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Sample records for higher prediction accuracy

  1. Global discriminative learning for higher-accuracy computational gene prediction.

    Directory of Open Access Journals (Sweden)

    Axel Bernal

    2007-03-01

    Full Text Available Most ab initio gene predictors use a probabilistic sequence model, typically a hidden Markov model, to combine separately trained models of genomic signals and content. By combining separate models of relevant genomic features, such gene predictors can exploit small training sets and incomplete annotations, and can be trained fairly efficiently. However, that type of piecewise training does not optimize prediction accuracy and has difficulty in accounting for statistical dependencies among different parts of the gene model. With genomic information being created at an ever-increasing rate, it is worth investigating alternative approaches in which many different types of genomic evidence, with complex statistical dependencies, can be integrated by discriminative learning to maximize annotation accuracy. Among discriminative learning methods, large-margin classifiers have become prominent because of the success of support vector machines (SVM in many classification tasks. We describe CRAIG, a new program for ab initio gene prediction based on a conditional random field model with semi-Markov structure that is trained with an online large-margin algorithm related to multiclass SVMs. Our experiments on benchmark vertebrate datasets and on regions from the ENCODE project show significant improvements in prediction accuracy over published gene predictors that use intrinsic features only, particularly at the gene level and on genes with long introns.

  2. Final Technical Report: Increasing Prediction Accuracy.

    Energy Technology Data Exchange (ETDEWEB)

    King, Bruce Hardison [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Hansen, Clifford [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Stein, Joshua [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

    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.

  3. Audiovisual biofeedback improves motion prediction accuracy.

    Science.gov (United States)

    Pollock, Sean; Lee, Danny; Keall, Paul; Kim, Taeho

    2013-04-01

    The accuracy of motion prediction, utilized to overcome the system latency of motion management radiotherapy systems, is hampered by irregularities present in the patients' respiratory pattern. Audiovisual (AV) biofeedback has been shown to reduce respiratory irregularities. The aim of this study was to test the hypothesis that AV biofeedback improves the accuracy of motion prediction. An AV biofeedback system combined with real-time respiratory data acquisition and MR images were implemented in this project. One-dimensional respiratory data from (1) the abdominal wall (30 Hz) and (2) the thoracic diaphragm (5 Hz) were obtained from 15 healthy human subjects across 30 studies. The subjects were required to breathe with and without the guidance of AV biofeedback during each study. The obtained respiratory signals were then implemented in a kernel density estimation prediction algorithm. For each of the 30 studies, five different prediction times ranging from 50 to 1400 ms were tested (150 predictions performed). Prediction error was quantified as the root mean square error (RMSE); the RMSE was calculated from the difference between the real and predicted respiratory data. The statistical significance of the prediction results was determined by the Student's t-test. Prediction accuracy was considerably improved by the implementation of AV biofeedback. Of the 150 respiratory predictions performed, prediction accuracy was improved 69% (103/150) of the time for abdominal wall data, and 78% (117/150) of the time for diaphragm data. The average reduction in RMSE due to AV biofeedback over unguided respiration was 26% (p biofeedback improves prediction accuracy. This would result in increased efficiency of motion management techniques affected by system latencies used in radiotherapy.

  4. Assessing Genomic Selection Prediction Accuracy in a Dynamic Barley Breeding Population

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    A. H. Sallam

    2015-03-01

    Full Text Available Prediction accuracy of genomic selection (GS has been previously evaluated through simulation and cross-validation; however, validation based on progeny performance in a plant breeding program has not been investigated thoroughly. We evaluated several prediction models in a dynamic barley breeding population comprised of 647 six-row lines using four traits differing in genetic architecture and 1536 single nucleotide polymorphism (SNP markers. The breeding lines were divided into six sets designated as one parent set and five consecutive progeny sets comprised of representative samples of breeding lines over a 5-yr period. We used these data sets to investigate the effect of model and training population composition on prediction accuracy over time. We found little difference in prediction accuracy among the models confirming prior studies that found the simplest model, random regression best linear unbiased prediction (RR-BLUP, to be accurate across a range of situations. In general, we found that using the parent set was sufficient to predict progeny sets with little to no gain in accuracy from generating larger training populations by combining the parent set with subsequent progeny sets. The prediction accuracy ranged from 0.03 to 0.99 across the four traits and five progeny sets. We explored characteristics of the training and validation populations (marker allele frequency, population structure, and linkage disequilibrium, LD as well as characteristics of the trait (genetic architecture and heritability, . Fixation of markers associated with a trait over time was most clearly associated with reduced prediction accuracy for the mycotoxin trait DON. Higher trait in the training population and simpler trait architecture were associated with greater prediction accuracy.

  5. The accuracy of new wheelchair users' predictions about their future wheelchair use.

    Science.gov (United States)

    Hoenig, Helen; Griffiths, Patricia; Ganesh, Shanti; Caves, Kevin; Harris, Frances

    2012-06-01

    This study examined the accuracy of new wheelchair user predictions about their future wheelchair use. This was a prospective cohort study of 84 community-dwelling veterans provided a new manual wheelchair. The association between predicted and actual wheelchair use was strong at 3 mos (ϕ coefficient = 0.56), with 90% of those who anticipated using the wheelchair at 3 mos still using it (i.e., positive predictive value = 0.96) and 60% of those who anticipated not using it indeed no longer using the wheelchair (i.e., negative predictive value = 0.60, overall accuracy = 0.92). Predictive accuracy diminished over time, with overall accuracy declining from 0.92 at 3 mos to 0.66 at 6 mos. At all time points, and for all types of use, patients better predicted use as opposed to disuse, with correspondingly higher positive than negative predictive values. Accuracy of prediction of use in specific indoor and outdoor locations varied according to location. This study demonstrates the importance of better understanding the potential mismatch between the anticipated and actual patterns of wheelchair use. The findings suggest that users can be relied upon to accurately predict their basic wheelchair-related needs in the short-term. Further exploration is needed to identify characteristics that will aid users and their providers in more accurately predicting mobility needs for the long-term.

  6. Improving orbit prediction accuracy through supervised machine learning

    Science.gov (United States)

    Peng, Hao; Bai, Xiaoli

    2018-05-01

    Due to the lack of information such as the space environment condition and resident space objects' (RSOs') body characteristics, current orbit predictions that are solely grounded on physics-based models may fail to achieve required accuracy for collision avoidance and have led to satellite collisions already. This paper presents a methodology to predict RSOs' trajectories with higher accuracy than that of the current methods. Inspired by the machine learning (ML) theory through which the models are learned based on large amounts of observed data and the prediction is conducted without explicitly modeling space objects and space environment, the proposed ML approach integrates physics-based orbit prediction algorithms with a learning-based process that focuses on reducing the prediction errors. Using a simulation-based space catalog environment as the test bed, the paper demonstrates three types of generalization capability for the proposed ML approach: (1) the ML model can be used to improve the same RSO's orbit information that is not available during the learning process but shares the same time interval as the training data; (2) the ML model can be used to improve predictions of the same RSO at future epochs; and (3) the ML model based on a RSO can be applied to other RSOs that share some common features.

  7. Human In Silico Drug Trials Demonstrate Higher Accuracy than Animal Models in Predicting Clinical Pro-Arrhythmic Cardiotoxicity.

    Science.gov (United States)

    Passini, Elisa; Britton, Oliver J; Lu, Hua Rong; Rohrbacher, Jutta; Hermans, An N; Gallacher, David J; Greig, Robert J H; Bueno-Orovio, Alfonso; Rodriguez, Blanca

    2017-01-01

    Early prediction of cardiotoxicity is critical for drug development. Current animal models raise ethical and translational questions, and have limited accuracy in clinical risk prediction. Human-based computer models constitute a fast, cheap and potentially effective alternative to experimental assays, also facilitating translation to human. Key challenges include consideration of inter-cellular variability in drug responses and integration of computational and experimental methods in safety pharmacology. Our aim is to evaluate the ability of in silico drug trials in populations of human action potential (AP) models to predict clinical risk of drug-induced arrhythmias based on ion channel information, and to compare simulation results against experimental assays commonly used for drug testing. A control population of 1,213 human ventricular AP models in agreement with experimental recordings was constructed. In silico drug trials were performed for 62 reference compounds at multiple concentrations, using pore-block drug models (IC 50 /Hill coefficient). Drug-induced changes in AP biomarkers were quantified, together with occurrence of repolarization/depolarization abnormalities. Simulation results were used to predict clinical risk based on reports of Torsade de Pointes arrhythmias, and further evaluated in a subset of compounds through comparison with electrocardiograms from rabbit wedge preparations and Ca 2+ -transient recordings in human induced pluripotent stem cell-derived cardiomyocytes (hiPS-CMs). Drug-induced changes in silico vary in magnitude depending on the specific ionic profile of each model in the population, thus allowing to identify cell sub-populations at higher risk of developing abnormal AP phenotypes. Models with low repolarization reserve (increased Ca 2+ /late Na + currents and Na + /Ca 2+ -exchanger, reduced Na + /K + -pump) are highly vulnerable to drug-induced repolarization abnormalities, while those with reduced inward current density

  8. Human In Silico Drug Trials Demonstrate Higher Accuracy than Animal Models in Predicting Clinical Pro-Arrhythmic Cardiotoxicity

    Directory of Open Access Journals (Sweden)

    Elisa Passini

    2017-09-01

    Full Text Available Early prediction of cardiotoxicity is critical for drug development. Current animal models raise ethical and translational questions, and have limited accuracy in clinical risk prediction. Human-based computer models constitute a fast, cheap and potentially effective alternative to experimental assays, also facilitating translation to human. Key challenges include consideration of inter-cellular variability in drug responses and integration of computational and experimental methods in safety pharmacology. Our aim is to evaluate the ability of in silico drug trials in populations of human action potential (AP models to predict clinical risk of drug-induced arrhythmias based on ion channel information, and to compare simulation results against experimental assays commonly used for drug testing. A control population of 1,213 human ventricular AP models in agreement with experimental recordings was constructed. In silico drug trials were performed for 62 reference compounds at multiple concentrations, using pore-block drug models (IC50/Hill coefficient. Drug-induced changes in AP biomarkers were quantified, together with occurrence of repolarization/depolarization abnormalities. Simulation results were used to predict clinical risk based on reports of Torsade de Pointes arrhythmias, and further evaluated in a subset of compounds through comparison with electrocardiograms from rabbit wedge preparations and Ca2+-transient recordings in human induced pluripotent stem cell-derived cardiomyocytes (hiPS-CMs. Drug-induced changes in silico vary in magnitude depending on the specific ionic profile of each model in the population, thus allowing to identify cell sub-populations at higher risk of developing abnormal AP phenotypes. Models with low repolarization reserve (increased Ca2+/late Na+ currents and Na+/Ca2+-exchanger, reduced Na+/K+-pump are highly vulnerable to drug-induced repolarization abnormalities, while those with reduced inward current density

  9. Genomic Prediction Accuracy for Resistance Against Piscirickettsia salmonis in Farmed Rainbow Trout

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    Grazyella M. Yoshida

    2018-02-01

    Full Text Available Salmonid rickettsial syndrome (SRS, caused by the intracellular bacterium Piscirickettsia salmonis, is one of the main diseases affecting rainbow trout (Oncorhynchus mykiss farming. To accelerate genetic progress, genomic selection methods can be used as an effective approach to control the disease. The aims of this study were: (i to compare the accuracy of estimated breeding values using pedigree-based best linear unbiased prediction (PBLUP with genomic BLUP (GBLUP, single-step GBLUP (ssGBLUP, Bayes C, and Bayesian Lasso (LASSO; and (ii to test the accuracy of genomic prediction and PBLUP using different marker densities (0.5, 3, 10, 20, and 27 K for resistance against P. salmonis in rainbow trout. Phenotypes were recorded as number of days to death (DD and binary survival (BS from 2416 fish challenged with P. salmonis. A total of 1934 fish were genotyped using a 57 K single-nucleotide polymorphism (SNP array. All genomic prediction methods achieved higher accuracies than PBLUP. The relative increase in accuracy for different genomic models ranged from 28 to 41% for both DD and BS at 27 K SNP. Between different genomic models, the highest relative increase in accuracy was obtained with Bayes C (∼40%, where 3 K SNP was enough to achieve a similar accuracy to that of the 27 K SNP for both traits. For resistance against P. salmonis in rainbow trout, we showed that genomic predictions using GBLUP, ssGBLUP, Bayes C, and LASSO can increase accuracy compared with PBLUP. Moreover, it is possible to use relatively low-density SNP panels for genomic prediction without compromising accuracy predictions for resistance against P. salmonis in rainbow trout.

  10. Meditation experience predicts introspective accuracy.

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    Kieran C R Fox

    Full Text Available The accuracy of subjective reports, especially those involving introspection of one's own internal processes, remains unclear, and research has demonstrated large individual differences in introspective accuracy. It has been hypothesized that introspective accuracy may be heightened in persons who engage in meditation practices, due to the highly introspective nature of such practices. We undertook a preliminary exploration of this hypothesis, examining introspective accuracy in a cross-section of meditation practitioners (1-15,000 hrs experience. Introspective accuracy was assessed by comparing subjective reports of tactile sensitivity for each of 20 body regions during a 'body-scanning' meditation with averaged, objective measures of tactile sensitivity (mean size of body representation area in primary somatosensory cortex; two-point discrimination threshold as reported in prior research. Expert meditators showed significantly better introspective accuracy than novices; overall meditation experience also significantly predicted individual introspective accuracy. These results suggest that long-term meditators provide more accurate introspective reports than novices.

  11. Improving prediction accuracy of cooling load using EMD, PSR and RBFNN

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    Shen, Limin; Wen, Yuanmei; Li, Xiaohong

    2017-08-01

    To increase the accuracy for the prediction of cooling load demand, this work presents an EMD (empirical mode decomposition)-PSR (phase space reconstruction) based RBFNN (radial basis function neural networks) method. Firstly, analyzed the chaotic nature of the real cooling load demand, transformed the non-stationary cooling load historical data into several stationary intrinsic mode functions (IMFs) by using EMD. Secondly, compared the RBFNN prediction accuracies of each IMFs and proposed an IMF combining scheme that is combine the lower-frequency components (called IMF4-IMF6 combined) while keep the higher frequency component (IMF1, IMF2, IMF3) and the residual unchanged. Thirdly, reconstruct phase space for each combined components separately, process the highest frequency component (IMF1) by differential method and predict with RBFNN in the reconstructed phase spaces. Real cooling load data of a centralized ice storage cooling systems in Guangzhou are used for simulation. The results show that the proposed hybrid method outperforms the traditional methods.

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

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

    2017-09-01

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

  13. Accuracy assessment of landslide prediction models

    International Nuclear Information System (INIS)

    Othman, A N; Mohd, W M N W; Noraini, S

    2014-01-01

    The increasing population and expansion of settlements over hilly areas has greatly increased the impact of natural disasters such as landslide. Therefore, it is important to developed models which could accurately predict landslide hazard zones. Over the years, various techniques and models have been developed to predict landslide hazard zones. The aim of this paper is to access the accuracy of landslide prediction models developed by the authors. The methodology involved the selection of study area, data acquisition, data processing and model development and also data analysis. The development of these models are based on nine different landslide inducing parameters i.e. slope, land use, lithology, soil properties, geomorphology, flow accumulation, aspect, proximity to river and proximity to road. Rank sum, rating, pairwise comparison and AHP techniques are used to determine the weights for each of the parameters used. Four (4) different models which consider different parameter combinations are developed by the authors. Results obtained are compared to landslide history and accuracies for Model 1, Model 2, Model 3 and Model 4 are 66.7, 66.7%, 60% and 22.9% respectively. From the results, rank sum, rating and pairwise comparison can be useful techniques to predict landslide hazard zones

  14. Evaluation of approaches for estimating the accuracy of genomic prediction in plant breeding.

    Science.gov (United States)

    Ould Estaghvirou, Sidi Boubacar; Ogutu, Joseph O; Schulz-Streeck, Torben; Knaak, Carsten; Ouzunova, Milena; Gordillo, Andres; Piepho, Hans-Peter

    2013-12-06

    In genomic prediction, an important measure of accuracy is the correlation between the predicted and the true breeding values. Direct computation of this quantity for real datasets is not possible, because the true breeding value is unknown. Instead, the correlation between the predicted breeding values and the observed phenotypic values, called predictive ability, is often computed. In order to indirectly estimate predictive accuracy, this latter correlation is usually divided by an estimate of the square root of heritability. In this study we use simulation to evaluate estimates of predictive accuracy for seven methods, four (1 to 4) of which use an estimate of heritability to divide predictive ability computed by cross-validation. Between them the seven methods cover balanced and unbalanced datasets as well as correlated and uncorrelated genotypes. We propose one new indirect method (4) and two direct methods (5 and 6) for estimating predictive accuracy and compare their performances and those of four other existing approaches (three indirect (1 to 3) and one direct (7)) with simulated true predictive accuracy as the benchmark and with each other. The size of the estimated genetic variance and hence heritability exerted the strongest influence on the variation in the estimated predictive accuracy. Increasing the number of genotypes considerably increases the time required to compute predictive accuracy by all the seven methods, most notably for the five methods that require cross-validation (Methods 1, 2, 3, 4 and 6). A new method that we propose (Method 5) and an existing method (Method 7) used in animal breeding programs were the fastest and gave the least biased, most precise and stable estimates of predictive accuracy. Of the methods that use cross-validation Methods 4 and 6 were often the best. The estimated genetic variance and the number of genotypes had the greatest influence on predictive accuracy. Methods 5 and 7 were the fastest and produced the least

  15. An Information Theory Account of Preference Prediction Accuracy

    NARCIS (Netherlands)

    Pollmann, Monique; Scheibehenne, Benjamin

    2015-01-01

    Knowledge about other people's preferences is essential for successful social interactions, but what exactly are the driving factors that determine how well we can predict the likes and dislikes of people around us? To investigate the accuracy of couples’ preference predictions we outline and

  16. A New Approach to Improve Accuracy of Grey Model GMC(1,n in Time Series Prediction

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    Sompop Moonchai

    2015-01-01

    Full Text Available This paper presents a modified grey model GMC(1,n for use in systems that involve one dependent system behavior and n-1 relative factors. The proposed model was developed from the conventional GMC(1,n model in order to improve its prediction accuracy by modifying the formula for calculating the background value, the system of parameter estimation, and the model prediction equation. The modified GMC(1,n model was verified by two cases: the study of forecasting CO2 emission in Thailand and forecasting electricity consumption in Thailand. The results demonstrated that the modified GMC(1,n model was able to achieve higher fitting and prediction accuracy compared with the conventional GMC(1,n and D-GMC(1,n models.

  17. Genomic selection in mink yield higher accuracies with a Bayesian approach allowing for heterogeneous variance than a GBLUP model

    DEFF Research Database (Denmark)

    Villumsen, Trine Michelle; Su, Guosheng; Cai, Zexi

    2018-01-01

    by sequencing. Four live grading traits and four traits on dried pelts for size and quality were analysed. GWAS analysis detected significant SNPs for all the traits. The single-trait Bayesian model resulted in higher accuracies for the genomic predictions than the single-trait GBLUP model, especially......The accuracy of genomic prediction for mink was compared for single-trait and multiple-trait GBLUP models and Bayesian models that allowed for heterogeneous (co)variance structure over the genome. The mink population consisted of 2,103 brown minks genotyped with the method of genotyping...... for the traits measured on dried pelts. We expected the multiple-trait models to be superior to the single trait models since the multiple-trait model can make use of information when traits are correlated. However, we did not find a general improvement in accuracies with the multiple-trait models compared...

  18. Multiple-Trait Genomic Selection Methods Increase Genetic Value Prediction Accuracy

    Science.gov (United States)

    Jia, Yi; Jannink, Jean-Luc

    2012-01-01

    Genetic correlations between quantitative traits measured in many breeding programs are pervasive. These correlations indicate that measurements of one trait carry information on other traits. Current single-trait (univariate) genomic selection does not take advantage of this information. Multivariate genomic selection on multiple traits could accomplish this but has been little explored and tested in practical breeding programs. In this study, three multivariate linear models (i.e., GBLUP, BayesA, and BayesCπ) were presented and compared to univariate models using simulated and real quantitative traits controlled by different genetic architectures. We also extended BayesA with fixed hyperparameters to a full hierarchical model that estimated hyperparameters and BayesCπ to impute missing phenotypes. We found that optimal marker-effect variance priors depended on the genetic architecture of the trait so that estimating them was beneficial. We showed that the prediction accuracy for a low-heritability trait could be significantly increased by multivariate genomic selection when a correlated high-heritability trait was available. Further, multiple-trait genomic selection had higher prediction accuracy than single-trait genomic selection when phenotypes are not available on all individuals and traits. Additional factors affecting the performance of multiple-trait genomic selection were explored. PMID:23086217

  19. A Method of Calculating Functional Independence Measure at Discharge from Functional Independence Measure Effectiveness Predicted by Multiple Regression Analysis Has a High Degree of Predictive Accuracy.

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    Tokunaga, Makoto; Watanabe, Susumu; Sonoda, Shigeru

    2017-09-01

    Multiple linear regression analysis is often used to predict the outcome of stroke rehabilitation. However, the predictive accuracy may not be satisfactory. The objective of this study was to elucidate the predictive accuracy of a method of calculating motor Functional Independence Measure (mFIM) at discharge from mFIM effectiveness predicted by multiple regression analysis. The subjects were 505 patients with stroke who were hospitalized in a convalescent rehabilitation hospital. The formula "mFIM at discharge = mFIM effectiveness × (91 points - mFIM at admission) + mFIM at admission" was used. By including the predicted mFIM effectiveness obtained through multiple regression analysis in this formula, we obtained the predicted mFIM at discharge (A). We also used multiple regression analysis to directly predict mFIM at discharge (B). The correlation between the predicted and the measured values of mFIM at discharge was compared between A and B. The correlation coefficients were .916 for A and .878 for B. Calculating mFIM at discharge from mFIM effectiveness predicted by multiple regression analysis had a higher degree of predictive accuracy of mFIM at discharge than that directly predicted. Copyright © 2017 National Stroke Association. Published by Elsevier Inc. All rights reserved.

  20. Effects of sample size on robustness and prediction accuracy of a prognostic gene signature

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    Kim Seon-Young

    2009-05-01

    Full Text Available Abstract Background Few overlap between independently developed gene signatures and poor inter-study applicability of gene signatures are two of major concerns raised in the development of microarray-based prognostic gene signatures. One recent study suggested that thousands of samples are needed to generate a robust prognostic gene signature. Results A data set of 1,372 samples was generated by combining eight breast cancer gene expression data sets produced using the same microarray platform and, using the data set, effects of varying samples sizes on a few performances of a prognostic gene signature were investigated. The overlap between independently developed gene signatures was increased linearly with more samples, attaining an average overlap of 16.56% with 600 samples. The concordance between predicted outcomes by different gene signatures also was increased with more samples up to 94.61% with 300 samples. The accuracy of outcome prediction also increased with more samples. Finally, analysis using only Estrogen Receptor-positive (ER+ patients attained higher prediction accuracy than using both patients, suggesting that sub-type specific analysis can lead to the development of better prognostic gene signatures Conclusion Increasing sample sizes generated a gene signature with better stability, better concordance in outcome prediction, and better prediction accuracy. However, the degree of performance improvement by the increased sample size was different between the degree of overlap and the degree of concordance in outcome prediction, suggesting that the sample size required for a study should be determined according to the specific aims of the study.

  1. Empirical and deterministic accuracies of across-population genomic prediction

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    Wientjes, Y.C.J.; Veerkamp, R.F.; Bijma, P.; Bovenhuis, H.; Schrooten, C.; Calus, M.P.L.

    2015-01-01

    Background: Differences in linkage disequilibrium and in allele substitution effects of QTL (quantitative trait loci) may hinder genomic prediction across populations. Our objective was to develop a deterministic formula to estimate the accuracy of across-population genomic prediction, for which

  2. Improving the accuracy of protein secondary structure prediction using structural alignment

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    Gallin Warren J

    2006-06-01

    Full Text Available Abstract Background The accuracy of protein secondary structure prediction has steadily improved over the past 30 years. Now many secondary structure prediction methods routinely achieve an accuracy (Q3 of about 75%. We believe this accuracy could be further improved by including structure (as opposed to sequence database comparisons as part of the prediction process. Indeed, given the large size of the Protein Data Bank (>35,000 sequences, the probability of a newly identified sequence having a structural homologue is actually quite high. Results We have developed a method that performs structure-based sequence alignments as part of the secondary structure prediction process. By mapping the structure of a known homologue (sequence ID >25% onto the query protein's sequence, it is possible to predict at least a portion of that query protein's secondary structure. By integrating this structural alignment approach with conventional (sequence-based secondary structure methods and then combining it with a "jury-of-experts" system to generate a consensus result, it is possible to attain very high prediction accuracy. Using a sequence-unique test set of 1644 proteins from EVA, this new method achieves an average Q3 score of 81.3%. Extensive testing indicates this is approximately 4–5% better than any other method currently available. Assessments using non sequence-unique test sets (typical of those used in proteome annotation or structural genomics indicate that this new method can achieve a Q3 score approaching 88%. Conclusion By using both sequence and structure databases and by exploiting the latest techniques in machine learning it is possible to routinely predict protein secondary structure with an accuracy well above 80%. A program and web server, called PROTEUS, that performs these secondary structure predictions is accessible at http://wishart.biology.ualberta.ca/proteus. For high throughput or batch sequence analyses, the PROTEUS programs

  3. Quantitative accuracy assessment of thermalhydraulic code predictions with SARBM

    International Nuclear Information System (INIS)

    Prosek, A.

    2001-01-01

    In recent years, the nuclear reactor industry has focused significant attention on nuclear reactor systems code accuracy and uncertainty issues. A few methods suitable to quantify code accuracy of thermalhydraulic code calculations were proposed and applied in the past. In this study a Stochastic Approximation Ratio Based Method (SARBM) was adapted and proposed for accuracy quantification. The objective of the study was to qualify the SARBM. The study compare the accuracy obtained by SARBM with the results obtained by widely used Fast Fourier Transform Based Method (FFTBM). The methods were applied to RELAP5/MOD3.2 code calculations of various BETHSY experiments. The obtained results showed that the SARBM was able to satisfactorily predict the accuracy of the calculated trends when visually comparing plots and comparing the results with the qualified FFTBM. The analysis also showed that the new figure-of-merit called accuracy factor (AF) is more convenient than stochastic approximation ratio for combining single variable accuracy's into total accuracy. The accuracy results obtained for the selected tests suggest that the acceptability factors for the SAR method were reasonably defined. The results also indicate that AF is a useful quantitative measure of accuracy.(author)

  4. Age-related differences in the accuracy of web query-based predictions of influenza-like illness.

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    Alexander Domnich

    Full Text Available Web queries are now widely used for modeling, nowcasting and forecasting influenza-like illness (ILI. However, given that ILI attack rates vary significantly across ages, in terms of both magnitude and timing, little is known about whether the association between ILI morbidity and ILI-related queries is comparable across different age-groups. The present study aimed to investigate features of the association between ILI morbidity and ILI-related query volume from the perspective of age.Since Google Flu Trends is unavailable in Italy, Google Trends was used to identify entry terms that correlated highly with official ILI surveillance data. All-age and age-class-specific modeling was performed by means of linear models with generalized least-square estimation. Hold-out validation was used to quantify prediction accuracy. For purposes of comparison, predictions generated by exponential smoothing were computed.Five search terms showed high correlation coefficients of > .6. In comparison with exponential smoothing, the all-age query-based model correctly predicted the peak time and yielded a higher correlation coefficient with observed ILI morbidity (.978 vs. .929. However, query-based prediction of ILI morbidity was associated with a greater error. Age-class-specific query-based models varied significantly in terms of prediction accuracy. In the 0-4 and 25-44-year age-groups, these did well and outperformed exponential smoothing predictions; in the 15-24 and ≥ 65-year age-classes, however, the query-based models were inaccurate and highly overestimated peak height. In all but one age-class, peak timing predicted by the query-based models coincided with observed timing.The accuracy of web query-based models in predicting ILI morbidity rates could differ among ages. Greater age-specific detail may be useful in flu query-based studies in order to account for age-specific features of the epidemiology of ILI.

  5. Accuracy of ultrasound for the prediction of placenta accreta.

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

    2014-08-01

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

  6. Typing speed, spelling accuracy, and the use of word-prediction

    Directory of Open Access Journals (Sweden)

    Marina Herold

    2008-02-01

    Full Text Available Children with spelling difficulties are limited in their participation in all written school activities. We aimed to investigate the influence of word-prediction as a tool on spelling accuracy and typing speed. To this end, we selected 80 Grade 4 - 6 children with spelling difficulties in a school for special needs to participate in a research project involving a cross-over within-subject design. The research task took the form of entering 30 words through an on-screen keyboard, with and without the use of word-prediction software. The Graded Word Spelling Test served to investigate whether there was a relationship between the children's current spelling knowledge and word-prediction efficacy. The results indicated an increase in spelling accuracy with the use of word-prediction, but at the cost of time and the tendency to use word approximations, and no significant relationship between spelling knowledge and word-prediction efficacy.

  7. PREDICTIVE ACCURACY OF TRANSCEREBELLAR DIAMETER IN COMPARISON WITH OTHER FOETAL BIOMETRIC PARAMETERS FOR GESTATIONAL AGE ESTIMATION AMONG PREGNANT NIGERIAN WOMEN.

    Science.gov (United States)

    Adeyekun, A A; Orji, M O

    2014-04-01

    To compare the predictive accuracy of foetal trans-cerebellar diameter (TCD) with those of other biometric parameters in the estimation of gestational age (GA). A cross-sectional study. The University of Benin Teaching Hospital, Nigeria. Four hundred and fifty healthy singleton pregnant women, between 14-42 weeks gestation. Trans-cerebellar diameter (TCD), biparietal diameter (BPD), femur length (FL), abdominal circumference (AC) values across the gestational age range studied. Correlation and predictive values of TCD compared to those of other biometric parameters. The range of values for TCD was 11.9 - 59.7mm (mean = 34.2 ± 14.1mm). TCD correlated more significantly with menstrual age compared with other biometric parameters (r = 0.984, p = 0.000). TCD had a higher predictive accuracy of 96.9% ± 12 days), BPD (93.8% ± 14.1 days). AC (92.7% ± 15.3 days). TCD has a stronger predictive accuracy for gestational age compared to other routinely used foetal biometric parameters among Nigerian Africans.

  8. Quantum chemistry by random walk: Higher accuracy

    International Nuclear Information System (INIS)

    Anderson, J.B.

    1980-01-01

    The random walk method of solving the Schroedinger equation is extended to allow the calculation of eigenvalues of atomic and molecular systems with higher accuracy. The combination of direct calculation of the difference delta between a true wave function psi and a trial wave function psi/sub o/ with importance sampling greatly reduces systematic and statistical error. The method is illustrated with calculations for ground-state hydrogen and helium atoms using trial wave functions from variational calculations. The energies obtained are 20 to 100 times more accurate than those of the corresponding variational calculations

  9. Bias associated with delayed verification in test accuracy studies: accuracy of tests for endometrial hyperplasia may be much higher than we think!

    OpenAIRE

    Clark, T Justin; ter Riet, Gerben; Coomarasamy, Aravinthan; Khan, Khalid S

    2004-01-01

    Abstract Background To empirically evaluate bias in estimation of accuracy associated with delay in verification of diagnosis among studies evaluating tests for predicting endometrial hyperplasia. Methods Systematic reviews of all published research on accuracy of miniature endometrial biopsy and endometr ial ultrasonography for diagnosing endometrial hyperplasia identified 27 test accuracy studies (2,982 subjects). Of these, 16 had immediate histological verification of diagnosis while 11 ha...

  10. Estimation of genomic prediction accuracy from reference populations with varying degrees of relationship.

    Directory of Open Access Journals (Sweden)

    S Hong Lee

    Full Text Available Genomic prediction is emerging in a wide range of fields including animal and plant breeding, risk prediction in human precision medicine and forensic. It is desirable to establish a theoretical framework for genomic prediction accuracy when the reference data consists of information sources with varying degrees of relationship to the target individuals. A reference set can contain both close and distant relatives as well as 'unrelated' individuals from the wider population in the genomic prediction. The various sources of information were modeled as different populations with different effective population sizes (Ne. Both the effective number of chromosome segments (Me and Ne are considered to be a function of the data used for prediction. We validate our theory with analyses of simulated as well as real data, and illustrate that the variation in genomic relationships with the target is a predictor of the information content of the reference set. With a similar amount of data available for each source, we show that close relatives can have a substantially larger effect on genomic prediction accuracy than lesser related individuals. We also illustrate that when prediction relies on closer relatives, there is less improvement in prediction accuracy with an increase in training data or marker panel density. We release software that can estimate the expected prediction accuracy and power when combining different reference sources with various degrees of relationship to the target, which is useful when planning genomic prediction (before or after collecting data in animal, plant and human genetics.

  11. Genomic Selection Accuracy using Multifamily Prediction Models in a Wheat Breeding Program

    Directory of Open Access Journals (Sweden)

    Elliot L. Heffner

    2011-03-01

    Full Text Available Genomic selection (GS uses genome-wide molecular marker data to predict the genetic value of selection candidates in breeding programs. In plant breeding, the ability to produce large numbers of progeny per cross allows GS to be conducted within each family. However, this approach requires phenotypes of lines from each cross before conducting GS. This will prolong the selection cycle and may result in lower gains per year than approaches that estimate marker-effects with multiple families from previous selection cycles. In this study, phenotypic selection (PS, conventional marker-assisted selection (MAS, and GS prediction accuracy were compared for 13 agronomic traits in a population of 374 winter wheat ( L. advanced-cycle breeding lines. A cross-validation approach that trained and validated prediction accuracy across years was used to evaluate effects of model selection, training population size, and marker density in the presence of genotype × environment interactions (G×E. The average prediction accuracies using GS were 28% greater than with MAS and were 95% as accurate as PS. For net merit, the average accuracy across six selection indices for GS was 14% greater than for PS. These results provide empirical evidence that multifamily GS could increase genetic gain per unit time and cost in plant breeding.

  12. Accuracy of genomic breeding value prediction for intramuscular fat using different genomic relationship matrices in Hanwoo (Korean cattle).

    Science.gov (United States)

    Choi, Taejeong; Lim, Dajeong; Park, Byoungho; Sharma, Aditi; Kim, Jong-Joo; Kim, Sidong; Lee, Seung Hwan

    2017-07-01

    Intramuscular fat is one of the meat quality traits that is considered in the selection strategies for Hanwoo (Korean cattle). Different methods are used to estimate the breeding value of selection candidates. In the present work we focused on accuracy of different genotype relationship matrices as described by forni and pedigree based relationship matrix. The data set included a total of 778 animals that were genotyped for BovineSNP50 BeadChip. Among these 778 animals, 72 animals were sires for 706 reference animals and were used as a validation dataset. Single trait animal model (best linear unbiased prediction and genomic best linear unbiased prediction) was used to estimate the breeding values from genomic and pedigree information. The diagonal elements for the pedigree based coefficients were slightly higher for the genomic relationship matrices (GRM) based coefficients while off diagonal elements were considerably low for GRM based coefficients. The accuracy of breeding value for the pedigree based relationship matrix (A) was 13% while for GRM (GOF, G05, and Yang) it was 0.37, 0.45, and 0.38, respectively. Accuracy of GRM was 1.5 times higher than A in this study. Therefore, genomic information will be more beneficial than pedigree information in the Hanwoo breeding program.

  13. Accuracy of depolarization and delay spread predictions using advanced ray-based modeling in indoor scenarios

    Directory of Open Access Journals (Sweden)

    Mani Francesco

    2011-01-01

    Full Text Available Abstract This article investigates the prediction accuracy of an advanced deterministic propagation model in terms of channel depolarization and frequency selectivity for indoor wireless propagation. In addition to specular reflection and diffraction, the developed ray tracing tool considers penetration through dielectric blocks and/or diffuse scattering mechanisms. The sensitivity and prediction accuracy analysis is based on two measurement campaigns carried out in a warehouse and an office building. It is shown that the implementation of diffuse scattering into RT significantly increases the accuracy of the cross-polar discrimination prediction, whereas the delay-spread prediction is only marginally improved.

  14. Predictive Accuracy of Exercise Stress Testing the Healthy Adult.

    Science.gov (United States)

    Lamont, Linda S.

    1981-01-01

    Exercise stress testing provides information on the aerobic capacity, heart rate, and blood pressure responses to graded exercises of a healthy adult. The reliability of exercise tests as a diagnostic procedure is discussed in relation to sensitivity and specificity and predictive accuracy. (JN)

  15. Lessons learned from accuracy assessment of IAEA-SPE-4 experiment predictions

    International Nuclear Information System (INIS)

    Prosek, A.

    2002-01-01

    The use of methods for code accuracy assessment has strongly increased in the last years. The methods suitable to provide quantitative comparison between the thermalhydraulic code predictions and experimental measurements were proposed e.g. fast Fourier transform based method (FFTBM), stochastic approximation ratio based method (SARBM) and a few methods used in the frame of the recently developed automated code assessment program (ACAP). Further, in the frame of FFTBM also a procedure to quantify the whole calculation was proposed with averaging of the results. The problem is that averaging may hide discrepancies highlighted in the qualitative analysis when only quantitative results are published. The purpose of the study was therefore to propose additional accuracy measures. New proposed measures were tested with IAEA-SPE-4 pre- and post-test predictions. The obtained results showed that the proposed measures improve the whole picture of the code accuracy. This is important when the reader is not provided with the accompanied qualitative analysis. The study shows that proposed accuracy measures efficiently increase the confidence in the quantitative results.(author)

  16. Accuracy of taxonomy prediction for 16S rRNA and fungal ITS sequences

    Directory of Open Access Journals (Sweden)

    Robert C. Edgar

    2018-04-01

    Full Text Available Prediction of taxonomy for marker gene sequences such as 16S ribosomal RNA (rRNA is a fundamental task in microbiology. Most experimentally observed sequences are diverged from reference sequences of authoritatively named organisms, creating a challenge for prediction methods. I assessed the accuracy of several algorithms using cross-validation by identity, a new benchmark strategy which explicitly models the variation in distances between query sequences and the closest entry in a reference database. When the accuracy of genus predictions was averaged over a representative range of identities with the reference database (100%, 99%, 97%, 95% and 90%, all tested methods had ≤50% accuracy on the currently-popular V4 region of 16S rRNA. Accuracy was found to fall rapidly with identity; for example, better methods were found to have V4 genus prediction accuracy of ∼100% at 100% identity but ∼50% at 97% identity. The relationship between identity and taxonomy was quantified as the probability that a rank is the lowest shared by a pair of sequences with a given pair-wise identity. With the V4 region, 95% identity was found to be a twilight zone where taxonomy is highly ambiguous because the probabilities that the lowest shared rank between pairs of sequences is genus, family, order or class are approximately equal.

  17. Improving the accuracy of the structure prediction of the third hypervariable loop of the heavy chains of antibodies.

    KAUST Repository

    Messih, Mario Abdel; Lepore, Rosalba; Marcatili, Paolo; Tramontano, Anna

    2014-01-01

    MOTIVATION: Antibodies are able to recognize a wide range of antigens through their complementary determining regions formed by six hypervariable loops. Predicting the 3D structure of these loops is essential for the analysis and reengineering of novel antibodies with enhanced affinity and specificity. The canonical structure model allows high accuracy prediction for five of the loops. The third loop of the heavy chain, H3, is the hardest to predict because of its diversity in structure, length and sequence composition. RESULTS: We describe a method, based on the Random Forest automatic learning technique, to select structural templates for H3 loops among a dataset of candidates. These can be used to predict the structure of the loop with a higher accuracy than that achieved by any of the presently available methods. The method also has the advantage of being extremely fast and returning a reliable estimate of the model quality. AVAILABILITY AND IMPLEMENTATION: The source code is freely available at http://www.biocomputing.it/H3Loopred/ .

  18. Improving the accuracy of the structure prediction of the third hypervariable loop of the heavy chains of antibodies.

    KAUST Repository

    Messih, Mario Abdel

    2014-06-13

    MOTIVATION: Antibodies are able to recognize a wide range of antigens through their complementary determining regions formed by six hypervariable loops. Predicting the 3D structure of these loops is essential for the analysis and reengineering of novel antibodies with enhanced affinity and specificity. The canonical structure model allows high accuracy prediction for five of the loops. The third loop of the heavy chain, H3, is the hardest to predict because of its diversity in structure, length and sequence composition. RESULTS: We describe a method, based on the Random Forest automatic learning technique, to select structural templates for H3 loops among a dataset of candidates. These can be used to predict the structure of the loop with a higher accuracy than that achieved by any of the presently available methods. The method also has the advantage of being extremely fast and returning a reliable estimate of the model quality. AVAILABILITY AND IMPLEMENTATION: The source code is freely available at http://www.biocomputing.it/H3Loopred/ .

  19. Accuracy of algorithms to predict accessory pathway location in children with Wolff-Parkinson-White syndrome.

    Science.gov (United States)

    Wren, Christopher; Vogel, Melanie; Lord, Stephen; Abrams, Dominic; Bourke, John; Rees, Philip; Rosenthal, Eric

    2012-02-01

    The aim of this study was to examine the accuracy in predicting pathway location in children with Wolff-Parkinson-White syndrome for each of seven published algorithms. ECGs from 100 consecutive children with Wolff-Parkinson-White syndrome undergoing electrophysiological study were analysed by six investigators using seven published algorithms, six of which had been developed in adult patients. Accuracy and concordance of predictions were adjusted for the number of pathway locations. Accessory pathways were left-sided in 49, septal in 20 and right-sided in 31 children. Overall accuracy of prediction was 30-49% for the exact location and 61-68% including adjacent locations. Concordance between investigators varied between 41% and 86%. No algorithm was better at predicting septal pathways (accuracy 5-35%, improving to 40-78% including adjacent locations), but one was significantly worse. Predictive accuracy was 24-53% for the exact location of right-sided pathways (50-71% including adjacent locations) and 32-55% for the exact location of left-sided pathways (58-73% including adjacent locations). All algorithms were less accurate in our hands than in other authors' own assessment. None performed well in identifying midseptal or right anteroseptal accessory pathway locations.

  20. Probability of criminal acts of violence: a test of jury predictive accuracy.

    Science.gov (United States)

    Reidy, Thomas J; Sorensen, Jon R; Cunningham, Mark D

    2013-01-01

    The ability of capital juries to accurately predict future prison violence at the sentencing phase of aggravated murder trials was examined through retrospective review of the disciplinary records of 115 male inmates sentenced to either life (n = 65) or death (n = 50) in Oregon from 1985 through 2008, with a mean post-conviction time at risk of 15.3 years. Violent prison behavior was completely unrelated to predictions made by capital jurors, with bidirectional accuracy simply reflecting the base rate of assaultive misconduct in the group. Rejection of the special issue predicting future violence enjoyed 90% accuracy. Conversely, predictions that future violence was probable had 90% error rates. More than 90% of the assaultive rule violations committed by these offenders resulted in no harm or only minor injuries. Copyright © 2013 John Wiley & Sons, Ltd.

  1. Effect of genetic architecture on the prediction accuracy of quantitative traits in samples of unrelated individuals.

    Science.gov (United States)

    Morgante, Fabio; Huang, Wen; Maltecca, Christian; Mackay, Trudy F C

    2018-06-01

    Predicting complex phenotypes from genomic data is a fundamental aim of animal and plant breeding, where we wish to predict genetic merits of selection candidates; and of human genetics, where we wish to predict disease risk. While genomic prediction models work well with populations of related individuals and high linkage disequilibrium (LD) (e.g., livestock), comparable models perform poorly for populations of unrelated individuals and low LD (e.g., humans). We hypothesized that low prediction accuracies in the latter situation may occur when the genetics architecture of the trait departs from the infinitesimal and additive architecture assumed by most prediction models. We used simulated data for 10,000 lines based on sequence data from a population of unrelated, inbred Drosophila melanogaster lines to evaluate this hypothesis. We show that, even in very simplified scenarios meant as a stress test of the commonly used Genomic Best Linear Unbiased Predictor (G-BLUP) method, using all common variants yields low prediction accuracy regardless of the trait genetic architecture. However, prediction accuracy increases when predictions are informed by the genetic architecture inferred from mapping the top variants affecting main effects and interactions in the training data, provided there is sufficient power for mapping. When the true genetic architecture is largely or partially due to epistatic interactions, the additive model may not perform well, while models that account explicitly for interactions generally increase prediction accuracy. Our results indicate that accounting for genetic architecture can improve prediction accuracy for quantitative traits.

  2. Prediction of lung tumour position based on spirometry and on abdominal displacement: Accuracy and reproducibility

    International Nuclear Information System (INIS)

    Hoisak, Jeremy D.P.; Sixel, Katharina E.; Tirona, Romeo; Cheung, Patrick C.F.; Pignol, Jean-Philippe

    2006-01-01

    Background and purpose: A simulation investigating the accuracy and reproducibility of a tumour motion prediction model over clinical time frames is presented. The model is formed from surrogate and tumour motion measurements, and used to predict the future position of the tumour from surrogate measurements alone. Patients and methods: Data were acquired from five non-small cell lung cancer patients, on 3 days. Measurements of respiratory volume by spirometry and abdominal displacement by a real-time position tracking system were acquired simultaneously with X-ray fluoroscopy measurements of superior-inferior tumour displacement. A model of tumour motion was established and used to predict future tumour position, based on surrogate input data. The calculated position was compared against true tumour motion as seen on fluoroscopy. Three different imaging strategies, pre-treatment, pre-fraction and intrafractional imaging, were employed in establishing the fitting parameters of the prediction model. The impact of each imaging strategy upon accuracy and reproducibility was quantified. Results: When establishing the predictive model using pre-treatment imaging, four of five patients exhibited poor interfractional reproducibility for either surrogate in subsequent sessions. Simulating the formulation of the predictive model prior to each fraction resulted in improved interfractional reproducibility. The accuracy of the prediction model was only improved in one of five patients when intrafractional imaging was used. Conclusions: Employing a prediction model established from measurements acquired at planning resulted in localization errors. Pre-fractional imaging improved the accuracy and reproducibility of the prediction model. Intrafractional imaging was of less value, suggesting that the accuracy limit of a surrogate-based prediction model is reached with once-daily imaging

  3. GFR prediction from cystatin C and creatinine in children: body cell mass increases accuracy of the estimate

    DEFF Research Database (Denmark)

    Andersen, Trine Borup; Jødal, Lars; Bøgsted, Martin

    ) aged 2-14 years (mean 8.8 years). GFR was 14-147 mL/min/1.73m2 (mean 97 mL/min/1.73m2). BCM was estimated using bioimpedance spectroscopy (Xitron Hydra 4200). Log-transformed data on BCM/CysC, serum creatinine (SCr), body-surface-area (BSA), height x BSA/SCr, serum CysC, weight, sex, age, height, serum....... The present equation also had the highest R2 and the narrowest 95% limits of agreement. CONCLUSION: The new equation predicts GFR with higher accuracy than other equations. Endogenous methods are, however, still not accurate enough to replace exogenous markers when GFR must be determined with high accuracy...

  4. Enhancing Predictive Accuracy of Cardiac Autonomic Neuropathy Using Blood Biochemistry Features and Iterative Multitier Ensembles.

    Science.gov (United States)

    Abawajy, Jemal; Kelarev, Andrei; Chowdhury, Morshed U; Jelinek, Herbert F

    2016-01-01

    Blood biochemistry attributes form an important class of tests, routinely collected several times per year for many patients with diabetes. The objective of this study is to investigate the role of blood biochemistry for improving the predictive accuracy of the diagnosis of cardiac autonomic neuropathy (CAN) progression. Blood biochemistry contributes to CAN, and so it is a causative factor that can provide additional power for the diagnosis of CAN especially in the absence of a complete set of Ewing tests. We introduce automated iterative multitier ensembles (AIME) and investigate their performance in comparison to base classifiers and standard ensemble classifiers for blood biochemistry attributes. AIME incorporate diverse ensembles into several tiers simultaneously and combine them into one automatically generated integrated system so that one ensemble acts as an integral part of another ensemble. We carried out extensive experimental analysis using large datasets from the diabetes screening research initiative (DiScRi) project. The results of our experiments show that several blood biochemistry attributes can be used to supplement the Ewing battery for the detection of CAN in situations where one or more of the Ewing tests cannot be completed because of the individual difficulties faced by each patient in performing the tests. The results show that AIME provide higher accuracy as a multitier CAN classification paradigm. The best predictive accuracy of 99.57% has been obtained by the AIME combining decorate on top tier with bagging on middle tier based on random forest. Practitioners can use these findings to increase the accuracy of CAN diagnosis.

  5. Accuracy of predicting milk yield from alternative recording schemes

    NARCIS (Netherlands)

    Berry, D.P.; Olori, V.E.; Cromie, A.R.; Rath, M.; Veerkamp, R.F.; Dilon, P.

    2005-01-01

    The effect of reducing the frequency of official milk recording and the number of recorded samples per test-day on the accuracy of predicting daily yield and cumulative 305-day yield was investigated. A control data set consisting of 58 210 primiparous cows with milk test-day records every 4 weeks

  6. Joint modeling of genetically correlated diseases and functional annotations increases accuracy of polygenic risk prediction.

    Directory of Open Access Journals (Sweden)

    Yiming Hu

    2017-06-01

    Full Text Available Accurate prediction of disease risk based on genetic factors is an important goal in human genetics research and precision medicine. Advanced prediction models will lead to more effective disease prevention and treatment strategies. Despite the identification of thousands of disease-associated genetic variants through genome-wide association studies (GWAS in the past decade, accuracy of genetic risk prediction remains moderate for most diseases, which is largely due to the challenges in both identifying all the functionally relevant variants and accurately estimating their effect sizes. In this work, we introduce PleioPred, a principled framework that leverages pleiotropy and functional annotations in genetic risk prediction for complex diseases. PleioPred uses GWAS summary statistics as its input, and jointly models multiple genetically correlated diseases and a variety of external information including linkage disequilibrium and diverse functional annotations to increase the accuracy of risk prediction. Through comprehensive simulations and real data analyses on Crohn's disease, celiac disease and type-II diabetes, we demonstrate that our approach can substantially increase the accuracy of polygenic risk prediction and risk population stratification, i.e. PleioPred can significantly better separate type-II diabetes patients with early and late onset ages, illustrating its potential clinical application. Furthermore, we show that the increment in prediction accuracy is significantly correlated with the genetic correlation between the predicted and jointly modeled diseases.

  7. Bias associated with delayed verification in test accuracy studies: accuracy of tests for endometrial hyperplasia may be much higher than we think!

    Directory of Open Access Journals (Sweden)

    Coomarasamy Aravinthan

    2004-05-01

    Full Text Available Abstract Background To empirically evaluate bias in estimation of accuracy associated with delay in verification of diagnosis among studies evaluating tests for predicting endometrial hyperplasia. Methods Systematic reviews of all published research on accuracy of miniature endometrial biopsy and endometr ial ultrasonography for diagnosing endometrial hyperplasia identified 27 test accuracy studies (2,982 subjects. Of these, 16 had immediate histological verification of diagnosis while 11 had verification delayed > 24 hrs after testing. The effect of delay in verification of diagnosis on estimates of accuracy was evaluated using meta-regression with diagnostic odds ratio (dOR as the accuracy measure. This analysis was adjusted for study quality and type of test (miniature endometrial biopsy or endometrial ultrasound. Results Compared to studies with immediate verification of diagnosis (dOR 67.2, 95% CI 21.7–208.8, those with delayed verification (dOR 16.2, 95% CI 8.6–30.5 underestimated the diagnostic accuracy by 74% (95% CI 7%–99%; P value = 0.048. Conclusion Among studies of miniature endometrial biopsy and endometrial ultrasound, diagnostic accuracy is considerably underestimated if there is a delay in histological verification of diagnosis.

  8. Accuracy of High-Resolution MRI with Lumen Distention in Rectal Cancer Staging and Circumferential Margin Involvement Prediction

    International Nuclear Information System (INIS)

    Iannicelli, Elsa; Di Renzo, Sara; Ferri, Mario; Pilozzi, Emanuela; Di Girolamo, Marco; Sapori, Alessandra; Ziparo, Vincenzo; David, Vincenzo

    2014-01-01

    To evaluate the accuracy of magnetic resonance imaging (MRI) with lumen distention for rectal cancer staging and circumferential resection margin (CRM) involvement prediction. Seventy-three patients with primary rectal cancer underwent high-resolution MRI with a phased-array coil performed using 60-80 mL room air rectal distention, 1-3 weeks before surgery. MRI results were compared to postoperative histopathological findings. The overall MRI T staging accuracy was calculated. CRM involvement prediction and the N staging, the accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were assessed for each T stage. The agreement between MRI and histological results was assessed using weighted-kappa statistics. The overall MRI accuracy for T staging was 93.6% (k = 0.85). The accuracy, sensitivity, specificity, PPV and NPV for each T stage were as follows: 91.8%, 86.2%, 95.5%, 92.6% and 91.3% for the group ≤ T2; 90.4%, 94.6%, 86.1%, 87.5% and 94% for T3; 98,6%, 85.7%, 100%, 100% and 98.5% for T4, respectively. The predictive CRM accuracy was 94.5% (k = 0.86); the sensitivity, specificity, PPV and NPV were 89.5%, 96.3%, 89.5%, and 96.3% respectively. The N staging accuracy was 68.49% (k = 0.4). MRI performed with rectal lumen distention has proved to be an effective technique both for rectal cancer staging and involved CRM predicting

  9. Accuracy of High-Resolution MRI with Lumen Distention in Rectal Cancer Staging and Circumferential Margin Involvement Prediction

    Energy Technology Data Exchange (ETDEWEB)

    Iannicelli, Elsa; Di Renzo, Sara [Radiology Institute, Faculty of Medicine and Psychology, University of Rome, Sapienza, Sant' Andrea Hospital, Rome 00189 (Italy); Department of Surgical and Medical Sciences and Translational Medicine, Faculty of Medicine and Psychology, University of Rome, Sapienza, Sant' Andrea Hospital, Rome 00189 (Italy); Ferri, Mario [Department of Surgical and Medical Sciences and Translational Medicine, Faculty of Medicine and Psychology, University of Rome, Sapienza, Sant' Andrea Hospital, Rome 00189 (Italy); Pilozzi, Emanuela [Department of Clinical and Molecular Sciences, Faculty of Medicine and Psychology, University of Rome, Sapienza, Sant' Andrea Hospital, Rome 00189 (Italy); Di Girolamo, Marco; Sapori, Alessandra [Radiology Institute, Faculty of Medicine and Psychology, University of Rome, Sapienza, Sant' Andrea Hospital, Rome 00189 (Italy); Department of Surgical and Medical Sciences and Translational Medicine, Faculty of Medicine and Psychology, University of Rome, Sapienza, Sant' Andrea Hospital, Rome 00189 (Italy); Ziparo, Vincenzo [Department of Surgical and Medical Sciences and Translational Medicine, Faculty of Medicine and Psychology, University of Rome, Sapienza, Sant' Andrea Hospital, Rome 00189 (Italy); David, Vincenzo [Radiology Institute, Faculty of Medicine and Psychology, University of Rome, Sapienza, Sant' Andrea Hospital, Rome 00189 (Italy); Department of Surgical and Medical Sciences and Translational Medicine, Faculty of Medicine and Psychology, University of Rome, Sapienza, Sant' Andrea Hospital, Rome 00189 (Italy)

    2014-07-01

    To evaluate the accuracy of magnetic resonance imaging (MRI) with lumen distention for rectal cancer staging and circumferential resection margin (CRM) involvement prediction. Seventy-three patients with primary rectal cancer underwent high-resolution MRI with a phased-array coil performed using 60-80 mL room air rectal distention, 1-3 weeks before surgery. MRI results were compared to postoperative histopathological findings. The overall MRI T staging accuracy was calculated. CRM involvement prediction and the N staging, the accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were assessed for each T stage. The agreement between MRI and histological results was assessed using weighted-kappa statistics. The overall MRI accuracy for T staging was 93.6% (k = 0.85). The accuracy, sensitivity, specificity, PPV and NPV for each T stage were as follows: 91.8%, 86.2%, 95.5%, 92.6% and 91.3% for the group ≤ T2; 90.4%, 94.6%, 86.1%, 87.5% and 94% for T3; 98,6%, 85.7%, 100%, 100% and 98.5% for T4, respectively. The predictive CRM accuracy was 94.5% (k = 0.86); the sensitivity, specificity, PPV and NPV were 89.5%, 96.3%, 89.5%, and 96.3% respectively. The N staging accuracy was 68.49% (k = 0.4). MRI performed with rectal lumen distention has proved to be an effective technique both for rectal cancer staging and involved CRM predicting.

  10. Improvement of prediction accuracy of large eddy simulation on colocated grids; Colocation koshi wo mochiita LES no keisan seido kaizen ni kansuru ichikosatsu

    Energy Technology Data Exchange (ETDEWEB)

    Inagaki, M.; Abe, K. [Toyota Central Research and Development Labs., Inc., Aichi (Japan)

    1998-07-25

    With the recent advances in computers, large eddy simulation (LES) has become applicable to engineering prediction. However, most cases of the engineering applications need to use the nonorthgonal curvilimear coordinate systems. The staggered grids, usually used in LES in the orthgonal coordinates, don`t keep conservative properties in the nonorthgonal curvilinear coordinates. On the other hand, the colocated grids can be applied in the nonorthgonal curvilinear coordinates without losing its conservative properties, although its prediction accuracy isn`t so high as the staggered grid`s in the orthgonal coordinates especially with the coarse grids. In this research, the discretization method of the colocated grids is modified to improve its prediction accuracy. Plane channel flows are simulated on four grids of different resolution using the modified colocated grids and the original colocated grids. The results show that the modified colocated grids have higher accuracy than the original colocated grids. 17 refs., 13 figs., 1 tab.

  11. Link Prediction Methods and Their Accuracy for Different Social Networks and Network Metrics

    Directory of Open Access Journals (Sweden)

    Fei Gao

    2015-01-01

    Full Text Available Currently, we are experiencing a rapid growth of the number of social-based online systems. The availability of the vast amounts of data gathered in those systems brings new challenges that we face when trying to analyse it. One of the intensively researched topics is the prediction of social connections between users. Although a lot of effort has been made to develop new prediction approaches, the existing methods are not comprehensively analysed. In this paper we investigate the correlation between network metrics and accuracy of different prediction methods. We selected six time-stamped real-world social networks and ten most widely used link prediction methods. The results of the experiments show that the performance of some methods has a strong correlation with certain network metrics. We managed to distinguish “prediction friendly” networks, for which most of the prediction methods give good performance, as well as “prediction unfriendly” networks, for which most of the methods result in high prediction error. Correlation analysis between network metrics and prediction accuracy of prediction methods may form the basis of a metalearning system where based on network characteristics it will be able to recommend the right prediction method for a given network.

  12. The accuracy of prediction of genomic selection in elite hybrid rye populations surpasses the accuracy of marker-assisted selection and is equally augmented by multiple field evaluation locations and test years.

    Science.gov (United States)

    Wang, Yu; Mette, Michael Florian; Miedaner, Thomas; Gottwald, Marlen; Wilde, Peer; Reif, Jochen C; Zhao, Yusheng

    2014-07-04

    Marker-assisted selection (MAS) and genomic selection (GS) based on genome-wide marker data provide powerful tools to predict the genotypic value of selection material in plant breeding. However, case-to-case optimization of these approaches is required to achieve maximum accuracy of prediction with reasonable input. Based on extended field evaluation data for grain yield, plant height, starch content and total pentosan content of elite hybrid rye derived from testcrosses involving two bi-parental populations that were genotyped with 1048 molecular markers, we compared the accuracy of prediction of MAS and GS in a cross-validation approach. MAS delivered generally lower and in addition potentially over-estimated accuracies of prediction than GS by ridge regression best linear unbiased prediction (RR-BLUP). The grade of relatedness of the plant material included in the estimation and test sets clearly affected the accuracy of prediction of GS. Within each of the two bi-parental populations, accuracies differed depending on the relatedness of the respective parental lines. Across populations, accuracy increased when both populations contributed to estimation and test set. In contrast, accuracy of prediction based on an estimation set from one population to a test set from the other population was low despite that the two bi-parental segregating populations under scrutiny shared one parental line. Limiting the number of locations or years in field testing reduced the accuracy of prediction of GS equally, supporting the view that to establish robust GS calibration models a sufficient number of test locations is of similar importance as extended testing for more than one year. In hybrid rye, genomic selection is superior to marker-assisted selection. However, it achieves high accuracies of prediction only for selection candidates closely related to the plant material evaluated in field trials, resulting in a rather pessimistic prognosis for distantly related material

  13. A novel method for improved accuracy of transcription factor binding site prediction

    KAUST Repository

    Khamis, Abdullah M.; Motwalli, Olaa Amin; Oliva, Romina; Jankovic, Boris R.; Medvedeva, Yulia; Ashoor, Haitham; Essack, Magbubah; Gao, Xin; Bajic, Vladimir B.

    2018-01-01

    Identifying transcription factor (TF) binding sites (TFBSs) is important in the computational inference of gene regulation. Widely used computational methods of TFBS prediction based on position weight matrices (PWMs) usually have high false positive rates. Moreover, computational studies of transcription regulation in eukaryotes frequently require numerous PWM models of TFBSs due to a large number of TFs involved. To overcome these problems we developed DRAF, a novel method for TFBS prediction that requires only 14 prediction models for 232 human TFs, while at the same time significantly improves prediction accuracy. DRAF models use more features than PWM models, as they combine information from TFBS sequences and physicochemical properties of TF DNA-binding domains into machine learning models. Evaluation of DRAF on 98 human ChIP-seq datasets shows on average 1.54-, 1.96- and 5.19-fold reduction of false positives at the same sensitivities compared to models from HOCOMOCO, TRANSFAC and DeepBind, respectively. This observation suggests that one can efficiently replace the PWM models for TFBS prediction by a small number of DRAF models that significantly improve prediction accuracy. The DRAF method is implemented in a web tool and in a stand-alone software freely available at http://cbrc.kaust.edu.sa/DRAF.

  14. A novel method for improved accuracy of transcription factor binding site prediction

    KAUST Repository

    Khamis, Abdullah M.

    2018-03-20

    Identifying transcription factor (TF) binding sites (TFBSs) is important in the computational inference of gene regulation. Widely used computational methods of TFBS prediction based on position weight matrices (PWMs) usually have high false positive rates. Moreover, computational studies of transcription regulation in eukaryotes frequently require numerous PWM models of TFBSs due to a large number of TFs involved. To overcome these problems we developed DRAF, a novel method for TFBS prediction that requires only 14 prediction models for 232 human TFs, while at the same time significantly improves prediction accuracy. DRAF models use more features than PWM models, as they combine information from TFBS sequences and physicochemical properties of TF DNA-binding domains into machine learning models. Evaluation of DRAF on 98 human ChIP-seq datasets shows on average 1.54-, 1.96- and 5.19-fold reduction of false positives at the same sensitivities compared to models from HOCOMOCO, TRANSFAC and DeepBind, respectively. This observation suggests that one can efficiently replace the PWM models for TFBS prediction by a small number of DRAF models that significantly improve prediction accuracy. The DRAF method is implemented in a web tool and in a stand-alone software freely available at http://cbrc.kaust.edu.sa/DRAF.

  15. Quantifying and estimating the predictive accuracy for censored time-to-event data with competing risks.

    Science.gov (United States)

    Wu, Cai; Li, Liang

    2018-05-15

    This paper focuses on quantifying and estimating the predictive accuracy of prognostic models for time-to-event outcomes with competing events. We consider the time-dependent discrimination and calibration metrics, including the receiver operating characteristics curve and the Brier score, in the context of competing risks. To address censoring, we propose a unified nonparametric estimation framework for both discrimination and calibration measures, by weighting the censored subjects with the conditional probability of the event of interest given the observed data. The proposed method can be extended to time-dependent predictive accuracy metrics constructed from a general class of loss functions. We apply the methodology to a data set from the African American Study of Kidney Disease and Hypertension to evaluate the predictive accuracy of a prognostic risk score in predicting end-stage renal disease, accounting for the competing risk of pre-end-stage renal disease death, and evaluate its numerical performance in extensive simulation studies. Copyright © 2018 John Wiley & Sons, Ltd.

  16. Typing speed, spelling accuracy, and the use of word-prediction ...

    African Journals Online (AJOL)

    Children with spelling difficulties are limited in their participation in all written school activities. We aimed to investigate the influence of word-prediction as a tool on spelling accuracy and typing speed. To this end, we selected 80 Grade 4 – 6 children with spelling difficulties in a school for special needs to participate

  17. The Accuracy and Bias of Single-Step Genomic Prediction for Populations Under Selection

    Directory of Open Access Journals (Sweden)

    Wan-Ling Hsu

    2017-08-01

    Full Text Available In single-step analyses, missing genotypes are explicitly or implicitly imputed, and this requires centering the observed genotypes using the means of the unselected founders. If genotypes are only available for selected individuals, centering on the unselected founder mean is not straightforward. Here, computer simulation is used to study an alternative analysis that does not require centering genotypes but fits the mean μg of unselected individuals as a fixed effect. Starting with observed diplotypes from 721 cattle, a five-generation population was simulated with sire selection to produce 40,000 individuals with phenotypes, of which the 1000 sires had genotypes. The next generation of 8000 genotyped individuals was used for validation. Evaluations were undertaken with (J or without (N μg when marker covariates were not centered; and with (JC or without (C μg when all observed and imputed marker covariates were centered. Centering did not influence accuracy of genomic prediction, but fitting μg did. Accuracies were improved when the panel comprised only quantitative trait loci (QTL; models JC and J had accuracies of 99.4%, whereas models C and N had accuracies of 90.2%. When only markers were in the panel, the 4 models had accuracies of 80.4%. In panels that included QTL, fitting μg in the model improved accuracy, but had little impact when the panel contained only markers. In populations undergoing selection, fitting μg in the model is recommended to avoid bias and reduction in prediction accuracy due to selection.

  18. Model training across multiple breeding cycles significantly improves genomic prediction accuracy in rye (Secale cereale L.).

    Science.gov (United States)

    Auinger, Hans-Jürgen; Schönleben, Manfred; Lehermeier, Christina; Schmidt, Malthe; Korzun, Viktor; Geiger, Hartwig H; Piepho, Hans-Peter; Gordillo, Andres; Wilde, Peer; Bauer, Eva; Schön, Chris-Carolin

    2016-11-01

    Genomic prediction accuracy can be significantly increased by model calibration across multiple breeding cycles as long as selection cycles are connected by common ancestors. In hybrid rye breeding, application of genome-based prediction is expected to increase selection gain because of long selection cycles in population improvement and development of hybrid components. Essentially two prediction scenarios arise: (1) prediction of the genetic value of lines from the same breeding cycle in which model training is performed and (2) prediction of lines from subsequent cycles. It is the latter from which a reduction in cycle length and consequently the strongest impact on selection gain is expected. We empirically investigated genome-based prediction of grain yield, plant height and thousand kernel weight within and across four selection cycles of a hybrid rye breeding program. Prediction performance was assessed using genomic and pedigree-based best linear unbiased prediction (GBLUP and PBLUP). A total of 1040 S 2 lines were genotyped with 16 k SNPs and each year testcrosses of 260 S 2 lines were phenotyped in seven or eight locations. The performance gap between GBLUP and PBLUP increased significantly for all traits when model calibration was performed on aggregated data from several cycles. Prediction accuracies obtained from cross-validation were in the order of 0.70 for all traits when data from all cycles (N CS  = 832) were used for model training and exceeded within-cycle accuracies in all cases. As long as selection cycles are connected by a sufficient number of common ancestors and prediction accuracy has not reached a plateau when increasing sample size, aggregating data from several preceding cycles is recommended for predicting genetic values in subsequent cycles despite decreasing relatedness over time.

  19. Predictive analysis of beer quality by correlating sensory evaluation with higher alcohol and ester production using multivariate statistics methods.

    Science.gov (United States)

    Dong, Jian-Jun; Li, Qing-Liang; Yin, Hua; Zhong, Cheng; Hao, Jun-Guang; Yang, Pan-Fei; Tian, Yu-Hong; Jia, Shi-Ru

    2014-10-15

    Sensory evaluation is regarded as a necessary procedure to ensure a reproducible quality of beer. Meanwhile, high-throughput analytical methods provide a powerful tool to analyse various flavour compounds, such as higher alcohol and ester. In this study, the relationship between flavour compounds and sensory evaluation was established by non-linear models such as partial least squares (PLS), genetic algorithm back-propagation neural network (GA-BP), support vector machine (SVM). It was shown that SVM with a Radial Basis Function (RBF) had a better performance of prediction accuracy for both calibration set (94.3%) and validation set (96.2%) than other models. Relatively lower prediction abilities were observed for GA-BP (52.1%) and PLS (31.7%). In addition, the kernel function of SVM played an essential role of model training when the prediction accuracy of SVM with polynomial kernel function was 32.9%. As a powerful multivariate statistics method, SVM holds great potential to assess beer quality. Copyright © 2014 Elsevier Ltd. All rights reserved.

  20. High accuracy prediction of beta-turns and their types using propensities and multiple alignments.

    Science.gov (United States)

    Fuchs, Patrick F J; Alix, Alain J P

    2005-06-01

    We have developed a method that predicts both the presence and the type of beta-turns, using a straightforward approach based on propensities and multiple alignments. The propensities were calculated classically, but the way to use them for prediction was completely new: starting from a tetrapeptide sequence on which one wants to evaluate the presence of a beta-turn, the propensity for a given residue is modified by taking into account all the residues present in the multiple alignment at this position. The evaluation of a score is then done by weighting these propensities by the use of Position-specific score matrices generated by PSI-BLAST. The introduction of secondary structure information predicted by PSIPRED or SSPRO2 as well as taking into account the flanking residues around the tetrapeptide improved the accuracy greatly. This latter evaluated on a database of 426 reference proteins (previously used on other studies) by a sevenfold crossvalidation gave very good results with a Matthews Correlation Coefficient (MCC) of 0.42 and an overall prediction accuracy of 74.8%; this places our method among the best ones. A jackknife test was also done, which gave results within the same range. This shows that it is possible to reach neural networks accuracy with considerably less computional cost and complexity. Furthermore, propensities remain excellent descriptors of amino acid tendencies to belong to beta-turns, which can be useful for peptide or protein engineering and design. For beta-turn type prediction, we reached the best accuracy ever published in terms of MCC (except for the irregular type IV) in the range of 0.25-0.30 for types I, II, and I' and 0.13-0.15 for types VIII, II', and IV. To our knowledge, our method is the only one available on the Web that predicts types I' and II'. The accuracy evaluated on two larger databases of 547 and 823 proteins was not improved significantly. All of this was implemented into a Web server called COUDES (French acronym

  1. Prediction of renal function (GFR) from cystatin C and creatinine in children: Body cell mass increases accuracy of the estimate

    DEFF Research Database (Denmark)

    Andersen, Trine Borup; Jødal, Lars; Bøgsted, Martin

    ) aged 2-14 years (mean 8.8 years). GFR was 14-147 mL/min/1.73m2 (mean 97 mL/min/1.73m2). BCM was estimated using bioimpedance spectroscopy (Xitron Hydra 4200). Log-transformed data on BCM/CysC, serum creatinine (SCr), body-surface-area (BSA), height x BSA/SCr, serum CysC, weight, sex, age, height, serum....... The present equation also had the highest R2 and the narrowest 95% limits of agreement. CONCLUSION: The new equation predicts GFR with higher accuracy than other equations. Endogenous methods are, however, still not accurate enough to replace exogenous markers when GFR must be determined with high accuracy...

  2. A Kolmogorov-Smirnov Based Test for Comparing the Predictive Accuracy of Two Sets of Forecasts

    Directory of Open Access Journals (Sweden)

    Hossein Hassani

    2015-08-01

    Full Text Available This paper introduces a complement statistical test for distinguishing between the predictive accuracy of two sets of forecasts. We propose a non-parametric test founded upon the principles of the Kolmogorov-Smirnov (KS test, referred to as the KS Predictive Accuracy (KSPA test. The KSPA test is able to serve two distinct purposes. Initially, the test seeks to determine whether there exists a statistically significant difference between the distribution of forecast errors, and secondly it exploits the principles of stochastic dominance to determine whether the forecasts with the lower error also reports a stochastically smaller error than forecasts from a competing model, and thereby enables distinguishing between the predictive accuracy of forecasts. We perform a simulation study for the size and power of the proposed test and report the results for different noise distributions, sample sizes and forecasting horizons. The simulation results indicate that the KSPA test is correctly sized, and robust in the face of varying forecasting horizons and sample sizes along with significant accuracy gains reported especially in the case of small sample sizes. Real world applications are also considered to illustrate the applicability of the proposed KSPA test in practice.

  3. Using Genetic Distance to Infer the Accuracy of Genomic Prediction.

    Directory of Open Access Journals (Sweden)

    Marco Scutari

    2016-09-01

    Full Text Available The prediction of phenotypic traits using high-density genomic data has many applications such as the selection of plants and animals of commercial interest; and it is expected to play an increasing role in medical diagnostics. Statistical models used for this task are usually tested using cross-validation, which implicitly assumes that new individuals (whose phenotypes we would like to predict originate from the same population the genomic prediction model is trained on. In this paper we propose an approach based on clustering and resampling to investigate the effect of increasing genetic distance between training and target populations when predicting quantitative traits. This is important for plant and animal genetics, where genomic selection programs rely on the precision of predictions in future rounds of breeding. Therefore, estimating how quickly predictive accuracy decays is important in deciding which training population to use and how often the model has to be recalibrated. We find that the correlation between true and predicted values decays approximately linearly with respect to either FST or mean kinship between the training and the target populations. We illustrate this relationship using simulations and a collection of data sets from mice, wheat and human genetics.

  4. Prediction of parturition in dogs and cats: accuracy at different gestational ages.

    Science.gov (United States)

    Beccaglia, M; Luvoni, G C

    2012-12-01

    In bitches and queens, the ultrasonographic measurement of extrafoetal and foetal structures allows the evaluation of gestational age and the prediction of the parturition term for an extended period of time. The aim of this study was to investigate whether the accuracy of parturition date prediction is affected by the week of pregnancy when the ultrasonographic examination is performed. The results were obtained by retrospective analysis on the gestational period basis (from week 4 to week 9 of pregnancy) in 495 ultrasonographic examinations of pregnant bitches (small and medium size) and 60 of pregnant queens. They demonstrated that a similar accuracy (p > 0.05) was obtained by the measurement of inner chorionic cavity at week 4 and 5 of pregnancy (± 1 day, 81% vs 67.7%; ± 2 days, 93.1% vs 85.9%). Accuracy (± 1 day) based on biparietal (BP) measurement was similar at week 5 and 6 of pregnancy (78.6% vs 78.9%; p > 0.05), whereas a significant decrease (p parturition term is highly consistent for 6 and 8 weeks of gestation, respectively. © 2012 Blackwell Verlag GmbH.

  5. Appropriate Combination of Artificial Intelligence and Algorithms for Increasing Predictive Accuracy Management

    Directory of Open Access Journals (Sweden)

    Shahram Gilani Nia

    2010-03-01

    Full Text Available In this paper a simple and effective expert system to predict random data fluctuation in short-term period is established. Evaluation process includes introducing Fourier series, Markov chain model prediction and comparison (Gray combined with the model prediction Gray- Fourier- Markov that the mixed results, to create an expert system predicted with artificial intelligence, made this model to predict the effectiveness of random fluctuation in most data management programs to increase. The outcome of this study introduced artificial intelligence algorithms that help detect that the computer environment to create a system that experts predict the short-term and unstable situation happens correctly and accurately predict. To test the effectiveness of the algorithm presented studies (Chen Tzay len,2008, and predicted data of tourism demand for Iran model is used. Results for the two countries show output model has high accuracy.

  6. Development and application of a statistical methodology to evaluate the predictive accuracy of building energy baseline models

    Energy Technology Data Exchange (ETDEWEB)

    Granderson, Jessica [Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). Energy Technologies Area Div.; Price, Phillip N. [Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). Energy Technologies Area Div.

    2014-03-01

    This paper documents the development and application of a general statistical methodology to assess the accuracy of baseline energy models, focusing on its application to Measurement and Verification (M&V) of whole-­building energy savings. The methodology complements the principles addressed in resources such as ASHRAE Guideline 14 and the International Performance Measurement and Verification Protocol. It requires fitting a baseline model to data from a ``training period’’ and using the model to predict total electricity consumption during a subsequent ``prediction period.’’ We illustrate the methodology by evaluating five baseline models using data from 29 buildings. The training period and prediction period were varied, and model predictions of daily, weekly, and monthly energy consumption were compared to meter data to determine model accuracy. Several metrics were used to characterize the accuracy of the predictions, and in some cases the best-­performing model as judged by one metric was not the best performer when judged by another metric.

  7. Estimating the Accuracy of the Chedoke-McMaster Stroke Assessment Predictive Equations for Stroke Rehabilitation.

    Science.gov (United States)

    Dang, Mia; Ramsaran, Kalinda D; Street, Melissa E; Syed, S Noreen; Barclay-Goddard, Ruth; Stratford, Paul W; Miller, Patricia A

    2011-01-01

    To estimate the predictive accuracy and clinical usefulness of the Chedoke-McMaster Stroke Assessment (CMSA) predictive equations. A longitudinal prognostic study using historical data obtained from 104 patients admitted post cerebrovascular accident was undertaken. Data were abstracted for all patients undergoing rehabilitation post stroke who also had documented admission and discharge CMSA scores. Published predictive equations were used to determine predicted outcomes. To determine the accuracy and clinical usefulness of the predictive model, shrinkage coefficients and predictions with 95% confidence bands were calculated. Complete data were available for 74 patients with a mean age of 65.3±12.4 years. The shrinkage values for the six Impairment Inventory (II) dimensions varied from -0.05 to 0.09; the shrinkage value for the Activity Inventory (AI) was 0.21. The error associated with predictive values was greater than ±1.5 stages for the II dimensions and greater than ±24 points for the AI. This study shows that the large error associated with the predictions (as defined by the confidence band) for the CMSA II and AI limits their clinical usefulness as a predictive measure. Further research to establish predictive models using alternative statistical procedures is warranted.

  8. Accuracy test for link prediction in terms of similarity index: The case of WS and BA models

    Science.gov (United States)

    Ahn, Min-Woo; Jung, Woo-Sung

    2015-07-01

    Link prediction is a technique that uses the topological information in a given network to infer the missing links in it. Since past research on link prediction has primarily focused on enhancing performance for given empirical systems, negligible attention has been devoted to link prediction with regard to network models. In this paper, we thus apply link prediction to two network models: The Watts-Strogatz (WS) model and Barabási-Albert (BA) model. We attempt to gain a better understanding of the relation between accuracy and each network parameter (mean degree, the number of nodes and the rewiring probability in the WS model) through network models. Six similarity indices are used, with precision and area under the ROC curve (AUC) value as the accuracy metrics. We observe a positive correlation between mean degree and accuracy, and size independence of the AUC value.

  9. Explained variation and predictive accuracy in general parametric statistical models: the role of model misspecification

    DEFF Research Database (Denmark)

    Rosthøj, Susanne; Keiding, Niels

    2004-01-01

    When studying a regression model measures of explained variation are used to assess the degree to which the covariates determine the outcome of interest. Measures of predictive accuracy are used to assess the accuracy of the predictions based on the covariates and the regression model. We give a ...... a detailed and general introduction to the two measures and the estimation procedures. The framework we set up allows for a study of the effect of misspecification on the quantities estimated. We also introduce a generalization to survival analysis....

  10. Predictive Validity and Accuracy of Oral Reading Fluency for English Learners

    Science.gov (United States)

    Vanderwood, Michael L.; Tung, Catherine Y.; Checca, C. Jason

    2014-01-01

    The predictive validity and accuracy of an oral reading fluency (ORF) measure for a statewide assessment in English language arts was examined for second-grade native English speakers (NESs) and English learners (ELs) with varying levels of English proficiency. In addition to comparing ELs with native English speakers, the impact of English…

  11. Accuracy statistics in predicting Independent Activities of Daily Living (IADL) capacity with comprehensive and brief neuropsychological test batteries.

    Science.gov (United States)

    Karzmark, Peter; Deutsch, Gayle K

    2018-01-01

    This investigation was designed to determine the predictive accuracy of a comprehensive neuropsychological and brief neuropsychological test battery with regard to the capacity to perform instrumental activities of daily living (IADLs). Accuracy statistics that included measures of sensitivity, specificity, positive and negative predicted power and positive likelihood ratio were calculated for both types of batteries. The sample was drawn from a general neurological group of adults (n = 117) that included a number of older participants (age >55; n = 38). Standardized neuropsychological assessments were administered to all participants and were comprised of the Halstead Reitan Battery and portions of the Wechsler Adult Intelligence Scale-III. A comprehensive test battery yielded a moderate increase over base-rate in predictive accuracy that generalized to older individuals. There was only limited support for using a brief battery, for although sensitivity was high, specificity was low. We found that a comprehensive neuropsychological test battery provided good classification accuracy for predicting IADL capacity.

  12. Comparison of the accuracy of three algorithms in predicting accessory pathways among adult Wolff-Parkinson-White syndrome patients.

