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Sample records for molecular ensemble based

  1. Quantum metrology with molecular ensembles

    SciTech Connect

    Schaffry, Marcus; Gauger, Erik M.; Morton, John J. L.; Fitzsimons, Joseph; Benjamin, Simon C.; Lovett, Brendon W.

    2010-10-15

    The field of quantum metrology promises measurement devices that are fundamentally superior to conventional technologies. Specifically, when quantum entanglement is harnessed, the precision achieved is supposed to scale more favorably with the resources employed, such as system size and time required. Here, we consider measurement of magnetic-field strength using an ensemble of spin-active molecules. We identify a third essential resource: the change in ensemble polarization (entropy increase) during the metrology experiment. We find that performance depends crucially on the form of decoherence present; for a plausible dephasing model, we describe a quantum strategy, which can indeed beat the standard strategy.

  2. Cavity-Controlled Chemistry in Molecular Ensembles

    NASA Astrophysics Data System (ADS)

    Herrera, Felipe; Spano, Frank C.

    2016-06-01

    The demonstration of strong and ultrastrong coupling regimes of cavity QED with polyatomic molecules has opened new routes to control chemical dynamics at the nanoscale. We show that strong resonant coupling of a cavity field with an electronic transition can effectively decouple collective electronic and nuclear degrees of freedom in a disordered molecular ensemble, even for molecules with high-frequency quantum vibrational modes having strong electron-vibration interactions. This type of polaron decoupling can be used to control chemical reactions. We show that the rate of electron transfer reactions in a cavity can be orders of magnitude larger than in free space for a wide class of organic molecular species.

  3. Polarizing properties of molecular ensembles: new approaches to calculations

    NASA Astrophysics Data System (ADS)

    Bokarev, Andrey N.; Plastun, Inna L.

    2016-04-01

    Polarizing properties of molecular ensembles with different structures are investigated by numerical simulation. Carbon nanotubes with zigzag configuration and nucleobases are considered. By numerical simulation total polarizability is investigated for different structures of molecules ensembles. New semi-analytical procedure for calculation of total polarizability for ensembles with different configuration is offered and tested by its application to ensembles which contain single-wall carbon nanotubes and nucleobases.

  4. Emerging methods for ensemble-based virtual screening.

    PubMed

    Amaro, Rommie E; Li, Wilfred W

    2010-01-01

    Ensemble based virtual screening refers to the use of conformational ensembles from crystal structures, NMR studies or molecular dynamics simulations. It has gained greater acceptance as advances in the theoretical framework, computational algorithms, and software packages enable simulations at longer time scales. Here we focus on the use of computationally generated conformational ensembles and emerging methods that use these ensembles for discovery, such as the Relaxed Complex Scheme or Dynamic Pharmacophore Model. We also discuss the more rigorous physics-based computational techniques such as accelerated molecular dynamics and thermodynamic integration and their applications in improving conformational sampling or the ranking of virtual screening hits. Finally, technological advances that will help make virtual screening tools more accessible to a wider audience in computer aided drug design are discussed.

  5. Protein Structure Refinement through Structure Selection and Averaging from Molecular Dynamics Ensembles.

    PubMed

    Mirjalili, Vahid; Feig, Michael

    2013-02-12

    A molecular dynamics (MD) simulation based protocol for structure refinement of template-based model predictions is described. The protocol involves the application of restraints, ensemble averaging of selected subsets, interpolation between initial and refined structures, and assessment of refinement success. It is found that sub-microsecond MD-based sampling when combined with ensemble averaging can produce moderate but consistent refinement for most systems in the CASP targets considered here.

  6. Thermodynamics and kinetics of a molecular motor ensemble.

    PubMed

    Baker, J E; Thomas, D D

    2000-10-01

    If, contrary to conventional models of muscle, it is assumed that molecular forces equilibrate among rather than within molecular motors, an equation of state and an expression for energy output can be obtained for a near-equilibrium, coworking ensemble of molecular motors. These equations predict clear, testable relationships between motor structure, motor biochemistry, and ensemble motor function, and we discuss these relationships in the context of various experimental studies. In this model, net work by molecular motors is performed with the relaxation of a near-equilibrium intermediate step in a motor-catalyzed reaction. The free energy available for work is localized to this step, and the rate at which this free energy is transferred to work is accelerated by the free energy of a motor-catalyzed reaction. This thermodynamic model implicitly deals with a motile cell system as a dynamic network (not a rigid lattice) of molecular motors within which the mechanochemistry of one motor influences and is influenced by the mechanochemistry of other motors in the ensemble. PMID:11023881

  7. Thermodynamics and kinetics of a molecular motor ensemble.

    PubMed Central

    Baker, J E; Thomas, D D

    2000-01-01

    If, contrary to conventional models of muscle, it is assumed that molecular forces equilibrate among rather than within molecular motors, an equation of state and an expression for energy output can be obtained for a near-equilibrium, coworking ensemble of molecular motors. These equations predict clear, testable relationships between motor structure, motor biochemistry, and ensemble motor function, and we discuss these relationships in the context of various experimental studies. In this model, net work by molecular motors is performed with the relaxation of a near-equilibrium intermediate step in a motor-catalyzed reaction. The free energy available for work is localized to this step, and the rate at which this free energy is transferred to work is accelerated by the free energy of a motor-catalyzed reaction. This thermodynamic model implicitly deals with a motile cell system as a dynamic network (not a rigid lattice) of molecular motors within which the mechanochemistry of one motor influences and is influenced by the mechanochemistry of other motors in the ensemble. PMID:11023881

  8. MSEBAG: a dynamic classifier ensemble generation based on `minimum-sufficient ensemble' and bagging

    NASA Astrophysics Data System (ADS)

    Chen, Lei; Kamel, Mohamed S.

    2016-01-01

    In this paper, we propose a dynamic classifier system, MSEBAG, which is characterised by searching for the 'minimum-sufficient ensemble' and bagging at the ensemble level. It adopts an 'over-generation and selection' strategy and aims to achieve a good bias-variance trade-off. In the training phase, MSEBAG first searches for the 'minimum-sufficient ensemble', which maximises the in-sample fitness with the minimal number of base classifiers. Then, starting from the 'minimum-sufficient ensemble', a backward stepwise algorithm is employed to generate a collection of ensembles. The objective is to create a collection of ensembles with a descending fitness on the data, as well as a descending complexity in the structure. MSEBAG dynamically selects the ensembles from the collection for the decision aggregation. The extended adaptive aggregation (EAA) approach, a bagging-style algorithm performed at the ensemble level, is employed for this task. EAA searches for the competent ensembles using a score function, which takes into consideration both the in-sample fitness and the confidence of the statistical inference, and averages the decisions of the selected ensembles to label the test pattern. The experimental results show that the proposed MSEBAG outperforms the benchmarks on average.

  9. A space time-ensemble parallel nudged elastic band algorithm for molecular kinetics simulation

    NASA Astrophysics Data System (ADS)

    Nakano, Aiichiro

    2008-02-01

    A scalable parallel algorithm has been designed to study long-time dynamics of many-atom systems based on the nudged elastic band method, which performs mutually constrained molecular dynamics simulations for a sequence of atomic configurations (or states) to obtain a minimum energy path between initial and final local minimum-energy states. A directionally heated nudged elastic band method is introduced to search for thermally activated events without the knowledge of final states, which is then applied to an ensemble of bands in a path ensemble method for long-time simulation in the framework of the transition state theory. The resulting molecular kinetics (MK) simulation method is parallelized with a space-time-ensemble parallel nudged elastic band (STEP-NEB) algorithm, which employs spatial decomposition within each state, while temporal parallelism across the states within each band and band-ensemble parallelism are implemented using a hierarchy of communicator constructs in the Message Passing Interface library. The STEP-NEB algorithm exhibits good scalability with respect to spatial, temporal and ensemble decompositions on massively parallel computers. The MK simulation method is used to study low strain-rate deformation of amorphous silica.

  10. A class of energy-based ensembles in Tsallis statistics

    NASA Astrophysics Data System (ADS)

    Chandrashekar, R.; Naina Mohammed, S. S.

    2011-05-01

    A comprehensive investigation is carried out on the class of energy-based ensembles. The eight ensembles are divided into two main classes. In the isothermal class of ensembles the individual members are at the same temperature. A unified framework is evolved to describe the four isothermal ensembles using the currently accepted third constraint formalism. The isothermal-isobaric, grand canonical and generalized ensembles are illustrated through a study of the classical nonrelativistic and extreme relativistic ideal gas models. An exact calculation is possible only in the case of the isothermal-isobaric ensemble. The study of the ideal gas models in the grand canonical and the generalized ensembles has been carried out using a perturbative procedure with the nonextensivity parameter (1 - q) as the expansion parameter. Though all the thermodynamic quantities have been computed up to a particular order in (1 - q) the procedure can be extended up to any arbitrary order in the expansion parameter. In the adiabatic class of ensembles the individual members of the ensemble have the same value of the heat function and a unified formulation to described all four ensembles is given. The nonrelativistic and the extreme relativistic ideal gases are studied in the isoenthalpic-isobaric ensemble, the adiabatic ensemble with number fluctuations and the adiabatic ensemble with number and particle fluctuations.

  11. Formulation of Liouville's theorem for grand ensemble molecular simulations

    NASA Astrophysics Data System (ADS)

    Delle Site, Luigi

    2016-02-01

    Liouville's theorem in a grand ensemble, that is for situations where a system is in equilibrium with a reservoir of energy and particles, is a subject that, to our knowledge, has not been explicitly treated in literature related to molecular simulation. Instead, Liouville's theorem, a central concept for the correct employment of molecular simulation techniques, is implicitly considered only within the framework of systems where the total number of particles is fixed. However, the pressing demand of applied science in treating open systems leads to the question of the existence and possible exact formulation of Liouville's theorem when the number of particles changes during the dynamical evolution of the system. The intention of this paper is to stimulate a debate about this crucial issue for molecular simulation.

  12. Direct molecular dynamics observation of protein folding transition state ensemble.

    PubMed Central

    Ding, Feng; Dokholyan, Nikolay V; Buldyrev, Sergey V; Stanley, H Eugene; Shakhnovich, Eugene I

    2002-01-01

    The concept of the protein transition state ensemble (TSE), a collection of the conformations that have 50% probability to convert rapidly to the folded state and 50% chance to rapidly unfold, constitutes the basis of the modern interpretation of protein engineering experiments. It has been conjectured that conformations constituting the TSE in many proteins are the expanded and distorted forms of the native state built around a specific folding nucleus. This view has been supported by a number of on-lattice and off-lattice simulations. Here we report a direct observation and characterization of the TSE by molecular dynamic folding simulations of the C-Src SH3 domain, a small protein that has been extensively studied experimentally. Our analysis reveals a set of key interactions between residues, conserved by evolution, that must be formed to enter the kinetic basin of attraction of the native state. PMID:12496119

  13. Sampling-based learning control of inhomogeneous quantum ensembles

    NASA Astrophysics Data System (ADS)

    Chen, Chunlin; Dong, Daoyi; Long, Ruixing; Petersen, Ian R.; Rabitz, Herschel A.

    2014-02-01

    Compensation for parameter dispersion is a significant challenge for control of inhomogeneous quantum ensembles. In this paper, we present the systematic methodology of sampling-based learning control (SLC) for simultaneously steering the members of inhomogeneous quantum ensembles to the same desired state. The SLC method is employed for optimal control of the state-to-state transition probability for inhomogeneous quantum ensembles of spins as well as Λ-type atomic systems. The procedure involves the steps of (i) training and (ii) testing. In the training step, a generalized system is constructed by sampling members according to the distribution of inhomogeneous parameters drawn from the ensemble. A gradient flow based learning and optimization algorithm is adopted to find an optimal control for the generalized system. In the process of testing, a number of additional ensemble members are randomly selected to evaluate the control performance. Numerical results are presented, showing the effectiveness of the SLC method.

  14. Ensemble-based multi-scale assimilation

    NASA Astrophysics Data System (ADS)

    Ravela, S.; Hansen, J.; Hill, C.; Hill, H.; Marshall, J.

    2003-04-01

    We develop ensemble methods for constraining numerical models due to errors induced both by uncertain initial states and model structure. In the present paper, circulation phenomena are physically simulated in a laboratory and sensors are used to extract observations (velocity, temperature, etc.). Ensembles of the MITGCM constructed across variations in state and model-parameterizations are assimilated with observations over sliding multi-scale assimilation windows to regulate the trajectory of the model attractors vis a vis the system attractor. The novel contribution of this work is in bringing together the use of multi-scale assimilations, physical processes of moderate complexity, techniques for extracting flow and providing physically meaningful ways to alter analyses for minimizing model/data misfit.

  15. Genetic programming based ensemble system for microarray data classification.

    PubMed

    Liu, Kun-Hong; Tong, Muchenxuan; Xie, Shu-Tong; Yee Ng, Vincent To

    2015-01-01

    Recently, more and more machine learning techniques have been applied to microarray data analysis. The aim of this study is to propose a genetic programming (GP) based new ensemble system (named GPES), which can be used to effectively classify different types of cancers. Decision trees are deployed as base classifiers in this ensemble framework with three operators: Min, Max, and Average. Each individual of the GP is an ensemble system, and they become more and more accurate in the evolutionary process. The feature selection technique and balanced subsampling technique are applied to increase the diversity in each ensemble system. The final ensemble committee is selected by a forward search algorithm, which is shown to be capable of fitting data automatically. The performance of GPES is evaluated using five binary class and six multiclass microarray datasets, and results show that the algorithm can achieve better results in most cases compared with some other ensemble systems. By using elaborate base classifiers or applying other sampling techniques, the performance of GPES may be further improved.

  16. Protein Remote Homology Detection Based on an Ensemble Learning Approach.

    PubMed

    Chen, Junjie; Liu, Bingquan; Huang, Dong

    2016-01-01

    Protein remote homology detection is one of the central problems in bioinformatics. Although some computational methods have been proposed, the problem is still far from being solved. In this paper, an ensemble classifier for protein remote homology detection, called SVM-Ensemble, was proposed with a weighted voting strategy. SVM-Ensemble combined three basic classifiers based on different feature spaces, including Kmer, ACC, and SC-PseAAC. These features consider the characteristics of proteins from various perspectives, incorporating both the sequence composition and the sequence-order information along the protein sequences. Experimental results on a widely used benchmark dataset showed that the proposed SVM-Ensemble can obviously improve the predictive performance for the protein remote homology detection. Moreover, it achieved the best performance and outperformed other state-of-the-art methods.

  17. Protein Remote Homology Detection Based on an Ensemble Learning Approach

    PubMed Central

    Chen, Junjie; Liu, Bingquan; Huang, Dong

    2016-01-01

    Protein remote homology detection is one of the central problems in bioinformatics. Although some computational methods have been proposed, the problem is still far from being solved. In this paper, an ensemble classifier for protein remote homology detection, called SVM-Ensemble, was proposed with a weighted voting strategy. SVM-Ensemble combined three basic classifiers based on different feature spaces, including Kmer, ACC, and SC-PseAAC. These features consider the characteristics of proteins from various perspectives, incorporating both the sequence composition and the sequence-order information along the protein sequences. Experimental results on a widely used benchmark dataset showed that the proposed SVM-Ensemble can obviously improve the predictive performance for the protein remote homology detection. Moreover, it achieved the best performance and outperformed other state-of-the-art methods. PMID:27294123

  18. Knowledge based cluster ensemble for cancer discovery from biomolecular data.

    PubMed

    Yu, Zhiwen; Wongb, Hau-San; You, Jane; Yang, Qinmin; Liao, Hongying

    2011-06-01

    The adoption of microarray techniques in biological and medical research provides a new way for cancer diagnosis and treatment. In order to perform successful diagnosis and treatment of cancer, discovering and classifying cancer types correctly is essential. Class discovery is one of the most important tasks in cancer classification using biomolecular data. Most of the existing works adopt single clustering algorithms to perform class discovery from biomolecular data. However, single clustering algorithms have limitations, which include a lack of robustness, stability, and accuracy. In this paper, we propose a new cluster ensemble approach called knowledge based cluster ensemble (KCE) which incorporates the prior knowledge of the data sets into the cluster ensemble framework. Specifically, KCE represents the prior knowledge of a data set in the form of pairwise constraints. Then, the spectral clustering algorithm (SC) is adopted to generate a set of clustering solutions. Next, KCE transforms pairwise constraints into confidence factors for these clustering solutions. After that, a consensus matrix is constructed by considering all the clustering solutions and their corresponding confidence factors. The final clustering result is obtained by partitioning the consensus matrix. Comparison with single clustering algorithms and conventional cluster ensemble approaches, knowledge based cluster ensemble approaches are more robust, stable and accurate. The experiments on cancer data sets show that: 1) KCE works well on these data sets; 2) KCE not only outperforms most of the state-of-the-art single clustering algorithms, but also outperforms most of the state-of-the-art cluster ensemble approaches.

  19. Creating "Intelligent" Ensemble Averages Using a Process-Based Framework

    NASA Astrophysics Data System (ADS)

    Baker, Noel; Taylor, Patrick

    2014-05-01

    The CMIP5 archive contains future climate projections from over 50 models provided by dozens of modeling centers from around the world. Individual model projections, however, are subject to biases created by structural model uncertainties. As a result, ensemble averaging of multiple models is used to add value to individual model projections and construct a consensus projection. Previous reports for the IPCC establish climate change projections based on an equal-weighted average of all model projections. However, individual models reproduce certain climate processes better than other models. Should models be weighted based on performance? Unequal ensemble averages have previously been constructed using a variety of mean state metrics. What metrics are most relevant for constraining future climate projections? This project develops a framework for systematically testing metrics in models to identify optimal metrics for unequal weighting multi-model ensembles. The intention is to produce improved ("intelligent") unequal-weight ensemble averages. A unique aspect of this project is the construction and testing of climate process-based model evaluation metrics. A climate process-based metric is defined as a metric based on the relationship between two physically related climate variables—e.g., outgoing longwave radiation and surface temperature. Several climate process metrics are constructed using high-quality Earth radiation budget data from NASA's Clouds and Earth's Radiant Energy System (CERES) instrument in combination with surface temperature data sets. It is found that regional values of tested quantities can vary significantly when comparing the equal-weighted ensemble average and an ensemble weighted using the process-based metric. Additionally, this study investigates the dependence of the metric weighting scheme on the climate state using a combination of model simulations including a non-forced preindustrial control experiment, historical simulations, and

  20. Ensemble Kalman filter implementations based on shrinkage covariance matrix estimation

    NASA Astrophysics Data System (ADS)

    Nino-Ruiz, Elias D.; Sandu, Adrian

    2015-11-01

    This paper develops efficient ensemble Kalman filter (EnKF) implementations based on shrinkage covariance estimation. The forecast ensemble members at each step are used to estimate the background error covariance matrix via the Rao-Blackwell Ledoit and Wolf estimator, which has been specifically developed to approximate high-dimensional covariance matrices using a small number of samples. Two implementations are considered: in the EnKF full-space (EnKF-FS) approach, the assimilation process is performed in the model space, while the EnKF reduce-space (EnKF-RS) formulation performs the analysis in the subspace spanned by the ensemble members. In the context of EnKF-RS, additional samples are taken from the normal distribution described by the background ensemble mean and the estimated background covariance matrix, in order to increase the size of the ensemble and reduce the sampling error of the filter. This increase in the size of the ensemble is obtained without running the forward model. After the assimilation step, the additional samples are discarded and only the model-based ensemble members are propagated further. Methodologies to reduce the impact of spurious correlations and under-estimation of sample variances in the context of the EnKF-FS and EnKF-RS implementations are discussed. An adjoint-free four-dimensional extension of EnKF-RS is also discussed. Numerical experiments carried out with the Lorenz-96 model and a quasi-geostrophic model show that the use of shrinkage covariance matrix estimation can mitigate the impact of spurious correlations during the assimilation process.

  1. Ensemble Sampling vs. Time Sampling in Molecular Dynamics Simulations of Thermal Conductivity

    DOE PAGESBeta

    Gordiz, Kiarash; Singh, David J.; Henry, Asegun

    2015-01-29

    In this report we compare time sampling and ensemble averaging as two different methods available for phase space sampling. For the comparison, we calculate thermal conductivities of solid argon and silicon structures, using equilibrium molecular dynamics. We introduce two different schemes for the ensemble averaging approach, and show that both can reduce the total simulation time as compared to time averaging. It is also found that velocity rescaling is an efficient mechanism for phase space exploration. Although our methodology is tested using classical molecular dynamics, the ensemble generation approaches may find their greatest utility in computationally expensive simulations such asmore » first principles molecular dynamics. For such simulations, where each time step is costly, time sampling can require long simulation times because each time step must be evaluated sequentially and therefore phase space averaging is achieved through sequential operations. On the other hand, with ensemble averaging, phase space sampling can be achieved through parallel operations, since each ensemble is independent. For this reason, particularly when using massively parallel architectures, ensemble sampling can result in much shorter simulation times and exhibits similar overall computational effort.« less

  2. Ensemble Sampling vs. Time Sampling in Molecular Dynamics Simulations of Thermal Conductivity

    SciTech Connect

    Gordiz, Kiarash; Singh, David J.; Henry, Asegun

    2015-01-29

    In this report we compare time sampling and ensemble averaging as two different methods available for phase space sampling. For the comparison, we calculate thermal conductivities of solid argon and silicon structures, using equilibrium molecular dynamics. We introduce two different schemes for the ensemble averaging approach, and show that both can reduce the total simulation time as compared to time averaging. It is also found that velocity rescaling is an efficient mechanism for phase space exploration. Although our methodology is tested using classical molecular dynamics, the ensemble generation approaches may find their greatest utility in computationally expensive simulations such as first principles molecular dynamics. For such simulations, where each time step is costly, time sampling can require long simulation times because each time step must be evaluated sequentially and therefore phase space averaging is achieved through sequential operations. On the other hand, with ensemble averaging, phase space sampling can be achieved through parallel operations, since each ensemble is independent. For this reason, particularly when using massively parallel architectures, ensemble sampling can result in much shorter simulation times and exhibits similar overall computational effort.

  3. A Random Forest-based ensemble method for activity recognition.

    PubMed

    Feng, Zengtao; Mo, Lingfei; Li, Meng

    2015-01-01

    This paper presents a multi-sensor ensemble approach to human physical activity (PA) recognition, using random forest. We designed an ensemble learning algorithm, which integrates several independent Random Forest classifiers based on different sensor feature sets to build a more stable, more accurate and faster classifier for human activity recognition. To evaluate the algorithm, PA data collected from the PAMAP (Physical Activity Monitoring for Aging People), which is a standard, publicly available database, was utilized to train and test. The experimental results show that the algorithm is able to correctly recognize 19 PA types with an accuracy of 93.44%, while the training is faster than others. The ensemble classifier system based on the RF (Random Forest) algorithm can achieve high recognition accuracy and fast calculation. PMID:26737432

  4. Verification of the Forecast Errors Based on Ensemble Spread

    NASA Astrophysics Data System (ADS)

    Vannitsem, S.; Van Schaeybroeck, B.

    2014-12-01

    The use of ensemble prediction systems allows for an uncertainty estimation of the forecast. Most end users do not require all the information contained in an ensemble and prefer the use of a single uncertainty measure. This measure is the ensemble spread which serves to forecast the forecast error. It is however unclear how best the quality of these forecasts can be performed, based on spread and forecast error only. The spread-error verification is intricate for two reasons: First for each probabilistic forecast only one observation is substantiated and second, the spread is not meant to provide an exact prediction for the error. Despite these facts several advances were recently made, all based on traditional deterministic verification of the error forecast. In particular, Grimit and Mass (2007) and Hopson (2014) considered in detail the strengths and weaknesses of the spread-error correlation, while Christensen et al (2014) developed a proper-score extension of the mean squared error. However, due to the strong variance of the error given a certain spread, the error forecast should be preferably considered as probabilistic in nature. In the present work, different probabilistic error models are proposed depending on the spread-error metrics used. Most of these models allow for the discrimination of a perfect forecast from an imperfect one, independent of the underlying ensemble distribution. The new spread-error scores are tested on the ensemble prediction system of the European Centre of Medium-range forecasts (ECMWF) over Europe and Africa. ReferencesChristensen, H. M., Moroz, I. M. and Palmer, T. N., 2014, Evaluation of ensemble forecast uncertainty using a new proper score: application to medium-range and seasonal forecasts. In press, Quarterly Journal of the Royal Meteorological Society. Grimit, E. P., and C. F. Mass, 2007: Measuring the ensemble spread-error relationship with a probabilistic approach: Stochastic ensemble results. Mon. Wea. Rev., 135, 203

  5. Fluorescence lifetimes of molecular dye ensembles near interfaces

    SciTech Connect

    Danz, Norbert; Heber, Joerg; Braeuer, Andreas; Kowarschik, Richard

    2002-12-01

    Fluorescence lifetimes of thin, rhodamine 6G-doped polymer layers in front of a mirror have been determined as a function of the emitter-mirror separation and the conditions of excitation and observation. Lifetime is well known to depend on the spatial emitter-mirror separation. The explanation of experimental data needs to consider direction, polarization, and numerical aperture of the experimental system. As predicted theoretically, experimental results depend on the conditions of illumination and observation, because of the different lifetimes of emitters aligned horizontally or vertically with respect to the plane of interfaces and their selection by the experimental system. This effect is not observable when ions are used as a source of fluorescence, because ensemble averaging depends on the properties of sources.

  6. Knowledge-Based Methods To Train and Optimize Virtual Screening Ensembles

    PubMed Central

    2016-01-01

    Ensemble docking can be a successful virtual screening technique that addresses the innate conformational heterogeneity of macromolecular drug targets. Yet, lacking a method to identify a subset of conformational states that effectively segregates active and inactive small molecules, ensemble docking may result in the recommendation of a large number of false positives. Here, three knowledge-based methods that construct structural ensembles for virtual screening are presented. Each method selects ensembles by optimizing an objective function calculated using the receiver operating characteristic (ROC) curve: either the area under the ROC curve (AUC) or a ROC enrichment factor (EF). As the number of receptor conformations, N, becomes large, the methods differ in their asymptotic scaling. Given a set of small molecules with known activities and a collection of target conformations, the most resource intense method is guaranteed to find the optimal ensemble but scales as O(2N). A recursive approximation to the optimal solution scales as O(N2), and a more severe approximation leads to a faster method that scales linearly, O(N). The techniques are generally applicable to any system, and we demonstrate their effectiveness on the androgen nuclear hormone receptor (AR), cyclin-dependent kinase 2 (CDK2), and the peroxisome proliferator-activated receptor δ (PPAR-δ) drug targets. Conformations that consisted of a crystal structure and molecular dynamics simulation cluster centroids were used to form AR and CDK2 ensembles. Multiple available crystal structures were used to form PPAR-δ ensembles. For each target, we show that the three methods perform similarly to one another on both the training and test sets. PMID:27097522

  7. Study of critical dynamics in fluids via molecular dynamics in canonical ensemble.

    PubMed

    Roy, Sutapa; Das, Subir K

    2015-12-01

    With the objective of understanding the usefulness of thermostats in the study of dynamic critical phenomena in fluids, we present results for transport properties in a binary Lennard-Jones fluid that exhibits liquid-liquid phase transition. Various collective transport properties, calculated from the molecular dynamics (MD) simulations in canonical ensemble, with different thermostats, are compared with those obtained from MD simulations in microcanonical ensemble. It is observed that the Nosé-Hoover and dissipative particle dynamics thermostats are useful for the calculations of mutual diffusivity and shear viscosity. The Nosé-Hoover thermostat, however, as opposed to the latter, appears inadequate for the study of bulk viscosity. PMID:26687057

  8. Comparing climate change impacts on crops in Belgium based on CMIP3 and EU-ENSEMBLES multi-model ensembles

    NASA Astrophysics Data System (ADS)

    Vanuytrecht, E.; Raes, D.; Willems, P.; Semenov, M.

    2012-04-01

    Global Circulation Models (GCMs) are sophisticated tools to study the future evolution of the climate. Yet, the coarse scale of GCMs of hundreds of kilometers raises questions about the suitability for agricultural impact assessments. These assessments are often made at field level and require consideration of interactions at sub-GCM grid scale (e.g., elevation-dependent climatic changes). Regional climate models (RCMs) were developed to provide climate projections at a spatial scale of 25-50 km for limited regions, e.g. Europe (Giorgi and Mearns, 1991). Climate projections from GCMs or RCMs are available as multi-model ensembles. These ensembles are based on large data sets of simulations produced by modelling groups worldwide, who performed a set of coordinated climate experiments in which climate models were run for a common set of experiments and various emissions scenarios (Knutti et al., 2010). The use of multi-model ensembles in climate change studies is an important step in quantifying uncertainty in impact predictions, which will underpin more informed decisions for adaptation and mitigation to changing climate (Semenov and Stratonovitch, 2010). The objective of our study was to evaluate the effect of the spatial scale of climate projections on climate change impacts for cereals in Belgium. Climate scenarios were based on two multi-model ensembles, one comprising 15 GCMs of the Coupled Model Intercomparison Project phase 3 (CMIP3; Meehl et al., 2007) with spatial resolution of 200-300 km, the other comprising 9 RCMs of the EU-ENSEMBLES project (van der Linden and Mitchell, 2009) with spatial resolution of 25 km. To be useful for agricultural impact assessments, the projections of GCMs and RCMs were downscaled to the field level. Long series (240 cropping seasons) of local-scale climate scenarios were generated by the LARS-WG weather generator (Semenov et al., 2010) via statistical inference. Crop growth and development were simulated with the Aqua

  9. Stochastic dynamics of small ensembles of non-processive molecular motors: The parallel cluster model

    NASA Astrophysics Data System (ADS)

    Erdmann, Thorsten; Albert, Philipp J.; Schwarz, Ulrich S.

    2013-11-01

    Non-processive molecular motors have to work together in ensembles in order to generate appreciable levels of force or movement. In skeletal muscle, for example, hundreds of myosin II molecules cooperate in thick filaments. In non-muscle cells, by contrast, small groups with few tens of non-muscle myosin II motors contribute to essential cellular processes such as transport, shape changes, or mechanosensing. Here we introduce a detailed and analytically tractable model for this important situation. Using a three-state crossbridge model for the myosin II motor cycle and exploiting the assumptions of fast power stroke kinetics and equal load sharing between motors in equivalent states, we reduce the stochastic reaction network to a one-step master equation for the binding and unbinding dynamics (parallel cluster model) and derive the rules for ensemble movement. We find that for constant external load, ensemble dynamics is strongly shaped by the catch bond character of myosin II, which leads to an increase of the fraction of bound motors under load and thus to firm attachment even for small ensembles. This adaptation to load results in a concave force-velocity relation described by a Hill relation. For external load provided by a linear spring, myosin II ensembles dynamically adjust themselves towards an isometric state with constant average position and load. The dynamics of the ensembles is now determined mainly by the distribution of motors over the different kinds of bound states. For increasing stiffness of the external spring, there is a sharp transition beyond which myosin II can no longer perform the power stroke. Slow unbinding from the pre-power-stroke state protects the ensembles against detachment.

  10. Stochastic dynamics of small ensembles of non-processive molecular motors: The parallel cluster model

    SciTech Connect

    Erdmann, Thorsten; Albert, Philipp J.; Schwarz, Ulrich S.

    2013-11-07

    Non-processive molecular motors have to work together in ensembles in order to generate appreciable levels of force or movement. In skeletal muscle, for example, hundreds of myosin II molecules cooperate in thick filaments. In non-muscle cells, by contrast, small groups with few tens of non-muscle myosin II motors contribute to essential cellular processes such as transport, shape changes, or mechanosensing. Here we introduce a detailed and analytically tractable model for this important situation. Using a three-state crossbridge model for the myosin II motor cycle and exploiting the assumptions of fast power stroke kinetics and equal load sharing between motors in equivalent states, we reduce the stochastic reaction network to a one-step master equation for the binding and unbinding dynamics (parallel cluster model) and derive the rules for ensemble movement. We find that for constant external load, ensemble dynamics is strongly shaped by the catch bond character of myosin II, which leads to an increase of the fraction of bound motors under load and thus to firm attachment even for small ensembles. This adaptation to load results in a concave force-velocity relation described by a Hill relation. For external load provided by a linear spring, myosin II ensembles dynamically adjust themselves towards an isometric state with constant average position and load. The dynamics of the ensembles is now determined mainly by the distribution of motors over the different kinds of bound states. For increasing stiffness of the external spring, there is a sharp transition beyond which myosin II can no longer perform the power stroke. Slow unbinding from the pre-power-stroke state protects the ensembles against detachment.

  11. Weighted ensemble based automatic detection of exudates in fundus photographs.

    PubMed

    Prentasic, Pavle; Loncaric, Sven

    2014-01-01

    Diabetic retinopathy (DR) is a visual complication of diabetes, which has become one of the leading causes of preventable blindness in the world. Exudate detection is an important problem in automatic screening systems for detection of diabetic retinopathy using color fundus photographs. In this paper, we present a method for detection of exudates in color fundus photographs, which combines several preprocessing and candidate extraction algorithms to increase the exudate detection accuracy. The first stage of the method consists of an ensemble of several exudate candidate extraction algorithms. In the learning phase, simulated annealing is used to determine weights for combining the results of the ensemble candidate extraction algorithms. The second stage of the method uses a machine learning-based classification for detection of exudate regions. The experimental validation was performed using the DRiDB color fundus image set. The validation has demonstrated that the proposed method achieved higher accuracy in comparison to state-of-the art methods.

  12. Ensemble-Based Assimilation of Aerosol Observations in GEOS-5

    NASA Technical Reports Server (NTRS)

    Buchard, V.; Da Silva, A.

    2016-01-01

    MERRA-2 is the latest Aerosol Reanalysis produced at NASA's Global Modeling Assimilation Office (GMAO) from 1979 to present. This reanalysis is based on a version of the GEOS-5 model radiatively coupled to GOCART aerosols and includes assimilation of bias corrected Aerosol Optical Depth (AOD) from AVHRR over ocean, MODIS sensors on both Terra and Aqua satellites, MISR over bright surfaces and AERONET data. In order to assimilate lidar profiles of aerosols, we are updating the aerosol component of our assimilation system to an Ensemble Kalman Filter (EnKF) type of scheme using ensembles generated routinely by the meteorological assimilation. Following the work performed with the first NASA's aerosol reanalysis (MERRAero), we first validate the vertical structure of MERRA-2 aerosol assimilated fields using CALIOP data over regions of particular interest during 2008.

  13. Ensemble polarimetric SAR image classification based on contextual sparse representation

    NASA Astrophysics Data System (ADS)

    Zhang, Lamei; Wang, Xiao; Zou, Bin; Qiao, Zhijun

    2016-05-01

    Polarimetric SAR image interpretation has become one of the most interesting topics, in which the construction of the reasonable and effective technique of image classification is of key importance. Sparse representation represents the data using the most succinct sparse atoms of the over-complete dictionary and the advantages of sparse representation also have been confirmed in the field of PolSAR classification. However, it is not perfect, like the ordinary classifier, at different aspects. So ensemble learning is introduced to improve the issue, which makes a plurality of different learners training and obtained the integrated results by combining the individual learner to get more accurate and ideal learning results. Therefore, this paper presents a polarimetric SAR image classification method based on the ensemble learning of sparse representation to achieve the optimal classification.

  14. SVM ensemble based transfer learning for large-scale membrane proteins discrimination.

    PubMed

    Mei, Suyu

    2014-01-01

    Membrane proteins play important roles in molecular trans-membrane transport, ligand-receptor recognition, cell-cell interaction, enzyme catalysis, host immune defense response and infectious disease pathways. Up to present, discriminating membrane proteins remains a challenging problem from the viewpoints of biological experimental determination and computational modeling. This work presents SVM ensemble based transfer learning model for membrane proteins discrimination (SVM-TLM). To reduce the data constraints on computational modeling, this method investigates the effectiveness of transferring the homolog knowledge to the target membrane proteins under the framework of probability weighted ensemble learning. As compared to multiple kernel learning based transfer learning model, the method takes the advantages of sparseness based SVM optimization on large data, thus more computationally efficient for large protein data analysis. The experiments on large membrane protein benchmark dataset show that SVM-TLM achieves significantly better cross validation performance than the baseline model.

  15. Isomolar semigrand ensemble molecular dynamics: development and application to liquid-liquid equilibria.

    PubMed

    Morrow, Timothy I; Maginn, Edward J

    2005-02-01

    An extended system molecular dynamics method for the isomolar semigrand ensemble (fixed number of particles, pressure, temperature, and fugacity fraction) is developed and applied to the calculation of liquid-liquid equilibria (LLE) for two Lennard-Jones mixtures. The method utilizes an extended system variable to dynamically control the fugacity fraction xi of the mixture by gradually transforming the identity of particles in the system. Two approaches are used to compute coexistence points. The first approach uses multiple-histogram reweighting techniques to determine the coexistence xi and compositions of each phase at temperatures near the upper critical solution temperature. The second approach, useful for cases in which there is no critical solution temperature, is based on principles of small system thermodynamics. In this case a coexistence point is found by running N-P-T-xi simulations at a common temperature and pressure and varying the fugacity fraction to map out the difference in chemical potential between the two species A and B (mu(A)-mu(B)) as a function of composition. Once this curve is known the equal-distance/equal-area criterion is used to determine the coexistence point. Both approaches give results that are comparable to those of previous Monte Carlo (MC) simulations. By formulating this approach in a molecular dynamics framework, it should be easier to compute the LLE of complex molecules whose intramolecular degrees of freedom are often difficult to properly sample with MC techniques.

  16. Probabilistic infrasound propagation using ensemble based atmospheric perturbations

    NASA Astrophysics Data System (ADS)

    Smets, Pieter; Evers, Läslo

    2015-04-01

    The state of the atmosphere is of utmost importance for infrasound propagation. In propagation modelling, still, the true state of the atmosphere is mainly represented by the analysis. The analysis is the best deterministic estimate of the atmosphere using a data assimilation system existing of a General Circulation Model (GCM). However, the analysis excludes error variances of both model and observations. In addition, the coarse resolution of GCM results in averaging of, e.g., clouds or gravity waves, over larger regions known as parameterisation. Consequentially, arrivals due to fine-scale structure in wind and temperature can be missing. Therefore, infrasound propagation including the atmospheric best-estimate error variances based on the ensemble model is proposed. The ensemble system exists of model perturbations with an amplitude comparable to analysis error estimates to obtain a probability density function rather than one specific state as obtained from a deterministic system. The best-estimate analysis error variances are described by a set of perturbations using the European Centre for Medium-range Weather Forecasts (ECMWF) Ensemble Data Assimilation (EDA) system. Probabilistic infrasound propagation using 3-D ray tracing is demonstrated by one year of mining activity, e.g., blasting, in Gällivare, northern Sweden, observed at infrasound array IS37 in Norway, part of the International Monitoring System (IMS) for verification of the Comprehensive Nuclear-Test-Ban Treaty (CTBT). Probabilistic infrasound propagation is compared with the standard deterministic result obtained using the analysis.

  17. Toward Focusing Conformational Ensembles on Bioactive Conformations: A Molecular Mechanics/Quantum Mechanics Study.

    PubMed

    Avgy-David, Hannah H; Senderowitz, Hanoch

    2015-10-26

    The identification of bound conformations, namely, conformations adopted by ligands when binding their target is critical for target-based and ligand-based drug design. Bound conformations could be obtained computationally from unbound conformational ensembles generated by conformational search tools. However, these tools also generate many nonrelevant conformations thus requiring a focusing mechanism. To identify such a mechanism, this work focuses on a comparison of energies and structural properties of bound and unbound conformations for a set of FDA approved drugs whose complexes are available in the PDB. Unbound conformational ensembles were initially obtained with three force fields. These were merged, clustered, and reminimized using the same force fields and four QM methods. Bound conformations of all ligands were represented by their crystal structures or by approximations to these structures. Energy differences were calculated between global minima of the unbound state or the Boltzmann averaged energies of the unbound ensemble and the approximated bound conformations. Ligand conformations which resemble the X-ray conformation (RMSD < 1.0 Å) were obtained in 91%-97% and 96%-98% of the cases using the ensembles generated by the individual force fields and the reminimized ensembles, respectively, yet only in 52%-56% (original ensembles) and 47%-65% (reminimized ensembles) as global energy minima. The energy window within which the different methods identified the bound conformation (approximated by its closest local energy minimum) was found to be at 4-6 kcal/mol with respect to the global minimum and marginally lower with respect to a Boltzmann averaged energy of the unbound ensemble. Better approximations to the bound conformation obtained with a constrained minimization using the crystallographic B-factors or with a newly developed Knee Point Detection (KPD) method gave lower values (2-5 kcal/mol). Overall, QM methods gave lower energy differences than

  18. Ensemble-based evaluation for protein structure models

    PubMed Central

    Jamroz, Michal; Kolinski, Andrzej; Kihara, Daisuke

    2016-01-01

    Motivation: Comparing protein tertiary structures is a fundamental procedure in structural biology and protein bioinformatics. Structure comparison is important particularly for evaluating computational protein structure models. Most of the model structure evaluation methods perform rigid body superimposition of a structure model to its crystal structure and measure the difference of the corresponding residue or atom positions between them. However, these methods neglect intrinsic flexibility of proteins by treating the native structure as a rigid molecule. Because different parts of proteins have different levels of flexibility, for example, exposed loop regions are usually more flexible than the core region of a protein structure, disagreement of a model to the native needs to be evaluated differently depending on the flexibility of residues in a protein. Results: We propose a score named FlexScore for comparing protein structures that consider flexibility of each residue in the native state of proteins. Flexibility information may be extracted from experiments such as NMR or molecular dynamics simulation. FlexScore considers an ensemble of conformations of a protein described as a multivariate Gaussian distribution of atomic displacements and compares a query computational model with the ensemble. We compare FlexScore with other commonly used structure similarity scores over various examples. FlexScore agrees with experts’ intuitive assessment of computational models and provides information of practical usefulness of models. Availability and implementation: https://bitbucket.org/mjamroz/flexscore Contact: dkihara@purdue.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27307633

  19. Hyperspectral anomaly detection using sparse kernel-based ensemble learning

    NASA Astrophysics Data System (ADS)

    Gurram, Prudhvi; Han, Timothy; Kwon, Heesung

    2011-06-01

    In this paper, sparse kernel-based ensemble learning for hyperspectral anomaly detection is proposed. The proposed technique is aimed to optimize an ensemble of kernel-based one class classifiers, such as Support Vector Data Description (SVDD) classifiers, by estimating optimal sparse weights. In this method, hyperspectral signatures are first randomly sub-sampled into a large number of spectral feature subspaces. An enclosing hypersphere that defines the support of spectral data, corresponding to the normalcy/background data, in the Reproducing Kernel Hilbert Space (RKHS) of each respective feature subspace is then estimated using regular SVDD. The enclosing hypersphere basically represents the spectral characteristics of the background data in the respective feature subspace. The joint hypersphere is learned by optimally combining the hyperspheres from the individual RKHS, while imposing the l1 constraint on the combining weights. The joint hypersphere representing the most optimal compact support of the local hyperspectral data in the joint feature subspaces is then used to test each pixel in hyperspectral image data to determine if it belongs to the local background data or not. The outliers are considered to be targets. The performance comparison between the proposed technique and the regular SVDD is provided using the HYDICE hyperspectral images.

  20. Glucose Biosensors Based on Carbon Nanotube Nanoelectrode Ensembles

    SciTech Connect

    Lin, Yuehe ); Lu, Fang; Tu, Yi; Ren, Zhifeng

    2004-02-12

    This paper describes the development of glucose biosensors based on carbon nanotube (CNT) nanoelectrode ensembles (NEEs) for the selective detection of glucose. Glucose oxidase was covalently immobilized on CNT NEEs via carbodiimide chemistry by forming amide linkages between their amine residues and carboxylic acid groups on the CNT tips. The catalytic reduction of hydrogen peroxide liberated from the enzymatic reaction of glucose oxidase upon the glucose and oxygen on CNT NEEs leads to the selective detection of glucose. The biosensor effectively performs selective electrochemical analysis of glucose in the presence of common interferents (e.g. acetaminophen, uric and ascorbic acids), avoiding the generation of an overlapping signal from such interferents. Such an operation eliminates the need for permselective membrane barriers or artificial electron mediators, thus greatly simplifying the sensor design and fabrication.

  1. Uncertainty Visualization in HARDI based on Ensembles of ODFs.

    PubMed

    Jiao, Fangxiang; Phillips, Jeff M; Gur, Yaniv; Johnson, Chris R

    2012-12-31

    In this paper, we propose a new and accurate technique for uncertainty analysis and uncertainty visualization based on fiber orientation distribution function (ODF) glyphs, associated with high angular resolution diffusion imaging (HARDI). Our visualization applies volume rendering techniques to an ensemble of 3D ODF glyphs, which we call SIP functions of diffusion shapes, to capture their variability due to underlying uncertainty. This rendering elucidates the complex heteroscedastic structural variation in these shapes. Furthermore, we quantify the extent of this variation by measuring the fraction of the volume of these shapes, which is consistent across all noise levels, the certain volume ratio. Our uncertainty analysis and visualization framework is then applied to synthetic data, as well as to HARDI human-brain data, to study the impact of various image acquisition parameters and background noise levels on the diffusion shapes.

  2. Ensembler: Enabling High-Throughput Molecular Simulations at the Superfamily Scale

    PubMed Central

    Parton, Daniel L.; Grinaway, Patrick B.; Hanson, Sonya M.; Beauchamp, Kyle A.; Chodera, John D.

    2016-01-01

    The rapidly expanding body of available genomic and protein structural data provides a rich resource for understanding protein dynamics with biomolecular simulation. While computational infrastructure has grown rapidly, simulations on an omics scale are not yet widespread, primarily because software infrastructure to enable simulations at this scale has not kept pace. It should now be possible to study protein dynamics across entire (super)families, exploiting both available structural biology data and conformational similarities across homologous proteins. Here, we present a new tool for enabling high-throughput simulation in the genomics era. Ensembler takes any set of sequences—from a single sequence to an entire superfamily—and shepherds them through various stages of modeling and refinement to produce simulation-ready structures. This includes comparative modeling to all relevant PDB structures (which may span multiple conformational states of interest), reconstruction of missing loops, addition of missing atoms, culling of nearly identical structures, assignment of appropriate protonation states, solvation in explicit solvent, and refinement and filtering with molecular simulation to ensure stable simulation. The output of this pipeline is an ensemble of structures ready for subsequent molecular simulations using computer clusters, supercomputers, or distributed computing projects like Folding@home. Ensembler thus automates much of the time-consuming process of preparing protein models suitable for simulation, while allowing scalability up to entire superfamilies. A particular advantage of this approach can be found in the construction of kinetic models of conformational dynamics—such as Markov state models (MSMs)—which benefit from a diverse array of initial configurations that span the accessible conformational states to aid sampling. We demonstrate the power of this approach by constructing models for all catalytic domains in the human tyrosine

  3. Ensembler: Enabling High-Throughput Molecular Simulations at the Superfamily Scale.

    PubMed

    Parton, Daniel L; Grinaway, Patrick B; Hanson, Sonya M; Beauchamp, Kyle A; Chodera, John D

    2016-06-01

    The rapidly expanding body of available genomic and protein structural data provides a rich resource for understanding protein dynamics with biomolecular simulation. While computational infrastructure has grown rapidly, simulations on an omics scale are not yet widespread, primarily because software infrastructure to enable simulations at this scale has not kept pace. It should now be possible to study protein dynamics across entire (super)families, exploiting both available structural biology data and conformational similarities across homologous proteins. Here, we present a new tool for enabling high-throughput simulation in the genomics era. Ensembler takes any set of sequences-from a single sequence to an entire superfamily-and shepherds them through various stages of modeling and refinement to produce simulation-ready structures. This includes comparative modeling to all relevant PDB structures (which may span multiple conformational states of interest), reconstruction of missing loops, addition of missing atoms, culling of nearly identical structures, assignment of appropriate protonation states, solvation in explicit solvent, and refinement and filtering with molecular simulation to ensure stable simulation. The output of this pipeline is an ensemble of structures ready for subsequent molecular simulations using computer clusters, supercomputers, or distributed computing projects like Folding@home. Ensembler thus automates much of the time-consuming process of preparing protein models suitable for simulation, while allowing scalability up to entire superfamilies. A particular advantage of this approach can be found in the construction of kinetic models of conformational dynamics-such as Markov state models (MSMs)-which benefit from a diverse array of initial configurations that span the accessible conformational states to aid sampling. We demonstrate the power of this approach by constructing models for all catalytic domains in the human tyrosine kinase

  4. Ensembler: Enabling High-Throughput Molecular Simulations at the Superfamily Scale.

    PubMed

    Parton, Daniel L; Grinaway, Patrick B; Hanson, Sonya M; Beauchamp, Kyle A; Chodera, John D

    2016-06-01

    The rapidly expanding body of available genomic and protein structural data provides a rich resource for understanding protein dynamics with biomolecular simulation. While computational infrastructure has grown rapidly, simulations on an omics scale are not yet widespread, primarily because software infrastructure to enable simulations at this scale has not kept pace. It should now be possible to study protein dynamics across entire (super)families, exploiting both available structural biology data and conformational similarities across homologous proteins. Here, we present a new tool for enabling high-throughput simulation in the genomics era. Ensembler takes any set of sequences-from a single sequence to an entire superfamily-and shepherds them through various stages of modeling and refinement to produce simulation-ready structures. This includes comparative modeling to all relevant PDB structures (which may span multiple conformational states of interest), reconstruction of missing loops, addition of missing atoms, culling of nearly identical structures, assignment of appropriate protonation states, solvation in explicit solvent, and refinement and filtering with molecular simulation to ensure stable simulation. The output of this pipeline is an ensemble of structures ready for subsequent molecular simulations using computer clusters, supercomputers, or distributed computing projects like Folding@home. Ensembler thus automates much of the time-consuming process of preparing protein models suitable for simulation, while allowing scalability up to entire superfamilies. A particular advantage of this approach can be found in the construction of kinetic models of conformational dynamics-such as Markov state models (MSMs)-which benefit from a diverse array of initial configurations that span the accessible conformational states to aid sampling. We demonstrate the power of this approach by constructing models for all catalytic domains in the human tyrosine kinase

  5. A Sidekick for Membrane Simulations: Automated Ensemble Molecular Dynamics Simulations of Transmembrane Helices

    PubMed Central

    Hall, Benjamin A; Halim, Khairul Abd; Buyan, Amanda; Emmanouil, Beatrice; Sansom, Mark S P

    2016-01-01

    The interactions of transmembrane (TM) α-helices with the phospholipid membrane and with one another are central to understanding the structure and stability of integral membrane proteins. These interactions may be analysed via coarse-grained molecular dynamics (CGMD) simulations. To obtain statistically meaningful analysis of TM helix interactions, large (N ca. 100) ensembles of CGMD simulations are needed. To facilitate the running and analysis of such ensembles of simulations we have developed Sidekick, an automated pipeline software for performing high throughput CGMD simulations of α-helical peptides in lipid bilayer membranes. Through an end-to-end approach, which takes as input a helix sequence and outputs analytical metrics derived from CGMD simulations, we are able to predict the orientation and likelihood of insertion into a lipid bilayer of a given helix of family of helix sequences. We illustrate this software via analysis of insertion into a membrane of short hydrophobic TM helices containing a single cationic arginine residue positioned at different positions along the length of the helix. From analysis of these ensembles of simulations we estimate apparent energy barriers to insertion which are comparable to experimentally determined values. In a second application we use CGMD simulations to examine self-assembly of dimers of TM helices from the ErbB1 receptor tyrosine kinase, and analyse the numbers of simulation repeats necessary to obtain convergence of simple descriptors of the mode of packing of the two helices within a dimer. Our approach offers proof-of-principle platform for the further employment of automation in large ensemble CGMD simulations of membrane proteins. PMID:26580541

  6. Toward post-processing ensemble forecasts based on hindcasts

    NASA Astrophysics Data System (ADS)

    Van Schaeybroeck, B.; Vannitsem, S.

    2012-04-01

    Having in mind an operational implementation of post-processing at the Royal Meteorological Institute of Belgium (RMI), we study possible approaches of correcting the ECMWF ensemble forecast for stations in Belgium using the ensemble hindcast data set. This data set is each week enlarged by eighteen independent five-member ensemble forecasts using the current operational system. Therefore, the hindcasts constitute an ideal basis for the training cycle of post-processing. Combined with a forward predictor-selection procedure we propose to use a post-processing technique called error-in-variables model output statistics or EVMOS. This technique was recently proposed and is based on linear regression and suited for correcting ensemble forecasts. The corrected forecasts are produced for nine synoptic stations in Belgium. Different factors which influence the correction quality and which we aim to optimize are the number of weeks of training data, the number of predictors and the clustering of daily training data. We also investigate the influence of the training period, that is, the period of days over which the training forecasts are initialized. More specifically, we compare a training window which is centered around the forecast day with the case where the days of training precede the forecast day. Different results for the different training periods arise due to seasonal effects. We validate the different approaches against the bias-corrected forecasts using observations at nine stations for the ten-meter zonal and meridional wind speed and the two-meter temperature and for lead times up to one week. This is performed by cross-validation for a period of fourteen weeks. For the inland stations and for all lead times, a mean-square-error (MSE) improvement of around 1.5 m2/s2 and 0.5 (°C)2 for wind and temperature, respectively, is obtained. The MSE gain for wind at the two coastal stations is lower, especially for the meridional wind. Systematic biases are

  7. Ensemble regularized linear discriminant analysis classifier for P300-based brain-computer interface.

    PubMed

    Onishi, Akinari; Natsume, Kiyohisa

    2013-01-01

    This paper demonstrates a better classification performance of an ensemble classifier using a regularized linear discriminant analysis (LDA) for P300-based brain-computer interface (BCI). The ensemble classifier with an LDA is sensitive to the lack of training data because covariance matrices are estimated imprecisely. One of the solution against the lack of training data is to employ a regularized LDA. Thus we employed the regularized LDA for the ensemble classifier of the P300-based BCI. The principal component analysis (PCA) was used for the dimension reduction. As a result, an ensemble regularized LDA classifier showed significantly better classification performance than an ensemble un-regularized LDA classifier. Therefore the proposed ensemble regularized LDA classifier is robust against the lack of training data.

  8. Development of Ensemble Model Based Water Demand Forecasting Model

    NASA Astrophysics Data System (ADS)

    Kwon, Hyun-Han; So, Byung-Jin; Kim, Seong-Hyeon; Kim, Byung-Seop

    2014-05-01

    In recent years, Smart Water Grid (SWG) concept has globally emerged over the last decade and also gained significant recognition in South Korea. Especially, there has been growing interest in water demand forecast and optimal pump operation and this has led to various studies regarding energy saving and improvement of water supply reliability. Existing water demand forecasting models are categorized into two groups in view of modeling and predicting their behavior in time series. One is to consider embedded patterns such as seasonality, periodicity and trends, and the other one is an autoregressive model that is using short memory Markovian processes (Emmanuel et al., 2012). The main disadvantage of the abovementioned model is that there is a limit to predictability of water demands of about sub-daily scale because the system is nonlinear. In this regard, this study aims to develop a nonlinear ensemble model for hourly water demand forecasting which allow us to estimate uncertainties across different model classes. The proposed model is consist of two parts. One is a multi-model scheme that is based on combination of independent prediction model. The other one is a cross validation scheme named Bagging approach introduced by Brieman (1996) to derive weighting factors corresponding to individual models. Individual forecasting models that used in this study are linear regression analysis model, polynomial regression, multivariate adaptive regression splines(MARS), SVM(support vector machine). The concepts are demonstrated through application to observed from water plant at several locations in the South Korea. Keywords: water demand, non-linear model, the ensemble forecasting model, uncertainty. Acknowledgements This subject is supported by Korea Ministry of Environment as "Projects for Developing Eco-Innovation Technologies (GT-11-G-02-001-6)

  9. Ensembl 2013

    PubMed Central

    Flicek, Paul; Ahmed, Ikhlak; Amode, M. Ridwan; Barrell, Daniel; Beal, Kathryn; Brent, Simon; Carvalho-Silva, Denise; Clapham, Peter; Coates, Guy; Fairley, Susan; Fitzgerald, Stephen; Gil, Laurent; García-Girón, Carlos; Gordon, Leo; Hourlier, Thibaut; Hunt, Sarah; Juettemann, Thomas; Kähäri, Andreas K.; Keenan, Stephen; Komorowska, Monika; Kulesha, Eugene; Longden, Ian; Maurel, Thomas; McLaren, William M.; Muffato, Matthieu; Nag, Rishi; Overduin, Bert; Pignatelli, Miguel; Pritchard, Bethan; Pritchard, Emily; Riat, Harpreet Singh; Ritchie, Graham R. S.; Ruffier, Magali; Schuster, Michael; Sheppard, Daniel; Sobral, Daniel; Taylor, Kieron; Thormann, Anja; Trevanion, Stephen; White, Simon; Wilder, Steven P.; Aken, Bronwen L.; Birney, Ewan; Cunningham, Fiona; Dunham, Ian; Harrow, Jennifer; Herrero, Javier; Hubbard, Tim J. P.; Johnson, Nathan; Kinsella, Rhoda; Parker, Anne; Spudich, Giulietta; Yates, Andy; Zadissa, Amonida; Searle, Stephen M. J.

    2013-01-01

    The Ensembl project (http://www.ensembl.org) provides genome information for sequenced chordate genomes with a particular focus on human, mouse, zebrafish and rat. Our resources include evidenced-based gene sets for all supported species; large-scale whole genome multiple species alignments across vertebrates and clade-specific alignments for eutherian mammals, primates, birds and fish; variation data resources for 17 species and regulation annotations based on ENCODE and other data sets. Ensembl data are accessible through the genome browser at http://www.ensembl.org and through other tools and programmatic interfaces. PMID:23203987

  10. Ensemble Classifier Strategy Based on Transient Feature Fusion in Electronic Nose

    NASA Astrophysics Data System (ADS)

    Bagheri, Mohammad Ali; Montazer, Gholam Ali

    2011-09-01

    In this paper, we test the performance of several ensembles of classifiers and each base learner has been trained on different types of extracted features. Experimental results show the potential benefits introduced by the usage of simple ensemble classification systems for the integration of different types of transient features.

