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

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.

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.

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

Directory of Open Access Journals (Sweden)

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.

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.

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.

Empirical and deterministic accuracies of across-population genomic prediction

NARCIS (Netherlands)

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

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

Directory of Open Access Journals (Sweden)

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.

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.

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.

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....

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.

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

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.

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

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)

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.

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.

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.

Accuracy of ultrasound for the prediction of placenta accreta.

Science.gov (United States)

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.

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.

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.

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...

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.

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

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.

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.

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

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.

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

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.

Prediction-Oriented Marker Selection (PROMISE): With Application to High-Dimensional Regression.

Science.gov (United States)

Kim, Soyeon; Baladandayuthapani, Veerabhadran; Lee, J Jack

2017-06-01

In personalized medicine, biomarkers are used to select therapies with the highest likelihood of success based on an individual patient's biomarker/genomic profile. Two goals are to choose important biomarkers that accurately predict treatment outcomes and to cull unimportant biomarkers to reduce the cost of biological and clinical verifications. These goals are challenging due to the high dimensionality of genomic data. Variable selection methods based on penalized regression (e.g., the lasso and elastic net) have yielded promising results. However, selecting the right amount of penalization is critical to simultaneously achieving these two goals. Standard approaches based on cross-validation (CV) typically provide high prediction accuracy with high true positive rates but at the cost of too many false positives. Alternatively, stability selection (SS) controls the number of false positives, but at the cost of yielding too few true positives. To circumvent these issues, we propose prediction-oriented marker selection (PROMISE), which combines SS with CV to conflate the advantages of both methods. Our application of PROMISE with the lasso and elastic net in data analysis shows that, compared to CV, PROMISE produces sparse solutions, few false positives, and small type I + type II error, and maintains good prediction accuracy, with a marginal decrease in the true positive rates. Compared to SS, PROMISE offers better prediction accuracy and true positive rates. In summary, PROMISE can be applied in many fields to select regularization parameters when the goals are to minimize false positives and maximize prediction accuracy.

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.

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)

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)

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.

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

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

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.

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.

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.

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

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.

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

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.

Predictions versus high-throughput experiments in T-cell epitope discovery: competition or synergy?

DEFF Research Database (Denmark)

Lundegaard, Claus; Lund, Ole; Nielsen, Morten

2012-01-01

and limitations regarding the number of proteins and MHC alleles that are feasibly handled by such experimental methods have made in silico prediction models of high interest. MHC binding prediction methods are today of a very high quality and can predict MHC binding peptides with high accuracy. This is possible...

Reduced Set of Virulence Genes Allows High Accuracy Prediction of Bacterial Pathogenicity in Humans

Science.gov (United States)

Iraola, Gregorio; Vazquez, Gustavo; Spangenberg, Lucía; Naya, Hugo

2012-01-01

Although there have been great advances in understanding bacterial pathogenesis, there is still a lack of integrative information about what makes a bacterium a human pathogen. The advent of high-throughput sequencing technologies has dramatically increased the amount of completed bacterial genomes, for both known human pathogenic and non-pathogenic strains; this information is now available to investigate genetic features that determine pathogenic phenotypes in bacteria. In this work we determined presence/absence patterns of different virulence-related genes among more than finished bacterial genomes from both human pathogenic and non-pathogenic strains, belonging to different taxonomic groups (i.e: Actinobacteria, Gammaproteobacteria, Firmicutes, etc.). An accuracy of 95% using a cross-fold validation scheme with in-fold feature selection is obtained when classifying human pathogens and non-pathogens. A reduced subset of highly informative genes () is presented and applied to an external validation set. The statistical model was implemented in the BacFier v1.0 software (freely available at ), that displays not only the prediction (pathogen/non-pathogen) and an associated probability for pathogenicity, but also the presence/absence vector for the analyzed genes, so it is possible to decipher the subset of virulence genes responsible for the classification on the analyzed genome. Furthermore, we discuss the biological relevance for bacterial pathogenesis of the core set of genes, corresponding to eight functional categories, all with evident and documented association with the phenotypes of interest. Also, we analyze which functional categories of virulence genes were more distinctive for pathogenicity in each taxonomic group, which seems to be a completely new kind of information and could lead to important evolutionary conclusions. PMID:22916122

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

Science.gov (United States)

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.

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.

High Sensitivity TSS Prediction: Estimates of Locations Where TSS Cannot Occur

KAUST Repository

Schaefer, Ulf; Kodzius, Rimantas; Kai, Chikatoshi; Kawai, Jun; Carninci, Piero; Hayashizaki, Yoshihide; Bajic, Vladimir B.

2013-01-01

from mouse and human genomes, we developed a methodology that allows us, by performing computational TSS prediction with very high sensitivity, to annotate, with a high accuracy in a strand specific manner, locations of mammalian genomes that are highly

High Order Wavelet-Based Multiresolution Technology for Airframe Noise Prediction, Phase II

Data.gov (United States)

National Aeronautics and Space Administration — We propose to develop a novel, high-accuracy, high-fidelity, multiresolution (MRES), wavelet-based framework for efficient prediction of airframe noise sources and...

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.

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

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.

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/ .

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/ .

High accuracy FIONA-AFM hybrid imaging

International Nuclear Information System (INIS)

Fronczek, D.N.; Quammen, C.; Wang, H.; Kisker, C.; Superfine, R.; Taylor, R.; Erie, D.A.; Tessmer, I.

2011-01-01

Multi-protein complexes are ubiquitous and play essential roles in many biological mechanisms. Single molecule imaging techniques such as electron microscopy (EM) and atomic force microscopy (AFM) are powerful methods for characterizing the structural properties of multi-protein and multi-protein-DNA complexes. However, a significant limitation to these techniques is the ability to distinguish different proteins from one another. Here, we combine high resolution fluorescence microscopy and AFM (FIONA-AFM) to allow the identification of different proteins in such complexes. Using quantum dots as fiducial markers in addition to fluorescently labeled proteins, we are able to align fluorescence and AFM information to ≥8 nm accuracy. This accuracy is sufficient to identify individual fluorescently labeled proteins in most multi-protein complexes. We investigate the limitations of localization precision and accuracy in fluorescence and AFM images separately and their effects on the overall registration accuracy of FIONA-AFM hybrid images. This combination of the two orthogonal techniques (FIONA and AFM) opens a wide spectrum of possible applications to the study of protein interactions, because AFM can yield high resolution (5-10 nm) information about the conformational properties of multi-protein complexes and the fluorescence can indicate spatial relationships of the proteins in the complexes. -- Research highlights: → Integration of fluorescent signals in AFM topography with high (<10 nm) accuracy. → Investigation of limitations and quantitative analysis of fluorescence-AFM image registration using quantum dots. → Fluorescence center tracking and display as localization probability distributions in AFM topography (FIONA-AFM). → Application of FIONA-AFM to a biological sample containing damaged DNA and the DNA repair proteins UvrA and UvrB conjugated to quantum dots.

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.

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.

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.

Computer modeling of oil spill trajectories with a high accuracy method

International Nuclear Information System (INIS)

Garcia-Martinez, Reinaldo; Flores-Tovar, Henry

1999-01-01

This paper proposes a high accuracy numerical method to model oil spill trajectories using a particle-tracking algorithm. The Euler method, used to calculate oil trajectories, can give adequate solutions in most open ocean applications. However, this method may not predict accurate particle trajectories in certain highly non-uniform velocity fields near coastal zones or in river problems. Simple numerical experiments show that the Euler method may also introduce artificial numerical dispersion that could lead to overestimation of spill areas. This article proposes a fourth-order Runge-Kutta method with fourth-order velocity interpolation to calculate oil trajectories that minimise these problems. The algorithm is implemented in the OilTrack model to predict oil trajectories following the 'Nissos Amorgos' oil spill accident that occurred in the Gulf of Venezuela in 1997. Despite lack of adequate field information, model results compare well with observations in the impacted area. (Author)

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.

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

Directory of Open Access Journals (Sweden)

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.

Genome-wide prediction methods in highly diverse and heterozygous species: proof-of-concept through simulation in grapevine.

Directory of Open Access Journals (Sweden)

Agota Fodor

Full Text Available Nowadays, genome-wide association studies (GWAS and genomic selection (GS methods which use genome-wide marker data for phenotype prediction are of much potential interest in plant breeding. However, to our knowledge, no studies have been performed yet on the predictive ability of these methods for structured traits when using training populations with high levels of genetic diversity. Such an example of a highly heterozygous, perennial species is grapevine. The present study compares the accuracy of models based on GWAS or GS alone, or in combination, for predicting simple or complex traits, linked or not with population structure. In order to explore the relevance of these methods in this context, we performed simulations using approx 90,000 SNPs on a population of 3,000 individuals structured into three groups and corresponding to published diversity grapevine data. To estimate the parameters of the prediction models, we defined four training populations of 1,000 individuals, corresponding to these three groups and a core collection. Finally, to estimate the accuracy of the models, we also simulated four breeding populations of 200 individuals. Although prediction accuracy was low when breeding populations were too distant from the training populations, high accuracy levels were obtained using the sole core-collection as training population. The highest prediction accuracy was obtained (up to 0.9 using the combined GWAS-GS model. We thus recommend using the combined prediction model and a core-collection as training population for grapevine breeding or for other important economic crops with the same characteristics.

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.

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.

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...

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.

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.

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....

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…

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.

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.

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

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.

The use of low density high accuracy (LDHA) data for correction of high density low accuracy (HDLA) point cloud

Science.gov (United States)

Rak, Michal Bartosz; Wozniak, Adam; Mayer, J. R. R.

2016-06-01

Coordinate measuring techniques rely on computer processing of coordinate values of points gathered from physical surfaces using contact or non-contact methods. Contact measurements are characterized by low density and high accuracy. On the other hand optical methods gather high density data of the whole object in a short time but with accuracy at least one order of magnitude lower than for contact measurements. Thus the drawback of contact methods is low density of data, while for non-contact methods it is low accuracy. In this paper a method for fusion of data from two measurements of fundamentally different nature: high density low accuracy (HDLA) and low density high accuracy (LDHA) is presented to overcome the limitations of both measuring methods. In the proposed method the concept of virtual markers is used to find a representation of pairs of corresponding characteristic points in both sets of data. In each pair the coordinates of the point from contact measurements is treated as a reference for the corresponding point from non-contact measurement. Transformation enabling displacement of characteristic points from optical measurement to their match from contact measurements is determined and applied to the whole point cloud. The efficiency of the proposed algorithm was evaluated by comparison with data from a coordinate measuring machine (CMM). Three surfaces were used for this evaluation: plane, turbine blade and engine cover. For the planar surface the achieved improvement was of around 200 μm. Similar results were obtained for the turbine blade but for the engine cover the improvement was smaller. For both freeform surfaces the improvement was higher for raw data than for data after creation of mesh of triangles.

Class prediction for high-dimensional class-imbalanced data

Directory of Open Access Journals (Sweden)

Lusa Lara

2010-10-01

Full Text Available Abstract Background The goal of class prediction studies is to develop rules to accurately predict the class membership of new samples. The rules are derived using the values of the variables available for each subject: the main characteristic of high-dimensional data is that the number of variables greatly exceeds the number of samples. Frequently the classifiers are developed using class-imbalanced data, i.e., data sets where the number of samples in each class is not equal. Standard classification methods used on class-imbalanced data often produce classifiers that do not accurately predict the minority class; the prediction is biased towards the majority class. In this paper we investigate if the high-dimensionality poses additional challenges when dealing with class-imbalanced prediction. We evaluate the performance of six types of classifiers on class-imbalanced data, using simulated data and a publicly available data set from a breast cancer gene-expression microarray study. We also investigate the effectiveness of some strategies that are available to overcome the effect of class imbalance. Results Our results show that the evaluated classifiers are highly sensitive to class imbalance and that variable selection introduces an additional bias towards classification into the majority class. Most new samples are assigned to the majority class from the training set, unless the difference between the classes is very large. As a consequence, the class-specific predictive accuracies differ considerably. When the class imbalance is not too severe, down-sizing and asymmetric bagging embedding variable selection work well, while over-sampling does not. Variable normalization can further worsen the performance of the classifiers. Conclusions Our results show that matching the prevalence of the classes in training and test set does not guarantee good performance of classifiers and that the problems related to classification with class

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...

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...

Identification and delineation of areas flood hazard using high accuracy of DEM data

Science.gov (United States)

Riadi, B.; Barus, B.; Widiatmaka; Yanuar, M. J. P.; Pramudya, B.

2018-05-01

Flood incidents that often occur in Karawang regency need to be mitigated. These expectations exist on technologies that can predict, anticipate and reduce disaster risks. Flood modeling techniques using Digital Elevation Model (DEM) data can be applied in mitigation activities. High accuracy DEM data used in modeling, will result in better flooding flood models. The result of high accuracy DEM data processing will yield information about surface morphology which can be used to identify indication of flood hazard area. The purpose of this study was to identify and describe flood hazard areas by identifying wetland areas using DEM data and Landsat-8 images. TerraSAR-X high-resolution data is used to detect wetlands from landscapes, while land cover is identified by Landsat image data. The Topography Wetness Index (TWI) method is used to detect and identify wetland areas with basic DEM data, while for land cover analysis using Tasseled Cap Transformation (TCT) method. The result of TWI modeling yields information about potential land of flood. Overlay TWI map with land cover map that produces information that in Karawang regency the most vulnerable areas occur flooding in rice fields. The spatial accuracy of the flood hazard area in this study was 87%.

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.

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

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.

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.

Regional Scale High Resolution δ18O Prediction in Precipitation Using MODIS EVI

Science.gov (United States)

Huang, Cho-Ying; Wang, Chung-Ho; Lin, Shou-De; Lo, Yi-Chen; Huang, Bo-Wen; Hatch, Kent A.; Shiu, Hau-Jie; You, Cheng-Feng; Chang, Yuan-Mou; Shen, Sheng-Feng

2012-01-01

The natural variation in stable water isotope ratio data, also known as water isoscape, is a spatiotemporal fingerprint and a powerful natural tracer that has been widely applied in disciplines as diverse as hydrology, paleoclimatology, ecology and forensic investigation. Although much effort has been devoted to developing a predictive water isoscape model, it remains a central challenge for scientists to generate high accuracy, fine scale spatiotemporal water isoscape prediction. Here we develop a novel approach of using the MODIS-EVI (the Moderate Resolution Imagining Spectroradiometer-Enhanced Vegetation Index), to predict δ18O in precipitation at the regional scale. Using a structural equation model, we show that the EVI and precipitated δ18O are highly correlated and thus the EVI is a good predictor of precipitated δ18O. We then test the predictability of our EVI-δ18O model and demonstrate that our approach can provide high accuracy with fine spatial (250×250 m) and temporal (16 days) scale δ18O predictions (annual and monthly predictabilities [r] are 0.96 and 0.80, respectively). We conclude the merging of the EVI and δ18O in precipitation can greatly extend the spatial and temporal data availability and thus enhance the applicability for both the EVI and water isoscape. PMID:23029053

Fission product model for BWR analysis with improved accuracy in high burnup

International Nuclear Information System (INIS)

Ikehara, Tadashi; Yamamoto, Munenari; Ando, Yoshihira

1998-01-01

A new fission product (FP) chain model has been studied to be used in a BWR lattice calculation. In attempting to establish the model, two requirements, i.e. the accuracy in predicting burnup reactivity and the easiness in practical application, are simultaneously considered. The resultant FP model consists of 81 explicit FP nuclides and two lumped pseudo nuclides having the absorption cross sections independent of burnup history and fuel composition. For the verification, extensive numerical tests covering over a wide range of operational conditions and fuel compositions have been carried out. The results indicate that the estimated errors in burnup reactivity are within 0.1%Δk for exposures up to 100GWd/t. It is concluded that the present model can offer a high degree of accuracy for FP representation in BWR lattice calculation. (author)

Predicting Future High-Cost Schizophrenia Patients Using High-Dimensional Administrative Data

Directory of Open Access Journals (Sweden)

Yajuan Wang

2017-06-01

Full Text Available BackgroundThe burden of serious and persistent mental illness such as schizophrenia is substantial and requires health-care organizations to have adequate risk adjustment models to effectively allocate their resources to managing patients who are at the greatest risk. Currently available models underestimate health-care costs for those with mental or behavioral health conditions.ObjectivesThe study aimed to develop and evaluate predictive models for identification of future high-cost schizophrenia patients using advanced supervised machine learning methods.MethodsThis was a retrospective study using a payer administrative database. The study cohort consisted of 97,862 patients diagnosed with schizophrenia (ICD9 code 295.* from January 2009 to June 2014. Training (n = 34,510 and study evaluation (n = 30,077 cohorts were derived based on 12-month observation and prediction windows (PWs. The target was average total cost/patient/month in the PW. Three models (baseline, intermediate, final were developed to assess the value of different variable categories for cost prediction (demographics, coverage, cost, health-care utilization, antipsychotic medication usage, and clinical conditions. Scalable orthogonal regression, significant attribute selection in high dimensions method, and random forests regression were used to develop the models. The trained models were assessed in the evaluation cohort using the regression R2, patient classification accuracy (PCA, and cost accuracy (CA. The model performance was compared to the Centers for Medicare & Medicaid Services Hierarchical Condition Categories (CMS-HCC model.ResultsAt top 10% cost cutoff, the final model achieved 0.23 R2, 43% PCA, and 63% CA; in contrast, the CMS-HCC model achieved 0.09 R2, 27% PCA with 45% CA. The final model and the CMS-HCC model identified 33 and 22%, respectively, of total cost at the top 10% cost cutoff.ConclusionUsing advanced feature selection leveraging detailed

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.

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

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.

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.

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

Science.gov (United States)

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

2017-09-01

The aim of this study was to critically examine the prognostic validity of various clinical high-risk (CHR) criteria alone and in combination with additional clinical characteristics. A total of 188 CHR positive persons from the region of Zurich, Switzerland (mean age 20.5 years; 60.2% male), meeting ultra high-risk (UHR) and/or basic symptoms (BS) criteria, were followed over three years. The test battery included the Structured Interview for Prodromal Syndromes (SIPS), verbal IQ and many other screening tools. Conversion to psychosis was defined according to ICD-10 criteria for schizophrenia (F20) or brief psychotic disorder (F23). Altogether n=24 persons developed manifest psychosis within three years and according to Kaplan-Meier survival analysis, the projected conversion rate was 17.5%. The predictive accuracy of UHR was statistically significant but poor (area under the curve [AUC]=0.65, Pthinking of binary at-risk criteria is necessary in order to improve the prognosis of psychotic disorders. Copyright © 2017 Elsevier Masson SAS. All rights reserved.

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...

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.

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

Directory of Open Access Journals (Sweden)

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.

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.

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.

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.

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...

Statistical approach to predict compressive strength of high workability slag-cement mortars

International Nuclear Information System (INIS)

Memon, N.A.; Memon, N.A.; Sumadi, S.R.

2009-01-01

This paper reports an attempt made to develop empirical expressions to estimate/ predict the compressive strength of high workability slag-cement mortars. Experimental data of 54 mix mortars were used. The mortars were prepared with slag as cement replacement of the order of 0, 50 and 60%. The flow (workability) was maintained at 136+-3%. The numerical and statistical analysis was performed by using database computer software Microsoft Office Excel 2003. Three empirical mathematical models were developed to estimate/predict 28 days compressive strength of high workability slag cement-mortars with 0, 50 and 60% slag which predict the values accurate between 97 and 98%. Finally a generalized empirical mathematical model was proposed which can predict 28 days compressive strength of high workability mortars up to degree of accuracy 95%. (author)

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

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.

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.

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.

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...

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

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

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.

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.

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.

Research on Horizontal Accuracy Method of High Spatial Resolution Remotely Sensed Orthophoto Image

Science.gov (United States)

Xu, Y. M.; Zhang, J. X.; Yu, F.; Dong, S.

2018-04-01

At present, in the inspection and acceptance of high spatial resolution remotly sensed orthophoto image, the horizontal accuracy detection is testing and evaluating the accuracy of images, which mostly based on a set of testing points with the same accuracy and reliability. However, it is difficult to get a set of testing points with the same accuracy and reliability in the areas where the field measurement is difficult and the reference data with high accuracy is not enough. So it is difficult to test and evaluate the horizontal accuracy of the orthophoto image. The uncertainty of the horizontal accuracy has become a bottleneck for the application of satellite borne high-resolution remote sensing image and the scope of service expansion. Therefore, this paper proposes a new method to test the horizontal accuracy of orthophoto image. This method using the testing points with different accuracy and reliability. These points' source is high accuracy reference data and field measurement. The new method solves the horizontal accuracy detection of the orthophoto image in the difficult areas and provides the basis for providing reliable orthophoto images to the users.

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.