    Science.gov (United States)

    Maden, Orhan; Balci, Kevser Gülcihan; Selcuk, Mehmet Timur; Balci, Mustafa Mücahit; Açar, Burak; Unal, Sefa; Kara, Meryem; Selcuk, Hatice

    2015-12-01

    The aim of this study was to investigate the accuracy of three algorithms in predicting accessory pathway locations in adult patients with Wolff-Parkinson-White syndrome in Turkish population. A total of 207 adult patients with Wolff-Parkinson-White syndrome were retrospectively analyzed. The most preexcited 12-lead electrocardiogram in sinus rhythm was used for analysis. Two investigators blinded to the patient data used three algorithms for prediction of accessory pathway location. Among all locations, 48.5% were left-sided, 44% were right-sided, and 7.5% were located in the midseptum or anteroseptum. When only exact locations were accepted as match, predictive accuracy for Chiang was 71.5%, 72.4% for d'Avila, and 71.5% for Arruda. The percentage of predictive accuracy of all algorithms did not differ between the algorithms (p = 1.000; p = 0.875; p = 0.885, respectively). The best algorithm for prediction of right-sided, left-sided, and anteroseptal and midseptal accessory pathways was Arruda (p algorithms were similar in predicting accessory pathway location and the predicted accuracy was lower than previously reported by their authors. However, according to the accessory pathway site, the algorithm designed by Arruda et al. showed better predictions than the other algorithms and using this algorithm may provide advantages before a planned ablation.

  13. Resource allocation for maximizing prediction accuracy and genetic gain of genomic selection in plant breeding: a simulation experiment.

    Science.gov (United States)

    Lorenz, Aaron J

    2013-03-01

    Allocating resources between population size and replication affects both genetic gain through phenotypic selection and quantitative trait loci detection power and effect estimation accuracy for marker-assisted selection (MAS). It is well known that because alleles are replicated across individuals in quantitative trait loci mapping and MAS, more resources should be allocated to increasing population size compared with phenotypic selection. Genomic selection is a form of MAS using all marker information simultaneously to predict individual genetic values for complex traits and has widely been found superior to MAS. No studies have explicitly investigated how resource allocation decisions affect success of genomic selection. My objective was to study the effect of resource allocation on response to MAS and genomic selection in a single biparental population of doubled haploid lines by using computer simulation. Simulation results were compared with previously derived formulas for the calculation of prediction accuracy under different levels of heritability and population size. Response of prediction accuracy to resource allocation strategies differed between genomic selection models (ridge regression best linear unbiased prediction [RR-BLUP], BayesCπ) and multiple linear regression using ordinary least-squares estimation (OLS), leading to different optimal resource allocation choices between OLS and RR-BLUP. For OLS, it was always advantageous to maximize population size at the expense of replication, but a high degree of flexibility was observed for RR-BLUP. Prediction accuracy of doubled haploid lines included in the training set was much greater than of those excluded from the training set, so there was little benefit to phenotyping only a subset of the lines genotyped. Finally, observed prediction accuracies in the simulation compared well to calculated prediction accuracies, indicating these theoretical formulas are useful for making resource allocation

  14. Mortality Predicted Accuracy for Hepatocellular Carcinoma Patients with Hepatic Resection Using Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Herng-Chia Chiu

    2013-01-01

    Full Text Available The aim of this present study is firstly to compare significant predictors of mortality for hepatocellular carcinoma (HCC patients undergoing resection between artificial neural network (ANN and logistic regression (LR models and secondly to evaluate the predictive accuracy of ANN and LR in different survival year estimation models. We constructed a prognostic model for 434 patients with 21 potential input variables by Cox regression model. Model performance was measured by numbers of significant predictors and predictive accuracy. The results indicated that ANN had double to triple numbers of significant predictors at 1-, 3-, and 5-year survival models as compared with LR models. Scores of accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC of 1-, 3-, and 5-year survival estimation models using ANN were superior to those of LR in all the training sets and most of the validation sets. The study demonstrated that ANN not only had a great number of predictors of mortality variables but also provided accurate prediction, as compared with conventional methods. It is suggested that physicians consider using data mining methods as supplemental tools for clinical decision-making and prognostic evaluation.

  15. Mortality Predicted Accuracy for Hepatocellular Carcinoma Patients with Hepatic Resection Using Artificial Neural Network

    Science.gov (United States)

    Chiu, Herng-Chia; Ho, Te-Wei; Lee, King-Teh; Chen, Hong-Yaw; Ho, Wen-Hsien

    2013-01-01

    The aim of this present study is firstly to compare significant predictors of mortality for hepatocellular carcinoma (HCC) patients undergoing resection between artificial neural network (ANN) and logistic regression (LR) models and secondly to evaluate the predictive accuracy of ANN and LR in different survival year estimation models. We constructed a prognostic model for 434 patients with 21 potential input variables by Cox regression model. Model performance was measured by numbers of significant predictors and predictive accuracy. The results indicated that ANN had double to triple numbers of significant predictors at 1-, 3-, and 5-year survival models as compared with LR models. Scores of accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC) of 1-, 3-, and 5-year survival estimation models using ANN were superior to those of LR in all the training sets and most of the validation sets. The study demonstrated that ANN not only had a great number of predictors of mortality variables but also provided accurate prediction, as compared with conventional methods. It is suggested that physicians consider using data mining methods as supplemental tools for clinical decision-making and prognostic evaluation. PMID:23737707

  16. Physiologically-based, predictive analytics using the heart-rate-to-Systolic-Ratio significantly improves the timeliness and accuracy of sepsis prediction compared to SIRS.

    Science.gov (United States)

    Danner, Omar K; Hendren, Sandra; Santiago, Ethel; Nye, Brittany; Abraham, Prasad

    2017-04-01

    Enhancing the efficiency of diagnosis and treatment of severe sepsis by using physiologically-based, predictive analytical strategies has not been fully explored. We hypothesize assessment of heart-rate-to-systolic-ratio significantly increases the timeliness and accuracy of sepsis prediction after emergency department (ED) presentation. We evaluated the records of 53,313 ED patients from a large, urban teaching hospital between January and June 2015. The HR-to-systolic ratio was compared to SIRS criteria for sepsis prediction. There were 884 patients with discharge diagnoses of sepsis, severe sepsis, and/or septic shock. Variations in three presenting variables, heart rate, systolic BP and temperature were determined to be primary early predictors of sepsis with a 74% (654/884) accuracy compared to 34% (304/884) using SIRS criteria (p < 0.0001)in confirmed septic patients. Physiologically-based predictive analytics improved the accuracy and expediency of sepsis identification via detection of variations in HR-to-systolic ratio. This approach may lead to earlier sepsis workup and life-saving interventions. Copyright © 2017 Elsevier Inc. All rights reserved.

  17. Long-term prediction of reading accuracy and speed: The importance of paired-associate learning

    DEFF Research Database (Denmark)

    Poulsen, Mads; Asmussen, Vibeke; Elbro, Carsten

    Purpose: Several cross-sectional studies have found a correlation between paired-associate learning (PAL) and reading (e.g. Litt et al., 2013; Messbauer & de Jong, 2003, 2006). These findings suggest that verbal learning of phonological forms is important for reading. However, results from...... longitudinal studies have been mixed (e.g. Lervåg & Hulme, 2009; Horbach et al. 2015). The present study investigated the possibility that the mixed results may be a result of a conflation of accuracy and speed. It is possible that PAL is a stronger correlate of reading accuracy than speed (Litt et al., 2013...... of reading comprehension and isolated sight word reading accuracy and speed. Results: PAL predicted unique variance in sight word accuracy, but not speed. Furthermore, PAL was indirectly linked to reading comprehension through sight word accuracy. RAN correlated with both accuracy and speed...

  18. Test accuracy of metabolic indicators in predicting decreased fertility in dairy cows

    DEFF Research Database (Denmark)

    Lomander, H; Gustafsson, H; Svensson, C

    2012-01-01

    Negative energy balance is a known risk factor for decreased fertility in dairy cows. This study evaluated the accuracy of plasma concentrations of nonesterified fatty acids (NEFA), β-hydroxybutyrate (BHBA), and insulin-like growth factor 1 (IGF-1)—factors related to negative energy balance...... was low when metabolic indicators measured as single values in early lactation were used to predict fertility in dairy cows, but accuracy was influenced by cow-level factors such as parity. The prevalence of the target condition (in this case, decreased fertility) also influences test usefulness......—in predicting decreased fertility. One plasma sample per cow was collected from 480 cows in 12 herds during the period from d 4 to 21 in milk and analyzed for NEFA, BHBA, and IGF-1. For each cow, data on breed, parity, calving date, gynecological examinations, and insemination dates were obtained. Milk samples...

  19. Thoracic injury rule out criteria and NEXUS chest in predicting the risk of traumatic intra-thoracic injuries: A diagnostic accuracy study.

    Science.gov (United States)

    Safari, Saeed; Radfar, Fatemeh; Baratloo, Alireza

    2018-05-01

    This study aimed to compare the diagnostic accuracy of NEXUS chest and Thoracic Injury Rule out criteria (TIRC) models in predicting the risk of intra-thoracic injuries following blunt multiple trauma. In this diagnostic accuracy study, using the 2 mentioned models, blunt multiple trauma patients over the age of 15 years presenting to emergency department were screened regarding the presence of intra-thoracic injuries that are detectable via chest x-ray and screening performance characteristics of the models were compared. In this study, 3118 patients with the mean (SD) age of 37.4 (16.9) years were studied (57.4% male). Based on TIRC and NEXUS chest, respectively, 1340 (43%) and 1417 (45.4%) patients were deemed in need of radiography performance. Sensitivity, specificity, and positive and negative predictive values of TIRC were 98.95%, 62.70%, 21.19% and 99.83%. These values were 98.61%, 59.94%, 19.97% and 99.76%, for NEXUS chest, respectively. Accuracy of TIRC and NEXUS chest models were 66.04 (95% CI: 64.34-67.70) and 63.50 (95% CI: 61.78-65.19), respectively. TIRC and NEXUS chest models have proper and similar sensitivity in prediction of blunt traumatic intra-thoracic injuries that are detectable via chest x-ray. However, TIRC had a significantly higher specificity in this regard. Copyright © 2018 Elsevier Ltd. All rights reserved.

  20. Improving accuracy of genomic prediction in Brangus cattle by adding animals with imputed low-density SNP genotypes.

    Science.gov (United States)

    Lopes, F B; Wu, X-L; Li, H; Xu, J; Perkins, T; Genho, J; Ferretti, R; Tait, R G; Bauck, S; Rosa, G J M

    2018-02-01

    Reliable genomic prediction of breeding values for quantitative traits requires the availability of sufficient number of animals with genotypes and phenotypes in the training set. As of 31 October 2016, there were 3,797 Brangus animals with genotypes and phenotypes. These Brangus animals were genotyped using different commercial SNP chips. Of them, the largest group consisted of 1,535 animals genotyped by the GGP-LDV4 SNP chip. The remaining 2,262 genotypes were imputed to the SNP content of the GGP-LDV4 chip, so that the number of animals available for training the genomic prediction models was more than doubled. The present study showed that the pooling of animals with both original or imputed 40K SNP genotypes substantially increased genomic prediction accuracies on the ten traits. By supplementing imputed genotypes, the relative gains in genomic prediction accuracies on estimated breeding values (EBV) were from 12.60% to 31.27%, and the relative gain in genomic prediction accuracies on de-regressed EBV was slightly small (i.e. 0.87%-18.75%). The present study also compared the performance of five genomic prediction models and two cross-validation methods. The five genomic models predicted EBV and de-regressed EBV of the ten traits similarly well. Of the two cross-validation methods, leave-one-out cross-validation maximized the number of animals at the stage of training for genomic prediction. Genomic prediction accuracy (GPA) on the ten quantitative traits was validated in 1,106 newly genotyped Brangus animals based on the SNP effects estimated in the previous set of 3,797 Brangus animals, and they were slightly lower than GPA in the original data. The present study was the first to leverage currently available genotype and phenotype resources in order to harness genomic prediction in Brangus beef cattle. © 2018 Blackwell Verlag GmbH.

  1. Knowing right from wrong in mental arithmetic judgments: calibration of confidence predicts the development of accuracy.

    Science.gov (United States)

    Rinne, Luke F; Mazzocco, Michèle M M

    2014-01-01

    Does knowing when mental arithmetic judgments are right--and when they are wrong--lead to more accurate judgments over time? We hypothesize that the successful detection of errors (and avoidance of false alarms) may contribute to the development of mental arithmetic performance. Insight into error detection abilities can be gained by examining the "calibration" of mental arithmetic judgments-that is, the alignment between confidence in judgments and the accuracy of those judgments. Calibration may be viewed as a measure of metacognitive monitoring ability. We conducted a developmental longitudinal investigation of the relationship between the calibration of children's mental arithmetic judgments and their performance on a mental arithmetic task. Annually between Grades 5 and 8, children completed a problem verification task in which they rapidly judged the accuracy of arithmetic expressions (e.g., 25 + 50 = 75) and rated their confidence in each judgment. Results showed that calibration was strongly related to concurrent mental arithmetic performance, that calibration continued to develop even as mental arithmetic accuracy approached ceiling, that poor calibration distinguished children with mathematics learning disability from both low and typically achieving children, and that better calibration in Grade 5 predicted larger gains in mental arithmetic accuracy between Grades 5 and 8. We propose that good calibration supports the implementation of cognitive control, leading to long-term improvement in mental arithmetic accuracy. Because mental arithmetic "fluency" is critical for higher-level mathematics competence, calibration of confidence in mental arithmetic judgments may represent a novel and important developmental predictor of future mathematics performance.

  2. Estimating the Accuracy of the Chedoke–McMaster Stroke Assessment Predictive Equations for Stroke Rehabilitation

    Science.gov (United States)

    Dang, Mia; Ramsaran, Kalinda D.; Street, Melissa E.; Syed, S. Noreen; Barclay-Goddard, Ruth; Miller, Patricia A.

    2011-01-01

    ABSTRACT Purpose: To estimate the predictive accuracy and clinical usefulness of the Chedoke–McMaster Stroke Assessment (CMSA) predictive equations. Method: A longitudinal prognostic study using historical data obtained from 104 patients admitted post cerebrovascular accident was undertaken. Data were abstracted for all patients undergoing rehabilitation post stroke who also had documented admission and discharge CMSA scores. Published predictive equations were used to determine predicted outcomes. To determine the accuracy and clinical usefulness of the predictive model, shrinkage coefficients and predictions with 95% confidence bands were calculated. Results: Complete data were available for 74 patients with a mean age of 65.3±12.4 years. The shrinkage values for the six Impairment Inventory (II) dimensions varied from −0.05 to 0.09; the shrinkage value for the Activity Inventory (AI) was 0.21. The error associated with predictive values was greater than ±1.5 stages for the II dimensions and greater than ±24 points for the AI. Conclusions: This study shows that the large error associated with the predictions (as defined by the confidence band) for the CMSA II and AI limits their clinical usefulness as a predictive measure. Further research to establish predictive models using alternative statistical procedures is warranted. PMID:22654239

  3. Sensitivity, Specificity, Predictive Values, and Accuracy of Three Diagnostic Tests to Predict Inferior Alveolar Nerve Blockade Failure in Symptomatic Irreversible Pulpitis

    Directory of Open Access Journals (Sweden)

    Daniel Chavarría-Bolaños

    2017-01-01

    Full Text Available Introduction. The inferior alveolar nerve block (IANB is the most common anesthetic technique used on mandibular teeth during root canal treatment. Its success in the presence of preoperative inflammation is still controversial. The aim of this study was to evaluate the sensitivity, specificity, predictive values, and accuracy of three diagnostic tests used to predict IANB failure in symptomatic irreversible pulpitis (SIP. Methodology. A cross-sectional study was carried out on the mandibular molars of 53 patients with SIP. All patients received a single cartridge of mepivacaine 2% with 1 : 100000 epinephrine using the IANB technique. Three diagnostic clinical tests were performed to detect anesthetic failure. Anesthetic failure was defined as a positive painful response to any of the three tests. Sensitivity, specificity, predictive values, accuracy, and ROC curves were calculated and compared and significant differences were analyzed. Results. IANB failure was determined in 71.7% of the patients. The sensitivity scores for the three tests (lip numbness, the cold stimuli test, and responsiveness during endodontic access were 0.03, 0.35, and 0.55, respectively, and the specificity score was determined as 1 for all of the tests. Clinically, none of the evaluated tests demonstrated a high enough accuracy (0.30, 0.53, and 0.68 for lip numbness, the cold stimuli test, and responsiveness during endodontic access, resp.. A comparison of the areas under the curve in the ROC analyses showed statistically significant differences between the three tests (p<0.05. Conclusion. None of the analyzed tests demonstrated a high enough accuracy to be considered a reliable diagnostic tool for the prediction of anesthetic failure.

  4. Models of alien species richness show moderate predictive accuracy and poor transferability

    Directory of Open Access Journals (Sweden)

    César Capinha

    2018-06-01

    Full Text Available Robust predictions of alien species richness are useful to assess global biodiversity change. Nevertheless, the capacity to predict spatial patterns of alien species richness remains largely unassessed. Using 22 data sets of alien species richness from diverse taxonomic groups and covering various parts of the world, we evaluated whether different statistical models were able to provide useful predictions of absolute and relative alien species richness, as a function of explanatory variables representing geographical, environmental and socio-economic factors. Five state-of-the-art count data modelling techniques were used and compared: Poisson and negative binomial generalised linear models (GLMs, multivariate adaptive regression splines (MARS, random forests (RF and boosted regression trees (BRT. We found that predictions of absolute alien species richness had a low to moderate accuracy in the region where the models were developed and a consistently poor accuracy in new regions. Predictions of relative richness performed in a superior manner in both geographical settings, but still were not good. Flexible tree ensembles-type techniques (RF and BRT were shown to be significantly better in modelling alien species richness than parametric linear models (such as GLM, despite the latter being more commonly applied for this purpose. Importantly, the poor spatial transferability of models also warrants caution in assuming the generality of the relationships they identify, e.g. by applying projections under future scenario conditions. Ultimately, our results strongly suggest that predictability of spatial variation in richness of alien species richness is limited. The somewhat more robust ability to rank regions according to the number of aliens they have (i.e. relative richness, suggests that models of aliens species richness may be useful for prioritising and comparing regions, but not for predicting exact species numbers.

  5. 100% classification accuracy considered harmful: the normalized information transfer factor explains the accuracy paradox.

    Directory of Open Access Journals (Sweden)

    Francisco J Valverde-Albacete

    Full Text Available The most widely spread measure of performance, accuracy, suffers from a paradox: predictive models with a given level of accuracy may have greater predictive power than models with higher accuracy. Despite optimizing classification error rate, high accuracy models may fail to capture crucial information transfer in the classification task. We present evidence of this behavior by means of a combinatorial analysis where every possible contingency matrix of 2, 3 and 4 classes classifiers are depicted on the entropy triangle, a more reliable information-theoretic tool for classification assessment. Motivated by this, we develop from first principles a measure of classification performance that takes into consideration the information learned by classifiers. We are then able to obtain the entropy-modulated accuracy (EMA, a pessimistic estimate of the expected accuracy with the influence of the input distribution factored out, and the normalized information transfer factor (NIT, a measure of how efficient is the transmission of information from the input to the output set of classes. The EMA is a more natural measure of classification performance than accuracy when the heuristic to maximize is the transfer of information through the classifier instead of classification error count. The NIT factor measures the effectiveness of the learning process in classifiers and also makes it harder for them to "cheat" using techniques like specialization, while also promoting the interpretability of results. Their use is demonstrated in a mind reading task competition that aims at decoding the identity of a video stimulus based on magnetoencephalography recordings. We show how the EMA and the NIT factor reject rankings based in accuracy, choosing more meaningful and interpretable classifiers.

  6. Predictive accuracy of changes in transvaginal sonographic cervical length over time for preterm birth: a systematic review and metaanalysis.

    Science.gov (United States)

    Conde-Agudelo, Agustin; Romero, Roberto

    2015-12-01

    To determine the accuracy of changes in transvaginal sonographic cervical length over time in predicting preterm birth in women with singleton and twin gestations. PubMed, Embase, Cinahl, Lilacs, and Medion (all from inception to June 30, 2015), bibliographies, Google scholar, and conference proceedings. Cohort or cross-sectional studies reporting on the predictive accuracy for preterm birth of changes in cervical length over time. Two reviewers independently selected studies, assessed the risk of bias, and extracted the data. Summary receiver-operating characteristic curves, pooled sensitivities and specificities, and summary likelihood ratios were generated. Fourteen studies met the inclusion criteria, of which 7 provided data on singleton gestations (3374 women) and 8 on twin gestations (1024 women). Among women with singleton gestations, the shortening of cervical length over time had a low predictive accuracy for preterm birth at predictive accuracy for preterm birth at predictive accuracies for preterm birth of cervical length shortening over time and the single initial and/or final cervical length measurement in 8 of 11 studies that provided data for making these comparisons. In the largest and highest-quality study, a single measurement of cervical length obtained at 24 or 28 weeks of gestation was significantly more predictive of preterm birth than any decrease in cervical length between these gestational ages. Change in transvaginal sonographic cervical length over time is not a clinically useful test to predict preterm birth in women with singleton or twin gestations. A single cervical length measurement obtained between 18 and 24 weeks of gestation appears to be a better test to predict preterm birth than changes in cervical length over time. Published by Elsevier Inc.

  7. Both Reaction Time and Accuracy Measures of Intraindividual Variability Predict Cognitive Performance in Alzheimer's Disease

    Directory of Open Access Journals (Sweden)

    Björn U. Christ

    2018-04-01

    Full Text Available Dementia researchers around the world prioritize the urgent need for sensitive measurement tools that can detect cognitive and functional change at the earliest stages of Alzheimer's disease (AD. Sensitive indicators of underlying neural pathology assist in the early detection of cognitive change and are thus important for the evaluation of early-intervention clinical trials. One method that may be particularly well-suited to help achieve this goal involves the quantification of intraindividual variability (IIV in cognitive performance. The current study aimed to directly compare two methods of estimating IIV (fluctuations in accuracy-based scores vs. those in latency-based scores to predict cognitive performance in AD. Specifically, we directly compared the relative sensitivity of reaction time (RT—and accuracy-based estimates of IIV to cognitive compromise. The novelty of the present study, however, centered on the patients we tested [a group of patients with Alzheimer's disease (AD] and the outcome measures we used (a measure of general cognitive function and a measure of episodic memory function. Hence, we compared intraindividual standard deviations (iSDs from two RT tasks and three accuracy-based memory tasks in patients with possible or probable Alzheimer's dementia (n = 23 and matched healthy controls (n = 25. The main analyses modeled the relative contributions of RT vs. accuracy-based measures of IIV toward the prediction of performance on measures of (a overall cognitive functioning, and (b episodic memory functioning. Results indicated that RT-based IIV measures are superior predictors of neurocognitive impairment (as indexed by overall cognitive and memory performance than accuracy-based IIV measures, even after adjusting for the timescale of measurement. However, one accuracy-based IIV measure (derived from a recognition memory test also differentiated patients with AD from controls, and significantly predicted episodic memory

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

    International Nuclear Information System (INIS)

    Ko, P; Kurosawa, S

    2014-01-01

    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

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

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    Linden, Ariel

    2006-04-01

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

  11. Increasing imputation and prediction accuracy for Chinese Holsteins using joint Chinese-Nordic reference population

    DEFF Research Database (Denmark)

    Ma, Peipei; Lund, Mogens Sandø; Ding, X

    2015-01-01

    This study investigated the effect of including Nordic Holsteins in the reference population on the imputation accuracy and prediction accuracy for Chinese Holsteins. The data used in this study include 85 Chinese Holstein bulls genotyped with both 54K chip and 777K (HD) chip, 2862 Chinese cows...... was improved slightly when using the marker data imputed based on the combined HD reference data, compared with using the marker data imputed based on the Chinese HD reference data only. On the other hand, when using the combined reference population including 4398 Nordic Holstein bulls, the accuracy...... to increase reference population rather than increasing marker density...

  12. Joint analysis of psychiatric disorders increases accuracy of risk prediction for schizophrenia, bipolar disorder, and major depressive disorder

    DEFF Research Database (Denmark)

    Maier, Robert; Moser, Gerhard; Chen, Guo-Bo

    2015-01-01

    Genetic risk prediction has several potential applications in medical research and clinical practice and could be used, for example, to stratify a heterogeneous population of patients by their predicted genetic risk. However, for polygenic traits, such as psychiatric disorders, the accuracy of risk...... number of GWAS datasets of correlated traits, it is a flexible and powerful tool to maximize prediction accuracy. With current sample size, risk predictors are not useful in a clinical setting but already are a valuable research tool, for example in experimental designs comparing cases with high and low...

  13. Climatic associations of British species distributions show good transferability in time but low predictive accuracy for range change.

    Directory of Open Access Journals (Sweden)

    Giovanni Rapacciuolo

    Full Text Available Conservation planners often wish to predict how species distributions will change in response to environmental changes. Species distribution models (SDMs are the primary tool for making such predictions. Many methods are widely used; however, they all make simplifying assumptions, and predictions can therefore be subject to high uncertainty. With global change well underway, field records of observed range shifts are increasingly being used for testing SDM transferability. We used an unprecedented distribution dataset documenting recent range changes of British vascular plants, birds, and butterflies to test whether correlative SDMs based on climate change provide useful approximations of potential distribution shifts. We modelled past species distributions from climate using nine single techniques and a consensus approach, and projected the geographical extent of these models to a more recent time period based on climate change; we then compared model predictions with recent observed distributions in order to estimate the temporal transferability and prediction accuracy of our models. We also evaluated the relative effect of methodological and taxonomic variation on the performance of SDMs. Models showed good transferability in time when assessed using widespread metrics of accuracy. However, models had low accuracy to predict where occupancy status changed between time periods, especially for declining species. Model performance varied greatly among species within major taxa, but there was also considerable variation among modelling frameworks. Past climatic associations of British species distributions retain a high explanatory power when transferred to recent time--due to their accuracy to predict large areas retained by species--but fail to capture relevant predictors of change. We strongly emphasize the need for caution when using SDMs to predict shifts in species distributions: high explanatory power on temporally-independent records

  14. Accuracy of the Timed Up and Go test for predicting sarcopenia in elderly hospitalized patients.

    Science.gov (United States)

    Martinez, Bruno Prata; Gomes, Isabela Barboza; Oliveira, Carolina Santana de; Ramos, Isis Resende; Rocha, Mônica Diniz Marques; Forgiarini Júnior, Luiz Alberto; Camelier, Fernanda Warken Rosa; Camelier, Aquiles Assunção

    2015-05-01

    The ability of the Timed Up and Go test to predict sarcopenia has not been evaluated previously. The objective of this study was to evaluate the accuracy of the Timed Up and Go test for predicting sarcopenia in elderly hospitalized patients. This cross-sectional study analyzed 68 elderly patients (≥60 years of age) in a private hospital in the city of Salvador-BA, Brazil, between the 1st and 5th day of hospitalization. The predictive variable was the Timed Up and Go test score, and the outcome of interest was the presence of sarcopenia (reduced muscle mass associated with a reduction in handgrip strength and/or weak physical performance in a 6-m gait-speed test). After the descriptive data analyses, the sensitivity, specificity and accuracy of a test using the predictive variable to predict the presence of sarcopenia were calculated. In total, 68 elderly individuals, with a mean age 70.4±7.7 years, were evaluated. The subjects had a Charlson Comorbidity Index score of 5.35±1.97. Most (64.7%) of the subjects had a clinical admission profile; the main reasons for hospitalization were cardiovascular disorders (22.1%), pneumonia (19.1%) and abdominal disorders (10.2%). The frequency of sarcopenia in the sample was 22.1%, and the mean length of time spent performing the Timed Up and Go test was 10.02±5.38 s. A time longer than or equal to a cutoff of 10.85 s on the Timed Up and Go test predicted sarcopenia with a sensitivity of 67% and a specificity of 88.7%. The accuracy of this cutoff for the Timed Up and Go test was good (0.80; IC=0.66-0.94; p=0.002). The Timed Up and Go test was shown to be a predictor of sarcopenia in elderly hospitalized patients.

  15. Measuring Personality in Context: Improving Predictive Accuracy in Selection Decision Making

    OpenAIRE

    Hoffner, Rebecca Ann

    2009-01-01

    This study examines the accuracy of a context-sensitive (i.e., goal dimensions) measure of personality compared to a traditional measure of personality (NEO-PI-R) and generalized self-efficacy (GSE) to predict variance in task performance. The goal dimensions measure takes a unique perspective in the conceptualization of personality. While traditional measures differentiate within person and collapse across context (e.g., Big Five), the goal dimensions measure employs a hierarchical structure...

  16. Accuracy assessment of the ERP prediction method based on analysis of 100-year ERP series

    Science.gov (United States)

    Malkin, Z.; Tissen, V. M.

    2012-12-01

    A new method has been developed at the Siberian Research Institute of Metrology (SNIIM) for highly accurate prediction of UT1 and Pole motion (PM). In this study, a detailed comparison was made of real-time UT1 predictions made in 2006-2011 and PMpredictions made in 2009-2011making use of the SNIIM method with simultaneous predictions computed at the International Earth Rotation and Reference Systems Service (IERS), USNO. Obtained results have shown that proposed method provides better accuracy at different prediction lengths.

  17. General Theory versus ENA Theory: Comparing Their Predictive Accuracy and Scope.

    Science.gov (United States)

    Ellis, Lee; Hoskin, Anthony; Hartley, Richard; Walsh, Anthony; Widmayer, Alan; Ratnasingam, Malini

    2015-12-01

    General theory attributes criminal behavior primarily to low self-control, whereas evolutionary neuroandrogenic (ENA) theory envisions criminality as being a crude form of status-striving promoted by high brain exposure to androgens. General theory predicts that self-control will be negatively correlated with risk-taking, while ENA theory implies that these two variables should actually be positively correlated. According to ENA theory, traits such as pain tolerance and muscularity will be positively associated with risk-taking and criminality while general theory makes no predictions concerning these relationships. Data from Malaysia and the United States are used to test 10 hypotheses derived from one or both of these theories. As predicted by both theories, risk-taking was positively correlated with criminality in both countries. However, contrary to general theory and consistent with ENA theory, the correlation between self-control and risk-taking was positive in both countries. General theory's prediction of an inverse correlation between low self-control and criminality was largely supported by the U.S. data but only weakly supported by the Malaysian data. ENA theory's predictions of positive correlations between pain tolerance, muscularity, and offending were largely confirmed. For the 10 hypotheses tested, ENA theory surpassed general theory in predictive scope and accuracy. © The Author(s) 2014.

  18. A fuzzy set approach to assess the predictive accuracy of land use simulations

    NARCIS (Netherlands)

    van Vliet, J.; Hagen-Zanker, A.; Hurkens, J.; van van Delden, H.

    2013-01-01

    The predictive accuracy of land use models is frequently assessed by comparing two data sets: the simulated land use map and the observed land use map at the end of the simulation period. A common statistic for this is Kappa, which expresses the agreement between two categorical maps, corrected for

  19. Breeding Jatropha curcas by genomic selection: A pilot assessment of the accuracy of predictive models.

    Science.gov (United States)

    Azevedo Peixoto, Leonardo de; Laviola, Bruno Galvêas; Alves, Alexandre Alonso; Rosado, Tatiana Barbosa; Bhering, Leonardo Lopes

    2017-01-01

    Genomic wide selection is a promising approach for improving the selection accuracy in plant breeding, particularly in species with long life cycles, such as Jatropha. Therefore, the objectives of this study were to estimate the genetic parameters for grain yield (GY) and the weight of 100 seeds (W100S) using restricted maximum likelihood (REML); to compare the performance of GWS methods to predict GY and W100S; and to estimate how many markers are needed to train the GWS model to obtain the maximum accuracy. Eight GWS models were compared in terms of predictive ability. The impact that the marker density had on the predictive ability was investigated using a varying number of markers, from 2 to 1,248. Because the genetic variance between evaluated genotypes was significant, it was possible to obtain selection gain. All of the GWS methods tested in this study can be used to predict GY and W100S in Jatropha. A training model fitted using 1,000 and 800 markers is sufficient to capture the maximum genetic variance and, consequently, maximum prediction ability of GY and W100S, respectively. This study demonstrated the applicability of genome-wide prediction to identify useful genetic sources of GY and W100S for Jatropha breeding. Further research is needed to confirm the applicability of the proposed approach to other complex traits.

  20. Combined Scintigraphy and Tumor Marker Analysis Predicts Unfavorable Histopathology of Neuroblastic Tumors with High Accuracy.

    Directory of Open Access Journals (Sweden)

    Wolfgang Peter Fendler

    Full Text Available Our aim was to improve the prediction of unfavorable histopathology (UH in neuroblastic tumors through combined imaging and biochemical parameters.123I-MIBG SPECT and MRI was performed before surgical resection or biopsy in 47 consecutive pediatric patients with neuroblastic tumor. Semi-quantitative tumor-to-liver count-rate ratio (TLCRR, MRI tumor size and margins, urine catecholamine and NSE blood levels of neuron specific enolase (NSE were recorded. Accuracy of single and combined variables for prediction of UH was tested by ROC analysis with Bonferroni correction.34 of 47 patients had UH based on the International Neuroblastoma Pathology Classification (INPC. TLCRR and serum NSE both predicted UH with moderate accuracy. Optimal cut-off for TLCRR was 2.0, resulting in 68% sensitivity and 100% specificity (AUC-ROC 0.86, p < 0.001. Optimal cut-off for NSE was 25.8 ng/ml, resulting in 74% sensitivity and 85% specificity (AUC-ROC 0.81, p = 0.001. Combination of TLCRR/NSE criteria reduced false negative findings from 11/9 to only five, with improved sensitivity and specificity of 85% (AUC-ROC 0.85, p < 0.001.Strong 123I-MIBG uptake and high serum level of NSE were each predictive of UH. Combined analysis of both parameters improved the prediction of UH in patients with neuroblastic tumor. MRI parameters and urine catecholamine levels did not predict UH.

  1. Breeding Jatropha curcas by genomic selection: A pilot assessment of the accuracy of predictive models.

    Directory of Open Access Journals (Sweden)

    Leonardo de Azevedo Peixoto

    Full Text Available Genomic wide selection is a promising approach for improving the selection accuracy in plant breeding, particularly in species with long life cycles, such as Jatropha. Therefore, the objectives of this study were to estimate the genetic parameters for grain yield (GY and the weight of 100 seeds (W100S using restricted maximum likelihood (REML; to compare the performance of GWS methods to predict GY and W100S; and to estimate how many markers are needed to train the GWS model to obtain the maximum accuracy. Eight GWS models were compared in terms of predictive ability. The impact that the marker density had on the predictive ability was investigated using a varying number of markers, from 2 to 1,248. Because the genetic variance between evaluated genotypes was significant, it was possible to obtain selection gain. All of the GWS methods tested in this study can be used to predict GY and W100S in Jatropha. A training model fitted using 1,000 and 800 markers is sufficient to capture the maximum genetic variance and, consequently, maximum prediction ability of GY and W100S, respectively. This study demonstrated the applicability of genome-wide prediction to identify useful genetic sources of GY and W100S for Jatropha breeding. Further research is needed to confirm the applicability of the proposed approach to other complex traits.

  2. Accuracy of Genomic Prediction in Switchgrass (Panicum virgatum L. Improved by Accounting for Linkage Disequilibrium

    Directory of Open Access Journals (Sweden)

    Guillaume P. Ramstein

    2016-04-01

    Full Text Available Switchgrass is a relatively high-yielding and environmentally sustainable biomass crop, but further genetic gains in biomass yield must be achieved to make it an economically viable bioenergy feedstock. Genomic selection (GS is an attractive technology to generate rapid genetic gains in switchgrass, and meet the goals of a substantial displacement of petroleum use with biofuels in the near future. In this study, we empirically assessed prediction procedures for genomic selection in two different populations, consisting of 137 and 110 half-sib families of switchgrass, tested in two locations in the United States for three agronomic traits: dry matter yield, plant height, and heading date. Marker data were produced for the families’ parents by exome capture sequencing, generating up to 141,030 polymorphic markers with available genomic-location and annotation information. We evaluated prediction procedures that varied not only by learning schemes and prediction models, but also by the way the data were preprocessed to account for redundancy in marker information. More complex genomic prediction procedures were generally not significantly more accurate than the simplest procedure, likely due to limited population sizes. Nevertheless, a highly significant gain in prediction accuracy was achieved by transforming the marker data through a marker correlation matrix. Our results suggest that marker-data transformations and, more generally, the account of linkage disequilibrium among markers, offer valuable opportunities for improving prediction procedures in GS. Some of the achieved prediction accuracies should motivate implementation of GS in switchgrass breeding programs.

  3. Accuracy of serum uric acid as a predictive test for maternal complications in pre-eclampsia: Bivariate meta-analysis and decision analysis

    NARCIS (Netherlands)

    Koopmans, Corine M.; van Pampus, Maria G.; Groen, Henk; Aarnoudse, Jan G.; van den Berg, Paul P.; Mol, Ben W. J.

    2009-01-01

    The aim of this study is to determine the accuracy and clinical value of serum uric acid in predicting maternal complications in women with pre-eclampsia. An existing meta-analysis on the subject was updated. The accuracy of serum uric acid for the prediction of maternal complications was assessed

  4. Accuracy of serum uric acid as a predictive test for maternal complications in pre-eclampsia : Bivariate meta-analysis and decision analysis

    NARCIS (Netherlands)

    Koopmans, C.M.; van Pampus, Maria; Groen, H.; Aarnoudse, J.G.; van den Berg, P.P.; Mol, B.W.J.

    The aim of this study is to determine the accuracy and clinical value of serum uric acid in predicting maternal complications in women with pre-eclampsia. An existing meta-analysis on the subject was updated. The accuracy of serum uric acid for the prediction of maternal complications was assessed

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

    Science.gov (United States)

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

    2018-01-01

    Both ultrasound and biochemical markers either alone or in combination have been described in the literature for the prediction of miscarriage. We performed this systematic review and meta-analysis to determine the best combination of biochemical, ultrasound and demographic markers to predict miscarriage in women with viable intrauterine pregnancy. The electronic database search included Medline (1946-June 2017), Embase (1980-June 2017), CINAHL (1981-June 2017) and Cochrane library. Key MESH and Boolean terms were used for the search. Data extraction and collection was performed based on the eligibility criteria by two authors independently. Quality assessment of the individual studies was done using QUADAS 2 (Quality Assessment for Diagnostic Accuracy Studies-2: A Revised Tool) and statistical analysis performed using the Cochrane systematic review manager 5.3 and STATA vs.13.0. Due to the diversity of the combinations used for prediction in the included papers it was not possible to perform a meta-analysis on combination markers. Therefore, we proceeded to perform a meta-analysis on ultrasound markers alone to determine the best marker that can help to improve the diagnostic accuracy of predicting miscarriage in women with viable intrauterine pregnancy. The systematic review identified 18 eligible studies for the quantitative meta-analysis with a total of 5584 women. Among the ultrasound scan markers, fetal bradycardia (n=10 studies, n=1762 women) on hierarchical summary receiver operating characteristic showed sensitivity of 68.41%, specificity of 97.84%, positive likelihood ratio of 31.73 (indicating a large effect on increasing the probability of predicting miscarriage) and negative likelihood ratio of 0.32. In studies for women with threatened miscarriage (n=5 studies, n=771 women) fetal bradycardia showed further increase in sensitivity (84.18%) for miscarriage prediction. Although there is gestational age dependent variation in the fetal heart rate, a plot

  6. Evaluating the predictive accuracy and the clinical benefit of a nomogram aimed to predict survival in node-positive prostate cancer patients: External validation on a multi-institutional database.

    Science.gov (United States)

    Bianchi, Lorenzo; Schiavina, Riccardo; Borghesi, Marco; Bianchi, Federico Mineo; Briganti, Alberto; Carini, Marco; Terrone, Carlo; Mottrie, Alex; Gacci, Mauro; Gontero, Paolo; Imbimbo, Ciro; Marchioro, Giansilvio; Milanese, Giulio; Mirone, Vincenzo; Montorsi, Francesco; Morgia, Giuseppe; Novara, Giacomo; Porreca, Angelo; Volpe, Alessandro; Brunocilla, Eugenio

    2018-04-06

    To assess the predictive accuracy and the clinical value of a recent nomogram predicting cancer-specific mortality-free survival after surgery in pN1 prostate cancer patients through an external validation. We evaluated 518 prostate cancer patients treated with radical prostatectomy and pelvic lymph node dissection with evidence of nodal metastases at final pathology, at 10 tertiary centers. External validation was carried out using regression coefficients of the previously published nomogram. The performance characteristics of the model were assessed by quantifying predictive accuracy, according to the area under the curve in the receiver operating characteristic curve and model calibration. Furthermore, we systematically analyzed the specificity, sensitivity, positive predictive value and negative predictive value for each nomogram-derived probability cut-off. Finally, we implemented decision curve analysis, in order to quantify the nomogram's clinical value in routine practice. External validation showed inferior predictive accuracy as referred to in the internal validation (65.8% vs 83.3%, respectively). The discrimination (area under the curve) of the multivariable model was 66.7% (95% CI 60.1-73.0%) by testing with receiver operating characteristic curve analysis. The calibration plot showed an overestimation throughout the range of predicted cancer-specific mortality-free survival rates probabilities. However, in decision curve analysis, the nomogram's use showed a net benefit when compared with the scenarios of treating all patients or none. In an external setting, the nomogram showed inferior predictive accuracy and suboptimal calibration characteristics as compared to that reported in the original population. However, decision curve analysis showed a clinical net benefit, suggesting a clinical implication to correctly manage pN1 prostate cancer patients after surgery. © 2018 The Japanese Urological Association.

  7. Atomic-accuracy prediction of protein loop structures through an RNA-inspired Ansatz.

    Directory of Open Access Journals (Sweden)

    Rhiju Das

    Full Text Available Consistently predicting biopolymer structure at atomic resolution from sequence alone remains a difficult problem, even for small sub-segments of large proteins. Such loop prediction challenges, which arise frequently in comparative modeling and protein design, can become intractable as loop lengths exceed 10 residues and if surrounding side-chain conformations are erased. Current approaches, such as the protein local optimization protocol or kinematic inversion closure (KIC Monte Carlo, involve stages that coarse-grain proteins, simplifying modeling but precluding a systematic search of all-atom configurations. This article introduces an alternative modeling strategy based on a 'stepwise ansatz', recently developed for RNA modeling, which posits that any realistic all-atom molecular conformation can be built up by residue-by-residue stepwise enumeration. When harnessed to a dynamic-programming-like recursion in the Rosetta framework, the resulting stepwise assembly (SWA protocol enables enumerative sampling of a 12 residue loop at a significant but achievable cost of thousands of CPU-hours. In a previously established benchmark, SWA recovers crystallographic conformations with sub-Angstrom accuracy for 19 of 20 loops, compared to 14 of 20 by KIC modeling with a comparable expenditure of computational power. Furthermore, SWA gives high accuracy results on an additional set of 15 loops highlighted in the biological literature for their irregularity or unusual length. Successes include cis-Pro touch turns, loops that pass through tunnels of other side-chains, and loops of lengths up to 24 residues. Remaining problem cases are traced to inaccuracies in the Rosetta all-atom energy function. In five additional blind tests, SWA achieves sub-Angstrom accuracy models, including the first such success in a protein/RNA binding interface, the YbxF/kink-turn interaction in the fourth 'RNA-puzzle' competition. These results establish all-atom enumeration as

  8. Accuracy of the Timed Up and Go test for predicting sarcopenia in elderly hospitalized patients

    Directory of Open Access Journals (Sweden)

    Bruno Prata Martinez

    2015-05-01

    Full Text Available OBJECTIVES: The ability of the Timed Up and Go test to predict sarcopenia has not been evaluated previously. The objective of this study was to evaluate the accuracy of the Timed Up and Go test for predicting sarcopenia in elderly hospitalized patients. METHODS: This cross-sectional study analyzed 68 elderly patients (≥60 years of age in a private hospital in the city of Salvador-BA, Brazil, between the 1st and 5th day of hospitalization. The predictive variable was the Timed Up and Go test score, and the outcome of interest was the presence of sarcopenia (reduced muscle mass associated with a reduction in handgrip strength and/or weak physical performance in a 6-m gait-speed test. After the descriptive data analyses, the sensitivity, specificity and accuracy of a test using the predictive variable to predict the presence of sarcopenia were calculated. RESULTS: In total, 68 elderly individuals, with a mean age 70.4±7.7 years, were evaluated. The subjects had a Charlson Comorbidity Index score of 5.35±1.97. Most (64.7% of the subjects had a clinical admission profile; the main reasons for hospitalization were cardiovascular disorders (22.1%, pneumonia (19.1% and abdominal disorders (10.2%. The frequency of sarcopenia in the sample was 22.1%, and the mean length of time spent performing the Timed Up and Go test was 10.02±5.38 s. A time longer than or equal to a cutoff of 10.85 s on the Timed Up and Go test predicted sarcopenia with a sensitivity of 67% and a specificity of 88.7%. The accuracy of this cutoff for the Timed Up and Go test was good (0.80; IC=0.66-0.94; p=0.002. CONCLUSION: The Timed Up and Go test was shown to be a predictor of sarcopenia in elderly hospitalized patients.

  9. The accuracy with which adults who do not stutter predict stuttering-related communication attitudes.

    Science.gov (United States)

    Logan, Kenneth J; Willis, Julie R

    2011-12-01

    The purpose of this study was to examine the extent to which adults who do not stutter can predict communication-related attitudes of adults who do stutter. 40 participants (mean age of 22.5 years) evaluated speech samples from an adult with mild stuttering and an adult with severe stuttering via audio-only (n=20) or audio-visual (n=20) modes to predict how the adults had responded on the S24 scale of communication attitudes. Participants correctly predicted which speaker had the more favorable S24 score, and the predicted scores were significantly different between the severity conditions. Across the four subgroups, predicted S24 scores differed from actual scores by 4-9 points. Predicted values were greater than the actual values for 3 of 4 subgroups, but still relatively positive in relation to the S24 norm sample. Stimulus presentation mode interacted with stuttering severity to affect prediction accuracy. The participants predicted the speakers' negative self-attributions more accurately than their positive self-attributions. Findings suggest that adults who do not stutter estimate the communication-related attitudes of specific adults who stutter in a manner that is generally accurate, though, in some conditions, somewhat less favorable than the speaker's actual ratings. At a group level, adults who do not stutter demonstrate the ability to discern minimal versus average levels of attitudinal impact for speakers who stutter. The participants' complex prediction patterns are discussed in relation to stereotype accuracy and classic views of negative stereotyping. The reader will be able to (a) summarize main findings on research related to listeners' attitudes toward people who stutter, (b) describe the extent to which people who do not stutter can predict the communication attitudes of people who do stutter; and (c) discuss how findings from the present study relate to previous findings on stereotypes about people who stutter. Copyright © 2011 Elsevier Inc

  10. Continuous electroencephalography predicts delayed cerebral ischemia after subarachnoid hemorrhage: A prospective study of diagnostic accuracy.

    Science.gov (United States)

    Rosenthal, Eric S; Biswal, Siddharth; Zafar, Sahar F; O'Connor, Kathryn L; Bechek, Sophia; Shenoy, Apeksha V; Boyle, Emily J; Shafi, Mouhsin M; Gilmore, Emily J; Foreman, Brandon P; Gaspard, Nicolas; Leslie-Mazwi, Thabele M; Rosand, Jonathan; Hoch, Daniel B; Ayata, Cenk; Cash, Sydney S; Cole, Andrew J; Patel, Aman B; Westover, M Brandon

    2018-04-16

    Delayed cerebral ischemia (DCI) is a common, disabling complication of subarachnoid hemorrhage (SAH). Preventing DCI is a key focus of neurocritical care, but interventions carry risk and cannot be applied indiscriminately. Although retrospective studies have identified continuous electroencephalographic (cEEG) measures associated with DCI, no study has characterized the accuracy of cEEG with sufficient rigor to justify using it to triage patients to interventions or clinical trials. We therefore prospectively assessed the accuracy of cEEG for predicting DCI, following the Standards for Reporting Diagnostic Accuracy Studies. We prospectively performed cEEG in nontraumatic, high-grade SAH patients at a single institution. The index test consisted of clinical neurophysiologists prospectively reporting prespecified EEG alarms: (1) decreasing relative alpha variability, (2) decreasing alpha-delta ratio, (3) worsening focal slowing, or (4) late appearing epileptiform abnormalities. The diagnostic reference standard was DCI determined by blinded, adjudicated review. Primary outcome measures were sensitivity and specificity of cEEG for subsequent DCI, determined by multistate survival analysis, adjusted for baseline risk. One hundred three of 227 consecutive patients were eligible and underwent cEEG monitoring (7.7-day mean duration). EEG alarms occurred in 96.2% of patients with and 19.6% without subsequent DCI (1.9-day median latency, interquartile range = 0.9-4.1). Among alarm subtypes, late onset epileptiform abnormalities had the highest predictive value. Prespecified EEG findings predicted DCI among patients with low (91% sensitivity, 83% specificity) and high (95% sensitivity, 77% specificity) baseline risk. cEEG accurately predicts DCI following SAH and may help target therapies to patients at highest risk of secondary brain injury. Ann Neurol 2018. © 2018 American Neurological Association.

  11. Prediction of nodal involvement in primary rectal carcinoma without invasion to pelvic structures: accuracy of preoperative CT, MR, and DWIBS assessments relative to histopathologic findings.

    Directory of Open Access Journals (Sweden)

    Jun Zhou

    Full Text Available OBJECTIVE: To investigate the accuracy of preoperative computed tomography (CT, magnetic resonance (MR imaging and diffusion-weighted imaging with background body signal suppression (DWIBS in the prediction of nodal involvement in primary rectal carcinoma patients in the absence of tumor invasion into pelvic structures. METHODS AND MATERIALS: Fifty-two subjects with primary rectal cancer were preoperatively assessed by CT and MRI at 1.5 T with a phased-array coil. Preoperative lymph node staging with imaging modalities (CT, MRI, and DWIBS were compared with the final histological findings. RESULTS: The accuracy of CT, MRI, and DWIBS were 57.7%, 63.5%, and 40.4%. The accuracy of DWIBS with higher sensitivity and negative predictive value for evaluating primary rectal cancer patients was lower than that of CT and MRI. Nodal staging agreement between imaging and pathology was fairly strong for CT and MRI (Kappa value = 0.331 and 0.348, P<0.01 but was relatively weaker for DWIBS (Kappa value = 0.174, P<0.05. The accuracy was 57.7% and 59.6%, respectively, for CT and MRI when the lymph node border information was used as the criteria, and was 57.7% and 61.5%, respectively, for enhanced CT and MRI when the lymph node enhancement pattern was used as the criteria. CONCLUSION: MRI is more accurate than CT in predicting nodal involvement in primary rectal carcinoma patients in the absence of tumor invasion into pelvic structures. DWIBS has a great diagnostic value in differentiating small malignant from benign lymph nodes.

  12. Prediction of pre-eclampsia: a protocol for systematic reviews of test accuracy

    Directory of Open Access Journals (Sweden)

    Khan Khalid S

    2006-10-01

    Full Text Available Abstract Background Pre-eclampsia, a syndrome of hypertension and proteinuria, is a major cause of maternal and perinatal morbidity and mortality. Accurate prediction of pre-eclampsia is important, since high risk women could benefit from intensive monitoring and preventive treatment. However, decision making is currently hampered due to lack of precise and up to date comprehensive evidence summaries on estimates of risk of developing pre-eclampsia. Methods/Design A series of systematic reviews and meta-analyses will be undertaken to determine, among women in early pregnancy, the accuracy of various tests (history, examinations and investigations for predicting pre-eclampsia. We will search Medline, Embase, Cochrane Library, MEDION, citation lists of review articles and eligible primary articles and will contact experts in the field. Reviewers working independently will select studies, extract data, and assess study validity according to established criteria. Language restrictions will not be applied. Bivariate meta-analysis of sensitivity and specificity will be considered for tests whose studies allow generation of 2 × 2 tables. Discussion The results of the test accuracy reviews will be integrated with results of effectiveness reviews of preventive interventions to assess the impact of test-intervention combinations for prevention of pre-eclampsia.

  13. Accuracy of genomic selection in European maize elite breeding populations.

    Science.gov (United States)

    Zhao, Yusheng; Gowda, Manje; Liu, Wenxin; Würschum, Tobias; Maurer, Hans P; Longin, Friedrich H; Ranc, Nicolas; Reif, Jochen C

    2012-03-01

    Genomic selection is a promising breeding strategy for rapid improvement of complex traits. The objective of our study was to investigate the prediction accuracy of genomic breeding values through cross validation. The study was based on experimental data of six segregating populations from a half-diallel mating design with 788 testcross progenies from an elite maize breeding program. The plants were intensively phenotyped in multi-location field trials and fingerprinted with 960 SNP markers. We used random regression best linear unbiased prediction in combination with fivefold cross validation. The prediction accuracy across populations was higher for grain moisture (0.90) than for grain yield (0.58). The accuracy of genomic selection realized for grain yield corresponds to the precision of phenotyping at unreplicated field trials in 3-4 locations. As for maize up to three generations are feasible per year, selection gain per unit time is high and, consequently, genomic selection holds great promise for maize breeding programs.

  14. Accuracy of Consecutive Fecal Calprotectin Measurements to Predict Relapse in Inflammatory Bowel Disease Patients Under Maintenance With Anti-TNF Therapy: A Prospective Longitudinal Cohort Study.

    Science.gov (United States)

    Ferreiro-Iglesias, Rocio; Barreiro-de Acosta, Manuel; Lorenzo-Gonzalez, Aurelio; Dominguez-Muñoz, Juan E

    2018-03-01

    Predicting relapse in inflammatory bowel disease (IBD) patients could allow early changes in therapy. We aimed at evaluating the accuracy of consecutive fecal calprotectin (FC) measurements to predict flares in IBD patients under maintenance treatment with anti-tumor necrosis factor (TNF) drugs. A prospective longitudinal cohort study with 16-month follow-up period was designed. IBD patients in clinical remission for at least 6 months under anti-TNF therapy were included. FC was quantified at 4-month intervals for 1 year, and patients were clinically evaluated for relapse at 2-month intervals. Diagnostic accuracy of FC for predicting relapse was evaluated by receiver-operating characteristic curve analysis. In total, 95 of 106 included patients finalized the study and were analyzed (median age 44 y, 50.5% female, 75% with Crohn's disease). A total of 30 patients (31.6%) had a relapse over follow-up. FC concentration was significantly higher in patients who relapsed (477 μg/g) than in patients who maintained in remission (65 μg/g) (Ppredict remission was 130 μg/g (negative predictive value of 100%), and 300 μg/g to predict relapse (positive predictive value of 78.3%). FC is a good predictor of clinical relapse and a particularly good predictor of remission over the following 4 months in patients with IBD on maintenance therapy with anti-TNF drugs. FC levels 300 μg/g allow predicting relapse with a high probability at any time over the following 4 months.

  15. Genetic architecture of complex traits and accuracy of genomic prediction: coat colour, milk-fat percentage, and type in Holstein cattle as contrasting model traits.

    Directory of Open Access Journals (Sweden)

    Ben J Hayes

    2010-09-01

    Full Text Available Prediction of genetic merit using dense SNP genotypes can be used for estimation of breeding values for selection of livestock, crops, and forage species; for prediction of disease risk; and for forensics. The accuracy of these genomic predictions depends in part on the genetic architecture of the trait, in particular number of loci affecting the trait and distribution of their effects. Here we investigate the difference among three traits in distribution of effects and the consequences for the accuracy of genomic predictions. Proportion of black coat colour in Holstein cattle was used as one model complex trait. Three loci, KIT, MITF, and a locus on chromosome 8, together explain 24% of the variation of proportion of black. However, a surprisingly large number of loci of small effect are necessary to capture the remaining variation. A second trait, fat concentration in milk, had one locus of large effect and a host of loci with very small effects. Both these distributions of effects were in contrast to that for a third trait, an index of scores for a number of aspects of cow confirmation ("overall type", which had only loci of small effect. The differences in distribution of effects among the three traits were quantified by estimating the distribution of variance explained by chromosome segments containing 50 SNPs. This approach was taken to account for the imperfect linkage disequilibrium between the SNPs and the QTL affecting the traits. We also show that the accuracy of predicting genetic values is higher for traits with a proportion of large effects (proportion black and fat percentage than for a trait with no loci of large effect (overall type, provided the method of analysis takes advantage of the distribution of loci effects.

  16. Best Practices for Mudweight Window Generation and Accuracy Assessment between Seismic Based Pore Pressure Prediction Methodologies for a Near-Salt Field in Mississippi Canyon, Gulf of Mexico

    Science.gov (United States)

    Mannon, Timothy Patrick, Jr.

    Improving well design has and always will be the primary goal in drilling operations in the oil and gas industry. Oil and gas plays are continuing to move into increasingly hostile drilling environments, including near and/or sub-salt proximities. The ability to reduce the risk and uncertainly involved in drilling operations in unconventional geologic settings starts with improving the techniques for mudweight window modeling. To address this issue, an analysis of wellbore stability and well design improvement has been conducted. This study will show a systematic approach to well design by focusing on best practices for mudweight window projection for a field in Mississippi Canyon, Gulf of Mexico. The field includes depleted reservoirs and is in close proximity of salt intrusions. Analysis of offset wells has been conducted in the interest of developing an accurate picture of the subsurface environment by making connections between depth, non-productive time (NPT) events, and mudweights used. Commonly practiced petrophysical methods of pore pressure, fracture pressure, and shear failure gradient prediction have been applied to key offset wells in order to enhance the well design for two proposed wells. For the first time in the literature, the accuracy of the commonly accepted, seismic interval velocity based and the relatively new, seismic frequency based methodologies for pore pressure prediction are qualitatively and quantitatively compared for accuracy. Accuracy standards will be based on the agreement of the seismic outputs to pressure data obtained while drilling and petrophysically based pore pressure outputs for each well. The results will show significantly higher accuracy for the seismic frequency based approach in wells that were in near/sub-salt environments and higher overall accuracy for all of the wells in the study as a whole.

  17. ZCURVE 3.0: identify prokaryotic genes with higher accuracy as well as automatically and accurately select essential genes

    Science.gov (United States)

    Hua, Zhi-Gang; Lin, Yan; Yuan, Ya-Zhou; Yang, De-Chang; Wei, Wen; Guo, Feng-Biao

    2015-01-01

    In 2003, we developed an ab initio program, ZCURVE 1.0, to find genes in bacterial and archaeal genomes. In this work, we present the updated version (i.e. ZCURVE 3.0). Using 422 prokaryotic genomes, the average accuracy was 93.7% with the updated version, compared with 88.7% with the original version. Such results also demonstrate that ZCURVE 3.0 is comparable with Glimmer 3.02 and may provide complementary predictions to it. In fact, the joint application of the two programs generated better results by correctly finding more annotated genes while also containing fewer false-positive predictions. As the exclusive function, ZCURVE 3.0 contains one post-processing program that can identify essential genes with high accuracy (generally >90%). We hope ZCURVE 3.0 will receive wide use with the web-based running mode. The updated ZCURVE can be freely accessed from http://cefg.uestc.edu.cn/zcurve/ or http://tubic.tju.edu.cn/zcurveb/ without any restrictions. PMID:25977299

  18. Accuracy of Endoscopy in Predicting the Depth of Mucosal Injury Following Caustic Ingestion; a Cross-Sectional Study

    Directory of Open Access Journals (Sweden)

    Athena Alipour-Faz

    2017-01-01

    Full Text Available Introduction: Esophagogastroduodenoscopy (EGD is currently considered as the primary method of determining the degree of mucosal injury following caustic ingestion. The present study aimed to evaluate the screening performance characteristics of EGD in predicting the depth of gastrointestinal mucosal injuries following caustic ingestion.Methods: Adult patients who were referred to emergency department due to ingestion of corrosive materials, over a 7-year period, were enrolled to this diagnostic accuracy study. Sensitivity, specificity, positive and negative predictive values as well as negative and positive likelihood ratios of EGD in predicting the depth of mucosal injury was calculated using pathologic findings as the gold standard.Results: 54 cases with the mean age of 35 ± 11.2 years were enrolled (59.25% male. Primary endoscopic results defined 28 (51.85% cases as second grade and 26 (48.14% as third grade of mucosal injury. On the other hand, pathologic findings reported 21 (38.88% patients as first grade, 14 (25.92% as second, and 19 patients (35.18% as third grade. Sensitivity and specificity of endoscopy for determining grade II tissue injury were 50.00 (23.04-76.96 and 47.50 (31.51-63.87, respectively. These measures were 100.00 (82.35-100 and 80.00 (63.06-91.56, respectively for grade III. Accuracy of EGD was 87.03% for grade III and 48.14% for grade II.Conclusion: Based on the findings of the present study, endoscopic grading of caustic related mucosal injury based on the Zargar’s classification has good accuracy in predicting grade III (87% and fail accuracy in grade II injuries (48%. It seems that we should be cautious in planning treatment for these patients solely based on endoscopic results. 

  19. Quantifying and comparing dynamic predictive accuracy of joint models for longitudinal marker and time-to-event in presence of censoring and competing risks.

    Science.gov (United States)

    Blanche, Paul; Proust-Lima, Cécile; Loubère, Lucie; Berr, Claudine; Dartigues, Jean-François; Jacqmin-Gadda, Hélène

    2015-03-01

    Thanks to the growing interest in personalized medicine, joint modeling of longitudinal marker and time-to-event data has recently started to be used to derive dynamic individual risk predictions. Individual predictions are called dynamic because they are updated when information on the subject's health profile grows with time. We focus in this work on statistical methods for quantifying and comparing dynamic predictive accuracy of this kind of prognostic models, accounting for right censoring and possibly competing events. Dynamic area under the ROC curve (AUC) and Brier Score (BS) are used to quantify predictive accuracy. Nonparametric inverse probability of censoring weighting is used to estimate dynamic curves of AUC and BS as functions of the time at which predictions are made. Asymptotic results are established and both pointwise confidence intervals and simultaneous confidence bands are derived. Tests are also proposed to compare the dynamic prediction accuracy curves of two prognostic models. The finite sample behavior of the inference procedures is assessed via simulations. We apply the proposed methodology to compare various prediction models using repeated measures of two psychometric tests to predict dementia in the elderly, accounting for the competing risk of death. Models are estimated on the French Paquid cohort and predictive accuracies are evaluated and compared on the French Three-City cohort. © 2014, The International Biometric Society.

  20. Chronnectome fingerprinting: Identifying individuals and predicting higher cognitive functions using dynamic brain connectivity patterns.

    Science.gov (United States)

    Liu, Jin; Liao, Xuhong; Xia, Mingrui; He, Yong

    2018-02-01

    The human brain is a large, interacting dynamic network, and its architecture of coupling among brain regions varies across time (termed the "chronnectome"). However, very little is known about whether and how the dynamic properties of the chronnectome can characterize individual uniqueness, such as identifying individuals as a "fingerprint" of the brain. Here, we employed multiband resting-state functional magnetic resonance imaging data from the Human Connectome Project (N = 105) and a sliding time-window dynamic network analysis approach to systematically examine individual time-varying properties of the chronnectome. We revealed stable and remarkable individual variability in three dynamic characteristics of brain connectivity (i.e., strength, stability, and variability), which was mainly distributed in three higher order cognitive systems (i.e., default mode, dorsal attention, and fronto-parietal) and in two primary systems (i.e., visual and sensorimotor). Intriguingly, the spatial patterns of these dynamic characteristics of brain connectivity could successfully identify individuals with high accuracy and could further significantly predict individual higher cognitive performance (e.g., fluid intelligence and executive function), which was primarily contributed by the higher order cognitive systems. Together, our findings highlight that the chronnectome captures inherent functional dynamics of individual brain networks and provides implications for individualized characterization of health and disease. © 2017 Wiley Periodicals, Inc.

  1. Accuracy of Genomic Prediction in Synthetic Populations Depending on the Number of Parents, Relatedness, and Ancestral Linkage Disequilibrium.

    Science.gov (United States)

    Schopp, Pascal; Müller, Dominik; Technow, Frank; Melchinger, Albrecht E

    2017-01-01

    Synthetics play an important role in quantitative genetic research and plant breeding, but few studies have investigated the application of genomic prediction (GP) to these populations. Synthetics are generated by intermating a small number of parents ([Formula: see text] and thereby possess unique genetic properties, which make them especially suited for systematic investigations of factors contributing to the accuracy of GP. We generated synthetics in silico from [Formula: see text]2 to 32 maize (Zea mays L.) lines taken from an ancestral population with either short- or long-range linkage disequilibrium (LD). In eight scenarios differing in relatedness of the training and prediction sets and in the types of data used to calculate the relationship matrix (QTL, SNPs, tag markers, and pedigree), we investigated the prediction accuracy (PA) of Genomic best linear unbiased prediction (GBLUP) and analyzed contributions from pedigree relationships captured by SNP markers, as well as from cosegregation and ancestral LD between QTL and SNPs. The effects of training set size [Formula: see text] and marker density were also studied. Sampling few parents ([Formula: see text]) generates substantial sample LD that carries over into synthetics through cosegregation of alleles at linked loci. For fixed [Formula: see text], [Formula: see text] influences PA most strongly. If the training and prediction set are related, using [Formula: see text] parents yields high PA regardless of ancestral LD because SNPs capture pedigree relationships and Mendelian sampling through cosegregation. As [Formula: see text] increases, ancestral LD contributes more information, while other factors contribute less due to lower frequencies of closely related individuals. For unrelated prediction sets, only ancestral LD contributes information and accuracies were poor and highly variable for [Formula: see text] due to large sample LD. For large [Formula: see text], achieving moderate accuracy requires

  2. Subjective evaluation of the accuracy of video imaging prediction following orthognathic surgery in Chinese patients

    NARCIS (Netherlands)

    Chew, Ming Tak; Koh, Chay Hui; Sandham, John; Wong, Hwee Bee

    Purpose: The aims of this retrospective study were to assess the subjective accuracy of predictions generated by a computer imaging software in Chinese patients who had undergone orthognathic surgery and to determine the influence of initial dysgnathia and complexity of the surgical procedure on

  3. Accuracy Analysis of a Box-wing Theoretical SRP Model

    Science.gov (United States)

    Wang, Xiaoya; Hu, Xiaogong; Zhao, Qunhe; Guo, Rui

    2016-07-01

    For Beidou satellite navigation system (BDS) a high accuracy SRP model is necessary for high precise applications especially with Global BDS establishment in future. The BDS accuracy for broadcast ephemeris need be improved. So, a box-wing theoretical SRP model with fine structure and adding conical shadow factor of earth and moon were established. We verified this SRP model by the GPS Block IIF satellites. The calculation was done with the data of PRN 1, 24, 25, 27 satellites. The results show that the physical SRP model for POD and forecast for GPS IIF satellite has higher accuracy with respect to Bern empirical model. The 3D-RMS of orbit is about 20 centimeters. The POD accuracy for both models is similar but the prediction accuracy with the physical SRP model is more than doubled. We tested 1-day 3-day and 7-day orbit prediction. The longer is the prediction arc length, the more significant is the improvement. The orbit prediction accuracy with the physical SRP model for 1-day, 3-day and 7-day arc length are 0.4m, 2.0m, 10.0m respectively. But they are 0.9m, 5.5m and 30m with Bern empirical model respectively. We apply this means to the BDS and give out a SRP model for Beidou satellites. Then we test and verify the model with Beidou data of one month only for test. Initial results show the model is good but needs more data for verification and improvement. The orbit residual RMS is similar to that with our empirical force model which only estimate the force for along track, across track direction and y-bias. But the orbit overlap and SLR observation evaluation show some improvement. The remaining empirical force is reduced significantly for present Beidou constellation.

  4. Genomic prediction of reproduction traits for Merino sheep.

    Science.gov (United States)

    Bolormaa, S; Brown, D J; Swan, A A; van der Werf, J H J; Hayes, B J; Daetwyler, H D

    2017-06-01

    Economically important reproduction traits in sheep, such as number of lambs weaned and litter size, are expressed only in females and later in life after most selection decisions are made, which makes them ideal candidates for genomic selection. Accurate genomic predictions would lead to greater genetic gain for these traits by enabling accurate selection of young rams with high genetic merit. The aim of this study was to design and evaluate the accuracy of a genomic prediction method for female reproduction in sheep using daughter trait deviations (DTD) for sires and ewe phenotypes (when individual ewes were genotyped) for three reproduction traits: number of lambs born (NLB), litter size (LSIZE) and number of lambs weaned. Genomic best linear unbiased prediction (GBLUP), BayesR and pedigree BLUP analyses of the three reproduction traits measured on 5340 sheep (4503 ewes and 837 sires) with real and imputed genotypes for 510 174 SNPs were performed. The prediction of breeding values using both sire and ewe trait records was validated in Merino sheep. Prediction accuracy was evaluated by across sire family and random cross-validations. Accuracies of genomic estimated breeding values (GEBVs) were assessed as the mean Pearson correlation adjusted by the accuracy of the input phenotypes. The addition of sire DTD into the prediction analysis resulted in higher accuracies compared with using only ewe records in genomic predictions or pedigree BLUP. Using GBLUP, the average accuracy based on the combined records (ewes and sire DTD) was 0.43 across traits, but the accuracies varied by trait and type of cross-validations. The accuracies of GEBVs from random cross-validations (range 0.17-0.61) were higher than were those from sire family cross-validations (range 0.00-0.51). The GEBV accuracies of 0.41-0.54 for NLB and LSIZE based on the combined records were amongst the highest in the study. Although BayesR was not significantly different from GBLUP in prediction accuracy

  5. The contribution of educational class in improving accuracy of cardiovascular risk prediction across European regions

    DEFF Research Database (Denmark)

    Ferrario, Marco M; Veronesi, Giovanni; Chambless, Lloyd E

    2014-01-01

    OBJECTIVE: To assess whether educational class, an index of socioeconomic position, improves the accuracy of the SCORE cardiovascular disease (CVD) risk prediction equation. METHODS: In a pooled analysis of 68 455 40-64-year-old men and women, free from coronary heart disease at baseline, from 47...

  6. Diagnostic accuracy of the STRATIFY clinical prediction rule for falls: A systematic review and meta-analysis

    LENUS (Irish Health Repository)

    Billington, Jennifer

    2012-08-07

    AbstractBackgroundThe STRATIFY score is a clinical prediction rule (CPR) derived to assist clinicians to identify patients at risk of falling. The purpose of this systematic review and meta-analysis is to determine the overall diagnostic accuracy of the STRATIFY rule across a variety of clinical settings.MethodsA literature search was performed to identify all studies that validated the STRATIFY rule. The methodological quality of the studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies tool. A STRATIFY score of ≥2 points was used to identify individuals at higher risk of falling. All included studies were combined using a bivariate random effects model to generate pooled sensitivity and specificity of STRATIFY at ≥2 points. Heterogeneity was assessed using the variance of logit transformed sensitivity and specificity.ResultsSeventeen studies were included in our meta-analysis, incorporating 11,378 patients. At a score ≥2 points, the STRATIFY rule is more useful at ruling out falls in those classified as low risk, with a greater pooled sensitivity estimate (0.67, 95% CI 0.52–0.80) than specificity (0.57, 95% CI 0.45 – 0.69). The sensitivity analysis which examined the performance of the rule in different settings and subgroups also showed broadly comparable results, indicating that the STRATIFY rule performs in a similar manner across a variety of different ‘at risk’ patient groups in different clinical settings.ConclusionThis systematic review shows that the diagnostic accuracy of the STRATIFY rule is limited and should not be used in isolation for identifying individuals at high risk of falls in clinical practice.

  7. Higher-order QCD predictions for dark matter production at the LHC in simplified models with s-channel mediators.

    Science.gov (United States)

    Backović, Mihailo; Krämer, Michael; Maltoni, Fabio; Martini, Antony; Mawatari, Kentarou; Pellen, Mathieu

    Weakly interacting dark matter particles can be pair-produced at colliders and detected through signatures featuring missing energy in association with either QCD/EW radiation or heavy quarks. In order to constrain the mass and the couplings to standard model particles, accurate and precise predictions for production cross sections and distributions are of prime importance. In this work, we consider various simplified models with s -channel mediators. We implement such models in the FeynRules/MadGraph5_aMC@NLO framework, which allows to include higher-order QCD corrections in realistic simulations and to study their effect systematically. As a first phenomenological application, we present predictions for dark matter production in association with jets and with a top-quark pair at the LHC, at next-to-leading order accuracy in QCD, including matching/merging to parton showers. Our study shows that higher-order QCD corrections to dark matter production via s -channel mediators have a significant impact not only on total production rates, but also on shapes of distributions. We also show that the inclusion of next-to-leading order effects results in a sizeable reduction of the theoretical uncertainties.

  8. Higher-order QCD predictions for dark matter production at the LHC in simplified models with s-channel mediators

    Energy Technology Data Exchange (ETDEWEB)

    Backović, Mihailo [Centre for Cosmology, Particle Physics and Phenomenology (CP3), Université catholique de Louvain, 1348, Louvain-la-Neuve (Belgium); Krämer, Michael [Institute for Theoretical Particle Physics and Cosmology, RWTH Aachen University, 52056, Aachen (Germany); Maltoni, Fabio; Martini, Antony [Centre for Cosmology, Particle Physics and Phenomenology (CP3), Université catholique de Louvain, 1348, Louvain-la-Neuve (Belgium); Mawatari, Kentarou, E-mail: kentarou.mawatari@vub.ac.be [Theoretische Natuurkunde and IIHE/ELEM, Vrije Universiteit Brussel, and International Solvay Institutes, Pleinlaan 2, 1050, Brussels (Belgium); Pellen, Mathieu [Institute for Theoretical Particle Physics and Cosmology, RWTH Aachen University, 52056, Aachen (Germany)

    2015-10-07

    Weakly interacting dark matter particles can be pair-produced at colliders and detected through signatures featuring missing energy in association with either QCD/EW radiation or heavy quarks. In order to constrain the mass and the couplings to standard model particles, accurate and precise predictions for production cross sections and distributions are of prime importance. In this work, we consider various simplified models with s-channel mediators. We implement such models in the FeynRules/MadGraph5{sub a}MC@NLO framework, which allows to include higher-order QCD corrections in realistic simulations and to study their effect systematically. As a first phenomenological application, we present predictions for dark matter production in association with jets and with a top-quark pair at the LHC, at next-to-leading order accuracy in QCD, including matching/merging to parton showers. Our study shows that higher-order QCD corrections to dark matter production via s-channel mediators have a significant impact not only on total production rates, but also on shapes of distributions. We also show that the inclusion of next-to-leading order effects results in a sizeable reduction of the theoretical uncertainties.

  9. Higher-order QCD predictions for dark matter production at the LHC in simplified models with s-channel mediators

    Energy Technology Data Exchange (ETDEWEB)

    Backovic, Mihailo; Maltoni, Fabio; Martini, Antony [Universite catholique de Louvain, Centre for Cosmology, Particle Physics and Phenomenology (CP3), Louvain-la-Neuve (Belgium); Kraemer, Michael; Pellen, Mathieu [RWTH Aachen University, Institute for Theoretical Particle Physics and Cosmology, Aachen (Germany); Mawatari, Kentarou [Theoretische Natuurkunde and IIHE/ELEM, Vrije Universiteit Brussel, and International Solvay Institutes, Brussels (Belgium)

    2015-10-15

    Weakly interacting dark matter particles can be pair-produced at colliders and detected through signatures featuring missing energy in association with either QCD/EW radiation or heavy quarks. In order to constrain the mass and the couplings to standard model particles, accurate and precise predictions for production cross sections and distributions are of prime importance. In this work, we consider various simplified models with s-channel mediators. We implement such models in the FeynRules/MadGraph5{sub a}MC rate at NLO framework, which allows to include higher-order QCD corrections in realistic simulations and to study their effect systematically. As a first phenomenological application, we present predictions for dark matter production in association with jets and with a top-quark pair at the LHC, at next-to-leading order accuracy in QCD, including matching/merging to parton showers. Our study shows that higher-order QCD corrections to dark matter production via s-channel mediators have a significant impact not only on total production rates, but also on shapes of distributions. We also show that the inclusion of next-to-leading order effects results in a sizeable reduction of the theoretical uncertainties. (orig.)

  10. Higher-order QCD predictions for dark matter production at the LHC in simplified models with s-channel mediators

    International Nuclear Information System (INIS)

    Backovic, Mihailo; Maltoni, Fabio; Martini, Antony; Kraemer, Michael; Pellen, Mathieu; Mawatari, Kentarou

    2015-01-01

    Weakly interacting dark matter particles can be pair-produced at colliders and detected through signatures featuring missing energy in association with either QCD/EW radiation or heavy quarks. In order to constrain the mass and the couplings to standard model particles, accurate and precise predictions for production cross sections and distributions are of prime importance. In this work, we consider various simplified models with s-channel mediators. We implement such models in the FeynRules/MadGraph5 a MC rate at NLO framework, which allows to include higher-order QCD corrections in realistic simulations and to study their effect systematically. As a first phenomenological application, we present predictions for dark matter production in association with jets and with a top-quark pair at the LHC, at next-to-leading order accuracy in QCD, including matching/merging to parton showers. Our study shows that higher-order QCD corrections to dark matter production via s-channel mediators have a significant impact not only on total production rates, but also on shapes of distributions. We also show that the inclusion of next-to-leading order effects results in a sizeable reduction of the theoretical uncertainties. (orig.)

  11. The paradox of verbal autopsy in cause of death assignment: symptom question unreliability but predictive accuracy.

    Science.gov (United States)

    Serina, Peter; Riley, Ian; Hernandez, Bernardo; Flaxman, Abraham D; Praveen, Devarsetty; Tallo, Veronica; Joshi, Rohina; Sanvictores, Diozele; Stewart, Andrea; Mooney, Meghan D; Murray, Christopher J L; Lopez, Alan D

    2016-01-01

    We believe that it is important that governments understand the reliability of the mortality data which they have at their disposable to guide policy debates. In many instances, verbal autopsy (VA) will be the only source of mortality data for populations, yet little is known about how the accuracy of VA diagnoses is affected by the reliability of the symptom responses. We previously described the effect of the duration of time between death and VA administration on VA validity. In this paper, using the same dataset, we assess the relationship between the reliability and completeness of symptom responses and the reliability and accuracy of cause of death (COD) prediction. The study was based on VAs in the Population Health Metrics Research Consortium (PHMRC) VA Validation Dataset from study sites in Bohol and Manila, Philippines and Andhra Pradesh, India. The initial interview was repeated within 3-52 months of death. Question responses were assessed for reliability and completeness between the two survey rounds. COD was predicted by Tariff Method. A sample of 4226 VAs was collected for 2113 decedents, including 1394 adults, 349 children, and 370 neonates. Mean question reliability was unexpectedly low ( kappa  = 0.447): 42.5 % of responses positive at the first interview were negative at the second, and 47.9 % of responses positive at the second had been negative at the first. Question reliability was greater for the short form of the PHMRC instrument ( kappa  = 0.497) and when analyzed at the level of the individual decedent ( kappa  = 0.610). Reliability at the level of the individual decedent was associated with COD predictive reliability and predictive accuracy. Families give coherent accounts of events leading to death but the details vary from interview to interview for the same case. Accounts are accurate but inconsistent; different subsets of symptoms are identified on each occasion. However, there are sufficient accurate and consistent

  12. Real time shear wave elastography in chronic liver diseases: Accuracy for predicting liver fibrosis, in comparison with serum markers

    Science.gov (United States)

    Jeong, Jae Yoon; Kim, Tae Yeob; Sohn, Joo Hyun; Kim, Yongsoo; Jeong, Woo Kyoung; Oh, Young-Ha; Yoo, Kyo-Sang

    2014-01-01

    AIM: To evaluate the correlation between liver stiffness measurement (LSM) by real-time shear wave elastography (SWE) and liver fibrosis stage and the accuracy of LSM for predicting significant and advanced fibrosis, in comparison with serum markers. METHODS: We consecutively analyzed 70 patients with various chronic liver diseases. Liver fibrosis was staged from F0 to F4 according to the Batts and Ludwig scoring system. Significant and advanced fibrosis was defined as stage F ≥ 2 and F ≥ 3, respectively. The accuracy of prediction for fibrosis was analyzed using receiver operating characteristic curves. RESULTS: Seventy patients, 15 were belonged to F0-F1 stage, 20 F2, 13 F3 and 22 F4. LSM was increased with progression of fibrosis stage (F0-F1: 6.77 ± 1.72, F2: 9.98 ± 3.99, F3: 15.80 ± 7.73, and F4: 22.09 ± 10.09, P < 0.001). Diagnostic accuracies of LSM for prediction of F ≥ 2 and F ≥ 3 were 0.915 (95%CI: 0.824-0.968, P < 0.001) and 0.913 (95%CI: 0.821-0.967, P < 0.001), respectively. The cut-off values of LSM for prediction of F ≥ 2 and F ≥ 3 were 8.6 kPa with 78.2% sensitivity and 93.3% specificity and 10.46 kPa with 88.6% sensitivity and 80.0% specificity, respectively. However, there were no significant differences between LSM and serum hyaluronic acid and type IV collagen in diagnostic accuracy. CONCLUSION: SWE showed a significant correlation with the severity of liver fibrosis and was useful and accurate to predict significant and advanced fibrosis, comparable with serum markers. PMID:25320528

  13. ZCURVE 3.0: identify prokaryotic genes with higher accuracy as well as automatically and accurately select essential genes.