  11. g_contacts: Fast contact search in bio-molecular ensemble data

    NASA Astrophysics Data System (ADS)

    Blau, Christian; Grubmuller, Helmut

    2013-12-01

    Short-range interatomic interactions govern many bio-molecular processes. Therefore, identifying close interaction partners in ensemble data is an essential task in structural biology and computational biophysics. A contact search can be cast as a typical range search problem for which efficient algorithms have been developed. However, none of those has yet been adapted to the context of macromolecular ensembles, particularly in a molecular dynamics (MD) framework. Here a set-decomposition algorithm is implemented which detects all contacting atoms or residues in maximum O(Nlog(N)) run-time, in contrast to the O(N2) complexity of a brute-force approach. Catalogue identifier: AEQA_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEQA_v1_0.html Program obtainable from: CPC Program Library, Queen’s University, Belfast, N. Ireland Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 8945 No. of bytes in distributed program, including test data, etc.: 981604 Distribution format: tar.gz Programming language: C99. Computer: PC. Operating system: Linux. RAM: ≈Size of input frame Classification: 3, 4.14. External routines: Gromacs 4.6[1] Nature of problem: Finding atoms or residues that are closer to one another than a given cut-off. Solution method: Excluding distant atoms from distance calculations by decomposing the given set of atoms into disjoint subsets. Running time:≤O(Nlog(N)) References: [1] S. Pronk, S. Pall, R. Schulz, P. Larsson, P. Bjelkmar, R. Apostolov, M. R. Shirts, J.C. Smith, P. M. Kasson, D. van der Spoel, B. Hess and Erik Lindahl, Gromacs 4.5: a high-throughput and highly parallel open source molecular simulation toolkit, Bioinformatics 29 (7) (2013).

  12. The Use of Ensemble-Based Sensitivity with Observations to Improve Predictability of Severe Convective Events

    NASA Astrophysics Data System (ADS)

    Ancell, B. C.; Hill, A. J.; Burghardt, B.

    2014-12-01

    Ensemble sensitivity can reveal important weather features early in a forecast window relevant to the predictability of high-impact events later in time. Sensitivity has been shown on synoptic scales with simulated observations to be useful in identifying ensemble subsets that are more likely than the full ensemble mean, which may potentially add value to operational guidance of high-impact events. On convective scales, with highly nonlinear ensemble perturbation evolution and very non-Gaussian distributions of severe weather responses (e.g., simulated reflectivity above some threshold), it becomes more difficult to apply linear-based ensemble sensitivity to improve predictability of severe events. Here we test the ability of ensemble sensitivity to improve predictability of a severe convective event through identifying errors in sensitive regions of different members early in a forecast period using radar and surface-based observations. In this case, through the inspection of a number of operational models, an overnight mesoscale convective system (MCS) and its associated cold pool appeared to strongly influence whether or not severe convection would occur the following afternoon. Since both the overnight MCS and next-day convection are associated with strong nonlinearity and non-Gaussian distributions in the ensemble, this case allows a rigid test of using ensemble sensitivity and related techniques with observations for convective events. The performance of the sensitivity-based technique will be presented, and integration into an operational tool for severe convection will be discussed.

  13. Structure refinement with molecular dynamics and a Boltzmann-weighted ensemble.

    PubMed

    Fennen, J; Torda, A E; van Gunsteren, W F

    1995-09-01

    Time-averaging restraints in molecular dynamics simulations were introduced to account for the averaging implicit in spectroscopic data. Space- or molecule-averaging restraints have been used to overcome the fact that not all molecular conformations can be visited during the finite time of a simulation of a single molecule. In this work we address the issue of using the correct Boltzmann weighting for each member of an ensemble, both in time and in space. It is shown that the molecular- or space-averaging method is simple in theory, but requires a priori knowledge of the behaviour of a system. This is illustrated using a five-atom model system and the small cycle peptide analogue somatostatin. When different molecular conformers that are separated by energy barriers insurmountable on the time scale of a simulation contribute significantly to a measured NOE intensity, the use of space- or molecule-averaged distance restraints yields a more appropriate description of the measured data than conventional single-molecule refinement with or without application of time averaging.

  14. Molecular Dynamics and Monte Carlo simulations in the microcanonical ensemble: Quantitative comparison and reweighting techniques.

    PubMed

    Schierz, Philipp; Zierenberg, Johannes; Janke, Wolfhard

    2015-10-01

    Molecular Dynamics (MD) and Monte Carlo (MC) simulations are the most popular simulation techniques for many-particle systems. Although they are often applied to similar systems, it is unclear to which extent one has to expect quantitative agreement of the two simulation techniques. In this work, we present a quantitative comparison of MD and MC simulations in the microcanonical ensemble. For three test examples, we study first- and second-order phase transitions with a focus on liquid-gas like transitions. We present MD analysis techniques to compensate for conservation law effects due to linear and angular momentum conservation. Additionally, we apply the weighted histogram analysis method to microcanonical histograms reweighted from MD simulations. By this means, we are able to estimate the density of states from many microcanonical simulations at various total energies. This further allows us to compute estimates of canonical expectation values.

  15. Confidence-based ensemble for GBM brain tumor segmentation

    NASA Astrophysics Data System (ADS)

    Huo, Jing; van Rikxoort, Eva M.; Okada, Kazunori; Kim, Hyun J.; Pope, Whitney; Goldin, Jonathan; Brown, Matthew

    2011-03-01

    It is a challenging task to automatically segment glioblastoma multiforme (GBM) brain tumors on T1w post-contrast isotropic MR images. A semi-automated system using fuzzy connectedness has recently been developed for computing the tumor volume that reduces the cost of manual annotation. In this study, we propose a an ensemble method that combines multiple segmentation results into a final ensemble one. The method is evaluated on a dataset of 20 cases from a multi-center pharmaceutical drug trial and compared to the fuzzy connectedness method. Three individual methods were used in the framework: fuzzy connectedness, GrowCut, and voxel classification. The combination method is a confidence map averaging (CMA) method. The CMA method shows an improved ROC curve compared to the fuzzy connectedness method (p < 0.001). The CMA ensemble result is more robust compared to the three individual methods.

  16. A SAXS-based ensemble model of the native and phosphorylated regulatory domain of the CFTR.

    PubMed

    Marasini, Carlotta; Galeno, Lauretta; Moran, Oscar

    2013-03-01

    The cystic fibrosis transmembrane conductance regulator (CFTR), the defective protein in cystic fibrosis, is an anion channel activated by protein kinase A phosphorylation. The regulatory domain (RD) of CFTR has multiple phosphorylation sites, and is responsible for channel activation. This domain is intrinsically disordered, rendering the structural analysis a difficult task, as high-resolution techniques are barely applicable. In this work, we obtained a biophysical characterization of the native and phosphorylated RD in solution by employing complementary structural methods. The native RD has a gyration radius of 3.25 nm, and a maximum molecular dimension of 11.4 nm, larger than expected for a globular protein of the same molecular mass. Phosphorylation causes compaction of the structure, yielding a significant reduction of the gyration radius, to 2.92 nm, and on the maximum molecular dimension to 10.2 nm. Using an ensemble optimization method, we were able to generate a low-resolution, three-dimensional model of the native and the phosphorylated RD based on small-angle X-ray scattering data. We have obtained the first experiment-based model of the CFTR regulatory domain, which will be useful to understand the molecular mechanisms of normal and pathological CFTR functioning.

  17. Constructing support vector machine ensembles for cancer classification based on proteomic profiling.

    PubMed

    Mao, Yong; Zhou, Xiao Bo; Pi, Dao Ying; Sun, You Xian

    2005-11-01

    In this study, we present a constructive algorithm for training cooperative support vector machine ensembles (CSVMEs). CSVME combines ensemble architecture design with cooperative training for individual SVMs in ensembles. Unlike most previous studies on training ensembles, CSVME puts emphasis on both accuracy and collaboration among individual SVMs in an ensemble. A group of SVMs selected on the basis of recursive classifier elimination is used in CSVME, and the number of the individual SVMs selected to construct CSVME is determined by 10-fold cross-validation. This kind of SVME has been tested on two ovarian cancer datasets previously obtained by proteomic mass spectrometry. By combining several individual SVMs, the proposed method achieves better performance than the SVME of all base SVMs.

  18. Monte Carlo and Molecular Dynamics in the Multicanonical Ensemble: Connections between Wang-Landau Sampling and Metadynamics

    NASA Astrophysics Data System (ADS)

    Vogel, Thomas; Perez, Danny; Junghans, Christoph

    2014-03-01

    We show direct formal relationships between the Wang-Landau iteration [PRL 86, 2050 (2001)], metadynamics [PNAS 99, 12562 (2002)] and statistical temperature molecular dynamics [PRL 97, 050601 (2006)], the major Monte Carlo and molecular dynamics work horses for sampling from a generalized, multicanonical ensemble. We aim at helping to consolidate the developments in the different areas by indicating how methodological advancements can be transferred in a straightforward way, avoiding the parallel, largely independent, developments tracks observed in the past.

  19. Modeling Dynamic Systems with Efficient Ensembles of Process-Based Models.

    PubMed

    Simidjievski, Nikola; Todorovski, Ljupčo; Džeroski, Sašo

    2016-01-01

    Ensembles are a well established machine learning paradigm, leading to accurate and robust models, predominantly applied to predictive modeling tasks. Ensemble models comprise a finite set of diverse predictive models whose combined output is expected to yield an improved predictive performance as compared to an individual model. In this paper, we propose a new method for learning ensembles of process-based models of dynamic systems. The process-based modeling paradigm employs domain-specific knowledge to automatically learn models of dynamic systems from time-series observational data. Previous work has shown that ensembles based on sampling observational data (i.e., bagging and boosting), significantly improve predictive performance of process-based models. However, this improvement comes at the cost of a substantial increase of the computational time needed for learning. To address this problem, the paper proposes a method that aims at efficiently learning ensembles of process-based models, while maintaining their accurate long-term predictive performance. This is achieved by constructing ensembles with sampling domain-specific knowledge instead of sampling data. We apply the proposed method to and evaluate its performance on a set of problems of automated predictive modeling in three lake ecosystems using a library of process-based knowledge for modeling population dynamics. The experimental results identify the optimal design decisions regarding the learning algorithm. The results also show that the proposed ensembles yield significantly more accurate predictions of population dynamics as compared to individual process-based models. Finally, while their predictive performance is comparable to the one of ensembles obtained with the state-of-the-art methods of bagging and boosting, they are substantially more efficient.

  20. Modeling Dynamic Systems with Efficient Ensembles of Process-Based Models

    PubMed Central

    Simidjievski, Nikola; Todorovski, Ljupčo; Džeroski, Sašo

    2016-01-01

    Ensembles are a well established machine learning paradigm, leading to accurate and robust models, predominantly applied to predictive modeling tasks. Ensemble models comprise a finite set of diverse predictive models whose combined output is expected to yield an improved predictive performance as compared to an individual model. In this paper, we propose a new method for learning ensembles of process-based models of dynamic systems. The process-based modeling paradigm employs domain-specific knowledge to automatically learn models of dynamic systems from time-series observational data. Previous work has shown that ensembles based on sampling observational data (i.e., bagging and boosting), significantly improve predictive performance of process-based models. However, this improvement comes at the cost of a substantial increase of the computational time needed for learning. To address this problem, the paper proposes a method that aims at efficiently learning ensembles of process-based models, while maintaining their accurate long-term predictive performance. This is achieved by constructing ensembles with sampling domain-specific knowledge instead of sampling data. We apply the proposed method to and evaluate its performance on a set of problems of automated predictive modeling in three lake ecosystems using a library of process-based knowledge for modeling population dynamics. The experimental results identify the optimal design decisions regarding the learning algorithm. The results also show that the proposed ensembles yield significantly more accurate predictions of population dynamics as compared to individual process-based models. Finally, while their predictive performance is comparable to the one of ensembles obtained with the state-of-the-art methods of bagging and boosting, they are substantially more efficient. PMID:27078633

  1. Ensembl 2015.

    PubMed

    Cunningham, Fiona; Amode, M Ridwan; Barrell, Daniel; Beal, Kathryn; Billis, Konstantinos; Brent, Simon; Carvalho-Silva, Denise; Clapham, Peter; Coates, Guy; Fitzgerald, Stephen; Gil, Laurent; Girón, Carlos García; Gordon, Leo; Hourlier, Thibaut; Hunt, Sarah E; Janacek, Sophie H; Johnson, Nathan; Juettemann, Thomas; Kähäri, Andreas K; Keenan, Stephen; Martin, Fergal J; Maurel, Thomas; McLaren, William; Murphy, Daniel N; Nag, Rishi; Overduin, Bert; Parker, Anne; Patricio, Mateus; Perry, Emily; Pignatelli, Miguel; Riat, Harpreet Singh; Sheppard, Daniel; Taylor, Kieron; Thormann, Anja; Vullo, Alessandro; Wilder, Steven P; Zadissa, Amonida; Aken, Bronwen L; Birney, Ewan; Harrow, Jennifer; Kinsella, Rhoda; Muffato, Matthieu; Ruffier, Magali; Searle, Stephen M J; Spudich, Giulietta; Trevanion, Stephen J; Yates, Andy; Zerbino, Daniel R; Flicek, Paul

    2015-01-01

    Ensembl (http://www.ensembl.org) is a genomic interpretation system providing the most up-to-date annotations, querying tools and access methods for chordates and key model organisms. This year we released updated annotation (gene models, comparative genomics, regulatory regions and variation) on the new human assembly, GRCh38, although we continue to support researchers using the GRCh37.p13 assembly through a dedicated site (http://grch37.ensembl.org). Our Regulatory Build has been revamped to identify regulatory regions of interest and to efficiently highlight their activity across disparate epigenetic data sets. A number of new interfaces allow users to perform large-scale comparisons of their data against our annotations. The REST server (http://rest.ensembl.org), which allows programs written in any language to query our databases, has moved to a full service alongside our upgraded website tools. Our online Variant Effect Predictor tool has been updated to process more variants and calculate summary statistics. Lastly, the WiggleTools package enables users to summarize large collections of data sets and view them as single tracks in Ensembl. The Ensembl code base itself is more accessible: it is now hosted on our GitHub organization page (https://github.com/Ensembl) under an Apache 2.0 open source license. PMID:25352552

  2. Ensembl 2015

    PubMed Central

    Cunningham, Fiona; Amode, M. Ridwan; Barrell, Daniel; Beal, Kathryn; Billis, Konstantinos; Brent, Simon; Carvalho-Silva, Denise; Clapham, Peter; Coates, Guy; Fitzgerald, Stephen; Gil, Laurent; Girón, Carlos García; Gordon, Leo; Hourlier, Thibaut; Hunt, Sarah E.; Janacek, Sophie H.; Johnson, Nathan; Juettemann, Thomas; Kähäri, Andreas K.; Keenan, Stephen; Martin, Fergal J.; Maurel, Thomas; McLaren, William; Murphy, Daniel N.; Nag, Rishi; Overduin, Bert; Parker, Anne; Patricio, Mateus; Perry, Emily; Pignatelli, Miguel; Riat, Harpreet Singh; Sheppard, Daniel; Taylor, Kieron; Thormann, Anja; Vullo, Alessandro; Wilder, Steven P.; Zadissa, Amonida; Aken, Bronwen L.; Birney, Ewan; Harrow, Jennifer; Kinsella, Rhoda; Muffato, Matthieu; Ruffier, Magali; Searle, Stephen M.J.; Spudich, Giulietta; Trevanion, Stephen J.; Yates, Andy; Zerbino, Daniel R.; Flicek, Paul

    2015-01-01

    Ensembl (http://www.ensembl.org) is a genomic interpretation system providing the most up-to-date annotations, querying tools and access methods for chordates and key model organisms. This year we released updated annotation (gene models, comparative genomics, regulatory regions and variation) on the new human assembly, GRCh38, although we continue to support researchers using the GRCh37.p13 assembly through a dedicated site (http://grch37.ensembl.org). Our Regulatory Build has been revamped to identify regulatory regions of interest and to efficiently highlight their activity across disparate epigenetic data sets. A number of new interfaces allow users to perform large-scale comparisons of their data against our annotations. The REST server (http://rest.ensembl.org), which allows programs written in any language to query our databases, has moved to a full service alongside our upgraded website tools. Our online Variant Effect Predictor tool has been updated to process more variants and calculate summary statistics. Lastly, the WiggleTools package enables users to summarize large collections of data sets and view them as single tracks in Ensembl. The Ensembl code base itself is more accessible: it is now hosted on our GitHub organization page (https://github.com/Ensembl) under an Apache 2.0 open source license. PMID:25352552

  3. Cyclodextrin-based molecular machines.

    PubMed

    Hashidzume, Akihito; Yamaguchi, Hiroyasu; Harada, Akira

    2014-01-01

    This chapter overviews molecular machines based on cyclodextrins (CDs). The categories of CD-based molecular machines, external stimuli for CD-based molecular machines, and typical examples of CD-based molecular machines are briefly described.

  4. Force Sensor Based Tool Condition Monitoring Using a Heterogeneous Ensemble Learning Model

    PubMed Central

    Wang, Guofeng; Yang, Yinwei; Li, Zhimeng

    2014-01-01

    Tool condition monitoring (TCM) plays an important role in improving machining efficiency and guaranteeing workpiece quality. In order to realize reliable recognition of the tool condition, a robust classifier needs to be constructed to depict the relationship between tool wear states and sensory information. However, because of the complexity of the machining process and the uncertainty of the tool wear evolution, it is hard for a single classifier to fit all the collected samples without sacrificing generalization ability. In this paper, heterogeneous ensemble learning is proposed to realize tool condition monitoring in which the support vector machine (SVM), hidden Markov model (HMM) and radius basis function (RBF) are selected as base classifiers and a stacking ensemble strategy is further used to reflect the relationship between the outputs of these base classifiers and tool wear states. Based on the heterogeneous ensemble learning classifier, an online monitoring system is constructed in which the harmonic features are extracted from force signals and a minimal redundancy and maximal relevance (mRMR) algorithm is utilized to select the most prominent features. To verify the effectiveness of the proposed method, a titanium alloy milling experiment was carried out and samples with different tool wear states were collected to build the proposed heterogeneous ensemble learning classifier. Moreover, the homogeneous ensemble learning model and majority voting strategy are also adopted to make a comparison. The analysis and comparison results show that the proposed heterogeneous ensemble learning classifier performs better in both classification accuracy and stability. PMID:25405514

  5. A Gibbs-ensemble based technique for Monte Carlo simulation of electric double layer capacitors (EDLC) at constant voltage

    NASA Astrophysics Data System (ADS)

    Punnathanam, Sudeep N.

    2014-05-01

    Current methods for molecular simulations of Electric Double Layer Capacitors (EDLC) have both the electrodes and the electrolyte region in a single simulation box. This necessitates simulation of the electrode-electrolyte region interface. Typical capacitors have macroscopic dimensions where the fraction of the molecules at the electrode-electrolyte region interface is very low. Hence, large systems sizes are needed to minimize the electrode-electrolyte region interfacial effects. To overcome these problems, a new technique based on the Gibbs Ensemble is proposed for simulation of an EDLC. In the proposed technique, each electrode is simulated in a separate simulation box. Application of periodic boundary conditions eliminates the interfacial effects. This in addition to the use of constant voltage ensemble allows for a more convenient comparison of simulation results with experimental measurements on typical EDLCs.

  6. A Gibbs-ensemble based technique for Monte Carlo simulation of electric double layer capacitors (EDLC) at constant voltage.

    PubMed

    Punnathanam, Sudeep N

    2014-05-01

    Current methods for molecular simulations of Electric Double Layer Capacitors (EDLC) have both the electrodes and the electrolyte region in a single simulation box. This necessitates simulation of the electrode-electrolyte region interface. Typical capacitors have macroscopic dimensions where the fraction of the molecules at the electrode-electrolyte region interface is very low. Hence, large systems sizes are needed to minimize the electrode-electrolyte region interfacial effects. To overcome these problems, a new technique based on the Gibbs Ensemble is proposed for simulation of an EDLC. In the proposed technique, each electrode is simulated in a separate simulation box. Application of periodic boundary conditions eliminates the interfacial effects. This in addition to the use of constant voltage ensemble allows for a more convenient comparison of simulation results with experimental measurements on typical EDLCs. PMID:24811628

  7. A Gibbs-ensemble based technique for Monte Carlo simulation of electric double layer capacitors (EDLC) at constant voltage.

    PubMed

    Punnathanam, Sudeep N

    2014-05-01

    Current methods for molecular simulations of Electric Double Layer Capacitors (EDLC) have both the electrodes and the electrolyte region in a single simulation box. This necessitates simulation of the electrode-electrolyte region interface. Typical capacitors have macroscopic dimensions where the fraction of the molecules at the electrode-electrolyte region interface is very low. Hence, large systems sizes are needed to minimize the electrode-electrolyte region interfacial effects. To overcome these problems, a new technique based on the Gibbs Ensemble is proposed for simulation of an EDLC. In the proposed technique, each electrode is simulated in a separate simulation box. Application of periodic boundary conditions eliminates the interfacial effects. This in addition to the use of constant voltage ensemble allows for a more convenient comparison of simulation results with experimental measurements on typical EDLCs.

  8. AWE-WQ: fast-forwarding molecular dynamics using the accelerated weighted ensemble.

    PubMed

    Abdul-Wahid, Badi'; Feng, Haoyun; Rajan, Dinesh; Costaouec, Ronan; Darve, Eric; Thain, Douglas; Izaguirre, Jesús A

    2014-10-27

    A limitation of traditional molecular dynamics (MD) is that reaction rates are difficult to compute. This is due to the rarity of observing transitions between metastable states since high energy barriers trap the system in these states. Recently the weighted ensemble (WE) family of methods have emerged which can flexibly and efficiently sample conformational space without being trapped and allow calculation of unbiased rates. However, while WE can sample correctly and efficiently, a scalable implementation applicable to interesting biomolecular systems is not available. We provide here a GPLv2 implementation called AWE-WQ of a WE algorithm using the master/worker distributed computing WorkQueue (WQ) framework. AWE-WQ is scalable to thousands of nodes and supports dynamic allocation of computer resources, heterogeneous resource usage (such as central processing units (CPU) and graphical processing units (GPUs) concurrently), seamless heterogeneous cluster usage (i.e., campus grids and cloud providers), and support for arbitrary MD codes such as GROMACS, while ensuring that all statistics are unbiased. We applied AWE-WQ to a 34 residue protein which simulated 1.5 ms over 8 months with peak aggregate performance of 1000 ns/h. Comparison was done with a 200 μs simulation collected on a GPU over a similar timespan. The folding and unfolded rates were of comparable accuracy.

  9. AWE-WQ: Fast-Forwarding Molecular Dynamics Using the Accelerated Weighted Ensemble

    PubMed Central

    2015-01-01

    A limitation of traditional molecular dynamics (MD) is that reaction rates are difficult to compute. This is due to the rarity of observing transitions between metastable states since high energy barriers trap the system in these states. Recently the weighted ensemble (WE) family of methods have emerged which can flexibly and efficiently sample conformational space without being trapped and allow calculation of unbiased rates. However, while WE can sample correctly and efficiently, a scalable implementation applicable to interesting biomolecular systems is not available. We provide here a GPLv2 implementation called AWE-WQ of a WE algorithm using the master/worker distributed computing WorkQueue (WQ) framework. AWE-WQ is scalable to thousands of nodes and supports dynamic allocation of computer resources, heterogeneous resource usage (such as central processing units (CPU) and graphical processing units (GPUs) concurrently), seamless heterogeneous cluster usage (i.e., campus grids and cloud providers), and support for arbitrary MD codes such as GROMACS, while ensuring that all statistics are unbiased. We applied AWE-WQ to a 34 residue protein which simulated 1.5 ms over 8 months with peak aggregate performance of 1000 ns/h. Comparison was done with a 200 μs simulation collected on a GPU over a similar timespan. The folding and unfolded rates were of comparable accuracy. PMID:25207854

  10. An Ensemble-Based Smoother with Retrospectively Updated Weights for Highly Nonlinear Systems

    NASA Technical Reports Server (NTRS)

    Chin, T. M.; Turmon, M. J.; Jewell, J. B.; Ghil, M.

    2006-01-01

    Monte Carlo computational methods have been introduced into data assimilation for nonlinear systems in order to alleviate the computational burden of updating and propagating the full probability distribution. By propagating an ensemble of representative states, algorithms like the ensemble Kalman filter (EnKF) and the resampled particle filter (RPF) rely on the existing modeling infrastructure to approximate the distribution based on the evolution of this ensemble. This work presents an ensemble-based smoother that is applicable to the Monte Carlo filtering schemes like EnKF and RPF. At the minor cost of retrospectively updating a set of weights for ensemble members, this smoother has demonstrated superior capabilities in state tracking for two highly nonlinear problems: the double-well potential and trivariate Lorenz systems. The algorithm does not require retrospective adaptation of the ensemble members themselves, and it is thus suited to a streaming operational mode. The accuracy of the proposed backward-update scheme in estimating non-Gaussian distributions is evaluated by comparison to the more accurate estimates provided by a Markov chain Monte Carlo algorithm.

  11. Overlapped partitioning for ensemble classifiers of P300-based brain-computer interfaces.

    PubMed

    Onishi, Akinari; Natsume, Kiyohisa

    2014-01-01

    A P300-based brain-computer interface (BCI) enables a wide range of people to control devices that improve their quality of life. Ensemble classifiers with naive partitioning were recently applied to the P300-based BCI and these classification performances were assessed. However, they were usually trained on a large amount of training data (e.g., 15300). In this study, we evaluated ensemble linear discriminant analysis (LDA) classifiers with a newly proposed overlapped partitioning method using 900 training data. In addition, the classification performances of the ensemble classifier with naive partitioning and a single LDA classifier were compared. One of three conditions for dimension reduction was applied: the stepwise method, principal component analysis (PCA), or none. The results show that an ensemble stepwise LDA (SWLDA) classifier with overlapped partitioning achieved a better performance than the commonly used single SWLDA classifier and an ensemble SWLDA classifier with naive partitioning. This result implies that the performance of the SWLDA is improved by overlapped partitioning and the ensemble classifier with overlapped partitioning requires less training data than that with naive partitioning. This study contributes towards reducing the required amount of training data and achieving better classification performance.

  12. Overlapped Partitioning for Ensemble Classifiers of P300-Based Brain-Computer Interfaces

    PubMed Central

    Onishi, Akinari; Natsume, Kiyohisa

    2014-01-01

    A P300-based brain-computer interface (BCI) enables a wide range of people to control devices that improve their quality of life. Ensemble classifiers with naive partitioning were recently applied to the P300-based BCI and these classification performances were assessed. However, they were usually trained on a large amount of training data (e.g., 15300). In this study, we evaluated ensemble linear discriminant analysis (LDA) classifiers with a newly proposed overlapped partitioning method using 900 training data. In addition, the classification performances of the ensemble classifier with naive partitioning and a single LDA classifier were compared. One of three conditions for dimension reduction was applied: the stepwise method, principal component analysis (PCA), or none. The results show that an ensemble stepwise LDA (SWLDA) classifier with overlapped partitioning achieved a better performance than the commonly used single SWLDA classifier and an ensemble SWLDA classifier with naive partitioning. This result implies that the performance of the SWLDA is improved by overlapped partitioning and the ensemble classifier with overlapped partitioning requires less training data than that with naive partitioning. This study contributes towards reducing the required amount of training data and achieving better classification performance. PMID:24695550

  13. Intelligent Ensemble Forecasting System of Stock Market Fluctuations Based on Symetric and Asymetric Wavelet Functions

    NASA Astrophysics Data System (ADS)

    Lahmiri, Salim; Boukadoum, Mounir

    2015-08-01

    We present a new ensemble system for stock market returns prediction where continuous wavelet transform (CWT) is used to analyze return series and backpropagation neural networks (BPNNs) for processing CWT-based coefficients, determining the optimal ensemble weights, and providing final forecasts. Particle swarm optimization (PSO) is used for finding optimal weights and biases for each BPNN. To capture symmetry/asymmetry in the underlying data, three wavelet functions with different shapes are adopted. The proposed ensemble system was tested on three Asian stock markets: The Hang Seng, KOSPI, and Taiwan stock market data. Three statistical metrics were used to evaluate the forecasting accuracy; including, mean of absolute errors (MAE), root mean of squared errors (RMSE), and mean of absolute deviations (MADs). Experimental results showed that our proposed ensemble system outperformed the individual CWT-ANN models each with different wavelet function. In addition, the proposed ensemble system outperformed the conventional autoregressive moving average process. As a result, the proposed ensemble system is suitable to capture symmetry/asymmetry in financial data fluctuations for better prediction accuracy.

  14. Ensembles of satellite aerosol retrievals based on three AATSR algorithms within aerosol_cci

    NASA Astrophysics Data System (ADS)

    Kosmale, Miriam; Popp, Thomas

    2016-04-01

    Ensemble techniques are widely used in the modelling community, combining different modelling results in order to reduce uncertainties. This approach could be also adapted to satellite measurements. Aerosol_cci is an ESA funded project, where most of the European aerosol retrieval groups work together. The different algorithms are homogenized as far as it makes sense, but remain essentially different. Datasets are compared with ground based measurements and between each other. Three AATSR algorithms (Swansea university aerosol retrieval, ADV aerosol retrieval by FMI and Oxford aerosol retrieval ORAC) provide within this project 17 year global aerosol records. Each of these algorithms provides also uncertainty information on pixel level. Within the presented work, an ensembles of the three AATSR algorithms is performed. The advantage over each single algorithm is the higher spatial coverage due to more measurement pixels per gridbox. A validation to ground based AERONET measurements shows still a good correlation of the ensemble, compared to the single algorithms. Annual mean maps show the global aerosol distribution, based on a combination of the three aerosol algorithms. In addition, pixel level uncertainties of each algorithm are used for weighting the contributions, in order to reduce the uncertainty of the ensemble. Results of different versions of the ensembles for aerosol optical depth will be presented and discussed. The results are validated against ground based AERONET measurements. A higher spatial coverage on daily basis allows better results in annual mean maps. The benefit of using pixel level uncertainties is analysed.

  15. Validating Solution Ensembles from Molecular Dynamics Simulation by Wide-Angle X-ray Scattering Data

    PubMed Central

    Chen, Po-chia; Hub, Jochen S.

    2014-01-01

    Wide-angle x-ray scattering (WAXS) experiments of biomolecules in solution have become increasingly popular because of technical advances in light sources and detectors. However, the structural interpretation of WAXS profiles is problematic, partly because accurate calculations of WAXS profiles from structural models have remained challenging. In this work, we present the calculation of WAXS profiles from explicit-solvent molecular dynamics (MD) simulations of five different proteins. Using only a single fitting parameter that accounts for experimental uncertainties because of the buffer subtraction and dark currents, we find excellent agreement to experimental profiles both at small and wide angles. Because explicit solvation eliminates free parameters associated with the solvation layer or the excluded solvent, which would require fitting to experimental data, we minimize the risk of overfitting. We further find that the influence from water models and protein force fields on calculated profiles are insignificant up to q≈15nm−1. Using a series of simulations that allow increasing flexibility of the proteins, we show that incorporating thermal fluctuations into the calculations significantly improves agreement with experimental data, demonstrating the importance of protein dynamics in the interpretation of WAXS profiles. In addition, free MD simulations up to one microsecond suggest that the calculated profiles are highly sensitive with respect to minor conformational rearrangements of proteins, such as an increased flexibility of a loop or an increase of the radius of gyration by < 1%. The present study suggests that quantitative comparison between MD simulations and experimental WAXS profiles emerges as an accurate tool to validate solution ensembles of biomolecules. PMID:25028885

  16. Genetic algorithm based adaptive neural network ensemble and its application in predicting carbon flux

    USGS Publications Warehouse

    Xue, Y.; Liu, S.; Hu, Y.; Yang, J.; Chen, Q.

    2007-01-01

    To improve the accuracy in prediction, Genetic Algorithm based Adaptive Neural Network Ensemble (GA-ANNE) is presented. Intersections are allowed between different training sets based on the fuzzy clustering analysis, which ensures the diversity as well as the accuracy of individual Neural Networks (NNs). Moreover, to improve the accuracy of the adaptive weights of individual NNs, GA is used to optimize the cluster centers. Empirical results in predicting carbon flux of Duke Forest reveal that GA-ANNE can predict the carbon flux more accurately than Radial Basis Function Neural Network (RBFNN), Bagging NN ensemble, and ANNE. ?? 2007 IEEE.

  17. Ensemble-based Regional Climate Prediction: Political Impacts

    NASA Astrophysics Data System (ADS)

    Miguel, E.; Dykema, J.; Satyanath, S.; Anderson, J. G.

    2008-12-01

    Accurate forecasts of regional climate, including temperature and precipitation, have significant implications for human activities, not just economically but socially. Sub Saharan Africa is a region that has displayed an exceptional propensity for devastating civil wars. Recent research in political economy has revealed a strong statistical relationship between year to year fluctuations in precipitation and civil conflict in this region in the 1980s and 1990s. To investigate how climate change may modify the regional risk of civil conflict in the future requires a probabilistic regional forecast that explicitly accounts for the community's uncertainty in the evolution of rainfall under anthropogenic forcing. We approach the regional climate prediction aspect of this question through the application of a recently demonstrated method called generalized scalar prediction (Leroy et al. 2009), which predicts arbitrary scalar quantities of the climate system. This prediction method can predict change in any variable or linear combination of variables of the climate system averaged over a wide range spatial scales, from regional to hemispheric to global. Generalized scalar prediction utilizes an ensemble of model predictions to represent the community's uncertainty range in climate modeling in combination with a timeseries of any type of observational data that exhibits sensitivity to the scalar of interest. It is not necessary to prioritize models in deriving with the final prediction. We present the results of the application of generalized scalar prediction for regional forecasts of temperature and precipitation and Sub Saharan Africa. We utilize the climate predictions along with the established statistical relationship between year-to-year rainfall variability in Sub Saharan Africa to investigate the potential impact of climate change on civil conflict within that region.

  18. Navigating a Path Toward Operational, Short-term, Ensemble Based, Probablistic Streamflow Forecasts

    NASA Astrophysics Data System (ADS)

    Hartman, R. K.; Schaake, J.

    2004-12-01

    The National Weather Service (NWS) has federal responsibility for issuing public flood warnings in the United States. Additionally, the NWS has been engaged in longer range water resources forecasts for many years, particularly in the Western U.S. In the past twenty years, longer range forecasts have increasingly incorporated ensemble techniques. Ensemble techniques are attractive because they allow a great deal of flexibility, both temporally and in content. This technique also provides for the influence of additional forcings (i.e. ENSO), through either pre or post processing techniques. More recently, attention has turned to the use of ensemble techniques in the short-term streamflow forecasting process. While considerably more difficult, the development of reliable short-term probabilistic streamflow forecasts has clear application and value for many NWS customers and partners. During flood episodes, expensive mitigation actions are initialed or withheld and critical reservoir management decisions are made in the absence of uncertainty and risk information. Limited emergency services resources and the optimal use of water resources facilities necessitates the development of a risk-based decision making process. The development of reliable short-term probabilistic streamflow forecasts are an essential ingredient in the decision making process. This paper addresses the utility of short-term ensemble streamflow forecasts and the considerations that must be addressed as techniques and operational capabilities are developed. Verification and validation information are discussed from both a scientific and customer perspective. Education and training related to the interpretation and use of ensemble products are also addressed.

  19. Determination of the DFN modeling domain size based on ensemble variability of equivalent permeability

    NASA Astrophysics Data System (ADS)

    Ji, S. H.; Koh, Y. K.

    2015-12-01

    Conceptualization of the fracture network in a disposal site is important for the safety assessment of a subsurface repository for radioactive waste. To consider the uncertainty of the stochastically conceptualized discrete fracture networks (DFNs), the ensemble variability of equivalent permeability was evaluated by defining different network structures with various fracture densities and characterization levels, and analyzing the ensemble mean and variability of the equivalent permeability of the networks, where the characterization level was defined as the ratio of the number of deterministically conceptualized fractures to the total number of fractures in the domain. The results show that the hydraulic property of the generated fractures were similar among the ensembles when the fracture density was larger than the specific fracture density where the domain size was equal to the correlation length of a given fracture network. In a sparsely fracture network where the fracture density was smaller than the specific fracture density, the ensemble variability was too large to ensure the consistent property from the stochastic DFN modeling. Deterministic information for a portion of a fracture network could reduce the uncertainty of the hydraulic property only when the fracture density was larger than the specific fracture density. Based on these results, the DFN modeling domain size for KAERI's (Korea Atomic Energy Research Institute) URT (Underground Research Tunnel) site to guarantee a less variable hydraulic property of the fracture network was determined by calculating the correlation length, and verified by evaluating the ensemble variability of the equivalent permeability.

  20. Creating "Intelligent" Climate Model Ensemble Averages Using a Process-Based Framework

    NASA Astrophysics Data System (ADS)

    Baker, N. C.; Taylor, P. C.

    2014-12-01

    The CMIP5 archive contains future climate projections from over 50 models provided by dozens of modeling centers from around the world. Individual model projections, however, are subject to biases created by structural model uncertainties. As a result, ensemble averaging of multiple models is often used to add value to model projections: consensus projections have been shown to consistently outperform individual models. Previous reports for the IPCC establish climate change projections based on an equal-weighted average of all model projections. However, certain models reproduce climate processes better than other models. Should models be weighted based on performance? Unequal ensemble averages have previously been constructed using a variety of mean state metrics. What metrics are most relevant for constraining future climate projections? This project develops a framework for systematically testing metrics in models to identify optimal metrics for unequal weighting multi-model ensembles. A unique aspect of this project is the construction and testing of climate process-based model evaluation metrics. A climate process-based metric is defined as a metric based on the relationship between two physically related climate variables—e.g., outgoing longwave radiation and surface temperature. Metrics are constructed using high-quality Earth radiation budget data from NASA's Clouds and Earth's Radiant Energy System (CERES) instrument and surface temperature data sets. It is found that regional values of tested quantities can vary significantly when comparing weighted and unweighted model ensembles. For example, one tested metric weights the ensemble by how well models reproduce the time-series probability distribution of the cloud forcing component of reflected shortwave radiation. The weighted ensemble for this metric indicates lower simulated precipitation (up to .7 mm/day) in tropical regions than the unweighted ensemble: since CMIP5 models have been shown to

  1. A user credit assessment model based on clustering ensemble for broadband network new media service supervision

    NASA Astrophysics Data System (ADS)

    Liu, Fang; Cao, San-xing; Lu, Rui

    2012-04-01

    This paper proposes a user credit assessment model based on clustering ensemble aiming to solve the problem that users illegally spread pirated and pornographic media contents within the user self-service oriented broadband network new media platforms. Its idea is to do the new media user credit assessment by establishing indices system based on user credit behaviors, and the illegal users could be found according to the credit assessment results, thus to curb the bad videos and audios transmitted on the network. The user credit assessment model based on clustering ensemble proposed by this paper which integrates the advantages that swarm intelligence clustering is suitable for user credit behavior analysis and K-means clustering could eliminate the scattered users existed in the result of swarm intelligence clustering, thus to realize all the users' credit classification automatically. The model's effective verification experiments are accomplished which are based on standard credit application dataset in UCI machine learning repository, and the statistical results of a comparative experiment with a single model of swarm intelligence clustering indicates this clustering ensemble model has a stronger creditworthiness distinguishing ability, especially in the aspect of predicting to find user clusters with the best credit and worst credit, which will facilitate the operators to take incentive measures or punitive measures accurately. Besides, compared with the experimental results of Logistic regression based model under the same conditions, this clustering ensemble model is robustness and has better prediction accuracy.

  2. Ensembl 2007.

    PubMed

    Hubbard, T J P; Aken, B L; Beal, K; Ballester, B; Caccamo, M; Chen, Y; Clarke, L; Coates, G; Cunningham, F; Cutts, T; Down, T; Dyer, S C; Fitzgerald, S; Fernandez-Banet, J; Graf, S; Haider, S; Hammond, M; Herrero, J; Holland, R; Howe, K; Howe, K; Johnson, N; Kahari, A; Keefe, D; Kokocinski, F; Kulesha, E; Lawson, D; Longden, I; Melsopp, C; Megy, K; Meidl, P; Ouverdin, B; Parker, A; Prlic, A; Rice, S; Rios, D; Schuster, M; Sealy, I; Severin, J; Slater, G; Smedley, D; Spudich, G; Trevanion, S; Vilella, A; Vogel, J; White, S; Wood, M; Cox, T; Curwen, V; Durbin, R; Fernandez-Suarez, X M; Flicek, P; Kasprzyk, A; Proctor, G; Searle, S; Smith, J; Ureta-Vidal, A; Birney, E

    2007-01-01

    The Ensembl (http://www.ensembl.org/) project provides a comprehensive and integrated source of annotation of chordate genome sequences. Over the past year the number of genomes available from Ensembl has increased from 15 to 33, with the addition of sites for the mammalian genomes of elephant, rabbit, armadillo, tenrec, platypus, pig, cat, bush baby, common shrew, microbat and european hedgehog; the fish genomes of stickleback and medaka and the second example of the genomes of the sea squirt (Ciona savignyi) and the mosquito (Aedes aegypti). Some of the major features added during the year include the first complete gene sets for genomes with low-sequence coverage, the introduction of new strain variation data and the introduction of new orthology/paralog annotations based on gene trees.

  3. Operational optimization of irrigation scheduling for citrus trees using an ensemble based data assimilation approach

    NASA Astrophysics Data System (ADS)

    Hendricks Franssen, H.; Han, X.; Martinez, F.; Jimenez, M.; Manzano, J.; Chanzy, A.; Vereecken, H.

    2013-12-01

    Data assimilation (DA) techniques, like the local ensemble transform Kalman filter (LETKF) not only offer the opportunity to update model predictions by assimilating new measurement data in real time, but also provide an improved basis for real-time (DA-based) control. This study focuses on the optimization of real-time irrigation scheduling for fields of citrus trees near Picassent (Spain). For three selected fields the irrigation was optimized with DA-based control, and for other fields irrigation was optimized on the basis of a more traditional approach where reference evapotranspiration for citrus trees was estimated using the FAO-method. The performance of the two methods is compared for the year 2013. The DA-based real-time control approach is based on ensemble predictions of soil moisture profiles, using the Community Land Model (CLM). The uncertainty in the model predictions is introduced by feeding the model with weather predictions from an ensemble prediction system (EPS) and uncertain soil hydraulic parameters. The model predictions are updated daily by assimilating soil moisture data measured by capacitance probes. The measurement data are assimilated with help of LETKF. The irrigation need was calculated for each of the ensemble members, averaged, and logistic constraints (hydraulics, energy costs) were taken into account for the final assigning of irrigation in space and time. For the operational scheduling based on this approach only model states and no model parameters were updated by the model. Other, non-operational simulation experiments for the same period were carried out where (1) neither ensemble weather forecast nor DA were used (open loop), (2) Only ensemble weather forecast was used, (3) Only DA was used, (4) also soil hydraulic parameters were updated in data assimilation and (5) both soil hydraulic and plant specific parameters were updated. The FAO-based and DA-based real-time irrigation control are compared in terms of soil moisture

  4. Recognition of multiple imbalanced cancer types based on DNA microarray data using ensemble classifiers.

    PubMed

    Yu, Hualong; Hong, Shufang; Yang, Xibei; Ni, Jun; Dan, Yuanyuan; Qin, Bin

    2013-01-01

    DNA microarray technology can measure the activities of tens of thousands of genes simultaneously, which provides an efficient way to diagnose cancer at the molecular level. Although this strategy has attracted significant research attention, most studies neglect an important problem, namely, that most DNA microarray datasets are skewed, which causes traditional learning algorithms to produce inaccurate results. Some studies have considered this problem, yet they merely focus on binary-class problem. In this paper, we dealt with multiclass imbalanced classification problem, as encountered in cancer DNA microarray, by using ensemble learning. We utilized one-against-all coding strategy to transform multiclass to multiple binary classes, each of them carrying out feature subspace, which is an evolving version of random subspace that generates multiple diverse training subsets. Next, we introduced one of two different correction technologies, namely, decision threshold adjustment or random undersampling, into each training subset to alleviate the damage of class imbalance. Specifically, support vector machine was used as base classifier, and a novel voting rule called counter voting was presented for making a final decision. Experimental results on eight skewed multiclass cancer microarray datasets indicate that unlike many traditional classification approaches, our methods are insensitive to class imbalance. PMID:24078908

  5. Application of dynamic linear regression to improve the skill of ensemble-based deterministic ozone forecasts

    SciTech Connect

    Pagowski, M O; Grell, G A; Devenyi, D; Peckham, S E; McKeen, S A; Gong, W; Monache, L D; McHenry, J N; McQueen, J; Lee, P

    2006-02-02

    Forecasts from seven air quality models and surface ozone data collected over the eastern USA and southern Canada during July and August 2004 provide a unique opportunity to assess benefits of ensemble-based ozone forecasting and devise methods to improve ozone forecasts. In this investigation, past forecasts from the ensemble of models and hourly surface ozone measurements at over 350 sites are used to issue deterministic 24-h forecasts using a method based on dynamic linear regression. Forecasts of hourly ozone concentrations as well as maximum daily 8-h and 1-h averaged concentrations are considered. It is shown that the forecasts issued with the application of this method have reduced bias and root mean square error and better overall performance scores than any of the ensemble members and the ensemble average. Performance of the method is similar to another method based on linear regression described previously by Pagowski et al., but unlike the latter, the current method does not require measurements from multiple monitors since it operates on individual time series. Improvement in the forecasts can be easily implemented and requires minimal computational cost.

  6. Fast Computation of Solvation Free Energies with Molecular Density Functional Theory: Thermodynamic-Ensemble Partial Molar Volume Corrections.

    PubMed

    Sergiievskyi, Volodymyr P; Jeanmairet, Guillaume; Levesque, Maximilien; Borgis, Daniel

    2014-06-01

    Molecular density functional theory (MDFT) offers an efficient implicit-solvent method to estimate molecule solvation free-energies, whereas conserving a fully molecular representation of the solvent. Even within a second-order approximation for the free-energy functional, the so-called homogeneous reference fluid approximation, we show that the hydration free-energies computed for a data set of 500 organic compounds are of similar quality as those obtained from molecular dynamics free-energy perturbation simulations, with a computer cost reduced by 2-3 orders of magnitude. This requires to introduce the proper partial volume correction to transform the results from the grand canonical to the isobaric-isotherm ensemble that is pertinent to experiments. We show that this correction can be extended to 3D-RISM calculations, giving a sound theoretical justification to empirical partial molar volume corrections that have been proposed recently.

  7. Reducing false positive incidental findings with ensemble genotyping and logistic regression-based variant filtering methods

    PubMed Central

    Hwang, Kyu-Baek; Lee, In-Hee; Park, Jin-Ho; Hambuch, Tina; Choi, Yongjoon; Kim, MinHyeok; Lee, Kyungjoon; Song, Taemin; Neu, Matthew B.; Gupta, Neha; Kohane, Isaac S.; Green, Robert C.; Kong, Sek Won

    2014-01-01

    As whole genome sequencing (WGS) uncovers variants associated with rare and common diseases, an immediate challenge is to minimize false positive findings due to sequencing and variant calling errors. False positives can be reduced by combining results from orthogonal sequencing methods, but costly. Here we present variant filtering approaches using logistic regression (LR) and ensemble genotyping to minimize false positives without sacrificing sensitivity. We evaluated the methods using paired WGS datasets of an extended family prepared using two sequencing platforms and a validated set of variants in NA12878. Using LR or ensemble genotyping based filtering, false negative rates were significantly reduced by 1.1- to 17.8-fold at the same levels of false discovery rates (5.4% for heterozygous and 4.5% for homozygous SNVs; 30.0% for heterozygous and 18.7% for homozygous insertions; 25.2% for heterozygous and 16.6% for homozygous deletions) compared to the filtering based on genotype quality scores. Moreover, ensemble genotyping excluded > 98% (105,080 of 107,167) of false positives while retaining > 95% (897 of 937) of true positives in de novo mutation (DNM) discovery, and performed better than a consensus method using two sequencing platforms. Our proposed methods were effective in prioritizing phenotype-associated variants, and ensemble genotyping would be essential to minimize false positive DNM candidates. PMID:24829188

  8. Characterization of the Free State Ensemble of the CoRNR Box Motif by Molecular Dynamics Simulations.

    PubMed

    Cino, Elio A; Choy, Wing-Yiu; Karttunen, Mikko

    2016-02-18

    Intrinsically disordered proteins (IDPs) and regions are highly prevalent in eukaryotic proteomes, and like folded proteins, they perform essential biological functions. Interaction sites in folded proteins are generally formed by tertiary structures, whereas IDPs use short segments called linear motifs (LMs). Despite their short length and lack of stable structure, LMs may have considerable structural propensities, which often resemble bound-state conformations with targets. Structural data is crucial for understanding the molecular basis of protein interactions and development of targeted pharmaceuticals, but IDPs present considerable challenges to experimental techniques. As a result, IDPs are largely underrepresented in the Protein Data Bank. In the face of experimental challenges, molecular dynamics (MD) simulations have proven to be a useful tool for structural characterization of IDPs. Here, the free state ensemble of the nuclear receptor corepressor 1 (NCOR1) CoRNR box 3 motif, which is important for binding to nuclear receptors to control gene expression, is studied using MD simulations of a total of 8 μs. Transitions between disordered and α-helical conformations resembling a bound-state structure were observed throughout the trajectory, indicating that the motif may have a natural conformational bias toward bound-state structures. The data shows that the disordered and folded populations are separated by a low energy (4-6 kJ/mol) barrier, and the presence of off-pathway intermediates, leading to a C-terminally folded species that cannot efficiently transition into a completely folded conformation. Structural transitions and folding pathways within the free state ensemble were well-described by principal component analysis (PCA) of the peptide backbone dihedral angles, with the analysis providing insight for increasing structural homogeneity of the ensemble.

  9. Three-dimensional theory of quantum memories based on {Lambda}-type atomic ensembles

    SciTech Connect

    Zeuthen, Emil; Grodecka-Grad, Anna; Soerensen, Anders S.

    2011-10-15

    We develop a three-dimensional theory for quantum memories based on light storage in ensembles of {Lambda}-type atoms, where two long-lived atomic ground states are employed. We consider light storage in an ensemble of finite spatial extent and we show that within the paraxial approximation the Fresnel number of the atomic ensemble and the optical depth are the only important physical parameters determining the quality of the quantum memory. We analyze the influence of these parameters on the storage of light followed by either forward or backward read-out from the quantum memory. We show that for small Fresnel numbers the forward memory provides higher efficiencies, whereas for large Fresnel numbers the backward memory is advantageous. The optimal light modes to store in the memory are presented together with the corresponding spin waves and outcoming light modes. We show that for high optical depths such {Lambda}-type atomic ensembles allow for highly efficient backward and forward memories even for small Fresnel numbers F(greater-or-similar sign)0.1.

  10. The effect of sampling noise in ensemble-based Kalman filters

    NASA Astrophysics Data System (ADS)

    Sacher, William

    Ensemble-based Kalman filters have drawn a lot of attention in the atmospheric and ocean scientific community because of their potential to be used as a data assimilation tool for numerical prediction in a strongly nonlinear context at an affordable cost. However, many studies have noted practical problems in their implementation. Indeed, being Monte-Carlo methods, the useful parameters are estimated from a sample of limited size of independent realizations of the process. As a consequence, the unavoidable sampling noise impacts the quality of the analysis. An idealized perfect model context is considered in which the analytical expression for the analysis accuracy and reliability as a function of the ensemble size is established, from a second-order moment perspective. It is proved that one can analytically explain the general tendency for ensemble-based Kalman filters to underestimate, on average, the analysis variance and therefore the likeliness for these filters to diverge. Performance of alternative methods, designed to reduce or eliminate sampling error effects, such as the double ensemble Kalman filter or covariance inflation are also analytically explored. For methods using perturbed observations, it is shown that the covariance inflation is the easiest and least expensive method to obtain the most accurate and reliable analysis. These analytical results agreed well with means over a large number of experiments using a perfect, low-resolution, and quasi-geostrophic barotropic model, in a series of observation system simulation experiments of single analysis cycles as well as in a simulated forecast system. In one-analysis cycle experiments with rank histograms, non-perturbed-observation methods show a lack of reliability regardless of the number of members. For small ensemble sizes, sampling error effects are dominant but have a smaller impact than in the perturbed observation method, making non-perturbed-observation method filters much less subject to

  11. Predicting human intestinal absorption of diverse chemicals using ensemble learning based QSAR modeling approaches.

    PubMed

    Basant, Nikita; Gupta, Shikha; Singh, Kunwar P

    2016-04-01

    Human intestinal absorption (HIA) of the drugs administered through the oral route constitutes an important criterion for the candidate molecules. The computational approach for predicting the HIA of molecules may potentiate the screening of new drugs. In this study, ensemble learning (EL) based qualitative and quantitative structure-activity relationship (SAR) models (gradient boosted tree, GBT and bagged decision tree, BDT) have been established for the binary classification and HIA prediction of the chemicals, using the selected molecular descriptors. The structural diversity of the chemicals and the nonlinear structure in the considered data were tested by the similarity index and Brock-Dechert-Scheinkman statistics. The external predictive power of the developed SAR models was evaluated through the internal and external validation procedures recommended in the literature. All the statistical criteria parameters derived for the performance of the constructed SAR models were above their respective thresholds suggesting for their robustness for future applications. In complete data, the qualitative SAR models rendered classification accuracy of >99%, while the quantitative SAR models yielded correlation (R(2)) of >0.91 between the measured and predicted HIA values. The performances of the EL-based SAR models were also compared with the linear models (linear discriminant analysis, LDA and multiple linear regression, MLR). The GBT and BDT SAR models performed better than the LDA and MLR methods. A comparison of our models with the previously reported QSARs for HIA prediction suggested for their better performance. The results suggest for the appropriateness of the developed SAR models to reliably predict the HIA of structurally diverse chemicals and can serve as useful tools for the initial screening of the molecules in the drug development process.

  12. A Remodeled Hsp90 Molecular Chaperone Ensemble with the Novel Cochaperone Aarsd1 Is Required for Muscle Differentiation

    PubMed Central

    Echeverría, Pablo C.; Briand, Pierre-André

    2016-01-01

    Hsp90 is the ATP-consuming core component of a very abundant molecular chaperone machine that handles a substantial portion of the cytosolic proteome. Rather than one machine, it is in fact an ensemble of molecular machines, since most mammalian cells express two cytosolic isoforms of Hsp90 and a subset of up to 40 to 50 cochaperones and regulate their interactions and functions by a variety of posttranslational modifications. We demonstrate that the Hsp90 ensemble is fundamentally remodeled during muscle differentiation and that this remodeling is not just a consequence of muscle differentiation but possibly one of the drivers to accompany and to match the vast proteomic changes associated with this process. As myoblasts differentiate into myotubes, Hsp90α disappears and only Hsp90β remains, which is the only isoform capable of interacting with the novel muscle-specific Hsp90 cochaperone Aarsd1L. Artificially maintaining Hsp90α or knocking down Aarsd1L expression interferes with the differentiation of C2C12 myotubes. During muscle differentiation, Aarsd1L replaces the more ubiquitous cochaperone p23 and in doing so dampens the activity of the glucocorticoid receptor, one of the Hsp90 clients relevant to muscle functions. This cochaperone switch protects muscle cells against the inhibitory effects of glucocorticoids and may contribute to preventing muscle wasting induced by excess glucocorticoids. PMID:26884463

  13. A hybrid ensemble method based on double disturbance for classifying microarray data.

    PubMed

    Chen, Tao; Xue, Huifeng; Hong, Zenglin; Cui, Man; Zhao, Hui

    2015-01-01

    Microarray data has small samples and high dimension, and it contains a significant amount of irrelevant and redundant genes. This paper proposes a hybrid ensemble method based on double disturbance to improve classification performance. Firstly, original genes are ranked through reliefF algorithm and part of the genes are selected from the original genes set, and then a new training set is generated from the original training set according to the previously selected genes. Secondly, D bootstrap training subsets are produced from the previously generated training set by bootstrap technology. Thirdly, an attribute reduction method based on neighborhood mutual information with a different radius is used to reduce genes on each bootstrap training subset to produce new training subsets. Each new training subset is applied to train a base classifier. Finally, a part of the base classifiers are selected based on the teaching-learning-based optimization to build an ensemble by weighted voting. Experimental results on six benchmark cancer microarray datasets showed proposed method decreased ensemble size and obtained higher classification performance compared with Bagging, AdaBoost, and Random Forest.

