results obtained from the ab initio-based Matsuoka-Clementi-Yoshimine non-additive (MCYna) [J. Li, Z. Zhou, and R. J. Sadus, J. Chem. Phys.more » 127, 154509 (2007)] potential, which includes polarization contributions. The data clearly indicate that both the SPC/E and TIP4P/2005 potentials are only in qualitative agreement with experiment, whereas the polarizable MCYna potential predicts some properties within experimental uncertainty. This highlights the importance of polarizability for the accurate prediction of the thermodynamic properties of water, particularly at temperatures beyond 298 K.« less

  3. ILT based defect simulation of inspection images accurately predicts mask defect printability on wafer

    NASA Astrophysics Data System (ADS)

    Deep, Prakash; Paninjath, Sankaranarayanan; Pereira, Mark; Buck, Peter

    2016-05-01

    At advanced technology nodes mask complexity has been increased because of large-scale use of resolution enhancement technologies (RET) which includes Optical Proximity Correction (OPC), Inverse Lithography Technology (ILT) and Source Mask Optimization (SMO). The number of defects detected during inspection of such mask increased drastically and differentiation of critical and non-critical defects are more challenging, complex and time consuming. Because of significant defectivity of EUVL masks and non-availability of actinic inspection, it is important and also challenging to predict the criticality of defects for printability on wafer. This is one of the significant barriers for the adoption of EUVL for semiconductor manufacturing. Techniques to decide criticality of defects from images captured using non actinic inspection images is desired till actinic inspection is not available. High resolution inspection of photomask images detects many defects which are used for process and mask qualification. Repairing all defects is not practical and probably not required, however it's imperative to know which defects are severe enough to impact wafer before repair. Additionally, wafer printability check is always desired after repairing a defect. AIMSTM review is the industry standard for this, however doing AIMSTM review for all defects is expensive and very time consuming. Fast, accurate and an economical mechanism is desired which can predict defect printability on wafer accurately and quickly from images captured using high resolution inspection machine. Predicting defect printability from such images is challenging due to the fact that the high resolution images do not correlate with actual mask contours. The challenge is increased due to use of different optical condition during inspection other than actual scanner condition, and defects found in such images do not have correlation with actual impact on wafer. Our automated defect simulation tool predicts

  4. TankSIM: A Cryogenic Tank Performance Prediction Program

    NASA Technical Reports Server (NTRS)

    Bolshinskiy, L. G.; Hedayat, A.; Hastings, L. J.; Moder, J. P.; Schnell, A. R.; Sutherlin, S. G.

    2015-01-01

    Accurate prediction of the thermodynamic state of the cryogenic propellants in launch vehicle tanks is necessary for mission planning and successful execution. Cryogenic propellant storage and transfer in space environments requires that tank pressure be controlled. The pressure rise rate is determined by the complex interaction of external heat leak, fluid temperature stratification, and interfacial heat and mass transfer. If the required storage duration of a space mission is longer than the period in which the tank pressure reaches its allowable maximum, an appropriate pressure control method must be applied. Therefore, predictions of the pressurization rate and performance of pressure control techniques in cryogenic tanks are required for development of cryogenic fluid long-duration storage technology and planning of future space exploration missions. This paper describes an analytical tool, Tank System Integrated Model (TankSIM), which can be used for modeling pressure control and predicting the behavior of cryogenic propellant for long-term storage for future space missions. It is written in the FORTRAN 90 language and can be compiled with any Visual FORTRAN compiler. A thermodynamic vent system (TVS) is used to achieve tank pressure control. Utilizing TankSIM, the following processes can be modeled: tank self-pressurization, boiloff, ullage venting, and mixing. Details of the TankSIM program and comparisons of its predictions with test data for liquid hydrogen and liquid methane will be presented in the final paper.

  5. Predicted osteotomy planes are accurate when using patient-specific instrumentation for total knee arthroplasty in cadavers: a descriptive analysis.

    PubMed

    Kievit, A J; Dobbe, J G G; Streekstra, G J; Blankevoort, L; Schafroth, M U

    2018-06-01

    Malalignment of implants is a major source of failure during total knee arthroplasty. To achieve more accurate 3D planning and execution of the osteotomy cuts during surgery, the Signature (Biomet, Warsaw) patient-specific instrumentation (PSI) was used to produce pin guides for the positioning of the osteotomy blocks by means of computer-aided manufacture based on CT scan images. The research question of this study is: what is the transfer accuracy of osteotomy planes predicted by the Signature PSI system for preoperative 3D planning and intraoperative block-guided pin placement to perform total knee arthroplasty procedures? The transfer accuracy achieved by using the Signature PSI system was evaluated by comparing the osteotomy planes predicted preoperatively with the osteotomy planes seen intraoperatively in human cadaveric legs. Outcomes were measured in terms of translational and rotational errors (varus, valgus, flexion, extension and axial rotation) for both tibia and femur osteotomies. Average translational errors between the osteotomy planes predicted using the Signature system and the actual osteotomy planes achieved was 0.8 mm (± 0.5 mm) for the tibia and 0.7 mm (± 4.0 mm) for the femur. Average rotational errors in relation to predicted and achieved osteotomy planes were 0.1° (± 1.2°) of varus and 0.4° (± 1.7°) of anterior slope (extension) for the tibia, and 2.8° (± 2.0°) of varus and 0.9° (± 2.7°) of flexion and 1.4° (± 2.2°) of external rotation for the femur. The similarity between osteotomy planes predicted using the Signature system and osteotomy planes actually achieved was excellent for the tibia although some discrepancies were seen for the femur. The use of 3D system techniques in TKA surgery can provide accurate intraoperative guidance, especially for patients with deformed bone, tailored to individual patients and ensure better placement of the implant.

  6. Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model.

    PubMed

    Wang, Sheng; Sun, Siqi; Li, Zhen; Zhang, Renyu; Xu, Jinbo

    2017-01-01

    Protein contacts contain key information for the understanding of protein structure and function and thus, contact prediction from sequence is an important problem. Recently exciting progress has been made on this problem, but the predicted contacts for proteins without many sequence homologs is still of low quality and not very useful for de novo structure prediction. This paper presents a new deep learning method that predicts contacts by integrating both evolutionary coupling (EC) and sequence conservation information through an ultra-deep neural network formed by two deep residual neural networks. The first residual network conducts a series of 1-dimensional convolutional transformation of sequential features; the second residual network conducts a series of 2-dimensional convolutional transformation of pairwise information including output of the first residual network, EC information and pairwise potential. By using very deep residual networks, we can accurately model contact occurrence patterns and complex sequence-structure relationship and thus, obtain higher-quality contact prediction regardless of how many sequence homologs are available for proteins in question. Our method greatly outperforms existing methods and leads to much more accurate contact-assisted folding. Tested on 105 CASP11 targets, 76 past CAMEO hard targets, and 398 membrane proteins, the average top L long-range prediction accuracy obtained by our method, one representative EC method CCMpred and the CASP11 winner MetaPSICOV is 0.47, 0.21 and 0.30, respectively; the average top L/10 long-range accuracy of our method, CCMpred and MetaPSICOV is 0.77, 0.47 and 0.59, respectively. Ab initio folding using our predicted contacts as restraints but without any force fields can yield correct folds (i.e., TMscore>0.6) for 203 of the 579 test proteins, while that using MetaPSICOV- and CCMpred-predicted contacts can do so for only 79 and 62 of them, respectively. Our contact-assisted models also have

  7. Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model

    PubMed Central

    Li, Zhen; Zhang, Renyu

    2017-01-01

    Motivation Protein contacts contain key information for the understanding of protein structure and function and thus, contact prediction from sequence is an important problem. Recently exciting progress has been made on this problem, but the predicted contacts for proteins without many sequence homologs is still of low quality and not very useful for de novo structure prediction. Method This paper presents a new deep learning method that predicts contacts by integrating both evolutionary coupling (EC) and sequence conservation information through an ultra-deep neural network formed by two deep residual neural networks. The first residual network conducts a series of 1-dimensional convolutional transformation of sequential features; the second residual network conducts a series of 2-dimensional convolutional transformation of pairwise information including output of the first residual network, EC information and pairwise potential. By using very deep residual networks, we can accurately model contact occurrence patterns and complex sequence-structure relationship and thus, obtain higher-quality contact prediction regardless of how many sequence homologs are available for proteins in question. Results Our method greatly outperforms existing methods and leads to much more accurate contact-assisted folding. Tested on 105 CASP11 targets, 76 past CAMEO hard targets, and 398 membrane proteins, the average top L long-range prediction accuracy obtained by our method, one representative EC method CCMpred and the CASP11 winner MetaPSICOV is 0.47, 0.21 and 0.30, respectively; the average top L/10 long-range accuracy of our method, CCMpred and MetaPSICOV is 0.77, 0.47 and 0.59, respectively. Ab initio folding using our predicted contacts as restraints but without any force fields can yield correct folds (i.e., TMscore>0.6) for 203 of the 579 test proteins, while that using MetaPSICOV- and CCMpred-predicted contacts can do so for only 79 and 62 of them, respectively. Our contact

  8. Obtaining Accurate Probabilities Using Classifier Calibration

    ERIC Educational Resources Information Center

    Pakdaman Naeini, Mahdi

    2016-01-01

    Learning probabilistic classification and prediction models that generate accurate probabilities is essential in many prediction and decision-making tasks in machine learning and data mining. One way to achieve this goal is to post-process the output of classification models to obtain more accurate probabilities. These post-processing methods are…

  9. Optimization of Biomathematical Model Predictions for Cognitive Performance Impairment in Individuals: Accounting for Unknown Traits and Uncertain States in Homeostatic and Circadian Processes

    PubMed Central

    Van Dongen, Hans P. A.; Mott, Christopher G.; Huang, Jen-Kuang; Mollicone, Daniel J.; McKenzie, Frederic D.; Dinges, David F.

    2007-01-01

    Current biomathematical models of fatigue and performance do not accurately predict cognitive performance for individuals with a priori unknown degrees of trait vulnerability to sleep loss, do not predict performance reliably when initial conditions are uncertain, and do not yield statistically valid estimates of prediction accuracy. These limitations diminish their usefulness for predicting the performance of individuals in operational environments. To overcome these 3 limitations, a novel modeling approach was developed, based on the expansion of a statistical technique called Bayesian forecasting. The expanded Bayesian forecasting procedure was implemented in the two-process model of sleep regulation, which has been used to predict performance on the basis of the combination of a sleep homeostatic process and a circadian process. Employing the two-process model with the Bayesian forecasting procedure to predict performance for individual subjects in the face of unknown traits and uncertain states entailed subject-specific optimization of 3 trait parameters (homeostatic build-up rate, circadian amplitude, and basal performance level) and 2 initial state parameters (initial homeostatic state and circadian phase angle). Prior information about the distribution of the trait parameters in the population at large was extracted from psychomotor vigilance test (PVT) performance measurements in 10 subjects who had participated in a laboratory experiment with 88 h of total sleep deprivation. The PVT performance data of 3 additional subjects in this experiment were set aside beforehand for use in prospective computer simulations. The simulations involved updating the subject-specific model parameters every time the next performance measurement became available, and then predicting performance 24 h ahead. Comparison of the predictions to the subjects' actual data revealed that as more data became available for the individuals at hand, the performance predictions became

  10. Computer-Assisted Decision Support for Student Admissions Based on Their Predicted Academic Performance.

    PubMed

    Muratov, Eugene; Lewis, Margaret; Fourches, Denis; Tropsha, Alexander; Cox, Wendy C

    2017-04-01

    Objective. To develop predictive computational models forecasting the academic performance of students in the didactic-rich portion of a doctor of pharmacy (PharmD) curriculum as admission-assisting tools. Methods. All PharmD candidates over three admission cycles were divided into two groups: those who completed the PharmD program with a GPA ≥ 3; and the remaining candidates. Random Forest machine learning technique was used to develop a binary classification model based on 11 pre-admission parameters. Results. Robust and externally predictive models were developed that had particularly high overall accuracy of 77% for candidates with high or low academic performance. These multivariate models were highly accurate in predicting these groups to those obtained using undergraduate GPA and composite PCAT scores only. Conclusion. The models developed in this study can be used to improve the admission process as preliminary filters and thus quickly identify candidates who are likely to be successful in the PharmD curriculum.

  11. EVA Performance Prediction

    NASA Technical Reports Server (NTRS)

    Peacock, Brian; Maida, James; Rajulu, Sudhakar

    2004-01-01

    Astronaut physical performance capabilities in micro gravity EV A or on planetary surfaces when encumbered by a life support suit and debilitated by a long exposure to micro gravity will be less than unencumbered pre flight capabilities. The big question addressed by human factors engineers is: what can the astronaut be expected to do on EVA or when we arrive at a planetary surface? A second question is: what aids to performance will be needed to enhance the human physical capability? These questions are important for a number of reasons. First it is necessary to carry out accurate planning of human physical demands to ensure that time and energy critical tasks can be carried out with confidence. Second it is important that the crew members (and their ground or planetary base monitors) have a realistic picture of their own capabilities, as excessive fatigue can lead to catastrophic failure. Third it is important to design appropriate equipment to enhance human sensory capabilities, locomotion, materials handling and manipulation. The evidence from physiological research points to musculoskeletal, cardiovascular and neurovestibular degradation during long duration exposure to micro gravity . The evidence from the biomechanics laboratory (and the Neutral Buoyancy Laboratory) points to a reduction in range of motion, strength and stamina when encumbered by a pressurized suit. The evidence from a long history of EVAs is that crewmembers are indeed restricted in their physical capabilities. There is a wealth of evidence in the literature on the causes and effects of degraded human performance in the laboratory, in sports and athletics, in industry and in other physically demanding jobs. One approach to this challenge is through biomechanical and performance modeling. Such models must be based on thorough task analysis, reliable human performance data from controlled studies, and functional extrapolations validated in analog contexts. The task analyses currently carried

  12. A Deep Learning Framework for Robust and Accurate Prediction of ncRNA-Protein Interactions Using Evolutionary Information.

    PubMed

    Yi, Hai-Cheng; You, Zhu-Hong; Huang, De-Shuang; Li, Xiao; Jiang, Tong-Hai; Li, Li-Ping

    2018-06-01

    The interactions between non-coding RNAs (ncRNAs) and proteins play an important role in many biological processes, and their biological functions are primarily achieved by binding with a variety of proteins. High-throughput biological techniques are used to identify protein molecules bound with specific ncRNA, but they are usually expensive and time consuming. Deep learning provides a powerful solution to computationally predict RNA-protein interactions. In this work, we propose the RPI-SAN model by using the deep-learning stacked auto-encoder network to mine the hidden high-level features from RNA and protein sequences and feed them into a random forest (RF) model to predict ncRNA binding proteins. Stacked assembling is further used to improve the accuracy of the proposed method. Four benchmark datasets, including RPI2241, RPI488, RPI1807, and NPInter v2.0, were employed for the unbiased evaluation of five established prediction tools: RPI-Pred, IPMiner, RPISeq-RF, lncPro, and RPI-SAN. The experimental results show that our RPI-SAN model achieves much better performance than other methods, with accuracies of 90.77%, 89.7%, 96.1%, and 99.33%, respectively. It is anticipated that RPI-SAN can be used as an effective computational tool for future biomedical researches and can accurately predict the potential ncRNA-protein interacted pairs, which provides reliable guidance for biological research. Copyright © 2018 The Author(s). Published by Elsevier Inc. All rights reserved.

  13. CodingQuarry: highly accurate hidden Markov model gene prediction in fungal genomes using RNA-seq transcripts.

    PubMed

    Testa, Alison C; Hane, James K; Ellwood, Simon R; Oliver, Richard P

    2015-03-11

    The impact of gene annotation quality on functional and comparative genomics makes gene prediction an important process, particularly in non-model species, including many fungi. Sets of homologous protein sequences are rarely complete with respect to the fungal species of interest and are often small or unreliable, especially when closely related species have not been sequenced or annotated in detail. In these cases, protein homology-based evidence fails to correctly annotate many genes, or significantly improve ab initio predictions. Generalised hidden Markov models (GHMM) have proven to be invaluable tools in gene annotation and, recently, RNA-seq has emerged as a cost-effective means to significantly improve the quality of automated gene annotation. As these methods do not require sets of homologous proteins, improving gene prediction from these resources is of benefit to fungal researchers. While many pipelines now incorporate RNA-seq data in training GHMMs, there has been relatively little investigation into additionally combining RNA-seq data at the point of prediction, and room for improvement in this area motivates this study. CodingQuarry is a highly accurate, self-training GHMM fungal gene predictor designed to work with assembled, aligned RNA-seq transcripts. RNA-seq data informs annotations both during gene-model training and in prediction. Our approach capitalises on the high quality of fungal transcript assemblies by incorporating predictions made directly from transcript sequences. Correct predictions are made despite transcript assembly problems, including those caused by overlap between the transcripts of adjacent gene loci. Stringent benchmarking against high-confidence annotation subsets showed CodingQuarry predicted 91.3% of Schizosaccharomyces pombe genes and 90.4% of Saccharomyces cerevisiae genes perfectly. These results are 4-5% better than those of AUGUSTUS, the next best performing RNA-seq driven gene predictor tested. Comparisons against

  14. Accurate prediction of protein-protein interactions by integrating potential evolutionary information embedded in PSSM profile and discriminative vector machine classifier.

    PubMed

    Li, Zheng-Wei; You, Zhu-Hong; Chen, Xing; Li, Li-Ping; Huang, De-Shuang; Yan, Gui-Ying; Nie, Ru; Huang, Yu-An

    2017-04-04

    Identification of protein-protein interactions (PPIs) is of critical importance for deciphering the underlying mechanisms of almost all biological processes of cell and providing great insight into the study of human disease. Although much effort has been devoted to identifying PPIs from various organisms, existing high-throughput biological techniques are time-consuming, expensive, and have high false positive and negative results. Thus it is highly urgent to develop in silico methods to predict PPIs efficiently and accurately in this post genomic era. In this article, we report a novel computational model combining our newly developed discriminative vector machine classifier (DVM) and an improved Weber local descriptor (IWLD) for the prediction of PPIs. Two components, differential excitation and orientation, are exploited to build evolutionary features for each protein sequence. The main characteristics of the proposed method lies in introducing an effective feature descriptor IWLD which can capture highly discriminative evolutionary information from position-specific scoring matrixes (PSSM) of protein data, and employing the powerful and robust DVM classifier. When applying the proposed method to Yeast and H. pylori data sets, we obtained excellent prediction accuracies as high as 96.52% and 91.80%, respectively, which are significantly better than the previous methods. Extensive experiments were then performed for predicting cross-species PPIs and the predictive results were also pretty promising. To further validate the performance of the proposed method, we compared it with the state-of-the-art support vector machine (SVM) classifier on Human data set. The experimental results obtained indicate that our method is highly effective for PPIs prediction and can be taken as a supplementary tool for future proteomics research.

  15. Real-time Tsunami Inundation Prediction Using High Performance Computers

    NASA Astrophysics Data System (ADS)

    Oishi, Y.; Imamura, F.; Sugawara, D.

    2014-12-01

    earthquake occurs took about 2 minutes, which would be sufficient for a practical tsunami inundation predictions. In the presentation, the computational performance of our faster-than-real-time tsunami inundation model will be shown, and preferable tsunami wave source analysis for an accurate inundation prediction will also be discussed.

  16. Combining Structural Modeling with Ensemble Machine Learning to Accurately Predict Protein Fold Stability and Binding Affinity Effects upon Mutation

    PubMed Central

    Garcia Lopez, Sebastian; Kim, Philip M.

    2014-01-01

    Advances in sequencing have led to a rapid accumulation of mutations, some of which are associated with diseases. However, to draw mechanistic conclusions, a biochemical understanding of these mutations is necessary. For coding mutations, accurate prediction of significant changes in either the stability of proteins or their affinity to their binding partners is required. Traditional methods have used semi-empirical force fields, while newer methods employ machine learning of sequence and structural features. Here, we show how combining both of these approaches leads to a marked boost in accuracy. We introduce ELASPIC, a novel ensemble machine learning approach that is able to predict stability effects upon mutation in both, domain cores and domain-domain interfaces. We combine semi-empirical energy terms, sequence conservation, and a wide variety of molecular details with a Stochastic Gradient Boosting of Decision Trees (SGB-DT) algorithm. The accuracy of our predictions surpasses existing methods by a considerable margin, achieving correlation coefficients of 0.77 for stability, and 0.75 for affinity predictions. Notably, we integrated homology modeling to enable proteome-wide prediction and show that accurate prediction on modeled structures is possible. Lastly, ELASPIC showed significant differences between various types of disease-associated mutations, as well as between disease and common neutral mutations. Unlike pure sequence-based prediction methods that try to predict phenotypic effects of mutations, our predictions unravel the molecular details governing the protein instability, and help us better understand the molecular causes of diseases. PMID:25243403

  17. Toward accurate prediction of pKa values for internal protein residues: the importance of conformational relaxation and desolvation energy.

    PubMed

    Wallace, Jason A; Wang, Yuhang; Shi, Chuanyin; Pastoor, Kevin J; Nguyen, Bao-Linh; Xia, Kai; Shen, Jana K

    2011-12-01

    Proton uptake or release controls many important biological processes, such as energy transduction, virus replication, and catalysis. Accurate pK(a) prediction informs about proton pathways, thereby revealing detailed acid-base mechanisms. Physics-based methods in the framework of molecular dynamics simulations not only offer pK(a) predictions but also inform about the physical origins of pK(a) shifts and provide details of ionization-induced conformational relaxation and large-scale transitions. One such method is the recently developed continuous constant pH molecular dynamics (CPHMD) method, which has been shown to be an accurate and robust pK(a) prediction tool for naturally occurring titratable residues. To further examine the accuracy and limitations of CPHMD, we blindly predicted the pK(a) values for 87 titratable residues introduced in various hydrophobic regions of staphylococcal nuclease and variants. The predictions gave a root-mean-square deviation of 1.69 pK units from experiment, and there were only two pK(a)'s with errors greater than 3.5 pK units. Analysis of the conformational fluctuation of titrating side-chains in the context of the errors of calculated pK(a) values indicate that explicit treatment of conformational flexibility and the associated dielectric relaxation gives CPHMD a distinct advantage. Analysis of the sources of errors suggests that more accurate pK(a) predictions can be obtained for the most deeply buried residues by improving the accuracy in calculating desolvation energies. Furthermore, it is found that the generalized Born implicit-solvent model underlying the current CPHMD implementation slightly distorts the local conformational environment such that the inclusion of an explicit-solvent representation may offer improvement of accuracy. Copyright © 2011 Wiley-Liss, Inc.

  18. Accurate perception of negative emotions predicts functional capacity in schizophrenia.

    PubMed

    Abram, Samantha V; Karpouzian, Tatiana M; Reilly, James L; Derntl, Birgit; Habel, Ute; Smith, Matthew J

    2014-04-30

    Several studies suggest facial affect perception (FAP) deficits in schizophrenia are linked to poorer social functioning. However, whether reduced functioning is associated with inaccurate perception of specific emotional valence or a global FAP impairment remains unclear. The present study examined whether impairment in the perception of specific emotional valences (positive, negative) and neutrality were uniquely associated with social functioning, using a multimodal social functioning battery. A sample of 59 individuals with schizophrenia and 41 controls completed a computerized FAP task, and measures of functional capacity, social competence, and social attainment. Participants also underwent neuropsychological testing and symptom assessment. Regression analyses revealed that only accurately perceiving negative emotions explained significant variance (7.9%) in functional capacity after accounting for neurocognitive function and symptoms. Partial correlations indicated that accurately perceiving anger, in particular, was positively correlated with functional capacity. FAP for positive, negative, or neutral emotions were not related to social competence or social attainment. Our findings were consistent with prior literature suggesting negative emotions are related to functional capacity in schizophrenia. Furthermore, the observed relationship between perceiving anger and performance of everyday living skills is novel and warrants further exploration. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

  19. Prognostic breast cancer signature identified from 3D culture model accurately predicts clinical outcome across independent datasets

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

    Martin, Katherine J.; Patrick, Denis R.; Bissell, Mina J.

    2008-10-20

    One of the major tenets in breast cancer research is that early detection is vital for patient survival by increasing treatment options. To that end, we have previously used a novel unsupervised approach to identify a set of genes whose expression predicts prognosis of breast cancer patients. The predictive genes were selected in a well-defined three dimensional (3D) cell culture model of non-malignant human mammary epithelial cell morphogenesis as down-regulated during breast epithelial cell acinar formation and cell cycle arrest. Here we examine the ability of this gene signature (3D-signature) to predict prognosis in three independent breast cancer microarray datasetsmore » having 295, 286, and 118 samples, respectively. Our results show that the 3D-signature accurately predicts prognosis in three unrelated patient datasets. At 10 years, the probability of positive outcome was 52, 51, and 47 percent in the group with a poor-prognosis signature and 91, 75, and 71 percent in the group with a good-prognosis signature for the three datasets, respectively (Kaplan-Meier survival analysis, p<0.05). Hazard ratios for poor outcome were 5.5 (95% CI 3.0 to 12.2, p<0.0001), 2.4 (95% CI 1.6 to 3.6, p<0.0001) and 1.9 (95% CI 1.1 to 3.2, p = 0.016) and remained significant for the two larger datasets when corrected for estrogen receptor (ER) status. Hence the 3D-signature accurately predicts breast cancer outcome in both ER-positive and ER-negative tumors, though individual genes differed in their prognostic ability in the two subtypes. Genes that were prognostic in ER+ patients are AURKA, CEP55, RRM2, EPHA2, FGFBP1, and VRK1, while genes prognostic in ER patients include ACTB, FOXM1 and SERPINE2 (Kaplan-Meier p<0.05). Multivariable Cox regression analysis in the largest dataset showed that the 3D-signature was a strong independent factor in predicting breast cancer outcome. The 3D-signature accurately predicts breast cancer outcome across multiple datasets and holds

  20. MetaPSICOV: combining coevolution methods for accurate prediction of contacts and long range hydrogen bonding in proteins.

    PubMed

    Jones, David T; Singh, Tanya; Kosciolek, Tomasz; Tetchner, Stuart

    2015-04-01

    Recent developments of statistical techniques to infer direct evolutionary couplings between residue pairs have rendered covariation-based contact prediction a viable means for accurate 3D modelling of proteins, with no information other than the sequence required. To extend the usefulness of contact prediction, we have designed a new meta-predictor (MetaPSICOV) which combines three distinct approaches for inferring covariation signals from multiple sequence alignments, considers a broad range of other sequence-derived features and, uniquely, a range of metrics which describe both the local and global quality of the input multiple sequence alignment. Finally, we use a two-stage predictor, where the second stage filters the output of the first stage. This two-stage predictor is additionally evaluated on its ability to accurately predict the long range network of hydrogen bonds, including correctly assigning the donor and acceptor residues. Using the original PSICOV benchmark set of 150 protein families, MetaPSICOV achieves a mean precision of 0.54 for top-L predicted long range contacts-around 60% higher than PSICOV, and around 40% better than CCMpred. In de novo protein structure prediction using FRAGFOLD, MetaPSICOV is able to improve the TM-scores of models by a median of 0.05 compared with PSICOV. Lastly, for predicting long range hydrogen bonding, MetaPSICOV-HB achieves a precision of 0.69 for the top-L/10 hydrogen bonds compared with just 0.26 for the baseline MetaPSICOV. MetaPSICOV is available as a freely available web server at http://bioinf.cs.ucl.ac.uk/MetaPSICOV. Raw data (predicted contact lists and 3D models) and source code can be downloaded from http://bioinf.cs.ucl.ac.uk/downloads/MetaPSICOV. Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press.

  1. Quasi-closed phase forward-backward linear prediction analysis of speech for accurate formant detection and estimation.

    PubMed

    Gowda, Dhananjaya; Airaksinen, Manu; Alku, Paavo

    2017-09-01

    Recently, a quasi-closed phase (QCP) analysis of speech signals for accurate glottal inverse filtering was proposed. However, the QCP analysis which belongs to the family of temporally weighted linear prediction (WLP) methods uses the conventional forward type of sample prediction. This may not be the best choice especially in computing WLP models with a hard-limiting weighting function. A sample selective minimization of the prediction error in WLP reduces the effective number of samples available within a given window frame. To counter this problem, a modified quasi-closed phase forward-backward (QCP-FB) analysis is proposed, wherein each sample is predicted based on its past as well as future samples thereby utilizing the available number of samples more effectively. Formant detection and estimation experiments on synthetic vowels generated using a physical modeling approach as well as natural speech utterances show that the proposed QCP-FB method yields statistically significant improvements over the conventional linear prediction and QCP methods.

  2. WegoLoc: accurate prediction of protein subcellular localization using weighted Gene Ontology terms.

    PubMed

    Chi, Sang-Mun; Nam, Dougu

    2012-04-01

    We present an accurate and fast web server, WegoLoc for predicting subcellular localization of proteins based on sequence similarity and weighted Gene Ontology (GO) information. A term weighting method in the text categorization process is applied to GO terms for a support vector machine classifier. As a result, WegoLoc surpasses the state-of-the-art methods for previously used test datasets. WegoLoc supports three eukaryotic kingdoms (animals, fungi and plants) and provides human-specific analysis, and covers several sets of cellular locations. In addition, WegoLoc provides (i) multiple possible localizations of input protein(s) as well as their corresponding probability scores, (ii) weights of GO terms representing the contribution of each GO term in the prediction, and (iii) a BLAST E-value for the best hit with GO terms. If the similarity score does not meet a given threshold, an amino acid composition-based prediction is applied as a backup method. WegoLoc and User's guide are freely available at the website http://www.btool.org/WegoLoc smchiks@ks.ac.kr; dougnam@unist.ac.kr Supplementary data is available at http://www.btool.org/WegoLoc.

  3. Sex-specific lean body mass predictive equations are accurate in the obese paediatric population

    PubMed Central

    Jackson, Lanier B.; Henshaw, Melissa H.; Carter, Janet; Chowdhury, Shahryar M.

    2015-01-01

    Background The clinical assessment of lean body mass (LBM) is challenging in obese children. A sex-specific predictive equation for LBM derived from anthropometric data was recently validated in children. Aim The purpose of this study was to independently validate these predictive equations in the obese paediatric population. Subjects and methods Obese subjects aged 4–21 were analysed retrospectively. Predicted LBM (LBMp) was calculated using equations previously developed in children. Measured LBM (LBMm) was derived from dual-energy x-ray absorptiometry. Agreement was expressed as [(LBMm-LBMp)/LBMm] with 95% limits of agreement. Results Of 310 enrolled patients, 195 (63%) were females. The mean age was 11.8 ± 3.4 years and mean BMI Z-score was 2.3 ± 0.4. The average difference between LBMm and LBMp was −0.6% (−17.0%, 15.8%). Pearson’s correlation revealed a strong linear relationship between LBMm and LBMp (r=0.97, p<0.01). Conclusion This study validates the use of these clinically-derived sex-specific LBM predictive equations in the obese paediatric population. Future studies should use these equations to improve the ability to accurately classify LBM in obese children. PMID:26287383

  4. Accurate prediction of cation-π interaction energy using substituent effects.

    PubMed

    Sayyed, Fareed Bhasha; Suresh, Cherumuttathu H

    2012-06-14

    (M(+))' and ΔV(min). All the Φ-X···M(+) systems showed good agreement between the calculated and predicted E(M(+))() values, suggesting that the ΔV(min) approach to substituent effect is accurate and useful for predicting the interactive behavior of substituted π-systems with cations.

  5. Prediction of pump cavitation performance

    NASA Technical Reports Server (NTRS)

    Moore, R. D.

    1974-01-01

    A method for predicting pump cavitation performance with various liquids, liquid temperatures, and rotative speeds is presented. Use of the method requires that two sets of test data be available for the pump of interest. Good agreement between predicted and experimental results of cavitation performance was obtained for several pumps operated in liquids which exhibit a wide range of properties. Two cavitation parameters which qualitatively evaluate pump cavitation performance are also presented.

  6. A systematic review of breast cancer incidence risk prediction models with meta-analysis of their performance.

    PubMed

    Meads, Catherine; Ahmed, Ikhlaaq; Riley, Richard D

    2012-04-01

    A risk prediction model is a statistical tool for estimating the probability that a currently healthy individual with specific risk factors will develop a condition in the future such as breast cancer. Reliably accurate prediction models can inform future disease burdens, health policies and individual decisions. Breast cancer prediction models containing modifiable risk factors, such as alcohol consumption, BMI or weight, condom use, exogenous hormone use and physical activity, are of particular interest to women who might be considering how to reduce their risk of breast cancer and clinicians developing health policies to reduce population incidence rates. We performed a systematic review to identify and evaluate the performance of prediction models for breast cancer that contain modifiable factors. A protocol was developed and a sensitive search in databases including MEDLINE and EMBASE was conducted in June 2010. Extensive use was made of reference lists. Included were any articles proposing or validating a breast cancer prediction model in a general female population, with no language restrictions. Duplicate data extraction and quality assessment were conducted. Results were summarised qualitatively, and where possible meta-analysis of model performance statistics was undertaken. The systematic review found 17 breast cancer models, each containing a different but often overlapping set of modifiable and other risk factors, combined with an estimated baseline risk that was also often different. Quality of reporting was generally poor, with characteristics of included participants and fitted model results often missing. Only four models received independent validation in external data, most notably the 'Gail 2' model with 12 validations. None of the models demonstrated consistently outstanding ability to accurately discriminate between those who did and those who did not develop breast cancer. For example, random-effects meta-analyses of the performance of the

  7. Accuracy of the actuator disc-RANS approach for predicting the performance and wake of tidal turbines.

    PubMed

    Batten, W M J; Harrison, M E; Bahaj, A S

    2013-02-28

    The actuator disc-RANS model has widely been used in wind and tidal energy to predict the wake of a horizontal axis turbine. The model is appropriate where large-scale effects of the turbine on a flow are of interest, for example, when considering environmental impacts, or arrays of devices. The accuracy of the model for modelling the wake of tidal stream turbines has not been demonstrated, and flow predictions presented in the literature for similar modelled scenarios vary significantly. This paper compares the results of the actuator disc-RANS model, where the turbine forces have been derived using a blade-element approach, to experimental data measured in the wake of a scaled turbine. It also compares the results with those of a simpler uniform actuator disc model. The comparisons show that the model is accurate and can predict up to 94 per cent of the variation in the experimental velocity data measured on the centreline of the wake, therefore demonstrating that the actuator disc-RANS model is an accurate approach for modelling a turbine wake, and a conservative approach to predict performance and loads. It can therefore be applied to similar scenarios with confidence.

  8. Accurate predictions of iron redox state in silicate glasses: A multivariate approach using X-ray absorption spectroscopy

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

    Dyar, M. Darby; McCanta, Molly; Breves, Elly

    2016-03-01

    Pre-edge features in the K absorption edge of X-ray absorption spectra are commonly used to predict Fe3+ valence state in silicate glasses. However, this study shows that using the entire spectral region from the pre-edge into the extended X-ray absorption fine-structure region provides more accurate results when combined with multivariate analysis techniques. The least absolute shrinkage and selection operator (lasso) regression technique yields %Fe3+ values that are accurate to ±3.6% absolute when the full spectral region is employed. This method can be used across a broad range of glass compositions, is easily automated, and is demonstrated to yield accurate resultsmore » from different synchrotrons. It will enable future studies involving X-ray mapping of redox gradients on standard thin sections at 1 × 1 μm pixel sizes.« less

  9. Accurate predictions of iron redox state in silicate glasses: A multivariate approach using X-ray absorption spectroscopy

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

    Dyar, M. Darby; McCanta, Molly; Breves, Elly

    2016-03-01

    Pre-edge features in the K absorption edge of X-ray absorption spectra are commonly used to predict Fe 3+ valence state in silicate glasses. However, this study shows that using the entire spectral region from the pre-edge into the extended X-ray absorption fine-structure region provides more accurate results when combined with multivariate analysis techniques. The least absolute shrinkage and selection operator (lasso) regression technique yields %Fe 3+ values that are accurate to ±3.6% absolute when the full spectral region is employed. This method can be used across a broad range of glass compositions, is easily automated, and is demonstrated to yieldmore » accurate results from different synchrotrons. It will enable future studies involving X-ray mapping of redox gradients on standard thin sections at 1 × 1 μm pixel sizes.« less

  10. Accurate indel prediction using paired-end short reads

    PubMed Central

    2013-01-01

    Background One of the major open challenges in next generation sequencing (NGS) is the accurate identification of structural variants such as insertions and deletions (indels). Current methods for indel calling assign scores to different types of evidence or counter-evidence for the presence of an indel, such as the number of split read alignments spanning the boundaries of a deletion candidate or reads that map within a putative deletion. Candidates with a score above a manually defined threshold are then predicted to be true indels. As a consequence, structural variants detected in this manner contain many false positives. Results Here, we present a machine learning based method which is able to discover and distinguish true from false indel candidates in order to reduce the false positive rate. Our method identifies indel candidates using a discriminative classifier based on features of split read alignment profiles and trained on true and false indel candidates that were validated by Sanger sequencing. We demonstrate the usefulness of our method with paired-end Illumina reads from 80 genomes of the first phase of the 1001 Genomes Project ( http://www.1001genomes.org) in Arabidopsis thaliana. Conclusion In this work we show that indel classification is a necessary step to reduce the number of false positive candidates. We demonstrate that missing classification may lead to spurious biological interpretations. The software is available at: http://agkb.is.tuebingen.mpg.de/Forschung/SV-M/. PMID:23442375

  11. Combining Mean and Standard Deviation of Hounsfield Unit Measurements from Preoperative CT Allows More Accurate Prediction of Urinary Stone Composition Than Mean Hounsfield Units Alone.

    PubMed

    Tailly, Thomas; Larish, Yaniv; Nadeau, Brandon; Violette, Philippe; Glickman, Leonard; Olvera-Posada, Daniel; Alenezi, Husain; Amann, Justin; Denstedt, John; Razvi, Hassan

    2016-04-01

    The mineral composition of a urinary stone may influence its surgical and medical treatment. Previous attempts at identifying stone composition based on mean Hounsfield Units (HUm) have had varied success. We aimed to evaluate the additional use of standard deviation of HU (HUsd) to more accurately predict stone composition. We identified patients from two centers who had undergone urinary stone treatment between 2006 and 2013 and had mineral stone analysis and a computed tomography (CT) available. HUm and HUsd of the stones were compared with ANOVA. Receiver operative characteristic analysis with area under the curve (AUC), Youden index, and likelihood ratio calculations were performed. Data were available for 466 patients. The major components were calcium oxalate monohydrate (COM), uric acid, hydroxyapatite, struvite, brushite, cystine, and CO dihydrate (COD) in 41.4%, 19.3%, 12.4%, 7.5%, 5.8%, 5.4%, and 4.7% of patients, respectively. The HUm of UA and Br was significantly lower and higher than the HUm of any other stone type, respectively. HUm and HUsd were most accurate in predicting uric acid with an AUC of 0.969 and 0.851, respectively. The combined use of HUm and HUsd resulted in increased positive predictive value and higher likelihood ratios for identifying a stone's mineral composition for all stone types but COM. To the best of our knowledge, this is the first report of CT data aiding in the prediction of brushite stone composition. Both HUm and HUsd can help predict stone composition and their combined use results in higher likelihood ratios influencing probability.

  12. Using radiance predicted by the P3 approximation in a spherical geometry to predict tissue optical properties

    NASA Astrophysics Data System (ADS)

    Dickey, Dwayne J.; Moore, Ronald B.; Tulip, John

    2001-01-01

    For photodynamic therapy of solid tumors, such as prostatic carcinoma, to be achieved, an accurate model to predict tissue parameters and light dose must be found. Presently, most analytical light dosimetry models are fluence based and are not clinically viable for tissue characterization. Other methods of predicting optical properties, such as Monet Carlo, are accurate but far too time consuming for clinical application. However, radiance predicted by the P3-Approximation, an anaylitical solution to the transport equation, may be a viable and accurate alternative. The P3-Approximation accurately predicts optical parameters in intralipid/methylene blue based phantoms in a spherical geometry. The optical parameters furnished by the radiance, when introduced into fluence predicted by both P3- Approximation and Grosjean Theory, correlate well with experimental data. The P3-Approximation also predicts the optical properties of prostate tissue, agreeing with documented optical parameters. The P3-Approximation could be the clinical tool necessary to facilitate PDT of solid tumors because of the limited number of invasive measurements required and the speed in which accurate calculations can be performed.

  13. Initial Cognitive Performance Predicts Longitudinal Aviator Performance

    PubMed Central

    Jo, Booil; Adamson, Maheen M.; Kennedy, Quinn; Noda, Art; Hernandez, Beatriz; Zeitzer, Jamie M.; Friedman, Leah F.; Fairchild, Kaci; Scanlon, Blake K.; Murphy, Greer M.; Taylor, Joy L.

    2011-01-01

    Objectives. The goal of the study was to improve prediction of longitudinal flight simulator performance by studying cognitive factors that may moderate the influence of chronological age. Method. We examined age-related change in aviation performance in aircraft pilots in relation to baseline cognitive ability measures and aviation expertise. Participants were aircraft pilots (N = 276) aged 40–77.9. Flight simulator performance and cognition were tested yearly; there were an average of 4.3 (± 2.7; range 1–13) data points per participant. Each participant was classified into one of the three levels of aviation expertise based on Federal Aviation Administration pilot proficiency ratings: least, moderate, or high expertise. Results. Addition of measures of cognitive processing speed and executive function to a model of age-related change in aviation performance significantly improved the model. Processing speed and executive function performance interacted such that the slowest rate of decline in flight simulator performance was found in aviators with the highest scores on tests of these abilities. Expertise was beneficial to pilots across the age range studied; however, expertise did not show evidence of reducing the effect of age. Discussion. These data suggest that longitudinal performance on an important real-world activity can be predicted by initial assessment of relevant cognitive abilities. PMID:21586627

  14. Molecular Dynamics in Mixed Solvents Reveals Protein-Ligand Interactions, Improves Docking, and Allows Accurate Binding Free Energy Predictions.

    PubMed

    Arcon, Juan Pablo; Defelipe, Lucas A; Modenutti, Carlos P; López, Elias D; Alvarez-Garcia, Daniel; Barril, Xavier; Turjanski, Adrián G; Martí, Marcelo A

    2017-04-24

    One of the most important biological processes at the molecular level is the formation of protein-ligand complexes. Therefore, determining their structure and underlying key interactions is of paramount relevance and has direct applications in drug development. Because of its low cost relative to its experimental sibling, molecular dynamics (MD) simulations in the presence of different solvent probes mimicking specific types of interactions have been increasingly used to analyze protein binding sites and reveal protein-ligand interaction hot spots. However, a systematic comparison of different probes and their real predictive power from a quantitative and thermodynamic point of view is still missing. In the present work, we have performed MD simulations of 18 different proteins in pure water as well as water mixtures of ethanol, acetamide, acetonitrile and methylammonium acetate, leading to a total of 5.4 μs simulation time. For each system, we determined the corresponding solvent sites, defined as space regions adjacent to the protein surface where the probability of finding a probe atom is higher than that in the bulk solvent. Finally, we compared the identified solvent sites with 121 different protein-ligand complexes and used them to perform molecular docking and ligand binding free energy estimates. Our results show that combining solely water and ethanol sites allows sampling over 70% of all possible protein-ligand interactions, especially those that coincide with ligand-based pharmacophoric points. Most important, we also show how the solvent sites can be used to significantly improve ligand docking in terms of both accuracy and precision, and that accurate predictions of ligand binding free energies, along with relative ranking of ligand affinity, can be performed.

  15. Development and Validation of a Multidisciplinary Tool for Accurate and Efficient Rotorcraft Noise Prediction (MUTE)

    NASA Technical Reports Server (NTRS)

    Liu, Yi; Anusonti-Inthra, Phuriwat; Diskin, Boris

    2011-01-01

    A physics-based, systematically coupled, multidisciplinary prediction tool (MUTE) for rotorcraft noise was developed and validated with a wide range of flight configurations and conditions. MUTE is an aggregation of multidisciplinary computational tools that accurately and efficiently model the physics of the source of rotorcraft noise, and predict the noise at far-field observer locations. It uses systematic coupling approaches among multiple disciplines including Computational Fluid Dynamics (CFD), Computational Structural Dynamics (CSD), and high fidelity acoustics. Within MUTE, advanced high-order CFD tools are used around the rotor blade to predict the transonic flow (shock wave) effects, which generate the high-speed impulsive noise. Predictions of the blade-vortex interaction noise in low speed flight are also improved by using the Particle Vortex Transport Method (PVTM), which preserves the wake flow details required for blade/wake and fuselage/wake interactions. The accuracy of the source noise prediction is further improved by utilizing a coupling approach between CFD and CSD, so that the effects of key structural dynamics, elastic blade deformations, and trim solutions are correctly represented in the analysis. The blade loading information and/or the flow field parameters around the rotor blade predicted by the CFD/CSD coupling approach are used to predict the acoustic signatures at far-field observer locations with a high-fidelity noise propagation code (WOPWOP3). The predicted results from the MUTE tool for rotor blade aerodynamic loading and far-field acoustic signatures are compared and validated with a variation of experimental data sets, such as UH60-A data, DNW test data and HART II test data.

  16. Limited Sampling Strategy for Accurate Prediction of Pharmacokinetics of Saroglitazar: A 3-point Linear Regression Model Development and Successful Prediction of Human Exposure.

    PubMed

    Joshi, Shuchi N; Srinivas, Nuggehally R; Parmar, Deven V

    2018-03-01

    Our aim was to develop and validate the extrapolative performance of a regression model using a limited sampling strategy for accurate estimation of the area under the plasma concentration versus time curve for saroglitazar. Healthy subject pharmacokinetic data from a well-powered food-effect study (fasted vs fed treatments; n = 50) was used in this work. The first 25 subjects' serial plasma concentration data up to 72 hours and corresponding AUC 0-t (ie, 72 hours) from the fasting group comprised a training dataset to develop the limited sampling model. The internal datasets for prediction included the remaining 25 subjects from the fasting group and all 50 subjects from the fed condition of the same study. The external datasets included pharmacokinetic data for saroglitazar from previous single-dose clinical studies. Limited sampling models were composed of 1-, 2-, and 3-concentration-time points' correlation with AUC 0-t of saroglitazar. Only models with regression coefficients (R 2 ) >0.90 were screened for further evaluation. The best R 2 model was validated for its utility based on mean prediction error, mean absolute prediction error, and root mean square error. Both correlations between predicted and observed AUC 0-t of saroglitazar and verification of precision and bias using Bland-Altman plot were carried out. None of the evaluated 1- and 2-concentration-time points models achieved R 2 > 0.90. Among the various 3-concentration-time points models, only 4 equations passed the predefined criterion of R 2 > 0.90. Limited sampling models with time points 0.5, 2, and 8 hours (R 2 = 0.9323) and 0.75, 2, and 8 hours (R 2 = 0.9375) were validated. Mean prediction error, mean absolute prediction error, and root mean square error were <30% (predefined criterion) and correlation (r) was at least 0.7950 for the consolidated internal and external datasets of 102 healthy subjects for the AUC 0-t prediction of saroglitazar. The same models, when applied to the AUC 0-t

  17. Local Debonding and Fiber Breakage in Composite Materials Modeled Accurately

    NASA Technical Reports Server (NTRS)

    Bednarcyk, Brett A.; Arnold, Steven M.

    2001-01-01

    A prerequisite for full utilization of composite materials in aerospace components is accurate design and life prediction tools that enable the assessment of component performance and reliability. Such tools assist both structural analysts, who design and optimize structures composed of composite materials, and materials scientists who design and optimize the composite materials themselves. NASA Glenn Research Center's Micromechanics Analysis Code with Generalized Method of Cells (MAC/GMC) software package (http://www.grc.nasa.gov/WWW/LPB/mac) addresses this need for composite design and life prediction tools by providing a widely applicable and accurate approach to modeling composite materials. Furthermore, MAC/GMC serves as a platform for incorporating new local models and capabilities that are under development at NASA, thus enabling these new capabilities to progress rapidly to a stage in which they can be employed by the code's end users.

  18. Predicting performance for ecological restoration: A case study using Spartina altemiflora

    USGS Publications Warehouse

    Travis, S.E.; Grace, J.B.

    2010-01-01

    The success of population-based ecological restoration relies on the growth and reproductive performance of selected donor materials, whether consisting of whole plants or seed. Accurately predicting performance requires an understanding of a variety of underlying processes, particularly gene flow and selection, which can be measured, at least in part, using surrogates such as neutral marker genetic distances and simple latitudinal effects. Here we apply a structural equation modeling approach to understanding and predicting performance in a widespread salt marsh grass, Spartina alterniflora, commonly used for ecological restoration throughout its native range in North America. We collected source materials from throughout this range, consisting of eight clones each from 23 populations, for transplantation to a common garden site in coastal Louisiana and monitored their performance. We modeled performance as a latent process described by multiple indicator variables (e.g., clone diameter, stem number) and estimated direct and indirect influences of geographic and genetic distances on performance. Genetic distances were determined by comparison of neutral molecular markers with those from a local population at the common garden site. Geographic distance metrics included dispersal distance (the minimum distance over water between donor and experimental sites) and latitude. Model results indicate direct effects of genetic distance and latitude on performance variation among the donor sites. Standardized effect strengths indicate that performance was roughly twice as sensitive to variation in genetic distance as to latitudinal variation. Dispersal distance had an indirect influence on performance through effects on genetic distance, indicating a typical pattern of genetic isolation by distance. Latitude also had an indirect effect on genetic distance through its linear relationship with dispersal distance. Three performance indicators had significant loadings on

  19. Improved predictive modeling of white LEDs with accurate luminescence simulation and practical inputs with TracePro opto-mechanical design software

    NASA Astrophysics Data System (ADS)

    Tsao, Chao-hsi; Freniere, Edward R.; Smith, Linda

    2009-02-01

    The use of white LEDs for solid-state lighting to address applications in the automotive, architectural and general illumination markets is just emerging. LEDs promise greater energy efficiency and lower maintenance costs. However, there is a significant amount of design and cost optimization to be done while companies continue to improve semiconductor manufacturing processes and begin to apply more efficient and better color rendering luminescent materials such as phosphor and quantum dot nanomaterials. In the last decade, accurate and predictive opto-mechanical software modeling has enabled adherence to performance, consistency, cost, and aesthetic criteria without the cost and time associated with iterative hardware prototyping. More sophisticated models that include simulation of optical phenomenon, such as luminescence, promise to yield designs that are more predictive - giving design engineers and materials scientists more control over the design process to quickly reach optimum performance, manufacturability, and cost criteria. A design case study is presented where first, a phosphor formulation and excitation source are optimized for a white light. The phosphor formulation, the excitation source and other LED components are optically and mechanically modeled and ray traced. Finally, its performance is analyzed. A blue LED source is characterized by its relative spectral power distribution and angular intensity distribution. YAG:Ce phosphor is characterized by relative absorption, excitation and emission spectra, quantum efficiency and bulk absorption coefficient. Bulk scatter properties are characterized by wavelength dependent scatter coefficients, anisotropy and bulk absorption coefficient.

  20. Accurate First-Principles Spectra Predictions for Planetological and Astrophysical Applications at Various T-Conditions

    NASA Astrophysics Data System (ADS)

    Rey, M.; Nikitin, A. V.; Tyuterev, V.

    2014-06-01

    Knowledge of near infrared intensities of rovibrational transitions of polyatomic molecules is essential for the modeling of various planetary atmospheres, brown dwarfs and for other astrophysical applications 1,2,3. For example, to analyze exoplanets, atmospheric models have been developed, thus making the need to provide accurate spectroscopic data. Consequently, the spectral characterization of such planetary objects relies on the necessity of having adequate and reliable molecular data in extreme conditions (temperature, optical path length, pressure). On the other hand, in the modeling of astrophysical opacities, millions of lines are generally involved and the line-by-line extraction is clearly not feasible in laboratory measurements. It is thus suggested that this large amount of data could be interpreted only by reliable theoretical predictions. There exists essentially two theoretical approaches for the computation and prediction of spectra. The first one is based on empirically-fitted effective spectroscopic models. Another way for computing energies, line positions and intensities is based on global variational calculations using ab initio surfaces. They do not yet reach the spectroscopic accuracy stricto sensu but implicitly account for all intramolecular interactions including resonance couplings in a wide spectral range. The final aim of this work is to provide reliable predictions which could be quantitatively accurate with respect to the precision of available observations and as complete as possible. All this thus requires extensive first-principles quantum mechanical calculations essentially based on three necessary ingredients which are (i) accurate intramolecular potential energy surface and dipole moment surface components well-defined in a large range of vibrational displacements and (ii) efficient computational methods combined with suitable choices of coordinates to account for molecular symmetry properties and to achieve a good numerical

  1. A probabilistic and adaptive approach to modeling performance of pavement infrastructure

    DOT National Transportation Integrated Search

    2007-08-01

    Accurate prediction of pavement performance is critical to pavement management agencies. Reliable and accurate predictions of pavement infrastructure performance can save significant amounts of money for pavement infrastructure management agencies th...

  2. Accurate prediction of vaccine stability under real storage conditions and during temperature excursions.

    PubMed

    Clénet, Didier

    2018-04-01

    Due to their thermosensitivity, most vaccines must be kept refrigerated from production to use. To successfully carry out global immunization programs, ensuring the stability of vaccines is crucial. In this context, two important issues are critical, namely: (i) predicting vaccine stability and (ii) preventing product damage due to excessive temperature excursions outside of the recommended storage conditions (cold chain break). We applied a combination of advanced kinetics and statistical analyses on vaccine forced degradation data to accurately describe the loss of antigenicity for a multivalent freeze-dried inactivated virus vaccine containing three variants. The screening of large amounts of kinetic models combined with a statistical model selection approach resulted in the identification of two-step kinetic models. Predictions based on kinetic analysis and experimental stability data were in agreement, with approximately five percentage points difference from real values for long-term stability storage conditions, after excursions of temperature and during experimental shipments of freeze-dried products. Results showed that modeling a few months of forced degradation can be used to predict various time and temperature profiles endured by vaccines, i.e. long-term stability, short time excursions outside the labeled storage conditions or shipments at ambient temperature, with high accuracy. Pharmaceutical applications of the presented kinetics-based approach are discussed. Copyright © 2018 The Author. Published by Elsevier B.V. All rights reserved.

  3. Accurate prediction of X-ray pulse properties from a free-electron laser using machine learning

    DOE PAGES

    Sanchez-Gonzalez, A.; Micaelli, P.; Olivier, C.; ...

    2017-06-05

    Free-electron lasers providing ultra-short high-brightness pulses of X-ray radiation have great potential for a wide impact on science, and are a critical element for unravelling the structural dynamics of matter. To fully harness this potential, we must accurately know the X-ray properties: intensity, spectrum and temporal profile. Owing to the inherent fluctuations in free-electron lasers, this mandates a full characterization of the properties for each and every pulse. While diagnostics of these properties exist, they are often invasive and many cannot operate at a high-repetition rate. Here, we present a technique for circumventing this limitation. Employing a machine learning strategy,more » we can accurately predict X-ray properties for every shot using only parameters that are easily recorded at high-repetition rate, by training a model on a small set of fully diagnosed pulses. Lastly, this opens the door to fully realizing the promise of next-generation high-repetition rate X-ray lasers.« less

  4. Accurate prediction of X-ray pulse properties from a free-electron laser using machine learning

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

    Sanchez-Gonzalez, A.; Micaelli, P.; Olivier, C.

    Free-electron lasers providing ultra-short high-brightness pulses of X-ray radiation have great potential for a wide impact on science, and are a critical element for unravelling the structural dynamics of matter. To fully harness this potential, we must accurately know the X-ray properties: intensity, spectrum and temporal profile. Owing to the inherent fluctuations in free-electron lasers, this mandates a full characterization of the properties for each and every pulse. While diagnostics of these properties exist, they are often invasive and many cannot operate at a high-repetition rate. Here, we present a technique for circumventing this limitation. Employing a machine learning strategy,more » we can accurately predict X-ray properties for every shot using only parameters that are easily recorded at high-repetition rate, by training a model on a small set of fully diagnosed pulses. Lastly, this opens the door to fully realizing the promise of next-generation high-repetition rate X-ray lasers.« less

  5. Prediction of Slot Shape and Slot Size for Improving the Performance of Microstrip Antennas Using Knowledge-Based Neural Networks.

    PubMed

    Khan, Taimoor; De, Asok

    2014-01-01

    In the last decade, artificial neural networks have become very popular techniques for computing different performance parameters of microstrip antennas. The proposed work illustrates a knowledge-based neural networks model for predicting the appropriate shape and accurate size of the slot introduced on the radiating patch for achieving desired level of resonance, gain, directivity, antenna efficiency, and radiation efficiency for dual-frequency operation. By incorporating prior knowledge in neural model, the number of required training patterns is drastically reduced. Further, the neural model incorporated with prior knowledge can be used for predicting response in extrapolation region beyond the training patterns region. For validation, a prototype is also fabricated and its performance parameters are measured. A very good agreement is attained between measured, simulated, and predicted results.

  6. Learning a weighted sequence model of the nucleosome core and linker yields more accurate predictions in Saccharomyces cerevisiae and Homo sapiens.