    Science.gov (United States)

    Hua, Zhi-Gang; Lin, Yan; Yuan, Ya-Zhou; Yang, De-Chang; Wei, Wen; Guo, Feng-Biao

    2015-07-01

    In 2003, we developed an ab initio program, ZCURVE 1.0, to find genes in bacterial and archaeal genomes. In this work, we present the updated version (i.e. ZCURVE 3.0). Using 422 prokaryotic genomes, the average accuracy was 93.7% with the updated version, compared with 88.7% with the original version. Such results also demonstrate that ZCURVE 3.0 is comparable with Glimmer 3.02 and may provide complementary predictions to it. In fact, the joint application of the two programs generated better results by correctly finding more annotated genes while also containing fewer false-positive predictions. As the exclusive function, ZCURVE 3.0 contains one post-processing program that can identify essential genes with high accuracy (generally >90%). We hope ZCURVE 3.0 will receive wide use with the web-based running mode. The updated ZCURVE can be freely accessed from http://cefg.uestc.edu.cn/zcurve/ or http://tubic.tju.edu.cn/zcurveb/ without any restrictions. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

  14. High-accuracy CFD prediction methods for fluid and structure temperature fluctuations at T-junction for thermal fatigue evaluation

    Energy Technology Data Exchange (ETDEWEB)

    Qian, Shaoxiang, E-mail: qian.shaoxiang@jgc.com [EN Technology Center, Process Technology Division, JGC Corporation, 2-3-1 Minato Mirai, Nishi-ku, Yokohama 220-6001 (Japan); Kanamaru, Shinichiro [EN Technology Center, Process Technology Division, JGC Corporation, 2-3-1 Minato Mirai, Nishi-ku, Yokohama 220-6001 (Japan); Kasahara, Naoto [Nuclear Engineering and Management, School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656 (Japan)

    2015-07-15

    Highlights: • Numerical methods for accurate prediction of thermal loading were proposed. • Predicted fluid temperature fluctuation (FTF) intensity is close to the experiment. • Predicted structure temperature fluctuation (STF) range is close to the experiment. • Predicted peak frequencies of FTF and STF also agree well with the experiment. • CFD results show the proposed numerical methods are of sufficiently high accuracy. - Abstract: Temperature fluctuations generated by the mixing of hot and cold fluids at a T-junction, which is widely used in nuclear power and process plants, can cause thermal fatigue failure. The conventional methods for evaluating thermal fatigue tend to provide insufficient accuracy, because they were developed based on limited experimental data and a simplified one-dimensional finite element analysis (FEA). CFD/FEA coupling analysis is expected as a useful tool for the more accurate evaluation of thermal fatigue. The present paper aims to verify the accuracy of proposed numerical methods of simulating fluid and structure temperature fluctuations at a T-junction for thermal fatigue evaluation. The dynamic Smagorinsky model (DSM) is used for large eddy simulation (LES) sub-grid scale (SGS) turbulence model, and a hybrid scheme (HS) is adopted for the calculation of convective terms in the governing equations. Also, heat transfer between fluid and structure is calculated directly through thermal conduction by creating a mesh with near wall resolution (NWR) by allocating grid points within the thermal boundary sub-layer. The simulation results show that the distribution of fluid temperature fluctuation intensity and the range of structure temperature fluctuation are remarkably close to the experimental results. Moreover, the peak frequencies of power spectrum density (PSD) of both fluid and structure temperature fluctuations also agree well with the experimental results. Therefore, the numerical methods used in the present paper are

  15. Phishtest: Measuring the Impact of Email Headers on the Predictive Accuracy of Machine Learning Techniques

    Science.gov (United States)

    Tout, Hicham

    2013-01-01

    The majority of documented phishing attacks have been carried by email, yet few studies have measured the impact of email headers on the predictive accuracy of machine learning techniques in detecting email phishing attacks. Research has shown that the inclusion of a limited subset of email headers as features in training machine learning…

  16. A comparison of accuracy validation methods for genomic and pedigree-based predictions of swine litter size traits using Large White and simulated data.

    Science.gov (United States)

    Putz, A M; Tiezzi, F; Maltecca, C; Gray, K A; Knauer, M T

    2018-02-01

    The objective of this study was to compare and determine the optimal validation method when comparing accuracy from single-step GBLUP (ssGBLUP) to traditional pedigree-based BLUP. Field data included six litter size traits. Simulated data included ten replicates designed to mimic the field data in order to determine the method that was closest to the true accuracy. Data were split into training and validation sets. The methods used were as follows: (i) theoretical accuracy derived from the prediction error variance (PEV) of the direct inverse (iLHS), (ii) approximated accuracies from the accf90(GS) program in the BLUPF90 family of programs (Approx), (iii) correlation between predictions and the single-step GEBVs from the full data set (GEBV Full ), (iv) correlation between predictions and the corrected phenotypes of females from the full data set (Y c ), (v) correlation from method iv divided by the square root of the heritability (Y ch ) and (vi) correlation between sire predictions and the average of their daughters' corrected phenotypes (Y cs ). Accuracies from iLHS increased from 0.27 to 0.37 (37%) in the Large White. Approximation accuracies were very consistent and close in absolute value (0.41 to 0.43). Both iLHS and Approx were much less variable than the corrected phenotype methods (ranging from 0.04 to 0.27). On average, simulated data showed an increase in accuracy from 0.34 to 0.44 (29%) using ssGBLUP. Both iLHS and Y ch approximated the increase well, 0.30 to 0.46 and 0.36 to 0.45, respectively. GEBV Full performed poorly in both data sets and is not recommended. Results suggest that for within-breed selection, theoretical accuracy using PEV was consistent and accurate. When direct inversion is infeasible to get the PEV, correlating predictions to the corrected phenotypes divided by the square root of heritability is adequate given a large enough validation data set. © 2017 Blackwell Verlag GmbH.

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

    Science.gov (United States)

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

    2015-05-13

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

  18. EMUDRA: Ensemble of Multiple Drug Repositioning Approaches to Improve Prediction Accuracy.

    Science.gov (United States)

    Zhou, Xianxiao; Wang, Minghui; Katsyv, Igor; Irie, Hanna; Zhang, Bin

    2018-04-24

    Availability of large-scale genomic, epigenetic and proteomic data in complex diseases makes it possible to objectively and comprehensively identify therapeutic targets that can lead to new therapies. The Connectivity Map has been widely used to explore novel indications of existing drugs. However, the prediction accuracy of the existing methods, such as Kolmogorov-Smirnov statistic remains low. Here we present a novel high-performance drug repositioning approach that improves over the state-of-the-art methods. We first designed an expression weighted cosine method (EWCos) to minimize the influence of the uninformative expression changes and then developed an ensemble approach termed EMUDRA (Ensemble of Multiple Drug Repositioning Approaches) to integrate EWCos and three existing state-of-the-art methods. EMUDRA significantly outperformed individual drug repositioning methods when applied to simulated and independent evaluation datasets. We predicted using EMUDRA and experimentally validated an antibiotic rifabutin as an inhibitor of cell growth in triple negative breast cancer. EMUDRA can identify drugs that more effectively target disease gene signatures and will thus be a useful tool for identifying novel therapies for complex diseases and predicting new indications for existing drugs. The EMUDRA R package is available at doi:10.7303/syn11510888. bin.zhang@mssm.edu or zhangb@hotmail.com. Supplementary data are available at Bioinformatics online.

  19. Geopositioning with a quadcopter: Extracted feature locations and predicted accuracy without a priori sensor attitude information

    Science.gov (United States)

    Dolloff, John; Hottel, Bryant; Edwards, David; Theiss, Henry; Braun, Aaron

    2017-05-01

    This paper presents an overview of the Full Motion Video-Geopositioning Test Bed (FMV-GTB) developed to investigate algorithm performance and issues related to the registration of motion imagery and subsequent extraction of feature locations along with predicted accuracy. A case study is included corresponding to a video taken from a quadcopter. Registration of the corresponding video frames is performed without the benefit of a priori sensor attitude (pointing) information. In particular, tie points are automatically measured between adjacent frames using standard optical flow matching techniques from computer vision, an a priori estimate of sensor attitude is then computed based on supplied GPS sensor positions contained in the video metadata and a photogrammetric/search-based structure from motion algorithm, and then a Weighted Least Squares adjustment of all a priori metadata across the frames is performed. Extraction of absolute 3D feature locations, including their predicted accuracy based on the principles of rigorous error propagation, is then performed using a subset of the registered frames. Results are compared to known locations (check points) over a test site. Throughout this entire process, no external control information (e.g. surveyed points) is used other than for evaluation of solution errors and corresponding accuracy.

  20. Predictive accuracy of backpropagation neural network ...

    Indian Academy of Sciences (India)

    incorporated into the BP model for high accuracy management purpose of irrigation water, which relies on accurate values of ET ... as seen from the recent food crisis demonstra- tion in most .... layers by using Geographical Information System.

  1. Predicting child maltreatment: A meta-analysis of the predictive validity of risk assessment instruments.

    Science.gov (United States)

    van der Put, Claudia E; Assink, Mark; Boekhout van Solinge, Noëlle F

    2017-11-01

    Risk assessment is crucial in preventing child maltreatment since it can identify high-risk cases in need of child protection intervention. Despite widespread use of risk assessment instruments in child welfare, it is unknown how well these instruments predict maltreatment and what instrument characteristics are associated with higher levels of predictive validity. Therefore, a multilevel meta-analysis was conducted to examine the predictive accuracy of (characteristics of) risk assessment instruments. A literature search yielded 30 independent studies (N=87,329) examining the predictive validity of 27 different risk assessment instruments. From these studies, 67 effect sizes could be extracted. Overall, a medium significant effect was found (AUC=0.681), indicating a moderate predictive accuracy. Moderator analyses revealed that onset of maltreatment can be better predicted than recurrence of maltreatment, which is a promising finding for early detection and prevention of child maltreatment. In addition, actuarial instruments were found to outperform clinical instruments. To bring risk and needs assessment in child welfare to a higher level, actuarial instruments should be further developed and strengthened by distinguishing risk assessment from needs assessment and by integrating risk assessment with case management. Copyright © 2017 Elsevier Ltd. All rights reserved.

  2. Accuracy assessment of pharmacogenetically predictive warfarin dosing algorithms in patients of an academic medical center anticoagulation clinic.

    Science.gov (United States)

    Shaw, Paul B; Donovan, Jennifer L; Tran, Maichi T; Lemon, Stephenie C; Burgwinkle, Pamela; Gore, Joel

    2010-08-01

    The objectives of this retrospective cohort study are to evaluate the accuracy of pharmacogenetic warfarin dosing algorithms in predicting therapeutic dose and to determine if this degree of accuracy warrants the routine use of genotyping to prospectively dose patients newly started on warfarin. Seventy-one patients of an outpatient anticoagulation clinic at an academic medical center who were age 18 years or older on a stable, therapeutic warfarin dose with international normalized ratio (INR) goal between 2.0 and 3.0, and cytochrome P450 isoenzyme 2C9 (CYP2C9) and vitamin K epoxide reductase complex subunit 1 (VKORC1) genotypes available between January 1, 2007 and September 30, 2008 were included. Six pharmacogenetic warfarin dosing algorithms were identified from the medical literature. Additionally, a 5 mg fixed dose approach was evaluated. Three algorithms, Zhu et al. (Clin Chem 53:1199-1205, 2007), Gage et al. (J Clin Ther 84:326-331, 2008), and International Warfarin Pharmacogenetic Consortium (IWPC) (N Engl J Med 360:753-764, 2009) were similar in the primary accuracy endpoints with mean absolute error (MAE) ranging from 1.7 to 1.8 mg/day and coefficient of determination R (2) from 0.61 to 0.66. However, the Zhu et al. algorithm severely over-predicted dose (defined as >or=2x or >or=2 mg/day more than actual dose) in twice as many (14 vs. 7%) patients as Gage et al. 2008 and IWPC 2009. In conclusion, the algorithms published by Gage et al. 2008 and the IWPC 2009 were the two most accurate pharmacogenetically based equations available in the medical literature in predicting therapeutic warfarin dose in our study population. However, the degree of accuracy demonstrated does not support the routine use of genotyping to prospectively dose all patients newly started on warfarin.

  3. Sensitivity, specificity, predictive value and accuracy of ultrasonography in pregnancy rate prediction in Sahelian goats after progesterone impregnated sponge synchronization

    Directory of Open Access Journals (Sweden)

    Justin Kouamo

    2014-09-01

    Full Text Available Aim: This study was aimed to evaluate the sensitivity, specificity, predictive value and accuracy of ultrasonography in pregnancy rate (PR prediction in Sahelian goats after progesterone impregnated sponge synchronization within the framework of caprine artificial insemination (AI program in Fatick (Senegal. Materials and Methods: Of 193 candidate goats in AI program, 167 were selected (day 50 in six villages. Estrus was synchronized by progesterone impregnated sponges installed for 11 days. Two days before the time of sponge removal (day 4, each goat was treated with 500 IU of equine chorionic gonadotropin and 50 μg of dcloprostenol. All goats were inseminated (day 0 with alpine goat semen from France at 45±3 h after sponge removal (day 2. Real-time B-mode ultrasonography was performed at day 50, day 13, day 0, day 40 and day 60 post-AI. Results: Selection rate, estrus response rate, AI rate, PR at days 40 and days 60 were 86.53%; 71.85%; 83.34%; 51% and 68% (p<0.05 respectively. Value of sensitivity, specificity, positive and negative predictive value, accuracy, total conformity, conformity of correct positive, conformity of correct negative and discordance of pregnancy diagnosis by trans-abdominal ultrasonography (TU were 98.03%; 63.26%; 73.52%; 3.12%; 81%; 81%; 50%; 31% and 19%, respectively. Conclusion: These results indicate that the TU can be performed in goats under traditional condition and emphasized the importance of re-examination of goats with negative or doubtful TU diagnoses performed at day 40 post-AI.

  4. Accuracy of some simple models for predicting particulate interception and retention in agricultural systems

    International Nuclear Information System (INIS)

    Pinder, J.E. III; McLeod, K.W.; Adriano, D.C.

    1989-01-01

    The accuracy of three radionuclide transfer models for predicting the interception and retention of airborne particles by agricultural crops was tested using Pu-bearing aerosols released to the atmosphere from nuclear fuel facilities on the U.S. Department of Energy's Savannah River Plant, near Aiken, SC. The models evaluated were: (1) NRC, the model defined in U.S. Nuclear Regulatory Guide 1.109; (2) FOOD, a model similar to the NRC model that also predicts concentrations in grains; and (3) AGNS, a model developed from the NRC model for the southeastern United States. Plutonium concentrations in vegetation and grain were predicted from measured deposition rates and compared to concentrations observed in the field. Crops included wheat, soybeans, corn and cabbage. Although predictions of the three models differed by less than a factor of 4, they showed different abilities to predict concentrations observed in the field. The NRC and FOOD models consistently underpredicted the observed Pu concentrations for vegetation. The AGNS model was a more accurate predictor of Pu concentrations for vegetation. Both the FOOD and AGNS models accurately predicted the Pu concentrations for grains

  5. Base Oils Biodegradability Prediction with Data Mining Techniques

    Directory of Open Access Journals (Sweden)

    Malika Trabelsi

    2010-02-01

    Full Text Available In this paper, we apply various data mining techniques including continuous numeric and discrete classification prediction models of base oils biodegradability, with emphasis on improving prediction accuracy. The results show that highly biodegradable oils can be better predicted through numeric models. In contrast, classification models did not uncover a similar dichotomy. With the exception of Memory Based Reasoning and Decision Trees, tested classification techniques achieved high classification prediction. However, the technique of Decision Trees helped uncover the most significant predictors. A simple classification rule derived based on this predictor resulted in good classification accuracy. The application of this rule enables efficient classification of base oils into either low or high biodegradability classes with high accuracy. For the latter, a higher precision biodegradability prediction can be obtained using continuous modeling techniques.

  6. A Critical Analysis and Validation of the Accuracy of Wave Overtopping Prediction Formulae for OWECs

    Directory of Open Access Journals (Sweden)

    David Gallach-Sánchez

    2018-01-01

    Full Text Available The development of wave energy devices is growing in recent years. One type of device is the overtopping wave energy converter (OWEC, for which the knowledge of the wave overtopping rates is a basic and crucial aspect in their design. In particular, the most interesting range to study is for OWECs with steep slopes to vertical walls, and with very small freeboards and zero freeboards where the overtopping rate is maximized, and which can be generalized as steep low-crested structures. Recently, wave overtopping prediction formulae have been published for this type of structures, although their accuracy has not been fully assessed, as the overtopping data available in this range is scarce. We performed a critical analysis of the overtopping prediction formulae for steep low-crested structures and the validation of the accuracy of these formulae, based on new overtopping data for steep low-crested structures obtained at Ghent University. This paper summarizes the existing knowledge about average wave overtopping, describes the physical model tests performed, analyses the results and compares them to existing prediction formulae. The new dataset extends the wave overtopping data towards vertical walls and zero freeboard structures. In general, the new dataset validated the more recent overtopping formulae focused on steep slopes with small freeboards, although the formulae are underpredicting the average overtopping rates for very small and zero relative crest freeboards.

  7. Improving accuracy of protein-protein interaction prediction by considering the converse problem for sequence representation

    Directory of Open Access Journals (Sweden)

    Wang Yong

    2011-10-01

    Full Text Available Abstract Background With the development of genome-sequencing technologies, protein sequences are readily obtained by translating the measured mRNAs. Therefore predicting protein-protein interactions from the sequences is of great demand. The reason lies in the fact that identifying protein-protein interactions is becoming a bottleneck for eventually understanding the functions of proteins, especially for those organisms barely characterized. Although a few methods have been proposed, the converse problem, if the features used extract sufficient and unbiased information from protein sequences, is almost untouched. Results In this study, we interrogate this problem theoretically by an optimization scheme. Motivated by the theoretical investigation, we find novel encoding methods for both protein sequences and protein pairs. Our new methods exploit sufficiently the information of protein sequences and reduce artificial bias and computational cost. Thus, it significantly outperforms the available methods regarding sensitivity, specificity, precision, and recall with cross-validation evaluation and reaches ~80% and ~90% accuracy in Escherichia coli and Saccharomyces cerevisiae respectively. Our findings here hold important implication for other sequence-based prediction tasks because representation of biological sequence is always the first step in computational biology. Conclusions By considering the converse problem, we propose new representation methods for both protein sequences and protein pairs. The results show that our method significantly improves the accuracy of protein-protein interaction predictions.

  8. Predicting extreme rainfall over eastern Asia by using complex networks

    International Nuclear Information System (INIS)

    He Su-Hong; Gong Yan-Chun; Huang Yan-Hua; Wu Cheng-Guo; Feng Tai-Chen; Gong Zhi-Qiang

    2014-01-01

    A climate network of extreme rainfall over eastern Asia is constructed for the period of 1971–2000, employing the tools of complex networks and a measure of nonlinear correlation called event synchronization (ES). Using this network, we predict the extreme rainfall for several cases without delay and with n-day delay (1 ≤ n ≤ 10). The prediction accuracy can reach 58% without delay, 21% with 1-day delay, and 12% with n-day delay (2 ≤ n ≤ 10). The results reveal that the prediction accuracy is low in years of a weak east Asia summer monsoon (EASM) or 1 year later and high in years of a strong EASM or 1 year later. Furthermore, the prediction accuracy is higher due to the many more links that represent correlations between different grid points and a higher extreme rainfall rate during strong EASM years. (geophysics, astronomy, and astrophysics)

  9. Bagging Approach for Increasing Classification Accuracy of CART on Family Participation Prediction in Implementation of Elderly Family Development Program

    Directory of Open Access Journals (Sweden)

    Wisoedhanie Widi Anugrahanti

    2017-06-01

    Full Text Available Classification and Regression Tree (CART was a method of Machine Learning where data exploration was done by decision tree technique. CART was a classification technique with binary recursive reconciliation algorithms where the sorting was performed on a group of data collected in a space called a node / node into two child nodes (Lewis, 2000. The aim of this study was to predict family participation in Elderly Family Development program based on family behavior in providing physical, mental, social care for the elderly. Family involvement accuracy using Bagging CART method was calculated based on 1-APER value, sensitivity, specificity, and G-Means. Based on CART method, classification accuracy was obtained 97,41% with Apparent Error Rate value 2,59%. The most important determinant of family behavior as a sorter was society participation (100,00000, medical examination (98,95988, providing nutritious food (68.60476, establishing communication (67,19877 and worship (57,36587. To improved the stability and accuracy of CART prediction, used CART Bootstrap Aggregating (Bagging with 100% accuracy result. Bagging CART classifies a total of 590 families (84.77% were appropriately classified into implement elderly Family Development program class.

  10. Higher Order Corrections in the CoLoRFulNNLO Framework

    Science.gov (United States)

    Somogyi, G.; Kardos, A.; Szőr, Z.; Trócsányi, Z.

    We discuss the CoLoRFulNNLO method for computing higher order radiative corrections to jet cross sections in perturbative QCD. We apply our method to the calculation of event shapes and jet rates in three-jet production in electron-positron annihilation. We validate our code by comparing our predictions to previous results in the literature and present the jet cone energy fraction distribution at NNLO accuracy. We also present preliminary NNLO results for the three-jet rate using the Durham jet clustering algorithm matched to resummed predictions at NLL accuracy, and a comparison to LEP data.

  11. Theoretical study on new bias factor methods to effectively use critical experiments for improvement of prediction accuracy of neutronic characteristics

    International Nuclear Information System (INIS)

    Kugo, Teruhiko; Mori, Takamasa; Takeda, Toshikazu

    2007-01-01

    Extended bias factor methods are proposed with two new concepts, the LC method and the PE method, in order to effectively use critical experiments and to enhance the applicability of the bias factor method for the improvement of the prediction accuracy of neutronic characteristics of a target core. Both methods utilize a number of critical experimental results and produce a semifictitious experimental value with them. The LC and PE methods define the semifictitious experimental values by a linear combination of experimental values and the product of exponentiated experimental values, respectively, and the corresponding semifictitious calculation values by those of calculation values. A bias factor is defined by the ratio of the semifictitious experimental value to the semifictitious calculation value in both methods. We formulate how to determine weights for the LC method and exponents for the PE method in order to minimize the variance of the design prediction value obtained by multiplying the design calculation value by the bias factor. From a theoretical comparison of these new methods with the conventional method which utilizes a single experimental result and the generalized bias factor method which was previously proposed to utilize a number of experimental results, it is concluded that the PE method is the most useful method for improving the prediction accuracy. The main advantages of the PE method are summarized as follows. The prediction accuracy is necessarily improved compared with the design calculation value even when experimental results include large experimental errors. This is a special feature that the other methods do not have. The prediction accuracy is most effectively improved by utilizing all the experimental results. From these facts, it can be said that the PE method effectively utilizes all the experimental results and has a possibility to make a full-scale-mockup experiment unnecessary with the use of existing and future benchmark

  12. Improving the Accuracy of the Hyperspectral Model for Apple Canopy Water Content Prediction using the Equidistant Sampling Method.

    Science.gov (United States)

    Zhao, Huan-San; Zhu, Xi-Cun; Li, Cheng; Wei, Yu; Zhao, Geng-Xing; Jiang, Yuan-Mao

    2017-09-11

    The influence of the equidistant sampling method was explored in a hyperspectral model for the accurate prediction of the water content of apple tree canopy. The relationship between spectral reflectance and water content was explored using the sample partition methods of equidistant sampling and random sampling, and a stepwise regression model of the apple canopy water content was established. The results showed that the random sampling model was Y = 0.4797 - 721787.3883 × Z 3 - 766567.1103 × Z 5 - 771392.9030 × Z 6 ; the equidistant sampling model was Y = 0.4613 - 480610.4213 × Z 2 - 552189.0450 × Z 5 - 1006181.8358 × Z 6 . After verification, the equidistant sampling method was verified to offer a superior prediction ability. The calibration set coefficient of determination of 0.6599 and validation set coefficient of determination of 0.8221 were higher than that of the random sampling model by 9.20% and 10.90%, respectively. The root mean square error (RMSE) of 0.0365 and relative error (RE) of 0.0626 were lower than that of the random sampling model by 17.23% and 17.09%, respectively. Dividing the calibration set and validation set by the equidistant sampling method can improve the prediction accuracy of the hyperspectral model of apple canopy water content.

  13. Analysis of spatial distribution of land cover maps accuracy

    Science.gov (United States)

    Khatami, R.; Mountrakis, G.; Stehman, S. V.

    2017-12-01

    Land cover maps have become one of the most important products of remote sensing science. However, classification errors will exist in any classified map and affect the reliability of subsequent map usage. Moreover, classification accuracy often varies over different regions of a classified map. These variations of accuracy will affect the reliability of subsequent analyses of different regions based on the classified maps. The traditional approach of map accuracy assessment based on an error matrix does not capture the spatial variation in classification accuracy. Here, per-pixel accuracy prediction methods are proposed based on interpolating accuracy values from a test sample to produce wall-to-wall accuracy maps. Different accuracy prediction methods were developed based on four factors: predictive domain (spatial versus spectral), interpolation function (constant, linear, Gaussian, and logistic), incorporation of class information (interpolating each class separately versus grouping them together), and sample size. Incorporation of spectral domain as explanatory feature spaces of classification accuracy interpolation was done for the first time in this research. Performance of the prediction methods was evaluated using 26 test blocks, with 10 km × 10 km dimensions, dispersed throughout the United States. The performance of the predictions was evaluated using the area under the curve (AUC) of the receiver operating characteristic. Relative to existing accuracy prediction methods, our proposed methods resulted in improvements of AUC of 0.15 or greater. Evaluation of the four factors comprising the accuracy prediction methods demonstrated that: i) interpolations should be done separately for each class instead of grouping all classes together; ii) if an all-classes approach is used, the spectral domain will result in substantially greater AUC than the spatial domain; iii) for the smaller sample size and per-class predictions, the spectral and spatial domain

  14. Prediction of renal function (GFR) from cystatin C and creatinine in children: Body cell mass increases accuracy of the estimate

    DEFF Research Database (Denmark)

    Andersen, Trine Borup; Jødal, Lars; Bøgsted, Martin

    using robust regression in a forward, stepwise procedure. GFR (mL/min) was the dependent variable. The accuracy and precision of the prediction model were compared to other prediction models from the literature, using k-fold cross-validation. Local constants and coefficients were calculated for all...

  15. To compare the accuracy of Prayer's sign and Mallampatti test in predicting difficult intubation in Diabetic patients

    International Nuclear Information System (INIS)

    Baig, M. M. A.; Khan, F. H.

    2014-01-01

    Objective: To determine the accuracy of Prayer's sign and Mallampatti test in predicting difficult endotracheal intubation in diabetic patients. Methods: The cross-sectional study was performed at Aga Khan University Hospital, Karachi, over a period from January 2009 to April 2010, and comprised 357 patients who required endotracheal intubation for elective surgical procedures. Prayer's sign and Mallampatti tests were performed for the assessment of airway by trained observers. Ease or difficulty of laryngoscopy after the patient was fully anaesthetised with standard technique were observed and laryngoscopic view of first attempt was rated according to Cormack-Lehan grade of intubation. SPSS 15 was used for statistical analysis. Results: Of the 357 patients, 125(35%) were classified as difficult to intubate. Prayer's sign showed significantly lower accuracy, positive and negative predictive values than Mallampatti test. The sensitivity of Prayer's sign was lower 29.6 (95% Confidence Interval, 21.9-38.5) than Mallampatti test 79.3 (95% confidence interval, 70.8-85.7) while specificity of both the tests was not found to be significantly different. Conclusion: Prayer's sign is not acceptable as a single best bedside test for prediction of difficult intubation. (author)

  16. Prediction of novel pre-microRNAs with high accuracy through boosting and SVM.

    Science.gov (United States)

    Zhang, Yuanwei; Yang, Yifan; Zhang, Huan; Jiang, Xiaohua; Xu, Bo; Xue, Yu; Cao, Yunxia; Zhai, Qian; Zhai, Yong; Xu, Mingqing; Cooke, Howard J; Shi, Qinghua

    2011-05-15

    High-throughput deep-sequencing technology has generated an unprecedented number of expressed short sequence reads, presenting not only an opportunity but also a challenge for prediction of novel microRNAs. To verify the existence of candidate microRNAs, we have to show that these short sequences can be processed from candidate pre-microRNAs. However, it is laborious and time consuming to verify these using existing experimental techniques. Therefore, here, we describe a new method, miRD, which is constructed using two feature selection strategies based on support vector machines (SVMs) and boosting method. It is a high-efficiency tool for novel pre-microRNA prediction with accuracy up to 94.0% among different species. miRD is implemented in PHP/PERL+MySQL+R and can be freely accessed at http://mcg.ustc.edu.cn/rpg/mird/mird.php.

  17. Accuracy of genomic selection for alfalfa biomass yield in different reference populations.

    Science.gov (United States)

    Annicchiarico, Paolo; Nazzicari, Nelson; Li, Xuehui; Wei, Yanling; Pecetti, Luciano; Brummer, E Charles

    2015-12-01

    Genomic selection based on genotyping-by-sequencing (GBS) data could accelerate alfalfa yield gains, if it displayed moderate ability to predict parent breeding values. Its interest would be enhanced by predicting ability also for germplasm/reference populations other than those for which it was defined. Predicting accuracy may be influenced by statistical models, SNP calling procedures and missing data imputation strategies. Landrace and variety material from two genetically-contrasting reference populations, i.e., 124 elite genotypes adapted to the Po Valley (sub-continental climate; PV population) and 154 genotypes adapted to Mediterranean-climate environments (Me population), were genotyped by GBS and phenotyped in separate environments for dry matter yield of their dense-planted half-sib progenies. Both populations showed no sub-population genetic structure. Predictive accuracy was higher by joint rather than separate SNP calling for the two data sets, and using random forest imputation of missing data. Highest accuracy was obtained using Support Vector Regression (SVR) for PV, and Ridge Regression BLUP and SVR for Me germplasm. Bayesian methods (Bayes A, Bayes B and Bayesian Lasso) tended to be less accurate. Random Forest Regression was the least accurate model. Accuracy attained about 0.35 for Me in the range of 0.30-0.50 missing data, and 0.32 for PV at 0.50 missing data, using at least 10,000 SNP markers. Cross-population predictions based on a smaller subset of common SNPs implied a relative loss of accuracy of about 25% for Me and 30% for PV. Genome-wide association analyses based on large subsets of M. truncatula-aligned markers revealed many SNPs with modest association with yield, and some genome areas hosting putative QTLs. A comparison of genomic vs. conventional selection for parent breeding value assuming 1-year vs. 5-year selection cycles, respectively, indicated over three-fold greater predicted yield gain per unit time for genomic selection

  18. Impact of sampling interval in training data acquisition on intrafractional predictive accuracy of indirect dynamic tumor-tracking radiotherapy.

    Science.gov (United States)

    Mukumoto, Nobutaka; Nakamura, Mitsuhiro; Akimoto, Mami; Miyabe, Yuki; Yokota, Kenji; Matsuo, Yukinori; Mizowaki, Takashi; Hiraoka, Masahiro

    2017-08-01

    To explore the effect of sampling interval of training data acquisition on the intrafractional prediction error of surrogate signal-based dynamic tumor-tracking using a gimbal-mounted linac. Twenty pairs of respiratory motions were acquired from 20 patients (ten lung, five liver, and five pancreatic cancer patients) who underwent dynamic tumor-tracking with the Vero4DRT. First, respiratory motions were acquired as training data for an initial construction of the prediction model before the irradiation. Next, additional respiratory motions were acquired for an update of the prediction model due to the change of the respiratory pattern during the irradiation. The time elapsed prior to the second acquisition of the respiratory motion was 12.6 ± 3.1 min. A four-axis moving phantom reproduced patients' three dimensional (3D) target motions and one dimensional surrogate motions. To predict the future internal target motion from the external surrogate motion, prediction models were constructed by minimizing residual prediction errors for training data acquired at 80 and 320 ms sampling intervals for 20 s, and at 500, 1,000, and 2,000 ms sampling intervals for 60 s using orthogonal kV x-ray imaging systems. The accuracies of prediction models trained with various sampling intervals were estimated based on training data with each sampling interval during the training process. The intrafractional prediction errors for various prediction models were then calculated on intrafractional monitoring images taken for 30 s at the constant sampling interval of a 500 ms fairly to evaluate the prediction accuracy for the same motion pattern. In addition, the first respiratory motion was used for the training and the second respiratory motion was used for the evaluation of the intrafractional prediction errors for the changed respiratory motion to evaluate the robustness of the prediction models. The training error of the prediction model was 1.7 ± 0.7 mm in 3D for all sampling

  19. Accuracy of Igenity genomically estimated breeding values for predicting Australian Angus BREEDPLAN traits.

    Science.gov (United States)

    Boerner, V; Johnston, D; Wu, X-L; Bauck, S

    2015-02-01

    Genomically estimated breeding values (GEBV) for Angus beef cattle are available from at least 2 commercial suppliers (Igenity [http://www.igenity.com] and Zoetis [http://www.zoetis.com]). The utility of these GEBV for improving genetic evaluation depends on their accuracies, which can be estimated by the genetic correlation with phenotypic target traits. Genomically estimated breeding values of 1,032 Angus bulls calculated from prediction equations (PE) derived by 2 different procedures in the U.S. Angus population were supplied by Igenity. Both procedures were based on Illuminia BovineSNP50 BeadChip genotypes. In procedure sg, GEBV were calculated from PE that used subsets of only 392 SNP, where these subsets were individually selected for each trait by BayesCπ. In procedure rg GEBV were calculated from PE derived in a ridge regression approach using all available SNP. Because the total set of 1,032 bulls with GEBV contained 732 individuals used in the Igenity training population, GEBV subsets were formed characterized by a decreasing average relationship between individuals in the subsets and individuals in the training population. Accuracies of GEBV were estimated as genetic correlations between GEBV and their phenotypic target traits modeling GEBV as trait observations in a bivariate REML approach, in which phenotypic observations were those recorded in the commercial Australian Angus seed stock sector. Using results from the GEBV subset excluding all training individuals as a reference, estimated accuracies were generally in agreement with those already published, with both types of GEBV (sg and rg) yielding similar results. Accuracies for growth traits ranged from 0.29 to 0.45, for reproductive traits from 0.11 to 0.53, and for carcass traits from 0.3 to 0.75. Accuracies generally decreased with an increasing genetic distance between the training and the validation population. However, for some carcass traits characterized by a low number of phenotypic

  20. Improved protein structure reconstruction using secondary structures, contacts at higher distance thresholds, and non-contacts.

    Science.gov (United States)

    Adhikari, Badri; Cheng, Jianlin

    2017-08-29

    Residue-residue contacts are key features for accurate de novo protein structure prediction. For the optimal utilization of these predicted contacts in folding proteins accurately, it is important to study the challenges of reconstructing protein structures using true contacts. Because contact-guided protein modeling approach is valuable for predicting the folds of proteins that do not have structural templates, it is necessary for reconstruction studies to focus on hard-to-predict protein structures. Using a data set consisting of 496 structural domains released in recent CASP experiments and a dataset of 150 representative protein structures, in this work, we discuss three techniques to improve the reconstruction accuracy using true contacts - adding secondary structures, increasing contact distance thresholds, and adding non-contacts. We find that reconstruction using secondary structures and contacts can deliver accuracy higher than using full contact maps. Similarly, we demonstrate that non-contacts can improve reconstruction accuracy not only when the used non-contacts are true but also when they are predicted. On the dataset consisting of 150 proteins, we find that by simply using low ranked predicted contacts as non-contacts and adding them as additional restraints, can increase the reconstruction accuracy by 5% when the reconstructed models are evaluated using TM-score. Our findings suggest that secondary structures are invaluable companions of contacts for accurate reconstruction. Confirming some earlier findings, we also find that larger distance thresholds are useful for folding many protein structures which cannot be folded using the standard definition of contacts. Our findings also suggest that for more accurate reconstruction using predicted contacts it is useful to predict contacts at higher distance thresholds (beyond 8 Å) and predict non-contacts.

  1. The Promise and Peril of Predictive Analytics in Higher Education: A Landscape Analysis

    Science.gov (United States)

    Ekowo, Manuela; Palmer, Iris

    2016-01-01

    Predictive analytics in higher education is a hot-button topic among educators and administrators as institutions strive to better serve students by becoming more data-informed. In this paper, the authors describe how predictive analytics are used in higher education to identify students who need extra support, steer students in courses they will…

  2. Pigeons exhibit higher accuracy for chosen memory tests than for forced memory tests in duration matching-to-sample.

    Science.gov (United States)

    Adams, Allison; Santi, Angelo

    2011-03-01

    Following training to match 2- and 8-sec durations of feederlight to red and green comparisons with a 0-sec baseline delay, pigeons were allowed to choose to take a memory test or to escape the memory test. The effects of sample omission, increases in retention interval, and variation in trial spacing on selection of the escape option and accuracy were studied. During initial testing, escaping the test did not increase as the task became more difficult, and there was no difference in accuracy between chosen and forced memory tests. However, with extended training, accuracy for chosen tests was significantly greater than for forced tests. In addition, two pigeons exhibited higher accuracy on chosen tests than on forced tests at the short retention interval and greater escape rates at the long retention interval. These results have not been obtained in previous studies with pigeons when the choice to take the test or to escape the test is given before test stimuli are presented. It appears that task-specific methodological factors may determine whether a particular species will exhibit the two behavioral effects that were initially proposed as potentially indicative of metacognition.

  3. Implementation of genomic prediction in Lolium perenne (L. breeding populations

    Directory of Open Access Journals (Sweden)

    Nastasiya F Grinberg

    2016-02-01

    Full Text Available Perennial ryegrass (Lolium perenne L. is one of the most widely grown forage grasses in temperate agriculture. In order to maintain and increase its usage as forage in livestock agriculture, there is a continued need for improvement in biomass yield, quality, disease resistance and seed yield. Genetic gain for traits such as biomass yield has been relatively modest. This has been attributed to its long breeding cycle, and the necessity to use population based breeding methods. Thanks to recent advances in genotyping techniques there is increasing interest in genomic selection from which genomically estimated breeding values (GEBV are derived. In this paper we compare the classical RRBLUP model with state-of-the-art machine learning (ML techniques that should yield themselves easily to use in GS and demonstrate their application to predicting quantitative traits in a breeding population of L. perenne. Prediction accuracies varied from 0 to 0.59 depending on trait, prediction model and composition of the training population. The BLUP model produced the highest prediction accuracies for most traits and training populations. Forage quality traits had the highest accuracies compared to yield related traits. There appeared to be no clear pattern to the effect of the training population composition on the prediction accuracies. The heritability of the forage quality traits was generally higher than for the yield related traits, and could partly explain the difference in accuracy. Some population structure was evident in the breeding populations, and probably contributed to the varying effects of training population on the predictions. The average linkage disequilibrium (LD between adjacent markers ranged from 0.121 to 0.215. Higher marker density and larger training population closely related with the test population are likely to improve the prediction accuracy.

  4. Does ADHD in adults affect the relative accuracy of metamemory judgments?

    Science.gov (United States)

    Knouse, Laura E; Paradise, Matthew J; Dunlosky, John

    2006-11-01

    Prior research suggests that individuals with ADHD overestimate their performance across domains despite performing more poorly in these domains. The authors introduce measures of accuracy from the larger realm of judgment and decision making--namely, relative accuracy and calibration--to the study of self-evaluative judgment accuracy in adults with ADHD. Twenty-eight adults with ADHD and 28 matched controls participate in a computer-administered paired-associate learning task and predict their future recall using immediate and delayed judgments of learning (JOLs). Retrospective confidence judgments are also collected. Groups perform equally in terms of judgment magnitude and absolute judgment accuracy as measured by discrepancy scores and calibration curves. Both groups benefit equally from making their JOL at a delay, and the group with ADHD show higher relative accuracy for delayed judgments. Results suggest that under certain circumstances, adults with ADHD can make accurate judgments about their future memory.

  5. Evaluation of accuracy of linear regression models in predicting urban stormwater discharge characteristics.

    Science.gov (United States)

    Madarang, Krish J; Kang, Joo-Hyon

    2014-06-01

    Stormwater runoff has been identified as a source of pollution for the environment, especially for receiving waters. In order to quantify and manage the impacts of stormwater runoff on the environment, predictive models and mathematical models have been developed. Predictive tools such as regression models have been widely used to predict stormwater discharge characteristics. Storm event characteristics, such as antecedent dry days (ADD), have been related to response variables, such as pollutant loads and concentrations. However it has been a controversial issue among many studies to consider ADD as an important variable in predicting stormwater discharge characteristics. In this study, we examined the accuracy of general linear regression models in predicting discharge characteristics of roadway runoff. A total of 17 storm events were monitored in two highway segments, located in Gwangju, Korea. Data from the monitoring were used to calibrate United States Environmental Protection Agency's Storm Water Management Model (SWMM). The calibrated SWMM was simulated for 55 storm events, and the results of total suspended solid (TSS) discharge loads and event mean concentrations (EMC) were extracted. From these data, linear regression models were developed. R(2) and p-values of the regression of ADD for both TSS loads and EMCs were investigated. Results showed that pollutant loads were better predicted than pollutant EMC in the multiple regression models. Regression may not provide the true effect of site-specific characteristics, due to uncertainty in the data. Copyright © 2014 The Research Centre for Eco-Environmental Sciences, Chinese Academy of Sciences. Published by Elsevier B.V. All rights reserved.

  6. Increasing the predictive accuracy of amyloid-β blood-borne biomarkers in Alzheimer's disease.

    Science.gov (United States)

    Watt, Andrew D; Perez, Keyla A; Faux, Noel G; Pike, Kerryn E; Rowe, Christopher C; Bourgeat, Pierrick; Salvado, Olivier; Masters, Colin L; Villemagne, Victor L; Barnham, Kevin J

    2011-01-01

    Diagnostic measures for Alzheimer's disease (AD) commonly rely on evaluating the levels of amyloid-β (Aβ) peptides within the cerebrospinal fluid (CSF) of affected individuals. These levels are often combined with levels of an additional non-Aβ marker to increase predictive accuracy. Recent efforts to overcome the invasive nature of CSF collection led to the observation of Aβ species within the blood cellular fraction, however, little is known of what additional biomarkers may be found in this membranous fraction. The current study aimed to undertake a discovery-based proteomic investigation of the blood cellular fraction from AD patients (n = 18) and healthy controls (HC; n = 15) using copper immobilized metal affinity capture and Surface Enhanced Laser Desorption/Ionisation Time-Of-Flight Mass Spectrometry. Three candidate biomarkers were observed which could differentiate AD patients from HC (ROC AUC > 0.8). Bivariate pairwise comparisons revealed significant correlations between these markers and measures of AD severity including; MMSE, composite memory, brain amyloid burden, and hippocampal volume. A partial least squares regression model was generated using the three candidate markers along with blood levels of Aβ. This model was able to distinguish AD from HC with high specificity (90%) and sensitivity (77%) and was able to separate individuals with mild cognitive impairment (MCI) who converted to AD from MCI non-converters. While requiring further characterization, these candidate biomarkers reaffirm the potential efficacy of blood-based investigations into neurodegenerative conditions. Furthermore, the findings indicate that the incorporation of non-amyloid markers into predictive models, function to increase the accuracy of the diagnostic potential of Aβ.

  7. THE ACCURACY AND BIAS EVALUATION OF THE USA UNEMPLOYMENT RATE FORECASTS. METHODS TO IMPROVE THE FORECASTS ACCURACY

    Directory of Open Access Journals (Sweden)

    MIHAELA BRATU (SIMIONESCU

    2012-12-01

    Full Text Available In this study some alternative forecasts for the unemployment rate of USA made by four institutions (International Monetary Fund (IMF, Organization for Economic Co-operation and Development (OECD, Congressional Budget Office (CBO and Blue Chips (BC are evaluated regarding the accuracy and the biasness. The most accurate predictions on the forecasting horizon 201-2011 were provided by IMF, followed by OECD, CBO and BC.. These results were gotten using U1 Theil’s statistic and a new method that has not been used before in literature in this context. The multi-criteria ranking was applied to make a hierarchy of the institutions regarding the accuracy and five important accuracy measures were taken into account at the same time: mean errors, mean squared error, root mean squared error, U1 and U2 statistics of Theil. The IMF, OECD and CBO predictions are unbiased. The combined forecasts of institutions’ predictions are a suitable strategy to improve the forecasts accuracy of IMF and OECD forecasts when all combination schemes are used, but INV one is the best. The filtered and smoothed original predictions based on Hodrick-Prescott filter, respectively Holt-Winters technique are a good strategy of improving only the BC expectations. The proposed strategies to improve the accuracy do not solve the problem of biasness. The assessment and improvement of forecasts accuracy have an important contribution in growing the quality of decisional process.

  8. Comparing the accuracy of perturbative and variational calculations for predicting fundamental vibrational frequencies of dihalomethanes

    Science.gov (United States)

    Krasnoshchekov, Sergey V.; Schutski, Roman S.; Craig, Norman C.; Sibaev, Marat; Crittenden, Deborah L.

    2018-02-01

    Three dihalogenated methane derivatives (CH2F2, CH2FCl, and CH2Cl2) were used as model systems to compare and assess the accuracy of two different approaches for predicting observed fundamental frequencies: canonical operator Van Vleck vibrational perturbation theory (CVPT) and vibrational configuration interaction (VCI). For convenience and consistency, both methods employ the Watson Hamiltonian in rectilinear normal coordinates, expanding the potential energy surface (PES) as a Taylor series about equilibrium and constructing the wavefunction from a harmonic oscillator product basis. At the highest levels of theory considered here, fourth-order CVPT and VCI in a harmonic oscillator basis with up to 10 quanta of vibrational excitation in conjunction with a 4-mode representation sextic force field (SFF-4MR) computed at MP2/cc-pVTZ with replacement CCSD(T)/aug-cc-pVQZ harmonic force constants, the agreement between computed fundamentals is closer to 0.3 cm-1 on average, with a maximum difference of 1.7 cm-1. The major remaining accuracy-limiting factors are the accuracy of the underlying electronic structure model, followed by the incompleteness of the PES expansion. Nonetheless, computed and experimental fundamentals agree to within 5 cm-1, with an average difference of 2 cm-1, confirming the utility and accuracy of both theoretical models. One exception to this rule is the formally IR-inactive but weakly allowed through Coriolis-coupling H-C-H out-of-plane twisting mode of dichloromethane, whose spectrum we therefore revisit and reassign. We also investigate convergence with respect to order of CVPT, VCI excitation level, and order of PES expansion, concluding that premature truncation substantially decreases accuracy, although VCI(6)/SFF-4MR results are still of acceptable accuracy, and some error cancellation is observed with CVPT2 using a quartic force field.

  9. Functional knowledge transfer for high-accuracy prediction of under-studied biological processes.

    Directory of Open Access Journals (Sweden)

    Christopher Y Park

    Full Text Available A key challenge in genetics is identifying the functional roles of genes in pathways. Numerous functional genomics techniques (e.g. machine learning that predict protein function have been developed to address this question. These methods generally build from existing annotations of genes to pathways and thus are often unable to identify additional genes participating in processes that are not already well studied. Many of these processes are well studied in some organism, but not necessarily in an investigator's organism of interest. Sequence-based search methods (e.g. BLAST have been used to transfer such annotation information between organisms. We demonstrate that functional genomics can complement traditional sequence similarity to improve the transfer of gene annotations between organisms. Our method transfers annotations only when functionally appropriate as determined by genomic data and can be used with any prediction algorithm to combine transferred gene function knowledge with organism-specific high-throughput data to enable accurate function prediction. We show that diverse state-of-art machine learning algorithms leveraging functional knowledge transfer (FKT dramatically improve their accuracy in predicting gene-pathway membership, particularly for processes with little experimental knowledge in an organism. We also show that our method compares favorably to annotation transfer by sequence similarity. Next, we deploy FKT with state-of-the-art SVM classifier to predict novel genes to 11,000 biological processes across six diverse organisms and expand the coverage of accurate function predictions to processes that are often ignored because of a dearth of annotated genes in an organism. Finally, we perform in vivo experimental investigation in Danio rerio and confirm the regulatory role of our top predicted novel gene, wnt5b, in leftward cell migration during heart development. FKT is immediately applicable to many bioinformatics

  10. Predictive accuracy of Edinburgh Postnatal Depression Scale assessment during pregnancy for the risk of developing postpartum depressive symptoms : a prospective cohort study

    NARCIS (Netherlands)

    Meijer, J. L.; Beijers, C.; van Pampus, M. G.; Verbeek, T.; Stolk, R. P.; Milgrom, J.; Bockting, C. L. H.; Burger, H.

    2014-01-01

    ObjectiveTo investigate whether the 10-item Edinburgh Postnatal Depression Scale (EPDS) administered antenatally is accurate in predicting postpartum depressive symptoms, and whether a two-item EPDS has similar predictive accuracy. DesignProspective cohort study. SettingObstetric care in the

  11. The Accuracy of Urinalysis in Predicting Intra-Abdominal Injury Following Blunt Traumas.

    Science.gov (United States)

    Sabzghabaei, Anita; Shojaee, Majid; Safari, Saeed; Hatamabadi, Hamid Reza; Shirvani, Reza

    2016-01-01

    In cases of blunt abdominal traumas, predicting the possible intra-abdominal injuries is still a challenge for the physicians involved with these patients. Therefore, this study was designed, to evaluate the accuracy of urinalysis in predicting intra-abdominal injuries. Patients aged 15 to 65 years with blunt abdominal trauma who were admitted to emergency departments were enrolled. Abdominopelvic computed tomography (CT) scan with intravenous contrast and urinalysis were requested for all the included patients. Demographic data, trauma mechanism, the results of urinalysis, and the results of abdominopelvic CT scan were gathered. Finally, the correlation between the results of abdominopelvic CT scan, and urinalysis was determined. Urinalysis was considered positive in case of at least one positive value in gross appearance, blood in dipstick, or red blood cell count. 325 patients with blunt abdominal trauma were admitted to the emergency departments (83% male with the mean age of 32.63±17.48 years). Sensitivity, specificity, positive and negative predictive values, and positive and negative likelihood ratios of urinalysis, were 77.9% (95% CI: 69.6-84.4), 58.5% (95% CI: 51.2-65.5), 56% (95% CI: 48.5-63.3), 79.6% (95% CI: 71.8-85.7), 1.27% (95% CI: 1.30-1.57), and 0.25% (95% CI: 0.18-0.36), respectively. The diagnostic value of urinalysis in prediction of blunt traumatic intra-abdominal injuries is low and it seems that it should be considered as an adjuvant diagnostic tool, in conjunction with other sources such as clinical findings and imaging.

  12. Validity of Predictive Equations for Resting Energy Expenditure Developed for Obese Patients: Impact of Body Composition Method

    Science.gov (United States)

    Achamrah, Najate; Jésus, Pierre; Grigioni, Sébastien; Rimbert, Agnès; Petit, André; Déchelotte, Pierre; Folope, Vanessa; Coëffier, Moïse

    2018-01-01

    Predictive equations have been specifically developed for obese patients to estimate resting energy expenditure (REE). Body composition (BC) assessment is needed for some of these equations. We assessed the impact of BC methods on the accuracy of specific predictive equations developed in obese patients. REE was measured (mREE) by indirect calorimetry and BC assessed by bioelectrical impedance analysis (BIA) and dual-energy X-ray absorptiometry (DXA). mREE, percentages of prediction accuracy (±10% of mREE) were compared. Predictive equations were studied in 2588 obese patients. Mean mREE was 1788 ± 6.3 kcal/24 h. Only the Müller (BIA) and Harris & Benedict (HB) equations provided REE with no difference from mREE. The Huang, Müller, Horie-Waitzberg, and HB formulas provided a higher accurate prediction (>60% of cases). The use of BIA provided better predictions of REE than DXA for the Huang and Müller equations. Inversely, the Horie-Waitzberg and Lazzer formulas provided a higher accuracy using DXA. Accuracy decreased when applied to patients with BMI ≥ 40, except for the Horie-Waitzberg and Lazzer (DXA) formulas. Müller equations based on BIA provided a marked improvement of REE prediction accuracy than equations not based on BC. The interest of BC to improve REE predictive equations accuracy in obese patients should be confirmed. PMID:29320432

  13. Influence of radiation on predictive accuracy in numerical simulations of the thermal environment in industrial buildings with buoyancy-driven natural ventilation

    International Nuclear Information System (INIS)

    Meng, Xiaojing; Wang, Yi; Liu, Tiening; Xing, Xiao; Cao, Yingxue; Zhao, Jiangping

    2016-01-01

    Highlights: • The effects of radiation on predictive accuracy in numerical simulations were studied. • A scaled experimental model with a high-temperature heat source was set up. • Simulation results were discussed considering with and without radiation model. • The buoyancy force and the ventilation rate were investigated. - Abstract: This paper investigates the effects of radiation on predictive accuracy in the numerical simulations of industrial buildings. A scaled experimental model with a high-temperature heat source is set up and the buoyancy-driven natural ventilation performance is presented. Besides predicting ventilation performance in an industrial building, the scaled model in this paper is also used to generate data to validate the numerical simulations. The simulation results show good agreement with the experiment data. The effects of radiation on predictive accuracy in the numerical simulations are studied for both pure convection model and combined convection and radiation model. Detailed results are discussed regarding the temperature and velocity distribution, the buoyancy force and the ventilation rate. The temperature and velocity distributions through the middle plane are presented for the pure convection model and the combined convection and radiation model. It is observed that the overall temperature and velocity magnitude predicted by the simulations for pure convection were significantly greater than those for the combined convection and radiation model. In addition, the Grashof number and the ventilation rate are investigated. The results show that the Grashof number and the ventilation rate are greater for the pure convection model than for the combined convection and radiation model.

  14. Accuracy of gastrocnemius muscles forces in walking and running goats predicted by one-element and two-element Hill-type models.

    Science.gov (United States)

    Lee, Sabrina S M; Arnold, Allison S; Miara, Maria de Boef; Biewener, Andrew A; Wakeling, James M

    2013-09-03

    Hill-type models are commonly used to estimate muscle forces during human and animal movement-yet the accuracy of the forces estimated during walking, running, and other tasks remains largely unknown. Further, most Hill-type models assume a single contractile element, despite evidence that faster and slower motor units, which have different activation-deactivation dynamics, may be independently or collectively excited. This study evaluated a novel, two-element Hill-type model with "differential" activation of fast and slow contractile elements. Model performance was assessed using a comprehensive data set (including measures of EMG intensity, fascicle length, and tendon force) collected from the gastrocnemius muscles of goats during locomotor experiments. Muscle forces predicted by the new two-element model were compared to the forces estimated using traditional one-element models and to the forces measured in vivo using tendon buckle transducers. Overall, the two-element model resulted in the best predictions of in vivo gastrocnemius force. The coefficient of determination, r(2), was up to 26.9% higher and the root mean square error, RMSE, was up to 37.4% lower for the two-element model than for the one-element models tested. All models captured salient features of the measured muscle force during walking, trotting, and galloping (r(2)=0.26-0.51), and all exhibited some errors (RMSE=9.63-32.2% of the maximum in vivo force). These comparisons provide important insight into the accuracy of Hill-type models. The results also show that incorporation of fast and slow contractile elements within muscle models can improve estimates of time-varying, whole muscle force during locomotor tasks. Copyright © 2013 Elsevier Ltd. All rights reserved.

  15. When high working memory capacity is and is not beneficial for predicting nonlinear processes.

    Science.gov (United States)

    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.

  16. Effect of length of measurement period on accuracy of predicted annual heating energy consumption of buildings

    International Nuclear Information System (INIS)

    Cho, Sung-Hwan; Kim, Won-Tae; Tae, Choon-Soeb; Zaheeruddin, M.

    2004-01-01

    This study examined the temperature dependent regression models of energy consumption as a function of the length of the measurement period. The methodology applied was to construct linear regression models of daily energy consumption from 1 day to 3 months data sets and compare the annual heating energy consumption predicted by these models with actual annual heating energy consumption. A commercial building in Daejon was selected, and the energy consumption was measured over a heating season. The results from the investigation show that the predicted energy consumption based on 1 day of measurements to build the regression model could lead to errors of 100% or more. The prediction error decreased to 30% when 1 week of data was used to build the regression model. Likewise, the regression model based on 3 months of measured data predicted the annual energy consumption within 6% of the measured energy consumption. These analyses show that the length of the measurement period has a significant impact on the accuracy of the predicted annual energy consumption of buildings

  17. The accuracy of body mass prediction for elderly specimens: Implications for paleoanthropology and legal medicine.

    Science.gov (United States)

    Chevalier, Tony; Lefèvre, Philippe; Clarys, Jan Pieter; Beauthier, Jean-Pol

    2016-10-01

    Different practices in paleoanthropology and legal medicine raise questions concerning the robustness of body mass (BM) prediction. Integrating personal identification from body mass estimation with skeleton is not a classic approach in legal medicine. The originality of our study is the use of an elderly sample in order to push prediction methods to their limits and to discuss about implications in paleoanthropology and legal medicine. The aim is to observe the accuracy of BM prediction in relation to the body mass index (BMI, index of classification) using five femoral head (FH) methods and one shaft (FSH) method. The sample is composed of 41 dry femurs obtained from dissection where age (c. 82 years) and gender are known, and weight (c. 59.5 kg) and height are measured upon admission to the body leg service. We show that the estimation of the mean BM of the elderly sample is not significantly different to the real mean BM when the appropriate formula is used for the femoral head diameter. In fact, the best prediction is obtained with the McHenry formula (1992), which was based on a sample with an equivalent average mass to that of our sample. In comparison, external shaft diameters, which are known to be more influenced by mechanical stimuli than femoral head diameters, yield less satisfactory results with the McHenry formula (1992) for shaft diameters. Based on all the methods used and the distinctive selected sample, overestimation (always observed with the different femoral head methods) can be restricted to 1.1%. The observed overestimation with the shaft method can be restricted to 7%. However, the estimation of individual BM is much less reliable. The BMI has a strong impact on the accuracy of individual BM prediction, and is unquestionably more reliable for individuals with normal BMI (9.6% vs 16.7% for the best prediction error). In this case, the FH method is also the better predictive method but not if we integrate the total sample (i.e., the FSH

  18. A function accounting for training set size and marker density to model the average accuracy of genomic prediction.

    Science.gov (United States)

    Erbe, Malena; Gredler, Birgit; Seefried, Franz Reinhold; Bapst, Beat; Simianer, Henner

    2013-01-01

    Prediction of genomic breeding values is of major practical relevance in dairy cattle breeding. Deterministic equations have been suggested to predict the accuracy of genomic breeding values in a given design which are based on training set size, reliability of phenotypes, and the number of independent chromosome segments ([Formula: see text]). The aim of our study was to find a general deterministic equation for the average accuracy of genomic breeding values that also accounts for marker density and can be fitted empirically. Two data sets of 5'698 Holstein Friesian bulls genotyped with 50 K SNPs and 1'332 Brown Swiss bulls genotyped with 50 K SNPs and imputed to ∼600 K SNPs were available. Different k-fold (k = 2-10, 15, 20) cross-validation scenarios (50 replicates, random assignment) were performed using a genomic BLUP approach. A maximum likelihood approach was used to estimate the parameters of different prediction equations. The highest likelihood was obtained when using a modified form of the deterministic equation of Daetwyler et al. (2010), augmented by a weighting factor (w) based on the assumption that the maximum achievable accuracy is [Formula: see text]. The proportion of genetic variance captured by the complete SNP sets ([Formula: see text]) was 0.76 to 0.82 for Holstein Friesian and 0.72 to 0.75 for Brown Swiss. When modifying the number of SNPs, w was found to be proportional to the log of the marker density up to a limit which is population and trait specific and was found to be reached with ∼20'000 SNPs in the Brown Swiss population studied.

  19. A function accounting for training set size and marker density to model the average accuracy of genomic prediction.

    Directory of Open Access Journals (Sweden)

    Malena Erbe

    Full Text Available Prediction of genomic breeding values is of major practical relevance in dairy cattle breeding. Deterministic equations have been suggested to predict the accuracy of genomic breeding values in a given design which are based on training set size, reliability of phenotypes, and the number of independent chromosome segments ([Formula: see text]. The aim of our study was to find a general deterministic equation for the average accuracy of genomic breeding values that also accounts for marker density and can be fitted empirically. Two data sets of 5'698 Holstein Friesian bulls genotyped with 50 K SNPs and 1'332 Brown Swiss bulls genotyped with 50 K SNPs and imputed to ∼600 K SNPs were available. Different k-fold (k = 2-10, 15, 20 cross-validation scenarios (50 replicates, random assignment were performed using a genomic BLUP approach. A maximum likelihood approach was used to estimate the parameters of different prediction equations. The highest likelihood was obtained when using a modified form of the deterministic equation of Daetwyler et al. (2010, augmented by a weighting factor (w based on the assumption that the maximum achievable accuracy is [Formula: see text]. The proportion of genetic variance captured by the complete SNP sets ([Formula: see text] was 0.76 to 0.82 for Holstein Friesian and 0.72 to 0.75 for Brown Swiss. When modifying the number of SNPs, w was found to be proportional to the log of the marker density up to a limit which is population and trait specific and was found to be reached with ∼20'000 SNPs in the Brown Swiss population studied.

  20. Dynamic Filtering Improves Attentional State Prediction with fNIRS

    Science.gov (United States)

    Harrivel, Angela R.; Weissman, Daniel H.; Noll, Douglas C.; Huppert, Theodore; Peltier, Scott J.

    2016-01-01

    Brain activity can predict a person's level of engagement in an attentional task. However, estimates of brain activity are often confounded by measurement artifacts and systemic physiological noise. The optimal method for filtering this noise - thereby increasing such state prediction accuracy - remains unclear. To investigate this, we asked study participants to perform an attentional task while we monitored their brain activity with functional near infrared spectroscopy (fNIRS). We observed higher state prediction accuracy when noise in the fNIRS hemoglobin [Hb] signals was filtered with a non-stationary (adaptive) model as compared to static regression (84% +/- 6% versus 72% +/- 15%).

  1. A Framework for the Objective Assessment of Registration Accuracy

    Directory of Open Access Journals (Sweden)

    Francesca Pizzorni Ferrarese

    2014-01-01

    Full Text Available Validation and accuracy assessment are the main bottlenecks preventing the adoption of image processing algorithms in the clinical practice. In the classical approach, a posteriori analysis is performed through objective metrics. In this work, a different approach based on Petri nets is proposed. The basic idea consists in predicting the accuracy of a given pipeline based on the identification and characterization of the sources of inaccuracy. The concept is demonstrated on a case study: intrasubject rigid and affine registration of magnetic resonance images. Both synthetic and real data are considered. While synthetic data allow the benchmarking of the performance with respect to the ground truth, real data enable to assess the robustness of the methodology in real contexts as well as to determine the suitability of the use of synthetic data in the training phase. Results revealed a higher correlation and a lower dispersion among the metrics for simulated data, while the opposite trend was observed for pathologic ones. Results show that the proposed model not only provides a good prediction performance but also leads to the optimization of the end-to-end chain in terms of accuracy and robustness, setting the ground for its generalization to different and more complex scenarios.

  2. The Accuracy of Urinalysis in Predicting Intra-Abdominal Injury Following Blunt Traumas

    Directory of Open Access Journals (Sweden)

    Anita Sabzghabaei

    2016-01-01

    Full Text Available Introduction: In cases of blunt abdominal traumas, predicting the possible intra-abdominal injuries is still a challenge for the physicians involved with these patients. Therefore, this study was designed, to evaluate the accuracy of urinalysis in predicting intra-abdominal injuries. Methods: Patients aged 15 to 65 years with blunt abdominal trauma who were admitted to emergency departments were enrolled. Abdominopelvic computed tomography (CT scan with intravenous contrast and urinalysis were requested for all the included patients. Demographic data, trauma mechanism, the results of urinalysis, and the results of abdominopelvic CT scan were gathered. Finally, the correlation between the results of abdominopelvic CT scan, and urinalysis was determined. Urinalysis was considered positive in case of at least one positive value in gross appearance, blood in dipstick, or red blood cell count. Results: 325 patients with blunt abdominal trauma were admitted to the emergency departments (83% male with the mean age of 32.63±17.48 years. Sensitivity, specificity, positive and negative predictive values, and positive and negative likelihood ratios of urinalysis, were 77.9% (95% CI: 69.6-84.4, 58.5% (95% CI: 51.2-65.5, 56% (95% CI: 48.5-63.3, 79.6% (95% CI: 71.8-85.7, 1.27% (95% CI: 1.30-1.57, and 0.25% (95% CI: 0.18-0.36, respectively. Conclusion: The diagnostic value of urinalysis in prediction of blunt traumatic intra-abdominal injuries is low and it seems that it should be considered as an adjuvant diagnostic tool, in conjunction with other sources such as clinical findings and imaging.

  3. Screening Characteristics of TIMI Score in Predicting Acute Coronary Syndrome Outcome; a Diagnostic Accuracy Study

    Directory of Open Access Journals (Sweden)

    Mostafa Alavi-Moghaddam

    2017-01-01

    Full Text Available Introduction: In cases with potential diagnosis of ischemic chest pain, screening high risk patients for adverse outcomes would be very helpful. The present study was designed aiming to determine the diagnostic accuracy of thrombolysis in myocardial infarction (TIMI score in Patients with potential diagnosis of ischemic chest pain.Method: This diagnostic accuracy study was designed to evaluate the screening performance characteristics of TIMI score in predicting 30-day outcomes of mortality, myocardial infarction (MI, and need for revascularization in patients presenting to ED with complaint of typical chest pain and diagnosis of unstable angina or Non-ST elevation MI.Results: 901 patients with the mean age of 58.17 ± 15.00 years (19-90 were studied (52.9% male. Mean TIMI score of the studied patients was 0.97 ± 0.93 (0-5 and the highest frequency of the score belonged to 0 to 2 with 37.2%, 35.3%, and 21.4%, respectively. In total, 170 (18.8% patients experienced the outcomes evaluated in this study. Total sensitivity, specificity, positive and negative predictive value, and positive and negative likelihood ratio of TIMI score were 20 (95% CI: 17 – 24, 99 (95% CI: 97 – 100, 98 (95% CI: 93 – 100, 42 (95% CI: 39 – 46, 58 (95% CI: 14 – 229, and 1.3 (95% CI: 1.2 – 1.4, respectively. Area under the ROC curve of this system for prediction of 30-day mortality, MI, and need for revascularization were 0.51 (95% CI: 0.47 – 0.55, 0.58 (95% CI: 0.54 – 0.62 and 0.56 (95% CI: 0.52 – 0.60, respectively.Conclusion: Based on the findings of the present study, it seems that TIMI score has a high specificity in predicting 30-day adverse outcomes of mortality, MI, and need for revascularization following acute coronary syndrome. However, since its sensitivity, negative predictive value, and negative likelihood ratio are low, it cannot be used as a proper screening tool for ruling out low risk patients in ED.

  4. Accuracy of formulas used to predict post-transfusion packed cell volume rise in anemic dogs.

    Science.gov (United States)

    Short, Jacqueline L; Diehl, Shenandoah; Seshadri, Ravi; Serrano, Sergi

    2012-08-01

    To assess the accuracy of published formulas used to guide packed red blood cell (pRBC) transfusions in anemic dogs and to compare the predicted rise in packed cell volume (PCV) to the actual post-transfusion rise in PCV. Prospective observational study from April 2009 through July 2009. A small animal emergency and specialty hospital. Thirty-one anemic client-owned dogs that received pRBC transfusions for treatment of anemia. None Four formulas were evaluated to determine their predictive ability with respect to rise in PCV following transfusion with pRBC. Post-transfusion rise in PCV were compared to calculated rise in PCV using 4 different formulas. Bias and limits of agreement were investigated using Bland-Altman analyses. Accuracy of existing formulas to predict rise in PCV following transfusion varied significantly. Formula 1 (volume to be transfused [VT] [mL] = 1 mL × % PCV rise × kg body weight [BW]) overestimated the expected rise in PCV (mean difference, 6.30), while formula 2 (VT [mL] = 2 mL ×% PCV rise × kg BW) underestimated the rise in PCV (mean difference, -3.01). Formula 3 (VT [mL] = 90 mL × kg BW × [(desired PCV - Patient PCV)/PCV of donor blood]) and formula 4 (VT [mL] = 1.5 mL ×% PCV rise × kg BW) performed well (mean difference 0.23 and 0.09, respectively) in predicting rise in PCV following pRBC transfusion. Agreement between 2 formulas, "VT (mL) = kg BW × blood volume (90 mL) × [(desired PCV - recipient PCV)/Donor PCV]" and "VT (mL) = 1.5 ×desired rise in PCV × kg BW," was found when they were compared to the actual rise in PCV following pRBC transfusion in anemic dogs. Further research is warranted to determine whether these formulas perform similarly well for other species. © Veterinary Emergency and Critical Care Society 2012.

  5. Improving protein fold recognition and structural class prediction accuracies using physicochemical properties of amino acids.

    Science.gov (United States)

    Raicar, Gaurav; Saini, Harsh; Dehzangi, Abdollah; Lal, Sunil; Sharma, Alok

    2016-08-07

    Predicting the three-dimensional (3-D) structure of a protein is an important task in the field of bioinformatics and biological sciences. However, directly predicting the 3-D structure from the primary structure is hard to achieve. Therefore, predicting the fold or structural class of a protein sequence is generally used as an intermediate step in determining the protein's 3-D structure. For protein fold recognition (PFR) and structural class prediction (SCP), two steps are required - feature extraction step and classification step. Feature extraction techniques generally utilize syntactical-based information, evolutionary-based information and physicochemical-based information to extract features. In this study, we explore the importance of utilizing the physicochemical properties of amino acids for improving PFR and SCP accuracies. For this, we propose a Forward Consecutive Search (FCS) scheme which aims to strategically select physicochemical attributes that will supplement the existing feature extraction techniques for PFR and SCP. An exhaustive search is conducted on all the existing 544 physicochemical attributes using the proposed FCS scheme and a subset of physicochemical attributes is identified. Features extracted from these selected attributes are then combined with existing syntactical-based and evolutionary-based features, to show an improvement in the recognition and prediction performance on benchmark datasets. Copyright © 2016 Elsevier Ltd. All rights reserved.

  6. Increased prediction accuracy in wheat breeding trials using a marker × environment interaction genomic selection model.

    Science.gov (United States)

    Lopez-Cruz, Marco; Crossa, Jose; Bonnett, David; Dreisigacker, Susanne; Poland, Jesse; Jannink, Jean-Luc; Singh, Ravi P; Autrique, Enrique; de los Campos, Gustavo

    2015-02-06

    Genomic selection (GS) models use genome-wide genetic information to predict genetic values of candidates of selection. Originally, these models were developed without considering genotype × environment interaction(G×E). Several authors have proposed extensions of the single-environment GS model that accommodate G×E using either covariance functions or environmental covariates. In this study, we model G×E using a marker × environment interaction (M×E) GS model; the approach is conceptually simple and can be implemented with existing GS software. We discuss how the model can be implemented by using an explicit regression of phenotypes on markers or using co-variance structures (a genomic best linear unbiased prediction-type model). We used the M×E model to analyze three CIMMYT wheat data sets (W1, W2, and W3), where more than 1000 lines were genotyped using genotyping-by-sequencing and evaluated at CIMMYT's research station in Ciudad Obregon, Mexico, under simulated environmental conditions that covered different irrigation levels, sowing dates and planting systems. We compared the M×E model with a stratified (i.e., within-environment) analysis and with a standard (across-environment) GS model that assumes that effects are constant across environments (i.e., ignoring G×E). The prediction accuracy of the M×E model was substantially greater of that of an across-environment analysis that ignores G×E. Depending on the prediction problem, the M×E model had either similar or greater levels of prediction accuracy than the stratified analyses. The M×E model decomposes marker effects and genomic values into components that are stable across environments (main effects) and others that are environment-specific (interactions). Therefore, in principle, the interaction model could shed light over which variants have effects that are stable across environments and which ones are responsible for G×E. The data set and the scripts required to reproduce the analysis are

  7. Higher Levels of Psychopathy Predict Poorer Motor Control: Implications for Understanding the Psychopathy Construct

    OpenAIRE

    Robinson, Michael D.; Bresin, Konrad

    2014-01-01

    A review of the literature suggests that higher levels of psychopathy may be linked to less effective behavioral control. However, several commentators have urged caution in making statements of this type in the absence of direct evidence. In two studies (total N = 142), moment-to-moment accuracy in a motor control task was examined as a function of dimensional variations in psychopathy in an undergraduate population. As hypothesized, motor control was distinctively worse at higher levels of ...

  8. Quantifying and comparing dynamic predictive accuracy of joint models for longitudinal marker and time-to-event in presence of censoring and competing risks

    DEFF Research Database (Denmark)

    Blanche, Paul; Proust-Lima, Cécile; Loubère, Lucie

    2015-01-01

    to quantify predictive accuracy. Nonparametric inverse probability of censoring weighting is used to estimate dynamic curves of AUC and BS as functions of the time at which predictions are made. Asymptotic results are established and both pointwise confidence intervals and simultaneous confidence bands...

  9. Predicting watershed post-fire sediment yield with the InVEST sediment retention model: Accuracy and uncertainties

    Science.gov (United States)

    Sankey, Joel B.; McVay, Jason C.; Kreitler, Jason R.; Hawbaker, Todd J.; Vaillant, Nicole; Lowe, Scott

    2015-01-01

    Increased sedimentation following wildland fire can negatively impact water supply and water quality. Understanding how changing fire frequency, extent, and location will affect watersheds and the ecosystem services they supply to communities is of great societal importance in the western USA and throughout the world. In this work we assess the utility of the InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) Sediment Retention Model to accurately characterize erosion and sedimentation of burned watersheds. InVEST was developed by the Natural Capital Project at Stanford University (Tallis et al., 2014) and is a suite of GIS-based implementations of common process models, engineered for high-end computing to allow the faster simulation of larger landscapes and incorporation into decision-making. The InVEST Sediment Retention Model is based on common soil erosion models (e.g., USLE – Universal Soil Loss Equation) and determines which areas of the landscape contribute the greatest sediment loads to a hydrological network and conversely evaluate the ecosystem service of sediment retention on a watershed basis. In this study, we evaluate the accuracy and uncertainties for InVEST predictions of increased sedimentation after fire, using measured postfire sediment yields available for many watersheds throughout the western USA from an existing, published large database. We show that the model can be parameterized in a relatively simple fashion to predict post-fire sediment yield with accuracy. Our ultimate goal is to use the model to accurately predict variability in post-fire sediment yield at a watershed scale as a function of future wildfire conditions.

  10. Genomic selection prediction accuracy in a perennial crop: case study of oil palm (Elaeis guineensis Jacq.).

    Science.gov (United States)

    Cros, David; Denis, Marie; Sánchez, Leopoldo; Cochard, Benoit; Flori, Albert; Durand-Gasselin, Tristan; Nouy, Bruno; Omoré, Alphonse; Pomiès, Virginie; Riou, Virginie; Suryana, Edyana; Bouvet, Jean-Marc

    2015-03-01

    Genomic selection empirically appeared valuable for reciprocal recurrent selection in oil palm as it could account for family effects and Mendelian sampling terms, despite small populations and low marker density. Genomic selection (GS) can increase the genetic gain in plants. In perennial crops, this is expected mainly through shortened breeding cycles and increased selection intensity, which requires sufficient GS accuracy in selection candidates, despite often small training populations. Our objective was to obtain the first empirical estimate of GS accuracy in oil palm (Elaeis guineensis), the major world oil crop. We used two parental populations involved in conventional reciprocal recurrent selection (Deli and Group B) with 131 individuals each, genotyped with 265 SSR. We estimated within-population GS accuracies when predicting breeding values of non-progeny-tested individuals for eight yield traits. We used three methods to sample training sets and five statistical methods to estimate genomic breeding values. The results showed that GS could account for family effects and Mendelian sampling terms in Group B but only for family effects in Deli. Presumably, this difference between populations originated from their contrasting breeding history. The GS accuracy ranged from -0.41 to 0.94 and was positively correlated with the relationship between training and test sets. Training sets optimized with the so-called CDmean criterion gave the highest accuracies, ranging from 0.49 (pulp to fruit ratio in Group B) to 0.94 (fruit weight in Group B). The statistical methods did not affect the accuracy. Finally, Group B could be preselected for progeny tests by applying GS to key yield traits, therefore increasing the selection intensity. Our results should be valuable for breeding programs with small populations, long breeding cycles, or reduced effective size.

  11. Predicting early academic achievement: The role of higher-versus lower-order personality traits

    Directory of Open Access Journals (Sweden)

    Zupančič Maja

    2011-01-01

    Full Text Available The study explored the role of children’s (N = 193 individual differences and parental characteristics at the beginning of the first year of schooling in predicting students’ attainment of academic standards at the end of the year. Special attention was paid to children’s personality as perceived by the teachers’ assistants. Along with parents’ education, parenting practices and first-graders’ cognitive ability, the incremental predictive power of children’s higher-order (robust personality traits was compared to the contribution of lower-order (specific traits in explaining academic achievement. The specific traits provided a somewhat more accurate prediction than the robust traits. Unique contributions of maternal authoritative parenting, children’s cognitive ability, and personality to academic achievement were established. The ratings of first-graders’ conscientiousness (a higher-order trait improved the prediction of academic achievement based on parenting and cognitive ability by 12%, whereas assistant teacher’s perceived children’s intelligence and low antagonism (lower-order traits improved the prediction by 17%.

  12. Accuracy of liver function tests for predicting adverse maternal and fetal outcomes in women with preeclampsia: a systematic review

    NARCIS (Netherlands)

    Thangaratinam, Shakila; Koopmans, Corine M.; Iyengar, Shalini; Zamora, Javier; Ismail, Khaled M. K.; Mol, Ben W. J.; Khan, Khalid S.

    2011-01-01

    Background. Liver function tests are routinely performed in women as part of a battery of investigations to assess severity at admission and later to guide appropriate management. Objective. To determine the accuracy with which liver function tests predict complications in women with preeclampsia by

  13. Accuracy of liver function tests for predicting adverse maternal and fetal outcomes in women with preeclampsia : a systematic review

    NARCIS (Netherlands)

    Thangaratinam, Shakila; Koopmans, Corine M.; Iyengar, Shalini; Zamora, Javier; Ismail, Khaled M. K.; Mol, Ben W. J.; Khan, Khalid S.

    Background. Liver function tests are routinely performed in women as part of a battery of investigations to assess severity at admission and later to guide appropriate management. Objective. To determine the accuracy with which liver function tests predict complications in women with preeclampsia by

  14. Higher-accuracy van der Waals density functional

    DEFF Research Database (Denmark)

    Lee, Kyuho; Murray, Éamonn D.; Kong, Lingzhu

    2010-01-01

    We propose a second version of the van der Waals density functional of Dion et al. [Phys. Rev. Lett. 92, 246401 (2004)], employing a more accurate semilocal exchange functional and the use of a large-N asymptote gradient correction in determining the vdW kernel. The predicted binding energy...

  15. Accuracy of Dolphin visual treatment objective (VTO prediction software on class III patients treated with maxillary advancement and mandibular setback

    Directory of Open Access Journals (Sweden)

    Robert J. Peterman

    2016-06-01

    Full Text Available Abstract Background Dolphin® visual treatment objective (VTO prediction software is routinely utilized by orthodontists during the treatment planning of orthognathic cases to help predict post-surgical soft tissue changes. Although surgical soft tissue prediction is considered to be a vital tool, its accuracy is not well understood in tow-jaw surgical procedures. The objective of this study was to quantify the accuracy of Dolphin Imaging’s VTO soft tissue prediction software on class III patients treated with maxillary advancement and mandibular setback and to validate the efficacy of the software in such complex cases. Methods This retrospective study analyzed the records of 14 patients treated with comprehensive orthodontics in conjunction with two-jaw orthognathic surgery. Pre- and post-treatment radiographs were traced and superimposed to determine the actual skeletal movements achieved in surgery. This information was then used to simulate surgery in the software and generate a final soft tissue patient profile prediction. Prediction images were then compared to the actual post-treatment profile photos to determine differences. Results Dolphin Imaging’s software was determined to be accurate within an error range of +/− 2 mm in the X-axis at most landmarks. The lower lip predictions were most inaccurate. Conclusions Clinically, the observed error suggests that the VTO may be used for demonstration and communication with a patient or consulting practitioner. However, Dolphin should not be useful for precise treatment planning of surgical movements. This program should be used with caution to prevent unrealistic patient expectations and dissatisfaction.

  16. Wake-Model Effects on Induced Drag Prediction of Staggered Boxwings

    Directory of Open Access Journals (Sweden)

    Julian Schirra

    2018-01-01

    Full Text Available For staggered boxwings the predictions of induced drag that rely on common potential-flow methods can be of limited accuracy. For example, linear, freestream-fixed wake models cannot resolve effects related to wake deflection and roll-up, which can have significant affects on the induced drag projection of these systems. The present work investigates the principle impact of wake modelling on the accuracy of induced drag prediction of boxwings with stagger. The study compares induced drag predictions of a higher-order potential-flow method that uses fixed and relaxed-wake models, and of an Euler-flow method. Positive-staggered systems at positive angles of attack are found to be particularly prone to higher-order wake effects due to vertical contraction of wakes trajectories, which results in smaller effective height-to-span ratios than compared with negative stagger and thus closer interactions between trailing wakes and lifting surfaces. Therefore, when trying to predict induced drag of positive staggered boxwings, only a potential-flow method with a fully relaxed-wake model will provide the high-degree of accuracy that rivals that of an Euler method while being computationally significantly more efficient.

