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Sample records for 10-fold cross-validation approach

  1. Prediction of maize single cross hybrids using the total effects of associated markers approach assessed by cross-validation and regional trials.

    PubMed

    Melo, Wagner Mateus Costa; Pinho, Renzo Garcia Von; Balestre, Marcio

    2014-01-01

    The present study aimed to predict the performance of maize hybrids and assess whether the total effects of associated markers (TEAM) method can correctly predict hybrids using cross-validation and regional trials. The training was performed in 7 locations of Southern Brazil during the 2010/11 harvest. The regional assays were conducted in 6 different South Brazilian locations during the 2011/12 harvest. In the training trial, 51 lines from different backgrounds were used to create 58 single cross hybrids. Seventy-nine microsatellite markers were used to genotype these 51 lines. In the cross-validation method the predictive accuracy ranged from 0.10 to 0.96, depending on the sample size. Furthermore, the accuracy was 0.30 when the values of hybrids that were not used in the training population (119) were predicted for the regional assays. Regarding selective loss, the TEAM method correctly predicted 50% of the hybrids selected in the regional assays. There was also loss in only 33% of cases; that is, only 33% of the materials predicted to be good in training trial were considered to be bad in regional assays. Our results show that the predictive validation of different crop conditions is possible, and the cross-validation results strikingly represented the field performance.

  2. Modified cross-validation as a method for estimating parameter

    NASA Astrophysics Data System (ADS)

    Shi, Chye Rou; Adnan, Robiah

    2014-12-01

    Best subsets regression is an effective approach to distinguish models that can attain objectives with as few predictors as would be prudent. Subset models might really estimate the regression coefficients and predict future responses with smaller variance than the full model using all predictors. The inquiry of how to pick subset size λ depends on the bias and variance. There are various method to pick subset size λ. Regularly pick the smallest model that minimizes an estimate of the expected prediction error. Since data are regularly small, so Repeated K-fold cross-validation method is the most broadly utilized method to estimate prediction error and select model. The data is reshuffled and re-stratified before each round. However, the "one-standard-error" rule of Repeated K-fold cross-validation method always picks the most stingy model. The objective of this research is to modify the existing cross-validation method to avoid overfitting and underfitting model, a modified cross-validation method is proposed. This paper compares existing cross-validation and modified cross-validation. Our results reasoned that the modified cross-validation method is better at submodel selection and evaluation than other methods.

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

    PubMed Central

    2014-01-01

    Background We address the problem of selecting and assessing classification and regression models using cross-validation. Current state-of-the-art methods can yield models with high variance, rendering them unsuitable for a number of practical applications including QSAR. In this paper we describe and evaluate best practices which improve reliability and increase confidence in selected models. A key operational component of the proposed methods is cloud computing which enables routine use of previously infeasible approaches. Methods We describe in detail an algorithm for repeated grid-search V-fold cross-validation for parameter tuning in classification and regression, and we define a repeated nested cross-validation algorithm for model assessment. As regards variable selection and parameter tuning we define two algorithms (repeated grid-search cross-validation and double cross-validation), and provide arguments for using the repeated grid-search in the general case. Results We show results of our algorithms on seven QSAR datasets. The variation of the prediction performance, which is the result of choosing different splits of the dataset in V-fold cross-validation, needs to be taken into account when selecting and assessing classification and regression models. Conclusions We demonstrate the importance of repeating cross-validation when selecting an optimal model, as well as the importance of repeating nested cross-validation when assessing a prediction error. PMID:24678909

  4. Cross-validated detection of crack initiation in aerospace materials

    NASA Astrophysics Data System (ADS)

    Vanniamparambil, Prashanth A.; Cuadra, Jefferson; Guclu, Utku; Bartoli, Ivan; Kontsos, Antonios

    2014-03-01

    A cross-validated nondestructive evaluation approach was employed to in situ detect the onset of damage in an Aluminum alloy compact tension specimen. The approach consisted of the coordinated use primarily the acoustic emission, combined with the infrared thermography and digital image correlation methods. Both tensile loads were applied and the specimen was continuously monitored using the nondestructive approach. Crack initiation was witnessed visually and was confirmed by the characteristic load drop accompanying the ductile fracture process. The full field deformation map provided by the nondestructive approach validated the formation of a pronounced plasticity zone near the crack tip. At the time of crack initiation, a burst in the temperature field ahead of the crack tip as well as a sudden increase of the acoustic recordings were observed. Although such experiments have been attempted and reported before in the literature, the presented approach provides for the first time a cross-validated nondestructive dataset that can be used for quantitative analyses of the crack initiation information content. It further allows future development of automated procedures for real-time identification of damage precursors including the rarely explored crack incubation stage in fatigue conditions.

  5. Cross validation in LASSO and its acceleration

    NASA Astrophysics Data System (ADS)

    Obuchi, Tomoyuki; Kabashima, Yoshiyuki

    2016-05-01

    We investigate leave-one-out cross validation (CV) as a determinator of the weight of the penalty term in the least absolute shrinkage and selection operator (LASSO). First, on the basis of the message passing algorithm and a perturbative discussion assuming that the number of observations is sufficiently large, we provide simple formulas for approximately assessing two types of CV errors, which enable us to significantly reduce the necessary cost of computation. These formulas also provide a simple connection of the CV errors to the residual sums of squares between the reconstructed and the given measurements. Second, on the basis of this finding, we analytically evaluate the CV errors when the design matrix is given as a simple random matrix in the large size limit by using the replica method. Finally, these results are compared with those of numerical simulations on finite-size systems and are confirmed to be correct. We also apply the simple formulas of the first type of CV error to an actual dataset of the supernovae.

  6. A cross-validation package driving Netica with python

    USGS Publications Warehouse

    Fienen, Michael N.; Plant, Nathaniel G.

    2014-01-01

    Bayesian networks (BNs) are powerful tools for probabilistically simulating natural systems and emulating process models. Cross validation is a technique to avoid overfitting resulting from overly complex BNs. Overfitting reduces predictive skill. Cross-validation for BNs is known but rarely implemented due partly to a lack of software tools designed to work with available BN packages. CVNetica is open-source, written in Python, and extends the Netica software package to perform cross-validation and read, rebuild, and learn BNs from data. Insights gained from cross-validation and implications on prediction versus description are illustrated with: a data-driven oceanographic application; and a model-emulation application. These examples show that overfitting occurs when BNs become more complex than allowed by supporting data and overfitting incurs computational costs as well as causing a reduction in prediction skill. CVNetica evaluates overfitting using several complexity metrics (we used level of discretization) and its impact on performance metrics (we used skill).

  7. Cross-Validation in Canonical Analysis.

    ERIC Educational Resources Information Center

    Taylor, Dianne L.

    The need for using invariance procedures to establish the external validity or generalizability of statistical results has been well documented. Invariance analysis is a tool that can be used to establish confidence in the replicability of research findings. Several approaches to invariance analysis are available that are broadly applicable across…

  8. Cross-Validation for Nonlinear Mixed Effects Models

    PubMed Central

    Colby, Emily; Bair, Eric

    2013-01-01

    Cross-validation is frequently used for model selection in a variety of applications. However, it is difficult to apply cross-validation to mixed effects models (including nonlinear mixed effects models or NLME models) due to the fact that cross-validation requires “out-of-sample” predictions of the outcome variable, which cannot be easily calculated when random effects are present. We describe two novel variants of cross-validation that can be applied to nonlinear mixed effects models. One variant, where out-of-sample predictions are based on post hoc estimates of the random effects, can be used to select the overall structural model. Another variant, where cross-validation seeks to minimize the estimated random effects rather than the estimated residuals, can be used to select covariates to include in the model. We show that these methods produce accurate results in a variety of simulated data sets and apply them to two publicly available population pharmacokinetic data sets. PMID:23532511

  9. A Cross-Validation Study of the Posttraumatic Growth Inventory

    ERIC Educational Resources Information Center

    Sheikh, Alia I.; Marotta, Sylvia A.

    2005-01-01

    This article is a cross-validation of R. G. Tedeschi and L. G. Calhoun's (1996) original study of the development of the Posttraumatic Growth Inventory (PTGI). It describes several psychometric properties of scores on the PTGI in a sample of middle- to old-aged adults with a history of cardiovascular disease. The results did not support the…

  10. Attrition from an Adolescent Addiction Treatment Program: A Cross Validation.

    ERIC Educational Resources Information Center

    Mathisen, Kenneth S.; Meyers, Kathleen

    Treatment attrition is a major problem for programs treating adolescent substance abusers. To isolate and cross validate factors which are predictive of addiction treatment attrition among adolescent substance abusers, screening interview and diagnostic variables from 119 adolescent in-patients were submitted to a discriminant equation analysis.…

  11. The Cross Validation of the Attitudes toward Mainstreaming Scale (ATMS).

    ERIC Educational Resources Information Center

    Berryman, Joan D.; Neal, W. R. Jr.

    1980-01-01

    Reliability and factorial validity of the Attitudes Toward Mainstreaming Scale was supported in a cross-validation study with teachers. Three factors emerged: learning capability, general mainstreaming, and traditional limiting disabilities. Factor intercorrelations varied from .42 to .55; correlations between total scores and individual factors…

  12. A leave-one-out cross-validation SAS macro for the identification of markers associated with survival.

    PubMed

    Rushing, Christel; Bulusu, Anuradha; Hurwitz, Herbert I; Nixon, Andrew B; Pang, Herbert

    2015-02-01

    A proper internal validation is necessary for the development of a reliable and reproducible prognostic model for external validation. Variable selection is an important step for building prognostic models. However, not many existing approaches couple the ability to specify the number of covariates in the model with a cross-validation algorithm. We describe a user-friendly SAS macro that implements a score selection method and a leave-one-out cross-validation approach. We discuss the method and applications behind this algorithm, as well as details of the SAS macro.

  13. Cross-Validation of Predictor Equations for Armor Crewman Performance

    DTIC Science & Technology

    1980-01-01

    assignment rather than specify the contents of the test battery to be used. Eaton, Bessemer , and Kristiansen (1979) evaluated the relationship... Bessemer , & Kristiansen, 1979). Consideration of their results suggested the potential for two cross-validation strategies. The first was to attempt to...formulas and their correlations with the criteria data from Eaton, Bessemer , and Kristiansen (1979) are shown in Table 2. By comparing Tables 1 and 2, one

  14. Free kick instead of cross-validation in maximum-likelihood refinement of macromolecular crystal structures

    SciTech Connect

    Pražnikar, Jure; Turk, Dušan

    2014-12-01

    The maximum-likelihood free-kick target, which calculates model error estimates from the work set and a randomly displaced model, proved superior in the accuracy and consistency of refinement of crystal structures compared with the maximum-likelihood cross-validation target, which calculates error estimates from the test set and the unperturbed model. The refinement of a molecular model is a computational procedure by which the atomic model is fitted to the diffraction data. The commonly used target in the refinement of macromolecular structures is the maximum-likelihood (ML) function, which relies on the assessment of model errors. The current ML functions rely on cross-validation. They utilize phase-error estimates that are calculated from a small fraction of diffraction data, called the test set, that are not used to fit the model. An approach has been developed that uses the work set to calculate the phase-error estimates in the ML refinement from simulating the model errors via the random displacement of atomic coordinates. It is called ML free-kick refinement as it uses the ML formulation of the target function and is based on the idea of freeing the model from the model bias imposed by the chemical energy restraints used in refinement. This approach for the calculation of error estimates is superior to the cross-validation approach: it reduces the phase error and increases the accuracy of molecular models, is more robust, provides clearer maps and may use a smaller portion of data for the test set for the calculation of R{sub free} or may leave it out completely.

  15. Predicting IQ change from brain structure: A cross-validation study

    PubMed Central

    Price, C.J.; Ramsden, S.; Hope, T.M.H.; Friston, K.J.; Seghier, M.L.

    2013-01-01

    Procedures that can predict cognitive abilities from brain imaging data are potentially relevant to educational assessments and studies of functional anatomy in the developing brain. Our aim in this work was to quantify the degree to which IQ change in the teenage years could be predicted from structural brain changes. Two well-known k-fold cross-validation analyses were applied to data acquired from 33 healthy teenagers – each tested at Time 1 and Time 2 with a 3.5 year interval. One approach, a Leave-One-Out procedure, predicted IQ change for each subject on the basis of structural change in a brain region that was identified from all other subjects (i.e., independent data). This approach predicted 53% of verbal IQ change and 14% of performance IQ change. The other approach used half the sample, to identify regions for predicting IQ change in the other half (i.e., a Split half approach); however – unlike the Leave-One-Out procedure – regions identified using half the sample were not significant. We discuss how these out-of-sample estimates compare to in-sample estimates; and draw some recommendations for k-fold cross-validation procedures when dealing with small datasets that are typical in the neuroimaging literature. PMID:23567505

  16. Predicting IQ change from brain structure: a cross-validation study.

    PubMed

    Price, C J; Ramsden, S; Hope, T M H; Friston, K J; Seghier, M L

    2013-07-01

    Procedures that can predict cognitive abilities from brain imaging data are potentially relevant to educational assessments and studies of functional anatomy in the developing brain. Our aim in this work was to quantify the degree to which IQ change in the teenage years could be predicted from structural brain changes. Two well-known k-fold cross-validation analyses were applied to data acquired from 33 healthy teenagers - each tested at Time 1 and Time 2 with a 3.5 year interval. One approach, a Leave-One-Out procedure, predicted IQ change for each subject on the basis of structural change in a brain region that was identified from all other subjects (i.e., independent data). This approach predicted 53% of verbal IQ change and 14% of performance IQ change. The other approach used half the sample, to identify regions for predicting IQ change in the other half (i.e., a Split half approach); however--unlike the Leave-One-Out procedure--regions identified using half the sample were not significant. We discuss how these out-of-sample estimates compare to in-sample estimates; and draw some recommendations for k-fold cross-validation procedures when dealing with small datasets that are typical in the neuroimaging literature.

  17. An adaptive deep learning approach for PPG-based identification.

    PubMed

    Jindal, V; Birjandtalab, J; Pouyan, M Baran; Nourani, M

    2016-08-01

    Wearable biosensors have become increasingly popular in healthcare due to their capabilities for low cost and long term biosignal monitoring. This paper presents a novel two-stage technique to offer biometric identification using these biosensors through Deep Belief Networks and Restricted Boltzman Machines. Our identification approach improves robustness in current monitoring procedures within clinical, e-health and fitness environments using Photoplethysmography (PPG) signals through deep learning classification models. The approach is tested on TROIKA dataset using 10-fold cross validation and achieved an accuracy of 96.1%.

  18. Genomic predictions in Angus cattle: comparisons of sample size, response variables, and clustering methods for cross-validation.

    PubMed

    Boddhireddy, P; Kelly, M J; Northcutt, S; Prayaga, K C; Rumph, J; DeNise, S

    2014-02-01

    Advances in genomics, molecular biology, and statistical genetics have created a paradigm shift in the way livestock producers pursue genetic improvement in their herds. The nexus of these technologies has resulted in combining genotypic and phenotypic information to compute genomically enhanced measures of genetic merit of individual animals. However, large numbers of genotyped and phenotyped animals are required to produce robust estimates of the effects of SNP that are summed together to generate direct genomic breeding values (DGV). Data on 11,756 Angus animals genotyped with the Illumina BovineSNP50 Beadchip were used to develop genomic predictions for 17 traits reported by the American Angus Association through Angus Genetics Inc. in their National Cattle Evaluation program. Marker effects were computed using a 5-fold cross-validation approach and a Bayesian model averaging algorithm. The accuracies were examined with EBV and deregressed EBV (DEBV) response variables and with K-means and identical by state (IBS)-based cross-validation methodologies. The cross-validation accuracies obtained using EBV response variables were consistently greater than those obtained using DEBV (average correlations were 0.64 vs. 0.57). The accuracies obtained using K-means cross-validation were consistently smaller than accuracies obtained with the IBS-based cross-validation approach (average correlations were 0.58 vs. 0.64 with EBV used as a response variable). Comparing the results from the current study with the results from a similar study consisting of only 2,253 records indicated that larger training population size resulted in higher accuracies in validation animals and explained on average 18% (69% improvement) additional genetic variance across all traits.

  19. Cross-validation analysis of bias models in Bayesian multi-model projections of climate

    NASA Astrophysics Data System (ADS)

    Huttunen, J. M. J.; Räisänen, J.; Nissinen, A.; Lipponen, A.; Kolehmainen, V.

    2017-03-01

    Climate change projections are commonly based on multi-model ensembles of climate simulations. In this paper we consider the choice of bias models in Bayesian multimodel predictions. Buser et al. (Clim Res 44(2-3):227-241, 2010a) introduced a hybrid bias model which combines commonly used constant bias and constant relation bias assumptions. The hybrid model includes a weighting parameter which balances these bias models. In this study, we use a cross-validation approach to study which bias model or bias parameter leads to, in a specific sense, optimal climate change projections. The analysis is carried out for summer and winter season means of 2 m-temperatures spatially averaged over the IPCC SREX regions, using 19 model runs from the CMIP5 data set. The cross-validation approach is applied to calculate optimal bias parameters (in the specific sense) for projecting the temperature change from the control period (1961-2005) to the scenario period (2046-2090). The results are compared to the results of the Buser et al. (Clim Res 44(2-3):227-241, 2010a) method which includes the bias parameter as one of the unknown parameters to be estimated from the data.

  20. The Religious Support Scale: construction, validation, and cross-validation.

    PubMed

    Fiala, William E; Bjorck, Jeffrey P; Gorsuch, Richard

    2002-12-01

    Cutrona and Russell's social support model was used to develop a religious support measure (C. E. Cutrona & D. W. Russell, 1987), including 3 distinct but related subscales respectively measuring support from God, the congregation, and church leadership. Factor analyses with the main sample's data (249 Protestants) and cross-validation (93 additional Protestants) supported the scales' reliability and validity. All 3 types of religious support were related to lower depression and greater life satisfaction. Moreover, several relationships between the 3 subscales and psychological functioning variables remained significant after controlling for variance because of church attendance and social support. Results suggest that religious attendance does not automatically imply religious support, and that religious support can provide unique resources for religious persons, above and beyond those furnished by social support. Findings are discussed regarding relevance to community psychology.

  1. Robust cross-validation of linear regression QSAR models.

    PubMed

    Konovalov, Dmitry A; Llewellyn, Lyndon E; Vander Heyden, Yvan; Coomans, Danny

    2008-10-01

    A quantitative structure-activity relationship (QSAR) model is typically developed to predict the biochemical activity of untested compounds from the compounds' molecular structures. "The gold standard" of model validation is the blindfold prediction when the model's predictive power is assessed from how well the model predicts the activity values of compounds that were not considered in any way during the model development/calibration. However, during the development of a QSAR model, it is necessary to obtain some indication of the model's predictive power. This is often done by some form of cross-validation (CV). In this study, the concepts of the predictive power and fitting ability of a multiple linear regression (MLR) QSAR model were examined in the CV context allowing for the presence of outliers. Commonly used predictive power and fitting ability statistics were assessed via Monte Carlo cross-validation when applied to percent human intestinal absorption, blood-brain partition coefficient, and toxicity values of saxitoxin QSAR data sets, as well as three known benchmark data sets with known outlier contamination. It was found that (1) a robust version of MLR should always be preferred over the ordinary-least-squares MLR, regardless of the degree of outlier contamination and that (2) the model's predictive power should only be assessed via robust statistics. The Matlab and java source code used in this study is freely available from the QSAR-BENCH section of www.dmitrykonovalov.org for academic use. The Web site also contains the java-based QSAR-BENCH program, which could be run online via java's Web Start technology (supporting Windows, Mac OSX, Linux/Unix) to reproduce most of the reported results or apply the reported procedures to other data sets.

  2. Cross-validating a bidimensional mathematics anxiety scale.

    PubMed

    Haiyan Bai

    2011-03-01

    The psychometric properties of a 14-item bidimensional Mathematics Anxiety Scale-Revised (MAS-R) were empirically cross-validated with two independent samples consisting of 647 secondary school students. An exploratory factor analysis on the scale yielded strong construct validity with a clear two-factor structure. The results from a confirmatory factor analysis indicated an excellent model-fit (χ(2) = 98.32, df = 62; normed fit index = .92, comparative fit index = .97; root mean square error of approximation = .04). The internal consistency (.85), test-retest reliability (.71), interfactor correlation (.26, p < .001), and positive discrimination power indicated that MAS-R is a psychometrically reliable and valid instrument for measuring mathematics anxiety. Math anxiety, as measured by MAS-R, correlated negatively with student achievement scores (r = -.38), suggesting that MAS-R may be a useful tool for classroom teachers and other educational personnel tasked with identifying students at risk of reduced math achievement because of anxiety.

  3. Correcting evaluation bias of relational classifiers with network cross validation

    DOE PAGES

    Neville, Jennifer; Gallagher, Brian; Eliassi-Rad, Tina; ...

    2011-01-04

    Recently, a number of modeling techniques have been developed for data mining and machine learning in relational and network domains where the instances are not independent and identically distributed (i.i.d.). These methods specifically exploit the statistical dependencies among instances in order to improve classification accuracy. However, there has been little focus on how these same dependencies affect our ability to draw accurate conclusions about the performance of the models. More specifically, the complex link structure and attribute dependencies in relational data violate the assumptions of many conventional statistical tests and make it difficult to use these tests to assess themore » models in an unbiased manner. In this work, we examine the task of within-network classification and the question of whether two algorithms will learn models that will result in significantly different levels of performance. We show that the commonly used form of evaluation (paired t-test on overlapping network samples) can result in an unacceptable level of Type I error. Furthermore, we show that Type I error increases as (1) the correlation among instances increases and (2) the size of the evaluation set increases (i.e., the proportion of labeled nodes in the network decreases). Lastly, we propose a method for network cross-validation that combined with paired t-tests produces more acceptable levels of Type I error while still providing reasonable levels of statistical power (i.e., 1–Type II error).« less

  4. Correcting evaluation bias of relational classifiers with network cross validation

    SciTech Connect

    Neville, Jennifer; Gallagher, Brian; Eliassi-Rad, Tina; Wang, Tao

    2011-01-04

    Recently, a number of modeling techniques have been developed for data mining and machine learning in relational and network domains where the instances are not independent and identically distributed (i.i.d.). These methods specifically exploit the statistical dependencies among instances in order to improve classification accuracy. However, there has been little focus on how these same dependencies affect our ability to draw accurate conclusions about the performance of the models. More specifically, the complex link structure and attribute dependencies in relational data violate the assumptions of many conventional statistical tests and make it difficult to use these tests to assess the models in an unbiased manner. In this work, we examine the task of within-network classification and the question of whether two algorithms will learn models that will result in significantly different levels of performance. We show that the commonly used form of evaluation (paired t-test on overlapping network samples) can result in an unacceptable level of Type I error. Furthermore, we show that Type I error increases as (1) the correlation among instances increases and (2) the size of the evaluation set increases (i.e., the proportion of labeled nodes in the network decreases). Lastly, we propose a method for network cross-validation that combined with paired t-tests produces more acceptable levels of Type I error while still providing reasonable levels of statistical power (i.e., 1–Type II error).

  5. Assessing the performance of spectroscopic models for cancer diagnostics using cross-validation and permutation testing

    NASA Astrophysics Data System (ADS)

    Lloyd, G. R.; Hutchings, J.; Almond, L. M.; Barr, H.; Kendall, C.; Stone, N.

    2012-01-01

    Multivariate classifiers (such as Linear Discriminant Analysis, Support Vector Machines etc) are known to be useful tools for making diagnostic decisions based on spectroscopic data. However, robust techniques for assessing their performance (e.g. by sensitivity and specificity) are vital if the application of these methods is to be successful in the clinic. In this work the application of repeated cross-validation for estimating confidence intervals for sensitivity and specificity of multivariate classifiers is presented. Furthermore, permutation testing is presented as a suitable technique for estimating the probability of obtaining the observed sensitivity and specificity by chance. Both approaches are demonstrated through their application to a Raman spectroscopic model of gastrointestinal cancer.

  6. Splenectomy Causes 10-Fold Increased Risk of Portal Venous System Thrombosis in Liver Cirrhosis Patients.

    PubMed

    Qi, Xingshun; Han, Guohong; Ye, Chun; Zhang, Yongguo; Dai, Junna; Peng, Ying; Deng, Han; Li, Jing; Hou, Feifei; Ning, Zheng; Zhao, Jiancheng; Zhang, Xintong; Wang, Ran; Guo, Xiaozhong

    2016-07-19

    BACKGROUND Portal venous system thrombosis (PVST) is a life-threatening complication of liver cirrhosis. We conducted a retrospective study to comprehensively analyze the prevalence and risk factors of PVST in liver cirrhosis. MATERIAL AND METHODS All cirrhotic patients without malignancy admitted between June 2012 and December 2013 were eligible if they underwent contrast-enhanced CT or MRI scans. Independent predictors of PVST in liver cirrhosis were calculated in multivariate analyses. Subgroup analyses were performed according to the severity of PVST (any PVST, main portal vein [MPV] thrombosis >50%, and clinically significant PVST) and splenectomy. Odds ratios (ORs) and 95% confidence intervals (CIs) were reported. RESULTS Overall, 113 cirrhotic patients were enrolled. The prevalence of PVST was 16.8% (19/113). Splenectomy (any PVST: OR=11.494, 95%CI=2.152-61.395; MPV thrombosis >50%: OR=29.987, 95%CI=3.247-276.949; clinically significant PVST: OR=40.415, 95%CI=3.895-419.295) and higher hemoglobin (any PVST: OR=0.974, 95%CI=0.953-0.996; MPV thrombosis >50%: OR=0.936, 95%CI=0.895-0.980; clinically significant PVST: OR=0.935, 95%CI=0.891-0.982) were the independent predictors of PVST. The prevalence of PVST was 13.3% (14/105) after excluding splenectomy. Higher hemoglobin was the only independent predictor of MPV thrombosis >50% (OR=0.952, 95%CI=0.909-0.997). No independent predictors of any PVST or clinically significant PVST were identified in multivariate analyses. Additionally, PVST patients who underwent splenectomy had a significantly higher proportion of clinically significant PVST but lower MELD score than those who did not undergo splenectomy. In all analyses, the in-hospital mortality was not significantly different between cirrhotic patient with and without PVST. CONCLUSIONS Splenectomy may increase by at least 10-fold the risk of PVST in liver cirrhosis independent of severity of liver dysfunction.

  7. Double Cross-Validation in Multiple Regression: A Method of Estimating the Stability of Results.

    ERIC Educational Resources Information Center

    Rowell, R. Kevin

    In multiple regression analysis, where resulting predictive equation effectiveness is subject to shrinkage, it is especially important to evaluate result replicability. Double cross-validation is an empirical method by which an estimate of invariance or stability can be obtained from research data. A procedure for double cross-validation is…

  8. Block-Regularized m × 2 Cross-Validated Estimator of the Generalization Error.

    PubMed

    Wang, Ruibo; Wang, Yu; Li, Jihong; Yang, Xingli; Yang, Jing

    2017-02-01

    A cross-validation method based on [Formula: see text] replications of two-fold cross validation is called an [Formula: see text] cross validation. An [Formula: see text] cross validation is used in estimating the generalization error and comparing of algorithms' performance in machine learning. However, the variance of the estimator of the generalization error in [Formula: see text] cross validation is easily affected by random partitions. Poor data partitioning may cause a large fluctuation in the number of overlapping samples between any two training (test) sets in [Formula: see text] cross validation. This fluctuation results in a large variance in the [Formula: see text] cross-validated estimator. The influence of the random partitions on variance becomes serious as [Formula: see text] increases. Thus, in this study, the partitions with a restricted number of overlapping samples between any two training (test) sets are defined as a block-regularized partition set. The corresponding cross validation is called block-regularized [Formula: see text] cross validation ([Formula: see text] BCV). It can effectively reduce the influence of random partitions. We prove that the variance of the [Formula: see text] BCV estimator of the generalization error is smaller than the variance of [Formula: see text] cross-validated estimator and reaches the minimum in a special situation. An analytical expression of the variance can also be derived in this special situation. This conclusion is validated through simulation experiments. Furthermore, a practical construction method of [Formula: see text] BCV by a two-level orthogonal array is provided. Finally, a conservative estimator is proposed for the variance of estimator of the generalization error.

  9. Comparison of cross-validation and bootstrap aggregating for building a seasonal streamflow forecast model

    NASA Astrophysics Data System (ADS)

    Schick, Simon; Rössler, Ole; Weingartner, Rolf

    2016-10-01

    Based on a hindcast experiment for the period 1982-2013 in 66 sub-catchments of the Swiss Rhine, the present study compares two approaches of building a regression model for seasonal streamflow forecasting. The first approach selects a single "best guess" model, which is tested by leave-one-out cross-validation. The second approach implements the idea of bootstrap aggregating, where bootstrap replicates are employed to select several models, and out-of-bag predictions provide model testing. The target value is mean streamflow for durations of 30, 60 and 90 days, starting with the 1st and 16th day of every month. Compared to the best guess model, bootstrap aggregating reduces the mean squared error of the streamflow forecast by seven percent on average. Thus, if resampling is anyway part of the model building procedure, bootstrap aggregating seems to be a useful strategy in statistical seasonal streamflow forecasting. Since the improved accuracy comes at the cost of a less interpretable model, the approach might be best suited for pure prediction tasks, e.g. as in operational applications.

  10. Estimators of the Squared Cross-Validity Coefficient: A Monte Carlo Investigation.

    ERIC Educational Resources Information Center

    And Others; Drasgow, Fritz

    1979-01-01

    A Monte Carlo experiment was used to evaluate four procedures for estimating the population squared cross-validity of a sample least squares regression equation. One estimator was particularly recommended. (Author/BH)

  11. The Performance of Cross-Validation Indices Used to Select among Competing Covariance Structure Models under Multivariate Nonnormality Conditions

    ERIC Educational Resources Information Center

    Whittaker, Tiffany A.; Stapleton, Laura M.

    2006-01-01

    Cudeck and Browne (1983) proposed using cross-validation as a model selection technique in structural equation modeling. The purpose of this study is to examine the performance of eight cross-validation indices under conditions not yet examined in the relevant literature, such as nonnormality and cross-validation design. The performance of each…

  12. Revealing Latent Value of Clinically Acquired CTs of Traumatic Brain Injury Through Multi-Atlas Segmentation in a Retrospective Study of 1,003 with External Cross-Validation.

    PubMed

    Plassard, Andrew J; Kelly, Patrick D; Asman, Andrew J; Kang, Hakmook; Patel, Mayur B; Landman, Bennett A

    2015-03-20

    Medical imaging plays a key role in guiding treatment of traumatic brain injury (TBI) and for diagnosing intracranial hemorrhage; most commonly rapid computed tomography (CT) imaging is performed. Outcomes for patients with TBI are variable and difficult to predict upon hospital admission. Quantitative outcome scales (e.g., the Marshall classification) have been proposed to grade TBI severity on CT, but such measures have had relatively low value in staging patients by prognosis. Herein, we examine a cohort of 1,003 subjects admitted for TBI and imaged clinically to identify potential prognostic metrics using a "big data" paradigm. For all patients, a brain scan was segmented with multi-atlas labeling, and intensity/volume/texture features were computed in a localized manner. In a 10-fold cross-validation approach, the explanatory value of the image-derived features is assessed for length of hospital stay (days), discharge disposition (five point scale from death to return home), and the Rancho Los Amigos functional outcome score (Rancho Score). Image-derived features increased the predictive R(2) to 0.38 (from 0.18) for length of stay, to 0.51 (from 0.4) for discharge disposition, and to 0.31 (from 0.16) for Rancho Score (over models consisting only of non-imaging admission metrics, but including positive/negative radiological CT findings). This study demonstrates that high volume retrospective analysis of clinical imaging data can reveal imaging signatures with prognostic value. These targets are suited for follow-up validation and represent targets for future feature selection efforts. Moreover, the increase in prognostic value would improve staging for intervention assessment and provide more reliable guidance for patients.

  13. Biased binomial assessment of cross-validated estimation of classification accuracies illustrated in diagnosis predictions.

    PubMed

    Noirhomme, Quentin; Lesenfants, Damien; Gomez, Francisco; Soddu, Andrea; Schrouff, Jessica; Garraux, Gaëtan; Luxen, André; Phillips, Christophe; Laureys, Steven

    2014-01-01

    Multivariate classification is used in neuroimaging studies to infer brain activation or in medical applications to infer diagnosis. Their results are often assessed through either a binomial or a permutation test. Here, we simulated classification results of generated random data to assess the influence of the cross-validation scheme on the significance of results. Distributions built from classification of random data with cross-validation did not follow the binomial distribution. The binomial test is therefore not adapted. On the contrary, the permutation test was unaffected by the cross-validation scheme. The influence of the cross-validation was further illustrated on real-data from a brain-computer interface experiment in patients with disorders of consciousness and from an fMRI study on patients with Parkinson disease. Three out of 16 patients with disorders of consciousness had significant accuracy on binomial testing, but only one showed significant accuracy using permutation testing. In the fMRI experiment, the mental imagery of gait could discriminate significantly between idiopathic Parkinson's disease patients and healthy subjects according to the permutation test but not according to the binomial test. Hence, binomial testing could lead to biased estimation of significance and false positive or negative results. In our view, permutation testing is thus recommended for clinical application of classification with cross-validation.

  14. Exact Analysis of Squared Cross-Validity Coefficient in Predictive Regression Models.

    PubMed

    Shieh, Gwowen

    2009-01-01

    In regression analysis, the notion of population validity is of theoretical interest for describing the usefulness of the underlying regression model, whereas the presumably more important concept of population cross-validity represents the predictive effectiveness for the regression equation in future research. It appears that the inference procedures of the squared multiple correlation coefficient have been extensively developed. In contrast, a full range of statistical methods for the analysis of the squared cross-validity coefficient is considerably far from complete. This article considers a distinct expression for the definition of the squared cross-validity coefficient as the direct connection and monotone transformation to the squared multiple correlation coefficient. Therefore, all the currently available exact methods for interval estimation, power calculation, and sample size determination of the squared multiple correlation coefficient are naturally modified and extended to the analysis of the squared cross-validity coefficient. The adequacies of the existing approximate procedures and the suggested exact method are evaluated through a Monte Carlo study. Furthermore, practical applications in areas of psychology and management are presented to illustrate the essential features of the proposed methodologies. The first empirical example uses 6 control variables related to driver characteristics and traffic congestion and their relation to stress in bus drivers, and the second example relates skills, cognitive performance, and personality to team performance measures. The results in this article can facilitate the recommended practice of cross-validation in psychological and other areas of social science research.

  15. Boosted leave-many-out cross-validation: the effect of training and test set diversity on PLS statistics.

    PubMed

    Clark, Robert D

    2003-01-01

    It is becoming increasingly common in quantitative structure/activity relationship (QSAR) analyses to use external test sets to evaluate the likely stability and predictivity of the models obtained. In some cases, such as those involving variable selection, an internal test set--i.e., a cross-validation set--is also used. Care is sometimes taken to ensure that the subsets used exhibit response and/or property distributions similar to those of the data set as a whole, but more often the individual observations are simply assigned 'at random.' In the special case of MLR without variable selection, it can be analytically demonstrated that this strategy is inferior to others. Most particularly, D-optimal design performs better if the form of the regression equation is known and the variables involved are well behaved. This report introduces an alternative, non-parametric approach termed 'boosted leave-many-out' (boosted LMO) cross-validation. In this method, relatively small training sets are chosen by applying optimizable k-dissimilarity selection (OptiSim) using a small subsample size (k = 4, in this case), with the unselected observations being reserved as a test set for the corresponding reduced model. Predictive errors for the full model are then estimated by aggregating results over several such analyses. The countervailing effects of training and test set size, diversity, and representativeness on PLS model statistics are described for CoMFA analysis of a large data set of COX2 inhibitors.

  16. The generalized cross-validation method applied to geophysical linear traveltime tomography

    NASA Astrophysics Data System (ADS)

    Bassrei, A.; Oliveira, N. P.

    2009-12-01

    The oil industry is the major user of Applied Geophysics methods for the subsurface imaging. Among different methods, the so-called seismic (or exploration seismology) methods are the most important. Tomography was originally developed for medical imaging and was introduced in exploration seismology in the 1980's. There are two main classes of geophysical tomography: those that use only the traveltimes between sources and receivers, which is a cinematic approach and those that use the wave amplitude itself, being a dynamic approach. Tomography is a kind of inverse problem, and since inverse problems are usually ill-posed, it is necessary to use some method to reduce their deficiencies. These difficulties of the inverse procedure are associated with the fact that the involved matrix is ill-conditioned. To compensate this shortcoming, it is appropriate to use some technique of regularization. In this work we make use of regularization with derivative matrices, also called smoothing. There is a crucial problem in regularization, which is the selection of the regularization parameter lambda. We use generalized cross validation (GCV) as a tool for the selection of lambda. GCV chooses the regularization parameter associated with the best average prediction for all possible omissions of one datum, corresponding to the minimizer of GCV function. GCV is used for an application in traveltime tomography, where the objective is to obtain the 2-D velocity distribution from the measured values of the traveltimes between sources and receivers. We present results with synthetic data, using a geological model that simulates different features, like a fault and a reservoir. The results using GCV are very good, including those contaminated with noise, and also using different regularization orders, attesting the feasibility of this technique.

  17. Cross Validation Through Two-dimensional Solution Surface for Cost-Sensitive SVM.

    PubMed

    Gu, Bin; Sheng, Victor; Tay, Keng; Romano, Walter; Li, Shuo

    2016-06-08

    Model selection plays an important role in cost-sensitive SVM (CS-SVM). It has been proven that the global minimum cross validation (CV) error can be efficiently computed based on the solution path for one parameter learning problems. However, it is a challenge to obtain the global minimum CV error for CS-SVM based on one-dimensional solution path and traditional grid search, because CS-SVM is with two regularization parameters. In this paper, we propose a solution and error surfaces based CV approach (CV-SES). More specifically, we first compute a two-dimensional solution surface for CS-SVM based on a bi-parameter space partition algorithm, which can fit solutions of CS-SVM for all values of both regularization parameters. Then, we compute a two-dimensional validation error surface for each CV fold, which can fit validation errors of CS-SVM for all values of both regularization parameters. Finally, we obtain the CV error surface by superposing K validation error surfaces, which can find the global minimum CV error of CS-SVM. Experiments are conducted on seven datasets for cost sensitive learning and on four datasets for imbalanced learning. Experimental results not only show that our proposed CV-SES has a better generalization ability than CS-SVM with various hybrids between grid search and solution path methods, and than recent proposed cost-sensitive hinge loss SVM with three-dimensional grid search, but also show that CV-SES uses less running time.

  18. Cross-Validating Chinese Language Mental Health Recovery Measures in Hong Kong

    ERIC Educational Resources Information Center

    Bola, John; Chan, Tiffany Hill Ching; Chen, Eric HY; Ng, Roger

    2016-01-01

    Objectives: Promoting recovery in mental health services is hampered by a shortage of reliable and valid measures, particularly in Hong Kong. We seek to cross validate two Chinese language measures of recovery and one of recovery-promoting environments. Method: A cross-sectional survey of people recovering from early episode psychosis (n = 121)…

  19. A Cross-Validation of MMPI Scales of Aggression on Male Criminal Criterion Groups

    ERIC Educational Resources Information Center

    Deiker, Thomas E.

    1974-01-01

    The 13 basic Minnesota Multiphasic Personality Inventory (MMPI) scales, 21 experimental scales of hostility and control, and four response-bias scales are cross-validated on 168 male criminals assigned to four aggressive criterion groups (nonviolent, threat, battery, and homicide). (Author)

  20. Reliable Digit Span: A Systematic Review and Cross-Validation Study

    ERIC Educational Resources Information Center

    Schroeder, Ryan W.; Twumasi-Ankrah, Philip; Baade, Lyle E.; Marshall, Paul S.

    2012-01-01

    Reliable Digit Span (RDS) is a heavily researched symptom validity test with a recent literature review yielding more than 20 studies ranging in dates from 1994 to 2011. Unfortunately, limitations within some of the research minimize clinical generalizability. This systematic review and cross-validation study was conducted to address these…

  1. The Employability of Psychologists in Academic Settings: A Cross-Validation.

    ERIC Educational Resources Information Center

    Quereshi, M. Y.

    1983-01-01

    Analyzed the curriculum vitae (CV) of 117 applicants for the position of assistant professor of psychology to yield four cross-validated factors. Comparisons of the results with those of four years ago indicated considerable stability of the factors. Scholarly publications remain an important factor. (JAC)

  2. A Cross-Validation of Sex Differences in the Expression of Depression.

    ERIC Educational Resources Information Center

    Chino, Allan F.; Funabiki, Dean

    1984-01-01

    Presents results of a cross-validational test of previous findings that men and women express depression differently. Reports that when depressed, females are more prone to somatic symptoms, self-deprecating statements, and less selectiveness than males in seeking out others. Qualifies findings, however, by posting a possible gap between reported…

  3. Cross-Validation of the Risk Matrix 2000 Sexual and Violent Scales

    ERIC Educational Resources Information Center

    Craig, Leam A.; Beech, Anthony; Browne, Kevin D.

    2006-01-01

    The predictive accuracy of the newly developed actuarial risk measures Risk Matrix 2000 Sexual/Violence (RMS, RMV) were cross validated and compared with two risk assessment measures (SVR-20 and Static-99) in a sample of sexual (n = 85) and nonsex violent (n = 46) offenders. The sexual offense reconviction rate for the sex offender group was 18%…

  4. Validity Evidence in Scale Development: The Application of Cross Validation and Classification-Sequencing Validation

    ERIC Educational Resources Information Center

    Acar, Tu¨lin

    2014-01-01

    In literature, it has been observed that many enhanced criteria are limited by factor analysis techniques. Besides examinations of statistical structure and/or psychological structure, such validity studies as cross validation and classification-sequencing studies should be performed frequently. The purpose of this study is to examine cross…

  5. Cross-Validation of FITNESSGRAM® Health-Related Fitness Standards in Hungarian Youth

    ERIC Educational Resources Information Center

    Laurson, Kelly R.; Saint-Maurice, Pedro F.; Karsai, István; Csányi, Tamás

    2015-01-01

    Purpose: The purpose of this study was to cross-validate FITNESSGRAM® aerobic and body composition standards in a representative sample of Hungarian youth. Method: A nationally representative sample (N = 405) of Hungarian adolescents from the Hungarian National Youth Fitness Study (ages 12-18.9 years) participated in an aerobic capacity assessment…

  6. A New Symptom Model for Autism Cross-Validated in an Independent Sample

    ERIC Educational Resources Information Center

    Boomsma, A.; Van Lang, N. D. J.; De Jonge, M. V.; De Bildt, A. A.; Van Engeland, H.; Minderaa, R. B.

    2008-01-01

    Background: Results from several studies indicated that a symptom model other than the DSM triad might better describe symptom domains of autism. The present study focused on a) investigating the stability of a new symptom model for autism by cross-validating it in an independent sample and b) examining the invariance of the model regarding three…

  7. Exact Analysis of Squared Cross-Validity Coefficient in Predictive Regression Models

    ERIC Educational Resources Information Center

    Shieh, Gwowen

    2009-01-01

    In regression analysis, the notion of population validity is of theoretical interest for describing the usefulness of the underlying regression model, whereas the presumably more important concept of population cross-validity represents the predictive effectiveness for the regression equation in future research. It appears that the inference…

  8. Cross-Validation of the Quick Word Test as an Estimator of Adult Mental Ability

    ERIC Educational Resources Information Center

    Grotelueschen, Arden; McQuarrie, Duncan

    1970-01-01

    This report provides additional evidence that the Quick Word Test (Level 2, Form AM) is valid for estimating adult mental ability as defined by the Wechsler Adult Intelligence Scale. The validation sample is also described to facilitate use of the conversion table developed in the cross-validation analysis. (Author/LY)

  9. Simulating California Reservoir Operation Using the Classification and Regression Tree Algorithm Combined with a Shuffled Cross-Validation Scheme

    NASA Astrophysics Data System (ADS)

    Yang, T.; Gao, X.; Sorooshian, S.; Li, X.

    2015-12-01

    The controlled outflows from a reservoir or dam are highly dependent on the decisions made by the reservoir operators, instead of a natural hydrological process. Difference exists between the natural upstream inflows to reservoirs, and the controlled outflows from reservoirs that supply the downstream users. With the decision maker's awareness of changing climate, reservoir management requires adaptable means to incorporate more information into decision making, such as the consideration of policy and regulation, environmental constraints, dry/wet conditions, etc. In this paper, a reservoir outflow simulation model is presented, which incorporates one of the well-developed data-mining models (Classification and Regression Tree) to predict the complicated human-controlled reservoir outflows and extract the reservoir operation patterns. A shuffled cross-validation approach is further implemented to improve model's predictive performance. An application study of 9 major reservoirs in California is carried out and the simulated results from different decision tree approaches are compared with observation, including original CART and Random Forest. The statistical measurements show that CART combined with the shuffled cross-validation scheme gives a better predictive performance over the other two methods, especially in simulating the peak flows. The results for simulated controlled outflow, storage changes and storage trajectories also show that the proposed model is able to consistently and reasonably predict the human's reservoir operation decisions. In addition, we found that the operation in the Trinity Lake, Oroville Lake and Shasta Lake are greatly influenced by policy and regulation, while low elevation reservoirs are more sensitive to inflow amount than others.

  10. Methodology Review: Estimation of Population Validity and Cross-Validity, and the Use of Equal Weights in Prediction.

    ERIC Educational Resources Information Center

    Raju, Nambury S.; Bilgic, Reyhan; Edwards, Jack E.; Fleer, Paul F.

    1997-01-01

    This review finds that formula-based procedures can be used in place of empirical validation for estimating population validity or in place of empirical cross-validation for estimating population cross-validity. Discusses conditions under which the equal weights procedure is a viable alternative. (SLD)

  11. Cross-Validation of easyCBM Reading Cut Scores in Oregon: 2009-2010. Technical Report #1108

    ERIC Educational Resources Information Center

    Park, Bitnara Jasmine; Irvin, P. Shawn; Anderson, Daniel; Alonzo, Julie; Tindal, Gerald

    2011-01-01

    This technical report presents results from a cross-validation study designed to identify optimal cut scores when using easyCBM[R] reading tests in Oregon. The cross-validation study analyzes data from the 2009-2010 academic year for easyCBM[R] reading measures. A sample of approximately 2,000 students per grade, randomly split into two groups of…

  12. Automatic regularization parameter selection by generalized cross-validation for total variational Poisson noise removal.

    PubMed

    Zhang, Xiongjun; Javidi, Bahram; Ng, Michael K

    2017-03-20

    In this paper, we propose an alternating minimization algorithm with an automatic selection of the regularization parameter for image reconstruction of photon-counted images. By using the generalized cross-validation technique, the regularization parameter can be updated in the iterations of the alternating minimization algorithm. Experimental results show that our proposed algorithm outperforms the two existing methods, the maximum likelihood expectation maximization estimator with total variation regularization and the primal dual method, where the parameters must be set in advance.

  13. Application of robust Generalised Cross-Validation to the inverse problem of electrocardiology.

    PubMed

    Barnes, Josef P; Johnston, Peter R

    2016-02-01

    Robust Generalised Cross-Validation was proposed recently as a method for determining near optimal regularisation parameters in inverse problems. It was introduced to overcome a problem with the regular Generalised Cross-Validation method in which the function that is minimised to obtain the regularisation parameter often has a broad, flat minimum, resulting in a poor estimate for the parameter. The robust method defines a new function to be minimised which has a narrower minimum, but at the expense of introducing a new parameter called the robustness parameter. In this study, the Robust Generalised Cross-Validation method is applied to the inverse problem of electrocardiology. It is demonstrated that, for realistic situations, the robustness parameter can be set to zero. With this choice of robustness parameter, it is shown that the robust method is able to obtain estimates of the regularisation parameter in the inverse problem of electrocardiology that are comparable to, or better than, many of the standard methods that are applied to this inverse problem.

  14. Cross-validation of matching correlation analysis by resampling matching weights.

    PubMed

    Shimodaira, Hidetoshi

    2016-03-01

    The strength of association between a pair of data vectors is represented by a nonnegative real number, called matching weight. For dimensionality reduction, we consider a linear transformation of data vectors, and define a matching error as the weighted sum of squared distances between transformed vectors with respect to the matching weights. Given data vectors and matching weights, the optimal linear transformation minimizing the matching error is solved by the spectral graph embedding of Yan et al. (2007). This method is a generalization of the canonical correlation analysis, and will be called as matching correlation analysis (MCA). In this paper, we consider a novel sampling scheme where the observed matching weights are randomly sampled from underlying true matching weights with small probability, whereas the data vectors are treated as constants. We then investigate a cross-validation by resampling the matching weights. Our asymptotic theory shows that the cross-validation, if rescaled properly, computes an unbiased estimate of the matching error with respect to the true matching weights. Existing ideas of cross-validation for resampling data vectors, instead of resampling matching weights, are not applicable here. MCA can be used for data vectors from multiple domains with different dimensions via an embarrassingly simple idea of coding the data vectors. This method will be called as cross-domain matching correlation analysis (CDMCA), and an interesting connection to the classical associative memory model of neural networks is also discussed.

  15. A statistical method (cross-validation) for bone loss region detection after spaceflight

    PubMed Central

    Zhao, Qian; Li, Wenjun; Li, Caixia; Chu, Philip W.; Kornak, John; Lang, Thomas F.

    2010-01-01

    Astronauts experience bone loss after the long spaceflight missions. Identifying specific regions that undergo the greatest losses (e.g. the proximal femur) could reveal information about the processes of bone loss in disuse and disease. Methods for detecting such regions, however, remains an open problem. This paper focuses on statistical methods to detect such regions. We perform statistical parametric mapping to get t-maps of changes in images, and propose a new cross-validation method to select an optimum suprathreshold for forming clusters of pixels. Once these candidate clusters are formed, we use permutation testing of longitudinal labels to derive significant changes. PMID:20632144

  16. Using cross-validation to evaluate predictive accuracy of survival risk classifiers based on high-dimensional data.

    PubMed

    Simon, Richard M; Subramanian, Jyothi; Li, Ming-Chung; Menezes, Supriya

    2011-05-01

    Developments in whole genome biotechnology have stimulated statistical focus on prediction methods. We review here methodology for classifying patients into survival risk groups and for using cross-validation to evaluate such classifications. Measures of discrimination for survival risk models include separation of survival curves, time-dependent ROC curves and Harrell's concordance index. For high-dimensional data applications, however, computing these measures as re-substitution statistics on the same data used for model development results in highly biased estimates. Most developments in methodology for survival risk modeling with high-dimensional data have utilized separate test data sets for model evaluation. Cross-validation has sometimes been used for optimization of tuning parameters. In many applications, however, the data available are too limited for effective division into training and test sets and consequently authors have often either reported re-substitution statistics or analyzed their data using binary classification methods in order to utilize familiar cross-validation. In this article we have tried to indicate how to utilize cross-validation for the evaluation of survival risk models; specifically how to compute cross-validated estimates of survival distributions for predicted risk groups and how to compute cross-validated time-dependent ROC curves. We have also discussed evaluation of the statistical significance of a survival risk model and evaluation of whether high-dimensional genomic data adds predictive accuracy to a model based on standard covariates alone.

  17. Interrelations between temperament, character, and parental rearing among delinquent adolescents: a cross-validation.

    PubMed

    Richter, Jörg; Krecklow, Beate; Eisemann, Matrin

    2002-01-01

    We performed a cross-validation of results from investigations in juvenile delinquents in Russia and Germany concerning relationships of personality characteristics in terms of temperament and character with parental rearing. Both studies used the Temperament and Character Inventory (TCI) based on Cloninger's psychobiological theory, and the Own Memories on Parenting (Egna Minnen Beträffande Uppfostran-Swedish [EMBU]) questionnaire on parental rearing based on Perris' vulnerability model. The inter-relatedness of parental rearing, temperament, and character traits in socially normally integrated adolescents, as well as in delinquent adolescents, implying direct and indirect pathways from personality and parental rearing to delinquency, could be cross-validated. Differences between delinquents and socially normally integrated adolescents are rather based on different levels of expressions of various temperament traits, harm avoidance and novelty seeking in particular, and the character trait self-directedness, as well as on parental rearing behavior (predominantly parental rejection and emotional warmth) than on different structures within related developmental processes.

  18. Evaluation of a cross-validation stopping rule in MLE SPECT reconstruction.

    PubMed

    Falcón, C; Juvells, I; Pavía, J; Ros, D

    1998-05-01

    One of the problems in the routine use of the maximum-likelihood estimator method-expectation maximization (MLE-EM) algorithm is to decide when the iterative process should be stopped. We studied a cross-validation stopping rule to assess its usefulness in SPECT. We tested this stopping rule criterion in the MLE-EM algorithm without acceleration as well as in two accelerating algorithms, the successive substitutions algorithm (SSA) and the additive algorithm (AA). Different values of an acceleration factor were tested in SSA and AA. Our results from numerical and physical phantoms show that the stopping rule based on the cross-validation ratio (CVR) takes into account the similarity of the reconstructed image to the ideal image, noise and the contrast of the image. CVR yields reconstructed images with balanced values of the figures of merit (FOM) employed to assess the image quality. The CVR criterion can be used in the original MLE-EM algorithm as well as in SSA and AA. The reconstructed images obtained with SSA and AA showed FOM values that were very similar. These results were justified by considering AA to be an approximate form of SSA. The range of validity for the acceleration factor in SSA and AA was found to be [1, 2]. In this range, an inverse function connects the acceleration factor to the number of iterations needed to attain prefixed values of FOMs.

  19. Variational cross-validation of slow dynamical modes in molecular kinetics

    PubMed Central

    Pande, Vijay S.

    2015-01-01

    Markov state models are a widely used method for approximating the eigenspectrum of the molecular dynamics propagator, yielding insight into the long-timescale statistical kinetics and slow dynamical modes of biomolecular systems. However, the lack of a unified theoretical framework for choosing between alternative models has hampered progress, especially for non-experts applying these methods to novel biological systems. Here, we consider cross-validation with a new objective function for estimators of these slow dynamical modes, a generalized matrix Rayleigh quotient (GMRQ), which measures the ability of a rank-m projection operator to capture the slow subspace of the system. It is shown that a variational theorem bounds the GMRQ from above by the sum of the first m eigenvalues of the system’s propagator, but that this bound can be violated when the requisite matrix elements are estimated subject to statistical uncertainty. This overfitting can be detected and avoided through cross-validation. These result make it possible to construct Markov state models for protein dynamics in a way that appropriately captures the tradeoff between systematic and statistical errors. PMID:25833563

  20. Applicability of Monte Carlo cross validation technique for model development and validation using generalised least squares regression

    NASA Astrophysics Data System (ADS)

    Haddad, Khaled; Rahman, Ataur; A Zaman, Mohammad; Shrestha, Surendra

    2013-03-01

    SummaryIn regional hydrologic regression analysis, model selection and validation are regarded as important steps. Here, the model selection is usually based on some measurements of goodness-of-fit between the model prediction and observed data. In Regional Flood Frequency Analysis (RFFA), leave-one-out (LOO) validation or a fixed percentage leave out validation (e.g., 10%) is commonly adopted to assess the predictive ability of regression-based prediction equations. This paper develops a Monte Carlo Cross Validation (MCCV) technique (which has widely been adopted in Chemometrics and Econometrics) in RFFA using Generalised Least Squares Regression (GLSR) and compares it with the most commonly adopted LOO validation approach. The study uses simulated and regional flood data from the state of New South Wales in Australia. It is found that when developing hydrologic regression models, application of the MCCV is likely to result in a more parsimonious model than the LOO. It has also been found that the MCCV can provide a more realistic estimate of a model's predictive ability when compared with the LOO.

  1. Simulating California reservoir operation using the classification and regression-tree algorithm combined with a shuffled cross-validation scheme

    NASA Astrophysics Data System (ADS)

    Yang, Tiantian; Gao, Xiaogang; Sorooshian, Soroosh; Li, Xin

    2016-03-01

    The controlled outflows from a reservoir or dam are highly dependent on the decisions made by the reservoir operators, instead of a natural hydrological process. Difference exists between the natural upstream inflows to reservoirs and the controlled outflows from reservoirs that supply the downstream users. With the decision maker's awareness of changing climate, reservoir management requires adaptable means to incorporate more information into decision making, such as water delivery requirement, environmental constraints, dry/wet conditions, etc. In this paper, a robust reservoir outflow simulation model is presented, which incorporates one of the well-developed data-mining models (Classification and Regression Tree) to predict the complicated human-controlled reservoir outflows and extract the reservoir operation patterns. A shuffled cross-validation approach is further implemented to improve CART's predictive performance. An application study of nine major reservoirs in California is carried out. Results produced by the enhanced CART, original CART, and random forest are compared with observation. The statistical measurements show that the enhanced CART and random forest overperform the CART control run in general, and the enhanced CART algorithm gives a better predictive performance over random forest in simulating the peak flows. The results also show that the proposed model is able to consistently and reasonably predict the expert release decisions. Experiments indicate that the release operation in the Oroville Lake is significantly dominated by SWP allocation amount and reservoirs with low elevation are more sensitive to inflow amount than others.

  2. Test, revision, and cross-validation of the Physical Activity Self-Definition Model.

    PubMed

    Kendzierski, Deborah; Morganstein, Mara S

    2009-08-01

    Structural equation modeling was used to test an extended version of the Kendzierski, Furr, and Schiavoni (1998) Physical Activity Self-Definition Model. A revised model using data from 622 runners fit the data well. Cross-validation indices supported the revised model, and this model also provided a good fit to data from 397 cyclists. Partial invariance was found across activities. In both samples, perceived commitment and perceived ability had direct effects on self-definition, and perceived wanting, perceived trying, and enjoyment had indirect effects. The contribution of perceived ability to self-definition did not differ across activities. Implications concerning the original model, indirect effects, skill salience, and the role of context in self-definition are discussed.

  3. Feature selection based on fusing mutual information and cross-validation

    NASA Astrophysics Data System (ADS)

    Li, Wei-wei; Liu, Chun-ping; Chen, Ning-qiang; Wang, Zhao-hui

    2009-10-01

    Many algorithms have been proposed in literature for feature selection; unfortunately, none of them ensures a perfect result. Here we propose an adaptive sequential floating forward feature selection algorithm which achieves accuracy results higher than that of already existing algorithms and naturally adaptive for implementation into the number of best feature subset to be selected. The basic idea of the proposed algorithm is to adopt two relatively well-settled algorithms for the problem at hand and combine mutual information and Cross-Validation through suitable fusion techniques, with the aim of taking advantage of the adopted algorithms' capabilities, at the same time, limiting their deficiencies. This method adaptively obtains the number of features to be selected according to dimensions of original feature set, and Dempster-Shafer Evidential Theory is used to fuse Max-Relevance, Min-Redundancy and CVFS. Extensive experiments show that the higher accuracy of classification and the less redundancy of features could be achieved.

  4. Cross Validation for Selection of Cortical Interaction Models From Scalp EEG or MEG

    PubMed Central

    Cheung, Bing Leung Patrick; Nowak, Robert; Lee, Hyong Chol; van Drongelen, Wim; Van Veen, Barry D.

    2012-01-01

    A cross-validation (CV) method based on state-space framework is introduced for comparing the fidelity of different cortical interaction models to the measured scalp electroencephalogram (EEG) or magnetoencephalography (MEG) data being modeled. A state equation models the cortical interaction dynamics and an observation equation represents the scalp measurement of cortical activity and noise. The measured data are partitioned into training and test sets. The training set is used to estimate model parameters and the model quality is evaluated by computing test data innovations for the estimated model. Two CV metrics normalized mean square error and log-likelihood are estimated by averaging over different training/test partitions of the data. The effectiveness of this method of model selection is illustrated by comparing two linear modeling methods and two nonlinear modeling methods on simulated EEG data derived using both known dynamic systems and measured electrocorticography data from an epilepsy patient. PMID:22084038

  5. SU-E-T-231: Cross-Validation of 3D Gamma Comparison Tools

    SciTech Connect

    Alexander, KM; Jechel, C; Pinter, C; Lasso, A; Fichtinger, G; Salomons, G; Schreiner, LJ

    2015-06-15

    Purpose: Moving the computational analysis for 3D gel dosimetry into the 3D Slicer (www.slicer.org) environment has made gel dosimetry more clinically accessible. To ensure accuracy, we cross-validate the 3D gamma comparison module in 3D Slicer with an independently developed algorithm using simulated and measured dose distributions. Methods: Two reference dose distributions were generated using the Varian Eclipse treatment planning system. The first distribution consisted of a four-field box irradiation delivered to a plastic water phantom and the second, a VMAT plan delivered to a gel dosimeter phantom. The first reference distribution was modified within Eclipse to create an evaluated dose distribution by spatially shifting one field by 3mm, increasing the monitor units of the second field, applying a dynamic wedge for the third field, and leaving the fourth field unchanged. The VMAT plan was delivered to a gel dosimeter and the evaluated dose in the gel was calculated from optical CT measurements. Results from the gamma comparison tool built into the SlicerRT toolbox were compared to results from our in-house gamma algorithm implemented in Matlab (via MatlabBridge in 3D Slicer). The effects of noise, resolution and the exchange of reference and evaluated designations on the gamma comparison were also examined. Results: Perfect agreement was found between the gamma results obtained using the SlicerRT tool and our Matlab implementation for both the four-field box and gel datasets. The behaviour of the SlicerRT comparison with respect to changes in noise, resolution and the role of the reference and evaluated dose distributions was consistent with previous findings. Conclusion: Two independently developed gamma comparison tools have been cross-validated and found to be identical. As we transition our gel dosimetry analysis from Matlab to 3D Slicer, this validation serves as an important test towards ensuring the consistency of dose comparisons using the 3D Slicer

  6. Cross-validation of the 20- versus 30-s Wingate anaerobic test.

    PubMed

    Laurent, C Matthew; Meyers, Michael C; Robinson, Clay A; Green, J Matt

    2007-08-01

    The 30-s Wingate anaerobic test (30-WAT) is the most widely accepted protocol for measuring anaerobic response, despite documented physical side effects. Abbreviation of the 30-WAT without loss of data could enhance subject compliance while maintaining test applicability. The intent of this study was to quantify the validity of the 20-s Wingate anaerobic test (20-WAT) versus the traditional 30-WAT. Fifty males (mean +/- SEM; age = 20.5 +/- 0.3 years; Ht = 1.6 +/- 0.01 m; Wt = 75.5 +/- 2.6 kg) were randomly selected to either a validation (N = 35) or cross-validation group (N = 15) and completed a 20-WAT and 30-WAT in double blind, random order on separate days to determine peak power (PP; W kg(-1)), mean power (MP; W kg(-1)), and fatigue index (FI; %). Utilizing power outputs (relative to body mass) recorded during each second of both protocols, a non-linear regression equation (Y (20WAT+10 )= 31.4697 e(-0.5)[ln(X (second)/1174.3961)/2.6369(2)]; r (2) = 0.97; SEE = 0.56 W kg(-1)) successfully predicted (error approximately 10%) the final 10 s of power outputs in the cross-validation population. There were no significant differences between MP and FI between the 20-WAT that included the predicted 10 s of power outputs (20-WAT+10) and the 30-WAT. When derived data were subjected to Bland-Altman analyses, the majority of plots (93%) fell within the limits of agreement (+/-2SD). Therefore, when compared to the 30-WAT, the 20-WAT may be considered a valid alternative when used with the predictive non-linear regression equation to derive the final power output values.

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

    USGS Publications Warehouse

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

    2007-01-01

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

  8. Cross-validation and Peeling Strategies for Survival Bump Hunting using Recursive Peeling Methods

    PubMed Central

    Dazard, Jean-Eudes; Choe, Michael; LeBlanc, Michael; Rao, J. Sunil

    2015-01-01

    We introduce a framework to build a survival/risk bump hunting model with a censored time-to-event response. Our Survival Bump Hunting (SBH) method is based on a recursive peeling procedure that uses a specific survival peeling criterion derived from non/semi-parametric statistics such as the hazards-ratio, the log-rank test or the Nelson--Aalen estimator. To optimize the tuning parameter of the model and validate it, we introduce an objective function based on survival or prediction-error statistics, such as the log-rank test and the concordance error rate. We also describe two alternative cross-validation techniques adapted to the joint task of decision-rule making by recursive peeling and survival estimation. Numerical analyses show the importance of replicated cross-validation and the differences between criteria and techniques in both low and high-dimensional settings. Although several non-parametric survival models exist, none addresses the problem of directly identifying local extrema. We show how SBH efficiently estimates extreme survival/risk subgroups unlike other models. This provides an insight into the behavior of commonly used models and suggests alternatives to be adopted in practice. Finally, our SBH framework was applied to a clinical dataset. In it, we identified subsets of patients characterized by clinical and demographic covariates with a distinct extreme survival outcome, for which tailored medical interventions could be made. An R package PRIMsrc (Patient Rule Induction Method in Survival, Regression and Classification settings) is available on CRAN (Comprehensive R Archive Network) and GitHub. PMID:27034730

  9. 8x8 and 10x10 Hyperspace Representations of SU(3) and 10-fold Point-Symmetry Group of Quasicrystals

    NASA Astrophysics Data System (ADS)

    Animalu, Alexander

    2012-02-01

    In order to further elucidate the unexpected 10-fold point-symmetry group structure of quasi-crystals for which the 2011 Nobel Prize in chemistry was awarded to Daniel Shechtman, we explore a correspondence principle between the number of (projective) geometric elements (points[vertices] + lines[edges] + planes[faces]) of primitive cells of periodic or quasi-periodic arrangement of hard or deformable spheres in 3-dimensional space of crystallography and elements of quantum field theory of particle physics [points ( particles, lines ( particles, planes ( currents] and hence construct 8x8 =64 = 28+36 = 26 + 38, and 10x10 =100= 64 + 36 = 74 + 26 hyperspace representations of the SU(3) symmetry of elementary particle physics and quasicrystals of condensed matter (solid state) physics respectively, As a result, we predict the Cabibbo-like angles in leptonic decay of hadrons in elementary-particle physics and the observed 10-fold symmetric diffraction pattern of quasi-crystals.

  10. A self-adaptive genetic algorithm-artificial neural network algorithm with leave-one-out cross validation for descriptor selection in QSAR study.

    PubMed

    Wu, Jingheng; Mei, Juan; Wen, Sixiang; Liao, Siyan; Chen, Jincan; Shen, Yong

    2010-07-30

    Based on the quantitative structure-activity relationships (QSARs) models developed by artificial neural networks (ANNs), genetic algorithm (GA) was used in the variable-selection approach with molecule descriptors and helped to improve the back-propagation training algorithm as well. The cross validation techniques of leave-one-out investigated the validity of the generated ANN model and preferable variable combinations derived in the GAs. A self-adaptive GA-ANN model was successfully established by using a new estimate function for avoiding over-fitting phenomenon in ANN training. Compared with the variables selected in two recent QSAR studies that were based on stepwise multiple linear regression (MLR) models, the variables selected in self-adaptive GA-ANN model are superior in constructing ANN model, as they revealed a higher cross validation (CV) coefficient (Q(2)) and a lower root mean square deviation both in the established model and biological activity prediction. The introduced methods for validation, including leave-multiple-out, Y-randomization, and external validation, proved the superiority of the established GA-ANN models over MLR models in both stability and predictive power. Self-adaptive GA-ANN showed us a prospect of improving QSAR model.

  11. Cross-validation and hypothesis testing in neuroimaging: an irenic comment on the exchange between Friston and Lindquist et al

    PubMed Central

    Reiss, Philip T.

    2016-01-01

    The “ten ironic rules for statistical reviewers” presented by Friston (2012) prompted a rebuttal by Lindquist et al. (2013), which was followed by a rejoinder by Friston (2013). A key issue left unresolved in this discussion is the use of cross-validation to test the significance of predictive analyses. This note discusses the role that cross-validation-based and related hypothesis tests have come to play in modern data analyses, in neuroimaging and other fields. It is shown that such tests need not be suboptimal and can fill otherwise-unmet inferential needs. PMID:25918034

  12. Credible Intervals for Precision and Recall Based on a K-Fold Cross-Validated Beta Distribution.

    PubMed

    Wang, Yu; Li, Jihong

    2016-08-01

    In typical machine learning applications such as information retrieval, precision and recall are two commonly used measures for assessing an algorithm's performance. Symmetrical confidence intervals based on K-fold cross-validated t distributions are widely used for the inference of precision and recall measures. As we confirmed through simulated experiments, however, these confidence intervals often exhibit lower degrees of confidence, which may easily lead to liberal inference results. Thus, it is crucial to construct faithful confidence (credible) intervals for precision and recall with a high degree of confidence and a short interval length. In this study, we propose two posterior credible intervals for precision and recall based on K-fold cross-validated beta distributions. The first credible interval for precision (or recall) is constructed based on the beta posterior distribution inferred by all K data sets corresponding to K confusion matrices from a K-fold cross-validation. Second, considering that each data set corresponding to a confusion matrix from a K-fold cross-validation can be used to infer a beta posterior distribution of precision (or recall), the second proposed credible interval for precision (or recall) is constructed based on the average of K beta posterior distributions. Experimental results on simulated and real data sets demonstrate that the first credible interval proposed in this study almost always resulted in degrees of confidence greater than 95%. With an acceptable degree of confidence, both of our two proposed credible intervals have shorter interval lengths than those based on a corrected K-fold cross-validated t distribution. Meanwhile, the average ranks of these two credible intervals are superior to that of the confidence interval based on a K-fold cross-validated t distribution for the degree of confidence and are superior to that of the confidence interval based on a corrected K-fold cross-validated t distribution for the

  13. Cross-validation and hypothesis testing in neuroimaging: An irenic comment on the exchange between Friston and Lindquist et al.

    PubMed

    Reiss, Philip T

    2015-08-01

    The "ten ironic rules for statistical reviewers" presented by Friston (2012) prompted a rebuttal by Lindquist et al. (2013), which was followed by a rejoinder by Friston (2013). A key issue left unresolved in this discussion is the use of cross-validation to test the significance of predictive analyses. This note discusses the role that cross-validation-based and related hypothesis tests have come to play in modern data analyses, in neuroimaging and other fields. It is shown that such tests need not be suboptimal and can fill otherwise-unmet inferential needs.

  14. A cross-validated cytoarchitectonic atlas of the human ventral visual stream.

    PubMed

    Rosenke, M; Weiner, K S; Barnett, M A; Zilles, K; Amunts, K; Goebel, R; Grill-Spector, K

    2017-02-14

    The human ventral visual stream consists of several areas considered processing stages essential for perception and recognition. A fundamental microanatomical feature differentiating areas is cytoarchitecture, which refers to the distribution, size, and density of cells across cortical layers. Because cytoarchitectonic structure is measured in 20-micron-thick histological slices of postmortem tissue, it is difficult to assess (a) how anatomically consistent these areas are across brains and (b) how they relate to brain parcellations obtained with prevalent neuroimaging methods, acquired at the millimeter and centimeter scale. Therefore, the goal of this study was to (a) generate a cross-validated cytoarchitectonic atlas of the human ventral visual stream on a whole brain template that is commonly used in neuroimaging studies and (b) to compare this atlas to a recently published retinotopic parcellation of visual cortex (Wang, 2014). To achieve this goal, we generated an atlas of eight cytoarchitectonic areas: four areas in the occipital lobe (hOc1-hOc4v) and four in the fusiform gyrus (FG1-FG4) and tested how alignment technique affects the accuracy of the atlas. Results show that both cortex-based alignment (CBA) and nonlinear volumetric alignment (NVA) generate an atlas with better cross-validation performance than affine volumetric alignment (AVA). Additionally, CBA outperformed NVA in 6/8 of the cytoarchitectonic areas. Finally, the comparison of the cytoarchitectonic atlas to a retinotopic atlas shows a clear correspondence between cytoarchitectonic and retinotopic areas in the ventral visual stream. The successful performance of CBA suggests a coupling between cytoarchitectonic areas and macroanatomical landmarks in the human ventral visual stream, and furthermore that this coupling can be utilized towards generating an accurate group atlas. In addition, the coupling between cytoarchitecture and retinotopy highlights the potential use of this atlas in

  15. A Cross-Validation of easyCBM[R] Mathematics Cut Scores in Oregon: 2009-2010. Technical Report #1104

    ERIC Educational Resources Information Center

    Anderson, Daniel; Alonzo, Julie; Tindal, Gerald

    2011-01-01

    In this technical report, we document the results of a cross-validation study designed to identify optimal cut-scores for the use of the easyCBM[R] mathematics test in Oregon. A large sample, randomly split into two groups of roughly equal size, was used for this study. Students' performance classification on the Oregon state test was used as the…

  16. How Nonrecidivism Affects Predictive Accuracy: Evidence from a Cross-Validation of the Ontario Domestic Assault Risk Assessment (ODARA)

    ERIC Educational Resources Information Center

    Hilton, N. Zoe; Harris, Grant T.