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

Fine Particulate Matter Predictions Using High Resolution Aerosol Optical Depth (AOD) Retrievals

Science.gov (United States)

Chudnovsky, Alexandra A.; Koutrakis, Petros; Kloog, Itai; Melly, Steven; Nordio, Francesco; Lyapustin, Alexei; Wang, Jujie; Schwartz, Joel

2014-01-01

To date, spatial-temporal patterns of particulate matter (PM) within urban areas have primarily been examined using models. On the other hand, satellites extend spatial coverage but their spatial resolution is too coarse. In order to address this issue, here we report on spatial variability in PM levels derived from high 1 km resolution AOD product of Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm developed for MODIS satellite. We apply day-specific calibrations of AOD data to predict PM(sub 2.5) concentrations within the New England area of the United States. To improve the accuracy of our model, land use and meteorological variables were incorporated. We used inverse probability weighting (IPW) to account for nonrandom missingness of AOD and nested regions within days to capture spatial variation. With this approach we can control for the inherent day-to-day variability in the AOD-PM(sub 2.5) relationship, which depends on time-varying parameters such as particle optical properties, vertical and diurnal concentration profiles and ground surface reflectance among others. Out-of-sample "ten-fold" cross-validation was used to quantify the accuracy of model predictions. Our results show that the model-predicted PM(sub 2.5) mass concentrations are highly correlated with the actual observations, with out-of- sample R(sub 2) of 0.89. Furthermore, our study shows that the model captures the pollution levels along highways and many urban locations thereby extending our ability to investigate the spatial patterns of urban air quality, such as examining exposures in areas with high traffic. Our results also show high accuracy within the cities of Boston and New Haven thereby indicating that MAIAC data can be used to examine intra-urban exposure contrasts in PM(sub 2.5) levels.

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.

High accuracy 3-D laser radar

DEFF Research Database (Denmark)

Busck, Jens; Heiselberg, Henning

2004-01-01

We have developed a mono-static staring 3-D laser radar based on gated viewing with range accuracy below 1 m at 10 m and 1 cm at 100. We use a high sensitivity, fast, intensified CCD camera, and a Nd:Yag passively Q-switched 32.4 kHz pulsed green laser at 532 nm. The CCD has 752x582 pixels. Camera...

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.

Pre-drilling prediction techniques on the high-temperature high-pressure hydrocarbon reservoirs offshore Hainan Island, China

Science.gov (United States)

Zhang, Hanyu; Liu, Huaishan; Wu, Shiguo; Sun, Jin; Yang, Chaoqun; Xie, Yangbing; Chen, Chuanxu; Gao, Jinwei; Wang, Jiliang

2018-02-01

Decreasing the risks and geohazards associated with drilling engineering in high-temperature high-pressure (HTHP) geologic settings begins with the implementation of pre-drilling prediction techniques (PPTs). To improve the accuracy of geopressure prediction in HTHP hydrocarbon reservoirs offshore Hainan Island, we made a comprehensive summary of current PPTs to identify existing problems and challenges by analyzing the global distribution of HTHP hydrocarbon reservoirs, the research status of PPTs, and the geologic setting and its HTHP formation mechanism. Our research results indicate that the HTHP formation mechanism in the study area is caused by multiple factors, including rapid loading, diapir intrusions, hydrocarbon generation, and the thermal expansion of pore fluids. Due to this multi-factor interaction, a cloud of HTHP hydrocarbon reservoirs has developed in the Ying-Qiong Basin, but only traditional PPTs have been implemented, based on the assumption of conditions that do not conform to the actual geologic environment, e.g., Bellotti's law and Eaton's law. In this paper, we focus on these issues, identify some challenges and solutions, and call for further PPT research to address the drawbacks of previous works and meet the challenges associated with the deepwater technology gap. In this way, we hope to contribute to the improved accuracy of geopressure prediction prior to drilling and provide support for future HTHP drilling offshore Hainan Island.

High accuracy autonomous navigation using the global positioning system (GPS)

Science.gov (United States)

Truong, Son H.; Hart, Roger C.; Shoan, Wendy C.; Wood, Terri; Long, Anne C.; Oza, Dipak H.; Lee, Taesul

1997-01-01

The application of global positioning system (GPS) technology to the improvement of the accuracy and economy of spacecraft navigation, is reported. High-accuracy autonomous navigation algorithms are currently being qualified in conjunction with the GPS attitude determination flyer (GADFLY) experiment for the small satellite technology initiative Lewis spacecraft. Preflight performance assessments indicated that these algorithms are able to provide a real time total position accuracy of better than 10 m and a velocity accuracy of better than 0.01 m/s, with selective availability at typical levels. It is expected that the position accuracy will be increased to 2 m if corrections are provided by the GPS wide area augmentation system.

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.

High accuracy of family history of melanoma in Danish melanoma cases.

Science.gov (United States)

Wadt, Karin A W; Drzewiecki, Krzysztof T; Gerdes, Anne-Marie

2015-12-01

The incidence of melanoma in Denmark has immensely increased over the last 10 years making Denmark a high risk country for melanoma. In the last two decades multiple public campaigns have sought to increase the awareness of melanoma. Family history of melanoma is a known major risk factor but previous studies have shown that self-reported family history of melanoma is highly inaccurate. These studies are 15 years old and we wanted to examine if a higher awareness of melanoma has increased the accuracy of self-reported family history of melanoma. We examined the family history of 181 melanoma probands who reported 199 cases of melanoma in relatives, of which 135 cases where in first degree relatives. We confirmed the diagnosis of melanoma in 77% of all relatives, and in 83% of first degree relatives. In 181 probands we validated the negative family history of melanoma in 748 first degree relatives and found only 1 case of melanoma which was not reported in a 3 case melanoma family. Melanoma patients in Denmark report family history of melanoma in first and second degree relatives with a high level of accuracy with a true positive predictive value between 77 and 87%. In 99% of probands reporting a negative family history of melanoma in first degree relatives this information is correct. In clinical practice we recommend that melanoma diagnosis in relatives should be verified if possible, but even unverified reported melanoma cases in relatives should be included in the indication of genetic testing and assessment of melanoma risk in the family.

Predicting High-Power Performance in Professional Cyclists.

Science.gov (United States)

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

2017-03-01

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

High accuracy wavelength calibration for a scanning visible spectrometer

Energy Technology Data Exchange (ETDEWEB)

Scotti, Filippo; Bell, Ronald E. [Princeton Plasma Physics Laboratory, Princeton, New Jersey 08543 (United States)

2010-10-15

Spectroscopic applications for plasma velocity measurements often require wavelength accuracies {<=}0.2 A. An automated calibration, which is stable over time and environmental conditions without the need to recalibrate after each grating movement, was developed for a scanning spectrometer to achieve high wavelength accuracy over the visible spectrum. This method fits all relevant spectrometer parameters using multiple calibration spectra. With a stepping-motor controlled sine drive, an accuracy of {approx}0.25 A has been demonstrated. With the addition of a high resolution (0.075 arc sec) optical encoder on the grating stage, greater precision ({approx}0.005 A) is possible, allowing absolute velocity measurements within {approx}0.3 km/s. This level of precision requires monitoring of atmospheric temperature and pressure and of grating bulk temperature to correct for changes in the refractive index of air and the groove density, respectively.

Switched-capacitor techniques for high-accuracy filter and ADC design

NARCIS (Netherlands)

Quinn, P.J.; Roermund, van A.H.M.

2007-01-01

Switched capacitor (SC) techniques are well proven to be excellent candidates for implementing critical analogue functions with high accuracy, surpassing other analogue techniques when embedded in mixed-signal CMOS VLSI. Conventional SC circuits are primarily limited in accuracy by a) capacitor

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

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

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

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. 

High-accuracy mass spectrometry for fundamental studies.

Science.gov (United States)

Kluge, H-Jürgen

2010-01-01

Mass spectrometry for fundamental studies in metrology and atomic, nuclear and particle physics requires extreme sensitivity and efficiency as well as ultimate resolving power and accuracy. An overview will be given on the global status of high-accuracy mass spectrometry for fundamental physics and metrology. Three quite different examples of modern mass spectrometric experiments in physics are presented: (i) the retardation spectrometer KATRIN at the Forschungszentrum Karlsruhe, employing electrostatic filtering in combination with magnetic-adiabatic collimation-the biggest mass spectrometer for determining the smallest mass, i.e. the mass of the electron anti-neutrino, (ii) the Experimental Cooler-Storage Ring at GSI-a mass spectrometer of medium size, relative to other accelerators, for determining medium-heavy masses and (iii) the Penning trap facility, SHIPTRAP, at GSI-the smallest mass spectrometer for determining the heaviest masses, those of super-heavy elements. Finally, a short view into the future will address the GSI project HITRAP at GSI for fundamental studies with highly-charged ions.

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.

Accuracy Assessment for the Three-Dimensional Coordinates by High-Speed Videogrammetric Measurement

Directory of Open Access Journals (Sweden)

Xianglei Liu

2018-01-01

Full Text Available High-speed CMOS camera is a new kind of transducer to make the videogrammetric measurement for monitoring the displacement of high-speed shaking table structure. The purpose of this paper is to validate the three-dimensional coordinate accuracy of the shaking table structure acquired from the presented high-speed videogrammetric measuring system. In the paper, all of the key intermediate links are discussed, including the high-speed CMOS videogrammetric measurement system, the layout of the control network, the elliptical target detection, and the accuracy validation of final 3D spatial results. Through the accuracy analysis, the submillimeter accuracy can be made for the final the three-dimensional spatial coordinates which certify that the proposed high-speed videogrammetric technique is a better alternative technique which can replace the traditional transducer technique for monitoring the dynamic response for the shaking table structure.

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

An angle encoder for super-high resolution and super-high accuracy using SelfA

Science.gov (United States)

Watanabe, Tsukasa; Kon, Masahito; Nabeshima, Nobuo; Taniguchi, Kayoko

2014-06-01

Angular measurement technology at high resolution for applications such as in hard disk drive manufacturing machines, precision measurement equipment and aspherical process machines requires a rotary encoder with high accuracy, high resolution and high response speed. However, a rotary encoder has angular deviation factors during operation due to scale error or installation error. It has been assumed to be impossible to achieve accuracy below 0.1″ in angular measurement or control after the installation onto the rotating axis. Self-calibration (Lu and Trumper 2007 CIRP Ann. 56 499; Kim et al 2011 Proc. MacroScale; Probst 2008 Meas. Sci. Technol. 19 015101; Probst et al Meas. Sci. Technol. 9 1059; Tadashi and Makoto 1993 J. Robot. Mechatronics 5 448; Ralf et al 2006 Meas. Sci. Technol. 17 2811) and cross-calibration (Probst et al 1998 Meas. Sci. Technol. 9 1059; Just et al 2009 Precis. Eng. 33 530; Burnashev 2013 Quantum Electron. 43 130) technologies for a rotary encoder have been actively discussed on the basis of the principle of circular closure. This discussion prompted the development of rotary tables which achieve reliable and high accuracy angular verification. We apply these technologies for the development of a rotary encoder not only to meet the requirement of super-high accuracy but also to meet that of super-high resolution. This paper presents the development of an encoder with 221 = 2097 152 resolutions per rotation (360°), that is, corresponding to a 0.62″ signal period, achieved by the combination of a laser rotary encoder supplied by Magnescale Co., Ltd and a self-calibratable encoder (SelfA) supplied by The National Institute of Advanced Industrial Science & Technology (AIST). In addition, this paper introduces the development of a rotary encoder to guarantee ±0.03″ accuracy at any point of the interpolated signal, with respect to the encoder at the minimum resolution of 233, that is, corresponding to a 0.0015″ signal period after

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.

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.

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.

Employing Tropospheric Numerical Weather Prediction Model for High-Precision GNSS Positioning

Science.gov (United States)

Alves, Daniele; Gouveia, Tayna; Abreu, Pedro; Magário, Jackes

2014-05-01

In the past few years is increasing the necessity of realizing high accuracy positioning. In this sense, the spatial technologies have being widely used. The GNSS (Global Navigation Satellite System) has revolutionized the geodetic positioning activities. Among the existent methods one can emphasize the Precise Point Positioning (PPP) and network-based positioning. But, to get high accuracy employing these methods, mainly in real time, is indispensable to realize the atmospheric modeling (ionosphere and troposphere) accordingly. Related to troposphere, there are the empirical models (for example Saastamoinen and Hopfield). But when highly accuracy results (error of few centimeters) are desired, maybe these models are not appropriated to the Brazilian reality. In order to minimize this limitation arises the NWP (Numerical Weather Prediction) models. In Brazil the CPTEC/INPE (Center for Weather Prediction and Climate Studies / Brazilian Institute for Spatial Researches) provides a regional NWP model, currently used to produce Zenithal Tropospheric Delay (ZTD) predictions (http://satelite.cptec.inpe.br/zenital/). The actual version, called eta15km model, has a spatial resolution of 15 km and temporal resolution of 3 hours. In this paper the main goal is to accomplish experiments and analysis concerning the use of troposphere NWP model (eta15km model) in PPP and network-based positioning. Concerning PPP it was used data from dozens of stations over the Brazilian territory, including Amazon forest. The results obtained with NWP model were compared with Hopfield one. NWP model presented the best results in all experiments. Related to network-based positioning it was used data from GNSS/SP Network in São Paulo State, Brazil. This network presents the best configuration in the country to realize this kind of positioning. Actually the network is composed by twenty stations (http://www.fct.unesp.br/#!/pesquisa/grupos-de-estudo-e-pesquisa/gege//gnss-sp-network2789/). The

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...

Pretreatment data is highly predictive of liver chemistry signals in clinical trials.

Science.gov (United States)

Cai, Zhaohui; Bresell, Anders; Steinberg, Mark H; Silberg, Debra G; Furlong, Stephen T

2012-01-01

The goal of this retrospective analysis was to assess how well predictive models could determine which patients would develop liver chemistry signals during clinical trials based on their pretreatment (baseline) information. Based on data from 24 late-stage clinical trials, classification models were developed to predict liver chemistry outcomes using baseline information, which included demographics, medical history, concomitant medications, and baseline laboratory results. Predictive models using baseline data predicted which patients would develop liver signals during the trials with average validation accuracy around 80%. Baseline levels of individual liver chemistry tests were most important for predicting their own elevations during the trials. High bilirubin levels at baseline were not uncommon and were associated with a high risk of developing biochemical Hy's law cases. Baseline γ-glutamyltransferase (GGT) level appeared to have some predictive value, but did not increase predictability beyond using established liver chemistry tests. It is possible to predict which patients are at a higher risk of developing liver chemistry signals using pretreatment (baseline) data. Derived knowledge from such predictions may allow proactive and targeted risk management, and the type of analysis described here could help determine whether new biomarkers offer improved performance over established ones.

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

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

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

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

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.

Accuracy of High-Resolution Ultrasonography in the Detection of Extensor Tendon Lacerations.

Science.gov (United States)

Dezfuli, Bobby; Taljanovic, Mihra S; Melville, David M; Krupinski, Elizabeth A; Sheppard, Joseph E

2016-02-01

Lacerations to the extensor mechanism are usually diagnosed clinically. Ultrasound (US) has been a growing diagnostic tool for tendon injuries since the 1990s. To date, there has been no publication establishing the accuracy and reliability of US in the evaluation of extensor mechanism lacerations in the hand. The purpose of this study is to determine the accuracy of US to detect extensor tendon injuries in the hand. Sixteen fingers and 4 thumbs in 4 fresh-frozen and thawed cadaveric hands were used. Sixty-eight 0.5-cm transverse skin lacerations were created. Twenty-seven extensor tendons were sharply transected. The remaining skin lacerations were used as sham dissection controls. One US technologist and one fellowship-trained musculoskeletal radiologist performed real-time dynamic US studies in and out of water bath. A second fellowship trained musculoskeletal radiologist subsequently reviewed the static US images. Dynamic and static US interpretation accuracy was assessed using dissection as "truth." All 27 extensor tendon lacerations and controls were identified correctly with dynamic imaging as either injury models that had a transected extensor tendon or sham controls with intact extensor tendons (sensitivity = 100%, specificity = 100%, positive predictive value = 1.0; all significantly greater than chance). Static imaging had a sensitivity of 85%, specificity of 89%, and accuracy of 88% (all significantly greater than chance). The results of the dynamic real time versus static US imaging were clearly different but did not reach statistical significance. Diagnostic US is a very accurate noninvasive study that can identify extensor mechanism injuries. Clinically suspected cases of acute extensor tendon injury scanned by high-frequency US can aid and/or confirm the diagnosis, with dynamic imaging providing added value compared to static. Ultrasonography, to aid in the diagnosis of extensor mechanism lacerations, can be successfully used in a reliable and

An angle encoder for super-high resolution and super-high accuracy using SelfA

International Nuclear Information System (INIS)

Watanabe, Tsukasa; Kon, Masahito; Nabeshima, Nobuo; Taniguchi, Kayoko

2014-01-01

Angular measurement technology at high resolution for applications such as in hard disk drive manufacturing machines, precision measurement equipment and aspherical process machines requires a rotary encoder with high accuracy, high resolution and high response speed. However, a rotary encoder has angular deviation factors during operation due to scale error or installation error. It has been assumed to be impossible to achieve accuracy below 0.1″ in angular measurement or control after the installation onto the rotating axis. Self-calibration (Lu and Trumper 2007 CIRP Ann. 56 499; Kim et al 2011 Proc. MacroScale; Probst 2008 Meas. Sci. Technol. 19 015101; Probst et al Meas. Sci. Technol. 9 1059; Tadashi and Makoto 1993 J. Robot. Mechatronics 5 448; Ralf et al 2006 Meas. Sci. Technol. 17 2811) and cross-calibration (Probst et al 1998 Meas. Sci. Technol. 9 1059; Just et al 2009 Precis. Eng. 33 530; Burnashev 2013 Quantum Electron. 43 130) technologies for a rotary encoder have been actively discussed on the basis of the principle of circular closure. This discussion prompted the development of rotary tables which achieve reliable and high accuracy angular verification. We apply these technologies for the development of a rotary encoder not only to meet the requirement of super-high accuracy but also to meet that of super-high resolution. This paper presents the development of an encoder with 2 21 = 2097 152 resolutions per rotation (360°), that is, corresponding to a 0.62″ signal period, achieved by the combination of a laser rotary encoder supplied by Magnescale Co., Ltd and a self-calibratable encoder (SelfA) supplied by The National Institute of Advanced Industrial Science and Technology (AIST). In addition, this paper introduces the development of a rotary encoder to guarantee ±0.03″ accuracy at any point of the interpolated signal, with respect to the encoder at the minimum resolution of 2 33 , that is, corresponding to a 0.0015″ signal period

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…

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.

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

Compound Structure-Independent Activity Prediction in High-Dimensional Target Space.

Science.gov (United States)

Balfer, Jenny; Hu, Ye; Bajorath, Jürgen

2014-08-01

Profiling of compound libraries against arrays of targets has become an important approach in pharmaceutical research. The prediction of multi-target compound activities also represents an attractive task for machine learning with potential for drug discovery applications. Herein, we have explored activity prediction in high-dimensional target space. Different types of models were derived to predict multi-target activities. The models included naïve Bayesian (NB) and support vector machine (SVM) classifiers based upon compound structure information and NB models derived on the basis of activity profiles, without considering compound structure. Because the latter approach can be applied to incomplete training data and principally depends on the feature independence assumption, SVM modeling was not applicable in this case. Furthermore, iterative hybrid NB models making use of both activity profiles and compound structure information were built. In high-dimensional target space, NB models utilizing activity profile data were found to yield more accurate activity predictions than structure-based NB and SVM models or hybrid models. An in-depth analysis of activity profile-based models revealed the presence of correlation effects across different targets and rationalized prediction accuracy. Taken together, the results indicate that activity profile information can be effectively used to predict the activity of test compounds against novel targets. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Fast and High Accuracy Wire Scanner

CERN Document Server

Koujili, M; Koopman, J; Ramos, D; Sapinski, M; De Freitas, J; Ait Amira, Y; Djerdir, A

2009-01-01

Scanning of a high intensity particle beam imposes challenging requirements on a Wire Scanner system. It is expected to reach a scanning speed of 20 m.s-1 with a position accuracy of the order of 1 μm. In addition a timing accuracy better than 1 millisecond is needed. The adopted solution consists of a fork holding a wire rotating by a maximum of 200°. Fork, rotor and angular position sensor are mounted on the same axis and located in a chamber connected to the beam vacuum. The requirements imply the design of a system with extremely low vibration, vacuum compatibility, radiation and temperature tolerance. The adopted solution consists of a rotary brushless synchronous motor with the permanent magnet rotor installed inside of the vacuum chamber and the stator installed outside. The accurate position sensor will be mounted on the rotary shaft inside of the vacuum chamber, has to resist a bake-out temperature of 200°C and ionizing radiation up to a dozen of kGy/year. A digital feedback controller allows maxi...

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.

Social Power Increases Interoceptive Accuracy

Directory of Open Access Journals (Sweden)

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.

Prostatectomy-based validation of combined urine and plasma test for predicting high grade prostate cancer.