  17. Assessing Predictive Properties of Genome-Wide Selection in Soybeans

    Directory of Open Access Journals (Sweden)

    Alencar Xavier

    2016-08-01

    Full Text Available Many economically important traits in plant breeding have low heritability or are difficult to measure. For these traits, genomic selection has attractive features and may boost genetic gains. Our goal was to evaluate alternative scenarios to implement genomic selection for yield components in soybean (Glycine max L. merr. We used a nested association panel with cross validation to evaluate the impacts of training population size, genotyping density, and prediction model on the accuracy of genomic prediction. Our results indicate that training population size was the factor most relevant to improvement in genome-wide prediction, with greatest improvement observed in training sets up to 2000 individuals. We discuss assumptions that influence the choice of the prediction model. Although alternative models had minor impacts on prediction accuracy, the most robust prediction model was the combination of reproducing kernel Hilbert space regression and BayesB. Higher genotyping density marginally improved accuracy. Our study finds that breeding programs seeking efficient genomic selection in soybeans would best allocate resources by investing in a representative training set.

  18. Assessing Predictive Properties of Genome-Wide Selection in Soybeans.

    Science.gov (United States)

    Xavier, Alencar; Muir, William M; Rainey, Katy Martin

    2016-08-09

    Many economically important traits in plant breeding have low heritability or are difficult to measure. For these traits, genomic selection has attractive features and may boost genetic gains. Our goal was to evaluate alternative scenarios to implement genomic selection for yield components in soybean (Glycine max L. merr). We used a nested association panel with cross validation to evaluate the impacts of training population size, genotyping density, and prediction model on the accuracy of genomic prediction. Our results indicate that training population size was the factor most relevant to improvement in genome-wide prediction, with greatest improvement observed in training sets up to 2000 individuals. We discuss assumptions that influence the choice of the prediction model. Although alternative models had minor impacts on prediction accuracy, the most robust prediction model was the combination of reproducing kernel Hilbert space regression and BayesB. Higher genotyping density marginally improved accuracy. Our study finds that breeding programs seeking efficient genomic selection in soybeans would best allocate resources by investing in a representative training set. Copyright © 2016 Xavie et al.

  19. Effectiveness of link prediction for face-to-face behavioral networks.

    Science.gov (United States)

    Tsugawa, Sho; Ohsaki, Hiroyuki

    2013-01-01

    Research on link prediction for social networks has been actively pursued. In link prediction for a given social network obtained from time-windowed observation, new link formation in the network is predicted from the topology of the obtained network. In contrast, recent advances in sensing technology have made it possible to obtain face-to-face behavioral networks, which are social networks representing face-to-face interactions among people. However, the effectiveness of link prediction techniques for face-to-face behavioral networks has not yet been explored in depth. To clarify this point, here we investigate the accuracy of conventional link prediction techniques for networks obtained from the history of face-to-face interactions among participants at an academic conference. Our findings were (1) that conventional link prediction techniques predict new link formation with a precision of 0.30-0.45 and a recall of 0.10-0.20, (2) that prolonged observation of social networks often degrades the prediction accuracy, (3) that the proposed decaying weight method leads to higher prediction accuracy than can be achieved by observing all records of communication and simply using them unmodified, and (4) that the prediction accuracy for face-to-face behavioral networks is relatively high compared to that for non-social networks, but not as high as for other types of social networks.

  20. Effect of Trait Heritability, Training Population Size and Marker Density on Genomic Prediction Accuracy Estimation in 22 bi-parental Tropical Maize Populations.

    Science.gov (United States)

    Zhang, Ao; Wang, Hongwu; Beyene, Yoseph; Semagn, Kassa; Liu, Yubo; Cao, Shiliang; Cui, Zhenhai; Ruan, Yanye; Burgueño, Juan; San Vicente, Felix; Olsen, Michael; Prasanna, Boddupalli M; Crossa, José; Yu, Haiqiu; Zhang, Xuecai

    2017-01-01

    Genomic selection is being used increasingly in plant breeding to accelerate genetic gain per unit time. One of the most important applications of genomic selection in maize breeding is to predict and select the best un-phenotyped lines in bi-parental populations based on genomic estimated breeding values. In the present study, 22 bi-parental tropical maize populations genotyped with low density SNPs were used to evaluate the genomic prediction accuracy ( r MG ) of the six trait-environment combinations under various levels of training population size (TPS) and marker density (MD), and assess the effect of trait heritability ( h 2 ), TPS and MD on r MG estimation. Our results showed that: (1) moderate r MG values were obtained for different trait-environment combinations, when 50% of the total genotypes was used as training population and ~200 SNPs were used for prediction; (2) r MG increased with an increase in h 2 , TPS and MD, both correlation and variance analyses showed that h 2 is the most important factor and MD is the least important factor on r MG estimation for most of the trait-environment combinations; (3) predictions between pairwise half-sib populations showed that the r MG values for all the six trait-environment combinations were centered around zero, 49% predictions had r MG values above zero; (4) the trend observed in r MG differed with the trend observed in r MG / h , and h is the square root of heritability of the predicted trait, it indicated that both r MG and r MG / h values should be presented in GS study to show the accuracy of genomic selection and the relative accuracy of genomic selection compared with phenotypic selection, respectively. This study provides useful information to maize breeders to design genomic selection workflow in their breeding programs.

  1. Effect of Trait Heritability, Training Population Size and Marker Density on Genomic Prediction Accuracy Estimation in 22 bi-parental Tropical Maize Populations

    Directory of Open Access Journals (Sweden)

    Ao Zhang

    2017-11-01

    Full Text Available Genomic selection is being used increasingly in plant breeding to accelerate genetic gain per unit time. One of the most important applications of genomic selection in maize breeding is to predict and select the best un-phenotyped lines in bi-parental populations based on genomic estimated breeding values. In the present study, 22 bi-parental tropical maize populations genotyped with low density SNPs were used to evaluate the genomic prediction accuracy (rMG of the six trait-environment combinations under various levels of training population size (TPS and marker density (MD, and assess the effect of trait heritability (h2, TPS and MD on rMG estimation. Our results showed that: (1 moderate rMG values were obtained for different trait-environment combinations, when 50% of the total genotypes was used as training population and ~200 SNPs were used for prediction; (2 rMG increased with an increase in h2, TPS and MD, both correlation and variance analyses showed that h2 is the most important factor and MD is the least important factor on rMG estimation for most of the trait-environment combinations; (3 predictions between pairwise half-sib populations showed that the rMG values for all the six trait-environment combinations were centered around zero, 49% predictions had rMG values above zero; (4 the trend observed in rMG differed with the trend observed in rMG/h, and h is the square root of heritability of the predicted trait, it indicated that both rMG and rMG/h values should be presented in GS study to show the accuracy of genomic selection and the relative accuracy of genomic selection compared with phenotypic selection, respectively. This study provides useful information to maize breeders to design genomic selection workflow in their breeding programs.

  2. Mortality Risk After Transcatheter Aortic Valve Implantation: Analysis of the Predictive Accuracy of the Transcatheter Valve Therapy Registry Risk Assessment Model.

    Science.gov (United States)

    Codner, Pablo; Malick, Waqas; Kouz, Remi; Patel, Amisha; Chen, Cheng-Han; Terre, Juan; Landes, Uri; Vahl, Torsten Peter; George, Isaac; Nazif, Tamim; Kirtane, Ajay J; Khalique, Omar K; Hahn, Rebecca T; Leon, Martin B; Kodali, Susheel

    2018-05-08

    Risk assessment tools currently used to predict mortality in transcatheter aortic valve implantation (TAVI) were designed for patients undergoing cardiac surgery. We aim to assess the accuracy of the TAVI dedicated American College of Cardiology / Transcatheter Valve Therapies (ACC/TVT) risk score in predicting mortality outcomes. Consecutive patients (n=1038) undergoing TAVI at a single institution from 2014 to 2016 were included. The ACC/TVT registry mortality risk score, the Society of Thoracic Surgeons - Patient Reported Outcomes (STS-PROM) score and the EuroSCORE II were calculated for all patients. In hospital and 30-day all-cause mortality rates were 1.3% and 2.9%, respectively. The ACC/TVT risk stratification tool scored higher for patients who died in-hospital than in those who survived the index hospitalization (6.4 ± 4.6 vs. 3.5 ± 1.6, p = 0.03; respectively). The ACC/TVT score showed a high level of discrimination, C-index for in-hospital mortality 0.74, 95% CI [0.59 - 0.88]. There were no significant differences between the performance of the ACC/TVT registry risk score, the EuroSCORE II and the STS-PROM for in hospital and 30-day mortality rates. The ACC/TVT registry risk model is a dedicated tool to aid in the prediction of in-hospital mortality risk after TAVI.

  3. Model Prediction Control For Water Management Using Adaptive Prediction Accuracy

    NARCIS (Netherlands)

    Tian, X.; Negenborn, R.R.; Van Overloop, P.J.A.T.M.; Mostert, E.

    2014-01-01

    In the field of operational water management, Model Predictive Control (MPC) has gained popularity owing to its versatility and flexibility. The MPC controller, which takes predictions, time delay and uncertainties into account, can be designed for multi-objective management problems and for

  4. Comparison of accuracy in predicting emotional instability from MMPI data: fisherian versus contingent probability statistics

    Energy Technology Data Exchange (ETDEWEB)

    Berghausen, P.E. Jr.; Mathews, T.W.

    1987-01-01

    The security plans of nuclear power plants generally require that all personnel who are to have access to protected areas or vital islands be screened for emotional stability. In virtually all instances, the screening involves the administration of one or more psychological tests, usually including the Minnesota Multiphasic Personality Inventory (MMPI). At some plants, all employees receive a structured clinical interview after they have taken the MMPI and results have been obtained. At other plants, only those employees with dirty MMPI are interviewed. This latter protocol is referred to as interviews by exception. Behaviordyne Psychological Corp. has succeeded in removing some of the uncertainty associated with interview-by-exception protocols by developing an empirically based, predictive equation. This equation permits utility companies to make informed choices regarding the risks they are assuming. A conceptual problem exists with the predictive equation, however. Like most predictive equations currently in use, it is based on Fisherian statistics, involving least-squares analyses. Consequently, Behaviordyne Psychological Corp., in conjunction with T.W. Mathews and Associates, has just developed a second predictive equation, one based on contingent probability statistics. The particular technique used in the multi-contingent analysis of probability systems (MAPS) approach. The present paper presents a comparison of predictive accuracy of the two equations: the one derived using Fisherian techniques versus the one thing contingent probability techniques.

  5. Comparison of accuracy in predicting emotional instability from MMPI data: fisherian versus contingent probability statistics

    International Nuclear Information System (INIS)

    Berghausen, P.E. Jr.; Mathews, T.W.

    1987-01-01

    The security plans of nuclear power plants generally require that all personnel who are to have access to protected areas or vital islands be screened for emotional stability. In virtually all instances, the screening involves the administration of one or more psychological tests, usually including the Minnesota Multiphasic Personality Inventory (MMPI). At some plants, all employees receive a structured clinical interview after they have taken the MMPI and results have been obtained. At other plants, only those employees with dirty MMPI are interviewed. This latter protocol is referred to as interviews by exception. Behaviordyne Psychological Corp. has succeeded in removing some of the uncertainty associated with interview-by-exception protocols by developing an empirically based, predictive equation. This equation permits utility companies to make informed choices regarding the risks they are assuming. A conceptual problem exists with the predictive equation, however. Like most predictive equations currently in use, it is based on Fisherian statistics, involving least-squares analyses. Consequently, Behaviordyne Psychological Corp., in conjunction with T.W. Mathews and Associates, has just developed a second predictive equation, one based on contingent probability statistics. The particular technique used in the multi-contingent analysis of probability systems (MAPS) approach. The present paper presents a comparison of predictive accuracy of the two equations: the one derived using Fisherian techniques versus the one thing contingent probability techniques

  6. Medium- and Long-term Prediction of LOD Change by the Leap-step Autoregressive Model

    Science.gov (United States)

    Wang, Qijie

    2015-08-01

    The accuracy of medium- and long-term prediction of length of day (LOD) change base on combined least-square and autoregressive (LS+AR) deteriorates gradually. Leap-step autoregressive (LSAR) model can significantly reduce the edge effect of the observation sequence. Especially, LSAR model greatly improves the resolution of signals’ low-frequency components. Therefore, it can improve the efficiency of prediction. In this work, LSAR is used to forecast the LOD change. The LOD series from EOP 08 C04 provided by IERS is modeled by both the LSAR and AR models. The results of the two models are analyzed and compared. When the prediction length is between 10-30 days, the accuracy improvement is less than 10%. When the prediction length amounts to above 30 day, the accuracy improved obviously, with the maximum being around 19%. The results show that the LSAR model has higher prediction accuracy and stability in medium- and long-term prediction.

  7. Genomic Prediction Within and Across Biparental Families: Means and Variances of Prediction Accuracy and Usefulness of Deterministic Equations

    Directory of Open Access Journals (Sweden)

    Pascal Schopp

    2017-11-01

    Full Text Available A major application of genomic prediction (GP in plant breeding is the identification of superior inbred lines within families derived from biparental crosses. When models for various traits were trained within related or unrelated biparental families (BPFs, experimental studies found substantial variation in prediction accuracy (PA, but little is known about the underlying factors. We used SNP marker genotypes of inbred lines from either elite germplasm or landraces of maize (Zea mays L. as parents to generate in silico 300 BPFs of doubled-haploid lines. We analyzed PA within each BPF for 50 simulated polygenic traits, using genomic best linear unbiased prediction (GBLUP models trained with individuals from either full-sib (FSF, half-sib (HSF, or unrelated families (URF for various sizes (Ntrain of the training set and different heritabilities (h2 . In addition, we modified two deterministic equations for forecasting PA to account for inbreeding and genetic variance unexplained by the training set. Averaged across traits, PA was high within FSF (0.41–0.97 with large variation only for Ntrain < 50 and h2 < 0.6. For HSF and URF, PA was on average ∼40–60% lower and varied substantially among different combinations of BPFs used for model training and prediction as well as different traits. As exemplified by HSF results, PA of across-family GP can be very low if causal variants not segregating in the training set account for a sizeable proportion of the genetic variance among predicted individuals. Deterministic equations accurately forecast the PA expected over many traits, yet cannot capture trait-specific deviations. We conclude that model training within BPFs generally yields stable PA, whereas a high level of uncertainty is encountered in across-family GP. Our study shows the extent of variation in PA that must be at least reckoned with in practice and offers a starting point for the design of training sets composed of multiple BPFs.

  8. The importance of the accuracy of the experimental data for the prediction of solubility

    Directory of Open Access Journals (Sweden)

    SLAVICA ERIĆ

    2010-04-01

    Full Text Available Aqueous solubility is an important factor influencing several aspects of the pharmacokinetic profile of a drug. Numerous publications present different methodologies for the development of reliable computational models for the prediction of solubility from structure. The quality of such models can be significantly affected by the accuracy of the employed experimental solubility data. In this work, the importance of the accuracy of the experimental solubility data used for model training was investigated. Three data sets were used as training sets – data set 1, containing solubility data collected from various literature sources using a few criteria (n = 319, data set 2, created by substituting 28 values from data set 1 with uniformly determined experimental data from one laboratory (n = 319, and data set 3, created by including 56 additional components, for which the solubility was also determined under uniform conditions in the same laboratory, in the data set 2 (n = 375. The selection of the most significant descriptors was performed by the heuristic method, using one-parameter and multi-parameter analysis. The correlations between the most significant descriptors and solubility were established using multi-linear regression analysis (MLR for all three investigated data sets. Notable differences were observed between the equations corresponding to different data sets, suggesting that models updated with new experimental data need to be additionally optimized. It was successfully shown that the inclusion of uniform experimental data consistently leads to an improvement in the correlation coefficients. These findings contribute to an emerging consensus that improving the reliability of solubility prediction requires the inclusion of many diverse compounds for which solubility was measured under standardized conditions in the data set.

  9. Prediction of Tubal Ectopic Pregnancy Using Offline Analysis of 3-Dimensional Transvaginal Ultrasonographic Data Sets: An Interobserver and Diagnostic Accuracy Study.

    Science.gov (United States)

    Infante, Fernando; Espada Vaquero, Mercedes; Bignardi, Tommaso; Lu, Chuan; Testa, Antonia C; Fauchon, David; Epstein, Elisabeth; Leone, Francesco P G; Van den Bosch, Thierry; Martins, Wellington P; Condous, George

    2017-12-08

    To assess interobserver reproducibility in detecting tubal ectopic pregnancies by reading data sets from 3-dimensional (3D) transvaginal ultrasonography (TVUS) and comparing it with real-time 2-dimensional (2D) TVUS. Images were initially classified as showing pregnancies of unknown location or tubal ectopic pregnancies on real time 2D TVUS by an experienced sonologist, who acquired 5 3D volumes. Data sets were analyzed offline by 5 observers who had to classify each case as ectopic pregnancy or pregnancy of unknown location. The interobserver reproducibility was evaluated by the Fleiss κ statistic. The performance of each observer in predicting ectopic pregnancies was compared to that of the experienced sonologist. Women were followed until they were reclassified as follows: (1) failed pregnancy of unknown location; (2) intrauterine pregnancy; (3) ectopic pregnancy; or (4) persistent pregnancy of unknown location. Sixty-one women were included. The agreement between reading offline 3D data sets and the first real-time 2D TVUS was very good (80%-82%; κ = 0.89). The overall interobserver agreement among observers reading offline 3D data sets was moderate (κ = 0.52). The diagnostic performance of experienced observers reading offline 3D data sets had accuracy of 78.3% to 85.0%, sensitivity of 66.7% to 81.3%, specificity of 79.5% to 88.4%, positive predictive value of 57.1% to 72.2%, and negative predictive value of 87.5% to 91.3%, compared to the experienced sonologist's real-time 2D TVUS: accuracy of 94.5%, sensitivity of 94.4%, specificity of 94.5%, positive predictive value of 85.0%, and negative predictive value of 98.1%. The diagnostic accuracy of 3D TVUS by reading offline data sets for predicting ectopic pregnancies is dependent on experience. Reading only static 3D data sets without clinical information does not match the diagnostic performance of real time 2D TVUS combined with clinical information obtained during the scan. © 2017 by the American

  10. Genomic predictions across Nordic Holstein and Nordic Red using the genomic best linear unbiased prediction model with different genomic relationship matrices.

    Science.gov (United States)

    Zhou, L; Lund, M S; Wang, Y; Su, G

    2014-08-01

    This study investigated genomic predictions across Nordic Holstein and Nordic Red using various genomic relationship matrices. Different sources of information, such as consistencies of linkage disequilibrium (LD) phase and marker effects, were used to construct the genomic relationship matrices (G-matrices) across these two breeds. Single-trait genomic best linear unbiased prediction (GBLUP) model and two-trait GBLUP model were used for single-breed and two-breed genomic predictions. The data included 5215 Nordic Holstein bulls and 4361 Nordic Red bulls, which was composed of three populations: Danish Red, Swedish Red and Finnish Ayrshire. The bulls were genotyped with 50 000 SNP chip. Using the two-breed predictions with a joint Nordic Holstein and Nordic Red reference population, accuracies increased slightly for all traits in Nordic Red, but only for some traits in Nordic Holstein. Among the three subpopulations of Nordic Red, accuracies increased more for Danish Red than for Swedish Red and Finnish Ayrshire. This is because closer genetic relationships exist between Danish Red and Nordic Holstein. Among Danish Red, individuals with higher genomic relationship coefficients with Nordic Holstein showed more increased accuracies in the two-breed predictions. Weighting the two-breed G-matrices by LD phase consistencies, marker effects or both did not further improve accuracies of the two-breed predictions. © 2014 Blackwell Verlag GmbH.

  11. Video image analysis in the Australian meat industry - precision and accuracy of predicting lean meat yield in lamb carcasses.

    Science.gov (United States)

    Hopkins, D L; Safari, E; Thompson, J M; Smith, C R

    2004-06-01

    A wide selection of lamb types of mixed sex (ewes and wethers) were slaughtered at a commercial abattoir and during this process images of 360 carcasses were obtained online using the VIAScan® system developed by Meat and Livestock Australia. Soft tissue depth at the GR site (thickness of tissue over the 12th rib 110 mm from the midline) was measured by an abattoir employee using the AUS-MEAT sheep probe (PGR). Another measure of this thickness was taken in the chiller using a GR knife (NGR). Each carcass was subsequently broken down to a range of trimmed boneless retail cuts and the lean meat yield determined. The current industry model for predicting meat yield uses hot carcass weight (HCW) and tissue depth at the GR site. A low level of accuracy and precision was found when HCW and PGR were used to predict lean meat yield (R(2)=0.19, r.s.d.=2.80%), which could be improved markedly when PGR was replaced by NGR (R(2)=0.41, r.s.d.=2.39%). If the GR measures were replaced by 8 VIAScan® measures then greater prediction accuracy could be achieved (R(2)=0.52, r.s.d.=2.17%). A similar result was achieved when the model was based on principal components (PCs) computed from the 8 VIAScan® measures (R(2)=0.52, r.s.d.=2.17%). The use of PCs also improved the stability of the model compared to a regression model based on HCW and NGR. The transportability of the models was tested by randomly dividing the data set and comparing coefficients and the level of accuracy and precision. Those models based on PCs were superior to those based on regression. It is demonstrated that with the appropriate modeling the VIAScan® system offers a workable method for predicting lean meat yield automatically.

  12. Accuracy evaluation of Fourier series analysis and singular spectrum analysis for predicting the volume of motorcycle sales in Indonesia

    Science.gov (United States)

    Sasmita, Yoga; Darmawan, Gumgum

    2017-08-01

    This research aims to evaluate the performance of forecasting by Fourier Series Analysis (FSA) and Singular Spectrum Analysis (SSA) which are more explorative and not requiring parametric assumption. Those methods are applied to predicting the volume of motorcycle sales in Indonesia from January 2005 to December 2016 (monthly). Both models are suitable for seasonal and trend component data. Technically, FSA defines time domain as the result of trend and seasonal component in different frequencies which is difficult to identify in the time domain analysis. With the hidden period is 2,918 ≈ 3 and significant model order is 3, FSA model is used to predict testing data. Meanwhile, SSA has two main processes, decomposition and reconstruction. SSA decomposes the time series data into different components. The reconstruction process starts with grouping the decomposition result based on similarity period of each component in trajectory matrix. With the optimum of window length (L = 53) and grouping effect (r = 4), SSA predicting testing data. Forecasting accuracy evaluation is done based on Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The result shows that in the next 12 month, SSA has MAPE = 13.54 percent, MAE = 61,168.43 and RMSE = 75,244.92 and FSA has MAPE = 28.19 percent, MAE = 119,718.43 and RMSE = 142,511.17. Therefore, to predict volume of motorcycle sales in the next period should use SSA method which has better performance based on its accuracy.

  13. Diagnostic accuracy of surgeons and trainees in assessment of patients with acute abdominal pain.

    Science.gov (United States)

    2016-09-01

    Diagnostic accuracy in the assessment of patients with acute abdominal pain in the emergency ward is not adequate. It has been argued that this is because the investigations are carried out predominantly by a trainee. Resource utilization could be lowered if surgeons had a higher initial diagnostic accuracy. Patients with acute abdominal pain were included in a prospective cohort study. A surgical trainee and a surgeon made independent assessments in the emergency department, recording the clinical diagnosis and proposed diagnostic investigations. A reference standard diagnosis was established by an expert panel, and the proportion of correct diagnoses was calculated. Diagnostic accuracy was expressed in terms of sensitivity, specificity, positive predictive value and negative predictive value. Interobserver agreement for the diagnosis and elements of history-taking and physical examination were expressed by means of Cohen's κ. Certainty of diagnosis was recorded using a visual analogue scale. A trainee and a surgeon independently assessed 126 patients. Trainees made a correct diagnosis in 44·4 per cent of patients and surgeons in 42·9 per cent (P = 0·839). Surgeons, however, recorded a higher level of diagnostic certainty. Diagnostic accuracy was comparable in distinguishing urgent from non-urgent diagnoses, and for the most common diseases. Interobserver agreement for the clinical diagnosis varied from fair to moderate (κ = 0·28-0·57). The diagnostic accuracy of the initial clinical assessment is not improved when a surgeon rather than a surgical trainee assesses a patient with abdominal pain in the emergency department. © 2016 BJS Society Ltd Published by John Wiley & Sons Ltd.

  14. Sparse Density, Leaf-Off Airborne Laser Scanning Data in Aboveground Biomass Component Prediction

    Directory of Open Access Journals (Sweden)

    Ville Kankare

    2015-05-01

    Full Text Available The demand for cost-efficient forest aboveground biomass (AGB prediction methods is growing worldwide. The National Land Survey of Finland (NLS began collecting airborne laser scanning (ALS data throughout Finland in 2008 to provide a new high-detailed terrain elevation model. Similar data sets are being collected in an increasing number of countries worldwide. These data sets offer great potential in forest mapping related applications. The objectives of our study were (i to evaluate the AGB component prediction accuracy at a resolution of 300 m2 using sparse density, leaf-off ALS data (collected by NLS derived metrics as predictor variables; (ii to compare prediction accuracies with existing large-scale forest mapping techniques (Multi-source National Forest Inventory, MS-NFI based on Landsat TM satellite imagery; and (iii to evaluate the accuracy and effect of canopy height model (CHM derived metrics on AGB component prediction when ALS data were acquired with multiple sensors and varying scanning parameters. Results showed that ALS point metrics can be used to predict component AGBs with an accuracy of 29.7%–48.3%. AGB prediction accuracy was slightly improved using CHM-derived metrics but CHM metrics had a more clear effect on the estimated bias. Compared to the MS-NFI, the prediction accuracy was considerably higher, which was caused by differences in the remote sensing data utilized.

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

    Science.gov (United States)

    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.

  16. Accuracy of magnetic resonance in identifying traumatic intraarticular knee lesions

    International Nuclear Information System (INIS)

    Vaz, Carlos Eduardo Sanches; Camargo, Olavo Pires de; Santana, Paulo Jose de; Valezi, Antonio Carlos

    2005-01-01

    Purpose: To evaluate the diagnostic accuracy of magnetic resonance imaging of the knee in identifying traumatic intraarticular knee lesions. Method: 300 patients with a clinical diagnosis of traumatic intraarticular knee lesions underwent prearthoscopic magnetic resonance imaging. The sensitivity, specificity, positive predictive value, negative predictive value, likelihood ratio for a positive test, likelihood ratio for a negative test, and accuracy of magnetic resonance imaging were calculated relative to the findings during arthroscopy in the studied structures of the knee (medial meniscus, lateral meniscus, anterior cruciate ligament, posterior cruciate ligament, and articular cartilage). Results: Magnetic resonance imaging produced the following results regarding detection of lesions: medial meniscus: sensitivity 97.5%, specificity 92.9%, positive predictive value 93.9%, positive negative value 97%, likelihood positive ratio 13.7, likelihood negative ratio 0.02, and accuracy 95.3%; lateral meniscus: sensitivity 91.9%, specificity 93.6%, positive predictive value 92.7%, positive negative value 92.9%, likelihood positive ratio 14.3, likelihood negative ratio 0.08, and accuracy 93.6%; anterior cruciate ligament: sensitivity 99.0%, specificity 95.9%, positive predictive value 91.9%, positive negative value 99.5%, likelihood positive ratio 21.5, likelihood negative ratio 0.01, and accuracy 96.6%; posterior cruciate ligament: sensitivity 100%, specificity 99%, positive predictive value 80.0%, positive negative value 100%, likelihood positive ratio 100, likelihood negative ratio 0.01, and accuracy 99.6%; articular cartilage: sensitivity 76.1%, specificity 94.9%, positive predictive value 94.7%, positive negative value 76.9%, likelihood positive ratio 14.9, likelihood negative ratio 0.25, and accuracy 84.6%. Conclusion: Magnetic resonance imaging is a satisfactory diagnostic tool for evaluating meniscal and ligamentous lesions of the knee, but it is unable to clearly

  17. Accuracy of magnetic resonance in identifying traumatic intraarticular knee lesions

    Directory of Open Access Journals (Sweden)

    Vaz Carlos Eduardo Sanches

    2005-01-01

    Full Text Available PURPOSE: To evaluate the diagnostic accuracy of magnetic resonance imaging of the knee in identifying traumatic intraarticular knee lesions. METHOD: 300 patients with a clinical diagnosis of traumatic intraarticular knee lesions underwent prearthoscopic magnetic resonance imaging. The sensitivity, specificity, positive predictive value, negative predictive value, likelihood ratio for a positive test, likelihood ratio for a negative test, and accuracy of magnetic resonance imaging were calculated relative to the findings during arthroscopy in the studied structures of the knee (medial meniscus, lateral meniscus, anterior cruciate ligament, posterior cruciate ligament, and articular cartilage. RESULTS: Magnetic resonance imaging produced the following results regarding detection of lesions: medial meniscus: sensitivity 97.5%, specificity 92.9%, positive predictive value 93.9%, positive negative value 97%, likelihood positive ratio 13.7, likelihood negative ratio 0.02, and accuracy 95.3%; lateral meniscus: sensitivity 91.9%, specificity 93.6%, positive predictive value 92.7%, positive negative value 92.9%, likelihood positive ratio 14.3, likelihood negative ratio 0.08, and accuracy 93.6%; anterior cruciate ligament: sensitivity 99.0%, specificity 95.9%, positive predictive value 91.9%, positive negative value 99.5%, likelihood positive ratio 21.5, likelihood negative ratio 0.01, and accuracy 96.6%; posterior cruciate ligament: sensitivity 100%, specificity 99%, positive predictive value 80.0%, positive negative value 100%, likelihood positive ratio 100, likelihood negative ratio 0.01, and accuracy 99.6%; articular cartilage: sensitivity 76.1%, specificity 94.9%, positive predictive value 94.7%, positive negative value 76.9%, likelihood positive ratio 14.9, likelihood negative ratio 0.25, and accuracy 84.6%. CONCLUSION: Magnetic resonance imaging is a satisfactory diagnostic tool for evaluating meniscal and ligamentous lesions of the knee, but it is

  18. Accuracy of cone-beam computed tomography in predicting the diameter of unerupted teeth.

    Science.gov (United States)

    Nguyen, Emerald; Boychuk, Darrell; Orellana, Maria

    2011-08-01

    An accurate prediction of the mesiodistal diameter (MDD) of the erupting permanent teeth is essential in orthodontic diagnosis and treatment planning during the mixed dentition period. Our objective was to test the accuracy and reproducibility of cone-beam computed tomography (CBCT) in predicting the MDD of unerupted teeth. Our secondary objective was to determine the accuracy and reproducibility of 3 viewing methods by using 2 CBCT software programs, InVivoDental (version 4.0; Anatomage, San Jose, Calif) and CBWorks (version 3.0, CyberMed, Seoul, Korea) in measuring the MDD of teeth in models simulating unerupted teeth. CBCT data were collected on the CB MercuRay (Hitachi Medical Corporation, Tokyo, Japan). Models of unerupted teeth (n = 25), created by embedding 25 tooth samples into a polydimethylsiloxane polymer with a similar density to tissues surrounding teeth, were scanned and measured by 2 investigators. Repeated MDD measurements of each sample were made by using 3 CBCT viewing methods: InVivo Section, InVivo Volume Render (both Anatomage), and CBWorks Volume Render (version 3.0, CyberMed). These measurements were then compared with the MDD physically measured by digital calipers before the teeth were embedded and scanned. All 3 of the new methods had mean measurements that were statistically significantly less (P <0.0001) than the physical method, adjusting for investigator and tooth effects. Specifically, InVivo Section measurements were 0.3 mm (95% CI, -0.4 to -0.2) less than the measurements with calipers, InVivo Volume Render measurements were 0.5 mm less (95% CI, -0.6 to -0.4) than those with calipers, and CBWorks Volume Render measurements were 0.4 mm less (95% CI, -0.4 to -0.3) than those with calipers. Overall, there were high correlation values among the 3 viewing methods, indicating that CBCT can be used to measure the MDD of unerupted teeth. The InVivo Section method had the greatest correlation with the calipers. Copyright © 2011 American

  19. Higher-order predictions for splitting functions and coefficient functions from physical evolution kernels

    International Nuclear Information System (INIS)

    Vogt, A; Soar, G.; Vermaseren, J.A.M.

    2010-01-01

    We have studied the physical evolution kernels for nine non-singlet observables in deep-inelastic scattering (DIS), semi-inclusive e + e - annihilation and the Drell-Yan (DY) process, and for the flavour-singlet case of the photon- and heavy-top Higgs-exchange structure functions (F 2 , F φ ) in DIS. All known contributions to these kernels show an only single-logarithmic large-x enhancement at all powers of (1-x). Conjecturing that this behaviour persists to (all) higher orders, we have predicted the highest three (DY: two) double logarithms of the higher-order non-singlet coefficient functions and of the four-loop singlet splitting functions. The coefficient-function predictions can be written as exponentiations of 1/N-suppressed contributions in Mellin-N space which, however, are less predictive than the well-known exponentiation of the ln k N terms. (orig.)

  20. Accuracy of clinical signs, SEP, and EEG in predicting outcome of hypoxic coma: a meta-analysis.

    Science.gov (United States)

    Lee, Y C; Phan, T G; Jolley, D J; Castley, H C; Ingram, D A; Reutens, D C

    2010-02-16

    Accurate prediction of neurologic outcome after hypoxic coma is important. Previous systematic reviews have not used summary statistics to summarize and formally compare the accuracy of different prognostic tests. We therefore used summary receiver operating characteristic curve (SROC) and cluster regression methods to compare motor and pupillary responses with sensory evoked potential (SEP) and EEG in predicting outcome after hypoxic coma. We searched PubMed, MEDLINE, and Embase (1966-2007) for reports in English, German, and French and identified 25 suitable studies. An SROC was constructed for each marker (SEP, EEG, M1 and M SEP was larger than those for M1, M SEP (AUC 0.891) and that for M1 (AUC 0.786) was small (0.105, 95% confidence interval 0.023-0.187), only reaching significance on day 1 after coma onset. The use of M SEP) is marginally better than M1 at predicting outcome after hypoxic coma. However, the superiority of SEP diminishes after day 1 and when M SEP is a better marker than clinical signs.

  1. EEG Beta Oscillations in the Temporoparietal Area Related to the Accuracy in Estimating Others' Preference

    Directory of Open Access Journals (Sweden)

    Jonghyeok Park

    2018-02-01

    Full Text Available Humans often attempt to predict what others prefer based on a narrow slice of experience, called thin-slicing. According to the theoretical bases for how humans can predict the preference of others, one tends to estimate the other's preference using a perceived difference between the other and self. Previous neuroimaging studies have revealed that the network of dorsal medial prefrontal cortex (dmPFC and right temporoparietal junction (rTPJ is related to the ability of predicting others' preference. However, it still remains unknown about the temporal patterns of neural activities for others' preference prediction through thin-slicing. To investigate such temporal aspects of neural activities, we investigated human electroencephalography (EEG recorded during the task of predicting the preference of others while only a facial picture of others was provided. Twenty participants (all female, average age: 21.86 participated in the study. In each trial of the task, participants were shown a picture of either a target person or self for 3 s, followed by the presentation of a movie poster over which participants predicted the target person's preference as liking or disliking. The time-frequency EEG analysis was employed to analyze temporal changes in the amplitudes of brain oscillations. Participants could predict others' preference for movies with accuracy of 56.89 ± 3.16% and 10 out of 20 participants exhibited prediction accuracy higher than a chance level (95% interval. There was a significant difference in the power of the parietal alpha (10~13 Hz oscillation 0.6~0.8 s after the onset of poster presentation between the cases when participants predicted others' preference and when they reported self-preference (p < 0.05. The power of brain oscillations at any frequency band and time period during the trial did not show a significant correlation with individual prediction accuracy. However, when we measured differences of the power between the

  2. Prediction of welding shrinkage deformation of bridge steel box girder based on wavelet neural network

    Science.gov (United States)

    Tao, Yulong; Miao, Yunshui; Han, Jiaqi; Yan, Feiyun

    2018-05-01

    Aiming at the low accuracy of traditional forecasting methods such as linear regression method, this paper presents a prediction method for predicting the relationship between bridge steel box girder and its displacement with wavelet neural network. Compared with traditional forecasting methods, this scheme has better local characteristics and learning ability, which greatly improves the prediction ability of deformation. Through analysis of the instance and found that after compared with the traditional prediction method based on wavelet neural network, the rigid beam deformation prediction accuracy is higher, and is superior to the BP neural network prediction results, conform to the actual demand of engineering design.

  3. High accuracy satellite drag model (HASDM)

    Science.gov (United States)

    Storz, Mark F.; Bowman, Bruce R.; Branson, Major James I.; Casali, Stephen J.; Tobiska, W. Kent

    The dominant error source in force models used to predict low-perigee satellite trajectories is atmospheric drag. Errors in operational thermospheric density models cause significant errors in predicted satellite positions, since these models do not account for dynamic changes in atmospheric drag for orbit predictions. The Air Force Space Battlelab's High Accuracy Satellite Drag Model (HASDM) estimates and predicts (out three days) a dynamically varying global density field. HASDM includes the Dynamic Calibration Atmosphere (DCA) algorithm that solves for the phases and amplitudes of the diurnal and semidiurnal variations of thermospheric density near real-time from the observed drag effects on a set of Low Earth Orbit (LEO) calibration satellites. The density correction is expressed as a function of latitude, local solar time and altitude. In HASDM, a time series prediction filter relates the extreme ultraviolet (EUV) energy index E10.7 and the geomagnetic storm index ap, to the DCA density correction parameters. The E10.7 index is generated by the SOLAR2000 model, the first full spectrum model of solar irradiance. The estimated and predicted density fields will be used operationally to significantly improve the accuracy of predicted trajectories for all low-perigee satellites.

  4. A Real-time Breakdown Prediction Method for Urban Expressway On-ramp Bottlenecks

    Science.gov (United States)

    Ye, Yingjun; Qin, Guoyang; Sun, Jian; Liu, Qiyuan

    2018-01-01

    Breakdown occurrence on expressway is considered to relate with various factors. Therefore, to investigate the association between breakdowns and these factors, a Bayesian network (BN) model is adopted in this paper. Based on the breakdown events identified at 10 urban expressways on-ramp in Shanghai, China, 23 parameters before breakdowns are extracted, including dynamic environment conditions aggregated with 5-minutes and static geometry features. Different time periods data are used to predict breakdown. Results indicate that the models using 5-10 min data prior to breakdown performs the best prediction, with the prediction accuracies higher than 73%. Moreover, one unified model for all bottlenecks is also built and shows reasonably good prediction performance with the classification accuracy of breakdowns about 75%, at best. Additionally, to simplify the model parameter input, the random forests (RF) model is adopted to identify the key variables. Modeling with the selected 7 parameters, the refined BN model can predict breakdown with adequate accuracy.

  5. Protein docking prediction using predicted protein-protein interface

    Directory of Open Access Journals (Sweden)

    Li Bin

    2012-01-01

    Full Text Available Abstract Background 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. Results 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. Conclusion 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.

  6. Protein docking prediction using predicted protein-protein interface.

    Science.gov (United States)

    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.

  7. Evaluation of Accuracy of Calculational Prediction of Criticality Based on ICSBEP Handbook Experiments

    International Nuclear Information System (INIS)

    Golovko, Yury; Rozhikhin, Yevgeniy; Tsibulya, Anatoly; Koscheev, Vladimir

    2008-01-01

    Experiments with plutonium, low enriched uranium and uranium-233 from the ICSBEP Handbook are being considered in this paper. Among these experiments it was selected only those, which seem to be the most relevant to the evaluation of uncertainty of critical mass of mixtures of plutonium or low enriched uranium or uranium-233 with light water. All selected experiments were examined and covariance matrices of criticality uncertainties were developed along with some uncertainties were revised. Statistical analysis of these experiments was performed and some contradictions were discovered and eliminated. Evaluation of accuracy of prediction of criticality calculations was performed using the internally consistent set of experiments with plutonium, low enriched uranium and uranium-233 remained after the statistical analyses. The application objects for the evaluation of calculational prediction of criticality were water-reflected spherical systems of homogeneous aqueous mixtures of plutonium or low enriched uranium or uranium-233 of different concentrations which are simplified models of apparatus of external fuel cycle. It is shows that the procedure allows to considerably reduce uncertainty in k eff caused by the uncertainties in neutron cross-sections. Also it is shows that the results are practically independent of initial covariance matrices of nuclear data uncertainties. (authors)

  8. Predictive Accuracy of a Cardiovascular Disease Risk Prediction Model in Rural South India – A Community Based Retrospective Cohort Study

    Directory of Open Access Journals (Sweden)

    Farah N Fathima

    2015-03-01

    Full Text Available Background: Identification of individuals at risk of developing cardiovascular diseases by risk stratification is the first step in primary prevention. Aims & Objectives: To assess the five year risk of developing a cardiovascular event from retrospective data and to assess the predictive accuracy of the non laboratory based National Health and Nutrition Examination Survey (NHANES risk prediction model among individuals in a rural South Indian population. Materials & Methods: A community based retrospective cohort study was conducted in three villages where risk stratification was done for all eligible adults aged between 35-74 years at the time of initial assessment using the NHANES risk prediction charts. Household visits were made after a period of five years by trained doctors to determine cardiovascular outcomes. Results: 521 people fulfilled the eligibility criteria of whom 486 (93.3% could be traced after five years. 56.8% were in low risk, 36.6% were in moderate risk and 6.6% were in high risk categories. 29 persons (5.97% had had cardiovascular events over the last five years of which 24 events (82.7% were nonfatal and five (17.25% were fatal. The mean age of the people who developed cardiovascular events was 57.24 ± 9.09 years. The odds ratios for the three levels of risk showed a linear trend with the odds ratios for the moderate risk and high risk category being 1.35 and 1.94 respectively with the low risk category as baseline. Conclusion: The non laboratory based NHANES charts did not accurately predict the occurrence of cardiovascular events in any of the risk categories.

  9. The Multi-center Evaluation of the Accuracy of the Contrast MEdium INduced Pd/Pa RaTiO in Predicting FFR (MEMENTO-FFR) Study.

    Science.gov (United States)

    Leone, Antonio Maria; Martin-Reyes, Roberto; Baptista, Sergio B; Amabile, Nicolas; Raposo, Luis; Franco Pelaez, Juan Antonio; Trani, Carlo; Cialdella, Pio; Basile, Eloisa; Zimbardo, Giuseppe; Burzotta, Francesco; Porto, Italo; Aurigemma, Cristina; Rebuzzi, Antonio G; Faustino, Mariana; Niccoli, Giampaolo; Abreu, Pedro F; Slama, Michel S; Spagnoli, Vincent; Telleria Arrieta, Miren; Amat Santos, Ignacio J; de la Torre Hernandez, Jose M; Lopez Palop, Ramon; Crea, Filippo

    2016-08-20

    Adenosine administration is needed for the achievement of maximal hyperaemia fractional flow reserve (FFR) assessment. The objective was to test the accuracy of Pd/Pa ratio registered during submaximal hyperaemia induced by non-ionic contrast medium (contrast FFR [cFFR]) in predicting FFR and comparing it to the performance of resting Pd/Pa in a collaborative registry of 926 patients enrolled in 10 hospitals from four European countries (Italy, Spain, France and Portugal). Resting Pd/Pa, cFFR and FFR were measured in 1,026 coronary stenoses functionally evaluated using commercially available pressure wires. cFFR was obtained after intracoronary injection of contrast medium, while FFR was measured after administration of adenosine. Resting Pd/Pa and cFFR were significantly higher than FFR (0.93±0.05 vs. 0.87±0.08 vs. 0.84±0.08, ptime and costs.

  10. Accuracy of Inferior Vena Cava Ultrasound for Predicting Dehydration in Children with Acute Diarrhea in Resource-Limited Settings.

    Science.gov (United States)

    Modi, Payal; Glavis-Bloom, Justin; Nasrin, Sabiha; Guy, Allysia; Chowa, Erika P; Dvor, Nathan; Dworkis, Daniel A; Oh, Michael; Silvestri, David M; Strasberg, Stephen; Rege, Soham; Noble, Vicki E; Alam, Nur H; Levine, Adam C

    2016-01-01

    Although dehydration from diarrhea is a leading cause of morbidity and mortality in children under five, existing methods of assessing dehydration status in children have limited accuracy. To assess the accuracy of point-of-care ultrasound measurement of the aorta-to-IVC ratio as a predictor of dehydration in children. A prospective cohort study of children under five years with acute diarrhea was conducted in the rehydration unit of the International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b). Ultrasound measurements of aorta-to-IVC ratio and dehydrated weight were obtained on patient arrival. Percent weight change was monitored during rehydration to classify children as having "some dehydration" with weight change 3-9% or "severe dehydration" with weight change > 9%. Logistic regression analysis and Receiver-Operator Characteristic (ROC) curves were used to evaluate the accuracy of aorta-to-IVC ratio as a predictor of dehydration severity. 850 children were enrolled, of which 771 were included in the final analysis. Aorta to IVC ratio was a significant predictor of the percent dehydration in children with acute diarrhea, with each 1-point increase in the aorta to IVC ratio predicting a 1.1% increase in the percent dehydration of the child. However, the area under the ROC curve (0.60), sensitivity (67%), and specificity (49%), for predicting severe dehydration were all poor. Point-of-care ultrasound of the aorta-to-IVC ratio was statistically associated with volume status, but was not accurate enough to be used as an independent screening tool for dehydration in children under five years presenting with acute diarrhea in a resource-limited setting.

  11. Accuracy of Inferior Vena Cava Ultrasound for Predicting Dehydration in Children with Acute Diarrhea in Resource-Limited Settings.

    Directory of Open Access Journals (Sweden)

    Payal Modi

    Full Text Available Although dehydration from diarrhea is a leading cause of morbidity and mortality in children under five, existing methods of assessing dehydration status in children have limited accuracy.To assess the accuracy of point-of-care ultrasound measurement of the aorta-to-IVC ratio as a predictor of dehydration in children.A prospective cohort study of children under five years with acute diarrhea was conducted in the rehydration unit of the International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b. Ultrasound measurements of aorta-to-IVC ratio and dehydrated weight were obtained on patient arrival. Percent weight change was monitored during rehydration to classify children as having "some dehydration" with weight change 3-9% or "severe dehydration" with weight change > 9%. Logistic regression analysis and Receiver-Operator Characteristic (ROC curves were used to evaluate the accuracy of aorta-to-IVC ratio as a predictor of dehydration severity.850 children were enrolled, of which 771 were included in the final analysis. Aorta to IVC ratio was a significant predictor of the percent dehydration in children with acute diarrhea, with each 1-point increase in the aorta to IVC ratio predicting a 1.1% increase in the percent dehydration of the child. However, the area under the ROC curve (0.60, sensitivity (67%, and specificity (49%, for predicting severe dehydration were all poor.Point-of-care ultrasound of the aorta-to-IVC ratio was statistically associated with volume status, but was not accurate enough to be used as an independent screening tool for dehydration in children under five years presenting with acute diarrhea in a resource-limited setting.

  12. Small angle X-ray scattering and cross-linking for data assisted protein structure prediction in CASP 12 with prospects for improved accuracy

    KAUST Repository

    Ogorzalek, Tadeusz L.

    2018-01-04

    Experimental data offers empowering constraints for structure prediction. These constraints can be used to filter equivalently scored models or more powerfully within optimization functions toward prediction. In CASP12, Small Angle X-ray Scattering (SAXS) and Cross-Linking Mass Spectrometry (CLMS) data, measured on an exemplary set of novel fold targets, were provided to the CASP community of protein structure predictors. As HT, solution-based techniques, SAXS and CLMS can efficiently measure states of the full-length sequence in its native solution conformation and assembly. However, this experimental data did not substantially improve prediction accuracy judged by fits to crystallographic models. One issue, beyond intrinsic limitations of the algorithms, was a disconnect between crystal structures and solution-based measurements. Our analyses show that many targets had substantial percentages of disordered regions (up to 40%) or were multimeric or both. Thus, solution measurements of flexibility and assembly support variations that may confound prediction algorithms trained on crystallographic data and expecting globular fully-folded monomeric proteins. Here, we consider the CLMS and SAXS data collected, the information in these solution measurements, and the challenges in incorporating them into computational prediction. As improvement opportunities were only partly realized in CASP12, we provide guidance on how data from the full-length biological unit and the solution state can better aid prediction of the folded monomer or subunit. We furthermore describe strategic integrations of solution measurements with computational prediction programs with the aim of substantially improving foundational knowledge and the accuracy of computational algorithms for biologically-relevant structure predictions for proteins in solution. This article is protected by copyright. All rights reserved.

  13. Small angle X-ray scattering and cross-linking for data assisted protein structure prediction in CASP 12 with prospects for improved accuracy

    KAUST Repository

    Ogorzalek, Tadeusz L.; Hura, Greg L.; Belsom, Adam; Burnett, Kathryn H.; Kryshtafovych, Andriy; Tainer, John A.; Rappsilber, Juri; Tsutakawa, Susan E.; Fidelis, Krzysztof

    2018-01-01

    Experimental data offers empowering constraints for structure prediction. These constraints can be used to filter equivalently scored models or more powerfully within optimization functions toward prediction. In CASP12, Small Angle X-ray Scattering (SAXS) and Cross-Linking Mass Spectrometry (CLMS) data, measured on an exemplary set of novel fold targets, were provided to the CASP community of protein structure predictors. As HT, solution-based techniques, SAXS and CLMS can efficiently measure states of the full-length sequence in its native solution conformation and assembly. However, this experimental data did not substantially improve prediction accuracy judged by fits to crystallographic models. One issue, beyond intrinsic limitations of the algorithms, was a disconnect between crystal structures and solution-based measurements. Our analyses show that many targets had substantial percentages of disordered regions (up to 40%) or were multimeric or both. Thus, solution measurements of flexibility and assembly support variations that may confound prediction algorithms trained on crystallographic data and expecting globular fully-folded monomeric proteins. Here, we consider the CLMS and SAXS data collected, the information in these solution measurements, and the challenges in incorporating them into computational prediction. As improvement opportunities were only partly realized in CASP12, we provide guidance on how data from the full-length biological unit and the solution state can better aid prediction of the folded monomer or subunit. We furthermore describe strategic integrations of solution measurements with computational prediction programs with the aim of substantially improving foundational knowledge and the accuracy of computational algorithms for biologically-relevant structure predictions for proteins in solution. This article is protected by copyright. All rights reserved.

  14. Viral IRES prediction system - a web server for prediction of the IRES secondary structure in silico.

    Directory of Open Access Journals (Sweden)

    Jun-Jie Hong

    Full Text Available The internal ribosomal entry site (IRES functions as cap-independent translation initiation sites in eukaryotic cells. IRES elements have been applied as useful tools for bi-cistronic expression vectors. Current RNA structure prediction programs are unable to predict precisely the potential IRES element. We have designed a viral IRES prediction system (VIPS to perform the IRES secondary structure prediction. In order to obtain better results for the IRES prediction, the VIPS can evaluate and predict for all four different groups of IRESs with a higher accuracy. RNA secondary structure prediction, comparison, and pseudoknot prediction programs were implemented to form the three-stage procedure for the VIPS. The backbone of VIPS includes: the RNAL fold program, aimed to predict local RNA secondary structures by minimum free energy method; the RNA Align program, intended to compare predicted structures; and pknotsRG program, used to calculate the pseudoknot structure. VIPS was evaluated by using UTR database, IRES database and Virus database, and the accuracy rate of VIPS was assessed as 98.53%, 90.80%, 82.36% and 80.41% for IRES groups 1, 2, 3, and 4, respectively. This advance useful search approach for IRES structures will facilitate IRES related studies. The VIPS on-line website service is available at http://140.135.61.250/vips/.

  15. Genomic breeding value prediction:methods and procedures

    NARCIS (Netherlands)

    Calus, M.P.L.

    2010-01-01

    Animal breeding faces one of the most significant changes of the past decades – the implementation of genomic selection. Genomic selection uses dense marker maps to predict the breeding value of animals with reported accuracies that are up to 0.31 higher than those of pedigree indexes, without the

  16. Prediction of non-canonical polyadenylation signals in human genomic sequences based on a novel algorithm using a fuzzy membership function.

    Science.gov (United States)

    Kamasawa, Masami; Horiuchi, Jun-Ichi

    2009-05-01

    Computational prediction of polyadenylation signals (PASes) is essential for analysis of alternative polyadenylation that plays crucial roles in gene regulations by generating heterogeneity of 3'-UTR of mRNAs. To date, several algorithms that are mostly based on machine learning methods have been developed to predict PASes. Accuracies of predictions by those algorithms have improved significantly for the last decade. However, they are designed primarily for prediction of the most canonical AAUAAA and its common variant AUUAAA whereas other variants have been ignored in their predictions despite recent studies indicating that non-canonical variants of AAUAAA are more important in the polyadenylation process than commonly recognized. Here we present a new algorithm "PolyF" employing fuzzy logic to confer an advance in computational PAS prediction--enable prediction of the non-canonical variants, and improve the accuracies for the canonical A(A/U)UAAA prediction. PolyF is a simple computational algorithm that is composed of membership functions defining sequence features of downstream sequence element (DSE) and upstream sequence element (USE), together with an inference engine. As a result, PolyF successfully identified the 10 single-nucleotide variants with approximately the same or higher accuracies compared to those for A(A/U)UAAA. PolyF also achieved higher accuracies for A(A/U)UAAA prediction than those by commonly known PAS finder programs, Polyadq and Erpin. Incorporating the USE into the PolyF algorithm was found to enhance prediction accuracies for all the 12 PAS hexamers compared to those using only the DSE, suggesting an important contribution of the USE in the polyadenylation process.

  17. Prediction of Spirometric Forced Expiratory Volume (FEV1) Data Using Support Vector Regression

    Science.gov (United States)

    Kavitha, A.; Sujatha, C. M.; Ramakrishnan, S.

    2010-01-01

    In this work, prediction of forced expiratory volume in 1 second (FEV1) in pulmonary function test is carried out using the spirometer and support vector regression analysis. Pulmonary function data are measured with flow volume spirometer from volunteers (N=175) using a standard data acquisition protocol. The acquired data are then used to predict FEV1. Support vector machines with polynomial kernel function with four different orders were employed to predict the values of FEV1. The performance is evaluated by computing the average prediction accuracy for normal and abnormal cases. Results show that support vector machines are capable of predicting FEV1 in both normal and abnormal cases and the average prediction accuracy for normal subjects was higher than that of abnormal subjects. Accuracy in prediction was found to be high for a regularization constant of C=10. Since FEV1 is the most significant parameter in the analysis of spirometric data, it appears that this method of assessment is useful in diagnosing the pulmonary abnormalities with incomplete data and data with poor recording.

  18. Prediction of beta-turns at over 80% accuracy based on an ensemble of predicted secondary structures and multiple alignments.

    Science.gov (United States)

    Zheng, Ce; Kurgan, Lukasz

    2008-10-10

    beta-turn is a secondary protein structure type that plays significant role in protein folding, stability, and molecular recognition. To date, several methods for prediction of beta-turns from protein sequences were developed, but they are characterized by relatively poor prediction quality. The novelty of the proposed sequence-based beta-turn predictor stems from the usage of a window based information extracted from four predicted three-state secondary structures, which together with a selected set of position specific scoring matrix (PSSM) values serve as an input to the support vector machine (SVM) predictor. We show that (1) all four predicted secondary structures are useful; (2) the most useful information extracted from the predicted secondary structure includes the structure of the predicted residue, secondary structure content in a window around the predicted residue, and features that indicate whether the predicted residue is inside a secondary structure segment; (3) the PSSM values of Asn, Asp, Gly, Ile, Leu, Met, Pro, and Val were among the top ranked features, which corroborates with recent studies. The Asn, Asp, Gly, and Pro indicate potential beta-turns, while the remaining four amino acids are useful to predict non-beta-turns. Empirical evaluation using three nonredundant datasets shows favorable Q total, Q predicted and MCC values when compared with over a dozen of modern competing methods. Our method is the first to break the 80% Q total barrier and achieves Q total = 80.9%, MCC = 0.47, and Q predicted higher by over 6% when compared with the second best method. We use feature selection to reduce the dimensionality of the feature vector used as the input for the proposed prediction method. The applied feature set is smaller by 86, 62 and 37% when compared with the second and two third-best (with respect to MCC) competing methods, respectively. Experiments show that the proposed method constitutes an improvement over the competing prediction

  19. Accuracy of rockall score for in hospital re bleeding among cirrhotic patients with variceal bleed

    International Nuclear Information System (INIS)

    Asgher, S.; Saleem, M.K.

    2015-01-01

    To assess the diagnostic accuracy of Roc kall scoring system for predicting in-hospital re-ble- eding in cirrhotic patients presenting with variceal bleed. Material and Methods: This descriptive case series study was conducted at Department of Medicine Combined Military Hospital Lahore from December 2013 to May 2014. We included patients with liver cirrhosis who presented with upper GI bleeding and showed varices as the cause of bleeding on endoscopy. Clinical and endoscopic features were noted to calculate Rockall score. Patients with score < 2 and > 8 were included. After treating with appropriate pharmacological and endoscopic therapy, patients were followed for re-bleeding for 10 days. Diagnostic accuracy was assessed by calculating sensitivity, specificity, positive and negative predictive values using 2 x 2 tables. Results: In the study, 175 patients were included. Mean age was 51.5 ± 1.22 years. Male to female ratio was 1.5 to 1.0 out of 175 patients, 157 patients (89.7%) were of low risk group (score = 2) while 18 patients (10.3%) were in high risk group (score > 8). In low risk group, re-bleeding occurred only in 2 patients (1.2%) while in high risk group, re-bleeding occurred in 14 patients (78%). Rockall score was found to have good diagnostic accuracy with sensitivity of 87.5%, specificity of 97.48%, positive predictive value of 77.8% and negative predictive value of 98.7%. Conclusion: In cases of variceal bleed, frequency of re-bleed is less in patients who are in low risk category with lower Rockall score and high in high risk patients with higher rockall score. The Rockall score has a good diagnostic accuracy in prediction of re-bleed in variceal bleeding. (author)

  20. Integrated Computational Solution for Predicting Skin Sensitization Potential of Molecules.

    Directory of Open Access Journals (Sweden)

    Konda Leela Sarath Kumar

    Full Text Available Skin sensitization forms a major toxicological endpoint for dermatology and cosmetic products. Recent ban on animal testing for cosmetics demands for alternative methods. We developed an integrated computational solution (SkinSense that offers a robust solution and addresses the limitations of existing computational tools i.e. high false positive rate and/or limited coverage.The key components of our solution include: QSAR models selected from a combinatorial set, similarity information and literature-derived sub-structure patterns of known skin protein reactive groups. Its prediction performance on a challenge set of molecules showed accuracy = 75.32%, CCR = 74.36%, sensitivity = 70.00% and specificity = 78.72%, which is better than several existing tools including VEGA (accuracy = 45.00% and CCR = 54.17% with 'High' reliability scoring, DEREK (accuracy = 72.73% and CCR = 71.44% and TOPKAT (accuracy = 60.00% and CCR = 61.67%. Although, TIMES-SS showed higher predictive power (accuracy = 90.00% and CCR = 92.86%, the coverage was very low (only 10 out of 77 molecules were predicted reliably.Owing to improved prediction performance and coverage, our solution can serve as a useful expert system towards Integrated Approaches to Testing and Assessment for skin sensitization. It would be invaluable to cosmetic/ dermatology industry for pre-screening their molecules, and reducing time, cost and animal testing.

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

    Science.gov (United States)

    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…

  2. The accuracy of transvaginal sonography to detect endometriosis cyst

    Science.gov (United States)

    Diantika, M.; Gunardi, E. R.

    2017-08-01

    Endometriosis is common in women of reproductive age. Late diagnosis is still the main concern. Currently, noninvasive diagnostic testing, such as transvaginal sonography, is recommended. The aim of the current study was to evaluate the accuracy of transvaginal sonography in diagnosing endometrial cysts in patients in Cipto Mangunkusumo Hospital, Jakarta, Indonesia. This diagnostic study was carried out at Cipto Mangunkusumo Hospital between January 2014 and June 2015. Outpatients suspected have an endometrial cyst based on the patient history and a clinical examination was recruited. The patients were then evaluated using transvaginal sonography by an experienced sonologist, according to the research protocol. The gold standard test was a histological finding in the removed surgical mass. Ninety-eight patients were analyzed. An endometrial cyst was confirmed by histology in 85 patients (87%). The accuracy, sensitivity, specificity, positive predictive value and negative predictive value of transvaginal sonography was established to be 85% (a range of 71-99%), 93%, 77%, 96%, and 63%, respectively. A significantly higher area under the curve was identified using transvaginal sonogpraphy compared to that achieved with a clinical examination alone (85% versus 79%). Transvaginal sonography was useful in diagnosing endometrial cysts in outpatients and is recommended in daily clinical practice.

  3. Prediction of beta-turns at over 80% accuracy based on an ensemble of predicted secondary structures and multiple alignments

    Directory of Open Access Journals (Sweden)

    Kurgan Lukasz

    2008-10-01

    Full Text Available Abstract Background β-turn is a secondary protein structure type that plays significant role in protein folding, stability, and molecular recognition. To date, several methods for prediction of β-turns from protein sequences were developed, but they are characterized by relatively poor prediction quality. The novelty of the proposed sequence-based β-turn predictor stems from the usage of a window based information extracted from four predicted three-state secondary structures, which together with a selected set of position specific scoring matrix (PSSM values serve as an input to the support vector machine (SVM predictor. Results We show that (1 all four predicted secondary structures are useful; (2 the most useful information extracted from the predicted secondary structure includes the structure of the predicted residue, secondary structure content in a window around the predicted residue, and features that indicate whether the predicted residue is inside a secondary structure segment; (3 the PSSM values of Asn, Asp, Gly, Ile, Leu, Met, Pro, and Val were among the top ranked features, which corroborates with recent studies. The Asn, Asp, Gly, and Pro indicate potential β-turns, while the remaining four amino acids are useful to predict non-β-turns. Empirical evaluation using three nonredundant datasets shows favorable Qtotal, Qpredicted and MCC values when compared with over a dozen of modern competing methods. Our method is the first to break the 80% Qtotal barrier and achieves Qtotal = 80.9%, MCC = 0.47, and Qpredicted higher by over 6% when compared with the second best method. We use feature selection to reduce the dimensionality of the feature vector used as the input for the proposed prediction method. The applied feature set is smaller by 86, 62 and 37% when compared with the second and two third-best (with respect to MCC competing methods, respectively. Conclusion Experiments show that the proposed method constitutes an

  4. An assessment of the accuracy of contrast enema for the diagnosis ...

    African Journals Online (AJOL)

    Diagnostic accuracy levels were calculated by comparing radiological results with histology results, which is the gold standard. Results: Diagnostic accuracy of contrast enema was 78%, sensitivity was 94.4% and the negative predictive value was 95.7%. Specificity (68.8%) and positive predictive values (63%) were ...

  5. Accuracy of the paracetamol-aminotransferase multiplication product to predict hepatotoxicity in modified-release paracetamol overdose.

    Science.gov (United States)

    Wong, Anselm; Sivilotti, Marco L A; Graudins, Andis

    2017-06-01

    The paracetamol-aminotransferase multiplication product (APAP × ALT) is a risk predictor of hepatotoxicity that is somewhat independent of time and type of ingestion. However, its accuracy following ingestion of modified-release formulations is not known, as the product has been derived and validated after immediate-release paracetamol overdoses. The aim of this retrospective cohort study was to evaluate the accuracy of the multiplication product to predict hepatotoxicity in a cohort of patients with modified-release paracetamol overdose. We assessed all patients with modified-release paracetamol overdose presenting to our hospital network from October 2009 to July 2016. Ingestion of a modified-release formulation was identified by patient self-report or retrieval of the original container. Hepatotoxicity was defined as peak alanine aminotransferase ≥1000 IU/L, and acute liver injury (ALI) as a doubling of baseline ALT to more than 50 IU/L. Of 1989 paracetamol overdose presentations, we identified 73 modified-release paracetamol exposures treated with acetylcysteine. Five patients developed hepatotoxicity, including one who received acetylcysteine within eight hours of an acute ingestion. No patient with an initial multiplication product paracetamol overdose treated with acetylcysteine, the paracetamol-aminotransferase multiplication product demonstrated similar accuracy and temporal profile to previous reports involving mostly immediate-release formulations. Above a cut-point of 10,000 mg/L × IU/L, it was very strongly associated with the development of acute liver injury and hepatotoxicity, especially when calculated more than eight hours post-ingestion. When below 1500 mg/L × IU/L the likelihood of developing hepatotoxicity was very low. Persistently high serial multiplication product calculations were associated with the greatest risk of hepatotoxicity.

  6. Higher accuracy analytical approximations to a nonlinear oscillator with discontinuity by He's homotopy perturbation method

    International Nuclear Information System (INIS)

    Belendez, A.; Hernandez, A.; Belendez, T.; Neipp, C.; Marquez, A.

    2008-01-01

    He's homotopy perturbation method is used to calculate higher-order approximate periodic solutions of a nonlinear oscillator with discontinuity for which the elastic force term is proportional to sgn(x). We find He's homotopy perturbation method works very well for the whole range of initial amplitudes, and the excellent agreement of the approximate frequencies and periodic solutions with the exact ones has been demonstrated and discussed. Only one iteration leads to high accuracy of the solutions with a maximal relative error for the approximate period of less than 1.56% for all values of oscillation amplitude, while this relative error is 0.30% for the second iteration and as low as 0.057% when the third-order approximation is considered. Comparison of the result obtained using this method with those obtained by different harmonic balance methods reveals that He's homotopy perturbation method is very effective and convenient

  7. The Diagnostic Accuracy of Clinical and External Pelvimetry in Prediction of Dystocia in Nulliparous Women

    Directory of Open Access Journals (Sweden)

    R Alijahan

    2011-08-01

    Full Text Available Introduction: Clinical pelvimetry is very uncomfortable for the patient and is associated with subjective error, while external pelvimetry is a simple and acceptable method for patients. The objective of this study was to compare the diagnostic accuracy of clinical and external pelvimetry in prediction of dystocia in nulliparous women. Methods: In this study between December 2008 and January 2009, 447 nulliparous women with a single pregnancy in vertex presentation and gestational age 38-42 weeks referring to the Ommolbanin hospital of Mashhad were included. External pelvic dimensions were assessed at the time of admission and clinical pelvimetry was performed by another examiner. These measurements were not available to the clinician in charge of the delivery. Dystocia was defined as caesarean section and vacuum or forceps delivery for abnormal progress of labor ( active uterine contractions, arrest of cervical dilatation or cervical dilatation less than 1 cm /h in the active phase for 2 hours, prolongation of second stage beyond 2 hours or fetal head descent less than 1cm/h. Statistical tests included Fisher exact test and Chi- square test. Results: The highest sensitivity obtained from clinical pelvimetry was 33.3% and related to diagonal conjugate less than 11.5 cm. The sensitivity of external pelvic dimensions was higher than clinical pelvimetry that was highest for the Michaelis transverse diameter(60.72%. Conclusion: External pelvimetry in comparison to clinical pelvimetry is a better method for identifying dystocia in nulliparous women and can replace clinical pelvimetry in antenatal care programs.

  8. Relative accuracy of spatial predictive models for lynx Lynx canadensis derived using logistic regression-AIC, multiple criteria evaluation and Bayesian approaches

    Directory of Open Access Journals (Sweden)

    Shelley M. ALEXANDER

    2009-02-01

    Full Text Available We compared probability surfaces derived using one set of environmental variables in three Geographic Information Systems (GIS-based approaches: logistic regression and Akaike’s Information Criterion (AIC, Multiple Criteria Evaluation (MCE, and Bayesian Analysis (specifically Dempster-Shafer theory. We used lynx Lynx canadensis as our focal species, and developed our environment relationship model using track data collected in Banff National Park, Alberta, Canada, during winters from 1997 to 2000. The accuracy of the three spatial models were compared using a contingency table method. We determined the percentage of cases in which both presence and absence points were correctly classified (overall accuracy, the failure to predict a species where it occurred (omission error and the prediction of presence where there was absence (commission error. Our overall accuracy showed the logistic regression approach was the most accurate (74.51%. The multiple criteria evaluation was intermediate (39.22%, while the Dempster-Shafer (D-S theory model was the poorest (29.90%. However, omission and commission error tell us a different story: logistic regression had the lowest commission error, while D-S theory produced the lowest omission error. Our results provide evidence that habitat modellers should evaluate all three error measures when ascribing confidence in their model. We suggest that for our study area at least, the logistic regression model is optimal. However, where sample size is small or the species is very rare, it may also be useful to explore and/or use a more ecologically cautious modelling approach (e.g. Dempster-Shafer that would over-predict, protect more sites, and thereby minimize the risk of missing critical habitat in conservation plans[Current Zoology 55(1: 28 – 40, 2009].

  9. Predicting Academic Success in Higher Education: What's More Important than Being Smart?

    Science.gov (United States)

    Kappe, Rutger; van der Flier, Henk

    2012-01-01

    This study investigated the combined predictive validity of intelligence and personality factors on multiple measures of academic achievement. Students in a college of higher education in the Netherlands (N = 137) completed a survey that measured intelligence, the Big Five personality traits, motivation, and four specific personality traits.…

  10. Diagnostic accuracy of soluble urokinase plasminogen activator receptor (suPAR) for prediction of bacteremia in patients with systemic inflammatory response syndrome.

    Science.gov (United States)

    Hoenigl, Martin; Raggam, Reinhard B; Wagner, Jasmin; Valentin, Thomas; Leitner, Eva; Seeber, Katharina; Zollner-Schwetz, Ines; Krammer, Werner; Prüller, Florian; Grisold, Andrea J; Krause, Robert

    2013-02-01

    Soluble urokinase plasminogen activator receptor (suPAR) serum concentrations have recently been described to reflect the severity status of systemic inflammation. In this study, the diagnostic accuracy of suPAR, C-reactive protein (CRP), procalcitonin (PCT), and interleukin-6 (IL-6) to predict bacteremia in patients with systemic inflammatory response syndrome (SIRS) was compared. A total of 132 patients with SIRS were included. In 55 patients blood cultures had resulted positive (study group 1, Gram positive bacteria: Staphylococcus aureus and Streptococcus spp., n=15; study group 2, Gram-negative bacteria, n=40) and 77 patients had negative blood culture results (control group, n=77). Simultaneously with blood cultures suPAR, CRP, PCT, IL-6 and white blood count (WBC) were determined. SuPAR values were significantly higher in study group 1 (median 8.11; IQR 5.78-15.53; p=0.006) and study group 2 (median 9.62; IQR 6.52-11.74; p<0.001) when compared with the control group (median 5.65; IQR 4.30-7.83). ROC curve analysis revealed an AUC of 0.726 for suPAR in differentiating SIRS patients with bacteremia from those without. The biomarkers PCT and IL-6 showed comparable results. Regarding combinations of biomarkers multiplying suPAR, PCT and IL-6 was most promising and resulted in an AUC value of 0.804. Initial suPAR serum concentrations were significantly higher (p=0.028) in patients who died within 28 days than in those who survived. No significant difference was seen for PCT, IL-6 and CRP. In conclusion, suPAR, IL-6 and PCT may contribute to predicting bacteremia in SIRS patients. Copyright © 2012 The Canadian Society of Clinical Chemists. Published by Elsevier Inc. All rights reserved.

  11. On accuracy of the wave finite element predictions of wavenumbers and power flow: A benchmark problem

    Science.gov (United States)

    Søe-Knudsen, Alf; Sorokin, Sergey

    2011-06-01

    This rapid communication is concerned with justification of the 'rule of thumb', which is well known to the community of users of the finite element (FE) method in dynamics, for the accuracy assessment of the wave finite element (WFE) method. An explicit formula linking the size of a window in the dispersion diagram, where the WFE method is trustworthy, with the coarseness of a FE mesh employed is derived. It is obtained by the comparison of the exact Pochhammer-Chree solution for an elastic rod having the circular cross-section with its WFE approximations. It is shown that the WFE power flow predictions are also valid within this window.

  12. Persistence of accuracy of genomic estimated breeding values over generations in layer chickens

    Directory of Open Access Journals (Sweden)

    Fernando Rohan

    2011-06-01

    Full Text Available Abstract Background The predictive ability of genomic estimated breeding values (GEBV originates both from associations between high-density markers and QTL (Quantitative Trait Loci and from pedigree information. Thus, GEBV are expected to provide more persistent accuracy over successive generations than breeding values estimated using pedigree-based methods. The objective of this study was to evaluate the accuracy of GEBV in a closed population of layer chickens and to quantify their persistence over five successive generations using marker or pedigree information. Methods The training data consisted of 16 traits and 777 genotyped animals from two generations of a brown-egg layer breeding line, 295 of which had individual phenotype records, while others had phenotypes on 2,738 non-genotyped relatives, or similar data accumulated over up to five generations. Validation data included phenotyped and genotyped birds from five subsequent generations (on average 306 birds/generation. Birds were genotyped for 23,356 segregating SNP. Animal models using genomic or pedigree relationship matrices and Bayesian model averaging methods were used for training analyses. Accuracy was evaluated as the correlation between EBV and phenotype in validation divided by the square root of trait heritability. Results Pedigree relationships in outbred populations are reduced by 50% at each meiosis, therefore accuracy is expected to decrease by the square root of 0.5 every generation, as observed for pedigree-based EBV (Estimated Breeding Values. In contrast the GEBV accuracy was more persistent, although the drop in accuracy was substantial in the first generation. Traits that were considered to be influenced by fewer QTL and to have a higher heritability maintained a higher GEBV accuracy over generations. In conclusion, GEBV capture information beyond pedigree relationships, but retraining every generation is recommended for genomic selection in closed breeding

  13. Accuracy of Genomic Prediction in a Commercial Perennial Ryegrass Breeding Program

    Directory of Open Access Journals (Sweden)

    Dario Fè

    2016-11-01

    Full Text Available The implementation of genomic selection (GS in plant breeding, so far, has been mainly evaluated in crops farmed as homogeneous varieties, and the results have been generally positive. Fewer results are available for species, such as forage grasses, that are grown as heterogenous families (developed from multiparent crosses in which the control of the genetic variation is far more complex. Here we test the potential for implementing GS in the breeding of perennial ryegrass ( L. using empirical data from a commercial forage breeding program. Biparental F and multiparental synthetic (SYN families of diploid perennial ryegrass were genotyped using genotyping-by-sequencing, and phenotypes for five different traits were analyzed. Genotypes were expressed as family allele frequencies, and phenotypes were recorded as family means. Different models for genomic prediction were compared by using practically relevant cross-validation strategies. All traits showed a highly significant level of genetic variance, which could be traced using the genotyping assay. While there was significant genotype × environment (G × E interaction for some traits, accuracies were high among F families and between biparental F and multiparental SYN families. We have demonstrated that the implementation of GS in grass breeding is now possible and presents an opportunity to make significant gains for various traits.

  14. Evidence for a confidence-accuracy relationship in memory for same- and cross-race faces.

    Science.gov (United States)

    Nguyen, Thao B; Pezdek, Kathy; Wixted, John T

    2017-12-01

    Discrimination accuracy is usually higher for same- than for cross-race faces, a phenomenon known as the cross-race effect (CRE). According to prior research, the CRE occurs because memories for same- and cross-race faces rely on qualitatively different processes. However, according to a continuous dual-process model of recognition memory, memories that rely on qualitatively different processes do not differ in recognition accuracy when confidence is equated. Thus, although there are differences in overall same- and cross-race discrimination accuracy, confidence-specific accuracy (i.e., recognition accuracy at a particular level of confidence) may not differ. We analysed datasets from four recognition memory studies on same- and cross-race faces to test this hypothesis. Confidence ratings reliably predicted recognition accuracy when performance was above chance levels (Experiments 1, 2, and 3) but not when performance was at chance levels (Experiment 4). Furthermore, at each level of confidence, confidence-specific accuracy for same- and cross-race faces did not significantly differ when overall performance was above chance levels (Experiments 1, 2, and 3) but significantly differed when overall performance was at chance levels (Experiment 4). Thus, under certain conditions, high-confidence same-race and cross-race identifications may be equally reliable.

  15. Accuracy and responses of genomic selection on key traits in apple breeding

    NARCIS (Netherlands)

    Muranty, Hélène; Troggio, Michela; Sadok, Ben Inès; Rifaï, Al Mehdi; Auwerkerken, Annemarie; Banchi, E.; Velasco, Riccardo; Stevanato, P.; Weg, van de W.E.; Guardo, Di M.; Kumar, S.; Laurens, François; Bink, M.C.A.M.

    2015-01-01

    The application of genomic selection in fruit tree crops is expected to enhance breeding efficiency by increasing prediction accuracy, increasing selection intensity and decreasing generation interval. The objectives of this study were to assess the accuracy of prediction and selection response in

  16. The use of imprecise processing to improve accuracy in weather and climate prediction

    Energy Technology Data Exchange (ETDEWEB)

    Düben, Peter D., E-mail: dueben@atm.ox.ac.uk [University of Oxford, Atmospheric, Oceanic and Planetary Physics (United Kingdom); McNamara, Hugh [University of Oxford, Mathematical Institute (United Kingdom); Palmer, T.N. [University of Oxford, Atmospheric, Oceanic and Planetary Physics (United Kingdom)

    2014-08-15

    The use of stochastic processing hardware and low precision arithmetic in atmospheric models is investigated. Stochastic processors allow hardware-induced faults in calculations, sacrificing bit-reproducibility and precision in exchange for improvements in performance and potentially accuracy of forecasts, due to a reduction in power consumption that could allow higher resolution. A similar trade-off is achieved using low precision arithmetic, with improvements in computation and communication speed and savings in storage and memory requirements. As high-performance computing becomes more massively parallel and power intensive, these two approaches may be important stepping stones in the pursuit of global cloud-resolving atmospheric modelling. The impact of both hardware induced faults and low precision arithmetic is tested using the Lorenz '96 model and the dynamical core of a global atmosphere model. In the Lorenz '96 model there is a natural scale separation; the spectral discretisation used in the dynamical core also allows large and small scale dynamics to be treated separately within the code. Such scale separation allows the impact of lower-accuracy arithmetic to be restricted to components close to the truncation scales and hence close to the necessarily inexact parametrised representations of unresolved processes. By contrast, the larger scales are calculated using high precision deterministic arithmetic. Hardware faults from stochastic processors are emulated using a bit-flip model with different fault rates. Our simulations show that both approaches to inexact calculations do not substantially affect the large scale behaviour, provided they are restricted to act only on smaller scales. By contrast, results from the Lorenz '96 simulations are superior when small scales are calculated on an emulated stochastic processor than when those small scales are parametrised. This suggests that inexact calculations at the small scale could reduce

  17. The use of imprecise processing to improve accuracy in weather and climate prediction

    International Nuclear Information System (INIS)

    Düben, Peter D.; McNamara, Hugh; Palmer, T.N.

    2014-01-01

    The use of stochastic processing hardware and low precision arithmetic in atmospheric models is investigated. Stochastic processors allow hardware-induced faults in calculations, sacrificing bit-reproducibility and precision in exchange for improvements in performance and potentially accuracy of forecasts, due to a reduction in power consumption that could allow higher resolution. A similar trade-off is achieved using low precision arithmetic, with improvements in computation and communication speed and savings in storage and memory requirements. As high-performance computing becomes more massively parallel and power intensive, these two approaches may be important stepping stones in the pursuit of global cloud-resolving atmospheric modelling. The impact of both hardware induced faults and low precision arithmetic is tested using the Lorenz '96 model and the dynamical core of a global atmosphere model. In the Lorenz '96 model there is a natural scale separation; the spectral discretisation used in the dynamical core also allows large and small scale dynamics to be treated separately within the code. Such scale separation allows the impact of lower-accuracy arithmetic to be restricted to components close to the truncation scales and hence close to the necessarily inexact parametrised representations of unresolved processes. By contrast, the larger scales are calculated using high precision deterministic arithmetic. Hardware faults from stochastic processors are emulated using a bit-flip model with different fault rates. Our simulations show that both approaches to inexact calculations do not substantially affect the large scale behaviour, provided they are restricted to act only on smaller scales. By contrast, results from the Lorenz '96 simulations are superior when small scales are calculated on an emulated stochastic processor than when those small scales are parametrised. This suggests that inexact calculations at the small scale could reduce computation and

  18. Does aging impair first impression accuracy? Differentiating emotion recognition from complex social inferences.

    Science.gov (United States)

    Krendl, Anne C; Rule, Nicholas O; Ambady, Nalini

    2014-09-01

    Young adults can be surprisingly accurate at making inferences about people from their faces. Although these first impressions have important consequences for both the perceiver and the target, it remains an open question whether first impression accuracy is preserved with age. Specifically, could age differences in impressions toward others stem from age-related deficits in accurately detecting complex social cues? Research on aging and impression formation suggests that young and older adults show relative consensus in their first impressions, but it is unknown whether they differ in accuracy. It has been widely shown that aging disrupts emotion recognition accuracy, and that these impairments may predict deficits in other social judgments, such as detecting deceit. However, it is unclear whether general impression formation accuracy (e.g., emotion recognition accuracy, detecting complex social cues) relies on similar or distinct mechanisms. It is important to examine this question to evaluate how, if at all, aging might affect overall accuracy. Here, we examined whether aging impaired first impression accuracy in predicting real-world outcomes and categorizing social group membership. Specifically, we studied whether emotion recognition accuracy and age-related cognitive decline (which has been implicated in exacerbating deficits in emotion recognition) predict first impression accuracy. Our results revealed that emotion recognition accuracy did not predict first impression accuracy, nor did age-related cognitive decline impair it. These findings suggest that domains of social perception outside of emotion recognition may rely on mechanisms that are relatively unimpaired by aging. PsycINFO Database Record (c) 2014 APA, all rights reserved.