  14. Integrating heterogeneous classifier ensembles for EMG signal decomposition based on classifier agreement.

    PubMed

    Rasheed, Sarbast; Stashuk, Daniel W; Kamel, Mohamed S

    2010-05-01

    In this paper, we present a design methodology for integrating heterogeneous classifier ensembles by employing a diversity-based hybrid classifier fusion approach, whose aggregator module consists of two classifier combiners, to achieve an improved classification performance for motor unit potential classification during electromyographic (EMG) signal decomposition. Following the so-called overproduce and choose strategy to classifier ensemble combination, the developed system allows the construction of a large set of base classifiers, and then automatically chooses subsets of classifiers to form candidate classifier ensembles for each combiner. The system exploits kappa statistic diversity measure to design classifier teams through estimating the level of agreement between base classifier outputs. The pool of base classifiers consists of different kinds of classifiers: the adaptive certainty-based, the adaptive fuzzy k -NN, and the adaptive matched template filter classifiers; and utilizes different types of features. Performance of the developed system was evaluated using real and simulated EMG signals, and was compared with the performance of the constituent base classifiers. Across the EMG signal datasets used, the developed system had better average classification performance overall, especially in terms of reducing classification errors. For simulated signals of varying intensity, the developed system had an average correct classification rate CCr of 93.8% and an error rate Er of 2.2% compared to 93.6% and 3.2%, respectively, for the best base classifier in the ensemble. For simulated signals with varying amounts of shape and/or firing pattern variability, the developed system had a CCr of 89.1% with an Er of 4.7% compared to 86.3% and 5.6%, respectively, for the best classifier. For real signals, the developed system had a CCr of 89.4% with an Er of 3.9% compared to 84.6% and 7.1%, respectively, for the best classifier.

  15. System for NIS Forecasting Based on Ensembles Analysis

    SciTech Connect

    2014-01-02

    BMA-NIS is a package/library designed to be called by a script (e.g. Perl or Python). The software itself is written in the language of R. The software assists electric power delivery systems in planning resource availability and demand, based on historical data and current data variables. Net Interchange Schedule (NIS) is the algebraic sum of all energy scheduled to flow into or out of a balancing area during any interval. Accurate forecasts for NIS are important so that the Area Control Error (ACE) stays within an acceptable limit. To date, there are many approaches for forecasting NIS but all none of these are based on single models that can be sensitive to time of day and day of week effects.

  16. System for NIS Forecasting Based on Ensembles Analysis

    2014-01-02

    BMA-NIS is a package/library designed to be called by a script (e.g. Perl or Python). The software itself is written in the language of R. The software assists electric power delivery systems in planning resource availability and demand, based on historical data and current data variables. Net Interchange Schedule (NIS) is the algebraic sum of all energy scheduled to flow into or out of a balancing area during any interval. Accurate forecasts for NIS aremore » important so that the Area Control Error (ACE) stays within an acceptable limit. To date, there are many approaches for forecasting NIS but all none of these are based on single models that can be sensitive to time of day and day of week effects.« less

  17. Ensemble method: Community detection based on game theory

    NASA Astrophysics Data System (ADS)

    Zhang, Xia; Xia, Zhengyou; Xu, Shengwu; Wang, J. D.

    2014-08-01

    Timely and cost-effective analytics over social network has emerged as a key ingredient for success in many businesses and government endeavors. Community detection is an active research area of relevance to analyze online social network. The problem of selecting a particular community detection algorithm is crucial if the aim is to unveil the community structure of a network. The choice of a given methodology could affect the outcome of the experiments because different algorithms have different advantages and depend on tuning specific parameters. In this paper, we propose a community division model based on the notion of game theory, which can combine advantages of previous algorithms effectively to get a better community classification result. By making experiments on some standard dataset, it verifies that our community detection model based on game theory is valid and better.

  18. Fluorescent Ensemble Based on Bispyrene Fluorophore and Surfactant Assemblies: Sensing and Discriminating Proteins in Aqueous Solution.

    PubMed

    Fan, Junmei; Ding, Liping; Bo, Yu; Fang, Yu

    2015-10-14

    A particular bispyrene fluorophore (1) with two pyrene moieties covalently linked via a hydrophilic spacer was synthesized. Fluorescence measurements reveal that the fluorescence emission of 1 could be well modulated by a cationic surfactant, dodecyltrimethylammonium bromide (DTAB). Protein sensing studies illustrate that the selected ensemble based on 1/DTAB assemblies exhibits ratiometric responses to nonmetalloproteins and turn-off responses to metalloproteins, which can be used to differentiate the two types of proteins. Moreover, negatively charged nonmetalloproteins can be discriminated from the positively charged ones according to the difference in ratiometric responses. Fluorescence sensing studies with control bispyrenes indicate that the polarity of the spacer connecting two pyrene moieties plays an important role in locating bispyrene fluorophore in DTAB assemblies, which further influences its sensing behaviors to noncovalent interacting proteins. This study sheds light on the influence of the probe structure on the sensing performance of a fluorescent ensemble based on probe and surfactant assemblies.

  19. Ensemble velocity of non-processive molecular motors with multiple chemical states

    PubMed Central

    Vilfan, Andrej

    2014-01-01

    We study the ensemble velocity of non-processive motor proteins, described with multiple chemical states. In particular, we discuss the velocity as a function of ATP concentration. Even a simple model which neglects the strain dependence of transition rates, reverse transition rates and nonlinearities in the elasticity can show interesting functional dependencies, which deviate significantly from the frequently assumed Michaelis–Menten form. We discuss how the order of events in the duty cycle can be inferred from the measured dependence. The model also predicts the possibility of velocity reversal at a certain ATP concentration if the duty cycle contains several conformational changes of opposite directionalities. PMID:25485083

  20. Protein complex detection via weighted ensemble clustering based on Bayesian nonnegative matrix factorization.

    PubMed

    Ou-Yang, Le; Dai, Dao-Qing; Zhang, Xiao-Fei

    2013-01-01

    Detecting protein complexes from protein-protein interaction (PPI) networks is a challenging task in computational biology. A vast number of computational methods have been proposed to undertake this task. However, each computational method is developed to capture one aspect of the network. The performance of different methods on the same network can differ substantially, even the same method may have different performance on networks with different topological characteristic. The clustering result of each computational method can be regarded as a feature that describes the PPI network from one aspect. It is therefore desirable to utilize these features to produce a more accurate and reliable clustering. In this paper, a novel Bayesian Nonnegative Matrix Factorization (NMF)-based weighted Ensemble Clustering algorithm (EC-BNMF) is proposed to detect protein complexes from PPI networks. We first apply different computational algorithms on a PPI network to generate some base clustering results. Then we integrate these base clustering results into an ensemble PPI network, in the form of weighted combination. Finally, we identify overlapping protein complexes from this network by employing Bayesian NMF model. When generating an ensemble PPI network, EC-BNMF can automatically optimize the values of weights such that the ensemble algorithm can deliver better results. Experimental results on four PPI networks of Saccharomyces cerevisiae well verify the effectiveness of EC-BNMF in detecting protein complexes. EC-BNMF provides an effective way to integrate different clustering results for more accurate and reliable complex detection. Furthermore, EC-BNMF has a high degree of flexibility in the choice of base clustering results. It can be coupled with existing clustering methods to identify protein complexes.

  1. Using multi-compartment ensemble modeling as an investigative tool of spatially distributed biophysical balances: application to hippocampal oriens-lacunosum/moleculare (O-LM) cells.

    PubMed

    Sekulić, Vladislav; Lawrence, J Josh; Skinner, Frances K

    2014-01-01

    Multi-compartmental models of neurons provide insight into the complex, integrative properties of dendrites. Because it is not feasible to experimentally determine the exact density and kinetics of each channel type in every neuronal compartment, an essential goal in developing models is to help characterize these properties. To address biological variability inherent in a given neuronal type, there has been a shift away from using hand-tuned models towards using ensembles or populations of models. In collectively capturing a neuron's output, ensemble modeling approaches uncover important conductance balances that control neuronal dynamics. However, conductances are never entirely known for a given neuron class in terms of its types, densities, kinetics and distributions. Thus, any multi-compartment model will always be incomplete. In this work, our main goal is to use ensemble modeling as an investigative tool of a neuron's biophysical balances, where the cycling between experiment and model is a design criterion from the start. We consider oriens-lacunosum/moleculare (O-LM) interneurons, a prominent interneuron subtype that plays an essential gating role of information flow in hippocampus. O-LM cells express the hyperpolarization-activated current (Ih). Although dendritic Ih could have a major influence on the integrative properties of O-LM cells, the compartmental distribution of Ih on O-LM dendrites is not known. Using a high-performance computing cluster, we generated a database of models that included those with or without dendritic Ih. A range of conductance values for nine different conductance types were used, and different morphologies explored. Models were quantified and ranked based on minimal error compared to a dataset of O-LM cell electrophysiological properties. Co-regulatory balances between conductances were revealed, two of which were dependent on the presence of dendritic Ih. These findings inform future experiments that differentiate between

  2. Clustering-based ensemble learning for activity recognition in smart homes.

    PubMed

    Jurek, Anna; Nugent, Chris; Bi, Yaxin; Wu, Shengli

    2014-01-01

    Application of sensor-based technology within activity monitoring systems is becoming a popular technique within the smart environment paradigm. Nevertheless, the use of such an approach generates complex constructs of data, which subsequently requires the use of intricate activity recognition techniques to automatically infer the underlying activity. This paper explores a cluster-based ensemble method as a new solution for the purposes of activity recognition within smart environments. With this approach activities are modelled as collections of clusters built on different subsets of features. A classification process is performed by assigning a new instance to its closest cluster from each collection. Two different sensor data representations have been investigated, namely numeric and binary. Following the evaluation of the proposed methodology it has been demonstrated that the cluster-based ensemble method can be successfully applied as a viable option for activity recognition. Results following exposure to data collected from a range of activities indicated that the ensemble method had the ability to perform with accuracies of 94.2% and 97.5% for numeric and binary data, respectively. These results outperformed a range of single classifiers considered as benchmarks.

  3. Ensemble and Arithmetic Recombination-Based Speciation Differential Evolution for Multimodal Optimization.

    PubMed

    Hui, Sheldon; Suganthan, Ponnuthurai N

    2016-01-01

    Multimodal optimization problems consists of multiple equal or comparable spatially distributed solutions. Niching and clustering differential evolution (DE) techniques have been demonstrated to be highly effective for solving such problems. The key challenge in the speciation niching technique is to balance between local solution exploitation and global exploration. Our proposal enhances exploration by applying arithmetic recombination with speciation and improves exploitation of individual peaks by applying neighborhood mutation with ensemble strategies. Our novel algorithm, called ensemble and arithmetic recombination-based speciation DE, is shown to either outperform or perform comparably to the state-of-the-art algorithms on 29 common multimodal benchmark problems. Comparable performance is observed only when some problems are solved perfectly by the algorithms in the literature. PMID:25781971

  4. An ensemble classification-based approach applied to retinal blood vessel segmentation.

    PubMed

    Fraz, Muhammad Moazam; Remagnino, Paolo; Hoppe, Andreas; Uyyanonvara, Bunyarit; Rudnicka, Alicja R; Owen, Christopher G; Barman, Sarah A

    2012-09-01

    This paper presents a new supervised method for segmentation of blood vessels in retinal photographs. This method uses an ensemble system of bagged and boosted decision trees and utilizes a feature vector based on the orientation analysis of gradient vector field, morphological transformation, line strength measures, and Gabor filter responses. The feature vector encodes information to handle the healthy as well as the pathological retinal image. The method is evaluated on the publicly available DRIVE and STARE databases, frequently used for this purpose and also on a new public retinal vessel reference dataset CHASE_DB1 which is a subset of retinal images of multiethnic children from the Child Heart and Health Study in England (CHASE) dataset. The performance of the ensemble system is evaluated in detail and the incurred accuracy, speed, robustness, and simplicity make the algorithm a suitable tool for automated retinal image analysis. PMID:22736688

  5. Ensembl 2012

    PubMed Central

    Flicek, Paul; Amode, M. Ridwan; Barrell, Daniel; Beal, Kathryn; Brent, Simon; Carvalho-Silva, Denise; Clapham, Peter; Coates, Guy; Fairley, Susan; Fitzgerald, Stephen; Gil, Laurent; Gordon, Leo; Hendrix, Maurice; Hourlier, Thibaut; Johnson, Nathan; Kähäri, Andreas K.; Keefe, Damian; Keenan, Stephen; Kinsella, Rhoda; Komorowska, Monika; Koscielny, Gautier; Kulesha, Eugene; Larsson, Pontus; Longden, Ian; McLaren, William; Muffato, Matthieu; Overduin, Bert; Pignatelli, Miguel; Pritchard, Bethan; Riat, Harpreet Singh; Ritchie, Graham R. S.; Ruffier, Magali; Schuster, Michael; Sobral, Daniel; Tang, Y. Amy; Taylor, Kieron; Trevanion, Stephen; Vandrovcova, Jana; White, Simon; Wilson, Mark; Wilder, Steven P.; Aken, Bronwen L.; Birney, Ewan; Cunningham, Fiona; Dunham, Ian; Durbin, Richard; Fernández-Suarez, Xosé M.; Harrow, Jennifer; Herrero, Javier; Hubbard, Tim J. P.; Parker, Anne; Proctor, Glenn; Spudich, Giulietta; Vogel, Jan; Yates, Andy; Zadissa, Amonida; Searle, Stephen M. J.

    2012-01-01

    The Ensembl project (http://www.ensembl.org) provides genome resources for chordate genomes with a particular focus on human genome data as well as data for key model organisms such as mouse, rat and zebrafish. Five additional species were added in the last year including gibbon (Nomascus leucogenys) and Tasmanian devil (Sarcophilus harrisii) bringing the total number of supported species to 61 as of Ensembl release 64 (September 2011). Of these, 55 species appear on the main Ensembl website and six species are provided on the Ensembl preview site (Pre!Ensembl; http://pre.ensembl.org) with preliminary support. The past year has also seen improvements across the project. PMID:22086963

  6. Planetary gearbox condition monitoring of ship-based satellite communication antennas using ensemble multiwavelet analysis method

    NASA Astrophysics Data System (ADS)

    Chen, Jinglong; Zhang, Chunlin; Zhang, Xiaoyan; Zi, Yanyang; He, Shuilong; Yang, Zhe

    2015-03-01

    Satellite communication antennas are key devices of a measurement ship to support voice, data, fax and video integration services. Condition monitoring of mechanical equipment from the vibration measurement data is significant for guaranteeing safe operation and avoiding the unscheduled breakdown. So, condition monitoring system for ship-based satellite communication antennas is designed and developed. Planetary gearboxes play an important role in the transmission train of satellite communication antenna. However, condition monitoring of planetary gearbox still faces challenges due to complexity and weak condition feature. This paper provides a possibility for planetary gearbox condition monitoring by proposing ensemble a multiwavelet analysis method. Benefit from the property on multi-resolution analysis and the multiple wavelet basis functions, multiwavelet has the advantage over characterizing the non-stationary signal. In order to realize the accurate detection of the condition feature and multi-resolution analysis in the whole frequency band, adaptive multiwavelet basis function is constructed via increasing multiplicity and then vibration signal is processed by the ensemble multiwavelet transform. Finally, normalized ensemble multiwavelet transform information entropy is computed to describe the condition of planetary gearbox. The effectiveness of proposed method is first validated through condition monitoring of experimental planetary gearbox. Then this method is used for planetary gearbox condition monitoring of ship-based satellite communication antennas and the results support its feasibility.

  7. Coupling ensemble weather predictions based on TIGGE database with Grid-Xinanjiang model for flood forecast

    NASA Astrophysics Data System (ADS)

    Bao, H.-J.; Zhao, L.-N.; He, Y.; Li, Z.-J.; Wetterhall, F.; Cloke, H. L.; Pappenberger, F.; Manful, D.

    2011-02-01

    The incorporation of numerical weather predictions (NWP) into a flood forecasting system can increase forecast lead times from a few hours to a few days. A single NWP forecast from a single forecast centre, however, is insufficient as it involves considerable non-predictable uncertainties and lead to a high number of false alarms. The availability of global ensemble numerical weather prediction systems through the THORPEX Interactive Grand Global Ensemble' (TIGGE) offers a new opportunity for flood forecast. The Grid-Xinanjiang distributed hydrological model, which is based on the Xinanjiang model theory and the topographical information of each grid cell extracted from the Digital Elevation Model (DEM), is coupled with ensemble weather predictions based on the TIGGE database (CMC, CMA, ECWMF, UKMO, NCEP) for flood forecast. This paper presents a case study using the coupled flood forecasting model on the Xixian catchment (a drainage area of 8826 km2) located in Henan province, China. A probabilistic discharge is provided as the end product of flood forecast. Results show that the association of the Grid-Xinanjiang model and the TIGGE database gives a promising tool for an early warning of flood events several days ahead.

  8. Exploring the Alzheimer amyloid-β peptide conformational ensemble: A review of molecular dynamics approaches.

    PubMed

    Tran, Linh; Ha-Duong, Tâp

    2015-07-01

    Alzheimer's disease is one of the most common dementia among elderly worldwide. There is no therapeutic drugs until now to treat effectively this disease. One main reason is due to the poorly understood mechanism of Aβ peptide aggregation, which plays a crucial role in the development of Alzheimer's disease. It remains challenging to experimentally or theoretically characterize the secondary and tertiary structures of the Aβ monomer because of its high flexibility and aggregation propensity, and its conformations that lead to the aggregation are not fully identified. In this review, we highlight various structural ensembles of Aβ peptide revealed and characterized by computational approaches in order to find converging structures of Aβ monomer. Understanding how Aβ peptide forms transiently stable structures prior to aggregation will contribute to the design of new therapeutic molecules against the Alzheimer's disease.

  9. Exploring the Alzheimer amyloid-β peptide conformational ensemble: A review of molecular dynamics approaches.

    PubMed

    Tran, Linh; Ha-Duong, Tâp

    2015-07-01

    Alzheimer's disease is one of the most common dementia among elderly worldwide. There is no therapeutic drugs until now to treat effectively this disease. One main reason is due to the poorly understood mechanism of Aβ peptide aggregation, which plays a crucial role in the development of Alzheimer's disease. It remains challenging to experimentally or theoretically characterize the secondary and tertiary structures of the Aβ monomer because of its high flexibility and aggregation propensity, and its conformations that lead to the aggregation are not fully identified. In this review, we highlight various structural ensembles of Aβ peptide revealed and characterized by computational approaches in order to find converging structures of Aβ monomer. Understanding how Aβ peptide forms transiently stable structures prior to aggregation will contribute to the design of new therapeutic molecules against the Alzheimer's disease. PMID:25908410

  10. Coastal aquifer management under parameter uncertainty: Ensemble surrogate modeling based simulation-optimization

    NASA Astrophysics Data System (ADS)

    Janardhanan, S.; Datta, B.

    2011-12-01

    Surrogate models are widely used to develop computationally efficient simulation-optimization models to solve complex groundwater management problems. Artificial intelligence based models are most often used for this purpose where they are trained using predictor-predictand data obtained from a numerical simulation model. Most often this is implemented with the assumption that the parameters and boundary conditions used in the numerical simulation model are perfectly known. However, in most practical situations these values are uncertain. Under these circumstances the application of such approximation surrogates becomes limited. In our study we develop a surrogate model based coupled simulation optimization methodology for determining optimal pumping strategies for coastal aquifers considering parameter uncertainty. An ensemble surrogate modeling approach is used along with multiple realization optimization. The methodology is used to solve a multi-objective coastal aquifer management problem considering two conflicting objectives. Hydraulic conductivity and the aquifer recharge are considered as uncertain values. Three dimensional coupled flow and transport simulation model FEMWATER is used to simulate the aquifer responses for a number of scenarios corresponding to Latin hypercube samples of pumping and uncertain parameters to generate input-output patterns for training the surrogate models. Non-parametric bootstrap sampling of this original data set is used to generate multiple data sets which belong to different regions in the multi-dimensional decision and parameter space. These data sets are used to train and test multiple surrogate models based on genetic programming. The ensemble of surrogate models is then linked to a multi-objective genetic algorithm to solve the pumping optimization problem. Two conflicting objectives, viz, maximizing total pumping from beneficial wells and minimizing the total pumping from barrier wells for hydraulic control of

  11. Super Ensemble-based Aviation Turbulence Guidance (SEATG) for Air Traffic Management (ATM)

    NASA Astrophysics Data System (ADS)

    Kim, Jung-Hoon; Chan, William; Sridhar, Banavar; Sharman, Robert

    2014-05-01

    Super Ensemble (ensemble of ten turbulence metrics from time-lagged ensemble members of weather forecast data)-based Aviation Turbulence Guidance (SEATG) is developed using Weather Research and Forecasting (WRF) model and in-situ eddy dissipation rate (EDR) observations equipped on commercial aircraft over the contiguous United States. SEATG is a sequence of five procedures including weather modeling, calculating turbulence metrics, mapping EDR-scale, evaluating metrics, and producing final SEATG forecast. This uses similar methodology to the operational Graphic Turbulence Guidance (GTG) with three major improvements. First, SEATG use a higher resolution (3-km) WRF model to capture cloud-resolving scale phenomena. Second, SEATG computes turbulence metrics for multiple forecasts that are combined at the same valid time resulting in an time-lagged ensemble of multiple turbulence metrics. Third, SEATG provides both deterministic and probabilistic turbulence forecasts to take into account weather uncertainties and user demands. It is found that the SEATG forecasts match well with observed radar reflectivity along a surface front as well as convectively induced turbulence outside the clouds on 7-8 Sep 2012. And, overall performance skill of deterministic SEATG against the observed EDR data during this period is superior to any single turbulence metrics. Finally, probabilistic SEATG is used as an example application of turbulence forecast for air-traffic management. In this study, a simple Wind-Optimal Route (WOR) passing through the potential areas of probabilistic SEATG and Lateral Turbulence Avoidance Route (LTAR) taking into account the SEATG are calculated at z = 35000 ft (z = 12 km) from Los Angeles to John F. Kennedy international airports. As a result, WOR takes total of 239 minutes with 16 minutes of SEATG areas for 40% of moderate turbulence potential, while LTAR takes total of 252 minutes travel time that 5% of fuel would be additionally consumed to entirely

  12. BODIPY-based azamacrocyclic ensemble for selective fluorescence detection and quantification of homocysteine in biological applications.

    PubMed

    Li, Zan; Geng, Zhi-Rong; Zhang, Cui; Wang, Xiao-Bo; Wang, Zhi-Lin

    2015-10-15

    Considering the significant role of plasma homocysteine in physiological processes, two ensembles (F465-Cu(2+) and F508-Cu(2+)) were constructed based on a BODIPY (4,4-difluoro-1,3,5,7-tetramethyl-4-bora-3a,4a-diaza-s-indacene) scaffold conjugated with an azamacrocyclic (1,4,7-triazacyclononane and 1,4,7,10-tetraazacyclododecane) Cu(2+) complex. The results of this effort demonstrated that the F465-Cu(2+) ensemble could be employed to detect homocysteine in the presence of other biologically relevant species, including cysteine and glutathione, under physiological conditions with high selectivity and sensitivity in the turn-on fluorescence mode, while the F508-Cu(2+) ensemble showed no fluorescence responses toward biothiols. A possible mechanism for this homocysteine-specific specificity involving the formation of a homocysteine-induced six-membered ring sandwich structure was proposed and confirmed for the first time by time-dependent fluorescence spectra, ESI-MS and EPR. The detection limit of homocysteine in deproteinized human serum was calculated to be 241.4 nM with a linear range of 0-90.0 μM and the detection limit of F465 for Cu(2+) is 74.7 nM with a linear range of 0-6.0 μM (F508, 80.2 nM, 0-7.0 μM). We have demonstrated the application of the F465-Cu(2+) ensemble for detecting homocysteine in human serum and monitoring the activity of cystathionine β-synthase in vitro. PMID:25951084

  13. Application of new methods based on ECMWF ensemble model for predicting severe convective weather situations

    NASA Astrophysics Data System (ADS)

    Lazar, Dora; Ihasz, Istvan

    2013-04-01

    The short and medium range operational forecasts, warning and alarm of the severe weather are one of the most important activities of the Hungarian Meteorological Service. Our study provides comprehensive summary of newly developed methods based on ECMWF ensemble forecasts to assist successful prediction of the convective weather situations. . In the first part of the study a brief overview is given about the components of atmospheric convection, which are the atmospheric lifting force, convergence and vertical wind shear. The atmospheric instability is often used to characterize the so-called instability index; one of the most popular and often used indexes is the convective available potential energy. Heavy convective events, like intensive storms, supercells and tornadoes are needed the vertical instability, adequate moisture and vertical wind shear. As a first step statistical studies of these three parameters are based on nine years time series of 51-member ensemble forecasting model based on convective summer time period, various statistical analyses were performed. Relationship of the rate of the convective and total precipitation and above three parameters was studied by different statistical methods. Four new visualization methods were applied for supporting successful forecasts of severe weathers. Two of the four visualization methods the ensemble meteogram and the ensemble vertical profiles had been available at the beginning of our work. Both methods show probability of the meteorological parameters for the selected location. Additionally two new methods have been developed. First method provides probability map of the event exceeding predefined values, so the incident of the spatial uncertainty is well-defined. The convective weather events are characterized by the incident of space often rhapsodic occurs rather have expected the event area can be selected so that the ensemble forecasts give very good support. Another new visualization tool shows time

  14. Hybrid Molecular and Spin Dynamics Simulations for Ensembles of Magnetic Nanoparticles for Magnetoresistive Systems

    PubMed Central

    Teich, Lisa; Schröder, Christian

    2015-01-01

    The development of magnetoresistive sensors based on magnetic nanoparticles which are immersed in conductive gel matrices requires detailed information about the corresponding magnetoresistive properties in order to obtain optimal sensor sensitivities. Here, crucial parameters are the particle concentration, the viscosity of the gel matrix and the particle structure. Experimentally, it is not possible to obtain detailed information about the magnetic microstructure, i.e., orientations of the magnetic moments of the particles that define the magnetoresistive properties, however, by using numerical simulations one can study the magnetic microstructure theoretically, although this requires performing classical spin dynamics and molecular dynamics simulations simultaneously. Here, we present such an approach which allows us to calculate the orientation and the trajectory of every single magnetic nanoparticle. This enables us to study not only the static magnetic microstructure, but also the dynamics of the structuring process in the gel matrix itself. With our hybrid approach, arbitrary sensor configurations can be investigated and their magnetoresistive properties can be optimized. PMID:26580623

  15. Nonequilibrium and generalized-ensemble molecular dynamics simulations for amyloid fibril

    SciTech Connect

    Okumura, Hisashi

    2015-12-31

    Amyloids are insoluble and misfolded fibrous protein aggregates and associated with more than 20 serious human diseases. We perform all-atom molecular dynamics simulations of amyloid fibril assembly and disassembly.

  16. Characterization of reservoir simulation models using a polynomial chaos-based ensemble Kalman filter

    NASA Astrophysics Data System (ADS)

    Saad, George; Ghanem, Roger

    2009-04-01

    Model-based predictions of flow in porous media are critically dependent on assumptions and hypotheses that are not always based on first principles and that cannot necessarily be justified on the basis of known prevalent physics. Constitutive models, for instance, fall under this category. While these predictive tools are usually calibrated using observational data, the scatter in the resulting parameters has typically been ignored. In this paper, this scatter is used to construct stochastic process models of the parameters which are then used as the cornerstone in a novel model validation methodology useful for ascertaining the confidence in model-based predictions. The uncertainties are first quantified by representing the unknown model parameters via their polynomial chaos decompositions. These are descriptions of stochastic processes in terms of their coordinates with respect to an orthogonal basis. This is followed by a filtering step to update these representations with measurements as they become available. In order to account for the non-Gaussian nature of model parameters and model errors, an adaptation of the ensemble Kalman filter is developed. Instead of propagating an ensemble of model states forward in time as is suggested within the framework of the ensemble Kalman filter, the proposed approach allows the propagation of a stochastic representation of unknown variables using their respective polynomial chaos decompositions. The model is propagated forward in time by solving the system of partial differential equations using a stochastic projection method. Whenever measurements are available, the proposed data assimilation technique is used to update the stochastic parameters of the numerical model. The proposed method is applied to a black oil reservoir simulation model where measurements are used to stochastically characterize the flow medium and to verify the model validity with specified confidence bounds. The updated model can then be employed to

  17. Raman quantum memory based on an ensemble of nitrogen-vacancy centers coupled to a microcavity

    NASA Astrophysics Data System (ADS)

    Heshami, Khabat; Santori, Charles; Khanaliloo, Behzad; Healey, Chris; Acosta, Victor M.; Barclay, Paul E.; Simon, Christoph

    2014-04-01

    We propose a scheme to realize optical quantum memories in an ensemble of nitrogen-vacancy centers in diamond that are coupled to a microcavity. The scheme is based on off-resonant Raman coupling, which allows one to circumvent optical inhomogeneous broadening and store optical photons in the electronic spin coherence. This approach promises a storage time of order 1 s and a time-bandwidth product of order 107. We include all possible optical transitions in a nine-level configuration, numerically evaluate the efficiencies, and discuss the requirements for achieving high efficiency and fidelity.

  18. A new strategy for snow-cover mapping using remote sensing data and ensemble based systems techniques

    NASA Astrophysics Data System (ADS)

    Roberge, S.; Chokmani, K.; De Sève, D.

    2012-04-01

    The snow cover plays an important role in the hydrological cycle of Quebec (Eastern Canada). Consequently, evaluating its spatial extent interests the authorities responsible for the management of water resources, especially hydropower companies. The main objective of this study is the development of a snow-cover mapping strategy using remote sensing data and ensemble based systems techniques. Planned to be tested in a near real-time operational mode, this snow-cover mapping strategy has the advantage to provide the probability of a pixel to be snow covered and its uncertainty. Ensemble systems are made of two key components. First, a method is needed to build an ensemble of classifiers that is diverse as much as possible. Second, an approach is required to combine the outputs of individual classifiers that make up the ensemble in such a way that correct decisions are amplified, and incorrect ones are cancelled out. In this study, we demonstrate the potential of ensemble systems to snow-cover mapping using remote sensing data. The chosen classifier is a sequential thresholds algorithm using NOAA-AVHRR data adapted to conditions over Eastern Canada. Its special feature is the use of a combination of six sequential thresholds varying according to the day in the winter season. Two versions of the snow-cover mapping algorithm have been developed: one is specific for autumn (from October 1st to December 31st) and the other for spring (from March 16th to May 31st). In order to build the ensemble based system, different versions of the algorithm are created by varying randomly its parameters. One hundred of the versions are included in the ensemble. The probability of a pixel to be snow, no-snow or cloud covered corresponds to the amount of votes the pixel has been classified as such by all classifiers. The overall performance of ensemble based mapping is compared to the overall performance of the chosen classifier, and also with ground observations at meteorological

  19. Ensemble-based evaluation of extreme water levels for the eastern Baltic Sea

    NASA Astrophysics Data System (ADS)

    Eelsalu, Maris; Soomere, Tarmo

    2016-04-01

    The risks and damages associated with coastal flooding that are naturally associated with an increase in the magnitude of extreme storm surges are one of the largest concerns of countries with extensive low-lying nearshore areas. The relevant risks are even more contrast for semi-enclosed water bodies such as the Baltic Sea where subtidal (weekly-scale) variations in the water volume of the sea substantially contribute to the water level and lead to large spreading of projections of future extreme water levels. We explore the options for using large ensembles of projections to more reliably evaluate return periods of extreme water levels. Single projections of the ensemble are constructed by means of fitting several sets of block maxima with various extreme value distributions. The ensemble is based on two simulated data sets produced in the Swedish Meteorological and Hydrological Institute. A hindcast by the Rossby Centre Ocean model is sampled with a resolution of 6 h and a similar hindcast by the circulation model NEMO with a resolution of 1 h. As the annual maxima of water levels in the Baltic Sea are not always uncorrelated, we employ maxima for calendar years and for stormy seasons. As the shape parameter of the Generalised Extreme Value distribution changes its sign and substantially varies in magnitude along the eastern coast of the Baltic Sea, the use of a single distribution for the entire coast is inappropriate. The ensemble involves projections based on the Generalised Extreme Value, Gumbel and Weibull distributions. The parameters of these distributions are evaluated using three different ways: maximum likelihood method and method of moments based on both biased and unbiased estimates. The total number of projections in the ensemble is 40. As some of the resulting estimates contain limited additional information, the members of pairs of projections that are highly correlated are assigned weights 0.6. A comparison of the ensemble-based projection of

  20. Accurate determination of imaging modality using an ensemble of text- and image-based classifiers.

    PubMed

    Kahn, Charles E; Kalpathy-Cramer, Jayashree; Lam, Cesar A; Eldredge, Christina E

    2012-02-01

    Imaging modality can aid retrieval of medical images for clinical practice, research, and education. We evaluated whether an ensemble classifier could outperform its constituent individual classifiers in determining the modality of figures from radiology journals. Seventeen automated classifiers analyzed 77,495 images from two radiology journals. Each classifier assigned one of eight imaging modalities--computed tomography, graphic, magnetic resonance imaging, nuclear medicine, positron emission tomography, photograph, ultrasound, or radiograph-to each image based on visual and/or textual information. Three physicians determined the modality of 5,000 randomly selected images as a reference standard. A "Simple Vote" ensemble classifier assigned each image to the modality that received the greatest number of individual classifiers' votes. A "Weighted Vote" classifier weighted each individual classifier's vote based on performance over a training set. For each image, this classifier's output was the imaging modality that received the greatest weighted vote score. We measured precision, recall, and F score (the harmonic mean of precision and recall) for each classifier. Individual classifiers' F scores ranged from 0.184 to 0.892. The simple vote and weighted vote classifiers correctly assigned 4,565 images (F score, 0.913; 95% confidence interval, 0.905-0.921) and 4,672 images (F score, 0.934; 95% confidence interval, 0.927-0.941), respectively. The weighted vote classifier performed significantly better than all individual classifiers. An ensemble classifier correctly determined the imaging modality of 93% of figures in our sample. The imaging modality of figures published in radiology journals can be determined with high accuracy, which will improve systems for image retrieval.

  1. An investigation of ensemble-based assimilation of satellite altimetry and tide gauge data in storm surge prediction

    NASA Astrophysics Data System (ADS)

    Etala, Paula; Saraceno, Martín; Echevarría, Pablo

    2015-03-01

    Cyclogenesis and long-fetched winds along the southeastern coast of South America may lead to floods in populated areas, as the Buenos Aires Province, with important economic and social impacts. A numerical model (SMARA) has already been implemented in the region to forecast storm surges. The propagation time of the surge in such extensive and shallow area allows the detection of anomalies based on observations from several hours up to the order of a day prior to the event. Here, we investigate the impact and potential benefit of storm surge level data assimilation into the SMARA model, with the objective of improving the forecast. In the experiments, the surface wind stress from an ensemble prediction system drives a storm surge model ensemble, based on the operational 2-D depth-averaged SMARA model. A 4-D Local Ensemble Transform Kalman Filter (4D-LETKF) initializes the ensemble in a 6-h cycle, assimilating the very few tide gauge observations available along the northern coast and satellite altimeter data. The sparse coverage of the altimeters is a challenge to data assimilation; however, the 4D-LETKF evolving covariance of the ensemble perturbations provides realistic cross-track analysis increments. Improvements on the forecast ensemble mean show the potential of an effective use of the sparse satellite altimeter and tidal gauges observations in the data assimilation prototype. Furthermore, the effects of the localization scale and of the observational errors of coastal altimetry and tidal gauges in the data assimilation approach are assessed.

  2. Data assimilation based on Ensemble Kalman filtering for ice sheet initialisation

    NASA Astrophysics Data System (ADS)

    Bonan, Bertrand; Nodet, Maëlle; Ritz, Catherine

    2013-04-01

    A hot topic in ice sheet modelling is to run prognostic simulations over the next 100 years to investigate the impact of Antarctica and Greenland ice sheets on sea level change. Such simulations require an initial state of ice sheets which must be as close as possible to what is currently observed. Large scale ice sheet dynamical models are mostly governed by the following input parameters and variables: basal dragging coefficient, bedrock topography, surface elevation, temperature field. But we do not have satisfying initial states for simulations. Fortunately, some observations are available such as surface and (sparse) bedrock topography, surface velocities, surface elevation trend. The use of advanced inverse methods appears to be the adequate tool to produce satisfying initial states. We develop ensemble methods based on Ensemble Kalman Filter (EnKF) to infer optimal actual states for ice sheet model initialisation thanks to available observations. EnKF is an efficient Monte-Carlo method based on a Gaussian approximation. Contrary to variational approach or traditional Kalman Filter, EnKF does not require full or reduced state error covariance matrices but represents them by a large stochastic ensemble of model realisations. Furthermore, the size of ensembles are generally smaller than other stochastic methods. EnKF has been successfully used in a large community, including ocean and atmospheric sciences. As we first want to assess the validity of the method we begin with twin experiments (simulated observations) with a simple flowline large scale model, Winnie, as a first step toward data assimilation for a full 3D ice sheet model, GRISLI. Winnie (as GRISLI) is an hybrid SIA/SSA ice sheet model and, as a flowline model, is a good prototype to valide our methods. Here we try here to retrieve the prescribed following input parameters and variables: basal sliding coefficients, bedrock topography and ice thickness thanks to our simulated observations of surface

  3. Basin-scale runoff prediction: An Ensemble Kalman Filter framework based on global hydrometeorological data sets

    NASA Astrophysics Data System (ADS)

    Lorenz, Christof; Tourian, Mohammad J.; Devaraju, Balaji; Sneeuw, Nico; Kunstmann, Harald

    2015-10-01

    In order to cope with the steady decline of the number of in situ gauges worldwide, there is a growing need for alternative methods to estimate runoff. We present an Ensemble Kalman Filter based approach that allows us to conclude on runoff for poorly or irregularly gauged basins. The approach focuses on the application of publicly available global hydrometeorological data sets for precipitation (GPCC, GPCP, CRU, UDEL), evapotranspiration (MODIS, FLUXNET, GLEAM, ERA interim, GLDAS), and water storage changes (GRACE, WGHM, GLDAS, MERRA LAND). Furthermore, runoff data from the GRDC and satellite altimetry derived estimates are used. We follow a least squares prediction that exploits the joint temporal and spatial auto- and cross-covariance structures of precipitation, evapotranspiration, water storage changes and runoff. We further consider time-dependent uncertainty estimates derived from all data sets. Our in-depth analysis comprises of 29 large river basins of different climate regions, with which runoff is predicted for a subset of 16 basins. Six configurations are analyzed: the Ensemble Kalman Filter (Smoother) and the hard (soft) Constrained Ensemble Kalman Filter (Smoother). Comparing the predictions to observed monthly runoff shows correlations larger than 0.5, percentage biases lower than ± 20%, and NSE-values larger than 0.5. A modified NSE-metric, stressing the difference to the mean annual cycle, shows an improvement of runoff predictions for 14 of the 16 basins. The proposed method is able to provide runoff estimates for nearly 100 poorly gauged basins covering an area of more than 11,500,000 km2 with a freshwater discharge, in volume, of more than 125,000 m3/s.

  4. Basin-scale runoff prediction: An Ensemble Kalman Filter framework based on global hydrometeorological data sets

    NASA Astrophysics Data System (ADS)

    Kunstmann, Harald; Lorenz, Christof; Tourian, Mohammad; Devaraju, Balaji; Sneeuw, Nico

    2016-04-01

    In order to cope with the steady decline of the number of in situ gauges worldwide, there is a growing need for alternative methods to estimate runoff. We present an Ensemble Kalman Filter based approach that allows us to conclude on runoff for poorly or irregularly gauged basins. The approach focuses on the application of publicly available global hydrometeorological data sets for precipitation (GPCC, GPCP, CRU, UDEL), evapotranspiration (MODIS, FLUXNET, GLEAM, ERA interim, GLDAS), and water storage changes (GRACE, WGHM, GLDAS, MERRA LAND). Furthermore, runoff data from the GRDC and satellite altimetry derived estimates are used. We follow a least squares prediction that exploits the joint temporal and spatial auto- and cross-covariance structures of precipitation, evapotranspiration, water storage changes and runoff. We further consider time-dependent uncertainty estimates derived from all data sets. Our in-depth analysis comprises of 29 large river basins of different climate regions, with which runoff is predicted for a subset of 16 basins. Six configurations are analyzed: the Ensemble Kalman Filter (Smoother) and the hard (soft) Constrained Ensemble Kalman Filter (Smoother). Comparing the predictions to observed monthly runoff shows correlations larger than 0.5, percentage biases lower than ± 20%, and NSE-values larger than 0.5. A modified NSE-metric, stressing the difference to the mean annual cycle, shows an improvement of runoff predictions for 14 of the 16 basins. The proposed method is able to provide runoff estimates for nearly 100 poorly gauged basins covering an area of more than 11,500,000 km2 with a freshwater discharge, in volume, of more than 125,000 m3/s.

  5. An ensemble method based on uninformative variable elimination and mutual information for spectral multivariate calibration.

    PubMed

    Tan, Chao; Wang, Jinyue; Wu, Tong; Qin, Xin; Li, Menglong

    2010-12-01

    Based on the combination of uninformative variable elimination (UVE), bootstrap and mutual information (MI), a simple ensemble algorithm, named ESPLS, is proposed for spectral multivariate calibration (MVC). In ESPLS, those uninformative variables are first removed; and then a preparatory training set is produced by bootstrap, on which a MI spectrum of retained variables is calculated. The variables that exhibit higher MI than a defined threshold form a subspace on which a candidate partial least-squares (PLS) model is constructed. This process is repeated. After a number of candidate models are obtained, a small part of models is picked out to construct an ensemble model by simple/weighted average. Four near/mid-infrared (NIR/MIR) spectral datasets concerning the determination of six components are used to verify the proposed ESPLS. The results indicate that ESPLS is superior to UVEPLS and its combination with MI-based variable selection (SPLS) in terms of both the accuracy and robustness. Besides, from the perspective of end-users, ESPLS does not increase the complexity of a calibration when enhancing its performance.

  6. Applications of Ensemble-based Data Assimilation Techniques for Aquifer Characterization using Tracer Data at Hanford 300 Area

    SciTech Connect

    Chen, Xingyuan; Hammond, Glenn E.; Murray, Christopher J.; Rockhold, Mark L.; Vermeul, Vincent R.; Zachara, John M.

    2013-10-31

    Subsurface aquifer characterization often involves high parameter dimensionality and requires tremendous computational resources if employing a full Bayesian approach. Ensemble-based data assimilation techniques, including filtering and smoothing, are computationally efficient alternatives. Despite the increasing number of applications of ensemble-based methods in assimilating flow and transport related data for subsurface aquifer charaterization, most are limited to either synthetic studies or two-dimensional problems. In this study, we applied ensemble-based techniques for assimilating field tracer experimental data obtained from the Integrated Field Research Challenge (IFRC) site at the Hanford 300 Area. The forward problem was simulated using the massively-parallel three-dimensional flow and transport code PFLOTRAN to effectively deal with the highly transient flow boundary conditions at the site and to meet the computational demands of ensemble-based methods. This study demonstrates the effectiveness of ensemble-based methods for characterizing a heterogeneous aquifer by sequentially assimilating multiple types of data. The necessity of employing high performance computing is shown to enable increasingly mechanistic non-linear forward simulations to be performed within the data assimilation framework for a complex system with reasonable turnaround time.

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

    SciTech Connect

    Singh, Kunwar P. Gupta, Shikha

    2014-03-15

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

  8. Properties of the galactic molecular cloud ensemble from observations of /sup 13/CO

    SciTech Connect

    Liszt, H.S.; Delin, X.; Burton, W.B.

    1981-10-15

    We have observed galactic /sup 13/CO over the region b = 0/sup 0/, l = 28/sup 0/--40/sup 0/, at 3' spacings of the 36 foot (11 m) telescope antenna pattern. The major results of interpretation of these observations are as follows: (1) The size distribution of molecular clouds, inferred from correction measured sizes of features for the bias introduced by sampling only at b = 0/sup 0/, is characterized by the moments < or approx. =25 pc, /sup 1/2/< or approx. =27 pc, /sup 1/3/< or approx. =29 pc. (2) The smallest mean density of hydrogen molecules derived for the region Rroughly-equal5 kpc consistent with available constraints is roughly-equal2.5 cm/sup -3/. Higher values may easily be derived; lower ones are difficult to justify. (3) The radial abundance variations of the /sup 13/CO emissivity follows the general behavior established earlier from /sup 12/CO, but with prominent enhancements (in the longitude region observed) at Rroughly-equal9--10 kpc and Rroughly-equal7--8 kpc. (4) Comparison of the longitudinal variation of terminal velocities measured in /sup 13/CO and in H I indicates that the galactic kinematics of the dense centered molecular clouds are essentially identical to those found for H I. (5) Comparison of /sup 13/CO and H I integrated intensities indicates that one may find substantial regions over each of which the atomic and molecular intensities may be strongly positively or anticorrelated, or statistically independent. Reasons for this behavior are summarized here and considered in more detail by Liszt, Burton, and Bania.

  9. Prognostics of Proton Exchange Membrane Fuel Cells stack using an ensemble of constraints based connectionist networks

    NASA Astrophysics Data System (ADS)

    Javed, Kamran; Gouriveau, Rafael; Zerhouni, Noureddine; Hissel, Daniel

    2016-08-01

    Proton Exchange Membrane Fuel Cell (PEMFC) is considered the most versatile among available fuel cell technologies, which qualify for diverse applications. However, the large-scale industrial deployment of PEMFCs is limited due to their short life span and high exploitation costs. Therefore, ensuring fuel cell service for a long duration is of vital importance, which has led to Prognostics and Health Management of fuel cells. More precisely, prognostics of PEMFC is major area of focus nowadays, which aims at identifying degradation of PEMFC stack at early stages and estimating its Remaining Useful Life (RUL) for life cycle management. This paper presents a data-driven approach for prognostics of PEMFC stack using an ensemble of constraint based Summation Wavelet- Extreme Learning Machine (SW-ELM) models. This development aim at improving the robustness and applicability of prognostics of PEMFC for an online application, with limited learning data. The proposed approach is applied to real data from two different PEMFC stacks and compared with ensembles of well known connectionist algorithms. The results comparison on long-term prognostics of both PEMFC stacks validates our proposition.

  10. Fault Diagnosis of Rotating Machinery Based on an Adaptive Ensemble Empirical Mode Decomposition

    PubMed Central

    Lei, Yaguo; Li, Naipeng; Lin, Jing; Wang, Sizhe

    2013-01-01

    The vibration based signal processing technique is one of the principal tools for diagnosing faults of rotating machinery. Empirical mode decomposition (EMD), as a time-frequency analysis technique, has been widely used to process vibration signals of rotating machinery. But it has the shortcoming of mode mixing in decomposing signals. To overcome this shortcoming, ensemble empirical mode decomposition (EEMD) was proposed accordingly. EEMD is able to reduce the mode mixing to some extent. The performance of EEMD, however, depends on the parameters adopted in the EEMD algorithms. In most of the studies on EEMD, the parameters were selected artificially and subjectively. To solve the problem, a new adaptive ensemble empirical mode decomposition method is proposed in this paper. In the method, the sifting number is adaptively selected, and the amplitude of the added noise changes with the signal frequency components during the decomposition process. The simulation, the experimental and the application results demonstrate that the adaptive EEMD provides the improved results compared with the original EEMD in diagnosing rotating machinery. PMID:24351666

  11. Ensemble-based source apportionment of fine particulate matter and emergency department visits for pediatric asthma.

    PubMed

    Gass, Katherine; Balachandran, Sivaraman; Chang, Howard H; Russell, Armistead G; Strickland, Matthew J

    2015-04-01

    Epidemiologic studies utilizing source apportionment (SA) of fine particulate matter have shown that particles from certain sources might be more detrimental to health than others; however, it is difficult to quantify the uncertainty associated with a given SA approach. In the present study, we examined associations between source contributions of fine particulate matter and emergency department visits for pediatric asthma in Atlanta, Georgia (2002-2010) using a novel ensemble-based SA technique. Six daily source contributions from 4 SA approaches were combined into an ensemble source contribution. To better account for exposure uncertainty, 10 source profiles were sampled from their posterior distributions, resulting in 10 time series with daily SA concentrations. For each of these time series, Poisson generalized linear models with varying lag structures were used to estimate the health associations for the 6 sources. The rate ratios for the source-specific health associations from the 10 imputed source contribution time series were combined, resulting in health associations with inflated confidence intervals to better account for exposure uncertainty. Adverse associations with pediatric asthma were observed for 8-day exposure to particles generated from diesel-fueled vehicles (rate ratio = 1.06, 95% confidence interval: 1.01, 1.10) and gasoline-fueled vehicles (rate ratio = 1.10, 95% confidence interval: 1.04, 1.17). PMID:25776011

  12. Ensemble-based snow data assimilation for an operational snow model

    NASA Astrophysics Data System (ADS)

    Liu, Y.; He, M.; Seo, D.; Laurine, D.; Lee, H.

    2010-12-01

    In mountainous regions of the western United States, seasonal snow pack evolution dominates the generation of snowmelt and streamflow. In the National Weather Service (NWS), the conceptual SNOW-17 model is used for operational forecasting of snowmelt, which then serves as an input to a rainfall-runoff model for streamflow forecasts in snow-affected areas. To improve snowmelt estimates and therefore streamflow forecasts, some River Forecast Centers (RFCs) of the NWS operate a snow updating system to update areal Snow Water Equivalent (SWE) estimates by using a regression technique to reconcile the differences between the observed SWE (e.g., from SNOTEL stations) and the modeled SWE. While this method is parsimonious and easy to use in operations, it does not capitalize on the full capabilities offered by advanced data assimilation techniques to quantify, reduce, and propagate forecast uncertainty in a statistically and dynamically consistent fashion. This study describes an application of the ensemble Kalman filter (EnKF) which automatically and systematically assimilates SNOTEL SWE observations into the SNOW-17 model to reduce uncertainties in model initial conditions. The robustness of the ensemble filter as compared to the operational regression-based method is evaluated for both snow and streamflow forecasts at several operational basins in the service area of the Northwest River Forecast Center (NWRFC). This presentation describes the implementation of the EnKF into the SNOW-17 model and summarizes the preliminary evaluation results.

  13. Ensemble-Based Source Apportionment of Fine Particulate Matter and Emergency Department Visits for Pediatric Asthma

    PubMed Central

    Gass, Katherine; Balachandran, Sivaraman; Chang, Howard H.; Russell, Armistead G.; Strickland, Matthew J.

    2015-01-01

    Epidemiologic studies utilizing source apportionment (SA) of fine particulate matter have shown that particles from certain sources might be more detrimental to health than others; however, it is difficult to quantify the uncertainty associated with a given SA approach. In the present study, we examined associations between source contributions of fine particulate matter and emergency department visits for pediatric asthma in Atlanta, Georgia (2002–2010) using a novel ensemble-based SA technique. Six daily source contributions from 4 SA approaches were combined into an ensemble source contribution. To better account for exposure uncertainty, 10 source profiles were sampled from their posterior distributions, resulting in 10 time series with daily SA concentrations. For each of these time series, Poisson generalized linear models with varying lag structures were used to estimate the health associations for the 6 sources. The rate ratios for the source-specific health associations from the 10 imputed source contribution time series were combined, resulting in health associations with inflated confidence intervals to better account for exposure uncertainty. Adverse associations with pediatric asthma were observed for 8-day exposure to particles generated from diesel-fueled vehicles (rate ratio = 1.06, 95% confidence interval: 1.01, 1.10) and gasoline-fueled vehicles (rate ratio = 1.10, 95% confidence interval: 1.04, 1.17). PMID:25776011

  14. Deconvoluting Protein (Un)folding Structural Ensembles Using X-Ray Scattering, Nuclear Magnetic Resonance Spectroscopy and Molecular Dynamics Simulation

    PubMed Central

    Nasedkin, Alexandr; Marcellini, Moreno; Religa, Tomasz L.; Freund, Stefan M.; Menzel, Andreas; Fersht, Alan R.; Jemth, Per; van der Spoel, David; Davidsson, Jan

    2015-01-01

    The folding and unfolding of protein domains is an apparently cooperative process, but transient intermediates have been detected in some cases. Such (un)folding intermediates are challenging to investigate structurally as they are typically not long-lived and their role in the (un)folding reaction has often been questioned. One of the most well studied (un)folding pathways is that of Drosophila melanogaster Engrailed homeodomain (EnHD): this 61-residue protein forms a three helix bundle in the native state and folds via a helical intermediate. Here we used molecular dynamics simulations to derive sample conformations of EnHD in the native, intermediate, and unfolded states and selected the relevant structural clusters by comparing to small/wide angle X-ray scattering data at four different temperatures. The results are corroborated using residual dipolar couplings determined by NMR spectroscopy. Our results agree well with the previously proposed (un)folding pathway. However, they also suggest that the fully unfolded state is present at a low fraction throughout the investigated temperature interval, and that the (un)folding intermediate is highly populated at the thermal midpoint in line with the view that this intermediate can be regarded to be the denatured state under physiological conditions. Further, the combination of ensemble structural techniques with MD allows for determination of structures and populations of multiple interconverting structures in solution. PMID:25946337

  15. Deconvoluting Protein (Un)folding Structural Ensembles Using X-Ray Scattering, Nuclear Magnetic Resonance Spectroscopy and Molecular Dynamics Simulation.

    PubMed

    Nasedkin, Alexandr; Marcellini, Moreno; Religa, Tomasz L; Freund, Stefan M; Menzel, Andreas; Fersht, Alan R; Jemth, Per; van der Spoel, David; Davidsson, Jan

    2015-01-01

    The folding and unfolding of protein domains is an apparently cooperative process, but transient intermediates have been detected in some cases. Such (un)folding intermediates are challenging to investigate structurally as they are typically not long-lived and their role in the (un)folding reaction has often been questioned. One of the most well studied (un)folding pathways is that of Drosophila melanogaster Engrailed homeodomain (EnHD): this 61-residue protein forms a three helix bundle in the native state and folds via a helical intermediate. Here we used molecular dynamics simulations to derive sample conformations of EnHD in the native, intermediate, and unfolded states and selected the relevant structural clusters by comparing to small/wide angle X-ray scattering data at four different temperatures. The results are corroborated using residual dipolar couplings determined by NMR spectroscopy. Our results agree well with the previously proposed (un)folding pathway. However, they also suggest that the fully unfolded state is present at a low fraction throughout the investigated temperature interval, and that the (un)folding intermediate is highly populated at the thermal midpoint in line with the view that this intermediate can be regarded to be the denatured state under physiological conditions. Further, the combination of ensemble structural techniques with MD allows for determination of structures and populations of multiple interconverting structures in solution.

  16. On the structure of crystalline and molten cryolite: Insights from the ab initio molecular dynamics in NpT ensemble

    NASA Astrophysics Data System (ADS)

    Bučko, Tomáš; Šimko, František

    2016-02-01

    Ab initio molecular dynamics simulations in isobaric-isothermal ensemble have been performed to study the low- and the high-temperature crystalline and liquid phases of cryolite. The temperature induced transitions from the low-temperature solid (α) to the high-temperature solid phase (β) and from the phase β to the liquid phase have been simulated using a series of MD runs performed at gradually increasing temperature. The structure of crystalline and liquid phases is analysed in detail and our computational approach is shown to reliably reproduce the available experimental data for a wide range of temperatures. Relatively frequent reorientations of the AlF6 octahedra observed in our simulation of the phase β explain the thermal disorder in positions of the F- ions observed in X-ray diffraction experiments. The isolated AlF63-, AlF52-, AlF4-, as well as the bridged Al 2 Fm 6 - m ionic entities have been identified as the main constituents of cryolite melt. In accord with the previous high-temperature NMR and Raman spectroscopic experiments, the compound AlF5 2 - has been shown to be the most abundant Al-containing species formed in the melt. The characteristic vibrational frequencies for the AlFn 3 - n species in realistic environment have been determined and the computed values have been found to be in a good agreement with experiment.

  17. On the structure of crystalline and molten cryolite: Insights from the ab initio molecular dynamics in NpT ensemble.

    PubMed

    Bučko, Tomáš; Šimko, František

    2016-02-14

    Ab initio molecular dynamics simulations in isobaric-isothermal ensemble have been performed to study the low- and the high-temperature crystalline and liquid phases of cryolite. The temperature induced transitions from the low-temperature solid (α) to the high-temperature solid phase (β) and from the phase β to the liquid phase have been simulated using a series of MD runs performed at gradually increasing temperature. The structure of crystalline and liquid phases is analysed in detail and our computational approach is shown to reliably reproduce the available experimental data for a wide range of temperatures. Relatively frequent reorientations of the AlF6 octahedra observed in our simulation of the phase β explain the thermal disorder in positions of the F(-) ions observed in X-ray diffraction experiments. The isolated AlF6(3-), AlF5(2-), AlF4(-), as well as the bridged Al2Fm(6-m) ionic entities have been identified as the main constituents of cryolite melt. In accord with the previous high-temperature NMR and Raman spectroscopic experiments, the compound AlF5(2-) has been shown to be the most abundant Al-containing species formed in the melt. The characteristic vibrational frequencies for the AlFn(3-n) species in realistic environment have been determined and the computed values have been found to be in a good agreement with experiment. PMID:26874492

  18. Metal oxide gas sensor drift compensation using a dynamic classifier ensemble based on fitting.

    PubMed

    Liu, Hang; Tang, Zhenan

    2013-01-01

    Sensor drift is currently the most challenging problem in gas sensing. We propose a novel ensemble method with dynamic weights based on fitting (DWF) to solve the gas discrimination problem, regardless of the gas concentration, with high accuracy over extended periods of time. The DWF method uses a dynamic weighted combination of support vector machine (SVM) classifiers trained by the datasets that are collected at different time periods. In the testing of future datasets, the classifier weights are predicted by fitting functions, which are obtained by the proper fitting of the optimal weights during training. We compare the performance of the DWF method with that of competing methods in an experiment based on a public dataset that was compiled over a period of three years. The experimental results demonstrate that the DWF method outperforms the other methods considered. Furthermore, the DWF method can be further optimized by applying a fitting function that more closely matches the variation of the optimal weight over time.

  19. Hidden Conformation Events in DNA Base Extrusions: A Generalized Ensemble Path Optimization and Equilibrium Simulation Study.