Science.gov (United States)

Albitar, Maher; Ma, Wanlong; Lund, Lars; Shahbaba, Babak; Uchio, Edward; Feddersen, Søren; Moylan, Donald; Wojno, Kirk; Shore, Neal

2018-03-01

Distinguishing between low- and high-grade prostate cancers (PCa) is important, but biopsy may underestimate the actual grade of cancer. We have previously shown that urine/plasma-based prostate-specific biomarkers can predict high grade PCa. Our objective was to determine the accuracy of a test using cell-free RNA levels of biomarkers in predicting prostatectomy results. This multicenter community-based prospective study was conducted using urine/blood samples collected from 306 patients. All recruited patients were treatment-naïve, without metastases, and had been biopsied, designated a Gleason Score (GS) based on biopsy, and assigned to prostatectomy prior to participation in the study. The primary outcome measure was the urine/plasma test accuracy in predicting high grade PCa on prostatectomy compared with biopsy findings. Sensitivity and specificity were calculated using standard formulas, while comparisons between groups were performed using the Wilcoxon Rank Sum, Kruskal-Wallis, Chi-Square, and Fisher's exact test. GS as assigned by standard 10-12 core biopsies was 3 + 3 in 90 (29.4%), 3 + 4 in 122 (39.8%), 4 + 3 in 50 (16.3%), and > 4 + 3 in 44 (14.4%) patients. The urine/plasma assay confirmed a previous validation and was highly accurate in predicting the presence of high-grade PCa (Gleason ≥3 + 4) with sensitivity between 88% and 95% as verified by prostatectomy findings. GS was upgraded after prostatectomy in 27% of patients and downgraded in 12% of patients. This plasma/urine biomarker test accurately predicts high grade cancer as determined by prostatectomy with a sensitivity at 92-97%, while the sensitivity of core biopsies was 78%. © 2018 Wiley Periodicals, Inc.

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.

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.

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

Automated, high accuracy classification of Parkinsonian disorders: a pattern recognition approach.

Directory of Open Access Journals (Sweden)

Andre F Marquand

Full Text Available Progressive supranuclear palsy (PSP, multiple system atrophy (MSA and idiopathic Parkinson's disease (IPD can be clinically indistinguishable, especially in the early stages, despite distinct patterns of molecular pathology. Structural neuroimaging holds promise for providing objective biomarkers for discriminating these diseases at the single subject level but all studies to date have reported incomplete separation of disease groups. In this study, we employed multi-class pattern recognition to assess the value of anatomical patterns derived from a widely available structural neuroimaging sequence for automated classification of these disorders. To achieve this, 17 patients with PSP, 14 with IPD and 19 with MSA were scanned using structural MRI along with 19 healthy controls (HCs. An advanced probabilistic pattern recognition approach was employed to evaluate the diagnostic value of several pre-defined anatomical patterns for discriminating the disorders, including: (i a subcortical motor network; (ii each of its component regions and (iii the whole brain. All disease groups could be discriminated simultaneously with high accuracy using the subcortical motor network. The region providing the most accurate predictions overall was the midbrain/brainstem, which discriminated all disease groups from one another and from HCs. The subcortical network also produced more accurate predictions than the whole brain and all of its constituent regions. PSP was accurately predicted from the midbrain/brainstem, cerebellum and all basal ganglia compartments; MSA from the midbrain/brainstem and cerebellum and IPD from the midbrain/brainstem only. This study demonstrates that automated analysis of structural MRI can accurately predict diagnosis in individual patients with Parkinsonian disorders, and identifies distinct patterns of regional atrophy particularly useful for this process.

BOOGIE: Predicting Blood Groups from High Throughput Sequencing Data.

Science.gov (United States)

Giollo, Manuel; Minervini, Giovanni; Scalzotto, Marta; Leonardi, Emanuela; Ferrari, Carlo; Tosatto, Silvio C E

2015-01-01

Over the last decade, we have witnessed an incredible growth in the amount of available genotype data due to high throughput sequencing (HTS) techniques. This information may be used to predict phenotypes of medical relevance, and pave the way towards personalized medicine. Blood phenotypes (e.g. ABO and Rh) are a purely genetic trait that has been extensively studied for decades, with currently over thirty known blood groups. Given the public availability of blood group data, it is of interest to predict these phenotypes from HTS data which may translate into more accurate blood typing in clinical practice. Here we propose BOOGIE, a fast predictor for the inference of blood groups from single nucleotide variant (SNV) databases. We focus on the prediction of thirty blood groups ranging from the well known ABO and Rh, to the less studied Junior or Diego. BOOGIE correctly predicted the blood group with 94% accuracy for the Personal Genome Project whole genome profiles where good quality SNV annotation was available. Additionally, our tool produces a high quality haplotype phase, which is of interest in the context of ethnicity-specific polymorphisms or traits. The versatility and simplicity of the analysis make it easily interpretable and allow easy extension of the protocol towards other phenotypes. BOOGIE can be downloaded from URL http://protein.bio.unipd.it/download/.

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.

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.

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

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.

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

High-accuracy measurements of the normal specular reflectance

International Nuclear Information System (INIS)

Voarino, Philippe; Piombini, Herve; Sabary, Frederic; Marteau, Daniel; Dubard, Jimmy; Hameury, Jacques; Filtz, Jean Remy

2008-01-01

The French Laser Megajoule (LMJ) is designed and constructed by the French Commissariata l'Energie Atomique (CEA). Its amplifying section needs highly reflective multilayer mirrors for the flash lamps. To monitor and improve the coating process, the reflectors have to be characterized to high accuracy. The described spectrophotometer is designed to measure normal specular reflectance with high repeatability by using a small spot size of 100 μm. Results are compared with ellipsometric measurements. The instrument can also perform spatial characterization to detect coating nonuniformity

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.

A high accuracy land use/cover retrieval system

Directory of Open Access Journals (Sweden)

Alaa Hefnawy

2012-03-01

Full Text Available The effects of spatial resolution on the accuracy of mapping land use/cover types have received increasing attention as a large number of multi-scale earth observation data become available. Although many methods of semi automated image classification of remotely sensed data have been established for improving the accuracy of land use/cover classification during the past 40 years, most of them were employed in single-resolution image classification, which led to unsatisfactory results. In this paper, we propose a multi-resolution fast adaptive content-based retrieval system of satellite images. Through our proposed system, we apply a Super Resolution technique for the Landsat-TM images to have a high resolution dataset. The human–computer interactive system is based on modified radial basis function for retrieval of satellite database images. We apply the backpropagation supervised artificial neural network classifier for both the multi and single resolution datasets. The results show significant improved land use/cover classification accuracy for the multi-resolution approach compared with those from single-resolution approach.

A highly accurate predictive-adaptive method for lithium-ion battery remaining discharge energy prediction in electric vehicle applications

International Nuclear Information System (INIS)

Liu, Guangming; Ouyang, Minggao; Lu, Languang; Li, Jianqiu; Hua, Jianfeng

2015-01-01

Highlights: • An energy prediction (EP) method is introduced for battery E RDE determination. • EP determines E RDE through coupled prediction of future states, parameters, and output. • The PAEP combines parameter adaptation and prediction to update model parameters. • The PAEP provides improved E RDE accuracy compared with DC and other EP methods. - Abstract: In order to estimate the remaining driving range (RDR) in electric vehicles, the remaining discharge energy (E RDE ) of the applied battery system needs to be precisely predicted. Strongly affected by the load profiles, the available E RDE varies largely in real-world applications and requires specific determination. However, the commonly-used direct calculation (DC) method might result in certain energy prediction errors by relating the E RDE directly to the current state of charge (SOC). To enhance the E RDE accuracy, this paper presents a battery energy prediction (EP) method based on the predictive control theory, in which a coupled prediction of future battery state variation, battery model parameter change, and voltage response, is implemented on the E RDE prediction horizon, and the E RDE is subsequently accumulated and real-timely optimized. Three EP approaches with different model parameter updating routes are introduced, and the predictive-adaptive energy prediction (PAEP) method combining the real-time parameter identification and the future parameter prediction offers the best potential. Based on a large-format lithium-ion battery, the performance of different E RDE calculation methods is compared under various dynamic profiles. Results imply that the EP methods provide much better accuracy than the traditional DC method, and the PAEP could reduce the E RDE error by more than 90% and guarantee the relative energy prediction error under 2%, proving as a proper choice in online E RDE prediction. The correlation of SOC estimation and E RDE calculation is then discussed to illustrate the

A New Three-Dimensional High-Accuracy Automatic Alignment System For Single-Mode Fibers

Science.gov (United States)

Yun-jiang, Rao; Shang-lian, Huang; Ping, Li; Yu-mei, Wen; Jun, Tang

1990-02-01

In order to achieve the low-loss splices of single-mode fibers, a new three-dimension high-accuracy automatic alignment system for single -mode fibers has been developed, which includes a new-type three-dimension high-resolution microdisplacement servo stage driven by piezoelectric elements, a new high-accuracy measurement system for the misalignment error of the fiber core-axis, and a special single chip microcomputer processing system. The experimental results show that alignment accuracy of ±0.1 pin with a movable stroke of -±20μm has been obtained. This new system has more advantages than that reported.

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

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...

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.

Stock Market Prediction on High-Frequency Data Using Generative Adversarial Nets

Directory of Open Access Journals (Sweden)

Xingyu Zhou

2018-01-01

Full Text Available Stock price prediction is an important issue in the financial world, as it contributes to the development of effective strategies for stock exchange transactions. In this paper, we propose a generic framework employing Long Short-Term Memory (LSTM and convolutional neural network (CNN for adversarial training to forecast high-frequency stock market. This model takes the publicly available index provided by trading software as input to avoid complex financial theory research and difficult technical analysis, which provides the convenience for the ordinary trader of nonfinancial specialty. Our study simulates the trading mode of the actual trader and uses the method of rolling partition training set and testing set to analyze the effect of the model update cycle on the prediction performance. Extensive experiments show that our proposed approach can effectively improve stock price direction prediction accuracy and reduce forecast error.

Building predictive in vitro pulmonary toxicity assays using high-throughput imaging and artificial intelligence.

Science.gov (United States)

Lee, Jia-Ying Joey; Miller, James Alastair; Basu, Sreetama; Kee, Ting-Zhen Vanessa; Loo, Lit-Hsin

2018-06-01

Human lungs are susceptible to the toxicity induced by soluble xenobiotics. However, the direct cellular effects of many pulmonotoxic chemicals are not always clear, and thus, a general in vitro assay for testing pulmonotoxicity applicable to a wide variety of chemicals is not currently available. Here, we report a study that uses high-throughput imaging and artificial intelligence to build an in vitro pulmonotoxicity assay by automatically comparing and selecting human lung-cell lines and their associated quantitative phenotypic features most predictive of in vivo pulmonotoxicity. This approach is called "High-throughput In vitro Phenotypic Profiling for Toxicity Prediction" (HIPPTox). We found that the resulting assay based on two phenotypic features of a human bronchial epithelial cell line, BEAS-2B, can accurately classify 33 reference chemicals with human pulmonotoxicity information (88.8% balance accuracy, 84.6% sensitivity, and 93.0% specificity). In comparison, the predictivity of a standard cell-viability assay on the same set of chemicals is much lower (77.1% balanced accuracy, 84.6% sensitivity, and 69.5% specificity). We also used the assay to evaluate 17 additional test chemicals with unknown/unclear human pulmonotoxicity, and experimentally confirmed that many of the pulmonotoxic reference and predicted-positive test chemicals induce DNA strand breaks and/or activation of the DNA-damage response (DDR) pathway. Therefore, HIPPTox helps us to uncover these common modes-of-action of pulmonotoxic chemicals. HIPPTox may also be applied to other cell types or models, and accelerate the development of predictive in vitro assays for other cell-type- or organ-specific toxicities.

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)

High-precision predictions for the light CP-even Higgs boson mass of the MSSM

Energy Technology Data Exchange (ETDEWEB)

Hahn, T.; Hollik, W. [Max-Planck-Institut fuer Physik, Muenchen (Germany); Heinemeyer, S. [Instituto de Fisica de Cantabria (CSIC-UC), Santander (Spain); Rzehak, H. [Freiburg Univ. (Germany). Physikalisches Inst.; Weiglein, G. [Deutsches Elektronen-Synchrotron (DESY), Hamburg (Germany)

2014-03-15

For the interpretation of the signal discovered in the Higgs searches at the LHC it will be crucial in particular to discriminate between the minimal Higgs sector realised in the Standard Model (SM) and its most commonly studied extension, the Minimal Supersymmetric SM (MSSM). The measured mass value, having already reached the level of a precision observable with an experimental accuracy of about 500 MeV, plays an important role in this context. In the MSSM the mass of the light CP-even Higgs boson, M{sub h}, can directly be predicted from the other parameters of the model. The accuracy of this prediction should at least match the one of the experimental result. The relatively high mass value of about 126 GeV has led to many investigations where the scalar top quarks are in the multi-TeV range. We improve the prediction for M{sub h} in the MSSM by combining the existing fixed-order result, comprising the full one-loop and leading and subleading two-loop corrections, with a resummation of the leading and subleading logarithmic contributions from the scalar top sector to all orders. In this way for the first time a high-precision prediction for the mass of the light CP-even Higgs boson in the MSSM is possible all the way up to the multi-TeV region of the relevant supersymmetric particles. The results are included in the code FeynHiggs.

High Sensitivity TSS Prediction: Estimates of Locations Where TSS Cannot Occur

KAUST Repository

Schaefer, Ulf

2013-10-10

Background Although transcription in mammalian genomes can initiate from various genomic positions (e.g., 3′UTR, coding exons, etc.), most locations on genomes are not prone to transcription initiation. It is of practical and theoretical interest to be able to estimate such collections of non-TSS locations (NTLs). The identification of large portions of NTLs can contribute to better focusing the search for TSS locations and thus contribute to promoter and gene finding. It can help in the assessment of 5′ completeness of expressed sequences, contribute to more successful experimental designs, as well as more accurate gene annotation. Methodology Using comprehensive collections of Cap Analysis of Gene Expression (CAGE) and other transcript data from mouse and human genomes, we developed a methodology that allows us, by performing computational TSS prediction with very high sensitivity, to annotate, with a high accuracy in a strand specific manner, locations of mammalian genomes that are highly unlikely to harbor transcription start sites (TSSs). The properties of the immediate genomic neighborhood of 98,682 accurately determined mouse and 113,814 human TSSs are used to determine features that distinguish genomic transcription initiation locations from those that are not likely to initiate transcription. In our algorithm we utilize various constraining properties of features identified in the upstream and downstream regions around TSSs, as well as statistical analyses of these surrounding regions. Conclusions Our analysis of human chromosomes 4, 21 and 22 estimates ~46%, ~41% and ~27% of these chromosomes, respectively, as being NTLs. This suggests that on average more than 40% of the human genome can be expected to be highly unlikely to initiate transcription. Our method represents the first one that utilizes high-sensitivity TSS prediction to identify, with high accuracy, large portions of mammalian genomes as NTLs. The server with our algorithm implemented is

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.

Adaptive sensor-based ultra-high accuracy solar concentrator tracker

Science.gov (United States)

Brinkley, Jordyn; Hassanzadeh, Ali

2017-09-01

Conventional solar trackers use information of the sun's position, either by direct sensing or by GPS. Our method uses the shading of the receiver. This, coupled with nonimaging optics design allows us to achieve ultra-high concentration. Incorporating a sensor based shadow tracking method with a two stage concentration solar hybrid parabolic trough allows the system to maintain high concentration with acute accuracy.

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.

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

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

High accuracy digital aging monitor based on PLL-VCO circuit

International Nuclear Information System (INIS)

Zhang Yuejun; Jiang Zhidi; Wang Pengjun; Zhang Xuelong

2015-01-01

As the manufacturing process is scaled down to the nanoscale, the aging phenomenon significantly affects the reliability and lifetime of integrated circuits. Consequently, the precise measurement of digital CMOS aging is a key aspect of nanoscale aging tolerant circuit design. This paper proposes a high accuracy digital aging monitor using phase-locked loop and voltage-controlled oscillator (PLL-VCO) circuit. The proposed monitor eliminates the circuit self-aging effect for the characteristic of PLL, whose frequency has no relationship with circuit aging phenomenon. The PLL-VCO monitor is implemented in TSMC low power 65 nm CMOS technology, and its area occupies 303.28 × 298.94 μm 2 . After accelerating aging tests, the experimental results show that PLL-VCO monitor improves accuracy about high temperature by 2.4% and high voltage by 18.7%. (semiconductor integrated circuits)

TH-CD-207A-07: Prediction of High Dimensional State Subject to Respiratory Motion: A Manifold Learning Approach

International Nuclear Information System (INIS)

Liu, W; Sawant, A; Ruan, D

2016-01-01

Purpose: The development of high dimensional imaging systems (e.g. volumetric MRI, CBCT, photogrammetry systems) in image-guided radiotherapy provides important pathways to the ultimate goal of real-time volumetric/surface motion monitoring. This study aims to develop a prediction method for the high dimensional state subject to respiratory motion. Compared to conventional linear dimension reduction based approaches, our method utilizes manifold learning to construct a descriptive feature submanifold, where more efficient and accurate prediction can be performed. Methods: We developed a prediction framework for high-dimensional state subject to respiratory motion. The proposed method performs dimension reduction in a nonlinear setting to permit more descriptive features compared to its linear counterparts (e.g., classic PCA). Specifically, a kernel PCA is used to construct a proper low-dimensional feature manifold, where low-dimensional prediction is performed. A fixed-point iterative pre-image estimation method is applied subsequently to recover the predicted value in the original state space. We evaluated and compared the proposed method with PCA-based method on 200 level-set surfaces reconstructed from surface point clouds captured by the VisionRT system. The prediction accuracy was evaluated with respect to root-mean-squared-error (RMSE) for both 200ms and 600ms lookahead lengths. Results: The proposed method outperformed PCA-based approach with statistically higher prediction accuracy. In one-dimensional feature subspace, our method achieved mean prediction accuracy of 0.86mm and 0.89mm for 200ms and 600ms lookahead lengths respectively, compared to 0.95mm and 1.04mm from PCA-based method. The paired t-tests further demonstrated the statistical significance of the superiority of our method, with p-values of 6.33e-3 and 5.78e-5, respectively. Conclusion: The proposed approach benefits from the descriptiveness of a nonlinear manifold and the prediction

A proposal for limited criminal liability in high-accuracy endoscopic sinus surgery.

Science.gov (United States)

Voultsos, P; Casini, M; Ricci, G; Tambone, V; Midolo, E; Spagnolo, A G

2017-02-01

The aim of the present study is to propose legal reform limiting surgeons' criminal liability in high-accuracy and high-risk surgery such as endoscopic sinus surgery (ESS). The study includes a review of the medical literature, focusing on identifying and examining reasons why ESS carries a very high risk of serious complications related to inaccurate surgical manoeuvers and reviewing British and Italian legal theory and case-law on medical negligence, especially with regard to Italian Law 189/2012 (so called "Balduzzi" Law). It was found that serious complications due to inaccurate surgical manoeuvers may occur in ESS regardless of the skill, experience and prudence/diligence of the surgeon. Subjectivity should be essential to medical negligence, especially regarding high-accuracy surgery. Italian Law 189/2012 represents a good basis for the limitation of criminal liability resulting from inaccurate manoeuvres in high-accuracy surgery such as ESS. It is concluded that ESS surgeons should be relieved of criminal liability in cases of simple/ordinary negligence where guidelines have been observed. © Copyright by Società Italiana di Otorinolaringologia e Chirurgia Cervico-Facciale, Rome, Italy.

High current high accuracy IGBT pulse generator

International Nuclear Information System (INIS)

Nesterov, V.V.; Donaldson, A.R.

1995-05-01

A solid state pulse generator capable of delivering high current triangular or trapezoidal pulses into an inductive load has been developed at SLAC. Energy stored in a capacitor bank of the pulse generator is switched to the load through a pair of insulated gate bipolar transistors (IGBT). The circuit can then recover the remaining energy and transfer it back to the capacitor bank without reversing the capacitor voltage. A third IGBT device is employed to control the initial charge to the capacitor bank, a command charging technique, and to compensate for pulse to pulse power losses. The rack mounted pulse generator contains a 525 μF capacitor bank. It can deliver 500 A at 900V into inductive loads up to 3 mH. The current amplitude and discharge time are controlled to 0.02% accuracy by a precision controller through the SLAC central computer system. This pulse generator drives a series pair of extraction dipoles

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.

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.

A Systematic Review and Meta-analysis of the Diagnostic Accuracy of Prostate Health Index and 4-Kallikrein Panel Score in Predicting Overall and High-grade Prostate Cancer.