  19. The diagnostic accuracy of the rapid dipstick test to predict asymptomatic urinary tract infection of pregnancy.

    Science.gov (United States)

    Eigbefoh, J O; Isabu, P; Okpere, E; Abebe, J

    2008-07-01

    Untreated urinary tract infection can have devastating maternal and neonatal effects. Thus, routine screening for bacteriuria is advocated. This study was designed to evaluate the diagnostic accuracy of the rapid dipstick test to predict urinary tract infection in pregnancy with the gold standard of urine microscopy, culture and sensitivity acting as the control. The urine dipstick test uses the leucocyte esterase, nitrite and test for protein singly and in combination. The result of the dipstick was compared with the gold standard, urine microscopy, culture and sensitivity using confidence interval for proportions. The reliability and validity of the urine dipstick was also evaluated. Overall, the urine dipstick test has a poor correlation with urine culture (p = 0.125, CI 95%). The same holds true for individual components of the dipstick test. The overall sensitivity of the urine dipstick test was poor at 2.3%. Individual sensitivity of the various components varied between 9.1% for leucocyte esterase and the nitrite test to 56.8% for leucocyte esterase alone. The other components of the dipstick test, the test of nitrite, test for protein and combination of the test (leucocyte esterase, nitrite and proteinuria) appear to decrease the sensitivity of the leucocyte esterase test alone. The ability of the urine dipstick test to correctly rule out urinary tract infection (specificity) was high. The positive predictive value for the dipstick test was high, with the leucocyte esterase test having the highest positive predictive value compared with the other components of the dipstick test. The negative predictive value (NPV) was expectedly highest for the leucocyte esterase test alone with values higher than the other components of the urine dipstick test singly and in various combinations. Compared with the other parameters of the urine dipstick test, singly and in combination, leucocyte esterase appears to be the most accurate (90.25%). The dipstick test has a

  20. Social Power Increases Interoceptive Accuracy

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    Mehrad Moeini-Jazani

    2017-08-01

    Full Text Available Building on recent psychological research showing that power increases self-focused attention, we propose that having power increases accuracy in perception of bodily signals, a phenomenon known as interoceptive accuracy. Consistent with our proposition, participants in a high-power experimental condition outperformed those in the control and low-power conditions in the Schandry heartbeat-detection task. We demonstrate that the effect of power on interoceptive accuracy is not explained by participants’ physiological arousal, affective state, or general intention for accuracy. Rather, consistent with our reasoning that experiencing power shifts attentional resources inward, we show that the effect of power on interoceptive accuracy is dependent on individuals’ chronic tendency to focus on their internal sensations. Moreover, we demonstrate that individuals’ chronic sense of power also predicts interoceptive accuracy similar to, and independent of, how their situationally induced feeling of power does. We therefore provide further support on the relation between power and enhanced perception of bodily signals. Our findings offer a novel perspective–a psychophysiological account–on how power might affect judgments and behavior. We highlight and discuss some of these intriguing possibilities for future research.

  1. The accuracy of Internet search engines to predict diagnoses from symptoms can be assessed with a validated scoring system.

    Science.gov (United States)

    Shenker, Bennett S

    2014-02-01

    To validate a scoring system that evaluates the ability of Internet search engines to correctly predict diagnoses when symptoms are used as search terms. We developed a five point scoring system to evaluate the diagnostic accuracy of Internet search engines. We identified twenty diagnoses common to a primary care setting to validate the scoring system. One investigator entered the symptoms for each diagnosis into three Internet search engines (Google, Bing, and Ask) and saved the first five webpages from each search. Other investigators reviewed the webpages and assigned a diagnostic accuracy score. They rescored a random sample of webpages two weeks later. To validate the five point scoring system, we calculated convergent validity and test-retest reliability using Kendall's W and Spearman's rho, respectively. We used the Kruskal-Wallis test to look for differences in accuracy scores for the three Internet search engines. A total of 600 webpages were reviewed. Kendall's W for the raters was 0.71 (psearch engines is a valid and reliable instrument. The scoring system may be used in future Internet research. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

  2. Accuracy and Predictability of PANC-3 Scoring System over APACHE II in Acute Pancreatitis: A Prospective Study.

    Science.gov (United States)

    Rathnakar, Surag Kajoor; Vishnu, Vikram Hubbanageri; Muniyappa, Shridhar; Prasath, Arun

    2017-02-01

    Acute Pancreatitis (AP) is one of the common conditions encountered in the emergency room. The course of the disease ranges from mild form to severe acute form. Most of these episodes are mild and spontaneously subsiding within 3 to 5 days. In contrast, Severe Acute Pancreatitis (SAP) occurring in around 15-20% of all cases, mortality can range between 10 to 85% across various centres and countries. In such a situation we need an indicator which can predict the outcome of an attack, as severe or mild, as early as possible and such an indicator should be sensitive and specific enough to trust upon. PANC-3 scoring is such a scoring system in predicting the outcome of an attack of AP. To assess the accuracy and predictability of PANC-3 scoring system over APACHE II in predicting severity in an attack of AP. This prospective study was conducted on 82 patients admitted with the diagnosis of pancreatitis. Investigations to evaluate PANC-3 and APACHE II were done on all the patients and the PANC-3 and APACHE II score was calculated. PANC-3 score has a sensitivity of 82.6% and specificity of 77.9%, the test had a Positive Predictive Value (PPV) of 0.59 and Negative Predictive Value (NPV) of 0.92. Sensitivity of APACHE II in predicting SAP was 91.3% and specificity was 96.6% with PPV of 0.91, NPV was 0.96. Our study shows that PANC-3 can be used to predict the severity of pancreatitis as efficiently as APACHE II. The interpretation of PANC-3 does not need expertise and can be applied at the time of admission which is an advantage when compared to classical scoring systems.

  3. Pre-Operative Prediction of Advanced Prostatic Cancer Using Clinical Decision Support Systems: Accuracy Comparison between Support Vector Machine and Artificial Neural Network

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Sang Youn; Moon, Sung Kyoung; Hwang, Sung Il; Sung, Chang Kyu; Cho, Jeong Yeon; Kim, Seung Hyup; Lee, Hak Jong [Seoul National University College of Medicine, Seoul (Korea, Republic of); Jung, Dae Chul [National Cancer Center, Ilsan (Korea, Republic of); Lee, Ji Won [Kangwon National University College of Medicine, Chuncheon (Korea, Republic of)

    2011-10-15

    The purpose of the current study was to develop support vector machine (SVM) and artificial neural network (ANN) models for the pre-operative prediction of advanced prostate cancer by using the parameters acquired from transrectal ultrasound (TRUS)-guided prostate biopsies, and to compare the accuracies between the two models. Five hundred thirty-two consecutive patients who underwent prostate biopsies and prostatectomies for prostate cancer were divided into the training and test groups (n = 300 versus n 232). From the data in the training group, two clinical decision support systems (CDSSs-[SVM and ANN]) were constructed with input (age, prostate specific antigen level, digital rectal examination, and five biopsy parameters) and output data (the probability for advanced prostate cancer [> pT3a]). From the data of the test group, the accuracy of output data was evaluated. The areas under the receiver operating characteristic (ROC) curve (AUC) were calculated to summarize the overall performances, and a comparison of the ROC curves was performed (p < 0.05). The AUC of SVM and ANN is 0.805 and 0.719, respectively (p = 0.020), in the pre-operative prediction of advanced prostate cancer. Te performance of SVM is superior to ANN in the pre-operative prediction of advanced prostate cancer.

  4. Persistency of Prediction Accuracy and Genetic Gain in Synthetic Populations Under Recurrent Genomic Selection

    Directory of Open Access Journals (Sweden)

    Dominik Müller

    2017-03-01

    Full Text Available Recurrent selection (RS has been used in plant breeding to successively improve synthetic and other multiparental populations. Synthetics are generated from a limited number of parents ( Np , but little is known about how Np affects genomic selection (GS in RS, especially the persistency of prediction accuracy (rg , g ^ and genetic gain. Synthetics were simulated by intermating Np= 2–32 parent lines from an ancestral population with short- or long-range linkage disequilibrium (LDA and subjected to multiple cycles of GS. We determined rg , g ^ and genetic gain across 30 cycles for different training set (TS sizes, marker densities, and generations of recombination before model training. Contributions to rg , g ^ and genetic gain from pedigree relationships, as well as from cosegregation and LDA between QTL and markers, were analyzed via four scenarios differing in (i the relatedness between TS and selection candidates and (ii whether selection was based on markers or pedigree records. Persistency of rg , g ^ was high for small Np , where predominantly cosegregation contributed to rg , g ^ , but also for large Np , where LDA replaced cosegregation as the dominant information source. Together with increasing genetic variance, this compensation resulted in relatively constant long- and short-term genetic gain for increasing Np > 4, given long-range LDA in the ancestral population. Although our scenarios suggest that information from pedigree relationships contributed to rg , g ^ for only very few generations in GS, we expect a longer contribution than in pedigree BLUP, because capturing Mendelian sampling by markers reduces selective pressure on pedigree relationships. Larger TS size (NTS and higher marker density improved persistency of rg , g ^ and hence genetic gain, but additional recombinations could not increase genetic gain.

  5. Exploring the genetic architecture and improving genomic prediction accuracy for mastitis and milk production traits in dairy cattle by mapping variants to hepatic transcriptomic regions responsive to intra-mammary infection.

    Science.gov (United States)

    Fang, Lingzhao; Sahana, Goutam; Ma, Peipei; Su, Guosheng; Yu, Ying; Zhang, Shengli; Lund, Mogens Sandø; Sørensen, Peter

    2017-05-12

    A better understanding of the genetic architecture of complex traits can contribute to improve genomic prediction. We hypothesized that genomic variants associated with mastitis and milk production traits in dairy cattle are enriched in hepatic transcriptomic regions that are responsive to intra-mammary infection (IMI). Genomic markers [e.g. single nucleotide polymorphisms (SNPs)] from those regions, if included, may improve the predictive ability of a genomic model. We applied a genomic feature best linear unbiased prediction model (GFBLUP) to implement the above strategy by considering the hepatic transcriptomic regions responsive to IMI as genomic features. GFBLUP, an extension of GBLUP, includes a separate genomic effect of SNPs within a genomic feature, and allows differential weighting of the individual marker relationships in the prediction equation. Since GFBLUP is computationally intensive, we investigated whether a SNP set test could be a computationally fast way to preselect predictive genomic features. The SNP set test assesses the association between a genomic feature and a trait based on single-SNP genome-wide association studies. We applied these two approaches to mastitis and milk production traits (milk, fat and protein yield) in Holstein (HOL, n = 5056) and Jersey (JER, n = 1231) cattle. We observed that a majority of genomic features were enriched in genomic variants that were associated with mastitis and milk production traits. Compared to GBLUP, the accuracy of genomic prediction with GFBLUP was marginally improved (3.2 to 3.9%) in within-breed prediction. The highest increase (164.4%) in prediction accuracy was observed in across-breed prediction. The significance of genomic features based on the SNP set test were correlated with changes in prediction accuracy of GFBLUP (P layers of biological knowledge to provide novel insights into the biological basis of complex traits, and to improve the accuracy of genomic prediction. The SNP set

  6. Predicting academic success in higher education: what’s more important than being smart?

    NARCIS (Netherlands)

    Kappe, F.R.; van der Flier, H.

    2012-01-01

    This study investigated the combined predictive validity of intelligence and personality factors on multiple measures of academic achievement. Students in a college of higher education in the Netherlands (N0137) completed a survey that measured intelligence, the Big Five personality traits,

  7. Accuracy of endoscopic ultrasonography for diagnosing ulcerative early gastric cancers

    Science.gov (United States)

    Park, Jin-Seok; Kim, Hyungkil; Bang, Byongwook; Kwon, Kyesook; Shin, Youngwoon

    2016-01-01

    Abstract Although endoscopic ultrasonography (EUS) is the first-choice imaging modality for predicting the invasion depth of early gastric cancer (EGC), the prediction accuracy of EUS is significantly decreased when EGC is combined with ulceration. The aim of present study was to compare the accuracy of EUS and conventional endoscopy (CE) for determining the depth of EGC. In addition, the various clinic-pathologic factors affecting the diagnostic accuracy of EUS, with a particular focus on endoscopic ulcer shapes, were evaluated. We retrospectively reviewed data from 236 consecutive patients with ulcerative EGC. All patients underwent EUS for estimating tumor invasion depth, followed by either curative surgery or endoscopic treatment. The diagnostic accuracy of EUS and CE was evaluated by comparing the final histologic result of resected specimen. The correlation between accuracy of EUS and characteristics of EGC (tumor size, histology, location in stomach, tumor invasion depth, and endoscopic ulcer shapes) was analyzed. Endoscopic ulcer shapes were classified into 3 groups: definite ulcer, superficial ulcer, and ill-defined ulcer. The overall accuracy of EUS and CE for predicting the invasion depth in ulcerative EGC was 68.6% and 55.5%, respectively. Of the 236 patients, 36 patients were classified as definite ulcers, 98 were superficial ulcers, and 102 were ill-defined ulcers, In univariate analysis, EUS accuracy was associated with invasion depth (P = 0.023), tumor size (P = 0.034), and endoscopic ulcer shapes (P = 0.001). In multivariate analysis, there is a significant association between superficial ulcer in CE and EUS accuracy (odds ratio: 2.977; 95% confidence interval: 1.255–7.064; P = 0.013). The accuracy of EUS for determining tumor invasion depth in ulcerative EGC was superior to that of CE. In addition, ulcer shape was an important factor that affected EUS accuracy. PMID:27472672

  8. Predictive Accuracy of Sweep Frequency Impedance Technology in Identifying Conductive Conditions in Newborns.

    Science.gov (United States)

    Aithal, Venkatesh; Kei, Joseph; Driscoll, Carlie; Murakoshi, Michio; Wada, Hiroshi

    2018-02-01

    Diagnosing conductive conditions in newborns is challenging for both audiologists and otolaryngologists. Although high-frequency tympanometry (HFT), acoustic stapedial reflex tests, and wideband absorbance measures are useful diagnostic tools, there is performance measure variability in their detection of middle ear conditions. Additional diagnostic sensitivity and specificity measures gained through new technology such as sweep frequency impedance (SFI) measures may assist in the diagnosis of middle ear dysfunction in newborns. The purpose of this study was to determine the test performance of SFI to predict the status of the outer and middle ear in newborns against commonly used reference standards. Automated auditory brainstem response (AABR), HFT (1000 Hz), transient evoked otoacoustic emission (TEOAE), distortion product otoacoustic emission (DPOAE), and SFI tests were administered to the study sample. A total of 188 neonates (98 males and 90 females) with a mean gestational age of 39.4 weeks were included in the sample. Mean age at the time of testing was 44.4 hr. Diagnostic accuracy of SFI was assessed in terms of its ability to identify conductive conditions in neonates when compared with nine different reference standards (including four single tests [AABR, HFT, TEOAE, and DPOAE] and five test batteries [HFT + DPOAE, HFT + TEOAE, DPOAE + TEOAE, DPOAE + AABR, and TEOAE + AABR]), using receiver operating characteristic (ROC) analysis and traditional test performance measures such as sensitivity and specificity. The test performance of SFI against the test battery reference standard of HFT + DPOAE and single reference standard of HFT was high with an area under the ROC curve (AROC) of 0.87 and 0.82, respectively. Although the HFT + DPOAE test battery reference standard performed better than the HFT reference standard in predicting middle ear conductive conditions in neonates, the difference in AROC was not significant. Further analysis revealed that the

  9. Gene network inherent in genomic big data improves the accuracy of prognostic prediction for cancer patients.

    Science.gov (United States)

    Kim, Yun Hak; Jeong, Dae Cheon; Pak, Kyoungjune; Goh, Tae Sik; Lee, Chi-Seung; Han, Myoung-Eun; Kim, Ji-Young; Liangwen, Liu; Kim, Chi Dae; Jang, Jeon Yeob; Cha, Wonjae; Oh, Sae-Ock

    2017-09-29

    Accurate prediction of prognosis is critical for therapeutic decisions regarding cancer patients. Many previously developed prognostic scoring systems have limitations in reflecting recent progress in the field of cancer biology such as microarray, next-generation sequencing, and signaling pathways. To develop a new prognostic scoring system for cancer patients, we used mRNA expression and clinical data in various independent breast cancer cohorts (n=1214) from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) and Gene Expression Omnibus (GEO). A new prognostic score that reflects gene network inherent in genomic big data was calculated using Network-Regularized high-dimensional Cox-regression (Net-score). We compared its discriminatory power with those of two previously used statistical methods: stepwise variable selection via univariate Cox regression (Uni-score) and Cox regression via Elastic net (Enet-score). The Net scoring system showed better discriminatory power in prediction of disease-specific survival (DSS) than other statistical methods (p=0 in METABRIC training cohort, p=0.000331, 4.58e-06 in two METABRIC validation cohorts) when accuracy was examined by log-rank test. Notably, comparison of C-index and AUC values in receiver operating characteristic analysis at 5 years showed fewer differences between training and validation cohorts with the Net scoring system than other statistical methods, suggesting minimal overfitting. The Net-based scoring system also successfully predicted prognosis in various independent GEO cohorts with high discriminatory power. In conclusion, the Net-based scoring system showed better discriminative power than previous statistical methods in prognostic prediction for breast cancer patients. This new system will mark a new era in prognosis prediction for cancer patients.

  10. The impact of training strategies on the accuracy of genomic predictors in United States Red Angus cattle.

    Science.gov (United States)

    Lee, J; Kachman, S D; Spangler, M L

    2017-08-01

    resulted in higher accuracies of MBV than those obtained by DEPD for growth and carcass traits. When DEPD were used as the response variable, accuracies were greater for threshold traits and those that are sex limited, likely due to the fact that these traits suffer from a lack of information content and excluding animals in training with only parental information substantially decreases the training population size. It is recommended that the contribution of parental average to deregressed EPD should be removed in the construction of genomic prediction equations. The difference in terms of prediction accuracies between the 2 SNP panels or the number of folds compared herein was negligible.

  11. MultiLoc2: integrating phylogeny and Gene Ontology terms improves subcellular protein localization prediction

    Directory of Open Access Journals (Sweden)

    Kohlbacher Oliver

    2009-09-01

    Full Text Available Abstract Background Knowledge of subcellular localization of proteins is crucial to proteomics, drug target discovery and systems biology since localization and biological function are highly correlated. In recent years, numerous computational prediction methods have been developed. Nevertheless, there is still a need for prediction methods that show more robustness and higher accuracy. Results We extended our previous MultiLoc predictor by incorporating phylogenetic profiles and Gene Ontology terms. Two different datasets were used for training the system, resulting in two versions of this high-accuracy prediction method. One version is specialized for globular proteins and predicts up to five localizations, whereas a second version covers all eleven main eukaryotic subcellular localizations. In a benchmark study with five localizations, MultiLoc2 performs considerably better than other methods for animal and plant proteins and comparably for fungal proteins. Furthermore, MultiLoc2 performs clearly better when using a second dataset that extends the benchmark study to all eleven main eukaryotic subcellular localizations. Conclusion MultiLoc2 is an extensive high-performance subcellular protein localization prediction system. By incorporating phylogenetic profiles and Gene Ontology terms MultiLoc2 yields higher accuracies compared to its previous version. Moreover, it outperforms other prediction systems in two benchmarks studies. MultiLoc2 is available as user-friendly and free web-service, available at: http://www-bs.informatik.uni-tuebingen.de/Services/MultiLoc2.

  12. Accuracy of the paracetamol-aminotransferase product to predict hepatotoxicity in paracetamol overdose treated with a 2-bag acetylcysteine regimen.

    Science.gov (United States)

    Wong, Anselm; Sivilotti, Marco L A; Gunja, Naren; McNulty, Richard; Graudins, Andis

    2018-03-01

    Paracetamol concentration is a highly accurate risk predictor for hepatotoxicity following overdose with known time of ingestion. However, the paracetamol-aminotransferase multiplication product can be used as a risk predictor independent of timing or ingestion type. Validated in patients treated with the traditional, "three-bag" intravenous acetylcysteine regimen, we evaluated the accuracy of the multiplication product in paracetamol overdose treated with a two-bag acetylcysteine regimen. We examined consecutive patients treated with the two-bag regimen from five emergency departments over a two-year period. We assessed the predictive accuracy of initial multiplication product for the primary outcome of hepatotoxicity (peak alanine aminotransferase ≥1000IU/L), as well as for acute liver injury (ALI), defined peak alanine aminotransferase ≥2× baseline and above 50IU/L). Of 447 paracetamol overdoses treated with the two-bag acetylcysteine regimen, 32 (7%) developed hepatotoxicity and 73 (16%) ALI. The pre-specified cut-off points of 1500 mg/L × IU/L (sensitivity 100% [95% CI 82%, 100%], specificity 62% [56%, 67%]) and 10,000 mg/L × IU/L (sensitivity 70% [47%, 87%], specificity of 97% [95%, 99%]) were highly accurate for predicting hepatotoxicity. There were few cases of hepatotoxicity irrespective of the product when acetylcysteine was administered within eight hours of overdose, when the product was largely determined by a high paracetamol concentration but normal aminotransferase. The multiplication product accurately predicts hepatotoxicity when using a two-bag acetylcysteine regimen, especially in patients treated more than eight hours post-overdose. Further studies are needed to assess the product as a method to adjust for exposure severity when testing efficacy of modified acetylcysteine regimens.

  13. Accuracy of Prediction Equations to Assess Percentage of Body Fat in Children and Adolescents with Down Syndrome Compared to Air Displacement Plethysmography

    Science.gov (United States)

    Gonzalez-Aguero, A.; Vicente-Rodriguez, G.; Ara, I.; Moreno, L. A.; Casajus, J. A.

    2011-01-01

    To determine the accuracy of the published percentage body fat (%BF) prediction equations (Durnin et al., Johnston et al., Brook and Slaughter et al.) from skinfold thickness compared to air displacement plethysmography (ADP) in children and adolescents with Down syndrome (DS). Twenty-eight children and adolescents with DS (10-20 years old; 12…

  14. Relationship between the Prediction Accuracy of Tsunami Inundation and Relative Distribution of Tsunami Source and Observation Arrays: A Case Study in Tokyo Bay

    Science.gov (United States)

    Takagawa, T.

    2017-12-01

    A rapid and precise tsunami forecast based on offshore monitoring is getting attention to reduce human losses due to devastating tsunami inundation. We developed a forecast method based on the combination of hierarchical Bayesian inversion with pre-computed database and rapid post-computing of tsunami inundation. The method was applied to Tokyo bay to evaluate the efficiency of observation arrays against three tsunamigenic earthquakes. One is a scenario earthquake at Nankai trough and the other two are historic ones of Genroku in 1703 and Enpo in 1677. In general, rich observation array near the tsunami source has an advantage in both accuracy and rapidness of tsunami forecast. To examine the effect of observation time length we used four types of data with the lengths of 5, 10, 20 and 45 minutes after the earthquake occurrences. Prediction accuracy of tsunami inundation was evaluated by the simulated tsunami inundation areas around Tokyo bay due to target earthquakes. The shortest time length of accurate prediction varied with target earthquakes. Here, accurate prediction means the simulated values fall within the 95% credible intervals of prediction. In Enpo earthquake case, 5-minutes observation is enough for accurate prediction for Tokyo bay, but 10-minutes and 45-minutes are needed in the case of Nankai trough and Genroku, respectively. The difference of the shortest time length for accurate prediction shows the strong relationship with the relative distance from the tsunami source and observation arrays. In the Enpo case, offshore tsunami observation points are densely distributed even in the source region. So, accurate prediction can be rapidly achieved within 5 minutes. This precise prediction is useful for early warnings. Even in the worst case of Genroku, where less observation points are available near the source, accurate prediction can be obtained within 45 minutes. This information can be useful to figure out the outline of the hazard in an early

  15. Effects of field plot size on prediction accuracy of aboveground biomass in airborne laser scanning-assisted inventories in tropical rain forests of Tanzania.

    Science.gov (United States)

    Mauya, Ernest William; Hansen, Endre Hofstad; Gobakken, Terje; Bollandsås, Ole Martin; Malimbwi, Rogers Ernest; Næsset, Erik

    2015-12-01

    Airborne laser scanning (ALS) has recently emerged as a promising tool to acquire auxiliary information for improving aboveground biomass (AGB) estimation in sample-based forest inventories. Under design-based and model-assisted inferential frameworks, the estimation relies on a model that relates the auxiliary ALS metrics to AGB estimated on ground plots. The size of the field plots has been identified as one source of model uncertainty because of the so-called boundary effects which increases with decreasing plot size. Recent research in tropical forests has aimed to quantify the boundary effects on model prediction accuracy, but evidence of the consequences for the final AGB estimates is lacking. In this study we analyzed the effect of field plot size on model prediction accuracy and its implication when used in a model-assisted inferential framework. The results showed that the prediction accuracy of the model improved as the plot size increased. The adjusted R 2 increased from 0.35 to 0.74 while the relative root mean square error decreased from 63.6 to 29.2%. Indicators of boundary effects were identified and confirmed to have significant effects on the model residuals. Variance estimates of model-assisted mean AGB relative to corresponding variance estimates of pure field-based AGB, decreased with increasing plot size in the range from 200 to 3000 m 2 . The variance ratio of field-based estimates relative to model-assisted variance ranged from 1.7 to 7.7. This study showed that the relative improvement in precision of AGB estimation when increasing field-plot size, was greater for an ALS-assisted inventory compared to that of a pure field-based inventory.

  16. Accuracy of CNV Detection from GWAS Data.

    Directory of Open Access Journals (Sweden)

    Dandan Zhang

    2011-01-01

    Full Text Available Several computer programs are available for detecting copy number variants (CNVs using genome-wide SNP arrays. We evaluated the performance of four CNV detection software suites--Birdsuite, Partek, HelixTree, and PennCNV-Affy--in the identification of both rare and common CNVs. Each program's performance was assessed in two ways. The first was its recovery rate, i.e., its ability to call 893 CNVs previously identified in eight HapMap samples by paired-end sequencing of whole-genome fosmid clones, and 51,440 CNVs identified by array Comparative Genome Hybridization (aCGH followed by validation procedures, in 90 HapMap CEU samples. The second evaluation was program performance calling rare and common CNVs in the Bipolar Genome Study (BiGS data set (1001 bipolar cases and 1033 controls, all of European ancestry as measured by the Affymetrix SNP 6.0 array. Accuracy in calling rare CNVs was assessed by positive predictive value, based on the proportion of rare CNVs validated by quantitative real-time PCR (qPCR, while accuracy in calling common CNVs was assessed by false positive/false negative rates based on qPCR validation results from a subset of common CNVs. Birdsuite recovered the highest percentages of known HapMap CNVs containing >20 markers in two reference CNV datasets. The recovery rate increased with decreased CNV frequency. In the tested rare CNV data, Birdsuite and Partek had higher positive predictive values than the other software suites. In a test of three common CNVs in the BiGS dataset, Birdsuite's call was 98.8% consistent with qPCR quantification in one CNV region, but the other two regions showed an unacceptable degree of accuracy. We found relatively poor consistency between the two "gold standards," the sequence data of Kidd et al., and aCGH data of Conrad et al. Algorithms for calling CNVs especially common ones need substantial improvement, and a "gold standard" for detection of CNVs remains to be established.

  17. Prediction of expected years of life using whole-genome markers.

    Directory of Open Access Journals (Sweden)

    Gustavo de los Campos

    Full Text Available Genetic factors are believed to account for 25% of the interindividual differences in Years of Life (YL among humans. However, the genetic loci that have thus far been found to be associated with YL explain a very small proportion of the expected genetic variation in this trait, perhaps reflecting the complexity of the trait and the limitations of traditional association studies when applied to traits affected by a large number of small-effect genes. Using data from the Framingham Heart Study and statistical methods borrowed largely from the field of animal genetics (whole-genome prediction, WGP, we developed a WGP model for the study of YL and evaluated the extent to which thousands of genetic variants across the genome examined simultaneously can be used to predict interindividual differences in YL. We find that a sizable proportion of differences in YL--which were unexplained by age at entry, sex, smoking and BMI--can be accounted for and predicted using WGP methods. The contribution of genomic information to prediction accuracy was even higher than that of smoking and body mass index (BMI combined; two predictors that are considered among the most important life-shortening factors. We evaluated the impacts of familial relationships and population structure (as described by the first two marker-derived principal components and concluded that in our dataset population structure explained partially, but not fully the gains in prediction accuracy obtained with WGP. Further inspection of prediction accuracies by age at death indicated that most of the gains in predictive ability achieved with WGP were due to the increased accuracy of prediction of early mortality, perhaps reflecting the ability of WGP to capture differences in genetic risk to deadly diseases such as cancer, which are most often responsible for early mortality in our sample.

  18. The accuracy of the SONOBREAST statistical model in comparison to BI-RADS for the prediction of malignancy in solid breast nodules detected at ultrasonography.

    Science.gov (United States)

    Paulinelli, Regis R; Oliveira, Luis-Fernando P; Freitas-Junior, Ruffo; Soares, Leonardo R

    2016-01-01

    The objective of the present study was to compare the accuracy of SONOBREAST for the prediction of malignancy in solid breast nodules detected at ultrasonography with that of the BI-RADS system and to assess the agreement between these two methods. This prospective study included 274 women and evaluated 500 breast nodules detected at ultrasonography. The probability of malignancy was calculated based on the SONOBREAST model, available at www.sonobreast.com.br, and on the BI-RADS system, with results being compared with the anatomopathology report. The lesions were considered suspect in 171 cases (34.20%), according to both SONOBREAST and BI-RADS. Agreement between the methods was perfect, as shown by a Kappa coefficient of 1 (pBI-RADS proved identical insofar as sensitivity (95.40%), specificity (78.69%), positive predictive value (48.54%), negative predictive value (98.78%) and accuracy (81.60%) are concerned. With respect to the categorical variables (BI-RADS categories 3, 4 and 5), the area under the receiver operating characteristic (ROC) curve was 94.41 for SONOBREAST (range 92.20-96.62) and 89.99 for BI-RADS (range 86.60-93.37). The accuracy of the SONOBREAST model is identical to that found with BI-RADS when the same parameters are used with respect to the cut-off point at which malignancy is suspected. Regarding the continuous probability of malignancy with BI-RADS categories 3, 4 and 5, SONOBREAST permits a more precise and individualized evaluation. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  19. Astudy on accuracy of predicted breeding value for body weight at eighth week of age in Khorasan native chickens

    Directory of Open Access Journals (Sweden)

    faeze ghorbani

    2015-12-01

    Full Text Available Introduction: Genetic resources in any country are valuable materials which needed to be conserved for a sustainable agriculture. An animal phenotype is generally affected by genetic and environmental factors. To increase mean performance in a population under consideration not only environmental conditions, but also genetic potential of the animals should be improved. Although, environmental improvement could increase the level of animals’ production in a more rapid way, it is not a permanent and non-cumulative progress. In any breeding schemes prediction breeding value of the candidate animals is needed to be obtained with a high precision and accuracy for making a remarkable genetic gain for the traits over the time. The main objective of the present research was to study accuracy of predicted breeding value for body weight at eighth week of age in indigenous chickens of Khorasan Razavi province. Materials and methods: A set of 47,000 body weight (at the age of eight weeks records belonging to 47,000 head of male and female chicks (progeny of 753 sires and 5,154 dams collected during seven generations (2006-2012 was used. The data were obtained in Khorasan Razavi native chicken breeding center. An animal model was applied for analyzing the records. In the model, contemporary group of generation*hatch*sex (GHS as a fixed effect, weight at birth as a covariable, as well as direct and maternal additive genetic random effects were taken into account. In an initial analysis using SAS software, all fixed and covariate factors included in the model were detected to be significant for the trait. All additive genetic relationships among the animals in the pedigree file (47,880 animals were accounted for. Variance and covariance components of direct and maternal additive genetic effects were estimated through restricted maximum likelihood (REML method. Breeding value of the animals was obtained by best linear unbiased prediction (BLUP. Selection

  20. Concurrent and predictive evaluation of malnutrition diagnostic measures in hip fracture inpatients: a diagnostic accuracy study.

    Science.gov (United States)

    Bell, J J; Bauer, J D; Capra, S; Pulle, R C

    2014-03-01

    Differences in malnutrition diagnostic measures impact malnutrition prevalence and outcomes data in hip fracture. This study investigated the concurrent and predictive validity of commonly reported malnutrition diagnostic measures in patients admitted to a metropolitan hospital acute hip fracture unit. A prospective, consecutive level II diagnostic accuracy study (n=142; 8 exclusions) including the International Classification of Disease, 10th Revision, Australian Modification (ICD10-AM) protein-energy malnutrition criteria, a body mass index (BMI) Patients were predominantly elderly (median age 83.5, range 50-100 years), female (68%), multimorbid (median five comorbidities), with 15% 4-month mortality. Malnutrition prevalence was lowest when assessed by BMI (13%), followed by MNA-SF (27%), ICD10-AM (48%), albumin (53%) and geriatrician assessment (55%). Agreement between measures was highest between ICD10-AM and geriatrician assessment (κ=0.61) followed by ICD10-AM and MNA-SF measures (κ=0.34). ICD10-AM diagnosed malnutrition was the only measure associated with 48-h mobilisation (35.0 vs 55.3%; P=0.018). Reduced likelihood of home discharge was predicted by ICD-10-AM (20.6 vs 57.1%; P=0.001) and MNA-SF (18.8 vs 47.8%; P=0.035). Bivariate analysis demonstrated ICD10-AM (relative risk (RR)1.2; 1.05-1.42) and MNA-SF (RR1.2; 1.0-1.5) predicted 4-month mortality. When adjusted for age, usual place of residency, comorbidities and time to surgery only ICD-10AM criteria predicted mortality (odds ratio 3.59; 1.10-11.77). Albumin, BMI and geriatrician assessment demonstrated limited concurrent and predictive validity. Malnutrition prevalence in hip fracture varies substantially depending on the diagnostic measure applied. ICD-10AM criteria or the MNA-SF should be considered for the diagnosis of protein-energy malnutrition in frail, multi-morbid hip fracture inpatients.

  1. Ditection of coronary artery disease: accuracy of 64- slice computed tomography versus converntional invasive angiography

    Directory of Open Access Journals (Sweden)

    Taghizadeh M

    2008-11-01

    Full Text Available "nBackground: Multislice computed tomography (MSCT is a noninvasive method of detecting coronary artery disease (CAD. The purpose of the present study was to investigate the accuracy of 64-slice MSCT (64-MSCT in daily practice, without patient selection. "nMethods: Sixty-four consecutive suspected CAD patients underwent both 64-MSCT and quantitative coronary angiography (QCA. The CT system The mean time span between MSCT and QCA was 7.2±3.9 days. For the 64-MSCT, detection or exclusion of CAD, defined as one or more areas of >50% stenosis within major epicardial coronary arteries, the sensitivity, specificity, diagnostic accuracy, positive predictive value (PPV, and negative predictive value (NPV were evaluated both per patient and per segment. "nResults: Sixty-one of the 64 coronary CT angiograms (95% were of diagnostic image quality. QCA showed significant CAD in 64% (39/61 of the patients, with the other 36% (22/61 showing nonsignificant disease or no disease. Sensitivity, specificity, accuracy, PPV, and NPV of 64-MSCT per patient were 92%, 86%, 90%, 92% and 96%, respectively. By the per-segment analysis, 695 of 791 coronary artery segments were assessable (88%. Of these, 64-MSCT showed a sensitivity of 80%, specificity of 92%, accuracy of 90%, PPV of 65%, and NPV of 96%, respectively, in detecting CAD. "nConclusions: Both per patient and per segment analyses for coronary 64-MSCT showed a higher diagnostic accuracy than QCA. This suggests 64-MSCT should primarily be used for risk stratification on a per patient basis as a noninvasive gate-keeper diagnostic method.

  2. Bottom-up coarse-grained models with predictive accuracy and transferability for both structural and thermodynamic properties of heptane-toluene mixtures.

    Science.gov (United States)

    Dunn, Nicholas J H; Noid, W G

    2016-05-28

    This work investigates the promise of a "bottom-up" extended ensemble framework for developing coarse-grained (CG) models that provide predictive accuracy and transferability for describing both structural and thermodynamic properties. We employ a force-matching variational principle to determine system-independent, i.e., transferable, interaction potentials that optimally model the interactions in five distinct heptane-toluene mixtures. Similarly, we employ a self-consistent pressure-matching approach to determine a system-specific pressure correction for each mixture. The resulting CG potentials accurately reproduce the site-site rdfs, the volume fluctuations, and the pressure equations of state that are determined by all-atom (AA) models for the five mixtures. Furthermore, we demonstrate that these CG potentials provide similar accuracy for additional heptane-toluene mixtures that were not included their parameterization. Surprisingly, the extended ensemble approach improves not only the transferability but also the accuracy of the calculated potentials. Additionally, we observe that the required pressure corrections strongly correlate with the intermolecular cohesion of the system-specific CG potentials. Moreover, this cohesion correlates with the relative "structure" within the corresponding mapped AA ensemble. Finally, the appendix demonstrates that the self-consistent pressure-matching approach corresponds to minimizing an appropriate relative entropy.

  3. Accuracy of topographic index models at identifying ephemeral gully trajectories on agricultural fields

    Science.gov (United States)

    Sheshukov, Aleksey Y.; Sekaluvu, Lawrence; Hutchinson, Stacy L.

    2018-04-01

    Topographic index (TI) models have been widely used to predict trajectories and initiation points of ephemeral gullies (EGs) in agricultural landscapes. Prediction of EGs strongly relies on the selected value of critical TI threshold, and the accuracy depends on topographic features, agricultural management, and datasets of observed EGs. This study statistically evaluated the predictions by TI models in two paired watersheds in Central Kansas that had different levels of structural disturbances due to implemented conservation practices. Four TI models with sole dependency on topographic factors of slope, contributing area, and planform curvature were used in this study. The observed EGs were obtained by field reconnaissance and through the process of hydrological reconditioning of digital elevation models (DEMs). The Kernel Density Estimation analysis was used to evaluate TI distribution within a 10-m buffer of the observed EG trajectories. The EG occurrence within catchments was analyzed using kappa statistics of the error matrix approach, while the lengths of predicted EGs were compared with the observed dataset using the Nash-Sutcliffe Efficiency (NSE) statistics. The TI frequency analysis produced bi-modal distribution of topographic indexes with the pixels within the EG trajectory having a higher peak. The graphs of kappa and NSE versus critical TI threshold showed similar profile for all four TI models and both watersheds with the maximum value representing the best comparison with the observed data. The Compound Topographic Index (CTI) model presented the overall best accuracy with NSE of 0.55 and kappa of 0.32. The statistics for the disturbed watershed showed higher best critical TI threshold values than for the undisturbed watershed. Structural conservation practices implemented in the disturbed watershed reduced ephemeral channels in headwater catchments, thus producing less variability in catchments with EGs. The variation in critical thresholds for all

  4. Adaboost Ensemble with Simple Genetic Algorithm for Student Prediction Mode

    OpenAIRE

    AhmedSharaf ElDen; ElDen1Malaka A. Moustafa2Hany; M. Harb; AbdelH.Emara

    2013-01-01

    Predicting the student performance is a great concern to the higher education managements.Thisprediction helps to identify and to improve students' performance.Several factors may improve thisperformance.In the present study, we employ the data mining processes, particularly classification, toenhance the quality of the higher educational system. Recently, a new direction is used for the improvementof the classification accuracy by combining classifiers.In thispaper, we design and evaluate a f...

  5. Energy-energy correlation in electron-positron annihilation at NNLL + NNLO accuracy

    Energy Technology Data Exchange (ETDEWEB)

    Tulipant, Zoltan; Kardos, Adam; Somogyi, Gabor [University of Debrecen, MTA-DE Particle Physics Research Group, Debrecen (Hungary)

    2017-11-15

    We present the computation of energy-energy correlation in e{sup +}e{sup -} collisions in the back-to-back region at next-to-next-to-leading logarithmic accuracy matched with the next-to-next-to-leading order perturbative prediction. We study the effect of the fixed higher-order corrections in a comparison of our results to LEP and SLC data. The next-to-next-to-leading order correction has a sizable impact on the extracted value of α{sub S}(M{sub Z}), hence its inclusion is mandatory for a precise measurement of the strong coupling using energy-energy correlation. (orig.)

  6. Energy-energy correlation in electron-positron annihilation at NNLL + NNLO accuracy

    Science.gov (United States)

    Tulipánt, Zoltán; Kardos, Adam; Somogyi, Gábor

    2017-11-01

    We present the computation of energy-energy correlation in e^+e^- collisions in the back-to-back region at next-to-next-to-leading logarithmic accuracy matched with the next-to-next-to-leading order perturbative prediction. We study the effect of the fixed higher-order corrections in a comparison of our results to LEP and SLC data. The next-to-next-to-leading order correction has a sizable impact on the extracted value of α S(M_Z), hence its inclusion is mandatory for a precise measurement of the strong coupling using energy-energy correlation.

  7. Predicting in vivo glioma growth with the reaction diffusion equation constrained by quantitative magnetic resonance imaging data

    International Nuclear Information System (INIS)

    Hormuth II, David A; Weis, Jared A; Barnes, Stephanie L; Miga, Michael I; Yankeelov, Thomas E; Rericha, Erin C; Quaranta, Vito

    2015-01-01

    Reaction–diffusion models have been widely used to model glioma growth. However, it has not been shown how accurately this model can predict future tumor status using model parameters (i.e., tumor cell diffusion and proliferation) estimated from quantitative in vivo imaging data. To this end, we used in silico studies to develop the methods needed to accurately estimate tumor specific reaction–diffusion model parameters, and then tested the accuracy with which these parameters can predict future growth. The analogous study was then performed in a murine model of glioma growth. The parameter estimation approach was tested using an in silico tumor ‘grown’ for ten days as dictated by the reaction–diffusion equation. Parameters were estimated from early time points and used to predict subsequent growth. Prediction accuracy was assessed at global (total volume and Dice value) and local (concordance correlation coefficient, CCC) levels. Guided by the in silico study, rats (n = 9) with C6 gliomas, imaged with diffusion weighted magnetic resonance imaging, were used to evaluate the model’s accuracy for predicting in vivo tumor growth. The in silico study resulted in low global (tumor volume error 0.92) and local (CCC values >0.80) level errors for predictions up to six days into the future. The in vivo study showed higher global (tumor volume error >11.7%, Dice <0.81) and higher local (CCC <0.33) level errors over the same time period. The in silico study shows that model parameters can be accurately estimated and used to accurately predict future tumor growth at both the global and local scale. However, the poor predictive accuracy in the experimental study suggests the reaction–diffusion equation is an incomplete description of in vivo C6 glioma biology and may require further modeling of intra-tumor interactions including segmentation of (for example) proliferative and necrotic regions. (paper)

  8. Genomic Prediction of Gene Bank Wheat Landraces

    Directory of Open Access Journals (Sweden)

    José Crossa

    2016-07-01

    Full Text Available This study examines genomic prediction within 8416 Mexican landrace accessions and 2403 Iranian landrace accessions stored in gene banks. The Mexican and Iranian collections were evaluated in separate field trials, including an optimum environment for several traits, and in two separate environments (drought, D and heat, H for the highly heritable traits, days to heading (DTH, and days to maturity (DTM. Analyses accounting and not accounting for population structure were performed. Genomic prediction models include genotype × environment interaction (G × E. Two alternative prediction strategies were studied: (1 random cross-validation of the data in 20% training (TRN and 80% testing (TST (TRN20-TST80 sets, and (2 two types of core sets, “diversity” and “prediction”, including 10% and 20%, respectively, of the total collections. Accounting for population structure decreased prediction accuracy by 15–20% as compared to prediction accuracy obtained when not accounting for population structure. Accounting for population structure gave prediction accuracies for traits evaluated in one environment for TRN20-TST80 that ranged from 0.407 to 0.677 for Mexican landraces, and from 0.166 to 0.662 for Iranian landraces. Prediction accuracy of the 20% diversity core set was similar to accuracies obtained for TRN20-TST80, ranging from 0.412 to 0.654 for Mexican landraces, and from 0.182 to 0.647 for Iranian landraces. The predictive core set gave similar prediction accuracy as the diversity core set for Mexican collections, but slightly lower for Iranian collections. Prediction accuracy when incorporating G × E for DTH and DTM for Mexican landraces for TRN20-TST80 was around 0.60, which is greater than without the G × E term. For Iranian landraces, accuracies were 0.55 for the G × E model with TRN20-TST80. Results show promising prediction accuracies for potential use in germplasm enhancement and rapid introgression of exotic germplasm

  9. Impact of an intra-cycle motion correction algorithm on overall evaluability and diagnostic accuracy of computed tomography coronary angiography

    Energy Technology Data Exchange (ETDEWEB)

    Pontone, Gianluca; Bertella, Erika; Baggiano, Andrea; Mushtaq, Saima; Loguercio, Monica; Segurini, Chiara; Conte, Edoardo; Beltrama, Virginia; Annoni, Andrea; Formenti, Alberto; Petulla, Maria; Trabattoni, Daniela; Pepi, Mauro [Centro Cardiologico Monzino, IRCCS, Milan (Italy); Andreini, Daniele; Montorsi, Piero; Bartorelli, Antonio L. [Centro Cardiologico Monzino, IRCCS, Milan (Italy); University of Milan, Department of Cardiovascular Sciences and Community Health, Milan (Italy); Guaricci, Andrea I. [University of Foggia, Department of Cardiology, Foggia (Italy)

    2016-01-15

    The aim of this study was to evaluate the impact of a novel intra-cycle motion correction algorithm (MCA) on overall evaluability and diagnostic accuracy of cardiac computed tomography coronary angiography (CCT). From a cohort of 900 consecutive patients referred for CCT for suspected coronary artery disease (CAD), we enrolled 160 (18 %) patients (mean age 65.3 ± 11.7 years, 101 male) with at least one coronary segment classified as non-evaluable for motion artefacts. The CCT data sets were evaluated using a standard reconstruction algorithm (SRA) and MCA and compared in terms of subjective image quality, evaluability and diagnostic accuracy. The mean heart rate during the examination was 68.3 ± 9.4 bpm. The MCA showed a higher Likert score (3.1 ± 0.9 vs. 2.5 ± 1.1, p < 0.001) and evaluability (94%vs.79 %, p < 0.001) than the SRA. In a 45-patient subgroup studied by clinically indicated invasive coronary angiography, specificity, positive predictive value and accuracy were higher in MCA vs. SRA in segment-based and vessel-based models, respectively (87%vs.73 %, 50%vs.34 %, 85%vs.73 %, p < 0.001 and 62%vs.28 %, 66%vs.51 % and 75%vs.57 %, p < 0.001). In a patient-based model, MCA showed higher accuracy vs. SCA (93%vs.76 %, p < 0.05). MCA can significantly improve subjective image quality, overall evaluability and diagnostic accuracy of CCT. (orig.)

  10. The influence of the new ECMWF Ensemble Prediction System resolution on wind power forecast accuracy and uncertainty estimation

    DEFF Research Database (Denmark)

    Alessandrini, S.; Pinson, Pierre; Sperati, S.

    2011-01-01

    The importance of wind power forecasting (WPF) is nowadays commonly recognized because it represents a useful tool to reduce problems of grid integration and to facilitate energy trading. If on one side the prediction accuracy is fundamental to these scopes, on the other it has become also clear...... by a recalibration procedure that allowed obtaining a more uniform distribution among the 51 intervals, making the ensemble spread large enough to include the observations. After that it was observed that the EPS power spread seemed to have enough correlation with the error calculated on the deterministic forecast...

  11. Bioinformatics analysis of the predicted polyprenol reductase genes in higher plants

    Science.gov (United States)

    Basyuni, M.; Wati, R.

    2018-03-01

    The present study evaluates the bioinformatics methods to analyze twenty-four predicted polyprenol reductase genes from higher plants on GenBank as well as predicted the structure, composition, similarity, subcellular localization, and phylogenetic. The physicochemical properties of plant polyprenol showed diversity among the observed genes. The percentage of the secondary structure of plant polyprenol genes followed the ratio order of α helix > random coil > extended chain structure. The values of chloroplast but not signal peptide were too low, indicated that few chloroplast transit peptide in plant polyprenol reductase genes. The possibility of the potential transit peptide showed variation among the plant polyprenol reductase, suggested the importance of understanding the variety of peptide components of plant polyprenol genes. To clarify this finding, a phylogenetic tree was drawn. The phylogenetic tree shows several branches in the tree, suggested that plant polyprenol reductase genes grouped into divergent clusters in the tree.

  12. Impact of Different Active-Speech-Ratios on PESQ’s Predictions in Case of Independent and Dependent Losses (in Presence of Receiver-Side Comfort-Noise

    Directory of Open Access Journals (Sweden)

    P. Pocta

    2010-04-01

    Full Text Available This paper deals with the investigation of PESQ’s behavior under independent and dependent loss conditions from an Active-Speech-Ratio perspective in presence of receiver-side comfort-noise. This reference signal characteristic is defined very broadly by ITU-T Recommendation P.862.3. That is the reason to investigate an impact of this characteristic on speech quality prediction more in-depth. We assess the variability of PESQ’s predictions with respect to Active-Speech-Ratios and loss conditions, as well as their accuracy, by comparing the predictions with subjective assessments. Our results show that an increase in amount of speech in the reference signal (expressed by the Active-Speech-Ratio characteristic may result in an increase of the reference signal sensitivity to packet loss change. Interestingly, we have found two additional effects in this investigated case. The use of higher Active-Speech-Ratios may lead to negative shifting effect in MOS domain and also PESQ’s predictions accuracy declining. Predictions accuracy could be improved by higher packet losses.

  13. Effects of the number of markers per haplotype and clustering of haplotypes on the accuracy of QTL mapping and prediction of genomic breeding values

    NARCIS (Netherlands)

    Calus, M.P.L.; Meuwissen, T.H.E.; Windig, J.J.; Knol, E.F.; Schrooten, C.; Vereijken, A.L.J.; Veerkamp, R.F.

    2009-01-01

    The aim of this paper was to compare the effect of haplotype definition on the precision of QTL-mapping and on the accuracy of predicted genomic breeding values. In a multiple QTL model using identity-by-descent (IBD) probabilities between haplotypes, various haplotype definitions were tested i.e.

  14. SU-E-T-802: Verification of Implanted Cardiac Pacemaker Doses in Intensity-Modulated Radiation Therapy: Dose Prediction Accuracy and Reduction Effect of a Lead Sheet

    Energy Technology Data Exchange (ETDEWEB)

    Lee, J [Dept. of Radiation Oncology, Konkuk University Medical Center, Seoul (Korea, Republic of); Chung, J [Dept. of Radiation Oncology, Seoul National University Bundang Hospital, Seongnam (Korea, Republic of)

    2015-06-15

    Purpose: To verify delivered doses on the implanted cardiac pacemaker, predicted doses with and without dose reduction method were verified using the MOSFET detectors in terms of beam delivery and dose calculation techniques in intensity-modulated radiation therapy (IMRT). Methods: The pacemaker doses for a patient with a tongue cancer were predicted according to the beam delivery methods [step-and-shoot (SS) and sliding window (SW)], intensity levels for dose optimization, and dose calculation algorithms. Dosimetric effects on the pacemaker were calculated three dose engines: pencil-beam convolution (PBC), analytical anisotropic algorithm (AAA), and Acuros-XB. A lead shield of 2 mm thickness was designed for minimizing irradiated doses to the pacemaker. Dose variations affected by the heterogeneous material properties of the pacemaker and effectiveness of the lead shield were predicted by the Acuros-XB. Dose prediction accuracy and the feasibility of the dose reduction strategy were verified based on the measured skin doses right above the pacemaker using mosfet detectors during the radiation treatment. Results: The Acuros-XB showed underestimated skin doses and overestimated doses by the lead-shield effect, even though the lower dose disagreement was observed. It led to improved dose prediction with higher intensity level of dose optimization in IMRT. The dedicated tertiary lead sheet effectively achieved reduction of pacemaker dose up to 60%. Conclusion: The current SS technique could deliver lower scattered doses than recommendation criteria, however, use of the lead sheet contributed to reduce scattered doses.Thin lead plate can be a useful tertiary shielder and it could not acuse malfunction or electrical damage of the implanted pacemaker in IMRT. It is required to estimate more accurate scattered doses of the patient with medical device to design proper dose reduction strategy.

  15. SU-E-T-802: Verification of Implanted Cardiac Pacemaker Doses in Intensity-Modulated Radiation Therapy: Dose Prediction Accuracy and Reduction Effect of a Lead Sheet

    International Nuclear Information System (INIS)

    Lee, J; Chung, J

    2015-01-01

    Purpose: To verify delivered doses on the implanted cardiac pacemaker, predicted doses with and without dose reduction method were verified using the MOSFET detectors in terms of beam delivery and dose calculation techniques in intensity-modulated radiation therapy (IMRT). Methods: The pacemaker doses for a patient with a tongue cancer were predicted according to the beam delivery methods [step-and-shoot (SS) and sliding window (SW)], intensity levels for dose optimization, and dose calculation algorithms. Dosimetric effects on the pacemaker were calculated three dose engines: pencil-beam convolution (PBC), analytical anisotropic algorithm (AAA), and Acuros-XB. A lead shield of 2 mm thickness was designed for minimizing irradiated doses to the pacemaker. Dose variations affected by the heterogeneous material properties of the pacemaker and effectiveness of the lead shield were predicted by the Acuros-XB. Dose prediction accuracy and the feasibility of the dose reduction strategy were verified based on the measured skin doses right above the pacemaker using mosfet detectors during the radiation treatment. Results: The Acuros-XB showed underestimated skin doses and overestimated doses by the lead-shield effect, even though the lower dose disagreement was observed. It led to improved dose prediction with higher intensity level of dose optimization in IMRT. The dedicated tertiary lead sheet effectively achieved reduction of pacemaker dose up to 60%. Conclusion: The current SS technique could deliver lower scattered doses than recommendation criteria, however, use of the lead sheet contributed to reduce scattered doses.Thin lead plate can be a useful tertiary shielder and it could not acuse malfunction or electrical damage of the implanted pacemaker in IMRT. It is required to estimate more accurate scattered doses of the patient with medical device to design proper dose reduction strategy

  16. [Prediction of regional soil quality based on mutual information theory integrated with decision tree algorithm].

    Science.gov (United States)

    Lin, Fen-Fang; Wang, Ke; Yang, Ning; Yan, Shi-Guang; Zheng, Xin-Yu

    2012-02-01

    In this paper, some main factors such as soil type, land use pattern, lithology type, topography, road, and industry type that affect soil quality were used to precisely obtain the spatial distribution characteristics of regional soil quality, mutual information theory was adopted to select the main environmental factors, and decision tree algorithm See 5.0 was applied to predict the grade of regional soil quality. The main factors affecting regional soil quality were soil type, land use, lithology type, distance to town, distance to water area, altitude, distance to road, and distance to industrial land. The prediction accuracy of the decision tree model with the variables selected by mutual information was obviously higher than that of the model with all variables, and, for the former model, whether of decision tree or of decision rule, its prediction accuracy was all higher than 80%. Based on the continuous and categorical data, the method of mutual information theory integrated with decision tree could not only reduce the number of input parameters for decision tree algorithm, but also predict and assess regional soil quality effectively.

  17. THE ACCURACY OF SCHEFFE'S THIRD DEGREE OVER SECOND ...

    African Journals Online (AJOL)

    NIJOTECH

    Scheffe's (1958), simplex theory to optimize the compressive strength of concrete made from RHA pozzolan based on (4,2) and (4,3) simplex lattices. The strengths predicted by the models are in good agreement with their corresponding experimentally observed values. The accuracy of strength predicted by the third degree ...

  18. Ensemble-based prediction of RNA secondary structures.

    Science.gov (United States)

    Aghaeepour, Nima; Hoos, Holger H

    2013-04-24

    Accurate structure prediction methods play an important role for the understanding of RNA function. Energy-based, pseudoknot-free secondary structure prediction is one of the most widely used and versatile approaches, and improved methods for this task have received much attention over the past five years. Despite the impressive progress that as been achieved in this area, existing evaluations of the prediction accuracy achieved by various algorithms do not provide a comprehensive, statistically sound assessment. Furthermore, while there is increasing evidence that no prediction algorithm consistently outperforms all others, no work has been done to exploit the complementary strengths of multiple approaches. In this work, we present two contributions to the area of RNA secondary structure prediction. Firstly, we use state-of-the-art, resampling-based statistical methods together with a previously published and increasingly widely used dataset of high-quality RNA structures to conduct a comprehensive evaluation of existing RNA secondary structure prediction procedures. The results from this evaluation clarify the performance relationship between ten well-known existing energy-based pseudoknot-free RNA secondary structure prediction methods and clearly demonstrate the progress that has been achieved in recent years. Secondly, we introduce AveRNA, a generic and powerful method for combining a set of existing secondary structure prediction procedures into an ensemble-based method that achieves significantly higher prediction accuracies than obtained from any of its component procedures. Our new, ensemble-based method, AveRNA, improves the state of the art for energy-based, pseudoknot-free RNA secondary structure prediction by exploiting the complementary strengths of multiple existing prediction procedures, as demonstrated using a state-of-the-art statistical resampling approach. In addition, AveRNA allows an intuitive and effective control of the trade-off between

  19. Diagnostic accuracy of calculated serum osmolarity to predict dehydration in older people: adding value to pathology laboratory reports.

    Science.gov (United States)

    Hooper, Lee; Abdelhamid, Asmaa; Ali, Adam; Bunn, Diane K; Jennings, Amy; John, W Garry; Kerry, Susan; Lindner, Gregor; Pfortmueller, Carmen A; Sjöstrand, Fredrik; Walsh, Neil P; Fairweather-Tait, Susan J; Potter, John F; Hunter, Paul R; Shepstone, Lee

    2015-10-21

    To assess which osmolarity equation best predicts directly measured serum/plasma osmolality and whether its use could add value to routine blood test results through screening for dehydration in older people. Diagnostic accuracy study. Older people (≥65 years) in 5 cohorts: Dietary Strategies for Healthy Ageing in Europe (NU-AGE, living in the community), Dehydration Recognition In our Elders (DRIE, living in residential care), Fortes (admitted to acute medical care), Sjöstrand (emergency room) or Pfortmueller cohorts (hospitalised with liver cirrhosis). Directly measured serum/plasma osmolality: current dehydration (serum osmolality>300 mOsm/kg), impending/current dehydration (≥295 mOsm/kg). 39 osmolarity equations calculated using serum indices from the same blood draw as directly measured osmolality. Across 5 cohorts 595 older people were included, of whom 19% were dehydrated (directly measured osmolality>300 mOsm/kg). Of 39 osmolarity equations, 5 showed reasonable agreement with directly measured osmolality and 3 had good predictive accuracy in subgroups with diabetes and poor renal function. Two equations were characterised by narrower limits of agreement, low levels of differential bias and good diagnostic accuracy in receiver operating characteristic plots (areas under the curve>0.8). The best equation was osmolarity=1.86×(Na++K+)+1.15×glucose+urea+14 (all measured in mmol/L). It appeared useful in people aged ≥65 years with and without diabetes, poor renal function, dehydration, in men and women, with a range of ages, health, cognitive and functional status. Some commonly used osmolarity equations work poorly, and should not be used. Given costs and prevalence of dehydration in older people we suggest use of the best formula by pathology laboratories using a cutpoint of 295 mOsm/L (sensitivity 85%, specificity 59%), to report dehydration risk opportunistically when serum glucose, urea and electrolytes are measured for other reasons in

  20. Genomic Prediction of Manganese Efficiency in Winter Barley

    Directory of Open Access Journals (Sweden)

    Florian Leplat

    2016-07-01

    Full Text Available Manganese efficiency is a quantitative abiotic stress trait controlled by several genes each with a small effect. Manganese deficiency leads to yield reduction in winter barley ( L.. Breeding new cultivars for this trait remains difficult because of the lack of visual symptoms and the polygenic features of the trait. Hence, Mn efficiency is a potential suitable trait for a genomic selection (GS approach. A collection of 248 winter barley varieties was screened for Mn efficiency using Chlorophyll (Chl fluorescence in six environments prone to induce Mn deficiency. Two models for genomic prediction were implemented to predict future performance and breeding value of untested varieties. Predictions were obtained using multivariate mixed models: best linear unbiased predictor (BLUP and genomic best linear unbiased predictor (G-BLUP. In the first model, predictions were based on the phenotypic evaluation, whereas both phenotypic and genomic marker data were included in the second model. Accuracy of predicting future phenotype, , and accuracy of predicting true breeding values, , were calculated and compared for both models using six cross-validation (CV schemes; these were designed to mimic plant breeding programs. Overall, the CVs showed that prediction accuracies increased when using the G-BLUP model compared with the prediction accuracies using the BLUP model. Furthermore, the accuracies [] of predicting breeding values were more accurate than accuracy of predicting future phenotypes []. The study confirms that genomic data may enhance the prediction accuracy. Moreover it indicates that GS is a suitable breeding approach for quantitative abiotic stress traits.

  1. Persistency of Prediction Accuracy and Genetic Gain in Synthetic Populations Under Recurrent Genomic Selection.

    Science.gov (United States)

    Müller, Dominik; Schopp, Pascal; Melchinger, Albrecht E

    2017-03-10

    Recurrent selection (RS) has been used in plant breeding to successively improve synthetic and other multiparental populations. Synthetics are generated from a limited number of parents [Formula: see text] but little is known about how [Formula: see text] affects genomic selection (GS) in RS, especially the persistency of prediction accuracy ([Formula: see text]) and genetic gain. Synthetics were simulated by intermating [Formula: see text]= 2-32 parent lines from an ancestral population with short- or long-range linkage disequilibrium ([Formula: see text]) and subjected to multiple cycles of GS. We determined [Formula: see text] and genetic gain across 30 cycles for different training set ( TS ) sizes, marker densities, and generations of recombination before model training. Contributions to [Formula: see text] and genetic gain from pedigree relationships, as well as from cosegregation and [Formula: see text] between QTL and markers, were analyzed via four scenarios differing in (i) the relatedness between TS and selection candidates and (ii) whether selection was based on markers or pedigree records. Persistency of [Formula: see text] was high for small [Formula: see text] where predominantly cosegregation contributed to [Formula: see text], but also for large [Formula: see text] where [Formula: see text] replaced cosegregation as the dominant information source. Together with increasing genetic variance, this compensation resulted in relatively constant long- and short-term genetic gain for increasing [Formula: see text] > 4, given long-range LD A in the ancestral population. Although our scenarios suggest that information from pedigree relationships contributed to [Formula: see text] for only very few generations in GS, we expect a longer contribution than in pedigree BLUP, because capturing Mendelian sampling by markers reduces selective pressure on pedigree relationships. Larger TS size ([Formula: see text]) and higher marker density improved persistency of

  2. Factors affecting GEBV accuracy with single-step Bayesian models.

    Science.gov (United States)

    Zhou, Lei; Mrode, Raphael; Zhang, Shengli; Zhang, Qin; Li, Bugao; Liu, Jian-Feng

    2018-01-01

    A single-step approach to obtain genomic prediction was first proposed in 2009. Many studies have investigated the components of GEBV accuracy in genomic selection. However, it is still unclear how the population structure and the relationships between training and validation populations influence GEBV accuracy in terms of single-step analysis. Here, we explored the components of GEBV accuracy in single-step Bayesian analysis with a simulation study. Three scenarios with various numbers of QTL (5, 50, and 500) were simulated. Three models were implemented to analyze the simulated data: single-step genomic best linear unbiased prediction (GBLUP; SSGBLUP), single-step BayesA (SS-BayesA), and single-step BayesB (SS-BayesB). According to our results, GEBV accuracy was influenced by the relationships between the training and validation populations more significantly for ungenotyped animals than for genotyped animals. SS-BayesA/BayesB showed an obvious advantage over SSGBLUP with the scenarios of 5 and 50 QTL. SS-BayesB model obtained the lowest accuracy with the 500 QTL in the simulation. SS-BayesA model was the most efficient and robust considering all QTL scenarios. Generally, both the relationships between training and validation populations and LD between markers and QTL contributed to GEBV accuracy in the single-step analysis, and the advantages of single-step Bayesian models were more apparent when the trait is controlled by fewer QTL.

  3. Bottom-up coarse-grained models with predictive accuracy and transferability for both structural and thermodynamic properties of heptane-toluene mixtures

    Energy Technology Data Exchange (ETDEWEB)

    Dunn, Nicholas J. H.; Noid, W. G., E-mail: wnoid@chem.psu.edu [Department of Chemistry, The Pennsylvania State University, University Park, Pennsylvania 16802 (United States)

    2016-05-28

    This work investigates the promise of a “bottom-up” extended ensemble framework for developing coarse-grained (CG) models that provide predictive accuracy and transferability for describing both structural and thermodynamic properties. We employ a force-matching variational principle to determine system-independent, i.e., transferable, interaction potentials that optimally model the interactions in five distinct heptane-toluene mixtures. Similarly, we employ a self-consistent pressure-matching approach to determine a system-specific pressure correction for each mixture. The resulting CG potentials accurately reproduce the site-site rdfs, the volume fluctuations, and the pressure equations of state that are determined by all-atom (AA) models for the five mixtures. Furthermore, we demonstrate that these CG potentials provide similar accuracy for additional heptane-toluene mixtures that were not included their parameterization. Surprisingly, the extended ensemble approach improves not only the transferability but also the accuracy of the calculated potentials. Additionally, we observe that the required pressure corrections strongly correlate with the intermolecular cohesion of the system-specific CG potentials. Moreover, this cohesion correlates with the relative “structure” within the corresponding mapped AA ensemble. Finally, the appendix demonstrates that the self-consistent pressure-matching approach corresponds to minimizing an appropriate relative entropy.

  4. Accuracy Enhancement with Processing Error Prediction and Compensation of a CNC Flame Cutting Machine Used in Spatial Surface Operating Conditions

    Directory of Open Access Journals (Sweden)

    Shenghai Hu

    2017-04-01

    Full Text Available This study deals with the precision performance of the CNC flame-cutting machine used in spatial surface operating conditions and presents an accuracy enhancement method based on processing error modeling prediction and real-time compensation. Machining coordinate systems and transformation matrix models were established for the CNC flame processing system considering both geometric errors and thermal deformation effects. Meanwhile, prediction and compensation models were constructed related to the actual cutting situation. Focusing on the thermal deformation elements, finite element analysis was used to measure the testing data of thermal errors, the grey system theory was applied to optimize the key thermal points, and related thermal dynamics models were carried out to achieve high-precision prediction values. Comparison experiments between the proposed method and the teaching method were conducted on the processing system after performing calibration. The results showed that the proposed method is valid and the cutting quality could be improved by more than 30% relative to the teaching method. Furthermore, the proposed method can be used under any working condition by making a few adjustments to the prediction and compensation models.

  5. Analysis of energy-based algorithms for RNA secondary structure prediction

    Directory of Open Access Journals (Sweden)

    Hajiaghayi Monir

    2012-02-01

    . Second, on our large datasets, the algorithm with best overall accuracy is a pseudo MEA-based algorithm of Hamada et al. that uses a generalized centroid estimator of base pairs. However, between MFE and other MEA-based methods, there is no clear winner in the sense that the relative accuracy of the MFE versus MEA-based algorithms changes depending on the underlying energy parameters. Third, of the four parameter sets we considered, the best accuracy for the MFE-, MEA-based, and pseudo-MEA-based methods is 0.686, 0.680, and 0.711, respectively (on a scale from 0 to 1 with 1 meaning perfect structure predictions and is obtained with a thermodynamic parameter set obtained by Andronescu et al. called BL* (named after the Boltzmann likelihood method by which the parameters were derived. Conclusions Large datasets should be used to obtain reliable measures of the accuracy of RNA structure prediction algorithms, and average accuracies on specific classes (such as Group I introns and Transfer RNAs should be interpreted with caution, considering the relatively small size of currently available datasets for such classes. The accuracy of the MEA-based methods is significantly higher when using the BL* parameter set of Andronescu et al. than when using the parameters of Mathews and Turner, and there is no significant difference between the accuracy of MEA-based methods and MFE when using the BL* parameters. The pseudo-MEA-based method of Hamada et al. with the BL* parameter set significantly outperforms all other MFE and MEA-based algorithms on our large data sets.

  6. Clinical accuracy of point-of-care urine culture in general practice

    DEFF Research Database (Denmark)

    Holm, Anne; Cordoba, Gloria; Sørensen, Tina Møller

    2017-01-01

    OBJECTIVE: To assess the clinical accuracy (sensitivity (SEN), specificity (SPE), positive predictive value and negative predictive value) of two point-of-care (POC) urine culture tests for the identification of urinary tract infection (UTI) in general practice. DESIGN: Prospective diagnostic...... uncomplicated, symptomatic UTI. MAIN OUTCOME MEASURES: (1) Overall accuracy of POC urine culture in general practice. (2) Individual accuracy of each of the two POC tests in this study. (3) Accuracy of POC urine culture in general practice with enterococci excluded, since enterococci are known to multiply...... general practices recruited 341 patients with suspected uncomplicated UTI. The overall agreement between index test and reference was 0.76 (CI: 0.71-0.80), SEN 0.88 (CI: 0.83-0.92) and SPE 0.55 (CI: 0.46-0.64). The two POC tests produced similar results individually. Overall agreement with enterococci...

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

    Science.gov (United States)

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

    2017-09-01

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

  8. Automatically explaining machine learning prediction results: a demonstration on type 2 diabetes risk prediction.

    Science.gov (United States)

    Luo, Gang

    2016-01-01

    Predictive modeling is a key component of solutions to many healthcare problems. Among all predictive modeling approaches, machine learning methods often achieve the highest prediction accuracy, but suffer from a long-standing open problem precluding their widespread use in healthcare. Most machine learning models give no explanation for their prediction results, whereas interpretability is essential for a predictive model to be adopted in typical healthcare settings. This paper presents the first complete method for automatically explaining results for any machine learning predictive model without degrading accuracy. We did a computer coding implementation of the method. Using the electronic medical record data set from the Practice Fusion diabetes classification competition containing patient records from all 50 states in the United States, we demonstrated the method on predicting type 2 diabetes diagnosis within the next year. For the champion machine learning model of the competition, our method explained prediction results for 87.4 % of patients who were correctly predicted by the model to have type 2 diabetes diagnosis within the next year. Our demonstration showed the feasibility of automatically explaining results for any machine learning predictive model without degrading accuracy.

  9. Comparative accuracy of Computed Tomography and lymphoangiography in detecting lymph node metastases from epithelial cancer of the ovary

    International Nuclear Information System (INIS)

    La Fianza, A.; Dore, R.; Campani, R.; Babilonti, L.; Tateo, S.

    1991-01-01

    The accuracy is investigated of both lymphangiography and CT in detecting lymph nodes metastases in 59 patients evaluated preoperatively and subsequently submitted to surgery with selective/systemic pelvic and paraaortic lymphadenectomy. CT accuracy was also investigated in 46 patients with a clinically suspected relapse of ovarian cancer (verified by means of clinical and/or CT follow-up in 36 patients, by laparotomy in 7, by fine-needle biopsy in 1 and by necroscopy in the last 2). In the first group (previously untreated patients) the overall results in the pelvis were, respectively, for lymphangiography and CT: 94.9% vs 89.8% accuracy, 86.6% vs 60% sensitivity, 97.7% vs 100% specificity, and 92.8% vs 100%, 95.5% vs 88% positive and negative predictive values. In the paraaortic region the results were: 89.1% vs 86.5% accuracy, 73.3% vs 66.6% sensitivity, 100% specificity for both techniques, 100% positive predictive value, and 84.6% vs 81.5% negative predictive value. In the second group (clinically suspected relapse), CT accuracy, sensitivity and specificity were, respectively: 91.3%, 81.8%,and 100%. Our experience demonstrated a high incidence of lymph node metastases in ovarian cancer, both in pelvic (15/49; 25.5%) and especially in aortic (15/37; 40.5%) locations in untreated patients, and an even higher incidence in relapses (22/42; 52.5%). The high specificity and positive predictive value of CT depended on the fact that there were no false positives. We arbitrarily considered as metastatic a lymph node with diameter >2cm, and this threshold seemed to be of clinical value since it made a good predictor of metastases. Among diagnostic imaging modalities, CT is suggested as the method of choice for the evaluation of pelvic and paraaortic lymph node metastases in untreated and relapsing ovarian cancers. Lymphangiography, a more invasive- though more accurate- technique, is indicated after normal CT

  10. Does filler database size influence identification accuracy?

    Science.gov (United States)

    Bergold, Amanda N; Heaton, Paul

    2018-06-01

    Police departments increasingly use large photo databases to select lineup fillers using facial recognition software, but this technological shift's implications have been largely unexplored in eyewitness research. Database use, particularly if coupled with facial matching software, could enable lineup constructors to increase filler-suspect similarity and thus enhance eyewitness accuracy (Fitzgerald, Oriet, Price, & Charman, 2013). However, with a large pool of potential fillers, such technologies might theoretically produce lineup fillers too similar to the suspect (Fitzgerald, Oriet, & Price, 2015; Luus & Wells, 1991; Wells, Rydell, & Seelau, 1993). This research proposes a new factor-filler database size-as a lineup feature affecting eyewitness accuracy. In a facial recognition experiment, we select lineup fillers in a legally realistic manner using facial matching software applied to filler databases of 5,000, 25,000, and 125,000 photos, and find that larger databases are associated with a higher objective similarity rating between suspects and fillers and lower overall identification accuracy. In target present lineups, witnesses viewing lineups created from the larger databases were less likely to make correct identifications and more likely to select known innocent fillers. When the target was absent, database size was associated with a lower rate of correct rejections and a higher rate of filler identifications. Higher algorithmic similarity ratings were also associated with decreases in eyewitness identification accuracy. The results suggest that using facial matching software to select fillers from large photograph databases may reduce identification accuracy, and provides support for filler database size as a meaningful system variable. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

  11. Genomic prediction of traits related to canine hip dysplasia

    Directory of Open Access Journals (Sweden)

    Enrique eSanchez-Molano

    2015-03-01

    Full Text Available Increased concern for the welfare of pedigree dogs has led to development of selection programs against inherited diseases. An example is canine hip dysplasia (CHD, which has a moderate heritability and a high prevalence in some large-sized breeds. To date, selection using phenotypes has led to only modest improvement, and alternative strategies such as genomic selection may prove more effective. The primary aims of this study were to compare the performance of pedigree- and genomic-based breeding against CHD in the UK Labrador retriever population and to evaluate the performance of different genomic selection methods. A sample of 1179 Labrador Retrievers evaluated for CHD according to the UK scoring method (hip score, HS was genotyped with the Illumina CanineHD BeadChip. Twelve functions of HS and its component traits were analyzed using different statistical methods (GBLUP, Bayes C and Single-Step methods, and results were compared with a pedigree-based approach (BLUP using cross-validation. Genomic methods resulted in similar or higher accuracies than pedigree-based methods with training sets of 944 individuals for all but the untransformed HS, suggesting that genomic selection is an effective strategy. GBLUP and Bayes C gave similar prediction accuracies for HS and related traits, indicating a polygenic architecture. This conclusion was also supported by the low accuracies obtained in additional GBLUP analyses performed using only the SNPs with highest test statistics, also indicating that marker-assisted selection would not be as effective as genomic selection. A Single-Step method that combines genomic and pedigree information also showed higher accuracy than GBLUP and Bayes C for the log-transformed HS, which is currently used for pedigree based evaluations in UK. In conclusion, genomic selection is a promising alternative to pedigree-based selection against CHD, requiring more phenotypes with genomic data to improve further the accuracy

  12. Circulating irisin levels are lower in patients with either stable coronary artery disease (CAD) or myocardial infarction (MI) versus healthy controls, whereas follistatin and activin A levels are higher and can discriminate MI from CAD with similar to CK-MB accuracy.

    Science.gov (United States)

    Anastasilakis, Athanasios D; Koulaxis, Dimitrios; Kefala, Nikoleta; Polyzos, Stergios A; Upadhyay, Jagriti; Pagkalidou, Eirini; Economou, Fotios; Anastasilakis, Chrysostomos D; Mantzoros, Christos S

    2017-08-01

    Several myokines are produced by cardiac muscle. We investigated changes in myokine levels at the time of acute myocardial infarction (MI) and following reperfusion in relation to controls. Patients with MI (MI Group, n=31) treated with percutaneous coronary intervention (PCI) were compared to patients with stable coronary artery disease (CAD) subjected to scheduled PCI (CAD Group, n=40) and controls with symptoms mimicking CAD without stenosis in angiography (Control Group, n=43). The number and degree of stenosis were recorded. Irisin, follistatin, follistatin-like 3, activin A and B, ALT, AST, CK and CK-MB were measured at baseline and 6 or 24h after the intervention. MI and CAD patients had lower irisin than controls (p<0.001). MI patients had higher follistatin, activin A, CK, CK-MB and AST than CAD patients and controls (all p≤0.001). None of the myokines changed following reperfusion. Circulating irisin was associated with the degree of stenosis in all patients (p=0.05). Irisin was not inferior to CK-MB in predicting MI while folistatin and activin A could discriminate MI from CAD patients with similar to CK-MB accuracy. None of these myokines was altered following PCI in contrast to CK-MB. Irisin levels are lower in MI and CAD implying that their production may depend on myocadial blood supply. Follistatin and activin A are higher in MI than in CAD suggesting increased release due to myocardial necrosis. They can predict MI with accuracy similar to CK-MB and their role in the diagnosis of MI remains to be confirmed by prospective large clinical studies. Copyright © 2017 Elsevier Inc. All rights reserved.

  13. Prospects and Potential Uses of Genomic Prediction of Key Performance Traits in Tetraploid Potato

    Directory of Open Access Journals (Sweden)

    Benjamin Stich

    2018-03-01

    Full Text Available Genomic prediction is a routine tool in breeding programs of most major animal and plant species. However, its usefulness for potato breeding has not yet been evaluated in detail. The objectives of this study were to (i examine the prospects of genomic prediction of key performance traits in a diversity panel of tetraploid potato modeling additive, dominance, and epistatic effects, (ii investigate the effects of size and make up of training set, number of test environments and molecular markers on prediction accuracy, and (iii assess the effect of including markers from candidate genes on the prediction accuracy. With genomic best linear unbiased prediction (GBLUP, BayesA, BayesCπ, and Bayesian LASSO, four different prediction methods were used for genomic prediction of relative area under disease progress curve after a Phytophthora infestans infection, plant maturity, maturity corrected resistance, tuber starch content, tuber starch yield (TSY, and tuber yield (TY of 184 tetraploid potato clones or subsets thereof genotyped with the SolCAP 8.3k SNP array. The cross-validated prediction accuracies with GBLUP and the three Bayesian approaches for the six evaluated traits ranged from about 0.5 to about 0.8. For traits with a high expected genetic complexity, such as TSY and TY, we observed an 8% higher prediction accuracy using a model with additive and dominance effects compared with a model with additive effects only. Our results suggest that for oligogenic traits in general and when diagnostic markers are available in particular, the use of Bayesian methods for genomic prediction is highly recommended and that the diagnostic markers should be modeled as fixed effects. The evaluation of the relative performance of genomic prediction vs. phenotypic selection indicated that the former is superior, assuming cycle lengths and selection intensities that are possible to realize in commercial potato breeding programs.

  14. Culture shapes whether the pursuit of happiness predicts higher or lower well-being

    Science.gov (United States)

    Ford, Brett Q.; Dmitrieva, Julia O.; Heller, Daniel; Chentsova-Dutton, Yulia; Grossmann, Igor; Tamir, Maya; Uchida, Yukiko; Koopmann-Holm, Birgit; Floerke, Victoria A.; Uhrig, Meike; Bokhan, Tatiana; Mauss, Iris B.

    2015-01-01

    Pursuing happiness can paradoxically impair well-being. Here, we propose the potential downsides to pursuing happiness may be specific to individualistic cultures. In collectivistic (vs. individualistic) cultures, pursuing happiness may be more successful because happiness is viewed – and thus pursued – in relatively socially-engaged ways. In four geographical regions that vary in level of collectivism (U.S., Germany, Russia, East Asia), we assessed participants’ well-being, motivation to pursue happiness, and to what extent they pursued happiness in socially-engaged ways. Motivation to pursue happiness predicted lower well-being in the U.S., did not predict well-being in Germany, and predicted higher well-being in Russia and in East Asia. These cultural differences in the link between motivation to pursue happiness and well-being were explained by cultural differences in the socially-engaged pursuit of happiness. These findings suggest that culture shapes whether the pursuit of happiness is linked with better or worse well-being, perhaps via how people pursue happiness. PMID:26347945

  15. Neural Fuzzy Inference System-Based Weather Prediction Model and Its Precipitation Predicting Experiment

    Directory of Open Access Journals (Sweden)

    Jing Lu

    2014-11-01

    Full Text Available We propose a weather prediction model in this article based on neural network and fuzzy inference system (NFIS-WPM, and then apply it to predict daily fuzzy precipitation given meteorological premises for testing. The model consists of two parts: the first part is the “fuzzy rule-based neural network”, which simulates sequential relations among fuzzy sets using artificial neural network; and the second part is the “neural fuzzy inference system”, which is based on the first part, but could learn new fuzzy rules from the previous ones according to the algorithm we proposed. NFIS-WPM (High Pro and NFIS-WPM (Ave are improved versions of this model. It is well known that the need for accurate weather prediction is apparent when considering the benefits. However, the excessive pursuit of accuracy in weather prediction makes some of the “accurate” prediction results meaningless and the numerical prediction model is often complex and time-consuming. By adapting this novel model to a precipitation prediction problem, we make the predicted outcomes of precipitation more accurate and the prediction methods simpler than by using the complex numerical forecasting model that would occupy large computation resources, be time-consuming and which has a low predictive accuracy rate. Accordingly, we achieve more accurate predictive precipitation results than by using traditional artificial neural networks that have low predictive accuracy.