    PubMed

    Reynolds, Sheila M; Bilmes, Jeff A; Noble, William Stafford

    2010-07-08

    DNA in eukaryotes is packaged into a chromatin complex, the most basic element of which is the nucleosome. The precise positioning of the nucleosome cores allows for selective access to the DNA, and the mechanisms that control this positioning are important pieces of the gene expression puzzle. We describe a large-scale nucleosome pattern that jointly characterizes the nucleosome core and the adjacent linkers and is predominantly characterized by long-range oscillations in the mono, di- and tri-nucleotide content of the DNA sequence, and we show that this pattern can be used to predict nucleosome positions in both Homo sapiens and Saccharomyces cerevisiae more accurately than previously published methods. Surprisingly, in both H. sapiens and S. cerevisiae, the most informative individual features are the mono-nucleotide patterns, although the inclusion of di- and tri-nucleotide features results in improved performance. Our approach combines a much longer pattern than has been previously used to predict nucleosome positioning from sequence-301 base pairs, centered at the position to be scored-with a novel discriminative classification approach that selectively weights the contributions from each of the input features. The resulting scores are relatively insensitive to local AT-content and can be used to accurately discriminate putative dyad positions from adjacent linker regions without requiring an additional dynamic programming step and without the attendant edge effects and assumptions about linker length modeling and overall nucleosome density. Our approach produces the best dyad-linker classification results published to date in H. sapiens, and outperforms two recently published models on a large set of S. cerevisiae nucleosome positions. Our results suggest that in both genomes, a comparable and relatively small fraction of nucleosomes are well-positioned and that these positions are predictable based on sequence alone. We believe that the bulk of the

  7. Learning a Weighted Sequence Model of the Nucleosome Core and Linker Yields More Accurate Predictions in Saccharomyces cerevisiae and Homo sapiens

    PubMed Central

    Reynolds, Sheila M.; Bilmes, Jeff A.; Noble, William Stafford

    2010-01-01

    DNA in eukaryotes is packaged into a chromatin complex, the most basic element of which is the nucleosome. The precise positioning of the nucleosome cores allows for selective access to the DNA, and the mechanisms that control this positioning are important pieces of the gene expression puzzle. We describe a large-scale nucleosome pattern that jointly characterizes the nucleosome core and the adjacent linkers and is predominantly characterized by long-range oscillations in the mono, di- and tri-nucleotide content of the DNA sequence, and we show that this pattern can be used to predict nucleosome positions in both Homo sapiens and Saccharomyces cerevisiae more accurately than previously published methods. Surprisingly, in both H. sapiens and S. cerevisiae, the most informative individual features are the mono-nucleotide patterns, although the inclusion of di- and tri-nucleotide features results in improved performance. Our approach combines a much longer pattern than has been previously used to predict nucleosome positioning from sequence—301 base pairs, centered at the position to be scored—with a novel discriminative classification approach that selectively weights the contributions from each of the input features. The resulting scores are relatively insensitive to local AT-content and can be used to accurately discriminate putative dyad positions from adjacent linker regions without requiring an additional dynamic programming step and without the attendant edge effects and assumptions about linker length modeling and overall nucleosome density. Our approach produces the best dyad-linker classification results published to date in H. sapiens, and outperforms two recently published models on a large set of S. cerevisiae nucleosome positions. Our results suggest that in both genomes, a comparable and relatively small fraction of nucleosomes are well-positioned and that these positions are predictable based on sequence alone. We believe that the bulk of the

  8. Accurate prediction of severe allergic reactions by a small set of environmental parameters (NDVI, temperature).

    PubMed

    Notas, George; Bariotakis, Michail; Kalogrias, Vaios; Andrianaki, Maria; Azariadis, Kalliopi; Kampouri, Errika; Theodoropoulou, Katerina; Lavrentaki, Katerina; Kastrinakis, Stelios; Kampa, Marilena; Agouridakis, Panagiotis; Pirintsos, Stergios; Castanas, Elias

    2015-01-01

    Severe allergic reactions of unknown etiology,necessitating a hospital visit, have an important impact in the life of affected individuals and impose a major economic burden to societies. The prediction of clinically severe allergic reactions would be of great importance, but current attempts have been limited by the lack of a well-founded applicable methodology and the wide spatiotemporal distribution of allergic reactions. The valid prediction of severe allergies (and especially those needing hospital treatment) in a region, could alert health authorities and implicated individuals to take appropriate preemptive measures. In the present report we have collecterd visits for serious allergic reactions of unknown etiology from two major hospitals in the island of Crete, for two distinct time periods (validation and test sets). We have used the Normalized Difference Vegetation Index (NDVI), a satellite-based, freely available measurement, which is an indicator of live green vegetation at a given geographic area, and a set of meteorological data to develop a model capable of describing and predicting severe allergic reaction frequency. Our analysis has retained NDVI and temperature as accurate identifiers and predictors of increased hospital severe allergic reactions visits. Our approach may contribute towards the development of satellite-based modules, for the prediction of severe allergic reactions in specific, well-defined geographical areas. It could also probably be used for the prediction of other environment related diseases and conditions.

  9. Accurate Prediction of Severe Allergic Reactions by a Small Set of Environmental Parameters (NDVI, Temperature)

    PubMed Central

    Andrianaki, Maria; Azariadis, Kalliopi; Kampouri, Errika; Theodoropoulou, Katerina; Lavrentaki, Katerina; Kastrinakis, Stelios; Kampa, Marilena; Agouridakis, Panagiotis; Pirintsos, Stergios; Castanas, Elias

    2015-01-01

    Severe allergic reactions of unknown etiology,necessitating a hospital visit, have an important impact in the life of affected individuals and impose a major economic burden to societies. The prediction of clinically severe allergic reactions would be of great importance, but current attempts have been limited by the lack of a well-founded applicable methodology and the wide spatiotemporal distribution of allergic reactions. The valid prediction of severe allergies (and especially those needing hospital treatment) in a region, could alert health authorities and implicated individuals to take appropriate preemptive measures. In the present report we have collecterd visits for serious allergic reactions of unknown etiology from two major hospitals in the island of Crete, for two distinct time periods (validation and test sets). We have used the Normalized Difference Vegetation Index (NDVI), a satellite-based, freely available measurement, which is an indicator of live green vegetation at a given geographic area, and a set of meteorological data to develop a model capable of describing and predicting severe allergic reaction frequency. Our analysis has retained NDVI and temperature as accurate identifiers and predictors of increased hospital severe allergic reactions visits. Our approach may contribute towards the development of satellite-based modules, for the prediction of severe allergic reactions in specific, well-defined geographical areas. It could also probably be used for the prediction of other environment related diseases and conditions. PMID:25794106

  10. A Machine Learned Classifier That Uses Gene Expression Data to Accurately Predict Estrogen Receptor Status

    PubMed Central

    Bastani, Meysam; Vos, Larissa; Asgarian, Nasimeh; Deschenes, Jean; Graham, Kathryn; Mackey, John; Greiner, Russell

    2013-01-01

    Background Selecting the appropriate treatment for breast cancer requires accurately determining the estrogen receptor (ER) status of the tumor. However, the standard for determining this status, immunohistochemical analysis of formalin-fixed paraffin embedded samples, suffers from numerous technical and reproducibility issues. Assessment of ER-status based on RNA expression can provide more objective, quantitative and reproducible test results. Methods To learn a parsimonious RNA-based classifier of hormone receptor status, we applied a machine learning tool to a training dataset of gene expression microarray data obtained from 176 frozen breast tumors, whose ER-status was determined by applying ASCO-CAP guidelines to standardized immunohistochemical testing of formalin fixed tumor. Results This produced a three-gene classifier that can predict the ER-status of a novel tumor, with a cross-validation accuracy of 93.17±2.44%. When applied to an independent validation set and to four other public databases, some on different platforms, this classifier obtained over 90% accuracy in each. In addition, we found that this prediction rule separated the patients' recurrence-free survival curves with a hazard ratio lower than the one based on the IHC analysis of ER-status. Conclusions Our efficient and parsimonious classifier lends itself to high throughput, highly accurate and low-cost RNA-based assessments of ER-status, suitable for routine high-throughput clinical use. This analytic method provides a proof-of-principle that may be applicable to developing effective RNA-based tests for other biomarkers and conditions. PMID:24312637

  11. Combining first-principles and data modeling for the accurate prediction of the refractive index of organic polymers

    NASA Astrophysics Data System (ADS)

    Afzal, Mohammad Atif Faiz; Cheng, Chong; Hachmann, Johannes

    2018-06-01

    Organic materials with a high index of refraction (RI) are attracting considerable interest due to their potential application in optic and optoelectronic devices. However, most of these applications require an RI value of 1.7 or larger, while typical carbon-based polymers only exhibit values in the range of 1.3-1.5. This paper introduces an efficient computational protocol for the accurate prediction of RI values in polymers to facilitate in silico studies that can guide the discovery and design of next-generation high-RI materials. Our protocol is based on the Lorentz-Lorenz equation and is parametrized by the polarizability and number density values of a given candidate compound. In the proposed scheme, we compute the former using first-principles electronic structure theory and the latter using an approximation based on van der Waals volumes. The critical parameter in the number density approximation is the packing fraction of the bulk polymer, for which we have devised a machine learning model. We demonstrate the performance of the proposed RI protocol by testing its predictions against the experimentally known RI values of 112 optical polymers. Our approach to combine first-principles and data modeling emerges as both a successful and a highly economical path to determining the RI values for a wide range of organic polymers.

  12. A NEW CLINICAL PREDICTION CRITERION ACCURATELY DETERMINES A SUBSET OF PATIENTS WITH BILATERAL PRIMARY ALDOSTERONISM BEFORE ADRENAL VENOUS SAMPLING.

    PubMed

    Kocjan, Tomaz; Janez, Andrej; Stankovic, Milenko; Vidmar, Gaj; Jensterle, Mojca

    2016-05-01

    Adrenal venous sampling (AVS) is the only available method to distinguish bilateral from unilateral primary aldosteronism (PA). AVS has several drawbacks, so it is reasonable to avoid this procedure when the results would not affect clinical management. Our objective was to identify a clinical criterion that can reliably predict nonlateralized AVS as a surrogate for bilateral PA that is not treated surgically. A retrospective diagnostic cross-sectional study conducted at Slovenian national endocrine referral center included 69 consecutive patients (mean age 56 ± 8 years, 21 females) with PA who underwent AVS. PA was confirmed with the saline infusion test (SIT). AVS was performed sequentially during continuous adrenocorticotrophic hormone (ACTH) infusion. The main outcome measures were variables associated with nonlateralized AVS to derive a clinical prediction rule. Sixty-seven (97%) patients had a successful AVS and were included in the statistical analysis. A total of 39 (58%) patients had nonlateralized AVS. The combined criterion of serum potassium ≥3.5 mmol/L, post-SIT aldosterone <18 ng/dL, and either no or bilateral tumor found on computed tomography (CT) imaging had perfect estimated specificity (and thus 100% positive predictive value) for bilateral PA, saving an estimated 16% of the patients (11/67) from unnecessary AVS. The best overall classification accuracy (50/67 = 75%) was achieved using the post-SIT aldosterone level <18 ng/dL alone, which yielded 74% sensitivity and 75% specificity for predicting nonlateralized AVS. Our clinical prediction criterion appears to accurately determine a subset of patients with bilateral PA who could avoid unnecessary AVS and immediately commence with medical treatment.

  13. The Real World Significance of Performance Prediction

    ERIC Educational Resources Information Center

    Pardos, Zachary A.; Wang, Qing Yang; Trivedi, Shubhendu

    2012-01-01

    In recent years, the educational data mining and user modeling communities have been aggressively introducing models for predicting student performance on external measures such as standardized tests as well as within-tutor performance. While these models have brought statistically reliable improvement to performance prediction, the real world…

  14. A link prediction approach to cancer drug sensitivity prediction.

    PubMed

    Turki, Turki; Wei, Zhi

    2017-10-03

    Predicting the response to a drug for cancer disease patients based on genomic information is an important problem in modern clinical oncology. This problem occurs in part because many available drug sensitivity prediction algorithms do not consider better quality cancer cell lines and the adoption of new feature representations; both lead to the accurate prediction of drug responses. By predicting accurate drug responses to cancer, oncologists gain a more complete understanding of the effective treatments for each patient, which is a core goal in precision medicine. In this paper, we model cancer drug sensitivity as a link prediction, which is shown to be an effective technique. We evaluate our proposed link prediction algorithms and compare them with an existing drug sensitivity prediction approach based on clinical trial data. The experimental results based on the clinical trial data show the stability of our link prediction algorithms, which yield the highest area under the ROC curve (AUC) and are statistically significant. We propose a link prediction approach to obtain new feature representation. Compared with an existing approach, the results show that incorporating the new feature representation to the link prediction algorithms has significantly improved the performance.

  15. Exchange-Hole Dipole Dispersion Model for Accurate Energy Ranking in Molecular Crystal Structure Prediction.

    PubMed

    Whittleton, Sarah R; Otero-de-la-Roza, A; Johnson, Erin R

    2017-02-14

    Accurate energy ranking is a key facet to the problem of first-principles crystal-structure prediction (CSP) of molecular crystals. This work presents a systematic assessment of B86bPBE-XDM, a semilocal density functional combined with the exchange-hole dipole moment (XDM) dispersion model, for energy ranking using 14 compounds from the first five CSP blind tests. Specifically, the set of crystals studied comprises 11 rigid, planar compounds and 3 co-crystals. The experimental structure was correctly identified as the lowest in lattice energy for 12 of the 14 total crystals. One of the exceptions is 4-hydroxythiophene-2-carbonitrile, for which the experimental structure was correctly identified once a quasi-harmonic estimate of the vibrational free-energy contribution was included, evidencing the occasional importance of thermal corrections for accurate energy ranking. The other exception is an organic salt, where charge-transfer error (also called delocalization error) is expected to cause the base density functional to be unreliable. Provided the choice of base density functional is appropriate and an estimate of temperature effects is used, XDM-corrected density-functional theory is highly reliable for the energetic ranking of competing crystal structures.

  16. Prediction of Slot Shape and Slot Size for Improving the Performance of Microstrip Antennas Using Knowledge-Based Neural Networks

    PubMed Central

    De, Asok

    2014-01-01

    In the last decade, artificial neural networks have become very popular techniques for computing different performance parameters of microstrip antennas. The proposed work illustrates a knowledge-based neural networks model for predicting the appropriate shape and accurate size of the slot introduced on the radiating patch for achieving desired level of resonance, gain, directivity, antenna efficiency, and radiation efficiency for dual-frequency operation. By incorporating prior knowledge in neural model, the number of required training patterns is drastically reduced. Further, the neural model incorporated with prior knowledge can be used for predicting response in extrapolation region beyond the training patterns region. For validation, a prototype is also fabricated and its performance parameters are measured. A very good agreement is attained between measured, simulated, and predicted results. PMID:27382616

  17. Numerical Investigation on the Effects of Self-Excited Tip Flow Unsteadiness and Blade Row Interactions on the Performance Predictions of Low Speed and Transonic Compressor Rotors

    NASA Astrophysics Data System (ADS)

    Lee, Daniel H.

    The impact blade row interactions can have on the performance of compressor rotors has been well documented. It is also well known that rotor tip clearance flows can have a large effect on compressor performance and stall margin and recent research has shown that tip leakage flows can exhibit self-excited unsteadiness at near stall conditions. However, the impact of tip leakage flow on the performance and operating range of a compressor rotor, relative to other important flow features such as upstream stator wakes or downstream potential effects, has not been explored. To this end, a numerical investigation has been conducted to determine the effects of self-excited tip flow unsteadiness, upstream stator wakes, and downstream blade row interactions on the performance prediction of low speed and transonic compressor rotors. Calculations included a single blade-row rotor configuration as well as two multi-blade row configurations: one where the rotor was modeled with an upstream stator and a second where the rotor was modeled with a downstream stator. Steady-state and time accurate calculations were performed using a RANS solver and the results were compared with detailed experimental data obtained in the GE Low Speed Research Compressor and the Notre Dame Transonic Rig at several operating conditions including near stall. Differences in the performance predictions between the three configurations were then used to determine the effect of the upstream stator wakes and the downstream blade row interactions. Results obtained show that for both the low speed and transonic research compressors used in this investigation time-accurate RANS analysis is necessary to accurately predict the stalling character of the rotor. Additionally, for the first time it is demonstrated that capturing the unsteady tip flow can have a larger impact on rotor performance predictions than adjacent blade row interactions.

  18. SnowyOwl: accurate prediction of fungal genes by using RNA-Seq and homology information to select among ab initio models

    PubMed Central

    2014-01-01

    Background Locating the protein-coding genes in novel genomes is essential to understanding and exploiting the genomic information but it is still difficult to accurately predict all the genes. The recent availability of detailed information about transcript structure from high-throughput sequencing of messenger RNA (RNA-Seq) delineates many expressed genes and promises increased accuracy in gene prediction. Computational gene predictors have been intensively developed for and tested in well-studied animal genomes. Hundreds of fungal genomes are now or will soon be sequenced. The differences of fungal genomes from animal genomes and the phylogenetic sparsity of well-studied fungi call for gene-prediction tools tailored to them. Results SnowyOwl is a new gene prediction pipeline that uses RNA-Seq data to train and provide hints for the generation of Hidden Markov Model (HMM)-based gene predictions and to evaluate the resulting models. The pipeline has been developed and streamlined by comparing its predictions to manually curated gene models in three fungal genomes and validated against the high-quality gene annotation of Neurospora crassa; SnowyOwl predicted N. crassa genes with 83% sensitivity and 65% specificity. SnowyOwl gains sensitivity by repeatedly running the HMM gene predictor Augustus with varied input parameters and selectivity by choosing the models with best homology to known proteins and best agreement with the RNA-Seq data. Conclusions SnowyOwl efficiently uses RNA-Seq data to produce accurate gene models in both well-studied and novel fungal genomes. The source code for the SnowyOwl pipeline (in Python) and a web interface (in PHP) is freely available from http://sourceforge.net/projects/snowyowl/. PMID:24980894

  19. Do subjective measures of attention and memory predict actual performance? Metacognition in older couples.

    PubMed

    Volz-Sidiropoulou, Eftychia; Gauggel, Siegfried

    2012-06-01

    Older individuals who recognize their cognitive difficulties are more likely to adjust their everyday life to their actual cognitive functioning, particularly when they are able to estimate their abilities accurately. We assessed self- and spouse-ratings of memory and attention difficulties in everyday life of healthy, older individuals and compared them with the respective test performance. Eighty-four older individuals (women's age, M = 67.4 years, SD = 5.2; men's age, M = 68.5 years, SD = 4.9) completed both the self and the spouse versions of the Attention Deficit Questionnaire and the Everyday Memory Questionnaire and completed two neuropsychological tests. Using the residual score approach, subjective metacognitive measures of memory and attention were created and compared with actual test performance. Significant associations between subjective and objective scores were found only for men and only for episodic memory measures. Men who underreported memory difficulties performed more poorly; men who overreported memory difficulties performed better. Men's recognition performance was best predicted by subjective measures (R² = .25), followed by delayed recall (R² = .14) and forgetting rate (R² = .13). The results indicate gender-specific differences in metacognitive accuracy and predictive validity of subjective ratings. PsycINFO Database Record (c) 2012 APA, all rights reserved

  20. Performance Reports: Mirror alignment system performance prediction comparison between SAO and EKC

    NASA Technical Reports Server (NTRS)

    Tananbaum, H. D.; Zhang, J. P.

    1994-01-01

    The objective of this study is to perform an independent analysis of the residual high resolution mirror assembly (HRMA) mirror distortions caused by force and moment errors in the mirror alignment system (MAS) to statistically predict the HRMA performance. These performance predictions are then compared with those performed by Kodak to verify their analysis results.

  1. Foveated model observers to predict human performance in 3D images

    NASA Astrophysics Data System (ADS)

    Lago, Miguel A.; Abbey, Craig K.; Eckstein, Miguel P.

    2017-03-01

    We evaluate 3D search requires model observers that take into account the peripheral human visual processing (foveated models) to predict human observer performance. We show that two different 3D tasks, free search and location-known detection, influence the relative human visual detectability of two signals of different sizes in synthetic backgrounds mimicking the noise found in 3D digital breast tomosynthesis. One of the signals resembled a microcalcification (a small and bright sphere), while the other one was designed to look like a mass (a larger Gaussian blob). We evaluated current standard models observers (Hotelling; Channelized Hotelling; non-prewhitening matched filter with eye filter, NPWE; and non-prewhitening matched filter model, NPW) and showed that they incorrectly predict the relative detectability of the two signals in 3D search. We propose a new model observer (3D Foveated Channelized Hotelling Observer) that incorporates the properties of the visual system over a large visual field (fovea and periphery). We show that the foveated model observer can accurately predict the rank order of detectability of the signals in 3D images for each task. Together, these results motivate the use of a new generation of foveated model observers for predicting image quality for search tasks in 3D imaging modalities such as digital breast tomosynthesis or computed tomography.

  2. Markers of preparatory attention predict visual short-term memory performance.

    PubMed

    Murray, Alexandra M; Nobre, Anna C; Stokes, Mark G

    2011-05-01

    Visual short-term memory (VSTM) is limited in capacity. Therefore, it is important to encode only visual information that is most likely to be relevant to behaviour. Here we asked which aspects of selective biasing of VSTM encoding predict subsequent memory-based performance. We measured EEG during a selective VSTM encoding task, in which we varied parametrically the memory load and the precision of recall required to compare a remembered item to a subsequent probe item. On half the trials, a spatial cue indicated that participants only needed to encode items from one hemifield. We observed a typical sequence of markers of anticipatory spatial attention: early attention directing negativity (EDAN), anterior attention directing negativity (ADAN), late directing attention positivity (LDAP); as well as of VSTM maintenance: contralateral delay activity (CDA). We found that individual differences in preparatory brain activity (EDAN/ADAN) predicted cue-related changes in recall accuracy, indexed by memory-probe discrimination sensitivity (d'). Importantly, our parametric manipulation of memory-probe similarity also allowed us to model the behavioural data for each participant, providing estimates for the quality of the memory representation and the probability that an item could be retrieved. We found that selective encoding primarily increased the probability of accurate memory recall; that ERP markers of preparatory attention predicted the cue-related changes in recall probability. Copyright © 2011. Published by Elsevier Ltd.

  3. PREVAPORATION PERFORMANCE PREDICTION SOFTWARE

    EPA Science Inventory

    The Pervaporation, Performance, Prediction Software and Database (PPPS&D) computer software program is currently being developed within the USEPA, NRMRL. The purpose of the PPPS&D program is to educate and assist potential users in identifying opportunities for using pervaporati...

  4. NMRDSP: an accurate prediction of protein shape strings from NMR chemical shifts and sequence data.

    PubMed

    Mao, Wusong; Cong, Peisheng; Wang, Zhiheng; Lu, Longjian; Zhu, Zhongliang; Li, Tonghua

    2013-01-01

    Shape string is structural sequence and is an extremely important structure representation of protein backbone conformations. Nuclear magnetic resonance chemical shifts give a strong correlation with the local protein structure, and are exploited to predict protein structures in conjunction with computational approaches. Here we demonstrate a novel approach, NMRDSP, which can accurately predict the protein shape string based on nuclear magnetic resonance chemical shifts and structural profiles obtained from sequence data. The NMRDSP uses six chemical shifts (HA, H, N, CA, CB and C) and eight elements of structure profiles as features, a non-redundant set (1,003 entries) as the training set, and a conditional random field as a classification algorithm. For an independent testing set (203 entries), we achieved an accuracy of 75.8% for S8 (the eight states accuracy) and 87.8% for S3 (the three states accuracy). This is higher than only using chemical shifts or sequence data, and confirms that the chemical shift and the structure profile are significant features for shape string prediction and their combination prominently improves the accuracy of the predictor. We have constructed the NMRDSP web server and believe it could be employed to provide a solid platform to predict other protein structures and functions. The NMRDSP web server is freely available at http://cal.tongji.edu.cn/NMRDSP/index.jsp.

  5. Performance Prediction of a Synchronization Link for Distributed Aerospace Wireless Systems

    PubMed Central

    Shao, Huaizong

    2013-01-01

    For reasons of stealth and other operational advantages, distributed aerospace wireless systems have received much attention in recent years. In a distributed aerospace wireless system, since the transmitter and receiver placed on separated platforms which use independent master oscillators, there is no cancellation of low-frequency phase noise as in the monostatic cases. Thus, high accurate time and frequency synchronization techniques are required for distributed wireless systems. The use of a dedicated synchronization link to quantify and compensate oscillator frequency instability is investigated in this paper. With the mathematical statistical models of phase noise, closed-form analytic expressions for the synchronization link performance are derived. The possible error contributions including oscillator, phase-locked loop, and receiver noise are quantified. The link synchronization performance is predicted by utilizing the knowledge of the statistical models, system error contributions, and sampling considerations. Simulation results show that effective synchronization error compensation can be achieved by using this dedicated synchronization link. PMID:23970828

  6. Performance prediction of a synchronization link for distributed aerospace wireless systems.

    PubMed

    Wang, Wen-Qin; Shao, Huaizong

    2013-01-01

    For reasons of stealth and other operational advantages, distributed aerospace wireless systems have received much attention in recent years. In a distributed aerospace wireless system, since the transmitter and receiver placed on separated platforms which use independent master oscillators, there is no cancellation of low-frequency phase noise as in the monostatic cases. Thus, high accurate time and frequency synchronization techniques are required for distributed wireless systems. The use of a dedicated synchronization link to quantify and compensate oscillator frequency instability is investigated in this paper. With the mathematical statistical models of phase noise, closed-form analytic expressions for the synchronization link performance are derived. The possible error contributions including oscillator, phase-locked loop, and receiver noise are quantified. The link synchronization performance is predicted by utilizing the knowledge of the statistical models, system error contributions, and sampling considerations. Simulation results show that effective synchronization error compensation can be achieved by using this dedicated synchronization link.

  7. Accurate prediction of complex free surface flow around a high speed craft using a single-phase level set method

    NASA Astrophysics Data System (ADS)

    Broglia, Riccardo; Durante, Danilo

    2017-11-01

    This paper focuses on the analysis of a challenging free surface flow problem involving a surface vessel moving at high speeds, or planing. The investigation is performed using a general purpose high Reynolds free surface solver developed at CNR-INSEAN. The methodology is based on a second order finite volume discretization of the unsteady Reynolds-averaged Navier-Stokes equations (Di Mascio et al. in A second order Godunov—type scheme for naval hydrodynamics, Kluwer Academic/Plenum Publishers, Dordrecht, pp 253-261, 2001; Proceedings of 16th international offshore and polar engineering conference, San Francisco, CA, USA, 2006; J Mar Sci Technol 14:19-29, 2009); air/water interface dynamics is accurately modeled by a non standard level set approach (Di Mascio et al. in Comput Fluids 36(5):868-886, 2007a), known as the single-phase level set method. In this algorithm the governing equations are solved only in the water phase, whereas the numerical domain in the air phase is used for a suitable extension of the fluid dynamic variables. The level set function is used to track the free surface evolution; dynamic boundary conditions are enforced directly on the interface. This approach allows to accurately predict the evolution of the free surface even in the presence of violent breaking waves phenomena, maintaining the interface sharp, without any need to smear out the fluid properties across the two phases. This paper is aimed at the prediction of the complex free-surface flow field generated by a deep-V planing boat at medium and high Froude numbers (from 0.6 up to 1.2). In the present work, the planing hull is treated as a two-degree-of-freedom rigid object. Flow field is characterized by the presence of thin water sheets, several energetic breaking waves and plungings. The computational results include convergence of the trim angle, sinkage and resistance under grid refinement; high-quality experimental data are used for the purposes of validation, allowing to

  8. Does mesenteric venous imaging assessment accurately predict pathologic invasion in localized pancreatic ductal adenocarcinoma?

    PubMed

    Clanton, Jesse; Oh, Stephen; Kaplan, Stephen J; Johnson, Emily; Ross, Andrew; Kozarek, Richard; Alseidi, Adnan; Biehl, Thomas; Picozzi, Vincent J; Helton, William S; Coy, David; Dorer, Russell; Rocha, Flavio G

    2018-05-09

    Accurate prediction of mesenteric venous involvement in pancreatic ductal adenocarcinoma (PDAC) is necessary for adequate staging and treatment. A retrospective cohort study was conducted in PDAC patients at a single institution. All patients with resected PDAC and staging CT and EUS between 2003 and 2014 were included and sub-divided into "upfront resected" and "neoadjuvant chemotherapy (NAC)" groups. Independent imaging re-review was correlated to venous resection and venous invasion. Sensitivity, specificity, positive and negative predictive values were then calculated. A total of 109 patients underwent analysis, 60 received upfront resection, and 49 NAC. Venous resection (30%) and vein invasion (13%) was less common in patients resected upfront than those who received NAC (53% and 16%, respectively). Both CT and EUS had poor sensitivity (14-44%) but high specificity (75-95%) for detecting venous resection and vein invasion in patients resected upfront, whereas sensitivity was high (84-100%) and specificity was low (27-44%) after NAC. Preoperative CT and EUS in PDAC have similar efficacy but different predictive capacity in assessing mesenteric venous involvement depending on whether patients are resected upfront or received NAC. Both modalities appear to significantly overestimate true vascular involvement and should be interpreted in the appropriate clinical context. Copyright © 2018 International Hepato-Pancreato-Biliary Association Inc. Published by Elsevier Ltd. All rights reserved.

  9. Accurate quantum chemical calculations

    NASA Technical Reports Server (NTRS)

    Bauschlicher, Charles W., Jr.; Langhoff, Stephen R.; Taylor, Peter R.

    1989-01-01

    An important goal of quantum chemical calculations is to provide an understanding of chemical bonding and molecular electronic structure. A second goal, the prediction of energy differences to chemical accuracy, has been much harder to attain. First, the computational resources required to achieve such accuracy are very large, and second, it is not straightforward to demonstrate that an apparently accurate result, in terms of agreement with experiment, does not result from a cancellation of errors. Recent advances in electronic structure methodology, coupled with the power of vector supercomputers, have made it possible to solve a number of electronic structure problems exactly using the full configuration interaction (FCI) method within a subspace of the complete Hilbert space. These exact results can be used to benchmark approximate techniques that are applicable to a wider range of chemical and physical problems. The methodology of many-electron quantum chemistry is reviewed. Methods are considered in detail for performing FCI calculations. The application of FCI methods to several three-electron problems in molecular physics are discussed. A number of benchmark applications of FCI wave functions are described. Atomic basis sets and the development of improved methods for handling very large basis sets are discussed: these are then applied to a number of chemical and spectroscopic problems; to transition metals; and to problems involving potential energy surfaces. Although the experiences described give considerable grounds for optimism about the general ability to perform accurate calculations, there are several problems that have proved less tractable, at least with current computer resources, and these and possible solutions are discussed.

  10. Method for Accurately Calibrating a Spectrometer Using Broadband Light

    NASA Technical Reports Server (NTRS)

    Simmons, Stephen; Youngquist, Robert

    2011-01-01

    A novel method has been developed for performing very fine calibration of a spectrometer. This process is particularly useful for modern miniature charge-coupled device (CCD) spectrometers where a typical factory wavelength calibration has been performed and a finer, more accurate calibration is desired. Typically, the factory calibration is done with a spectral line source that generates light at known wavelengths, allowing specific pixels in the CCD array to be assigned wavelength values. This method is good to about 1 nm across the spectrometer s wavelength range. This new method appears to be accurate to about 0.1 nm, a factor of ten improvement. White light is passed through an unbalanced Michelson interferometer, producing an optical signal with significant spectral variation. A simple theory can be developed to describe this spectral pattern, so by comparing the actual spectrometer output against this predicted pattern, errors in the wavelength assignment made by the spectrometer can be determined.

  11. Accurate load prediction by BEM with airfoil data from 3D RANS simulations

    NASA Astrophysics Data System (ADS)

    Schneider, Marc S.; Nitzsche, Jens; Hennings, Holger

    2016-09-01

    In this paper, two methods for the extraction of airfoil coefficients from 3D CFD simulations of a wind turbine rotor are investigated, and these coefficients are used to improve the load prediction of a BEM code. The coefficients are extracted from a number of steady RANS simulations, using either averaging of velocities in annular sections, or an inverse BEM approach for determination of the induction factors in the rotor plane. It is shown that these 3D rotor polars are able to capture the rotational augmentation at the inner part of the blade as well as the load reduction by 3D effects close to the blade tip. They are used as input to a simple BEM code and the results of this BEM with 3D rotor polars are compared to the predictions of BEM with 2D airfoil coefficients plus common empirical corrections for stall delay and tip loss. While BEM with 2D airfoil coefficients produces a very different radial distribution of loads than the RANS simulation, the BEM with 3D rotor polars manages to reproduce the loads from RANS very accurately for a variety of load cases, as long as the blade pitch angle is not too different from the cases from which the polars were extracted.

  12. What predicts performance during clinical psychology training?

    PubMed Central

    Scior, Katrina; Bradley, Caroline E; Potts, Henry W W; Woolf, Katherine; de C Williams, Amanda C

    2014-01-01

    Objectives While the question of who is likely to be selected for clinical psychology training has been studied, evidence on performance during training is scant. This study explored data from seven consecutive intakes of the UK's largest clinical psychology training course, aiming to identify what factors predict better or poorer outcomes. Design Longitudinal cross-sectional study using prospective and retrospective data. Method Characteristics at application were analysed in relation to a range of in-course assessments for 274 trainee clinical psychologists who had completed or were in the final stage of their training. Results Trainees were diverse in age, pre-training experience, and academic performance at A-level (advanced level certificate required for university admission), but not in gender or ethnicity. Failure rates across the three performance domains (academic, clinical, research) were very low, suggesting that selection was successful in screening out less suitable candidates. Key predictors of good performance on the course were better A-levels and better degree class. Non-white students performed less well on two outcomes. Type and extent of pre-training clinical experience on outcomes had varied effects on outcome. Research supervisor ratings emerged as global indicators and predicted nearly all outcomes, but may have been biased as they were retrospective. Referee ratings predicted only one of the seven outcomes examined, and interview ratings predicted none of the outcomes. Conclusions Predicting who will do well or poorly in clinical psychology training is complex. Interview and referee ratings may well be successful in screening out unsuitable candidates, but appear to be a poor guide to performance on the course. Practitioner points While referee and selection interview ratings did not predict performance during training, they may be useful in screening out unsuitable candidates at the application stage High school final academic performance

  13. Accurate secondary structure prediction and fold recognition for circular dichroism spectroscopy

    PubMed Central

    Micsonai, András; Wien, Frank; Kernya, Linda; Lee, Young-Ho; Goto, Yuji; Réfrégiers, Matthieu; Kardos, József

    2015-01-01

    Circular dichroism (CD) spectroscopy is a widely used technique for the study of protein structure. Numerous algorithms have been developed for the estimation of the secondary structure composition from the CD spectra. These methods often fail to provide acceptable results on α/β-mixed or β-structure–rich proteins. The problem arises from the spectral diversity of β-structures, which has hitherto been considered as an intrinsic limitation of the technique. The predictions are less reliable for proteins of unusual β-structures such as membrane proteins, protein aggregates, and amyloid fibrils. Here, we show that the parallel/antiparallel orientation and the twisting of the β-sheets account for the observed spectral diversity. We have developed a method called β-structure selection (BeStSel) for the secondary structure estimation that takes into account the twist of β-structures. This method can reliably distinguish parallel and antiparallel β-sheets and accurately estimates the secondary structure for a broad range of proteins. Moreover, the secondary structure components applied by the method are characteristic to the protein fold, and thus the fold can be predicted to the level of topology in the CATH classification from a single CD spectrum. By constructing a web server, we offer a general tool for a quick and reliable structure analysis using conventional CD or synchrotron radiation CD (SRCD) spectroscopy for the protein science research community. The method is especially useful when X-ray or NMR techniques fail. Using BeStSel on data collected by SRCD spectroscopy, we investigated the structure of amyloid fibrils of various disease-related proteins and peptides. PMID:26038575

  14. Personalized long-term prediction of cognitive function: Using sequential assessments to improve model performance.

    PubMed

    Chi, Chih-Lin; Zeng, Wenjun; Oh, Wonsuk; Borson, Soo; Lenskaia, Tatiana; Shen, Xinpeng; Tonellato, Peter J

    2017-12-01

    Prediction of onset and progression of cognitive decline and dementia is important both for understanding the underlying disease processes and for planning health care for populations at risk. Predictors identified in research studies are typically accessed at one point in time. In this manuscript, we argue that an accurate model for predicting cognitive status over relatively long periods requires inclusion of time-varying components that are sequentially assessed at multiple time points (e.g., in multiple follow-up visits). We developed a pilot model to test the feasibility of using either estimated or observed risk factors to predict cognitive status. We developed two models, the first using a sequential estimation of risk factors originally obtained from 8 years prior, then improved by optimization. This model can predict how cognition will change over relatively long time periods. The second model uses observed rather than estimated time-varying risk factors and, as expected, results in better prediction. This model can predict when newly observed data are acquired in a follow-up visit. Performances of both models that are evaluated in10-fold cross-validation and various patient subgroups show supporting evidence for these pilot models. Each model consists of multiple base prediction units (BPUs), which were trained using the same set of data. The difference in usage and function between the two models is the source of input data: either estimated or observed data. In the next step of model refinement, we plan to integrate the two types of data together to flexibly predict dementia status and changes over time, when some time-varying predictors are measured only once and others are measured repeatedly. Computationally, both data provide upper and lower bounds for predictive performance. Copyright © 2017 Elsevier Inc. All rights reserved.

  15. Lower NIH stroke scale scores are required to accurately predict a good prognosis in posterior circulation stroke.

    PubMed

    Inoa, Violiza; Aron, Abraham W; Staff, Ilene; Fortunato, Gilbert; Sansing, Lauren H

    2014-01-01

    The NIH stroke scale (NIHSS) is an indispensable tool that aids in the determination of acute stroke prognosis and decision making. Patients with posterior circulation (PC) strokes often present with lower NIHSS scores, which may result in the withholding of thrombolytic treatment from these patients. However, whether these lower initial NIHSS scores predict better long-term prognoses is uncertain. We aimed to assess the utility of the NIHSS at presentation for predicting the functional outcome at 3 months in anterior circulation (AC) versus PC strokes. This was a retrospective analysis of a large prospectively collected database of adults with acute ischemic stroke. Univariate and multivariate analyses were conducted to identify factors associated with outcome. Additional analyses were performed to determine the receiver operating characteristic (ROC) curves for NIHSS scores and outcomes in AC and PC infarctions. Both the optimal cutoffs for maximal diagnostic accuracy and the cutoffs to obtain >80% sensitivity for poor outcomes were determined in AC and PC strokes. The analysis included 1,197 patients with AC stroke and 372 with PC stroke. The median initial NIHSS score for patients with AC strokes was 7 and for PC strokes it was 2. The majority (71%) of PC stroke patients had baseline NIHSS scores ≤4, and 15% of these 'minor' stroke patients had a poor outcome at 3 months. ROC analysis identified that the optimal NIHSS cutoff for outcome prediction after infarction in the AC was 8 and for infarction in the PC it was 4. To achieve >80% sensitivity for detecting patients with a subsequent poor outcome, the NIHSS cutoff for infarctions in the AC was 4 and for infarctions in the PC it was 2. The NIHSS cutoff that most accurately predicts outcomes is 4 points higher in AC compared to PC infarctions. There is potential for poor outcomes in patients with PC strokes and low NIHSS scores, suggesting that thrombolytic treatment should not be withheld from these patients

  16. FragBag, an accurate representation of protein structure, retrieves structural neighbors from the entire PDB quickly and accurately.

    PubMed

    Budowski-Tal, Inbal; Nov, Yuval; Kolodny, Rachel

    2010-02-23

    Fast identification of protein structures that are similar to a specified query structure in the entire Protein Data Bank (PDB) is fundamental in structure and function prediction. We present FragBag: An ultrafast and accurate method for comparing protein structures. We describe a protein structure by the collection of its overlapping short contiguous backbone segments, and discretize this set using a library of fragments. Then, we succinctly represent the protein as a "bags-of-fragments"-a vector that counts the number of occurrences of each fragment-and measure the similarity between two structures by the similarity between their vectors. Our representation has two additional benefits: (i) it can be used to construct an inverted index, for implementing a fast structural search engine of the entire PDB, and (ii) one can specify a structure as a collection of substructures, without combining them into a single structure; this is valuable for structure prediction, when there are reliable predictions only of parts of the protein. We use receiver operating characteristic curve analysis to quantify the success of FragBag in identifying neighbor candidate sets in a dataset of over 2,900 structures. The gold standard is the set of neighbors found by six state of the art structural aligners. Our best FragBag library finds more accurate candidate sets than the three other filter methods: The SGM, PRIDE, and a method by Zotenko et al. More interestingly, FragBag performs on a par with the computationally expensive, yet highly trusted structural aligners STRUCTAL and CE.

  17. A Numerical Analysis on the Effects of Self-Excited Tip Flow Unsteadiness and Upstream Blade Row Interactions on the Performance Predictions of a Transonic Compressor

    NASA Astrophysics Data System (ADS)

    Heberling, Brian

    Computational fluid dynamics (CFD) simulations can offer a detailed view of the complex flow fields within an axial compressor and greatly aid the design process. However, the desire for quick turnaround times raises the question of how exact the model must be. At design conditions, steady CFD simulating an isolated blade row can accurately predict the performance of a rotor. However, as a compressor is throttled and mass flow rate decreased, axial flow becomes weaker making the capturing of unsteadiness, wakes, or other flow features more important. The unsteadiness of the tip clearance flow and upstream blade wake can have a significant impact on a rotor. At off-design conditions, time-accurate simulations or modeling multiple blade rows can become necessary in order to receive accurate performance predictions. Unsteady and multi- bladerow simulations are computationally expensive, especially when used in conjunction. It is important to understand which features are important to model in order to accurately capture a compressor's performance. CFD simulations of a transonic axial compressor throttling from the design point to stall are presented. The importance of capturing the unsteadiness of the rotor tip clearance flow versus capturing upstream blade-row interactions is examined through steady and unsteady, single- and multi-bladerow computations. It is shown that there are significant differences at near stall conditions between the different types of simulations.

  18. Towards accurate cosmological predictions for rapidly oscillating scalar fields as dark matter

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

    Ureña-López, L. Arturo; Gonzalez-Morales, Alma X., E-mail: lurena@ugto.mx, E-mail: alma.gonzalez@fisica.ugto.mx

    2016-07-01

    As we are entering the era of precision cosmology, it is necessary to count on accurate cosmological predictions from any proposed model of dark matter. In this paper we present a novel approach to the cosmological evolution of scalar fields that eases their analytic and numerical analysis at the background and at the linear order of perturbations. The new method makes use of appropriate angular variables that simplify the writing of the equations of motion, and which also show that the usual field variables play a secondary role in the cosmological dynamics. We apply the method to a scalar fieldmore » endowed with a quadratic potential and revisit its properties as dark matter. Some of the results known in the literature are recovered, and a better understanding of the physical properties of the model is provided. It is confirmed that there exists a Jeans wavenumber k {sub J} , directly related to the suppression of linear perturbations at wavenumbers k > k {sub J} , and which is verified to be k {sub J} = a √ mH . We also discuss some semi-analytical results that are well satisfied by the full numerical solutions obtained from an amended version of the CMB code CLASS. Finally we draw some of the implications that this new treatment of the equations of motion may have in the prediction of cosmological observables from scalar field dark matter models.« less

  19. Turbine Performance Optimization Task Status

    NASA Technical Reports Server (NTRS)

    Griffin, Lisa W.; Turner, James E. (Technical Monitor)

    2001-01-01

    Capability to optimize for turbine performance and accurately predict unsteady loads will allow for increased reliability, Isp, and thrust-to-weight. The development of a fast, accurate aerodynamic design, analysis, and optimization system is required.

  20. Predicting performance: relative importance of students' background and past performance.

    PubMed

    Stegers-Jager, Karen M; Themmen, Axel P N; Cohen-Schotanus, Janke; Steyerberg, Ewout W

    2015-09-01

    Despite evidence for the predictive value of both pre-admission characteristics and past performance at medical school, their relative contribution to predicting medical school performance has not been thoroughly investigated. This study was designed to determine the relative importance of pre-admission characteristics and past performance in medical school in predicting student performance in pre-clinical and clinical training. This longitudinal prospective study followed six cohorts of students admitted to a Dutch, 6-year, undergraduate medical course during 2002-2007 (n = 2357). Four prediction models were developed using multivariate logistic regression analysis. Main outcome measures were 'Year 1 course completion within 1 year' (models 1a, 1b), 'Pre-clinical course completion within 4 years' (model 2) and 'Achievement of at least three of five clerkship grades of ≥ 8.0' (model 3). Pre-admission characteristics (models 1a, 1b, 2, 3) and past performance at medical school (models 1b, 2, 3) were included as predictor variables. In model 1a - including pre-admission characteristics only - the strongest predictor for Year 1 course completion was pre-university grade point average (GPA). Success factors were 'selected by admission testing' and 'age > 21 years'; risk factors were 'Surinamese/Antillean background', 'foreign pre-university degree', 'doctor parent' and male gender. In model 1b, number of attempts and GPA at 4 months were the strongest predictors for Year 1 course completion, and male gender remained a risk factor. Year 1 GPA was the strongest predictor for pre-clinical course completion, whereas being male or aged 19-21 years were risk factors. Pre-clinical course GPA positively predicted clinical performance, whereas being non-Dutch or a first-generation university student were important risk factors for lower clinical grades. Nagelkerke's R(2) ranged from 0.16 to 0.62. This study not only confirms the importance of past performance as a predictor

  1. Accurate prediction of cellular co-translational folding indicates proteins can switch from post- to co-translational folding

    PubMed Central

    Nissley, Daniel A.; Sharma, Ajeet K.; Ahmed, Nabeel; Friedrich, Ulrike A.; Kramer, Günter; Bukau, Bernd; O'Brien, Edward P.

    2016-01-01

    The rates at which domains fold and codons are translated are important factors in determining whether a nascent protein will co-translationally fold and function or misfold and malfunction. Here we develop a chemical kinetic model that calculates a protein domain's co-translational folding curve during synthesis using only the domain's bulk folding and unfolding rates and codon translation rates. We show that this model accurately predicts the course of co-translational folding measured in vivo for four different protein molecules. We then make predictions for a number of different proteins in yeast and find that synonymous codon substitutions, which change translation-elongation rates, can switch some protein domains from folding post-translationally to folding co-translationally—a result consistent with previous experimental studies. Our approach explains essential features of co-translational folding curves and predicts how varying the translation rate at different codon positions along a transcript's coding sequence affects this self-assembly process. PMID:26887592

  2. Laser line scan performance prediction

    NASA Astrophysics Data System (ADS)

    Mahoney, Kevin L.; Schofield, Oscar; Kerfoot, John; Giddings, Tom; Shirron, Joe; Twardowski, Mike

    2007-09-01

    The effectiveness of sensors that use optical measurements for the laser detection and identification of subsurface mines is directly related to water clarity. The primary objective of the work presented here was to use the optical data collected by UUV (Slocum Glider) surveys of an operational areas to estimate the performance of an electro-optical identification (EOID) Laser Line Scan (LLS) system during RIMPAC 06, an international naval exercise off the coast of Hawaii. Measurements of optical backscattering and beam attenuation were made with a Wet Labs, Inc. Scattering Absorption Meter (SAM), mounted on a Rutgers University/Webb Research Slocum glider. The optical data universally indicated extremely clear water in the operational area, except very close to shore. The beam-c values from the SAM sensor were integrated to three attenuation lengths to provide an estimate of how well the LLS would perform in detecting and identifying mines in the operational areas. Additionally, the processed in situ optical data served as near-real-time input to the Electro-Optic Detection Simulator, ver. 3 (EODES-3; Metron, Inc.) model for EOID performance prediction. Both methods of predicting LLS performance suggested a high probability of detection and probability of identification. These predictions were validated by the actual performance of the LLS as the EOID system yielded imagery from which reliable mine identification could be made. Future plans include repeating this work in more optically challenging water types to demonstrate the utility of pre-mission UUV surveys of operational areas as a tactical decision aid for planning EOID missions.

  3. A statistical model for predicting muscle performance

    NASA Astrophysics Data System (ADS)

    Byerly, Diane Leslie De Caix

    The objective of these studies was to develop a capability for predicting muscle performance and fatigue to be utilized for both space- and ground-based applications. To develop this predictive model, healthy test subjects performed a defined, repetitive dynamic exercise to failure using a Lordex spinal machine. Throughout the exercise, surface electromyography (SEMG) data were collected from the erector spinae using a Mega Electronics ME3000 muscle tester and surface electrodes placed on both sides of the back muscle. These data were analyzed using a 5th order Autoregressive (AR) model and statistical regression analysis. It was determined that an AR derived parameter, the mean average magnitude of AR poles, significantly correlated with the maximum number of repetitions (designated Rmax) that a test subject was able to perform. Using the mean average magnitude of AR poles, a test subject's performance to failure could be predicted as early as the sixth repetition of the exercise. This predictive model has the potential to provide a basis for improving post-space flight recovery, monitoring muscle atrophy in astronauts and assessing the effectiveness of countermeasures, monitoring astronaut performance and fatigue during Extravehicular Activity (EVA) operations, providing pre-flight assessment of the ability of an EVA crewmember to perform a given task, improving the design of training protocols and simulations for strenuous International Space Station assembly EVA, and enabling EVA work task sequences to be planned enhancing astronaut performance and safety. Potential ground-based, medical applications of the predictive model include monitoring muscle deterioration and performance resulting from illness, establishing safety guidelines in the industry for repetitive tasks, monitoring the stages of rehabilitation for muscle-related injuries sustained in sports and accidents, and enhancing athletic performance through improved training protocols while reducing

  4. Can single empirical algorithms accurately predict inland shallow water quality status from high resolution, multi-sensor, multi-temporal satellite data?

    NASA Astrophysics Data System (ADS)

    Theologou, I.; Patelaki, M.; Karantzalos, K.

    2015-04-01

    Assessing and monitoring water quality status through timely, cost effective and accurate manner is of fundamental importance for numerous environmental management and policy making purposes. Therefore, there is a current need for validated methodologies which can effectively exploit, in an unsupervised way, the enormous amount of earth observation imaging datasets from various high-resolution satellite multispectral sensors. To this end, many research efforts are based on building concrete relationships and empirical algorithms from concurrent satellite and in-situ data collection campaigns. We have experimented with Landsat 7 and Landsat 8 multi-temporal satellite data, coupled with hyperspectral data from a field spectroradiometer and in-situ ground truth data with several physico-chemical and other key monitoring indicators. All available datasets, covering a 4 years period, in our case study Lake Karla in Greece, were processed and fused under a quantitative evaluation framework. The performed comprehensive analysis posed certain questions regarding the applicability of single empirical models across multi-temporal, multi-sensor datasets towards the accurate prediction of key water quality indicators for shallow inland systems. Single linear regression models didn't establish concrete relations across multi-temporal, multi-sensor observations. Moreover, the shallower parts of the inland system followed, in accordance with the literature, different regression patterns. Landsat 7 and 8 resulted in quite promising results indicating that from the recreation of the lake and onward consistent per-sensor, per-depth prediction models can be successfully established. The highest rates were for chl-a (r2=89.80%), dissolved oxygen (r2=88.53%), conductivity (r2=88.18%), ammonium (r2=87.2%) and pH (r2=86.35%), while the total phosphorus (r2=70.55%) and nitrates (r2=55.50%) resulted in lower correlation rates.

  5. Accurate prediction of RNA-binding protein residues with two discriminative structural descriptors.

    PubMed

    Sun, Meijian; Wang, Xia; Zou, Chuanxin; He, Zenghui; Liu, Wei; Li, Honglin

    2016-06-07

    RNA-binding proteins participate in many important biological processes concerning RNA-mediated gene regulation, and several computational methods have been recently developed to predict the protein-RNA interactions of RNA-binding proteins. Newly developed discriminative descriptors will help to improve the prediction accuracy of these prediction methods and provide further meaningful information for researchers. In this work, we designed two structural features (residue electrostatic surface potential and triplet interface propensity) and according to the statistical and structural analysis of protein-RNA complexes, the two features were powerful for identifying RNA-binding protein residues. Using these two features and other excellent structure- and sequence-based features, a random forest classifier was constructed to predict RNA-binding residues. The area under the receiver operating characteristic curve (AUC) of five-fold cross-validation for our method on training set RBP195 was 0.900, and when applied to the test set RBP68, the prediction accuracy (ACC) was 0.868, and the F-score was 0.631. The good prediction performance of our method revealed that the two newly designed descriptors could be discriminative for inferring protein residues interacting with RNAs. To facilitate the use of our method, a web-server called RNAProSite, which implements the proposed method, was constructed and is freely available at http://lilab.ecust.edu.cn/NABind .

  6. Accurate genomic predictions for BCWD resistance in rainbow trout are achieved using low-density SNP panels: Evidence that long-range LD is a major contributing factor.

    PubMed

    Vallejo, Roger L; Silva, Rafael M O; Evenhuis, Jason P; Gao, Guangtu; Liu, Sixin; Parsons, James E; Martin, Kyle E; Wiens, Gregory D; Lourenco, Daniela A L; Leeds, Timothy D; Palti, Yniv

    2018-06-05

    Previously accurate genomic predictions for Bacterial cold water disease (BCWD) resistance in rainbow trout were obtained using a medium-density single nucleotide polymorphism (SNP) array. Here, the impact of lower-density SNP panels on the accuracy of genomic predictions was investigated in a commercial rainbow trout breeding population. Using progeny performance data, the accuracy of genomic breeding values (GEBV) using 35K, 10K, 3K, 1K, 500, 300 and 200 SNP panels as well as a panel with 70 quantitative trait loci (QTL)-flanking SNP was compared. The GEBVs were estimated using the Bayesian method BayesB, single-step GBLUP (ssGBLUP) and weighted ssGBLUP (wssGBLUP). The accuracy of GEBVs remained high despite the sharp reductions in SNP density, and even with 500 SNP accuracy was higher than the pedigree-based prediction (0.50-0.56 versus 0.36). Furthermore, the prediction accuracy with the 70 QTL-flanking SNP (0.65-0.72) was similar to the panel with 35K SNP (0.65-0.71). Genomewide linkage disequilibrium (LD) analysis revealed strong LD (r 2  ≥ 0.25) spanning on average over 1 Mb across the rainbow trout genome. This long-range LD likely contributed to the accurate genomic predictions with the low-density SNP panels. Population structure analysis supported the hypothesis that long-range LD in this population may be caused by admixture. Results suggest that lower-cost, low-density SNP panels can be used for implementing genomic selection for BCWD resistance in rainbow trout breeding programs. © 2018 The Authors. This article is a U.S. Government work and is in the public domain in the USA. Journal of Animal Breeding and Genetics published by Blackwell Verlag GmbH.

  7. Predicting academic performance and clinical competency for international dental students: seeking the most efficient and effective measures.

    PubMed

    Stacey, D Graham; Whittaker, John M

    2005-02-01

    Measures used in the selection of international dental students to a U.S. D.D.S. program were examined to identify the grouping that most effectively and efficiently predicted academic performance and clinical competency. Archival records from the International Dental Program (IDP) at Loma Linda University provided data on 171 students who had trained in countries outside the United States. The students sought admission to the D.D.S. degree program, successful completion of which qualified them to sit for U.S. licensure. As with most dental schools, competition is high for admission to the D.D.S. program. The study's goal was to identify what measures contributed to a fair and accurate selection process for dental school applicants from other nations. Multiple regression analyses identified National Board Part II and dexterity measures as significant predictors of academic performance and clinical competency. National Board Part I, TOEFL, and faculty interviews added no significant additional help in predicting eventual academic performance and clinical competency.

  8. A gene expression biomarker accurately predicts estrogen ...

    EPA Pesticide Factsheets

    The EPA’s vision for the Endocrine Disruptor Screening Program (EDSP) in the 21st Century (EDSP21) includes utilization of high-throughput screening (HTS) assays coupled with computational modeling to prioritize chemicals with the goal of eventually replacing current Tier 1 screening tests. The ToxCast program currently includes 18 HTS in vitro assays that evaluate the ability of chemicals to modulate estrogen receptor α (ERα), an important endocrine target. We propose microarray-based gene expression profiling as a complementary approach to predict ERα modulation and have developed computational methods to identify ERα modulators in an existing database of whole-genome microarray data. The ERα biomarker consisted of 46 ERα-regulated genes with consistent expression patterns across 7 known ER agonists and 3 known ER antagonists. The biomarker was evaluated as a predictive tool using the fold-change rank-based Running Fisher algorithm by comparison to annotated gene expression data sets from experiments in MCF-7 cells. Using 141 comparisons from chemical- and hormone-treated cells, the biomarker gave a balanced accuracy for prediction of ERα activation or suppression of 94% or 93%, respectively. The biomarker was able to correctly classify 18 out of 21 (86%) OECD ER reference chemicals including “very weak” agonists and replicated predictions based on 18 in vitro ER-associated HTS assays. For 114 chemicals present in both the HTS data and the MCF-7 c

  9. Accurate in silico prediction of species-specific methylation sites based on information gain feature optimization.

    PubMed

    Wen, Ping-Ping; Shi, Shao-Ping; Xu, Hao-Dong; Wang, Li-Na; Qiu, Jian-Ding

    2016-10-15

    As one of the most important reversible types of post-translational modification, protein methylation catalyzed by methyltransferases carries many pivotal biological functions as well as many essential biological processes. Identification of methylation sites is prerequisite for decoding methylation regulatory networks in living cells and understanding their physiological roles. Experimental methods are limitations of labor-intensive and time-consuming. While in silicon approaches are cost-effective and high-throughput manner to predict potential methylation sites, but those previous predictors only have a mixed model and their prediction performances are not fully satisfactory now. Recently, with increasing availability of quantitative methylation datasets in diverse species (especially in eukaryotes), there is a growing need to develop a species-specific predictor. Here, we designed a tool named PSSMe based on information gain (IG) feature optimization method for species-specific methylation site prediction. The IG method was adopted to analyze the importance and contribution of each feature, then select the valuable dimension feature vectors to reconstitute a new orderly feature, which was applied to build the finally prediction model. Finally, our method improves prediction performance of accuracy about 15% comparing with single features. Furthermore, our species-specific model significantly improves the predictive performance compare with other general methylation prediction tools. Hence, our prediction results serve as useful resources to elucidate the mechanism of arginine or lysine methylation and facilitate hypothesis-driven experimental design and validation. The tool online service is implemented by C# language and freely available at http://bioinfo.ncu.edu.cn/PSSMe.aspx CONTACT: jdqiu@ncu.edu.cnSupplementary information: Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights

  10. A Performance Prediction Model for a Fault-Tolerant Computer During Recovery and Restoration

    NASA Technical Reports Server (NTRS)

    Obando, Rodrigo A.; Stoughton, John W.