Science.gov (United States)

Russo, Giorgio Ivan; Regis, Federica; Castelli, Tommaso; Favilla, Vincenzo; Privitera, Salvatore; Giardina, Raimondo; Cimino, Sebastiano; Morgia, Giuseppe

2017-08-01

Markers for prostate cancer (PCa) have progressed over recent years. In particular, the prostate health index (PHI) and the 4-kallikrein (4K) panel have been demonstrated to improve the diagnosis of PCa. We aimed to review the diagnostic accuracy of PHI and the 4K panel for PCa detection. We performed a systematic literature search of PubMed, EMBASE, Cochrane, and Academic One File databases until July 2016. We included diagnostic accuracy studies that used PHI or 4K panel for the diagnosis of PCa or high-grade PCa. The methodological quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. Twenty-eight studies including 16,762 patients have been included for the analysis. The pooled data showed a sensitivity of 0.89 and 0.74 for PHI and 4K panel, respectively, for PCa detection and a pooled specificity of 0.34 and 0.60 for PHI and 4K panel, respectively. The derived area under the curve (AUC) from the hierarchical summary receiver operating characteristic (HSROC) showed an accuracy of 0.76 and 0.72 for PHI and 4K panel respectively. For high-grade PCa detection, the pooled sensitivity was 0.93 and 0.87 for PHI and 4K panel, respectively, whereas the pooled specificity was 0.34 and 0.61 for PHI and 4K panel, respectively. The derived AUC from the HSROC showed an accuracy of 0.82 and 0.81 for PHI and 4K panel, respectively. Both PHI and the 4K panel provided good diagnostic accuracy in detecting overall and high-grade PCa. Copyright © 2016 Elsevier Inc. All rights reserved.

High-accuracy determination for optical indicatrix rotation in ferroelectric DTGS

OpenAIRE

O.S.Kushnir; O.A.Bevz; O.G.Vlokh

2000-01-01

Optical indicatrix rotation in deuterated ferroelectric triglycine sulphate is studied with the high-accuracy null-polarimetric technique. The behaviour of the effect in ferroelectric phase is referred to quadratic spontaneous electrooptics.

Achieving High Accuracy in Calculations of NMR Parameters

DEFF Research Database (Denmark)

Faber, Rasmus

quantum chemical methods have been developed, the calculation of NMR parameters with quantitative accuracy is far from trivial. In this thesis I address some of the issues that makes accurate calculation of NMR parameters so challenging, with the main focus on SSCCs. High accuracy quantum chemical......, but no programs were available to perform such calculations. As part of this thesis the CFOUR program has therefore been extended to allow the calculation of SSCCs using the CC3 method. CC3 calculations of SSCCs have then been performed for several molecules, including some difficult cases. These results show...... vibrations must be included. The calculation of vibrational corrections to NMR parameters has been reviewed as part of this thesis. A study of the basis set convergence of vibrational corrections to nuclear shielding constants has also been performed. The basis set error in vibrational correction...

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.

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.

Coronary CT angiography using prospective ECG triggering. High diagnostic accuracy with low radiation dose

International Nuclear Information System (INIS)

Arnoldi, E.; Ramos-Duran, L.; Abro, J.A.; Costello, P.; Zwerner, P.L.; Schoepf, U.J.; Nikolaou, K.; Reiser, M.F.

2010-01-01

The purpose of this study was to evaluate the diagnostic performance of coronary CT angiography (coronary CTA) using prospective ECG triggering (PT) for the detection of significant coronary artery stenosis compared to invasive coronary angiography (ICA). A total of 20 patients underwent coronary CTA with PT using a 128-slice CT scanner (Definition trademark AS+, Siemens) and ICA. All coronary CTA studies were evaluated for significant coronary artery stenoses (≥50% luminal narrowing) by 2 observers in consensus using the AHA-15-segment model. Findings in CTA were compared to those in ICA. Coronary CTA using PT had 88% sensitivity in comparison to 100% with ICA, 95% to 88% specificity, 80% to 92% positive predictive value and 97% to 100% negative predictive value for diagnosing significant coronary artery stenosis on per segment per patient analysis, respectively. Mean effective radiation dose-equivalent of CTA was 2.6±1 mSv. Coronary CTA using PT enables non-invasive diagnosis of significant coronary artery stenosis with high diagnostic accuracy in comparison to ICA and is associated with comparably low radiation exposure. (orig.) [de

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.

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

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...

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.

Pretreatment data is highly predictive of liver chemistry signals in clinical trials

Directory of Open Access Journals (Sweden)

Cai Z

2012-11-01

Full Text Available Zhaohui Cai,1,* Anders Bresell,2,* Mark H Steinberg,1 Debra G Silberg,1 Stephen T Furlong11AstraZeneca Pharmaceuticals, Wilmington, DE, USA; 2AstraZeneca Pharmaceuticals, Södertälje, Sweden*These authors contributed equally to this workPurpose: The goal of this retrospective analysis was to assess how well predictive models could determine which patients would develop liver chemistry signals during clinical trials based on their pretreatment (baseline information.Patients and methods: Based on data from 24 late-stage clinical trials, classification models were developed to predict liver chemistry outcomes using baseline information, which included demographics, medical history, concomitant medications, and baseline laboratory results.Results: Predictive models using baseline data predicted which patients would develop liver signals during the trials with average validation accuracy around 80%. Baseline levels of individual liver chemistry tests were most important for predicting their own elevations during the trials. High bilirubin levels at baseline were not uncommon and were associated with a high risk of developing biochemical Hy’s law cases. Baseline γ-glutamyltransferase (GGT level appeared to have some predictive value, but did not increase predictability beyond using established liver chemistry tests.Conclusion: It is possible to predict which patients are at a higher risk of developing liver chemistry signals using pretreatment (baseline data. Derived knowledge from such predictions may allow proactive and targeted risk management, and the type of analysis described here could help determine whether new biomarkers offer improved performance over established ones.Keywords: bilirubin, Hy’s Law, ALT, GGT, baseline, prediction

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.

High-Accuracy Spherical Near-Field Measurements for Satellite Antenna Testing

DEFF Research Database (Denmark)

Breinbjerg, Olav

2017-01-01

The spherical near-field antenna measurement technique is unique in combining several distinct advantages and it generally constitutes the most accurate technique for experimental characterization of radiation from antennas. From the outset in 1970, spherical near-field antenna measurements have...... matured into a well-established technique that is widely used for testing antennas for many wireless applications. In particular, for high-accuracy applications, such as remote sensing satellite missions in ESA's Earth Observation Programme with uncertainty requirements at the level of 0.05dB - 0.10d......B, the spherical near-field antenna measurement technique is generally superior. This paper addresses the means to achieving high measurement accuracy; these include the measurement technique per se, its implementation in terms of proper measurement procedures, the use of uncertainty estimates, as well as facility...

High Accuracy Piezoelectric Kinemometer; Cinemometro piezoelectrico de alta exactitud (VUAE)

Energy Technology Data Exchange (ETDEWEB)

Jimenez Martinez, F. J.; Frutos, J. de; Pastor, C.; Vazquez Rodriguez, M.

2012-07-01

We have developed a portable computerized and low consumption, our system is called High Accuracy Piezoelectric Kinemometer measurement, herein VUAE. By the high accuracy obtained by VUAE it make able to use the VUAE to obtain references measurements of system for measuring Speeds in Vehicles. Therefore VUAE could be used how reference equipment to estimate the error of installed kinemometers. The VUAE was created with n (n=2) pairs of ultrasonic transmitter-receiver, herein E-Rult. The transmitters used in the n couples E-Rult generate n ultrasonic barriers and receivers receive the echoes when the vehicle crosses the barriers. Digital processing of the echoes signals let us to obtain acceptable signals. Later, by mean of cross correlation technics is possible make a highly exact estimation of speed of the vehicle. The log of the moments of interception and the distance between each of the n ultrasounds allows for a highly exact estimation of speed of the vehicle. VUAE speed measurements were compared to a speed reference system based on piezoelectric cables. (Author) 11 refs.

High-Accuracy Measurements of Total Column Water Vapor From the Orbiting Carbon Observatory-2

Science.gov (United States)

Nelson, Robert R.; Crisp, David; Ott, Lesley E.; O'Dell, Christopher W.

2016-01-01

Accurate knowledge of the distribution of water vapor in Earth's atmosphere is of critical importance to both weather and climate studies. Here we report on measurements of total column water vapor (TCWV) from hyperspectral observations of near-infrared reflected sunlight over land and ocean surfaces from the Orbiting Carbon Observatory-2 (OCO-2). These measurements are an ancillary product of the retrieval algorithm used to measure atmospheric carbon dioxide concentrations, with information coming from three highly resolved spectral bands. Comparisons to high-accuracy validation data, including ground-based GPS and microwave radiometer data, demonstrate that OCO-2 TCWV measurements have maximum root-mean-square deviations of 0.9-1.3mm. Our results indicate that OCO-2 is the first space-based sensor to accurately and precisely measure the two most important greenhouse gases, water vapor and carbon dioxide, at high spatial resolution [1.3 x 2.3 km(exp. 2)] and that OCO-2 TCWV measurements may be useful in improving numerical weather predictions and reanalysis products.

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

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

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.

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.

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.

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.

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

Why is a high accuracy needed in dosimetry

International Nuclear Information System (INIS)

Lanzl, L.H.

1976-01-01

Dose and exposure intercomparisons on a national or international basis have become an important component of quality assurance in the practice of good radiotherapy. A high degree of accuracy of γ and x radiation dosimetry is essential in our international society, where medical information is so readily exchanged and used. The value of accurate dosimetry lies mainly in the avoidance of complications in normal tissue and an optimal degree of tumor control

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.

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.

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

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.

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.

High accuracy in silico sulfotransferase models.

Science.gov (United States)

Cook, Ian; Wang, Ting; Falany, Charles N; Leyh, Thomas S

2013-11-29

Predicting enzymatic behavior in silico is an integral part of our efforts to understand biology. Hundreds of millions of compounds lie in targeted in silico libraries waiting for their metabolic potential to be discovered. In silico "enzymes" capable of accurately determining whether compounds can inhibit or react is often the missing piece in this endeavor. This problem has now been solved for the cytosolic sulfotransferases (SULTs). SULTs regulate the bioactivities of thousands of compounds--endogenous metabolites, drugs and other xenobiotics--by transferring the sulfuryl moiety (SO3) from 3'-phosphoadenosine 5'-phosphosulfate to the hydroxyls and primary amines of these acceptors. SULT1A1 and 2A1 catalyze the majority of sulfation that occurs during human Phase II metabolism. Here, recent insights into the structure and dynamics of SULT binding and reactivity are incorporated into in silico models of 1A1 and 2A1 that are used to identify substrates and inhibitors in a structurally diverse set of 1,455 high value compounds: the FDA-approved small molecule drugs. The SULT1A1 models predict 76 substrates. Of these, 53 were known substrates. Of the remaining 23, 21 were tested, and all were sulfated. The SULT2A1 models predict 22 substrates, 14 of which are known substrates. Of the remaining 8, 4 were tested, and all are substrates. The models proved to be 100% accurate in identifying substrates and made no false predictions at Kd thresholds of 100 μM. In total, 23 "new" drug substrates were identified, and new linkages to drug inhibitors are predicted. It now appears to be possible to accurately predict Phase II sulfonation in silico.

The accuracy of {sup 68}Ga-PSMA PET/CT in primary lymph node staging in high-risk prostate cancer

Energy Technology Data Exchange (ETDEWEB)

Oebek, Can; Doganca, Tuenkut [Acibadem Taksim Hospital, Department of Urology, Istanbul (Turkey); Demirci, Emre [Sisli Etfal Training and Research Hospital, Department of Nuclear Medicine, Istanbul (Turkey); Ocak, Meltem [Istanbul University, Faculty of Pharmacy, Department of Pharmaceutical Technology, Istanbul (Turkey); Kural, Ali Riza [Acibadem University, Department of Urology, Istanbul (Turkey); Yildirim, Asif [Istanbul Medeniyet University, Department of Urology, Istanbul (Turkey); Yuecetas, Ugur [Istanbul Training and Research Hospital, Department of Urology, Istanbul (Turkey); Demirdag, Cetin [Istanbul University, Cerrahpasa School of Medicine, Department of Urology, Istanbul (Turkey); Erdogan, Sarper M. [Istanbul University, Cerrahpasa School of Medicine, Department of Public Health, Istanbul (Turkey); Kabasakal, Levent [Istanbul University, Cerrahpasa School of Medicine, Department of Nuclear Medicine, Istanbul (Turkey); Collaboration: Members of Urooncology Association, Turkey

2017-10-15

To assess the diagnostic accuracy of {sup 68}Ga-PSMA PET in predicting lymph node (LN) metastases in primary N staging in high-risk and very high-risk nonmetastatic prostate cancer in comparison with morphological imaging. This was a multicentre trial of the Society of Urologic Oncology in Turkey in conjunction with the Nuclear Medicine Department of Cerrahpasa School of Medicine, Istanbul University. Patients were accrued from eight centres. Patients with high-risk and very high-risk disease scheduled to undergo surgical treatment with extended LN dissection between July 2014 and October 2015 were included. Either MRI or CT was used for morphological imaging. PSMA PET/CT was performed and evaluated at a single centre. Sensitivity, specificity and accuracy were calculated for the detection of lymphatic metastases by PSMA PET/CT and morphological imaging. Kappa values were calculated to evaluate the correlation between the numbers of LN metastases detected by PSMA PET/CT and by histopathology. Data on 51 eligible patients are presented. The sensitivity, specificity and accuracy of PSMA PET in detecting LN metastases in the primary setting were 53%, 86% and 76%, and increased to 67%, 88% and 81% in the subgroup with of patients with ≥15 LN removed. Kappa values for the correlation between imaging and pathology were 0.41 for PSMA PET and 0.18 for morphological imaging. PSMA PET/CT is superior to morphological imaging for the detection of metastatic LNs in patients with primary prostate cancer. Surgical dissection remains the gold standard for precise lymphatic staging. (orig.)

Innovative Fiber-Optic Gyroscopes (FOGs) for High Accuracy Space Applications, Phase II

Data.gov (United States)

National Aeronautics and Space Administration — This project aims to develop a compact, highly innovative Inertial Reference/Measurement Unit (IRU/IMU) that pushes the state-of-the-art in high accuracy performance...

High accuracy acoustic relative humidity measurement in duct flow with air.

Science.gov (United States)

van Schaik, Wilhelm; Grooten, Mart; Wernaart, Twan; van der Geld, Cees

2010-01-01

An acoustic relative humidity sensor for air-steam mixtures in duct flow is designed and tested. Theory, construction, calibration, considerations on dynamic response and results are presented. The measurement device is capable of measuring line averaged values of gas velocity, temperature and relative humidity (RH) instantaneously, by applying two ultrasonic transducers and an array of four temperature sensors. Measurement ranges are: gas velocity of 0-12 m/s with an error of ± 0.13 m/s, temperature 0-100 °C with an error of ± 0.07 °C and relative humidity 0-100% with accuracy better than 2 % RH above 50 °C. Main advantage over conventional humidity sensors is the high sensitivity at high RH at temperatures exceeding 50 °C, with accuracy increasing with increasing temperature. The sensors are non-intrusive and resist highly humid environments.

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.

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.

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

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.

Accuracy of Combined Computed Tomography Colonography and Dual Energy Iiodine Map Imaging for Detecting Colorectal masses using High-pitch Dual-source CT.

Science.gov (United States)

Sun, Kai; Han, Ruijuan; Han, Yang; Shi, Xuesen; Hu, Jiang; Lu, Bin

2018-02-28

To evaluate the diagnostic accuracy of combined computed tomography colonography (CTC) and dual-energy iodine map imaging for detecting colorectal masses using high-pitch dual-source CT, compared with optical colonography (OC) and histopathologic findings. Twenty-eight consecutive patients were prospectively enrolled in this study. All patients were underwent contrast-enhanced CTC acquisition using dual-energy mode and OC and pathologic examination. The size of the space-occupied mass, the CT value after contrast enhancement, and the iodine value were measured and statistically compared. The sensitivity, specificity, accuracy rate, and positive predictive and negative predictive values of dual-energy contrast-enhanced CTC were calculated and compared between conventional CTC and dual-energy iodine images. The iodine value of stool was significantly lower than the colonic neoplasia (P dual-energy iodine maps imaging was 95.6% (95% CI = 77.9%-99.2%). The specificity of the two methods was 42.8% (95% CI = 15.4%-93.5%) and 100% (95% CI = 47.9%-100%; P = 0.02), respectively. Compared with optical colonography and histopathology, combined CTC and dual-energy iodine maps imaging can distinguish stool and colonic neoplasia, distinguish between benign and malignant tumors initially and improve the diagnostic accuracy of CTC for colorectal cancer screening.

Two high accuracy digital integrators for Rogowski current transducers

Science.gov (United States)

Luo, Pan-dian; Li, Hong-bin; Li, Zhen-hua

2014-01-01

The Rogowski current transducers have been widely used in AC current measurement, but their accuracy is mainly subject to the analog integrators, which have typical problems such as poor long-term stability and being susceptible to environmental conditions. The digital integrators can be another choice, but they cannot obtain a stable and accurate output for the reason that the DC component in original signal can be accumulated, which will lead to output DC drift. Unknown initial conditions can also result in integral output DC offset. This paper proposes two improved digital integrators used in Rogowski current transducers instead of traditional analog integrators for high measuring accuracy. A proportional-integral-derivative (PID) feedback controller and an attenuation coefficient have been applied in improving the Al-Alaoui integrator to change its DC response and get an ideal frequency response. For the special design in the field of digital signal processing, the improved digital integrators have better performance than analog integrators. Simulation models are built for the purpose of verification and comparison. The experiments prove that the designed integrators can achieve higher accuracy than analog integrators in steady-state response, transient-state response, and temperature changing condition.

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.

Differential Predictive Validity of High School GPA and College Entrance Test Scores for University Students in Yemen

Science.gov (United States)

Al-Hattami, Abdulghani Ali Dawod

2012-01-01

High school grade point average and college entrance test scores are two admission criteria that are currently used by most colleges in Yemen to select their prospective students. Given their widespread use, it is important to investigate their predictive validity to ensure the accuracy of the admission decisions in these institutions. This study…

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...

Electron ray tracing with high accuracy

International Nuclear Information System (INIS)

Saito, K.; Okubo, T.; Takamoto, K.; Uno, Y.; Kondo, M.

1986-01-01

An electron ray tracing program is developed to investigate the overall geometrical and chromatic aberrations in electron optical systems. The program also computes aberrations due to manufacturing errors in lenses and deflectors. Computation accuracy is improved by (1) calculating electrostatic and magnetic scalar potentials using the finite element method with third-order isoparametric elements, and (2) solving the modified ray equation which the aberrations satisfy. Computation accuracy of 4 nm is achieved for calculating optical properties of the system with an electrostatic lens

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.

High accuracy 3D electromagnetic finite element analysis

International Nuclear Information System (INIS)

Nelson, Eric M.

1997-01-01

A high accuracy 3D electromagnetic finite element field solver employing quadratic hexahedral elements and quadratic mixed-order one-form basis functions will be described. The solver is based on an object-oriented C++ class library. Test cases demonstrate that frequency errors less than 10 ppm can be achieved using modest workstations, and that the solutions have no contamination from spurious modes. The role of differential geometry and geometrical physics in finite element analysis will also be discussed

High accuracy 3D electromagnetic finite element analysis

International Nuclear Information System (INIS)

Nelson, E.M.

1996-01-01

A high accuracy 3D electromagnetic finite element field solver employing quadratic hexahedral elements and quadratic mixed-order one-form basis functions will be described. The solver is based on an object-oriented C++ class library. Test cases demonstrate that frequency errors less than 10 ppm can be achieved using modest workstations, and that the solutions have no contamination from spurious modes. The role of differential geometry and geometrical physics in finite element analysis will also be discussed

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

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

A High-Throughput, High-Accuracy System-Level Simulation Framework for System on Chips

Directory of Open Access Journals (Sweden)

Guanyi Sun

2011-01-01

Full Text Available Today's System-on-Chips (SoCs design is extremely challenging because it involves complicated design tradeoffs and heterogeneous design expertise. To explore the large solution space, system architects have to rely on system-level simulators to identify an optimized SoC architecture. In this paper, we propose a system-level simulation framework, System Performance Simulation Implementation Mechanism, or SPSIM. Based on SystemC TLM2.0, the framework consists of an executable SoC model, a simulation tool chain, and a modeling methodology. Compared with the large body of existing research in this area, this work is aimed at delivering a high simulation throughput and, at the same time, guaranteeing a high accuracy on real industrial applications. Integrating the leading TLM techniques, our simulator can attain a simulation speed that is not slower than that of the hardware execution by a factor of 35 on a set of real-world applications. SPSIM incorporates effective timing models, which can achieve a high accuracy after hardware-based calibration. Experimental results on a set of mobile applications proved that the difference between the simulated and measured results of timing performance is within 10%, which in the past can only be attained by cycle-accurate models.

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.

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)

Accuracy of hiatal hernia detection with esophageal high-resolution manometry

NARCIS (Netherlands)

Weijenborg, P. W.; van Hoeij, F. B.; Smout, A. J. P. M.; Bredenoord, A. J.

