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Sample records for intarna prediction combined

  1. IntaRNA 2.0: enhanced and customizable prediction of RNA-RNA interactions.

    Science.gov (United States)

    Mann, Martin; Wright, Patrick R; Backofen, Rolf

    2017-07-03

    The IntaRNA algorithm enables fast and accurate prediction of RNA-RNA hybrids by incorporating seed constraints and interaction site accessibility. Here, we introduce IntaRNAv2, which enables enhanced parameterization as well as fully customizable control over the prediction modes and output formats. Based on up to date benchmark data, the enhanced predictive quality is shown and further improvements due to more restrictive seed constraints are highlighted. The extended web interface provides visualizations of the new minimal energy profiles for RNA-RNA interactions. These allow a detailed investigation of interaction alternatives and can reveal potential interaction site multiplicity. IntaRNAv2 is freely available (source and binary), and distributed via the conda package manager. Furthermore, it has been included into the Galaxy workflow framework and its already established web interface enables ad hoc usage. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

  2. IntaRNA 2.0: enhanced and customizable prediction of RNA–RNA interactions

    Science.gov (United States)

    Mann, Martin; Wright, Patrick R.

    2017-01-01

    Abstract The IntaRNA algorithm enables fast and accurate prediction of RNA–RNA hybrids by incorporating seed constraints and interaction site accessibility. Here, we introduce IntaRNAv2, which enables enhanced parameterization as well as fully customizable control over the prediction modes and output formats. Based on up to date benchmark data, the enhanced predictive quality is shown and further improvements due to more restrictive seed constraints are highlighted. The extended web interface provides visualizations of the new minimal energy profiles for RNA–RNA interactions. These allow a detailed investigation of interaction alternatives and can reveal potential interaction site multiplicity. IntaRNAv2 is freely available (source and binary), and distributed via the conda package manager. Furthermore, it has been included into the Galaxy workflow framework and its already established web interface enables ad hoc usage. PMID:28472523

  3. Freiburg RNA Tools: a web server integrating INTARNA, EXPARNA and LOCARNA.

    Science.gov (United States)

    Smith, Cameron; Heyne, Steffen; Richter, Andreas S; Will, Sebastian; Backofen, Rolf

    2010-07-01

    The Freiburg RNA tools web server integrates three tools for the advanced analysis of RNA in a common web-based user interface. The tools IntaRNA, ExpaRNA and LocARNA support the prediction of RNA-RNA interaction, exact RNA matching and alignment of RNA, respectively. The Freiburg RNA tools web server and the software packages of the stand-alone tools are freely accessible at http://rna.informatik.uni-freiburg.de.

  4. Combining resummed Higgs predictions across jet bins

    Energy Technology Data Exchange (ETDEWEB)

    Boughezal, Radja [Argonne National Laboratory, IL (United States). High Energy Physics Division; Liu, Xiaohui; Petriello, Frank [Argonne National Laboratory, IL (United States). High Energy Physics Division; Northwestern Univ., Evanston, IL (United States). Dept. of Physics and Astronomy; Tackmann, Frank J. [Deutsches Elektronen-Synchrotron (DESY), Hamburg (Germany); Walsh, Jonathan R. [California Univ., Berkeley, CA (United States). Ernest Orlando Lawrence Berkeley Laboratory; California Univ., Berkeley, CA (United States). Center for Theoretical Physics

    2013-12-15

    Experimental analyses often use jet binning to distinguish between different kinematic regimes and separate contributions from background processes. To accurately model theoretical uncertainties in these measurements, a consistent description of the jet bins is required. We present a complete framework for the combination of resummed results for production processes in different exclusive jet bins, focusing on Higgs production in gluon fusion as an example. We extend the resummation of the H+1-jet cross section into the challenging low transverse momentum region, lowering the uncertainties considerably. We provide combined predictions with resummation for cross sections in the H+0-jet and H+1-jet bins, and give an improved theory covariance matrix for use in experimental studies. We estimate that the relevant theoretical uncertainties on the signal strength in the H{yields}WW{sup *} analysis are reduced by nearly a factor of 2 compared to the current value.

  5. Combining GPS measurements and IRI model predictions

    International Nuclear Information System (INIS)

    Hernandez-Pajares, M.; Juan, J.M.; Sanz, J.; Bilitza, D.

    2002-01-01

    The free electrons distributed in the ionosphere (between one hundred and thousands of km in height) produce a frequency-dependent effect on Global Positioning System (GPS) signals: a delay in the pseudo-orange and an advance in the carrier phase. These effects are proportional to the columnar electron density between the satellite and receiver, i.e. the integrated electron density along the ray path. Global ionospheric TEC (total electron content) maps can be obtained with GPS data from a network of ground IGS (international GPS service) reference stations with an accuracy of few TEC units. The comparison with the TOPEX TEC, mainly measured over the oceans far from the IGS stations, shows a mean bias and standard deviation of about 2 and 5 TECUs respectively. The discrepancies between the STEC predictions and the observed values show an RMS typically below 5 TECUs (which also includes the alignment code noise). he existence of a growing database 2-hourly global TEC maps and with resolution of 5x2.5 degrees in longitude and latitude can be used to improve the IRI prediction capability of the TEC. When the IRI predictions and the GPS estimations are compared for a three month period around the Solar Maximum, they are in good agreement for middle latitudes. An over-determination of IRI TEC has been found at the extreme latitudes, the IRI predictions being, typically two times higher than the GPS estimations. Finally, local fits of the IRI model can be done by tuning the SSN from STEC GPS observations

  6. Life prediction methods for the combined creep-fatigue endurance

    International Nuclear Information System (INIS)

    Wareing, J.; Lloyd, G.J.

    1980-09-01

    The basis and current status of development of the various approaches to the prediction of the combined creep-fatigue endurance are reviewed. It is concluded that an inadequate materials data base makes it difficult to draw sensible conclusions about the prediction capabilities of each of the available methods. Correlation with data for stainless steel 304 and 316 is presented. (U.K.)

  7. Combining clinical variables to optimize prediction of antidepressant treatment outcomes.

    Science.gov (United States)

    Iniesta, Raquel; Malki, Karim; Maier, Wolfgang; Rietschel, Marcella; Mors, Ole; Hauser, Joanna; Henigsberg, Neven; Dernovsek, Mojca Zvezdana; Souery, Daniel; Stahl, Daniel; Dobson, Richard; Aitchison, Katherine J; Farmer, Anne; Lewis, Cathryn M; McGuffin, Peter; Uher, Rudolf

    2016-07-01

    The outcome of treatment with antidepressants varies markedly across people with the same diagnosis. A clinically significant prediction of outcomes could spare the frustration of trial and error approach and improve the outcomes of major depressive disorder through individualized treatment selection. It is likely that a combination of multiple predictors is needed to achieve such prediction. We used elastic net regularized regression to optimize prediction of symptom improvement and remission during treatment with escitalopram or nortriptyline and to identify contributing predictors from a range of demographic and clinical variables in 793 adults with major depressive disorder. A combination of demographic and clinical variables, with strong contributions from symptoms of depressed mood, reduced interest, decreased activity, indecisiveness, pessimism and anxiety significantly predicted treatment outcomes, explaining 5-10% of variance in symptom improvement with escitalopram. Similar combinations of variables predicted remission with area under the curve 0.72, explaining approximately 15% of variance (pseudo R(2)) in who achieves remission, with strong contributions from body mass index, appetite, interest-activity symptom dimension and anxious-somatizing depression subtype. Escitalopram-specific outcome prediction was more accurate than generic outcome prediction, and reached effect sizes that were near or above a previously established benchmark for clinical significance. Outcome prediction on the nortriptyline arm did not significantly differ from chance. These results suggest that easily obtained demographic and clinical variables can predict therapeutic response to escitalopram with clinically meaningful accuracy, suggesting a potential for individualized prescription of this antidepressant drug. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

  8. A joint calibration model for combining predictive distributions

    Directory of Open Access Journals (Sweden)

    Patrizia Agati

    2013-05-01

    Full Text Available In many research fields, as for example in probabilistic weather forecasting, valuable predictive information about a future random phenomenon may come from several, possibly heterogeneous, sources. Forecast combining methods have been developed over the years in order to deal with ensembles of sources: the aim is to combine several predictions in such a way to improve forecast accuracy and reduce risk of bad forecasts.In this context, we propose the use of a Bayesian approach to information combining, which consists in treating the predictive probability density functions (pdfs from the individual ensemble members as data in a Bayesian updating problem. The likelihood function is shown to be proportional to the product of the pdfs, adjusted by a joint “calibration function” describing the predicting skill of the sources (Morris, 1977. In this paper, after rephrasing Morris’ algorithm in a predictive context, we propose to model the calibration function in terms of bias, scale and correlation and to estimate its parameters according to the least squares criterion. The performance of our method is investigated and compared with that of Bayesian Model Averaging (Raftery, 2005 on simulated data.

  9. Combining gene prediction methods to improve metagenomic gene annotation

    Directory of Open Access Journals (Sweden)

    Rosen Gail L

    2011-01-01

    Full Text Available Abstract Background Traditional gene annotation methods rely on characteristics that may not be available in short reads generated from next generation technology, resulting in suboptimal performance for metagenomic (environmental samples. Therefore, in recent years, new programs have been developed that optimize performance on short reads. In this work, we benchmark three metagenomic gene prediction programs and combine their predictions to improve metagenomic read gene annotation. Results We not only analyze the programs' performance at different read-lengths like similar studies, but also separate different types of reads, including intra- and intergenic regions, for analysis. The main deficiencies are in the algorithms' ability to predict non-coding regions and gene edges, resulting in more false-positives and false-negatives than desired. In fact, the specificities of the algorithms are notably worse than the sensitivities. By combining the programs' predictions, we show significant improvement in specificity at minimal cost to sensitivity, resulting in 4% improvement in accuracy for 100 bp reads with ~1% improvement in accuracy for 200 bp reads and above. To correctly annotate the start and stop of the genes, we find that a consensus of all the predictors performs best for shorter read lengths while a unanimous agreement is better for longer read lengths, boosting annotation accuracy by 1-8%. We also demonstrate use of the classifier combinations on a real dataset. Conclusions To optimize the performance for both prediction and annotation accuracies, we conclude that the consensus of all methods (or a majority vote is the best for reads 400 bp and shorter, while using the intersection of GeneMark and Orphelia predictions is the best for reads 500 bp and longer. We demonstrate that most methods predict over 80% coding (including partially coding reads on a real human gut sample sequenced by Illumina technology.

  10. Technical note: Combining quantile forecasts and predictive distributions of streamflows

    Science.gov (United States)

    Bogner, Konrad; Liechti, Katharina; Zappa, Massimiliano

    2017-11-01

    The enhanced availability of many different hydro-meteorological modelling and forecasting systems raises the issue of how to optimally combine this great deal of information. Especially the usage of deterministic and probabilistic forecasts with sometimes widely divergent predicted future streamflow values makes it even more complicated for decision makers to sift out the relevant information. In this study multiple streamflow forecast information will be aggregated based on several different predictive distributions, and quantile forecasts. For this combination the Bayesian model averaging (BMA) approach, the non-homogeneous Gaussian regression (NGR), also known as the ensemble model output statistic (EMOS) techniques, and a novel method called Beta-transformed linear pooling (BLP) will be applied. By the help of the quantile score (QS) and the continuous ranked probability score (CRPS), the combination results for the Sihl River in Switzerland with about 5 years of forecast data will be compared and the differences between the raw and optimally combined forecasts will be highlighted. The results demonstrate the importance of applying proper forecast combination methods for decision makers in the field of flood and water resource management.

  11. Prediction of a service demand using combined forecasting approach

    Science.gov (United States)

    Zhou, Ling

    2017-08-01

    Forecasting facilitates cutting down operational and management costs while ensuring service level for a logistics service provider. Our case study here is to investigate how to forecast short-term logistic demand for a LTL carrier. Combined approach depends on several forecasting methods simultaneously, instead of a single method. It can offset the weakness of a forecasting method with the strength of another, which could improve the precision performance of prediction. Main issues of combined forecast modeling are how to select methods for combination, and how to find out weight coefficients among methods. The principles of method selection include that each method should apply to the problem of forecasting itself, also methods should differ in categorical feature as much as possible. Based on these principles, exponential smoothing, ARIMA and Neural Network are chosen to form the combined approach. Besides, least square technique is employed to settle the optimal weight coefficients among forecasting methods. Simulation results show the advantage of combined approach over the three single methods. The work done in the paper helps manager to select prediction method in practice.

  12. Combining neural networks for protein secondary structure prediction

    DEFF Research Database (Denmark)

    Riis, Søren Kamaric

    1995-01-01

    In this paper structured neural networks are applied to the problem of predicting the secondary structure of proteins. A hierarchical approach is used where specialized neural networks are designed for each structural class and then combined using another neural network. The submodels are designed...... by using a priori knowledge of the mapping between protein building blocks and the secondary structure and by using weight sharing. Since none of the individual networks have more than 600 adjustable weights over-fitting is avoided. When ensembles of specialized experts are combined the performance...

  13. Combining Gene Signatures Improves Prediction of Breast Cancer Survival

    Science.gov (United States)

    Zhao, Xi; Naume, Bjørn; Langerød, Anita; Frigessi, Arnoldo; Kristensen, Vessela N.; Børresen-Dale, Anne-Lise; Lingjærde, Ole Christian

    2011-01-01

    Background Several gene sets for prediction of breast cancer survival have been derived from whole-genome mRNA expression profiles. Here, we develop a statistical framework to explore whether combination of the information from such sets may improve prediction of recurrence and breast cancer specific death in early-stage breast cancers. Microarray data from two clinically similar cohorts of breast cancer patients are used as training (n = 123) and test set (n = 81), respectively. Gene sets from eleven previously published gene signatures are included in the study. Principal Findings To investigate the relationship between breast cancer survival and gene expression on a particular gene set, a Cox proportional hazards model is applied using partial likelihood regression with an L2 penalty to avoid overfitting and using cross-validation to determine the penalty weight. The fitted models are applied to an independent test set to obtain a predicted risk for each individual and each gene set. Hierarchical clustering of the test individuals on the basis of the vector of predicted risks results in two clusters with distinct clinical characteristics in terms of the distribution of molecular subtypes, ER, PR status, TP53 mutation status and histological grade category, and associated with significantly different survival probabilities (recurrence: p = 0.005; breast cancer death: p = 0.014). Finally, principal components analysis of the gene signatures is used to derive combined predictors used to fit a new Cox model. This model classifies test individuals into two risk groups with distinct survival characteristics (recurrence: p = 0.003; breast cancer death: p = 0.001). The latter classifier outperforms all the individual gene signatures, as well as Cox models based on traditional clinical parameters and the Adjuvant! Online for survival prediction. Conclusion Combining the predictive strength of multiple gene signatures improves prediction of breast

  14. Combining gene signatures improves prediction of breast cancer survival.

    Directory of Open Access Journals (Sweden)

    Xi Zhao

    Full Text Available BACKGROUND: Several gene sets for prediction of breast cancer survival have been derived from whole-genome mRNA expression profiles. Here, we develop a statistical framework to explore whether combination of the information from such sets may improve prediction of recurrence and breast cancer specific death in early-stage breast cancers. Microarray data from two clinically similar cohorts of breast cancer patients are used as training (n = 123 and test set (n = 81, respectively. Gene sets from eleven previously published gene signatures are included in the study. PRINCIPAL FINDINGS: To investigate the relationship between breast cancer survival and gene expression on a particular gene set, a Cox proportional hazards model is applied using partial likelihood regression with an L2 penalty to avoid overfitting and using cross-validation to determine the penalty weight. The fitted models are applied to an independent test set to obtain a predicted risk for each individual and each gene set. Hierarchical clustering of the test individuals on the basis of the vector of predicted risks results in two clusters with distinct clinical characteristics in terms of the distribution of molecular subtypes, ER, PR status, TP53 mutation status and histological grade category, and associated with significantly different survival probabilities (recurrence: p = 0.005; breast cancer death: p = 0.014. Finally, principal components analysis of the gene signatures is used to derive combined predictors used to fit a new Cox model. This model classifies test individuals into two risk groups with distinct survival characteristics (recurrence: p = 0.003; breast cancer death: p = 0.001. The latter classifier outperforms all the individual gene signatures, as well as Cox models based on traditional clinical parameters and the Adjuvant! Online for survival prediction. CONCLUSION: Combining the predictive strength of multiple gene signatures improves

  15. Predicting Bobsled Pushing Ability from Various Combine Testing Events.

    Science.gov (United States)

    Tomasevicz, Curtis L; Ransone, Jack W; Bach, Christopher W

    2018-03-12

    The requisite combination of speed, power, and strength necessary for a bobsled push athlete coupled with the difficulty in directly measuring pushing ability makes selecting effective push crews challenging. Current practices by USA Bobsled and Skeleton (USABS) utilize field combine testing to assess and identify specifically selected performance variables in an attempt to best predict push performance abilities. Combine data consisting of 11 physical performance variables were collected from 75 subjects across two winter Olympic qualification years (2009 and 2013). These variables were sprints of 15-, 30-, and 60 m, a flying 30 m sprint, a standing broad jump, a shot toss, squat, power clean, body mass, and dry-land brake and side bobsled pushes. Discriminant Analysis (DA) in addition to Principle Component Analysis (PCA) was used to investigate two cases (Case 1: Olympians vs. non-Olympians; Case 2: National Team vs. non-National Team). Using these 11 variables, DA led to a classification rule that proved capable of identifying Olympians from non-Olympians and National Team members from non-National Team members with 9.33% and 14.67% misclassification rates, respectively. The PCA was used to find similar test variables within the combine that provided redundant or useless data. After eliminating the unnecessary variables, DA on the new combinations showed that 8 (Case 1) and 20 (Case 2) other combinations with fewer performance variables yielded misclassification rates as low as 6.67% and 13.33% respectively. Utilizing fewer performance variables can allow governing bodies in many other sports to create more appropriate combine testing that maximize accuracy, while minimizing irrelevant and redundant strategies.

  16. The utility of combining RSA indices in depression prediction.

    Science.gov (United States)

    Yaroslavsky, Ilya; Rottenberg, Jonathan; Kovacs, Maria

    2013-05-01

    Depression is associated with protracted despondent mood, blunted emotional reactivity, and dysregulated parasympathetic nervous system (PNS) activity. PNS activity is commonly indexed via cardiac output, using indictors of its level (resting respiratory sinus arrhythmia [RSA]) or fluctuations (RSA reactivity). RSA reactivity can reflect increased or decreased PNS cardiac output (RSA augmentation and RSA withdrawal, respectively). Because a single index of a dynamic physiological system may be inadequate to characterize interindividual differences, we investigated whether the interaction of RSA reactivity and resting RSA is a better predictor of depression. Adult probands with childhood-onset depressive disorder histories (n = 113) and controls with no history of major mental disorders (n = 93) completed a psychophysiology protocol involving assessment of RSA at multiple rest periods and while watching a sad film. When examined independently, resting RSA and RSA reactivity were unrelated to depression, but their interaction predicted latent depression levels and proband status. In the context of high resting RSA, RSA withdrawal from the sad film predicted the lowest levels of depressive symptoms (irrespective of depression histories) and the greatest likelihood of having had no history of major mental disorder (irrespective of current distress). Our findings highlight the utility of combining indices of physiological responses in studying depression; combinations of RSA indices should be given future consideration as reflecting depression endophenotypes. © 2013 American Psychological Association

  17. Nonparametric predictive inference for combined competing risks data

    International Nuclear Information System (INIS)

    Coolen-Maturi, Tahani; Coolen, Frank P.A.

    2014-01-01

    The nonparametric predictive inference (NPI) approach for competing risks data has recently been presented, in particular addressing the question due to which of the competing risks the next unit will fail, and also considering the effects of unobserved, re-defined, unknown or removed competing risks. In this paper, we introduce how the NPI approach can be used to deal with situations where units are not all at risk from all competing risks. This may typically occur if one combines information from multiple samples, which can, e.g. be related to further aspects of units that define the samples or groups to which the units belong or to different applications where the circumstances under which the units operate can vary. We study the effect of combining the additional information from these multiple samples, so effectively borrowing information on specific competing risks from other units, on the inferences. Such combination of information can be relevant to competing risks scenarios in a variety of application areas, including engineering and medical studies

  18. Model Predictive Control of Integrated Gasification Combined Cycle Power Plants

    Energy Technology Data Exchange (ETDEWEB)

    B. Wayne Bequette; Priyadarshi Mahapatra

    2010-08-31

    The primary project objectives were to understand how the process design of an integrated gasification combined cycle (IGCC) power plant affects the dynamic operability and controllability of the process. Steady-state and dynamic simulation models were developed to predict the process behavior during typical transients that occur in plant operation. Advanced control strategies were developed to improve the ability of the process to follow changes in the power load demand, and to improve performance during transitions between power levels. Another objective of the proposed work was to educate graduate and undergraduate students in the application of process systems and control to coal technology. Educational materials were developed for use in engineering courses to further broaden this exposure to many students. ASPENTECH software was used to perform steady-state and dynamic simulations of an IGCC power plant. Linear systems analysis techniques were used to assess the steady-state and dynamic operability of the power plant under various plant operating conditions. Model predictive control (MPC) strategies were developed to improve the dynamic operation of the power plants. MATLAB and SIMULINK software were used for systems analysis and control system design, and the SIMULINK functionality in ASPEN DYNAMICS was used to test the control strategies on the simulated process. Project funds were used to support a Ph.D. student to receive education and training in coal technology and the application of modeling and simulation techniques.

  19. Nonparametric predictive inference for combining diagnostic tests with parametric copula

    Science.gov (United States)

    Muhammad, Noryanti; Coolen, F. P. A.; Coolen-Maturi, T.

    2017-09-01

    Measuring the accuracy of diagnostic tests is crucial in many application areas including medicine and health care. The Receiver Operating Characteristic (ROC) curve is a popular statistical tool for describing the performance of diagnostic tests. The area under the ROC curve (AUC) is often used as a measure of the overall performance of the diagnostic test. In this paper, we interest in developing strategies for combining test results in order to increase the diagnostic accuracy. We introduce nonparametric predictive inference (NPI) for combining two diagnostic test results with considering dependence structure using parametric copula. NPI is a frequentist statistical framework for inference on a future observation based on past data observations. NPI uses lower and upper probabilities to quantify uncertainty and is based on only a few modelling assumptions. While copula is a well-known statistical concept for modelling dependence of random variables. A copula is a joint distribution function whose marginals are all uniformly distributed and it can be used to model the dependence separately from the marginal distributions. In this research, we estimate the copula density using a parametric method which is maximum likelihood estimator (MLE). We investigate the performance of this proposed method via data sets from the literature and discuss results to show how our method performs for different family of copulas. Finally, we briefly outline related challenges and opportunities for future research.

  20. Towards predictive scenario simulations combining LH, ICRH and ECRH heating

    International Nuclear Information System (INIS)

    Basiuk, V.; Artaud, J.F.; Becoulet, A.; Eriksson, L.G.; Hoang, G.T.; Huysmans, G.; Imbeaux, F.; Litaudon, X.; Mazon, D.; Passeron, C.; Peysson, Y.

    2003-01-01

    Reliable predictive simulations, combining current, heat and matter transport equation with a 2D equilibrium allowing diagnostic reconstruction such as Faraday angle and MSE angle are of a great interest for existing and future tokamak. The Cronos code with its various power deposition codes (Delphine, Rema, Pion) is a powerful tool to prepare such scenario in a reasonable CPU time (a few hours, for one minute plasma discharge). An example of such advanced scenario, with a negative seed of current at the center of the discharge is shown in this paper. It allows also testing new concept of feedback control, which will be directly implemented on the new real-time network of Tore-Supra. In this concept, the algorithm as to find itself the best and safe way to reach enhance performance (i.e. best plasma fusion power D-D) using different actuators (injected power,...). On this paper, we will focus on a simple example where the initial and final states are known and we will show why a steady state tokamak allowing long pulse operation is necessary for such control. (authors)

  1. Prediction of organic combined sewer sediment release and transport

    OpenAIRE

    Seco, Raquel Irene; Schellart, Alma Neeltje Antonia; Gómez Valentín, Manuel; Tait, Simon

    2018-01-01

    Accurate predictions of sediment loads released by sewer overflow discharges are important for being able to provide protection to vulnerable receiving waters. These predictions are sensitive to the estimated sediment characteristics and on the site conditions of in-pipe deposit formation. Their application without a detailed analysis and understanding of the initial conditions under which in-sewer deposits were formed normally results in very poor estimations. In this study, in-sewer sedimen...

  2. Prediction of Protein–Protein Interactions by Evidence Combining Methods

    Directory of Open Access Journals (Sweden)

    Ji-Wei Chang

    2016-11-01

    Full Text Available Most cellular functions involve proteins’ features based on their physical interactions with other partner proteins. Sketching a map of protein–protein interactions (PPIs is therefore an important inception step towards understanding the basics of cell functions. Several experimental techniques operating in vivo or in vitro have made significant contributions to screening a large number of protein interaction partners, especially high-throughput experimental methods. However, computational approaches for PPI predication supported by rapid accumulation of data generated from experimental techniques, 3D structure definitions, and genome sequencing have boosted the map sketching of PPIs. In this review, we shed light on in silico PPI prediction methods that integrate evidence from multiple sources, including evolutionary relationship, function annotation, sequence/structure features, network topology and text mining. These methods are developed for integration of multi-dimensional evidence, for designing the strategies to predict novel interactions, and for making the results consistent with the increase of prediction coverage and accuracy.

  3. Combining disparate data sources for improved poverty prediction and mapping.

    Science.gov (United States)

    Pokhriyal, Neeti; Jacques, Damien Christophe

    2017-11-14

    More than 330 million people are still living in extreme poverty in Africa. Timely, accurate, and spatially fine-grained baseline data are essential to determining policy in favor of reducing poverty. The potential of "Big Data" to estimate socioeconomic factors in Africa has been proven. However, most current studies are limited to using a single data source. We propose a computational framework to accurately predict the Global Multidimensional Poverty Index (MPI) at a finest spatial granularity and coverage of 552 communes in Senegal using environmental data (related to food security, economic activity, and accessibility to facilities) and call data records (capturing individualistic, spatial, and temporal aspects of people). Our framework is based on Gaussian Process regression, a Bayesian learning technique, providing uncertainty associated with predictions. We perform model selection using elastic net regularization to prevent overfitting. Our results empirically prove the superior accuracy when using disparate data (Pearson correlation of 0.91). Our approach is used to accurately predict important dimensions of poverty: health, education, and standard of living (Pearson correlation of 0.84-0.86). All predictions are validated using deprivations calculated from census. Our approach can be used to generate poverty maps frequently, and its diagnostic nature is, likely, to assist policy makers in designing better interventions for poverty eradication. Copyright © 2017 the Author(s). Published by PNAS.

  4. Combining specificity determining and conserved residues improves functional site prediction

    Directory of Open Access Journals (Sweden)

    Gelfand Mikhail S

    2009-06-01

    Full Text Available Abstract Background Predicting the location of functionally important sites from protein sequence and/or structure is a long-standing problem in computational biology. Most current approaches make use of sequence conservation, assuming that amino acid residues conserved within a protein family are most likely to be functionally important. Most often these approaches do not consider many residues that act to define specific sub-functions within a family, or they make no distinction between residues important for function and those more relevant for maintaining structure (e.g. in the hydrophobic core. Many protein families bind and/or act on a variety of ligands, meaning that conserved residues often only bind a common ligand sub-structure or perform general catalytic activities. Results Here we present a novel method for functional site prediction based on identification of conserved positions, as well as those responsible for determining ligand specificity. We define Specificity-Determining Positions (SDPs, as those occupied by conserved residues within sub-groups of proteins in a family having a common specificity, but differ between groups, and are thus likely to account for specific recognition events. We benchmark the approach on enzyme families of known 3D structure with bound substrates, and find that in nearly all families residues predicted by SDPsite are in contact with the bound substrate, and that the addition of SDPs significantly improves functional site prediction accuracy. We apply SDPsite to various families of proteins containing known three-dimensional structures, but lacking clear functional annotations, and discusse several illustrative examples. Conclusion The results suggest a better means to predict functional details for the thousands of protein structures determined prior to a clear understanding of molecular function.

  5. Predicting the Toxicity of Adjuvant Breast Cancer Drug Combination Therapy

    Science.gov (United States)

    2013-03-01

    Neratinib Versus Lapatinib Plus Capecitabine For ErbB2 Positive Advanced Breast Cancer Active, not recruiting No Results Available YES neratinib -9...Drug: Neratinib |Drug: Lapatinib|Drug: Capecitabine Efficacy and Safety of BMS-690514 in Combination With Letrozole to Treat Metastatic Breast Cancer

  6. Learning Combinations of Multiple Feature Representations for Music Emotion Prediction

    DEFF Research Database (Denmark)

    Madsen, Jens; Jensen, Bjørn Sand; Larsen, Jan

    2015-01-01

    Music consists of several structures and patterns evolving through time which greatly influences the human decoding of higher-level cognitive aspects of music like the emotions expressed in music. For tasks, such as genre, tag and emotion recognition, these structures have often been identified...... and used as individual and non-temporal features and representations. In this work, we address the hypothesis whether using multiple temporal and non-temporal representations of different features is beneficial for modeling music structure with the aim to predict the emotions expressed in music. We test...

  7. Reduce manual curation by combining gene predictions from multiple annotation engines, a case study of start codon prediction.

    Directory of Open Access Journals (Sweden)

    Thomas H A Ederveen

    Full Text Available Nowadays, prokaryotic genomes are sequenced faster than the capacity to manually curate gene annotations. Automated genome annotation engines provide users a straight-forward and complete solution for predicting ORF coordinates and function. For many labs, the use of AGEs is therefore essential to decrease the time necessary for annotating a given prokaryotic genome. However, it is not uncommon for AGEs to provide different and sometimes conflicting predictions. Combining multiple AGEs might allow for more accurate predictions. Here we analyzed the ab initio open reading frame (ORF calling performance of different AGEs based on curated genome annotations of eight strains from different bacterial species with GC% ranging from 35-52%. We present a case study which demonstrates a novel way of comparative genome annotation, using combinations of AGEs in a pre-defined order (or path to predict ORF start codons. The order of AGE combinations is from high to low specificity, where the specificity is based on the eight genome annotations. For each AGE combination we are able to derive a so-called projected confidence value, which is the average specificity of ORF start codon prediction based on the eight genomes. The projected confidence enables estimating likeliness of a correct prediction for a particular ORF start codon by a particular AGE combination, pinpointing ORFs notoriously difficult to predict start codons. We correctly predict start codons for 90.5±4.8% of the genes in a genome (based on the eight genomes with an accuracy of 81.1±7.6%. Our consensus-path methodology allows a marked improvement over majority voting (9.7±4.4% and with an optimal path ORF start prediction sensitivity is gained while maintaining a high specificity.

  8. A comparison of LOD and UT1-UTC forecasts by different combined prediction techniques

    Science.gov (United States)

    Kosek, W.; Kalarus, M.; Johnson, T. J.; Wooden, W. H.; McCarthy, D. D.; Popiński, W.

    Stochastic prediction techniques including autocovariance, autoregressive, autoregressive moving average, and neural networks were applied to the UT1-UTC and Length of Day (LOD) International Earth Rotation and Reference Systems Servive (IERS) EOPC04 time series to evaluate the capabilities of each method. All known effects such as leap seconds and solid Earth zonal tides were first removed from the observed values of UT1-UTC and LOD. Two combination procedures were applied to predict the resulting LODR time series: 1) the combination of the least-squares (LS) extrapolation with a stochastic predition method, and 2) the combination of the discrete wavelet transform (DWT) filtering and a stochastic prediction method. The results of the combination of the LS extrapolation with different stochastic prediction techniques were compared with the results of the UT1-UTC prediction method currently used by the IERS Rapid Service/Prediction Centre (RS/PC). It was found that the prediction accuracy depends on the starting prediction epochs, and for the combined forecast methods, the mean prediction errors for 1 to about 70 days in the future are of the same order as those of the method used by the IERS RS/PC.

  9. Predictive Validity of National Basketball Association Draft Combine on Future Performance.

    Science.gov (United States)

    Teramoto, Masaru; Cross, Chad L; Rieger, Randall H; Maak, Travis G; Willick, Stuart E

    2018-02-01

    Teramoto, M, Cross, CL, Rieger, RH, Maak, TG, and Willick, SE. Predictive validity of national basketball association draft combine on future performance. J Strength Cond Res 32(2): 396-408, 2018-The National Basketball Association (NBA) Draft Combine is an annual event where prospective players are evaluated in terms of their athletic abilities and basketball skills. Data collected at the Combine should help NBA teams select right the players for the upcoming NBA draft; however, its value for predicting future performance of players has not been examined. This study investigated predictive validity of the NBA Draft Combine on future performance of basketball players. We performed a principal component analysis (PCA) on the 2010-2015 Combine data to reduce correlated variables (N = 234), a correlation analysis on the Combine data and future on-court performance to examine relationships (maximum pairwise N = 217), and a robust principal component regression (PCR) analysis to predict first-year and 3-year on-court performance from the Combine measures (N = 148 and 127, respectively). Three components were identified within the Combine data through PCA (= Combine subscales): length-size, power-quickness, and upper-body strength. As per the correlation analysis, the individual Combine items for anthropometrics, including height without shoes, standing reach, weight, wingspan, and hand length, as well as the Combine subscale of length-size, had positive, medium-to-large-sized correlations (r = 0.313-0.545) with defensive performance quantified by Defensive Box Plus/Minus. The robust PCR analysis showed that the Combine subscale of length-size was a predictor most significantly associated with future on-court performance (p ≤ 0.05), including Win Shares, Box Plus/Minus, and Value Over Replacement Player, followed by upper-body strength. In conclusion, the NBA Draft Combine has value for predicting future performance of players.

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

    Directory of Open Access Journals (Sweden)

    Shahram Gilani Nia

    2010-03-01

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

  11. The predictive value of quantitative fibronectin testing in combination with cervical length measurement in symptomatic women

    NARCIS (Netherlands)

    Bruijn, Merel M. C.; Kamphuis, Esme I.; Hoesli, Irene M.; Martinez de Tejada, Begoña; Loccufier, Anne R.; Kühnert, Maritta; Helmer, Hanns; Franz, Marie; Porath, Martina M.; Oudijk, Martijn A.; Jacquemyn, Yves; Schulzke, Sven M.; Vetter, Grit; Hoste, Griet; Vis, Jolande Y.; Kok, Marjolein; Mol, Ben W. J.; van Baaren, Gert-Jan

    2016-01-01

    The combination of the qualitative fetal fibronectin test and cervical length measurement has a high negative predictive value for preterm birth within 7 days; however, positive prediction is poor. A new bedside quantitative fetal fibronectin test showed potential additional value over the

  12. Combining Predictive Densities using Nonlinear Filtering with Applications to US Economics Data

    NARCIS (Netherlands)

    M. Billio (Monica); R. Casarin (Roberto); F. Ravazzolo (Francesco); H.K. van Dijk (Herman)

    2011-01-01

    textabstractWe propose a multivariate combination approach to prediction based on a distributional state space representation of the weights belonging to a set of Bayesian predictive densities which have been obtained from alternative models. Several specifications of multivariate time-varying

  13. Bayesian Combinations of Stock Price Predictions with an Application to the Amsterdam Exchange Index

    NARCIS (Netherlands)

    M. Billio (Monica); R. Casarin (Roberto); F. Ravazzolo (Francesco); H.K. van Dijk (Herman)

    2011-01-01

    textabstractWe summarize the general combination approach by Billio et al. [2010]. In the combination model the weights follow logistic autoregressive processes, change over time and their dynamics are possible driven by the past forecasting performances of the predictive densities. For illustrative

  14. Combination of serum angiopoietin-2 and uterine artery Doppler for prediction of preeclampsia.

    Science.gov (United States)

    Puttapitakpong, Ploynin; Phupong, Vorapong

    2016-02-01

    The aim of this study was to determine the predictive value of the combination of serum angiopoietin-2 (Ang-2) levels and uterine artery Doppler for the detection of preeclampsia in women at 16-18 weeks of gestation and to identify other pregnancy complications that could be predicted with these combined tests. Maternal serum Ang-2 levels were measured, and uterine artery Doppler was performed in 400 pregnant women. The main outcome was preeclampsia. The predictive values of this combination were calculated. Twenty-five women (6.3%) developed preeclampsia. The sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of uterine artery Doppler combined with serum Ang-2 levels for the prediction of preeclampsia were 24.0%, 94.4%, 22.2% and 94.9%, respectively. For the prediction of early-onset preeclampsia, the sensitivity, specificity, PPV and NPV were 57.1%, 94.1%, 14.8% and 99.2%, respectively. Patients with abnormal uterine artery Doppler and abnormal serum Ang-2 levels (above 19.5 ng ml(-1)) were at higher risk for preterm delivery (relative risk=2.7, 95% confidence interval 1.2-5.8). Our findings revealed that the combination of uterine artery Doppler and serum Ang-2 levels at 16-18 weeks of gestation can be used to predict early-onset preeclampsia but not overall preeclampsia. Thus, this combination may be a useful early second trimester screening test for the prediction of early-onset preeclampsia.

  15. Studies of the Raman Spectra of Cyclic and Acyclic Molecules: Combination and Prediction Spectrum Methods

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Taijin; Assary, Rajeev S.; Marshall, Christopher L.; Gosztola, David J.; Curtiss, Larry A.; Stair, Peter C.

    2012-04-02

    A combination of Raman spectroscopy and density functional methods was employed to investigate the spectral features of selected molecules: furfural, 5-hydroxymethyl furfural (HMF), methanol, acetone, acetic acid, and levulinic acid. The computed spectra and measured spectra are in excellent agreement, consistent with previous studies. Using the combination and prediction spectrum method (CPSM), we were able to predict the important spectral features of two platform chemicals, HMF and levulinic acid.The results have shown that CPSM is a useful alternative method for predicting vibrational spectra of complex molecules in the biomass transformation process.

  16. Prediction of Effective Drug Combinations by Chemical Interaction, Protein Interaction and Target Enrichment of KEGG Pathways

    Directory of Open Access Journals (Sweden)

    Lei Chen

    2013-01-01

    Full Text Available Drug combinatorial therapy could be more effective in treating some complex diseases than single agents due to better efficacy and reduced side effects. Although some drug combinations are being used, their underlying molecular mechanisms are still poorly understood. Therefore, it is of great interest to deduce a novel drug combination by their molecular mechanisms in a robust and rigorous way. This paper attempts to predict effective drug combinations by a combined consideration of: (1 chemical interaction between drugs, (2 protein interactions between drugs’ targets, and (3 target enrichment of KEGG pathways. A benchmark dataset was constructed, consisting of 121 confirmed effective combinations and 605 random combinations. Each drug combination was represented by 465 features derived from the aforementioned three properties. Some feature selection techniques, including Minimum Redundancy Maximum Relevance and Incremental Feature Selection, were adopted to extract the key features. Random forest model was built with its performance evaluated by 5-fold cross-validation. As a result, 55 key features providing the best prediction result were selected. These important features may help to gain insights into the mechanisms of drug combinations, and the proposed prediction model could become a useful tool for screening possible drug combinations.

  17. A Combined Cooperative Braking Model with a Predictive Control Strategy in an Electric Vehicle

    Directory of Open Access Journals (Sweden)

    Hongqiang Guo

    2013-12-01

    Full Text Available Cooperative braking with regenerative braking and mechanical braking plays an important role in electric vehicles for energy-saving control. Based on the parallel and the series cooperative braking models, a combined model with a predictive control strategy to get a better cooperative braking performance is presented. The balance problem between the maximum regenerative energy recovery efficiency and the optimum braking stability is solved through an off-line process optimization stream with the collaborative optimization algorithm (CO. To carry out the process optimization stream, the optimal Latin hypercube design (Opt LHD is presented to discrete the continuous design space. To solve the poor real-time problem of the optimization, a high-precision predictive model based on the off-line optimization data of the combined model is built, and a predictive control strategy is proposed and verified through simulation. The simulation results demonstrate that the predictive control strategy and the combined model are reasonable and effective.

  18. New in vitro system to predict chemotherapeutic efficacy of drug combinations in fresh tumor samples

    Directory of Open Access Journals (Sweden)

    Frank Christian Kischkel

    2017-03-01

    Full Text Available Background To find the best individual chemotherapy for cancer patients, the efficacy of different chemotherapeutic drugs can be predicted by pretesting tumor samples in vitro via the chemotherapy-resistance (CTR-Test®. Although drug combinations are widely used among cancer therapy, so far only single drugs are tested by this and other tests. However, several first line chemotherapies are combining two or more chemotherapeutics, leading to the necessity of drug combination testing methods. Methods We established a system to measure and predict the efficacy of chemotherapeutic drug combinations with the help of the Loewe additivity concept in combination with the CTR-test. A combination is measured by using half of the monotherapy’s concentration of both drugs simultaneously. With this method, the efficacy of a combination can also be calculated based on single drug measurements. Results The established system was tested on a data set of ovarian carcinoma samples using the combination carboplatin and paclitaxel and confirmed by using other tumor species and chemotherapeutics. Comparing the measured and the calculated values of the combination testings revealed a high correlation. Additionally, in 70% of the cases the measured and the calculated values lead to the same chemotherapeutic resistance category of the tumor. Conclusion Our data suggest that the best drug combination consists of the most efficient single drugs and the worst drug combination of the least efficient single drugs. Our results showed that single measurements are sufficient to predict combinations in specific cases but there are exceptions in which it is necessary to measure combinations, which is possible with the presented system.

  19. BAYESIAN FORECASTS COMBINATION TO IMPROVE THE ROMANIAN INFLATION PREDICTIONS BASED ON ECONOMETRIC MODELS

    Directory of Open Access Journals (Sweden)

    Mihaela Simionescu

    2014-12-01

    Full Text Available There are many types of econometric models used in predicting the inflation rate, but in this study we used a Bayesian shrinkage combination approach. This methodology is used in order to improve the predictions accuracy by including information that is not captured by the econometric models. Therefore, experts’ forecasts are utilized as prior information, for Romania these predictions being provided by Institute for Economic Forecasting (Dobrescu macromodel, National Commission for Prognosis and European Commission. The empirical results for Romanian inflation show the superiority of a fixed effects model compared to other types of econometric models like VAR, Bayesian VAR, simultaneous equations model, dynamic model, log-linear model. The Bayesian combinations that used experts’ predictions as priors, when the shrinkage parameter tends to infinite, improved the accuracy of all forecasts based on individual models, outperforming also zero and equal weights predictions and naïve forecasts.

  20. Ensemble approach combining multiple methods improves human transcription start site prediction

    LENUS (Irish Health Repository)

    Dineen, David G

    2010-11-30

    Abstract Background The computational prediction of transcription start sites is an important unsolved problem. Some recent progress has been made, but many promoters, particularly those not associated with CpG islands, are still difficult to locate using current methods. These methods use different features and training sets, along with a variety of machine learning techniques and result in different prediction sets. Results We demonstrate the heterogeneity of current prediction sets, and take advantage of this heterogeneity to construct a two-level classifier (\\'Profisi Ensemble\\') using predictions from 7 programs, along with 2 other data sources. Support vector machines using \\'full\\' and \\'reduced\\' data sets are combined in an either\\/or approach. We achieve a 14% increase in performance over the current state-of-the-art, as benchmarked by a third-party tool. Conclusions Supervised learning methods are a useful way to combine predictions from diverse sources.

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

    Directory of Open Access Journals (Sweden)

    Wolfgang Peter Fendler

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

  2. Prediction of Combine Economic Life Based on Repair and Maintenance Costs Model

    Directory of Open Access Journals (Sweden)

    A Rohani

    2014-09-01

    Full Text Available Farm machinery managers often need to make complex economic decisions on machinery replacement. Repair and maintenance costs can have significant impacts on this economic decision. The farm manager must be able to predict farm machinery repair and maintenance costs. This study aimed to identify a regression model that can adequately represent the repair and maintenance costs in terms of machine age in cumulative hours of use. The regression model has the ability to predict the repair and maintenance costs for longer time periods. Therefore, it can be used for the estimation of the economic life. The study was conducted using field data collected from 11 John-Deer 955 combine harvesters used in several western provinces of Iran. It was found that power model has a better performance for the prediction of combine repair and maintenance costs. The results showed that the optimum replacement age of John-Deer 955 combine was 54300 cumulative hours.

  3. Wind Turbine Generator Efficiency Based on Powertrain Combination and Annual Power Generation Prediction

    Directory of Open Access Journals (Sweden)

    Dongmyung Kim

    2018-05-01

    Full Text Available Wind turbine generators are eco-friendly generators that produce electric energy using wind energy. In this study, wind turbine generator efficiency is examined using a powertrain combination and annual power generation prediction, by employing an analysis model. Performance testing was conducted in order to analyze the efficiency of a hydraulic pump and a motor, which are key components, and so as to verify the analysis model. The annual wind speed occurrence frequency for the expected installation areas was used to predict the annual power generation of the wind turbine generators. It was found that the parallel combination of the induction motors exhibited a higher efficiency when the wind speed was low and the serial combination showed higher efficiency when wind speed was high. The results of predicting the annual power generation considering the regional characteristics showed that the power generation was the highest when the hydraulic motors were designed in parallel and the induction motors were designed in series.

  4. Methods to improve genomic prediction and GWAS using combined Holstein populations

    DEFF Research Database (Denmark)

    Li, Xiujin

    The thesis focuses on methods to improve GWAS and genomic prediction using combined Holstein populations and investigations G by E interaction. The conclusions are: 1) Prediction reliabilities for Brazilian Holsteins can be increased by adding Nordic and Frensh genotyped bulls and a large G by E...... interaction exists between populations. 2) Combining data from Chinese and Danish Holstein populations increases the power of GWAS and detects new QTL regions for milk fatty acid traits. 3) The novel multi-trait Bayesian model efficiently estimates region-specific genomic variances, covariances...

  5. Research of combination model for prediction of the trend of outbreak of hepatitis B

    Directory of Open Access Journals (Sweden)

    Yin-ping CHEN

    2014-03-01

    Full Text Available Objective To establish a combination model of autoregressive integrated moving average model and the grey dynamics (ARIMA-GM of hepatitis B incidence rate (1/100 000 to predict the trend of outbreak of hepatitis B, as to provide a scientific basis for the early discovery of the infectious diseases for the performance of countermeasures of controlling its spread. Methods The monthly incidence of hepatitis B in Qian'an city, Hebei province, was collected from Jan 2004 to Dec 2012, and a model (ARIMA was reproduced with SPSS software. The GM (1,1 model was used to correct the residual sequence with a threshold value, and a combined forecasting model was reproduced. This combination model was used to predict the monthly incidence rate in this city in 2013. Results The model ARIMA(0,1,1(0,1,112 was established successfully and the residual sequence was a white noise sequence. Then the GM (1,1 model with a threshold of 3 was used to correct its residuals and obtain its nonlinear feature extraction of information. The forecasting model met required precision standards (C=0.673, P=0.877, the fitting accuracy of which was basically qualified. The results showed that the MAE, MAPE of the ARIMA-GM combined model were smaller than that of a single model, and the combined model could improve the prediction accuracy. Using the combined model to forecast the incidence of hepatitis B during Jan 2013 to Dec 2013, the overall trend was relatively consistent with the condition of previous years. Conclusion The ARIMA-GM combined model can better fit the incidence rate of hepatitis B with a greater accuracy than the seasonal ARIMA model. The prediction results can provide the reference for the early warning system of HBV. DOI: 10.11855/j.issn.0577-7402.2014.01.12

  6. Combining a weed traits database with a population dynamics model predicts shifts in weed communities

    DEFF Research Database (Denmark)

    Storkey, Jonathan; Holst, Niels; Bøjer, Ole Mission

    2015-01-01

    A functional approach to predicting shifts in weed floras in response to management or environmental change requires the combination of data on weed traits with analytical frameworks that capture the filtering effect of selection pressures on traits. A weed traits database (WTDB) was designed, po...

  7. An inventory of the Aspergillus niger secretome by combining in silico predictions with shotgun proteomics data

    Directory of Open Access Journals (Sweden)

    Martens-Uzunova Elena S

    2010-10-01

    Full Text Available Abstract Background The ecological niche occupied by a fungal species, its pathogenicity and its usefulness as a microbial cell factory to a large degree depends on its secretome. Protein secretion usually requires the presence of a N-terminal signal peptide (SP and by scanning for this feature using available highly accurate SP-prediction tools, the fraction of potentially secreted proteins can be directly predicted. However, prediction of a SP does not guarantee that the protein is actually secreted and current in silico prediction methods suffer from gene-model errors introduced during genome annotation. Results A majority rule based classifier that also evaluates signal peptide predictions from the best homologs of three neighbouring Aspergillus species was developed to create an improved list of potential signal peptide containing proteins encoded by the Aspergillus niger genome. As a complement to these in silico predictions, the secretome associated with growth and upon carbon source depletion was determined using a shotgun proteomics approach. Overall, some 200 proteins with a predicted signal peptide were identified to be secreted proteins. Concordant changes in the secretome state were observed as a response to changes in growth/culture conditions. Additionally, two proteins secreted via a non-classical route operating in A. niger were identified. Conclusions We were able to improve the in silico inventory of A. niger secretory proteins by combining different gene-model predictions from neighbouring Aspergilli and thereby avoiding prediction conflicts associated with inaccurate gene-models. The expected accuracy of signal peptide prediction for proteins that lack homologous sequences in the proteomes of related species is 85%. An experimental validation of the predicted proteome confirmed in silico predictions.

  8. An inventory of the Aspergillus niger secretome by combining in silico predictions with shotgun proteomics data.

    Science.gov (United States)

    Braaksma, Machtelt; Martens-Uzunova, Elena S; Punt, Peter J; Schaap, Peter J

    2010-10-19

    The ecological niche occupied by a fungal species, its pathogenicity and its usefulness as a microbial cell factory to a large degree depends on its secretome. Protein secretion usually requires the presence of a N-terminal signal peptide (SP) and by scanning for this feature using available highly accurate SP-prediction tools, the fraction of potentially secreted proteins can be directly predicted. However, prediction of a SP does not guarantee that the protein is actually secreted and current in silico prediction methods suffer from gene-model errors introduced during genome annotation. A majority rule based classifier that also evaluates signal peptide predictions from the best homologs of three neighbouring Aspergillus species was developed to create an improved list of potential signal peptide containing proteins encoded by the Aspergillus niger genome. As a complement to these in silico predictions, the secretome associated with growth and upon carbon source depletion was determined using a shotgun proteomics approach. Overall, some 200 proteins with a predicted signal peptide were identified to be secreted proteins. Concordant changes in the secretome state were observed as a response to changes in growth/culture conditions. Additionally, two proteins secreted via a non-classical route operating in A. niger were identified. We were able to improve the in silico inventory of A. niger secretory proteins by combining different gene-model predictions from neighbouring Aspergilli and thereby avoiding prediction conflicts associated with inaccurate gene-models. The expected accuracy of signal peptide prediction for proteins that lack homologous sequences in the proteomes of related species is 85%. An experimental validation of the predicted proteome confirmed in silico predictions.

  9. Predicting combined sewer overflows chamber depth using artificial neural networks with rainfall radar data.

    Science.gov (United States)

    Mounce, S R; Shepherd, W; Sailor, G; Shucksmith, J; Saul, A J

    2014-01-01

    Combined sewer overflows (CSOs) represent a common feature in combined urban drainage systems and are used to discharge excess water to the environment during heavy storms. To better understand the performance of CSOs, the UK water industry has installed a large number of monitoring systems that provide data for these assets. This paper presents research into the prediction of the hydraulic performance of CSOs using artificial neural networks (ANN) as an alternative to hydraulic models. Previous work has explored using an ANN model for the prediction of chamber depth using time series for depth and rain gauge data. Rainfall intensity data that can be provided by rainfall radar devices can be used to improve on this approach. Results are presented using real data from a CSO for a catchment in the North of England, UK. An ANN model trained with the pseudo-inverse rule was shown to be capable of predicting CSO depth with less than 5% error for predictions more than 1 hour ahead for unseen data. Such predictive approaches are important to the future management of combined sewer systems.

  10. Calculation of solar irradiation prediction intervals combining volatility and kernel density estimates

    International Nuclear Information System (INIS)

    Trapero, Juan R.

    2016-01-01

    In order to integrate solar energy into the grid it is important to predict the solar radiation accurately, where forecast errors can lead to significant costs. Recently, the increasing statistical approaches that cope with this problem is yielding a prolific literature. In general terms, the main research discussion is centred on selecting the “best” forecasting technique in accuracy terms. However, the need of the users of such forecasts require, apart from point forecasts, information about the variability of such forecast to compute prediction intervals. In this work, we will analyze kernel density estimation approaches, volatility forecasting models and combination of both of them in order to improve the prediction intervals performance. The results show that an optimal combination in terms of prediction interval statistical tests can achieve the desired confidence level with a lower average interval width. Data from a facility located in Spain are used to illustrate our methodology. - Highlights: • This work explores uncertainty forecasting models to build prediction intervals. • Kernel density estimators, exponential smoothing and GARCH models are compared. • An optimal combination of methods provides the best results. • A good compromise between coverage and average interval width is shown.

  11. Combining Traditional Cyber Security Audit Data with Psychosocial Data: Towards Predictive Modeling for Insider Threat Mitigation

    Science.gov (United States)

    Greitzer, Frank L.; Frincke, Deborah A.

    The purpose of this chapter is to motivate the combination of traditional cyber security audit data with psychosocial data, to support a move from an insider threat detection stance to one that enables prediction of potential insider presence. Twodistinctiveaspects of the approach are the objectiveof predicting or anticipating potential risksandthe useoforganizational datain additiontocyber datato support the analysis. The chapter describes the challenges of this endeavor and reports on progressin definingausablesetof predictiveindicators,developingaframeworkfor integratingthe analysisoforganizationalandcyber securitydatatoyield predictions about possible insider exploits, and developing the knowledge base and reasoning capabilityof the system.We also outline the typesof errors that oneexpectsina predictive system versus a detection system and discuss how those errors can affect the usefulness of the results.

  12. Combining modularity, conservation, and interactions of proteins significantly increases precision and coverage of protein function prediction

    Directory of Open Access Journals (Sweden)

    Sers Christine T

    2010-12-01

    Full Text Available Abstract Background While the number of newly sequenced genomes and genes is constantly increasing, elucidation of their function still is a laborious and time-consuming task. This has led to the development of a wide range of methods for predicting protein functions in silico. We report on a new method that predicts function based on a combination of information about protein interactions, orthology, and the conservation of protein networks in different species. Results We show that aggregation of these independent sources of evidence leads to a drastic increase in number and quality of predictions when compared to baselines and other methods reported in the literature. For instance, our method generates more than 12,000 novel protein functions for human with an estimated precision of ~76%, among which are 7,500 new functional annotations for 1,973 human proteins that previously had zero or only one function annotated. We also verified our predictions on a set of genes that play an important role in colorectal cancer (MLH1, PMS2, EPHB4 and could confirm more than 73% of them based on evidence in the literature. Conclusions The combination of different methods into a single, comprehensive prediction method infers thousands of protein functions for every species included in the analysis at varying, yet always high levels of precision and very good coverage.

  13. Transformer Incipient Fault Prediction Using Combined Artificial Neural Network and Various Particle Swarm Optimisation Techniques.

    Directory of Open Access Journals (Sweden)

    Hazlee Azil Illias

    Full Text Available It is important to predict the incipient fault in transformer oil accurately so that the maintenance of transformer oil can be performed correctly, reducing the cost of maintenance and minimise the error. Dissolved gas analysis (DGA has been widely used to predict the incipient fault in power transformers. However, sometimes the existing DGA methods yield inaccurate prediction of the incipient fault in transformer oil because each method is only suitable for certain conditions. Many previous works have reported on the use of intelligence methods to predict the transformer faults. However, it is believed that the accuracy of the previously proposed methods can still be improved. Since artificial neural network (ANN and particle swarm optimisation (PSO techniques have never been used in the previously reported work, this work proposes a combination of ANN and various PSO techniques to predict the transformer incipient fault. The advantages of PSO are simplicity and easy implementation. The effectiveness of various PSO techniques in combination with ANN is validated by comparison with the results from the actual fault diagnosis, an existing diagnosis method and ANN alone. Comparison of the results from the proposed methods with the previously reported work was also performed to show the improvement of the proposed methods. It was found that the proposed ANN-Evolutionary PSO method yields the highest percentage of correct identification for transformer fault type than the existing diagnosis method and previously reported works.

  14. Transformer Incipient Fault Prediction Using Combined Artificial Neural Network and Various Particle Swarm Optimisation Techniques.

    Science.gov (United States)

    Illias, Hazlee Azil; Chai, Xin Rui; Abu Bakar, Ab Halim; Mokhlis, Hazlie

    2015-01-01

    It is important to predict the incipient fault in transformer oil accurately so that the maintenance of transformer oil can be performed correctly, reducing the cost of maintenance and minimise the error. Dissolved gas analysis (DGA) has been widely used to predict the incipient fault in power transformers. However, sometimes the existing DGA methods yield inaccurate prediction of the incipient fault in transformer oil because each method is only suitable for certain conditions. Many previous works have reported on the use of intelligence methods to predict the transformer faults. However, it is believed that the accuracy of the previously proposed methods can still be improved. Since artificial neural network (ANN) and particle swarm optimisation (PSO) techniques have never been used in the previously reported work, this work proposes a combination of ANN and various PSO techniques to predict the transformer incipient fault. The advantages of PSO are simplicity and easy implementation. The effectiveness of various PSO techniques in combination with ANN is validated by comparison with the results from the actual fault diagnosis, an existing diagnosis method and ANN alone. Comparison of the results from the proposed methods with the previously reported work was also performed to show the improvement of the proposed methods. It was found that the proposed ANN-Evolutionary PSO method yields the highest percentage of correct identification for transformer fault type than the existing diagnosis method and previously reported works.

  15. Prediction of HDR quality by combining perceptually transformed display measurements with machine learning

    Science.gov (United States)

    Choudhury, Anustup; Farrell, Suzanne; Atkins, Robin; Daly, Scott

    2017-09-01

    We present an approach to predict overall HDR display quality as a function of key HDR display parameters. We first performed subjective experiments on a high quality HDR display that explored five key HDR display parameters: maximum luminance, minimum luminance, color gamut, bit-depth and local contrast. Subjects rated overall quality for different combinations of these display parameters. We explored two models | a physical model solely based on physically measured display characteristics and a perceptual model that transforms physical parameters using human vision system models. For the perceptual model, we use a family of metrics based on a recently published color volume model (ICT-CP), which consists of the PQ luminance non-linearity (ST2084) and LMS-based opponent color, as well as an estimate of the display point spread function. To predict overall visual quality, we apply linear regression and machine learning techniques such as Multilayer Perceptron, RBF and SVM networks. We use RMSE and Pearson/Spearman correlation coefficients to quantify performance. We found that the perceptual model is better at predicting subjective quality than the physical model and that SVM is better at prediction than linear regression. The significance and contribution of each display parameter was investigated. In addition, we found that combined parameters such as contrast do not improve prediction. Traditional perceptual models were also evaluated and we found that models based on the PQ non-linearity performed better.

  16. Transformer Incipient Fault Prediction Using Combined Artificial Neural Network and Various Particle Swarm Optimisation Techniques

    Science.gov (United States)

    2015-01-01

    It is important to predict the incipient fault in transformer oil accurately so that the maintenance of transformer oil can be performed correctly, reducing the cost of maintenance and minimise the error. Dissolved gas analysis (DGA) has been widely used to predict the incipient fault in power transformers. However, sometimes the existing DGA methods yield inaccurate prediction of the incipient fault in transformer oil because each method is only suitable for certain conditions. Many previous works have reported on the use of intelligence methods to predict the transformer faults. However, it is believed that the accuracy of the previously proposed methods can still be improved. Since artificial neural network (ANN) and particle swarm optimisation (PSO) techniques have never been used in the previously reported work, this work proposes a combination of ANN and various PSO techniques to predict the transformer incipient fault. The advantages of PSO are simplicity and easy implementation. The effectiveness of various PSO techniques in combination with ANN is validated by comparison with the results from the actual fault diagnosis, an existing diagnosis method and ANN alone. Comparison of the results from the proposed methods with the previously reported work was also performed to show the improvement of the proposed methods. It was found that the proposed ANN-Evolutionary PSO method yields the highest percentage of correct identification for transformer fault type than the existing diagnosis method and previously reported works. PMID:26103634

  17. Music-induced emotions can be predicted from a combination of brain activity and acoustic features.

    Science.gov (United States)

    Daly, Ian; Williams, Duncan; Hallowell, James; Hwang, Faustina; Kirke, Alexis; Malik, Asad; Weaver, James; Miranda, Eduardo; Nasuto, Slawomir J

    2015-12-01

    It is widely acknowledged that music can communicate and induce a wide range of emotions in the listener. However, music is a highly-complex audio signal composed of a wide range of complex time- and frequency-varying components. Additionally, music-induced emotions are known to differ greatly between listeners. Therefore, it is not immediately clear what emotions will be induced in a given individual by a piece of music. We attempt to predict the music-induced emotional response in a listener by measuring the activity in the listeners electroencephalogram (EEG). We combine these measures with acoustic descriptors of the music, an approach that allows us to consider music as a complex set of time-varying acoustic features, independently of any specific music theory. Regression models are found which allow us to predict the music-induced emotions of our participants with a correlation between the actual and predicted responses of up to r=0.234,pmusic induced emotions can be predicted by their neural activity and the properties of the music. Given the large amount of noise, non-stationarity, and non-linearity in both EEG and music, this is an encouraging result. Additionally, the combination of measures of brain activity and acoustic features describing the music played to our participants allows us to predict music-induced emotions with significantly higher accuracies than either feature type alone (p<0.01). Copyright © 2015 Elsevier Inc. All rights reserved.

  18. Combined Prediction Model of Death Toll for Road Traffic Accidents Based on Independent and Dependent Variables

    Directory of Open Access Journals (Sweden)

    Feng Zhong-xiang

    2014-01-01

    Full Text Available In order to build a combined model which can meet the variation rule of death toll data for road traffic accidents and can reflect the influence of multiple factors on traffic accidents and improve prediction accuracy for accidents, the Verhulst model was built based on the number of death tolls for road traffic accidents in China from 2002 to 2011; and car ownership, population, GDP, highway freight volume, highway passenger transportation volume, and highway mileage were chosen as the factors to build the death toll multivariate linear regression model. Then the two models were combined to be a combined prediction model which has weight coefficient. Shapley value method was applied to calculate the weight coefficient by assessing contributions. Finally, the combined model was used to recalculate the number of death tolls from 2002 to 2011, and the combined model was compared with the Verhulst and multivariate linear regression models. The results showed that the new model could not only characterize the death toll data characteristics but also quantify the degree of influence to the death toll by each influencing factor and had high accuracy as well as strong practicability.

  19. Combined prediction model of death toll for road traffic accidents based on independent and dependent variables.

    Science.gov (United States)

    Feng, Zhong-xiang; Lu, Shi-sheng; Zhang, Wei-hua; Zhang, Nan-nan

    2014-01-01

    In order to build a combined model which can meet the variation rule of death toll data for road traffic accidents and can reflect the influence of multiple factors on traffic accidents and improve prediction accuracy for accidents, the Verhulst model was built based on the number of death tolls for road traffic accidents in China from 2002 to 2011; and car ownership, population, GDP, highway freight volume, highway passenger transportation volume, and highway mileage were chosen as the factors to build the death toll multivariate linear regression model. Then the two models were combined to be a combined prediction model which has weight coefficient. Shapley value method was applied to calculate the weight coefficient by assessing contributions. Finally, the combined model was used to recalculate the number of death tolls from 2002 to 2011, and the combined model was compared with the Verhulst and multivariate linear regression models. The results showed that the new model could not only characterize the death toll data characteristics but also quantify the degree of influence to the death toll by each influencing factor and had high accuracy as well as strong practicability.

  20. Combining mid infrared and total X-ray fluorescence spectroscopy for prediction of soil properties

    Science.gov (United States)

    Towett, Erick; Shepherd, Keith; Sila, Andrew; Aynekulu, Ermias; Cadisch, Georg

    2015-04-01

    Mid-infrared diffuse reflectance spectroscopy (MIR) can predict many soil properties but extractable nutrients are often predicted poorly. We evaluated the potential of MIR and total elemental analysis using total X-ray fluorescence spectroscopy (TXRF), both individually and combined, to predict results of conventional soil tests. Total multi-elemental analysis provides a fingerprint of soil mineralogy and could predict some soil properties and help improve MIR predictions. A set of 700 georeferenced soil samples associated with the Africa Soil Information Service (AfSIS) (www.africasoils.net) from 44 stratified randomly-located 100-km2 sentinel sites distributed across sub-Saharan Africa were analysed for physico-chemical composition using conventional reference methods, and compared to MIR and TXRF spectra using the Random Forests regression algorithm and an internal out-of-bag validation. MIR spectra resulted in good prediction models (R2 >0.80) for organic C and total N, Mehlich-3 Ca and Al, and pH. To test the combined spectroscopic approach, TXRF element concentration data was included as a property predictor along with the first derivative of MIR spectral data using the RF algorithm. Including TXRF did not improve prediction of these properties. TXRF was poorer (R2 0.86) as these elements are not directly determined with TXRF, however the variance explained is still quite high and may be attributable to TXRF signatures relating to mineralogy correlated with protection of soil organic matter. TXRF model for Mehlich-3 Al had excellent prediction capability explaining 81% of the observed variation in extractable Al content and was comparable to that of MIR (R2 = 0.86). However, models for pH and Mehlich-3 exchangeable Ca exhibited R2 values of 0.74 and 0.79 respectively and thus had moderate predictive accuracy, compared to MIR alone with R2 values of 0.82 and 0.84 respectively. Both MIR and TXRF methods predicted soil properties that relate to nutrient

  1. Assessing Long-Term Wind Conditions by Combining Different Measure-Correlate-Predict Algorithms: Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Zhang, J.; Chowdhury, S.; Messac, A.; Hodge, B. M.

    2013-08-01

    This paper significantly advances the hybrid measure-correlate-predict (MCP) methodology, enabling it to account for variations of both wind speed and direction. The advanced hybrid MCP method uses the recorded data of multiple reference stations to estimate the long-term wind condition at a target wind plant site. The results show that the accuracy of the hybrid MCP method is highly sensitive to the combination of the individual MCP algorithms and reference stations. It was also found that the best combination of MCP algorithms varies based on the length of the correlation period.

  2. Combining Spot Sign and Intracerebral Hemorrhage Score to Estimate Functional Outcome: Analysis From the PREDICT Cohort.

    Science.gov (United States)

    Schneider, Hauke; Huynh, Thien J; Demchuk, Andrew M; Dowlatshahi, Dar; Rodriguez-Luna, David; Silva, Yolanda; Aviv, Richard; Dzialowski, Imanuel

    2018-06-01

    The intracerebral hemorrhage (ICH) score is the most commonly used grading scale for stratifying functional outcome in patients with acute ICH. We sought to determine whether a combination of the ICH score and the computed tomographic angiography spot sign may improve outcome prediction in the cohort of a prospective multicenter hemorrhage trial. Prospectively collected data from 241 patients from the observational PREDICT study (Prediction of Hematoma Growth and Outcome in Patients With Intracerebral Hemorrhage Using the CT-Angiography Spot Sign) were analyzed. Functional outcome at 3 months was dichotomized using the modified Rankin Scale (0-3 versus 4-6). Performance of (1) the ICH score and (2) the spot sign ICH score-a scoring scale combining ICH score and spot sign number-was tested. Multivariable analysis demonstrated that ICH score (odds ratio, 3.2; 95% confidence interval, 2.2-4.8) and spot sign number (n=1: odds ratio, 2.7; 95% confidence interval, 1.1-7.4; n>1: odds ratio, 3.8; 95% confidence interval, 1.2-17.1) were independently predictive of functional outcome at 3 months with similar odds ratios. Prediction of functional outcome was not significantly different using the spot sign ICH score compared with the ICH score alone (spot sign ICH score area under curve versus ICH score area under curve: P =0.14). In the PREDICT cohort, a prognostic score adding the computed tomographic angiography-based spot sign to the established ICH score did not improve functional outcome prediction compared with the ICH score. © 2018 American Heart Association, Inc.

  3. Combined heat transfer and kinetic models to predict cooking loss during heat treatment of beef meat.

    Science.gov (United States)

    Kondjoyan, Alain; Oillic, Samuel; Portanguen, Stéphane; Gros, Jean-Bernard

    2013-10-01

    A heat transfer model was used to simulate the temperature in 3 dimensions inside the meat. This model was combined with a first-order kinetic models to predict cooking losses. Identification of the parameters of the kinetic models and first validations were performed in a water bath. Afterwards, the performance of the combined model was determined in a fan-assisted oven under different air/steam conditions. Accurate knowledge of the heat transfer coefficient values and consideration of the retraction of the meat pieces are needed for the prediction of meat temperature. This is important since the temperature at the center of the product is often used to determine the cooking time. The combined model was also able to predict cooking losses from meat pieces of different sizes and subjected to different air/steam conditions. It was found that under the studied conditions, most of the water loss comes from the juice expelled by protein denaturation and contraction and not from evaporation. Copyright © 2013 Elsevier Ltd. All rights reserved.

  4. NUMERICAL PREDICTION OF COMPOSITE BEAM SUBJECTED TO COMBINED NEGATIVE BENDING AND AXIAL TENSION

    Directory of Open Access Journals (Sweden)

    MAHESAN BAVAN

    2013-08-01

    Full Text Available The present study has investigated the finite element method (FEM techniques of composite beam subjected to combined axial tension and negative bending. The negative bending regions of composite beams are influenced by worsen failures due to various levels of axial tensile loads on steel section especially in the regions near internal supports. Three dimensional solid FEM model was developed to accurately predict the unfavourable phenomenon of cracking of concrete and compression of steel in the negative bending regions of composite beam due to axial tensile loads. The prediction of quasi-static solution was extensively analysed with various deformation speeds and energy stabilities. The FEM model was then validated with existing experimental data. Reasonable agreements were observed between the results of FEM model and experimental analysis in the combination of vertical-axial forces and failure modes on ultimate limit state behaviour. The local failure modes known as shear studs failure, excess yielding on steel beam and crushing on concrete were completely verified by extensive similarity between the numerical and experimental results. Finally, a proper way of modelling techniques for large FEM models by considering uncertainties of material behaviour due to biaxial loadings and complex contact interactions is discussed. Further, the model is suggested for the limit state prediction of composite beam with calibrating necessary degree of the combined axial loads.

  5. Calibration and combination of monthly near-surface temperature and precipitation predictions over Europe

    Science.gov (United States)

    Rodrigues, Luis R. L.; Doblas-Reyes, Francisco J.; Coelho, Caio A. S.

    2018-02-01

    A Bayesian method known as the Forecast Assimilation (FA) was used to calibrate and combine monthly near-surface temperature and precipitation outputs from seasonal dynamical forecast systems. The simple multimodel (SMM), a method that combines predictions with equal weights, was used as a benchmark. This research focuses on Europe and adjacent regions for predictions initialized in May and November, covering the boreal summer and winter months. The forecast quality of the FA and SMM as well as the single seasonal dynamical forecast systems was assessed using deterministic and probabilistic measures. A non-parametric bootstrap method was used to account for the sampling uncertainty of the forecast quality measures. We show that the FA performs as well as or better than the SMM in regions where the dynamical forecast systems were able to represent the main modes of climate covariability. An illustration with the near-surface temperature over North Atlantic, the Mediterranean Sea and Middle-East in summer months associated with the well predicted first mode of climate covariability is offered. However, the main modes of climate covariability are not well represented in most situations discussed in this study as the seasonal dynamical forecast systems have limited skill when predicting the European climate. In these situations, the SMM performs better more often.

  6. Seasonal streamflow prediction by a combined climate-hydrologic system for river basins of Taiwan

    Science.gov (United States)

    Kuo, Chun-Chao; Gan, Thian Yew; Yu, Pao-Shan

    2010-06-01

    SummaryA combined, climate-hydrologic system with three components to predict the streamflow of two river basins of Taiwan at one season (3-month) lead time for the NDJ and JFM seasons was developed. The first component consists of the wavelet-based, ANN-GA model (Artificial Neural Network calibrated by Genetic Algorithm) which predicts the seasonal rainfall by using selected sea surface temperature (SST) as predictors, given that SST are generally predictable by climate models up to 6-month lead time. For the second component, three disaggregation models, Valencia and Schaake (VS), Lane, and Canonical Random Cascade Model (CRCM), were tested to compare the accuracy of seasonal rainfall disaggregated by these three models to 3-day time scale rainfall data. The third component consists of the continuous rainfall-runoff model modified from HBV (called the MHBV) and calibrated by a global optimization algorithm against the observed rainfall and streamflow data of the Shihmen and Tsengwen river basins of Taiwan. The proposed system was tested, first by disaggregating the predicted seasonal rainfall of ANN-GA to rainfall of 3-day time step using the Lane model; then the disaggregated rainfall data was used to drive the calibrated MHBV to predict the streamflow for both river basins at 3-day time step up to a season's lead time. Overall, the streamflow predicted by this combined system for the NDJ season, which is better than that of the JFM season, will be useful for the seasonal planning and management of water resources of these two river basins of Taiwan.

  7. Improving prediction of heterodimeric protein complexes using combination with pairwise kernel.

    Science.gov (United States)

    Ruan, Peiying; Hayashida, Morihiro; Akutsu, Tatsuya; Vert, Jean-Philippe

    2018-02-19

    Since many proteins become functional only after they interact with their partner proteins and form protein complexes, it is essential to identify the sets of proteins that form complexes. Therefore, several computational methods have been proposed to predict complexes from the topology and structure of experimental protein-protein interaction (PPI) network. These methods work well to predict complexes involving at least three proteins, but generally fail at identifying complexes involving only two different proteins, called heterodimeric complexes or heterodimers. There is however an urgent need for efficient methods to predict heterodimers, since the majority of known protein complexes are precisely heterodimers. In this paper, we use three promising kernel functions, Min kernel and two pairwise kernels, which are Metric Learning Pairwise Kernel (MLPK) and Tensor Product Pairwise Kernel (TPPK). We also consider the normalization forms of Min kernel. Then, we combine Min kernel or its normalization form and one of the pairwise kernels by plugging. We applied kernels based on PPI, domain, phylogenetic profile, and subcellular localization properties to predicting heterodimers. Then, we evaluate our method by employing C-Support Vector Classification (C-SVC), carrying out 10-fold cross-validation, and calculating the average F-measures. The results suggest that the combination of normalized-Min-kernel and MLPK leads to the best F-measure and improved the performance of our previous work, which had been the best existing method so far. We propose new methods to predict heterodimers, using a machine learning-based approach. We train a support vector machine (SVM) to discriminate interacting vs non-interacting protein pairs, based on informations extracted from PPI, domain, phylogenetic profiles and subcellular localization. We evaluate in detail new kernel functions to encode these data, and report prediction performance that outperforms the state-of-the-art.