    PubMed

    Cao, Liaoran; Lv, Chao; Yang, Wei

    2013-08-13

    DNA base extrusion is a crucial component of many biomolecular processes. Elucidating how bases are selectively extruded from the interiors of double-strand DNAs is pivotal to accurately understanding and efficiently sampling this general type of conformational transitions. In this work, the on-the-path random walk (OTPRW) method, which is the first generalized ensemble sampling scheme designed for finite-temperature-string path optimizations, was improved and applied to obtain the minimum free energy path (MFEP) and the free energy profile of a classical B-DNA major-groove base extrusion pathway. Along the MFEP, an intermediate state and the corresponding transition state were located and characterized. The MFEP result suggests that a base-plane-elongation event rather than the commonly focused base-flipping event is dominant in the transition state formation portion of the pathway; and the energetic penalty at the transition state is mainly introduced by the stretching of the Watson-Crick base pair. Moreover to facilitate the essential base-plane-elongation dynamics, the surrounding environment of the flipped base needs to be intimately involved. Further taking the advantage of the extended-dynamics nature of the OTPRW Hamiltonian, an equilibrium generalized ensemble simulation was performed along the optimized path; and based on the collected samples, several base-flipping (opening) angle collective variables were evaluated. In consistence with the MFEP result, the collective variable analysis result reveals that none of these commonly employed flipping (opening) angles alone can adequately represent the base extrusion pathway, especially in the pre-transition-state portion. As further revealed by the collective variable analysis, the base-pairing partner of the extrusion target undergoes a series of in-plane rotations to facilitate the base-plane-elongation dynamics. A base-plane rotation angle is identified to be a possible reaction coordinate to represent

  20. Database decomposition of a knowledge-based CAD system in mammography: an ensemble approach to improve detection

    NASA Astrophysics Data System (ADS)

    Mazurowski, Maciej A.; Zurada, Jacek M.; Tourassi, Georgia D.

    2008-03-01

    Although ensemble techniques have been investigated in supervised machine learning, their potential with knowledge-based systems is unexplored. The purpose of this study is to investigate the ensemble approach with a knowledge-based (KB) CAD system for the detection of masses in screening mammograms. The system is designed to determine the presence of a mass in a query mammographic region of interest (ROI) based on its similarity with previously acquired examples of mass and normal cases. Similarity between images is assessed using normalized mutual information. Two different approaches of knowledge database decomposition were investigated to create the ensemble. The first approach was random division of the knowledge database into a pre-specified number of equal size, separate groups. The second approach was based on k-means clustering of the knowledge cases according to common texture features extracted from the ROIs. The ensemble components were fused using a linear classifier. Based on a database of 1820 ROIs (901 masses and 919 and the leave-one-out crossvalidation scheme, the ensemble techniques improved the performance of the original KB-CAD system (A z = 0.86+/-0.01). Specifically, random division resulted in ROC area index of A z = 0.90 +/- 0.01 while k-means clustering provided further improvement (A z = 0.91 +/- 0.01). Although marginally better, the improvement was statistically significant. The superiority of the k-means clustering scheme was robust regardless of the number of clusters. This study supports the idea of incorporation of ensemble techniques with knowledge-based systems in mammography.

  1. The possibility of improving aerosol prediction with ensemble-based data assimilation method

    NASA Astrophysics Data System (ADS)

    Chen, Y.; Takemura, T.; Higurashi, A.

    2012-12-01

    Airborne aerosol particles impact not only air quality but also climate through their direct and indirect effects. It is also well known that the influence of aerosols on the air quality is directly linked to the human health. Thus an aerosol prediction system with better accuracy on the temporal and spatial distributions is necessary to provide their information with the public. It is thought that an aerosol prediction system with data assimilation may provide better aerosol forecast than ever. Data assimilation integrates observations and numerical simulations to obtain the optimal solution and reduce the uncertainty. Yumimoto and Takemura (2011) have developed an ensemble based data assimilation system with the local ensemble transformed Kalman filter (LETKF; Hunt et al., 2007) based on a global aerosol climate model (SPRINTARS; Takemura et al., 2002, 2005). In their result, it has been shown that the global distribution of aerosol can be improved. They also indicated that the LETKF data assimilation method may be a good tool to improve the accuracy of the aerosol prediction system. In this study, we try to develop an aerosol prediction system with the data assimilation system shown by Yumimoto and Takemura (2011). We assimilate semi-real time data of the aerosol optical thickness measured by MODIS onboard TERRA and AQUA in the East Asian region into SPRINTARS and use the assimilated data as the initial condition to forecast the temporal and spatial distribution of aerosol particles. Differences between predicted results with/without data assimilation will be revealed in the presentation to show whether the data assimilation with the retrieved satellite data is useful to improve prediction of the aerosol distribution in the East Asian region. Ground-based semi-real time observation data in East Asia from sunphotometer and lidar will be used in the aerosol forecast system with assimilation. This study is supported by the Funding Program for Next Generation World

  2. Comparison of ensemble post-processing approaches, based on empirical and dynamical error modelisation of rainfall-runoff model forecasts

    NASA Astrophysics Data System (ADS)

    Chardon, J.; Mathevet, T.; Le Lay, M.; Gailhard, J.

    2012-04-01

    In the context of a national energy company (EDF : Electricité de France), hydro-meteorological forecasts are necessary to ensure safety and security of installations, meet environmental standards and improve water ressources management and decision making. Hydrological ensemble forecasts allow a better representation of meteorological and hydrological forecasts uncertainties and improve human expertise of hydrological forecasts, which is essential to synthesize available informations, coming from different meteorological and hydrological models and human experience. An operational hydrological ensemble forecasting chain has been developed at EDF since 2008 and is being used since 2010 on more than 30 watersheds in France. This ensemble forecasting chain is characterized ensemble pre-processing (rainfall and temperature) and post-processing (streamflow), where a large human expertise is solicited. The aim of this paper is to compare 2 hydrological ensemble post-processing methods developed at EDF in order improve ensemble forecasts reliability (similar to Monatanari &Brath, 2004; Schaefli et al., 2007). The aim of the post-processing methods is to dress hydrological ensemble forecasts with hydrological model uncertainties, based on perfect forecasts. The first method (called empirical approach) is based on a statistical modelisation of empirical error of perfect forecasts, by streamflow sub-samples of quantile class and lead-time. The second method (called dynamical approach) is based on streamflow sub-samples of quantile class and streamflow variation, and lead-time. On a set of 20 watersheds used for operational forecasts, results show that both approaches are necessary to ensure a good post-processing of hydrological ensemble, allowing a good improvement of reliability, skill and sharpness of ensemble forecasts. The comparison of the empirical and dynamical approaches shows the limits of the empirical approach which is not able to take into account hydrological

  3. A Human ECG Identification System Based on Ensemble Empirical Mode Decomposition

    PubMed Central

    Zhao, Zhidong; Yang, Lei; Chen, Diandian; Luo, Yi

    2013-01-01

    In this paper, a human electrocardiogram (ECG) identification system based on ensemble empirical mode decomposition (EEMD) is designed. A robust preprocessing method comprising noise elimination, heartbeat normalization and quality measurement is proposed to eliminate the effects of noise and heart rate variability. The system is independent of the heart rate. The ECG signal is decomposed into a number of intrinsic mode functions (IMFs) and Welch spectral analysis is used to extract the significant heartbeat signal features. Principal component analysis is used reduce the dimensionality of the feature space, and the K-nearest neighbors (K-NN) method is applied as the classifier tool. The proposed human ECG identification system was tested on standard MIT-BIH ECG databases: the ST change database, the long-term ST database, and the PTB database. The system achieved an identification accuracy of 95% for 90 subjects, demonstrating the effectiveness of the proposed method in terms of accuracy and robustness. PMID:23698274

  4. Prediction intervals for a noisy nonlinear time series based on a bootstrapping reservoir computing network ensemble.

    PubMed

    Sheng, Chunyang; Zhao, Jun; Wang, Wei; Leung, Henry

    2013-07-01

    Prediction intervals that provide estimated values as well as the corresponding reliability are applied to nonlinear time series forecast. However, constructing reliable prediction intervals for noisy time series is still a challenge. In this paper, a bootstrapping reservoir computing network ensemble (BRCNE) is proposed and a simultaneous training method based on Bayesian linear regression is developed. In addition, the structural parameters of the BRCNE, that is, the number of reservoir computing networks and the reservoir dimension, are determined off-line by the 0.632 bootstrap cross-validation. To verify the effectiveness of the proposed method, two kinds of time series data, including the multisuperimposed oscillator problem with additive noises and a practical gas flow in steel industry are employed here. The experimental results indicate that the proposed approach has a satisfactory performance on prediction intervals for practical applications.

  5. Ensemble Methods

    NASA Astrophysics Data System (ADS)

    Re, Matteo; Valentini, Giorgio

    2012-03-01

    Ensemble methods are statistical and computational learning procedures reminiscent of the human social learning behavior of seeking several opinions before making any crucial decision. The idea of combining the opinions of different "experts" to obtain an overall “ensemble” decision is rooted in our culture at least from the classical age of ancient Greece, and it has been formalized during the Enlightenment with the Condorcet Jury Theorem[45]), which proved that the judgment of a committee is superior to those of individuals, provided the individuals have reasonable competence. Ensembles are sets of learning machines that combine in some way their decisions, or their learning algorithms, or different views of data, or other specific characteristics to obtain more reliable and more accurate predictions in supervised and unsupervised learning problems [48,116]. A simple example is represented by the majority vote ensemble, by which the decisions of different learning machines are combined, and the class that receives the majority of “votes” (i.e., the class predicted by the majority of the learning machines) is the class predicted by the overall ensemble [158]. In the literature, a plethora of terms other than ensembles has been used, such as fusion, combination, aggregation, and committee, to indicate sets of learning machines that work together to solve a machine learning problem [19,40,56,66,99,108,123], but in this chapter we maintain the term ensemble in its widest meaning, in order to include the whole range of combination methods. Nowadays, ensemble methods represent one of the main current research lines in machine learning [48,116], and the interest of the research community on ensemble methods is witnessed by conferences and workshops specifically devoted to ensembles, first of all the multiple classifier systems (MCS) conference organized by Roli, Kittler, Windeatt, and other researchers of this area [14,62,85,149,173]. Several theories have been

  6. Ensemble Methods

    NASA Astrophysics Data System (ADS)

    Re, Matteo; Valentini, Giorgio

    2012-03-01

    Ensemble methods are statistical and computational learning procedures reminiscent of the human social learning behavior of seeking several opinions before making any crucial decision. The idea of combining the opinions of different "experts" to obtain an overall “ensemble” decision is rooted in our culture at least from the classical age of ancient Greece, and it has been formalized during the Enlightenment with the Condorcet Jury Theorem[45]), which proved that the judgment of a committee is superior to those of individuals, provided the individuals have reasonable competence. Ensembles are sets of learning machines that combine in some way their decisions, or their learning algorithms, or different views of data, or other specific characteristics to obtain more reliable and more accurate predictions in supervised and unsupervised learning problems [48,116]. A simple example is represented by the majority vote ensemble, by which the decisions of different learning machines are combined, and the class that receives the majority of “votes” (i.e., the class predicted by the majority of the learning machines) is the class predicted by the overall ensemble [158]. In the literature, a plethora of terms other than ensembles has been used, such as fusion, combination, aggregation, and committee, to indicate sets of learning machines that work together to solve a machine learning problem [19,40,56,66,99,108,123], but in this chapter we maintain the term ensemble in its widest meaning, in order to include the whole range of combination methods. Nowadays, ensemble methods represent one of the main current research lines in machine learning [48,116], and the interest of the research community on ensemble methods is witnessed by conferences and workshops specifically devoted to ensembles, first of all the multiple classifier systems (MCS) conference organized by Roli, Kittler, Windeatt, and other researchers of this area [14,62,85,149,173]. Several theories have been

  7. Data-worth analysis through probabilistic collocation-based Ensemble Kalman Filter

    NASA Astrophysics Data System (ADS)

    Dai, Cheng; Xue, Liang; Zhang, Dongxiao; Guadagnini, Alberto

    2016-09-01

    We propose a new and computationally efficient data-worth analysis and quantification framework keyed to the characterization of target state variables in groundwater systems. We focus on dynamically evolving plumes of dissolved chemicals migrating in randomly heterogeneous aquifers. An accurate prediction of the detailed features of solute plumes requires collecting a substantial amount of data. Otherwise, constraints dictated by the availability of financial resources and ease of access to the aquifer system suggest the importance of assessing the expected value of data before these are actually collected. Data-worth analysis is targeted to the quantification of the impact of new potential measurements on the expected reduction of predictive uncertainty based on a given process model. Integration of the Ensemble Kalman Filter method within a data-worth analysis framework enables us to assess data worth sequentially, which is a key desirable feature for monitoring scheme design in a contaminant transport scenario. However, it is remarkably challenging because of the (typically) high computational cost involved, considering that repeated solutions of the inverse problem are required. As a computationally efficient scheme, we embed in the data-worth analysis framework a modified version of the Probabilistic Collocation Method-based Ensemble Kalman Filter proposed by Zeng et al. (2011) so that we take advantage of the ability to assimilate data sequentially in time through a surrogate model constructed via the polynomial chaos expansion. We illustrate our approach on a set of synthetic scenarios involving solute migrating in a two-dimensional random permeability field. Our results demonstrate the computational efficiency of our approach and its ability to quantify the impact of the design of the monitoring network on the reduction of uncertainty associated with the characterization of a migrating contaminant plume.

  8. Predicting Ground Based Magnetometer Measurements Using the Ensemble Transform Kalman Filter

    NASA Astrophysics Data System (ADS)

    Lynch, E. M.; Sharma, A. S.; Kalnay, E.; Ide, K.

    2015-12-01

    Ensemble data assimilation techniques, including the Ensemble Transform Kalman Filter (ETKF), have been successfully used to improve prediction skill in cases where a numerical model for forecasting has been developed. These techniques for systems for which no model exists are developed using the reconstruction of phase space from time series data. For many natural systems, the complete set of equations governing their evolution are not known and observational data of only a limited number of physical variables are available However, for a dissipative system in which the variables are coupled nonlinearly, the dimensionality of the phase space is greatly reduced, and it is possible to reconstruct the details of the phase space from a single scalar time series of observations. A combination of the phase phase reconstruction with ETKF yields a new technique of forecasting using only time series data. This technique is used to forecast magnetic field variations in th magnetosphere, which exhibits low dimensional behavior on the substorm time scale. The time series data of the magnetic field variations monitored by the network of groundbased magnetometers in the auroral region are used for forecasting at two stages.. In the first stage, the auroral electrojet indices computed from the data from the magnetometers are used for forecasting and yields forecasts that are better than persistence. In the second stage, the multivariate time series from several auroral region magnetometers is used to reconstruct the phase space of the magnetosphere-solar wind system using Multi-channel Singular Spectrum Analysis. The ETKF is applied to ensemble forecasts made using model data constructed from long time series of the data from each magnetometer and observations of the magnetometer measurements. The improved prediction skill, e.g., with respect to persistence, is achieved from the use of the dynamical behavior of nearby trajectories. The near-real time forecasts of space weather

  9. Constructing better classifier ensemble based on weighted accuracy and diversity measure.

    PubMed

    Zeng, Xiaodong; Wong, Derek F; Chao, Lidia S

    2014-01-01

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

  10. An exact approach for studying cargo transport by an ensemble of molecular motors

    PubMed Central

    2013-01-01

    Background Intracellular transport is crucial for many cellular processes where a large fraction of the cargo is transferred by motor-proteins over a network of microtubules. Malfunctions in the transport mechanism underlie a number of medical maladies. Existing methods for studying how motor-proteins coordinate the transfer of a shared cargo over a microtubule are either analytical or are based on Monte-Carlo simulations. Approaches that yield analytical results, while providing unique insights into transport mechanism, make simplifying assumptions, where a detailed characterization of important transport modalities is difficult to reach. On the other hand, Monte-Carlo based simulations can incorporate detailed characteristics of the transport mechanism; however, the quality of the results depend on the number and quality of simulation runs used in arriving at results. Here, for example, it is difficult to simulate and study rare-events that can trigger abnormalities in transport. Results In this article, a semi-analytical methodology that determines the probability distribution function of motor-protein behavior in an exact manner is developed. The method utilizes a finite-dimensional projection of the underlying infinite-dimensional Markov model, which retains the Markov property, and enables the detailed and exact determination of motor configurations, from which meaningful inferences on transport characteristics of the original model can be derived. Conclusions Under this novel probabilistic approach new insights about the mechanisms of action of these proteins are found, suggesting hypothesis about their behavior and driving the design and realization of new experiments. The advantages provided in accuracy and efficiency make it possible to detect rare events in the motor protein dynamics, that could otherwise pass undetected using standard simulation methods. In this respect, the model has allowed to provide a possible explanation for possible mechanisms

  11. Ensemble Models

    EPA Science Inventory

    Ensemble forecasting has been used for operational numerical weather prediction in the United States and Europe since the early 1990s. An ensemble of weather or climate forecasts is used to characterize the two main sources of uncertainty in computer models of physical systems: ...

  12. Microarray gene cluster identification and annotation through cluster ensemble and EM-based informative textual summarization.

    PubMed

    Hu, Xiaohua; Park, E K; Zhang, Xiaodan

    2009-09-01

    Generating high-quality gene clusters and identifying the underlying biological mechanism of the gene clusters are the important goals of clustering gene expression analysis. To get high-quality cluster results, most of the current approaches rely on choosing the best cluster algorithm, in which the design biases and assumptions meet the underlying distribution of the dataset. There are two issues for this approach: 1) usually, the underlying data distribution of the gene expression datasets is unknown and 2) there are so many clustering algorithms available and it is very challenging to choose the proper one. To provide a textual summary of the gene clusters, the most explored approach is the extractive approach that essentially builds upon techniques borrowed from the information retrieval, in which the objective is to provide terms to be used for query expansion, and not to act as a stand-alone summary for the entire document sets. Another drawback is that the clustering quality and cluster interpretation are treated as two isolated research problems and are studied separately. In this paper, we design and develop a unified system Gene Expression Miner to address these challenging issues in a principled and general manner by integrating cluster ensemble, text clustering, and multidocument summarization and provide an environment for comprehensive gene expression data analysis. We present a novel cluster ensemble approach to generate high-quality gene cluster. In our text summarization module, given a gene cluster, our expectation-maximization based algorithm can automatically identify subtopics and extract most probable terms for each topic. Then, the extracted top k topical terms from each subtopic are combined to form the biological explanation of each gene cluster. Experimental results demonstrate that our system can obtain high-quality clusters and provide informative key terms for the gene clusters.

  13. The impact of Ensemble-based data assimilation on the predictability of landfalling Hurricane Katrina (2005)

    NASA Astrophysics Data System (ADS)

    Zhang, H.; Pu, Z.

    2012-12-01

    Accurate forecasts of the track, intensity and structure of a landfalling hurricane can save lives and mitigate social impacts. Over the last two decades, significant improvements have been achieved for hurricane forecasts. However, only a few of studies have emphasized landfalling hurricanes. Specifically, there are difficulties in predicting hurricane landfall due to the uncertainties in representing the atmospheric near-surface conditions in numerical weather prediction models, the complicated interaction between the atmosphere and the ocean, and the multiple-scale dynamical and physical processes accompanying storm development. In this study, the impact of the assimilation of conventional and satellite observations on the predictability of landfalling hurricanes is examined by using a mesoscale community Weather Research and Forecasting (WRF) model and an ensemble Kalman filter developed by NCAR Data Assimilation Research Testbed (DART). Hurricane Katrina (2005) was chosen as a case study since it was one of the deadliest disasters in US history. The minimum sea level pressure from the best track, QuikScat ocean surface wind vectors, surface mesonet observations, airborne Doppler radar derived wind components and available conventional observations are assimilated in a series of experiments to examine the data impacts on the predictability of Hurricane Katrina. The analyses and forecasts show that ensemble-based data assimilation significantly improves the forecast of Hurricane Katrina. The assimilation improves the track forecast through modifying the storm structures and related environmental fields. Cyclonic increments are clearly seen in vorticity and wind analyses. Temperature and humidity fields are also modified by the data assimilation. The changes in relevant fields help organize the structure of the storm, intensify the circulation, and result in a positive impact on the evolution of the storm in both analyses and forecasts. The forecasts in the

  14. Ensemble-based characterization of unbound and bound states on protein energy landscape

    PubMed Central

    Ruvinsky, Anatoly M; Kirys, Tatsiana; Tuzikov, Alexander V; Vakser, Ilya A

    2013-01-01

    Physicochemical description of numerous cell processes is fundamentally based on the energy landscapes of protein molecules involved. Although the whole energy landscape is difficult to reconstruct, increased attention to particular targets has provided enough structures for mapping functionally important subspaces associated with the unbound and bound protein structures. The subspace mapping produces a discrete representation of the landscape, further called energy spectrum. We compiled and characterized ensembles of bound and unbound conformations of six small proteins and explored their spectra in implicit solvent. First, the analysis of the unbound-to-bound changes points to conformational selection as the binding mechanism for four proteins. Second, results show that bound and unbound spectra often significantly overlap. Moreover, the larger the overlap the smaller the root mean square deviation (RMSD) between the bound and unbound conformational ensembles. Third, the center of the unbound spectrum has a higher energy than the center of the corresponding bound spectrum of the dimeric and multimeric states for most of the proteins. This suggests that the unbound states often have larger entropy than the bound states. Fourth, the exhaustively long minimization, making small intrarotamer adjustments (all-atom RMSD ≤ 0.7 Å), dramatically reduces the distance between the centers of the bound and unbound spectra as well as the spectra extent. It condenses unbound and bound energy levels into a thin layer at the bottom of the energy landscape with the energy spacing that varies between 0.8–4.6 and 3.5–10.5 kcal/mol for the unbound and bound states correspondingly. Finally, the analysis of protein energy fluctuations showed that protein vibrations itself can excite the interstate transitions, including the unbound-to-bound ones. PMID:23526684

  15. Bayesian model aggregation for ensemble-based estimates of protein pKa values

    SciTech Connect

    Gosink, Luke J.; Hogan, Emilie A.; Pulsipher, Trenton C.; Baker, Nathan A.

    2014-03-01

    This paper investigates an ensemble-based technique called Bayesian Model Averaging (BMA) to improve the performance of protein amino acid p$K_a$ predictions. Structure-based p$K_a$ calculations play an important role in the mechanistic interpretation of protein structure and are also used to determine a wide range of protein properties. A diverse set of methods currently exist for p$K_a$ prediction, ranging from empirical statistical models to {\\it ab initio} quantum mechanical approaches. However, each of these methods are based on a set of assumptions that have inherent bias and sensitivities that can effect a model's accuracy and generalizability for p$K_a$ prediction in complicated biomolecular systems. We use BMA to combine eleven diverse prediction methods that each estimate pKa values of amino acids in staphylococcal nuclease. These methods are based on work conducted for the pKa Cooperative and the pKa measurements are based on experimental work conducted by the Garc{\\'i}a-Moreno lab. Our study demonstrates that the aggregated estimate obtained from BMA outperforms all individual prediction methods in our cross-validation study with improvements from 40-70\\% over other method classes. This work illustrates a new possible mechanism for improving the accuracy of p$K_a$ prediction and lays the foundation for future work on aggregate models that balance computational cost with prediction accuracy.

  16. Generic Learning-Based Ensemble Framework for Small Sample Size Face Recognition in Multi-Camera Networks.

    PubMed

    Zhang, Cuicui; Liang, Xuefeng; Matsuyama, Takashi

    2014-01-01

    Multi-camera networks have gained great interest in video-based surveillance systems for security monitoring, access control, etc. Person re-identification is an essential and challenging task in multi-camera networks, which aims to determine if a given individual has already appeared over the camera network. Individual recognition often uses faces as a trial and requires a large number of samples during the training phrase. This is difficult to fulfill due to the limitation of the camera hardware system and the unconstrained image capturing conditions. Conventional face recognition algorithms often encounter the "small sample size" (SSS) problem arising from the small number of training samples compared to the high dimensionality of the sample space. To overcome this problem, interest in the combination of multiple base classifiers has sparked research efforts in ensemble methods. However, existing ensemble methods still open two questions: (1) how to define diverse base classifiers from the small data; (2) how to avoid the diversity/accuracy dilemma occurring during ensemble. To address these problems, this paper proposes a novel generic learning-based ensemble framework, which augments the small data by generating new samples based on a generic distribution and introduces a tailored 0-1 knapsack algorithm to alleviate the diversity/accuracy dilemma. More diverse base classifiers can be generated from the expanded face space, and more appropriate base classifiers are selected for ensemble. Extensive experimental results on four benchmarks demonstrate the higher ability of our system to cope with the SSS problem compared to the state-of-the-art system. PMID:25494350

  17. Generic Learning-Based Ensemble Framework for Small Sample Size Face Recognition in Multi-Camera Networks

    PubMed Central

    Zhang, Cuicui; Liang, Xuefeng; Matsuyama, Takashi

    2014-01-01

    Multi-camera networks have gained great interest in video-based surveillance systems for security monitoring, access control, etc. Person re-identification is an essential and challenging task in multi-camera networks, which aims to determine if a given individual has already appeared over the camera network. Individual recognition often uses faces as a trial and requires a large number of samples during the training phrase. This is difficult to fulfill due to the limitation of the camera hardware system and the unconstrained image capturing conditions. Conventional face recognition algorithms often encounter the “small sample size” (SSS) problem arising from the small number of training samples compared to the high dimensionality of the sample space. To overcome this problem, interest in the combination of multiple base classifiers has sparked research efforts in ensemble methods. However, existing ensemble methods still open two questions: (1) how to define diverse base classifiers from the small data; (2) how to avoid the diversity/accuracy dilemma occurring during ensemble. To address these problems, this paper proposes a novel generic learning-based ensemble framework, which augments the small data by generating new samples based on a generic distribution and introduces a tailored 0–1 knapsack algorithm to alleviate the diversity/accuracy dilemma. More diverse base classifiers can be generated from the expanded face space, and more appropriate base classifiers are selected for ensemble. Extensive experimental results on four benchmarks demonstrate the higher ability of our system to cope with the SSS problem compared to the state-of-the-art system. PMID:25494350

  18. Uncertainty analysis of neural network based flood forecasting models: An ensemble based approach for constructing prediction interval

    NASA Astrophysics Data System (ADS)

    Kasiviswanathan, K.; Sudheer, K.

    2013-05-01

    Artificial neural network (ANN) based hydrologic models have gained lot of attention among water resources engineers and scientists, owing to their potential for accurate prediction of flood flows as compared to conceptual or physics based hydrologic models. The ANN approximates the non-linear functional relationship between the complex hydrologic variables in arriving at the river flow forecast values. Despite a large number of applications, there is still some criticism that ANN's point prediction lacks in reliability since the uncertainty of predictions are not quantified, and it limits its use in practical applications. A major concern in application of traditional uncertainty analysis techniques on neural network framework is its parallel computing architecture with large degrees of freedom, which makes the uncertainty assessment a challenging task. Very limited studies have considered assessment of predictive uncertainty of ANN based hydrologic models. In this study, a novel method is proposed that help construct the prediction interval of ANN flood forecasting model during calibration itself. The method is designed to have two stages of optimization during calibration: at stage 1, the ANN model is trained with genetic algorithm (GA) to obtain optimal set of weights and biases vector, and during stage 2, the optimal variability of ANN parameters (obtained in stage 1) is identified so as to create an ensemble of predictions. During the 2nd stage, the optimization is performed with multiple objectives, (i) minimum residual variance for the ensemble mean, (ii) maximum measured data points to fall within the estimated prediction interval and (iii) minimum width of prediction interval. The method is illustrated using a real world case study of an Indian basin. The method was able to produce an ensemble that has an average prediction interval width of 23.03 m3/s, with 97.17% of the total validation data points (measured) lying within the interval. The derived

  19. An approach for identifying cytokines based on a novel ensemble classifier.

    PubMed

    Zou, Quan; Wang, Zhen; Guan, Xinjun; Liu, Bin; Wu, Yunfeng; Lin, Ziyu

    2013-01-01

    Biology is meaningful and important to identify cytokines and investigate their various functions and biochemical mechanisms. However, several issues remain, including the large scale of benchmark datasets, serious imbalance of data, and discovery of new gene families. In this paper, we employ the machine learning approach based on a novel ensemble classifier to predict cytokines. We directly selected amino acids sequences as research objects. First, we pretreated the benchmark data accurately. Next, we analyzed the physicochemical properties and distribution of whole amino acids and then extracted a group of 120-dimensional (120D) valid features to represent sequences. Third, in the view of the serious imbalance in benchmark datasets, we utilized a sampling approach based on the synthetic minority oversampling technique algorithm and K-means clustering undersampling algorithm to rebuild the training set. Finally, we built a library for dynamic selection and circulating combination based on clustering (LibD3C) and employed the new training set to realize cytokine classification. Experiments showed that the geometric mean of sensitivity and specificity obtained through our approach is as high as 93.3%, which proves that our approach is effective for identifying cytokines.

  20. Investigating properties of the cardiovascular system using innovative analysis algorithms based on ensemble empirical mode decomposition.

    PubMed

    Yeh, Jia-Rong; Lin, Tzu-Yu; Chen, Yun; Sun, Wei-Zen; Abbod, Maysam F; Shieh, Jiann-Shing

    2012-01-01

    Cardiovascular system is known to be nonlinear and nonstationary. Traditional linear assessments algorithms of arterial stiffness and systemic resistance of cardiac system accompany the problem of nonstationary or inconvenience in practical applications. In this pilot study, two new assessment methods were developed: the first is ensemble empirical mode decomposition based reflection index (EEMD-RI) while the second is based on the phase shift between ECG and BP on cardiac oscillation. Both methods utilise the EEMD algorithm which is suitable for nonlinear and nonstationary systems. These methods were used to investigate the properties of arterial stiffness and systemic resistance for a pig's cardiovascular system via ECG and blood pressure (BP). This experiment simulated a sequence of continuous changes of blood pressure arising from steady condition to high blood pressure by clamping the artery and an inverse by relaxing the artery. As a hypothesis, the arterial stiffness and systemic resistance should vary with the blood pressure due to clamping and relaxing the artery. The results show statistically significant correlations between BP, EEMD-based RI, and the phase shift between ECG and BP on cardiac oscillation. The two assessments results demonstrate the merits of the EEMD for signal analysis.

  1. Multiple time step molecular dynamics in the optimized isokinetic ensemble steered with the molecular theory of solvation: Accelerating with advanced extrapolation of effective solvation forces

    SciTech Connect

    Omelyan, Igor E-mail: omelyan@icmp.lviv.ua; Kovalenko, Andriy

    2013-12-28

    We develop efficient handling of solvation forces in the multiscale method of multiple time step molecular dynamics (MTS-MD) of a biomolecule steered by the solvation free energy (effective solvation forces) obtained from the 3D-RISM-KH molecular theory of solvation (three-dimensional reference interaction site model complemented with the Kovalenko-Hirata closure approximation). To reduce the computational expenses, we calculate the effective solvation forces acting on the biomolecule by using advanced solvation force extrapolation (ASFE) at inner time steps while converging the 3D-RISM-KH integral equations only at large outer time steps. The idea of ASFE consists in developing a discrete non-Eckart rotational transformation of atomic coordinates that minimizes the distances between the atomic positions of the biomolecule at different time moments. The effective solvation forces for the biomolecule in a current conformation at an inner time step are then extrapolated in the transformed subspace of those at outer time steps by using a modified least square fit approach applied to a relatively small number of the best force-coordinate pairs. The latter are selected from an extended set collecting the effective solvation forces obtained from 3D-RISM-KH at outer time steps over a broad time interval. The MTS-MD integration with effective solvation forces obtained by converging 3D-RISM-KH at outer time steps and applying ASFE at inner time steps is stabilized by employing the optimized isokinetic Nosé-Hoover chain (OIN) ensemble. Compared to the previous extrapolation schemes used in combination with the Langevin thermostat, the ASFE approach substantially improves the accuracy of evaluation of effective solvation forces and in combination with the OIN thermostat enables a dramatic increase of outer time steps. We demonstrate on a fully flexible model of alanine dipeptide in aqueous solution that the MTS-MD/OIN/ASFE/3D-RISM-KH multiscale method of molecular dynamics

  2. Multiple time step molecular dynamics in the optimized isokinetic ensemble steered with the molecular theory of solvation: Accelerating with advanced extrapolation of effective solvation forces

    NASA Astrophysics Data System (ADS)

    Omelyan, Igor; Kovalenko, Andriy

    2013-12-01

    We develop efficient handling of solvation forces in the multiscale method of multiple time step molecular dynamics (MTS-MD) of a biomolecule steered by the solvation free energy (effective solvation forces) obtained from the 3D-RISM-KH molecular theory of solvation (three-dimensional reference interaction site model complemented with the Kovalenko-Hirata closure approximation). To reduce the computational expenses, we calculate the effective solvation forces acting on the biomolecule by using advanced solvation force extrapolation (ASFE) at inner time steps while converging the 3D-RISM-KH integral equations only at large outer time steps. The idea of ASFE consists in developing a discrete non-Eckart rotational transformation of atomic coordinates that minimizes the distances between the atomic positions of the biomolecule at different time moments. The effective solvation forces for the biomolecule in a current conformation at an inner time step are then extrapolated in the transformed subspace of those at outer time steps by using a modified least square fit approach applied to a relatively small number of the best force-coordinate pairs. The latter are selected from an extended set collecting the effective solvation forces obtained from 3D-RISM-KH at outer time steps over a broad time interval. The MTS-MD integration with effective solvation forces obtained by converging 3D-RISM-KH at outer time steps and applying ASFE at inner time steps is stabilized by employing the optimized isokinetic Nosé-Hoover chain (OIN) ensemble. Compared to the previous extrapolation schemes used in combination with the Langevin thermostat, the ASFE approach substantially improves the accuracy of evaluation of effective solvation forces and in combination with the OIN thermostat enables a dramatic increase of outer time steps. We demonstrate on a fully flexible model of alanine dipeptide in aqueous solution that the MTS-MD/OIN/ASFE/3D-RISM-KH multiscale method of molecular dynamics

  3. An efficient tree classifier ensemble-based approach for pedestrian detection.

    PubMed

    Xu, Yanwu; Cao, Xianbin; Qiao, Hong

    2011-02-01

    Classification-based pedestrian detection systems (PDSs) are currently a hot research topic in the field of intelligent transportation. A PDS detects pedestrians in real time on moving vehicles. A practical PDS demands not only high detection accuracy but also high detection speed. However, most of the existing classification-based approaches mainly seek for high detection accuracy, while the detection speed is not purposely optimized for practical application. At the same time, the performance, particularly the speed, is primarily tuned based on experiments without theoretical foundations, leading to a long training procedure. This paper starts with measuring and optimizing detection speed, and then a practical classification-based pedestrian detection solution with high detection speed and training speed is described. First, an extended classification/detection speed metric, named feature-per-object (fpo), is proposed to measure the detection speed independently from execution. Then, an fpo minimization model with accuracy constraints is formulated based on a tree classifier ensemble, where the minimum fpo can guarantee the highest detection speed. Finally, the minimization problem is solved efficiently by using nonlinear fitting based on radial basis function neural networks. In addition, the optimal solution is directly used to instruct classifier training; thus, the training speed could be accelerated greatly. Therefore, a rapid and accurate classification-based detection technique is proposed for the PDS. Experimental results on urban traffic videos show that the proposed method has a high detection speed with an acceptable detection rate and a false-alarm rate for onboard detection; moreover, the training procedure is also very fast.

  4. Determination of knock characteristics in spark ignition engines: an approach based on ensemble empirical mode decomposition

    NASA Astrophysics Data System (ADS)

    Li, Ning; Yang, Jianguo; Zhou, Rui; Liang, Caiping

    2016-04-01

    Knock is one of the major constraints to improve the performance and thermal efficiency of spark ignition (SI) engines. It can also result in severe permanent engine damage under certain operating conditions. Based on the ensemble empirical mode decomposition (EEMD), this paper proposes a new approach to determine the knock characteristics in SI engines. By adding a uniformly distributed and finite white Gaussian noise, the EEMD can preserve signal continuity in different scales and therefore alleviates the mode-mixing problem occurring in the classic empirical mode decomposition (EMD). The feasibilities of applying the EEMD to detect the knock signatures of a test SI engine via the pressure signal measured from combustion chamber and the vibration signal measured from cylinder head are investigated. Experimental results show that the EEMD-based method is able to detect the knock signatures from both the pressure signal and vibration signal, even in initial stage of knock. Finally, by comparing the application results with those obtained by short-time Fourier transform (STFT), Wigner-Ville distribution (WVD) and discrete wavelet transform (DWT), the superiority of the EEMD method in determining knock characteristics is demonstrated.

  5. An ensemble-based approach to imputation of moderate-density genotypes for genomic selection with application to Angus cattle.

    PubMed

    Sun, Chuanyu; Wu, Xiao-Lin; Weigel, Kent A; Rosa, Guilherme J M; Bauck, Stewart; Woodward, Brent W; Schnabel, Robert D; Taylor, Jeremy F; Gianola, Daniel

    2012-06-01

    Summary Imputation of moderate-density genotypes from low-density panels is of increasing interest in genomic selection, because it can dramatically reduce genotyping costs. Several imputation software packages have been developed, but they vary in imputation accuracy, and imputed genotypes may be inconsistent among methods. An AdaBoost-like approach is proposed to combine imputation results from several independent software packages, i.e. Beagle(v3.3), IMPUTE(v2.0), fastPHASE(v1.4), AlphaImpute, findhap(v2) and Fimpute(v2), with each package serving as a basic classifier in an ensemble-based system. The ensemble-based method computes weights sequentially for all classifiers, and combines results from component methods via weighted majority 'voting' to determine unknown genotypes. The data included 3078 registered Angus cattle, each genotyped with the Illumina BovineSNP50 BeadChip. SNP genotypes on three chromosomes (BTA1, BTA16 and BTA28) were used to compare imputation accuracy among methods, and the application involved the imputation of 50K genotypes covering 29 chromosomes based on a set of 5K genotypes. Beagle and Fimpute had the greatest accuracy among the six imputation packages, which ranged from 0·8677 to 0·9858. The proposed ensemble method was better than any of these packages, but the sequence of independent classifiers in the voting scheme affected imputation accuracy. The ensemble systems yielding the best imputation accuracies were those that had Beagle as first classifier, followed by one or two methods that utilized pedigree information. A salient feature of the proposed ensemble method is that it can solve imputation inconsistencies among different imputation methods, hence leading to a more reliable system for imputing genotypes relative to independent methods.

  6. Development of web-based services for an ensemble flood forecasting and risk assessment system

    NASA Astrophysics Data System (ADS)

    Yaw Manful, Desmond; He, Yi; Cloke, Hannah; Pappenberger, Florian; Li, Zhijia; Wetterhall, Fredrik; Huang, Yingchun; Hu, Yuzhong

    2010-05-01

    Flooding is a wide spread and devastating natural disaster worldwide. Floods that took place in the last decade in China were ranked the worst amongst recorded floods worldwide in terms of the number of human fatalities and economic losses (Munich Re-Insurance). Rapid economic development and population expansion into low lying flood plains has worsened the situation. Current conventional flood prediction systems in China are neither suited to the perceptible climate variability nor the rapid pace of urbanization sweeping the country. Flood prediction, from short-term (a few hours) to medium-term (a few days), needs to be revisited and adapted to changing socio-economic and hydro-climatic realities. The latest technology requires implementation of multiple numerical weather prediction systems. The availability of twelve global ensemble weather prediction systems through the ‘THORPEX Interactive Grand Global Ensemble' (TIGGE) offers a good opportunity for an effective state-of-the-art early forecasting system. A prototype of a Novel Flood Early Warning System (NEWS) using the TIGGE database is tested in the Huai River basin in east-central China. It is the first early flood warning system in China that uses the massive TIGGE database cascaded with river catchment models, the Xinanjiang hydrologic model and a 1-D hydraulic model, to predict river discharge and flood inundation. The NEWS algorithm is also designed to provide web-based services to a broad spectrum of end-users. The latter presents challenges as both databases and proprietary codes reside in different locations and converge at dissimilar times. NEWS will thus make use of a ready-to-run grid system that makes distributed computing and data resources available in a seamless and secure way. An ability to run or function on different operating systems and provide an interface or front that is accessible to broad spectrum of end-users is additional requirement. The aim is to achieve robust interoperability

  7. NMR-based conformational ensembles explain pH-gated opening and closing of OmpG channel.

    PubMed

    Zhuang, Tiandi; Chisholm, Christina; Chen, Min; Tamm, Lukas K

    2013-10-01

    The outer membrane protein G (OmpG) is a monomeric 33 kDa 14-stranded β-barrel membrane protein functioning as a nonspecific porin for the uptake of oligosaccharides in Escherichia coli. Two different crystal structures of OmpG obtained at different values of pH suggest a pH-gated pore opening mechanism. In these structures, extracellular loop 6 extends away from the barrel wall at neutral pH but is folded back into the pore lumen at low pH, blocking transport through the pore. Loop 6 was invisible in a previously published solution NMR structure of OmpG in n-dodecylphosphocholine micelles, presumably due to conformational exchange on an intermediate NMR time scale. Here we present an NMR paramagnetic relaxation enhancement (PRE)-based approach to visualize the conformational dynamics of loop 6 and to calculate conformational ensembles that explain the pH-gated opening and closing of the OmpG channel. The different loop conformers detected by the PRE ensemble calculations were validated by disulfide cross-linking of strategically engineered cysteines and electrophysiological single channel recordings. The results indicate a more dynamically regulated channel opening and closing than previously thought and reveal additional membrane-associated conformational ensembles at pH 6.3 and 7.0. We anticipate this approach to be generally applicable to detect and characterize functionally important conformational ensembles of membrane proteins.

  8. Simulating rare events using a weighted ensemble-based string method.

    PubMed

    Adelman, Joshua L; Grabe, Michael

    2013-01-28

    We introduce an extension to the weighted ensemble (WE) path sampling method to restrict sampling to a one-dimensional path through a high dimensional phase space. Our method, which is based on the finite-temperature string method, permits efficient sampling of both equilibrium and non-equilibrium systems. Sampling obtained from the WE method guides the adaptive refinement of a Voronoi tessellation of order parameter space, whose generating points, upon convergence, coincide with the principle reaction pathway. We demonstrate the application of this method to several simple, two-dimensional models of driven Brownian motion and to the conformational change of the nitrogen regulatory protein C receiver domain using an elastic network model. The simplicity of the two-dimensional models allows us to directly compare the efficiency of the WE method to conventional brute force simulations and other path sampling algorithms, while the example of protein conformational change demonstrates how the method can be used to efficiently study transitions in the space of many collective variables.

  9. Enhancement of light depolarization by random ensembles of titania-based low-dimensional nanoparticles

    NASA Astrophysics Data System (ADS)

    Zimnyakov, D. A.; Zdrajevsky, R. A.; Yuvchenko, S. A.; Ushakova, O. V.; Angelsky, O. V.; Yermolenko, S. B.

    2015-02-01

    Depolarization peculiarities of the light scattered by the random ensembles of titania-based low-dimensional nanoparticles are studied during the experiments with aqueous suspensions of potassium polytitanate nanoplatelets and nanoribbons. The obtained experimental results are compared with the theoretical data obtained for the random systems of oblate and prolate flattened ellipsoidal nanoparticles with various values of the shape factor and dielectric function corresponding the parent material (titanium dioxide). The possibility to recover the effective dielectric function from the depolarization ratio spectra using the ellipsoidal shape model is considered. Ellipsoidal approximation is appropriate for both the nanoplatelets and nanoribbons in the spectral region for which the real part of nanoparticles permittivity is sufficiently negative and the near-resonant excitation of longitudinal mode of charge oscillations in nanoparticles occurs. Also, ellipsoidal approximation is appropriate for nanoplatelets in the region of sufficiently po sitive real part of permittivity but gives remarkably underestimated values of the depolarization ratio for nanoribbons in the region. This is presumably caused by considerable difference in the light-induced charge distributions for nanoribbons and prolate flattened ellipsoidal nanoparticles with the decreasing efficiency in longitudinal mode excitation. The recovered values of nanoparticle permittivity exhibit the red shift with respect to the permittivity values of the parent material due to its modification in the course of nanoparticles synthesis.

  10. An Efficient Data-worth Analysis Framework via Probabilistic Collocation Method Based Ensemble Kalman Filter

    NASA Astrophysics Data System (ADS)

    Xue, L.; Dai, C.; Zhang, D.; Guadagnini, A.

    2015-12-01

    It is critical to predict contaminant plume in an aquifer under uncertainty, which can help assess environmental risk and design rational management strategies. An accurate prediction of contaminant plume requires the collection of data to help characterize the system. Due to the limitation of financial resources, ones should estimate the expectative value of data collected from each optional monitoring scheme before carried out. Data-worth analysis is believed to be an effective approach to identify the value of the data in some problems, which quantifies the uncertainty reduction assuming that the plausible data has been collected. However, it is difficult to apply the data-worth analysis to a dynamic simulation of contaminant transportation model owning to its requirement of large number of inverse-modeling. In this study, a novel efficient data-worth analysis framework is proposed by developing the Probabilistic Collocation Method based Ensemble Kalman Filter (PCKF). The PCKF constructs polynomial chaos expansion surrogate model to replace the original complex numerical model. Consequently, the inverse modeling can perform on the proxy rather than the original model. An illustrative example, considering the dynamic change of the contaminant concentration, is employed to demonstrate the proposed approach. The Results reveal that schemes with different sampling frequencies, monitoring networks location, prior data content will have significant impact on the uncertainty reduction of the estimation of contaminant plume. Our proposition is validated to provide the reasonable value of data from various schemes.

  11. Effective Visualization of Temporal Ensembles.

    PubMed

    Hao, Lihua; Healey, Christopher G; Bass, Steffen A

    2016-01-01

    An ensemble is a collection of related datasets, called members, built from a series of runs of a simulation or an experiment. Ensembles are large, temporal, multidimensional, and multivariate, making them difficult to analyze. Another important challenge is visualizing ensembles that vary both in space and time. Initial visualization techniques displayed ensembles with a small number of members, or presented an overview of an entire ensemble, but without potentially important details. Recently, researchers have suggested combining these two directions, allowing users to choose subsets of members to visualization. This manual selection process places the burden on the user to identify which members to explore. We first introduce a static ensemble visualization system that automatically helps users locate interesting subsets of members to visualize. We next extend the system to support analysis and visualization of temporal ensembles. We employ 3D shape comparison, cluster tree visualization, and glyph based visualization to represent different levels of detail within an ensemble. This strategy is used to provide two approaches for temporal ensemble analysis: (1) segment based ensemble analysis, to capture important shape transition time-steps, clusters groups of similar members, and identify common shape changes over time across multiple members; and (2) time-step based ensemble analysis, which assumes ensemble members are aligned in time by combining similar shapes at common time-steps. Both approaches enable users to interactively visualize and analyze a temporal ensemble from different perspectives at different levels of detail. We demonstrate our techniques on an ensemble studying matter transition from hadronic gas to quark-gluon plasma during gold-on-gold particle collisions.

  12. Effective Visualization of Temporal Ensembles.

    PubMed

    Hao, Lihua; Healey, Christopher G; Bass, Steffen A

    2016-01-01

    An ensemble is a collection of related datasets, called members, built from a series of runs of a simulation or an experiment. Ensembles are large, temporal, multidimensional, and multivariate, making them difficult to analyze. Another important challenge is visualizing ensembles that vary both in space and time. Initial visualization techniques displayed ensembles with a small number of members, or presented an overview of an entire ensemble, but without potentially important details. Recently, researchers have suggested combining these two directions, allowing users to choose subsets of members to visualization. This manual selection process places the burden on the user to identify which members to explore. We first introduce a static ensemble visualization system that automatically helps users locate interesting subsets of members to visualize. We next extend the system to support analysis and visualization of temporal ensembles. We employ 3D shape comparison, cluster tree visualization, and glyph based visualization to represent different levels of detail within an ensemble. This strategy is used to provide two approaches for temporal ensemble analysis: (1) segment based ensemble analysis, to capture important shape transition time-steps, clusters groups of similar members, and identify common shape changes over time across multiple members; and (2) time-step based ensemble analysis, which assumes ensemble members are aligned in time by combining similar shapes at common time-steps. Both approaches enable users to interactively visualize and analyze a temporal ensemble from different perspectives at different levels of detail. We demonstrate our techniques on an ensemble studying matter transition from hadronic gas to quark-gluon plasma during gold-on-gold particle collisions. PMID:26529728

  13. Accelerating Monte Carlo molecular simulations by reweighting and reconstructing Markov chains: Extrapolation of canonical ensemble averages and second derivatives to different temperature and density conditions

    SciTech Connect

    Kadoura, Ahmad; Sun, Shuyu Salama, Amgad

    2014-08-01

    Accurate determination of thermodynamic properties of petroleum reservoir fluids is of great interest to many applications, especially in petroleum engineering and chemical engineering. Molecular simulation has many appealing features, especially its requirement of fewer tuned parameters but yet better predicting capability; however it is well known that molecular simulation is very CPU expensive, as compared to equation of state approaches. We have recently introduced an efficient thermodynamically consistent technique to regenerate rapidly Monte Carlo Markov Chains (MCMCs) at different thermodynamic conditions from the existing data points that have been pre-computed with expensive classical simulation. This technique can speed up the simulation more than a million times, making the regenerated molecular simulation almost as fast as equation of state approaches. In this paper, this technique is first briefly reviewed and then numerically investigated in its capability of predicting ensemble averages of primary quantities at different neighboring thermodynamic conditions to the original simulated MCMCs. Moreover, this extrapolation technique is extended to predict second derivative properties (e.g. heat capacity and fluid compressibility). The method works by reweighting and reconstructing generated MCMCs in canonical ensemble for Lennard-Jones particles. In this paper, system's potential energy, pressure, isochoric heat capacity and isothermal compressibility along isochors, isotherms and paths of changing temperature and density from the original simulated points were extrapolated. Finally, an optimized set of Lennard-Jones parameters (ε, σ) for single site models were proposed for methane, nitrogen and carbon monoxide.

  14. Process-based assessment of an ensemble of climate projections for West Africa

    NASA Astrophysics Data System (ADS)

    James, Rachel; Washington, Richard; Jones, Richard

    2015-02-01

    Determining the level of confidence in regional climate model projections could be very useful for designing climate change adaptation, particularly for vulnerable regions. The majority of previous research to evaluate models has been based on the mean state, but for confidence in projections the plausibility of the mechanisms for change is just as, if not more, important. In this study we demonstrate a methodology for process-based assessment of projections, whereby circulation changes accompanying future responses are examined and then compared to atmospheric dynamics during historical years in models and reanalyses. We apply this methodology to an ensemble of five global and regional model experiments and focus on West Africa, where these models project a strong drying trend. The analysis reveals that this drying is associated with anomalous subsidence in the upper atmosphere, and large warming of the Saharan heat low region, with potential feedback effects via the African easterly jet and West African monsoon. This mode occurs during dry years in the historical period, and dominates in the future experiments. However, the same mode is not found in dry years in reanalysis data, which casts doubt on the reasons for strong drying in these models. The regional models show a very similar response to their driving global models, and are therefore no more trustworthy in this case. This result underlines the importance of assessing model credibility on a case-by-case basis and implies that process-based methodologies should be applied to other model projections before their outputs are used to inform decision making.

  15. Determining optimal clothing ensembles based on weather forecasts, with particular reference to outdoor winter military activities.

    PubMed

    Morabito, Marco; Pavlinic, Daniela Z; Crisci, Alfonso; Capecchi, Valerio; Orlandini, Simone; Mekjavic, Igor B

    2011-07-01

    Military and civil defense personnel are often involved in complex activities in a variety of outdoor environments. The choice of appropriate clothing ensembles represents an important strategy to establish the success of a military mission. The main aim of this study was to compare the known clothing insulation of the garment ensembles worn by soldiers during two winter outdoor field trials (hike and guard duty) with the estimated optimal clothing thermal insulations recommended to maintain thermoneutrality, assessed by using two different biometeorological procedures. The overall aim was to assess the applicability of such biometeorological procedures to weather forecast systems, thereby developing a comprehensive biometeorological tool for military operational forecast purposes. Military trials were carried out during winter 2006 in Pokljuka (Slovenia) by Slovene Armed Forces personnel. Gastrointestinal temperature, heart rate and environmental parameters were measured with portable data acquisition systems. The thermal characteristics of the clothing ensembles worn by the soldiers, namely thermal resistance, were determined with a sweating thermal manikin. Results showed that the clothing ensemble worn by the military was appropriate during guard duty but generally inappropriate during the hike. A general under-estimation of the biometeorological forecast model in predicting the optimal clothing insulation value was observed and an additional post-processing calibration might further improve forecast accuracy. This study represents the first step in the development of a comprehensive personalized biometeorological forecast system aimed at improving recommendations regarding the optimal thermal insulation of military garment ensembles for winter activities.

  16. Determining optimal clothing ensembles based on weather forecasts, with particular reference to outdoor winter military activities

    NASA Astrophysics Data System (ADS)

    Morabito, Marco; Pavlinic, Daniela Z.; Crisci, Alfonso; Capecchi, Valerio; Orlandini, Simone; Mekjavic, Igor B.

    2011-07-01

    Military and civil defense personnel are often involved in complex activities in a variety of outdoor environments. The choice of appropriate clothing ensembles represents an important strategy to establish the success of a military mission. The main aim of this study was to compare the known clothing insulation of the garment ensembles worn by soldiers during two winter outdoor field trials (hike and guard duty) with the estimated optimal clothing thermal insulations recommended to maintain thermoneutrality, assessed by using two different biometeorological procedures. The overall aim was to assess the applicability of such biometeorological procedures to weather forecast systems, thereby developing a comprehensive biometeorological tool for military operational forecast purposes. Military trials were carried out during winter 2006 in Pokljuka (Slovenia) by Slovene Armed Forces personnel. Gastrointestinal temperature, heart rate and environmental parameters were measured with portable data acquisition systems. The thermal characteristics of the clothing ensembles worn by the soldiers, namely thermal resistance, were determined with a sweating thermal manikin. Results showed that the clothing ensemble worn by the military was appropriate during guard duty but generally inappropriate during the hike. A general under-estimation of the biometeorological forecast model in predicting the optimal clothing insulation value was observed and an additional post-processing calibration might further improve forecast accuracy. This study represents the first step in the development of a comprehensive personalized biometeorological forecast system aimed at improving recommendations regarding the optimal thermal insulation of military garment ensembles for winter activities.

  17. Constructing prediction interval for artificial neural network rainfall runoff models based on ensemble simulations

    NASA Astrophysics Data System (ADS)

    Kasiviswanathan, K. S.; Cibin, R.; Sudheer, K. P.; Chaubey, I.

    2013-08-01

    This paper presents a method of constructing prediction interval for artificial neural network (ANN) rainfall runoff models during calibration with a consideration of generating ensemble predictions. A two stage optimization procedure is envisaged in this study for construction of prediction interval for the ANN output. In Stage 1, ANN model is trained with genetic algorithm (GA) to obtain optimal set of weights and biases vector. In Stage 2, possible variability of ANN parameters (obtained in Stage 1) is optimized so as to create an ensemble of models with the consideration of minimum residual variance for the ensemble mean, while ensuring a maximum of the measured data to fall within the estimated prediction interval. The width of the prediction interval is also minimized simultaneously. The method is demonstrated using a real world case study of rainfall runoff data for an Indian basin. The method was able to produce ensembles with a prediction interval (average width) of 26.49 m3/s with 97.17% of the total observed data points lying within the interval in validation. One specific advantage of the method is that when ensemble mean value is considered as a forecast, the peak flows are predicted with improved accuracy by this method compared to traditional single point forecasted ANNs.

  18. Ensemble Tractography

    PubMed Central

    Wandell, Brian A.

    2016-01-01

    Tractography uses diffusion MRI to estimate the trajectory and cortical projection zones of white matter fascicles in the living human brain. There are many different tractography algorithms and each requires the user to set several parameters, such as curvature threshold. Choosing a single algorithm with specific parameters poses two challenges. First, different algorithms and parameter values produce different results. Second, the optimal choice of algorithm and parameter value may differ between different white matter regions or different fascicles, subjects, and acquisition parameters. We propose using ensemble methods to reduce algorithm and parameter dependencies. To do so we separate the processes of fascicle generation and evaluation. Specifically, we analyze the value of creating optimized connectomes by systematically combining candidate streamlines from an ensemble of algorithms (deterministic and probabilistic) and systematically varying parameters (curvature and stopping criterion). The ensemble approach leads to optimized connectomes that provide better cross-validated prediction error of the diffusion MRI data than optimized connectomes generated using a single-algorithm or parameter set. Furthermore, the ensemble approach produces connectomes that contain both short- and long-range fascicles, whereas single-parameter connectomes are biased towards one or the other. In summary, a systematic ensemble tractography approach can produce connectomes that are superior to standard single parameter estimates both for predicting the diffusion measurements and estimating white matter fascicles. PMID:26845558

  19. AxTract: Microstructure-Driven Tractography Based on the Ensemble Average Propagator.

    PubMed

    Girard, Gabriel; Rutger Fick; Descoteaux, Maxime; Deriche, Rachid; Wassermann, Demian

    2015-01-01

    We propose a novel method to simultaneously trace brain white matter (WM) fascicles and estimate WM microstructure characteristics. Recent advancements in diffusion-weighted imaging (DWI) allow multi-shell acquisitions with b-values of up to 10,000 s/mm2 in human subjects, enabling the measurement of the ensemble average propagator (EAP) at distances as short as 10 μm. Coupled with continuous models of the full 3D DWI signal and the EAP such as Mean Apparent Propagator (MAP) MRI, these acquisition schemes provide unparalleled means to probe the WM tissue in vivo. Presently, there are two complementary limitations in tractography and microstructure measurement techniques. Tractography techniques are based on models of the DWI signal geometry without taking specific hypotheses of the WM structure. This hinders the tracing of fascicles through certain WM areas with complex organization such as branching, crossing, merging, and bottlenecks that are indistinguishable using the orientation-only part of the DWI signal. Microstructure measuring techniques, such as AxCaliber, require the direction of the axons within the probed tissue before the acquisition as well as the tissue to be highly organized. Our contributions are twofold. First, we extend the theoretical DWI models proposed by Callaghan et al. to characterize the distribution of axonal calibers within the probed tissue taking advantage of the MAP-MRI model. Second, we develop a simultaneous tractography and axonal caliber distribution algorithm based on the hypothesis that axonal caliber distribution varies smoothly along a WM fascicle. To validate our model we test it on insilico phantoms and on the HCP dataset.