    1995-01-01

    The modeling and design of a fault-tolerant multiprocessor system is addressed. Of interest is the behavior of the system during recovery and restoration after a fault has occurred. The multiprocessor systems are based on the Algorithm to Architecture Mapping Model (ATAMM) and the fault considered is the death of a processor. The developed model is useful in the determination of performance bounds of the system during recovery and restoration. The performance bounds include time to recover from the fault, time to restore the system, and determination of any permanent delay in the input to output latency after the system has regained steady state. Implementation of an ATAMM based computer was developed for a four-processor generic VHSIC spaceborne computer (GVSC) as the target system. A simulation of the GVSC was also written on the code used in the ATAMM Multicomputer Operating System (AMOS). The simulation is used to verify the new model for tracking the propagation of the delay through the system and predicting the behavior of the transient state of recovery and restoration. The model is shown to accurately predict the transient behavior of an ATAMM based multicomputer during recovery and restoration.

  11. PredSTP: a highly accurate SVM based model to predict sequential cystine stabilized peptides.

    PubMed

    Islam, S M Ashiqul; Sajed, Tanvir; Kearney, Christopher Michel; Baker, Erich J

    2015-07-05

    Numerous organisms have evolved a wide range of toxic peptides for self-defense and predation. Their effective interstitial and macro-environmental use requires energetic and structural stability. One successful group of these peptides includes a tri-disulfide domain arrangement that offers toxicity and high stability. Sequential tri-disulfide connectivity variants create highly compact disulfide folds capable of withstanding a variety of environmental stresses. Their combination of toxicity and stability make these peptides remarkably valuable for their potential as bio-insecticides, antimicrobial peptides and peptide drug candidates. However, the wide sequence variation, sources and modalities of group members impose serious limitations on our ability to rapidly identify potential members. As a result, there is a need for automated high-throughput member classification approaches that leverage their demonstrated tertiary and functional homology. We developed an SVM-based model to predict sequential tri-disulfide peptide (STP) toxins from peptide sequences. One optimized model, called PredSTP, predicted STPs from training set with sensitivity, specificity, precision, accuracy and a Matthews correlation coefficient of 94.86%, 94.11%, 84.31%, 94.30% and 0.86, respectively, using 200 fold cross validation. The same model outperforms existing prediction approaches in three independent out of sample testsets derived from PDB. PredSTP can accurately identify a wide range of cystine stabilized peptide toxins directly from sequences in a species-agnostic fashion. The ability to rapidly filter sequences for potential bioactive peptides can greatly compress the time between peptide identification and testing structural and functional properties for possible antimicrobial and insecticidal candidates. A web interface is freely available to predict STP toxins from http://crick.ecs.baylor.edu/.

  12. Final Technical Report: Increasing Prediction Accuracy.

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

    King, Bruce Hardison; Hansen, Clifford; Stein, Joshua

    2015-12-01

    PV performance models are used to quantify the value of PV plants in a given location. They combine the performance characteristics of the system, the measured or predicted irradiance and weather at a site, and the system configuration and design into a prediction of the amount of energy that will be produced by a PV system. These predictions must be as accurate as possible in order for finance charges to be minimized. Higher accuracy equals lower project risk. The Increasing Prediction Accuracy project at Sandia focuses on quantifying and reducing uncertainties in PV system performance models.

  13. PSSP-RFE: accurate prediction of protein structural class by recursive feature extraction from PSI-BLAST profile, physical-chemical property and functional annotations.

    PubMed

    Li, Liqi; Cui, Xiang; Yu, Sanjiu; Zhang, Yuan; Luo, Zhong; Yang, Hua; Zhou, Yue; Zheng, Xiaoqi

    2014-01-01

    Protein structure prediction is critical to functional annotation of the massively accumulated biological sequences, which prompts an imperative need for the development of high-throughput technologies. As a first and key step in protein structure prediction, protein structural class prediction becomes an increasingly challenging task. Amongst most homological-based approaches, the accuracies of protein structural class prediction are sufficiently high for high similarity datasets, but still far from being satisfactory for low similarity datasets, i.e., below 40% in pairwise sequence similarity. Therefore, we present a novel method for accurate and reliable protein structural class prediction for both high and low similarity datasets. This method is based on Support Vector Machine (SVM) in conjunction with integrated features from position-specific score matrix (PSSM), PROFEAT and Gene Ontology (GO). A feature selection approach, SVM-RFE, is also used to rank the integrated feature vectors through recursively removing the feature with the lowest ranking score. The definitive top features selected by SVM-RFE are input into the SVM engines to predict the structural class of a query protein. To validate our method, jackknife tests were applied to seven widely used benchmark datasets, reaching overall accuracies between 84.61% and 99.79%, which are significantly higher than those achieved by state-of-the-art tools. These results suggest that our method could serve as an accurate and cost-effective alternative to existing methods in protein structural classification, especially for low similarity datasets.

  14. Accurate Prediction of Protein Contact Maps by Coupling Residual Two-Dimensional Bidirectional Long Short-Term Memory with Convolutional Neural Networks.

    PubMed

    Hanson, Jack; Paliwal, Kuldip; Litfin, Thomas; Yang, Yuedong; Zhou, Yaoqi

    2018-06-19

    Accurate prediction of a protein contact map depends greatly on capturing as much contextual information as possible from surrounding residues for a target residue pair. Recently, ultra-deep residual convolutional networks were found to be state-of-the-art in the latest Critical Assessment of Structure Prediction techniques (CASP12, (Schaarschmidt et al., 2018)) for protein contact map prediction by attempting to provide a protein-wide context at each residue pair. Recurrent neural networks have seen great success in recent protein residue classification problems due to their ability to propagate information through long protein sequences, especially Long Short-Term Memory (LSTM) cells. Here we propose a novel protein contact map prediction method by stacking residual convolutional networks with two-dimensional residual bidirectional recurrent LSTM networks, and using both one-dimensional sequence-based and two-dimensional evolutionary coupling-based information. We show that the proposed method achieves a robust performance over validation and independent test sets with the Area Under the receiver operating characteristic Curve (AUC)>0.95 in all tests. When compared to several state-of-the-art methods for independent testing of 228 proteins, the method yields an AUC value of 0.958, whereas the next-best method obtains an AUC of 0.909. More importantly, the improvement is over contacts at all sequence-position separations. Specifically, a 8.95%, 5.65% and 2.84% increase in precision were observed for the top L∕10 predictions over the next best for short, medium and long-range contacts, respectively. This confirms the usefulness of ResNets to congregate the short-range relations and 2D-BRLSTM to propagate the long-range dependencies throughout the entire protein contact map 'image'. SPOT-Contact server url: http://sparks-lab.org/jack/server/SPOT-Contact/. Supplementary data is available at Bioinformatics online.

  15. Performance predictions affect attentional processes of event-based prospective memory.

    PubMed

    Rummel, Jan; Kuhlmann, Beatrice G; Touron, Dayna R

    2013-09-01

    To investigate whether making performance predictions affects prospective memory (PM) processing, we asked one group of participants to predict their performance in a PM task embedded in an ongoing task and compared their performance with a control group that made no predictions. A third group gave not only PM predictions but also ongoing-task predictions. Exclusive PM predictions resulted in slower ongoing-task responding both in a nonfocal (Experiment 1) and in a focal (Experiment 2) PM task. Only in the nonfocal task was the additional slowing accompanied by improved PM performance. Even in the nonfocal task, however, was the correlation between ongoing-task speed and PM performance reduced after predictions, suggesting that the slowing was not completely functional for PM. Prediction-induced changes could be avoided by asking participants to additionally predict their performance in the ongoing task. In sum, the present findings substantiate a role of metamemory for attention-allocation strategies of PM. Copyright © 2013 Elsevier Inc. All rights reserved.

  16. Accurate multimodal probabilistic prediction of conversion to Alzheimer's disease in patients with mild cognitive impairment.

    PubMed

    Young, Jonathan; Modat, Marc; Cardoso, Manuel J; Mendelson, Alex; Cash, Dave; Ourselin, Sebastien

    2013-01-01

    Accurately identifying the patients that have mild cognitive impairment (MCI) who will go on to develop Alzheimer's disease (AD) will become essential as new treatments will require identification of AD patients at earlier stages in the disease process. Most previous work in this area has centred around the same automated techniques used to diagnose AD patients from healthy controls, by coupling high dimensional brain image data or other relevant biomarker data to modern machine learning techniques. Such studies can now distinguish between AD patients and controls as accurately as an experienced clinician. Models trained on patients with AD and control subjects can also distinguish between MCI patients that will convert to AD within a given timeframe (MCI-c) and those that remain stable (MCI-s), although differences between these groups are smaller and thus, the corresponding accuracy is lower. The most common type of classifier used in these studies is the support vector machine, which gives categorical class decisions. In this paper, we introduce Gaussian process (GP) classification to the problem. This fully Bayesian method produces naturally probabilistic predictions, which we show correlate well with the actual chances of converting to AD within 3 years in a population of 96 MCI-s and 47 MCI-c subjects. Furthermore, we show that GPs can integrate multimodal data (in this study volumetric MRI, FDG-PET, cerebrospinal fluid, and APOE genotype with the classification process through the use of a mixed kernel). The GP approach aids combination of different data sources by learning parameters automatically from training data via type-II maximum likelihood, which we compare to a more conventional method based on cross validation and an SVM classifier. When the resulting probabilities from the GP are dichotomised to produce a binary classification, the results for predicting MCI conversion based on the combination of all three types of data show a balanced accuracy

  17. Predicting Performance in Higher Education Using Proximal Predictors.

    PubMed

    Niessen, A Susan M; Meijer, Rob R; Tendeiro, Jorge N

    2016-01-01

    We studied the validity of two methods for predicting academic performance and student-program fit that were proximal to important study criteria. Applicants to an undergraduate psychology program participated in a selection procedure containing a trial-studying test based on a work sample approach, and specific skills tests in English and math. Test scores were used to predict academic achievement and progress after the first year, achievement in specific course types, enrollment, and dropout after the first year. All tests showed positive significant correlations with the criteria. The trial-studying test was consistently the best predictor in the admission procedure. We found no significant differences between the predictive validity of the trial-studying test and prior educational performance, and substantial shared explained variance between the two predictors. Only applicants with lower trial-studying scores were significantly less likely to enroll in the program. In conclusion, the trial-studying test yielded predictive validities similar to that of prior educational performance and possibly enabled self-selection. In admissions aimed at student-program fit, or in admissions in which past educational performance is difficult to use, a trial-studying test is a good instrument to predict academic performance.

  18. Instrument Landing System performance prediction

    DOT National Transportation Integrated Search

    1974-01-01

    Further achievements made in fiscal year 1973 on the development : of an Instrument Landing System (ILS) performance prediction model : are reported. These include (ILS) localizer scattering from generalized : slanted rectangular, triangular and cyli...

  19. Changes in Memory Prediction Accuracy: Age and Performance Effects

    ERIC Educational Resources Information Center

    Pearman, Ann; Trujillo, Amanda

    2013-01-01

    Memory performance predictions are subjective estimates of possible memory task performance. The purpose of this study was to examine possible factors related to changes in word list performance predictions made by younger and older adults. Factors included memory self-efficacy, actual performance, and perceptions of performance. The current study…

  20. A computer program for performance prediction of tripropellant rocket engines with tangential slot injection

    NASA Technical Reports Server (NTRS)

    Dang, Anthony; Nickerson, Gary R.

    1987-01-01

    For the development of a Heavy Lift Launch Vehicle (HLLV) several engines with different operating cycles and using LOX/Hydrocarbon propellants are presently being examined. Some concepts utilize hydrogen for thrust chamber wall cooling followed by a gas generator turbine drive cycle with subsequent dumping of H2/O2 combustion products into the nozzle downstream of the throat. In the Space Transportation Booster Engine (STBE) selection process the specific impulse will be one of the optimization criteria; however, the current performance prediction programs do not have the capability to include a third propellant in this process, nor to account for the effect of dumping the gas-generator product tangentially inside the nozzle. The purpose is to describe a computer program for accurately predicting the performance of such an engine. The code consists of two modules; one for the inviscid performance, and the other for the viscous loss. For the first module, the two-dimensional kinetics program (TDK) was modified to account for tripropellant chemistry, and for the effect of tangential slot injection. For the viscous loss, the Mass Addition Boundary Layer program (MABL) was modified to include the effects of the boundary layer-shear layer interaction, and tripropellant chemistry. Calculations were made for a real engine and compared with available data.

  1. Towards more accurate vegetation mortality predictions

    DOE PAGES

    Sevanto, Sanna Annika; Xu, Chonggang

    2016-09-26

    Predicting the fate of vegetation under changing climate is one of the major challenges of the climate modeling community. Here, terrestrial vegetation dominates the carbon and water cycles over land areas, and dramatic changes in vegetation cover resulting from stressful environmental conditions such as drought feed directly back to local and regional climate, potentially leading to a vicious cycle where vegetation recovery after a disturbance is delayed or impossible.

  2. Rotary-wing aerodynamics. Volume 2: Performance prediction of helicopters

    NASA Technical Reports Server (NTRS)

    Keys, C. N.; Stephniewski, W. Z. (Editor)

    1979-01-01

    Application of theories, as well as, special methods of procedures applicable to performance prediction are illustrated first, on an example of the conventional helicopter and then, winged and tandem configurations. Performance prediction of conventional helicopters in hover and vertical ascent are investigated. Various approaches to performance prediction in forward translation are presented. Performance problems are discussed only this time, a wing is added to the baseline configuration, and both aircraft are compared with respect to their performance. This comparison is extended to a tandem. Appendices on methods for estimating performance guarantees and growth of aircraft concludes this volume.

  3. What predicts performance during clinical psychology training?

    PubMed

    Scior, Katrina; Bradley, Caroline E; Potts, Henry W W; Woolf, Katherine; de C Williams, Amanda C

    2014-06-01

    While the question of who is likely to be selected for clinical psychology training has been studied, evidence on performance during training is scant. This study explored data from seven consecutive intakes of the UK's largest clinical psychology training course, aiming to identify what factors predict better or poorer outcomes. Longitudinal cross-sectional study using prospective and retrospective data. Characteristics at application were analysed in relation to a range of in-course assessments for 274 trainee clinical psychologists who had completed or were in the final stage of their training. Trainees were diverse in age, pre-training experience, and academic performance at A-level (advanced level certificate required for university admission), but not in gender or ethnicity. Failure rates across the three performance domains (academic, clinical, research) were very low, suggesting that selection was successful in screening out less suitable candidates. Key predictors of good performance on the course were better A-levels and better degree class. Non-white students performed less well on two outcomes. Type and extent of pre-training clinical experience on outcomes had varied effects on outcome. Research supervisor ratings emerged as global indicators and predicted nearly all outcomes, but may have been biased as they were retrospective. Referee ratings predicted only one of the seven outcomes examined, and interview ratings predicted none of the outcomes. Predicting who will do well or poorly in clinical psychology training is complex. Interview and referee ratings may well be successful in screening out unsuitable candidates, but appear to be a poor guide to performance on the course. © 2013 The Authors. British Journal of Clinical Psychology published by John Wiley & Sons Ltd on behalf of the British Psychological Society.

  4. In vitro models for the prediction of in vivo performance of oral dosage forms.

    PubMed

    Kostewicz, Edmund S; Abrahamsson, Bertil; Brewster, Marcus; Brouwers, Joachim; Butler, James; Carlert, Sara; Dickinson, Paul A; Dressman, Jennifer; Holm, René; Klein, Sandra; Mann, James; McAllister, Mark; Minekus, Mans; Muenster, Uwe; Müllertz, Anette; Verwei, Miriam; Vertzoni, Maria; Weitschies, Werner; Augustijns, Patrick

    2014-06-16

    Accurate prediction of the in vivo biopharmaceutical performance of oral drug formulations is critical to efficient drug development. Traditionally, in vitro evaluation of oral drug formulations has focused on disintegration and dissolution testing for quality control (QC) purposes. The connection with in vivo biopharmaceutical performance has often been ignored. More recently, the switch to assessing drug products in a more biorelevant and mechanistic manner has advanced the understanding of drug formulation behavior. Notwithstanding this evolution, predicting the in vivo biopharmaceutical performance of formulations that rely on complex intraluminal processes (e.g. solubilization, supersaturation, precipitation…) remains extremely challenging. Concomitantly, the increasing demand for complex formulations to overcome low drug solubility or to control drug release rates urges the development of new in vitro tools. Development and optimizing innovative, predictive Oral Biopharmaceutical Tools is the main target of the OrBiTo project within the Innovative Medicines Initiative (IMI) framework. A combination of physico-chemical measurements, in vitro tests, in vivo methods, and physiology-based pharmacokinetic modeling is expected to create a unique knowledge platform, enabling the bottlenecks in drug development to be removed and the whole process of drug development to become more efficient. As part of the basis for the OrBiTo project, this review summarizes the current status of predictive in vitro assessment tools for formulation behavior. Both pharmacopoeia-listed apparatus and more advanced tools are discussed. Special attention is paid to major issues limiting the predictive power of traditional tools, including the simulation of dynamic changes in gastrointestinal conditions, the adequate reproduction of gastrointestinal motility, the simulation of supersaturation and precipitation, and the implementation of the solubility-permeability interplay. It is

  5. A high order accurate finite element algorithm for high Reynolds number flow prediction

    NASA Technical Reports Server (NTRS)

    Baker, A. J.

    1978-01-01

    A Galerkin-weighted residuals formulation is employed to establish an implicit finite element solution algorithm for generally nonlinear initial-boundary value problems. Solution accuracy, and convergence rate with discretization refinement, are quantized in several error norms, by a systematic study of numerical solutions to several nonlinear parabolic and a hyperbolic partial differential equation characteristic of the equations governing fluid flows. Solutions are generated using selective linear, quadratic and cubic basis functions. Richardson extrapolation is employed to generate a higher-order accurate solution to facilitate isolation of truncation error in all norms. Extension of the mathematical theory underlying accuracy and convergence concepts for linear elliptic equations is predicted for equations characteristic of laminar and turbulent fluid flows at nonmodest Reynolds number. The nondiagonal initial-value matrix structure introduced by the finite element theory is determined intrinsic to improved solution accuracy and convergence. A factored Jacobian iteration algorithm is derived and evaluated to yield a consequential reduction in both computer storage and execution CPU requirements while retaining solution accuracy.

  6. Accurate Monitoring and Fault Detection in Wind Measuring Devices through Wireless Sensor Networks

    PubMed Central

    Khan, Komal Saifullah; Tariq, Muhammad

    2014-01-01

    Many wind energy projects report poor performance as low as 60% of the predicted performance. The reason for this is poor resource assessment and the use of new untested technologies and systems in remote locations. Predictions about the potential of an area for wind energy projects (through simulated models) may vary from the actual potential of the area. Hence, introducing accurate site assessment techniques will lead to accurate predictions of energy production from a particular area. We solve this problem by installing a Wireless Sensor Network (WSN) to periodically analyze the data from anemometers installed in that area. After comparative analysis of the acquired data, the anemometers transmit their readings through a WSN to the sink node for analysis. The sink node uses an iterative algorithm which sequentially detects any faulty anemometer and passes the details of the fault to the central system or main station. We apply the proposed technique in simulation as well as in practical implementation and study its accuracy by comparing the simulation results with experimental results to analyze the variation in the results obtained from both simulation model and implemented model. Simulation results show that the algorithm indicates faulty anemometers with high accuracy and low false alarm rate when as many as 25% of the anemometers become faulty. Experimental analysis shows that anemometers incorporating this solution are better assessed and performance level of implemented projects is increased above 86% of the simulated models. PMID:25421739

  7. Reliable and accurate point-based prediction of cumulative infiltration using soil readily available characteristics: A comparison between GMDH, ANN, and MLR

    NASA Astrophysics Data System (ADS)

    Rahmati, Mehdi

    2017-08-01

    Developing accurate and reliable pedo-transfer functions (PTFs) to predict soil non-readily available characteristics is one of the most concerned topic in soil science and selecting more appropriate predictors is a crucial factor in PTFs' development. Group method of data handling (GMDH), which finds an approximate relationship between a set of input and output variables, not only provide an explicit procedure to select the most essential PTF input variables, but also results in more accurate and reliable estimates than other mostly applied methodologies. Therefore, the current research was aimed to apply GMDH in comparison with multivariate linear regression (MLR) and artificial neural network (ANN) to develop several PTFs to predict soil cumulative infiltration point-basely at specific time intervals (0.5-45 min) using soil readily available characteristics (RACs). In this regard, soil infiltration curves as well as several soil RACs including soil primary particles (clay (CC), silt (Si), and sand (Sa)), saturated hydraulic conductivity (Ks), bulk (Db) and particle (Dp) densities, organic carbon (OC), wet-aggregate stability (WAS), electrical conductivity (EC), and soil antecedent (θi) and field saturated (θfs) water contents were measured at 134 different points in Lighvan watershed, northwest of Iran. Then, applying GMDH, MLR, and ANN methodologies, several PTFs have been developed to predict cumulative infiltrations using two sets of selected soil RACs including and excluding Ks. According to the test data, results showed that developed PTFs by GMDH and MLR procedures using all soil RACs including Ks resulted in more accurate (with E values of 0.673-0.963) and reliable (with CV values lower than 11 percent) predictions of cumulative infiltrations at different specific time steps. In contrast, ANN procedure had lower accuracy (with E values of 0.356-0.890) and reliability (with CV values up to 50 percent) compared to GMDH and MLR. The results also revealed

  8. Weather-based prediction of Plasmodium falciparum malaria in epidemic-prone regions of Ethiopia II. Weather-based prediction systems perform comparably to early detection systems in identifying times for interventions.

    PubMed

    Teklehaimanot, Hailay D; Schwartz, Joel; Teklehaimanot, Awash; Lipsitch, Marc

    2004-11-19

    Timely and accurate information about the onset of malaria epidemics is essential for effective control activities in epidemic-prone regions. Early warning methods that provide earlier alerts (usually by the use of weather variables) may permit control measures to interrupt transmission earlier in the epidemic, perhaps at the expense of some level of accuracy. Expected case numbers were modeled using a Poisson regression with lagged weather factors in a 4th-degree polynomial distributed lag model. For each week, the numbers of malaria cases were predicted using coefficients obtained using all years except that for which the prediction was being made. The effectiveness of alerts generated by the prediction system was compared against that of alerts based on observed cases. The usefulness of the prediction system was evaluated in cold and hot districts. The system predicts the overall pattern of cases well, yet underestimates the height of the largest peaks. Relative to alerts triggered by observed cases, the alerts triggered by the predicted number of cases performed slightly worse, within 5% of the detection system. The prediction-based alerts were able to prevent 10-25% more cases at a given sensitivity in cold districts than in hot ones. The prediction of malaria cases using lagged weather performed well in identifying periods of increased malaria cases. Weather-derived predictions identified epidemics with reasonable accuracy and better timeliness than early detection systems; therefore, the prediction of malarial epidemics using weather is a plausible alternative to early detection systems.

  9. Generating highly accurate prediction hypotheses through collaborative ensemble learning

    NASA Astrophysics Data System (ADS)

    Arsov, Nino; Pavlovski, Martin; Basnarkov, Lasko; Kocarev, Ljupco

    2017-03-01

    Ensemble generation is a natural and convenient way of achieving better generalization performance of learning algorithms by gathering their predictive capabilities. Here, we nurture the idea of ensemble-based learning by combining bagging and boosting for the purpose of binary classification. Since the former improves stability through variance reduction, while the latter ameliorates overfitting, the outcome of a multi-model that combines both strives toward a comprehensive net-balancing of the bias-variance trade-off. To further improve this, we alter the bagged-boosting scheme by introducing collaboration between the multi-model’s constituent learners at various levels. This novel stability-guided classification scheme is delivered in two flavours: during or after the boosting process. Applied among a crowd of Gentle Boost ensembles, the ability of the two suggested algorithms to generalize is inspected by comparing them against Subbagging and Gentle Boost on various real-world datasets. In both cases, our models obtained a 40% generalization error decrease. But their true ability to capture details in data was revealed through their application for protein detection in texture analysis of gel electrophoresis images. They achieve improved performance of approximately 0.9773 AUROC when compared to the AUROC of 0.9574 obtained by an SVM based on recursive feature elimination.

  10. Iowa calibration of MEPDG performance prediction models.

    DOT National Transportation Integrated Search

    2013-06-01

    This study aims to improve the accuracy of AASHTO Mechanistic-Empirical Pavement Design Guide (MEPDG) pavement : performance predictions for Iowa pavement systems through local calibration of MEPDG prediction models. A total of 130 : representative p...

  11. Predictive Variables of Half-Marathon Performance for Male Runners.

    PubMed

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

    2017-06-01

    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 (VO 2max , 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.

  12. Program Predicts Nonlinear Inverter Performance

    NASA Technical Reports Server (NTRS)

    Al-Ayoubi, R. R.; Oepomo, T. S.

    1985-01-01

    Program developed for ac power distribution system on Shuttle orbiter predicts total load on inverters and node voltages at each of line replaceable units (LRU's). Mathematical model simulates inverter performance at each change of state in power distribution system.

  13. Unprecedently Large-Scale Kinase Inhibitor Set Enabling the Accurate Prediction of Compound–Kinase Activities: A Way toward Selective Promiscuity by Design?

    PubMed Central

    2016-01-01

    Drug discovery programs frequently target members of the human kinome and try to identify small molecule protein kinase inhibitors, primarily for cancer treatment, additional indications being increasingly investigated. One of the challenges is controlling the inhibitors degree of selectivity, assessed by in vitro profiling against panels of protein kinases. We manually extracted, compiled, and standardized such profiles published in the literature: we collected 356 908 data points corresponding to 482 protein kinases, 2106 inhibitors, and 661 patents. We then analyzed this data set in terms of kinome coverage, results reproducibility, popularity, and degree of selectivity of both kinases and inhibitors. We used the data set to create robust proteochemometric models capable of predicting kinase activity (the ligand–target space was modeled with an externally validated RMSE of 0.41 ± 0.02 log units and R02 0.74 ± 0.03), in order to account for missing or unreliable measurements. The influence on the prediction quality of parameters such as number of measurements, Murcko scaffold frequency or inhibitor type was assessed. Interpretation of the models enabled to highlight inhibitors and kinases properties correlated with higher affinities, and an analysis in the context of kinases crystal structures was performed. Overall, the models quality allows the accurate prediction of kinase-inhibitor activities and their structural interpretation, thus paving the way for the rational design of compounds with a targeted selectivity profile. PMID:27482722

  14. Deformation, Failure, and Fatigue Life of SiC/Ti-15-3 Laminates Accurately Predicted by MAC/GMC

    NASA Technical Reports Server (NTRS)

    Bednarcyk, Brett A.; Arnold, Steven M.

    2002-01-01

    NASA Glenn Research Center's Micromechanics Analysis Code with Generalized Method of Cells (MAC/GMC) (ref.1) has been extended to enable fully coupled macro-micro deformation, failure, and fatigue life predictions for advanced metal matrix, ceramic matrix, and polymer matrix composites. Because of the multiaxial nature of the code's underlying micromechanics model, GMC--which allows the incorporation of complex local inelastic constitutive models--MAC/GMC finds its most important application in metal matrix composites, like the SiC/Ti-15-3 composite examined here. Furthermore, since GMC predicts the microscale fields within each constituent of the composite material, submodels for local effects such as fiber breakage, interfacial debonding, and matrix fatigue damage can and have been built into MAC/GMC. The present application of MAC/GMC highlights the combination of these features, which has enabled the accurate modeling of the deformation, failure, and life of titanium matrix composites.

  15. Object Detection in Natural Backgrounds Predicted by Discrimination Performance and Models

    NASA Technical Reports Server (NTRS)

    Ahumada, A. J., Jr.; Watson, A. B.; Rohaly, A. M.; Null, Cynthia H. (Technical Monitor)

    1995-01-01

    In object detection, an observer looks for an object class member in a set of backgrounds. In discrimination, an observer tries to distinguish two images. Discrimination models predict the probability that an observer detects a difference between two images. We compare object detection and image discrimination with the same stimuli by: (1) making stimulus pairs of the same background with and without the target object and (2) either giving many consecutive trials with the same background (discrimination) or intermixing the stimuli (object detection). Six images of a vehicle in a natural setting were altered to remove the vehicle and mixed with the original image in various proportions. Detection observers rated the images for vehicle presence. Discrimination observers rated the images for any difference from the background image. Estimated detectabilities of the vehicles were found by maximizing the likelihood of a Thurstone category scaling model. The pattern of estimated detectabilities is similar for discrimination and object detection, and is accurately predicted by a Cortex Transform discrimination model. Predictions of a Contrast- Sensitivity- Function filter model and a Root-Mean-Square difference metric based on the digital image values are less accurate. The discrimination detectabilities averaged about twice those of object detection.

  16. Flight test evaluation of predicted light aircraft drag, performance, and stability

    NASA Technical Reports Server (NTRS)

    Smetana, F. O.; Fox, S. R.

    1979-01-01

    A technique was developed which permits simultaneous extraction of complete lift, drag, and thrust power curves from time histories of a single aircraft maneuver such as a pullup (from V sub max to V sub stall) and pushover (to sub V max for level flight.) The technique is an extension to non-linear equations of motion of the parameter identification methods of lliff and Taylor and includes provisions for internal data compatibility improvement as well. The technique was show to be capable of correcting random errors in the most sensitive data channel and yielding highly accurate results. This technique was applied to flight data taken on the ATLIT aircraft. The drag and power values obtained from the initial least squares estimate are about 15% less than the 'true' values. If one takes into account the rather dirty wing and fuselage existing at the time of the tests, however, the predictions are reasonably accurate. The steady state lift measurements agree well with the extracted values only for small values of alpha. The predicted value of the lift at alpha = 0 is about 33% below that found in steady state tests while the predicted lift slope is 13% below the steady state value.

  17. Predictive validity of pre-admission assessments on medical student performance.

    PubMed

    Dabaliz, Al-Awwab; Kaadan, Samy; Dabbagh, M Marwan; Barakat, Abdulaziz; Shareef, Mohammad Abrar; Al-Tannir, Mohamad; Obeidat, Akef; Mohamed, Ayman

    2017-11-24

    To examine the predictive validity of pre-admission variables on students' performance in a medical school in Saudi Arabia. In this retrospective study, we collected admission and college performance data for 737 students in preclinical and clinical years. Data included high school scores and other standardized test scores, such as those of the National Achievement Test and the General Aptitude Test. Additionally, we included the scores of the Test of English as a Foreign Language (TOEFL) and the International English Language Testing System (IELTS) exams. Those datasets were then compared with college performance indicators, namely the cumulative Grade Point Average (cGPA) and progress test, using multivariate linear regression analysis. In preclinical years, both the National Achievement Test (p=0.04, B=0.08) and TOEFL (p=0.017, B=0.01) scores were positive predictors of cGPA, whereas the General Aptitude Test (p=0.048, B=-0.05) negatively predicted cGPA. Moreover, none of the pre-admission variables were predictive of progress test performance in the same group. On the other hand, none of the pre-admission variables were predictive of cGPA in clinical years. Overall, cGPA strongly predict-ed students' progress test performance (p<0.001 and B=19.02). Only the National Achievement Test and TOEFL significantly predicted performance in preclinical years. However, these variables do not predict progress test performance, meaning that they do not predict the functional knowledge reflected in the progress test. We report various strengths and deficiencies in the current medical college admission criteria, and call for employing more sensitive and valid ones that predict student performance and functional knowledge, especially in the clinical years.

  18. Accurate prediction of cardiorespiratory fitness using cycle ergometry in minimally disabled persons with relapsing-remitting multiple sclerosis.

    PubMed

    Motl, Robert W; Fernhall, Bo

    2012-03-01

    To examine the accuracy of predicting peak oxygen consumption (VO(2peak)) primarily from peak work rate (WR(peak)) recorded during a maximal, incremental exercise test on a cycle ergometer among persons with relapsing-remitting multiple sclerosis (RRMS) who had minimal disability. Cross-sectional study. Clinical research laboratory. Women with RRMS (n=32) and sex-, age-, height-, and weight-matched healthy controls (n=16) completed an incremental exercise test on a cycle ergometer to volitional termination. Not applicable. Measured and predicted VO(2peak) and WR(peak). There were strong, statistically significant associations between measured and predicted VO(2peak) in the overall sample (R(2)=.89, standard error of the estimate=127.4 mL/min) and subsamples with (R(2)=.89, standard error of the estimate=131.3 mL/min) and without (R(2)=.85, standard error of the estimate=126.8 mL/min) multiple sclerosis (MS) based on the linear regression analyses. Based on the 95% confidence limits for worst-case errors, the equation predicted VO(2peak) within 10% of its true value in 95 of every 100 subjects with MS. Peak VO(2) can be accurately predicted in persons with RRMS who have minimal disability as it is in controls by using established equations and WR(peak) recorded from a maximal, incremental exercise test on a cycle ergometer. Copyright © 2012 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

  19. Predictive performance models and multiple task performance

    NASA Technical Reports Server (NTRS)

    Wickens, Christopher D.; Larish, Inge; Contorer, Aaron

    1989-01-01

    Five models that predict how performance of multiple tasks will interact in complex task scenarios are discussed. The models are shown in terms of the assumptions they make about human operator divided attention. The different assumptions about attention are then empirically validated in a multitask helicopter flight simulation. It is concluded from this simulation that the most important assumption relates to the coding of demand level of different component tasks.

  20. 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

  1. Predicting the Plate Dent Test Output in Order to Assess the Performance of Condensed High Explosives

    NASA Astrophysics Data System (ADS)

    Frem, Dany

    2017-01-01

    In the present study, a relationship is proposed that is capable of predicting the output of the plate dent test. It is shown that the initial density ?; condensed phase heat of formation ?; the number of carbon (C), nitrogen (N), oxygen (O); and the composition molecular weight (MW) are the most important parameters needed in order to accurately predict the absolute dent depth ? produced on 1018 cold-rolled steel by a detonating organic explosive. The estimated ? values can be used to predict the detonation pressure (P) of high explosives; furthermore, we show that a correlation exists between ? and the Gurney velocity ? parameter. The new correlation is used to accurately estimate ? for several C-H-N-O explosive compositions.

  2. Do Skilled Elementary Teachers Hold Scientific Conceptions and Can They Accurately Predict the Type and Source of Students' Preconceptions of Electric Circuits?

    ERIC Educational Resources Information Center

    Lin, Jing-Wen

    2016-01-01

    Holding scientific conceptions and having the ability to accurately predict students' preconceptions are a prerequisite for science teachers to design appropriate constructivist-oriented learning experiences. This study explored the types and sources of students' preconceptions of electric circuits. First, 438 grade 3 (9 years old) students were…

  3. Predictive validity of pre-admission assessments on medical student performance

    PubMed Central

    Dabaliz, Al-Awwab; Kaadan, Samy; Dabbagh, M. Marwan; Barakat, Abdulaziz; Shareef, Mohammad Abrar; Al-Tannir, Mohamad; Obeidat, Akef

    2017-01-01

    Objectives To examine the predictive validity of pre-admission variables on students’ performance in a medical school in Saudi Arabia.  Methods In this retrospective study, we collected admission and college performance data for 737 students in preclinical and clinical years. Data included high school scores and other standardized test scores, such as those of the National Achievement Test and the General Aptitude Test. Additionally, we included the scores of the Test of English as a Foreign Language (TOEFL) and the International English Language Testing System (IELTS) exams. Those datasets were then compared with college performance indicators, namely the cumulative Grade Point Average (cGPA) and progress test, using multivariate linear regression analysis. Results In preclinical years, both the National Achievement Test (p=0.04, B=0.08) and TOEFL (p=0.017, B=0.01) scores were positive predictors of cGPA, whereas the General Aptitude Test (p=0.048, B=-0.05) negatively predicted cGPA. Moreover, none of the pre-admission variables were predictive of progress test performance in the same group. On the other hand, none of the pre-admission variables were predictive of cGPA in clinical years. Overall, cGPA strongly predict-ed students’ progress test performance (p<0.001 and B=19.02). Conclusions Only the National Achievement Test and TOEFL significantly predicted performance in preclinical years. However, these variables do not predict progress test performance, meaning that they do not predict the functional knowledge reflected in the progress test. We report various strengths and deficiencies in the current medical college admission criteria, and call for employing more sensitive and valid ones that predict student performance and functional knowledge, especially in the clinical years. PMID:29176032

  4. Daily FOUR score assessment provides accurate prognosis of long-term outcome in out-of-hospital cardiac arrest.

    PubMed

    Weiss, N; Venot, M; Verdonk, F; Chardon, A; Le Guennec, L; Llerena, M C; Raimbourg, Q; Taldir, G; Luque, Y; Fagon, J-Y; Guerot, E; Diehl, J-L

    2015-05-01

    The accurate prediction of outcome after out-of-hospital cardiac arrest (OHCA) is of major importance. The recently described Full Outline of UnResponsiveness (FOUR) is well adapted to mechanically ventilated patients and does not depend on verbal response. To evaluate the ability of FOUR assessed by intensivists to accurately predict outcome in OHCA. We prospectively identified patients admitted for OHCA with a Glasgow Coma Scale below 8. Neurological assessment was performed daily. Outcome was evaluated at 6 months using Glasgow-Pittsburgh Cerebral Performance Categories (GP-CPC). Eighty-five patients were included. At 6 months, 19 patients (22%) had a favorable outcome, GP-CPC 1-2, and 66 (78%) had an unfavorable outcome, GP-CPC 3-5. Compared to both brainstem responses at day 3 and evolution of Glasgow Coma Scale, evolution of FOUR score over the three first days was able to predict unfavorable outcome more precisely. Thus, absence of improvement or worsening from day 1 to day 3 of FOUR had 0.88 (0.79-0.97) specificity, 0.71 (0.66-0.76) sensitivity, 0.94 (0.84-1.00) PPV and 0.54 (0.49-0.59) NPV to predict unfavorable outcome. Similarly, the brainstem response of FOUR score at 0 evaluated at day 3 had 0.94 (0.89-0.99) specificity, 0.60 (0.50-0.70) sensitivity, 0.96 (0.92-1.00) PPV and 0.47 (0.37-0.57) NPV to predict unfavorable outcome. The absence of improvement or worsening from day 1 to day 3 of FOUR evaluated by intensivists provides an accurate prognosis of poor neurological outcome in OHCA. Copyright © 2015 Elsevier Masson SAS. All rights reserved.

  5. Calibration of PMIS pavement performance prediction models.

    DOT National Transportation Integrated Search

    2012-02-01

    Improve the accuracy of TxDOTs existing pavement performance prediction models through calibrating these models using actual field data obtained from the Pavement Management Information System (PMIS). : Ensure logical performance superiority patte...

  6. Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties

    NASA Astrophysics Data System (ADS)

    Xie, Tian; Grossman, Jeffrey C.

    2018-04-01

    The use of machine learning methods for accelerating the design of crystalline materials usually requires manually constructed feature vectors or complex transformation of atom coordinates to input the crystal structure, which either constrains the model to certain crystal types or makes it difficult to provide chemical insights. Here, we develop a crystal graph convolutional neural networks framework to directly learn material properties from the connection of atoms in the crystal, providing a universal and interpretable representation of crystalline materials. Our method provides a highly accurate prediction of density functional theory calculated properties for eight different properties of crystals with various structure types and compositions after being trained with 1 04 data points. Further, our framework is interpretable because one can extract the contributions from local chemical environments to global properties. Using an example of perovskites, we show how this information can be utilized to discover empirical rules for materials design.

  7. PredictSNP2: A Unified Platform for Accurately Evaluating SNP Effects by Exploiting the Different Characteristics of Variants in Distinct Genomic Regions

    PubMed Central

    Brezovský, Jan

    2016-01-01

    An important message taken from human genome sequencing projects is that the human population exhibits approximately 99.9% genetic similarity. Variations in the remaining parts of the genome determine our identity, trace our history and reveal our heritage. The precise delineation of phenotypically causal variants plays a key role in providing accurate personalized diagnosis, prognosis, and treatment of inherited diseases. Several computational methods for achieving such delineation have been reported recently. However, their ability to pinpoint potentially deleterious variants is limited by the fact that their mechanisms of prediction do not account for the existence of different categories of variants. Consequently, their output is biased towards the variant categories that are most strongly represented in the variant databases. Moreover, most such methods provide numeric scores but not binary predictions of the deleteriousness of variants or confidence scores that would be more easily understood by users. We have constructed three datasets covering different types of disease-related variants, which were divided across five categories: (i) regulatory, (ii) splicing, (iii) missense, (iv) synonymous, and (v) nonsense variants. These datasets were used to develop category-optimal decision thresholds and to evaluate six tools for variant prioritization: CADD, DANN, FATHMM, FitCons, FunSeq2 and GWAVA. This evaluation revealed some important advantages of the category-based approach. The results obtained with the five best-performing tools were then combined into a consensus score. Additional comparative analyses showed that in the case of missense variations, protein-based predictors perform better than DNA sequence-based predictors. A user-friendly web interface was developed that provides easy access to the five tools’ predictions, and their consensus scores, in a user-understandable format tailored to the specific features of different categories of variations

  8. PredictSNP2: A Unified Platform for Accurately Evaluating SNP Effects by Exploiting the Different Characteristics of Variants in Distinct Genomic Regions.

    PubMed

    Bendl, Jaroslav; Musil, Miloš; Štourač, Jan; Zendulka, Jaroslav; Damborský, Jiří; Brezovský, Jan

    2016-05-01

    An important message taken from human genome sequencing projects is that the human population exhibits approximately 99.9% genetic similarity. Variations in the remaining parts of the genome determine our identity, trace our history and reveal our heritage. The precise delineation of phenotypically causal variants plays a key role in providing accurate personalized diagnosis, prognosis, and treatment of inherited diseases. Several computational methods for achieving such delineation have been reported recently. However, their ability to pinpoint potentially deleterious variants is limited by the fact that their mechanisms of prediction do not account for the existence of different categories of variants. Consequently, their output is biased towards the variant categories that are most strongly represented in the variant databases. Moreover, most such methods provide numeric scores but not binary predictions of the deleteriousness of variants or confidence scores that would be more easily understood by users. We have constructed three datasets covering different types of disease-related variants, which were divided across five categories: (i) regulatory, (ii) splicing, (iii) missense, (iv) synonymous, and (v) nonsense variants. These datasets were used to develop category-optimal decision thresholds and to evaluate six tools for variant prioritization: CADD, DANN, FATHMM, FitCons, FunSeq2 and GWAVA. This evaluation revealed some important advantages of the category-based approach. The results obtained with the five best-performing tools were then combined into a consensus score. Additional comparative analyses showed that in the case of missense variations, protein-based predictors perform better than DNA sequence-based predictors. A user-friendly web interface was developed that provides easy access to the five tools' predictions, and their consensus scores, in a user-understandable format tailored to the specific features of different categories of variations. To

  9. Accurate X-Ray Spectral Predictions: An Advanced Self-Consistent-Field Approach Inspired by Many-Body Perturbation Theory

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

    Liang, Yufeng; Vinson, John; Pemmaraju, Sri

    Constrained-occupancy delta-self-consistent-field (ΔSCF) methods and many-body perturbation theories (MBPT) are two strategies for obtaining electronic excitations from first principles. Using the two distinct approaches, we study the O 1s core excitations that have become increasingly important for characterizing transition-metal oxides and understanding strong electronic correlation. The ΔSCF approach, in its current single-particle form, systematically underestimates the pre-edge intensity for chosen oxides, despite its success in weakly correlated systems. By contrast, the Bethe-Salpeter equation within MBPT predicts much better line shapes. This motivates one to reexamine the many-electron dynamics of x-ray excitations. We find that the single-particle ΔSCF approach can bemore » rectified by explicitly calculating many-electron transition amplitudes, producing x-ray spectra in excellent agreement with experiments. This study paves the way to accurately predict x-ray near-edge spectral fingerprints for physics and materials science beyond the Bethe-Salpether equation.« less

  10. Accurate X-Ray Spectral Predictions: An Advanced Self-Consistent-Field Approach Inspired by Many-Body Perturbation Theory

    DOE PAGES

    Liang, Yufeng; Vinson, John; Pemmaraju, Sri; ...

    2017-03-03

    Constrained-occupancy delta-self-consistent-field (ΔSCF) methods and many-body perturbation theories (MBPT) are two strategies for obtaining electronic excitations from first principles. Using the two distinct approaches, we study the O 1s core excitations that have become increasingly important for characterizing transition-metal oxides and understanding strong electronic correlation. The ΔSCF approach, in its current single-particle form, systematically underestimates the pre-edge intensity for chosen oxides, despite its success in weakly correlated systems. By contrast, the Bethe-Salpeter equation within MBPT predicts much better line shapes. This motivates one to reexamine the many-electron dynamics of x-ray excitations. We find that the single-particle ΔSCF approach can bemore » rectified by explicitly calculating many-electron transition amplitudes, producing x-ray spectra in excellent agreement with experiments. This study paves the way to accurately predict x-ray near-edge spectral fingerprints for physics and materials science beyond the Bethe-Salpether equation.« less

  11. Accurate X-Ray Spectral Predictions: An Advanced Self-Consistent-Field Approach Inspired by Many-Body Perturbation Theory.

    PubMed

    Liang, Yufeng; Vinson, John; Pemmaraju, Sri; Drisdell, Walter S; Shirley, Eric L; Prendergast, David

    2017-03-03

    Constrained-occupancy delta-self-consistent-field (ΔSCF) methods and many-body perturbation theories (MBPT) are two strategies for obtaining electronic excitations from first principles. Using the two distinct approaches, we study the O 1s core excitations that have become increasingly important for characterizing transition-metal oxides and understanding strong electronic correlation. The ΔSCF approach, in its current single-particle form, systematically underestimates the pre-edge intensity for chosen oxides, despite its success in weakly correlated systems. By contrast, the Bethe-Salpeter equation within MBPT predicts much better line shapes. This motivates one to reexamine the many-electron dynamics of x-ray excitations. We find that the single-particle ΔSCF approach can be rectified by explicitly calculating many-electron transition amplitudes, producing x-ray spectra in excellent agreement with experiments. This study paves the way to accurately predict x-ray near-edge spectral fingerprints for physics and materials science beyond the Bethe-Salpether equation.

  12. Accurate prediction of acute fish toxicity of fragrance chemicals with the RTgill-W1 cell assay.

    PubMed

    Natsch, Andreas; Laue, Heike; Haupt, Tina; von Niederhäusern, Valentin; Sanders, Gordon

    2018-03-01

    Testing for acute fish toxicity is an integral part of the environmental safety assessment of chemicals. A true replacement of primary fish tissue was recently proposed using cell viability in a fish gill cell line (RTgill-W1) as a means of predicting acute toxicity, showing good predictivity on 35 chemicals. To promote regulatory acceptance, the predictivity and applicability domain of novel tests need to be carefully evaluated on chemicals with existing high-quality in vivo data. We applied the RTgill-W1 cell assay to 38 fragrance chemicals with a wide range of both physicochemical properties and median lethal concentration (LC50) values and representing a diverse range of chemistries. A strong correlation (R 2  = 0.90-0.94) between the logarithmic in vivo LC50 values, based on fish mortality, and the logarithmic in vitro median effect concentration (EC50) values based on cell viability was observed. A leave-one-out analysis illustrates a median under-/overprediction from in vitro EC50 values to in vivo LC50 values by a factor of 1.5. This assay offers a simple, accurate, and reliable alternative to in vivo acute fish toxicity testing for chemicals, presumably acting mainly by a narcotic mode of action. Furthermore, the present study provides validation of the predictivity of the RTgill-W1 assay on a completely independent set of chemicals that had not been previously tested and indicates that fragrance chemicals are clearly within the applicability domain. Environ Toxicol Chem 2018;37:931-941. © 2017 SETAC. © 2017 SETAC.

  13. A new method of estimating thermal performance of embryonic development rate yields accurate prediction of embryonic age in wild reptile nests.

    PubMed

    Rollinson, Njal; Holt, Sarah M; Massey, Melanie D; Holt, Richard C; Nancekivell, E Graham; Brooks, Ronald J

    2018-05-01

    Temperature has a strong effect on ectotherm development rate. It is therefore possible to construct predictive models of development that rely solely on temperature, which have applications in a range of biological fields. Here, we leverage a reference series of development stages for embryos of the turtle Chelydra serpentina, which was described at a constant temperature of 20 °C. The reference series acts to map each distinct developmental stage onto embryonic age (in days) at 20 °C. By extension, an embryo taken from any given incubation environment, once staged, can be assigned an equivalent age at 20 °C. We call this concept "Equivalent Development", as it maps the development stage of an embryo incubated at a given temperature to its equivalent age at a reference temperature. In the laboratory, we used the concept of Equivalent Development to estimate development rate of embryos of C. serpentina across a series of constant temperatures. Using these estimates of development rate, we created a thermal performance curve measured in units of Equivalent Development (TPC ED ). We then used the TPC ED to predict developmental stage of embryos in several natural turtle nests across six years. We found that 85% of the variation of development stage in natural nests could be explained. Further, we compared the predictive accuracy of the model based on the TPC ED to the predictive accuracy of a degree-day model, where development is assumed to be linearly related to temperature and the amount of accumulated heat is summed over time. Information theory suggested that the model based on the TPC ED better describes variation in developmental stage in wild nests than the degree-day model. We suggest the concept of Equivalent Development has several strengths and can be broadly applied. In particular, studies on temperature-dependent sex determination may be facilitated by the concept of Equivalent Development, as development age maps directly onto the

  14. Small RNA-based prediction of hybrid performance in maize.

    PubMed

    Seifert, Felix; Thiemann, Alexander; Schrag, Tobias A; Rybka, Dominika; Melchinger, Albrecht E; Frisch, Matthias; Scholten, Stefan

    2018-05-21

    Small RNA (sRNA) sequences are known to have a broad impact on gene regulation by various mechanisms. Their performance for the prediction of hybrid traits has not yet been analyzed. Our objective was to analyze the relation of parental sRNA expression with the performance of their hybrids, to develop a sRNA-based prediction approach, and to compare it to more common SNP and mRNA transcript based predictions using a factorial mating scheme of a maize hybrid breeding program. Correlation of genomic differences and messenger RNA (mRNA) or sRNA expression differences between parental lines with hybrid performance of their hybrids revealed that sRNAs showed an inverse relationship in contrast to the other two data types. We associated differences for SNPs, mRNA and sRNA expression between parental inbred lines with the performance of their hybrid combinations and developed two prediction approaches using distance measures based on associated markers. Cross-validations revealed parental differences in sRNA expression to be strong predictors for hybrid performance for grain yield in maize, comparable to genomic and mRNA data. The integration of both positively and negatively associated markers in the prediction approaches enhanced the prediction accurary. The associated sRNAs belong predominantly to the canonical size classes of 22- and 24-nt that show specific genomic mapping characteristics. Expression profiles of sRNA are a promising alternative to SNPs or mRNA expression profiles for hybrid prediction, especially for plant species without reference genome or transcriptome information. The characteristics of the sRNAs we identified suggest that association studies based on breeding populations facilitate the identification of sRNAs involved in hybrid performance.

  15. Coarse-Graining Polymer Field Theory for Fast and Accurate Simulations of Directed Self-Assembly

    NASA Astrophysics Data System (ADS)

    Liu, Jimmy; Delaney, Kris; Fredrickson, Glenn

    To design effective manufacturing processes using polymer directed self-assembly (DSA), the semiconductor industry benefits greatly from having a complete picture of stable and defective polymer configurations. Field-theoretic simulations are an effective way to study these configurations and predict defect populations. Self-consistent field theory (SCFT) is a particularly successful theory for studies of DSA. Although other models exist that are faster to simulate, these models are phenomenological or derived through asymptotic approximations, often leading to a loss of accuracy relative to SCFT. In this study, we employ our recently-developed method to produce an accurate coarse-grained field theory for diblock copolymers. The method uses a force- and stress-matching strategy to map output from SCFT simulations into parameters for an optimized phase field model. This optimized phase field model is just as fast as existing phenomenological phase field models, but makes more accurate predictions of polymer self-assembly, both in bulk and in confined systems. We study the performance of this model under various conditions, including its predictions of domain spacing, morphology and defect formation energies. Samsung Electronics.

  16. Predicting the performance and innovativeness of scientists and engineers.

    PubMed

    Keller, Robert T

    2012-01-01

    A study of 644 scientists and engineers from 5 corporate research and development organizations investigated hypotheses generated from an interactionist framework of 4 individual characteristics as longitudinal predictors of performance and innovativeness. An innovative orientation predicted 1-year-later and 5-years-later supervisory job performance ratings and 5-years-later counts of patents and publications. An internal locus of control predicted 5-years-later patents and publications, and self-esteem predicted performance ratings for both times and patents. Team-level nonroutine tasks moderated the individual-level relationships between an innovative orientation and performance ratings and patents such that the relationships were stronger in a nonroutine task environment. Implications for an interactionist framework of performance and innovativeness for knowledge workers are discussed.

  17. A cross-race effect in metamemory: Predictions of face recognition are more accurate for members of our own race

    PubMed Central

    Hourihan, Kathleen L.; Benjamin, Aaron S.; Liu, Xiping

    2012-01-01

    The Cross-Race Effect (CRE) in face recognition is the well-replicated finding that people are better at recognizing faces from their own race, relative to other races. The CRE reveals systematic limitations on eyewitness identification accuracy and suggests that some caution is warranted in evaluating cross-race identification. The CRE is a problem because jurors value eyewitness identification highly in verdict decisions. In the present paper, we explore how accurate people are in predicting their ability to recognize own-race and other-race faces. Caucasian and Asian participants viewed photographs of Caucasian and Asian faces, and made immediate judgments of learning during study. An old/new recognition test replicated the CRE: both groups displayed superior discriminability of own-race faces, relative to other-race faces. Importantly, relative metamnemonic accuracy was also greater for own-race faces, indicating that the accuracy of predictions about face recognition is influenced by race. This result indicates another source of concern when eliciting or evaluating eyewitness identification: people are less accurate in judging whether they will or will not recognize a face when that face is of a different race than they are. This new result suggests that a witness’s claim of being likely to recognize a suspect from a lineup should be interpreted with caution when the suspect is of a different race than the witness. PMID:23162788

  18. Remaining dischargeable time prediction for lithium-ion batteries using unscented Kalman filter

    NASA Astrophysics Data System (ADS)

    Dong, Guangzhong; Wei, Jingwen; Chen, Zonghai; Sun, Han; Yu, Xiaowei

    2017-10-01

    To overcome the range anxiety, one of the important strategies is to accurately predict the range or dischargeable time of the battery system. To accurately predict the remaining dischargeable time (RDT) of a battery, a RDT prediction framework based on accurate battery modeling and state estimation is presented in this paper. Firstly, a simplified linearized equivalent-circuit-model is developed to simulate the dynamic characteristics of a battery. Then, an online recursive least-square-algorithm method and unscented-Kalman-filter are employed to estimate the system matrices and SOC at every prediction point. Besides, a discrete wavelet transform technique is employed to capture the statistical information of past dynamics of input currents, which are utilized to predict the future battery currents. Finally, the RDT can be predicted based on the battery model, SOC estimation results and predicted future battery currents. The performance of the proposed methodology has been verified by a lithium-ion battery cell. Experimental results indicate that the proposed method can provide an accurate SOC and parameter estimation and the predicted RDT can solve the range anxiety issues.

  19. An Assessment of NASA Glenn's Aeroacoustic Experimental and Predictive Capabilities for Installed Cooling Fans. Part 1; Aerodynamic Performance

    NASA Technical Reports Server (NTRS)

    VanZante, Dale E.; Koch, L. Danielle; Wernet, Mark P.; Podboy, Gary G.

    2006-01-01

    Driven by the need for low production costs, electronics cooling fans have evolved differently than the bladed components of gas turbine engines which incorporate multiple technologies to enhance performance and durability while reducing noise emissions. Drawing upon NASA Glenn's experience in the measurement and prediction of gas turbine engine aeroacoustic performance, tests have been conducted to determine if these tools and techniques can be extended for application to the aerodynamics and acoustics of electronics cooling fans. An automated fan plenum installed in NASA Glenn's Acoustical Testing Laboratory was used to map the overall aerodynamic and acoustic performance of a spaceflight qualified 80 mm diameter axial cooling fan. In order to more accurately identify noise sources, diagnose performance limiting aerodynamic deficiencies, and validate noise prediction codes, additional aerodynamic measurements were recorded for two operating points: free delivery and a mild stall condition. Non-uniformities in the fan s inlet and exhaust regions captured by Particle Image Velocimetry measurements, and rotor blade wakes characterized by hot wire anemometry measurements provide some assessment of the fan aerodynamic performance. The data can be used to identify fan installation/design changes which could enlarge the stable operating region for the fan and improve its aerodynamic performance and reduce noise emissions.

  20. Accurate RNA 5-methylcytosine site prediction based on heuristic physical-chemical properties reduction and classifier ensemble.

    PubMed

    Zhang, Ming; Xu, Yan; Li, Lei; Liu, Zi; Yang, Xibei; Yu, Dong-Jun

    2018-06-01

    RNA 5-methylcytosine (m 5 C) is an important post-transcriptional modification that plays an indispensable role in biological processes. The accurate identification of m 5 C sites from primary RNA sequences is especially useful for deeply understanding the mechanisms and functions of m 5 C. Due to the difficulty and expensive costs of identifying m 5 C sites with wet-lab techniques, developing fast and accurate machine-learning-based prediction methods is urgently needed. In this study, we proposed a new m 5 C site predictor, called M5C-HPCR, by introducing a novel heuristic nucleotide physicochemical property reduction (HPCR) algorithm and classifier ensemble. HPCR extracts multiple reducts of physical-chemical properties for encoding discriminative features, while the classifier ensemble is applied to integrate multiple base predictors, each of which is trained based on a separate reduct of the physical-chemical properties obtained from HPCR. Rigorous jackknife tests on two benchmark datasets demonstrate that M5C-HPCR outperforms state-of-the-art m 5 C site predictors, with the highest values of MCC (0.859) and AUC (0.962). We also implemented the webserver of M5C-HPCR, which is freely available at http://cslab.just.edu.cn:8080/M5C-HPCR/. Copyright © 2018 Elsevier Inc. All rights reserved.