  8. Multi-Marker Strategy in Heart Failure: Combination of ST2 and CRP Predicts Poor Outcome.

    Directory of Open Access Journals (Sweden)

    Anne Marie Dupuy

    Full Text Available Natriuretic peptides (BNP and NT-proBNP are recognized as gold-standard predictive markers in Heart Failure (HF. However, currently ST2 (member of the interleukin 1 receptor family has emerged as marker of inflammation, fibrosis and cardiac stress. We evaluated ST2 and CRP as prognostic markers in 178 patients with chronic heart failure in comparison with other classical markers such as clinical established parameters but also biological markers: NT-proBNP, hs-cTnT alone or in combination. In multivariate analysis, subsequent addition of ST2 led to age, CRP and ST2 as the only remaining predictors of all-cause mortality (HR 1.03, HR 1.61 and HR 2.75, respectively as well as of cardiovascular mortality (HR 1.00, HR 2.27 and HR 3.78, respectively. The combined increase of ST2 and CRP was significant for predicting worsened outcomes leading to identify a high risk subgroup that individual assessment of either marker. The same analysis was performed with ST2 in combination with Barcelona score. Overall, our findings extend previous data demonstrating that ST2 in combination with CRP as a valuable tool for identifying patients at risk of death.

  9. Multistrain models predict sequential multidrug treatment strategies to result in less antimicrobial resistance than combination treatment

    DEFF Research Database (Denmark)

    Ahmad, Amais; Zachariasen, Camilla; Christiansen, Lasse Engbo

    2016-01-01

    Background: Combination treatment is increasingly used to fight infections caused by bacteria resistant to two or more antimicrobials. While multiple studies have evaluated treatment strategies to minimize the emergence of resistant strains for single antimicrobial treatment, fewer studies have...... the sensitive fraction of the commensal flora.Growth parameters for competing bacterial strains were estimated from the combined in vitro pharmacodynamic effect of two antimicrobials using the relationship between concentration and net bacterial growth rate. Predictions of in vivo bacterial growth were...... (how frequently antibiotics are alternated in a sequential treatment) of the two drugs was dependent upon the order in which the two drugs were used.Conclusion: Sequential treatment was more effective in preventing the growth of resistant strains when compared to the combination treatment. The cycling...

  10. Synergistic target combination prediction from curated signaling networks: Machine learning meets systems biology and pharmacology.

    Science.gov (United States)

    Chua, Huey Eng; Bhowmick, Sourav S; Tucker-Kellogg, Lisa

    2017-10-01

    Given a signaling network, the target combination prediction problem aims to predict efficacious and safe target combinations for combination therapy. State-of-the-art in silico methods use Monte Carlo simulated annealing (mcsa) to modify a candidate solution stochastically, and use the Metropolis criterion to accept or reject the proposed modifications. However, such stochastic modifications ignore the impact of the choice of targets and their activities on the combination's therapeutic effect and off-target effects, which directly affect the solution quality. In this paper, we present mascot, a method that addresses this limitation by leveraging two additional heuristic criteria to minimize off-target effects and achieve synergy for candidate modification. Specifically, off-target effects measure the unintended response of a signaling network to the target combination and is often associated with toxicity. Synergy occurs when a pair of targets exerts effects that are greater than the sum of their individual effects, and is generally a beneficial strategy for maximizing effect while minimizing toxicity. mascot leverages on a machine learning-based target prioritization method which prioritizes potential targets in a given disease-associated network to select more effective targets (better therapeutic effect and/or lower off-target effects); and on Loewe additivity theory from pharmacology which assesses the non-additive effects in a combination drug treatment to select synergistic target activities. Our experimental study on two disease-related signaling networks demonstrates the superiority of mascot in comparison to existing approaches. Copyright © 2017 Elsevier Inc. All rights reserved.

  11. Advanced validation of CFD-FDTD combined method using highly applicable solver for reentry blackout prediction

    International Nuclear Information System (INIS)

    Takahashi, Yusuke

    2016-01-01

    An analysis model of plasma flow and electromagnetic waves around a reentry vehicle for radio frequency blackout prediction during aerodynamic heating was developed in this study. The model was validated based on experimental results from the radio attenuation measurement program. The plasma flow properties, such as electron number density, in the shock layer and wake region were obtained using a newly developed unstructured grid solver that incorporated real gas effect models and could treat thermochemically non-equilibrium flow. To predict the electromagnetic waves in plasma, a frequency-dependent finite-difference time-domain method was used. Moreover, the complicated behaviour of electromagnetic waves in the plasma layer during atmospheric reentry was clarified at several altitudes. The prediction performance of the combined model was evaluated with profiles and peak values of the electron number density in the plasma layer. In addition, to validate the models, the signal losses measured during communication with the reentry vehicle were directly compared with the predicted results. Based on the study, it was suggested that the present analysis model accurately predicts the radio frequency blackout and plasma attenuation of electromagnetic waves in plasma in communication. (paper)

  12. Predictive saccades in children and adults: A combined fMRI and eye tracking study.

    Directory of Open Access Journals (Sweden)

    Katerina Lukasova

    Full Text Available Saccades were assessed in 21 adults (age 24 years, SD = 4 and 15 children (age 11 years, SD = 1, using combined functional magnetic resonance imaging (fMRI and eye-tracking. Subjects visually tracked a point on a horizontal line in four conditions: time and position predictable task (PRED, position predictable (pPRED, time predictable (tPRED and visually guided saccades (SAC. Both groups in the PRED but not in pPRED, tPRED and SAC produced predictive saccades with latency below 80 ms. In task versus group comparisons, children's showed less efficient learning compared to adults for predictive saccades (adults = 48%, children = 34%, p = 0.05. In adults brain activation was found in the frontal and occipital regions in the PRED, in the intraparietal sulcus in pPRED and in the frontal eye field, posterior intraparietal sulcus and medial regions in the tPRED task. Group-task interaction was found in the supplementary eye field and visual cortex in the PRED task, and the frontal cortex including the right frontal eye field and left frontal pole, in the pPRED condition. These results indicate that, the basic visuomotor circuitry is present in both adults and children, but fine-tuning of the activation according to the task temporal and spatial demand mature late in child development.

  13. A combination of compositional index and genetic algorithm for predicting transmembrane helical segments.

    Directory of Open Access Journals (Sweden)

    Nazar Zaki

    Full Text Available Transmembrane helix (TMH topology prediction is becoming a focal problem in bioinformatics because the structure of TM proteins is difficult to determine using experimental methods. Therefore, methods that can computationally predict the topology of helical membrane proteins are highly desirable. In this paper we introduce TMHindex, a method for detecting TMH segments using only the amino acid sequence information. Each amino acid in a protein sequence is represented by a Compositional Index, which is deduced from a combination of the difference in amino acid occurrences in TMH and non-TMH segments in training protein sequences and the amino acid composition information. Furthermore, a genetic algorithm was employed to find the optimal threshold value for the separation of TMH segments from non-TMH segments. The method successfully predicted 376 out of the 378 TMH segments in a dataset consisting of 70 test protein sequences. The sensitivity and specificity for classifying each amino acid in every protein sequence in the dataset was 0.901 and 0.865, respectively. To assess the generality of TMHindex, we also tested the approach on another standard 73-protein 3D helix dataset. TMHindex correctly predicted 91.8% of proteins based on TM segments. The level of the accuracy achieved using TMHindex in comparison to other recent approaches for predicting the topology of TM proteins is a strong argument in favor of our proposed method.The datasets, software together with supplementary materials are available at: http://faculty.uaeu.ac.ae/nzaki/TMHindex.htm.

  14. Ant colony optimization algorithm for interpretable Bayesian classifiers combination: application to medical predictions.

    Directory of Open Access Journals (Sweden)

    Salah Bouktif

    Full Text Available Prediction and classification techniques have been well studied by machine learning researchers and developed for several real-word problems. However, the level of acceptance and success of prediction models are still below expectation due to some difficulties such as the low performance of prediction models when they are applied in different environments. Such a problem has been addressed by many researchers, mainly from the machine learning community. A second problem, principally raised by model users in different communities, such as managers, economists, engineers, biologists, and medical practitioners, etc., is the prediction models' interpretability. The latter is the ability of a model to explain its predictions and exhibit the causality relationships between the inputs and the outputs. In the case of classification, a successful way to alleviate the low performance is to use ensemble classiers. It is an intuitive strategy to activate collaboration between different classifiers towards a better performance than individual classier. Unfortunately, ensemble classifiers method do not take into account the interpretability of the final classification outcome. It even worsens the original interpretability of the individual classifiers. In this paper we propose a novel implementation of classifiers combination approach that does not only promote the overall performance but also preserves the interpretability of the resulting model. We propose a solution based on Ant Colony Optimization and tailored for the case of Bayesian classifiers. We validate our proposed solution with case studies from medical domain namely, heart disease and Cardiotography-based predictions, problems where interpretability is critical to make appropriate clinical decisions.The datasets, Prediction Models and software tool together with supplementary materials are available at http://faculty.uaeu.ac.ae/salahb/ACO4BC.htm.

  15. Splice site prediction in Arabidopsis thaliana pre-mRNA by combining local and global sequence information

    DEFF Research Database (Denmark)

    Hebsgaard, Stefan M.; Korning, Peter G.; Tolstrup, Niels

    1996-01-01

    Artificial neural networks have been combined with a rule based system to predict intron splice sites in the dicot plant Arabidopsis thaliana. A two step prediction scheme, where a global prediction of the coding potential regulates a cutoff level for a local predicition of splice sites, is refin...

  16. Predicting Global Fund grant disbursements for procurement of artemisinin-based combination therapies

    Directory of Open Access Journals (Sweden)

    O'Brien Megan E

    2008-10-01

    Full Text Available Abstract Background An accurate forecast of global demand is essential to stabilize the market for artemisinin-based combination therapy (ACT and to ensure access to high-quality, life-saving medications at the lowest sustainable prices by avoiding underproduction and excessive overproduction, each of which can have negative consequences for the availability of affordable drugs. A robust forecast requires an understanding of the resources available to support procurement of these relatively expensive antimalarials, in particular from the Global Fund, at present the single largest source of ACT funding. Methods Predictive regression models estimating the timing and rate of disbursements from the Global Fund to recipient countries for each malaria grant were derived using a repeated split-sample procedure intended to avoid over-fitting. Predictions were compared against actual disbursements in a group of validation grants, and forecasts of ACT procurement extrapolated from disbursement predictions were evaluated against actual procurement in two sub-Saharan countries. Results Quarterly forecasts were correlated highly with actual smoothed disbursement rates (r = 0.987, p Conclusion This analysis derived predictive regression models that successfully forecasted disbursement patterning for individual Global Fund malaria grants. These results indicate the utility of this approach for demand forecasting of ACT and, potentially, for other commodities procured using funding from the Global Fund. Further validation using data from other countries in different regions and environments will be necessary to confirm its generalizability.

  17. Combining Review Text Content and Reviewer-Item Rating Matrix to Predict Review Rating

    Directory of Open Access Journals (Sweden)

    Bingkun Wang

    2016-01-01

    Full Text Available E-commerce develops rapidly. Learning and taking good advantage of the myriad reviews from online customers has become crucial to the success in this game, which calls for increasingly more accuracy in sentiment classification of these reviews. Therefore the finer-grained review rating prediction is preferred over the rough binary sentiment classification. There are mainly two types of method in current review rating prediction. One includes methods based on review text content which focus almost exclusively on textual content and seldom relate to those reviewers and items remarked in other relevant reviews. The other one contains methods based on collaborative filtering which extract information from previous records in the reviewer-item rating matrix, however, ignoring review textual content. Here we proposed a framework for review rating prediction which shows the effective combination of the two. Then we further proposed three specific methods under this framework. Experiments on two movie review datasets demonstrate that our review rating prediction framework has better performance than those previous methods.

  18. Combining Review Text Content and Reviewer-Item Rating Matrix to Predict Review Rating

    Science.gov (United States)

    Wang, Bingkun; Huang, Yongfeng; Li, Xing

    2016-01-01

    E-commerce develops rapidly. Learning and taking good advantage of the myriad reviews from online customers has become crucial to the success in this game, which calls for increasingly more accuracy in sentiment classification of these reviews. Therefore the finer-grained review rating prediction is preferred over the rough binary sentiment classification. There are mainly two types of method in current review rating prediction. One includes methods based on review text content which focus almost exclusively on textual content and seldom relate to those reviewers and items remarked in other relevant reviews. The other one contains methods based on collaborative filtering which extract information from previous records in the reviewer-item rating matrix, however, ignoring review textual content. Here we proposed a framework for review rating prediction which shows the effective combination of the two. Then we further proposed three specific methods under this framework. Experiments on two movie review datasets demonstrate that our review rating prediction framework has better performance than those previous methods. PMID:26880879

  19. Combining Review Text Content and Reviewer-Item Rating Matrix to Predict Review Rating.

    Science.gov (United States)

    Wang, Bingkun; Huang, Yongfeng; Li, Xing

    2016-01-01

    E-commerce develops rapidly. Learning and taking good advantage of the myriad reviews from online customers has become crucial to the success in this game, which calls for increasingly more accuracy in sentiment classification of these reviews. Therefore the finer-grained review rating prediction is preferred over the rough binary sentiment classification. There are mainly two types of method in current review rating prediction. One includes methods based on review text content which focus almost exclusively on textual content and seldom relate to those reviewers and items remarked in other relevant reviews. The other one contains methods based on collaborative filtering which extract information from previous records in the reviewer-item rating matrix, however, ignoring review textual content. Here we proposed a framework for review rating prediction which shows the effective combination of the two. Then we further proposed three specific methods under this framework. Experiments on two movie review datasets demonstrate that our review rating prediction framework has better performance than those previous methods.

  20. A Novel approach for predicting monthly water demand by combining singular spectrum analysis with neural networks

    Science.gov (United States)

    Zubaidi, Salah L.; Dooley, Jayne; Alkhaddar, Rafid M.; Abdellatif, Mawada; Al-Bugharbee, Hussein; Ortega-Martorell, Sandra

    2018-06-01

    Valid and dependable water demand prediction is a major element of the effective and sustainable expansion of municipal water infrastructures. This study provides a novel approach to quantifying water demand through the assessment of climatic factors, using a combination of a pretreatment signal technique, a hybrid particle swarm optimisation algorithm and an artificial neural network (PSO-ANN). The Singular Spectrum Analysis (SSA) technique was adopted to decompose and reconstruct water consumption in relation to six weather variables, to create a seasonal and stochastic time series. The results revealed that SSA is a powerful technique, capable of decomposing the original time series into many independent components including trend, oscillatory behaviours and noise. In addition, the PSO-ANN algorithm was shown to be a reliable prediction model, outperforming the hybrid Backtracking Search Algorithm BSA-ANN in terms of fitness function (RMSE). The findings of this study also support the view that water demand is driven by climatological variables.

  1. Wind gust estimation by combining numerical weather prediction model and statistical post-processing

    Science.gov (United States)

    Patlakas, Platon; Drakaki, Eleni; Galanis, George; Spyrou, Christos; Kallos, George

    2017-04-01

    The continuous rise of off-shore and near-shore activities as well as the development of structures, such as wind farms and various offshore platforms, requires the employment of state-of-the-art risk assessment techniques. Such analysis is used to set the safety standards and can be characterized as a climatologically oriented approach. Nevertheless, a reliable operational support is also needed in order to minimize cost drawbacks and human danger during the construction and the functioning stage as well as during maintenance activities. One of the most important parameters for this kind of analysis is the wind speed intensity and variability. A critical measure associated with this variability is the presence and magnitude of wind gusts as estimated in the reference level of 10m. The latter can be attributed to different processes that vary among boundary-layer turbulence, convection activities, mountain waves and wake phenomena. The purpose of this work is the development of a wind gust forecasting methodology combining a Numerical Weather Prediction model and a dynamical statistical tool based on Kalman filtering. To this end, the parameterization of Wind Gust Estimate method was implemented to function within the framework of the atmospheric model SKIRON/Dust. The new modeling tool combines the atmospheric model with a statistical local adaptation methodology based on Kalman filters. This has been tested over the offshore west coastline of the United States. The main purpose is to provide a useful tool for wind analysis and prediction and applications related to offshore wind energy (power prediction, operation and maintenance). The results have been evaluated by using observational data from the NOAA's buoy network. As it was found, the predicted output shows a good behavior that is further improved after the local adjustment post-process.

  2. Combining many interaction networks to predict gene function and analyze gene lists.

    Science.gov (United States)

    Mostafavi, Sara; Morris, Quaid

    2012-05-01

    In this article, we review how interaction networks can be used alone or in combination in an automated fashion to provide insight into gene and protein function. We describe the concept of a "gene-recommender system" that can be applied to any large collection of interaction networks to make predictions about gene or protein function based on a query list of proteins that share a function of interest. We discuss these systems in general and focus on one specific system, GeneMANIA, that has unique features and uses different algorithms from the majority of other systems. © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  3. Combined Active and Reactive Power Control of Wind Farms based on Model Predictive Control

    DEFF Research Database (Denmark)

    Zhao, Haoran; Wu, Qiuwei; Wang, Jianhui

    2017-01-01

    This paper proposes a combined wind farm controller based on Model Predictive Control (MPC). Compared with the conventional decoupled active and reactive power control, the proposed control scheme considers the significant impact of active power on voltage variations due to the low X=R ratio...... of wind farm collector systems. The voltage control is improved. Besides, by coordination of active and reactive power, the Var capacity is optimized to prevent potential failures due to Var shortage, especially when the wind farm operates close to its full load. An analytical method is used to calculate...... the sensitivity coefficients to improve the computation efficiency and overcome the convergence problem. Two control modes are designed for both normal and emergency conditions. A wind farm with 20 wind turbines was used to verify the proposed combined control scheme....

  4. Structure Elucidation of Unknown Metabolites in Metabolomics by Combined NMR and MS/MS Prediction.

    Science.gov (United States)

    Boiteau, Rene M; Hoyt, David W; Nicora, Carrie D; Kinmonth-Schultz, Hannah A; Ward, Joy K; Bingol, Kerem

    2018-01-17

    We introduce a cheminformatics approach that combines highly selective and orthogonal structure elucidation parameters; accurate mass, MS/MS (MS²), and NMR into a single analysis platform to accurately identify unknown metabolites in untargeted studies. The approach starts with an unknown LC-MS feature, and then combines the experimental MS/MS and NMR information of the unknown to effectively filter out the false positive candidate structures based on their predicted MS/MS and NMR spectra. We demonstrate the approach on a model mixture, and then we identify an uncatalogued secondary metabolite in Arabidopsis thaliana . The NMR/MS² approach is well suited to the discovery of new metabolites in plant extracts, microbes, soils, dissolved organic matter, food extracts, biofuels, and biomedical samples, facilitating the identification of metabolites that are not present in experimental NMR and MS metabolomics databases.

  5. Multistrain models predict sequential multidrug treatment strategies to result in less antimicrobial resistance than combination treatment

    DEFF Research Database (Denmark)

    Ahmad, Amais; Zachariasen, Camilla; Christiansen, Lasse Engbo

    2016-01-01

    generated by a mathematical model of the competitive growth of multiple strains of Escherichia coli.Results: Simulation studies showed that sequential use of tetracycline and ampicillin reduced the level of double resistance, when compared to the combination treatment. The effect of the cycling frequency...... frequency did not play a role in suppressing the growth of resistant strains, but the specific order of the two antimicrobials did. Predictions made from the study could be used to redesign multidrug treatment strategies not only for intramuscular treatment in pigs, but also for other dosing routes.......Background: Combination treatment is increasingly used to fight infections caused by bacteria resistant to two or more antimicrobials. While multiple studies have evaluated treatment strategies to minimize the emergence of resistant strains for single antimicrobial treatment, fewer studies have...

  6. Prediction of Effective Drug Combinations by an Improved Naïve Bayesian Algorithm.

    Science.gov (United States)

    Bai, Li-Yue; Dai, Hao; Xu, Qin; Junaid, Muhammad; Peng, Shao-Liang; Zhu, Xiaolei; Xiong, Yi; Wei, Dong-Qing

    2018-02-05

    Drug combinatorial therapy is a promising strategy for combating complex diseases due to its fewer side effects, lower toxicity and better efficacy. However, it is not feasible to determine all the effective drug combinations in the vast space of possible combinations given the increasing number of approved drugs in the market, since the experimental methods for identification of effective drug combinations are both labor- and time-consuming. In this study, we conducted systematic analysis of various types of features to characterize pairs of drugs. These features included information about the targets of the drugs, the pathway in which the target protein of a drug was involved in, side effects of drugs, metabolic enzymes of the drugs, and drug transporters. The latter two features (metabolic enzymes and drug transporters) were related to the metabolism and transportation properties of drugs, which were not analyzed or used in previous studies. Then, we devised a novel improved naïve Bayesian algorithm to construct classification models to predict effective drug combinations by using the individual types of features mentioned above. Our results indicated that the performance of our proposed method was indeed better than the naïve Bayesian algorithm and other conventional classification algorithms such as support vector machine and K-nearest neighbor.

  7. Prediction of Effective Drug Combinations by an Improved Naïve Bayesian Algorithm

    Directory of Open Access Journals (Sweden)

    Li-Yue Bai

    2018-02-01

    Full Text Available Drug combinatorial therapy is a promising strategy for combating complex diseases due to its fewer side effects, lower toxicity and better efficacy. However, it is not feasible to determine all the effective drug combinations in the vast space of possible combinations given the increasing number of approved drugs in the market, since the experimental methods for identification of effective drug combinations are both labor- and time-consuming. In this study, we conducted systematic analysis of various types of features to characterize pairs of drugs. These features included information about the targets of the drugs, the pathway in which the target protein of a drug was involved in, side effects of drugs, metabolic enzymes of the drugs, and drug transporters. The latter two features (metabolic enzymes and drug transporters were related to the metabolism and transportation properties of drugs, which were not analyzed or used in previous studies. Then, we devised a novel improved naïve Bayesian algorithm to construct classification models to predict effective drug combinations by using the individual types of features mentioned above. Our results indicated that the performance of our proposed method was indeed better than the naïve Bayesian algorithm and other conventional classification algorithms such as support vector machine and K-nearest neighbor.

  8. Modeling plant interspecific interactions from experiments with perennial crop mixtures to predict optimal combinations.

    Science.gov (United States)

    Halty, Virginia; Valdés, Matías; Tejera, Mauricio; Picasso, Valentín; Fort, Hugo

    2017-12-01

    The contribution of plant species richness to productivity and ecosystem functioning is a longstanding issue in ecology, with relevant implications for both conservation and agriculture. Both experiments and quantitative modeling are fundamental to the design of sustainable agroecosystems and the optimization of crop production. We modeled communities of perennial crop mixtures by using a generalized Lotka-Volterra model, i.e., a model such that the interspecific interactions are more general than purely competitive. We estimated model parameters -carrying capacities and interaction coefficients- from, respectively, the observed biomass of monocultures and bicultures measured in a large diversity experiment of seven perennial forage species in Iowa, United States. The sign and absolute value of the interaction coefficients showed that the biological interactions between species pairs included amensalism, competition, and parasitism (asymmetric positive-negative interaction), with various degrees of intensity. We tested the model fit by simulating the combinations of more than two species and comparing them with the polycultures experimental data. Overall, theoretical predictions are in good agreement with the experiments. Using this model, we also simulated species combinations that were not sown. From all possible mixtures (sown and not sown) we identified which are the most productive species combinations. Our results demonstrate that a combination of experiments and modeling can contribute to the design of sustainable agricultural systems in general and to the optimization of crop production in particular. © 2017 by the Ecological Society of America.

  9. A Novel Risk prediction Model for Patients with Combined Hepatocellular-Cholangiocarcinoma.

    Science.gov (United States)

    Tian, Meng-Xin; He, Wen-Jun; Liu, Wei-Ren; Yin, Jia-Cheng; Jin, Lei; Tang, Zheng; Jiang, Xi-Fei; Wang, Han; Zhou, Pei-Yun; Tao, Chen-Yang; Ding, Zhen-Bin; Peng, Yuan-Fei; Dai, Zhi; Qiu, Shuang-Jian; Zhou, Jian; Fan, Jia; Shi, Ying-Hong

    2018-01-01

    Backgrounds: Regarding the difficulty of CHC diagnosis and potential adverse outcomes or misuse of clinical therapies, an increasing number of patients have undergone liver transplantation, transcatheter arterial chemoembolization (TACE) or other treatments. Objective: To construct a convenient and reliable risk prediction model for identifying high-risk individuals with combined hepatocellular-cholangiocarcinoma (CHC). Methods: 3369 patients who underwent surgical resection for liver cancer at Zhongshan Hospital were enrolled in this study. The epidemiological and clinical characteristics of the patients were collected at the time of tumor diagnosis. Variables ( P model discrimination. Calibration was performed using the Hosmer-Lemeshow test and a calibration curve. Internal validation was performed using a bootstrapping approach. Results: Among the entire study population, 250 patients (7.42%) were pathologically defined with CHC. Age, HBcAb, red blood cells (RBC), blood urea nitrogen (BUN), AFP, CEA and portal vein tumor thrombus (PVTT) were included in the final risk prediction model (area under the curve, 0.69; 95% confidence interval, 0.51-0.77). Bootstrapping validation presented negligible optimism. When the risk threshold of the prediction model was set at 20%, 2.73% of the patients diagnosed with liver cancer would be diagnosed definitely, which could identify CHC patients with 12.40% sensitivity, 98.04% specificity, and a positive predictive value of 33.70%. Conclusions: Herein, the study established a risk prediction model which incorporates the clinical risk predictors and CT/MRI-presented PVTT status that could be adopted to facilitate the diagnosis of CHC patients preoperatively.

  10. Short-term traffic flow prediction model using particle swarm optimization–based combined kernel function-least squares support vector machine combined with chaos theory

    Directory of Open Access Journals (Sweden)

    Qiang Shang

    2016-08-01

    Full Text Available Short-term traffic flow prediction is an important part of intelligent transportation systems research and applications. For further improving the accuracy of short-time traffic flow prediction, a novel hybrid prediction model (multivariate phase space reconstruction–combined kernel function-least squares support vector machine based on multivariate phase space reconstruction and combined kernel function-least squares support vector machine is proposed. The C-C method is used to determine the optimal time delay and the optimal embedding dimension of traffic variables’ (flow, speed, and occupancy time series for phase space reconstruction. The G-P method is selected to calculate the correlation dimension of attractor which is an important index for judging chaotic characteristics of the traffic variables’ series. The optimal input form of combined kernel function-least squares support vector machine model is determined by multivariate phase space reconstruction, and the model’s parameters are optimized by particle swarm optimization algorithm. Finally, case validation is carried out using the measured data of an expressway in Xiamen, China. The experimental results suggest that the new proposed model yields better predictions compared with similar models (combined kernel function-least squares support vector machine, multivariate phase space reconstruction–generalized kernel function-least squares support vector machine, and phase space reconstruction–combined kernel function-least squares support vector machine, which indicates that the new proposed model exhibits stronger prediction ability and robustness.

  11. A Combined High and Low Cycle Fatigue Model for Life Prediction of Turbine Blades

    Directory of Open Access Journals (Sweden)

    Shun-Peng Zhu

    2017-06-01

    Full Text Available Combined high and low cycle fatigue (CCF generally induces the failure of aircraft gas turbine attachments. Based on the aero-engine load spectrum, accurate assessment of fatigue damage due to the interaction of high cycle fatigue (HCF resulting from high frequency vibrations and low cycle fatigue (LCF from ground-air-ground engine cycles is of critical importance for ensuring structural integrity of engine components, like turbine blades. In this paper, the influence of combined damage accumulation on the expected CCF life are investigated for turbine blades. The CCF behavior of a turbine blade is usually studied by testing with four load-controlled parameters, including high cycle stress amplitude and frequency, and low cycle stress amplitude and frequency. According to this, a new damage accumulation model is proposed based on Miner’s rule to consider the coupled damage due to HCF-LCF interaction by introducing the four load parameters. Five experimental datasets of turbine blade alloys and turbine blades were introduced for model validation and comparison between the proposed Miner, Manson-Halford, and Trufyakov-Kovalchuk models. Results show that the proposed model provides more accurate predictions than others with lower mean and standard deviation values of model prediction errors.

  12. Combined visual and motor evoked potentials predict multiple sclerosis disability after 20 years.

    Science.gov (United States)

    Schlaeger, Regina; Schindler, Christian; Grize, Leticia; Dellas, Sophie; Radue, Ernst W; Kappos, Ludwig; Fuhr, Peter

    2014-09-01

    The development of predictors of multiple sclerosis (MS) disability is difficult due to the complex interplay of pathophysiological and adaptive processes. The purpose of this study was to investigate whether combined evoked potential (EP)-measures allow prediction of MS disability after 20 years. We examined 28 patients with clinically definite MS according to Poser's criteria with Expanded Disability Status Scale (EDSS) scores, combined visual and motor EPs at entry (T0), 6 (T1), 12 (T2) and 24 (T3) months, and a cranial magnetic resonance imaging (MRI) scan at T0 and T2. EDSS testing was repeated at year 14 (T4) and year 20 (T5). Spearman rank correlation was used. We performed a multivariable regression analysis to examine predictive relationships of the sum of z-transformed EP latencies (s-EPT0) and other baseline variables with EDSST5. We found that s-EPT0 correlated with EDSST5 (rho=0.72, pdisability in MS. © The Author(s) 2014.

  13. Prediction of UT1-UTC, LOD and AAM χ3 by combination of least-squares and multivariate stochastic methods

    Science.gov (United States)

    Niedzielski, Tomasz; Kosek, Wiesław

    2008-02-01

    This article presents the application of a multivariate prediction technique for predicting universal time (UT1-UTC), length of day (LOD) and the axial component of atmospheric angular momentum (AAM χ 3). The multivariate predictions of LOD and UT1-UTC are generated by means of the combination of (1) least-squares (LS) extrapolation of models for annual, semiannual, 18.6-year, 9.3-year oscillations and for the linear trend, and (2) multivariate autoregressive (MAR) stochastic prediction of LS residuals (LS + MAR). The MAR technique enables the use of the AAM χ 3 time-series as the explanatory variable for the computation of LOD or UT1-UTC predictions. In order to evaluate the performance of this approach, two other prediction schemes are also applied: (1) LS extrapolation, (2) combination of LS extrapolation and univariate autoregressive (AR) prediction of LS residuals (LS + AR). The multivariate predictions of AAM χ 3 data, however, are computed as a combination of the extrapolation of the LS model for annual and semiannual oscillations and the LS + MAR. The AAM χ 3 predictions are also compared with LS extrapolation and LS + AR prediction. It is shown that the predictions of LOD and UT1-UTC based on LS + MAR taking into account the axial component of AAM are more accurate than the predictions of LOD and UT1-UTC based on LS extrapolation or on LS + AR. In particular, the UT1-UTC predictions based on LS + MAR during El Niño/La Niña events exhibit considerably smaller prediction errors than those calculated by means of LS or LS + AR. The AAM χ 3 time-series is predicted using LS + MAR with higher accuracy than applying LS extrapolation itself in the case of medium-term predictions (up to 100 days in the future). However, the predictions of AAM χ 3 reveal the best accuracy for LS + AR.

  14. Combining Satellite Measurements and Numerical Flood Prediction Models to Save Lives and Property from Flooding

    Science.gov (United States)

    Saleh, F.; Garambois, P. A.; Biancamaria, S.

    2017-12-01

    Floods are considered the major natural threats to human societies across all continents. Consequences of floods in highly populated areas are more dramatic with losses of human lives and substantial property damage. This risk is projected to increase with the effects of climate change, particularly sea-level rise, increasing storm frequencies and intensities and increasing population and economic assets in such urban watersheds. Despite the advances in computational resources and modeling techniques, significant gaps exist in predicting complex processes and accurately representing the initial state of the system. Improving flood prediction models and data assimilation chains through satellite has become an absolute priority to produce accurate flood forecasts with sufficient lead times. The overarching goal of this work is to assess the benefits of the Surface Water Ocean Topography SWOT satellite data from a flood prediction perspective. The near real time methodology is based on combining satellite data from a simulator that mimics the future SWOT data, numerical models, high resolution elevation data and real-time local measurement in the New York/New Jersey area.

  15. Handling imbalance data in churn prediction using combined SMOTE and RUS with bagging method

    Science.gov (United States)

    Pura Hartati, Eka; Adiwijaya; Arif Bijaksana, Moch

    2018-03-01

    Customer churn has become a significant problem and also a challenge for Telecommunication company such as PT. Telkom Indonesia. It is necessary to evaluate whether the big problems of churn customer and the company’s managements will make appropriate strategies to minimize the churn and retaining the customer. Churn Customer data which categorized churn Atas Permintaan Sendiri (APS) in this Company is an imbalance data, and this issue is one of the challenging tasks in machine learning. This study will investigate how is handling class imbalance in churn prediction using combined Synthetic Minority Over-Sampling (SMOTE) and Random Under-Sampling (RUS) with Bagging method for a better churn prediction performance’s result. The dataset that used is Broadband Internet data which is collected from Telkom Regional 6 Kalimantan. The research firstly using data preprocessing to balance the imbalanced dataset and also to select features by sampling technique SMOTE and RUS, and then building churn prediction model using Bagging methods and C4.5.

  16. Exploring the Combination of Dempster-Shafer Theory and Neural Network for Predicting Trust and Distrust

    Directory of Open Access Journals (Sweden)

    Xin Wang

    2016-01-01

    Full Text Available In social media, trust and distrust among users are important factors in helping users make decisions, dissect information, and receive recommendations. However, the sparsity and imbalance of social relations bring great difficulties and challenges in predicting trust and distrust. Meanwhile, there are numerous inducing factors to determine trust and distrust relations. The relationship among inducing factors may be dependency, independence, and conflicting. Dempster-Shafer theory and neural network are effective and efficient strategies to deal with these difficulties and challenges. In this paper, we study trust and distrust prediction based on the combination of Dempster-Shafer theory and neural network. We firstly analyze the inducing factors about trust and distrust, namely, homophily, status theory, and emotion tendency. Then, we quantify inducing factors of trust and distrust, take these features as evidences, and construct evidence prototype as input nodes of multilayer neural network. Finally, we propose a framework of predicting trust and distrust which uses multilayer neural network to model the implementing process of Dempster-Shafer theory in different hidden layers, aiming to overcome the disadvantage of Dempster-Shafer theory without optimization method. Experimental results on a real-world dataset demonstrate the effectiveness of the proposed framework.

  17. Predictive inference for best linear combination of biomarkers subject to limits of detection.

    Science.gov (United States)

    Coolen-Maturi, Tahani

    2017-08-15

    Measuring the accuracy of diagnostic tests is crucial in many application areas including medicine, machine learning and credit scoring. The receiver operating characteristic (ROC) curve is a useful tool to assess the ability of a diagnostic test to discriminate between two classes or groups. In practice, multiple diagnostic tests or biomarkers are combined to improve diagnostic accuracy. Often, biomarker measurements are undetectable either below or above the so-called limits of detection (LoD). In this paper, nonparametric predictive inference (NPI) for best linear combination of two or more biomarkers subject to limits of detection is presented. NPI is a frequentist statistical method that is explicitly aimed at using few modelling assumptions, enabled through the use of lower and upper probabilities to quantify uncertainty. The NPI lower and upper bounds for the ROC curve subject to limits of detection are derived, where the objective function to maximize is the area under the ROC curve. In addition, the paper discusses the effect of restriction on the linear combination's coefficients on the analysis. Examples are provided to illustrate the proposed method. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

  18. Predictions of new AB O3 perovskite compounds by combining machine learning and density functional theory

    Science.gov (United States)

    Balachandran, Prasanna V.; Emery, Antoine A.; Gubernatis, James E.; Lookman, Turab; Wolverton, Chris; Zunger, Alex

    2018-04-01

    We apply machine learning (ML) methods to a database of 390 experimentally reported A B O3 compounds to construct two statistical models that predict possible new perovskite materials and possible new cubic perovskites. The first ML model classified the 390 compounds into 254 perovskites and 136 that are not perovskites with a 90% average cross-validation (CV) accuracy; the second ML model further classified the perovskites into 22 known cubic perovskites and 232 known noncubic perovskites with a 94% average CV accuracy. We find that the most effective chemical descriptors affecting our classification include largely geometric constructs such as the A and B Shannon ionic radii, the tolerance and octahedral factors, the A -O and B -O bond length, and the A and B Villars' Mendeleev numbers. We then construct an additional list of 625 A B O3 compounds assembled from charge conserving combinations of A and B atoms absent from our list of known compounds. Then, using the two ML models constructed on the known compounds, we predict that 235 of the 625 exist in a perovskite structure with a confidence greater than 50% and among them that 20 exist in the cubic structure (albeit, the latter with only ˜50 % confidence). We find that the new perovskites are most likely to occur when the A and B atoms are a lanthanide or actinide, when the A atom is an alkali, alkali earth, or late transition metal atom, or when the B atom is a p -block atom. We also compare the ML findings with the density functional theory calculations and convex hull analyses in the Open Quantum Materials Database (OQMD), which predicts the T =0 K ground-state stability of all the A B O3 compounds. We find that OQMD predicts 186 of 254 of the perovskites in the experimental database to be thermodynamically stable within 100 meV/atom of the convex hull and predicts 87 of the 235 ML-predicted perovskite compounds to be thermodynamically stable within 100 meV/atom of the convex hull, including 6 of these to

  19. Combining multiple regression and principal component analysis for accurate predictions for column ozone in Peninsular Malaysia

    Science.gov (United States)

    Rajab, Jasim M.; MatJafri, M. Z.; Lim, H. S.

    2013-06-01

    This study encompasses columnar ozone modelling in the peninsular Malaysia. Data of eight atmospheric parameters [air surface temperature (AST), carbon monoxide (CO), methane (CH4), water vapour (H2Ovapour), skin surface temperature (SSKT), atmosphere temperature (AT), relative humidity (RH), and mean surface pressure (MSP)] data set, retrieved from NASA's Atmospheric Infrared Sounder (AIRS), for the entire period (2003-2008) was employed to develop models to predict the value of columnar ozone (O3) in study area. The combined method, which is based on using both multiple regressions combined with principal component analysis (PCA) modelling, was used to predict columnar ozone. This combined approach was utilized to improve the prediction accuracy of columnar ozone. Separate analysis was carried out for north east monsoon (NEM) and south west monsoon (SWM) seasons. The O3 was negatively correlated with CH4, H2Ovapour, RH, and MSP, whereas it was positively correlated with CO, AST, SSKT, and AT during both the NEM and SWM season periods. Multiple regression analysis was used to fit the columnar ozone data using the atmospheric parameter's variables as predictors. A variable selection method based on high loading of varimax rotated principal components was used to acquire subsets of the predictor variables to be comprised in the linear regression model of the atmospheric parameter's variables. It was found that the increase in columnar O3 value is associated with an increase in the values of AST, SSKT, AT, and CO and with a drop in the levels of CH4, H2Ovapour, RH, and MSP. The result of fitting the best models for the columnar O3 value using eight of the independent variables gave about the same values of the R (≈0.93) and R2 (≈0.86) for both the NEM and SWM seasons. The common variables that appeared in both regression equations were SSKT, CH4 and RH, and the principal precursor of the columnar O3 value in both the NEM and SWM seasons was SSKT.

  20. Performance of third-trimester combined screening model for prediction of adverse perinatal outcome.

    Science.gov (United States)

    Miranda, J; Triunfo, S; Rodriguez-Lopez, M; Sairanen, M; Kouru, H; Parra-Saavedra, M; Crovetto, F; Figueras, F; Crispi, F; Gratacós, E

    2017-09-01

    To explore the potential value of third-trimester combined screening for the prediction of adverse perinatal outcome (APO) in the general population and among small-for-gestational-age (SGA) fetuses. This was a nested case-control study within a prospective cohort of 1590 singleton gestations undergoing third-trimester evaluation (32 + 0 to 36 + 6 weeks' gestation). Maternal baseline characteristics, mean arterial blood pressure, fetoplacental ultrasound and circulating biochemical markers (placental growth factor (PlGF), lipocalin-2, unconjugated estriol and inhibin A) were assessed in all women who subsequently had an APO (n = 148) and in a control group without perinatal complications (n = 902). APO was defined as the occurrence of stillbirth, umbilical artery cord blood pH < 7.15, 5-min Apgar score < 7 or emergency operative delivery for fetal distress. Logistic regression models were developed for the prediction of APO in the general population and among SGA cases (defined as customized birth weight < 10 th centile). The prevalence of APO was 9.3% in the general population and 27.4% among SGA cases. In the general population, a combined screening model including a-priori risk (maternal characteristics), estimated fetal weight (EFW) centile, umbilical artery pulsatility index (UA-PI), estriol and PlGF achieved a detection rate for APO of 26% (area under receiver-operating characteristics curve (AUC), 0.59 (95% CI, 0.54-0.65)), at a 10% false-positive rate (FPR). Among SGA cases, a model including a-priori risk, EFW centile, UA-PI, cerebroplacental ratio, estriol and PlGF predicted 62% of APO (AUC, 0.86 (95% CI, 0.80-0.92)) at a FPR of 10%. The use of fetal ultrasound and maternal biochemical markers at 32-36 weeks provides a poor prediction of APO in the general population. Although it remains limited, the performance of the screening model is improved when applied to fetuses with suboptimal fetal growth. Copyright © 2016 ISUOG. Published by John Wiley & Sons

  1. Combining Pathway Identification and Breast Cancer Survival Prediction via Screening-Network Methods

    Directory of Open Access Journals (Sweden)

    Antonella Iuliano

    2018-06-01

    Full Text Available Breast cancer is one of the most common invasive tumors causing high mortality among women. It is characterized by high heterogeneity regarding its biological and clinical characteristics. Several high-throughput assays have been used to collect genome-wide information for many patients in large collaborative studies. This knowledge has improved our understanding of its biology and led to new methods of diagnosing and treating the disease. In particular, system biology has become a valid approach to obtain better insights into breast cancer biological mechanisms. A crucial component of current research lies in identifying novel biomarkers that can be predictive for breast cancer patient prognosis on the basis of the molecular signature of the tumor sample. However, the high dimension and low sample size of data greatly increase the difficulty of cancer survival analysis demanding for the development of ad-hoc statistical methods. In this work, we propose novel screening-network methods that predict patient survival outcome by screening key survival-related genes and we assess the capability of the proposed approaches using METABRIC dataset. In particular, we first identify a subset of genes by using variable screening techniques on gene expression data. Then, we perform Cox regression analysis by incorporating network information associated with the selected subset of genes. The novelty of this work consists in the improved prediction of survival responses due to the different types of screenings (i.e., a biomedical-driven, data-driven and a combination of the two before building the network-penalized model. Indeed, the combination of the two screening approaches allows us to use the available biological knowledge on breast cancer and complement it with additional information emerging from the data used for the analysis. Moreover, we also illustrate how to extend the proposed approaches to integrate an additional omic layer, such as copy number

  2. Model predictive control system and method for integrated gasification combined cycle power generation

    Science.gov (United States)

    Kumar, Aditya; Shi, Ruijie; Kumar, Rajeeva; Dokucu, Mustafa

    2013-04-09

    Control system and method for controlling an integrated gasification combined cycle (IGCC) plant are provided. The system may include a controller coupled to a dynamic model of the plant to process a prediction of plant performance and determine a control strategy for the IGCC plant over a time horizon subject to plant constraints. The control strategy may include control functionality to meet a tracking objective and control functionality to meet an optimization objective. The control strategy may be configured to prioritize the tracking objective over the optimization objective based on a coordinate transformation, such as an orthogonal or quasi-orthogonal projection. A plurality of plant control knobs may be set in accordance with the control strategy to generate a sequence of coordinated multivariable control inputs to meet the tracking objective and the optimization objective subject to the prioritization resulting from the coordinate transformation.

  3. Prediction of ash deposition using CFD simulation combined to thermodynamic calculation

    Energy Technology Data Exchange (ETDEWEB)

    Takeshi Muratani; Takashi Hongo [UBE Industries, Ltd., Yamaguchi (Japan). Coal Department, Energy and Environment Division

    2007-07-01

    This study focused on the advanced ash deposition prediction using computational fluid dynamics (CFD) analysis combined to thermodynamic calculation, considering both combustion characteristics and ash fusibility. Combustion field in pulverised coal-fired boiler was calculated through the normal CFD process. As the post process of combustion calculation, ash particles were injected into the combustion field to calculate ash deposition by CFD, in which particle sticking sub-program was newly employed. In this post process, ash deposition condition for CFD calculation was defined with the ash fusibility data obtained from thermodynamic analysis. These results of ash deposition on the furnace wall showed good agreement with the plant observation. Furthermore, in order to improve the plant operation, some virtual cases were simulated, which might reduce ash deposition. 7 refs., 14 figs., 6 tabs.

  4. Prognostic durability of liver fibrosis tests and improvement in predictive performance for mortality by combining tests.

    Science.gov (United States)

    Bertrais, Sandrine; Boursier, Jérôme; Ducancelle, Alexandra; Oberti, Frédéric; Fouchard-Hubert, Isabelle; Moal, Valérie; Calès, Paul

    2017-06-01

    There is currently no recommended time interval between noninvasive fibrosis measurements for monitoring chronic liver diseases. We determined how long a single liver fibrosis evaluation may accurately predict mortality, and assessed whether combining tests improves prognostic performance. We included 1559 patients with chronic liver disease and available baseline liver stiffness measurement (LSM) by Fibroscan, aspartate aminotransferase to platelet ratio index (APRI), FIB-4, Hepascore, and FibroMeter V2G . Median follow-up was 2.8 years during which 262 (16.8%) patients died, with 115 liver-related deaths. All fibrosis tests were able to predict mortality, although APRI (and FIB-4 for liver-related mortality) showed lower overall discriminative ability than the other tests (differences in Harrell's C-index: P fibrosis, 1 year in patients with significant fibrosis, and liver disease (MELD) score testing sets. In the training set, blood tests and LSM were independent predictors of all-cause mortality. The best-fit multivariate model included age, sex, LSM, and FibroMeter V2G with C-index = 0.834 (95% confidence interval, 0.803-0.862). The prognostic model for liver-related mortality included the same covariates with C-index = 0.868 (0.831-0.902). In the testing set, the multivariate models had higher prognostic accuracy than FibroMeter V2G or LSM alone for all-cause mortality and FibroMeter V2G alone for liver-related mortality. The prognostic durability of a single baseline fibrosis evaluation depends on the liver fibrosis level. Combining LSM with a blood fibrosis test improves mortality risk assessment. © 2016 Journal of Gastroenterology and Hepatology Foundation and John Wiley & Sons Australia, Ltd.

  5. Combined Molecular Dynamics Simulation-Molecular-Thermodynamic Theory Framework for Predicting Surface Tensions.

    Science.gov (United States)

    Sresht, Vishnu; Lewandowski, Eric P; Blankschtein, Daniel; Jusufi, Arben

    2017-08-22

    A molecular modeling approach is presented with a focus on quantitative predictions of the surface tension of aqueous surfactant solutions. The approach combines classical Molecular Dynamics (MD) simulations with a molecular-thermodynamic theory (MTT) [ Y. J. Nikas, S. Puvvada, D. Blankschtein, Langmuir 1992 , 8 , 2680 ]. The MD component is used to calculate thermodynamic and molecular parameters that are needed in the MTT model to determine the surface tension isotherm. The MD/MTT approach provides the important link between the surfactant bulk concentration, the experimental control parameter, and the surfactant surface concentration, the MD control parameter. We demonstrate the capability of the MD/MTT modeling approach on nonionic alkyl polyethylene glycol surfactants at the air-water interface and observe reasonable agreement of the predicted surface tensions and the experimental surface tension data over a wide range of surfactant concentrations below the critical micelle concentration. Our modeling approach can be extended to ionic surfactants and their mixtures with both ionic and nonionic surfactants at liquid-liquid interfaces.

  6. Combining a weed traits database with a population dynamics model predicts shifts in weed communities.

    Science.gov (United States)

    Storkey, J; Holst, N; Bøjer, O Q; Bigongiali, F; Bocci, G; Colbach, N; Dorner, Z; Riemens, M M; Sartorato, I; Sønderskov, M; Verschwele, A

    2015-04-01

    A functional approach to predicting shifts in weed floras in response to management or environmental change requires the combination of data on weed traits with analytical frameworks that capture the filtering effect of selection pressures on traits. A weed traits database (WTDB) was designed, populated and analysed, initially using data for 19 common European weeds, to begin to consolidate trait data in a single repository. The initial choice of traits was driven by the requirements of empirical models of weed population dynamics to identify correlations between traits and model parameters. These relationships were used to build a generic model, operating at the level of functional traits, to simulate the impact of increasing herbicide and fertiliser use on virtual weeds along gradients of seed weight and maximum height. The model generated 'fitness contours' (defined as population growth rates) within this trait space in different scenarios, onto which two sets of weed species, defined as common or declining in the UK, were mapped. The effect of increasing inputs on the weed flora was successfully simulated; 77% of common species were predicted to have stable or increasing populations under high fertiliser and herbicide use, in contrast with only 29% of the species that have declined. Future development of the WTDB will aim to increase the number of species covered, incorporate a wider range of traits and analyse intraspecific variability under contrasting management and environments.

  7. Taller-than-wide sign for predicting thyroid microcarcinoma: comparison and combination of two ultrasonographic planes.

    Science.gov (United States)

    Chen, Shun-Ping; Hu, Yuan-Ping; Chen, Bin

    2014-09-01

    The aims of this study were to investigate the accuracy of using the taller-than-wide (TTW) sign in two ultrasonographic planes to predict thyroid microcarcinoma, and to confirm the hypothesis that the presence of a TTW sign in both the transverse and longitudinal ultrasonographic planes strongly suggests thyroid microcarcinoma. Nine hundred forty-two thyroid nodules ≤1 cm were submitted to surgical-histopathologic and ultrasonographic examination. TTW signs were divided into three types based on their detection only in the transverse plane (TTTW type, n = 100), only in the longitudinal plane (LTTW type, n = 61) or in both planes (BTTW type, n = 131). The areas under the receiver operating characteristic curves (A(z)) for the three different TTW signs, as well as for the combination of all TTW signs (ATTW, n = 292), were compared. The results indicated that the A(z) values of the TTTW, LTTW, BTTW and ATTW signs in predicting thyroid microcarcinoma were 0.544, 0.531, 0.627 and 0.702, respectively. The ATTW sign was the most accurate (p 0.05). Therefore, both the LTTW and TTTW signs are reliable markers of thyroid microcarcinoma. The BTTW sign strongly suggests thyroid microcarcinoma. Copyright © 2014 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.

  8. Pressure and fluid saturation prediction in a multicomponent reservoir, using combined seismic and electromagnetic imaging

    International Nuclear Information System (INIS)

    Hoversten, G.M.; Gritto, Roland; Washbourne, John; Daley, Tom

    2002-01-01

    This paper presents a method for combining seismic and electromagnetic measurements to predict changes in water saturation, pressure, and CO 2 gas/oil ratio in a reservoir undergoing CO 2 flood. Crosswell seismic and electromagnetic data sets taken before and during CO 2 flooding of an oil reservoir are inverted to produce crosswell images of the change in compressional velocity, shear velocity, and electrical conductivity during a CO 2 injection pilot study. A rock properties model is developed using measured log porosity, fluid saturations, pressure, temperature, bulk density, sonic velocity, and electrical conductivity. The parameters of the rock properties model are found by an L1-norm simplex minimization of predicted and observed differences in compressional velocity and density. A separate minimization, using Archie's law, provides parameters for modeling the relations between water saturation, porosity, and the electrical conductivity. The rock-properties model is used to generate relationships between changes in geophysical parameters and changes in reservoir parameters. Electrical conductivity changes are directly mapped to changes in water saturation; estimated changes in water saturation are used along with the observed changes in shear wave velocity to predict changes in reservoir pressure. The estimation of the spatial extent and amount of CO 2 relies on first removing the effects of the water saturation and pressure changes from the observed compressional velocity changes, producing a residual compressional velocity change. This velocity change is then interpreted in terms of increases in the CO 2 /oil ratio. Resulting images of the CO 2 /oil ratio show CO 2 -rich zones that are well correlated to the location of injection perforations, with the size of these zones also correlating to the amount of injected CO 2 . The images produced by this process are better correlated to the location and amount of injected CO 2 than are any of the individual

  9. Combining Spatial Models for Shallow Landslides and Debris-Flows Prediction

    Directory of Open Access Journals (Sweden)

    Eurípedes Vargas do Amaral

    2013-05-01

    Full Text Available Mass movements in Brazil are common phenomena, especially during strong rainfall events that occur frequently in the summer season. These phenomena cause losses of lives and serious damage to roads, bridges, and properties. Moreover, the illegal occupation by slums on the slopes around the cities intensifies the effect of the mass movement. This study aimed to develop a methodology that combines models of shallow landslides and debris-flows in order to create a map with landslides initiation and debris-flows volume and runout distance. The study area comprised of two catchments in Rio de Janeiro city: Quitite and Papagaio that drained side by side the west flank of the Maciço da Tijuca, with an area of 5 km2. The method included the following steps: (a location of the susceptible areas to landslides using SHALSTAB model; (b determination of rheological parameters of debris-flow from the back-analysis technique; and (c combination of SHALSTAB and FLO-2D models to delineate the areas more susceptible to mass movements. These scenarios were compared with the landslide and debris-flow event of February 1996. Many FLO-2D simulations were exhaustively made to estimate the rheological parameters from the back-analysis technique. Those rheological coefficients of single simulation were back-calculated by adjusting with area and depth of the debris-flow obtained from field data. The initial material volume in the FLO-2D simulations was estimated from SHALSTAB model. The combination of these two mathematical models, SHALSTAB and FLO-2D, was able to predict both landslides and debris-flow events. Such procedures can reduce the casualties and property damage, delineating hazard areas, to estimate hazard intensities for input into risk studies providing information for public policy and planning.

  10. Developing a theoretical predictive model for cellular response to combined actions of low radiation and hyperthermia

    International Nuclear Information System (INIS)

    Jin Kyu Kim; Petin, V.G.; Mishra, K.P.

    2007-01-01

    Complete text of publication follows. Background: Organisms in their living environment are not exposed to merely a single stress agent. Several factors such as radiation and heat may simultaneously exert their stressful effect to the organisms. The combined exposure to two stressors can result in an enhanced effect that would be expected from the addition of the separate exposures to individual agents. Objective: This study has been undertaken to develop a theoretical model for assessment of combined effects of low dose radiation and mild heat for predictive cellular response assay. Rationale: Present study was motivated from the belief that synergism may occur in terms of lethal lesions arising from the interaction of non-lethal sub-lesions induced by individual agents. The sub-lesions induced by each agent may be negligible or undetectable. But, there exists a possibility of some cross talk between sublesions produced by radiation and heat. These processes may reflect the real mechanisms for inflicting the lethal damage by otherwise ignorable or undetectable insults to exposed organisms. Results: A theoretically developed mathematical model of the synergy was formulated which was tested for validation on the experimental data. The model predictions fairly closely corresponded with several experimental results. .The significance of synergistic effects for radiation biology has been demonstrated. A number of common peculiarities of synergistic interactions were found to play their roles. A unified biophysical concept for synergistic interaction has been suggested. Conclusions: For a constant dose rate, synergistic interaction between radiation and hyperthermia especially at low intensity is realized only within a certain range of temperature, independently of the target object analyzed. For temperatures below the range, the synergistic effect was not observed and cell killing was mainly determined by the damage induced by ionizing radiation. On the contrary, the

  11. Combined-modality treatment and organ preservation in bladder cancer. Do molecular markers predict outcome?

    International Nuclear Information System (INIS)

    Weiss, C.; Roedel, F.; Wolf, I.; Sauer, R.; Roedel, C.; Papadopoulos, T.; Engehausen, D.G.; Schrott, K.M.

    2005-01-01

    Purpose: in invasive bladder cancer, several groups have reported the value of organ preservation by a combined-treatment approach, including transurethral resection (TUR-BT) and radiochemotherapy (RCT). As more experience is acquired with this organ-sparing treatment, patient selection needs to be optimized. Clinical factors are limited in their potential to identify patients most likely to respond to RCT, thus, additional molecular markers for predicting treatment response of individual lesions are sorely needed. Patients and methods: the apoptotic index (AI) and Ki-67 index were evaluated by immunohistochemistry on pretreatment biopsies of 134 patients treated for bladder cancer by TUR-BT and RCT. Expression of each marker as well as clinicopathologic factors were then correlated with initial response, local control and cancer-specific survival with preserved bladder in univariate and multivariate analysis. Results: the median AI for all patients was 1.5% (range 0.2-7.4%). The percentage of Ki-67-positive cells in the tumors ranged from 0.2% to 85% with a median of 14.2%. A significant correlation was found for AI and tumor differentiation (G1/2: AI = 1.3% vs. G3/4: AI = 1.6%; p = 0.01). A complete response at restaging TUR-BT was achieved in 76% of patients. Factors predictive of complete response included T-category (p < 0.0001), resection status (p = 0.02), lymphovascular invasion (p = 0.01), and Ki-67 index (p = 0.02). For local control, AI (p = 0.04) and Ki-67 index (p = 0.05) as well as T-category (p = 0.005), R-status (p = 0.05), and lymphatic vessel invasion (p = 0.05) reached statistical significance. Out of the molecular markers only high Ki-67 levels were associated to cause-specific survival with preserved bladder. On multivariate analysis, T-category was the strongest independent factor for initial response, local control and cancer-specific survival with preserved bladder. Conclusion: The indices of pretreatment apoptosis and Ki-67 predict a

  12. Combining structural modeling with ensemble machine learning to accurately predict protein fold stability and binding affinity effects upon mutation.

    Directory of Open Access Journals (Sweden)

    Niklas Berliner

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

  13. A genetic risk score combining ten psoriasis risk loci improves disease prediction.

    Directory of Open Access Journals (Sweden)

    Haoyan Chen

    2011-04-01

    Full Text Available Psoriasis is a chronic, immune-mediated skin disease affecting 2-3% of Caucasians. Recent genetic association studies have identified multiple psoriasis risk loci; however, most of these loci contribute only modestly to disease risk. In this study, we investigated whether a genetic risk score (GRS combining multiple loci could improve psoriasis prediction. Two approaches were used: a simple risk alleles count (cGRS and a weighted (wGRS approach. Ten psoriasis risk SNPs were genotyped in 2815 case-control samples and 858 family samples. We found that the total number of risk alleles in the cases was significantly higher than in controls, mean 13.16 (SD 1.7 versus 12.09 (SD 1.8, p = 4.577×10(-40. The wGRS captured considerably more risk than any SNP considered alone, with a psoriasis OR for high-low wGRS quartiles of 10.55 (95% CI 7.63-14.57, p = 2.010×10(-65. To compare the discriminatory ability of the GRS models, receiver operating characteristic curves were used to calculate the area under the curve (AUC. The AUC for wGRS was significantly greater than for cGRS (72.0% versus 66.5%, p = 2.13×10(-8. Additionally, the AUC for HLA-C alone (rs10484554 was equivalent to the AUC for all nine other risk loci combined (66.2% versus 63.8%, p = 0.18, highlighting the dominance of HLA-C as a risk locus. Logistic regression revealed that the wGRS was significantly associated with two subphenotypes of psoriasis, age of onset (p = 4.91×10(-6 and family history (p = 0.020. Using a liability threshold model, we estimated that the 10 risk loci account for only 11.6% of the genetic variance in psoriasis. In summary, we found that a GRS combining 10 psoriasis risk loci captured significantly more risk than any individual SNP and was associated with early onset of disease and a positive family history. Notably, only a small fraction of psoriasis heritability is captured by the common risk variants identified to date.

  14. Towards predictive resistance models for agrochemicals by combining chemical and protein similarity via proteochemometric modelling.

    Science.gov (United States)

    van Westen, Gerard J P; Bender, Andreas; Overington, John P

    2014-10-01

    Resistance to pesticides is an increasing problem in agriculture. Despite practices such as phased use and cycling of 'orthogonally resistant' agents, resistance remains a major risk to national and global food security. To combat this problem, there is a need for both new approaches for pesticide design, as well as for novel chemical entities themselves. As summarized in this opinion article, a technique termed 'proteochemometric modelling' (PCM), from the field of chemoinformatics, could aid in the quantification and prediction of resistance that acts via point mutations in the target proteins of an agent. The technique combines information from both the chemical and biological domain to generate bioactivity models across large numbers of ligands as well as protein targets. PCM has previously been validated in prospective, experimental work in the medicinal chemistry area, and it draws on the growing amount of bioactivity information available in the public domain. Here, two potential applications of proteochemometric modelling to agrochemical data are described, based on previously published examples from the medicinal chemistry literature.

  15. An Approach to Flooding Inundation Combining the Streamflow Prediction Tool (SPT) and Downscaled Soil Moisture

    Science.gov (United States)

    Cotterman, K. A.; Follum, M. L.; Pradhan, N. R.; Niemann, J. D.

    2017-12-01

    Flooding impacts numerous aspects of society, from localized flash floods to continental-scale flood events. Many numerical flood models focus solely on riverine flooding, with some capable of capturing both localized and continental-scale flood events. However, these models neglect flooding away from channels that are related to excessive ponding, typically found in areas with flat terrain and poorly draining soils. In order to obtain a holistic view of flooding, we combine flood results from the Streamflow Prediction Tool (SPT), a riverine flood model, with soil moisture downscaling techniques to determine if a better representation of flooding is obtained. This allows for a more holistic understanding of potential flood prone areas, increasing the opportunity for more accurate warnings and evacuations during flooding conditions. Thirty-five years of near-global historical streamflow is reconstructed with continental-scale flow routing of runoff from global land surface models. Elevation data was also obtained worldwide, to establish a relationship between topographic attributes and soil moisture patterns. Derived soil moisture data is validated against observed soil moisture, increasing confidence in the ability to accurately capture soil moisture patterns. Potential flooding situations can be examined worldwide, with this study focusing on the United States, Central America, and the Philippines.

  16. Combined UMC- DFT prediction of electron-hole coupling in unit cells of pentacene crystals.

    Science.gov (United States)

    Leal, Luciano Almeida; de Souza Júnior, Rafael Timóteo; de Almeida Fonseca, Antonio Luciano; Ribeiro Junior, Luiz Antonio; Blawid, Stefan; da Silva Filho, Demetrio Antonio; da Cunha, Wiliam Ferreira

    2017-05-01

    Pentacene is an organic semiconductor that draws special attention from the scientific community due to the high mobility of its charge carriers. As electron-hole interactions are important aspects in the regard of such property, a computationally inexpensive method to predict the coupling between these quasi-particles is highly desired. In this work, we propose a hybrid methodology of combining Uncoupled Monte Carlo Simulations (UMC) and Density functional Theory (DFT) methodologies to obtain a good compromise between computational feasibility and accuracy. As a first step in considering a Pentacene crystal, we describe its unit cell: the Pentacene Dimer. Because many conformations can be encountered for the dimer and considering the complexity of the system, we make use of UMC in order to find the most probable structures and relative orientations for the Pentacene-Pentacene complex. Following, we carry out electronic structure calculations in the scope of DFT with the goal of describing the electron-hole coupling on the most probable configurations obtained by UMC. The comparison of our results with previously reported data on the literature suggests that the methodology is well suited for describing transfer integrals of organic semiconductors. The observed accuracy together with the smaller computational cost required by our approach allows us to conclude that such methodology might be an important tool towards the description of systems with higher complexity.

  17. Multi-parametric MRI in cervical cancer. Early prediction of response to concurrent chemoradiotherapy in combination with clinical prognostic factors

    Energy Technology Data Exchange (ETDEWEB)

    Yang, Wei; Chen, Bing; Wang, Ai Jun; Zhao, Jian Guo [The General Hospital of Ningxia Medical University, Department of Radiology, Yinchuan (China); Qiang, Jin Wei [Fudan University, Department of Radiology, Jinshan Hospital, Shanghai (China); Tian, Hai Ping [The General Hospital of Ningxia Medical University, Department of Pathology, Yinchuan (China)

    2018-01-15

    To investigate the prediction of response to concurrent chemoradiotherapy (CCRT) through a combination of pretreatment multi-parametric magnetic resonance imaging (MRI) with clinical prognostic factors (CPF) in cervical cancer patients. Sixty-five patients underwent conventional MRI, diffusion-weighted imaging (DWI), and dynamic contrast-enhanced MRI (DCE-MRI) before CCRT. The patients were divided into non- and residual tumour groups according to post-treatment MRI. Pretreatment MRI parameters and CPF between the two groups were compared and prognostic factors, optimal thresholds, and predictive performance for post-treatment residual tumour occurrence were estimated. The residual group showed a lower maximum slope of increase (MSI{sub L}) and signal enhancement ratio (SER{sub L}) in low-perfusion subregions, a higher apparent diffusion coefficient (ADC) value, and a higher stage than the non-residual tumour group (p < 0.001, p = 0.003, p < 0.001, and p < 0.001, respectively). MSI{sub L} and ADC were independent prognostic factors. The combination of both measures improved the diagnostic performance compared with individual MRI parameters. A further combination of these two factors with CPF exhibited the highest predictive performance. Pretreatment MSI{sub L} and ADC were independent prognostic factors for cervical cancer. The predictive capacity of multi-parametric MRI was superior to individual MRI parameters. The combination of multi-parametric MRI with CPF further improved the predictive performance. (orig.)

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

    Science.gov (United States)

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

    2015-05-13

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

  19. ATM and p53 combined analysis predicts survival in glioblastoma multiforme patients: A clinicopathologic study.

    Science.gov (United States)

    Romano, Francesco Jacopo; Guadagno, Elia; Solari, Domenico; Borrelli, Giorgio; Pignatiello, Sara; Cappabianca, Paolo; Del Basso De Caro, Marialaura

    2018-06-01

    Glioblastoma is one of the most malignant cancers, with a distinguishing dismal prognosis: surgery followed by chemo- and radiotherapy represents the current standard of care, and chemo- and radioresistance underlie disease recurrence and short overall survival of patients suffering from this malignancy. ATM is a kinase activated by autophosphorylation upon DNA doublestrand breaks arising from errors during replication, byproducts of metabolism, chemotherapy or ionizing radiations; TP53 is one of the most popular tumor suppressor, with a preeminent role in DNA damage response and repair. To study the effects of the immunohistochemical expression of p-ATM and p53 in glioblastoma patients, 21 cases were retrospectively examined. In normal brain tissue, p-ATM was expressed only in neurons; conversely, in tumors cells, the protein showed a variable cytoplasmic expression (score: +,++,+++), with being completely undetectable in three cases. Statistical analysis revealed that high p-ATM score (++/+++) strongly correlated to shorter survival (P = 0.022). No difference in overall survival was registered between p53 normally expressed (NE) and overexpressed (OE) glioblastoma patients (P = 0.669). Survival analysis performed on the results from combined assessment of the two proteins showed that patients with NE p53 /low pATM score had longer overall survival than the NE p53/ high pATM score counterpart. Cox-regression analysis confirmed this finding (HR = 0.025; CI 95% = 0.002-0.284; P = 0.003). Our study outlined the immunohistochemical expression of p-ATM/p53 in glioblastomas and provided data on their possible prognostic/predictive of response role. A "non-oncogene addiction" to ATM for NEp53 glioblastoma could be postulated, strengthening the rationale for development of ATM inhibiting drugs. © 2018 Wiley Periodicals, Inc.

  20. Combined prediction model for supply risk in nuclear power equipment manufacturing industry based on support vector machine and decision tree

    International Nuclear Information System (INIS)

    Shi Chunsheng; Meng Dapeng

    2011-01-01

    The prediction index for supply risk is developed based on the factor identifying of nuclear equipment manufacturing industry. The supply risk prediction model is established with the method of support vector machine and decision tree, based on the investigation on 3 important nuclear power equipment manufacturing enterprises and 60 suppliers. Final case study demonstrates that the combination model is better than the single prediction model, and demonstrates the feasibility and reliability of this model, which provides a method to evaluate the suppliers and measure the supply risk. (authors)

  1. Simultaneous learning and filtering without delusions: a Bayes-optimal combination of Predictive Inference and Adaptive Filtering.

    Science.gov (United States)

    Kneissler, Jan; Drugowitsch, Jan; Friston, Karl; Butz, Martin V

    2015-01-01

    Predictive coding appears to be one of the fundamental working principles of brain processing. Amongst other aspects, brains often predict the sensory consequences of their own actions. Predictive coding resembles Kalman filtering, where incoming sensory information is filtered to produce prediction errors for subsequent adaptation and learning. However, to generate prediction errors given motor commands, a suitable temporal forward model is required to generate predictions. While in engineering applications, it is usually assumed that this forward model is known, the brain has to learn it. When filtering sensory input and learning from the residual signal in parallel, a fundamental problem arises: the system can enter a delusional loop when filtering the sensory information using an overly trusted forward model. In this case, learning stalls before accurate convergence because uncertainty about the forward model is not properly accommodated. We present a Bayes-optimal solution to this generic and pernicious problem for the case of linear forward models, which we call Predictive Inference and Adaptive Filtering (PIAF). PIAF filters incoming sensory information and learns the forward model simultaneously. We show that PIAF is formally related to Kalman filtering and to the Recursive Least Squares linear approximation method, but combines these procedures in a Bayes optimal fashion. Numerical evaluations confirm that the delusional loop is precluded and that the learning of the forward model is more than 10-times faster when compared to a naive combination of Kalman filtering and Recursive Least Squares.

  2. Simultaneous Learning and Filtering without Delusions: A Bayes-Optimal Derivation of Combining Predictive Inference and AdaptiveFiltering

    Directory of Open Access Journals (Sweden)

    Jan eKneissler

    2015-04-01

    Full Text Available Predictive coding appears to be one of the fundamental working principles of brain processing. Amongst other aspects, brains often predict the sensory consequences of their own actions. Predictive coding resembles Kalman filtering, where incoming sensory information is filtered to produce prediction errors for subsequent adaptation and learning. However, to generate prediction errors given motor commands, a suitable temporal forward model is required to generate predictions. While in engineering applications, it is usually assumed that this forward model is known, the brain has to learn it. When filtering sensory input and learning from the residual signal in parallel, a fundamental problem arises: the system can enter a delusional loop when filtering the sensory information using an overly trusted forward model. In this case, learning stalls before accurate convergence because uncertainty about the forward model is not properly accommodated. We present a Bayes-optimal solution to this generic and pernicious problem for the case of linear forward models, which we call Predictive Inference and Adaptive Filtering (PIAF. PIAF filters incoming sensory information and learns the forward model simultaneously. We show that PIAF is formally related to Kalman filtering and to the Recursive Least Squares linear approximation method, but combines these procedures in a Bayes optimal fashion. Numerical evaluations confirm that the delusional loop is precluded and that the learning of the forward model is more than ten-times faster when compared to a naive combination of Kalman filtering and Recursive Least Squares.

  3. Prediction of quality attributes of chicken breast fillets by using Vis/NIR spectroscopy combined with factor analysis method

    Science.gov (United States)

    Visible/near-infrared (Vis/NIR) spectroscopy with wavelength range between 400 and 2500 nm combined with factor analysis method was tested to predict quality attributes of chicken breast fillets. Quality attributes, including color (L*, a*, b*), pH, and drip loss were analyzed using factor analysis ...

  4. Combining University Student Self-Regulated Learning Indicators and Engagement with Online Learning Events to Predict Academic Performance

    Science.gov (United States)

    Pardo, Abelardo; Han, Feifei; Ellis, Robert A.

    2017-01-01

    Self-regulated learning theories are used to understand the reasons for different levels of university student academic performance. Similarly, learning analytics research proposes the combination of detailed data traces derived from technology-mediated tasks with a variety of algorithms to predict student academic performance. The former approach…

  5. An inventory of the Aspergillus niger secretome by combining in silico predictions with shotgun proteomics data

    OpenAIRE

    Braaksma, Machtelt; Martens-Uzunova, Elena S; Punt, Peter J; Schaap, Peter J

    2010-01-01

    Abstract Background The ecological niche occupied by a fungal species, its pathogenicity and its usefulness as a microbial cell factory to a large degree depends on its secretome. Protein secretion usually requires the presence of a N-terminal signal peptide (SP) and by scanning for this feature using available highly accurate SP-prediction tools, the fraction of potentially secreted proteins can be directly predicted. However, prediction of a SP does not guarantee that the protein is actuall...

  6. Combining sequence-based prediction methods and circular dichroism and infrared spectroscopic data to improve protein secondary structure determinations

    Directory of Open Access Journals (Sweden)

    Lees Jonathan G

    2008-01-01

    Full Text Available Abstract Background A number of sequence-based methods exist for protein secondary structure prediction. Protein secondary structures can also be determined experimentally from circular dichroism, and infrared spectroscopic data using empirical analysis methods. It has been proposed that comparable accuracy can be obtained from sequence-based predictions as from these biophysical measurements. Here we have examined the secondary structure determination accuracies of sequence prediction methods with the empirically determined values from the spectroscopic data on datasets of proteins for which both crystal structures and spectroscopic data are available. Results In this study we show that the sequence prediction methods have accuracies nearly comparable to those of spectroscopic methods. However, we also demonstrate that combining the spectroscopic and sequences techniques produces significant overall improvements in secondary structure determinations. In addition, combining the extra information content available from synchrotron radiation circular dichroism data with sequence methods also shows improvements. Conclusion Combining sequence prediction with experimentally determined spectroscopic methods for protein secondary structure content significantly enhances the accuracy of the overall results obtained.

  7. A Novel Grey Prediction Model Combining Markov Chain with Functional-Link Net and Its Application to Foreign Tourist Forecasting

    Directory of Open Access Journals (Sweden)

    Yi-Chung Hu

    2017-10-01

    Full Text Available Grey prediction models for time series have been widely applied to demand forecasting because only limited data are required for them to build a time series model without any statistical assumptions. Previous studies have demonstrated that the combination of grey prediction with neural networks helps grey prediction perform better. Some methods have been presented to improve the prediction accuracy of the popular GM(1,1 model by using the Markov chain to estimate the residual needed to modify a predicted value. Compared to the previous Grey-Markov models, this study contributes to apply the functional-link net to estimate the degree to which a predicted value obtained from the GM(1,1 model can be adjusted. Furthermore, the troublesome number of states and their bounds that are not easily specified in Markov chain have been determined by a genetic algorithm. To verify prediction performance, the proposed grey prediction model was applied to an important grey system problem—foreign tourist forecasting. Experimental results show that the proposed model provides satisfactory results compared to the other Grey-Markov models considered.

  8. Ensemble approach combining multiple methods improves human transcription start site prediction.

    LENUS (Irish Health Repository)

    Dineen, David G

    2010-01-01

    The computational prediction of transcription start sites is an important unsolved problem. Some recent progress has been made, but many promoters, particularly those not associated with CpG islands, are still difficult to locate using current methods. These methods use different features and training sets, along with a variety of machine learning techniques and result in different prediction sets.

  9. Predicting Lung Radiotherapy-Induced Pneumonitis Using a Model Combining Parametric Lyman Probit With Nonparametric Decision Trees

    International Nuclear Information System (INIS)

    Das, Shiva K.; Zhou Sumin; Zhang, Junan; Yin, F.-F.; Dewhirst, Mark W.; Marks, Lawrence B.

    2007-01-01

    Purpose: To develop and test a model to predict for lung radiation-induced Grade 2+ pneumonitis. Methods and Materials: The model was built from a database of 234 lung cancer patients treated with radiotherapy (RT), of whom 43 were diagnosed with pneumonitis. The model augmented the predictive capability of the parametric dose-based Lyman normal tissue complication probability (LNTCP) metric by combining it with weighted nonparametric decision trees that use dose and nondose inputs. The decision trees were sequentially added to the model using a 'boosting' process that enhances the accuracy of prediction. The model's predictive capability was estimated by 10-fold cross-validation. To facilitate dissemination, the cross-validation result was used to extract a simplified approximation to the complicated model architecture created by boosting. Application of the simplified model is demonstrated in two example cases. Results: The area under the model receiver operating characteristics curve for cross-validation was 0.72, a significant improvement over the LNTCP area of 0.63 (p = 0.005). The simplified model used the following variables to output a measure of injury: LNTCP, gender, histologic type, chemotherapy schedule, and treatment schedule. For a given patient RT plan, injury prediction was highest for the combination of pre-RT chemotherapy, once-daily treatment, female gender and lowest for the combination of no pre-RT chemotherapy and nonsquamous cell histologic type. Application of the simplified model to the example cases revealed that injury prediction for a given treatment plan can range from very low to very high, depending on the settings of the nondose variables. Conclusions: Radiation pneumonitis prediction was significantly enhanced by decision trees that added the influence of nondose factors to the LNTCP formulation

  10. Improvement of Risk Prediction After Transcatheter Aortic Valve Replacement by Combining Frailty With Conventional Risk Scores.