    2009-01-01

    Prediction effect sizes such as ROC area are important for demonstrating a risk assessment's generalizability and utility. How a study defines recidivism might affect predictive accuracy. Nonrecidivism is problematic when predicting specialized violence (e.g., domestic violence). The present study cross-validates the ability of the Ontario…

  17. Population Validity and Cross-Validity: Applications of Distribution Theory for Testing Hypotheses, Setting Confidence Intervals, and Determining Sample Size

    ERIC Educational Resources Information Center

    Algina, James; Keselman, H. J.

    2008-01-01

    Applications of distribution theory for the squared multiple correlation coefficient and the squared cross-validation coefficient are reviewed, and computer programs for these applications are made available. The applications include confidence intervals, hypothesis testing, and sample size selection. (Contains 2 tables.)

  18. Density-preserving sampling: robust and efficient alternative to cross-validation for error estimation.

    PubMed

    Budka, Marcin; Gabrys, Bogdan

    2013-01-01

    Estimation of the generalization ability of a classification or regression model is an important issue, as it indicates the expected performance on previously unseen data and is also used for model selection. Currently used generalization error estimation procedures, such as cross-validation (CV) or bootstrap, are stochastic and, thus, require multiple repetitions in order to produce reliable results, which can be computationally expensive, if not prohibitive. The correntropy-inspired density-preserving sampling (DPS) procedure proposed in this paper eliminates the need for repeating the error estimation procedure by dividing the available data into subsets that are guaranteed to be representative of the input dataset. This allows the production of low-variance error estimates with an accuracy comparable to 10 times repeated CV at a fraction of the computations required by CV. This method can also be used for model ranking and selection. This paper derives the DPS procedure and investigates its usability and performance using a set of public benchmark datasets and standard classifiers.

  19. Approximate l-fold cross-validation with Least Squares SVM and Kernel Ridge Regression

    SciTech Connect

    Edwards, Richard E; Zhang, Hao; Parker, Lynne Edwards; New, Joshua Ryan

    2013-01-01

    Kernel methods have difficulties scaling to large modern data sets. The scalability issues are based on computational and memory requirements for working with a large matrix. These requirements have been addressed over the years by using low-rank kernel approximations or by improving the solvers scalability. However, Least Squares Support VectorMachines (LS-SVM), a popular SVM variant, and Kernel Ridge Regression still have several scalability issues. In particular, the O(n^3) computational complexity for solving a single model, and the overall computational complexity associated with tuning hyperparameters are still major problems. We address these problems by introducing an O(n log n) approximate l-fold cross-validation method that uses a multi-level circulant matrix to approximate the kernel. In addition, we prove our algorithm s computational complexity and present empirical runtimes on data sets with approximately 1 million data points. We also validate our approximate method s effectiveness at selecting hyperparameters on real world and standard benchmark data sets. Lastly, we provide experimental results on using a multi-level circulant kernel approximation to solve LS-SVM problems with hyperparameters selected using our method.

  20. Cross-validation of a Shortened Battery for the Assessment of Dysexecutive Disorders in Alzheimer Disease.

    PubMed

    Godefroy, Olivier; Martinaud, Olivier; Verny, Marc; Mosca, Chrystèle; Lenoir, Hermine; Bretault, Eric; Devendeville, Agnès; Diouf, Momar; Pere, Jean-Jacques; Bakchine, Serge; Delabrousse-Mayoux, Jean-Philippe; Roussel, Martine

    2016-01-01

    The frequency of executive disorders in mild-to-moderate Alzheimer disease (AD) has been demonstrated by the application of a comprehensive battery. The present study analyzed data from 2 recent multicenter studies based on the same executive battery. The objective was to derive a shortened battery by using the GREFEX population as a training dataset and by cross-validating the results in the REFLEX population. A total of 102 AD patients of the GREFEX study (MMSE=23.2±2.9) and 72 patients of the REFLEX study (MMSE=20.8±3.5) were included. Tests were selected and receiver operating characteristic curves were generated relative to the performance of 780 controls from the GREFEX study. Stepwise logistic regression identified 3 cognitive tests (Six Elements Task, categorical fluency and Trail Making Test B error) and behavioral disorders globally referred as global hypoactivity (P=0.0001, all). This shortened battery was as accurate as the entire GREFEX battery in diagnosing dysexecutive disorders in both training group and the validation group. Bootstrap procedure confirmed the stability of AUC. A shortened battery based on 3 cognitive tests and 3 behavioral domains provides a high diagnosis accuracy of executive disorders in mild-to-moderate AD.

  1. Sound quality indicators for urban places in Paris cross-validated by Milan data.

    PubMed

    Ricciardi, Paola; Delaitre, Pauline; Lavandier, Catherine; Torchia, Francesca; Aumond, Pierre

    2015-10-01

    A specific smartphone application was developed to collect perceptive and acoustic data in Paris. About 3400 questionnaires were analyzed, regarding the global sound environment characterization, the perceived loudness of some emergent sources and the presence time ratio of sources that do not emerge from the background. Sound pressure level was recorded each second from the mobile phone's microphone during a 10-min period. The aim of this study is to propose indicators of urban sound quality based on linear regressions with perceptive variables. A cross validation of the quality models extracted from Paris data was carried out by conducting the same survey in Milan. The proposed sound quality general model is correlated with the real perceived sound quality (72%). Another model without visual amenity and familiarity is 58% correlated with perceived sound quality. In order to improve the sound quality indicator, a site classification was performed by Kohonen's Artificial Neural Network algorithm, and seven specific class models were developed. These specific models attribute more importance on source events and are slightly closer to the individual data than the global model. In general, the Parisian models underestimate the sound quality of Milan environments assessed by Italian people.

  2. Cross-validation of Waist-Worn GENEA Accelerometer Cut-Points

    PubMed Central

    Welch, Whitney A.; Bassett, David R.; Freedson, Patty S.; John, Dinesh; Steeves, Jeremy A.; Conger, Scott A.; Ceaser, Tyrone G.; Howe, Cheryl A.; Sasaki, Jeffer E.

    2014-01-01

    Purpose The purpose of this study was to determine the classification accuracy of the waist GENEA cut-points developed by Esliger et al. for predicting intensity categories across a range of lifestyle activities. Methods Each participant performed one of two routines, consisting of seven lifestyle activities (home/office, ambulatory, and sport). The GENEA was worn on the right waist and oxygen uptake was continuously measured using the Oxycon mobile. A one-way chi-square was used to determine the classification accuracy of the GENEA cut-points. Cross tabulation tables provided information on under- and over-estimations, and sensitivity and specificity analyses of the waist cut-points were also performed. Results Spearman’s rank order correlation for the GENEA SVMgs and Oxycon mobile MET values was 0.73. For all activities combined, the GENEA accurately predicted intensity classification 55.3% of the time, and increased to 58.3% when stationary cycling was removed from the analysis. The sensitivity of the cut-points for the four intensity categories ranged from 0.244 to 0.958 and the specificity ranged from 0.576 to 0.943. Conclusion In this cross-validation study, the proposed GENEA cut-points had a low overall accuracy rate for classifying intensity (55.3%) when engaging in 14 different lifestyle activities. PMID:24496118

  3. Electrochemical Performance and Stability of the Cathode for Solid Oxide Fuel Cells. I. Cross Validation of Polarization Measurements by Impedance Spectroscopy and Current-Potential Sweep

    SciTech Connect

    Zhou, Xiao Dong; Pederson, Larry R.; Templeton, Jared W.; Stevenson, Jeffry W.

    2009-12-09

    The aim of this paper is to address three issues in solid oxide fuel cells: (1) cross-validation of the polarization of a single cell measured using both dc and ac approaches, (2) the precise determination of the total areal specific resistance (ASR), and (3) understanding cathode polarization with LSCF cathodes. The ASR of a solid oxide fuel cell is a dynamic property, meaning that it changes with current density. The ASR measured using ac impedance spectroscopy (low frequency interception with real Z´ axis of ac impedance spectrum) matches with that measured from a dc IV sweep (the tangent of dc i-V curve). Due to the dynamic nature of ASR, we found that an ac impedance spectrum measured under open circuit voltage or on a half cell may not represent cathode performance under real operating conditions, particularly at high current density. In this work, the electrode polarization was governed by the cathode activation polarization; the anode contribution was negligible.

  4. Airborne environmental endotoxin: a cross-validation of sampling and analysis techniques.

    PubMed Central

    Walters, M; Milton, D; Larsson, L; Ford, T

    1994-01-01

    A standard method for measurement of airborne environmental endotoxin was developed and field tested in a fiberglass insulation-manufacturing facility. This method involved sampling with a capillary-pore membrane filter, extraction in buffer using a sonication bath, and analysis by the kinetic-Limulus assay with resistant-parallel-line estimation (KLARE). Cross-validation of the extraction and assay method was performed by comparison with methanolysis of samples followed by 3-hydroxy fatty acid (3-OHFA) analysis by gas chromatography-mass spectrometry. Direct methanolysis of filter samples and methanolysis of buffer extracts of the filters yielded similar 3-OHFA content (P = 0.72); the average difference was 2.1%. Analysis of buffer extracts for endotoxin content by the KLARE method and by gas chromatography-mass spectrometry for 3-OHFA content produced similar results (P = 0.23); the average difference was 0.88%. The source of endotoxin was gram-negative bacteria growing in recycled washwater used to clean the insulation-manufacturing equipment. The endotoxin and bacteria become airborne during spray cleaning operations. The types of 3-OHFAs in bacteria cultured from the washwater, present in the washwater and in the air, were similar. Virtually all of the bacteria cultured from air and water were gram negative composed mostly of two species, Deleya aesta and Acinetobacter johnsonii. Airborne countable bacteria correlated well with endotoxin (r2 = 0.64). Replicate sampling showed that results with the standard sampling, extraction, and Limulus assay by the KLARE method were highly reproducible (95% confidence interval for endotoxin measurement +/- 0.28 log10). These results demonstrate the accuracy, precision, and sensitivity of the standard procedure proposed for airborne environmental endotoxin. PMID:8161191

  5. The computation of generalized cross-validation functions through householder tridiagonalization with applications to the fitting of interaction spline models

    NASA Technical Reports Server (NTRS)

    Gu, Chong; Bates, Douglas M.; Chen, Zehua; Wahba, Grace

    1989-01-01

    An efficient algorithm for computing the generalized cross-validation function for the general cross-validated regularization/smoothing problem is provided. This algorithm is appropriate for problems where no natural structure is available, and the regularization/smoothing problem is solved (exactly) in a reproducing kernel Hilbert space. It is particularly appropriate for certain multivariate smoothing problems with irregularly spaced data, and certain remote sensing problems, such as those that occur in meteorology, where the sensors are arranged irregularly. The algorithm is applied to the fitting of interaction spline models with irregularly spaced data and two smoothing parameters; favorable timing results are presented. The algorithm may be extended to the computation of certain generalized maximum likelihood (GML) functions. Application of the GML algorithm to a problem in numerical weather forecasting, and to a broad class of hypothesis testing problems, is noted.

  6. Predicting Chinese Children and Youth's Energy Expenditure Using ActiGraph Accelerometers: A Calibration and Cross-Validation Study

    ERIC Educational Resources Information Center

    Zhu, Zheng; Chen, Peijie; Zhuang, Jie

    2013-01-01

    Purpose: The purpose of this study was to develop and cross-validate an equation based on ActiGraph accelerometer GT3X output to predict children and youth's energy expenditure (EE) of physical activity (PA). Method: Participants were 367 Chinese children and youth (179 boys and 188 girls, aged 9 to 17 years old) who wore 1 ActiGraph GT3X…

  7. The female sexual function index (FSFI): cross-validation and development of clinical cutoff scores.

    PubMed

    Wiegel, Markus; Meston, Cindy; Rosen, Raymond

    2005-01-01

    , independently. Discriminant validity testing confirmed the ability of both total and domain scores to differentiate between functional and nondysfunctional women. On the basis of sensitivity and specificity analyses and the CART procedure, we found an FSFI total score of 26.55 to be the optimal cut score for differentiating women with and without sexual dysfunction. On the basis of this cut-off we found 70.7% of women with sexual dysfunction and 88.1% of the sexually functional women in the cross-validation sample to be correctly classified. Addition of the lubrication score in the model resulted in slightly improved specificity (from .707 to .772) at a slight cost of sensitivity (from .881 to .854) for identifying women without sexual dysfunction. We discuss the results in terms of potential strengths and weaknesses of the FSFI, as well in terms of further clinical and research implications.

  8. Multi-parameter machine learning approach to the neuroanatomical basis of developmental dyslexia.

    PubMed

    Płoński, Piotr; Gradkowski, Wojciech; Altarelli, Irene; Monzalvo, Karla; van Ermingen-Marbach, Muna; Grande, Marion; Heim, Stefan; Marchewka, Artur; Bogorodzki, Piotr; Ramus, Franck; Jednoróg, Katarzyna

    2017-02-01

    Despite decades of research, the anatomical abnormalities associated with developmental dyslexia are still not fully described. Studies have focused on between-group comparisons in which different neuroanatomical measures were generally explored in isolation, disregarding potential interactions between regions and measures. Here, for the first time a multivariate classification approach was used to investigate grey matter disruptions in children with dyslexia in a large (N = 236) multisite sample. A variety of cortical morphological features, including volumetric (volume, thickness and area) and geometric (folding index and mean curvature) measures were taken into account and generalizability of classification was assessed with both 10-fold and leave-one-out cross validation (LOOCV) techniques. Classification into control vs. dyslexic subjects achieved above chance accuracy (AUC = 0.66 and ACC = 0.65 in the case of 10-fold CV, and AUC = 0.65 and ACC = 0.64 using LOOCV) after principled feature selection. Features that discriminated between dyslexic and control children were exclusively situated in the left hemisphere including superior and middle temporal gyri, subparietal sulcus and prefrontal areas. They were related to geometric properties of the cortex, with generally higher mean curvature and a greater folding index characterizing the dyslexic group. Our results support the hypothesis that an atypical curvature pattern with extra folds in left hemispheric perisylvian regions characterizes dyslexia. Hum Brain Mapp 38:900-908, 2017. © 2016 Wiley Periodicals, Inc.

  9. Bayesian cross-validation for model evaluation and selection, with application to the North American Breeding Bird Survey

    USGS Publications Warehouse

    Link, William; Sauer, John R.

    2016-01-01

    The analysis of ecological data has changed in two important ways over the last 15 years. The development and easy availability of Bayesian computational methods has allowed and encouraged the fitting of complex hierarchical models. At the same time, there has been increasing emphasis on acknowledging and accounting for model uncertainty. Unfortunately, the ability to fit complex models has outstripped the development of tools for model selection and model evaluation: familiar model selection tools such as Akaike's information criterion and the deviance information criterion are widely known to be inadequate for hierarchical models. In addition, little attention has been paid to the evaluation of model adequacy in context of hierarchical modeling, i.e., to the evaluation of fit for a single model. In this paper, we describe Bayesian cross-validation, which provides tools for model selection and evaluation. We describe the Bayesian predictive information criterion and a Bayesian approximation to the BPIC known as the Watanabe-Akaike information criterion. We illustrate the use of these tools for model selection, and the use of Bayesian cross-validation as a tool for model evaluation, using three large data sets from the North American Breeding Bird Survey.

  10. Cross-validation of the reduced form of the Food Craving Questionnaire-Trait using confirmatory factor analysis

    PubMed Central

    Iani, Luca; Barbaranelli, Claudio; Lombardo, Caterina

    2015-01-01

    Objective: The Food Craving Questionnaire-Trait (FCQ-T) is commonly used to assess habitual food cravings among individuals. Previous studies have shown that a brief version of this instrument (FCQ-T-r) has good reliability and validity. This article is the first to use Confirmatory factor analysis to examine the psychometric properties of the FCQ-T-r in a cross-validation study. Method: Habitual food cravings, as well as emotion regulation strategies, affective states, and disordered eating behaviors, were investigated in two independent samples of non-clinical adult volunteers (Sample 1: N = 368; Sample 2: N = 246). Confirmatory factor analyses were conducted to simultaneously test model fit statistics and dimensionality of the instrument. FCQ-T-r reliability was assessed by computing the composite reliability coefficient. Results: Analysis supported the unidimensional structure of the scale and fit indices were acceptable for both samples. The FCQ-T-r showed excellent reliability and moderate to high correlations with negative affect and disordered eating. Conclusion: Our results indicate that the FCQ-T-r scores can be reliably used to assess habitual cravings in an Italian non-clinical sample of adults. The robustness of these results is tested by a cross-validation of the model using two independent samples. Further research is required to expand on these findings, particularly in children and adolescents. PMID:25918510

  11. Fear factors: cross validation of specific phobia domains in a community-based sample of African American adults.

    PubMed

    Chapman, L Kevin; Vines, Lauren; Petrie, Jenny

    2011-05-01

    The current study attempted a cross-validation of specific phobia domains in a community-based sample of African American adults based on a previous model of phobia domains in a college student sample of African Americans. Subjects were 100 African American community-dwelling adults who completed the Fear Survey Schedule-Second Edition (FSS-II). Domains of fear were created using a similar procedure as the original, college sample of African American adults. A model including all of the phobia domains from the FSS-II was initially tested and resulted in poor model fit. Cross-validation was subsequently attempted through examining the original factor pattern of specific phobia domains from the college sample (Chapman, Kertz, Zurlage, & Woodruff-Borden, 2008). Data from the current, community based sample of African American adults provided poor fit to this model. The trimmed model for the current sample included the animal and social anxiety factors as in the original model. The natural environment-type specific phobia factor did not provide adequate fit for the community-based sample of African Americans. Results indicated that although different factor loading patterns of fear may exist among community-based African Americans as compared to African American college students, both animal and social fears are nearly identical in both groups, indicating a possible cultural homogeneity for phobias in African Americans. Potential explanations of these findings and future directions are discussed.

  12. Bayesian cross-validation for model evaluation and selection, with application to the North American Breeding Bird Survey.

    PubMed

    Link, William A; Sauer, John R

    2016-07-01

    The analysis of ecological data has changed in two important ways over the last 15 years. The development and easy availability of Bayesian computational methods has allowed and encouraged the fitting of complex hierarchical models. At the same time, there has been increasing emphasis on acknowledging and accounting for model uncertainty. Unfortunately, the ability to fit complex models has outstripped the development of tools for model selection and model evaluation: familiar model selection tools such as Akaike's information criterion and the deviance information criterion are widely known to be inadequate for hierarchical models. In addition, little attention has been paid to the evaluation of model adequacy in context of hierarchical modeling, i.e., to the evaluation of fit for a single model. In this paper, we describe Bayesian cross-validation, which provides tools for model selection and evaluation. We describe the Bayesian predictive information criterion and a Bayesian approximation to the BPIC known as the Watanabe-Akaike information criterion. We illustrate the use of these tools for model selection, and the use of Bayesian cross-validation as a tool for model evaluation, using three large data sets from the North American Breeding Bird Survey.

  13. The joint WAIS-III and WMS-III factor structure: development and cross-validation of a six-factor model of cognitive functioning.

    PubMed

    Tulsky, David S; Price, Larry R

    2003-06-01

    During the standardization of the Wechsler Adult Intelligence Scale (3rd ed.; WAIS-III) and the Wechsler Memory Scale (3rd ed.; WMS-III) the participants in the normative study completed both scales. This "co-norming" methodology set the stage for full integration of the 2 tests and the development of an expanded structure of cognitive functioning. Until now, however, the WAIS-III and WMS-III had not been examined together in a factor analytic study. This article presents a series of confirmatory factor analyses to determine the joint WAIS-III and WMS-III factor structure. Using a structural equation modeling approach, a 6-factor model that included verbal, perceptual, processing speed, working memory, auditory memory, and visual memory constructs provided the best model fit to the data. Allowing select subtests to load simultaneously on 2 factors improved model fit and indicated that some subtests are multifaceted. The results were then replicated in a large cross-validation sample (N = 858).

  14. Adaptive smoothing of high angular resolution diffusion-weighted imaging data by generalized cross-validation improves Q-ball orientation distribution function reconstruction.

    PubMed

    Metwalli, Nader S; Hu, Xiaoping P; Carew, John D

    2010-09-01

    Q-ball imaging (QBI) is a high angular resolution diffusion-weighted imaging (HARDI) technique for reconstructing the orientation distribution function (ODF). Some form of smoothing or regularization is typically required in the ODF reconstruction from low signal-to-noise ratio HARDI data. The amount of smoothing or regularization is usually set a priori at the discretion of the investigator. In this article, we apply an adaptive and objective means of smoothing the raw HARDI data using the smoothing splines on the sphere method with generalized cross-validation (GCV) to estimate the diffusivity profile in each voxel. Subsequently, we reconstruct the ODF, from the smoothed data, based on the Funk-Radon transform (FRT) used in QBI. The spline method was applied to both simulated data and in vivo human brain data. Simulated data show that the smoothing splines on the sphere method with GCV smoothing reduces the mean squared error in estimates of the ODF as compared with the standard analytical QBI approach. The human data demonstrate the utility of the method for estimating smooth ODFs.

  15. A cross-validation procedure for stopping the EM algorithm and deconvolution of neutron depth profiling spectra

    SciTech Connect

    Coakley, K.J. )

    1991-02-01

    The iterative EM algorithm is used to deconvolve neutron depth profiling spectra. Because of statistical noise in the data, artifacts in the estimated particle emission rate profile appear after too many iterations of the EM algorithm. To avoid artifacts, the EM algorithm is stopped using a cross-validation procedure. The data are split into two independent halves. The EM algorithm is applied to one half of the data to get an estimate of the emission rates. The algorithm is stopped when the conditional likelihood of the other half of the data passes through its maximum. The roles of the two halves of the data are then switched to get a second estimate of the emission rates. The two estimates are then averaged.

  16. A bayesian cross-validation approach to evaluate genetic baselines and forecast the necessary number of informative single nucleotide polymorphisms

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Mixed stock analysis (MSA) is a powerful tool used in the management and conservation of numerous species. Its function is to estimate the sources of contributions in a mixture of populations of a species, as well as to estimate the probabilities that individuals originated at a source. Considerable...

  17. Significant Association of Urinary Toxic Metals and Autism-Related Symptoms—A Nonlinear Statistical Analysis with Cross Validation

    PubMed Central

    Adams, James; Kruger, Uwe; Geis, Elizabeth; Gehn, Eva; Fimbres, Valeria; Pollard, Elena; Mitchell, Jessica; Ingram, Julie; Hellmers, Robert; Quig, David; Hahn, Juergen

    2017-01-01

    Introduction A number of previous studies examined a possible association of toxic metals and autism, and over half of those studies suggest that toxic metal levels are different in individuals with Autism Spectrum Disorders (ASD). Additionally, several studies found that those levels correlate with the severity of ASD. Methods In order to further investigate these points, this paper performs the most detailed statistical analysis to date of a data set in this field. First morning urine samples were collected from 67 children and adults with ASD and 50 neurotypical controls of similar age and gender. The samples were analyzed to determine the levels of 10 urinary toxic metals (UTM). Autism-related symptoms were assessed with eleven behavioral measures. Statistical analysis was used to distinguish participants on the ASD spectrum and neurotypical participants based upon the UTM data alone. The analysis also included examining the association of autism severity with toxic metal excretion data using linear and nonlinear analysis. “Leave-one-out” cross-validation was used to ensure statistical independence of results. Results and Discussion Average excretion levels of several toxic metals (lead, tin, thallium, antimony) were significantly higher in the ASD group. However, ASD classification using univariate statistics proved difficult due to large variability, but nonlinear multivariate statistical analysis significantly improved ASD classification with Type I/II errors of 15% and 18%, respectively. These results clearly indicate that the urinary toxic metal excretion profiles of participants in the ASD group were significantly different from those of the neurotypical participants. Similarly, nonlinear methods determined a significantly stronger association between the behavioral measures and toxic metal excretion. The association was strongest for the Aberrant Behavior Checklist (including subscales on Irritability, Stereotypy, Hyperactivity, and Inappropriate

  18. Evidence cross-validation and Bayesian inference of MAST plasma equilibria

    NASA Astrophysics Data System (ADS)

    von Nessi, G. T.; Hole, M. J.; Svensson, J.; Appel, L.

    2012-01-01

    In this paper, current profiles for plasma discharges on the mega-ampere spherical tokamak are directly calculated from pickup coil, flux loop, and motional-Stark effect observations via methods based in the statistical theory of Bayesian analysis. By representing toroidal plasma current as a series of axisymmetric current beams with rectangular cross-section and inferring the current for each one of these beams, flux-surface geometry and q-profiles are subsequently calculated by elementary application of Biot-Savart's law. The use of this plasma model in the context of Bayesian analysis was pioneered by Svensson and Werner on the joint-European tokamak [Svensson and Werner,Plasma Phys. Controlled Fusion 50(8), 085002 (2008)]. In this framework, linear forward models are used to generate diagnostic predictions, and the probability distribution for the currents in the collection of plasma beams was subsequently calculated directly via application of Bayes' formula. In this work, we introduce a new diagnostic technique to identify and remove outlier observations associated with diagnostics falling out of calibration or suffering from an unidentified malfunction. These modifications enable a good agreement between Bayesian inference of the last-closed flux-surface with other corroborating data, such as that from force balance considerations using EFIT++ [Appel et al., "A unified approach to equilibrium reconstruction" Proceedings of the 33rd EPS Conference on Plasma Physics (Rome, Italy, 2006)]. In addition, this analysis also yields errors on the plasma current profile and flux-surface geometry as well as directly predicting the Shafranov shift of the plasma core.

  19. Evidence cross-validation and Bayesian inference of MAST plasma equilibria

    SciTech Connect

    Nessi, G. T. von; Hole, M. J.; Svensson, J.; Appel, L.

    2012-01-15

    In this paper, current profiles for plasma discharges on the mega-ampere spherical tokamak are directly calculated from pickup coil, flux loop, and motional-Stark effect observations via methods based in the statistical theory of Bayesian analysis. By representing toroidal plasma current as a series of axisymmetric current beams with rectangular cross-section and inferring the current for each one of these beams, flux-surface geometry and q-profiles are subsequently calculated by elementary application of Biot-Savart's law. The use of this plasma model in the context of Bayesian analysis was pioneered by Svensson and Werner on the joint-European tokamak [Svensson and Werner,Plasma Phys. Controlled Fusion 50(8), 085002 (2008)]. In this framework, linear forward models are used to generate diagnostic predictions, and the probability distribution for the currents in the collection of plasma beams was subsequently calculated directly via application of Bayes' formula. In this work, we introduce a new diagnostic technique to identify and remove outlier observations associated with diagnostics falling out of calibration or suffering from an unidentified malfunction. These modifications enable a good agreement between Bayesian inference of the last-closed flux-surface with other corroborating data, such as that from force balance considerations using EFIT++[Appel et al., ''A unified approach to equilibrium reconstruction'' Proceedings of the 33rd EPS Conference on Plasma Physics (Rome, Italy, 2006)]. In addition, this analysis also yields errors on the plasma current profile and flux-surface geometry as well as directly predicting the Shafranov shift of the plasma core.

  20. An Improved Systematic Approach to Predicting Transcription Factor Target Genes Using Support Vector Machine

    PubMed Central

    Cui, Song; Youn, Eunseog; Lee, Joohyun; Maas, Stephan J.

    2014-01-01

    Biological prediction of transcription factor binding sites and their corresponding transcription factor target genes (TFTGs) makes great contribution to understanding the gene regulatory networks. However, these approaches are based on laborious and time-consuming biological experiments. Numerous computational approaches have shown great potential to circumvent laborious biological methods. However, the majority of these algorithms provide limited performances and fail to consider the structural property of the datasets. We proposed a refined systematic computational approach for predicting TFTGs. Based on previous work done on identifying auxin response factor target genes from Arabidopsis thaliana co-expression data, we adopted a novel reverse-complementary distance-sensitive n-gram profile algorithm. This algorithm converts each upstream sub-sequence into a high-dimensional vector data point and transforms the prediction task into a classification problem using support vector machine-based classifier. Our approach showed significant improvement compared to other computational methods based on the area under curve value of the receiver operating characteristic curve using 10-fold cross validation. In addition, in the light of the highly skewed structure of the dataset, we also evaluated other metrics and their associated curves, such as precision-recall curves and cost curves, which provided highly satisfactory results. PMID:24743548

  1. Assessing genomic prediction accuracy for Holstein sires using bootstrap aggregation sampling and leave-one-out cross validation.

    PubMed

    Mikshowsky, Ashley A; Gianola, Daniel; Weigel, Kent A

    2017-01-01

    Since the introduction of genome-enabled prediction for dairy cattle in 2009, genomic selection has markedly changed many aspects of the dairy genetics industry and enhanced the rate of response to selection for most economically important traits. Young dairy bulls are genotyped to obtain their genomic predicted transmitting ability (GPTA) and reliability (REL) values. These GPTA are a main factor in most purchasing, marketing, and culling decisions until bulls reach 5 yr of age and their milk-recorded offspring become available. At that time, daughter yield deviations (DYD) can be compared with the GPTA computed several years earlier. For most bulls, the DYD align well with the initial predictions. However, for some bulls, the difference between DYD and corresponding GPTA is quite large, and published REL are of limited value in identifying such bulls. A method of bootstrap aggregation sampling (bagging) using genomic BLUP (GBLUP) was applied to predict the GPTA of 2,963, 2,963, and 2,803 young Holstein bulls for protein yield, somatic cell score, and daughter pregnancy rate (DPR), respectively. For each trait, 50 bootstrap samples from a reference population comprising 2011 DYD of 8,610, 8,405, and 7,945 older Holstein bulls were used. Leave-one-out cross validation was also performed to assess prediction accuracy when removing specific bulls from the reference population. The main objectives of this study were (1) to assess the extent to which current REL values and alternative measures of variability, such as the bootstrap standard deviation (SD) of predictions, could detect bulls whose daughter performance deviates significantly from early genomic predictions, and (2) to identify factors associated with the reference population that inform about inaccurate genomic predictions. The SD of bootstrap predictions was a mildly useful metric for identifying bulls whose future daughter performance may deviate significantly from early GPTA for protein and DPR. Leave

  2. An intercomparison of a large ensemble of statistical downscaling methods for Europe: Overall results from the VALUE perfect predictor cross-validation experiment

    NASA Astrophysics Data System (ADS)

    Gutiérrez, Jose Manuel; Maraun, Douglas; Widmann, Martin; Huth, Radan; Hertig, Elke; Benestad, Rasmus; Roessler, Ole; Wibig, Joanna; Wilcke, Renate; Kotlarski, Sven

    2016-04-01

    VALUE is an open European network to validate and compare downscaling methods for climate change research (http://www.value-cost.eu). A key deliverable of VALUE is the development of a systematic validation framework to enable the assessment and comparison of both dynamical and statistical downscaling methods. This framework is based on a user-focused validation tree, guiding the selection of relevant validation indices and performance measures for different aspects of the validation (marginal, temporal, spatial, multi-variable). Moreover, several experiments have been designed to isolate specific points in the downscaling procedure where problems may occur (assessment of intrinsic performance, effect of errors inherited from the global models, effect of non-stationarity, etc.). The list of downscaling experiments includes 1) cross-validation with perfect predictors, 2) GCM predictors -aligned with EURO-CORDEX experiment- and 3) pseudo reality predictors (see Maraun et al. 2015, Earth's Future, 3, doi:10.1002/2014EF000259, for more details). The results of these experiments are gathered, validated and publicly distributed through the VALUE validation portal, allowing for a comprehensive community-open downscaling intercomparison study. In this contribution we describe the overall results from Experiment 1), consisting of a European wide 5-fold cross-validation (with consecutive 6-year periods from 1979 to 2008) using predictors from ERA-Interim to downscale precipitation and temperatures (minimum and maximum) over a set of 86 ECA&D stations representative of the main geographical and climatic regions in Europe. As a result of the open call for contribution to this experiment (closed in Dec. 2015), over 40 methods representative of the main approaches (MOS and Perfect Prognosis, PP) and techniques (linear scaling, quantile mapping, analogs, weather typing, linear and generalized regression, weather generators, etc.) were submitted, including information both data

  3. Accuracy of Population Validity and Cross-Validity Estimation: An Empirical Comparison of Formula-Based, Traditional Empirical, and Equal Weights Procedures.

    ERIC Educational Resources Information Center

    Raju, Nambury S.; Bilgic, Reyhan; Edwards, Jack E.; Fleer, Paul F.

    1999-01-01

    Performed an empirical Monte Carlo study using predictor and criterion data from 84,808 U.S. Air Force enlistees. Compared formula-based, traditional empirical, and equal-weights procedures. Discusses issues for basic research on validation and cross-validation. (SLD)

  4. Cross-validation of generalised body composition equations with diverse young men and women: the Training Intervention and Genetics of Exercise Response (TIGER) Study

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Generalised skinfold equations developed in the 1970s are commonly used to estimate laboratory-measured percentage fat (BF%). The equations were developed on predominately white individuals using Siri's two-component percentage fat equation (BF%-GEN). We cross-validated the Jackson-Pollock (JP) gene...

  5. Cross-Validation of a Recently Published Equation Predicting Energy Expenditure to Run or Walk a Mile in Normal-Weight and Overweight Adults

    ERIC Educational Resources Information Center

    Morris, Cody E.; Owens, Scott G.; Waddell, Dwight E.; Bass, Martha A.; Bentley, John P.; Loftin, Mark

    2014-01-01

    An equation published by Loftin, Waddell, Robinson, and Owens (2010) was cross-validated using ten normal-weight walkers, ten overweight walkers, and ten distance runners. Energy expenditure was measured at preferred walking (normal-weight walker and overweight walkers) or running pace (distance runners) for 5 min and corrected to a mile. Energy…

  6. Actuarial assessment of sex offender recidivism risk: a cross-validation of the RRASOR and the Static-99 in Sweden.

    PubMed

    Sjöstedt, G; Långström, N

    2001-12-01

    We cross-validated two actuarial risk assessment tools, the RRASOR (R. K. Hanson, 1997) and the Static-99 (R. K. Hanson & D. Thornton, 1999), in a retrospective follow-up (mean follow-up time = 3.69 years) of all sex offenders released from Swedish prisons during 1993-1997 (N = 1,400, all men, age > or =18 years). File-based data were collected by a researcher blind to the outcome (registered criminal recidivism), and individual risk factors as well as complete instrument characteristics were explored. Both the RRASOR and the Static-99 showed similar and moderate predictive accuracy for sexual reconvictions whereas the Static-99 exhibited a significantly higher accuracy for the prediction of any violent recidivism as compared to the RRASOR. Although particularly the Static-99 proved moderately robust as an actuarial measure of recidivism risk among sexual offenders in Sweden, both procedures may need further evaluation, for example, with sex offender subpopulations differing ethnically or with respect to offense characteristics. The usefulness of actuarial methods for the assessment of sex offender recidivism risk is discussed in the context of current practice.

  7. Cross-validation and discriminant validity of Adolescent Health Promotion Scale among overweight and nonoverweight adolescents in Taiwan.

    PubMed

    Chen, Mei-Yen; Wang, Edward K; Chang, Chee-Jen

    2006-01-01

    This study used cross-validation and discriminant analysis to evaluate the construct and discriminant validity of Adolescent Health Promotion (AHP) scale between the overweight and nonoverweight adolescents in Taiwan. A cross-sectional survey method was used and 660 adolescents participated in this study. Cluster and discriminant analyses were used to analyze the data. Our findings indicate that the AHP is a valid and reliable scale to discriminate between the health-promoting behaviors of overweight and nonoverweight adolescents. For the total scale, cluster analyses revealed two distinct patterns, which we designated the healthy and unhealthy groups. Discriminate analysis supported this clustering as having good discriminant validity, as nonoverweight adolescents tended to be classified as healthy, while the overweight tended to be in the unhealthy group. In general, overweight adolescents practiced health-related behaviors at a significantly lower frequency than the nonoverweight. These included exercise behavior, stress management, life appreciation, health responsibility, and social support. These findings can be used to further develop and refine knowledge of adolescent overweight and related strategies for intervention.

  8. An Efficient Leave-One-Out Cross-Validation-Based Extreme Learning Machine (ELOO-ELM) With Minimal User Intervention.

    PubMed

    Shao, Zhifei; Er, Meng Joo; Wang, Ning

    2016-08-01

    It is well known that the architecture of the extreme learning machine (ELM) significantly affects its performance and how to determine a suitable set of hidden neurons is recognized as a key issue to some extent. The leave-one-out cross-validation (LOO-CV) is usually used to select a model with good generalization performance among potential candidates. The primary reason for using the LOO-CV is that it is unbiased and reliable as long as similar distribution exists in the training and testing data. However, the LOO-CV has rarely been implemented in practice because of its notorious slow execution speed. In this paper, an efficient LOO-CV formula and an efficient LOO-CV-based ELM (ELOO-ELM) algorithm are proposed. The proposed ELOO-ELM algorithm can achieve fast learning speed similar to the original ELM without compromising the reliability feature of the LOO-CV. Furthermore, minimal user intervention is required for the ELOO-ELM, thus it can be easily adopted by nonexperts and implemented in automation processes. Experimentation studies on benchmark datasets demonstrate that the proposed ELOO-ELM algorithm can achieve good generalization with limited user intervention while retaining the efficiency feature.

  9. Crossing the North Sea seems to make DCD disappear: cross-validation of Movement Assessment Battery for Children-2 norms.

    PubMed

    Niemeijer, Anuschka S; van Waelvelde, Hilde; Smits-Engelsman, Bouwien C M

    2015-02-01

    The Movement Assessment Battery for Children has been revised as the Movement ABC-2 (Henderson, Sugden, & Barnett, 2007). In Europe, the 15th percentile score on this test is recommended for one of the DSM-IV diagnostic criteria for Developmental Coordination Disorder (DCD). A representative sample of Dutch and Flemish children was tested to cross-validate the UK standard scores, including the 15th percentile score. First, the mean, SD and percentile scores of Dutch children were compared to those of UK normative samples. Item standard scores of Dutch speaking children deviated from the UK reference values suggesting necessary adjustments. Except for very young children, the Dutch-speaking samples performed better. Second, based on the mean and SD and clinical relevant cut-off scores (5th and 15th percentile), norms were adjusted for the Dutch population. For diagnostic use, researchers and clinicians should use the reference norms that are valid for the group of children they are testing. The results indicate that there possibly is an effect of testing procedure in other countries that validated the UK norms and/or cultural influence on the age norms of the Movement ABC-2. It is suggested to formulate criterion-based norms for age groups in addition to statistical norms.

  10. Comparison, cross-validation and consolidation of the results from two different geodynamic projects working in the eastern Carpathians

    NASA Astrophysics Data System (ADS)

    Ambrosious, B.; Dinter, G.; van der Hoeven, A.; Mocanu, V.; Nutto, M.; Schmitt, G.; Spakman, W.

    2003-04-01

    Since 1995 several projects/programmes are working in the Vrancea-region in Romania with partly different intensions. First of all, the CERGOP project installed the CEGRN-network and performed GPS-measurements ('95,'96,'97,'99,'01), mainly to realise a geodetic reference frame for local geodynamic projects. In the framework of the Collaborative Research Center CRC461 "Strong Earthquakes" the Geodetic Institute of the University Karlsruhe (GIK) Densified the network up to 35 stations and carried out three GPS-campaigns ('97, '98 and '00). First results of this project were presented at the EGS-meeting 2001 in Nice. In 2002 a new geodynamic research project was initiated at the Delft Institute of Earth-Oriented Space Research (DEOS). In the context of this project, 4 permanent stations and 10 new campaign stations were installed, which leads to a common network of about 50 stations. In tight cooperation with the GIK and the University of Bucarest (Departement of Geophysics) the currently last GPS-campaign was successfully carried out in 2002. Now the great challenge and at the same time the great difficulty is a correct combination of all available GPS datasets particularly in consideration of station excentricities and variations of antenna- and receiver-types. Different evalutation strategies and software packages (Bernese-GPS-Software, GIPSY) were used to analyse the GPS data and to estimate the station velocities. Main focus of this joint-presentation is the comparison of the results from the German and Dutch geodynamic projects. The results of the two working groups are cross-validated and finally joined together in a most reasonable solution. Even if three-dimensional analysis is in work, the presentation is limited to the horizontal component.

  11. Quantification of rainfall prediction uncertainties using a cross-validation based technique. Methodology description and experimental validation.

    NASA Astrophysics Data System (ADS)

    Fraga, Ignacio; Cea, Luis; Puertas, Jerónimo; Salsón, Santiago; Petazzi, Alberto

    2016-04-01

    In this paper we present a new methodology to compute rainfall fields including the quantification of predictions uncertainties using raingauge network data. The proposed methodology comprises two steps. Firstly, the ordinary krigging technique is used to determine the estimated rainfall depth in every point of the study area. Then multiple equi-probable errors fields, which comprise both interpolation and measuring uncertainties, are added to the krigged field resulting in multiple rainfall predictions. To compute these error fields first the standard deviation of the krigging estimation is determined following the cross-validation based procedure described in Delrieu et al. (2014). Then, the standard deviation field is sampled using non-conditioned Gaussian random fields. The proposed methodology was applied to study 7 rain events in a 60x60 km area of the west coast of Galicia, in the Northwest of Spain. Due to its location at the junction between tropical and polar regions, the study area suffers from frequent intense rainfalls characterized by a great variability in terms of both space and time. Rainfall data from the tipping bucket raingauge network operated by MeteoGalicia were used to estimate the rainfall fields using the proposed methodology. The obtained predictions were then validated using rainfall data from 3 additional rain gauges installed within the CAPRI project (Probabilistic flood prediction with high resolution hydrologic models from radar rainfall estimates, funded by the Spanish Ministry of Economy and Competitiveness. Reference CGL2013-46245-R.). Results show that both the mean hyetographs and the peak intensities are correctly predicted. The computed hyetographs present a good fit to the experimental data and most of the measured values fall within the 95% confidence intervals. Also, most of the experimental values outside the confidence bounds correspond to time periods of low rainfall depths, where the inaccuracy of the measuring devices

  12. Evaluation of a generalizable approach to clinical information retrieval using the automated retrieval console (ARC)

    PubMed Central

    Nguyen, Thien M; Farwell, Wildon R; Chen, Yongming; Fitzmeyer, Felicia; Harris, Owen M; Fiore, Louis D

    2010-01-01

    Reducing custom software development effort is an important goal in information retrieval (IR). This study evaluated a generalizable approach involving with no custom software or rules development. The study used documents “consistent with cancer” to evaluate system performance in the domains of colorectal (CRC), prostate (PC), and lung (LC) cancer. Using an end-user-supplied reference set, the automated retrieval console (ARC) iteratively calculated performance of combinations of natural language processing-derived features and supervised classification algorithms. Training and testing involved 10-fold cross-validation for three sets of 500 documents each. Performance metrics included recall, precision, and F-measure. Annotation time for five physicians was also measured. Top performing algorithms had recall, precision, and F-measure values as follows: for CRC, 0.90, 0.92, and 0.89, respectively; for PC, 0.97, 0.95, and 0.94; and for LC, 0.76, 0.80, and 0.75. In all but one case, conditional random fields outperformed maximum entropy-based classifiers. Algorithms had good performance without custom code or rules development, but performance varied by specific application. PMID:20595303

  13. Feature selection and classification of protein-protein complexes based on their binding affinities using machine learning approaches.

    PubMed

    Yugandhar, K; Gromiha, M Michael

    2014-09-01

    Protein-protein interactions are intrinsic to virtually every cellular process. Predicting the binding affinity of protein-protein complexes is one of the challenging problems in computational and molecular biology. In this work, we related sequence features of protein-protein complexes with their binding affinities using machine learning approaches. We set up a database of 185 protein-protein complexes for which the interacting pairs are heterodimers and their experimental binding affinities are available. On the other hand, we have developed a set of 610 features from the sequences of protein complexes and utilized Ranker search method, which is the combination of Attribute evaluator and Ranker method for selecting specific features. We have analyzed several machine learning algorithms to discriminate protein-protein complexes into high and low affinity groups based on their Kd values. Our results showed a 10-fold cross-validation accuracy of 76.1% with the combination of nine features using support vector machines. Further, we observed accuracy of 83.3% on an independent test set of 30 complexes. We suggest that our method would serve as an effective tool for identifying the interacting partners in protein-protein interaction networks and human-pathogen interactions based on the strength of interactions.

  14. An Efficient Diagnosis System for Parkinson's Disease Using Kernel-Based Extreme Learning Machine with Subtractive Clustering Features Weighting Approach

    PubMed Central

    Ma, Chao; Ouyang, Jihong; Chen, Hui-Ling; Zhao, Xue-Hua

    2014-01-01

    A novel hybrid method named SCFW-KELM, which integrates effective subtractive clustering features weighting and a fast classifier kernel-based extreme learning machine (KELM), has been introduced for the diagnosis of PD. In the proposed method, SCFW is used as a data preprocessing tool, which aims at decreasing the variance in features of the PD dataset, in order to further improve the diagnostic accuracy of the KELM classifier. The impact of the type of kernel functions on the performance of KELM has been investigated in detail. The efficiency and effectiveness of the proposed method have been rigorously evaluated against the PD dataset in terms of classification accuracy, sensitivity, specificity, area under the receiver operating characteristic (ROC) curve (AUC), f-measure, and kappa statistics value. Experimental results have demonstrated that the proposed SCFW-KELM significantly outperforms SVM-based, KNN-based, and ELM-based approaches and other methods in the literature and achieved highest classification results reported so far via 10-fold cross validation scheme, with the classification accuracy of 99.49%, the sensitivity of 100%, the specificity of 99.39%, AUC of 99.69%, the f-measure value of 0.9964, and kappa value of 0.9867. Promisingly, the proposed method might serve as a new candidate of powerful methods for the diagnosis of PD with excellent performance. PMID:25484912

  15. New approach for automatic classification of Alzheimer's disease, mild cognitive impairment and healthy brain magnetic resonance images.

    PubMed

    Lahmiri, Salim; Boukadoum, Mounir

    2014-01-01

    Explored is the utility of modelling brain magnetic resonance images as a fractal object for the classification of healthy brain images against those with Alzheimer's disease (AD) or mild cognitive impairment (MCI). More precisely, fractal multi-scale analysis is used to build feature vectors from the derived Hurst's exponents. These are then classified by support vector machines (SVMs). Three experiments were conducted: in the first the SVM was trained to classify AD against healthy images. In the second experiment, the SVM was trained to classify AD against MCI and, in the third experiment, a multiclass SVM was trained to classify all three types of images. The experimental results, using the 10-fold cross-validation technique, indicate that the SVM achieved 97.08% ± 0.05 correct classification rate, 98.09% ± 0.04 sensitivity and 96.07% ± 0.07 specificity for the classification of healthy against MCI images, thus outperforming recent works found in the literature. For the classification of MCI against AD, the SVM achieved 97.5% ± 0.04 correct classification rate, 100% sensitivity and 94.93% ± 0.08 specificity. The third experiment also showed that the multiclass SVM provided highly accurate classification results. The processing time for a given image was 25 s. These findings suggest that this approach is efficient and may be promising for clinical applications.

  16. New approach for automatic classification of Alzheimer's disease, mild cognitive impairment and healthy brain magnetic resonance images

    PubMed Central

    Boukadoum, Mounir

    2014-01-01

    Explored is the utility of modelling brain magnetic resonance images as a fractal object for the classification of healthy brain images against those with Alzheimer's disease (AD) or mild cognitive impairment (MCI). More precisely, fractal multi-scale analysis is used to build feature vectors from the derived Hurst's exponents. These are then classified by support vector machines (SVMs). Three experiments were conducted: in the first the SVM was trained to classify AD against healthy images. In the second experiment, the SVM was trained to classify AD against MCI and, in the third experiment, a multiclass SVM was trained to classify all three types of images. The experimental results, using the 10-fold cross-validation technique, indicate that the SVM achieved 97.08% ± 0.05 correct classification rate, 98.09% ± 0.04 sensitivity and 96.07% ± 0.07 specificity for the classification of healthy against MCI images, thus outperforming recent works found in the literature. For the classification of MCI against AD, the SVM achieved 97.5% ± 0.04 correct classification rate, 100% sensitivity and 94.93% ± 0.08 specificity. The third experiment also showed that the multiclass SVM provided highly accurate classification results. The processing time for a given image was 25 s. These findings suggest that this approach is efficient and may be promising for clinical applications. PMID:26609373

  17. Carboxylation of cytosine (5caC) in the CG dinucleotide in the E-box motif (CGCAG|GTG) increases binding of the Tcf3|Ascl1 helix-loop-helix heterodimer 10-fold.

    PubMed

    Golla, Jaya Prakash; Zhao, Jianfei; Mann, Ishminder K; Sayeed, Syed K; Mandal, Ajeet; Rose, Robert B; Vinson, Charles

    2014-06-27

    Three oxidative products of 5-methylcytosine (5mC) occur in mammalian genomes. We evaluated if these cytosine modifications in a CG dinucleotide altered DNA binding of four B-HLH homodimers and three heterodimers to the E-Box motif CGCAG|GTG. We examined 25 DNA probes containing all combinations of cytosine in a CG dinucleotide and none changed binding except for carboxylation of cytosine (5caC) in the strand CGCAG|GTG. 5caC enhanced binding of all examined B-HLH homodimers and heterodimers, particularly the Tcf3|Ascl1 heterodimer which increased binding ~10-fold. These results highlight a potential function of the oxidative products of 5mC, changing the DNA binding of sequence-specific transcription factors.

  18. Global mapping of highly pathogenic avian influenza H5N1 and H5Nx clade 2.3.4.4 viruses with spatial cross-validation

    PubMed Central

    Dhingra, Madhur S; Artois, Jean; Robinson, Timothy P; Linard, Catherine; Chaiban, Celia; Xenarios, Ioannis; Engler, Robin; Liechti, Robin; Kuznetsov, Dmitri; Xiao, Xiangming; Dobschuetz, Sophie Von; Claes, Filip; Newman, Scott H; Dauphin, Gwenaëlle; Gilbert, Marius

    2016-01-01

    Global disease suitability models are essential tools to inform surveillance systems and enable early detection. We present the first global suitability model of highly pathogenic avian influenza (HPAI) H5N1 and demonstrate that reliable predictions can be obtained at global scale. Best predictions are obtained using spatial predictor variables describing host distributions, rather than land use or eco-climatic spatial predictor variables, with a strong association with domestic duck and extensively raised chicken densities. Our results also support a more systematic use of spatial cross-validation in large-scale disease suitability modelling compared to standard random cross-validation that can lead to unreliable measure of extrapolation accuracy. A global suitability model of the H5 clade 2.3.4.4 viruses, a group of viruses that recently spread extensively in Asia and the US, shows in comparison a lower spatial extrapolation capacity than the HPAI H5N1 models, with a stronger association with intensively raised chicken densities and anthropogenic factors. DOI: http://dx.doi.org/10.7554/eLife.19571.001 PMID:27885988

  19. Derivation and Cross-Validation of Cutoff Scores for Patients With Schizophrenia Spectrum Disorders on WAIS-IV Digit Span-Based Performance Validity Measures.

    PubMed

    Glassmire, David M; Toofanian Ross, Parnian; Kinney, Dominique I; Nitch, Stephen R

    2016-06-01

    Two studies were conducted to identify and cross-validate cutoff scores on the Wechsler Adult Intelligence Scale-Fourth Edition Digit Span-based embedded performance validity (PV) measures for individuals with schizophrenia spectrum disorders. In Study 1, normative scores were identified on Digit Span-embedded PV measures among a sample of patients (n = 84) with schizophrenia spectrum diagnoses who had no known incentive to perform poorly and who put forth valid effort on external PV tests. Previously identified cutoff scores resulted in unacceptable false positive rates and lower cutoff scores were adopted to maintain specificity levels ≥90%. In Study 2, the revised cutoff scores were cross-validated within a sample of schizophrenia spectrum patients (n = 96) committed as incompetent to stand trial. Performance on Digit Span PV measures was significantly related to Full Scale IQ in both studies, indicating the need to consider the intellectual functioning of examinees with psychotic spectrum disorders when interpreting scores on Digit Span PV measures.

  20. A novel approach for exposure assessment in air pollution epidemiological studies using neuro-fuzzy inference systems: Comparison of exposure estimates and exposure-health associations.

    PubMed

    Blanes-Vidal, Victoria; Cantuaria, Manuella Lech; Nadimi, Esmaeil S

    2017-04-01

    Many epidemiological studies have used proximity to sources as air pollution exposure assessment method. However, proximity measures are not generally good surrogates because of their complex non-linear relationship with exposures. Neuro-fuzzy inference systems (NFIS) can be used to map complex non-linear systems, but its usefulness in exposure assessment has not been extensively explored. We present a novel approach for exposure assessment using NFIS, where the inputs of the model were easily-obtainable proximity measures, and the output was residential exposure to an air pollutant. We applied it to a case-study on NH3 pollution, and compared health effects and exposures estimated from NFIS, with those obtained from emission-dispersion models, and linear and non-linear regression proximity models, using 10-fold cross validation. The agreement between emission-dispersion and NFIS exposures was high (Root-mean-square error (RMSE) =0.275, correlation coefficient (r)=0.91) and resulted in similar health effect estimates. Linear models showed poor performance (RMSE=0.527, r=0.59), while non-linear regression models resulted in heterocedasticity, non-normality and clustered data. NFIS could be a useful tool for estimating individual air pollution exposures in epidemiological studies on large populations, when emission-dispersion data are not available. The tradeoff between simplicity and accuracy needs to be considered.

  1. SILAC-Pulse Proteolysis: A Mass Spectrometry-Based Method for Discovery and Cross-Validation in Proteome-Wide Studies of Ligand Binding

    NASA Astrophysics Data System (ADS)

    Adhikari, Jagat; Fitzgerald, Michael C.

    2014-12-01

    Reported here is the use of stable isotope labeling with amino acids in cell culture (SILAC) and pulse proteolysis (PP) for detection and quantitation of protein-ligand binding interactions on the proteomic scale. The incorporation of SILAC into PP enables the PP technique to be used for the unbiased detection and quantitation of protein-ligand binding interactions in complex biological mixtures (e.g., cell lysates) without the need for prefractionation. The SILAC-PP technique is demonstrated in two proof-of-principle experiments using proteins in a yeast cell lysate and two test ligands including a well-characterized drug, cyclosporine A (CsA), and a non-hydrolyzable adenosine triphosphate (ATP) analogue, adenylyl imidodiphosphate (AMP-PNP). The well-known tight-binding interaction between CsA and cyclophilin A was successfully detected and quantified in replicate analyses, and a total of 33 proteins from a yeast cell lysate were found to have AMP-PNP-induced stability changes. In control experiments, the method's false positive rate of protein target discovery was found to be in the range of 2.1% to 3.6%. SILAC-PP and the previously reported stability of protein from rates of oxidation (SPROX) technique both report on the same thermodynamic properties of proteins and protein-ligand complexes. However, they employ different probes and mass spectrometry-based readouts. This creates the opportunity to cross-validate SPROX results with SILAC-PP results, and vice-versa. As part of this work, the SILAC-PP results obtained here were cross-validated with previously reported SPROX results on the same model systems to help differentiate true positives from false positives in the two experiments.

  2. Body fat measurement by bioelectrical impedance and air displacement plethysmography: a cross-validation study to design bioelectrical impedance equations in Mexican adults

    PubMed Central

    Macias, Nayeli; Alemán-Mateo, Heliodoro; Esparza-Romero, Julián; Valencia, Mauro E

    2007-01-01

    Background The study of body composition in specific populations by techniques such as bio-impedance analysis (BIA) requires validation based on standard reference methods. The aim of this study was to develop and cross-validate a predictive equation for bioelectrical impedance using air displacement plethysmography (ADP) as standard method to measure body composition in Mexican adult men and women. Methods This study included 155 male and female subjects from northern Mexico, 20–50 years of age, from low, middle, and upper income levels. Body composition was measured by ADP. Body weight (BW, kg) and height (Ht, cm) were obtained by standard anthropometric techniques. Resistance, R (ohms) and reactance, Xc (ohms) were also measured. A random-split method was used to obtain two samples: one was used to derive the equation by the "all possible regressions" procedure and was cross-validated in the other sample to test predicted versus measured values of fat-free mass (FFM). Results and Discussion The final model was: FFM (kg) = 0.7374 * (Ht2 /R) + 0.1763 * (BW) - 0.1773 * (Age) + 0.1198 * (Xc) - 2.4658. R2 was 0.97; the square root of the mean square error (SRMSE) was 1.99 kg, and the pure error (PE) was 2.96. There was no difference between FFM predicted by the new equation (48.57 ± 10.9 kg) and that measured by ADP (48.43 ± 11.3 kg). The new equation did not differ from the line of identity, had a high R2 and a low SRMSE, and showed no significant bias (0.87 ± 2.84 kg). Conclusion The new bioelectrical impedance equation based on the two-compartment model (2C) was accurate, precise, and free of bias. This equation can be used to assess body composition and nutritional status in populations similar in anthropometric and physical characteristics to this sample. PMID:17697388

  3. Remote sensing and GIS-based landslide hazard analysis and cross-validation using multivariate logistic regression model on three test areas in Malaysia

    NASA Astrophysics Data System (ADS)

    Pradhan, Biswajeet

    2010-05-01

    This paper presents the results of the cross-validation of a multivariate logistic regression model using remote sensing data and GIS for landslide hazard analysis on the Penang, Cameron, and Selangor areas in Malaysia. Landslide locations in the study areas were identified by interpreting aerial photographs and satellite images, supported by field surveys. SPOT 5 and Landsat TM satellite imagery were used to map landcover and vegetation index, respectively. Maps of topography, soil type, lineaments and land cover were constructed from the spatial datasets. Ten factors which influence landslide occurrence, i.e., slope, aspect, curvature, distance from drainage, lithology, distance from lineaments, soil type, landcover, rainfall precipitation, and normalized difference vegetation index (ndvi), were extracted from the spatial database and the logistic regression coefficient of each factor was computed. Then the landslide hazard was analysed using the multivariate logistic regression coefficients derived not only from the data for the respective area but also using the logistic regression coefficients calculated from each of the other two areas (nine hazard maps in all) as a cross-validation of the model. For verification of the model, the results of the analyses were then compared with the field-verified landslide locations. Among the three cases of the application of logistic regression coefficient in the same study area, the case of Selangor based on the Selangor logistic regression coefficients showed the highest accuracy (94%), where as Penang based on the Penang coefficients showed the lowest accuracy (86%). Similarly, among the six cases from the cross application of logistic regression coefficient in other two areas, the case of Selangor based on logistic coefficient of Cameron showed highest (90%) prediction accuracy where as the case of Penang based on the Selangor logistic regression coefficients showed the lowest accuracy (79%). Qualitatively, the cross

  4. Annotation of protein residues based on a literature analysis: cross-validation against UniProtKb

    PubMed Central

    Nagel, Kevin; Jimeno-Yepes, Antonio; Rebholz-Schuhmann, Dietrich

    2009-01-01

    Background A protein annotation database, such as the Universal Protein Resource knowledge base (UniProtKb), is a valuable resource for the validation and interpretation of predicted 3D structure patterns in proteins. Existing studies have focussed on point mutation extraction methods from biomedical literature which can be used to support the time consuming work of manual database curation. However, these methods were limited to point mutation extraction and do not extract features for the annotation of proteins at the residue level. Results This work introduces a system that identifies protein residues in MEDLINE abstracts and annotates them with features extracted from the context written in the surrounding text. MEDLINE abstract texts have been processed to identify protein mentions in combination with taxonomic species and protein residues (F1-measure 0.52). The identified protein-species-residue triplets have been validated and benchmarked against reference data resources (UniProtKb, average F1-measure of 0.54). Then, contextual features were extracted through shallow and deep parsing and the features have been classified into predefined categories (F1-measure ranges from 0.15 to 0.67). Furthermore, the feature sets have been aligned with annotation types in UniProtKb to assess the relevance of the annotations for ongoing curation projects. Altogether, the annotations have been assessed automatically and manually against reference data resources. Conclusion This work proposes a solution for the automatic extraction of functional annotation for protein residues from biomedical articles. The presented approach is an extension to other existing systems in that a wider range of residue entities are considered and that features of residues are extracted as annotations. PMID:19758468

  5. Improved GRACE regional mass balance estimates of the Greenland ice sheet cross-validated with the input-output method

    NASA Astrophysics Data System (ADS)

    Xu, Zheng; Schrama, Ernst J. O.; van der Wal, Wouter; van den Broeke, Michiel; Enderlin, Ellyn M.

    2016-04-01

    In this study, we use satellite gravimetry data from the Gravity Recovery and Climate Experiment (GRACE) to estimate regional mass change of the Greenland ice sheet (GrIS) and neighboring glaciated regions using a least squares inversion approach. We also consider results from the input-output method (IOM). The IOM quantifies the difference between the mass input and output of the GrIS by studying the surface mass balance (SMB) and the ice discharge (D). We use the Regional Atmospheric Climate Model version 2.3 (RACMO2.3) to model the SMB and derive the ice discharge from 12 years of high-precision ice velocity and thickness surveys. We use a simulation model to quantify and correct for GRACE approximation errors in mass change between different subregions of the GrIS, and investigate the reliability of pre-1990s ice discharge estimates, which are based on the modeled runoff. We find that the difference between the IOM and our improved GRACE mass change estimates is reduced in terms of the long-term mass change when using a reference discharge derived from runoff estimates in several subareas. In most regions our GRACE and IOM solutions are consistent with other studies, but differences remain in the northwestern GrIS. We validate the GRACE mass balance in that region by considering several different GIA models and mass change estimates derived from data obtained by the Ice, Cloud and land Elevation Satellite (ICESat). We conclude that the approximated mass balance between GRACE and IOM is consistent in most GrIS regions. The difference in the northwest is likely due to underestimated uncertainties in the IOM solutions.

  6. Geostatistical validation and cross-validation of magnetometric measurements of soil pollution with Potentially Toxic Elements in problematic areas

    NASA Astrophysics Data System (ADS)

    Fabijańczyk, Piotr; Zawadzki, Jarosław

    2016-04-01

    Field magnetometry is fast method that was previously effectively used to assess the potential soil pollution. One of the most popular devices that are used to measure the soil magnetic susceptibility on the soil surface is a MS2D Bartington. Single reading using MS2D device of soil magnetic susceptibility is low time-consuming but often characterized by considerable errors related to the instrument or environmental and lithogenic factors. In this connection, measured values of soil magnetic susceptibility have to be usually validated using more precise, but also much more expensive, chemical measurements. The goal of this study was to analyze validation methods of magnetometric measurements using chemical analyses of a concentration of elements in soil. Additionally, validation of surface measurements of soil magnetic susceptibility was performed using selected parameters of a distribution of magnetic susceptibility in a soil profile. Validation was performed using selected geostatistical measures of cross-correlation. The geostatistical approach was compared with validation performed using the classic statistics. Measurements were performed at selected areas located in the Upper Silesian Industrial Area in Poland, and in the selected parts of Norway. In these areas soil magnetic susceptibility was measured on the soil surface using a MS2D Bartington device and in the soil profile using MS2C Bartington device. Additionally, soil samples were taken in order to perform chemical measurements. Acknowledgment The research leading to these results has received funding from the Polish-Norwegian Research Programme operated by the National Centre for Research and Development under the Norwegian Financial Mechanism 2009-2014 in the frame of Project IMPACT - Contract No Pol-Nor/199338/45/2013.

  7. A Cross-Validation Approach to Approximate Basis Function Selection of the Stall Flutter Response of a Rectangular Wing in a Wind Tunnel

    NASA Technical Reports Server (NTRS)

    Kukreja, Sunil L.; Vio, Gareth A.; Andrianne, Thomas; azak, Norizham Abudl; Dimitriadis, Grigorios

    2012-01-01

    The stall flutter response of a rectangular wing in a low speed wind tunnel is modelled using a nonlinear difference equation description. Static and dynamic tests are used to select a suitable model structure and basis function. Bifurcation criteria such as the Hopf condition and vibration amplitude variation with airspeed were used to ensure the model was representative of experimentally measured stall flutter phenomena. Dynamic test data were used to estimate model parameters and estimate an approximate basis function.

  8. Calibration and Cross-Validation of the ActiGraph wGT3X+ Accelerometer for the Estimation of Physical Activity Intensity in Children with Intellectual Disabilities

    PubMed Central

    McGarty, Arlene M.; Penpraze, Victoria; Melville, Craig A.

    2016-01-01

    Background Valid objective measurement is integral to increasing our understanding of physical activity and sedentary behaviours. However, no population-specific cut points have been calibrated for children with intellectual disabilities. Therefore, this study aimed to calibrate and cross-validate the first population-specific accelerometer intensity cut points for children with intellectual disabilities. Methods Fifty children with intellectual disabilities were randomly assigned to the calibration (n = 36; boys = 28, 9.53±1.08yrs) or cross-validation (n = 14; boys = 9, 9.57±1.16yrs) group. Participants completed a semi-structured school-based activity session, which included various activities ranging from sedentary to vigorous intensity. Direct observation (SOFIT tool) was used to calibrate the ActiGraph wGT3X+, which participants wore on the right hip. Receiver Operating Characteristic curve analyses determined the optimal cut points for sedentary, moderate, and vigorous intensity activity for the vertical axis and vector magnitude. Classification agreement was investigated using sensitivity, specificity, total agreement, and Cohen’s kappa scores against the criterion measure of SOFIT. Results The optimal (AUC = .87−.94) vertical axis cut points (cpm) were ≤507 (sedentary), 1008−2300 (moderate), and ≥2301 (vigorous), which demonstrated high sensitivity (81−88%) and specificity (81−85%). The optimal (AUC = .86−.92) vector magnitude cut points (cpm) of ≤1863 (sedentary), 2610−4214 (moderate), and ≥4215 (vigorous) demonstrated comparable, albeit marginally lower, accuracy than the vertical axis cut points (sensitivity = 80−86%; specificity = 77−82%). Classification agreement ranged from moderate to almost perfect (κ = .51−.85) with high sensitivity and specificity, and confirmed the trend that accuracy increased with intensity, and vertical axis cut points provide higher classification agreement than vector magnitude cut points

  9. Slips of Action and Sequential Decisions: A Cross-Validation Study of Tasks Assessing Habitual and Goal-Directed Action Control.

    PubMed

    Sjoerds, Zsuzsika; Dietrich, Anja; Deserno, Lorenz; de Wit, Sanne; Villringer, Arno; Heinze, Hans-Jochen; Schlagenhauf, Florian; Horstmann, Annette

    2016-01-01

    Instrumental learning and decision-making rely on two parallel systems: a goal-directed and a habitual system. In the past decade, several paradigms have been developed to study these systems in animals and humans by means of e.g., overtraining, devaluation procedures and sequential decision-making. These different paradigms are thought to measure the same constructs, but cross-validation has rarely been investigated. In this study we compared two widely used paradigms that assess aspects of goal-directed and habitual behavior. We correlated parameters from a two-step sequential decision-making task that assesses model-based (MB) and model-free (MF) learning with a slips-of-action paradigm that assesses the ability to suppress cue-triggered, learnt responses when the outcome has been devalued and is therefore no longer desirable. MB control during the two-step task showed a very moderately positive correlation with goal-directed devaluation sensitivity, whereas MF control did not show any associations. Interestingly, parameter estimates of MB and goal-directed behavior in the two tasks were positively correlated with higher-order cognitive measures (e.g., visual short-term memory). These cognitive measures seemed to (at least partly) mediate the association between MB control during sequential decision-making and goal-directed behavior after instructed devaluation. This study provides moderate support for a common framework to describe the propensity towards goal-directed behavior as measured with two frequently used tasks. However, we have to caution that the amount of shared variance between the goal-directed and MB system in both tasks was rather low, suggesting that each task does also pick up distinct aspects of goal-directed behavior. Further investigation of the commonalities and differences between the MF and habit systems as measured with these, and other, tasks is needed. Also, a follow-up cross-validation on the neural systems driving these constructs

  10. Slips of Action and Sequential Decisions: A Cross-Validation Study of Tasks Assessing Habitual and Goal-Directed Action Control

    PubMed Central

    Sjoerds, Zsuzsika; Dietrich, Anja; Deserno, Lorenz; de Wit, Sanne; Villringer, Arno; Heinze, Hans-Jochen; Schlagenhauf, Florian; Horstmann, Annette

    2016-01-01

    Instrumental learning and decision-making rely on two parallel systems: a goal-directed and a habitual system. In the past decade, several paradigms have been developed to study these systems in animals and humans by means of e.g., overtraining, devaluation procedures and sequential decision-making. These different paradigms are thought to measure the same constructs, but cross-validation has rarely been investigated. In this study we compared two widely used paradigms that assess aspects of goal-directed and habitual behavior. We correlated parameters from a two-step sequential decision-making task that assesses model-based (MB) and model-free (MF) learning with a slips-of-action paradigm that assesses the ability to suppress cue-triggered, learnt responses when the outcome has been devalued and is therefore no longer desirable. MB control during the two-step task showed a very moderately positive correlation with goal-directed devaluation sensitivity, whereas MF control did not show any associations. Interestingly, parameter estimates of MB and goal-directed behavior in the two tasks were positively correlated with higher-order cognitive measures (e.g., visual short-term memory). These cognitive measures seemed to (at least partly) mediate the association between MB control during sequential decision-making and goal-directed behavior after instructed devaluation. This study provides moderate support for a common framework to describe the propensity towards goal-directed behavior as measured with two frequently used tasks. However, we have to caution that the amount of shared variance between the goal-directed and MB system in both tasks was rather low, suggesting that each task does also pick up distinct aspects of goal-directed behavior. Further investigation of the commonalities and differences between the MF and habit systems as measured with these, and other, tasks is needed. Also, a follow-up cross-validation on the neural systems driving these constructs

  11. A Novel Local Learning based Approach With Application to Breast Cancer Diagnosis

    SciTech Connect

    Xu, Songhua; Tourassi, Georgia

    2012-01-01

    The purpose of this study is to develop and evaluate a novel local learning-based approach for computer-assisted diagnosis of breast cancer. Our new local learning based algorithm using the linear logistic regression method as its base learner is described. Overall, our algorithm will perform its stochastic searching process until the total allowed computing time is used up by our random walk process in identifying the most suitable population subdivision scheme and their corresponding individual base learners. The proposed local learning-based approach was applied for the prediction of breast cancer given 11 mammographic and clinical findings reported by physicians using the BI-RADS lexicon. Our database consisted of 850 patients with biopsy confirmed diagnosis (290 malignant and 560 benign). We also compared the performance of our method with a collection of publicly available state-of-the-art machine learning methods. Predictive performance for all classifiers was evaluated using 10-fold cross validation and Receiver Operating Characteristics (ROC) analysis. Figure 1 reports the performance of 54 machine learning methods implemented in the machine learning toolkit Weka (version 3.0). We introduced a novel local learning-based classifier and compared it with an extensive list of other classifiers for the problem of breast cancer diagnosis. Our experiments show that the algorithm superior prediction performance outperforming a wide range of other well established machine learning techniques. Our conclusion complements the existing understanding in the machine learning field that local learning may capture complicated, non-linear relationships exhibited by real-world datasets.

  12. PLS/OPLS models in metabolomics: the impact of permutation of dataset rows on the K-fold cross-validation quality parameters.

    PubMed

    Triba, Mohamed N; Le Moyec, Laurence; Amathieu, Roland; Goossens, Corentine; Bouchemal, Nadia; Nahon, Pierre; Rutledge, Douglas N; Savarin, Philippe

    2015-01-01

    Among all the software packages available for discriminant analyses based on projection to latent structures (PLS-DA) or orthogonal projection to latent structures (OPLS-DA), SIMCA (Umetrics, Umeå Sweden) is the more widely used in the metabolomics field. SIMCA proposes many parameters or tests to assess the quality of the computed model (the number of significant components, R2, Q2, pCV-ANOVA, and the permutation test). Significance thresholds for these parameters are strongly application-dependent. Concerning the Q2 parameter, a significance threshold of 0.5 is generally admitted. However, during the last few years, many PLS-DA/OPLS-DA models built using SIMCA have been published with Q2 values lower than 0.5. The purpose of this opinion note is to point out that, in some circumstances frequently encountered in metabolomics, the values of these parameters strongly depend on the individuals that constitute the validation subsets. As a result of the way in which the software selects members of the calibration and validation subsets, a simple permutation of dataset rows can, in several cases, lead to contradictory conclusions about the significance of the models when a K-fold cross-validation is used. We believe that, when Q2 values lower than 0.5 are obtained, SIMCA users should at least verify that the quality parameters are stable towards permutation of the rows in their dataset.