  20. AxTract: Microstructure-Driven Tractography Based on the Ensemble Average Propagator.

    PubMed

    Girard, Gabriel; Rutger Fick; Descoteaux, Maxime; Deriche, Rachid; Wassermann, Demian

    2015-01-01

    We propose a novel method to simultaneously trace brain white matter (WM) fascicles and estimate WM microstructure characteristics. Recent advancements in diffusion-weighted imaging (DWI) allow multi-shell acquisitions with b-values of up to 10,000 s/mm2 in human subjects, enabling the measurement of the ensemble average propagator (EAP) at distances as short as 10 μm. Coupled with continuous models of the full 3D DWI signal and the EAP such as Mean Apparent Propagator (MAP) MRI, these acquisition schemes provide unparalleled means to probe the WM tissue in vivo. Presently, there are two complementary limitations in tractography and microstructure measurement techniques. Tractography techniques are based on models of the DWI signal geometry without taking specific hypotheses of the WM structure. This hinders the tracing of fascicles through certain WM areas with complex organization such as branching, crossing, merging, and bottlenecks that are indistinguishable using the orientation-only part of the DWI signal. Microstructure measuring techniques, such as AxCaliber, require the direction of the axons within the probed tissue before the acquisition as well as the tissue to be highly organized. Our contributions are twofold. First, we extend the theoretical DWI models proposed by Callaghan et al. to characterize the distribution of axonal calibers within the probed tissue taking advantage of the MAP-MRI model. Second, we develop a simultaneous tractography and axonal caliber distribution algorithm based on the hypothesis that axonal caliber distribution varies smoothly along a WM fascicle. To validate our model we test it on insilico phantoms and on the HCP dataset. PMID:26221712

  1. Multi-model ensemble-based probabilistic prediction of tropical cyclogenesis using TIGGE model forecasts

    NASA Astrophysics Data System (ADS)

    Jaiswal, Neeru; Kishtawal, C. M.; Bhomia, Swati; Pal, P. K.

    2016-10-01

    An extended range tropical cyclogenesis forecast model has been developed using the forecasts of global models available from TIGGE portal. A scheme has been developed to detect the signatures of cyclogenesis in the global model forecast fields [i.e., the mean sea level pressure and surface winds (10 m horizontal winds)]. For this, a wind matching index was determined between the synthetic cyclonic wind fields and the forecast wind fields. The thresholds of 0.4 for wind matching index and 1005 hpa for pressure were determined to detect the cyclonic systems. These detected cyclonic systems in the study region are classified into different cyclone categories based on their intensity (maximum wind speed). The forecasts of up to 15 days from three global models viz., ECMWF, NCEP and UKMO have been used to predict cyclogenesis based on multi-model ensemble approach. The occurrence of cyclonic events of different categories in all the forecast steps in the grided region (10 × 10 km2) was used to estimate the probability of the formation of cyclogenesis. The probability of cyclogenesis was estimated by computing the grid score using the wind matching index by each model and at each forecast step and convolving it with Gaussian filter. The proposed method is used to predict the cyclogenesis of five named tropical cyclones formed during the year 2013 in the north Indian Ocean. The 6-8 days advance cyclogenesis of theses systems were predicted using the above approach. The mean lead prediction time for the cyclogenesis event of the proposed model has been found as 7 days.

  2. Endowing a Content-Based Medical Image Retrieval System with Perceptual Similarity Using Ensemble Strategy.

    PubMed

    Bedo, Marcos Vinicius Naves; Pereira Dos Santos, Davi; Ponciano-Silva, Marcelo; de Azevedo-Marques, Paulo Mazzoncini; Ferreira de Carvalho, André Ponce de León; Traina, Caetano

    2016-02-01

    Content-based medical image retrieval (CBMIR) is a powerful resource to improve differential computer-aided diagnosis. The major problem with CBMIR applications is the semantic gap, a situation in which the system does not follow the users' sense of similarity. This gap can be bridged by the adequate modeling of similarity queries, which ultimately depends on the combination of feature extractor methods and distance functions. In this study, such combinations are referred to as perceptual parameters, as they impact on how images are compared. In a CBMIR, the perceptual parameters must be manually set by the users, which imposes a heavy burden on the specialists; otherwise, the system will follow a predefined sense of similarity. This paper presents a novel approach to endow a CBMIR with a proper sense of similarity, in which the system defines the perceptual parameter depending on the query element. The method employs ensemble strategy, where an extreme learning machine acts as a meta-learner and identifies the most suitable perceptual parameter according to a given query image. This parameter defines the search space for the similarity query that retrieves the most similar images. An instance-based learning classifier labels the query image following the query result set. As the concept implementation, we integrated the approach into a mammogram CBMIR. For each query image, the resulting tool provided a complete second opinion, including lesion class, system certainty degree, and set of most similar images. Extensive experiments on a large mammogram dataset showed that our proposal achieved a hit ratio up to 10% higher than the traditional CBMIR approach without requiring external parameters from the users. Our database-driven solution was also up to 25% faster than content retrieval traditional approaches.

  3. Ensemble Modeling of Cancer Metabolism

    PubMed Central

    Khazaei, Tahmineh; McGuigan, Alison; Mahadevan, Radhakrishnan

    2012-01-01

    The metabolic behavior of cancer cells is adapted to meet their proliferative needs, with notable changes such as enhanced lactate secretion and glucose uptake rates. In this work, we use the Ensemble Modeling (EM) framework to gain insight and predict potential drug targets for tumor cells. EM generates a set of models which span the space of kinetic parameters that are constrained by thermodynamics. Perturbation data based on known targets are used to screen the entire ensemble of models to obtain a sub-set, which is increasingly predictive. EM allows for incorporation of regulatory information and captures the behavior of enzymatic reactions at the molecular level by representing reactions in the elementary reaction form. In this study, a metabolic network consisting of 58 reactions is considered and accounts for glycolysis, the pentose phosphate pathway, lipid metabolism, amino acid metabolism, and includes allosteric regulation of key enzymes. Experimentally measured intracellular and extracellular metabolite concentrations are used for developing the ensemble of models along with information on established drug targets. The resulting models predicted transaldolase (TALA) and succinyl-CoA ligase (SUCOAS1m) to cause a significant reduction in growth rate when repressed, relative to currently known drug targets. Furthermore, the results suggest that the synergistic repression of transaldolase and glycine hydroxymethyltransferase (GHMT2r) will lead to a threefold decrease in growth rate compared to the repression of single enzyme targets. PMID:22623918

  4. An Ensemble-of-Classifiers Based Approach for Early Diagnosis of Alzheimer's Disease: Classification Using Structural Features of Brain Images

    PubMed Central

    Farhan, Saima; Tauseef, Huma

    2014-01-01

    Structural brain imaging is playing a vital role in identification of changes that occur in brain associated with Alzheimer's disease. This paper proposes an automated image processing based approach for the identification of AD from MRI of the brain. The proposed approach is novel in a sense that it has higher specificity/accuracy values despite the use of smaller feature set as compared to existing approaches. Moreover, the proposed approach is capable of identifying AD patients in early stages. The dataset selected consists of 85 age and gender matched individuals from OASIS database. The features selected are volume of GM, WM, and CSF and size of hippocampus. Three different classification models (SVM, MLP, and J48) are used for identification of patients and controls. In addition, an ensemble of classifiers, based on majority voting, is adopted to overcome the error caused by an independent base classifier. Ten-fold cross validation strategy is applied for the evaluation of our scheme. Moreover, to evaluate the performance of proposed approach, individual features and combination of features are fed to individual classifiers and ensemble based classifier. Using size of left hippocampus as feature, the accuracy achieved with ensemble of classifiers is 93.75%, with 100% specificity and 87.5% sensitivity. PMID:25276224

  5. Self-Adaptive Prediction of Cloud Resource Demands Using Ensemble Model and Subtractive-Fuzzy Clustering Based Fuzzy Neural Network

    PubMed Central

    Chen, Zhijia; Zhu, Yuanchang; Di, Yanqiang; Feng, Shaochong

    2015-01-01

    In IaaS (infrastructure as a service) cloud environment, users are provisioned with virtual machines (VMs). To allocate resources for users dynamically and effectively, accurate resource demands predicting is essential. For this purpose, this paper proposes a self-adaptive prediction method using ensemble model and subtractive-fuzzy clustering based fuzzy neural network (ESFCFNN). We analyze the characters of user preferences and demands. Then the architecture of the prediction model is constructed. We adopt some base predictors to compose the ensemble model. Then the structure and learning algorithm of fuzzy neural network is researched. To obtain the number of fuzzy rules and the initial value of the premise and consequent parameters, this paper proposes the fuzzy c-means combined with subtractive clustering algorithm, that is, the subtractive-fuzzy clustering. Finally, we adopt different criteria to evaluate the proposed method. The experiment results show that the method is accurate and effective in predicting the resource demands. PMID:25691896

  6. Self-adaptive prediction of cloud resource demands using ensemble model and subtractive-fuzzy clustering based fuzzy neural network.

    PubMed

    Chen, Zhijia; Zhu, Yuanchang; Di, Yanqiang; Feng, Shaochong

    2015-01-01

    In IaaS (infrastructure as a service) cloud environment, users are provisioned with virtual machines (VMs). To allocate resources for users dynamically and effectively, accurate resource demands predicting is essential. For this purpose, this paper proposes a self-adaptive prediction method using ensemble model and subtractive-fuzzy clustering based fuzzy neural network (ESFCFNN). We analyze the characters of user preferences and demands. Then the architecture of the prediction model is constructed. We adopt some base predictors to compose the ensemble model. Then the structure and learning algorithm of fuzzy neural network is researched. To obtain the number of fuzzy rules and the initial value of the premise and consequent parameters, this paper proposes the fuzzy c-means combined with subtractive clustering algorithm, that is, the subtractive-fuzzy clustering. Finally, we adopt different criteria to evaluate the proposed method. The experiment results show that the method is accurate and effective in predicting the resource demands. PMID:25691896

  7. Texture Descriptors Ensembles Enable Image-Based Classification of Maturation of Human Stem Cell-Derived Retinal Pigmented Epithelium

    PubMed Central

    Caetano dos Santos, Florentino Luciano; Skottman, Heli; Juuti-Uusitalo, Kati; Hyttinen, Jari

    2016-01-01

    Aims A fast, non-invasive and observer-independent method to analyze the homogeneity and maturity of human pluripotent stem cell (hPSC) derived retinal pigment epithelial (RPE) cells is warranted to assess the suitability of hPSC-RPE cells for implantation or in vitro use. The aim of this work was to develop and validate methods to create ensembles of state-of-the-art texture descriptors and to provide a robust classification tool to separate three different maturation stages of RPE cells by using phase contrast microscopy images. The same methods were also validated on a wide variety of biological image classification problems, such as histological or virus image classification. Methods For image classification we used different texture descriptors, descriptor ensembles and preprocessing techniques. Also, three new methods were tested. The first approach was an ensemble of preprocessing methods, to create an additional set of images. The second was the region-based approach, where saliency detection and wavelet decomposition divide each image in two different regions, from which features were extracted through different descriptors. The third method was an ensemble of Binarized Statistical Image Features, based on different sizes and thresholds. A Support Vector Machine (SVM) was trained for each descriptor histogram and the set of SVMs combined by sum rule. The accuracy of the computer vision tool was verified in classifying the hPSC-RPE cell maturation level. Dataset and Results The RPE dataset contains 1862 subwindows from 195 phase contrast images. The final descriptor ensemble outperformed the most recent stand-alone texture descriptors, obtaining, for the RPE dataset, an area under ROC curve (AUC) of 86.49% with the 10-fold cross validation and 91.98% with the leave-one-image-out protocol. The generality of the three proposed approaches was ascertained with 10 more biological image datasets, obtaining an average AUC greater than 97%. Conclusions Here we

  8. Are Charge-State Distributions a Reliable Tool Describing Molecular Ensembles of Intrinsically Disordered Proteins by Native MS?

    NASA Astrophysics Data System (ADS)

    Natalello, Antonino; Santambrogio, Carlo; Grandori, Rita

    2016-10-01

    Native mass spectrometry (MS) has become a central tool of structural proteomics, but its applicability to the peculiar class of intrinsically disordered proteins (IDPs) is still object of debate. IDPs lack an ordered tridimensional structure and are characterized by high conformational plasticity. Since they represent valuable targets for cancer and neurodegeneration research, there is an urgent need of methodological advances for description of the conformational ensembles populated by these proteins in solution. However, structural rearrangements during electrospray-ionization (ESI) or after the transfer to the gas phase could affect data obtained by native ESI-MS. In particular, charge-state distributions (CSDs) are affected by protein conformation inside ESI droplets, while ion mobility (IM) reflects protein conformation in the gas phase. This review focuses on the available evidence relating IDP solution ensembles with CSDs, trying to summarize cases of apparent consistency or discrepancy. The protein-specificity of ionization patterns and their responses to ligands and buffer conditions suggests that CSDs are imprinted to protein structural features also in the case of IDPs. Nevertheless, it seems that these proteins are more easily affected by electrospray conditions, leading in some cases to rearrangements of the conformational ensembles.

  9. Towards the knowledge-based design of universal influenza epitope ensemble vaccines

    PubMed Central

    Sheikh, Qamar M.; Gatherer, Derek; Reche, Pedro A; Flower, Darren R.

    2016-01-01

    Motivation: Influenza A viral heterogeneity remains a significant threat due to unpredictable antigenic drift in seasonal influenza and antigenic shifts caused by the emergence of novel subtypes. Annual review of multivalent influenza vaccines targets strains of influenza A and B likely to be predominant in future influenza seasons. This does not induce broad, cross protective immunity against emergent subtypes. Better strategies are needed to prevent future pandemics. Cross-protection can be achieved by activating CD8+ and CD4+ T cells against highly conserved regions of the influenza genome. We combine available experimental data with informatics-based immunological predictions to help design vaccines potentially able to induce cross-protective T-cells against multiple influenza subtypes. Results: To exemplify our approach we designed two epitope ensemble vaccines comprising highly conserved and experimentally verified immunogenic influenza A epitopes as putative non-seasonal influenza vaccines; one specifically targets the US population and the other is a universal vaccine. The USA-specific vaccine comprised 6 CD8+ T cell epitopes (GILGFVFTL, FMYSDFHFI, GMDPRMCSL, SVKEKDMTK, FYIQMCTEL, DTVNRTHQY) and 3 CD4+ epitopes (KGILGFVFTLTVPSE, EYIMKGVYINTALLN, ILGFVFTLTVPSERG). The universal vaccine comprised 8 CD8+ epitopes: (FMYSDFHFI, GILGFVFTL, ILRGSVAHK, FYIQMCTEL, ILKGKFQTA, YYLEKANKI, VSDGGPNLY, YSHGTGTGY) and the same 3 CD4+ epitopes. Our USA-specific vaccine has a population protection coverage (portion of the population potentially responsive to one or more component epitopes of the vaccine, PPC) of over 96 and 95% coverage of observed influenza subtypes. The universal vaccine has a PPC value of over 97 and 88% coverage of observed subtypes. Availability and Implementation: http://imed.med.ucm.es/Tools/episopt.html. Contact: d.r.flower@aston.ac.uk PMID:27402904

  10. [Estimation and forecast of chlorophyll a concentration in Taihu Lake based on ensemble square root filters].

    PubMed

    Li, Yuan; Li, Yun-Mei; Wang, Qiao; Zhang, Zhuo; Guo, Fei; Lü, Heng; Bi, Kun; Huang, Chang-Chun; Guo, Yu-Long

    2013-01-01

    Chlorophyll a concentration is one of the important parameters for the characterization of water quality, which reflects the degree of eutrophication and algae content in the water body. It is also an important factor in determining water spectral reflectance. Chlorophyll a concentration is an important water quality parameter in water quality remote sensing. Remote sensing quantitative retrieval of chlorophyll a concentration can provide new ideas and methods for the monitoring and evaluation of lake water quality. In this work, we developed a data assimilation scheme based on ensemble square root filters and three-dimensional numerical modeling for wind-driven circulation and pollutant transport to assimilate the concentration of chlorophyll a. We also conducted some assimilation experiments using buoy observation data on May 20, 2010. We estimated the concentration of chlorophyll a in Taihu Lake, and then used this result to forecast the concentration of chlorophyll a. During the assimilation stage, the root mean square error reduced from 1.58, 1.025, and 2.76 to 0.465, 0.276, and 1.01, respectively, and the average relative error reduced from 0.2 to 0.05, 0.046, and 0.069, respectively. During the prediction stage, the root mean square error reduced from 1.486, 1.143, and 2.38 to 0.017, 0.147, and 0.23, respectively, and the average relative error reduced from 0.2 to 0.002, 0.025, and 0.019, respectively. The final results indicate that the method of data assimilation can significantly improve the accuracy in the estimation and prediction of chlorophyll a concentration in Taihu Lake.

  11. Ensemble-Based Instrumental Music Instruction: Dead-End Tradition or Opportunity for Socially Enlightened Teaching

    ERIC Educational Resources Information Center

    Heuser, Frank

    2011-01-01

    Public school music education in the USA remains wedded to large ensemble performance. Instruction tends to be teacher directed, relies on styles from the Western canon and exhibits little concern for musical interests of students. The idea that a fundamental purpose of education is the creation of a just society is difficult for many music…

  12. Forecasting European cold waves based on subsampling strategies of CMIP5 and Euro-CORDEX ensembles

    NASA Astrophysics Data System (ADS)

    Cordero-Llana, Laura; Braconnot, Pascale; Vautard, Robert; Vrac, Mathieu; Jezequel, Aglae

    2016-04-01

    Forecasting future extreme events under the present changing climate represents a difficult task. Currently there are a large number of ensembles of simulations for climate projections that take in account different models and scenarios. However, there is a need for reducing the size of the ensemble to make the interpretation of these simulations more manageable for impact studies or climate risk assessment. This can be achieved by developing subsampling strategies to identify a limited number of simulations that best represent the ensemble. In this study, cold waves are chosen to test different approaches for subsampling available simulations. The definition of cold waves depends on the criteria used, but they are generally defined using a minimum temperature threshold, the duration of the cold spell as well as their geographical extend. These climate indicators are not universal, highlighting the difficulty of directly comparing different studies. As part of the of the CLIPC European project, we use daily surface temperature data obtained from CMIP5 outputs as well as Euro-CORDEX simulations to predict future cold waves events in Europe. From these simulations a clustering method is applied to minimise the number of ensembles required. Furthermore, we analyse the different uncertainties that arise from the different model characteristics and definitions of climate indicators. Finally, we will test if the same subsampling strategy can be used for different climate indicators. This will facilitate the use of the subsampling results for a wide number of impact assessment studies.

  13. Comparison of Ensemble Kalman Filter groundwater-data assimilation methods based on stochastic moment equations and Monte Carlo simulation

    NASA Astrophysics Data System (ADS)

    Panzeri, M.; Riva, M.; Guadagnini, A.; Neuman, S. P.

    2014-04-01

    Traditional Ensemble Kalman Filter (EnKF) data assimilation requires computationally intensive Monte Carlo (MC) sampling, which suffers from filter inbreeding unless the number of simulations is large. Recently we proposed an alternative EnKF groundwater-data assimilation method that obviates the need for sampling and is free of inbreeding issues. In our new approach, theoretical ensemble moments are approximated directly by solving a system of corresponding stochastic groundwater flow equations. Like MC-based EnKF, our moment equations (ME) approach allows Bayesian updating of system states and parameters in real-time as new data become available. Here we compare the performances and accuracies of the two approaches on two-dimensional transient groundwater flow toward a well pumping water in a synthetic, randomly heterogeneous confined aquifer subject to prescribed head and flux boundary conditions.

  14. A new data assimilation technique based on ensemble Kalman filter and Brownian bridges: An application to Richards' equation

    NASA Astrophysics Data System (ADS)

    Berardi, Marco; Andrisani, Andrea; Lopez, Luciano; Vurro, Michele

    2016-11-01

    In this paper a new data assimilation technique is proposed which is based on the ensemble Kalman filter (EnKF). Such a technique will be effective if few observations of a dynamical system are available and a large model error occurs. The idea is to acquire a fine grid of synthetic observations in two steps: (1) first we interpolate the real observations with suitable polynomial curves; (2) then we estimate the relative measurement errors by means of Brownian bridges. This technique has been tested on the Richards' equation, which governs the water flow in unsaturated soils, where a large model error has been introduced by solving the Richards' equation by means of an explicit numerical scheme. The application of this technique to some synthetic experiments has shown improvements with respect to the classical ensemble Kalman filter, in particular for problems with a large model error.

  15. Ensembl 2016.

    PubMed

    Yates, Andrew; Akanni, Wasiu; Amode, M Ridwan; Barrell, Daniel; Billis, Konstantinos; Carvalho-Silva, Denise; Cummins, Carla; Clapham, Peter; Fitzgerald, Stephen; Gil, Laurent; Girón, Carlos García; Gordon, Leo; Hourlier, Thibaut; Hunt, Sarah E; Janacek, Sophie H; Johnson, Nathan; Juettemann, Thomas; Keenan, Stephen; Lavidas, Ilias; Martin, Fergal J; Maurel, Thomas; McLaren, William; Murphy, Daniel N; Nag, Rishi; Nuhn, Michael; Parker, Anne; Patricio, Mateus; Pignatelli, Miguel; Rahtz, Matthew; Riat, Harpreet Singh; Sheppard, Daniel; Taylor, Kieron; Thormann, Anja; Vullo, Alessandro; Wilder, Steven P; Zadissa, Amonida; Birney, Ewan; Harrow, Jennifer; Muffato, Matthieu; Perry, Emily; Ruffier, Magali; Spudich, Giulietta; Trevanion, Stephen J; Cunningham, Fiona; Aken, Bronwen L; Zerbino, Daniel R; Flicek, Paul

    2016-01-01

    The Ensembl project (http://www.ensembl.org) is a system for genome annotation, analysis, storage and dissemination designed to facilitate the access of genomic annotation from chordates and key model organisms. It provides access to data from 87 species across our main and early access Pre! websites. This year we introduced three newly annotated species and released numerous updates across our supported species with a concentration on data for the latest genome assemblies of human, mouse, zebrafish and rat. We also provided two data updates for the previous human assembly, GRCh37, through a dedicated website (http://grch37.ensembl.org). Our tools, in particular the VEP, have been improved significantly through integration of additional third party data. REST is now capable of larger-scale analysis and our regulatory data BioMart can deliver faster results. The website is now capable of displaying long-range interactions such as those found in cis-regulated datasets. Finally we have launched a website optimized for mobile devices providing views of genes, variants and phenotypes. Our data is made available without restriction and all code is available from our GitHub organization site (http://github.com/Ensembl) under an Apache 2.0 license.

  16. Ensembl 2016.

    PubMed

    Yates, Andrew; Akanni, Wasiu; Amode, M Ridwan; Barrell, Daniel; Billis, Konstantinos; Carvalho-Silva, Denise; Cummins, Carla; Clapham, Peter; Fitzgerald, Stephen; Gil, Laurent; Girón, Carlos García; Gordon, Leo; Hourlier, Thibaut; Hunt, Sarah E; Janacek, Sophie H; Johnson, Nathan; Juettemann, Thomas; Keenan, Stephen; Lavidas, Ilias; Martin, Fergal J; Maurel, Thomas; McLaren, William; Murphy, Daniel N; Nag, Rishi; Nuhn, Michael; Parker, Anne; Patricio, Mateus; Pignatelli, Miguel; Rahtz, Matthew; Riat, Harpreet Singh; Sheppard, Daniel; Taylor, Kieron; Thormann, Anja; Vullo, Alessandro; Wilder, Steven P; Zadissa, Amonida; Birney, Ewan; Harrow, Jennifer; Muffato, Matthieu; Perry, Emily; Ruffier, Magali; Spudich, Giulietta; Trevanion, Stephen J; Cunningham, Fiona; Aken, Bronwen L; Zerbino, Daniel R; Flicek, Paul

    2016-01-01

    The Ensembl project (http://www.ensembl.org) is a system for genome annotation, analysis, storage and dissemination designed to facilitate the access of genomic annotation from chordates and key model organisms. It provides access to data from 87 species across our main and early access Pre! websites. This year we introduced three newly annotated species and released numerous updates across our supported species with a concentration on data for the latest genome assemblies of human, mouse, zebrafish and rat. We also provided two data updates for the previous human assembly, GRCh37, through a dedicated website (http://grch37.ensembl.org). Our tools, in particular the VEP, have been improved significantly through integration of additional third party data. REST is now capable of larger-scale analysis and our regulatory data BioMart can deliver faster results. The website is now capable of displaying long-range interactions such as those found in cis-regulated datasets. Finally we have launched a website optimized for mobile devices providing views of genes, variants and phenotypes. Our data is made available without restriction and all code is available from our GitHub organization site (http://github.com/Ensembl) under an Apache 2.0 license. PMID:26687719

  17. Ensembl 2016

    PubMed Central

    Yates, Andrew; Akanni, Wasiu; Amode, M. Ridwan; Barrell, Daniel; Billis, Konstantinos; Carvalho-Silva, Denise; Cummins, Carla; Clapham, Peter; Fitzgerald, Stephen; Gil, Laurent; Girón, Carlos García; Gordon, Leo; Hourlier, Thibaut; Hunt, Sarah E.; Janacek, Sophie H.; Johnson, Nathan; Juettemann, Thomas; Keenan, Stephen; Lavidas, Ilias; Martin, Fergal J.; Maurel, Thomas; McLaren, William; Murphy, Daniel N.; Nag, Rishi; Nuhn, Michael; Parker, Anne; Patricio, Mateus; Pignatelli, Miguel; Rahtz, Matthew; Riat, Harpreet Singh; Sheppard, Daniel; Taylor, Kieron; Thormann, Anja; Vullo, Alessandro; Wilder, Steven P.; Zadissa, Amonida; Birney, Ewan; Harrow, Jennifer; Muffato, Matthieu; Perry, Emily; Ruffier, Magali; Spudich, Giulietta; Trevanion, Stephen J.; Cunningham, Fiona; Aken, Bronwen L.; Zerbino, Daniel R.; Flicek, Paul

    2016-01-01

    The Ensembl project (http://www.ensembl.org) is a system for genome annotation, analysis, storage and dissemination designed to facilitate the access of genomic annotation from chordates and key model organisms. It provides access to data from 87 species across our main and early access Pre! websites. This year we introduced three newly annotated species and released numerous updates across our supported species with a concentration on data for the latest genome assemblies of human, mouse, zebrafish and rat. We also provided two data updates for the previous human assembly, GRCh37, through a dedicated website (http://grch37.ensembl.org). Our tools, in particular the VEP, have been improved significantly through integration of additional third party data. REST is now capable of larger-scale analysis and our regulatory data BioMart can deliver faster results. The website is now capable of displaying long-range interactions such as those found in cis-regulated datasets. Finally we have launched a website optimized for mobile devices providing views of genes, variants and phenotypes. Our data is made available without restriction and all code is available from our GitHub organization site (http://github.com/Ensembl) under an Apache 2.0 license. PMID:26687719

  18. Ensembl comparative genomics resources

    PubMed Central

    Muffato, Matthieu; Beal, Kathryn; Fitzgerald, Stephen; Gordon, Leo; Pignatelli, Miguel; Vilella, Albert J.; Searle, Stephen M. J.; Amode, Ridwan; Brent, Simon; Spooner, William; Kulesha, Eugene; Yates, Andrew; Flicek, Paul

    2016-01-01

    Evolution provides the unifying framework with which to understand biology. The coherent investigation of genic and genomic data often requires comparative genomics analyses based on whole-genome alignments, sets of homologous genes and other relevant datasets in order to evaluate and answer evolutionary-related questions. However, the complexity and computational requirements of producing such data are substantial: this has led to only a small number of reference resources that are used for most comparative analyses. The Ensembl comparative genomics resources are one such reference set that facilitates comprehensive and reproducible analysis of chordate genome data. Ensembl computes pairwise and multiple whole-genome alignments from which large-scale synteny, per-base conservation scores and constrained elements are obtained. Gene alignments are used to define Ensembl Protein Families, GeneTrees and homologies for both protein-coding and non-coding RNA genes. These resources are updated frequently and have a consistent informatics infrastructure and data presentation across all supported species. Specialized web-based visualizations are also available including synteny displays, collapsible gene tree plots, a gene family locator and different alignment views. The Ensembl comparative genomics infrastructure is extensively reused for the analysis of non-vertebrate species by other projects including Ensembl Genomes and Gramene and much of the information here is relevant to these projects. The consistency of the annotation across species and the focus on vertebrates makes Ensembl an ideal system to perform and support vertebrate comparative genomic analyses. We use robust software and pipelines to produce reference comparative data and make it freely available. Database URL: http://www.ensembl.org. PMID:26896847

  19. Ensembl comparative genomics resources.

    PubMed

    Herrero, Javier; Muffato, Matthieu; Beal, Kathryn; Fitzgerald, Stephen; Gordon, Leo; Pignatelli, Miguel; Vilella, Albert J; Searle, Stephen M J; Amode, Ridwan; Brent, Simon; Spooner, William; Kulesha, Eugene; Yates, Andrew; Flicek, Paul

    2016-01-01

    Evolution provides the unifying framework with which to understand biology. The coherent investigation of genic and genomic data often requires comparative genomics analyses based on whole-genome alignments, sets of homologous genes and other relevant datasets in order to evaluate and answer evolutionary-related questions. However, the complexity and computational requirements of producing such data are substantial: this has led to only a small number of reference resources that are used for most comparative analyses. The Ensembl comparative genomics resources are one such reference set that facilitates comprehensive and reproducible analysis of chordate genome data. Ensembl computes pairwise and multiple whole-genome alignments from which large-scale synteny, per-base conservation scores and constrained elements are obtained. Gene alignments are used to define Ensembl Protein Families, GeneTrees and homologies for both protein-coding and non-coding RNA genes. These resources are updated frequently and have a consistent informatics infrastructure and data presentation across all supported species. Specialized web-based visualizations are also available including synteny displays, collapsible gene tree plots, a gene family locator and different alignment views. The Ensembl comparative genomics infrastructure is extensively reused for the analysis of non-vertebrate species by other projects including Ensembl Genomes and Gramene and much of the information here is relevant to these projects. The consistency of the annotation across species and the focus on vertebrates makes Ensembl an ideal system to perform and support vertebrate comparative genomic analyses. We use robust software and pipelines to produce reference comparative data and make it freely available. Database URL: http://www.ensembl.org.

  20. Input Decimated Ensembles

    NASA Technical Reports Server (NTRS)

    Tumer, Kagan; Oza, Nikunj C.; Clancy, Daniel (Technical Monitor)

    2001-01-01

    Using an ensemble of classifiers instead of a single classifier has been shown to improve generalization performance in many pattern recognition problems. However, the extent of such improvement depends greatly on the amount of correlation among the errors of the base classifiers. Therefore, reducing those correlations while keeping the classifiers' performance levels high is an important area of research. In this article, we explore input decimation (ID), a method which selects feature subsets for their ability to discriminate among the classes and uses them to decouple the base classifiers. We provide a summary of the theoretical benefits of correlation reduction, along with results of our method on two underwater sonar data sets, three benchmarks from the Probenl/UCI repositories, and two synthetic data sets. The results indicate that input decimated ensembles (IDEs) outperform ensembles whose base classifiers use all the input features; randomly selected subsets of features; and features created using principal components analysis, on a wide range of domains.

  1. A Sensitive Electrochemiluminescence Immunosensor for Celiac Disease Diagnosis Based on Nanoelectrode Ensembles.

    PubMed

    Habtamu, Henok B; Sentic, Milica; Silvestrini, Morena; De Leo, Luigina; Not, Tarcisio; Arbault, Stephane; Manojlovic, Dragan; Sojic, Neso; Ugo, Paolo

    2015-12-15

    We report here the design of a novel immunosensor and its application for celiac disease diagnosis, based on an electrogenerated chemiluminescence (ECL) readout, using membrane-templated gold nanoelectrode ensembles (NEEs) as a detection platform. An original sensing strategy is presented by segregating spatially the initial electrochemical reaction and the location of the immobilized biomolecules where ECL is finally emitted. The recognition scaffold is the following: tissue transglutaminase (tTG) is immobilized as a capturing agent on the polycarbonate (PC) surface of the track-etched templating membrane. It captures the target tissue transglutaminase antibody (anti-tTG), and finally allows the immobilization of a streptavidin-modified ruthenium-based ECL label via reaction with a suitable biotinylated secondary antibody. The application of an oxidizing potential in a tri-n-propylamine (TPrA) solution generates an intense and sharp ECL signal, suitable for analytical purposes. Voltammetric and ECL analyses evidenced that the ruthenium complex is not oxidized directly at the surface of the nanoelectrodes; instead ECL is generated following the TPrA oxidation, which produces the TPrA•+ and TPrA• radicals. With NEEs operating under total overlap diffusion conditions, high local fluxes of these reactive radicals are produced by the nanoelectrodes in the immediate vicinity of the ECL labels, so that they efficiently generate the ECL signal. The radicals can diffuse over short distances and react with the Ru(bpy)32+ label. In addition, the ECL emission is obtained by applying a potential of 0.88 V versus Ag/AgCl, which is about 0.3 V lower than when ECL is initiated by the electrochemical oxidation of Ru(bpy)3(2+). The immunosensor provides ECL signals which scale with anti-tTG concentration with a linearity range between 1.5 ng·mL–1 and 10 μg·mL–1 and a detection limit of 0.5 ng·mL–1. The sensor is finally applied to the analysis of anti-tTG in human

  2. Ensemble-based air quality forecasts: A multimodel approach applied to ozone

    NASA Astrophysics Data System (ADS)

    Mallet, Vivien; Sportisse, Bruno

    2006-09-01

    The potential of ensemble techniques to improve ozone forecasts is investigated. Ensembles with up to 48 members (models) are generated using the modeling system Polyphemus. Members differ in their physical parameterizations, their numerical approximations, and their input data. Each model is evaluated during 4 months (summer 2001) over Europe with hundreds of stations from three ozone-monitoring networks. We found that several linear combinations of models have the potential to drastically increase the performances of model-to-data comparisons. Optimal weights associated with each model are not robust in time or space. Forecasting these weights therefore requires relevant methods, such as selection of adequate learning data sets, or specific learning algorithms. Significant performance improvements are accomplished by the resulting forecasted combinations. A decrease of about 10% of the root-mean-square error is obtained on ozone daily peaks. Ozone hourly concentrations show stronger improvements.

  3. Hydantoin-based molecular photoswitches.

    PubMed

    Martínez-López, David; Yu, Meng-Long; García-Iriepa, Cristina; Campos, Pedro J; Frutos, Luis Manuel; Golen, James A; Rasapalli, Sivappa; Sampedro, Diego

    2015-04-17

    A new family of molecular photoswitches based on arylidenehydantoins is described together with their synthesis and photochemical and photophysical studies. A series of hydantoin derivatives have been prepared as single isomers using simple and versatile chemistry in good yields. Our studies show that the photostationary states of these compounds can be easily controlled by means of external factors, such as the light source or filters. Moreover, the detailed investigations proved that these switches are efficient (i.e., they make efficient use of the light energy, are high fatigue resistant, and are very photostable). In some cases, the switches can be completely turned on/off, a desirable feature for specific applications. A series of theoretical calculations have also been carried out to understand the photoisomerization mechanism at the molecular level. PMID:25806596

  4. Development of web-based services for a novel ensemble flood forecasting and risk assessment system

    NASA Astrophysics Data System (ADS)

    He, Y.; Manful, D. Y.; Cloke, H. L.; Wetterhall, F.; Li, Z.; Bao, H.; Pappenberger, F.; Wesner, S.; Schubert, L.; Yang, L.; Hu, Y.

    2009-12-01

    Flooding is a wide spread and devastating natural disaster worldwide. Floods that took place in the last decade in China were ranked the worst amongst recorded floods worldwide in terms of the number of human fatalities and economic losses (Munich Re-Insurance). Rapid economic development and population expansion into low lying flood plains has worsened the situation. Current conventional flood prediction systems in China are neither suited to the perceptible climate variability nor the rapid pace of urbanization sweeping the country. Flood prediction, from short-term (a few hours) to medium-term (a few days), needs to be revisited and adapted to changing socio-economic and hydro-climatic realities. The latest technology requires implementation of multiple numerical weather prediction systems. The availability of twelve global ensemble weather prediction systems through the ‘THORPEX Interactive Grand Global Ensemble’ (TIGGE) offers a good opportunity for an effective state-of-the-art early forecasting system. A prototype of a Novel Flood Early Warning System (NEWS) using the TIGGE database is tested in the Huai River basin in east-central China. It is the first early flood warning system in China that uses the massive TIGGE database cascaded with river catchment models, the Xinanjiang hydrologic model and a 1-D hydraulic model, to predict river discharge and flood inundation. The NEWS algorithm is also designed to provide web-based services to a broad spectrum of end-users. The latter presents challenges as both databases and proprietary codes reside in different locations and converge at dissimilar times. NEWS will thus make use of a ready-to-run grid system that makes distributed computing and data resources available in a seamless and secure way. An ability to run or function on different operating systems and provide an interface or front that is accessible to broad spectrum of end-users is additional requirement. The aim is to achieve robust

  5. Genre-based image classification using ensemble learning for online flyers

    NASA Astrophysics Data System (ADS)

    Pourashraf, Payam; Tomuro, Noriko; Apostolova, Emilia

    2015-07-01

    This paper presents an image classification model developed to classify images embedded in commercial real estate flyers. It is a component in a larger, multimodal system which uses texts as well as images in the flyers to automatically classify them by the property types. The role of the image classifier in the system is to provide the genres of the embedded images (map, schematic drawing, aerial photo, etc.), which to be combined with the texts in the flyer to do the overall classification. In this work, we used an ensemble learning approach and developed a model where the outputs of an ensemble of support vector machines (SVMs) are combined by a k-nearest neighbor (KNN) classifier. In this model, the classifiers in the ensemble are strong classifiers, each of which is trained to predict a given/assigned genre. Not only is our model intuitive by taking advantage of the mutual distinctness of the image genres, it is also scalable. We tested the model using over 3000 images extracted from online real estate flyers. The result showed that our model outperformed the baseline classifiers by a large margin.

  6. Maximum Likelihood Ensemble Filter-based Data Assimilation with HSPF for Improving Water Quality Forecasting

    NASA Astrophysics Data System (ADS)

    Kim, S.; Riazi, H.; Shin, C.; Seo, D.

    2013-12-01

    Due to the large dimensionality of the state vector and sparsity of observations, the initial conditions (IC) of water quality models are subject to large uncertainties. To reduce the IC uncertainties in operational water quality forecasting, an ensemble data assimilation (DA) procedure for the Hydrologic Simulation Program - Fortran (HSPF) model has been developed and evaluated for the Kumho River Subcatchment of the Nakdong River Basin in Korea. The procedure, referred to herein as MLEF-HSPF, uses maximum likelihood ensemble filter (MLEF) which combines strengths of variational assimilation (VAR) and ensemble Kalman filter (EnKF). The Control variables involved in the DA procedure include the bias correction factors for mean areal precipitation and mean areal potential evaporation, the hydrologic state variables, and the water quality state variables such as water temperature, dissolved oxygen (DO), biochemical oxygen demand (BOD), ammonium (NH4), nitrate (NO3), phosphate (PO4) and chlorophyll a (CHL-a). Due to the very large dimensionality of the inverse problem, accurately specifying the parameters for the DA procdedure is a challenge. Systematic sensitivity analysis is carried out for identifying the optimal parameter settings. To evaluate the robustness of MLEF-HSPF, we use multiple subcatchments of the Nakdong River Basin. In evaluation, we focus on the performance of MLEF-HSPF on prediction of extreme water quality events.

  7. High teleportation rates using cold-atom-ensemble-based quantum repeaters with Rydberg blockade

    NASA Astrophysics Data System (ADS)

    Solmeyer, Neal; Li, Xiao; Quraishi, Qudsia

    2016-04-01

    We present a simplified version of a repeater protocol in a cold neutral-atom ensemble with Rydberg excitations optimized for two-node entanglement generation and describe a protocol for quantum teleportation. Our proposal draws from previous proposals [B. Zhao et al., Phys. Rev. A 81, 052329 (2010), 10.1103/PhysRevA.81.052329; Y. Han et al., Phys. Rev. A 81, 052311 (2010), 10.1103/PhysRevA.81.052311] that described efficient and robust protocols for long-distance entanglement with many nodes. Using realistic experimental values, we predict an entanglement generation rate of ˜25 Hz and a teleportation rate of ˜5 Hz . Our predicted rates match the current state-of-the-art experiments for entanglement generation and teleportation between quantum memories. With improved efficiencies we predict entanglement generation and teleportation rates of ˜7.8 and ˜3.6 kHz, respectively, representing a two-order-of-magnitude improvement over the currently realized values. Cold-atom ensembles with Rydberg excitations are promising candidates for repeater nodes because collective effects in the ensemble can be used to deterministically generate a long-lived ground-state memory which may be efficiently mapped onto a directionally emitted single photon.

  8. Ensembl 2014

    PubMed Central

    Flicek, Paul; Amode, M. Ridwan; Barrell, Daniel; Beal, Kathryn; Billis, Konstantinos; Brent, Simon; Carvalho-Silva, Denise; Clapham, Peter; Coates, Guy; Fitzgerald, Stephen; Gil, Laurent; Girón, Carlos García; Gordon, Leo; Hourlier, Thibaut; Hunt, Sarah; Johnson, Nathan; Juettemann, Thomas; Kähäri, Andreas K.; Keenan, Stephen; Kulesha, Eugene; Martin, Fergal J.; Maurel, Thomas; McLaren, William M.; Murphy, Daniel N.; Nag, Rishi; Overduin, Bert; Pignatelli, Miguel; Pritchard, Bethan; Pritchard, Emily; Riat, Harpreet S.; Ruffier, Magali; Sheppard, Daniel; Taylor, Kieron; Thormann, Anja; Trevanion, Stephen J.; Vullo, Alessandro; Wilder, Steven P.; Wilson, Mark; Zadissa, Amonida; Aken, Bronwen L.; Birney, Ewan; Cunningham, Fiona; Harrow, Jennifer; Herrero, Javier; Hubbard, Tim J.P.; Kinsella, Rhoda; Muffato, Matthieu; Parker, Anne; Spudich, Giulietta; Yates, Andy; Zerbino, Daniel R.; Searle, Stephen M.J.

    2014-01-01

    Ensembl (http://www.ensembl.org) creates tools and data resources to facilitate genomic analysis in chordate species with an emphasis on human, major vertebrate model organisms and farm animals. Over the past year we have increased the number of species that we support to 77 and expanded our genome browser with a new scrollable overview and improved variation and phenotype views. We also report updates to our core datasets and improvements to our gene homology relationships from the addition of new species. Our REST service has been extended with additional support for comparative genomics and ontology information. Finally, we provide updated information about our methods for data access and resources for user training. PMID:24316576

  9. Loss of conformational entropy in protein folding calculated using realistic ensembles and its implications for NMR-based calculations.

    PubMed

    Baxa, Michael C; Haddadian, Esmael J; Jumper, John M; Freed, Karl F; Sosnick, Tobin R

    2014-10-28

    The loss of conformational entropy is a major contribution in the thermodynamics of protein folding. However, accurate determination of the quantity has proven challenging. We calculate this loss using molecular dynamic simulations of both the native protein and a realistic denatured state ensemble. For ubiquitin, the total change in entropy is TΔSTotal = 1.4 kcal⋅mol(-1) per residue at 300 K with only 20% from the loss of side-chain entropy. Our analysis exhibits mixed agreement with prior studies because of the use of more accurate ensembles and contributions from correlated motions. Buried side chains lose only a factor of 1.4 in the number of conformations available per rotamer upon folding (ΩU/ΩN). The entropy loss for helical and sheet residues differs due to the smaller motions of helical residues (TΔShelix-sheet = 0.5 kcal⋅mol(-1)), a property not fully reflected in the amide N-H and carbonyl C=O bond NMR order parameters. The results have implications for the thermodynamics of folding and binding, including estimates of solvent ordering and microscopic entropies obtained from NMR.

  10. Loss of conformational entropy in protein folding calculated using realistic ensembles and its implications for NMR-based calculations

    PubMed Central

    Baxa, Michael C.; Haddadian, Esmael J.; Jumper, John M.; Freed, Karl F.; Sosnick, Tobin R.

    2014-01-01

    The loss of conformational entropy is a major contribution in the thermodynamics of protein folding. However, accurate determination of the quantity has proven challenging. We calculate this loss using molecular dynamic simulations of both the native protein and a realistic denatured state ensemble. For ubiquitin, the total change in entropy is TΔSTotal = 1.4 kcal⋅mol−1 per residue at 300 K with only 20% from the loss of side-chain entropy. Our analysis exhibits mixed agreement with prior studies because of the use of more accurate ensembles and contributions from correlated motions. Buried side chains lose only a factor of 1.4 in the number of conformations available per rotamer upon folding (ΩU/ΩN). The entropy loss for helical and sheet residues differs due to the smaller motions of helical residues (TΔShelix−sheet = 0.5 kcal⋅mol−1), a property not fully reflected in the amide N-H and carbonyl C=O bond NMR order parameters. The results have implications for the thermodynamics of folding and binding, including estimates of solvent ordering and microscopic entropies obtained from NMR. PMID:25313044

  11. Complex System Ensemble Analysis

    NASA Astrophysics Data System (ADS)

    Pearson, Carl A.

    A new measure for interaction network ensembles and their dynamics is presented: the ensemble transition matrix, T, the proportions of networks in an ensemble that support particular transitions. That presentation begins with generation of the ensemble and application of constraint perturbations to compute T, including estimating alternatives to accommodate cases where the problem size becomes computationally intractable. Then, T is used to predict ensemble dynamics properties in expected-value like calculations. Finally, analyses from the two complementary assumptions about T - that it represents uncertainty about a unique system or that it represents stochasticity around a unique constraint - are presented: entropy-based experiment selection and generalized potentials/heat dissipation of the system, respectively. Extension of these techniques to more general graph models is described, but not demonstrated. Future directions for research using T are proposed in the summary chapter. Throughout this work, the presentation of various calculations involving T are motivated by the Budding Yeast Cell Cycle example, with argument for the generality of the approaches presented by the results of their application to a database of pseudo-randomly generated dynamic constraints.

  12. Ensemble-based analysis of extreme precipitation events from 2007-2011

    NASA Astrophysics Data System (ADS)

    Lynch, Samantha

    From 2007 to 2011, 22 widespread, multiday rain events occurred across the United States. This study makes use of the European Centre for Medium-Range Weather Forecasts (ECMWF), the National Centers of Environmental Prediction (NCEP), and the United Kingdom Office of Meteorology (UKMET) ensemble prediction systems (EPS) in order to assess their forecast skill of these 22 widespread, precipitation events. Overall, the ECMWF had a skillful forecast for almost every event, with an exception of the 25-30 June 2007 event, the mesoscale convective vortex (MCV) over the southern plains of the United States. Additionally, the ECMWF EPS generally outperformed both the NCEP and UKMET EPS. To better evaluate the ECMWF, two widespread, multiday precipitation events were selected for closer examination: 29 April-4 May 2010 and 23-28 April 2011. The 29 April-4 May 2010 case study used ECMWF ensemble forecasts to explore the processes responsible for the development and maintenance of a multiday precipitation event that occurred in early May 2010, due to two successive quasi-stationary mesoscale convective systems. Locations in central Tennessee accumulated more than 483 millimeters (19 inches) of rain and the city of Nashville experienced a historic flash flood. Differences between ensemble members that correctly predicted heavy precipitation and those that did not were determined in order to determine the processes that were favorable or detrimental to the system's development. Statistical analysis was used to determine how synoptic-scale flows were correlated to area- averaged precipitation. For this particular case, the distribution of precipitation was found to be closely related to the strength of an upper-level trough in the central United States and an associated surface cyclone, with a weaker trough and cyclone being associated with more precipitation in the area of interest. The 23-28 April 2011 case study also used ECMWF ensemble forecasts to explore the processes

  13. A generalized polynomial chaos based ensemble Kalman filter with high accuracy

    SciTech Connect

    Li Jia; Xiu Dongbin

    2009-08-20

    As one of the most adopted sequential data assimilation methods in many areas, especially those involving complex nonlinear dynamics, the ensemble Kalman filter (EnKF) has been under extensive investigation regarding its properties and efficiency. Compared to other variants of the Kalman filter (KF), EnKF is straightforward to implement, as it employs random ensembles to represent solution states. This, however, introduces sampling errors that affect the accuracy of EnKF in a negative manner. Though sampling errors can be easily reduced by using a large number of samples, in practice this is undesirable as each ensemble member is a solution of the system of state equations and can be time consuming to compute for large-scale problems. In this paper we present an efficient EnKF implementation via generalized polynomial chaos (gPC) expansion. The key ingredients of the proposed approach involve (1) solving the system of stochastic state equations via the gPC methodology to gain efficiency; and (2) sampling the gPC approximation of the stochastic solution with an arbitrarily large number of samples, at virtually no additional computational cost, to drastically reduce the sampling errors. The resulting algorithm thus achieves a high accuracy at reduced computational cost, compared to the classical implementations of EnKF. Numerical examples are provided to verify the convergence property and accuracy improvement of the new algorithm. We also prove that for linear systems with Gaussian noise, the first-order gPC Kalman filter method is equivalent to the exact Kalman filter.

  14. Application of an ensemble technique based on singular spectrum analysis to daily rainfall forecasting.

    PubMed

    Baratta, Daniela; Cicioni, Giovambattista; Masulli, Francesco; Studer, Léonard

    2003-01-01

    In previous work, we have proposed a constructive methodology for temporal data learning supported by results and prescriptions related to the embedding theorem, and using the singular spectrum analysis both in order to reduce the effects of the possible discontinuity of the signal and to implement an efficient ensemble method. In this paper we present new results concerning the application of this approach to the forecasting of the individual rain-fall intensities series collected by 135 stations distributed in the Tiber basin. The average RMS error of the obtained forecasting is less than 3mm of rain. PMID:12672433

  15. A decision support system based on an ensemble of random forests for improving the management of women with abnormal findings at cervical cancer screening.

    PubMed

    Bountris, Panagiotis; Haritou, Maria; Pouliakis, Abraham; Karakitsos, Petros; Koutsouris, Dimitrios

    2015-08-01

    In most cases, cervical cancer (CxCa) develops due to underestimated abnormalities in the Pap test. Today, there are ancillary molecular biology techniques available that provide important information related to CxCa and the Human Papillomavirus (HPV) natural history, including HPV DNA tests, HPV mRNA tests and immunocytochemistry techniques such as overexpression of p16. These techniques are either highly sensitive or highly specific, however not both at the same time, thus no perfect method is available today. In this paper we present a decision support system (DSS) based on an ensemble of Random Forests (RFs) for the intelligent combination of the results of classic and ancillary techniques that are available for CxCa detection, in order to exploit the benefits of each technique and produce more accurate results. The proposed system achieved both, high sensitivity (86.1%) and high specificity (93.3%), as well as high overall accuracy (91.8%), in detecting cervical intraepithelial neoplasia grade 2 or worse (CIN2+). The system's performance was better than any other single test involved in this study. Moreover, the proposed architecture of employing an ensemble of RFs proved to be better than the single classifier approach. The presented system can handle cases with missing tests and more importantly cases with inadequate cytological outcome, thus it can also produce accurate results in the case of stand-alone HPV-based screening, where Pap test is not applied. The proposed system may identify women at true risk of developing CxCa and guide personalised management and therapeutic interventions.

  16. Synthetic molecular systems based on porphyrins as models for the study of energy transfer in photosynthesis

    NASA Astrophysics Data System (ADS)

    Konovalova, Nadezhda V.; Evstigneeva, Rima P.; Luzgina, Valentina N.

    2001-11-01

    The published data on the synthesis and photochemical properties of porphyrin-based molecular ensembles which represent models of natural photosynthetic light-harvesting complexes are generalised and systematised. The dependence of the transfer of excitation energy on the distance between donor and acceptor components, their mutual arrangement, electronic and environmental factors are discussed. Two mechanisms of energy transfer reactions, viz., 'through space' and 'through bond', are considered. The bibliography includes 96 references.

  17. Selective ensemble modeling load parameters of ball mill based on multi-scale frequency spectral features and sphere criterion

    NASA Astrophysics Data System (ADS)

    Tang, Jian; Yu, Wen; Chai, Tianyou; Liu, Zhuo; Zhou, Xiaojie

    2016-01-01

    It is difficult to model multi-frequency signal, such as mechanical vibration and acoustic signals of wet ball mill in the mineral grinding process. In this paper, these signals are decomposed into multi-scale intrinsic mode functions (IMFs) by the empirical mode decomposition (EMD) technique. A new adaptive multi-scale spectral features selection approach based on sphere criterion (SC) is applied to these IMFs frequency spectra. The candidate sub-models are constructed by the partial least squares (PLS) with the selected features. Finally, the branch and bound based selective ensemble (BBSEN) algorithm is applied to select and combine these ensemble sub-models. This method can be easily extended to regression and classification problems with multi-time scale signal. We successfully apply this approach to a laboratory-scale ball mill. The shell vibration and acoustic signals are used to model mill load parameters. The experimental results demonstrate that this novel approach is more effective than the other modeling methods based on multi-scale frequency spectral features.

  18. Hierarchical Conformational Analysis of Native Lysozyme Based on Sub-Millisecond Molecular Dynamics Simulations

    PubMed Central

    Wang, Kai; Long, Shiyang; Tian, Pu

    2015-01-01

    Hierarchical organization of free energy landscape (FEL) for native globular proteins has been widely accepted by the biophysics community. However, FEL of native proteins is usually projected onto one or a few dimensions. Here we generated collectively 0.2 milli-second molecular dynamics simulation trajectories in explicit solvent for hen egg white lysozyme (HEWL), and carried out detailed conformational analysis based on backbone torsional degrees of freedom (DOF). Our results demonstrated that at micro-second and coarser temporal resolutions, FEL of HEWL exhibits hub-like topology with crystal structures occupying the dominant structural ensemble that serves as the hub of conformational transitions. However, at 100ns and finer temporal resolutions, conformational substates of HEWL exhibit network-like topology, crystal structures are associated with kinetic traps that are important but not dominant ensembles. Backbone torsional state transitions on time scales ranging from nanoseconds to beyond microseconds were found to be associated with various types of molecular interactions. Even at nanoseconds temporal resolution, the number of conformational substates that are of statistical significance is quite limited. These observations suggest that detailed analysis of conformational substates at multiple temporal resolutions is both important and feasible. Transition state ensembles among various conformational substates at microsecond temporal resolution were observed to be considerably disordered. Life times of these transition state ensembles are found to be nearly independent of the time scales of the participating torsional DOFs. PMID:26057625

  19. Carbon Nanotube Based Molecular Electronics

    NASA Technical Reports Server (NTRS)

    Srivastava, Deepak; Saini, Subhash; Menon, Madhu

    1998-01-01

    Carbon nanotubes and the nanotube heterojunctions have recently emerged as excellent candidates for nanoscale molecular electronic device components. Experimental measurements on the conductivity, rectifying behavior and conductivity-chirality correlation have also been made. While quasi-one dimensional simple heterojunctions between nanotubes with different electronic behavior can be generated by introduction of a pair of heptagon-pentagon defects in an otherwise all hexagon graphene sheet. Other complex 3- and 4-point junctions may require other mechanisms. Structural stability as well as local electronic density of states of various nanotube junctions are investigated using a generalized tight-binding molecular dynamics (GDBMD) scheme that incorporates non-orthogonality of the orbitals. The junctions investigated include straight and small angle heterojunctions of various chiralities and diameters; as well as more complex 'T' and 'Y' junctions which do not always obey the usual pentagon-heptagon pair rule. The study of local density of states (LDOS) reveal many interesting features, most prominent among them being the defect-induced states in the gap. The proposed three and four pointjunctions are one of the smallest possible tunnel junctions made entirely of carbon atoms. Furthermore the electronic behavior of the nanotube based device components can be taylored by doping with group III-V elements such as B and N, and BN nanotubes as a wide band gap semiconductor has also been realized in experiments. Structural properties of heteroatomic nanotubes comprising C, B and N will be discussed.

  20. Predicting protein dynamics from structural ensembles

    NASA Astrophysics Data System (ADS)

    Copperman, J.; Guenza, M. G.

    2015-12-01

    The biological properties of proteins are uniquely determined by their structure and dynamics. A protein in solution populates a structural ensemble of metastable configurations around the global fold. From overall rotation to local fluctuations, the dynamics of proteins can cover several orders of magnitude in time scales. We propose a simulation-free coarse-grained approach which utilizes knowledge of the important metastable folded states of the protein to predict the protein dynamics. This approach is based upon the Langevin Equation for Protein Dynamics (LE4PD), a Langevin formalism in the coordinates of the protein backbone. The linear modes of this Langevin formalism organize the fluctuations of the protein, so that more extended dynamical cooperativity relates to increasing energy barriers to mode diffusion. The accuracy of the LE4PD is verified by analyzing the predicted dynamics across a set of seven different proteins for which both relaxation data and NMR solution structures are available. Using experimental NMR conformers as the input structural ensembles, LE4PD predicts quantitatively accurate results, with correlation coefficient ρ = 0.93 to NMR backbone relaxation measurements for the seven proteins. The NMR solution structure derived ensemble and predicted dynamical relaxation is compared with molecular dynamics simulation-derived structural ensembles and LE4PD predictions and is consistent in the time scale of the simulations. The use of the experimental NMR conformers frees the approach from computationally demanding simulations.