    Science.gov (United States)

    Schoenenberger, Andreas W; Moser, André; Bertschi, Dominic; Wenaweser, Peter; Windecker, Stephan; Carrel, Thierry; Stuck, Andreas E; Stortecky, Stefan

    2018-02-26

    This study sought to evaluate whether frailty improves mortality prediction in combination with the conventional scores. European System for Cardiac Operative Risk Evaluation (EuroSCORE) or Society of Thoracic Surgeons (STS) score have not been evaluated in combined models with frailty for mortality prediction after transcatheter aortic valve replacement (TAVR). This prospective cohort comprised 330 consecutive TAVR patients ≥70 years of age. Conventional scores and a frailty index (based on assessment of cognition, mobility, nutrition, and activities of daily living) were evaluated to predict 1-year all-cause mortality using Cox proportional hazards regression (providing hazard ratios [HRs] with confidence intervals [CIs]) and measures of test performance (providing likelihood ratio [LR] chi-square test statistic and C-statistic [CS]). All risk scores were predictive of the outcome (EuroSCORE, HR: 1.90 [95% CI: 1.45 to 2.48], LR chi-square test statistic 19.29, C-statistic 0.67; STS score, HR: 1.51 [95% CI: 1.21 to 1.88], LR chi-square test statistic 11.05, C-statistic 0.64; frailty index, HR: 3.29 [95% CI: 1.98 to 5.47], LR chi-square test statistic 22.28, C-statistic 0.66). A combination of the frailty index with either EuroSCORE (LR chi-square test statistic 38.27, C-statistic 0.72) or STS score (LR chi-square test statistic 28.71, C-statistic 0.68) improved mortality prediction. The frailty index accounted for 58.2% and 77.6% of the predictive information in the combined model with EuroSCORE and STS score, respectively. Net reclassification improvement and integrated discrimination improvement confirmed that the added frailty index improved risk prediction. This is the first study showing that the assessment of frailty significantly enhances prediction of 1-year mortality after TAVR in combined risk models with conventional risk scores and relevantly contributes to this improvement. Copyright © 2018 American College of Cardiology Foundation

  11. Advantages of combined transmembrane topology and signal peptide prediction--the Phobius web server

    DEFF Research Database (Denmark)

    Käll, Lukas; Krogh, Anders; Sonnhammer, Erik L L

    2007-01-01

    . The method makes an optimal choice between transmembrane segments and signal peptides, and also allows constrained and homology-enriched predictions. We here present a web interface (http://phobius.cgb.ki.se and http://phobius.binf.ku.dk) to access Phobius. Udgivelsesdato: 2007-Jul......When using conventional transmembrane topology and signal peptide predictors, such as TMHMM and SignalP, there is a substantial overlap between these two types of predictions. Applying these methods to five complete proteomes, we found that 30-65% of all predicted signal peptides and 25-35% of all...

  12. A Combination of Terrain Prediction and Correction for Search and Rescue Robot Autonomous Navigation

    Directory of Open Access Journals (Sweden)

    Yan Guo

    2009-09-01

    Full Text Available This paper presents a novel two-step autonomous navigation method for search and rescue robot. The algorithm based on the vision is proposed for terrain identification to give a prediction of the safest path with the support vector regression machine (SVRM trained off-line with the texture feature and color features. And correction algorithm of the prediction based the vibration information is developed during the robot traveling, using the judgment function given in the paper. The region with fault prediction will be corrected with the real traversability value and be used to update the SVRM. The experiment demonstrates that this method could help the robot to find the optimal path and be protected from the trap brought from the error between prediction and the real environment.

  13. A Nomogram to Predict Radiation Pneumonitis, Derived From a Combined Analysis of RTOG 9311 and Institutional Data

    International Nuclear Information System (INIS)

    Bradley, Jeffrey D.; Hope, Andrew; El Naqa, Issam; Apte, Aditya M.S.; Lindsay, Patricia E.; Bosch, Walter D.Sc.; Matthews, John D.Sc.; Sause, William; Graham, Mary V.; Deasy, Joseph O.

    2007-01-01

    Purpose: To test the Washington University (WU) patient dataset, analysis of which suggested that superior-to-inferior tumor position, maximum dose, and D35 (minimum dose to the hottest 35% of the lung volume) were valuable to predict radiation pneumonitis (RP), against the patient database from Radiation Therapy Oncology Group (RTOG) trial 9311. Methods and Materials: The entire dataset consisted of 324 patients receiving definitive conformal radiotherapy for non-small-cell lung cancer (WU = 219, RTOG 9311 = 129). Clinical, dosimetric, and tumor location parameters were modeled to predict RP in the individual datasets and in a combined dataset. Association quality with RP was assessed using Spearman's rank correlation (r) for univariate analysis and multivariate analysis; comparison between subgroups was tested using the Wilcoxon rank sum test. Results: The WU model to predict RP performed poorly for the RTOG 9311 data. The most predictive model in the RTOG 9311 dataset was a single-parameter model, D15 (r = 0.28). Combining the datasets, the best derived model was a two-parameter model consisting of mean lung dose and superior-to-inferior gross tumor volume position (r = 0.303). An equation and nomogram to predict the probability of RP was derived using the combined patient population. Conclusions: Statistical models derived from a large pool of candidate models resulted in well-tuned models for each subset (WU or RTOG 9311), which did not perform well when applied to the other dataset. However, when the data were combined, a model was generated that performed well on each data subset. The final model incorporates two effects: greater risk due to inferior lung irradiation, and greater risk for increasing normal lung mean dose. This formula and nomogram may aid clinicians during radiation treatment planning for lung cancer

  14. Using Combined Computational Techniques to Predict the Glass Transition Temperatures of Aromatic Polybenzoxazines

    Science.gov (United States)

    Mhlanga, Phumzile; Wan Hassan, Wan Aminah; Hamerton, Ian; Howlin, Brendan J.

    2013-01-01

    The Molecular Operating Environment software (MOE) is used to construct a series of benzoxazine monomers for which a variety of parameters relating to the structures (e.g. water accessible surface area, negative van der Waals surface area, hydrophobic volume and the sum of atomic polarizabilities, etc.) are obtained and quantitative structure property relationships (QSPR) models are formulated. Three QSPR models (formulated using up to 5 descriptors) are first used to make predictions for the initiator data set (n = 9) and compared to published thermal data; in all of the QSPR models there is a high level of agreement between the actual data and the predicted data (within 0.63–1.86 K of the entire dataset). The water accessible surface area is found to be the most important descriptor in the prediction of Tg. Molecular modelling simulations of the benzoxazine polymer (minus initiator) carried out at the same time using the Materials Studio software suite provide an independent prediction of Tg. Predicted Tg values from molecular modelling fall in the middle of the range of the experimentally determined Tg values, indicating that the structure of the network is influenced by the nature of the initiator used. Hence both techniques can provide predictions of glass transition temperatures and provide complementary data for polymer design. PMID:23326419

  15. Using combined computational techniques to predict the glass transition temperatures of aromatic polybenzoxazines.

    Directory of Open Access Journals (Sweden)

    Phumzile Mhlanga

    Full Text Available The Molecular Operating Environment software (MOE is used to construct a series of benzoxazine monomers for which a variety of parameters relating to the structures (e.g. water accessible surface area, negative van der Waals surface area, hydrophobic volume and the sum of atomic polarizabilities, etc. are obtained and quantitative structure property relationships (QSPR models are formulated. Three QSPR models (formulated using up to 5 descriptors are first used to make predictions for the initiator data set (n = 9 and compared to published thermal data; in all of the QSPR models there is a high level of agreement between the actual data and the predicted data (within 0.63-1.86 K of the entire dataset. The water accessible surface area is found to be the most important descriptor in the prediction of T(g. Molecular modelling simulations of the benzoxazine polymer (minus initiator carried out at the same time using the Materials Studio software suite provide an independent prediction of T(g. Predicted T(g values from molecular modelling fall in the middle of the range of the experimentally determined T(g values, indicating that the structure of the network is influenced by the nature of the initiator used. Hence both techniques can provide predictions of glass transition temperatures and provide complementary data for polymer design.

  16. Can We Predict Individual Combined Benefit and Harm of Therapy? Warfarin Therapy for Atrial Fibrillation as a Test Case.

    Directory of Open Access Journals (Sweden)

    Guowei Li

    Full Text Available To construct and validate a prediction model for individual combined benefit and harm outcomes (stroke with no major bleeding, major bleeding with no stroke, neither event, or both in patients with atrial fibrillation (AF with and without warfarin therapy.Using the Kaiser Permanente Colorado databases, we included patients newly diagnosed with AF between January 1, 2005 and December 31, 2012 for model construction and validation. The primary outcome was a prediction model of composite of stroke or major bleeding using polytomous logistic regression (PLR modelling. The secondary outcome was a prediction model of all-cause mortality using the Cox regression modelling.We included 9074 patients with 4537 and 4537 warfarin users and non-users, respectively. In the derivation cohort (n = 4632, there were 136 strokes (2.94%, 280 major bleedings (6.04% and 1194 deaths (25.78% occurred. In the prediction models, warfarin use was not significantly associated with risk of stroke, but increased the risk of major bleeding and decreased the risk of death. Both the PLR and Cox models were robust, internally and externally validated, and with acceptable model performances.In this study, we introduce a new methodology for predicting individual combined benefit and harm outcomes associated with warfarin therapy for patients with AF. Should this approach be validated in other patient populations, it has potential advantages over existing risk stratification approaches as a patient-physician aid for shared decision-making.

  17. Predicting human splicing branchpoints by combining sequence-derived features and multi-label learning methods.

    Science.gov (United States)

    Zhang, Wen; Zhu, Xiaopeng; Fu, Yu; Tsuji, Junko; Weng, Zhiping

    2017-12-01

    Alternative splicing is the critical process in a single gene coding, which removes introns and joins exons, and splicing branchpoints are indicators for the alternative splicing. Wet experiments have identified a great number of human splicing branchpoints, but many branchpoints are still unknown. In order to guide wet experiments, we develop computational methods to predict human splicing branchpoints. Considering the fact that an intron may have multiple branchpoints, we transform the branchpoint prediction as the multi-label learning problem, and attempt to predict branchpoint sites from intron sequences. First, we investigate a variety of intron sequence-derived features, such as sparse profile, dinucleotide profile, position weight matrix profile, Markov motif profile and polypyrimidine tract profile. Second, we consider several multi-label learning methods: partial least squares regression, canonical correlation analysis and regularized canonical correlation analysis, and use them as the basic classification engines. Third, we propose two ensemble learning schemes which integrate different features and different classifiers to build ensemble learning systems for the branchpoint prediction. One is the genetic algorithm-based weighted average ensemble method; the other is the logistic regression-based ensemble method. In the computational experiments, two ensemble learning methods outperform benchmark branchpoint prediction methods, and can produce high-accuracy results on the benchmark dataset.

  18. Combined effects of form- and meaning-based predictability on perceived clarity of speech.

    Science.gov (United States)

    Signoret, Carine; Johnsrude, Ingrid; Classon, Elisabet; Rudner, Mary

    2018-02-01

    The perceptual clarity of speech is influenced by more than just the acoustic quality of the sound; it also depends on contextual support. For example, a degraded sentence is perceived to be clearer when the content of the speech signal is provided with matching text (i.e., form-based predictability) before hearing the degraded sentence. Here, we investigate whether sentence-level semantic coherence (i.e., meaning-based predictability), enhances perceptual clarity of degraded sentences, and if so, whether the mechanism is the same as that underlying enhancement by matching text. We also ask whether form- and meaning-based predictability are related to individual differences in cognitive abilities. Twenty participants listened to spoken sentences that were either clear or degraded by noise vocoding and rated the clarity of each item. The sentences had either high or low semantic coherence. Each spoken word was preceded by the homologous printed word (matching text), or by a meaningless letter string (nonmatching text). Cognitive abilities were measured with a working memory test. Results showed that perceptual clarity was significantly enhanced both by matching text and by semantic coherence. Importantly, high coherence enhanced the perceptual clarity of the degraded sentences even when they were preceded by matching text, suggesting that the effects of form- and meaning-based predictions on perceptual clarity are independent and additive. However, when working memory capacity indexed by the Size-Comparison Span Test was controlled for, only form-based predictions enhanced perceptual clarity, and then only at some sound quality levels, suggesting that prediction effects are to a certain extent dependent on cognitive abilities. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

  19. Evaluation on the model of performance predictions for on-line monitoring system for combined-cycle power plant

    International Nuclear Information System (INIS)

    Kim, Si Moon

    2002-01-01

    This paper presents the simulation model developed to predict design and off-design performance of an actual combined cycle power plant(S-Station in Korea), which would be running combined with on-line performance monitoring system in an on-line real-time fashion. The first step in thermal performance analysis is to build an accurate performance model of the power plant, in order to achieve this goal, GateCycle program has been employed in developing the model. This developed models predict design and off-design performance with a precision of one percent over a wide range of operating conditions so that on-line real-time performance monitoring can accurately establish both current performance and expected performance and also help the operator identify problems before they would be noticed

  20. Combining multiple models to generate consensus: Application to radiation-induced pneumonitis prediction

    Energy Technology Data Exchange (ETDEWEB)

    Das, Shiva K.; Chen Shifeng; Deasy, Joseph O.; Zhou Sumin; Yin Fangfang; Marks, Lawrence B. [Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina 27710 (United States); Department of Radiation Oncology, Washington University School of Medicine, St. Louis, Missouri 63110 (United States); Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina 27710 (United States); Department of Radiation Oncology, University of North Carolina School of Medicine, Chapel Hill, North Carolina 27599 (United States)

    2008-11-15

    The fusion of predictions from disparate models has been used in several fields to obtain a more realistic and robust estimate of the ''ground truth'' by allowing the models to reinforce each other when consensus exists, or, conversely, negate each other when there is no consensus. Fusion has been shown to be most effective when the models have some complementary strengths arising from different approaches. In this work, we fuse the results from four common but methodologically different nonlinear multivariate models (Decision Trees, Neural Networks, Support Vector Machines, Self-Organizing Maps) that were trained to predict radiation-induced pneumonitis risk on a database of 219 lung cancer patients treated with radiotherapy (34 with Grade 2+ postradiotherapy pneumonitis). Each model independently incorporated a small number of features from the available set of dose and nondose patient variables to predict pneumonitis; no two models had all features in common. Fusion was achieved by simple averaging of the predictions for each patient from all four models. Since a model's prediction for a patient can be dependent on the patient training set used to build the model, the average of several different predictions from each model was used in the fusion (predictions were made by repeatedly testing each patient with a model built from different cross-validation training sets that excluded the patient being tested). The area under the receiver operating characteristics curve for the fused cross-validated results was 0.79, with lower variance than the individual component models. From the fusion, five features were extracted as the consensus among all four models in predicting radiation pneumonitis. Arranged in order of importance, the features are (1) chemotherapy; (2) equivalent uniform dose (EUD) for exponent a=1.2 to 3; (3) EUD for a=0.5 to 1.2, lung volume receiving >20-30 Gy; (4) female sex; and (5) squamous cell histology. To facilitate

  1. Combining Results from Distinct MicroRNA Target Prediction Tools Enhances the Performance of Analyses

    Directory of Open Access Journals (Sweden)

    Arthur C. Oliveira

    2017-05-01

    Full Text Available Target prediction is generally the first step toward recognition of bona fide microRNA (miRNA-target interactions in living cells. Several target prediction tools are now available, which use distinct criteria and stringency to provide the best set of candidate targets for a single miRNA or a subset of miRNAs. However, there are many false-negative predictions, and consensus about the optimum strategy to select and use the output information provided by the target prediction tools is lacking. We compared the performance of four tools cited in literature—TargetScan (TS, miRanda-mirSVR (MR, Pita, and RNA22 (R22, and we determined the most effective approach for analyzing target prediction data (individual, union, or intersection. For this purpose, we calculated the sensitivity, specificity, precision, and correlation of these approaches using 10 miRNAs (miR-1-3p, miR-17-5p, miR-21-5p, miR-24-3p, miR-29a-3p, miR-34a-5p, miR-124-3p, miR-125b-5p, miR-145-5p, and miR-155-5p and 1,400 genes (700 validated and 700 non-validated as targets of these miRNAs. The four tools provided a subset of high-quality predictions and returned few false-positive predictions; however, they could not identify several known true targets. We demonstrate that union of TS/MR and TS/MR/R22 enhanced the quality of in silico prediction analysis of miRNA targets. We conclude that the union rather than the intersection of the aforementioned tools is the best strategy for maximizing performance while minimizing the loss of time and resources in subsequent in vivo and in vitro experiments for functional validation of miRNA-target interactions.

  2. Numerical Predictions of Wind Turbine Power and Aerodynamic Loads for the NREL Phase II and IV Combined Experiment Rotor

    Science.gov (United States)

    Duque, Earl P. N.; Johnson, Wayne; vanDam, C. P.; Chao, David D.; Cortes, Regina; Yee, Karen

    1999-01-01

    Accurate, reliable and robust numerical predictions of wind turbine rotor power remain a challenge to the wind energy industry. The literature reports various methods that compare predictions to experiments. The methods vary from Blade Element Momentum Theory (BEM), Vortex Lattice (VL), to variants of Reynolds-averaged Navier-Stokes (RaNS). The BEM and VL methods consistently show discrepancies in predicting rotor power at higher wind speeds mainly due to inadequacies with inboard stall and stall delay models. The RaNS methodologies show promise in predicting blade stall. However, inaccurate rotor vortex wake convection, boundary layer turbulence modeling and grid resolution has limited their accuracy. In addition, the inherently unsteady stalled flow conditions become computationally expensive for even the best endowed research labs. Although numerical power predictions have been compared to experiment. The availability of good wind turbine data sufficient for code validation experimental data that has been extracted from the IEA Annex XIV download site for the NREL Combined Experiment phase II and phase IV rotor. In addition, the comparisons will show data that has been further reduced into steady wind and zero yaw conditions suitable for comparisons to "steady wind" rotor power predictions. In summary, the paper will present and discuss the capabilities and limitations of the three numerical methods and make available a database of experimental data suitable to help other numerical methods practitioners validate their own work.

  3. A combination of preoperative CT findings and postoperative serum CEA levels improves recurrence prediction for stage I lung adenocarcinoma

    Energy Technology Data Exchange (ETDEWEB)

    Yamazaki, Motohiko, E-mail: xackey2001@gmail.com [Department of Radiology, Niigata University Graduate School of Medical and Dental Sciences (Japan); Ishikawa, Hiroyuki [Department of Radiology, Niigata University Graduate School of Medical and Dental Sciences (Japan); Kunii, Ryosuke [Division of Cellular and Molecular Pathology, Niigata University Graduate School of Medical and Dental Sciences (Japan); Tasaki, Akiko; Sato, Suguru; Ikeda, Yohei; Yoshimura, Norihiko [Department of Radiology, Niigata University Graduate School of Medical and Dental Sciences (Japan); Hashimoto, Takehisa; Tsuchida, Masanori [Division of Thoracic and Cardiovascular Surgery, Niigata University Graduate School of Medical and Dental Sciences (Japan); Aoyama, Hidefumi [Department of Radiology, Niigata University Graduate School of Medical and Dental Sciences (Japan)

    2015-01-15

    Objectives: To assess the prognostic value of combined evaluation of preoperative CT findings and pre/postoperative serum carcinoembryonic antigen (CEA) levels for pathological stage I lung adenocarcinoma. Methods: This retrospective study included 250 consecutive patients who underwent complete resection for ≤3-cm pathological stage I (T1–2aN0M0) adenocarcinomas (132 men, 118 women; mean age, 67.8 years). Radiologists evaluated following CT findings: maximum tumor diameter, percentage of solid component (%solid), air bronchogram, spiculation, adjacency of bullae or interstitial pneumonia (IP) around the tumor, notch, and pleural indent. These CT findings, pre/postoperative CEA levels, age, gender, and Brinkman index were assessed by Cox proportional hazards model to determine the best prognostic model. Prognostic accuracy was examined using the area under the receiver operating characteristic curve (AUC). Results: Median follow-up period was 73.2 months. In multivariate analysis, high %solid, adjacency of bullae or IP around the tumor, and high postoperative CEA levels comprised the best combination for predicting recurrence (P < 0.05). A combination of these three findings had a greater accuracy in predicting 5-year disease-free survival than did %solid alone (AUC = 0.853 versus 0.792; P = 0.023), with a sensitivity of 85.7% and a specificity of 74.3% at the optimal threshold. The best cut-off values of %solid and postoperative CEA levels for predicting high-risk patients were ≥48% and ≥3.7 ng/mL, respectively. Conclusion: Compared to %solid alone, combined evaluation of %solid, adjacency of bullae or IP change around the tumor, and postoperative CEA levels improves recurrence prediction for stage I lung adenocarcinoma.

  4. Prediction of resistance development against drug combinations by collateral responses to component drugs

    DEFF Research Database (Denmark)

    Munck, Christian; Gumpert, Heidi; Nilsson Wallin, Annika

    2014-01-01

    the genomes of all evolved E. coli lineages, we identified the mutational events that drive the differences in drug resistance levels and found that the degree of resistance development against drug combinations can be understood in terms of collateral sensitivity and resistance that occurred during...... adaptation to the component drugs. Then, using engineered E. coli strains, we confirmed that drug resistance mutations that imposed collateral sensitivity were suppressed in a drug pair growth environment. These results provide a framework for rationally selecting drug combinations that limit resistance......Resistance arises quickly during chemotherapeutic selection and is particularly problematic during long-term treatment regimens such as those for tuberculosis, HIV infections, or cancer. Although drug combination therapy reduces the evolution of drug resistance, drug pairs vary in their ability...

  5. Prediction of inflows into Lake Kariba using a combination of physical and empirical models

    CSIR Research Space (South Africa)

    Muchuru, S

    2015-10-01

    Full Text Available the upper Zambezi catchment as predictor in a statistical model for estimating seasonal inflows into Lake Kariba. The second and more sophisticated method uses predicted low-level atmospheric circulation of a coupled ocean–atmosphere general circulation...

  6. A Combined Thermodynamic and Kinetic Model for Barite Prediction at Oil Reservoir Conditions

    DEFF Research Database (Denmark)

    Zhen Wu, Bi Yun

    of the literature (PhD Study 1). The reviewed dataset was used as starting point for geochemical speciation modelling and applied to predict the stability of sulphate minerals in North Sea oil field brines. Second, for modelling of high salinity solutions using the Pitzer ion interaction approach, the temperature...... observations. This information can help planning mitigation and optimise costs in oil production....

  7. Dropout Prediction in E-Learning Courses through the Combination of Machine Learning Techniques

    Science.gov (United States)

    Lykourentzou, Ioanna; Giannoukos, Ioannis; Nikolopoulos, Vassilis; Mpardis, George; Loumos, Vassili

    2009-01-01

    In this paper, a dropout prediction method for e-learning courses, based on three popular machine learning techniques and detailed student data, is proposed. The machine learning techniques used are feed-forward neural networks, support vector machines and probabilistic ensemble simplified fuzzy ARTMAP. Since a single technique may fail to…

  8. Model Predictive Controller Combined with LQG Controller and Velocity Feedback to Control the Stewart Platform

    DEFF Research Database (Denmark)

    Nadimi, Esmaeil Sharak; Bak, Thomas; Izadi-Zamanabadi, Roozbeh

    2006-01-01

    The main objective of this paper is to investigate the erformance and applicability of two GPC (generalized predictive control) based control methods on a complete benchmark model of the Stewart platform made in MATLAB V6.5. The first method involves an LQG controller (Linear Quadratic Gaussian...

  9. Combining quantitative trait loci analysis with physiological models to predict genotype-specific transpiration rates.

    Science.gov (United States)

    Reuning, Gretchen A; Bauerle, William L; Mullen, Jack L; McKay, John K

    2015-04-01

    Transpiration is controlled by evaporative demand and stomatal conductance (gs ), and there can be substantial genetic variation in gs . A key parameter in empirical models of transpiration is minimum stomatal conductance (g0 ), a trait that can be measured and has a large effect on gs and transpiration. In Arabidopsis thaliana, g0 exhibits both environmental and genetic variation, and quantitative trait loci (QTL) have been mapped. We used this information to create a genetically parameterized empirical model to predict transpiration of genotypes. For the parental lines, this worked well. However, in a recombinant inbred population, the predictions proved less accurate. When based only upon their genotype at a single g0 QTL, genotypes were less distinct than our model predicted. Follow-up experiments indicated that both genotype by environment interaction and a polygenic inheritance complicate the application of genetic effects into physiological models. The use of ecophysiological or 'crop' models for predicting transpiration of novel genetic lines will benefit from incorporating further knowledge of the genetic control and degree of independence of core traits/parameters underlying gs variation. © 2014 John Wiley & Sons Ltd.

  10. Predicting targeted drug combinations based on Pareto optimal patterns of coexpression network connectivity.

    Science.gov (United States)

    Penrod, Nadia M; Greene, Casey S; Moore, Jason H

    2014-01-01

    Molecularly targeted drugs promise a safer and more effective treatment modality than conventional chemotherapy for cancer patients. However, tumors are dynamic systems that readily adapt to these agents activating alternative survival pathways as they evolve resistant phenotypes. Combination therapies can overcome resistance but finding the optimal combinations efficiently presents a formidable challenge. Here we introduce a new paradigm for the design of combination therapy treatment strategies that exploits the tumor adaptive process to identify context-dependent essential genes as druggable targets. We have developed a framework to mine high-throughput transcriptomic data, based on differential coexpression and Pareto optimization, to investigate drug-induced tumor adaptation. We use this approach to identify tumor-essential genes as druggable candidates. We apply our method to a set of ER(+) breast tumor samples, collected before (n = 58) and after (n = 60) neoadjuvant treatment with the aromatase inhibitor letrozole, to prioritize genes as targets for combination therapy with letrozole treatment. We validate letrozole-induced tumor adaptation through coexpression and pathway analyses in an independent data set (n = 18). We find pervasive differential coexpression between the untreated and letrozole-treated tumor samples as evidence of letrozole-induced tumor adaptation. Based on patterns of coexpression, we identify ten genes as potential candidates for combination therapy with letrozole including EPCAM, a letrozole-induced essential gene and a target to which drugs have already been developed as cancer therapeutics. Through replication, we validate six letrozole-induced coexpression relationships and confirm the epithelial-to-mesenchymal transition as a process that is upregulated in the residual tumor samples following letrozole treatment. To derive the greatest benefit from molecularly targeted drugs it is critical to design combination

  11. Combining logistic regression with classification and regression tree to predict quality of care in a home health nursing data set.

    Science.gov (United States)

    Guo, Huey-Ming; Shyu, Yea-Ing Lotus; Chang, Her-Kun

    2006-01-01

    In this article, the authors provide an overview of a research method to predict quality of care in home health nursing data set. The results of this study can be visualized through classification an regression tree (CART) graphs. The analysis was more effective, and the results were more informative since the home health nursing dataset was analyzed with a combination of the logistic regression and CART, these two techniques complete each other. And the results more informative that more patients' characters were related to quality of care in home care. The results contributed to home health nurse predict patient outcome in case management. Improved prediction is needed for interventions to be appropriately targeted for improved patient outcome and quality of care.

  12. Prediction of high-temperature point defect formation in TiO2 from combined ab initio and thermodynamic calculations

    International Nuclear Information System (INIS)

    He, J.; Behera, R.K.; Finnis, M.W.; Li, X.; Dickey, E.C.; Phillpot, S.R.; Sinnott, S.B.

    2007-01-01

    A computational approach that integrates ab initio electronic structure and thermodynamic calculations is used to determine point defect stability in rutile TiO 2 over a range of temperatures, oxygen partial pressures and stoichiometries. Both donors (titanium interstitials and oxygen vacancies) and acceptors (titanium vacancies) are predicted to have shallow defect transition levels in the electronic-structure calculations. The resulting defect formation energies for all possible charge states are then used in thermodynamic calculations to predict the influence of temperature and oxygen partial pressure on the relative stabilities of the point defects. Their ordering is found to be the same as temperature increases and oxygen partial pressure decreases: titanium vacancy → oxygen vacancy → titanium interstitial. The charges on these defects, however, are quite sensitive to the Fermi level. Finally, the combined formation energies of point defect complexes, including Schottky, Frenkel and anti-Frenkel defects, are predicted to limit the further formation of point defects

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

    Science.gov (United States)

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

    2018-03-01

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

  14. Prediction of a Visible Plume from a Dry and Wet Combined Cooling Tower and Its Mechanism of Abatement

    Directory of Open Access Journals (Sweden)

    Kazutaka Takata

    2016-04-01

    Full Text Available Heated moist air from a cooling tower forms a visible plume and needs to be predicted, not only for the performance design of the cooling tower, but also for environmental impact assessments. In this study, a computational fluid dynamics analysis is conducted to predict the scale of a visible plume rising from a cross flow cooling tower with mechanical draft (provided by a rotating fan. The results of computational fluid dynamics analysis are verified by comparing predictions with an actual observed plume. The results show that the predicted visible plume represents the observed plume in an error range of 15%–20%, which is permissible for designing a cooling tower. Additionally, the mixing condition of heated dry air and moist air under dry and wet combined operation is examined, and the condition is thought to affect the scale of the visible plume. It is found that, in the case of a mechanical-draft cooling tower, the fan has a mixing function which performs the complete mixing of wet and dry air, and this suggests that the generation of the plume can be determined by the intersection of the operation line and saturation line. Additionally, the effect of external wind on the scale of the visible plume is large, especially for dry and wet combined operation.

  15. Human and server docking prediction for CAPRI round 30-35 using LZerD with combined scoring functions.

    Science.gov (United States)

    Peterson, Lenna X; Kim, Hyungrae; Esquivel-Rodriguez, Juan; Roy, Amitava; Han, Xusi; Shin, Woong-Hee; Zhang, Jian; Terashi, Genki; Lee, Matt; Kihara, Daisuke

    2017-03-01

    We report the performance of protein-protein docking predictions by our group for recent rounds of the Critical Assessment of Prediction of Interactions (CAPRI), a community-wide assessment of state-of-the-art docking methods. Our prediction procedure uses a protein-protein docking program named LZerD developed in our group. LZerD represents a protein surface with 3D Zernike descriptors (3DZD), which are based on a mathematical series expansion of a 3D function. The appropriate soft representation of protein surface with 3DZD makes the method more tolerant to conformational change of proteins upon docking, which adds an advantage for unbound docking. Docking was guided by interface residue prediction performed with BindML and cons-PPISP as well as literature information when available. The generated docking models were ranked by a combination of scoring functions, including PRESCO, which evaluates the native-likeness of residues' spatial environments in structure models. First, we discuss the overall performance of our group in the CAPRI prediction rounds and investigate the reasons for unsuccessful cases. Then, we examine the performance of several knowledge-based scoring functions and their combinations for ranking docking models. It was found that the quality of a pool of docking models generated by LZerD, that is whether or not the pool includes near-native models, can be predicted by the correlation of multiple scores. Although the current analysis used docking models generated by LZerD, findings on scoring functions are expected to be universally applicable to other docking methods. Proteins 2017; 85:513-527. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

  16. Identification and prediction of diabetic sensorimotor polyneuropathy using individual and simple combinations of nerve conduction study parameters.

    Directory of Open Access Journals (Sweden)

    Alanna Weisman

    Full Text Available OBJECTIVE: Evaluation of diabetic sensorimotor polyneuropathy (DSP is hindered by the need for complex nerve conduction study (NCS protocols and lack of predictive biomarkers. We aimed to determine the performance of single and simple combinations of NCS parameters for identification and future prediction of DSP. MATERIALS AND METHODS: 406 participants (61 with type 1 diabetes and 345 with type 2 diabetes with a broad spectrum of neuropathy, from none to severe, underwent NCS to determine presence or absence of DSP for cross-sectional (concurrent validity analysis. The 109 participants without baseline DSP were re-evaluated for its future onset (predictive validity. Performance of NCS parameters was compared by area under the receiver operating characteristic curve (AROC. RESULTS: At baseline there were 246 (60% Prevalent Cases. After 3.9 years mean follow-up, 25 (23% of the 109 Prevalent Controls that were followed became Incident DSP Cases. Threshold values for peroneal conduction velocity and sural amplitude potential best identified Prevalent Cases (AROC 0.90 and 0.83, sensitivity 80 and 83%, specificity 89 and 72%, respectively. Baseline tibial F-wave latency, peroneal conduction velocity and the sum of three lower limb nerve conduction velocities (sural, peroneal, and tibial best predicted 4-year incidence (AROC 0.79, 0.79, and 0.85; sensitivity 79, 70, and 81%; specificity 63, 74 and 77%, respectively. DISCUSSION: Individual NCS parameters or their simple combinations are valid measures for identification and future prediction of DSP. Further research into the predictive roles of tibial F-wave latencies, peroneal conduction velocity, and sum of conduction velocities as markers of incipient nerve injury is needed to risk-stratify individuals for clinical and research protocols.

  17. Applicability of Alignment and Combination Rules to Burst Pressure Prediction of Multiple-flawed Steam Generator Tube

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Myeong Woo; Kim, Ji Seok; Kim, Yun Jae [Korea University, Seoul (Korea, Republic of); Jeon, Jun Young [Doosan Heavy Industries and Consruction, Seoul (Korea, Republic of); Lee, Dong Min [Korea Plant Service and Engineering, Technical Research and Development Institute, Naju (Korea, Republic of)

    2016-05-15

    Alignment and combination rules are provided by various codes and standards. These rules are used to determine whether multiple flaws should be treated as non-aligned or as coplanar, and independent or combined flaws. Experimental results on steam generator (SG) tube specimens containing multiple axial part-through-wall (PTW) flaws at room temperature (RT) are compared with assessment results based on the alignment and combination rules of the codes and standards. In case of axial collinear flaws, ASME, JSME, and BS7910 treated multiple flaws as independent flaws and API 579, A16, and FKM treated multiple flaws as combined single flaw. Assessment results of combined flaws were conservative. In case of axial non-aligned flaws, almost flaws were aligned and assessment results well correlate with experimental data. In case of axial parallel flaws, both effective flaw lengths of aligned flaws and separated flaws was are same because of each flaw length were same. This study investigates the applicability of alignment and combination rules for multiple flaws on the failure behavior of Alloy 690TT steam generator (SG) tubes that widely used in the nuclear power plan. Experimental data of burst tests on Alloy 690TT tubes with single and multiple flaws that conducted at room temperature (RT) by Kim el al. compared with the alignment rules of these codes and standards. Burst pressure of SG tubes with flaws are predicted using limit load solutions that provide by EPRI Handbook.

  18. PERPADUAN COMBINED SAMPLING DAN ENSEMBLE OF SUPPORT VECTOR MACHINE (ENSVM UNTUK MENANGANI KASUS CHURN PREDICTION PERUSAHAAN TELEKOMUNIKASI

    Directory of Open Access Journals (Sweden)

    Fernandy Marbun

    2010-07-01

    Full Text Available Churn prediction adalah suatu cara untuk memprediksi pelanggan yang berpotensial untuk churn. Data mining khususnya klasifikasi tampaknya dapat menjadi alternatif solusi dalam membuat model churn prediction yang akurat. Namun hasil klasifikasi menjadi tidak akurat disebabkan karena data churn bersifat imbalance. Kelas data menjadi tidak stabil karena data akan lebih condong ke bagian data yang memiliki komposisi data yang lebih besar. Salah satu cara untuk menangani permasalahan ini adalah dengan memodifikasi dataset yang digunakan atau yang lebih dikenal dengan metode resampling. Teknik resampling ini meliputi over-sampling, under-sampling, dan combined-sampling. Metode Ensemble of SVM (EnSVM diharapkan dapat meminimalisir kesalahan klasifikasi kelas mayor dan minor yang dihasilkan oleh classifier SVM tunggal. Dalam penelitian ini akan dicoba untuk memadukan combined sampling dan EnSVM untuk churn predicition. Pengujian dilakukan dengan membandingkan hasil klasifikasi CombinedSampling-EnSVM dengan SMOTE-SVM (perpaduan oversamping-SVM dan pure-SVM. Hasil pengujian menunjukkan bahwa metode CombinedSampling-EnSVM secara umum hanya mampu menghasilkan performansi Gini Index yang lebih baik daripada metode SMOTE-SVM dan tanpa resampling (pure-SVM.

  19. The development of artificial neural networks to predict virological response to combination HIV therapy

    NARCIS (Netherlands)

    Larder, Brendan; Wang, Dechao; Revell, Andrew; Montaner, Julio; Harrigan, Richard; de Wolf, Frank; Lange, Joep; Wegner, Scott; Ruiz, Lidia; Pérez-Elías, Maria Jésus; Emery, Sean; Gatell, Jose; D'Arminio Monforte, Antonella; Torti, Carlo; Zazzi, Maurizio; Lane, Clifford

    2007-01-01

    When used in combination, antiretroviral drugs are highly effective for suppressing HIV replication. Nevertheless, treatment failure commonly occurs and is generally associated with viral drug resistance. The choice of an alternative regimen may be guided by a drug-resistance test. However,

  20. Combined crystal structure prediction and high-pressure crystallization in rational pharmaceutical polymorph screening

    DEFF Research Database (Denmark)

    Neumann, M A; van de Streek, J; Fabbiani, F P A

    2015-01-01

    Organic molecules, such as pharmaceuticals, agro-chemicals and pigments, frequently form several crystal polymorphs with different physicochemical properties. Finding polymorphs has long been a purely experimental game of trial-and-error. Here we utilize in silico polymorph screening in combination...

  1. Reliabilities of genomic prediction using combined reference data of the Nordic Red dairy cattle production

    DEFF Research Database (Denmark)

    Brøndum, Rasmus Froberg; Rius-Vilarrasa, E; Strandén, I

    2011-01-01

    This study investigated the possibility of increasing the reliability of direct genomic values (DGV) by combining reference opulations. The data were from 3,735 bulls from Danish, Swedish, and Finnish Red dairy cattle populations. Single nucleotide polymorphism markers were fitted as random varia...

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

    DEFF Research Database (Denmark)

    Albitar, Maher; Ma, Wanlong; Lund, Lars

    2018-01-01

    standard formulas, while comparisons between groups were performed using the Wilcoxon Rank Sum, Kruskal-Wallis, Chi-Square, and Fisher's exact test. RESULTS: GS as assigned by standard 10-12 core biopsies was 3 + 3 in 90 (29.4%), 3 + 4 in 122 (39.8%), 4 + 3 in 50 (16.3%), and > 4 + 3 in 44 (14.4%) patients....... CONCLUSIONS: This plasma/urine biomarker test accurately predicts high grade cancer as determined by prostatectomy with a sensitivity at 92-97%, while the sensitivity of core biopsies was 78%....... of a test using cell-free RNA levels of biomarkers in predicting prostatectomy results. METHODS: This multicenter community-based prospective study was conducted using urine/blood samples collected from 306 patients. All recruited patients were treatment-naïve, without metastases, and had been biopsied...

  3. GI-POP: a combinational annotation and genomic island prediction pipeline for ongoing microbial genome projects.

    Science.gov (United States)

    Lee, Chi-Ching; Chen, Yi-Ping Phoebe; Yao, Tzu-Jung; Ma, Cheng-Yu; Lo, Wei-Cheng; Lyu, Ping-Chiang; Tang, Chuan Yi

    2013-04-10

    Sequencing of microbial genomes is important because of microbial-carrying antibiotic and pathogenetic activities. However, even with the help of new assembling software, finishing a whole genome is a time-consuming task. In most bacteria, pathogenetic or antibiotic genes are carried in genomic islands. Therefore, a quick genomic island (GI) prediction method is useful for ongoing sequencing genomes. In this work, we built a Web server called GI-POP (http://gipop.life.nthu.edu.tw) which integrates a sequence assembling tool, a functional annotation pipeline, and a high-performance GI predicting module, in a support vector machine (SVM)-based method called genomic island genomic profile scanning (GI-GPS). The draft genomes of the ongoing genome projects in contigs or scaffolds can be submitted to our Web server, and it provides the functional annotation and highly probable GI-predicting results. GI-POP is a comprehensive annotation Web server designed for ongoing genome project analysis. Researchers can perform annotation and obtain pre-analytic information include possible GIs, coding/non-coding sequences and functional analysis from their draft genomes. This pre-analytic system can provide useful information for finishing a genome sequencing project. Copyright © 2012 Elsevier B.V. All rights reserved.

  4. Can We Predict Functional Outcome in Neonates with Hypoxic Ischemic Encephalopathy by the Combination of Neuroimaging and Electroencephalography?

    Science.gov (United States)

    Nanavati, Tania; Seemaladinne, Nirupama; Regier, Michael; Yossuck, Panitan; Pergami, Paola

    2015-01-01

    Background Neonatal hypoxic ischemic encephalopathy (HIE) is a major cause of mortality, morbidity, and long-term neurological deficits. Despite the availability of neuroimaging and neurophysiological testing, tools for accurate early diagnosis and prediction of developmental outcome are still lacking. The goal of this study was to determine if combined use of magnetic resonance imaging (MRI) and electroencephalography (EEG) findings could support outcome prediction. Methods We retrospectively reviewed records of 17 HIE neonates, classified brain MRI and EEG findings based on severity, and assessed clinical outcome up to 48 months. We determined the relation between MRI/EEG findings and clinical outcome. Results We demonstrated a significant relationship between MRI findings and clinical outcome (Fisher’s exact test, p = 0.017). EEG provided no additional information about the outcome beyond that contained in the MRI score. The statistical model for outcome prediction based on random forests suggested that EEG readings at 24 hours and 72 hours could be important variables for outcome prediction, but this needs to be investigated further. Conclusion Caution should be used when discussing prognosis for neonates with mild-to-moderate HIE based on early MR imaging and EEG findings. A robust, quantitative marker of HIE severity that allows for accurate prediction of long-term outcome, particularly for mild-to-moderate cases, is still needed. PMID:25862075

  5. STRUCTURAL SCALE LIFE PREDICTION OF AERO STRUCTURES EXPERIENCING COMBINED EXTREME ENVIRONMENTS

    Science.gov (United States)

    2017-07-01

    complex loading environments. Today’s state of the art methods cannot address structural reliability under combined environment conditions due to...probabilistically assess the structural life under complex loading environments. Today’s state of the art methods cannot address structural reliability...Institute of Aeronautics and Astronautics, San Diego, CA, January 4th‐8th, 2016. Clark, L. D., Bae, H., Gobal, K., and Penmetsa, R., “ Engineering

  6. Benefit of hepatitis C virus core antigen assay in prediction of therapeutic response to interferon and ribavirin combination therapy.

    Science.gov (United States)

    Takahashi, Masahiko; Saito, Hidetsugu; Higashimoto, Makiko; Atsukawa, Kazuhiro; Ishii, Hiromasa

    2005-01-01

    A highly sensitive second-generation hepatitis C virus (HCV) core antigen assay has recently been developed. We compared viral disappearance and first-phase kinetics between commercially available core antigen (Ag) assays, Lumipulse Ortho HCV Ag (Lumipulse-Ag), and a quantitative HCV RNA PCR assay, Cobas Amplicor HCV Monitor test, version 2 (Amplicor M), to estimate the predictive benefit of a sustained viral response (SVR) and non-SVR in 44 genotype 1b patients treated with interferon (IFN) and ribavirin. HCV core Ag negativity could predict SVR on day 1 (sensitivity = 100%, specificity = 85.0%, accuracy = 86.4%), whereas RNA negativity could predict SVR on day 7 (sensitivity = 100%, specificity = 87.2%, accuracy = 88.6%). None of the patients who had detectable serum core Ag or RNA on day 14 achieved SVR (specificity = 100%). The predictive accuracy on day 14 was higher by RNA negativity (93.2%) than that by core Ag negativity (75.0%). The combined predictive criterion of both viral load decline during the first 24 h and basal viral load was also predictive for SVR; the sensitivities of Lumipulse-Ag and Amplicor-M were 45.5 and 47.6%, respectively, and the specificity was 100%. Amplicor-M had better predictive accuracy than Lumipulse-Ag in 2-week disappearance tests because it had better sensitivity. On the other hand, estimates of kinetic parameters were similar regardless of the detection method. Although the correlations between Lumipulse-Ag and Amplicor-M were good both before and 24 h after IFN administration, HCV core Ag seemed to be relatively lower 24 h after IFN administration than before administration. Lumipulse-Ag seems to be useful for detecting the HCV concentration during IFN therapy; however, we still need to understand the characteristics of the assay.

  7. Mortality prediction to hospitalized patients with influenza pneumonia: PO2 /FiO2 combined lymphocyte count is the answer.

    Science.gov (United States)

    Shi, Shu Jing; Li, Hui; Liu, Meng; Liu, Ying Mei; Zhou, Fei; Liu, Bo; Qu, Jiu Xin; Cao, Bin

    2017-05-01

    Community-acquired pneumonia (CAP) severity scores perform well in predicting mortality of CAP patients, but their applicability in influenza pneumonia is powerless. The aim of our research was to test the efficiency of PO 2 /FiO 2 and CAP severity scores in predicting mortality and intensive care unit (ICU) admission with influenza pneumonia patients. We reviewed all patients with positive influenza virus RNA detection in Beijing Chao-Yang Hospital during the 2009-2014 influenza seasons. Outpatients, inpatients with no pneumonia and incomplete data were excluded. We used receiver operating characteristic curves (ROCs) to verify the accuracy of severity scores or indices as mortality predictors in the study patients. Among 170 hospitalized patients with influenza pneumonia, 30 (17.6%) died. Among those who were classified as low-risk (predicted mortality 0.1%-2.1%) by pneumonia severity index (PSI) or confusion, urea, respiratory rate, blood pressure, age ≥65 year (CURB-65), the actual mortality ranged from 5.9 to 22.1%. Multivariate logistic regression indicated that hypoxia (PO 2 /FiO 2  ≤ 250) and lymphopenia (peripheral blood lymphocyte count pneumonia confirmed a similar pattern and PO 2 /FiO 2 combined lymphocyte count was also the best predictor for predicting ICU admission. In conclusion, we found that PO 2 /FiO 2 combined lymphocyte count is simple and reliable predictor of hospitalized patients with influenza pneumonia in predicting mortality and ICU admission. When PO 2 /FiO 2  ≤ 250 or peripheral blood lymphocyte count pneumonia. © 2015 The Authors. The Clinical Respiratory Journal published by John Wiley & Sons Ltd.

  8. Predictive error detection in pianists: A combined ERP and motion capture study

    Directory of Open Access Journals (Sweden)

    Clemens eMaidhof

    2013-09-01

    Full Text Available Performing a piece of music involves the interplay of several cognitive and motor processes and requires extensive training to achieve a high skill level. However, even professional musicians commit errors occasionally. Previous event-related potential (ERP studies have investigated the neurophysiological correlates of pitch errors during piano performance, and reported pre-error negativity already occurring approximately 70-100 ms before the error had been committed and audible. It was assumed that this pre-error negativity reflects predictive control processes that compare predicted consequences with actual consequences of one’s own actions. However, in previous investigations, correct and incorrect pitch events were confounded by their different tempi. In addition, no data about the underlying movements were available. In the present study, we exploratively recorded the ERPs and 3D movement data of pianists’ fingers simultaneously while they performed fingering exercises from memory. Results showed a pre-error negativity for incorrect keystrokes when both correct and incorrect keystrokes were performed with comparable tempi. Interestingly, even correct notes immediately preceding erroneous keystrokes elicited a very similar negativity. In addition, we explored the possibility of computing ERPs time-locked to a kinematic landmark in the finger motion trajectories defined by when a finger makes initial contact with the key surface, that is, at the onset of tactile feedback. Results suggest that incorrect notes elicited a small difference after the onset of tactile feedback, whereas correct notes preceding incorrect ones elicited negativity before the onset of tactile feedback. The results tentatively suggest that tactile feedback plays an important role in error-monitoring during piano performance, because the comparison between predicted and actual sensory (tactile feedback may provide the information necessary for the detection of an

  9. Predictions from a flavour GUT model combined with a SUSY breaking sector

    Science.gov (United States)

    Antusch, Stefan; Hohl, Christian

    2017-10-01

    We discuss how flavour GUT models in the context of supergravity can be completed with a simple SUSY breaking sector, such that the flavour-dependent (non-universal) soft breaking terms can be calculated. As an example, we discuss a model based on an SU(5) GUT symmetry and A 4 family symmetry, plus additional discrete "shaping symmetries" and a ℤ 4 R symmetry. We calculate the soft terms and identify the relevant high scale input parameters, and investigate the resulting predictions for the low scale observables, such as flavour violating processes, the sparticle spectrum and the dark matter relic density.

  10. Predictability of copper, irgarol, and diuron combined effects on sea urchin Paracentrotus lividus.

    Science.gov (United States)

    Manzo, S; Buono, S; Cremisini, C

    2008-01-01

    The aim of this work was to investigate the mixture toxicity of Irgarol (2-methylthio-4-t-butylamino-6-cyclopropylamino-s-triazine), Diuron (3-(3,4-dichlorophenyl)-1,1-dimethylurea), and copper upon the sea urchin Paracentrotus lividus and to compare the observed data with the predictions derived from approaches of Concentration Addition (CA) and Independent Action (IA). Copper spermiotoxicity was more sensitive (EC50 = 0.018 mg/L) than embryotoxicity (EC50 = 0.046 mg/L). The offspring malformations were mainly P1 type (skeletal alterations) in both cases, probably because copper competes to fix Ca2+. Irgarol and Diuron toxicity has been previously investigated. EC50 mixture embryotoxicity showed an EC50 of 1.79 mg/L, whereas spermiotoxicity mixture effects were lower than 11%. Both CA and IA modeling approaches failed to predict accurately mixture toxicity. For embryotoxicity, the IA model overestimated the mixture toxicity at effect levels of <80%. CA does not represent the worst-case approach showing values lower than IA (embryotoxicity) or similar (spermiotoxicity).

  11. Bimanual coordination positively predicts episodic memory: A combined behavioral and MRI investigation.

    Science.gov (United States)

    Lyle, Keith B; Dombroski, Brynn A; Faul, Leonard; Hopkins, Robin F; Naaz, Farah; Switala, Andrew E; Depue, Brendan E

    2017-11-01

    Some people remember events more completely and accurately than other people, but the origins of individual differences in episodic memory are poorly understood. One way to advance understanding is by identifying characteristics of individuals that reliably covary with memory performance. Recent research suggests motor behavior is related to memory performance, with individuals who consistently use a single preferred hand for unimanual actions performing worse than individuals who make greater use of both hands. This research has relied on self-reports of behavior. It is unknown whether objective measures of motor behavior also predict memory performance. Here, we tested the predictive power of bimanual coordination, an important form of manual dexterity. Bimanual coordination, as measured objectively on the Purdue Pegboard Test, was positively related to correct recall on the California Verbal Learning Test-II and negatively related to false recall. Furthermore, MRI data revealed that cortical surface area in right lateral prefrontal regions was positively related to correct recall. In one of these regions, cortical thickness was negatively related to bimanual coordination. These results suggest that individual differences in episodic memory may partially reflect morphological variation in right lateral prefrontal cortex and suggest a relationship between neural correlates of episodic memory and motor behavior. Copyright © 2017 Elsevier Inc. All rights reserved.

  12. A combination of routine blood analytes predicts fitness decrement in elderly endurance athletes.

    Directory of Open Access Journals (Sweden)

    Helmuth Haslacher

    Full Text Available Endurance sports are enjoying greater popularity, particularly among new target groups such as the elderly. Predictors of future physical capacities providing a basis for training adaptations are in high demand. We therefore aimed to estimate the future physical performance of elderly marathoners (runners/bicyclists using a set of easily accessible standard laboratory parameters. To this end, 47 elderly marathon athletes underwent physical examinations including bicycle ergometry and a blood draw at baseline and after a three-year follow-up period. In order to compile a statistical model containing baseline laboratory results allowing prediction of follow-up ergometry performance, the cohort was subgrouped into a model training (n = 25 and a test sample (n = 22. The model containing significant predictors in univariate analysis (alanine aminotransferase, urea, folic acid, myeloperoxidase and total cholesterol presented with high statistical significance and excellent goodness of fit (R2 = 0.789, ROC-AUC = 0.951±0.050 in the model training sample and was validated in the test sample (ROC-AUC = 0.786±0.098. Our results suggest that standard laboratory parameters could be particularly useful for predicting future physical capacity in elderly marathoners. It hence merits further research whether these conclusions can be translated to other disciplines or age groups.

  13. Combined Analysis of Plasma Amphiregulin and Heregulin Predicts Response to Cetuximab in Metastatic Colorectal Cancer.

    Directory of Open Access Journals (Sweden)

    Kimio Yonesaka

    Full Text Available Amphiregulin, a ligand of the epidermal growth factor receptor (EGFR, is associated with the efficacy of cetuximab, an antibody against EGFR, as treatment for colorectal cancer (CRC. In contrast, the HER3 ligand heregulin correlates with cetuximab resistance. In this study, we evaluated how the combined levels of circulating amphiregulin and heregulin affect clinical outcomes in patients who receive cetuximab as therapy against advanced CRC.Plasma levels of amphiregulin and heregulin were measured by enzyme-linked immunosorbent assay in 50 patients with CRC in a training cohort, and in 10 patients in a validation cohort. The combined expression was then assessed with clinical outcome after receiver operating characteristics analysis.Overall response rate was 26%, and median progression-free survival was 110 days in the training cohort. Patients with high amphiregulin and low heregulin had significantly higher objective response rate at 58% and significantly longer progression-free survival of 216 days. This result was confirmed in the validation cohort.A subgroup of CRC patients with high amphiregulin and low heregulin respond to cetuximab therapy better than other patients.

  14. Factors predictive of sustained virological response following 72 weeks of combination therapy for genotype 1b hepatitis C.

    Science.gov (United States)

    Chayama, Kazuaki; Hayes, C Nelson; Yoshioka, Kentaro; Moriwaki, Hisataka; Okanoue, Takashi; Sakisaka, Shotaro; Takehara, Tetsuo; Oketani, Makoto; Toyota, Joji; Izumi, Namiki; Hiasa, Yoichi; Matsumoto, Akihiro; Nomura, Hideyuki; Seike, Masataka; Ueno, Yoshiyuki; Yotsuyanagi, Hiroshi; Kumada, Hiromitsu

    2011-04-01

    Treatment of genotype 1b chronic hepatitis C virus (HCV) infection has been improved by extending peg-interferon plus ribavirin combination therapy to 72 weeks, but predictive factors are needed to identify those patients who are likely to respond to long-term therapy. We analyzed amino acid (aa) substitutions in the core protein and the interferon sensitivity determining region (ISDR) of nonstructural protein (NS) 5A in 840 genotype 1b chronic hepatitis C patients with high viral load. We used logistic regression and classification and regression tree (CART) analysis to identify predictive factors for sustained virological response (SVR) for patients undergoing 72 weeks of treatment. When patients were separately analyzed by treatment duration using multivariate logistic regression, several factors, including sex, age, viral load, and core aa70 and ISDR substitutions (P = 0.0003, P = 0.02, P = 0.01, P = 0.0001, and P = 0.0004, respectively) were significant predictive factors for SVR with 48 weeks of treatment, whereas age, previous interferon treatment history, and ISDR substitutions (P = 0.03, P = 0.01, and P = 0.02, respectively) were the only significant predictive factors with 72 weeks of treatment. Using CART analysis, a decision tree was generated that identified age, cholesterol, sex, treatment length, and aa70 and ISDR substitutions as the most important predictive factors. The CART model had a sensitivity of 69.2% and specificity of 60%, with a positive predictive value of 68.4%. Complementary statistical and data mining approaches were used to identify a subgroup of patients likely to benefit from 72 weeks of therapy.

  15. Predicting cell types and genetic variations contributing to disease by combining GWAS and epigenetic data.

    Directory of Open Access Journals (Sweden)

    Anna Gerasimova

    Full Text Available Genome-wide association studies (GWASs identify single nucleotide polymorphisms (SNPs that are enriched in individuals suffering from a given disease. Most disease-associated SNPs fall into non-coding regions, so that it is not straightforward to infer phenotype or function; moreover, many SNPs are in tight genetic linkage, so that a SNP identified as associated with a particular disease may not itself be causal, but rather signify the presence of a linked SNP that is functionally relevant to disease pathogenesis. Here, we present an analysis method that takes advantage of the recent rapid accumulation of epigenomics data to address these problems for some SNPs. Using asthma as a prototypic example; we show that non-coding disease-associated SNPs are enriched in genomic regions that function as regulators of transcription, such as enhancers and promoters. Identifying enhancers based on the presence of the histone modification marks such as H3K4me1 in different cell types, we show that the location of enhancers is highly cell-type specific. We use these findings to predict which SNPs are likely to be directly contributing to disease based on their presence in regulatory regions, and in which cell types their effect is expected to be detectable. Moreover, we can also predict which cell types contribute to a disease based on overlap of the disease-associated SNPs with the locations of enhancers present in a given cell type. Finally, we suggest that it will be possible to re-analyze GWAS studies with much higher power by limiting the SNPs considered to those in coding or regulatory regions of cell types relevant to a given disease.

  16. Predicting the oral uptake efficiency of chemicals in mammals: Combining the hydrophilic and lipophilic range

    Energy Technology Data Exchange (ETDEWEB)

    O' Connor, Isabel A., E-mail: i.oconnor@science.ru.nl [Radboud University Nijmegen, Institute for Water and Wetland Research, Department of Environmental Science, P.O. Box 9010, NL-6500 GL, Nijmegen (Netherlands); Huijbregts, Mark A.J., E-mail: m.huijbregts@science.ru.nl [Radboud University Nijmegen, Institute for Water and Wetland Research, Department of Environmental Science, P.O. Box 9010, NL-6500 GL, Nijmegen (Netherlands); Ragas, Ad M.J., E-mail: a.ragas@science.ru.nl [Radboud University Nijmegen, Institute for Water and Wetland Research, Department of Environmental Science, P.O. Box 9010, NL-6500 GL, Nijmegen (Netherlands); Open University, School of Science, P.O. Box 2960,6401 DL Heerlen (Netherlands); Hendriks, A. Jan, E-mail: a.j.hendriks@science.ru.nl [Radboud University Nijmegen, Institute for Water and Wetland Research, Department of Environmental Science, P.O. Box 9010, NL-6500 GL, Nijmegen (Netherlands)

    2013-01-01

    Environmental risk assessment requires models for estimating the bioaccumulation of untested compounds. So far, bioaccumulation models have focused on lipophilic compounds, and only a few have included hydrophilic compounds. Our aim was to extend an existing bioaccumulation model to estimate the oral uptake efficiency of pollutants in mammals for compounds over a wide K{sub ow} range with an emphasis on hydrophilic compounds, i.e. compounds in the lower K{sub ow} range. Usually, most models use octanol as a single surrogate for the membrane and thus neglect the bilayer structure of the membrane. However, compounds with polar groups can have different affinities for the different membrane regions. Therefore, an existing bioaccumulation model was extended by dividing the diffusion resistance through the membrane into an outer and inner membrane resistance, where the solvents octanol and heptane were used as surrogates for these membrane regions, respectively. The model was calibrated with uptake efficiencies of environmental pollutants measured in different mammals during feeding studies combined with human oral uptake efficiencies of pharmaceuticals. The new model estimated the uptake efficiency of neutral (RMSE = 14.6) and dissociating (RMSE = 19.5) compounds with logK{sub ow} ranging from − 10 to + 8. The inclusion of the K{sub hw} improved uptake estimation for 33% of the hydrophilic compounds (logK{sub ow} < 0) (r{sup 2} = 0.51, RMSE = 22.8) compared with the model based on K{sub ow} only (r{sup 2} = 0.05, RMSE = 34.9), while hydrophobic compounds (logK{sub ow} > 0) were estimated equally by both model versions with RMSE = 15.2 (K{sub ow} and K{sub hw}) and RMSE = 15.7 (K{sub ow} only). The model can be used to estimate the oral uptake efficiency for both hydrophilic and hydrophobic compounds. -- Highlights: ► A mechanistic model was developed to estimate oral uptake efficiency. ► Model covers wide logK{sub ow} range (- 10 to + 8) and several mammalian

  17. Adaptive adjustment of interval predictive control based on combined model and application in shell brand petroleum distillation tower

    Science.gov (United States)

    Sun, Chao; Zhang, Chunran; Gu, Xinfeng; Liu, Bin

    2017-10-01

    Constraints of the optimization objective are often unable to be met when predictive control is applied to industrial production process. Then, online predictive controller will not find a feasible solution or a global optimal solution. To solve this problem, based on Back Propagation-Auto Regressive with exogenous inputs (BP-ARX) combined control model, nonlinear programming method is used to discuss the feasibility of constrained predictive control, feasibility decision theorem of the optimization objective is proposed, and the solution method of soft constraint slack variables is given when the optimization objective is not feasible. Based on this, for the interval control requirements of the controlled variables, the slack variables that have been solved are introduced, the adaptive weighted interval predictive control algorithm is proposed, achieving adaptive regulation of the optimization objective and automatically adjust of the infeasible interval range, expanding the scope of the feasible region, and ensuring the feasibility of the interval optimization objective. Finally, feasibility and effectiveness of the algorithm is validated through the simulation comparative experiments.

  18. Combination of Biorthogonal Wavelet Hybrid Kernel OCSVM with Feature Weighted Approach Based on EVA and GRA in Financial Distress Prediction

    Directory of Open Access Journals (Sweden)

    Chao Huang

    2014-01-01

    Full Text Available Financial distress prediction plays an important role in the survival of companies. In this paper, a novel biorthogonal wavelet hybrid kernel function is constructed by combining linear kernel function with biorthogonal wavelet kernel function. Besides, a new feature weighted approach is presented based on economic value added (EVA and grey relational analysis (GRA. Considering the imbalance between financially distressed companies and normal ones, the feature weighted one-class support vector machine based on biorthogonal wavelet hybrid kernel (BWH-FWOCSVM is further put forward for financial distress prediction. The empirical study with real data from the listed companies on Growth Enterprise Market (GEM in China shows that the proposed approach has good performance.

  19. Predicting the impact of combined therapies on myeloma cell growth using a hybrid multi-scale agent-based model.

    Science.gov (United States)

    Ji, Zhiwei; Su, Jing; Wu, Dan; Peng, Huiming; Zhao, Weiling; Nlong Zhao, Brian; Zhou, Xiaobo

    2017-01-31

    Multiple myeloma is a malignant still incurable plasma cell disorder. This is due to refractory disease relapse, immune impairment, and development of multi-drug resistance. The growth of malignant plasma cells is dependent on the bone marrow (BM) microenvironment and evasion of the host's anti-tumor immune response. Hence, we hypothesized that targeting tumor-stromal cell interaction and endogenous immune system in BM will potentially improve the response of multiple myeloma (MM). Therefore, we proposed a computational simulation of the myeloma development in the complicated microenvironment which includes immune cell components and bone marrow stromal cells and predicted the effects of combined treatment with multi-drugs on myeloma cell growth. We constructed a hybrid multi-scale agent-based model (HABM) that combines an ODE system and Agent-based model (ABM). The ODEs was used for modeling the dynamic changes of intracellular signal transductions and ABM for modeling the cell-cell interactions between stromal cells, tumor, and immune components in the BM. This model simulated myeloma growth in the bone marrow microenvironment and revealed the important role of immune system in this process. The predicted outcomes were consistent with the experimental observations from previous studies. Moreover, we applied this model to predict the treatment effects of three key therapeutic drugs used for MM, and found that the combination of these three drugs potentially suppress the growth of myeloma cells and reactivate the immune response. In summary, the proposed model may serve as a novel computational platform for simulating the formation of MM and evaluating the treatment response of MM to multiple drugs.

  20. Combination of Intra-Hematomal Hypodensity on CT and BRAIN Scoring Improves Prediction of Hemorrhage Expansion in ICH.

    Science.gov (United States)

    VanDerWerf, Joshua; Kurowski, Donna; Siegler, James; Ganguly, Taneeta; Cucchiara, Brett

    2018-02-06

    Hematoma expansion (HE) occurs in 1/3 of ICH patients and is associated with poor outcome. Intra-hematomal hypodensity (IHH) on CT has been reported to predict HE, as has the "BRAIN" score. We sought to assess the predictive value of these markers alone and in combination. We performed a retrospective single-center study of ICH patients with CT IHH on initial CT. Two HE definitions were examined: > 6 ml and > 6 ml or > 33%. Multivariable logistic regression was used to determine the relationship between IHH and HE. Predictive value of the BRAIN score alone and integrated with IHH was assessed. In 122 included patients, median ICH volume was 13 ml, median time to CT 2.0 h; HE > 6 ml occurred in 31% and > 6 ml/> 33% in 43% of subjects. IHH were identified in 61% of patients with moderate inter-rater agreement (κ = 0.59). In multivariable analysis, IHH was associated with HE using > 6 ml definition (OR 8.3, 95% CI, 2.6-32.8, P  6 ml/> 33% definition (OR 1.9, 95% CI 0.84-4.3, P = 0.12). Rate of HE (> 6 ml) increased across increasing BRAIN score quartiles (Q1:11%, Q2:23%, Q3:43%, Q4:57%, P for trend  6 ml in patients with BRAIN score ≥ 10 and IHH was 55%, with either alone was 33%, and with neither was 3%. Combining IHH on non-contrast CT and a simple clinical BRAIN score is a potentially powerful way to predict those patients at very high and very low risk of HE.

  1. Combining process-based and correlative models improves predictions of climate change effects on Schistosoma mansoni transmission in eastern Africa

    Directory of Open Access Journals (Sweden)

    Anna-Sofie Stensgaard

    2016-03-01

    Full Text Available Currently, two broad types of approach for predicting the impact of climate change on vector-borne diseases can be distinguished: i empirical-statistical (correlative approaches that use statistical models of relationships between vector and/or pathogen presence and environmental factors; and ii process-based (mechanistic approaches that seek to simulate detailed biological or epidemiological processes that explicitly describe system behavior. Both have advantages and disadvantages, but it is generally acknowledged that both approaches have value in assessing the response of species in general to climate change. Here, we combine a previously developed dynamic, agentbased model of the temperature-sensitive stages of the Schistosoma mansoni and intermediate host snail lifecycles, with a statistical model of snail habitat suitability for eastern Africa. Baseline model output compared to empirical prevalence data suggest that the combined model performs better than a temperature-driven model alone, and highlights the importance of including snail habitat suitability when modeling schistosomiasis risk. There was general agreement among models in predicting changes in risk, with 24-36% of the eastern Africa region predicted to experience an increase in risk of up-to 20% as a result of increasing temperatures over the next 50 years. Vice versa the models predicted a general decrease in risk in 30-37% of the study area. The snail habitat suitability models also suggest that anthropogenically altered habitat play a vital role for the current distribution of the intermediate snail host, and hence we stress the importance of accounting for land use changes in models of future changes in schistosomiasis risk.

  2. Pharmacodynamically optimized erythropoietin treatment combined with phlebotomy reduction predicted to eliminate blood transfusions in selected preterm infants.

    Science.gov (United States)

    Rosebraugh, Matthew R; Widness, John A; Nalbant, Demet; Cress, Gretchen; Veng-Pedersen, Peter

    2014-02-01

    Preterm very-low-birth-weight (VLBW) infants weighing eliminated by reducing laboratory blood loss in combination with pharmacodynamically optimized erythropoietin (Epo) treatment. Twenty-six VLBW ventilated infants receiving RBCTx were studied during the first month of life. RBCTx simulations were based on previously published RBCTx criteria and data-driven Epo pharmacodynamic optimization of literature-derived RBC life span and blood volume data corrected for phlebotomy loss. Simulated pharmacodynamic optimization of Epo administration and reduction in phlebotomy by ≥ 55% predicted a complete elimination of RBCTx in 1.0-1.5 kg infants. In infants 1.0 kg.

  3. HemeBIND: a novel method for heme binding residue prediction by combining structural and sequence information

    Directory of Open Access Journals (Sweden)

    Hu Jianjun

    2011-05-01

    Full Text Available Abstract Background Accurate prediction of binding residues involved in the interactions between proteins and small ligands is one of the major challenges in structural bioinformatics. Heme is an essential and commonly used ligand that plays critical roles in electron transfer, catalysis, signal transduction and gene expression. Although much effort has been devoted to the development of various generic algorithms for ligand binding site prediction over the last decade, no algorithm has been specifically designed to complement experimental techniques for identification of heme binding residues. Consequently, an urgent need is to develop a computational method for recognizing these important residues. Results Here we introduced an efficient algorithm HemeBIND for predicting heme binding residues by integrating structural and sequence information. We systematically investigated the characteristics of binding interfaces based on a non-redundant dataset of heme-protein complexes. It was found that several sequence and structural attributes such as evolutionary conservation, solvent accessibility, depth and protrusion clearly illustrate the differences between heme binding and non-binding residues. These features can then be separately used or combined to build the structure-based classifiers using support vector machine (SVM. The results showed that the information contained in these features is largely complementary and their combination achieved the best performance. To further improve the performance, an attempt has been made to develop a post-processing procedure to reduce the number of false positives. In addition, we built a sequence-based classifier based on SVM and sequence profile as an alternative when only sequence information can be used. Finally, we employed a voting method to combine the outputs of structure-based and sequence-based classifiers, which demonstrated remarkably better performance than the individual classifier alone

  4. Predicting protein complexes using a supervised learning method combined with local structural information.

    Science.gov (United States)

    Dong, Yadong; Sun, Yongqi; Qin, Chao

    2018-01-01

    The existing protein complex detection methods can be broadly divided into two categories: unsupervised and supervised learning methods. Most of the unsupervised learning methods assume that protein complexes are in dense regions of protein-protein interaction (PPI) networks even though many true complexes are not dense subgraphs. Supervised learning methods utilize the informative properties of known complexes; they often extract features from existing complexes and then use the features to train a classification model. The trained model is used to guide the search process for new complexes. However, insufficient extracted features, noise in the PPI data and the incompleteness of complex data make the classification model imprecise. Consequently, the classification model is not sufficient for guiding the detection of complexes. Therefore, we propose a new robust score function that combines the classification model with local structural information. Based on the score function, we provide a search method that works both forwards and backwards. The results from experiments on six benchmark PPI datasets and three protein complex datasets show that our approach can achieve better performance compared with the state-of-the-art supervised, semi-supervised and unsupervised methods for protein complex detection, occasionally significantly outperforming such methods.

  5. Results of fatigue tests and prediction of fatigue life under superposed stress wave and combined superposed stress wave

    International Nuclear Information System (INIS)

    Takasugi, Shunji; Horikawa, Takeshi; Tsunenari, Toshiyasu; Nakamura, Hiroshi

    1983-01-01

    In order to examine fatigue life prediction methods at high temperatures where creep damage need not be taken into account, fatigue tests were carried out on plane bending specimens of alloy steels (SCM 435, 2 1/4Cr-1Mo) under superposed and combined superposed stress waves at room temperature and 500 0 C. The experimental data were compared with the fatigue lives predicted by using the cycle counting methods (range pair, range pair mean and zero-cross range pair mean methods), the modified Goodman's equation and the modified Miner's rule. The main results were as follows. (1) The fatigue life prediction method which is being used for the data at room temperature is also applicable to predict the life at high temperatures. The range pair mean method is especially better than other cycle counting methods. The zero-cross range pair mean method gives the estimated lives on the safe side of the experimental lives. (2) The scatter bands of N-bar/N-barsub(es) (experimental life/estimated life) becomes narrower when the following equation is used instead of the modified Goodman's equation for predicting the effect of mean stress on fatigue life. σ sub(t) = σ sub(a) / (1 - Sigma-s sub(m) / kσ sub(B)) σ sub(t); stress amplitude at zero mean stress (kg/mm 2 ) σ sub(B); tensile strength (kg/mm 2 ) σ sub(m); mean stress (kg/mm 2 ) σ sub(a); stress amplitude (kg/mm 2 ) k; modified coefficient of σ sub(B) (author)

  6. Prediction of canine and premolar size using the widths of various permanent teeth combinations: A cross-sectional study

    Directory of Open Access Journals (Sweden)

    Kalasandhya Vanjari

    2015-01-01

    Full Text Available Aims: To suggest the best predictor/s for determining the mesio-distal widths (MDWs of canines (C and premolars (Ps, and propose regression equation/s for hitherto unreported population. Methods: Impressions of maxillary and mandibular arches were made for 201 children (100 boys and 101 girls; age range: 11–15 years who met the inclusion criteria and poured with dental stone. The maximum MDWs of all the permanent teeth were measured using digital vernier caliper. Thirty-three possible combinations (patterns of permanent maxillary and mandibular first molars, central and lateral incisors were framed and correlated with MDWs of C and Ps using Pearson correlation test. Results: There were significant correlations between the considered patterns and MDWs of C and Ps, with difference noted between girls (range of r: 0.34–0.66 and boys (range of r: 0.28–0.77. Simple linear and multiple regression equations for boys, girls, and combined sample were determined to predict MDW of C and Ps in both the arches. Conclusions: The accuracy of prediction improved considerably with the inclusion of as many teeth as possible in the regression equations. The newly proposed equations based on the erupted teeth may be considered clinically useful for space analysis in the considered population.

  7. Prediction of fetal growth restriction using estimated fetal weight vs a combined screening model in the third trimester.

    Science.gov (United States)

    Miranda, J; Rodriguez-Lopez, M; Triunfo, S; Sairanen, M; Kouru, H; Parra-Saavedra, M; Crovetto, F; Figueras, F; Crispi, F; Gratacós, E

    2017-11-01

    To compare the performance of third-trimester screening, based on estimated fetal weight centile (EFWc) vs a combined model including maternal baseline characteristics, fetoplacental ultrasound and maternal biochemical markers, for the prediction of small-for-gestational-age (SGA) neonates and late-onset fetal growth restriction (FGR). This was a nested case-control study within a prospective cohort of 1590 singleton gestations undergoing third-trimester (32 + 0 to 36 + 6 weeks' gestation) evaluation. Maternal baseline characteristics, mean arterial pressure, fetoplacental ultrasound and circulating biochemical markers (placental growth factor (PlGF), lipocalin-2, unconjugated estriol and inhibin A) were assessed in all women who subsequently delivered a SGA neonate (n = 175), defined as birth weight < 10 th centile according to customized standards, and in a control group (n = 875). Among SGA cases, those with birth weight < 3 rd centile and/or abnormal uterine artery pulsatility index (UtA-PI) and/or abnormal cerebroplacental ratio (CPR) were classified as FGR. Logistic regression predictive models were developed for SGA and FGR, and their performance was compared with that obtained using EFWc alone. In SGA cases, EFWc, CPR Z-score and maternal serum concentrations of unconjugated estriol and PlGF were significantly lower, while mean UtA-PI Z-score and lipocalin-2 and inhibin A concentrations were significantly higher, compared with controls. Using EFWc alone, 52% (area under receiver-operating characteristics curve (AUC), 0.82 (95% CI, 0.77-0.85)) of SGA and 64% (AUC, 0.86 (95% CI, 0.81-0.91)) of FGR cases were predicted at a 10% false-positive rate. A combined screening model including a-priori risk (maternal characteristics), EFWc, UtA-PI, PlGF and estriol (with lipocalin-2 for SGA) achieved a detection rate of 61% (AUC, 0.86 (95% CI, 0.83-0.89)) for SGA cases and 77% (AUC, 0.92 (95% CI, 0.88-0.95)) for FGR. The combined model for the

  8. Accurate prediction of stability changes in protein mutants by combining machine learning with structure based computational mutagenesis.