2015-01-01

The diagnosis of a sliding hiatal hernia is classically made with endoscopy or barium esophagogram. Spatial separation of the lower esophageal sphincter (LES) and diaphragm, the hallmark of hiatal hernia, can also be observed on high-resolution manometry (HRM), but the diagnostic accuracy of this

Prediction of thermal coagulation from the instantaneous strain distribution induced by high-intensity focused ultrasound

Science.gov (United States)

Iwasaki, Ryosuke; Takagi, Ryo; Tomiyasu, Kentaro; Yoshizawa, Shin; Umemura, Shin-ichiro

2017-07-01

The targeting of the ultrasound beam and the prediction of thermal lesion formation in advance are the requirements for monitoring high-intensity focused ultrasound (HIFU) treatment with safety and reproducibility. To visualize the HIFU focal zone, we utilized an acoustic radiation force impulse (ARFI) imaging-based method. After inducing displacements inside tissues with pulsed HIFU called the push pulse exposure, the distribution of axial displacements started expanding and moving. To acquire RF data immediately after and during the HIFU push pulse exposure to improve prediction accuracy, we attempted methods using extrapolation estimation and applying HIFU noise elimination. The distributions going back in the time domain from the end of push pulse exposure are in good agreement with tissue coagulation at the center. The results suggest that the proposed focal zone visualization employing pulsed HIFU entailing the high-speed ARFI imaging method is useful for the prediction of thermal coagulation in advance.

Cox-nnet: An artificial neural network method for prognosis prediction of high-throughput omics data.

Science.gov (United States)

Ching, Travers; Zhu, Xun; Garmire, Lana X

2018-04-01

Artificial neural networks (ANN) are computing architectures with many interconnections of simple neural-inspired computing elements, and have been applied to biomedical fields such as imaging analysis and diagnosis. We have developed a new ANN framework called Cox-nnet to predict patient prognosis from high throughput transcriptomics data. In 10 TCGA RNA-Seq data sets, Cox-nnet achieves the same or better predictive accuracy compared to other methods, including Cox-proportional hazards regression (with LASSO, ridge, and mimimax concave penalty), Random Forests Survival and CoxBoost. Cox-nnet also reveals richer biological information, at both the pathway and gene levels. The outputs from the hidden layer node provide an alternative approach for survival-sensitive dimension reduction. In summary, we have developed a new method for accurate and efficient prognosis prediction on high throughput data, with functional biological insights. The source code is freely available at https://github.com/lanagarmire/cox-nnet.

High Accuracy Acoustic Relative Humidity Measurement inDuct Flow with Air

Directory of Open Access Journals (Sweden)

Cees van der Geld

2010-08-01

Full Text Available An acoustic relative humidity sensor for air-steam mixtures in duct flow is designed and tested. Theory, construction, calibration, considerations on dynamic response and results are presented. The measurement device is capable of measuring line averaged values of gas velocity, temperature and relative humidity (RH instantaneously, by applying two ultrasonic transducers and an array of four temperature sensors. Measurement ranges are: gas velocity of 0–12 m/s with an error of ±0.13 m/s, temperature 0–100 °C with an error of ±0.07 °C and relative humidity 0–100% with accuracy better than 2 % RH above 50 °C. Main advantage over conventional humidity sensors is the high sensitivity at high RH at temperatures exceeding 50 °C, with accuracy increasing with increasing temperature. The sensors are non-intrusive and resist highly humid environments.

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

Diagnostic accuracy for X-ray chest in interstitial lung disease as confirmed by high resolution computed tomography (HRCT) chest

International Nuclear Information System (INIS)

Afzal, F.; Raza, S.; Shafique, M.

2017-01-01

Objective: To determine the diagnostic accuracy of x-ray chest in interstitial lung disease as confirmed by high resolution computed tomography (HRCT) chest. Study Design: A cross-sectional validational study. Place and Duration of Study: Department of Diagnostic Radiology, Combined Military Hospital Rawalpindi, from Oct 2013 to Apr 2014. Material and Method: A total of 137 patients with clinical suspicion of interstitial lung disease (ILD) aged 20-50 years of both genders were included in the study. Patients with h/o previous histopathological diagnosis, already taking treatment and pregnant females were excluded. All the patients had chest x-ray and then HRCT. The x-ray and HRCT findings were recorded as presence or absence of the ILD. Results: Mean age was 40.21 ± 4.29 years. Out of 137 patients, 79 (57.66 percent) were males and 58 (42.34 percent) were females with male to female ratio of 1.36:1. Chest x-ray detected ILD in 80 (58.39 percent) patients, out of which, 72 (true positive) had ILD and 8 (false positive) had no ILD on HRCT. Overall sensitivity, specificity, positive predictive value, negative predictive value and diagnostic accuracy of chest x-ray in diagnosing ILD was 80.0 percent, 82.98 percent, 90.0 percent, 68.42 percent and 81.02 percent respectively. Conclusion: This study concluded that chest x-ray is simple, non-invasive, economical and readily available alternative to HRCT with an acceptable diagnostic accuracy of 81 percent in the diagnosis of ILD. (author)

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.

Prediction of high frequency core loss for electrical steel using the data provided by manufacturer

Energy Technology Data Exchange (ETDEWEB)

Roy, Rakesh [National Institute of Technology Meghalaya, Shillong (India); Dalal, Ankit; Kumar, Praveen [Indian Institute of Technology Guwahati, Assam (India)

2016-07-15

This paper describes a technique to determine the core loss data, at high frequencies, using the loss data provided by the lamination manufacturer. Steinmetz equation is used in this proposed method to determine core loss at high frequency. This Steinmetz equation consists of static hysteresis and eddy current loss. The presented technique considers the coefficients of Steinmetz equation as variable with frequency and peak magnetic flux density. The high frequency core loss data, predicted using this model is compared with the catalogue data given by manufacturer and very good accuracy has been obtained for a wide range of frequency. - Highlights: • A curve fitting algorithm is proposed to predict core loss at high frequency. • The loss data given by the steel manufacturers are used in curve fitting algorithm. • The algorithm is tested on nine different material’s data set given by the manufacturer.

Prediction of high frequency core loss for electrical steel using the data provided by manufacturer

International Nuclear Information System (INIS)

Roy, Rakesh; Dalal, Ankit; Kumar, Praveen

2016-01-01

This paper describes a technique to determine the core loss data, at high frequencies, using the loss data provided by the lamination manufacturer. Steinmetz equation is used in this proposed method to determine core loss at high frequency. This Steinmetz equation consists of static hysteresis and eddy current loss. The presented technique considers the coefficients of Steinmetz equation as variable with frequency and peak magnetic flux density. The high frequency core loss data, predicted using this model is compared with the catalogue data given by manufacturer and very good accuracy has been obtained for a wide range of frequency. - Highlights: • A curve fitting algorithm is proposed to predict core loss at high frequency. • The loss data given by the steel manufacturers are used in curve fitting algorithm. • The algorithm is tested on nine different material’s data set given by the manufacturer.

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.

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.

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

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

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

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.

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

Prediction of high frequency core loss for electrical steel using the data provided by manufacturer

Science.gov (United States)

Roy, Rakesh; Dalal, Ankit; Kumar, Praveen

2016-07-01

This paper describes a technique to determine the core loss data, at high frequencies, using the loss data provided by the lamination manufacturer. Steinmetz equation is used in this proposed method to determine core loss at high frequency. This Steinmetz equation consists of static hysteresis and eddy current loss. The presented technique considers the coefficients of Steinmetz equation as variable with frequency and peak magnetic flux density. The high frequency core loss data, predicted using this model is compared with the catalogue data given by manufacturer and very good accuracy has been obtained for a wide range of frequency.

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.

High accuracy 3D electromagnetic finite element analysis

International Nuclear Information System (INIS)

Nelson, E.M.

1997-01-01

A high accuracy 3D electromagnetic finite element field solver employing quadratic hexahedral elements and quadratic mixed-order one-form basis functions will be described. The solver is based on an object-oriented C++ class library. Test cases demonstrate that frequency errors less than 10 ppm can be achieved using modest workstations, and that the solutions have no contamination from spurious modes. The role of differential geometry and geometrical physics in finite element analysis will also be discussed. copyright 1997 American Institute of Physics

DIAGNOSTIC ACCURACY OF CLINICAL AND MAGNETIC RESONANCE IN KNEE MENISCI AND LIGAMENTOUS INJURIES

Directory of Open Access Journals (Sweden)

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.

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

High-precision predictions for the light CP-even Higgs boson mass of the minimal supersymmetric standard model.

Science.gov (United States)

Hahn, T; Heinemeyer, S; Hollik, W; Rzehak, H; Weiglein, G

2014-04-11

For the interpretation of the signal discovered in the Higgs searches at the LHC it will be crucial in particular to discriminate between the minimal Higgs sector realized in the standard model (SM) and its most commonly studied extension, the minimal supersymmetric standard model (MSSM). The measured mass value, having already reached the level of a precision observable with an experimental accuracy of about 500 MeV, plays an important role in this context. In the MSSM the mass of the light CP-even Higgs boson, Mh, can directly be predicted from the other parameters of the model. The accuracy of this prediction should at least match the one of the experimental result. The relatively high mass value of about 126 GeV has led to many investigations where the scalar top quarks are in the multi-TeV range. We improve the prediction for Mh in the MSSM by combining the existing fixed-order result, comprising the full one-loop and leading and subleading two-loop corrections, with a resummation of the leading and subleading logarithmic contributions from the scalar top sector to all orders. In this way for the first time a high-precision prediction for the mass of the light CP-even Higgs boson in the MSSM is possible all the way up to the multi-TeV region of the relevant supersymmetric particles. The results are included in the code FEYNHIGGS.

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.

A Study on Accuracy Improvement of Dual Micro Patterns Using Magnetic Abrasive Deburring

Energy Technology Data Exchange (ETDEWEB)

Jin, Dong-Hyun; Kwak, Jae-Seob [Pukyong Nat’l Univ., Busan (Korea, Republic of)

2016-11-15

In recent times, the requirement of a micro pattern on the surface of products has been increasing, and high precision in the fabrication of the pattern is required. Hence, in this study, dual micro patterns were fabricated on a cylindrical workpiece, and deburring was performed by magnetic abrasive deburring (MAD) process. A prediction model was developed, and the MAD process was optimized using the response surface method. When the predicted values were compared with the experimental results, the average prediction error was found to be approximately 7%. Experimental verification shows fabrication of high accuracy dual micro pattern and reliability of prediction model.

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.

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

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.

Prediction of high pressure vapor-liquid equilibria with mixing rule using ASOG group contribution method

Energy Technology Data Exchange (ETDEWEB)

Tochigi, K.; Kojima, K.; Kurihara, K.

1985-02-01

To develop a widely applicable method for predicting high-pressure vapor-liquid equilibria by the equation of state, a mixing rule is proposed in which mixture energy parameter ''..cap alpha..'' of theSoave-RedlichKwong, Peng-Robinson, and Martin cubic equations of state is expressed by using the ASOG group contribution method. The group pair parameters are then determined for 14 group pairs constituted by six groups, i.e. CH/sub 4/, CH/sub 3/, CH/sub 2/, N/sub 2/, H/sub 2/, and CO/sub 2/ groups. By using the group pair parameters determined, high-pressure vapor-liquid equilibria are predicted with good accuracy for binary and ternary systems constituted by n-paraffins, nitrogen, hydrogen, and carbon dioxide in the temperature range of 100 - 450K.

Achieving Climate Change Absolute Accuracy in Orbit

Science.gov (United States)

Wielicki, Bruce A.; Young, D. F.; Mlynczak, M. G.; Thome, K. J; Leroy, S.; Corliss, J.; Anderson, J. G.; Ao, C. O.; Bantges, R.; Best, F.;

The effect of pattern overlap on the accuracy of high resolution electron backscatter diffraction measurements

Energy Technology Data Exchange (ETDEWEB)

Tong, Vivian, E-mail: v.tong13@imperial.ac.uk [Department of Materials, Imperial College London, Prince Consort Road, London SW7 2AZ (United Kingdom); Jiang, Jun [Department of Materials, Imperial College London, Prince Consort Road, London SW7 2AZ (United Kingdom); Wilkinson, Angus J. [Department of Materials, University of Oxford, Parks Road, Oxford OX1 3PH (United Kingdom); Britton, T. Ben [Department of Materials, Imperial College London, Prince Consort Road, London SW7 2AZ (United Kingdom)

2015-08-15

High resolution, cross-correlation-based, electron backscatter diffraction (EBSD) measures the variation of elastic strains and lattice rotations from a reference state. Regions near grain boundaries are often of interest but overlap of patterns from the two grains could reduce accuracy of the cross-correlation analysis. To explore this concern, patterns from the interior of two grains have been mixed to simulate the interaction volume crossing a grain boundary so that the effect on the accuracy of the cross correlation results can be tested. It was found that the accuracy of HR-EBSD strain measurements performed in a FEG-SEM on zirconium remains good until the incident beam is less than 18 nm from a grain boundary. A simulated microstructure was used to measure how often pattern overlap occurs at any given EBSD step size, and a simple relation was found linking the probability of overlap with step size. - Highlights: • Pattern overlap occurs at grain boundaries and reduces HR-EBSD accuracy. • A test is devised to measure the accuracy of HR-EBSD in the presence of overlap. • High pass filters can sometimes, but not generally, improve HR-EBSD measurements. • Accuracy of HR-EBSD remains high until the reference pattern intensity is <72%. • 9% of points near a grain boundary will have significant error for 200nm step size in Zircaloy-4.

Read-only high accuracy volume holographic optical correlator

Science.gov (United States)

Zhao, Tian; Li, Jingming; Cao, Liangcai; He, Qingsheng; Jin, Guofan

2011-10-01

A read-only volume holographic correlator (VHC) is proposed. After the recording of all of the correlation database pages by angular multiplexing, a stand-alone read-only high accuracy VHC will be separated from the VHC recording facilities which include the high-power laser and the angular multiplexing system. The stand-alone VHC has its own low power readout laser and very compact and simple structure. Since there are two lasers that are employed for recording and readout, respectively, the optical alignment tolerance of the laser illumination on the SLM is very sensitive. The twodimensional angular tolerance is analyzed based on the theoretical model of the volume holographic correlator. The experimental demonstration of the proposed read-only VHC is introduced and discussed.

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....

Optical System Error Analysis and Calibration Method of High-Accuracy Star Trackers

Directory of Open Access Journals (Sweden)

Zheng You

2013-04-01

Full Text Available The star tracker is a high-accuracy attitude measurement device widely used in spacecraft. Its performance depends largely on the precision of the optical system parameters. Therefore, the analysis of the optical system parameter errors and a precise calibration model are crucial to the accuracy of the star tracker. Research in this field is relatively lacking a systematic and universal analysis up to now. This paper proposes in detail an approach for the synthetic error analysis of the star tracker, without the complicated theoretical derivation. This approach can determine the error propagation relationship of the star tracker, and can build intuitively and systematically an error model. The analysis results can be used as a foundation and a guide for the optical design, calibration, and compensation of the star tracker. A calibration experiment is designed and conducted. Excellent calibration results are achieved based on the calibration model. To summarize, the error analysis approach and the calibration method are proved to be adequate and precise, and could provide an important guarantee for the design, manufacture, and measurement of high-accuracy star trackers.

Optical system error analysis and calibration method of high-accuracy star trackers.

Science.gov (United States)

Sun, Ting; Xing, Fei; You, Zheng

2013-04-08

The star tracker is a high-accuracy attitude measurement device widely used in spacecraft. Its performance depends largely on the precision of the optical system parameters. Therefore, the analysis of the optical system parameter errors and a precise calibration model are crucial to the accuracy of the star tracker. Research in this field is relatively lacking a systematic and universal analysis up to now. This paper proposes in detail an approach for the synthetic error analysis of the star tracker, without the complicated theoretical derivation. This approach can determine the error propagation relationship of the star tracker, and can build intuitively and systematically an error model. The analysis results can be used as a foundation and a guide for the optical design, calibration, and compensation of the star tracker. A calibration experiment is designed and conducted. Excellent calibration results are achieved based on the calibration model. To summarize, the error analysis approach and the calibration method are proved to be adequate and precise, and could provide an important guarantee for the design, manufacture, and measurement of high-accuracy star trackers.

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....

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.

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

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

Innovative Fiber-Optic Gyroscopes (FOGs) for High Accuracy Space Applications, Phase I

Data.gov (United States)

National Aeronautics and Space Administration — NASA's future science and exploratory missions will require much lighter, smaller, and longer life rate sensors that can provide high accuracy navigational...

MUSCLE: multiple sequence alignment with high accuracy and high throughput.

Science.gov (United States)

Edgar, Robert C

2004-01-01

We describe MUSCLE, a new computer program for creating multiple alignments of protein sequences. Elements of the algorithm include fast distance estimation using kmer counting, progressive alignment using a new profile function we call the log-expectation score, and refinement using tree-dependent restricted partitioning. The speed and accuracy of MUSCLE are compared with T-Coffee, MAFFT and CLUSTALW on four test sets of reference alignments: BAliBASE, SABmark, SMART and a new benchmark, PREFAB. MUSCLE achieves the highest, or joint highest, rank in accuracy on each of these sets. Without refinement, MUSCLE achieves average accuracy statistically indistinguishable from T-Coffee and MAFFT, and is the fastest of the tested methods for large numbers of sequences, aligning 5000 sequences of average length 350 in 7 min on a current desktop computer. The MUSCLE program, source code and PREFAB test data are freely available at http://www.drive5. com/muscle.

High Accuracy Evaluation of the Finite Fourier Transform Using Sampled Data

Science.gov (United States)

Morelli, Eugene A.

1997-01-01

Many system identification and signal processing procedures can be done advantageously in the frequency domain. A required preliminary step for this approach is the transformation of sampled time domain data into the frequency domain. The analytical tool used for this transformation is the finite Fourier transform. Inaccuracy in the transformation can degrade system identification and signal processing results. This work presents a method for evaluating the finite Fourier transform using cubic interpolation of sampled time domain data for high accuracy, and the chirp Zeta-transform for arbitrary frequency resolution. The accuracy of the technique is demonstrated in example cases where the transformation can be evaluated analytically. Arbitrary frequency resolution is shown to be important for capturing details of the data in the frequency domain. The technique is demonstrated using flight test data from a longitudinal maneuver of the F-18 High Alpha Research Vehicle.

Accounting of fundamental components of the rotation parameters of the Earth in the formation of a high-accuracy orbit of navigation satellites

Science.gov (United States)

Markov, Yu. G.; Mikhailov, M. V.; Pochukaev, V. N.

2012-07-01

An analysis of perturbing factors influencing the motion of a navigation satellite (NS) is carried out, and the degree of influence of each factor on the GLONASS orbit is estimated. It is found that fundamental components of the Earth's rotation parameters (ERP) are one substantial factor commensurable with maximum perturbations. Algorithms for the calculation of orbital perturbations caused by these parameters are given; these algorithms can be implemented in a consumer's equipment. The daily prediction of NS coordinates is performed on the basis of real GLONASS satellite ephemerides transmitted to a consumer, using the developed prediction algorithms taking the ERP into account. The obtained accuracy of the daily prediction of GLONASS ephemerides exceeds by tens of times the accuracy of the daily prediction performed using algorithms recommended in interface control documents.

High accuracy interface characterization of three phase material systems in three dimensions

DEFF Research Database (Denmark)

Jørgensen, Peter Stanley; Hansen, Karin Vels; Larsen, Rasmus

2010-01-01

Quantification of interface properties such as two phase boundary area and triple phase boundary length is important in the characterization ofmanymaterial microstructures, in particular for solid oxide fuel cell electrodes. Three-dimensional images of these microstructures can be obtained...... by tomography schemes such as focused ion beam serial sectioning or micro-computed tomography. We present a high accuracy method of calculating two phase surface areas and triple phase length of triple phase systems from subvoxel accuracy segmentations of constituent phases. The method performs a three phase...... polygonization of the interface boundaries which results in a non-manifold mesh of connected faces. We show how the triple phase boundaries can be extracted as connected curve loops without branches. The accuracy of the method is analyzed by calculations on geometrical primitives...

Extrinsic Cognitive Load Impairs Spoken Word Recognition in High- and Low-Predictability Sentences.

Science.gov (United States)

Hunter, Cynthia R; Pisoni, David B

Listening effort (LE) induced by speech degradation reduces performance on concurrent cognitive tasks. However, a converse effect of extrinsic cognitive load on recognition of spoken words in sentences has not been shown. The aims of the present study were to (a) examine the impact of extrinsic cognitive load on spoken word recognition in a sentence recognition task and (b) determine whether cognitive load and/or LE needed to understand spectrally degraded speech would differentially affect word recognition in high- and low-predictability sentences. Downstream effects of speech degradation and sentence predictability on the cognitive load task were also examined. One hundred twenty young adults identified sentence-final spoken words in high- and low-predictability Speech Perception in Noise sentences. Cognitive load consisted of a preload of short (low-load) or long (high-load) sequences of digits, presented visually before each spoken sentence and reported either before or after identification of the sentence-final word. LE was varied by spectrally degrading sentences with four-, six-, or eight-channel noise vocoding. Level of spectral degradation and order of report (digits first or words first) were between-participants variables. Effects of cognitive load, sentence predictability, and speech degradation on accuracy of sentence-final word identification as well as recall of preload digit sequences were examined. In addition to anticipated main effects of sentence predictability and spectral degradation on word recognition, we found an effect of cognitive load, such that words were identified more accurately under low load than high load. However, load differentially affected word identification in high- and low-predictability sentences depending on the level of sentence degradation. Under severe spectral degradation (four-channel vocoding), the effect of cognitive load on word identification was present for high-predictability sentences but not for low-predictability

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

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.