  16. Improve accuracy and sensibility in glycan structure prediction by matching glycan isotope abundance

    International Nuclear Information System (INIS)

    Xu Guang; Liu Xin; Liu Qingyan; Zhou Yanhong; Li Jianjun

    2012-01-01

    Highlights: ► A glycan isotope pattern recognition strategy for glycomics. ► A new data preprocessing procedure to detect ion peaks in a giving MS spectrum. ► A linear soft margin SVM classification for isotope pattern recognition. - Abstract: Mass Spectrometry (MS) is a powerful technique for the determination of glycan structures and is capable of providing qualitative and quantitative information. Recent development in computational method offers an opportunity to use glycan structure databases and de novo algorithms for extracting valuable information from MS or MS/MS data. However, detecting low-intensity peaks that are buried in noisy data sets is still a challenge and an algorithm for accurate prediction and annotation of glycan structures from MS data is highly desirable. The present study describes a novel algorithm for glycan structure prediction by matching glycan isotope abundance (mGIA), which takes isotope masses, abundances, and spacing into account. We constructed a comprehensive database containing 808 glycan compositions and their corresponding isotope abundance. Unlike most previously reported methods, not only did we take into count the m/z values of the peaks but also their corresponding logarithmic Euclidean distance of the calculated and detected isotope vectors. Evaluation against a linear classifier, obtained by training mGIA algorithm with datasets of three different human tissue samples from Consortium for Functional Glycomics (CFG) in association with Support Vector Machine (SVM), was proposed to improve the accuracy of automatic glycan structure annotation. In addition, an effective data preprocessing procedure, including baseline subtraction, smoothing, peak centroiding and composition matching for extracting correct isotope profiles from MS data was incorporated. The algorithm was validated by analyzing the mouse kidney MS data from CFG, resulting in the identification of 6 more glycan compositions than the previous annotation

  17. Does diagnosis affect the predictive accuracy of risk assessment tools for juvenile offenders: Conduct Disorder and Attention Deficit Hyperactivity Disorder.

    Science.gov (United States)

    Khanna, Dinesh; Shaw, Jenny; Dolan, Mairead; Lennox, Charlotte

    2014-10-01

    Studies have suggested an increased risk of criminality in juveniles if they suffer from co-morbid Attention Deficit Hyperactivity Disorder (ADHD) along with Conduct Disorder. The Structured Assessment of Violence Risk in Youth (SAVRY), the Psychopathy Checklist Youth Version (PCL:YV), and Youth Level of Service/Case Management Inventory (YLS/CMI) have been shown to be good predictors of violent and non-violent re-offending. The aim was to compare the accuracy of these tools to predict violent and non-violent re-offending in young people with co-morbid ADHD and Conduct Disorder and Conduct Disorder only. The sample included 109 White-British adolescent males in secure settings. Results revealed no significant differences between the groups for re-offending. SAVRY factors had better predictive values than PCL:YV or YLS/CMI. Tools generally had better predictive values for the Conduct Disorder only group than the co-morbid group. Possible reasons for these findings have been discussed along with limitations of the study. Copyright © 2014 The Foundation for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.

  18. Protein secondary structure prediction for a single-sequence using hidden semi-Markov models

    Directory of Open Access Journals (Sweden)

    Borodovsky Mark

    2006-03-01

    Full Text Available Abstract Background The accuracy of protein secondary structure prediction has been improving steadily towards the 88% estimated theoretical limit. There are two types of prediction algorithms: Single-sequence prediction algorithms imply that information about other (homologous proteins is not available, while algorithms of the second type imply that information about homologous proteins is available, and use it intensively. The single-sequence algorithms could make an important contribution to studies of proteins with no detected homologs, however the accuracy of protein secondary structure prediction from a single-sequence is not as high as when the additional evolutionary information is present. Results In this paper, we further refine and extend the hidden semi-Markov model (HSMM initially considered in the BSPSS algorithm. We introduce an improved residue dependency model by considering the patterns of statistically significant amino acid correlation at structural segment borders. We also derive models that specialize on different sections of the dependency structure and incorporate them into HSMM. In addition, we implement an iterative training method to refine estimates of HSMM parameters. The three-state-per-residue accuracy and other accuracy measures of the new method, IPSSP, are shown to be comparable or better than ones for BSPSS as well as for PSIPRED, tested under the single-sequence condition. Conclusions We have shown that new dependency models and training methods bring further improvements to single-sequence protein secondary structure prediction. The results are obtained under cross-validation conditions using a dataset with no pair of sequences having significant sequence similarity. As new sequences are added to the database it is possible to augment the dependency structure and obtain even higher accuracy. Current and future advances should contribute to the improvement of function prediction for orphan proteins inscrutable

  19. Eyeball Position in Facial Approximation: Accuracy of Methods for Predicting Globe Positioning in Lateral View.

    Science.gov (United States)

    Zednikova Mala, Pavla; Veleminska, Jana

    2018-01-01

    This study measured the accuracy of traditional and validated newly proposed methods for globe positioning in lateral view. Eighty lateral head cephalograms of adult subjects from Central Europe were taken, and the actual and predicted dimensions were compared. The anteroposterior eyeball position was estimated as the most accurate method based on the proportion of the orbital height (SEE = 1.9 mm) and was followed by the "tangent to the iris method" showing SEE = 2.4 mm. The traditional "tangent to the cornea method" underestimated the eyeball projection by SEE = 5.8 mm. Concerning the superoinferior eyeball position, the results showed a deviation from a central to a more superior position by 0.3 mm, on average, and the traditional method of central positioning of the globe could not be rejected as inaccurate (SEE = 0.3 mm). Based on regression analyzes or proportionality of the orbital height, the SEE = 2.1 mm. © 2017 American Academy of Forensic Sciences.

  20. Accuracy of Body Mass Index Versus Lean Mass Index for Prediction of Sarcopenia in Older Women.

    Science.gov (United States)

    Benton, M J; Silva-Smith, A L

    2018-01-01

    We compared accuracy of body mass index (BMI) versus lean mass index (LMI) to predict sarcopenia in 58 community-dwelling women (74.1±0.9 years). Lean mass was measured with multi-frequency bioelectrical impedance analysis, and strength was measured with Arm Curl test, Chair Stand test, and handgrip dynamometry. Sarcopenia was defined as low LMI. When categorized by BMI, normal women had less absolute lean mass (37.6±1.0 vs. 42.6±0.9 kg; Plean mass (14.1±0.2 vs. 16.1±0.2 kg/m2; Plean mass (44.0±0.7 vs. 35.7±0.7 kg; Plean mass (16.2±0.2 vs. 13.8±0.2 kg/m2; Plean mass and strength. For clinical assessment, calculation of LMI rather than BMI is appropriate.

  1. FDG-PET and CSF biomarker accuracy in prediction of conversion to different dementias in a large multicentre MCI cohort.

    Science.gov (United States)

    Caminiti, Silvia Paola; Ballarini, Tommaso; Sala, Arianna; Cerami, Chiara; Presotto, Luca; Santangelo, Roberto; Fallanca, Federico; Vanoli, Emilia Giovanna; Gianolli, Luigi; Iannaccone, Sandro; Magnani, Giuseppe; Perani, Daniela

    2018-01-01

    In this multicentre study in clinical settings, we assessed the accuracy of optimized procedures for FDG-PET brain metabolism and CSF classifications in predicting or excluding the conversion to Alzheimer's disease (AD) dementia and non-AD dementias. We included 80 MCI subjects with neurological and neuropsychological assessments, FDG-PET scan and CSF measures at entry, all with clinical follow-up. FDG-PET data were analysed with a validated voxel-based SPM method. Resulting single-subject SPM maps were classified by five imaging experts according to the disease-specific patterns, as "typical-AD", "atypical-AD" (i.e. posterior cortical atrophy, asymmetric logopenic AD variant, frontal-AD variant), "non-AD" (i.e. behavioural variant FTD, corticobasal degeneration, semantic variant FTD; dementia with Lewy bodies) or "negative" patterns. To perform the statistical analyses, the individual patterns were grouped either as "AD dementia vs. non-AD dementia (all diseases)" or as "FTD vs. non-FTD (all diseases)". Aβ42, total and phosphorylated Tau CSF-levels were classified dichotomously, and using the Erlangen Score algorithm. Multivariate logistic models tested the prognostic accuracy of FDG-PET-SPM and CSF dichotomous classifications. Accuracy of Erlangen score and Erlangen Score aided by FDG-PET SPM classification was evaluated. The multivariate logistic model identified FDG-PET "AD" SPM classification (Expβ = 19.35, 95% C.I. 4.8-77.8, p CSF Aβ42 (Expβ = 6.5, 95% C.I. 1.64-25.43, p CSF biomarkers.

  2. A Modified LS+AR Model to Improve the Accuracy of the Short-term Polar Motion Prediction

    Science.gov (United States)

    Wang, Z. W.; Wang, Q. X.; Ding, Y. Q.; Zhang, J. J.; Liu, S. S.

    2017-03-01

    There are two problems of the LS (Least Squares)+AR (AutoRegressive) model in polar motion forecast: the inner residual value of LS fitting is reasonable, but the residual value of LS extrapolation is poor; and the LS fitting residual sequence is non-linear. It is unsuitable to establish an AR model for the residual sequence to be forecasted, based on the residual sequence before forecast epoch. In this paper, we make solution to those two problems with two steps. First, restrictions are added to the two endpoints of LS fitting data to fix them on the LS fitting curve. Therefore, the fitting values next to the two endpoints are very close to the observation values. Secondly, we select the interpolation residual sequence of an inward LS fitting curve, which has a similar variation trend as the LS extrapolation residual sequence, as the modeling object of AR for the residual forecast. Calculation examples show that this solution can effectively improve the short-term polar motion prediction accuracy by the LS+AR model. In addition, the comparison results of the forecast models of RLS (Robustified Least Squares)+AR, RLS+ARIMA (AutoRegressive Integrated Moving Average), and LS+ANN (Artificial Neural Network) confirm the feasibility and effectiveness of the solution for the polar motion forecast. The results, especially for the polar motion forecast in the 1-10 days, show that the forecast accuracy of the proposed model can reach the world level.

  3. Cognitive Abilities Underlying Reading Accuracy, Fluency and Spelling Acquisition in Korean Hangul Learners from Grades 1 to 4: A Cross-Sectional Study.

    Science.gov (United States)

    Park, Hyun-Rin; Uno, Akira

    2015-08-01

    The purpose of this cross-sectional study was to examine the cognitive abilities that predict reading and spelling performance in Korean children in Grades 1 to 4, depending on expertise and reading experience. As a result, visual cognition, phonological awareness, naming speed and receptive vocabulary significantly predicted reading accuracy in children in Grades 1 and 2, whereas visual cognition, phonological awareness and rapid naming speed did not predict reading accuracy in children in higher grades. For reading, fluency, phonological awareness, rapid naming speed and receptive vocabulary were crucial abilities in children in Grades 1 to 3, whereas phonological awareness was not a significant predictor in children in Grade 4. In spelling, reading ability and receptive vocabulary were the most important abilities for accurate Hangul spelling. The results suggested that the degree of cognitive abilities required for reading and spelling changed depending on expertise and reading experience. Copyright © 2015 John Wiley & Sons, Ltd.

  4. Artificial neural network application for predicting soil distribution coefficient of nickel

    International Nuclear Information System (INIS)

    Falamaki, Amin

    2013-01-01

    The distribution (or partition) coefficient (K d ) is an applicable parameter for modeling contaminant and radionuclide transport as well as risk analysis. Selection of this parameter may cause significant error in predicting the impacts of contaminant migration or site-remediation options. In this regards, various models were presented to predict K d values for different contaminants specially heavy metals and radionuclides. In this study, artificial neural network (ANN) is used to present simplified model for predicting K d of nickel. The main objective is to develop a more accurate model with a minimal number of parameters, which can be determined experimentally or select by review of different studies. In addition, the effects of training as well as the type of the network are considered. The K d values of Ni is strongly dependent on pH of the soil and mathematical relationships were presented between pH and K d of nickel recently. In this study, the same database of these presented models was used to verify that neural network may be more useful tools for predicting of K d . Two different types of ANN, multilayer perceptron and redial basis function, were used to investigate the effect of the network geometry on the results. In addition, each network was trained by 80 and 90% of the data and tested for 20 and 10% of the rest data. Then the results of the networks compared with the results of the mathematical models. Although the networks trained by 80 and 90% of the data the results show that all the networks predict with higher accuracy relative to mathematical models which were derived by 100% of data. More training of a network increases the accuracy of the network. Multilayer perceptron network used in this study predicts better than redial basis function network. - Highlights: ► Simplified models for predicting K d of nickel presented using artificial neural networks. ► Multilayer perceptron and redial basis function used to predict K d of nickel in

  5. Effects of the number of markers per haplotype and clustering of haplotypes on the accuracy of QTL mapping and prediction of genomic breeding values

    Directory of Open Access Journals (Sweden)

    Schrooten Chris

    2009-01-01

    Full Text Available Abstract The aim of this paper was to compare the effect of haplotype definition on the precision of QTL-mapping and on the accuracy of predicted genomic breeding values. In a multiple QTL model using identity-by-descent (IBD probabilities between haplotypes, various haplotype definitions were tested i.e. including 2, 6, 12 or 20 marker alleles and clustering base haplotypes related with an IBD probability of > 0.55, 0.75 or 0.95. Simulated data contained 1100 animals with known genotypes and phenotypes and 1000 animals with known genotypes and unknown phenotypes. Genomes comprising 3 Morgan were simulated and contained 74 polymorphic QTL and 383 polymorphic SNP markers with an average r2 value of 0.14 between adjacent markers. The total number of haplotypes decreased up to 50% when the window size was increased from two to 20 markers and decreased by at least 50% when haplotypes related with an IBD probability of > 0.55 instead of > 0.95 were clustered. An intermediate window size led to more precise QTL mapping. Window size and clustering had a limited effect on the accuracy of predicted total breeding values, ranging from 0.79 to 0.81. Our conclusion is that different optimal window sizes should be used in QTL-mapping versus genome-wide breeding value prediction.

  6. Accuracy of Referring Provider and Endoscopist Impressions of Colonoscopy Indication.

    Science.gov (United States)

    Naveed, Mariam; Clary, Meredith; Ahn, Chul; Kubiliun, Nisa; Agrawal, Deepak; Cryer, Byron; Murphy, Caitlin; Singal, Amit G

    2017-07-01

    Background: Referring provider and endoscopist impressions of colonoscopy indication are used for clinical care, reimbursement, and quality reporting decisions; however, the accuracy of these impressions is unknown. This study assessed the sensitivity, specificity, positive and negative predictive value, and overall accuracy of methods to classify colonoscopy indication, including referring provider impression, endoscopist impression, and administrative algorithm compared with gold standard chart review. Methods: We randomly sampled 400 patients undergoing a colonoscopy at a Veterans Affairs health system between January 2010 and December 2010. Referring provider and endoscopist impressions of colonoscopy indication were compared with gold-standard chart review. Indications were classified into 4 mutually exclusive categories: diagnostic, surveillance, high-risk screening, or average-risk screening. Results: Of 400 colonoscopies, 26% were performed for average-risk screening, 7% for high-risk screening, 26% for surveillance, and 41% for diagnostic indications. Accuracy of referring provider and endoscopist impressions of colonoscopy indication were 87% and 84%, respectively, which were significantly higher than that of the administrative algorithm (45%; P 90%) for determining screening (vs nonscreening) indication, but specificity of the administrative algorithm was lower (40.3%) compared with referring provider (93.7%) and endoscopist (84.0%) impressions. Accuracy of endoscopist, but not referring provider, impression was lower in patients with a family history of colon cancer than in those without (65% vs 84%; P =.001). Conclusions: Referring provider and endoscopist impressions of colonoscopy indication are both accurate and may be useful data to incorporate into algorithms classifying colonoscopy indication. Copyright © 2017 by the National Comprehensive Cancer Network.

  7. Prediction of drug synergy in cancer using ensemble-based machine learning techniques

    Science.gov (United States)

    Singh, Harpreet; Rana, Prashant Singh; Singh, Urvinder

    2018-04-01

    Drug synergy prediction plays a significant role in the medical field for inhibiting specific cancer agents. It can be developed as a pre-processing tool for therapeutic successes. Examination of different drug-drug interaction can be done by drug synergy score. It needs efficient regression-based machine learning approaches to minimize the prediction errors. Numerous machine learning techniques such as neural networks, support vector machines, random forests, LASSO, Elastic Nets, etc., have been used in the past to realize requirement as mentioned above. However, these techniques individually do not provide significant accuracy in drug synergy score. Therefore, the primary objective of this paper is to design a neuro-fuzzy-based ensembling approach. To achieve this, nine well-known machine learning techniques have been implemented by considering the drug synergy data. Based on the accuracy of each model, four techniques with high accuracy are selected to develop ensemble-based machine learning model. These models are Random forest, Fuzzy Rules Using Genetic Cooperative-Competitive Learning method (GFS.GCCL), Adaptive-Network-Based Fuzzy Inference System (ANFIS) and Dynamic Evolving Neural-Fuzzy Inference System method (DENFIS). Ensembling is achieved by evaluating the biased weighted aggregation (i.e. adding more weights to the model with a higher prediction score) of predicted data by selected models. The proposed and existing machine learning techniques have been evaluated on drug synergy score data. The comparative analysis reveals that the proposed method outperforms others in terms of accuracy, root mean square error and coefficient of correlation.

  8. Accuracy in Orbital Propagation: A Comparison of Predictive Software Models

    Science.gov (United States)

    2017-06-01

    30] M. Lane and K. Cranford, "An improved analytical drag theory for the artificial satellite problem," American Institute of Aeronautics and...which have a foundation in similar theory . Since their first operational use, both propagators have incorporated updated theory and mathematical...propagators should therefore utilize the most current TLE data available to avoid accuracy errors. 14. SUBJECT TERMS orbital mechanics , orbital

  9. Selecting Optimal Random Forest Predictive Models: A Case Study on Predicting the Spatial Distribution of Seabed Hardness

    Science.gov (United States)

    Li, Jin; Tran, Maggie; Siwabessy, Justy

    2016-01-01

    Spatially continuous predictions of seabed hardness are important baseline environmental information for sustainable management of Australia’s marine jurisdiction. Seabed hardness is often inferred from multibeam backscatter data with unknown accuracy and can be inferred from underwater video footage at limited locations. In this study, we classified the seabed into four classes based on two new seabed hardness classification schemes (i.e., hard90 and hard70). We developed optimal predictive models to predict seabed hardness using random forest (RF) based on the point data of hardness classes and spatially continuous multibeam data. Five feature selection (FS) methods that are variable importance (VI), averaged variable importance (AVI), knowledge informed AVI (KIAVI), Boruta and regularized RF (RRF) were tested based on predictive accuracy. Effects of highly correlated, important and unimportant predictors on the accuracy of RF predictive models were examined. Finally, spatial predictions generated using the most accurate models were visually examined and analysed. This study confirmed that: 1) hard90 and hard70 are effective seabed hardness classification schemes; 2) seabed hardness of four classes can be predicted with a high degree of accuracy; 3) the typical approach used to pre-select predictive variables by excluding highly correlated variables needs to be re-examined; 4) the identification of the important and unimportant predictors provides useful guidelines for further improving predictive models; 5) FS methods select the most accurate predictive model(s) instead of the most parsimonious ones, and AVI and Boruta are recommended for future studies; and 6) RF is an effective modelling method with high predictive accuracy for multi-level categorical data and can be applied to ‘small p and large n’ problems in environmental sciences. Additionally, automated computational programs for AVI need to be developed to increase its computational efficiency and

  10. Proposed Testing to Assess the Accuracy of Glass-To-Metal Seal Stress Analyses.

    Energy Technology Data Exchange (ETDEWEB)

    Chambers, Robert S.; Emery, John M; Tandon, Rajan; Antoun, Bonnie R.; Stavig, Mark E.; Newton, Clay S.; Gibson, Cory S; Bencoe, Denise N.

    2014-09-01

    The material characterization tests conducted on 304L VAR stainless steel and Schott 8061 glass have provided higher fidelity data for calibration of material models used in Glass - T o - Metal (GTM) seal analyses. Specifically, a Thermo - Multi - Linear Elastic Plastic ( thermo - MLEP) material model has be en defined for S S304L and the Simplified Potential Energy Clock nonlinear visc oelastic model has been calibrated for the S8061 glass. To assess the accuracy of finite element stress analyses of GTM seals, a suite of tests are proposed to provide data for comparison to mo del predictions.

  11. Slat Noise Predictions Using Higher-Order Finite-Difference Methods on Overset Grids

    Science.gov (United States)

    Housman, Jeffrey A.; Kiris, Cetin

    2016-01-01

    Computational aeroacoustic simulations using the structured overset grid approach and higher-order finite difference methods within the Launch Ascent and Vehicle Aerodynamics (LAVA) solver framework are presented for slat noise predictions. The simulations are part of a collaborative study comparing noise generation mechanisms between a conventional slat and a Krueger leading edge flap. Simulation results are compared with experimental data acquired during an aeroacoustic test in the NASA Langley Quiet Flow Facility. Details of the structured overset grid, numerical discretization, and turbulence model are provided.

  12. Accounting for taxonomic distance in accuracy assessment of soil class predictions

    NARCIS (Netherlands)

    Rossiter, David G.; Zeng, Rong; Zhang, Gan Lin

    2017-01-01

    Evaluating the accuracy of allocation to classes in monothetic hierarchical soil classification systems, including the World Reference Base for Soil Classification, US Soil Taxonomy, and Chinese Soil Taxonomy, is poorly-served by binomial methods (correct/incorrect allocation per evaluation

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

    Directory of Open Access Journals (Sweden)

    Santana Isabel

    2011-08-01

    Full Text Available Abstract Background Dementia and cognitive impairment associated with aging are a major medical and social concern. Neuropsychological testing is a key element in the diagnostic procedures of Mild Cognitive Impairment (MCI, but has presently a limited value in the prediction of progression to dementia. We advance the hypothesis that newer statistical classification methods derived from data mining and machine learning methods like Neural Networks, Support Vector Machines and Random Forests can improve accuracy, sensitivity and specificity of predictions obtained from neuropsychological testing. Seven non parametric classifiers derived from data mining methods (Multilayer Perceptrons Neural Networks, Radial Basis Function Neural Networks, Support Vector Machines, CART, CHAID and QUEST Classification Trees and Random Forests were compared to three traditional classifiers (Linear Discriminant Analysis, Quadratic Discriminant Analysis and Logistic Regression in terms of overall classification accuracy, specificity, sensitivity, Area under the ROC curve and Press'Q. Model predictors were 10 neuropsychological tests currently used in the diagnosis of dementia. Statistical distributions of classification parameters obtained from a 5-fold cross-validation were compared using the Friedman's nonparametric test. Results Press' Q test showed that all classifiers performed better than chance alone (p Conclusions When taking into account sensitivity, specificity and overall classification accuracy Random Forests and Linear Discriminant analysis rank first among all the classifiers tested in prediction of dementia using several neuropsychological tests. These methods may be used to improve accuracy, sensitivity and specificity of Dementia predictions from neuropsychological testing.

  14. The accuracy of {sup 18}F-FDG PET/CT in predicting the pathological response to neoadjuvant chemotherapy in patients with breast cancer. A meta-analysis and systematic review

    Energy Technology Data Exchange (ETDEWEB)

    Tian, Fangfang; Shen, Guohua; Diao, Wei; Jia, Zhiyun [West China Hospital of Sichuan University, Department of Nuclear Medicine, Chengdu, Sichuan (China); Deng, Yunfu [West China Hospital of Sichuan University, Department of Oncology, Chengdu (China)

    2017-11-15

    The aim of this meta-analysis was to evaluate the accuracy of {sup 18}F-FDG PET/CT in predicting the pathological response to neoadjuvant chemotherapy (NAC) in breast cancer (BC) patients. PubMed, Embase, the Cochrane Library (Central), and the Web of Science (SCI-Expanded) were systematically searched to identify pertinent studies. The methodologic quality of the included studies was assessed by the Quality Assessment of Diagnostic Accuracy Studies-2. The Spearman correlation coefficient was used to explore the existence of a threshold effect. Heterogeneity was assessed by the likelihood ratio I {sup 2} index. The pooled values calculated with a mixed-effects model for the sensitivity, specificity and diagnostic odds ratio with 95% confidence intervals were 81.9% (76.0-86.6%), 79.3% (72.1-85.1%) and 17.35 (10.98-27.42), respectively. {sup 18}F-FDG PET/CT has a moderate accuracy in predicting the pathological response during the early process of NAC in breast cancer patients. To increase the role of {sup 18}F-FDG PET/CT in monitoring the therapy response, future prospective studies are needed to explore how chemotherapy regimens and different subtypes affect the levels of glucose metabolism. (orig.)

  15. Application of FFTBM with signal mirroring to improve accuracy assessment of MELCOR code

    International Nuclear Information System (INIS)

    Saghafi, Mahdi; Ghofrani, Mohammad Bagher; D’Auria, Francesco

    2016-01-01

    Highlights: • FFTBM-SM is an improved Fast Fourier Transform Base Method by signal mirroring. • FFTBM-SM has been applied to accuracy assessment of MELCOR code predictions. • The case studied was Station Black-Out accident in PSB-VVER integral test facility. • FFTBM-SM eliminates fluctuations of accuracy indices when signals sharply change. • Accuracy assessment is performed in a more realistic and consistent way by FFTBM-SM. - Abstract: This paper deals with the application of Fast Fourier Transform Base Method (FFTBM) with signal mirroring (FFTBM-SM) to assess accuracy of MELCOR code. This provides deeper insights into how the accuracy of MELCOR code in predictions of thermal-hydraulic parameters varies during transients. The case studied was modeling of Station Black-Out (SBO) accident in PSB-VVER integral test facility by MELCOR code. The accuracy of this thermal-hydraulic modeling was previously quantified using original FFTBM in a few number of time-intervals, based on phenomenological windows of SBO accident. Accuracy indices calculated by original FFTBM in a series of time-intervals unreasonably fluctuate when the investigated signals sharply increase or decrease. In the current study, accuracy of MELCOR code is quantified using FFTBM-SM in a series of increasing time-intervals, and the results are compared to those with original FFTBM. Also, differences between the accuracy indices of original FFTBM and FFTBM-SM are investigated and correction factors calculated to eliminate unphysical effects in original FFTBM. The main findings are: (1) replacing limited number of phenomena-based time-intervals by a series of increasing time-intervals provides deeper insights about accuracy variation of the MELCOR calculations, and (2) application of FFTBM-SM for accuracy evaluation of the MELCOR predictions, provides more reliable results than original FFTBM by eliminating the fluctuations of accuracy indices when experimental signals sharply increase or

  16. Application of FFTBM with signal mirroring to improve accuracy assessment of MELCOR code

    Energy Technology Data Exchange (ETDEWEB)

    Saghafi, Mahdi [Department of Energy Engineering, Sharif University of Technology, Azadi Avenue, Tehran (Iran, Islamic Republic of); Ghofrani, Mohammad Bagher, E-mail: ghofrani@sharif.edu [Department of Energy Engineering, Sharif University of Technology, Azadi Avenue, Tehran (Iran, Islamic Republic of); D’Auria, Francesco [San Piero a Grado Nuclear Research Group (GRNSPG), University of Pisa, Via Livornese 1291, San Piero a Grado, Pisa (Italy)

    2016-11-15

    Highlights: • FFTBM-SM is an improved Fast Fourier Transform Base Method by signal mirroring. • FFTBM-SM has been applied to accuracy assessment of MELCOR code predictions. • The case studied was Station Black-Out accident in PSB-VVER integral test facility. • FFTBM-SM eliminates fluctuations of accuracy indices when signals sharply change. • Accuracy assessment is performed in a more realistic and consistent way by FFTBM-SM. - Abstract: This paper deals with the application of Fast Fourier Transform Base Method (FFTBM) with signal mirroring (FFTBM-SM) to assess accuracy of MELCOR code. This provides deeper insights into how the accuracy of MELCOR code in predictions of thermal-hydraulic parameters varies during transients. The case studied was modeling of Station Black-Out (SBO) accident in PSB-VVER integral test facility by MELCOR code. The accuracy of this thermal-hydraulic modeling was previously quantified using original FFTBM in a few number of time-intervals, based on phenomenological windows of SBO accident. Accuracy indices calculated by original FFTBM in a series of time-intervals unreasonably fluctuate when the investigated signals sharply increase or decrease. In the current study, accuracy of MELCOR code is quantified using FFTBM-SM in a series of increasing time-intervals, and the results are compared to those with original FFTBM. Also, differences between the accuracy indices of original FFTBM and FFTBM-SM are investigated and correction factors calculated to eliminate unphysical effects in original FFTBM. The main findings are: (1) replacing limited number of phenomena-based time-intervals by a series of increasing time-intervals provides deeper insights about accuracy variation of the MELCOR calculations, and (2) application of FFTBM-SM for accuracy evaluation of the MELCOR predictions, provides more reliable results than original FFTBM by eliminating the fluctuations of accuracy indices when experimental signals sharply increase or

  17. Culture shapes whether the pursuit of happiness predicts higher or lower well-being.

    Science.gov (United States)

    Ford, Brett Q; Dmitrieva, Julia O; Heller, Daniel; Chentsova-Dutton, Yulia; Grossmann, Igor; Tamir, Maya; Uchida, Yukiko; Koopmann-Holm, Birgit; Floerke, Victoria A; Uhrig, Meike; Bokhan, Tatiana; Mauss, Iris B

    2015-12-01

    Pursuing happiness can paradoxically impair well-being. Here, the authors propose the potential downsides to pursuing happiness may be specific to individualistic cultures. In collectivistic (vs. individualistic) cultures, pursuing happiness may be more successful because happiness is viewed--and thus pursued--in relatively socially engaged ways. In 4 geographical regions that vary in level of collectivism (United States, Germany, Russia, East Asia), we assessed participants' well-being, motivation to pursue happiness, and to what extent they pursued happiness in socially engaged ways. Motivation to pursue happiness predicted lower well-being in the United States, did not predict well-being in Germany, and predicted higher well-being in Russia and in East Asia. These cultural differences in the link between motivation to pursue happiness and well-being were explained by cultural differences in the socially engaged pursuit of happiness. These findings suggest that culture shapes whether the pursuit of happiness is linked with better or worse well-being, perhaps via how people pursue happiness. (c) 2015 APA, all rights reserved).

  18. Predicting Streptococcal Pharyngitis in Adults in Primary Care: A Systematic Review of the Diagnostic Accuracy of Symptoms and Signs and Validation of the Centor Score

    LENUS (Irish Health Repository)

    Aalbers, Jolien

    2011-06-01

    Abstract Background Stratifying patients with a sore throat into the probability of having an underlying bacterial or viral cause may be helpful in targeting antibiotic treatment. We sought to assess the diagnostic accuracy of signs and symptoms and validate a clinical prediction rule (CPR), the Centor score, for predicting group A β-haemolytic streptococcal (GABHS) pharyngitis in adults (> 14 years of age) presenting with sore throat symptoms. Methods A systematic literature search was performed up to July 2010. Studies that assessed the diagnostic accuracy of signs and symptoms and\\/or validated the Centor score were included. For the analysis of the diagnostic accuracy of signs and symptoms and the Centor score, studies were combined using a bivariate random effects model, while for the calibration analysis of the Centor score, a random effects model was used. Results A total of 21 studies incorporating 4,839 patients were included in the meta-analysis on diagnostic accuracy of signs and symptoms. The results were heterogeneous and suggest that individual signs and symptoms generate only small shifts in post-test probability (range positive likelihood ratio (+LR) 1.45-2.33, -LR 0.54-0.72). As a decision rule for considering antibiotic prescribing (score ≥ 3), the Centor score has reasonable specificity (0.82, 95% CI 0.72 to 0.88) and a post-test probability of 12% to 40% based on a prior prevalence of 5% to 20%. Pooled calibration shows no significant difference between the numbers of patients predicted and observed to have GABHS pharyngitis across strata of Centor score (0-1 risk ratio (RR) 0.72, 95% CI 0.49 to 1.06; 2-3 RR 0.93, 95% CI 0.73 to 1.17; 4 RR 1.14, 95% CI 0.95 to 1.37). Conclusions Individual signs and symptoms are not powerful enough to discriminate GABHS pharyngitis from other types of sore throat. The Centor score is a well calibrated CPR for estimating the probability of GABHS pharyngitis. The Centor score can enhance appropriate

  19. Modeling Chronic Toxicity: A Comparison of Experimental Variability With (QSAR/Read-Across Predictions

    Directory of Open Access Journals (Sweden)

    Christoph Helma

    2018-04-01

    Full Text Available This study compares the accuracy of (QSAR/read-across predictions with the experimental variability of chronic lowest-observed-adverse-effect levels (LOAELs from in vivo experiments. We could demonstrate that predictions of the lazy structure-activity relationships (lazar algorithm within the applicability domain of the training data have the same variability as the experimental training data. Predictions with a lower similarity threshold (i.e., a larger distance from the applicability domain are also significantly better than random guessing, but the errors to be expected are higher and a manual inspection of prediction results is highly recommended.

  20. The Use of Linear Programming for Prediction.

    Science.gov (United States)

    Schnittjer, Carl J.

    The purpose of the study was to develop a linear programming model to be used for prediction, test the accuracy of the predictions, and compare the accuracy with that produced by curvilinear multiple regression analysis. (Author)

  1. Higher resting heart rate variability predicts skill in expressing some emotions.

    Science.gov (United States)

    Tuck, Natalie L; Grant, Rosemary C I; Sollers, John J; Booth, Roger J; Consedine, Nathan S

    2016-12-01

    Vagally mediated heart rate variability (vmHRV) is a measure of cardiac vagal tone, and is widely viewed as a physiological index of the capacity to regulate emotions. However, studies have not directly tested whether vmHRV is associated with the ability to facially express emotions. In extending prior work, the current report tested links between resting vmHRV and the objectively assessed ability to facially express emotions, hypothesizing that higher vmHRV would predict greater expressive skill. Eighty healthy women completed self-reported measures, before attending a laboratory session in which vmHRV and the ability to express six emotions in the face were assessed. A repeated measures analysis of variance revealed a marginal main effect for vmHRV on skill overall; individuals with higher resting vmHRV were only better able to deliberately facially express anger and interest. Findings suggest that differences in resting vmHRV are associated with the objectively assessed ability to facially express some, but not all, emotions, with potential implications for health and well-being. © 2016 Society for Psychophysiological Research.

  2. Improved prediction of higher heating value of biomass using an artificial neural network model based on proximate analysis.

    Science.gov (United States)

    Uzun, Harun; Yıldız, Zeynep; Goldfarb, Jillian L; Ceylan, Selim

    2017-06-01

    As biomass becomes more integrated into our energy feedstocks, the ability to predict its combustion enthalpies from routine data such as carbon, ash, and moisture content enables rapid decisions about utilization. The present work constructs a novel artificial neural network model with a 3-3-1 tangent sigmoid architecture to predict biomasses' higher heating values from only their proximate analyses, requiring minimal specificity as compared to models based on elemental composition. The model presented has a considerably higher correlation coefficient (0.963) and lower root mean square (0.375), mean absolute (0.328), and mean bias errors (0.010) than other models presented in the literature which, at least when applied to the present data set, tend to under-predict the combustion enthalpy. Copyright © 2017 Elsevier Ltd. All rights reserved.

  3. Eye movement accuracy determines natural interception strategies.

    Science.gov (United States)

    Fooken, Jolande; Yeo, Sang-Hoon; Pai, Dinesh K; Spering, Miriam

    2016-11-01

    Eye movements aid visual perception and guide actions such as reaching or grasping. Most previous work on eye-hand coordination has focused on saccadic eye movements. Here we show that smooth pursuit eye movement accuracy strongly predicts both interception accuracy and the strategy used to intercept a moving object. We developed a naturalistic task in which participants (n = 42 varsity baseball players) intercepted a moving dot (a "2D fly ball") with their index finger in a designated "hit zone." Participants were instructed to track the ball with their eyes, but were only shown its initial launch (100-300 ms). Better smooth pursuit resulted in more accurate interceptions and determined the strategy used for interception, i.e., whether interception was early or late in the hit zone. Even though early and late interceptors showed equally accurate interceptions, they may have relied on distinct tactics: early interceptors used cognitive heuristics, whereas late interceptors' performance was best predicted by pursuit accuracy. Late interception may be beneficial in real-world tasks as it provides more time for decision and adjustment. Supporting this view, baseball players who were more senior were more likely to be late interceptors. Our findings suggest that interception strategies are optimally adapted to the proficiency of the pursuit system.

  4. Predicting Performance in Higher Education Using Proximal Predictors

    Science.gov (United States)

    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. PMID:27073859

  5. Diagnostic accuracy of computed tomography and magnetic resonance imaging obtained after neoadjuvant chemoradiotherapy in predicting the local tumor stage and circumferential resection margin status of rectal cancer

    Energy Technology Data Exchange (ETDEWEB)

    Park, Jin Hoon; Kim, Young Hoon; Lee, Yoon Jin; Lee, Kyoung Ho; Kang, Sung Bum; Kim, Duck Woo; Kim, Jae Hyun; Kim, Jae Sung; Lee, Hye Seung [Dept. of Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam (Korea, Republic of); Lee, Sang Min [Dept. of Radiology, Seoul National University Hospital, Seoul (Korea, Republic of)

    2014-02-15

    To measure the diagnostic accuracy of computed tomography (CT) and magnetic resonance imaging (MRI) obtained after neoadjuvant chemoradiotherapy (CRT) in patients with rectal cancer for a prediction of the local tumor stage and circumferential resection margin (CRM). Two independent radiologists reviewed CT and MRI obtained after neoadjuvant CRT. The accuracy of the local tumor staging and the diagnostic performance for the prediction of CRM involvement were calculated. The agreement between the measurements of the distance to potential CRM on both imaging modalities and the histopathology findings was assessed using Bland-Altman plots. 57 patients (mean age, 59.2 years; 24 females) were included. The accuracy of T and N staging were 43.9% (95% confidence interval, 30.8-57.7%) and 77.2% (64.2-87.3%) on CT and 63.2% (49.4-75.6%) and 77.2% (64.2-87.3%) on MRI for Observer 1. The accuracy of T and N staging were 54.4% (40.7-67.7%) and 77.2% (64.2-87.3%) on CT and 68.4% (54.7-80.1%) and 80.7% (68.1-90.0%) on MRI for Observer 2. Sensitivity and specificity on CRM involvement were 83.3% (43.7-97.0%) and 88.2% (76.6-94.5%) on CT and 100% (61.0-100%) and 90.2% (79.0-95.7%) on MRI for Observer 1. Sensitivity and specificity on CRM involvement were 66.7% (30.0-90.3%) and 88.2% (76.7-94.5%) on CT and 100% (61.0-100%) and 90.2% (79.0-95.7%) on MRI for Observer 2. Bland-Altman plots showed wide discrepancies between measurements of the distance to CRM on each CT and MRI and those on histopathology findings. CT and MRI showed limited performance in predicting the local tumor staging and CRM involvement in patients with neoadjuvant CRT although MRI tended to show a better performance than CT.

  6. Diagnostic accuracy of computed tomography and magnetic resonance imaging obtained after neoadjuvant chemoradiotherapy in predicting the local tumor stage and circumferential resection margin status of rectal cancer

    International Nuclear Information System (INIS)

    Park, Jin Hoon; Kim, Young Hoon; Lee, Yoon Jin; Lee, Kyoung Ho; Kang, Sung Bum; Kim, Duck Woo; Kim, Jae Hyun; Kim, Jae Sung; Lee, Hye Seung; Lee, Sang Min

    2014-01-01

    To measure the diagnostic accuracy of computed tomography (CT) and magnetic resonance imaging (MRI) obtained after neoadjuvant chemoradiotherapy (CRT) in patients with rectal cancer for a prediction of the local tumor stage and circumferential resection margin (CRM). Two independent radiologists reviewed CT and MRI obtained after neoadjuvant CRT. The accuracy of the local tumor staging and the diagnostic performance for the prediction of CRM involvement were calculated. The agreement between the measurements of the distance to potential CRM on both imaging modalities and the histopathology findings was assessed using Bland-Altman plots. 57 patients (mean age, 59.2 years; 24 females) were included. The accuracy of T and N staging were 43.9% (95% confidence interval, 30.8-57.7%) and 77.2% (64.2-87.3%) on CT and 63.2% (49.4-75.6%) and 77.2% (64.2-87.3%) on MRI for Observer 1. The accuracy of T and N staging were 54.4% (40.7-67.7%) and 77.2% (64.2-87.3%) on CT and 68.4% (54.7-80.1%) and 80.7% (68.1-90.0%) on MRI for Observer 2. Sensitivity and specificity on CRM involvement were 83.3% (43.7-97.0%) and 88.2% (76.6-94.5%) on CT and 100% (61.0-100%) and 90.2% (79.0-95.7%) on MRI for Observer 1. Sensitivity and specificity on CRM involvement were 66.7% (30.0-90.3%) and 88.2% (76.7-94.5%) on CT and 100% (61.0-100%) and 90.2% (79.0-95.7%) on MRI for Observer 2. Bland-Altman plots showed wide discrepancies between measurements of the distance to CRM on each CT and MRI and those on histopathology findings. CT and MRI showed limited performance in predicting the local tumor staging and CRM involvement in patients with neoadjuvant CRT although MRI tended to show a better performance than CT

  7. Impact of selective genotyping in the training population on accuracy and bias of genomic selection.

    Science.gov (United States)

    Zhao, Yusheng; Gowda, Manje; Longin, Friedrich H; Würschum, Tobias; Ranc, Nicolas; Reif, Jochen C

    2012-08-01

    Estimating marker effects based on routinely generated phenotypic data of breeding programs is a cost-effective strategy to implement genomic selection. Truncation selection in breeding populations, however, could have a strong impact on the accuracy to predict genomic breeding values. The main objective of our study was to investigate the influence of phenotypic selection on the accuracy and bias of genomic selection. We used experimental data of 788 testcross progenies from an elite maize breeding program. The testcross progenies were evaluated in unreplicated field trials in ten environments and fingerprinted with 857 SNP markers. Random regression best linear unbiased prediction method was used in combination with fivefold cross-validation based on genotypic sampling. We observed a substantial loss in the accuracy to predict genomic breeding values in unidirectional selected populations. In contrast, estimating marker effects based on bidirectional selected populations led to only a marginal decrease in the prediction accuracy of genomic breeding values. We concluded that bidirectional selection is a valuable approach to efficiently implement genomic selection in applied plant breeding programs.

  8. Assessing the accuracy of forecasting: applying standard diagnostic assessment tools to a health technology early warning system.

    Science.gov (United States)

    Simpson, Sue; Hyde, Chris; Cook, Alison; Packer, Claire; Stevens, Andrew

    2004-01-01

    Early warning systems are an integral part of many health technology assessment programs. Despite this finding, to date, there have been no quantitative evaluations of the accuracy of predictions made by these systems. We report a study evaluating the accuracy of predictions made by the main United Kingdom early warning system. As prediction of impact is analogous to diagnosis, a method normally applied to determine the accuracy of diagnostic tests was used. The sensitivity, specificity, and predictive values of the National Horizon Scanning Centre's prediction methods were estimated with reference to an (imperfect) gold standard, that is, expert opinion of impact 3 to 5 years after prediction. The sensitivity of predictions was 71 percent (95 percent confidence interval [CI], 0.36-0.92), and the specificity was 73 percent (95 percent CI, 0.64-0.8). The negative predictive value was 98 percent (95 percent CI, 0.92-0.99), and the positive predictive value was 14 percent (95 percent CI, 0.06-0.3). Forecasting is difficult, but the results suggest that this early warning system's predictions have an acceptable level of accuracy. However, there are caveats. The first is that early warning systems may themselves reduce the impact of a technology, as helping to control adoption and diffusion is their main purpose. The second is that the use of an imperfect gold standard may bias the results. As early warning systems are viewed as an increasingly important component of health technology assessment and decision making, their outcomes must be evaluated. The method used here should be investigated further and the accuracy of other early warning systems explored.

  9. Improved accuracy of intraocular lens power calculation with the Zeiss IOLMaster.

    Science.gov (United States)

    Olsen, Thomas

    2007-02-01

    This study aimed to demonstrate how the level of accuracy in intraocular lens (IOL) power calculation can be improved with optical biometry using partial optical coherence interferometry (PCI) (Zeiss IOLMaster) and current anterior chamber depth (ACD) prediction algorithms. Intraocular lens power in 461 consecutive cataract operations was calculated using both PCI and ultrasound and the accuracy of the results of each technique were compared. To illustrate the importance of ACD prediction per se, predictions were calculated using both a recently published 5-variable method and the Haigis 2-variable method and the results compared. All calculations were optimized in retrospect to account for systematic errors, including IOL constants and other off-set errors. The average absolute IOL prediction error (observed minus expected refraction) was 0.65 dioptres with ultrasound and 0.43 D with PCI using the 5-variable ACD prediction method (p ultrasound, respectively (p power calculation can be significantly improved using calibrated axial length readings obtained with PCI and modern IOL power calculation formulas incorporating the latest generation ACD prediction algorithms.

  10. Improving consensus contact prediction via server correlation reduction.

    Science.gov (United States)

    Gao, Xin; Bu, Dongbo; Xu, Jinbo; Li, Ming

    2009-05-06

    Protein inter-residue contacts play a crucial role in the determination and prediction of protein structures. Previous studies on contact prediction indicate that although template-based consensus methods outperform sequence-based methods on targets with typical templates, such consensus methods perform poorly on new fold targets. However, we find out that even for new fold targets, the models generated by threading programs can contain many true contacts. The challenge is how to identify them. In this paper, we develop an integer linear programming model for consensus contact prediction. In contrast to the simple majority voting method assuming that all the individual servers are equally important and independent, the newly developed method evaluates their correlation by using maximum likelihood estimation and extracts independent latent servers from them by using principal component analysis. An integer linear programming method is then applied to assign a weight to each latent server to maximize the difference between true contacts and false ones. The proposed method is tested on the CASP7 data set. If the top L/5 predicted contacts are evaluated where L is the protein size, the average accuracy is 73%, which is much higher than that of any previously reported study. Moreover, if only the 15 new fold CASP7 targets are considered, our method achieves an average accuracy of 37%, which is much better than that of the majority voting method, SVM-LOMETS, SVM-SEQ, and SAM-T06. These methods demonstrate an average accuracy of 13.0%, 10.8%, 25.8% and 21.2%, respectively. Reducing server correlation and optimally combining independent latent servers show a significant improvement over the traditional consensus methods. This approach can hopefully provide a powerful tool for protein structure refinement and prediction use.

  11. Improving consensus contact prediction via server correlation reduction

    Directory of Open Access Journals (Sweden)

    Xu Jinbo

    2009-05-01

    Full Text Available Abstract Background Protein inter-residue contacts play a crucial role in the determination and prediction of protein structures. Previous studies on contact prediction indicate that although template-based consensus methods outperform sequence-based methods on targets with typical templates, such consensus methods perform poorly on new fold targets. However, we find out that even for new fold targets, the models generated by threading programs can contain many true contacts. The challenge is how to identify them. Results In this paper, we develop an integer linear programming model for consensus contact prediction. In contrast to the simple majority voting method assuming that all the individual servers are equally important and independent, the newly developed method evaluates their correlation by using maximum likelihood estimation and extracts independent latent servers from them by using principal component analysis. An integer linear programming method is then applied to assign a weight to each latent server to maximize the difference between true contacts and false ones. The proposed method is tested on the CASP7 data set. If the top L/5 predicted contacts are evaluated where L is the protein size, the average accuracy is 73%, which is much higher than that of any previously reported study. Moreover, if only the 15 new fold CASP7 targets are considered, our method achieves an average accuracy of 37%, which is much better than that of the majority voting method, SVM-LOMETS, SVM-SEQ, and SAM-T06. These methods demonstrate an average accuracy of 13.0%, 10.8%, 25.8% and 21.2%, respectively. Conclusion Reducing server correlation and optimally combining independent latent servers show a significant improvement over the traditional consensus methods. This approach can hopefully provide a powerful tool for protein structure refinement and prediction use.

  12. Group-regularized individual prediction: theory and application to pain.

    Science.gov (United States)

    Lindquist, Martin A; Krishnan, Anjali; López-Solà, Marina; Jepma, Marieke; Woo, Choong-Wan; Koban, Leonie; Roy, Mathieu; Atlas, Lauren Y; Schmidt, Liane; Chang, Luke J; Reynolds Losin, Elizabeth A; Eisenbarth, Hedwig; Ashar, Yoni K; Delk, Elizabeth; Wager, Tor D

    2017-01-15

    Multivariate pattern analysis (MVPA) has become an important tool for identifying brain representations of psychological processes and clinical outcomes using fMRI and related methods. Such methods can be used to predict or 'decode' psychological states in individual subjects. Single-subject MVPA approaches, however, are limited by the amount and quality of individual-subject data. In spite of higher spatial resolution, predictive accuracy from single-subject data often does not exceed what can be accomplished using coarser, group-level maps, because single-subject patterns are trained on limited amounts of often-noisy data. Here, we present a method that combines population-level priors, in the form of biomarker patterns developed on prior samples, with single-subject MVPA maps to improve single-subject prediction. Theoretical results and simulations motivate a weighting based on the relative variances of biomarker-based prediction-based on population-level predictive maps from prior groups-and individual-subject, cross-validated prediction. Empirical results predicting pain using brain activity on a trial-by-trial basis (single-trial prediction) across 6 studies (N=180 participants) confirm the theoretical predictions. Regularization based on a population-level biomarker-in this case, the Neurologic Pain Signature (NPS)-improved single-subject prediction accuracy compared with idiographic maps based on the individuals' data alone. The regularization scheme that we propose, which we term group-regularized individual prediction (GRIP), can be applied broadly to within-person MVPA-based prediction. We also show how GRIP can be used to evaluate data quality and provide benchmarks for the appropriateness of population-level maps like the NPS for a given individual or study. Copyright © 2015 Elsevier Inc. All rights reserved.

  13. Gender differences in the accuracy of time-dependent blood pressure indices for predicting coronary heart disease: A random-effects modeling approach.

    Science.gov (United States)

    Brant, Larry J; Ferrucci, Luigi; Sheng, Shan L; Concin, Hans; Zonderman, Alan B; Kelleher, Cecily C; Longo, Dan L; Ulmer, Hanno; Strasak, Alexander M

    2010-12-01

    Previous studies on blood pressure (BP) indices as a predictor of coronary heart disease (CHD) have provided equivocal results and generally relied on Cox proportional hazards regression methodology, with age and sex accounting for most of the predictive capability of the model. The aim of the present study was to use serially collected BP measurements to examine age-and gender-related differences in BP indices for predicting CHD. The predictive accuracy of time-dependent BP indices for CHD was investigated using a method of risk prediction based on posterior probabilities calculated from mixed-effects regression to utilize intraindividual differences in serial BP measurements according to age changes within gender groups. Data were collected prospectively from 2 community-dwelling cohort studies in the United States (Baltimore Longitudinal Study of Aging [BLSA]) and Europe (Vorarlberg Health Monitoring and Promotion Program [VHM&PP]). The study comprised 152,633 participants (aged 30-74 years) and 610,061 BP measurements. During mean follow-up of 7.5 years, 2457 nonfatal and fatal CHD events were observed. In both study populations, pulse pressure (PP) and systolic blood pressure (SBP) performed best as individual predictors of CHD in women (area under the receiver operating characteristic curve [AUC(ROC)] was between 0.83 and 0.85 for PP, and between 0.77 and 0.81 for SBP). Mean arterial pressure (MAP) and diastolic blood pressure (DBP) performed better for men (AUC(ROC) = 0.67 and 0.65 for MAP and DBP, respectively, in the BLSA; AUC(ROC) = 0.77 and 0.75 in the VHM&PP) than for women (AUC(ROC) = 0.60 for both MAP and DBP in the BLSA; AUC(ROC) = 0.75 and 0.52, respectively, in the VHM&PP). The degree of discrimination in both populations was overall greater but more varied for all BP indices for women (AUC(ROC) estimates between 0.85 [PP in the VHM&PP] and 0.52 [DBP in the VHM&PP]) than for men (AUC(ROC) estimates between 0.78 [MAP + PP in the VHM&PP] and 0.63 [PP

  14. DNA template dependent accuracy variation of nucleotide selection in transcription.

    Directory of Open Access Journals (Sweden)

    Harriet Mellenius

    Full Text Available It has been commonly assumed that the effect of erroneous transcription of DNA genes into messenger RNAs on peptide sequence errors are masked by much more frequent errors of mRNA translation to protein. We present a theoretical model of transcriptional accuracy. It uses experimentally estimated standard free energies of double-stranded DNA and RNA/DNA hybrids and predicts a DNA template dependent transcriptional accuracy variation spanning several orders of magnitude. The model also identifies high-error as well a high-accuracy transcription motifs. The source of the large accuracy span is the context dependent variation of the stacking free energy of pairs of correct and incorrect base pairs in the ever moving transcription bubble. Our model predictions have direct experimental support from recent single molecule based identifications of transcriptional errors in the C. elegans transcriptome. Our conclusions challenge the general view that amino acid substitution errors in proteins are mainly caused by translational errors. It suggests instead that transcriptional error hotspots are the dominating source of peptide sequence errors in some DNA template contexts, while mRNA translation is the major cause of protein errors in other contexts.

  15. A polynomial based model for cell fate prediction in human diseases.

    Science.gov (United States)

    Ma, Lichun; Zheng, Jie

    2017-12-21

    Cell fate regulation directly affects tissue homeostasis and human health. Research on cell fate decision sheds light on key regulators, facilitates understanding the mechanisms, and suggests novel strategies to treat human diseases that are related to abnormal cell development. In this study, we proposed a polynomial based model to predict cell fate. This model was derived from Taylor series. As a case study, gene expression data of pancreatic cells were adopted to test and verify the model. As numerous features (genes) are available, we employed two kinds of feature selection methods, i.e. correlation based and apoptosis pathway based. Then polynomials of different degrees were used to refine the cell fate prediction function. 10-fold cross-validation was carried out to evaluate the performance of our model. In addition, we analyzed the stability of the resultant cell fate prediction model by evaluating the ranges of the parameters, as well as assessing the variances of the predicted values at randomly selected points. Results show that, within both the two considered gene selection methods, the prediction accuracies of polynomials of different degrees show little differences. Interestingly, the linear polynomial (degree 1 polynomial) is more stable than others. When comparing the linear polynomials based on the two gene selection methods, it shows that although the accuracy of the linear polynomial that uses correlation analysis outcomes is a little higher (achieves 86.62%), the one within genes of the apoptosis pathway is much more stable. Considering both the prediction accuracy and the stability of polynomial models of different degrees, the linear model is a preferred choice for cell fate prediction with gene expression data of pancreatic cells. The presented cell fate prediction model can be extended to other cells, which may be important for basic research as well as clinical study of cell development related diseases.

  16. A network security situation prediction model based on wavelet neural network with optimized parameters

    Directory of Open Access Journals (Sweden)

    Haibo Zhang

    2016-08-01

    Full Text Available The security incidents ion networks are sudden and uncertain, it is very hard to precisely predict the network security situation by traditional methods. In order to improve the prediction accuracy of the network security situation, we build a network security situation prediction model based on Wavelet Neural Network (WNN with optimized parameters by the Improved Niche Genetic Algorithm (INGA. The proposed model adopts WNN which has strong nonlinear ability and fault-tolerance performance. Also, the parameters for WNN are optimized through the adaptive genetic algorithm (GA so that WNN searches more effectively. Considering the problem that the adaptive GA converges slowly and easily turns to the premature problem, we introduce a novel niche technology with a dynamic fuzzy clustering and elimination mechanism to solve the premature convergence of the GA. Our final simulation results show that the proposed INGA-WNN prediction model is more reliable and effective, and it achieves faster convergence-speed and higher prediction accuracy than the Genetic Algorithm-Wavelet Neural Network (GA-WNN, Genetic Algorithm-Back Propagation Neural Network (GA-BPNN and WNN.

  17. Diagnostic Accuracy of Preoperative Neutrophil-to-Lymphocyte and Platelet-to-Lymphocyte Ratios in Detecting Occult Papillary Thyroid Microcarcinomas in Benign Multinodular Goitres

    Directory of Open Access Journals (Sweden)

    Dimitrios K. Manatakis

    2018-01-01

    Full Text Available Objective. To investigate the diagnostic accuracy of neutrophil-to-lymphocyte (NLR and platelet-to-lymphocyte (PLR ratios in detecting occult papillary thyroid microcarcinomas in benign, multinodular goitres. Methods. 397 total thyroidectomy patients were identified from the institutional thyroid surgery database between 2007 and 2016 (94 males, 303 females, mean age 53 ± 14.5 years. NLR and PLR were calculated as the absolute neutrophil and absolute platelet counts divided by the absolute lymphocyte count, respectively, based on the preoperative complete blood cell count. Results. NLR was significantly higher in carcinomas and microcarcinomas compared to benign pathology (p=0.026, whereas a direct association could not be established for PLR. Both NLR and PLR scored low in all parameters of diagnostic accuracy, with overall accuracy ranging between 45 and 50%. Conclusions. As surrogate indices of the systemic inflammatory response, NLR and PLR are inexpensive and universally available from routine blood tests. Although we found higher NLR values in cases of malignancy, NLR and PLR cannot effectively predict the presence of occult papillary microcarcinomas in otherwise benign, multinodular goitres.

  18. EFFECT OF BASIC SKILLS IN ANY SITUATION TESTS ACCURACY YOUNG PLAYERS

    Directory of Open Access Journals (Sweden)

    Artan R. Kryeziu

    2013-07-01

    Full Text Available The paper aims to establish the impact of basic motor skills in some situational accuracy tests in youth basketball game. In a sample of 60 tested age of 17 year + / - 6 months. We have applied eigjt variables to the tested persons, five of which are predicting, while the other three are criterion tests. Connectivity between motor variables is handled through the correlation of Pearson. The impact of variables predicting in those criteria used in regression analysis. Also significant importance is given to the introduction of connectivity between basic motor skills in several accuracy tests typical for the basketball game situations. Regarding the basic motor skills variables have attained significant coherence, however situational space test throwing the ball in the basket in the same direction and shooting the ball in the basket in the corner 450 degres no coherence with any test has been shown from the space of the appility of basic motor skills. Through regression analysis procedure we’ve reviewed the impact of basic motor skills in some situational accuracy tests. Treat analysis of criterion variables in those predicting we can say that accuracy to situational variables impact junior league players had explosive force. Therefore we can conclude that explosive strength is a quite important factor in the game between young basketball players, with there moving actions the playes can achive their goal of scoring points.

  19. IFE Target Injection Tracking and Position Prediction Update

    International Nuclear Information System (INIS)

    Petzoldt, Ronald W.; Jonestrask, Kevin

    2005-01-01

    To achieve high gain in an inertial fusion energy power plant, driver beams must hit direct drive targets with ±20 μm accuracy (±100 μm for indirect drive). Targets will have to be tracked with even greater accuracy. The conceptual design for our tracking system, which predicts target arrival position and timing based on position measurements outside of the reaction chamber was previously described. The system has been built and has begun tracking targets at the first detector station. Additional detector stations are being modified for increased field of view. After three tracking stations are operational, position predictions at the final station will be compared to position measurements at that station as a measure of target position prediction accuracy.The as-installed design will be described together with initial target tracking and position prediction accuracy results. Design modifications that allow for improved accuracy and/or in-chamber target tracking will also be presented

  20. New prediction of chaotic time series based on local Lyapunov exponent

    International Nuclear Information System (INIS)

    Zhang Yong

    2013-01-01

    A new method of predicting chaotic time series is presented based on a local Lyapunov exponent, by quantitatively measuring the exponential rate of separation or attraction of two infinitely close trajectories in state space. After reconstructing state space from one-dimensional chaotic time series, neighboring multiple-state vectors of the predicting point are selected to deduce the prediction formula by using the definition of the local Lyapunov exponent. Numerical simulations are carried out to test its effectiveness and verify its higher precision over two older methods. The effects of the number of referential state vectors and added noise on forecasting accuracy are also studied numerically. (general)

  1. Effect of accuracy of wind power prediction on power system operator

    Science.gov (United States)

    Schlueter, R. A.; Sigari, G.; Costi, T.

    1985-01-01

    This research project proposed a modified unit commitment that schedules connection and disconnection of generating units in response to load. A modified generation control is also proposed that controls steam units under automatic generation control, fast responding diesels, gas turbines and hydro units under a feedforward control, and wind turbine array output under a closed loop array control. This modified generation control and unit commitment require prediction of trend wind power variation one hour ahead and the prediction of error in this trend wind power prediction one half hour ahead. An improved meter for predicting trend wind speed variation is developed. Methods for accurately simulating the wind array power from a limited number of wind speed prediction records was developed. Finally, two methods for predicting the error in the trend wind power prediction were developed. This research provides a foundation for testing and evaluating the modified unit commitment and generation control that was developed to maintain operating reliability at a greatly reduced overall production cost for utilities with wind generation capacity.

  2. EVALUATING RISK-PREDICTION MODELS USING DATA FROM ELECTRONIC HEALTH RECORDS.

    Science.gov (United States)

    Wang, L E; Shaw, Pamela A; Mathelier, Hansie M; Kimmel, Stephen E; French, Benjamin

    2016-03-01

    The availability of data from electronic health records facilitates the development and evaluation of risk-prediction models, but estimation of prediction accuracy could be limited by outcome misclassification, which can arise if events are not captured. We evaluate the robustness of prediction accuracy summaries, obtained from receiver operating characteristic curves and risk-reclassification methods, if events are not captured (i.e., "false negatives"). We derive estimators for sensitivity and specificity if misclassification is independent of marker values. In simulation studies, we quantify the potential for bias in prediction accuracy summaries if misclassification depends on marker values. We compare the accuracy of alternative prognostic models for 30-day all-cause hospital readmission among 4548 patients discharged from the University of Pennsylvania Health System with a primary diagnosis of heart failure. Simulation studies indicate that if misclassification depends on marker values, then the estimated accuracy improvement is also biased, but the direction of the bias depends on the direction of the association between markers and the probability of misclassification. In our application, 29% of the 1143 readmitted patients were readmitted to a hospital elsewhere in Pennsylvania, which reduced prediction accuracy. Outcome misclassification can result in erroneous conclusions regarding the accuracy of risk-prediction models.

  3. Outcome Prediction in Mathematical Models of Immune Response to Infection.

    Directory of Open Access Journals (Sweden)

    Manuel Mai

    Full Text Available Clinicians need to predict patient outcomes with high accuracy as early as possible after disease inception. In this manuscript, we show that patient-to-patient variability sets a fundamental limit on outcome prediction accuracy for a general class of mathematical models for the immune response to infection. However, accuracy can be increased at the expense of delayed prognosis. We investigate several systems of ordinary differential equations (ODEs that model the host immune response to a pathogen load. Advantages of systems of ODEs for investigating the immune response to infection include the ability to collect data on large numbers of 'virtual patients', each with a given set of model parameters, and obtain many time points during the course of the infection. We implement patient-to-patient variability v in the ODE models by randomly selecting the model parameters from distributions with coefficients of variation v that are centered on physiological values. We use logistic regression with one-versus-all classification to predict the discrete steady-state outcomes of the system. We find that the prediction algorithm achieves near 100% accuracy for v = 0, and the accuracy decreases with increasing v for all ODE models studied. The fact that multiple steady-state outcomes can be obtained for a given initial condition, i.e. the basins of attraction overlap in the space of initial conditions, limits the prediction accuracy for v > 0. Increasing the elapsed time of the variables used to train and test the classifier, increases the prediction accuracy, while adding explicit external noise to the ODE models decreases the prediction accuracy. Our results quantify the competition between early prognosis and high prediction accuracy that is frequently encountered by clinicians.

  4. The accuracy of time dependent transport equation ergodic approximation

    International Nuclear Information System (INIS)

    Stancic, V.

    1995-01-01

    In order to predict the accuracy of the ergodic approximation for solving the time dependent transport equation, a comparison with respect to multiple collision and time finite difference methods, has been considered. (author)

  5. Comparison of Two Predictive Models for Short-Term Mortality in Patients after Severe Traumatic Brain Injury.

    Science.gov (United States)

    Kesmarky, Klara; Delhumeau, Cecile; Zenobi, Marie; Walder, Bernhard

    2017-07-15

    The Glasgow Coma Scale (GCS) and the Abbreviated Injury Score of the head region (HAIS) are validated prognostic factors in traumatic brain injury (TBI). The aim of this study was to compare the prognostic performance of an alternative predictive model including motor GCS, pupillary reactivity, age, HAIS, and presence of multi-trauma for short-term mortality with a reference predictive model including motor GCS, pupil reaction, and age (IMPACT core model). A secondary analysis of a prospective epidemiological cohort study in Switzerland including patients after severe TBI (HAIS >3) with the outcome death at 14 days was performed. Performance of prediction, accuracy of discrimination (area under the receiver operating characteristic curve [AUROC]), calibration, and validity of the two predictive models were investigated. The cohort included 808 patients (median age, 56; interquartile range, 33-71), median GCS at hospital admission 3 (3-14), abnormal pupil reaction 29%, with a death rate of 29.7% at 14 days. The alternative predictive model had a higher accuracy of discrimination to predict death at 14 days than the reference predictive model (AUROC 0.852, 95% confidence interval [CI] 0.824-0.880 vs. AUROC 0.826, 95% CI 0.795-0.857; p predictive model had an equivalent calibration, compared with the reference predictive model Hosmer-Lemeshow p values (Chi2 8.52, Hosmer-Lemeshow p = 0.345 vs. Chi2 8.66, Hosmer-Lemeshow p = 0.372). The optimism-corrected value of AUROC for the alternative predictive model was 0.845. After severe TBI, a higher performance of prediction for short-term mortality was observed with the alternative predictive model, compared with the reference predictive model.

  6. The diagnostic accuracy of endovaginal and transperineal ultrasound for detecting anal sphincter defects: The PREDICT study

    Energy Technology Data Exchange (ETDEWEB)

    Roos, A.-M., E-mail: annemarie.roos@gmail.com [Department of Obstetrics and Gynaecology, Mayday University Hospital, Croydon (United Kingdom); Abdool, Z. [Department of Obstetrics and Gynaecology, University of Pretoria, Pretoria (South Africa); Sultan, A.H.; Thakar, R. [Department of Obstetrics and Gynaecology, Mayday University Hospital, Croydon (United Kingdom)

    2011-07-15

    Aim: To determine the accuracy and predictive value of transperineal (TPU) and endovaginal ultrasound (EVU) in the detection of anal sphincter defects in women with obstetric anal sphincter injuries and/or postpartum symptoms of faecal incontinence. Materials and methods: One hundred and sixty-five women were recruited, four women were excluded as they were seen years after their last delivery. TPU and EVU, followed by endonanal ultrasound (EAU), were performed using the B and K Viking 2400 scanner. Sensitivity and specificity, as well as predictive values with 95% confidence intervals, for detecting anal sphincter defects were calculated for EVU and TPU, using EAU as the reference standard. Results: On EAU a defect was found in 42 (26%) women: 39 (93%) had an external (EAS) and 23 (55%) an internal anal sphincter (IAS) defect. Analysable images of one level of the EAS combined with an analysable IAS were available in 140 (87%) women for EVU and in 131 (81%) for TPU. The sensitivity and specificity for the detection of any defect was 48% (30-67%) and 85% (77-91%) for EVU and 64% (44-81%) and 85% (77-91%) for TPU, respectively. Conclusion: Although EAU using a rotating endoprobe is the validated reference standard in the identification of anal sphincter defects, it is not universally available. However while TPU and/or EVU with conventional ultrasound probes can be useful in identifying normality, for clinical purposes they are not sensitive enough to identify an underlying sphincter defect.

  7. The diagnostic accuracy of endovaginal and transperineal ultrasound for detecting anal sphincter defects: The PREDICT study

    International Nuclear Information System (INIS)

    Roos, A.-M.; Abdool, Z.; Sultan, A.H.; Thakar, R.

    2011-01-01

    Aim: To determine the accuracy and predictive value of transperineal (TPU) and endovaginal ultrasound (EVU) in the detection of anal sphincter defects in women with obstetric anal sphincter injuries and/or postpartum symptoms of faecal incontinence. Materials and methods: One hundred and sixty-five women were recruited, four women were excluded as they were seen years after their last delivery. TPU and EVU, followed by endonanal ultrasound (EAU), were performed using the B and K Viking 2400 scanner. Sensitivity and specificity, as well as predictive values with 95% confidence intervals, for detecting anal sphincter defects were calculated for EVU and TPU, using EAU as the reference standard. Results: On EAU a defect was found in 42 (26%) women: 39 (93%) had an external (EAS) and 23 (55%) an internal anal sphincter (IAS) defect. Analysable images of one level of the EAS combined with an analysable IAS were available in 140 (87%) women for EVU and in 131 (81%) for TPU. The sensitivity and specificity for the detection of any defect was 48% (30-67%) and 85% (77-91%) for EVU and 64% (44-81%) and 85% (77-91%) for TPU, respectively. Conclusion: Although EAU using a rotating endoprobe is the validated reference standard in the identification of anal sphincter defects, it is not universally available. However while TPU and/or EVU with conventional ultrasound probes can be useful in identifying normality, for clinical purposes they are not sensitive enough to identify an underlying sphincter defect.

  8. Cadastral Database Positional Accuracy Improvement

    Science.gov (United States)

    Hashim, N. M.; Omar, A. H.; Ramli, S. N. M.; Omar, K. M.; Din, N.

    2017-10-01

    Positional Accuracy Improvement (PAI) is the refining process of the geometry feature in a geospatial dataset to improve its actual position. This actual position relates to the absolute position in specific coordinate system and the relation to the neighborhood features. With the growth of spatial based technology especially Geographical Information System (GIS) and Global Navigation Satellite System (GNSS), the PAI campaign is inevitable especially to the legacy cadastral database. Integration of legacy dataset and higher accuracy dataset like GNSS observation is a potential solution for improving the legacy dataset. However, by merely integrating both datasets will lead to a distortion of the relative geometry. The improved dataset should be further treated to minimize inherent errors and fitting to the new accurate dataset. The main focus of this study is to describe a method of angular based Least Square Adjustment (LSA) for PAI process of legacy dataset. The existing high accuracy dataset known as National Digital Cadastral Database (NDCDB) is then used as bench mark to validate the results. It was found that the propose technique is highly possible for positional accuracy improvement of legacy spatial datasets.

  9. Prediction of a missing higher charmonium around 4.26 GeV in J/ψ family

    International Nuclear Information System (INIS)

    He, Li-Ping; Chen, Dian-Yong; Liu, Xiang; Matsuki, Takayuki

    2014-01-01

    Inspired by the similarity between the mass gaps of the J/ψ and Υ families, the prediction of a missing higher charmonium with mass 4,263 MeV and very narrow width is made. In addition, the properties of two charmonium-like states, X(3940) and X(4160), and charmonium ψ(4415) are discussed, where our calculation shows that X(3940) as η c (3S) is established, while the explanation of X(4160) to be η c (4S) is fully excluded and that η c (4S) is typically a very narrow state. These predictions might be accessible at BESIII, Belle, and BelleII in near future

  10. The effect of using genealogy-based haplotypes for genomic prediction.

    Science.gov (United States)

    Edriss, Vahid; Fernando, Rohan L; Su, Guosheng; Lund, Mogens S; Guldbrandtsen, Bernt

    2013-03-06

    Genomic prediction uses two sources of information: linkage disequilibrium between markers and quantitative trait loci, and additive genetic relationships between individuals. One way to increase the accuracy of genomic prediction is to capture more linkage disequilibrium by regression on haplotypes instead of regression on individual markers. The aim of this study was to investigate the accuracy of genomic prediction using haplotypes based on local genealogy information. A total of 4429 Danish Holstein bulls were genotyped with the 50K SNP chip. Haplotypes were constructed using local genealogical trees. Effects of haplotype covariates were estimated with two types of prediction models: (1) assuming that effects had the same distribution for all haplotype covariates, i.e. the GBLUP method and (2) assuming that a large proportion (π) of the haplotype covariates had zero effect, i.e. a Bayesian mixture method. About 7.5 times more covariate effects were estimated when fitting haplotypes based on local genealogical trees compared to fitting individuals markers. Genealogy-based haplotype clustering slightly increased the accuracy of genomic prediction and, in some cases, decreased the bias of prediction. With the Bayesian method, accuracy of prediction was less sensitive to parameter π when fitting haplotypes compared to fitting markers. Use of haplotypes based on genealogy can slightly increase the accuracy of genomic prediction. Improved methods to cluster the haplotypes constructed from local genealogy could lead to additional gains in accuracy.

  11. Comparison of predictive accuracy of pre surgical serum parathormone (PTH) level with that of parathyroid scan in case of primary hyperparathyroidism

    International Nuclear Information System (INIS)

    Nasreen, F.; Yasmeen, S.; Ahsan, A.S.M.; Mandal, T.; Sultana, K.S.A.; Shirin, A.

    2007-01-01

    Full text: Aims and Objective: Parathyroid scintigraphy with Tc-99m Sestamibi is a sensitive and specific test for pre operative localization of parathyroid adenoma (PA) in patients with primary hyperparathyroidism. However false ve studies are not uncommon. Our aim was to find out the predictive accuracy of pre surgical parathormone (PTH) level with that of parathyroid scan in case of primary hyperparathyroidism. Materials And Method: A total of 54 patients (29 male, 25 female) with a mean age of 41. 24+14.26 years suspected of primary hyperparathyroidism were included in this study. All patients had serum PTH and calcium level higher than the normal limit. Parathyroid scintigraphy was done by subtraction method using 185 MBq of Tc-99m PO4 which was given first and images were taken by planar gamma camera after 20 minutes followed by Tc-99m Sestamibi (740MBq) injection without moving the patient. We calculated the sensitivity and specificity at different cut off values of PTH such as >70pg/ml, >80pg/ml, >90pg/ml and >100pg/ml and observed the changes in sensitivity, specificity, PPV and NPV against scintigraphic diagnosis of PA. Result: Parathyroid scintigraphy revealed 15 positive cases (27.8%) amongst 54 patients, which were surgically proven to be so. The sensitivity of PTH in predicting positive parathyroid scan revealed to be 86.7% at serum PTH level of 70-90pg/ml. Then the sensitivity declines steadily to 73.3% at PTH level of >100pg/ml. The specificity increases gradually from 20.5% at serum PTH level >70pg/ml to 53.8% at serum PTH level >100pg/ml. However, PPV and NPV of serum PTH did not experience significant change like sensitivity and specificity with the increase of cut off values. Conclusion: We can use a cut off value of pre surgical serum PTH level at 90pg/ml before doing parathyroid scan as this has maximum sensitivity and optimum specificity. It will help to predict the outcome of scan and avoid unnecessary parathyroid scan and false ve cases

  12. Research on Improved Depth Belief Network-Based Prediction of Cardiovascular Diseases

    Directory of Open Access Journals (Sweden)

    Peng Lu

    2018-01-01

    Full Text Available Quantitative analysis and prediction can help to reduce the risk of cardiovascular disease. Quantitative prediction based on traditional model has low accuracy. The variance of model prediction based on shallow neural network is larger. In this paper, cardiovascular disease prediction model based on improved deep belief network (DBN is proposed. Using the reconstruction error, the network depth is determined independently, and unsupervised training and supervised optimization are combined. It ensures the accuracy of model prediction while guaranteeing stability. Thirty experiments were performed independently on the Statlog (Heart and Heart Disease Database data sets in the UCI database. Experimental results showed that the mean of prediction accuracy was 91.26% and 89.78%, respectively. The variance of prediction accuracy was 5.78 and 4.46, respectively.

  13. Predicting and validating protein interactions using network structure.

    Directory of Open Access Journals (Sweden)

    Pao-Yang Chen

    2008-07-01

    Full Text Available Protein interactions play a vital part in the function of a cell. As experimental techniques for detection and validation of protein interactions are time consuming, there is a need for computational methods for this task. Protein interactions appear to form a network with a relatively high degree of local clustering. In this paper we exploit this clustering by suggesting a score based on triplets of observed protein interactions. The score utilises both protein characteristics and network properties. Our score based on triplets is shown to complement existing techniques for predicting protein interactions, outperforming them on data sets which display a high degree of clustering. The predicted interactions score highly against test measures for accuracy. Compared to a similar score derived from pairwise interactions only, the triplet score displays higher sensitivity and specificity. By looking at specific examples, we show how an experimental set of interactions can be enriched and validated. As part of this work we also examine the effect of different prior databases upon the accuracy of prediction and find that the interactions from the same kingdom give better results than from across kingdoms, suggesting that there may be fundamental differences between the networks. These results all emphasize that network structure is important and helps in the accurate prediction of protein interactions. The protein interaction data set and the program used in our analysis, and a list of predictions and validations, are available at http://www.stats.ox.ac.uk/bioinfo/resources/PredictingInteractions.

  14. Research on cardiovascular disease prediction based on distance metric learning

    Science.gov (United States)

    Ni, Zhuang; Liu, Kui; Kang, Guixia

    2018-04-01

    Distance metric learning algorithm has been widely applied to medical diagnosis and exhibited its strengths in classification problems. The k-nearest neighbour (KNN) is an efficient method which treats each feature equally. The large margin nearest neighbour classification (LMNN) improves the accuracy of KNN by learning a global distance metric, which did not consider the locality of data distributions. In this paper, we propose a new distance metric algorithm adopting cosine metric and LMNN named COS-SUBLMNN which takes more care about local feature of data to overcome the shortage of LMNN and improve the classification accuracy. The proposed methodology is verified on CVDs patient vector derived from real-world medical data. The Experimental results show that our method provides higher accuracy than KNN and LMNN did, which demonstrates the effectiveness of the Risk predictive model of CVDs based on COS-SUBLMNN.

  15. Measuring Study Habits in Higher Education: The Way Forward?

    International Nuclear Information System (INIS)

    Fitkov-Norris, E D; Yeghiazarian, A

    2013-01-01

    This article reviews existing study habit measurement instruments and discusses their drawbacks, in the light of new evidence from neuroscience on the workings of the brain. It is suggested that in addition to traditional frequency based past behavioural measures, the predictive accuracy of study habit measurement instruments could be improved by including measures of habit strength that take into account behaviour automaticity and efficacy, such as the Self-Report Habit Index (SRHI) developed by [1]. The SRHI has shown high reliability and internal validity in a wide range of contexts and its applicability and validity in the context of learning and higher education as an enhancement to study habit measurement instruments is as yet to be tested

  16. Quantitative analysis and prediction of regional lymph node status in rectal cancer based on computed tomography imaging

    Energy Technology Data Exchange (ETDEWEB)

    Cui, Chunyan; Liu, Lizhi; Li, Li [Sun Yat-sen University, State Key Laboratory of Oncology in Southern China, Imaging Diagnosis and Interventional Center, Cancer Center, Guangzhou, Guangdong (China); Cai, Hongmin; Tian, Haiying [Sun Yat-Sen University, Department of Automation, School of Science Information and Technology, Guangzhou (China); Li, Liren [Sun Yat-sen University, State Key Laboratory of Oncology in Southern China, Department of Abdominal (colon and rectal) Surgery, Cancer Center, Guangzhou (China)

    2011-11-15

    To quantitatively evaluate regional lymph nodes in rectal cancer patients by using an automated, computer-aided approach, and to assess the accuracy of this approach in differentiating benign and malignant lymph nodes. Patients (228) with newly diagnosed rectal cancer, confirmed by biopsy, underwent enhanced computed tomography (CT). Patients were assigned to the benign node or malignant node group according to histopathological analysis of node samples. All CT-detected lymph nodes were segmented using the edge detection method, and seven quantitative parameters of each node were measured. To increase the prediction accuracy, a hierarchical model combining the merits of the support and relevance vector machines was proposed to achieve higher performance. Of the 220 lymph nodes evaluated, 125 were positive and 95 were negative for metastases. Fractal dimension obtained by the Minkowski box-counting approach was higher in malignant nodes than in benign nodes, and there was a significant difference in heterogeneity between metastatic and non-metastatic lymph nodes. The overall performance of the proposed model is shown to have accuracy as high as 88% using morphological characterisation of lymph nodes. Computer-aided quantitative analysis can improve the prediction of node status in rectal cancer. (orig.)

  17. The diagnostic accuracy of integrated positron emission tomography ...

    African Journals Online (AJOL)

    The PET-CT findings, including maximum standardised uptake value (SUVmax), were compared with the gold standard (tissue or microbiological diagnosis). The sensitivity, specificity, positive and negative predictive values and diagnostic accuracy for malignant disease were calculated according to the SUVmax cut-off of ...

  18. Factors affecting the accuracy of genomic selection for growth and wood quality traits in an advanced-breeding population of black spruce (Picea mariana).