    Science.gov (United States)

    Masso, Majid; Vaisman, Iosif I

    2008-09-15

    Accurate predictive models for the impact of single amino acid substitutions on protein stability provide insight into protein structure and function. Such models are also valuable for the design and engineering of new proteins. Previously described methods have utilized properties of protein sequence or structure to predict the free energy change of mutants due to thermal (DeltaDeltaG) and denaturant (DeltaDeltaG(H2O)) denaturations, as well as mutant thermal stability (DeltaT(m)), through the application of either computational energy-based approaches or machine learning techniques. However, accuracy associated with applying these methods separately is frequently far from optimal. We detail a computational mutagenesis technique based on a four-body, knowledge-based, statistical contact potential. For any mutation due to a single amino acid replacement in a protein, the method provides an empirical normalized measure of the ensuing environmental perturbation occurring at every residue position. A feature vector is generated for the mutant by considering perturbations at the mutated position and it's ordered six nearest neighbors in the 3-dimensional (3D) protein structure. These predictors of stability change are evaluated by applying machine learning tools to large training sets of mutants derived from diverse proteins that have been experimentally studied and described. Predictive models based on our combined approach are either comparable to, or in many cases significantly outperform, previously published results. A web server with supporting documentation is available at http://proteins.gmu.edu/automute.

  9. Sugar and acid content of Citrus prediction modeling using FT-IR fingerprinting in combination with multivariate statistical analysis.

    Science.gov (United States)

    Song, Seung Yeob; Lee, Young Koung; Kim, In-Jung

    2016-01-01

    A high-throughput screening system for Citrus lines were established with higher sugar and acid contents using Fourier transform infrared (FT-IR) spectroscopy in combination with multivariate analysis. FT-IR spectra confirmed typical spectral differences between the frequency regions of 950-1100 cm(-1), 1300-1500 cm(-1), and 1500-1700 cm(-1). Principal component analysis (PCA) and subsequent partial least square-discriminant analysis (PLS-DA) were able to discriminate five Citrus lines into three separate clusters corresponding to their taxonomic relationships. The quantitative predictive modeling of sugar and acid contents from Citrus fruits was established using partial least square regression algorithms from FT-IR spectra. The regression coefficients (R(2)) between predicted values and estimated sugar and acid content values were 0.99. These results demonstrate that by using FT-IR spectra and applying quantitative prediction modeling to Citrus sugar and acid contents, excellent Citrus lines can be early detected with greater accuracy. Copyright © 2015 Elsevier Ltd. All rights reserved.

  10. Combining PubMed knowledge and EHR data to develop a weighted bayesian network for pancreatic cancer prediction.

    Science.gov (United States)

    Zhao, Di; Weng, Chunhua

    2011-10-01

    In this paper, we propose a novel method that combines PubMed knowledge and Electronic Health Records to develop a weighted Bayesian Network Inference (BNI) model for pancreatic cancer prediction. We selected 20 common risk factors associated with pancreatic cancer and used PubMed knowledge to weigh the risk factors. A keyword-based algorithm was developed to extract and classify PubMed abstracts into three categories that represented positive, negative, or neutral associations between each risk factor and pancreatic cancer. Then we designed a weighted BNI model by adding the normalized weights into a conventional BNI model. We used this model to extract the EHR values for patients with or without pancreatic cancer, which then enabled us to calculate the prior probabilities for the 20 risk factors in the BNI. The software iDiagnosis was designed to use this weighted BNI model for predicting pancreatic cancer. In an evaluation using a case-control dataset, the weighted BNI model significantly outperformed the conventional BNI and two other classifiers (k-Nearest Neighbor and Support Vector Machine). We conclude that the weighted BNI using PubMed knowledge and EHR data shows remarkable accuracy improvement over existing representative methods for pancreatic cancer prediction. Copyright © 2011 Elsevier Inc. All rights reserved.

  11. Biopsychosocial influence on exercise-induced injury: genetic and psychological combinations are predictive of shoulder pain phenotypes.

    Science.gov (United States)

    George, Steven Z; Parr, Jeffrey J; Wallace, Margaret R; Wu, Samuel S; Borsa, Paul A; Dai, Yunfeng; Fillingim, Roger B

    2014-01-01

    Chronic pain is influenced by biological, psychological, social, and cultural factors. The current study investigated potential roles for combinations of genetic and psychological factors in the development and/or maintenance of chronic musculoskeletal pain. An exercise-induced shoulder injury model was used, and a priori selected genetic (ADRB2, COMT, OPRM1, AVPR1 A, GCH1, and KCNS1) and psychological (anxiety, depressive symptoms, pain catastrophizing, fear of pain, and kinesiophobia) factors were included as predictors. Pain phenotypes were shoulder pain intensity (5-day average and peak reported on numerical rating scale), upper extremity disability (5-day average and peak reported on the QuickDASH), and shoulder pain duration (in days). After controlling for age, sex, and race, the genetic and psychological predictors were entered as main effects and interaction terms in separate regression models for the different pain phenotypes. Results from the recruited cohort (N = 190) indicated strong statistical evidence for interactions between the COMT diplotype and 1) pain catastrophizing for 5-day average upper extremity disability and 2) depressive symptoms for pain duration. There was moderate statistical evidence for interactions for other shoulder pain phenotypes between additional genes (ADRB2, AVPR1 A, and KCNS1) and depressive symptoms, pain catastrophizing, or kinesiophobia. These findings confirm the importance of the combined predictive ability of COMT with psychological distress and reveal other novel combinations of genetic and psychological factors that may merit additional investigation in other pain cohorts. Interactions between genetic and psychological factors were investigated as predictors of different exercise-induced shoulder pain phenotypes. The strongest statistical evidence was for interactions between the COMT diplotype and pain catastrophizing (for upper extremity disability) or depressive symptoms (for pain duration). Other novel

  12. SU-D-BRB-02: Combining a Commercial Autoplanning Engine with Database Dose Predictions to Further Improve Plan Quality

    Energy Technology Data Exchange (ETDEWEB)

    Robertson, SP; Moore, JA; Hui, X; Cheng, Z; McNutt, TR [Johns Hopkins University, Baltimore, MD (United States); DeWeese, TL; Tran, P; Quon, H [John Hopkins Hospital, Baltimore, MD (United States); Bzdusek, K [Philips, Fitchburg, WI (United States); Kumar, P [Philips India Limited, Bangalore, Karnataka (India)

    2016-06-15

    Purpose: Database dose predictions and a commercial autoplanning engine both improve treatment plan quality in different but complimentary ways. The combination of these planning techniques is hypothesized to further improve plan quality. Methods: Four treatment plans were generated for each of 10 head and neck (HN) and 10 prostate cancer patients, including Plan-A: traditional IMRT optimization using clinically relevant default objectives; Plan-B: traditional IMRT optimization using database dose predictions; Plan-C: autoplanning using default objectives; and Plan-D: autoplanning using database dose predictions. One optimization was used for each planning method. Dose distributions were normalized to 95% of the planning target volume (prostate: 8000 cGy; HN: 7000 cGy). Objectives used in plan optimization and analysis were the larynx (25%, 50%, 90%), left and right parotid glands (50%, 85%), spinal cord (0%, 50%), rectum and bladder (0%, 20%, 50%, 80%), and left and right femoral heads (0%, 70%). Results: All objectives except larynx 25% and 50% resulted in statistically significant differences between plans (Friedman’s χ{sup 2} ≥ 11.2; p ≤ 0.011). Maximum dose to the rectum (Plans A-D: 8328, 8395, 8489, 8537 cGy) and bladder (Plans A-D: 8403, 8448, 8527, 8569 cGy) were significantly increased. All other significant differences reflected a decrease in dose. Plans B-D were significantly different from Plan-A for 3, 17, and 19 objectives, respectively. Plans C-D were also significantly different from Plan-B for 8 and 13 objectives, respectively. In one case (cord 50%), Plan-D provided significantly lower dose than plan C (p = 0.003). Conclusion: Combining database dose predictions with a commercial autoplanning engine resulted in significant plan quality differences for the greatest number of objectives. This translated to plan quality improvements in most cases, although special care may be needed for maximum dose constraints. Further evaluation is warranted

  13. Improving predictions for collider observables by consistently combining fixed order calculations with resummed results in perturbation theory

    International Nuclear Information System (INIS)

    Schoenherr, Marek

    2011-01-01

    With the constantly increasing precision of experimental data acquired at the current collider experiments Tevatron and LHC the theoretical uncertainty on the prediction of multiparticle final states has to decrease accordingly in order to have meaningful tests of the underlying theories such as the Standard Model. A pure leading order calculation, defined in the perturbative expansion of said theory in the interaction constant, represents the classical limit to such a quantum field theory and was already found to be insufficient at past collider experiments, e.g. LEP or HERA. Such a leading order calculation can be systematically improved in various limits. If the typical scales of a process are large and the respective coupling constants are small, the inclusion of fixed-order higher-order corrections then yields quickly converging predictions with much reduced uncertainties. In certain regions of the phase space, still well within the perturbative regime of the underlying theory, a clear hierarchy of the inherent scales, however, leads to large logarithms occurring at every order in perturbation theory. In many cases these logarithms are universal and can be resummed to all orders leading to precise predictions in these limits. Multiparticle final states now exhibit both small and large scales, necessitating a description using both resummed and fixed-order results. This thesis presents the consistent combination of two such resummation schemes with fixed-order results. The main objective therefor is to identify and properly treat terms that are present in both formulations in a process and observable independent manner. In the first part the resummation scheme introduced by Yennie, Frautschi and Suura (YFS), resumming large logarithms associated with the emission of soft photons in massive QED, is combined with fixed-order next-to-leading matrix elements. The implementation of a universal algorithm is detailed and results are studied for various precision

  14. Precise prediction for the light MSSM Higgs-boson mass combining effective field theory and fixed-order calculations

    Energy Technology Data Exchange (ETDEWEB)

    Bahl, Henning; Hollik, Wolfgang [Max-Planck-Institut fuer Physik (Werner-Heisenberg-Institut), Munich (Germany)

    2016-09-15

    In the Minimal Supersymmetric Standard Model heavy superparticles introduce large logarithms in the calculation of the lightest CP-even Higgs-boson mass. These logarithmic contributions can be resummed using effective field theory techniques. For light superparticles, however, fixed-order calculations are expected to be more accurate. To gain a precise prediction also for intermediate mass scales, the two approaches have to be combined. Here, we report on an improvement of this method in various steps: the inclusion of electroweak contributions, of separate electroweakino and gluino thresholds, as well as resummation at the NNLL level. These improvements can lead to significant numerical effects. In most cases, the lightest CP-even Higgs-boson mass is shifted downwards by about 1 GeV. This is mainly caused by higher-order corrections to the MS top-quark mass. We also describe the implementation of the new contributions in the code FeynHiggs. (orig.)

  15. Use of the Charlson Combined Comorbidity Index To Predict Postradiotherapy Quality of Life for Prostate Cancer Patients

    International Nuclear Information System (INIS)

    Wahlgren, Thomas; Levitt, Seymour; Kowalski, Jan; Nilsson, Sten; Brandberg, Yvonne

    2011-01-01

    Purpose: To determine the impact of pretreatment comorbidity on late health-related quality of life (HRQoL) scores after patients have undergone combined radiotherapy for prostate cancer, including high-dose rate brachytherapy boost and hormonal deprivation therapy. Methods and Materials: Results from the European Organization for Research and Treatment of Cancer QLQ-C30 questionnaire survey of 158 patients 5 years or more after completion of therapy were used from consecutively accrued subjects treated with curative radiotherapy at our institution, with no signs of disease at the time of questionnaire completion. HRQoL scores were compared with the Charlson combined comorbidity index (CCI), using analysis of covariance and multivariate regression models together with pretreatment factors including tumor stage, tumor grade, pretreatment prostate-specific antigen level, neoadjuvant hormonal treatment, diabetes status, cardiovascular status, and age and Charlson score as separate variables or the composite CCI. Results: An inverse correlation between the two HRQoL domains, long-term global health (QL) and physical function (PF) scores, and the CCI score was observed, indicating an impact of comorbidity in these function areas. Selected pretreatment factors poorly explained the variation in functional HRQoL in the multivariate models; however, a statistically significant impact was found for the CCI (with QL and PF scores) and the presence of diabetes (with QL and emotional function). Cognitive function and social function were not statistically significantly predicted by any of the pretreatment factors. Conclusions: The CCI proved to be valid in this context, but it seems useful mainly in predicting long-term QL and PF scores. Of the other variables investigated, diabetes had more impact than cardiovascular morbidity on HRQoL outcomes in prostate cancer.

  16. Use of the Charlson Combined Comorbidity Index To Predict Postradiotherapy Quality of Life for Prostate Cancer Patients

    Energy Technology Data Exchange (ETDEWEB)

    Wahlgren, Thomas, E-mail: thomas.wahlgren@telia.com [Department of Oncology-Pathology, Karolinska University Hospital and Institutet, Stockholm (Sweden); Levitt, Seymour [Department of Oncology-Pathology, Karolinska University Hospital and Institutet, Stockholm (Sweden); Department of Therapeutic Radiology, University of Minnesota, Minneapolis, Minnesota (United States); Kowalski, Jan [Department of Clinical Science, Intervention and Technology, Karolinska Institutet, Stockholm (Sweden); Nilsson, Sten; Brandberg, Yvonne [Department of Oncology-Pathology, Karolinska University Hospital and Institutet, Stockholm (Sweden)

    2011-11-15

    Purpose: To determine the impact of pretreatment comorbidity on late health-related quality of life (HRQoL) scores after patients have undergone combined radiotherapy for prostate cancer, including high-dose rate brachytherapy boost and hormonal deprivation therapy. Methods and Materials: Results from the European Organization for Research and Treatment of Cancer QLQ-C30 questionnaire survey of 158 patients 5 years or more after completion of therapy were used from consecutively accrued subjects treated with curative radiotherapy at our institution, with no signs of disease at the time of questionnaire completion. HRQoL scores were compared with the Charlson combined comorbidity index (CCI), using analysis of covariance and multivariate regression models together with pretreatment factors including tumor stage, tumor grade, pretreatment prostate-specific antigen level, neoadjuvant hormonal treatment, diabetes status, cardiovascular status, and age and Charlson score as separate variables or the composite CCI. Results: An inverse correlation between the two HRQoL domains, long-term global health (QL) and physical function (PF) scores, and the CCI score was observed, indicating an impact of comorbidity in these function areas. Selected pretreatment factors poorly explained the variation in functional HRQoL in the multivariate models; however, a statistically significant impact was found for the CCI (with QL and PF scores) and the presence of diabetes (with QL and emotional function). Cognitive function and social function were not statistically significantly predicted by any of the pretreatment factors. Conclusions: The CCI proved to be valid in this context, but it seems useful mainly in predicting long-term QL and PF scores. Of the other variables investigated, diabetes had more impact than cardiovascular morbidity on HRQoL outcomes in prostate cancer.

  17. A novel model to combine clinical and pathway-based transcriptomic information for the prognosis prediction of breast cancer.

    Directory of Open Access Journals (Sweden)

    Sijia Huang

    2014-09-01

    Full Text Available Breast cancer is the most common malignancy in women worldwide. With the increasing awareness of heterogeneity in breast cancers, better prediction of breast cancer prognosis is much needed for more personalized treatment and disease management. Towards this goal, we have developed a novel computational model for breast cancer prognosis by combining the Pathway Deregulation Score (PDS based pathifier algorithm, Cox regression and L1-LASSO penalization method. We trained the model on a set of 236 patients with gene expression data and clinical information, and validated the performance on three diversified testing data sets of 606 patients. To evaluate the performance of the model, we conducted survival analysis of the dichotomized groups, and compared the areas under the curve based on the binary classification. The resulting prognosis genomic model is composed of fifteen pathways (e.g., P53 pathway that had previously reported cancer relevance, and it successfully differentiated relapse in the training set (log rank p-value = 6.25e-12 and three testing data sets (log rank p-value < 0.0005. Moreover, the pathway-based genomic models consistently performed better than gene-based models on all four data sets. We also find strong evidence that combining genomic information with clinical information improved the p-values of prognosis prediction by at least three orders of magnitude in comparison to using either genomic or clinical information alone. In summary, we propose a novel prognosis model that harnesses the pathway-based dysregulation as well as valuable clinical information. The selected pathways in our prognosis model are promising targets for therapeutic intervention.

  18. RVMAB: Using the Relevance Vector Machine Model Combined with Average Blocks to Predict the Interactions of Proteins from Protein Sequences

    Directory of Open Access Journals (Sweden)

    Ji-Yong An

    2016-05-01

    Full Text Available Protein-Protein Interactions (PPIs play essential roles in most cellular processes. Knowledge of PPIs is becoming increasingly more important, which has prompted the development of technologies that are capable of discovering large-scale PPIs. Although many high-throughput biological technologies have been proposed to detect PPIs, there are unavoidable shortcomings, including cost, time intensity, and inherently high false positive and false negative rates. For the sake of these reasons, in silico methods are attracting much attention due to their good performances in predicting PPIs. In this paper, we propose a novel computational method known as RVM-AB that combines the Relevance Vector Machine (RVM model and Average Blocks (AB to predict PPIs from protein sequences. The main improvements are the results of representing protein sequences using the AB feature representation on a Position Specific Scoring Matrix (PSSM, reducing the influence of noise using a Principal Component Analysis (PCA, and using a Relevance Vector Machine (RVM based classifier. We performed five-fold cross-validation experiments on yeast and Helicobacter pylori datasets, and achieved very high accuracies of 92.98% and 95.58% respectively, which is significantly better than previous works. In addition, we also obtained good prediction accuracies of 88.31%, 89.46%, 91.08%, 91.55%, and 94.81% on other five independent datasets C. elegans, M. musculus, H. sapiens, H. pylori, and E. coli for cross-species prediction. To further evaluate the proposed method, we compare it with the state-of-the-art support vector machine (SVM classifier on the yeast dataset. The experimental results demonstrate that our RVM-AB method is obviously better than the SVM-based method. The promising experimental results show the efficiency and simplicity of the proposed method, which can be an automatic decision support tool. To facilitate extensive studies for future proteomics research, we developed

  19. Alcoholic fermentation under oenological conditions. Use of a combination of data analysis and neural networks to predict sluggish and stuck fermentations

    Energy Technology Data Exchange (ETDEWEB)

    Insa, G. [Inst. National de la Recherche Agronomique, Inst. des Produits de la Vigne, Lab. de Microbiologie et Technologie des Fermentations, 34 - Montpellier (France); Sablayrolles, J.M. [Inst. National de la Recherche Agronomique, Inst. des Produits de la Vigne, Lab. de Microbiologie et Technologie des Fermentations, 34 - Montpellier (France); Douzal, V. [Centre National du Machinisme Agricole du Genie Rural des Eaux et Forets, 92 - Antony (France)

    1995-09-01

    The possibility of predicting sluggish fermentations under oenological conditions was investigated by studying 117 musts of different French grape varieties using an automatic device for fermentation monitoring. The objective was to detect sluggish or stuck fermentations at the halfway point of fermentation. Seventy nine percent of fermentations were correctly predicted by combining data analysis and neural networks. (orig.)

  20. Predicting adherence to combination antiretroviral therapy for HIV in Tanzania: A test of an extended theory of planned behaviour model.

    Science.gov (United States)

    Banas, Kasia; Lyimo, Ramsey A; Hospers, Harm J; van der Ven, Andre; de Bruin, Marijn

    2017-10-01

    Combination antiretroviral therapy (cART) for HIV is widely available in sub-Saharan Africa. Adherence is crucial to successful treatment. This study aimed to apply an extended theory of planned behaviour (TPB) model to predict objectively measured adherence to cART in Tanzania. Prospective observational study (n = 158) where patients completed questionnaires on demographics (Month 0), socio-cognitive variables including intentions (Month 1), and action planning and self-regulatory processes hypothesised to mediate the intention-behaviour relationship (Month 3), to predict adherence (Month 5). Taking adherence was measured objectively using the Medication Events Monitoring System (MEMS) caps. Model tests were conducted using regression and bootstrap mediation analyses. Perceived behavioural control (PBC) was positively (β = .767, p behavioural measure, identified PBC as the main driver of adherence intentions. The effect of intentions on adherence was only indirect through self-regulatory processes, which were the main predictor of objectively assessed adherence.

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

    Science.gov (United States)

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

    2018-06-01

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

  2. Effects and Predictive Factors of Immunosuppressive Therapy Combined with Umbilical Cord Blood Infusion in Patients with Severe Aplastic Anemia.

    Science.gov (United States)

    Zhang, Xia; Li, Zhangzhi; Geng, Wei; Song, Bin; Wan, Chucheng

    2018-07-01

    To investigate the efficacy and safety of umbilical cord blood (UCB) infusion (UCBI) plus immunosuppressive therapy (IST) treatment in comparison to IST treatment, as well as predictive factors for clinical responses, in severe aplastic anemia (SAA) patients. Totally, 93 patients with SAA were enrolled in this cohort study. In the IST group, rabbit antithymocyte globulin (r-ATG) combined with cyclosporine A (CsA) was administered, while in the IST+UBCI group, r-ATG, CsA, and UCB were used. After 6 months of treatment, UCBI+IST achieved a higher complete response (CR) rate (p=0.002) and an elevated overall response rate (ORR) (p=0.004), compared to IST. Regarding hematopoietic recovery at month 6, platelet responses in the UCBI+IST group were better than those in the IST group (p=0.002), and UCBI+IST treatment facilitated increasing trends in absolute neutrophil count (ANC) response (p=0.056). Kaplan-Meier curves illuminated UCBI+IST achieved faster ANC response (paplastic anemia (VSAA) and ANC could predict clinical responses as well. However, Cox proportional hazard regression indicated that VSAA (p=0.003), but not UCBI+IST, affected OS. Safety profiles showed that UCBI+IST therapy did not elevate adverse events, compared with IST treatment. UCBI+IST achieved better clinical responses and hematopoietic recovery than IST, and was well tolerated in SAA patients. © Copyright: Yonsei University College of Medicine 2018.

  3. QSPR Models for Predicting Log Pliver Values for Volatile Organic Compounds Combining Statistical Methods and Domain Knowledge

    Directory of Open Access Journals (Sweden)

    Mónica F. Díaz

    2012-12-01

    Full Text Available Volatile organic compounds (VOCs are contained in a variety of chemicals that can be found in household products and may have undesirable effects on health. Thereby, it is important to model blood-to-liver partition coefficients (log Pliver for VOCs in a fast and inexpensive way. In this paper, we present two new quantitative structure-property relationship (QSPR models for the prediction of log Pliver, where we also propose a hybrid approach for the selection of the descriptors. This hybrid methodology combines a machine learning method with a manual selection based on expert knowledge. This allows obtaining a set of descriptors that is interpretable in physicochemical terms. Our regression models were trained using decision trees and neural networks and validated using an external test set. Results show high prediction accuracy compared to previous log Pliver models, and the descriptor selection approach provides a means to get a small set of descriptors that is in agreement with theoretical understanding of the target property.

  4. Predicting Transport of 3,5,6-Trichloro-2-Pyridinol Into Saliva Using a Combination Experimental and Computational Approach

    Energy Technology Data Exchange (ETDEWEB)

    Smith, Jordan Ned; Carver, Zana A.; Weber, Thomas J.; Timchalk, Charles

    2017-04-11

    A combination experimental and computational approach was developed to predict chemical transport into saliva. A serous-acinar chemical transport assay was established to measure chemical transport with non-physiological (standard cell culture medium) and physiological (using surrogate plasma and saliva medium) conditions using 3,5,6-trichloro-2-pyridinol (TCPy) a metabolite of the pesticide chlorpyrifos. High levels of TCPy protein binding was observed in cell culture medium and rat plasma resulting in different TCPy transport behaviors in the two experimental conditions. In the non-physiological transport experiment, TCPy reached equilibrium at equivalent concentrations in apical and basolateral chambers. At higher TCPy doses, increased unbound TCPy was observed, and TCPy concentrations in apical and basolateral chambers reached equilibrium faster than lower doses, suggesting only unbound TCPy is able to cross the cellular monolayer. In the physiological experiment, TCPy transport was slower than non-physiological conditions, and equilibrium was achieved at different concentrations in apical and basolateral chambers at a comparable ratio (0.034) to what was previously measured in rats dosed with TCPy (saliva:blood ratio: 0.049). A cellular transport computational model was developed based on TCPy protein binding kinetics and accurately simulated all transport experiments using different permeability coefficients for the two experimental conditions (1.4 vs 0.4 cm/hr for non-physiological and physiological experiments, respectively). The computational model was integrated into a physiologically based pharmacokinetic (PBPK) model and accurately predicted TCPy concentrations in saliva of rats dosed with TCPy. Overall, this study demonstrates an approach to predict chemical transport in saliva potentially increasing the utility of salivary biomonitoring in the future.

  5. Predicting Transport of 3,5,6-Trichloro-2-Pyridinol Into Saliva Using a Combination Experimental and Computational Approach.

    Science.gov (United States)

    Smith, Jordan Ned; Carver, Zana A; Weber, Thomas J; Timchalk, Charles

    2017-06-01

    A combination experimental and computational approach was developed to predict chemical transport into saliva. A serous-acinar chemical transport assay was established to measure chemical transport with nonphysiological (standard cell culture medium) and physiological (using surrogate plasma and saliva medium) conditions using 3,5,6-trichloro-2-pyridinol (TCPy) a metabolite of the pesticide chlorpyrifos. High levels of TCPy protein binding were observed in cell culture medium and rat plasma resulting in different TCPy transport behaviors in the 2 experimental conditions. In the nonphysiological transport experiment, TCPy reached equilibrium at equivalent concentrations in apical and basolateral chambers. At higher TCPy doses, increased unbound TCPy was observed, and TCPy concentrations in apical and basolateral chambers reached equilibrium faster than lower doses, suggesting only unbound TCPy is able to cross the cellular monolayer. In the physiological experiment, TCPy transport was slower than nonphysiological conditions, and equilibrium was achieved at different concentrations in apical and basolateral chambers at a comparable ratio (0.034) to what was previously measured in rats dosed with TCPy (saliva:blood ratio: 0.049). A cellular transport computational model was developed based on TCPy protein binding kinetics and simulated all transport experiments reasonably well using different permeability coefficients for the 2 experimental conditions (1.14 vs 0.4 cm/h for nonphysiological and physiological experiments, respectively). The computational model was integrated into a physiologically based pharmacokinetic model and accurately predicted TCPy concentrations in saliva of rats dosed with TCPy. Overall, this study demonstrates an approach to predict chemical transport in saliva, potentially increasing the utility of salivary biomonitoring in the future. © The Author 2017. Published by Oxford University Press on behalf of the Society of Toxicology. All rights

  6. Semi-quantitative prediction of a multiple API solid dosage form with a combination of vibrational spectroscopy methods.

    Science.gov (United States)

    Hertrampf, A; Sousa, R M; Menezes, J C; Herdling, T

    2016-05-30

    Quality control (QC) in the pharmaceutical industry is a key activity in ensuring medicines have the required quality, safety and efficacy for their intended use. QC departments at pharmaceutical companies are responsible for all release testing of final products but also all incoming raw materials. Near-infrared spectroscopy (NIRS) and Raman spectroscopy are important techniques for fast and accurate identification and qualification of pharmaceutical samples. Tablets containing two different active pharmaceutical ingredients (API) [bisoprolol, hydrochlorothiazide] in different commercially available dosages were analysed using Raman- and NIR Spectroscopy. The goal was to define multivariate models based on each vibrational spectroscopy to discriminate between different dosages (identity) and predict their dosage (semi-quantitative). Furthermore the combination of spectroscopic techniques was investigated. Therefore, two different multiblock techniques based on PLS have been applied: multiblock PLS (MB-PLS) and sequential-orthogonalised PLS (SO-PLS). NIRS showed better results compared to Raman spectroscopy for both identification and quantitation. The multiblock techniques investigated showed that each spectroscopy contains information not present or captured with the other spectroscopic technique, thus demonstrating that there is a potential benefit in their combined use for both identification and quantitation purposes. Copyright © 2016 Elsevier B.V. All rights reserved.

  7. Specific cognitive-neurophysiological processes predict impulsivity in the childhood attention-deficit/hyperactivity disorder combined subtype.

    Science.gov (United States)

    Bluschke, A; Roessner, V; Beste, C

    2016-04-01

    Attention-deficit/hyperactivity disorder (ADHD) is one of the most prevalent neuropsychiatric disorders in childhood. Besides inattention and hyperactivity, impulsivity is the third core symptom leading to diverse and serious problems. However, the neuronal mechanisms underlying impulsivity in ADHD are still not fully understood. This is all the more the case when patients with the ADHD combined subtype (ADHD-C) are considered who are characterized by both symptoms of inattention and hyperactivity/impulsivity. Combining high-density electroencephalography (EEG) recordings with source localization analyses, we examined what information processing stages are dysfunctional in ADHD-C (n = 20) compared with controls (n = 18). Patients with ADHD-C made more impulsive errors in a Go/No-go task than healthy controls. Neurophysiologically, different subprocesses from perceptual gating to attentional selection, resource allocation and response selection processes are altered in this patient group. Perceptual gating, stimulus-driven attention selection and resource allocation processes were more pronounced in ADHD-C, are related to activation differences in parieto-occipital networks and suggest attentional filtering deficits. However, only response selection processes, associated with medial prefrontal networks, predicted impulsive errors in ADHD-C. Although the clinical picture of ADHD-C is complex and a multitude of processing steps are altered, only a subset of processes seems to directly modulate impulsive behaviour. The present findings improve the understanding of mechanisms underlying impulsivity in patients with ADHD-C and might help to refine treatment algorithms focusing on impulsivity.

  8. Combined use of serum MCP-1/IL-10 ratio and uterine artery Doppler index significantly improves the prediction of preeclampsia.

    Science.gov (United States)

    Cui, Shihong; Gao, Yanan; Zhang, Linlin; Wang, Yuan; Zhang, Lindong; Liu, Pingping; Liu, Ling; Chen, Juan

    2017-10-01

    Monocyte chemotactic protein-1 (MCP-1, or CCL2) is a member of the chemokine subfamily involved in recruitment of monocytes in inflammatory tissues. IL-10 is a key regulator for maintaining the balance of anti-inflammatory and pro-inflammatory milieu at the feto-maternal interface. Doppler examination has been routinely performed for the monitoring and management of preeclampsia patients. This study evaluates the efficiency of these factors alone, or in combination, for the predication of preeclampsia. The serum levels of MCP-1 and IL-10 in 78 preeclampsia patients and 143 age-matched normal controls were measured. The Doppler ultrasonography was performed and Artery Pulsatility Index (PI) and Resistance Index (RI) were calculated for the same subjects. It was found that while the second-trimester serum MCP-1, IL-10, MCP-1/IL-10 ratio, PI, and RI showed some power in predicting preeclampsia, the combination of MCP-1/IL-10 and PI and RI accomplishes the highest efficiency, achieving an AUC of 0.973 (95% CI, 0.000-1.000, Ppreeclampsia. Future studies using a larger sample can be conducted to construct an algorithm capable of quantitative assessment on the risk of preeclampsia. Copyright © 2016 Elsevier B.V. All rights reserved.

  9. Use of net reclassification improvement (NRI method confirms the utility of combined genetic risk score to predict type 2 diabetes.

    Directory of Open Access Journals (Sweden)

    Claudia H T Tam

    Full Text Available BACKGROUND: Recent genome-wide association studies (GWAS identified more than 70 novel loci for type 2 diabetes (T2D, some of which have been widely replicated in Asian populations. In this study, we investigated their individual and combined effects on T2D in a Chinese population. METHODOLOGY: We selected 14 single nucleotide polymorphisms (SNPs in T2D genes relating to beta-cell function validated in Asian populations and genotyped them in 5882 Chinese T2D patients and 2569 healthy controls. A combined genetic score (CGS was calculated by summing up the number of risk alleles or weighted by the effect size for each SNP under an additive genetic model. We tested for associations by either logistic or linear regression analysis for T2D and quantitative traits, respectively. The contribution of the CGS for predicting T2D risk was evaluated by receiver operating characteristic (ROC analysis and net reclassification improvement (NRI. RESULTS: We observed consistent and significant associations of IGF2BP2, WFS1, CDKAL1, SLC30A8, CDKN2A/B, HHEX, TCF7L2 and KCNQ1 (8.5×10(-18predictive ability on T2D risk by 11.2% and 11.3% for unweighted and weighted CGS, respectively using the NRI approach (P<0.001. CONCLUSION: In a Chinese population, the use of a CGS of 8 SNPs modestly but significantly improved its discriminative ability to predict T2D above and beyond that attributed to clinical risk factors (sex, age and BMI.

  10. The Combination of Platelet Count and Neutrophil Lymphocyte Ratio Is a Predictive Factor in Patients with Esophageal Squamous Cell Carcinoma

    Directory of Open Access Journals (Sweden)

    Ji-Feng Feng

    2014-10-01

    Full Text Available OBJECTIVE: The prognostic value of inflammation indexes in esophageal cancer was not established. In this study, therefore, both prognostic values of Glasgow prognostic score (GPS and combination of platelet count and neutrophil lymphocyte ratio (COP-NLR in patients with esophageal squamous cell carcinoma (ESCC were investigated and compared. METHODS: This retrospective study included 375 patients who underwent esophagectomy for ESCC. The cancer-specific survival (CSS was calculated by the Kaplan-Meier method, and the difference was assessed by the log-rank test. The GPS was calculated as follows: patients with elevated C-reactive protein (>10 mg/l and hypoalbuminemia (300 × 109/l and neutrophil lymphocyte ratio (>3 were assigned to COP-NLR2. Patients with one or no abnormal value were assigned to COP-NLR1 or COP-NLR0, respectively. RESULTS: The 5-year CSS in patients with GPS0, 1, and 2 was 50.0%, 27.0%, and 12.5%, respectively (P < .001. The 5-year CSS in patients with COP-NLR0, 1, and 2 was 51.8%, 27.0%, and 11.6%, respectively (P < .001. Multivariate analysis showed that both GPS (P = .003 and COP-NLR (P = .003 were significant predictors in such patients. In addition, our study demonstrated a similar hazard ratio (HR between COP-NLR and GPS (HR = 1.394 vs HR = 1.367. CONCLUSIONS: COP-NLR is an independent predictive factor in patients with ESCC. We conclude that COP-NLR predicts survival in ESCC similar to GPS.

  11. Predicting mercury retention in utility gas cleaning systems with SCR/ESP/FGD combinations or activated carbon injection

    Energy Technology Data Exchange (ETDEWEB)

    Krishnakumar, Balaji; Naik, Chitralkumar V.; Niksa, Stephen [Niksa Energy Associates LLC, Belmont, CA (United States); Fujiwara, Naoki [Idemitsu Kosan Co., Ltd, Chiba (Japan). Coal and Environment Research Lab.

    2013-07-01

    This paper presents validations of the Hg speciation predicted by NEA's MercuRator trademark package with an American field test database for 28 full-scale utility gas cleaning systems. It emphasizes SCR/ESP/FGD combinations and activated carbon injection because these two applications present the best long- term prospects for Hg control by coal-burning utilities. Validations of the extents of Hg{sup 0} oxidation across SCRs and of Hg retention in wet FGDs gave correlation coefficients greater than 0.9 for both units. A transport-based FGD analysis correctly assessed the potential for Hg{sup 0} re-emission in one limestone wet FGD. Among the ten stations in the SCR/ESP/FGD validations, the simulations correctly identified 3 of 4 of the relatively high Hg emissions rates; all four of the sites with moderate emissions rates; and both sites with the lowest emission rates. The validations for ACI applications demonstrated that Hg removals can be accurately estimated for the full domain of coal quality, LOI, and ACI rates for both untreated and brominated carbon sorbents. The predictions for ACI depict the test-to-test variations in most cases, and accurately describe the impact of ACI configuration and sorbent type. ACI into FFs is the most effective configuration, although ACI into ESPs often removes 90% or more Hg, provided that there is sufficient residence time and Cl in the flue gas. Brominated sorbents perform better than untreated carbons, unless SO{sub 3} condensation inhibits Hg adsorption.

  12. The combination of platelet count and neutrophil lymphocyte ratio is a predictive factor in patients with esophageal squamous cell carcinoma.

    Science.gov (United States)

    Feng, Ji-Feng; Huang, Ying; Chen, Qi-Xun

    2014-10-01

    The prognostic value of inflammation indexes in esophageal cancer was not established. In this study, therefore, both prognostic values of Glasgow prognostic score (GPS) and combination of platelet count and neutrophil lymphocyte ratio (COP-NLR) in patients with esophageal squamous cell carcinoma (ESCC) were investigated and compared. This retrospective study included 375 patients who underwent esophagectomy for ESCC. The cancer-specific survival (CSS) was calculated by the Kaplan-Meier method, and the difference was assessed by the log-rank test. The GPS was calculated as follows: patients with elevated C-reactive protein (> 10 mg/l) and hypoalbuminemia (l) were assigned to GPS2. Patients with one or no abnormal value were assigned to GPS1 or GPS0, respectively. The COP-NLR was calculated as follows: patients with elevated platelet count (> 300 × 10(9)/l) and neutrophil lymphocyte ratio (> 3) were assigned to COP-NLR2. Patients with one or no abnormal value were assigned to COP-NLR1 or COP-NLR0, respectively. The 5-year CSS in patients with GPS0, 1, and 2 was 50.0%, 27.0%, and 12.5%, respectively (P GPS (P = .003) and COP-NLR (P = .003) were significant predictors in such patients. In addition, our study demonstrated a similar hazard ratio (HR) between COP-NLR and GPS (HR = 1.394 vs HR = 1.367). COP-NLR is an independent predictive factor in patients with ESCC. We conclude that COP-NLR predicts survival in ESCC similar to GPS.

  13. Differentiation of solitary brain metastasis from glioblastoma multiforme: a predictive multiparametric approach using combined MR diffusion and perfusion

    Energy Technology Data Exchange (ETDEWEB)

    Bauer, Adam Herman; Moser, Franklin G.; Maya, Marcel [Cedars-Sinai Medical Center, Department of Medical Imaging, Los Angeles, CA (United States); Erly, William; Nael, Kambiz [University of Arizona Medical Center, Department of Medical Imaging, Tucson, AZ (United States)

    2015-07-15

    Solitary brain metastasis (MET) and glioblastoma multiforme (GBM) can appear similar on conventional MRI. The purpose of this study was to identify magnetic resonance (MR) perfusion and diffusion-weighted biomarkers that can differentiate MET from GBM. In this retrospective study, patients were included if they met the following criteria: underwent resection of a solitary enhancing brain tumor and had preoperative 3.0 T MRI encompassing diffusion tensor imaging (DTI), dynamic contrast-enhanced (DCE), and dynamic susceptibility contrast (DSC) perfusion. Using co-registered images, voxel-based fractional anisotropy (FA), mean diffusivity (MD), K{sup trans}, and relative cerebral blood volume (rCBV) values were obtained in the enhancing tumor and non-enhancing peritumoral T2 hyperintense region (NET2). Data were analyzed by logistic regression and analysis of variance. Receiver operating characteristic (ROC) analysis was performed to determine the optimal parameter/s and threshold for predicting of GBM vs. MET. Twenty-three patients (14 M, age 32-78 years old) met our inclusion criteria. Pathology revealed 13 GBMs and 10 METs. In the enhancing tumor, rCBV, K{sup trans}, and FA were higher in GBM, whereas MD was lower, neither without statistical significance. In the NET2, rCBV was significantly higher (p = 0.05) in GBM, but MD was significantly lower (p < 0.01) in GBM. FA and K{sup trans} were higher in GBM, though not reaching significance. The best discriminative power was obtained in NET2 from a combination of rCBV, FA, and MD, resulting in an area under the curve (AUC) of 0.98. The combination of MR diffusion and perfusion matrices in NET2 can help differentiate GBM over solitary MET with diagnostic accuracy of 98 %. (orig.)

  14. Differentiation of solitary brain metastasis from glioblastoma multiforme: a predictive multiparametric approach using combined MR diffusion and perfusion

    International Nuclear Information System (INIS)

    Bauer, Adam Herman; Moser, Franklin G.; Maya, Marcel; Erly, William; Nael, Kambiz

    2015-01-01

    Solitary brain metastasis (MET) and glioblastoma multiforme (GBM) can appear similar on conventional MRI. The purpose of this study was to identify magnetic resonance (MR) perfusion and diffusion-weighted biomarkers that can differentiate MET from GBM. In this retrospective study, patients were included if they met the following criteria: underwent resection of a solitary enhancing brain tumor and had preoperative 3.0 T MRI encompassing diffusion tensor imaging (DTI), dynamic contrast-enhanced (DCE), and dynamic susceptibility contrast (DSC) perfusion. Using co-registered images, voxel-based fractional anisotropy (FA), mean diffusivity (MD), K trans , and relative cerebral blood volume (rCBV) values were obtained in the enhancing tumor and non-enhancing peritumoral T2 hyperintense region (NET2). Data were analyzed by logistic regression and analysis of variance. Receiver operating characteristic (ROC) analysis was performed to determine the optimal parameter/s and threshold for predicting of GBM vs. MET. Twenty-three patients (14 M, age 32-78 years old) met our inclusion criteria. Pathology revealed 13 GBMs and 10 METs. In the enhancing tumor, rCBV, K trans , and FA were higher in GBM, whereas MD was lower, neither without statistical significance. In the NET2, rCBV was significantly higher (p = 0.05) in GBM, but MD was significantly lower (p < 0.01) in GBM. FA and K trans were higher in GBM, though not reaching significance. The best discriminative power was obtained in NET2 from a combination of rCBV, FA, and MD, resulting in an area under the curve (AUC) of 0.98. The combination of MR diffusion and perfusion matrices in NET2 can help differentiate GBM over solitary MET with diagnostic accuracy of 98 %. (orig.)

  15. Maternal nutritional status predicts adverse birth outcomes among HIV-infected rural Ugandan women receiving combination antiretroviral therapy.

    Directory of Open Access Journals (Sweden)

    Sera Young

    Full Text Available Maternal nutritional status is an important predictor of birth outcomes, yet little is known about the nutritional status of HIV-infected pregnant women treated with combination antiretroviral therapy (cART. We therefore examined the relationship between maternal BMI at study enrollment, gestational weight gain (GWG, and hemoglobin concentration (Hb among 166 women initiating cART in rural Uganda.Prospective cohort.HIV-infected, ART-naïve pregnant women were enrolled between 12 and 28 weeks gestation and treated with a protease inhibitor or non-nucleoside reverse transcriptase inhibitor-based combination regimen. Nutritional status was assessed monthly. Neonatal anthropometry was examined at birth. Outcomes were evaluated using multivariate analysis.Mean GWG was 0.17 kg/week, 14.6% of women experienced weight loss during pregnancy, and 44.9% were anemic. Adverse fetal outcomes included low birth weight (LBW (19.6%, preterm delivery (17.7%, fetal death (3.9%, stunting (21.1%, small-for-gestational age (15.1%, and head-sparing growth restriction (26%. No infants were HIV-infected. Gaining <0.1 kg/week was associated with LBW, preterm delivery, and a composite adverse obstetric/fetal outcome. Maternal weight at 7 months gestation predicted LBW. For each g/dL higher mean Hb, the odds of small-for-gestational age decreased by 52%.In our cohort of HIV-infected women initiating cART during pregnancy, grossly inadequate GWG was common. Infants whose mothers gained <0.1 kg/week were at increased risk for LBW, preterm delivery, and composite adverse birth outcomes. cART by itself may not be sufficient for decreasing the burden of adverse birth outcomes among HIV-infected women.Clinicaltrials.gov NCT00993031.

  16. Predictive control strategy of a gas turbine for improvement of combined cycle power plant dynamic performance and efficiency.

    Science.gov (United States)

    Mohamed, Omar; Wang, Jihong; Khalil, Ashraf; Limhabrash, Marwan

    2016-01-01

    This paper presents a novel strategy for implementing model predictive control (MPC) to a large gas turbine power plant as a part of our research progress in order to improve plant thermal efficiency and load-frequency control performance. A generalized state space model for a large gas turbine covering the whole steady operational range is designed according to subspace identification method with closed loop data as input to the identification algorithm. Then the model is used in developing a MPC and integrated into the plant existing control strategy. The strategy principle is based on feeding the reference signals of the pilot valve, natural gas valve, and the compressor pressure ratio controller with the optimized decisions given by the MPC instead of direct application of the control signals. If the set points for the compressor controller and turbine valves are sent in a timely manner, there will be more kinetic energy in the plant to release faster responses on the output and the overall system efficiency is improved. Simulation results have illustrated the feasibility of the proposed application that has achieved significant improvement in the frequency variations and load following capability which are also translated to be improvements in the overall combined cycle thermal efficiency of around 1.1 % compared to the existing one.

  17. Predicting Short-Term Electricity Demand by Combining the Advantages of ARMA and XGBoost in Fog Computing Environment

    Directory of Open Access Journals (Sweden)

    Chuanbin Li

    2018-01-01

    Full Text Available With the rapid development of IoT, the disadvantages of Cloud framework have been exposed, such as high latency, network congestion, and low reliability. Therefore, the Fog Computing framework has emerged, with an extended Fog Layer between the Cloud and terminals. In order to address the real-time prediction on electricity demand, we propose an approach based on XGBoost and ARMA in Fog Computing environment. By taking the advantages of Fog Computing framework, we first propose a prototype-based clustering algorithm to divide enterprise users into several categories based on their total electricity consumption; we then propose a model selection approach by analyzing users’ historical records of electricity consumption and identifying the most important features. Generally speaking, if the historical records pass the test of stationarity and white noise, ARMA is used to model the user’s electricity consumption in time sequence; otherwise, if the historical records do not pass the test, and some discrete features are the most important, such as weather and whether it is weekend, XGBoost will be used. The experiment results show that our proposed approach by combining the advantage of ARMA and XGBoost is more accurate than the classical models.

  18. Using the Relevance Vector Machine Model Combined with Local Phase Quantization to Predict Protein-Protein Interactions from Protein Sequences

    Directory of Open Access Journals (Sweden)

    Ji-Yong An

    2016-01-01

    Full Text Available We propose a novel computational method known as RVM-LPQ that combines the Relevance Vector Machine (RVM model and Local Phase Quantization (LPQ to predict PPIs from protein sequences. The main improvements are the results of representing protein sequences using the LPQ feature representation on a Position Specific Scoring Matrix (PSSM, reducing the influence of noise using a Principal Component Analysis (PCA, and using a Relevance Vector Machine (RVM based classifier. We perform 5-fold cross-validation experiments on Yeast and Human datasets, and we achieve very high accuracies of 92.65% and 97.62%, respectively, which is significantly better than previous works. To further evaluate the proposed method, we compare it with the state-of-the-art support vector machine (SVM classifier on the Yeast dataset. The experimental results demonstrate that our RVM-LPQ method is obviously better than the SVM-based method. The promising experimental results show the efficiency and simplicity of the proposed method, which can be an automatic decision support tool for future proteomics research.

  19. Predicting the aquatic stage sustainability of a restored backwater channel combining in-situ and airborne remotely sensed bathymetric models.

    Science.gov (United States)

    Jérôme, Lejot; Jérémie, Riquier; Hervé, Piégay

    2014-05-01

    As other large river floodplain worldwide, the floodplain of the Rhône has been deeply altered by human activities and infrastructures over the last centuries both in term of structure and functioning. An ambitious restoration plan of selected by-passed reaches has been implemented since 1999, in order to improve their ecological conditions. One of the main action aimed to increase the aquatic areas in floodplain channels (i.e. secondary channels, backwaters, …). In practice, fine and/or coarse alluvium were dredged, either locally or over the entire cut-off channel length. Sometimes the upstream or downstream alluvial plugs were also removed to reconnect the restored feature to the main channel. Such operation aims to restore forms and associated habitats of biotic communities, which are no more created or maintained by the river itself. In this context, assessing the sustainability of such restoration actions is a major issue. In this study, we focus on 1 of the 24 floodplain channels which have been restored along the Rhône River since 1999, the Malourdie channel (Chautagne reach, France). A monitoring of the geomorphologic evolution of the channel has been conducted during a decade to assess the aquatic stage sustainability of this former fully isolated channel, which has been restored as a backwater in 2004. Two main types of measures were performed: (a) water depth and fine sediment thickness were surveyed with an auger every 10 m along the channel centerline in average every year and a half allowing to establish an exponential decay model of terrestrialization rates through time; (b) three airborne campaigns (2006, 2007, 2012) by Ultra Aerial Vehicle (UAV) provided images from which bathymetry were inferred in combination with observed field measures. Coupling field and airborne models allows us to simulate different states of terrestrialization at the scale of the whole restore feature (e.g. 2020/2030/2050). Raw results indicate that terrestrialization

  20. Prediction of hot spot residues at protein-protein interfaces by combining machine learning and energy-based methods

    Directory of Open Access Journals (Sweden)

    Pontil Massimiliano

    2009-10-01

    Full Text Available Abstract Background Alanine scanning mutagenesis is a powerful experimental methodology for investigating the structural and energetic characteristics of protein complexes. Individual amino-acids are systematically mutated to alanine and changes in free energy of binding (ΔΔG measured. Several experiments have shown that protein-protein interactions are critically dependent on just a few residues ("hot spots" at the interface. Hot spots make a dominant contribution to the free energy of binding and if mutated they can disrupt the interaction. As mutagenesis studies require significant experimental efforts, there is a need for accurate and reliable computational methods. Such methods would also add to our understanding of the determinants of affinity and specificity in protein-protein recognition. Results We present a novel computational strategy to identify hot spot residues, given the structure of a complex. We consider the basic energetic terms that contribute to hot spot interactions, i.e. van der Waals potentials, solvation energy, hydrogen bonds and Coulomb electrostatics. We treat them as input features and use machine learning algorithms such as Support Vector Machines and Gaussian Processes to optimally combine and integrate them, based on a set of training examples of alanine mutations. We show that our approach is effective in predicting hot spots and it compares favourably to other available methods. In particular we find the best performances using Transductive Support Vector Machines, a semi-supervised learning scheme. When hot spots are defined as those residues for which ΔΔG ≥ 2 kcal/mol, our method achieves a precision and a recall respectively of 56% and 65%. Conclusion We have developed an hybrid scheme in which energy terms are used as input features of machine learning models. This strategy combines the strengths of machine learning and energy-based methods. Although so far these two types of approaches have mainly been

  1. The Preoperative Composite Physiologic Index May Predict Mortality in Lung Cancer Patients with Combined Pulmonary Fibrosis and Emphysema.

    Science.gov (United States)

    Ueno, Fumika; Kitaguchi, Yoshiaki; Shiina, Takayuki; Asaka, Shiho; Miura, Kentaro; Yasuo, Masanori; Wada, Yosuke; Yoshizawa, Akihiko; Hanaoka, Masayuki

    2017-01-01

    It remains unclear whether the preoperative pulmonary function parameters and prognostic indices that are indicative of nutritional and immunological status are associated with prognosis in lung cancer patients with combined pulmonary fibrosis and emphysema (CPFE) who have undergone surgery. The aim of this study is to identify prognostic determinants in these patients. The medical records of all patients with lung cancer associated with CPFE who had undergone surgery at Shinshu University Hospital were retrospectively reviewed to obtain clinical data, including the results of preoperative pulmonary function tests and laboratory examinations, chest high-resolution computed tomography (HRCT), and survival. Univariate Cox proportional hazards regression analysis showed that a high pathological stage of the lung cancer, a higher preoperative serum carcinoembryonic antigen level, and a higher preoperative composite physiologic index (CPI) were associated with a high risk of death. Multivariate analysis showed that a high pathological stage of the lung cancer (HR: 1.579; p = 0.0305) and a higher preoperative CPI (HR: 1.034; p = 0.0174) were independently associated with a high risk of death. In contrast, the severity of fibrosis or emphysema on chest HRCT, the individual pulmonary function parameters, the prognostic nutritional index, the neutrophil-to-lymphocyte ratio, and the platelet-to-lymphocyte ratio were not associated with prognosis. In the Kaplan-Meier analysis, the log-rank test showed significant differences in survival between the high-CPI and the low-CPI group (p = 0.0234). The preoperative CPI may predict mortality and provide more powerful prognostic information than individual pulmonary function parameters in lung cancer patients with CPFE who have undergone surgery. © 2017 S. Karger AG, Basel.

  2. Landslide prediction using combined deterministic and probabilistic methods in hilly area of Mt. Medvednica in Zagreb City, Croatia

    Science.gov (United States)

    Wang, Chunxiang; Watanabe, Naoki; Marui, Hideaki

    2013-04-01

    The hilly slopes of Mt. Medvednica are stretched in the northwestern part of Zagreb City, Croatia, and extend to approximately 180km2. In this area, landslides, e.g. Kostanjek landslide and Črešnjevec landslide, have brought damage to many houses, roads, farmlands, grassland and etc. Therefore, it is necessary to predict the potential landslides and to enhance landslide inventory for hazard mitigation and security management of local society in this area. We combined deterministic method and probabilistic method to assess potential landslides including their locations, size and sliding surfaces. Firstly, this study area is divided into several slope units that have similar topographic and geological characteristics using the hydrology analysis tool in ArcGIS. Then, a GIS-based modified three-dimensional Hovland's method for slope stability analysis system is developed to identify the sliding surface and corresponding three-dimensional safety factor for each slope unit. Each sliding surface is assumed to be the lower part of each ellipsoid. The direction of inclination of the ellipsoid is considered to be the same as the main dip direction of the slope unit. The center point of the ellipsoid is randomly set to the center point of a grid cell in the slope unit. The minimum three-dimensional safety factor and corresponding critical sliding surface are also obtained for each slope unit. Thirdly, since a single value of safety factor is insufficient to evaluate the slope stability of a slope unit, the ratio of the number of calculation cases in which the three-dimensional safety factor values less than 1.0 to the total number of trial calculation is defined as the failure probability of the slope unit. If the failure probability is more than 80%, the slope unit is distinguished as 'unstable' from other slope units and the landslide hazard can be mapped for the whole study area.

  3. Overexpression of RBBP6, alone or combined with mutant TP53, is predictive of poor prognosis in colon cancer.

    Directory of Open Access Journals (Sweden)

    Jian Chen

    Full Text Available Retinoblastoma binding protein 6 (RBBP6 plays an important role in chaperone-mediated ubiquitination and interacts with TP53 in carcinogenesis. However, the clinicopathologic significance of RBBP6 expression in colon cancer is unknown; in particular, the prognostic value of RBBP6 combined with TP53 expression has not been explored. Therefore, quantitative real-time PCR and western blot analyses were performed to detect RBBP6 expression in colon cancer tissues. RBBP6 and TP53 expression were assessed by immunohistochemistry in a tissue microarray format, in which the primary colon cancer tissue was paired with noncancerous tissue. Tissue specimens were obtained from 203 patients. We found that RBBP6 was overexpressed in colon tumorous tissues and was significantly associated with clinical stage, depth of tumor invasion, lymph node metastasis (LNM, distant metastasis, and histologic grade. Further studies revealed that a corresponding correlation between RBBP6 overexpression and mutant TP53 was evident in colon cancer (r = 0.450; P<0.001. RBBP6 expression was an independent prognostic factor for overall survival (OS and disease free survival (DFS. Interestingly, patients with tumors that had both RBBP6 overexpression and mutant TP53 protein accumulation relapsed and died within a significantly short period after surgery (P<0.001. Multivariate analysis showed that patients with LNM and patients with both RBBP6- and TP53-positive tumors had extremely poor OS (HR 6.75; 95% CI 2.63-17.35; P<0.001 and DFS (HR 8.08; 95% CI 2.80-23.30; P<0.001. These clinical findings indicate that the assessment of both RBBP6 and mutant TP53 expression will be helpful in predicting colon cancer prognosis.

  4. Predicting Lymph Node Metastasis in Endometrial Cancer Using Serum CA125 Combined with Immunohistochemical Markers PR and Ki67, and a Comparison with Other Prediction Models.

    Directory of Open Access Journals (Sweden)

    Bingyi Yang

    Full Text Available We aimed to evaluate the value of immunohistochemical markers and serum CA125 in predicting the risk of lymph node metastasis (LNM in women with endometrial cancer and to identify a low-risk group of LNM. The medical records of 370 patients with endometrial endometrioid adenocarcinoma who underwent surgical staging in the Obstetrics & Gynecology Hospital of Fudan University were collected and retrospectively reviewed. Immunohistochemical markers were screened. A model using serum cancer antigen 125 (CA125 level, the immunohistochemical markers progesterone receptor (PR and Ki67 was created for prediction of LNM. A predicted probability of 4% among these patients was defined as low risk. The developed model was externally validated in 200 patients from Shanghai Cancer Center. The efficiency of the model was compared with three other reported prediction models. Patients with serum CA125 50% and Ki67 < 40% in cancer lesion were defined as low risk for LNM. The model showed good discrimination with an area under the receiver operating characteristic curve of 0.82. The model classified 61.9% (229/370 of patients as being at low risk for LNM. Among these 229 patients, 6 patients (2.6% had LNM and the negative predictive value was 97.4% (223/229. The sensitivity and specificity of the model were 84.6% and 67.4% respectively. In the validation cohort, the model classified 59.5% (119/200 of patients as low-risk, 3 out of these 119 patients (2.5% has LNM. Our model showed a predictive power similar to those of two previously reported prediction models. The prediction model using serum CA125 and the immunohistochemical markers PR and Ki67 is useful to predict patients with a low risk of LNM and has the potential to provide valuable guidance to clinicians in the treatment of patients with endometrioid endometrial cancer.

  5. Role of Combining Peripheral with Sublingual Perfusion on Evaluating Microcirculation and Predicting Prognosis in Patients with Septic Shock.

    Science.gov (United States)

    Pan, Pan; Liu, Da-Wei; Su, Long-Xiang; He, Huai-Wu; Wang, Xiao-Ting; Yu, Chao

    2018-05-20

    Organ Failure Assessment score (14.5 ± 2.9) was in the low PI and low △PPV group (F = 13.7, P < 0.001). Post hoc tests showed significant differences in 28-day survival rates among these four groups (log rank [Mantel-Cox], 20.931; P < 0.05). PI and △PPV in septic shock patients are related to 6 h LC, and combining these two parameters to assess microcirculation can predict organ dysfunction and 28-day mortality in patients with septic shock.

  6. A comparison of the embryonic stem cell test and whole embryo culture assay combined with the BeWo placental passage model for predicting the embryotoxicity of azoles.

    NARCIS (Netherlands)

    Dimopoulou, Myrto; Verhoef, Aart; Gomes, Caroline A; van Dongen, Catharina W; Rietjens, Ivonne M C M; Piersma, Aldert H; van Ravenzwaay, Bennard

    2018-01-01

    In the present study, we show the value of combining toxico-dynamic and -kinetic in vitro approaches for embryotoxicity testing of azoles. Both the whole embryo culture (WEC) and the embryonic stem cells test (EST) predicted the in vivo potency ranking of twelve tested azoles with moderate accuracy.

  7. Partial least squares modeling of combined infrared, 1H NMR and 13C NMR spectra to predict long residue properties of crude oils

    NARCIS (Netherlands)

    de Peinder, P.; Visser, T.; Petrauskas, D.D.; Salvatori, F.; Soulimani, F.; Weckhuysen, B.M.

    2009-01-01

    Research has been carried out to determine the potential of partial least squares (PLS) modeling of mid-infrared (IR) spectra of crude oils combined with the corresponding 1H and 13C nuclear magnetic resonance (NMR) data, to predict the long residue (LR) properties of these substances. The study

  8. Predicting dermal penetration for ToxCast chemicals using in silico estimates for diffusion in combination with physiologically based pharmacokinetic (PBPK) modeling.

    Science.gov (United States)

    Predicting dermal penetration for ToxCast chemicals using in silico estimates for diffusion in combination with physiologically based pharmacokinetic (PBPK) modeling.Evans, M.V., Sawyer, M.E., Isaacs, K.K, and Wambaugh, J.With the development of efficient high-throughput (HT) in ...

  9. A Combined Pharmacokinetic and Radiologic Assessment of Dynamic Contrast-Enhanced Magnetic Resonance Imaging Predicts Response to Chemoradiation in Locally Advanced Cervical Cancer

    International Nuclear Information System (INIS)

    Semple, Scott; Harry, Vanessa N. MRCOG.; Parkin, David E.; Gilbert, Fiona J.

    2009-01-01

    Purpose: To investigate the combination of pharmacokinetic and radiologic assessment of dynamic contrast-enhanced magnetic resonance imaging (MRI) as an early response indicator in women receiving chemoradiation for advanced cervical cancer. Methods and Materials: Twenty women with locally advanced cervical cancer were included in a prospective cohort study. Dynamic contrast-enhanced MRI was carried out before chemoradiation, after 2 weeks of therapy, and at the conclusion of therapy using a 1.5-T MRI scanner. Radiologic assessment of uptake parameters was obtained from resultant intensity curves. Pharmacokinetic analysis using a multicompartment model was also performed. General linear modeling was used to combine radiologic and pharmacokinetic parameters and correlated with eventual response as determined by change in MRI tumor size and conventional clinical response. A subgroup of 11 women underwent repeat pretherapy MRI to test pharmacokinetic reproducibility. Results: Pretherapy radiologic parameters and pharmacokinetic K trans correlated with response (p < 0.01). General linear modeling demonstrated that a combination of radiologic and pharmacokinetic assessments before therapy was able to predict more than 88% of variance of response. Reproducibility of pharmacokinetic modeling was confirmed. Conclusions: A combination of radiologic assessment with pharmacokinetic modeling applied to dynamic MRI before the start of chemoradiation improves the predictive power of either by more than 20%. The potential improvements in therapy response prediction using this type of combined analysis of dynamic contrast-enhanced MRI may aid in the development of more individualized, effective therapy regimens for this patient group.

  10. Prediction of Increasing Production Activities using Combination of Query Aggregation on Complex Events Processing and Neural Network

    Directory of Open Access Journals (Sweden)

    Achmad Arwan

    2016-07-01

    Full Text Available AbstrakProduksi, order, penjualan, dan pengiriman adalah serangkaian event yang saling terkait dalam industri manufaktur. Selanjutnya hasil dari event tersebut dicatat dalam event log. Complex Event Processing adalah metode yang digunakan untuk menganalisis apakah terdapat pola kombinasi peristiwa tertentu (peluang/ancaman yang terjadi pada sebuah sistem, sehingga dapat ditangani secara cepat dan tepat. Jaringan saraf tiruan adalah metode yang digunakan untuk mengklasifikasi data peningkatan proses produksi. Hasil pencatatan rangkaian proses yang menyebabkan peningkatan produksi digunakan sebagai data latih untuk mendapatkan fungsi aktivasi dari jaringan saraf tiruan. Penjumlahan hasil catatan event log dimasukkan ke input jaringan saraf tiruan untuk perhitungan nilai aktivasi. Ketika nilai aktivasi lebih dari batas yang ditentukan, maka sistem mengeluarkan sinyal untuk meningkatkan produksi, jika tidak, sistem tetap memantau kejadian. Hasil percobaan menunjukkan bahwa akurasi dari metode ini adalah 77% dari 39 rangkaian aliran event.Kata kunci: complex event processing, event, jaringan saraf tiruan, prediksi peningkatan produksi, proses. AbstractProductions, orders, sales, and shipments are series of interrelated events within manufacturing industry. Further these events were recorded in the event log. Complex event processing is a method that used to analyze whether there are patterns of combinations of certain events (opportunities / threats that occur in a system, so it can be addressed quickly and appropriately. Artificial neural network is a method that we used to classify production increase activities. The series of events that cause the increase of the production used as a dataset to train the weight of neural network which result activation value. An aggregate stream of events inserted into the neural network input to compute the value of activation. When the value is over a certain threshold (the activation value results

  11. The combined status of estrogen receptor (ER) and epidermal growth factor (EGFR) predicts a poor outcome in primary breast cancer

    International Nuclear Information System (INIS)

    Artagaveytia, N.; Román, E.; Alonso, I.; Sabini, G.; Garófalo, E.

    2004-01-01

    had significance prognostic (SVLE, p = 0.005 and SVG, p = 0.01, respectively). In conclusion, the content of RE is better than the status indicator evolutionary RE. The combined ER-positive status and REGFpositivo in primary breast cancer predicts a poor outcome of the disease-The RE-b is emerging as a potential marker of poor prognosis

  12. Discussion of “Prediction intervals for short-term wind farm generation forecasts” and “Combined nonparametric prediction intervals for wind power generation”

    DEFF Research Database (Denmark)

    Pinson, Pierre; Tastu, Julija

    2014-01-01

    A new score for the evaluation of interval forecasts, the so-called coverage width-based criterion (CWC), was proposed and utilized.. This score has been used for the tuning (in-sample) and genuine evaluation (out-ofsample) of prediction intervals for various applications, e.g., electric load [1......], electricity prices [2], general purpose prediction [3], and wind power generation [4], [5]. Indeed, two papers by the same authors appearing in the IEEE Transactions On Sustainable Energy employ that score and use it to conclude on the comparative quality of alternative approaches to interval forecasting...

  13. Improving model predictions for RNA interference activities that use support vector machine regression by combining and filtering features

    Directory of Open Access Journals (Sweden)

    Peek Andrew S

    2007-06-01

    Full Text Available Abstract Background RNA interference (RNAi is a naturally occurring phenomenon that results in the suppression of a target RNA sequence utilizing a variety of possible methods and pathways. To dissect the factors that result in effective siRNA sequences a regression kernel Support Vector Machine (SVM approach was used to quantitatively model RNA interference activities. Results Eight overall feature mapping methods were compared in their abilities to build SVM regression models that predict published siRNA activities. The primary factors in predictive SVM models are position specific nucleotide compositions. The secondary factors are position independent sequence motifs (N-grams and guide strand to passenger strand sequence thermodynamics. Finally, the factors that are least contributory but are still predictive of efficacy are measures of intramolecular guide strand secondary structure and target strand secondary structure. Of these, the site of the 5' most base of the guide strand is the most informative. Conclusion The capacity of specific feature mapping methods and their ability to build predictive models of RNAi activity suggests a relative biological importance of these features. Some feature mapping methods are more informative in building predictive models and overall t-test filtering provides a method to remove some noisy features or make comparisons among datasets. Together, these features can yield predictive SVM regression models with increased predictive accuracy between predicted and observed activities both within datasets by cross validation, and between independently collected RNAi activity datasets. Feature filtering to remove features should be approached carefully in that it is possible to reduce feature set size without substantially reducing predictive models, but the features retained in the candidate models become increasingly distinct. Software to perform feature prediction and SVM training and testing on nucleic acid

  14. Accuracy of combined maxillary and mandibular repositioning and of soft tissue prediction in relation to maxillary antero-superior repositioning combined with mandibular set back A computerized cephalometric evaluation of the immediate postsurgical outcome using the TIOPS planning system

    DEFF Research Database (Denmark)

    Donatsky, Ole; Bjørn-Jørgensen, Jens; Hermund, Niels Ulrich

    2009-01-01

    surgical planning system (TIOPS). MATERIAL AND METHODS: Out of 100 prospectively and consecutively treated patients, 52 patients manifested dentofacial deformities requiring bimaxillary orthognathic surgery with maxillary antero-superior repositioning combined with mandibular set back and so were included......AIM: The purpose of the present study was to evaluate the immediate postsurgical outcome of planned and predicted hard and soft tissue positional changes in relation to maxillary antero-superior repositioning combined with mandibular set back using the computerized, cephalometric, orthognathic...... positional changes were transferred to model surgery on a three-dimensional articulator system (SAM) and finally to surgery. Five to six weeks after surgery, the actually obtained hard and soft tissue profile changes were cephalometricly assessed. RESULTS: The mean accuracy of the planned and predicted hard...

  15. Prediction of phenotypic susceptibility to antiretroviral drugs using physiochemical properties of the primary enzymatic structure combined with artificial neural networks

    DEFF Research Database (Denmark)

    Kjaer, J; Høj, L; Fox, Z

    2008-01-01

    OBJECTIVES: Genotypic interpretation systems extrapolate observed associations in datasets to predict viral susceptibility to antiretroviral drugs (ARVs) for given isolates. We aimed to develop and validate an approach using artificial neural networks (ANNs) that employ descriptors...

  16. An integrative approach to CTL epitope prediction: A combined algorithm integrating MHC class I binding, TAP transport efficiency, and proteasomal cleavage predictions

    DEFF Research Database (Denmark)

    Larsen, Mette Voldby; Lundegaard, Claus; Lamberth, K

    2005-01-01

    Reverse immunogenetic approaches attempt to optimize the selection of candidate epitopes, and thus minimize the experimental effort needed to identify new epitopes. When predicting cytotoxic T cell epitopes, the main focus has been on the highly specific MHC class I binding event. Methods have al.......The method is available at http://www.cbs.dtu.dk/services/NetCTL. Supplementary material is available at http://www.cbs.dtu.dk/suppl/immunology/CTL.php....

  17. Middle and long-term prediction of UT1-UTC based on combination of Gray Model and Autoregressive Integrated Moving Average

    Science.gov (United States)

    Jia, Song; Xu, Tian-he; Sun, Zhang-zhen; Li, Jia-jing

    2017-02-01

    UT1-UTC is an important part of the Earth Orientation Parameters (EOP). The high-precision predictions of UT1-UTC play a key role in practical applications of deep space exploration, spacecraft tracking and satellite navigation and positioning. In this paper, a new prediction method with combination of Gray Model (GM(1, 1)) and Autoregressive Integrated Moving Average (ARIMA) is developed. The main idea is as following. Firstly, the UT1-UTC data are preprocessed by removing the leap second and Earth's zonal harmonic tidal to get UT1R-TAI data. Periodic terms are estimated and removed by the least square to get UT2R-TAI. Then the linear terms of UT2R-TAI data are modeled by the GM(1, 1), and the residual terms are modeled by the ARIMA. Finally, the UT2R-TAI prediction can be performed based on the combined model of GM(1, 1) and ARIMA, and the UT1-UTC predictions are obtained by adding the corresponding periodic terms, leap second correction and the Earth's zonal harmonic tidal correction. The results show that the proposed model can be used to predict UT1-UTC effectively with higher middle and long-term (from 32 to 360 days) accuracy than those of LS + AR, LS + MAR and WLS + MAR.

  18. A model predictive control approach combined unscented Kalman filter vehicle state estimation in intelligent vehicle trajectory tracking

    Directory of Open Access Journals (Sweden)

    Hongxiao Yu

    2015-05-01

    Full Text Available Trajectory tracking and state estimation are significant in the motion planning and intelligent vehicle control. This article focuses on the model predictive control approach for the trajectory tracking of the intelligent vehicles and state estimation of the nonlinear vehicle system. The constraints of the system states are considered when applying the model predictive control method to the practical problem, while 4-degree-of-freedom vehicle model and unscented Kalman filter are proposed to estimate the vehicle states. The estimated states of the vehicle are used to provide model predictive control with real-time control and judge vehicle stability. Furthermore, in order to decrease the cost of solving the nonlinear optimization, the linear time-varying model predictive control is used at each time step. The effectiveness of the proposed vehicle state estimation and model predictive control method is tested by driving simulator. The results of simulations and experiments show that great and robust performance is achieved for trajectory tracking and state estimation in different scenarios.

  19. The combination of kinetic and flow cytometric semen parameters as a tool to predict fertility in cryopreserved bull semen.

    Science.gov (United States)

    Gliozzi, T M; Turri, F; Manes, S; Cassinelli, C; Pizzi, F

    2017-11-01

    Within recent years, there has been growing interest in the prediction of bull fertility through in vitro assessment of semen quality. A model for fertility prediction based on early evaluation of semen quality parameters, to exclude sires with potentially low fertility from breeding programs, would therefore be useful. The aim of the present study was to identify the most suitable parameters that would provide reliable prediction of fertility. Frozen semen from 18 Italian Holstein-Friesian proven bulls was analyzed using computer-assisted semen analysis (CASA) (motility and kinetic parameters) and flow cytometry (FCM) (viability, acrosomal integrity, mitochondrial function, lipid peroxidation, plasma membrane stability and DNA integrity). Bulls were divided into two groups (low and high fertility) based on the estimated relative conception rate (ERCR). Significant differences were found between fertility groups for total motility, active cells, straightness, linearity, viability and percentage of DNA fragmented sperm. Correlations were observed between ERCR and some kinetic parameters, and membrane instability and some DNA integrity indicators. In order to define a model with high relation between semen quality parameters and ERCR, backward stepwise multiple regression analysis was applied. Thus, we obtained a prediction model that explained almost half (R 2=0.47, P<0.05) of the variation in the conception rate and included nine variables: five kinetic parameters measured by CASA (total motility, active cells, beat cross frequency, curvilinear velocity and amplitude of lateral head displacement) and four parameters related to DNA integrity evaluated by FCM (degree of chromatin structure abnormality Alpha-T, extent of chromatin structure abnormality (Alpha-T standard deviation), percentage of DNA fragmented sperm and percentage of sperm with high green fluorescence representative of immature cells). A significant relationship (R 2=0.84, P<0.05) was observed between

  20. Predicting in-patient falls in a geriatric clinic: a clinical study combining assessment data and simple sensory gait measurements.

    Science.gov (United States)

    Marschollek, M; Nemitz, G; Gietzelt, M; Wolf, K H; Meyer Zu Schwabedissen, H; Haux, R

    2009-08-01

    Falls are among the predominant causes for morbidity and mortality in elderly persons and occur most often in geriatric clinics. Despite several studies that have identified parameters associated with elderly patients' fall risk, prediction models -- e.g., based on geriatric assessment data -- are currently not used on a regular basis. Furthermore, technical aids to objectively assess mobility-associated parameters are currently not used. To assess group differences in clinical as well as common geriatric assessment data and sensory gait measurements between fallers and non-fallers in a geriatric sample, and to derive and compare two prediction models based on assessment data alone (model #1) and added sensory measurement data (model #2). For a sample of n=110 geriatric in-patients (81 women, 29 men) the following fall risk-associated assessments were performed: Timed 'Up & Go' (TUG) test, STRATIFY score and Barthel index. During the TUG test the subjects wore a triaxial accelerometer, and sensory gait parameters were extracted from the data recorded. Group differences between fallers (n=26) and non-fallers (n=84) were compared using Student's t-test. Two classification tree prediction models were computed and compared. Significant differences between the two groups were found for the following parameters: time to complete the TUG test, transfer item (Barthel), recent falls (STRATIFY), pelvic sway while walking and step length. Prediction model #1 (using common assessment data only) showed a sensitivity of 38.5% and a specificity of 97.6%, prediction model #2 (assessment data plus sensory gait parameters) performed with 57.7% and 100%, respectively. Significant differences between fallers and non-fallers among geriatric in-patients can be detected for several assessment subscores as well as parameters recorded by simple accelerometric measurements during a common mobility test. Existing geriatric assessment data may be used for falls prediction on a regular basis

  1. Predicting High or Low Transfer Efficiency of Photovoltaic Systems Using a Novel Hybrid Methodology Combining Rough Set Theory, Data Envelopment Analysis and Genetic Programming

    Directory of Open Access Journals (Sweden)

    Lee-Ing Tong

    2012-02-01

    Full Text Available Solar energy has become an important energy source in recent years as it generates less pollution than other energies. A photovoltaic (PV system, which typically has many components, converts solar energy into electrical energy. With the development of advanced engineering technologies, the transfer efficiency of a PV system has been increased from low to high. The combination of components in a PV system influences its transfer efficiency. Therefore, when predicting the transfer efficiency of a PV system, one must consider the relationship among system components. This work accurately predicts whether transfer efficiency of a PV system is high or low using a novel hybrid model that combines rough set theory (RST, data envelopment analysis (DEA, and genetic programming (GP. Finally, real data-set are utilized to demonstrate the accuracy of the proposed method.