  13. Partial cross-validation of the Wechsler Memory Scale-Revised (WMS-R) General Memory-Attention/Concentration Malingering Index in a nonlitigating sample.

    PubMed

    Hilsabeck, Robin C; Thompson, Matthew D; Irby, James W; Adams, Russell L; Scott, James G; Gouvier, Wm Drew

    2003-01-01

    The Wechsler Memory Scale-Revised (WMS-R) malingering indices proposed by Mittenberg, Azrin, Millsaps, and Heilbronner [Psychol Assess 5 (1993) 34.] were partially cross-validated in a sample of 200 nonlitigants. Nine diagnostic categories were examined, including participants with traumatic brain injury (TBI), brain tumor, stroke/vascular, senile dementia of the Alzheimer's type (SDAT), epilepsy, depression/anxiety, medical problems, and no diagnosis. Results showed that the discriminant function using WMS-R subtests misclassified only 6.5% of the sample as malingering, with significantly higher misclassification rates of SDAT and stroke/vascular groups. The General Memory Index-Attention/Concentration Index (GMI-ACI) difference score misclassified only 8.5% of the sample as malingering when a difference score of greater than 25 points was used as the cutoff criterion. No diagnostic group was significantly more likely to be misclassified. Results support the utility of the GMI-ACI difference score, as well as the WMS-R subtest discriminant function score, in detecting malingering.

  14. Estimation of influential points in any data set from coefficient of determination and its leave-one-out cross-validated counterpart.

    PubMed

    Tóth, Gergely; Bodai, Zsolt; Héberger, Károly

    2013-10-01

    Coefficient of determination (R (2)) and its leave-one-out cross-validated analogue (denoted by Q (2) or R cv (2) ) are the most frequantly published values to characterize the predictive performance of models. In this article we use R (2) and Q (2) in a reversed aspect to determine uncommon points, i.e. influential points in any data sets. The term (1 - Q (2))/(1 - R (2)) corresponds to the ratio of predictive residual sum of squares and the residual sum of squares. The ratio correlates to the number of influential points in experimental and random data sets. We propose an (approximate) F test on (1 - Q (2))/(1 - R (2)) term to quickly pre-estimate the presence of influential points in training sets of models. The test is founded upon the routinely calculated Q (2) and R (2) values and warns the model builders to verify the training set, to perform influence analysis or even to change to robust modeling.

  15. Classification of technical pitfalls in objective universal hearing screening by otoacoustic emissions, using an ARMA model of the stimulus waveform and bootstrap cross-validation.

    PubMed

    Vannier, E; Avan, P

    2005-10-01

    Transient-evoked otoacoustic emissions (TEOAE) are widely used for objective hearing screening in neonates. Their main shortcoming is their sensitivity to adverse conditions for sound transmission through the middle-ear, to and from the cochlea. We study here whether a close examination of the stimulus waveform (SW) recorded in the ear canal in the course of a screening test can pinpoint the most frequent middle-ear dysfunctions, thus allowing screeners to avoid misclassifying the corresponding babies as deaf for lack of TEOAE. Three groups of SWs were defined in infants (6-36 months of age) according to middle-ear impairment as assessed by independent testing procedures, and analyzed in the frequency domain where their properties are more readily interpreted than in the time domain. Synthetic SW parameters were extracted with the help of an autoregressive and moving average (ARMA) model, then classified using a maximum likelihood criterion and a bootstrap cross-validation. The best classification performance was 79% with a lower limit (with 90% confidence) of 60%, showing the results' consistency. We therefore suggest that new parameters and methodology based upon a more thorough analysis of SWs can improve the efficiency of TEOAE-based tests by helping the most frequent technical pitfalls to be identified.

  16. Shuffling cross-validation-bee algorithm as a new descriptor selection method for retention studies of pesticides in biopartitioning micellar chromatography.

    PubMed

    Zarei, Kobra; Atabati, Morteza; Ahmadi, Monire

    2017-02-22

    Bee algorithm (BA) is an optimization algorithm inspired by the natural foraging behaviour of honey bees to find the optimal solution which can be proposed to feature selection. In this paper, shuffling cross-validation-BA (CV-BA) was applied to select the best descriptors that could describe the retention factor (log k) in the biopartitioning micellar chromatography (BMC) of 79 heterogeneous pesticides. Six descriptors were obtained using BA and then the selected descriptors were applied for model development using multiple linear regression (MLR). The descriptor selection was also performed using stepwise, genetic algorithm and simulated annealing methods and MLR was applied to model development and then the results were compared with those obtained from shuffling CV-BA. The results showed that shuffling CV-BA can be applied as a powerful descriptor selection method. Support vector machine (SVM) was also applied for model development using six selected descriptors by BA. The obtained statistical results using SVM were better than those obtained using MLR, as the root mean square error (RMSE) and correlation coefficient (R) for whole data set (training and test), using shuffling CV-BA-MLR, were obtained as 0.1863 and 0.9426, respectively, while these amounts for the shuffling CV-BA-SVM method were obtained as 0.0704 and 0.9922, respectively.

  17. Sediment transport patterns in the San Francisco Bay Coastal System from cross-validation of bedform asymmetry and modeled residual flux

    USGS Publications Warehouse

    Barnard, Patrick L.; Erikson, Li H.; Elias, Edwin P.L.; Dartnell, Peter; Barnard, P.L.; Jaffee, B.E.; Schoellhamer, D.H.

    2013-01-01

    The morphology of ~ 45,000 bedforms from 13 multibeam bathymetry surveys was used as a proxy for identifying net bedload sediment transport directions and pathways throughout the San Francisco Bay estuary and adjacent outer coast. The spatially-averaged shape asymmetry of the bedforms reveals distinct pathways of ebb and flood transport. Additionally, the region-wide, ebb-oriented asymmetry of 5% suggests net seaward-directed transport within the estuarine-coastal system, with significant seaward asymmetry at the mouth of San Francisco Bay (11%), through the northern reaches of the Bay (7–8%), and among the largest bedforms (21% for λ > 50 m). This general indication for the net transport of sand to the open coast strongly suggests that anthropogenic removal of sediment from the estuary, particularly along clearly defined seaward transport pathways, will limit the supply of sand to chronically eroding, open-coast beaches. The bedform asymmetry measurements significantly agree (up to ~ 76%) with modeled annual residual transport directions derived from a hydrodynamically-calibrated numerical model, and the orientation of adjacent, flow-sculpted seafloor features such as mega-flute structures, providing a comprehensive validation of the technique. The methods described in this paper to determine well-defined, cross-validated sediment transport pathways can be applied to estuarine-coastal systems globally where bedforms are present. The results can inform and improve regional sediment management practices to more efficiently utilize often limited sediment resources and mitigate current and future sediment supply-related impacts.

  18. Sediment transport patterns in the San Francisco Bay Coastal System from cross-validation of bedform asymmetry and modeled residual flux

    USGS Publications Warehouse

    Barnard, Patrick L.; Erikson, Li H.; Elias, Edwin P.L.; Dartnell, Peter

    2013-01-01

    The morphology of ~ 45,000 bedforms from 13 multibeam bathymetry surveys was used as a proxy for identifying net bedload sediment transport directions and pathways throughout the San Francisco Bay estuary and adjacent outer coast. The spatially-averaged shape asymmetry of the bedforms reveals distinct pathways of ebb and flood transport. Additionally, the region-wide, ebb-oriented asymmetry of 5% suggests net seaward-directed transport within the estuarine-coastal system, with significant seaward asymmetry at the mouth of San Francisco Bay (11%), through the northern reaches of the Bay (7-8%), and among the largest bedforms (21% for λ > 50 m). This general indication for the net transport of sand to the open coast strongly suggests that anthropogenic removal of sediment from the estuary, particularly along clearly defined seaward transport pathways, will limit the supply of sand to chronically eroding, open-coast beaches. The bedform asymmetry measurements significantly agree (up to ~ 76%) with modeled annual residual transport directions derived from a hydrodynamically-calibrated numerical model, and the orientation of adjacent, flow-sculpted seafloor features such as mega-flute structures, providing a comprehensive validation of the technique. The methods described in this paper to determine well-defined, cross-validated sediment transport pathways can be applied to estuarine-coastal systems globally where bedforms are present. The results can inform and improve regional sediment management practices to more efficiently utilize often limited sediment resources and mitigate current and future sediment supply-related impacts.

  19. Cross-validation of serial optical coherence scanning and diffusion tensor imaging: a study on neural fiber maps in human medulla oblongata.

    PubMed

    Wang, Hui; Zhu, Junfeng; Reuter, Martin; Vinke, Louis N; Yendiki, Anastasia; Boas, David A; Fischl, Bruce; Akkin, Taner

    2014-10-15

    We established a strategy to perform cross-validation of serial optical coherence scanner imaging (SOCS) and diffusion tensor imaging (DTI) on a postmortem human medulla. Following DTI, the sample was serially scanned by SOCS, which integrates a vibratome slicer and a multi-contrast optical coherence tomography rig for large-scale three-dimensional imaging at microscopic resolution. The DTI dataset was registered to the SOCS space. An average correlation coefficient of 0.9 was found between the co-registered fiber maps constructed by fractional anisotropy and retardance contrasts. Pixelwise comparison of fiber orientations demonstrated good agreement between the DTI and SOCS measures. Details of the comparison were studied in regions exhibiting a variety of fiber organizations. DTI estimated the preferential orientation of small fiber tracts; however, it didn't capture their complex patterns as SOCS did. In terms of resolution and imaging depth, SOCS and DTI complement each other, and open new avenues for cross-modality investigations of the brain.

  20. FDDS: A Cross Validation Study.

    ERIC Educational Resources Information Center

    Sawyer, Judy Parsons

    The Family Drawing Depression Scale (FDDS) was created by Wright and McIntyre to provide a clear and reliable scoring method for the Kinetic Family Drawing as a procedure for detecting depression. A study was conducted to confirm the value of the FDDS as a systematic tool for interpreting family drawings with populations of depressed individuals.…

  1. 2D-RNA-coupling numbers: a new computational chemistry approach to link secondary structure topology with biological function.

    PubMed

    González-Díaz, Humberto; Agüero-Chapin, Guillermín; Varona, Javier; Molina, Reinaldo; Delogu, Giovanna; Santana, Lourdes; Uriarte, Eugenio; Podda, Gianni

    2007-04-30

    Methods for prediction of proteins, DNA, or RNA function and mapping it onto sequence often rely on bioinformatics alignment approach instead of chemical structure. Consequently, it is interesting to develop computational chemistry approaches based on molecular descriptors. In this sense, many researchers used sequence-coupling numbers and our group extended them to 2D proteins representations. However, no coupling numbers have been reported for 2D-RNA topology graphs, which are highly branched and contain useful information. Here, we use a computational chemistry scheme: (a) transforming sequences into RNA secondary structures, (b) defining and calculating new 2D-RNA-coupling numbers, (c) seek a structure-function model, and (d) map biological function onto the folded RNA. We studied as example 1-aminocyclopropane-1-carboxylic acid (ACC) oxidases known as ACO, which control fruit ripening having importance for biotechnology industry. First, we calculated tau(k)(2D-RNA) values to a set of 90-folded RNAs, including 28 transcripts of ACO and control sequences. Afterwards, we compared the classification performance of 10 different classifiers implemented in the software WEKA. In particular, the logistic equation ACO = 23.8 . tau(1)(2D-RNA) + 41.4 predicts ACOs with 98.9%, 98.0%, and 97.8% of accuracy in training, leave-one-out and 10-fold cross-validation, respectively. Afterwards, with this equation we predict ACO function to a sequence isolated in this work from Coffea arabica (GenBank accession DQ218452). The tau(1)(2D-RNA) also favorably compare with other descriptors. This equation allows us to map the codification of ACO activity on different mRNA topology features. The present computational-chemistry approach is general and could be extended to connect RNA secondary structure topology to other functions.

  2. Methane cross-validation between three Fourier transform spectrometers: SCISAT ACE-FTS, GOSAT TANSO-FTS, and ground-based FTS measurements in the Canadian high Arctic

    NASA Astrophysics Data System (ADS)

    Holl, Gerrit; Walker, Kaley A.; Conway, Stephanie; Saitoh, Naoko; Boone, Chris D.; Strong, Kimberly; Drummond, James R.

    2016-05-01

    We present cross-validation of remote sensing measurements of methane profiles in the Canadian high Arctic. Accurate and precise measurements of methane are essential to understand quantitatively its role in the climate system and in global change. Here, we show a cross-validation between three data sets: two from spaceborne instruments and one from a ground-based instrument. All are Fourier transform spectrometers (FTSs). We consider the Canadian SCISAT Atmospheric Chemistry Experiment (ACE)-FTS, a solar occultation infrared spectrometer operating since 2004, and the thermal infrared band of the Japanese Greenhouse Gases Observing Satellite (GOSAT) Thermal And Near infrared Sensor for carbon Observation (TANSO)-FTS, a nadir/off-nadir scanning FTS instrument operating at solar and terrestrial infrared wavelengths, since 2009. The ground-based instrument is a Bruker 125HR Fourier transform infrared (FTIR) spectrometer, measuring mid-infrared solar absorption spectra at the Polar Environment Atmospheric Research Laboratory (PEARL) Ridge Laboratory at Eureka, Nunavut (80° N, 86° W) since 2006. For each pair of instruments, measurements are collocated within 500 km and 24 h. An additional collocation criterion based on potential vorticity values was found not to significantly affect differences between measurements. Profiles are regridded to a common vertical grid for each comparison set. To account for differing vertical resolutions, ACE-FTS measurements are smoothed to the resolution of either PEARL-FTS or TANSO-FTS, and PEARL-FTS measurements are smoothed to the TANSO-FTS resolution. Differences for each pair are examined in terms of profile and partial columns. During the period considered, the number of collocations for each pair is large enough to obtain a good sample size (from several hundred to tens of thousands depending on pair and configuration). Considering full profiles, the degrees of freedom for signal (DOFS) are between 0.2 and 0.7 for TANSO-FTS and

  3. Methane cross-validation between three Fourier Transform Spectrometers: SCISAT ACE-FTS, GOSAT TANSO-FTS, and ground-based FTS measurements in the Canadian high Arctic

    NASA Astrophysics Data System (ADS)

    Holl, G.; Walker, K. A.; Conway, S.; Saitoh, N.; Boone, C. D.; Strong, K.; Drummond, J. R.

    2015-12-01

    We present cross-validation of remote sensing measurements of methane profiles in the Canadian high Arctic. Accurate and precise measurements of methane are essential to understand quantitatively its role in the climate system and in global change. Here, we show a cross-validation between three datasets: two from spaceborne instruments and one from a ground-based instrument. All are Fourier Transform Spectrometers (FTSs). We consider the Canadian SCISAT Atmospheric Chemistry Experiment (ACE)-FTS, a solar occultation infrared spectrometer operating since 2004, and the thermal infrared band of the Japanese Greenhouse Gases Observing Satellite (GOSAT) Thermal And Near infrared Sensor for carbon Observation (TANSO)-FTS, a nadir/off-nadir scanning FTS instrument operating at solar and terrestrial infrared wavelengths, since 2009. The ground-based instrument is a Bruker 125HR Fourier Transform Infrared (FTIR) spectrometer, measuring mid-infrared solar absorption spectra at the Polar Environment Atmospheric Research Laboratory (PEARL) Ridge Lab at Eureka, Nunavut (80° N, 86° W) since 2006. For each pair of instruments, measurements are collocated within 500 km and 24 h. An additional criterion based on potential vorticity values was found not to significantly affect differences between measurements. Profiles are regridded to a common vertical grid for each comparison set. To account for differing vertical resolutions, ACE-FTS measurements are smoothed to the resolution of either PEARL-FTS or TANSO-FTS, and PEARL-FTS measurements are smoothed to the TANSO-FTS resolution. Differences for each pair are examined in terms of profile and partial columns. During the period considered, the number of collocations for each pair is large enough to obtain a good sample size (from several hundred to tens of thousands depending on pair and configuration). Considering full profiles, the degrees of freedom for signal (DOFS) are between 0.2 and 0.7 for TANSO-FTS and between 1.5 and 3

  4. Cross-validation of a portable, six-degree-of-freedom load cell for use in lower-limb prosthetics research.

    PubMed

    Koehler, Sara R; Dhaher, Yasin Y; Hansen, Andrew H

    2014-04-11

    The iPecs load cell is a lightweight, six-degree-of-freedom force transducer designed to fit easily into an endoskeletal prosthesis via a universal mounting interface. Unlike earlier tethered systems, it is capable of wireless data transmission and on-board memory storage, which facilitate its use in both clinical and real-world settings. To date, however, the validity of the iPecs load cell has not been rigorously established, particularly for loading conditions that represent typical prosthesis use. The aim of this study was to assess the accuracy of an iPecs load cell during in situ human subject testing by cross-validating its force and moment measurements with those of a typical gait analysis laboratory. Specifically, the gait mechanics of a single person with transtibial amputation were simultaneously measured using an iPecs load cell, multiple floor-mounted force platforms, and a three-dimensional motion capture system. Overall, the forces and moments measured by the iPecs were highly correlated with those measured by the gait analysis laboratory (r>0.86) and RMSEs were less than 3.4% and 5.2% full scale output across all force and moment channels, respectively. Despite this favorable comparison, however, the results of a sensitivity analysis suggest that care should be taken to accurately identify the axes and instrumentation center of the load cell in situations where iPecs data will be interpreted in a coordinate system other than its own (e.g., inverse dynamics analysis).

  5. Use of n-fold cross-validation to evaluate three methods to calculate heavy truck annual average daily traffic and vehicle miles traveled.

    PubMed

    Hallmark, Shauna L; Souleyrette, Reginald; Lamptey, Stephen

    2007-01-01

    Reliable estimates of heavy-truck volumes in the United States are important in a number of transportation applications including pavement design and management, traffic safety, and traffic operations. Additionally, because heavy vehicles emit pollutants at much higher rates than passenger vehicles, reliable volume estimates are critical to computing accurate inventories of on-road emissions. Accurate baseline inventories are also necessary to forecast future scenarios. The research presented in this paper evaluated three different methods commonly used by transportation agencies to estimate annual average daily traffic (AADT), which is used to determine vehicle miles traveled (VMT). Traffic data from continuous count stations provided by the Iowa Department of Transportation were used to estimate AADT for single-unit and multiunit trucks for rural freeways and rural primary highways using the three methods. The first method developed general expansion factors, which apply to all vehicles. AADT, representing all vehicles, was estimated for short-term counts and was multiplied by statewide average truck volumes for the corresponding roadway type to obtain AADT for each truck category. The second method also developed general expansion factors and AADT estimates. Truck AADT for the second method was calculated by multiplying the general AADT by truck volumes from the short-term counts. The third method developed expansion factors specific to each truck group. AADT estimates for each truck group were estimated from short-term counts using corresponding expansion factors. Accuracy of the three methods was determined by comparing actual AADT from count station data to estimates from the three methods. Accuracy of the three methods was compared using n-fold cross-validation. Mean squared error of prediction was used to estimate the difference between estimated and actual AADT. Prediction error was lowest for the method that developed separate expansion factors for trucks

  6. A cross validation study of deep brain stimulation targeting: from experts to atlas-based, segmentation-based and automatic registration algorithms.

    PubMed

    Castro, F Javier Sanchez; Pollo, Claudio; Meuli, Reto; Maeder, Philippe; Cuisenaire, Olivier; Cuadra, Meritxell Bach; Villemure, Jean-Guy; Thiran, Jean-Philippe

    2006-11-01

    Validation of image registration algorithms is a difficult task and open-ended problem, usually application-dependent. In this paper, we focus on deep brain stimulation (DBS) targeting for the treatment of movement disorders like Parkinson's disease and essential tremor. DBS involves implantation of an electrode deep inside the brain to electrically stimulate specific areas shutting down the disease's symptoms. The subthalamic nucleus (STN) has turned out to be the optimal target for this kind of surgery. Unfortunately, the STN is in general not clearly distinguishable in common medical imaging modalities. Usual techniques to infer its location are the use of anatomical atlases and visible surrounding landmarks. Surgeons have to adjust the electrode intraoperatively using electrophysiological recordings and macrostimulation tests. We constructed a ground truth derived from specific patients whose STNs are clearly visible on magnetic resonance (MR) T2-weighted images. A patient is chosen as atlas both for the right and left sides. Then, by registering each patient with the atlas using different methods, several estimations of the STN location are obtained. Two studies are driven using our proposed validation scheme. First, a comparison between different atlas-based and nonrigid registration algorithms with a evaluation of their performance and usability to locate the STN automatically. Second, a study of which visible surrounding structures influence the STN location. The two studies are cross validated between them and against expert's variability. Using this scheme, we evaluated the expert's ability against the estimation error provided by the tested algorithms and we demonstrated that automatic STN targeting is possible and as accurate as the expert-driven techniques currently used. We also show which structures have to be taken into account to accurately estimate the STN location.

  7. Revealing latent value of clinically acquired CTs of traumatic brain injury through multi-atlas segmentation in a retrospective study of 1,003 with external cross-validation

    NASA Astrophysics Data System (ADS)

    Plassard, Andrew J.; Kelly, Patrick D.; Asman, Andrew J.; Kang, Hakmook; Patel, Mayur B.; Landman, Bennett A.

    2015-03-01

    Medical imaging plays a key role in guiding treatment of traumatic brain injury (TBI) and for diagnosing intracranial hemorrhage; most commonly rapid computed tomography (CT) imaging is performed. Outcomes for patients with TBI are variable and difficult to predict upon hospital admission. Quantitative outcome scales (e.g., the Marshall classification) have been proposed to grade TBI severity on CT, but such measures have had relatively low value in staging patients by prognosis. Herein, we examine a cohort of 1,003 subjects admitted for TBI and imaged clinically to identify potential prognostic metrics using a "big data" paradigm. For all patients, a brain scan was segmented with multi-atlas labeling, and intensity/volume/texture features were computed in a localized manner. In a 10-fold crossvalidation approach, the explanatory value of the image-derived features is assessed for length of hospital stay (days), discharge disposition (five point scale from death to return home), and the Rancho Los Amigos functional outcome score (Rancho Score). Image-derived features increased the predictive R2 to 0.38 (from 0.18) for length of stay, to 0.51 (from 0.4) for discharge disposition, and to 0.31 (from 0.16) for Rancho Score (over models consisting only of non-imaging admission metrics, but including positive/negative radiological CT findings). This study demonstrates that high volume retrospective analysis of clinical imaging data can reveal imaging signatures with prognostic value. These targets are suited for follow-up validation and represent targets for future feature selection efforts. Moreover, the increase in prognostic value would improve staging for intervention assessment and provide more reliable guidance for patients.

  8. Automated detection of amnestic mild cognitive impairment in community-dwelling elderly adults: a combined spatial atrophy and white matter alteration approach.

    PubMed

    Cui, Yue; Wen, Wei; Lipnicki, Darren M; Beg, Mirza Faisal; Jin, Jesse S; Luo, Suhuai; Zhu, Wanlin; Kochan, Nicole A; Reppermund, Simone; Zhuang, Lin; Raamana, Pradeep Reddy; Liu, Tao; Trollor, Julian N; Wang, Lei; Brodaty, Henry; Sachdev, Perminder S

    2012-01-16

    Amnestic mild cognitive impairment (aMCI) is a syndrome widely considered to be prodromal Alzheimer's disease. Accurate diagnosis of aMCI would enable earlier treatment, and could thus help minimize the prevalence of Alzheimer's disease. The aim of the present study was to evaluate a magnetic resonance imaging-based automated classification schema for identifying aMCI. This was carried out in a sample of community-dwelling adults aged 70-90 years old: 79 with a clinical diagnosis of aMCI and 204 who were cognitively normal. Our schema was novel in using measures of both spatial atrophy, derived from T1-weighted images, and white matter alterations, assessed with diffusion tensor imaging (DTI) tract-based spatial statistics (TBSS). Subcortical volumetric features were extracted using a FreeSurfer-initialized Large Deformation Diffeomorphic Metric Mapping (FS+LDDMM) segmentation approach, and fractional anisotropy (FA) values obtained for white matter regions of interest. Features were ranked by their ability to discriminate between aMCI and normal cognition, and a support vector machine (SVM) selected an optimal feature subset that was used to train SVM classifiers. As evaluated via 10-fold cross-validation, the classification performance characteristics achieved by our schema were: accuracy, 71.09%; sensitivity, 51.96%; specificity, 78.40%; and area under the curve, 0.7003. Additionally, we identified numerous socio-demographic, lifestyle, health and other factors potentially implicated in the misclassification of individuals by our schema and those previously used by others. Given its high level of performance, our classification schema could facilitate the early detection of aMCI in community-dwelling elderly adults.

  9. Methane Cross-Validation Between Spaceborne Solar Occultation Observations from ACE-FTS, Spaceborne Nadir Sounding from Gosat, and Ground-Based Solar Absorption Measurements, at a High Arctic Site.

    NASA Astrophysics Data System (ADS)

    Holl, G.; Walker, K. A.; Conway, S. A.; Saitoh, N.; Boone, C. D.; Strong, K.; Drummond, J. R.

    2014-12-01

    We present cross-validation of remote sensing observations of methane profiles in the Canadian High Arctic. Methane is the third most important greenhouse gas on Earth, and second only to carbon dioxide in its contribution to anthropogenic global warming. Accurate and precise observations of methane are essential to understand quantitatively its role in the climate system and in global change. The Arctic is a particular region of concern, as melting permafrost and disappearing sea ice might lead to accelerated release of methane into the atmosphere. Global observations require spaceborne instruments, in particular in the Arctic, where surface measurements are sparse and expensive to perform. Satellite-based remote sensing is an underconstrained problem, and specific validation under Arctic circumstances is required. Here, we show a cross-validation between two spaceborne instruments and ground-based measurements, all Fourier Transform Spectrometers (FTS). We consider the Canadian SCISAT ACE-FTS, a solar occultation spectrometer operating since 2004, and the Japanese GOSAT TANSO-FTS, a nadir-pointing FTS operating at solar and terrestrial infrared wavelengths, since 2009. The ground-based instrument is a Bruker Fourier Transform Infrared (FTIR) spectrometer, measuring mid-infrared solar absorption spectra at the Polar Environmental and Atmospheric Research Laboratory (PEARL) at Eureka, Nunavut (80°N, 86°W) since 2006. Measurements are collocated considering temporal, spatial, and geophysical criteria and regridded to a common vertical grid. We perform smoothing on the higher-resolution instrument results to account for different vertical resolutions. Then, profiles of differences for each pair of instruments are examined. Any bias between instruments, or any accuracy that is worse than expected, needs to be understood prior to using the data. The results of the study will serve as a guideline on how to use the vertically resolved methane products from ACE and

  10. In silico prediction of toxicity of non-congeneric industrial chemicals using ensemble learning based modeling approaches

    SciTech Connect

    Singh, Kunwar P. Gupta, Shikha

    2014-03-15

    Ensemble learning approach based decision treeboost (DTB) and decision tree forest (DTF) models are introduced in order to establish quantitative structure–toxicity relationship (QSTR) for the prediction of toxicity of 1450 diverse chemicals. Eight non-quantum mechanical molecular descriptors were derived. Structural diversity of the chemicals was evaluated using Tanimoto similarity index. Stochastic gradient boosting and bagging algorithms supplemented DTB and DTF models were constructed for classification and function optimization problems using the toxicity end-point in T. pyriformis. Special attention was drawn to prediction ability and robustness of the models, investigated both in external and 10-fold cross validation processes. In complete data, optimal DTB and DTF models rendered accuracies of 98.90%, 98.83% in two-category and 98.14%, 98.14% in four-category toxicity classifications. Both the models further yielded classification accuracies of 100% in external toxicity data of T. pyriformis. The constructed regression models (DTB and DTF) using five descriptors yielded correlation coefficients (R{sup 2}) of 0.945, 0.944 between the measured and predicted toxicities with mean squared errors (MSEs) of 0.059, and 0.064 in complete T. pyriformis data. The T. pyriformis regression models (DTB and DTF) applied to the external toxicity data sets yielded R{sup 2} and MSE values of 0.637, 0.655; 0.534, 0.507 (marine bacteria) and 0.741, 0.691; 0.155, 0.173 (algae). The results suggest for wide applicability of the inter-species models in predicting toxicity of new chemicals for regulatory purposes. These approaches provide useful strategy and robust tools in the screening of ecotoxicological risk or environmental hazard potential of chemicals. - Graphical abstract: Importance of input variables in DTB and DTF classification models for (a) two-category, and (b) four-category toxicity intervals in T. pyriformis data. Generalization and predictive abilities of the

  11. puffMarker: A Multi-Sensor Approach for Pinpointing the Timing of First Lapse in Smoking Cessation.

    PubMed

    Saleheen, Nazir; Ali, Amin Ahsan; Hossain, Syed Monowar; Sarker, Hillol; Chatterjee, Soujanya; Marlin, Benjamin; Ertin, Emre; al'Absi, Mustafa; Kumar, Santosh

    2015-09-01

    Recent researches have demonstrated the feasibility of detecting smoking from wearable sensors, but their performance on real-life smoking lapse detection is unknown. In this paper, we propose a new model and evaluate its performance on 61 newly abstinent smokers for detecting a first lapse. We use two wearable sensors - breathing pattern from respiration and arm movements from 6-axis inertial sensors worn on wrists. In 10-fold cross-validation on 40 hours of training data from 6 daily smokers, our model achieves a recall rate of 96.9%, for a false positive rate of 1.1%. When our model is applied to 3 days of post-quit data from 32 lapsers, it correctly pinpoints the timing of first lapse in 28 participants. Only 2 false episodes are detected on 20 abstinent days of these participants. When tested on 84 abstinent days from 28 abstainers, the false episode per day is limited to 1/6.

  12. Can lncRNAs be indicators for the diagnosis of early onset or acute schizophrenia and distinguish major depressive disorder and generalized anxiety disorder?-A cross validation analysis.

    PubMed

    Cui, Xuelian; Niu, Wei; Kong, Lingming; He, Mingjun; Jiang, Kunhong; Chen, Shengdong; Zhong, Aifang; Li, Wanshuai; Lu, Jim; Zhang, Liyi

    2017-03-28

    Depression and anxiety are apparent symptoms in the early onset or acute phase of schizophrenia (SZ), which complicate timely diagnosis and treatment. It is imperative to seek an indicator to distinguish schizophrenia from depressive and anxiety disorders. Using lncRNA microarray profiling and RT-PCR, three up-regulated lncRNAs in SZ, six down-regulated lncRNAs in major depressive disorder (MDD), and three up-regulated lncRNAs in generalized anxiety disorder (GAD) had been identified as potential biomarkers. All the lncRNAs were, then, cross-validated in 40 SZ patients, 40 MDD patients, 40 GAD patients, and 40 normal controls. Compared with controls, three up-regulated SZ lncRNAs had a significantly down-regulated expression in GAD, and no remarkable differences existed between MDD and the controls. Additionally, the six down-regulated MDD lncRNAs were expressed in an opposite fashion in SZ, and the expression of the three up-regulated GAD lncRNAs were significantly different between SZ and GAD. These results indicate that the expression patterns of the three up-regulated SZ lncRNAs could not be completely replicated in MDD and GAD, and vice versa. Thus, these three SZ lncRNAs seem to be established as potential indicators for diagnosis of schizophrenia and distinguishing it from MDD and GAD.© 2017 Wiley Periodicals, Inc.

  13. Exploring Mouse Protein Function via Multiple Approaches

    PubMed Central

    Huang, Tao; Kong, Xiangyin; Zhang, Yunhua; Zhang, Ning

    2016-01-01

    Although the number of available protein sequences is growing exponentially, functional protein annotations lag far behind. Therefore, accurate identification of protein functions remains one of the major challenges in molecular biology. In this study, we presented a novel approach to predict mouse protein functions. The approach was a sequential combination of a similarity-based approach, an interaction-based approach and a pseudo amino acid composition-based approach. The method achieved an accuracy of about 0.8450 for the 1st-order predictions in the leave-one-out and ten-fold cross-validations. For the results yielded by the leave-one-out cross-validation, although the similarity-based approach alone achieved an accuracy of 0.8756, it was unable to predict the functions of proteins with no homologues. Comparatively, the pseudo amino acid composition-based approach alone reached an accuracy of 0.6786. Although the accuracy was lower than that of the previous approach, it could predict the functions of almost all proteins, even proteins with no homologues. Therefore, the combined method balanced the advantages and disadvantages of both approaches to achieve efficient performance. Furthermore, the results yielded by the ten-fold cross-validation indicate that the combined method is still effective and stable when there are no close homologs are available. However, the accuracy of the predicted functions can only be determined according to known protein functions based on current knowledge. Many protein functions remain unknown. By exploring the functions of proteins for which the 1st-order predicted functions are wrong but the 2nd-order predicted functions are correct, the 1st-order wrongly predicted functions were shown to be closely associated with the genes encoding the proteins. The so-called wrongly predicted functions could also potentially be correct upon future experimental verification. Therefore, the accuracy of the presented method may be much higher in

  14. Reference measurement procedure for CSF amyloid beta (Aβ)1-42 and the CSF Aβ1-42 /Aβ1-40 ratio - a cross-validation study against amyloid PET.

    PubMed

    Pannee, Josef; Portelius, Erik; Minthon, Lennart; Gobom, Johan; Andreasson, Ulf; Zetterberg, Henrik; Hansson, Oskar; Blennow, Kaj

    2016-11-01

    A clinical diagnosis of Alzheimer's disease is currently made on the basis of results from cognitive tests in combination with medical history and general clinical evaluation, but the peptide amyloid-beta (Aβ) in cerebrospinal fluid (CSF) is increasingly used as a biomarker for amyloid pathology in clinical trials and in recently proposed revised clinical criteria for Alzheimer's disease. Recent analytical developments have resulted in mass spectrometry (MS) reference measurement procedures for absolute quantification of Aβ1-42 in CSF. The CSF Aβ1-42 /Aβ1-40 ratio has been suggested to improve the detection of cerebral amyloid deposition, by compensating for inter-individual variations in total Aβ production. Our aim was to cross-validate the reference measurement procedure as well as the Aβ1-42 /Aβ1-40 and Aβ1-42 /Aβ1-38 ratios in CSF, measured by high-resolution MS, with the cortical level of Aβ fibrils as measured by amyloid ((18) F-flutemetamol) positron emission tomography (PET). We included 100 non-demented patients with cognitive symptoms from the Swedish BioFINDER study, all of whom had undergone both lumbar puncture and (18) F-flutemetamol PET. Comparing CSF Aβ1-42 concentrations with (18) F-flutemetamol PET showed high concordance with an area under the receiver operating characteristic curve of 0.85 and a sensitivity and specificity of 82% and 81%, respectively. The ratio of Aβ1-42 /Aβ1-40 or Aβ1-42 /Aβ1-38 significantly improved concordance with an area under the receiver operating characteristic curve of 0.95 and a sensitivity and specificity of 96% and 91%, respectively. These results show that the CSF Aβ1-42 /Aβ1-40 and Aβ1-42 /Aβ1-38 ratios using the described MS method are strongly associated with cortical Aβ fibrils measured by (18) F-flutemetamol PET.

  15. Cross-validated stable-isotope dilution GC-MS and LC-MS/MS assays for monoacylglycerol lipase (MAGL) activity by measuring arachidonic acid released from the endocannabinoid 2-arachidonoyl glycerol.

    PubMed

    Kayacelebi, Arslan Arinc; Schauerte, Celina; Kling, Katharina; Herbers, Jan; Beckmann, Bibiana; Engeli, Stefan; Jordan, Jens; Zoerner, Alexander A; Tsikas, Dimitrios

    2017-03-15

    2-Arachidonoyl glycerol (2AG) is an endocannabinoid that activates cannabinoid (CB) receptors CB1 and CB2. Monoacylglycerol lipase (MAGL) inactivates 2AG through hydrolysis to arachidonic acid (AA) and glycerol, thus modulating the activity at CB receptors. In the brain, AA released from 2AG by the action of MAGL serves as a substrate for cyclooxygenases which produce pro-inflammatory prostaglandins. Here we report stable-isotope GC-MS and LC-MS/MS assays for the reliable measurement of MAGL activity. The assays utilize deuterium-labeled 2AG (d8-2AG; 10μM) as the MAGL substrate and measure deuterium-labeled AA (d8-AA; range 0-1μM) as the MAGL product. Unlabelled AA (d0-AA, 1μM) serves as the internal standard. d8-AA and d0-AA are extracted from the aqueous buffered incubation mixtures by ethyl acetate. Upon solvent evaporation the residue is reconstituted in the mobile phase prior to LC-MS/MS analysis or in anhydrous acetonitrile for GC-MS analysis. LC-MS/MS analysis is performed in the negative electrospray ionization mode by selected-reaction monitoring the mass transitions [M-H](-)→[M-H - CO2](-), i.e., m/z 311→m/z 267 for d8-AA and m/z 303→m/z 259 for d0-AA. Prior to GC-MS analysis d8-AA and d0-AA were converted to their pentafluorobenzyl (PFB) esters by means of PFB-Br. GC-MS analysis is performed in the electron-capture negative-ion chemical ionization mode by selected-ion monitoring the ions [M-PFB](-), i.e., m/z 311 for d8-AA and m/z 303 for d0-AA. The GC-MS and LC-MS/MS assays were cross-validated. Linear regression analysis between the concentration (range, 0-1μM) of d8-AA measured by LC-MS/MS (y) and that by GC-MS (x) revealed a straight line (r(2)=0.9848) with the regression equation y=0.003+0.898x, indicating a good agreement. In dog liver, we detected MAGL activity that was inhibitable by the MAGL inhibitor JZL-184. Exogenous eicosatetraynoic acid is suitable as internal standard for the quantitative determination of d8-AA produced from d8

  16. Outlier detection and removal improves accuracy of machine learning approach to multispectral burn diagnostic imaging.

    PubMed

    Li, Weizhi; Mo, Weirong; Zhang, Xu; Squiers, John J; Lu, Yang; Sellke, Eric W; Fan, Wensheng; DiMaio, J Michael; Thatcher, Jeffrey E

    2015-12-01

    Multispectral imaging (MSI) was implemented to develop a burn tissue classification device to assist burn surgeons in planning and performing debridement surgery. To build a classification model via machine learning, training data accurately representing the burn tissue was needed, but assigning raw MSI data to appropriate tissue classes is prone to error. We hypothesized that removing outliers from the training dataset would improve classification accuracy. A swine burn model was developed to build an MSI training database and study an algorithm’s burn tissue classification abilities. After the ground-truth database was generated, we developed a multistage method based on Z -test and univariate analysis to detect and remove outliers from the training dataset. Using 10-fold cross validation, we compared the algorithm’s accuracy when trained with and without the presence of outliers. The outlier detection and removal method reduced the variance of the training data. Test accuracy was improved from 63% to 76%, matching the accuracy of clinical judgment of expert burn surgeons, the current gold standard in burn injury assessment. Given that there are few surgeons and facilities specializing in burn care, this technology may improve the standard of burn care for patients without access to specialized facilities.

  17. A PSO-Based Approach for Pathway Marker Identification From Gene Expression Data.

    PubMed

    Mandal, Monalisa; Mondal, Jyotirmay; Mukhopadhyay, Anirban

    2015-09-01

    In this article, a new and robust pathway activity inference scheme is proposed from gene expression data using Particle Swarm Optimization (PSO). From microarray gene expression data, the corresponding pathway information of the genes are collected from a public database. For identifying the pathway markers, the expression values of each pathway consisting of genes, termed as pathway activity, are summarized. To measure the goodness of a pathway activity vector, t-score is widely used in the existing literature. The weakness of existing techniques for inferring pathway activity is that they intend to consider all the member genes of a pathway. But in reality, all the member genes may not be significant to the corresponding pathway. Therefore, those genes, which are responsible in the corresponding pathway, should be included only. Motivated by this, in the proposed method, using PSO, important genes with respect to each pathway are identified. The objective is to maximize the average t-score. For the pathway activities inferred from different percentage of significant pathways, the average absolute t -scores are plotted. In addition, the top 50% pathway markers are evaluated using 10-fold cross validation and its performance is compared with that of other existing techniques. Biological relevance of the results is also studied.

  18. Sex estimation from the tarsal bones in a Portuguese sample: a machine learning approach.

    PubMed

    Navega, David; Vicente, Ricardo; Vieira, Duarte N; Ross, Ann H; Cunha, Eugénia

    2015-05-01

    Sex estimation is extremely important in the analysis of human remains as many of the subsequent biological parameters are sex specific (e.g., age at death, stature, and ancestry). When dealing with incomplete or fragmented remains, metric analysis of the tarsal bones of the feet has proven valuable. In this study, the utility of 18 width, length, and height tarsal measurements were assessed for sex-related variation in a Portuguese sample. A total of 300 males and females from the Coimbra Identified Skeletal Collection were used to develop sex prediction models based on statistical and machine learning algorithm such as discriminant function analysis, logistic regression, classification trees, and artificial neural networks. All models were evaluated using 10-fold cross-validation and an independent test sample composed of 60 males and females from the Identified Skeletal Collection of the 21st Century. Results showed that tarsal bone sex-related variation can be easily captured with a high degree of repeatability. A simple tree-based multivariate algorithm involving measurements from the calcaneus, talus, first and third cuneiforms, and cuboid resulted in 88.3% correct sex estimation both on training and independent test sets. Traditional statistical classifiers such as the discriminant function analysis were outperformed by machine learning techniques. Results obtained show that machine learning algorithm are an important tool the forensic practitioners should consider when developing new standards for sex estimation.

  19. Outlier detection and removal improves accuracy of machine learning approach to multispectral burn diagnostic imaging

    NASA Astrophysics Data System (ADS)

    Li, Weizhi; Mo, Weirong; Zhang, Xu; Squiers, John J.; Lu, Yang; Sellke, Eric W.; Fan, Wensheng; DiMaio, J. Michael; Thatcher, Jeffrey E.

    2015-12-01

    Multispectral imaging (MSI) was implemented to develop a burn tissue classification device to assist burn surgeons in planning and performing debridement surgery. To build a classification model via machine learning, training data accurately representing the burn tissue was needed, but assigning raw MSI data to appropriate tissue classes is prone to error. We hypothesized that removing outliers from the training dataset would improve classification accuracy. A swine burn model was developed to build an MSI training database and study an algorithm's burn tissue classification abilities. After the ground-truth database was generated, we developed a multistage method based on Z-test and univariate analysis to detect and remove outliers from the training dataset. Using 10-fold cross validation, we compared the algorithm's accuracy when trained with and without the presence of outliers. The outlier detection and removal method reduced the variance of the training data. Test accuracy was improved from 63% to 76%, matching the accuracy of clinical judgment of expert burn surgeons, the current gold standard in burn injury assessment. Given that there are few surgeons and facilities specializing in burn care, this technology may improve the standard of burn care for patients without access to specialized facilities.

  20. puffMarker: A Multi-Sensor Approach for Pinpointing the Timing of First Lapse in Smoking Cessation

    PubMed Central

    Saleheen, Nazir; Ali, Amin Ahsan; Hossain, Syed Monowar; Sarker, Hillol; Chatterjee, Soujanya; Marlin, Benjamin; Ertin, Emre; al’Absi, Mustafa; Kumar, Santosh

    2015-01-01

    Recent researches have demonstrated the feasibility of detecting smoking from wearable sensors, but their performance on real-life smoking lapse detection is unknown. In this paper, we propose a new model and evaluate its performance on 61 newly abstinent smokers for detecting a first lapse. We use two wearable sensors — breathing pattern from respiration and arm movements from 6-axis inertial sensors worn on wrists. In 10-fold cross-validation on 40 hours of training data from 6 daily smokers, our model achieves a recall rate of 96.9%, for a false positive rate of 1.1%. When our model is applied to 3 days of post-quit data from 32 lapsers, it correctly pinpoints the timing of first lapse in 28 participants. Only 2 false episodes are detected on 20 abstinent days of these participants. When tested on 84 abstinent days from 28 abstainers, the false episode per day is limited to 1/6. PMID:26543927

  1. Microsatellite mutation rates in the eastern tiger salamander (Ambystoma tigrinum tigrinum) differ 10-fold across loci.

    PubMed

    Bulut, Zafer; McCormick, Cory R; Gopurenko, David; Williams, Rod N; Bos, David H; DeWoody, J Andrew

    2009-07-01

    Microsatellites are commonly used for mapping and population genetics because of their high heterozygosities and allelic variability (i.e., polymorphism). Microsatellite markers are generally more polymorphic than other types of molecular markers such as allozymes or SNPs because the insertions/deletions that give rise to microsatellite variability are relatively common compared to nucleotide substitutions. Nevertheless, direct evidence of microsatellite mutation rates (MMRs) is lacking in most vertebrate groups despite the importance of such estimates to key population parameters (e.g., genetic differentiation or theta = 4N (e)micro). Herein, we present empirical data on MMRs in eastern tiger salamanders (Ambystoma tigrinum tigrinum). We conducted captive breeding trials and genotyped over 1,000 offspring at a suite of microsatellite loci. These data on 7,906 allele transfers provide the first direct estimates of MMRs in amphibians, and they illustrate that MMRs can vary by more than an order of magnitude across loci within a given species (one locus had ten mutations whereas the others had none).

  2. Cesarean Delivery Rates Vary 10-Fold Among US Hospitals; Reducing Variation May Address Quality, Cost Issues

    PubMed Central

    Kozhimannil, Katy Backes; Law, Michael R.; Virnig, Beth A.

    2013-01-01

    Cesarean delivery is the most commonly performed surgical procedure in the United States, and cesarean rates are increasing. Working with 2009 data from 593 US hospitals nationwide, we found that cesarean rates varied tenfold across hospitals, from 7.1 percent to 69.9 percent. Even for women with lower-risk pregnancies, in which more limited variation might be expected, cesarean rates varied fifteen-fold, from 2.4 percent to 36.5 percent. Thus, vast differences in practice patterns are likely to be driving the costly overuse of cesarean delivery in many US hospitals. Because Medicaid pays for nearly half of US births, government efforts to decrease variation are warranted. We focus on four promising directions for reducing these variations, including better coordination of maternity care, more data collection and measurement, tying Medicaid payment to quality improvement, and enhancing patient-centered decision making through public reporting. PMID:23459732

  3. Linear Models for Field Trials, Smoothing, and Cross-Validation.

    DTIC Science & Technology

    1984-01-01

    in fertility and other environmental factors. Blocking methods are customarily used, even when blocks have no physical meaning in thei% experiment, but... TEST CHART NATIONAL BUREAU nT STANDARDS 196b3 A . - -. .. .• • ei .. o .’ ’."., " ". " ’ . .J . . .~i• j,... . . . . . .. . . . . . . . . .,.. - i...34-""bution/ __Availability Code..""s’’ Technical Summary Report #2779 December 1984 1J ABSTRACT Spatial methods for the analysis of agricultural field

  4. Internet Attack Traceback: Cross-Validation and Pebble-Trace

    DTIC Science & Technology

    2013-02-28

    of key: a permutation of values in 0— 255 • Entropy verifier: the candidate key with largest entropy drop is the real key Pattern filter...Characteristics of symmetric keys • Randomness of ciphertext mostly from symmetric encryption schemes Pattern filter Entropy filter Verifier...Characteristic verifer + Entropy verifier) Correct keys Candidate key regions Candidate key regions Memory image Network traffic Step 2

  5. Cross Validation of Selection of Variables in Multiple Regression.

    DTIC Science & Technology

    1979-12-01

    Bomber IBMNAV * BOMNAV Navigation-Cargo * * CARNAV Sensory-Fighter * SF FGTSEN Sensory - Bomber * SB BOMSEN Communication - Fighter IFGCOM CF FGTCOM...of Variables Variable No. Recode FGTNAV 1 0 LESS THAN 1 1 OR OVER BONNAV 2 0 LESS THAN S1 OR OVER CARNAV 3 0 LESS THAN S1 OR OVER FGTSEN 4 0 LESS THAN...cc x x x x x x x CARNAV X X X X X X x XMTR x X X X X x PD X x X X X X UP x- *Those which AID determined. 44 This value was lowered to 3 in the

  6. Cross-Validation of the Self-Motivation Inventory.

    ERIC Educational Resources Information Center

    Heiby, Elaine M.; And Others

    Because the literature suggests that aerobic exercise is associated with physical health and psychological well-being, there is a concern with discovering how to improve adherence to such exercise. There is growing evidence that self-motivation, as measured by the Dishman Self-Motivation Inventory (SMI), is a redictor of adherence to regular…

  7. Evaluation and cross-validation of Environmental Models

    NASA Astrophysics Data System (ADS)

    Lemaire, Joseph

    Before scientific models (statistical or empirical models based on experimental measurements; physical or mathematical models) can be proposed and selected as ISO Environmental Standards, a Commission of professional experts appointed by an established International Union or Association (e.g. IAGA for Geomagnetism and Aeronomy, . . . ) should have been able to study, document, evaluate and validate the best alternative models available at a given epoch. Examples will be given, indicating that different values for the Earth radius have been employed in different data processing laboratories, institutes or agencies, to process, analyse or retrieve series of experimental observations. Furthermore, invariant magnetic coordinates like B and L, commonly used in the study of Earth's radiation belts fluxes and for their mapping, differ from one space mission data center to the other, from team to team, and from country to country. Worse, users of empirical models generally fail to use the original magnetic model which had been employed to compile B and L , and thus to build these environmental models. These are just some flagrant examples of inconsistencies and misuses identified so far; there are probably more of them to be uncovered by careful, independent examination and benchmarking. A meter prototype, the standard unit length that has been determined on 20 May 1875, during the Diplomatic Conference of the Meter, and deposited at the BIPM (Bureau International des Poids et Mesures). In the same token, to coordinate and safeguard progress in the field of Space Weather, similar initiatives need to be undertaken, to prevent wild, uncontrolled dissemination of pseudo Environmental Models and Standards. Indeed, unless validation tests have been performed, there is guaranty, a priori, that all models on the market place have been built consistently with the same units system, and that they are based on identical definitions for the coordinates systems, etc... Therefore, preliminary analyses should be carried out under the control and authority of an established international professional Organization or Association, before any final political decision is made by ISO to select a specific Environmental Models, like for example IGRF and DGRF. Of course, Commissions responsible for checking the consistency of definitions, methods and algorithms for data processing might consider to delegate specific tasks (e.g. bench-marking the technical tools, the calibration procedures, the methods of data analysis, and the software algorithms employed in building the different types of models, as well as their usage) to private, intergovernmental or international organization/agencies (e.g.: NASA, ESA, AGU, EGU, COSPAR, . . . ); eventually, the latter should report conclusions to the Commissions members appointed by IAGA or any established authority like IUGG.

  8. Cross-validation of resting metabolic rate prediction equations

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Background: Knowledge of the resting metabolic rate (RMR) is necessary for determining individual total energy requirements. Measurement of RMR is time consuming and requires specialized equipment. Prediction equations provide an easy method to estimate RMR; however, the accuracy of these equations...

  9. Cross-Validation of Experimental USAF Pilot Training Performance Models

    DTIC Science & Technology

    1990-05-01

    BAT) battery Is a computerized test battery designed to measure individual differences In psychomotor skills , information processing abilities...differences In psychomotor skills , information processing abilities, personality and attitudes helped to reduce uncertainty In making pilot candidate

  10. Correcting Evaluation Bias of Relational Classifiers with Network Cross Validation

    DTIC Science & Technology

    2010-01-01

    Computer Sciences (with a minor in Mathematical Statistics ) at the University of Wisconsin-Madison in 2001. Broadly speaking, Tina’s research interests...These methods specifically exploit the statistical dependencies among instances in order to improve classification accuracy. However, there has been...the complex link structure and attribute dependencies in relational data violate the assumptions of many conventional statistical tests and make it

  11. Cross-Ethnic Cross Validation of Aptitude Batteries.

    ERIC Educational Resources Information Center

    Pike, Lewis W.; Mahoney, Margaret H.

    How well an aptitude test battery predicts rated job performance for Negroes and whites, and how well a battery selected for one group predicts performance for the other, is examined. Supervisory ratings were used as the criterion of job performance. Tests selected to predict performance in the job of Medical Laboratory technicians were validated…

  12. [Cross validity of the UCLA Loneliness Scale factorization].

    PubMed

    Borges, Africa; Prieto, Pedro; Ricchetti, Giacinto; Hernández-Jorge, Carmen; Rodríguez-Naveiras, Elena

    2008-11-01

    Loneliness is an unpleasant experience that takes place when a person's network of social relationships is significantly deficient in quality and quantity, and it is associated with negative feelings. Loneliness is a fundamental construct that provides information about several psychological processes, especially in the clinical setting. It is well known that this construct is related to isolation and emotional loneliness. One of the most well-known psychometric instruments to measure loneliness is the revised UCLA Loneliness Scale, which has been factorized in several populations. A controversial issue related to the UCLA Loneliness Scale is its factor structure, because the test was first created based on a unidimensional structure; however, subsequent research has proved that its structure may be bipolar or even multidimensional. In the present work, the UCLA Loneliness Scale was completed by two populations: Spanish and Italian undergraduate university students. Results show a multifactorial structure in both samples. This research presents a theoretically and analytically coherent bifactorial structure.

  13. AnL1 smoothing spline algorithm with cross validation

    NASA Astrophysics Data System (ADS)

    Bosworth, Ken W.; Lall, Upmanu

    1993-08-01

    We propose an algorithm for the computation ofL1 (LAD) smoothing splines in the spacesWM(D), with . We assume one is given data of the formyiD(f(ti) +ɛi, iD1,...,N with {itti}iD1N ⊂D, theɛi are errors withE(ɛi)D0, andf is assumed to be inWM. The LAD smoothing spline, for fixed smoothing parameterλ?;0, is defined as the solution,sλ, of the optimization problem (1/N)∑iD1N yi-g(ti +λJM(g), whereJM(g) is the seminorm consisting of the sum of the squaredL2 norms of theMth partial derivatives ofg. Such an LAD smoothing spline,sλ, would be expected to give robust smoothed estimates off in situations where theɛi are from a distribution with heavy tails. The solution to such a problem is a "thin plate spline" of known form. An algorithm for computingsλ is given which is based on considering a sequence of quadratic programming problems whose structure is guided by the optimality conditions for the above convex minimization problem, and which are solved readily, if a good initial point is available. The "data driven" selection of the smoothing parameter is achieved by minimizing aCV(λ) score of the form .The combined LAD-CV smoothing spline algorithm is a continuation scheme in λ↘0 taken on the above SQPs parametrized inλ, with the optimal smoothing parameter taken to be that value ofλ at which theCV(λ) score first begins to increase. The feasibility of constructing the LAD-CV smoothing spline is illustrated by an application to a problem in environment data interpretation.

  14. A fast approach for detection of erythemato-squamous diseases based on extreme learning machine with maximum relevance minimum redundancy feature selection

    NASA Astrophysics Data System (ADS)

    Liu, Tong; Hu, Liang; Ma, Chao; Wang, Zhi-Yan; Chen, Hui-Ling

    2015-04-01

    In this paper, a novel hybrid method, which integrates an effective filter maximum relevance minimum redundancy (MRMR) and a fast classifier extreme learning machine (ELM), has been introduced for diagnosing erythemato-squamous (ES) diseases. In the proposed method, MRMR is employed as a feature selection tool for dimensionality reduction in order to further improve the diagnostic accuracy of the ELM classifier. The impact of the type of activation functions, the number of hidden neurons and the size of the feature subsets on the performance of ELM have been investigated in detail. The effectiveness of the proposed method has been rigorously evaluated against the ES disease dataset, a benchmark dataset, from UCI machine learning database in terms of classification accuracy. Experimental results have demonstrated that our method has achieved the best classification accuracy of 98.89% and an average accuracy of 98.55% via 10-fold cross-validation technique. The proposed method might serve as a new candidate of powerful methods for diagnosing ES diseases.

  15. A novel approach to CAD system for the detection of lung nodules in CT images.

    PubMed

    Javaid, Muzzamil; Javid, Moazzam; Rehman, Muhammad Zia Ur; Shah, Syed Irtiza Ali

    2016-10-01

    Detection of pulmonary nodule plays a significant role in the diagnosis of lung cancer in early stage that improves the chances of survival of an individual. In this paper, a computer aided nodule detection method is proposed for the segmentation and detection of challenging nodules like juxtavascular and juxtapleural nodules. Lungs are segmented from computed tomography (CT) images using intensity thresholding; brief analysis of CT image histogram is done to select a suitable threshold value for better segmentation results. Simple morphological closing is used to include juxtapleural nodules in segmented lung regions. K-means clustering is applied for the initial detection and segmentation of potential nodules; shape specific morphological opening is implemented to refine segmentation outcomes. These segmented potential nodules are then divided into six groups on the basis of their thickness and percentage connectivity with lung walls. Grouping not only helped in improving system's efficiency but also reduced computational time, otherwise consumed in calculating and analyzing unnecessary features for all nodules. Different sets of 2D and 3D features are extracted from nodules in each group to eliminate false positives. Small size nodules are differentiated from false positives (FPs) on the basis of their salient features; sensitivity of the system for small nodules is 83.33%. SVM classifier is used for the classification of large nodules, for which the sensitivity of the proposed system is 93.8% applying 10-fold cross-validation. Receiver Operating Characteristic (ROC) curve is used for the analysis of CAD system. Overall sensitivity of the system is 91.65% with 3.19 FPs per case, and accuracy is 96.22%. The system took 3.8 seconds to analyze each image.

  16. A methodological approach to the classification of dermoscopy images

    PubMed Central

    Celebi, M. Emre; Kingravi, Hassan A.; Uddin, Bakhtiyar; Iyatomi, Hitoshi; Aslandogan, Y. Alp; Stoecker, William V.; Moss, Randy H.

    2011-01-01

    In this paper a methodological approach to the classification of pigmented skin lesions in dermoscopy images is presented. First, automatic border detection is performed to separate the lesion from the background skin. Shape features are then extracted from this border. For the extraction of color and texture related features, the image is divided into various clinically significant regions using the Euclidean distance transform. This feature data is fed into an optimization framework, which ranks the features using various feature selection algorithms and determines the optimal feature subset size according to the area under the ROC curve measure obtained from support vector machine classification. The issue of class imbalance is addressed using various sampling strategies, and the classifier generalization error is estimated using Monte Carlo cross validation. Experiments on a set of 564 images yielded a specificity of 92.34% and a sensitivity of 93.33%. PMID:17387001

  17. Active subspace approach to reliability and safety assessments of small satellite separation

    NASA Astrophysics Data System (ADS)

    Hu, Xingzhi; Chen, Xiaoqian; Zhao, Yong; Tuo, Zhouhui; Yao, Wen

    2017-02-01

    Ever-increasing launch of small satellites demands an effective and efficient computer-aided analysis approach to shorten the ground test cycle and save the economic cost. However, the multiple influencing factors hamper the efficiency and accuracy of separation reliability assessment. In this study, a novel evaluation approach based on active subspace identification and response surface construction is established and verified. The formulation of small satellite separation is firstly derived, including equations of motion, separation and gravity forces, and quantity of interest. The active subspace reduces the dimension of uncertain inputs with minimum precision loss and a 4th degree multivariate polynomial regression (MPR) using cross validation is hand-coded for the propagation and error analysis. A common spring separation of small satellites is employed to demonstrate the accuracy and efficiency of the approach, which exhibits its potential use in widely existing needs of satellite separation analysis.

  18. A Bayesian Shrinkage Approach for AMMI Models

    PubMed Central

    de Oliveira, Luciano Antonio; Nuvunga, Joel Jorge; Pamplona, Andrezza Kéllen Alves

    2015-01-01

    Linear-bilinear models, especially the additive main effects and multiplicative interaction (AMMI) model, are widely applicable to genotype-by-environment interaction (GEI) studies in plant breeding programs. These models allow a parsimonious modeling of GE interactions, retaining a small number of principal components in the analysis. However, one aspect of the AMMI model that is still debated is the selection criteria for determining the number of multiplicative terms required to describe the GE interaction pattern. Shrinkage estimators have been proposed as selection criteria for the GE interaction components. In this study, a Bayesian approach was combined with the AMMI model with shrinkage estimators for the principal components. A total of 55 maize genotypes were evaluated in nine different environments using a complete blocks design with three replicates. The results show that the traditional Bayesian AMMI model produces low shrinkage of singular values but avoids the usual pitfalls in determining the credible intervals in the biplot. On the other hand, Bayesian shrinkage AMMI models have difficulty with the credible interval for model parameters, but produce stronger shrinkage of the principal components, converging to GE matrices that have more shrinkage than those obtained using mixed models. This characteristic allowed more parsimonious models to be chosen, and resulted in models being selected that were similar to those obtained by the Cornelius F-test (α = 0.05) in traditional AMMI models and cross validation based on leave-one-out. This characteristic allowed more parsimonious models to be chosen and more GEI pattern retained on the first two components. The resulting model chosen by posterior distribution of singular value was also similar to those produced by the cross-validation approach in traditional AMMI models. Our method enables the estimation of credible interval for AMMI biplot plus the choice of AMMI model based on direct posterior

  19. A Bayesian Shrinkage Approach for AMMI Models.

    PubMed

    da Silva, Carlos Pereira; de Oliveira, Luciano Antonio; Nuvunga, Joel Jorge; Pamplona, Andrezza Kéllen Alves; Balestre, Marcio

    2015-01-01

    Linear-bilinear models, especially the additive main effects and multiplicative interaction (AMMI) model, are widely applicable to genotype-by-environment interaction (GEI) studies in plant breeding programs. These models allow a parsimonious modeling of GE interactions, retaining a small number of principal components in the analysis. However, one aspect of the AMMI model that is still debated is the selection criteria for determining the number of multiplicative terms required to describe the GE interaction pattern. Shrinkage estimators have been proposed as selection criteria for the GE interaction components. In this study, a Bayesian approach was combined with the AMMI model with shrinkage estimators for the principal components. A total of 55 maize genotypes were evaluated in nine different environments using a complete blocks design with three replicates. The results show that the traditional Bayesian AMMI model produces low shrinkage of singular values but avoids the usual pitfalls in determining the credible intervals in the biplot. On the other hand, Bayesian shrinkage AMMI models have difficulty with the credible interval for model parameters, but produce stronger shrinkage of the principal components, converging to GE matrices that have more shrinkage than those obtained using mixed models. This characteristic allowed more parsimonious models to be chosen, and resulted in models being selected that were similar to those obtained by the Cornelius F-test (α = 0.05) in traditional AMMI models and cross validation based on leave-one-out. This characteristic allowed more parsimonious models to be chosen and more GEI pattern retained on the first two components. The resulting model chosen by posterior distribution of singular value was also similar to those produced by the cross-validation approach in traditional AMMI models. Our method enables the estimation of credible interval for AMMI biplot plus the choice of AMMI model based on direct posterior

  20. A novel fractal approach for predicting G-protein-coupled receptors and their subfamilies with support vector machines.

    PubMed

    Nie, Guoping; Li, Yong; Wang, Feichi; Wang, Siwen; Hu, Xuehai

    2015-01-01

    G-protein-coupled receptors (GPCRs) are seven membrane-spanning proteins and regulate many important physiological processes, such as vision, neurotransmission, immune response and so on. GPCRs-related pathways are the targets of a large number of marketed drugs. Therefore, the design of a reliable computational model for predicting GPCRs from amino acid sequence has long been a significant biomedical problem. Chaos game representation (CGR) reveals the fractal patterns hidden in protein sequences, and then fractal dimension (FD) is an important feature of these highly irregular geometries with concise mathematical expression. Here, in order to extract important features from GPCR protein sequences, CGR algorithm, fractal dimension and amino acid composition (AAC) are employed to formulate the numerical features of protein samples. Four groups of features are considered, and each group is evaluated by support vector machine (SVM) and 10-fold cross-validation test. To test the performance of the present method, a new non-redundant dataset was built based on latest GPCRDB database. Comparing the results of numerical experiments, the group of combined features with AAC and FD gets the best result, the accuracy is 99.22% and Matthew's correlation coefficient (MCC) is 0.9845 for identifying GPCRs from non-GPCRs. Moreover, if it is classified as a GPCR, it will be further put into the second level, which will classify a GPCR into one of the five main subfamilies. At this level, the group of combined features with AAC and FD also gets best accuracy 85.73%. Finally, the proposed predictor is also compared with existing methods and shows better performances.

  1. QNA-based 'Star Track' QSAR approach.

    PubMed

    Filimonov, D A; Zakharov, A V; Lagunin, A A; Poroikov, V V

    2009-10-01

    In the existing quantitative structure-activity relationship (QSAR) methods any molecule is represented as a single point in a many-dimensional space of molecular descriptors. We propose a new QSAR approach based on Quantitative Neighbourhoods of Atoms (QNA) descriptors, which characterize each atom of a molecule and depend on the whole molecule structure. In the 'Star Track' methodology any molecule is represented as a set of points in a two-dimensional space of QNA descriptors. With our new method the estimate of the target property of a chemical compound is calculated as the average value of the function of QNA descriptors in the points of the atoms of a molecule in QNA descriptor space. Substantially, we propose the use of only two descriptors rather than more than 3000 molecular descriptors that apply in the QSAR method. On the basis of this approach we have developed the computer program GUSAR and compared it with several widely used QSAR methods including CoMFA, CoMSIA, Golpe/GRID, HQSAR and others, using ten data sets representing various chemical series and diverse types of biological activity. We show that in the majority of cases the accuracy and predictivity of GUSAR models appears to be better than those for the reference QSAR methods. High predictive ability and robustness of GUSAR are also shown in the leave-20%-out cross-validation procedure.

  2. Predicting drug-induced liver injury in human with Naïve Bayes classifier approach

    NASA Astrophysics Data System (ADS)

    Zhang, Hui; Ding, Lan; Zou, Yi; Hu, Shui-Qing; Huang, Hai-Guo; Kong, Wei-Bao; Zhang, Ji

    2016-10-01

    Drug-induced liver injury (DILI) is one of the major safety concerns in drug development. Although various toxicological studies assessing DILI risk have been developed, these methods were not sufficient in predicting DILI in humans. Thus, developing new tools and approaches to better predict DILI risk in humans has become an important and urgent task. In this study, we aimed to develop a computational model for assessment of the DILI risk with using a larger scale human dataset and Naïve Bayes classifier. The established Naïve Bayes prediction model was evaluated by 5-fold cross validation and an external test set. For the training set, the overall prediction accuracy of the 5-fold cross validation was 94.0 %. The sensitivity, specificity, positive predictive value and negative predictive value were 97.1, 89.2, 93.5 and 95.1 %, respectively. The test set with the concordance of 72.6 %, sensitivity of 72.5 %, specificity of 72.7 %, positive predictive value of 80.4 %, negative predictive value of 63.2 %. Furthermore, some important molecular descriptors related to DILI risk and some toxic/non-toxic fragments were identified. Thus, we hope the prediction model established here would be employed for the assessment of human DILI risk, and the obtained molecular descriptors and substructures should be taken into consideration in the design of new candidate compounds to help medicinal chemists rationally select the chemicals with the best prospects to be effective and safe.

  3. Sexual Dimorphism of the First Rib: A Comparative Approach Using Metric and Geometric Morphometric Analyses.

    PubMed

    Lynch, Jeffrey James; Cross, Peter; Heaton, Vivienne

    2017-02-07

    This research investigated the sexual dimorphism of the first human rib using geometric morphometric and metric approaches on a sample of 285 specimens containing European Americans and African Americans from the Hamann-Todd collection. Metric measurements were investigated for sexual dimorphism and ancestral differences using univariate statistics. Four type II landmarks and 40 sliding semi-landmarks were placed outlining the dorsal and ventral curvatures of the ribs. Landmark data were processed using Generalized Procrustes Analyses with Procrustes distance sliding, and the subsequent coordinates were investigated for sexual dimorphism and ancestral differences using Procrustes ANOVAs. Both geometric morphometric and metric data were analyzed using cross-validated discriminant function analyses to test the hypothesis that variables from both approaches can be combined to increase sex classification rate. European Americans had sex correctly classified as high as 88.05% and African Americans as high as 70.86% using a combination of metric and geometric morphometric variables.

  4. Estimating hazardous concentrations by an informative Bayesian approach.

    PubMed

    Ciffroy, Philippe; Keller, Merlin; Pasanisi, Alberto

    2013-03-01

    The species sensitivity distribution (SSD) approach is recommended for assessing chemical risk. In practice, however, it can be used only for the few substances for which large-scale ecotoxicological results are available. Indeed, the statistical frequentist approaches used for building SSDs and for deriving hazardous concentrations (HC5) inherently require extensive data to guarantee goodness-of-fit. An alternative Bayesian approach to estimating HC5 from small data sets was developed. In contrast to the noninformative Bayesian approaches that have been tested to date, the authors' method used informative priors related to the expected species sensitivity variance. This method was tested on actual ecotoxicological data for 21 well-informed substances. A cross-validation compared the HC5 values calculated using frequentist approaches with the results of our Bayesian approach, using both complete and truncated data samples. The authors' informative Bayesian approach was compared with noninformative Bayesian methods published in the past, including those incorporating loss functions. The authors found that even for the truncated sample the HC5 values derived from the informative Bayesian approach were generally close to those obtained using the frequentist approach, which requires more data. In addition, the probability of overestimating an HC5 is rather limited. More robust HC5 estimates can be practically obtained from additional data without impairing regulatory protection levels, which will encourage collecting new ecotoxicological data. In conclusion, the Bayesian informative approach was shown to be relatively robust and could be a good surrogate approach for deriving HC5 values from small data sets.

  5. Classification of Human Pregnane X Receptor (hPXR) Activators and Non-Activators by Machine Learning Techniques: A Multifaceted Approach.

    PubMed

    Rathod, Vijay; Belekar, Vilas; Garg, Prabha; Sangamwar, Abhay T

    2016-01-01

    The Human Pregnane X Receptor (hPXR) is a regulator of drug metabolising enzymes (DME) and efflux transporters (ET). The prediction of hPXR activators and non-activators has pharmaceutical importance to predict the multiple drug resistance (MDR) and drug-drug interactions (DDI). In this study, we developed and validated the computational prediction models to classify hPXR activators and non-activators. We employed four machine learning methods support vector machine (SVM), k-nearest neighbour (k-NN), random forest (RF) and naïve bayesian (NB). These methods were used to develop molecular and fingerprint based descriptors for the prediction of hPXR activators and non-activators. Total 529 molecules consitsting of 317 activators and 212 non-activators were used for model development. The overall prediction accuracy of models was 69% to 99% to classify hPXR activators and nonactivators using RDkit descriptors. In case of 5 and 10-fold cross validation the prediction accuracy for training set is 74% to 82% and 79% to 83% for hPXR activators respectively and 50% to 62% and 49% to 65% non-activators, respectively. The external test prediction is between 59% to 73% for hPXR activators and 55% to 68% for hPXR non-activators. In addition, consensus models were developed in which the best model shows overall 75% to 83% accuracy for fingerprint and RDkit descriptors, respectively. The best developed model will be utilized for the prediction of hPXR activators and non-activators.

  6. The H50Q Mutation Induces a 10-fold Decrease in the Solubility of α-Synuclein*

    PubMed Central

    Porcari, Riccardo; Proukakis, Christos; Waudby, Christopher A.; Bolognesi, Benedetta; Mangione, P. Patrizia; Paton, Jack F. S.; Mullin, Stephen; Cabrita, Lisa D.; Penco, Amanda; Relini, Annalisa; Verona, Guglielmo; Vendruscolo, Michele; Stoppini, Monica; Tartaglia, Gian Gaetano; Camilloni, Carlo; Christodoulou, John; Schapira, Anthony H. V.; Bellotti, Vittorio

    2015-01-01

    The conversion of α-synuclein from its intrinsically disordered monomeric state into the fibrillar cross-β aggregates characteristically present in Lewy bodies is largely unknown. The investigation of α-synuclein variants causative of familial forms of Parkinson disease can provide unique insights into the conditions that promote or inhibit aggregate formation. It has been shown recently that a newly identified pathogenic mutation of α-synuclein, H50Q, aggregates faster than the wild-type. We investigate here its aggregation propensity by using a sequence-based prediction algorithm, NMR chemical shift analysis of secondary structure populations in the monomeric state, and determination of thermodynamic stability of the fibrils. Our data show that the H50Q mutation induces only a small increment in polyproline II structure around the site of the mutation and a slight increase in the overall aggregation propensity. We also find, however, that the H50Q mutation strongly stabilizes α-synuclein fibrils by 5.0 ± 1.0 kJ mol−1, thus increasing the supersaturation of monomeric α-synuclein within the cell, and strongly favors its aggregation process. We further show that wild-type α-synuclein can decelerate the aggregation kinetics of the H50Q variant in a dose-dependent manner when coaggregating with it. These last findings suggest that the precise balance of α-synuclein synthesized from the wild-type and mutant alleles may influence the natural history and heterogeneous clinical phenotype of Parkinson disease. PMID:25505181

  7. Prediction of 10-fold coordinated TiO2 and SiO2 structures at multimegabar pressures

    PubMed Central

    Lyle, Matthew J.; Pickard, Chris J.; Needs, Richard J.