  1. Fluid trajectory evaluation based on an ensemble-averaged cross-correlation in time-resolved PIV

    NASA Astrophysics Data System (ADS)

    Jeon, Young Jin; Chatellier, Ludovic; David, Laurent

    2014-07-01

    A novel multi-frame particle image velocimetry (PIV) method, able to evaluate a fluid trajectory by means of an ensemble-averaged cross-correlation, is introduced. The method integrates the advantages of the state-of-art time-resolved PIV (TR-PIV) methods to further enhance both robustness and dynamic range. The fluid trajectory follows a polynomial model with a prescribed order. A set of polynomial coefficients, which maximizes the ensemble-averaged cross-correlation value across the frames, is regarded as the most appropriate solution. To achieve a convergence of the trajectory in terms of polynomial coefficients, an ensemble-averaged cross-correlation map is constructed by sampling cross-correlation values near the predictor trajectory with respect to an imposed change of each polynomial coefficient. A relation between the given change and corresponding cross-correlation maps, which could be calculated from the ordinary cross-correlation, is derived. A disagreement between computational domain and corresponding physical domain is compensated by introducing the Jacobian matrix based on the image deformation scheme in accordance with the trajectory. An increased cost of the convergence calculation, associated with the nonlinearity of the fluid trajectory, is moderated by means of a V-cycle iteration. To validate enhancements of the present method, quantitative comparisons with the state-of-arts TR-PIV methods, e.g., the adaptive temporal interval, the multi-frame pyramid correlation and the fluid trajectory correlation, were carried out by using synthetically generated particle image sequences. The performances of the tested methods are discussed in algorithmic terms. A high-rate TR-PIV experiment of a flow over an airfoil demonstrates the effectiveness of the present method. It is shown that the present method is capable of reducing random errors in both velocity and material acceleration while suppressing spurious temporal fluctuations due to measurement noise.

  2. BWM*: A Novel, Provable, Ensemble-based Dynamic Programming Algorithm for Sparse Approximations of Computational Protein Design.

    PubMed

    Jou, Jonathan D; Jain, Swati; Georgiev, Ivelin S; Donald, Bruce R

    2016-06-01

    Sparse energy functions that ignore long range interactions between residue pairs are frequently used by protein design algorithms to reduce computational cost. Current dynamic programming algorithms that fully exploit the optimal substructure produced by these energy functions only compute the GMEC. This disproportionately favors the sequence of a single, static conformation and overlooks better binding sequences with multiple low-energy conformations. Provable, ensemble-based algorithms such as A* avoid this problem, but A* cannot guarantee better performance than exhaustive enumeration. We propose a novel, provable, dynamic programming algorithm called Branch-Width Minimization* (BWM*) to enumerate a gap-free ensemble of conformations in order of increasing energy. Given a branch-decomposition of branch-width w for an n-residue protein design with at most q discrete side-chain conformations per residue, BWM* returns the sparse GMEC in O([Formula: see text]) time and enumerates each additional conformation in merely O([Formula: see text]) time. We define a new measure, Total Effective Search Space (TESS), which can be computed efficiently a priori before BWM* or A* is run. We ran BWM* on 67 protein design problems and found that TESS discriminated between BWM*-efficient and A*-efficient cases with 100% accuracy. As predicted by TESS and validated experimentally, BWM* outperforms A* in 73% of the cases and computes the full ensemble or a close approximation faster than A*, enumerating each additional conformation in milliseconds. Unlike A*, the performance of BWM* can be predicted in polynomial time before running the algorithm, which gives protein designers the power to choose the most efficient algorithm for their particular design problem.

  3. A modified Shockley equation taking into account the multi-element nature of light emitting diodes based on nanowire ensembles.

    PubMed

    Musolino, M; Tahraoui, A; Treeck, D van; Geelhaar, L; Riechert, H

    2016-07-01

    In this work we study how the multi-element nature of light emitting diodes (LEDs) based on nanowire (NW) ensembles influences their current voltage (I-V) characteristics. We systematically address critical issues of the fabrication process that can result in significant fluctuations of the electrical properties among the individual NWs in such LEDs, paying particular attention to the planarization step. Electroluminescence (EL) maps acquired for two nominally identical NW-LEDs reveal that small processing variations can result in a large difference in the number of individual nano-devices emitting EL. The lower number of EL spots in one of the LEDs is caused by its inhomogeneous electrical properties. The I-V characteristics of this LED cannot be described well by the classical Shockley model. We are able to take into account the multi-element nature of such LEDs and fit the I-V characteristics in the forward bias regime by employing an ad hoc adjusted version of the Shockley equation. More specifically, we introduce a bias dependence of the ideality factor. The basic considerations of our model should remain valid also for other types of devices based on ensembles of interconnected p-n junctions with inhomogeneous electrical properties, regardless of the employed material system.

  4. A modified Shockley equation taking into account the multi-element nature of light emitting diodes based on nanowire ensembles

    NASA Astrophysics Data System (ADS)

    Musolino, M.; Tahraoui, A.; van Treeck, D.; Geelhaar, L.; Riechert, H.

    2016-07-01

    In this work we study how the multi-element nature of light emitting diodes (LEDs) based on nanowire (NW) ensembles influences their current voltage (I-V) characteristics. We systematically address critical issues of the fabrication process that can result in significant fluctuations of the electrical properties among the individual NWs in such LEDs, paying particular attention to the planarization step. Electroluminescence (EL) maps acquired for two nominally identical NW-LEDs reveal that small processing variations can result in a large difference in the number of individual nano-devices emitting EL. The lower number of EL spots in one of the LEDs is caused by its inhomogeneous electrical properties. The I-V characteristics of this LED cannot be described well by the classical Shockley model. We are able to take into account the multi-element nature of such LEDs and fit the I-V characteristics in the forward bias regime by employing an ad hoc adjusted version of the Shockley equation. More specifically, we introduce a bias dependence of the ideality factor. The basic considerations of our model should remain valid also for other types of devices based on ensembles of interconnected p-n junctions with inhomogeneous electrical properties, regardless of the employed material system.

  5. Hybrid MPI/OpenMP Implementation of the ORAC Molecular Dynamics Program for Generalized Ensemble and Fast Switching Alchemical Simulations.

    PubMed

    Procacci, Piero

    2016-06-27

    We present a new release (6.0β) of the ORAC program [Marsili et al. J. Comput. Chem. 2010, 31, 1106-1116] with a hybrid OpenMP/MPI (open multiprocessing message passing interface) multilevel parallelism tailored for generalized ensemble (GE) and fast switching double annihilation (FS-DAM) nonequilibrium technology aimed at evaluating the binding free energy in drug-receptor system on high performance computing platforms. The production of the GE or FS-DAM trajectories is handled using a weak scaling parallel approach on the MPI level only, while a strong scaling force decomposition scheme is implemented for intranode computations with shared memory access at the OpenMP level. The efficiency, simplicity, and inherent parallel nature of the ORAC implementation of the FS-DAM algorithm, project the code as a possible effective tool for a second generation high throughput virtual screening in drug discovery and design. The code, along with documentation, testing, and ancillary tools, is distributed under the provisions of the General Public License and can be freely downloaded at www.chim.unifi.it/orac . PMID:27231982

  6. A novel hybrid decomposition-and-ensemble model based on CEEMD and GWO for short-term PM2.5 concentration forecasting

    NASA Astrophysics Data System (ADS)

    Niu, Mingfei; Wang, Yufang; Sun, Shaolong; Li, Yongwu

    2016-06-01

    To enhance prediction reliability and accuracy, a hybrid model based on the promising principle of "decomposition and ensemble" and a recently proposed meta-heuristic called grey wolf optimizer (GWO) is introduced for daily PM2.5 concentration forecasting. Compared with existing PM2.5 forecasting methods, this proposed model has improved the prediction accuracy and hit rates of directional prediction. The proposed model involves three main steps, i.e., decomposing the original PM2.5 series into several intrinsic mode functions (IMFs) via complementary ensemble empirical mode decomposition (CEEMD) for simplifying the complex data; individually predicting each IMF with support vector regression (SVR) optimized by GWO; integrating all predicted IMFs for the ensemble result as the final prediction by another SVR optimized by GWO. Seven benchmark models, including single artificial intelligence (AI) models, other decomposition-ensemble models with different decomposition methods and models with the same decomposition-ensemble method but optimized by different algorithms, are considered to verify the superiority of the proposed hybrid model. The empirical study indicates that the proposed hybrid decomposition-ensemble model is remarkably superior to all considered benchmark models for its higher prediction accuracy and hit rates of directional prediction.

  7. An ensemble-based algorithm for optimizing the configuration of an in situ soil moisture monitoring network

    NASA Astrophysics Data System (ADS)

    De Vleeschouwer, Niels; Verhoest, Niko E. C.; Gobeyn, Sacha; De Baets, Bernard; Verwaeren, Jan; Pauwels, Valentijn R. N.

    2015-04-01

    factors that will influence the outcome of the algorithm are the following: the choice of the hydrological model, the uncertainty model applied for ensemble generation, the general wetness of the catchment during which the error covariance is computed, etc. In this research the influence of the latter two is examined more in-depth. Furthermore, the optimal network configuration resulting from the newly developed algorithm is compared to network configurations obtained by two other algorithms. The first algorithm is based on a temporal stability analysis of the modeled soil moisture in order to identify catchment representative monitoring locations with regard to average conditions. The second algorithm involves the clustering of available spatially distributed data (e.g. land cover and soil maps) that is not obtained by hydrological modeling.

  8. An efficient ensemble of radial basis functions method based on quadratic programming

    NASA Astrophysics Data System (ADS)

    Shi, Renhe; Liu, Li; Long, Teng; Liu, Jian

    2016-07-01

    Radial basis function (RBF) surrogate models have been widely applied in engineering design optimization problems to approximate computationally expensive simulations. Ensemble of radial basis functions (ERBF) using the weighted sum of stand-alone RBFs improves the approximation performance. To achieve a good trade-off between the accuracy and efficiency of the modelling process, this article presents a novel efficient ERBF method to determine the weights through solving a quadratic programming subproblem, denoted ERBF-QP. Several numerical benchmark functions are utilized to test the performance of the proposed ERBF-QP method. The results show that ERBF-QP can significantly improve the modelling efficiency compared with several existing ERBF methods. Moreover, ERBF-QP also provides satisfactory performance in terms of approximation accuracy. Finally, the ERBF-QP method is applied to a satellite multidisciplinary design optimization problem to illustrate its practicality and effectiveness for real-world engineering applications.

  9. Single-photon-level quantum image memory based on cold atomic ensembles

    PubMed Central

    Ding, Dong-Sheng; Zhou, Zhi-Yuan; Shi, Bao-Sen; Guo, Guang-Can

    2013-01-01

    A quantum memory is a key component for quantum networks, which will enable the distribution of quantum information. Its successful development requires storage of single-photon light. Encoding photons with spatial shape through higher-dimensional states significantly increases their information-carrying capability and network capacity. However, constructing such quantum memories is challenging. Here we report the first experimental realization of a true single-photon-carrying orbital angular momentum stored via electromagnetically induced transparency in a cold atomic ensemble. Our experiments show that the non-classical pair correlation between trigger photon and retrieved photon is retained, and the spatial structure of input and retrieved photons exhibits strong similarity. More importantly, we demonstrate that single-photon coherence is preserved during storage. The ability to store spatial structure at the single-photon level opens the possibility for high-dimensional quantum memories. PMID:24084711

  10. Dynamic State Estimation and Parameter Calibration of DFIG based on Ensemble Kalman Filter

    SciTech Connect

    Fan, Rui; Huang, Zhenyu; Wang, Shaobu; Diao, Ruisheng; Meng, Da

    2015-07-30

    With the growing interest in the application of wind energy, doubly fed induction generator (DFIG) plays an essential role in the industry nowadays. To deal with the increasing stochastic variations introduced by intermittent wind resource and responsive loads, dynamic state estimation (DSE) are introduced in any power system associated with DFIGs. However, sometimes this dynamic analysis canould not work because the parameters of DFIGs are not accurate enough. To solve the problem, an ensemble Kalman filter (EnKF) method is proposed for the state estimation and parameter calibration tasks. In this paper, a DFIG is modeled and implemented with the EnKF method. Sensitivity analysis is demonstrated regarding the measurement noise, initial state errors and parameter errors. The results indicate this EnKF method has a robust performance on the state estimation and parameter calibration of DFIGs.

  11. [Simulation of cropland soil moisture based on an ensemble Kalman filter].

    PubMed

    Liu, Zhao; Zhou, Yan-Lian; Ju, Wei-Min; Gao, Ping

    2011-11-01

    By using an ensemble Kalman filter (EnKF) to assimilate the observed soil moisture data, the modified boreal ecosystem productivity simulator (BEPS) model was adopted to simulate the dynamics of soil moisture in winter wheat root zones at Xuzhou Agro-meteorological Station, Jiangsu Province of China during the growth seasons in 2000-2004. After the assimilation of observed data, the determination coefficient, root mean square error, and average absolute error of simulated soil moisture were in the ranges of 0.626-0.943, 0.018-0.042, and 0.021-0.041, respectively, with the simulation precision improved significantly, as compared with that before assimilation, indicating the applicability of data assimilation in improving the simulation of soil moisture. The experimental results at single point showed that the errors in the forcing data and observations and the frequency and soil depth of the assimilation of observed data all had obvious effects on the simulated soil moisture.

  12. Effects of ensembles on methane hydrate nucleation kinetics.

    PubMed

    Zhang, Zhengcai; Liu, Chan-Juan; Walsh, Matthew R; Guo, Guang-Jun

    2016-06-21

    By performing molecular dynamics simulations to form a hydrate with a methane nano-bubble in liquid water at 250 K and 50 MPa, we report how different ensembles, such as the NPT, NVT, and NVE ensembles, affect the nucleation kinetics of the methane hydrate. The nucleation trajectories are monitored using the face-saturated incomplete cage analysis (FSICA) and the mutually coordinated guest (MCG) order parameter (OP). The nucleation rate and the critical nucleus are obtained using the mean first-passage time (MFPT) method based on the FS cages and the MCG-1 OPs, respectively. The fitting results of MFPT show that hydrate nucleation and growth are coupled together, consistent with the cage adsorption hypothesis which emphasizes that the cage adsorption of methane is a mechanism for both hydrate nucleation and growth. For the three different ensembles, the hydrate nucleation rate is quantitatively ordered as follows: NPT > NVT > NVE, while the sequence of hydrate crystallinity is exactly reversed. However, the largest size of the critical nucleus appears in the NVT ensemble, rather than in the NVE ensemble. These results are helpful for choosing a suitable ensemble when to study hydrate formation via computer simulations, and emphasize the importance of the order degree of the critical nucleus.

  13. Conformational Ensemble of hIAPP Dimer: Insight into the Molecular Mechanism by which a Green Tea Extract inhibits hIAPP Aggregation

    PubMed Central

    Mo, Yuxiang; Lei, Jiangtao; Sun, Yunxiang; Zhang, Qingwen; Wei, Guanghong

    2016-01-01

    Small oligomers formed early along human islet amyloid polypeptide (hIAPP) aggregation is responsible for the cell death in Type II diabetes. The epigallocatechin gallate (EGCG), a green tea extract, was found to inhibit hIAPP fibrillation. However, the inhibition mechanism and the conformational distribution of the smallest hIAPP oligomer – dimer are mostly unknown. Herein, we performed extensive replica exchange molecular dynamic simulations on hIAPP dimer with and without EGCG molecules. Extended hIAPP dimer conformations, with a collision cross section value similar to that observed by ion mobility-mass spectrometry, were observed in our simulations. Notably, these dimers adopt a three-stranded antiparallel β-sheet and contain the previously reported β-hairpin amyloidogenic precursor. We find that EGCG binding strongly blocks both the inter-peptide hydrophobic and aromatic-stacking interactions responsible for inter-peptide β-sheet formation and intra-peptide interaction crucial for β-hairpin formation, thus abolishes the three-stranded β-sheet structures and leads to the formation of coil-rich conformations. Hydrophobic, aromatic-stacking, cation-π and hydrogen-bonding interactions jointly contribute to the EGCG-induced conformational shift. This study provides, on atomic level, the conformational ensemble of hIAPP dimer and the molecular mechanism by which EGCG inhibits hIAPP aggregation. PMID:27620620

  14. Conformational Ensemble of hIAPP Dimer: Insight into the Molecular Mechanism by which a Green Tea Extract inhibits hIAPP Aggregation.

    PubMed

    Mo, Yuxiang; Lei, Jiangtao; Sun, Yunxiang; Zhang, Qingwen; Wei, Guanghong

    2016-01-01

    Small oligomers formed early along human islet amyloid polypeptide (hIAPP) aggregation is responsible for the cell death in Type II diabetes. The epigallocatechin gallate (EGCG), a green tea extract, was found to inhibit hIAPP fibrillation. However, the inhibition mechanism and the conformational distribution of the smallest hIAPP oligomer - dimer are mostly unknown. Herein, we performed extensive replica exchange molecular dynamic simulations on hIAPP dimer with and without EGCG molecules. Extended hIAPP dimer conformations, with a collision cross section value similar to that observed by ion mobility-mass spectrometry, were observed in our simulations. Notably, these dimers adopt a three-stranded antiparallel β-sheet and contain the previously reported β-hairpin amyloidogenic precursor. We find that EGCG binding strongly blocks both the inter-peptide hydrophobic and aromatic-stacking interactions responsible for inter-peptide β-sheet formation and intra-peptide interaction crucial for β-hairpin formation, thus abolishes the three-stranded β-sheet structures and leads to the formation of coil-rich conformations. Hydrophobic, aromatic-stacking, cation-π and hydrogen-bonding interactions jointly contribute to the EGCG-induced conformational shift. This study provides, on atomic level, the conformational ensemble of hIAPP dimer and the molecular mechanism by which EGCG inhibits hIAPP aggregation. PMID:27620620

  15. The Ensembl gene annotation system.

    PubMed

    Aken, Bronwen L; Ayling, Sarah; Barrell, Daniel; Clarke, Laura; Curwen, Valery; Fairley, Susan; Fernandez Banet, Julio; Billis, Konstantinos; García Girón, Carlos; Hourlier, Thibaut; Howe, Kevin; Kähäri, Andreas; Kokocinski, Felix; Martin, Fergal J; Murphy, Daniel N; Nag, Rishi; Ruffier, Magali; Schuster, Michael; Tang, Y Amy; Vogel, Jan-Hinnerk; White, Simon; Zadissa, Amonida; Flicek, Paul; Searle, Stephen M J

    2016-01-01

    The Ensembl gene annotation system has been used to annotate over 70 different vertebrate species across a wide range of genome projects. Furthermore, it generates the automatic alignment-based annotation for the human and mouse GENCODE gene sets. The system is based on the alignment of biological sequences, including cDNAs, proteins and RNA-seq reads, to the target genome in order to construct candidate transcript models. Careful assessment and filtering of these candidate transcripts ultimately leads to the final gene set, which is made available on the Ensembl website. Here, we describe the annotation process in detail.Database URL: http://www.ensembl.org/index.html. PMID:27337980

  16. The Ensembl gene annotation system

    PubMed Central

    Aken, Bronwen L.; Ayling, Sarah; Barrell, Daniel; Clarke, Laura; Curwen, Valery; Fairley, Susan; Fernandez Banet, Julio; Billis, Konstantinos; García Girón, Carlos; Hourlier, Thibaut; Howe, Kevin; Kähäri, Andreas; Kokocinski, Felix; Martin, Fergal J.; Murphy, Daniel N.; Nag, Rishi; Ruffier, Magali; Schuster, Michael; Tang, Y. Amy; Vogel, Jan-Hinnerk; White, Simon; Zadissa, Amonida; Flicek, Paul

    2016-01-01

    The Ensembl gene annotation system has been used to annotate over 70 different vertebrate species across a wide range of genome projects. Furthermore, it generates the automatic alignment-based annotation for the human and mouse GENCODE gene sets. The system is based on the alignment of biological sequences, including cDNAs, proteins and RNA-seq reads, to the target genome in order to construct candidate transcript models. Careful assessment and filtering of these candidate transcripts ultimately leads to the final gene set, which is made available on the Ensembl website. Here, we describe the annotation process in detail. Database URL: http://www.ensembl.org/index.html PMID:27337980

  17. [Removal Algorithm of Power Line Interference in Electrocardiogram Based on Morphological Component Analysis and Ensemble Empirical Mode Decomposition].

    PubMed

    Zhao, Wei; Xiao, Shixiao; Zhang, Baocan; Huang, Xiaojing; You, Rongyi

    2015-12-01

    Electrocardiogram (ECG) signals are susceptible to be disturbed by 50 Hz power line interference (PLI) in the process of acquisition and conversion. This paper, therefore, proposes a novel PLI removal algorithm based on morphological component analysis (MCA) and ensemble empirical mode decomposition (EEMD). Firstly, according to the morphological differences in ECG waveform characteristics, the noisy ECG signal was decomposed into the mutated component, the smooth component and the residual component by MCA. Secondly, intrinsic mode functions (IMF) of PLI was filtered. The noise suppression rate (NSR) and the signal distortion ratio (SDR) were used to evaluate the effect of de-noising algorithm. Finally, the ECG signals were re-constructed. Based on the experimental comparison, it was concluded that the proposed algorithm had better filtering functions than the improved Levkov algorithm, because it could not only effectively filter the PLI, but also have smaller SDR value. PMID:27079083

  18. Structure-based ensemble-QSAR model: a novel approach to the study of the EGFR tyrosine kinase and its inhibitors

    PubMed Central

    Sun, Xian-qiang; Chen, Lei; Li, Yao-zong; Li, Wei-hua; Liu, Gui-xia; Tu, Yao-quan; Tang, Yun

    2014-01-01

    Aim: To develop a novel 3D-QSAR approach for study of the epidermal growth factor receptor tyrosine kinase (EGFR TK) and its inhibitors. Methods: One hundred thirty nine EGFR TK inhibitors were classified into 3 clusters. Ensemble docking of these inhibitors with 19 EGFR TK crystal structures was performed. Three protein structures that showed the best recognition of each cluster were selected based on the docking results. Then, a novel QSAR (ensemble-QSAR) building method was developed based on the ligand conformations determined by the corresponding protein structures. Results: Compared with the 3D-QSAR model, in which the ligand conformations were determined by a single protein structure, ensemble-QSAR exhibited higher R2 (0.87) and Q2 (0.78) values and thus appeared to be a more reliable and better predictive model. Ensemble-QSAR was also able to more accurately describe the interactions between the target and the ligands. Conclusion: The novel ensemble-QSAR model built in this study outperforms the traditional 3D-QSAR model in rationality, and provides a good example of selecting suitable protein structures for docking prediction and for building structure-based QSAR using available protein structures. PMID:24335842

  19. Molecular memory based on nanowire-molecular wire heterostructures.

    PubMed

    Li, Chao; Lei, Bo; Fan, Wendy; Zhang, Daihua; Meyyappan, M; Zhou, Chongwu

    2007-01-01

    This article reviews the recent research of molecular memory based on self-assembled nanowire-molecular wire heterostructures. These devices exploit a novel concept of using redox-active molecules as charge storage flash nodes for nanowire transistors, and thus boast many advantages such as room-temperature processing and nanoscale device area. Various key elements of this technology will be reviewed, including the synthesis of the nanowires and molecular wires, and fabrication and characterization of the molecular memory devices. In particular, multilevel memory has been demonstrated using In2O3 nanowires with self-assembled Fe-bis(terpyridine) molecules, which serve to multiple the charge storage density without increasing the device size. Furthermore, in-depth studies on memory devices made with different molecules or with different functionalization techniques will be reviewed and analyzed. These devices represent a conceptual breakthrough in molecular memory and may work as building blocks for future beyond-CMOS nanoelectronic circuits.

  20. Progress in Multi-Center Probabilistic Wave Forecasting and Ensemble-Based Data Assimilation using LETKF at the US National Weather Service

    NASA Astrophysics Data System (ADS)

    Alves, Jose-Henrique; Bernier, Natacha; Etala, Paula; Wittmann, Paul

    2015-04-01

    The combination of ensemble predictions of Hs made by the US National Weather Service (NEW) and the US Navy Fleet Numerical Meteorological and Oceanography Center (FNMOC) has established the NFCENS, a probabilistic wave forecast system in operations at NCEP since 2011. Computed from 41 combined wave ensemble members, the new product outperforms deterministic and probabilistic forecasts and nowcasts of Hs issued separately at each forecast center, at all forecast ranges. The successful implementation of the NFCENS has brought new opportunities for collaboration with Environment Canada (EC). EC is in the process of adding new global wave model ensemble products to its existing suite of operational regional products. The planned upgrade to the current NFCENS wave multi-center ensemble includes the addition of 20 members from the Canadian WES. With this upgrade, the NFCENS will be renamed North American Wave Ensemble System (NAWES). As part of the new system implementation, new higher-resolution grids and upgrades to model physics using recent advances in source-term parameterizations are being tested. We provide results of a first validation of NAWES relative to global altimeter data, and buoy measurements of waves, as well as its ability to forecast waves during the 2012 North Atlantic hurricane Sandy. A second line of research involving wave ensembles at the NWS is the implementation of a LETKF-based data assimilation system developed in collaboration with the Argentinian Navy Meteorological Service. The project involves an implementation of the 4D-LETKF in the NWS global wave ensemble forecast system GWES. The 4-D scheme initializes a full 81-member ensemble in a 6-hour cycle. The LETKF determines the analysis ensemble locally in the space spanned by the ensemble, as a linear combination of the background perturbations. Observations from three altimeters and one scatterometer were used. Preliminary results for a prototype system running at the NWS, including

  1. Cardiopulmonary Resuscitation Pattern Evaluation Based on Ensemble Empirical Mode Decomposition Filter via Nonlinear Approaches

    PubMed Central

    Ma, Matthew Huei-Ming

    2016-01-01

    Good quality cardiopulmonary resuscitation (CPR) is the mainstay of treatment for managing patients with out-of-hospital cardiac arrest (OHCA). Assessment of the quality of the CPR delivered is now possible through the electrocardiography (ECG) signal that can be collected by an automated external defibrillator (AED). This study evaluates a nonlinear approximation of the CPR given to the asystole patients. The raw ECG signal is filtered using ensemble empirical mode decomposition (EEMD), and the CPR-related intrinsic mode functions (IMF) are chosen to be evaluated. In addition, sample entropy (SE), complexity index (CI), and detrended fluctuation algorithm (DFA) are collated and statistical analysis is performed using ANOVA. The primary outcome measure assessed is the patient survival rate after two hours. CPR pattern of 951 asystole patients was analyzed for quality of CPR delivered. There was no significant difference observed in the CPR-related IMFs peak-to-peak interval analysis for patients who are younger or older than 60 years of age, similarly to the amplitude difference evaluation for SE and DFA. However, there is a difference noted for the CI (p < 0.05). The results show that patients group younger than 60 years have higher survival rate with high complexity of the CPR-IMFs amplitude differences. PMID:27529068

  2. Cardiopulmonary Resuscitation Pattern Evaluation Based on Ensemble Empirical Mode Decomposition Filter via Nonlinear Approaches.

    PubMed

    Sadrawi, Muammar; Sun, Wei-Zen; Ma, Matthew Huei-Ming; Dai, Chun-Yi; Abbod, Maysam F; Shieh, Jiann-Shing

    2016-01-01

    Good quality cardiopulmonary resuscitation (CPR) is the mainstay of treatment for managing patients with out-of-hospital cardiac arrest (OHCA). Assessment of the quality of the CPR delivered is now possible through the electrocardiography (ECG) signal that can be collected by an automated external defibrillator (AED). This study evaluates a nonlinear approximation of the CPR given to the asystole patients. The raw ECG signal is filtered using ensemble empirical mode decomposition (EEMD), and the CPR-related intrinsic mode functions (IMF) are chosen to be evaluated. In addition, sample entropy (SE), complexity index (CI), and detrended fluctuation algorithm (DFA) are collated and statistical analysis is performed using ANOVA. The primary outcome measure assessed is the patient survival rate after two hours. CPR pattern of 951 asystole patients was analyzed for quality of CPR delivered. There was no significant difference observed in the CPR-related IMFs peak-to-peak interval analysis for patients who are younger or older than 60 years of age, similarly to the amplitude difference evaluation for SE and DFA. However, there is a difference noted for the CI (p < 0.05). The results show that patients group younger than 60 years have higher survival rate with high complexity of the CPR-IMFs amplitude differences. PMID:27529068

  3. Efficient ensemble system based on the copper binding motif for highly sensitive and selective detection of cyanide ions in 100% aqueous solutions by fluorescent and colorimetric changes.

    PubMed

    Jung, Kwan Ho; Lee, Keun-Hyeung

    2015-09-15

    A peptide-based ensemble for the detection of cyanide ions in 100% aqueous solutions was designed on the basis of the copper binding motif. 7-Nitro-2,1,3-benzoxadiazole-labeled tripeptide (NBD-SSH, NBD-SerSerHis) formed the ensemble with Cu(2+), leading to a change in the color of the solution from yellow to orange and a complete decrease of fluorescence emission. The ensemble (NBD-SSH-Cu(2+)) sensitively and selectively detected a low concentration of cyanide ions in 100% aqueous solutions by a colorimetric change as well as a fluorescent change. The addition of cyanide ions instantly removed Cu(2+) from the ensemble (NBD-SSH-Cu(2+)) in 100% aqueous solutions, resulting in a color change of the solution from orange to yellow and a "turn-on" fluorescent response. The detection limits for cyanide ions were lower than the maximum allowable level of cyanide ions in drinking water set by the World Health Organization. The peptide-based ensemble system is expected to be a potential and practical way for the detection of submicromolar concentrations of cyanide ions in 100% aqueous solutions.

  4. Future changes to drought characteristics over the Canadian Prairie Provinces based on NARCCAP multi-RCM ensemble

    NASA Astrophysics Data System (ADS)

    Masud, M. B.; Khaliq, M. N.; Wheater, H. S.

    2016-06-01

    This study assesses projected changes to drought characteristics in Alberta, Saskatchewan and Manitoba, the prairie provinces of Canada, using a multi-regional climate model (RCM) ensemble available through the North American Regional Climate Change Assessment Program. Simulations considered include those performed with six RCMs driven by National Center for Environmental Prediction reanalysis II for the 1981-2003 period and those driven by four Atmosphere-Ocean General Circulation Models for the 1970-1999 and 2041-2070 periods (i.e. eleven current and the same number of corresponding future period simulations). Drought characteristics are extracted using two drought indices, namely the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI). Regional frequency analysis is used to project changes to selected 20- and 50-year regional return levels of drought characteristics for fifteen homogeneous regions, covering the study area. In addition, multivariate analyses of drought characteristics, derived on the basis of 6-month SPI and SPEI values, are developed using the copula approach for each region. Analysis of multi-RCM ensemble-averaged projected changes to mean and selected return levels of drought characteristics show increases over the southern and south-western parts of the study area. Based on bi- and trivariate joint occurrence probabilities of drought characteristics, the southern regions along with the central regions are found highly drought vulnerable, followed by the southwestern and southeastern regions. Compared to the SPI-based analysis, the results based on SPEI suggest drier conditions over many regions in the future, indicating potential effects of rising temperatures on drought risks. These projections will be useful in the development of appropriate adaptation strategies for the water and agricultural sectors, which play an important role in the economy of the study area.

  5. Probabilistic Assessment of Meteorological Drought Risk over the Canadian Prairie Provinces Based on the NARCCAP multi-RCM Ensemble

    NASA Astrophysics Data System (ADS)

    Masud, M. B.; Khaliq, N.; Wheater, H. S.; Zilefac, E.

    2015-12-01

    This study assesses projected changes to drought characteristics in Alberta, Saskatchewan and Manitoba, the prairie provinces of Canada using a multi-Regional Climate Model (RCM) ensemble available through the North American Regional Climate Change Assessment Program. Simulations considered include those performed with six RCMs for the 1981-2003 period driven by National Centre for Environmental Prediction reanalysis II and by four Atmosphere-Ocean General Circulation Models for the 1970-1999 and 2041-2070 periods (11 current-to-future period simulation pairs). Drought characteristics are extracted using two drought indices, namely Standardized Precipitation Index (SPI), which is solely based on precipitation, and the Standardized Precipitation Evapotranspiration Index (SPEI), which is based on both precipitation and temperature in the form of evapotranspiration. Regional frequency analysis is used to project changes to selected 20- and 50-yr regional return levels of drought for fifteen homogeneous regions. In addition, multivariate analyses of drought characteristics, derived on the basis of SPI and SPEI values of six month time scale, are developed using the copula approach for each region. Results reveal that analysis of multi-RCM ensemble-averaged projected changes to drought characteristics and various return levels of drought characteristics show increases over the southern, western and eastern parts of the study area. Based on bi- and trivariate joint occurrence probabilities of drought characteristics, the southern regions along with the central regions are found highly drought vulnerable, followed by southwestern and southeastern regions. These projections will be useful in the development of appropriate adaptation strategies for the water and agricultural sectors, which play an important role in the economy of the study area.

  6. Interference-based molecular transistors

    PubMed Central

    Li, Ying; Mol, Jan A.; Benjamin, Simon C.; Briggs, G. Andrew D.

    2016-01-01

    Molecular transistors have the potential for switching with lower gate voltages than conventional field-effect transistors. We have calculated the performance of a single-molecule device in which there is interference between electron transport through the highest occupied molecular orbital and the lowest unoccupied molecular orbital of a single molecule. Quantum interference results in a subthreshold slope that is independent of temperature. For realistic parameters the change in gate potential required for a change in source-drain current of two decades is 20 mV, which is a factor of six smaller than the theoretical limit for a metal-oxide-semiconductor field-effect transistor. PMID:27646692

  7. Interference-based molecular transistors.

    PubMed

    Li, Ying; Mol, Jan A; Benjamin, Simon C; Briggs, G Andrew D

    2016-01-01

    Molecular transistors have the potential for switching with lower gate voltages than conventional field-effect transistors. We have calculated the performance of a single-molecule device in which there is interference between electron transport through the highest occupied molecular orbital and the lowest unoccupied molecular orbital of a single molecule. Quantum interference results in a subthreshold slope that is independent of temperature. For realistic parameters the change in gate potential required for a change in source-drain current of two decades is 20 mV, which is a factor of six smaller than the theoretical limit for a metal-oxide-semiconductor field-effect transistor. PMID:27646692

  8. Analysis of Vibration and Noise of Construction Machinery Based on Ensemble Empirical Mode Decomposition and Spectral Correlation Analysis Method

    NASA Astrophysics Data System (ADS)

    Chen, Yuebiao; Zhou, Yiqi; Yu, Gang; Lu, Dan

    In order to analyze the effect of engine vibration on cab noise of construction machinery in multi-frequency bands, a new method based on ensemble empirical mode decomposition (EEMD) and spectral correlation analysis is proposed. Firstly, the intrinsic mode functions (IMFs) of vibration and noise signals were obtained by EEMD method, and then the IMFs which have the same frequency bands were selected. Secondly, we calculated the spectral correlation coefficients between the selected IMFs, getting the main frequency bands in which engine vibration has significant impact on cab noise. Thirdly, the dominated frequencies were picked out and analyzed by spectral analysis method. The study result shows that the main frequency bands and dominated frequencies in which engine vibration have serious impact on cab noise can be identified effectively by the proposed method, which provides effective guidance to noise reduction of construction machinery.

  9. [Molecular bases of prion diseases].

    PubMed

    Pokrovskiĭ, V I; Kiselev, O I

    1998-01-01

    The paper briefly analyzes the origin of priones and their association with the cellular gene and homologous protein of diseases in man and animals. There is evidence for a direct relationship of the agents that cause spongious encephalitis in the cattle and a new type of Creutzfeldt-Jacob disease in man. The molecular organization of priones and the conformational cellular protein changes underlying the infectious activation of the cell homologue of priones. Emphasis is first laid on the capacity of the cell homologue of priones and their infectiously active derivative to bind to DNA or RNA. In the context of concepts of the priones yeasts an attempt was made to explain the reproduction through the altered control of translation of mRNA that encodes the cellular homologue of priones, which accounts for the duration of the incubation period of the disease. The infections caused by priones are referred to as the so-called slow infections. But in the context of the proposed hypothesis, an infective process in the tissues did not really have some typical signs of infection and resembles accumulation diseases more without the replicative burst typical of infectious processes. The paper gives data on the vital cycle of priones in infected animals and changes in the accumulation of an infective agent. This assesses the currently available diagnostic methods and gives preference to the methods which will be based on the use of monoclonal antibodies that specifically recognize the conformationally altered form of an infectious prione or on the identification of primary oligomeric forms which manifest the onset of amyloidization of the damaged tissues. The main conclusion of the paper is that protein prionization is a common biological phenomenon and the diseases caused by these processes will increase in number in the near future, which makes it necessary to develop diagnostic methods and universal treatments of diseases, such as bacterial infections by using antibiotics.

  10. Ensemble Empirical Mode Decomposition based methodology for ultrasonic testing of coarse grain austenitic stainless steels.

    PubMed

    Sharma, Govind K; Kumar, Anish; Jayakumar, T; Purnachandra Rao, B; Mariyappa, N

    2015-03-01

    A signal processing methodology is proposed in this paper for effective reconstruction of ultrasonic signals in coarse grained high scattering austenitic stainless steel. The proposed methodology is comprised of the Ensemble Empirical Mode Decomposition (EEMD) processing of ultrasonic signals and application of signal minimisation algorithm on selected Intrinsic Mode Functions (IMFs) obtained by EEMD. The methodology is applied to ultrasonic signals obtained from austenitic stainless steel specimens of different grain size, with and without defects. The influence of probe frequency and data length of a signal on EEMD decomposition is also investigated. For a particular sampling rate and probe frequency, the same range of IMFs can be used to reconstruct the ultrasonic signal, irrespective of the grain size in the range of 30-210 μm investigated in this study. This methodology is successfully employed for detection of defects in a 50mm thick coarse grain austenitic stainless steel specimens. Signal to noise ratio improvement of better than 15 dB is observed for the ultrasonic signal obtained from a 25 mm deep flat bottom hole in 200 μm grain size specimen. For ultrasonic signals obtained from defects at different depths, a minimum of 7 dB extra enhancement in SNR is achieved as compared to the sum of selected IMF approach. The application of minimisation algorithm with EEMD processed signal in the proposed methodology proves to be effective for adaptive signal reconstruction with improved signal to noise ratio. This methodology was further employed for successful imaging of defects in a B-scan.

  11. A Multiobjective Genetic Programming-Based Ensemble for Simultaneous Feature Selection and Classification.

    PubMed

    Nag, Kaustuv; Pal, Nikhil R

    2016-02-01

    We present an integrated algorithm for simultaneous feature selection (FS) and designing of diverse classifiers using a steady state multiobjective genetic programming (GP), which minimizes three objectives: 1) false positives (FPs); 2) false negatives (FNs); and 3) the number of leaf nodes in the tree. Our method divides a c -class problem into c binary classification problems. It evolves c sets of genetic programs to create c ensembles. During mutation operation, our method exploits the fitness as well as unfitness of features, which dynamically change with generations with a view to using a set of highly relevant features with low redundancy. The classifiers of i th class determine the net belongingness of an unknown data point to the i th class using a weighted voting scheme, which makes use of the FP and FN mistakes made on the training data. We test our method on eight microarray and 11 text data sets with diverse number of classes (from 2 to 44), large number of features (from 2000 to 49 151), and high feature-to-sample ratio (from 1.03 to 273.1). We compare our method with a bi-objective GP scheme that does not use any FS and rule size reduction strategy. It depicts the effectiveness of the proposed FS and rule size reduction schemes. Furthermore, we compare our method with four classification methods in conjunction with six features selection algorithms and full feature set. Our scheme performs the best for 380 out of 474 combinations of data sets, algorithm and FS method.

  12. Gold price analysis based on ensemble empirical model decomposition and independent component analysis

    NASA Astrophysics Data System (ADS)

    Xian, Lu; He, Kaijian; Lai, Kin Keung

    2016-07-01

    In recent years, the increasing level of volatility of the gold price has received the increasing level of attention from the academia and industry alike. Due to the complexity and significant fluctuations observed in the gold market, however, most of current approaches have failed to produce robust and consistent modeling and forecasting results. Ensemble Empirical Model Decomposition (EEMD) and Independent Component Analysis (ICA) are novel data analysis methods that can deal with nonlinear and non-stationary time series. This study introduces a new methodology which combines the two methods and applies it to gold price analysis. This includes three steps: firstly, the original gold price series is decomposed into several Intrinsic Mode Functions (IMFs) by EEMD. Secondly, IMFs are further processed with unimportant ones re-grouped. Then a new set of data called Virtual Intrinsic Mode Functions (VIMFs) is reconstructed. Finally, ICA is used to decompose VIMFs into statistically Independent Components (ICs). The decomposition results reveal that the gold price series can be represented by the linear combination of ICs. Furthermore, the economic meanings of ICs are analyzed and discussed in detail, according to the change trend and ICs' transformation coefficients. The analyses not only explain the inner driving factors and their impacts but also conduct in-depth analysis on how these factors affect gold price. At the same time, regression analysis has been conducted to verify our analysis. Results from the empirical studies in the gold markets show that the EEMD-ICA serve as an effective technique for gold price analysis from a new perspective.

  13. An ensemble-based reanalysis approach for estimating river bathymetry from the upcoming SWOT mission

    NASA Astrophysics Data System (ADS)

    Yoon, Y.; Durand, M. T.; Merry, C. J.; Clark, E.; Alsdorf, D. E.

    2011-12-01

    In spite of the critical role of river discharge in land surface hydrology, global gauging networks are sparse and even have been in decline. Over the past decade, researchers have been trying to better estimate river discharge using remote sensing techniques to complement the existing in-situ gage networks. The upcoming Surface Water and Ocean Topography (SWOT) mission will directly provide simultaneous spatial mapping of inundation area (A) and inland water surface elevation (WSE) data (i.e., river, lakes, wetlands, and reservoirs), both temporally (dh/dt) and spatially (dh/dx), with the Ka-band Radar INterferometer (KaRIN). With these observations, the SWOT mission will provide the measurements of water storage changes in terrestrial surface water bodies. However, because the SWOT will measure WSE, not the true depth to the river bottom, the cross section channel bathymetry will not be fully measured. Thus, estimating bathymetry is important in order to produce accurate estimates of river discharge from the SWOT data. In previous work, a local ensemble Kalman filter (LEnKF) was used to estimate the river bathymetry, given synthetic SWOT observations and WSE predictions by the LISFLOOD-FP hydrodynamic model. However, the accuracy of river bathymetry was highly affected by the severe bias of boundary inflows due to the mathematical relationship for the assimilation. The bias in model is not accounted for the data assimilation. Here, we focus on correcting the forecast bias for the LEnKF scheme to result in the improvement of river bathymetry estimates. To correct the forecast bias and improve the accuracy, we combined the LEnKF scheme with continuity and momentum equations. To evaluate the reanalysis approach, the error of bathymetry was evaluated by comparing with the true value and previous work. In addition, we examined the sensitivity to the bathymetry estimate for estimating the river discharge.

  14. Three-dimensional visualization of ensemble weather forecasts - Part 2: Forecasting warm conveyor belt situations for aircraft-based field campaigns

    NASA Astrophysics Data System (ADS)

    Rautenhaus, M.; Grams, C. M.; Schäfler, A.; Westermann, R.

    2015-07-01

    We present the application of interactive three-dimensional (3-D) visualization of ensemble weather predictions to forecasting warm conveyor belt situations during aircraft-based atmospheric research campaigns. Motivated by forecast requirements of the T-NAWDEX-Falcon 2012 (THORPEX - North Atlantic Waveguide and Downstream Impact Experiment) campaign, a method to predict 3-D probabilities of the spatial occurrence of warm conveyor belts (WCBs) has been developed. Probabilities are derived from Lagrangian particle trajectories computed on the forecast wind fields of the European Centre for Medium Range Weather Forecasts (ECMWF) ensemble prediction system. Integration of the method into the 3-D ensemble visualization tool Met.3D, introduced in the first part of this study, facilitates interactive visualization of WCB features and derived probabilities in the context of the ECMWF ensemble forecast. We investigate the sensitivity of the method with respect to trajectory seeding and grid spacing of the forecast wind field. Furthermore, we propose a visual analysis method to quantitatively analyse the contribution of ensemble members to a probability region and, thus, to assist the forecaster in interpreting the obtained probabilities. A case study, revisiting a forecast case from T-NAWDEX-Falcon, illustrates the practical application of Met.3D and demonstrates the use of 3-D and uncertainty visualization for weather forecasting and for planning flight routes in the medium forecast range (3 to 7 days before take-off).

  15. The origin of emissive states of carbon nanoparticles derived from ensemble-averaged and single-molecular studies.

    PubMed

    Demchenko, Alexander P; Dekaliuk, Mariia O

    2016-08-01

    At present, there is no consensus understanding on the origin of photoluminescence of carbon nanoparticles, particularly the so-called carbon dots. Providing comparative analysis of spectroscopic studies in solution and on a single-molecular level, we demonstrate that these particles behave collectively as fixed single dipoles and probably are the quantum emitter entities. Their spectral and lifetime heterogeneity in solutions is explained by variation of the local chemical environment within and around luminescence centers. Hence, the carbon dots possess a unique hybrid combination of fluorescence properties peculiar to dye molecules, their conjugates and semiconductor nanocrystals. It is proposed that their optical properties are due to generation of H-aggregate-type excitonic states with their coherence spreading over the whole nanoparticles.

  16. The origin of emissive states of carbon nanoparticles derived from ensemble-averaged and single-molecular studies

    NASA Astrophysics Data System (ADS)

    Demchenko, Alexander P.; Dekaliuk, Mariia O.

    2016-07-01

    At present, there is no consensus understanding on the origin of photoluminescence of carbon nanoparticles, particularly the so-called carbon dots. Providing comparative analysis of spectroscopic studies in solution and on a single-molecular level, we demonstrate that these particles behave collectively as fixed single dipoles and probably are the quantum emitter entities. Their spectral and lifetime heterogeneity in solutions is explained by variation of the local chemical environment within and around luminescence centers. Hence, the carbon dots possess a unique hybrid combination of fluorescence properties peculiar to dye molecules, their conjugates and semiconductor nanocrystals. It is proposed that their optical properties are due to generation of H-aggregate-type excitonic states with their coherence spreading over the whole nanoparticles.

  17. ENBFS+kNN: Hybrid ensemble classifier using entropy-based naïve Bayes with feature selection and k-nearest neighbor

    NASA Astrophysics Data System (ADS)

    Sainin, Mohd Shamrie; Alfred, Rayner; Ahmad, Faudziah

    2016-08-01

    A hybrid ensemble classifier which combines the entropy based naive Bayes (ENB) classifier strategy and k-nearest neighbor (k-NN) is examined. The classifiers are joined in light of the fact that naive Bayes gives prior estimations taking into account entropy while k-NN gives neighborhood estimate to model for a deferred characterization. While original NB utilizes the probabilities, this study utilizes the entropy as priors for class estimations. The result of the hybrid ensemble classifier demonstrates that by consolidating the classifiers, the proposed technique accomplishes promising execution on several benchmark datasets.

  18. A mapping of an ensemble of mitochondrial sequences for various organisms into 3D space based on the word composition.

    PubMed

    Aita, Takuyo; Nishigaki, Koichi

    2012-11-01

    To visualize a bird's-eye view of an ensemble of mitochondrial genome sequences for various species, we recently developed a novel method of mapping a biological sequence ensemble into Three-Dimensional (3D) vector space. First, we represented a biological sequence of a species s by a word-composition vector x(s), where its length [absolute value]x(s)[absolute value] represents the sequence length, and its unit vector x(s)/[absolute value]x(s)[absolute value] represents the relative composition of the K-tuple words through the sequence and the size of the dimension, N=4(K), is the number of all possible words with the length of K. Second, we mapped the vector x(s) to the 3D position vector y(s), based on the two following simple principles: (1) [absolute value]y(s)[absolute value]=[absolute value]x(s)[absolute value] and (2) the angle between y(s) and y(t) maximally correlates with the angle between x(s) and x(t). The mitochondrial genome sequences for 311 species, including 177 Animalia, 85 Fungi and 49 Green plants, were mapped into 3D space by using K=7. The mapping was successful because the angles between vectors before and after the mapping highly correlated with each other (correlation coefficients were 0.92-0.97). Interestingly, the Animalia kingdom is distributed along a single arc belt (just like the Milky Way on a Celestial Globe), and the Fungi and Green plant kingdoms are distributed in a similar arc belt. These two arc belts intersect at their respective middle regions and form a cross structure just like a jet aircraft fuselage and its wings. This new mapping method will allow researchers to intuitively interpret the visual information presented in the maps in a highly effective manner.

  19. A mapping of an ensemble of mitochondrial sequences for various organisms into 3D space based on the word composition.

    PubMed

    Aita, Takuyo; Nishigaki, Koichi

    2012-11-01

    To visualize a bird's-eye view of an ensemble of mitochondrial genome sequences for various species, we recently developed a novel method of mapping a biological sequence ensemble into Three-Dimensional (3D) vector space. First, we represented a biological sequence of a species s by a word-composition vector x(s), where its length [absolute value]x(s)[absolute value] represents the sequence length, and its unit vector x(s)/[absolute value]x(s)[absolute value] represents the relative composition of the K-tuple words through the sequence and the size of the dimension, N=4(K), is the number of all possible words with the length of K. Second, we mapped the vector x(s) to the 3D position vector y(s), based on the two following simple principles: (1) [absolute value]y(s)[absolute value]=[absolute value]x(s)[absolute value] and (2) the angle between y(s) and y(t) maximally correlates with the angle between x(s) and x(t). The mitochondrial genome sequences for 311 species, including 177 Animalia, 85 Fungi and 49 Green plants, were mapped into 3D space by using K=7. The mapping was successful because the angles between vectors before and after the mapping highly correlated with each other (correlation coefficients were 0.92-0.97). Interestingly, the Animalia kingdom is distributed along a single arc belt (just like the Milky Way on a Celestial Globe), and the Fungi and Green plant kingdoms are distributed in a similar arc belt. These two arc belts intersect at their respective middle regions and form a cross structure just like a jet aircraft fuselage and its wings. This new mapping method will allow researchers to intuitively interpret the visual information presented in the maps in a highly effective manner. PMID:22776549

  20. Quantifying the Usefulness of Ensemble-Based Precipitation Forecasts with Respect to Water Use and Yield during a Field Trial

    NASA Astrophysics Data System (ADS)

    Christ, E.; Webster, P. J.; Collins, G.; Byrd, S.

    2014-12-01

    Recent droughts and the continuing water wars between the states of Georgia, Alabama and Florida have made agricultural producers more aware of the importance of managing their irrigation systems more efficiently. Many southeastern states are beginning to consider laws that will require monitoring and regulation of water used for irrigation. Recently, Georgia suspended issuing irrigation permits in some areas of the southwestern portion of the state to try and limit the amount of water being used in irrigation. However, even in southern Georgia, which receives on average between 23 and 33 inches of rain during the growing season, irrigation can significantly impact crop yields. In fact, studies have shown that when fields do not receive rainfall at the most critical stages in the life of cotton, yield for irrigated fields can be up to twice as much as fields for non-irrigated cotton. This leads to the motivation for this study, which is to produce a forecast tool that will enable producers to make more efficient irrigation management decisions. We will use the ECMWF (European Centre for Medium-Range Weather Forecasts) vars EPS (Ensemble Prediction System) model precipitation forecasts for the grid points included in the 1◦ x 1◦ lat/lon square surrounding the point of interest. We will then apply q-to-q bias corrections to the forecasts. Once we have applied the bias corrections, we will use the check-book method of irrigation scheduling to determine the probability of receiving the required amount of rainfall for each week of the growing season. These forecasts will be used during a field trial conducted at the CM Stripling Irrigation Research Park in Camilla, Georgia. This research will compare differences in yield and water use among the standard checkbook method of irrigation, which uses no precipitation forecast knowledge, the weather.com forecast, a dry land plot, and the ensemble-based forecasts mentioned above.

  1. Molecular simulation of aqueous electrolyte solubility. 2. Osmotic ensemble Monte Carlo methodology for free energy and solubility calculations and application to NaCl.

    PubMed

    Moučka, Filip; Lísal, Martin; Škvor, Jiří; Jirsák, Jan; Nezbeda, Ivo; Smith, William R

    2011-06-23

    We present a new and computationally efficient methodology using osmotic ensemble Monte Carlo (OEMC) simulation to calculate chemical potential-concentration curves and the solubility of aqueous electrolytes. The method avoids calculations for the solid phase, incorporating readily available data from thermochemical tables that are based on well-defined reference states. It performs simulations of the aqueous solution at a fixed number of water molecules, pressure, temperature, and specified overall electrolyte chemical potential. Insertion/deletion of ions to/from the system is implemented using fractional ions, which are coupled to the system via a coupling parameter λ that varies between 0 (no interaction between the fractional ions and the other particles in the system) and 1 (full interaction between the fractional ions and the other particles of the system). Transitions between λ-states are accepted with a probability following from the osmotic ensemble partition function. Biasing weights associated with the λ-states are used in order to efficiently realize transitions between them; these are determined by means of the Wang-Landau method. We also propose a novel scaling procedure for λ, which can be used for both nonpolarizable and polarizable models of aqueous electrolyte systems. The approach is readily extended to involve other solvents, multiple electrolytes, and species complexation reactions. The method is illustrated for NaCl, using SPC/E water and several force field models for NaCl from the literature, and the results are compared with experiment at ambient conditions. Good agreement is obtained for the chemical potential-concentration curve and the solubility prediction is reasonable. Future improvements to the predictions will require improved force field models.

  2. Gold nanoclusters-Cu(2+) ensemble-based fluorescence turn-on and real-time assay for acetylcholinesterase activity and inhibitor screening.

    PubMed

    Sun, Jian; Yang, Xiurong

    2015-12-15

    Based on the specific binding of Cu(2+) ions to the 11-mercaptoundecanoic acid (11-MUA)-protected AuNCs with intense orange-red emission, we have proposed and constructed a novel fluorescent nanomaterials-metal ions ensemble at a nonfluorescence off-state. Subsequently, an AuNCs@11-MUA-Cu(2+) ensemble-based fluorescent chemosensor, which is amenable to convenient, sensitive, selective, turn-on and real-time assay of acetylcholinesterase (AChE), could be developed by using acetylthiocholine (ATCh) as the substrate. Herein, the sensing ensemble solution exhibits a marvelous fluorescent enhancement in the presence of AChE and ATCh, where AChE hydrolyzes its active substrate ATCh into thiocholine (TCh), and then TCh captures Cu(2+) from the ensemble, accompanied by the conversion from fluorescence off-state to on-state of the AuNCs. The AChE activity could be detected less than 0.05 mU/mL within a good linear range from 0.05 to 2.5 mU/mL. Our proposed fluorescence assay can be utilized to evaluate the AChE activity quantitatively in real biological sample, and furthermore to screen the inhibitor of AChE. As far as we know, the present study has reported the first analytical proposal for sensing AChE activity in real time by using a fluorescent nanomaterials-Cu(2+) ensemble or focusing on the Cu(2+)-triggered fluorescence quenching/recovery. This strategy paves a new avenue for exploring the biosensing applications of fluorescent AuNCs, and presents the prospect of AuNCs@11-MUA-Cu(2+) ensemble as versatile enzyme activity assay platforms by means of other appropriate substrates/analytes. PMID:26141104

  3. A comparison of breeding and ensemble transform vectors for global ensemble generation

    NASA Astrophysics Data System (ADS)

    Deng, Guo; Tian, Hua; Li, Xiaoli; Chen, Jing; Gong, Jiandong; Jiao, Meiyan

    2012-02-01

    To compare the initial perturbation techniques using breeding vectors and ensemble transform vectors, three ensemble prediction systems using both initial perturbation methods but with different ensemble member sizes based on the spectral model T213/L31 are constructed at the National Meteorological Center, China Meteorological Administration (NMC/CMA). A series of ensemble verification scores such as forecast skill of the ensemble mean, ensemble resolution, and ensemble reliability are introduced to identify the most important attributes of ensemble forecast systems. The results indicate that the ensemble transform technique is superior to the breeding vector method in light of the evaluation of anomaly correlation coefficient (ACC), which is a deterministic character of the ensemble mean, the root-mean-square error (RMSE) and spread, which are of probabilistic attributes, and the continuous ranked probability score (CRPS) and its decomposition. The advantage of the ensemble transform approach is attributed to its orthogonality among ensemble perturbations as well as its consistence with the data assimilation system. Therefore, this study may serve as a reference for configuration of the best ensemble prediction system to be used in operation.

  4. Climate Change and Regional Agricultural Production Risk in China: A New Super-ensemble-based Probabilistic Projection

    NASA Astrophysics Data System (ADS)

    Tao, F.