  2. Accurate prediction of subcellular location of apoptosis proteins combining Chou’s PseAAC and PsePSSM based on wavelet denoising

    Science.gov (United States)

    Chen, Cheng; Chen, Rui-Xin; Wang, Lei; Wang, Ming-Hui; Zhang, Yan

    2017-01-01

    Apoptosis proteins subcellular localization information are very important for understanding the mechanism of programmed cell death and the development of drugs. The prediction of subcellular localization of an apoptosis protein is still a challenging task because the prediction of apoptosis proteins subcellular localization can help to understand their function and the role of metabolic processes. In this paper, we propose a novel method for protein subcellular localization prediction. Firstly, the features of the protein sequence are extracted by combining Chou's pseudo amino acid composition (PseAAC) and pseudo-position specific scoring matrix (PsePSSM), then the feature information of the extracted is denoised by two-dimensional (2-D) wavelet denoising. Finally, the optimal feature vectors are input to the SVM classifier to predict subcellular location of apoptosis proteins. Quite promising predictions are obtained using the jackknife test on three widely used datasets and compared with other state-of-the-art methods. The results indicate that the method proposed in this paper can remarkably improve the prediction accuracy of apoptosis protein subcellular localization, which will be a supplementary tool for future proteomics research. PMID:29296195

  3. Combined use of the National Early Warning Score and D-dimer levels to predict 30-day and 365-day mortality in medical patients

    DEFF Research Database (Denmark)

    Nickel, Christian H; Kellett, John; Cooksley, Tim

    2016-01-01

    AIM: To investigate the combined use of NEWS and D-dimer levels to predict the 30-day and 365-day mortality rates of a cohort of Danish patients with complete follow-up. METHODS: Post-hoc retrospective observational study of acutely admitted medical patients aged 18 years or older who had D...... with a NEWS on admission levels below 0.50mgL(-1) appears to identify patients of low risk...

  4. A combined neural network and mechanistic approach for the prediction of corrosion rate and yield strength of magnesium-rare earth alloys

    Energy Technology Data Exchange (ETDEWEB)

    Birbilis, N., E-mail: nick.birbilis@monash.ed [ARC Centre of Excellence for Design in Light Metals, Monash University (Australia); CAST Co-operative Research Centre, Monash University (Australia); Cavanaugh, M.K. [Department of Materials Science and Engineering, The Ohio State University (United States); Sudholz, A.D. [ARC Centre of Excellence for Design in Light Metals, Monash University (Australia); Zhu, S.M.; Easton, M.A. [CAST Co-operative Research Centre, Monash University (Australia); Gibson, M.A. [CSIRO Division of Process Science and Engineering (Australia)

    2011-01-15

    Research highlights: This study presents a body of corrosion data for a set of custom alloys and displays this in multivariable space. These alloys represent the next generation of Mg alloys for auto applications. The data is processed using an ANN model, which makes it possible to yield a single expression for prediction of corrosion rate (and strength) as a function of any input composition (of Ce, La or Nd between 0 and 6 wt.%). The relative influence of the various RE elements on corrosion is assessed, with the outcome that Nd additions can offer comparable strength with minimal rise in corrosion rate. The morphology and solute present in the eutectic region itself (as opposed to just the intermetallic presence) was shown - for the first time - to also be a key contributor to corrosion. The above approach sets the foundation for rational alloy design of alloys with corrosion performance in mind. - Abstract: Additions of Ce, La and Nd to Mg were made in binary, ternary and quaternary combinations up to {approx}6 wt.%. This provided a dataset that was used in developing a neural network model for predicting corrosion rate and yield strength. Whilst yield strength increased with RE additions, corrosion rates also systematically increased, however, this depended on the type of RE element added and the combination of elements added (along with differences in intermetallic morphology). This work is permits an understanding of Mg-RE alloy performance, and can be exploited in Mg alloy design for predictable combinations of strength and corrosion resistance.

  5. Energy Consumption and Indoor Environment Predicted by a Combination of Computational Fluid Dynamics and Building Energy Performance Simulation

    DEFF Research Database (Denmark)

    Nielsen, Peter Vilhelm

    2003-01-01

    An interconnection between a building energy performance simulation program and a Computational Fluid Dynamics program (CFD) for room air distribution is introduced for improvement of the predictions of both the energy consumption and the indoor environment.The article describes a calculation...

  6. Prediction of harmful water quality parameters combining weather, air quality and ecosystem models with in situ measurement

    Science.gov (United States)

    The ability to predict water quality in lakes is important since lakes are sources of water for agriculture, drinking, and recreational uses. Lakes are also home to a dynamic ecosystem of lacustrine wetlands and deep waters. They are sensitive to pH changes and are dependent on d...

  7. Cephalometric variables predicting the long-term success or failure of combined rapid maxillary expansion and facial mask therapy.

    Science.gov (United States)

    Baccetti, Tiziano; Franchi, Lorenzo; McNamara, James A

    2004-07-01

    The aim of this study was to select a model of cephalometric variables to predict the results of early treatment of Class III malocclusion with rapid maxillary expansion and facemask therapy followed by comprehensive treatment with fixed appliances. Lateral cephalograms of 42 patients (20 boys, 22 girls) with Class III malocclusion were analyzed at the start of treatment (mean age 8 years 6 months +/- 2 years, at stage I in cervical vertebral maturation). All patients were reevaluated after a mean period of 6 years 6 months (at stage IV or V in cervical vertebral maturation) that included active treatment plus retention. At this time, the sample was divided into 2 groups according to occlusal criteria: a successful group (30 patients) and an unsuccessful group (12 patients). Discriminant analysis was applied to select pretreatment predictive variables of long-term treatment outcome. Stepwise variable selection of the cephalometric measurements at the first observation identified 3 predictive variables. Orthopedic treatment of Class III malocclusion might be unfavorable over the long term when a patient's pretreatment cephalometric records exhibit a long mandibular ramus (ie, increased posterior facial height), an acute cranial base angle, and a steep mandibular plane angle. On the basis of the equation generated by the multivariate statistical method, the outcome of interceptive orthopedic treatment for each new patient with Class III malocclusion can be predicted with a probability error of 16.7%.

  8. Combination of c-reactive protein and squamous cell carcinoma antigen in predicting postoperative prognosis for patients with squamous cell carcinoma of the esophagus.

    Science.gov (United States)

    Feng, Ji-Feng; Chen, Sheng; Yang, Xun

    2017-09-08

    We initially proposed a useful and novel prognostic model, named CCS [Combination of c-reactive protein (CRP) and squamous cell carcinoma antigen (SCC)], for predicting the postoperative survival in patients with esophageal squamous cell carcinoma (ESCC). Two hundred and fifty-two patients with resectable ESCC were included in this retrospective study. A logistic regression was performed and yielded a logistic equation. The CCS was calculated by the combined CRP and SCC. The optimal cut-off value for CCS was evaluated by X-tile program. Univariate and multivariate analyses were used to evaluate the predictive factors. In addition, a novel nomogram model was also performed to predict the prognosis for patients with ESCC. In the current study, CCS was calculated as CRP+6.33 SCC according to the logistic equation. The optimal cut-off value was 15.8 for CCS according to the X-tile program. Kaplan-Meier analyses demonstrated that high CCS group had a significantly poor 5-year cancer-specific survival (CSS) than low CCS group (10.3% vs. 47.3%, P <0.001). According to multivariate analyses, CCS ( P =0.004), but not CRP ( P =0.466) or SCC ( P =0.926), was an independent prognostic factor. A nomogram could be more accuracy for CSS (Harrell's c-index: 0.70). The CCS is a usefull and independent predictive factor in patients with ESCC.

  9. Predictive value of PET response combined with baseline metabolic tumor volume in peripheral T-cell lymphoma patients

    DEFF Research Database (Denmark)

    Cottereau, Anne-Segolene; El-Galaly, Tarec C; Becker, Stéphanie

    2018-01-01

    Peripheral T-cell lymphoma (PTCL) is a heterogeneous group of aggressive non-Hodgkin lymphomas with poor outcomes with current therapy. We investigated if response assessed with Positron Emission Tomography/computed tomography (PET/CT) combined with baseline total metabolic tumor volume (TMTV) co...

  10. Improving malignancy prediction in breast lesions with the combination of apparent diffusion coefficient and dynamic contrast-enhanced kinetic descriptors

    International Nuclear Information System (INIS)

    Nogueira, Luisa; Brandão, Sofia; Matos, Eduarda; Gouveia Nunes, Rita; Ferreira, Hugo Alexandre; Loureiro, Joana; Ramos, Isabel

    2015-01-01

    Aim: To assess how the joint use of apparent diffusion coefficient (ADC) and kinetic parameters (uptake phase and delayed enhancement characteristics) from dynamic contrast-enhanced (DCE) can boost the ability to predict breast lesion malignancy. Materials and methods: Breast magnetic resonance examinations including DCE and diffusion-weighted imaging (DWI) were performed on 51 women. The association between kinetic parameters and ADC were evaluated and compared between lesion types. Models with binary outcome of malignancy were studied using generalized estimating equations (GEE), (GEE), and using kinetic parameters and ADC values as malignancy predictors. Model accuracy was assessed using the corrected maximum quasi-likelihood under the independence confidence criterion (QICC). Predicted probability of malignancy was estimated for the best model. Results: ADC values were significantly associated with kinetic parameters: medium and rapid uptake phase (p<0.001) and plateau and washout curve types (p=0.004). Comparison between lesion type showed significant differences for ADC (p=0.001), early phase (p<0.001), and curve type (p<0.001). The predicted probabilities of malignancy for the first ADC quartile (≤1.17×10 −3  mm 2 /s) and persistent, plateau and washout curves, were 54.6%, 86.9%, and 97.8%, respectively, and for the third ADC quartile (≥1.51×10 −3  mm 2 /s) were 3.2%, 15.5%, and 54.8%, respectively. The predicted probability of malignancy was less than 5% for 18.8% of the lesions and greater than 33% for 50.7% of the lesions (24/35 lesions, corresponding to a malignancy rate of 68.6%). Conclusion: The best malignancy predictors were low ADCs and washout curves. ADC and kinetic parameters provide differentiated information on the microenvironment of the lesion, with joint models displaying improved predictive performance. -- Highlights: •ADC and kinetic parameters provide diverse information regarding lesion environment. •The best predictors

  11. Combined spatial prediction of schistosomiasis and soil-transmitted helminthiasis in Sierra Leone: a tool for integrated disease control.

    Directory of Open Access Journals (Sweden)

    Mary H Hodges

    Full Text Available BACKGROUND: A national mapping of Schistosoma haematobium was conducted in Sierra Leone before the mass drug administration (MDA with praziquantel. Together with the separate mapping of S. mansoni and soil-transmitted helminths, the national control programme was able to plan the MDA strategies according to the World Health Organization guidelines for preventive chemotherapy for these diseases. METHODOLOGY/PRINCIPAL FINDINGS: A total of 52 sites/schools were selected according to prior knowledge of S. haematobium endemicity taking into account a good spatial coverage within each district, and a total of 2293 children aged 9-14 years were examined. Spatial analysis showed that S. haematobium is heterogeneously distributed in the country with significant spatial clustering in the central and eastern regions of the country, most prevalent in Bo (24.6% and 8.79 eggs/10 ml, Koinadugu (20.4% and 3.53 eggs/10 ml and Kono (25.3% and 7.91 eggs/10 ml districts. By combining this map with the previously reported maps on intestinal schistosomiasis using a simple probabilistic model, the combined schistosomiasis prevalence map highlights the presence of high-risk communities in an extensive area in the northeastern half of the country. By further combining the hookworm prevalence map, the at-risk population of school-age children requiring integrated schistosomiasis/soil-transmitted helminth treatment regimens according to the coendemicity was estimated. CONCLUSIONS/SIGNIFICANCE: The first comprehensive national mapping of urogenital schistosomiasis in Sierra Leone was conducted. Using a new method for calculating the combined prevalence of schistosomiasis using estimates from two separate surveys, we provided a robust coendemicity mapping for overall urogenital and intestinal schistosomiasis. We also produced a coendemicity map of schistosomiasis and hookworm. These coendemicity maps can be used to guide the decision making for MDA strategies in combination

  12. Combined spatial prediction of schistosomiasis and soil-transmitted helminthiasis in Sierra Leone: a tool for integrated disease control.

    Science.gov (United States)

    Hodges, Mary H; Soares Magalhães, Ricardo J; Paye, Jusufu; Koroma, Joseph B; Sonnie, Mustapha; Clements, Archie; Zhang, Yaobi

    2012-01-01

    A national mapping of Schistosoma haematobium was conducted in Sierra Leone before the mass drug administration (MDA) with praziquantel. Together with the separate mapping of S. mansoni and soil-transmitted helminths, the national control programme was able to plan the MDA strategies according to the World Health Organization guidelines for preventive chemotherapy for these diseases. A total of 52 sites/schools were selected according to prior knowledge of S. haematobium endemicity taking into account a good spatial coverage within each district, and a total of 2293 children aged 9-14 years were examined. Spatial analysis showed that S. haematobium is heterogeneously distributed in the country with significant spatial clustering in the central and eastern regions of the country, most prevalent in Bo (24.6% and 8.79 eggs/10 ml), Koinadugu (20.4% and 3.53 eggs/10 ml) and Kono (25.3% and 7.91 eggs/10 ml) districts. By combining this map with the previously reported maps on intestinal schistosomiasis using a simple probabilistic model, the combined schistosomiasis prevalence map highlights the presence of high-risk communities in an extensive area in the northeastern half of the country. By further combining the hookworm prevalence map, the at-risk population of school-age children requiring integrated schistosomiasis/soil-transmitted helminth treatment regimens according to the coendemicity was estimated. The first comprehensive national mapping of urogenital schistosomiasis in Sierra Leone was conducted. Using a new method for calculating the combined prevalence of schistosomiasis using estimates from two separate surveys, we provided a robust coendemicity mapping for overall urogenital and intestinal schistosomiasis. We also produced a coendemicity map of schistosomiasis and hookworm. These coendemicity maps can be used to guide the decision making for MDA strategies in combination with the local knowledge and programme needs.

  13. Combining on-chip synthesis of a focused combinatorial library with computational target prediction reveals imidazopyridine GPCR ligands.

    Science.gov (United States)

    Reutlinger, Michael; Rodrigues, Tiago; Schneider, Petra; Schneider, Gisbert

    2014-01-07

    Using the example of the Ugi three-component reaction we report a fast and efficient microfluidic-assisted entry into the imidazopyridine scaffold, where building block prioritization was coupled to a new computational method for predicting ligand-target associations. We identified an innovative GPCR-modulating combinatorial chemotype featuring ligand-efficient adenosine A1/2B and adrenergic α1A/B receptor antagonists. Our results suggest the tight integration of microfluidics-assisted synthesis with computer-based target prediction as a viable approach to rapidly generate bioactivity-focused combinatorial compound libraries with high success rates. Copyright © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  14. Combining public participatory surveillance and occupancy modelling to predict the distributional response of Ixodes scapularis to climate change.

    Science.gov (United States)

    Lieske, David J; Lloyd, Vett K

    2018-03-01

    Ixodes scapularis, a known vector of Borrelia burgdorferi sensu stricto (Bbss), is undergoing range expansion in many parts of Canada. The province of New Brunswick, which borders jurisdictions with established populations of I. scapularis, constitutes a range expansion zone for this species. To better understand the current and potential future distribution of this tick under climate change projections, this study applied occupancy modelling to distributional records of adult ticks that successfully overwintered, obtained through passive surveillance. This study indicates that I. scapularis occurs throughout the southern-most portion of the province, in close proximity to coastlines and major waterways. Milder winter conditions, as indicated by the number of degree days model with a predictive accuracy of 0.845 (range: 0.828-0.893). Both RCP 4.5 and RCP 8.5 climate projections predict that a significant proportion of the province (roughly a quarter to a third) will be highly suitable for I. scapularis by the 2080s. Comparison with cases of canine infection show good spatial agreement with baseline model predictions, but the presence of canine Borrelia infections beyond the climate envelope, defined by the highest probabilities of tick occurrence, suggest the presence of Bbss-carrying ticks distributed by long-range dispersal events. This research demonstrates that predictive statistical modelling of multi-year surveillance information is an efficient way to identify areas where I. scapularis is most likely to occur, and can be used to guide subsequent active sampling efforts in order to better understand fine scale species distributional patterns. Copyright © 2018 The Authors. Published by Elsevier GmbH.. All rights reserved.

  15. Benefit of Hepatitis C Virus Core Antigen Assay in Prediction of Therapeutic Response to Interferon and Ribavirin Combination Therapy

    OpenAIRE

    Takahashi, Masahiko; Saito, Hidetsugu; Higashimoto, Makiko; Atsukawa, Kazuhiro; Ishii, Hiromasa

    2005-01-01

    A highly sensitive second-generation hepatitis C virus (HCV) core antigen assay has recently been developed. We compared viral disappearance and first-phase kinetics between commercially available core antigen (Ag) assays, Lumipulse Ortho HCV Ag (Lumipulse-Ag), and a quantitative HCV RNA PCR assay, Cobas Amplicor HCV Monitor test, version 2 (Amplicor M), to estimate the predictive benefit of a sustained viral response (SVR) and non-SVR in 44 genotype 1b patients treated with interferon (IFN) ...

  16. Prediction of HIFU Propagation in a Dispersive Medium via Khokhlov–Zabolotskaya–Kuznetsov Model Combined with a Fractional Order Derivative

    OpenAIRE

    Shilei Liu; Yanye Yang; Chenghai Li; Xiasheng Guo; Juan Tu; Dong Zhang

    2018-01-01

    High intensity focused ultrasound (HIFU) has been proven to be promising in non-invasive therapies, in which precise prediction of the focused ultrasound field is crucial for its accurate and safe application. Although the Khokhlov–Zabolotskaya–Kuznetsov (KZK) equation has been widely used in the calculation of the nonlinear acoustic field of HIFU, some deviations still exist when it comes to dispersive medium. This problem also exists as an obstacle to the Westervelt model and the Spherical ...

  17. Through-Thickness Residual Stress Profiles in Austenitic Stainless Steel Welds: A Combined Experimental and Prediction Study

    Science.gov (United States)

    Mathew, J.; Moat, R. J.; Paddea, S.; Francis, J. A.; Fitzpatrick, M. E.; Bouchard, P. J.

    2017-12-01

    Economic and safe management of nuclear plant components relies on accurate prediction of welding-induced residual stresses. In this study, the distribution of residual stress through the thickness of austenitic stainless steel welds has been measured using neutron diffraction and the contour method. The measured data are used to validate residual stress profiles predicted by an artificial neural network approach (ANN) as a function of welding heat input and geometry. Maximum tensile stresses with magnitude close to the yield strength of the material were observed near the weld cap in both axial and hoop direction of the welds. Significant scatter of more than 200 MPa was found within the residual stress measurements at the weld center line and are associated with the geometry and welding conditions of individual weld passes. The ANN prediction is developed in an attempt to effectively quantify this phenomenon of `innate scatter' and to learn the non-linear patterns in the weld residual stress profiles. Furthermore, the efficacy of the ANN method for defining through-thickness residual stress profiles in welds for application in structural integrity assessments is evaluated.

  18. Combining in vitro and in silico methods for better prediction of surfactant effects on the absorption of poorly water soluble drugs-a fenofibrate case example.

    Science.gov (United States)

    Berthelsen, Ragna; Sjögren, Erik; Jacobsen, Jette; Kristensen, Jakob; Holm, René; Abrahamsson, Bertil; Müllertz, Anette

    2014-10-01

    The aim of this study was to develop a sensitive and discriminative in vitro-in silico model able to simulate the in vivo performance of three fenofibrate immediate release formulations containing different surfactants. In addition, the study was designed to investigate the effect of dissolution volume when predicting the oral bioavailability of the formulations. In vitro dissolution studies were carried out using the USP apparatus 2 or a mini paddle assembly, containing 1000 mL or 100mL fasted state biorelevant medium, respectively. In silico simulations of small intestinal absorption were performed using the GI-Sim absorption model. All simulation runs were performed twice adopting either a total small intestinal volume of 533 mL or 105 mL, in order to examine the implication of free luminal water volumes for the in silico predictions. For the tested formulations, the use of a small biorelevant dissolution volume was critical for in vitro-in silico prediction of drug absorption. Good predictions, demonstrating rank order in vivo-in vitro-in silico correlations for Cmax, were obtained with in silico predictions utilizing a 105 mL estimate for the human intestinal water content combined with solubility and dissolution data performed in a mini paddle apparatus with 100mL fasted state simulated media. Copyright © 2014. Published by Elsevier B.V.

  19. Prediction of intramuscular fat content and shear force in Texel lamb loins using combinations of different X-ray computed tomography (CT) scanning techniques.

    Science.gov (United States)

    Clelland, N; Bunger, L; McLean, K A; Knott, S; Matthews, K R; Lambe, N R

    2018-06-01

    Computed tomography (CT) parameters, including spiral computed tomography scanning (SCTS) parameters, intramuscular fat (IMF) and mechanically measured shear force were derived from two previously published studies. Purebred Texel (n = 377) of both sexes, females (n = 206) and intact males (n = 171) were used to investigate the prediction of IMF and shear force in the loin. Two and three dimensional CT density information was available. Accuracies in the prediction of shear force and IMF ranged from R 2 0.02 to R 2 0.13 and R 2 0.51 to R 2 0.71 respectively, using combinations of SCTS and CT scan information. The prediction of mechanical shear force could not be achieved at an acceptable level of accuracy employing SCTS information. However, the prediction of IMF in the loin employing information from SCTS and additional information from standard CT scans was successful, providing evidence that the prediction of IMF and related meat eating quality (MEQ) traits for Texel lambs in vivo can be achieved. Copyright © 2018 Elsevier Ltd. All rights reserved.

  20. A Combined Hydrological and Hydraulic Model for Flood Prediction in Vietnam Applied to the Huong River Basin as a Test Case Study

    Directory of Open Access Journals (Sweden)

    Dang Thanh Mai

    2017-11-01

    Full Text Available A combined hydrological and hydraulic model is presented for flood prediction in Vietnam. This model is applied to the Huong river basin as a test case study. Observed flood flows and water surface levels of the 2002–2005 flood seasons are used for model calibration, and those of the 2006–2007 flood seasons are used for validation of the model. The physically based distributed hydrologic model WetSpa is used for predicting the generation and propagation of flood flows in the mountainous upper sub-basins, and proves to predict flood flows accurately. The Hydrologic Engineering Center River Analysis System (HEC-RAS hydraulic model is applied to simulate flood flows and inundation levels in the downstream floodplain, and also proves to predict water levels accurately. The predicted water profiles are used for mapping of inundations in the floodplain. The model may be useful in developing flood forecasting and early warning systems to mitigate losses due to flooding in Vietnam.

  1. Combining Physical and Biologic Parameters to Predict Radiation-Induced Lung Toxicity in Patients With Non-Small-Cell Lung Cancer Treated With Definitive Radiation Therapy

    Energy Technology Data Exchange (ETDEWEB)

    Stenmark, Matthew H. [Department of Radiation Oncology, University of Michigan Medical Center, Ann Arbor, Michigan (United States); Cai Xuwei [Department of Radiation Oncology, University of Michigan Medical Center, Ann Arbor, Michigan (United States); Radiation Oncology, Shanghai Cancer Hospital, Fudan University, Shanghai (China); Shedden, Kerby [Department of Biostatistics, University of Michigan Medical Center, Ann Arbor, Michigan (United States); Hayman, James A. [Department of Radiation Oncology, University of Michigan Medical Center, Ann Arbor, Michigan (United States); Yuan Shuanghu [Department of Radiation Oncology, University of Michigan Medical Center, Ann Arbor, Michigan (United States); Radiation Oncology, Shangdong Cancer Hospital, Jinan (China); Ritter, Timothy [Veterans Affairs Medical Center, Ann Arbor, Michigan (United States); Ten Haken, Randall K.; Lawrence, Theodore S. [Department of Radiation Oncology, University of Michigan Medical Center, Ann Arbor, Michigan (United States); Kong Fengming, E-mail: fengkong@med.umich.edu [Department of Radiation Oncology, University of Michigan Medical Center, Ann Arbor, Michigan (United States); Veterans Affairs Medical Center, Ann Arbor, Michigan (United States)

    2012-10-01

    Purpose: To investigate the plasma dynamics of 5 proinflammatory/fibrogenic cytokines, including interleukin-1beta (IL-1{beta}), IL-6, IL-8, tumor necrosis factor alpha (TNF-{alpha}), and transforming growth factor beta1 (TGF-{beta}1) to ascertain their value in predicting radiation-induced lung toxicity (RILT), both individually and in combination with physical dosimetric parameters. Methods and Materials: Treatments of patients receiving definitive conventionally fractionated radiation therapy (RT) on clinical trial for inoperable stages I-III lung cancer were prospectively evaluated. Circulating cytokine levels were measured prior to and at weeks 2 and 4 during RT. The primary endpoint was symptomatic RILT, defined as grade 2 and higher radiation pneumonitis or symptomatic pulmonary fibrosis. Minimum follow-up was 18 months. Results: Of 58 eligible patients, 10 (17.2%) patients developed RILT. Lower pretreatment IL-8 levels were significantly correlated with development of RILT, while radiation-induced elevations of TGF-ss1 were weakly correlated with RILT. Significant correlations were not found for any of the remaining 3 cytokines or for any clinical or dosimetric parameters. Using receiver operator characteristic curves for predictive risk assessment modeling, we found both individual cytokines and dosimetric parameters were poor independent predictors of RILT. However, combining IL-8, TGF-ss1, and mean lung dose into a single model yielded an improved predictive ability (P<.001) compared to either variable alone. Conclusions: Combining inflammatory cytokines with physical dosimetric factors may provide a more accurate model for RILT prediction. Future study with a larger number of cases and events is needed to validate such findings.

  2. Biopsychosocial influence on exercise-induced injury: genetic and psychological combinations are predictive of shoulder pain phenotypes

    OpenAIRE

    George, Steven Z.; Parr, Jeffrey J.; Wallace, Margaret R.; Wu, Samuel S.; Borsa, Paul A.; Dai, Yunfeng; Fillingim, Roger B.

    2013-01-01

    Chronic pain is influenced by biological, psychological, social, and cultural factors. The current study investigated potential roles for combinations of genetic and psychological factors in the development and/or maintenance of chronic musculoskeletal pain. An exercise-induced shoulder injury model was used and a priori selected genetic (ADRB2, COMT, OPRM1, AVPR1A, GCH1, and KCNS1) and psychological (anxiety, depressive symptoms, pain catastrophizing, fear of pain, and kinesiophobia) factors...

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

    Science.gov (United States)

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

    2016-04-01

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

  4. Effective prediction of bacterial type IV secreted effectors by combined features of both C-termini and N-termini.

    Science.gov (United States)

    Wang, Yu; Guo, Yanzhi; Pu, Xuemei; Li, Menglong

    2017-11-01

    Various bacterial pathogens can deliver their secreted substrates also called as effectors through type IV secretion systems (T4SSs) into host cells and cause diseases. Since T4SS secreted effectors (T4SEs) play important roles in pathogen-host interactions, identifying them is crucial to our understanding of the pathogenic mechanisms of T4SSs. A few computational methods using machine learning algorithms for T4SEs prediction have been developed by using features of C-terminal residues. However, recent studies have shown that targeting information can also be encoded in the N-terminal region of at least some T4SEs. In this study, we present an effective method for T4SEs prediction by novelly integrating both N-terminal and C-terminal sequence information. First, we collected a comprehensive dataset across multiple bacterial species of known T4SEs and non-T4SEs from literatures. Then, three types of distinctive features, namely amino acid composition, composition, transition and distribution and position-specific scoring matrices were calculated for 50 N-terminal and 100 C-terminal residues. After that, we employed information gain represent to rank the importance score of the 150 different position residues for T4SE secretion signaling. At last, 125 distinctive position residues were singled out for the prediction model to classify T4SEs and non-T4SEs. The support vector machine model yields a high receiver operating curve of 0.916 in the fivefold cross-validation and an accuracy of 85.29% for the independent test set.

  5. Prediction of severe pre-eclampsia/HELLP syndrome by combination of sFlt-1, CT-pro-ET-1 and blood pressure: exploratory study.

    Science.gov (United States)

    Lind Malte, A; Uldbjerg, N; Wright, D; Tørring, N

    2018-06-01

    To evaluate the performance of a combination of angiogenic and vasoactive biomarkers to predict the development of severe pre-eclampsia (PE)/HELLP syndrome in the third trimester. Included were 215 women referred in the third trimester to an obstetric outpatient clinic with suspected PE (mean gestational age, 35 + 4 weeks), and 94 with normal pregnancy attending a midwife clinic. Cases were categorized as having subclinical PE, essential hypertension, gestational hypertension, moderate PE, and severe PE/HELLP syndrome. Blood samples were analyzed by immunoassay and groups were compared with respect to potential clinical and biochemical biomarkers, with the primary outcome being development of severe PE/HELLP syndrome within 1 week and within 2 weeks of analysis. The most promising markers were also assessed in combination. In the patients presenting with mild to moderate symptoms of PE, the individual markers which performed best for the prediction of progression to severe PE/HELLP syndrome within 1 week and within 2 weeks of biomarker evaluation were C-terminal pro-endothelin-1 (CT-pro-ET-1) (area under the receiver-operating characteristics curve (AUC), 0.82 and 0.78, respectively), soluble fms-like tyrosine kinase-1 (sFlt-1) (AUC, 0.81 and 0.76), systolic blood pressure (AUC, 0.80 and 0.68) and midregional pro-atrial natriuretic peptide (AUC, 0.79 and 0.77). The combination of biomarkers with the best performance was CT-pro-ET-1, sFlt-1 and systolic blood pressure, achieving an AUC of 0.94 for prediction of development of severe PE/HELLP syndrome within 1 week and an AUC of 0.83 for prediction of their development within 2 weeks of biomarker evaluation. The performance of CT-pro-ET-1 for prediction of the development of PE/HELLP syndrome in the third trimester was promising, especially in combination with sFlt-1 and systolic blood pressure. This was an exploratory study and our findings should be confirmed in further studies. Copyright © 2017

  6. Integrated predictive modeling of high-mode tokamak plasmas using a combination of core and pedestal models

    International Nuclear Information System (INIS)

    Bateman, Glenn; Bandres, Miguel A.; Onjun, Thawatchai; Kritz, Arnold H.; Pankin, Alexei

    2003-01-01

    A new integrated modeling protocol is developed using a model for the temperature and density pedestal at the edge of high-mode (H-mode) plasmas [Onjun et al., Phys. Plasmas 9, 5018 (2002)] together with the Multi-Mode core transport model (MMM95) [Bateman et al., Phys. Plasmas 5, 1793 (1998)] in the BALDUR integrated modeling code to predict the temperature and density profiles of 33 H-mode discharges. The pedestal model is used to provide the boundary conditions in the simulations, once the heating power rises above the H-mode power threshold. Simulations are carried out for 20 discharges in the Joint European Torus and 13 discharges in the DIII-D tokamak. These discharges include systematic scans in normalized gyroradius, plasma pressure, collisionality, isotope mass, elongation, heating power, and plasma density. The average rms deviation between experimental data and the predicted profiles of temperature and density, normalized by central values, is found to be about 10%. It is found that the simulations tend to overpredict the temperature profiles in discharges with low heating power per plasma particle and to underpredict the temperature profiles in discharges with high heating power per particle. Variations of the pedestal model are used to test the sensitivity of the simulation results

  7. Prediction of HIFU Propagation in a Dispersive Medium via Khokhlov–Zabolotskaya–Kuznetsov Model Combined with a Fractional Order Derivative

    Directory of Open Access Journals (Sweden)

    Shilei Liu

    2018-04-01

    Full Text Available High intensity focused ultrasound (HIFU has been proven to be promising in non-invasive therapies, in which precise prediction of the focused ultrasound field is crucial for its accurate and safe application. Although the Khokhlov–Zabolotskaya–Kuznetsov (KZK equation has been widely used in the calculation of the nonlinear acoustic field of HIFU, some deviations still exist when it comes to dispersive medium. This problem also exists as an obstacle to the Westervelt model and the Spherical Beam Equation. Considering that the KZK equation is the most prevalent model in HIFU applications due to its accurate and simple simulation algorithms, there is an urgent need to improve its performance in dispersive medium. In this work, a modified KZK (mKZK equation derived from a fractional order derivative is proposed to calculate the nonlinear acoustic field in a dispersive medium. By correcting the power index in the attenuation term, this model is capable of providing improved prediction accuracy, especially in the axial position of the focal area. Simulation results using the obtained model were further compared with the experimental results from a gel phantom. Good agreements were found, indicating the applicability of the proposed model. The findings of this work will be helpful in making more accurate treatment plans for HIFU therapies, as well as facilitating the application of ultrasound in acoustic hyperthermia therapy.

  8. New Combined Scoring System for Predicting Respiratory Failure in Iraqi Patients with Guillain-Barré Syndrome

    Directory of Open Access Journals (Sweden)

    Zaki Noah Hasan

    2010-09-01

    Full Text Available The Guillain-Barré syndrome (GBS is an acute post-infective autoimmune polyradiculoneuropathy, it is the commonest peripheral neuropathy causing respiratory failure. The aim of the study is to use the New Combined Scoring System in anticipating respiratory failure in order to perform elective measures without waiting for emergency situations to occur.
    Patients and methods: Fifty patients with GBS were studied. Eight clinical parameters (including progression of patients to maximum weakness, respiratory rate/minute, breath holding
    count (the number of digits the patient can count in holding his breath, presence of facial muscle weakness (unilateral or bilateral, presence of weakness of the bulbar muscle, weakness of the neck flexor muscle, and limbs weakness were assessed for each patient and a certain score was given to
    each parameter, a designed combined score being constructed by taking into consideration all the above mentioned clinical parameters. Results and discussion: Fifteen patients (30% that were enrolled in our study developed respiratory failure. There was a highly significant statistical association between the development of respiratory failure and the lower grades of (bulbar muscle weakness score, breath holding count scores, neck muscle weakness score, lower limbs and upper limbs weakness score , respiratory rate score and the total sum score above 16 out of 30 (p-value=0.000 . No significant statistical difference was found regarding the progression to maximum weakness (p-value=0.675 and facial muscle weakness (p-value=0.482.
    Conclusion: The patients who obtained a combined score (above 16’30 are at great risk of having respiratory failure.

  9. Development and validation of a combined phased acoustical radiosity and image source model for predicting sound fields in rooms

    DEFF Research Database (Denmark)

    Marbjerg, Gerd Høy; Brunskog, Jonas; Jeong, Cheol-Ho

    2015-01-01

    A model, combining acoustical radiosity and the image source method, including phase shifts on reflection, has been developed. The model is denoted Phased Acoustical Radiosity and Image Source Method (PARISM), and it has been developed in order to be able to model both specular and diffuse...... radiosity by regarding the model as being stochastic. Three methods of implementation are proposed and investigated, and finally, recommendations are made for their use. Validation of the image source method is done by comparison with finite element simulations of a rectangular room with a porous absorber...

  10. Predicting prices of agricultural commodities in Thailand using combined approach emphasizing on data pre-processing technique

    Directory of Open Access Journals (Sweden)

    Thoranin Sujjaviriyasup

    2018-02-01

    Full Text Available In this research, a combined approach emphasizing on data pre-processing technique is developed to forecast prices of agricultural commodities in Thailand. The future prices play significant role in decision making to cultivate crops in next year. The proposed model takes ability of MODWT to decompose original time series data into more stable and explicit subseries, and SVR model to formulate complex function of forecasting. The experimental results indicated that the proposed model outperforms traditional forecasting models based on MAE and MAPE criteria. Furthermore, the proposed model reveals that it is able to be a useful forecasting tool for prices of agricultural commodities in Thailand

  11. Condensational Growth of Combination Drug-Excipient Submicrometer Particles for Targeted High Efficiency Pulmonary Delivery: Comparison of CFD Predictions with Experimental Results

    Science.gov (United States)

    Hindle, Michael

    2011-01-01

    Purpose The objective of this study was to investigate the hygroscopic growth of combination drug and excipient submicrometer aerosols for respiratory drug delivery using in vitro experiments and a newly developed computational fluid dynamics (CFD) model. Methods Submicrometer combination drug and excipient particles were generated experimentally using both the capillary aerosol generator and the Respimat inhaler. Aerosol hygroscopic growth was evaluated in vitro and with CFD in a coiled tube geometry designed to provide residence times and thermodynamic conditions consistent with the airways. Results The in vitro results and CFD predictions both indicated that the initially submicrometer particles increased in mean size to a range of 1.6–2.5 µm for the 50:50 combination of a non-hygroscopic drug (budesonide) and different hygroscopic excipients. CFD results matched the in vitro predictions to within 10% and highlighted gradual and steady size increase of the droplets, which will be effective for minimizing extrathoracic deposition and producing deposition deep within the respiratory tract. Conclusions Enhanced excipient growth (EEG) appears to provide an effective technique to increase pharmaceutical aerosol size, and the developed CFD model will provide a powerful design tool for optimizing this technique to produce high efficiency pulmonary delivery. PMID:21948458

  12. Condensational growth of combination drug-excipient submicrometer particles for targeted high efficiency pulmonary delivery: comparison of CFD predictions with experimental results.

    Science.gov (United States)

    Longest, P Worth; Hindle, Michael

    2012-03-01

    The objective of this study was to investigate the hygroscopic growth of combination drug and excipient submicrometer aerosols for respiratory drug delivery using in vitro experiments and a newly developed computational fluid dynamics (CFD) model. Submicrometer combination drug and excipient particles were generated experimentally using both the capillary aerosol generator and the Respimat inhaler. Aerosol hygroscopic growth was evaluated in vitro and with CFD in a coiled tube geometry designed to provide residence times and thermodynamic conditions consistent with the airways. The in vitro results and CFD predictions both indicated that the initially submicrometer particles increased in mean size to a range of 1.6-2.5 μm for the 50:50 combination of a non-hygroscopic drug (budesonide) and different hygroscopic excipients. CFD results matched the in vitro predictions to within 10% and highlighted gradual and steady size increase of the droplets, which will be effective for minimizing extrathoracic deposition and producing deposition deep within the respiratory tract. Enhanced excipient growth (EEG) appears to provide an effective technique to increase pharmaceutical aerosol size, and the developed CFD model will provide a powerful design tool for optimizing this technique to produce high efficiency pulmonary delivery.

  13. Maternal Nutritional Status Predicts Adverse Birth Outcomes among HIV-Infected Rural Ugandan Women Receiving Combination Antiretroviral Therapy

    Science.gov (United States)

    Young, Sera; Murray, Katherine; Mwesigwa, Julia; Natureeba, Paul; Osterbauer, Beth; Achan, Jane; Arinaitwe, Emmanuel; Clark, Tamara; Ades, Veronica; Plenty, Albert; Charlebois, Edwin; Ruel, Theodore; Kamya, Moses; Havlir, Diane; Cohan, Deborah

    2012-01-01

    Objective Maternal nutritional status is an important predictor of birth outcomes, yet little is known about the nutritional status of HIV-infected pregnant women treated with combination antiretroviral therapy (cART). We therefore examined the relationship between maternal BMI at study enrollment, gestational weight gain (GWG), and hemoglobin concentration (Hb) among 166 women initiating cART in rural Uganda. Design Prospective cohort. Methods HIV-infected, ART-naïve pregnant women were enrolled between 12 and 28 weeks gestation and treated with a protease inhibitor or non-nucleoside reverse transcriptase inhibitor-based combination regimen. Nutritional status was assessed monthly. Neonatal anthropometry was examined at birth. Outcomes were evaluated using multivariate analysis. Results Mean GWG was 0.17 kg/week, 14.6% of women experienced weight loss during pregnancy, and 44.9% were anemic. Adverse fetal outcomes included low birth weight (LBW) (19.6%), preterm delivery (17.7%), fetal death (3.9%), stunting (21.1%), small-for-gestational age (15.1%), and head-sparing growth restriction (26%). No infants were HIV-infected. Gaining pregnancy, grossly inadequate GWG was common. Infants whose mothers gained <0.1 kg/week were at increased risk for LBW, preterm delivery, and composite adverse birth outcomes. cART by itself may not be sufficient for decreasing the burden of adverse birth outcomes among HIV-infected women. Trial Registration Clinicaltrials.gov NCT00993031 PMID:22879899

  14. Development and validation of a combined phased acoustical radiosity and image source model for predicting sound fields in rooms.

    Science.gov (United States)

    Marbjerg, Gerd; Brunskog, Jonas; Jeong, Cheol-Ho; Nilsson, Erling

    2015-09-01

    A model, combining acoustical radiosity and the image source method, including phase shifts on reflection, has been developed. The model is denoted Phased Acoustical Radiosity and Image Source Method (PARISM), and it has been developed in order to be able to model both specular and diffuse reflections with complex-valued and angle-dependent boundary conditions. This paper mainly describes the combination of the two models and the implementation of the angle-dependent boundary conditions. It furthermore describes how a pressure impulse response is obtained from the energy-based acoustical radiosity by regarding the model as being stochastic. Three methods of implementation are proposed and investigated, and finally, recommendations are made for their use. Validation of the image source method is done by comparison with finite element simulations of a rectangular room with a porous absorber ceiling. Results from the full model are compared with results from other simulation tools and with measurements. The comparisons of the full model are done for real-valued and angle-independent surface properties. The proposed model agrees well with both the measured results and the alternative theories, and furthermore shows a more realistic spatial variation than energy-based methods due to the fact that interference is considered.

  15. Seminal vesicle invasion in prostate cancer: prediction with combined T2-weighted and diffusion-weighted MR imaging

    International Nuclear Information System (INIS)

    Ren, Jing; Huan, Yi; Ge, YaLi; Chang, YingJuan; Yin, Hong; Sun, LiJun; Wang, He

    2009-01-01

    The aim of this study was to evaluate the usefulness of diffusion-weighted imaging (DWI) in detecting seminal vesicle invasion (SVI). A total of 283 patients underwent conventional MRI and DWI before prostate cancer surgery. The apparent diffusion coefficient (ADC) values of all PCa foci, SVI and seminal vesicle were measured. T2 images alone and then T2 images combined with DWI were scored for the likelihood of SVI. The area under the receiver operating characteristic curve (AUC) was used to assess accuracy. Statistical significance was inferred at P<0.05. On pathological analysis, 39 patients had SVI. The ADC values of SVI were significantly lower than those of SV. The AUC for T2-weighted imaging plus DW imaging (0.897) was significantly larger (P<0.05) than that for T2-weighted imaging alone (0.779). T2 images combined with DWI shows significantly higher accuracy than T2-weighted imaging alone in the detection of SVI. (orig.)

  16. Combined application of Sentinel2A data and growth modelling for novel monitoring and prediction of pasture yields

    Science.gov (United States)

    Verhoef, A.; Punalekar, S.; Quaife, T. L.; Humphries, D.; Reynolds, C.

    2017-12-01

    Currently, 30% of the world's land area is covered by permanent pasture. Grazing ruminants convert forage materials into milk and meat for human consumption; ruminant production is a key agricultural enterprise. Management of pasture farms (nutrient and herbi-/pesticides application, grazing rotations) is often suboptimal. Furthermore, adverse weather can have negative effects on pasture growth and quality. Near real-time herbage monitoring and prediction could help improve farm profitability. While the use of remote sensing (RS) in the context of arable crop growth prediction is becoming more established, the same is not true for pasture. However, recently launched Sentinel satellites offer real opportunities to exploit high spatio-temporal resolution datasets for effective monitoring of pastures, as well as crops. A perennial grazed ryegrass field in the Southwest of the UK was monitored regularly using field hyperspectral spectro-radiometers. Simultaneously, leaf area index (LAI) was measured using a ceptometer, and yield was measured, indirectly using a `plate meter' and directly by destructive sampling. Two sets of spectral data were used to retrieve LAI with the PROSAIL radiative transfer model: (i) Sentinel-2A bands convolved from field spectral data, (ii) actual Sentinel-2A image pixels for the sampling plots. Retrieved LAI was compared against field observations. LAI estimates were assimilated in a bespoke growth model (including grazing and management), driven by weather data, for calibration of sensitive parameters using a 4D-Var scheme, to obtain pasture biomass. The developed approach was used to study a pasture farm in the South of the UK, for which a large number of Sentinel-2A images were available throughout 2016-17. Retrieved LAI compared well with in-situ LAI, and significantly improved yield estimates. The calibrated model parameters compared well with literature values. The model, guided by satellite data and general information on farm

  17. Predicting the density of structural timber members in service. The combine use of wood cores and drill resistance data

    Directory of Open Access Journals (Sweden)

    Morales-Condea, M. J.

    2014-09-01

    Full Text Available Drilling devices is used to get information about the cross-section properties and internal defects of structural members. Drill resistance is correlated with density which is often used to predict the mechanical properties of timber elements. However in situ a regression curve cannot be obtained and pre-existent curves provides unreliable predictions. The present paper proposes a procedure for in situ “calibration” of drill resistance data. The “calibration” is based on density values from wood cores taken in the close vicinity of drill holes. Two approaches were tested. One approach based on a regression curve built using wood cores density and drill resistance values obtained from a limited number of members. The other approach uses the information of one wood core to “calibrate” the drill resistance profile taken at the same member. Following this procedure a density prediction is obtained showing a low mean percentage error and a medium coefficient of determination.Los dispositivos de perforación se utilizan a menudo para obtener información sobre las propiedades de la sección transversal y defectos internos de los elementos estructurales de madera. La resistencia a la perforación se correlaciona con la densidad que, a menudo, se utiliza para obtener la predicción de las propiedades mecánicas de los elementos de madera. Sin embargo, una curva de regresión no puede ser obtenida in situ y las curvas de preexistentes proporcionan predicciones poco fiables. En el presente trabajo se propone un procedimiento de “calibración” in situ de los datos de resistencia de perforación obtenidos en cada caso. La “calibración” se basa en los valores de densidad de pequeñas probetas de madera tomadas en las inmediaciones de los taladros. Para ello se plantean 2 métodos: Un primer enfoque basado en obtener una curva de regresión a partir de los valores de densidad de pequeñas probetas de madera y los valores de resistencia a

  18. Combining X-ray computed tomography and visible near-infrared spectroscopy for prediction of soil structural properties

    DEFF Research Database (Denmark)

    Katuwal, Sheela; Hermansen, Cecilie; Knadel, Maria

    2018-01-01

    agricultural fields within Denmark with a wide range of textural properties and organic C (OC) contents were studied. Macroporosity (>1.2 mm in diameter) and CTmatrix (the density of the field-moist soil matrix devoid of large macropores and stones) were determined from X-ray CT scans of undisturbed soil cores...... (19 by 20 cm). Both macroporosity and CTmatix are soil structural properties that affect the degree of preferential transport. Bulk soils from the 127 sampling locations were scanned with a vis-NIR spectrometer (400–2500 nm). Macroporosity and CTmatrix were statistically predicted with partial least......Soil structure is a key soil property affecting a soil’s flow and transport behavior. X-ray computed tomography (CT) is increasingly used to quantify soil structure. However, the availability, cost, time, and skills required for processing are still limiting the number of soils studied. Visible...

  19. Kiwifruit fibre level influences the predicted production and absorption of SCFA in the hindgut of growing pigs using a combined in vivo-in vitro digestion methodology.

    Science.gov (United States)

    Montoya, Carlos A; Rutherfurd, Shane M; Moughan, Paul J

    2016-04-01

    Combined in vivo (ileal cannulated pig) and in vitro (faecal inoculum-based fermentation) digestion methodologies were used to predict the production and absorption of SCFA in the hindgut of growing pigs. Ileal and faecal samples were collected from animals (n 7) fed diets containing either 25 or 50 g/kg DM of kiwifruit fibre from added kiwifruit for 14 d. Ileal and faecal SCFA concentrations normalised for food DM intake (DMI) and nutrient digestibility were determined. Ileal digesta were collected and fermented for 38 h using a fresh pig faecal inoculum to predict SCFA production. The predicted hindgut SCFA production along with the determined ileal and faecal SCFA were then used to predict SCFA absorption in the hindgut and total tract organic matter digestibility. The determined ileal and faecal SCFA concentrations (e.g. 8·5 and 4·4 mmol/kg DMI, respectively, for acetic acid for the low-fibre diet) represented only 0·2-3·2 % of the predicted hindgut SCFA production (e.g. 270 mmol/kg DMI for acetic acid). Predicted production and absorption of acetic, butyric and propionic acids were the highest for the high-fibre diet (P0·05). In conclusion, determined ileal and faecal SCFA concentrations represent only a small fraction of total SCFA production, and may therefore be misleading in relation to the effect of diets on SCFA production and absorption. Considerable quantities of SCFA are produced and absorbed in the hindgut of the pig by the fermentation of kiwifruit.

  20. Spatial prediction of landslide susceptibility using an adaptive neuro-fuzzy inference system combined with frequency ratio, generalized additive model, and support vector machine techniques

    Science.gov (United States)

    Chen, Wei; Pourghasemi, Hamid Reza; Panahi, Mahdi; Kornejady, Aiding; Wang, Jiale; Xie, Xiaoshen; Cao, Shubo

    2017-11-01

    The spatial prediction of landslide susceptibility is an important prerequisite for the analysis of landslide hazards and risks in any area. This research uses three data mining techniques, such as an adaptive neuro-fuzzy inference system combined with frequency ratio (ANFIS-FR), a generalized additive model (GAM), and a support vector machine (SVM), for landslide susceptibility mapping in Hanyuan County, China. In the first step, in accordance with a review of the previous literature, twelve conditioning factors, including slope aspect, altitude, slope angle, topographic wetness index (TWI), plan curvature, profile curvature, distance to rivers, distance to faults, distance to roads, land use, normalized difference vegetation index (NDVI), and lithology, were selected. In the second step, a collinearity test and correlation analysis between the conditioning factors and landslides were applied. In the third step, we used three advanced methods, namely, ANFIS-FR, GAM, and SVM, for landslide susceptibility modeling. Subsequently, the results of their accuracy were validated using a receiver operating characteristic curve. The results showed that all three models have good prediction capabilities, while the SVM model has the highest prediction rate of 0.875, followed by the ANFIS-FR and GAM models with prediction rates of 0.851 and 0.846, respectively. Thus, the landslide susceptibility maps produced in the study area can be applied for management of hazards and risks in landslide-prone Hanyuan County.

  1. Improved Prediction of Blood-Brain Barrier Permeability Through Machine Learning with Combined Use of Molecular Property-Based Descriptors and Fingerprints.

    Science.gov (United States)

    Yuan, Yaxia; Zheng, Fang; Zhan, Chang-Guo

    2018-03-21

    Blood-brain barrier (BBB) permeability of a compound determines whether the compound can effectively enter the brain. It is an essential property which must be accounted for in drug discovery with a target in the brain. Several computational methods have been used to predict the BBB permeability. In particular, support vector machine (SVM), which is a kernel-based machine learning method, has been used popularly in this field. For SVM training and prediction, the compounds are characterized by molecular descriptors. Some SVM models were based on the use of molecular property-based descriptors (including 1D, 2D, and 3D descriptors) or fragment-based descriptors (known as the fingerprints of a molecule). The selection of descriptors is critical for the performance of a SVM model. In this study, we aimed to develop a generally applicable new SVM model by combining all of the features of the molecular property-based descriptors and fingerprints to improve the accuracy for the BBB permeability prediction. The results indicate that our SVM model has improved accuracy compared to the currently available models of the BBB permeability prediction.

  2. Cooling-load prediction by the combination of rough set theory and an artificial neural-network based on data-fusion technique

    International Nuclear Information System (INIS)

    Hou Zhijian; Lian Zhiwei; Yao Ye; Yuan Xinjian

    2006-01-01

    A novel method integrating rough sets (RS) theory and an artificial neural network (ANN) based on data-fusion technique is presented to forecast an air-conditioning load. Data-fusion technique is the process of combining multiple sensors data or related information to estimate or predict entity states. In this paper, RS theory is applied to find relevant factors to the load, which are used as inputs of an artificial neural-network to predict the cooling load. To improve the accuracy and enhance the robustness of load forecasting results, a general load-prediction model, by synthesizing multi-RSAN (MRAN), is presented so as to make full use of redundant information. The optimum principle is employed to deduce the weights of each RSAN model. Actual prediction results from a real air-conditioning system show that, the MRAN forecasting model is better than the individual RSAN and moving average (AMIMA) ones, whose relative error is within 4%. In addition, individual RSAN forecasting results are better than that of ARIMA

  3. Combining biological and psychosocial baseline variables did not improve prediction of outcome of a very-low-energy diet in a clinic referral population.

    Science.gov (United States)

    Sumithran, P; Purcell, K; Kuyruk, S; Proietto, J; Prendergast, L A

    2018-02-01

    Consistent, strong predictors of obesity treatment outcomes have not been identified. It has been suggested that broadening the range of predictor variables examined may be valuable. We explored methods to predict outcomes of a very-low-energy diet (VLED)-based programme in a clinically comparable setting, using a wide array of pre-intervention biological and psychosocial participant data. A total of 61 women and 39 men (mean ± standard deviation [SD] body mass index: 39.8 ± 7.3 kg/m 2 ) underwent an 8-week VLED and 12-month follow-up. At baseline, participants underwent a blood test and assessment of psychological, social and behavioural factors previously associated with treatment outcomes. Logistic regression, linear discriminant analysis, decision trees and random forests were used to model outcomes from baseline variables. Of the 100 participants, 88 completed the VLED and 42 attended the Week 60 visit. Overall prediction rates for weight loss of ≥10% at weeks 8 and 60, and attrition at Week 60, using combined data were between 77.8 and 87.6% for logistic regression, and lower for other methods. When logistic regression analyses included only baseline demographic and anthropometric variables, prediction rates were 76.2-86.1%. In this population, considering a wide range of biological and psychosocial data did not improve outcome prediction compared to simply-obtained baseline characteristics. © 2017 World Obesity Federation.

  4. The macrophage activation marker sCD163 combined with markers of the Enhanced Liver Fibrosis (ELF) score predicts clinically significant portal hypertension in patients with cirrhosis

    DEFF Research Database (Denmark)

    Sandahl, T D; McGrail, R; Møller, H J

    2016-01-01

    BACKGROUND: Noninvasive identification of significant portal hypertension in patients with cirrhosis is needed in hepatology practice. AIM: To investigate whether the combination of sCD163 as a hepatic inflammation marker and the fibrosis markers of the Enhanced Liver Fibrosis score (ELF) can...... predict portal hypertension in patients with cirrhosis. METHODS: We measured sCD163 and the ELF components (hyaluronic acid, tissue inhibitor of metalloproteinase-1 and procollagen-III aminopeptide) in two separate cohorts of cirrhosis patients that underwent hepatic vein catheterisation. To test...... the predictive accuracy we developed a CD163-fibrosis portal hypertension score in an estimation cohort (n = 80) and validated the score in an independent cohort (n = 80). A HVPG ≥10 mmHg was considered clinically significant. RESULTS: Both sCD163 and the ELF components increased in a stepwise manner...

  5. Predicting the drying shrinkage behavior of high strength portland cement mortar under the combined influence of fine aggregate and steel micro fiber

    International Nuclear Information System (INIS)

    Li, Zhengqi

    2017-01-01

    The workability, 28-day compressive strength and free drying shrinkage of a very high strength (121-142 MPa) steel micro fiber reinforced portland cement mortar were studied under a combined influence of fine aggregate content and fiber content. The test results showed that an increase in the fine aggregate content resulted in decreases in the workability, 28-day compressive strength and drying shrinkage of mortar at a fixed fiber content. An increase in the fiber content resulted in decreases in the workability and drying shrinkage of mortar, but an increase in the 28-day compressive strength of mortar at a fixed fine aggregate content. The modified Gardner model most accurately predicted the drying shrinkage development of the high strength mortars, followed by the Ross model and the ACI 209R-92 model. The Gardner model gave the least accurate prediction for it was developed based on a database of normal strength concrete. [es

  6. Predicting the Inflow Distortion Tone Noise of the NASA Glenn Advanced Noise Control Fan with a Combined Quadrupole-Dipole Model

    Science.gov (United States)

    Koch, L. Danielle

    2012-01-01

    A combined quadrupole-dipole model of fan inflow distortion tone noise has been extended to calculate tone sound power levels generated by obstructions arranged in circumferentially asymmetric locations upstream of a rotor. Trends in calculated sound power level agreed well with measurements from tests conducted in 2007 in the NASA Glenn Advanced Noise Control Fan. Calculated values of sound power levels radiated upstream were demonstrated to be sensitive to the accuracy of the modeled wakes from the cylindrical rods that were placed upstream of the fan to distort the inflow. Results indicate a continued need to obtain accurate aerodynamic predictions and measurements at the fan inlet plane as engineers work towards developing fan inflow distortion tone noise prediction tools.

  7. Predicting the drying shrinkage behavior of high strength portland cement mortar under the combined influence of fine aggregate and steel micro fiber

    Directory of Open Access Journals (Sweden)

    Zhengqi Li

    2017-03-01

    Full Text Available The workability, 28-day compressive strength and free drying shrinkage of a very high strength (121-142 MPa steel micro fiber reinforced portland cement mortar were studied under a combined influence of fine aggregate content and fiber content. The test results showed that an increase in the fine aggregate content resulted in decreases in the workability, 28-day compressive strength and drying shrinkage of mortar at a fixed fiber content. An increase in the fiber content resulted in decreases in the workability and drying shrinkage of mortar, but an increase in the 28-day compressive strength of mortar at a fixed fine aggregate content. The modified Gardner model most accurately predicted the drying shrinkage development of the high strength mortars, followed by the Ross model and the ACI 209R-92 model. The Gardner model gave the least accurate prediction for it was developed based on a database of normal strength concrete.

  8. A combined neural network and mechanistic approach for the prediction of corrosion rate and yield strength of magnesium-rare earth alloys

    International Nuclear Information System (INIS)

    Birbilis, N.; Cavanaugh, M.K.; Sudholz, A.D.; Zhu, S.M.; Easton, M.A.; Gibson, M.A.

    2011-01-01

    Research highlights: → This study presents a body of corrosion data for a set of custom alloys and displays this in multivariable space. These alloys represent the next generation of Mg alloys for auto applications. → The data is processed using an ANN model, which makes it possible to yield a single expression for prediction of corrosion rate (and strength) as a function of any input composition (of Ce, La or Nd between 0 and 6 wt.%). → The relative influence of the various RE elements on corrosion is assessed, with the outcome that Nd additions can offer comparable strength with minimal rise in corrosion rate. → The morphology and solute present in the eutectic region itself (as opposed to just the intermetallic presence) was shown - for the first time - to also be a key contributor to corrosion. → The above approach sets the foundation for rational alloy design of alloys with corrosion performance in mind. - Abstract: Additions of Ce, La and Nd to Mg were made in binary, ternary and quaternary combinations up to ∼6 wt.%. This provided a dataset that was used in developing a neural network model for predicting corrosion rate and yield strength. Whilst yield strength increased with RE additions, corrosion rates also systematically increased, however, this depended on the type of RE element added and the combination of elements added (along with differences in intermetallic morphology). This work is permits an understanding of Mg-RE alloy performance, and can be exploited in Mg alloy design for predictable combinations of strength and corrosion resistance.

  9. Pretreatment Endorectal Coil Magnetic Resonance Imaging Findings Predict Biochemical Tumor Control in Prostate Cancer Patients Treated With Combination Brachytherapy and External-Beam Radiotherapy

    International Nuclear Information System (INIS)

    Riaz, Nadeem; Afaq, Asim; Akin, Oguz; Pei Xin; Kollmeier, Marisa A.; Cox, Brett; Hricak, Hedvig; Zelefsky, Michael J.

    2012-01-01

    Purpose: To investigate the utility of endorectal coil magenetic resonance imaging (eMRI) in predicting biochemical relapse in prostate cancer patients treated with combination brachytherapy and external-beam radiotherapy. Methods and Materials: Between 2000 and 2008, 279 men with intermediate- or high-risk prostate cancer underwent eMRI of their prostate before receiving brachytherapy and supplemental intensity-modulated radiotherapy. Endorectal coil MRI was performed before treatment and retrospectively reviewed by two radiologists experienced in genitourinary MRI. Image-based variables, including tumor diameter, location, number of sextants involved, and the presence of extracapsular extension (ECE), were incorporated with other established clinical variables to predict biochemical control outcomes. The median follow-up was 49 months (range, 1–13 years). Results: The 5-year biochemical relapse-free survival for the cohort was 92%. Clinical findings predicting recurrence on univariate analysis included Gleason score (hazard ratio [HR] 3.6, p = 0.001), PSA (HR 1.04, p = 0.005), and National Comprehensive Cancer Network risk group (HR 4.1, p = 0.002). Clinical T stage and the use of androgen deprivation therapy were not correlated with biochemical failure. Imaging findings on univariate analysis associated with relapse included ECE on MRI (HR 3.79, p = 0.003), tumor size (HR 2.58, p = 0.04), and T stage (HR 1.71, p = 0.004). On multivariate analysis incorporating both clinical and imaging findings, only ECE on MRI and Gleason score were independent predictors of recurrence. Conclusions: Pretreatment eMRI findings predict for biochemical recurrence in intermediate- and high-risk prostate cancer patients treated with combination brachytherapy and external-beam radiotherapy. Gleason score and the presence of ECE on MRI were the only significant predictors of biochemical relapse in this group of patients.

  10. Classification of protein fold classes by knot theory and prediction of folds by neural networks: A combined theoretical and experimental approach

    DEFF Research Database (Denmark)

    Ramnarayan, K.; Bohr, Henrik; Jalkanen, Karl J.

    2008-01-01

    We present different means of classifying protein structure. One is made rigorous by mathematical knot invariants that coincide reasonably well with ordinary graphical fold classification and another classification is by packing analysis. Furthermore when constructing our mathematical fold...... classifications, we utilize standard neural network methods for predicting protein fold classes from amino acid sequences. We also make an analysis of the redundancy of the structural classifications in relation to function and ligand binding. Finally we advocate the use of combining the measurement of the VA...

  11. Combining areal DXA bone mineral density and vertebrae postero-anterior width improves the prediction of vertebral strength

    Energy Technology Data Exchange (ETDEWEB)

    Taton, Grzegorz; Rokita, Eugeniusz [Jagiellonian University Medical College, Department of Biophysics, Krakow (Poland); Wrobel, Andrzej [Jagiellonian University, Institute of Physics, Krakow (Poland); Korkosz, Mariusz [Jagiellonian University Medical College, Department of Internal Medicine and Gerontology, Division of Rheumatology, Krakow (Poland)

    2013-12-15

    Areal bone mineral density (aBMD) measured by dual-energy X-ray absorptiometry (DXA) is an important determinant of bone strength (BS), despite the fact that the correlation between aBMD and BS is relatively weak. Parameters that describe BS more accurately are desired. The aim of this study was to determine whether the geometrical corrections applied to aBMD would improve its ability for BS prediction. We considered new parameters, estimated from a single DXA measurement, as well as BMAD (bone mineral apparent density) reported in the literature. In vitro studies were performed with the L3 vertebrae from 20 cadavers, which were studied with DXA and quantitative computed tomography (QCT). A mechanical strength assessment was carried out. Two new parameters were introduced: vBMD{sub min} = (aBMD)/(W{sub PA}{sup min}) and vBMD{sub av} = (aBMD)/(W{sub PA}{sup av}) (W{sub PA}{sup min} - minimal vertebral body width in postero-anterior (PA) view, W{sub PA}{sup av} - average PA vertebral body width). Volumetric BMD measured by QCT (vBMD), aBMD, BMAD, vBMD{sub min}, and vBMD{sub av} were correlated to ultimate load and ultimate stress (P{sub max}) to find the best predictor of vertebrae BS. The coefficients of correlation between P{sub max} and vBMD{sub min}, vBMD{sub av}, as well as BMAD, were r = 0.626 (p = 0.005), r = 0.610 (p = 0.006) and r = 0.567 (p = 0.012), respectively. Coefficients for vBMD and aBMD are r = 0.648 (p = 0.003) and r = 0.511 (p = 0.03), respectively. Our results showed that aBMD normalized by vertebrae dimensions describes vertebrae BS better than aBMD alone. The considered indices vBMD{sub av}, vBMD{sub min}, and BMAD can be measured in routine PA DXA and considerably improve BS variability prediction. vBMD{sub min} is superior compared to vBMD{sub av} and BMAD. (orig.)

  12. Improving the prediction of strength and rigidity of structural timber by combining ultrasound techniques with visual grading parameters

    Directory of Open Access Journals (Sweden)

    Hermoso Prieto, E.

    2007-12-01

    Full Text Available The present study explores the possibility of using longitudinal ultrasound transmission to evaluate the bending strength and modulus of elasticity in structural timber made from the two species most commonly found in Spanish construction and rehabilitation works: Scots pine (Pinus sylvestris L. and Laricio pine (Pinus nigra Arn.. An analysis of 1305 Scots pine and 852 Laricio pine beams shows that ultrasound transmission velocity alone can predict neither the bending strength nor the modulus of elasticity and that other predictive variables are required.A series of models are proposed based on ultrasound transmission velocity measurements, the relative size of the largest face and edge knots, length and density. After running models for each species individually and for the two jointly, a single model is found to be suitable for both. The models proposed explain from 63 to 73 per cent of bending strength and modulus of elasticity variability.Se analiza la posibilidad de aplicar la técnica de transmisión longitudinal de ultrasonidos para la evaluación de la resistencia y módulo de elasticidad a flexión de la madera estructural de las dos especies de mayor interés constructivo y más amplia presencia en obras de rehabilitación: el pino silvestre (Pinus sylvestris L. y el pino laricio (Pinus nigra Arn.. Trabajando sobre un total de 1.305 vigas de pino silvestre y 852 de pino laricio se concluye que por sí sola la velocidad de transmisión de ultrasonidos no es un buen predictor ni de la resistencia ni del módulo de elasticidad en flexión, necesitando el complemento de otras variables predictoras. Se proponen diversos modelos basados en la medición de la velocidad de transmisión de ultrasonidos, de los diámetros relativos del nudo máximo de cara y de canto, de la longitud y de la densidad. Los modelos se proponen tanto a nivel especie como global, comprobándose que es posible emplear un modelo único para ambas especies. Los modelos

  13. TP53 hotspot mutations are predictive of survival in primary central nervous system lymphoma patients treated with combination chemotherapy

    DEFF Research Database (Denmark)

    Munch-Petersen, Helga D; Asmar, Fazila; Dimopoulos, Konstantinos

    2016-01-01

    regimens (high-dose methotrexate/whole brain radiation therapy, 6.0 months, or no therapy, 0.83 months), P hotspot/direct DNA contact mutations. CCT-treated patients with PCNSL harboring a hotspot/direct DNA contact MUT......-TP53 (n = 9) had a significantly worse OS and progression free survival (PFS) compared to patients with non-hotspot/non-direct DNA contact MUT-TP53 or wild-type TP53 (median PFS 4.6 versus 18.2 or 45.7 months), P = 0.041 and P = 0.00076, respectively. Multivariate Cox regression analysis confirmed...... that hotspot/direct DNA contact MUT-TP53 was predictive of poor outcome in CCT-treated PCNSL patients, P = 0.012 and P = 0.008; HR: 1.86 and 1.95, for OS and PFS, respectively. MIR34A, MIR34B/C, and DAPK promoter methylation were detected in 53/93 (57.0 %), 80/84 (95.2 %), and 70/75 (93.3 %) of the PCNSL...

  14. Discovery of candidate KEN-box motifs using cell cycle keyword enrichment combined with native disorder prediction and motif conservation.

    Science.gov (United States)

    Michael, Sushama; Travé, Gilles; Ramu, Chenna; Chica, Claudia; Gibson, Toby J

    2008-02-15

    KEN-box-mediated target selection is one of the mechanisms used in the proteasomal destruction of mitotic cell cycle proteins via the APC/C complex. While annotating the Eukaryotic Linear Motif resource (ELM, http://elm.eu.org/), we found that KEN motifs were significantly enriched in human protein entries with cell cycle keywords in the UniProt/Swiss-Prot database-implying that KEN-boxes might be more common than reported. Matches to short linear motifs in protein database searches are not, per se, significant. KEN-box enrichment with cell cycle Gene Ontology terms suggests that collectively these motifs are functional but does not prove that any given instance is so. Candidates were surveyed for native disorder prediction using GlobPlot and IUPred and for motif conservation in homologues. Among >25 strong new candidates, the most notable are human HIPK2, CHFR, CDC27, Dab2, Upf2, kinesin Eg5, DNA Topoisomerase 1 and yeast Cdc5 and Swi5. A similar number of weaker candidates were present. These proteins have yet to be tested for APC/C targeted destruction, providing potential new avenues of research.

  15. Combining LDL-C and HDL-C to predict survival in late life: The InChianti study.

    Directory of Open Access Journals (Sweden)

    Giovanni Zuliani

    Full Text Available While the relationship between total cholesterol (TC and cardiovascular disease (CVD progressively weakens with aging, several studies have shown that low TC is associated with increased mortality in older individuals. However, the possible additive/synergic contribution of the two most important cholesterol rich fractions (LDL-C and HDL-C to mortality risk has not been previously investigated. Our study aimed to investigate the relationship between baseline LDL-C and HDL-C, both separately and combined, and 9-years mortality in a sample of community dwelling older individuals from the InCHIANTI study.1044 individuals over 64 years were included. CVD and cancer mortality were defined by ICD-9 codes 390-459 and 140-239, respectively. LDL-C <130 mg/dL (3.36 mmol/L was defined as "optimal/near optimal". Low HDL-C was defined as <40/50 mg/dL (1.03/1.29 mmol/L in males/females, respectively. Nine-years mortality risk was calculated by multivariate Cox proportional hazards model. We found that, compared to subjects with high LDL-C and normal HDL-C (reference group, total mortality was significantly increased in subjects with optimal/near optimal LDL-C and low HDL-C (H.R.:1.58; 95%CI:1.11-2.25. As regards the specific cause of death, CVD mortality was not affected by LDL-C/HDL-C levels, while cancer mortality was significantly increased in all subjects with optimal/near optimal LDL-C (with normal HDL-C: H.R.: 2.49; with low HDL-C: H.R.: 4.52. Results were unchanged after exclusion of the first three years of follow-up, and of subjects with low TC (<160 g/dL-4.13 mmol/L.Our findings suggest that, in community dwelling older individuals, the combined presence of optimal/near optimal LDL-C and low HDL-C represents a marker of increased future mortality.

  16. Calibration and combination of dynamical seasonal forecasts to enhance the value of predicted probabilities for managing risk

    Science.gov (United States)

    Dutton, John A.; James, Richard P.; Ross, Jeremy D.

    2013-06-01

    Seasonal probability forecasts produced with numerical dynamics on supercomputers offer great potential value in managing risk and opportunity created by seasonal variability. The skill and reliability of contemporary forecast systems can be increased by calibration methods that use the historical performance of the forecast system to improve the ongoing real-time forecasts. Two calibration methods are applied to seasonal surface temperature forecasts of the US National Weather Service, the European Centre for Medium Range Weather Forecasts, and to a World Climate Service multi-model ensemble created by combining those two forecasts with Bayesian methods. As expected, the multi-model is somewhat more skillful and more reliable than the original models taken alone. The potential value of the multimodel in decision making is illustrated with the profits achieved in simulated trading of a weather derivative. In addition to examining the seasonal models, the article demonstrates that calibrated probability forecasts of weekly average temperatures for leads of 2-4 weeks are also skillful and reliable. The conversion of ensemble forecasts into probability distributions of impact variables is illustrated with degree days derived from the temperature forecasts. Some issues related to loss of stationarity owing to long-term warming are considered. The main conclusion of the article is that properly calibrated probabilistic forecasts possess sufficient skill and reliability to contribute to effective decisions in government and business activities that are sensitive to intraseasonal and seasonal climate variability.

  17. Predictive Toxicology: Modeling Chemical Induced Toxicological Response Combining Circular Fingerprints with Random Forest and Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Alexios eKoutsoukas

    2016-03-01

    Full Text Available Modern drug discovery and toxicological research are under pressure, as the cost of developing and testing new chemicals for potential toxicological risk is rising. Extensive evaluation of chemical products for potential adverse effects is a challenging task, due to the large number of chemicals and the possible hazardous effects on human health. Safety regulatory agencies around the world are dealing with two major challenges. First, the growth of chemicals introduced every year in household products and medicines that need to be tested, and second the need to protect public welfare. Hence, alternative and more efficient toxicological risk assessment methods are in high demand. The Toxicology in the 21st Century (Tox21 consortium a collaborative effort was formed to develop and investigate alternative assessment methods. A collection of 10,000 compounds composed of environmental chemicals and approved drugs were screened for interference in biochemical pathways and released for crowdsourcing data analysis. The physicochemical space covered by Tox21 library was explored, measured by Molecular Weight (MW and the octanol/water partition coefficient (cLogP. It was found that on average chemical structures had MW of 272.6 Daltons. In case of cLogP the average value was 2.476. Next relationships between assays were examined based on compounds activity profiles across the assays utilizing the Pearson correlation coefficient r. A cluster was observed between the Androgen and Estrogen Receptors and their ligand bind domains accordingly indicating presence of cross talks among the receptors. The highest correlations observed were between NR.AR and NR.AR_LBD, where it was r=0.66 and between NR.ER and NR.ER_LBD, where it was r=0.5.Our approach to model the Tox21 data consisted of utilizing circular molecular fingerprints combined with Random Forest and Support Vector Machine by modeling each assay independently. In all of the 12 sub-challenges our modeling

  18. Response to a combination of oxygen and a hypnotic as treatment for obstructive sleep apnoea is predicted by a patient's therapeutic CPAP requirement.

    Science.gov (United States)

    Landry, Shane A; Joosten, Simon A; Sands, Scott A; White, David P; Malhotra, Atul; Wellman, Andrew; Hamilton, Garun S; Edwards, Bradley A

    2017-08-01

    Upper airway collapsibility predicts the response to several non-continuous positive airway pressure (CPAP) interventions for obstructive sleep apnoea (OSA). Measures of upper airway collapsibility cannot be easily performed in a clinical context; however, a patient's therapeutic CPAP requirement may serve as a surrogate measure of collapsibility. The present work aimed to compare the predictive use of CPAP level with detailed physiological measures of collapsibility. Therapeutic CPAP levels and gold-standard pharyngeal collapsibility measures (passive pharyngeal critical closing pressure (P crit ) and ventilation at CPAP level of 0 cmH 2 O (V passive )) were retrospectively analysed from a randomized controlled trial (n = 20) comparing the combination of oxygen and eszopiclone (treatment) versus placebo/air control. Responders (9/20) to treatment were defined as those who exhibited a 50% reduction in apnoea/hypopnoea index (AHI) plus an AHICPAP requirement compared with non-responders (6.6 (5.4-8.1)  cmH 2 O vs 8.9 (8.4-10.4) cmH 2 O, P = 0.007), consistent with their reduced collapsibility (lower P crit , P = 0.017, higher V passive P = 0.025). Therapeutic CPAP level provided the highest predictive accuracy for differentiating responders from non-responders (area under the curve (AUC) = 0.86 ± 0.9, 95% CI: 0.68-1.00, P = 0.007). However, both P crit (AUC = 0.83 ± 0.11, 95% CI: 0.62-1.00, P = 0.017) and V passive (AUC = 0.77 ± 0.12, 95% CI: 0.53-1.00, P = 0.44) performed well, and the difference in AUC for these three metrics was not statistically different. A therapeutic CPAP level ≤8 cmH 2 O provided 78% sensitivity and 82% specificity (positive predictive value = 78%, negative predictive value = 82%) for predicting a response to these therapies. Therapeutic CPAP requirement, as a surrogate measure of pharyngeal collapsibility, predicts the response to non-anatomical therapy (oxygen and

  19. Combining empirical approaches and error modelling to enhance predictive uncertainty estimation in extrapolation for operational flood forecasting. Tests on flood events on the Loire basin, France.