    2015-01-01

    We predict by first-principles methods a phase transition in TiO2 at 6.5 Mbar from the Fe2P-type polymorph to a ten-coordinated structure with space group I4/mmm. This is the first report, to our knowledge, of the pressure-induced phase transition to the I4/mmm structure among all dioxide compounds. The I4/mmm structure was found to be up to 3.3% denser across all pressures investigated. Significant differences were found in the electronic properties of the two structures, and the metallization of TiO2 was calculated to occur concomitantly with the phase transition to I4/mmm. The implications of our findings were extended to SiO2, and an analogous Fe2P-type to I4/mmm transition was found to occur at 10 TPa. This is consistent with the lower-pressure phase transitions of TiO2, which are well-established models for the phase transitions in other AX2 compounds, including SiO2. As in TiO2, the transition to I4/mmm corresponds to the metallization of SiO2. This transformation is in the pressure range reached in the interiors of recently discovered extrasolar planets and calls for a reformulation of the equations of state used to model them. PMID:25991859

  8. 13 Years of TOPEX/POSEIDON Precision Orbit Determination and the 10-fold Improvement in Expected Orbit Accuracy

    NASA Technical Reports Server (NTRS)

    Lemoine, F. G.; Zelensky, N. P.; Luthcke, S. B.; Rowlands, D. D.; Beckley, B. D.; Klosko, S. M.

    2006-01-01

    Launched in the summer of 1992, TOPEX/POSEIDON (T/P) was a joint mission between NASA and the Centre National d Etudes Spatiales (CNES), the French Space Agency, to make precise radar altimeter measurements of the ocean surface. After the remarkably successful 13-years of mapping the ocean surface T/P lost its ability to maneuver and was de-commissioned January 2006. T/P revolutionized the study of the Earth s oceans by vastly exceeding pre-launch estimates of surface height accuracy recoverable from radar altimeter measurements. The precision orbit lies at the heart of the altimeter measurement providing the reference frame from which the radar altimeter measurements are made. The expected quality of orbit knowledge had limited the measurement accuracy expectations of past altimeter missions, and still remains a major component in the error budget of all altimeter missions. This paper describes critical improvements made to the T/P orbit time series over the 13-years of precise orbit determination (POD) provided by the GSFC Space Geodesy Laboratory. The POD improvements from the pre-launch T/P expectation of radial orbit accuracy and Mission requirement of 13-cm to an expected accuracy of about 1.5-cm with today s latest orbits will be discussed. The latest orbits with 1.5 cm RMS radial accuracy represent a significant improvement to the 2.0-cm accuracy orbits currently available on the T/P Geophysical Data Record (GDR) altimeter product.

  9. Coastal water quality estimation from Geostationary Ocean Color Imager (GOCI) satellite data using machine learning approaches

    NASA Astrophysics Data System (ADS)

    Im, Jungho; Ha, Sunghyun; Kim, Yong Hoon; Ha, Hokyung; Choi, Jongkuk; Kim, Miae

    2014-05-01

    It is important to monitor coastal water quality using key parameters such as chlorophyll-a concentration and suspended sediment to better manage coastal areas as well as to better understand the nature of biophysical processes in coastal seawater. Remote sensing technology has been commonly used to monitor coastal water quality due to its ability of covering vast areas at high temporal resolution. While it is relatively straightforward to estimate water quality in open ocean (i.e., Case I water) using remote sensing, coastal water quality estimation is still challenging as many factors can influence water quality, including various materials coming from inland water systems and tidal circulation. There are continued efforts to accurately estimate water quality parameters in coastal seawater from remote sensing data in a timely manner. In this study, two major water quality indicators, chlorophyll-a concentration and the amount of suspended sediment, were estimated using Geostationary Ocean Color Imager (GOCI) satellite data. GOCI, launched in June 2010, is the first geostationary ocean color observation satellite in the world. GOCI collects data hourly for 8 hours a day at 6 visible and 2 near-infrared bands at a 500 m resolution with 2,500 x 2,500 km square around Korean peninsula. Along with conventional statistical methods (i.e., various linear and non-linear regression), three machine learning approaches such as random forest, Cubist, and support vector regression were evaluated for coastal water quality estimation. In situ measurements (63 samples; including location, two water quality parameters, and the spectra of surface water using a hand-held spectroradiometer) collected during four days between 2011 and 2012 were used as reference data. Due to the small sample size, leave-one-out cross validation was used to assess the performance of the water quality estimation models. Atmospherically corrected radiance data and selected band-ratioed images were used

  10. A Predictive Approach to Network Reverse-Engineering

    NASA Astrophysics Data System (ADS)

    Wiggins, Chris

    2005-03-01

    A central challenge of systems biology is the ``reverse engineering" of transcriptional networks: inferring which genes exert regulatory control over which other genes. Attempting such inference at the genomic scale has only recently become feasible, via data-intensive biological innovations such as DNA microrrays (``DNA chips") and the sequencing of whole genomes. In this talk we present a predictive approach to network reverse-engineering, in which we integrate DNA chip data and sequence data to build a model of the transcriptional network of the yeast S. cerevisiae capable of predicting the response of genes in unseen experiments. The technique can also be used to extract ``motifs,'' sequence elements which act as binding sites for regulatory proteins. We validate by a number of approaches and present comparison of theoretical prediction vs. experimental data, along with biological interpretations of the resulting model. En route, we will illustrate some basic notions in statistical learning theory (fitting vs. over-fitting; cross- validation; assessing statistical significance), highlighting ways in which physicists can make a unique contribution in data- driven approaches to reverse engineering.

  11. Artificial neural network approach to modelling of metal contents in different types of chocolates.

    PubMed

    Podunavac-Kuzmanović, Sanja; Jevrić, Lidija; Švarc-Gajić, Jaroslava; Kovačević, Strahinja; Vasiljević, Ivana; Kecojević, Isidora; Ivanović, Evica

    2015-01-01

    The relationships between the contents of various metals in different types of chocolates were studied using chemometric approach. Chemometric analysis was based on the application of artificial neural networks (ANN). ANN was performed in order to select the significant models for predicting the metal contents. ANN equations, that represent the content of one metal as a function of the contents of other metals were established. The statistical quality of the generated mathematical models was determined by standard statistical measures and cross-validation parameters. High agreement between experimental and predicted values, obtained in the validation procedure, indicated the good quality of the models. The obtained results indicate the possibility of predicting the metal contents in different types of chocolate.

  12. An efficient approach to the quantitative analysis of humic acid in water.

    PubMed

    Wang, Xue; Li, Bao Qiong; Zhai, Hong Lin; Xiong, Meng Yi; Liu, Ying

    2016-01-01

    Rayleigh and Raman scatterings inevitably appear in fluorescence measurements, which make the quantitative analysis more difficult, especially in the overlap of target signals and scattering signals. Based on the grayscale images of three-dimensional fluorescence spectra, the linear model with two selected Zernike moments was established for the determination of humic acid, and applied to the quantitative analysis of the real sample taken from the Yellow River. The correlation coefficient (R(2)) and leave-one-out cross validation correlation coefficient (R(2)cv) were up to 0.9994 and 0.9987, respectively. The average recoveries were reached 96.28%. Compared with N-way partial least square and alternating trilinear decomposition methods, our approach was immune from the scattering and noise signals owing to its powerful multi-resolution characteristic and the obtained results were more reliable and accurate, which could be applied in food analyses.

  13. Ordinary kriging approach to predicting long-term particulate matter concentrations in seven major Korean cities

    PubMed Central

    Kim, Sun-Young; Yi, Seon-Ju; Eum, Young Seob; Choi, Hae-Jin; Shin, Hyesop; Ryou, Hyoung Gon; Kim, Ho

    2014-01-01

    Objectives Cohort studies of associations between air pollution and health have used exposure prediction approaches to estimate individual-level concentrations. A common prediction method used in Korean cohort studies is ordinary kriging. In this study, performance of ordinary kriging models for long-term particulate matter less than or equal to 10 μm in diameter (PM10) concentrations in seven major Korean cities was investigated with a focus on spatial prediction ability. Methods We obtained hourly PM10 data for 2010 at 226 urban-ambient monitoring sites in South Korea and computed annual average PM10 concentrations at each site. Given the annual averages, we developed ordinary kriging prediction models for each of the seven major cities and for the entire country by using an exponential covariance reference model and a maximum likelihood estimation method. For model evaluation, cross-validation was performed and mean square error and R-squared (R2) statistics were computed. Results Mean annual average PM10 concentrations in the seven major cities ranged between 45.5 and 66.0 μg/m3 (standard deviation=2.40 and 9.51 μg/m3, respectively). Cross-validated R2 values in Seoul and Busan were 0.31 and 0.23, respectively, whereas the other five cities had R2 values of zero. The national model produced a higher crossvalidated R2 (0.36) than those for the city-specific models. Conclusions In general, the ordinary kriging models performed poorly for the seven major cities and the entire country of South Korea, but the model performance was better in the national model. To improve model performance, future studies should examine different prediction approaches that incorporate PM10 source characteristics. PMID:25262773

  14. Carcinogenicity prediction of noncongeneric chemicals by a support vector machine.

    PubMed

    Zhong, Min; Nie, Xianglei; Yan, Aixia; Yuan, Qipeng

    2013-05-20

    The ability to identify carcinogenic compounds is of fundamental importance to the safe application of chemicals. In this study, we generated an array of in silico models allowing the classification of compounds into carcinogenic and noncarcinogenic agents based on a data set of 852 noncongeneric chemicals collected from the Carcinogenic Potency Database (CPDBAS). Twenty-four molecular descriptors were selected by Pearson correlation, F-score, and stepwise regression analysis. These descriptors cover a range of physicochemical properties, including electrophilicity, geometry, molecular weight, size, and solubility. The descriptor mutagenic showed the highest correlation coefficient with carcinogenicity. On the basis of these descriptors, a support vector machine-based (SVM) classification model was developed and fine-tuned by a 10-fold cross-validation approach. Both the SVM model (Model A1) and the best model from the 10-fold cross-validation (Model B3) runs gave good results on the test set with prediction accuracy over 80%, sensitivity over 76%, and specificity over 82%. In addition, extended connectivity fingerprints (ECFPs) and the Toxtree software were used to analyze the functional groups and substructures linked to carcinogenicity. It was found that the results of both methods are in good agreement.

  15. Selecting Relevant Descriptors for Classification by Bayesian Estimates: A Comparison with Decision Trees and Support Vector Machines Approaches for Disparate Data Sets.

    PubMed

    Carbon-Mangels, Miriam; Hutter, Michael C

    2011-10-01

    Classification algorithms suffer from the curse of dimensionality, which leads to overfitting, particularly if the problem is over-determined. Therefore it is of particular interest to identify the most relevant descriptors to reduce the complexity. We applied Bayesian estimates to model the probability distribution of descriptors values used for binary classification using n-fold cross-validation. As a measure for the discriminative power of the classifiers, the symmetric form of the Kullback-Leibler divergence of their probability distributions was computed. We found that the most relevant descriptors possess a Gaussian-like distribution of their values, show the largest divergences, and therefore appear most often in the cross-validation scenario. The results were compared to those of the LASSO feature selection method applied to multiple decision trees and support vector machine approaches for data sets of substrates and nonsubstrates of three Cytochrome P450 isoenzymes, which comprise strongly unbalanced compound distributions. In contrast to decision trees and support vector machines, the performance of Bayesian estimates is less affected by unbalanced data sets. This strategy reveals those descriptors that allow a simple linear separation of the classes, whereas the superior accuracy of decision trees and support vector machines can be attributed to nonlinear separation, which are in turn more prone to overfitting.

  16. Predicting drug-induced liver injury in human with Naïve Bayes classifier approach.

    PubMed

    Zhang, Hui; Ding, Lan; Zou, Yi; Hu, Shui-Qing; Huang, Hai-Guo; Kong, Wei-Bao; Zhang, Ji

    2016-10-01

    Drug-induced liver injury (DILI) is one of the major safety concerns in drug development. Although various toxicological studies assessing DILI risk have been developed, these methods were not sufficient in predicting DILI in humans. Thus, developing new tools and approaches to better predict DILI risk in humans has become an important and urgent task. In this study, we aimed to develop a computational model for assessment of the DILI risk with using a larger scale human dataset and Naïve Bayes classifier. The established Naïve Bayes prediction model was evaluated by 5-fold cross validation and an external test set. For the training set, the overall prediction accuracy of the 5-fold cross validation was 94.0 %. The sensitivity, specificity, positive predictive value and negative predictive value were 97.1, 89.2, 93.5 and 95.1 %, respectively. The test set with the concordance of 72.6 %, sensitivity of 72.5 %, specificity of 72.7 %, positive predictive value of 80.4 %, negative predictive value of 63.2 %. Furthermore, some important molecular descriptors related to DILI risk and some toxic/non-toxic fragments were identified. Thus, we hope the prediction model established here would be employed for the assessment of human DILI risk, and the obtained molecular descriptors and substructures should be taken into consideration in the design of new candidate compounds to help medicinal chemists rationally select the chemicals with the best prospects to be effective and safe.

  17. Chronic subdural hematoma: Surgical management and outcome in 986 cases: A classification and regression tree approach

    PubMed Central

    Rovlias, Aristedis; Theodoropoulos, Spyridon; Papoutsakis, Dimitrios

    2015-01-01

    Background: Chronic subdural hematoma (CSDH) is one of the most common clinical entities in daily neurosurgical practice which carries a most favorable prognosis. However, because of the advanced age and medical problems of patients, surgical therapy is frequently associated with various complications. This study evaluated the clinical features, radiological findings, and neurological outcome in a large series of patients with CSDH. Methods: A classification and regression tree (CART) technique was employed in the analysis of data from 986 patients who were operated at Asclepeion General Hospital of Athens from January 1986 to December 2011. Burr holes evacuation with closed system drainage has been the operative technique of first choice at our institution for 29 consecutive years. A total of 27 prognostic factors were examined to predict the outcome at 3-month postoperatively. Results: Our results indicated that neurological status on admission was the best predictor of outcome. With regard to the other data, age, brain atrophy, thickness and density of hematoma, subdural accumulation of air, and antiplatelet and anticoagulant therapy were found to correlate significantly with prognosis. The overall cross-validated predictive accuracy of CART model was 85.34%, with a cross-validated relative error of 0.326. Conclusions: Methodologically, CART technique is quite different from the more commonly used methods, with the primary benefit of illustrating the important prognostic variables as related to outcome. Since, the ideal therapy for the treatment of CSDH is still under debate, this technique may prove useful in developing new therapeutic strategies and approaches for patients with CSDH. PMID:26257985

  18. A rapid-screening approach to detect and quantify microplastics based on fluorescent tagging with Nile Red

    NASA Astrophysics Data System (ADS)

    Maes, Thomas; Jessop, Rebecca; Wellner, Nikolaus; Haupt, Karsten; Mayes, Andrew G.

    2017-03-01

    A new approach is presented for analysis of microplastics in environmental samples, based on selective fluorescent staining using Nile Red (NR), followed by density-based extraction and filtration. The dye adsorbs onto plastic surfaces and renders them fluorescent when irradiated with blue light. Fluorescence emission is detected using simple photography through an orange filter. Image-analysis allows fluorescent particles to be identified and counted. Magnified images can be recorded and tiled to cover the whole filter area, allowing particles down to a few micrometres to be detected. The solvatochromic nature of Nile Red also offers the possibility of plastic categorisation based on surface polarity characteristics of identified particles. This article details the development of this staining method and its initial cross-validation by comparison with infrared (IR) microscopy. Microplastics of different sizes could be detected and counted in marine sediment samples. The fluorescence staining identified the same particles as those found by scanning a filter area with IR-microscopy.

  19. A rapid-screening approach to detect and quantify microplastics based on fluorescent tagging with Nile Red.

    PubMed

    Maes, Thomas; Jessop, Rebecca; Wellner, Nikolaus; Haupt, Karsten; Mayes, Andrew G

    2017-03-16

    A new approach is presented for analysis of microplastics in environmental samples, based on selective fluorescent staining using Nile Red (NR), followed by density-based extraction and filtration. The dye adsorbs onto plastic surfaces and renders them fluorescent when irradiated with blue light. Fluorescence emission is detected using simple photography through an orange filter. Image-analysis allows fluorescent particles to be identified and counted. Magnified images can be recorded and tiled to cover the whole filter area, allowing particles down to a few micrometres to be detected. The solvatochromic nature of Nile Red also offers the possibility of plastic categorisation based on surface polarity characteristics of identified particles. This article details the development of this staining method and its initial cross-validation by comparison with infrared (IR) microscopy. Microplastics of different sizes could be detected and counted in marine sediment samples. The fluorescence staining identified the same particles as those found by scanning a filter area with IR-microscopy.

  20. Finding Important Terms for Patients in Their Electronic Health Records: A Learning-to-Rank Approach Using Expert Annotations

    PubMed Central

    Zheng, Jiaping; Yu, Hong

    2016-01-01

    Background Many health organizations allow patients to access their own electronic health record (EHR) notes through online patient portals as a way to enhance patient-centered care. However, EHR notes are typically long and contain abundant medical jargon that can be difficult for patients to understand. In addition, many medical terms in patients’ notes are not directly related to their health care needs. One way to help patients better comprehend their own notes is to reduce information overload and help them focus on medical terms that matter most to them. Interventions can then be developed by giving them targeted education to improve their EHR comprehension and the quality of care. Objective We aimed to develop a supervised natural language processing (NLP) system called Finding impOrtant medical Concepts most Useful to patientS (FOCUS) that automatically identifies and ranks medical terms in EHR notes based on their importance to the patients. Methods First, we built an expert-annotated corpus. For each EHR note, 2 physicians independently identified medical terms important to the patient. Using the physicians’ agreement as the gold standard, we developed and evaluated FOCUS. FOCUS first identifies candidate terms from each EHR note using MetaMap and then ranks the terms using a support vector machine-based learn-to-rank algorithm. We explored rich learning features, including distributed word representation, Unified Medical Language System semantic type, topic features, and features derived from consumer health vocabulary. We compared FOCUS with 2 strong baseline NLP systems. Results Physicians annotated 90 EHR notes and identified a mean of 9 (SD 5) important terms per note. The Cohen’s kappa annotation agreement was .51. The 10-fold cross-validation results show that FOCUS achieved an area under the receiver operating characteristic curve (AUC-ROC) of 0.940 for ranking candidate terms from EHR notes to identify important terms. When including term

  1. A Multiscale Approach to InSAR Time Series Analysis

    NASA Astrophysics Data System (ADS)

    Hetland, E. A.; Muse, P.; Simons, M.; Lin, N.; Dicaprio, C. J.

    2010-12-01

    We present a technique to constrain time-dependent deformation from repeated satellite-based InSAR observations of a given region. This approach, which we call MInTS (Multiscale InSAR Time Series analysis), relies on a spatial wavelet decomposition to permit the inclusion of distance based spatial correlations in the observations while maintaining computational tractability. As opposed to single pixel InSAR time series techniques, MInTS takes advantage of both spatial and temporal characteristics of the deformation field. We use a weighting scheme which accounts for the presence of localized holes due to decorrelation or unwrapping errors in any given interferogram. We represent time-dependent deformation using a dictionary of general basis functions, capable of detecting both steady and transient processes. The estimation is regularized using a model resolution based smoothing so as to be able to capture rapid deformation where there are temporally dense radar acquisitions and to avoid oscillations during time periods devoid of acquisitions. MInTS also has the flexibility to explicitly parametrize known time-dependent processes that are expected to contribute to a given set of observations (e.g., co-seismic steps and post-seismic transients, secular variations, seasonal oscillations, etc.). We use cross validation to choose the regularization penalty parameter in the inversion of for the time-dependent deformation field. We demonstrate MInTS using a set of 63 ERS-1/2 and 29 Envisat interferograms for Long Valley Caldera.

  2. Predicting dispersal distance in mammals: a trait-based approach.

    PubMed

    Whitmee, Sarah; Orme, C David L

    2013-01-01

    Dispersal is one of the principal mechanisms influencing ecological and evolutionary processes but quantitative empirical data are unfortunately scarce. As dispersal is likely to influence population responses to climate change, whether by adaptation or by migration, there is an urgent need to obtain estimates of dispersal distance. Cross-species correlative approaches identifying predictors of dispersal distance can provide much-needed insights into this data-scarce area. Here, we describe the compilation of a new data set of natal dispersal distances and use it to test life-history predictors of dispersal distance in mammals and examine the strength of the phylogenetic signal in dispersal distance. We find that both maximum and median dispersal distances have strong phylogenetic signals. No single model performs best in describing either maximum or median dispersal distances when phylogeny is taken into account but many models show high explanatory power, suggesting that dispersal distance per generation can be estimated for mammals with comparatively little data availability. Home range area, geographic range size and body mass are identified as the most important terms across models. Cross-validation of models supports the ability of these variables to predict dispersal distances, suggesting that models may be extended to species where dispersal distance is unknown.

  3. A multiscale approach to InSAR time series analysis

    NASA Astrophysics Data System (ADS)

    Simons, M.; Hetland, E. A.; Muse, P.; Lin, Y. N.; Dicaprio, C.; Rickerby, A.

    2008-12-01

    We describe a new technique to constrain time-dependent deformation from repeated satellite-based InSAR observations of a given region. This approach, which we call MInTS (Multiscale analysis of InSAR Time Series), relies on a spatial wavelet decomposition to permit the inclusion of distance based spatial correlations in the observations while maintaining computational tractability. This approach also permits a consistent treatment of all data independent of the presence of localized holes in any given interferogram. In essence, MInTS allows one to considers all data at the same time (as opposed to one pixel at a time), thereby taking advantage of both spatial and temporal characteristics of the deformation field. In terms of the temporal representation, we have the flexibility to explicitly parametrize known processes that are expected to contribute to a given set of observations (e.g., co-seismic steps and post-seismic transients, secular variations, seasonal oscillations, etc.). Our approach also allows for the temporal parametrization to includes a set of general functions (e.g., splines) in order to account for unexpected processes. We allow for various forms of model regularization using a cross-validation approach to select penalty parameters. The multiscale analysis allows us to consider various contributions (e.g., orbit errors) that may affect specific scales but not others. The methods described here are all embarrassingly parallel and suitable for implementation on a cluster computer. We demonstrate the use of MInTS using a large suite of ERS-1/2 and Envisat interferograms for Long Valley Caldera, and validate our results by comparing with ground-based observations.

  4. An Example-Based Multi-Atlas Approach to Automatic Labeling of White Matter Tracts

    PubMed Central

    Yoo, Sang Wook; Guevara, Pamela; Jeong, Yong; Yoo, Kwangsun; Shin, Joseph S.; Mangin, Jean-Francois; Seong, Joon-Kyung

    2015-01-01

    We present an example-based multi-atlas approach for classifying white matter (WM) tracts into anatomic bundles. Our approach exploits expert-provided example data to automatically classify the WM tracts of a subject. Multiple atlases are constructed to model the example data from multiple subjects in order to reflect the individual variability of bundle shapes and trajectories over subjects. For each example subject, an atlas is maintained to allow the example data of a subject to be added or deleted flexibly. A voting scheme is proposed to facilitate the multi-atlas exploitation of example data. For conceptual simplicity, we adopt the same metrics in both example data construction and WM tract labeling. Due to the huge number of WM tracts in a subject, it is time-consuming to label each WM tract individually. Thus, the WM tracts are grouped according to their shape similarity, and WM tracts within each group are labeled simultaneously. To further enhance the computational efficiency, we implemented our approach on the graphics processing unit (GPU). Through nested cross-validation we demonstrated that our approach yielded high classification performance. The average sensitivities for bundles in the left and right hemispheres were 89.5% and 91.0%, respectively, and their average false discovery rates were 14.9% and 14.2%, respectively. PMID:26225419

  5. A land use regression model for ambient ultrafine particles in Montreal, Canada: A comparison of linear regression and a machine learning approach.

    PubMed

    Weichenthal, Scott; Ryswyk, Keith Van; Goldstein, Alon; Bagg, Scott; Shekkarizfard, Maryam; Hatzopoulou, Marianne

    2016-04-01

    Existing evidence suggests that ambient ultrafine particles (UFPs) (<0.1µm) may contribute to acute cardiorespiratory morbidity. However, few studies have examined the long-term health effects of these pollutants owing in part to a need for exposure surfaces that can be applied in large population-based studies. To address this need, we developed a land use regression model for UFPs in Montreal, Canada using mobile monitoring data collected from 414 road segments during the summer and winter months between 2011 and 2012. Two different approaches were examined for model development including standard multivariable linear regression and a machine learning approach (kernel-based regularized least squares (KRLS)) that learns the functional form of covariate impacts on ambient UFP concentrations from the data. The final models included parameters for population density, ambient temperature and wind speed, land use parameters (park space and open space), length of local roads and rail, and estimated annual average NOx emissions from traffic. The final multivariable linear regression model explained 62% of the spatial variation in ambient UFP concentrations whereas the KRLS model explained 79% of the variance. The KRLS model performed slightly better than the linear regression model when evaluated using an external dataset (R(2)=0.58 vs. 0.55) or a cross-validation procedure (R(2)=0.67 vs. 0.60). In general, our findings suggest that the KRLS approach may offer modest improvements in predictive performance compared to standard multivariable linear regression models used to estimate spatial variations in ambient UFPs. However, differences in predictive performance were not statistically significant when evaluated using the cross-validation procedure.

  6. Cross-Validation of a Psychological Test Battery to Detect Faked Insanity.

    ERIC Educational Resources Information Center

    Schretlen, David; And Others

    1992-01-01

    Eight predictor variables from the Minnesota Multiphasic Personality Inventory, the Bender Gestalt, and a Malingering Scale differentiated 20 prison inmates faking insanity from 40 nonfaking controls. A second experiment with 22 substance abusers faking insanity and 20 schizophrenics also supports the use of the test battery to detect faking. (SLD)

  7. National Cross-Validation of the Computerized Adaptive Screening Test (CAST)

    DTIC Science & Technology

    1987-11-01

    Correlation Between CAST subtests andi AF~r bv Subtest Lencrth Ccrbination WK 5 6 7 8 9 10 11 12 13 14 15 5 76 77 78 79 79 79 80 80 80 80 81 6 78 78 79 80 80 80 81...81 81 81 81 7 78 79 80 80 80 81 81 81 82 82 82 AR 8 79 80 80 81 81 81 82 82 82 82 82 9 80 80 81 81 81 82 82 82 82 83 83 1 80 81 81 82 82 8 82

  8. Cross-Validation of Mental Health Recovery Measures in a Hong Kong Chinese Sample

    ERIC Educational Resources Information Center

    Ye, Shengquan; Pan, Jia-Yan; Wong, Daniel Fu Keung; Bola, John Robert

    2013-01-01

    Objectives: The concept of recovery has begun shifting mental health service delivery from a medical perspective toward a client-centered recovery orientation. This shift is also beginning in Hong Kong, but its development is hampered by a dearth of available measures in Chinese. Method: This article translates two measures of recovery (mental…

  9. The African American Acculturation Scale II: Cross-Validation and Short Form.

    ERIC Educational Resources Information Center

    Landrine, Hope; Klonoff, Elizabeth A.

    1995-01-01

    Studied African American culture, using a new, shortened, 33-item African American Acculturation Scale (AAAS-33) to assess the scale's validity and reliability. Comparisons between the original form and AAAS-33 reveal high correlations, however, the longer form may be sensitive to some beliefs, practices, and attitudes not assessed by the short…

  10. The Adolescent Religious Coping Scale: Development, Validation, and Cross-Validation

    ERIC Educational Resources Information Center

    Bjorck, Jeffrey P.; Braese, Robert W.; Tadie, Joseph T.; Gililland, David D.

    2010-01-01

    Research literature on adolescent coping is growing, but typically such studies have ignored religious coping strategies and their potential impact on functioning. To address this lack, we developed the Adolescent Religious Coping Scale and used its seven subscales to examine the relationship between religious coping and emotional functioning. A…

  11. Cross-Validation of the PAI Negative Distortion Scale for Feigned Mental Disorders: A Research Report

    ERIC Educational Resources Information Center

    Rogers, Richard; Gillard, Nathan D.; Wooley, Chelsea N.; Kelsey, Katherine R.

    2013-01-01

    A major strength of the Personality Assessment Inventory (PAI) is its systematic assessment of response styles, including feigned mental disorders. Recently, Mogge, Lepage, Bell, and Ragatz developed and provided the initial validation for the Negative Distortion Scale (NDS). Using rare symptoms as its detection strategy for feigning, the…

  12. Potential of cognitive plasticity as a diagnostic instrument: a cross-validation and extension.

    PubMed

    Baltes, M M; Kühl, K P; Gutzmann, H; Sowarka, D

    1995-06-01

    Examination of the range and limits of cognitive developmental reserve capacity (plasticity) by means of cognitive training has been proffered as a promising diagnostic strategy for the early identification of Alzheimer's disease. Previous findings of differential gains after cognitive training for healthy older persons and older persons at risk for dementia were supported, rendering cognitive plasticity a criterion by which the overlap in performance distributions between healthy older persons and older persons at risk can be reduced. Stepwise hierarchical regression analyses demonstrated that posttraining scores, which represented developmental reserve capacity, explained significantly more variance in mental health status than pretest or baseline performance. Older persons at risk profited significantly less from training in 2 components of fluid intelligence, figural relations, and inductive reasoning. The authors discuss the possibilities of turning this testing-the-limits procedure into an instrument for screening purposes in clinical practice.

  13. Preliminary Report on a National Cross-Validation of the Computerized Adaptive Screening Test (CAST).

    ERIC Educational Resources Information Center

    Knapp, Deirdre J.; Pliske, Rebecca M.

    A study was conducted to validate the Army's Computerized Adaptive Screening Test (CAST), using data from 2,240 applicants from 60 army recruiting stations across the nation. CAST is a computer-assisted adaptive test used to predict performance on the Armed Forces Qualification Test (AFQT). AFQT scores are computed by adding four subtest scores of…

  14. Prediction of treatment outcome in social phobia: a cross-validation.

    PubMed

    Scholing, A; Emmelkamp, P M

    1999-07-01

    This study was a replication of a study on the prediction of treatment outcome in social phobic patients [Chambless, D. L., Tran, G. Q. Glass, C.R. (1997). Predictors of response to cognitive-behavioral group therapy for social phobia. Journal of Anxiety Disorders, 11 221-240]. Results at the posttest and the 18-months follow-up were analyzed for DSM-III-R social phobic patients, with either a generalized social phobia (n = 50) or a nongeneralized fear, i.e. fear of blushing, trembling or sweating in social situations (n = 26). Predictors were pretreatment depression, personality disorder traits, clinician rated severity of impairment and frequency of negative self-statements during social interactions. The criterium variable was (the residual gain score of) self-reported avoidance of social situations. In line with Chambless et al., pretreatment depression showed some predictive value, but smaller and only at the posttest. Change in the frequency of negative self-statements paralleled, but did not predict, change in social phobia symptoms. In contrast with Chambless et al., clinician rated severity was (slightly) predictive for treatment outcome, whereas avoidant personality traits had reverse correlations with outcome in both subgroups. The results are discussed and directions for further research are given.

  15. Cross-validation of recent and longstanding resting metabolic rate prediction equations

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Resting metabolic rate (RMR) measurement is time consuming and requires specialized equipment. Prediction equations provide an easy method to estimate RMR; however, their accuracy likely varies across individuals. Understanding the factors that influence predicted RMR accuracy at the individual lev...

  16. A cross-validation study of nurses' attitudes and commitment to organ donation in Hong Kong.

    PubMed

    Boey, Kam Weng

    2002-01-01

    This study examined the attitudes and commitment to posthumous organ donation among a group of nurses (N=314) in a teaching hospital in Hong Kong. Attitude was operationally defined by a self-report measure of favorable or unfavorable feelings and beliefs about organ donation, whereas commitment was defined by having signed a donor card. Consistent with findings reported in the West, nurses who exhibited favorable attitudes towards organ donation were of greater proportion than those who had signed a donor card. Younger and unmarried nurses were more likely to commit to posthumous organ donation. More than half (55%) of the nurses were undecided about commitment to organ donation, but most of them were likely to sign a donor card. Principal component analysis confirmed the conceptual structure of the Organ Donation Attitude Scale developed by Parisi and Katz (Health Psychol. 5 (1986) 565-580). Reliability of the factor scores (Humanitarian and moral conviction, Fears of bodily mutilation, and Fears of medical neglect) was satisfactory (Cronbach alpha ranged from 0.80 to 0.86). Fears of bodily mutilation were most significantly related to unwillingness to commit to organ donation. Implications of the findings for nursing education are discussed and suggestions for future research made.

  17. Cross-validation of murine UV signal transduction pathways in human skin.

    PubMed

    Einspahr, Janine G; Bowden, G Timothy; Alberts, David S; McKenzie, Naja; Saboda, Kathylynn; Warneke, James; Salasche, Stuart; Ranger-Moore, James; Curiel-Lewandrowski, Clara; Nagle, Raymond B; Nickoloff, Brian J; Brooks, Christine; Dong, Zigang; Stratton, Steven P

    2008-01-01

    Acute UVB irradiation of mouse skin results in activation of phospatidyinositol-3 (PI-3) kinase and mitogen-activated protein kinase (MAPK) pathways leading to altered protein phosphorylation and downstream transcription of genes. We determined whether activation of these pathways also occurs in human skin exposed to 4x minimal erythemic dose of UVB in 23 volunteers. Biopsies were taken prior to, at 30 min, 1 and 24 h post-UVB. In agreement with mouse studies, the earliest UV-induced changes in epidermis were seen in phospho-CREB (two- and five-fold at 30 min and 1 h) and in phospho-MAPKAPK-2 (three-fold at both 30 min and 1 h). At 1 h, phospho-c-JUN and phospho-p38 were increased five- and two-fold, respectively. Moreover, phospho-c-JUN and phospho-p38 were further increased at 24 h (12- and six-fold, respectively). Phospho-GSK-3beta was similarly increased at all time points. Increases in phospho-p53 (12-fold), COX-2 (four-fold), c-FOS (14-fold) and apoptosis were not seen until 24 h. Our data suggest that UVB acts through MAPK p38 and PI-3 kinase with phosphorylation of MAPKAPK-2, CREB, c-JUN, p38, GSK-3beta and p53 leading to marked increases in c-FOS, COX-2 and apoptosis. Validation of murine models in human skin will aid in development of effective skin cancer chemoprevention and prevention strategies.

  18. Bias of Exploratory and Cross-Validated DETECT Index under Unidimensionality

    ERIC Educational Resources Information Center

    Monahan, Patrick O.; Stump, Timothy E.; Finch, Holmes; Hambleton, Ronald K.

    2007-01-01

    DETECT is a nonparametric "full" dimensionality assessment procedure that clusters dichotomously scored items into dimensions and provides a DETECT index of magnitude of multidimensionality. Four factors (test length, sample size, item response theory [IRT] model, and DETECT index) were manipulated in a Monte Carlo study of bias, standard error,…

  19. Cross-Validation of the Norwegian Teacher's Self-Efficacy Scale (NTSES)

    ERIC Educational Resources Information Center

    Avanzi, Lorenzo; Miglioretti, Massimo; Velasco, Veronica; Balducci, Cristian; Vecchio, Luca; Fraccaroli, Franco; Skaalvik, Einar M.

    2013-01-01

    The study assesses the psychometric properties of the Italian version of the Norwegian Teacher Self-Efficacy Scale--NTSES. Multiple group confirmatory factor analysis was used to explore the measurement invariance of the scale across two countries. Analyses performed on Italian and Norwegian samples confirmed a six-factor structure of the scale…

  20. Three-factor structure for Epistemic Belief Inventory: A cross-validation study

    PubMed Central

    2017-01-01

    Research on epistemic beliefs has been hampered by lack of validated models and measurement instruments. The most widely used instrument is the Epistemological Questionnaire, which has been criticized for validity, and it has been proposed a new instrument based in the Epistemological Questionnaire: the Epistemic Belief Inventory. The Spanish-language version of Epistemic Belief Inventory was applied to 1,785 Chilean high school students. Exploratory and confirmatory factor analyses in independent subsamples were performed. A three factor structure emerged and was confirmed. Reliability was comparable to other studies, and the factor structure was invariant among randomized subsamples. The structure that was found does not replicate the one proposed originally, but results are interpreted in light of embedded systemic model of epistemological beliefs. PMID:28278258

  1. Cross-validation of satellite products over France through their integration into a land surface model

    NASA Astrophysics Data System (ADS)

    Calvet, Jean-Christophe; Barbu, Alina; Carrer, Dominique; Meurey, Catherine

    2014-05-01

    Long (more than 30 years) time series of satellite-derived products over land are now available. They concern Essential Climate Variables (ECV) such as LAI, FAPAR, surface albedo, and soil moisture. The direct validation of such Climate Data Records (CDR) is not easy, as in situ observations are limited in space and time. Therefore, indirect validation has a key role. It consists in comparing the products with similar preexisting products derived from satellite observations or from land surface model (LSM) simulations. The most advanced indirect validation technique consists in integrating the products into a LSM using a data assimilation scheme. The obtained reanalysis accounts for the synergies of the various upstream products and provides statistics which can be used to monitor the quality of the assimilated observations. Meteo-France develops the ISBA-A-gs generic LSM able to represent the diurnal cycle of the surface fluxes together with the seasonal, interannual and decadal variability of the vegetation biomass. The LSM is embedded in the SURFEX modeling platform together with a simplified extended Kalman filter. These tools form a Land Data Assimilation System (LDAS). The current version of the LDAS assimilates SPOT-VGT LAI and ASCAT surface soil moisture (SSM) products over France (8km x 8km), and a passive monitoring of albedo, FAPAR and Land Surface temperature (LST) is performed (i.e., the simulated values are compared with the satellite products). The LDAS-France system is used in the European Copernicus Global Land Service (http://land.copernicus.eu/global/) to monitor the quality of upstream products. The LDAS generates statistics whose trends can be analyzed in order to detect possible drifts in the quality of the products: (1) for LAI and SSM, metrics derived from the active monitoring (i.e. assimilation) such as innovations (observations vs. model forecast), residuals (observations vs. analysis), and increments (analysis vs. model forecast) ; (2) for albedo, LST, and FAPAR, metrics derived from the passive monitoring such as the Pearson correlation coefficient, z-score, RMSD, SDD, mean bias. The results obtained over the 2007-2013 period are presented. The added value of computing a prognostic FAPAR is shown, as this quantity can be used to better estimate the LAI observation error used by the LDAS. In the near future, the LDAS will be upgraded in order to assimilate FAPAR and surface albedo, and it will be extended to a global scale. At the same time, the coupling to hydrological models (now in a testing phase) will be consolidated, and this will allow the use of in situ river discharge observations for the validation of the whole system.

  2. Evaluating the Replicability of Sample Results: A Tutorial of Double Cross-Validation Methods

    ERIC Educational Resources Information Center

    Guan, Jianmin; Xiang, Ping; Keating, Xiaofen Deng

    2004-01-01

    Although replication is important to the validity of a study and is endorsed by more and more scholars, few researchers in kinesiology attend to this issue. Some researchers may believe that statistical significance and effect size are the most important statistical issues in their research and thereby may have ignored the importance of result…

  3. Repeated holdout Cross-Validation of Model to Estimate Risk of Lyme Disease by Landscape Attributes

    EPA Science Inventory

    We previously modeled Lyme disease (LD) risk at the landscape scale; here we evaluate the model's overall goodness-of-fit using holdout validation. Landscapes were characterized within road-bounded analysis units (AU). Observed LD cases (obsLD) were ascertained per AU. Data were ...

  4. Categories of Counselors Behavior as Defined from Cross-Validated Factoral Descriptions.

    ERIC Educational Resources Information Center

    Zimmer, Jules M.; And Others

    The intent of the study was to explore and categorize counselor responses. Three separate filmed presentations were shown. Participating with the same client were Albert Ellis, Frederick Perls, and Carl Rogers. At the beginning of each counselor statement, a number was inserted in sequence and remained on the videotape until completion of that…

  5. Skill Versus Chance Activity Preferences as Alternative Measures of Locus of Control: An Attempted Cross Validation

    ERIC Educational Resources Information Center

    Berzins, Juris I.; And Others

    1970-01-01

    The skill chance and Rotter's Locus of Control scales were administered to 97 male Ss. The two variables were unrelated, although the skill chance inventory showed an acceptable degree of internal consistency. The utility of the skill chance inventory with noncollegiate samples was questioned. (Author)

  6. Laplacian Smoothing Splines with Generalized Cross Validation for Objective Analysis of Meteorological Data.

    DTIC Science & Technology

    1985-08-01

    REPOR NUMUERVi ACCESO NO3. RECIPIENT’S CATALOG HUMMER NPS-53-85-0008 __ 4. TITLE (and ,Subite) S. TYPE OF REPORT & PERIOD COVER[’D Technical Report...is best. Though not discernable from the table, the GCV func- tion generally was found to have multiple local minima, especial- ly for the larger data

  7. Computational Steroidogenesis Model To Predict Biochemical Responses to Endocrine Active Chemicals: Model Development and Cross Validation

    EPA Science Inventory

    Steroids, which have an important role in a wide range of physiological processes, are synthesized primarily in the gonads and adrenal glands through a series of enzyme-mediated reactions. The activity of steroidogenic enzymes can be altered by a variety of endocrine active chem...

  8. Cross-Validation of a PACER Prediction Equation for Assessing Aerobic Capacity in Hungarian Youth

    ERIC Educational Resources Information Center

    Saint-Maurice, Pedro F.; Welk, Gregory J.; Finn, Kevin J.; Kaj, Mónika

    2015-01-01

    Purpose: The purpose of this article was to evaluate the validity of the Progressive Aerobic Cardiovascular and Endurance Run (PACER) test in a sample of Hungarian youth. Method: Approximately 500 participants (aged 10-18 years old) were randomly selected across Hungary to complete both laboratory (maximal treadmill protocol) and field assessments…

  9. Cross-Validation of the Emotion Awareness Questionnaire for Children in Three Populations

    ERIC Educational Resources Information Center

    Lahaye, Magali; Mikolajczak, Moira; Rieffe, Carolien; Villanueva, Lidon; Van Broeck, Nady; Bodart, Eddy; Luminet, Olivier

    2011-01-01

    The main aim of the present study was to examine the cross-cultural equivalence of a newly developed questionnaire, the Emotion Awareness Questionnaire (EAQ30) that assesses emotional awareness of children through self-report. Participants were recruited in three countries: the Netherlands (N = 665), Spain (N = 464), and Belgium (N = 707),…

  10. The Bland-Altman Method Should Not Be Used in Regression Cross-Validation Studies

    ERIC Educational Resources Information Center

    O'Connor, Daniel P.; Mahar, Matthew T.; Laughlin, Mitzi S.; Jackson, Andrew S.

    2011-01-01

    The purpose of this study was to demonstrate the bias in the Bland-Altman (BA) limits of agreement method when it is used to validate regression models. Data from 1,158 men were used to develop three regression equations to estimate maximum oxygen uptake (R[superscript 2] = 0.40, 0.61, and 0.82, respectively). The equations were evaluated in a…

  11. Estimation of Posterior Probabilities Using Multivariate Smoothing Splines and Generalized Cross-Validation.

    DTIC Science & Technology

    1983-09-01

    nonlinear optimization problem in n -M variables but there is still the problem of choos - ing the value of the smoothing parameter. Since the conditional...method, The Annals of Statistics 10, 3. pp. 795-810. Smith. C.A.B. (1947). Some exrmples of discrimination, Annals of Eugenics 13, pp 272-282. Tapia. R. A

  12. Cross-Validation of a Creativity Scale for the Adjective Check List

    ERIC Educational Resources Information Center

    Albaum, Gerald; Baker, Kenneth

    1977-01-01

    The validity of the creativity scale of the Adjective Check List was investigated with a sample of inventors and non-inventors. Eight scales of the Adjective Check List showed significant differences for the two groups, including the creativity scale. (Author/JKS)

  13. Faculty's Acceptance of Computer Based Technology: Cross-Validation of an Extended Model

    ERIC Educational Resources Information Center

    Ahmad, Tunku Badariah Tunku; Madarsha, Kamal Basha; Zainuddin, Ahmad Marzuki; Ismail, Nik Ahmad Hisham; Nordin, Mohamad Sahari

    2010-01-01

    The first aim of the present study is to validate an extended technology acceptance model (TAME) on the data derived from the faculty members of a university in an ongoing, computer mediated work setting. The study extended the original TAM model by including an intrinsic motivation component--computer self efficacy. In so doing, the study…

  14. Embedded Performance Validity Measures with Postdeployment Veterans: Cross-Validation and Efficiency with Multiple Measures.

    PubMed

    Shura, Robert D; Miskey, Holly M; Rowland, Jared A; Yoash-Gantz, Ruth E; Denning, John H

    2016-01-01

    Embedded validity measures support comprehensive assessment of performance validity. The purpose of this study was to evaluate the accuracy of individual embedded measures and to reduce them to the most efficient combination. The sample included 212 postdeployment veterans (average age = 35 years, average education = 14 years). Thirty embedded measures were initially identified as predictors of Green's Word Memory Test (WMT) and were derived from the California Verbal Learning Test-Second Edition (CVLT-II), Conners' Continuous Performance Test-Second Edition (CPT-II), Trail Making Test, Stroop, Wisconsin Card Sorting Test-64, the Wechsler Adult Intelligence Scale-Third Edition Letter-Number Sequencing, Rey Complex Figure Test (RCFT), Brief Visuospatial Memory Test-Revised, and the Finger Tapping Test. Eight nonoverlapping measures with the highest area-under-the-curve (AUC) values were retained for entry into a logistic regression analysis. Embedded measure accuracy was also compared to cutoffs found in the existing literature. Twenty-one percent of the sample failed the WMT. Previously developed cutoffs for individual measures showed poor sensitivity (SN) in the current sample except for the CPT-II (Total Errors, SN = .41). The CVLT-II (Trials 1-5 Total) showed the best overall accuracy (AUC = .80). After redundant measures were statistically eliminated, the model included the RCFT (Recognition True Positives), CPT-II (Total Errors), and CVLT-II (Trials 1-5 Total) and increased overall accuracy compared with the CVLT-II alone (AUC = .87). The combination of just 3 measures from the CPT-II, CVLT-II, and RCFT was the most accurate/efficient in predicting WMT performance.

  15. The structure of peritraumatic dissociation: a cross validation in clinical and nonclinical samples.

    PubMed

    Sijbrandij, Marit; Engelhard, Iris M; Opmeer, Brent C; van de Schoot, Rens; Carlier, Ingrid V E; Gersons, Berthold P R; Olff, Miranda

    2012-08-01

    Empirical data have challenged the unidimensionality of the Peritraumatic Dissociative Experiences Questionnaire (PDEQ), a widely used measure for peritraumatic dissociation. The aim of this study was to assess the factor structure of the PDEQ in 3 trauma-exposed samples: (a) trauma-exposed police officers (N = 219); (b) trauma-exposed civilians (N = 158); and (c) treatment-seeking trauma-exposed civilians (N = 185). Confirmatory factor analyses using measurement invariance testing supported a 2-factor structure (CFIs .96-.98; RMSEAs .07-.09), but excluded 2 of the original items. Factor 1 was termed Altered Awareness; Factor 2 was termed Derealization. Altered Awareness reflected disturbances in information processing during the traumatic event, whereas Derealization reflected distortions in perception. Hierarchical linear regression analysis showed that Derealization predicted posttraumatic stress severity at 26.5 weeks follow-up only in the sample of police officers (R(2) = .45). Future longitudinal research shortly following trauma is required to elucidate causality and underlying mechanisms of peritraumatic dissociation, which may contribute to the development of more accurate screening strategies, as well as more effective strategies for prevention and early intervention.

  16. Cross validation of experts versus registration methods for target localization in deep brain stimulation.

    PubMed

    Sánchez Castro, F Javier; Pollo, Claudio; Meuli, Reto; Maeder, Philippe; Cuadra, Meritxell Bach; Cuisenaire, Olivier; Villemure, Jean-Guy; Thiran, Jean-Philippe

    2005-01-01

    In the last five years, Deep Brain Stimulation (DBS) has become the most popular and effective surgical technique for the treatent of Parkinson's disease (PD). The Subthalamic Nucleus (STN) is the usual target involved when applying DBS. Unfortunately, the STN is in general not visible in common medical imaging modalities. Therefore, atlas-based segmentation is commonly considered to locate it in the images. In this paper, we propose a scheme that allows both, to perform a comparison between different registration algorithms and to evaluate their ability to locate the STN automatically. Using this scheme we can evaluate the expert variability against the error of the algorithms and we demonstrate that automatic STN location is possible and as accurate as the methods currently used.

  17. Cross Validity of the Behavior Style Questionnaire and Child Personality Scale in Nursery School Children.

    ERIC Educational Resources Information Center

    Simonds, John F.; Simonds, M. Patricia

    1982-01-01

    Mothers of 182 nursery school children completed the Behavior Style Questionnaire (BSQ) and the Child Personality Scale (CPS). Intercorrelational analyses showed many significantly correlated items. Scores of the five CPS factors clearly distinguished between subjects in easy and difficult BSQ clusters. Found boys significantly more introverted…

  18. Cross-Validation of Levenson's Psychopathy Scale in a Sample of Federal Female Inmates

    ERIC Educational Resources Information Center

    Brinkley, Chad A.; Diamond, Pamela M.; Magaletta, Philip R.; Heigel, Caron P.

    2008-01-01

    Levenson, Kiehl, and Fitzpatrick's Self-Report Psychopathy Scale (LSRPS) is evaluated to determine the factor structure and concurrent validity of the instrument among 430 federal female inmates. Confirmatory factor analysis fails to validate the expected 2-factor structure. Subsequent exploratory factor analysis reveals a 3-factor structure…

  19. Feature Selection Method Based on Artificial Bee Colony Algorithm and Support Vector Machines for Medical Datasets Classification

    PubMed Central

    Yilmaz, Nihat; Inan, Onur

    2013-01-01

    This paper offers a hybrid approach that uses the artificial bee colony (ABC) algorithm for feature selection and support vector machines for classification. The purpose of this paper is to test the effect of elimination of the unimportant and obsolete features of the datasets on the success of the classification, using the SVM classifier. The developed approach conventionally used in liver diseases and diabetes diagnostics, which are commonly observed and reduce the quality of life, is developed. For the diagnosis of these diseases, hepatitis, liver disorders and diabetes datasets from the UCI database were used, and the proposed system reached a classification accuracies of 94.92%, 74.81%, and 79.29%, respectively. For these datasets, the classification accuracies were obtained by the help of the 10-fold cross-validation method. The results show that the performance of the method is highly successful compared to other results attained and seems very promising for pattern recognition applications. PMID:23983632

  20. Predicting lipase types by improved Chou's pseudo-amino acid composition.

    PubMed

    Zhang, Guang-Ya; Li, Hong-Chun; Gao, Jia-Qiang; Fang, Bai-Shan

    2008-01-01

    By proposing a improved Chou's pseudo amino acid composition approach to extract the features of the sequences, a powerful predictor based on k-nearest neighbor was introduced to identify the types of lipases according to their sequences. To avoid redundancy and bias, demonstrations were performed on a dataset where none of the proteins has > or =25% sequence identity to any other. The overall success rate thus obtained by the 10-fold cross-validation test was over 90%, indicating that the improved Chou's pseudo amino acid composition might be a useful tool for extracting the features of protein sequences, or at lease can play a complementary role to many of the other existing approaches.

  1. Ensemble forecasting of sub-seasonal to seasonal streamflow by a Bayesian joint probability modelling approach

    NASA Astrophysics Data System (ADS)

    Zhao, Tongtiegang; Schepen, Andrew; Wang, Q. J.

    2016-10-01

    The Bayesian joint probability (BJP) modelling approach is used operationally to produce seasonal (three-month-total) ensemble streamflow forecasts in Australia. However, water resource managers are calling for more informative sub-seasonal forecasts. Taking advantage of BJP's capability of handling multiple predictands, ensemble forecasting of sub-seasonal to seasonal streamflows is investigated for 23 catchments around Australia. Using antecedent streamflow and climate indices as predictors, monthly forecasts are developed for the three-month period ahead. Forecast reliability and skill are evaluated for the period 1982-2011 using a rigorous leave-five-years-out cross validation strategy. BJP ensemble forecasts of monthly streamflow volumes are generally reliable in ensemble spread. Forecast skill, relative to climatology, is positive in 74% of cases in the first month, decreasing to 57% and 46% respectively for streamflow forecasts for the final two months of the season. As forecast skill diminishes with increasing lead time, the monthly forecasts approach climatology. Seasonal forecasts accumulated from monthly forecasts are found to be similarly skilful to forecasts from BJP models based on seasonal totals directly. The BJP modelling approach is demonstrated to be a viable option for producing ensemble time-series sub-seasonal to seasonal streamflow forecasts.

  2. Future Performance Trend Indicators: A Current Value Approach to Human Resources Accounting. Report III. Multivariate Predictions of Organizational Performance Across Time.

    ERIC Educational Resources Information Center

    Pecorella, Patricia A.; Bowers, David G.

    Multiple regression in a double cross-validated design was used to predict two performance measures (total variable expense and absence rate) by multi-month period in five industrial firms. The regressions do cross-validate, and produce multiple coefficients which display both concurrent and predictive effects, peaking 18 months to two years…

  3. COMPUTING THERAPY FOR PRECISION MEDICINE: COLLABORATIVE FILTERING INTEGRATES AND PREDICTS MULTI-ENTITY INTERACTIONS

    PubMed Central

    REGENBOGEN, SAM; WILKINS, ANGELA D.; LICHTARGE, OLIVIER

    2015-01-01

    Biomedicine produces copious information it cannot fully exploit. Specifically, there is considerable need to integrate knowledge from disparate studies to discover connections across domains. Here, we used a Collaborative Filtering approach, inspired by online recommendation algorithms, in which non-negative matrix factorization (NMF) predicts interactions among chemicals, genes, and diseases only from pairwise information about their interactions. Our approach, applied to matrices derived from the Comparative Toxicogenomics Database, successfully recovered Chemical-Disease, Chemical-Gene, and Disease-Gene networks in 10-fold cross-validation experiments. Additionally, we could predict each of these interaction matrices from the other two. Integrating all three CTD interaction matrices with NMF led to good predictions of STRING, an independent, external network of protein-protein interactions. Finally, this approach could integrate the CTD and STRING interaction data to improve Chemical-Gene cross-validation performance significantly, and, in a time-stamped study, it predicted information added to CTD after a given date, using only data prior to that date. We conclude that collaborative filtering can integrate information across multiple types of biological entities, and that as a first step towards precision medicine it can compute drug repurposing hypotheses. PMID:26776170

  4. Automated Learning of Temporal Expressions.

    PubMed

    Redd, Douglas; Shaoa, YiJun; Yang, Jing; Divita, Guy; Zeng-Treitler, Qing

    2015-01-01

    Clinical notes contain important temporal information that are critical for making clinical diagnosis and treatment as well as for retrospective analyses. Manually created regular expressions are commonly used for the extraction of temporal information; however, this can be a time consuming and brittle approach. We describe a novel algorithm for automatic learning of regular expressions in recognizing temporal expressions. Five classes of temporal expressions are identified. Keywords specific to those classes are used to retrieve snippets of text representing the same keywords in context. Those snippets are used for Regular Expression Discovery Extraction (REDEx). These learned regular expressions are then evaluated using 10-fold cross validation. Precision and recall are very high, above 0.95 for most classes.

  5. Supervised learning for neural manifold using spatiotemporal brain activity

    NASA Astrophysics Data System (ADS)

    Kuo, Po-Chih; Chen, Yong-Sheng; Chen, Li-Fen

    2015-12-01

    Objective. Determining the means by which perceived stimuli are compactly represented in the human brain is a difficult task. This study aimed to develop techniques for the construction of the neural manifold as a representation of visual stimuli. Approach. We propose a supervised locally linear embedding method to construct the embedded manifold from brain activity, taking into account similarities between corresponding stimuli. In our experiments, photographic portraits were used as visual stimuli and brain activity was calculated from magnetoencephalographic data using a source localization method. Main results. The results of 10 × 10-fold cross-validation revealed a strong correlation between manifolds of brain activity and the orientation of faces in the presented images, suggesting that high-level information related to image content can be revealed in the brain responses represented in the manifold. Significance. Our experiments demonstrate that the proposed method is applicable to investigation into the inherent patterns of brain activity.

  6. Multilevel ensemble model for prediction of IgA and IgG antibodies.

    PubMed

    Khanna, Divya; Rana, Prashant Singh

    2017-04-01

    Identification of antigen for inducing specific class of antibody is prime objective in peptide based vaccine designs, immunodiagnosis, and antibody productions. It's urge to introduce a reliable system with high accuracy and efficiency for prediction. In the present study, a novel multilevel ensemble model is developed for prediction of antibodies IgG and IgA. Epitope length is important in training the model and it is efficient to use variable length of epitopes. In this ensemble approach, seven different machine learning models are combined to predict variable length of epitopes (4 to 50). The proposed model of IgG specific epitopes achieves 94.43% of accuracy and IgA specific epitopes achieves 97.56% of accuracy with repeated 10-fold cross validation. The proposed model is compared with the existing system i.e. IgPred model and outcome of proposed model is improved.

  7. Modeling of autocatalytic hydrolysis of adefovir dipivoxil in solid formulations.

    PubMed

    Dong, Ying; Zhang, Yan; Xiang, Bingren; Deng, Haishan; Wu, Jingfang

    2011-04-01

    The stability and hydrolysis kinetics of a phosphate prodrug, adefovir dipivoxil, in solid formulations were studied. The stability relationship between five solid formulations was explored. An autocatalytic mechanism for hydrolysis could be proposed according to the kinetic behavior which fits the Prout-Tompkins model well. For the classical kinetic models could hardly describe and predict the hydrolysis kinetics of adefovir dipivoxil in solid formulations accurately when the temperature is high, a feedforward multilayer perceptron (MLP) neural network was constructed to model the hydrolysis kinetics. The build-in approaches in Weka, such as lazy classifiers and rule-based learners (IBk, KStar, DecisionTable and M5Rules), were used to verify the performance of MLP. The predictability of the models was evaluated by 10-fold cross-validation and an external test set. It reveals that MLP should be of general applicability proposing an alternative efficient way to model and predict autocatalytic hydrolysis kinetics for phosphate prodrugs.

  8. On approaches to analyze the sensitivity of simulated hydrologic fluxes to model parameters in the community land model

    DOE PAGES

    Bao, Jie; Hou, Zhangshuan; Huang, Maoyi; ...

    2015-12-04

    Here, effective sensitivity analysis approaches are needed to identify important parameters or factors and their uncertainties in complex Earth system models composed of multi-phase multi-component phenomena and multiple biogeophysical-biogeochemical processes. In this study, the impacts of 10 hydrologic parameters in the Community Land Model on simulations of runoff and latent heat flux are evaluated using data from a watershed. Different metrics, including residual statistics, the Nash-Sutcliffe coefficient, and log mean square error, are used as alternative measures of the deviations between the simulated and field observed values. Four sensitivity analysis (SA) approaches, including analysis of variance based on the generalizedmore » linear model, generalized cross validation based on the multivariate adaptive regression splines model, standardized regression coefficients based on a linear regression model, and analysis of variance based on support vector machine, are investigated. Results suggest that these approaches show consistent measurement of the impacts of major hydrologic parameters on response variables, but with differences in the relative contributions, particularly for the secondary parameters. The convergence behaviors of the SA with respect to the number of sampling points are also examined with different combinations of input parameter sets and output response variables and their alternative metrics. This study helps identify the optimal SA approach, provides guidance for the calibration of the Community Land Model parameters to improve the model simulations of land surface fluxes, and approximates the magnitudes to be adjusted in the parameter values during parametric model optimization.« less

  9. On approaches to analyze the sensitivity of simulated hydrologic fluxes to model parameters in the community land model

    SciTech Connect

    Bao, Jie; Hou, Zhangshuan; Huang, Maoyi; Liu, Ying

    2015-12-04

    Here, effective sensitivity analysis approaches are needed to identify important parameters or factors and their uncertainties in complex Earth system models composed of multi-phase multi-component phenomena and multiple biogeophysical-biogeochemical processes. In this study, the impacts of 10 hydrologic parameters in the Community Land Model on simulations of runoff and latent heat flux are evaluated using data from a watershed. Different metrics, including residual statistics, the Nash-Sutcliffe coefficient, and log mean square error, are used as alternative measures of the deviations between the simulated and field observed values. Four sensitivity analysis (SA) approaches, including analysis of variance based on the generalized linear model, generalized cross validation based on the multivariate adaptive regression splines model, standardized regression coefficients based on a linear regression model, and analysis of variance based on support vector machine, are investigated. Results suggest that these approaches show consistent measurement of the impacts of major hydrologic parameters on response variables, but with differences in the relative contributions, particularly for the secondary parameters. The convergence behaviors of the SA with respect to the number of sampling points are also examined with different combinations of input parameter sets and output response variables and their alternative metrics. This study helps identify the optimal SA approach, provides guidance for the calibration of the Community Land Model parameters to improve the model simulations of land surface fluxes, and approximates the magnitudes to be adjusted in the parameter values during parametric model optimization.

  10. Application of Machine Learning Approaches for Classifying Sitting Posture Based on Force and Acceleration Sensors

    PubMed Central

    Tanadini, Matteo; Plüss, Stefan; Schnüriger, Karin; Singh, Navrag B.

    2016-01-01

    Occupational musculoskeletal disorders, particularly chronic low back pain (LBP), are ubiquitous due to prolonged static sitting or nonergonomic sitting positions. Therefore, the aim of this study was to develop an instrumented chair with force and acceleration sensors to determine the accuracy of automatically identifying the user's sitting position by applying five different machine learning methods (Support Vector Machines, Multinomial Regression, Boosting, Neural Networks, and Random Forest). Forty-one subjects were requested to sit four times in seven different prescribed sitting positions (total 1148 samples). Sixteen force sensor values and the backrest angle were used as the explanatory variables (features) for the classification. The different classification methods were compared by means of a Leave-One-Out cross-validation approach. The best performance was achieved using the Random Forest classification algorithm, producing a mean classification accuracy of 90.9% for subjects with which the algorithm was not familiar. The classification accuracy varied between 81% and 98% for the seven different sitting positions. The present study showed the possibility of accurately classifying different sitting positions by means of the introduced instrumented office chair combined with machine learning analyses. The use of such novel approaches for the accurate assessment of chair usage could offer insights into the relationships between sitting position, sitting behaviour, and the occurrence of musculoskeletal disorders. PMID:27868066

  11. A rational approach to identify inhibitors of Mycobacterium tuberculosis enoyl acyl carrier protein reductase.

    PubMed

    Chhabria, Mahesh T; Parmar, Kailash B; Brahmkshatriya, Pathik S

    2013-01-01

    Mycobacterial enoyl acyl carrier protein (ACP) reductase is an attractive target for focused design of novel antitubercular agents. Structural information available on enoyl-ACP reductase in complex with different ligands was used to generate receptor-based pharmacophore model in Discovery Studio (DS). In parallel, pharmacophore models were also generated using ligand-based approach (HypoGen module in DS). Statistically significant models were generated (r(2) = 0.85) which were found to be predictive as indicated from internal and external cross-validations. The model was used as a query tool to search Zinc and Maybridge databases to identify lead compounds and predict their activity in silico. Database searching retrieved many potential lead compounds having better estimated IC50 values than the training set compounds. These compounds were then evaluated for their drug-likeliness and pharmacokinetic properties using DS. Few selected compounds were then docked into the crystal structure of enoyl-ACP reductase using Dock 6.5. Most compounds were found to have high score values, which was found to be consistent with the results from pharmacophore mapping. Additionally, molecular docking provided useful insights into the nature of binding of the identified hit molecules. In summary, we show a useful strategy employing ligand- and structure-based approaches (pharmacophore modeling coupled with molecular docking) to identify new enoyl- ACP reductase inhibitors for antimycobacterial chemotherapy.

  12. A Systematic Approach to Predicting Spring Force for Sagittal Craniosynostosis Surgery.

    PubMed

    Zhang, Guangming; Tan, Hua; Qian, Xiaohua; Zhang, Jian; Li, King; David, Lisa R; Zhou, Xiaobo

    2016-05-01

    Spring-assisted surgery (SAS) can effectively treat scaphocephaly by reshaping crania with the appropriate spring force. However, it is difficult to accurately estimate spring force without considering biomechanical properties of tissues. This study presents and validates a reliable system to accurately predict the spring force for sagittal craniosynostosis surgery. The authors randomly chose 23 patients who underwent SAS and had been followed for at least 2 years. An elastic model was designed to characterize the biomechanical behavior of calvarial bone tissue for each individual. After simulating the contact force on accurate position of the skull strip with the springs, the finite element method was applied to calculating the stress of each tissue node based on the elastic model. A support vector regression approach was then used to model the relationships between biomechanical properties generated from spring force, bone thickness, and the change of cephalic index after surgery. Therefore, for a new patient, the optimal spring force can be predicted based on the learned model with virtual spring simulation and dynamic programming approach prior to SAS. Leave-one-out cross-validation was implemented to assess the accuracy of our prediction. As a result, the mean prediction accuracy of this model was 93.35%, demonstrating the great potential of this model as a useful adjunct for preoperative planning tool.

  13. HOW LONG WILL MY MOUSE LIVE? MACHINE LEARNING APPROACHES FOR PREDICTION OF MOUSE LIFESPAN

    PubMed Central

    Swindell, William R.; Harper, James M.; Miller, Richard A.

    2009-01-01

    Prediction of individual lifespan based upon characteristics evaluated at middle-age represents a challenging objective for aging research. In this study, we used machine learning algorithms to construct models that predict lifespan in a stock of genetically heterogeneous mice. Lifespan-prediction accuracy of 22 algorithms was evaluated using a cross-validation approach, in which models were trained and tested with distinct subsets of data. Using a combination of body weight and T-cell subset measures evaluated before two years of age, we show that the lifespan quartile to which an individual mouse belongs can be predicted with an accuracy of 35.3% (± 0.10%). This result provides a new benchmark for the development of lifespan-predictive models, but improvement can be expected through identification of new predictor variables and development of computational approaches. Future work in this direction can provide tools for aging research and will shed light on associations between phenotypic traits and longevity. PMID:18840793

  14. A Market-Basket Approach to Predict the Acute Aquatic Toxicity of Munitions and Energetic Materials.

    PubMed

    Burgoon, Lyle D

    2016-06-01

    An ongoing challenge in chemical production, including the production of insensitive munitions and energetics, is the ability to make predictions about potential environmental hazards early in the process. To address this challenge, a quantitative structure activity relationship model was developed to predict acute fathead minnow toxicity of insensitive munitions and energetic materials. Computational predictive toxicology models like this one may be used to identify and prioritize environmentally safer materials early in their development. The developed model is based on the Apriori market-basket/frequent itemset mining approach to identify probabilistic prediction rules using chemical atom-pairs and the lethality data for 57 compounds from a fathead minnow acute toxicity assay. Lethality data were discretized into four categories based on the Globally Harmonized System of Classification and Labelling of Chemicals. Apriori identified toxicophores for categories two and three. The model classified 32 of the 57 compounds correctly, with a fivefold cross-validation classification rate of 74 %. A structure-based surrogate approach classified the remaining 25 chemicals correctly at 48 %. This result is unsurprising as these 25 chemicals were fairly unique within the larger set.

  15. Evaluating the reliability of the stream tracer approach to characterize stream-subsurface water exchange

    USGS Publications Warehouse

    Harvey, J.W.; Wagner, B.J.; Bencala, K.E.

    1996-01-01

    Stream water was locally recharged into shallow groundwater flow paths that returned to the stream (hyporheic exchange) in St. Kevin Gulch, a Rocky Mountain stream in Colorado contaminated by acid mine drainage. Two approaches were used to characterize hyporheic exchange: sub- reach-scale measurement of hydraulic heads and hydraulic conductivity to compute streambed fluxes (hydrometric approach) and reachscale modeling of in- stream solute tracer injections to determine characteristic length and timescales of exchange with storage zones (stream tracer approach). Subsurface data were the standard of comparison used to evaluate the reliability of the stream tracer approach to characterize hyporheic exchange. The reach-averaged hyporheic exchange flux (1.5 mL s-1 m-1), determined by hydrometric methods, was largest when stream base flow was low (10 L s-1); hyporheic exchange persisted when base flow was 10- fold higher, decreasing by approximately 30%. Reliability of the stream tracer approach to detect hyporheic exchange was assessed using first- order uncertainty analysis that considered model parameter sensitivity. The stream tracer approach did not reliably characterize hyporheic exchange at high base flow: the model was apparently more sensitive to exchange with surface water storage zones than with the hyporheic zone. At low base flow the stream tracer approach reliably characterized exchange between the stream and gravel streambed (timescale of hours) but was relatively insensitive to slower exchange with deeper alluvium (timescale of tens of hours) that was detected by subsurface measurements. The stream tracer approach was therefore not equally sensitive to all timescales of hyporheic exchange. We conclude that while the stream tracer approach is an efficient means to characterize surface-subsurface exchange, future studies will need to more routinely consider decreasing sensitivities of tracer methods at higher base flow and a potential bias toward

  16. Neural Network Based Response Prediction of rTMS in Major Depressive Disorder Using QEEG Cordance

    PubMed Central

    Ozekes, Serhat; Gultekin, Selahattin; Tarhan, Nevzat; Hizli Sayar, Gokben; Bayram, Ali

    2015-01-01

    Objective The combination of repetitive transcranial magnetic stimulation (rTMS), a non-pharmacological form of therapy for treating major depressive disorder (MDD), and electroencephalogram (EEG) is a valuable tool for investigating the functional connectivity in the brain. This study aims to explore whether pre-treating frontal quantitative EEG (QEEG) cordance is associated with response to rTMS treatment among MDD patients by using an artificial intelligence approach, artificial neural network (ANN). Methods The artificial neural network using pre-treatment cordance of frontal QEEG classification was carried out to identify responder or non-responder to rTMS treatment among 55 MDD subjects. The classification performance was evaluated using k-fold cross-validation. Results The ANN classification identified responders to rTMS treatment with a sensitivity of 93.33%, and its overall accuracy reached to 89.09%. Area under Receiver Operating Characteristic (ROC) curve (AUC) value for responder detection using 6, 8 and 10 fold cross validation were 0.917, 0.823 and 0.894 respectively. Conclusion Potential utility of ANN approach method can be used as a clinical tool in administering rTMS therapy to a targeted group of subjects suffering from MDD. This methodology is more potentially useful to the clinician as prediction is possible using EEG data collected before this treatment process is initiated. It is worth using feature selection algorithms to raise the sensitivity and accuracy values. PMID:25670947

  17. The Object-analogue approach for probabilistic forecasting

    NASA Astrophysics Data System (ADS)

    Frediani, M. E.; Hopson, T. M.; Anagnostou, E. N.; Hacker, J.