    2010-05-01

    A warming trend has become pronounced since the 1980s and is projected to accelerate in the future. Concerns about the vulnerability of agricultural production to climate change are increasing. However estimates of climate change impacts are plague with uncertainties from many physical, biological, and social-economic processes. Among the urgent research priorities, more comprehensive assessments of impacts that better represent the uncertainties are needed. Here, we develop a new super-ensemble-based probabilistic projection system to account for the uncertainties from CO2 emission scenarios, climate change scenarios, and biophysical processes in impact assessment model. We demonstrate the system in addressing the probabilistic changes of maize production in the North China Plain in future. The new process-based general crop model, MCWLA [Tao, F., Yokozawa, M. Zhang, Z., 2009. Modelling the impacts of weather and climate variability on crop productivity over a large area: a new process-based model development, optimization, and uncertainties analysis. Agric. For. Meteorol. 149, 831-850], is used. MCWLA accounts for the key impact mechanisms of climate variability and is accurate over a large area. We use 10 climate scenarios consisting of the combinations of five GCMs and two emission scenarios, the corresponding atmospheric CO2 concentration range, and 60 sets of crop model parameters derived using the Bayesian probability inversion and a Markov chain Monte Carlo (MCMC) technique, representing the biophysical uncertainties from crop models. The resulting probability distributions indicate expected yield changes of -9.7% to -9.1%, -19.0% to -15.7%, and -25.5% to -24.7%, during 2020s, 2050s, and 2080s, respectively. We also investigate the temporal and spatial pattern of changes and variability in maize yield across the region. Besides the new findings on the probabilistic changes of maize productivity in the North China Plain, our study demonstrated an advanced

  5. Niobate-based octahedral molecular sieves

    DOEpatents

    Nenoff, Tina M.; Nyman, May D.

    2006-10-17

    Niobate-based octahedral molecular sieves having significant activity for multivalent cations and a method for synthesizing such sieves are disclosed. The sieves have a net negatively charged octahedral framework, comprising niobium, oxygen, and octahedrally coordinated lower valence transition metals. The framework can be charge balanced by the occluded alkali cation from the synthesis method. The alkali cation can be exchanged for other contaminant metal ions. The ion-exchanged niobate-based octahedral molecular sieve can be backexchanged in acidic solutions to yield a solution concentrated in the contaminant metal. Alternatively, the ion-exchanged niobate-based octahedral molecular sieve can be thermally converted to a durable perovskite phase waste form.

  6. Niobate-based octahedral molecular sieves

    DOEpatents

    Nenoff, Tina M.; Nyman, May D.

    2003-07-22

    Niobate-based octahedral molecular sieves having significant activity for multivalent cations and a method for synthesizing such sieves are disclosed. The sieves have a net negatively charged octahedral framework, comprising niobium, oxygen, and octahedrally coordinated lower valence transition metals. The framework can be charge balanced by the occluded alkali cation from the synthesis method. The alkali cation can be exchanged for other contaminant metal ions. The ion-exchanged niobate-based octahedral molecular sieve can be backexchanged in acidic solutions to yield a solution concentrated in the contaminant metal. Alternatively, the ion-exchanged niobate-based octahedral molecular sieve can be thermally converted to a durable perovskite phase waste form.

  7. The Protein Ensemble Database.

    PubMed

    Varadi, Mihaly; Tompa, Peter

    2015-01-01

    The scientific community's major conceptual notion of structural biology has recently shifted in emphasis from the classical structure-function paradigm due to the emergence of intrinsically disordered proteins (IDPs). As opposed to their folded cousins, these proteins are defined by the lack of a stable 3D fold and a high degree of inherent structural heterogeneity that is closely tied to their function. Due to their flexible nature, solution techniques such as small-angle X-ray scattering (SAXS), nuclear magnetic resonance (NMR) spectroscopy and fluorescence resonance energy transfer (FRET) are particularly well-suited for characterizing their biophysical properties. Computationally derived structural ensembles based on such experimental measurements provide models of the conformational sampling displayed by these proteins, and they may offer valuable insights into the functional consequences of inherent flexibility. The Protein Ensemble Database (http://pedb.vib.be) is the first openly accessible, manually curated online resource storing the ensemble models, protocols used during the calculation procedure, and underlying primary experimental data derived from SAXS and/or NMR measurements. By making this previously inaccessible data freely available to researchers, this novel resource is expected to promote the development of more advanced modelling methodologies, facilitate the design of standardized calculation protocols, and consequently lead to a better understanding of how function arises from the disordered state.

  8. A method for extracting human gait series from accelerometer signals based on the ensemble empirical mode decomposition

    NASA Astrophysics Data System (ADS)

    Fu, Mao-Jing; Zhuang, Jian-Jun; Hou, Feng-Zhen; Zhan, Qing-Bo; Shao, Yi; Ning, Xin-Bao

    2010-05-01

    In this paper, the ensemble empirical mode decomposition (EEMD) is applied to analyse accelerometer signals collected during normal human walking. First, the self-adaptive feature of EEMD is utilised to decompose the accelerometer signals, thus sifting out several intrinsic mode functions (IMFs) at disparate scales. Then, gait series can be extracted through peak detection from the eigen IMF that best represents gait rhythmicity. Compared with the method based on the empirical mode decomposition (EMD), the EEMD-based method has the following advantages: it remarkably improves the detection rate of peak values hidden in the original accelerometer signal, even when the signal is severely contaminated by the intermittent noises; this method effectively prevents the phenomenon of mode mixing found in the process of EMD. And a reasonable selection of parameters for the stop-filtering criteria can improve the calculation speed of the EEMD-based method. Meanwhile, the endpoint effect can be suppressed by using the auto regressive and moving average model to extend a short-time series in dual directions. The results suggest that EEMD is a powerful tool for extraction of gait rhythmicity and it also provides valuable clues for extracting eigen rhythm of other physiological signals.

  9. Prediction of conversion from mild cognitive impairment to Alzheimer disease based on bayesian data mining with ensemble learning.

    PubMed

    Chen, R; Young, K; Chao, L L; Miller, B; Yaffe, K; Weiner, M W; Herskovits, E H

    2012-03-01

    Prediction of disease progress is of great importance to Alzheimer disease (AD) researchers and clinicians. Previous attempts at constructing predictive models have been hindered by undersampling, and restriction to linear associations among variables, among other problems. To address these problems, we propose a novel Bayesian data-mining method called Bayesian Outcome Prediction with Ensemble Learning (BOPEL). BOPEL uses a Bayesian-network representation with boosting, to allow the detection of nonlinear multivariate associations, and incorporates resampling-based feature selection to prevent over-fitting caused by undersampling. We demonstrate the use of this approach in predicting conversion to AD in individuals with mild cognitive impairment (MCI), based on structural magnetic-resonance and magnetic-resonance- spectroscopy data. This study includes 26 subjects with amnestic MCI: the converter group (n = 8) met MCI criteria at baseline, but converted to AD within five years, whereas the non-converter group (n = 18) met MCI criteria at baseline and at follow-up. We found that BOPEL accurately differentiates MCI converters from non-converters, based on the baseline volumes of the left hippocampus, the banks of the right superior temporal sulcus, the right entorhinal cortex, the left lingual gyrus, and the rostral aspect of the left middle frontal gyrus. Prediction accuracy was 0.81, sensitivity was 0.63 and specificity was 0.89. We validated the generated predictive model with an independent data set constructed from the Alzheimer Disease Neuroimaging Initiative database, and again found high predictive accuracy (0.75).

  10. Emotion Recognition of Weblog Sentences Based on an Ensemble Algorithm of Multi-label Classification and Word Emotions

    NASA Astrophysics Data System (ADS)

    Li, Ji; Ren, Fuji

    Weblogs have greatly changed the communication ways of mankind. Affective analysis of blog posts is found valuable for many applications such as text-to-speech synthesis or computer-assisted recommendation. Traditional emotion recognition in text based on single-label classification can not satisfy higher requirements of affective computing. In this paper, the automatic identification of sentence emotion in weblogs is modeled as a multi-label text categorization task. Experiments are carried out on 12273 blog sentences from the Chinese emotion corpus Ren_CECps with 8-dimension emotion annotation. An ensemble algorithm RAKEL is used to recognize dominant emotions from the writer's perspective. Our emotion feature using detailed intensity representation for word emotions outperforms the other main features such as the word frequency feature and the traditional lexicon-based feature. In order to deal with relatively complex sentences, we integrate grammatical characteristics of punctuations, disjunctive connectives, modification relations and negation into features. It achieves 13.51% and 12.49% increases for Micro-averaged F1 and Macro-averaged F1 respectively compared to the traditional lexicon-based feature. Result shows that multiple-dimension emotion representation with grammatical features can efficiently classify sentence emotion in a multi-label problem.

  11. One-day-ahead streamflow forecasting via super-ensembles of several neural network architectures based on the Multi-Level Diversity Model

    NASA Astrophysics Data System (ADS)

    Brochero, Darwin; Hajji, Islem; Pina, Jasson; Plana, Queralt; Sylvain, Jean-Daniel; Vergeynst, Jenna; Anctil, Francois

    2015-04-01

    Theories about generalization error with ensembles are mainly based on the diversity concept, which promotes resorting to many members of different properties to support mutually agreeable decisions. Kuncheva (2004) proposed the Multi Level Diversity Model (MLDM) to promote diversity in model ensembles, combining different data subsets, input subsets, models, parameters, and including a combiner level in order to optimize the final ensemble. This work tests the hypothesis about the minimisation of the generalization error with ensembles of Neural Network (NN) structures. We used the MLDM to evaluate two different scenarios: (i) ensembles from a same NN architecture, and (ii) a super-ensemble built by a combination of sub-ensembles of many NN architectures. The time series used correspond to the 12 basins of the MOdel Parameter Estimation eXperiment (MOPEX) project that were used by Duan et al. (2006) and Vos (2013) as benchmark. Six architectures are evaluated: FeedForward NN (FFNN) trained with the Levenberg Marquardt algorithm (Hagan et al., 1996), FFNN trained with SCE (Duan et al., 1993), Recurrent NN trained with a complex method (Weins et al., 2008), Dynamic NARX NN (Leontaritis and Billings, 1985), Echo State Network (ESN), and leak integrator neuron (L-ESN) (Lukosevicius and Jaeger, 2009). Each architecture performs separately an Input Variable Selection (IVS) according to a forward stepwise selection (Anctil et al., 2009) using mean square error as objective function. Post-processing by Predictor Stepwise Selection (PSS) of the super-ensemble has been done following the method proposed by Brochero et al. (2011). IVS results showed that the lagged stream flow, lagged precipitation, and Standardized Precipitation Index (SPI) (McKee et al., 1993) were the most relevant variables. They were respectively selected as one of the firsts three selected variables in 66, 45, and 28 of the 72 scenarios. A relationship between aridity index (Arora, 2002) and NN

  12. One-day-ahead streamflow forecasting via super-ensembles of several neural network architectures based on the Multi-Level Diversity Model

    NASA Astrophysics Data System (ADS)

    Brochero, Darwin; Hajji, Islem; Pina, Jasson; Plana, Queralt; Sylvain, Jean-Daniel; Vergeynst, Jenna; Anctil, Francois

    2015-04-01

    Theories about generalization error with ensembles are mainly based on the diversity concept, which promotes resorting to many members of different properties to support mutually agreeable decisions. Kuncheva (2004) proposed the Multi Level Diversity Model (MLDM) to promote diversity in model ensembles, combining different data subsets, input subsets, models, parameters, and including a combiner level in order to optimize the final ensemble. This work tests the hypothesis about the minimisation of the generalization error with ensembles of Neural Network (NN) structures. We used the MLDM to evaluate two different scenarios: (i) ensembles from a same NN architecture, and (ii) a super-ensemble built by a combination of sub-ensembles of many NN architectures. The time series used correspond to the 12 basins of the MOdel Parameter Estimation eXperiment (MOPEX) project that were used by Duan et al. (2006) and Vos (2013) as benchmark. Six architectures are evaluated: FeedForward NN (FFNN) trained with the Levenberg Marquardt algorithm (Hagan et al., 1996), FFNN trained with SCE (Duan et al., 1993), Recurrent NN trained with a complex method (Weins et al., 2008), Dynamic NARX NN (Leontaritis and Billings, 1985), Echo State Network (ESN), and leak integrator neuron (L-ESN) (Lukosevicius and Jaeger, 2009). Each architecture performs separately an Input Variable Selection (IVS) according to a forward stepwise selection (Anctil et al., 2009) using mean square error as objective function. Post-processing by Predictor Stepwise Selection (PSS) of the super-ensemble has been done following the method proposed by Brochero et al. (2011). IVS results showed that the lagged stream flow, lagged precipitation, and Standardized Precipitation Index (SPI) (McKee et al., 1993) were the most relevant variables. They were respectively selected as one of the firsts three selected variables in 66, 45, and 28 of the 72 scenarios. A relationship between aridity index (Arora, 2002) and NN

  13. Coordination-Cluster-Based Molecular Magnetic Refrigerants.

    PubMed

    Zhang, Shaowei; Cheng, Peng

    2016-08-01

    Coordination polymers serving as molecular magnetic refrigerants have been attracting great interest. In particular, coordination cluster compounds that demonstrate their apparent advantages on cryogenic magnetic refrigerants have attracted more attention in the last five years. Herein, we mainly focus on depicting aspects of syntheses, structures, and magnetothermal properties of coordination clusters that serve as magnetic refrigerants on account of the magnetocaloric effect. The documented molecular magnetic refrigerants are classified into two primary categories according to the types of metal centers, namely, homo- and heterometallic clusters. Every section is further divided into several subgroups based on the metal nuclearity and their dimensionalities, including discrete molecular clusters and those with extended structures constructed from molecular clusters. The objective is to present a rough overview of recent progress in coordination-cluster-based molecular magnetic refrigerants and provide a tutorial for researchers who are interested in the field. PMID:27381662

  14. FRET-based Molecular Tension Microscopy.

    PubMed

    Gayrard, Charlène; Borghi, Nicolas

    2016-02-01

    Cells generate and experience mechanical forces that may shape tissues and regulate signaling pathways in a variety of physiological or pathological situations. How forces propagate and transduce signals at the molecular level is poorly understood. The advent of FRET-based Molecular Tension Microscopy now allows to achieve mechanical force measurements at a molecular scale with molecular specificity in situ, and thereby better understand the mechanical architecture of cells and tissues, and mechanotransduction pathways. In this review, we will first expose the basic principles of FRET-based MTM and its various incarnations. We will describe different ways of measuring FRET, their advantages and drawbacks. Then, throughout the range of proteins of interest, cells and organisms to which it has been applied, we will review the tests developed to validate the approach, how molecular tension was related to cell functions, and conclude with possible developments and offshoots.

  15. [Research on ECG de-noising method based on ensemble empirical mode decomposition and wavelet transform using improved threshold function].

    PubMed

    Ye, Linlin; Yang, Dan; Wang, Xu

    2014-06-01

    A de-noising method for electrocardiogram (ECG) based on ensemble empirical mode decomposition (EEMD) and wavelet threshold de-noising theory is proposed in our school. We decomposed noised ECG signals with the proposed method using the EEMD and calculated a series of intrinsic mode functions (IMFs). Then we selected IMFs and reconstructed them to realize the de-noising for ECG. The processed ECG signals were filtered again with wavelet transform using improved threshold function. In the experiments, MIT-BIH ECG database was used for evaluating the performance of the proposed method, contrasting with de-noising method based on EEMD and wavelet transform with improved threshold function alone in parameters of signal to noise ratio (SNR) and mean square error (MSE). The results showed that the ECG waveforms de-noised with the proposed method were smooth and the amplitudes of ECG features did not attenuate. In conclusion, the method discussed in this paper can realize the ECG denoising and meanwhile keep the characteristics of original ECG signal. PMID:25219236

  16. A Compound fault diagnosis for rolling bearings method based on blind source separation and ensemble empirical mode decomposition.

    PubMed

    Wang, Huaqing; Li, Ruitong; Tang, Gang; Yuan, Hongfang; Zhao, Qingliang; Cao, Xi

    2014-01-01

    A Compound fault signal usually contains multiple characteristic signals and strong confusion noise, which makes it difficult to separate week fault signals from them through conventional ways, such as FFT-based envelope detection, wavelet transform or empirical mode decomposition individually. In order to improve the compound faults diagnose of rolling bearings via signals' separation, the present paper proposes a new method to identify compound faults from measured mixed-signals, which is based on ensemble empirical mode decomposition (EEMD) method and independent component analysis (ICA) technique. With the approach, a vibration signal is firstly decomposed into intrinsic mode functions (IMF) by EEMD method to obtain multichannel signals. Then, according to a cross correlation criterion, the corresponding IMF is selected as the input matrix of ICA. Finally, the compound faults can be separated effectively by executing ICA method, which makes the fault features more easily extracted and more clearly identified. Experimental results validate the effectiveness of the proposed method in compound fault separating, which works not only for the outer race defect, but also for the rollers defect and the unbalance fault of the experimental system. PMID:25289644

  17. [Research on ECG de-noising method based on ensemble empirical mode decomposition and wavelet transform using improved threshold function].

    PubMed

    Ye, Linlin; Yang, Dan; Wang, Xu

    2014-06-01

    A de-noising method for electrocardiogram (ECG) based on ensemble empirical mode decomposition (EEMD) and wavelet threshold de-noising theory is proposed in our school. We decomposed noised ECG signals with the proposed method using the EEMD and calculated a series of intrinsic mode functions (IMFs). Then we selected IMFs and reconstructed them to realize the de-noising for ECG. The processed ECG signals were filtered again with wavelet transform using improved threshold function. In the experiments, MIT-BIH ECG database was used for evaluating the performance of the proposed method, contrasting with de-noising method based on EEMD and wavelet transform with improved threshold function alone in parameters of signal to noise ratio (SNR) and mean square error (MSE). The results showed that the ECG waveforms de-noised with the proposed method were smooth and the amplitudes of ECG features did not attenuate. In conclusion, the method discussed in this paper can realize the ECG denoising and meanwhile keep the characteristics of original ECG signal.

  18. [MicroRNA Target Prediction Based on Support Vector Machine Ensemble Classification Algorithm of Under-sampling Technique].

    PubMed

    Chen, Zhiru; Hong, Wenxue

    2016-02-01

    Considering the low accuracy of prediction in the positive samples and poor overall classification effects caused by unbalanced sample data of MicroRNA (miRNA) target, we proposes a support vector machine (SVM)-integration of under-sampling and weight (IUSM) algorithm in this paper, an under-sampling based on the ensemble learning algorithm. The algorithm adopts SVM as learning algorithm and AdaBoost as integration framework, and embeds clustering-based under-sampling into the iterative process, aiming at reducing the degree of unbalanced distribution of positive and negative samples. Meanwhile, in the process of adaptive weight adjustment of the samples, the SVM-IUSM algorithm eliminates the abnormal ones in negative samples with robust sample weights smoothing mechanism so as to avoid over-learning. Finally, the prediction of miRNA target integrated classifier is achieved with the combination of multiple weak classifiers through the voting mechanism. The experiment revealed that the SVM-IUSW, compared with other algorithms on unbalanced dataset collection, could not only improve the accuracy of positive targets and the overall effect of classification, but also enhance the generalization ability of miRNA target classifier.

  19. A Clustered Multiclass Likelihood-Ratio Ensemble Method for Family-Based Association Analysis Accounting for Phenotypic Heterogeneity.

    PubMed

    Wen, Yalu; Lu, Qing

    2016-09-01

    Although compelling evidence suggests that the genetic etiology of complex diseases could be heterogeneous in subphenotype groups, little attention has been paid to phenotypic heterogeneity in genetic association analysis of complex diseases. Simply ignoring phenotypic heterogeneity in association analysis could result in attenuated estimates of genetic effects and low power of association tests if subphenotypes with similar clinical manifestations have heterogeneous underlying genetic etiologies. To facilitate the family-based association analysis allowing for phenotypic heterogeneity, we propose a clustered multiclass likelihood-ratio ensemble (CMLRE) method. The proposed method provides an alternative way to model the complex relationship between disease outcomes and genetic variants. It allows for heterogeneous genetic causes of disease subphenotypes and can be applied to various pedigree structures. Through simulations, we found CMLRE outperformed the commonly adopted strategies in a variety of underlying disease scenarios. We further applied CMLRE to a family-based dataset from the International Consortium to Identify Genes and Interactions Controlling Oral Clefts (ICOC) to investigate the genetic variants and interactions predisposing to subphenotypes of oral clefts. The analysis suggested that two subphenotypes, nonsyndromic cleft lip without palate (CL) and cleft lip with palate (CLP), shared similar genetic etiologies, while cleft palate only (CP) had its own genetic mechanism. The analysis further revealed that rs10863790 (IRF6), rs7017252 (8q24), and rs7078160 (VAX1) were jointly associated with CL/CLP, while rs7969932 (TBK1), rs227731 (17q22), and rs2141765 (TBK1) jointly contributed to CP. PMID:27321816

  20. [MicroRNA Target Prediction Based on Support Vector Machine Ensemble Classification Algorithm of Under-sampling Technique].

    PubMed

    Chen, Zhiru; Hong, Wenxue

    2016-02-01

    Considering the low accuracy of prediction in the positive samples and poor overall classification effects caused by unbalanced sample data of MicroRNA (miRNA) target, we proposes a support vector machine (SVM)-integration of under-sampling and weight (IUSM) algorithm in this paper, an under-sampling based on the ensemble learning algorithm. The algorithm adopts SVM as learning algorithm and AdaBoost as integration framework, and embeds clustering-based under-sampling into the iterative process, aiming at reducing the degree of unbalanced distribution of positive and negative samples. Meanwhile, in the process of adaptive weight adjustment of the samples, the SVM-IUSM algorithm eliminates the abnormal ones in negative samples with robust sample weights smoothing mechanism so as to avoid over-learning. Finally, the prediction of miRNA target integrated classifier is achieved with the combination of multiple weak classifiers through the voting mechanism. The experiment revealed that the SVM-IUSW, compared with other algorithms on unbalanced dataset collection, could not only improve the accuracy of positive targets and the overall effect of classification, but also enhance the generalization ability of miRNA target classifier. PMID:27382743

  1. A Cutting Pattern Recognition Method for Shearers Based on Improved Ensemble Empirical Mode Decomposition and a Probabilistic Neural Network

    PubMed Central

    Xu, Jing; Wang, Zhongbin; Tan, Chao; Si, Lei; Liu, Xinhua

    2015-01-01

    In order to guarantee the stable operation of shearers and promote construction of an automatic coal mining working face, an online cutting pattern recognition method with high accuracy and speed based on Improved Ensemble Empirical Mode Decomposition (IEEMD) and Probabilistic Neural Network (PNN) is proposed. An industrial microphone is installed on the shearer and the cutting sound is collected as the recognition criterion to overcome the disadvantages of giant size, contact measurement and low identification rate of traditional detectors. To avoid end-point effects and get rid of undesirable intrinsic mode function (IMF) components in the initial signal, IEEMD is conducted on the sound. The end-point continuation based on the practical storage data is performed first to overcome the end-point effect. Next the average correlation coefficient, which is calculated by the correlation of the first IMF with others, is introduced to select essential IMFs. Then the energy and standard deviation of the reminder IMFs are extracted as features and PNN is applied to classify the cutting patterns. Finally, a simulation example, with an accuracy of 92.67%, and an industrial application prove the efficiency and correctness of the proposed method. PMID:26528985

  2. A Clustered Multiclass Likelihood-Ratio Ensemble Method for Family-Based Association Analysis Accounting for Phenotypic Heterogeneity.

    PubMed

    Wen, Yalu; Lu, Qing

    2016-09-01

    Although compelling evidence suggests that the genetic etiology of complex diseases could be heterogeneous in subphenotype groups, little attention has been paid to phenotypic heterogeneity in genetic association analysis of complex diseases. Simply ignoring phenotypic heterogeneity in association analysis could result in attenuated estimates of genetic effects and low power of association tests if subphenotypes with similar clinical manifestations have heterogeneous underlying genetic etiologies. To facilitate the family-based association analysis allowing for phenotypic heterogeneity, we propose a clustered multiclass likelihood-ratio ensemble (CMLRE) method. The proposed method provides an alternative way to model the complex relationship between disease outcomes and genetic variants. It allows for heterogeneous genetic causes of disease subphenotypes and can be applied to various pedigree structures. Through simulations, we found CMLRE outperformed the commonly adopted strategies in a variety of underlying disease scenarios. We further applied CMLRE to a family-based dataset from the International Consortium to Identify Genes and Interactions Controlling Oral Clefts (ICOC) to investigate the genetic variants and interactions predisposing to subphenotypes of oral clefts. The analysis suggested that two subphenotypes, nonsyndromic cleft lip without palate (CL) and cleft lip with palate (CLP), shared similar genetic etiologies, while cleft palate only (CP) had its own genetic mechanism. The analysis further revealed that rs10863790 (IRF6), rs7017252 (8q24), and rs7078160 (VAX1) were jointly associated with CL/CLP, while rs7969932 (TBK1), rs227731 (17q22), and rs2141765 (TBK1) jointly contributed to CP.

  3. Self-assembly of [3]catenanes and a [4]molecular necklace based on a cryptand/paraquat recognition motif.

    PubMed

    Ye, Yang; Wang, Shu-Ping; Zhu, Bin; Cook, Timothy R; Wu, Jing; Li, Shijun; Stang, Peter J

    2015-06-01

    Hierarchical self-assembly centered on metallacyclic scaffolds greatly facilitates the construction of mechanically interlocked structures. The formation of two [3]catenanes and one [4]molecular necklace is presented by utilizing the orthogonality of coordination-driven self-assembly and crown ether-based cryptand/paraquat derivative complexation. The threaded [3]catenanes and [4]molecular necklace were fabricated by using ten and nine total molecular components, respectively, from four and three unique species in solution, respectively. In all cases single supramolecular ensembles were obtained, attesting to the high degree of structural complexity made possible via self-assembly approaches. PMID:25996900

  4. Self-assembly of [3]catenanes and a [4]molecular necklace based on a cryptand/paraquat recognition motif.

    PubMed

    Ye, Yang; Wang, Shu-Ping; Zhu, Bin; Cook, Timothy R; Wu, Jing; Li, Shijun; Stang, Peter J

    2015-06-01

    Hierarchical self-assembly centered on metallacyclic scaffolds greatly facilitates the construction of mechanically interlocked structures. The formation of two [3]catenanes and one [4]molecular necklace is presented by utilizing the orthogonality of coordination-driven self-assembly and crown ether-based cryptand/paraquat derivative complexation. The threaded [3]catenanes and [4]molecular necklace were fabricated by using ten and nine total molecular components, respectively, from four and three unique species in solution, respectively. In all cases single supramolecular ensembles were obtained, attesting to the high degree of structural complexity made possible via self-assembly approaches.

  5. ENCORE: Software for Quantitative Ensemble Comparison.

    PubMed

    Tiberti, Matteo; Papaleo, Elena; Bengtsen, Tone; Boomsma, Wouter; Lindorff-Larsen, Kresten

    2015-10-01

    There is increasing evidence that protein dynamics and conformational changes can play an important role in modulating biological function. As a result, experimental and computational methods are being developed, often synergistically, to study the dynamical heterogeneity of a protein or other macromolecules in solution. Thus, methods such as molecular dynamics simulations or ensemble refinement approaches have provided conformational ensembles that can be used to understand protein function and biophysics. These developments have in turn created a need for algorithms and software that can be used to compare structural ensembles in the same way as the root-mean-square-deviation is often used to compare static structures. Although a few such approaches have been proposed, these can be difficult to implement efficiently, hindering a broader applications and further developments. Here, we present an easily accessible software toolkit, called ENCORE, which can be used to compare conformational ensembles generated either from simulations alone or synergistically with experiments. ENCORE implements three previously described methods for ensemble comparison, that each can be used to quantify the similarity between conformational ensembles by estimating the overlap between the probability distributions that underlie them. We demonstrate the kinds of insights that can be obtained by providing examples of three typical use-cases: comparing ensembles generated with different molecular force fields, assessing convergence in molecular simulations, and calculating differences and similarities in structural ensembles refined with various sources of experimental data. We also demonstrate efficient computational scaling for typical analyses, and robustness against both the size and sampling of the ensembles. ENCORE is freely available and extendable, integrates with the established MDAnalysis software package, reads ensemble data in many common formats, and can work with large

  6. ENCORE: Software for Quantitative Ensemble Comparison

    PubMed Central

    Tiberti, Matteo; Papaleo, Elena; Bengtsen, Tone; Boomsma, Wouter; Lindorff-Larsen, Kresten

    2015-01-01

    There is increasing evidence that protein dynamics and conformational changes can play an important role in modulating biological function. As a result, experimental and computational methods are being developed, often synergistically, to study the dynamical heterogeneity of a protein or other macromolecules in solution. Thus, methods such as molecular dynamics simulations or ensemble refinement approaches have provided conformational ensembles that can be used to understand protein function and biophysics. These developments have in turn created a need for algorithms and software that can be used to compare structural ensembles in the same way as the root-mean-square-deviation is often used to compare static structures. Although a few such approaches have been proposed, these can be difficult to implement efficiently, hindering a broader applications and further developments. Here, we present an easily accessible software toolkit, called ENCORE, which can be used to compare conformational ensembles generated either from simulations alone or synergistically with experiments. ENCORE implements three previously described methods for ensemble comparison, that each can be used to quantify the similarity between conformational ensembles by estimating the overlap between the probability distributions that underlie them. We demonstrate the kinds of insights that can be obtained by providing examples of three typical use-cases: comparing ensembles generated with different molecular force fields, assessing convergence in molecular simulations, and calculating differences and similarities in structural ensembles refined with various sources of experimental data. We also demonstrate efficient computational scaling for typical analyses, and robustness against both the size and sampling of the ensembles. ENCORE is freely available and extendable, integrates with the established MDAnalysis software package, reads ensemble data in many common formats, and can work with large

  7. ENCORE: Software for Quantitative Ensemble Comparison.

    PubMed

    Tiberti, Matteo; Papaleo, Elena; Bengtsen, Tone; Boomsma, Wouter; Lindorff-Larsen, Kresten

    2015-10-01

    There is increasing evidence that protein dynamics and conformational changes can play an important role in modulating biological function. As a result, experimental and computational methods are being developed, often synergistically, to study the dynamical heterogeneity of a protein or other macromolecules in solution. Thus, methods such as molecular dynamics simulations or ensemble refinement approaches have provided conformational ensembles that can be used to understand protein function and biophysics. These developments have in turn created a need for algorithms and software that can be used to compare structural ensembles in the same way as the root-mean-square-deviation is often used to compare static structures. Although a few such approaches have been proposed, these can be difficult to implement efficiently, hindering a broader applications and further developments. Here, we present an easily accessible software toolkit, called ENCORE, which can be used to compare conformational ensembles generated either from simulations alone or synergistically with experiments. ENCORE implements three previously described methods for ensemble comparison, that each can be used to quantify the similarity between conformational ensembles by estimating the overlap between the probability distributions that underlie them. We demonstrate the kinds of insights that can be obtained by providing examples of three typical use-cases: comparing ensembles generated with different molecular force fields, assessing convergence in molecular simulations, and calculating differences and similarities in structural ensembles refined with various sources of experimental data. We also demonstrate efficient computational scaling for typical analyses, and robustness against both the size and sampling of the ensembles. ENCORE is freely available and extendable, integrates with the established MDAnalysis software package, reads ensemble data in many common formats, and can work with large

  8. Evaluating Model Performance of an Ensemble-based Chemical Data Assimilation System During INTEX-B Field Mission

    NASA Technical Reports Server (NTRS)

    Arellano, A. F., Jr.; Raeder, K.; Anderson, J. L.; Hess, P. G.; Emmons, L. K.; Edwards, D. P.; Pfister, G. G.; Campos, T. L.; Sachse, G. W.

    2007-01-01

    We present a global chemical data assimilation system using a global atmosphere model, the Community Atmosphere Model (CAM3) with simplified chemistry and the Data Assimilation Research Testbed (DART) assimilation package. DART is a community software facility for assimilation studies using the ensemble Kalman filter approach. Here, we apply the assimilation system to constrain global tropospheric carbon monoxide (CO) by assimilating meteorological observations of temperature and horizontal wind velocity and satellite CO retrievals from the Measurement of Pollution in the Troposphere (MOPITT) satellite instrument. We verify the system performance using independent CO observations taken on board the NSFINCAR C-130 and NASA DC-8 aircrafts during the April 2006 part of the Intercontinental Chemical Transport Experiment (INTEX-B). Our evaluations show that MOPITT data assimilation provides significant improvements in terms of capturing the observed CO variability relative to no MOPITT assimilation (i.e. the correlation improves from 0.62 to 0.71, significant at 99% confidence). The assimilation provides evidence of median CO loading of about 150 ppbv at 700 hPa over the NE Pacific during April 2006. This is marginally higher than the modeled CO with no MOPITT assimilation (-140 ppbv). Our ensemble-based estimates of model uncertainty also show model overprediction over the source region (i.e. China) and underprediction over the NE Pacific, suggesting model errors that cannot be readily explained by emissions alone. These results have important implications for improving regional chemical forecasts and for inverse modeling of CO sources and further demonstrate the utility of the assimilation system in comparing non-coincident measurements, e.g. comparing satellite retrievals of CO with in-situ aircraft measurements. The work described above also brought to light several short-comings of the data assimilation approach for CO profiles. Because of the limited vertical

  9. Simulation of future groundwater recharge using a climate model ensemble and SAR-image based soil parameter distributions - A case study in an intensively-used Mediterranean catchment.

    PubMed

    Herrmann, Frank; Baghdadi, Nicolas; Blaschek, Michael; Deidda, Roberto; Duttmann, Rainer; La Jeunesse, Isabelle; Sellami, Haykel; Vereecken, Harry; Wendland, Frank

    2016-02-01

    We used observed climate data, an ensemble of four GCM-RCM combinations (global and regional climate models) and the water balance model mGROWA to estimate present and future groundwater recharge for the intensively-used Thau lagoon catchment in southern France. In addition to a highly resolved soil map, soil moisture distributions obtained from SAR-images (Synthetic Aperture Radar) were used to derive the spatial distribution of soil parameters covering the full simulation domain. Doing so helped us to assess the impact of different soil parameter sources on the modelled groundwater recharge levels. Groundwater recharge was simulated in monthly time steps using the ensemble approach and analysed in its spatial and temporal variability. The soil parameters originating from both sources led to very similar groundwater recharge rates, proving that soil parameters derived from SAR images may replace traditionally used soil maps in regions where soil maps are sparse or missing. Additionally, we showed that the variance in different GCM-RCMs influences the projected magnitude of future groundwater recharge change significantly more than the variance in the soil parameter distributions derived from the two different sources. For the period between 1950 and 2100, climate change impacts based on the climate model ensemble indicated that overall groundwater recharge will possibly show a low to moderate decrease in the Thau catchment. However, as no clear trend resulted from the ensemble simulations, reliable recommendations for adapting the regional groundwater management to changed available groundwater volumes could not be derived.

  10. Simulation of future groundwater recharge using a climate model ensemble and SAR-image based soil parameter distributions - A case study in an intensively-used Mediterranean catchment.

    PubMed

    Herrmann, Frank; Baghdadi, Nicolas; Blaschek, Michael; Deidda, Roberto; Duttmann, Rainer; La Jeunesse, Isabelle; Sellami, Haykel; Vereecken, Harry; Wendland, Frank

    2016-02-01

    We used observed climate data, an ensemble of four GCM-RCM combinations (global and regional climate models) and the water balance model mGROWA to estimate present and future groundwater recharge for the intensively-used Thau lagoon catchment in southern France. In addition to a highly resolved soil map, soil moisture distributions obtained from SAR-images (Synthetic Aperture Radar) were used to derive the spatial distribution of soil parameters covering the full simulation domain. Doing so helped us to assess the impact of different soil parameter sources on the modelled groundwater recharge levels. Groundwater recharge was simulated in monthly time steps using the ensemble approach and analysed in its spatial and temporal variability. The soil parameters originating from both sources led to very similar groundwater recharge rates, proving that soil parameters derived from SAR images may replace traditionally used soil maps in regions where soil maps are sparse or missing. Additionally, we showed that the variance in different GCM-RCMs influences the projected magnitude of future groundwater recharge change significantly more than the variance in the soil parameter distributions derived from the two different sources. For the period between 1950 and 2100, climate change impacts based on the climate model ensemble indicated that overall groundwater recharge will possibly show a low to moderate decrease in the Thau catchment. However, as no clear trend resulted from the ensemble simulations, reliable recommendations for adapting the regional groundwater management to changed available groundwater volumes could not be derived. PMID:26190446

  11. Exploring Ensemble Visualization

    PubMed Central

    Phadke, Madhura N.; Pinto, Lifford; Alabi, Femi; Harter, Jonathan; Taylor, Russell M.; Wu, Xunlei; Petersen, Hannah; Bass, Steffen A.; Healey, Christopher G.

    2012-01-01

    An ensemble is a collection of related datasets. Each dataset, or member, of an ensemble is normally large, multidimensional, and spatio-temporal. Ensembles are used extensively by scientists and mathematicians, for example, by executing a simulation repeatedly with slightly different input parameters and saving the results in an ensemble to see how parameter choices affect the simulation. To draw inferences from an ensemble, scientists need to compare data both within and between ensemble members. We propose two techniques to support ensemble exploration and comparison: a pairwise sequential animation method that visualizes locally neighboring members simultaneously, and a screen door tinting method that visualizes subsets of members using screen space subdivision. We demonstrate the capabilities of both techniques, first using synthetic data, then with simulation data of heavy ion collisions in high-energy physics. Results show that both techniques are capable of supporting meaningful comparisons of ensemble data. PMID:22347540

  12. Risk assessment of agricultural water requirement based on a multi-model ensemble framework, southwest of Iran

    NASA Astrophysics Data System (ADS)

    Zamani, Reza; Akhond-Ali, Ali-Mohammad; Roozbahani, Abbas; Fattahi, Rouhollah

    2016-06-01

    Water shortage and climate change are the most important issues of sustainable agricultural and water resources development. Given the importance of water availability in crop production, the present study focused on risk assessment of climate change impact on agricultural water requirement in southwest of Iran, under two emission scenarios (A2 and B1) for the future period (2025-2054). A multi-model ensemble framework based on mean observed temperature-precipitation (MOTP) method and a combined probabilistic approach Long Ashton Research Station-Weather Generator (LARS-WG) and change factor (CF) have been used for downscaling to manage the uncertainty of outputs of 14 general circulation models (GCMs). The results showed an increasing temperature in all months and irregular changes of precipitation (either increasing or decreasing) in the future period. In addition, the results of the calculated annual net water requirement for all crops affected by climate change indicated an increase between 4 and 10 %. Furthermore, an increasing process is also expected regarding to the required water demand volume. The most and the least expected increase in the water demand volume is about 13 and 5 % for A2 and B1 scenarios, respectively. Considering the results and the limited water resources in the study area, it is crucial to provide water resources planning in order to reduce the negative effects of climate change. Therefore, the adaptation scenarios with the climate change related to crop pattern and water consumption should be taken into account.

  13. Multi-fault diagnosis for rolling element bearings based on ensemble empirical mode decomposition and optimized support vector machines

    NASA Astrophysics Data System (ADS)

    Zhang, Xiaoyuan; Zhou, Jianzhong

    2013-12-01

    This study presents a novel procedure based on ensemble empirical mode decomposition (EEMD) and optimized support vector machine (SVM) for multi-fault diagnosis of rolling element bearings. The vibration signal is adaptively decomposed into a number of intrinsic mode functions (IMFs) by EEMD. Two types of features, the EEMD energy entropy and singular values of the matrix whose rows are IMFs, are extracted. EEMD energy entropy is used to specify whether the bearing has faults or not. If the bearing has faults, singular values are input to multi-class SVM optimized by inter-cluster distance in the feature space (ICDSVM) to specify the fault type. The proposed method was tested on a system with an electric motor which has two rolling bearings with 8 normal working conditions and 48 fault working conditions. Five groups of experiments were done to evaluate the effectiveness of the proposed method. The results show that the proposed method outperforms other methods both mentioned in this paper and published in other literatures.

  14. Modification of input datasets for the Ensemble Streamflow Prediction based on large-scale climatic indices and weather generator

    NASA Astrophysics Data System (ADS)

    Šípek, Václav; Daňhelka, Jan

    2015-09-01

    Ensemble Streamflow Prediction (ESP) provides an efficient tool for seasonal hydrological forecasts. In this study, we propose a new modification of input data series for the ESP system used for the runoff volume prediction with a lead of one month. These series are not represented by short historical weather datasets but by longer generated synthetic weather data series. Before their submission to the hydrological model, their number is restricted by relations among observed meteorological variables (average monthly precipitation and temperature) and large-scale climatic patterns and indices (e.g. North Atlantic Oscillation, sea level pressure values and two geopotential heights). This modification was tested over a four-year testing period using the river basin in central Europe. The LARS-WG weather generator proved to be a suitable tool for the extension of the historical weather records. The modified ESP approach proved to be more efficient in the majority of months compared both to the original ESP method and reference forecast (based on probability distribution of historical discharges). The improvement over traditional ESP was most obvious in the narrower forecast interval of the expected runoff volume. The inefficient forecasts of the modified ESP scheme (compared to traditional ESP) were conditioned by an insufficient restriction of input synthetic weather datasets by the climate forecast.

  15. Development of polyvinylpyrrolidone-based spray-dried solid dispersions using response surface model and ensemble artificial neural network.

    PubMed

    Patel, Ashwinkumar D; Agrawal, Anjali; Dave, Rutesh H

    2013-06-01

    A model for spray drying processes was developed using polyvinylpyrrolidone (PVP)-K29/32 as a placebo formulation to predict quality attributes (process yield, outlet temperature, and particle size) for binary solid dispersions (SDs). The experiments were designed to achieve a better understanding of the spray drying process. The obtained powders were analyzed by modulated differential scanning calorimetry, thermogravimetric analysis, X-ray diffraction, polarized light microscopy, and particle size analysis. On the basis of the experimental data, a response surface model and an ensemble artificial neural network were developed. Both models showed significant correlation between experimental and predicted data for all quality attributes. In addition, a Pearson correlation analysis, response surface curves, Kohonen's self-organizing maps, and contribution plots were used to evaluate the effect of individual process parameters on quality attributes. The predictive abilities of both models were compared using separate validation datasets. These datasets contained binary SDs of four model drugs with PVP based on root mean square error and mean absolute error for each quality attribute. The results indicate that both models show reliable predictivity for all quality attributes. The present methodology provides a useful tool for designing a spray drying process, which will help formulation scientists save time, drug usage, and resources in the development of spray-dried SDs.

  16. Faults Diagnostics of Railway Axle Bearings Based on IMF’s Confidence Index Algorithm for Ensemble EMD

    PubMed Central

    Yi, Cai; Lin, Jianhui; Zhang, Weihua; Ding, Jianming

    2015-01-01

    As train loads and travel speeds have increased over time, railway axle bearings have become critical elements which require more efficient non-destructive inspection and fault diagnostics methods. This paper presents a novel and adaptive procedure based on ensemble empirical mode decomposition (EEMD) and Hilbert marginal spectrum for multi-fault diagnostics of axle bearings. EEMD overcomes the limitations that often hypothesize about data and computational efforts that restrict the application of signal processing techniques. The outputs of this adaptive approach are the intrinsic mode functions that are treated with the Hilbert transform in order to obtain the Hilbert instantaneous frequency spectrum and marginal spectrum. Anyhow, not all the IMFs obtained by the decomposition should be considered into Hilbert marginal spectrum. The IMFs’ confidence index arithmetic proposed in this paper is fully autonomous, overcoming the major limit of selection by user with experience, and allows the development of on-line tools. The effectiveness of the improvement is proven by the successful diagnosis of an axle bearing with a single fault or multiple composite faults, e.g., outer ring fault, cage fault and pin roller fault. PMID:25970256

  17. Molecular bases of protein halotolerance.

    PubMed

    Graziano, Giuseppe; Merlino, Antonello

    2014-04-01

    Halophilic proteins are stable and function at high salt concentration. Understanding how these molecules maintain their fold stable and avoid aggregation under harsh conditions is of great interest for biotechnological applications. This mini-review describes what is known about the molecular determinants of protein halotolerance. Comparisons between the sequences of halophilic/non-halophilic homologous protein pairs indicated that Asp and Glu are significantly more frequent, while Lys, Ile and Leu are less frequent in halophilic proteins. Homologous halophilic and non-halophilic proteins have similar overall structure, secondary structure content, and number of residues involved in the formation of H-bonds. On the other hand, on the halophilic protein surface, a decrease of nonpolar residues and an increase of charged residues are observed. Particularly, halophilic adaptation correlates with an increase of Asp and Glu, compensated by a decrease of basic residues, mainly Lys, on protein surface. A thermodynamic model, that provides a reliable explanation of the salt effect on the conformational stability of globular proteins, is presented.

  18. World Music Ensemble: Kulintang

    ERIC Educational Resources Information Center

    Beegle, Amy C.

    2012-01-01

    As instrumental world music ensembles such as steel pan, mariachi, gamelan and West African drums are becoming more the norm than the exception in North American school music programs, there are other world music ensembles just starting to gain popularity in particular parts of the United States. The kulintang ensemble, a drum and gong ensemble…

  19. Carbon-based ion and molecular channels

    NASA Astrophysics Data System (ADS)

    Sint, Kyaw; Wang, Boyang; Kral, Petr

    2008-03-01

    We design ion and molecular channels based on layered carboneous materials, with chemically-functionalized pore entrances. Our molecular dynamics simulations demonstrate that these ultra-narrow pores, with diameters around 1 nm, are highly selective to the charges and sizes of the passing (Na^+ and Cl^-) ions and short alkanes. We demonstrate that the molecular flows through these pores can be easily controlled by electrical and mechanical means. These artificial pores could be integrated in fluidic nanodevices and lab-on-a-chip techniques with numerous potential applications. [1] Kyaw Sint, Boyang Wang and Petr Kral, submitted. [2] Boyang Wang and Petr Kral, JACS 128, 15984 (2006).

  20. Physics-based protein structure refinement through multiple molecular dynamics trajectories and structure averaging.

    PubMed

    Mirjalili, Vahid; Noyes, Keenan; Feig, Michael

    2014-02-01

    We used molecular dynamics (MD) simulations for structure refinement of Critical Assessment of Techniques for Protein Structure Prediction 10 (CASP10) targets. Refinement was achieved by selecting structures from the MD-based ensembles followed by structural averaging. The overall performance of this method in CASP10 is described, and specific aspects are analyzed in detail to provide insight into key components. In particular, the use of different restraint types, sampling from multiple short simulations versus a single long simulation, the success of a quality assessment criterion, the application of scoring versus averaging, and the impact of a final refinement step are discussed in detail.

  1. An Ensemble Based Top Performing Approach for NCI-DREAM Drug Sensitivity Prediction Challenge

    PubMed Central

    Wan, Qian; Pal, Ranadip

    2014-01-01

    We consider the problem of predicting sensitivity of cancer cell lines to new drugs based on supervised learning on genomic profiles. The genetic and epigenetic characterization of a cell line provides observations on various aspects of regulation including DNA copy number variations, gene expression, DNA methylation and protein abundance. To extract relevant information from the various data types, we applied a random forest based approach to generate sensitivity predictions from each type of data and combined the predictions in a linear regression model to generate the final drug sensitivity prediction. Our approach when applied to the NCI-DREAM drug sensitivity prediction challenge was a top performer among 47 teams and produced high accuracy predictions. Our results show that the incorporation of multiple genomic characterizations lowered the mean and variance of the estimated bootstrap prediction error. We also applied our approach to the Cancer Cell Line Encyclopedia database for sensitivity prediction and the ability to extract the top targets of an anti-cancer drug. The results illustrate the effectiveness of our approach in predicting drug sensitivity from heterogeneous genomic datasets. PMID:24978814

  2. Ultrathin inorganic molecular nanowire based on polyoxometalates

    PubMed Central

    Zhang, Zhenxin; Murayama, Toru; Sadakane, Masahiro; Ariga, Hiroko; Yasuda, Nobuhiro; Sakaguchi, Norihito; Asakura, Kiyotaka; Ueda, Wataru

    2015-01-01

    The development of metal oxide-based molecular wires is important for fundamental research and potential practical applications. However, examples of these materials are rare. Here we report an all-inorganic transition metal oxide molecular wire prepared by disassembly of larger crystals. The wires are comprised of molybdenum(VI) with either tellurium(IV) or selenium(IV): {(NH4)2[XMo6O21]}n (X=tellurium(IV) or selenium(IV)). The ultrathin molecular nanowires with widths of 1.2 nm grow to micrometre-scale crystals and are characterized by single-crystal X-ray analysis, Rietveld analysis, scanning electron microscopy, X-ray photoelectron spectroscopy, ultraviolet–visible spectroscopy, thermal analysis and elemental analysis. The crystals can be disassembled into individual molecular wires through cation exchange and subsequent ultrasound treatment, as visualized by atomic force microscopy and transmission electron microscopy. The ultrathin molecular wire-based material exhibits high activity as an acid catalyst, and the band gap of the molecular wire-based crystal is tunable by heat treatment. PMID:26139011

  3. A two-stage method of quantitative flood risk analysis for reservoir real-time operation using ensemble-based hydrologic forecasts

    NASA Astrophysics Data System (ADS)

    Liu, P.

    2013-12-01

    Quantitative analysis of the risk for reservoir real-time operation is a hard task owing to the difficulty of accurate description of inflow uncertainties. The ensemble-based hydrologic forecasts directly depict the inflows not only the marginal distributions but also their persistence via scenarios. This motivates us to analyze the reservoir real-time operating risk with ensemble-based hydrologic forecasts as inputs. A method is developed by using the forecast horizon point to divide the future time into two stages, the forecast lead-time and the unpredicted time. The risk within the forecast lead-time is computed based on counting the failure number of forecast scenarios, and the risk in the unpredicted time is estimated using reservoir routing with the design floods and the reservoir water levels of forecast horizon point. As a result, a two-stage risk analysis method is set up to quantify the entire flood risks by defining the ratio of the number of scenarios that excessive the critical value to the total number of scenarios. The China's Three Gorges Reservoir (TGR) is selected as a case study, where the parameter and precipitation uncertainties are implemented to produce ensemble-based hydrologic forecasts. The Bayesian inference, Markov Chain Monte Carlo, is used to account for the parameter uncertainty. Two reservoir operation schemes, the real operated and scenario optimization, are evaluated for the flood risks and hydropower profits analysis. With the 2010 flood, it is found that the improvement of the hydrologic forecast accuracy is unnecessary to decrease the reservoir real-time operation risk, and most risks are from the forecast lead-time. It is therefore valuable to decrease the avarice of ensemble-based hydrologic forecasts with less bias for a reservoir operational purpose.

  4. Projected changes to winter temperature characteristics over Canada based on an RCM ensemble

    NASA Astrophysics Data System (ADS)

    Jeong, Dae Il; Sushama, Laxmi; Diro, Gulilat Tefera; Khaliq, M. Naveed

    2016-09-01

    Cold temperature and associated extremes often impact adversely human health and environment and bring disruptions in economic activities during winter over Canada. This study investigates projected changes in winter (December to March) period cold extreme days (i.e., cold nights, cold days, frost days, and ice days) and cold spells over Canada based on 11 regional climate model (RCM) simulations for the future 2040-2069 period with respect to the current 1970-1999 period. These simulations, available from the North American Regional Climate Change Assessment Program, were obtained with six different RCMs, when driven by four different Atmosphere-Ocean General Circulation Models, under the Special Report on Emissions Scenarios A2 scenario. Based on the reanalysis boundary conditions, the RCM simulations reproduce spatial patterns of observed mean values of the daily minimum and maximum temperatures and inter-annual variability of the number of cold nights over different Canadian climatic regions considered in the study. A comparison of current and future period simulations suggests decreases in the frequency of cold extreme events (i.e., cold nights, cold days and cold spells) and in selected return levels of maximum duration of cold spells over the entire study domain. Important regional differences are noticed as the simulations generally indicate smaller decreases in the characteristics of extreme cold events over western Canada compared to the other regions. The analysis also suggests an increase in the frequency of midwinter freeze-thaw events, due mainly to a decrease in the number of frost days and ice days for all Canadian regions. Especially, densely populated southern and coastal Canadian regions will require in depth studies to facilitate appropriate adaptation strategies as these regions are clearly expected to experience large increases in the frequency of freeze-thaw events.

  5. Application of ensemble-based methods for assimilating 4D ERT data at the groundwater-river water interaction zone based on a coupled hydrogeophysical model

    NASA Astrophysics Data System (ADS)

    Chen, X.; Johnson, T.; Hammond, G. E.; Zachara, J. M.

    2013-12-01

    Dynamic groundwater-river water exchange between the Columbia River and the Hanford 300 Area has substantial influence on flow and transport processes and biogeochemical cycles at the site. Existing research efforts have shown that the groundwater-river water interaction zone is a heterogeneous and highly dynamic region exhibiting variability over a range of space and time scales. Since it is insufficient to rely on well-based information to characterize the spatially variable subsurface properties within this interaction zone, we have installed a large-scale (300 m by 300 m) 3-dimensional electrical resistivity tomography (ERT) array to monitor river water intrusion and retreat at a temporal resolution of four images per day, using a novel time lapse ERT imaging methodology that explicitly accommodates the sharp, transient bulk conductivity contrast at the water table. The 4-dimensional electrical geophysical data is incorporated into ensemble-based data assimilation algorithms (e.g., ensemble Kalman filter and ensemble smoother) to statistically estimate the heterogeneous permeability field at the groundwater-river water interaction zone, which is critical for modeling flow and biogeochemical transport processes at the site. A new high performance computing capability has been developed to couple the ERT imaging code E4D (Johnson et al., 2010) with the site-scale flow and transport code, PFLOTRAN (Hammond et al., 2012), which serves as the forward simulator of the hydrogeophysical data assimilation. The joint, parallel, multi-physics code is able to simulate well-based pressure and pore-fluid conductivity measurements, as well as spatially continuous ERT measurements collected throughout the experiment. The data assimilation framework integrates both the well-based point measurements and spatially continuous ERT measurements in a sequential Bayesian manner. Our study demonstrates the effectiveness of ERT data for large-scale characterization of subsurface

  6. Ensemble simulation of land evapotranspiration in China based on a multi-forcing and multi-model approach

    NASA Astrophysics Data System (ADS)

    Liu, Jianguo; Jia, Binghao; Xie, Zhenghui; Shi, Chunxiang

    2016-06-01

    In order to reduce the uncertainty of offline land surface model (LSM) simulations of land evapotranspiration (ET), we used ensemble simulations based on three meteorological forcing datasets [Princeton, ITPCAS (Institute of Tibetan Plateau Research, Chinese Academy of Sciences), Qian] and four LSMs (BATS, VIC, CLM3.0 and CLM3.5), to explore the trends and spatiotemporal characteristics of ET, as well as the spatiotemporal pattern of ET in response to climate factors over mainland China during 1982-2007. The results showed that various simulations of each member and their arithmetic mean (Ens Mean) could capture the spatial distribution and seasonal pattern of ET sufficiently well, where they exhibited more significant spatial and seasonal variation in the ET compared with observation-based ET estimates (Obs MTE). For the mean annual ET, we found that the BATS forced by Princeton forcing overestimated the annual mean ET compared with Obs MTE for most of the basins in China, whereas the VIC forced by Princeton forcing showed underestimations. By contrast, the Ens Mean was closer to Obs MTE, although the results were underestimated over Southeast China. Furthermore, both the Obs MTE and Ens Mean exhibited a significant increasing trend during 1982-98; whereas after 1998, when the last big EI Ni˜no event occurred, the Ens Mean tended to decrease significantly between 1999 and 2007, although the change was not significant for Obs MTE. Changes in air temperature and shortwave radiation played key roles in the long-term variation in ET over the humid area of China, but precipitation mainly controlled the long-term variation in ET in arid and semi-arid areas of China.

  7. A general framework for multivariate multi-index drought prediction based on Multivariate Ensemble Streamflow Prediction (MESP)

    NASA Astrophysics Data System (ADS)

    Hao, Zengchao; Hao, Fanghua; Singh, Vijay P.

    2016-08-01

    Drought is among the costliest natural hazards worldwide and extreme drought events in recent years have caused huge losses to various sectors. Drought prediction is therefore critically important for providing early warning information to aid decision making to cope with drought. Due to the complicated nature of drought, it has been recognized that the univariate drought indicator may not be sufficient for drought characterization and hence multivariate drought indices have been developed for drought monitoring. Alongside the substantial effort in drought monitoring with multivariate drought indices, it is of equal importance to develop a drought prediction method with multivariate drought indices to integrate drought information from various sources. This study proposes a general framework for multivariate multi-index drought prediction that is capable of integrating complementary prediction skills from multiple drought indices. The Multivariate Ensemble Streamflow Prediction (MESP) is employed to sample from historical records for obtaining statistical prediction of multiple variables, which is then used as inputs to achieve multivariate prediction. The framework is illustrated with a linearly combined drought index (LDI), which is a commonly used multivariate drought index, based on climate division data in California and New York in the United States with different seasonality of precipitation. The predictive skill of LDI (represented with persistence) is assessed by comparison with the univariate drought index and results show that the LDI prediction skill is less affected by seasonality than the meteorological drought prediction based on SPI. Prediction results from the case study show that the proposed multivariate drought prediction outperforms the persistence prediction, implying a satisfactory performance of multivariate drought prediction. The proposed method would be useful for drought prediction to integrate drought information from various sources

  8. Global and regional past sea level from an ensemble of reconstructions based on Altimetry, OGCM runs and tide gauges

    NASA Astrophysics Data System (ADS)

    Meyssignac, B.; Palanisamy, H. K.; Cazenave, A. A.; Shum, C.

    2013-12-01

    over the period 1950-2012. Then, we present a ';mean' reconstruction based on the ensemble average of the 8 individual reconstructions. The dominant modes of temporal variability and the spatial trend patterns of this mean reconstruction are discussed.