Resolution, Scales and Predictability: Is High Resolution Detrimental To Predictability At Extended Forecast Times?

Science.gov (United States)

Mesinger, F.

The traditional views hold that high-resolution limited area models (LAMs) down- scale large-scale lateral boundary information, and that predictability of small scales is short. Inspection of various rms fits/errors has contributed to these views. It would follow that the skill of LAMs should visibly deteriorate compared to that of their driver models at more extended forecast times. The limited area Eta Model at NCEP has an additional handicap of being driven by LBCs of the previous Avn global model run, at 0000 and 1200 UTC estimated to amount to about an 8 h loss in accuracy. This should make its relative skill compared to that of the Avn deteriorate even faster. These views are challenged by various Eta results including rms fits to raobs out to 84 h. It is argued that it is the largest scales that contribute the most to the skill of the Eta relative to that of the Avn.

Real-time Tsunami Inundation Prediction Using High Performance Computers

Science.gov (United States)

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

2014-12-01

Recently off-shore tsunami observation stations based on cabled ocean bottom pressure gauges are actively being deployed especially in Japan. These cabled systems are designed to provide real-time tsunami data before tsunamis reach coastlines for disaster mitigation purposes. To receive real benefits of these observations, real-time analysis techniques to make an effective use of these data are necessary. A representative study was made by Tsushima et al. (2009) that proposed a method to provide instant tsunami source prediction based on achieving tsunami waveform data. As time passes, the prediction is improved by using updated waveform data. After a tsunami source is predicted, tsunami waveforms are synthesized from pre-computed tsunami Green functions of linear long wave equations. Tsushima et al. (2014) updated the method by combining the tsunami waveform inversion with an instant inversion of coseismic crustal deformation and improved the prediction accuracy and speed in the early stages. For disaster mitigation purposes, real-time predictions of tsunami inundation are also important. In this study, we discuss the possibility of real-time tsunami inundation predictions, which require faster-than-real-time tsunami inundation simulation in addition to instant tsunami source analysis. Although the computational amount is large to solve non-linear shallow water equations for inundation predictions, it has become executable through the recent developments of high performance computing technologies. We conducted parallel computations of tsunami inundation and achieved 6.0 TFLOPS by using 19,000 CPU cores. We employed a leap-frog finite difference method with nested staggered grids of which resolution range from 405 m to 5 m. The resolution ratio of each nested domain was 1/3. Total number of grid points were 13 million, and the time step was 0.1 seconds. Tsunami sources of 2011 Tohoku-oki earthquake were tested. The inundation prediction up to 2 hours after the

Predictive business process monitoring with LSTM neural networks

NARCIS (Netherlands)

Tax, N.; Verenich, I.; La Rosa, M.; Dumas, M.; Pohl, Klaus; Dubois, Eric

2017-01-01

Predictive business process monitoring methods exploit logs of completed cases of a process in order to make predictions about running cases thereof. Existing methods in this space are tailor-made for specific prediction tasks. Moreover, their relative accuracy is highly sensitive to the dataset at

A consensus approach for estimating the predictive accuracy of dynamic models in biology.

Science.gov (United States)

Villaverde, Alejandro F; Bongard, Sophia; Mauch, Klaus; Müller, Dirk; Balsa-Canto, Eva; Schmid, Joachim; Banga, Julio R

2015-04-01

Mathematical models that predict the complex dynamic behaviour of cellular networks are fundamental in systems biology, and provide an important basis for biomedical and biotechnological applications. However, obtaining reliable predictions from large-scale dynamic models is commonly a challenging task due to lack of identifiability. The present work addresses this challenge by presenting a methodology for obtaining high-confidence predictions from dynamic models using time-series data. First, to preserve the complex behaviour of the network while reducing the number of estimated parameters, model parameters are combined in sets of meta-parameters, which are obtained from correlations between biochemical reaction rates and between concentrations of the chemical species. Next, an ensemble of models with different parameterizations is constructed and calibrated. Finally, the ensemble is used for assessing the reliability of model predictions by defining a measure of convergence of model outputs (consensus) that is used as an indicator of confidence. We report results of computational tests carried out on a metabolic model of Chinese Hamster Ovary (CHO) cells, which are used for recombinant protein production. Using noisy simulated data, we find that the aggregated ensemble predictions are on average more accurate than the predictions of individual ensemble models. Furthermore, ensemble predictions with high consensus are statistically more accurate than ensemble predictions with large variance. The procedure provides quantitative estimates of the confidence in model predictions and enables the analysis of sufficiently complex networks as required for practical applications. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

A Smart High Accuracy Silicon Piezoresistive Pressure Sensor Temperature Compensation System

Directory of Open Access Journals (Sweden)

Guanwu Zhou

2014-07-01

Full Text Available Theoretical analysis in this paper indicates that the accuracy of a silicon piezoresistive pressure sensor is mainly affected by thermal drift, and varies nonlinearly with the temperature. Here, a smart temperature compensation system to reduce its effect on accuracy is proposed. Firstly, an effective conditioning circuit for signal processing and data acquisition is designed. The hardware to implement the system is fabricated. Then, a program is developed on LabVIEW which incorporates an extreme learning machine (ELM as the calibration algorithm for the pressure drift. The implementation of the algorithm was ported to a micro-control unit (MCU after calibration in the computer. Practical pressure measurement experiments are carried out to verify the system’s performance. The temperature compensation is solved in the interval from −40 to 85 °C. The compensated sensor is aimed at providing pressure measurement in oil-gas pipelines. Compared with other algorithms, ELM acquires higher accuracy and is more suitable for batch compensation because of its higher generalization and faster learning speed. The accuracy, linearity, zero temperature coefficient and sensitivity temperature coefficient of the tested sensor are 2.57% FS, 2.49% FS, 8.1 × 10−5/°C and 29.5 × 10−5/°C before compensation, and are improved to 0.13%FS, 0.15%FS, 1.17 × 10−5/°C and 2.1 × 10−5/°C respectively, after compensation. The experimental results demonstrate that the proposed system is valid for the temperature compensation and high accuracy requirement of the sensor.

Improving virtual screening predictive accuracy of Human kallikrein 5 inhibitors using machine learning models.

Science.gov (United States)

Fang, Xingang; Bagui, Sikha; Bagui, Subhash

2017-08-01

The readily available high throughput screening (HTS) data from the PubChem database provides an opportunity for mining of small molecules in a variety of biological systems using machine learning techniques. From the thousands of available molecular descriptors developed to encode useful chemical information representing the characteristics of molecules, descriptor selection is an essential step in building an optimal quantitative structural-activity relationship (QSAR) model. For the development of a systematic descriptor selection strategy, we need the understanding of the relationship between: (i) the descriptor selection; (ii) the choice of the machine learning model; and (iii) the characteristics of the target bio-molecule. In this work, we employed the Signature descriptor to generate a dataset on the Human kallikrein 5 (hK 5) inhibition confirmatory assay data and compared multiple classification models including logistic regression, support vector machine, random forest and k-nearest neighbor. Under optimal conditions, the logistic regression model provided extremely high overall accuracy (98%) and precision (90%), with good sensitivity (65%) in the cross validation test. In testing the primary HTS screening data with more than 200K molecular structures, the logistic regression model exhibited the capability of eliminating more than 99.9% of the inactive structures. As part of our exploration of the descriptor-model-target relationship, the excellent predictive performance of the combination of the Signature descriptor and the logistic regression model on the assay data of the Human kallikrein 5 (hK 5) target suggested a feasible descriptor/model selection strategy on similar targets. Copyright © 2017 Elsevier Ltd. All rights reserved.

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.

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.

Predicting visual acuity from detection thresholds.

Science.gov (United States)

Newacheck, J S; Haegerstrom-Portnoy, G; Adams, A J

1990-03-01

Visual performance based exclusively on high luminance and high contrast letter acuity measures often fails to predict individual performance at low contrast and low luminance. Here we measured visual acuity over a wide range of contrasts and luminances (low mesopic to photopic) for 17 young normal observers. Acuity vs. contrast functions appear to fit a single template which can be displaced laterally along the log contrast axis. The magnitude of this lateral displacement for different luminances was well predicted by the contrast threshold difference for a 4 min arc spot. The acuity vs. contrast template, taken from the mean of all 17 subjects, was used in conjunction with individual spot contrast threshold measures to predict an individual's visual acuity over a wide range of luminance and contrast levels. The accuracy of the visual acuity predictions from this simple procedure closely approximates test-retest accuracy for both positive (projected Landolt rings) and negative contrast (Bailey-Lovie charts).

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)

Experimental Investigation and Prediction of Compressive Strength of Ultra-High Performance Concrete Containing Supplementary Cementitious Materials

Directory of Open Access Journals (Sweden)

Jisong Zhang

2017-01-01

Full Text Available Ultra-high performance concrete (UHPC has superior mechanical properties and durability to normal strength concrete. However, the high amount of cement, high environmental impact, and initial cost are regarded as disadvantages, restricting its wider application. Incorporation of supplementary cementitious materials (SCMs in UHPC is an effective way to reduce the amount of cement needed while contributing to the sustainability and cost. This paper investigates the mechanical properties and microstructure of UHPC containing fly ash (FA and silica fume (SF with the aim of contributing to this issue. The results indicate that, on the basis of 30% FA replacement, the incorporation of 10% and 20% SF showed equivalent or higher mechanical properties compared to the reference samples. The microstructure and pore volume of the UHPCs were also examined. Furthermore, to minimise the experimental workload of future studies, a prediction model is developed to predict the compressive strength of the UHPC using artificial neural networks (ANNs. The results indicate that the developed ANN model has high accuracy and can be used for the prediction of the compressive strength of UHPC with these SCMs.

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.

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.

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.

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.

Ultra-high accuracy optical testing: creating diffraction-limitedshort-wavelength optical systems

Energy Technology Data Exchange (ETDEWEB)

Goldberg, Kenneth A.; Naulleau, Patrick P.; Rekawa, Senajith B.; Denham, Paul E.; Liddle, J. Alexander; Gullikson, Eric M.; Jackson, KeithH.; Anderson, Erik H.; Taylor, John S.; Sommargren, Gary E.; Chapman,Henry N.; Phillion, Donald W.; Johnson, Michael; Barty, Anton; Soufli,Regina; Spiller, Eberhard A.; Walton, Christopher C.; Bajt, Sasa

2005-08-03

Since 1993, research in the fabrication of extreme ultraviolet (EUV) optical imaging systems, conducted at Lawrence Berkeley National Laboratory (LBNL) and Lawrence Livermore National Laboratory (LLNL), has produced the highest resolution optical systems ever made. We have pioneered the development of ultra-high-accuracy optical testing and alignment methods, working at extreme ultraviolet wavelengths, and pushing wavefront-measuring interferometry into the 2-20-nm wavelength range (60-600 eV). These coherent measurement techniques, including lateral shearing interferometry and phase-shifting point-diffraction interferometry (PS/PDI) have achieved RMS wavefront measurement accuracies of 0.5-1-{angstrom} and better for primary aberration terms, enabling the creation of diffraction-limited EUV optics. The measurement accuracy is established using careful null-testing procedures, and has been verified repeatedly through high-resolution imaging. We believe these methods are broadly applicable to the advancement of short-wavelength optical systems including space telescopes, microscope objectives, projection lenses, synchrotron beamline optics, diffractive and holographic optics, and more. Measurements have been performed on a tunable undulator beamline at LBNL's Advanced Light Source (ALS), optimized for high coherent flux; although many of these techniques should be adaptable to alternative ultraviolet, EUV, and soft x-ray light sources. To date, we have measured nine prototype all-reflective EUV optical systems with NA values between 0.08 and 0.30 (f/6.25 to f/1.67). These projection-imaging lenses were created for the semiconductor industry's advanced research in EUV photolithography, a technology slated for introduction in 2009-13. This paper reviews the methods used and our program's accomplishments to date.

NiftyPET: a High-throughput Software Platform for High Quantitative Accuracy and Precision PET Imaging and Analysis.

Science.gov (United States)

Markiewicz, Pawel J; Ehrhardt, Matthias J; Erlandsson, Kjell; Noonan, Philip J; Barnes, Anna; Schott, Jonathan M; Atkinson, David; Arridge, Simon R; Hutton, Brian F; Ourselin, Sebastien

2018-01-01

We present a standalone, scalable and high-throughput software platform for PET image reconstruction and analysis. We focus on high fidelity modelling of the acquisition processes to provide high accuracy and precision quantitative imaging, especially for large axial field of view scanners. All the core routines are implemented using parallel computing available from within the Python package NiftyPET, enabling easy access, manipulation and visualisation of data at any processing stage. The pipeline of the platform starts from MR and raw PET input data and is divided into the following processing stages: (1) list-mode data processing; (2) accurate attenuation coefficient map generation; (3) detector normalisation; (4) exact forward and back projection between sinogram and image space; (5) estimation of reduced-variance random events; (6) high accuracy fully 3D estimation of scatter events; (7) voxel-based partial volume correction; (8) region- and voxel-level image analysis. We demonstrate the advantages of this platform using an amyloid brain scan where all the processing is executed from a single and uniform computational environment in Python. The high accuracy acquisition modelling is achieved through span-1 (no axial compression) ray tracing for true, random and scatter events. Furthermore, the platform offers uncertainty estimation of any image derived statistic to facilitate robust tracking of subtle physiological changes in longitudinal studies. The platform also supports the development of new reconstruction and analysis algorithms through restricting the axial field of view to any set of rings covering a region of interest and thus performing fully 3D reconstruction and corrections using real data significantly faster. All the software is available as open source with the accompanying wiki-page and test data.

The urine dipstick test useful to rule out infections. A meta-analysis of the accuracy

Directory of Open Access Journals (Sweden)

Devillé Walter LJM

2004-06-01

Full Text Available Abstract Background Many studies have evaluated the accuracy of dipstick tests as rapid detectors of bacteriuria and urinary tract infections (UTI. The lack of an adequate explanation for the heterogeneity of the dipstick accuracy stimulates an ongoing debate. The objective of the present meta-analysis was to summarise the available evidence on the diagnostic accuracy of the urine dipstick test, taking into account various pre-defined potential sources of heterogeneity. Methods Literature from 1990 through 1999 was searched in Medline and Embase, and by reference tracking. Selected publications should be concerned with the diagnosis of bacteriuria or urinary tract infections, investigate the use of dipstick tests for nitrites and/or leukocyte esterase, and present empirical data. A checklist was used to assess methodological quality. Results 70 publications were included. Accuracy of nitrites was high in pregnant women (Diagnostic Odds Ratio = 165 and elderly people (DOR = 108. Positive predictive values were ≥80% in elderly and in family medicine. Accuracy of leukocyte-esterase was high in studies in urology patients (DOR = 276. Sensitivities were highest in family medicine (86%. Negative predictive values were high in both tests in all patient groups and settings, except for in family medicine. The combination of both test results showed an important increase in sensitivity. Accuracy was high in studies in urology patients (DOR = 52, in children (DOR = 46, and if clinical information was present (DOR = 28. Sensitivity was highest in studies carried out in family medicine (90%. Predictive values of combinations of positive test results were low in all other situations. Conclusions Overall, this review demonstrates that the urine dipstick test alone seems to be useful in all populations to exclude the presence of infection if the results of both nitrites and leukocyte-esterase are negative. Sensitivities of the combination of both tests vary

Applying Machine Learning and High Performance Computing to Water Quality Assessment and Prediction

Directory of Open Access Journals (Sweden)

Ruijian Zhang

2017-12-01

Full Text Available Water quality assessment and prediction is a more and more important issue. Traditional ways either take lots of time or they can only do assessments. In this research, by applying machine learning algorithm to a long period time of water attributes’ data; we can generate a decision tree so that it can predict the future day’s water quality in an easy and efficient way. The idea is to combine the traditional ways and the computer algorithms together. Using machine learning algorithms, the assessment of water quality will be far more efficient, and by generating the decision tree, the prediction will be quite accurate. The drawback of the machine learning modeling is that the execution takes quite long time, especially when we employ a better accuracy but more time-consuming algorithm in clustering. Therefore, we applied the high performance computing (HPC System to deal with this problem. Up to now, the pilot experiments have achieved very promising preliminary results. The visualized water quality assessment and prediction obtained from this project would be published in an interactive website so that the public and the environmental managers could use the information for their decision making.

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.

High-accuracy self-mixing interferometer based on single high-order orthogonally polarized feedback effects.

Science.gov (United States)

Zeng, Zhaoli; Qu, Xueming; Tan, Yidong; Tan, Runtao; Zhang, Shulian

2015-06-29

A simple and high-accuracy self-mixing interferometer based on single high-order orthogonally polarized feedback effects is presented. The single high-order feedback effect is realized when dual-frequency laser reflects numerous times in a Fabry-Perot cavity and then goes back to the laser resonator along the same route. In this case, two orthogonally polarized feedback fringes with nanoscale resolution are obtained. This self-mixing interferometer has the advantages of higher sensitivity to weak signal than that of conventional interferometer. In addition, two orthogonally polarized fringes are useful for discriminating the moving direction of measured object. The experiment of measuring 2.5nm step is conducted, which shows a great potential in nanometrology.

Automated novel high-accuracy miniaturized positioning system for use in analytical instrumentation

Science.gov (United States)

Siomos, Konstadinos; Kaliakatsos, John; Apostolakis, Manolis; Lianakis, John; Duenow, Peter

1996-01-01

The development of three-dimensional automotive devices (micro-robots) for applications in analytical instrumentation, clinical chemical diagnostics and advanced laser optics, depends strongly on the ability of such a device: firstly to be positioned with high accuracy, reliability, and automatically, by means of user friendly interface techniques; secondly to be compact; and thirdly to operate under vacuum conditions, free of most of the problems connected with conventional micropositioners using stepping-motor gear techniques. The objective of this paper is to develop and construct a mechanically compact computer-based micropositioning system for coordinated motion in the X-Y-Z directions with: (1) a positioning accuracy of less than 1 micrometer, (the accuracy of the end-position of the system is controlled by a hard/software assembly using a self-constructed optical encoder); (2) a heat-free propulsion mechanism for vacuum operation; and (3) synchronized X-Y motion.

Accuracy of Estimating Highly Eccentric Binary Black Hole Parameters with Gravitational-wave Detections

Science.gov (United States)

Gondán, László; Kocsis, Bence; Raffai, Péter; Frei, Zsolt

2018-03-01

Mergers of stellar-mass black holes on highly eccentric orbits are among the targets for ground-based gravitational-wave detectors, including LIGO, VIRGO, and KAGRA. These sources may commonly form through gravitational-wave emission in high-velocity dispersion systems or through the secular Kozai–Lidov mechanism in triple systems. Gravitational waves carry information about the binaries’ orbital parameters and source location. Using the Fisher matrix technique, we determine the measurement accuracy with which the LIGO–VIRGO–KAGRA network could measure the source parameters of eccentric binaries using a matched filtering search of the repeated burst and eccentric inspiral phases of the waveform. We account for general relativistic precession and the evolution of the orbital eccentricity and frequency during the inspiral. We find that the signal-to-noise ratio and the parameter measurement accuracy may be significantly higher for eccentric sources than for circular sources. This increase is sensitive to the initial pericenter distance, the initial eccentricity, and the component masses. For instance, compared to a 30 {M}ȯ –30 {M}ȯ non-spinning circular binary, the chirp mass and sky-localization accuracy can improve by a factor of ∼129 (38) and ∼2 (11) for an initially highly eccentric binary assuming an initial pericenter distance of 20 M tot (10 M tot).

Automatic selection of reference taxa for protein-protein interaction prediction with phylogenetic profiling

DEFF Research Database (Denmark)

Simonsen, Martin; Maetschke, S.R.; Ragan, M.A.

2012-01-01

Motivation: Phylogenetic profiling methods can achieve good accuracy in predicting protein–protein interactions, especially in prokaryotes. Recent studies have shown that the choice of reference taxa (RT) is critical for accurate prediction, but with more than 2500 fully sequenced taxa publicly......: We present three novel methods for automating the selection of RT, using machine learning based on known protein–protein interaction networks. One of these methods in particular, Tree-Based Search, yields greatly improved prediction accuracies. We further show that different methods for constituting...... phylogenetic profiles often require very different RT sets to support high prediction accuracy....

High accuracy navigation information estimation for inertial system using the multi-model EKF fusing adams explicit formula applied to underwater gliders.