    2015-12-01

    The object-analogue is a new method to estimate forecast uncertainty and to derive probabilistic predictions of gridded forecast fields over larger regions rather than point locations. The method has been developed for improving the forecast of 10-meter wind speed over the northeast US, and it can be extended to other forecast variables, vertical levels, and other regions. The object-analogue approach combines the analog post-processing technique (Hopson 2005; Hamill 2006; Delle Monache 2011) with the Method for Object-based Diagnostic Evaluation (MODE) for forecast verification (Davis et al 2006a, b). Originally, MODE is used to verify mainly precipitation forecasts using features of a forecast region represented by an object. The analog technique is used to reduce the NWP systematic and random errors of a gridded forecast field. In this study we use MODE-derived objects to characterize the wind fields forecasts into attributes such as object area, centroid location, and intensity percentiles, and apply the analogue concept to these objects. The object-analogue method uses a database of objects derived from reforecasts and their respective reanalysis. Given a real-time forecast field, it searches the database and selects the top-ranked objects with the most similar set of attributes using the MODE fuzzy logic algorithm for object matching. The attribute probabilities obtained with the set of selected object-analogues are used to derive a multi-layer probabilistic prediction. The attribute probabilities are combined into three uncertainty layers that address the main concerns of most applications: location, area, and magnitude. The multi-layer uncertainty can be weighted and combined or used independently in such that it provides a more accurate prediction, adjusted according to the application interest. In this study we present preliminary results of the object-analogue method. Using a database with one hundred storms we perform a leave-one-out cross-validation to

  18. A practical and automated approach to large area forest disturbance mapping with remote sensing.

    PubMed

    Ozdogan, Mutlu

    2014-01-01

    In this paper, I describe a set of procedures that automate forest disturbance mapping using a pair of Landsat images. The approach is built on the traditional pair-wise change detection method, but is designed to extract training data without user interaction and uses a robust classification algorithm capable of handling incorrectly labeled training data. The steps in this procedure include: i) creating masks for water, non-forested areas, clouds, and cloud shadows; ii) identifying training pixels whose value is above or below a threshold defined by the number of standard deviations from the mean value of the histograms generated from local windows in the short-wave infrared (SWIR) difference image; iii) filtering the original training data through a number of classification algorithms using an n-fold cross validation to eliminate mislabeled training samples; and finally, iv) mapping forest disturbance using a supervised classification algorithm. When applied to 17 Landsat footprints across the U.S. at five-year intervals between 1985 and 2010, the proposed approach produced forest disturbance maps with 80 to 95% overall accuracy, comparable to those obtained from traditional approaches to forest change detection. The primary sources of mis-classification errors included inaccurate identification of forests (errors of commission), issues related to the land/water mask, and clouds and cloud shadows missed during image screening. The approach requires images from the peak growing season, at least for the deciduous forest sites, and cannot readily distinguish forest harvest from natural disturbances or other types of land cover change. The accuracy of detecting forest disturbance diminishes with the number of years between the images that make up the image pair. Nevertheless, the relatively high accuracies, little or no user input needed for processing, speed of map production, and simplicity of the approach make the new method especially practical for forest cover

  19. Long-read sequencing improves assembly of Trichinella genomes 10-fold, revealing substantial synteny between lineages diverged over seven million years

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Genome evolution influences a parasite’s’s pathogenicity, host-pathogen interactions, environmental constraints, and invasion biology, while genome assemblies form the basis of comparative sequence analyses. Given that closely related organisms typically maintain appreciable synteny, the genome asse...

  20. Mechanistic role of each metal ion in Streptomyces dinuclear aminopeptidase: PEPTIDE hydrolysis and 7x10(10)-fold rate enhancement of phosphodiester hydrolysis.

    PubMed

    Ercan, Altan; Tay, William M; Grossman, Steven H; Ming, Li-June

    2010-01-01

    The dinuclear aminopeptidase from Streptomyces griseus (SgAP) and its metal derivatives catalyze the hydrolysis of the phosphoester bis(p-nitrophenyl) phosphate (BNPP) and the phosphonate ester p-nitrophenyl phenylphosphonate with extraordinary rate enhancements at pH 7.0 and 25 degrees C [A. Ercan, H. I. Park, L.-J. Ming, Biochemistry 45, (2006) 13779-13793.], reaching 6.7 billion-fold in terms of the first-order rate constant of the di-Co(II) derivative with respect to the autohydrolytic rates. Since phosphoesters are transition state-like inhibitors in peptide hydrolysis, their hydrolysis by SgAP is quite novel. Herein, we report the investigation of this proficient alternative catalysis of SgAP and the role of each metal ion in the dinuclear site toward peptide and BNPP hydrolysis. Mn(II) selectively binds to one of the dinuclear metal sites (M1), affording MnE-SgAP with an empty (E) second site for the binding of another metal (M2), including Mn(II), Co(II), Ni(II), Zn(II), and Cd(II). Peptide hydrolysis is controlled by M2, wherein the k(cat) values for the derivatives MnM2-SgAP are different yet similar between MnCo- and CoCo-SgAP and pairs of other metal derivatives. On the other hand, BNPP hydrolysis is affected by metals in both sites. Thus, the two hydrolytic catalyses must follow different mechanisms. Based on crystal structures, docking, and the results presented herein, the M1 site is close to the hydrophobic specific site and the M2 site is next to Tyr246 that is H-bonded to a coordinated nucleophilic water molecule in peptide hydrolysis; whereas a coordinated water molecule on M1 becomes available as the nucleophile in phosphodiester hydrolysis.

  1. A Multiscale Approach to InSAR Time Series Analysis

    NASA Astrophysics Data System (ADS)

    Simons, M.; Hetland, E. A.; Muse, P.; Lin, Y.; Dicaprio, C. J.

    2009-12-01

    We describe progress in the development of MInTS (Multiscale analysis of InSAR Time Series), an approach to constructed self-consistent time-dependent deformation observations from repeated satellite-based InSAR images of a given region. MInTS relies on a spatial wavelet decomposition to permit the inclusion of distance based spatial correlations in the observations while maintaining computational tractability. In essence, MInTS allows one to considers all data at the same time as opposed to one pixel at a time, thereby taking advantage of both spatial and temporal characteristics of the deformation field. This approach also permits a consistent treatment of all data independent of the presence of localized holes due to unwrapping issues in any given interferogram. Specifically, the presence of holes is accounted for through a weighting scheme that accounts for the extent of actual data versus the area of holes associated with any given wavelet. In terms of the temporal representation, we have the flexibility to explicitly parametrize known processes that are expected to contribute to a given set of observations (e.g., co-seismic steps and post-seismic transients, secular variations, seasonal oscillations, etc.). Our approach also allows for the temporal parametrization to include a set of general functions in order to account for unexpected processes. We allow for various forms of model regularization using a cross-validation approach to select penalty parameters. We also experiment with the use of sparsity inducing regularization as a way to select from a large dictionary of time functions. The multiscale analysis allows us to consider various contributions (e.g., orbit errors) that may affect specific scales but not others. The methods described here are all embarrassingly parallel and suitable for implementation on a cluster computer. We demonstrate the use of MInTS using a large suite of ERS-1/2 and Envisat interferograms for Long Valley Caldera, and validate

  2. Feature Selection and Classification of Electroencephalographic Signals: An Artificial Neural Network and Genetic Algorithm Based Approach.

    PubMed

    Erguzel, Turker Tekin; Ozekes, Serhat; Tan, Oguz; Gultekin, Selahattin

    2015-10-01

    Feature selection is an important step in many pattern recognition systems aiming to overcome the so-called curse of dimensionality. In this study, an optimized classification method was tested in 147 patients with major depressive disorder (MDD) treated with repetitive transcranial magnetic stimulation (rTMS). The performance of the combination of a genetic algorithm (GA) and a back-propagation (BP) neural network (BPNN) was evaluated using 6-channel pre-rTMS electroencephalographic (EEG) patterns of theta and delta frequency bands. The GA was first used to eliminate the redundant and less discriminant features to maximize classification performance. The BPNN was then applied to test the performance of the feature subset. Finally, classification performance using the subset was evaluated using 6-fold cross-validation. Although the slow bands of the frontal electrodes are widely used to collect EEG data for patients with MDD and provide quite satisfactory classification results, the outcomes of the proposed approach indicate noticeably increased overall accuracy of 89.12% and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.904 using the reduced feature set.

  3. A Metabolomic Approach to Target Compounds from the Asteraceae Family for Dual COX and LOX Inhibition

    PubMed Central

    Chagas-Paula, Daniela A.; Zhang, Tong; Da Costa, Fernando B.; Edrada-Ebel, RuAngelie

    2015-01-01

    The application of metabolomics in phytochemical analysis is an innovative strategy for targeting active compounds from a complex plant extract. Species of the Asteraceae family are well-known to exhibit potent anti-inflammatory (AI) activity. Dual inhibition of the enzymes COX-1 and 5-LOX is essential for the treatment of several inflammatory diseases, but there is not much investigation reported in the literature for natural products. In this study, 57 leaf extracts (EtOH-H2O 7:3, v/v) from different genera and species of the Asteraceae family were tested against COX-1 and 5-LOX while HPLC-ESI-HRMS analysis of the extracts indicated high diversity in their chemical compositions. Using O2PLS-DA (R2 > 0.92; VIP > 1 and positive Y-correlation values), dual inhibition potential of low-abundance metabolites was determined. The O2PLS-DA results exhibited good validation values (cross-validation = Q2 > 0.7 and external validation = P2 > 0.6) with 0% of false positive predictions. The metabolomic approach determined biomarkers for the required biological activity and detected active compounds in the extracts displaying unique mechanisms of action. In addition, the PCA data also gave insights on the chemotaxonomy of the family Asteraceae across its diverse range of genera and tribes. PMID:26184333

  4. Predicting dissolved oxygen concentration using kernel regression modeling approaches with nonlinear hydro-chemical data.

    PubMed

    Singh, Kunwar P; Gupta, Shikha; Rai, Premanjali

    2014-05-01

    Kernel function-based regression models were constructed and applied to a nonlinear hydro-chemical dataset pertaining to surface water for predicting the dissolved oxygen levels. Initial features were selected using nonlinear approach. Nonlinearity in the data was tested using BDS statistics, which revealed the data with nonlinear structure. Kernel ridge regression, kernel principal component regression, kernel partial least squares regression, and support vector regression models were developed using the Gaussian kernel function and their generalization and predictive abilities were compared in terms of several statistical parameters. Model parameters were optimized using the cross-validation procedure. The proposed kernel regression methods successfully captured the nonlinear features of the original data by transforming it to a high dimensional feature space using the kernel function. Performance of all the kernel-based modeling methods used here were comparable both in terms of predictive and generalization abilities. Values of the performance criteria parameters suggested for the adequacy of the constructed models to fit the nonlinear data and their good predictive capabilities.

  5. An exometabolomics approach to monitoring microbial contamination in microalgal fermentation processes by using metabolic footprint analysis.

    PubMed

    Sue, Tiffany; Obolonkin, Victor; Griffiths, Hywel; Villas-Bôas, Silas Granato

    2011-11-01

    The early detection of microbial contamination is crucial to avoid process failure and costly delays in fermentation industries. However, traditional detection methods such as plate counting and microscopy are labor-intensive, insensitive, and time-consuming. Modern techniques that can detect microbial contamination rapidly and cost-effectively are therefore sought. In the present study, we propose gas chromatography-mass spectrometry (GC-MS)-based metabolic footprint analysis as a rapid and reliable method for the detection of microbial contamination in fermentation processes. Our metabolic footprint analysis detected statistically significant differences in metabolite profiles of axenic and contaminated batch cultures of microalgae as early as 3 h after contamination was introduced, while classical detection methods could detect contamination only after 24 h. The data were analyzed by discriminant function analysis and were validated by leave-one-out cross-validation. We obtained a 97% success rate in correctly classifying samples coming from contaminated or axenic cultures. Therefore, metabolic footprint analysis combined with discriminant function analysis presents a rapid and cost-effective approach to monitor microbial contamination in industrial fermentation processes.

  6. A machine learning approach for classification of anatomical coverage in CT

    NASA Astrophysics Data System (ADS)

    Wang, Xiaoyong; Lo, Pechin; Ramakrishna, Bharath; Goldin, Johnathan; Brown, Matthew

    2016-03-01

    Automatic classification of anatomical coverage of medical images is critical for big data mining and as a pre-processing step to automatically trigger specific computer aided diagnosis systems. The traditional way to identify scans through DICOM headers has various limitations due to manual entry of series descriptions and non-standardized naming conventions. In this study, we present a machine learning approach where multiple binary classifiers were used to classify different anatomical coverages of CT scans. A one-vs-rest strategy was applied. For a given training set, a template scan was selected from the positive samples and all other scans were registered to it. Each registered scan was then evenly split into k × k × k non-overlapping blocks and for each block the mean intensity was computed. This resulted in a 1 × k3 feature vector for each scan. The feature vectors were then used to train a SVM based classifier. In this feasibility study, four classifiers were built to identify anatomic coverages of brain, chest, abdomen-pelvis, and chest-abdomen-pelvis CT scans. Each classifier was trained and tested using a set of 300 scans from different subjects, composed of 150 positive samples and 150 negative samples. Area under the ROC curve (AUC) of the testing set was measured to evaluate the performance in a two-fold cross validation setting. Our results showed good classification performance with an average AUC of 0.96.

  7. A machine learning approach to predicting protein-ligand binding affinity with applications to molecular docking

    PubMed Central

    Ballester, Pedro J.; Mitchell, John B.O.

    2012-01-01

    Motivation Accurately predicting the binding affinities of large sets of diverse protein-ligand complexes is an extremely challenging task. The scoring functions that attempt such computational prediction are essential for analysing the outputs of Molecular Docking, which is in turn an important technique for drug discovery, chemical biology and structural biology. Each scoring function assumes a predetermined theory-inspired functional form for the relationship between the variables that characterise the complex, which also include parameters fitted to experimental or simulation data, and its predicted binding affinity. The inherent problem of this rigid approach is that it leads to poor predictivity for those complexes that do not conform to the modelling assumptions. Moreover, resampling strategies, such as cross-validation or bootstrapping, are still not systematically used to guard against the overfitting of calibration data in parameter estimation for scoring functions. Results We propose a novel scoring function (RF-Score) that circumvents the need for problematic modelling assumptions via non-parametric machine learning. In particular, Random Forest was used to implicitly capture binding effects that are hard to model explicitly. RF-Score is compared with the state of the art on the demanding PDBbind benchmark. Results show that RF-Score is a very competitive scoring function. Importantly, RF-Score’s performance was shown to improve dramatically with training set size and hence the future availability of more high quality structural and interaction data is expected to lead to improved versions of RF-Score. PMID:20236947

  8. Heterogeneous ensemble approach with discriminative features and modified-SMOTEbagging for pre-miRNA classification

    PubMed Central

    Lertampaiporn, Supatcha; Thammarongtham, Chinae; Nukoolkit, Chakarida; Kaewkamnerdpong, Boonserm; Ruengjitchatchawalya, Marasri

    2013-01-01

    An ensemble classifier approach for microRNA precursor (pre-miRNA) classification was proposed based upon combining a set of heterogeneous algorithms including support vector machine (SVM), k-nearest neighbors (kNN) and random forest (RF), then aggregating their prediction through a voting system. Additionally, the proposed algorithm, the classification performance was also improved using discriminative features, self-containment and its derivatives, which have shown unique structural robustness characteristics of pre-miRNAs. These are applicable across different species. By applying preprocessing methods—both a correlation-based feature selection (CFS) with genetic algorithm (GA) search method and a modified-Synthetic Minority Oversampling Technique (SMOTE) bagging rebalancing method—improvement in the performance of this ensemble was observed. The overall prediction accuracies obtained via 10 runs of 5-fold cross validation (CV) was 96.54%, with sensitivity of 94.8% and specificity of 98.3%—this is better in trade-off sensitivity and specificity values than those of other state-of-the-art methods. The ensemble model was applied to animal, plant and virus pre-miRNA and achieved high accuracy, >93%. Exploiting the discriminative set of selected features also suggests that pre-miRNAs possess high intrinsic structural robustness as compared with other stem loops. Our heterogeneous ensemble method gave a relatively more reliable prediction than those using single classifiers. Our program is available at http://ncrna-pred.com/premiRNA.html. PMID:23012261

  9. An Approach for Predicting Essential Genes Using Multiple Homology Mapping and Machine Learning Algorithms

    PubMed Central

    Hua, Hong-Li; Zhang, Fa-Zhan; Labena, Abraham Alemayehu; Dong, Chuan; Jin, Yan-Ting

    2016-01-01

    Investigation of essential genes is significant to comprehend the minimal gene sets of cell and discover potential drug targets. In this study, a novel approach based on multiple homology mapping and machine learning method was introduced to predict essential genes. We focused on 25 bacteria which have characterized essential genes. The predictions yielded the highest area under receiver operating characteristic (ROC) curve (AUC) of 0.9716 through tenfold cross-validation test. Proper features were utilized to construct models to make predictions in distantly related bacteria. The accuracy of predictions was evaluated via the consistency of predictions and known essential genes of target species. The highest AUC of 0.9552 and average AUC of 0.8314 were achieved when making predictions across organisms. An independent dataset from Synechococcus elongatus, which was released recently, was obtained for further assessment of the performance of our model. The AUC score of predictions is 0.7855, which is higher than other methods. This research presents that features obtained by homology mapping uniquely can achieve quite great or even better results than those integrated features. Meanwhile, the work indicates that machine learning-based method can assign more efficient weight coefficients than using empirical formula based on biological knowledge. PMID:27660763

  10. Derivation of molecular signatures for breast cancer recurrence prediction using a two-way validation approach

    PubMed Central

    Sun, Yijun; Urquidi, Virginia

    2010-01-01

    Previous studies have demonstrated the potential value of gene expression signatures in assessing the risk of post-surgical breast cancer recurrence, however, many of these predictive models have been derived using simple computational algorithms and validated internally or using one-way validation on a single dataset. We have recently developed a new feature selection algorithm that overcomes some limitations inherent to high-dimensional data analysis. In this study, we applied this algorithm to two publicly available gene expression datasets obtained from over 400 patients with breast cancer to investigate whether we could derive more accurate prognostic signatures and reveal common predictive factors across independent datasets. We compared the performance of three advanced computational algorithms using a robust two-way validation method, where one dataset was used for training and to establish a prediction model that was then blindly tested on the other dataset. The experiment was then repeated in the reverse direction. Analyses identified prognostic signatures that while comprised of only 10–13 genes, significantly outperformed previously reported signatures for breast cancer evaluation. The cross-validation approach revealed CEGP1 and PRAME as major candidates for breast cancer biomarker development. PMID:19291396

  11. Vocal individuality cues in the African penguin (Spheniscus demersus): a source-filter theory approach

    PubMed Central

    Favaro, Livio; Gamba, Marco; Alfieri, Chiara; Pessani, Daniela; McElligott, Alan G.

    2015-01-01

    The African penguin is a nesting seabird endemic to southern Africa. In penguins of the genus Spheniscus vocalisations are important for social recognition. However, it is not clear which acoustic features of calls can encode individual identity information. We recorded contact calls and ecstatic display songs of 12 adult birds from a captive colony. For each vocalisation, we measured 31 spectral and temporal acoustic parameters related to both source and filter components of calls. For each parameter, we calculated the Potential of Individual Coding (PIC). The acoustic parameters showing PIC ≥ 1.1 were used to perform a stepwise cross-validated discriminant function analysis (DFA). The DFA correctly classified 66.1% of the contact calls and 62.5% of display songs to the correct individual. The DFA also resulted in the further selection of 10 acoustic features for contact calls and 9 for display songs that were important for vocal individuality. Our results suggest that studying the anatomical constraints that influence nesting penguin vocalisations from a source-filter perspective, can lead to a much better understanding of the acoustic cues of individuality contained in their calls. This approach could be further extended to study and understand vocal communication in other bird species. PMID:26602001

  12. A Bayesian Approach to the Design and Analysis of Computer Experiments

    SciTech Connect

    Currin, C.

    1988-01-01

    We consider the problem of designing and analyzing experiments for prediction of the function y(f), t {element_of} T, where y is evaluated by means of a computer code (typically by solving complicated equations that model a physical system), and T represents the domain of inputs to the code. We use a Bayesian approach, in which uncertainty about y is represented by a spatial stochastic process (random function); here we restrict attention to stationary Gaussian processes. The posterior mean function can be used as an interpolating function, with uncertainties given by the posterior standard deviations. Instead of completely specifying the prior process, we consider several families of priors, and suggest some cross-validational methods for choosing one that performs relatively well on the function at hand. As a design criterion, we use the expected reduction in the entropy of the random vector y (T*), where T* {contained_in} T is a given finite set of ''sites'' (input configurations) at which predictions are to be made. We describe an exchange algorithm for constructing designs that are optimal with respect to this criterion. To demonstrate the use of these design and analysis methods, several examples are given, including one experiment on a computer model of a thermal energy storage device and another on an integrated circuit simulator.

  13. Vocal individuality cues in the African penguin (Spheniscus demersus): a source-filter theory approach.

    PubMed

    Favaro, Livio; Gamba, Marco; Alfieri, Chiara; Pessani, Daniela; McElligott, Alan G

    2015-11-25

    The African penguin is a nesting seabird endemic to southern Africa. In penguins of the genus Spheniscus vocalisations are important for social recognition. However, it is not clear which acoustic features of calls can encode individual identity information. We recorded contact calls and ecstatic display songs of 12 adult birds from a captive colony. For each vocalisation, we measured 31 spectral and temporal acoustic parameters related to both source and filter components of calls. For each parameter, we calculated the Potential of Individual Coding (PIC). The acoustic parameters showing PIC ≥ 1.1 were used to perform a stepwise cross-validated discriminant function analysis (DFA). The DFA correctly classified 66.1% of the contact calls and 62.5% of display songs to the correct individual. The DFA also resulted in the further selection of 10 acoustic features for contact calls and 9 for display songs that were important for vocal individuality. Our results suggest that studying the anatomical constraints that influence nesting penguin vocalisations from a source-filter perspective, can lead to a much better understanding of the acoustic cues of individuality contained in their calls. This approach could be further extended to study and understand vocal communication in other bird species.

  14. Critical Evaluation of Human Oral Bioavailability for Pharmaceutical Drugs by Using Various Cheminformatics Approaches

    PubMed Central

    Kim, Marlene; Sedykh, Alexander; Chakravarti, Suman K.; Saiakhov, Roustem D.; Zhu, Hao

    2014-01-01

    Purpose Oral bioavailability (%F) is a key factor that determines the fate of a new drug in clinical trials. Traditionally, %F is measured using costly and time -consuming experimental tests. Developing computational models to evaluate the %F of new drugs before they are synthesized would be beneficial in the drug discovery process. Methods We employed Combinatorial Quantitative Structure-Activity Relationship approach to develop several computational %F models. We compiled a %F dataset of 995 drugs from public sources. After generating chemical descriptors for each compound, we used random forest, support vector machine, k nearest neighbor, and CASE Ultra to develop the relevant QSAR models. The resulting models were validated using five-fold cross-validation. Results The external predictivity of %F values was poor (R2=0.28, n=995, MAE=24), but was improved (R2=0.40, n=362, MAE=21) by filtering unreliable predictions that had a high probability of interacting with MDR1 and MRP2 transporters. Furthermore, classifying the compounds according to the %F values (%F<50% as “low”, %F≥50% as ‘high”) and developing category QSAR models resulted in an external accuracy of 76%. Conclusions In this study, we developed predictive %F QSAR models that could be used to evaluate new drug compounds, and integrating drug-transporter interactions data greatly benefits the resulting models. PMID:24306326

  15. Sonoelastomics for Breast Tumor Classification: A Radiomics Approach with Clustering-Based Feature Selection on Sonoelastography.

    PubMed

    Zhang, Qi; Xiao, Yang; Suo, Jingfeng; Shi, Jun; Yu, Jinhua; Guo, Yi; Wang, Yuanyuan; Zheng, Hairong

    2017-02-20

    A radiomics approach to sonoelastography, called "sonoelastomics," is proposed for classification of benign and malignant breast tumors. From sonoelastograms of breast tumors, a high-throughput 364-dimensional feature set was calculated consisting of shape features, intensity statistics, gray-level co-occurrence matrix texture features and contourlet texture features, which quantified the shape, hardness and hardness heterogeneity of a tumor. The high-throughput features were then selected for feature reduction using hierarchical clustering and three-feature selection metrics. For a data set containing 42 malignant and 75 benign tumors from 117 patients, seven selected sonoelastomic features achieved an area under the receiver operating characteristic curve of 0.917, an accuracy of 88.0%, a sensitivity of 85.7% and a specificity of 89.3% in a validation set via the leave-one-out cross-validation, revealing superiority over the principal component analysis, deep polynomial networks and manually selected features. The sonoelastomic features are valuable in breast tumor differentiation.

  16. Concept mapping as an approach for expert-guided model building: The example of health literacy.

    PubMed

    Soellner, Renate; Lenartz, Norbert; Rudinger, Georg

    2017-02-01

    Concept mapping served as the starting point for the aim of capturing the comprehensive structure of the construct of 'health literacy.' Ideas about health literacy were generated by 99 experts and resulted in 105 statements that were subsequently organized by 27 experts in an unstructured card sorting. Multidimensional scaling was applied to the sorting data and a two and three-dimensional solution was computed. The three dimensional solution was used in subsequent cluster analysis and resulted in a concept map of nine "clusters": (1) self-regulation, (2) self-perception, (3) proactive approach to health, (4) basic literacy and numeracy skills, (5) information appraisal, (6) information search, (7) health care system knowledge and acting, (8) communication and cooperation, and (9) beneficial personality traits. Subsequently, this concept map served as a starting point for developing a "qualitative" structural model of health literacy and a questionnaire for the measurement of health literacy. On the basis of questionnaire data, a "quantitative" structural model was created by first applying exploratory factor analyses (EFA) and then cross-validating the model with confirmatory factor analyses (CFA). Concept mapping proved to be a highly valuable tool for the process of model building up to translational research in the "real world".

  17. Prediction of community prevalence of human onchocerciasis in the Amazonian onchocerciasis focus: Bayesian approach.

    PubMed Central

    Carabin, Hélène; Escalona, Marisela; Marshall, Clare; Vivas-Martínez, Sarai; Botto, Carlos; Joseph, Lawrence; Basáñez, María-Gloria

    2003-01-01

    OBJECTIVE: To develop a Bayesian hierarchical model for human onchocerciasis with which to explore the factors that influence prevalence of microfilariae in the Amazonian focus of onchocerciasis and predict the probability of any community being at least mesoendemic (>20% prevalence of microfilariae), and thus in need of priority ivermectin treatment. METHODS: Models were developed with data from 732 individuals aged > or =15 years who lived in 29 Yanomami communities along four rivers of the south Venezuelan Orinoco basin. The models' abilities to predict prevalences of microfilariae in communities were compared. The deviance information criterion, Bayesian P-values, and residual values were used to select the best model with an approximate cross-validation procedure. FINDINGS: A three-level model that acknowledged clustering of infection within communities performed best, with host age and sex included at the individual level, a river-dependent altitude effect at the community level, and additional clustering of communities along rivers. This model correctly classified 25/29 (86%) villages with respect to their need for priority ivermectin treatment. CONCLUSION: Bayesian methods are a flexible and useful approach for public health research and control planning. Our model acknowledges the clustering of infection within communities, allows investigation of links between individual- or community-specific characteristics and infection, incorporates additional uncertainty due to missing covariate data, and informs policy decisions by predicting the probability that a new community is at least mesoendemic. PMID:12973640

  18. Large-scale retrospective evaluation of regulated liquid chromatography-mass spectrometry bioanalysis projects using different total error approaches.

    PubMed

    Tan, Aimin; Saffaj, Taoufiq; Musuku, Adrien; Awaiye, Kayode; Ihssane, Bouchaib; Jhilal, Fayçal; Sosse, Saad Alaoui; Trabelsi, Fethi

    2015-03-01

    The current approach in regulated LC-MS bioanalysis, which evaluates the precision and trueness of an assay separately, has long been criticized for inadequate balancing of lab-customer risks. Accordingly, different total error approaches have been proposed. The aims of this research were to evaluate the aforementioned risks in reality and the difference among four common total error approaches (β-expectation, β-content, uncertainty, and risk profile) through retrospective analysis of regulated LC-MS projects. Twenty-eight projects (14 validations and 14 productions) were randomly selected from two GLP bioanalytical laboratories, which represent a wide variety of assays. The results show that the risk of accepting unacceptable batches did exist with the current approach (9% and 4% of the evaluated QC levels failed for validation and production, respectively). The fact that the risk was not wide-spread was only because the precision and bias of modern LC-MS assays are usually much better than the minimum regulatory requirements. Despite minor differences in magnitude, very similar accuracy profiles and/or conclusions were obtained from the four different total error approaches. High correlation was even observed in the width of bias intervals. For example, the mean width of SFSTP's β-expectation is 1.10-fold (CV=7.6%) of that of Saffaj-Ihssane's uncertainty approach, while the latter is 1.13-fold (CV=6.0%) of that of Hoffman-Kringle's β-content approach. To conclude, the risk of accepting unacceptable batches was real with the current approach, suggesting that total error approaches should be used instead. Moreover, any of the four total error approaches may be used because of their overall similarity. Lastly, the difficulties/obstacles associated with the application of total error approaches in routine analysis and their desirable future improvements are discussed.

  19. Differential label-free quantitative proteomic analysis of Shewanella oneidensis cultured under aerobic and suboxic conditions by accurate mass and time tag approach.

    PubMed

    Fang, Ruihua; Elias, Dwayne A; Monroe, Matthew E; Shen, Yufeng; McIntosh, Martin; Wang, Pei; Goddard, Carrie D; Callister, Stephen J; Moore, Ronald J; Gorby, Yuri A; Adkins, Joshua N; Fredrickson, Jim K; Lipton, Mary S; Smith, Richard D

    2006-04-01

    We describe the application of LC-MS without the use of stable isotope labeling for differential quantitative proteomic analysis of whole cell lysates of Shewanella oneidensis MR-1 cultured under aerobic and suboxic conditions. LC-MS/MS was used to initially identify peptide sequences, and LC-FTICR was used to confirm these identifications as well as measure relative peptide abundances. 2343 peptides covering 668 proteins were identified with high confidence and quantified. Among these proteins, a subset of 56 changed significantly using statistical approaches such as statistical analysis of microarrays, whereas another subset of 56 that were annotated as performing housekeeping functions remained essentially unchanged in relative abundance. Numerous proteins involved in anaerobic energy metabolism exhibited up to a 10-fold increase in relative abundance when S. oneidensis was transitioned from aerobic to suboxic conditions.

  20. Differential Label-free Quantitative Proteomic Analysis of Shewanella oneidensis Cultured under Aerobic and Suboxic Conditions by Accurate Mass and Time Tag Approach

    SciTech Connect

    Fang, Ruihua; Elias, Dwayne A.; Monroe, Matthew E.; Shen, Yufeng; McIntosh, Martin; Wang, Pei; Goddard, Carrie D.; Callister, Stephen J.; Moore, Ronald J.; Gorby, Yuri A.; Adkins, Joshua N.; Fredrickson, Jim K.; Lipton, Mary S.; Smith, Richard D.

    2006-04-01

    We describe the application of liquid chromatography coupled to mass spectrometry (LC/MS) without the use of stable isotope labeling for differential quantitative proteomics analysis of whole cell lysates of Shewanella oneidensis MR-1 cultured under aerobic and sub-oxic conditions. Liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) was used to initially identify peptide sequences, and LC coupled to Fourier transform ion cyclotron resonance mass spectrometry (LC-FTICR) was used to confirm these identifications, as well as measure relative peptide abundances. 2343 peptides, covering 668 proteins were identified with high confidence and quantified. Among these proteins, a subset of 56 changed significantly using statistical approaches such as SAM, while another subset of 56 that were annotated as performing housekeeping functions remained essentially unchanged in relative abundance. Numerous proteins involved in anaerobic energy metabolism exhibited up to a 10-fold increase in relative abundance when S. oneidensis is transitioned from aerobic to sub-oxic conditions.

  1. Evaluating fossil calibrations for dating phylogenies in light of rates of molecular evolution: a comparison of three approaches.

    PubMed

    Lukoschek, Vimoksalehi; Scott Keogh, J; Avise, John C

    2012-01-01

    Evolutionary and biogeographic studies increasingly rely on calibrated molecular clocks to date key events. Although there has been significant recent progress in development of the techniques used for molecular dating, many issues remain. In particular, controversies abound over the appropriate use and placement of fossils for calibrating molecular clocks. Several methods have been proposed for evaluating candidate fossils; however, few studies have compared the results obtained by different approaches. Moreover, no previous study has incorporated the effects of nucleotide saturation from different data types in the evaluation of candidate fossils. In order to address these issues, we compared three approaches for evaluating fossil calibrations: the single-fossil cross-validation method of Near, Meylan, and Shaffer (2005. Assessing concordance of fossil calibration points in molecular clock studies: an example using turtles. Am. Nat. 165:137-146), the empirical fossil coverage method of Marshall (2008. A simple method for bracketing absolute divergence times on molecular phylogenies using multiple fossil calibration points. Am. Nat. 171:726-742), and the Bayesian multicalibration method of Sanders and Lee (2007. Evaluating molecular clock calibrations using Bayesian analyses with soft and hard bounds. Biol. Lett. 3:275-279) and explicitly incorporate the effects of data type (nuclear vs. mitochondrial DNA) for identifying the most reliable or congruent fossil calibrations. We used advanced (Caenophidian) snakes as a case study; however, our results are applicable to any taxonomic group with multiple candidate fossils, provided appropriate taxon sampling and sufficient molecular sequence data are available. We found that data type strongly influenced which fossil calibrations were identified as outliers, regardless of which method was used. Despite the use of complex partitioned models of sequence evolution and multiple calibrations throughout the tree, saturation

  2. Structure–activity relationships study of mTOR kinase inhibition using QSAR and structure-based drug design approaches

    PubMed Central

    Lakhlili, Wiame; Yasri, Abdelaziz; Ibrahimi, Azeddine

    2016-01-01

    The discovery of clinically relevant inhibitors of mammalian target of rapamycin (mTOR) for anticancer therapy has proved to be a challenging task. The quantitative structure–activity relationship (QSAR) approach is a very useful and widespread technique for ligand-based drug design, which can be used to identify novel and potent mTOR inhibitors. In this study, we performed two-dimensional QSAR tests, and molecular docking validation tests of a series of mTOR ATP-competitive inhibitors to elucidate their structural properties associated with their activity. The QSAR tests were performed using partial least square method with a correlation coefficient of r2=0.799 and a cross-validation of q2=0.714. The chemical library screening was done by associating ligand-based to structure-based approach using the three-dimensional structure of mTOR developed by homology modeling. We were able to select 22 compounds from two databases as inhibitors of the mTOR kinase active site. We believe that the method and applications highlighted in this study will help future efforts toward the design of selective ATP-competitive inhibitors. PMID:27980424

  3. A hydrometeorological approach for probabilistic simulation of monthly soil moisture under bare and crop land conditions

    NASA Astrophysics Data System (ADS)

    Das, Sarit Kumar; Maity, Rajib

    2015-04-01

    This study focuses on the probabilistic estimation of monthly soil moisture variation by considering (a) the influence of hydrometeorological forcing to model the temporal variation and (b) the information of Hydrological Soil Groups (HSGs) and Agro-Climatic Zones (ACZs) to capture the spatial variation. The innovative contributions of this study are: (i) development of a Combined Hydro-Meteorological (CHM) index to extract the information of different influencing hydrometeorological variables, (ii) consideration of soil-hydrologic characteristics (through HSGs) and climate regime-based zoning for agriculture (through ACZs), and (iii) quantification of uncertainty range of the estimated soil moisture. Usage of Supervised Principal Component Analysis (SPCA) in the development of the CHM index helps to eliminate the "curse of dimensionality," typically arises in the multivariate analysis. The usage of SPCA also ensures the maximum possible association between the developed CHM index and soil moisture variation. The association between these variables is modeled through their joint distribution which is obtained by using the theory of copula. The proposed approach is also spatially transferable, since the information on HSGs and ACZs is considered. The "leave-one-out" cross-validation (LOO-CV) approach is adopted for stations belong to a particular HSG to examine the spatial transferability. The simulated soil moisture values are also compared with a few existing soil moisture data sets, derived from different Land Surface Models (LSMs) or retrieved from different satellite-based missions. The potential of the proposed approach is found to be promising and even applicable to crop land also, though with a lesser degree of efficiency as compared to bare land conditions.

  4. Estimation of extreme daily precipitation: comparison between regional and geostatistical approaches.

    NASA Astrophysics Data System (ADS)

    Hellies, Matteo; Deidda, Roberto; Langousis, Andreas

    2016-04-01

    addition, KUD avoids separation of the study region in contiguous areas, allowing for a continuous representation of the spatial variation of distribution parameters. Comparisons based on different error metrics, conducted with the method of cross-validation, show better performance of the geostatistical approach relative to the regional one. In addition, the geostatistical approach better represents local features of the spatial variability of rainfall, while overcoming the issue of abrupt shifts of distribution parameters at the boundaries between contiguous homogeneous regions.

  5. Modeling particulate matter concentrations measured through mobile monitoring in a deletion/substitution/addition approach

    NASA Astrophysics Data System (ADS)

    Su, Jason G.; Hopke, Philip K.; Tian, Yilin; Baldwin, Nichole; Thurston, Sally W.; Evans, Kristin; Rich, David Q.

    2015-12-01

    Land use regression modeling (LUR) through local scale circular modeling domains has been used to predict traffic-related air pollution such as nitrogen oxides (NOX). LUR modeling for fine particulate matters (PM), which generally have smaller spatial gradients than NOX, has been typically applied for studies involving multiple study regions. To increase the spatial coverage for fine PM and key constituent concentrations, we designed a mobile monitoring network in Monroe County, New York to measure pollutant concentrations of black carbon (BC, wavelength at 880 nm), ultraviolet black carbon (UVBC, wavelength at 3700 nm) and Delta-C (the difference between the UVBC and BC concentrations) using the Clarkson University Mobile Air Pollution Monitoring Laboratory (MAPL). A Deletion/Substitution/Addition (D/S/A) algorithm was conducted, which used circular buffers as a basis for statistics. The algorithm maximizes the prediction accuracy for locations without measurements using the V-fold cross-validation technique, and it reduces overfitting compared to other approaches. We found that the D/S/A LUR modeling approach could achieve good results, with prediction powers of 60%, 63%, and 61%, respectively, for BC, UVBC, and Delta-C. The advantage of mobile monitoring is that it can monitor pollutant concentrations at hundreds of spatial points in a region, rather than the typical less than 100 points from a fixed site saturation monitoring network. This research indicates that a mobile saturation sampling network, when combined with proper modeling techniques, can uncover small area variations (e.g., 10 m) in particulate matter concentrations.

  6. Dynamic frequency feature selection based approach for classification of motor imageries.

    PubMed

    Luo, Jing; Feng, Zuren; Zhang, Jun; Lu, Na

    2016-08-01

    Electroencephalography (EEG) is one of the most popular techniques to record the brain activities such as motor imagery, which is of low signal-to-noise ratio and could lead to high classification error. Therefore, selection of the most discriminative features could be crucial to improve the classification performance. However, the traditional feature selection methods employed in brain-computer interface (BCI) field (e.g. Mutual Information-based Best Individual Feature (MIBIF), Mutual Information-based Rough Set Reduction (MIRSR) and cross-validation) mainly focus on the overall performance on all the trials in the training set, and thus may have very poor performance on some specific samples, which is not acceptable. To address this problem, a novel sequential forward feature selection approach called Dynamic Frequency Feature Selection (DFFS) is proposed in this paper. The DFFS method emphasized the importance of the samples that got misclassified while only pursuing high overall classification performance. In the DFFS based classification scheme, the EEG data was first transformed to frequency domain using Wavelet Packet Decomposition (WPD), which is then employed as the candidate set for further discriminatory feature selection. The features are selected one by one in a boosting manner. After one feature being selected, the importance of the correctly classified samples based on the feature will be decreased, which is equivalent to increasing the importance of the misclassified samples. Therefore, a complement feature to the current features could be selected in the next run. The selected features are then fed to a classifier trained by random forest algorithm. Finally, a time series voting-based method is utilized to improve the classification performance. Comparisons between the DFFS-based approach and state-of-art methods on BCI competition IV data set 2b have been conducted, which have shown the superiority of the proposed algorithm.

  7. A Novel Approach of Understanding and Incorporating Error of Chemical Transport Models into a Geostatistical Framework

    NASA Astrophysics Data System (ADS)

    Reyes, J.; Vizuete, W.; Serre, M. L.; Xu, Y.

    2015-12-01

    The EPA employs a vast monitoring network to measure ambient PM2.5 concentrations across the United States with one of its goals being to quantify exposure within the population. However, there are several areas of the country with sparse monitoring spatially and temporally. One means to fill in these monitoring gaps is to use PM2.5 modeled estimates from Chemical Transport Models (CTMs) specifically the Community Multi-scale Air Quality (CMAQ) model. CMAQ is able to provide complete spatial coverage but is subject to systematic and random error due to model uncertainty. Due to the deterministic nature of CMAQ, often these uncertainties are not quantified. Much effort is employed to quantify the efficacy of these models through different metrics of model performance. Currently evaluation is specific to only locations with observed data. Multiyear studies across the United States are challenging because the error and model performance of CMAQ are not uniform over such large space/time domains. Error changes regionally and temporally. Because of the complex mix of species that constitute PM2.5, CMAQ error is also a function of increasing PM2.5 concentration. To address this issue we introduce a model performance evaluation for PM2.5 CMAQ that is regionalized and non-linear. This model performance evaluation leads to error quantification for each CMAQ grid. Areas and time periods of error being better qualified. The regionalized error correction approach is non-linear and is therefore more flexible at characterizing model performance than approaches that rely on linearity assumptions and assume homoscedasticity of CMAQ predictions errors. Corrected CMAQ data are then incorporated into the modern geostatistical framework of Bayesian Maximum Entropy (BME). Through cross validation it is shown that incorporating error-corrected CMAQ data leads to more accurate estimates than just using observed data by themselves.

  8. Prediction of drug-induced eosinophilia adverse effect by using SVM and naïve Bayesian approaches.

    PubMed

    Zhang, Hui; Yu, Peng; Xiang, Ming-Li; Li, Xi-Bo; Kong, Wei-Bao; Ma, Jun-Yi; Wang, Jun-Long; Zhang, Jin-Ping; Zhang, Ji

    2016-03-01

    Drug-induced eosinophilia is a potentially life-threatening adverse effect; clinical manifestations, eosinophilia-myalgia syndrome, mainly include severe skin eruption, fever, hematologic abnormalities, and organ system dysfunction. Using experimental methods to evaluate drug-induced eosinophilia is very complicated, time-consuming, and costly in the early stage of drug development. Thus, in this investigation, we established computational prediction models of drug-induced eosinophilia using SVM and naïve Bayesian approaches. For the SVM modeling, the overall prediction accuracy for the training set by means of fivefold cross-validation is 91.6 and for the external test set is 82.9 %. For the naïve Bayesian modeling, the overall prediction accuracy for the training set is 92.5 and for the external test set is 85.4 %. Moreover, some molecular descriptors and substructures considered as important for drug-induced eosinophilia were identified. Thus, we hope the prediction models of drug-induced eosinophilia built in this work should be applied to filter early-stage molecules for potential eosinophilia adverse effect, and the selected molecular descriptors and substructures of toxic compounds should be taken into consideration in the design of new candidate drugs to help medicinal chemists rationally select the chemicals with the best prospects to be effective and safe.

  9. Automatic approach to solve the morphological galaxy classification problem using the sparse representation technique and dictionary learning

    NASA Astrophysics Data System (ADS)

    Diaz-Hernandez, R.; Ortiz-Esquivel, A.; Peregrina-Barreto, H.; Altamirano-Robles, L.; Gonzalez-Bernal, J.

    2016-06-01

    The observation of celestial objects in the sky is a practice that helps astronomers to understand the way in which the Universe is structured. However, due to the large number of observed objects with modern telescopes, the analysis of these by hand is a difficult task. An important part in galaxy research is the morphological structure classification based on the Hubble sequence. In this research, we present an approach to solve the morphological galaxy classification problem in an automatic way by using the Sparse Representation technique and dictionary learning with K-SVD. For the tests in this work, we use a database of galaxies extracted from the Principal Galaxy Catalog (PGC) and the APM Equatorial Catalogue of Galaxies obtaining a total of 2403 useful galaxies. In order to represent each galaxy frame, we propose to calculate a set of 20 features such as Hu's invariant moments, galaxy nucleus eccentricity, gabor galaxy ratio and some other features commonly used in galaxy classification. A stage of feature relevance analysis was performed using Relief-f in order to determine which are the best parameters for the classification tests using 2, 3, 4, 5, 6 and 7 galaxy classes making signal vectors of different length values with the most important features. For the classification task, we use a 20-random cross-validation technique to evaluate classification accuracy with all signal sets achieving a score of 82.27 % for 2 galaxy classes and up to 44.27 % for 7 galaxy classes.

  10. Identify submitochondria and subchloroplast locations with pseudo amino acid composition: approach from the strategy of discrete wavelet transform feature extraction.

    PubMed

    Shi, Shao-Ping; Qiu, Jian-Ding; Sun, Xing-Yu; Huang, Jian-Hua; Huang, Shu-Yun; Suo, Sheng-Bao; Liang, Ru-Ping; Zhang, Li

    2011-03-01

    It is very challenging and complicated to predict protein locations at the sub-subcellular level. The key to enhancing the prediction quality for protein sub-subcellular locations is to grasp the core features of a protein that can discriminate among proteins with different subcompartment locations. In this study, a different formulation of pseudoamino acid composition by the approach of discrete wavelet transform feature extraction was developed to predict submitochondria and subchloroplast locations. As a result of jackknife cross-validation, with our method, it can efficiently distinguish mitochondrial proteins from chloroplast proteins with total accuracy of 98.8% and obtained a promising total accuracy of 93.38% for predicting submitochondria locations. Especially the predictive accuracy for mitochondrial outer membrane and chloroplast thylakoid lumen were 82.93% and 82.22%, respectively, showing an improvement of 4.88% and 27.22% when other existing methods were compared. The results indicated that the proposed method might be employed as a useful assistant technique for identifying sub-subcellular locations. We have implemented our algorithm as an online service called SubIdent (http://bioinfo.ncu.edu.cn/services.aspx).

  11. A rapid-screening approach to detect and quantify microplastics based on fluorescent tagging with Nile Red

    PubMed Central

    Maes, Thomas; Jessop, Rebecca; Wellner, Nikolaus; Haupt, Karsten; Mayes, Andrew G.

    2017-01-01

    A new approach is presented for analysis of microplastics in environmental samples, based on selective fluorescent staining using Nile Red (NR), followed by density-based extraction and filtration. The dye adsorbs onto plastic surfaces and renders them fluorescent when irradiated with blue light. Fluorescence emission is detected using simple photography through an orange filter. Image-analysis allows fluorescent particles to be identified and counted. Magnified images can be recorded and tiled to cover the whole filter area, allowing particles down to a few micrometres to be detected. The solvatochromic nature of Nile Red also offers the possibility of plastic categorisation based on surface polarity characteristics of identified particles. This article details the development of this staining method and its initial cross-validation by comparison with infrared (IR) microscopy. Microplastics of different sizes could be detected and counted in marine sediment samples. The fluorescence staining identified the same particles as those found by scanning a filter area with IR-microscopy. PMID:28300146

  12. Combined SERS and flow linear dichroism approach to monitoring the interaction of pharmaceuticals with their target

    NASA Astrophysics Data System (ADS)

    Ianoul, Anatoli I.; Fleury, Fabrice; Duval, Olivier; Jardillier, Jean-Claude; Alix, Alain J.; Nabiev, Igor R.

    1999-04-01

    Surface-Enhanced Raman Scattering (SERS) spectroscopy and Flow Linear Dichroism (FLD) technique have been employed to study the anticancer agent fagaronine and its derivative ethoxidine - double inhibitors of DNA topoisomerases I and II. Cooperative use of two methods permitted (i) to determine the molecular determinants of the drug-DNA interactions; (ii) to monitor in real time the process of topo I inhibition by these anticancer agents. FLD technique allowed us to identify the mode of drug interactions with the DNA as a 'major groove intercalation' and to determine orientation of the drugs chromophores within the complexes. Using SERS spectroscopy we have determined the drugs molecular determinants interacting with the DNA. FLD was also used for real time monitoring of the process of sc DNA relaxation by topo I and of inhibition of relaxation with the pharmaceuticals. Ethoxidine was found to exhibit the same activity of inhibition of sc DNA relaxation as fagaronine at the 10-fold less concentration. The proposed SERS-FLD combined approach demonstrates the new perspectives for screening new pharmaceuticals due to its relative simplicity and low expense, high sensitivity and selectivity, and, finally, possibility of real-time monitoring of the structure-function correlation within the series of drug derivatives.

  13. The Treatment of Differentiated Thyroid Cancer in Children: Emphasis on Surgical Approach and Radioactive Iodine Therapy

    PubMed Central

    Mazzaferri, Ernest L.; Verburg, Frederik A.; Reiners, Christoph; Luster, Markus; Breuer, Christopher K.; Dinauer, Catherine A.; Udelsman, Robert

    2011-01-01

    Pediatric thyroid cancer is a rare disease with an excellent prognosis. Compared with adults, epithelial-derived differentiated thyroid cancer (DTC), which includes papillary and follicular thyroid cancer, presents at more advanced stages in children and is associated with higher rates of recurrence. Because of its uncommon occurrence, randomized trials have not been applied to test best-care options in children. Even in adults that have a 10-fold or higher incidence of thyroid cancer than children, few prospective trials have been executed to compare treatment approaches. We recognize that treatment recommendations have changed over the past few decades and will continue to do so. Respecting the aggressiveness of pediatric thyroid cancer, high recurrence rates, and the problems associated with decades of long-term follow-up, a premium should be placed on treatments that minimize risk of recurrence and the adverse effects of treatments and facilitate follow-up. We recommend that total thyroidectomy and central compartment lymph node dissection is the surgical procedure of choice for children with DTC if it can be performed by a high-volume thyroid surgeon. We recommend radioactive iodine therapy for remnant ablation or residual disease for most children with DTC. We recommend long-term follow-up because disease can recur decades after initial diagnosis and therapy. Considering the complexity of DTC management and the potential complications associated with therapy, it is essential that pediatric DTC be managed by physicians with expertise in this area. PMID:21880704

  14. Some Approaches to Reading.

    ERIC Educational Resources Information Center

    Smith, Nila Banton; Strickland, Ruth

    This pamphlet discusses some beginning approaches and technological approaches to reading instruction, and the relationship between children's language and reading. The first section looks at several approaches to reading instruction: "The Language Experience Approach,""The Initial Teaching Alphabet,""Linguistic Approaches to Reading,""Programed…

  15. iLOGP: a simple, robust, and efficient description of n-octanol/water partition coefficient for drug design using the GB/SA approach.

    PubMed

    Daina, Antoine; Michielin, Olivier; Zoete, Vincent

    2014-12-22

    The n-octanol/water partition coefficient (log Po/w) is a key physicochemical parameter for drug discovery, design, and development. Here, we present a physics-based approach that shows a strong linear correlation between the computed solvation free energy in implicit solvents and the experimental log Po/w on a cleansed data set of more than 17,500 molecules. After internal validation by five-fold cross-validation and data randomization, the predictive power of the most interesting multiple linear model, based on two GB/SA parameters solely, was tested on two different external sets of molecules. On the Martel druglike test set, the predictive power of the best model (N = 706, r = 0.64, MAE = 1.18, and RMSE = 1.40) is similar to six well-established empirical methods. On the 17-drug test set, our model outperformed all compared empirical methodologies (N = 17, r = 0.94, MAE = 0.38, and RMSE = 0.52). The physical basis of our original GB/SA approach together with its predictive capacity, computational efficiency (1 to 2 s per molecule), and tridimensional molecular graphics capability lay the foundations for a promising predictor, the implicit log P method (iLOGP), to complement the portfolio of drug design tools developed and provided by the SIB Swiss Institute of Bioinformatics.

  16. Biomedical system based on the Discrete Hidden Markov Model using the Rocchio-Genetic approach for the classification of internal carotid artery Doppler signals.

    PubMed

    Uğuz, Harun; Güraksın, Gür Emre; Ergün, Uçman; Saraçoğlu, Rıdvan

    2011-07-01

    When the maximum likelihood approach (ML) is used during the calculation of the Discrete Hidden Markov Model (DHMM) parameters, DHMM parameters of the each class are only calculated using the training samples (positive training samples) of the same class. The training samples (negative training samples) not belonging to that class are not used in the calculation of DHMM model parameters. With the aim of supplying that deficiency, by involving the training samples of all classes in calculating processes, a Rocchio algorithm based approach is suggested. During the calculation period, in order to determine the most appropriate values of parameters for adjusting the relative effect of the positive and negative training samples, a Genetic algorithm is used as an optimization technique. The purposed method is used to classify the internal carotid artery Doppler signals recorded from 136 patients as well as of 55 healthy people. Our proposed method reached 97.38% classification accuracy with fivefold cross-validation (CV) technique. The classification results showed that the proposed method was effective for the classification of internal carotid artery Doppler signals.

  17. An Approach to Developing a Prediction Model of Fertility Intent Among HIV-Positive Women and Men in Cape Town, South Africa: A Case Study.

    PubMed

    Bai, Dan; Leu, Cheng-Shiun; Mantell, Joanne E; Exner, Theresa M; Cooper, Diane; Hoffman, Susie; Kelvin, Elizabeth A; Myer, Landon; Constant, Debbie; Moodley, Jennifer

    2017-02-01

    As a 'case-study' to demonstrate an approach to establishing a fertility-intent prediction model, we used data collected from recently diagnosed HIV-positive women (N = 69) and men (N = 55) who reported inconsistent condom use and were enrolled in a sexual and reproductive health intervention in public sector HIV care clinics in Cape Town, South Africa. Three theoretically-driven prediction models showed reasonable sensitivity (0.70-1.00), specificity (0.66-0.94), and area under the receiver operating characteristic curve (0.79-0.89) for predicting fertility intent at the 6-month visit. A k-fold cross-validation approach was employed to reduce bias due to over-fitting of data in estimating sensitivity, specificity, and area under the curve. We discuss how the methods presented might be used in future studies to develop a clinical screening tool to identify HIV-positive individuals likely to have future fertility intent and who could therefore benefit from sexual and reproductive health counseling around fertility options.

  18. Identification of potential Gly/NMDA receptor antagonists by cheminformatics approach: a combination of pharmacophore modelling, virtual screening and molecular docking studies.

    PubMed

    Ugale, V G; Bari, S B

    2016-01-01

    The Gly/NMDA receptor has become known as potential target for the management of neurodegenerative diseases. Discovery of Gly/NMDA antagonists has thus attracted much attention in recent years. In the present research, a cheminformatics approach has been used to determine structural requirements for Gly/NMDA antagonism and to identify potential antagonists. Here, 37 quinoxaline derivatives were selected to develop a significant pharmacophore model with good certainty. The selected model was validated by leave-one-out cross-validation, an external test set, decoy set and Y-randomization test. Applicability domain was verified by the standardization approach. The validated 3D-QSAR model was used to screen virtual hits from the ZINC database by pharmacophore mapping. Molecular docking was used for assessment of receptor-ligand binding modes and binding affinities. The GlideScore and molecular interactions with critical amino acids were considered as crucial features to identify final hits. Furthermore, hits were analysed for in silico pharmacokinetic parameters and Lipinski's rule of five, demonstrating their potential as drug-like candidates. The PubChem and SciFinder search tools were used to authenticate the novelty of leads retrieved. Finally, five different leads have been suggested as putative novel candidates for the exploration of potent Gly/NMDA receptor antagonists.

  19. Homology modeling and virtual screening of inhibitors against TEM- and SHV-type-resistant mutants: A multilayer filtering approach.

    PubMed

    Baig, Mohammad H; Balaramnavar, Vishal M; Wadhwa, Gulshan; Khan, Asad U

    2015-01-01

    TEM and SHV are class-A-type β-lactamases commonly found in Escherichia coli and Klebsiella pneumoniae. Previous studies reported S130G and K234R mutations in SHVs to be 41- and 10-fold more resistant toward clavulanic acid than SHV-1, respectively, whereas TEM S130G and R244S also showed the same level of resistance. These selected mutants confer higher level of resistance against clavulanic acid. They also show little susceptibility against other commercially available β-lactamase inhibitors. In this study, we have used docking-based virtual screening approach in order to screen potential inhibitors against some of the major resistant mutants of SHV and TEM types β-lactamase. Two different inhibitor-resistant mutants from SHV and TEM were selected. Moreover, we have retained the active site water molecules within each enzyme. Active site water molecules were placed within modeled structure of the mutant whose structure was unavailable with protein databank. The novelty of this work lies in the use of multilayer virtual screening approach for the prediction of best and accurate results. We are reporting five inhibitors on the basis of their efficacy against all the selected resistant mutants. These inhibitors were selected on the basis of their binding efficacies and pharmacophore features.

  20. The Cross-Validation of the United States Air Force Submaximal Cycle Ergometer Test to Estimate Aerobic Capacity

    DTIC Science & Technology

    1994-06-01

    and Febiger, 199’. 2. Arts, F.JP., H. Kuipers , A.E. Jeukendrup, et al. A short cycle ergometry test to predict workload and maximal oxygen uptake. Int...133Description of Monark Cycle Ergometer Calibration Monark Cycle Ergometer calibration is achieved by first turning the resistance belt to zero on the free

  1. The Beck Depression Inventory: A Cross-Validated Test of Second-Order Factorial Structure for Bulgarian Adolescents.

    ERIC Educational Resources Information Center

    Byrne, Barbara M.; Baron, Pierre; Balev, Jorj

    1998-01-01

    The validity of a higher order factorial structure of a Bulgarian version of the Beck Depression Inventory (BDI) (A. Beck et al, 1961) for nonclinical adolescents was studied with 3 samples totaling 691. Findings yielded strong support for the hypothesized second-order factor, adding to a growing cross-cultural agglomerate of construct validity…

  2. A fast cross-validation method for alignment of electron tomography images based on Beer-Lambert law.

    PubMed

    Yan, Rui; Edwards, Thomas J; Pankratz, Logan M; Kuhn, Richard J; Lanman, Jason K; Liu, Jun; Jiang, Wen

    2015-11-01

    In electron tomography, accurate alignment of tilt series is an essential step in attaining high-resolution 3D reconstructions. Nevertheless, quantitative assessment of alignment quality has remained a challenging issue, even though many alignment methods have been reported. Here, we report a fast and accurate method, tomoAlignEval, based on the Beer-Lambert law, for the evaluation of alignment quality. Our method is able to globally estimate the alignment accuracy by measuring the goodness of log-linear relationship of the beam intensity attenuations at different tilt angles. Extensive tests with experimental data demonstrated its robust performance with stained and cryo samples. Our method is not only significantly faster but also more sensitive than measurements of tomogram resolution using Fourier shell correlation method (FSCe/o). From these tests, we also conclude that while current alignment methods are sufficiently accurate for stained samples, inaccurate alignments remain a major limitation for high resolution cryo-electron tomography.

  3. Cross-Validation of the YMCA Submaximal Cycle Ergometer Test to Predict V[o.sub.2] Max

    ERIC Educational Resources Information Center

    Beekley, Matthew D.; Brechue, William F.; deHoyos, Diego V.; Garzarella, Linda; Werber-Zion, Galila; Pollock, Michael L.

    2004-01-01

    Maximal oxygen uptake (V[O.sub.2]max) is an important indicator of health-risk status, specifically for coronary heart disease (Blair et al., 1989). Direct measurement of V[O.sub.2]max is considered to be the most accurate means of determining cardiovascular fitness level. Typically, this measurement is taken using a progressive exercise test on a…

  4. Two-receiver measurements of phase velocity: cross-validation of ambient-noise and earthquake-based observations

    NASA Astrophysics Data System (ADS)

    Kästle, Emanuel D.; Soomro, Riaz; Weemstra, Cornelis; Boschi, Lapo; Meier, Thomas

    2016-12-01

    Phase velocities derived from ambient-noise cross-correlation are compared with phase velocities calculated from cross-correlations of waveform recordings of teleseismic earthquakes whose epicentres are approximately on the station-station great circle. The comparison is conducted both for Rayleigh and Love waves using over 1000 station pairs in central Europe. We describe in detail our signal-processing method which allows for automated processing of large amounts of data. Ambient-noise data are collected in the 5-80 s period range, whereas teleseismic data are available between about 8 and 250 s, resulting in a broad common period range between 8 and 80 s. At intermediate periods around 30 s and for shorter interstation distances, phase velocities measured from ambient noise are on average between 0.5 per cent and 1.5 per cent lower than those observed via the earthquake-based method. This discrepancy is small compared to typical phase-velocity heterogeneities (10 per cent peak-to-peak or more) observed in this period range.We nevertheless conduct a suite of synthetic tests to evaluate whether known biases in ambient-noise cross-correlation measurements could account for this discrepancy; we specifically evaluate the effects of heterogeneities in source distribution, of azimuthal anisotropy in surface-wave velocity and of the presence of near-field, rather than far-field only, sources of seismic noise. We find that these effects can be quite important comparing individual station pairs. The systematic discrepancy is presumably due to a combination of factors, related to differences in sensitivity of earthquake versus noise data to lateral heterogeneity. The data sets from both methods are used to create some preliminary tomographic maps that are characterized by velocity heterogeneities of similar amplitude and pattern, confirming the overall agreement between the two measurement methods.

  5. Cross validation of geotechnical and geophysical site characterization methods: near surface data from selected accelerometric stations in Crete (Greece)

    NASA Astrophysics Data System (ADS)

    Loupasakis, C.; Tsangaratos, P.; Rozos, D.; Rondoyianni, Th.; Vafidis, A.; Kritikakis, G.; Steiakakis, M.; Agioutantis, Z.; Savvaidis, A.; Soupios, P.; Papadopoulos, I.; Papadopoulos, N.; Sarris, A.; Mangriotis, M.-D.; Dikmen, U.

    2015-06-01

    The specification of the near surface ground conditions is highly important for the design of civil constructions. These conditions determine primarily the ability of the foundation formations to bear loads, the stress - strain relations and the corresponding settlements, as well as the soil amplification and corresponding peak ground motion in case of dynamic loading. The static and dynamic geotechnical parameters as well as the ground-type/soil-category can be determined by combining geotechnical and geophysical methods, such as engineering geological surface mapping, geotechnical drilling, in situ and laboratory testing and geophysical investigations. The above mentioned methods were combined, through the Thalis ″Geo-Characterization″ project, for the site characterization in selected sites of the Hellenic Accelerometric Network (HAN) in the area of Crete Island. The combination of the geotechnical and geophysical methods in thirteen (13) sites provided sufficient information about their limitations, setting up the minimum tests requirements in relation to the type of the geological formations. The reduced accuracy of the surface mapping in urban sites, the uncertainties introduced by the geophysical survey in sites with complex geology and the 1D data provided by the geotechnical drills are some of the causes affecting the right order and the quantity of the necessary investigation methods. Through this study the gradual improvement on the accuracy of site characterization data is going to be presented by providing characteristic examples from a total number of thirteen sites. Selected examples present sufficiently the ability, the limitations and the right order of the investigation methods.

  6. Suicidal Ideation, Parent-Child Relationships, and Adverse Childhood Experiences: A Cross-Validation Study Using a Graphical Markov Model

    ERIC Educational Resources Information Center

    Hardt, Jochen; Herke, Max; Schier, Katarzyna

    2011-01-01

    Suicide is one of the leading causes of death in many Western countries. An exploration of factors associated with suicidality may help to understand the mechanisms that lead to suicide. Two samples in Germany (n = 500 and n = 477) were examined via Internet regarding suicidality, depression, alcohol abuse, adverse childhood experiences, and…

  7. A fast cross-validation method for alignment of electron tomography images based on Beer-Lambert law

    PubMed Central

    Yan, Rui; Edwards, Thomas J.; Pankratz, Logan M.; Kuhn, Richard J.; Lanman, Jason K.; Liu, Jun; Jiang, Wen

    2015-01-01

    In electron tomography, accurate alignment of tilt series is an essential step in attaining high-resolution 3D reconstructions. Nevertheless, quantitative assessment of alignment quality has remained a challenging issue, even though many alignment methods have been reported. Here, we report a fast and accurate method, tomoAlignEval, based on the Beer-Lambert law, for the evaluation of alignment quality. Our method is able to globally estimate the alignment accuracy by measuring the goodness of log-linear relationship of the beam intensity attenuations at different tilt angles. Extensive tests with experimental data demonstrated its robust performance with stained and cryo samples. Our method is not only significantly faster but also more sensitive than measurements of tomogram resolution using Fourier shell correlation method (FSCe/o). From these tests, we also conclude that while current alignment methods are sufficiently accurate for stained samples, inaccurate alignments remain a major limitation for high resolution cryo-electron tomography. PMID:26455556

  8. The Relationships Between Low-Inference Measures of Classroom Behavior and Pupil Growth: A Cross-Validation.

    ERIC Educational Resources Information Center

    Lorentz, Jeffrey L.; Coker, Homer

    As part of the Competency Based Teacher Certification Project in Carroll County, Georgia, large samples of elementary and secondary school teachers and students were observed during a two-year period. Four low-inference observation measures were used to record teacher behaviors and student-teacher interactions: (1) Teacher Practices Observation…

  9. Anticipating Mathematics Performance: A Cross-Validation Comparison of AID3 and Regression. AIR 1988 Annual Forum Paper.

    ERIC Educational Resources Information Center

    Bloom, Allan M.; And Others

    In response to the increasing importance of student performance in required classes, research was conducted to compare two prediction procedures, linear modeling using multiple regression and nonlinear modeling using AID3. Performance in the first college math course (College Mathematics, Calculus, or Business Calculus Matrices) was the dependent…

  10. Development of a human dihydroorotate dehydrogenase (hDHODH) pharma-similarity index approach with scaffold-hopping strategy for the design of novel potential inhibitors.

    PubMed

    Shih, Kuei-Chung; Lee, Chi-Ching; Tsai, Chi-Neu; Lin, Yu-Shan; Tang, Chuan-Yi

    2014-01-01

    Human dihydroorotate dehydrogenase (hDHODH) is a class-2 dihydroorotate dehydrogenase. Because it is extensively used by proliferating cells, its inhibition in autoimmune and inflammatory diseases, cancers, and multiple sclerosis is of substantial clinical importance. In this study, we had two aims. The first was to develop an hDHODH pharma-similarity index approach (PhSIA) using integrated molecular dynamics calculations, pharmacophore hypothesis, and comparative molecular similarity index analysis (CoMSIA) contour information techniques. The approach, for the discovery and design of novel inhibitors, was based on 25 diverse known hDHODH inhibitors. Three statistical methods were used to verify the performance of hDHODH PhSIA. Fischer's cross-validation test provided a 98% confidence level and the goodness of hit (GH) test score was 0.61. The q(2), r(2), and predictive r(2) values were 0.55, 0.97, and 0.92, respectively, for a partial least squares validation method. In our approach, each diverse inhibitor structure could easily be aligned with contour information, and common substructures were unnecessary. For our second aim, we used the proposed approach to design 13 novel hDHODH inhibitors using a scaffold-hopping strategy. Chemical features of the approach were divided into two groups, and the Vitas-M Laboratory fragment was used to create de novo inhibitors. This approach provides a useful tool for the discovery and design of potential inhibitors of hDHODH, and does not require docking analysis; thus, our method can assist medicinal chemists in their efforts to identify novel inhibitors.

  11. A machine learning approach to investigate the relationship between shape features and numerically predicted risk of ascending aortic aneurysm.

    PubMed

    Liang, Liang; Liu, Minliang; Martin, Caitlin; Elefteriades, John A; Sun, Wei

    2017-04-06

    Geometric features of the aorta are linked to patient risk of rupture in the clinical decision to electively repair an ascending aortic aneurysm (AsAA). Previous approaches have focused on relationship between intuitive geometric features (e.g., diameter and curvature) and wall stress. This work investigates the feasibility of a machine learning approach to establish the linkages between shape features and FEA-predicted AsAA rupture risk, and it may serve as a faster surrogate for FEA associated with long simulation time and numerical convergence issues. This method consists of four main steps: (1) constructing a statistical shape model (SSM) from clinical 3D CT images of AsAA patients; (2) generating a dataset of representative aneurysm shapes and obtaining FEA-predicted risk scores defined as systolic pressure divided by rupture pressure (rupture is determined by a threshold criterion); (3) establishing relationship between shape features and risk by using classifiers and regressors; and (4) evaluating such relationship in cross-validation. The results show that SSM parameters can be used as strong shape features to make predictions of risk scores consistent with FEA, which lead to an average risk classification accuracy of 95.58% by using support vector machine and an average regression error of 0.0332 by using support vector regression, while intuitive geometric features have relatively weak performance. Compared to FEA, this machine learning approach is magnitudes faster. In our future studies, material properties and inhomogeneous thickness will be incorporated into the models and learning algorithms, which may lead to a practical system for clinical applications.

  12. Intelligent Data Analysis: the Best Approach for Chronic Heart Failure (CHF) Follow Up Management

    PubMed Central

    Mohammadzadeh, Niloofar; Safdari, Reza; Baraani, Alireza; Mohammadzadeh, Farshid

    2014-01-01

    Objective: Intelligent data analysis has ability to prepare and present complex relations between symptoms and diseases, medical and treatment consequences and definitely has significant role in improving follow-up management of chronic heart failure (CHF) patients, increasing speed ​​and accuracy in diagnosis and treatments; reducing costs, designing and implementation of clinical guidelines. The aim: The aim of this article is to describe intelligent data analysis methods in order to improve patient monitoring in follow and treatment of chronic heart failure patients as the best approach for CHF follow up management. Methods: Minimum data set (MDS) requirements for monitoring and follow up of CHF patient designed in checklist with six main parts. All CHF patients that discharged in 2013 from Tehran heart center have been selected. The MDS for monitoring CHF patient status were collected during 5 months in three different times of follow up. Gathered data was imported in RAPIDMINER 5 software. Results: Modeling was based on decision trees methods such as C4.5, CHAID, ID3 and k-Nearest Neighbors algorithm (K-NN) with k=1. Final analysis was based on voting method. Decision trees and K-NN evaluate according to Cross-Validation. Conclusion: Creating and using standard terminologies and databases consistent with these terminologies help to meet the challenges related to data collection from various places and data application in intelligent data analysis. It should be noted that intelligent analysis of health data and intelligent system can never replace cardiologists. It can only act as a helpful tool for the cardiologist’s decisions making. PMID:25395730

  13. Smart-card-based automatic meal record system intervention tool for analysis using data mining approach.

    PubMed

    Zenitani, Satoko; Nishiuchi, Hiromu; Kiuchi, Takahiro

    2010-04-01

    The Smart-card-based Automatic Meal Record system for company cafeterias (AutoMealRecord system) was recently developed and used to monitor employee eating habits. The system could be a unique nutrition assessment tool for automatically monitoring the meal purchases of all employees, although it only focuses on company cafeterias and has never been validated. Before starting an interventional study, we tested the reliability of the data collected by the system using the data mining approach. The AutoMealRecord data were examined to determine if it could predict current obesity. All data used in this study (n = 899) were collected by a major electric company based in Tokyo, which has been operating the AutoMealRecord system for several years. We analyzed dietary patterns by principal component analysis using data from the system and extracted 5 major dietary patterns: healthy, traditional Japanese, Chinese, Japanese noodles, and pasta. The ability to predict current body mass index (BMI) with dietary preference was assessed with multiple linear regression analyses, and in the current study, BMI was positively correlated with male gender, preference for "Japanese noodles," mean energy intake, protein content, and frequency of body measurement at a body measurement booth in the cafeteria. There was a negative correlation with age, dietary fiber, and lunchtime cafeteria use (R(2) = 0.22). This regression model predicted "would-be obese" participants (BMI >or= 23) with 68.8% accuracy by leave-one-out cross validation. This shows that there was sufficient predictability of BMI based on data from the AutoMealRecord System. We conclude that the AutoMealRecord system is valuable for further consideration as a health care intervention tool.

  14. A pragmatic approach to estimate the number of days in exceedance of PM10 limit value

    NASA Astrophysics Data System (ADS)

    Beauchamp, Maxime; Malherbe, Laure; de Fouquet, Chantal

    2015-06-01

    European legislation on ambient air quality requests that Member States report the annual number of exceedances of short-term concentration regulatory thresholds for PM10 and delimit the concerned areas. Measurements at the monitoring stations do not allow to fully describe those areas. We present a methodology to estimate the number of exceedances of the daily limit value over a year, that can be extended to any similar issue. This methodology is applied to PM10 concentrations in France for which the daily limit value is 50 μg m-3, not to be exceeded more that 35 days. A probabilistic model is built using preliminary mapping of daily mean concentrations. First, daily atmospheric concentration fields are estimated at 1 km resolution by external drift kriging, combining surface monitoring observations and outputs from the CHIMERE chemistry transport model. Setting a conventional Gaussian hypothesis for the estimation error, the kriging variance is used to compute the probability of exceeding the daily limit value and to identify three areas: those where we can suppose as certain that the concentrations exceed or not the daily limit value and those where the situation is indeterminate because of the estimation uncertainty. Then, from the set of 365 daily mappings of the probability to exceed the daily limit value, the parameters of a translated Poisson distribution is fitted on the annual number of exceedances of the daily limit value at each grid cell, which enables to compute the probability for this number to exceed 35. The methodology is tested for three years (2007, 2009 and 2011) which present numerous exceedances of the daily limit concentration at some monitoring stations. A cross-validation analysis is carried out to check the efficiency of the methodology. The way to interpret probability maps is discussed. A comparison is made with simpler kriging approaches using indicator kriging of exceedances. Lastly, estimation of the population exposed to PM10

  15. Advances in the regionalization approach: geostatistical techniques for estimating flood quantiles

    NASA Astrophysics Data System (ADS)

    Chiarello, Valentina; Caporali, Enrica; Matthies, Hermann G.