    Science.gov (United States)

    Berthet, Lionel; Marty, Renaud; Bourgin, François; Viatgé, Julie; Piotte, Olivier; Perrin, Charles

    2017-04-01

    An increasing number of operational flood forecasting centres assess the predictive uncertainty associated with their forecasts and communicate it to the end users. This information can match the end-users needs (i.e. prove to be useful for an efficient crisis management) only if it is reliable: reliability is therefore a key quality for operational flood forecasts. In 2015, the French flood forecasting national and regional services (Vigicrues network; www.vigicrues.gouv.fr) implemented a framework to compute quantitative discharge and water level forecasts and to assess the predictive uncertainty. Among the possible technical options to achieve this goal, a statistical analysis of past forecasting errors of deterministic models has been selected (QUOIQUE method, Bourgin, 2014). It is a data-based and non-parametric approach based on as few assumptions as possible about the forecasting error mathematical structure. In particular, a very simple assumption is made regarding the predictive uncertainty distributions for large events outside the range of the calibration data: the multiplicative error distribution is assumed to be constant, whatever the magnitude of the flood. Indeed, the predictive distributions may not be reliable in extrapolation. However, estimating the predictive uncertainty for these rare events is crucial when major floods are of concern. In order to improve the forecasts reliability for major floods, an attempt at combining the operational strength of the empirical statistical analysis and a simple error modelling is done. Since the heteroscedasticity of forecast errors can considerably weaken the predictive reliability for large floods, this error modelling is based on the log-sinh transformation which proved to reduce significantly the heteroscedasticity of the transformed error in a simulation context, even for flood peaks (Wang et al., 2012). Exploratory tests on some operational forecasts issued during the recent floods experienced in

  20. Predictions of NO{sub x} formation in an NH{sub 3}-doped syngas flame using CFD combined with a detailed reaction mechanism

    Energy Technology Data Exchange (ETDEWEB)

    Brink, A; Norstroem, T; Kilpinen, P; Hupa, M [Aabo Akademi, Turku (Finland). Combustion Chemistry Research Group

    1998-12-31

    The formation of NO{sub x} in a CO/H{sub 2}/CH{sub 4}/NH{sub 3} jet in a co-flowing air stream was modeled by use of CFD combined with a comprehensive detailed reaction mechanism. The comprehensive mechanism involved 340 reversible elementary reactions between 55 species. Three different approaches to include the detailed reaction mechanism were tested. In approach I, all chemistry was described with the comprehensive mechanism. In approaches IIa and IIb the comprehensive mechanism was used in post-processing calculations of the nitrogen chemistry. In approach IIa, the temperatures of the reacting structures obtained in the main calculations were used, whereas in approach IIb, the inlet temperatures to the reacting structures were taken from the main calculation. In approach IIIa and IIIb, empirical reaction mechanisms describing the nitrogen chemistry were tested. The turbulence-chemistry interaction was accounted for with a new model, which combines the Eddy-Dissipation Concept with a model based on the `Exchange by Interaction with the Mean`. There was a clear difference between the computed results and the measured ones. The use of approach I resulted in an obvious overprediction of the lift-off height. The predicted molar NO{sub x} yield with the approaches IIa and IIb were 89 % and 85 %, respectively, whereas a yield of 23 % had been measured. With the empirical mechanisms used in approach IIIa, a similar NO{sub x} yield was predicted as with approaches IIa and IIb. With IIIb the predicted NO{sub x} yield was 40 %. However, in this case 67 % of the NH{sub 3} remained unreacted. The reason for the large difference between the calculated NO{sub x} yield and the measured one reported in the literature is a poor modeling of the initial part of the fuel jet. A possible reason for this is the coarse grid. (author) 15 refs.

  1. Predictions of NO{sub x} formation in an NH{sub 3}-doped syngas flame using CFD combined with a detailed reaction mechanism

    Energy Technology Data Exchange (ETDEWEB)

    Brink, A.; Norstroem, T.; Kilpinen, P.; Hupa, M. [Aabo Akademi, Turku (Finland). Combustion Chemistry Research Group

    1997-12-31

    The formation of NO{sub x} in a CO/H{sub 2}/CH{sub 4}/NH{sub 3} jet in a co-flowing air stream was modeled by use of CFD combined with a comprehensive detailed reaction mechanism. The comprehensive mechanism involved 340 reversible elementary reactions between 55 species. Three different approaches to include the detailed reaction mechanism were tested. In approach I, all chemistry was described with the comprehensive mechanism. In approaches IIa and IIb the comprehensive mechanism was used in post-processing calculations of the nitrogen chemistry. In approach IIa, the temperatures of the reacting structures obtained in the main calculations were used, whereas in approach IIb, the inlet temperatures to the reacting structures were taken from the main calculation. In approach IIIa and IIIb, empirical reaction mechanisms describing the nitrogen chemistry were tested. The turbulence-chemistry interaction was accounted for with a new model, which combines the Eddy-Dissipation Concept with a model based on the `Exchange by Interaction with the Mean`. There was a clear difference between the computed results and the measured ones. The use of approach I resulted in an obvious overprediction of the lift-off height. The predicted molar NO{sub x} yield with the approaches IIa and IIb were 89 % and 85 %, respectively, whereas a yield of 23 % had been measured. With the empirical mechanisms used in approach IIIa, a similar NO{sub x} yield was predicted as with approaches IIa and IIb. With IIIb the predicted NO{sub x} yield was 40 %. However, in this case 67 % of the NH{sub 3} remained unreacted. The reason for the large difference between the calculated NO{sub x} yield and the measured one reported in the literature is a poor modeling of the initial part of the fuel jet. A possible reason for this is the coarse grid. (author) 15 refs.

  2. Improved predictive value of GRACE risk score combined with platelet reactivity for 1-year cardiovascular risk in patients with acute coronary syndrome who underwent coronary stent implantation.

    Science.gov (United States)

    Li, Shan; Liu, Hongbin; Liu, Jianfeng; Wang, Haijun

    2016-11-01

    Both high platelet reactivity (HPR) and Global Registry of Acute Coronary Events (GRACE) risk score have moderate predictive value for major adverse cardiovascular disease (CVD) events in patients with acute coronary syndrome (ACS) who underwent percutaneous coronary intervention (PCI), whereas the prognostic significance of GRACE risk score combined with platelet function testing remains unclear. A total of 596 patients with non-ST elevation ACS who underwent PCI were enrolled. The P2Y 12 reaction unit (PRU) value was measured by VerifyNow P2Y 12 assay and GRACE score was calculated by GRACE risk 2.0 calculator. Patients were stratified by a pre-specified cutoff value of PRU 230 and GRACE score 140 to assess 1-year risk of cardiovascular death, non-fatal myocardial infarction (MI), and stent thrombosis. Seventy-two (12.1%) patients developed CVD events during 1-year follow-up. Patients with CVD events had a higher PRU value (244.6 ± 50.9 vs. 203.7 ± 52.0, p risk independently. Compared to patients with normal platelet reactivity (NPR) and GRACE score risk (HR: 5.048; 95% CI: 2.268-11.237; p risk score yielded superior risk predictive capacity beyond GRACE score alone, which is shown by improved c-statistic value (0.871, p = 0.002) as well as net reclassification improvement (NRI 0.263, p risk of adverse CVD events. The combination of platelet function testing and GRACE score predicted 1-year CVD risk better.

  3. A semi-supervised learning approach to predict synthetic genetic interactions by combining functional and topological properties of functional gene network

    Directory of Open Access Journals (Sweden)

    Han Kyungsook

    2010-06-01

    Full Text Available Abstract Background Genetic interaction profiles are highly informative and helpful for understanding the functional linkages between genes, and therefore have been extensively exploited for annotating gene functions and dissecting specific pathway structures. However, our understanding is rather limited to the relationship between double concurrent perturbation and various higher level phenotypic changes, e.g. those in cells, tissues or organs. Modifier screens, such as synthetic genetic arrays (SGA can help us to understand the phenotype caused by combined gene mutations. Unfortunately, exhaustive tests on all possible combined mutations in any genome are vulnerable to combinatorial explosion and are infeasible either technically or financially. Therefore, an accurate computational approach to predict genetic interaction is highly desirable, and such methods have the potential of alleviating the bottleneck on experiment design. Results In this work, we introduce a computational systems biology approach for the accurate prediction of pairwise synthetic genetic interactions (SGI. First, a high-coverage and high-precision functional gene network (FGN is constructed by integrating protein-protein interaction (PPI, protein complex and gene expression data; then, a graph-based semi-supervised learning (SSL classifier is utilized to identify SGI, where the topological properties of protein pairs in weighted FGN is used as input features of the classifier. We compare the proposed SSL method with the state-of-the-art supervised classifier, the support vector machines (SVM, on a benchmark dataset in S. cerevisiae to validate our method's ability to distinguish synthetic genetic interactions from non-interaction gene pairs. Experimental results show that the proposed method can accurately predict genetic interactions in S. cerevisiae (with a sensitivity of 92% and specificity of 91%. Noticeably, the SSL method is more efficient than SVM, especially for

  4. Can Clinical and Surgical Parameters Be Combined to Predict How Long It Will Take a Tibia Fracture to Heal? A Prospective Multicentre Observational Study: The FRACTING Study

    Directory of Open Access Journals (Sweden)

    Leo Massari

    2018-01-01

    Full Text Available Background. Healing of tibia fractures occurs over a wide time range of months, with a number of risk factors contributing to prolonged healing. In this prospective, multicentre, observational study, we investigated the capability of FRACTING (tibia FRACTure prediction healING days score, calculated soon after tibia fracture treatment, to predict healing time. Methods. The study included 363 patients. Information on patient health, fracture morphology, and surgical treatment adopted were combined to calculate the FRACTING score. Fractures were considered healed when the patient was able to fully weight-bear without pain. Results. 319 fractures (88% healed within 12 months from treatment. Forty-four fractures healed after 12 months or underwent a second surgery. FRACTING score positively correlated with days to healing: r=0.63 (p<0.0001. Average score value was 7.3 ± 2.5; ROC analysis showed strong reliability of the score in separating patients healing before versus after 6 months: AUC = 0.823. Conclusions. This study shows that the FRACTING score can be employed both to predict months needed for fracture healing and to identify immediately after treatment patients at risk of prolonged healing. In patients with high score values, new pharmacological and nonpharmacological treatments to enhance osteogenesis could be tested selectively, which may finally result in reduced disability time and health cost savings.

  5. The usefulness of twenty-four molecular markers in predicting treatment outcome with combination therapy of amodiaquine plus sulphadoxine-pyrimethamine against falciparum malaria in Papua New Guinea

    Directory of Open Access Journals (Sweden)

    Reeder John C

    2008-04-01

    Full Text Available Abstract Background In Papua New Guinea (PNG, combination therapy with amodiaquine (AQ or chloroquine (CQ plus sulphadoxine-pyrimethamine (SP was introduced as first-line treatment against uncomplicated malaria in 2000. Methods We assessed in vivo treatment failure rates with AQ+SP in two different areas in PNG and twenty-four molecular drug resistance markers of Plasmodium falciparum were characterized in pre-treatment samples. The aim of the study was to investigate the association between infecting genotype and treatment response in order to identify useful predictors of treatment failure with AQ+SP. Results In 2004, Day-28 treatment failure rates for AQ+SP were 29% in the Karimui and 19% in the South Wosera area, respectively. The strongest independent predictors for treatment failure with AQ+SP were pfmdr1 N86Y (OR = 7.87, p pfdhps A437G (OR = 3.44, p pfcrt K76T, A220S, N326D, and I356L did not help to increase the predictive value, the most likely reason being that these mutations reached almost fixed levels. Though mutations in SP related markers pfdhfr S108N and C59R were not associated with treatment failure, they increased the predictive value of pfdhps A437G. The difference in treatment failure rate in the two sites was reflected in the corresponding genetic profile of the parasite populations, with significant differences seen in the allele frequencies of mutant pfmdr1 N86Y, pfmdr1 Y184F, pfcrt A220S, and pfdhps A437G. Conclusion The study provides evidence for high levels of resistance to the combination regimen of AQ+SP in PNG and indicates which of the many molecular markers analysed are useful for the monitoring of parasite resistance to combinations with AQ+SP.

  6. Self-perceived quality of life predicts mortality risk better than a multi-biomarker panel, but the combination of both does best

    Directory of Open Access Journals (Sweden)

    Nauck Matthias

    2011-07-01

    Full Text Available Abstract Background Associations between measures of subjective health and mortality risk have previously been shown. We assessed the impact and comparative predictive performance of a multi-biomarker panel on this association. Methods Data from 4,261 individuals aged 20-79 years recruited for the population-based Study of Health in Pomerania was used. During an average 9.7 year follow-up, 456 deaths (10.7% occurred. Subjective health was assessed by SF-12 derived physical (PCS-12 and mental component summaries (MCS-12, and a single-item self-rated health (SRH question. We implemented Cox proportional-hazards regression models to investigate the association of subjective health with mortality and to assess the impact of a combination of 10 biomarkers on this association. Variable selection procedures were used to identify a parsimonious set of subjective health measures and biomarkers, whose predictive ability was compared using receiver operating characteristic (ROC curves, C-statistics, and reclassification methods. Results In age- and gender-adjusted Cox models, poor SRH (hazard ratio (HR, 2.07; 95% CI, 1.34-3.20 and low PCS-12 scores (lowest vs. highest quartile: HR, 1.75; 95% CI, 1.31-2.33 were significantly associated with increased risk of all-cause mortality; an association independent of various covariates and biomarkers. Furthermore, selected subjective health measures yielded a significantly higher C-statistic (0.883 compared to the selected biomarker panel (0.872, whereas a combined assessment showed the highest C-statistic (0.887 with a highly significant integrated discrimination improvement of 1.5% (p Conclusion Adding biomarker information did not affect the association of subjective health measures with mortality, but significantly improved risk stratification. Thus, a combined assessment of self-reported subjective health and measured biomarkers may be useful to identify high-risk individuals for intensified monitoring.

  7. Falls Risk Prediction for Older Inpatients in Acute Care Medical Wards: Is There an Interest to Combine an Early Nurse Assessment and the Artificial Neural Network Analysis?

    Science.gov (United States)

    Beauchet, O; Noublanche, F; Simon, R; Sekhon, H; Chabot, J; Levinoff, E J; Kabeshova, A; Launay, C P

    2018-01-01

    Identification of the risk of falls is important among older inpatients. This study aims to examine performance criteria (i.e.; sensitivity, specificity, positive predictive value, negative predictive value and accuracy) for fall prediction resulting from a nurse assessment and an artificial neural networks (ANNs) analysis in older inpatients hospitalized in acute care medical wards. A total of 848 older inpatients (mean age, 83.0±7.2 years; 41.8% female) admitted to acute care medical wards in Angers University hospital (France) were included in this study using an observational prospective cohort design. Within 24 hours after admission of older inpatients, nurses performed a bedside clinical assessment. Participants were separated into non-fallers and fallers (i.e.; ≥1 fall during hospitalization stay). The analysis was conducted using three feed forward ANNs (multilayer perceptron [MLP], averaged neural network, and neuroevolution of augmenting topologies [NEAT]). Seventy-three (8.6%) participants fell at least once during their hospital stay. ANNs showed a high specificity, regardless of which ANN was used, and the highest value reported was with MLP (99.8%). In contrast, sensitivity was lower, with values ranging between 98.4 to 14.8%. MLP had the highest accuracy (99.7). Performance criteria for fall prediction resulting from a bedside nursing assessment and an ANNs analysis was associated with a high specificity but a low sensitivity, suggesting that this combined approach should be used more as a diagnostic test than a screening test when considering older inpatients in acute care medical ward.

  8. Prediction of the fertility of stallion frozen-thawed semen using a combination of computer-assisted motility analysis, microscopical observation and flow cytometry.

    Science.gov (United States)

    Battut, I Barrier; Kempfer, A; Lemasson, N; Chevrier, L; Camugli, S

    2017-07-15

    Spermatozoa from some stallions do not maintain an acceptable fertility after freezing and thawing. The selection of frozen ejaculates that would be suitable for insemination is mainly based on post-thaw motility, but the prediction of fertility remains limited. A recent study in our laboratory has enabled the determination of a new protocol for the evaluation of fresh stallion semen, combining microscopical observation, computer-assisted motility analysis and flow cytometry, and providing a high level of fertility prediction. The purpose of the present experiment was to perform similar investigations on frozen semen. A panel of tests evaluating a large number of compartments or functions of the spermatozoa was applied to a population of 42 stallions, 33 of which showing widely differing fertilities (17-67% pregnancy rate per cycle [PRC]). Variability was evaluated by calculating the coefficient of variation (CV=SD/mean) and the intra-class correlation or "repeatability" for each variable. For paired variables, mean within-stallion CV% was significantly lower than between-stallion CV%, which was significantly lower than total CV%. Within-ejaculate repeatability, determined by analysing 6 straws for each of 10 ejaculates, ranged from 0.60 to 0.97. Within-stallion repeatability, determined by analysing at least 5 ejaculates for each of 38 stallions, ranged from 0.12 to 0.95. Principal component regression using a combination of 25 variables, including motility, morphology, viability, oxidation level, acrosome integrity, DNA integrity and hypoosmotic resistance, accounted for 94.5% of the variability regarding fertility, and was used to calculate a prediction of the PRC with a mean standard deviation of 2.2. The difference between the observed PRC and the calculated value ranged from -3.4 to 4.2. The 90% confidence interval (90CI) for the prediction of the PRC for the stallions of unknown fertility ranged from 8 to 30 (mean = 17). The best-fit model using only

  9. Prediction of Small for Gestational Age Infants in Healthy Nulliparous Women Using Clinical and Ultrasound Risk Factors Combined with Early Pregnancy Biomarkers.

    Directory of Open Access Journals (Sweden)

    Lesley M E McCowan

    Full Text Available Most small for gestational age pregnancies are unrecognised before birth, resulting in substantial avoidable perinatal mortality and morbidity. Our objective was to develop multivariable prediction models for small for gestational age combining clinical risk factors and biomarkers at 15±1 weeks' with ultrasound parameters at 20±1 weeks' gestation.Data from 5606 participants in the Screening for Pregnancy Endpoints (SCOPE cohort study were divided into Training (n = 3735 and Validation datasets (n = 1871. The primary outcomes were All-SGA (small for gestational age with birthweight <10th customised centile, Normotensive-SGA (small for gestational age with a normotensive mother and Hypertensive-SGA (small for gestational age with an hypertensive mother. The comparison group comprised women without the respective small for gestational age phenotype. Multivariable analysis was performed using stepwise logistic regression beginning with clinical variables, and subsequent additions of biomarker and then ultrasound (biometry and Doppler variables. Model performance was assessed in Training and Validation datasets by calculating area under the curve.633 (11.2% infants were All-SGA, 465(8.2% Normotensive-SGA and 168 (3% Hypertensive-SGA. Area under the curve (95% Confidence Intervals for All-SGA using 15±1 weeks' clinical variables, 15±1 weeks' clinical+ biomarker variables and clinical + biomarkers + biometry /Doppler at 20±1 weeks' were: 0.63 (0.59-0.67, 0.64 (0.60-0.68 and 0.69 (0.66-0.73 respectively in the Validation dataset; Normotensive-SGA results were similar: 0.61 (0.57-0.66, 0.61 (0.56-0.66 and 0.68 (0.64-0.73 with small increases in performance in the Training datasets. Area under the curve (95% Confidence Intervals for Hypertensive-SGA were: 0.76 (0.70-0.82, 0.80 (0.75-0.86 and 0.84 (0.78-0.89 with minimal change in the Training datasets.Models for prediction of small for gestational age, which combine biomarkers, clinical and

  10. DARTAB: a program to combine airborne radionuclide environmental exposure data with dosimetric and health effects data to generate tabulations of predicted health impacts

    International Nuclear Information System (INIS)

    Begovich, C.L.; Eckerman, K.F.; Schlatter, E.C.; Ohr, S.Y.; Chester, R.O.

    1981-08-01

    The DARTAB computer code combines radionuclide environmental exposure data with dosimetric and health effects data to generate tabulations of the predicted impact of radioactive airborne effluents. DARTAB is independent of the environmental transport code used to generate the environmental exposure data and the codes used to produce the dosimetric and health effects data. Therefore human dose and risk calculations need not be added to every environmental transport code. Options are included in DARTAB to permit the user to request tabulations by various topics (e.g., cancer site, exposure pathway, etc.) to facilitate characterization of the human health impacts of the effluents. The DARTAB code was written at ORNL for the US Environmental Protection Agency, Office of Radiation Programs

  11. A Hybrid Approach Based on the Combination of Adaptive Neuro-Fuzzy Inference System and Imperialist Competitive Algorithm: Oil Flow Rate of the Wells Prediction Case Study

    Directory of Open Access Journals (Sweden)

    Shahram Mollaiy Berneti

    2013-04-01

    Full Text Available In this paper, a novel hybrid approach composed of adaptive neuro-fuzzy inference system (ANFIS and imperialist competitive algorithm is proposed. The imperialist competitive algorithm (ICA is used in this methodology to determine the most suitable initial membership functions of the ANFIS. The proposed model combines the global search ability of ICA with local search ability of gradient descent method. To illustrate the suitability and capability of the proposed model, this model is applied to predict oil flow rate of the wells utilizing data set of 31 wells in one of the northern Persian Gulf oil fields of Iran. The data set collected in a three month period for each well from Dec. 2002 to Nov. 2010. For the sake of performance evaluation, the results of the proposed model are compared with the conventional ANFIS model. The results show that the significant improvements are achievable using the proposed model in comparison with the results obtained by conventional ANFIS.

  12. Improving case-based reasoning systems by combining k-nearest neighbour algorithm with logistic regression in the prediction of patients' registration on the renal transplant waiting list.

    Directory of Open Access Journals (Sweden)

    Boris Campillo-Gimenez

    Full Text Available Case-based reasoning (CBR is an emerging decision making paradigm in medical research where new cases are solved relying on previously solved similar cases. Usually, a database of solved cases is provided, and every case is described through a set of attributes (inputs and a label (output. Extracting useful information from this database can help the CBR system providing more reliable results on the yet to be solved cases.We suggest a general framework where a CBR system, viz. K-Nearest Neighbour (K-NN algorithm, is combined with various information obtained from a Logistic Regression (LR model, in order to improve prediction of access to the transplant waiting list.LR is applied, on the case database, to assign weights to the attributes as well as the solved cases. Thus, five possible decision making systems based on K-NN and/or LR were identified: a standalone K-NN, a standalone LR and three soft K-NN algorithms that rely on the weights based on the results of the LR. The evaluation was performed under two conditions, either using predictive factors known to be related to registration, or using a combination of factors related and not related to registration.The results show that our suggested approach, where the K-NN algorithm relies on both weighted attributes and cases, can efficiently deal with non relevant attributes, whereas the four other approaches suffer from this kind of noisy setups. The robustness of this approach suggests interesting perspectives for medical problem solving tools using CBR methodology.

  13. Sudden cardiac death and pump failure death prediction in chronic heart failure by combining ECG and clinical markers in an integrated risk model

    Science.gov (United States)

    Orini, Michele; Mincholé, Ana; Monasterio, Violeta; Cygankiewicz, Iwona; Bayés de Luna, Antonio; Martínez, Juan Pablo

    2017-01-01

    Background Sudden cardiac death (SCD) and pump failure death (PFD) are common endpoints in chronic heart failure (CHF) patients, but prevention strategies are different. Currently used tools to specifically predict these endpoints are limited. We developed risk models to specifically assess SCD and PFD risk in CHF by combining ECG markers and clinical variables. Methods The relation of clinical and ECG markers with SCD and PFD risk was assessed in 597 patients enrolled in the MUSIC (MUerte Súbita en Insuficiencia Cardiaca) study. ECG indices included: turbulence slope (TS), reflecting autonomic dysfunction; T-wave alternans (TWA), reflecting ventricular repolarization instability; and T-peak-to-end restitution (ΔαTpe) and T-wave morphology restitution (TMR), both reflecting changes in dispersion of repolarization due to heart rate changes. Standard clinical indices were also included. Results The indices with the greatest SCD prognostic impact were gender, New York Heart Association (NYHA) class, left ventricular ejection fraction, TWA, ΔαTpe and TMR. For PFD, the indices were diabetes, NYHA class, ΔαTpe and TS. Using a model with only clinical variables, the hazard ratios (HRs) for SCD and PFD for patients in the high-risk group (fifth quintile of risk score) with respect to patients in the low-risk group (first and second quintiles of risk score) were both greater than 4. HRs for SCD and PFD increased to 9 and 11 when using a model including only ECG markers, and to 14 and 13, when combining clinical and ECG markers. Conclusion The inclusion of ECG markers capturing complementary pro-arrhythmic and pump failure mechanisms into risk models based only on standard clinical variables substantially improves prediction of SCD and PFD in CHF patients. PMID:29020031

  14. Combination of baseline metabolic tumour volume and early response on PET/CT improves progression-free survival prediction in DLBCL

    Energy Technology Data Exchange (ETDEWEB)

    Mikhaeel, N.G.; Smith, Daniel [Guy' s and St Thomas' NHS Foundation Trust, Department of Clinical Oncology, London (United Kingdom); Dunn, Joel T.; Phillips, Michael; Barrington, Sally F. [King' s College London, PET Imaging Centre at St Thomas' Hospital, Division of Imaging Sciences and Biomedical Engineering, London (United Kingdom); Moeller, Henrik [King' s College London, Department of Cancer Epidemiology and Population Health, London (United Kingdom); Fields, Paul A.; Wrench, David [Guy' s and St Thomas' NHS Foundation Trust, Department of Haematology, London (United Kingdom)

    2016-07-15

    The study objectives were to assess the prognostic value of quantitative PET and to test whether combining baseline metabolic tumour burden with early PET response could improve predictive power in DLBCL. A total of 147 patients with DLBCL underwent FDG-PET/CT scans before and after two cycles of RCHOP. Quantitative parameters including metabolic tumour volume (MTV) and total lesion glycolysis (TLG) were measured, as well as the percentage change in these parameters. Cox regression analysis was used to test the relationship between progression-free survival (PFS) and the study variables. Receiver operator characteristics (ROC) analysis determined the optimal cut-off for quantitative variables, and Kaplan-Meier survival analysis was performed. The median follow-up was 3.8 years. As MTV and TLG measures correlated strongly, only MTV measures were used for multivariate analysis (MVA). Baseline MTV (MTV-0) was the only statistically significant predictor of PFS on MVA. The optimal cut-off for MTV-0 was 396 cm{sup 3}. A model combing MTV-0 and Deauville score (DS) separated the population into three distinct prognostic groups: good (MTV-0 < 400; 5-year PFS > 90 %), intermediate (MTV-0 ≥ 400+ DS1-3; 5-year PFS 58.5 %) and poor (MTV-0 ≥ 400+ DS4-5; 5-year PFS 29.7 %) MTV-0 is an important prognostic factor in DLBCL. Combining MTV-0 and early PET/CT response improves the predictive power of interim PET and defines a poor-prognosis group in whom most of the events occur. (orig.)

  15. Development of a special approach of the mineralization localization zones prediction based on the combination and the geoinformation analysis of heterogeneous geodata

    Science.gov (United States)

    Ivanova, Julia

    2014-05-01

    The complexity of any task solving, including tasks in the Earth Sciences, depends on the completeness of the information that is available. The prediction of the mineralization zone localization is a task with incomplete information. The tasks of prediction are complicated because of search data difficult formalize, and the absent of single information structures of the representation of the search data. These facts complicate the process of structuring, processing and analysis of information. Geodata that need to process are presented in various formats: raster two-dimensional and three-dimensional fields, vector layers of polygons and lines, point markable layers, the spectral and discrete, quantized and continuous, analog and digital forms, as well as chemical formalization. In this form representative data cannot be combining into superclasses. At the same time the information content of geodata that are applied individually is very small. While a number of low informative features, which can be obtained in the process of research of mineralization zones are usually redundant. As a result the quality of knowledge that can be obtained from the search data decreases, as well as the technological cycle of information processing increases. Additionally, that leads to exploitation of datasets, and production of large shared datasets [1]. To solve efficiently the tasks of predicting, it is necessary to use union heterogeneous search features, accumulated factual data and modern science-based mathematical apparatus of processing and analysis of the information. As well young branches of human knowledge help to solve this task: remote sensing, geoinformatics, Earth and Space Science Informatics [2], apparatus of catastrophe theory and nonlinear dynamics, game theory. The purpose of the suggested approach is to increase informational content, and to reduce of geodata redundancy to improve the accuracy of the prediction of mineralization zones. The developed algorithm

  16. Stochastic simulation of time-series models combined with geostatistics to predict water-table scenarios in a Guarani Aquifer System outcrop area, Brazil

    Science.gov (United States)

    Manzione, Rodrigo L.; Wendland, Edson; Tanikawa, Diego H.

    2012-11-01

    Stochastic methods based on time-series modeling combined with geostatistics can be useful tools to describe the variability of water-table levels in time and space and to account for uncertainty. Monitoring water-level networks can give information about the dynamic of the aquifer domain in both dimensions. Time-series modeling is an elegant way to treat monitoring data without the complexity of physical mechanistic models. Time-series model predictions can be interpolated spatially, with the spatial differences in water-table dynamics determined by the spatial variation in the system properties and the temporal variation driven by the dynamics of the inputs into the system. An integration of stochastic methods is presented, based on time-series modeling and geostatistics as a framework to predict water levels for decision making in groundwater management and land-use planning. The methodology is applied in a case study in a Guarani Aquifer System (GAS) outcrop area located in the southeastern part of Brazil. Communication of results in a clear and understandable form, via simulated scenarios, is discussed as an alternative, when translating scientific knowledge into applications of stochastic hydrogeology in large aquifers with limited monitoring network coverage like the GAS.

  17. Combining chemoinformatics with bioinformatics: in silico prediction of bacterial flavor-forming pathways by a chemical systems biology approach "reverse pathway engineering".

    Science.gov (United States)

    Liu, Mengjin; Bienfait, Bruno; Sacher, Oliver; Gasteiger, Johann; Siezen, Roland J; Nauta, Arjen; Geurts, Jan M W

    2014-01-01

    The incompleteness of genome-scale metabolic models is a major bottleneck for systems biology approaches, which are based on large numbers of metabolites as identified and quantified by metabolomics. Many of the revealed secondary metabolites and/or their derivatives, such as flavor compounds, are non-essential in metabolism, and many of their synthesis pathways are unknown. In this study, we describe a novel approach, Reverse Pathway Engineering (RPE), which combines chemoinformatics and bioinformatics analyses, to predict the "missing links" between compounds of interest and their possible metabolic precursors by providing plausible chemical and/or enzymatic reactions. We demonstrate the added-value of the approach by using flavor-forming pathways in lactic acid bacteria (LAB) as an example. Established metabolic routes leading to the formation of flavor compounds from leucine were successfully replicated. Novel reactions involved in flavor formation, i.e. the conversion of alpha-hydroxy-isocaproate to 3-methylbutanoic acid and the synthesis of dimethyl sulfide, as well as the involved enzymes were successfully predicted. These new insights into the flavor-formation mechanisms in LAB can have a significant impact on improving the control of aroma formation in fermented food products. Since the input reaction databases and compounds are highly flexible, the RPE approach can be easily extended to a broad spectrum of applications, amongst others health/disease biomarker discovery as well as synthetic biology.

  18. Validation of the MARS (Medical Admission Risk System): A combined physiological and laboratory risk prediction tool for 5- to 7-day in-hospital mortality.

    Science.gov (United States)

    Ohman, Malin Charlotta; Atkins, Tara E Holm; Cooksley, Tim; Brabrand, Mikkel

    2018-03-10

    The MARS (Medical Admission Risk System) uses 11 physiological and laboratory data and had promising results in its derivation study for predicting 5 and 7 day mortality. To perform an external independent validation of the MARS score. An unplanned secondary cohort study. Patients admitted to the medical admission unit (MAU) at The Hospital of South West Jutland were included from 2 October 2008 until 19 February 2009 and 23 February 2010 until 26 May 2010 were analysed. Validation of the MARS score using 5 and 7 day mortality was the primary endpoint. 5858 patients were included in the study. 2923 (49.9%) patients were women with a median age of 65 years (15-107). The MARS score had an AUROC of 0.858 (95% CI: 0.831-0.884) for 5-day mortality and 0.844 (0.818-0.870) for 7 day mortality with poor calibration for both outcomes. The MARS score had excellent discriminatory power but poor calibration in predicting both 5 and 7-day mortality. The development of accurate combination physiological/laboratory data risk scores has the potential to improve the recognition of at risk patients.

  19. Predictable topography simulation of SiO2 etching by C5F8 gas combined with a plasma simulation, sheath model and chemical reaction model

    International Nuclear Information System (INIS)

    Takagi, S; Onoue, S; Iyanagi, K; Nishitani, K; Shinmura, T; Kanoh, M; Itoh, H; Shioyama, Y; Akiyama, T; Kishigami, D

    2003-01-01

    We have developed a simulation for predicting reactive ion etching (RIE) topography, which is a combination of plasma simulation, the gas reaction model, the sheath model and the surface reaction model. The simulation is applied to the SiO 2 etching process of a high-aspect-ratio contact hole using C 5 F 8 gas. A capacitively coupled plasma (CCP) reactor of an 8-in. wafer was used in the etching experiments. The baseline conditions are RF power of 1500 W and gas pressure of 4.0 Pa in a gas mixture of Ar, O 2 and C 5 F 8 . The plasma simulation reproduces the tendency that CF 2 radical density increases rapidly and the electron density decreases gradually with increasing gas flow rate of C 5 F 8 . In the RIE topography simulation, the etching profiles such as bowing and taper shape at the bottom are reproduced in deep holes with aspect ratios greater than 19. Moreover, the etching profile, the dependence of the etch depth on the etching time, and the bottom diameter can be predicted by this simulation

  20. Predicting Keto-Enol Equilibrium from Combining UV/Visible Absorption Spectroscopy with Quantum Chemical Calculations of Vibronic Structures for Many Excited States. A Case Study on Salicylideneanilines.

    Science.gov (United States)

    Zutterman, Freddy; Louant, Orian; Mercier, Gabriel; Leyssens, Tom; Champagne, Benoît

    2018-06-21

    Salicylideneanilines are characterized by a tautomer equilibrium, between an enol and a keto form of different colors, at the origin of their remarkable thermochromic, solvatochromic, and photochromic properties. The enol form is usually the most stable but appropriate choice of substituents and conditions (solvent, crystal, host compound) can displace the equilibrium toward the keto form so that there is a need for fast prediction of the keto:enol abundance ratio. Here we demonstrate the reliability of a combined theoretical-experimental method, based on comparing simulated and measured UV/visible absorption spectra, to determine this keto/enol ratio. The calculations of the excitation energies, oscillator strengths, and vibronic structures of both enol and keto forms are performed for all excited states absorbing in the relevant (visible and near-UV) wavelength range at the time-dependent density functional theory level by accounting for solvent effects using the polarizable continuum model. This approach is illustrated for two salicylideneaniline derivatives, which are present, in solution, under the form of keto-enol mixtures. The results are compared to those of chemometric analysis as well as ab initio predictions of the reaction free enthalpies.

  1. Prediction of EPR Spectra of Lyotropic Liquid Crystals using a Combination of Molecular Dynamics Simulations and the Model-Free Approach.

    Science.gov (United States)

    Prior, Christopher; Oganesyan, Vasily S

    2017-09-21

    We report the first application of fully atomistic molecular dynamics (MD) simulations to the prediction of the motional electron paramagnetic resonance (EPR) spectra of lyotropic liquid crystals in different aggregation states doped with a paramagnetic spin probe. The purpose of this study is twofold. First, given that EPR spectra are highly sensitive to the motions and order of the spin probes doped within lyotropic aggregates, simulation of EPR line shapes from the results of MD modelling provides an ultimate test bed for the force fields currently employed to model such systems. Second, the EPR line shapes are simulated using the motional parameters extracted from MD trajectories using the Model-Free (MF) approach. Thus a combined MD-EPR methodology allowed us to test directly the validity of the application of the MF approach to systems with multi-component molecular motions. All-atom MD simulations using the General AMBER Force Field (GAFF) have been performed on sodium dodecyl sulfate (SDS) and dodecyltrimethylammonium chloride (DTAC) liquid crystals. The resulting MD trajectories were used to predict and interpret the EPR spectra of pre-micellar, micellar, rod and lamellar aggregates. The predicted EPR spectra demonstrate good agreement with most of experimental line shapes thus confirming the validity of both the force fields employed and the MF approach for the studied systems. At the same time simulation results confirm that GAFF tends to overestimate the packing and the order of the carbonyl chains of the surfactant molecules. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

  2. Utility of the combination of serum highly-sensitive C-reactive protein level at discharge and a risk index in predicting readmission for acute exacerbation of COPD,

    Directory of Open Access Journals (Sweden)

    Chun Chang

    2014-10-01

    Full Text Available OBJECTIVE: Frequent readmissions for acute exacerbations of COPD (AECOPD are an independent risk factor for increased mortality and use of health-care resources. Disease severity and C-reactive protein (CRP level are validated predictors of long-term prognosis in such patients. This study investigated the utility of combining serum CRP level with the Global Initiative for Chronic Obstructive Lung Disease (GOLD exacerbation risk classification for predicting readmission for AECOPD. METHODS: This was a prospective observational study of consecutive patients hospitalized for AECOPD at Peking University Third Hospital, in Beijing, China. We assessed patient age; gender; smoking status and history (pack-years; lung function; AECOPD frequency during the last year; quality of life; GOLD risk category (A-D; D indicating the greatest risk; and serum level of high-sensitivity CRP at discharge (hsCRP-D. RESULTS: The final sample comprised 135 patients. Of those, 71 (52.6% were readmitted at least once during the 12-month follow-up period. The median (interquartile time to readmission was 78 days (42-178 days. Multivariate analysis revealed that serum hsCRP-D ≥ 3 mg/L and GOLD category D were independent predictors of readmission (hazard ratio = 3.486; 95% CI: 1.968-6.175; p < 0.001 and hazard ratio = 2.201; 95% CI: 1.342-3.610; p = 0.002, respectively. The ordering of the factor combinations by cumulative readmission risk, from highest to lowest, was as follows: hsCRP-D ≥ 3 mg/L and GOLD category D; hsCRP-D ≥ 3 mg/L and GOLD categories A-C; hsCRP-D < 3 mg/L and GOLD category D; hsCRP-D < 3 mg/L and GOLD categories A-C. CONCLUSIONS: Serum hsCRP-D and GOLD classification are independent predictors of readmission for AECOPD, and their predictive value increases when they are used in combination.

  3. Predicted sub-populations in a marine shrimp proteome as revealed by combined EST and cDNA data from multiple Penaeus species

    Directory of Open Access Journals (Sweden)

    Kotewong Rattanawadee

    2010-11-01

    Full Text Available Abstract Background Many species of marine shrimp in the Family Penaeidae, viz. Penaeus (Litopenaeus vannamei, Penaeus monodon, Penaeus (Fenneropenaeus chinensis, and Penaeus (Marsupenaeus japonicus, are animals of economic importance in the aquaculture industry. Yet information about their DNA and protein sequences is lacking. In order to predict their collective proteome, we combined over 270,000 available EST and cDNA sequences from the 4 shrimp species with all protein sequences of Drosophila melanogaster and Caenorhabditis elegans. EST data from 4 other crustaceans, the crab Carcinus maenas, the lobster Homarus americanus (Decapoda, the water flea Daphnia pulex, and the brine shrimp Artemia franciscana were also used. Findings Similarity searches from EST collections of the 4 shrimp species matched 64% of the protein sequences of the fruit fly, but only 45% of nematode proteins, indicating that the shrimp proteome content is more similar to that of an insect than a nematode. Combined results with 4 additional non-shrimp crustaceans increased matching to 78% of fruit fly and 56% of nematode proteins, suggesting that present shrimp EST collections still lack sequences for many conserved crustacean proteins. Analysis of matching data revealed the presence of 4 EST groups from shrimp, namely sequences for proteins that are both fruit fly-like and nematode-like, fruit fly-like only, nematode-like only, and non-matching. Gene ontology profiles of proteins for the 3 matching EST groups were analyzed. For non-matching ESTs, a small fraction matched protein sequences from other species in the UniProt database, including other crustacean-specific proteins. Conclusions Shrimp ESTs indicated that the shrimp proteome is comprised of sub-populations of proteins similar to those common to both insect and nematode models, those present specifically in either model, or neither. Combining small EST collections from related species to compensate for their

  4. Critical combinations of radiation dose and volume predict intelligence quotient and academic achievement scores after craniospinal irradiation in children with medulloblastoma.

    Science.gov (United States)

    Merchant, Thomas E; Schreiber, Jane E; Wu, Shengjie; Lukose, Renin; Xiong, Xiaoping; Gajjar, Amar

    2014-11-01

    To prospectively follow children treated with craniospinal irradiation to determine critical combinations of radiation dose and volume that would predict for cognitive effects. Between 1996 and 2003, 58 patients (median age 8.14 years, range 3.99-20.11 years) with medulloblastoma received risk-adapted craniospinal irradiation followed by dose-intense chemotherapy and were followed longitudinally with multiple cognitive evaluations (through 5 years after treatment) that included intelligence quotient (estimated intelligence quotient, full-scale, verbal, and performance) and academic achievement (math, reading, spelling) tests. Craniospinal irradiation consisted of 23.4 Gy for average-risk patients (nonmetastatic) and 36-39.6 Gy for high-risk patients (metastatic or residual disease >1.5 cm(2)). The primary site was treated using conformal or intensity modulated radiation therapy using a 2-cm clinical target volume margin. The effect of clinical variables and radiation dose to different brain volumes were modeled to estimate cognitive scores after treatment. A decline with time for all test scores was observed for the entire cohort. Sex, race, and cerebrospinal fluid shunt status had a significant impact on baseline scores. Age and mean radiation dose to specific brain volumes, including the temporal lobes and hippocampi, had a significant impact on longitudinal scores. Dichotomized dose distributions at 25 Gy, 35 Gy, 45 Gy, and 55 Gy were modeled to show the impact of the high-dose volume on longitudinal test scores. The 50% risk of a below-normal cognitive test score was calculated according to mean dose and dose intervals between 25 Gy and 55 Gy at 10-Gy increments according to brain volume and age. The ability to predict cognitive outcomes in children with medulloblastoma using dose-effects models for different brain subvolumes will improve treatment planning, guide intervention, and help estimate the value of newer methods of irradiation. Copyright © 2014

  5. Pretreatment combination of platelet counts and neutrophil–lymphocyte ratio predicts survival of nasopharyngeal cancer patients receiving intensity-modulated radiotherapy

    Directory of Open Access Journals (Sweden)

    Lin YH

    2017-05-01

    Full Text Available Yu-Hsuan Lin,1 Kuo-Ping Chang,2 Yaoh-Shiang Lin,2,3 Ting-Shou Chang2–4 1Department of Otolaryngology, Head and Neck Surgery, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, 2Department of Otolaryngology, Head and Neck Surgery, Kaohsiung Veterans General Hospital, Kaohsiung, 3Department of Otolaryngology, Head and Neck Surgery, National Defense Medical Center, Taipei, 4Institute of Public Health, College of Medicine, National Cheng Kung University, Tainan, Taiwan, Republic of China Background: Increased cancer-related inflammation has been associated with unfavorable clinical outcomes. The combination of platelet count and neutrophil–lymphocyte ratio (COP-NLR has related outcomes in several cancers, except for nasopharyngeal carcinoma (NPC. This study evaluated the prognostic value of COP-NLR in predicting outcome in NPC patients treated with intensity-modulated radiotherapy (IMRT.Materials and methods: We analyzed the data collected from 232 NPC patients. Pretreatment total platelet counts, neutrophil–lymphocyte ratio (NLR, and COP-NLR score were evaluated as potential predictors. Optimal cutoff values for NLR and platelets were determined using receiver operating curve. Patients with both elevated NLR (>3 and platelet counts (>300×109/L were assigned a COP-NLR score of 2; those with one elevated or no elevated value were assigned a COP-NLR a score of 1 or 0. Cox proportional hazards model was used to test the association of these factors and relevant 3-year survivals.Results: Patients (COP-NLR scores 1 and 2=85; score 0=147 were followed up for 55.19 months. Univariate analysis showed no association between pretreatment NLR >2.23 and platelet counts >290.5×109/L and worse outcomes. Multivariate analysis revealed that those with COP-NLR scores of 0 had better 3-year disease-specific survival (P=0.02, overall survival (P=0.024, locoregional relapse-free survival (P=0.004, and distant

  6. Improvement of high floods predictability in the Red River of the North basin using combined remote-sensed, gauge-based and assimilated precipitation data

    Science.gov (United States)

    Semenova, O.; Restrepo, P. J.

    2011-12-01

    The Red River of the North basin (USA) is considered to be under high risk of flood danger, having experienced serious flooding during the last few years. The region climate can be characterized as cold and, during winter, it exhibits continuous snowcover modified by wind redistribution. High-hazard runoff regularly occurs as a major spring snowmelt event resulting from the relatively rapid release of water from the snowpack on frozen soils. Although in summer/autumn most rainfall occurs from convective storms over small areas and does not generate dangerous floods, the pre-winter state of the soils may radically influence spring maximum flows. Large amount of artificial agricultural tiles and numerous small post-glacial depressions influencing the redistribution of runoff complicates the predictions of high floods. In such conditions any hydrological model would not be successful without proper precipitation input. In this study the simulation of runoff processes for two watersheds in the basin of the Red River of the North, USA, was undertaken using the Hydrograph model developed at the State Hydrological Institute (St. Petersburg, Russia). The Hydrograph is a robust process-based model, where the processes have a physical basis combined with some strategic conceptual simplifications that give it the ability to be applied in the conditions of low information availability. It accounts for the processes of frost and thaw of soils, snow redistribution and depression storage impacts. The assessment of the model parameters was conducted based on the characteristics of soil and vegetation cover. While performing the model runs, the parameters of depression storage and the parameters of different types of flow were manually calibrated to reproduce the observed flow. The model provided satisfactory simulation results in terms not only of river runoff but also variable sates of soil like moisture and temperature over a simulation period 2005 - 2010. For experimental runs

  7. Modelling sewer sediment deposition, erosion, and transport processes to predict acute influent and reduce combined sewer overflows and CO(2) emissions.

    Science.gov (United States)

    Mouri, Goro; Oki, Taikan

    2010-01-01

    Understanding of solids deposition, erosion, and transport processes in sewer systems has improved considerably in the past decade. This has provided guidance for controlling sewer solids and associated acute pollutants to protect the environment and improve the operation of wastewater systems. Although measures to decrease combined sewer overflow (CSO) events have reduced the amount of discharged pollution, overflows continue to occur during rainy weather in combined sewer systems. The solution lies in the amount of water allotted to various processes in an effluent treatment system, in impact evaluation of water quality and prediction technology, and in stressing the importance of developing a control technology. Extremely contaminated inflow has been a serious research subject, especially in connection with the influence of rainy weather on nitrogen and organic matter removal efficiency in wastewater treatment plants (WWTP). An intensive investigation of an extremely polluted inflow load to WWTP during rainy weather was conducted in the city of Matsuyama, the region used for the present research on total suspended solid (TSS) concentration. Since the inflow during rainy weather can be as much as 400 times that in dry weather, almost all sewers are unsettled and overflowing when a rain event is more than moderate. Another concern is the energy consumed by wastewater treatment; this problem has become important from the viewpoint of reducing CO(2) emissions and overall costs. Therefore, while establishing a prediction technology for the inflow water quality characteristics of a sewage disposal plant is an important priority, the development of a management/control method for an effluent treatment system that minimises energy consumption and CO(2) emissions due to water disposal is also a pressing research topic with regards to the quality of treated water. The procedure to improve water quality must make use of not only water quality and biotic criteria, but also

  8. Combined use of brain natriuretic peptide and C-reactive protein for predicting cardiovascular risk in outpatients with type 2 diabetes mellitus

    Directory of Open Access Journals (Sweden)

    Toshihiro Tsuruda

    2007-09-01

    Full Text Available Toshihiro Tsuruda1, Johji Kato2, Takahiro Sumi1, Kazuya Mishima1, Hiroyuki Masuyama1, Hiroyuki Nakao3, Takuroh Imamura1, Tanenao Eto1, Kazuo Kitamura11Department of Internal Medicine, Circulatory and Body Fluid Regulation, Faculty of Medicine, University of Miyazaki, Miyazaki, Japan; 2Frontier Research Center, University of Miyazaki, Miyazaki, Japan; 3Department of Public Health, Faculty of Medicine, University of Miyazaki, Miyazaki, Japan Abstract: Appropriate tools are necessary for predicting cardiovascular events in patients with diabetes mellitus because of their high incidence. In this study, we assessed whether a combination of brain natriuretic peptide (BNP and C-reactive protein (CRP measurement were useful prognosticators in patients with type 2 diabetes mellitus. One hundred and nine patients with type 2 diabetes mellitus, aged 52 to 93 years, were examined at outpatient clinics for blood, urinary samples, and echocardiography. They were then followed prospectively. During the average follow-up period of 30 months (range, 3 to 37, 15 patients (14% had cardiovascular events: This was the first event in 5 patients and a recurrence in 10. Cox regression analysis showed that the past event (hazard ratio [HR] 4.819 [95% confidence interval (CI: 1.299–17.881]; p = 0.019 and plasma BNP level (HR 1.007 [95% CI: 1.002–1.012]; p = 0.010] were independently significant factors for the cardiovascular events during the follow-up period. Patients with plasma BNP ≥53 pg/mL and CRP ≥0.95 mg/dL demonstrated the highest incidence in cardiovascular event, compared to those categorized into either or both low levels of BNP and CRP. This study suggests that combination of plasma BNP and CRP measurement provides the additive prognostic information of cardiovascular events in patients with type 2 diabetes mellitus.Keywords: diabetes mellitus; natriuretic peptide; inflammation; mortality and morbidity

  9. PREDICTION OF GAS HOLD-UP IN A COMBINED LOOP AIR LIFT FLUIDIZED BED REACTOR USING NEWTONIAN AND NON-NEWTONIAN LIQUIDS

    Directory of Open Access Journals (Sweden)

    Sivakumar Venkatachalam

    2011-09-01

    Full Text Available Many experiments have been conducted to study the hydrodynamic characteristics of column reactors and loop reactors. In this present work, a novel combined loop airlift fluidized bed reactor was developed to study the effect of superficial gas and liquid velocities, particle diameter, fluid properties on gas holdup by using Newtonian and non-Newtonian liquids. Compressed air was used as gas phase. Water, 5% n-butanol, various concentrations of glycerol (60 and 80% were used as Newtonian liquids, and different concentrations of carboxy methyl cellulose aqueous solutions (0.25, 0.6 and 1.0% were used as non-Newtonian liquids. Different sizes of spheres, Bearl saddles and Raschig rings were used as solid phases. From the experimental results, it was found that the increase in superficial gas velocity increases the gas holdup, but it decreases with increase in superficial liquid velocity and viscosity of liquids. Based on the experimental results a correlation was developed to predict the gas hold-up for Newtonian and non-Newtonian liquids for a wide range of operating conditions at a homogeneous flow regime where the superficial gas velocity is approximately less than 5 cm/s

  10. Differential effect of IP- and IV-injected nitrogen mustard on subsequently-irradiated intestinal crypts: implications for 'dose-effect factors' predicted by experimental, combined modality therapy

    International Nuclear Information System (INIS)

    Moore, J.V.

    1984-01-01

    In experimental chemotherapy-radiotherapy, cytotoxic drugs are almost invariably injected by the intraperitoneal (IP) route. This contrasts with normal clinical practice, which is to employ the intravenous (IV) route. We have used a clonogenic assay of gastrointestinal (GI) injury in mice to show that a given administered dose of nitrogen mustard (HN 2 ), injected IP, results in a much greater reduction in the subsequent radiation dose required to achieve an isoeffect, than if the drug is injected IV. At an administered dose of 3.5 mg kg -1 of HN 2 (the animal LDsub(10/30) for IP injection), the radiation dose-reduction factor for 10% survival of intestinal crypts, was 1.94 for IP HN 2 and only 1.28 for IV HN 2 . Even the grossly-equitoxic (mouse LDsub(10/30)) dose of IV HN 2 resulted in a smaller predicted radiation dose reduction for GI injury, by a factor of 1.45. The validity of using the IP route in combined chemotherapy-radiotherapy studies designed to generate quantitative estimates of toxicity is discussed. (author)

  11. Resting state glutamate predicts elevated pre-stimulus alpha during self-relatedness: A combined EEG-MRS study on "rest-self overlap".

    Science.gov (United States)

    Bai, Yu; Nakao, Takashi; Xu, Jiameng; Qin, Pengmin; Chaves, Pedro; Heinzel, Alexander; Duncan, Niall; Lane, Timothy; Yen, Nai-Shing; Tsai, Shang-Yueh; Northoff, Georg

    2016-01-01

    Recent studies have demonstrated neural overlap between resting state activity and self-referential processing. This "rest-self" overlap occurs especially in anterior cortical midline structures like the perigenual anterior cingulate cortex (PACC). However, the exact neurotemporal and biochemical mechanisms remain to be identified. Therefore, we conducted a combined electroencephalography (EEG)-magnetic resonance spectroscopy (MRS) study. EEG focused on pre-stimulus (e.g., prior to stimulus presentation or perception) power changes to assess the degree to which those changes can predict subjects' perception (and judgment) of subsequent stimuli as high or low self-related. MRS measured resting state concentration of glutamate, focusing on PACC. High pre-stimulus (e.g., prior to stimulus presentation or perception) alpha power significantly correlated with both perception of stimuli judged to be highly self-related and with resting state glutamate concentrations in the PACC. In sum, our results show (i) pre-stimulus (e.g., prior to stimulus presentation or perception) alpha power and resting state glutamate concentration to mediate rest-self overlap that (ii) dispose or incline subjects to assign high degrees of self-relatedness to perceptual stimuli.

  12. Critical Combinations of Radiation Dose and Volume Predict Intelligence Quotient and Academic Achievement Scores After Craniospinal Irradiation in Children With Medulloblastoma

    Energy Technology Data Exchange (ETDEWEB)

    Merchant, Thomas E., E-mail: thomas.merchant@stjude.org [Division of Radiation Oncology, St. Jude Children' s Research Hospital, Memphis, Tennessee (United States); Schreiber, Jane E. [Department of Psychology, St. Jude Children' s Research Hospital, Memphis, Tennessee (United States); Wu, Shengjie [Department of Biostatistcs, St. Jude Children' s Research Hospital, Memphis, Tennessee (United States); Lukose, Renin [Division of Radiation Oncology, St. Jude Children' s Research Hospital, Memphis, Tennessee (United States); Xiong, Xiaoping [Department of Biostatistcs, St. Jude Children' s Research Hospital, Memphis, Tennessee (United States); Gajjar, Amar [Department of Oncology, St. Jude Children' s Research Hospital, Memphis, Tennessee (United States)

    2014-11-01

    Purpose: To prospectively follow children treated with craniospinal irradiation to determine critical combinations of radiation dose and volume that would predict for cognitive effects. Methods and Materials: Between 1996 and 2003, 58 patients (median age 8.14 years, range 3.99-20.11 years) with medulloblastoma received risk-adapted craniospinal irradiation followed by dose-intense chemotherapy and were followed longitudinally with multiple cognitive evaluations (through 5 years after treatment) that included intelligence quotient (estimated intelligence quotient, full-scale, verbal, and performance) and academic achievement (math, reading, spelling) tests. Craniospinal irradiation consisted of 23.4 Gy for average-risk patients (nonmetastatic) and 36-39.6 Gy for high-risk patients (metastatic or residual disease >1.5 cm{sup 2}). The primary site was treated using conformal or intensity modulated radiation therapy using a 2-cm clinical target volume margin. The effect of clinical variables and radiation dose to different brain volumes were modeled to estimate cognitive scores after treatment. Results: A decline with time for all test scores was observed for the entire cohort. Sex, race, and cerebrospinal fluid shunt status had a significant impact on baseline scores. Age and mean radiation dose to specific brain volumes, including the temporal lobes and hippocampi, had a significant impact on longitudinal scores. Dichotomized dose distributions at 25 Gy, 35 Gy, 45 Gy, and 55 Gy were modeled to show the impact of the high-dose volume on longitudinal test scores. The 50% risk of a below-normal cognitive test score was calculated according to mean dose and dose intervals between 25 Gy and 55 Gy at 10-Gy increments according to brain volume and age. Conclusions: The ability to predict cognitive outcomes in children with medulloblastoma using dose-effects models for different brain subvolumes will improve treatment planning, guide intervention, and help

  13. Critical Combinations of Radiation Dose and Volume Predict Intelligence Quotient and Academic Achievement Scores After Craniospinal Irradiation in Children With Medulloblastoma

    International Nuclear Information System (INIS)

    Merchant, Thomas E.; Schreiber, Jane E.; Wu, Shengjie; Lukose, Renin; Xiong, Xiaoping; Gajjar, Amar

    2014-01-01

    Purpose: To prospectively follow children treated with craniospinal irradiation to determine critical combinations of radiation dose and volume that would predict for cognitive effects. Methods and Materials: Between 1996 and 2003, 58 patients (median age 8.14 years, range 3.99-20.11 years) with medulloblastoma received risk-adapted craniospinal irradiation followed by dose-intense chemotherapy and were followed longitudinally with multiple cognitive evaluations (through 5 years after treatment) that included intelligence quotient (estimated intelligence quotient, full-scale, verbal, and performance) and academic achievement (math, reading, spelling) tests. Craniospinal irradiation consisted of 23.4 Gy for average-risk patients (nonmetastatic) and 36-39.6 Gy for high-risk patients (metastatic or residual disease >1.5 cm 2 ). The primary site was treated using conformal or intensity modulated radiation therapy using a 2-cm clinical target volume margin. The effect of clinical variables and radiation dose to different brain volumes were modeled to estimate cognitive scores after treatment. Results: A decline with time for all test scores was observed for the entire cohort. Sex, race, and cerebrospinal fluid shunt status had a significant impact on baseline scores. Age and mean radiation dose to specific brain volumes, including the temporal lobes and hippocampi, had a significant impact on longitudinal scores. Dichotomized dose distributions at 25 Gy, 35 Gy, 45 Gy, and 55 Gy were modeled to show the impact of the high-dose volume on longitudinal test scores. The 50% risk of a below-normal cognitive test score was calculated according to mean dose and dose intervals between 25 Gy and 55 Gy at 10-Gy increments according to brain volume and age. Conclusions: The ability to predict cognitive outcomes in children with medulloblastoma using dose-effects models for different brain subvolumes will improve treatment planning, guide intervention, and help estimate

  14. Prediction of local failures with a combination of pretreatment tumor volume and apparent diffusion coefficient in patients treated with definitive radiotherapy for hypopharyngeal or oropharyngeal squamous cell carcinoma

    International Nuclear Information System (INIS)

    Ohnishi, Kayoko; Shioyama, Yoshiyuki; Hatakenaka, Masamitsu

    2011-01-01

    The purpose of this study was to investigate the clinical factors for predicting local failure after definitive radiotherapy in oropharyngeal or hypopharyngeal squamous cell carcinoma. Between July 2006 and December 2008, 64 consecutive patients with squamous cell carcinoma of the hypopharynx or the oropharynx treated with definitive radiotherapy were included in this study. Clinical factors, such as pretreatment hemoglobin (Hb) level, T-stage, gross tumor volume of primary tumors (pGTV), and maximum standardized uptake value (SUV max ) on 18 F-fluorodeoxyglucose positron emission tomography (FDG-PET), were evaluated for the correlation with local failure. A subset analysis of 32 patients with MR images including diffusion-weighted images (DWI) as a pretreatment evaluation was also performed. The Kaplan-Meier curves, the log-rank test, and the Cox proportional hazards model were used to evaluate these clinical factors. Eleven of 64 patients experienced local recurrence, with a median follow-up time of 15 months. In the univariate analysis, Hb level (p=0.0261), T-stage (p=0.012), pGTV (p=0.0025), and SUV max (p=0.024) were significantly associated with local failure. In the multivariate analysis, pGTV (p=0.0070) remained an adverse factor for local control. In the subset analysis of 32 patients with DWI, the median apparent diffusion coefficient (ADC) value of primary tumors on DWI was 0.79 x 10 -3 mm 2 /s (range, 0.40-1.60 x 10 -3 mm 2 /s). Patients with a high ADC value (>0.79 x 10 -3 mm 2 /s) had a significantly lower local control rate than patients with a low ADC value (100% vs. 44%, p=0.0019). The rate of local failure among patients with a large pGTV and a high ADC value was 55% (6/11), whereas no local failures occurred (0%, 0/21) among patients with a small pGTV or a low ADC. These results suggest that a combination of a large tumor volume and a high ADC value could be predictive of local recurrence after definitive radiotherapy in hypopharyngeal or

  15. Use of Biofeedback Combined With Diet for Treatment of Obstructed Defecation Associated With Paradoxical Puborectalis Contraction (Anismus): Predictive Factors and Short-term Outcome.

    Science.gov (United States)

    Murad-Regadas, Sthela M; Regadas, Francisco S Pinheiro; Bezerra, Carla C Rocha; de Oliveira, Maura T Coutinho Cajazeiras; Regadas Filho, Francisco S Pinheiro; Rodrigues, Lusmar Veras; Almeida, Saulo Santiago; da Silva Fernandes, Graziela O

    2016-02-01

    Numerous studies have described the use of biofeedback therapy for the treatment of anismus. Success rates vary widely, but few data are available regarding factors predictive of success. Our aim was to evaluate short-term results of biofeedback associated with diet in patients with obstructed defecation because of anismus and to investigate factors that may affect the results. Patients were identified from a single-institution prospectively maintained database. This study was conducted in a tertiary hospital. Consecutive patients who had obstructed defecation associated with anismus and were treated with biofeedback associated with diet were eligible. Each patient underwent anal manometry and/or dynamic anal ultrasound. Patients with anismus and were treated with biofeedback associated with diet. Patients classed as having a satisfactory response to therapy and those classed as having an unsatisfactory response were compared with regard to sex, age, Cleveland Clinic Florida constipation score, functional factors (anal resting and squeeze pressures and reversal of paradoxical puborectalis contraction on manometry), and anatomic factors in women (history of vaginal delivery, number of vaginal deliveries, menopause, hysterectomy, and previous anorectal surgery). A total of 116 patients were included (75 women and 41 men). Overall, 59% were classed as having a satisfactory response (decrease in constipation score, >50%). Patients with satisfactory responses to biofeedback plus diet did not differ from those with unsatisfactory responses with regard to clinical, anatomic, and physiological factors. This was not a randomized controlled trial. Biofeedback combined with diet is a valuable treatment option for patients with obstructed defecation syndrome associated with anismus, and more than half of our patients of both sexes achieved a satisfactory response. Improvement was not related to reversal of paradoxical contraction of puborectalis muscles at manometry. Patient

  16. Combining water-rock interaction experiments with reaction path and reactive transport modelling to predict reservoir rock evolution in an enhanced geothermal system

    Science.gov (United States)

    Kuesters, Tim; Mueller, Thomas; Renner, Joerg

    2016-04-01

    different grain sizes (<120 μm and 120 - 180 μm), (II) cubes of the intact rock (˜ 1 cm3) and (III) thermally cracked rock cubes. Run durations were up to 60 days and the bulk fluid reservoir was regularly sampled to monitor the compositional evolution (Na, K, Ca, Si, Al, Fe, and Mg) and pH. The temporal evolution of the fluid was compared to a numerical simulation which combines the iPhreeqC application library (thermodynamic calculations) with a self-coded FORTRAN program (dissolution / growth kinetic, mineral nucleation, crystal size distribution and reactive surface area). Experimental and modelling results both indicate a fast increase of Na, Ca, K and Si related to kinetically controlled dissolution of plg, Kfs and qtz. The concentrations of Al, Mg, and Fe reach a maximum in the first two days followed by a rapid decrease caused by clay mineral precipitation. Measured rates depend on the properties of the starting material controlling the effective element flux. The reaction path modelling based on new kinetic data constrained by our experiments provides a quantitative basis for a model of polycrystalline rocks that exhibits the potential for upscaling and thus an improved prediction of large scale reactive transport for EGS.

  17. Gas-surface interactions using accommodation coefficients for a dilute and a dense gas in a micro/nano-channel : heat flux predictions using combined molecular dynamics and Monte Carlo techniques

    NARCIS (Netherlands)

    Gaastra - Nedea, S.V.; Steenhoven, van A.A.; Markvoort, A.J.; Spijker, P.; Giordano, D.

    2014-01-01

    The influence of gas-surface interactions of a dilute gas confined between two parallel walls on the heat flux predictions is investigated using a combined Monte Carlo (MC) and molecular dynamics (MD) approach. The accommodation coefficients are computed from the temperature of incident and

  18. Updating the Skating Multistage Aerobic Test and Correction for V[Combining Dot Above]O2max Prediction Using a New Skating Economy Index in Elite Youth Ice Hockey Players.

    Science.gov (United States)

    Allisse, Maxime; Bui, Hung Tien; Léger, Luc; Comtois, Alain-Steve; Leone, Mario

    2018-05-07

    Allisse, M, Bui, HT, Léger, L, Comtois, A-S, and Leone, M. Updating the skating multistage aerobic test and correction for V[Combining Dot Above]O2max prediction using a new skating economy index in elite youth ice hockey players. J Strength Cond Res XX(X): 000-000, 2018-A number of field tests, including the skating multistage aerobic test (SMAT), have been developed to predict V[Combining Dot Above]O2max in ice hockey players. The SMAT, like most field tests, assumes that participants who reach a given stage have the same oxygen uptake, which is not usually true. Thus, the objectives of this research are to update the V[Combining Dot Above]O2 values during the SMAT using a portable breath-by-breath metabolic analyzer and to propose a simple index of skating economy to improve the prediction of oxygen uptake. Twenty-six elite hockey players (age 15.8 ± 1.3 years) participated in this study. The oxygen uptake was assessed using a portable metabolic analyzer (K4b) during an on-ice maximal shuttle skate test. To develop an index of skating economy called the skating stride index (SSI), the number of skating strides was compiled for each stage of the test. The SMAT enabled the prediction of the V[Combining Dot Above]O2max (ml·kg·min) from the maximal velocity (m·s) and the SSI (skating strides·kg) using the following regression equation: V[Combining Dot Above]O2max = (14.94 × maximal velocity) + (3.68 × SSI) - 24.98 (r = 0.95, SEE = 1.92). This research allowed for the update of the oxygen uptake values of the SMAT and proposed a simple measure of skating efficiency for a more accurate evaluation of V[Combining Dot Above]O2max in elite youth hockey players. By comparing the highest and lowest observed SSI scores in our sample, it was noted that the V[Combining Dot Above]O2 values can vary by up to 5 ml·kg·min. Our results suggest that skating economy should be included in the prediction of V[Combining Dot Above]O2max to improve prediction accuracy.

  19. Prediction of ingredient quality and the effect of a combination of xylanase, amylase, protease and phytase in the diets of broiler chicks. 2. Energy and nutrient utilisation.

    Science.gov (United States)

    Cowieson, A J; Singh, D N; Adeola, O

    2006-08-01

    1. In order to investigate the effects of xylanase, amylase, protease and phytase in the diets of broiler chickens containing graded concentrations of metabolisable energy (ME), two 42-d experiments were conducted using a total of 2208 broiler chicks (8 treatments with 12 replicate pens in each experiment). 2. Four diets including one positive and three negative control diets were used. Three maize/soybean meal-based negative control (NC) diets were formulated to be identical in available phosphorus (P), calcium (Ca) and amino acids but NC1 contained approximately 0.17 MJ/kg less ME than NC2 and approximately 0.34 MJ/kg less ME than NC3. A positive control (PC) was fed for comparison and was formulated to be adequate in all nutrients, providing approximately 0.63 MJ/kg ME, 0.13% available P, 0.12% Ca and 1 to 2% amino acids more than NC1. 3. The reduction in nutrient density between NC1 and PC was determined using ingredient quality models Avichecktrade mark Corn and Phychecktrade mark that can predict the response to exogenous enzymes in maize/soybean meal-based broiler diets. Supplementation of each diet with or without a cocktail of xylanase, amylase, protease and phytase gave a total of 8 dietary treatments in a 4 x 2 factorial arrangement. The same treatments and diet designs were used in both experiments but conducted in different locations using different batches of maize, soybean meal and minor ingredients. 4. In both experiments, digestibility was improved by the addition of exogenous enzymes, particularly those for P, Ca and certain amino acids. In addition, the supplementation of the PC with enzymes elicited a positive response indicating that over-the-top addition of xylanase, amylase, protease and phytase may offer a nutritionally and economically viable alternative to feed cost reduction. 5. It can be concluded that the digestibility of nutrients by broilers fed on maize/soybean meal-based diets can be improved by the use of a combination of xylanase

  20. Combined heavy smoking and drinking predicts overall but not disease-free survival after curative resection of locoregional esophageal squamous cell carcinoma

    Directory of Open Access Journals (Sweden)

    Sun P

    2016-07-01

    neither-users (95% CI =1.020–1.901, P=0.037.Conclusion: We identified that combined heavy smoking and drinking might predict poor prognosis in ESCC patients. Keywords: esophageal squamous cell carcinoma, smoking, drinking, survival, prognosis

  1. Interval to Biochemical Failure Predicts Clinical Outcomes in Patients With High-Risk Prostate Cancer Treated by Combined-Modality Radiation Therapy

    International Nuclear Information System (INIS)

    Shilkrut, Mark; McLaughlin, P. William; Merrick, Gregory S.; Vainshtein, Jeffrey M.; Feng, Felix Y.; Hamstra, Daniel A.

    2013-01-01

    Purpose: To validate the prognostic value of interval to biochemical failure (IBF) in patients with high-risk prostate cancer (HiRPCa) treated with combined-modality radiation therapy (CMRT) with or without androgen deprivation therapy (ADT). Methods and Materials: We conducted a retrospective review of HiRPCa (prostate-specific antigen >20 ng/mL, Gleason score [GS] 8-10, or clinical T stage T3-T4) treated with either dose-escalated external beam radiation therapy (EBRT) or CMRT. Interval to biochemical failure was classified as ≤18 or >18 months from the end of all therapy to the date of biochemical failure (BF). Kaplan-Meier methods and Cox proportional hazards regression were used to evaluate the prognostic value of IBF ≤18 months for distant metastasis (DM) and prostate cancer-specific mortality (PCSM). Results: Of 958 patients with a median follow-up of 63.2 months, 175 patients experienced BF. In those with BF, there were no differences in pretreatment clinical characteristics between the EBRT and CMRT groups, except for a higher proportion of patients with GS 8-10 in the CMRT group (70% vs 52%, P=.02). Median IBF after all therapy was 24.0 months (interquartile range 9.6-46.0) in the EBRT group and 18.9 months (interquartile range 9.2-34.5) in the CMRT group (P=.055). On univariate analysis, IBF ≤18 months was associated with increased risk of DM and PCSM in the entire cohort and the individual EBRT and CMRT groups. On multivariate analysis, only GS 9-10 and IBF ≤18 months, but not the radiation therapy regimen or ADT use, predicted DM (hazard ratio [HR] 3.7, P<.01, 95% confidence interval [CI] 1.4-10.3 for GS 9-10; HR 3.9, P<.0001, 95% CI 2.4-6.5 for IBF ≤18 months) and PCSM (HR 14.8, P<.009, 95% CI 2.0-110 for GS 9-10; HR 4.4, P<.0001, 95% CI 2.4-8.1 for IBF ≤18 months). Conclusions: Short IBF was highly prognostic for higher DM and PCSM in patients with HiRPCa. The prognostic value of IBF for DM and PCSM was not affected by the radiation

  2. Assessment of the Prognostic and Treatment-Predictive Performance of the Combined HOXB13:IL17BR-MGI Gene Expression Signature in the Trans-ATAC Cohort

    Science.gov (United States)

    2013-12-01

    Shak S, Tang G, et al. A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med 2004; 351: 2817–26. 7...Barlow WE, Shak S, et al, for The Breast Cancer Intergroup of North America. Prognostic and predictive value of the 21-gene recurrence score assay in

  3. The Bi-Directional Prediction of Carbon Fiber Production Using a Combination of Improved Particle Swarm Optimization and Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Chuncai Xiao

    2014-12-01

    Full Text Available This paper creates a bi-directional prediction model to predict the performance of carbon fiber and the productive parameters based on a support vector machine (SVM and improved particle swarm optimization (IPSO algorithm (SVM-IPSO. In the SVM, it is crucial to select the parameters that have an important impact on the performance of prediction. The IPSO is proposed to optimize them, and then the SVM-IPSO model is applied to the bi-directional prediction of carbon fiber production. The predictive accuracy of SVM is mainly dependent on its parameters, and IPSO is thus exploited to seek the optimal parameters for SVM in order to improve its prediction capability. Inspired by a cell communication mechanism, we propose IPSO by incorporating information of the global best solution into the search strategy to improve exploitation, and we employ IPSO to establish the bi-directional prediction model: in the direction of the forward prediction, we consider productive parameters as input and property indexes as output; in the direction of the backward prediction, we consider property indexes as input and productive parameters as output, and in this case, the model becomes a scheme design for novel style carbon fibers. The results from a set of the experimental data show that the proposed model can outperform the radial basis function neural network (RNN, the basic particle swarm optimization (PSO method and the hybrid approach of genetic algorithm and improved particle swarm optimization (GA-IPSO method in most of the experiments. In other words, simulation results demonstrate the effectiveness and advantages of the SVM-IPSO model in dealing with the problem of forecasting.

  4. The Bi-Directional Prediction of Carbon Fiber Production Using a Combination of Improved Particle Swarm Optimization and Support Vector Machine.

    Science.gov (United States)

    Xiao, Chuncai; Hao, Kuangrong; Ding, Yongsheng

    2014-12-30

    This paper creates a bi-directional prediction model to predict the performance of carbon fiber and the productive parameters based on a support vector machine (SVM) and improved particle swarm optimization (IPSO) algorithm (SVM-IPSO). In the SVM, it is crucial to select the parameters that have an important impact on the performance of prediction. The IPSO is proposed to optimize them, and then the SVM-IPSO model is applied to the bi-directional prediction of carbon fiber production. The predictive accuracy of SVM is mainly dependent on its parameters, and IPSO is thus exploited to seek the optimal parameters for SVM in order to improve its prediction capability. Inspired by a cell communication mechanism, we propose IPSO by incorporating information of the global best solution into the search strategy to improve exploitation, and we employ IPSO to establish the bi-directional prediction model: in the direction of the forward prediction, we consider productive parameters as input and property indexes as output; in the direction of the backward prediction, we consider property indexes as input and productive parameters as output, and in this case, the model becomes a scheme design for novel style carbon fibers. The results from a set of the experimental data show that the proposed model can outperform the radial basis function neural network (RNN), the basic particle swarm optimization (PSO) method and the hybrid approach of genetic algorithm and improved particle swarm optimization (GA-IPSO) method in most of the experiments. In other words, simulation results demonstrate the effectiveness and advantages of the SVM-IPSO model in dealing with the problem of forecasting.