Science.gov (United States)

Huang, Haoqian; Chen, Xiyuan; Zhang, Bo; Wang, Jian

2017-01-01

The underwater navigation system, mainly consisting of MEMS inertial sensors, is a key technology for the wide application of underwater gliders and plays an important role in achieving high accuracy navigation and positioning for a long time of period. However, the navigation errors will accumulate over time because of the inherent errors of inertial sensors, especially for MEMS grade IMU (Inertial Measurement Unit) generally used in gliders. The dead reckoning module is added to compensate the errors. In the complicated underwater environment, the performance of MEMS sensors is degraded sharply and the errors will become much larger. It is difficult to establish the accurate and fixed error model for the inertial sensor. Therefore, it is very hard to improve the accuracy of navigation information calculated by sensors. In order to solve the problem mentioned, the more suitable filter which integrates the multi-model method with an EKF approach can be designed according to different error models to give the optimal estimation for the state. The key parameters of error models can be used to determine the corresponding filter. The Adams explicit formula which has an advantage of high precision prediction is simultaneously fused into the above filter to achieve the much more improvement in attitudes estimation accuracy. The proposed algorithm has been proved through theory analyses and has been tested by both vehicle experiments and lake trials. Results show that the proposed method has better accuracy and effectiveness in terms of attitudes estimation compared with other methods mentioned in the paper for inertial navigation applied to underwater gliders. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

The power within: The experimental manipulation of power interacts with trait BDD symptoms to predict interoceptive accuracy.

Science.gov (United States)

Kunstman, Jonathan W; Clerkin, Elise M; Palmer, Kateyln; Peters, M Taylar; Dodd, Dorian R; Smith, April R

2016-03-01

This study tested whether relatively low levels of interoceptive accuracy (IAcc) are associated with body dysmorphic disorder (BDD) symptoms. Additionally, given research indicating that power attunes individuals to their internal states, we sought to determine if state interoceptive accuracy could be improved through an experimental manipulation of power.. Undergraduate women (N = 101) completed a baseline measure of interoceptive accuracy and then were randomized to a power or control condition. Participants were primed with power or a neutral control topic and then completed a post-manipulation measure of state IAcc. Trait BDD symptoms were assessed with a self-report measure. Controlling for baseline IAcc, within the control condition, there was a significant inverse relationship between trait BDD symptoms and interoceptive accuracy. Continuing to control for baseline IAcc, within the power condition, there was not a significant relationship between trait BDD symptoms and IAcc, suggesting that power may have attenuated this relationship. At high levels of BDD symptomology, there was also a significant simple effect of experimental condition, such that participants in the power (vs. control) condition had better interoceptive accuracy. These results provide initial evidence that power may positively impact interoceptive accuracy among those with high levels of BDD symptoms.. This cross-sectional study utilized a demographically homogenous sample of women that reflected a broad range of symptoms; thus, although there were a number of participants reporting elevated BDD symptoms, these findings might not generalize to other populations or clinical samples. This study provides the first direct test of the relationship between trait BDD symptoms and IAcc, and provides preliminary evidence that among those with severe BDD symptoms, power may help connect individuals with their internal states. Future research testing the mechanisms linking BDD symptoms with IAcc, as

High accuracy positioning using carrier-phases with the opensource GPSTK software

OpenAIRE

Salazar Hernández, Dagoberto José; Hernández Pajares, Manuel; Juan Zornoza, José Miguel; Sanz Subirana, Jaume

2008-01-01

The objective of this work is to show how using a proper GNSS data management strategy, combined with the flexibility provided by the open source "GPS Toolkit" (GPSTk), it is possible to easily develop both simple code-based processing strategies as well as basic high accuracy carrier-phase positioning techniques like Precise Point Positioning (PPP

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.

Global skin colour prediction from DNA

NARCIS (Netherlands)

S. Walsh (Susan); L.C. Chaitanya (Lakshmi); Breslin, K. (Krystal); Muralidharan, C. (Charanya); Bronikowska, A. (Agnieszka); E. Pośpiech (Ewelina); Koller, J. (Julia); L. Kovatsi (Leda); A. Wollstein (Andreas); W. Branicki (Wojciech); F. Liu; M.H. Kayser (Manfred)

2017-01-01

textabstractHuman skin colour is highly heritable and externally visible with relevance in medical, forensic, and anthropological genetics. Although eye and hair colour can already be predicted with high accuracies from small sets of carefully selected DNA markers, knowledge about the genetic

A New Approach to High-accuracy Road Orthophoto Mapping Based on Wavelet Transform

Directory of Open Access Journals (Sweden)

Ming Yang

2011-12-01

Full Text Available Existing orthophoto map based on satellite photography and aerial photography is not precise enough for road marking. This paper proposes a new approach to high-accuracy orthophoto mapping. The approach uses inverse perspective transformation to process the image information and generates the orthophoto fragment. The offline interpolation algorithm is used to process the location information. It processes the dead reckoning and the EKF location information, and uses the result to transform the fragments to the global coordinate system. At last it uses wavelet transform to divides the image to two frequency bands and uses weighted median algorithm to deal with them separately. The result of experiment shows that the map produced with this method has high accuracy.

Evaluation of machine learning algorithms for prediction of regions of high Reynolds averaged Navier Stokes uncertainty

Science.gov (United States)

Ling, J.; Templeton, J.

2015-08-01

Reynolds Averaged Navier Stokes (RANS) models are widely used in industry to predict fluid flows, despite their acknowledged deficiencies. Not only do RANS models often produce inaccurate flow predictions, but there are very limited diagnostics available to assess RANS accuracy for a given flow configuration. If experimental or higher fidelity simulation results are not available for RANS validation, there is no reliable method to evaluate RANS accuracy. This paper explores the potential of utilizing machine learning algorithms to identify regions of high RANS uncertainty. Three different machine learning algorithms were evaluated: support vector machines, Adaboost decision trees, and random forests. The algorithms were trained on a database of canonical flow configurations for which validated direct numerical simulation or large eddy simulation results were available, and were used to classify RANS results on a point-by-point basis as having either high or low uncertainty, based on the breakdown of specific RANS modeling assumptions. Classifiers were developed for three different basic RANS eddy viscosity model assumptions: the isotropy of the eddy viscosity, the linearity of the Boussinesq hypothesis, and the non-negativity of the eddy viscosity. It is shown that these classifiers are able to generalize to flows substantially different from those on which they were trained. Feature selection techniques, model evaluation, and extrapolation detection are discussed in the context of turbulence modeling applications.

A new ultra-high-accuracy angle generator: current status and future direction

Science.gov (United States)

Guertin, Christian F.; Geckeler, Ralf D.

2017-09-01

Lack of an extreme high-accuracy angular positioning device available in the United States has left a gap in industrial and scientific efforts conducted there, requiring certain user groups to undertake time-consuming work with overseas laboratories. Specifically, in x-ray mirror metrology the global research community is advancing the state-of-the-art to unprecedented levels. We aim to fill this U.S. gap by developing a versatile high-accuracy angle generator as a part of the national metrology tool set for x-ray mirror metrology and other important industries. Using an established calibration technique to measure the errors of the encoder scale graduations for full-rotation rotary encoders, we implemented an optimized arrangement of sensors positioned to minimize propagation of calibration errors. Our initial feasibility research shows that upon scaling to a full prototype and including additional calibration techniques we can expect to achieve uncertainties at the level of 0.01 arcsec (50 nrad) or better and offer the immense advantage of a highly automatable and customizable product to the commercial market.

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.

Genomic prediction applied to high-biomass sorghum for bioenergy production.

Science.gov (United States)

de Oliveira, Amanda Avelar; Pastina, Maria Marta; de Souza, Vander Filipe; da Costa Parrella, Rafael Augusto; Noda, Roberto Willians; Simeone, Maria Lúcia Ferreira; Schaffert, Robert Eugene; de Magalhães, Jurandir Vieira; Damasceno, Cynthia Maria Borges; Margarido, Gabriel Rodrigues Alves

2018-01-01

The increasing cost of energy and finite oil and gas reserves have created a need to develop alternative fuels from renewable sources. Due to its abiotic stress tolerance and annual cultivation, high-biomass sorghum ( Sorghum bicolor L. Moench) shows potential as a bioenergy crop. Genomic selection is a useful tool for accelerating genetic gains and could restructure plant breeding programs by enabling early selection and reducing breeding cycle duration. This work aimed at predicting breeding values via genomic selection models for 200 sorghum genotypes comprising landrace accessions and breeding lines from biomass and saccharine groups. These genotypes were divided into two sub-panels, according to breeding purpose. We evaluated the following phenotypic biomass traits: days to flowering, plant height, fresh and dry matter yield, and fiber, cellulose, hemicellulose, and lignin proportions. Genotyping by sequencing yielded more than 258,000 single-nucleotide polymorphism markers, which revealed population structure between subpanels. We then fitted and compared genomic selection models BayesA, BayesB, BayesCπ, BayesLasso, Bayes Ridge Regression and random regression best linear unbiased predictor. The resulting predictive abilities varied little between the different models, but substantially between traits. Different scenarios of prediction showed the potential of using genomic selection results between sub-panels and years, although the genotype by environment interaction negatively affected accuracies. Functional enrichment analyses performed with the marker-predicted effects suggested several interesting associations, with potential for revealing biological processes relevant to the studied quantitative traits. This work shows that genomic selection can be successfully applied in biomass sorghum breeding programs.

High accuracy line positions of the ν1 fundamental band of 14N216O

Science.gov (United States)

AlSaif, Bidoor; Lamperti, Marco; Gatti, Davide; Laporta, Paolo; Fermann, Martin; Farooq, Aamir; Lyulin, Oleg; Campargue, Alain; Marangoni, Marco

2018-05-01

The ν1 fundamental band of N2O is examined by a novel spectrometer that relies on the frequency locking of an external-cavity quantum cascade laser around 7.8 μm to a near-infrared Tm:based frequency comb at 1.9 μm. Due to the large tunability, nearly 70 lines in the 1240-1310 cm-1 range of the ν1 band of N2O, from P(40) to R(31), are for the first time measured with an absolute frequency calibration and an uncertainty from 62 to 180 kHz, depending on the line. Accurate values of the spectroscopic constants of the upper state are derived from a fit of the line centers (rms ≈ 4.8 × 10-6 cm-1 or 144 kHz). The ν1 transitions presently measured in a Doppler regime validate high accuracy predictions based on sub-Doppler measurements of the ν3 and ν3-ν1 transitions.

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 ...

Ultra-high accuracy optical testing: creating diffraction-limited short-wavelength optical systems

International Nuclear Information System (INIS)

Goldberg, Kenneth A.; Naulleau, Patrick P.; Rekawa, Senajith B.; Denham, Paul E.; Liddle, J. Alexander; Gullikson, Eric M.; Jackson, KeithH.; Anderson, Erik H.; Taylor, John S.; Sommargren, Gary E.; Chapman, Henry N.; Phillion, Donald W.; Johnson, Michael; Barty, Anton; Soufli, Regina; Spiller, Eberhard A.; Walton, Christopher C.; Bajt, Sasa

2005-01-01

Since 1993, research in the fabrication of extreme ultraviolet (EUV) optical imaging systems, conducted at Lawrence Berkeley National Laboratory (LBNL) and Lawrence Livermore National Laboratory (LLNL), has produced the highest resolution optical systems ever made. We have pioneered the development of ultra-high-accuracy optical testing and alignment methods, working at extreme ultraviolet wavelengths, and pushing wavefront-measuring interferometry into the 2-20-nm wavelength range (60-600 eV). These coherent measurement techniques, including lateral shearing interferometry and phase-shifting point-diffraction interferometry (PS/PDI) have achieved RMS wavefront measurement accuracies of 0.5-1-(angstrom) and better for primary aberration terms, enabling the creation of diffraction-limited EUV optics. The measurement accuracy is established using careful null-testing procedures, and has been verified repeatedly through high-resolution imaging. We believe these methods are broadly applicable to the advancement of short-wavelength optical systems including space telescopes, microscope objectives, projection lenses, synchrotron beamline optics, diffractive and holographic optics, and more. Measurements have been performed on a tunable undulator beamline at LBNL's Advanced Light Source (ALS), optimized for high coherent flux; although many of these techniques should be adaptable to alternative ultraviolet, EUV, and soft x-ray light sources. To date, we have measured nine prototype all-reflective EUV optical systems with NA values between 0.08 and 0.30 (f/6.25 to f/1.67). These projection-imaging lenses were created for the semiconductor industry's advanced research in EUV photolithography, a technology slated for introduction in 2009-13. This paper reviews the methods used and our program's accomplishments to date

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.

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.

High DNA melting temperature predicts transcription start site location in human and mouse.

LENUS (Irish Health Repository)

Dineen, David G

2009-12-01

The accurate computational prediction of transcription start sites (TSS) in vertebrate genomes is a difficult problem. The physicochemical properties of DNA can be computed in various ways and a many combinations of DNA features have been tested in the past for use as predictors of transcription. We looked in detail at melting temperature, which measures the temperature, at which two strands of DNA separate, considering the cooperative nature of this process. We find that peaks in melting temperature correspond closely to experimentally determined transcription start sites in human and mouse chromosomes. Using melting temperature alone, and with simple thresholding, we can predict TSS with accuracy that is competitive with the most accurate state-of-the-art TSS prediction methods. Accuracy is measured using both experimentally and manually determined TSS. The method works especially well with CpG island containing promoters, but also works when CpG islands are absent. This result is clear evidence of the important role of the physical properties of DNA in the process of transcription. It also points to the importance for TSS prediction methods to include melting temperature as prior information.

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

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].

Automated analysis of free speech predicts psychosis onset in high-risk youths

Science.gov (United States)

Bedi, Gillinder; Carrillo, Facundo; Cecchi, Guillermo A; Slezak, Diego Fernández; Sigman, Mariano; Mota, Natália B; Ribeiro, Sidarta; Javitt, Daniel C; Copelli, Mauro; Corcoran, Cheryl M

2015-01-01

Background/Objectives: Psychiatry lacks the objective clinical tests routinely used in other specializations. Novel computerized methods to characterize complex behaviors such as speech could be used to identify and predict psychiatric illness in individuals. AIMS: In this proof-of-principle study, our aim was to test automated speech analyses combined with Machine Learning to predict later psychosis onset in youths at clinical high-risk (CHR) for psychosis. Methods: Thirty-four CHR youths (11 females) had baseline interviews and were assessed quarterly for up to 2.5 years; five transitioned to psychosis. Using automated analysis, transcripts of interviews were evaluated for semantic and syntactic features predicting later psychosis onset. Speech features were fed into a convex hull classification algorithm with leave-one-subject-out cross-validation to assess their predictive value for psychosis outcome. The canonical correlation between the speech features and prodromal symptom ratings was computed. Results: Derived speech features included a Latent Semantic Analysis measure of semantic coherence and two syntactic markers of speech complexity: maximum phrase length and use of determiners (e.g., which). These speech features predicted later psychosis development with 100% accuracy, outperforming classification from clinical interviews. Speech features were significantly correlated with prodromal symptoms. Conclusions: Findings support the utility of automated speech analysis to measure subtle, clinically relevant mental state changes in emergent psychosis. Recent developments in computer science, including natural language processing, could provide the foundation for future development of objective clinical tests for psychiatry. PMID:27336038

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.

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.

High-accuracy user identification using EEG biometrics.

Science.gov (United States)

Koike-Akino, Toshiaki; Mahajan, Ruhi; Marks, Tim K; Ye Wang; Watanabe, Shinji; Tuzel, Oncel; Orlik, Philip

2016-08-01

We analyze brain waves acquired through a consumer-grade EEG device to investigate its capabilities for user identification and authentication. First, we show the statistical significance of the P300 component in event-related potential (ERP) data from 14-channel EEGs across 25 subjects. We then apply a variety of machine learning techniques, comparing the user identification performance of various different combinations of a dimensionality reduction technique followed by a classification algorithm. Experimental results show that an identification accuracy of 72% can be achieved using only a single 800 ms ERP epoch. In addition, we demonstrate that the user identification accuracy can be significantly improved to more than 96.7% by joint classification of multiple epochs.

Prediction and measurement of the electromagnetic environment of high-power medium-wave and short-wave broadcast antennas in far field.

Science.gov (United States)

Tang, Zhanghong; Wang, Qun; Ji, Zhijiang; Shi, Meiwu; Hou, Guoyan; Tan, Danjun; Wang, Pengqi; Qiu, Xianbo

2014-12-01

With the increasing city size, high-power electromagnetic radiation devices such as high-power medium-wave (MW) and short-wave (SW) antennas have been inevitably getting closer and closer to buildings, which resulted in the pollution of indoor electromagnetic radiation becoming worsened. To avoid such radiation exceeding the exposure limits by national standards, it is necessary to predict and survey the electromagnetic radiation by MW and SW antennas before constructing the buildings. In this paper, a modified prediction method for the far-field electromagnetic radiation is proposed and successfully applied to predict the electromagnetic environment of an area close to a group of typical high-power MW and SW wave antennas. Different from currently used simplified prediction method defined in the Radiation Protection Management Guidelines (H J/T 10. 3-1996), the new method in this article makes use of more information such as antennas' patterns to predict the electromagnetic environment. Therefore, it improves the prediction accuracy significantly by the new feature of resolution at different directions. At the end of this article, a comparison between the prediction data and the measured results is given to demonstrate the effectiveness of the proposed new method. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Hyperbolic Method for Dispersive PDEs: Same High-Order of Accuracy for Solution, Gradient, and Hessian

Science.gov (United States)

Mazaheri, Alireza; Ricchiuto, Mario; Nishikawa, Hiroaki

2016-01-01

In this paper, we introduce a new hyperbolic first-order system for general dispersive partial differential equations (PDEs). We then extend the proposed system to general advection-diffusion-dispersion PDEs. We apply the fourth-order RD scheme of Ref. 1 to the proposed hyperbolic system, and solve time-dependent dispersive equations, including the classical two-soliton KdV and a dispersive shock case. We demonstrate that the predicted results, including the gradient and Hessian (second derivative), are in a very good agreement with the exact solutions. We then show that the RD scheme applied to the proposed system accurately captures dispersive shocks without numerical oscillations. We also verify that the solution, gradient and Hessian are predicted with equal order of accuracy.

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

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

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.

High Accuracy Attitude Control System Design for Satellite with Flexible Appendages

Directory of Open Access Journals (Sweden)

Wenya Zhou

2014-01-01

Full Text Available In order to realize the high accuracy attitude control of satellite with flexible appendages, attitude control system consisting of the controller and structural filter was designed. When the low order vibration frequency of flexible appendages is approximating the bandwidth of attitude control system, the vibration signal will enter the control system through measurement device to bring impact on the accuracy or even the stability. In order to reduce the impact of vibration of appendages on the attitude control system, the structural filter is designed in terms of rejecting the vibration of flexible appendages. Considering the potential problem of in-orbit frequency variation of the flexible appendages, the design method for the adaptive notch filter is proposed based on the in-orbit identification technology. Finally, the simulation results are given to demonstrate the feasibility and effectiveness of the proposed design techniques.

High-fidelity large eddy simulation for supersonic jet noise prediction

Science.gov (United States)

Aikens, Kurt M.

The problem of intense sound radiation from supersonic jets is a concern for both civil and military applications. As a result, many experimental and computational efforts are focused at evaluating possible noise suppression techniques. Large-eddy simulation (LES) is utilized in many computational studies to simulate the turbulent jet flowfield. Integral methods such as the Ffowcs Williams-Hawkings (FWH) method are then used for propagation of the sound waves to the farfield. Improving the accuracy of this two-step methodology and evaluating beveled converging-diverging nozzles for noise suppression are the main tasks of this work. First, a series of numerical experiments are undertaken to ensure adequate numerical accuracy of the FWH methodology. This includes an analysis of different treatments for the downstream integration surface: with or without including an end-cap, averaging over multiple end-caps, and including an approximate surface integral correction term. Secondly, shock-capturing methods based on characteristic filtering and adaptive spatial filtering are used to extend a highly-parallelizable multiblock subsonic LES code to enable simulations of supersonic jets. The code is based on high-order numerical methods for accurate prediction of the acoustic sources and propagation of the sound waves. Furthermore, this new code is more efficient than the legacy version, allows cylindrical multiblock topologies, and is capable of simulating nozzles with resolved turbulent boundary layers when coupled with an approximate turbulent inflow boundary condition. Even though such wall-resolved simulations are more physically accurate, their expense is often prohibitive. To make simulations more economical, a wall model is developed and implemented. The wall modeling methodology is validated for turbulent quasi-incompressible and compressible zero pressure gradient flat plate boundary layers, and for subsonic and supersonic jets. The supersonic code additions and the

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.

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.

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.

Technics study on high accuracy crush dressing and sharpening of diamond grinding wheel

Science.gov (United States)

Jia, Yunhai; Lu, Xuejun; Li, Jiangang; Zhu, Lixin; Song, Yingjie

2011-05-01

Mechanical grinding of artificial diamond grinding wheel was traditional wheel dressing process. The rotate speed and infeed depth of tool wheel were main technics parameters. The suitable technics parameters of metals-bonded diamond grinding wheel and resin-bonded diamond grinding wheel high accuracy crush dressing were obtained by a mount of experiment in super-hard material wheel dressing grind machine and by analysis of grinding force. In the same time, the effect of machine sharpening and sprinkle granule sharpening was contrasted. These analyses and lots of experiments had extent instruction significance to artificial diamond grinding wheel accuracy crush dressing.