    Science.gov (United States)

    Lenz, Patrick R N; Beaulieu, Jean; Mansfield, Shawn D; Clément, Sébastien; Desponts, Mireille; Bousquet, Jean

    2017-04-28

    Genomic selection (GS) uses information from genomic signatures consisting of thousands of genetic markers to predict complex traits. As such, GS represents a promising approach to accelerate tree breeding, which is especially relevant for the genetic improvement of boreal conifers characterized by long breeding cycles. In the present study, we tested GS in an advanced-breeding population of the boreal black spruce (Picea mariana [Mill.] BSP) for growth and wood quality traits, and concurrently examined factors affecting GS model accuracy. The study relied on 734 25-year-old trees belonging to 34 full-sib families derived from 27 parents and that were established on two contrasting sites. Genomic profiles were obtained from 4993 Single Nucleotide Polymorphisms (SNPs) representative of as many gene loci distributed among the 12 linkage groups common to spruce. GS models were obtained for four growth and wood traits. Validation using independent sets of trees showed that GS model accuracy was high, related to trait heritability and equivalent to that of conventional pedigree-based models. In forward selection, gains per unit of time were three times higher with the GS approach than with conventional selection. In addition, models were also accurate across sites, indicating little genotype-by-environment interaction in the area investigated. Using information from half-sibs instead of full-sibs led to a significant reduction in model accuracy, indicating that the inclusion of relatedness in the model contributed to its higher accuracies. About 500 to 1000 markers were sufficient to obtain GS model accuracy almost equivalent to that obtained with all markers, whether they were well spread across the genome or from a single linkage group, further confirming the implication of relatedness and potential long-range linkage disequilibrium (LD) in the high accuracy estimates obtained. Only slightly higher model accuracy was obtained when using marker subsets that were

  19. Accuracy of Single Frequency GPS Observations Processing In Near Real-time With Use of Code Predicted Products

    Science.gov (United States)

    Wielgosz, P. A.

    In this year, the system of active geodetic GPS permanent stations is going to be estab- lished in Poland. This system should provide GPS observations for a wide spectrum of users, especially it will be a great opportunity for surveyors. Many of surveyors still use cheaper, single frequency receivers. This paper focuses on processing of single frequency GPS observations only. During processing of such observations the iono- sphere plays an important role, so we concentrated on the influence of the ionosphere on the positional coordinates. Twenty consecutive days of GPS data from 2001 year were processed to analyze the accuracy of a derived three-dimensional relative vec- tor position between GPS stations. Observations from two Polish EPN/IGS stations: BOGO and JOZE were used. In addition to, a new test station - IGIK was created. In this paper, the results of single frequency GPS observations processing in near real- time are presented. Baselines of 15, 27 and 42 kilometers and sessions of 1, 2, 3, 4, and 6 hours long were processed. While processing we used CODE (Centre for Orbit De- termination in Europe, Bern, Switzerland) predicted products: orbits and ionosphere info. These products are available in real-time and enable near real-time processing. Software Bernese v. 4.2 for Linux and BPE (Bernese Processing Engine) mode were used. These results are shown with a reference to dual frequency weekly solution (the best solution). Obtained GPS positional time and GPS baseline length dependency accuracy is presented for single frequency GPS observations.

  20. The accuracy of placental alpha-microglobuline-1 test in diagnosis of premature rupture of the membranes

    Directory of Open Access Journals (Sweden)

    Maryam Khooshideh

    2015-06-01

    Full Text Available Background: Premature rupture of membranes (PROM is a common obstetric issue during pregnancy which might lead to serious fetal or maternal problems. Therefore, an appropriate diagnosis and management of PROM are of significant importance in patients. Objective: The aim of this study was to determine the accuracy of placental alpha microglobuline-1 (PAMG-1 test in PROM diagnosis and compare this diagnostic method with other standard tests in diagnosis of PROM. Materials and Methods: In this prospective diagnostic accuracy study, patients with symptoms of membrane rupture in 16-39 weeks of gestation were involved. Three tests including Fern, Nitrazine and PAMG-1 were performed at the same time. Results: PROM was confirmed in 86 patients out of 100. The sensitivity and specificity were respectively 81.3% and 100% for Fern test, 93% and 92.8% for Nitrazine test, 98.9% and 92.8% for PAMG-1 test. PAMG-1 test showed higher sensitivity (98.9% with p<0.001 and accuracy (98% compared with conventional tests. Although PAMG-1showed a lower positive predictive value (PPV compared to conventional tests such as Fern test (100%, it was shown to be more accurate. Conclusion: The accuracy of PAMG-1 test was superior to both Fern and Nitrazine test in PROM diagnosis.

  1. Interrelated Dimensional Chains in Predicting Accuracy of Turbine Wheel Assembly Parameters

    Science.gov (United States)

    Yanyukina, M. V.; Bolotov, M. A.; Ruzanov, N. V.

    2018-03-01

    The working capacity of any device primarily depends on the assembly accuracy which, in its turn, is determined by the quality of each part manufactured, i.e., the degree of conformity between final geometrical parameters and the set ones. However, the assembly accuracy depends not only on a qualitative manufacturing process but also on the assembly process correctness. In this connection, there were preliminary calculations of assembly stages in terms of conformity to real geometrical parameters with their permissible values. This task is performed by means of the calculation of dimensional chains. The calculation of interrelated dimensional chains in the aircraft industry requires particular attention. The article considers the issues of dimensional chain calculation modelling by the example of the turbine wheel assembly process. The authors described the solution algorithm in terms of mathematical statistics implemented in Matlab. The paper demonstrated the results of a dimensional chain calculation for a turbine wheel in relation to the draw of turbine blades to the shroud ring diameter. Besides, the article provides the information on the influence of a geometrical parameter tolerance for the dimensional chain link elements on a closing one.

  2. Martial arts striking hand peak acceleration, accuracy and consistency.

    Science.gov (United States)

    Neto, Osmar Pinto; Marzullo, Ana Carolina De Miranda; Bolander, Richard P; Bir, Cynthia A

    2013-01-01

    The goal of this paper was to investigate the possible trade-off between peak hand acceleration and accuracy and consistency of hand strikes performed by martial artists of different training experiences. Ten male martial artists with training experience ranging from one to nine years volunteered to participate in the experiment. Each participant performed 12 maximum effort goal-directed strikes. Hand acceleration during the strikes was obtained using a tri-axial accelerometer block. A pressure sensor matrix was used to determine the accuracy and consistency of the strikes. Accuracy was estimated by the radial distance between the centroid of each subject's 12 strikes and the target, whereas consistency was estimated by the square root of the 12 strikes mean squared distance from their centroid. We found that training experience was significantly correlated to hand peak acceleration prior to impact (r(2)=0.456, p =0.032) and accuracy (r(2)=0. 621, p=0.012). These correlations suggest that more experienced participants exhibited higher hand peak accelerations and at the same time were more accurate. Training experience, however, was not correlated to consistency (r(2)=0.085, p=0.413). Overall, our results suggest that martial arts training may lead practitioners to achieve higher striking hand accelerations with better accuracy and no change in striking consistency.

  3. SHORT-TERM PRECIPITATION OCCURRENCE PREDICTION FOR STRONG CONVECTIVE WEATHER USING FY2-G SATELLITE DATA: A CASE STUDY OF SHENZHEN,SOUTH CHINA

    Directory of Open Access Journals (Sweden)

    K. Chen

    2016-06-01

    Full Text Available Short-term precipitation commonly occurs in south part of China, which brings intensive precipitation in local region for very short time. Massive water would cause the intensive flood inside of city when precipitation amount beyond the capacity of city drainage system. Thousands people’s life could be influenced by those short-term disasters and the higher city managements are required to facing these challenges. How to predict the occurrence of heavy precipitation accurately is one of the worthwhile scientific questions in meteorology. According to recent studies, the accuracy of short-term precipitation prediction based on numerical simulation model still remains low reliability, in some area where lack of local observations, the accuracy may be as low as 10%. The methodology for short term precipitation occurrence prediction still remains a challenge. In this paper, a machine learning method based on SVM was presented to predict short-term precipitation occurrence by using FY2-G satellite imagery and ground in situ observation data. The results were validated by traditional TS score which commonly used in evaluation of weather prediction. The results indicate that the proposed algorithm can present overall accuracy up to 90% for one-hour to six-hour forecast. The result implies the prediction accuracy could be improved by using machine learning method combining with satellite image. This prediction model can be further used to evaluated to predicted other characteristics of weather in Shenzhen in future.

  4. Accuracy of Self-Esteem Judgments at Zero Acquaintance.

    Science.gov (United States)

    Hirschmüller, Sarah; Schmukle, Stefan C; Krause, Sascha; Back, Mitja D; Egloff, Boris

    2018-04-01

    Perceptions of strangers' self-esteem can have wide-ranging interpersonal consequences. Aiming to reconcile inconsistent results from previous research that had predominantly suggested that self-esteem is a trait that can hardly be accurately judged at zero acquaintance, we examined unaquainted others' accuracy in inferring individuals' actual self-esteem. Ninety-nine target participants (77 female; M age  = 23.5 years) were videotaped in a self-introductory situation, and self-esteem self-reports and reports by well-known informants were obtained as separate accuracy criteria. Forty unacquainted observers judged targets' self-esteem on the basis of these short video sequences (M = 23s, SD = 7.7). Results showed that both self-reported (r = .31, p = .002) and informant-reported self-esteem (r = .21, p = .040) of targets could be inferred by strangers. The degree of accuracy in self-esteem judgments could be explained with lens model analyses: Self- and informant-reported self-esteem predicted nonverbal and vocal friendliness, both of which predicted self-esteem judgments by observers. In addition, observers' accuracy in inferring informant-reported self-esteem was mediated by the utilization of targets' physical attractiveness. Besides using valid behavioral information to infer strangers' self-esteem, observers inappropriately relied on invalid behavioral information reflecting nonverbal, vocal, and verbal self-assuredness. Our findings show that strangers can quite accurately detect individuals' self-reported and informant-reported self-esteem when targets are observed in a public self-presentational situation. © 2017 Wiley Periodicals, Inc.

  5. Accuracy of Genomic Selection in a Rice Synthetic Population Developed for Recurrent Selection Breeding.

    Science.gov (United States)

    Grenier, Cécile; Cao, Tuong-Vi; Ospina, Yolima; Quintero, Constanza; Châtel, Marc Henri; Tohme, Joe; Courtois, Brigitte; Ahmadi, Nourollah

    2015-01-01

    Genomic selection (GS) is a promising strategy for enhancing genetic gain. We investigated the accuracy of genomic estimated breeding values (GEBV) in four inter-related synthetic populations that underwent several cycles of recurrent selection in an upland rice-breeding program. A total of 343 S2:4 lines extracted from those populations were phenotyped for flowering time, plant height, grain yield and panicle weight, and genotyped with an average density of one marker per 44.8 kb. The relative effect of the linkage disequilibrium (LD) and minor allele frequency (MAF) thresholds for selecting markers, the relative size of the training population (TP) and of the validation population (VP), the selected trait and the genomic prediction models (frequentist and Bayesian) on the accuracy of GEBVs was investigated in 540 cross validation experiments with 100 replicates. The effect of kinship between the training and validation populations was tested in an additional set of 840 cross validation experiments with a single genomic prediction model. LD was high (average r2 = 0.59 at 25 kb) and decreased slowly, distribution of allele frequencies at individual loci was markedly skewed toward unbalanced frequencies (MAF average value 15.2% and median 9.6%), and differentiation between the four synthetic populations was low (FST ≤0.06). The accuracy of GEBV across all cross validation experiments ranged from 0.12 to 0.54 with an average of 0.30. Significant differences in accuracy were observed among the different levels of each factor investigated. Phenotypic traits had the biggest effect, and the size of the incidence matrix had the smallest. Significant first degree interaction was observed for GEBV accuracy between traits and all the other factors studied, and between prediction models and LD, MAF and composition of the TP. The potential of GS to accelerate genetic gain and breeding options to increase the accuracy of predictions are discussed.

  6. Prediction of a missing higher charmonium around 4.26 GeV in J/ψ family

    Energy Technology Data Exchange (ETDEWEB)

    He, Li-Ping; Liu, Xiang [Institute of Modern Physics of CAS, Lanzhou University, Research Center for Hadron and CSR Physics, Lanzhou (China); Lanzhou University, School of Physical Science and Technology, Lanzhou (China); Chen, Dian-Yong [Institute of Modern Physics of CAS, Lanzhou University, Research Center for Hadron and CSR Physics, Lanzhou (China); Institute of Modern Physics of CAS, Nuclear Theory Group, Lanzhou (China); Matsuki, Takayuki [Tokyo Kasei University, Itabashi, Tokyo (Japan); Nishina Center, RIKEN, Theoretical Research Division, Saitama (Japan)

    2014-12-01

    Inspired by the similarity between the mass gaps of the J/ψ and Υ families, the prediction of a missing higher charmonium with mass 4,263 MeV and very narrow width is made. In addition, the properties of two charmonium-like states, X(3940) and X(4160), and charmonium ψ(4415) are discussed, where our calculation shows that X(3940) as η{sub c}(3S) is established, while the explanation of X(4160) to be η{sub c}(4S) is fully excluded and that η{sub c}(4S) is typically a very narrow state. These predictions might be accessible at BESIII, Belle, and BelleII in near future. (orig.)

  7. Prediction of a missing higher charmonium around 4.26 GeV in J/ψ family

    Energy Technology Data Exchange (ETDEWEB)

    He, Li-Ping, E-mail: help08@lzu.edu.cn [Research Center for Hadron and CSR Physics, Institute of Modern Physics of CAS, Lanzhou University, 730000, Lanzhou (China); School of Physical Science and Technology, Lanzhou University, 730000, Lanzhou (China); Chen, Dian-Yong, E-mail: chendy@impcas.ac.cn [Research Center for Hadron and CSR Physics, Institute of Modern Physics of CAS, Lanzhou University, 730000, Lanzhou (China); Nuclear Theory Group, Institute of Modern Physics of CAS, 730000, Lanzhou (China); Liu, Xiang, E-mail: xiangliu@lzu.edu.cn [Research Center for Hadron and CSR Physics, Institute of Modern Physics of CAS, Lanzhou University, 730000, Lanzhou (China); School of Physical Science and Technology, Lanzhou University, 730000, Lanzhou (China); Matsuki, Takayuki, E-mail: matsuki@tokyo-kasei.ac.jp [Tokyo Kasei University, 1-18-1 Kaga, 173-8602, Itabashi, Tokyo (Japan); Theoretical Research Division, Nishina Center, RIKEN, 351-0198, Saitama (Japan)

    2014-12-11

    Inspired by the similarity between the mass gaps of the J/ψ and Υ families, the prediction of a missing higher charmonium with mass 4,263 MeV and very narrow width is made. In addition, the properties of two charmonium-like states, X(3940) and X(4160), and charmonium ψ(4415) are discussed, where our calculation shows that X(3940) as η{sub c}(3S) is established, while the explanation of X(4160) to be η{sub c}(4S) is fully excluded and that η{sub c}(4S) is typically a very narrow state. These predictions might be accessible at BESIII, Belle, and BelleII in near future.

  8. Genomic Prediction of Seed Quality Traits Using Advanced Barley Breeding Lines

    Science.gov (United States)

    Nielsen, Nanna Hellum; Jahoor, Ahmed; Jensen, Jens Due; Orabi, Jihad; Cericola, Fabio; Edriss, Vahid; Jensen, Just

    2016-01-01

    Genomic selection was recently introduced in plant breeding. The objective of this study was to develop genomic prediction for important seed quality parameters in spring barley. The aim was to predict breeding values without expensive phenotyping of large sets of lines. A total number of 309 advanced spring barley lines tested at two locations each with three replicates were phenotyped and each line was genotyped by Illumina iSelect 9Kbarley chip. The population originated from two different breeding sets, which were phenotyped in two different years. Phenotypic measurements considered were: seed size, protein content, protein yield, test weight and ergosterol content. A leave-one-out cross-validation strategy revealed high prediction accuracies ranging between 0.40 and 0.83. Prediction across breeding sets resulted in reduced accuracies compared to the leave-one-out strategy. Furthermore, predicting across full and half-sib-families resulted in reduced prediction accuracies. Additionally, predictions were performed using reduced marker sets and reduced training population sets. In conclusion, using less than 200 lines in the training set can result in low prediction accuracy, and the accuracy will then be highly dependent on the family structure of the selected training set. However, the results also indicate that relatively small training sets (200 lines) are sufficient for genomic prediction in commercial barley breeding. In addition, our results indicate a minimum marker set of 1,000 to decrease the risk of low prediction accuracy for some traits or some families. PMID:27783639

  9. Genomic Prediction of Seed Quality Traits Using Advanced Barley Breeding Lines.

    Directory of Open Access Journals (Sweden)

    Nanna Hellum Nielsen

    Full Text Available Genomic selection was recently introduced in plant breeding. The objective of this study was to develop genomic prediction for important seed quality parameters in spring barley. The aim was to predict breeding values without expensive phenotyping of large sets of lines. A total number of 309 advanced spring barley lines tested at two locations each with three replicates were phenotyped and each line was genotyped by Illumina iSelect 9Kbarley chip. The population originated from two different breeding sets, which were phenotyped in two different years. Phenotypic measurements considered were: seed size, protein content, protein yield, test weight and ergosterol content. A leave-one-out cross-validation strategy revealed high prediction accuracies ranging between 0.40 and 0.83. Prediction across breeding sets resulted in reduced accuracies compared to the leave-one-out strategy. Furthermore, predicting across full and half-sib-families resulted in reduced prediction accuracies. Additionally, predictions were performed using reduced marker sets and reduced training population sets. In conclusion, using less than 200 lines in the training set can result in low prediction accuracy, and the accuracy will then be highly dependent on the family structure of the selected training set. However, the results also indicate that relatively small training sets (200 lines are sufficient for genomic prediction in commercial barley breeding. In addition, our results indicate a minimum marker set of 1,000 to decrease the risk of low prediction accuracy for some traits or some families.

  10. Higher order polynomial expansion nodal method for hexagonal core neutronics analysis

    International Nuclear Information System (INIS)

    Jin, Young Cho; Chang, Hyo Kim

    1998-01-01

    A higher-order polynomial expansion nodal(PEN) method is newly formulated as a means to improve the accuracy of the conventional PEN method solutions to multi-group diffusion equations in hexagonal core geometry. The new method is applied to solving various hexagonal core neutronics benchmark problems. The computational accuracy of the higher order PEN method is then compared with that of the conventional PEN method, the analytic function expansion nodal (AFEN) method, and the ANC-H method. It is demonstrated that the higher order PEN method improves the accuracy of the conventional PEN method and that it compares very well with the other nodal methods like the AFEN and ANC-H methods in accuracy

  11. A Bayesian method and its variational approximation for prediction of genomic breeding values in multiple traits

    Directory of Open Access Journals (Sweden)

    Hayashi Takeshi

    2013-01-01

    Full Text Available Abstract Background Genomic selection is an effective tool for animal and plant breeding, allowing effective individual selection without phenotypic records through the prediction of genomic breeding value (GBV. To date, genomic selection has focused on a single trait. However, actual breeding often targets multiple correlated traits, and, therefore, joint analysis taking into consideration the correlation between traits, which might result in more accurate GBV prediction than analyzing each trait separately, is suitable for multi-trait genomic selection. This would require an extension of the prediction model for single-trait GBV to multi-trait case. As the computational burden of multi-trait analysis is even higher than that of single-trait analysis, an effective computational method for constructing a multi-trait prediction model is also needed. Results We described a Bayesian regression model incorporating variable selection for jointly predicting GBVs of multiple traits and devised both an MCMC iteration and variational approximation for Bayesian estimation of parameters in this multi-trait model. The proposed Bayesian procedures with MCMC iteration and variational approximation were referred to as MCBayes and varBayes, respectively. Using simulated datasets of SNP genotypes and phenotypes for three traits with high and low heritabilities, we compared the accuracy in predicting GBVs between multi-trait and single-trait analyses as well as between MCBayes and varBayes. The results showed that, compared to single-trait analysis, multi-trait analysis enabled much more accurate GBV prediction for low-heritability traits correlated with high-heritability traits, by utilizing the correlation structure between traits, while the prediction accuracy for uncorrelated low-heritability traits was comparable or less with multi-trait analysis in comparison with single-trait analysis depending on the setting for prior probability that a SNP has zero

  12. Accuracy of non-fasting lipid profile for the assessment of lipoprotein coronary risk

    International Nuclear Information System (INIS)

    Fatima, S.; Ijaz, A.; Sharif, T.; Khan, D.A.; Siddique, A.

    2016-01-01

    To determine the diagnostic accuracy of non-fasting lipid profile in the diagnosis of hyperlipidemia, taking fasting lipid profile as gold standard, in adult population. Study Design: Cross sectional validation study. Place and Duration of Study: Department of chemical pathology and endocrinology, armed forces institute of pathology, rawalpindi, from july to december 2014. Methodology: One hundred seventy five adult patients coming for fasting lipid prodile were included; their non-fasting samples were taken on the next day. patients on anti-cholesterol treatment and indoor patients were excluded. Total cholesterol (TC), high density lipoprotein-cholestrol (HDL-C), and triglycerides were measured by direct enzymatic calorimetric method by modular p-800 rate. Low density lipoprotein-cholesterol (LDL-C) was calculated by friendewald's formula but when triglyceride was greater than 4.5mol/l, then LDL-C was measured directly by homogenous enzymatic colorimetric method. non-fasting lipid profile had 93% specificity, 51% sensitivity, 94% positive predictive value and 49% negative predictive value and 65% accuracy with 7.28 positive likehood ratio and 0.52 negative likelihood ratio. Non fasting TC and non-HDLC were significantly higher than fasting TC and non-HDL-c by mean difference of 0.2 mmol/l each with p=0.001 and p=0.004, respectively. fasting and on fasting HDLC-are comparable to each other with mean difference of 0.01 mmol/l (p=0.745) Receiver operating curve (ROC) of non fasting non HDLC-C showed 0.84 (95% Cl (0.738-0.870), p=0.000) area under the curve (AUC) indicating that it was a significant test for ruling out hyperlipdemia. Bland-altmann plot showed a significant difference between non fasting, non HDLC-C and fasting LDL-C and non fasting, non-HDL-C -0.087540 with base -0.00109; therefore, these cannot be alternative to each other. Conclusion: Diagnostic accuracy of non-fasting lipid profile was found significantly higher than fasting lipid profile (p=0

  13. Validation and diagnostic accuracy of predictive curves for age-associated longitudinal cognitive decline in older adults

    Science.gov (United States)

    Bernier, Patrick J.; Gourdeau, Christian; Carmichael, Pierre-Hugues; Beauchemin, Jean-Pierre; Verreault, René; Bouchard, Rémi W.; Kröger, Edeltraut; Laforce, Robert

    2017-01-01

    BACKGROUND: The Mini-Mental State Examination continues to be used frequently to screen for cognitive impairment in older adults, but it remains unclear how to interpret changes in its score over time to distinguish age-associated cognitive decline from an early degenerative process. We aimed to generate cognitive charts for use in clinical practice for longitudinal evaluation of age-associated cognitive decline. METHODS: We used data from the Canadian Study of Health and Aging from 7569 participants aged 65 years or older who completed a Mini-Mental State Examination at baseline, and at 5 and 10 years later to develop a linear regression model for the Mini-Mental State Examination score as a function of age and education. Based on this model, we generated cognitive charts designed to optimize accuracy for distinguishing participants with dementia from healthy controls. We validated our model using a separate data set of 6501 participants from the National Alzheimer’s Coordinating Center’s Uniform Data Set. RESULTS: For baseline measurement, the cognitive charts had a sensitivity of 80% (95% confidence interval [CI] 75% to 84%) and a specificity of 89% (95% CI 88% to 90%) for distinguishing healthy controls from participants with dementia. Similar sensitivities and specificities were observed for a decline over time greater than 1 percentile zone from the first measurement. Results in the validation sample were comparable, albeit with lower sensitivities. Negative predictive value was 99%. INTERPRETATION: Our innovative model, which factors in age and education, showed validity and diagnostic accuracy for determining whether older patients show abnormal performance on serial Mini-Mental State Examination measurements. Similar to growth curves used in pediatrics, cognitive charts allow longitudinal cognitive evaluation and enable prompt initiation of investigation and treatment when appropriate. PMID:29203616

  14. Maternal Accuracy in Predicting Toddlers' Behaviors and Associations with Toddlers' Fearful Temperament

    Science.gov (United States)

    Kiel, Elizabeth J.; Buss, Kristin A.

    2006-01-01

    Past research provides associations between maternal parenting behaviors and characteristics such as depression and toddlers' fearful temperament. Less is known about how maternal cognitive characteristics and normal personality relate to fearful temperament. This study examined associations among the maternal cognitive characteristic of accuracy,…

  15. The diagnostic accuracy of endovaginal and transperineal ultrasound for detecting anal sphincter defects: The PREDICT study.

    Science.gov (United States)

    Roos, A-M; Abdool, Z; Sultan, A H; Thakar, R

    2011-07-01

    To determine the accuracy and predictive value of transperineal (TPU) and endovaginal ultrasound (EVU) in the detection of anal sphincter defects in women with obstetric anal sphincter injuries and/or postpartum symptoms of faecal incontinence. One hundred and sixty-five women were recruited, four women were excluded as they were seen years after their last delivery. TPU and EVU, followed by endonanal ultrasound (EAU), were performed using the B&K Viking 2400 scanner. Sensitivity and specificity, as well as predictive values with 95% confidence intervals, for detecting anal sphincter defects were calculated for EVU and TPU, using EAU as the reference standard. On EAU a defect was found in 42 (26%) women: 39 (93%) had an external (EAS) and 23 (55%) an internal anal sphincter (IAS) defect. Analysable images of one level of the EAS combined with an analysable IAS were available in 140 (87%) women for EVU and in 131 (81%) for TPU. The sensitivity and specificity for the detection of any defect was 48% (30-67%) and 85% (77-91%) for EVU and 64% (44-81%) and 85% (77-91%) for TPU, respectively. Although EAU using a rotating endoprobe is the validated reference standard in the identification of anal sphincter defects, it is not universally available. However while TPU and/or EVU with conventional ultrasound probes can be useful in identifying normality, for clinical purposes they are not sensitive enough to identify an underlying sphincter defect. Copyright © 2011 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

  16. Improving Accuracy of Processing Through Active Control

    Directory of Open Access Journals (Sweden)

    N. N. Barbashov

    2016-01-01

    Full Text Available An important task of modern mathematical statistics with its methods based on the theory of probability is a scientific estimate of measurement results. There are certain costs under control, and under ineffective control when a customer has got defective products these costs are significantly higher because of parts recall.When machining the parts, under the influence of errors a range scatter of part dimensions is offset towards the tolerance limit. To improve a processing accuracy and avoid defective products involves reducing components of error in machining, i.e. to improve the accuracy of machine and tool, tool life, rigidity of the system, accuracy of the adjustment. In a given time it is also necessary to adapt machine.To improve an accuracy and a machining rate there, currently  become extensively popular various the in-process gaging devices and controlled machining that uses adaptive control systems for the process monitoring. Improving the accuracy in this case is compensation of a majority of technological errors. The in-cycle measuring sensors (sensors of active control allow processing accuracy improvement by one or two quality and provide a capability for simultaneous operation of several machines.Efficient use of in-cycle measuring sensors requires development of methods to control the accuracy through providing the appropriate adjustments. Methods based on the moving average, appear to be the most promising for accuracy control since they include data on the change in some last measured values of the parameter under control.

  17. Predicting AD conversion

    DEFF Research Database (Denmark)

    Liu, Yawu; Mattila, Jussi; Ruiz, Miguel �ngel Mu�oz

    2013-01-01

    To compare the accuracies of predicting AD conversion by using a decision support system (PredictAD tool) and current research criteria of prodromal AD as identified by combinations of episodic memory impairment of hippocampal type and visual assessment of medial temporal lobe atrophy (MTA) on MRI...

  18. Breed-specific fetal biometry and factors affecting the prediction of whelping date in the German shepherd dog.

    Science.gov (United States)

    Groppetti, D; Vegetti, F; Bronzo, V; Pecile, A

    2015-01-01

    To date many studies have been published about predicting parturition by ultrasonographic fetal measurements in the bitch. Given that accuracy in such prediction is a key point for clinicians and breeders, formulas to calculate the whelping date were mainly obtained from small and medium sized dogs, which means poor accuracy when applied to large or giant breeds. Based on the evidence that ethnicity significantly affects fetal biometry in humans, this study aimed at developing a breed-specific linear regression model for estimating parturition date in the German shepherd dog. For this purpose, serial ultrasonographic measurements of the inner chorionic cavity diameter (ICC) and the fetal biparietal diameter (BP) were collected in 40 pregnant German shepherd bitches. The quality of the regression models for estimating parturition date was further verified in 22 other pregnant German shepherd bitches. Accuracy related to the prediction of parturition date was higher than previously reported: 94.5% and 91.7% within ±2 days interval based on ICC and BP measurements, respectively. Additional investigation was performed on the effects of maternal weight, age and litter size in relation to fetal biometry and to accuracy of parturition estimation. Moreover, the study included a comparison between hormonal and fetal ultrasound (ICC and BP) measurements connected to the estimation of whelping date. We suggest that specific equations from a single breed are likely to offer excellent accuracy, comparable to that of periovulatory progesteronemia, in parturition prediction and to avoid morphological variables present in dogs of different breeds even with the same size/weight. Copyright © 2014 Elsevier B.V. All rights reserved.

  19. Numerical methods of higher order of accuracy for incompressible flows

    Czech Academy of Sciences Publication Activity Database

    Kozel, K.; Louda, Petr; Příhoda, Jaromír

    2010-01-01

    Roč. 80, č. 8 (2010), s. 1734-1745 ISSN 0378-4754 Institutional research plan: CEZ:AV0Z20760514 Keywords : higher order methods * upwind methods * backward-facing step Subject RIV: BK - Fluid Dynamics Impact factor: 0.812, year: 2010

  20. Traffic Flow Prediction Model for Large-Scale Road Network Based on Cloud Computing

    Directory of Open Access Journals (Sweden)

    Zhaosheng Yang

    2014-01-01

    Full Text Available To increase the efficiency and precision of large-scale road network traffic flow prediction, a genetic algorithm-support vector machine (GA-SVM model based on cloud computing is proposed in this paper, which is based on the analysis of the characteristics and defects of genetic algorithm and support vector machine. In cloud computing environment, firstly, SVM parameters are optimized by the parallel genetic algorithm, and then this optimized parallel SVM model is used to predict traffic flow. On the basis of the traffic flow data of Haizhu District in Guangzhou City, the proposed model was verified and compared with the serial GA-SVM model and parallel GA-SVM model based on MPI (message passing interface. The results demonstrate that the parallel GA-SVM model based on cloud computing has higher prediction accuracy, shorter running time, and higher speedup.

  1. Accuracy Rates of Ancestry Estimation by Forensic Anthropologists Using Identified Forensic Cases.

    Science.gov (United States)

    Thomas, Richard M; Parks, Connie L; Richard, Adam H

    2017-07-01

    A common task in forensic anthropology involves the estimation of the ancestry of a decedent by comparing their skeletal morphology and measurements to skeletons of individuals from known geographic groups. However, the accuracy rates of ancestry estimation methods in actual forensic casework have rarely been studied. This article uses 99 forensic cases with identified skeletal remains to develop accuracy rates for ancestry estimations conducted by forensic anthropologists. The overall rate of correct ancestry estimation from these cases is 90.9%, which is comparable to most research-derived rates and those reported by individual practitioners. Statistical tests showed no significant difference in accuracy rates depending on examiner education level or on the estimated or identified ancestry. More recent cases showed a significantly higher accuracy rate. The incorporation of metric analyses into the ancestry estimate in these cases led to a higher accuracy rate. © 2017 American Academy of Forensic Sciences.

  2. Artificial neural networks: Predicting head CT findings in elderly patients presenting with minor head injury after a fall.

    Science.gov (United States)

    Dusenberry, Michael W; Brown, Charles K; Brewer, Kori L

    2017-02-01

    To construct an artificial neural network (ANN) model that can predict the presence of acute CT findings with both high sensitivity and high specificity when applied to the population of patients≥age 65years who have incurred minor head injury after a fall. An ANN was created in the Python programming language using a population of 514 patients ≥ age 65 years presenting to the ED with minor head injury after a fall. The patient dataset was divided into three parts: 60% for "training", 20% for "cross validation", and 20% for "testing". Sensitivity, specificity, positive and negative predictive values, and accuracy were determined by comparing the model's predictions to the actual correct answers for each patient. On the "cross validation" data, the model attained a sensitivity ("recall") of 100.00%, specificity of 78.95%, PPV ("precision") of 78.95%, NPV of 100.00%, and accuracy of 88.24% in detecting the presence of positive head CTs. On the "test" data, the model attained a sensitivity of 97.78%, specificity of 89.47%, PPV of 88.00%, NPV of 98.08%, and accuracy of 93.14% in detecting the presence of positive head CTs. ANNs show great potential for predicting CT findings in the population of patients ≥ 65 years of age presenting with minor head injury after a fall. As a good first step, the ANN showed comparable sensitivity, predictive values, and accuracy, with a much higher specificity than the existing decision rules in clinical usage for predicting head CTs with acute intracranial findings. Copyright © 2016 Elsevier Inc. All rights reserved.

  3. Accuracy of Clinicians in Predicting the Bacterial Cause of Clinical Bovine Mastitis

    OpenAIRE

    White, Maurice E.; Glickman, Lawrence T.; Barnes-Pallesen, Frances D.; Stem, Edgar S.; Dinsmore, Page; Powers, Michael S.; Powers, Pamela; Smith, Mary C.; Jasko, David

    1986-01-01

    We examined the ability of clinicians to predict the causative organism of bovine mastitis in our practice. We obtained 118 milk culture results from 112 mastitic cows and compared the culture results to the predictions of clinicians at the time of milk sample collection. Sixty of 118 culture results were accurately predicted. The positive predictive value for coliform mastitis was 42% and the negative predictive value was 79% in a study population with a 31% prevalence of coliform mastitis. ...

  4. A Copula Based Approach for Design of Multivariate Random Forests for Drug Sensitivity Prediction.

    Science.gov (United States)

    Haider, Saad; Rahman, Raziur; Ghosh, Souparno; Pal, Ranadip

    2015-01-01

    Modeling sensitivity to drugs based on genetic characterizations is a significant challenge in the area of systems medicine. Ensemble based approaches such as Random Forests have been shown to perform well in both individual sensitivity prediction studies and team science based prediction challenges. However, Random Forests generate a deterministic predictive model for each drug based on the genetic characterization of the cell lines and ignores the relationship between different drug sensitivities during model generation. This application motivates the need for generation of multivariate ensemble learning techniques that can increase prediction accuracy and improve variable importance ranking by incorporating the relationships between different output responses. In this article, we propose a novel cost criterion that captures the dissimilarity in the output response structure between the training data and node samples as the difference in the two empirical copulas. We illustrate that copulas are suitable for capturing the multivariate structure of output responses independent of the marginal distributions and the copula based multivariate random forest framework can provide higher accuracy prediction and improved variable selection. The proposed framework has been validated on genomics of drug sensitivity for cancer and cancer cell line encyclopedia database.

  5. Utility of the whole-kidney and parenchymal time-activity curves for a prediction of diuretic response

    International Nuclear Information System (INIS)

    Samal, M.; Mostbeck, A.; Bergmann, H.; Nimmon, C.C.; Staudenherz, A.; Dudczak, R.

    2002-01-01

    Full text: In a retrospective study, MAG3 dynamic renal data (90 kidneys in 57 children) have been analyzed with the aim to test a prediction of diuretic response. Whole-kidney (WK) and parenchymal (PA) curves were extracted from 20 min pre-diuretic phase using standard and fuzzy ROIs. Peak time (PT), half time (HT), ratio of the curve value in 20th min to the curve maximum (RM), mean transit time (TT), and output efficiency (OE) were calculated for each curve. With PA curves, also the transit time index (PI) was calculated. The curve parameters were compared with the maximum elimination rate of urine after diuretic (EM) using paired correlation and Fisher's linear discriminate function. The highest correlation was found between ln EM and OE-PA (0.61), RM-PA (-0.58), TT-PA (-0.57), and PI (-0.57). Best diagnostic accuracy in prediction of EM ≤ 7 % (a sign of obstruction) was obtained with OE-PA (87 %), PI (87 %), and both PT-PA and RM-PA (83 %). Parameters of WK curves had higher sensitivity, those of PA curves higher specificity. Most parameters had a high predictive value of negative result (NPV > 90 %) but low predictive value of positive result (PPV < 50 %). Best discrimination of low EM was obtained with a combination of both WK and PA parameters (diagnostic accuracy of 90 %). Using PA curves in kidneys with late PT-WK made possible to increase the diagnostic accuracy from 70 - 80 % (with WK parameters only) to 95 %. Our results demonstrate that PA curves carry additional clinical information and may help to predict and Interpret a diuretic response especially in kidneys with late peak of the WK curves. (author)

  6. Predictive Accuracy of the PanCan Lung Cancer Risk Prediction Model -External Validation based on CT from the Danish Lung Cancer Screening Trial

    DEFF Research Database (Denmark)

    Winkler Wille, Mathilde M.; van Riel, Sarah J.; Saghir, Zaigham

    2015-01-01

    Objectives: Lung cancer risk models should be externally validated to test generalizability and clinical usefulness. The Danish Lung Cancer Screening Trial (DLCST) is a population-based prospective cohort study, used to assess the discriminative performances of the PanCan models. Methods: From...... the DLCST database, 1,152 nodules from 718 participants were included. Parsimonious and full PanCan risk prediction models were applied to DLCST data, and also coefficients of the model were recalculated using DLCST data. Receiver operating characteristics (ROC) curves and area under the curve (AUC) were...... used to evaluate risk discrimination. Results: AUCs of 0.826–0.870 were found for DLCST data based on PanCan risk prediction models. In the DLCST, age and family history were significant predictors (p = 0.001 and p = 0.013). Female sex was not confirmed to be associated with higher risk of lung cancer...

  7. Deriving Physical Properties from Broadband Photometry with Prospector: Description of the Model and a Demonstration of its Accuracy Using 129 Galaxies in the Local Universe

    Energy Technology Data Exchange (ETDEWEB)

    Leja, Joel; Johnson, Benjamin D.; Conroy, Charlie [Harvard-Smithsonian Center for Astrophysics, 60 Garden Street, Cambridge, MA 02138 (United States); Dokkum, Pieter G. van [Department of Astronomy, Yale University, New Haven, CT 06511 (United States); Byler, Nell [Department of Astronomy, University of Washington, Seattle, WA 98185 (United States)

    2017-03-10

    Broadband photometry of galaxies measures an unresolved mix of complex stellar populations, gas, and dust. Interpreting these data is a challenge for models: many studies have shown that properties derived from modeling galaxy photometry are uncertain by a factor of two or more, and yet answering key questions in the field now requires higher accuracy than this. Here, we present a new model framework specifically designed for these complexities. Our model, Prospector- α , includes dust attenuation and re-radiation, a flexible attenuation curve, nebular emission, stellar metallicity, and a six-component nonparametric star formation history. The flexibility and range of the parameter space, coupled with Monte Carlo Markov chain sampling within the Prospector inference framework, is designed to provide unbiased parameters and realistic error bars. We assess the accuracy of the model with aperture-matched optical spectroscopy, which was excluded from the fits. We compare spectral features predicted solely from fits to the broadband photometry to the observed spectral features. Our model predicts H α luminosities with a scatter of ∼0.18 dex and an offset of ∼0.1 dex across a wide range of morphological types and stellar masses. This agreement is remarkable, as the H α luminosity is dependent on accurate star formation rates, dust attenuation, and stellar metallicities. The model also accurately predicts dust-sensitive Balmer decrements, spectroscopic stellar metallicities, polycyclic aromatic hydrocarbon mass fractions, and the age- and metallicity-sensitive features D{sub n}4000 and H δ . Although the model passes all these tests, we caution that we have not yet assessed its performance at higher redshift or the accuracy of recovered stellar masses.

  8. Reliability and accuracy of Crystaleye spectrophotometric system.

    Science.gov (United States)

    Chen, Li; Tan, Jian Guo; Zhou, Jian Feng; Yang, Xu; Du, Yang; Wang, Fang Ping

    2010-01-01

    to develop an in vitro shade-measuring model to evaluate the reliability and accuracy of the Crystaleye spectrophotometric system, a newly developed spectrophotometer. four shade guides, VITA Classical, VITA 3D-Master, Chromascop and Vintage Halo NCC, were measured with the Crystaleye spectrophotometer in a standardised model, ten times for 107 shade tabs. The shade-matching results and the CIE L*a*b* values of the cervical, body and incisal regions for each measurement were automatically analysed using the supporting software. Reliability and accuracy were calculated for each shade tab both in percentage and in colour difference (ΔE). Difference was analysed by one-way ANOVA in the cervical, body and incisal regions. range of reliability was 88.81% to 98.97% and 0.13 to 0.24 ΔE units, and that of accuracy was 44.05% to 91.25% and 1.03 to 1.89 ΔE units. Significant differences in reliability and accuracy were found between the body region and the cervical and incisal regions. Comparisons made among regions and shade guides revealed that evaluation in ΔE was prone to disclose the differences. measurements with the Crystaleye spectrophotometer had similar, high reliability in different shade guides and regions, indicating predictable repeated measurements. Accuracy in the body region was high and less variable compared with the cervical and incisal regions.

  9. Accuracy of pre-operative hysteroscopic guided biopsy for predicting final pathology in uterine malignancies.

    Science.gov (United States)

    Martinelli, Fabio; Ditto, Antonino; Bogani, Giorgio; Signorelli, Mauro; Chiappa, Valentina; Lorusso, Domenica; Haeusler, Edward; Raspagliesi, Francesco

    2017-07-01

    To evaluate concordance (C) between pre-operative hysteroscopic-directed sampling and final pathology in uterine cancers. A retrospective cross-sectional evaluation of prospectively collected data of women who underwent hysterectomy for uterine malignancies and a previous hysteroscopic-guided biopsy was performed. Diagnostic concordance between pre-operative (hysteroscopic biopsy) and postoperative (uterine specimen) histology was evaluated. In endometrioid-endometrial cancers cases Kappa (k) statistics was applied to evaluate agreement for grading (G) between the preoperative and final pathology. A total 101 hysterectomies for uterine malignancies were evaluated. There were 23 non-endometrioid cancers: 7 serous (C:5/7, 71.4%); 10 carcinosarcomas (C:7/10, 70%, remaining 3 cases only epithelial component diagnosed); 3 clear cell (C:3/3, 100%); 3 sarcomas (C:3/3, 100%). In 78 cases an endometrioid endometrial cancer was found. In 63 cases there was a histological C (63/78, 80.8%) between hysteroscopic-guided biopsy and final pathology, while in 15 cases (19.2%) only hyperplasia (with/without atypia) was found preoperatively. Overall accuracy to detect endometrial cancer was 80.2%. In 50 out of 63 endometrial cancers (79.4%) grading was concordant. The overall level of agreement between preoperative and postoperative grading was "substantial" according to Kappa (k) statistics (k 0.64; 95% CI: 0.449-0.83; p < 0.001), as well as for G1 (0.679; 95% CI: 0.432-0.926; p < 0.001) and G3 (0.774; 94% CI: 0.534-1; p < 0.001), while for G2 (0.531; 95% CI: 0.286-0.777; p < 0.001) it was moderate. In our series we found an 80% C between pre-operative hysteroscopic-guided biopsy and final pathology, in uterine malignancies. Moreover, hysteroscopic biopsy accurately predicted endometrial cancer in 80% of cases and "substantially" predicted histological grading. Hysteroscopic-guided uterine sampling could be a useful tool to tailor treatment in patients with uterine

  10. Diagnostic accuracies of MR enterography and CT enterography in symptomatic Crohn's disease

    DEFF Research Database (Denmark)

    Jensen, Michael Dam; Kjeldsen, Jens; Rafaelsen, Søren Rafael

    2011-01-01

    diagnostic accuracies for detection of small bowel CD and stenosis. In symptomatic patients with CD and high disease prevalence, positive predictive values are favorable but negative predictive values are low. Consequently, MRE and CTE can be relied upon, if a positive result is obtained whereas a negative...

  11. A fresh look at the predictors of naming accuracy and errors in Alzheimer's disease.

    Science.gov (United States)

    Cuetos, Fernando; Rodríguez-Ferreiro, Javier; Sage, Karen; Ellis, Andrew W

    2012-09-01

    In recent years, a considerable number of studies have tried to establish which characteristics of objects and their names predict the responses of patients with Alzheimer's disease (AD) in the picture-naming task. The frequency of use of words and their age of acquisition (AoA) have been implicated as two of the most influential variables, with naming being best preserved for objects with high-frequency, early-acquired names. The present study takes a fresh look at the predictors of naming success in Spanish and English AD patients using a range of measures of word frequency and AoA along with visual complexity, imageability, and word length as predictors. Analyses using generalized linear mixed modelling found that naming accuracy was better predicted by AoA ratings taken from older adults than conventional ratings from young adults. Older frequency measures based on written language samples predicted accuracy better than more modern measures based on the frequencies of words in film subtitles. Replacing adult frequency with an estimate of cumulative (lifespan) frequency did not reduce the impact of AoA. Semantic error rates were predicted by both written word frequency and senior AoA while null response errors were only predicted by frequency. Visual complexity, imageability, and word length did not predict naming accuracy or errors. ©2012 The British Psychological Society.

  12. Genomic prediction of complex human traits: relatedness, trait architecture and predictive meta-models

    Science.gov (United States)

    Spiliopoulou, Athina; Nagy, Reka; Bermingham, Mairead L.; Huffman, Jennifer E.; Hayward, Caroline; Vitart, Veronique; Rudan, Igor; Campbell, Harry; Wright, Alan F.; Wilson, James F.; Pong-Wong, Ricardo; Agakov, Felix; Navarro, Pau; Haley, Chris S.

    2015-01-01

    We explore the prediction of individuals' phenotypes for complex traits using genomic data. We compare several widely used prediction models, including Ridge Regression, LASSO and Elastic Nets estimated from cohort data, and polygenic risk scores constructed using published summary statistics from genome-wide association meta-analyses (GWAMA). We evaluate the interplay between relatedness, trait architecture and optimal marker density, by predicting height, body mass index (BMI) and high-density lipoprotein level (HDL) in two data cohorts, originating from Croatia and Scotland. We empirically demonstrate that dense models are better when all genetic effects are small (height and BMI) and target individuals are related to the training samples, while sparse models predict better in unrelated individuals and when some effects have moderate size (HDL). For HDL sparse models achieved good across-cohort prediction, performing similarly to the GWAMA risk score and to models trained within the same cohort, which indicates that, for predicting traits with moderately sized effects, large sample sizes and familial structure become less important, though still potentially useful. Finally, we propose a novel ensemble of whole-genome predictors with GWAMA risk scores and demonstrate that the resulting meta-model achieves higher prediction accuracy than either model on its own. We conclude that although current genomic predictors are not accurate enough for diagnostic purposes, performance can be improved without requiring access to large-scale individual-level data. Our methodologically simple meta-model is a means of performing predictive meta-analysis for optimizing genomic predictions and can be easily extended to incorporate multiple population-level summary statistics or other domain knowledge. PMID:25918167

  13. On the accuracy and reliability of predictions by control-system theory.

    Science.gov (United States)

    Bourbon, W T; Copeland, K E; Dyer, V R; Harman, W K; Mosley, B L

    1990-12-01

    In three experiments we used control-system theory (CST) to predict the results of tracking tasks on which people held a handle to keep a cursor even with a target on a computer screen. 10 people completed a total of 104 replications of the task. In each experiment, there were two conditions: in one, only the handle affected the position of the cursor; in the other, a random disturbance also affected the cursor. From a person's performance during Condition 1, we derived constants used in the CST model to predict the results of Condition 2. In two experiments, predictions occurred a few minutes before Condition 2; in one experiment, the delay was 1 yr. During a 1-min. experimental run, the positions of handle and cursor, produced by the person, were each sampled 1800 times, once every 1/30 sec. During a modeling run, the model predicted the positions of the handle and target for each of the 1800 intervals sampled in the experimental run. In 104 replications, the mean correlation between predicted and actual positions of the handle was .996; SD = .002.

  14. Social class, contextualism, and empathic accuracy.

    Science.gov (United States)

    Kraus, Michael W; Côté, Stéphane; Keltner, Dacher

    2010-11-01

    Recent research suggests that lower-class individuals favor explanations of personal and political outcomes that are oriented to features of the external environment. We extended this work by testing the hypothesis that, as a result, individuals of a lower social class are more empathically accurate in judging the emotions of other people. In three studies, lower-class individuals (compared with upper-class individuals) received higher scores on a test of empathic accuracy (Study 1), judged the emotions of an interaction partner more accurately (Study 2), and made more accurate inferences about emotion from static images of muscle movements in the eyes (Study 3). Moreover, the association between social class and empathic accuracy was explained by the tendency for lower-class individuals to explain social events in terms of features of the external environment. The implications of class-based patterns in empathic accuracy for well-being and relationship outcomes are discussed.

  15. Prediction of selected Indian stock using a partitioning–interpolation based ARIMA–GARCH model

    Directory of Open Access Journals (Sweden)

    C. Narendra Babu

    2015-07-01

    Full Text Available Accurate long-term prediction of time series data (TSD is a very useful research challenge in diversified fields. As financial TSD are highly volatile, multi-step prediction of financial TSD is a major research problem in TSD mining. The two challenges encountered are, maintaining high prediction accuracy and preserving the data trend across the forecast horizon. The linear traditional models such as autoregressive integrated moving average (ARIMA and generalized autoregressive conditional heteroscedastic (GARCH preserve data trend to some extent, at the cost of prediction accuracy. Non-linear models like ANN maintain prediction accuracy by sacrificing data trend. In this paper, a linear hybrid model, which maintains prediction accuracy while preserving data trend, is proposed. A quantitative reasoning analysis justifying the accuracy of proposed model is also presented. A moving-average (MA filter based pre-processing, partitioning and interpolation (PI technique are incorporated by the proposed model. Some existing models and the proposed model are applied on selected NSE India stock market data. Performance results show that for multi-step ahead prediction, the proposed model outperforms the others in terms of both prediction accuracy and preserving data trend.

  16. Older but not wiser—Predicting a partner's preferences gets worse with age

    OpenAIRE

    Scheibehenne, Benjamin; Todd, Peter M.; Mata, Jutta

    2011-01-01

    To test the influence of relationship length on ability to predict a partner's preferences, 58 younger (M = 24.1 years) and 20 older (M = 68.7 years) couples made predictions in three domains that varied in daily importance. While prediction accuracy was generally better than chance, longer relationship length correlated with lower prediction accuracy and greater overconfidence. The difference in accuracy between older and younger couples increased for strong preferences and when controlling ...

  17. Using support vector machine to predict beta- and gamma-turns in proteins.

    Science.gov (United States)

    Hu, Xiuzhen; Li, Qianzhong

    2008-09-01

    By using the composite vector with increment of diversity, position conservation scoring function, and predictive secondary structures to express the information of sequence, a support vector machine (SVM) algorithm for predicting beta- and gamma-turns in the proteins is proposed. The 426 and 320 nonhomologous protein chains described by Guruprasad and Rajkumar (Guruprasad and Rajkumar J. Biosci 2000, 25,143) are used for training and testing the predictive model of the beta- and gamma-turns, respectively. The overall prediction accuracy and the Matthews correlation coefficient in 7-fold cross-validation are 79.8% and 0.47, respectively, for the beta-turns. The overall prediction accuracy in 5-fold cross-validation is 61.0% for the gamma-turns. These results are significantly higher than the other algorithms in the prediction of beta- and gamma-turns using the same datasets. In addition, the 547 and 823 nonhomologous protein chains described by Fuchs and Alix (Fuchs and Alix Proteins: Struct Funct Bioinform 2005, 59, 828) are used for training and testing the predictive model of the beta- and gamma-turns, and better results are obtained. This algorithm may be helpful to improve the performance of protein turns' prediction. To ensure the ability of the SVM method to correctly classify beta-turn and non-beta-turn (gamma-turn and non-gamma-turn), the receiver operating characteristic threshold independent measure curves are provided. (c) 2008 Wiley Periodicals, Inc.

  18. Prediction of electricity and lpg consumption in a hotel using artificial neural networks

    International Nuclear Information System (INIS)

    Montero, L Reiners; Perez T, Carlos; Gongora L, Ever; Marrero, R Secundino

    2009-01-01

    This work was developed in order to improve the current tools for energy planning. This makes possible to predict electricity and LPG consumption in a tourist facility with accuracy higher than 90% by using Artificial Neuronal Networks (ANN) as fitting and predictive models. Local climatology and occupational patterns were used as entering variables for the models. Parametric modeling was performed as starting conditions and then improved with ANN. Matlab tools were used for calculations. The average deviation when predicting electricity consumption was 0.6% with a standard deviation of 4%. For LPG consumption the average deviation was less than 1% with a standard deviation of 1.3%.

  19. Assessing the accuracy of software predictions of mammalian and microbial metabolites

    Science.gov (United States)

    New chemical development and hazard assessments benefit from accurate predictions of mammalian and microbial metabolites. Fourteen biotransformation libraries encoded in eight software packages that predict metabolite structures were assessed for their sensitivity (proportion of ...

  20. Gas Emission Prediction Model of Coal Mine Based on CSBP Algorithm

    Directory of Open Access Journals (Sweden)

    Xiong Yan

    2016-01-01

    Full Text Available In view of the nonlinear characteristics of gas emission in a coal working face, a prediction method is proposed based on cuckoo search algorithm optimized BP neural network (CSBP. In the CSBP algorithm, the cuckoo search is adopted to optimize weight and threshold parameters of BP network, and obtains the global optimal solutions. Furthermore, the twelve main affecting factors of the gas emission in the coal working face are taken as input vectors of CSBP algorithm, the gas emission is acted as output vector, and then the prediction model of BP neural network with optimal parameters is established. The results show that the CSBP algorithm has batter generalization ability and higher prediction accuracy, and can be utilized effectively in the prediction of coal mine gas emission.

  1. Accuracy of CT-guided joint aspiration in patients with suspected infection status post-total hip arthroplasty

    Energy Technology Data Exchange (ETDEWEB)

    Tomas, Xavier; Garcia-Diez, Ana Isabel; Pomes, Jaime [Universidad de Barcelona, Department of Radiology, Hospital Clinic, Barcelona (Spain); Bori, Guillem; Garcia, Sebastian; Gallart, Xavier; Martinez, Juan Carlos; Riba, Josep [Universidad de Barcelona, Department of Orthopaedics, Hospital Clinic, Barcelona (Spain); Soriano, Alex; Mensa, Josep [Universidad de Barcelona, Department of Infectious Diseases, Hospital Clinic, Barcelona (Spain); Rios, Jose [Statistical Unit de Suport a la Estadistica I Metodologia IDIBAPS, Barcelona (Spain); Almela, Manel [Universidad de Barcelona, Department of Microbiology, Hospital Clinic, Barcelona (Spain)

    2011-01-15

    To determine the accuracy of guided computed tomography aspiration in the detection of septic hip prosthesis before surgery. Sixty-three patients (35 women and 28 men; age range, 29-86 years; mean age, 71 years) with clinically suspected septic hip prosthesis were prospectively studied with independent review board (IRB) approval. Volume and microbiological cultures of aspirated fluid and several computed tomography imaging findings such as periprosthetic fluid collections, prosthetic acetabular malposition, and heterotopic ossification were analyzed. All patients underwent revision surgery and infection was finally diagnosed in 33 patients. Statistical comparative analysis was performed comparing computed tomography aspiration and surgical findings (95% CI; level of significance at P = 0.05 two-sided) with 70% sensitivity, 100% specificity, 84% accuracy, 100% positive predictive value, and 75% negative predictive value. Using Fisher's exact test, the presence of periprosthetic fluid collections (P = 0.001), prosthetic acetabular malposition (P = 0.025) and aspirated fluid volume (P = 0.009) were significantly higher in infected than in non-infected prostheses, whereas heterotopic ossification was not (P = 0.429). Computed tomography aspiration is accurate to preoperatively diagnose septic hip prosthesis on the basis of volume and bacterial cultures of aspirated joint fluid. Furthermore, imaging findings such as periprosthetic fluid collections and prosthetic acetabular malposition strongly suggest infected prosthesis. (orig.)

  2. Accuracy of CT-guided joint aspiration in patients with suspected infection status post-total hip arthroplasty

    International Nuclear Information System (INIS)

    Tomas, Xavier; Garcia-Diez, Ana Isabel; Pomes, Jaime; Bori, Guillem; Garcia, Sebastian; Gallart, Xavier; Martinez, Juan Carlos; Riba, Josep; Soriano, Alex; Mensa, Josep; Rios, Jose; Almela, Manel

    2011-01-01

    To determine the accuracy of guided computed tomography aspiration in the detection of septic hip prosthesis before surgery. Sixty-three patients (35 women and 28 men; age range, 29-86 years; mean age, 71 years) with clinically suspected septic hip prosthesis were prospectively studied with independent review board (IRB) approval. Volume and microbiological cultures of aspirated fluid and several computed tomography imaging findings such as periprosthetic fluid collections, prosthetic acetabular malposition, and heterotopic ossification were analyzed. All patients underwent revision surgery and infection was finally diagnosed in 33 patients. Statistical comparative analysis was performed comparing computed tomography aspiration and surgical findings (95% CI; level of significance at P = 0.05 two-sided) with 70% sensitivity, 100% specificity, 84% accuracy, 100% positive predictive value, and 75% negative predictive value. Using Fisher's exact test, the presence of periprosthetic fluid collections (P = 0.001), prosthetic acetabular malposition (P = 0.025) and aspirated fluid volume (P = 0.009) were significantly higher in infected than in non-infected prostheses, whereas heterotopic ossification was not (P = 0.429). Computed tomography aspiration is accurate to preoperatively diagnose septic hip prosthesis on the basis of volume and bacterial cultures of aspirated joint fluid. Furthermore, imaging findings such as periprosthetic fluid collections and prosthetic acetabular malposition strongly suggest infected prosthesis. (orig.)

  3. Early static {sup 18}F-FET-PET scans have a higher accuracy for glioma grading than the standard 20-40 min scans

    Energy Technology Data Exchange (ETDEWEB)

    Albert, Nathalie L.; Winkelmann, Isabel; Wenter, Vera; Mille, Erik; Todica, Andrei; Brendel, Matthias; Bartenstein, Peter [Ludwig-Maximilians-University Munich, Department of Nuclear Medicine, Munich (Germany); Suchorska, Bogdana; Tonn, Joerg-Christian [Ludwig-Maximilians-University Munich, Department of Neurosurgery, Munich (Germany); Schmid-Tannwald, Christine [Ludwig-Maximilians-University Munich, Institute for Clinical Radiology, Munich (Germany); La Fougere, Christian [University of Tuebingen, Division of Nuclear Medicine and Clinical Molecular Imaging, Department of Radiology, Tuebingen (Germany)

    2016-06-15

    Current guidelines for glioma imaging by positron emission tomography (PET) using the amino acid analogue O-(2-[{sup 18}F]fluoroethyl)-L-tyrosine ({sup 18}F-FET) recommend image acquisition from 20-40 min post injection (p.i.). The maximal tumour-to-background evaluation (TBR{sub max}) obtained in these summation images does not enable reliable differentiation between low and high grade glioma (LGG and HGG), which, however, can be achieved by dynamic {sup 18}F-FET-PET. We investigated the accuracy of tumour grading using TBR{sub max} values at different earlier time points after tracer injection. Three hundred and fourteen patients with histologically proven primary diagnosis of glioma (131 LGG, 183 HGG) who had undergone 40-min dynamic {sup 18}F-FET-PET scans were retrospectively evaluated. TBR{sub max} was assessed in the standard 20-40 min summation images, as well as in summation images from 0-10 min, 5-15 min, 5-20 min, and 15-30 min p.i., and kinetic analysis was performed. TBR{sub max} values and kinetic analysis were correlated with histological classification. ROC analyses were performed for each time frame and sensitivity, specificity, and accuracy were assessed. TBR{sub max} values in the earlier summation images were significantly better for tumour grading (P < 0.001) when compared to standard 20-40 min scans, with best results for the early 5-15 min scan. This was due to higher TBR{sub max} in the HGG (3.9 vs. 3.3; p < 0.001), while TBR{sub max} remained nearly stable in the LGG (2.2 vs. 2.1). Overall, accuracy increased from 70 % in the 20-40 min analysis to 77 % in the 5-15 min images, but did not reach the accuracy of dynamic analysis (80 %). Early TBR{sub max} assessment (5-15 min p.i.) is more accurate for the differentiation between LGG and HGG than the standard static scan (20-40 min p.i.) mainly caused by the characteristic high {sup 18}F-FET uptake of HGG in the initial phase. Therefore, when dynamic {sup 18}F-FET-PET cannot be performed

  4. Accuracy and Training Population Design for Genomic Selection on Quantitative Traits in Elite North American Oats

    Directory of Open Access Journals (Sweden)

    Franco G. Asoro

    2011-07-01

    Full Text Available Genomic selection (GS is a method to estimate the breeding values of individuals by using markers throughout the genome. We evaluated the accuracies of GS using data from five traits on 446 oat ( L. lines genotyped with 1005 Diversity Array Technology (DArT markers and two GS methods (ridge regression–best linear unbiased prediction [RR-BLUP] and BayesCπ under various training designs. Our objectives were to (i determine accuracy under increasing marker density and training population size, (ii assess accuracies when data is divided over time, and (iii examine accuracy in the presence of population structure. Accuracy increased as the number of markers and training size become larger. Including older lines in the training population increased or maintained accuracy, indicating that older generations retained information useful for predicting validation populations. The presence of population structure affected accuracy: when training and validation subpopulations were closely related accuracy was greater than when they were distantly related, implying that linkage disequilibrium (LD relationships changed across subpopulations. Across many scenarios involving large training populations, the accuracy of BayesCπ and RR-BLUP did not differ. This empirical study provided evidence regarding the application of GS to hasten the delivery of cultivars through the use of inexpensive and abundant molecular markers available to the public sector.

  5. Development of motion image prediction method using principal component analysis

    International Nuclear Information System (INIS)

    Chhatkuli, Ritu Bhusal; Demachi, Kazuyuki; Kawai, Masaki; Sakakibara, Hiroshi; Kamiaka, Kazuma

    2012-01-01

    Respiratory motion can induce the limit in the accuracy of area irradiated during lung cancer radiation therapy. Many methods have been introduced to minimize the impact of healthy tissue irradiation due to the lung tumor motion. The purpose of this research is to develop an algorithm for the improvement of image guided radiation therapy by the prediction of motion images. We predict the motion images by using principal component analysis (PCA) and multi-channel singular spectral analysis (MSSA) method. The images/movies were successfully predicted and verified using the developed algorithm. With the proposed prediction method it is possible to forecast the tumor images over the next breathing period. The implementation of this method in real time is believed to be significant for higher level of tumor tracking including the detection of sudden abdominal changes during radiation therapy. (author)

  6. Diagnostic accuracy of tuberculous lymphadenitis fine needle aspiration biopsy confirmed by PCR as gold standard

    Science.gov (United States)

    DSuryadi; Delyuzar; Soekimin

    2018-03-01

    Indonesia is the second country with the TB (tuberculosis) burden in the world. Improvement in controlling TB and reducing the complications can accelerate early diagnosis and correct treatment. PCR test is a gold standard. However, it is quite expensive for routine diagnosis. Therefore, an accurate and cheaper diagnostic method such as fine needle aspiration biopsy is needed. The study aimsto determine the accuracy of fine needle aspiration biopsy cytology in the diagnosis of tuberculous lymphadenitis. A cross-sectional analytic study was conducted to the samples from patients suspected with tuberculous lymphadenitis. The fine needle aspiration biopsy (FNAB)test was performed and confirmed by PCR test.There is a comparison to the sensitivity, specificity, accuracy, positive predictive value and negative predictive value of both methods. Sensitivity (92.50%), specificity (96.49%), accuracy (94.85%), positive predictive value (94.87%) and negative predictive value (94.83%) were in FNAB test compared to gold standard. We concluded that fine needle aspiration biopsy is a recommendation for a cheaper and accurate diagnostic test for tuberculous lymphadenitis diagnosis.

  7. A systems biology approach to transcription factor binding site prediction.

    Directory of Open Access Journals (Sweden)

    Xiang Zhou

    2010-03-01

    Full Text Available The elucidation of mammalian transcriptional regulatory networks holds great promise for both basic and translational research and remains one the greatest challenges to systems biology. Recent reverse engineering methods deduce regulatory interactions from large-scale mRNA expression profiles and cross-species conserved regulatory regions in DNA. Technical challenges faced by these methods include distinguishing between direct and indirect interactions, associating transcription regulators with predicted transcription factor binding sites (TFBSs, identifying non-linearly conserved binding sites across species, and providing realistic accuracy estimates.We address these challenges by closely integrating proven methods for regulatory network reverse engineering from mRNA expression data, linearly and non-linearly conserved regulatory region discovery, and TFBS evaluation and discovery. Using an extensive test set of high-likelihood interactions, which we collected in order to provide realistic prediction-accuracy estimates, we show that a careful integration of these methods leads to significant improvements in prediction accuracy. To verify our methods, we biochemically validated TFBS predictions made for both transcription factors (TFs and co-factors; we validated binding site predictions made using a known E2F1 DNA-binding motif on E2F1 predicted promoter targets, known E2F1 and JUND motifs on JUND predicted promoter targets, and a de novo discovered motif for BCL6 on BCL6 predicted promoter targets. Finally, to demonstrate accuracy of prediction using an external dataset, we showed that sites matching predicted motifs for ZNF263 are significantly enriched in recent ZNF263 ChIP-seq data.Using an integrative framework, we were able to address technical challenges faced by state of the art network reverse engineering methods, leading to significant improvement in direct-interaction detection and TFBS-discovery accuracy. We estimated the accuracy

  8. A range-based predictive localization algorithm for WSID networks

    Science.gov (United States)

    Liu, Yuan; Chen, Junjie; Li, Gang

    2017-11-01

    Most studies on localization algorithms are conducted on the sensor networks with densely distributed nodes. However, the non-localizable problems are prone to occur in the network with sparsely distributed sensor nodes. To solve this problem, a range-based predictive localization algorithm (RPLA) is proposed in this paper for the wireless sensor networks syncretizing the RFID (WSID) networks. The Gaussian mixture model is established to predict the trajectory of a mobile target. Then, the received signal strength indication is used to reduce the residence area of the target location based on the approximate point-in-triangulation test algorithm. In addition, collaborative localization schemes are introduced to locate the target in the non-localizable situations. Simulation results verify that the RPLA achieves accurate localization for the network with sparsely distributed sensor nodes. The localization accuracy of the RPLA is 48.7% higher than that of the APIT algorithm, 16.8% higher than that of the single Gaussian model-based algorithm and 10.5% higher than that of the Kalman filtering-based algorithm.

  9. The value of predicting restriction of fetal growth and compromise of its wellbeing: Systematic quantitative overviews (meta-analysis) of test accuracy literature.

    Science.gov (United States)

    Morris, Rachel K; Khan, Khalid S; Coomarasamy, Aravinthan; Robson, Stephen C; Kleijnen, Jos

    2007-03-08

    Restriction of fetal growth and compromise of fetal wellbeing remain significant causes of perinatal death and childhood disability. At present, there is a lack of scientific consensus about the best strategies for predicting these conditions before birth. Therefore, there is uncertainty about the best management of pregnant women who might have a growth restricted baby. This is likely to be due to a dearth of clear collated information from individual research studies drawn from different sources on this subject. A series of systematic reviews and meta-analyses will be undertaken to determine, among pregnant women, the accuracy of various tests to predict and/or diagnose fetal growth restriction and compromise of fetal wellbeing. We will search Medline, Embase, Cochrane Library, MEDION, citation lists of review articles and eligible primary articles and will contact experts in the field. Independent reviewers will select studies, extract data and assess study quality according to established criteria. Language restrictions will not be applied. Data synthesis will involve meta-analysis (where appropriate), exploration of heterogeneity and publication bias. The project will collate and synthesise the available evidence regarding the value of the tests for predicting restriction of fetal growth and compromise of fetal wellbeing. The systematic overviews will assess the quality of the available evidence, estimate the magnitude of potential benefits, identify those tests with good predictive value and help formulate practice recommendations.

  10. High Accuracy Transistor Compact Model Calibrations

    Energy Technology Data Exchange (ETDEWEB)

    Hembree, Charles E. [Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States); Mar, Alan [Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States); Robertson, Perry J. [Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)

    2015-09-01

    Typically, transistors are modeled by the application of calibrated nominal and range models. These models consists of differing parameter values that describe the location and the upper and lower limits of a distribution of some transistor characteristic such as current capacity. Correspond- ingly, when using this approach, high degrees of accuracy of the transistor models are not expected since the set of models is a surrogate for a statistical description of the devices. The use of these types of models describes expected performances considering the extremes of process or transistor deviations. In contrast, circuits that have very stringent accuracy requirements require modeling techniques with higher accuracy. Since these accurate models have low error in transistor descriptions, these models can be used to describe part to part variations as well as an accurate description of a single circuit instance. Thus, models that meet these stipulations also enable the calculation of quantifi- cation of margins with respect to a functional threshold and uncertainties in these margins. Given this need, new model high accuracy calibration techniques for bipolar junction transis- tors have been developed and are described in this report.

  11. Alternatives to accuracy and bias metrics based on percentage errors for radiation belt modeling applications

    Energy Technology Data Exchange (ETDEWEB)

    Morley, Steven Karl [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2016-07-01

    This report reviews existing literature describing forecast accuracy metrics, concentrating on those based on relative errors and percentage errors. We then review how the most common of these metrics, the mean absolute percentage error (MAPE), has been applied in recent radiation belt modeling literature. Finally, we describe metrics based on the ratios of predicted to observed values (the accuracy ratio) that address the drawbacks inherent in using MAPE. Specifically, we define and recommend the median log accuracy ratio as a measure of bias and the median symmetric accuracy as a measure of accuracy.

  12. Genomic prediction in a breeding program of perennial ryegrass

    DEFF Research Database (Denmark)

    Fé, Dario; Ashraf, Bilal; Greve-Pedersen, Morten

    2015-01-01

    We present a genomic selection study performed on 1918 rye grass families (Lolium perenne L.), which were derived from a commercial breeding program at DLF-Trifolium, Denmark. Phenotypes were recorded on standard plots, across 13 years and in 6 different countries. Variants were identified...... this set. Estimated Breeding Value and prediction accuracies were calculated trough two different cross-validation schemes: (i) k-fold (k=10); (ii) leaving out one parent combination at the time, in order to test for accuracy of predicting new families. Accuracies ranged between 0.56 and 0.97 for scheme (i....... A larger set of 1791 F2s were used as training set to predict EBVs of 127 synthetic families (originated from poly-crosses between 5-11 single plants) for heading date and crown rust resistance. Prediction accuracies were 0.93 and 0.57 respectively. Results clearly demonstrate considerable potential...

  13. Systematic Calibration for Ultra-High Accuracy Inertial Measurement Units

    Directory of Open Access Journals (Sweden)

    Qingzhong Cai

    2016-06-01

    Full Text Available An inertial navigation system (INS has been widely used in challenging GPS environments. With the rapid development of modern physics, an atomic gyroscope will come into use in the near future with a predicted accuracy of 5 × 10−6°/h or better. However, existing calibration methods and devices can not satisfy the accuracy requirements of future ultra-high accuracy inertial sensors. In this paper, an improved calibration model is established by introducing gyro g-sensitivity errors, accelerometer cross-coupling errors and lever arm errors. A systematic calibration method is proposed based on a 51-state Kalman filter and smoother. Simulation results show that the proposed calibration method can realize the estimation of all the parameters using a common dual-axis turntable. Laboratory and sailing tests prove that the position accuracy in a five-day inertial navigation can be improved about 8% by the proposed calibration method. The accuracy can be improved at least 20% when the position accuracy of the atomic gyro INS can reach a level of 0.1 nautical miles/5 d. Compared with the existing calibration methods, the proposed method, with more error sources and high order small error parameters calibrated for ultra-high accuracy inertial measurement units (IMUs using common turntables, has a great application potential in future atomic gyro INSs.

  14. Accuracy of radiologic and endoscopic diagnosis of gastric ulcer

    International Nuclear Information System (INIS)

    Gjoerup, T.; Agner, E.; Bording Jensen, L.; Moerup Jensen, A.; Moellmann, K.M.; Copenhagen Univ., Herlev; Copenhagen Univ.

    1987-01-01

    Patients with upper abdominal pain are often examined with both double contrast study of the stomach and endoscopy. On the basis of the results of the two examinations four diagnostic criteria of an ulcer can be formed: 1) radiography reveals an ulcer, 2) endoscopy reveals an ulcer, 3) both radiography and endoscopy reveal an ulcer, and 4) radiography and/or endoscopy reveals an ulcer. In a prospective study the accuracy of each of the four diagnostic criteria was examined. Eighty-two randomly selected outpatients had a double contrast barium examination and an upper gastrointestinal endoscopy performed by staff personnel. The diagnosis of a specialist in upper gastrointestinal endoscopy was used as the standard. For the four diagnostic criteria the overall accuracy ranged from 0.80 to 0.88. The predictive value of a positive test result was around 0.70 and the predictive value of a negative test result ranged from 0.81 to 0.96. The specificity ranged from 0.87 to 0.95, and the sensitivity from 0.38 to 0.90. It is concluded that from a clinical point of view, the accuracy of the four diagnostic criteria does not differ to an extent that justifies recommendation of one diagnostic criterion of gastric ulcer rather than the other. (orig.)

  15. The effect of using cow genomic information on accuracy and bias of genomic breeding values in a simulated Holstein dairy cattle population.

    Science.gov (United States)

    Dehnavi, E; Mahyari, S Ansari; Schenkel, F S; Sargolzaei, M

    2018-06-01

    Using cow data in the training population is attractive as a way to mitigate bias due to highly selected training bulls and to implement genomic selection for countries with no or limited proven bull data. However, one potential issue with cow data is a bias due to the preferential treatment. The objectives of this study were to (1) investigate the effect of including cow genotype and phenotype data into the training population on accuracy and bias of genomic predictions and (2) assess the effect of preferential treatment for different proportions of elite cows. First, a 4-pathway Holstein dairy cattle population was simulated for 2 traits with low (0.05) and moderate (0.3) heritability. Then different numbers of cows (0, 2,500, 5,000, 10,000, 15,000, or 20,000) were randomly selected and added to the training group composed of different numbers of top bulls (0, 2,500, 5,000, 10,000, or 15,000). Reliability levels of de-regressed estimated breeding values for training cows and bulls were 30 and 75% for traits with low heritability and were 60 and 90% for traits with moderate heritability, respectively. Preferential treatment was simulated by introducing upward bias equal to 35% of phenotypic variance to 5, 10, and 20% of elite bull dams in each scenario. Two different validation data sets were considered: (1) all animals in the last generation of both elite and commercial tiers (n = 42,000) and (2) only animals in the last generation of the elite tier (n = 12,000). Adding cow data into the training population led to an increase in accuracy (r) and decrease in bias of genomic predictions in all considered scenarios without preferential treatment. The gain in r was higher for the low heritable trait (from 0.004 to 0.166 r points) compared with the moderate heritable trait (from 0.004 to 0.116 r points). The gain in accuracy in scenarios with a lower number of training bulls was relatively higher (from 0.093 to 0.166 r points) than with a higher number of training

  16. PockDrug: A Model for Predicting Pocket Druggability That Overcomes Pocket Estimation Uncertainties.

    Science.gov (United States)

    Borrel, Alexandre; Regad, Leslie; Xhaard, Henri; Petitjean, Michel; Camproux, Anne-Claude

    2015-04-27

    Predicting protein druggability is a key interest in the target identification phase of drug discovery. Here, we assess the pocket estimation methods' influence on druggability predictions by comparing statistical models constructed from pockets estimated using different pocket estimation methods: a proximity of either 4 or 5.5 Å to a cocrystallized ligand or DoGSite and fpocket estimation methods. We developed PockDrug, a robust pocket druggability model that copes with uncertainties in pocket boundaries. It is based on a linear discriminant analysis from a pool of 52 descriptors combined with a selection of the most stable and efficient models using different pocket estimation methods. PockDrug retains the best combinations of three pocket properties which impact druggability: geometry, hydrophobicity, and aromaticity. It results in an average accuracy of 87.9% ± 4.7% using a test set and exhibits higher accuracy (∼5-10%) than previous studies that used an identical apo set. In conclusion, this study confirms the influence of pocket estimation on pocket druggability prediction and proposes PockDrug as a new model that overcomes pocket estimation variability.

  17. A Prediction Mechanism of Energy Consumption in Residential Buildings Using Hidden Markov Model

    Directory of Open Access Journals (Sweden)

    Israr Ullah

    2018-02-01

    Full Text Available Internet of Things (IoT is considered as one of the future disruptive technologies, which has the potential to bring positive change in human lifestyle and uplift living standards. Many IoT-based applications have been designed in various fields, e.g., security, health, education, manufacturing, transportation, etc. IoT has transformed conventional homes into Smart homes. By attaching small IoT devices to various appliances, we cannot only monitor but also control indoor environment as per user demand. Intelligent IoT devices can also be used for optimal energy utilization by operating the associated equipment only when it is needed. In this paper, we have proposed a Hidden Markov Model based algorithm to predict energy consumption in Korean residential buildings using data collected through smart meters. We have used energy consumption data collected from four multi-storied buildings located in Seoul, South Korea for model validation and results analysis. Proposed model prediction results are compared with three well-known prediction algorithms i.e., Support Vector Machine (SVM, Artificial Neural Network (ANN and Classification and Regression Trees (CART. Comparative analysis shows that our proposed model achieves 2.96 % better than ANN results in terms of root mean square error metric, 6.09 % better than SVM and 9.03 % better than CART results. To further establish and validate prediction results of our proposed model, we have performed temporal granularity analysis. For this purpose, we have evaluated our proposed model for hourly, daily and weekly data aggregation. Prediction accuracy in terms of root mean square error metric for hourly, daily and weekly data is 2.62, 1.54 and 0.46, respectively. This shows that our model prediction accuracy improves for coarse grain data. Higher prediction accuracy gives us confidence to further explore its application in building control systems for achieving better energy efficiency.

  18. Feedback from the heart: Emotional learning and memory is controlled by cardiac cycle, interoceptive accuracy and personality.

    Science.gov (United States)

    Pfeifer, Gaby; Garfinkel, Sarah N; Gould van Praag, Cassandra D; Sahota, Kuljit; Betka, Sophie; Critchley, Hugo D

    2017-05-01

    Feedback processing is critical to trial-and-error learning. Here, we examined whether interoceptive signals concerning the state of cardiovascular arousal influence the processing of reinforcing feedback during the learning of 'emotional' face-name pairs, with subsequent effects on retrieval. Participants (N=29) engaged in a learning task of face-name pairs (fearful, neutral, happy faces). Correct and incorrect learning decisions were reinforced by auditory feedback, which was delivered either at cardiac systole (on the heartbeat, when baroreceptors signal the contraction of the heart to the brain), or at diastole (between heartbeats during baroreceptor quiescence). We discovered a cardiac influence on feedback processing that enhanced the learning of fearful faces in people with heightened interoceptive ability. Individuals with enhanced accuracy on a heartbeat counting task learned fearful face-name pairs better when feedback was given at systole than at diastole. This effect was not present for neutral and happy faces. At retrieval, we also observed related effects of personality: First, individuals scoring higher for extraversion showed poorer retrieval accuracy. These individuals additionally manifested lower resting heart rate and lower state anxiety, suggesting that attenuated levels of cardiovascular arousal in extraverts underlies poorer performance. Second, higher extraversion scores predicted higher emotional intensity ratings of fearful faces reinforced at systole. Third, individuals scoring higher for neuroticism showed higher retrieval confidence for fearful faces reinforced at diastole. Our results show that cardiac signals shape feedback processing to influence learning of fearful faces, an effect underpinned by personality differences linked to psychophysiological arousal. Copyright © 2017 Elsevier B.V. All rights reserved.

  19. Macrocell path loss prediction using artificial intelligence techniques

    Science.gov (United States)

    Usman, Abraham U.; Okereke, Okpo U.; Omizegba, Elijah E.

    2014-04-01

    The prediction of propagation loss is a practical non-linear function approximation problem which linear regression or auto-regression models are limited in their ability to handle. However, some computational Intelligence techniques such as artificial neural networks (ANNs) and adaptive neuro-fuzzy inference systems (ANFISs) have been shown to have great ability to handle non-linear function approximation and prediction problems. In this study, the multiple layer perceptron neural network (MLP-NN), radial basis function neural network (RBF-NN) and an ANFIS network were trained using actual signal strength measurement taken at certain suburban areas of Bauchi metropolis, Nigeria. The trained networks were then used to predict propagation losses at the stated areas under differing conditions. The predictions were compared with the prediction accuracy of the popular Hata model. It was observed that ANFIS model gave a better fit in all cases having higher R2 values in each case and on average is more robust than MLP and RBF models as it generalises better to a different data.