  1. Determination of accurate vertical atmospheric profiles of extinction and turbulence

    NASA Astrophysics Data System (ADS)

    Hammel, Steve; Campbell, James; Hallenborg, Eric

    2017-09-01

    Our ability to generate an accurate vertical profile characterizing the atmosphere from the surface to a point above the boundary layer top is quite rudimentary. The region from a land or sea surface to an altitude of 3000 meters is dynamic and particularly important to the performance of many active optical systems. Accurate and agile instruments are necessary to provide measurements in various conditions, and models are needed to provide the framework and predictive capability necessary for system design and optimization. We introduce some of the path characterization instruments and describe the first work to calibrate and validate them. Along with a verification of measurement accuracy, the tests must also establish each instruments performance envelope. Measurement of these profiles in the field is a problem, and we will present a discussion of recent field test activity to address this issue. The Comprehensive Atmospheric Boundary Layer Extinction/Turbulence Resolution Analysis eXperiment (CABLE/TRAX) was conducted late June 2017. There were two distinct objectives for the experiment: 1) a comparison test of various scintillometers and transmissometers on a homogeneous horizontal path; 2) a vertical profile experiment. In this paper we discuss only the vertical profiling effort, and we describe the instruments that generated data for vertical profiles of absorption, scattering, and turbulence. These three profiles are the core requirements for an accurate assessment of laser beam propagation.

  2. Accurate Simulation of MPPT Methods Performance When Applied to Commercial Photovoltaic Panels

    PubMed Central

    2015-01-01

    A new, simple, and quick-calculation methodology to obtain a solar panel model, based on the manufacturers' datasheet, to perform MPPT simulations, is described. The method takes into account variations on the ambient conditions (sun irradiation and solar cells temperature) and allows fast MPPT methods comparison or their performance prediction when applied to a particular solar panel. The feasibility of the described methodology is checked with four different MPPT methods applied to a commercial solar panel, within a day, and under realistic ambient conditions. PMID:25874262

  3. Accurate simulation of MPPT methods performance when applied to commercial photovoltaic panels.

    PubMed

    Cubas, Javier; Pindado, Santiago; Sanz-Andrés, Ángel

    2015-01-01

    A new, simple, and quick-calculation methodology to obtain a solar panel model, based on the manufacturers' datasheet, to perform MPPT simulations, is described. The method takes into account variations on the ambient conditions (sun irradiation and solar cells temperature) and allows fast MPPT methods comparison or their performance prediction when applied to a particular solar panel. The feasibility of the described methodology is checked with four different MPPT methods applied to a commercial solar panel, within a day, and under realistic ambient conditions.

  4. Metabolite signal identification in accurate mass metabolomics data with MZedDB, an interactive m/z annotation tool utilising predicted ionisation behaviour 'rules'

    PubMed Central

    Draper, John; Enot, David P; Parker, David; Beckmann, Manfred; Snowdon, Stuart; Lin, Wanchang; Zubair, Hassan

    2009-01-01

    Background Metabolomics experiments using Mass Spectrometry (MS) technology measure the mass to charge ratio (m/z) and intensity of ionised molecules in crude extracts of complex biological samples to generate high dimensional metabolite 'fingerprint' or metabolite 'profile' data. High resolution MS instruments perform routinely with a mass accuracy of < 5 ppm (parts per million) thus providing potentially a direct method for signal putative annotation using databases containing metabolite mass information. Most database interfaces support only simple queries with the default assumption that molecules either gain or lose a single proton when ionised. In reality the annotation process is confounded by the fact that many ionisation products will be not only molecular isotopes but also salt/solvent adducts and neutral loss fragments of original metabolites. This report describes an annotation strategy that will allow searching based on all potential ionisation products predicted to form during electrospray ionisation (ESI). Results Metabolite 'structures' harvested from publicly accessible databases were converted into a common format to generate a comprehensive archive in MZedDB. 'Rules' were derived from chemical information that allowed MZedDB to generate a list of adducts and neutral loss fragments putatively able to form for each structure and calculate, on the fly, the exact molecular weight of every potential ionisation product to provide targets for annotation searches based on accurate mass. We demonstrate that data matrices representing populations of ionisation products generated from different biological matrices contain a large proportion (sometimes > 50%) of molecular isotopes, salt adducts and neutral loss fragments. Correlation analysis of ESI-MS data features confirmed the predicted relationships of m/z signals. An integrated isotope enumerator in MZedDB allowed verification of exact isotopic pattern distributions to corroborate experimental data

  5. Persistent overconfidence despite practice: the role of task experience in preschoolers' recall predictions.

    PubMed

    Lipko, Amanda R; Dunlosky, John; Merriman, William E

    2009-06-01

    In three experiments, preschoolers' ability to predict their picture recall was examined. Children studied 10 pictures, predicted how many they would recall, and then attempted to recall them. This study-prediction-recall trial was repeated multiple times with new pictures on each trial. In Experiment 1, children were overconfident on the initial trial, and this overconfidence persisted across three trials. In Experiment 2, children predicted either their own performance or another child's performance. Their predictions were overconfident across all trials regardless of whether they made predictions for themselves or for another child, suggesting that wishful thinking cannot fully account for their overconfidence. In Experiment 3, some children postdicted their previous recall performance prior to making each prediction. Although their postdictions were quite accurate, their predictions were still overconfident across five trials. Preschoolers' overconfidence was remarkably resistant to the repeated experience of recalling fewer pictures than the children had predicted. Even asking them to report the number that they recalled on a previous trial, which they could do accurately, did not cause them to lower their predictions across trials.

  6. The NAFLD Index: A Simple and Accurate Screening Tool for the Prediction of Non-Alcoholic Fatty Liver Disease.

    PubMed

    Ichino, Naohiro; Osakabe, Keisuke; Sugimoto, Keiko; Suzuki, Koji; Yamada, Hiroya; Takai, Hiroji; Sugiyama, Hiroko; Yukitake, Jun; Inoue, Takashi; Ohashi, Koji; Hata, Tadayoshi; Hamajima, Nobuyuki; Nishikawa, Toru; Hashimoto, Senju; Kawabe, Naoto; Yoshioka, Kentaro

    2015-01-01

    Non-alcoholic fatty liver disease (NAFLD) is a common debilitating condition in many industrialized countries that increases the risk of cardiovascular disease. The aim of this study was to derive a simple and accurate screening tool for the prediction of NAFLD in the Japanese population. A total of 945 participants, 279 men and 666 women living in Hokkaido, Japan, were enrolled among residents who attended a health check-up program from 2010 to 2014. Participants with an alcohol consumption > 20 g/day and/or a chronic liver disease, such as chronic hepatitis B, chronic hepatitis C or autoimmune hepatitis, were excluded from this study. Clinical and laboratory data were examined to identify predictive markers of NAFLD. A new predictive index for NAFLD, the NAFLD index, was constructed for men and for women. The NAFLD index for men = -15.5693+0.3264 [BMI] +0.0134 [triglycerides (mg/dl)], and for women = -31.4686+0.3683 [BMI] +2.5699 [albumin (g/dl)] +4.6740[ALT/AST] -0.0379 [HDL cholesterol (mg/dl)]. The AUROC of the NAFLD index for men and for women was 0.87(95% CI 0.88-1.60) and 0.90 (95% CI 0.66-1.02), respectively. The cut-off point of -5.28 for men predicted NAFLD with an accuracy of 82.8%. For women, the cut-off point of -7.65 predicted NAFLD with an accuracy of 87.7%. A new index for the non-invasive prediction of NAFLD, the NAFLD index, was constructed using available clinical and laboratory data. This index is a simple screening tool to predict the presence of NAFLD.

  7. Accurate van der Waals force field for gas adsorption in porous materials.

    PubMed

    Sun, Lei; Yang, Li; Zhang, Ya-Dong; Shi, Qi; Lu, Rui-Feng; Deng, Wei-Qiao

    2017-09-05

    An accurate van der Waals force field (VDW FF) was derived from highly precise quantum mechanical (QM) calculations. Small molecular clusters were used to explore van der Waals interactions between gas molecules and porous materials. The parameters of the accurate van der Waals force field were determined by QM calculations. To validate the force field, the prediction results from the VDW FF were compared with standard FFs, such as UFF, Dreiding, Pcff, and Compass. The results from the VDW FF were in excellent agreement with the experimental measurements. This force field can be applied to the prediction of the gas density (H 2 , CO 2 , C 2 H 4 , CH 4 , N 2 , O 2 ) and adsorption performance inside porous materials, such as covalent organic frameworks (COFs), zeolites and metal organic frameworks (MOFs), consisting of H, B, N, C, O, S, Si, Al, Zn, Mg, Ni, and Co. This work provides a solid basis for studying gas adsorption in porous materials. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

  8. Temporal prediction errors modulate task-switching performance

    PubMed Central

    Limongi, Roberto; Silva, Angélica M.; Góngora-Costa, Begoña

    2015-01-01

    We have previously shown that temporal prediction errors (PEs, the differences between the expected and the actual stimulus’ onset times) modulate the effective connectivity between the anterior cingulate cortex and the right anterior insular cortex (rAI), causing the activity of the rAI to decrease. The activity of the rAI is associated with efficient performance under uncertainty (e.g., changing a prepared behavior when a change demand is not expected), which leads to hypothesize that temporal PEs might disrupt behavior-change performance under uncertainty. This hypothesis has not been tested at a behavioral level. In this work, we evaluated this hypothesis within the context of task switching and concurrent temporal predictions. Our participants performed temporal predictions while observing one moving ball striking a stationary ball which bounced off with a variable temporal gap. Simultaneously, they performed a simple color comparison task. In some trials, a change signal made the participants change their behaviors. Performance accuracy decreased as a function of both the temporal PE and the delay. Explaining these results without appealing to ad hoc concepts such as “executive control” is a challenge for cognitive neuroscience. We provide a predictive coding explanation. We hypothesize that exteroceptive and proprioceptive minimization of PEs would converge in a fronto-basal ganglia network which would include the rAI. Both temporal gaps (or uncertainty) and temporal PEs would drive and modulate this network respectively. Whereas the temporal gaps would drive the activity of the rAI, the temporal PEs would modulate the endogenous excitatory connections of the fronto-striatal network. We conclude that in the context of perceptual uncertainty, the system is not able to minimize perceptual PE, causing the ongoing behavior to finalize and, in consequence, disrupting task switching. PMID:26379568

  9. Temporal prediction errors modulate task-switching performance.

    PubMed

    Limongi, Roberto; Silva, Angélica M; Góngora-Costa, Begoña

    2015-01-01

    We have previously shown that temporal prediction errors (PEs, the differences between the expected and the actual stimulus' onset times) modulate the effective connectivity between the anterior cingulate cortex and the right anterior insular cortex (rAI), causing the activity of the rAI to decrease. The activity of the rAI is associated with efficient performance under uncertainty (e.g., changing a prepared behavior when a change demand is not expected), which leads to hypothesize that temporal PEs might disrupt behavior-change performance under uncertainty. This hypothesis has not been tested at a behavioral level. In this work, we evaluated this hypothesis within the context of task switching and concurrent temporal predictions. Our participants performed temporal predictions while observing one moving ball striking a stationary ball which bounced off with a variable temporal gap. Simultaneously, they performed a simple color comparison task. In some trials, a change signal made the participants change their behaviors. Performance accuracy decreased as a function of both the temporal PE and the delay. Explaining these results without appealing to ad hoc concepts such as "executive control" is a challenge for cognitive neuroscience. We provide a predictive coding explanation. We hypothesize that exteroceptive and proprioceptive minimization of PEs would converge in a fronto-basal ganglia network which would include the rAI. Both temporal gaps (or uncertainty) and temporal PEs would drive and modulate this network respectively. Whereas the temporal gaps would drive the activity of the rAI, the temporal PEs would modulate the endogenous excitatory connections of the fronto-striatal network. We conclude that in the context of perceptual uncertainty, the system is not able to minimize perceptual PE, causing the ongoing behavior to finalize and, in consequence, disrupting task switching.

  10. Predicting subcontractor performance using web-based Evolutionary Fuzzy Neural Networks.

    PubMed

    Ko, Chien-Ho

    2013-01-01

    Subcontractor performance directly affects project success. The use of inappropriate subcontractors may result in individual work delays, cost overruns, and quality defects throughout the project. This study develops web-based Evolutionary Fuzzy Neural Networks (EFNNs) to predict subcontractor performance. EFNNs are a fusion of Genetic Algorithms (GAs), Fuzzy Logic (FL), and Neural Networks (NNs). FL is primarily used to mimic high level of decision-making processes and deal with uncertainty in the construction industry. NNs are used to identify the association between previous performance and future status when predicting subcontractor performance. GAs are optimizing parameters required in FL and NNs. EFNNs encode FL and NNs using floating numbers to shorten the length of a string. A multi-cut-point crossover operator is used to explore the parameter and retain solution legality. Finally, the applicability of the proposed EFNNs is validated using real subcontractors. The EFNNs are evolved using 22 historical patterns and tested using 12 unseen cases. Application results show that the proposed EFNNs surpass FL and NNs in predicting subcontractor performance. The proposed approach improves prediction accuracy and reduces the effort required to predict subcontractor performance, providing field operators with web-based remote access to a reliable, scientific prediction mechanism.

  11. Predicting Subcontractor Performance Using Web-Based Evolutionary Fuzzy Neural Networks

    PubMed Central

    2013-01-01

    Subcontractor performance directly affects project success. The use of inappropriate subcontractors may result in individual work delays, cost overruns, and quality defects throughout the project. This study develops web-based Evolutionary Fuzzy Neural Networks (EFNNs) to predict subcontractor performance. EFNNs are a fusion of Genetic Algorithms (GAs), Fuzzy Logic (FL), and Neural Networks (NNs). FL is primarily used to mimic high level of decision-making processes and deal with uncertainty in the construction industry. NNs are used to identify the association between previous performance and future status when predicting subcontractor performance. GAs are optimizing parameters required in FL and NNs. EFNNs encode FL and NNs using floating numbers to shorten the length of a string. A multi-cut-point crossover operator is used to explore the parameter and retain solution legality. Finally, the applicability of the proposed EFNNs is validated using real subcontractors. The EFNNs are evolved using 22 historical patterns and tested using 12 unseen cases. Application results show that the proposed EFNNs surpass FL and NNs in predicting subcontractor performance. The proposed approach improves prediction accuracy and reduces the effort required to predict subcontractor performance, providing field operators with web-based remote access to a reliable, scientific prediction mechanism. PMID:23864830

  12. Accurate, reliable prototype earth horizon sensor head

    NASA Technical Reports Server (NTRS)

    Schwarz, F.; Cohen, H.

    1973-01-01

    The design and performance is described of an accurate and reliable prototype earth sensor head (ARPESH). The ARPESH employs a detection logic 'locator' concept and horizon sensor mechanization which should lead to high accuracy horizon sensing that is minimally degraded by spatial or temporal variations in sensing attitude from a satellite in orbit around the earth at altitudes in the 500 km environ 1,2. An accuracy of horizon location to within 0.7 km has been predicted, independent of meteorological conditions. This corresponds to an error of 0.015 deg-at 500 km altitude. Laboratory evaluation of the sensor indicates that this accuracy is achieved. First, the basic operating principles of ARPESH are described; next, detailed design and construction data is presented and then performance of the sensor under laboratory conditions in which the sensor is installed in a simulator that permits it to scan over a blackbody source against background representing the earth space interface for various equivalent plant temperatures.

  13. Accurate and Reliable Prediction of the Binding Affinities of Macrocycles to Their Protein Targets.

    PubMed

    Yu, Haoyu S; Deng, Yuqing; Wu, Yujie; Sindhikara, Dan; Rask, Amy R; Kimura, Takayuki; Abel, Robert; Wang, Lingle

    2017-12-12

    Macrocycles have been emerging as a very important drug class in the past few decades largely due to their expanded chemical diversity benefiting from advances in synthetic methods. Macrocyclization has been recognized as an effective way to restrict the conformational space of acyclic small molecule inhibitors with the hope of improving potency, selectivity, and metabolic stability. Because of their relatively larger size as compared to typical small molecule drugs and the complexity of the structures, efficient sampling of the accessible macrocycle conformational space and accurate prediction of their binding affinities to their target protein receptors poses a great challenge of central importance in computational macrocycle drug design. In this article, we present a novel method for relative binding free energy calculations between macrocycles with different ring sizes and between the macrocycles and their corresponding acyclic counterparts. We have applied the method to seven pharmaceutically interesting data sets taken from recent drug discovery projects including 33 macrocyclic ligands covering a diverse chemical space. The predicted binding free energies are in good agreement with experimental data with an overall root-mean-square error (RMSE) of 0.94 kcal/mol. This is to our knowledge the first time where the free energy of the macrocyclization of linear molecules has been directly calculated with rigorous physics-based free energy calculation methods, and we anticipate the outstanding accuracy demonstrated here across a broad range of target classes may have significant implications for macrocycle drug discovery.

  14. Assessing the performance of quantitative image features on early stage prediction of treatment effectiveness for ovary cancer patients: a preliminary investigation

    NASA Astrophysics Data System (ADS)

    Zargari, Abolfazl; Du, Yue; Thai, Theresa C.; Gunderson, Camille C.; Moore, Kathleen; Mannel, Robert S.; Liu, Hong; Zheng, Bin; Qiu, Yuchen

    2018-02-01

    The objective of this study is to investigate the performance of global and local features to better estimate the characteristics of highly heterogeneous metastatic tumours, for accurately predicting the treatment effectiveness of the advanced stage ovarian cancer patients. In order to achieve this , a quantitative image analysis scheme was developed to estimate a total of 103 features from three different groups including shape and density, Wavelet, and Gray Level Difference Method (GLDM) features. Shape and density features are global features, which are directly applied on the entire target image; wavelet and GLDM features are local features, which are applied on the divided blocks of the target image. To assess the performance, the new scheme was applied on a retrospective dataset containing 120 recurrent and high grade ovary cancer patients. The results indicate that the three best performed features are skewness, root-mean-square (rms) and mean of local GLDM texture, indicating the importance of integrating local features. In addition, the averaged predicting performance are comparable among the three different categories. This investigation concluded that the local features contains at least as copious tumour heterogeneity information as the global features, which may be meaningful on improving the predicting performance of the quantitative image markers for the diagnosis and prognosis of ovary cancer patients.

  15. Predictive Performance Assessment: Trait and State Dimensions Should not be Confused

    NASA Astrophysics Data System (ADS)

    Pattyn, N.; Migeotte, P.-F.; Morais, J.; Cluydts, R.; Soetens, E.; Meeusen, R.; de Schutter, G.; Nederhof, E.; Kolinsky, R.

    2008-06-01

    One of the major aims of performance investigation is to obtain a measure predicting real-life performance, in order to prevent consequences of a potential decrement. Whereas the predictive validity of such assessment has been extensively described for long-term outcomes, as is the case for testing in selection context, equivalent evidence is lacking regarding the short-term predictive value of cognitive testing, i.e., whether these results reflect real-life performance on an immediately subsequent task. In this series of experiments, we investigated both medium-term and short-term predictive value of psychophysiological testing with regard to real-life performance in two operational settings: military student pilots with regard to their success on an evaluation flight, and special forces candidates with regard to their performance on their training course. Our results showed some relationships between test performance and medium-term outcomes. However, no short-term predictive value could be identified for cognitive testing, despite the fact physiological data showed interesting trends. We recommend a critical distinction between "state" and "trait" dimensions of performance with regard to the predictive value of testing.

  16. Diagnostic Performance 1 H after Simulation Training Predicts Learning

    ERIC Educational Resources Information Center

    Consoli, Anna; Fraser, Kristin; Ma, Irene; Sobczak, Matthew; Wright, Bruce; McLaughlin, Kevin

    2013-01-01

    Although simulation training improves post-training performance, it is unclear how well performance soon after simulation training predicts longer term outcomes (i.e., learning). Here our objective was to assess the predictive value of performance 1 h post-training of performance 6 weeks later. We trained 84 first year medical students a simulated…

  17. Predicting DNA hybridization kinetics from sequence

    NASA Astrophysics Data System (ADS)

    Zhang, Jinny X.; Fang, John Z.; Duan, Wei; Wu, Lucia R.; Zhang, Angela W.; Dalchau, Neil; Yordanov, Boyan; Petersen, Rasmus; Phillips, Andrew; Zhang, David Yu

    2018-01-01

    Hybridization is a key molecular process in biology and biotechnology, but so far there is no predictive model for accurately determining hybridization rate constants based on sequence information. Here, we report a weighted neighbour voting (WNV) prediction algorithm, in which the hybridization rate constant of an unknown sequence is predicted based on similarity reactions with known rate constants. To construct this algorithm we first performed 210 fluorescence kinetics experiments to observe the hybridization kinetics of 100 different DNA target and probe pairs (36 nt sub-sequences of the CYCS and VEGF genes) at temperatures ranging from 28 to 55 °C. Automated feature selection and weighting optimization resulted in a final six-feature WNV model, which can predict hybridization rate constants of new sequences to within a factor of 3 with ∼91% accuracy, based on leave-one-out cross-validation. Accurate prediction of hybridization kinetics allows the design of efficient probe sequences for genomics research.

  18. Prediction of Muscle Performance During Dynamic Repetitive Exercise

    NASA Technical Reports Server (NTRS)

    Byerly, D. L.; Byerly, K. A.; Sognier, M. A.; Squires, W. G.

    2002-01-01

    A method for predicting human muscle performance was developed. Eight test subjects performed a repetitive dynamic exercise to failure using a Lordex spinal machine. Electromyography (EMG) data was collected from the erector spinae. Evaluation of the EMG data using a 5th order Autoregressive (AR) model and statistical regression analysis revealed that an AR parameter, the mean average magnitude of AR poles, can predict performance to failure as early as the second repetition of the exercise. Potential applications to the space program include evaluating on-orbit countermeasure effectiveness, maximizing post-flight recovery, and future real-time monitoring capability during Extravehicular Activity.

  19. Can Medical School Performance Predict Residency Performance? Resident Selection and Predictors of Successful Performance in Obstetrics and Gynecology

    PubMed Central

    Stohl, Hindi E.; Hueppchen, Nancy A.; Bienstock, Jessica L.

    2010-01-01

    Background During the evaluation process, Residency Admissions Committees typically gather data on objective and subjective measures of a medical student's performance through the Electronic Residency Application Service, including medical school grades, standardized test scores, research achievements, nonacademic accomplishments, letters of recommendation, the dean's letter, and personal statements. Using these data to identify which medical students are likely to become successful residents in an academic residency program in obstetrics and gynecology is difficult and to date, not well studied. Objective To determine whether objective information in medical students' applications can help predict resident success. Method We performed a retrospective cohort study of all residents who matched into the Johns Hopkins University residency program in obstetrics and gynecology between 1994 and 2004 and entered the program through the National Resident Matching Program as a postgraduate year-1 resident. Residents were independently evaluated by faculty and ranked in 4 groups according to perceived level of success. Applications from residents in the highest and lowest group were abstracted. Groups were compared using the Fisher exact test and the Student t test. Results Seventy-five residents met inclusion criteria and 29 residents were ranked in the highest and lowest quartiles (15 in highest, 14 in lowest). Univariate analysis identified no variables as consistent predictors of resident success. Conclusion In a program designed to train academic obstetrician-gynecologists, objective data from medical students' applications did not correlate with successful resident performance in our obstetrics-gynecology residency program. We need to continue our search for evaluation criteria that can accurately and reliably select the medical students that are best fit for our specialty. PMID:21976076

  20. SWAT system performance predictions

    NASA Astrophysics Data System (ADS)

    Parenti, Ronald R.; Sasiela, Richard J.

    1993-03-01

    In the next phase of Lincoln Laboratory's SWAT (Short-Wavelength Adaptive Techniques) program, the performance of a 241-actuator adaptive-optics system will be measured using a variety of synthetic-beacon geometries. As an aid in this experimental investigation, a detailed set of theoretical predictions has also been assembled. The computational tools that have been applied in this study include a numerical approach in which Monte-Carlo ray-trace simulations of accumulated phase error are developed, and an analytical analysis of the expected system behavior. This report describes the basis of these two computational techniques and compares their estimates of overall system performance. Although their regions of applicability tend to be complementary rather than redundant, good agreement is usually obtained when both sets of results can be derived for the same engagement scenario.

  1. Prediction of muscle performance during dynamic repetitive movement

    NASA Technical Reports Server (NTRS)

    Byerly, D. L.; Byerly, K. A.; Sognier, M. A.; Squires, W. G.

    2003-01-01

    BACKGROUND: During long-duration spaceflight, astronauts experience progressive muscle atrophy and often perform strenuous extravehicular activities. Post-flight, there is a lengthy recovery period with an increased risk for injury. Currently, there is a critical need for an enabling tool to optimize muscle performance and to minimize the risk of injury to astronauts while on-orbit and during post-flight recovery. Consequently, these studies were performed to develop a method to address this need. METHODS: Eight test subjects performed a repetitive dynamic exercise to failure at 65% of their upper torso weight using a Lordex spinal machine. Surface electromyography (SEMG) data was collected from the erector spinae back muscle. The SEMG data was evaluated using a 5th order autoregressive (AR) model and linear regression analysis. RESULTS: The best predictor found was an AR parameter, the mean average magnitude of AR poles, with r = 0.75 and p = 0.03. This parameter can predict performance to failure as early as the second repetition of the exercise. CONCLUSION: A method for predicting human muscle performance early during dynamic repetitive exercise was developed. The capability to predict performance to failure has many potential applications to the space program including evaluating countermeasure effectiveness on-orbit, optimizing post-flight recovery, and potential future real-time monitoring capability during extravehicular activity.

  2. Predicting survival across chronic interstitial lung disease: the ILD-GAP model.

    PubMed

    Ryerson, Christopher J; Vittinghoff, Eric; Ley, Brett; Lee, Joyce S; Mooney, Joshua J; Jones, Kirk D; Elicker, Brett M; Wolters, Paul J; Koth, Laura L; King, Talmadge E; Collard, Harold R

    2014-04-01

    Risk prediction is challenging in chronic interstitial lung disease (ILD) because of heterogeneity in disease-specific and patient-specific variables. Our objective was to determine whether mortality is accurately predicted in patients with chronic ILD using the GAP model, a clinical prediction model based on sex, age, and lung physiology, that was previously validated in patients with idiopathic pulmonary fibrosis. Patients with idiopathic pulmonary fibrosis (n=307), chronic hypersensitivity pneumonitis (n=206), connective tissue disease-associated ILD (n=281), idiopathic nonspecific interstitial pneumonia (n=45), or unclassifiable ILD (n=173) were selected from an ongoing database (N=1,012). Performance of the previously validated GAP model was compared with novel prediction models in each ILD subtype and the combined cohort. Patients with follow-up pulmonary function data were used for longitudinal model validation. The GAP model had good performance in all ILD subtypes (c-index, 74.6 in the combined cohort), which was maintained at all stages of disease severity and during follow-up evaluation. The GAP model had similar performance compared with alternative prediction models. A modified ILD-GAP Index was developed for application across all ILD subtypes to provide disease-specific survival estimates using a single risk prediction model. This was done by adding a disease subtype variable that accounted for better adjusted survival in connective tissue disease-associated ILD, chronic hypersensitivity pneumonitis, and idiopathic nonspecific interstitial pneumonia. The GAP model accurately predicts risk of death in chronic ILD. The ILD-GAP model accurately predicts mortality in major chronic ILD subtypes and at all stages of disease.

  3. Internal performance predictions for Langley scramjet engine module

    NASA Technical Reports Server (NTRS)

    Pinckney, S. Z.

    1978-01-01

    A one dimensional theoretical method for the prediction of the internal performance of a scramjet engine is presented. The effects of changes in vehicle forebody flow parameters and characteristics on predicted thrust for the scramjet engine were evaluated using this method, and results are presented. A theoretical evaluation of the effects of changes in the scramjet engine's internal parameters is also presented. Theoretical internal performance predictions, in terms thrust coefficient and specific impulse, are provided for the scramjet engine for free stream Mach numbers of 5, 6, and 7 free stream dynamic pressure of 23,940 N/sq m forebody surface angles of 4.6 deg to 14.6 deg, and fuel equivalence ratio of 1.0.

  4. New and Accurate Predictive Model for the Efficacy of Extracorporeal Shock Wave Therapy in Managing Patients With Chronic Plantar Fasciitis.

    PubMed

    Yin, Mengchen; Chen, Ni; Huang, Quan; Marla, Anastasia Sulindro; Ma, Junming; Ye, Jie; Mo, Wen

    2017-12-01

    To identify factors for the outcome of a minimum clinically successful therapy and to establish a predictive model of extracorporeal shock wave therapy (ESWT) in managing patients with chronic plantar fasciitis. Randomized, controlled, prospective study. Outpatient of local medical center settings. Patients treated for symptomatic chronic plantar fasciitis between 2014 and 2016 (N=278). ESWT was performed by the principal authors to treat chronic plantar fasciitis. ESWT was administered in 3 sessions, with an interval of 2 weeks (±4d). In the low-, moderate-, and high-intensity groups, 2400 impulses total of ESWT with an energy flux density of 0.2, 0.4, and 0.6mJ/mm 2 , respectively (a rate of 8 impulses per second), were applied. The independent variables were patient age, sex, body mass index, affected side, duration of symptoms, Roles and Maudsley score, visual analog scale (VAS) score when taking first steps in the morning, edema, bone spurs, and intensity grade of ESWT. A minimal reduction of 50% in the VAS score was considered as minimum clinically successful therapy. The correlations between the achievement of minimum clinically successful therapy and independent variables were analyzed. The statistically significant factors identified were further analyzed by multivariate logistic regression, and the predictive model was established. The success rate of ESWT was 66.9%. Univariate analysis found that VAS score when taking first steps in the morning, edema, and the presence of heel spur in radiograph significantly affected the outcome of the treatment. Logistic regression drew the equation: minimum clinically successful therapy=(1+e [.011+42.807×heel spur+.109×edema+5.395×VAS score] ) -1 .The sensitivity of the predictive factors was 96.77%, 87.63%, and 86.02%, respectively. The specificity of the predictive factors was 45.65%, 42.39%, and 85.87%, respectively. The area under the curve of the predictive factors was .751, .650, and .859, respectively. The

  5. Estimating energy expenditure in vascular surgery patients: Are predictive equations accurate enough?

    PubMed

    Suen, J; Thomas, J M; Delaney, C L; Spark, J I; Miller, M D

    2016-12-01

    Malnutrition is prevalent in vascular surgical patients who commonly seek tertiary care at advanced stages of disease. Adjunct nutrition support is therefore pertinent to optimise patient outcomes. To negate consequences related to excessive or suboptimal dietary energy intake, it is essential to accurately determine energy expenditure and subsequent requirements. This study aims to compare resting energy expenditure (REE) measured by indirect calorimetry, a commonly used comparator, to REE estimated by predictive equations (Schofield, Harris-Benedict equations and Miller equation) to determine the most suitable equation for vascular surgery patients. Data were collected from four studies that measured REE in 77 vascular surgery patients. Bland-Altman analyses were conducted to explore agreement. Presence of fixed or proportional bias was assessed by linear regression analyses. In comparison to measured REE, on average REE was overestimated when Schofield (+857 kJ/day), Harris-Benedict (+801 kJ/day) and Miller (+71 kJ/day) equations were used. Wide limits of agreement led to an over or underestimation from 1552 to 1755 kJ. Proportional bias was absent in Schofield (R 2  = 0.005, p = 0.54) and Harris-Benedict equations (R 2  = 0.045, p = 0.06) but was present in the Miller equation (R 2  = 0.210, p < 0.01) even after logarithmic transformation (R 2  = 0.213, p < 0.01). Whilst the Miller equation tended to overestimate resting energy expenditure and was affected by proportional bias, the limits of agreement and mean bias were smaller compared to Schofield and Harris-Benedict equations. This suggested that it is the preferred predictive equation for vascular surgery patients. Future research to refine the Miller equation to improve its overall accuracy will better inform the provision of nutritional support for vascular surgery patients and subsequently improve outcomes. Alternatively, an equation might be developed specifically for use with

  6. A fast and accurate method to predict 2D and 3D aerodynamic boundary layer flows

    NASA Astrophysics Data System (ADS)

    Bijleveld, H. A.; Veldman, A. E. P.

    2014-12-01

    A quasi-simultaneous interaction method is applied to predict 2D and 3D aerodynamic flows. This method is suitable for offshore wind turbine design software as it is a very accurate and computationally reasonably cheap method. This study shows the results for a NACA 0012 airfoil. The two applied solvers converge to the experimental values when the grid is refined. We also show that in separation the eigenvalues remain positive thus avoiding the Goldstein singularity at separation. In 3D we show a flow over a dent in which separation occurs. A rotating flat plat is used to show the applicability of the method for rotating flows. The shown capabilities of the method indicate that the quasi-simultaneous interaction method is suitable for design methods for offshore wind turbine blades.

  7. Third Graders' Performance Predictions: Calibration Deflections and Academic Success

    ERIC Educational Resources Information Center

    Ots, Aivar

    2013-01-01

    This study focuses on third grade pupils' (9 to 10 years old) ability to predict their performance in a given task and on the correspondence between the accuracy and adequacy of the predictions on the one hand, and the academic achievement on the other. The study involved 713 pupils from 29 Estonian schools. The pupils' performance predictions…

  8. Assessing the performance of MM/PBSA and MM/GBSA methods. 8. Predicting binding free energies and poses of protein-RNA complexes.

    PubMed

    Chen, Fu; Sun, Huiyong; Wang, Junmei; Zhu, Feng; Liu, Hui; Wang, Zhe; Lei, Tailong; Li, Youyong; Hou, Tingjun

    2018-06-21

    Molecular docking provides a computationally efficient way to predict the atomic structural details of protein-RNA interactions (PRI), but accurate prediction of the three-dimensional structures and binding affinities for PRI is still notoriously difficult, partly due to the unreliability of the existing scoring functions for PRI. MM/PBSA and MM/GBSA are more theoretically rigorous than most scoring functions for protein-RNA docking, but their prediction performance for protein-RNA systems remains unclear. Here, we systemically evaluated the capability of MM/PBSA and MM/GBSA to predict the binding affinities and recognize the near-native binding structures for protein-RNA systems with different solvent models and interior dielectric constants (ϵ in ). For predicting the binding affinities, the predictions given by MM/GBSA based on the minimized structures in explicit solvent and the GBGBn1 model with ϵ in = 2 yielded the highest correlation with the experimental data. Moreover, the MM/GBSA calculations based on the minimized structures in implicit solvent and the GBGBn1 model distinguished the near-native binding structures within the top 10 decoys for 118 out of the 149 protein-RNA systems (79.2%). This performance is better than all docking scoring functions studied here. Therefore, the MM/GBSA rescoring is an efficient way to improve the prediction capability of scoring functions for protein-RNA systems. Published by Cold Spring Harbor Laboratory Press for the RNA Society.

  9. Performance of immunological response in predicting virological failure.

    PubMed

    Ingole, Nayana; Mehta, Preeti; Pazare, Amar; Paranjpe, Supriya; Sarkate, Purva

    2013-03-01

    In HIV-infected individuals on antiretroviral therapy (ART), the decision on when to switch from first-line to second-line therapy is dictated by treatment failure, and this can be measured in three ways: clinically, immunologically, and virologically. While viral load (VL) decreases and CD4 cell increases typically occur together after starting ART, discordant responses may be seen. Hence the current study was designed to determine the immunological and virological response to ART and to evaluate the utility of immunological response to predict virological failure. All treatment-naive HIV-positive individuals aged >18 years who were eligible for ART were enrolled and assessed at baseline, 6 months, and 12 months clinically and by CD4 cell count and viral load estimations. The patients were categorized as showing concordant favorable (CF), immunological only (IO), virological only (VO), and concordant unfavorable responses (CU). The efficiency of immunological failure to predict virological failure was analyzed across various levels of virological failure (VL>50, >500, and >5,000 copies/ml). At 6 months, 87(79.81%), 7(5.5%), 13 (11.92%), and 2 (1.83%) patients and at 12 months 61(69.3%), 9(10.2%), 16 (18.2%), and 2 (2.3%) patients had CF, IO, VO, and CU responses, respectively. Immunological failure criteria had a very low sensitivity (11.1-40%) and positive predictive value (8.3-25%) to predict virological failure. Immunological criteria do not accurately predict virological failure resulting in significant misclassification of therapeutic responses. There is an urgent need for inclusion of viral load testing in the initiation and monitoring of ART.

  10. Accuracy of Kindergarten Teachers' Predictions of Their Pupils' Subsequent Performance. Research Report.

    ERIC Educational Resources Information Center

    Virgin, Albert E.; Shecter, H. E.

    This study is part of a series of three studies related to the incidence and effects of grade repetition on elementary school children. The focus of this study was to determine whether kindergarten teachers are able to make accurate predictions about their pupils' general academic and reading achievement when they reach Grade 3. In the 1970/71…

  11. Objective Prediction of Hearing Aid Benefit Across Listener Groups Using Machine Learning: Speech Recognition Performance With Binaural Noise-Reduction Algorithms.

    PubMed

    Schädler, Marc R; Warzybok, Anna; Kollmeier, Birger

    2018-01-01

    The simulation framework for auditory discrimination experiments (FADE) was adopted and validated to predict the individual speech-in-noise recognition performance of listeners with normal and impaired hearing with and without a given hearing-aid algorithm. FADE uses a simple automatic speech recognizer (ASR) to estimate the lowest achievable speech reception thresholds (SRTs) from simulated speech recognition experiments in an objective way, independent from any empirical reference data. Empirical data from the literature were used to evaluate the model in terms of predicted SRTs and benefits in SRT with the German matrix sentence recognition test when using eight single- and multichannel binaural noise-reduction algorithms. To allow individual predictions of SRTs in binaural conditions, the model was extended with a simple better ear approach and individualized by taking audiograms into account. In a realistic binaural cafeteria condition, FADE explained about 90% of the variance of the empirical SRTs for a group of normal-hearing listeners and predicted the corresponding benefits with a root-mean-square prediction error of 0.6 dB. This highlights the potential of the approach for the objective assessment of benefits in SRT without prior knowledge about the empirical data. The predictions for the group of listeners with impaired hearing explained 75% of the empirical variance, while the individual predictions explained less than 25%. Possibly, additional individual factors should be considered for more accurate predictions with impaired hearing. A competing talker condition clearly showed one limitation of current ASR technology, as the empirical performance with SRTs lower than -20 dB could not be predicted.

  12. Ensemble MD simulations restrained via crystallographic data: Accurate structure leads to accurate dynamics

    PubMed Central

    Xue, Yi; Skrynnikov, Nikolai R

    2014-01-01

    Currently, the best existing molecular dynamics (MD) force fields cannot accurately reproduce the global free-energy minimum which realizes the experimental protein structure. As a result, long MD trajectories tend to drift away from the starting coordinates (e.g., crystallographic structures). To address this problem, we have devised a new simulation strategy aimed at protein crystals. An MD simulation of protein crystal is essentially an ensemble simulation involving multiple protein molecules in a crystal unit cell (or a block of unit cells). To ensure that average protein coordinates remain correct during the simulation, we introduced crystallography-based restraints into the MD protocol. Because these restraints are aimed at the ensemble-average structure, they have only minimal impact on conformational dynamics of the individual protein molecules. So long as the average structure remains reasonable, the proteins move in a native-like fashion as dictated by the original force field. To validate this approach, we have used the data from solid-state NMR spectroscopy, which is the orthogonal experimental technique uniquely sensitive to protein local dynamics. The new method has been tested on the well-established model protein, ubiquitin. The ensemble-restrained MD simulations produced lower crystallographic R factors than conventional simulations; they also led to more accurate predictions for crystallographic temperature factors, solid-state chemical shifts, and backbone order parameters. The predictions for 15N R1 relaxation rates are at least as accurate as those obtained from conventional simulations. Taken together, these results suggest that the presented trajectories may be among the most realistic protein MD simulations ever reported. In this context, the ensemble restraints based on high-resolution crystallographic data can be viewed as protein-specific empirical corrections to the standard force fields. PMID:24452989

  13. Application of the aberration ring test (ARTEMIS) to determine lens quality and predict its lithographic performance

    NASA Astrophysics Data System (ADS)

    Moers, Marco H. P.; van der Laan, Hans; Zellenrath, Mark; de Boeij, Wim; Beaudry, Neil A.; Cummings, Kevin D.; van Zwol, Adriaan; Brecht, Arthur; Willekers, Rob

    2001-09-01

    ARTEMISTM (Aberration Ring Test Exposed at Multiple Illumination Settings) is a technique to determine in-situ, full-field, low and high order lens aberrations. In this paper we are analyzing the ARTEMISTM data of PAS5500/750TM DUV Step & Scan systems and its use as a lithographic prediction tool. ARTEMISTM is capable of determining Zernike coefficients up to Z25 with a 3(sigma) reproducibility range from 1.5 to 4.5 nm depending on the aberration type. 3D electric field simulations, that take the extended geometry of the phase shift feature into account, have been used for an improved treatment of the extraction of the spherical Zernike coefficients. Knowledge of the extracted Zernike coefficients allows an accurate prediction of the lithographic performance of the scanner system. This ability is demonstrated for a two bar pattern and an isolation pattern. The RMS difference between the ARTEMISTM-based lithographic prediction and the lithographic measurement is 2.5 nm for the two bar pattern and 3 nm for the isolation pattern. The 3(sigma) reproducibility of the prediction for the two bar pattern is 2.5 nm and 1 nm for the isolation pattern. This is better than the reproducibility of the lithographic measurements themselves.

  14. Accurate disulfide-bonding network predictions improve ab initio structure prediction of cysteine-rich proteins

    PubMed Central

    Yang, Jing; He, Bao-Ji; Jang, Richard; Zhang, Yang; Shen, Hong-Bin

    2015-01-01

    Abstract Motivation: Cysteine-rich proteins cover many important families in nature but there are currently no methods specifically designed for modeling the structure of these proteins. The accuracy of disulfide connectivity pattern prediction, particularly for the proteins of higher-order connections, e.g. >3 bonds, is too low to effectively assist structure assembly simulations. Results: We propose a new hierarchical order reduction protocol called Cyscon for disulfide-bonding prediction. The most confident disulfide bonds are first identified and bonding prediction is then focused on the remaining cysteine residues based on SVR training. Compared with purely machine learning-based approaches, Cyscon improved the average accuracy of connectivity pattern prediction by 21.9%. For proteins with more than 5 disulfide bonds, Cyscon improved the accuracy by 585% on the benchmark set of PDBCYS. When applied to 158 non-redundant cysteine-rich proteins, Cyscon predictions helped increase (or decrease) the TM-score (or RMSD) of the ab initio QUARK modeling by 12.1% (or 14.4%). This result demonstrates a new avenue to improve the ab initio structure modeling for cysteine-rich proteins. Availability and implementation: http://www.csbio.sjtu.edu.cn/bioinf/Cyscon/ Contact: zhng@umich.edu or hbshen@sjtu.edu.cn Supplementary information: Supplementary data are available at Bioinformatics online. PMID:26254435

  15. Do measures of surgical effectiveness at 1 year after lumbar spine surgery accurately predict 2-year outcomes?

    PubMed

    Adogwa, Owoicho; Elsamadicy, Aladine A; Han, Jing L; Cheng, Joseph; Karikari, Isaac; Bagley, Carlos A

    2016-12-01

    OBJECTIVE With the recent passage of the Patient Protection and Affordable Care Act, there has been a dramatic shift toward critical analyses of quality and longitudinal assessment of subjective and objective outcomes after lumbar spine surgery. Accordingly, the emergence and routine use of real-world institutional registries have been vital to the longitudinal assessment of quality. However, prospectively obtaining longitudinal outcomes for patients at 24 months after spine surgery remains a challenge. The aim of this study was to assess if 12-month measures of treatment effectiveness accurately predict long-term outcomes (24 months). METHODS A nationwide, multiinstitutional, prospective spine outcomes registry was used for this study. Enrollment criteria included available demographic, surgical, and clinical outcomes data. All patients had prospectively collected outcomes measures and a minimum 2-year follow-up. Patient-reported outcomes instruments (Oswestry Disability Index [ODI], SF-36, and visual analog scale [VAS]-back pain/leg pain) were completed before surgery and then at 3, 6, 12, and 24 months after surgery. The Health Transition Index of the SF-36 was used to determine the 1- and 2-year minimum clinically important difference (MCID), and logistic regression modeling was performed to determine if achieving MCID at 1 year adequately predicted improvement and achievement of MCID at 24 months. RESULTS The study group included 969 patients: 300 patients underwent anterior lumbar interbody fusion (ALIF), 606 patients underwent transforaminal lumbar interbody fusion (TLIF), and 63 patients underwent lateral interbody fusion (LLIF). There was a significant correlation between the 12- and 24-month ODI (r = 0.82; p < 0.0001), SF-36 Physical Component Summary score (r = 0.89; p < 0.0001), VAS-back pain (r = 0.90; p < 0.0001), and VAS-leg pain (r = 0.85; p < 0.0001). For the ALIF cohort, patients achieving MCID thresholds for ODI at 12 months were 13-fold (p < 0

  16. Accurate prediction of polarised high order electrostatic interactions for hydrogen bonded complexes using the machine learning method kriging.

    PubMed

    Hughes, Timothy J; Kandathil, Shaun M; Popelier, Paul L A

    2015-02-05

    As intermolecular interactions such as the hydrogen bond are electrostatic in origin, rigorous treatment of this term within force field methodologies should be mandatory. We present a method able of accurately reproducing such interactions for seven van der Waals complexes. It uses atomic multipole moments up to hexadecupole moment mapped to the positions of the nuclear coordinates by the machine learning method kriging. Models were built at three levels of theory: HF/6-31G(**), B3LYP/aug-cc-pVDZ and M06-2X/aug-cc-pVDZ. The quality of the kriging models was measured by their ability to predict the electrostatic interaction energy between atoms in external test examples for which the true energies are known. At all levels of theory, >90% of test cases for small van der Waals complexes were predicted within 1 kJ mol(-1), decreasing to 60-70% of test cases for larger base pair complexes. Models built on moments obtained at B3LYP and M06-2X level generally outperformed those at HF level. For all systems the individual interactions were predicted with a mean unsigned error of less than 1 kJ mol(-1). Copyright © 2013 Elsevier B.V. All rights reserved.

  17. Predicting the Impacts of Intravehicular Displays on Driving Performance with Human Performance Modeling

    NASA Technical Reports Server (NTRS)

    Mitchell, Diane Kuhl; Wojciechowski, Josephine; Samms, Charneta

    2012-01-01

    A challenge facing the U.S. National Highway Traffic Safety Administration (NHTSA), as well as international safety experts, is the need to educate car drivers about the dangers associated with performing distraction tasks while driving. Researchers working for the U.S. Army Research Laboratory have developed a technique for predicting the increase in mental workload that results when distraction tasks are combined with driving. They implement this technique using human performance modeling. They have predicted workload associated with driving combined with cell phone use. In addition, they have predicted the workload associated with driving military vehicles combined with threat detection. Their technique can be used by safety personnel internationally to demonstrate the dangers of combining distracter tasks with driving and to mitigate the safety risks.

  18. Folding molecular dynamics simulations accurately predict the effect of mutations on the stability and structure of a vammin-derived peptide.

    PubMed

    Koukos, Panagiotis I; Glykos, Nicholas M

    2014-08-28

    Folding molecular dynamics simulations amounting to a grand total of 4 μs of simulation time were performed on two peptides (with native and mutated sequences) derived from loop 3 of the vammin protein and the results compared with the experimentally known peptide stabilities and structures. The simulations faithfully and accurately reproduce the major experimental findings and show that (a) the native peptide is mostly disordered in solution, (b) the mutant peptide has a well-defined and stable structure, and (c) the structure of the mutant is an irregular β-hairpin with a non-glycine β-bulge, in excellent agreement with the peptide's known NMR structure. Additionally, the simulations also predict the presence of a very small β-hairpin-like population for the native peptide but surprisingly indicate that this population is structurally more similar to the structure of the native peptide as observed in the vammin protein than to the NMR structure of the isolated mutant peptide. We conclude that, at least for the given system, force field, and simulation protocol, folding molecular dynamics simulations appear to be successful in reproducing the experimentally accessible physical reality to a satisfactory level of detail and accuracy.

  19. Accurate Predictions of Mean Geomagnetic Dipole Excursion and Reversal Frequencies, Mean Paleomagnetic Field Intensity, and the Radius of Earth's Core Using McLeod's Rule

    NASA Technical Reports Server (NTRS)

    Voorhies, Coerte V.; Conrad, Joy

    1996-01-01

    The geomagnetic spatial power spectrum R(sub n)(r) is the mean square magnetic induction represented by degree n spherical harmonic coefficients of the internal scalar potential averaged over the geocentric sphere of radius r. McLeod's Rule for the magnetic field generated by Earth's core geodynamo says that the expected core surface power spectrum (R(sub nc)(c)) is inversely proportional to (2n + 1) for 1 less than n less than or equal to N(sub E). McLeod's Rule is verified by locating Earth's core with main field models of Magsat data; the estimated core radius of 3485 kn is close to the seismologic value for c of 3480 km. McLeod's Rule and similar forms are then calibrated with the model values of R(sub n) for 3 less than or = n less than or = 12. Extrapolation to the degree 1 dipole predicts the expectation value of Earth's dipole moment to be about 5.89 x 10(exp 22) Am(exp 2)rms (74.5% of the 1980 value) and the expected geomagnetic intensity to be about 35.6 (mu)T rms at Earth's surface. Archeo- and paleomagnetic field intensity data show these and related predictions to be reasonably accurate. The probability distribution chi(exp 2) with 2n+1 degrees of freedom is assigned to (2n + 1)R(sub nc)/(R(sub nc). Extending this to the dipole implies that an exceptionally weak absolute dipole moment (less than or = 20% of the 1980 value) will exist during 2.5% of geologic time. The mean duration for such major geomagnetic dipole power excursions, one quarter of which feature durable axial dipole reversal, is estimated from the modern dipole power time-scale and the statistical model of excursions. The resulting mean excursion duration of 2767 years forces us to predict an average of 9.04 excursions per million years, 2.26 axial dipole reversals per million years, and a mean reversal duration of 5533 years. Paleomagnetic data show these predictions to be quite accurate. McLeod's Rule led to accurate predictions of Earth's core radius, mean paleomagnetic field

  20. Performance and acoustic prediction of counterrotating propeller configurations

    NASA Technical Reports Server (NTRS)

    Denner, B. W.; Korkan, K. D.

    1989-01-01

    The Davidson (1981) numerical method is used to predict the performance of a counterrotating propeller configuration over a range of different front and back disk rotation speeds with constant-speed propellers; this has yielded such overall performance parameters as integrated thrust, torque, and power, as well as the radial variation of blade torque and thrust. Since the unsteady component of the noise from a counterrotating propeller configuration is minimal in the plane of the propeller disk, this approach is restricted to noise-level predictions for observer locations in this region.

  1. Radio interferometric measurements for accurate planetary orbiter navigation

    NASA Technical Reports Server (NTRS)

    Poole, S. R.; Ananda, M.; Hildebrand, C. E.

    1979-01-01

    The use of narrowband delta-VLBI to achieve accurate orbit determination is presented by viewing a spacecraft from widely separated stations followed by viewing a nearby quasar from the same stations. Current analysis is examined that establishes the orbit determination accuracy achieved with data arcs spanning up to 3.5 d. Strategies for improving prediction accuracy are given, and the performance of delta-VLBI is compared with conventional radiometric tracking data. It is found that accuracy 'within the fit' is on the order of 0.5 km for data arcs having delta-VLBI on the ends of the arcs and for arc lengths varying from one baseline to 3.5 d. The technique is discussed with reference to the proposed Venus Orbiting Imaging Radar mission.

  2. Experimental evaluation of radiosity for room sound-field prediction.

    PubMed

    Hodgson, Murray; Nosal, Eva-Marie

    2006-08-01

    An acoustical radiosity model was evaluated for how it performs in predicting real room sound fields. This was done by comparing radiosity predictions with experimental results for three existing rooms--a squash court, a classroom, and an office. Radiosity predictions were also compared with those by ray tracing--a "reference" prediction model--for both specular and diffuse surface reflection. Comparisons were made for detailed and discretized echograms, sound-decay curves, sound-propagation curves, and the variations with frequency of four room-acoustical parameters--EDT, RT, D50, and C80. In general, radiosity and diffuse ray tracing gave very similar predictions. Predictions by specular ray tracing were often very different. Radiosity agreed well with experiment in some cases, less well in others. Definitive conclusions regarding the accuracy with which the rooms were modeled, or the accuracy of the radiosity approach, were difficult to draw. The results suggest that radiosity predicts room sound fields with some accuracy, at least as well as diffuse ray tracing and, in general, better than specular ray tracing. The predictions of detailed echograms are less accurate, those of derived room-acoustical parameters more accurate. The results underline the need to develop experimental methods for accurately characterizing the absorptive and reflective characteristics of room surfaces, possible including phase.

  3. Robust and accurate decoding of motoneuron behavior and prediction of the resulting force output.

    PubMed

    Thompson, Christopher K; Negro, Francesco; Johnson, Michael D; Holmes, Matthew R; McPherson, Laura Miller; Powers, Randall K; Farina, Dario; Heckman, Charles J

    2018-05-03

    The spinal alpha motoneuron is the only cell in the human CNS whose discharge can be routinely recorded in humans. We have reengineered motor unit collection and decomposition approaches, originally developed in humans, to measure the neural drive to muscle and estimate muscle force generation in the decerebrate cat model. Experimental, computational, and predictive approaches are used to demonstrate the validity of this approach across a wide range of modes to activate the motor pool. The utility of this approach is shown through the ability to track individual motor units across trials, allowing for better predictions of muscle force than the electromyography signal, and providing insights in to the stereotypical discharge characteristics in response to synaptic activation of the motor pool. This approach now allows for a direct link between the intracellular data of single motoneurons, the discharge properties of motoneuron populations, and muscle force generation in the same preparation. The discharge of a spinal alpha motoneuron and the resulting contraction of its muscle fibers represents the functional quantum of the motor system. Recent advances in the recording and decomposition of the electromyographic signal allows for the identification of several tens of concurrently active motor units. These detailed population data provide the potential to achieve deep insights into the synaptic organization of motor commands. Yet most of our understanding of the synaptic input to motoneurons is derived from intracellular recordings in animal preparations. Thus, it is necessary to extend the new electrode and decomposition methods to recording of motor unit populations in these same preparations. To achieve this goal, we use high-density electrode arrays and decomposition techniques, analogous to those developed for humans, to record and decompose the activity of tens of concurrently active motor units in a hindlimb muscle in the decerebrate cat. Our results showed

  4. Microarray-based cancer prediction using soft computing approach.

    PubMed

    Wang, Xiaosheng; Gotoh, Osamu

    2009-05-26

    One of the difficulties in using gene expression profiles to predict cancer is how to effectively select a few informative genes to construct accurate prediction models from thousands or ten thousands of genes. We screen highly discriminative genes and gene pairs to create simple prediction models involved in single genes or gene pairs on the basis of soft computing approach and rough set theory. Accurate cancerous prediction is obtained when we apply the simple prediction models for four cancerous gene expression datasets: CNS tumor, colon tumor, lung cancer and DLBCL. Some genes closely correlated with the pathogenesis of specific or general cancers are identified. In contrast with other models, our models are simple, effective and robust. Meanwhile, our models are interpretable for they are based on decision rules. Our results demonstrate that very simple models may perform well on cancerous molecular prediction and important gene markers of cancer can be detected if the gene selection approach is chosen reasonably.

  5. Numerical predictions of EML (electromagnetic launcher) system performance

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

    Schnurr, N.M.; Kerrisk, J.F.; Davidson, R.F.

    1987-01-01

    The performance of an electromagnetic launcher (EML) depends on a large number of parameters, including the characteristics of the power supply, rail geometry, rail and insulator material properties, injection velocity, and projectile mass. EML system performance is frequently limited by structural or thermal effects in the launcher (railgun). A series of computer codes has been developed at the Los Alamos National Laboratory to predict EML system performance and to determine the structural and thermal constraints on barrel design. These codes include FLD, a two-dimensional electrostatic code used to calculate the high-frequency inductance gradient and surface current density distribution for themore » rails; TOPAZRG, a two-dimensional finite-element code that simultaneously analyzes thermal and electromagnetic diffusion in the rails; and LARGE, a code that predicts the performance of the entire EML system. Trhe NIKE2D code, developed at the Lawrence Livermore National Laboratory, is used to perform structural analyses of the rails. These codes have been instrumental in the design of the Lethality Test System (LTS) at Los Alamos, which has an ultimate goal of accelerating a 30-g projectile to a velocity of 15 km/s. The capabilities of the individual codes and the coupling of these codes to perform a comprehensive analysis is discussed in relation to the LTS design. Numerical predictions are compared with experimental data and presented for the LTS prototype tests.« less

  6. Improving Computational Efficiency of Prediction in Model-Based Prognostics Using the Unscented Transform

    NASA Technical Reports Server (NTRS)

    Daigle, Matthew John; Goebel, Kai Frank

    2010-01-01

    Model-based prognostics captures system knowledge in the form of physics-based models of components, and how they fail, in order to obtain accurate predictions of end of life (EOL). EOL is predicted based on the estimated current state distribution of a component and expected profiles of future usage. In general, this requires simulations of the component using the underlying models. In this paper, we develop a simulation-based prediction methodology that achieves computational efficiency by performing only the minimal number of simulations needed in order to accurately approximate the mean and variance of the complete EOL distribution. This is performed through the use of the unscented transform, which predicts the means and covariances of a distribution passed through a nonlinear transformation. In this case, the EOL simulation acts as that nonlinear transformation. In this paper, we review the unscented transform, and describe how this concept is applied to efficient EOL prediction. As a case study, we develop a physics-based model of a solenoid valve, and perform simulation experiments to demonstrate improved computational efficiency without sacrificing prediction accuracy.