    2015-04-01

    The knowledge of peak flow discharges and associated floods is of primary importance in engineering practice for planning of water resources and risk assessment. Streamflow characteristics are usually estimated starting from measurements of river discharges at stream gauging stations. However, the lack of observations at site of interest as well as the measurement inaccuracies, bring inevitably to the necessity of developing predictive models. Regional analysis is a classical approach to estimate river flow characteristics at sites where little or no data exists. Specific techniques are needed to regionalize the hydrological variables over the considered area. Top-kriging or topological kriging, is a kriging interpolation procedure that takes into account the geometric organization and structure of hydrographic network, the catchment area and the nested nature of catchments. The continuous processes in space defined for the point variables are represented by a variogram. In Top-kriging, the measurements are not point values but are defined over a non-zero catchment area. Top-kriging is applied here over the geographical space of Tuscany Region, in Central Italy. The analysis is carried out on the discharge data of 57 consistent runoff gauges, recorded from 1923 to 2014. Top-kriging give also an estimation of the prediction uncertainty in addition to the prediction itself. The results are validated using a cross-validation procedure implemented in the package rtop of the open source statistical environment R The results are compared through different error measurement methods. Top-kriging seems to perform better in nested catchments and larger scale catchments but no for headwater or where there is a high variability for neighbouring catchments.

  16. Time series segmentation: a new approach based on Genetic Algorithm and Hidden Markov Model

    NASA Astrophysics Data System (ADS)

    Toreti, A.; Kuglitsch, F. G.; Xoplaki, E.; Luterbacher, J.

    2009-04-01

    The subdivision of a time series into homogeneous segments has been performed using various methods applied to different disciplines. In climatology, for example, it is accompanied by the well-known homogenization problem and the detection of artificial change points. In this context, we present a new method (GAMM) based on Hidden Markov Model (HMM) and Genetic Algorithm (GA), applicable to series of independent observations (and easily adaptable to autoregressive processes). A left-to-right hidden Markov model, estimating the parameters and the best-state sequence, respectively, with the Baum-Welch and Viterbi algorithms, was applied. In order to avoid the well-known dependence of the Baum-Welch algorithm on the initial condition, a Genetic Algorithm was developed. This algorithm is characterized by mutation, elitism and a crossover procedure implemented with some restrictive rules. Moreover the function to be minimized was derived following the approach of Kehagias (2004), i.e. it is the so-called complete log-likelihood. The number of states was determined applying a two-fold cross-validation procedure (Celeux and Durand, 2008). Being aware that the last issue is complex, and it influences all the analysis, a Multi Response Permutation Procedure (MRPP; Mielke et al., 1981) was inserted. It tests the model with K+1 states (where K is the state number of the best model) if its likelihood is close to K-state model. Finally, an evaluation of the GAMM performances, applied as a break detection method in the field of climate time series homogenization, is shown. 1. G. Celeux and J.B. Durand, Comput Stat 2008. 2. A. Kehagias, Stoch Envir Res 2004. 3. P.W. Mielke, K.J. Berry, G.W. Brier, Monthly Wea Rev 1981.

  17. A volatolomic approach for studying plant variability: the case of selected Helichrysum species (Asteraceae).

    PubMed

    Giuliani, Claudia; Lazzaro, Lorenzo; Calamassi, Roberto; Calamai, Luca; Romoli, Riccardo; Fico, Gelsomina; Foggi, Bruno; Mariotti Lippi, Marta

    2016-10-01

    The species of Helichrysum sect. Stoechadina (Asteraceae) are well-known for their secondary metabolite content and the characteristic aromatic bouquets. In the wild, populations exhibit a wide phenotypic plasticity which makes critical the circumscription of species and infraspecific ranks. Previous investigations on Helichrysum italicum complex focused on a possible phytochemical typification based on hydrodistilled essential oils. Aims of this paper are three-fold: (i) characterizing the volatile profiles of different populations, testing (ii) how these profiles vary across populations and (iii) how the phytochemical diversity may contribute in solving taxonomic problems. Nine selected Helichrysum populations, included within the H. italicum complex, Helichrysum litoreum and Helichrysum stoechas, were investigated. H. stoechas was chosen as outgroup for validating the method. After collection in the wild, plants were cultivated in standard growing conditions for over one year. Annual leafy shoots were screened in the post-blooming period for the emissions of volatile organic compounds (VOCs) by means of headspace solid phase microextraction coupled with gas-chromatography and mass spectrometry (HS-SPME-GC/MS). The VOC composition analysis revealed the production of overall 386 different compounds, with terpenes being the most represented compound class. Statistical data processing allowed the identification of the indicator compounds that differentiate the single populations, revealing the influence of the geographical provenance area in determining the volatile profiles. These results suggested the potential use of VOCs as valuable diacritical characters in discriminating the Helichrysum populations. In addition, the cross-validation analysis hinted the potentiality of this volatolomic study in the discrimination of the Helichrysum species and subspecies, highlighting a general congruence with the current taxonomic treatment of the genus. The consistency

  18. A local space–time kriging approach applied to a national outpatient malaria data set

    PubMed Central

    Gething, P.W.; Atkinson, P.M.; Noor, A.M.; Gikandi, P.W.; Hay, S.I.; Nixon, M.S.

    2007-01-01

    Increases in the availability of reliable health data are widely recognised as essential for efforts to strengthen health-care systems in resource-poor settings worldwide. Effective health-system planning requires comprehensive and up-to-date information on a range of health metrics and this requirement is generally addressed by a Health Management Information System (HMIS) that coordinates the routine collection of data at individual health facilities and their compilation into national databases. In many resource-poor settings, these systems are inadequate and national databases often contain only a small proportion of the expected records. In this paper, we take an important health metric in Kenya (the proportion of outpatient treatments for malaria (MP)) from the national HMIS database and predict the values of MP at facilities where monthly records are missing. The available MP data were densely distributed across a spatiotemporal domain and displayed second-order heterogeneity. We used three different kriging methodologies to make cross-validation predictions of MP in order to test the effect on prediction accuracy of (a) the extension of a spatial-only to a space–time prediction approach, and (b) the replacement of a globally stationary with a locally varying random function model. Space–time kriging was found to produce predictions with 98.4% less mean bias and 14.8% smaller mean imprecision than conventional spatial-only kriging. A modification of space–time kriging that allowed space–time variograms to be recalculated for every prediction location within a spatially local neighbourhood resulted in a larger decrease in mean imprecision over ordinary kriging (18.3%) although the mean bias was reduced less (87.5%). PMID:19424510

  19. A Machine Learning Approach to Estimate Riverbank Geotechnical Parameters from Sediment Particle Size Data

    NASA Astrophysics Data System (ADS)

    Iwashita, Fabio; Brooks, Andrew; Spencer, John; Borombovits, Daniel; Curwen, Graeme; Olley, Jon

    2015-04-01

    Assessing bank stability using geotechnical models traditionally involves the laborious collection of data on the bank and floodplain stratigraphy, as well as in-situ geotechnical data for each sedimentary unit within a river bank. The application of geotechnical bank stability models are limited to those sites where extensive field data has been collected, where their ability to provide predictions of bank erosion at the reach scale are limited without a very extensive and expensive field data collection program. Some challenges in the construction and application of riverbank erosion and hydraulic numerical models are their one-dimensionality, steady-state requirements, lack of calibration data, and nonuniqueness. Also, numerical models commonly can be too rigid with respect to detecting unexpected features like the onset of trends, non-linear relations, or patterns restricted to sub-samples of a data set. These shortcomings create the need for an alternate modelling approach capable of using available data. The application of the Self-Organizing Maps (SOM) approach is well-suited to the analysis of noisy, sparse, nonlinear, multidimensional, and scale-dependent data. It is a type of unsupervised artificial neural network with hybrid competitive-cooperative learning. In this work we present a method that uses a database of geotechnical data collected at over 100 sites throughout Queensland State, Australia, to develop a modelling approach that enables geotechnical parameters (soil effective cohesion, friction angle, soil erodibility and critical stress) to be derived from sediment particle size data (PSD). The model framework and predicted values were evaluated using two methods, splitting the dataset into training and validation set, and through a Bootstrap approach. The basis of Bootstrap cross-validation is a leave-one-out strategy. This requires leaving one data value out of the training set while creating a new SOM to estimate that missing value based on the

  20. Markov blanket-based approach for learning multi-dimensional Bayesian network classifiers: an application to predict the European Quality of Life-5 Dimensions (EQ-5D) from the 39-item Parkinson's Disease Questionnaire (PDQ-39).

    PubMed

    Borchani, Hanen; Bielza, Concha; Martı Nez-Martı N, Pablo; Larrañaga, Pedro

    2012-12-01

    Multi-dimensional Bayesian network classifiers (MBCs) are probabilistic graphical models recently proposed to deal with multi-dimensional classification problems, where each instance in the data set has to be assigned to more than one class variable. In this paper, we propose a Markov blanket-based approach for learning MBCs from data. Basically, it consists of determining the Markov blanket around each class variable using the HITON algorithm, then specifying the directionality over the MBC subgraphs. Our approach is applied to the prediction problem of the European Quality of Life-5 Dimensions (EQ-5D) from the 39-item Parkinson's Disease Questionnaire (PDQ-39) in order to estimate the health-related quality of life of Parkinson's patients. Fivefold cross-validation experiments were carried out on randomly generated synthetic data sets, Yeast data set, as well as on a real-world Parkinson's disease data set containing 488 patients. The experimental study, including comparison with additional Bayesian network-based approaches, back propagation for multi-label learning, multi-label k-nearest neighbor, multinomial logistic regression, ordinary least squares, and censored least absolute deviations, shows encouraging results in terms of predictive accuracy as well as the identification of dependence relationships among class and feature variables.

  1. Bifactor Approach to Modeling Multidimensionality of Physical Self-Perception Profile

    ERIC Educational Resources Information Center

    Chung, ChihMing; Liao, Xiaolan; Song, Hairong; Lee, Taehun

    2016-01-01

    The multi-dimensionality of Physical Self-Perception Profile (PSPP) has been acknowledged by the use of correlated-factor model and second-order model. In this study, the authors critically endorse the bifactor model, as a substitute to address the multi-dimensionality of PSPP. To cross-validate the models, analyses are conducted first in…

  2. A new approach for simultaneous screening and quantification of toxic pyrrolizidine alkaloids in some potential pyrrolizidine alkaloid-containing plants by using ultra performance liquid chromatography-tandem quadrupole mass spectrometry.

    PubMed

    Zhou, Yan; Li, Na; Choi, Franky Fung-Kei; Qiao, Chun-Feng; Song, Jing-Zheng; Li, Song-Lin; Liu, Xin; Cai, Zong-Wei; Fu, Peter P; Lin, Ge; Xu, Hong-Xi

    2010-11-29

    A rapid, but sensitive and selective method for simultaneous screening and quantification of toxic pyrrolizidine alkaloids (PAs) by ultra performance liquid-chromatography (UPLC) coupled with tandem mass spectrometry (MS/MS) on a tandem quadrupole mass spectrometer (TQ-MS) is described. This was accomplished by incorporating the precursor ion scan (PIS) acquisition and multiple reaction monitoring (MRM) acquisition in the same UPLC-MS/MS run. Notably, the developed PIS approach for detecting two pairs of characteristic product ions at m/z 120/138 or 168/150, allowed specific identification of toxic retronecine and otonecine types PAs. This PIS method is highly sensitive with over 10-fold sensitivity improvement upon previously published LC-MS method. Moreover, this new approach is suitable for high sample throughput and was applied to the screening and quantifying toxic PAs in 22 samples collected from seven Parasenecio species and four Senecio species. In addition, coupling the MRM with PIS approach generated quantitative results equivalent to those obtained by conventional MRM-only approach. This coupled MRM with PIS approach could provide both qualitative and quantitative results without the need of repetitive analyses.

  3. Unbiased estimation of chloroplast number in mesophyll cells: advantage of a genuine three-dimensional approach

    PubMed Central

    Kubínová, Zuzana

    2014-01-01

    Chloroplast number per cell is a frequently examined quantitative anatomical parameter, often estimated by counting chloroplast profiles in two-dimensional (2D) sections of mesophyll cells. However, a mesophyll cell is a three-dimensional (3D) structure and this has to be taken into account when quantifying its internal structure. We compared 2D and 3D approaches to chloroplast counting from different points of view: (i) in practical measurements of mesophyll cells of Norway spruce needles, (ii) in a 3D model of a mesophyll cell with chloroplasts, and (iii) using a theoretical analysis. We applied, for the first time, the stereological method of an optical disector based on counting chloroplasts in stacks of spruce needle optical cross-sections acquired by confocal laser-scanning microscopy. This estimate was compared with counting chloroplast profiles in 2D sections from the same stacks of sections. Comparing practical measurements of mesophyll cells, calculations performed in a 3D model of a cell with chloroplasts as well as a theoretical analysis showed that the 2D approach yielded biased results, while the underestimation could be up to 10-fold. We proved that the frequently used method for counting chloroplasts in a mesophyll cell by counting their profiles in 2D sections did not give correct results. We concluded that the present disector method can be efficiently used for unbiased estimation of chloroplast number per mesophyll cell. This should be the method of choice, especially in coniferous needles and leaves with mesophyll cells with lignified cell walls where maceration methods are difficult or impossible to use. PMID:24336344

  4. Complementary Health Approaches

    MedlinePlus

    ... on some complementary approaches, such as acupuncture and yoga, but there have been fewer studies on other approaches, so much less is known about them. The National Institutes of Health (NIH) is sponsoring research to learn more about ...

  5. Morpheus Surface Approach

    NASA Video Gallery

    This animation shows the Project Morpheus lander flying a kilometer-long simulated surface approach while avoiding hazards in a landing field. The approach takes place at the Shuttle Landing Facili...

  6. A similarity learning approach to content-based image retrieval: application to digital mammography.

    PubMed

    El-Naqa, Issam; Yang, Yongyi; Galatsanos, Nikolas P; Nishikawa, Robert M; Wernick, Miles N

    2004-10-01

    In this paper, we describe an approach to content-based retrieval of medical images from a database, and provide a preliminary demonstration of our approach as applied to retrieval of digital mammograms. Content-based image retrieval (CBIR) refers to the retrieval of images from a database using information derived from the images themselves, rather than solely from accompanying text indices. In the medical-imaging context, the ultimate aim of CBIR is to provide radiologists with a diagnostic aid in the form of a display of relevant past cases, along with proven pathology and other suitable information. CBIR may also be useful as a training tool for medical students and residents. The goal of information retrieval is to recall from a database information that is relevant to the user's query. The most challenging aspect of CBIR is the definition of relevance (similarity), which is used to guide the retrieval machine. In this paper, we pursue a new approach, in which similarity is learned from training examples provided by human observers. Specifically, we explore the use of neural networks and support vector machines to predict the user's notion of similarity. Within this framework we propose using a hierarchal learning approach, which consists of a cascade of a binary classifier and a regression module to optimize retrieval effectiveness and efficiency. We also explore how to incorporate online human interaction to achieve relevance feedback in this learning framework. Our experiments are based on a database consisting of 76 mammograms, all of which contain clustered microcalcifications (MCs). Our goal is to retrieve mammogram images containing similar MC clusters to that in a query. The performance of the retrieval system is evaluated using precision-recall curves computed using a cross-validation procedure. Our experimental results demonstrate that: 1) the learning framework can accurately predict the perceptual similarity reported by human observers, thereby

  7. Mountain permafrost detection inferred by a combined remote sensing, geomorphological and geophysical approach

    NASA Astrophysics Data System (ADS)

    Klug, Christoph; Mössinger, Max; Ott, Patrick; Rieg, Lorenzo; Sailer, Rudolf; Sass, Oliver; Stötter, Johann

    2013-04-01

    of the single datasets, a tomography including all results was created. For a further investigation of the occurrence and distribution of permafrost, 15 temperature-loggers were installed, which measured the base temperature of the snowpack (BTS) during the whole winter and so recorded potential freezing and thawing of the ground. This offered a further possibility for validation of the geophysical measurements. Additionally, the BTS was measured two times in the area close to the end of the winter snow accumulation period, to get a general idea of the possible distribution of permafrost or ice in the underground. The results of the measurements at Rofenberg show good correlation. In the areas detected within the multitemporal ALS dataset permafrost is assumed at a depth between 2 meters and 8 meters and similar ground structures can be spotted for every geophysical method. The combined approach of geophysical methods, remote sensing and field investigations allowed a profound cross-validation of the different methods.

  8. Anti-spoofing for display and print attacks on palmprint verification systems

    NASA Astrophysics Data System (ADS)

    Kanhangad, Vivek; Bhilare, Shruti; Garg, Pragalbh; Singh, Pranjalya; Chaudhari, Narendra

    2015-05-01

    A number of approaches for personal authentication using palmprint features have been proposed in the literature, majority of which focus on improving the matching performance. However, of late, preventing potential attacks on biometric systems has become a major concern as more and more biometric systems get deployed for wide range of applications. Among various types of attacks, sensor level attack, commonly known as spoof attack, has emerged as the most common attack due to simplicity in its execution. In this paper, we present an approach for detection of display and print based spoof attacks on palmprint verifcation systems. The approach is based on the analysis of acquired hand images for estimating surface re ectance. First and higher order statistical features computed from the distributions of pixel intensities and sub-band wavelet coeefficients form the feature set. A trained binary classifier utilizes the discriminating information to determine if the acquired image is of real hand or a fake one. Experiments are performed on a publicly available hand image dataset, containing 1300 images corresponding to 230 subjects. Experimental results show that the real hand biometrics samples can be substituted by the fake digital or print copies with an alarming spoof acceptance rate as high as 79.8%. Experimental results also show that the proposed spoof detection approach is very effective for discriminating between real and fake palmprint images. The proposed approach consistently achieves over 99% average 10-fold cross validation classification accuracy in our experiments.

  9. Data-driven approach to Type Ia supernovae: variable selection on the peak luminosity and clustering in visual analytics

    NASA Astrophysics Data System (ADS)

    Uemura, Makoto; Kawabata, Koji S.; Ikeda, Shiro; Maeda, Keiichi; Wu, Hsiang-Yun; Watanabe, Kazuho; Takahashi, Shigeo; Fujishiro, Issei

    2016-03-01

    Type Ia supernovae (SNIa) have an almost uniform peak luminosity, so that they are used as “standard candle” to estimate distances to galaxies in cosmology. In this article, we introduce our two recent works on SNIa based on data-driven approach. The diversity in the peak luminosity of SNIa can be reduced by corrections in several variables. The color and decay rate have been used as the explanatory variables of the peak luminosity in past studies. However, it is proposed that their spectral data could give a better model of the peak luminosity. We use cross-validation in order to control the generalization error and a LASSO-type estimator in order to choose the set of variables. Using 78 samples and 276 candidates of variables, we confirm that the peak luminosity depends on the color and decay rate. Our analysis does not support adding any other variables in order to have a better generalization error. On the other hand, this analysis is based on the assumption that SNIa originate in a single population, while it is not trivial. Indeed, several sub-types possibly having different nature have been proposed. We used a visual analytics tool for the asymmetric biclustering method to find both a good set of variables and samples at the same time. Using 14 variables and 132 samples, we found that SNIa can be divided into two categories by the expansion velocity of ejecta. Those examples demonstrate that the data-driven approach is useful for high-dimensional large-volume data which becomes common in modern astronomy.

  10. Computer-aided detection of microcalcifications in digital breast tomosynthesis (DBT): a multichannel signal detection approach on projection views

    NASA Astrophysics Data System (ADS)

    Wei, Jun; Chan, Heang-Ping; Hadjiiski, Lubomir; Helvie, Mark A.; Zhou, Chuan; Lu, Yao

    2012-03-01

    DBT is one of the promising imaging modalities that may improve the sensitivity and specificity for breast cancer detection. We are developing a computer-aided detection (CADe) system for clustered microcalcifications (MC) in DBT. A data set of two-view DBTs from 42 breasts was collected with a GE prototype system. We investigated a 2D approach to MC detection using projection view (PV) images rather than reconstructed 3D DBT volume. Our 2D approach consisted of two major stages: 1) detecting individual MC candidates on each PV, and 2) correlating the MC candidates from the different PVs and detecting clusters in the breast volume. With the MC candidates detected by prescreening on PVs, a trained multi-channel (MCH) filter bank was used to extract signal response from each MC candidate. A ray-tracing process was performed to fuse the MCH responses and localize the MC candidates in 3D using the geometrical information of the DBT system. Potential MC clusters were then identified by dynamic clustering of the MCs in 3D. A two-fold cross-validation method was used to train and test the CADe system. The detection performance of clustered MCs was assessed by free receiver operating characteristic (FROC) analysis. It was found that the CADe system achieved a case-based sensitivity of 90% at an average false positive rate of 2.1 clusters per DBT volume. Our study demonstrated that the CADe system using 2D MCH filter bank is promising for detection of clustered MCs in DBT.

  11. Development and validation of a streamlined autism case confirmation approach for use in epidemiologic risk factor research in prospective cohorts.

    PubMed

    Newschaffer, Craig J; Schriver, Emily; Berrigan, Lindsay; Landa, Rebecca; Stone, Wendy L; Bishop, Somer; Burkom, Diane; Golden, Anne; Ibanez, Lisa; Kuo, Alice; Lakes, Kimberly D; Messinger, Daniel S; Paterson, Sarah; Warren, Zachary E

    2017-03-01

    The cost associated with incorporating standardized observational assessments and diagnostic interviews in large-scale epidemiologic studies of autism spectrum disorders (ASD) risk factors can be substantial. Streamlined approaches for confirming ASD case status would benefit these studies. We conducted a multi-site, cross-sectional criterion validity study in a convenience sample of 382 three-year olds scheduled for neurodevelopmental evaluation. ASD case classification as determined by three novel assessment instruments (the Early Video-guided Autism Screener E-VAS; the Autism Symptom Interview, ASI; the Screening Tool for Autism in Toddlers Expanded, STAT-E) each designed to be administered in less than 30 minutes by lay staff, was compared to ADOS scores and DSM-based diagnostic assessment from a qualified clinician. Sensitivity and specificity of each instrument alone and in combination were estimated. Alternative cutpoints were identified under different criteria and two-stage cross validation was used to avoid overfitting. Findings were interpreted in the context of a large, prospective pregnancy cohort study utilizing a two-stage approach to case identification. Under initial cutpoints, sensitivity ranged from 0.63 to 0.92 and specificity from 0.35 to 0.70. Cutpoints giving equal weight to sensitivity and specificity resulted in sensitivity estimates ranging from 0.45 to 0.83 and specificity ranging from 0.49 to 0.86. Several strategies were well-suited for application as a second-stage case-confirmation. These included the STAT-E alone and the parallel administration of both the E-VAS and the ASI. Use of more streamlined methods of case-confirmation in large-scale prospective cohort epidemiologic investigations of ASD risk factors appears feasible. Autism Res 2017, 10: 485-501. © 2016 International Society for Autism Research, Wiley Periodicals, Inc.

  12. Small-molecule interferon inducers. Toward the comprehension of the molecular determinants through ligand-based approaches.

    PubMed

    Musmuca, Ira; Simeoni, Silvia; Caroli, Antonia; Ragno, Rino

    2009-07-01

    Hepatitis C is becoming an increasingly common cause of mortality especially in the HIV-coinfected group. Due to the efficacy of interferon (IFN) based therapy in the treatment of hepatitis C, various compounds possessing IFN-inducing activity have been hitherto reported. In the present study, we describe how steric, electrostatic, hydrophobic, and hydrogen-bonding interactions might influence the biological activity of a published set of IFN inducers, using a three-dimensional quantitative structure-activity relationship (3-D QSAR) approach. Analyses were conducted evaluating different series of compounds structurally related to 8-hydroxyadenines and 1H-imidazo[4,5-c]quinolines. A ligand-based alignment protocol in combination with the GRID/GOLPE approach was applied: 62 3-D QSAR models were derived using different GRID probes and several training sets. Performed 3-D QSAR investigations proved to be of good statistical value displaying r2, q2CV-LOO, and cross-validated SDEP values of 0.73, 0.61, 0.61 and 0.89, 0.64, 0.58 using the OH or the DRY probe, respectively. Additionally, the predictive performance was evaluated using an external test set of 20 compounds. Analyses of the resulting models led to the definition of a pharmacophore model that can be of interest to explain the observed affinities of known compounds as well as to design novel low molecular weight IFN inducers (IFNIs). To the best of our knowledge, this is the first 3-D QSAR application on IFN-inducing agents.

  13. A Novel Approach to Determining Violence Risk in Schizophrenia: Developing a Stepped Strategy in 13,806 Discharged Patients

    PubMed Central

    Singh, Jay P.; Grann, Martin; Lichtenstein, Paul; Långström, Niklas; Fazel, Seena

    2012-01-01

    Clinical guidelines recommend that violence risk be assessed in schizophrenia. Current approaches are resource-intensive as they employ detailed clinical assessments of dangerousness for most patients. An alternative approach would be to first screen out patients at very low risk of future violence prior to more costly and time-consuming assessments. In order to implement such a stepped strategy, we developed a simple tool to screen out individuals with schizophrenia at very low risk of violent offending. We merged high quality Swedish national registers containing information on psychiatric diagnoses, socio-demographic factors, and violent crime. A cohort of 13,806 individuals with hospital discharge diagnoses of schizophrenia was identified and followed for up to 33 years for violent crime. Cox regression was used to determine risk factors for violent crime and construct the screening tool, the predictive validity of which was measured using four outcome statistics. The instrument was calibrated on 6,903 participants and cross-validated using three independent replication samples of 2,301 participants each. Regression analyses resulted in a tool composed of five items: male sex, previous criminal conviction, young age at assessment, comorbid alcohol abuse, and comorbid drug abuse. At 5 years after discharge, the instrument had a negative predictive value of 0.99 (95% CI = 0.98–0.99), meaning that very few individuals who the tool screened out (n = 2,359 out of original sample of 6,903) were subsequently convicted of a violent offence. Screening out patients who are at very low risk of violence prior to more detailed clinical assessment may assist the risk assessment process in schizophrenia. PMID:22359622

  14. Application of electron conformational-genetic algorithm approach to 1,4-dihydropyridines as calcium channel antagonists: pharmacophore identification and bioactivity prediction.

    PubMed

    Geçen, Nazmiye; Sarıpınar, Emin; Yanmaz, Ersin; Sahin, Kader

    2012-01-01

    Two different approaches, namely the electron conformational and genetic algorithm methods (EC-GA), were combined to identify a pharmacophore group and to predict the antagonist activity of 1,4-dihydropyridines (known calcium channel antagonists) from molecular structure descriptors. To identify the pharmacophore, electron conformational matrices of congruity (ECMC)-which include atomic charges as diagonal elements and bond orders and interatomic distances as off-diagonal elements-were arranged for all compounds. The ECMC of the compound with the highest activity was chosen as a template and compared with the ECMCs of other compounds within given tolerances to reveal the electron conformational submatrix of activity (ECSA) that refers to the pharmacophore. The genetic algorithm was employed to search for the best subset of parameter combinations that contributes the most to activity. Applying the model with the optimum 10 parameters to training (50 compounds) and test (22 compounds) sets gave satisfactory results (R(2)(training)= 0.848, R(2)(test))= 0.904, with a cross-validated q(2) = 0.780).

  15. Prediction and validation of protein-protein interactors from genome-wide DNA-binding data using a knowledge-based machine-learning approach.

    PubMed

    Waardenberg, Ashley J; Homan, Bernou; Mohamed, Stephanie; Harvey, Richard P; Bouveret, Romaric

    2016-09-01

    The ability to accurately predict the DNA targets and interacting cofactors of transcriptional regulators from genome-wide data can significantly advance our understanding of gene regulatory networks. NKX2-5 is a homeodomain transcription factor that sits high in the cardiac gene regulatory network and is essential for normal heart development. We previously identified genomic targets for NKX2-5 in mouse HL-1 atrial cardiomyocytes using DNA-adenine methyltransferase identification (DamID). Here, we apply machine learning algorithms and propose a knowledge-based feature selection method for predicting NKX2-5 protein : protein interactions based on motif grammar in genome-wide DNA-binding data. We assessed model performance using leave-one-out cross-validation and a completely independent DamID experiment performed with replicates. In addition to identifying previously described NKX2-5-interacting proteins, including GATA, HAND and TBX family members, a number of novel interactors were identified, with direct protein : protein interactions between NKX2-5 and retinoid X receptor (RXR), paired-related homeobox (PRRX) and Ikaros zinc fingers (IKZF) validated using the yeast two-hybrid assay. We also found that the interaction of RXRα with NKX2-5 mutations found in congenital heart disease (Q187H, R189G and R190H) was altered. These findings highlight an intuitive approach to accessing protein-protein interaction information of transcription factors in DNA-binding experiments.

  16. On the reliability of a geometric morphometric approach to sex determination: a blind test of six criteria of the juvenile ilium.

    PubMed

    Wilson, Laura A B; Cardoso, Hugo F V; Humphrey, Louise T

    2011-03-20

    Despite the attention of many studies, researchers still struggle to identify criteria with which to sex juvenile remains at levels of accuracy and reproducibility comparable with those documented for adults. This study uses a sample of 82 juvenile ilia from an identified Portuguese population (Lisbon collection) to test the cross-applicability of a new approach by Wilson et al. [23] that uses geometric morphometric methods to sex the subadult ilium. Further, we evaluate the wider applicability of these methods for forensic casework, extending the age range of the original study by examining an additional 19 juvenile ilia from the St. Brides and Spitalfields collections, housed in London. Levels of accuracy for the Portuguese sample (62.2-89.0%) indicate that the methods can be used to document dimorphism in another sample. Discriminant functions are sample-specific, indicated by not better than average classification using cross-validation. We propose a methodological update, whereby we recommend disuse of the auricular surface morphology criterion, based upon reduced success rates and inadequate accuracy of female identification. We show, in addition to population differences, differences in the ontogeny of dimorphism may lead to differing degrees of success for female identification using some criteria. The success rates are highest between the ages of 11.00 and 14.99 years (93.3% males, 80.0% females).

  17. Prediction and validation of protein–protein interactors from genome-wide DNA-binding data using a knowledge-based machine-learning approach

    PubMed Central

    Homan, Bernou; Mohamed, Stephanie; Harvey, Richard P.; Bouveret, Romaric

    2016-01-01

    The ability to accurately predict the DNA targets and interacting cofactors of transcriptional regulators from genome-wide data can significantly advance our understanding of gene regulatory networks. NKX2-5 is a homeodomain transcription factor that sits high in the cardiac gene regulatory network and is essential for normal heart development. We previously identified genomic targets for NKX2-5 in mouse HL-1 atrial cardiomyocytes using DNA-adenine methyltransferase identification (DamID). Here, we apply machine learning algorithms and propose a knowledge-based feature selection method for predicting NKX2-5 protein : protein interactions based on motif grammar in genome-wide DNA-binding data. We assessed model performance using leave-one-out cross-validation and a completely independent DamID experiment performed with replicates. In addition to identifying previously described NKX2-5-interacting proteins, including GATA, HAND and TBX family members, a number of novel interactors were identified, with direct protein : protein interactions between NKX2-5 and retinoid X receptor (RXR), paired-related homeobox (PRRX) and Ikaros zinc fingers (IKZF) validated using the yeast two-hybrid assay. We also found that the interaction of RXRα with NKX2-5 mutations found in congenital heart disease (Q187H, R189G and R190H) was altered. These findings highlight an intuitive approach to accessing protein–protein interaction information of transcription factors in DNA-binding experiments. PMID:27683156

  18. A statistical approach towards the derivation of predictive gene sets for potency ranking of chemicals in the mouse embryonic stem cell test.

    PubMed

    Schulpen, Sjors H W; Pennings, Jeroen L A; Tonk, Elisa C M; Piersma, Aldert H

    2014-03-21

    The embryonic stem cell test (EST) is applied as a model system for detection of embryotoxicants. The application of transcriptomics allows a more detailed effect assessment compared to the morphological endpoint. Genes involved in cell differentiation, modulated by chemical exposures, may be useful as biomarkers of developmental toxicity. We describe a statistical approach to obtain a predictive gene set for toxicity potency ranking of compounds within one class. This resulted in a gene set based on differential gene expression across concentration-response series of phthalatic monoesters. We determined the concentration at which gene expression was changed at least 1.5-fold. Genes responding with the same potency ranking in vitro and in vivo embryotoxicity were selected. A leave-one-out cross-validation showed that the relative potency of each phthalate was always predicted correctly. The classical morphological 50% effect level (ID50) in EST was similar to the predicted concentration using gene set expression responses. A general down-regulation of development-related genes and up-regulation of cell-cycle related genes was observed, reminiscent of the differentiation inhibition in EST. This study illustrates the feasibility of applying dedicated gene set selections as biomarkers for developmental toxicity potency ranking on the basis of in vitro testing in the EST.

  19. Charge-controlled nanoprecipitation as a modular approach to ultrasmall polymer nanocarriers: making bright and stable nanoparticles.

    PubMed

    Reisch, Andreas; Runser, Anne; Arntz, Youri; Mély, Yves; Klymchenko, Andrey S

    2015-05-26

    Ultrasmall polymer nanoparticles are rapidly gaining importance as nanocarriers for drugs and contrast agents. Here, a straightforward modular approach to efficiently loaded and stable sub-20-nm polymer particles is developed. In order to obtain ultrasmall polymer nanoparticles, we investigated the influence of one to two charged groups per polymer chain on the size of particles obtained by nanoprecipitation. Negatively charged carboxylate and sulfonate or positively charged trimethylammonium groups were introduced into the polymers poly(d,l-lactide-co-glycolide) (PLGA), polycaprolactone (PCL), and poly(methyl methacrylate) (PMMA). According to dynamic light scattering, atomic force and electron microscopy, the presence of one to two charged groups per polymer chain can strongly reduce the size of polymer nanoparticles made by nanoprecipitation. The particle size can be further decreased to less than 15 nm by decreasing the concentration of polymer in the solvent used for nanoprecipitation. We then show that even very small nanocarriers of 15 nm size preserve the capacity to encapsulate large amounts of ionic dyes with bulky counterions at efficiencies >90%, which generates polymer nanoparticles 10-fold brighter than quantum dots of the same size. Postmodification of their surface with the PEG containing amphiphiles Tween 80 and pluronic F-127 led to particles that were stable under physiological conditions and in the presence of 10% fetal bovine serum. This modular route could become a general method for the preparation of ultrasmall polymer nanoparticles as nanocarriers of contrast agents and drugs.

  20. Comparative residue interaction analysis (CoRIA): a 3D-QSAR approach to explore the binding contributions of active site residues with ligands

    NASA Astrophysics Data System (ADS)

    Datar, Prasanna A.; Khedkar, Santosh A.; Malde, Alpeshkumar K.; Coutinho, Evans C.

    2006-06-01

    A novel approach termed comparative residue-interaction analysis (CoRIA), emphasizing the trends and principles of QSAR in a ligand-receptor environment has been developed to analyze and predict the binding affinity of enzyme inhibitors. To test this new approach, a training set of 36 COX-2 inhibitors belonging to nine families was selected. The putative binding (bioactive) conformations of inhibitors in the COX-2 active site were searched using the program DOCK. The docked configurations were further refined by a combination of Monte Carlo and simulated annealing methods with the Affinity program. The non-bonded interaction energies of the inhibitors with the individual amino acid residues in the active site were then computed. These interaction energies, plus specific terms describing the thermodynamics of ligand-enzyme binding, were correlated to the biological activity with G/PLS. The various QSAR models obtained were validated internally by cross validation and boot strapping, and externally using a test set of 13 molecules. The QSAR models developed on the CoRIA formalism were robust with good r 2, q 2 and r pred 2 values. The major highlights of the method are: adaptation of the QSAR formalism in a receptor setting to answer both the type (qualitative) and the extent (quantitative) of ligand-receptor binding, and use of descriptors that account for the complete thermodynamics of the ligand-receptor binding. The CoRIA approach can be used to identify crucial interactions of inhibitors with the enzyme at the residue level, which can be gainfully exploited in optimizing the inhibitory activity of ligands. Furthermore, it can be used with advantage to guide point mutation studies. As regards the COX-2 dataset, the CoRIA approach shows that improving Coulombic interaction with Pro528 and reducing van der Waals interaction with Tyr385 will improve the binding affinity of inhibitors.

  1. Information theory applied to the sparse gene ontology annotation network to predict novel gene function

    PubMed Central

    Tao, Ying; Li, Jianrong

    2010-01-01

    Motivation Despite advances in the gene annotation process, the functions of a large portion of the gene products remain insufficiently characterized. In addition, the “in silico” prediction of novel Gene Ontology (GO) annotations for partially characterized gene functions or processes is highly dependent on reverse genetic or function genomics approaches. Results We propose a novel approach, Information Theory-based Semantic Similarity (ITSS), to automatically predict molecular functions of genes based on Gene Ontology annotations. We have demonstrated using a 10-fold cross-validation that the ITSS algorithm obtains prediction accuracies (Precision 97%, Recall 77%) comparable to other machine learning algorithms when applied to similarly dense annotated portions of the GO datasets. In addition, such method can generate highly accurate predictions in sparsely annotated portions of GO, in which previous algorithm failed to do so. As a result, our technique generates an order of magnitude more gene function predictions than previous methods. Further, this paper presents the first historical rollback validation for the predicted GO annotations, which may represent more realistic conditions for an evaluation than generally used cross-validations type of evaluations. By manually assessing a random sample of 100 predictions conducted in a historical roll-back evaluation, we estimate that a minimum precision of 51% (95% confidence interval: 43%–58%) can be achieved for the human GO Annotation file dated 2003. Availability The program is available on request. The 97,732 positive predictions of novel gene annotations from the 2005 GO Annotation dataset are available at http://phenos.bsd.uchicago.edu/mphenogo/prediction_result_2005.txt. PMID:17646340

  2. Modeling temporal sequences of cognitive state changes based on a combination of EEG-engagement, EEG-workload, and heart rate metrics

    PubMed Central

    Stikic, Maja; Berka, Chris; Levendowski, Daniel J.; Rubio, Roberto F.; Tan, Veasna; Korszen, Stephanie; Barba, Douglas; Wurzer, David

    2014-01-01

    The objective of this study was to investigate the feasibility of physiological metrics such as ECG-derived heart rate and EEG-derived cognitive workload and engagement as potential predictors of performance on different training tasks. An unsupervised approach based on self-organizing neural network (NN) was utilized to model cognitive state changes over time. The feature vector comprised EEG-engagement, EEG-workload, and heart rate metrics, all self-normalized to account for individual differences. During the competitive training process, a linear topology was developed where the feature vectors similar to each other activated the same NN nodes. The NN model was trained and auto-validated on combat marksmanship training data from 51 participants that were required to make “deadly force decisions” in challenging combat scenarios. The trained NN model was cross validated using 10-fold cross-validation. It was also validated on a golf study in which additional 22 participants were asked to complete 10 sessions of 10 putts each. Temporal sequences of the activated nodes for both studies followed the same pattern of changes, demonstrating the generalization capabilities of the approach. Most node transition changes were local, but important events typically caused significant changes in the physiological metrics, as evidenced by larger state changes. This was investigated by calculating a transition score as the sum of subsequent state transitions between the activated NN nodes. Correlation analysis demonstrated statistically significant correlations between the transition scores and subjects' performances in both studies. This paper explored the hypothesis that temporal sequences of physiological changes comprise the discriminative patterns for performance prediction. These physiological markers could be utilized in future training improvement systems (e.g., through neurofeedback), and applied across a variety of training environments. PMID:25414629

  3. A Practical Approach for Designing Breeding Groups to Maximize Genetic Diversity in a Large Colony of Captive Rhesus Macaques (Macaca mulatta).

    PubMed

    Vinson, Amanda; Raboin, Michael J

    2015-11-01

    Limited guidance is available on practical approaches for maintaining genetic diversity in large NHP colonies that support biomedical research, despite the fact that reduced diversity in these colonies is likely to compromise the application of findings in NHP to human disease. In particular, constraints related to simultaneously housing, breeding, and providing ongoing veterinary care for thousands of animals with a highly complex social structure creates unique challenges for genetic management in these colonies. Because the composition of new breeding groups is a critical component of genetic management, here we outline a 3-stage protocol for forming new breeding groups of NHP that is aimed at maximizing genetic diversity in the face of frequent restrictions on age, sex, and numbers of animals per breeding group. As an example application of this protocol, we describe optimal combinations of rhesus macaques from an analysis of candidate animals available for breeding in July 2013, selected from among the approximately 4000 macaques maintained at the Oregon National Primate Research Center. In addition, a simulation study to explore the genetic diversity in breeding groups formed by using this protocol, indicated an approximate 10-fold higher genome uniqueness, 50% lower mean kinship, and an 84-fold lower mean inbreeding coefficient among potential offspring within groups, when compared with a suboptimal group design. We conclude that this protocol provides a practical and effective approach to breeding group design for colony managers who want to prevent the loss of genetic diversity in large, semiisolated NHP colonies.

  4. Endoscopic thyroidectomy: retroauricular approach

    PubMed Central

    Lee, Doh Young; Baek, Seung-Kuk

    2016-01-01

    The incidence of thyroid cancer has abruptly increased recently, with a female predominance. Conventional thyroidectomy using transcervical incision inevitably leaves an unfavorable neck scar; therefore, various extracervical approaches have been introduced to improve cosmetic satisfaction after thyroidectomy. Several reports demonstrated that these extracervical approaches have advantages not only in terms of cosmesis but also in terms of surgical outcomes and postoperative functional preservation. The retroauricular approach has advantages as the dissection area is smaller than that in the transaxillary approach (TA) and surgical anatomy is familiar to the head and neck surgeons. In addition, there is no concern about paresthesia around the nipple or anterior chest, and surgical direction makes central neck dissection easier than with the other extracervical approaches. Herein, we aim to introduce the surgical procedure of retroauricular approach thyroidectomy and present our experiences of postoperative outcomes. PMID:27294041

  5. [Surgical approaches in rhinoplasty].

    PubMed

    Nguyen, P S; Duron, J-B; Bardot, J; Levet, Y; Aiach, G

    2014-12-01

    In the first step of rhinoplasty, the surgical approach will expose through different types of incisions and dissection planes the osteocartilaginous framework of the nasal pyramid prior to performing actions to reduce or increase the latter. This exposure can be performed by a closed approach or by an external approach--the choice depends on the type of nose and the habits of the surgeon. Far from being opposites, closed and external approaches are complementary and should be known and mastered by surgeons performing rhinoplasty.

  6. Modular Approach for Ethics

    ERIC Educational Resources Information Center

    Wyne, Mudasser F.

    2010-01-01

    It is hard to define a single set of ethics that will cover an entire computer users community. In this paper, the issue is addressed in reference to code of ethics implemented by various professionals, institutes and organizations. The paper presents a higher level model using hierarchical approach. The code developed using this approach could be…

  7. Approaches to Truancy Prevention.

    ERIC Educational Resources Information Center

    Mogulescu, Sara; Segal, Heidi J.

    This report examines how New York counties can systematically and programmatically improve approaches to managing persons in need of supervision (PINS), describing approaches to truancy prevention and diversion that have been instituted nationwide and may be applicable to the PINS operating system. Researchers surveyed truancy-specific programs…

  8. Alternative Approaches to Negotiating.

    ERIC Educational Resources Information Center

    Ramming, Thomas M.

    1997-01-01

    The wait-and-react and laundry-list approaches to combating teachers' collective-bargaining demands are ineffective. An alternative goals-setting approach requires management and the district negotiations team to identify important needs and objectives. West Seneca Central School District ended contentious negotiations by presenting unions with…

  9. The TLC Approach.

    ERIC Educational Resources Information Center

    Welker, William A.

    2002-01-01

    Notes how the author has developed the Teaching and Learning Cues (TLC) approach, an offspring of textbook organizational patterns instruction that stresses the significance of certain words and phrases in reading. Concludes that with the TLC approach, students learn to appreciate the important role cue words and phrases play in understanding…

  10. Approaches to Beginning Reading.

    ERIC Educational Resources Information Center

    Aukerman, Robert C.

    The more than one hundred approaches to initial reading instruction can be grouped under ten headings: basal reader, phonemics, phonemic reading, "linguistics," total language arts, language-experience, one-to-one sound symbol, individualized reading, early reading, and perceptual discrimination. Although the basal reader approach is used in more…

  11. Stuttering-Psycholinguistic Approach

    ERIC Educational Resources Information Center

    Hategan, Carolina Bodea; Anca, Maria; Prihoi, Lacramioara

    2012-01-01

    This research promotes psycholinguistic paradigm, it focusing in delimitating several specific particularities in stuttering pathology. Structural approach, on language sides proves both the recurrent aspects found within specialized national and international literature and the psycholinguistic approaches dependence on the features of the…

  12. Ten practice redesign approaches.

    PubMed

    Slayton, Val

    2013-01-01

    As healthcare delivery continues to evolve at a rapid pace, practices need to consider redesign approaches to stay ahead of the pack. From national policy and private payer initiatives to societal macro trends and the growing use of mobile technologies, delivering value, understanding customer needs, and assessing satisfaction are important elements to achieve and maintain success. This article discusses 10 practice redesign approaches.

  13. Topological and canonical kriging for design flood prediction in ungauged catchments: an improvement over a traditional regional regression approach?

    USGS Publications Warehouse

    Archfield, Stacey A.; Pugliese, Alessio; Castellarin, Attilio; Skøien, Jon O.; Kiang, Julie E.

    2013-01-01

    In the United States, estimation of flood frequency quantiles at ungauged locations has been largely based on regional regression techniques that relate measurable catchment descriptors to flood quantiles. More recently, spatial interpolation techniques of point data have been shown to be effective for predicting streamflow statistics (i.e., flood flows and low-flow indices) in ungauged catchments. Literature reports successful applications of two techniques, canonical kriging, CK (or physiographical-space-based interpolation, PSBI), and topological kriging, TK (or top-kriging). CK performs the spatial interpolation of the streamflow statistic of interest in the two-dimensional space of catchment descriptors. TK predicts the streamflow statistic along river networks taking both the catchment area and nested nature of catchments into account. It is of interest to understand how these spatial interpolation methods compare with generalized least squares (GLS) regression, one of the most common approaches to estimate flood quantiles at ungauged locations. By means of a leave-one-out cross-validation procedure, the performance of CK and TK was compared to GLS regression equations developed for the prediction of 10, 50, 100 and 500 yr floods for 61 streamgauges in the southeast United States. TK substantially outperforms GLS and CK for the study area, particularly for large catchments. The performance of TK over GLS highlights an important distinction between the treatments of spatial correlation when using regression-based or spatial interpolation methods to estimate flood quantiles at ungauged locations. The analysis also shows that coupling TK with CK slightly improves the performance of TK; however, the improvement is marginal when compared to the improvement in performance over GLS.

  14. Discriminative Analysis of Migraine without Aura: Using Functional and Structural MRI with a Multi-Feature Classification Approach

    PubMed Central

    Zhang, Junran; He, Ling; Huang, Jiangtao; Zhang, Jiang; Huang, Hua; Gong, Qiyong

    2016-01-01

    Magnetic resonance imaging (MRI) is by nature a multi-modality technique that provides complementary information about different aspects of diseases. So far no attempts have been reported to assess the potential of multi-modal MRI in discriminating individuals with and without migraine, so in this study, we proposed a classification approach to examine whether or not the integration of multiple MRI features could improve the classification performance between migraine patients without aura (MWoA) and healthy controls. Twenty-one MWoA patients and 28 healthy controls participated in this study. Resting-state functional MRI data was acquired to derive three functional measures: the amplitude of low-frequency fluctuations, regional homogeneity and regional functional correlation strength; and structural MRI data was obtained to measure the regional gray matter volume. For each measure, the values of 116 pre-defined regions of interest were extracted as classification features. Features were first selected and combined by a multi-kernel strategy; then a support vector machine classifier was trained to distinguish the subjects at individual level. The performance of the classifier was evaluated using a leave-one-out cross-validation method, and the final classification accuracy obtained was 83.67% (with a sensitivity of 92.86% and a specificity of 71.43%). The anterior cingulate cortex, prefrontal cortex, orbitofrontal cortex and the insula contributed the most discriminative features. In general, our proposed framework shows a promising classification capability for MWoA by integrating information from multiple MRI features. PMID:27690138

  15. A wrapper-based approach for feature selection and classification of major depressive disorder-bipolar disorders.

    PubMed

    Tekin Erguzel, Turker; Tas, Cumhur; Cebi, Merve

    2015-09-01

    Feature selection (FS) and classification are consecutive artificial intelligence (AI) methods used in data analysis, pattern classification, data mining and medical informatics. Beside promising studies in the application of AI methods to health informatics, working with more informative features is crucial in order to contribute to early diagnosis. Being one of the prevalent psychiatric disorders, depressive episodes of bipolar disorder (BD) is often misdiagnosed as major depressive disorder (MDD), leading to suboptimal therapy and poor outcomes. Therefore discriminating MDD and BD at earlier stages of illness could help to facilitate efficient and specific treatment. In this study, a nature inspired and novel FS algorithm based on standard Ant Colony Optimization (ACO), called improved ACO (IACO), was used to reduce the number of features by removing irrelevant and redundant data. The selected features were then fed into support vector machine (SVM), a powerful mathematical tool for data classification, regression, function estimation and modeling processes, in order to classify MDD and BD subjects. Proposed method used coherence, a promising quantitative electroencephalography (EEG) biomarker, values calculated from alpha, theta and delta frequency bands. The noteworthy performance of novel IACO-SVM approach stated that it is possible to discriminate 46 BD and 55 MDD subjects using 22 of 48 features with 80.19% overall classification accuracy. The performance of IACO algorithm was also compared to the performance of standard ACO, genetic algorithm (GA) and particle swarm optimization (PSO) algorithms in terms of their classification accuracy and number of selected features. In order to provide an almost unbiased estimate of classification error, the validation process was performed using nested cross-validation (CV) procedure.

  16. Data Mining Approach for Evaluating Vegetation Dynamics in Earth System Models (ESMs) Using Satellite Remote Sensing Products

    NASA Astrophysics Data System (ADS)

    Shu, S.; Hoffman, F. M.; Kumar, J.; Hargrove, W. W.; Jain, A. K.

    2014-12-01

    biome types. However, Mapcurves results showed a relatively low goodness of fit score for modeled phenology projected onto observations. This study demonstrates the utility of a data mining approach for cross-validation of observations and evaluation of model performance.

  17. DisoMCS: Accurately Predicting Protein Intrinsically Disordered Regions Using a Multi-Class Conservative Score Approach

    PubMed Central

    Wang, Zhiheng; Yang, Qianqian; Li, Tonghua; Cong, Peisheng

    2015-01-01

    The precise prediction of protein intrinsically disordered regions, which play a crucial role in biological procedures, is a necessary prerequisite to further the understanding of the principles and mechanisms of protein function. Here, we propose a novel predictor, DisoMCS, which is a more accurate predictor of protein intrinsically disordered regions. The DisoMCS bases on an original multi-class conservative score (MCS) obtained by sequence-order/disorder alignment. Initially, near-disorder regions are defined on fragments located at both the terminus of an ordered region connecting a disordered region. Then the multi-class conservative score is generated by sequence alignment against a known structure database and represented as order, near-disorder and disorder conservative scores. The MCS of each amino acid has three elements: order, near-disorder and disorder profiles. Finally, the MCS is exploited as features to identify disordered regions in sequences. DisoMCS utilizes a non-redundant data set as the training set, MCS and predicted secondary structure as features, and a conditional random field as the classification algorithm. In predicted near-disorder regions a residue is determined as an order or a disorder according to the optimized decision threshold. DisoMCS was evaluated by cross-validation, large-scale prediction, independent tests and CASP (Critical Assessment of Techniques for Protein Structure Prediction) tests. All results confirmed that DisoMCS was very competitive in terms of accuracy of prediction when compared with well-established publicly available disordered region predictors. It also indicated our approach was more accurate when a query has higher homologous with the knowledge database. Availability The DisoMCS is available at http://cal.tongji.edu.cn/disorder/. PMID:26090958

  18. VOXEL-LEVEL MAPPING OF TRACER KINETICS IN PET STUDIES: A STATISTICAL APPROACH EMPHASIZING TISSUE LIFE TABLES1

    PubMed Central

    O’Sullivan, Finbarr; Muzi, Mark; Mankoff, David A.; Eary, Janet F.; Spence, Alexander M.; Krohn, Kenneth A.

    2014-01-01

    Most radiotracers used in dynamic positron emission tomography (PET) scanning act in a linear time-invariant fashion so that the measured time-course data are a convolution between the time course of the tracer in the arterial supply and the local tissue impulse response, known as the tissue residue function. In statistical terms the residue is a life table for the transit time of injected radiotracer atoms. The residue provides a description of the tracer kinetic information measurable by a dynamic PET scan. Decomposition of the residue function allows separation of rapid vascular kinetics from slower blood-tissue exchanges and tissue retention. For voxel-level analysis, we propose that residues be modeled by mixtures of nonparametrically derived basis residues obtained by segmentation of the full data volume. Spatial and temporal aspects of diagnostics associated with voxel-level model fitting are emphasized. Illustrative examples, some involving cancer imaging studies, are presented. Data from cerebral PET scanning with 18F fluoro-deoxyglucose (FDG) and 15O water (H2O) in normal subjects is used to evaluate the approach. Cross-validation is used to make regional comparisons between residues estimated using adaptive mixture models with more conventional compartmental modeling techniques. Simulations studies are used to theoretically examine mean square error performance and to explore the benefit of voxel-level analysis when the primary interest is a statistical summary of regional kinetics. The work highlights the contribution that multivariate analysis tools and life-table concepts can make in the recovery of local metabolic information from dynamic PET studies, particularly ones in which the assumptions of compartmental-like models, with residues that are sums of exponentials, might not be certain. PMID:25392718

  19. Risk Prediction of One-Year Mortality in Patients with Cardiac Arrhythmias Using Random Survival Forest.

    PubMed

    Miao, Fen; Cai, Yun-Peng; Zhang, Yu-Xiao; Li, Ye; Zhang, Yuan-Ting

    2015-01-01

    Existing models for predicting mortality based on traditional Cox proportional hazard approach (CPH) often have low prediction accuracy. This paper aims to develop a clinical risk model with good accuracy for predicting 1-year mortality in cardiac arrhythmias patients using random survival forest (RSF), a robust approach for survival analysis. 10,488 cardiac arrhythmias patients available in the public MIMIC II clinical database were investigated, with 3,452 deaths occurring within 1-year followups. Forty risk factors including demographics and clinical and laboratory information and antiarrhythmic agents were analyzed as potential predictors of all-cause mortality. RSF was adopted to build a comprehensive survival model and a simplified risk model composed of 14 top risk factors. The built comprehensive model achieved a prediction accuracy of 0.81 measured by c-statistic with 10-fold cross validation. The simplified risk model also achieved a good accuracy of 0.799. Both results outperformed traditional CPH (which achieved a c-statistic of 0.733 for the comprehensive model and 0.718 for the simplified model). Moreover, various factors are observed to have nonlinear impact on cardiac arrhythmias prognosis. As a result, RSF based model which took nonlinearity into account significantly outperformed traditional Cox proportional hazard model and has great potential to be a more effective approach for survival analysis.

  20. Combining radiomic features with a miRNA classifier may improve prediction of malignant pathology for pancreatic intraductal papillary mucinous neoplasms

    PubMed Central

    Permuth, Jennifer B.; Choi, Jung; Balarunathan, Yoganand; Kim, Jongphil; Chen, Dung-Tsa; Chen, Lu; Orcutt, Sonia; Doepker, Matthew P.; Gage, Kenneth; Zhang, Geoffrey; Latifi, Kujtim; Hoffe, Sarah; Jiang, Kun; Coppola, Domenico; Centeno, Barbara A.; Magliocco, Anthony; Li, Qian; Trevino, Jose; Merchant, Nipun; Gillies, Robert; Malafa, Mokenge

    2016-01-01

    Intraductal papillary mucinous neoplasms (IPMNs) are pancreatic cancer precursors incidentally discovered by cross-sectional imaging. Consensus guidelines for IPMN management rely on standard radiologic features to predict pathology, but they lack accuracy. Using a retrospective cohort of 38 surgically-resected, pathologically-confirmed IPMNs (20 benign; 18 malignant) with preoperative computed tomography (CT) images and matched plasma-based ‘miRNA genomic classifier (MGC)’ data, we determined whether quantitative ‘radiomic’ CT features (+/- the MGC) can more accurately predict IPMN pathology than standard radiologic features ‘high-risk’ or ‘worrisome’ for malignancy. Logistic regression, principal component analyses, and cross-validation were used to examine associations. Sensitivity, specificity, positive and negative predictive value (PPV, NPV) were estimated. The MGC, ‘high-risk,’ and ‘worrisome’ radiologic features had area under the receiver operating characteristic curve (AUC) values of 0.83, 0.84, and 0.54, respectively. Fourteen radiomic features differentiated malignant from benign IPMNs (p<0.05) and collectively had an AUC=0.77. Combining radiomic features with the MGC revealed an AUC=0.92 and superior sensitivity (83%), specificity (89%), PPV (88%), and NPV (85%) than other models. Evaluation of uncertainty by 10-fold cross-validation retained an AUC>0.80 (0.87 (95% CI:0.84-0.89)). This proof-of-concept study suggests a noninvasive radiogenomic approach may more accurately predict IPMN pathology than ‘worrisome’ radiologic features considered in consensus guidelines. PMID:27589689

  1. A hybrid approach for rapid, accurate, and direct kilovoltage radiation dose calculations in CT voxel space

    SciTech Connect

    Kouznetsov, Alexei; Tambasco, Mauro

    2011-03-15

    Purpose: To develop and validate a fast and accurate method that uses computed tomography (CT) voxel data to estimate absorbed radiation dose at a point of interest (POI) or series of POIs from a kilovoltage (kV) imaging procedure. Methods: The authors developed an approach that computes absorbed radiation dose at a POI by numerically evaluating the linear Boltzmann transport equation (LBTE) using a combination of deterministic and Monte Carlo (MC) techniques. This hybrid approach accounts for material heterogeneity with a level of accuracy comparable to the general MC algorithms. Also, the dose at a POI is computed within seconds using the Intel Core i7 CPU 920 2.67 GHz quad core architecture, and the calculations are performed using CT voxel data, making it flexible and feasible for clinical applications. To validate the method, the authors constructed and acquired a CT scan of a heterogeneous block phantom consisting of a succession of slab densities: Tissue (1.29 cm), bone (2.42 cm), lung (4.84 cm), bone (1.37 cm), and tissue (4.84 cm). Using the hybrid transport method, the authors computed the absorbed doses at a set of points along the central axis and x direction of the phantom for an isotropic 125 kVp photon spectral point source located along the central axis 92.7 cm above the phantom surface. The accuracy of the results was compared to those computed with MCNP, which was cross-validated with EGSnrc, and served as the benchmark for validation. Results: The error in the depth dose ranged from -1.45% to +1.39% with a mean and standard deviation of -0.12% and 0.66%, respectively. The error in the x profile ranged from -1.3% to +0.9%, with standard deviations of -0.3% and 0.5%, respectively. The number of photons required to achieve these results was 1x10{sup 6}. Conclusions: The voxel-based hybrid method evaluates the LBTE rapidly and accurately to estimate the absorbed x-ray dose at any POI or series of POIs from a kV imaging procedure.

  2. Introducing Systems Approaches

    NASA Astrophysics Data System (ADS)

    Reynolds, Martin; Holwell, Sue

    Systems Approaches to Managing Change brings together five systems approaches to managing complex issues, each having a proven track record of over 25 years. The five approaches are: System Dynamics (SD) developed originally in the late 1950s by Jay Forrester Viable Systems Model (VSM) developed originally in the late 1960s by Stafford Beer Strategic Options Development and Analysis (SODA: with cognitive mapping) developed originally in the 1970s by Colin Eden Soft Systems Methodology (SSM) developed originally in the 1970s by Peter Checkland Critical Systems Heuristics (CSH) developed originally in the late 1970s by Werner Ulrich

  3. Otoplasty: A graduated approach.

    PubMed

    Foda, H M

    1999-01-01

    Numerous otoplastic techniques have been described for the correction of protruding ears. Technique selection in otoplasty should be done only after careful analysis of the abnormal anatomy responsible for the protruding ear deformity. A graduated surgical approach is presented which is designed to address all contributing factors to the presenting auricular deformity. The approach starts with the more conservative cartilage-sparing suturing techniques, then proceeds to incorporate other more aggressive cartilage weakening maneuvers. Applying this approach resulted in better long-term results with less postoperative lateralization than that encountered on using the cartilage-sparing techniques alone.

  4. Validation of the Chinese Version of the Life Orientation Test with a Robust Weighted Least Squares Approach

    ERIC Educational Resources Information Center

    Li, Cheng-Hsien

    2012-01-01

    Of the several measures of optimism presently available in the literature, the Life Orientation Test (LOT; Scheier & Carver, 1985) has been the most widely used in empirical research. This article explores, confirms, and cross-validates the factor structure of the Chinese version of the LOT with ordinal data by using robust weighted least…

  5. Approaches to Human Communication.

    ERIC Educational Resources Information Center

    Budd, Richard W., Ed.; Ruben, Brent D., Ed.

    This anthology of essays approaches human communication from the points of view of: anthropology, art biology, economics, encounter groups, semantics, general system theory, history, information theory, international behavior, journalism, linguistics, mass media, neurophysiology, nonverbal behavior, organizational behavior, philosophy, political…

  6. SOHO Sees Venus' Approach

    NASA Video Gallery

    This video taken by the Solar and Heliospheric Observatory (SOHO) shows the Sun's corona and Venus' approach for the transit. This was taken with the Extreme ultraviolet Imaging Telescope (EIT) in ...

  7. Tiny Asteroid Approaches Earth

    NASA Video Gallery

    On Oct. 15, 2010, NASA astronomer Rob Suggs captured this view of the tiny asteroid 2010 TG19 as it made its way among the stars of the constellation Pegasus. It will continue to approach during th...

  8. Evaluation of the predictive capacity of DNA variants associated with straight hair in Europeans.

    PubMed

    Pośpiech, Ewelina; Karłowska-Pik, Joanna; Marcińska, Magdalena; Abidi, Sarah; Andersen, Jeppe Dyrberg; van den Berge, Margreet; Carracedo, Ángel; Eduardoff, Mayra; Freire-Aradas, Ana; Morling, Niels; Sijen, Titia; Skowron, Małgorzata; Söchtig, Jens; Syndercombe-Court, Denise; Weiler, Natalie; Schneider, Peter M; Ballard, David; Børsting, Claus; Parson, Walther; Phillips, Chris; Branicki, Wojciech

    2015-11-01

    DNA-based prediction of hair morphology, defined as straight, curly or wavy hair, could contribute to an improved description of an unknown offender and allow more accurate forensic reconstructions of physical appearance in the field of forensic DNA phenotyping. Differences in scalp hair morphology are significant at the worldwide scale and within Europe. The only genome-wide association study made to date revealed the Trichohyalin gene (TCHH) to be significantly associated with hair morphology in Europeans and reported weaker associations for WNT10A and FRAS1 genes. We conducted a study that centered on six SNPs located in these three genes with a sample of 528 individuals from Poland. The predictive capacity of the candidate DNA variants was evaluated using logistic regression; classification and regression trees; and neural networks, by applying a 10-fold cross validation procedure. Additionally, an independent test set of 142 males from six European populations was used to verify performance of the developed prediction models. Our study confirmed association of rs11803731 (TCHH), rs7349332 (WNT10A) and rs1268789 (FRAS1) SNPs with hair morphology. The combined genotype risk score for straight hair had an odds ratio of 2.7 and these predictors explained ∼ 8.2% of the total variance. The selected three SNPs were found to predict straight hair with a high sensitivity but low specificity when a 10-fold cross validation procedure was applied and the best results were obtained using the neural networks approach (AUC=0.688, sensitivity=91.2%, specificity=23.0%). Application of the neural networks model with 65% probability threshold on an additional test set gave high sensitivity (81.4%) and improved specificity (50.0%) with a total of 78.7% correct calls, but a high non-classification rate (66.9%). The combined TTGGGG SNP genotype for rs11803731, rs7349332, rs1268789 (European frequency=4.5%) of all six straight hair-associated alleles was identified as the best

  9. Cow genotyping strategies for genomic selection in a small dairy cattle population.

    PubMed

    Jenko, J; Wiggans, G R; Cooper, T A; Eaglen, S A E; Luff, W G de L; Bichard, M; Pong-Wong, R; Woolliams, J A

    2017-01-01

    This study compares how different cow genotyping strategies increase the accuracy of genomic estimated breeding values (EBV) in dairy cattle breeds with low numbers. In these breeds, few sires have progeny records, and genotyping cows can improve the accuracy of genomic EBV. The Guernsey breed is a small dairy cattle breed with approximately 14,000 recorded individuals worldwide. Predictions of phenotypes of milk yield, fat yield, protein yield, and calving interval were made for Guernsey cows from England and Guernsey Island using genomic EBV, with training sets including 197 de-regressed proofs of genotyped bulls, with cows selected from among 1,440 genotyped cows using different genotyping strategies. Accuracies of predictions were tested using 10-fold cross-validation among the cows. Genomic EBV were predicted using 4 different methods: (1) pedigree BLUP, (2) genomic BLUP using only bulls, (3) univariate genomic BLUP using bulls and cows, and (4) bivariate genomic BLUP. Genotyping cows with phenotypes and using their data for the prediction of single nucleotide polymorphism effects increased the correlation between genomic EBV and phenotypes compared with using only bulls by 0.163±0.022 for milk yield, 0.111±0.021 for fat yield, and 0.113±0.018 for protein yield; a decrease of 0.014±0.010 for calving interval from a low base was the only exception. Genetic correlation between phenotypes from bulls and cows were approximately 0.6 for all yield traits and significantly different from 1. Only a very small change occurred in correlation between genomic EBV and phenotypes when using the bivariate model. It was always better to genotype all the cows, but when only half of the cows were genotyped, a divergent selection strategy was better compared with the random or directional selection approach. Divergent selection of 30% of the cows remained superior for the yield traits in 8 of 10 folds.

  10. A combined ligand-based and target-based drug design approach for G-protein coupled receptors: application to salvinorin A, a selective kappa opioid receptor agonist

    NASA Astrophysics Data System (ADS)

    Singh, Nidhi; Chevé, Gwénaël; Ferguson, David M.; McCurdy, Christopher R.

    2006-08-01

    Combined ligand-based and target-based drug design approaches provide a synergistic advantage over either method individually. Therefore, we set out to develop a powerful virtual screening model to identify novel molecular scaffolds as potential leads for the human KOP (hKOP) receptor employing a combined approach. Utilizing a set of recently reported derivatives of salvinorin A, a structurally unique KOP receptor agonist, a pharmacophore model was developed that consisted of two hydrogen bond acceptor and three hydrophobic features. The model was cross-validated by randomizing the data using the CatScramble technique. Further validation was carried out using a test set that performed well in classifying active and inactive molecules correctly. Simultaneously, a bovine rhodopsin based "agonist-bound" hKOP receptor model was also generated. The model provided more accurate information about the putative binding site of salvinorin A based ligands. Several protein structure-checking programs were used to validate the model. In addition, this model was in agreement with the mutation experiments carried out on KOP receptor. The predictive ability of the model was evaluated by docking a set of known KOP receptor agonists into the active site of this model. The docked scores correlated reasonably well with experimental p K i values. It is hypothesized that the integration of these two independently generated models would enable a swift and reliable identification of new lead compounds that could reduce time and cost of hit finding within the drug discovery and development process, particularly in the case of GPCRs.

  11. Development of 3D-QSAR Model for Acetylcholinesterase Inhibitors Using a Combination of Fingerprint, Molecular Docking, and Structure-Based Pharmacophore Approaches.

    PubMed

    Lee, Sehan; Barron, Mace G

    2015-11-01

    Acetylcholinesterase (AChE), a serine hydrolase vital for regulating the neurotransmitter acetylcholine in animals, has been used as a target for drugs and pesticides. With the increasing availability of AChE crystal structures, with or without ligands bound, structure-based approaches have been successfully applied to AChE inhibitors (AChEIs). The major limitation of these approaches has been the small applicability domain due to the lack of structural diversity in the training set. In this study, we developed a 3 dimensional quantitative structure-activity relationship (3D-QSAR) for inhibitory activity of 89 reversible and irreversible AChEIs including drugs and insecticides. A 3D-fingerprint descriptor encoding protein-ligand interactions was developed using molecular docking and structure-based pharmacophore to rationalize the structural requirements responsible for the activity of these compounds. The obtained 3D-QSAR model exhibited high correlation value (R(2) = 0.93) and low mean absolute error (MAE = 0.32 log units) for the training set (n = 63). The model was predictive across a range of structures as shown by the leave-one-out cross-validated correlation coefficient (Q(2) = 0.89) and external validation results (n = 26, R(2) = 0.89, and MAE = 0.38 log units). The model revealed that the compounds with high inhibition potency had proper conformation in the active site gorge and interacted with key amino acid residues, in particular Trp84 and Phe330 at the catalytic anionic site, Trp279 at the peripheral anionic site, and Gly118, Gly119, and Ala201 at the oxyanion hole. The resulting universal 3D-QSAR model provides insight into the multiple molecular interactions determining AChEI potency that may guide future chemical design and regulation of toxic AChEIs.

  12. An in vitro approach for lipolysis measurement using high-resolution mass spectrometry and partial least squares based analysis.

    PubMed

    Chang, Wen-Qi; Zhou, Jian-Liang; Li, Yi; Shi, Zi-Qi; Wang, Li; Yang, Jie; Li, Ping; Liu, Li-Fang; Xin, Gui-Zhong

    2017-01-15

    The elevation of free fatty acids (FFAs) has been regarded as a universal metabolic signature of excessive adipocyte lipolysis. Nowadays, in vitro lipolysis assay is generally essential for drug screening prior to the animal study. Here, we present a novel in vitro approach for lipolysis measurement combining UHPLC-Orbitrap and partial least squares (PLS) based analysis. Firstly, the calibration matrix was constructed by serial proportions of mixed samples (blended with control and model samples). Then, lipidome profiling was performed by UHPLC-Orbitrap, and 403 variables were extracted and aligned as dataset. Owing to the high resolution of Orbitrap analyzer and open source lipid identification software, 28 FFAs were further screened and identified. Based on the relative intensity of the screened FFAs, PLS regression model was constructed for lipolysis measurement. After leave-one-out cross-validation, ten principal components have been designated to build the final PLS model with excellent performances (RMSECV, 0.0268; RMSEC, 0.0173; R(2), 0.9977). In addition, the high predictive accuracy (R(2) = 0.9907 and RMSEP = 0.0345) of the trained PLS model was also demonstrated using test samples. Finally, taking curcumin as a model compound, its antilipolytic effect on palmitic acid-induced lipolysis was successfully predicted as 31.78% by the proposed approach. Besides, supplementary evidences of curcumin induced modification in FFAs compositions as well as lipidome were given by PLS extended methods. Different from general biological assays, high resolution MS-based method provide more sophisticated information included in biological events. Thus, the novel biological evaluation model proposed here showed promising perspectives for drug evaluation or disease diagnosis.

  13. Analysis of alcoholic EEG signals based on horizontal visibility graph entropy.

    PubMed

    Zhu, Guohun; Li, Yan; Wen, Peng Paul; Wang, Shuaifang

    2014-12-01

    This paper proposes a novel horizontal visibility graph entropy (HVGE) approach to evaluate EEG signals from alcoholic subjects and controlled drinkers and compare with a sample entropy (SaE) method. Firstly, HVGEs and SaEs are extracted from 1,200 recordings of biomedical signals, respectively. A statistical analysis method is employed to choose the optimal channels to identify the abnormalities in alcoholics. Five group channels are selected and forwarded to a K-Nearest Neighbour (K-NN) and a support vector machine (SVM) to conduct classification, respectively. The experimental results show that the HVGEs associated with left hemisphere, [Formula: see text]1, [Formula: see text]3 and FC5 electrodes, of alcoholics are significantly abnormal. The accuracy of classification with 10-fold cross-validation is 87.5 [Formula: see text] with about three HVGE features. By using just optimal 13-dimension HVGE features, the accuracy is 95.8 [Formula: see text]. In contrast, SaE features associated cannot identify the left hemisphere disorder for alcoholism and the maximum classification ratio based on SaE is just 95.2 [Formula: see text] even using all channel signals. These results demonstrate that the HVGE method is a promising approach for alcoholism identification by EEG signals.