  20. Validation of Models Used to Inform Colorectal Cancer Screening Guidelines: Accuracy and Implications.

    Science.gov (United States)

    Rutter, Carolyn M; Knudsen, Amy B; Marsh, Tracey L; Doria-Rose, V Paul; Johnson, Eric; Pabiniak, Chester; Kuntz, Karen M; van Ballegooijen, Marjolein; Zauber, Ann G; Lansdorp-Vogelaar, Iris

    2016-07-01

    Microsimulation models synthesize evidence about disease processes and interventions, providing a method for predicting long-term benefits and harms of prevention, screening, and treatment strategies. Because models often require assumptions about unobservable processes, assessing a model's predictive accuracy is important. We validated 3 colorectal cancer (CRC) microsimulation models against outcomes from the United Kingdom Flexible Sigmoidoscopy Screening (UKFSS) Trial, a randomized controlled trial that examined the effectiveness of one-time flexible sigmoidoscopy screening to reduce CRC mortality. The models incorporate different assumptions about the time from adenoma initiation to development of preclinical and symptomatic CRC. Analyses compare model predictions to study estimates across a range of outcomes to provide insight into the accuracy of model assumptions. All 3 models accurately predicted the relative reduction in CRC mortality 10 years after screening (predicted hazard ratios, with 95% percentile intervals: 0.56 [0.44, 0.71], 0.63 [0.51, 0.75], 0.68 [0.53, 0.83]; estimated with 95% confidence interval: 0.56 [0.45, 0.69]). Two models with longer average preclinical duration accurately predicted the relative reduction in 10-year CRC incidence. Two models with longer mean sojourn time accurately predicted the number of screen-detected cancers. All 3 models predicted too many proximal adenomas among patients referred to colonoscopy. Model accuracy can only be established through external validation. Analyses such as these are therefore essential for any decision model. Results supported the assumptions that the average time from adenoma initiation to development of preclinical cancer is long (up to 25 years), and mean sojourn time is close to 4 years, suggesting the window for early detection and intervention by screening is relatively long. Variation in dwell time remains uncertain and could have important clinical and policy implications. © The

  1. Prediction of Machine Tool Condition Using Support Vector Machine

    International Nuclear Information System (INIS)

    Wang Peigong; Meng Qingfeng; Zhao Jian; Li Junjie; Wang Xiufeng

    2011-01-01

    Condition monitoring and predicting of CNC machine tools are investigated in this paper. Considering the CNC machine tools are often small numbers of samples, a condition predicting method for CNC machine tools based on support vector machines (SVMs) is proposed, then one-step and multi-step condition prediction models are constructed. The support vector machines prediction models are used to predict the trends of working condition of a certain type of CNC worm wheel and gear grinding machine by applying sequence data of vibration signal, which is collected during machine processing. And the relationship between different eigenvalue in CNC vibration signal and machining quality is discussed. The test result shows that the trend of vibration signal Peak-to-peak value in surface normal direction is most relevant to the trend of surface roughness value. In trends prediction of working condition, support vector machine has higher prediction accuracy both in the short term ('One-step') and long term (multi-step) prediction compared to autoregressive (AR) model and the RBF neural network. Experimental results show that it is feasible to apply support vector machine to CNC machine tool condition prediction.

  2. Unfamiliar voice identification: Effect of post-event information on accuracy and voice ratings

    Directory of Open Access Journals (Sweden)

    Harriet Mary Jessica Smith

    2014-04-01

    Full Text Available This study addressed the effect of misleading post-event information (PEI on voice ratings, identification accuracy, and confidence, as well as the link between verbal recall and accuracy. Participants listened to a dialogue between male and female targets, then read misleading information about voice pitch. Participants engaged in verbal recall, rated voices on a feature checklist, and made a lineup decision. Accuracy rates were low, especially on target-absent lineups. Confidence and accuracy were unrelated, but the number of facts recalled about the voice predicted later lineup accuracy. There was a main effect of misinformation on ratings of target voice pitch, but there was no effect on identification accuracy or confidence ratings. As voice lineup evidence from earwitnesses is used in courts, the findings have potential applied relevance.

  3. Influence of Elevation Data Resolution on Spatial Prediction of Colluvial Soils in a Luvisol Region

    Science.gov (United States)

    Penížek, Vít; Zádorová, Tereza; Kodešová, Radka; Vaněk, Aleš

    2016-01-01

    The development of a soil cover is a dynamic process. Soil cover can be altered within a few decades, which requires updating of the legacy soil maps. Soil erosion is one of the most important processes quickly altering soil cover on agriculture land. Colluvial soils develop in concave parts of the landscape as a consequence of sedimentation of eroded material. Colluvial soils are recognised as important soil units because they are a vast sink of soil organic carbon. Terrain derivatives became an important tool in digital soil mapping and are among the most popular auxiliary data used for quantitative spatial prediction. Prediction success rates are often directly dependent on raster resolution. In our study, we tested how raster resolution (1, 2, 3, 5, 10, 20 and 30 meters) influences spatial prediction of colluvial soils. Terrain derivatives (altitude, slope, plane curvature, topographic position index, LS factor and convergence index) were calculated for the given raster resolutions. Four models were applied (boosted tree, neural network, random forest and Classification/Regression Tree) to spatially predict the soil cover over a 77 ha large study plot. Models training and validation was based on 111 soil profiles surveyed on a regular sampling grid. Moreover, the predicted real extent and shape of the colluvial soil area was examined. In general, no clear trend in the accuracy prediction was found without the given raster resolution range. Higher maximum prediction accuracy for colluvial soil, compared to prediction accuracy of total soil cover of the study plot, can be explained by the choice of terrain derivatives that were best for Colluvial soils differentiation from other soil units. Regarding the character of the predicted Colluvial soils area, maps of 2 to 10 m resolution provided reasonable delineation of the colluvial soil as part of the cover over the study area. PMID:27846230

  4. Influence of Elevation Data Resolution on Spatial Prediction of Colluvial Soils in a Luvisol Region.

    Directory of Open Access Journals (Sweden)

    Vít Penížek

    Full Text Available The development of a soil cover is a dynamic process. Soil cover can be altered within a few decades, which requires updating of the legacy soil maps. Soil erosion is one of the most important processes quickly altering soil cover on agriculture land. Colluvial soils develop in concave parts of the landscape as a consequence of sedimentation of eroded material. Colluvial soils are recognised as important soil units because they are a vast sink of soil organic carbon. Terrain derivatives became an important tool in digital soil mapping and are among the most popular auxiliary data used for quantitative spatial prediction. Prediction success rates are often directly dependent on raster resolution. In our study, we tested how raster resolution (1, 2, 3, 5, 10, 20 and 30 meters influences spatial prediction of colluvial soils. Terrain derivatives (altitude, slope, plane curvature, topographic position index, LS factor and convergence index were calculated for the given raster resolutions. Four models were applied (boosted tree, neural network, random forest and Classification/Regression Tree to spatially predict the soil cover over a 77 ha large study plot. Models training and validation was based on 111 soil profiles surveyed on a regular sampling grid. Moreover, the predicted real extent and shape of the colluvial soil area was examined. In general, no clear trend in the accuracy prediction was found without the given raster resolution range. Higher maximum prediction accuracy for colluvial soil, compared to prediction accuracy of total soil cover of the study plot, can be explained by the choice of terrain derivatives that were best for Colluvial soils differentiation from other soil units. Regarding the character of the predicted Colluvial soils area, maps of 2 to 10 m resolution provided reasonable delineation of the colluvial soil as part of the cover over the study area.

  5. Higher schizotypy predicts better metabolic profile in unaffected siblings of patients with schizophrenia.

    Science.gov (United States)

    Atbasoglu, E Cem; Gumus-Akay, Guvem; Guloksuz, Sinan; Saka, Meram Can; Ucok, Alp; Alptekin, Koksal; Gullu, Sevim; van Os, Jim

    2018-04-01

    Type 2 diabetes (T2D) is more frequent in schizophrenia (Sz) than in the general population. This association is partly accounted for by shared susceptibility genetic variants. We tested the hypotheses that a genetic predisposition to Sz would be associated with higher likelihood of insulin resistance (IR), and that IR would be predicted by subthreshold psychosis phenotypes. Unaffected siblings of Sz patients (n = 101) were compared with a nonclinical sample (n = 305) in terms of IR, schizotypy (SzTy), and a behavioural experiment of "jumping to conclusions". The measures, respectively, were the Homeostatic Model Assessment of Insulin Resistance (HOMA-IR), Structured Interview for Schizotypy-Revised (SIS-R), and the Beads Task (BT). The likelihood of IR was examined in multiple regression models that included sociodemographic, metabolic, and cognitive parameters alongside group status, SIS-R scores, and BT performance. Insulin resistance was less frequent in siblings (31.7%) compared to controls (43.3%) (p model that examined all relevant parameters included the tSzTy tertiles, TG and HDL-C levels, and BMI, as significant predictors of IR. Lack of IR was predicted by the highest as compared to the lowest SzTy tertile [OR (95%CI): 0.43 (0.21-0.85), p = 0.015]. Higher dopaminergic activity may contribute to both schizotypal features and a favourable metabolic profile in the same individual. This is compatible with dopamine's regulatory role in glucose metabolism via indirect central actions and a direct action on pancreatic insulin secretion. The relationship between dopaminergic activity and metabolic profile in Sz must be examined in longitudinal studies with younger unaffected siblings.

  6. Source localization of rhythmic ictal EEG activity: a study of diagnostic accuracy following STARD criteria.

    Science.gov (United States)

    Beniczky, Sándor; Lantz, Göran; Rosenzweig, Ivana; Åkeson, Per; Pedersen, Birthe; Pinborg, Lars H; Ziebell, Morten; Jespersen, Bo; Fuglsang-Frederiksen, Anders

    2013-10-01

    Although precise identification of the seizure-onset zone is an essential element of presurgical evaluation, source localization of ictal electroencephalography (EEG) signals has received little attention. The aim of our study was to estimate the accuracy of source localization of rhythmic ictal EEG activity using a distributed source model. Source localization of rhythmic ictal scalp EEG activity was performed in 42 consecutive cases fulfilling inclusion criteria. The study was designed according to recommendations for studies on diagnostic accuracy (STARD). The initial ictal EEG signals were selected using a standardized method, based on frequency analysis and voltage distribution of the ictal activity. A distributed source model-local autoregressive average (LAURA)-was used for the source localization. Sensitivity, specificity, and measurement of agreement (kappa) were determined based on the reference standard-the consensus conclusion of the multidisciplinary epilepsy surgery team. Predictive values were calculated from the surgical outcome of the operated patients. To estimate the clinical value of the ictal source analysis, we compared the likelihood ratios of concordant and discordant results. Source localization was performed blinded to the clinical data, and before the surgical decision. Reference standard was available for 33 patients. The ictal source localization had a sensitivity of 70% and a specificity of 76%. The mean measurement of agreement (kappa) was 0.61, corresponding to substantial agreement (95% confidence interval (CI) 0.38-0.84). Twenty patients underwent resective surgery. The positive predictive value (PPV) for seizure freedom was 92% and the negative predictive value (NPV) was 43%. The likelihood ratio was nine times higher for the concordant results, as compared with the discordant ones. Source localization of rhythmic ictal activity using a distributed source model (LAURA) for the ictal EEG signals selected with a standardized method

  7. Genomic-Enabled Prediction in Maize Using Kernel Models with Genotype × Environment Interaction.

    Science.gov (United States)

    Bandeira E Sousa, Massaine; Cuevas, Jaime; de Oliveira Couto, Evellyn Giselly; Pérez-Rodríguez, Paulino; Jarquín, Diego; Fritsche-Neto, Roberto; Burgueño, Juan; Crossa, Jose

    2017-06-07

    Multi-environment trials are routinely conducted in plant breeding to select candidates for the next selection cycle. In this study, we compare the prediction accuracy of four developed genomic-enabled prediction models: (1) single-environment, main genotypic effect model (SM); (2) multi-environment, main genotypic effects model (MM); (3) multi-environment, single variance G×E deviation model (MDs); and (4) multi-environment, environment-specific variance G×E deviation model (MDe). Each of these four models were fitted using two kernel methods: a linear kernel Genomic Best Linear Unbiased Predictor, GBLUP (GB), and a nonlinear kernel Gaussian kernel (GK). The eight model-method combinations were applied to two extensive Brazilian maize data sets (HEL and USP data sets), having different numbers of maize hybrids evaluated in different environments for grain yield (GY), plant height (PH), and ear height (EH). Results show that the MDe and the MDs models fitted with the Gaussian kernel (MDe-GK, and MDs-GK) had the highest prediction accuracy. For GY in the HEL data set, the increase in prediction accuracy of SM-GK over SM-GB ranged from 9 to 32%. For the MM, MDs, and MDe models, the increase in prediction accuracy of GK over GB ranged from 9 to 49%. For GY in the USP data set, the increase in prediction accuracy of SM-GK over SM-GB ranged from 0 to 7%. For the MM, MDs, and MDe models, the increase in prediction accuracy of GK over GB ranged from 34 to 70%. For traits PH and EH, gains in prediction accuracy of models with GK compared to models with GB were smaller than those achieved in GY. Also, these gains in prediction accuracy decreased when a more difficult prediction problem was studied. Copyright © 2017 Bandeira e Sousa et al.

  8. Genomic-Enabled Prediction in Maize Using Kernel Models with Genotype × Environment Interaction

    Directory of Open Access Journals (Sweden)

    Massaine Bandeira e Sousa

    2017-06-01

    Full Text Available Multi-environment trials are routinely conducted in plant breeding to select candidates for the next selection cycle. In this study, we compare the prediction accuracy of four developed genomic-enabled prediction models: (1 single-environment, main genotypic effect model (SM; (2 multi-environment, main genotypic effects model (MM; (3 multi-environment, single variance G×E deviation model (MDs; and (4 multi-environment, environment-specific variance G×E deviation model (MDe. Each of these four models were fitted using two kernel methods: a linear kernel Genomic Best Linear Unbiased Predictor, GBLUP (GB, and a nonlinear kernel Gaussian kernel (GK. The eight model-method combinations were applied to two extensive Brazilian maize data sets (HEL and USP data sets, having different numbers of maize hybrids evaluated in different environments for grain yield (GY, plant height (PH, and ear height (EH. Results show that the MDe and the MDs models fitted with the Gaussian kernel (MDe-GK, and MDs-GK had the highest prediction accuracy. For GY in the HEL data set, the increase in prediction accuracy of SM-GK over SM-GB ranged from 9 to 32%. For the MM, MDs, and MDe models, the increase in prediction accuracy of GK over GB ranged from 9 to 49%. For GY in the USP data set, the increase in prediction accuracy of SM-GK over SM-GB ranged from 0 to 7%. For the MM, MDs, and MDe models, the increase in prediction accuracy of GK over GB ranged from 34 to 70%. For traits PH and EH, gains in prediction accuracy of models with GK compared to models with GB were smaller than those achieved in GY. Also, these gains in prediction accuracy decreased when a more difficult prediction problem was studied.

  9. Accuracy of Genomic Selection in a Rice Synthetic Population Developed for Recurrent Selection Breeding.

    Directory of Open Access Journals (Sweden)

    Cécile Grenier

    Full Text Available Genomic selection (GS is a promising strategy for enhancing genetic gain. We investigated the accuracy of genomic estimated breeding values (GEBV in four inter-related synthetic populations that underwent several cycles of recurrent selection in an upland rice-breeding program. A total of 343 S2:4 lines extracted from those populations were phenotyped for flowering time, plant height, grain yield and panicle weight, and genotyped with an average density of one marker per 44.8 kb. The relative effect of the linkage disequilibrium (LD and minor allele frequency (MAF thresholds for selecting markers, the relative size of the training population (TP and of the validation population (VP, the selected trait and the genomic prediction models (frequentist and Bayesian on the accuracy of GEBVs was investigated in 540 cross validation experiments with 100 replicates. The effect of kinship between the training and validation populations was tested in an additional set of 840 cross validation experiments with a single genomic prediction model. LD was high (average r2 = 0.59 at 25 kb and decreased slowly, distribution of allele frequencies at individual loci was markedly skewed toward unbalanced frequencies (MAF average value 15.2% and median 9.6%, and differentiation between the four synthetic populations was low (FST ≤0.06. The accuracy of GEBV across all cross validation experiments ranged from 0.12 to 0.54 with an average of 0.30. Significant differences in accuracy were observed among the different levels of each factor investigated. Phenotypic traits had the biggest effect, and the size of the incidence matrix had the smallest. Significant first degree interaction was observed for GEBV accuracy between traits and all the other factors studied, and between prediction models and LD, MAF and composition of the TP. The potential of GS to accelerate genetic gain and breeding options to increase the accuracy of predictions are discussed.

  10. Predictive Manufacturing: A Classification Strategy to Predict Product Failures

    DEFF Research Database (Denmark)

    Khan, Abdul Rauf; Schiøler, Henrik; Kulahci, Murat

    2018-01-01

    manufacturing analytics model that employs a big data approach to predicting product failures; third, we illustrate the issue of high dimensionality, along with statistically redundant information; and, finally, our proposed method will be compared against the well-known classification methods (SVM, K......-nearest neighbor, artificial neural networks). The results from real data show that our predictive manufacturing analytics approach, using genetic algorithms and Voronoi tessellations, is capable of predicting product failure with reasonable accuracy. The potential application of this method contributes...... to accurately predicting product failures, which would enable manufacturers to reduce production costs without compromising product quality....

  11. Diagnostic Accuracy of Perioperative Measurement of Basal Anterior Pituitary and Target Gland Hormones in Predicting Adrenal Insufficiency After Pituitary Surgery.

    Science.gov (United States)

    Cerina, Vatroslav; Kruljac, Ivan; Radosevic, Jelena Marinkovic; Kirigin, Lora Stanka; Stipic, Darko; Pecina, Hrvoje Ivan; Vrkljan, Milan

    2016-03-01

    The insulin tolerance test (ITT) is the gold standard for diagnosing adrenal insufficiency (AI) after pituitary surgery. The ITT is unpleasant for patients, requires close medical supervision and is contraindicated in several comorbidities. The aim of this study was to analyze whether tumor size, remission rate, preoperative, and early postoperative baseline hormone concentrations could serve as predictors of AI in order to increase the diagnostic accuracy of morning serum cortisol. This prospective study enrolled 70 consecutive patients with newly diagnosed pituitary adenomas. Thirty-seven patients had nonfunctioning pituitary adenomas (NPA), 28 had prolactinomas and 5 had somatotropinomas. Thyroxin (T4), thyrotropin (TSH), prolactin, follicle-stimulating hormone (FSH), luteinizing hormone (LH), testosterone, and insulin-like growth factor 1 (IGF-I) were measured preoperatively and on the sixth postoperative day. Serum morning cortisol was measured on the third postoperative day (CORT3) as well as the sixth postoperative day (CORT6). Tumor mass was measured preoperatively and remission was assessed 3 months after surgery. An ITT was performed 3 to 6 months postoperatively. Remission was achieved in 48% of patients and AI occurred in 51%. Remission rates and tumor type were not associated with AI. CORT3 had the best predictive value for AI (area under the curve (AUC) 0.868, sensitivity 82.4%, specificity 83.3%). Tumor size, preoperative T4, postoperative T4, and TSH were also associated with AI in a multivariate regression model. A combination of all preoperative and postoperative variables (excluding serum cortisol) had a sensitivity of 75.0% and specificity of 77.8%. The predictive power of CORT3 substantially improved by adding those variables into the model (AUC 0.921, sensitivity 94.1%, specificity 78.3%, PPV 81.9%, NPV of 92.7%). In a subgroup analysis that included only female patients with NPA, LH had exactly the same predictive value as CORT3. The addition

  12. The Current and Future Use of Ridge Regression for Prediction in Quantitative Genetics

    Directory of Open Access Journals (Sweden)

    Ronald de Vlaming

    2015-01-01

    Full Text Available In recent years, there has been a considerable amount of research on the use of regularization methods for inference and prediction in quantitative genetics. Such research mostly focuses on selection of markers and shrinkage of their effects. In this review paper, the use of ridge regression for prediction in quantitative genetics using single-nucleotide polymorphism data is discussed. In particular, we consider (i the theoretical foundations of ridge regression, (ii its link to commonly used methods in animal breeding, (iii the computational feasibility, and (iv the scope for constructing prediction models with nonlinear effects (e.g., dominance and epistasis. Based on a simulation study we gauge the current and future potential of ridge regression for prediction of human traits using genome-wide SNP data. We conclude that, for outcomes with a relatively simple genetic architecture, given current sample sizes in most cohorts (i.e., N<10,000 the predictive accuracy of ridge regression is slightly higher than the classical genome-wide association study approach of repeated simple regression (i.e., one regression per SNP. However, both capture only a small proportion of the heritability. Nevertheless, we find evidence that for large-scale initiatives, such as biobanks, sample sizes can be achieved where ridge regression compared to the classical approach improves predictive accuracy substantially.

  13. Office gel sonovaginography for the prediction of posterior deep infiltrating endometriosis: a multicenter prospective observational study.

    Science.gov (United States)

    Reid, S; Lu, C; Hardy, N; Casikar, I; Reid, G; Cario, G; Chou, D; Almashat, D; Condous, G

    2014-12-01

    To use office gel sonovaginography (SVG) to predict posterior deep infiltrating endometriosis (DIE) in women undergoing laparoscopy. This was a multicenter prospective observational study carried out between January 2009 and February 2013. All women were of reproductive age, had a history of chronic pelvic pain and underwent office gel SVG assessment for the prediction of posterior compartment DIE prior to laparoscopic endometriosis surgery. Gel SVG findings were compared with laparoscopic findings to determine the diagnostic accuracy of office gel SVG for the prediction of posterior compartment DIE. In total, 189 women underwent preoperative gel SVG and laparoscopy for endometriosis. At laparoscopy, 57 (30%) women had posterior DIE and 43 (23%) had rectosigmoid/anterior rectal DIE. For the prediction of rectosigmoid/anterior rectal (i.e. bowel) DIE, gel SVG had an accuracy of 92%, sensitivity of 88%, specificity of 93%, positive predictive value (PPV) of 79%, negative predictive value (NPV) of 97%, positive likelihood ratio (LR+) of 12.9 and negative likelihood ratio (LR-) of 0.12 (P = 3.98E-25); for posterior vaginal wall and rectovaginal septum (RVS) DIE, respectively, the accuracy was 95% and 95%, sensitivity was 18% and 18%, specificity was 99% and 100%, PPV was 67% and 100%, NPV was 95% and 95%, LR+ was 32.4 and infinity and LR- was 0.82 and 0.82 (P = 0.009 and P = 0.003). Office gel SVG appears to be an effective outpatient imaging technique for the prediction of bowel DIE, with a higher accuracy for the prediction of rectosigmoid compared with anterior rectal DIE. Although the sensitivity for vaginal and RVS DIE was limited, gel SVG had a high specificity and NPV for all forms of posterior DIE, indicating that a negative gel SVG examination is highly suggestive of the absence of DIE at laparoscopy. Copyright © 2014 ISUOG. Published by John Wiley & Sons Ltd.

  14. Validity of predictive equations for basal metabolic rate in Japanese adults.

    Science.gov (United States)

    Miyake, Rieko; Tanaka, Shigeho; Ohkawara, Kazunori; Ishikawa-Takata, Kazuko; Hikihara, Yuki; Taguri, Emiko; Kayashita, Jun; Tabata, Izumi

    2011-01-01

    Many predictive equations for basal metabolic rate (BMR) based on anthropometric measurements, age, and sex have been developed, mainly for healthy Caucasians. However, it has been reported that many of these equations, used widely, overestimate BMR not only for Asians, but also for Caucasians. The present study examined the accuracy of several predictive equations for BMR in Japanese subjects. In 365 healthy Japanese male and female subjects, aged 18 to 79 y, BMR was measured in the post-absorptive state using a mask and Douglas bag. Six predictive equations were examined. Total error was used as an index of the accuracy of each equation's prediction. Predicted BMR values by Dietary Reference Intakes for Japanese (Japan-DRI), Adjusted Dietary Reference Intakes for Japanese (Adjusted-DRI), and Ganpule equations were not significantly different from the measured BMR in either sex. On the other hand, Harris-Benedict, Schofield, and Food and Agriculture Organization of the United Nations/World Health Organization/United Nations University equations were significantly higher than the measured BMR in both sexes. The prediction error by Japan-DRI, Adjusted-DRI, and Harris-Benedict equations was significantly correlated with body weight in both sexes. Total error using the Ganpule equation was low in both males and females (125 and 99 kcal/d, respectively). In addition, total error using the Adjusted-DRI equation was low in females (95 kcal/d). Thus, the Ganpule equation was the most accurate in predicting BMR in our healthy Japanese subjects, because the difference between the predicted and measured BMR was relatively small, and body weight had no effect on the prediction error.

  15. Improved imputation accuracy of rare and low-frequency variants using population-specific high-coverage WGS-based imputation reference panel.

    Science.gov (United States)

    Mitt, Mario; Kals, Mart; Pärn, Kalle; Gabriel, Stacey B; Lander, Eric S; Palotie, Aarno; Ripatti, Samuli; Morris, Andrew P; Metspalu, Andres; Esko, Tõnu; Mägi, Reedik; Palta, Priit

    2017-06-01

    Genetic imputation is a cost-efficient way to improve the power and resolution of genome-wide association (GWA) studies. Current publicly accessible imputation reference panels accurately predict genotypes for common variants with minor allele frequency (MAF)≥5% and low-frequency variants (0.5≤MAF<5%) across diverse populations, but the imputation of rare variation (MAF<0.5%) is still rather limited. In the current study, we evaluate imputation accuracy achieved with reference panels from diverse populations with a population-specific high-coverage (30 ×) whole-genome sequencing (WGS) based reference panel, comprising of 2244 Estonian individuals (0.25% of adult Estonians). Although the Estonian-specific panel contains fewer haplotypes and variants, the imputation confidence and accuracy of imputed low-frequency and rare variants was significantly higher. The results indicate the utility of population-specific reference panels for human genetic studies.

  16. Instrument uncertainty predictions

    International Nuclear Information System (INIS)

    Coutts, D.A.

    1991-07-01

    The accuracy of measurements and correlations should normally be provided for most experimental activities. The uncertainty is a measure of the accuracy of a stated value or equation. The uncertainty term reflects a combination of instrument errors, modeling limitations, and phenomena understanding deficiencies. This report provides several methodologies to estimate an instrument's uncertainty when used in experimental work. Methods are shown to predict both the pretest and post-test uncertainty

  17. Financial distress prediction and operating leases

    NARCIS (Netherlands)

    Lückerath – Rovers, M.

    2009-01-01

    This study investigates whether including operating lease commitments in financial distress prediction models would increase the classification accuracy of these models. Classification accuracy measures the percentages of correctly classified companies in either of the two categories (healthy or

  18. Engaging Students Emotionally: The Role of Emotional Intelligence in Predicting Cognitive and Affective Engagement in Higher Education

    Science.gov (United States)

    Maguire, Rebecca; Egan, Arlene; Hyland, Philip; Maguire, Phil

    2017-01-01

    Student engagement is a key predictor of academic performance, persistence and retention in higher education. While many studies have identified how aspects of the college environment influence engagement, fewer have specifically focused on emotional intelligence (EI). In this study, we sought to explore whether EI could predict cognitive and/or…

  19. Students-as-Customers' Satisfaction, Predictive Retention with Marketing Implications: The Case of Malaysian Higher Education Business Students

    Science.gov (United States)

    Carter, Stephen; Yeo, Amy Chu-May

    2016-01-01

    Purpose: The purpose of this paper is to investigate two areas of interest: first, to determine business student customer satisfiers that could be contributors to students' current and predicted retention in a higher educational institution (HEI) and second, to use these satisfiers to inform HEI marketing planning. Design/Methodology/Approach: The…

  20. Accuracy of plain films, and the effect of experience, in the assessment of ankle effusions

    International Nuclear Information System (INIS)

    Karchevsky, Michael; Schweitzer, Mark E.

    2004-01-01

    To investigate the accuracy of plain radiographs, and the effect of observer experience, in the assessment of ankle effusions compared with an MRI gold standard. Anteroposterior (AP) and lateral radiographs of the ankle of 39 patients were evaluated by four observers, ranging from first-year radiology resident to an attending musculoskeletal radiologist. Observers independently graded the lateral films from 0 to5 at one sitting, and the AP films at a second sitting. All patients had an MRI scan performed within 48 h of the ankle radiographs, on which distention of the anterior recess was used as the gold standard for an effusion. Lateral radiographs had variable sensitivity (range 17 - 63%), but specificity (81-94%) was usually high. AP radiographs similarly had variable sensitivity (15-55%), but their specificity (63-75%) was surprisingly good. Overall, sensitivity and specificity were inversely proportional and more related to individual variability than experience (observer 1, 53% and 81%; observer 2, 17% and 94%; observer 3, 63% and 88%; observer 4, 21% and 94%); however, individual sensitivity and specificity were consistent between AP and lateral radiographs (observer 1, 53% and 81%, 50% and 65%; observer 2, 17% and 94%, 15% and 75%), observer 3, 63% and 88%, 55% and 63%; observer 4, 21% and 94%, 25% and 70%. Positive predictive value was reasonably good for lateral radiographs (range 75 - 86%); however, it was fairly low for AP radiographs (38-61%). Negative predictive value was low for both lateral (50-67%) and AP (47-58%) radiographs. Accuracy was low for both AP (45-59%) and lateral (53-74%) radiographs. As expected, individual accuracy was consistently higher for lateral radiographs than for AP radiographs (observer 1, 65% and 58%; observer 2, 53% and 45%; observer 3, 74% and 59%; observer 4, 54% and 48%). For the diagnosis of ankle effusions the overall accuracy of radiographs was surprisingly low. Quite surprisingly, the diagnosis of effusions on AP

  1. Improving Prediction Accuracy of a Rate-Based Model of an MEA-Based Carbon Capture Process for Large-Scale Commercial Deployment

    Directory of Open Access Journals (Sweden)

    Xiaobo Luo

    2017-04-01

    Full Text Available Carbon capture and storage (CCS technology will play a critical role in reducing anthropogenic carbon dioxide (CO2 emission from fossil-fired power plants and other energy-intensive processes. However, the increment of energy cost caused by equipping a carbon capture process is the main barrier to its commercial deployment. To reduce the capital and operating costs of carbon capture, great efforts have been made to achieve optimal design and operation through process modeling, simulation, and optimization. Accurate models form an essential foundation for this purpose. This paper presents a study on developing a more accurate rate-based model in Aspen Plus® for the monoethanolamine (MEA-based carbon capture process by multistage model validations. The modeling framework for this process was established first. The steady-state process model was then developed and validated at three stages, which included a thermodynamic model, physical properties calculations, and a process model at the pilot plant scale, covering a wide range of pressures, temperatures, and CO2 loadings. The calculation correlations of liquid density and interfacial area were updated by coding Fortran subroutines in Aspen Plus®. The validation results show that the correlation combination for the thermodynamic model used in this study has higher accuracy than those of three other key publications and the model prediction of the process model has a good agreement with the pilot plant experimental data. A case study was carried out for carbon capture from a 250 MWe combined cycle gas turbine (CCGT power plant. Shorter packing height and lower specific duty were achieved using this accurate model.

  2. Criteria of GenCall score to edit marker data and methods to handle missing markers have an influence on accuracy of genomic predictions

    DEFF Research Database (Denmark)

    Edriss, Vahid; Guldbrandtsen, Bernt; Lund, Mogens Sandø

    2013-01-01

    The aim of this study was to investigate the effect of different strategies for handling low-quality or missing data on prediction accuracy for direct genomic values of protein yield, mastitis and fertility using a Bayesian variable model and a GBLUP model in the Danish Jersey population. The data...... contained 1071 Jersey bulls that were genotyped with the Illumina Bovine 50K chip. After preliminary editing, 39227 SNP remained in the dataset. Four methods to handle missing genotypes were: 1) BEAGLE: missing markers were imputed using Beagle 3.3 software, 2) COMMON: missing genotypes at a locus were...

  3. Rectal cancer staging: Multidetector-row computed tomography diagnostic accuracy in assessment of mesorectal fascia invasion

    Science.gov (United States)

    Ippolito, Davide; Drago, Silvia Girolama; Franzesi, Cammillo Talei; Fior, Davide; Sironi, Sandro

    2016-01-01

    AIM: To assess the diagnostic accuracy of multidetector-row computed tomography (MDCT) as compared with conventional magnetic resonance imaging (MRI), in identifying mesorectal fascia (MRF) invasion in rectal cancer patients. METHODS: Ninety-one patients with biopsy proven rectal adenocarcinoma referred for thoracic and abdominal CT staging were enrolled in this study. The contrast-enhanced MDCT scans were performed on a 256 row scanner (ICT, Philips) with the following acquisition parameters: tube voltage 120 KV, tube current 150-300 mAs. Imaging data were reviewed as axial and as multiplanar reconstructions (MPRs) images along the rectal tumor axis. MRI study, performed on 1.5 T with dedicated phased array multicoil, included multiplanar T2 and axial T1 sequences and diffusion weighted images (DWI). Axial and MPR CT images independently were compared to MRI and MRF involvement was determined. Diagnostic accuracy of both modalities was compared and statistically analyzed. RESULTS: According to MRI, the MRF was involved in 51 patients and not involved in 40 patients. DWI allowed to recognize the tumor as a focal mass with high signal intensity on high b-value images, compared with the signal of the normal adjacent rectal wall or with the lower tissue signal intensity background. The number of patients correctly staged by the native axial CT images was 71 out of 91 (41 with involved MRF; 30 with not involved MRF), while by using the MPR 80 patients were correctly staged (45 with involved MRF; 35 with not involved MRF). Local tumor staging suggested by MDCT agreed with those of MRI, obtaining for CT axial images sensitivity and specificity of 80.4% and 75%, positive predictive value (PPV) 80.4%, negative predictive value (NPV) 75% and accuracy 78%; while performing MPR the sensitivity and specificity increased to 88% and 87.5%, PPV was 90%, NPV 85.36% and accuracy 88%. MPR images showed higher diagnostic accuracy, in terms of MRF involvement, than native axial images

  4. Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores

    DEFF Research Database (Denmark)

    Vilhjálmsson, Bjarni J; Yang, Jian; Finucane, Hilary K

    2015-01-01

    to association statistics, but this discards information and can reduce predictive accuracy. We introduce LDpred, a method that infers the posterior mean effect size of each marker by using a prior on effect sizes and LD information from an external reference panel. Theory and simulations show that LDpred...

  5. Capability Database of Injection Molding Process— Requirements Study for Wider Suitability and Higher Accuracy

    DEFF Research Database (Denmark)

    Boorla, Srinivasa Murthy; Eifler, Tobias; Jepsen, Jens Dines O.

    2017-01-01

    for an improved applicability of corresponding database solutions in an industrial context. A survey of database users at all phases of product value chain in the plastic industry revealed that 59% of the participating companies use their own, internally created databases, although reported to be not fully...... adequate in most cases. Essential influences are the suitability of the provided data, defined by the content such as material, tolerance types, etc. covered, as well as its accuracy, largely influenced by the updating frequency. Forming a consortium with stakeholders, linking database update to technology...

  6. The accuracy of ab initio calculations without ab initio calculations for charged systems: Kriging predictions of atomistic properties for ions in aqueous solutions

    Science.gov (United States)

    Di Pasquale, Nicodemo; Davie, Stuart J.; Popelier, Paul L. A.

    2018-06-01

    Using the machine learning method kriging, we predict the energies of atoms in ion-water clusters, consisting of either Cl- or Na+ surrounded by a number of water molecules (i.e., without Na+Cl- interaction). These atomic energies are calculated following the topological energy partitioning method called Interacting Quantum Atoms (IQAs). Kriging predicts atomic properties (in this case IQA energies) by a model that has been trained over a small set of geometries with known property values. The results presented here are part of the development of an advanced type of force field, called FFLUX, which offers quantum mechanical information to molecular dynamics simulations without the limiting computational cost of ab initio calculations. The results reported for the prediction of the IQA components of the energy in the test set exhibit an accuracy of a few kJ/mol, corresponding to an average error of less than 5%, even when a large cluster of water molecules surrounding an ion is considered. Ions represent an important chemical system and this work shows that they can be correctly taken into account in the framework of the FFLUX force field.

  7. Fast Algorithms for High-Order Sparse Linear Prediction with Applications to Speech Processing

    DEFF Research Database (Denmark)

    Jensen, Tobias Lindstrøm; Giacobello, Daniele; van Waterschoot, Toon

    2016-01-01

    In speech processing applications, imposing sparsity constraints on high-order linear prediction coefficients and prediction residuals has proven successful in overcoming some of the limitation of conventional linear predictive modeling. However, this modeling scheme, named sparse linear prediction...... problem with lower accuracy than in previous work. In the experimental analysis, we clearly show that a solution with lower accuracy can achieve approximately the same performance as a high accuracy solution both objectively, in terms of prediction gain, as well as with perceptual relevant measures, when...... evaluated in a speech reconstruction application....

  8. Higher Self-Control Capacity Predicts Lower Anxiety-Impaired Cognition during Math Examinations.

    Science.gov (United States)

    Bertrams, Alex; Baumeister, Roy F; Englert, Chris

    2016-01-01

    We assumed that self-control capacity, self-efficacy, and self-esteem would enable students to keep attentional control during tests. Therefore, we hypothesized that the three personality traits would be negatively related to anxiety-impaired cognition during math examinations. Secondary school students (N = 158) completed measures of self-control capacity, self-efficacy, and self-esteem at the beginning of the school year. Five months later, anxiety-impaired cognition during math examinations was assessed. Higher self-control capacity, but neither self-efficacy nor self-esteem, predicted lower anxiety-impaired cognition 5 months later, over and above baseline anxiety-impaired cognition. Moreover, self-control capacity was indirectly related to math grades via anxiety-impaired cognition. The findings suggest that improving self-control capacity may enable students to deal with anxiety-related problems during school tests.

  9. Evaluation of accuracy of intra operative imprint cytology for detection of breast lesions

    International Nuclear Information System (INIS)

    Mahmood, Z.; Shahbaz, A.; Qureshi, A.; Aziz, N.; Niazi, S.; Qureshi, S.; Bukhari, M.H.

    2010-01-01

    Objective: To determine the accuracy of imprint cytology as an intraoperative diagnostic procedure for breast lesions with histopathological correlation. Materials and Methods: This was a descriptive study on 40 cases of breast lesions comprising of inflammatory, benign and malignant lesions including their margins etc. It was conducted at King Edward Medical University, Lahore in collaboration with all Surgical Departments of Mayo Hospital. Relevant clinical data was recorded in a proforma. Both touch and scrape imprints were prepared from all the lesions and stained with May-Grunwaled Giemsa and Haematoxylin and Eosin stains. The imprints were subsequently compared with histopathology sections. Results: When we used atypical cases as negative both touch and scrape imprints gave sensitivity, specificity, positive predictive value, negative predictive value and accuracy at 100%. However when we used cases with atypia as positive, sensitivity and negative predictive value were 100% with both touch and scrape imprints. Specificity, positive predictive value and accuracy were 71%, 86%, 85.5% respectively with touch imprints and 78%, 89%, 89% respectively with scrape imprints. No diagnostic difference was noted between the results of both stains. All the imprints were well correlated with histopathological diagnosis. Conclusion: Imprint cytology is an accurate and simple intraoperative method for diagnosing breast lesions. It can provide the surgeons with information regarding immediate clinical and surgical interventions. (author)

  10. Testing an Automated Accuracy Assessment Method on Bibliographic Data

    Directory of Open Access Journals (Sweden)

    Marlies Olensky

    2014-12-01

    Full Text Available This study investigates automated data accuracy assessment as described in data quality literature for its suitability to assess bibliographic data. The data samples comprise the publications of two Nobel Prize winners in the field of Chemistry for a 10-year-publication period retrieved from the two bibliometric data sources, Web of Science and Scopus. The bibliographic records are assessed against the original publication (gold standard and an automatic assessment method is compared to a manual one. The results show that the manual assessment method reflects truer accuracy scores. The automated assessment method would need to be extended by additional rules that reflect specific characteristics of bibliographic data. Both data sources had higher accuracy scores per field than accumulated per record. This study contributes to the research on finding a standardized assessment method of bibliographic data accuracy as well as defining the impact of data accuracy on the citation matching process.

  11. Accuracy improvement of irradiation data by combining ground and satellite measurements

    Energy Technology Data Exchange (ETDEWEB)

    Betcke, J. [Energy and Semiconductor Research Laboratory, Carl von Ossietzky University, Oldenburg (Germany); Beyer, H.G. [Department of Electrical Engineering, University of Applied Science (F.H.) Magdeburg-Stendal, Magdeburg (Germany)

    2004-07-01

    Accurate and site-specific irradiation data are essential input for optimal planning, monitoring and operation of solar energy technologies. A concrete example is the performance check of grid connected PV systems with the PVSAT-2 procedure. This procedure detects system faults in an early stage by a daily comparison of an individual reference yield with the actual yield. Calculation of the reference yield requires hourly irradiation data with a known accuracy. A field test of the predecessing PVSAT-1 procedure showed that the accuracy of the irradiation input is the determining factor for the overall accuracy of the yield calculation. In this paper we will investigate if it is possible to improve the accuracy of sitespeci.c irradiation data by combining accurate localised pyranometer data with semi-continuous satellite data.We will therefore introduce the ''Kriging of Differences'' data fusion method. Kriging of Differences also offers the possibility to estimate it's own accuracy. The obtainable accuracy gain and the effectiveness of the accuracy prediction will be investigated by validation on monthly and daily irradiation datasets. Results will be compared with the Heliosat method and interpolation of ground data. (orig.)

  12. The value of predicting restriction of fetal growth and compromise of its wellbeing: Systematic quantitative overviews (meta-analysis of test accuracy literature

    Directory of Open Access Journals (Sweden)

    Robson Stephen C

    2007-03-01

    Full Text Available Abstract Background Restriction of fetal growth and compromise of fetal wellbeing remain significant causes of perinatal death and childhood disability. At present, there is a lack of scientific consensus about the best strategies for predicting these conditions before birth. Therefore, there is uncertainty about the best management of pregnant women who might have a growth restricted baby. This is likely to be due to a dearth of clear collated information from individual research studies drawn from different sources on this subject. Methods/Design A series of systematic reviews and meta-analyses will be undertaken to determine, among pregnant women, the accuracy of various tests to predict and/or diagnose fetal growth restriction and compromise of fetal wellbeing. We will search Medline, Embase, Cochrane Library, MEDION, citation lists of review articles and eligible primary articles and will contact experts in the field. Independent reviewers will select studies, extract data and assess study quality according to established criteria. Language restrictions will not be applied. Data synthesis will involve meta-analysis (where appropriate, exploration of heterogeneity and publication bias. Discussion The project will collate and synthesise the available evidence regarding the value of the tests for predicting restriction of fetal growth and compromise of fetal wellbeing. The systematic overviews will assess the quality of the available evidence, estimate the magnitude of potential benefits, identify those tests with good predictive value and help formulate practice recommendations.

  13. Prediction of Commuter’s Daily Time Allocation

    Directory of Open Access Journals (Sweden)

    Fang Zong

    2013-10-01

    Full Text Available This paper presents a model system to predict the time allocation in commuters’ daily activity-travel pattern. The departure time and the arrival time are estimated with Ordered Probit model and Support Vector Regression is introduced for travel time and activity duration prediction. Applied in a real-world time allocation prediction experiment, the model system shows a satisfactory level of prediction accuracy. This study provides useful insights into commuters’ activity-travel time allocation decision by identifying the important influences, and the results are readily applied to a wide range of transportation practice, such as travel information system, by providing reliable forecast for variations in travel demand over time. By introducing the Support Vector Regression, it also makes a methodological contribution in enhancing prediction accuracy of travel time and activity duration prediction.

  14. Effects of accuracy motivation and anchoring on metacomprehension judgment and accuracy.

    Science.gov (United States)

    Zhao, Qin

    2012-01-01

    The current research investigates how accuracy motivation impacts anchoring and adjustment in metacomprehension judgment and how accuracy motivation and anchoring affect metacomprehension accuracy. Participants were randomly assigned to one of six conditions produced by the between-subjects factorial design involving accuracy motivation (incentive or no) and peer performance anchor (95%, 55%, or no). Two studies showed that accuracy motivation did not impact anchoring bias, but the adjustment-from-anchor process occurred. Accuracy incentive increased anchor-judgment gap for the 95% anchor but not for the 55% anchor, which induced less certainty about the direction of adjustment. The findings offer support to the integrative theory of anchoring. Additionally, the two studies revealed a "power struggle" between accuracy motivation and anchoring in influencing metacomprehension accuracy. Accuracy motivation could improve metacomprehension accuracy in spite of anchoring effect, but if anchoring effect is too strong, it could overpower the motivation effect. The implications of the findings were discussed.

  15. Accuracy of a heart failure diagnosis in administrative registers

    DEFF Research Database (Denmark)

    Kümler, Thomas; Gislason, Gunnar Hilmar; Kirk, Vibeke

    2008-01-01

    BACKGROUND: The incidence of heart failure is frequently reported using hospital discharge diagnoses. The specificity of a diagnosis has been shown to be high but the sensitivity of a reported diagnosis is unknown. PURPOSE: To study the accuracy of a heart failure diagnosis reported to the Danish...... a specificity of 99% and a sensitivity of 29% for all patients. The positive predictive value was 81%, the negative predictive value 90%. CONCLUSION: The diagnosis of Heart Failure in the Danish National Registers is underreported, but very specific....

  16. [Application of ARIMA model to predict number of malaria cases in China].

    Science.gov (United States)

    Hui-Yu, H; Hua-Qin, S; Shun-Xian, Z; Lin, A I; Yan, L U; Yu-Chun, C; Shi-Zhu, L I; Xue-Jiao, T; Chun-Li, Y; Wei, H U; Jia-Xu, C

    2017-08-15

    Objective To study the application of autoregressive integrated moving average (ARIMA) model to predict the monthly reported malaria cases in China, so as to provide a reference for prevention and control of malaria. Methods SPSS 24.0 software was used to construct the ARIMA models based on the monthly reported malaria cases of the time series of 20062015 and 2011-2015, respectively. The data of malaria cases from January to December, 2016 were used as validation data to compare the accuracy of the two ARIMA models. Results The models of the monthly reported cases of malaria in China were ARIMA (2, 1, 1) (1, 1, 0) 12 and ARIMA (1, 0, 0) (1, 1, 0) 12 respectively. The comparison between the predictions of the two models and actual situation of malaria cases showed that the ARIMA model based on the data of 2011-2015 had a higher accuracy of forecasting than the model based on the data of 2006-2015 had. Conclusion The establishment and prediction of ARIMA model is a dynamic process, which needs to be adjusted unceasingly according to the accumulated data, and in addition, the major changes of epidemic characteristics of infectious diseases must be considered.

  17. Nuclear data for fission reactor core design and safety analysis: Requirements and status of accuracy of nuclear data

    International Nuclear Information System (INIS)

    Rowlands, J.L.

    1984-01-01

    The types of nuclear data required for fission reactor design and safety analysis, and the ways in which the data are represented and approximated for use in reactor calculations, are summarised first. The relative importance of different items of nuclear data in the prediction of reactor parameters is described and ways of investigating the accuracy of these data by evaluating related integral measurements are discussed. The use of sensitivity analysis, together with estimates of the uncertainties in nuclear data and relevant integral measurements, in assessing the accuracy of prediction of reactor parameters is described. The inverse procedure for deciding nuclear data requirements from the target accuracies for prediction of reactor parameters follows on from this. The need for assessments of the uncertainties in nuclear data evaluations and the form of the uncertainty information is discussed. The status of the accuracies of predictions and nuclear data requirements are then summarised. The reactor parameters considered include: (a) Criticality conditions, conversion and burn-up effects. (b) Energy production and deposition, decay heating, irradiation damage, dosimetry and induced radioactivity. (c) Kinetics characteristics and control, including temperature, power and coolant density coefficients, delayed neutrons and control absorbers. (author)

  18. Genomic prediction in a nuclear population of layers using single-step models.

    Science.gov (United States)

    Yan, Yiyuan; Wu, Guiqin; Liu, Aiqiao; Sun, Congjiao; Han, Wenpeng; Li, Guangqi; Yang, Ning

    2018-02-01

    Single-step genomic prediction method has been proposed to improve the accuracy of genomic prediction by incorporating information of both genotyped and ungenotyped animals. The objective of this study is to compare the prediction performance of single-step model with a 2-step models and the pedigree-based models in a nuclear population of layers. A total of 1,344 chickens across 4 generations were genotyped by a 600 K SNP chip. Four traits were analyzed, i.e., body weight at 28 wk (BW28), egg weight at 28 wk (EW28), laying rate at 38 wk (LR38), and Haugh unit at 36 wk (HU36). In predicting offsprings, individuals from generation 1 to 3 were used as training data and females from generation 4 were used as validation set. The accuracies of predicted breeding values by pedigree BLUP (PBLUP), genomic BLUP (GBLUP), SSGBLUP and single-step blending (SSBlending) were compared for both genotyped and ungenotyped individuals. For genotyped females, GBLUP performed no better than PBLUP because of the small size of training data, while the 2 single-step models predicted more accurately than the PBLUP model. The average predictive ability of SSGBLUP and SSBlending were 16.0% and 10.8% higher than the PBLUP model across traits, respectively. Furthermore, the predictive abilities for ungenotyped individuals were also enhanced. The average improvements of prediction abilities were 5.9% and 1.5% for SSGBLUP and SSBlending model, respectively. It was concluded that single-step models, especially the SSGBLUP model, can yield more accurate prediction of genetic merits and are preferable for practical implementation of genomic selection in layers. © 2017 Poultry Science Association Inc.

  19. Accuracy of pedicle screw placement in patients with Marfan syndrome.

    Science.gov (United States)

    Qiao, Jun; Zhu, Feng; Xu, Leilei; Liu, Zhen; Sun, Xu; Qian, Bangping; Jiang, Qing; Zhu, Zezhang; Qiu, Yong

    2017-03-21

    There is no study concerning safety and accuracy of pedicle screw placement in Marfan syndrome. The objective of this study is to investigate accuracy and safety of pedicle screw placement in scoliosis associated with Marfan syndrome. CT scanning was performed to analyze accuracy of pedicle screw placement. Pedicle perforations were classified as medial, lateral or anterior and categorized to four grades: ≤ 2 mm as Grade 1, 2.1-4.0 mm as Grade 2, 4.1-6.0 mm as Grade 3, ≥6.1 mm as Grade 4. Fully contained screws or with medial wall perforation ≤ 2 mm or with lateral wall perforation ≤ 6 mm and without injury of visceral organs were considered acceptable, otherwise were unacceptable. 976 pedicle screws were placed, 713 screws (73.1%) were fully contained within the cortical boundaries of the pedicle. 924 (94.7%) screws were considered as acceptable, and 52 (5.3%) as unacceptable. The perforation rate was higher using free-hand technique than O-arm navigation technique (30.8% VS. 11.4%, P Marfan syndrome is accuracy and safe. O-arm navigation was an effective modality to ensure the safety and accuracy of screw placement. Special attention should be paid when screws were placed at the lumber spine and the concave side of spine deformity to avoid the higher rate of complications.

  20. Relevance of intracellular polarity to accuracy of eukaryotic chemotaxis

    International Nuclear Information System (INIS)

    Hiraiwa, Tetsuya; Nishikawa, Masatoshi; Shibata, Tatsuo; Nagamatsu, Akihiro; Akuzawa, Naohiro

    2014-01-01

    Eukaryotic chemotaxis is usually mediated by intracellular signals that tend to localize at the front or back of the cell. Such intracellular polarities frequently require no extracellular guidance cues, indicating that spontaneous polarization occurs in the signal network. Spontaneous polarization activity is considered relevant to the persistent motions in random cell migrations and chemotaxis. In this study, we propose a theoretical model that connects spontaneous intracellular polarity and motile ability in a chemoattractant solution. We demonstrate that the intracellular polarity can enhance the accuracy of chemotaxis. Chemotactic accuracy should also depend on chemoattractant concentration through the concentration-dependent correlation time in the polarity direction. Both the polarity correlation time and the chemotactic accuracy depend on the degree of responsiveness to the chemical gradient. We show that optimally accurate chemotaxis occurs at an intermediate responsiveness of intracellular polarity. Experimentally, we find that the persistence time of randomly migrating Dictyostelium cells depends on the chemoattractant concentration, as predicted by our theory. At the optimum responsiveness, this ameboid cell can enhance its chemotactic accuracy tenfold. (paper)

  1. Comparisons of Faulting-Based Pavement Performance Prediction Models

    Directory of Open Access Journals (Sweden)

    Weina Wang

    2017-01-01

    Full Text Available Faulting prediction is the core of concrete pavement maintenance and design. Highway agencies are always faced with the problem of lower accuracy for the prediction which causes costly maintenance. Although many researchers have developed some performance prediction models, the accuracy of prediction has remained a challenge. This paper reviews performance prediction models and JPCP faulting models that have been used in past research. Then three models including multivariate nonlinear regression (MNLR model, artificial neural network (ANN model, and Markov Chain (MC model are tested and compared using a set of actual pavement survey data taken on interstate highway with varying design features, traffic, and climate data. It is found that MNLR model needs further recalibration, while the ANN model needs more data for training the network. MC model seems a good tool for pavement performance prediction when the data is limited, but it is based on visual inspections and not explicitly related to quantitative physical parameters. This paper then suggests that the further direction for developing the performance prediction model is incorporating the advantages and disadvantages of different models to obtain better accuracy.

  2. SU-F-J-218: Predicting Radiation-Induced Xerostomia by Dosimetrically Accounting for Daily Setup Uncertainty During Head and Neck IMRT

    International Nuclear Information System (INIS)

    Park, S; Quon, H; McNutt, T; Lee, J; Plishker, W; Shekhar, R

    2016-01-01

    Purpose: To determine if the accumulated parotid dosimetry using planning CT to daily CBCT deformation and dose re-calculation can predict for radiation-induced xerostomia. Methods: To track and dosimetrically account for the effects of anatomical changes on the parotid glands, we propagated physicians’ contours from planning CT to daily CBCT using a deformable registration with iterative CBCT intensity correction. A surface mesh for each OAR was created with the deformation applied to the mesh to obtain the deformed parotid volumes. Daily dose was computed on the deformed CT and accumulated to the last fraction. For both the accumulated and the planned parotid dosimetry, we tested the prediction power of different dosimetric parameters including D90, D50, D10, mean, standard deviation, min/max dose to the combined parotids and patient age to severe xerostomia (NCI-CTCAE grade≥2 at 6 mo follow-up). We also tested the dosimetry to parotid sub-volumes. Three classification algorithms, random tree, support vector machine, and logistic regression were tested to predict severe xerostomia using a leave-one-out validation approach. Results: We tested our prediction model on 35 HN IMRT cases. Parameters from the accumulated dosimetry model demonstrated an 89% accuracy for predicting severe xerostomia. Compared to the planning dosimetry, the accumulated dose consistently demonstrated higher prediction power with all three classification algorithms, including 11%, 5% and 30% higher accuracy, sensitivity and specificity, respectively. Geometric division of the combined parotid glands into superior-inferior regions demonstrated ∼5% increased accuracy than the whole volume. The most influential ranked features include age, mean accumulated dose of the submandibular glands and the accumulated D90 of the superior parotid glands. Conclusion: We demonstrated that the accumulated parotid dosimetry using CT-CBCT registration and dose re-calculation more accurately predicts for

  3. SU-F-J-218: Predicting Radiation-Induced Xerostomia by Dosimetrically Accounting for Daily Setup Uncertainty During Head and Neck IMRT

    Energy Technology Data Exchange (ETDEWEB)

    Park, S; Quon, H; McNutt, T; Lee, J [Johns Hopkins University, Baltimor, MD (United States); Plishker, W [IGI Technologies, Inc., College Park, MD (United States); Shekhar, R [IGI Technologies, Inc., College Park, MD (United States); Children’s National Medical Center, Washington, DC (United States)

    2016-06-15

    Purpose: To determine if the accumulated parotid dosimetry using planning CT to daily CBCT deformation and dose re-calculation can predict for radiation-induced xerostomia. Methods: To track and dosimetrically account for the effects of anatomical changes on the parotid glands, we propagated physicians’ contours from planning CT to daily CBCT using a deformable registration with iterative CBCT intensity correction. A surface mesh for each OAR was created with the deformation applied to the mesh to obtain the deformed parotid volumes. Daily dose was computed on the deformed CT and accumulated to the last fraction. For both the accumulated and the planned parotid dosimetry, we tested the prediction power of different dosimetric parameters including D90, D50, D10, mean, standard deviation, min/max dose to the combined parotids and patient age to severe xerostomia (NCI-CTCAE grade≥2 at 6 mo follow-up). We also tested the dosimetry to parotid sub-volumes. Three classification algorithms, random tree, support vector machine, and logistic regression were tested to predict severe xerostomia using a leave-one-out validation approach. Results: We tested our prediction model on 35 HN IMRT cases. Parameters from the accumulated dosimetry model demonstrated an 89% accuracy for predicting severe xerostomia. Compared to the planning dosimetry, the accumulated dose consistently demonstrated higher prediction power with all three classification algorithms, including 11%, 5% and 30% higher accuracy, sensitivity and specificity, respectively. Geometric division of the combined parotid glands into superior-inferior regions demonstrated ∼5% increased accuracy than the whole volume. The most influential ranked features include age, mean accumulated dose of the submandibular glands and the accumulated D90 of the superior parotid glands. Conclusion: We demonstrated that the accumulated parotid dosimetry using CT-CBCT registration and dose re-calculation more accurately predicts for

  4. How accurate is anatomic limb alignment in predicting mechanical limb alignment after total knee arthroplasty?

    Science.gov (United States)

    Lee, Seung Ah; Choi, Sang-Hee; Chang, Moon Jong

    2015-10-27

    Anatomic limb alignment often differs from mechanical limb alignment after total knee arthroplasty (TKA). We sought to assess the accuracy, specificity, and sensitivity for each of three commonly used ranges for anatomic limb alignment (3-9°, 5-10° and 2-10°) in predicting an acceptable range (neutral ± 3°) for mechanical limb alignment after TKA. We also assessed whether the accuracy of anatomic limb alignment was affected by anatomic variation. This retrospective study included 314 primary TKAs. The alignment of the limb was measured with both anatomic and mechanical methods of measurement. We also measured anatomic variation, including the femoral bowing angle, tibial bowing angle, and neck-shaft angle of the femur. All angles were measured on the same full-length standing anteroposterior radiographs. The accuracy, specificity, and sensitivity for each range of anatomic limb alignment were calculated and compared using mechanical limb alignment as the reference standard. The associations between the accuracy of anatomic limb alignment and anatomic variation were also determined. The range of 2-10° for anatomic limb alignment showed the highest accuracy, but it was only 73 % (3-9°, 65 %; 5-10°, 67 %). The specificity of the 2-10° range was 81 %, which was higher than that of the other ranges (3-9°, 69 %; 5-10°, 67 %). However, the sensitivity of the 2-10° range to predict varus malalignment was only 16 % (3-9°, 35 %; 5-10°, 68 %). In addition, the sensitivity of the 2-10° range to predict valgus malalignment was only 43 % (3-9°, 71 %; 5-10°, 43 %). The accuracy of anatomical limb alignment was lower for knees with greater femoral (odds ratio = 1.2) and tibial (odds ratio = 1.2) bowing. Anatomic limb alignment did not accurately predict mechanical limb alignment after TKA, and its accuracy was affected by anatomic variation. Thus, alignment after TKA should be assessed by measuring mechanical alignment rather than anatomic

  5. Benefit of hepatitis C virus core antigen assay in prediction of therapeutic response to interferon and ribavirin combination therapy.

    Science.gov (United States)

    Takahashi, Masahiko; Saito, Hidetsugu; Higashimoto, Makiko; Atsukawa, Kazuhiro; Ishii, Hiromasa

    2005-01-01

    A highly sensitive second-generation hepatitis C virus (HCV) core antigen assay has recently been developed. We compared viral disappearance and first-phase kinetics between commercially available core antigen (Ag) assays, Lumipulse Ortho HCV Ag (Lumipulse-Ag), and a quantitative HCV RNA PCR assay, Cobas Amplicor HCV Monitor test, version 2 (Amplicor M), to estimate the predictive benefit of a sustained viral response (SVR) and non-SVR in 44 genotype 1b patients treated with interferon (IFN) and ribavirin. HCV core Ag negativity could predict SVR on day 1 (sensitivity = 100%, specificity = 85.0%, accuracy = 86.4%), whereas RNA negativity could predict SVR on day 7 (sensitivity = 100%, specificity = 87.2%, accuracy = 88.6%). None of the patients who had detectable serum core Ag or RNA on day 14 achieved SVR (specificity = 100%). The predictive accuracy on day 14 was higher by RNA negativity (93.2%) than that by core Ag negativity (75.0%). The combined predictive criterion of both viral load decline during the first 24 h and basal viral load was also predictive for SVR; the sensitivities of Lumipulse-Ag and Amplicor-M were 45.5 and 47.6%, respectively, and the specificity was 100%. Amplicor-M had better predictive accuracy than Lumipulse-Ag in 2-week disappearance tests because it had better sensitivity. On the other hand, estimates of kinetic parameters were similar regardless of the detection method. Although the correlations between Lumipulse-Ag and Amplicor-M were good both before and 24 h after IFN administration, HCV core Ag seemed to be relatively lower 24 h after IFN administration than before administration. Lumipulse-Ag seems to be useful for detecting the HCV concentration during IFN therapy; however, we still need to understand the characteristics of the assay.

  6. Prediction of the binding affinities of peptides to class II MHC using a regularized thermodynamic model

    Directory of Open Access Journals (Sweden)

    Mittelmann Hans D

    2010-01-01

    Full Text Available Abstract Background The binding of peptide fragments of extracellular peptides to class II MHC is a crucial event in the adaptive immune response. Each MHC allotype generally binds a distinct subset of peptides and the enormous number of possible peptide epitopes prevents their complete experimental characterization. Computational methods can utilize the limited experimental data to predict the binding affinities of peptides to class II MHC. Results We have developed the Regularized Thermodynamic Average, or RTA, method for predicting the affinities of peptides binding to class II MHC. RTA accounts for all possible peptide binding conformations using a thermodynamic average and includes a parameter constraint for regularization to improve accuracy on novel data. RTA was shown to achieve higher accuracy, as measured by AUC, than SMM-align on the same data for all 17 MHC allotypes examined. RTA also gave the highest accuracy on all but three allotypes when compared with results from 9 different prediction methods applied to the same data. In addition, the method correctly predicted the peptide binding register of 17 out of 18 peptide-MHC complexes. Finally, we found that suboptimal peptide binding registers, which are often ignored in other prediction methods, made significant contributions of at least 50% of the total binding energy for approximately 20% of the peptides. Conclusions The RTA method accurately predicts peptide binding affinities to class II MHC and accounts for multiple peptide binding registers while reducing overfitting through regularization. The method has potential applications in vaccine design and in understanding autoimmune disorders. A web server implementing the RTA prediction method is available at http://bordnerlab.org/RTA/.

  7. Is Perceived Expressivity of Game Players a Cue to Game Outcome Prediction Accuracy?

    NARCIS (Netherlands)

    Mui, H.C.; Goudbeek, M.B.; Swerts, M.G.J.

    Games can be won or lost, and the outcome of the game often determines our facial expression. Thus, game players’ facial expression possibly provides information about the game outcome. The connection between such nonverbal cues and accuracy at which game outcome could be deduced is investigated in

  8. Comparison of linear and non-linear models for predicting energy expenditure from raw accelerometer data.

    Science.gov (United States)

    Montoye, Alexander H K; Begum, Munni; Henning, Zachary; Pfeiffer, Karin A

    2017-02-01

    This study had three purposes, all related to evaluating energy expenditure (EE) prediction accuracy from body-worn accelerometers: (1) compare linear regression to linear mixed models, (2) compare linear models to artificial neural network models, and (3) compare accuracy of accelerometers placed on the hip, thigh, and wrists. Forty individuals performed 13 activities in a 90 min semi-structured, laboratory-based protocol. Participants wore accelerometers on the right hip, right thigh, and both wrists and a portable metabolic analyzer (EE criterion). Four EE prediction models were developed for each accelerometer: linear regression, linear mixed, and two ANN models. EE prediction accuracy was assessed using correlations, root mean square error (RMSE), and bias and was compared across models and accelerometers using repeated-measures analysis of variance. For all accelerometer placements, there were no significant differences for correlations or RMSE between linear regression and linear mixed models (correlations: r  =  0.71-0.88, RMSE: 1.11-1.61 METs; p  >  0.05). For the thigh-worn accelerometer, there were no differences in correlations or RMSE between linear and ANN models (ANN-correlations: r  =  0.89, RMSE: 1.07-1.08 METs. Linear models-correlations: r  =  0.88, RMSE: 1.10-1.11 METs; p  >  0.05). Conversely, one ANN had higher correlations and lower RMSE than both linear models for the hip (ANN-correlation: r  =  0.88, RMSE: 1.12 METs. Linear models-correlations: r  =  0.86, RMSE: 1.18-1.19 METs; p  linear models for the wrist-worn accelerometers (ANN-correlations: r  =  0.82-0.84, RMSE: 1.26-1.32 METs. Linear models-correlations: r  =  0.71-0.73, RMSE: 1.55-1.61 METs; p  models offer a significant improvement in EE prediction accuracy over linear models. Conversely, linear models showed similar EE prediction accuracy to machine learning models for hip- and thigh

  9. ACCURACY OF MEASUREMENTS IN OBLIQUE AERIAL IMAGES FOR URBAN ENVIRONMENT

    Directory of Open Access Journals (Sweden)

    W. Ostrowski

    2016-10-01

    Full Text Available Oblique aerial images have been a source of data for urban areas for several years. However, the accuracy of measurements in oblique images during this time has been limited to a single meter due to the use of direct -georeferencing technology and the underlying digital elevation model. Therefore, oblique images have been used mostly for visualization purposes. This situation changed in recent years as new methods, which allowed for a higher accuracy of exterior orientation, were developed. Current developments include the process of determining exterior orientation and the previous but still crucial process of tie point extraction. Progress in this area was shown in the ISPRS/EUROSDR Benchmark on Multi-Platform Photogrammetry and is also noticeable in the growing interest in the use of this kind of imagery. The higher level of accuracy in the orientation of oblique aerial images that has become possible in the last few years should result in a higher level of accuracy in the measurements of these types of images. The main goal of this research was to set and empirically verify the accuracy of measurements in oblique aerial images. The research focused on photogrammetric measurements composed of many images, which use a high overlap within an oblique dataset and different view angles. During the experiments, two series of images of urban areas were used. Both were captured using five DigiCam cameras in a Maltese cross configuration. The tilt angles of the oblique cameras were 45 degrees, and the position of the cameras during flight used a high grade GPS/INS navigation system. The orientation of the images was set using the Pix4D Mapper Pro software with both measurements of the in-flight camera position and the ground control points (measured with GPS RTK technology. To control the accuracy, check points were used (which were also measured with GPS RTK technology. As reference data for the whole study, an area of the city-based map was used

  10. High-accuracy Subdaily ERPs from the IGS

    Science.gov (United States)

    Ray, J. R.; Griffiths, J.

    2012-04-01

    Since November 2000 the International GNSS Service (IGS) has published Ultra-rapid (IGU) products for near real-time (RT) and true real-time applications. They include satellite orbits and clocks, as well as Earth rotation parameters (ERPs) for a sliding 48-hr period. The first day of each update is based on the most recent GPS and GLONASS observational data from the IGS hourly tracking network. At the time of release, these observed products have an initial latency of 3 hr. The second day of each update consists of predictions. So the predictions between about 3 and 9 hr into the second half are relevant for true RT uses. Originally updated twice daily, the IGU products since April 2004 have been issued every 6 hr, at 3, 9, 15, and 21 UTC. Up to seven Analysis Centers (ACs) contribute to the IGU combinations. Two sets of ERPs are published with each IGU update, observed values at the middle epoch of the first half and predicted values at the middle epoch of the second half. The latency of the near RT ERPs is 15 hr while the predicted ERPs, based on projections of each AC's most recent determinations, are issued 9 hr ahead of their reference epoch. While IGU ERPs are issued every 6 hr, each set represents an integrated estimate over the surrounding 24 hr. So successive values are temporally correlated with about 75% of the data being common; this fact should be taken into account in user assimilations. To evaluate the accuracy of these near RT and predicted ERPs, they have been compared to the IGS Final ERPs, available about 11 to 17 d after data collection. The IGU products improved dramatically in the earlier years but since about 2008.0 the performance has been stable and excellent. During the last three years, RMS differences for the observed IGU ERPs have been about 0.036 mas and 0.0101 ms for each polar motion component and LOD respectively. (The internal precision of the reference IGS ERPs over the same period is about 0.016 mas for polar motion and 0

  11. Short-term wind power prediction based on LSSVM–GSA model

    International Nuclear Information System (INIS)

    Yuan, Xiaohui; Chen, Chen; Yuan, Yanbin; Huang, Yuehua; Tan, Qingxiong

    2015-01-01

    Highlights: • A hybrid model is developed for short-term wind power prediction. • The model is based on LSSVM and gravitational search algorithm. • Gravitational search algorithm is used to optimize parameters of LSSVM. • Effect of different kernel function of LSSVM on wind power prediction is discussed. • Comparative studies show that prediction accuracy of wind power is improved. - Abstract: Wind power forecasting can improve the economical and technical integration of wind energy into the existing electricity grid. Due to its intermittency and randomness, it is hard to forecast wind power accurately. For the purpose of utilizing wind power to the utmost extent, it is very important to make an accurate prediction of the output power of a wind farm under the premise of guaranteeing the security and the stability of the operation of the power system. In this paper, a hybrid model (LSSVM–GSA) based on the least squares support vector machine (LSSVM) and gravitational search algorithm (GSA) is proposed to forecast the short-term wind power. As the kernel function and the related parameters of the LSSVM have a great influence on the performance of the prediction model, the paper establishes LSSVM model based on different kernel functions for short-term wind power prediction. And then an optimal kernel function is determined and the parameters of the LSSVM model are optimized by using GSA. Compared with the Back Propagation (BP) neural network and support vector machine (SVM) model, the simulation results show that the hybrid LSSVM–GSA model based on exponential radial basis kernel function and GSA has higher accuracy for short-term wind power prediction. Therefore, the proposed LSSVM–GSA is a better model for short-term wind power prediction

  12. Do Shared Interests Affect the Accuracy of Budgets?

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    Ilse Maria Beuren

    2015-04-01

    Full Text Available The creation of budgetary slack is a phenomenon associated with various behavioral aspects. This study focuses on accuracy in budgeting when the benefit of the slack is shared between the unit manager and his/her assistant. In this study, accuracy is measured by the level of slack in the budget, and the benefit of slack represents a financial consideration for the manager and the assistant. The study aims to test how shared interests in budgetary slack affect the accuracy of budget reports in an organization. To this end, an experimental study was conducted with a sample of 90 employees in management and other leadership positions at a cooperative that has a variable compensation plan based on the achievement of organizational goals. The experiment conducted in this study is consubstantiated by the study of Church, Hannan and Kuang (2012, which was conducted with a sample of undergraduate students in the United States and used a quantitative approach to analyze the results. In the first part of the experiment, the results show that when budgetary slack is not shared, managers tend to create greater slack when the assistant is not aware of the creation of slack; these managers thus generate a lower accuracy index than managers whose assistants are aware of the creation of slack. When budgetary slack is shared, there is higher average slack when the assistant is aware of the creation of slack. In the second part of the experiment, the accuracy index is higher for managers who prepare the budget with the knowledge that their assistants prefer larger slack values. However, the accuracy level differs between managers who know that their assistants prefer maximizing slack values and managers who do not know their assistants' preference regarding slack. These results contribute to the literature by presenting evidence of managers' behavior in the creation of budgetary slack in scenarios in which they share the benefits of slack with their assistants.

  13. Accuracy of five intraoral scanners compared to indirect digitalization.

    Science.gov (United States)

    Güth, Jan-Frederik; Runkel, Cornelius; Beuer, Florian; Stimmelmayr, Michael; Edelhoff, Daniel; Keul, Christine

    2017-06-01

    Direct and indirect digitalization offer two options for computer-aided design (CAD)/ computer-aided manufacturing (CAM)-generated restorations. The aim of this study was to evaluate the accuracy of different intraoral scanners and compare them to the process of indirect digitalization. A titanium testing model was directly digitized 12 times with each intraoral scanner: (1) CS 3500 (CS), (2) Zfx Intrascan (ZFX), (3) CEREC AC Bluecam (BLU), (4) CEREC AC Omnicam (OC) and (5) True Definition (TD). As control, 12 polyether impressions were taken and the referring plaster casts were digitized indirectly with the D-810 laboratory scanner (CON). The accuracy (trueness/precision) of the datasets was evaluated by an analysing software (Geomagic Qualify 12.1) using a "best fit alignment" of the datasets with a highly accurate reference dataset of the testing model, received from industrial computed tomography. Direct digitalization using the TD showed the significant highest overall "trueness", followed by CS. Both performed better than CON. BLU, ZFX and OC showed higher differences from the reference dataset than CON. Regarding the overall "precision", the CS 3500 intraoral scanner and the True Definition showed the best performance. CON, BLU and OC resulted in significantly higher precision than ZFX did. Within the limitations of this in vitro study, the accuracy of the ascertained datasets was dependent on the scanning system. The direct digitalization was not superior to indirect digitalization for all tested systems. Regarding the accuracy, all tested intraoral scanning technologies seem to be able to reproduce a single quadrant within clinical acceptable accuracy. However, differences were detected between the tested systems.

  14. OXBench: A benchmark for evaluation of protein multiple sequence alignment accuracy

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    Searle Stephen MJ

    2003-10-01

    Full Text Available Abstract Background The alignment of two or more protein sequences provides a powerful guide in the prediction of the protein structure and in identifying key functional residues, however, the utility of any prediction is completely dependent on the accuracy of the alignment. In this paper we describe a suite of reference alignments derived from the comparison of protein three-dimensional structures together with evaluation measures and software that allow automatically generated alignments to be benchmarked. We test the OXBench benchmark suite on alignments generated by the AMPS multiple alignment method, then apply the suite to compare eight different multiple alignment algorithms. The benchmark shows the current state-of-the art for alignment accuracy and provides a baseline against which new alignment algorithms may be judged. Results The simple hierarchical multiple alignment algorithm, AMPS, performed as well as or better than more modern methods such as CLUSTALW once the PAM250 pair-score matrix was replaced by a BLOSUM series matrix. AMPS gave an accuracy in Structurally Conserved Regions (SCRs of 89.9% over a set of 672 alignments. The T-COFFEE method on a data set of families with http://www.compbio.dundee.ac.uk. Conclusions The OXBench suite of reference alignments, evaluation software and results database provide a convenient method to assess progress in sequence alignment techniques. Evaluation measures that were dependent on comparison to a reference alignment were found to give good discrimination between methods. The STAMP Sc Score which is independent of a reference alignment also gave good discrimination. Application of OXBench in this paper shows that with the exception of T-COFFEE, the majority of the improvement in alignment accuracy seen since 1985 stems from improved pair-score matrices rather than algorithmic refinements. The maximum theoretical alignment accuracy obtained by pooling results over all methods was 94

  15. Monitoring and regulation of learning in medical education: the need for predictive cues.

    Science.gov (United States)

    de Bruin, Anique B H; Dunlosky, John; Cavalcanti, Rodrigo B

    2017-06-01

    Being able to accurately monitor learning activities is a key element in self-regulated learning in all settings, including medical schools. Yet students' ability to monitor their progress is often limited, leading to inefficient use of study time. Interventions that improve the accuracy of students' monitoring can optimise self-regulated learning, leading to higher achievement. This paper reviews findings from cognitive psychology and explores potential applications in medical education, as well as areas for future research. Effective monitoring depends on students' ability to generate information ('cues') that accurately reflects their knowledge and skills. The ability of these 'cues' to predict achievement is referred to as 'cue diagnosticity'. Interventions that improve the ability of students to elicit predictive cues typically fall into two categories: (i) self-generation of cues and (ii) generation of cues that is delayed after self-study. Providing feedback and support is useful when cues are predictive but may be too complex to be readily used. Limited evidence exists about interventions to improve the accuracy of self-monitoring among medical students or trainees. Developing interventions that foster use of predictive cues can enhance the accuracy of self-monitoring, thereby improving self-study and clinical reasoning. First, insight should be gained into the characteristics of predictive cues used by medical students and trainees. Next, predictive cue prompts should be designed and tested to improve monitoring and regulation of learning. Finally, the use of predictive cues should be explored in relation to teaching and learning clinical reasoning. Improving self-regulated learning is important to help medical students and trainees efficiently acquire knowledge and skills necessary for clinical practice. Interventions that help students generate and use predictive cues hold the promise of improved self-regulated learning and achievement. This framework is

  16. Prediction of Potential Hit Song and Musical Genre Using Artificial Neural Networks

    Science.gov (United States)

    Monterola, Christopher; Abundo, Cheryl; Tugaff, Jeric; Venturina, Lorcel Ericka

    Accurately quantifying the goodness of music based on the seemingly subjective taste of the public is a multi-million industry. Recording companies can make sound decisions on which songs or artists to prioritize if accurate forecasting is achieved. We extract 56 single-valued musical features (e.g. pitch and tempo) from 380 Original Pilipino Music (OPM) songs (190 are hit songs) released from 2004 to 2006. Based on an effect size criterion which measures a variable's discriminating power, the 20 highest ranked features are fed to a classifier tasked to predict hit songs. We show that regardless of musical genre, a trained feed-forward neural network (NN) can predict potential hit songs with an average accuracy of ΦNN = 81%. The accuracy is about +20% higher than those of standard classifiers such as linear discriminant analysis (LDA, ΦLDA = 61%) and classification and regression trees (CART, ΦCART = 57%). Both LDA and CART are above the proportional chance criterion (PCC, ΦPCC = 50%) but are slightly below the suggested acceptable classifier requirement of 1.25*ΦPCC = 63%. Utilizing a similar procedure, we demonstrate that different genres (ballad, alternative rock or rock) of OPM songs can be automatically classified with near perfect accuracy using LDA or NN but only around 77% using CART.

  17. Higher Self-Control Capacity Predicts Lower Anxiety-Impaired Cognition During Math Examinations

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    Alex eBertrams

    2016-03-01

    Full Text Available We assumed that self-control capacity, self-efficacy, and self-esteem would enable students to keep attentional control during tests. Therefore, we hypothesized that the three personality traits would be negatively related to anxiety-impaired cognition during math examinations. Secondary school students (N = 158 completed measures of self-control capacity, self-efficacy, and self-esteem at the beginning of the school year. Five months later, anxiety-impaired cognition during math examinations was assessed. Higher self-control capacity, but neither self-efficacy nor self-esteem, predicted lower anxiety-impaired cognition five months later, over and above baseline anxiety-impaired cognition. Moreover, self-control capacity was indirectly related to math grades via anxiety-impaired cognition. The findings suggest that improving self-control capacity may enable students to deal with anxiety-related problems during school tests.

  18. INFLUENCE OF STRUCTURE COMPONENTS ON MACHINE TOOL ACCURACY

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    ConstantinSANDU

    2017-11-01

    Full Text Available For machine tools, the accuracy of the parts of the machine tool structure (after roughing should be subject to relief and natural or artificial aging. The performance of the current accuracy of machine tools as linearity or flatness was higher than 5 μm/m. Under this value there are great difficulties. The performance of the structure of the machine tools in the manufacture of structural parts of machine tools, with a flatness accuracy that the linearity of about 2 μm/m, are significant deviations form of their half-finished. This article deals with the influence of errors of form of semifinished and machined parts on them, on their shape and especially what happens to structure machine tools when the components of the structure were assembling this.

  19. Augmented chaos-multiple linear regression approach for prediction of wave parameters

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    M.A. Ghorbani

    2017-06-01

    The inter-comparisons demonstrated that the Chaos-MLR and pure MLR models yield almost the same accuracy in predicting the significant wave heights and the zero-up-crossing wave periods. Whereas, the augmented Chaos-MLR model is performed better results in term of the prediction accuracy vis-a-vis the previous prediction applications of the same case study.

  20. DIAGNOSTIC ACCURACY OF CLINICAL AND MAGNETIC RESONANCE IN KNEE MENISCI AND LIGAMENTOUS INJURIES

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    Nilesh

    2016-03-01

    Full Text Available OBJECTIVE The purpose of this study was to evaluate the reliability of clinical diagnosis compared to MRI findings in ligamentous and meniscal injuries with respect to arthroscopic confirmation as a gold standard. METHODS 485 patients with knee injuries were prospectively assessed by clinical evaluation and magnetic resonance imaging and correlated after therapeutic arthroscopy. The overall accuracy, clinically productive values of sensitivity and specificity was derived. The actual value of the test with respect to positive predictive and negative predictive value was also derived, taking arthroscopic findings as confirmatory. The overall partial and total agreement among the clinical, MRI and arthroscopy was documented. RESULTS The overall accuracy for clinical examination was 85, 92, 100 and 100 and accuracy for MRI was 90, 97, 97 and 97 for detecting medial meniscus, lateral meniscus, ACL and PCL tears respectively. Clinically lateral meniscus tears are difficult to diagnose clinically with negative predictive value (90 whereas ACL injuries do not need MRI for diagnosis as evident by a high negative predictive value (100 of clinical examination. Total agreement with the clinical findings confirmed by arthroscopy was 64.40% which was relatively high as compared to total agreement of MRI findings which was only 31.50%. We found similar total agreement versus total disagreement of both clinical and MRI to be only 2.74% indicating very high accuracy in clinical diagnosis of meniscal and ligamentous injuries combined. CONCLUSION The clinical evaluation alone is sufficient to diagnose meniscal and ACL/PCL pathologies and MRI should be considered only as a powerful negative diagnostic tool. The arthroscopy decision should not be heavily dependent on MRI for ligamentous injuries but reverse is true for meniscal lesions. MR evaluation functions as a powerful negative diagnostic tool to rule out doubtful and complex knee injuries.