  7. Accurate prediction of collapse temperature using optical coherence tomography-based freeze-drying microscopy.

    PubMed

    Greco, Kristyn; Mujat, Mircea; Galbally-Kinney, Kristin L; Hammer, Daniel X; Ferguson, R Daniel; Iftimia, Nicusor; Mulhall, Phillip; Sharma, Puneet; Kessler, William J; Pikal, Michael J

    2013-06-01

    The objective of this study was to assess the feasibility of developing and applying a laboratory tool that can provide three-dimensional product structural information during freeze-drying and which can accurately characterize the collapse temperature (Tc ) of pharmaceutical formulations designed for freeze-drying. A single-vial freeze dryer coupled with optical coherence tomography freeze-drying microscopy (OCT-FDM) was developed to investigate the structure and Tc of formulations in pharmaceutically relevant products containers (i.e., freeze-drying in vials). OCT-FDM was used to measure the Tc and eutectic melt of three formulations in freeze-drying vials. The Tc as measured by OCT-FDM was found to be predictive of freeze-drying with a batch of vials in a conventional laboratory freeze dryer. The freeze-drying cycles developed using OCT-FDM data, as compared with traditional light transmission freeze-drying microscopy (LT-FDM), resulted in a significant reduction in primary drying time, which could result in a substantial reduction of manufacturing costs while maintaining product quality. OCT-FDM provides quantitative data to justify freeze-drying at temperatures higher than the Tc measured by LT-FDM and provides a reliable upper limit to setting a product temperature in primary drying. Copyright © 2013 Wiley Periodicals, Inc.

  8. Hindered rotor models with variable kinetic functions for accurate thermodynamic and kinetic predictions

    NASA Astrophysics Data System (ADS)

    Reinisch, Guillaume; Leyssale, Jean-Marc; Vignoles, Gérard L.

    2010-10-01

    We present an extension of some popular hindered rotor (HR) models, namely, the one-dimensional HR (1DHR) and the degenerated two-dimensional HR (d2DHR) models, allowing for a simple and accurate treatment of internal rotations. This extension, based on the use of a variable kinetic function in the Hamiltonian instead of a constant reduced moment of inertia, is extremely suitable in the case of rocking/wagging motions involved in dissociation or atom transfer reactions. The variable kinetic function is first introduced in the framework of a classical 1DHR model. Then, an effective temperature and potential dependent constant is proposed in the cases of quantum 1DHR and classical d2DHR models. These methods are finally applied to the atom transfer reaction SiCl3+BCl3→SiCl4+BCl2. We show, for this particular case, that a proper accounting of internal rotations greatly improves the accuracy of thermodynamic and kinetic predictions. Moreover, our results confirm (i) that using a suitably defined kinetic function appears to be very adapted to such problems; (ii) that the separability assumption of independent rotations seems justified; and (iii) that a quantum mechanical treatment is not a substantial improvement with respect to a classical one.

  9. A Complete Procedure for Predicting and Improving the Performance of HAWT's

    NASA Astrophysics Data System (ADS)

    Al-Abadi, Ali; Ertunç, Özgür; Sittig, Florian; Delgado, Antonio

    2014-06-01

    A complete procedure for predicting and improving the performance of the horizontal axis wind turbine (HAWT) has been developed. The first process is predicting the power extracted by the turbine and the derived rotor torque, which should be identical to that of the drive unit. The BEM method and a developed post-stall treatment for resolving stall-regulated HAWT is incorporated in the prediction. For that, a modified stall-regulated prediction model, which can predict the HAWT performance over the operating range of oncoming wind velocity, is derived from existing models. The model involves radius and chord, which has made it more general in applications for predicting the performance of different scales and rotor shapes of HAWTs. The second process is modifying the rotor shape by an optimization process, which can be applied to any existing HAWT, to improve its performance. A gradient- based optimization is used for adjusting the chord and twist angle distribution of the rotor blade to increase the extraction of the power while keeping the drive torque constant, thus the same drive unit can be kept. The final process is testing the modified turbine to predict its enhanced performance. The procedure is applied to NREL phase-VI 10kW as a baseline turbine. The study has proven the applicability of the developed model in predicting the performance of the baseline as well as the optimized turbine. In addition, the optimization method has shown that the power coefficient can be increased while keeping same design rotational speed.

  10. Accurate prediction of hot spot residues through physicochemical characteristics of amino acid sequences.

    PubMed

    Chen, Peng; Li, Jinyan; Wong, Limsoon; Kuwahara, Hiroyuki; Huang, Jianhua Z; Gao, Xin

    2013-08-01

    Hot spot residues of proteins are fundamental interface residues that help proteins perform their functions. Detecting hot spots by experimental methods is costly and time-consuming. Sequential and structural information has been widely used in the computational prediction of hot spots. However, structural information is not always available. In this article, we investigated the problem of identifying hot spots using only physicochemical characteristics extracted from amino acid sequences. We first extracted 132 relatively independent physicochemical features from a set of the 544 properties in AAindex1, an amino acid index database. Each feature was utilized to train a classification model with a novel encoding schema for hot spot prediction by the IBk algorithm, an extension of the K-nearest neighbor algorithm. The combinations of the individual classifiers were explored and the classifiers that appeared frequently in the top performing combinations were selected. The hot spot predictor was built based on an ensemble of these classifiers and to work in a voting manner. Experimental results demonstrated that our method effectively exploited the feature space and allowed flexible weights of features for different queries. On the commonly used hot spot benchmark sets, our method significantly outperformed other machine learning algorithms and state-of-the-art hot spot predictors. The program is available at http://sfb.kaust.edu.sa/pages/software.aspx. Copyright © 2013 Wiley Periodicals, Inc.

  11. A Simple and Accurate Model to Predict Responses to Multi-electrode Stimulation in the Retina

    PubMed Central

    Maturana, Matias I.; Apollo, Nicholas V.; Hadjinicolaou, Alex E.; Garrett, David J.; Cloherty, Shaun L.; Kameneva, Tatiana; Grayden, David B.; Ibbotson, Michael R.; Meffin, Hamish

    2016-01-01

    Implantable electrode arrays are widely used in therapeutic stimulation of the nervous system (e.g. cochlear, retinal, and cortical implants). Currently, most neural prostheses use serial stimulation (i.e. one electrode at a time) despite this severely limiting the repertoire of stimuli that can be applied. Methods to reliably predict the outcome of multi-electrode stimulation have not been available. Here, we demonstrate that a linear-nonlinear model accurately predicts neural responses to arbitrary patterns of stimulation using in vitro recordings from single retinal ganglion cells (RGCs) stimulated with a subretinal multi-electrode array. In the model, the stimulus is projected onto a low-dimensional subspace and then undergoes a nonlinear transformation to produce an estimate of spiking probability. The low-dimensional subspace is estimated using principal components analysis, which gives the neuron’s electrical receptive field (ERF), i.e. the electrodes to which the neuron is most sensitive. Our model suggests that stimulation proportional to the ERF yields a higher efficacy given a fixed amount of power when compared to equal amplitude stimulation on up to three electrodes. We find that the model captures the responses of all the cells recorded in the study, suggesting that it will generalize to most cell types in the retina. The model is computationally efficient to evaluate and, therefore, appropriate for future real-time applications including stimulation strategies that make use of recorded neural activity to improve the stimulation strategy. PMID:27035143

  12. Objective Prediction of Hearing Aid Benefit Across Listener Groups Using Machine Learning: Speech Recognition Performance With Binaural Noise-Reduction Algorithms

    PubMed Central

    Schädler, Marc R.; Warzybok, Anna; Kollmeier, Birger

    2018-01-01

    The simulation framework for auditory discrimination experiments (FADE) was adopted and validated to predict the individual speech-in-noise recognition performance of listeners with normal and impaired hearing with and without a given hearing-aid algorithm. FADE uses a simple automatic speech recognizer (ASR) to estimate the lowest achievable speech reception thresholds (SRTs) from simulated speech recognition experiments in an objective way, independent from any empirical reference data. Empirical data from the literature were used to evaluate the model in terms of predicted SRTs and benefits in SRT with the German matrix sentence recognition test when using eight single- and multichannel binaural noise-reduction algorithms. To allow individual predictions of SRTs in binaural conditions, the model was extended with a simple better ear approach and individualized by taking audiograms into account. In a realistic binaural cafeteria condition, FADE explained about 90% of the variance of the empirical SRTs for a group of normal-hearing listeners and predicted the corresponding benefits with a root-mean-square prediction error of 0.6 dB. This highlights the potential of the approach for the objective assessment of benefits in SRT without prior knowledge about the empirical data. The predictions for the group of listeners with impaired hearing explained 75% of the empirical variance, while the individual predictions explained less than 25%. Possibly, additional individual factors should be considered for more accurate predictions with impaired hearing. A competing talker condition clearly showed one limitation of current ASR technology, as the empirical performance with SRTs lower than −20 dB could not be predicted. PMID:29692200

  13. Two-structured solid particle model for predicting and analyzing supercritical extraction performance.

    PubMed

    Samadi, Sara; Vaziri, Behrooz Mahmoodzadeh

    2017-07-14

    Solid extraction process, using the supercritical fluid, is a modern science and technology, which has come in vogue regarding its considerable advantages. In the present article, a new and comprehensive model is presented for predicting the performance and separation yield of the supercritical extraction process. The base of process modeling is partial differential mass balances. In the proposed model, the solid particles are considered twofold: (a) particles with intact structure, (b) particles with destructed structure. A distinct mass transfer coefficient has been used for extraction of each part of solid particles to express different extraction regimes and to evaluate the process accurately (internal mass transfer coefficient was used for the intact-structure particles and external mass transfer coefficient was employed for the destructed-structure particles). In order to evaluate and validate the proposed model, the obtained results from simulations were compared with two series of available experimental data for extraction of chamomile extract with supercritical carbon dioxide, which had an excellent agreement. This is indicative of high potentiality of the model in predicting the extraction process, precisely. In the following, the effect of major parameters on supercritical extraction process, like pressure, temperature, supercritical fluid flow rate, and the size of solid particles was evaluated. The model can be used as a superb starting point for scientific and experimental applications. Copyright © 2017 Elsevier B.V. All rights reserved.

  14. GPM Microwave Imager Design, Predicted Performance and Status

    NASA Technical Reports Server (NTRS)

    Krimchansky, Sergey; Newell, David

    2010-01-01

    The Global Precipitation Measurement (GPM) Microwave Imager (GMI) Instrument is being developed by Ball Aerospace and Technology Corporation (BATC) for the GPM program at NASA Goddard. The Global Precipitation Measurement (GPM) mission is an international effort managed by the National Aeronautics and Space Administration (t.JASA) to improve climate, weather, and hydro-meteorological predictions through more accurate and more frequent precipitation measurements. The GPM Microwave Imager (GMI) will be used to make calibrated, radiometric measurements from space at multiple microwave frequencies and polarizations. GMI will be placed on the GPM Core Spacecraft together with the Dual-frequency Precipitation Radar (DPR). The DPR is two-frequency precipitation measurement radar, which will operate in the Ku-band and Ka-band of the microwave spectrum. The Core Spacecraft will make radiometric and radar measurements of clouds and precipitation and will be the central element of GPM's space segment. The data products from GPM will provide information concerning global precipitation on a frequent, near-global basis to meteorologists and scientists making weather forecasts and performing research on the global energy and water cycle, precipitation, hydrology, and related disciplines. In addition, radiometric measurements from GMI and radar measurements from the DPR will be used together to develop a retrieval transfer standard for the purpose of calibrating precipitation retrieval algorithms. This calibration standard will establish a reference against which other retrieval algorithms using only microwave radiometers (and without the benefit of the DPR) on other satellites in the GPM constellation will be compared.

  15. Can medical students accurately predict their learning? A study comparing perceived and actual performance in neuroanatomy.

    PubMed

    Hall, Samuel R; Stephens, Jonny R; Seaby, Eleanor G; Andrade, Matheus Gesteira; Lowry, Andrew F; Parton, Will J C; Smith, Claire F; Border, Scott

    2016-10-01

    It is important that clinicians are able to adequately assess their level of knowledge and competence in order to be safe practitioners of medicine. The medical literature contains numerous examples of poor self-assessment accuracy amongst medical students over a range of subjects however this ability in neuroanatomy has yet to be observed. Second year medical students attending neuroanatomy revision sessions at the University of Southampton and the competitors of the National Undergraduate Neuroanatomy Competition were asked to rate their level of knowledge in neuroanatomy. The responses from the former group were compared to performance on a ten item multiple choice question examination and the latter group were compared to their performance within the competition. In both cohorts, self-assessments of perceived level of knowledge correlated weakly to their performance in their respective objective knowledge assessments (r = 0.30 and r = 0.44). Within the NUNC, this correlation improved when students were instead asked to rate their performance on a specific examination within the competition (spotter, rS = 0.68; MCQ, rS = 0.58). Despite its inherent difficulty, medical student self-assessment accuracy in neuroanatomy is comparable to other subjects within the medical curriculum. Anat Sci Educ 9: 488-495. © 2016 American Association of Anatomists. © 2016 American Association of Anatomists.

  16. Clinical assessment is an accurate predictor of which patients will need septoplasty.

    PubMed

    Sedaghat, Ahmad R; Busaba, Nicolas Y; Cunningham, Michael J; Kieff, David A

    2013-01-01

    Septoplasty is a frequently performed surgical procedure with the most common indication being nasal airway obstruction. Almost universally, health insurance companies mandate a trial of medical therapy consisting of intranasal corticosteroids prior to performance of septoplasty regardless of clinical assessment. Evidence for this requirement is lacking. We sought to evaluate the initial clinical assessment as a predictor of response to this mandated trial of medical treatment. Retrospective review of prospectively collected data on 137 consecutive patients who presented with symptoms of nasal obstruction and a deviated nasal septum on physical examination. Patients were placed into one of three cohorts based on prediction of 1) failure of medical therapy with subsequent septoplasty, 2) success of medical therapy without subsequent septoplasty, or 3) unable to make a prediction. Patients from each cohort were assessed for subsequent response to medical therapy and ultimate need for septoplasty. Overall clinical assessment had a sensitivity of 86.9%, specificity of 91.8%, positive predictive value of 93.6%, and negative predictive value of 96.4% for detecting/predicting need for septoplasty. The accuracy of the overall clinical assessment is considerably better than severe deviation at any one septal anatomical site. Of patients whose response to medical therapy could not be predicted, 61.3% failed medical therapy and needed surgery; this is statistically equivalent to a 50/50 distribution between either needing septoplasty or not. Clinical assessment at initial presentation of patients with nasal obstruction and deviated septum is highly accurate in predicting which patients will need septoplasty. Copyright © 2012 The American Laryngological, Rhinological, and Otological Society, Inc.

  17. Children's construction task performance and spatial ability: controlling task complexity and predicting mathematics performance.

    PubMed

    Richardson, Miles; Hunt, Thomas E; Richardson, Cassandra

    2014-12-01

    This paper presents a methodology to control construction task complexity and examined the relationships between construction performance and spatial and mathematical abilities in children. The study included three groups of children (N = 96); ages 7-8, 10-11, and 13-14 years. Each group constructed seven pre-specified objects. The study replicated and extended previous findings that indicated that the extent of component symmetry and variety, and the number of components for each object and available for selection, significantly predicted construction task difficulty. Results showed that this methodology is a valid and reliable technique for assessing and predicting construction play task difficulty. Furthermore, construction play performance predicted mathematical attainment independently of spatial ability.

  18. Predicting course performance in freshman and sophomore physics courses: Women are more predictable than men

    NASA Astrophysics Data System (ADS)

    McCammon, Susan; Golden, Jeannie; Wuensch, Karl L.

    This study investigated the extent to which thinking skills and mathematical competency would predict the course performance of freshman and sophomore science majors enrolled in physics courses. Multiple-regression equations revealed that algebra and critical thinking skills were the best overall predictors across several physics courses. Although arithmetic skills, math anxiety, and primary mental abilities scores also correlated with performance, they were redundant with the algebra and critical thinking. The most surprising finding of the study was the differential validity by sex; predictor variables were successful in predicting course performance for women but not for men.

  19. Beyond Genomic Prediction: Combining Different Types of omics Data Can Improve Prediction of Hybrid Performance in Maize.

    PubMed

    Schrag, Tobias A; Westhues, Matthias; Schipprack, Wolfgang; Seifert, Felix; Thiemann, Alexander; Scholten, Stefan; Melchinger, Albrecht E

    2018-04-01

    The ability to predict the agronomic performance of single-crosses with high precision is essential for selecting superior candidates for hybrid breeding. With recent technological advances, thousands of new parent lines, and, consequently, millions of new hybrid combinations are possible in each breeding cycle, yet only a few hundred can be produced and phenotyped in multi-environment yield trials. Well established prediction approaches such as best linear unbiased prediction (BLUP) using pedigree data and whole-genome prediction using genomic data are limited in capturing epistasis and interactions occurring within and among downstream biological strata such as transcriptome and metabolome. Because mRNA and small RNA (sRNA) sequences are involved in transcriptional, translational and post-translational processes, we expect them to provide information influencing several biological strata. However, using sRNA data of parent lines to predict hybrid performance has not yet been addressed. Here, we gathered genomic, transcriptomic (mRNA and sRNA) and metabolomic data of parent lines to evaluate the ability of the data to predict the performance of untested hybrids for important agronomic traits in grain maize. We found a considerable interaction for predictive ability between predictor and trait, with mRNA data being a superior predictor for grain yield and genomic data for grain dry matter content, while sRNA performed relatively poorly for both traits. Combining mRNA and genomic data as predictors resulted in high predictive abilities across both traits and combining other predictors improved prediction over that of the individual predictors alone. We conclude that downstream "omics" can complement genomics for hybrid prediction, and, thereby, contribute to more efficient selection of hybrid candidates. Copyright © 2018 by the Genetics Society of America.

  20. International Low-Earth-Orbit Spacecraft Materials Test Program Initiated for Better Prediction of Durability and Performance

    NASA Technical Reports Server (NTRS)

    Rutledge, Sharon K.

    1999-01-01

    Spacecraft in low Earth orbit (LEO) are subjected to many components of the environment, which can cause them to degrade much more rapidly than intended and greatly shorten their functional life. The atomic oxygen, ultraviolet radiation, and cross contamination present in LEO can affect sensitive surfaces such as thermal control paints, multilayer insulation, solar array surfaces, and optical surfaces. The LEO Spacecraft Materials Test (LEO-SMT) program is being conducted to assess the effects of simulated LEO exposure on current spacecraft materials to increase understanding of LEO degradation processes as well as to enable the prediction of in-space performance and durability. Using ground-based simulation facilities to test the durability of materials currently flying in LEO will allow researchers to compare the degradation evidenced in the ground-based facilities with that evidenced on orbit. This will allow refinement of ground laboratory test systems and the development of algorithms to predict the durability and performance of new materials in LEO from ground test results. Accurate predictions based on ground tests could reduce development costs and increase reliability. The wide variety of national and international materials being tested represent materials being functionally used on spacecraft in LEO. The more varied the types of materials tested, the greater the probability that researchers will develop and validate predictive models for spacecraft long-term performance and durability. Organizations that are currently participating in the program are ITT Research Institute (USA), Lockheed Martin (USA), MAP (France), SOREQ Nuclear Research Center (Israel), TNO Institute of Applied Physics (The Netherlands), and UBE Industries, Ltd. (Japan). These represent some of the major suppliers of thermal control and sensor materials currently flying in LEO. The participants provide materials that are exposed to selected levels of atomic oxygen, vacuum ultraviolet

  1. ILS Glide Slope Performance Prediction. Volume B

    DTIC Science & Technology

    1974-09-01

    figures are identical in both volumes. 󈧔. Abottec A mathematical model for predicting the performance of ILS glide slope arrays in the presence of...irregularities on the performance of ILS Glide Slope antenna systems, a mathematical -electromagnetic scattering computer model has been developed. This work was...Antenna ........... 4-4 9. Test Case Results ..................................... r-3 ix PART I. IEO -j 1.INTRODUCTION IA mathematical model has been

  2. Improving the Accuracy of Predicting Maximal Oxygen Consumption (VO2pk)

    NASA Technical Reports Server (NTRS)

    Downs, Meghan E.; Lee, Stuart M. C.; Ploutz-Snyder, Lori; Feiveson, Alan

    2016-01-01

    Maximal oxygen (VO2pk) is the maximum amount of oxygen that the body can use during intense exercise and is used for benchmarking endurance exercise capacity. The most accurate method to determineVO2pk requires continuous measurements of ventilation and gas exchange during an exercise test to maximal effort, which necessitates expensive equipment, a trained staff, and time to set-up the equipment. For astronauts, accurate VO2pk measures are important to assess mission critical task performance capabilities and to prescribe exercise intensities to optimize performance. Currently, astronauts perform submaximal exercise tests during flight to predict VO2pk; however, while submaximal VO2pk prediction equations provide reliable estimates of mean VO2pk for populations, they can be unacceptably inaccurate for a given individual. The error in current predictions and logistical limitations of measuring VO2pk, particularly during spaceflight, highlights the need for improved estimation methods.

  3. Accurate pan-specific prediction of peptide-MHC class II binding affinity with improved binding core identification.

    PubMed

    Andreatta, Massimo; Karosiene, Edita; Rasmussen, Michael; Stryhn, Anette; Buus, Søren; Nielsen, Morten

    2015-11-01

    A key event in the generation of a cellular response against malicious organisms through the endocytic pathway is binding of peptidic antigens by major histocompatibility complex class II (MHC class II) molecules. The bound peptide is then presented on the cell surface where it can be recognized by T helper lymphocytes. NetMHCIIpan is a state-of-the-art method for the quantitative prediction of peptide binding to any human or mouse MHC class II molecule of known sequence. In this paper, we describe an updated version of the method with improved peptide binding register identification. Binding register prediction is concerned with determining the minimal core region of nine residues directly in contact with the MHC binding cleft, a crucial piece of information both for the identification and design of CD4(+) T cell antigens. When applied to a set of 51 crystal structures of peptide-MHC complexes with known binding registers, the new method NetMHCIIpan-3.1 significantly outperformed the earlier 3.0 version. We illustrate the impact of accurate binding core identification for the interpretation of T cell cross-reactivity using tetramer double staining with a CMV epitope and its variants mapped to the epitope binding core. NetMHCIIpan is publicly available at http://www.cbs.dtu.dk/services/NetMHCIIpan-3.1 .

  4. Moving Toward Integrating Gene Expression Profiling Into High-Throughput Testing: A Gene Expression Biomarker Accurately Predicts Estrogen Receptor α Modulation in a Microarray Compendium

    PubMed Central

    Ryan, Natalia; Chorley, Brian; Tice, Raymond R.; Judson, Richard; Corton, J. Christopher

    2016-01-01

    Microarray profiling of chemical-induced effects is being increasingly used in medium- and high-throughput formats. Computational methods are described here to identify molecular targets from whole-genome microarray data using as an example the estrogen receptor α (ERα), often modulated by potential endocrine disrupting chemicals. ERα biomarker genes were identified by their consistent expression after exposure to 7 structurally diverse ERα agonists and 3 ERα antagonists in ERα-positive MCF-7 cells. Most of the biomarker genes were shown to be directly regulated by ERα as determined by ESR1 gene knockdown using siRNA as well as through chromatin immunoprecipitation coupled with DNA sequencing analysis of ERα-DNA interactions. The biomarker was evaluated as a predictive tool using the fold-change rank-based Running Fisher algorithm by comparison to annotated gene expression datasets from experiments using MCF-7 cells, including those evaluating the transcriptional effects of hormones and chemicals. Using 141 comparisons from chemical- and hormone-treated cells, the biomarker gave a balanced accuracy for prediction of ERα activation or suppression of 94% and 93%, respectively. The biomarker was able to correctly classify 18 out of 21 (86%) ER reference chemicals including “very weak” agonists. Importantly, the biomarker predictions accurately replicated predictions based on 18 in vitro high-throughput screening assays that queried different steps in ERα signaling. For 114 chemicals, the balanced accuracies were 95% and 98% for activation or suppression, respectively. These results demonstrate that the ERα gene expression biomarker can accurately identify ERα modulators in large collections of microarray data derived from MCF-7 cells. PMID:26865669

  5. A Course Specific Perspective in the Prediction of Academic Success.

    ERIC Educational Resources Information Center

    Beaulieu, R. P.

    1990-01-01

    Students (N=94) enrolled in a senior-level management course over six semesters were used to investigate the ability of four measures from two industrial tests to predict course performance. The resulting multiple regression equation with four predictors could accurately predict achievement of males, but not of females. (Author/TE)

  6. Genomic Prediction of Testcross Performance in Canola (Brassica napus)

    PubMed Central

    Jan, Habib U.; Abbadi, Amine; Lücke, Sophie; Nichols, Richard A.; Snowdon, Rod J.

    2016-01-01

    Genomic selection (GS) is a modern breeding approach where genome-wide single-nucleotide polymorphism (SNP) marker profiles are simultaneously used to estimate performance of untested genotypes. In this study, the potential of genomic selection methods to predict testcross performance for hybrid canola breeding was applied for various agronomic traits based on genome-wide marker profiles. A total of 475 genetically diverse spring-type canola pollinator lines were genotyped at 24,403 single-copy, genome-wide SNP loci. In parallel, the 950 F1 testcross combinations between the pollinators and two representative testers were evaluated for a number of important agronomic traits including seedling emergence, days to flowering, lodging, oil yield and seed yield along with essential seed quality characters including seed oil content and seed glucosinolate content. A ridge-regression best linear unbiased prediction (RR-BLUP) model was applied in combination with 500 cross-validations for each trait to predict testcross performance, both across the whole population as well as within individual subpopulations or clusters, based solely on SNP profiles. Subpopulations were determined using multidimensional scaling and K-means clustering. Genomic prediction accuracy across the whole population was highest for seed oil content (0.81) followed by oil yield (0.75) and lowest for seedling emergence (0.29). For seed yieId, seed glucosinolate, lodging resistance and days to onset of flowering (DTF), prediction accuracies were 0.45, 0.61, 0.39 and 0.56, respectively. Prediction accuracies could be increased for some traits by treating subpopulations separately; a strategy which only led to moderate improvements for some traits with low heritability, like seedling emergence. No useful or consistent increase in accuracy was obtained by inclusion of a population substructure covariate in the model. Testcross performance prediction using genome-wide SNP markers shows considerable

  7. Accurate, predictable, repeatable micro-assembly technology for polymer, microfluidic modules.

    PubMed

    Lee, Tae Yoon; Han, Kyudong; Barrett, Dwhyte O; Park, Sunggook; Soper, Steven A; Murphy, Michael C

    2018-01-01

    A method for the design, construction, and assembly of modular, polymer-based, microfluidic devices using simple micro-assembly technology was demonstrated to build an integrated fluidic system consisting of vertically stacked modules for carrying out multi-step molecular assays. As an example of the utility of the modular system, point mutation detection using the ligase detection reaction (LDR) following amplification by the polymerase chain reaction (PCR) was carried out. Fluid interconnects and standoffs ensured that temperatures in the vertically stacked reactors were within ± 0.2 C° at the center of the temperature zones and ± 1.1 C° overall. The vertical spacing between modules was confirmed using finite element models (ANSYS, Inc., Canonsburg, PA) to simulate the steady-state temperature distribution for the assembly. Passive alignment structures, including a hemispherical pin-in-hole, a hemispherical pin-in-slot, and a plate-plate lap joint, were developed using screw theory to enable accurate exactly constrained assembly of the microfluidic reactors, cover sheets, and fluid interconnects to facilitate the modular approach. The mean mismatch between the centers of adjacent through holes was 64 ± 7.7 μm, significantly reducing the dead volume necessary to accommodate manufacturing variation. The microfluidic components were easily assembled by hand and the assembly of several different configurations of microfluidic modules for executing the assay was evaluated. Temperatures were measured in the desired range in each reactor. The biochemical performance was comparable to that obtained with benchtop instruments, but took less than 45 min to execute, half the time.

  8. Deriving Points of Departure and Performance Baselines for Predictive Modeling of Systemic Toxicity using ToxRefDB (SOT)

    EPA Science Inventory

    A primary goal of computational toxicology is to generate predictive models of toxicity. An elusive target of alternative test methods and models has been the accurate prediction of systemic toxicity points of departure (PoD). We aim not only to provide a large and valuable resou...

  9. Predicting space telerobotic operator training performance from human spatial ability assessment

    NASA Astrophysics Data System (ADS)

    Liu, Andrew M.; Oman, Charles M.; Galvan, Raquel; Natapoff, Alan

    2013-11-01

    Our goal was to determine whether existing tests of spatial ability can predict an astronaut's qualification test performance after robotic training. Because training astronauts to be qualified robotics operators is so long and expensive, NASA is interested in tools that can predict robotics performance before training begins. Currently, the Astronaut Office does not have a validated tool to predict robotics ability as part of its astronaut selection or training process. Commonly used tests of human spatial ability may provide such a tool to predict robotics ability. We tested the spatial ability of 50 active astronauts who had completed at least one robotics training course, then used logistic regression models to analyze the correlation between spatial ability test scores and the astronauts' performance in their evaluation test at the end of the training course. The fit of the logistic function to our data is statistically significant for several spatial tests. However, the prediction performance of the logistic model depends on the criterion threshold assumed. To clarify the critical selection issues, we show how the probability of correct classification vs. misclassification varies as a function of the mental rotation test criterion level. Since the costs of misclassification are low, the logistic models of spatial ability and robotic performance are reliable enough only to be used to customize regular and remedial training. We suggest several changes in tracking performance throughout robotics training that could improve the range and reliability of predictive models.

  10. A time accurate prediction of the viscous flow in a turbine stage including a rotor in motion

    NASA Astrophysics Data System (ADS)

    Shavalikul, Akamol

    in the relative frame of reference; the boundary conditions for the computations were obtained from inlet flow measurements performed in the AFTRF. A complete turbine stage, including an NGV and a rotor row was simulated using the RANS solver with the SST kappa -- o turbulence model, with two different computational models for the interface between the rotating component and the stationary component. The first interface model, the circumferentially averaged mixing plane model, was solved for a fixed position of the rotor blades relative to the NGV in the stationary frame of reference. The information transferred between the NGV and rotor domains is obtained by averaging across the entire interface. The quasi-steady state flow characteristics of the AFTRF can be obtained from this interface model. After the model was validated with the existing experimental data, this model was not only used to investigate the flow characteristics in the turbine stage but also the effects of using pressure side rotor tip extensions. The tip leakage flow fields simulated from this model and from the linear cascade model show similar trends. More detailed understanding of unsteady characteristics of a turbine flow field can be obtained using the second type of interface model, the time accurate sliding mesh model. The potential flow interactions, wake characteristics, their effects on secondary flow formation, and the wake mixing process in a rotor passage were examined using this model. Furthermore, turbine stage efficiency and effects of tip clearance height on the turbine stage efficiency were also investigated. A comparison between the results from the circumferential average model and the time accurate flow model results is presented. It was found that the circumferential average model cannot accurately simulate flow interaction characteristics on the interface plane between the NGV trailing edge and the rotor leading edge. However, the circumferential average model does give

  11. Accurate thermoelastic tensor and acoustic velocities of NaCl

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

    Marcondes, Michel L., E-mail: michel@if.usp.br; Chemical Engineering and Material Science, University of Minnesota, Minneapolis, 55455; Shukla, Gaurav, E-mail: shukla@physics.umn.edu

    Despite the importance of thermoelastic properties of minerals in geology and geophysics, their measurement at high pressures and temperatures are still challenging. Thus, ab initio calculations are an essential tool for predicting these properties at extreme conditions. Owing to the approximate description of the exchange-correlation energy, approximations used in calculations of vibrational effects, and numerical/methodological approximations, these methods produce systematic deviations. Hybrid schemes combining experimental data and theoretical results have emerged as a way to reconcile available information and offer more reliable predictions at experimentally inaccessible thermodynamics conditions. Here we introduce a method to improve the calculated thermoelastic tensor bymore » using highly accurate thermal equation of state (EoS). The corrective scheme is general, applicable to crystalline solids with any symmetry, and can produce accurate results at conditions where experimental data may not exist. We apply it to rock-salt-type NaCl, a material whose structural properties have been challenging to describe accurately by standard ab initio methods and whose acoustic/seismic properties are important for the gas and oil industry.« less

  12. Utilizing Machine Learning and Automated Performance Metrics to Evaluate Robot-Assisted Radical Prostatectomy Performance and Predict Outcomes.

    PubMed

    Hung, Andrew J; Chen, Jian; Che, Zhengping; Nilanon, Tanachat; Jarc, Anthony; Titus, Micha; Oh, Paul J; Gill, Inderbir S; Liu, Yan

    2018-05-01

    Surgical performance is critical for clinical outcomes. We present a novel machine learning (ML) method of processing automated performance metrics (APMs) to evaluate surgical performance and predict clinical outcomes after robot-assisted radical prostatectomy (RARP). We trained three ML algorithms utilizing APMs directly from robot system data (training material) and hospital length of stay (LOS; training label) (≤2 days and >2 days) from 78 RARP cases, and selected the algorithm with the best performance. The selected algorithm categorized the cases as "Predicted as expected LOS (pExp-LOS)" and "Predicted as extended LOS (pExt-LOS)." We compared postoperative outcomes of the two groups (Kruskal-Wallis/Fisher's exact tests). The algorithm then predicted individual clinical outcomes, which we compared with actual outcomes (Spearman's correlation/Fisher's exact tests). Finally, we identified five most relevant APMs adopted by the algorithm during predicting. The "Random Forest-50" (RF-50) algorithm had the best performance, reaching 87.2% accuracy in predicting LOS (73 cases as "pExp-LOS" and 5 cases as "pExt-LOS"). The "pExp-LOS" cases outperformed the "pExt-LOS" cases in surgery time (3.7 hours vs 4.6 hours, p = 0.007), LOS (2 days vs 4 days, p = 0.02), and Foley duration (9 days vs 14 days, p = 0.02). Patient outcomes predicted by the algorithm had significant association with the "ground truth" in surgery time (p < 0.001, r = 0.73), LOS (p = 0.05, r = 0.52), and Foley duration (p < 0.001, r = 0.45). The five most relevant APMs, adopted by the RF-50 algorithm in predicting, were largely related to camera manipulation. To our knowledge, ours is the first study to show that APMs and ML algorithms may help assess surgical RARP performance and predict clinical outcomes. With further accrual of clinical data (oncologic and functional data), this process will become increasingly relevant and valuable in surgical assessment and

  13. Gender Differences in Performance Predictions: Evidence from the Cognitive Reflection Test.

    PubMed

    Ring, Patrick; Neyse, Levent; David-Barett, Tamas; Schmidt, Ulrich

    2016-01-01

    This paper studies performance predictions in the 7-item Cognitive Reflection Test (CRT) and whether they differ by gender. After participants completed the CRT, they predicted their own (i), the other participants' (ii), men's (iii), and women's (iv) number of correct answers. In keeping with existing literature, men scored higher on the CRT than women and both men and women were too optimistic about their own performance. When we compare gender-specific predictions, we observe that men think they perform significantly better than other men and do so significantly more than women. The equality between women's predictions about their own performance and their female peers cannot be rejected. Our findings contribute to the growing literature on the underpinnings of behavior in economics and in psychology by uncovering gender differences in confidence about one's ability relative to same and opposite sex peers.

  14. Mammalian choices: combining fast-but-inaccurate and slow-but-accurate decision-making systems.

    PubMed

    Trimmer, Pete C; Houston, Alasdair I; Marshall, James A R; Bogacz, Rafal; Paul, Elizabeth S; Mendl, Mike T; McNamara, John M

    2008-10-22

    Empirical findings suggest that the mammalian brain has two decision-making systems that act at different speeds. We represent the faster system using standard signal detection theory. We represent the slower (but more accurate) cortical system as the integration of sensory evidence over time until a certain level of confidence is reached. We then consider how two such systems should be combined optimally for a range of information linkage mechanisms. We conclude with some performance predictions that will hold if our representation is realistic.

  15. Accurate predictions of population-level changes in sequence and structural properties of HIV-1 Env using a volatility-controlled diffusion model

    PubMed Central

    DeLeon, Orlando; Hodis, Hagit; O’Malley, Yunxia; Johnson, Jacklyn; Salimi, Hamid; Zhai, Yinjie; Winter, Elizabeth; Remec, Claire; Eichelberger, Noah; Van Cleave, Brandon; Puliadi, Ramya; Harrington, Robert D.; Stapleton, Jack T.; Haim, Hillel

    2017-01-01

    The envelope glycoproteins (Envs) of HIV-1 continuously evolve in the host by random mutations and recombination events. The resulting diversity of Env variants circulating in the population and their continuing diversification process limit the efficacy of AIDS vaccines. We examined the historic changes in Env sequence and structural features (measured by integrity of epitopes on the Env trimer) in a geographically defined population in the United States. As expected, many Env features were relatively conserved during the 1980s. From this state, some features diversified whereas others remained conserved across the years. We sought to identify “clues” to predict the observed historic diversification patterns. Comparison of viruses that cocirculate in patients at any given time revealed that each feature of Env (sequence or structural) exists at a defined level of variance. The in-host variance of each feature is highly conserved among individuals but can vary between different HIV-1 clades. We designate this property “volatility” and apply it to model evolution of features as a linear diffusion process that progresses with increasing genetic distance. Volatilities of different features are highly correlated with their divergence in longitudinally monitored patients. Volatilities of features also correlate highly with their population-level diversification. Using volatility indices measured from a small number of patient samples, we accurately predict the population diversity that developed for each feature over the course of 30 years. Amino acid variants that evolved at key antigenic sites are also predicted well. Therefore, small “fluctuations” in feature values measured in isolated patient samples accurately describe their potential for population-level diversification. These tools will likely contribute to the design of population-targeted AIDS vaccines by effectively capturing the diversity of currently circulating strains and addressing properties

  16. Disturbance observer based model predictive control for accurate atmospheric entry of spacecraft

    NASA Astrophysics Data System (ADS)

    Wu, Chao; Yang, Jun; Li, Shihua; Li, Qi; Guo, Lei

    2018-05-01

    Facing the complex aerodynamic environment of Mars atmosphere, a composite atmospheric entry trajectory tracking strategy is investigated in this paper. External disturbances, initial states uncertainties and aerodynamic parameters uncertainties are the main problems. The composite strategy is designed to solve these problems and improve the accuracy of Mars atmospheric entry. This strategy includes a model predictive control for optimized trajectory tracking performance, as well as a disturbance observer based feedforward compensation for external disturbances and uncertainties attenuation. 500-run Monte Carlo simulations show that the proposed composite control scheme achieves more precise Mars atmospheric entry (3.8 km parachute deployment point distribution error) than the baseline control scheme (8.4 km) and integral control scheme (5.8 km).

  17. Predator personality and prey behavioural predictability jointly determine foraging performance.

    PubMed

    Chang, Chia-Chen; Teo, Huey Yee; Norma-Rashid, Y; Li, Daiqin

    2017-01-17

    Predator-prey interactions play important roles in ecological communities. Personality, consistent inter-individual differences in behaviour, of predators, prey or both are known to influence inter-specific interactions. An individual may also behave differently under the same situation and the level of such variability may differ between individuals. Such intra-individual variability (IIV) or predictability may be a trait on which selection can also act. A few studies have revealed the joint effect of personality types of both predators and prey on predator foraging performance. However, how personality type and IIV of both predators and prey jointly influence predator foraging performance remains untested empirically. Here, we addressed this using a specialized spider-eating jumping spider, Portia labiata (Salticidae), as the predator, and a jumping spider, Cosmophasis umbratica, as the prey. We examined personality types and IIVs of both P. labiata and C. umbratica and used their inter- and intra-individual behavioural variation as predictors of foraging performance (i.e., number of attempts to capture prey). Personality type and predictability had a joint effect on predator foraging performance. Aggressive predators performed better in capturing unpredictable (high IIV) prey than predictable (low IIV) prey, while docile predators demonstrated better performance when encountering predictable prey. This study highlights the importance of the joint effect of both predator and prey personality types and IIVs on predator-prey interactions.

  18. Predator personality and prey behavioural predictability jointly determine foraging performance

    PubMed Central

    Chang, Chia-chen; Teo, Huey Yee; Norma-Rashid, Y.; Li, Daiqin

    2017-01-01

    Predator-prey interactions play important roles in ecological communities. Personality, consistent inter-individual differences in behaviour, of predators, prey or both are known to influence inter-specific interactions. An individual may also behave differently under the same situation and the level of such variability may differ between individuals. Such intra-individual variability (IIV) or predictability may be a trait on which selection can also act. A few studies have revealed the joint effect of personality types of both predators and prey on predator foraging performance. However, how personality type and IIV of both predators and prey jointly influence predator foraging performance remains untested empirically. Here, we addressed this using a specialized spider-eating jumping spider, Portia labiata (Salticidae), as the predator, and a jumping spider, Cosmophasis umbratica, as the prey. We examined personality types and IIVs of both P. labiata and C. umbratica and used their inter- and intra-individual behavioural variation as predictors of foraging performance (i.e., number of attempts to capture prey). Personality type and predictability had a joint effect on predator foraging performance. Aggressive predators performed better in capturing unpredictable (high IIV) prey than predictable (low IIV) prey, while docile predators demonstrated better performance when encountering predictable prey. This study highlights the importance of the joint effect of both predator and prey personality types and IIVs on predator-prey interactions. PMID:28094288

  19. ILS Glide Slope Performance Prediction Multipath Scattering

    DOT National Transportation Integrated Search

    1976-12-01

    A mathematical model has been developed which predicts the performance of ILS glide slope systems subject to multipath scattering and the effects of irregular terrain contours. The model is discussed in detail and then applied to a test case for purp...

  20. Machine Learning and Neurosurgical Outcome Prediction: A Systematic Review.

    PubMed

    Senders, Joeky T; Staples, Patrick C; Karhade, Aditya V; Zaki, Mark M; Gormley, William B; Broekman, Marike L D; Smith, Timothy R; Arnaout, Omar

    2018-01-01

    Accurate measurement of surgical outcomes is highly desirable to optimize surgical decision-making. An important element of surgical decision making is identification of the patient cohort that will benefit from surgery before the intervention. Machine learning (ML) enables computers to learn from previous data to make accurate predictions on new data. In this systematic review, we evaluate the potential of ML for neurosurgical outcome prediction. A systematic search in the PubMed and Embase databases was performed to identify all potential relevant studies up to January 1, 2017. Thirty studies were identified that evaluated ML algorithms used as prediction models for survival, recurrence, symptom improvement, and adverse events in patients undergoing surgery for epilepsy, brain tumor, spinal lesions, neurovascular disease, movement disorders, traumatic brain injury, and hydrocephalus. Depending on the specific prediction task evaluated and the type of input features included, ML models predicted outcomes after neurosurgery with a median accuracy and area under the receiver operating curve of 94.5% and 0.83, respectively. Compared with logistic regression, ML models performed significantly better and showed a median absolute improvement in accuracy and area under the receiver operating curve of 15% and 0.06, respectively. Some studies also demonstrated a better performance in ML models compared with established prognostic indices and clinical experts. In the research setting, ML has been studied extensively, demonstrating an excellent performance in outcome prediction for a wide range of neurosurgical conditions. However, future studies should investigate how ML can be implemented as a practical tool supporting neurosurgical care. Copyright © 2017 Elsevier Inc. All rights reserved.

  1. Predictability of Brayton electric power system performance

    NASA Technical Reports Server (NTRS)

    Klann, J. L.; Hettel, H. J.

    1972-01-01

    Data from the first tests of the 2- to 15-kilowatt space power system in a vacuum chamber were compared with predictions of both a pretest analysis and a modified version of that analysis. The pretest analysis predicted test results with differences of no more than 9 percent of the largest measured value for each quantity. The modified analysis correlated measurements. Differences in conversion efficiency and power output were no greater than plus or minus 2.5 percent. This modified analysis was used to project space performance maps for the current test system.

  2. Automatic intraaortic balloon pump timing using an intrabeat dicrotic notch prediction algorithm.

    PubMed

    Schreuder, Jan J; Castiglioni, Alessandro; Donelli, Andrea; Maisano, Francesco; Jansen, Jos R C; Hanania, Ramzi; Hanlon, Pat; Bovelander, Jan; Alfieri, Ottavio

    2005-03-01

    The efficacy of intraaortic balloon counterpulsation (IABP) during arrhythmic episodes is questionable. A novel algorithm for intrabeat prediction of the dicrotic notch was used for real time IABP inflation timing control. A windkessel model algorithm was used to calculate real-time aortic flow from aortic pressure. The dicrotic notch was predicted using a percentage of calculated peak flow. Automatic inflation timing was set at intrabeat predicted dicrotic notch and was combined with automatic IAB deflation. Prophylactic IABP was applied in 27 patients with low ejection fraction (< 35%) undergoing cardiac surgery. Analysis of IABP at a 1:4 ratio revealed that IAB inflation occurred at a mean of 0.6 +/- 5 ms from the dicrotic notch. In all patients accurate automatic timing at a 1:1 assist ratio was performed. Seventeen patients had episodes of severe arrhythmia, the novel IABP inflation algorithm accurately assisted 318 of 320 arrhythmic beats at a 1:1 ratio. The novel real-time intrabeat IABP inflation timing algorithm performed accurately in all patients during both regular rhythms and severe arrhythmia, allowing fully automatic intrabeat IABP timing.

  3. Relationships between episodic memory performance prediction and sociodemographic variables among healthy older adults.

    PubMed

    de Oliveira, Glaucia Martins; Cachioni, Meire; Falcão, Deusivania; Batistoni, Samila; Lopes, Andrea; Guimarães, Vanessa; Lima-Silva, Thais Bento; Neri, Anita Liberalesso; Yassuda, Mônica Sanches

    2015-01-01

    Previous studies have suggested that performance prediction, an aspect of metamemory, may be associated with objective performance on memory tasks. The objective of the study was to describe memory prediction before performing an episodic memory task, in community-dwelling older adults, stratified by sex, age group and educational level. Additionally, the association between predicted and objective performance on a memory task was investigated. The study was based on data from 359 participants in the FIBRA study carried out at Ermelino Matarazzo, São Paulo. Memory prediction was assessed by posing the question: "If someone showed you a sheet with drawings of 10 pictures to observe for 30 seconds, how many pictures do you think you could remember without seeing the sheet?". Memory performance was assessed by the memorization of 10 black and white pictures from the Brief Cognitive Screening Battery (BCSB). No differences were found between men and women, nor for age group and educational level, in memory performance prediction before carrying out the memory task. There was a modest association (rho=0.11, p=0.041) between memory prediction and performance in immediate memory. On multivariate linear regression analyses, memory performance prediction was moderately significantly associated with immediate memory (p=0.061). In this study, sociodemographic variables did not influence memory prediction, which was only modestly associated with immediate memory on the Brief Cognitive Screening Battery (BCSB).

  4. Predictive performance of rainfall thresholds for shallow landslide triggering in Switzerland from daily gridded precipitation data

    NASA Astrophysics Data System (ADS)

    Leonarduzzi, E.; Molnar, P.; McArdell, B. W.

    2017-12-01

    In Switzerland floods are responsible for most of the damage caused by rainfall-triggered natural hazards (89%), followed by landslides (6%, almost 600 M USD) as reported in Hilker et al. (2009) for the period 1972-2007. A high-resolution gridded daily precipitation dataset is combined with a landslide inventory containing over 2000 events in the period 1972-2012 to analyze rainfall thresholds that lead to landsliding in Switzerland. First triggering rainfall and landslides are co-located obtaining the distributions of triggering and non-triggering rainfall event properties at the scale of the precipitation data (2*2 km2) and considering 1 day as the interarrival time to separate events. Then rainfall thresholds are obtained by maximizing true positives (accurate predictions) while minimizing false negatives (false alarms), using the True Skill Statistic. The best predictive performance is obtained by the intensity-duration ID threshold curve, followed by peak daily intensity (Imax) and mean event intensity (Imean). Event duration by itself has very low predictive power. In addition to country-wide thresholds, local ones are also defined by regionalization based on surface erodibility and local long-term climate (mean daily precipitation). Different Imax thresholds are determined for each of the regions separately. It is found that wetter local climate and lower erodibility lead to significantly higher rainfall thresholds required to trigger landslides. However, the improvement in model performance due to regionalization is marginal and much lower than what can be achieved by having a high quality landslide database. In order to validate the performance of the Imax rainfall threshold model, reference cases will be presented in which the landslide locations and timing are randomized and the landslide sample size is reduced. Jack-knife and cross-validation experiments demonstrate that the model is robust. The results highlight the potential of using rainfall I

  5. PSORTb 3.0: improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes.

    PubMed

    Yu, Nancy Y; Wagner, James R; Laird, Matthew R; Melli, Gabor; Rey, Sébastien; Lo, Raymond; Dao, Phuong; Sahinalp, S Cenk; Ester, Martin; Foster, Leonard J; Brinkman, Fiona S L

    2010-07-01

    PSORTb has remained the most precise bacterial protein subcellular localization (SCL) predictor since it was first made available in 2003. However, the recall needs to be improved and no accurate SCL predictors yet make predictions for archaea, nor differentiate important localization subcategories, such as proteins targeted to a host cell or bacterial hyperstructures/organelles. Such improvements should preferably be encompassed in a freely available web-based predictor that can also be used as a standalone program. We developed PSORTb version 3.0 with improved recall, higher proteome-scale prediction coverage, and new refined localization subcategories. It is the first SCL predictor specifically geared for all prokaryotes, including archaea and bacteria with atypical membrane/cell wall topologies. It features an improved standalone program, with a new batch results delivery system complementing its web interface. We evaluated the most accurate SCL predictors using 5-fold cross validation plus we performed an independent proteomics analysis, showing that PSORTb 3.0 is the most accurate but can benefit from being complemented by Proteome Analyst predictions. http://www.psort.org/psortb (download open source software or use the web interface). psort-mail@sfu.ca Supplementary data are available at Bioinformatics online.

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

    NASA Astrophysics Data System (ADS)

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

    2015-09-01

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

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

    PubMed

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

    2015-09-18

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

  8. Gender Differences in Performance Predictions: Evidence from the Cognitive Reflection Test

    PubMed Central

    Ring, Patrick; Neyse, Levent; David-Barett, Tamas; Schmidt, Ulrich

    2016-01-01

    This paper studies performance predictions in the 7-item Cognitive Reflection Test (CRT) and whether they differ by gender. After participants completed the CRT, they predicted their own (i), the other participants’ (ii), men’s (iii), and women’s (iv) number of correct answers. In keeping with existing literature, men scored higher on the CRT than women and both men and women were too optimistic about their own performance. When we compare gender-specific predictions, we observe that men think they perform significantly better than other men and do so significantly more than women. The equality between women’s predictions about their own performance and their female peers cannot be rejected. Our findings contribute to the growing literature on the underpinnings of behavior in economics and in psychology by uncovering gender differences in confidence about one’s ability relative to same and opposite sex peers. PMID:27847487

  9. Probability-based collaborative filtering model for predicting gene-disease associations.

    PubMed

    Zeng, Xiangxiang; Ding, Ningxiang; Rodríguez-Patón, Alfonso; Zou, Quan

    2017-12-28

    Accurately predicting pathogenic human genes has been challenging in recent research. Considering extensive gene-disease data verified by biological experiments, we can apply computational methods to perform accurate predictions with reduced time and expenses. We propose a probability-based collaborative filtering model (PCFM) to predict pathogenic human genes. Several kinds of data sets, containing data of humans and data of other nonhuman species, are integrated in our model. Firstly, on the basis of a typical latent factorization model, we propose model I with an average heterogeneous regularization. Secondly, we develop modified model II with personal heterogeneous regularization to enhance the accuracy of aforementioned models. In this model, vector space similarity or Pearson correlation coefficient metrics and data on related species are also used. We compared the results of PCFM with the results of four state-of-arts approaches. The results show that PCFM performs better than other advanced approaches. PCFM model can be leveraged for predictions of disease genes, especially for new human genes or diseases with no known relationships.

  10. Comparative values of medical school assessments in the prediction of internship performance.

    PubMed

    Lee, Ming; Vermillion, Michelle

    2018-02-01

    Multiple undergraduate achievements have been used for graduate admission consideration. Their relative values in the prediction of residency performance are not clear. This study compared the contributions of major undergraduate assessments to the prediction of internship performance. Internship performance ratings of the graduates of a medical school were collected from 2012 to 2015. Hierarchical multiple regression analyses were used to examine the predictive values of undergraduate measures assessing basic and clinical sciences knowledge and clinical performances, after controlling for differences in the Medical College Admission Test (MCAT). Four hundred eighty (75%) graduates' archived data were used in the study. Analyses revealed that clinical competencies, assessed by the USMLE Step 2 CK, NBME medicine exam, and an eight-station objective structured clinical examination (OSCE), were strong predictors of internship performance. Neither the USMLE Step 1 nor the inpatient internal medicine clerkship evaluation predicted internship performance. The undergraduate assessments as a whole showed a significant collective relationship with internship performance (ΔR 2  = 0.12, p < 0.001). The study supports the use of clinical competency assessments, instead of pre-clinical measures, in graduate admission consideration. It also provides validity evidence for OSCE scores in the prediction of workplace performance.

  11. Accurate experimental and theoretical comparisons between superconductor-insulator-superconductor mixers showing weak and strong quantum effects

    NASA Technical Reports Server (NTRS)

    Mcgrath, W. R.; Richards, P. L.; Face, D. W.; Prober, D. E.; Lloyd, F. L.

    1988-01-01

    A systematic study of the gain and noise in superconductor-insulator-superconductor mixers employing Ta based, Nb based, and Pb-alloy based tunnel junctions was made. These junctions displayed both weak and strong quantum effects at a signal frequency of 33 GHz. The effects of energy gap sharpness and subgap current were investigated and are quantitatively related to mixer performance. Detailed comparisons are made of the mixing results with the predictions of a three-port model approximation to the Tucker theory. Mixer performance was measured with a novel test apparatus which is accurate enough to allow for the first quantitative tests of theoretical noise predictions. It is found that the three-port model of the Tucker theory underestimates the mixer noise temperature by a factor of about 2 for all of the mixers. In addition, predicted values of available mixer gain are in reasonable agreement with experiment when quantum effects are weak. However, as quantum effects become strong, the predicted available gain diverges to infinity, which is in sharp contrast to the experimental results. Predictions of coupled gain do not always show such divergences.

  12. Player's success prediction in rugby union: From youth performance to senior level placing.

    PubMed

    Fontana, Federico Y; Colosio, Alessandro L; Da Lozzo, Giorgio; Pogliaghi, Silvia

    2017-04-01

    The study questioned if and to what extent specific anthropometric and functional characteristics measured in youth draft camps, can accurately predict subsequent career progression in rugby union. Original research. Anthropometric and functional characteristics of 531 male players (U16) were retrospectively analysed in relation to senior level team representation at age 21-24. Players were classified as International (Int: National team and international clubs) or National (Nat: 1st, 2nd and other divisions and dropout). Multivariate analysis of variance (one-way MANOVA) tested differences between Int and Nat, along a combination of anthropometric (body mass, height, body fat, fat-free mass) and functional variables (SJ, CMJ, t 15m , t 30m , VO 2max ). A discriminant function (DF) was determined to predict group assignment based on the linear combination of variables that best discriminate groups. Correct level assignment was expressed as % hit rate. A combination of anthropometric and functional characteristics reflects future level assignment (Int vs. Nat). Players' success can be accurately predicted (hit rate=81% and 77% for Int and Nat respectively) by a DF that combines anthropometric and functional variables as measured at ∼15 years of age, percent body fat and speed being the most influential predictors of group stratification. Within a group of 15 year-olds with exceptional physical characteristics, future players' success can be predicted using a linear combination of anthropometric and functional variables, among which a lower percent body fat and higher speed over a 15m sprint provide the most important predictors of the highest career success. Copyright © 2016 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.

  13. Predictive genomics DNA profiling for athletic performance.

    PubMed

    Kambouris, Marios; Ntalouka, Foteini; Ziogas, Georgios; Maffulli, Nicola

    2012-12-01

    Genes control biological processes such as muscle, cartilage and bone formation, muscle energy production and metabolism (mitochondriogenesis, lactic acid removal), blood and tissue oxygenation (erythropoiesis, angiogenesis, vasodilatation), all essential in sport and athletic performance. DNA sequence variations in such genes confer genetic advantages that can be exploited, or genetic 'barriers' that could be overcome to achieve optimal athletic performance. Predictive Genomic DNA Profiling for athletic performance reveals genetic variations that may be associated with better suitability for endurance, strength and speed sports, vulnerability to sports-related injuries and individualized nutritional requirements. Knowledge of genetic 'suitability' in respect to endurance capacity or strength and speed would lead to appropriate sport and athletic activity selection. Knowledge of genetic advantages and barriers would 'direct' an individualized training program, nutritional plan and nutritional supplementation to achieving optimal performance, overcoming 'barriers' that results from intense exercise and pressure under competition with minimum waste of time and energy and avoidance of health risks (hypertension, cardiovascular disease, inflammation, and musculoskeletal injuries) related to exercise, training and competition. Predictive Genomics DNA profiling for Athletics and Sports performance is developing into a tool for athletic activity and sport selection and for the formulation of individualized and personalized training and nutritional programs to optimize health and performance for the athlete. Human DNA sequences are patentable in some countries, while in others DNA testing methodologies [unless proprietary], are non patentable. On the other hand, gene and variant selection, genotype interpretation and the risk and suitability assigning algorithms based on the specific Genomic variants used are amenable to patent protection.