  14. Segmentation of skin strata in reflectance confocal microscopy depth stacks

    NASA Astrophysics Data System (ADS)

    Hames, Samuel C.; Ardigò, Marco; Soyer, H. Peter; Bradley, Andrew P.; Prow, Tarl W.

    2015-03-01

    Reflectance confocal microscopy is an emerging tool for imaging human skin, but currently requires expert human assessment. To overcome the need for human experts it is necessary to develop automated tools for automatically assessing reflectance confocal microscopy imagery. This work presents a novel approach to this task, using a bag of visual words approach to represent and classify en-face optical sections from four distinct strata of the skin. A dictionary of representative features is learned from whitened and normalised patches using hierarchical spherical k-means. Each image is then represented by extracting a dense array of patches and encoding each with the most similar element in the dictionary. Linear discriminant analysis is used as a simple linear classifier. The proposed framework was tested on 308 depth stacks from 54 volunteers. Parameters are tuned using 10 fold cross validation on a training sub-set of the data, and final evaluation was performed on a held out test set. The proposed method generated physically plausible profiles of the distinct strata of human skin, and correctly classified 81.4% of sections in the test set.

  15. Comparison of machine-learning methods for above-ground biomass estimation based on Landsat imagery

    NASA Astrophysics Data System (ADS)

    Wu, Chaofan; Shen, Huanhuan; Shen, Aihua; Deng, Jinsong; Gan, Muye; Zhu, Jinxia; Xu, Hongwei; Wang, Ke

    2016-07-01

    Biomass is one significant biophysical parameter of a forest ecosystem, and accurate biomass estimation on the regional scale provides important information for carbon-cycle investigation and sustainable forest management. In this study, Landsat satellite imagery data combined with field-based measurements were integrated through comparisons of five regression approaches [stepwise linear regression, K-nearest neighbor, support vector regression, random forest (RF), and stochastic gradient boosting] with two different candidate variable strategies to implement the optimal spatial above-ground biomass (AGB) estimation. The results suggested that RF algorithm exhibited the best performance by 10-fold cross-validation with respect to R2 (0.63) and root-mean-square error (26.44 ton/ha). Consequently, the map of estimated AGB was generated with a mean value of 89.34 ton/ha in northwestern Zhejiang Province, China, with a similar pattern to the distribution mode of local forest species. This research indicates that machine-learning approaches associated with Landsat imagery provide an economical way for biomass estimation. Moreover, ensemble methods using all candidate variables, especially for Landsat images, provide an alternative for regional biomass simulation.

  16. SVM Based Descriptor Selection and Classification of Neurodegenerative Disease Drugs for Pharmacological Modeling.

    PubMed

    Shahid, Mohammad; Shahzad Cheema, Muhammad; Klenner, Alexander; Younesi, Erfan; Hofmann-Apitius, Martin

    2013-03-01

    Systems pharmacological modeling of drug mode of action for the next generation of multitarget drugs may open new routes for drug design and discovery. Computational methods are widely used in this context amongst which support vector machines (SVM) have proven successful in addressing the challenge of classifying drugs with similar features. We have applied a variety of such SVM-based approaches, namely SVM-based recursive feature elimination (SVM-RFE). We use the approach to predict the pharmacological properties of drugs widely used against complex neurodegenerative disorders (NDD) and to build an in-silico computational model for the binary classification of NDD drugs from other drugs. Application of an SVM-RFE model to a set of drugs successfully classified NDD drugs from non-NDD drugs and resulted in overall accuracy of ∼80 % with 10 fold cross validation using 40 top ranked molecular descriptors selected out of total 314 descriptors. Moreover, SVM-RFE method outperformed linear discriminant analysis (LDA) based feature selection and classification. The model reduced the multidimensional descriptors space of drugs dramatically and predicted NDD drugs with high accuracy, while avoiding over fitting. Based on these results, NDD-specific focused libraries of drug-like compounds can be designed and existing NDD-specific drugs can be characterized by a well-characterized set of molecular descriptors.

  17. Combining Land-Use Regression and Chemical Transport Modeling in a Spatiotemporal Geostatistical Model for Ozone and PM2.5.

    PubMed

    Wang, Meng; Sampson, Paul D; Hu, Jianlin; Kleeman, Michael; Keller, Joshua P; Olives, Casey; Szpiro, Adam A; Vedal, Sverre; Kaufman, Joel D

    2016-05-17

    Assessments of long-term air pollution exposure in population studies have commonly employed land-use regression (LUR) or chemical transport modeling (CTM) techniques. Attempts to incorporate both approaches in one modeling framework are challenging. We present a novel geostatistical modeling framework, incorporating CTM predictions into a spatiotemporal LUR model with spatial smoothing to estimate spatiotemporal variability of ozone (O3) and particulate matter with diameter less than 2.5 μm (PM2.5) from 2000 to 2008 in the Los Angeles Basin. The observations include over 9 years' data from more than 20 routine monitoring sites and specific monitoring data at over 100 locations to provide more comprehensive spatial coverage of air pollutants. Our composite modeling approach outperforms separate CTM and LUR models in terms of root-mean-square error (RMSE) assessed by 10-fold cross-validation in both temporal and spatial dimensions, with larger improvement in the accuracy of predictions for O3 (RMSE [ppb] for CTM, 6.6; LUR, 4.6; composite, 3.6) than for PM2.5 (RMSE [μg/m(3)] CTM: 13.7, LUR: 3.2, composite: 3.1). Our study highlights the opportunity for future exposure assessment to make use of readily available spatiotemporal modeling methods and auxiliary gridded data that takes chemical reaction processes into account to improve the accuracy of predictions in a single spatiotemporal modeling framework.

  18. [Prediction of lipases types by different scale pseudo-amino acid composition].

    PubMed

    Zhang, Guangya; Li, Hongchun; Gao, Jiaqiang; Fang, Baishan

    2008-11-01

    Lipases are widely used enzymes in biotechnology. Although they catalyze the same reaction, their sequences vary. Therefore, it is highly desired to develop a fast and reliable method to identify the types of lipases according to their sequences, or even just to confirm whether they are lipases or not. By proposing two scales based pseudo amino acid composition approaches to extract the features of the sequences, a powerful predictor based on k-nearest neighbor was introduced to address the problems. The overall success rates thus obtained by the 10-fold cross-validation test were shown as below: for predicting lipases and nonlipase, the success rates were 92.8%, 91.4% and 91.3%, respectively. For lipase types, the success rates were 92.3%, 90.3% and 89.7%, respectively. Among them, the Z scales based pseudo amino acid composition was the best, T scales was the second. They outperformed significantly than 6 other frequently used sequence feature extraction methods. The high success rates yielded for such a stringent dataset indicate predicting the types of lipases is feasible and the different scales pseudo amino acid composition might be a useful tool for extracting the features of protein sequences, or at lease can play a complementary role to many of the other existing approaches.

  19. A Pathological Brain Detection System based on Extreme Learning Machine Optimized by Bat Algorithm.

    PubMed

    Lu, Siyuan; Qiu, Xin; Shi, Jianpin; Li, Na; Lu, Zhi-Hai; Chen, Peng; Yang, Meng-Meng; Liu, Fang-Yuan; Jia, Wen-Juan; Zhang, Yudong

    2016-10-19

    (Aim) It is beneficial to classify brain images as healthy or pathological automatically, because 3D brain images can generate so much information that it is time consuming and tedious for manual analysis. Among various 3D brain imaging techniques, magnetic resonance (MR) imaging is the most suitable for brain, and it is now widely applied in hospitals, because it is helpful in the four ways of diagnosis, prognosis, pre-surgical, and postsurgical procedures. Although there are automatic detection methods, they suffer from low accuracy. (Method) Therefore, we proposed a novel approach which employed 2D discrete wavelet transform (DWT), and calculated the entropies of the subbands as features. Then, a bat algorithm optimized extreme learning machine (BA-ELM) was trained to identify pathological brains from healthy controls. A 10x10-fold cross validation was performed to evaluate the out-of-sample performance. (Result) The method achieved a sensitivity of 99.04%, a specificity of 93.89%, and an overall accuracy of 98.33% over 132 MR brain images. (Conclusion) The experimental results suggest the proposed approach is accurate and robust in pathological brain detection.

  20. Using nanoinformatics methods for automatically identifying relevant nanotoxicology entities from the literature.

    PubMed

    García-Remesal, Miguel; García-Ruiz, Alejandro; Pérez-Rey, David; de la Iglesia, Diana; Maojo, Víctor

    2013-01-01

    Nanoinformatics is an emerging research field that uses informatics techniques to collect, process, store, and retrieve data, information, and knowledge on nanoparticles, nanomaterials, and nanodevices and their potential applications in health care. In this paper, we have focused on the solutions that nanoinformatics can provide to facilitate nanotoxicology research. For this, we have taken a computational approach to automatically recognize and extract nanotoxicology-related entities from the scientific literature. The desired entities belong to four different categories: nanoparticles, routes of exposure, toxic effects, and targets. The entity recognizer was trained using a corpus that we specifically created for this purpose and was validated by two nanomedicine/nanotoxicology experts. We evaluated the performance of our entity recognizer using 10-fold cross-validation. The precisions range from 87.6% (targets) to 93.0% (routes of exposure), while recall values range from 82.6% (routes of exposure) to 87.4% (toxic effects). These results prove the feasibility of using computational approaches to reliably perform different named entity recognition (NER)-dependent tasks, such as for instance augmented reading or semantic searches. This research is a "proof of concept" that can be expanded to stimulate further developments that could assist researchers in managing data, information, and knowledge at the nanolevel, thus accelerating research in nanomedicine.

  1. Using Nanoinformatics Methods for Automatically Identifying Relevant Nanotoxicology Entities from the Literature

    PubMed Central

    García-Remesal, Miguel; García-Ruiz, Alejandro; Pérez-Rey, David; de la Iglesia, Diana; Maojo, Víctor

    2013-01-01

    Nanoinformatics is an emerging research field that uses informatics techniques to collect, process, store, and retrieve data, information, and knowledge on nanoparticles, nanomaterials, and nanodevices and their potential applications in health care. In this paper, we have focused on the solutions that nanoinformatics can provide to facilitate nanotoxicology research. For this, we have taken a computational approach to automatically recognize and extract nanotoxicology-related entities from the scientific literature. The desired entities belong to four different categories: nanoparticles, routes of exposure, toxic effects, and targets. The entity recognizer was trained using a corpus that we specifically created for this purpose and was validated by two nanomedicine/nanotoxicology experts. We evaluated the performance of our entity recognizer using 10-fold cross-validation. The precisions range from 87.6% (targets) to 93.0% (routes of exposure), while recall values range from 82.6% (routes of exposure) to 87.4% (toxic effects). These results prove the feasibility of using computational approaches to reliably perform different named entity recognition (NER)-dependent tasks, such as for instance augmented reading or semantic searches. This research is a “proof of concept” that can be expanded to stimulate further developments that could assist researchers in managing data, information, and knowledge at the nanolevel, thus accelerating research in nanomedicine. PMID:23509721

  2. Unconventional approaches to fusion

    SciTech Connect

    Brunelli, B.; Leotta, G.G.

    1982-01-01

    This volume is dedicated to unconventional approaches to fusionthose thermonuclear reactors that, in comparison with Tokamak and other main lines, have received little attention in the worldwide scientific community. Many of the approaches considered are still in the embryonic stages. The authors-an international group of active nuclear scientists and engineers-focus on the parameters achieved in the use of these reactors and on the meaning of the most recent physical studies and their implications for the future. They also compare these approaches with conventional ones, the Tokamak in particular, stressing the non-plasma-physics requirements of fusion reactors. Unconventional compact toroids, linear systems, and multipoles are considered, as are the ''almost conventional'' fusion machines: stellarators, mirrors, reversed-field pinches, and EBT.

  3. Technical approach document

    SciTech Connect

    Not Available

    1989-12-01

    The Uranium Mill Tailings Radiation Control Act (UMTRCA) of 1978, Public Law 95-604 (PL95-604), grants the Secretary of Energy the authority and responsibility to perform such actions as are necessary to minimize radiation health hazards and other environmental hazards caused by inactive uranium mill sites. This Technical Approach Document (TAD) describes the general technical approaches and design criteria adopted by the US Department of Energy (DOE) in order to implement remedial action plans (RAPS) and final designs that comply with EPA standards. It does not address the technical approaches necessary for aquifer restoration at processing sites; a guidance document, currently in preparation, will describe aquifer restoration concerns and technical protocols. This document is a second revision to the original document issued in May 1986; the revision has been made in response to changes to the groundwater standards of 40 CFR 192, Subparts A--C, proposed by EPA as draft standards. New sections were added to define the design approaches and designs necessary to comply with the groundwater standards. These new sections are in addition to changes made throughout the document to reflect current procedures, especially in cover design, water resources protection, and alternate site selection; only minor revisions were made to some of the sections. Sections 3.0 is a new section defining the approach taken in the design of disposal cells; Section 4.0 has been revised to include design of vegetated covers; Section 8.0 discusses design approaches necessary for compliance with the groundwater standards; and Section 9.0 is a new section dealing with nonradiological hazardous constituents. 203 refs., 18 figs., 26 tabs.

  4. Financial Management: An Organic Approach

    ERIC Educational Resources Information Center

    Laux, Judy

    2013-01-01

    Although textbooks present corporate finance using a topical approach, good financial management requires an organic approach that integrates the various assignments financial managers confront every day. Breaking the tasks into meaningful subcategories, the current article offers one approach.

  5. Optimized approach to decision fusion of heterogeneous data for breast cancer diagnosis

    SciTech Connect

    Jesneck, Jonathan L.; Nolte, Loren W.; Baker, Jay A.; Floyd, Carey E.; Lo, Joseph Y.

    2006-08-15

    As more diagnostic testing options become available to physicians, it becomes more difficult to combine various types of medical information together in order to optimize the overall diagnosis. To improve diagnostic performance, here we introduce an approach to optimize a decision-fusion technique to combine heterogeneous information, such as from different modalities, feature categories, or institutions. For classifier comparison we used two performance metrics: The receiving operator characteristic (ROC) area under the curve [area under the ROC curve (AUC)] and the normalized partial area under the curve (pAUC). This study used four classifiers: Linear discriminant analysis (LDA), artificial neural network (ANN), and two variants of our decision-fusion technique, AUC-optimized (DF-A) and pAUC-optimized (DF-P) decision fusion. We applied each of these classifiers with 100-fold cross-validation to two heterogeneous breast cancer data sets: One of mass lesion features and a much more challenging one of microcalcification lesion features. For the calcification data set, DF-A outperformed the other classifiers in terms of AUC (p<0.02) and achieved AUC=0.85{+-}0.01. The DF-P surpassed the other classifiers in terms of pAUC (p<0.01) and reached pAUC=0.38{+-}0.02. For the mass data set, DF-A outperformed both the ANN and the LDA (p<0.04) and achieved AUC=0.94{+-}0.01. Although for this data set there were no statistically significant differences among the classifiers' pAUC values (pAUC=0.57{+-}0.07 to 0.67{+-}0.05, p>0.10), the DF-P did significantly improve specificity versus the LDA at both 98% and 100% sensitivity (p<0.04). In conclusion, decision fusion directly optimized clinically significant performance measures, such as AUC and pAUC, and sometimes outperformed two well-known machine-learning techniques when applied to two different breast cancer data sets.

  6. Predicting the efficacy of radiotherapy in individual glioblastoma patients in vivo: a mathematical modeling approach

    NASA Astrophysics Data System (ADS)

    Rockne, R.; Rockhill, J. K.; Mrugala, M.; Spence, A. M.; Kalet, I.; Hendrickson, K.; Lai, A.; Cloughesy, T.; Alvord, E. C., Jr.; Swanson, K. R.

    2010-06-01

    Glioblastoma multiforme (GBM) is the most malignant form of primary brain tumors known as gliomas. They proliferate and invade extensively and yield short life expectancies despite aggressive treatment. Response to treatment is usually measured in terms of the survival of groups of patients treated similarly, but this statistical approach misses the subgroups that may have responded to or may have been injured by treatment. Such statistics offer scant reassurance to individual patients who have suffered through these treatments. Furthermore, current imaging-based treatment response metrics in individual patients ignore patient-specific differences in tumor growth kinetics, which have been shown to vary widely across patients even within the same histological diagnosis and, unfortunately, these metrics have shown only minimal success in predicting patient outcome. We consider nine newly diagnosed GBM patients receiving diagnostic biopsy followed by standard-of-care external beam radiation therapy (XRT). We present and apply a patient-specific, biologically based mathematical model for glioma growth that quantifies response to XRT in individual patients in vivo. The mathematical model uses net rates of proliferation and migration of malignant tumor cells to characterize the tumor's growth and invasion along with the linear-quadratic model for the response to radiation therapy. Using only routinely available pre-treatment MRIs to inform the patient-specific bio-mathematical model simulations, we find that radiation response in these patients, quantified by both clinical and model-generated measures, could have been predicted prior to treatment with high accuracy. Specifically, we find that the net proliferation rate is correlated with the radiation response parameter (r = 0.89, p = 0.0007), resulting in a predictive relationship that is tested with a leave-one-out cross-validation technique. This relationship predicts the tumor size post-therapy to within inter

  7. Mapping Natural Terroir Units using a multivariate approach and legacy data

    NASA Astrophysics Data System (ADS)

    Priori, Simone; Barbetti, Roberto; L'Abate, Giovanni; Bucelli, Piero; Storchi, Paolo; Costantini, Edoardo A. C.

    2014-05-01

    Natural Terroir Unit (NTU) is a volume of earth's biosphere that is characterized by a stable set of variables related to the topography, climate, geology and soil. Methods to study the association soil-climate-vines are numerous, but the main question is always: which variables are actually important for the quality and the typicality of grapevines, and then wine, for a particular scale? This work aimed to setting up a multivariate methodology to define viticultural terroirs at the province scale (1:125,000), using viticultural and oenological legacy data. The study area was the Siena province in the Tuscany region (Central Italy). The reference grapevine cultivar was "Sangiovese", which is the most important cultivar of the region. The methodology was based upon the creation of a GIS storing several viticultural and oenological legacy data of 55 experimental vineyards (vintages between 1989-2009), the long term climate data, the digital elevation model, the soil-landscapes (land systems) and the soil profiles with the soil analysis. The selected viticultural and oenological parameters were: must sugar content, sugar accumulation rate from veraison to harvest, must titratable acidity, grape yield per vine, number of bunches for vine, mean bunch weight, and mean weight of berries. The environmental parameters related to viticulture, selected by an explorative PCA, were: elevation, mean annual temperature, mean soil temperature, annual precipitation, clay, sand and gravel content of soils, soil water availability, redoximorphic features and rooting depth. The geostatistical models of the variables interpolation were chosen on the best of mean standardize error, obtained by the cross-validation, between "Simple cokriging with varying local mean", "Multicollocated simple cokriging with varying local mean" and "Regression kriging". These variables were used for a k-means clustering aimed to map the Natural Terroirs Units (NTUs). The viticultural areas of Siena province

  8. Implementation of Communicative Approach

    ERIC Educational Resources Information Center

    Jabeen, Shazi Shah

    2014-01-01

    In the contemporary age of high professional requirements such as excellent communicative skills, the need for successful learning of communicative skills of English language suggests communicative ability to be the goal of language teaching. In other words, to teach English language using communicative approach becomes essential. Studies to…

  9. USEPA WATERSHED APPROACH

    EPA Science Inventory

    The U.S. Environmental Protection Agency's Office of Research and Development has developed a well defined research plan to evaluate pollutants within watersheds. This plan is defined by long term goals and annual performance measures. The first goal is to provide the approache...

  10. Adopting a Pluricentric Approach

    ERIC Educational Resources Information Center

    van Kerckvoorde, Colette

    2012-01-01

    This article argues for a "D-A-CH" approach, which stands for Germany (D), Austria (A), and Switzerland (CH), in language classes from the introductory level on. I begin by tracing the emergence and development of distinct Standard Swiss and Austrian German varieties. I then discuss marketing efforts for Swiss and Austrian German, and…

  11. External approach to rhinoplasty.

    PubMed

    Goodman, Wilfred S; Charbonneau, Paul A

    2015-07-01

    The technique of external rhinoplasty is outlined. Having reviewed 74 cases, its advantages and disadvantages are discussed. Reluctance to use this external approach seems to be based on emotional rather than radical grounds, for its seems to be the procedure of choice for many problems.

  12. NEW APPROACHES: Toppling trains

    NASA Astrophysics Data System (ADS)

    Parry, Malcolm

    1998-03-01

    This article explains a novel way of approaching centripetal force: theory is used to predict an orbital period at which a toy train will topple from a circular track. The demonstration has proved useful in A-level, GNVQ and undergraduate Physics and Engineering schemes.

  13. Approaches to acceptable risk

    SciTech Connect

    Whipple, C.

    1997-04-30

    Several alternative approaches to address the question {open_quotes}How safe is safe enough?{close_quotes} are reviewed and an attempt is made to apply the reasoning behind these approaches to the issue of acceptability of radiation exposures received in space. The approaches to the issue of the acceptability of technological risk described here are primarily analytical, and are drawn from examples in the management of environmental health risks. These include risk-based approaches, in which specific quantitative risk targets determine the acceptability of an activity, and cost-benefit and decision analysis, which generally focus on the estimation and evaluation of risks, benefits and costs, in a framework that balances these factors against each other. These analytical methods tend by their quantitative nature to emphasize the magnitude of risks, costs and alternatives, and to downplay other factors, especially those that are not easily expressed in quantitative terms, that affect acceptance or rejection of risk. Such other factors include the issues of risk perceptions and how and by whom risk decisions are made.

  14. Salt repository design approach

    SciTech Connect

    Matthews, S.C.

    1983-01-01

    This paper presents a summary discussion of the approaches that have been and will be taken in design of repository facilities for use with disposal of radioactive wastes in salt formations. Since specific sites have yet to be identified, the discussion is at a general level, supplemented with illustrative examples where appropriate. 5 references, 1 figure.

  15. Marxian Approaches to Education.

    ERIC Educational Resources Information Center

    Carnoy, Martin

    Traditional Marxist approaches to the state relegate superstructural institutions like the school to a minor role in the process of social change. More recent theories like those of Gramsci, Althusser, and Poulantzas raise the state and the class struggle in the state apparatuses to a much more prominent position: superstructure, including the…

  16. New Ideas and Approaches

    ERIC Educational Resources Information Center

    Lukov, V. A.

    2014-01-01

    The article examines theories of youth that have been proposed in the past few years by Russian scientists, and presents the author's original version of a theory of youth that is based on the thesaurus methodological approach. It addresses the ways in which biosocial characteristics may be reflected in new theories of youth.

  17. Dystonia: a clinical approach.

    PubMed

    Edwards, Mark J

    2008-12-01

    Dystonia is a common movement disorder characterised by abnormal postures of the affected body part. It has a very varied presentation and numerous causes, and this can create difficulties with diagnosis and appropriate investigation. This article aims to provide a clinical approach to patients with dystonia, focussing on how to create a differential diagnosis and to plan rational testing.

  18. A Fresh Approach

    ERIC Educational Resources Information Center

    Violino, Bob

    2011-01-01

    Facilities and services are a huge drain on community college budgets. They are also vital to the student experience. As funding dries up across the country, many institutions are taking a team approach, working with partner colleges and private service providers to offset costs and generate revenue without sacrificing the services and amenities…

  19. Islamic approach in counseling.

    PubMed

    Hanin Hamjah, Salasiah; Mat Akhir, Noor Shakirah

    2014-02-01

    A religious approach is one of the matters emphasized in counseling today. Many researchers find that there is a need to apply the religious element in counseling because religion is important in a client's life. The purpose of this research is to identify aspects of the Islamic approach applied in counseling clients by counselors at Pusat Kaunseling Majlis Agama Islam Negeri Sembilan (PKMAINS). In addition, this research also analyses the Islamic approach applied in counseling at PKMAINS with reference to al-Quran and al-Sunnah. This is a qualitative research in the form of case study at PKMAINS. The main method used in this research is interview. The research instrument used is interview protocol. The respondents in this study include 9 counselors who serve in one of the counseling centers in Malaysia. This study also uses questionnaire as an additional instrument, distributed to 36 clients who receive counseling service at the center. The findings of the study show that the Islamic approach applied in counseling at PKMAINS may be categorized into three main aspects: aqidah (faith), ibadah (worship/ultimate devotion and love for God) and akhlaq (moral conduct). Findings also show that the counseling in these aspects is in line with Islamic teachings as contained in al-Quran and al-Sunnah.

  20. SYSTEMS APPROACH TO LEARNING.

    ERIC Educational Resources Information Center

    WIENS, JACOB H.

    TO PERMIT COMPARATIVE ANALYSIS FOR PURPOSES OF EDUCATIONAL PLANNING AT SAN MATEO, FIVE INSTITUTIONS WITH SYSTEMS PROGRAMS ARE EVALUATED ON THE BASIS OF TRIP NOTES. OAKLAND COMMUNITY COLLEGE HAS BEEN COMPLETELY ORGANIZED AROUND THE VOLUNTARY WORK-STUDY LABORATORY APPROACH TO LEARNING. ORAL ROBERTS UNIVERSITY, OKLAHOMA CHRISTIAN COLLEGE, HENRY FORD…

  1. Predicting Optimal Outcomes in Cognitive Therapy or Interpersonal Psychotherapy for Depressed Individuals Using the Personalized Advantage Index Approach

    PubMed Central

    Huibers, Marcus J. H.; Cohen, Zachary D.; Lemmens, Lotte H. J. M.; Arntz, Arnoud; Peeters, Frenk P. M. L.; Cuijpers, Pim; DeRubeis, Robert J.

    2015-01-01

    Introduction Although psychotherapies for depression produce equivalent outcomes, individual patients respond differently to different therapies. Predictors of outcome have been identified in the context of randomized trials, but this information has not been used to predict which treatment works best for the depressed individual. In this paper, we aim to replicate a recently developed treatment selection method, using data from an RCT comparing the effects of cognitive therapy (CT) and interpersonal psychotherapy (IPT). Methods 134 depressed patients completed the pre- and post-treatment BDI-II assessment. First, we identified baseline predictors and moderators. Second, individual treatment recommendations were generated by combining the identified predictors and moderators in an algorithm that produces the Personalized Advantage Index (PAI), a measure of the predicted advantage in one therapy compared to the other, using standard regression analyses and the leave-one-out cross-validation approach. Results We found five predictors (gender, employment status, anxiety, personality disorder and quality of life) and six moderators (somatic complaints, cognitive problems, paranoid symptoms, interpersonal self-sacrificing, attributional style and number of life events) of treatment outcome. The mean average PAI value was 8.9 BDI points, and 63% of the sample was predicted to have a clinically meaningful advantage in one of the therapies. Those who were randomized to their predicted optimal treatment (either CT or IPT) had an observed mean end-BDI of 11.8, while those who received their predicted non-optimal treatment had an end-BDI of 17.8 (effect size for the difference = 0.51). Discussion Depressed patients who were randomized to their predicted optimal treatment fared much better than those randomized to their predicted non-optimal treatment. The PAI provides a great opportunity for formal decision-making to improve individual patient outcomes in depression. Although

  2. DISTRIBUTIONS OF INDIVIDUAL SUSCEPTIBILITY AMONG HUMANS FOR TOXIC EFFECTS--FOR WHAT FRACTION OF WHICH KINDS OF CHEMICALS AND EFFECTS DOES THE TRADITIONAL 10-FOLD FACTOR PROVIDE HOW MUCH PROTECTION? (R825360)

    EPA Science Inventory

    The perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Concl...

  3. Seasonal precipitation forecasts for selected regions in West Africa using circulation type classifications in combination with further statistical approaches - Conceptual framework and first results

    NASA Astrophysics Data System (ADS)

    Bliefernicht, Jan; Laux, Patrik; Waongo, Moussa; Kunstmann, Harald

    2015-04-01

    Providing valuable forecasts of the seasonal precipitation amount for the upcoming rainy season is one of the big challenges for the national weather services in West Africa. Every year a harmonized forecast of the seasonal precipitation amount for the West African region is issued by the national weather services within the PRESAO framework. The PREASO forecast is based on various statistical approaches ranging from a simple subjective analog method based on the experiences of a meteorological expert to objective regression-based approaches by using various sources of input information such as predicted monsoon winds or observed sea surface temperature anomalies close to the West African coastline. The objective of this study is to perform an evaluation of these techniques for selected West African regions and to introduce classification techniques in the current operational practices and to combine these approaches with further techniques for an additional refinement of the forecasting procedure. We use a fuzzy-rule based technique for a classification of (sub-) monthly large-scale atmospheric and oceanic patterns which are combined to further statistical approaches such as an analog method and a data depth approach for the prediction of the (sub-) seasonal precipitation amounts and additional precipitation indices. The study regions are located from the Edges of the Sahel region in the North of Burkina Faso to the coastline of Ghana. A novel precipitation archive based on daily observations provided by the meteorological services of Burkina Faso and Ghana is the basis for the predictands and is used as reference for model evaluation. The performance of the approach is evaluated over a long period (e.g. 50 years) using cross-validation techniques and sophisticated verification measures for an evaluation of a probability forecast. The precipitation forecast of the classification techniques are also compared to the techniques of the PREASAO community, the

  4. Domain Approach: An Alternative Approach in Moral Education

    ERIC Educational Resources Information Center

    Vengadasalam, Chander; Mamat, Wan Hasmah Wan; Mail, Fauziah; Sudramanian, Munimah

    2014-01-01

    This paper discusses the use of the domain approach in moral education in an upper secondary school in Malaysia. Moral Education needs a creative and an innovative approach. Therefore, a few forms of approaches are used in the teaching-learning of Moral Education. This research describes the use of domain approach which comprises the moral domain…

  5. Controlled incremental filtration: a simplified approach to design and fabrication of high-throughput microfluidic devices for selective enrichment of particles.

    PubMed

    Gifford, Sean C; Spillane, Angela M; Vignes, Seth M; Shevkoplyas, Sergey S

    2014-12-07

    The number of microfluidic strategies aimed at separating particles or cells of a specific size within a continuous flow system continues to grow. The wide array of biomedical and other applications that would benefit from successful development of such technology has motivated the extensive research in this area over the past 15 years. However, despite promising advancements in microfabrication capabilities, a versatile approach that is suitable for a large range of particle sizes and high levels of enrichment, with a volumetric throughput sufficient for large-scale applications, has yet to emerge. Here we describe a straightforward method that enables the rapid design of microfluidic devices that are capable of enriching/removing particles within a complex aqueous mixture, with an unprecedented range of potential cutoff diameter (below 1 μm to above 100 μm) and an easily scalable degree of enrichment/filtration (up to 10-fold and well beyond). A simplified model of a new approach to crossflow filtration - controlled incremental filtration - was developed and validated for its ability to generate microfluidic devices that efficiently separate particles on the order of 1-10 μm, with throughputs of tens of μL min(-1), without the use of a pump. Precise control of the amount of fluid incrementally diverted at each filtration "gap" of the device allows for the gap size (~20 μm) to be much larger than the particles of interest, while the simplicity of the model allows for many thousands of these filtration points to be readily incorporated into a desired device design. This new approach should enable truly high-throughput microfluidic particle-separation devices to be generated, even by users only minimally experienced in fluid mechanics and microfabrication techniques.

  6. Approach to hemorrhoids.

    PubMed

    Lohsiriwat, Varut

    2013-07-01

    Hemorrhoids are a very common anorectal disorder defined as the symptomatic enlargement and abnormally downward displacement of anal cushions. The current pathophysiologies of hemorrhoids include the degenerative change of supportive tissue within the anal cushions, vascular hyperplasia, and hyperperfusion of hemorrhoidal plexus. Low-grade hemorrhoids are easily and effectively treated with dietary and lifestyle modification, medical intervention, and some office-based procedures. An operation is usually indicated in symptomatic high-grade and/or complicated hemorrhoids. Whilst hemorrhoidectomy has been the mainstay of surgical treatment, more recently other approaches have been employed including Ligasure hemorrhoidectomy, stapled hemorrhoidopexy, and doppler-guided hemorrhoidal artery ligation. Post-procedural pain and disease recurrence remain the most challenging problems in the treatment of hemorrhoids. This article deals with modern approaches to hemorrhoids based on the latest evidence and reviews of the literature. The management of hemorrhoids in complicated situations is also discussed.

  7. Theoretical Approaches to Nanoparticles

    NASA Astrophysics Data System (ADS)

    Kempa, Krzysztof

    Nanoparticles can be viewed as wave resonators. Involved waves are, for example, carrier waves, plasmon waves, polariton waves, etc. A few examples of successful theoretical treatments that follow this approach are given. In one, an effective medium theory of a nanoparticle composite is presented. In another, plasmon polaritonic solutions allow to extend concepts of radio technology, such as an antenna and a coaxial transmission line, to the visible frequency range.

  8. NEW APPROACHES: Vision underwater

    NASA Astrophysics Data System (ADS)

    Steele, Alan L.

    1997-11-01

    A tutorial type problem examining the focusing performance of the human eye in air and in water is solved by two different approaches. Calculations show that light can be effectively focused on the retina when the eye is in air but not underwater, even with the usual accommodation. We then examine how some vertebrates have accommodation processes that permit them to see effectively both above and below water.

  9. The diesel approach

    SciTech Connect

    Anderson, J.L.

    1993-04-01

    Whether for standby or baseload capacity, diesel generator sets are being used in markets worldwide. Companies are taking a variety of approaches to tapping these markets. The markets for diesel generators follow two basic paths. In the US, they are used primarily for standby or peaking applications. Outside the US, the market includes standby applications but is more often for baseload or prime-power applications.

  10. AVOSS Development Approach

    NASA Technical Reports Server (NTRS)

    Hinton, David A.

    1997-01-01

    A concept is presented for development and implementation of prototype Aircraft Vortex Spacing System (AVOSS). The purpose of the AVOSS is to provide dynamical, weather dependent wake vortex separation criteria to ATC facilities with adequate stability and lead time for use in establishing arrival scheduling. This current paper discusses AVOSS development approach. The discussion includes: system model, AVOSS R&D effort scope, major development issues, concept system development process, AVOSS system testing, and concept demonstration.

  11. Parsec's astrometry direct approaches .

    NASA Astrophysics Data System (ADS)

    Andrei, A. H.

    Parallaxes - and hence the fundamental establishment of stellar distances - rank among the oldest, keyest, and hardest of astronomical determinations. Arguably amongst the most essential too. The direct approach to obtain trigonometric parallaxes, using a constrained set of equations to derive positions, proper motions, and parallaxes, has been labeled as risky. Properly so, because the axis of the parallactic apparent ellipse is smaller than one arcsec even for the nearest stars, and just a fraction of its perimeter can be followed. Thus the classical approach is of linearizing the description by locking the solution to a set of precise positions of the Earth at the instants of observation, rather than to the dynamics of its orbit, and of adopting a close examination of the never many points available. In the PARSEC program the parallaxes of 143 brown dwarfs were aimed at. Five years of observation of the fields were taken with the WIFI camera at the ESO 2.2m telescope, in Chile. The goal is to provide a statistically significant number of trigonometric parallaxes to BD sub-classes from L0 to T7. Taking advantage of the large, regularly spaced, quantity of observations, here we take the risky approach to fit an ellipse in ecliptical observed coordinates and derive the parallaxes. We also combine the solutions from different centroiding methods, widely proven in prior astrometric investigations. As each of those methods assess diverse properties of the PSFs, they are taken as independent measurements, and combined into a weighted least-square general solution.

  12. Three Approaches to Descriptive Research.

    ERIC Educational Resources Information Center

    Svensson, Lennart

    This report compares three approaches to descriptive research, focusing on the kinds of descriptions developed and on the methods used to develop the descriptions. The main emphasis in all three approaches is on verbal data. In these approaches the importance of interpretation and its intuitive nature are emphasized. The three approaches, however,…

  13. Current Approaches to Teaching Reading.

    ERIC Educational Resources Information Center

    Mackintosh, Helen K., Ed.

    Eight approaches to the teaching of elementary reading are described briefly. The Executive Committee of the Department of Elementary-Kindergarten-Nursery Education of the National Education Association selected the approaches to be discussed. They include (1) Language Experience Approach by R. V. Allen, (2) Phonic Approach by Charles E. Wingo,…

  14. Degeneration of penicillin production in ethanol-limited chemostat cultivations of Penicillium chrysogenum: A systems biology approach

    PubMed Central

    2011-01-01

    Background In microbial production of non-catabolic products such as antibiotics a loss of production capacity upon long-term cultivation (for example chemostat), a phenomenon called strain degeneration, is often observed. In this study a systems biology approach, monitoring changes from gene to produced flux, was used to study degeneration of penicillin production in a high producing Penicillium chrysogenum strain during prolonged ethanol-limited chemostat cultivations. Results During these cultivations, the biomass specific penicillin production rate decreased more than 10-fold in less than 22 generations. No evidence was obtained for a decrease of the copy number of the penicillin gene cluster, nor a significant down regulation of the expression of the penicillin biosynthesis genes. However, a strong down regulation of the biosynthesis pathway of cysteine, one of the precursors of penicillin, was observed. Furthermore the protein levels of the penicillin pathway enzymes L-α-(δ-aminoadipyl)-L-α-cystenyl-D-α-valine synthetase (ACVS) and isopenicillin-N synthase (IPNS), decreased significantly. Re-cultivation of fully degenerated cells in unlimited batch culture and subsequent C-limited chemostats did only result in a slight recovery of penicillin production. Conclusions Our findings indicate that the observed degeneration is attributed to a significant decrease of the levels of the first two enzymes of the penicillin biosynthesis pathway, ACVS and IPNS. This decrease is not caused by genetic instability of the penicillin amplicon, neither by down regulation of the penicillin biosynthesis pathway. Furthermore no indications were obtained for degradation of these enzymes as a result of autophagy. Possible causes for the decreased enzyme levels could be a decrease of the translation efficiency of ACVS and IPNS during degeneration, or the presence of a culture variant impaired in the biosynthesis of functional proteins of these enzymes, which outcompeted the high

  15. Detecting Lung Diseases from Exhaled Aerosols: Non-Invasive Lung Diagnosis Using Fractal Analysis and SVM Classification

    PubMed Central

    Xi, Jinxiang; Zhao, Weizhong; Yuan, Jiayao Eddie; Kim, JongWon; Si, Xiuhua; Xu, Xiaowei

    2015-01-01

    Background Each lung structure exhales a unique pattern of aerosols, which can be used to detect and monitor lung diseases non-invasively. The challenges are accurately interpreting the exhaled aerosol fingerprints and quantitatively correlating them to the lung diseases. Objective and Methods In this study, we presented a paradigm of an exhaled aerosol test that addresses the above two challenges and is promising to detect the site and severity of lung diseases. This paradigm consists of two steps: image feature extraction using sub-regional fractal analysis and data classification using a support vector machine (SVM). Numerical experiments were conducted to evaluate the feasibility of the breath test in four asthmatic lung models. A high-fidelity image-CFD approach was employed to compute the exhaled aerosol patterns under different disease conditions. Findings By employing the 10-fold cross-validation method, we achieved 100% classification accuracy among four asthmatic models using an ideal 108-sample dataset and 99.1% accuracy using a more realistic 324-sample dataset. The fractal-SVM classifier has been shown to be robust, highly sensitive to structural variations, and inherently suitable for investigating aerosol-disease correlations. Conclusion For the first time, this study quantitatively linked the exhaled aerosol patterns with their underlying diseases and set the stage for the development of a computer-aided diagnostic system for non-invasive detection of obstructive respiratory diseases. PMID:26422016

  16. Predicting drug-target interactions by dual-network integrated logistic matrix factorization

    NASA Astrophysics Data System (ADS)

    Hao, Ming; Bryant, Stephen H.; Wang, Yanli

    2017-01-01

    In this work, we propose a dual-network integrated logistic matrix factorization (DNILMF) algorithm to predict potential drug-target interactions (DTI). The prediction procedure consists of four steps: (1) inferring new drug/target profiles and constructing profile kernel matrix; (2) diffusing drug profile kernel matrix with drug structure kernel matrix; (3) diffusing target profile kernel matrix with target sequence kernel matrix; and (4) building DNILMF model and smoothing new drug/target predictions based on their neighbors. We compare our algorithm with the state-of-the-art method based on the benchmark dataset. Results indicate that the DNILMF algorithm outperforms the previously reported approaches in terms of AUPR (area under precision-recall curve) and AUC (area under curve of receiver operating characteristic) based on the 5 trials of 10-fold cross-validation. We conclude that the performance improvement depends on not only the proposed objective function, but also the used nonlinear diffusion technique which is important but under studied in the DTI prediction field. In addition, we also compile a new DTI dataset for increasing the diversity of currently available benchmark datasets. The top prediction results for the new dataset are confirmed by experimental studies or supported by other computational research.

  17. Computer-Aided Diagnosis for Breast Ultrasound Using Computerized BI-RADS Features and Machine Learning Methods.

    PubMed

    Shan, Juan; Alam, S Kaisar; Garra, Brian; Zhang, Yingtao; Ahmed, Tahira

    2016-04-01

    This work identifies effective computable features from the Breast Imaging Reporting and Data System (BI-RADS), to develop a computer-aided diagnosis (CAD) system for breast ultrasound. Computerized features corresponding to ultrasound BI-RADs categories were designed and tested using a database of 283 pathology-proven benign and malignant lesions. Features were selected based on classification performance using a "bottom-up" approach for different machine learning methods, including decision tree, artificial neural network, random forest and support vector machine. Using 10-fold cross-validation on the database of 283 cases, the highest area under the receiver operating characteristic (ROC) curve (AUC) was 0.84 from a support vector machine with 77.7% overall accuracy; the highest overall accuracy, 78.5%, was from a random forest with the AUC 0.83. Lesion margin and orientation were optimum features common to all of the different machine learning methods. These features can be used in CAD systems to help distinguish benign from worrisome lesions.

  18. Machine learning techniques for the prediction of the peptide mobility in capillary zone electrophoresis.

    PubMed

    Yu, Ke; Cheng, Yiyu

    2007-02-15

    Three machine learning techniques including back propagation artificial neural network (BP-ANN), radial basis function artificial neural network (RBF-ANN) and support vector regression (SVR) were applied to predicting the peptide mobility in capillary zone electrophoresis through the development of quantitative structure-mobility relationship (QSMR) models. A data set containing 102 peptides with a large range of size, charge and hydrophobicity was used as a typical study. The optimal modeling parameters of the models were determined by grid-searching approach using 10-fold cross-validation. The predicted results were compared with that obtained by the multiple linear regression (MLR) method. The results showed that the relative standard errors (R.S.E.) of the developed models for the test set obtained by MLR, BP-ANN, RBF-ANN and SVR were 11.21%, 7.47%, 5.79% and 5.75%, respectively, while the R.S.E.s for the external validation set were 11.18%, 7.87%, 7.54% and 7.18%, respectively. The better generalization ability of the QSMR models developed by machine learning techniques over MLR was exactly presented. It was shown that the machine learning techniques were effective for developing the accurate and relaible QSMR models.

  19. Classification of physiologically significant pumping states in an implantable rotary blood pump: patient trial results.

    PubMed

    Karantonis, Dean M; Mason, David G; Salamonsen, Robert F; Ayre, Peter J; Cloherty, Shaun L; Lovell, Nigel H

    2007-01-01

    An integral component in the development of a control strategy for implantable rotary blood pumps is the task of reliably detecting the occurrence of left ventricular collapse due to overpumping of the native heart. Using the noninvasive pump feedback signal of impeller speed, an approach to distinguish between overpumping (or ventricular collapse) and the normal pumping state has been developed. Noninvasive pump signals from 10 human pump recipients were collected, and the pumping state was categorized as either normal or suction, based on expert opinion aided by transesophageal echocardiographic images. A number of indices derived from the pump speed waveform were incorporated into a classification and regression tree model, which acted as the pumping state classifier. When validating the model on 12,990 segments of unseen data, this methodology yielded a peak sensitivity/specificity for detecting suction of 99.11%/98.76%. After performing a 10-fold cross-validation on all of the available data, a minimum estimated error of 0.53% was achieved. The results presented suggest that techniques for pumping state detection, previously investigated in preliminary in vivo studies, are applicable and sufficient for use in the clinical environment.

  20. Biomarker Discovery Based on Hybrid Optimization Algorithm and Artificial Neural Networks on Microarray Data for Cancer Classification.

    PubMed

    Moteghaed, Niloofar Yousefi; Maghooli, Keivan; Pirhadi, Shiva; Garshasbi, Masoud

    2015-01-01

    The improvement of high-through-put gene profiling based microarrays technology has provided monitoring the expression value of thousands of genes simultaneously. Detailed examination of changes in expression levels of genes can help physicians to have efficient diagnosing, classification of tumors and cancer's types as well as effective treatments. Finding genes that can classify the group of cancers correctly based on hybrid optimization algorithms is the main purpose of this paper. In this paper, a hybrid particle swarm optimization and genetic algorithm method are used for gene selection and also artificial neural network (ANN) is adopted as the classifier. In this work, we have improved the ability of the algorithm for the classification problem by finding small group of biomarkers and also best parameters of the classifier. The proposed approach is tested on three benchmark gene expression data sets: Blood (acute myeloid leukemia, acute lymphoblastic leukemia), colon and breast datasets. We used 10-fold cross-validation to achieve accuracy and also decision tree algorithm to find the relation between the biomarkers for biological point of view. To test the ability of the trained ANN models to categorize the cancers, we analyzed additional blinded samples that were not previously used for the training procedure. Experimental results show that the proposed method can reduce the dimension of the data set and confirm the most informative gene subset and improve classification accuracy with best parameters based on datasets.

  1. Using symbolic knowledge in the UMLS to disambiguate words in small datasets with a naïve Bayes classifier.

    PubMed

    Leroy, Gondy; Rindflesch, Thomas C

    2004-01-01

    Current approaches to word sense disambiguation use and combine various machine-learning techniques. Most refer to characteristics of the ambiguous word and surrounding words and are based on hundreds of examples. Unfortunately, developing large training sets is time-consuming. We investigate the use of symbolic knowledge to augment machine-learning techniques for small datasets. UMLS semantic types assigned to concepts found in the sentence and relationships between these semantic types form the knowledge base. A naïve Bayes classifier was trained for 15 words with 100 examples for each. The most frequent sense of a word served as the baseline. The effect of increasingly accurate symbolic knowledge was evaluated in eight experimental conditions. Performance was measured by accuracy based on 10-fold cross-validation. The best condition used only the semantic types of the words in the sentence. Accuracy was then on average 10% higher than the baseline; however, it varied from 8% deterioration to 29% improvement. In a follow-up evaluation, we noted a trend that the best disambiguation was found for words that were the least troublesome to the human evaluators.

  2. Prediction of fat-free body mass from bioelectrical impedance and anthropometry among 3-year-old children using DXA.

    PubMed

    Ejlerskov, Katrine T; Jensen, Signe M; Christensen, Line B; Ritz, Christian; Michaelsen, Kim F; Mølgaard, Christian

    2014-01-27

    For 3-year-old children suitable methods to estimate body composition are sparse. We aimed to develop predictive equations for estimating fat-free mass (FFM) from bioelectrical impedance (BIA) and anthropometry using dual-energy X-ray absorptiometry (DXA) as reference method using data from 99 healthy 3-year-old Danish children. Predictive equations were derived from two multiple linear regression models, a comprehensive model (height(2)/resistance (RI), six anthropometric measurements) and a simple model (RI, height, weight). Their uncertainty was quantified by means of 10-fold cross-validation approach. Prediction error of FFM was 3.0% for both equations (root mean square error: 360 and 356 g, respectively). The derived equations produced BIA-based prediction of FFM and FM near DXA scan results. We suggest that the predictive equations can be applied in similar population samples aged 2-4 years. The derived equations may prove useful for studies linking body composition to early risk factors and early onset of obesity.

  3. CSS-Palm 2.0: an updated software for palmitoylation sites prediction

    PubMed Central

    Ren, Jian; Wen, Longping; Gao, Xinjiao; Jin, Changjiang; Xue, Yu; Yao, Xuebiao

    2008-01-01

    Protein palmitoylation is an essential post-translational lipid modification of proteins, and reversibly orchestrates a variety of cellular processes. Identification of palmitoylated proteins with their sites is the foundation for understanding molecular mechanisms and regulatory roles of palmitoylation. Contrasting to the labor-intensive and time-consuming experimental approaches, in silico prediction of palmitoylation sites has attracted much attention as a popular strategy. In this work, we updated our previous CSS-Palm into version 2.0. An updated clustering and scoring strategy (CSS) algorithm was employed with great improvement. The leave-one-out validation and 4-, 6-, 8- and 10-fold cross-validations were adopted to evaluate the prediction performance of CSS-Palm 2.0. Also, an additional new data set not included in training was used to test the robustness of CSS-Palm 2.0. By comparison, the performance of CSS-Palm was much better than previous tools. As an application, we performed a small-scale annotation of palmitoylated proteins in budding yeast. The online service and local packages of CSS-Palm 2.0 were freely available at: http://bioinformatics.lcd-ustc.org/css_palm. PMID:18753194

  4. Generating One Biometric Feature from Another: Faces from Fingerprints

    PubMed Central

    Ozkaya, Necla; Sagiroglu, Seref

    2010-01-01

    This study presents a new approach based on artificial neural networks for generating one biometric feature (faces) from another (only fingerprints). An automatic and intelligent system was designed and developed to analyze the relationships among fingerprints and faces and also to model and to improve the existence of the relationships. The new proposed system is the first study that generates all parts of the face including eyebrows, eyes, nose, mouth, ears and face border from only fingerprints. It is also unique and different from similar studies recently presented in the literature with some superior features. The parameter settings of the system were achieved with the help of Taguchi experimental design technique. The performance and accuracy of the system have been evaluated with 10-fold cross validation technique using qualitative evaluation metrics in addition to the expanded quantitative evaluation metrics. Consequently, the results were presented on the basis of the combination of these objective and subjective metrics for illustrating the qualitative properties of the proposed methods as well as a quantitative evaluation of their performances. Experimental results have shown that one biometric feature can be determined from another. These results have once more indicated that there is a strong relationship between fingerprints and faces. PMID:22399877

  5. Expiratory and Inspiratory Cries Detection Using Different Signals' Decomposition Techniques.

    PubMed

    Abou-Abbas, Lina; Tadj, Chakib; Gargour, Christian; Montazeri, Leila

    2017-03-01

    This paper addresses the problem of automatic cry signal segmentation for the purposes of infant cry analysis. The main goal is to automatically detect expiratory and inspiratory phases from recorded cry signals. The approach used in this paper is made up of three stages: signal decomposition, features extraction, and classification. In the first stage, short-time Fourier transform, empirical mode decomposition (EMD), and wavelet packet transform have been considered. In the second stage, various set of features have been extracted, and in the third stage, two supervised learning methods, Gaussian mixture models and hidden Markov models, with four and five states, have been discussed as well. The main goal of this work is to investigate the EMD performance and to compare it with the other standard decomposition techniques. A combination of two and three intrinsic mode functions (IMFs) that resulted from EMD has been used to represent cry signal. The performance of nine different segmentation systems has been evaluated. The experiments for each system have been repeated several times with different training and testing datasets, randomly chosen using a 10-fold cross-validation procedure. The lowest global classification error rates of around 8.9% and 11.06% have been achieved using a Gaussian mixture models classifier and a hidden Markov models classifier, respectively. Among all IMF combinations, the winner combination is IMF3+IMF4+IMF5.

  6. Predicting drug-target interactions by dual-network integrated logistic matrix factorization

    PubMed Central

    Hao, Ming; Bryant, Stephen H.; Wang, Yanli

    2017-01-01

    In this work, we propose a dual-network integrated logistic matrix factorization (DNILMF) algorithm to predict potential drug-target interactions (DTI). The prediction procedure consists of four steps: (1) inferring new drug/target profiles and constructing profile kernel matrix; (2) diffusing drug profile kernel matrix with drug structure kernel matrix; (3) diffusing target profile kernel matrix with target sequence kernel matrix; and (4) building DNILMF model and smoothing new drug/target predictions based on their neighbors. We compare our algorithm with the state-of-the-art method based on the benchmark dataset. Results indicate that the DNILMF algorithm outperforms the previously reported approaches in terms of AUPR (area under precision-recall curve) and AUC (area under curve of receiver operating characteristic) based on the 5 trials of 10-fold cross-validation. We conclude that the performance improvement depends on not only the proposed objective function, but also the used nonlinear diffusion technique which is important but under studied in the DTI prediction field. In addition, we also compile a new DTI dataset for increasing the diversity of currently available benchmark datasets. The top prediction results for the new dataset are confirmed by experimental studies or supported by other computational research. PMID:28079135

  7. A novel method for in silico identification of regulatory SNPs in human genome.

    PubMed

    Li, Rong; Zhong, Dexing; Liu, Ruiling; Lv, Hongqiang; Zhang, Xinman; Liu, Jun; Han, Jiuqiang

    2017-02-21

    Regulatory single nucleotide polymorphisms (rSNPs), kind of functional noncoding genetic variants, can affect gene expression in a regulatory way, and they are thought to be associated with increased susceptibilities to complex diseases. Here a novel computational approach to identify potential rSNPs is presented. Different from most other rSNPs finding methods which based on hypothesis that SNPs causing large allele-specific changes in transcription factor binding affinities are more likely to play regulatory functions, we use a set of documented experimentally verified rSNPs and nonfunctional background SNPs to train classifiers, so the discriminating features are found. To characterize variants, an extensive range of characteristics, such as sequence context, DNA structure and evolutionary conservation etc. are analyzed. Support vector machine is adopted to build the classifier model together with an ensemble method to deal with unbalanced data. 10-fold cross-validation result shows that our method can achieve accuracy with sensitivity of ~78% and specificity of ~82%. Furthermore, our method performances better than some other algorithms based on aforementioned hypothesis in handling false positives. The original data and the source matlab codes involved are available at https://sourceforge.net/projects/rsnppredict/.

  8. Developing a QSAR model for hepatotoxicity screening of the active compounds in traditional Chinese medicines.

    PubMed

    Huang, Shan-Han; Tung, Chun-Wei; Fülöp, Ferenc; Li, Jih-Heng

    2015-04-01

    The perception that natural substances are deemed safe has made traditional Chinese medicine (TCM) popular in the treatment and prevention of disease globally. However, such an assumption is often misleading owing to a lack of scientific validation. To assess the safety of TCM, in silico screening provides major advantages over the classical laboratory approaches in terms of resource- and time-saving and full reproducibility. To screen the hepatotoxicity of the active compounds of TCMs, a quantitative structure-activity relationship (QSAR) model was firstly established by utilizing drugs from the Liver Toxicity Knowledge Base. These drugs were annotated with drug-induced liver injury information obtained from clinical trials and post-marketing surveillance. The performance of the model after nested 10-fold cross-validation was 79.1%, 91.2%, 53.8% for accuracy, sensitivity, and specificity, respectively. The external validation of 91 well-known ingredients of common herbal medicines yielded a high accuracy (87%). After screening the TCM Database@Taiwan, the world's largest TCM database, a total of 6853 (74.8%) ingredients were predicted to have hepatotoxic potential. The one-hundred chemical ingredients predicted to have the highest hepatotoxic potential by our model were further verified by published literatures. Our study indicated that this model can serve as a complementary tool to evaluate the safety of TCM.

  9. CSS-Palm 2.0: an updated software for palmitoylation sites prediction.

    PubMed

    Ren, Jian; Wen, Longping; Gao, Xinjiao; Jin, Changjiang; Xue, Yu; Yao, Xuebiao

    2008-11-01

    Protein palmitoylation is an essential post-translational lipid modification of proteins, and reversibly orchestrates a variety of cellular processes. Identification of palmitoylated proteins with their sites is the foundation for understanding molecular mechanisms and regulatory roles of palmitoylation. Contrasting to the labor-intensive and time-consuming experimental approaches, in silico prediction of palmitoylation sites has attracted much attention as a popular strategy. In this work, we updated our previous CSS-Palm into version 2.0. An updated clustering and scoring strategy (CSS) algorithm was employed with great improvement. The leave-one-out validation and 4-, 6-, 8- and 10-fold cross-validations were adopted to evaluate the prediction performance of CSS-Palm 2.0. Also, an additional new data set not included in training was used to test the robustness of CSS-Palm 2.0. By comparison, the performance of CSS-Palm was much better than previous tools. As an application, we performed a small-scale annotation of palmitoylated proteins in budding yeast. The online service and local packages of CSS-Palm 2.0 were freely available at: http://bioinformatics.lcd-ustc.org/css_palm.

  10. DisPredict: A Predictor of Disordered Protein Using Optimized RBF Kernel

    PubMed Central

    Iqbal, Sumaiya; Hoque, Md Tamjidul

    2015-01-01

    Intrinsically disordered proteins or, regions perform important biological functions through their dynamic conformations during binding. Thus accurate identification of these disordered regions have significant implications in proper annotation of function, induced fold prediction and drug design to combat critical diseases. We introduce DisPredict, a disorder predictor that employs a single support vector machine with RBF kernel and novel features for reliable characterization of protein structure. DisPredict yields effective performance. In addition to 10-fold cross validation, training and testing of DisPredict was conducted with independent test datasets. The results were consistent with both the training and test error minimal. The use of multiple data sources, makes the predictor generic. The datasets used in developing the model include disordered regions of various length which are categorized as short and long having different compositions, different types of disorder, ranging from fully to partially disordered regions as well as completely ordered regions. Through comparison with other state of the art approaches and case studies, DisPredict is found to be a useful tool with competitive performance. DisPredict is available at https://github.com/tamjidul/DisPredict_v1.0. PMID:26517719

  11. Systematic Characterization and Prediction of Post-Translational Modification Cross-Talk*

    PubMed Central

    Huang, Yuanhua; Xu, Bosen; Zhou, Xueya; Li, Ying; Lu, Ming; Jiang, Rui; Li, Tingting

    2015-01-01

    Post-translational modification (PTM)1 plays an important role in regulating the functions of proteins. PTMs of multiple residues on one protein may work together to determine a functional outcome, which is known as PTM cross-talk. Identification of PTM cross-talks is an emerging theme in proteomics and has elicited great interest, but their properties remain to be systematically characterized. To this end, we collected 193 PTM cross-talk pairs in 77 human proteins from the literature and then tested location preference and co-evolution at the residue and modification levels. We found that cross-talk events preferentially occurred among nearby PTM sites, especially in disordered protein regions, and cross-talk pairs tended to co-evolve. Given the properties of PTM cross-talk pairs, a naïve Bayes classifier integrating different features was built to predict cross-talks for pairwise combination of PTM sites. By using a 10-fold cross-validation, the integrated prediction model showed an area under the receiver operating characteristic (ROC) curve of 0.833, superior to using any individual feature alone. The prediction performance was also demonstrated to be robust to the biases in the collected PTM cross-talk pairs. The integrated approach has the potential for large-scale prioritization of PTM cross-talk candidates for functional validation and was implemented as a web server available at http://bioinfo.bjmu.edu.cn/ptm-x/. PMID:25605461

  12. Predicting the distribution of canine leishmaniasis in western Europe based on environmental variables.

    PubMed

    Franco, Ana O; Davies, Clive R; Mylne, Adrian; Dedet, Jean-Pierre; Gállego, Montserrat; Ballart, Cristina; Gramiccia, Marina; Gradoni, Luigi; Molina, Ricardo; Gálvez, Rosa; Morillas-Márquez, Francisco; Barón-López, Sergio; Pires, Carlos Alves; Afonso, Maria Odete; Ready, Paul D; Cox, Jonathan

    2011-12-01

    The domestic dog is the reservoir host of Leishmania infantum, the causative agent of zoonotic visceral leishmaniasis endemic in Mediterranean Europe. Targeted control requires predictive risk maps of canine leishmaniasis (CanL), which are now explored. We databased 2187 published and unpublished surveys of CanL in southern Europe. A total of 947 western surveys met inclusion criteria for analysis, including serological identification of infection (504, 369 dogs tested 1971-2006). Seroprevalence was 23 2% overall (median 10%). Logistic regression models within a GIS framework identified the main environmental predictors of CanL seroprevalence in Portugal, Spain, France and Italy, or in France alone. A 10-fold cross-validation approach determined model capacity to predict point-values of seroprevalence and the correct seroprevalence class (<5%, 5-20%, >20%). Both the four-country and France-only models performed reasonably well for predicting correctly the <5% and >20% seroprevalence classes (AUC >0 70). However, the France-only model performed much better for France than the four-country model. The four-country model adequately predicted regions of CanL emergence in northern Italy (<5% seroprevalence). Both models poorly predicted intermediate point seroprevalences (5-20%) within regional foci, because surveys were biased towards known rural foci and Mediterranean bioclimates. Our recommendations for standardizing surveys would permit higher-resolution risk mapping.

  13. Mapping Prosopis spp. with Landsat 8 data in arid environments: Evaluating effectiveness of different methods and temporal imagery selection for Hargeisa, Somaliland

    NASA Astrophysics Data System (ADS)

    Ng, Wai-Tim; Meroni, Michele; Immitzer, Markus; Böck, Sebastian; Leonardi, Ugo; Rembold, Felix; Gadain, Hussein; Atzberger, Clement

    2016-12-01

    Prosopis spp. is a fast and aggressive invader threatening many arid and semi-arid areas globally. The species is native to the American dry zones and was introduced in Somaliland for dune stabilization and fuel wood production in the 1970⿿s and 1980⿿s. Its deep rooting system is capable of tapping into the groundwater table thereby reducing its reliance on infrequent rainfalls and near-surface water. The competitive advantage of Prosopis is further fuelled by the hybridization of the many introduced subspecies that made the plant capable of adapting to the new environment and replacing endemic species. This study aimed to test the mapping accuracy achievable with Landsat 8 data acquired during the wet and the dry seasons within a Random Forest (RF) classifier, using both pixel- and object-based approaches. Maps are produced for the Hargeisa area (Somaliland), where reference data was collected during the dry season of 2015. Results were assessed through a 10-fold cross-validation procedure. In our study, the highest overall accuracy (74%) was achieved when applying a pixel-based classification using a combination of the wet and dry season Earth observation data. Object-based mapping were less reliable due to the limitations in spatial resolution of the Landsat data (15⿿30 m) and problems in finding an appropriate segmentation scale.

  14. PSNO: predicting cysteine S-nitrosylation sites by incorporating various sequence-derived features into the general form of Chou's PseAAC.

    PubMed

    Zhang, Jian; Zhao, Xiaowei; Sun, Pingping; Ma, Zhiqiang

    2014-06-25

    S-nitrosylation (SNO) is one of the most universal reversible post-translational modifications involved in many biological processes. Malfunction or dysregulation of SNO leads to a series of severe diseases, such as developmental abnormalities and various diseases. Therefore, the identification of SNO sites (SNOs) provides insights into disease progression and drug development. In this paper, a new bioinformatics tool, named PSNO, is proposed to identify SNOs from protein sequences. Firstly, we explore various promising sequence-derived discriminative features, including the evolutionary profile, the predicted secondary structure and the physicochemical properties. Secondly, rather than simply combining the features, which may bring about information redundancy and unwanted noise, we use the relative entropy selection and incremental feature selection approach to select the optimal feature subsets. Thirdly, we train our model by the technique of the k-nearest neighbor algorithm. Using both informative features and an elaborate feature selection scheme, our method, PSNO, achieves good prediction performance with a mean Mathews correlation coefficient (MCC) value of about 0.5119 on the training dataset using 10-fold cross-validation. These results indicate that PSNO can be used as a competitive predictor among the state-of-the-art SNOs prediction tools. A web-server, named PSNO, which implements the proposed method, is freely available at http://59.73.198.144:8088/PSNO/.

  15. Generating one biometric feature from another: faces from fingerprints.

    PubMed

    Ozkaya, Necla; Sagiroglu, Seref

    2010-01-01

    This study presents a new approach based on artificial neural networks for generating one biometric feature (faces) from another (only fingerprints). An automatic and intelligent system was designed and developed to analyze the relationships among fingerprints and faces and also to model and to improve the existence of the relationships. The new proposed system is the first study that generates all parts of the face including eyebrows, eyes, nose, mouth, ears and face border from only fingerprints. It is also unique and different from similar studies recently presented in the literature with some superior features. The parameter settings of the system were achieved with the help of Taguchi experimental design technique. The performance and accuracy of the system have been evaluated with 10-fold cross validation technique using qualitative evaluation metrics in addition to the expanded quantitative evaluation metrics. Consequently, the results were presented on the basis of the combination of these objective and subjective metrics for illustrating the qualitative properties of the proposed methods as well as a quantitative evaluation of their performances. Experimental results have shown that one biometric feature can be determined from another. These results have once more indicated that there is a strong relationship between fingerprints and faces.

  16. ChemStable: a web server for rule-embedded naïve Bayesian learning approach to predict compound stability.

    PubMed

    Liu, Zhihong; Zheng, Minghao; Yan, Xin; Gu, Qiong; Gasteiger, Johann; Tijhuis, Johan; Maas, Peter; Li, Jiabo; Xu, Jun

    2014-09-01

    Predicting compound chemical stability is important because unstable compounds can lead to either false positive or to false negative conclusions in bioassays. Experimental data (COMDECOM) measured from DMSO/H2O solutions stored at 50 °C for 105 days were used to predicted stability by applying rule-embedded naïve Bayesian learning, based upon atom center fragment (ACF) features. To build the naïve Bayesian classifier, we derived ACF features from 9,746 compounds in the COMDECOM dataset. By recursively applying naïve Bayesian learning from the data set, each ACF is assigned with an expected stable probability (p(s)) and an unstable probability (p(uns)). 13,340 ACFs, together with their p(s) and p(uns) data, were stored in a knowledge base for use by the Bayesian classifier. For a given compound, its ACFs were derived from its structure connection table with the same protocol used to drive ACFs from the training data. Then, the Bayesian classifier assigned p(s) and p(uns) values to the compound ACFs by a structural pattern recognition algorithm, which was implemented in-house. Compound instability is calculated, with Bayes' theorem, based upon the p(s) and p(uns) values of the compound ACFs. We were able to achieve performance with an AUC value of 84% and a tenfold cross validation accuracy of 76.5%. To reduce false negatives, a rule-based approach has been embedded in the classifier. The rule-based module allows the program to improve its predictivity by expanding its compound instability knowledge base, thus further reducing the possibility of false negatives. To our knowledge, this is the first in silico prediction service for the prediction of the stabilities of organic compounds.

  17. ChemStable: a web server for rule-embedded naïve Bayesian learning approach to predict compound stability

    NASA Astrophysics Data System (ADS)

    Liu, Zhihong; Zheng, Minghao; Yan, Xin; Gu, Qiong; Gasteiger, Johann; Tijhuis, Johan; Maas, Peter; Li, Jiabo; Xu, Jun

    2014-09-01

    Predicting compound chemical stability is important because unstable compounds can lead to either false positive or to false negative conclusions in bioassays. Experimental data (COMDECOM) measured from DMSO/H2O solutions stored at 50 °C for 105 days were used to predicted stability by applying rule-embedded naïve Bayesian learning, based upon atom center fragment (ACF) features. To build the naïve Bayesian classifier, we derived ACF features from 9,746 compounds in the COMDECOM dataset. By recursively applying naïve Bayesian learning from the data set, each ACF is assigned with an expected stable probability ( p s ) and an unstable probability ( p uns ). 13,340 ACFs, together with their p s and p uns data, were stored in a knowledge base for use by the Bayesian classifier. For a given compound, its ACFs were derived from its structure connection table with the same protocol used to drive ACFs from the training data. Then, the Bayesian classifier assigned p s and p uns values to the compound ACFs by a structural pattern recognition algorithm, which was implemented in-house. Compound instability is calculated, with Bayes' theorem, based upon the p s and p uns values of the compound ACFs. We were able to achieve performance with an AUC value of 84 % and a tenfold cross validation accuracy of 76.5 %. To reduce false negatives, a rule-based approach has been embedded in the classifier. The rule-based module allows the program to improve its predictivity by expanding its compound instability knowledge base, thus further reducing the possibility of false negatives. To our knowledge, this is the first in silico prediction service for the prediction of the stabilities of organic compounds.

  18. Spatiotemporal Modeling of Ozone Levels in Quebec (Canada): A Comparison of Kriging, Land-Use Regression (LUR), and Combined Bayesian Maximum Entropy–LUR Approaches

    PubMed Central

    Adam-Poupart, Ariane; Brand, Allan; Fournier, Michel; Jerrett, Michael

    2014-01-01

    Background: Ambient air ozone (O3) is a pulmonary irritant that has been associated with respiratory health effects including increased lung inflammation and permeability, airway hyperreactivity, respiratory symptoms, and decreased lung function. Estimation of O3 exposure is a complex task because the pollutant exhibits complex spatiotemporal patterns. To refine the quality of exposure estimation, various spatiotemporal methods have been developed worldwide. Objectives: We sought to compare the accuracy of three spatiotemporal models to predict summer ground-level O3 in Quebec, Canada. Methods: We developed a land-use mixed-effects regression (LUR) model based on readily available data (air quality and meteorological monitoring data, road networks information, latitude), a Bayesian maximum entropy (BME) model incorporating both O3 monitoring station data and the land-use mixed model outputs (BME-LUR), and a kriging method model based only on available O3 monitoring station data (BME kriging). We performed leave-one-station-out cross-validation and visually assessed the predictive capability of each model by examining the mean temporal and spatial distributions of the average estimated errors. Results: The BME-LUR was the best predictive model (R2 = 0.653) with the lowest root mean-square error (RMSE ;7.06 ppb), followed by the LUR model (R2 = 0.466, RMSE = 8.747) and the BME kriging model (R2 = 0.414, RMSE = 9.164). Conclusions: Our findings suggest that errors of estimation in the interpolation of O3 concentrations with BME can be greatly reduced by incorporating outputs from a LUR model developed with readily available data. Citation: Adam-Poupart A, Brand A, Fournier M, Jerrett M, Smargiassi A. 2014. Spatiotemporal modeling of ozone levels in Quebec (Canada): a comparison of kriging, land-use regression (LUR), and combined Bayesian maximum entropy–LUR approaches. Environ Health Perspect 122:970–976; http://dx.doi.org/10.1289/ehp.1306566 PMID:24879650

  19. Three-dimensional quantitative structure-activity relationship for several bioactive peptides searched by a convex hull-comparative molecular field analysis approach.

    PubMed

    Lin, T H; Lin, J J

    2001-09-01

    Three-dimensional (3D) convex hulls are computed for theoretically generated structures of a group of 18 bioactive tachykinin peptides. The number of peptides treated as a training set is 14, whereas that treated as a test set is four. The frequency of atoms of the same atomic type lying at the vertices of all the hulls computed for all the structures in a structural set is counted. Vertex atoms with non-zero frequency counted are collected together as a set of commonly exposed groups. These commonly exposed atoms are then treated as a set of correspondences for aligning all the other 13 structures in a structural set against a common template, which is the structure of the most potent peptide in the set using the FIT module of the SYBYL 6.6 program. Each aligned structural set is then analyzed by the comparative molecular field analysis (CoMFA) module using the C.3 probe having a charge of +1.0. The corresponding cross-validated r2 values range from -0.99 to 0.57 for a number of 73 structural sets studied. The comparative molecular similarity indices analysis (CoMSIA) module within the SYBYL 6.6 package is also used to analyze some of these aligned structural sets. Although the CoMSIA results are in accord with those of CoMFA, it is also found that the CoMFA results of several structural sets can be improved somewhat for conformations of the structures in the sets that are adjusted by constraint energy minimization and then constraint molecular dynamics simulation runs using distance constraints derived from some commonly exposed groups determined for them. This result further implies that the convex hull-CoMFA is a feasible approach to screen the bioactive conformations for molecules of this class.