  5. Modifications to the Patient Rule-Induction Method that utilize non-additive combinations of genetic and environmental effects to define partitions that predict ischemic heart disease

    DEFF Research Database (Denmark)

    Dyson, Greg; Frikke-Schmidt, Ruth; Nordestgaard, Børge G

    2009-01-01

    This article extends the Patient Rule-Induction Method (PRIM) for modeling cumulative incidence of disease developed by Dyson et al. (Genet Epidemiol 31:515-527) to include the simultaneous consideration of non-additive combinations of predictor variables, a significance test of each combination,...

  6. Prognostic value of combined CT angiography and myocardial perfusion imaging versus invasive coronary angiography and nuclear stress perfusion imaging in the prediction of major adverse cardiovascular events

    DEFF Research Database (Denmark)

    Chen, Marcus Y.; Rochitte, Carlos E.; Arbab-Zadeh, Armin

    2017-01-01

    Purpose: To compare the prognostic importance (time to major adverse cardiovascular event [MACE]) of combined computed tomography (CT) angiography and CT myocardial stress perfusion imaging with that of combined invasive coronary angiography (ICA) and stress single photon emission CT myocardial p...

  7. Neither Baseline nor Changes in Serum Triiodothyronine during Levothyroxine/Liothyronine Combination Therapy Predict a Positive Response to This Treatment Modality in Hypothyroid Patients with Persistent Symptoms

    DEFF Research Database (Denmark)

    Medici, Bjarke Borregaard; la Cour, Jeppe Lerche; Michaelsson, Luba Freja

    2017-01-01

    BACKGROUND: Despite biochemical euthyroidism, some levothyroxine (L-T4)-treated hypothyroid patients report persisting symptoms and some of these patients are tentatively treated with a combination of L-T4 and liothyronine (L-T3). Combination therapy and the appropriate choice of blood tests to m...

  8. Fatigue-creep life prediction of 21/4Cr-1Mo steel under combined tension-torsion at 600degC

    International Nuclear Information System (INIS)

    Inoue, Tatsuo; Kishi, Shigeru; Koto, Hiroyuki; Takahashi, Yukio.

    1991-01-01

    From the above discussion on the comparison between predicted and experimental lives in this project, the followings are suggested. The Mises equivalent stress is not a successful parameter to describe the multiaxial fatigue-creep damage, while the Huddleston's stress gives better prediction. SRP, Ostergren method and Lemaitre-Plumtree-Chaboche method give comparatively good estimation of life, though these methods predict more or less the greater damage than the experiment under pure torsion and in-phase cycling, and cannot predict the life under out-of-phase cycling. The reason is considered to be owing to employment of the Mises equivalent strain, which might be possibly improved by using more sophisticated equivalent strain such as the strain defined on the Γ-plane. The life prediction based on the analytical stress-strain relation gives almost similar results as that based on the experimental data. It suggests the applicability of the inelastic constitutive moeles to give the fundamental data for life prediction. The subcommittee wishes to express her gratitude to the head Committee on High Temperature Strength, JSMS, for supporting the project, and acknowledgement is due to the Ministry of Education and Culture for providing Grant-in-Aid for Co-operative Research A (No. 61302036 and 01302026). (author)

  9. The Combination of GIS and Biphasic to Better Predict In Vivo Dissolution of BCS Class IIb Drugs, Ketoconazole and Raloxifene.

    Science.gov (United States)

    Tsume, Yasuhiro; Igawa, Naoto; Drelich, Adam J; Amidon, Gregory E; Amidon, Gordon L

    2018-01-01

    The formulation developments and the in vivo assessment of Biopharmaceutical Classification System (BCS) class II drugs are challenging due to their low solubility and high permeability in the human gastrointestinal (GI) tract. Since the GI environment influences the drug dissolution of BCS class II drugs, the human GI characteristics should be incorporated into the in vitro dissolution system to predict bioperformance of BCS class II drugs. An absorptive compartment may be important in dissolution apparatus for BCS class II drugs, especially for bases (BCS IIb) because of high permeability, precipitation, and supersaturation. Thus, the in vitro dissolution system with an absorptive compartment may help predicting the in vivo phenomena of BCS class II drugs better than compendial dissolution apparatuses. In this study, an absorptive compartment (a biphasic device) was introduced to a gastrointestinal simulator. This addition was evaluated if this in vitro system could improve the prediction of in vivo dissolution for BCS class IIb drugs, ketoconazole and raloxifene, and subsequent absorption. The gastrointestinal simulator is a practical in vivo predictive tool and exhibited an improved in vivo prediction utilizing the biphasic format and thus a better tool for evaluating the bioperformance of BCS class IIb drugs than compendial apparatuses. Copyright © 2018. Published by Elsevier Inc.

  10. Predicting the equilibrium solubility of solid polycyclic aromatic hydrocarbons and dibenzothiophene using a combination of MOSCED plus molecular simulation or electronic structure calculations

    Science.gov (United States)

    Phifer, Jeremy R.; Cox, Courtney E.; da Silva, Larissa Ferreira; Nogueira, Gabriel Gonçalves; Barbosa, Ana Karolyne Pereira; Ley, Ryan T.; Bozada, Samantha M.; O'Loughlin, Elizabeth J.; Paluch, Andrew S.

    2017-06-01

    Methods to predict the equilibrium solubility of non-electrolyte solids are important for the design of novel separation processes. Here we demonstrate how conventional molecular simulation free energy calculations or electronic structure calculations in a continuum solvent, here SMD or SM8, can be used to predict parameters for the MOdified Separation of Cohesive Energy Density (MOSCED) method. The method is applied to the solutes naphthalene, anthracene, phenanthrene, pyrene and dibenzothiophene, compounds of interested to the petroleum industry and for environmental remediation. Adopting the melting point temperature and enthalpy of fusion of these compounds from experiment, we are able to predict equilibrium solubilities. Comparing to a total of 422 non-aqueous and 193 aqueous experimental solubilities, we find the proposed method is able to well correlate the data. The use of MOSCED is additionally advantageous as it is a solubility parameter-based method useful for intuitive solvent selection and formulation.

  11. Best of both worlds: combining pharma data and state of the art modeling technology to improve in Silico pKa prediction.

    Science.gov (United States)

    Fraczkiewicz, Robert; Lobell, Mario; Göller, Andreas H; Krenz, Ursula; Schoenneis, Rolf; Clark, Robert D; Hillisch, Alexander

    2015-02-23

    In a unique collaboration between a software company and a pharmaceutical company, we were able to develop a new in silico pKa prediction tool with outstanding prediction quality. An existing pKa prediction method from Simulations Plus based on artificial neural network ensembles (ANNE), microstates analysis, and literature data was retrained with a large homogeneous data set of drug-like molecules from Bayer. The new model was thus built with curated sets of ∼14,000 literature pKa values (∼11,000 compounds, representing literature chemical space) and ∼19,500 pKa values experimentally determined at Bayer Pharma (∼16,000 compounds, representing industry chemical space). Model validation was performed with several test sets consisting of a total of ∼31,000 new pKa values measured at Bayer. For the largest and most difficult test set with >16,000 pKa values that were not used for training, the original model achieved a mean absolute error (MAE) of 0.72, root-mean-square error (RMSE) of 0.94, and squared correlation coefficient (R(2)) of 0.87. The new model achieves significantly improved prediction statistics, with MAE = 0.50, RMSE = 0.67, and R(2) = 0.93. It is commercially available as part of the Simulations Plus ADMET Predictor release 7.0. Good predictions are only of value when delivered effectively to those who can use them. The new pKa prediction model has been integrated into Pipeline Pilot and the PharmacophorInformatics (PIx) platform used by scientists at Bayer Pharma. Different output formats allow customized application by medicinal chemists, physical chemists, and computational chemists.

  12. Combining NMR ensembles and molecular dynamics simulations provides more realistic models of protein structures in solution and leads to better chemical shift prediction

    International Nuclear Information System (INIS)

    Lehtivarjo, Juuso; Tuppurainen, Kari; Hassinen, Tommi; Laatikainen, Reino; Peräkylä, Mikael

    2012-01-01

    While chemical shifts are invaluable for obtaining structural information from proteins, they also offer one of the rare ways to obtain information about protein dynamics. A necessary tool in transforming chemical shifts into structural and dynamic information is chemical shift prediction. In our previous work we developed a method for 4D prediction of protein 1 H chemical shifts in which molecular motions, the 4th dimension, were modeled using molecular dynamics (MD) simulations. Although the approach clearly improved the prediction, the X-ray structures and single NMR conformers used in the model cannot be considered fully realistic models of protein in solution. In this work, NMR ensembles (NMRE) were used to expand the conformational space of proteins (e.g. side chains, flexible loops, termini), followed by MD simulations for each conformer to map the local fluctuations. Compared with the non-dynamic model, the NMRE+MD model gave 6–17% lower root-mean-square (RMS) errors for different backbone nuclei. The improved prediction indicates that NMR ensembles with MD simulations can be used to obtain a more realistic picture of protein structures in solutions and moreover underlines the importance of short and long time-scale dynamics for the prediction. The RMS errors of the NMRE+MD model were 0.24, 0.43, 0.98, 1.03, 1.16 and 2.39 ppm for 1 Hα, 1 HN, 13 Cα, 13 Cβ, 13 CO and backbone 15 N chemical shifts, respectively. The model is implemented in the prediction program 4DSPOT, available at http://www.uef.fi/4dspothttp://www.uef.fi/4dspot.

  13. Combining NMR ensembles and molecular dynamics simulations provides more realistic models of protein structures in solution and leads to better chemical shift prediction

    Energy Technology Data Exchange (ETDEWEB)

    Lehtivarjo, Juuso, E-mail: juuso.lehtivarjo@uef.fi; Tuppurainen, Kari; Hassinen, Tommi; Laatikainen, Reino [University of Eastern Finland, School of Pharmacy (Finland); Peraekylae, Mikael [University of Eastern Finland, Institute of Biomedicine (Finland)

    2012-03-15

    While chemical shifts are invaluable for obtaining structural information from proteins, they also offer one of the rare ways to obtain information about protein dynamics. A necessary tool in transforming chemical shifts into structural and dynamic information is chemical shift prediction. In our previous work we developed a method for 4D prediction of protein {sup 1}H chemical shifts in which molecular motions, the 4th dimension, were modeled using molecular dynamics (MD) simulations. Although the approach clearly improved the prediction, the X-ray structures and single NMR conformers used in the model cannot be considered fully realistic models of protein in solution. In this work, NMR ensembles (NMRE) were used to expand the conformational space of proteins (e.g. side chains, flexible loops, termini), followed by MD simulations for each conformer to map the local fluctuations. Compared with the non-dynamic model, the NMRE+MD model gave 6-17% lower root-mean-square (RMS) errors for different backbone nuclei. The improved prediction indicates that NMR ensembles with MD simulations can be used to obtain a more realistic picture of protein structures in solutions and moreover underlines the importance of short and long time-scale dynamics for the prediction. The RMS errors of the NMRE+MD model were 0.24, 0.43, 0.98, 1.03, 1.16 and 2.39 ppm for {sup 1}H{alpha}, {sup 1}HN, {sup 13}C{alpha}, {sup 13}C{beta}, {sup 13}CO and backbone {sup 15}N chemical shifts, respectively. The model is implemented in the prediction program 4DSPOT, available at http://www.uef.fi/4dspothttp://www.uef.fi/4dspot.

  14. Comparison of Predictable Smooth Ocular and Combined Eye-Head Tracking Behaviour in Patients with Lesions Affecting the Brainstem and Cerebellum

    Science.gov (United States)

    Grant, Michael P.; Leigh, R. John; Seidman, Scott H.; Riley, David E.; Hanna, Joseph P.

    1992-01-01

    We compared the ability of eight normal subjects and 15 patients with brainstem or cerebellar disease to follow a moving visual stimulus smoothly with either the eyes alone or with combined eye-head tracking. The visual stimulus was either a laser spot (horizontal and vertical planes) or a large rotating disc (torsional plane), which moved at one sinusoidal frequency for each subject. The visually enhanced Vestibulo-Ocular Reflex (VOR) was also measured in each plane. In the horizontal and vertical planes, we found that if tracking gain (gaze velocity/target velocity) for smooth pursuit was close to 1, the gain of combined eye-hand tracking was similar. If the tracking gain during smooth pursuit was less than about 0.7, combined eye-head tracking was usually superior. Most patients, irrespective of diagnosis, showed combined eye-head tracking that was superior to smooth pursuit; only two patients showed the converse. In the torsional plane, in which optokinetic responses were weak, combined eye-head tracking was much superior, and this was the case in both subjects and patients. We found that a linear model, in which an internal ocular tracking signal cancelled the VOR, could account for our findings in most normal subjects in the horizontal and vertical planes, but not in the torsional plane. The model failed to account for tracking behaviour in most patients in any plane, and suggested that the brain may use additional mechanisms to reduce the internal gain of the VOR during combined eye-head tracking. Our results confirm that certain patients who show impairment of smooth-pursuit eye movements preserve their ability to smoothly track a moving target with combined eye-head tracking.

  15. Combined Value of Red Blood Cell Distribution Width and Global Registry of Acute Coronary Events Risk Score for Predicting Cardiovascular Events in Patients with Acute Coronary Syndrome Undergoing Percutaneous Coronary Intervention.

    Directory of Open Access Journals (Sweden)

    Na Zhao

    Full Text Available Global Registry of Acute Coronary Events (GRACE risk score and red blood cell distribution width (RDW content can both independently predict major adverse cardiac events (MACEs in patients with acute coronary syndrome (ACS. We investigated the combined predictive value of RDW and GRACE risk score for cardiovascular events in patients with ACS undergoing percutaneous coronary intervention (PCI for the first time. We enrolled 480 ACS patients. During a median follow-up time of 37.2 months, 70 (14.58% patients experienced MACEs. Patients were divided into tertiles according to the baseline RDW content (11.30-12.90, 13.00-13.50, 13.60-16.40. GRACE score was positively correlated with RDW content. Multivariate Cox analysis showed that both GRACE score and RDW content were independent predictors of MACEs (hazard ratio 1.039; 95% confidence interval [CI] 1.024-1.055; p < 0.001; 1.699; 1.294-2.232; p < 0.001; respectively. Furthermore, Kaplan-Meier analysis demonstrated that the risk of MACEs increased with increasing RDW content (p < 0.001. For GRACE score alone, the area under the receiver operating characteristic (ROC curve for MACEs was 0.749 (95% CI: 0.707-0.787. The area under the ROC curve for MACEs increased to 0.805 (0.766-0.839, p = 0.034 after adding RDW content. The incremental predictive value of combining RDW content and GRACE risk score was significantly improved, also shown by the net reclassification improvement (NRI = 0.352, p < 0.001 and integrated discrimination improvement (IDI = 0.023, p = 0.002. Combining the predictive value of RDW and GRACE risk score yielded a more accurate predictive value for long-term cardiovascular events in ACS patients who underwent PCI as compared to each measure alone.

  16. The combined application of the Caco-2 cell bioassay coupled with in vivo (Gallus gallus) feeding trial represents an effective approach to predicting Fe bioavailability in humans

    Science.gov (United States)

    Research methods that predict Fe bioavailability for humans can be extremely useful in evaluating food fortification strategies, developing Fe-biofortified enhanced staple food crops and assessing the Fe bioavailability of meal plans that include such crops. In this review, research from four recent...

  17. Aberrant TP53 detected by combining immunohistochemistry and DNA-FISH improves Barrett's esophagus progression prediction: a prospective follow-up study

    NARCIS (Netherlands)

    Davelaar, Akueni L.; Calpe, Silvia; Lau, Liana; Timmer, Margriet R.; Visser, Mike; ten Kate, Fiebo J.; Parikh, Kaushal B.; Meijer, Sybren L.; Bergman, Jacques J.; Fockens, Paul; Krishnadath, Kausilia K.

    2015-01-01

    Barrett's esophagus (BE) goes through a sequence of low grade dysplasia (LGD) and high grade dysplasia (HGD) to esophageal adenocarcinoma (EAC). The current gold standard for BE outcome prediction, histopathological staging, can be unreliable. TP53 abnormalities may serve as prognostic biomarkers.

  18. Aberrant TP53 detected by combining immunohistochemistry and DNA-FISH improves Barrett's esophagus progression prediction : A prospective follow-up study

    NARCIS (Netherlands)

    Davelaar, Akueni L.; Calpe, Silvia; Lau, Liana; Timmer, Margriet R.; Visser, Mike; ten Kate, Fiebo J.; Parikh, Kaushal B.; Meijer, Sybren L.; Bergman, Jacques J.; Fockens, Paul; Krishnadath, Kausilia K.

    2015-01-01

    Barrett's esophagus (BE) goes through a sequence of low grade dysplasia (LGD) and high grade dysplasia (HGD) to esophageal adenocarcinoma (EAC). The current gold standard for BE outcome prediction, histopathological staging, can be unreliable. TP53 abnormalities may serve as prognostic biomarkers.

  19. Human glycemic response curves after intake of carbohydrate foods are accurately predicted by combining in vitro gastrointestinal digestion with in silico kinetic modeling

    Directory of Open Access Journals (Sweden)

    Susann Bellmann

    2018-02-01

    Conclusion: Based on the demonstrated accuracy and predictive quality, this in vitro–in silico technology can be used for the testing of food products on their glycemic response under standardized conditions and may stimulate the production of (slow carbs for the prevention of metabolic diseases.

  20. Baseline prediction of combination therapy outcome in hepatitis C virus 1b infected patients by discriminant analysis using viral and host factors.

    Science.gov (United States)

    Saludes, Verónica; Bracho, Maria Alma; Valero, Oliver; Ardèvol, Mercè; Planas, Ramón; González-Candelas, Fernando; Ausina, Vicente; Martró, Elisa

    2010-11-30

    Current treatment of chronic hepatitis C virus (HCV) infection has limited efficacy -especially among genotype 1 infected patients-, is costly, and involves severe side effects. Thus, predicting non-response is of major interest for both patient wellbeing and health care expense. At present, treatment cannot be individualized on the basis of any baseline predictor of response. We aimed to identify pre-treatment clinical and virological parameters associated with treatment failure, as well as to assess whether therapy outcome could be predicted at baseline. Forty-three HCV subtype 1b (HCV-1b) chronically infected patients treated with pegylated-interferon alpha plus ribavirin were retrospectively studied (21 responders and 22 non-responders). Host (gender, age, weight, transaminase levels, fibrosis stage, and source of infection) and viral-related factors (viral load, and genetic variability in the E1-E2 and Core regions) were assessed. Logistic regression and discriminant analyses were used to develop predictive models. A "leave-one-out" cross-validation method was used to assess the reliability of the discriminant models. Lower alanine transaminase levels (ALT, p=0.009), a higher number of quasispecies variants in the E1-E2 region (number of haplotypes, nHap_E1-E2) (p=0.003), and the absence of both amino acid arginine at position 70 and leucine at position 91 in the Core region (p=0.039) were significantly associated with treatment failure. Therapy outcome was most accurately predicted by discriminant analysis (90.5% sensitivity and 95.5% specificity, 85.7% sensitivity and 81.8% specificity after cross-validation); the most significant variables included in the predictive model were the Core amino acid pattern, the nHap_E1-E2, and gamma-glutamyl transferase and ALT levels. Discriminant analysis has been shown as a useful tool to predict treatment outcome using baseline HCV genetic variability and host characteristics. The discriminant models obtained in this

  1. Combined effect of pulse density and grid cell size on predicting and mapping aboveground carbon in fast-growing Eucalyptus forest plantation using airborne LiDAR data.

    Science.gov (United States)

    Silva, Carlos Alberto; Hudak, Andrew Thomas; Klauberg, Carine; Vierling, Lee Alexandre; Gonzalez-Benecke, Carlos; de Padua Chaves Carvalho, Samuel; Rodriguez, Luiz Carlos Estraviz; Cardil, Adrián

    2017-12-01

    LiDAR remote sensing is a rapidly evolving technology for quantifying a variety of forest attributes, including aboveground carbon (AGC). Pulse density influences the acquisition cost of LiDAR, and grid cell size influences AGC prediction using plot-based methods; however, little work has evaluated the effects of LiDAR pulse density and cell size for predicting and mapping AGC in fast-growing Eucalyptus forest plantations. The aim of this study was to evaluate the effect of LiDAR pulse density and grid cell size on AGC prediction accuracy at plot and stand-levels using airborne LiDAR and field data. We used the Random Forest (RF) machine learning algorithm to model AGC using LiDAR-derived metrics from LiDAR collections of 5 and 10 pulses m -2 (RF5 and RF10) and grid cell sizes of 5, 10, 15 and 20 m. The results show that LiDAR pulse density of 5 pulses m -2 provides metrics with similar prediction accuracy for AGC as when using a dataset with 10 pulses m -2 in these fast-growing plantations. Relative root mean square errors (RMSEs) for the RF5 and RF10 were 6.14 and 6.01%, respectively. Equivalence tests showed that the predicted AGC from the training and validation models were equivalent to the observed AGC measurements. The grid cell sizes for mapping ranging from 5 to 20 also did not significantly affect the prediction accuracy of AGC at stand level in this system. LiDAR measurements can be used to predict and map AGC across variable-age Eucalyptus plantations with adequate levels of precision and accuracy using 5 pulses m -2 and a grid cell size of 5 m. The promising results for AGC modeling in this study will allow for greater confidence in comparing AGC estimates with varying LiDAR sampling densities for Eucalyptus plantations and assist in decision making towards more cost effective and efficient forest inventory.

  2. A RSM-based predictive model to characterize heat treating parameters of D2 steel using combined Barkhausen noise and hysteresis loop methods

    International Nuclear Information System (INIS)

    Kahrobaee, Saeed; Hejazi, Taha-Hossein

    2017-01-01

    Highlights: • A statistical relationship between NDE inputs and heat treating outputs was provided. • Predicting austenitizing/tempering temperatures at unknown heat treating conditions. • An optimization model that achieves minimum error in prediction was developed. • Applying two simultaneous magnetic NDE methods led to better measuring reliability. - Abstract: Austenitizing and tempering temperatures are the effective characteristics in heat treating process of AISI D2 tool steel. Therefore, controlling them enables the heat treatment process to be designed more accurately which results in more balanced mechanical properties. The aim of this work is to develop a multiresponse predictive model that enables finding these characteristics based on nondestructive tests by a set of parameters of the magnetic Barkhausen noise technique and hysteresis loop method. To produce various microstructural changes, identical specimens from the AISI D2 steel sheet were austenitized in the range 1025–1130 °C, for 30 min, oil-quenched and finally tempered at various temperatures between 200 °C and 650 °C. A set of nondestructive data have been gathered based on general factorial design of experiments and used for training and testing the multiple response surface model. Finally, an optimization model has been proposed to achieve minimal error prediction. Results revealed that applying Barkhausen and hysteresis loop methods, simultaneously, coupling to the multiresponse model, has a potential to be used as a reliable and accurate nondestructive tool for predicting austenitizing and tempering temperatures (which, in turn, led to characterizing the microstructural changes) of the parts with unknown heat treating conditions.

  3. A RSM-based predictive model to characterize heat treating parameters of D2 steel using combined Barkhausen noise and hysteresis loop methods

    Energy Technology Data Exchange (ETDEWEB)

    Kahrobaee, Saeed, E-mail: kahrobaee@sadjad.ac.ir [Department of Mechanical and Materials Engineering, Sadjad University of Technology, P.O. Box 91881-48848, Mashhad (Iran, Islamic Republic of); Hejazi, Taha-Hossein [Department of Industrial Engineering and Management, Sadjad University of Technology, P.O. Box 91881-48848, Mashhad (Iran, Islamic Republic of)

    2017-07-01

    Highlights: • A statistical relationship between NDE inputs and heat treating outputs was provided. • Predicting austenitizing/tempering temperatures at unknown heat treating conditions. • An optimization model that achieves minimum error in prediction was developed. • Applying two simultaneous magnetic NDE methods led to better measuring reliability. - Abstract: Austenitizing and tempering temperatures are the effective characteristics in heat treating process of AISI D2 tool steel. Therefore, controlling them enables the heat treatment process to be designed more accurately which results in more balanced mechanical properties. The aim of this work is to develop a multiresponse predictive model that enables finding these characteristics based on nondestructive tests by a set of parameters of the magnetic Barkhausen noise technique and hysteresis loop method. To produce various microstructural changes, identical specimens from the AISI D2 steel sheet were austenitized in the range 1025–1130 °C, for 30 min, oil-quenched and finally tempered at various temperatures between 200 °C and 650 °C. A set of nondestructive data have been gathered based on general factorial design of experiments and used for training and testing the multiple response surface model. Finally, an optimization model has been proposed to achieve minimal error prediction. Results revealed that applying Barkhausen and hysteresis loop methods, simultaneously, coupling to the multiresponse model, has a potential to be used as a reliable and accurate nondestructive tool for predicting austenitizing and tempering temperatures (which, in turn, led to characterizing the microstructural changes) of the parts with unknown heat treating conditions.

  4. Prediction of severe pre-eclampsia by a combination of sFlt-1, CT-pro ET1 and blood pressure: exploratory study

    DEFF Research Database (Denmark)

    Malte, Anne Lind; Uldbjerg, Niels; Wright, David

    2018-01-01

    gestational age 35(+6) weeks) attending the obstetrical outpatient clinic, and 99 with normal pregnancies. RESULTS: In a group of patients presenting with mild to moderate symptoms of preeclampsia, a combination of C-terminal Pro Endothelin 1 (CT-Pro-ET1), sFlt-1 and systolic blood pressure reached an Area...

  5. Experiences with an outpatient relapse program (CRA) combined with naltrexone in the treatment of opioid dependence: effect on addictive behaviours and the predictive value of psychiatric comorbidity.

    NARCIS (Netherlands)

    Roozen, H.G.; Kerkhof, A.J.F.M.; van den Brink, W.

    2003-01-01

    Background: There is increasing interest in naltrexone, an opiate antagonist, in the treatment of opiate addicts. The effects of naltrexone are often compromised by a lack of compliance and drop-out. The effects of this compound are probably more favorable when combined with a psychosocial

  6. Evaluating the applicability of four recent satellite–gauge combined precipitation estimates for extreme precipitation and streamflow predictions over the upper Yellow river basin in China

    Science.gov (United States)

    This study aimed to statistically and hydrologically assess the performance of four latest and widely used satellite–gauge combined precipitation estimates (SGPEs), namely CRT, BLD, 3B42CDR, and 3B42 for the extreme precipitation and stream'ow scenarios over the upper Yellow river basin (UYRB) in ch...

  7. Early diffusion weighted magnetic resonance imaging can predict survival in women with locally advanced cancer of the cervix treated with combined chemo-radiation

    International Nuclear Information System (INIS)

    Somoye, Gbolahan; Parkin, David; Harry, Vanessa; Semple, Scott; Plataniotis, George; Scott, Neil; Gilbert, Fiona J.

    2012-01-01

    To assess the predictive value of diffusion weighted imaging (DWI) for survival in women treated for advanced cancer of the cervix with concurrent chemo-radiotherapy. Twenty women treated for advanced cancer of the cervix were recruited and followed up for a median of 26 (range -3 /mm 2 /s), respectively, P = 0.02. The median change in ADC 14 days after treatment commencement was significantly higher in the alive group compared to non-survivors, 0.28 and 0.14 (x 10 -3 /mm 2 /s), respectively, P = 0.02. There was no evidence of a difference between survivors and non-survivors for pretreatment baseline or post-therapy ADC values. Functional DWI early in the treatment of advanced cancer of the cervix may provide useful information in predicting survival. (orig.)

  8. Utility of combinations of biomarkers, cognitive markers, and risk factors to predict conversion from mild cognitive impairment to Alzheimer disease in patients in the Alzheimer's disease neuroimaging initiative.

    Science.gov (United States)

    Gomar, Jesus J; Bobes-Bascaran, Maria T; Conejero-Goldberg, Concepcion; Davies, Peter; Goldberg, Terry E

    2011-09-01

    Biomarkers have become increasingly important in understanding neurodegenerative processes associated with Alzheimer disease. Markers include regional brain volumes, cerebrospinal fluid measures of pathological Aβ1-42 and total tau, cognitive measures, and individual risk factors. To determine the discriminative utility of different classes of biomarkers and cognitive markers by examining their ability to predict a change in diagnostic status from mild cognitive impairment to Alzheimer disease. Longitudinal study. We analyzed the Alzheimer's Disease Neuroimaging Initiative database to study patients with mild cognitive impairment who converted to Alzheimer disease (n = 116) and those who did not convert (n = 204) within a 2-year period. We determined the predictive utility of 25 variables from all classes of markers, biomarkers, and risk factors in a series of logistic regression models and effect size analyses. The Alzheimer's Disease Neuroimaging Initiative public database. Primary outcome measures were odds ratios, pseudo- R(2)s, and effect sizes. In comprehensive stepwise logistic regression models that thus included variables from all classes of markers, the following baseline variables predicted conversion within a 2-year period: 2 measures of delayed verbal memory and middle temporal lobe cortical thickness. In an effect size analysis that examined rates of decline, change scores for biomarkers were modest for 2 years, but a change in an everyday functional activities measure (Functional Assessment Questionnaire) was considerably larger. Decline in scores on the Functional Assessment Questionnaire and Trail Making Test, part B, accounted for approximately 50% of the predictive variance in conversion from mild cognitive impairment to Alzheimer disease. Cognitive markers at baseline were more robust predictors of conversion than most biomarkers. Longitudinal analyses suggested that conversion appeared to be driven less by changes in the neurobiologic

  9. PCVMZM: Using the Probabilistic Classification Vector Machines Model Combined with a Zernike Moments Descriptor to Predict Protein-Protein Interactions from Protein Sequences.

    Science.gov (United States)

    Wang, Yanbin; You, Zhuhong; Li, Xiao; Chen, Xing; Jiang, Tonghai; Zhang, Jingting

    2017-05-11

    Protein-protein interactions (PPIs) are essential for most living organisms' process. Thus, detecting PPIs is extremely important to understand the molecular mechanisms of biological systems. Although many PPIs data have been generated by high-throughput technologies for a variety of organisms, the whole interatom is still far from complete. In addition, the high-throughput technologies for detecting PPIs has some unavoidable defects, including time consumption, high cost, and high error rate. In recent years, with the development of machine learning, computational methods have been broadly used to predict PPIs, and can achieve good prediction rate. In this paper, we present here PCVMZM, a computational method based on a Probabilistic Classification Vector Machines (PCVM) model and Zernike moments (ZM) descriptor for predicting the PPIs from protein amino acids sequences. Specifically, a Zernike moments (ZM) descriptor is used to extract protein evolutionary information from Position-Specific Scoring Matrix (PSSM) generated by Position-Specific Iterated Basic Local Alignment Search Tool (PSI-BLAST). Then, PCVM classifier is used to infer the interactions among protein. When performed on PPIs datasets of Yeast and H. Pylori , the proposed method can achieve the average prediction accuracy of 94.48% and 91.25%, respectively. In order to further evaluate the performance of the proposed method, the state-of-the-art support vector machines (SVM) classifier is used and compares with the PCVM model. Experimental results on the Yeast dataset show that the performance of PCVM classifier is better than that of SVM classifier. The experimental results indicate that our proposed method is robust, powerful and feasible, which can be used as a helpful tool for proteomics research.

  10. A RSM-based predictive model to characterize heat treating parameters of D2 steel using combined Barkhausen noise and hysteresis loop methods

    Science.gov (United States)

    Kahrobaee, Saeed; Hejazi, Taha-Hossein

    2017-07-01

    Austenitizing and tempering temperatures are the effective characteristics in heat treating process of AISI D2 tool steel. Therefore, controlling them enables the heat treatment process to be designed more accurately which results in more balanced mechanical properties. The aim of this work is to develop a multiresponse predictive model that enables finding these characteristics based on nondestructive tests by a set of parameters of the magnetic Barkhausen noise technique and hysteresis loop method. To produce various microstructural changes, identical specimens from the AISI D2 steel sheet were austenitized in the range 1025-1130 °C, for 30 min, oil-quenched and finally tempered at various temperatures between 200 °C and 650 °C. A set of nondestructive data have been gathered based on general factorial design of experiments and used for training and testing the multiple response surface model. Finally, an optimization model has been proposed to achieve minimal error prediction. Results revealed that applying Barkhausen and hysteresis loop methods, simultaneously, coupling to the multiresponse model, has a potential to be used as a reliable and accurate nondestructive tool for predicting austenitizing and tempering temperatures (which, in turn, led to characterizing the microstructural changes) of the parts with unknown heat treating conditions.

  11. FUNCTIONAL SUBCLONE PROFILING FOR PREDICTION OF TREATMENT-INDUCED INTRA-TUMOR POPULATION SHIFTS AND DISCOVERY OF RATIONAL DRUG COMBINATIONS IN HUMAN GLIOBLASTOMA

    Science.gov (United States)

    Reinartz, Roman; Wang, Shanshan; Kebir, Sied; Silver, Daniel J.; Wieland, Anja; Zheng, Tong; Küpper, Marius; Rauschenbach, Laurèl; Fimmers, Rolf; Shepherd, Timothy M.; Trageser, Daniel; Till, Andreas; Schäfer, Niklas; Glas, Martin; Hillmer, Axel M.; Cichon, Sven; Smith, Amy A.; Pietsch, Torsten; Liu, Ying; Reynolds, Brent A.; Yachnis, Anthony; Pincus, David W.; Simon, Matthias; Brüstle, Oliver; Steindler, Dennis A.; Scheffler, Björn

    2016-01-01

    Purpose Investigation of clonal heterogeneity may be key to understanding mechanisms of therapeutic failure in human cancer. However, little is known on the consequences of therapeutic intervention on the clonal composition of solid tumors. Experimental Design Here, we used 33 single cell-derived subclones generated from five clinical glioblastoma specimens for exploring intra- and inter-individual spectra of drug resistance profiles in vitro. In a personalized setting, we explored whether differences in pharmacological sensitivity among subclones could be employed to predict drug-dependent changes to the clonal composition of tumors. Results Subclones from individual tumors exhibited a remarkable heterogeneity of drug resistance to a library of potential anti-glioblastoma compounds. A more comprehensive intra-tumoral analysis revealed that stable genetic and phenotypic characteristics of co-existing subclones could be correlated with distinct drug sensitivity profiles. The data obtained from differential drug response analysis could be employed to predict clonal population shifts within the naïve parental tumor in vitro and in orthotopic xenografts. Furthermore, the value of pharmacological profiles could be shown for establishing rational strategies for individualized secondary lines of treatment. Conclusions Our data provide a previously unrecognized strategy for revealing functional consequences of intra-tumor heterogeneity by enabling predictive modeling of treatment-related subclone dynamics in human glioblastoma. PMID:27521447

  12. Lactate dehydrogenase predicts combined progression-free survival after sequential therapy with abiraterone and enzalutamide for patients with castration-resistant prostate cancer.

    Science.gov (United States)

    Mori, Keiichiro; Kimura, Takahiro; Onuma, Hajime; Kimura, Shoji; Yamamoto, Toshihiro; Sasaki, Hiroshi; Miki, Jun; Miki, Kenta; Egawa, Shin

    2017-07-01

    An array of clinical issues remains to be resolved for castration-resistant prostate cancer (CRPC), including the sequence of drug use and drug cross-resistance. At present, no clear guidelines are available for the optimal sequence of use of novel agents like androgen-receptor axis-targeted (ARAT) agents, particularly enzalutamide, and abiraterone. This study retrospectively analyzed a total of 69 patients with CRPC treated with sequential therapy using enzalutamide followed by abiraterone or vice versa. The primary outcome measure was the comparative combined progression-free survival (PFS) comprising symptomatic and/or radiographic PFS. Patients were also compared for total prostate-specific antigen (PSA)-PFS, overall survival (OS), and PSA response. The predictors of combined PFS and OS were analyzed with a backward-stepwise multivariate Cox model. Of the 69 patients, 46 received enzalutamide first, followed by abiraterone (E-A group), and 23 received abiraterone, followed by enzalutamide (A-E group). The two groups were not significantly different with regard to basic data, except for hemoglobin values. In a comparison with the E-A group, the A-E group was shown to be associated with better combined PFS in Kaplan-Meier analysis (P = 0.043). Similar results were obtained for total PSA-PFS (P = 0.049), while OS did not differ between groups (P = 0.62). Multivariate analysis demonstrated that pretreatment lactate dehydrogenase (LDH) values and age were significant predictors of longer combined PFS (P < 0.05). Likewise, multivariate analysis demonstrated that pretreatment hemoglobin values and performance status were significant predictors of longer OS (P < 0.05). The results of this study suggested the A-E sequence had longer combined PSA and total PSA-PFS compared to the E-A sequence in patients with CRPC. LDH values in sequential therapy may serve as a predictor of longer combined PFS. © 2017 Wiley Periodicals, Inc.

  13. Improving predictions of protein-protein interfaces by combining amino acid-specific classifiers based on structural and physicochemical descriptors with their weighted neighbor averages.

    Directory of Open Access Journals (Sweden)

    Fábio R de Moraes

    Full Text Available Protein-protein interactions are involved in nearly all regulatory processes in the cell and are considered one of the most important issues in molecular biology and pharmaceutical sciences but are still not fully understood. Structural and computational biology contributed greatly to the elucidation of the mechanism of protein interactions. In this paper, we present a collection of the physicochemical and structural characteristics that distinguish interface-forming residues (IFR from free surface residues (FSR. We formulated a linear discriminative analysis (LDA classifier to assess whether chosen descriptors from the BlueStar STING database (http://www.cbi.cnptia.embrapa.br/SMS/ are suitable for such a task. Receiver operating characteristic (ROC analysis indicates that the particular physicochemical and structural descriptors used for building the linear classifier perform much better than a random classifier and in fact, successfully outperform some of the previously published procedures, whose performance indicators were recently compared by other research groups. The results presented here show that the selected set of descriptors can be utilized to predict IFRs, even when homologue proteins are missing (particularly important for orphan proteins where no homologue is available for comparative analysis/indication or, when certain conformational changes accompany interface formation. The development of amino acid type specific classifiers is shown to increase IFR classification performance. Also, we found that the addition of an amino acid conservation attribute did not improve the classification prediction. This result indicates that the increase in predictive power associated with amino acid conservation is exhausted by adequate use of an extensive list of independent physicochemical and structural parameters that, by themselves, fully describe the nano-environment at protein-protein interfaces. The IFR classifier developed in this study

  14. Combining Microbial Enzyme Kinetics Models with Light Use Efficiency Models to Predict CO2 and CH4 Ecosystem Exchange from Flooded and Drained Peatland Systems

    Science.gov (United States)

    Oikawa, P. Y.; Jenerette, D.; Knox, S. H.; Sturtevant, C. S.; Verfaillie, J. G.; Baldocchi, D. D.

    2014-12-01

    Under California's Cap-and-Trade program, companies are looking to invest in land-use practices that will reduce greenhouse gas (GHG) emissions. The Sacramento-San Joaquin River Delta is a drained cultivated peatland system and a large source of CO2. To slow soil subsidence and reduce CO2 emissions, there is growing interest in converting drained peatlands to wetlands. However, wetlands are large sources of CH4 that could offset CO2-based GHG reductions. The goal of our research is to provide accurate measurements and model predictions of the changes in GHG budgets that occur when drained peatlands are restored to wetland conditions. We have installed a network of eddy covariance towers across multiple land use types in the Delta and have been measuring CO2 and CH4 ecosystem exchange for multiple years. In order to upscale these measurements through space and time we are using these data to parameterize and validate a process-based biogeochemical model. To predict gross primary productivity (GPP), we are using a simple light use efficiency (LUE) model which requires estimates of light, leaf area index and air temperature and can explain 90% of the observed variation in GPP in a mature wetland. To predict ecosystem respiration we have adapted the Dual Arrhenius Michaelis-Menten (DAMM) model. The LUE-DAMM model allows accurate simulation of half-hourly net ecosystem exchange (NEE) in a mature wetland (r2=0.85). We are working to expand the model to pasture, rice and alfalfa systems in the Delta. To predict methanogenesis, we again apply a modified DAMM model, using simple enzyme kinetics. However CH4 exchange is complex and we have thus expanded the model to predict not only microbial CH4 production, but also CH4 oxidation, CH4 storage and the physical processes regulating the release of CH4 to the atmosphere. The CH4-DAMM model allows accurate simulation of daily CH4 ecosystem exchange in a mature wetland (r2=0.55) and robust estimates of annual CH4 budgets. The LUE

  15. Improving predictions of protein-protein interfaces by combining amino acid-specific classifiers based on structural and physicochemical descriptors with their weighted neighbor averages.

    Science.gov (United States)

    de Moraes, Fábio R; Neshich, Izabella A P; Mazoni, Ivan; Yano, Inácio H; Pereira, José G C; Salim, José A; Jardine, José G; Neshich, Goran

    2014-01-01

    Protein-protein interactions are involved in nearly all regulatory processes in the cell and are considered one of the most important issues in molecular biology and pharmaceutical sciences but are still not fully understood. Structural and computational biology contributed greatly to the elucidation of the mechanism of protein interactions. In this paper, we present a collection of the physicochemical and structural characteristics that distinguish interface-forming residues (IFR) from free surface residues (FSR). We formulated a linear discriminative analysis (LDA) classifier to assess whether chosen descriptors from the BlueStar STING database (http://www.cbi.cnptia.embrapa.br/SMS/) are suitable for such a task. Receiver operating characteristic (ROC) analysis indicates that the particular physicochemical and structural descriptors used for building the linear classifier perform much better than a random classifier and in fact, successfully outperform some of the previously published procedures, whose performance indicators were recently compared by other research groups. The results presented here show that the selected set of descriptors can be utilized to predict IFRs, even when homologue proteins are missing (particularly important for orphan proteins where no homologue is available for comparative analysis/indication) or, when certain conformational changes accompany interface formation. The development of amino acid type specific classifiers is shown to increase IFR classification performance. Also, we found that the addition of an amino acid conservation attribute did not improve the classification prediction. This result indicates that the increase in predictive power associated with amino acid conservation is exhausted by adequate use of an extensive list of independent physicochemical and structural parameters that, by themselves, fully describe the nano-environment at protein-protein interfaces. The IFR classifier developed in this study is now

  16. Improving Predictions of Protein-Protein Interfaces by Combining Amino Acid-Specific Classifiers Based on Structural and Physicochemical Descriptors with Their Weighted Neighbor Averages

    Science.gov (United States)

    de Moraes, Fábio R.; Neshich, Izabella A. P.; Mazoni, Ivan; Yano, Inácio H.; Pereira, José G. C.; Salim, José A.; Jardine, José G.; Neshich, Goran

    2014-01-01

    Protein-protein interactions are involved in nearly all regulatory processes in the cell and are considered one of the most important issues in molecular biology and pharmaceutical sciences but are still not fully understood. Structural and computational biology contributed greatly to the elucidation of the mechanism of protein interactions. In this paper, we present a collection of the physicochemical and structural characteristics that distinguish interface-forming residues (IFR) from free surface residues (FSR). We formulated a linear discriminative analysis (LDA) classifier to assess whether chosen descriptors from the BlueStar STING database (http://www.cbi.cnptia.embrapa.br/SMS/) are suitable for such a task. Receiver operating characteristic (ROC) analysis indicates that the particular physicochemical and structural descriptors used for building the linear classifier perform much better than a random classifier and in fact, successfully outperform some of the previously published procedures, whose performance indicators were recently compared by other research groups. The results presented here show that the selected set of descriptors can be utilized to predict IFRs, even when homologue proteins are missing (particularly important for orphan proteins where no homologue is available for comparative analysis/indication) or, when certain conformational changes accompany interface formation. The development of amino acid type specific classifiers is shown to increase IFR classification performance. Also, we found that the addition of an amino acid conservation attribute did not improve the classification prediction. This result indicates that the increase in predictive power associated with amino acid conservation is exhausted by adequate use of an extensive list of independent physicochemical and structural parameters that, by themselves, fully describe the nano-environment at protein-protein interfaces. The IFR classifier developed in this study is now

  17. HER-2, p53, p21 and hormonal receptors proteins expression as predictive factors of response and prognosis in locally advanced breast cancer treated with neoadjuvant docetaxel plus epirubicin combination

    International Nuclear Information System (INIS)

    Tiezzi, Daniel G; Andrade, Jurandyr M; Ribeiro-Silva, Alfredo; Zola, Fábio E; Marana, Heitor RC; Tiezzi, Marcelo G

    2007-01-01

    Neoadjuvant chemotherapy has been considered the standard care in locally advanced breast cancer. However, about 20% of the patients do not benefit from this clinical treatment and, predictive factors of response were not defined yet. This study was designed to evaluate the importance of biological markers to predict response and prognosis in stage II and III breast cancer patients treated with taxane and anthracycline combination as neoadjuvant setting. Sixty patients received preoperative docetaxel (75 mg/m 2 ) in combination with epirubicin (50 mg/m 2 ) in i.v. infusion in D1 every 3 weeks after incisional biopsy. They received adjuvant chemotherapy with CMF or FEC, attaining axillary status following definitive breast surgery. Clinical and pathologic response rates were measured after preoperative therapy. We evaluated the response rate to neoadjuvant chemotherapy and the prognostic significance of clinicopathological and immunohistochemical parameters (ER, PR, p51, p21 and HER-2 protein expression). The median patient age was 50.5 years with a median follow up time 48 months after the time of diagnosis. Preoperative treatment achieved clinical response in 76.6% of patients and complete pathologic response in 5%. The clinical, pathological and immunohistochemical parameters were not able to predict response to therapy and, only HER2 protein overexpression was associated with a decrease in disease free and overall survival (P = 0.0007 and P = 0.003) as shown by multivariate analysis. Immunohistochemical phenotypes were not able to predict response to neoadjuvant chemotherapy. Clinical response is inversely correlated with a risk of death in patients submitted to neoadjuvant chemotherapy and HER2 overexpression is the major prognostic factor in stage II and III breast cancer patients treated with a neoadjuvant docetaxel and epirubicin combination

  18. An online study combining the constructs from the theory of planned behaviour and protection motivation theory in predicting intention to test for chlamydia in two testing contexts.

    Science.gov (United States)

    Powell, Rachael; Pattison, Helen M; Francis, Jill J

    2016-01-01

    Chlamydia is a common sexually transmitted infection that has potentially serious consequences unless detected and treated early. The health service in the UK offers clinic-based testing for chlamydia but uptake is low. Identifying the predictors of testing behaviours may inform interventions to increase uptake. Self-tests for chlamydia may facilitate testing and treatment in people who avoid clinic-based testing. Self-testing and being tested by a health care professional (HCP) involve two contrasting contexts that may influence testing behaviour. However, little is known about how predictors of behaviour differ as a function of context. In this study, theoretical models of behaviour were used to assess factors that may predict intention to test in two different contexts: self-testing and being tested by a HCP. Individuals searching for or reading about chlamydia testing online were recruited using Google Adwords. Participants completed an online questionnaire that addressed previous testing behaviour and measured constructs of the Theory of Planned Behaviour and Protection Motivation Theory, which propose a total of eight possible predictors of intention. The questionnaire was completed by 310 participants. Sufficient data for multiple regression were provided by 102 and 118 respondents for self-testing and testing by a HCP respectively. Intention to self-test was predicted by vulnerability and self-efficacy, with a trend-level effect for response efficacy. Intention to be tested by a HCP was predicted by vulnerability, attitude and subjective norm. Thus, intentions to carry out two testing behaviours with very similar goals can have different predictors depending on test context. We conclude that interventions to increase self-testing should be based on evidence specifically related to test context.

  19. The value of the combination of hemoglobin, albumin, lymphocyte and platelet in predicting platinum-based chemoradiotherapy response in male patients with esophageal squamous cell carcinoma.

    Science.gov (United States)

    Cong, Lihong; Hu, Likuan

    2017-05-01

    The predictive value of HALP in esophageal cancer is currently unclear. We aimed to evaluate the value of HALP in predicting platinum-based definitive chemoradiotherapy response in male patients with esophageal squamous cell carcinoma. Data from all newly diagnosed patients with esophageal squamous cell carcinoma (ESCC) were collected from January 1, 2010 to December 31, 2014 in Qilu Hospital. The treatment protocol was definitive chemoradiotherapy consisting of docetaxel plus cisplatin or carboplatin. The response assessment of the definitive chemoradiotherapy was based on computed tomography (CT) and barium meal test results. A total of 39 patients were included in the present study. The median value of HALP was 48.34. The chemoradiotherapy response rate of patients in the low HALP value group was 35%, compared with 78.95% of patients in the high HALP group (P=0.010). Additionally, the median progression-free survival in the 2 patient groups was significantly different (10.7 vs. 24.7m, P=0.041). In the multivariate analysis, patients with HALP higher than 48.34 had longer progression-free survival than patients with HALP of 48.34 or less (HR 2.745; 95% CI, 1.176-6.408; P=0.020). However, there was no significant difference for overall survival between the high HALP group and low HALP group. Our data suggested that pretreatment HALP could predict the platinum-based chemoradiotherapy response of tumors and progression free survival in male patients with ESCC. Therefore, HALP could be used in routine clinical practice to guide the therapeutic strategies for individual treatment in patients with ESCC. Copyright © 2017 Elsevier B.V. All rights reserved.

  20. Combination of prostate specific antigen and pathological stage regarding to gleason score to predict bone metastasis of newly diagnosed prostate cancer

    International Nuclear Information System (INIS)

    Wang Zhen; Zhou Liquan; Gao Jiangping; Shi Lixin; Zhao Xiaoyi; Hong Baofa

    2004-01-01

    To determine the value of tumor grade and serum prostate-specific antigen in predicting skeletal metastases in untreated prostate cancer, the results of bone scans were related retrospectively to levels of serum PSA and tumor Grade based on pathologyical examination in 202 patients with prostate cancer newly diagnosed. Skeletal metastases were present in 7% of patients with serum PSA 100 μg/L. Bone scans are omitted likely in a man newly diagnosed with prostate cancer who has no suggestive clinical features, a serum PSA 100 μg/L. (authors)

  1. Predicting tumor hypoxia in non-small cell lung cancer by combining CT, FDG PET and dynamic contrast-enhanced CT.

    Science.gov (United States)

    Even, Aniek J G; Reymen, Bart; La Fontaine, Matthew D; Das, Marco; Jochems, Arthur; Mottaghy, Felix M; Belderbos, José S A; De Ruysscher, Dirk; Lambin, Philippe; van Elmpt, Wouter

    2017-11-01

    Most solid tumors contain inadequately oxygenated (i.e., hypoxic) regions, which tend to be more aggressive and treatment resistant. Hypoxia PET allows visualization of hypoxia and may enable treatment adaptation. However, hypoxia PET imaging is expensive, time-consuming and not widely available. We aimed to predict hypoxia levels in non-small cell lung cancer (NSCLC) using more easily available imaging modalities: FDG-PET/CT and dynamic contrast-enhanced CT (DCE-CT). For 34 NSCLC patients, included in two clinical trials, hypoxia HX4-PET/CT, planning FDG-PET/CT and DCE-CT scans were acquired before radiotherapy. Scans were non-rigidly registered to the planning CT. Tumor blood flow (BF) and blood volume (BV) were calculated by kinetic analysis of DCE-CT images. Within the gross tumor volume, independent clusters, i.e., supervoxels, were created based on FDG-PET/CT. For each supervoxel, tumor-to-background ratios (TBR) were calculated (median SUV/aorta SUV mean ) for HX4-PET/CT and supervoxel features (median, SD, entropy) for the other modalities. Two random forest models (cross-validated: 10 folds, five repeats) were trained to predict the hypoxia TBR; one based on CT, FDG, BF and BV, and one with only CT and FDG features. Patients were split in a training (trial NCT01024829) and independent test set (trial NCT01210378). For each patient, predicted, and observed hypoxic volumes (HV) (TBR > 1.2) were compared. Fifteen patients (3291 supervoxels) were used for training and 19 patients (1502 supervoxels) for testing. The model with all features (RMSE training: 0.19 ± 0.01, test: 0.27) outperformed the model with only CT and FDG-PET features (RMSE training: 0.20 ± 0.01, test: 0.29). All tumors of the test set were correctly classified as normoxic or hypoxic (HV > 1 cm 3 ) by the best performing model. We created a data-driven methodology to predict hypoxia levels and hypoxia spatial patterns using CT, FDG-PET and DCE-CT features in NSCLC. The

  2. Combined Value of Red Blood Cell Distribution Width and Global Registry of Acute Coronary Events Risk Score for Predicting Cardiovascular Events in Patients with Acute Coronary Syndrome Undergoing Percutaneous Coronary Intervention.

    Science.gov (United States)

    Zhao, Na; Mi, Lan; Liu, Xiaojun; Pan, Shuo; Xu, Jiaojiao; Xia, Dongyu; Liu, Zhongwei; Zhang, Yong; Xiang, Yu; Yuan, Zuyi; Guan, Gongchang; Wang, Junkui

    2015-01-01

    Global Registry of Acute Coronary Events (GRACE) risk score and red blood cell distribution width (RDW) content can both independently predict major adverse cardiac events (MACEs) in patients with acute coronary syndrome (ACS). We investigated the combined predictive value of RDW and GRACE risk score for cardiovascular events in patients with ACS undergoing percutaneous coronary intervention (PCI) for the first time. We enrolled 480 ACS patients. During a median follow-up time of 37.2 months, 70 (14.58%) patients experienced MACEs. Patients were divided into tertiles according to the baseline RDW content (11.30-12.90, 13.00-13.50, 13.60-16.40). GRACE score was positively correlated with RDW content. Multivariate Cox analysis showed that both GRACE score and RDW content were independent predictors of MACEs (hazard ratio 1.039; 95% confidence interval [CI] 1.024-1.055; p risk of MACEs increased with increasing RDW content (p value of combining RDW content and GRACE risk score was significantly improved, also shown by the net reclassification improvement (NRI = 0.352, p value of RDW and GRACE risk score yielded a more accurate predictive value for long-term cardiovascular events in ACS patients who underwent PCI as compared to each measure alone.

  3. The Combined Application of the Caco-2 Cell Bioassay Coupled with In Vivo (Gallus gallus Feeding Trial Represents an Effective Approach to Predicting Fe Bioavailability in Humans

    Directory of Open Access Journals (Sweden)

    Elad Tako

    2016-11-01

    Full Text Available Research methods that predict Fe bioavailability for humans can be extremely useful in evaluating food fortification strategies, developing Fe-biofortified enhanced staple food crops and assessing the Fe bioavailability of meal plans that include such crops. In this review, research from four recent poultry (Gallus gallus feeding trials coupled with in vitro analyses of Fe-biofortified crops will be compared to the parallel human efficacy studies which used the same varieties and harvests of the Fe-biofortified crops. Similar to the human studies, these trials were aimed to assess the potential effects of regular consumption of these enhanced staple crops on maintenance or improvement of iron status. The results demonstrate a strong agreement between the in vitro/in vivo screening approach and the parallel human studies. These observations therefore indicate that the in vitro/Caco-2 cell and Gallus gallus models can be integral tools to develop varieties of staple food crops and predict their effect on iron status in humans. The cost-effectiveness of this approach also means that it can be used to monitor the nutritional stability of the Fe-biofortified crop once a variety has released and integrated into the food system. These screening tools therefore represent a significant advancement to the field for crop development and can be applied to ensure the sustainability of the biofortification approach.

  4. Combined computational-experimental approach to predict blood-brain barrier (BBB) permeation based on "green" salting-out thin layer chromatography supported by simple molecular descriptors.

    Science.gov (United States)

    Ciura, Krzesimir; Belka, Mariusz; Kawczak, Piotr; Bączek, Tomasz; Markuszewski, Michał J; Nowakowska, Joanna

    2017-09-05

    The objective of this paper is to build QSRR/QSAR model for predicting the blood-brain barrier (BBB) permeability. The obtained models are based on salting-out thin layer chromatography (SOTLC) constants and calculated molecular descriptors. Among chromatographic methods SOTLC was chosen, since the mobile phases are free of organic solvent. As consequences, there are less toxic, and have lower environmental impact compared to classical reserved phases liquid chromatography (RPLC). During the study three stationary phase silica gel, cellulose plates and neutral aluminum oxide were examined. The model set of solutes presents a wide range of log BB values, containing compounds which cross the BBB readily and molecules poorly distributed to the brain including drugs acting on the nervous system as well as peripheral acting drugs. Additionally, the comparison of three regression models: multiple linear regression (MLR), partial least-squares (PLS) and orthogonal partial least squares (OPLS) were performed. The designed QSRR/QSAR models could be useful to predict BBB of systematically synthesized newly compounds in the drug development pipeline and are attractive alternatives of time-consuming and demanding directed methods for log BB measurement. The study also shown that among several regression techniques, significant differences can be obtained in models performance, measured by R 2 and Q 2 , hence it is strongly suggested to evaluate all available options as MLR, PLS and OPLS. Copyright © 2017 Elsevier B.V. All rights reserved.

  5. Near-Infrared Spectroscopy Combined with Multivariate Calibration to Predict the Yield of Sesame Oil Produced by Traditional Aqueous Extraction Process

    Directory of Open Access Journals (Sweden)

    Yong-Dong Xu

    2017-01-01

    Full Text Available Sesame oil produced by the traditional aqueous extraction process (TAEP has been recognized by its pleasant flavor and high nutrition value. This paper developed a rapid and nondestructive method to predict the sesame oil yield by TAEP using near-infrared (NIR spectroscopy. A collection of 145 sesame seed samples was measured by NIR spectroscopy and the relationship between the TAEP oil yield and the spectra was modeled by least-squares support vector machine (LS-SVM. Smoothing, taking second derivatives (D2, and standard normal variate (SNV transformation were performed to remove the unwanted variations in the raw spectra. The results indicated that D2-LS-SVM (4000–9000 cm−1 obtained the most accurate calibration model with root mean square error of prediction (RMSEP of 1.15 (%, w/w. Moreover, the RMSEP was not significantly influenced by different initial values of LS-SVM parameters. The calibration model could be helpful to search for sesame seeds with higher TAEP oil yields.

  6. The Combined Application of the Caco-2 Cell Bioassay Coupled with In Vivo (Gallus gallus) Feeding Trial Represents an Effective Approach to Predicting Fe Bioavailability in Humans

    Science.gov (United States)

    Tako, Elad; Bar, Haim; Glahn, Raymond P.

    2016-01-01

    Research methods that predict Fe bioavailability for humans can be extremely useful in evaluating food fortification strategies, developing Fe-biofortified enhanced staple food crops and assessing the Fe bioavailability of meal plans that include such crops. In this review, research from four recent poultry (Gallus gallus) feeding trials coupled with in vitro analyses of Fe-biofortified crops will be compared to the parallel human efficacy studies which used the same varieties and harvests of the Fe-biofortified crops. Similar to the human studies, these trials were aimed to assess the potential effects of regular consumption of these enhanced staple crops on maintenance or improvement of iron status. The results demonstrate a strong agreement between the in vitro/in vivo screening approach and the parallel human studies. These observations therefore indicate that the in vitro/Caco-2 cell and Gallus gallus models can be integral tools to develop varieties of staple food crops and predict their effect on iron status in humans. The cost-effectiveness of this approach also means that it can be used to monitor the nutritional stability of the Fe-biofortified crop once a variety has released and integrated into the food system. These screening tools therefore represent a significant advancement to the field for crop development and can be applied to ensure the sustainability of the biofortification approach. PMID:27869705

  7. Combining an experimental study and ANFIS modeling to predict landfill leachate transport in underlying soil-a case study in north of Iran.

    Science.gov (United States)

    Yousefi Kebria, D; Ghavami, M; Javadi, S; Goharimanesh, M

    2017-12-16

    In the contemporary world, urbanization and progressive industrial activities increase the rate of waste material generated in many developed countries. Municipal solid waste landfills (MSWs) are designed to dispose the waste from urban areas. However, discharged landfill leachate, the soluble water mixture that filters through solid waste landfills, can potentially migrate into the soil and affect living organisms by making harmful biological changes in the ecosystem. Due to well-documented landfill problems involving contamination, it is necessary to investigate the long-term influence of discharged leachate on the consistency of the soil beds beneath MSW landfills. To do so, the current study collected vertical deep core samples from different locations in the same unlined landfill. The impacts of effluent leachate on physical and chemical properties of the soil and its propagation depth were studied, and the leachate-transport pattern between successive boreholes was predicted by a developed mathematical model using an adaptive neuro-fuzzy inference system (ANFIS). The decomposition of organic leachate admixtures in the landfill yield is to produce organic acids as well as carbon dioxide, which diminishes the pH level of the landfill soil. The chemical analysis of discharged leachate in the soil samples showed that the concentrations of heavy metals are much lower than those of chloride, COD, BOD 5 , and bicarbonate. Using linear regression and mean square errors between the measured and predicted data, the accuracy of the proposed ANFIS model has been validated. Results show a high correlation between observed and predicated data.

  8. Coordinating rule-based and system-wide model predictive control strategies to reduce storage expansion of combined urban drainage systems: The case study of Lundtofte, Denmark

    DEFF Research Database (Denmark)

    Meneses, Elbys Jose; Gaussens, Marion; Jakobsen, Carsten

    2018-01-01

    performance in terms of combined sewer overflow (CSO) volumes, environmental impacts, and utility costs, which were reduced by up to 10%. The risk-based optimization strategy provided slightly better performance in terms of reducing CSO volumes, with evident improvements in environmental impacts and utility...... a five-year period. This study illustrates that including RTC during the planning stages reduces the infrastructural costs while offering better environmental protection, and that dynamic risk-based optimisation allows prioritising environmental impact reduction for particularly sensitive locations....

  9. Combination of STOP-Bang Score with Mallampati Score fails to improve specificity in the prediction of sleep-disordered breathing.

    Science.gov (United States)

    Dette, Frank G; Graf, Juergen; Cassel, Werner; Lloyd-Jones, Carla; Boehm, Stefan; Zoremba, Martin; Schramm, Patrick; Pestel, Gunther; Thal, Serge C

    2016-06-01

    Sleep-disordered breathing (SDB) is closely associated with perioperative complications. STOP-Bang score was validated for preoperative screening of SDB. However, STOP-Bang Score lacks adequately high specificity. We aimed to improve it by combining it with the Mallampati Score. The study included 347 patients, in which we assessed both STOP-Bang and Mallampati scores. Overnight oxygen saturation was measured to calculate ODI4%. We calculated the sensitivity and specificity for AHI and ODI4% of both scores separately and in combination. We found that STOP-Bang Score ≥3 was present in 71%, ODI≥5/h (AHI ≥5/h) in 42.6% (39.3%) and ODI≥15/h (AHI ≥15/h) in 13.5% (17.8%). For ODI4%≥5/h (AHI ≥5/h) we observed in men a response rate for sensitivity and specificity of STOP-Bang of 94.5% and 17.1% (90.9% and 12.5%) and in women 66% and 51% (57.8% and 46.9%). For ODI4%≥15/h (AHI≥15/h) it was 92% and 12% (84.6% and 10.3%) and 93% and 49% (75% and 49.2%). For ODI4%≥5 (AHI≥5) sensitivity and specificity of Mallampati score were in men 38.4% and 78.6% (27.3% and 68.2%) and in women 25% and 82.7% (21.9% and 81.3%), for ODI≥15 (AHI ≥15/h) 38.5% and 71.8% (26.9% and 69.2%) and 33.3% and 81.4% (17.9% and 79.6%). In combination, for ODI4%≥15/h, we found sensitivity in men to be 92.3% and in women 93.3%, specificity 10.3% and 41.4%. STOP-Bang Score combined with Mallampati Score fails to increase specificity. Low specificity should be considered when using both scores for preoperative screening of SDB.

  10. WDL-RF: Predicting Bioactivities of Ligand Molecules Acting with G Protein-coupled Receptors by Combining Weighted Deep Learning and Random Forest.

    Science.gov (United States)

    Wu, Jiansheng; Zhang, Qiuming; Wu, Weijian; Pang, Tao; Hu, Haifeng; Chan, Wallace K B; Ke, Xiaoyan; Zhang, Yang; Wren, Jonathan

    2018-02-08

    Precise assessment of ligand bioactivities (including IC50, EC50, Ki, Kd, etc.) is essential for virtual screening and lead compound identification. However, not all ligands have experimentally-determined activities. In particular, many G protein-coupled receptors (GPCRs), which are the largest integral membrane protein family and represent targets of nearly 40% drugs on the market, lack published experimental data about ligand interactions. Computational methods with the ability to accurately predict the bioactivity of ligands can help efficiently address this problem. We proposed a new method, WDL-RF, using weighted deep learning and random forest, to model the bioactivity of GPCR-associated ligand molecules. The pipeline of our algorithm consists of two consecutive stages: 1) molecular fingerprint generation through a new weighted deep learning method, and 2) bioactivity calculations with a random forest model; where one uniqueness of the approach is that the model allows end-to-end learning of prediction pipelines with input ligands being of arbitrary size. The method was tested on a set of twenty-six non-redundant GPCRs that have a high number of active ligands, each with 200∼4000 ligand associations. The results from our benchmark show that WDL-RF can generate bioactivity predictions with an average root-mean square error 1.33 and correlation coefficient (r2) 0.80 compared to the experimental measurements, which are significantly more accurate than the control predictors with different molecular fingerprints and descriptors. In particular, data-driven molecular fingerprint features, as extracted from the weighted deep learning models, can help solve deficiencies stemming from the use of traditional hand-crafted features and significantly increase the efficiency of short molecular fingerprints in virtual screening. The WDL-RF web server, as well as source codes and datasets of WDL-RF, is freely available at https://zhanglab.ccmb.med.umich.edu/WDL-RF/ for

  11. Combining electronic structure and many-body theory with large databases: A method for predicting the nature of 4 f states in Ce compounds

    Science.gov (United States)

    Herper, H. C.; Ahmed, T.; Wills, J. M.; Di Marco, I.; Björkman, T.; Iuşan, D.; Balatsky, A. V.; Eriksson, O.

    2017-08-01

    Recent progress in materials informatics has opened up the possibility of a new approach to accessing properties of materials in which one assays the aggregate properties of a large set of materials within the same class in addition to a detailed investigation of each compound in that class. Here we present a large scale investigation of electronic properties and correlated magnetism in Ce-based compounds accompanied by a systematic study of the electronic structure and 4 f -hybridization function of a large body of Ce compounds. We systematically study the electronic structure and 4 f -hybridization function of a large body of Ce compounds with the goal of elucidating the nature of the 4 f states and their interrelation with the measured Kondo energy in these compounds. The hybridization function has been analyzed for more than 350 data sets (being part of the IMS database) of cubic Ce compounds using electronic structure theory that relies on a full-potential approach. We demonstrate that the strength of the hybridization function, evaluated in this way, allows us to draw precise conclusions about the degree of localization of the 4 f states in these compounds. The theoretical results are entirely consistent with all experimental information, relevant to the degree of 4 f localization for all investigated materials. Furthermore, a more detailed analysis of the electronic structure and the hybridization function allows us to make precise statements about Kondo correlations in these systems. The calculated hybridization functions, together with the corresponding density of states, reproduce the expected exponential behavior of the observed Kondo temperatures and prove a consistent trend in real materials. This trend allows us to predict which systems may be correctly identified as Kondo systems. A strong anticorrelation between the size of the hybridization function and the volume of the systems has been observed. The information entropy for this set of systems is

  12. Predicting Key Agronomic Soil Properties with UV-Vis Fluorescence Measurements Combined with Vis-NIR-SWIR Reflectance Spectroscopy: A Farm-Scale Study in a Mediterranean Viticultural Agroecosystem.

    Science.gov (United States)

    Vaudour, Emmanuelle; Cerovic, Zoran G; Ebengo, Dav M; Latouche, Gwendal

    2018-04-10

    For adequate crop and soil management, rapid and accurate techniques for monitoring soil properties are particularly important when a farmer starts up his activities and needs a diagnosis of his cultivated fields. This study aimed to evaluate the potential of fluorescence measured directly on 146 whole soil solid samples, for predicting key soil properties at the scale of a 6 ha Mediterranean wine estate with contrasting soils. UV-Vis fluorescence measurements were carried out in conjunction with reflectance measurements in the Vis-NIR-SWIR range. Combining PLSR predictions from Vis-NIR-SWIR reflectance spectra and from a set of fluorescence signals enabled us to improve the power of prediction of a number of key agronomic soil properties including SOC, N tot , CaCO₃, iron, fine particle-sizes (clay, fine silt, fine sand), CEC, pH and exchangeable Ca 2+ with cross-validation RPD ≥ 2 and R² ≥ 0.75, while exchangeable K⁺, Na⁺, Mg 2+ , coarse silt and coarse sand contents were fairly predicted (1.42 ≤ RPD < 2 and 0.54 ≤ R² < 0.75). Predictions of SOC, N tot , CaCO₃, iron contents, and pH were still good (RPD ≥ 1.8, R² ≥ 0.68) when using a single fluorescence signal or index such as SFR_R or FERARI, highlighting the unexpected importance of red excitations and indices derived from plant studies. The predictive ability of single fluorescence indices or original signals was very significant for topsoil: this is very important for a farmer who wishes to update information on soil nutrient for the purpose of fertility diagnosis and particularly nitrogen fertilization. These results open encouraging perspectives for using miniaturized fluorescence devices enabling red excitation coupled with red or far-red fluorescence emissions directly in the field.

  13. Predicting Key Agronomic Soil Properties with UV-Vis Fluorescence Measurements Combined with Vis-NIR-SWIR Reflectance Spectroscopy: A Farm-Scale Study in a Mediterranean Viticultural Agroecosystem

    Directory of Open Access Journals (Sweden)

    Emmanuelle Vaudour

    2018-04-01

    Full Text Available For adequate crop and soil management, rapid and accurate techniques for monitoring soil properties are particularly important when a farmer starts up his activities and needs a diagnosis of his cultivated fields. This study aimed to evaluate the potential of fluorescence measured directly on 146 whole soil solid samples, for predicting key soil properties at the scale of a 6 ha Mediterranean wine estate with contrasting soils. UV-Vis fluorescence measurements were carried out in conjunction with reflectance measurements in the Vis-NIR-SWIR range. Combining PLSR predictions from Vis-NIR-SWIR reflectance spectra and from a set of fluorescence signals enabled us to improve the power of prediction of a number of key agronomic soil properties including SOC, Ntot, CaCO3, iron, fine particle-sizes (clay, fine silt, fine sand, CEC, pH and exchangeable Ca2+ with cross-validation RPD ≥ 2 and R² ≥ 0.75, while exchangeable K+, Na+, Mg2+, coarse silt and coarse sand contents were fairly predicted (1.42 ≤ RPD < 2 and 0.54 ≤ R² < 0.75. Predictions of SOC, Ntot, CaCO3, iron contents, and pH were still good (RPD ≥ 1.8, R² ≥ 0.68 when using a single fluorescence signal or index such as SFR_R or FERARI, highlighting the unexpected importance of red excitations and indices derived from plant studies. The predictive ability of single fluorescence indices or original signals was very significant for topsoil: this is very important for a farmer who wishes to update information on soil nutrient for the purpose of fertility diagnosis and particularly nitrogen fertilization. These results open encouraging perspectives for using miniaturized fluorescence devices enabling red excitation coupled with red or far-red fluorescence emissions directly in the field.

  14. Bioaccumulation Potential Of Air Contaminants: Combining Biological Allometry, Chemical Equilibrium And Mass-Balances To Predict Accumulation Of Air Pollutants In Various Mammals

    Energy Technology Data Exchange (ETDEWEB)

    Veltman, Karin; McKone, Thomas E.; Huijbregts, Mark A.J.; Hendriks, A. Jan

    2009-03-01

    In the present study we develop and test a uniform model intended for single compartment analysis in the context of human and environmental risk assessment of airborne contaminants. The new aspects of the model are the integration of biological allometry with fugacity-based mass-balance theory to describe exchange of contaminants with air. The developed model is applicable to various mammalian species and a range of chemicals, while requiring few and typically well-known input parameters, such as the adult mass and composition of the species, and the octanol-water and air-water partition coefficient of the chemical. Accumulation of organic chemicals is typically considered to be a function of the chemical affinity forlipid components in tissues. Here, we use a generic description of chemical affinity for neutral and polar lipids and proteins to estimate blood-air partition coefficients (Kba) and tissue-air partition coefficients (Kta) for various mammals. This provides a more accurate prediction of blood-air partition coefficients, as proteins make up a large fraction of total blood components. The results show that 75percent of the modeled inhalation and exhalation rate constants are within a factor of 2 from independent empirical values for humans, rats and mice, and 87percent of the predicted blood-air partition coefficients are within a factor of 5 from empirical data. At steady-state, the bioaccumulation potential of air pollutants is shown to be mainly a function of the tissue-air partition coefficient and the biotransformation capacity of the species and depends weakly on the ventilation rate and the cardiac output of mammals.

  15. Predicting the Oxygen-Binding Properties of Platinum Nanoparticle Ensembles by Combining High-Precision Electron Microscopy and Density Functional Theory.

    Science.gov (United States)

    Aarons, Jolyon; Jones, Lewys; Varambhia, Aakash; MacArthur, Katherine E; Ozkaya, Dogan; Sarwar, Misbah; Skylaris, Chris-Kriton; Nellist, Peter D

    2017-07-12

    Many studies of heterogeneous catalysis, both experimental and computational, make use of idealized structures such as extended surfaces or regular polyhedral nanoparticles. This simplification neglects the morphological diversity in real commercial oxygen reduction reaction (ORR) catalysts used in fuel-cell cathodes. Here we introduce an approach that combines 3D nanoparticle structures obtained from high-throughput high-precision electron microscopy with density functional theory. Discrepancies between experimental observations and cuboctahedral/truncated-octahedral particles are revealed and discussed using a range of widely used descriptors, such as electron-density, d-band centers, and generalized coordination numbers. We use this new approach to determine the optimum particle size for which both detrimental surface roughness and particle shape effects are minimized.

  16. Comparison of in situ DGT measurement with ex situ methods for predicting cadmium bioavailability in soils with combined pollution to biotas.

    Science.gov (United States)

    Wang, Peifang; Liu, Cui; Yao, Yu; Wang, Chao; Wang, Teng; Yuan, Ye; Hou, Jun

    2017-05-01

    To assess the capabilities of the different techniques in predicting Cadmium (Cd) bioavailability in Cd-contaminated soils with the addition of Zn, one in situ technique (diffusive gradients in thin films; DGT) was compared with soil solution concentration and four widely used single-step extraction methods (acetic acid, EDTA, sodium acetate and CaCl 2 ). Wheat and maize were selected as tested species. The results demonstrated that single Cd-polluted soils inhibited the growth of wheat and maize significantly compared with control plants; the shoot and root biomasses of the plants both dropped significantly (P 0.9) between Cd concentrations in two plants and Cd bioavailability indicated by each method in soils. Consequently, the results indicated that the DGT technique could be regarded as a good predictor of Cd bioavailability to plants, comparable to soil solution concentration and the four single-step extraction methods. Because the DGT technique can offer in situ data, it is expected to be widely used in more areas.