  14. Why Do Spatial Abilities Predict Mathematical Performance?

    ERIC Educational Resources Information Center

    Tosto, Maria Grazia; Hanscombe, Ken B.; Haworth, Claire M. A.; Davis, Oliver S. P.; Petrill, Stephen A.; Dale, Philip S.; Malykh, Sergey; Plomin, Robert; Kovas, Yulia

    2014-01-01

    Spatial ability predicts performance in mathematics and eventual expertise in science, technology and engineering. Spatial skills have also been shown to rely on neuronal networks partially shared with mathematics. Understanding the nature of this association can inform educational practices and intervention for mathematical underperformance.…

  15. Predicting the performance of fingerprint similarity searching.

    PubMed

    Vogt, Martin; Bajorath, Jürgen

    2011-01-01

    Fingerprints are bit string representations of molecular structure that typically encode structural fragments, topological features, or pharmacophore patterns. Various fingerprint designs are utilized in virtual screening and their search performance essentially depends on three parameters: the nature of the fingerprint, the active compounds serving as reference molecules, and the composition of the screening database. It is of considerable interest and practical relevance to predict the performance of fingerprint similarity searching. A quantitative assessment of the potential that a fingerprint search might successfully retrieve active compounds, if available in the screening database, would substantially help to select the type of fingerprint most suitable for a given search problem. The method presented herein utilizes concepts from information theory to relate the fingerprint feature distributions of reference compounds to screening libraries. If these feature distributions do not sufficiently differ, active database compounds that are similar to reference molecules cannot be retrieved because they disappear in the "background." By quantifying the difference in feature distribution using the Kullback-Leibler divergence and relating the divergence to compound recovery rates obtained for different benchmark classes, fingerprint search performance can be quantitatively predicted.

  16. Children's biological responsivity to acute stress predicts concurrent cognitive performance.

    PubMed

    Roos, Leslie E; Beauchamp, Kathryn G; Giuliano, Ryan; Zalewski, Maureen; Kim, Hyoun K; Fisher, Philip A

    2018-04-10

    Although prior research has characterized stress system reactivity (i.e. hypothalamic-pituitary-adrenal axis, HPAA; autonomic nervous system, ANS) in children, it has yet to examine the extent to which biological reactivity predicts concurrent goal-directed behavior. Here, we employed a stressor paradigm that allowed concurrent assessment of both stress system reactivity and performance on a speeded-response task to investigate the links between biological reactivity and cognitive function under stress. We further investigated gender as a moderator given previous research suggesting that the ANS may be particularly predictive of behavior in males due to gender differences in socialization. In a sociodemographically diverse sample of young children (N = 58, M age = 5.38 yrs; 44% male), individual differences in sociodemographic covariates (age, household income), HPAA (i.e. cortisol), and ANS (i.e. respiratory sinus arrhythmia, RSA, indexing the parasympathetic branch; pre-ejection period, PEP, indexing the sympathetic branch) function were assessed as predictors of cognitive performance under stress. We hypothesized that higher income, older age, and greater cortisol reactivity would be associated with better performance overall, and flexible ANS responsivity (i.e. RSA withdrawal, PEP shortening) would be predictive of performance for males. Overall, females performed better than males. Two-group SEM analyses suggest that, for males, greater RSA withdrawal to the stressor was associated with better performance, while for females, older age, higher income, and greater cortisol reactivity were associated with better performance. Results highlight the relevance of stress system reactivity to cognitive performance under stress. Future research is needed to further elucidate for whom and in what situations biological reactivity predicts goal-directed behavior.

  17. Predicting driving performance in older adults: we are not there yet!

    PubMed

    Bédard, Michel; Weaver, Bruce; Darzins, Peteris; Porter, Michelle M

    2008-08-01

    We set up this study to determine the predictive value of approaches for which a statistical association with driving performance has been documented. We determined the statistical association (magnitude of association and probability of occurrence by chance alone) between four different predictors (the Mini-Mental State Examination, Trails A test, Useful Field of View [UFOV], and a composite measure of past driving incidents) and driving performance. We then explored the predictive value of these measures with receiver operating characteristic (ROC) curves and various cutoff values. We identified associations between the predictors and driving performance well beyond the play of chance (p < .01). Nonetheless, the predictors had limited predictive value with areas under the curve ranging from .51 to .82. Statistical associations are not sufficient to infer adequate predictive value, especially when crucial decisions such as whether one can continue driving are at stake. The predictors we examined have limited predictive value if used as stand-alone screening tests.

  18. Does the MCAT predict medical school and PGY-1 performance?

    PubMed

    Saguil, Aaron; Dong, Ting; Gingerich, Robert J; Swygert, Kimberly; LaRochelle, Jeffrey S; Artino, Anthony R; Cruess, David F; Durning, Steven J

    2015-04-01

    The Medical College Admissions Test (MCAT) is a high-stakes test required for entry to most U. S. medical schools; admissions committees use this test to predict future accomplishment. Although there is evidence that the MCAT predicts success on multiple choice-based assessments, there is little information on whether the MCAT predicts clinical-based assessments of undergraduate and graduate medical education performance. This study looked at associations between the MCAT and medical school grade point average (GPA), Medical Licensing Examination (USMLE) scores, observed patient care encounters, and residency performance assessments. This study used data collected as part of the Long-Term Career Outcome Study to determine associations between MCAT scores, USMLE Step 1, Step 2 clinical knowledge and clinical skill, and Step 3 scores, Objective Structured Clinical Examination performance, medical school GPA, and PGY-1 program director (PD) assessment of physician performance for students graduating 2010 and 2011. MCAT data were available for all students, and the PGY PD evaluation response rate was 86.2% (N = 340). All permutations of MCAT scores (first, last, highest, average) were weakly associated with GPA, Step 2 clinical knowledge scores, and Step 3 scores. MCAT scores were weakly to moderately associated with Step 1 scores. MCAT scores were not significantly associated with Step 2 clinical skills Integrated Clinical Encounter and Communication and Interpersonal Skills subscores, Objective Structured Clinical Examination performance or PGY-1 PD evaluations. MCAT scores were weakly to moderately associated with assessments that rely on multiple choice testing. The association is somewhat stronger for assessments occurring earlier in medical school, such as USMLE Step 1. The MCAT was not able to predict assessments relying on direct clinical observation, nor was it able to predict PD assessment of PGY-1 performance. Reprint & Copyright © 2015 Association of

  19. Performance Prediction Toolkit

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

    Chennupati, Gopinath; Santhi, Nanadakishore; Eidenbenz, Stephen

    The Performance Prediction Toolkit (PPT), is a scalable co-design tool that contains the hardware and middle-ware models, which accept proxy applications as input in runtime prediction. PPT relies on Simian, a parallel discrete event simulation engine in Python or Lua, that uses the process concept, where each computing unit (host, node, core) is a Simian entity. Processes perform their task through message exchanges to remain active, sleep, wake-up, begin and end. The PPT hardware model of a compute core (such as a Haswell core) consists of a set of parameters, such as clock speed, memory hierarchy levels, their respective sizes,more » cache-lines, access times for different cache levels, average cycle counts of ALU operations, etc. These parameters are ideally read off a spec sheet or are learned using regression models learned from hardware counters (PAPI) data. The compute core model offers an API to the software model, a function called time_compute(), which takes as input a tasklist. A tasklist is an unordered set of ALU, and other CPU-type operations (in particular virtual memory loads and stores). The PPT application model mimics the loop structure of the application and replaces the computational kernels with a call to the hardware model's time_compute() function giving tasklists as input that model the compute kernel. A PPT application model thus consists of tasklists representing kernels and the high-er level loop structure that we like to think of as pseudo code. The key challenge for the hardware model's time_compute-function is to translate virtual memory accesses into actual cache hierarchy level hits and misses.PPT also contains another CPU core level hardware model, Analytical Memory Model (AMM). The AMM solves this challenge soundly, where our previous alternatives explicitly include the L1,L2,L3 hit-rates as inputs to the tasklists. Explicit hit-rates inevitably only reflect the application modeler's best guess, perhaps informed by a few small

  20. Physics-based model for predicting the performance of a miniature wind turbine

    NASA Astrophysics Data System (ADS)

    Xu, F. J.; Hu, J. Z.; Qiu, Y. P.; Yuan, F. G.

    2011-04-01

    A comprehensive physics-based model for predicting the performance of the miniature wind turbine (MWT) for power wireless sensor systems was proposed in this paper. An approximation of the power coefficient of the turbine rotor was made after the turbine rotor performance was measured. Incorporation of the approximation with the equivalent circuit model which was proposed according to the principles of the MWT, the overall system performance of the MWT was predicted. To demonstrate the prediction, the MWT system comprised of a 7.6 cm thorgren plastic propeller as turbine rotor and a DC motor as generator was designed and its performance was tested experimentally. The predicted output voltage, power and system efficiency are matched well with the tested results, which imply that this study holds promise in estimating and optimizing the performance of the MWT.

  1. An accurate evaluation of the performance of asynchronous DS-CDMA systems with zero-correlation-zone coding in Rayleigh fading

    NASA Astrophysics Data System (ADS)

    Walker, Ernest; Chen, Xinjia; Cooper, Reginald L.

    2010-04-01

    An arbitrarily accurate approach is used to determine the bit-error rate (BER) performance for generalized asynchronous DS-CDMA systems, in Gaussian noise with Raleigh fading. In this paper, and the sequel, new theoretical work has been contributed which substantially enhances existing performance analysis formulations. Major contributions include: substantial computational complexity reduction, including a priori BER accuracy bounding; an analytical approach that facilitates performance evaluation for systems with arbitrary spectral spreading distributions, with non-uniform transmission delay distributions. Using prior results, augmented by these enhancements, a generalized DS-CDMA system model is constructed and used to evaluated the BER performance, in a variety of scenarios. In this paper, the generalized system modeling was used to evaluate the performance of both Walsh- Hadamard (WH) and Walsh-Hadamard-seeded zero-correlation-zone (WH-ZCZ) coding. The selection of these codes was informed by the observation that WH codes contain N spectral spreading values (0 to N - 1), one for each code sequence; while WH-ZCZ codes contain only two spectral spreading values (N/2 - 1,N/2); where N is the sequence length in chips. Since these codes span the spectral spreading range for DS-CDMA coding, by invoking an induction argument, the generalization of the system model is sufficiently supported. The results in this paper, and the sequel, support the claim that an arbitrary accurate performance analysis for DS-CDMA systems can be evaluated over the full range of binary coding, with minimal computational complexity.

  2. Predicting the Performance of an Axial-Flow Compressor

    NASA Technical Reports Server (NTRS)

    Steinke, R. J.

    1986-01-01

    Stage-stacking computer code (STGSTK) developed for predicting off-design performance of multi-stage axial-flow compressors. Code uses meanline stagestacking method. Stage and cumulative compressor performance calculated from representative meanline velocity diagrams located at rotor inlet and outlet meanline radii. Numerous options available within code. Code developed so user modify correlations to suit their needs.

  3. Academic performance, career potential, creativity, and job performance: can one construct predict them all?

    PubMed

    Kuncel, Nathan R; Hezlett, Sarah A; Ones, Deniz S

    2004-01-01

    This meta-analysis addresses the question of whether 1 general cognitive ability measure developed for predicting academic performance is valid for predicting performance in both educational and work domains. The validity of the Miller Analogies Test (MAT; W. S. Miller, 1960) for predicting 18 academic and work-related criteria was examined. MAT correlations with other cognitive tests (e.g., Raven's Matrices [J. C. Raven, 1965]; Graduate Record Examinations) also were meta-analyzed. The results indicate that the abilities measured by the MAT are shared with other cognitive ability instruments and that these abilities are generalizably valid predictors of academic and vocational criteria, as well as evaluations of career potential and creativity. These findings contradict the notion that intelligence at work is wholly different from intelligence at school, extending the voluminous literature that supports the broad importance of general cognitive ability (g).

  4. Performance predictions for an SSME configuration with an enlarged throat

    NASA Technical Reports Server (NTRS)

    Nickerson, G. R.; Dang, L. D.

    1985-01-01

    The Two Dimensional Kinetics (TDK) computer program that was recently developed for NASA was used to predict the performance of a Large Throat Configuration of the Space Shuttle Main Engine (SSME). Calculations indicate that the current design SSME contains a shock wave that is induced by the nozzle wall shape. In the Large Throat design an even stronger shock wave is predicted. Because of the presence of this shock wave, earlier performance predictions that have neglected shock wave effects have been questioned. The JANNAF thrust chamber performance prediction procedures given in a reference were applied. The analysis includes the effects of two dimensional reacting flow with a shock wave. The effects of the boundary layer with a regenatively cooled wall are also included. A Purdue computer program was used to compute axially symmetric supersonic nozzle flows with an induced shock, but is restricted to flows with a constant ratio of specific heats. Thus, the TDK program was also run with ths assumption and the results of the two programs were compared.

  5. Workflows and performances in the ranking prediction of 2016 D3R Grand Challenge 2: lessons learned from a collaborative effort.

    PubMed

    Gao, Ying-Duo; Hu, Yuan; Crespo, Alejandro; Wang, Deping; Armacost, Kira A; Fells, James I; Fradera, Xavier; Wang, Hongwu; Wang, Huijun; Sherborne, Brad; Verras, Andreas; Peng, Zhengwei

    2018-01-01

    The 2016 D3R Grand Challenge 2 includes both pose and affinity or ranking predictions. This article is focused exclusively on affinity predictions submitted to the D3R challenge from a collaborative effort of the modeling and informatics group. Our submissions include ranking of 102 ligands covering 4 different chemotypes against the FXR ligand binding domain structure, and the relative binding affinity predictions of the two designated free energy subsets of 15 and 18 compounds. Using all the complex structures prepared in the same way allowed us to cover many types of workflows and compare their performances effectively. We evaluated typical workflows used in our daily structure-based design modeling support, which include docking scores, force field-based scores, QM/MM, MMGBSA, MD-MMGBSA, and MacroModel interaction energy estimations. The best performing methods for the two free energy subsets are discussed. Our results suggest that affinity ranking still remains very challenging; that the knowledge of more structural information does not necessarily yield more accurate predictions; and that visual inspection and human intervention are considerably important for ranking. Knowledge of the mode of action and protein flexibility along with visualization tools that depict polar and hydrophobic maps are very useful for visual inspection. QM/MM-based workflows were found to be powerful in affinity ranking and are encouraged to be applied more often. The standardized input and output enable systematic analysis and support methodology development and improvement for high level blinded predictions.

  6. Workflows and performances in the ranking prediction of 2016 D3R Grand Challenge 2: lessons learned from a collaborative effort

    NASA Astrophysics Data System (ADS)

    Gao, Ying-Duo; Hu, Yuan; Crespo, Alejandro; Wang, Deping; Armacost, Kira A.; Fells, James I.; Fradera, Xavier; Wang, Hongwu; Wang, Huijun; Sherborne, Brad; Verras, Andreas; Peng, Zhengwei

    2018-01-01

    The 2016 D3R Grand Challenge 2 includes both pose and affinity or ranking predictions. This article is focused exclusively on affinity predictions submitted to the D3R challenge from a collaborative effort of the modeling and informatics group. Our submissions include ranking of 102 ligands covering 4 different chemotypes against the FXR ligand binding domain structure, and the relative binding affinity predictions of the two designated free energy subsets of 15 and 18 compounds. Using all the complex structures prepared in the same way allowed us to cover many types of workflows and compare their performances effectively. We evaluated typical workflows used in our daily structure-based design modeling support, which include docking scores, force field-based scores, QM/MM, MMGBSA, MD-MMGBSA, and MacroModel interaction energy estimations. The best performing methods for the two free energy subsets are discussed. Our results suggest that affinity ranking still remains very challenging; that the knowledge of more structural information does not necessarily yield more accurate predictions; and that visual inspection and human intervention are considerably important for ranking. Knowledge of the mode of action and protein flexibility along with visualization tools that depict polar and hydrophobic maps are very useful for visual inspection. QM/MM-based workflows were found to be powerful in affinity ranking and are encouraged to be applied more often. The standardized input and output enable systematic analysis and support methodology development and improvement for high level blinded predictions.

  7. Predictive Validity of National Basketball Association Draft Combine on Future Performance.

    PubMed

    Teramoto, Masaru; Cross, Chad L; Rieger, Randall H; Maak, Travis G; Willick, Stuart E

    2018-02-01

    Teramoto, M, Cross, CL, Rieger, RH, Maak, TG, and Willick, SE. Predictive validity of national basketball association draft combine on future performance. J Strength Cond Res 32(2): 396-408, 2018-The National Basketball Association (NBA) Draft Combine is an annual event where prospective players are evaluated in terms of their athletic abilities and basketball skills. Data collected at the Combine should help NBA teams select right the players for the upcoming NBA draft; however, its value for predicting future performance of players has not been examined. This study investigated predictive validity of the NBA Draft Combine on future performance of basketball players. We performed a principal component analysis (PCA) on the 2010-2015 Combine data to reduce correlated variables (N = 234), a correlation analysis on the Combine data and future on-court performance to examine relationships (maximum pairwise N = 217), and a robust principal component regression (PCR) analysis to predict first-year and 3-year on-court performance from the Combine measures (N = 148 and 127, respectively). Three components were identified within the Combine data through PCA (= Combine subscales): length-size, power-quickness, and upper-body strength. As per the correlation analysis, the individual Combine items for anthropometrics, including height without shoes, standing reach, weight, wingspan, and hand length, as well as the Combine subscale of length-size, had positive, medium-to-large-sized correlations (r = 0.313-0.545) with defensive performance quantified by Defensive Box Plus/Minus. The robust PCR analysis showed that the Combine subscale of length-size was a predictor most significantly associated with future on-court performance (p ≤ 0.05), including Win Shares, Box Plus/Minus, and Value Over Replacement Player, followed by upper-body strength. In conclusion, the NBA Draft Combine has value for predicting future performance of players.

  8. Predicting Student Academic Performance in an Engineering Dynamics Course: A Comparison of Four Types of Predictive Mathematical Models

    ERIC Educational Resources Information Center

    Huang, Shaobo; Fang, Ning

    2013-01-01

    Predicting student academic performance has long been an important research topic in many academic disciplines. The present study is the first study that develops and compares four types of mathematical models to predict student academic performance in engineering dynamics--a high-enrollment, high-impact, and core course that many engineering…

  9. Metamemory monitoring in mild cognitive impairment: Evidence of a less accurate episodic feeling-of-knowing.

    PubMed

    Perrotin, Audrey; Belleville, Sylvie; Isingrini, Michel

    2007-09-20

    This study aimed at exploring metamemory and specifically the accuracy of memory monitoring in mild cognitive impairment (MCI) using an episodic memory feeling-of-knowing (FOK) procedure. To this end, 20 people with MCI and 20 matched control participants were compared on the episodic FOK task. Results showed that the MCI group made less accurate FOK predictions than the control group by overestimating their memory performance on a recognition task. The MCI overestimation behavior was found to be critically related to the severity of their cognitive decline. In the light of recent neuroanatomical models showing the involvement of a temporal-frontal network underlying accurate FOK predictions, the role of memory and executive processes was evaluated. Thus, participants were also administered memory and executive neuropsychological tests. Correlation analysis revealed a between-group differential pattern indicating that FOK accuracy was primarily related to memory abilities in people with MCI, whereas it was specifically related to executive functioning in control participants. The lesser ability of people with MCI to assess their memory status accurately on an episodic FOK task is discussed in relation to both their subjective memory complaints and to their actual memory deficits which might be mediated by the brain vulnerability of their hippocampus and medial temporal system. It is suggested that their memory weakness may lead people with MCI to use other less reliable forms of memory monitoring.

  10. Point of Care Ultrasound Accurately Distinguishes Inflammatory from Noninflammatory Disease in Patients Presenting with Abdominal Pain and Diarrhea

    PubMed Central

    Novak, Kerri L.; Jacob, Deepti; Kaplan, Gilaad G.; Boyce, Emma; Ghosh, Subrata; Ma, Irene; Lu, Cathy; Wilson, Stephanie; Panaccione, Remo

    2016-01-01

    Background. Approaches to distinguish inflammatory bowel disease (IBD) from noninflammatory disease that are noninvasive, accurate, and readily available are desirable. Such approaches may decrease time to diagnosis and better utilize limited endoscopic resources. The aim of this study was to evaluate the diagnostic accuracy for gastroenterologist performed point of care ultrasound (POCUS) in the detection of luminal inflammation relative to gold standard ileocolonoscopy. Methods. A prospective, single-center study was conducted on convenience sample of patients presenting with symptoms of diarrhea and/or abdominal pain. Patients were offered POCUS prior to having ileocolonoscopy. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) with 95% confidence intervals (CI), as well as likelihood ratios, were calculated. Results. Fifty-eight patients were included in this study. The overall sensitivity, specificity, PPV, and NPV were 80%, 97.8%, 88.9%, and 95.7%, respectively, with positive and negative likelihood ratios (LR) of 36.8 and 0.20. Conclusion. POCUS can accurately be performed at the bedside to detect transmural inflammation of the intestine. This noninvasive approach may serve to expedite diagnosis, improve allocation of endoscopic resources, and facilitate initiation of appropriate medical therapy. PMID:27446838

  11. Comparison of measured efficiencies of nine turbine designs with efficiencies predicted by two empirical methods

    NASA Technical Reports Server (NTRS)

    English, Robert E; Cavicchi, Richard H

    1951-01-01

    Empirical methods of Ainley and Kochendorfer and Nettles were used to predict performances of nine turbine designs. Measured and predicted performances were compared. Appropriate values of blade-loss parameter were determined for the method of Kochendorfer and Nettles. The measured design-point efficiencies were lower than predicted by as much as 0.09 (Ainley and 0.07 (Kochendorfer and Nettles). For the method of Kochendorfer and Nettles, appropriate values of blade-loss parameter ranged from 0.63 to 0.87 and the off-design performance was accurately predicted.

  12. Stata Modules for Calculating Novel Predictive Performance Indices for Logistic Models

    PubMed Central

    Barkhordari, Mahnaz; Padyab, Mojgan; Hadaegh, Farzad; Azizi, Fereidoun; Bozorgmanesh, Mohammadreza

    2016-01-01

    Background Prediction is a fundamental part of prevention of cardiovascular diseases (CVD). The development of prediction algorithms based on the multivariate regression models loomed several decades ago. Parallel with predictive models development, biomarker researches emerged in an impressively great scale. The key question is how best to assess and quantify the improvement in risk prediction offered by new biomarkers or more basically how to assess the performance of a risk prediction model. Discrimination, calibration, and added predictive value have been recently suggested to be used while comparing the predictive performances of the predictive models’ with and without novel biomarkers. Objectives Lack of user-friendly statistical software has restricted implementation of novel model assessment methods while examining novel biomarkers. We intended, thus, to develop a user-friendly software that could be used by researchers with few programming skills. Materials and Methods We have written a Stata command that is intended to help researchers obtain cut point-free and cut point-based net reclassification improvement index and (NRI) and relative and absolute Integrated discriminatory improvement index (IDI) for logistic-based regression analyses.We applied the commands to a real data on women participating the Tehran lipid and glucose study (TLGS) to examine if information of a family history of premature CVD, waist circumference, and fasting plasma glucose can improve predictive performance of the Framingham’s “general CVD risk” algorithm. Results The command is addpred for logistic regression models. Conclusions The Stata package provided herein can encourage the use of novel methods in examining predictive capacity of ever-emerging plethora of novel biomarkers. PMID:27279830

  13. Stata Modules for Calculating Novel Predictive Performance Indices for Logistic Models.

    PubMed

    Barkhordari, Mahnaz; Padyab, Mojgan; Hadaegh, Farzad; Azizi, Fereidoun; Bozorgmanesh, Mohammadreza

    2016-01-01

    Prediction is a fundamental part of prevention of cardiovascular diseases (CVD). The development of prediction algorithms based on the multivariate regression models loomed several decades ago. Parallel with predictive models development, biomarker researches emerged in an impressively great scale. The key question is how best to assess and quantify the improvement in risk prediction offered by new biomarkers or more basically how to assess the performance of a risk prediction model. Discrimination, calibration, and added predictive value have been recently suggested to be used while comparing the predictive performances of the predictive models' with and without novel biomarkers. Lack of user-friendly statistical software has restricted implementation of novel model assessment methods while examining novel biomarkers. We intended, thus, to develop a user-friendly software that could be used by researchers with few programming skills. We have written a Stata command that is intended to help researchers obtain cut point-free and cut point-based net reclassification improvement index and (NRI) and relative and absolute Integrated discriminatory improvement index (IDI) for logistic-based regression analyses.We applied the commands to a real data on women participating the Tehran lipid and glucose study (TLGS) to examine if information of a family history of premature CVD, waist circumference, and fasting plasma glucose can improve predictive performance of the Framingham's "general CVD risk" algorithm. The command is addpred for logistic regression models. The Stata package provided herein can encourage the use of novel methods in examining predictive capacity of ever-emerging plethora of novel biomarkers.

  14. Accurate density functional prediction of molecular electron affinity with the scaling corrected Kohn–Sham frontier orbital energies

    NASA Astrophysics Data System (ADS)

    Zhang, DaDi; Yang, Xiaolong; Zheng, Xiao; Yang, Weitao

    2018-04-01

    Electron affinity (EA) is the energy released when an additional electron is attached to an atom or a molecule. EA is a fundamental thermochemical property, and it is closely pertinent to other important properties such as electronegativity and hardness. However, accurate prediction of EA is difficult with density functional theory methods. The somewhat large error of the calculated EAs originates mainly from the intrinsic delocalisation error associated with the approximate exchange-correlation functional. In this work, we employ a previously developed non-empirical global scaling correction approach, which explicitly imposes the Perdew-Parr-Levy-Balduz condition to the approximate functional, and achieve a substantially improved accuracy for the calculated EAs. In our approach, the EA is given by the scaling corrected Kohn-Sham lowest unoccupied molecular orbital energy of the neutral molecule, without the need to carry out the self-consistent-field calculation for the anion.

  15. Recent progress towards predicting aircraft ground handling performance

    NASA Technical Reports Server (NTRS)

    Yager, T. J.; White, E. J.

    1981-01-01

    Capability implemented in simulating aircraft ground handling performance is reviewed and areas for further expansion and improvement are identified. Problems associated with providing necessary simulator input data for adequate modeling of aircraft tire/runway friction behavior are discussed and efforts to improve tire/runway friction definition, and simulator fidelity are described. Aircraft braking performance data obtained on several wet runway surfaces are compared to ground vehicle friction measurements. Research to improve methods of predicting tire friction performance are discussed.

  16. Absolute Hounsfield unit measurement on noncontrast computed tomography cannot accurately predict struvite stone composition.

    PubMed

    Marchini, Giovanni Scala; Gebreselassie, Surafel; Liu, Xiaobo; Pynadath, Cindy; Snyder, Grace; Monga, Manoj

    2013-02-01

    The purpose of our study was to determine, in vivo, whether single-energy noncontrast computed tomography (NCCT) can accurately predict the presence/percentage of struvite stone composition. We retrospectively searched for all patients with struvite components on stone composition analysis between January 2008 and March 2012. Inclusion criteria were NCCT prior to stone analysis and stone size ≥4 mm. A single urologist, blinded to stone composition, reviewed all NCCT to acquire stone location, dimensions, and Hounsfield unit (HU). HU density (HUD) was calculated by dividing mean HU by the stone's largest transverse diameter. Stone analysis was performed via Fourier transform infrared spectrometry. Independent sample Student's t-test and analysis of variance (ANOVA) were used to compare HU/HUD among groups. Spearman's correlation test was used to determine the correlation between HU and stone size and also HU/HUD to % of each component within the stone. Significance was considered if p<0.05. Fourty-four patients met the inclusion criteria. Struvite was the most prevalent component with mean percentage of 50.1%±17.7%. Mean HU and HUD were 820.2±357.9 and 67.5±54.9, respectively. Struvite component analysis revealed a nonsignificant positive correlation with HU (R=0.017; p=0.912) and negative with HUD (R=-0.20; p=0.898). Overall, 3 (6.8%) had <20% of struvite component; 11 (25%), 25 (56.8%), and 5 (11.4%) had 21% to 40%, 41% to 60%, and 61% to 80% of struvite, respectively. ANOVA revealed no difference among groups regarding HU (p=0.68) and HUD (p=0.37), with important overlaps. When comparing pure struvite stones (n=5) with other miscellaneous stones (n=39), no difference was found for HU (p=0.09) but HUD was significantly lower for pure stones (27.9±23.6 v 72.5±55.9, respectively; p=0.006). Again, significant overlaps were seen. Pure struvite stones have significantly lower HUD than mixed struvite stones, but overlap exists. A low HUD may increase the

  17. Predictive value of background experiences and visual spatial ability testing on laparoscopic baseline performance among residents entering postgraduate surgical training.

    PubMed

    Louridas, Marisa; Quinn, Lauren E; Grantcharov, Teodor P

    2016-03-01

    Emerging evidence suggests that despite dedicated practice, not all surgical trainees have the ability to reach technical competency in minimally invasive techniques. While selecting residents that have the ability to reach technical competence is important, evidence to guide the incorporation of technical ability into selection processes is limited. Therefore, the purpose of the present study was to evaluate whether background experiences and 2D-3D visual spatial test results are predictive of baseline laparoscopic skill for the novice surgical trainee. First-year residents were studied. Demographic data and background surgical and non-surgical experiences were obtained using a questionnaire. Visual spatial ability was evaluated using the PicSOr, cube comparison (CC) and card rotation (CR) tests. Technical skill was assessed using the camera navigation (LCN) task and laparoscopic circle cut (LCC) task. Resident performance on these technical tasks was compared and correlated with the questionnaire and visual spatial findings. Previous experience in observing laparoscopic procedures was associated with significantly better LCN performance, and experience in navigating the laparoscopic camera was associated with significantly better LCC task results. Residents who scored higher on the CC test demonstrated a more accurate LCN path length score (r s(PL) = -0.36, p = 0.03) and angle path (r s(AP) = -0.426, p = 0.01) score when completing the LCN task. No other significant correlations were found between the visual spatial tests (PicSOr, CC or CR) and LCC performance. While identifying selection tests for incoming surgical trainees that predict technical skill performance is appealing, the surrogate markers evaluated correlate with specific metrics of surgical performance related to a single task but do not appear to reliably predict technical performance of different laparoscopic tasks. Predicting the acquisition of technical skills will require the development

  18. Do Maximal Roller Skiing Speed and Double Poling Performance Predict Youth Cross-Country Skiing Performance?

    PubMed Central

    Stöggl, Roland; Müller, Erich; Stöggl, Thomas

    2017-01-01

    The aims of the current study were to analyze whether specific roller skiing tests and cycle length are determinants of youth cross-country (XC) skiing performance, and to evaluate sex specific differences by applying non-invasive diagnostics. Forty-nine young XC skiers (33 boys; 13.8 ± 0.6 yrs and 16 girls; 13.4 ± 0.9 yrs) performed roller skiing tests consisting of both shorter (50 m) and longer durations (575 m). Test results were correlated with on snow XC skiing performance (PXC) based on 3 skating and 3 classical distance competitions (3 to 6 km). The main findings of the current study were: 1) Anthropometrics and maturity status were related to boys’, but not to girls’ PXC; 2) Significant moderate to acceptable correlations between girls’ and boys’ short duration maximal roller skiing speed (double poling, V2 skating, leg skating) and PXC were found; 3) Boys’ PXC was best predicted by double poling test performance on flat and uphill, while girls’ performance was mainly predicted by uphill double poling test performance; 4) When controlling for maturity offset, boys’ PXC was still highly associated with the roller skiing tests. The use of simple non-invasive roller skiing tests for determination of PXC represents practicable support for ski clubs, schools or skiing federations in the guidance and evaluation of young talent. Key points Double poling tests on flat and uphill terrain and short duration maximal speed tests were the highest cross-country skiing predicting factors in girls and boys. Only in the boys there was an effect of maturation on the performance outcomes, pointing out that girls seem to be almost fully matured at the age of 13 in contrast to the boys. Roller skiing tests over short distance (50-m) and longer distance 225 m and 350 m are stable and valid measures and suitable for performance prediction in youth cross-country skiers. PMID:28912656

  19. Performance evaluation of NCEP climate forecast system for the prediction of winter temperatures over India

    NASA Astrophysics Data System (ADS)

    Nageswararao, M. M.; Mohanty, U. C.; Kiran Prasad, S.; Osuri, Krishna K.; Ramakrishna, S. S. V. S.

    2016-11-01

    The surface air temperature during the winter season (December-February) in India adversely affects agriculture as well as day-to-day life. Therefore, the accurate prediction of winter temperature in extended range is of utmost importance. The National Center for Environmental Prediction (NCEP) has been providing climatic variables from the fully coupled global climate model, known as Climate Forecast System version 1 (CFSv1) on monthly to seasonal scale since 2004, and it has been upgraded to CFSv2 subsequently in 2011. In the present study, the performance of CFSv1 and CFSv2 in simulating the winter 2 m maximum, minimum, and mean temperatures ( T max, T min, and T mean, respectively) over India is evaluated with respect to India Meteorological Department (IMD) 1° × 1° observations. The hindcast data obtained from both versions of CFS from 1982 to 2009 (27 years) with November initial conditions (lead-1) are used. The analyses of winter ( T max, T min, and T mean) temperatures revealed that CFSv1 and CFSv2 are able to replicate the patterns of observed climatology, interannual variability, and coefficient of variation with a slight negative bias. Of the two, CFSv2 is appreciable in capturing increasing trends of winter temperatures like observed. The T max, T min, and T mean correlations from CFSv2 is significantly high (0.35, 0.53, and 0.51, respectively), while CFSv1 correlations are less (0.29, 0.15, and 0.12) and insignificant. This performance of CFSv2 may be due to the better estimation of surface heat budget terms and realistic CO2 concentration, which were absent in CFSv1. CFSv2 proved to have a high probability of detection in predicting different categories (below, near, and above normal) for winter T min, which are required for crop yield and public utility services, over north India.

  20. New analytical model for the ozone electronic ground state potential surface and accurate ab initio vibrational predictions at high energy range.

    PubMed

    Tyuterev, Vladimir G; Kochanov, Roman V; Tashkun, Sergey A; Holka, Filip; Szalay, Péter G

    2013-10-07

    An accurate description of the complicated shape of the potential energy surface (PES) and that of the highly excited vibration states is of crucial importance for various unsolved issues in the spectroscopy and dynamics of ozone and remains a challenge for the theory. In this work a new analytical representation is proposed for the PES of the ground electronic state of the ozone molecule in the range covering the main potential well and the transition state towards the dissociation. This model accounts for particular features specific to the ozone PES for large variations of nuclear displacements along the minimum energy path. The impact of the shape of the PES near the transition state (existence of the "reef structure") on vibration energy levels was studied for the first time. The major purpose of this work was to provide accurate theoretical predictions for ozone vibrational band centres at the energy range near the dissociation threshold, which would be helpful for understanding the very complicated high-resolution spectra and its analyses currently in progress. Extended ab initio electronic structure calculations were carried out enabling the determination of the parameters of a minimum energy path PES model resulting in a new set of theoretical vibrational levels of ozone. A comparison with recent high-resolution spectroscopic data on the vibrational levels gives the root-mean-square deviations below 1 cm(-1) for ozone band centres up to 90% of the dissociation energy. New ab initio vibrational predictions represent a significant improvement with respect to all previously available calculations.

  1. Moving Toward Integrating Gene Expression Profiling Into High-Throughput Testing: A Gene Expression Biomarker Accurately Predicts Estrogen Receptor α Modulation in a Microarray Compendium.

    PubMed

    Ryan, Natalia; Chorley, Brian; Tice, Raymond R; Judson, Richard; Corton, J Christopher

    2016-05-01

    Microarray profiling of chemical-induced effects is being increasingly used in medium- and high-throughput formats. Computational methods are described here to identify molecular targets from whole-genome microarray data using as an example the estrogen receptor α (ERα), often modulated by potential endocrine disrupting chemicals. ERα biomarker genes were identified by their consistent expression after exposure to 7 structurally diverse ERα agonists and 3 ERα antagonists in ERα-positive MCF-7 cells. Most of the biomarker genes were shown to be directly regulated by ERα as determined by ESR1 gene knockdown using siRNA as well as through chromatin immunoprecipitation coupled with DNA sequencing analysis of ERα-DNA interactions. The biomarker was evaluated as a predictive tool using the fold-change rank-based Running Fisher algorithm by comparison to annotated gene expression datasets from experiments using MCF-7 cells, including those evaluating the transcriptional effects of hormones and chemicals. Using 141 comparisons from chemical- and hormone-treated cells, the biomarker gave a balanced accuracy for prediction of ERα activation or suppression of 94% and 93%, respectively. The biomarker was able to correctly classify 18 out of 21 (86%) ER reference chemicals including "very weak" agonists. Importantly, the biomarker predictions accurately replicated predictions based on 18 in vitro high-throughput screening assays that queried different steps in ERα signaling. For 114 chemicals, the balanced accuracies were 95% and 98% for activation or suppression, respectively. These results demonstrate that the ERα gene expression biomarker can accurately identify ERα modulators in large collections of microarray data derived from MCF-7 cells. Published by Oxford University Press on behalf of the Society of Toxicology 2016. This work is written by US Government employees and is in the public domain in the US.

  2. Using Modeling and Simulation to Predict Operator Performance and Automation-Induced Complacency With Robotic Automation: A Case Study and Empirical Validation.

    PubMed

    Wickens, Christopher D; Sebok, Angelia; Li, Huiyang; Sarter, Nadine; Gacy, Andrew M

    2015-09-01

    The aim of this study was to develop and validate a computational model of the automation complacency effect, as operators work on a robotic arm task, supported by three different degrees of automation. Some computational models of complacency in human-automation interaction exist, but those are formed and validated within the context of fairly simplified monitoring failures. This research extends model validation to a much more complex task, so that system designers can establish, without need for human-in-the-loop (HITL) experimentation, merits and shortcomings of different automation degrees. We developed a realistic simulation of a space-based robotic arm task that could be carried out with three different levels of trajectory visualization and execution automation support. Using this simulation, we performed HITL testing. Complacency was induced via several trials of correctly performing automation and then was assessed on trials when automation failed. Following a cognitive task analysis of the robotic arm operation, we developed a multicomponent model of the robotic operator and his or her reliance on automation, based in part on visual scanning. The comparison of model predictions with empirical results revealed that the model accurately predicted routine performance and predicted the responses to these failures after complacency developed. However, the scanning models do not account for the entire attention allocation effects of complacency. Complacency modeling can provide a useful tool for predicting the effects of different types of imperfect automation. The results from this research suggest that focus should be given to supporting situation awareness in automation development. © 2015, Human Factors and Ergonomics Society.

  3. Sexual victimization history predicts academic performance in college women.

    PubMed

    Baker, Majel R; Frazier, Patricia A; Greer, Christiaan; Paulsen, Jacob A; Howard, Kelli; Meredith, Liza N; Anders, Samantha L; Shallcross, Sandra L

    2016-11-01

    College women frequently report having experienced sexual victimization (SV) in their lifetime, including child sexual abuse and adolescent/adult sexual assault. Although the harmful mental health sequelae of SV have been extensively studied, recent research suggests that SV is also a risk factor for poorer college academic performance. The current studies examined whether exposure to SV uniquely predicted poorer college academic performance, even beyond contributions from three well-established predictors of academic performance: high school rank, composite standardized test scores (i.e., American College Testing [ACT]), and conscientiousness. Study 1 analyzed longitudinal data from a sample of female college students (N = 192) who were assessed at the beginning and end of one semester. SV predicted poorer cumulative end-of-semester grade point average (GPA) while controlling for well-established predictors of academic performance. Study 2 replicated these findings in a second longitudinal study of female college students (N = 390) and extended the analyses to include follow-up data on the freshmen and sophomore students (n = 206) 4 years later. SV predicted students' GPA in their final term at the university above the contributions of well-established academic predictors, and it was the only factor related to leaving college. These findings highlight the importance of expanding the scope of outcomes of SV to include academic performance, and they underscore the need to assess SV and other adverse experiences on college campuses to target students who may be at risk of poor performance or leaving college. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

  4. Grid orthogonality effects on predicted turbine midspan heat transfer and performance

    NASA Technical Reports Server (NTRS)

    Boyle, R. J.; Ameri, A. A.

    1995-01-01

    The effect of five different C type grid geometries on the predicted heat transfer and aerodynamic performance of a turbine stator is examined. Predictions were obtained using two flow analysis codes. One was a finite difference analysis, and the other was a finite volume analysis. Differences among the grids in terms of heat transfer and overall performance were small. The most significant difference among the five grids occurred in the prediction of pitchwise variation in total pressure. There was consistency between results obtained with each of the flow analysis codes when the same grid was used. A grid generating procedure in which the viscous grid is embedded within an inviscid type grid resulted in the best overall performance.

  5. Comparing theories' performance in predicting violence.

    PubMed

    Haas, Henriette; Cusson, Maurice

    2015-01-01

    The stakes of choosing the best theory as a basis for violence prevention and offender rehabilitation are high. However, no single theory of violence has ever been universally accepted by a majority of established researchers. Psychiatry, psychology and sociology are each subdivided into different schools relying upon different premises. All theories can produce empirical evidence for their validity, some of them stating the opposite of each other. Calculating different models with multivariate logistic regression on a dataset of N = 21,312 observations and ninety-two influences allowed a direct comparison of the performance of operationalizations of some of the most important schools. The psychopathology model ranked as the best model in terms of predicting violence right after the comprehensive interdisciplinary model. Next came the rational choice and lifestyle model and third the differential association and learning theory model. Other models namely the control theory model, the childhood-trauma model and the social conflict and reaction model turned out to have low sensitivities for predicting violence. Nevertheless, all models produced acceptable results in predictions of a non-violent outcome. Copyright © 2015. Published by Elsevier Ltd.

  6. Characterizing Decision-Analysis Performances of Risk Prediction Models Using ADAPT Curves.

    PubMed

    Lee, Wen-Chung; Wu, Yun-Chun

    2016-01-01

    The area under the receiver operating characteristic curve is a widely used index to characterize the performance of diagnostic tests and prediction models. However, the index does not explicitly acknowledge the utilities of risk predictions. Moreover, for most clinical settings, what counts is whether a prediction model can guide therapeutic decisions in a way that improves patient outcomes, rather than to simply update probabilities.Based on decision theory, the authors propose an alternative index, the "average deviation about the probability threshold" (ADAPT).An ADAPT curve (a plot of ADAPT value against the probability threshold) neatly characterizes the decision-analysis performances of a risk prediction model.Several prediction models can be compared for their ADAPT values at a chosen probability threshold, for a range of plausible threshold values, or for the whole ADAPT curves. This should greatly facilitate the selection of diagnostic tests and prediction models.

  7. Cortical Measures of Binaural Processing Predict Spatial Release from Masking Performance

    PubMed Central

    Papesh, Melissa A.; Folmer, Robert L.; Gallun, Frederick J.

    2017-01-01

    Binaural sensitivity is an important contributor to the ability to understand speech in adverse acoustical environments such as restaurants and other social gatherings. The ability to accurately report on binaural percepts is not commonly measured, however, as extensive training is required before reliable measures can be obtained. Here, we investigated the use of auditory evoked potentials (AEPs) as a rapid physiological indicator of detection of interaural phase differences (IPDs) by assessing cortical responses to 180° IPDs embedded in amplitude-modulated carrier tones. We predicted that decrements in encoding of IPDs would be evident in middle age, with further declines found with advancing age and hearing loss. Thus, participants in experiment #1 were young to middle-aged adults with relatively good hearing thresholds while participants in experiment #2 were older individuals with typical age-related hearing loss. Results revealed that while many of the participants in experiment #1 could encode IPDs in stimuli up to 1,000 Hz, few of the participants in experiment #2 had discernable responses to stimuli above 750 Hz. These results are consistent with previous studies that have found that aging and hearing loss impose frequency limits on the ability to encode interaural phase information present in the fine structure of auditory stimuli. We further hypothesized that AEP measures of binaural sensitivity would be predictive of participants' ability to benefit from spatial separation between sound sources, a phenomenon known as spatial release from masking (SRM) which depends upon binaural cues. Results indicate that not only were objective IPD measures well correlated with and predictive of behavioral SRM measures in both experiments, but that they provided much stronger predictive value than age or hearing loss. Overall, the present work shows that objective measures of the encoding of interaural phase information can be readily obtained using commonly

  8. Cortical Measures of Binaural Processing Predict Spatial Release from Masking Performance.

    PubMed

    Papesh, Melissa A; Folmer, Robert L; Gallun, Frederick J

    2017-01-01

    Binaural sensitivity is an important contributor to the ability to understand speech in adverse acoustical environments such as restaurants and other social gatherings. The ability to accurately report on binaural percepts is not commonly measured, however, as extensive training is required before reliable measures can be obtained. Here, we investigated the use of auditory evoked potentials (AEPs) as a rapid physiological indicator of detection of interaural phase differences (IPDs) by assessing cortical responses to 180° IPDs embedded in amplitude-modulated carrier tones. We predicted that decrements in encoding of IPDs would be evident in middle age, with further declines found with advancing age and hearing loss. Thus, participants in experiment #1 were young to middle-aged adults with relatively good hearing thresholds while participants in experiment #2 were older individuals with typical age-related hearing loss. Results revealed that while many of the participants in experiment #1 could encode IPDs in stimuli up to 1,000 Hz, few of the participants in experiment #2 had discernable responses to stimuli above 750 Hz. These results are consistent with previous studies that have found that aging and hearing loss impose frequency limits on the ability to encode interaural phase information present in the fine structure of auditory stimuli. We further hypothesized that AEP measures of binaural sensitivity would be predictive of participants' ability to benefit from spatial separation between sound sources, a phenomenon known as spatial release from masking (SRM) which depends upon binaural cues. Results indicate that not only were objective IPD measures well correlated with and predictive of behavioral SRM measures in both experiments, but that they provided much stronger predictive value than age or hearing loss. Overall, the present work shows that objective measures of the encoding of interaural phase information can be readily obtained using commonly

  9. Light-Frame Wall Systems: Performance and Predictability.

    Treesearch

    David S. Gromala

    1983-01-01

    This paper compares results of all wall tests with analytical predictions of performance.Conventional wood-stud walls of one configuration failed at bending loads that were 4 to 6 times design load.The computer model overpredicted wall strength by and average of 10 percent and deflection by an average of 6 percent.

  10. Predicting Language Performance in Hearing Impaired Children.

    ERIC Educational Resources Information Center

    Monsees, Edna K.

    The 2-year study evaluated the language performance of 69 hearing impaired, preschool children born following the rubella epidemic of the early 1960's in order to develop an instrument for objectively assessing language achievement and a predictive index of language achievement. Two language rating scales were developed which were tied to the…

  11. Choosing the appropriate forecasting model for predictive parameter control.

    PubMed

    Aleti, Aldeida; Moser, Irene; Meedeniya, Indika; Grunske, Lars

    2014-01-01

    All commonly used stochastic optimisation algorithms have to be parameterised to perform effectively. Adaptive parameter control (APC) is an effective method used for this purpose. APC repeatedly adjusts parameter values during the optimisation process for optimal algorithm performance. The assignment of parameter values for a given iteration is based on previously measured performance. In recent research, time series prediction has been proposed as a method of projecting the probabilities to use for parameter value selection. In this work, we examine the suitability of a variety of prediction methods for the projection of future parameter performance based on previous data. All considered prediction methods have assumptions the time series data has to conform to for the prediction method to provide accurate projections. Looking specifically at parameters of evolutionary algorithms (EAs), we find that all standard EA parameters with the exception of population size conform largely to the assumptions made by the considered prediction methods. Evaluating the performance of these prediction methods, we find that linear regression provides the best results by a very small and statistically insignificant margin. Regardless of the prediction method, predictive parameter control outperforms state of the art parameter control methods when the performance data adheres to the assumptions made by the prediction method. When a parameter's performance data does not adhere to the assumptions made by the forecasting method, the use of prediction does not have a notable adverse impact on the algorithm's performance.

  12. Nonhydrostatic and surfbeat model predictions of extreme wave run-up in fringing reef environments

    USGS Publications Warehouse

    Lashley, Christopher H.; Roelvink, Dano; van Dongeren, Ap R.; Buckley, Mark L.; Lowe, Ryan J.

    2018-01-01

    The accurate prediction of extreme wave run-up is important for effective coastal engineering design and coastal hazard management. While run-up processes on open sandy coasts have been reasonably well-studied, very few studies have focused on understanding and predicting wave run-up at coral reef-fronted coastlines. This paper applies the short-wave resolving, Nonhydrostatic (XB-NH) and short-wave averaged, Surfbeat (XB-SB) modes of the XBeach numerical model to validate run-up using data from two 1D (alongshore uniform) fringing-reef profiles without roughness elements, with two objectives: i) to provide insight into the physical processes governing run-up in such environments; and ii) to evaluate the performance of both modes in accurately predicting run-up over a wide range of conditions. XBeach was calibrated by optimizing the maximum wave steepness parameter (maxbrsteep) in XB-NH and the dissipation coefficient (alpha) in XB-SB) using the first dataset; and then applied to the second dataset for validation. XB-NH and XB-SB predictions of extreme wave run-up (Rmax and R2%) and its components, infragravity- and sea-swell band swash (SIG and SSS) and shoreline setup (<η>), were compared to observations. XB-NH more accurately simulated wave transformation but under-predicted shoreline setup due to its exclusion of parameterized wave-roller dynamics. XB-SB under-predicted sea-swell band swash but overestimated shoreline setup due to an over-prediction of wave heights on the reef flat. Run-up (swash) spectra were dominated by infragravity motions, allowing the short-wave (but not wave group) averaged model (XB-SB) to perform comparably well to its more complete, short-wave resolving (XB-NH) counterpart. Despite their respective limitations, both modes were able to accurately predict Rmax and R2%.

  13. Does ultrasonography accurately diagnose acute cholecystitis? Improving diagnostic accuracy based on a review at a regional hospital

    PubMed Central

    Hwang, Hamish; Marsh, Ian; Doyle, Jason

    2014-01-01

    Background Acute cholecystitis is one of the most common diseases requiring emergency surgery. Ultrasonography is an accurate test for cholelithiasis but has a high false-negative rate for acute cholecystitis. The Murphy sign and laboratory tests performed independently are also not particularly accurate. This study was designed to review the accuracy of ultrasonography for diagnosing acute cholecystitis in a regional hospital. Methods We studied all emergency cholecystectomies performed over a 1-year period. All imaging studies were reviewed by a single radiologist, and all pathology was reviewed by a single pathologist. The reviewers were blinded to each other’s results. Results A total of 107 patients required an emergency cholecystectomy in the study period; 83 of them underwent ultrasonography. Interradiologist agreement was 92% for ultrasonography. For cholelithiasis, ultrasonography had 100% sensitivity, 18% specificity, 81% positive predictive value (PPV) and 100% negative predictive value (NPV). For acute cholecystitis, it had 54% sensitivity, 81% specificity, 85% PPV and 47% NPV. All patients had chronic cholecystitis and 67% had acute cholecystitis on histology. When combined with positive Murphy sign and elevated neutrophil count, an ultrasound showing cholelithiasis or acute cholecystitis yielded a sensitivity of 74%, specificity of 62%, PPV of 80% and NPV of 53% for the diagnosis of acute cholecystitis. Conclusion Ultrasonography alone has a high rate of false-negative studies for acute cholecystitis. However, a higher rate of accurate diagnosis can be achieved using a triad of positive Murphy sign, elevated neutrophil count and an ultrasound showing cholelithiasis or cholecystitis. PMID:24869607

  14. Just-in-Time Correntropy Soft Sensor with Noisy Data for Industrial Silicon Content Prediction.

    PubMed

    Chen, Kun; Liang, Yu; Gao, Zengliang; Liu, Yi

    2017-08-08

    Development of accurate data-driven quality prediction models for industrial blast furnaces encounters several challenges mainly because the collected data are nonlinear, non-Gaussian, and uneven distributed. A just-in-time correntropy-based local soft sensing approach is presented to predict the silicon content in this work. Without cumbersome efforts for outlier detection, a correntropy support vector regression (CSVR) modeling framework is proposed to deal with the soft sensor development and outlier detection simultaneously. Moreover, with a continuous updating database and a clustering strategy, a just-in-time CSVR (JCSVR) method is developed. Consequently, more accurate prediction and efficient implementations of JCSVR can be achieved. Better prediction performance of JCSVR is validated on the online silicon content prediction, compared with traditional soft sensors.

  15. Just-in-Time Correntropy Soft Sensor with Noisy Data for Industrial Silicon Content Prediction

    PubMed Central

    Chen, Kun; Liang, Yu; Gao, Zengliang; Liu, Yi

    2017-01-01

    Development of accurate data-driven quality prediction models for industrial blast furnaces encounters several challenges mainly because the collected data are nonlinear, non-Gaussian, and uneven distributed. A just-in-time correntropy-based local soft sensing approach is presented to predict the silicon content in this work. Without cumbersome efforts for outlier detection, a correntropy support vector regression (CSVR) modeling framework is proposed to deal with the soft sensor development and outlier detection simultaneously. Moreover, with a continuous updating database and a clustering strategy, a just-in-time CSVR (JCSVR) method is developed. Consequently, more accurate prediction and efficient implementations of JCSVR can be achieved. Better prediction performance of JCSVR is validated on the online silicon content prediction, compared with traditional soft sensors. PMID:28786957

  16. Age differences in recall and predicting recall of action events and words.

    PubMed

    McDonald-Miszczak, L; Hubley, A M; Hultsch, D F

    1996-03-01

    Age differences in recall and prediction of recall were examined with different memory tasks. We asked 36 younger (19-28 yrs) and 36 older (60-81 yrs) women to provide both global and item-by-item predictions of their recall, and then to recall either (a) Subject Performance Tasks (SPTs), (b) verb-noun word-pairs memorized in list-like fashion (Word-Pairs), or (c) nonsense verb-noun word-pairs (Nonsense-Pairs) over three experimental trials. Based on previous research, we hypothesized that these tasks would vary in relative difficulty and flexibility of encoding. The results indicated that (a) age differences in global predictions (task specific self-efficacy) and recall performance across trials were minimized with SPT as compared with verbal materials, (b) global predictions were higher and more accurate for SPT as compared to verbal materials, and (c) item-by-item predictions were most accurate for materials encoded with the most flexibility (Nonsense Pairs). The results suggest that SPTs may provide some level of environmental support to reduce age differences in performance and task-specific self-efficacy, but that memory monitoring may depend on specific characteristics of the stimuli (i.e., flexibility of encoding) rather than their verbal or nonverbal nature.

  17. Aqua/Aura Updated Inclination Adjust Maneuver Performance Prediction Model

    NASA Technical Reports Server (NTRS)

    Boone, Spencer

    2017-01-01

    This presentation will discuss the updated Inclination Adjust Maneuver (IAM) performance prediction model that was developed for Aqua and Aura following the 2017 IAM series. This updated model uses statistical regression methods to identify potential long-term trends in maneuver parameters, yielding improved predictions when re-planning past maneuvers. The presentation has been reviewed and approved by Eric Moyer, ESMO Deputy Project Manager.

  18. Predicting performance with traffic analysis tools : final report.

    DOT National Transportation Integrated Search

    2008-03-01

    This document provides insights into the common pitfalls and challenges associated with use of traffic analysis tools for predicting future performance of a transportation facility. It provides five in-depth case studies that demonstrate common ways ...

  19. Prediction of human adaptation and performance in underwater environments.

    PubMed

    Colodro Plaza, Joaquín; Garcés de los Fayos Ruiz, Enrique J; López García, Juan J; Colodro Conde, Lucía

    2014-01-01

    Environmental stressors require the professional diver to undergo a complex process of psychophysiological adaptation in order to overcome the demands of an extreme environment and carry out effective and efficient work under water. The influence of cognitive and personality traits in predicting underwater performance and adaptation has been a common concern for diving psychology, and definitive conclusions have not been reached. In this ex post facto study, psychological and academic data were analyzed from a large sample of personnel participating in scuba diving courses carried out in the Spanish Navy Diving Center. In order to verify the relevance of individual differences in adaptation to a hostile environment, we evaluated the predictive validity of general mental ability and personality traits with regression techniques. The data indicated the existence of psychological variables that can predict the performance ( R² = .30, p <.001) and adaptation ( R²(N) = .51, p <.001) of divers in underwater environment. These findings support the hypothesis that individual differences are related to the probability of successful adaptation and effective performance in professional diving. These results also verify that dispositional traits play a decisive role in diving training and are significant factors in divers' psychological fitness.

  20. Shedding light on the variability of optical skin properties: finding a path towards more accurate prediction of light propagation in human cutaneous compartments

    PubMed Central

    Mignon, C.; Tobin, D. J.; Zeitouny, M.; Uzunbajakava, N. E.

    2018-01-01

    Finding a path towards a more accurate prediction of light propagation in human skin remains an aspiration of biomedical scientists working on cutaneous applications both for diagnostic and therapeutic reasons. The objective of this study was to investigate variability of the optical properties of human skin compartments reported in literature, to explore the underlying rational of this variability and to propose a dataset of values, to better represent an in vivo case and recommend a solution towards a more accurate prediction of light propagation through cutaneous compartments. To achieve this, we undertook a novel, logical yet simple approach. We first reviewed scientific articles published between 1981 and 2013 that reported on skin optical properties, to reveal the spread in the reported quantitative values. We found variations of up to 100-fold. Then we extracted the most trust-worthy datasets guided by a rule that the spectral properties should reflect the specific biochemical composition of each of the skin layers. This resulted in the narrowing of the spread in the calculated photon densities to 6-fold. We conclude with a recommendation to use the identified most robust datasets when estimating light propagation in human skin using Monte Carlo simulations. Alternatively, otherwise follow our proposed strategy to screen any new datasets to determine their biological relevance. PMID:29552418

  1. Predicting College Students' First Year Success: Should Soft Skills Be Taken into Consideration to More Accurately Predict the Academic Achievement of College Freshmen?