  20. Approaches to Quantum Gravity

    NASA Astrophysics Data System (ADS)

    Oriti, Daniele

    2009-03-01

    Preface; Part I. Fundamental Ideas and General Formalisms: 1. Unfinished revolution C. Rovelli; 2. The fundamental nature of space and time G. 't Hooft; 3. Does locality fail at intermediate length scales R. Sorkin; 4. Prolegomena to any future quantum gravity J. Stachel; 5. Spacetime symmetries in histories canonical gravity N. Savvidou; 6. Categorical geometry and the mathematical foundations of quantum gravity L. Crane; 7. Emergent relativity O. Dreyer; 8. Asymptotic safety R. Percacci; 9. New directions in background independent quantum gravity F. Markopoulou; Questions and answers; Part II: 10. Gauge/gravity duality G. Horowitz and J. Polchinski; 11. String theory, holography and quantum gravity T. Banks; 12. String field theory W. Taylor; Questions and answers; Part III: 13. Loop Quantum Gravity T. Thiemann; 14. Covariant loop quantum gravity? E. LIvine; 15. The spin foam representation of loop quantum gravity A. Perez; 16. 3-dimensional spin foam quantum gravity L. Freidel; 17. The group field theory approach to quantum gravity D. Oriti; Questions and answers; Part IV. Discrete Quantum Gravity: 18. Quantum gravity: the art of building spacetime J. Ambjørn, J. Jurkiewicz and R. Loll; 19. Quantum Regge calculations R. Williams; 20. Consistent discretizations as a road to quantum gravity R. Gambini and J. Pullin; 21. The causal set approach to quantum gravity J. Henson; Questions and answers; Part V. Effective Models and Quantum Gravity Phenomenology: 22. Quantum gravity phenomenology G. Amelino-Camelia; 23. Quantum gravity and precision tests C. Burgess; 24. Algebraic approach to quantum gravity II: non-commutative spacetime F. Girelli; 25. Doubly special relativity J. Kowalski-Glikman; 26. From quantum reference frames to deformed special relativity F. Girelli; 27. Lorentz invariance violation and its role in quantum gravity phenomenology J. Collins, A. Perez and D. Sudarsky; 28. Generic predictions of quantum theories of gravity L. Smolin; Questions and

  1. Quantum Approach to Informatics

    NASA Astrophysics Data System (ADS)

    Stenholm, Stig; Suominen, Kalle-Antti

    2005-08-01

    An essential overview of quantum information Information, whether inscribed as a mark on a stone tablet or encoded as a magnetic domain on a hard drive, must be stored in a physical object and thus made subject to the laws of physics. Traditionally, information processing such as computation occurred in a framework governed by laws of classical physics. However, information can also be stored and processed using the states of matter described by non-classical quantum theory. Understanding this quantum information, a fundamentally different type of information, has been a major project of physicists and information theorists in recent years, and recent experimental research has started to yield promising results. Quantum Approach to Informatics fills the need for a concise introduction to this burgeoning new field, offering an intuitive approach for readers in both the physics and information science communities, as well as in related fields. Only a basic background in quantum theory is required, and the text keeps the focus on bringing this theory to bear on contemporary informatics. Instead of proofs and other highly formal structures, detailed examples present the material, making this a uniquely accessible introduction to quantum informatics. Topics covered include: * An introduction to quantum information and the qubit * Concepts and methods of quantum theory important for informatics * The application of information concepts to quantum physics * Quantum information processing and computing * Quantum gates * Error correction using quantum-based methods * Physical realizations of quantum computing circuits A helpful and economical resource for understanding this exciting new application of quantum theory to informatics, Quantum Approach to Informatics provides students and researchers in physics and information science, as well as other interested readers with some scientific background, with an essential overview of the field.

  2. Mitochondrial diseases: therapeutic approaches.

    PubMed

    DiMauro, Salvatore; Mancuso, Michelangelo

    2007-06-01

    Therapy of mitochondrial encephalomyopathies (defined restrictively as defects of the mitochondrial respiratory chain) is woefully inadequate, despite great progress in our understanding of the molecular bases of these disorders. In this review, we consider sequentially several different therapeutic approaches. Palliative therapy is dictated by good medical practice and includes anticonvulsant medication, control of endocrine dysfunction, and surgical procedures. Removal of noxious metabolites is centered on combating lactic acidosis, but extends to other metabolites. Attempts to bypass blocks in the respiratory chain by administration of electron acceptors have not been successful, but this may be amenable to genetic engineering. Administration of metabolites and cofactors is the mainstay of real-life therapy and is especially important in disorders due to primary deficiencies of specific compounds, such as carnitine or coenzyme Q10. There is increasing interest in the administration of reactive oxygen species scavengers both in primary mitochondrial diseases and in neurodegenerative diseases directly or indirectly related to mitochondrial dysfunction. Aerobic exercise and physical therapy prevent or correct deconditioning and improve exercise tolerance in patients with mitochondrial myopathies due to mitochondrial DNA (mtDNA) mutations. Gene therapy is a challenge because of polyplasmy and heteroplasmy, but interesting experimental approaches are being pursued and include, for example, decreasing the ratio of mutant to wild-type mitochondrial genomes (gene shifting), converting mutated mtDNA genes into normal nuclear DNA genes (allotopic expression), importing cognate genes from other species, or correcting mtDNA mutations with specific restriction endonucleases. Germline therapy raises ethical problems but is being considered for prevention of maternal transmission of mtDNA mutations. Preventive therapy through genetic counseling and prenatal diagnosis is

  3. Combined approach brings success.

    PubMed

    Law, Oliver

    2014-06-01

    Sixteen months ago, according to Trumpf Medical Systems, which managed the project, 'something out of the ordinary' happened at Leighton Hospital in Crewe. When making plans to upgrade ageing operating theatres and critical care units, the estates department took the decision to involve other disciplines from the very start of the process. Clinicians, nursing staff, architects, patient representatives, and suppliers, all played their part, with the estates team always at the hub. As Oliver Law, managing director of the UK medical technology specialist, explains, this multidisciplinary approach had a profound effect on the outcome.

  4. [Regenerative approach for COPD].

    PubMed

    Kubo, Hiroshi

    2011-10-01

    No treatment to cure of chronic obstructive pulmonary disease (COPD) is available. Regenerative medicine is one of promising areas for this intractable disease. Several reagents and growth factors are known to promote lung regeneration in small animal models. However, regenerative medicines for human lungs are not achieved yet. Recent advances in stem cell biology and tissue engineering have expanded our understanding of lung endogenous stem cells, and this new knowledge provides us with new ideas for future regenerative therapy for lung diseases. Although lungs are the most challenging organ for regenerative medicine, our cumulative knowledge of lung regeneration and of endogenous progenitor cells makes clear the possibilities for regenerative approach to COPD.

  5. The collaboratory approach

    SciTech Connect

    Peskin, A.M.

    1997-04-01

    A {open_quotes}collaboratory{close_quotes} has been defined as a center without walls, in which researchers can perform their work without regard to geographical location. To an increasing degree, engineering design and development is also taking the form of far-flung collaborations among divisions of a plant, subcontractors, university consultants and customers. It has long been recognized that quality engineering education presents the student with an environment that duplicates as much as possible that which the graduate will encounter in industry. To that end, it is important that engineering schools begin to introduce the collaboratory approach in its preparation, and even use it in delivery of subject matter to students.

  6. Engineering approaches to immunotherapy.

    PubMed

    Swartz, Melody A; Hirosue, Sachiko; Hubbell, Jeffrey A

    2012-08-22

    As the science of immunology grows increasingly mechanistic, motivation for developing quantitative, design-based engineering approaches has also evolved, both for therapeutic interventions and for elucidating immunological pathways in human disease. This has seeded the nascent field of "immunoengineering," which seeks to apply engineering analyses and design approaches to problems in translational immunology. For example, cell engineers are creating ways to tailor and use immune cells as living therapeutics; protein engineers are devising new methods of rapid antibody discovery; biomaterials scientists are guiding vaccine delivery and immune-cell activation with novel constructs; and systems immunologists are deciphering the evolution and maintenance of T and B cell receptor repertoires, which could help guide vaccine design. The field is multidisciplinary and collaborative, with engineers and immunologists working together to better understand and treat disease. We discuss the scientific progress in this young, yet rapidly evolving research area, which has yielded numerous start-up companies that are betting on impact in clinical and commercial translation in the near future.

  7. Modular Approach to Spintronics.

    PubMed

    Camsari, Kerem Yunus; Ganguly, Samiran; Datta, Supriyo

    2015-06-11

    There has been enormous progress in the last two decades, effectively combining spintronics and magnetics into a powerful force that is shaping the field of memory devices. New materials and phenomena continue to be discovered at an impressive rate, providing an ever-increasing set of building blocks that could be exploited in designing transistor-like functional devices of the future. The objective of this paper is to provide a quantitative foundation for this building block approach, so that new discoveries can be integrated into functional device concepts, quickly analyzed and critically evaluated. Through careful benchmarking against available theory and experiment we establish a set of elemental modules representing diverse materials and phenomena. These elemental modules can be integrated seamlessly to model composite devices involving both spintronic and nanomagnetic phenomena. We envision the library of modules to evolve both by incorporating new modules and by improving existing modules as the field progresses. The primary contribution of this paper is to establish the ground rules or protocols for a modular approach that can build a lasting bridge between materials scientists and circuit designers in the field of spintronics and nanomagnetics.

  8. Modular Approach to Spintronics

    PubMed Central

    Camsari, Kerem Yunus; Ganguly, Samiran; Datta, Supriyo

    2015-01-01

    There has been enormous progress in the last two decades, effectively combining spintronics and magnetics into a powerful force that is shaping the field of memory devices. New materials and phenomena continue to be discovered at an impressive rate, providing an ever-increasing set of building blocks that could be exploited in designing transistor-like functional devices of the future. The objective of this paper is to provide a quantitative foundation for this building block approach, so that new discoveries can be integrated into functional device concepts, quickly analyzed and critically evaluated. Through careful benchmarking against available theory and experiment we establish a set of elemental modules representing diverse materials and phenomena. These elemental modules can be integrated seamlessly to model composite devices involving both spintronic and nanomagnetic phenomena. We envision the library of modules to evolve both by incorporating new modules and by improving existing modules as the field progresses. The primary contribution of this paper is to establish the ground rules or protocols for a modular approach that can build a lasting bridge between materials scientists and circuit designers in the field of spintronics and nanomagnetics. PMID:26066079

  9. Breakfast: a multidisciplinary approach

    PubMed Central

    2013-01-01

    Background The role of breakfast as an essential part of an healthy diet has been only recently promoted even if breakfast practices were known since the Middle Age. The growing scientific evidences on this topic are extremely sector-based nevertheless breakfast could be regarded from different point of views and from different expertises. This approach, that take into account history, sociology, anthropology, medicine, psychology and pedagogy, is useful to better understand the value of this meal in our culture. The aim of this paper was to analyse breakfast-related issues based on a multidisciplinary approach with input by specialists from different fields of learning. Discussion Breakfast is now recommended as part of a diet because it is associated with healthier macro- and micronutrient intakes, body mass index and lifestyle. Moreover recent studies showed that breakfast improves cognitive function, intuitive perception and academic performance. Research demonstrates the importance of providing breakfast not only to children but in adults and elderly too. Although the important role breakfast plays in maintaining the health, epidemiological data from industrialised countries reveal that many individuals either eat a nutritionally unhealthy breakfast or skip it completely. Summary The historical, bio-psychological and educational value of breakfast in our culture is extremely important and should be recognized and stressed by the scientific community. Efforts should be done to promote this practice for the individual health and well-being. PMID:23842429

  10. Interstage Flammability Analysis Approach

    NASA Technical Reports Server (NTRS)

    Little, Jeffrey K.; Eppard, William M.

    2011-01-01

    The Interstage of the Ares I launch platform houses several key components which are on standby during First Stage operation: the Reaction Control System (ReCS), the Upper Stage (US) Thrust Vector Control (TVC) and the J-2X with the Main Propulsion System (MPS) propellant feed system. Therefore potentially dangerous leaks of propellants could develop. The Interstage leaks analysis addresses the concerns of localized mixing of hydrogen and oxygen gases to produce deflagration zones in the Interstage of the Ares I launch vehicle during First Stage operation. This report details the approach taken to accomplish the analysis. Specified leakage profiles and actual flammability results are not presented due to proprietary and security restrictions. The interior volume formed by the Interstage walls, bounding interfaces with the Upper and First Stages, and surrounding the J2-X engine was modeled using Loci-CHEM to assess the potential for flammable gas mixtures to develop during First Stage operations. The transient analysis included a derived flammability indicator based on mixture ratios to maintain achievable simulation times. Validation of results was based on a comparison to Interstage pressure profiles outlined in prior NASA studies. The approach proved useful in the bounding of flammability risk in supporting program hazard reviews.

  11. Systemic approaches to biodegradation.

    PubMed

    Trigo, Almudena; Valencia, Alfonso; Cases, Ildefonso

    2009-01-01

    Biodegradation, the ability of microorganisms to remove complex chemicals from the environment, is a multifaceted process in which many biotic and abiotic factors are implicated. The recent accumulation of knowledge about the biochemistry and genetics of the biodegradation process, and its categorization and formalization in structured databases, has recently opened the door to systems biology approaches, where the interactions of the involved parts are the main subject of study, and the system is analysed as a whole. The global analysis of the biodegradation metabolic network is beginning to produce knowledge about its structure, behaviour and evolution, such as its free-scale structure or its intrinsic robustness. Moreover, these approaches are also developing into useful tools such as predictors for compounds' degradability or the assisted design of artificial pathways. However, it is the environmental application of high-throughput technologies from the genomics, metagenomics, proteomics and metabolomics that harbours the most promising opportunities to understand the biodegradation process, and at the same time poses tremendous challenges from the data management and data mining point of view.

  12. Coordinated Parallel Runway Approaches

    NASA Technical Reports Server (NTRS)

    Koczo, Steve

    1996-01-01

    The current air traffic environment in airport terminal areas experiences substantial delays when weather conditions deteriorate to Instrument Meteorological Conditions (IMC). Expected future increases in air traffic will put additional pressures on the National Airspace System (NAS) and will further compound the high costs associated with airport delays. To address this problem, NASA has embarked on a program to address Terminal Area Productivity (TAP). The goals of the TAP program are to provide increased efficiencies in air traffic during the approach, landing, and surface operations in low-visibility conditions. The ultimate goal is to achieve efficiencies of terminal area flight operations commensurate with Visual Meteorological Conditions (VMC) at current or improved levels of safety.

  13. Neuroblastoma: A neurochemical approach

    SciTech Connect

    Schor, N.F. )

    1991-07-01

    Neuroblastoma is among the most common malignancies of childhood. Despite greatly improved therapy for some pediatric tumors, the prognosis for children with metastatic neuroblastoma has not changed significantly in the past 10 years. With conventional chemotherapy, radiation therapy, and surgery, children with metastatic neuroblastoma have a 20% long-term survival rate. The authors describe here approaches to neuroblastoma that target its neuronal characteristics. On the one hand, the neurotransmitter receptors on the surface of the neuroblastoma cells and, on the other hand, specific isozymes that distinguish neuroblastoma cells from their normal counterparts are the focus of these experimental therapies. In the former case, specificity for tumor cells is effected by (1) selective protection of normal neuronal elements from toxicity, or (2) selective potentiation of toxicity for neural tumor cells. It is hoped that these strategies will be generalizable to other neural crest-derived tumors. 32 references.

  14. Population approaches in paediatrics.

    PubMed

    Chatelut, Etienne

    2008-12-01

    Population pharmacokinetic (PK) approach is now often used to evaluate PK characteristics of a new compound during its clinical development. Recently, new legislation governing the development and authorization of medicines for use in children aged 0-17 years was introduced in the European Union. Among the strategies proposed in relation to clinical aspects, use of population PKs is stated. In this manuscript, comparison between standard PK and population PK methods will be briefly addressed to understand why the second is particularly adapted to perform PK studies in paediatrics. Then, specific patients' characteristics (covariates) in paediatrics will be presented. Examples of PK and PK-pharmacodynamic (PK-PD) studies will be finally given. The number of population PK studies published still exceeds largely those of PK-PD.

  15. An environmental approach

    SciTech Connect

    Geerling, C.

    1996-11-01

    The Shell Petroleum Development Company is operating in southern Nigeria in the delta of the Niger River. This delta covers an area 70,000 square kin of coastal ridge barriers, mangroves, freshwater swamp forest and lowland rain forests. Over the past decades considerable changes has occurred through coastal zone modifications, upstream urban and hydrological infrastructure, deforestation, agriculture, fisheries, industrial development, oil operation, as well as demographic changes. The problems associated with these changes are: (1) over-exploitation of renewable natural resources and breakdown of traditional management structures; (2) impact from industry such as pollution and physical changes, and (3) a perception of lack of social and economic equity. This paper describes approaches to help counteract theses problems.

  16. Editorial: Approaching 125.

    PubMed

    Goodman, Sherryl

    2012-02-01

    With this issue, beginning Volume 121, the editorial team shifts from the strong leadership of David Watson to a team under my direction. Approaching 125 years of publication, the Journal of Abnormal Psychology has earned its place as the preeminent outlet for research in psychopathology. With gratitude to the newly assembled team of associate editors (AEs), consulting editors, and ad hoc reviewers, I look forward to guiding the journal through this next term. Nine well-respected scholars have agreed to serve as AEs: Timothy Brown, Laurie Chassin, Jeff Epstein, Jutta Joormann, Pamela Keel, Kate Keenan, Scott Lilienfeld, Angus MacDonald, and Michael Young. The new team is dedicated to working tirelessly to maintain and enhance the journal's esteemed tradition of excellence. Given the well-established strengths of the journal, I will not suggest any fundamental changes.

  17. ATIS - A modular approach

    NASA Astrophysics Data System (ADS)

    Kirson, Allan

    The author describes a modular approach to the design of an in-vehicle navigation and route guidance system that supports a phased implementation of the technology, and anticipates expected differences in implementation in different parts of the world and for different makes and models of vehicle. A series of sensors in the vehicle are used to determine the vehicle's position by dead reckoning and map-matching. The system then calculates the best route to the selected destination, taking into account the real-time traffic information received from a traffic management center, and presents route guidance instructions to the user as the route is traversed. Attention is given to modularity considerations, vehicle positioning, driver support, vehicle-to-infrastructure communications, and the role of standards.

  18. Therapeutic approaches for shankopathies.

    PubMed

    Wang, Xiaoming; Bey, Alexandra L; Chung, Leeyup; Krystal, Andrew D; Jiang, Yong-Hui

    2014-02-01

    Despite recent advances in understanding the molecular mechanisms of autism spectrum disorders (ASD), the current treatments for these disorders are mostly focused on behavioral and educational approaches. The considerable clinical and molecular heterogeneity of ASD present a significant challenge to the development of an effective treatment targeting underlying molecular defects. Deficiency of SHANK family genes causing ASD represent an exciting opportunity for developing molecular therapies because of strong genetic evidence for SHANK as causative genes in ASD and the availability of a panel of Shank mutant mouse models. In this article, we review the literature suggesting the potential for developing therapies based on molecular characteristics and discuss several exciting themes that are emerging from studying Shank mutant mice at the molecular level and in terms of synaptic function.

  19. Television Criticism: A Multifarious Approach.

    ERIC Educational Resources Information Center

    Oseguera, A. Anthony

    Recognizing the need for a multifarious approach to television, this paper provides the reader with the following multidimensional approaches to television criticism: rhetorical, dramatic, literary, cinematic, content analysis, myth, linguistics, semiotics, phenomenalism, phenomenology, interpersonal communication, public relations, image,…

  20. Investigational Approaches for Mesothelioma

    PubMed Central

    Surmont, Veerle F.; van Thiel, Eric R. E.; Vermaelen, Karim; van Meerbeeck, Jan P.

    2011-01-01

    Malignant pleural mesothelioma (MPM) is a rare, aggressive tumor with a poor prognosis. In view of the poor survival benefit from first-line chemotherapy and the lack of subsequent effective treatment options, there is a strong need for the development of more effective treatment approaches for patients with MPM. This review will provide a comprehensive state of the art of new investigational approaches for mesothelioma. In an introductory section, the etiology, epidemiology, natural history, and standard of care treatment for MPM will be discussed. This review provide an update of the major clinical trials that impact mesothelioma treatment, discuss the impact of novel therapeutics, and provide perspective on where the clinical research in mesothelioma is moving. The evidence was collected by a systematic analysis of the literature (2000–2011) using the databases Medline (National Library of Medicine, USA), Embase (Elsevier, Netherlands), Cochrane Library (Great Britain), National Guideline Clearinghouse (USA), HTA Database (International Network of Agencies for Health Technology Assessment – INAHTA), NIH database (USA), International Pleural Mesothelioma Program – WHOLIS (WHO Database), with the following keywords and filters: mesothelioma, guidelines, treatment, surgery, chemotherapy, radiotherapy, review, investigational, drugs. Currently different targeted therapies and biologicals are under investigation for MPM. It is important that the molecular biologic research should first focus on mesothelioma-specific pathways and biomarkers in order to have more effective treatment options for this disease. The use of array technology will be certainly an implicit gain in the identification of new potential prognostic or biomarkers or important pathways in the MPM pathogenesis. Probably a central mesothelioma virtual tissue bank may contribute to the ultimate goal to identify druggable targets and to develop personalized treatment for the MPM patients. PMID

  1. Approaching attometer laser vibrometry

    SciTech Connect

    Rembe, Christian; Kadner, Lisa; Giesen, Moritz

    2014-05-27

    The heterodyne two-beam interferometer has been proven to be the optimal solution for laser-Doppler vibrometry regarding accuracy and signal robustness. The theoretical resolution limit for a two-beam interferometer of laser class 3R (up to 5 mW visible measurement-light) is in the regime of a few femtometer per square-root Hertz and well suited to study vibrations in microstructures. However, some new applications of RF-MEM resonators, nanostructures, and surface-nano-defect detection require resolutions beyond that limit. The resolution depends only on the noise and the sensor sensitivity to specimen displacements. The noise is already defined in nowadays systems by the quantum nature of light for a properly designed optical sensor and more light would lead to an inacceptable influence like heating of a very tiny structure. Thus, noise can only be improved by squeezed-light techniques which require a negligible loss of measurement light which is impossible for almost all technical measurement tasks. Thus, improving the sensitivity is the only possible path which could make attometer laser vibrometry possible. Decreasing the measurement wavelength would increase the sensitivity but would also increase the photon shot noise. In this paper, we discuss an approach to increase the sensitivity by assembling an additional mirror between interferometer and specimen to form an optical cavity. A detailed theoretical analysis of this setup is presented and we derive the resolution limit, discuss the main contributions to the uncertainty budget, and show a first experiment proving the sensitivity amplification of our approach.

  2. Graph theoretical similarity approach to compare molecular electrostatic potentials.

    PubMed

    Marín, Ray M; Aguirre, Nestor F; Daza, Edgar E

    2008-01-01

    In this work we introduce a graph theoretical method to compare MEPs, which is independent of molecular alignment. It is based on the edit distance of weighted rooted trees, which encode the geometrical and topological information of Negative Molecular Isopotential Surfaces. A meaningful chemical classification of a set of 46 molecules with different functional groups was achieved. Structure--activity relationships for the corticosteroid binding affinity (CBG) of 31 steroids by means of hierarchical clustering resulted in a clear partitioning in high, intermediate, and low activity groups, whereas the results from quantitative structure--activity relationships, obtained from a partial least-squares analysis, showed comparable or better cross-validated correlation coefficients than the ones reported for previous methods based solely in the MEP.

  3. A Narrative Approach to Research

    ERIC Educational Resources Information Center

    Bell, Anne

    2003-01-01

    In this paper I present a narrative approach to environmental education research. This approach evolved through a dynamic interplay between research questions, theory, experience, conversation, and reflection. I situate the approach with respect to narrative inquiry and clarify the key conceptual metaphors underpinning my study, including…

  4. A Blend Approach to P3HT Based Field Effect Transistor Performance Enhancement via Inclusion of 2,5-bis(3-dodecylthiophen-2-yl)thieno[3,2-b]thiophene

    NASA Astrophysics Data System (ADS)

    Chu, Ping-Hsun; Zhang, Lei; Park, Jung Ok; Srinivasarao, Mohan; Briseño, Alejandro L.; Reichmanis, Elsa

    2015-03-01

    Improved OFET performance through a polymer-small molecule semiconductor blend approach was demonstrated. However, a number of serious issues remain. For example, the threshold voltage (Vth) of the blend OFETs is still at a relatively high value (|Vth|>10V), which is incompatible with most of portable electronics. Moreover, electrode treatment or thermal annealing is required to avoid a sacrifice in the device performance. Herein, a small molecule, 2,5-bis(3-dodecylthiophen-2-yl)thieno[3,2-b]thiophene (BTTT), is proposed to be incorporated within poly(3-hexylthiophene) (P3HT) polymer thin-films and is demonstrated to lead to overall improvement in transistor performance. The resultant blend OFETs exhibited approximately a 5-fold increase in charge carrier mobility, 10-fold increase in on-off current ratio and concomitantly, controlled the Vth as low as 1.7 V. It is worth noting that no pre- or post-treatment is required during the blend OFET fabrication process. Further, the thin-film deposition was conducted under ambient conditions using a volatile low boiling point solvent, suggesting a promising method for low-cost, high-throughput, large-area flexible device fabrication under non-stringent conditions.

  5. Approaches to refractory epilepsy

    PubMed Central

    Engel, Jerome

    2014-01-01

    Epilepsy is one of the most common serious neurological conditions, and 30 to 40% of people with epilepsy have seizures that are not controlled by medication. Patients are considered to have refractory epilepsy if disabling seizures continue despite appropriate trials of two antiseizure drugs, either alone or in combination. At this point, patients should be referred to multidisciplinary epilepsy centers that perform specialized diagnostic testing to first determine whether they are, in fact, pharmacoresistant, and then, if so, offer alternative treatments. Apparent pharmacoresistance can result from a variety of situations, including noncompliance, seizures that are not epileptic, misdiagnosis of the seizure type or epilepsy syndrome, inappropriate use of medication, and lifestyle issues. For patients who are pharmacoresistant, surgical treatment offers the best opportunity for complete freedom from seizures. Surgically remediable epilepsy syndromes have been identified, but patients with more complicated epilepsy can also benefit from surgical treatment and require more specialized evaluation, including intracranial EEG monitoring. For patients who are not surgical candidates, or who are unwilling to consider surgery, a variety of other alternative treatments can be considered, including peripheral or central neurostimulation, ketogenic diet, and complementary and alternative approaches. When such alternative treatments are not appropriate or effective, quality of life can still be greatly improved by the psychological and social support services offered by multidisciplinary epilepsy centers. A major obstacle remains the fact that only a small proportion of patients with refractory epilepsy are referred for expert evaluation and treatment. PMID:24791078

  6. Halitosis: the multidisciplinary approach

    PubMed Central

    Bollen, Curd ML; Beikler, Thomas

    2012-01-01

    Halitosis, bad breath or oral malodour are all synonyms for the same pathology. Halitosis has a large social and economic impact. For the majority of patients suffering from bad breath, it causes embarrassment and affects their social communication and life. Moreover, halitosis can be indicative of underlying diseases. Only a limited number of scientific publications were presented in this field until 1995. Ever since, a large amount of research is published, often with lack of evidence. In general, intraoral conditions, like insufficient dental hygiene, periodontitis or tongue coating are considered to be the most important cause (85%) for halitosis. Therefore, dentists and periodontologists are the first-line professionals to be confronted with this problem. They should be well aware of the origin, the detection and especially of the treatment of this pathology. In addition, ear–nose–throat-associated (10%) or gastrointestinal/endocrinological (5%) disorders may contribute to the problem. In the case of halitophobia, psychiatrical or psychological problems may be present. Bad breath needs a multidisciplinary team approach: dentists, periodontologists, specialists in family medicine, ear–nose–throat surgeons, internal medicine and psychiatry need to be updated in this field, which still is surrounded by a large taboo. Multidisciplinary bad breath clinics offer the best environment to examine and treat this pathology that affects around 25% of the whole population. This article describes the origin, detection and treatment of halitosis, regarded from the different etiological origins. PMID:22722640

  7. The Stepping Stone Approach

    NASA Astrophysics Data System (ADS)

    Brumfitt, A.

    Education is a profession in its own right. It has its own parameters, passions and language. Having the responsibility both of educare and educere, education has a focus of delivering specific factual knowledge whilst drawing out the creative mind. Space Science is a special vehicle having the properties of both educare and educere. It has a magic and wonder that touches the very essence of an individual and his place in time and space; it offers the "wow" factor that all teachers strive for. Space Science is the wrapping paper for other elements in the curriculum, e.g. cross-curricula and skill-based activities, such as language development, creativity, etc. as well as the pure sciences which comprise of engineering, physics and other natural sciences from astronomy to chemistry to biology. Each of these spheres of influence are relevant from kindergarten to undergraduate studies and complement, and in addition support informal education in museums, science centers and the world of e-learning. ESA Science Education has devised the "Stepping Stone Approach" to maximize the greatest outreach to all education stakeholders in Europe. In this paper we illustrate how to best reach these target groups with very specific activities to trigger and sustain enthusiasm whilst supporting the pedagogical, subject content and skill-based needs of a prescribed curriculum.

  8. Approach to hypohidrosis.

    PubMed

    Chia, K Y; Tey, H L

    2013-07-01

    Hypohidrosis refers to diminished sweating in response to appropriate stimuli. This can cause hyperthermia, heat exhaustion and death. The aetiology of hypohidrosis can be divided into exogenous, dermatological and neurological causes. Exogenous causes act either by systemic neurohormonal inhibition of sweating or localised damage to the skin and sweat glands. Dermatological disorders can result from congenital disorders, wherein other ectodermal tissues may also be affected, or acquired disorders in which manifestations of the primary disease predominate. Neurological disorders should be classified based on an upper motor neuron or lower motor neuron pattern of disease. In the former, there is spasticity and hyperactive reflexes whereas in the latter, flaccidity and hypoactive reflexes predominate. Acquired idiopathic generalised anhidrois refers to isolated anhidrosis with no other detectable abnormalities. When approaching a patient with hypohidrois, exogenous causes should first be excluded. Physical examination, paying attention to mucocutaneous manifestations and neurological signs, will dichotomise if the lesion is dermatological or neurological. In the former, a skin biopsy is the investigation of choice. In the latter, one should consider magnetic resonance imaging of the brain and spinal cord for upper motor neuron lesions, nerve conduction tests for lower motor neuron lesions and autonomic nerve function tests for autonomic dysfunction. Finally, if a diagnosis of acquired idiopathic generalised anhidrosis is suspected, a quantitative sudomotor axon reflex test and serum immunoglobulin-E levels may be performed. Treatment involves addressing the underlying condition and avoidance of aggravating factors. Acquired idiopathic generalised anhidrosis responds well to high dose systemic corticosteroids.

  9. Cross-validation Methodology between Ground and GPM Satellite-based Radar Rainfall Product over Dallas-Fort Worth (DFW) Metroplex

    NASA Astrophysics Data System (ADS)

    Chen, H.; Chandrasekar, V.; Biswas, S.

    2015-12-01

    Over the past two decades, a large number of rainfall products have been developed based on satellite, radar, and/or rain gauge observations. However, to produce optimal rainfall estimation for a given region is still challenging due to the space time variability of rainfall at many scales and the spatial and temporal sampling difference of different rainfall instruments. In order to produce high-resolution rainfall products for urban flash flood applications and improve the weather sensing capability in urban environment, the center for Collaborative Adaptive Sensing of the Atmosphere (CASA), in collaboration with National Weather Service (NWS) and North Central Texas Council of Governments (NCTCOG), has developed an urban radar remote sensing network in DFW Metroplex. DFW is the largest inland metropolitan area in the U.S., that experiences a wide range of natural weather hazards such as flash flood and hailstorms. The DFW urban remote sensing network, centered by the deployment of eight dual-polarization X-band radars and a NWS WSR-88DP radar, is expected to provide impacts-based warning and forecasts for benefit of the public safety and economy. High-resolution quantitative precipitation estimation (QPE) is one of the major goals of the development of this urban test bed. In addition to ground radar-based rainfall estimation, satellite-based rainfall products for this area are also of interest for this study. Typical example is the rainfall rate product produced by the Dual-frequency Precipitation Radar (DPR) onboard Global Precipitation Measurement (GPM) Core Observatory satellite. Therefore, cross-comparison between ground and space-based rainfall estimation is critical to building an optimal regional rainfall system, which can take advantages of the sampling differences of different sensors. This paper presents the real-time high-resolution QPE system developed for DFW urban radar network, which is based upon the combination of S-band WSR-88DP and X-band CASA radars. In addition, we focuses on the cross-comparison between rainfall estimation from this ground based QPE system and GPM rainfall products. The observations collected during the GPM satellite overpasses over DFW area will be used extensively in this study. Data alignment for better comparison will also be presented.

  10. Cross-validation of δ15N and FishBase estimates of fish trophic position in a Mediterranean lagoon: The importance of the isotopic baseline

    NASA Astrophysics Data System (ADS)

    Mancinelli, Giorgio; Vizzini, Salvatrice; Mazzola, Antonio; Maci, Stefano; Basset, Alberto

    2013-12-01

    FishBase, a relational database freely available on the Internet, is to date widely used as a source of quantitative information on the trophic position of marine fish species. Here, we compared FishBase estimates for an assemblage of 30 fish species sampled in a Mediterranean lagoon (Acquatina lagoon, SE Italy) with their trophic positions calculated using nitrogen stable isotopes.

  11. The alpha-globin genotype does not influence sickle cell disease severity in a retrospective cross-validation study of the pediatric severity score.

    PubMed

    Joly, Philippe; Pondarré, Corinne; Bardel, Claire; Francina, Alain; Martin, Cyril

    2012-01-01

    To validate the recently proposed pediatric severity score (PSS) for sickle cell disease (SCD), we retrospectively assembled clinical data from a cohort of 122 patients with SCD (105 S/S or S/β(0) -thal. and 17 S/C) followed up for at least 2 years. Besides age and α- and β-globin genotypes, four new parameters were also tested against the PSS: duration of data assembly, neonatal screening, use of transcranial Doppler ultrasound to prevent vasculopathies and β-globin gene cluster haplotype. Once again, the PSS clearly differentiated patients by their β-globin genotype (P=0.004) but not by their age during data assembly (P=0.159). But, surprisingly, alpha-gene deletions were not associated with a lower PSS (P=0.604), possibly reflecting the opposite effects of α-thalassemia on global SCD severity. As for the newly tested parameters, the PSS appeared not to be influenced by the duration of data assembly (P=0.071) and neonatal screening (P=0.678) but rather by the introduction of transcranial Doppler ultrasound (P=0.006). Moreover, the Senegal haplotype at the homozygous state may be associated with a lower PSS. Methodologically, our data globally confirm the usefulness of the PSS to identify major etiological factors of SCD gravity. Nevertheless, the score is surely underestimated for patients who have been switched to a chronic therapy before the main SCD complications. Biologically, our study questions about the exact influence of α-thalassemia on global SCD severity.

  12. Early and brain region-specific decrease of de novo cholesterol biosynthesis in Huntington's disease: A cross-validation study in Q175 knock-in mice.

    PubMed

    Shankaran, Mahalakshmi; Di Paolo, Eleonora; Leoni, Valerio; Caccia, Claudio; Ferrari Bardile, Costanza; Mohammed, Hussein; Di Donato, Stefano; Kwak, Seung; Marchionini, Deanna; Turner, Scott; Cattaneo, Elena; Valenza, Marta

    2017-02-01

    Cholesterol precursors and cholesterol levels are reduced in brain regions of Huntington's disease (HD) mice. Here we quantified the rate of in vivo de novo cholesterol biosynthesis in the HD brain. Samples from different brain regions and blood of the heterozygous knock-in mouse model carrying 175 CAG repeats (Q175) at different phenotypic stages were processed independently by two research units to quantify cholesterol synthesis rate by (2)H2O labeling and measure the concentrations of lathosterol, cholesterol and its brain-specific cholesterol catabolite 24-hydroxy-cholesterol (24OHC) by isotope dilution mass spectrometry. The daily synthesis rate of cholesterol and the corresponding concentration of lathosterol were significantly reduced in the striatum of heterozygous Q175 mice early in the disease course. We also report that the decrease in lathosterol was inversely correlated with CAG-size at symptomatic stage, as observed in striatal samples from an allelic series of HD mice. There was also a significant correlation between the fractional synthesis rates of total cholesterol and 24OHC in brain of wild-type (WT) and Q175 mice, supporting the evidence that plasma 24OHC may reflect cholesterol synthesis in the adult brain. This comprehensive analysis demonstrates consistent cholesterol biosynthesis defects in HD mouse models and suggests that plasma 24OHC may serve as a biomarker of brain cholesterol metabolism.

  13. Sentimentality and Nostalgia in Elderly People in Bulgaria and Greece – Cross-Validity of the Questionnaire SNEP and Cross-Cultural Comparison

    PubMed Central

    Stoyanova, Stanislava Yordanova; Giannouli, Vaitsa; Gergov, Teodor Krasimirov

    2017-01-01

    Sentimentality and nostalgia are two similar psychological constructs, which play an important role in the emotional lives of elderly people who are usually focused on the past. There are two objectives of this study - making cross-cultural comparison of sentimentality and nostalgia among Bulgarian and Greek elderly people using a questionnaire, and establishing the psychometric properties of this questionnaire among Greek elderly people. Sentimentality and nostalgia in elderly people in Bulgaria and Greece were studied by means of Sentimentality and Nostalgia in Elderly People questionnaire (SNEP), created by Gergov and Stoyanova (2013). For the Greek version, one factor structure without sub-scales is proposed, while for the Bulgarian version of SNEP the factor structure had four sub-scales, besides the total score. Together with some similarities (medium level of nostalgia and sentimentality being widespread), the elderly people in Bulgaria and Greece differed cross-culturally in their sentimentality and nostalgia related to the past in direction of more increased sentimentality and nostalgia in the Bulgarian sample. Some gender and age differences revealed that the oldest male Bulgarians were the most sentimental. The psychometric properties of this questionnaire were examined for the first time in a Greek sample of elders and a trend was found for stability of sentimentality and nostalgia in elderly people that could be studied further in longitudinal studies. PMID:28344678

  14. Direct spectral analysis of tea samples using 266 nm UV pulsed laser-induced breakdown spectroscopy and cross validation of LIBS results with ICP-MS.

    PubMed

    Gondal, M A; Habibullah, Y B; Baig, Umair; Oloore, L E

    2016-05-15

    Tea is one of the most common and popular beverages spanning vast array of cultures all over the world. The main nutritional benefits of drinking tea are its anti-oxidant properties, presumed protection against certain cancers, inhibition of inflammation and possible protective effects against diabetes. Laser induced breakdown spectrometer (LIBS) was assembled as a powerful tool for qualitative and quantitative analysis of various brands of tea samples using 266 nm pulsed UV laser. LIBS spectra for six brands of tea samples in the wavelength range of 200-900 nm was recorded and all elements present in our tea samples were identified. The major toxic elements detected in several brands of tea samples were bromine, chromium and minerals like iron, calcium, potassium and silicon. The spectral assignment was conducted prior to the determination of concentration of each element. For quantitative analysis, calibration curves were drawn for each element using standard samples prepared in known concentration in the tea matrix. The plasma parameters (electron temperature and electron density) were also determined prior to the tea samples spectroscopic analysis. The concentration of iron, chromium, potassium, bromine, copper, silicon and calcium detected in all tea samples was between 378-656, 96-124, 1421-6785, 99-1476, 17-36, 2-11 and 92-130 mg L(-1) respectively. The limits of detection estimated for Fe, Cr, K, Br, Cu, Si, Ca in tea samples were 22, 12, 14, 11, 6, 1 and 12 mg L(-1) respectively. To further confirm the accuracy of our LIBS results, we determined the concentration of each element present in tea samples by using standard analytical technique like ICP-MS. The concentrations detected with our LIBS system are in excellent agreement with ICP-MS results. The system assembled for spectral analysis in this work could be highly applicable for testing the quality and purity of food and also pharmaceuticals products.

  15. Near surface geotechnical and geophysical data cross validated for site characterization applications. The cases of selected accelerometric stations in Crete island (Greece)

    NASA Astrophysics Data System (ADS)

    Loupasakis, Constantinos; Tsangaratos, Paraskevas; Rozos, Dimitrios; Rondoyianni, Theodora; Vafidis, Antonis; Steiakakis, Emanouil; Agioutantis, Zacharias; Savvaidis, Alexandros; Soupios, Pantelis; Papadopoulos, Ioannis; Papadopoulos, Nikos; Sarris, Apostolos; Mangriotis, Maria-Dafni; Dikmen, Unal

    2015-04-01

    The near surface ground conditions are highly important for the design of civil constructions. These conditions determine primarily the ability of the foundation formations to bear loads, the stress - strain relations and the corresponding deformations, as well as the soil amplification and corresponding peak ground motion in case of dynamic loading. The static and dynamic geotechnical parameters as well as the ground-type/soil-category can be determined by combining geotechnical and geophysical methods, such as engineering geological surface mapping, geotechnical drilling, in situ and laboratory testing and geophysical investigations. The above mentioned methods were combined for the site characterization in selected sites of the Hellenic Accelerometric Network (HAN) in the area of Crete Island. The combination of the geotechnical and geophysical methods in thirteen (13) sites provided sufficient information about their limitations, setting up the minimum tests requirements in relation to the type of the geological formations. The reduced accuracy of the surface mapping in urban sites, the uncertainties introduced by the geophysical survey in sites with complex geology and the 1-D data provided by the geotechnical drills are some of the causes affecting the right order and the quantity of the necessary investigation methods. Through this study the gradual improvement on the accuracy of the site characterization data in regards to the applied investigation techniques is presented by providing characteristic examples from the total number of thirteen sites. As an example of the gradual improvement of the knowledge about the ground conditions the case of AGN1 strong motion station, located at Agios Nikolaos city (Eastern Crete), is briefly presented. According to the medium scale geological map of IGME the station was supposed to be founded over limestone. The detailed geological mapping reveled that a few meters of loose alluvial deposits occupy the area, expected to lay over the Neogene marly formations and the Mesozoic limestone, identified at the surrounding area. This changes the ground type to E instead of A, based on the EC8 classification. According the geophysical survey the Neogene formations extend down several meters and the mean Vs30 is 476m/s, increasing the rank of the ground type to B. Finally, the geotechnical drill reviled that the loose alluvial deposits extend down 13m containing two clearly identified layers of liquefiable loose sand. Below the alluvial deposits a thin layer (1,5m thick) of Neogene marly formations and the karstified limestone was located, as expected. So finally it was proved that the ground type category at the site is S2, setting up the geotechnical drills as the determinant investigation technique for this site. Besides the above described case, all selected examples present sufficiently the ability, the limitations and the right order of the investigation methods aiming to the site characterization. This research has been co-financed by the European Union (European Social Fund - ESF) and Greek national funds through the Operational Program "Education and Lifelong Learning" of the National Strategic Reference Framework (NSRF) - Research Funding Program: THALES. Investing in knowledge society through the European Social Fund.

  16. Refinement and cross-validation of nickel bioavailability in PNEC-pro, a regulatory tool for site-specific risk assessment of metals in surface water.

    PubMed

    Verschoor, Anja J; Vijver, Martina G; Vink, Jos P M

    2017-02-22

    The European Water Framework Directive prescribes that the environmental quality standards for nickel in surface waters should be based on bioavailable concentrations. Biotic ligand models (BLMs) are powerful tools to account for site-specific bioavailability within risk assessments. Several BLMs and simplified tools are available. For nickel, most of them are based on the same toxicity dataset and chemical speciation methodology as laid down in the 2008 European Union Environmental-Risk Assessment Report (RAR). Since then, insights on toxic effects of nickel on aquatic species have progressed and new data and methodologies were generated and implemented in PNEC-pro tool. The aim of this study is to provide maximum transparency on data revisions and how this affects the derived environmental quality standards. A case study with seven different ecoregions was used to determine differences in species sensitivity distributions and in HC5-values between the original Ni-RAR BLMs and the PNEC-pro BLMs. The BLM parameters used were pH dependent, which extended the applicability domain of PNEC-pro up to a pH of 8.7 for surface waters. After inclusion of additional species, adjustment of cross-species extrapolation and the HC5s are well within the prediction range of the RAR. Based on the latest data and scientific insights, transfer functions in the user-friendly PNEC-pro tool have been updated accordingly without compromising the original considerations of the Ni-RAR. This article is protected by copyright. All rights reserved.

  17. Cross-Validation of the Spanish HP-Version of the Jefferson Scale of Empathy Confirmed with Some Cross-Cultural Differences

    PubMed Central

    Alcorta-Garza, Adelina; San-Martín, Montserrat; Delgado-Bolton, Roberto; Soler-González, Jorge; Roig, Helena; Vivanco, Luis

    2016-01-01

    Context: Medical educators agree that empathy is essential for physicians' professionalism. The Health Professional Version of the Jefferson Scale of Empathy (JSE-HP) was developed in response to a need for a psychometrically sound instrument to measure empathy in the context of patient care. Although extensive support for its validity and reliability is available, the authors recognize the necessity to examine psychometrics of the JSE-HP in different socio-cultural contexts to assure the psychometric soundness of this instrument. The first aim of this study was to confirm its psychometric properties in the cross-cultural context of Spain and Latin American countries. The second aim was to measure the influence of social and cultural factors on the development of medical empathy in health practitioners. Methods: The original English version of the JSE-HP was translated into International Spanish using back-translation procedures. The Spanish version of the JSE-HP was administered to 896 physicians from Spain and 13 Latin American countries. Data were subjected to exploratory factor analysis using principal component analysis (PCA) with oblique rotation (promax) to allow for correlation among the resulting factors, followed by a second analysis, using confirmatory factor analysis (CFA). Two theoretical models, one based on the English JSE-HP and another on the first Spanish student version of the JSE (JSE-S), were tested. Demographic variables were compared using group comparisons. Results: A total of 715 (80%) surveys were returned fully completed. Cronbach's alpha coefficient of the JSE for the entire sample was 0.84. The psychometric properties of the Spanish JSE-HP matched those of the original English JSE-HP. However, the Spanish JSE-S model proved more appropriate than the original English model for the sample in this study. Group comparisons among physicians classified by gender, medical specialties, cultural and cross-cultural backgrounds yielded statistically significant differences (p < 0.001). Conclusions: The findings support the underlying factor structure of the Jefferson Scale of Empathy (JSE). The results reveal the importance of culture in the development of medical empathy. The cross-cultural differences described could open gates for further lines of medical education research. PMID:27462282

  18. A cross-validation of the Youth Level of Service/Case Management Inventory (YLS/CMI) among Japanese juvenile offenders.

    PubMed

    Takahashi, Masaru; Mori, Takemi; Kroner, Daryl G

    2013-12-01

    The main purpose of the current research is to examine the applicability of the Youth Level of Service/Case Management Inventory (YLS/CMI) in a Japanese juvenile offender population. Three hundred eighty-nine youths who were released from the five Juvenile Classification Homes were followed for approximately one and half years. Results show that the YLS/CMI total score significantly predict recidivism. Survival time analysis demonstrates that the YLS/CMI total score also significantly predicted faster time to recidivism. The overall findings support adequate predictive validity of the YLS/CMI total score, but subscales lacked content representativeness and predictive validity in this sample. Canadian and Japanese cultural differences in criminal history and substance abuse are contributors to the lack of content representativeness.

  19. Cross-validation of IASI/MetOp derived tropospheric δD with TES and ground-based FTIR observations

    NASA Astrophysics Data System (ADS)

    Lacour, J.-L.; Clarisse, L.; Worden, J.; Schneider, M.; Barthlott, S.; Hase, F.; Risi, C.; Clerbaux, C.; Hurtmans, D.; Coheur, P.-F.

    2015-03-01

    The Infrared Atmospheric Sounding Interferometer (IASI) flying onboard MetOpA and MetOpB is able to capture fine isotopic variations of the HDO to H2O ratio (δD) in the troposphere. Such observations at the high spatio-temporal resolution of the sounder are of great interest to improve our understanding of the mechanisms controlling humidity in the troposphere. In this study we aim to empirically assess the validity of our error estimation previously evaluated theoretically. To achieve this, we compare IASI δD retrieved profiles with other available profiles of δD, from the TES infrared sounder onboard AURA and from three ground-based FTIR stations produced within the MUSICA project: the NDACC (Network for the Detection of Atmospheric Composition Change) sites Kiruna and Izaña, and the TCCON site Karlsruhe, which in addition to near-infrared TCCON spectra also records mid-infrared spectra. We describe the achievable level of agreement between the different retrievals and show that these theoretical errors are in good agreement with empirical differences. The comparisons are made at different locations from tropical to Arctic latitudes, above sea and above land. Generally IASI and TES are similarly sensitive to δD in the free troposphere which allows one to compare their measurements directly. At tropical latitudes where IASI's sensitivity is lower than that of TES, we show that the agreement improves when taking into account the sensitivity of IASI in the TES retrieval. For the comparison IASI-FTIR only direct comparisons are performed because the sensitivity profiles of the two observing systems do not allow to take into account their differences of sensitivity. We identify a quasi negligible bias in the free troposphere (-3‰) between IASI retrieved δD with the TES, which are bias corrected, but important with the ground-based FTIR reaching -47‰. We also suggest that model-satellite observation comparisons could be optimized with IASI thanks to its high spatial and temporal sampling.

  20. Cross-validation of IASI/MetOp derived tropospheric δD with TES and ground-based FTIR observations

    NASA Astrophysics Data System (ADS)

    Lacour, J.-L.; Clarisse, L.; Worden, J.; Schneider, M.; Barthlott, S.; Hase, F.; Risi, C.; Clerbaux, C.; Hurtmans, D.; Coheur, P.-F.

    2014-11-01

    The Infrared Atmospheric Sounding Interferometer (IASI) flying on-board MetOpA and MetOpB is able to capture fine isotopic variations of the HDO to H2O ratio (δD) in the troposphere. Such observations at the high spatio temporal resolution of the sounder are of great interest to improve our understanding of the mechanisms controlling humidity in the troposphere. In this study we aim to empirically assess the validity of our error estimation previously evaluated theoretically. To achieve this, we compare IASI δD retrieved profiles with other available profiles of δD, from the TES infrared sounder onboard AURA and from three ground-based FTIR stations produced within the MUSICA project: the NDACC (Network for the Detection of Atmospheric Composition Change) sites Kiruna and Izana, and the TCCON site Karlsruhe, which in addition to near-infrared TCCON spectra also records mid-infrared spectra. We describe the achievable level of agreement between the different retrievals and show that these theoretical errors are in good agreement with empirical differences. The comparisons are made at different locations from tropical to Arctic latitudes, above sea and above land. Generally IASI and TES are similarly sensitive to δD in the free troposphere which allows to compare their measurements directly. At tropical latitudes where IASI's sensitivity is lower than that of TES, we show that the agreement improves when taking into account the sensitivity of IASI in the TES retrieval. For the comparison IASI-FTIR only direct comparisons are performed because of similar sensitivities. We identify a quasi negligible bias in the free troposphere (-3‰) between IASI retrieved δD with the TES one, which are bias corrected, but an important with the ground-based FTIR reaching -47‰. We also suggest that model-satellite observations comparisons could be optimized with IASI thanks to its high spatial and temporal sampling.

  1. Cross-validation of the factorial structure of the Neighborhood Environment Walkability Scale (NEWS) and its abbreviated form (NEWS-A)

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The Neighborhood Environment Walkability Scale (NEWS) and its abbreviated form (NEWS-A) assess perceived environmental attributes believed to influence physical activity. A multilevel confirmatory factor analysis (MCFA) conducted on a sample from Seattle, WA, showed that, at the respondent level, th...

  2. RegulonDB v8.0: omics data sets, evolutionary conservation, regulatory phrases, cross-validated gold standards and more

    PubMed Central

    Salgado, Heladia; Peralta-Gil, Martin; Gama-Castro, Socorro; Santos-Zavaleta, Alberto; Muñiz-Rascado, Luis; García-Sotelo, Jair S.; Weiss, Verena; Solano-Lira, Hilda; Martínez-Flores, Irma; Medina-Rivera, Alejandra; Salgado-Osorio, Gerardo; Alquicira-Hernández, Shirley; Alquicira-Hernández, Kevin; López-Fuentes, Alejandra; Porrón-Sotelo, Liliana; Huerta, Araceli M.; Bonavides-Martínez, César; Balderas-Martínez, Yalbi I.; Pannier, Lucia; Olvera, Maricela; Labastida, Aurora; Jiménez-Jacinto, Verónica; Vega-Alvarado, Leticia; del Moral-Chávez, Victor; Hernández-Alvarez, Alfredo; Morett, Enrique; Collado-Vides, Julio

    2013-01-01

    This article summarizes our progress with RegulonDB (http://regulondb.ccg.unam.mx/) during the past 2 years. We have kept up-to-date the knowledge from the published literature regarding transcriptional regulation in Escherichia coli K-12. We have maintained and expanded our curation efforts to improve the breadth and quality of the encoded experimental knowledge, and we have implemented criteria for the quality of our computational predictions. Regulatory phrases now provide high-level descriptions of regulatory regions. We expanded the assignment of quality to various sources of evidence, particularly for knowledge generated through high-throughput (HT) technology. Based on our analysis of most relevant methods, we defined rules for determining the quality of evidence when multiple independent sources support an entry. With this latest release of RegulonDB, we present a new highly reliable larger collection of transcription start sites, a result of our experimental HT genome-wide efforts. These improvements, together with several novel enhancements (the tracks display, uploading format and curational guidelines), address the challenges of incorporating HT-generated knowledge into RegulonDB. Information on the evolutionary conservation of regulatory elements is also available now. Altogether, RegulonDB version 8.0 is a much better home for integrating knowledge on gene regulation from the sources of information currently available. PMID:23203884

  3. Endoscopic Endonasal Transsphenoidal Approach

    PubMed Central

    Cappabianca, Paolo; Alfieri, Alessandra; Colao, Annamaria; Ferone, Diego; Lombardi, Gaetano; de Divitiis, Enrico

    1999-01-01

    The outcome of endoscopic endonasal transsphenoidal surgery in 10 patients with pituitary adenomas was compared with that of traditional transnasal transsphenoidal approach (TTA) in 20 subjects. Among the 10 individuals subjected to “pure endoscopy,” 2 had a microadenoma, 1 an intrasellar macroadenoma, 4 had a macroadenoma with suprasellar expansion, 2 had a macroadenoma with supra-parasellar expansion, and 1 a residual tumor; 5 had acromegaly and 5 had a nonfunctioning adenoma (NFA). Among the patients subjected to TTA, 4 had a microadenoma, 2 had an intrasellar macroadenoma, 6 had a macroadenoma with suprasellar expansion, 4 had a macroadenoma with supra-parasellar expansion, and 4 had a residual tumor; 9 patients had acromegaly, 1 hyperprolactinemia, 1 Cushing's disease, and 9 a NFA. At the macroscopic evaluation, tumor removal was total (100%) after endoscopy in 9 patients and after TTA in 14 patients. Six months after surgery, magnetic resonance imaging (MRI) confirmed the total tumor removal in 21 of 23 patients (91.3%). Circulating growth hormone (GH) and insulin-like growth factor-I (IGF-I) significantly decreased 6 months after surgery in all 14 acromegalic patients: normalization of plasma IGF-I levels was obtained in 4 of 5 patients after the endoscopic procedure and in 4 of 9 patients after TTA. Before surgery, pituitary hormone deficiency was present in 14 out of 30 patients: pituitary function improved in 4 patients, remaining unchanged in the other 10 patients. Visual field defects were present before surgery in 4 patients, and improved in all. Early surgical results in the group of 10 patients who underwent endoscopic pituitary tumor removal were at least equivalent to those of standard TTA, with excellent postoperative course. Postsurgical hospital stay was significantly shorter (3.1 ± 0.4 vs. 6.2 ± 0.3 days, p < 0.001) after endoscopy as compared to TTA. ImagesFigure 1Figure 2 PMID:17171126

  4. Skull base approaches in neurosurgery

    PubMed Central

    2010-01-01

    The skull base surgery is one of the most demanding surgeries. There are different structures that can be injured easily, by operating in the skull base. It is very important for the neurosurgeon to choose the right approach in order to reach the lesion without harming the other intact structures. Due to the pioneering work of Cushing, Hirsch, Yasargil, Krause, Dandy and other dedicated neurosurgeons, it is possible to address the tumor and other lesions in the anterior, the mid-line and the posterior cranial base. With the transsphenoidal, the frontolateral, the pterional and the lateral suboccipital approach nearly every region of the skull base is exposable. In the current state many different skull base approaches are described for various neurosurgical diseases during the last 20 years. The selection of an approach may differ from country to country, e.g., in the United States orbitozygomaticotomy for special lesions of the anterior skull base or petrosectomy for clivus meningiomas, are found more frequently than in Europe. The reason for writing the review was the question: Are there keyhole approaches with which someone can deal with a vast variety of lesions in the neurosurgical field? In my opinion the different surgical approaches mentioned above cover almost 95% of all skull base tumors and lesions. In the following text these approaches will be described. These approaches are: 1) pterional approach 2) frontolateral approach 3) transsphenoidal approach 4) suboccipital lateral approach These approaches can be extended and combined with each other. In the following we want to enhance this philosophy. PMID:20602753

  5. LacSubPred: predicting subtypes of Laccases, an important lignin metabolism-related enzyme class, using in silico approaches

    PubMed Central

    2014-01-01

    , a supervised learning system was developed from the clusters. The models showed high performance with an overall accuracy of 99.03%, error of 0.49%, MCC of 0.9367, precision of 94.20%, sensitivity of 94.20%, and specificity of 99.47% in a 5-fold cross-validation test. In an independent test, our models still provide a high accuracy of 97.98%, error rate of 1.02%, MCC of 0.8678, precision of 87.88%, sensitivity of 87.88% and specificity of 98.90%. Conclusion This study provides a useful classification system for better understanding of Laccases from their physicochemical properties perspective. We also developed a publically available web tool for the characterization of Laccase protein sequences (http://lacsubpred.bioinfo.ucr.edu/). Finally, the programs used in the study are made available for researchers interested in applying the system to other enzyme classes (https://github.com/tweirick/SubClPred). PMID:25350584

  6. Training based on ligand efficiency improves prediction of bioactivities of ligands and drug target proteins in a machine learning approach.

    PubMed

    Sugaya, Nobuyoshi

    2013-10-28

    Machine learning methods based on ligand-protein interaction data in bioactivity databases are one of the current strategies for efficiently finding novel lead compounds as the first step in the drug discovery process. Although previous machine learning studies have succeeded in predicting novel ligand-protein interactions with high performance, all of the previous studies to date have been heavily dependent on the simple use of raw bioactivity data of ligand potencies measured by IC50, EC50, K(i), and K(d) deposited in databases. ChEMBL provides us with a unique opportunity to investigate whether a machine-learning-based classifier created by reflecting ligand efficiency other than the IC50, EC50, K(i), and Kd values can also offer high predictive performance. Here we report that classifiers created from training data based on ligand efficiency show higher performance than those from data based on IC50 or K(i) values. Utilizing GPCRSARfari and KinaseSARfari databases in ChEMBL, we created IC50- or K(i)-based training data and binding efficiency index (BEI) based training data then constructed classifiers using support vector machines (SVMs). The SVM classifiers from the BEI-based training data showed slightly higher area under curve (AUC), accuracy, sensitivity, and specificity in the cross-validation tests. Application of the classifiers to the validation data demonstrated that the AUCs and specificities of the BEI-based classifiers dramatically increased in comparison with the IC50- or K(i)-based classifiers. The improvement of the predictive power by the BEI-based classifiers can be attributed to (i) the more separated distributions of positives and negatives, (ii) the higher diversity of negatives in the BEI-based training data in a feature space of SVMs, and (iii) a more balanced number of positives and negatives in the BEI-based training data. These results strongly suggest that training data based on ligand efficiency as well as data based on classical IC50

  7. SPINE: an integrated tracking database and data mining approach for identifying feasible targets in high-throughput structural proteomics.

    PubMed

    Bertone, P; Kluger, Y; Lan, N; Zheng, D; Christendat, D; Yee, A; Edwards, A M; Arrowsmith, C H; Montelione, G T; Gerstein, M

    2001-07-01

    proteins tend to have significantly more acidic residues and fewer hydrophobic stretches than insoluble ones. One of the characteristics of proteomics data sets, currently and in the foreseeable future, is their intermediate size ( approximately 500-5000 data points). This creates a number of issues in relation to error estimation. Initially we estimate the overall error in our trees based on standard cross-validation. However, this leaves out a significant fraction of the data in model construction and does not give error estimates on individual rules. Therefore, we present alternative methods to estimate the error in particular rules.

  8. The narrative approach to personalisation

    NASA Astrophysics Data System (ADS)

    Conlan, Owen; Staikopoulos, Athanasios; Hampson, Cormac; Lawless, Séamus; O'keeffe, Ian

    2013-06-01

    This article describes the narrative approach to personalisation. This novel approach to the generation of personalised adaptive hypermedia experiences employs runtime reconciliation between a personalisation strategy and a number of contextual models (e.g. user and domain). The approach also advocates the late binding of suitable content and services to the generated personalised pathway resulting in an interactive composition that comprises services as well as content. This article provides a detailed definition of the narrative approach to personalisation and showcases the approach through the examination of two use-cases: the personalised digital educational games developed by the ELEKTRA and 80Days projects; and the personalised learning activities realised as part of the AMAS project. These use-cases highlight the general applicability of the narrative approach and how it has been applied to create a diverse range of real-world systems.

  9. Defining biocultural approaches to conservation.

    PubMed

    Gavin, Michael C; McCarter, Joe; Mead, Aroha; Berkes, Fikret; Stepp, John Richard; Peterson, Debora; Tang, Ruifei

    2015-03-01

    We contend that biocultural approaches to conservation can achieve effective and just conservation outcomes while addressing erosion of both cultural and biological diversity. Here, we propose a set of guidelines for the adoption of biocultural approaches to conservation. First, we draw lessons from work on biocultural diversity and heritage, social-ecological systems theory, integrated conservation and development, co-management, and community-based conservation to define biocultural approaches to conservation. Second, we describe eight principles that characterize such approaches. Third, we discuss reasons for adopting biocultural approaches and challenges. If used well, biocultural approaches to conservation can be a powerful tool for reducing the global loss of both biological and cultural diversity.

  10. Approaching cancer pain relief.

    PubMed

    Lickiss, J N

    2001-01-01

    Pain is defined as an unpleasant experience-it is subjective and achieving pain relief is achieving a change in the patient's experience. There needs to be an adequate concept of a human person (an ecological model will be discussed) and a logical process for approaching pain relief in an individual patient (e.g. the plan used in the Sydney Institute of Palliative Medicine). Communication with the patient is critical to get a grasp of him or her as a person, their environment, personal experience and cultural background. Then encourage him or her to tell the story of the cancer saga as they perceive it, listening carefully for the matters which may have given rise to acute distress (for example, delay in diagnosis) and how they adjusted to this. The individual is conveying a great deal about him or herself as they tell their story. Next the story of the treatment and their experience of it, and then the response of their tumour to it--then the story of their pain: when it began, its characteristics, how it evolved, what factors worsen the pain, what relieves it, etc. This is followed by careful clinical examination to clarify what could be the most likely mechanism(s) responsible for the noxious stimulus. Some investigation (e.g. X-ray) may be justified to assist clarification--but not before making a clinical diagnosis (best guess) and commencing treatment with drugs or other logical measures with some local action--depending on the most probable mechanism. Paracetamol/non-steroidal anti-inflammatory drugs (NSAIDs) etc may be logical. Threshold factors should be attended to--comfort, concern always, or anxiolytic or antidepressant drugs if the patient is pathologically anxious or depressed. The opioid drugs--with morphine still as the gold standard--should be appropriately used. This involves careful calibration of dose (below sedative level) normally with an immediate-release, preparation--and, in the case of morphine, specific counselling concerning 'myths' to

  11. Loran-C approach considerations

    NASA Technical Reports Server (NTRS)

    Lilley, Robert W.

    1987-01-01

    The use of Loran-C during approaches to landing is investigated. The Avionics Engineering Center has evaluated such approach applications at Galion, Ohio Municipal Airport and at Mansfield, Ohio Lahm airport. Loran-C data were referenced to ground tracker data to determine that the Loran-C approach path was straight, flyable, and parallel to the runway centerline. The Loran-C operational issues that were investigated are listed.

  12. New Approaches to Final Cooling

    SciTech Connect

    Neuffer, David

    2014-11-10

    A high-energy muon collider scenario require a “final cooling” system that reduces transverse emittances by a factor of ~10 while allowing longitudinal emittance increase. The baseline approach has low-energy transverse cooling within high-field solenoids, with strong longitudinal heating. This approach and its recent simulation are discussed. Alternative approaches which more explicitly include emittance exchange are also presented. Round-to-flat beam transform, transverse slicing, and longitudinal bunch coalescence are possible components of the alternative approach. A more explicit understanding of solenoidal cooling beam dynamics is introduced.

  13. Use of the design-of-experiments approach for the development of a refolding technology for progenipoietin-1, a recombinant human cytokine fusion protein from Escherichia coli inclusion bodies.

    PubMed

    Boyle, Denis M; Buckley, John J; Johnson, Gary V; Rathore, Anurag; Gustafson, Mark E

    2009-07-14

    Optimization of refolding conditions for progenipoietin was performed. The molecule has five disulfide bonds and, hence, is a challenge to refold. Variables studied included pH, DTT (dithiothreitol) concentration, cystine concentration, urea concentration, protein concentration, dissolution hold time and oxygen availability. In view of the complexity of the reaction with respect to the number of parameters that can impact the refold efficiency, some variables were examined via single-parameter studies, whereas others were looked at via a DOE (design of experiments) approach. The DOE approach allowed us to look at the effect of these variables over wide ranges, as well as their interactions, in a very efficient manner. We were able to obtain a maximal refolding efficiency of 57%, defined as a percentage of correctly folded, bioactive dimer protein from inclusion-body slurries produced from Escherichia coli. The final method involved dissolution of IBs for 30 min at 2 mg/ml protein, 6 M urea, 2 mM DTT and 50 mM Tris (pH 10.2) for approx. 30 min, followed by the addition of 4 mM cystine just prior to a 10-fold dilution with 50 mM Tris (pH 10.2) buffer and reaction for 72 h at 2-10 degrees C. The use of the DOE approach allowed us to understand the interactions between the various parameters, in particular those between cystine and urea concentrations. The results were used to create a process model that demonstrated satisfactory accuracy and that could be used during commercialization of the product.

  14. Microbial Burden Approach : New Monitoring Approach for Measuring Microbial Burden

    NASA Technical Reports Server (NTRS)

    Venkateswaran, Kasthuri; Vaishampayan, Parag; Barmatz, Martin

    2013-01-01

    Advantages of new approach for differentiating live cells/ spores from dead cells/spores. Four examples of Salmonella outbreaks leading to costly destruction of dairy products. List of possible collaboration activities between JPL and other industries (for future discussion). Limitations of traditional microbial monitoring approaches. Introduction to new approach for rapid measurement of viable (live) bacterial cells/spores and its areas of application. Detailed example for determining live spores using new approach (similar procedure for determining live cells). JPL has developed a patented approach for measuring amount of live and dead cells/spores. This novel "molecular" method takes less than 5 to 7 hrs. compared to the seven days required using conventional techniques. Conventional "molecular" techniques can not discriminate live cells/spores among dead cells/spores. The JPL-developed novel method eliminates false positive results obtained from conventional "molecular" techniques that lead to unnecessary delay in the processing and to unnecessary destruction of food products.

  15. Exomars Mission Verification Approach

    NASA Astrophysics Data System (ADS)

    Cassi, Carlo; Gilardi, Franco; Bethge, Boris

    According to the long-term cooperation plan established by ESA and NASA in June 2009, the ExoMars project now consists of two missions: A first mission will be launched in 2016 under ESA lead, with the objectives to demonstrate the European capability to safely land a surface package on Mars, to perform Mars Atmosphere investigation, and to provide communi-cation capability for present and future ESA/NASA missions. For this mission ESA provides a spacecraft-composite, made up of an "Entry Descent & Landing Demonstrator Module (EDM)" and a Mars Orbiter Module (OM), NASA provides the Launch Vehicle and the scientific in-struments located on the Orbiter for Mars atmosphere characterisation. A second mission with it launch foreseen in 2018 is lead by NASA, who provides spacecraft and launcher, the EDL system, and a rover. ESA contributes the ExoMars Rover Module (RM) to provide surface mobility. It includes a drill system allowing drilling down to 2 meter, collecting samples and to investigate them for signs of past and present life with exobiological experiments, and to investigate the Mars water/geochemical environment, In this scenario Thales Alenia Space Italia as ESA Prime industrial contractor is in charge of the design, manufacturing, integration and verification of the ESA ExoMars modules, i.e.: the Spacecraft Composite (OM + EDM) for the 2016 mission, the RM for the 2018 mission and the Rover Operations Control Centre, which will be located at Altec-Turin (Italy). The verification process of the above products is quite complex and will include some pecu-liarities with limited or no heritage in Europe. Furthermore the verification approach has to be optimised to allow full verification despite significant schedule and budget constraints. The paper presents the verification philosophy tailored for the ExoMars mission in line with the above considerations, starting from the model philosophy, showing the verification activities flow and the sharing of tests

  16. Project Approach: Teaching. Second Edition.

    ERIC Educational Resources Information Center

    Ho, Rose

    The primary objective of the action research chronicled (in English and Chinese) in this book was to shift the teaching method used by preschool teachers in Hong Kong from a teacher-directed mode by training them to use the Project Approach. The secondary objective was to measure children's achievement while using the Project Approach, focusing on…

  17. Integrated approach for biofouling control.

    PubMed

    Vrouwenvelder, J S; Kruithof, J C; Van Loosdrecht, M C M

    2010-01-01

    Despite extensive research efforts, past and present strategies to control biofouling problems in spiral-wound nanofiltration and reverse osmosis membranes have not been successful under all circumstances. Gaining insight in the biofouling process is a first necessity. Based on recent insights, an overview is given of 12 potential complementary approaches to solve biofouling. Combinations of approaches may be more efficient in biofouling control than a single approach. A single approach must be 100% effective, while in combination each individual approach can be partially effective while the combination is still efficient. An integrated Approach for Biofouling Control (ABC) is proposed, based on three corner stones: (i) equipment design and operation, (ii) biomass growth conditions, and (iii) cleaning agents as a framework to control biofouling. While past and present strategies addressed mainly membranes and microorganisms, i.e. removal or inactivation of biomass, this ABC-approach addresses the total membrane filtration system. It is anticipated that this integral approach will enable a more rational and effective control of biofouling. Although in this stage chemical cleaning and biofouling inhibitor dosage seem unavoidable to control biofouling, it is expected that in future--because of sustainability and costs reasons--membrane systems will be developed without or with minimal need for chemical cleaning and dosing. Three potential scenarios for biofouling control are proposed based on (i) biofouling tolerant spiral wound membrane systems, (ii) capillary membranes, and (iii) phosphate limitation.

  18. Science Focus: The Salters' Approach.

    ERIC Educational Resources Information Center

    Berg, Kevin de

    1995-01-01

    Outlines the Salter's approach to teaching and learning science at the Junior Secondary level by showing how the phenomenon of fire is treated in curriculum materials. Discusses contents of the teachers' guide, student texts, and assessment pack. Gives an evaluation of the usefulness of the approach in the Australian context. (Author/MKR)

  19. Approaches to Teaching Foreign Languages.

    ERIC Educational Resources Information Center

    Hesse, M. G., Ed.

    Works by European and American educators from the Renaissance to the twentieth century are presented. A historical re-evaluation of foreign-language teaching combined with the scientific approach of modern linguistics can provide valuable insights for current teaching and learning approaches. Selections are presented from the writings of the…

  20. [Endoscopic approaches to the orbit].

    PubMed

    Cebula, H; Lahlou, A; De Battista, J C; Debry, C; Froelich, S

    2010-01-01

    During the last decade, the use of endoscopic endonasal approaches to the pituitary has increased considerably. The endoscopic endonasal and transantral approaches offer a minimally invasive alternative to the classic transcranial or transconjunctival approaches to the medial aspect of the orbit. The medial wall of the orbit, the orbital apex, and the optic canal can be exposed through a middle meatal antrostomy, an anterior and posterior ethmoidectomy, and a sphenoidotomy. The inferomedial wall of the orbit can be also perfectly visualized through a sublabial antrostomy or an inferior meatal antrostomy. Several reports have described the use of an endoscopic approach for the resection or the biopsy of lesions located on the medial extraconal aspect of the orbit and orbital apex. However, the resection of intraconal lesions is still limited by inadequate instrumentation. Other indications for the endoscopic approach to the orbit are the decompression of the orbit for Graves' ophthalmopathy and traumatic optic neuropathy. However, the optimal management of traumatic optic neuropathy remains very controversial. Endoscopic endonasal decompression of the optic nerve in case of tumor compression could be a more valid indication in combination with radiation therapy. Finally, the endoscopic transantral treatment of blowout fracture of the floor of the orbit is an interesting option that avoids the eyelid or conjunctive incision of traditional approaches. The collaboration between the neurosurgeon and the ENT surgeon is mandatory and reduces the morbidity of the approach. Progress in instrumentation and optical devices will certainly make this approach promising for intraconal tumor of the orbit.