Dimensional analysis and prediction of dielectrophoretic crossover frequency of spherical particles

Directory of Open Access Journals (Sweden)

Che-Kai Yeh

2017-06-01

Full Text Available The manipulation of biological cells and micrometer-scale particles using dielectrophoresis (DEP is an indispensable technique for lab-on-a-chip systems for many biological and colloidal science applications. However, existing models, including the dipole model and numerical simulations based on Maxwell stress tensor (MST, cannot achieve high accuracy and high computation efficiency at the same time. The dipole model is widely used and provides adequate predictions on the crossover frequency of submicron particles, but cannot predict the crossover frequency for larger particles accurately; on the other hand, the MST method offers high accuracy for a wide variety of particle sizes and shapes, but is time-consuming and may lack predictive understanding of the interplay between key parameters. Here we present a mathematical model, using dimensional analysis and the Buckingham pi theorem, that permits high accuracy and efficiency in predicting the crossover frequency of spherical particles. The curve fitting and calculation are performed using commercial packages OriginLab and MATLAB, respectively. In addition, through this model we also can predict the conditions in which no crossover frequency exists. Also, we propose a pair of dimensionless parameters, forming a functional relation, that provide physical insights into the dependency of the crossover frequency on five key parameters. The model is verified under several scenarios using comprehensive MST simulations by COMSOL Multiphysics software (COMSOL, Inc. and some published experimental data.

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.

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

Science.gov (United States)

Wang, Ming; Long, Qi

2016-09-01

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

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

In-depth, high-accuracy proteomics of sea urchin tooth organic matrix

Directory of Open Access Journals (Sweden)

Mann Matthias

2008-12-01

Full Text Available Abstract Background The organic matrix contained in biominerals plays an important role in regulating mineralization and in determining biomineral properties. However, most components of biomineral matrices remain unknown at present. In sea urchin tooth, which is an important model for developmental biology and biomineralization, only few matrix components have been identified. The recent publication of the Strongylocentrotus purpuratus genome sequence rendered possible not only the identification of genes potentially coding for matrix proteins, but also the direct identification of proteins contained in matrices of skeletal elements by in-depth, high-accuracy proteomic analysis. Results We identified 138 proteins in the matrix of tooth powder. Only 56 of these proteins were previously identified in the matrices of test (shell and spine. Among the novel components was an interesting group of five proteins containing alanine- and proline-rich neutral or basic motifs separated by acidic glycine-rich motifs. In addition, four of the five proteins contained either one or two predicted Kazal protease inhibitor domains. The major components of tooth matrix were however largely identical to the set of spicule matrix proteins and MSP130-related proteins identified in test (shell and spine matrix. Comparison of the matrices of crushed teeth to intact teeth revealed a marked dilution of known intracrystalline matrix proteins and a concomitant increase in some intracellular proteins. Conclusion This report presents the most comprehensive list of sea urchin tooth matrix proteins available at present. The complex mixture of proteins identified may reflect many different aspects of the mineralization process. A comparison between intact tooth matrix, presumably containing odontoblast remnants, and crushed tooth matrix served to differentiate between matrix components and possible contributions of cellular remnants. Because LC-MS/MS-based methods directly

High accuracy subwavelength distance measurements: A variable-angle standing-wave total-internal-reflection optical microscope

International Nuclear Information System (INIS)

Haynie, A.; Min, T.-J.; Luan, L.; Mu, W.; Ketterson, J. B.

2009-01-01

We describe an extension of the total-internal-reflection microscopy technique that permits direct in-plane distance measurements with high accuracy (<10 nm) over a wide range of separations. This high position accuracy arises from the creation of a standing evanescent wave and the ability to sweep the nodal positions (intensity minima of the standing wave) in a controlled manner via both the incident angle and the relative phase of the incoming laser beams. Some control over the vertical resolution is available through the ability to scan the incoming angle and with it the evanescent penetration depth.

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...

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

Constructing Better Classifier Ensemble Based on Weighted Accuracy and Diversity Measure

Directory of Open Access Journals (Sweden)

Xiaodong Zeng

2014-01-01

Full Text Available A weighted accuracy and diversity (WAD method is presented, a novel measure used to evaluate the quality of the classifier ensemble, assisting in the ensemble selection task. The proposed measure is motivated by a commonly accepted hypothesis; that is, a robust classifier ensemble should not only be accurate but also different from every other member. In fact, accuracy and diversity are mutual restraint factors; that is, an ensemble with high accuracy may have low diversity, and an overly diverse ensemble may negatively affect accuracy. This study proposes a method to find the balance between accuracy and diversity that enhances the predictive ability of an ensemble for unknown data. The quality assessment for an ensemble is performed such that the final score is achieved by computing the harmonic mean of accuracy and diversity, where two weight parameters are used to balance them. The measure is compared to two representative measures, Kappa-Error and GenDiv, and two threshold measures that consider only accuracy or diversity, with two heuristic search algorithms, genetic algorithm, and forward hill-climbing algorithm, in ensemble selection tasks performed on 15 UCI benchmark datasets. The empirical results demonstrate that the WAD measure is superior to others in most cases.

Detection of dysregulated protein-association networks by high-throughput proteomics predicts cancer vulnerabilities.

Science.gov (United States)

Lapek, John D; Greninger, Patricia; Morris, Robert; Amzallag, Arnaud; Pruteanu-Malinici, Iulian; Benes, Cyril H; Haas, Wilhelm

2017-10-01

The formation of protein complexes and the co-regulation of the cellular concentrations of proteins are essential mechanisms for cellular signaling and for maintaining homeostasis. Here we use isobaric-labeling multiplexed proteomics to analyze protein co-regulation and show that this allows the identification of protein-protein associations with high accuracy. We apply this 'interactome mapping by high-throughput quantitative proteome analysis' (IMAHP) method to a panel of 41 breast cancer cell lines and show that deviations of the observed protein co-regulations in specific cell lines from the consensus network affects cellular fitness. Furthermore, these aberrant interactions serve as biomarkers that predict the drug sensitivity of cell lines in screens across 195 drugs. We expect that IMAHP can be broadly used to gain insight into how changing landscapes of protein-protein associations affect the phenotype of biological systems.

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.

Treatment accuracy of hypofractionated spine and other highly conformal IMRT treatments

International Nuclear Information System (INIS)

Sutherland, B.; Hanlon, P.; Charles, P.

2011-01-01

Full text: Spinal cord metastases pose difficult challenges for radiation treatment due to tight dose constraints and a concave PTY. This project aimed to thoroughly test the treatment accuracy of the Eclipse Treatment Planning System (TPS) for highly modulated IMRT treatments, in particular of the thoracic spine, using an Elekta Synergy Linear Accelerator. The increased understanding obtained through different quality assurance techniques allowed recommendations to be made for treatment site commissioning with improved accuracy at the Princess Alexandra Hospital (PAH). Three thoracic spine IMRT plans at the PAH were used for data collection. Complex phantom models were built using CT data, and fields simulated using Monte Carlo modelling. The simulated dose distributions were compared with the TPS using gamma analysis and DYH comparison. High resolution QA was done for all fields using the MatriXX ion chamber array, MapCHECK2 diode array shifted, and the EPlD to determine a procedure for commissioning new treatment sites. Basic spine simulations found the TPS overestimated absorbed dose to bone, however within spinal cord there was good agreement. High resolution QA found the average gamma pass rate of the fields to be 99.1 % for MatriXX, 96.5% for MapCHECK2 shifted and 97.7% for EPlD. Preliminary results indicate agreement between the TPS and delivered dose distributions higher than previously believed for the investigated IMRT plans. The poor resolution of the MatriXX, and normalisation issues with MapCHECK2 leads to probable recommendation of EPlD for future IMRT commissioning due to the high resolution and minimal setup required.

Diagnostic accuracy of liver fibrosis based on red cell distribution width (RDW) to platelet ratio with fibroscan in chronic hepatitis B

Science.gov (United States)

Sembiring, J.; Jones, F.

2018-03-01

Red cell Distribution Width (RDW) and platelet ratio (RPR) can predict liver fibrosis and cirrhosis in chronic hepatitis B with relatively high accuracy. RPR was superior to other non-invasive methods to predict liver fibrosis, such as AST and ALT ratio, AST and platelet ratio Index and FIB-4. The aim of this study was to assess diagnostic accuracy liver fibrosis by using RDW and platelets ratio in chronic hepatitis B patients based on compared with Fibroscan. This cross-sectional study was conducted at Adam Malik Hospital from January-June 2015. We examine 34 patients hepatitis B chronic, screen RDW, platelet, and fibroscan. Data were statistically analyzed. The result RPR with ROC procedure has an accuracy of 72.3% (95% CI: 84.1% - 97%). In this study, the RPR had a moderate ability to predict fibrosis degree (p = 0.029 with AUC> 70%). The cutoff value RPR was 0.0591, sensitivity and spesificity were 71.4% and 60%, Positive Prediction Value (PPV) was 55.6% and Negative Predictions Value (NPV) was 75%, positive likelihood ratio was 1.79 and negative likelihood ratio was 0.48. RPR have the ability to predict the degree of liver fibrosis in chronic hepatitis B patients with moderate accuracy.

High accuracy line positions of the ν 1 fundamental band of 14 N 2 16 O

KAUST Repository

Alsaif, Bidoor

2018-03-08

The ν1 fundamental band of N2O is examined by a novel spectrometer that relies on the frequency locking of an external-cavity quantum cascade laser around 7.8 μm to a near-infrared Tm:based frequency comb at 1.9 μm. Due to the large tunability, nearly 70 lines in the 1240 – 1310 cm−1 range of the ν1 band of N2O, from P(40) to R(31), are for the first time measured with an absolute frequency calibration and an uncertainty from 62 to 180 kHz, depending on the line. Accurate values of the spectroscopic constants of the upper state are derived from a fit of the line centers (rms ≈ 4.8 × 10−6 cm−1 or 144 kHz). The ν1 transitions presently measured in a Doppler regime validate high accuracy predictions based on sub-Doppler measurements of the ν3 and ν3-ν1 transitions.

High accuracy of family history of melanoma in Danish melanoma cases

DEFF Research Database (Denmark)

Wadt, Karin A W; Drzewiecki, Krzysztof T; Gerdes, Anne-Marie

2015-01-01

The incidence of melanoma in Denmark has immensely increased over the last 10 years making Denmark a high risk country for melanoma. In the last two decades multiple public campaigns have sought to increase the awareness of melanoma. Family history of melanoma is a known major risk factor...... but previous studies have shown that self-reported family history of melanoma is highly inaccurate. These studies are 15 years old and we wanted to examine if a higher awareness of melanoma has increased the accuracy of self-reported family history of melanoma. We examined the family history of 181 melanoma...

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…

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.

Mining geriatric assessment data for in-patient fall prediction models and high-risk subgroups.

Science.gov (United States)

Marschollek, Michael; Gövercin, Mehmet; Rust, Stefan; Gietzelt, Matthias; Schulze, Mareike; Wolf, Klaus-Hendrik; Steinhagen-Thiessen, Elisabeth

2012-03-14

Hospital in-patient falls constitute a prominent problem in terms of costs and consequences. Geriatric institutions are most often affected, and common screening tools cannot predict in-patient falls consistently. Our objectives are to derive comprehensible fall risk classification models from a large data set of geriatric in-patients' assessment data and to evaluate their predictive performance (aim#1), and to identify high-risk subgroups from the data (aim#2). A data set of n = 5,176 single in-patient episodes covering 1.5 years of admissions to a geriatric hospital were extracted from the hospital's data base and matched with fall incident reports (n = 493). A classification tree model was induced using the C4.5 algorithm as well as a logistic regression model, and their predictive performance was evaluated. Furthermore, high-risk subgroups were identified from extracted classification rules with a support of more than 100 instances. The classification tree model showed an overall classification accuracy of 66%, with a sensitivity of 55.4%, a specificity of 67.1%, positive and negative predictive values of 15% resp. 93.5%. Five high-risk groups were identified, defined by high age, low Barthel index, cognitive impairment, multi-medication and co-morbidity. Our results show that a little more than half of the fallers may be identified correctly by our model, but the positive predictive value is too low to be applicable. Non-fallers, on the other hand, may be sorted out with the model quite well. The high-risk subgroups and the risk factors identified (age, low ADL score, cognitive impairment, institutionalization, polypharmacy and co-morbidity) reflect domain knowledge and may be used to screen certain subgroups of patients with a high risk of falling. Classification models derived from a large data set using data mining methods can compete with current dedicated fall risk screening tools, yet lack diagnostic precision. High-risk subgroups may be identified

Mining geriatric assessment data for in-patient fall prediction models and high-risk subgroups

Directory of Open Access Journals (Sweden)

Marschollek Michael

2012-03-01

Full Text Available Abstract Background Hospital in-patient falls constitute a prominent problem in terms of costs and consequences. Geriatric institutions are most often affected, and common screening tools cannot predict in-patient falls consistently. Our objectives are to derive comprehensible fall risk classification models from a large data set of geriatric in-patients' assessment data and to evaluate their predictive performance (aim#1, and to identify high-risk subgroups from the data (aim#2. Methods A data set of n = 5,176 single in-patient episodes covering 1.5 years of admissions to a geriatric hospital were extracted from the hospital's data base and matched with fall incident reports (n = 493. A classification tree model was induced using the C4.5 algorithm as well as a logistic regression model, and their predictive performance was evaluated. Furthermore, high-risk subgroups were identified from extracted classification rules with a support of more than 100 instances. Results The classification tree model showed an overall classification accuracy of 66%, with a sensitivity of 55.4%, a specificity of 67.1%, positive and negative predictive values of 15% resp. 93.5%. Five high-risk groups were identified, defined by high age, low Barthel index, cognitive impairment, multi-medication and co-morbidity. Conclusions Our results show that a little more than half of the fallers may be identified correctly by our model, but the positive predictive value is too low to be applicable. Non-fallers, on the other hand, may be sorted out with the model quite well. The high-risk subgroups and the risk factors identified (age, low ADL score, cognitive impairment, institutionalization, polypharmacy and co-morbidity reflect domain knowledge and may be used to screen certain subgroups of patients with a high risk of falling. Classification models derived from a large data set using data mining methods can compete with current dedicated fall risk screening tools, yet lack

MEDEX 2015: Heart Rate Variability Predicts Development of Acute Mountain Sickness.

Science.gov (United States)

Sutherland, Angus; Freer, Joseph; Evans, Laura; Dolci, Alberto; Crotti, Matteo; Macdonald, Jamie Hugo

2017-09-01

Sutherland, Angus, Joseph Freer, Laura Evans, Alberto Dolci, Matteo Crotti, and Jamie Hugo Macdonald. MEDEX 2015: Heart rate variability predicts development of acute mountain sickness. High Alt Med Biol. 18: 199-208, 2017. Acute mountain sickness (AMS) develops when the body fails to acclimatize to atmospheric changes at altitude. Preascent prediction of susceptibility to AMS would be a useful tool to prevent subsequent harm. Changes to peripheral oxygen saturation (SpO 2 ) on hypoxic exposure have previously been shown to be of poor predictive value. Heart rate variability (HRV) has shown promise in the early prediction of AMS, but its use pre-expedition has not previously been investigated. We aimed to determine whether pre- and intraexpedition HRV assessment could predict susceptibility to AMS at high altitude with better diagnostic accuracy than SpO 2 . Forty-four healthy volunteers undertook an expedition in the Nepali Himalaya to >5000 m. SpO 2 and HRV parameters were recorded at rest in normoxia and in a normobaric hypoxic chamber before the expedition. On the expedition HRV parameters and SpO 2 were collected again at 3841 m. A daily Lake Louise Score was obtained to assess AMS symptomology. Low frequency/high frequency (LF/HF) ratio in normoxia (cutpoint ≤2.28 a.u.) and LF following 15 minutes of exposure to normobaric hypoxia had moderate (area under the curve ≥0.8) diagnostic accuracy. LF/HF ratio in normoxia had the highest sensitivity (85%) and specificity (88%) for predicting AMS on subsequent ascent to altitude. In contrast, pre-expedition SpO 2 measurements had poor (area under the curve <0.7) diagnostic accuracy and inferior sensitivity and specificity. Pre-ascent measurement of HRV in normoxia was found to be of better diagnostic accuracy for AMS prediction than all measures of HRV in hypoxia, and better than peripheral oxygen saturation monitoring.

Life prediction for high temperature low cycle fatigue of two kinds of titanium alloys based on exponential function

Science.gov (United States)

Mu, G. Y.; Mi, X. Z.; Wang, F.

2018-01-01

The high temperature low cycle fatigue tests of TC4 titanium alloy and TC11 titanium alloy are carried out under strain controlled. The relationships between cyclic stress-life and strain-life are analyzed. The high temperature low cycle fatigue life prediction model of two kinds of titanium alloys is established by using Manson-Coffin method. The relationship between failure inverse number and plastic strain range presents nonlinear in the double logarithmic coordinates. Manson-Coffin method assumes that they have linear relation. Therefore, there is bound to be a certain prediction error by using the Manson-Coffin method. In order to solve this problem, a new method based on exponential function is proposed. The results show that the fatigue life of the two kinds of titanium alloys can be predicted accurately and effectively by using these two methods. Prediction accuracy is within ±1.83 times scatter zone. The life prediction capability of new methods based on exponential function proves more effective and accurate than Manson-Coffin method for two kinds of titanium alloys. The new method based on exponential function can give better fatigue life prediction results with the smaller standard deviation and scatter zone than Manson-Coffin method. The life prediction results of two methods for TC4 titanium alloy prove better than TC11 titanium alloy.

RAPID COMMUNICATION: Improving prediction accuracy of GPS satellite clocks with periodic variation behaviour

Science.gov (United States)

Heo, Youn Jeong; Cho, Jeongho; Heo, Moon Beom

2010-07-01

The broadcast ephemeris and IGS ultra-rapid predicted (IGU-P) products are primarily available for use in real-time GPS applications. The IGU orbit precision has been remarkably improved since late 2007, but its clock products have not shown acceptably high-quality prediction performance. One reason for this fact is that satellite atomic clocks in space can be easily influenced by various factors such as temperature and environment and this leads to complicated aspects like periodic variations, which are not sufficiently described by conventional models. A more reliable prediction model is thus proposed in this paper in order to be utilized particularly in describing the periodic variation behaviour satisfactorily. The proposed prediction model for satellite clocks adds cyclic terms to overcome the periodic effects and adopts delay coordinate embedding, which offers the possibility of accessing linear or nonlinear coupling characteristics like satellite behaviour. The simulation results have shown that the proposed prediction model outperforms the IGU-P solutions at least on a daily basis.

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

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)

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.

Study on the improvement of the convective differencing scheme for the high-accuracy and stable resolution of the numerical solution

International Nuclear Information System (INIS)

Shin, J. K.; Choi, Y. D.

1992-01-01

QUICKER scheme has several attractive properties. However, under highly convective conditions, it produces overshoots and possibly some oscillations on each side of steps in the dependent variable when the flow is convected at an angle oblique to the grid line. Fortunately, it is possible to modify the QUICKER scheme using non-linear and linear functional relationship. Details of the development of polynomial upwinding scheme are given in this paper, where it is seen that this non-linear scheme has also third order accuracy. This polynomial upwinding scheme is used as the basis for the SHARPER and SMARTER schemes. Another revised scheme was developed by partial modification of QUICKER scheme using CDS and UPWIND schemes (QUICKUP). These revised schemes are tested at the well known bench mark flows, Two-Dimensional Pure Convection Flows in Oblique-Step, Lid Driven Cavity Flows and Buoyancy Driven Cavity Flows. For remain absolutely monotonic without overshoot and oscillation. QUICKUP scheme is more accurate than any other scheme in their relative accuracy. In high Reynolds number Lid Driven Catity Flow, SMARTER and SHARPER schemes retain lower computational cost than QUICKER and QUICKUP schemes, but computed velocity values in the revised schemes produced less predicted values than QUICKER scheme which is strongly effected by overshoot and undershoot values. Also, in Buoyancy Driven Cavity Flow, SMARTER, SHARPER and QUICKUP schemes give acceptable results. (Author)

Extrinsic factors affecting accuracy of ultrasonic flowmeters for LMFBRs

International Nuclear Information System (INIS)

Managan, W.W.

1976-08-01

Assuming that ultrasonic flowmeters of suitable intrinsic accuracy are feasible, this report explores factors extrinsic to the flowmeter which affect the accuracy such as asymmetric flow profile, regions of high turbulence and thermal stratification. By integrating isovelocity flow profile maps, the predicted performance of various flowmeter configurations may be compared to experimental data. For the two pipe arrangements analyzed, the single diametral path flowmeter results were within 5 percent of true flow rate. Theoretical correction factors could reduce the error for the straight pipe but increased the error for asymmetrical flow. On the same pipe arrangements a four path ultrasonic flowmeter spaced for Gaussian integration gave less than 1 percent error. For more general conclusions a range of flow profiles produced by typical LMFBR piping arrangements must be analyzed

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.