    ERIC Educational Resources Information Center

    Powell, Erica Dion

    2013-01-01

    This study presents a survey developed to measure the skills of entering college freshmen in the areas of responsibility, motivation, study habits, literacy, and stress management, and explores the predictive power of this survey as a measure of academic performance during the first semester of college. The survey was completed by 334 incoming…

  2. Burst muscle performance predicts the speed, acceleration, and turning performance of Anna’s hummingbirds

    PubMed Central

    Segre, Paolo S; Dakin, Roslyn; Zordan, Victor B; Dickinson, Michael H; Straw, Andrew D; Altshuler, Douglas L

    2015-01-01

    Despite recent advances in the study of animal flight, the biomechanical determinants of maneuverability are poorly understood. It is thought that maneuverability may be influenced by intrinsic body mass and wing morphology, and by physiological muscle capacity, but this hypothesis has not yet been evaluated because it requires tracking a large number of free flight maneuvers from known individuals. We used an automated tracking system to record flight sequences from 20 Anna's hummingbirds flying solo and in competition in a large chamber. We found that burst muscle capacity predicted most performance metrics. Hummingbirds with higher burst capacity flew with faster velocities, accelerations, and rotations, and they used more demanding complex turns. In contrast, body mass did not predict variation in maneuvering performance, and wing morphology predicted only the use of arcing turns and high centripetal accelerations. Collectively, our results indicate that burst muscle capacity is a key predictor of maneuverability. DOI: http://dx.doi.org/10.7554/eLife.11159.001 PMID:26583753

  3. Comparison of Performance Predictions for New Low-Thrust Trajectory Tools

    NASA Technical Reports Server (NTRS)

    Polsgrove, Tara; Kos, Larry; Hopkins, Randall; Crane, Tracie

    2006-01-01

    Several low thrust trajectory optimization tools have been developed over the last 3% years by the Low Thrust Trajectory Tools development team. This toolset includes both low-medium fidelity and high fidelity tools which allow the analyst to quickly research a wide mission trade space and perform advanced mission design. These tools were tested using a set of reference trajectories that exercised each tool s unique capabilities. This paper compares the performance predictions of the various tools against several of the reference trajectories. The intent is to verify agreement between the high fidelity tools and to quantify the performance prediction differences between tools of different fidelity levels.

  4. Predictions of H-mode performance in ITER

    NASA Astrophysics Data System (ADS)

    Budny, Robert

    2008-11-01

    Time-dependent integrated predictions of performance metrics such as the fusion power PDT, QDT≡ PDT/Pext, and alpha profiles are presented. The PTRANSP [1] code is used, along with GLF23 to predict plasma profiles, NUBEAM for NNBI and alpha heating, TORIC for ICRH, and TORAY for ECRH. Effects of sawteeth mixing, beam steering, beam shine-through, radiation loss, ash accumulation, and toroidal rotation are included. A total heating of Pext=73MW is assumed to achieve H-mode during the density and current ramp-up phase. Various mixes of NNBI, ICRH, and ECRH heating schemes are compared. After steady state conditions are achieved, Pext is stepped down to lower values to explore high QDT. Physics and computation uncertainties lead to ranges in predictions for PDT and QDT. Physics uncertainties include the L->H and H->L threshold powers, pedestal height, impurity and ash transport, and recycling. There are considerably more uncertainties predicting the peak value for QDT than for PDT. [0pt] [1] R.V. Budny, R. Andre, G. Bateman, F. Halpern, C.E. Kessel, A. Kritz, and D. McCune, Nuclear Fusion 48 (2008) 075005.

  5. A rapid and accurate approach for prediction of interactomes from co-elution data (PrInCE).

    PubMed

    Stacey, R Greg; Skinnider, Michael A; Scott, Nichollas E; Foster, Leonard J

    2017-10-23

    An organism's protein interactome, or complete network of protein-protein interactions, defines the protein complexes that drive cellular processes. Techniques for studying protein complexes have traditionally applied targeted strategies such as yeast two-hybrid or affinity purification-mass spectrometry to assess protein interactions. However, given the vast number of protein complexes, more scalable methods are necessary to accelerate interaction discovery and to construct whole interactomes. We recently developed a complementary technique based on the use of protein correlation profiling (PCP) and stable isotope labeling in amino acids in cell culture (SILAC) to assess chromatographic co-elution as evidence of interacting proteins. Importantly, PCP-SILAC is also capable of measuring protein interactions simultaneously under multiple biological conditions, allowing the detection of treatment-specific changes to an interactome. Given the uniqueness and high dimensionality of co-elution data, new tools are needed to compare protein elution profiles, control false discovery rates, and construct an accurate interactome. Here we describe a freely available bioinformatics pipeline, PrInCE, for the analysis of co-elution data. PrInCE is a modular, open-source library that is computationally inexpensive, able to use label and label-free data, and capable of detecting tens of thousands of protein-protein interactions. Using a machine learning approach, PrInCE offers greatly reduced run time, more predicted interactions at the same stringency, prediction of protein complexes, and greater ease of use over previous bioinformatics tools for co-elution data. PrInCE is implemented in Matlab (version R2017a). Source code and standalone executable programs for Windows and Mac OSX are available at https://github.com/fosterlab/PrInCE , where usage instructions can be found. An example dataset and output are also provided for testing purposes. PrInCE is the first fast and easy

  6. Machine learning and predictive data analytics enabling metrology and process control in IC fabrication

    NASA Astrophysics Data System (ADS)

    Rana, Narender; Zhang, Yunlin; Wall, Donald; Dirahoui, Bachir; Bailey, Todd C.

    2015-03-01

    Integrate circuit (IC) technology is going through multiple changes in terms of patterning techniques (multiple patterning, EUV and DSA), device architectures (FinFET, nanowire, graphene) and patterning scale (few nanometers). These changes require tight controls on processes and measurements to achieve the required device performance, and challenge the metrology and process control in terms of capability and quality. Multivariate data with complex nonlinear trends and correlations generally cannot be described well by mathematical or parametric models but can be relatively easily learned by computing machines and used to predict or extrapolate. This paper introduces the predictive metrology approach which has been applied to three different applications. Machine learning and predictive analytics have been leveraged to accurately predict dimensions of EUV resist patterns down to 18 nm half pitch leveraging resist shrinkage patterns. These patterns could not be directly and accurately measured due to metrology tool limitations. Machine learning has also been applied to predict the electrical performance early in the process pipeline for deep trench capacitance and metal line resistance. As the wafer goes through various processes its associated cost multiplies. It may take days to weeks to get the electrical performance readout. Predicting the electrical performance early on can be very valuable in enabling timely actionable decision such as rework, scrap, feedforward, feedback predicted information or information derived from prediction to improve or monitor processes. This paper provides a general overview of machine learning and advanced analytics application in the advanced semiconductor development and manufacturing.

  7. CUFID-query: accurate network querying through random walk based network flow estimation.

    PubMed

    Jeong, Hyundoo; Qian, Xiaoning; Yoon, Byung-Jun

    2017-12-28

    Functional modules in biological networks consist of numerous biomolecules and their complicated interactions. Recent studies have shown that biomolecules in a functional module tend to have similar interaction patterns and that such modules are often conserved across biological networks of different species. As a result, such conserved functional modules can be identified through comparative analysis of biological networks. In this work, we propose a novel network querying algorithm based on the CUFID (Comparative network analysis Using the steady-state network Flow to IDentify orthologous proteins) framework combined with an efficient seed-and-extension approach. The proposed algorithm, CUFID-query, can accurately detect conserved functional modules as small subnetworks in the target network that are expected to perform similar functions to the given query functional module. The CUFID framework was recently developed for probabilistic pairwise global comparison of biological networks, and it has been applied to pairwise global network alignment, where the framework was shown to yield accurate network alignment results. In the proposed CUFID-query algorithm, we adopt the CUFID framework and extend it for local network alignment, specifically to solve network querying problems. First, in the seed selection phase, the proposed method utilizes the CUFID framework to compare the query and the target networks and to predict the probabilistic node-to-node correspondence between the networks. Next, the algorithm selects and greedily extends the seed in the target network by iteratively adding nodes that have frequent interactions with other nodes in the seed network, in a way that the conductance of the extended network is maximally reduced. Finally, CUFID-query removes irrelevant nodes from the querying results based on the personalized PageRank vector for the induced network that includes the fully extended network and its neighboring nodes. Through extensive

  8. Towards Relaxing the Spherical Solar Radiation Pressure Model for Accurate Orbit Predictions

    NASA Astrophysics Data System (ADS)

    Lachut, M.; Bennett, J.

    2016-09-01

    The well-known cannonball model has been used ubiquitously to capture the effects of atmospheric drag and solar radiation pressure on satellites and/or space debris for decades. While it lends itself naturally to spherical objects, its validity in the case of non-spherical objects has been debated heavily for years throughout the space situational awareness community. One of the leading motivations to improve orbit predictions by relaxing the spherical assumption, is the ongoing demand for more robust and reliable conjunction assessments. In this study, we explore the orbit propagation of a flat plate in a near-GEO orbit under the influence of solar radiation pressure, using a Lambertian BRDF model. Consequently, this approach will account for the spin rate and orientation of the object, which is typically determined in practice using a light curve analysis. Here, simulations will be performed which systematically reduces the spin rate to demonstrate the point at which the spherical model no longer describes the orbital elements of the spinning plate. Further understanding of this threshold would provide insight into when a higher fidelity model should be used, thus resulting in improved orbit propagations. Therefore, the work presented here is of particular interest to organizations and researchers that maintain their own catalog, and/or perform conjunction analyses.

  9. Towards Accurate Prediction of Turbulent, Three-Dimensional, Recirculating Flows with the NCC

    NASA Technical Reports Server (NTRS)

    Iannetti, A.; Tacina, R.; Jeng, S.-M.; Cai, J.

    2001-01-01

    The National Combustion Code (NCC) was used to calculate the steady state, nonreacting flow field of a prototype Lean Direct Injection (LDI) swirler. This configuration used nine groups of eight holes drilled at a thirty-five degree angle to induce swirl. These nine groups created swirl in the same direction, or a corotating pattern. The static pressure drop across the holes was fixed at approximately four percent. Computations were performed on one quarter of the geometry, because the geometry is considered rotationally periodic every ninety degrees. The final computational grid used was approximately 2.26 million tetrahedral cells, and a cubic nonlinear k - epsilon model was used to model turbulence. The NCC results were then compared to time averaged Laser Doppler Velocimetry (LDV) data. The LDV measurements were performed on the full geometry, but four ninths of the geometry was measured. One-, two-, and three-dimensional representations of both flow fields are presented. The NCC computations compare both qualitatively and quantitatively well to the LDV data, but differences exist downstream. The comparison is encouraging, and shows that NCC can be used for future injector design studies. To improve the flow prediction accuracy of turbulent, three-dimensional, recirculating flow fields with the NCC, recommendations are given.

  10. Prediction of Gas Lubricated Foil Journal Bearing Performance

    NASA Technical Reports Server (NTRS)

    Carpino, Marc; Talmage, Gita

    2003-01-01

    This report summarizes the progress in the first eight months of the project. The objectives of this research project are to theoretically predict the steady operating conditions and the rotor dynamic coefficients of gas foil journal bearings. The project is currently on or ahead of schedule with the development of a finite element code that predicts steady bearing performance characteristics such as film thickness, pressure, load, and drag. Graphical results for a typical bearing are presented in the report. Project plans for the next year are discussed.

  11. Predicting Document Retrieval System Performance: An Expected Precision Measure.

    ERIC Educational Resources Information Center

    Losee, Robert M., Jr.

    1987-01-01

    Describes an expected precision (EP) measure designed to predict document retrieval performance. Highlights include decision theoretic models; precision and recall as measures of system performance; EP graphs; relevance feedback; and computing the retrieval status value of a document for two models, the Binary Independent Model and the Two Poisson…

  12. Static and transient performance prediction for CFB boilers using a Bayesian-Gaussian Neural Network

    NASA Astrophysics Data System (ADS)

    Ye, Haiwen; Ni, Weidou

    1997-06-01

    A Bayesian-Gaussian Neural Network (BGNN) is put forward in this paper to predict the static and transient performance of Circulating Fluidized Bed (CFB) boilers. The advantages of this network over Back-Propagation Neural Networks (BPNNs), easier determination of topology, simpler and time saving in training process as well as self-organizing ability, make this network more practical in on-line performance prediction for complicated processes. Simulation shows that this network is comparable to the BPNNs in predicting the performance of CFB boilers. Good and practical on-line performance predictions are essential for operation guide and model predictive control of CFB boilers, which are under research by the authors.

  13. Hounsfield unit density accurately predicts ESWL success.

    PubMed

    Magnuson, William J; Tomera, Kevin M; Lance, Raymond S

    2005-01-01

    Extracorporeal shockwave lithotripsy (ESWL) is a commonly used non-invasive treatment for urolithiasis. Helical CT scans provide much better and detailed imaging of the patient with urolithiasis including the ability to measure density of urinary stones. In this study we tested the hypothesis that density of urinary calculi as measured by CT can predict successful ESWL treatment. 198 patients were treated at Alaska Urological Associates with ESWL between January 2002 and April 2004. Of these 101 met study inclusion with accessible CT scans and stones ranging from 5-15 mm. Follow-up imaging demonstrated stone freedom in 74.2%. The overall mean Houndsfield density value for stone-free compared to residual stone groups were significantly different ( 93.61 vs 122.80 p < 0.0001). We determined by receiver operator curve (ROC) that HDV of 93 or less carries a 90% or better chance of stone freedom following ESWL for upper tract calculi between 5-15mm.

  14. An accurate behavioral model for single-photon avalanche diode statistical performance simulation

    NASA Astrophysics Data System (ADS)

    Xu, Yue; Zhao, Tingchen; Li, Ding

    2018-01-01

    An accurate behavioral model is presented to simulate important statistical performance of single-photon avalanche diodes (SPADs), such as dark count and after-pulsing noise. The derived simulation model takes into account all important generation mechanisms of the two kinds of noise. For the first time, thermal agitation, trap-assisted tunneling and band-to-band tunneling mechanisms are simultaneously incorporated in the simulation model to evaluate dark count behavior of SPADs fabricated in deep sub-micron CMOS technology. Meanwhile, a complete carrier trapping and de-trapping process is considered in afterpulsing model and a simple analytical expression is derived to estimate after-pulsing probability. In particular, the key model parameters of avalanche triggering probability and electric field dependence of excess bias voltage are extracted from Geiger-mode TCAD simulation and this behavioral simulation model doesn't include any empirical parameters. The developed SPAD model is implemented in Verilog-A behavioral hardware description language and successfully operated on commercial Cadence Spectre simulator, showing good universality and compatibility. The model simulation results are in a good accordance with the test data, validating high simulation accuracy.

  15. Accurate Characterization of Rain Drop Size Distribution Using Meteorological Particle Spectrometer and 2D Video Disdrometer for Propagation and Remote Sensing Applications

    NASA Technical Reports Server (NTRS)

    Thurai, Merhala; Bringi, Viswanathan; Kennedy, Patrick; Notaros, Branislav; Gatlin, Patrick

    2017-01-01

    Accurate measurements of rain drop size distributions (DSD), with particular emphasis on small and tiny drops, are presented. Measurements were conducted in two very different climate regions, namely Northern Colorado and Northern Alabama. Both datasets reveal a combination of (i) a drizzle mode for drop diameters less than 0.7 mm and (ii) a precipitation mode for larger diameters. Scattering calculations using the DSDs are performed at S and X bands and compared with radar observations for the first location. Our accurate DSDs will improve radar-based rain rate estimates as well as propagation predictions.

  16. Accurate Prediction of Inducible Transcription Factor Binding Intensities In Vivo

    PubMed Central

    Siepel, Adam; Lis, John T.

    2012-01-01

    DNA sequence and local chromatin landscape act jointly to determine transcription factor (TF) binding intensity profiles. To disentangle these influences, we developed an experimental approach, called protein/DNA binding followed by high-throughput sequencing (PB–seq), that allows the binding energy landscape to be characterized genome-wide in the absence of chromatin. We applied our methods to the Drosophila Heat Shock Factor (HSF), which inducibly binds a target DNA sequence element (HSE) following heat shock stress. PB–seq involves incubating sheared naked genomic DNA with recombinant HSF, partitioning the HSF–bound and HSF–free DNA, and then detecting HSF–bound DNA by high-throughput sequencing. We compared PB–seq binding profiles with ones observed in vivo by ChIP–seq and developed statistical models to predict the observed departures from idealized binding patterns based on covariates describing the local chromatin environment. We found that DNase I hypersensitivity and tetra-acetylation of H4 were the most influential covariates in predicting changes in HSF binding affinity. We also investigated the extent to which DNA accessibility, as measured by digital DNase I footprinting data, could be predicted from MNase–seq data and the ChIP–chip profiles for many histone modifications and TFs, and found GAGA element associated factor (GAF), tetra-acetylation of H4, and H4K16 acetylation to be the most predictive covariates. Lastly, we generated an unbiased model of HSF binding sequences, which revealed distinct biophysical properties of the HSF/HSE interaction and a previously unrecognized substructure within the HSE. These findings provide new insights into the interplay between the genomic sequence and the chromatin landscape in determining transcription factor binding intensity. PMID:22479205

  17. Performance of a process-based hydrodynamic model in predicting shoreline change

    NASA Astrophysics Data System (ADS)

    Safak, I.; Warner, J. C.; List, J. H.

    2012-12-01

    Shoreline change is controlled by a complex combination of processes that include waves, currents, sediment characteristics and availability, geologic framework, human interventions, and sea level rise. A comprehensive data set of shoreline position (14 shorelines between 1978-2002) along the continuous and relatively non-interrupted North Carolina Coast from Oregon Inlet to Cape Hatteras (65 km) reveals a spatial pattern of alternating erosion and accretion, with an erosional average shoreline change rate of -1.6 m/yr and up to -8 m/yr in some locations. This data set gives a unique opportunity to study long-term shoreline change in an area hit by frequent storm events while relatively uninfluenced by human interventions and the effects of tidal inlets. Accurate predictions of long-term shoreline change may require a model that accurately resolves surf zone processes and sediment transport patterns. Conventional methods for predicting shoreline change such as one-line models and regression of shoreline positions have been designed for computational efficiency. These methods, however, not only have several underlying restrictions (validity for small angle of wave approach, assuming bottom contours and shoreline to be parallel, depth of closure, etc.) but also their empirical estimates of sediment transport rates in the surf zone have been shown to vary greatly from the calculations of process-based hydrodynamic models. We focus on hind-casting long-term shoreline change using components of the process-based, three-dimensional coupled-ocean-atmosphere-wave-sediment transport modeling system (COAWST). COAWST is forced with historical predictions of atmospheric and oceanographic data from public-domain global models. Through a method of coupled concurrent grid-refinement approach in COAWST, the finest grid with resolution of O(10 m) that covers the surf zone along the section of interest is forced at its spatial boundaries with waves and currents computed on the grids

  18. The prediction of swimming performance in competition from behavioral information.

    PubMed

    Rushall, B S; Leet, D

    1979-06-01

    The swimming performances of the Canadian Team at the 1976 Olympic Games were categorized as being improved or worse than previous best times in the events contested. The two groups had been previously assessed on the Psychological Inventories for Competitive Swimmers. A stepwise multiple-discriminant analysis of the inventory responses revealed that 13 test questions produced a perfect discrimination of group membership. The resultant discriminant functions for predicting performance classification were applied to the test responses of 157 swimmers at the 1977 Canadian Winter National Swimming Championships. Using the same performance classification criteria the accuracy of prediction was not better than chance in three of four sex by performance classifications. This yielded a failure to locate a set of behavioral factors which determine swimming performance improvements in elite competitive circumstances. The possibility of sets of factors which do not discriminate between performances in similar environments or between similar groups of swimmers was raised.

  19. Are external knee load and EMG measures accurate indicators of internal knee contact forces during gait?

    PubMed

    Meyer, Andrew J; D'Lima, Darryl D; Besier, Thor F; Lloyd, David G; Colwell, Clifford W; Fregly, Benjamin J

    2013-06-01

    Mechanical loading is believed to be a critical factor in the development and treatment of knee osteoarthritis. However, the contact forces to which the knee articular surfaces are subjected during daily activities cannot be measured clinically. Thus, the ability to predict internal knee contact forces accurately using external measures (i.e., external knee loads and muscle electromyographic [EMG] signals) would be clinically valuable. We quantified how well external knee load and EMG measures predict internal knee contact forces during gait. A single subject with a force-measuring tibial prosthesis and post-operative valgus alignment performed four gait patterns (normal, medial thrust, walking pole, and trunk sway) to induce a wide range of external and internal knee joint loads. Linear regression analyses were performed to assess how much of the variability in internal contact forces was accounted for by variability in the external measures. Though the different gait patterns successfully induced significant changes in the external and internal quantities, changes in external measures were generally weak indicators of changes in total, medial, and lateral contact force. Our results suggest that when total contact force may be changing, caution should be exercised when inferring changes in knee contact forces based on observed changes in external knee load and EMG measures. Advances in musculoskeletal modeling methods may be needed for accurate estimation of in vivo knee contact forces. Copyright © 2012 Orthopaedic Research Society.

  20. A Comprehensive High Performance Predictive Tool for Fusion Liquid Metal Hydromagnetics

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

    Huang, Peter; Chhabra, Rupanshi; Munipalli, Ramakanth

    In Phase I SBIR project, HyPerComp and Texcel initiated the development of two induction-based MHD codes as a predictive tool for fusion hydro-magnetics. The newly-developed codes overcome the deficiency of other MHD codes based on the quasi static approximation by defining a more general mathematical model that utilizes the induced magnetic field rather than the electric potential as the main electromagnetic variable. The UCLA code is a finite-difference staggered-mesh code that serves as a supplementary tool to the massively-parallel finite-volume code developed by HyPerComp. As there is no suitable experimental data under blanket-relevant conditions for code validation, code-to-code comparisons andmore » comparisons against analytical solutions were successfully performed for three selected test cases: (1) lid-driven MHD flow, (2) flow in a rectangular duct in a transverse magnetic field, and (3) unsteady finite magnetic Reynolds number flow in a rectangular enclosure. The performed tests suggest that the developed codes are accurate and robust. Further work will focus on enhancing the code capabilities towards higher flow parameters and faster computations. At the conclusion of the current Phase-II Project we have completed the preliminary validation efforts in performing unsteady mixed-convection MHD flows (against limited data that is currently available in literature), and demonstrated flow behavior in large 3D channels including important geometrical features. Code enhancements such as periodic boundary conditions, unmatched mesh structures are also ready. As proposed, we have built upon these strengths and explored a much increased range of Grashof numbers and Hartmann numbers under various flow conditions, ranging from flows in a rectangular duct to prototypic blanket modules and liquid metal PFC. Parametric studies, numerical and physical model improvements to expand the scope of simulations, code demonstration, and continued validation activities have

  1. RTOD- RADIAL TURBINE OFF-DESIGN PERFORMANCE ANALYSIS

    NASA Technical Reports Server (NTRS)

    Glassman, A. J.

    1994-01-01

    The RTOD program was developed to accurately predict radial turbine off-design performance. The radial turbine has been used extensively in automotive turbochargers and aircraft auxiliary power units. It is now being given serious consideration for primary powerplant applications. In applications where the turbine will operate over a wide range of power settings, accurate off-design performance prediction is essential for a successful design. RTOD predictions have already illustrated a potential improvement in off-design performance offered by rotor back-sweep for high-work-factor radial turbines. RTOD can be used to analyze other potential performance enhancing design features. RTOD predicts the performance of a radial turbine (with or without rotor blade sweep) as a function of pressure ratio, speed, and stator setting. The program models the flow with the following: 1) stator viscous and trailing edge losses; 2) a vaneless space loss between the stator and the rotor; and 3) rotor incidence, viscous, trailing-edge, clearance, and disk friction losses. The stator and rotor viscous losses each represent the combined effects of profile, endwall, and secondary flow losses. The stator inlet and exit and the rotor inlet flows are modeled by a mean-line analysis, but a sector analysis is used at the rotor exit. The leakage flow through the clearance gap in a pivoting stator is also considered. User input includes gas properties, turbine geometry, and the stator and rotor viscous losses at a reference performance point. RTOD output includes predicted turbine performance over a specified operating range and any user selected flow parameters. The RTOD program is written in FORTRAN IV for batch execution and has been implemented on an IBM 370 series computer with a central memory requirement of approximately 100K of 8 bit bytes. The RTOD program was developed in 1983.

  2. Predicting Classifier Performance with Limited Training Data: Applications to Computer-Aided Diagnosis in Breast and Prostate Cancer

    PubMed Central

    Basavanhally, Ajay; Viswanath, Satish; Madabhushi, Anant

    2015-01-01

    Clinical trials increasingly employ medical imaging data in conjunction with supervised classifiers, where the latter require large amounts of training data to accurately model the system. Yet, a classifier selected at the start of the trial based on smaller and more accessible datasets may yield inaccurate and unstable classification performance. In this paper, we aim to address two common concerns in classifier selection for clinical trials: (1) predicting expected classifier performance for large datasets based on error rates calculated from smaller datasets and (2) the selection of appropriate classifiers based on expected performance for larger datasets. We present a framework for comparative evaluation of classifiers using only limited amounts of training data by using random repeated sampling (RRS) in conjunction with a cross-validation sampling strategy. Extrapolated error rates are subsequently validated via comparison with leave-one-out cross-validation performed on a larger dataset. The ability to predict error rates as dataset size increases is demonstrated on both synthetic data as well as three different computational imaging tasks: detecting cancerous image regions in prostate histopathology, differentiating high and low grade cancer in breast histopathology, and detecting cancerous metavoxels in prostate magnetic resonance spectroscopy. For each task, the relationships between 3 distinct classifiers (k-nearest neighbor, naive Bayes, Support Vector Machine) are explored. Further quantitative evaluation in terms of interquartile range (IQR) suggests that our approach consistently yields error rates with lower variability (mean IQRs of 0.0070, 0.0127, and 0.0140) than a traditional RRS approach (mean IQRs of 0.0297, 0.0779, and 0.305) that does not employ cross-validation sampling for all three datasets. PMID:25993029

  3. Predictions and Performance on the PACT Teaching Event: Case Studies of High and Low Performers

    ERIC Educational Resources Information Center

    Sandholtz, Judith Haymore

    2012-01-01

    In an earlier study, the author and her colleague explored the extent to which supervisors' perspectives about candidates' performance corresponded with outcomes from a summative performance assessment (Sandholtz & Shea, 2012). They specifically examined the relationship between university supervisors' predictions and candidates' performance…

  4. Accurate interatomic force fields via machine learning with covariant kernels

    NASA Astrophysics Data System (ADS)

    Glielmo, Aldo; Sollich, Peter; De Vita, Alessandro

    2017-06-01

    We present a novel scheme to accurately predict atomic forces as vector quantities, rather than sets of scalar components, by Gaussian process (GP) regression. This is based on matrix-valued kernel functions, on which we impose the requirements that the predicted force rotates with the target configuration and is independent of any rotations applied to the configuration database entries. We show that such covariant GP kernels can be obtained by integration over the elements of the rotation group SO (d ) for the relevant dimensionality d . Remarkably, in specific cases the integration can be carried out analytically and yields a conservative force field that can be recast into a pair interaction form. Finally, we show that restricting the integration to a summation over the elements of a finite point group relevant to the target system is sufficient to recover an accurate GP. The accuracy of our kernels in predicting quantum-mechanical forces in real materials is investigated by tests on pure and defective Ni, Fe, and Si crystalline systems.

  5. The joint effects of personality and workplace social exchange relationships in predicting task performance and citizenship performance.

    PubMed

    Kamdar, Dishan; Van Dyne, Linn

    2007-09-01

    This field study examines the joint effects of social exchange relationships at work (leader-member exchange and team-member exchange) and employee personality (conscientiousness and agreeableness) in predicting task performance and citizenship performance. Consistent with trait activation theory, matched data on 230 employees, their coworkers, and their supervisors demonstrated interactions in which high quality social exchange relationships weakened the positive relationships between personality and performance. Results demonstrate the benefits of consonant predictions in which predictors and outcomes are matched on the basis of specific targets. We discuss theoretical and practical implications. (c) 2007 APA.

  6. Prediction of Gymnastic Performance from Arousal and Anxiety Measures.

    ERIC Educational Resources Information Center

    Basler, Marilyn L.; And Others

    This study predicts gymnastic performance, arousal, and anxiety measures from past performances. Pulse rate and the Palmar Sweat Index were utilized as indicants of arousal. Anxiety was assessed by means of the State-Trait Anxiety Inventory. Eighteen members of the Ithaca College women's varsity gymnastic team were tested throughout the 1973-74…

  7. Mars Science Laboratory Interplanetary Navigation Performance

    NASA Technical Reports Server (NTRS)

    Martin-Mur, Tomas J.; Kruizinga, Gerhard; Wong, Mau

    2013-01-01

    The Mars Science Laboratory spacecraft, carrying the Curiosity rover to Mars, hit the top of the Martian atmosphere just 200 meters from where it had been predicted more than six days earlier, and 2.6 million kilometers away. This un-expected level of accuracy was achieved by a combination of factors including: spacecraft performance, tracking data processing, dynamical modeling choices, and navigation filter setup. This paper will describe our best understanding of what were the factors that contributed to this excellent interplanetary trajectory prediction performance. The accurate interplanetary navigation contributed to the very precise landing performance, and to the overall success of the mission.

  8. Knotty: Efficient and Accurate Prediction of Complex RNA Pseudoknot Structures.

    PubMed

    Jabbari, Hosna; Wark, Ian; Montemagno, Carlo; Will, Sebastian

    2018-06-01

    The computational prediction of RNA secondary structure by free energy minimization has become an important tool in RNA research. However in practice, energy minimization is mostly limited to pseudoknot-free structures or rather simple pseudoknots, not covering many biologically important structures such as kissing hairpins. Algorithms capable of predicting sufficiently complex pseudoknots (for sequences of length n) used to have extreme complexities, e.g. Pknots (Rivas and Eddy, 1999) has O(n6) time and O(n4) space complexity. The algorithm CCJ (Chen et al., 2009) dramatically improves the asymptotic run time for predicting complex pseudoknots (handling almost all relevant pseudoknots, while being slightly less general than Pknots), but this came at the cost of large constant factors in space and time, which strongly limited its practical application (∼200 bases already require 256GB space). We present a CCJ-type algorithm, Knotty, that handles the same comprehensive pseudoknot class of structures as CCJ with improved space complexity of Θ(n3 + Z)-due to the applied technique of sparsification, the number of "candidates", Z, appears to grow significantly slower than n4 on our benchmark set (which include pseudoknotted RNAs up to 400 nucleotides). In terms of run time over this benchmark, Knotty clearly outperforms Pknots and the original CCJ implementation, CCJ 1.0; Knotty's space consumption fundamentally improves over CCJ 1.0, being on a par with the space-economic Pknots. By comparing to CCJ 2.0, our unsparsified Knotty variant, we demonstrate the isolated effect of sparsification. Moreover, Knotty employs the state-of-the-art energy model of "HotKnots DP09", which results in superior prediction accuracy over Pknots. Our software is available at https://github.com/HosnaJabbari/Knotty. will@tbi.unvie.ac.at. Supplementary data are available at Bioinformatics online.

  9. Validity of Treadmill-Derived Critical Speed on Predicting 5000-Meter Track-Running Performance.

    PubMed

    Nimmerichter, Alfred; Novak, Nina; Triska, Christoph; Prinz, Bernhard; Breese, Brynmor C

    2017-03-01

    Nimmerichter, A, Novak, N, Triska, C, Prinz, B, and Breese, BC. Validity of treadmill-derived critical speed on predicting 5,000-meter track-running performance. J Strength Cond Res 31(3): 706-714, 2017-To evaluate 3 models of critical speed (CS) for the prediction of 5,000-m running performance, 16 trained athletes completed an incremental test on a treadmill to determine maximal aerobic speed (MAS) and 3 randomly ordered runs to exhaustion at the [INCREMENT]70% intensity, at 110% and 98% of MAS. Critical speed and the distance covered above CS (D') were calculated using the hyperbolic speed-time (HYP), the linear distance-time (LIN), and the linear speed inverse-time model (INV). Five thousand meter performance was determined on a 400-m running track. Individual predictions of 5,000-m running time (t = [5,000-D']/CS) and speed (s = D'/t + CS) were calculated across the 3 models in addition to multiple regression analyses. Prediction accuracy was assessed with the standard error of estimate (SEE) from linear regression analysis and the mean difference expressed in units of measurement and coefficient of variation (%). Five thousand meter running performance (speed: 4.29 ± 0.39 m·s; time: 1,176 ± 117 seconds) was significantly better than the predictions from all 3 models (p < 0.0001). The mean difference was 65-105 seconds (5.7-9.4%) for time and -0.22 to -0.34 m·s (-5.0 to -7.5%) for speed. Predictions from multiple regression analyses with CS and D' as predictor variables were not significantly different from actual running performance (-1.0 to 1.1%). The SEE across all models and predictions was approximately 65 seconds or 0.20 m·s and is therefore considered as moderate. The results of this study have shown the importance of aerobic and anaerobic energy system contribution to predict 5,000-m running performance. Using estimates of CS and D' is valuable for predicting performance over race distances of 5,000 m.

  10. Gender and Attraction: Predicting Middle School Performance Ensemble Participation

    ERIC Educational Resources Information Center

    Warnock, Emery C.

    2009-01-01

    This study was designed to predict middle school sixth graders' group membership in band (n = 81), chorus (n = 45), and as non-participants in music performance ensembles (n = 127), as determined by gender and factors on the Attraction Toward School Performance Ensemble (ATSPE) scale (alpha = 0.88). Students completed the ATSPE as elementary fifth…

  11. Improved Fuzzy Modelling to Predict the Academic Performance of Distance Education Students

    ERIC Educational Resources Information Center

    Yildiz, Osman; Bal, Abdullah; Gulsecen, Sevinc

    2013-01-01

    It is essential to predict distance education students' year-end academic performance early during the course of the semester and to take precautions using such prediction-based information. This will, in particular, help enhance their academic performance and, therefore, improve the overall educational quality. The present study was on the…

  12. Prediction of Scour below Flip Bucket using Soft Computing Techniques

    NASA Astrophysics Data System (ADS)

    Azamathulla, H. Md.; Ab Ghani, Aminuddin; Azazi Zakaria, Nor

    2010-05-01

    The accurate prediction of the depth of scour around hydraulic structure (trajectory spillways) has been based on the experimental studies and the equations developed are mainly empirical in nature. This paper evaluates the performance of the soft computing (intelligence) techiques, Adaptive Neuro-Fuzzy System (ANFIS) and Genetic expression Programming (GEP) approach, in prediction of scour below a flip bucket spillway. The results are very promising, which support the use of these intelligent techniques in prediction of highly non-linear scour parameters.

  13. Linear regression models for solvent accessibility prediction in proteins.

    PubMed

    Wagner, Michael; Adamczak, Rafał; Porollo, Aleksey; Meller, Jarosław

    2005-04-01

    The relative solvent accessibility (RSA) of an amino acid residue in a protein structure is a real number that represents the solvent exposed surface area of this residue in relative terms. The problem of predicting the RSA from the primary amino acid sequence can therefore be cast as a regression problem. Nevertheless, RSA prediction has so far typically been cast as a classification problem. Consequently, various machine learning techniques have been used within the classification framework to predict whether a given amino acid exceeds some (arbitrary) RSA threshold and would thus be predicted to be "exposed," as opposed to "buried." We have recently developed novel methods for RSA prediction using nonlinear regression techniques which provide accurate estimates of the real-valued RSA and outperform classification-based approaches with respect to commonly used two-class projections. However, while their performance seems to provide a significant improvement over previously published approaches, these Neural Network (NN) based methods are computationally expensive to train and involve several thousand parameters. In this work, we develop alternative regression models for RSA prediction which are computationally much less expensive, involve orders-of-magnitude fewer parameters, and are still competitive in terms of prediction quality. In particular, we investigate several regression models for RSA prediction using linear L1-support vector regression (SVR) approaches as well as standard linear least squares (LS) regression. Using rigorously derived validation sets of protein structures and extensive cross-validation analysis, we compare the performance of the SVR with that of LS regression and NN-based methods. In particular, we show that the flexibility of the SVR (as encoded by metaparameters such as the error insensitivity and the error penalization terms) can be very beneficial to optimize the prediction accuracy for buried residues. We conclude that the simple

  14. A Method for Using Player Tracking Data in Basketball to Learn Player Skills and Predict Team Performance.

    PubMed

    Skinner, Brian; Guy, Stephen J

    2015-01-01

    Player tracking data represents a revolutionary new data source for basketball analysis, in which essentially every aspect of a player's performance is tracked and can be analyzed numerically. We suggest a way by which this data set, when coupled with a network-style model of the offense that relates players' skills to the team's success at running different plays, can be used to automatically learn players' skills and predict the performance of untested 5-man lineups in a way that accounts for the interaction between players' respective skill sets. After developing a general analysis procedure, we present as an example a specific implementation of our method using a simplified network model. While player tracking data is not yet available in the public domain, we evaluate our model using simulated data and show that player skills can be accurately inferred by a simple statistical inference scheme. Finally, we use the model to analyze games from the 2011 playoff series between the Memphis Grizzlies and the Oklahoma City Thunder and we show that, even with a very limited data set, the model can consistently describe a player's interactions with a given lineup based only on his performance with a different lineup.

  15. Contextual predictability enhances reading performance in patients with schizophrenia.

    PubMed

    Fernández, Gerardo; Guinjoan, Salvador; Sapognikoff, Marcelo; Orozco, David; Agamennoni, Osvaldo

    2016-07-30

    In the present work we analyzed fixation duration in 40 healthy individuals and 18 patients with chronic, stable SZ during reading of regular sentences and proverbs. While they read, their eye movements were recorded. We used lineal mixed models to analyze fixation durations. The predictability of words N-1, N, and N+1 exerted a strong influence on controls and SZ patients. The influence of the predictabilities of preceding, current, and upcoming words on SZ was clearly reduced for proverbs in comparison to regular sentences. Both controls and SZ readers were able to use highly predictable fixated words for an easier reading. Our results suggest that SZ readers might compensate attentional and working memory deficiencies by using stored information of familiar texts for enhancing their reading performance. The predictabilities of words in proverbs serve as task-appropriate cues that are used by SZ readers. To the best of our knowledge, this is the first study using eyetracking for measuring how patients with SZ process well-defined words embedded in regular sentences and proverbs. Evaluation of the resulting changes in fixation durations might provide a useful tool for understanding how SZ patients could enhance their reading performance. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  16. How the study of online collaborative learning can guide teachers and predict students' performance in a medical course.

    PubMed

    Saqr, Mohammed; Fors, Uno; Tedre, Matti

    2018-02-06

    Collaborative learning facilitates reflection, diversifies understanding and stimulates skills of critical and higher-order thinking. Although the benefits of collaborative learning have long been recognized, it is still rarely studied by social network analysis (SNA) in medical education, and the relationship of parameters that can be obtained via SNA with students' performance remains largely unknown. The aim of this work was to assess the potential of SNA for studying online collaborative clinical case discussions in a medical course and to find out which activities correlate with better performance and help predict final grade or explain variance in performance. Interaction data were extracted from the learning management system (LMS) forum module of the Surgery course in Qassim University, College of Medicine. The data were analyzed using social network analysis. The analysis included visual as well as a statistical analysis. Correlation with students' performance was calculated, and automatic linear regression was used to predict students' performance. By using social network analysis, we were able to analyze a large number of interactions in online collaborative discussions and gain an overall insight of the course social structure, track the knowledge flow and the interaction patterns, as well as identify the active participants and the prominent discussion moderators. When augmented with calculated network parameters, SNA offered an accurate view of the course network, each user's position, and level of connectedness. Results from correlation coefficients, linear regression, and logistic regression indicated that a student's position and role in information relay in online case discussions, combined with the strength of that student's network (social capital), can be used as predictors of performance in relevant settings. By using social network analysis, researchers can analyze the social structure of an online course and reveal important information

  17. Prediction of antenna array performance from subarray measurements

    NASA Technical Reports Server (NTRS)

    Huisjen, M. A.

    1978-01-01

    Computer runs were used to determine the effect of mechanical distortions on array pattern performance. Subarray gain data, along with feed network insertion loss, and insertion phase data were combined with the analysis of Ruze on random errors to predict gain of a full array.

  18. Predicting School Performance with the Early Screening Inventory.

    ERIC Educational Resources Information Center

    Meisels, Samuel J.; And Others

    1984-01-01

    Proposes criteria for defining and selecting preschool developmental screening instruments and describes the Early Screening Inventory (ESI), a developmental screening instrument designed to satisfy these criteria. Presents results of several studies demonstrating that the ESI predicts school performance with moderate to excellent accuracy through…

  19. Predicting in-treatment performance and post-treatment outcomes in methamphetamine users.

    PubMed

    Hillhouse, Maureen P; Marinelli-Casey, Patricia; Gonzales, Rachel; Ang, Alfonso; Rawson, Richard A

    2007-04-01

    This study examines the utility of individual drug use and treatment characteristics for predicting in-treatment performance and post-treatment outcomes over a 1-year period. Data were collected from 420 adults who participated in the Methamphetamine Treatment Project (MTP), a multi-site study of randomly assigned treatment for methamphetamine dependence. Interviews were conducted at baseline, during treatment and during three follow-up time-points: treatment discharge and at 6 and 12 months following admission. The Addiction Severity Index (ASI); the Craving, Frequency, Intensity and Duration Estimate (CFIDE); and laboratory urinalysis results were used in the current study. Analyses addressed both in-treatment performance and post-treatment outcomes. The most consistent finding is that pre-treatment methamphetamine use predicts in-treatment performance and post-treatment outcomes. No one variable predicted all in-treatment performance measures; however, gender, route of administration and pre-treatment methamphetamine use were significant predictors. Similarly, post-treatment outcomes were predicted by a range of variables, although pre-treatment methamphetamine use was significantly associated with each post-treatment outcome. These findings provide useful empirical information about treatment outcomes for methamphetamine abusers, and highlight the utility of assessing individual and in-treatment characteristics in the development of appropriate treatment plans.

  20. Using the load-velocity relationship for 1RM prediction.

    PubMed

    Jidovtseff, Boris; Harris, Nigel K; Crielaard, Jean-Michel; Cronin, John B

    2011-01-01

    The purpose of this study was to investigate the ability of the load-velocity relationship to accurately predict a bench press 1 repetition maximum (1RM). Data from 3 different bench press studies (n = 112) that incorporated both 1RM assessment and submaximal load-velocity profiling were analyzed. Individual regression analysis was performed to determine the theoretical load at zero velocity (LD0). Data from each of the 3 studies were analyzed separately and also presented as overall group mean. Thereafter, correlation analysis provided quantification of the relationships between 1RM and LD0. Practically perfect correlations (r = ∼0.95) were observed in our samples, confirming the ability of the load-velocity profile to accurately predict bench press 1RM.

  1. Predicting BCI subject performance using probabilistic spatio-temporal filters.

    PubMed

    Suk, Heung-Il; Fazli, Siamac; Mehnert, Jan; Müller, Klaus-Robert; Lee, Seong-Whan

    2014-01-01

    Recently, spatio-temporal filtering to enhance decoding for Brain-Computer-Interfacing (BCI) has become increasingly popular. In this work, we discuss a novel, fully Bayesian-and thereby probabilistic-framework, called Bayesian Spatio-Spectral Filter Optimization (BSSFO) and apply it to a large data set of 80 non-invasive EEG-based BCI experiments. Across the full frequency range, the BSSFO framework allows to analyze which spatio-spectral parameters are common and which ones differ across the subject population. As expected, large variability of brain rhythms is observed between subjects. We have clustered subjects according to similarities in their corresponding spectral characteristics from the BSSFO model, which is found to reflect their BCI performances well. In BCI, a considerable percentage of subjects is unable to use a BCI for communication, due to their missing ability to modulate their brain rhythms-a phenomenon sometimes denoted as BCI-illiteracy or inability. Predicting individual subjects' performance preceding the actual, time-consuming BCI-experiment enhances the usage of BCIs, e.g., by detecting users with BCI inability. This work additionally contributes by using the novel BSSFO method to predict the BCI-performance using only 2 minutes and 3 channels of resting-state EEG data recorded before the actual BCI-experiment. Specifically, by grouping the individual frequency characteristics we have nicely classified them into the subject 'prototypes' (like μ - or β -rhythm type subjects) or users without ability to communicate with a BCI, and then by further building a linear regression model based on the grouping we could predict subjects' performance with the maximum correlation coefficient of 0.581 with the performance later seen in the actual BCI session.

  2. Improving Prediction Accuracy for WSN Data Reduction by Applying Multivariate Spatio-Temporal Correlation

    PubMed Central

    Carvalho, Carlos; Gomes, Danielo G.; Agoulmine, Nazim; de Souza, José Neuman

    2011-01-01

    This paper proposes a method based on multivariate spatial and temporal correlation to improve prediction accuracy in data reduction for Wireless Sensor Networks (WSN). Prediction of data not sent to the sink node is a technique used to save energy in WSNs by reducing the amount of data traffic. However, it may not be very accurate. Simulations were made involving simple linear regression and multiple linear regression functions to assess the performance of the proposed method. The results show a higher correlation between gathered inputs when compared to time, which is an independent variable widely used for prediction and forecasting. Prediction accuracy is lower when simple linear regression is used, whereas multiple linear regression is the most accurate one. In addition to that, our proposal outperforms some current solutions by about 50% in humidity prediction and 21% in light prediction. To the best of our knowledge, we believe that we are probably the first to address prediction based on multivariate correlation for WSN data reduction. PMID:22346626

  3. Evaluation and comparison of predictive individual-level general surrogates.

    PubMed

    Gabriel, Erin E; Sachs, Michael C; Halloran, M Elizabeth

    2018-07-01

    An intermediate response measure that accurately predicts efficacy in a new setting at the individual level could be used both for prediction and personalized medical decisions. In this article, we define a predictive individual-level general surrogate (PIGS), which is an individual-level intermediate response that can be used to accurately predict individual efficacy in a new setting. While methods for evaluating trial-level general surrogates, which are predictors of trial-level efficacy, have been developed previously, few, if any, methods have been developed to evaluate individual-level general surrogates, and no methods have formalized the use of cross-validation to quantify the expected prediction error. Our proposed method uses existing methods of individual-level surrogate evaluation within a given clinical trial setting in combination with cross-validation over a set of clinical trials to evaluate surrogate quality and to estimate the absolute prediction error that is expected in a new trial setting when using a PIGS. Simulations show that our method performs well across a variety of scenarios. We use our method to evaluate and to compare candidate individual-level general surrogates over a set of multi-national trials of a pentavalent rotavirus vaccine.

  4. Genome-Wide Polygenic Scores Predict Reading Performance Throughout the School Years.

    PubMed

    Selzam, Saskia; Dale, Philip S; Wagner, Richard K; DeFries, John C; Cederlöf, Martin; O'Reilly, Paul F; Krapohl, Eva; Plomin, Robert

    2017-07-04

    It is now possible to create individual-specific genetic scores, called genome-wide polygenic scores (GPS). We used a GPS for years of education ( EduYears ) to predict reading performance assessed at UK National Curriculum Key Stages 1 (age 7), 2 (age 12) and 3 (age 14) and on reading tests administered at ages 7 and 12 in a UK sample of 5,825 unrelated individuals. EduYears GPS accounts for up to 5% of the variance in reading performance at age 14. GPS predictions remained significant after accounting for general cognitive ability and family socioeconomic status. Reading performance of children in the lowest and highest 12.5% of the EduYears GPS distribution differed by a mean growth in reading ability of approximately two school years. It seems certain that polygenic scores will be used to predict strengths and weaknesses in education.

  5. Predicting intensity ranks of peptide fragment ions.

    PubMed

    Frank, Ari M

    2009-05-01

    Accurate modeling of peptide fragmentation is necessary for the development of robust scoring functions for peptide-spectrum matches, which are the cornerstone of MS/MS-based identification algorithms. Unfortunately, peptide fragmentation is a complex process that can involve several competing chemical pathways, which makes it difficult to develop generative probabilistic models that describe it accurately. However, the vast amounts of MS/MS data being generated now make it possible to use data-driven machine learning methods to develop discriminative ranking-based models that predict the intensity ranks of a peptide's fragment ions. We use simple sequence-based features that get combined by a boosting algorithm into models that make peak rank predictions with high accuracy. In an accompanying manuscript, we demonstrate how these prediction models are used to significantly improve the performance of peptide identification algorithms. The models can also be useful in the design of optimal multiple reaction monitoring (MRM) transitions, in cases where there is insufficient experimental data to guide the peak selection process. The prediction algorithm can also be run independently through PepNovo+, which is available for download from http://bix.ucsd.edu/Software/PepNovo.html.

  6. Predicting Intensity Ranks of Peptide Fragment Ions

    PubMed Central

    Frank, Ari M.

    2009-01-01

    Accurate modeling of peptide fragmentation is necessary for the development of robust scoring functions for peptide-spectrum matches, which are the cornerstone of MS/MS-based identification algorithms. Unfortunately, peptide fragmentation is a complex process that can involve several competing chemical pathways, which makes it difficult to develop generative probabilistic models that describe it accurately. However, the vast amounts of MS/MS data being generated now make it possible to use data-driven machine learning methods to develop discriminative ranking-based models that predict the intensity ranks of a peptide's fragment ions. We use simple sequence-based features that get combined by a boosting algorithm in to models that make peak rank predictions with high accuracy. In an accompanying manuscript, we demonstrate how these prediction models are used to significantly improve the performance of peptide identification algorithms. The models can also be useful in the design of optimal MRM transitions, in cases where there is insufficient experimental data to guide the peak selection process. The prediction algorithm can also be run independently through PepNovo+, which is available for download from http://bix.ucsd.edu/Software/PepNovo.html. PMID:19256476

  7. Large-scale structure prediction by improved contact predictions and model quality assessment.

    PubMed

    Michel, Mirco; Menéndez Hurtado, David; Uziela, Karolis; Elofsson, Arne

    2017-07-15

    Accurate contact predictions can be used for predicting the structure of proteins. Until recently these methods were limited to very big protein families, decreasing their utility. However, recent progress by combining direct coupling analysis with machine learning methods has made it possible to predict accurate contact maps for smaller families. To what extent these predictions can be used to produce accurate models of the families is not known. We present the PconsFold2 pipeline that uses contact predictions from PconsC3, the CONFOLD folding algorithm and model quality estimations to predict the structure of a protein. We show that the model quality estimation significantly increases the number of models that reliably can be identified. Finally, we apply PconsFold2 to 6379 Pfam families of unknown structure and find that PconsFold2 can, with an estimated 90% specificity, predict the structure of up to 558 Pfam families of unknown structure. Out of these, 415 have not been reported before. Datasets as well as models of all the 558 Pfam families are available at http://c3.pcons.net/ . All programs used here are freely available. arne@bioinfo.se. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

  8. The predictive accuracy of PREDICT: a personalized decision-making tool for Southeast Asian women with breast cancer.

    PubMed

    Wong, Hoong-Seam; Subramaniam, Shridevi; Alias, Zarifah; Taib, Nur Aishah; Ho, Gwo-Fuang; Ng, Char-Hong; Yip, Cheng-Har; Verkooijen, Helena M; Hartman, Mikael; Bhoo-Pathy, Nirmala

    2015-02-01

    Web-based prognostication tools may provide a simple and economically feasible option to aid prognostication and selection of chemotherapy in early breast cancers. We validated PREDICT, a free online breast cancer prognostication and treatment benefit tool, in a resource-limited setting. All 1480 patients who underwent complete surgical treatment for stages I to III breast cancer from 1998 to 2006 were identified from the prospective breast cancer registry of University Malaya Medical Centre, Kuala Lumpur, Malaysia. Calibration was evaluated by comparing the model-predicted overall survival (OS) with patients' actual OS. Model discrimination was tested using receiver-operating characteristic (ROC) analysis. Median age at diagnosis was 50 years. The median tumor size at presentation was 3 cm and 54% of patients had lymph node-negative disease. About 55% of women had estrogen receptor-positive breast cancer. Overall, the model-predicted 5 and 10-year OS was 86.3% and 77.5%, respectively, whereas the observed 5 and 10-year OS was 87.6% (difference: -1.3%) and 74.2% (difference: 3.3%), respectively; P values for goodness-of-fit test were 0.18 and 0.12, respectively. The program was accurate in most subgroups of patients, but significantly overestimated survival in patients aged <40 years, and in those receiving neoadjuvant chemotherapy. PREDICT performed well in terms of discrimination; areas under ROC curve were 0.78 (95% confidence interval [CI]: 0.74-0.81) and 0.73 (95% CI: 0.68-0.78) for 5 and 10-year OS, respectively. Based on its accurate performance in this study, PREDICT may be clinically useful in prognosticating women with breast cancer and personalizing breast cancer treatment in resource-limited settings.

  9. SAE for the prediction of road traffic status from taxicab operating data and bus smart card data

    NASA Astrophysics Data System (ADS)

    Zhengfeng, Huang; Pengjun, Zheng; Wenjun, Xu; Gang, Ren

    Road traffic status is significant for trip decision and traffic management, and thus should be predicted accurately. A contribution is that we consider multi-modal data for traffic status prediction than only using single source data. With the substantial data from Ningbo Passenger Transport Management Sector (NPTMS), we wished to determine whether it was possible to develop Stacked Autoencoders (SAEs) for accurately predicting road traffic status from taxicab operating data and bus smart card data. We show that SAE performed better than linear regression model and Back Propagation (BP) neural network for determining the relationship between road traffic status and those factors. In a 26-month data experiment using SAE, we show that it is possible to develop highly accurate predictions (91% test accuracy) of road traffic status from daily taxicab operating data and bus smart card data.

  10. Predicting falls in older adults using the four square step test.

    PubMed

    Cleary, Kimberly; Skornyakov, Elena

    2017-10-01

    The Four Square Step Test (FSST) is a performance-based balance tool involving stepping over four single-point canes placed on the floor in a cross configuration. The purpose of this study was to evaluate properties of the FSST in older adults who lived independently. Forty-five community dwelling older adults provided fall history and completed the FSST, Berg Balance Scale (BBS), Timed Up and Go (TUG), and Tinetti in random order. Future falls were recorded for 12 months following testing. The FSST accurately distinguished between non-fallers and multiple fallers, and the 15-second threshold score accurately distinguished multiple fallers from non-multiple fallers based on fall history. The FSST predicted future falls, and performance on the FSST was significantly correlated with performance on the BBS, TUG, and Tinetti. However, the test is not appropriate for older adults who use walkers. Overall, the FSST is a valid yet underutilized measure of balance performance and fall prediction tool that physical therapists should consider using in ambulatory community dwelling older adults.

  11. Predicting speech intelligibility in noise for hearing-critical jobs

    NASA Astrophysics Data System (ADS)

    Soli, Sigfrid D.; Laroche, Chantal; Giguere, Christian

    2003-10-01

    Many jobs require auditory abilities such as speech communication, sound localization, and sound detection. An employee for whom these abilities are impaired may constitute a safety risk for himself or herself, for fellow workers, and possibly for the general public. A number of methods have been used to predict these abilities from diagnostic measures of hearing (e.g., the pure-tone audiogram); however, these methods have not proved to be sufficiently accurate for predicting performance in the noise environments where hearing-critical jobs are performed. We have taken an alternative and potentially more accurate approach. A direct measure of speech intelligibility in noise, the Hearing in Noise Test (HINT), is instead used to screen individuals. The screening criteria are validated by establishing the empirical relationship between the HINT score and the auditory abilities of the individual, as measured in laboratory recreations of real-world workplace noise environments. The psychometric properties of the HINT enable screening of individuals with an acceptable amount of error. In this presentation, we will describe the predictive model and report the results of field measurements and laboratory studies used to provide empirical validation of the model. [Work supported by Fisheries and Oceans Canada.

  12. Convergence in parameters and predictions using computational experimental design.

    PubMed

    Hagen, David R; White, Jacob K; Tidor, Bruce

    2013-08-06

    Typically, biological models fitted to experimental data suffer from significant parameter uncertainty, which can lead to inaccurate or uncertain predictions. One school of thought holds that accurate estimation of the true parameters of a biological system is inherently problematic. Recent work, however, suggests that optimal experimental design techniques can select sets of experiments whose members probe complementary aspects of a biochemical network that together can account for its full behaviour. Here, we implemented an experimental design approach for selecting sets of experiments that constrain parameter uncertainty. We demonstrated with a model of the epidermal growth factor-nerve growth factor pathway that, after synthetically performing a handful of optimal experiments, the uncertainty in all 48 parameters converged below 10 per cent. Furthermore, the fitted parameters converged to their true values with a small error consistent with the residual uncertainty. When untested experimental conditions were simulated with the fitted models, the predicted species concentrations converged to their true values with errors that were consistent with the residual uncertainty. This paper suggests that accurate parameter estimation is achievable with complementary experiments specifically designed for the task, and that the resulting parametrized models are capable of accurate predictions.

  13. The development of performance prediction models for Virginia's interstate highway system.

    DOT National Transportation Integrated Search

    1995-01-01

    Performance prediction models are a key component of any well-designed pavement management system. In this study, data compiled from the condition surveys conducted annually on Virginia's pavement network were used to develop prediction models for mo...

  14. Ensemble framework based real-time respiratory motion prediction for adaptive radiotherapy applications.

    PubMed

    Tatinati, Sivanagaraja; Nazarpour, Kianoush; Tech Ang, Wei; Veluvolu, Kalyana C

    2016-08-01

    Successful treatment of tumors with motion-adaptive radiotherapy requires accurate prediction of respiratory motion, ideally with a prediction horizon larger than the latency in radiotherapy system. Accurate prediction of respiratory motion is however a non-trivial task due to the presence of irregularities and intra-trace variabilities, such as baseline drift and temporal changes in fundamental frequency pattern. In this paper, to enhance the accuracy of the respiratory motion prediction, we propose a stacked regression ensemble framework that integrates heterogeneous respiratory motion prediction algorithms. We further address two crucial issues for developing a successful ensemble framework: (1) selection of appropriate prediction methods to ensemble (level-0 methods) among the best existing prediction methods; and (2) finding a suitable generalization approach that can successfully exploit the relative advantages of the chosen level-0 methods. The efficacy of the developed ensemble framework is assessed with real respiratory motion traces acquired from 31 patients undergoing treatment. Results show that the developed ensemble framework improves the prediction performance significantly compared to the best existing methods. Copyright © 2016 IPEM. Published by Elsevier Ltd. All rights reserved.