  17. BCL-2, in combination with MVP and IGF-1R expression, improves prediction of clinical outcome in complete response cervical carcinoma patients treated by radiochemotherapy.

    Science.gov (United States)

    Henríquez-Hernández, Luis Alberto; Lloret, Marta; Pinar, Beatriz; Bordón, Elisa; Rey, Agustín; Lubrano, Amina; Lara, Pedro Carlos

    2011-09-01

    To investigate whether BCL-2 expression would improve MVP/IGF-1R prediction of clinical outcome in cervix carcinoma patients treated by radiochemotherapy, and suggest possible mechanisms behind this effect. Fifty consecutive patients, who achieved complete response to treatment, from a whole series of 60 cases suffering from non-metastatic localized cervical carcinoma, were prospectively included in this study from July 1999 to December 2003. Follow-up was closed in January 2011. All patients received pelvic radiation (45-64.80 Gy in 1.8-2 Gy fractions) with concomitant cisplatin at 40 mg/m2/week doses followed by brachytherapy. Oncoprotein expression was studied by immunohistochemistry in paraffin-embedded tumour tissue. No relation was found between BCL-2 and clinicopathological variables. High MVP/IGF-1R/BCL-2 tumour expression was strongly related to poor local and regional disease-free survival (PMVP, and IGF-1R overexpression were related to poorer clinical outcome in cervical cancer patients who achieved clinical complete response to radiochemotherapy. The NHEJ repair protein Ku70/80 expression could be involved in the regulation of these oncoproteins. Copyright © 2011 Elsevier Inc. All rights reserved.

  18. Exploring pyrazolo[3,4-d]pyrimidine phosphodiesterase 1 (PDE1) inhibitors: a predictive approach combining comparative validated multiple molecular modelling techniques.

    Science.gov (United States)

    Amin, Sk Abdul; Bhargava, Sonam; Adhikari, Nilanjan; Gayen, Shovanlal; Jha, Tarun

    2018-02-01

    Phosphodiesterase 1 (PDE1) is a potential target for a number of neurodegenerative disorders such as Schizophrenia, Parkinson's and Alzheimer's diseases. A number of pyrazolo[3,4-d]pyrimidine PDE1 inhibitors were subjected to different molecular modelling techniques [such as regression-based quantitative structure-activity relationship (QSAR): multiple linear regression, support vector machine and artificial neural network; classification-based QSAR: Bayesian modelling and Recursive partitioning; Monte Carlo based QSAR; Open3DQSAR; pharmacophore mapping and molecular docking analyses] to get a detailed knowledge about the physicochemical and structural requirements for higher inhibitory activity. The planarity of the pyrimidinone ring plays an important role for PDE1 inhibition. The N-methylated function at the 5th position of the pyrazolo[3,4-d]pyrimidine core is required for interacting with the PDE1 enzyme. The cyclopentyl ring fused with the parent scaffold is necessary for PDE1 binding potency. The phenylamino substitution at 3rd position is crucial for PDE1 inhibition. The N2-substitution at the pyrazole moiety is important for PDE1 inhibition compared to the N1-substituted analogues. Moreover, the p-substituted benzyl side chain at N2-position helps to enhance the PDE1 inhibitory profile. Depending on these observations, some new molecules are predicted that may possess better PDE1 inhibition.

  19. Nonstructural carbon dynamics are best predicted by the combination of photosynthesis and plant hydraulics during both bark beetle induced mortality and herbaceous plant response to drought

    Science.gov (United States)

    Ewers, B. E.; Mackay, D. S.; Guadagno, C.; Peckham, S. D.; Pendall, E.; Borkhuu, B.; Aston, T.; Frank, J. M.; Massman, W. J.; Reed, D. E.; Yarkhunova, Y.; Weinig, C.

    2012-12-01

    Recent work has shown that nonstructural carbon (NSC) provides both a signal and consequence of water stress in plants. The dynamics of NSC are likely not solely a result of the balance of photosynthesis and respiration (carbon starvation hypothesis) but also the availability of NSC for plant functions due to hydraulic condition. Further, plant hydraulics regulates photosynthesis both directly through stomatal conductance and indirectly through leaf water status control over leaf biochemistry. To test these hypotheses concerning NSC in response to a wide variety of plant perturbations, we used a model that combines leaf biochemical controls over photosynthesis (Farquhar model) with dynamic plant hydraulic conductance (Sperry model). This model (Terrestrial Regional Ecosystem Exchange Simulator; TREES) simulates the dynamics of NSC through a carbon budget approach that responds to plant hydraulic status. We tested TREES on two dramatically different datasets. The first dataset is from lodgepole pine and Engelmann spruce trees dying from bark beetles that carry blue-stain fungi which block xylem and cause hydraulic failure. The second data set is from Brassica rapa, a small herbaceous plant whose accessions are used in a variety of crops. The Brassica rapa plants include two parents whose circadian clock periods are different; NSC is known to provide inputs to the circadian clock likely modified by drought. Thus, drought may interact with clock control to constrain how NSC changes over the day. The Brassica rapa plants were grown in growth chamber conditions where drought was precisely controlled. The connection between these datasets is that both provide rigorous tests of our understanding of plant NSC dynamics and use similar leaf and whole plant gas exchange and NSC laboratory methods. Our results show that NSC decline (water stress. The model is able to capture this relatively small decline in NSC by limiting NSC utilization through loss of plant hydraulic

  20. Towards the development of multifunctional molecular indicators combining soil biogeochemical and microbiological variables to predict the ecological integrity of silvicultural practices.

    Science.gov (United States)

    Peck, Vincent; Quiza, Liliana; Buffet, Jean-Philippe; Khdhiri, Mondher; Durand, Audrey-Anne; Paquette, Alain; Thiffault, Nelson; Messier, Christian; Beaulieu, Nadyre; Guertin, Claude; Constant, Philippe

    2016-05-01

    The impact of mechanical site preparation (MSP) on soil biogeochemical structure in young larch plantations was investigated. Soil samples were collected in replicated plots comprising simple trenching, double trenching, mounding and inverting site preparation. Unlogged natural mixed forest areas were used as a reference. Analysis of soil nutrients, abundance of bacteria and gas exchanges unveiled no significant difference among the plots. However, inverting site preparation resulted in higher variations of gas exchanges when compared with trenching, mounding and unlogged natural forest. A combination of the biological and physicochemical variables was used to define a multifunctional classification of the soil samples into four distinct groups categorized as a function of their deviation from baseline ecological conditions. According to this classification model, simple trenching was the approach that represented the lowest ecological risk potential at the microsite level. No relationship was observed between MSP method and soil bacterial community structure as assessed by high-throughput sequencing of bacterial 16S rRNA gene; however, indicator genotypes were identified for each multifunctional soil class. This is the first identification of multifunctional molecular indicators for baseline and disturbed ecological conditions in soil, demonstrating the potential of applied microbial ecology to guide silvicultural practices and ecological risk assessment. © 2016 The Authors. Microbial Biotechnology published by John Wiley & Sons Ltd and Society for Applied Microbiology.

  1. Combined detection of preoperative serum CEA, CA19-9 and CA242 improve prognostic prediction of surgically treated colorectal cancer patients.

    Science.gov (United States)

    Wang, Jingtao; Wang, Xiao; Yu, Fudong; Chen, Jian; Zhao, Senlin; Zhang, Dongyuan; Yu, Yang; Liu, Xisheng; Tang, Huamei; Peng, Zhihai

    2015-01-01

    We assessed the prognostic significance of preoperative serum carcinoembryonic antigen (CEA), carbohydrate antigen 19-9 (CA19-9) and carbohydrate antigen 242 (CA242) levels in surgically treated colorectal cancer patients. The relationship of preoperative serum CEA, CA19-9 and CA242 levels with disease characteristics was investigated in 310 patients. Correlation between tumor markers was investigated using Pearson correlation test. Univariate and multivariate survival analyses were used to study the relationship between preoperative tumor markers and prognosis [disease free survival (DFS) and overall survival (OS)]. Kaplan-Meier analysis with log rank test was used to assess the impact of tumor marker levels on survival. Positive rate of preoperative serum CEA, CA19-9 and CA242 were 54.84%, 47.42% and 37.10%, respectively. High preoperative CEA level was associated with tumor size (P = 0.038), T stage (P tumor AJCC stage (P = 0.023). Preoperative CA242 positively correlated with CEA (P markers was of independent prognostic value in CRC (HR = 2.532, 95% CI: 1.400-4.579, P = 0.002 for OS; and HR = 2.366, 95% CI: 1.334-4.196, P = 0.003 for DFS). Combined detection of preoperative serum CEA, CA19-9 and CA242 is of independent prognostic value for management of CRC patients treated surgically.

  2. Microemulsion Electrokinetic Chromatography in Combination with Chemometric Methods to Evaluate the Holistic Quality Consistency and Predict the Antioxidant Activity of Ixeris sonchifolia (Bunge Hance Injection.

    Directory of Open Access Journals (Sweden)

    Lanping Yang

    Full Text Available In this paper, microemulsion electrokinetic chromatography (MEEKC fingerprints combined with quantification were successfully developed to monitor the holistic quality consistency of Ixeris sonchifolia (Bge. Hance Injection (ISHI. ISHI is a Chinese traditional patent medicine used for its anti-inflammatory and hemostatic effects. The effects of five crucial experimental variables on MEEKC were optimized by the central composite design. Under the optimized conditions, the MEEKC fingerprints of 28 ISHIs were developed. Quantitative determination of seven marker compounds was employed simultaneously, then 28 batches of samples from two manufacturers were clearly divided into two clusters by the principal component analysis. In fingerprint assessments, a systematic quantitative fingerprint method was established for the holistic quality consistency evaluation of ISHI from qualitative and quantitative perspectives, by which the qualities of 28 samples were well differentiated. In addition, the fingerprint-efficacy relationship between the fingerprints and the antioxidant activities was established utilizing orthogonal projection to latent structures, which provided important medicinal efficacy information for quality control. The present study offered a powerful and holistic approach to evaluating the quality consistency of herbal medicines and their preparations.

  3. Development of a Combined In Vitro Physiologically Based Kinetic (PBK) and Monte Carlo Modelling Approach to Predict Interindividual Human Variation in Phenol-Induced Developmental Toxicity.

    Science.gov (United States)

    Strikwold, Marije; Spenkelink, Bert; Woutersen, Ruud A; Rietjens, Ivonne M C M; Punt, Ans

    2017-06-01

    With our recently developed in vitro physiologically based kinetic (PBK) modelling approach, we could extrapolate in vitro toxicity data to human toxicity values applying PBK-based reverse dosimetry. Ideally information on kinetic differences among human individuals within a population should be considered. In the present study, we demonstrated a modelling approach that integrated in vitro toxicity data, PBK modelling and Monte Carlo simulations to obtain insight in interindividual human kinetic variation and derive chemical specific adjustment factors (CSAFs) for phenol-induced developmental toxicity. The present study revealed that UGT1A6 is the primary enzyme responsible for the glucuronidation of phenol in humans followed by UGT1A9. Monte Carlo simulations were performed taking into account interindividual variation in glucuronidation by these specific UGTs and in the oral absorption coefficient. Linking Monte Carlo simulations with PBK modelling, population variability in the maximum plasma concentration of phenol for the human population could be predicted. This approach provided a CSAF for interindividual variation of 2.0 which covers the 99th percentile of the population, which is lower than the default safety factor of 3.16 for interindividual human kinetic differences. Dividing the dose-response curve data obtained with in vitro PBK-based reverse dosimetry, with the CSAF provided a dose-response curve that reflects the consequences of the interindividual variability in phenol kinetics for the developmental toxicity of phenol. The strength of the presented approach is that it provides insight in the effect of interindividual variation in kinetics for phenol-induced developmental toxicity, based on only in vitro and in silico testing. © The Author 2017. Published by Oxford University Press on behalf of the Society of Toxicology. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  4. Pulmonary Vascular Input Impedance is a Combined Measure of Pulmonary Vascular Resistance and Stiffness and Predicts Clinical Outcomes Better than PVR Alone in Pediatric Patients with Pulmonary Hypertension

    Science.gov (United States)

    Hunter, Kendall S.; Lee, Po-Feng; Lanning, Craig J.; Ivy, D. Dunbar; Kirby, K. Scott; Claussen, Lori R.; Chan, K. Chen; Shandas, Robin

    2011-01-01

    Background Pulmonary vascular resistance (PVR) is the current standard for evaluating reactivity in children with pulmonary arterial hypertension (PAH). However, PVR measures only the mean component of right ventricular afterload and neglects pulsatile effects. We recently developed and validated an method to measure pulmonary vascular input impedance, which revealed excellent correlation between the zero-harmonic impedance value and PVR, and suggested a correlation between higher harmonic impedance values and pulmonary vascular stiffness (PVS). Here we show that input impedance can be measured routinely and easily in the catheterization laboratory, that impedance provides PVR and PVS from a single measurement, and that impedance is a better predictor of disease outcomes compared to PVR. Methods Pressure and velocity waveforms within the main PA were measured during right-heart catheterization of patients with normal PA hemodynamics (n=14) and those with PAH undergoing reactivity evaluation (49 subjects; 95 conditions). A correction factor needed to transform velocity into flow was obtained by calibrating against cardiac output. Input impedance was obtained off-line by dividing Fourier-transformed pressure and flow waveforms. Results Exceptional correlation was found between the indexed zero harmonic of impedance and indexed PVR (y=1.095·x+1.381, R2=0.9620). Additionally, the modulus sum of the first two harmonics of impedance was found to best correlate with indexed pulse pressure over stroke volume (PP/SV) (y=13.39·x-0.8058, R2=0.7962). Amongst a subset of PAH patients (n=25), cumulative logistic regression between outcomes to total indexed impedance was better (RL2=0.4012) than between outcomes and indexed PVR (RL2=0.3131). Conclusions Input impedance can be consistently and easily obtained from PW Doppler and a single catheter pressure measurement, provides comprehensive characterization of the main components of RV afterload, and better predicts patient

  5. Predictive transport modelling of type I ELMy H-mode dynamics using a theory-motivated combined ballooning-peeling model

    International Nuclear Information System (INIS)

    Loennroth, J-S; Parail, V; Dnestrovskij, A; Figarella, C; Garbet, X; Wilson, H

    2004-01-01

    This paper discusses predictive transport simulations of the type I ELMy high confinement mode (H-mode) with a theory-motivated edge localized mode (ELM) model based on linear ballooning and peeling mode stability theory. In the model, a total mode amplitude is calculated as a sum of the individual mode amplitudes given by two separate linear differential equations for the ballooning and peeling mode amplitudes. The ballooning and peeling mode growth rates are represented by mutually analogous terms, which differ from zero upon the violation of a critical pressure gradient and an analytical peeling mode stability criterion, respectively. The damping of the modes due to non-ideal magnetohydrodynamic effects is controlled by a term driving the mode amplitude towards the level of background fluctuations. Coupled to simulations with the JETTO transport code, the model qualitatively reproduces the experimental dynamics of type I ELMy H-mode, including an ELM frequency that increases with the external heating power. The dynamics of individual ELM cycles is studied. Each ELM is usually triggered by a ballooning mode instability. The ballooning phase of the ELM reduces the pressure gradient enough to make the plasma peeling unstable, whereby the ELM continues driven by the peeling mode instability, until the edge current density has been depleted to a stable level. Simulations with current ramp-up and ramp-down are studied as examples of situations in which pure peeling and pure ballooning mode ELMs, respectively, can be obtained. The sensitivity with respect to the ballooning and peeling mode growth rates is investigated. Some consideration is also given to an alternative formulation of the model as well as to a pure peeling model

  6. A COMBINATION OF GEOSPATIAL AND CLINICAL ANALYSIS IN PREDICTING DISABILITY OUTCOME AFTER ROAD TRAFFIC INJURY (RTI IN A DISTRICT IN MALAYSIA

    Directory of Open Access Journals (Sweden)

    R. Nik Hisamuddin

    2016-09-01

    Full Text Available This was a Prospective Cohort Study commencing from July 2011 until June 2013 involving all injuries related to motor vehicle crashes (MVC attended Emergency Departments (ED of two tertiary centers in a district in Malaysia. Selected attributes were geospatially analyzed by using ARCGIS (by ESRI software version 10.1 licensed to the institution and Google Map free software and multiple logistic regression was performed by using SPSS version 22.0. A total of 439 cases were recruited. The mean age (SD of the MVC victims was 26.04 years (s.d 15.26. Male comprised of 302 (71.7% of the cases. Motorcyclists were the commonest type of victims involved [351(80.0%]. Hotspot MVC locations occurred at certain intersections and on roads within borough of Kenali and Binjai. The number of severely injured and polytrauma are mostly on the road network within speed limit of 60 km/hour. A person with an increase in ISS of one score had a 37 % higher odd to have disability at hospital discharge (95% CI: 1.253, 1.499, p-value < 0.001. Pediatric age group (less than 19 years of age had 52.1% lesser odds to have disability at discharge from hospital (95% CI: 0.258, 0.889, p-value < 0.001 and patients who underwent operation for definitive management had 4.14 times odds to have disability at discharge from hospital (95% CI: 1.681, 10.218, p-value = 0.002. Overall this study has proven that GIS with a combination of traditional statistical analysis is still a powerful tool in road traffic injury (RTI related research.

  7. Prognostic Significance of 5-Year PSA Value for Predicting Prostate Cancer Recurrence After Brachytherapy Alone and Combined With Hormonal Therapy and/or External Beam Radiotherapy

    International Nuclear Information System (INIS)

    Stock, Richard G.; Klein, Thomas J.; Cesaretti, Jamie A.; Stone, Nelson N.

    2009-01-01

    Purpose: To analyze the prognosis and outcomes of patients who remain free of biochemical failure during the first 5 years after treatment. Methods and Materials: Between 1991 and 2002, 742 patients with prostate cancer were treated with brachytherapy alone (n = 306), brachytherapy and hormonal therapy (n = 212), or combined implantation and external beam radiotherapy (with or without hormonal therapy; n = 224). These patients were free of biochemical failure (American Society for Therapeutic Radiology and Oncology [ASTRO] definition) during the first 5 post-treatment years and had a documented 5-year prostate-specific antigen (PSA) value. The median follow-up was 6.93 years. Results: The actuarial 10-year freedom from PSA failure rate was 97% using the ASTRO definition and 95% using the Phoenix definition. The median 5-year PSA level was 0.03 ng/mL (range, 0-3.6). The 5-year PSA value was ≤0.01 in 47.7%, >0.01-0.10 in 31.1%, >0.10-0.2 in 10.2%, >0.2-0.5 in 7.82%, and >0.5 in 3.10%. The 5-year PSA value had prognostic significance, with a PSA value of ≤0.2 ng/mL (n = 661) corresponding to a 10-year freedom from PSA failure rate of 99% with the ASTRO definition and 98% with the Phoenix definition vs. 86% (ASTRO definition) and 81% (Phoenix definition) for a PSA value ≥0.2 ng/mL (n = 81; p < .0001). The treatment regimen had no effect on biochemical failure. None of the 742 patients in this study developed metastatic disease or died of prostate cancer. Conclusion: The results of this study have shown that the prognosis for patients treated with brachytherapy and who remain biochemically free of disease for ≥5 years is excellent and none developed metastatic disease during the first 10 years after treatment. The 5-year PSA value is prognostic, and patients with a PSA value <0.2 ng/mL are unlikely to develop subsequent biochemical relapse.

  8. Combination of equilibrium models and hybrid life cycle-input–output analysis to predict the environmental impacts of energy policy scenarios

    International Nuclear Information System (INIS)

    Igos, Elorri; Rugani, Benedetto; Rege, Sameer; Benetto, Enrico; Drouet, Laurent; Zachary, Daniel S.

    2015-01-01

    Highlights: • The environmental impacts of two energy policy scenarios in Luxembourg are assessed. • Computable General Equilibrium (CGE) and Partial Equilibrium (PE) models are used. • Results from coupling of CGE and PE are integrated in hybrid Life Cycle Assessment. • Impacts due to energy related production and imports are likely to grow over time. • Carbon mitigation policies seem to not substantially decrease the impacts’ trend. - Abstract: Nowadays, many countries adopt an active agenda to mitigate the impact of greenhouse gas emissions by moving towards less polluting energy generation technologies. The environmental costs, directly or indirectly generated to achieve such a challenging objective, remain however largely underexplored. Until now, research has focused either on pure economic approaches such as Computable General Equilibrium (CGE) and partial equilibrium (PE) models, or on (physical) energy supply scenarios. These latter could be used to evaluate the environmental impacts of various energy saving or cleaner technologies via Life Cycle Assessment (LCA) methodology. These modelling efforts have, however, been pursued in isolation, without exploring the possible complementarities and synergies. In this study, we have undertaken a practical combination of these approaches into a common framework: on the one hand, by coupling a CGE with a PE model, and, on the other hand, by linking the outcomes from the coupling with a hybrid input–output−process based life cycle inventory. The methodological framework aimed at assessing the environmental consequences of two energy policy scenarios in Luxembourg between 2010 and 2025. The study highlights the potential of coupling CGE and PE models but also the related methodological difficulties (e.g. small number of available technologies in Luxembourg, intrinsic limitations of the two approaches, etc.). The assessment shows both environmental synergies and trade-offs due to the implementation of

  9. New Substrate-Guided Method of Predicting Slow Conducting Isthmuses of Ventricular Tachycardia: Preliminary Analysis to the Combined Use of Voltage Limit Adjustment and Fast-Fourier Transform Analysis.

    Science.gov (United States)

    Kuroki, Kenji; Nogami, Akihiko; Igarashi, Miyako; Masuda, Keita; Kowase, Shinya; Kurosaki, Kenji; Komatsu, Yuki; Naruse, Yoshihisa; Machino, Takeshi; Yamasaki, Hiro; Xu, Dongzhu; Murakoshi, Nobuyuki; Sekiguchi, Yukio; Aonuma, Kazutaka

    2018-04-01

    Several conducting channels of ventricular tachycardia (VT) can be identified using voltage limit adjustment (VLA) of substrate mapping. However, the sensitivity or specificity to predict a VT isthmus is not high by using VLA alone. This study aimed to evaluate the efficacy of the combined use of VLA and fast-Fourier transform analysis to predict VT isthmuses. VLA and fast-Fourier transform analyses of local ventricular bipolar electrograms during sinus rhythm were performed in 9 postinfarction patients who underwent catheter ablation for a total of 13 monomorphic VTs. Relatively higher voltage areas on an electroanatomical map were defined as high voltage channels (HVCs), and relatively higher fast-Fourier transform areas were defined as high-frequency channels (HFCs). HVCs were classified into full or partial HVCs (the entire or >30% of HVC can be detectable, respectively). Twelve full HVCs were identified in 7 of 9 patients. HFCs were located on 7 of 12 full HVCs. Five VT isthmuses (71%) were included in the 7 full HVC+/HFC+ sites, whereas no VT isthmus was found in the 5 full HVC+/HFC- sites. HFCs were identical to 9 of 16 partial HVCs. Eight VT isthmuses (89%) were included in the 9 partial HVC+/HFC+ sites, whereas no VT isthmus was found in the 7 partial HVC+/HFC- sites. All HVC+/HFC+ sites predicted VT isthmus with a sensitivity of 100% and a specificity of 80%. Combined use of VLA and fast-Fourier transform analysis may be a useful method to detect VT isthmuses. © 2018 American Heart Association, Inc.

  10. The Predictive Value of Early In-Treatment 18F-FDG PET/CT Response to Chemotherapy in Combination with Bevacizumab in Advanced Nonsquamous Non-Small Cell Lung Cancer.

    Science.gov (United States)

    Usmanij, Edwin A; Natroshvili, Tinatin; Timmer-Bonte, Johanna N H; Oyen, Wim J G; van der Drift, Miep A; Bussink, Johan; Geus-Oei, Lioe-Fee de

    2017-08-01

    18 F-FDG PET/CT is potentially applicable to predict response to chemotherapy in combination with bevacizumab in patients with advanced non-small cell lung cancer (NSCLC). Methods: In 25 patients with advanced nonsquamous NSCLC, 18 F-FDG PET/CT was performed before treatment and after 2 wk, at the end of the second week of first cycle carboplatin-paclitaxel and bevacizumab (CPB) treatment. Patients received up to a total of 4 cycles of CPB treatment. Maintenance treatment with bevacizumab monotherapy was continued until progressive disease without significant treatment-related toxicities of first-line treatment. In the case of progressive disease, bevacizumab was combined with erlotinib. SUV corrected for lean body mass (SUL and SUL peak ) were obtained. PERCIST were used for response evaluation. These semiquantitative parameters were correlated with progression-free survival and overall survival (OS). Results: Metabolic response, defined by a significant reduction in SUL peak of 30% or more after 2 wk of CPB, was predictive of progression-free survival and OS. For partial metabolic responders ( n = 19), the median OS was 22.8 mo. One-year and 2-y OS were 79% and 47%, respectively. Nonmetabolic responders ( n = 6) (stable metabolic disease or progressive disease) showed a median OS of 4.4 mo (1-y and 2-y OS was 33% and 0%, respectively) ( P predictive of outcome to first-line chemotherapy with bevacizumab in patients with advanced nonsquamous NSCLC. This enables identification of patients at risk of treatment failure, permitting treatment alternatives such as early switch to a different therapy. © 2017 by the Society of Nuclear Medicine and Molecular Imaging.

  11. A combination of troponin T and 12-lead electrocardiography: a valuable tool for early prediction of long-term mortality in patients with chest pain without ST-segment elevation.

    Science.gov (United States)

    Jernberg, Tomas; Lindahl, Bertil

    2002-11-01

    Electrocardiography (ECG) obtained on admission and a troponin T (tn-T) level measured early after admission are simple and accessible methods for predicting outcome in patients with suspected unstable angina or myocardial infarction without persistent ST-elevations. However, there are few studies about the combination of these 2 methods as a means of predicting long-term outcome. ECG was obtained on admission, and a tn-T level was analyzed on admission and after 6 hours in 710 consecutive patients admitted because of chest pain and no ST-elevations. Patients were observed for a median time of 40 months for death. ST-segment depressions > or =0.05 mV were present in 266 patients (37%). These patients had a 9.7-fold increased risk of death, compared with patients with normal ECG results. Isolated T-Wave inversions or pathological signs other than ST-T changes were present in 196 patients (28%), who had a 4.5-fold increased risk of death compared with patients who had normal ECG results. At 6 hours after admission, 169 patients (24%) had at least 1 sample of tn-T > or =0.10 microg/L, which resulted in an 3.7-fold increased risk of death. In a multivariate analysis, both ECG on admission and tn-T level came out as independent predictors of outcome. When these methods were combined, patients could be divided into low- (tn-T level or =0.10 microg/L or ST-segment depression), and high-risk groups (tn-T level > or =0.10 microg/L and ST-segment depression). ECG and tn-T level are valuable tools to quickly risk stratify patients with chest pain. The combination of these methods is superior to either one alone.

  12. Forecast Combinations

    OpenAIRE

    Timmermann, Allan G

    2005-01-01

    Forecast combinations have frequently been found in empirical studies to produce better forecasts on average than methods based on the ex-ante best individual forecasting model. Moreover, simple combinations that ignore correlations between forecast errors often dominate more refined combination schemes aimed at estimating the theoretically optimal combination weights. In this paper we analyse theoretically the factors that determine the advantages from combining forecasts (for example, the d...

  13. Value of Combining Left Atrial Diameter and Amino-terminal Pro-brain Natriuretic Peptide to the CHA2DS2-VASc Score for Predicting Stroke and Death in Patients with Sick Sinus Syndrome after Pacemaker Implantation.

    Science.gov (United States)

    Mo, Bin-Feng; Lu, Qiu-Fen; Lu, Shang-Biao; Xie, Yu-Quan; Feng, Xiang-Fei; Li, Yi-Gang

    2017-08-20

    The CHA2DS2-VASc score is used clinically for stroke risk stratification in patients with atrial fibrillation (AF). We sought to investigate whether the CHA2DS2-VASc score predicts stroke and death in Chinese patients with sick sinus syndrome (SSS) after pacemaker implantation and to evaluate whether the predictive power of the CHA2DS2-VASc score could be improved by combining it with left atrial diameter (LAD) and amino-terminal pro-brain natriuretic peptide (NT-proBNP). A total of 481 consecutive patients with SSS who underwent pacemaker implantation from January 2004 to December 2014 in our department were included. The CHA2DS2-VASc scores were retrospectively calculated according to the hospital medical records before pacemaker implantation. The outcome data (stroke and death) were collected by pacemaker follow-up visits and telephonic follow-up until December 31, 2015. During 2151 person-years of follow-up, 46 patients (9.6%) suffered stroke and 52 (10.8%) died. The CHA2DS2-VASc score showed a significant association with the development of stroke (hazard ratio [HR] 1.45, 95% confidence interval [CI] 1.20-1.75, Ppacemaker implantation. The addition of LAD and NT-proBNP to the CHA2DS2-VASc score improved its predictive power for stroke and death, respectively, in this patient cohort. Future prospective studies are warranted to validate the benefit of adding LAD and NT-proBNP to the CHA2DS2-VASc score for predicting stroke and death risk in non-AF populations.

  14. A prototype methodology combining surface-enhanced laser desorption/ionization protein chip technology and artificial neural network algorithms to predict the chemoresponsiveness of breast cancer cell lines exposed to Paclitaxel and Doxorubicin under in vitro conditions.

    Science.gov (United States)

    Mian, Shahid; Ball, Graham; Hornbuckle, Jo; Holding, Finn; Carmichael, James; Ellis, Ian; Ali, Selman; Li, Geng; McArdle, Stephanie; Creaser, Colin; Rees, Robert

    2003-09-01

    An ability to predict the likelihood of cellular response towards particular chemotherapeutic agents based upon protein expression patterns could facilitate the identification of biological molecules with previously undefined roles in the process of chemoresistance/chemosensitivity, and if robust enough these patterns might also be exploited towards the development of novel predictive assays. To ascertain whether proteomic based molecular profiling in conjunction with artificial neural network (ANN) algorithms could be applied towards the specific recognition of phenotypic patterns between either control or drug treated and chemosensitive or chemoresistant cellular populations, a combined approach involving MALDI-TOF matrix-assisted laser desorption/ionization-time of flight mass spectrometry, Ciphergen protein chip technology and ANN algorithms have been applied to specifically identify proteomic 'fingerprints' indicative of treatment regimen for chemosensitive (MCF-7, T47D) and chemoresistant (MCF-7/ADR) breast cancer cell lines following exposure to Doxorubicin or Paclitaxel. The results indicate that proteomic patterns can be identified by ANN algorithms to correctly assign 'class' for treatment regimen (e.g. control/drug treated or chemosensitive/chemoresistant) with a high degree of accuracy using boot-strap statistical validation techniques and that biomarker ion patterns indicative of response/non-response phenotypes are associated with MCF-7 and MCF-7/ADR cells exposed to Doxorubicin. We have also examined the predictive capability of this approach towards MCF-7 and T47D cells to ascertain whether prediction could be made based upon treatment regimen irrespective of cell lineage. Models were identified that could correctly assign class (control or Paclitaxel treatment) for 35/38 samples of an independent dataset. A similar level of predictive capability was also found (> 92%; n = 28) when proteomic patterns derived from the drug resistant cell line MCF-7

  15. Identification of high-risk cutaneous melanoma tumors is improved when combining the online American Joint Committee on Cancer Individualized Melanoma Patient Outcome Prediction Tool with a 31-gene expression profile-based classification.

    Science.gov (United States)

    Ferris, Laura K; Farberg, Aaron S; Middlebrook, Brooke; Johnson, Clare E; Lassen, Natalie; Oelschlager, Kristen M; Maetzold, Derek J; Cook, Robert W; Rigel, Darrell S; Gerami, Pedram

    2017-05-01

    A significant proportion of patients with American Joint Committee on Cancer (AJCC)-defined early-stage cutaneous melanoma have disease recurrence and die. A 31-gene expression profile (GEP) that accurately assesses metastatic risk associated with primary cutaneous melanomas has been described. We sought to compare accuracy of the GEP in combination with risk determined using the web-based AJCC Individualized Melanoma Patient Outcome Prediction Tool. GEP results from 205 stage I/II cutaneous melanomas with sufficient clinical data for prognostication using the AJCC tool were classified as low (class 1) or high (class 2) risk. Two 5-year overall survival cutoffs (AJCC 79% and 68%), reflecting survival for patients with stage IIA or IIB disease, respectively, were assigned for binary AJCC risk. Cox univariate analysis revealed significant risk classification of distant metastasis-free and overall survival (hazard ratio range 3.2-9.4, P risk by GEP but low risk by AJCC. Specimens reflect tertiary care center referrals; more effective therapies have been approved for clinical use after accrual. The GEP provides valuable prognostic information and improves identification of high-risk melanomas when used together with the AJCC online prediction tool. Copyright © 2016 American Academy of Dermatology, Inc. Published by Elsevier Inc. All rights reserved.

  16. Preoperative weight loss with glucagon-like peptide-1 receptor agonist treatment predicts greater weight loss achieved by the combination of medical weight management and bariatric surgery in patients with type 2 diabetes: A longitudinal analysis.

    Science.gov (United States)

    Tang, Tien; Abbott, Sally; le Roux, Carel W; Wilson, Violet; Singhal, Rishi; Bellary, Srikanth; Tahrani, Abd A

    2018-03-01

    We examined the relationship between weight changes after preoperative glucagon-like peptide-1 receptor agonist (GLP-1RA) treatment and weight changes from the start of medical weight management (MWM) until 12 months after bariatric surgery in patients with type 2 diabetes in a retrospective cohort study. A total of 45 patients (64.4% women, median [interquartile range] age 49 [45-60] years) were included. The median (interquartile range) weight loss from start of MWM until 12 months post-surgery was 17.9% (13.0%-29.3%). GLP-1RA treatment during MWM resulted in 5.0% (1.9%-7.7%) weight loss. Weight loss during GLP-1RA treatment predicted weight loss from the start of MWM until 12 months post-surgery, but not postoperative weight loss after adjustment. The proportion of weight loss from start of MWM to 12 months post-surgery attributed to GLP-1RA treatment was negatively associated with that attributed to surgery, after adjustment. In conclusion, weight change after GLP-1RA treatment predicted the weight loss achieved by a combination of MWM and bariatric surgery, but not weight loss induced by surgery only. Failure to lose weight after GLP-1RA treatment should not be considered a barrier to undergoing bariatric surgery. © 2017 John Wiley & Sons Ltd.

  17. Thermodynamic model for predicting equilibrium conditions of clathrate hydrates of noble gases + light hydrocarbons: Combination of Van der Waals–Platteeuw model and sPC-SAFT EoS

    International Nuclear Information System (INIS)

    Abolala, Mostafa; Varaminian, Farshad

    2015-01-01

    Highlights: • Applying sPC-SAFT for phase equilibrium calculations. • Determining Kihara potential parameters for hydrate formers. • Successful usage of the model for systems with hydrate azeotropes. - Abstract: In this communication, equilibrium conditions of clathrate hydrates containing mixtures of noble gases (Argon, Krypton and Xenon) and light hydrocarbons (C 1 –C 3 ), which form structure I and II, are modeled. The thermodynamic model is based on the solid solution theory of Van der Waals–Platteeuw combined with the simplified Perturbed-Chain Statistical Association Fluid Theory equation of state (sPC-SAFT EoS). In dispersion term of sPC-SAFT EoS, the temperature dependent binary interaction parameters (k ij ) are adjusted; taking advantage of the well described (vapor + liquid) phase equilibria. Furthermore, the Kihara potential parameters are optimized based on the P–T data of pure hydrate former. Subsequently, these obtained parameters are used to predict the binary gas hydrate dissociation conditions. The equilibrium conditions of the binary gas hydrates predicted by this model agree well with experimental data (overall AAD P ∼ 2.17)

  18. Combination of Mean Platelet Volume/Platelet Count Ratio and the APACHE II Score Better Predicts the Short-Term Outcome in Patients with Acute Kidney Injury Receiving Continuous Renal Replacement Therapy.

    Science.gov (United States)

    Li, Junhui; Li, Yingchuan; Sheng, Xiaohua; Wang, Feng; Cheng, Dongsheng; Jian, Guihua; Li, Yongguang; Feng, Liang; Wang, Niansong

    2018-03-29

    Both the Acute physiology and Chronic Health Evaluation (APACHE II) score and mean platelet volume/platelet count Ratio (MPR) can independently predict adverse outcomes in critically ill patients. This study was aimed to investigate whether the combination of them could have a better performance in predicting prognosis of patients with acute kidney injury (AKI) who received continuous renal replacement therapy (CRRT). Two hundred twenty-three patients with AKI who underwent CRRT between January 2009 and December 2014 in a Chinese university hospital were enrolled. They were divided into survivals group and non-survivals group based on the situation at discharge. Receiver Operating Characteristic (ROC) curve was used for MPR and APACHE II score, and to determine the optimal cut-off value of MPR for in-hospital mortality. Factors associated with mortality were identified by univariate and multivariate logistic regression analysis. The mean age of the patients was 61.4 years, and the overall in-hospital mortality was 48.4%. Acute cardiorenal syndrome (ACRS) was the most common cause of AKI. The optimal cut-off value of MPR for mortality was 0.099 with an area under the ROC curve (AUC) of 0.636. The AUC increased to 0.851 with the addition of the APACHE II score. The mortality of patients with of MPR > 0.099 was 56.4%, which was significantly higher than that of the control group with of ≤ 0.099 (39.6%, P= 0.012). Logistic regression analysis showed that average number of organ failure (OR = 2.372), APACHE II score (OR = 1.187), age (OR = 1.028) and vasopressors administration (OR = 38.130) were significantly associated with poor prognosis. Severity of illness was significantly associated with prognosis of patients with AKI. The combination of MPR and APACHE II score may be helpful in predicting the short-term outcome of AKI. © 2018 The Author(s). Published by S. Karger AG, Basel.

  19. Correlation Between Squamous Cell Carcinoma Antigen Level and the Clinicopathological Features of Early-Stage Cervical Squamous Cell Carcinoma and the Predictive Value of Squamous Cell Carcinoma Antigen Combined With Computed Tomography Scan for Lymph Node Metastasis.

    Science.gov (United States)

    Xu, Dianbo; Wang, Danbo; Wang, Shuo; Tian, Ye; Long, Zaiqiu; Ren, Xuemei

    2017-11-01

    The aim of this study was to analyze the relationship between serum squamous cell carcinoma antigen (SCC-Ag) and the clinicopathological features of cervical squamous cell carcinoma. The value of SCC-Ag and computed tomography (CT) for predicting lymph node metastasis (LNM) was evaluated. A total of 197 patients with International Federation of Gynecology and Obstetrics stages IB to IIA cervical squamous cell carcinoma who underwent radical surgery were enrolled in this study. The SCC-Ag was measured, and CT scans were used for the preoperative assessment of lymph node status. Increased preoperative SCC-Ag levels were associated with International Federation of Gynecology and Obstetrics stage (P = 0.001), tumor diameter of greater than 4 cm (P 4 cm (P = 0.001, OR = 4.019), and greater than one half stromal infiltration (P = 0.002, OR = 3.680) as independent factors affecting SCC-Ag greater than or equal to 2.35 ng/mL. In the analysis of LNM, SCC-Ag greater than or equal to 2.35 ng/mL (P < 0.001, OR = 4.825) was an independent factor for LNM. The area under the receiver operator characteristic curve (AUC) of SCC-Ag was 0.763 for all patients, and 0.805 and 0.530 for IB1 + IIA1 and IB2 + IIA2 patients, respectively; 2.35 ng/mL was the optimum cutoff for predicting LNM. The combination of CT and SCC-Ag showed a sensitivity and specificity of 82.9% and 66% in parallel tests, and 29.8% and 93.3% in serial tests, respectively. The increase of SCC-Ag level in the preoperative phase means that there may be a pathological risk factor for postoperative outcomes. The SCC-Ag (≥2.35 ng/mL) may be a useful marker for predicting LNM of cervical cancer, especially in stages IB1 and IIA1, and the combination of SCC-Ag and CT may help identify patients with LNM to provide them with the most appropriate therapeutic approach.

  20. Combination of Mean Platelet Volume/Platelet Count Ratio and the APACHE II Score Better Predicts the Short-Term Outcome in Patients with Acute Kidney Injury Receiving Continuous Renal Replacement Therapy

    Directory of Open Access Journals (Sweden)

    Junhui Li

    2018-03-01

    Full Text Available Background/Aims: Both the Acute physiology and Chronic Health Evaluation (APACHE II score and mean platelet volume/platelet count Ratio (MPR can independently predict adverse outcomes in critically ill patients. This study was aimed to investigate whether the combination of them could have a better performance in predicting prognosis of patients with acute kidney injury (AKI who received continuous renal replacement therapy (CRRT. Methods: Two hundred twenty-three patients with AKI who underwent CRRT between January 2009 and December 2014 in a Chinese university hospital were enrolled. They were divided into survivals group and non-survivals group based on the situation at discharge. Receiver Operating Characteristic (ROC curve was used for MPR and APACHE II score, and to determine the optimal cut-off value of MPR for in-hospital mortality. Factors associated with mortality were identified by univariate and multivariate logistic regression analysis. Results: The mean age of the patients was 61.4 years, and the overall in-hospital mortality was 48.4%. Acute cardiorenal syndrome (ACRS was the most common cause of AKI. The optimal cut-off value of MPR for mortality was 0.099 with an area under the ROC curve (AUC of 0.636. The AUC increased to 0.851 with the addition of the APACHE II score. The mortality of patients with of MPR > 0.099 was 56.4%, which was significantly higher than that of the control group with of ≤ 0.099 (39.6%, P= 0.012. Logistic regression analysis showed that average number of organ failure (OR = 2.372, APACHE II score (OR = 1.187, age (OR = 1.028 and vasopressors administration (OR = 38.130 were significantly associated with poor prognosis. Conclusion: Severity of illness was significantly associated with prognosis of patients with AKI. The combination of MPR and APACHE II score may be helpful in predicting the short-term outcome of AKI.

  1. Elevation of Plasma TGF-β1 During Radiation Therapy Predicts Radiation-Induced Lung Toxicity in Patients With Non-Small-Cell Lung Cancer: A Combined Analysis From Beijing and Michigan

    International Nuclear Information System (INIS)

    Zhao Lujun; Wang Luhua; Ji Wei; Wang Xiaozhen; Zhu Xiangzhi; Hayman, James A.; Kalemkerian, Gregory P.; Yang Weizhi; Brenner, Dean; Lawrence, Theodore S.; Kong, F.-M.

    2009-01-01

    Purpose: To test whether radiation-induced elevations of transforming growth factor-β1 (TGF-β1) during radiation therapy (RT) correlate with radiation-induced lung toxicity (RILT) in patients with non-small-cell lung cancer (NSCLC) and to evaluate the ability of mean lung dose (MLD) to improve the predictive power. Methods and Materials: Eligible patients included those with Stage I-III NSCLC treated with RT with or without chemotherapy. Platelet-poor plasma was obtained pre-RT and at 4-5 weeks (40-50 Gy) during RT. TGF-β1 was measured using an enzyme-linked immunosorbent assay. The primary endpoint was ≥ Grade 2 RILT. Mann-Whitney U test, logistic regression, and chi-square were used for statistical analysis. Results: A total of 165 patients were enrolled in this study. The median radiation dose was 60 Gy, and the median MLD was 15.3 Gy. Twenty-nine patients (17.6%) experienced RILT. The incidence of RILT was 46.2% in patients with a TGF-β1 ratio > 1 vs. 7.9% in patients with a TGF-β1 ratio ≤ 1 (p 20 Gy vs. 17.4% if MLD ≤ 20 Gy (p = 0.024). The incidence was 4.3% in patients with a TGF-β1 ratio ≤ 1 and MLD ≤ 20 Gy, 47.4% in those with a TGF-β1 ratio >1 or MLD > 20 Gy, and 66.7% in those with a TGF-β1 ratio >1 and MLD > 20 Gy (p < 0.001). Conclusions: Radiation-induced elevation of plasma TGF-β1 level during RT is predictive of RILT. The combination of TGF- β1 and MLD may help stratify the patients for their risk of RILT.

  2. Development and validation of a prognostic model using blood biomarker information for prediction of survival of non-small-cell lung cancer patients treated with combined chemotherapy and radiation or radiotherapy alone (NCT00181519, NCT00573040, and NCT00572325).

    Science.gov (United States)

    Dehing-Oberije, Cary; Aerts, Hugo; Yu, Shipeng; De Ruysscher, Dirk; Menheere, Paul; Hilvo, Mika; van der Weide, Hiska; Rao, Bharat; Lambin, Philippe

    2011-10-01

    Currently, prediction of survival for non-small-cell lung cancer patients treated with (chemo)radiotherapy is mainly based on clinical factors. The hypothesis of this prospective study was that blood biomarkers related to hypoxia, inflammation, and tumor load would have an added prognostic value for predicting survival. Clinical data and blood samples were collected prospectively (NCT00181519, NCT00573040, and NCT00572325) from 106 inoperable non-small-cell lung cancer patients (Stages I-IIIB), treated with curative intent with radiotherapy alone or combined with chemotherapy. Blood biomarkers, including lactate dehydrogenase, C-reactive protein, osteopontin, carbonic anhydrase IX, interleukin (IL) 6, IL-8, carcinoembryonic antigen (CEA), and cytokeratin fragment 21-1, were measured. A multivariate model, built on a large patient population (N = 322) and externally validated, was used as a baseline model. An extended model was created by selecting additional biomarkers. The model's performance was expressed as the area under the curve (AUC) of the receiver operating characteristic and assessed by use of leave-one-out cross validation as well as a validation cohort (n = 52). The baseline model consisted of gender, World Health Organization performance status, forced expiratory volume, number of positive lymph node stations, and gross tumor volume and yielded an AUC of 0.72. The extended model included two additional blood biomarkers (CEA and IL-6) and resulted in a leave-one-out AUC of 0.81. The performance of the extended model was significantly better than the clinical model (p = 0.004). The AUC on the validation cohort was 0.66 and 0.76, respectively. The performance of the prognostic model for survival improved markedly by adding two blood biomarkers: CEA and IL-6. Copyright © 2011 Elsevier Inc. All rights reserved.

  3. Prediction of Path Deviation in Robot Based Incremental Sheet Metal Forming by Means of a New Solid-Shell Finite Element Technology and a Finite Elastoplastic Model with Combined Hardening

    Science.gov (United States)

    Kiliclar, Yalin; Laurischkat, Roman; Vladimirov, Ivaylo N.; Reese, Stefanie

    2011-08-01

    The presented project deals with a robot based incremental sheet metal forming process, which is called roboforming and has been developed at the Chair of Production Systems. It is characterized by flexible shaping using a freely programmable path-synchronous movement of two industrial robots. The final shape is produced by the incremental infeed of the forming tool in depth direction and its movement along the part contour in lateral direction. However, the resulting geometries formed in roboforming deviate several millimeters from the reference geometry. This results from the compliance of the involved machine structures and the springback effects of the workpiece. The project aims to predict these deviations caused by resiliences and to carry out a compensative path planning based on this prediction. Therefore a planning tool is implemented which compensates the robots's compliance and the springback effects of the sheet metal. The forming process is simulated by means of a finite element analysis using a material model developed at the Institute of Applied Mechanics (IFAM). It is based on the multiplicative split of the deformation gradient in the context of hyperelasticity and combines nonlinear kinematic and isotropic hardening. Low-order finite elements used to simulate thin sheet structures, such as used for the experiments, have the major problem of locking, a nonphysical stiffening effect. For an efficient finite element analysis a special solid-shell finite element formulation based on reduced integration with hourglass stabilization has been developed. To circumvent different locking effects, the enhanced assumed strain (EAS) and the assumed natural strain (ANS) concepts are included in this formulation. Having such powerful tools available we obtain more accurate geometries.

  4. Improving the Resilience of Major Ports and Critical Supply Chains to Extreme Coastal Flooding: a Combined Artificial Neural Network and Hydrodynamic Simulation Approach to Predicting Tidal Surge Inundation of Port Infrastructure and Impact on Operations.

    Science.gov (United States)

    French, J.

    2015-12-01

    Ports are vital to the global economy, but assessments of global exposure to flood risk have generally focused on major concentrations of population or asset values. Few studies have examined the impact of extreme inundation events on port operation and critical supply chains. Extreme water levels and recurrence intervals have conventionally been estimated via analysis of historic water level maxima, and these vary widely depending on the statistical assumptions made. This information is supplemented by near-term forecasts from operational surge-tide models, which give continuous water levels but at considerable computational cost. As part of a NERC Infrastructure and Risk project, we have investigated the impact of North Sea tidal surges on the Port of Immingham, eastern, UK. This handles the largest volume of bulk cargo in the UK and flows of coal and biomass that are critically important for national energy security. The port was partly flooded during a major tidal surge in 2013. This event highlighted the need for improved local forecasts of surge timing in relation to high water, with a better indication of flood depth and duration. We address this problem using a combination of data-driven and numerical hydrodynamic models. An Artificial Neural Network (ANN) is first used to predict the surge component of water level from meteorological data. The input vector comprises time-series of local wind (easterly and northerly wind stress) and pressure, as well as regional pressure and pressure gradients from stations between the Shetland Islands and the Humber estuary. The ANN achieves rms errors of around 0.1 m and can generate short-range (~ 3 to 12 hour) forecasts given real-time input data feeds. It can also synthesize water level events for a wider range of tidal and meteorological forcing combinations than contained in the observational records. These are used to force Telemac2D numerical floodplain simulations using a LiDAR digital elevation model of the port

  5. Non-empirical Prediction of the Photophysical and Magnetic Properties of Systems with Open d- and f-Shells Based on Combined Ligand Field and Density Functional Theory (LFDFT).

    Science.gov (United States)

    Daul, Claude

    2014-09-01

    Despite the important growth of ab initio and computational techniques, ligand field theory in molecular science or crystal field theory in condensed matter offers the most intuitive way to calculate multiplet energy levels arising from systems with open shells d and/or f electrons. Over the past decade we have developed a ligand field treatment of inorganic molecular modelling taking advantage of the dominant localization of the frontier orbitals within the metal-sphere. This feature, which is observed in any inorganic coordination compound, especially if treated by Density Functional Theory calculation, allows the determination of the electronic structure and properties with a surprising good accuracy. In ligand field theory, the theoretical concepts consider only a single atom center; and treat its interaction with the chemical environment essentially as a perturbation. Therefore success in the simple ligand field theory is no longer questionable, while the more accurate molecular orbital theory does in general over-estimate the metal-ligand covalence, thus yields wave functions that are too delocalized. Although LF theory has always been popular as a semi-empirical method when dealing with molecules of high symmetry e.g. cubic symmetry where the number of parameters needed is reasonably small (3 or 5), this is no more the case for molecules without symmetry and involving both an open d- and f-shell (# parameters ∼90). However, the combination of LF theory and Density Functional (DF) theory that we introduced twenty years ago can easily deal with complex molecules of any symmetry with two and more open shells. The accuracy of these predictions from 1(st) principles achieves quite a high accuracy (<5%) in terms of states energies. Hence, this approach is well suited to predict the magnetic and photo-physical properties arbitrary molecules and materials prior to their synthesis, which is the ultimate goal of each computational chemist. We will illustrate the

  6. combination Dictionary

    African Journals Online (AJOL)

    rbr

    of the idiomatic expression as a whole" (Crystal 2003: 225-226). Idiomatic .... nations and idioms.11 Nonetheless, free combinations of words have not been .... those who thronged Emmett place last night wanted to see the film, and they.

  7. Winning Combinations

    DEFF Research Database (Denmark)

    Criscuolo, Paola; Laursen, Keld; Reichstein, Toke

    2018-01-01

    examine the effectiveness of different combinations of knowledge sources for achieving innovative performance. We suggest that combinations involving integrative search strategies – combining internal and external knowledge – are the most likely to generate product and process innovation. In this context......, we present the idea that cognitively distant knowledge sources are helpful for innovation only when used in conjunction with knowledge sources that are closer to the focal firm. We also find important differences between product and process innovation, with the former associated with broader searches......Searching for the most rewarding sources of innovative ideas remains a key challenge in management of technological innovation. Yet, little is known about which combinations of internal and external knowledge sources are triggers for innovation. Extending theories about searching for innovation, we...

  8. Adverse drug reactions after intravenous rituximab infusion are more common in hematologic malignancies than in autoimmune disorders and can be predicted by the combination of few clinical and laboratory parameters: results from a retrospective, multicenter study of 374 patients.

    Science.gov (United States)

    D'Arena, Giovanni; Simeon, Vittorio; Laurenti, Luca; Cimminiello, Michele; Innocenti, Idanna; Gilio, Michele; Padula, Angela; Vigliotti, Maria Luigia; De Lorenzo, Sonya; Loseto, Giacomo; Passarelli, Anna; Di Minno, Matteo Nicola Dario; Tucci, Marco; De Feo, Vincenzo; D'Auria, Fiorella; Silvestris, Francesco; Di Minno, Giovanni; Musto, Pellegrino

    2017-11-01

    Rituximab is an effective treatment for CD20 + B-cell malignancies and autoimmune disorders. However, adverse drug reactions (ADRs) may occur after rituximab infusion, causing, in rare cases, its discontinuation. In this multicenter, retrospective study, among 374 patients treated with rituximab i.v., 23.5% experienced ADRs. Mean follow-up was 20.6 months (range 8-135). Overall, ADRs were significantly more frequent in non-Hodgkin lymphomas (NHL) and chronic lymphocytic leukemias (25-35.9%), than in autoimmune diseases (9.4-17.5%) (p < .0001). Grade 3-4 toxicity was observed in eight patients (2.1%), and in four of them (1% of all patients) definitive drug discontinuation was necessary. Interestingly, three groups of patients with different risk of developing ADR were identified, according to a predictive heat-map developed combining four parameters (splenomegaly, history of allergy, hemoglobin levels and gender) selected by multivariate analysis. This model may be useful in identifying patients at higher risk of ADRs, needing appropriate preventing therapies.

  9. The Prediction Value

    NARCIS (Netherlands)

    Koster, M.; Kurz, S.; Lindner, I.; Napel, S.

    2013-01-01

    We introduce the prediction value (PV) as a measure of players’ informational importance in probabilistic TU games. The latter combine a standard TU game and a probability distribution over the set of coalitions. Player i’s prediction value equals the difference between the conditional expectations

  10. Predicting AD conversion

    DEFF Research Database (Denmark)

    Liu, Yawu; Mattila, Jussi; Ruiz, Miguel �ngel Mu�oz

    2013-01-01

    To compare the accuracies of predicting AD conversion by using a decision support system (PredictAD tool) and current research criteria of prodromal AD as identified by combinations of episodic memory impairment of hippocampal type and visual assessment of medial temporal lobe atrophy (MTA) on MRI...

  11. Heterogeneity of HVR-1 quasispecies is predictive of early but not sustained virological response in genotype 1b-infected patients undergoing combined treatment with PEG- or STD-IFN plus RBV.

    Science.gov (United States)

    Abbate, I; Cappiello, G; Lo Iacono, O; Longo, R; Ferraro, D; Antonucci, G; Di Marco, V; Di Stefano, R; Craxì, A; Solmone, M C; Spanò, A; Ippolito, G; Capobianchi, M R

    2003-01-01

    ISDR mutation pattern and HVR-1 quasispecies were analyzed in HCV genotype 1b-infected patients treated with either PEG- or STD-IFN plus ribavirin, in order to find virological correlates of therapy outcome. ISDR region analysis, performed at baseline (T0) and at 4 weeks of therapy (T1), indicated that ISDR mutation pattern was not predictive of response to treatment. Moreover, no selection of putative resistant strains in the first month of therapy was observed. Viral load was not correlated with any parameter of HVR-1 heterogeneity. Among the HVR-1 heterogeneity parameters considered, complexity was inversely correlated to viral load decline at T1. In univariate analysis, complexity, proportion of non synonymous substitutions (NS) and NS/S ratio were lower in patients showing virological response at 6 months of treatment. Complexity was the only parameter independently associated with both decline of viral load at T1 and virological response after 6 months, even after adjustment for confounding variables. At the end of treatment or later, these correlations were lost. Evolution pattern of the HVR-1 quasispecies indicated a strong selective pressure in sustained responders, with complete substitution of pre-existing quasispecies, while minor changes occured in non responders. In relapsers both patterns were present at a similar rate. In conclusion, this study shows that HVR-1 heterogeneity may be involved in the early response to combined IFN-RBV therapy. The loss of correlation between viral heterogeneity and therapy outcome at 6 months of therapy, or later, suggests that other factors may play a role in maintaining sustained response to treatment.

  12. Role of Combined Post-Operative Venous Lactate and 48 Hours C-Reactive Protein Values on the Etiology and Predictive Capacity of Organ-Space Surgical Site Infection after Elective Colorectal Operation.

    Science.gov (United States)

    Juvany, Montserrat; Guirao, Xavier; Oliva, Joan Carles; Badía Pérez, Jose M

    2017-04-01

    C-reactive protein (CRP) has been assessed to detect organ-space surgical site infection (OSI). Nevertheless, data about peri-operative oxygen debt and surgical stress-elicited biologic markers to explain and allow for the early detection of OSI are lacking. We analyzed immediate post-operative venous lactate, early CRP levels, and intra-operative hemodynamic values on the capacity to predict OSI after elective colorectal operation. Patients undergoing an elective colorectal surgical procedure with anastomosis between March 2013 and August 2014 were included and assessed prospectively. Post-operative lactate values at L-0, L-6, and L-24 hours, CRP (basal and 48 h), and the percentage of operative time (POT) with systolic blood pressure below 100 mm Hg and heart rate above 90 beats per minute in patients with and without OSI were compared. Binary logistic regression was constructed for L-0 and CRP-48, and receiver-operating characteristic (ROC) was analyzed for sensitivity (S), specificity (Sp), positive (PPV) and negative (NPV) predictive values. Patients with OSI (11 of 100) showed higher L-0 and L-24 (3.2 ± 2.5 vs. 1.6 ± 0.8; p = 0.025 and 1.9 ± 1.2 vs. 1.2 ± 0.4 mmol/L; p = 0.025) and CRP-48 (188 ± 80 vs. 74 ± 52 mg/L; p = 0.001). The ROC from logistic regression showed area under the curve of 0.899 (95% confidence interval [CI] 0.805-0.992), S of 72% (95% CI 43.2%-90.5%), Sp of 95% (95% CI 88.6%-98.4%), PPV of 66% (95% CI 38.9%-86.4%) and NPV of 0.96 (95% CI 90%-99%). L-0 was higher in those patients with hypotension during more than 60% of the POT (2.4 ± 2.1 vs. 1.6 ± 0.8; p = 0.038). Patients with OSI had a higher POT with hypotension (50 ± 28% vs. 30 ± 28%; p = 0.032) and tachycardia (18 ± 27% vs. 5 ± 16%; p = 0,024). The combination of immediate post-operative lactate and CRP at 48 hours proved to be useful in predicting OSI after elective colorectal operation

  13. Forecast combinations

    OpenAIRE

    Aiolfi, Marco; Capistrán, Carlos; Timmermann, Allan

    2010-01-01

    We consider combinations of subjective survey forecasts and model-based forecasts from linear and non-linear univariate specifications as well as multivariate factor-augmented models. Empirical results suggest that a simple equal-weighted average of survey forecasts outperform the best model-based forecasts for a majority of macroeconomic variables and forecast horizons. Additional improvements can in some cases be gained by using a simple equal-weighted average of survey and model-based fore...

  14. Combined homicide

    Directory of Open Access Journals (Sweden)

    Slović Živana

    2017-01-01

    Full Text Available Introduction: Combined homicide is a combination of two or more different modes of killing. These homicides occur when multiple perpetrators have different mode of killing, to hide the true manner of death, or when an initially unsuccessful attack with one weapon is abandoned and changed by another mode which is more successful, or due to availability of weapons at the scene of homicide, or unexpected appearance of possible eyewitness, or else. Case report: This case report is about 65-year old woman who was found in her residence on the floor next to the bed lying on her back with two kitchen knives in her neck. Autopsy revealed an abrasion on the frontal part of the neck and a bruise of the soft tissues of the neck with a double fracture of both greater horns of the hyoid bone and a fracture of both superior horns of the thyroid cartilage. The cause of death was exsanguination into right half of the thoracic cavity from the left subclavian artery which was cut, on the spot of stab wound in the neck. Conclusion: Hemorrhage in the soft tissue near broken hyoid bone and thyroid cartilage indicate that the victim was first strangulated and then stabbed with kitchen knives. Combined homicides are caused by one or more killers in order to accelerate the killing, or to be sure to provide the fatal outcome. This case is also interesting because the killer left weapon in the victim's neck.

  15. Prostate Specific Antigen (PSA) as Predicting Marker for Clinical Outcome and Evaluation of Early Toxicity Rate after High-Dose Rate Brachytherapy (HDR-BT) in Combination with Additional External Beam Radiation Therapy (EBRT) for High Risk Prostate Cancer.

    Science.gov (United States)

    Ecke, Thorsten H; Huang-Tiel, Hui-Juan; Golka, Klaus; Selinski, Silvia; Geis, Berit Christine; Koswig, Stephan; Bathe, Katrin; Hallmann, Steffen; Gerullis, Holger

    2016-11-10

    High-dose-rate brachytherapy (HDR-BT) with external beam radiation therapy (EBRT) is a common treatment option for locally advanced prostate cancer (PCa). Seventy-nine male patients (median age 71 years, range 50 to 79) with high-risk PCa underwent HDR-BT following EBRT between December 2009 and January 2016 with a median follow-up of 21 months. HDR-BT was administered in two treatment sessions (one week interval) with 9 Gy per fraction using a planning system and the Ir192 treatment unit GammaMed Plus iX. EBRT was performed with CT-based 3D-conformal treatment planning with a total dose administration of 50.4 Gy with 1.8 Gy per fraction and five fractions per week. Follow-up for all patients was organized one, three, and five years after radiation therapy to evaluate early and late toxicity side effects, metastases, local recurrence, and prostate-specific antigen (PSA) value measured in ng/mL. The evaluated data included age, PSA at time of diagnosis, PSA density, BMI (body mass index), Gleason score, D'Amico risk classification for PCa, digital rectal examination (DRE), PSA value after one/three/five year(s) follow-up (FU), time of follow-up, TNM classification, prostate volume, and early toxicity rates. Early toxicity rates were 8.86% for gastrointestinal, and 6.33% for genitourinary side effects. Of all treated patients, 84.81% had no side effects. All reported complications in early toxicity were grade 1. PSA density at time of diagnosis ( p = 0.009), PSA on date of first HDR-BT ( p = 0.033), and PSA on date of first follow-up after one year ( p = 0.025) have statistical significance on a higher risk to get a local recurrence during follow-up. HDR-BT in combination with additional EBRT in the presented design for high-risk PCa results in high biochemical control rates with minimal side-effects. PSA is a negative predictive biomarker for local recurrence during follow-up. A longer follow-up is needed to assess long-term outcome and toxicities.

  16. Combination of prostate imaging reporting and data system (PI-RADS) score and prostate-specific antigen (PSA) density predicts biopsy outcome in prostate biopsy naïve patients.

    Science.gov (United States)

    Washino, Satoshi; Okochi, Tomohisa; Saito, Kimitoshi; Konishi, Tsuzumi; Hirai, Masaru; Kobayashi, Yutaka; Miyagawa, Tomoaki

    2017-02-01

    To assess the value of the Prostate Imaging Reporting and Data System (PI-RADS) scoring system, for prostate multi-parametric magnetic resonance imaging (mpMRI) to detect prostate cancer, and classical parameters, such as prostate-specific antigen (PSA) level, prostate volume and PSA density, for predicting biopsy outcome in biopsy naïve patients who have suspected prostate cancer. Patients who underwent mpMRI at our hospital, and who had their first prostate biopsy between July 2010 and April 2014, were analysed retrospectively. The prostate biopsies were taken transperineally under transrectal ultrasonography guidance. In all, 14 cores were biopsied as a systematic biopsy in all patients. Two cognitive fusion-targeted biopsy cores were added for each lesion in patients who had suspicious or equivocal lesions on mpMRI. The PI-RADS scoring system version 2.0 (PI-RADS v2) was used to describe the MRI findings. Univariate and multivariate analyses were performed to determine significant predictors of prostate cancer and clinically significant prostate cancer. In all, 288 patients were analysed. The median patient age, PSA level, prostate volume and PSA density were 69 years, 7.5 ng/mL, 28.7 mL, and 0.26 ng/mL/mL, respectively. The biopsy results were benign, clinically insignificant, and clinically significant prostate cancer in 129 (45%), 18 (6%) and 141 (49%) patients, respectively. The multivariate analysis revealed that PI-RADS v2 score and PSA density were independent predictors for prostate cancer and clinically significant prostate cancer. When PI-RADS v2 score and PSA density were combined, a PI-RADS v2 score of ≥4 and PSA density ≥0.15 ng/mL/mL, or PI-RADS v2 score of 3 and PSA density of ≥0.30 ng/mL/mL, was associated with the highest clinically significant prostate cancer detection rates (76-97%) on the first biopsy. Of the patients in this group with negative biopsy results, 22% were subsequently diagnosed as prostate cancer. In contrast, a PI

  17. WALS Prediction

    NARCIS (Netherlands)

    Magnus, J.R.; Wang, W.; Zhang, Xinyu

    2012-01-01

    Abstract: Prediction under model uncertainty is an important and difficult issue. Traditional prediction methods (such as pretesting) are based on model selection followed by prediction in the selected model, but the reported prediction and the reported prediction variance ignore the uncertainty

  18. Value of Combining Left Atrial Diameter and Amino-terminal Pro-brain Natriuretic Peptide to the CHA2DS2-VASc Score for Predicting Stroke and Death in Patients with Sick Sinus Syndrome after Pacemaker Implantation

    Directory of Open Access Journals (Sweden)

    Bin-Feng Mo

    2017-01-01

    Conclusions: CHA2DS2-VASc score is valuable for predicting stroke and death risk in patients with SSS after pacemaker implantation. The addition of LAD and NT-proBNP to the CHA2DS2-VASc score improved its predictive power for stroke and death, respectively, in this patient cohort. Future prospective studies are warranted to validate the benefit of adding LAD and NT-proBNP to the CHA2DS2-VASc score for predicting stroke and death risk in non-AF populations.

  19. ILSE combiner study

    International Nuclear Information System (INIS)

    Hahn, K.

    1994-03-01

    In a heavy ion inertial fusion (HIF) driver, the beam energy and current are increased several orders of magnitude from the injector to the final focus system. At low and high energy stages of the driver, electrostatic and magnetic focusing transport channels, respectively, can be used. At the electric-to-magnetic transition point, the beams may be combined to reduce the transverse dimensions of the system, which could have significant impact on the driver cost. In a presently envisioned combiner, four beams are brought together transversely into a single transport channel. A matching section follows the combiner in order to provide a smooth transition to the subsequent magnetic transport channel. This report summarizes a conceptual design study of possible combiner configurations for the proposed Introduction Linac Systems Experiment (ILSE). The conceptual design study includes subjects such as the expected technical difficulties, predicted emittance growth, particle loss, effect of geometric and chromatic aberrations, and the sensitivity of emittance growth on the initial beam position and angle errors

  20. Climate prediction and predictability

    Science.gov (United States)

    Allen, Myles

    2010-05-01

    Climate prediction is generally accepted to be one of the grand challenges of the Geophysical Sciences. What is less widely acknowledged is that fundamental issues have yet to be resolved concerning the nature of the challenge, even after decades of research in this area. How do we verify or falsify a probabilistic forecast of a singular event such as anthropogenic warming over the 21st century? How do we determine the information content of a climate forecast? What does it mean for a modelling system to be "good enough" to forecast a particular variable? How will we know when models and forecasting systems are "good enough" to provide detailed forecasts of weather at specific locations or, for example, the risks associated with global geo-engineering schemes. This talk will provide an overview of these questions in the light of recent developments in multi-decade climate forecasting, drawing on concepts from information theory, machine learning and statistics. I will draw extensively but not exclusively from the experience of the climateprediction.net project, running multiple versions of climate models on personal computers.

  1. Boosted regression trees, multivariate adaptive regression splines and their two-step combinations with multiple linear regression or partial least squares to predict blood-brain barrier passage: a case study.

    Science.gov (United States)

    Deconinck, E; Zhang, M H; Petitet, F; Dubus, E; Ijjaali, I; Coomans, D; Vander Heyden, Y

    2008-02-18

    The use of some unconventional non-linear modeling techniques, i.e. classification and regression trees and multivariate adaptive regression splines-based methods, was explored to model the blood-brain barrier (BBB) passage of drugs and drug-like molecules. The data set contains BBB passage values for 299 structural and pharmacological diverse drugs, originating from a structured knowledge-based database. Models were built using boosted regression trees (BRT) and multivariate adaptive regression splines (MARS), as well as their respective combinations with stepwise multiple linear regression (MLR) and partial least squares (PLS) regression in two-step approaches. The best models were obtained using combinations of MARS with either stepwise MLR or PLS. It could be concluded that the use of combinations of a linear with a non-linear modeling technique results in some improved properties compared to the individual linear and non-linear models and that, when the use of such a combination is appropriate, combinations using MARS as non-linear technique should be preferred over those with BRT, due to some serious drawbacks of the BRT approaches.

  2. Experiences with an outpatient relapse program (community reinforcement approach) combined with naltrexone in teh treament of opioid-dependence: effect on addictive behaviors and the predictive value of Psychiatric comorbidity.

    NARCIS (Netherlands)

    Roozen, H.G.; Kerkhof, A.J.F.M.; Brink, van den W.

    2003-01-01

    Background: There is increasing interest in naltrexone, an opiate antagonist, in the treatment of opiate addicts. The effects of naltrexone are often compromised by a lack of compliance and drop-out. The effects of this compound are probably more favorable when combined with a psychosocial

  3. Experiences with an outpatient relapse program (community reinforcement approach) combined with naltrexone in the treatment of opioid-dependence: Effect on addictive behaviors and the predictive value of psychiatric comorbidity

    NARCIS (Netherlands)

    Roozen, H. G.; Kerkhof, A. J. F. M.; van den Brink, W.

    2003-01-01

    BACKGROUND: There is increasing interest in naltrexone, an opiate antagonist, in the treatment of opiate addicts. The effects of naltrexone are often compromised by a lack of compliance and drop-out. The effects of this compound are probably more favorable when combined with a psychosocial

  4. Comparing nonsynergistic gamma models with interaction models to predict growth of emetic Bacillus cereus when using combinations of pH and individual undissociated acids as growth-limiting factors

    NARCIS (Netherlands)

    Biesta-Peters, E.G.; Reij, M.W.; Gorris, L.G.M.; Zwietering, M.H.

    2010-01-01

    A combination of multiple hurdles to limit microbial growth is frequently applied in foods to achieve an overall level of protection. Quantification of hurdle technology aims at identifying synergistic or multiplicative effects and is still being developed. The gamma hypothesis states that

  5. Prediction of forage intake using in vitro gas production methods: Comparison of multiphase fermentation kinetics measured in an automated gas test, and combined gas volume and substrate degradability measurements in a manual syringe system

    NARCIS (Netherlands)

    Blümmel, M.; Cone, J.W.; Gelder, van A.H.; Nshalai, I.; Umunna, N.N.; Makkar, H.P.S.; Becker, K.

    2005-01-01

    This study investigated two approaches to in vitro analysis of gas production data, being a three phase model with long (¿72 h) incubation times, to obtain kinetics and asymptotic values of gas production, and combination of gas volume measurements with residue determinations after a relatively

  6. Incremental value of a combination of cardiac troponin T, N-terminal pro-brain natriuretic peptide and C-reactive protein for prediction of mortality in end-stage renal disease

    DEFF Research Database (Denmark)

    Hallén, Jonas; Madsen, Lene Helleskov; Ladefoged, Søren

    2011-01-01

    Abstract Objective. To determine the relative prognostic merits of C-reactive protein (CRP), cardiac troponin T (cTnT) and N-terminal pro-brain natriuretic peptide (NT-pro-BNP) for prediction of all-cause death in patients with end-stage renal disease (ESRD) receiving haemodialysis. Material...... and methods. This prospective, controlled cohort study included 109 patients. Biomarkers were sampled at inclusion and considered as categorical and continuous variables in Cox proportional hazard models. Results. Mean follow-up ± SD was 926 ± 385 days, during which 52 patients (48%) died. All three markers...

  7. Earthquake prediction

    International Nuclear Information System (INIS)

    Ward, P.L.

    1978-01-01

    The state of the art of earthquake prediction is summarized, the possible responses to such prediction are examined, and some needs in the present prediction program and in research related to use of this new technology are reviewed. Three basic aspects of earthquake prediction are discussed: location of the areas where large earthquakes are most likely to occur, observation within these areas of measurable changes (earthquake precursors) and determination of the area and time over which the earthquake will occur, and development of models of the earthquake source in order to interpret the precursors reliably. 6 figures

  8. Combined assessment of polymorphisms in the LHCGR and FSHR genes predict chance of pregnancy after in vitro fertilization

    DEFF Research Database (Denmark)

    Lindgren, I; Bååth, M; Uvebrant, K

    2016-01-01

    -stimulating hormone receptor (FSHR) variant N680S can be utilized for the prediction of pregnancy chances in women undergoing IVF. WHAT IS KNOWN ALREADY: The FSHR N680S polymorphism has been shown to affect the ovarian response in response to gonadotrophin treatment, while no information is currently available...... regarding variants of the LHCGR in this context. STUDY DESIGN, SIZE, DURATION: Cross-sectional study, duration from September 2010 to February 2015. Women undergoing IVF were consecutively enrolled and genetic variants compared between those who became pregnant and those who did not. The study...... was subsequently replicated in an independent sample. Granulosa cells from a subset of women were investigated regarding functionality of the genetic variants. PARTICIPANTS/MATERIALS, SETTING, METHODS: Women undergoing IVF (n = 384) were enrolled in the study and genotyped. Clinical variables were retrieved from...

  9. Predictive medicine

    NARCIS (Netherlands)

    Boenink, Marianne; ten Have, Henk

    2015-01-01

    In the last part of the twentieth century, predictive medicine has gained currency as an important ideal in biomedical research and health care. Research in the genetic and molecular basis of disease suggested that the insights gained might be used to develop tests that predict the future health

  10. Predicting location-specific extreme coastal floods in the future climate by introducing a probabilistic method to calculate maximum elevation of the continuous water mass caused by a combination of water level variations and wind waves

    Science.gov (United States)

    Leijala, Ulpu; Björkqvist, Jan-Victor; Johansson, Milla M.; Pellikka, Havu

    2017-04-01

    Future coastal management continuously strives for more location-exact and precise methods to investigate possible extreme sea level events and to face flooding hazards in the most appropriate way. Evaluating future flooding risks by understanding the behaviour of the joint effect of sea level variations and wind waves is one of the means to make more comprehensive flooding hazard analysis, and may at first seem like a straightforward task to solve. Nevertheless, challenges and limitations such as availability of time series of the sea level and wave height components, the quality of data, significant locational variability of coastal wave height, as well as assumptions to be made depending on the study location, make the task more complicated. In this study, we present a statistical method for combining location-specific probability distributions of water level variations (including local sea level observations and global mean sea level rise) and wave run-up (based on wave buoy measurements). The goal of our method is to obtain a more accurate way to account for the waves when making flooding hazard analysis on the coast compared to the approach of adding a separate fixed wave action height on top of sea level -based flood risk estimates. As a result of our new method, we gain maximum elevation heights with different return periods of the continuous water mass caused by a combination of both phenomena, "the green water". We also introduce a sensitivity analysis to evaluate the properties and functioning of our meth