  15. Protein docking prediction using predicted protein-protein interface.

    PubMed

    Li, Bin; Kihara, Daisuke

    2012-01-10

    Many important cellular processes are carried out by protein complexes. To provide physical pictures of interacting proteins, many computational protein-protein prediction methods have been developed in the past. However, it is still difficult to identify the correct docking complex structure within top ranks among alternative conformations. We present a novel protein docking algorithm that utilizes imperfect protein-protein binding interface prediction for guiding protein docking. Since the accuracy of protein binding site prediction varies depending on cases, the challenge is to develop a method which does not deteriorate but improves docking results by using a binding site prediction which may not be 100% accurate. The algorithm, named PI-LZerD (using Predicted Interface with Local 3D Zernike descriptor-based Docking algorithm), is based on a pair wise protein docking prediction algorithm, LZerD, which we have developed earlier. PI-LZerD starts from performing docking prediction using the provided protein-protein binding interface prediction as constraints, which is followed by the second round of docking with updated docking interface information to further improve docking conformation. Benchmark results on bound and unbound cases show that PI-LZerD consistently improves the docking prediction accuracy as compared with docking without using binding site prediction or using the binding site prediction as post-filtering. We have developed PI-LZerD, a pairwise docking algorithm, which uses imperfect protein-protein binding interface prediction to improve docking accuracy. PI-LZerD consistently showed better prediction accuracy over alternative methods in the series of benchmark experiments including docking using actual docking interface site predictions as well as unbound docking cases.

  16. Occupational-Specific Strength Predicts Astronaut-Related Task Performance in a Weighted Suit.

    PubMed

    Taylor, Andrew; Kotarsky, Christopher J; Bond, Colin W; Hackney, Kyle J

    2018-01-01

    Future space missions beyond low Earth orbit will require deconditioned astronauts to perform occupationally relevant tasks within a planetary spacesuit. The prediction of time-to-completion (TTC) of astronaut tasks will be critical for crew safety, autonomous operations, and mission success. This exploratory study determined if the addition of task-specific strength testing to current standard lower body testing would enhance the prediction of TTC in a 1-G test battery. Eight healthy participants completed NASA lower body strength tests, occupationally specific strength tests, and performed six task simulations (hand drilling, construction wrenching, incline walking, collecting weighted samples, and dragging an unresponsive crewmember to safety) in a 48-kg weighted suit. The TTC for each task was recorded and summed to obtain a total TTC for the test battery. Linear regression was used to predict total TTC with two models: 1) NASA lower body strength tests; and 2) NASA lower body strength tests + occupationally specific strength tests. Total TTC of the test battery ranged from 20.2-44.5 min. The lower body strength test alone accounted for 61% of the variability in total TTC. The addition of hand drilling and wrenching strength tests accounted for 99% of the variability in total TTC. Adding occupationally specific strength tests (hand drilling and wrenching) to standard lower body strength tests successfully predicted total TTC in a performance test battery within a weighted suit. Future research should couple these strength tests with higher fidelity task simulations to determine the utility and efficacy of task performance prediction.Taylor A, Kotarsky CJ, Bond CW, Hackney KJ. Occupational-specific strength predicts astronaut-related task performance in a weighted suit. Aerosp Med Hum Perform. 2018; 89(1):58-62.

  17. Calibration between Undergraduate Students' Prediction of and Actual Performance: The Role of Gender and Performance Attributions

    ERIC Educational Resources Information Center

    Gutierrez, Antonio P.; Price, Addison F.

    2017-01-01

    This study investigated changes in male and female students' prediction and postdiction calibration accuracy and bias scores, and the predictive effects of explanatory styles on these variables beyond gender. Seventy undergraduate students rated their confidence in performance before and after a 40-item exam. There was an improvement in students'…

  18. Children Can Accurately Monitor and Control Their Number-Line Estimation Performance

    ERIC Educational Resources Information Center

    Wall, Jenna L.; Thompson, Clarissa A.; Dunlosky, John; Merriman, William E.

    2016-01-01

    Accurate monitoring and control are essential for effective self-regulated learning. These metacognitive abilities may be particularly important for developing math skills, such as when children are deciding whether a math task is difficult or whether they made a mistake on a particular item. The present experiments investigate children's ability…

  19. Accurate and scalable social recommendation using mixed-membership stochastic block models.

    PubMed

    Godoy-Lorite, Antonia; Guimerà, Roger; Moore, Cristopher; Sales-Pardo, Marta

    2016-12-13

    With increasing amounts of information available, modeling and predicting user preferences-for books or articles, for example-are becoming more important. We present a collaborative filtering model, with an associated scalable algorithm, that makes accurate predictions of users' ratings. Like previous approaches, we assume that there are groups of users and of items and that the rating a user gives an item is determined by their respective group memberships. However, we allow each user and each item to belong simultaneously to mixtures of different groups and, unlike many popular approaches such as matrix factorization, we do not assume that users in each group prefer a single group of items. In particular, we do not assume that ratings depend linearly on a measure of similarity, but allow probability distributions of ratings to depend freely on the user's and item's groups. The resulting overlapping groups and predicted ratings can be inferred with an expectation-maximization algorithm whose running time scales linearly with the number of observed ratings. Our approach enables us to predict user preferences in large datasets and is considerably more accurate than the current algorithms for such large datasets.

  20. Accurate and scalable social recommendation using mixed-membership stochastic block models

    PubMed Central

    Godoy-Lorite, Antonia; Moore, Cristopher

    2016-01-01

    With increasing amounts of information available, modeling and predicting user preferences—for books or articles, for example—are becoming more important. We present a collaborative filtering model, with an associated scalable algorithm, that makes accurate predictions of users’ ratings. Like previous approaches, we assume that there are groups of users and of items and that the rating a user gives an item is determined by their respective group memberships. However, we allow each user and each item to belong simultaneously to mixtures of different groups and, unlike many popular approaches such as matrix factorization, we do not assume that users in each group prefer a single group of items. In particular, we do not assume that ratings depend linearly on a measure of similarity, but allow probability distributions of ratings to depend freely on the user’s and item’s groups. The resulting overlapping groups and predicted ratings can be inferred with an expectation-maximization algorithm whose running time scales linearly with the number of observed ratings. Our approach enables us to predict user preferences in large datasets and is considerably more accurate than the current algorithms for such large datasets. PMID:27911773

  1. Prediction Of The Expected Safety Performance Of Rural Two-Lane Highways

    DOT National Transportation Integrated Search

    2000-12-01

    This report presents an algorithm for predicting the safety performance of a rural two-lane highway. The accident prediction algorithm consists of base models and accident modification factors for both roadway segments and at-grade intersections on r...

  2. MHA admission criteria and program performance: do they predict career performance?

    PubMed

    Porter, J; Galfano, V J

    1987-01-01

    The purpose of this study was to determine to what extent admission criteria predict graduate school and career performance. The study also analyzed which objective and subjective criteria served as the best predictors. MHA graduates of the University of Minnesota from 1974 to 1977 were surveyed to assess career performance. Student files served as the data base on admission criteria and program performance. Career performance was measured by four variables: total compensation, satisfaction, fiscal responsibility, and level of authority. High levels of MHA program performance were associated with women who had high undergraduate GPAs from highly selective undergraduate colleges, were undergraduate business majors, and participated in extracurricular activities. High levels of compensation were associated with relatively low undergraduate GPAs, high levels of participation in undergraduate extracurricular activities, and being single at admission to graduate school. Admission to MHA programs should be based upon both objective and subjective criteria. Emphasis should be placed upon the selection process for MHA students since admission criteria are shown to explain 30 percent of the variability in graduate program performance, and as much as 65 percent of the variance in level of position authority.

  3. Raoult's law revisited: accurately predicting equilibrium relative humidity points for humidity control experiments.

    PubMed

    Bowler, Michael G; Bowler, David R; Bowler, Matthew W

    2017-04-01

    The humidity surrounding a sample is an important variable in scientific experiments. Biological samples in particular require not just a humid atmosphere but often a relative humidity (RH) that is in equilibrium with a stabilizing solution required to maintain the sample in the same state during measurements. The controlled dehydration of macromolecular crystals can lead to significant increases in crystal order, leading to higher diffraction quality. Devices that can accurately control the humidity surrounding crystals while monitoring diffraction have led to this technique being increasingly adopted, as the experiments become easier and more reproducible. Matching the RH to the mother liquor is the first step in allowing the stable mounting of a crystal. In previous work [Wheeler, Russi, Bowler & Bowler (2012). Acta Cryst. F 68 , 111-114], the equilibrium RHs were measured for a range of concentrations of the most commonly used precipitants in macromolecular crystallography and it was shown how these related to Raoult's law for the equilibrium vapour pressure of water above a solution. However, a discrepancy between the measured values and those predicted by theory could not be explained. Here, a more precise humidity control device has been used to determine equilibrium RH points. The new results are in agreement with Raoult's law. A simple argument in statistical mechanics is also presented, demonstrating that the equilibrium vapour pressure of a solvent is proportional to its mole fraction in an ideal solution: Raoult's law. The same argument can be extended to the case where the solvent and solute molecules are of different sizes, as is the case with polymers. The results provide a framework for the correct maintenance of the RH surrounding a sample.

  4. How Accurately Can We Predict Eclipses for Algol? (Poster abstract)

    NASA Astrophysics Data System (ADS)

    Turner, D.

    2016-06-01

    (Abstract only) beta Persei, or Algol, is a very well known eclipsing binary system consisting of a late B-type dwarf that is regularly eclipsed by a GK subgiant every 2.867 days. Eclipses, which last about 8 hours, are regular enough that predictions for times of minima are published in various places, Sky & Telescope magazine and The Observer's Handbook, for example. But eclipse minimum lasts for less than a half hour, whereas subtle mistakes in the current ephemeris for the star can result in predictions that are off by a few hours or more. The Algol system is fairly complex, with the Algol A and Algol B eclipsing system also orbited by Algol C with an orbital period of nearly 2 years. Added to that are complex long-term O-C variations with a periodicity of almost two centuries that, although suggested by Hoffmeister to be spurious, fit the type of light travel time variations expected for a fourth star also belonging to the system. The AB sub-system also undergoes mass transfer events that add complexities to its O-C behavior. Is it actually possible to predict precise times of eclipse minima for Algol months in advance given such complications, or is it better to encourage ongoing observations of the star so that O-C variations can be tracked in real time?

  5. Prediction of Human Performance Using Electroencephalography under Different Indoor Room Temperatures

    PubMed Central

    Zhang, Tinghe; Mao, Zijing; Xu, Xiaojing; Zhang, Lin; Pack, Daniel J.; Dong, Bing; Huang, Yufei

    2018-01-01

    Varying indoor environmental conditions is known to affect office worker’s performance; wherein past research studies have reported the effects of unfavorable indoor temperature and air quality causing sick building syndrome (SBS) among office workers. Thus, investigating factors that can predict performance in changing indoor environments have become a highly important research topic bearing significant impact in our society. While past research studies have attempted to determine predictors for performance, they do not provide satisfactory prediction ability. Therefore, in this preliminary study, we attempt to predict performance during office-work tasks triggered by different indoor room temperatures (22.2 °C and 30 °C) from human brain signals recorded using electroencephalography (EEG). Seven participants were recruited, from whom EEG, skin temperature, heart rate and thermal survey questionnaires were collected. Regression analyses were carried out to investigate the effectiveness of using EEG power spectral densities (PSD) as predictors of performance. Our results indicate EEG PSDs as predictors provide the highest R2 (> 0.70), that is 17 times higher than using other physiological signals as predictors and is more robust. Finally, the paper provides insight on the selected predictors based on brain activity patterns for low- and high-performance levels under different indoor-temperatures. PMID:29690601

  6. Prediction of Human Performance Using Electroencephalography under Different Indoor Room Temperatures.

    PubMed

    Nayak, Tapsya; Zhang, Tinghe; Mao, Zijing; Xu, Xiaojing; Zhang, Lin; Pack, Daniel J; Dong, Bing; Huang, Yufei

    2018-04-23

    Varying indoor environmental conditions is known to affect office worker’s performance; wherein past research studies have reported the effects of unfavorable indoor temperature and air quality causing sick building syndrome (SBS) among office workers. Thus, investigating factors that can predict performance in changing indoor environments have become a highly important research topic bearing significant impact in our society. While past research studies have attempted to determine predictors for performance, they do not provide satisfactory prediction ability. Therefore, in this preliminary study, we attempt to predict performance during office-work tasks triggered by different indoor room temperatures (22.2 °C and 30 °C) from human brain signals recorded using electroencephalography (EEG). Seven participants were recruited, from whom EEG, skin temperature, heart rate and thermal survey questionnaires were collected. Regression analyses were carried out to investigate the effectiveness of using EEG power spectral densities (PSD) as predictors of performance. Our results indicate EEG PSDs as predictors provide the highest R ² (> 0.70), that is 17 times higher than using other physiological signals as predictors and is more robust. Finally, the paper provides insight on the selected predictors based on brain activity patterns for low- and high-performance levels under different indoor-temperatures.

  7. Prediction of Vigilant Attention and Cognitive Performance Using Self-Reported Alertness, Circadian Phase, Hours since Awakening, and Accumulated Sleep Loss

    PubMed Central

    Bermudez, Eduardo B.; Klerman, Elizabeth B.; Czeisler, Charles A.; Cohen, Daniel A.; Wyatt, James K.; Phillips, Andrew J. K.

    2016-01-01

    Sleep restriction causes impaired cognitive performance that can result in adverse consequences in many occupational settings. Individuals may rely on self-perceived alertness to decide if they are able to adequately perform a task. It is therefore important to determine the relationship between an individual’s self-assessed alertness and their objective performance, and how this relationship depends on circadian phase, hours since awakening, and cumulative lost hours of sleep. Healthy young adults (aged 18–34) completed an inpatient schedule that included forced desynchrony of sleep/wake and circadian rhythms with twelve 42.85-hour “days” and either a 1:2 (n = 8) or 1:3.3 (n = 9) ratio of sleep-opportunity:enforced-wakefulness. We investigated whether subjective alertness (visual analog scale), circadian phase (melatonin), hours since awakening, and cumulative sleep loss could predict objective performance on the Psychomotor Vigilance Task (PVT), an Addition/Calculation Test (ADD) and the Digit Symbol Substitution Test (DSST). Mathematical models that allowed nonlinear interactions between explanatory variables were evaluated using the Akaike Information Criterion (AIC). Subjective alertness was the single best predictor of PVT, ADD, and DSST performance. Subjective alertness alone, however, was not an accurate predictor of PVT performance. The best AIC scores for PVT and DSST were achieved when all explanatory variables were included in the model. The best AIC score for ADD was achieved with circadian phase and subjective alertness variables. We conclude that subjective alertness alone is a weak predictor of objective vigilant or cognitive performance. Predictions can, however, be improved by knowing an individual’s circadian phase, current wake duration, and cumulative sleep loss. PMID:27019198

  8. A probabilistic approach to photovoltaic generator performance prediction

    NASA Astrophysics Data System (ADS)

    Khallat, M. A.; Rahman, S.

    1986-09-01

    A method for predicting the performance of a photovoltaic (PV) generator based on long term climatological data and expected cell performance is described. The equations for cell model formulation are provided. Use of the statistical model for characterizing the insolation level is discussed. The insolation data is fitted to appropriate probability distribution functions (Weibull, beta, normal). The probability distribution functions are utilized to evaluate the capacity factors of PV panels or arrays. An example is presented revealing the applicability of the procedure.

  9. Computational Model-Based Prediction of Human Episodic Memory Performance Based on Eye Movements

    NASA Astrophysics Data System (ADS)

    Sato, Naoyuki; Yamaguchi, Yoko

    Subjects' episodic memory performance is not simply reflected by eye movements. We use a ‘theta phase coding’ model of the hippocampus to predict subjects' memory performance from their eye movements. Results demonstrate the ability of the model to predict subjects' memory performance. These studies provide a novel approach to computational modeling in the human-machine interface.

  10. Genome-Wide Polygenic Scores Predict Reading Performance Throughout the School Years

    PubMed Central

    Selzam, Saskia; Dale, Philip S.; Wagner, Richard K.; DeFries, John C.; Cederlöf, Martin; O’Reilly, Paul F.; Krapohl, Eva; Plomin, Robert

    2017-01-01

    ABSTRACT It is now possible to create individual-specific genetic scores, called genome-wide polygenic scores (GPS). We used a GPS for years of education (EduYears) to predict reading performance assessed at UK National Curriculum Key Stages 1 (age 7), 2 (age 12) and 3 (age 14) and on reading tests administered at ages 7 and 12 in a UK sample of 5,825 unrelated individuals. EduYears GPS accounts for up to 5% of the variance in reading performance at age 14. GPS predictions remained significant after accounting for general cognitive ability and family socioeconomic status. Reading performance of children in the lowest and highest 12.5% of the EduYears GPS distribution differed by a mean growth in reading ability of approximately two school years. It seems certain that polygenic scores will be used to predict strengths and weaknesses in education. PMID:28706435

  11. Estimating the state of a geophysical system with sparse observations: time delay methods to achieve accurate initial states for prediction

    NASA Astrophysics Data System (ADS)

    An, Zhe; Rey, Daniel; Ye, Jingxin; Abarbanel, Henry D. I.

    2017-01-01

    The problem of forecasting the behavior of a complex dynamical system through analysis of observational time-series data becomes difficult when the system expresses chaotic behavior and the measurements are sparse, in both space and/or time. Despite the fact that this situation is quite typical across many fields, including numerical weather prediction, the issue of whether the available observations are "sufficient" for generating successful forecasts is still not well understood. An analysis by Whartenby et al. (2013) found that in the context of the nonlinear shallow water equations on a β plane, standard nudging techniques require observing approximately 70 % of the full set of state variables. Here we examine the same system using a method introduced by Rey et al. (2014a), which generalizes standard nudging methods to utilize time delayed measurements. We show that in certain circumstances, it provides a sizable reduction in the number of observations required to construct accurate estimates and high-quality predictions. In particular, we find that this estimate of 70 % can be reduced to about 33 % using time delays, and even further if Lagrangian drifter locations are also used as measurements.

  12. Predicting Next Year's Resources--Short-Term Enrollment Forecasting for Accurate Budget Planning. AIR Forum Paper 1978.

    ERIC Educational Resources Information Center

    Salley, Charles D.

    Accurate enrollment forecasts are a prerequisite for reliable budget projections. This is because tuition payments make up a significant portion of a university's revenue, and anticipated revenue is the immediate constraint on current operating expenditures. Accurate forecasts are even more critical to revenue projections when a university's…

  13. Effectiveness of Genomic Prediction of Maize Hybrid Performance in Different Breeding Populations and Environments

    PubMed Central

    Windhausen, Vanessa S.; Atlin, Gary N.; Hickey, John M.; Crossa, Jose; Jannink, Jean-Luc; Sorrells, Mark E.; Raman, Babu; Cairns, Jill E.; Tarekegne, Amsal; Semagn, Kassa; Beyene, Yoseph; Grudloyma, Pichet; Technow, Frank; Riedelsheimer, Christian; Melchinger, Albrecht E.

    2012-01-01

    Genomic prediction is expected to considerably increase genetic gains by increasing selection intensity and accelerating the breeding cycle. In this study, marker effects estimated in 255 diverse maize (Zea mays L.) hybrids were used to predict grain yield, anthesis date, and anthesis-silking interval within the diversity panel and testcross progenies of 30 F2-derived lines from each of five populations. Although up to 25% of the genetic variance could be explained by cross validation within the diversity panel, the prediction of testcross performance of F2-derived lines using marker effects estimated in the diversity panel was on average zero. Hybrids in the diversity panel could be grouped into eight breeding populations differing in mean performance. When performance was predicted separately for each breeding population on the basis of marker effects estimated in the other populations, predictive ability was low (i.e., 0.12 for grain yield). These results suggest that prediction resulted mostly from differences in mean performance of the breeding populations and less from the relationship between the training and validation sets or linkage disequilibrium with causal variants underlying the predicted traits. Potential uses for genomic prediction in maize hybrid breeding are discussed emphasizing the need of (1) a clear definition of the breeding scenario in which genomic prediction should be applied (i.e., prediction among or within populations), (2) a detailed analysis of the population structure before performing cross validation, and (3) larger training sets with strong genetic relationship to the validation set. PMID:23173094

  14. Effectiveness of genomic prediction of maize hybrid performance in different breeding populations and environments.

    PubMed

    Windhausen, Vanessa S; Atlin, Gary N; Hickey, John M; Crossa, Jose; Jannink, Jean-Luc; Sorrells, Mark E; Raman, Babu; Cairns, Jill E; Tarekegne, Amsal; Semagn, Kassa; Beyene, Yoseph; Grudloyma, Pichet; Technow, Frank; Riedelsheimer, Christian; Melchinger, Albrecht E

    2012-11-01

    Genomic prediction is expected to considerably increase genetic gains by increasing selection intensity and accelerating the breeding cycle. In this study, marker effects estimated in 255 diverse maize (Zea mays L.) hybrids were used to predict grain yield, anthesis date, and anthesis-silking interval within the diversity panel and testcross progenies of 30 F(2)-derived lines from each of five populations. Although up to 25% of the genetic variance could be explained by cross validation within the diversity panel, the prediction of testcross performance of F(2)-derived lines using marker effects estimated in the diversity panel was on average zero. Hybrids in the diversity panel could be grouped into eight breeding populations differing in mean performance. When performance was predicted separately for each breeding population on the basis of marker effects estimated in the other populations, predictive ability was low (i.e., 0.12 for grain yield). These results suggest that prediction resulted mostly from differences in mean performance of the breeding populations and less from the relationship between the training and validation sets or linkage disequilibrium with causal variants underlying the predicted traits. Potential uses for genomic prediction in maize hybrid breeding are discussed emphasizing the need of (1) a clear definition of the breeding scenario in which genomic prediction should be applied (i.e., prediction among or within populations), (2) a detailed analysis of the population structure before performing cross validation, and (3) larger training sets with strong genetic relationship to the validation set.

  15. Quantifying the Performances of DFT for Predicting Vibrationally Resolved Optical Spectra: Asymmetric Fluoroborate Dyes as Working Examples.

    PubMed

    Bednarska, Joanna; Zaleśny, Robert; Bartkowiak, Wojciech; Ośmiałowski, Borys; Medved', Miroslav; Jacquemin, Denis

    2017-09-12

    This article aims at a quantitative assessment of the performances of a panel of exchange-correlation functionals, including semilocal (BLYP and PBE), global hybrids (B3LYP, PBE0, M06, BHandHLYP, M06-2X, and M06-HF), and range-separated hybrids (CAM-B3LYP, LC-ωPBE, LC-BLYP, ωB97X, and ωB97X-D), in predicting the vibrationally resolved absorption spectra of BF 2 -carrying compounds. To this end, for 19 difluoroborates as examples, we use, as a metric, the vibrational reorganization energy (λ vib ) that can be determined based on the computationally efficient linear coupling model (a.k.a. vertical gradient method). The reference values of λ vib were determined by employing the CC2 method combined with the cc-pVTZ basis set for a representative subset of molecules. To validate the performances of CC2, comparisons with experimental data have been carried out as well. This study shows that the vibrational reorganization energy, involving Huang-Rhys factors and normal-mode frequencies, can indeed be used to quantify the reliability of functionals in the calculations of the vibrational fine structure of absorption bands, i.e., an accurate prediction of the vibrational reorganization energy leads to absorption band shapes better fitting the selected reference. The CAM-B3LYP, M06-2X, ωB97X-D, ωB97X, and BHandHLYP functionals all deliver vibrational reorganization energies with absolute relative errors smaller than 20% compared to CC2, whereas 10% accuracy can be achieved with the first three functionals. Indeed, the set of examined exchange-correlation functionals can be divided into three groups: (i) BLYP, B3LYP, PBE, PBE0, and M06 yield inaccurate band shapes (λ vib,TDDFT < λ vib,CC2 ), (ii) BHandHLYP, CAM-B3LYP, M06-2X, ωB97X, and ωB97X-D provide accurate band shapes (λ vib,TDDFT ≈ λ vib,CC2 ), and (iii) LC-ωPBE, LC-BLYP, and M06-HF deliver rather poor band topologies (λ vib,TDDFT > λ vib,CC2 ). This study also demonstrates that λ vib can be reliably

  16. [Evaluation of performance of five bioinformatics software for the prediction of missense mutations].

    PubMed

    Chen, Qianting; Dai, Congling; Zhang, Qianjun; Du, Juan; Li, Wen

    2016-10-01

    To study the prediction performance evaluation with five kinds of bioinformatics software (SIFT, PolyPhen2, MutationTaster, Provean, MutationAssessor). From own database for genetic mutations collected over the past five years, Chinese literature database, Human Gene Mutation Database, and dbSNP, 121 missense mutations confirmed by functional studies, and 121 missense mutations suspected to be pathogenic by pedigree analysis were used as positive gold standard, while 242 missense mutations with minor allele frequency (MAF)>5% in dominant hereditary diseases were used as negative gold standard. The selected mutations were predicted with the five software. Based on the results, the performance of the five software was evaluated for their sensitivity, specificity, positive predict value, false positive rate, negative predict value, false negative rate, false discovery rate, accuracy, and receiver operating characteristic curve (ROC). In terms of sensitivity, negative predictive value and false negative rate, the rank was MutationTaster, PolyPhen2, Provean, SIFT, and MutationAssessor. For specificity and false positive rate, the rank was MutationTaster, Provean, MutationAssessor, SIFT, and PolyPhen2. For positive predict value and false discovery rate, the rank was MutationTaster, Provean, MutationAssessor, PolyPhen2, and SIFT. For area under the ROC curve (AUC) and accuracy, the rank was MutationTaster, Provean, PolyPhen2, MutationAssessor, and SIFT. The prediction performance of software may be different when using different parameters. Among the five software, MutationTaster has the best prediction performance.

  17. Do physiological measures predict selected CrossFit(®) benchmark performance?

    PubMed

    Butcher, Scotty J; Neyedly, Tyler J; Horvey, Karla J; Benko, Chad R

    2015-01-01

    CrossFit(®) is a new but extremely popular method of exercise training and competition that involves constantly varied functional movements performed at high intensity. Despite the popularity of this training method, the physiological determinants of CrossFit performance have not yet been reported. The purpose of this study was to determine whether physiological and/or muscle strength measures could predict performance on three common CrossFit "Workouts of the Day" (WODs). Fourteen CrossFit Open or Regional athletes completed, on separate days, the WODs "Grace" (30 clean and jerks for time), "Fran" (three rounds of thrusters and pull-ups for 21, 15, and nine repetitions), and "Cindy" (20 minutes of rounds of five pull-ups, ten push-ups, and 15 bodyweight squats), as well as the "CrossFit Total" (1 repetition max [1RM] back squat, overhead press, and deadlift), maximal oxygen consumption (VO2max), and Wingate anaerobic power/capacity testing. Performance of Grace and Fran was related to whole-body strength (CrossFit Total) (r=-0.88 and -0.65, respectively) and anaerobic threshold (r=-0.61 and -0.53, respectively); however, whole-body strength was the only variable to survive the prediction regression for both of these WODs (R (2)=0.77 and 0.42, respectively). There were no significant associations or predictors for Cindy. CrossFit benchmark WOD performance cannot be predicted by VO2max, Wingate power/capacity, or either respiratory compensation or anaerobic thresholds. Of the data measured, only whole-body strength can partially explain performance on Grace and Fran, although anaerobic threshold also exhibited association with performance. Along with their typical training, CrossFit athletes should likely ensure an adequate level of strength and aerobic endurance to optimize performance on at least some benchmark WODs.

  18. Do physiological measures predict selected CrossFit® benchmark performance?

    PubMed Central

    Butcher, Scotty J; Neyedly, Tyler J; Horvey, Karla J; Benko, Chad R

    2015-01-01

    Purpose CrossFit® is a new but extremely popular method of exercise training and competition that involves constantly varied functional movements performed at high intensity. Despite the popularity of this training method, the physiological determinants of CrossFit performance have not yet been reported. The purpose of this study was to determine whether physiological and/or muscle strength measures could predict performance on three common CrossFit “Workouts of the Day” (WODs). Materials and methods Fourteen CrossFit Open or Regional athletes completed, on separate days, the WODs “Grace” (30 clean and jerks for time), “Fran” (three rounds of thrusters and pull-ups for 21, 15, and nine repetitions), and “Cindy” (20 minutes of rounds of five pull-ups, ten push-ups, and 15 bodyweight squats), as well as the “CrossFit Total” (1 repetition max [1RM] back squat, overhead press, and deadlift), maximal oxygen consumption (VO2max), and Wingate anaerobic power/capacity testing. Results Performance of Grace and Fran was related to whole-body strength (CrossFit Total) (r=−0.88 and −0.65, respectively) and anaerobic threshold (r=−0.61 and −0.53, respectively); however, whole-body strength was the only variable to survive the prediction regression for both of these WODs (R2=0.77 and 0.42, respectively). There were no significant associations or predictors for Cindy. Conclusion CrossFit benchmark WOD performance cannot be predicted by VO2max, Wingate power/capacity, or either respiratory compensation or anaerobic thresholds. Of the data measured, only whole-body strength can partially explain performance on Grace and Fran, although anaerobic threshold also exhibited association with performance. Along with their typical training, CrossFit athletes should likely ensure an adequate level of strength and aerobic endurance to optimize performance on at least some benchmark WODs. PMID:26261428

  19. Time-Accurate Numerical Prediction of Free Flight Aerodynamics of a Finned Projectile

    DTIC Science & Technology

    2005-09-01

    develop (with fewer dollars) more lethal and effective munitions. The munitions must stay abreast of the latest technology available to our...consuming. Computer simulations can and have provided an effective means of determining the unsteady aerodynamics and flight mechanics of guided projectile...Recently, the time-accurate technique was used to obtain improved results for Magnus moment and roll damping moment of a spinning projectile at transonic

  20. Evaluation of three statistical prediction models for forensic age prediction based on DNA methylation.

    PubMed

    Smeers, Inge; Decorte, Ronny; Van de Voorde, Wim; Bekaert, Bram

    2018-05-01

    DNA methylation is a promising biomarker for forensic age prediction. A challenge that has emerged in recent studies is the fact that prediction errors become larger with increasing age due to interindividual differences in epigenetic ageing rates. This phenomenon of non-constant variance or heteroscedasticity violates an assumption of the often used method of ordinary least squares (OLS) regression. The aim of this study was to evaluate alternative statistical methods that do take heteroscedasticity into account in order to provide more accurate, age-dependent prediction intervals. A weighted least squares (WLS) regression is proposed as well as a quantile regression model. Their performances were compared against an OLS regression model based on the same dataset. Both models provided age-dependent prediction intervals which account for the increasing variance with age, but WLS regression performed better in terms of success rate in the current dataset. However, quantile regression might be a preferred method when dealing with a variance that is not only non-constant, but also not normally distributed. Ultimately the choice of which model to use should depend on the observed characteristics of the data. Copyright © 2018 Elsevier B.V. All rights reserved.

  1. When high working memory capacity is and is not beneficial for predicting nonlinear processes.

    PubMed

    Fischer, Helen; Holt, Daniel V

    2017-04-01

    Predicting the development of dynamic processes is vital in many areas of life. Previous findings are inconclusive as to whether higher working memory capacity (WMC) is always associated with using more accurate prediction strategies, or whether higher WMC can also be associated with using overly complex strategies that do not improve accuracy. In this study, participants predicted a range of systematically varied nonlinear processes based on exponential functions where prediction accuracy could or could not be enhanced using well-calibrated rules. Results indicate that higher WMC participants seem to rely more on well-calibrated strategies, leading to more accurate predictions for processes with highly nonlinear trajectories in the prediction region. Predictions of lower WMC participants, in contrast, point toward an increased use of simple exemplar-based prediction strategies, which perform just as well as more complex strategies when the prediction region is approximately linear. These results imply that with respect to predicting dynamic processes, working memory capacity limits are not generally a strength or a weakness, but that this depends on the process to be predicted.

  2. Predictive Monitoring for Improved Management of Glucose Levels

    PubMed Central

    Reifman, Jaques; Rajaraman, Srinivasan; Gribok, Andrei; Ward, W. Kenneth

    2007-01-01

    Background Recent developments and expected near-future improvements in continuous glucose monitoring (CGM) devices provide opportunities to couple them with mathematical forecasting models to produce predictive monitoring systems for early, proactive glycemia management of diabetes mellitus patients before glucose levels drift to undesirable levels. This article assesses the feasibility of data-driven models to serve as the forecasting engine of predictive monitoring systems. Methods We investigated the capabilities of data-driven autoregressive (AR) models to (1) capture the correlations in glucose time-series data, (2) make accurate predictions as a function of prediction horizon, and (3) be made portable from individual to individual without any need for model tuning. The investigation is performed by employing CGM data from nine type 1 diabetic subjects collected over a continuous 5-day period. Results With CGM data serving as the gold standard, AR model-based predictions of glucose levels assessed over nine subjects with Clarke error grid analysis indicated that, for a 30-minute prediction horizon, individually tuned models yield 97.6 to 100.0% of data in the clinically acceptable zones A and B, whereas cross-subject, portable models yield 95.8 to 99.7% of data in zones A and B. Conclusions This study shows that, for a 30-minute prediction horizon, data-driven AR models provide sufficiently-accurate and clinically-acceptable estimates of glucose levels for timely, proactive therapy and should be considered as the modeling engine for predictive monitoring of patients with type 1 diabetes mellitus. It also suggests that AR models can be made portable from individual to individual with minor performance penalties, while greatly reducing the burden associated with model tuning and data collection for model development. PMID:19885110

  3. Tissue resonance interaction accurately detects colon lesions: A double-blind pilot study.

    PubMed

    Dore, Maria P; Tufano, Marcello O; Pes, Giovanni M; Cuccu, Marianna; Farina, Valentina; Manca, Alessandra; Graham, David Y

    2015-07-07

    To investigated the performance of the tissue resonance interaction method (TRIM) for the non-invasive detection of colon lesions. We performed a prospective single-center blinded pilot study of consecutive adults undergoing colonoscopy at the University Hospital in Sassari, Italy. Before patients underwent colonoscopy, they were examined by the TRIMprobe which detects differences in electromagnetic properties between pathological and normal tissues. All patients had completed the polyethylene glycol-containing bowel prep for the colonoscopy procedure before being screened. During the procedure the subjects remained fully dressed. A hand-held probe was moved over the abdomen and variations in electromagnetic signals were recorded for 3 spectral lines (462-465 MHz, 930 MHz, and 1395 MHz). A single investigator, blind to any clinical information, performed the test using the TRIMprob system. Abnormal signals were identified and recorded as malignant or benign (adenoma or hyperplastic polyps). Findings were compared with those from colonoscopy with histologic confirmation. Statistical analysis was performed by χ(2) test. A total of 305 consecutive patients fulfilling the inclusion criteria were enrolled over a period of 12 months. The most frequent indication for colonoscopy was abdominal pain (33%). The TRIMprob was well accepted by all patients; none spontaneously complained about the procedure, and no adverse effects were observed. TRIM proved inaccurate for polyp detection in patients with inflammatory bowel disease (IBD) and they were excluded leaving 281 subjects (mean age 59 ± 13 years; 107 males). The TRIM detected and accurately characterized all 12 adenocarcinomas and 135/137 polyps (98.5%) including 64 adenomatous (100%) found. The method identified cancers and polyps with 98.7% sensitivity, 96.2% specificity, and 97.5% diagnostic accuracy, compared to colonoscopy and histology analyses. The positive predictive value was 96.7% and the negative predictive

  4. Tissue resonance interaction accurately detects colon lesions: A double-blind pilot study

    PubMed Central

    Dore, Maria P; Tufano, Marcello O; Pes, Giovanni M; Cuccu, Marianna; Farina, Valentina; Manca, Alessandra; Graham, David Y

    2015-01-01

    AIM: To investigated the performance of the tissue resonance interaction method (TRIM) for the non-invasive detection of colon lesions. METHODS: We performed a prospective single-center blinded pilot study of consecutive adults undergoing colonoscopy at the University Hospital in Sassari, Italy. Before patients underwent colonoscopy, they were examined by the TRIMprobe which detects differences in electromagnetic properties between pathological and normal tissues. All patients had completed the polyethylene glycol-containing bowel prep for the colonoscopy procedure before being screened. During the procedure the subjects remained fully dressed. A hand-held probe was moved over the abdomen and variations in electromagnetic signals were recorded for 3 spectral lines (462-465 MHz, 930 MHz, and 1395 MHz). A single investigator, blind to any clinical information, performed the test using the TRIMprob system. Abnormal signals were identified and recorded as malignant or benign (adenoma or hyperplastic polyps). Findings were compared with those from colonoscopy with histologic confirmation. Statistical analysis was performed by χ2 test. RESULTS: A total of 305 consecutive patients fulfilling the inclusion criteria were enrolled over a period of 12 months. The most frequent indication for colonoscopy was abdominal pain (33%). The TRIMprob was well accepted by all patients; none spontaneously complained about the procedure, and no adverse effects were observed. TRIM proved inaccurate for polyp detection in patients with inflammatory bowel disease (IBD) and they were excluded leaving 281 subjects (mean age 59 ± 13 years; 107 males). The TRIM detected and accurately characterized all 12 adenocarcinomas and 135/137 polyps (98.5%) including 64 adenomatous (100%) found. The method identified cancers and polyps with 98.7% sensitivity, 96.2% specificity, and 97.5% diagnostic accuracy, compared to colonoscopy and histology analyses. The positive predictive value was 96.7% and the

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

    NASA Astrophysics Data System (ADS)

    Mao, Lei; Jackson, Lisa

    2016-10-01

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

  6. Beta-band activity and connectivity in sensorimotor and parietal cortex are important for accurate motor performance.

    PubMed

    Chung, Jae W; Ofori, Edward; Misra, Gaurav; Hess, Christopher W; Vaillancourt, David E

    2017-01-01

    Accurate motor performance may depend on the scaling of distinct oscillatory activity within the motor cortex and effective neural communication between the motor cortex and other brain areas. Oscillatory activity within the beta-band (13-30Hz) has been suggested to provide distinct functional roles for attention and sensorimotor control, yet it remains unclear how beta-band and other oscillatory activity within and between cortical regions is coordinated to enhance motor performance. We explore this open issue by simultaneously measuring high-density cortical activity and elbow flexor and extensor neuromuscular activity during ballistic movements, and manipulating error using high and low visual gain across three target distances. Compared with low visual gain, high visual gain decreased movement errors at each distance. Group analyses in 3D source-space revealed increased theta-, alpha-, and beta-band desynchronization of the contralateral motor cortex and medial parietal cortex in high visual gain conditions and this corresponded to reduced movement error. Dynamic causal modeling was used to compute connectivity between motor cortex and parietal cortex. Analyses revealed that gain affected the directionally-specific connectivity across broadband frequencies from parietal to sensorimotor cortex but not from sensorimotor cortex to parietal cortex. These new findings provide support for the interpretation that broad-band oscillations in theta, alpha, and beta frequency bands within sensorimotor and parietal cortex coordinate to facilitate accurate upper limb movement. Our findings establish a link between sensorimotor oscillations in the context of online motor performance in common source space across subjects. Specifically, the extent and distinct role of medial parietal cortex to sensorimotor beta connectivity and local domain broadband activity combine in a time and frequency manner to assist ballistic movements. These findings can serve as a model to examine

  7. Performance of genomic prediction within and across generations in maritime pine.

    PubMed

    Bartholomé, Jérôme; Van Heerwaarden, Joost; Isik, Fikret; Boury, Christophe; Vidal, Marjorie; Plomion, Christophe; Bouffier, Laurent

    2016-08-11

    Genomic selection (GS) is a promising approach for decreasing breeding cycle length in forest trees. Assessment of progeny performance and of the prediction accuracy of GS models over generations is therefore a key issue. A reference population of maritime pine (Pinus pinaster) with an estimated effective inbreeding population size (status number) of 25 was first selected with simulated data. This reference population (n = 818) covered three generations (G0, G1 and G2) and was genotyped with 4436 single-nucleotide polymorphism (SNP) markers. We evaluated the effects on prediction accuracy of both the relatedness between the calibration and validation sets and validation on the basis of progeny performance. Pedigree-based (best linear unbiased prediction, ABLUP) and marker-based (genomic BLUP and Bayesian LASSO) models were used to predict breeding values for three different traits: circumference, height and stem straightness. On average, the ABLUP model outperformed genomic prediction models, with a maximum difference in prediction accuracies of 0.12, depending on the trait and the validation method. A mean difference in prediction accuracy of 0.17 was found between validation methods differing in terms of relatedness. Including the progenitors in the calibration set reduced this difference in prediction accuracy to 0.03. When only genotypes from the G0 and G1 generations were used in the calibration set and genotypes from G2 were used in the validation set (progeny validation), prediction accuracies ranged from 0.70 to 0.85. This study suggests that the training of prediction models on parental populations can predict the genetic merit of the progeny with high accuracy: an encouraging result for the implementation of GS in the maritime pine breeding program.

  8. Prediction of Tennis Performance in Junior Elite Tennis Players

    PubMed Central

    Kramer, Tamara; Huijgen, Barbara C.H.; Elferink-Gemser, Marije T.; Visscher, Chris

    2017-01-01

    Predicting current and future tennis performance can lead to improving the development of junior tennis players. The aim of this study is to investigate whether age, maturation, or physical fitness in junior elite tennis players in U13 can explain current and future tennis performance. The value of current tennis performance for future tennis performance is also investigated. A total of 86 junior elite tennis players (boys, n = 44; girls, n = 42) U13 (aged: 12.5 ± 0.3 years), and followed to U16, took part in this study. All players were top-30 ranked on the Dutch national ranking list at U13, and top-50 at U16. Age, maturation, and physical fitness, were measured at U13. A principal component analysis was used to extract four physical components from eight tests (medicine ball throwing overhead and reverse, ball throwing, SJ, CMJas, Sprint 5 and 10 meter, and the spider test). The possible relationship of age, maturation, and the physical components; “upper body power”, “lower body power”, “speed”, and “agility” with tennis performance at U13 and U16 was analyzed. Tennis performance was measured by using the ranking position on the Dutch national ranking list at U13 and U16. Regression analyses were conducted based on correlations between variables and tennis performance for boys and girls, separately. In boys U13, positive correlations were found between upper body power and tennis performance (R2 is 25%). In girls, positive correlations between maturation and lower body power with tennis performance were found at U13. Early maturing players were associated with a better tennis performance (R2 is 15%). In girls U16, only maturation correlated with tennis performance (R2 is 13%); later-maturing girls at U13 had better tennis performances at U16. Measuring junior elite tennis players at U13 is important for monitoring their development. These measurements did not predict future tennis performance of junior elite tennis players three years later

  9. Ozone Mapping and Profiler Suite: using mission performance data to refine predictive contamination modeling

    NASA Astrophysics Data System (ADS)

    Devaud, Genevieve; Jaross, Glen

    2014-09-01

    On October 28, 2011, the Suomi National Polar-orbiting Partnership (Suomi NPP) satellite launched at Vandenberg Air Force base aboard a United Launch Alliance Delta II rocket. Included among the five instruments was the Ozone Mapping and Profiler Suite (OMPS), an advanced suite of three hyperspectral instruments built by Ball Aerospace and Technologies Corporation (BATC) for the NASA Goddard Space Flight Center. Molecular transport modeling is used to predict optical throughput changes due to contaminant accumulation to ensure performance margin to End Of Life. The OMPS Nadir Profiler, operating at the lowest wavelengths of 250 - 310 nm, is most sensitive to contaminant accumulation. Geometry, thermal profile and material properties must be accurately modeled in order to have confidence in the results, yet it is well known that the complex chemistry and process dependent variability of aerospace materials presents a substantial challenge to the modeler. Assumptions about the absorption coefficients, desorption and diffusion kinetics of outgassing species from polymeric materials dramatically affect the model predictions, yet it is rare indeed that on-mission data is analyzed at a later date as a means to compare with modeling results. Optical throughput measurements for the Ozone and Mapping Profiler Suite on the Suomi NPP Satellite indicate that optical throughput degradation between day 145 and day 858 is less than 0.5%. We will show how assumptions about outgassing rates and desorption energies, in particular, dramatically affect the modeled optical throughput and what assumptions represent the on-orbit data.

  10. Modeling of capacitor charging dynamics in an energy harvesting system considering accurate electromechanical coupling effects

    NASA Astrophysics Data System (ADS)

    Bagheri, Shahriar; Wu, Nan; Filizadeh, Shaahin

    2018-06-01

    This paper presents an iterative numerical method that accurately models an energy harvesting system charging a capacitor with piezoelectric patches. The constitutive relations of piezoelectric materials connected with an external charging circuit with a diode bridge and capacitors lead to the electromechanical coupling effect and the difficulty of deriving accurate transient mechanical response, as well as the charging progress. The proposed model is built upon the Euler-Bernoulli beam theory and takes into account the electromechanical coupling effects as well as the dynamic process of charging an external storage capacitor. The model is validated through experimental tests on a cantilever beam coated with piezoelectric patches. Several parametric studies are performed and the functionality of the model is verified. The efficiency of power harvesting system can be predicted and tuned considering variations in different design parameters. Such a model can be utilized to design robust and optimal energy harvesting system.

  11. Predicting maximal strength in trained postmenopausal woman.

    PubMed

    Kemmler, Wolfgang K; Lauber, Dirk; Wassermann, Alfred; Mayhew, Jerry L

    2006-11-01

    The purpose of this study was to present an equation that accurately predicts 1 repetition maximum (RM) over a wide range of repetitions to fatigue (RTF) for 4 different machine resistance exercises in postmenopausal women. Seventy trained women (age = 57.4 +/- 3.1 years) performed maximal and submaximal repetitions on leg press, bench press, rowing, and leg adduction machines at the conclusion of a 2-year training program. Maximal repetitions were performed on each exercise in the following ranges: 3-5RM, 6-10RM, 11-15RM, and 16-20RM. Special regard was taken to maintain the identical execution of each test (i.e., range of motion, starting angle, speed of movement). One cubic polynomial (w(i) [0.988-0.0000584 r(i)(3) + 0.00190 r(i)(2) + 0.0104 r(i),] where w(i) is the load of measurement I, and r(i) is the number of repetitions) accurately predicted 1RM from RTF with mean absolute differences between actual 1RM and predicted 1RM for the 4 exercises of 1.5-3.1% and with coefficients of variation of <3.3%. Equation accuracy was independent of the exercise type or the number of RTF. Thus, this study supported the validity of RTF to adequately estimate 1RM over a wide range of repetitions and within different exercises in trained, older female subjects.

  12. Predicting problems in school performance from preschool health, developmental and behavioural assessments.

    PubMed Central

    Cadman, D; Walter, S D; Chambers, L W; Ferguson, R; Szatmari, P; Johnson, N; McNamee, J

    1988-01-01

    To determine the accuracy of various predictors of school problems, we conducted a 3-year prospective study of 1999 children who began school in the Niagara region of Ontario in 1980. During the year before school entry the parents gave a health, developmental and behavioural history during an interview with a community health nurse, and the children underwent vision and hearing screening tests and the Denver Developmental Screening Test (DDST). At the end of the 1980-81 school year the kindergarten teachers rated the children's learning problems. At the end of the 1982-83 school year the presence of school problems was ascertained, and the predictive accuracy of items from the preschool history and examination and of the kindergarten teachers' ratings was calculated. The health, developmental and behavioural history with or without the DDST was found to predict later school problems with acceptable accuracy. The kindergarten teachers' ratings gave slightly more accurate predictions. We conclude that in communities where prompt diagnostic evaluation and effective therapeutic or preventive help can be provided to children identified as being at high risk, health professionals may play a useful role in screening for future school problems. PMID:3383038

  13. Performance Models for the Spike Banded Linear System Solver

    DOE PAGES

    Manguoglu, Murat; Saied, Faisal; Sameh, Ahmed; ...

    2011-01-01

    With availability of large-scale parallel platforms comprised of tens-of-thousands of processors and beyond, there is significant impetus for the development of scalable parallel sparse linear system solvers and preconditioners. An integral part of this design process is the development of performance models capable of predicting performance and providing accurate cost models for the solvers and preconditioners. There has been some work in the past on characterizing performance of the iterative solvers themselves. In this paper, we investigate the problem of characterizing performance and scalability of banded preconditioners. Recent work has demonstrated the superior convergence properties and robustness of banded preconditioners,more » compared to state-of-the-art ILU family of preconditioners as well as algebraic multigrid preconditioners. Furthermore, when used in conjunction with efficient banded solvers, banded preconditioners are capable of significantly faster time-to-solution. Our banded solver, the Truncated Spike algorithm is specifically designed for parallel performance and tolerance to deep memory hierarchies. Its regular structure is also highly amenable to accurate performance characterization. Using these characteristics, we derive the following results in this paper: (i) we develop parallel formulations of the Truncated Spike solver, (ii) we develop a highly accurate pseudo-analytical parallel performance model for our solver, (iii) we show excellent predication capabilities of our model – based on which we argue the high scalability of our solver. Our pseudo-analytical performance model is based on analytical performance characterization of each phase of our solver. These analytical models are then parameterized using actual runtime information on target platforms. An important consequence of our performance models is that they reveal underlying performance bottlenecks in both serial and parallel formulations. All of our results are validated

  14. Effect of window length on performance of the elbow-joint angle prediction based on electromyography

    NASA Astrophysics Data System (ADS)

    Triwiyanto; Wahyunggoro, Oyas; Adi Nugroho, Hanung; Herianto

    2017-05-01

    The high performance of the elbow joint angle prediction is essential on the development of the devices based on electromyography (EMG) control. The performance of the prediction depends on the feature of extraction parameters such as window length. In this paper, we evaluated the effect of the window length on the performance of the elbow-joint angle prediction. The prediction algorithm consists of zero-crossing feature extraction and second order of Butterworth low pass filter. The feature was used to extract the EMG signal by varying window length. The EMG signal was collected from the biceps muscle while the elbow was moved in the flexion and extension motion. The subject performed the elbow motion by holding a 1-kg load and moved the elbow in different periods (12 seconds, 8 seconds and 6 seconds). The results indicated that the window length affected the performance of the prediction. The 250 window lengths yielded the best performance of the prediction algorithm of (mean±SD) root mean square error = 5.68%±1.53% and Person’s correlation = 0.99±0.0059.

  15. Readmission prediction via deep contextual embedding of clinical concepts.

    PubMed

    Xiao, Cao; Ma, Tengfei; Dieng, Adji B; Blei, David M; Wang, Fei

    2018-01-01

    Hospital readmission costs a lot of money every year. Many hospital readmissions are avoidable, and excessive hospital readmissions could also be harmful to the patients. Accurate prediction of hospital readmission can effectively help reduce the readmission risk. However, the complex relationship between readmission and potential risk factors makes readmission prediction a difficult task. The main goal of this paper is to explore deep learning models to distill such complex relationships and make accurate predictions. We propose CONTENT, a deep model that predicts hospital readmissions via learning interpretable patient representations by capturing both local and global contexts from patient Electronic Health Records (EHR) through a hybrid Topic Recurrent Neural Network (TopicRNN) model. The experiment was conducted using the EHR of a real world Congestive Heart Failure (CHF) cohort of 5,393 patients. The proposed model outperforms state-of-the-art methods in readmission prediction (e.g. 0.6103 ± 0.0130 vs. second best 0.5998 ± 0.0124 in terms of ROC-AUC). The derived patient representations were further utilized for patient phenotyping. The learned phenotypes provide more precise understanding of readmission risks. Embedding both local and global context in patient representation not only improves prediction performance, but also brings interpretable insights of understanding readmission risks for heterogeneous chronic clinical conditions. This is the first of its kind model that integrates the power of both conventional deep neural network and the probabilistic generative models for highly interpretable deep patient representation learning. Experimental results and case studies demonstrate the improved performance and interpretability of the model.

  16. Predicting biomedical metadata in CEDAR: A study of Gene Expression Omnibus (GEO).

    PubMed

    Panahiazar, Maryam; Dumontier, Michel; Gevaert, Olivier

    2017-08-01

    A crucial and limiting factor in data reuse is the lack of accurate, structured, and complete descriptions of data, known as metadata. Towards improving the quantity and quality of metadata, we propose a novel metadata prediction framework to learn associations from existing metadata that can be used to predict metadata values. We evaluate our framework in the context of experimental metadata from the Gene Expression Omnibus (GEO). We applied four rule mining algorithms to the most common structured metadata elements (sample type, molecular type, platform, label type and organism) from over 1.3million GEO records. We examined the quality of well supported rules from each algorithm and visualized the dependencies among metadata elements. Finally, we evaluated the performance of the algorithms in terms of accuracy, precision, recall, and F-measure. We found that PART is the best algorithm outperforming Apriori, Predictive Apriori, and Decision Table. All algorithms perform significantly better in predicting class values than the majority vote classifier. We found that the performance of the algorithms is related to the dimensionality of the GEO elements. The average performance of all algorithm increases due of the decreasing of dimensionality of the unique values of these elements (2697 platforms, 537 organisms, 454 labels, 9 molecules, and 5 types). Our work suggests that experimental metadata such as present in GEO can be accurately predicted using rule mining algorithms. Our work has implications for both prospective and retrospective augmentation of metadata quality, which are geared towards making data easier to find and reuse. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

  17. The Predictive Accuracy of PREDICT: A Personalized Decision-Making Tool for Southeast Asian Women With Breast Cancer

    PubMed Central

    Wong, Hoong-Seam; Subramaniam, Shridevi; Alias, Zarifah; Taib, Nur Aishah; Ho, Gwo-Fuang; Ng, Char-Hong; Yip, Cheng-Har; Verkooijen, Helena M.; Hartman, Mikael; Bhoo-Pathy, Nirmala

    2015-01-01

    Abstract Web-based prognostication tools may provide a simple and economically feasible option to aid prognostication and selection of chemotherapy in early breast cancers. We validated PREDICT, a free online breast cancer prognostication and treatment benefit tool, in a resource-limited setting. All 1480 patients who underwent complete surgical treatment for stages I to III breast cancer from 1998 to 2006 were identified from the prospective breast cancer registry of University Malaya Medical Centre, Kuala Lumpur, Malaysia. Calibration was evaluated by comparing the model-predicted overall survival (OS) with patients’ actual OS. Model discrimination was tested using receiver-operating characteristic (ROC) analysis. Median age at diagnosis was 50 years. The median tumor size at presentation was 3 cm and 54% of patients had lymph node-negative disease. About 55% of women had estrogen receptor-positive breast cancer. Overall, the model-predicted 5 and 10-year OS was 86.3% and 77.5%, respectively, whereas the observed 5 and 10-year OS was 87.6% (difference: −1.3%) and 74.2% (difference: 3.3%), respectively; P values for goodness-of-fit test were 0.18 and 0.12, respectively. The program was accurate in most subgroups of patients, but significantly overestimated survival in patients aged <40 years, and in those receiving neoadjuvant chemotherapy. PREDICT performed well in terms of discrimination; areas under ROC curve were 0.78 (95% confidence interval [CI]: 0.74–0.81) and 0.73 (95% CI: 0.68–0.78) for 5 and 10-year OS, respectively. Based on its accurate performance in this study, PREDICT may be clinically useful in prognosticating women with breast cancer and personalizing breast cancer treatment in resource-limited settings. PMID:25715267

  18. Ski jump takeoff performance predictions for a mixed-flow, remote-lift STOVL aircraft

    NASA Technical Reports Server (NTRS)

    Birckelbaw, Lourdes G.

    1992-01-01

    A ski jump model was developed to predict ski jump takeoff performance for a short takeoff and vertical landing (STOVL) aircraft. The objective was to verify the model with results from a piloted simulation of a mixed flow, remote lift STOVL aircraft. The prediction model is discussed. The predicted results are compared with the piloted simulation results. The ski jump model can be utilized for basic research of other thrust vectoring STOVL aircraft performing a ski jump takeoff.

  19. Array Simulations Platform (ASP) predicts NASA Data Link Module (NDLM) performance

    NASA Technical Reports Server (NTRS)

    Snook, Allen David

    1993-01-01

    Through a variety of imbedded theoretical and actual antenna patterns, the array simulation platform (ASP) enhanced analysis of the array antenna pattern effects for the KTx (Ku-Band Transmit) service of the NDLM (NASA Data Link Module). The ASP utilizes internally stored models of the NDLM antennas and can develop the overall pattern of antenna arrays through common array calculation techniques. ASP expertly assisted in the diagnosing of element phase shifter errors during KTx testing and was able to accurately predict the overall array pattern from combinations of the four internally held element patterns. This paper provides an overview of the use of the ASP software in the solving of array mis-phasing problems.

  20. Improvement of experimental testing and network training conditions with genome-wide microarrays for more accurate predictions of drug gene targets

    PubMed Central

    2014-01-01

    Background Genome-wide microarrays have been useful for predicting chemical-genetic interactions at the gene level. However, interpreting genome-wide microarray results can be overwhelming due to the vast output of gene expression data combined with off-target transcriptional responses many times induced by a drug treatment. This study demonstrates how experimental and computational methods can interact with each other, to arrive at more accurate predictions of drug-induced perturbations. We present a two-stage strategy that links microarray experimental testing and network training conditions to predict gene perturbations for a drug with a known mechanism of action in a well-studied organism. Results S. cerevisiae cells were treated with the antifungal, fluconazole, and expression profiling was conducted under different biological conditions using Affymetrix genome-wide microarrays. Transcripts were filtered with a formal network-based method, sparse simultaneous equation models and Lasso regression (SSEM-Lasso), under different network training conditions. Gene expression results were evaluated using both gene set and single gene target analyses, and the drug’s transcriptional effects were narrowed first by pathway and then by individual genes. Variables included: (i) Testing conditions – exposure time and concentration and (ii) Network training conditions – training compendium modifications. Two analyses of SSEM-Lasso output – gene set and single gene – were conducted to gain a better understanding of how SSEM-Lasso predicts perturbation targets. Conclusions This study demonstrates that genome-wide microarrays can be optimized using a two-stage strategy for a more in-depth understanding of how a cell manifests biological reactions to a drug treatment at the transcription level. Additionally, a more detailed understanding of how the statistical model, SSEM-Lasso, propagates perturbations through a network of gene regulatory interactions is achieved