  9. Bioassays Based on Molecular Nanomechanics

    PubMed Central

    Majumdar, Arun

    2002-01-01

    Recent experiments have shown that when specific biomolecular interactions are confined to one surface of a microcantilever beam, changes in intermolecular nanomechanical forces provide sufficient differential torque to bend the cantilever beam. This has been used to detect single base pair mismatches during DNA hybridization, as well as prostate specific antigen (PSA) at concentrations and conditions that are clinically relevant for prostate cancer diagnosis. Since cantilever motion originates from free energy change induced by specific biomolecular binding, this technique is now offering a common platform for label-free quantitative analysis of protein-protein binding, DNA hybridization DNA-protein interactions, and in general receptor-ligand interactions. Current work is focused on developing “universal microarrays” of microcantilever beams for high-throughput multiplexed bioassays. PMID:12590170

  10. Bioassays Based on Molecular Nanomechanics

    DOE PAGESBeta

    Majumdar, Arun

    2002-01-01

    Recent experiments have shown that when specific biomolecular interactions are confined to one surface of a microcantilever beam, changes in intermolecular nanomechanical forces provide sufficient differential torque to bend the cantilever beam. This has been used to detect single base pair mismatches during DNA hybridization, as well as prostate specific antigen (PSA) at concentrations and conditions that are clinically relevant for prostate cancer diagnosis. Since cantilever motion originates from free energy change induced by specific biomolecular binding, this technique is now offering a common platform for label-free quantitative analysis of protein-protein binding, DNA hybridization DNA-protein interactions, and in general receptor-ligandmore » interactions. Current work is focused on developing “universal microarrays” of microcantilever beams for high-throughput multiplexed bioassays.« less

  11. Projected changes to high temperature events for Canada based on a regional climate model ensemble

    NASA Astrophysics Data System (ADS)

    Jeong, Dae Il; Sushama, Laxmi; Diro, Gulilat Tefera; Khaliq, M. Naveed; Beltrami, Hugo; Caya, Daniel

    2016-05-01

    Extreme hot spells can have significant impacts on human society and ecosystems, and therefore it is important to assess how these extreme events will evolve in a changing climate. In this study, the impact of climate change on hot days, hot spells, and heat waves, over 10 climatic regions covering Canada, based on 11 regional climate model (RCM) simulations from the North American Regional Climate Change Assessment Program for the June to August summer period is presented. These simulations were produced with six RCMs driven by four Atmosphere-Ocean General Circulation Models (AOGCM), for the A2 emission scenario, for the current 1970-1999 and future 2040-2069 periods. Two types of hot days, namely HD-1 and HD-2, defined respectively as days with only daily maximum temperature (Tmax) and both Tmax and daily minimum temperature (Tmin) exceeding their respective thresholds (i.e., period-of-record 90th percentile of Tmax and Tmin values), are considered in the study. Analogous to these hot days, two types of hot spells, namely HS-1 and HS-2, are identified as spells of consecutive HD-1 and HD-2 type hot days. In the study, heat waves are defined as periods of three or more consecutive days, with Tmax above 32 °C threshold. Results suggest future increases in the number of both types of hot days and hot spell events for the 10 climatic regions considered. However, the projected changes show high spatial variability and are highly dependent on the RCM and driving AOGCM combination. Extreme hot spell events such as HS-2 type hot spells of longer duration are expected to experience relatively larger increases compared to hot spells of moderate duration, implying considerable heat related environmental and health risks. Regionally, the Great Lakes, West Coast, Northern Plains, and Maritimes regions are found to be more affected due to increases in the frequency and severity of hot spells and/or heat wave characteristics, requiring more in depth studies for these regions

  12. Toward improving the reliability of hydrologic prediction: Model structure uncertainty and its quantification using ensemble-based genetic programming framework

    NASA Astrophysics Data System (ADS)

    Parasuraman, Kamban; Elshorbagy, Amin

    2008-12-01

    Uncertainty analysis is starting to be widely acknowledged as an integral part of hydrological modeling. The conventional treatment of uncertainty analysis in hydrologic modeling is to assume a deterministic model structure, and treat its associated parameters as imperfectly known, thereby neglecting the uncertainty associated with the model structure. In this paper, a modeling framework that can explicitly account for the effect of model structure uncertainty has been proposed. The modeling framework is based on initially generating different realizations of the original data set using a non-parametric bootstrap method, and then exploiting the ability of the self-organizing algorithms, namely genetic programming, to evolve their own model structure for each of the resampled data sets. The resulting ensemble of models is then used to quantify the uncertainty associated with the model structure. The performance of the proposed modeling framework is analyzed with regards to its ability in characterizing the evapotranspiration process at the Southwest Sand Storage facility, located near Ft. McMurray, Alberta. Eddy-covariance-measured actual evapotranspiration is modeled as a function of net radiation, air temperature, ground temperature, relative humidity, and wind speed. Investigating the relation between model complexity, prediction accuracy, and uncertainty, two sets of experiments were carried out by varying the level of mathematical operators that can be used to define the predictand-predictor relationship. While the first set uses just the additive operators, the second set uses both the additive and the multiplicative operators to define the predictand-predictor relationship. The results suggest that increasing the model complexity may lead to better prediction accuracy but at an expense of increasing uncertainty. Compared to the model parameter uncertainty, the relative contribution of model structure uncertainty to the predictive uncertainty of a model is

  13. A hybrid model for PM₂.₅ forecasting based on ensemble empirical mode decomposition and a general regression neural network.

    PubMed

    Zhou, Qingping; Jiang, Haiyan; Wang, Jianzhou; Zhou, Jianling

    2014-10-15

    Exposure to high concentrations of fine particulate matter (PM₂.₅) can cause serious health problems because PM₂.₅ contains microscopic solid or liquid droplets that are sufficiently small to be ingested deep into human lungs. Thus, daily prediction of PM₂.₅ levels is notably important for regulatory plans that inform the public and restrict social activities in advance when harmful episodes are foreseen. A hybrid EEMD-GRNN (ensemble empirical mode decomposition-general regression neural network) model based on data preprocessing and analysis is firstly proposed in this paper for one-day-ahead prediction of PM₂.₅ concentrations. The EEMD part is utilized to decompose original PM₂.₅ data into several intrinsic mode functions (IMFs), while the GRNN part is used for the prediction of each IMF. The hybrid EEMD-GRNN model is trained using input variables obtained from principal component regression (PCR) model to remove redundancy. These input variables accurately and succinctly reflect the relationships between PM₂.₅ and both air quality and meteorological data. The model is trained with data from January 1 to November 1, 2013 and is validated with data from November 2 to November 21, 2013 in Xi'an Province, China. The experimental results show that the developed hybrid EEMD-GRNN model outperforms a single GRNN model without EEMD, a multiple linear regression (MLR) model, a PCR model, and a traditional autoregressive integrated moving average (ARIMA) model. The hybrid model with fast and accurate results can be used to develop rapid air quality warning systems. PMID:25089688

  14. Identifying representative trees from ensembles.

    PubMed

    Banerjee, Mousumi; Ding, Ying; Noone, Anne-Michelle

    2012-07-10

    Tree-based methods have become popular for analyzing complex data structures where the primary goal is risk stratification of patients. Ensemble techniques improve the accuracy in prediction and address the instability in a single tree by growing an ensemble of trees and aggregating. However, in the process, individual trees get lost. In this paper, we propose a methodology for identifying the most representative trees in an ensemble on the basis of several tree distance metrics. Although our focus is on binary outcomes, the methods are applicable to censored data as well. For any two trees, the distance metrics are chosen to (1) measure similarity of the covariates used to split the trees; (2) reflect similar clustering of patients in the terminal nodes of the trees; and (3) measure similarity in predictions from the two trees. Whereas the latter focuses on prediction, the first two metrics focus on the architectural similarity between two trees. The most representative trees in the ensemble are chosen on the basis of the average distance between a tree and all other trees in the ensemble. Out-of-bag estimate of error rate is obtained using neighborhoods of representative trees. Simulations and data examples show gains in predictive accuracy when averaging over such neighborhoods. We illustrate our methods using a dataset of kidney cancer treatment receipt (binary outcome) and a second dataset of breast cancer survival (censored outcome).

  15. Structure and Dynamics of Ribosomal Protein L12: An Ensemble Model Based on SAXS and NMR Relaxation

    PubMed Central

    Bernadó, Pau; Modig, Kristofer; Grela, Przemysław; Svergun, Dmitri I.; Tchorzewski, Marek; Pons, Miquel; Akke, Mikael

    2010-01-01

    Abstract Ribosomal protein L12 is a two-domain protein that forms dimers mediated by its N-terminal domains. A 20-residue linker separates the N- and C-terminal domains. This linker results in a three-lobe topology with significant flexibility, known to be critical for efficient translation. Here we present an ensemble model of spatial distributions and correlation times for the domain reorientations of L12 that reconciles experimental data from small-angle x-ray scattering and nuclear magnetic resonance. We generated an ensemble of L12 conformations in which the structure of each domain is fixed but the domain orientations are variable. The ensemble reproduces the small-angle x-ray scattering data and the optimized correlation times of its reorientational eigenmodes fit the 15N relaxation data. The ensemble model reveals intrinsic conformational properties of L12 that help explain its function on the ribosome. The two C-terminal domains sample a large volume and extend further away from the ribosome anchor than expected for a random-chain linker, indicating that the flexible linker has residual order. Furthermore, the distances between each C-terminal domain and the anchor are anticorrelated, indicating that one of them is more retracted on average. We speculate that these properties promote the function of L12 to recruit translation factors and control their activity on the ribosome. PMID:20483347

  16. An assessment of a North American Multi-Model Ensemble (NMME) based global drought early warning forecast system

    NASA Astrophysics Data System (ADS)

    Wood, E. F.; Yuan, X.; Sheffield, J.; Pan, M.; Roundy, J.

    2013-12-01

    One of the key recommendations of the WCRP Global Drought Information System (GDIS) workshop is to develop an experimental real-time global monitoring and prediction system. While great advances has been made in global drought monitoring based on satellite observations and model reanalysis data, global drought forecasting has been stranded in part due to the limited skill both in climate forecast models and global hydrologic predictions. Having been working on drought monitoring and forecasting over USA for more than a decade, the Princeton land surface hydrology group is now developing an experimental global drought early warning system that is based on multiple climate forecast models and a calibrated global hydrologic model. In this presentation, we will test its capability in seasonal forecasting of meteorological, agricultural and hydrologic droughts over global major river basins, using precipitation, soil moisture and streamflow forecasts respectively. Based on the joint probability distribution between observations using Princeton's global drought monitoring system and model hindcasts and real-time forecasts from North American Multi-Model Ensemble (NMME) project, we (i) bias correct the monthly precipitation and temperature forecasts from multiple climate forecast models, (ii) downscale them to a daily time scale, and (iii) use them to drive the calibrated VIC model to produce global drought forecasts at a 1-degree resolution. A parallel run using the ESP forecast method, which is based on resampling historical forcings, is also carried out for comparison. Analysis is being conducted over global major river basins, with multiple drought indices that have different time scales and characteristics. The meteorological drought forecast does not have uncertainty from hydrologic models and can be validated directly against observations - making the validation an 'apples-to-apples' comparison. Preliminary results for the evaluation of meteorological drought onset

  17. Potential and limitations of ensemble docking.

    PubMed

    Korb, Oliver; Olsson, Tjelvar S G; Bowden, Simon J; Hall, Richard J; Verdonk, Marcel L; Liebeschuetz, John W; Cole, Jason C

    2012-05-25

    A major problem in structure-based virtual screening applications is the appropriate selection of a single or even multiple protein structures to be used in the virtual screening process. A priori it is unknown which protein structure(s) will perform best in a virtual screening experiment. We investigated the performance of ensemble docking, as a function of ensemble size, for eight targets of pharmaceutical interest. Starting from single protein structure docking results, for each ensemble size up to 500,000 combinations of protein structures were generated, and, for each ensemble, pose prediction and virtual screening results were derived. Comparison of single to multiple protein structure results suggests improvements when looking at the performance of the worst and the average over all single protein structures to the performance of the worst and average over all protein ensembles of size two or greater, respectively. We identified several key factors affecting ensemble docking performance, including the sampling accuracy of the docking algorithm, the choice of the scoring function, and the similarity of database ligands to the cocrystallized ligands of ligand-bound protein structures in an ensemble. Due to these factors, the prospective selection of optimum ensembles is a challenging task, shown by a reassessment of published ensemble selection protocols. PMID:22482774

  18. SIENA: Efficient Compilation of Selective Protein Binding Site Ensembles.

    PubMed

    Bietz, Stefan; Rarey, Matthias

    2016-01-25

    Structural flexibility of proteins has an important influence on molecular recognition and enzymatic function. In modeling, structure ensembles are therefore often applied as a valuable source of alternative protein conformations. However, their usage is often complicated by structural artifacts and inconsistent data annotation. Here, we present SIENA, a new computational approach for the automated assembly and preprocessing of protein binding site ensembles. Starting with an arbitrarily defined binding site in a single protein structure, SIENA searches for alternative conformations of the same or sequentially closely related binding sites. The method is based on an indexed database for identifying perfect k-mer matches and a recently published algorithm for the alignment of protein binding site conformations. Furthermore, SIENA provides a new algorithm for the interaction-based selection of binding site conformations which aims at covering all known ligand-binding geometries. Various experiments highlight that SIENA is able to generate comprehensive and well selected binding site ensembles improving the compatibility to both known and unconsidered ligand molecules. Starting with the whole PDB as data source, the computation time of the whole ensemble generation takes only a few seconds. SIENA is available via a Web service at www.zbh.uni-hamburg.de/siena .

  19. A stochastic ensemble-based model to predict crop water requirements from numerical weather forecasts and VIS-NIR high resolution satellite images in Southern Italy

    NASA Astrophysics Data System (ADS)

    Pelosi, Anna; Falanga Bolognesi, Salvatore; De Michele, Carlo; Medina Gonzalez, Hanoi; Villani, Paolo; D'Urso, Guido; Battista Chirico, Giovanni

    2015-04-01

    Irrigation agriculture is one the biggest consumer of water in Europe, especially in southern regions, where it accounts for up to 70% of the total water consumption. The EU Common Agricultural Policy, combined with the Water Framework Directive, imposes to farmers and irrigation managers a substantial increase of the efficiency in the use of water in agriculture for the next decade. Ensemble numerical weather predictions can be valuable data for developing operational advisory irrigation services. We propose a stochastic ensemble-based model providing spatial and temporal estimates of crop water requirements, implemented within an advisory service offering detailed maps of irrigation water requirements and crop water consumption estimates, to be used by water irrigation managers and farmers. The stochastic model combines estimates of crop potential evapotranspiration retrieved from ensemble numerical weather forecasts (COSMO-LEPS, 16 members, 7 km resolution) and canopy parameters (LAI, albedo, fractional vegetation cover) derived from high resolution satellite images in the visible and near infrared wavelengths. The service provides users with daily estimates of crop water requirements for lead times up to five days. The temporal evolution of the crop potential evapotranspiration is simulated with autoregressive models. An ensemble Kalman filter is employed for updating model states by assimilating both ground based meteorological variables (where available) and numerical weather forecasts. The model has been applied in Campania region (Southern Italy), where a satellite assisted irrigation advisory service has been operating since 2006. This work presents the results of the system performance for one year of experimental service. The results suggest that the proposed model can be an effective support for a sustainable use and management of irrigation water, under conditions of water scarcity and drought. Since the evapotranspiration term represents a staple

  20. A probabilistic approach of the Flash Flood Early Warning System (FF-EWS) in Catalonia based on radar ensemble generation

    NASA Astrophysics Data System (ADS)

    Velasco, David; Sempere-Torres, Daniel; Corral, Carles; Llort, Xavier; Velasco, Enrique

    2010-05-01

    probabilistic component to the FF-EWS. As a first step, we have incorporated the uncertainty in rainfall estimates and forecasts based on an ensemble of equiprobable rainfall scenarios. The presented study has focused on a number of rainfall events and the performance of the FF-EWS evaluated in terms of its ability to produce probabilistic hazard warnings for decision-making support.

  1. [Molecular based targets and endometrial cancer].

    PubMed

    Stoyanov, St; Ananiev, J; Ivanova, K; Velev, V; Todorova, M; Gulubova, M

    2015-01-01

    In recent years, increasing attention has been paid to the rate of spread of endometrial carcinoma, especially in the postmenopausal period. Along with routine diagnostic methods, giving information on the location and progression of the disease, there are some morphological methods determining very accurately the correlations in the development of this type of cancer and his prognosis. Moreover--in recent years, the accumulated information about the molecular profile of this type of cancer made it possible to implement a number of new drugs against the so-called molecular therapy -'targets' in the neoplastic process. Significant proportion of cases show response rates, it is more hope in the development of more successful formulas and target -based therapy. In this review, we present and discuss the role of certain molecular markers as potential indicators of prognosis and development, as well as determining the target treatment of endometrial carcinoma.

  2. [Molecular based targets and endometrial cancer].

    PubMed

    Stoyanov, St; Ananiev, J; Ivanova, K; Velev, V; Todorova, M; Gulubova, M

    2015-01-01

    In recent years, increasing attention has been paid to the rate of spread of endometrial carcinoma, especially in the postmenopausal period. Along with routine diagnostic methods, giving information on the location and progression of the disease, there are some morphological methods determining very accurately the correlations in the development of this type of cancer and his prognosis. Moreover--in recent years, the accumulated information about the molecular profile of this type of cancer made it possible to implement a number of new drugs against the so-called molecular therapy -'targets' in the neoplastic process. Significant proportion of cases show response rates, it is more hope in the development of more successful formulas and target -based therapy. In this review, we present and discuss the role of certain molecular markers as potential indicators of prognosis and development, as well as determining the target treatment of endometrial carcinoma. PMID:25909140

  3. A benchmark for reaction coordinates in the transition path ensemble.

    PubMed

    Li, Wenjin; Ma, Ao

    2016-04-01

    The molecular mechanism of a reaction is embedded in its transition path ensemble, the complete collection of reactive trajectories. Utilizing the information in the transition path ensemble alone, we developed a novel metric, which we termed the emergent potential energy, for distinguishing reaction coordinates from the bath modes. The emergent potential energy can be understood as the average energy cost for making a displacement of a coordinate in the transition path ensemble. Where displacing a bath mode invokes essentially no cost, it costs significantly to move the reaction coordinate. Based on some general assumptions of the behaviors of reaction and bath coordinates in the transition path ensemble, we proved theoretically with statistical mechanics that the emergent potential energy could serve as a benchmark of reaction coordinates and demonstrated its effectiveness by applying it to a prototypical system of biomolecular dynamics. Using the emergent potential energy as guidance, we developed a committor-free and intuition-independent method for identifying reaction coordinates in complex systems. We expect this method to be applicable to a wide range of reaction processes in complex biomolecular systems.

  4. Finding diversity for building one-day ahead Hydrological Ensemble Prediction System based on artificial neural network stacks

    NASA Astrophysics Data System (ADS)

    Brochero, Darwin; Anctil, Francois; Gagné, Christian; López, Karol

    2013-04-01

    In this study, we addressed the application of Artificial Neural Networks (ANN) in the context of Hydrological Ensemble Prediction Systems (HEPS). Such systems have become popular in the past years as a tool to include the forecast uncertainty in the decision making process. HEPS considers fundamentally the uncertainty cascade model [4] for uncertainty representation. Analogously, the machine learning community has proposed models of multiple classifier systems that take into account the variability in datasets, input space, model structures, and parametric configuration [3]. This approach is based primarily on the well-known "no free lunch theorem" [1]. Consequently, we propose a framework based on two separate but complementary topics: data stratification and input variable selection (IVS). Thus, we promote an ANN prediction stack in which each predictor is trained based on input spaces defined by the IVS application on different stratified sub-samples. All this, added to the inherent variability of classical ANN optimization, leads us to our ultimate goal: diversity in the prediction, defined as the complementarity of the individual predictors. The stratification application on the 12 basins used in this study, which originate from the second and third workshop of the MOPEX project [2], shows that the informativeness of the data is far more important than the quantity used for ANN training. Additionally, the input space variability leads to ANN stacks that outperform an ANN stack model trained with 100% of the available information but with a random selection of dataset used in the early stopping method (scenario R100P). The results show that from a deterministic view, the main advantage focuses on the efficient selection of the training information, which is an equally important concept for the calibration of conceptual hydrological models. On the other hand, the diversity achieved is reflected in a substantial improvement in the scores that define the

  5. Generation of Daily Rainfall Scenario Based on Nonstationary Spatial-Temporal Downscaling Techniques with Multimodel Ensemble of Different GCMs

    NASA Astrophysics Data System (ADS)

    Kim, T. J.; Kwon, H. H.

    2014-12-01

    Recently, extreme weather occurrences associated with climate change are gradually increasing in frequency, causing unprecedented major weather-related disasters. General Circulation Models (GCMs) are the basic tool used for modelling climate. However, the discrepancy between the spatio-temporal scale at which the models deliver output and the scales that are generally required for applied studies has led to the development of various downscaling methods. Stochastic downscaling methods have been used extensively to generate long-term weather sequences from finite observed records. A primary objective of this study is to develop a forecasting scheme which is able to make use of a multimodel ensemble of different GCMs. This study employed a Nonstationary Hidden Markov Chain Model (NHMM) as a main tool for downscaling seasonal ensemble forecasts over 3 month period, providing daily forecasts. In particular, this study uses MMEs from the APEC Climate Center (APCC) as a predictor. Our results showed that the proposed downscaling scheme can provide the skillful forecasts as inputs for hydrologic modeling, which in turn may improve water resources management. An application to the Nakdong watershed in South Korea illustrates how the proposed approach can lead to potentially reliable information for water resources management. Acknowledgement: This research was supported by a grant (13SCIPA01) from Smart Civil Infrastructure Research Program funded by the Ministry of Land, Infrastructure and Transport (MOLIT) of Korea government and the Korea Agency for Infrastructure Technology Advancement (KAIA). Keywords: Climate Change, GCM, Hidden Markov Chain Model, Multi-Model Ensemble

  6. Probabilistic Forecast for 21st Century Climate Based on an Ensemble of Simulations using a Business-As-Usual Scenario

    NASA Astrophysics Data System (ADS)

    Scott, J. R.; Forest, C. E.; Sokolov, A. P.; Dutkiewicz, S.

    2011-12-01

    The behavior of the climate system is examined in an ensemble of runs using an Earth System Model of intermediate complexity. Climate "parameters" varied are the climate sensitivity, the aerosol forcing, and the strength of ocean heat uptake. Variations in the latter were accomplished by changing the strength of the oceans' background vertical mixing. While climate sensitivity and aerosol forcing can be varied over rather wide ranges, it is more difficult to create such variation in heat uptake while maintaining a realistic overturning ocean circulation. Therefore, separate ensembles were carried out for a few values of the vertical diffusion coefficient. Joint probability distributions for climate sensitivity and aerosol forcing are constructed by comparing results from 20th century simulations with available observational data. These distributions are then used to generate ensembles of 21st century simulations; results allow us to construct probabilistic distributions for changes in important climate change variables such as surface air temperature, sea level rise, and magnitude of the AMOC. Changes in the rate of air-sea flux of CO2 and the export of carbon into the deep ocean are also examined.

  7. Hydrological modelling using ensemble satellite rainfall estimates in a sparsely gauged river basin: The need for whole-ensemble calibration

    NASA Astrophysics Data System (ADS)

    Skinner, Christopher J.; Bellerby, Timothy J.; Greatrex, Helen; Grimes, David I. F.

    2015-03-01

    The potential for satellite rainfall estimates to drive hydrological models has been long understood, but at the high spatial and temporal resolutions often required by these models the uncertainties in satellite rainfall inputs are both significant in magnitude and spatiotemporally autocorrelated. Conditional stochastic modelling of ensemble observed fields provides one possible approach to representing this uncertainty in a form suitable for hydrological modelling. Previous studies have concentrated on the uncertainty within the satellite rainfall estimates themselves, sometimes applying ensemble inputs to a pre-calibrated hydrological model. This approach does not account for the interaction between input uncertainty and model uncertainty and in particular the impact of input uncertainty on model calibration. Moreover, it may not be appropriate to use deterministic inputs to calibrate a model that is intended to be driven by using an ensemble. A novel whole-ensemble calibration approach has been developed to overcome some of these issues. This study used ensemble rainfall inputs produced by a conditional satellite-driven stochastic rainfall generator (TAMSIM) to drive a version of the Pitman rainfall-runoff model, calibrated using the whole-ensemble approach. Simulated ensemble discharge outputs were assessed using metrics adapted from ensemble forecast verification, showing that the ensemble outputs produced using the whole-ensemble calibrated Pitman model outperformed equivalent ensemble outputs created using a Pitman model calibrated against either the ensemble mean or a theoretical infinite-ensemble expected value. Overall, for the verification period the whole-ensemble calibration provided a mean RMSE of 61.7% of the mean wet season discharge, compared to 83.6% using a calibration based on the daily mean of the ensemble estimates. Using a Brier's Skill Score to assess the performance of the ensemble against a climatic estimate, the whole-ensemble

  8. Graph-based molecular alignment (GMA).

    PubMed

    Marialke, J; Körner, R; Tietze, S; Apostolakis, Joannis

    2007-01-01

    We describe a combined 2D/3D approach for the superposition of flexible chemical structures, which is based on recent progress in the efficient identification of common subgraphs and a gradient-based torsion space optimization algorithm. The simplicity of the approach is reflected in its generality and computational efficiency: the suggested approach neither requires precalculated statistics on the conformations of the molecules nor does it make simplifying assumptions on the topology of the molecules being compared. Furthermore, graph-based molecular alignment produces alignments that are consistent with the chemistry of the molecules as well as their general structure, as it depends on both the local connectivities between atoms and the overall topology of the molecules. We validate this approach on benchmark sets taken from the literature and show that it leads to good results compared to computationally and algorithmically more involved methods. The results suggest that, for most practical purposes, graph-based molecular alignment is a viable alternative to molecular field alignment with respect to structural superposition and leads to structures of comparable quality in a fraction of the time. PMID:17381175

  9. Restoring electronic coherence/decoherence for a trajectory-based nonadiabatic molecular dynamics

    PubMed Central

    Zhu, Chaoyuan

    2016-01-01

    By utilizing the time-independent semiclassical phase integral, we obtained modified coupled time-dependent Schrödinger equations that restore coherences and induce decoherences within original simple trajectory-based nonadiabatic molecular dynamic algorithms. Nonadiabatic transition probabilities simulated from both Tully’s fewest switches and semiclassical Ehrenfest algorithms follow exact quantum electronic oscillations and amplitudes for three out of the four well-known model systems. Within the present theory, nonadiabatic transitions estimated from statistical ensemble of trajectories accurately follow those of the modified electronic wave functions. The present theory can be immediately applied to the molecular dynamic simulations of photochemical and photophysical processes involving electronic excited states. PMID:27063337

  10. Image Change Detection via Ensemble Learning

    SciTech Connect

    Martin, Benjamin W; Vatsavai, Raju

    2013-01-01

    The concept of geographic change detection is relevant in many areas. Changes in geography can reveal much information about a particular location. For example, analysis of changes in geography can identify regions of population growth, change in land use, and potential environmental disturbance. A common way to perform change detection is to use a simple method such as differencing to detect regions of change. Though these techniques are simple, often the application of these techniques is very limited. Recently, use of machine learning methods such as neural networks for change detection has been explored with great success. In this work, we explore the use of ensemble learning methodologies for detecting changes in bitemporal synthetic aperture radar (SAR) images. Ensemble learning uses a collection of weak machine learning classifiers to create a stronger classifier which has higher accuracy than the individual classifiers in the ensemble. The strength of the ensemble lies in the fact that the individual classifiers in the ensemble create a mixture of experts in which the final classification made by the ensemble classifier is calculated from the outputs of the individual classifiers. Our methodology leverages this aspect of ensemble learning by training collections of weak decision tree based classifiers to identify regions of change in SAR images collected of a region in the Staten Island, New York area during Hurricane Sandy. Preliminary studies show that the ensemble method has approximately 11.5% higher change detection accuracy than an individual classifier.

  11. On the Utility of a Modest Physics-Based High-Resolution WRF Ensemble for Hurricane Prediction: Hurricane Ivan as an Example

    NASA Astrophysics Data System (ADS)

    Tilley, J. S.; Bower, K. A.; Kumar, S. S.; Kucera, P. A.; Askelson, M. A.

    2005-05-01

    The remarkable 2004 Atlantic hurricane season featured six "major" hurricanes (according to the Saffir-Simpson (SS) intensity scale), four of which directly impacted the state of Florida (Charley, Frances, Ivan, Jeanne) within a six-week period. Of these, Hurricane Ivan distinguished itself with impressive statistics in terms of lifespan (22 days), maximum intensity (SS category 5), damage (est. 13 billion dollars) and U.S. deaths (26). A variety of tools are currently available to forecasters at the National Oceanographic and Atmospheric Administration's (NOAA) Tropical Prediction Center (TPC), including several deterministic and statistical models as well as the Florida State University superensemble (e.g., Shin and Krishnamurti, 2003a,b). Often a blend of solutions from the various packages is utilized, though in other cases the TPC forecasters will follow the solution from a preferred model based on recent performance for the tropical cyclone of interest. Given that the NOAA National Centers for Environmental Prediction (NCEP) continue to move towards an operational environment where a relatively modest ensemble (roughly 6 members), constructed from within the Weather Research and Forecasting (WRF) framework, will figure prominently in the near future (DiMego 2004), a timely question to ask is whether the performance of such an ensemble for tropical systems will add value to the tool box now available to TPC forecasters. While a fully robust answer to this question demands a period of extensive testing under operational conditions, individual case studies can provide significant insights into some aspects of the expected performance of such a modeling system. Therefore, in this presentation we will present early results and limited performance metrics for such a case study, focusing on the 30-hour period beginning with Hurricane Ivan's entrance into the Gulf of Mexico. We note that while our 7-member ensemble consists entirely of WRF model members, in line with

  12. Ensembl regulation resources.

    PubMed

    Zerbino, Daniel R; Johnson, Nathan; Juetteman, Thomas; Sheppard, Dan; Wilder, Steven P; Lavidas, Ilias; Nuhn, Michael; Perry, Emily; Raffaillac-Desfosses, Quentin; Sobral, Daniel; Keefe, Damian; Gräf, Stefan; Ahmed, Ikhlak; Kinsella, Rhoda; Pritchard, Bethan; Brent, Simon; Amode, Ridwan; Parker, Anne; Trevanion, Steven; Birney, Ewan; Dunham, Ian; Flicek, Paul

    2016-01-01

    New experimental techniques in epigenomics allow researchers to assay a diversity of highly dynamic features such as histone marks, DNA modifications or chromatin structure. The study of their fluctuations should provide insights into gene expression regulation, cell differentiation and disease. The Ensembl project collects and maintains the Ensembl regulation data resources on epigenetic marks, transcription factor binding and DNA methylation for human and mouse, as well as microarray probe mappings and annotations for a variety of chordate genomes. From this data, we produce a functional annotation of the regulatory elements along the human and mouse genomes with plans to expand to other species as data becomes available. Starting from well-studied cell lines, we will progressively expand our library of measurements to a greater variety of samples. Ensembl's regulation resources provide a central and easy-to-query repository for reference epigenomes. As with all Ensembl data, it is freely available at http://www.ensembl.org, from the Perl and REST APIs and from the public Ensembl MySQL database server at ensembldb.ensembl.org. Database URL: http://www.ensembl.org. PMID:26888907

  13. Net charge per residue modulates conformational ensembles of intrinsically disordered proteins

    PubMed Central

    Mao, Albert H.; Crick, Scott L.; Vitalis, Andreas; Chicoine, Caitlin L.; Pappu, Rohit V.

    2010-01-01

    Intrinsically disordered proteins (IDPs) adopt heterogeneous ensembles of conformations under physiological conditions. Understanding the relationship between amino acid sequence and conformational ensembles of IDPs can help clarify the role of disorder in physiological function. Recent studies revealed that polar IDPs favor collapsed ensembles in water despite the absence of hydrophobic groups—a result that holds for polypeptide backbones as well. By studying highly charged polypeptides, a different archetype of IDPs, we assess how charge content modulates the intrinsic preference of polypeptide backbones for collapsed structures. We characterized conformational ensembles for a set of protamines in aqueous milieus using molecular simulations and fluorescence measurements. Protamines are arginine-rich IDPs involved in the condensation of chromatin during spermatogenesis. Simulations based on the ABSINTH implicit solvation model predict the existence of a globule-to-coil transition, with net charge per residue serving as the discriminating order parameter. The transition is supported by quantitative agreement between simulation and experiment. Local conformational preferences partially explain the observed trends of polymeric properties. Our results lead to the proposal of a schematic protein phase diagram that should enable prediction of polymeric attributes for IDP conformational ensembles using easily calculated physicochemical properties of amino acid sequences. Although sequence composition allows the prediction of polymeric properties, interresidue contact preferences of protamines with similar polymeric attributes suggest that certain details of conformational ensembles depend on the sequence. This provides a plausible mechanism for specificity in the functions of IDPs. PMID:20404210

  14. Ensemble of Surrogates-based Optimization for Identifying an Optimal Surfactant-enhanced Aquifer Remediation Strategy at Heterogeneous DNAPL-contaminated Sites

    NASA Astrophysics Data System (ADS)

    Lu, W., Sr.; Xin, X.; Luo, J.; Jiang, X.; Zhang, Y.; Zhao, Y.; Chen, M.; Hou, Z.; Ouyang, Q.

    2015-12-01

    The purpose of this study was to identify an optimal surfactant-enhanced aquifer remediation (SEAR) strategy for aquifers contaminated by dense non-aqueous phase liquid (DNAPL) based on an ensemble of surrogates-based optimization technique. A saturated heterogeneous medium contaminated by nitrobenzene was selected as case study. A new kind of surrogate-based SEAR optimization employing an ensemble surrogate (ES) model together with a genetic algorithm (GA) is presented. Four methods, namely radial basis function artificial neural network (RBFANN), kriging (KRG), support vector regression (SVR), and kernel extreme learning machines (KELM), were used to create four individual surrogate models, which were then compared. The comparison enabled us to select the two most accurate models (KELM and KRG) to establish an ES model of the SEAR simulation model, and the developed ES model as well as these four stand-alone surrogate models was compared. The results showed that the average relative error of the average nitrobenzene removal rates between the ES model and the simulation model for 20 test samples was 0.8%, which is a high approximation accuracy, and which indicates that the ES model provides more accurate predictions than the stand-alone surrogate models. Then, a nonlinear optimization model was formulated for the minimum cost, and the developed ES model was embedded into this optimization model as a constrained condition. Besides, GA was used to solve the optimization model to provide the optimal SEAR strategy. The developed ensemble surrogate-optimization approach was effective in seeking a cost-effective SEAR strategy for heterogeneous DNAPL-contaminated sites. This research is expected to enrich and develop the theoretical and technical implications for the analysis of remediation strategy optimization of DNAPL-contaminated aquifers.

  15. Ensemble of surrogates-based optimization for identifying an optimal surfactant-enhanced aquifer remediation strategy at heterogeneous DNAPL-contaminated sites

    NASA Astrophysics Data System (ADS)

    Jiang, Xue; Lu, Wenxi; Hou, Zeyu; Zhao, Haiqing; Na, Jin

    2015-11-01

    The purpose of this study was to identify an optimal surfactant-enhanced aquifer remediation (SEAR) strategy for aquifers contaminated by dense non-aqueous phase liquid (DNAPL) based on an ensemble of surrogates-based optimization technique. A saturated heterogeneous medium contaminated by nitrobenzene was selected as case study. A new kind of surrogate-based SEAR optimization employing an ensemble surrogate (ES) model together with a genetic algorithm (GA) is presented. Four methods, namely radial basis function artificial neural network (RBFANN), kriging (KRG), support vector regression (SVR), and kernel extreme learning machines (KELM), were used to create four individual surrogate models, which were then compared. The comparison enabled us to select the two most accurate models (KELM and KRG) to establish an ES model of the SEAR simulation model, and the developed ES model as well as these four stand-alone surrogate models was compared. The results showed that the average relative error of the average nitrobenzene removal rates between the ES model and the simulation model for 20 test samples was 0.8%, which is a high approximation accuracy, and which indicates that the ES model provides more accurate predictions than the stand-alone surrogate models. Then, a nonlinear optimization model was formulated for the minimum cost, and the developed ES model was embedded into this optimization model as a constrained condition. Besides, GA was used to solve the optimization model to provide the optimal SEAR strategy. The developed ensemble surrogate-optimization approach was effective in seeking a cost-effective SEAR strategy for heterogeneous DNAPL-contaminated sites. This research is expected to enrich and develop the theoretical and technical implications for the analysis of remediation strategy optimization of DNAPL-contaminated aquifers.

  16. Generalized Ensemble Sampling of Enzyme Reaction Free Energy Pathways

    PubMed Central

    Wu, Dongsheng; Fajer, Mikolai I.; Cao, Liaoran; Cheng, Xiaolin; Yang, Wei

    2016-01-01

    Free energy path sampling plays an essential role in computational understanding of chemical reactions, particularly those occurring in enzymatic environments. Among a variety of molecular dynamics simulation approaches, the generalized ensemble sampling strategy is uniquely attractive for the fact that it not only can enhance the sampling of rare chemical events but also can naturally ensure consistent exploration of environmental degrees of freedom. In this review, we plan to provide a tutorial-like tour on an emerging topic: generalized ensemble sampling of enzyme reaction free energy path. The discussion is largely focused on our own studies, particularly ones based on the metadynamics free energy sampling method and the on-the-path random walk path sampling method. We hope that this mini presentation will provide interested practitioners some meaningful guidance for future algorithm formulation and application study. PMID:27498634

  17. Generalized Ensemble Sampling of Enzyme Reaction Free Energy Pathways.

    PubMed

    Wu, D; Fajer, M I; Cao, L; Cheng, X; Yang, W

    2016-01-01

    Free energy path sampling plays an essential role in computational understanding of chemical reactions, particularly those occurring in enzymatic environments. Among a variety of molecular dynamics simulation approaches, the generalized ensemble sampling strategy is uniquely attractive for the fact that it not only can enhance the sampling of rare chemical events but also can naturally ensure consistent exploration of environmental degrees of freedom. In this review, we plan to provide a tutorial-like tour on an emerging topic: generalized ensemble sampling of enzyme reaction free energy path. The discussion is largely focused on our own studies, particularly ones based on the metadynamics free energy sampling method and the on-the-path random walk path sampling method. We hope that this minipresentation will provide interested practitioners some meaningful guidance for future algorithm formulation and application study.

  18. Sampling the isothermal-isobaric ensemble by Langevin dynamics.

    PubMed

    Gao, Xingyu; Fang, Jun; Wang, Han

    2016-03-28

    We present a new method of conducting fully flexible-cell molecular dynamics simulation in isothermal-isobaric ensemble based on Langevin equations of motion. The stochastic coupling to all particle and cell degrees of freedoms is introduced in a correct way, in the sense that the stationary configurational distribution is proved to be consistent with that of the isothermal-isobaric ensemble. In order to apply the proposed method in computer simulations, a second order symmetric numerical integration scheme is developed by Trotter's splitting of the single-step propagator. Moreover, a practical guide of choosing working parameters is suggested for user specified thermo- and baro-coupling time scales. The method and software implementation are carefully validated by a numerical example. PMID:27036433

  19. Generalized Ensemble Sampling of Enzyme Reaction Free Energy Pathways.

    PubMed

    Wu, D; Fajer, M I; Cao, L; Cheng, X; Yang, W

    2016-01-01

    Free energy path sampling plays an essential role in computational understanding of chemical reactions, particularly those occurring in enzymatic environments. Among a variety of molecular dynamics simulation approaches, the generalized ensemble sampling strategy is uniquely attractive for the fact that it not only can enhance the sampling of rare chemical events but also can naturally ensure consistent exploration of environmental degrees of freedom. In this review, we plan to provide a tutorial-like tour on an emerging topic: generalized ensemble sampling of enzyme reaction free energy path. The discussion is largely focused on our own studies, particularly ones based on the metadynamics free energy sampling method and the on-the-path random walk path sampling method. We hope that this minipresentation will provide interested practitioners some meaningful guidance for future algorithm formulation and application study. PMID:27498634

  20. Photoswitchable gel assembly based on molecular recognition.

    PubMed

    Yamaguchi, Hiroyasu; Kobayashi, Yuichiro; Kobayashi, Ryosuke; Takashima, Yoshinori; Hashidzume, Akihito; Harada, Akira

    2012-01-03

    The formation of effective and precise linkages in bottom-up or top-down processes is important for the development of self-assembled materials. Self-assembly through molecular recognition events is a powerful tool for producing functionalized materials. Photoresponsive molecular recognition systems can permit the creation of photoregulated self-assembled macroscopic objects. Here we demonstrate that macroscopic gel assembly can be highly regulated through photoisomerization of an azobenzene moiety that interacts differently with two host molecules. A photoregulated gel assembly system is developed using polyacrylamide-based hydrogels functionalized with azobenzene (guest) or cyclodextrin (host) moieties. Reversible adhesion and dissociation of the host gel from the guest gel may be controlled by photoirradiation. The differential affinities of α-cyclodextrin or β-cyclodextrin for the trans-azobenzene and cis-azobenzene are employed in the construction of a photoswitchable gel assembly system.

  1. Photoswitchable gel assembly based on molecular recognition

    PubMed Central

    Yamaguchi, Hiroyasu; Kobayashi, Yuichiro; Kobayashi, Ryosuke; Takashima, Yoshinori; Hashidzume, Akihito; Harada, Akira

    2012-01-01

    The formation of effective and precise linkages in bottom-up or top-down processes is important for the development of self-assembled materials. Self-assembly through molecular recognition events is a powerful tool for producing functionalized materials. Photoresponsive molecular recognition systems can permit the creation of photoregulated self-assembled macroscopic objects. Here we demonstrate that macroscopic gel assembly can be highly regulated through photoisomerization of an azobenzene moiety that interacts differently with two host molecules. A photoregulated gel assembly system is developed using polyacrylamide-based hydrogels functionalized with azobenzene (guest) or cyclodextrin (host) moieties. Reversible adhesion and dissociation of the host gel from the guest gel may be controlled by photoirradiation. The differential affinities of α-cyclodextrin or β-cyclodextrin for the trans-azobenzene and cis-azobenzene are employed in the construction of a photoswitchable gel assembly system. PMID:22215078

  2. Photoswitchable gel assembly based on molecular recognition.

    PubMed

    Yamaguchi, Hiroyasu; Kobayashi, Yuichiro; Kobayashi, Ryosuke; Takashima, Yoshinori; Hashidzume, Akihito; Harada, Akira

    2012-01-01

    The formation of effective and precise linkages in bottom-up or top-down processes is important for the development of self-assembled materials. Self-assembly through molecular recognition events is a powerful tool for producing functionalized materials. Photoresponsive molecular recognition systems can permit the creation of photoregulated self-assembled macroscopic objects. Here we demonstrate that macroscopic gel assembly can be highly regulated through photoisomerization of an azobenzene moiety that interacts differently with two host molecules. A photoregulated gel assembly system is developed using polyacrylamide-based hydrogels functionalized with azobenzene (guest) or cyclodextrin (host) moieties. Reversible adhesion and dissociation of the host gel from the guest gel may be controlled by photoirradiation. The differential affinities of α-cyclodextrin or β-cyclodextrin for the trans-azobenzene and cis-azobenzene are employed in the construction of a photoswitchable gel assembly system. PMID:22215078

  3. Advanced ensemble modelling of flexible macromolecules using X-ray solution scattering.

    PubMed

    Tria, Giancarlo; Mertens, Haydyn D T; Kachala, Michael; Svergun, Dmitri I

    2015-03-01

    Dynamic ensembles of macromolecules mediate essential processes in biology. Understanding the mechanisms driving the function and molecular interactions of 'unstructured' and flexible molecules requires alternative approaches to those traditionally employed in structural biology. Small-angle X-ray scattering (SAXS) is an established method for structural characterization of biological macromolecules in solution, and is directly applicable to the study of flexible systems such as intrinsically disordered proteins and multi-domain proteins with unstructured regions. The Ensemble Optimization Method (EOM) [Bernadó et al. (2007 ▶). J. Am. Chem. Soc. 129, 5656-5664] was the first approach introducing the concept of ensemble fitting of the SAXS data from flexible systems. In this approach, a large pool of macromolecules covering the available conformational space is generated and a sub-ensemble of conformers coexisting in solution is selected guided by the fit to the experimental SAXS data. This paper presents a series of new developments and advancements to the method, including significantly enhanced functionality and also quantitative metrics for the characterization of the results. Building on the original concept of ensemble optimization, the algorithms for pool generation have been redesigned to allow for the construction of partially or completely symmetric oligomeric models, and the selection procedure was improved to refine the size of the ensemble. Quantitative measures of the flexibility of the system studied, based on the characteristic integral parameters of the selected ensemble, are introduced. These improvements are implemented in the new EOM version 2.0, and the capabilities as well as inherent limitations of the ensemble approach in SAXS, and of EOM 2.0 in particular, are discussed.

  4. Hybrid Data Assimilation without Ensemble Filtering

    NASA Technical Reports Server (NTRS)

    Todling, Ricardo; Akkraoui, Amal El

    2014-01-01

    The Global Modeling and Assimilation Office is preparing to upgrade its three-dimensional variational system to a hybrid approach in which the ensemble is generated using a square-root ensemble Kalman filter (EnKF) and the variational problem is solved using the Grid-point Statistical Interpolation system. As in most EnKF applications, we found it necessary to employ a combination of multiplicative and additive inflations, to compensate for sampling and modeling errors, respectively and, to maintain the small-member ensemble solution close to the variational solution; we also found it necessary to re-center the members of the ensemble about the variational analysis. During tuning of the filter we have found re-centering and additive inflation to play a considerably larger role than expected, particularly in a dual-resolution context when the variational analysis is ran at larger resolution than the ensemble. This led us to consider a hybrid strategy in which the members of the ensemble are generated by simply converting the variational analysis to the resolution of the ensemble and applying additive inflation, thus bypassing the EnKF. Comparisons of this, so-called, filter-free hybrid procedure with an EnKF-based hybrid procedure and a control non-hybrid, traditional, scheme show both hybrid strategies to provide equally significant improvement over the control; more interestingly, the filter-free procedure was found to give qualitatively similar results to the EnKF-based procedure.

  5. Supramolecular Ensembles Formed between Charged Conjugated Polymers and Glycoprobes for the Fluorogenic Recognition of Receptor Proteins.

    PubMed

    Dou, Wei-Tao; Zeng, Ya-Li; Lv, Ying; Wu, Jiatao; He, Xiao-Peng; Chen, Guo-Rong; Tan, Chunyan

    2016-06-01

    This paper describes the simple construction of a unique class of supramolecular ensembles formed by electrostatic self-assembly between charged conjugated polymers and fluorophore-coupled glycoligands (glycoprobes) for the selective fluorogenic detection of receptor proteins at both the molecular and cellular levels. We show that positively and negatively charged diazobenzene-containing poly(p-phenylethynylenes) (PPEs) can be used to form stable fluorogenic probes with fluorescein-based (negatively charged) and rhodamine B based (positively charged) glycoprobes by electrostatic interaction. The structures of the ensembles have been characterized by spectroscopic and microscopic techniques. The supramolecular probes formed show quenched fluorescence in an aqueous buffer solution, which can be specifically recovered, in a concentration-dependent manner, through competitive complexation with a selective protein receptor, over a range of other unselective proteins. The ensembles also show selective fluorescence enhancement with a live cell that expresses the glycoligand receptor but not a control cell without receptor expression. PMID:27159586

  6. Creating ensembles of decision trees through sampling

    DOEpatents

    Kamath, Chandrika; Cantu-Paz, Erick

    2005-08-30

    A system for decision tree ensembles that includes a module to read the data, a module to sort the data, a module to evaluate a potential split of the data according to some criterion using a random sample of the data, a module to split the data, and a module to combine multiple decision trees in ensembles. The decision tree method is based on statistical sampling techniques and includes the steps of reading the data; sorting the data; evaluating a potential split according to some criterion using a random sample of the data, splitting the data, and combining multiple decision trees in ensembles.

  7. Integrated logic gate for fluorescence turn-on detection of histidine and cysteine based on Ag/Au bimetallic nanoclusters-Cu²⁺ ensemble.

    PubMed

    Sun, Jian; Yang, Fan; Zhao, Dan; Chen, Chuanxia; Yang, Xiurong

    2015-04-01

    By means of employing 11-mercaptoundecanoic acid (11-MUA) as a reducing agent and protecting ligand, we present straightforward one-pot preparation of fluorescent Ag/Au bimetallic nanoclusters (namely AgAuNCs@11-MUA) from AgNO3 and HAuCl4 in alkaline aqueous solution at room temperature. It is found that the fluorescence of AgAuNCs@11-MUA has been selectively quenched by Cu(2+) ions, and the nonfluorescence off-state of the as-prepared AgAuNCs@11-MUA-Cu(2+) ensemble can be effectively switched on upon the addition of histidine and cysteine. By incorporating Ni(2+) ions and N-ethylmaleimide, this phenomenon is further exploited as an integrated logic gate and a specific fluorescence turn-on assay for selectively and sensitively sensing histidine and cysteine has been designed and established based on the original noncovalent AgAuNCs@11-MUA-Cu(2+) ensemble. Under the optimal conditions, histidine and cysteine can be detected in the concentration ranges of 0.25-9 and 0.25-7 μM; besides, the detection limits are found to be 87 and 111 nM (S/N = 3), respectively. Furthermore, we demonstrate that the proposed AgAuNCs@11-MUA-based fluorescent assay can be successfully utilized for biological fluids sample analysis.

  8. Integrated logic gate for fluorescence turn-on detection of histidine and cysteine based on Ag/Au bimetallic nanoclusters-Cu²⁺ ensemble.

    PubMed

    Sun, Jian; Yang, Fan; Zhao, Dan; Chen, Chuanxia; Yang, Xiurong

    2015-04-01

    By means of employing 11-mercaptoundecanoic acid (11-MUA) as a reducing agent and protecting ligand, we present straightforward one-pot preparation of fluorescent Ag/Au bimetallic nanoclusters (namely AgAuNCs@11-MUA) from AgNO3 and HAuCl4 in alkaline aqueous solution at room temperature. It is found that the fluorescence of AgAuNCs@11-MUA has been selectively quenched by Cu(2+) ions, and the nonfluorescence off-state of the as-prepared AgAuNCs@11-MUA-Cu(2+) ensemble can be effectively switched on upon the addition of histidine and cysteine. By incorporating Ni(2+) ions and N-ethylmaleimide, this phenomenon is further exploited as an integrated logic gate and a specific fluorescence turn-on assay for selectively and sensitively sensing histidine and cysteine has been designed and established based on the original noncovalent AgAuNCs@11-MUA-Cu(2+) ensemble. Under the optimal conditions, histidine and cysteine can be detected in the concentration ranges of 0.25-9 and 0.25-7 μM; besides, the detection limits are found to be 87 and 111 nM (S/N = 3), respectively. Furthermore, we demonstrate that the proposed AgAuNCs@11-MUA-based fluorescent assay can be successfully utilized for biological fluids sample analysis. PMID:25761537

  9. Ensemble Data Mining Methods

    NASA Technical Reports Server (NTRS)

    Oza, Nikunj C.

    2004-01-01

    Ensemble Data Mining Methods, also known as Committee Methods or Model Combiners, are machine learning methods that leverage the power of multiple models to achieve better prediction accuracy than any of the individual models could on their own. The basic goal when designing an ensemble is the same as when establishing a committee of people: each member of the committee should be as competent as possible, but the members should be complementary to one another. If the members are not complementary, Le., if they always agree, then the committee is unnecessary---any one member is sufficient. If the members are complementary, then when one or a few members make an error, the probability is high that the remaining members can correct this error. Research in ensemble methods has largely revolved around designing ensembles consisting of competent yet complementary models.

  10. Organic-based molecular switches for molecular electronics.

    PubMed

    Fuentes, Noelia; Martín-Lasanta, Ana; Alvarez de Cienfuegos, Luis; Ribagorda, Maria; Parra, Andres; Cuerva, Juan M

    2011-10-01

    In a general sense, molecular electronics (ME) is the branch of nanotechnology which studies the application of molecular building blocks for the fabrication of electronic components. Among the different types of molecules, organic compounds have been revealed as promising candidates for ME, due to the easy access, great structural diversity and suitable electronic and mechanical properties. Thanks to these useful capabilities, organic molecules have been used to emulate electronic devices at the nanoscopic scale. In this feature article, we present the diverse strategies used to develop organic switches towards ME with special attention to non-volatile systems.

  11. A mercuric ensemble based on a cycloruthenated complex as a visual probe for iodide in aqueous solution

    NASA Astrophysics Data System (ADS)

    Su, Xianlong; Guo, Lieping; Ma, Yajuan; Li, Xianghong

    2016-01-01

    A new water-soluble cycloruthenated complex Ru(bthiq)(dcbpy)2+ (1, Hbthiq = 1-(2-benzo[b]thiophenyl)isoquinoline, dcbpy = 4,4‧-dicarboxylate-2,2‧-bipyridine) was designed and synthesized to form its mercuric ensemble (1-Hg2+) to achieve visual detection of iodide anions. The binding constant of 1-Hg2+ is calculated to be 2.40 × 104 M-1, which is lower than that of HgI2. Therefore, the addition of I- to the aqueous solution of 1-Hg2+lead to significant color changes from yellow to deep-red by the release of 1. The results showed that iodide anions could be easily detected by the naked eyes. The detection limit of iodide anion is calculated as 0.77 μM. In addition, an easily-prepared test strip of 1-Hg2+ was obtained successfully to detect iodide anions.

  12. EAKF-CMAQ : introduction and evaluation of a data assimilation for CMAQ based on the ensemble adjustment Kalman filter.

    SciTech Connect

    Zubrow, A.; Chen, L.; Kotamarthi, V. R.; Environmental Science Division; Univ. of North Carolina; Univ. of Chicago

    2008-05-10

    A new approach is presented for data assimilation using the ensemble adjustment Kalman filter (EAKF) technique for surface measurements of carbon monoxide in a single tracer version of the community air quality model. An implementation of the EAKF known as the Data Assimilation Research Testbed at the National Center for Atmospheric Research was used for developing the model. Three different sets of numerical experiments were performed to test the effectiveness of the procedure and the range of key parameters used in implementing the procedure. The model domain includes much of the northeastern United States. The first two numerical experiments use idealized measurements derived from defined model runs, and the last test uses measurements of carbon monoxide from approximately 220 Air Quality System monitoring sites over the northeastern United States, maintained by the U.S. Environmental Protection Agency. In each case, the proposed method provided better results than the method without data assimilation.

  13. Molecular cloning of protein-based polymers.

    PubMed

    Mi, Lixin

    2006-07-01

    Protein-based biopolymers have become a promising class of materials for both biomedical and pharmaceutical applications, as they have well-defined molecular weights, monomer compositions, as well as tunable chemical, biological, and mechanical properties. Using standard molecular biology tools, it is possible to design and construct genes encoding artificial proteins or protein-based polymers containing multiple repeats of amino acid sequences. This article reviews some of the traditional methods used for constructing DNA duplexes encoding these repeat-containing genes, including monomer generation, concatemerization, iterative oligomerization, and seamless cloning. A facile and versatile method, called modules of degenerate codons (MDC), which uses PCR and codon degeneracy to overcome some of the disadvantages of traditional methods, is introduced. Re-engineering of the random coil spacer domain of a bioactive protein, WPT2-3R, is used to demonstrate the utility of the MDC method. MDC re-constructed coding sequences facilitate further manipulations, such as insertion, deletion, and swapping of various sequence modules. A summary of some promising emerging techniques for synthesizing repetitive sequence-containing artificial proteins is also provided. PMID:16827576

  14. An Ensemble Method to Distinguish Bacteriophage Virion from Non-Virion Proteins Based on Protein Sequence Characteristics.

    PubMed

    Zhang, Lina; Zhang, Chengjin; Gao, Rui; Yang, Runtao

    2015-01-01

    Bacteriophage virion proteins and non-virion proteins have distinct functions in biological processes, such as specificity determination for host bacteria, bacteriophage replication and transcription. Accurate identification of bacteriophage virion proteins from bacteriophage protein sequences is significant to understand the complex virulence mechanism in host bacteria and the influence of bacteriophages on the development of antibacterial drugs. In this study, an ensemble method for bacteriophage virion protein prediction from bacteriophage protein sequences is put forward with hybrid feature spaces incorporating CTD (composition, transition and distribution), bi-profile Bayes, PseAAC (pseudo-amino acid composition) and PSSM (position-specific scoring matrix). When performing on the training dataset 10-fold cross-validation, the presented method achieves a satisfactory prediction result with a sensitivity of 0.870, a specificity of 0.830, an accuracy of 0.850 and Matthew's correlation coefficient (MCC) of 0.701, respectively. To evaluate the prediction performance objectively, an independent testing dataset is used to evaluate the proposed method. Encouragingly, our proposed method performs better than previous studies with a sensitivity of 0.853, a specificity of 0.815, an accuracy of 0.831 and MCC of 0.662 on the independent testing dataset. These results suggest that the proposed method can be a potential candidate for bacteriophage virion protein prediction, which may provide a useful tool to find novel antibacterial drugs and to understand the relationship between bacteriophage and host bacteria. For the convenience of the vast majority of experimental Int. J. Mol. Sci. 2015, 16,21735 scientists, a user-friendly and publicly-accessible web-server for the proposed ensemble method is established.

  15. An Ensemble Method to Distinguish Bacteriophage Virion from Non-Virion Proteins Based on Protein Sequence Characteristics

    PubMed Central

    Zhang, Lina; Zhang, Chengjin; Gao, Rui; Yang, Runtao

    2015-01-01

    Bacteriophage virion proteins and non-virion proteins have distinct functions in biological processes, such as specificity determination for host bacteria, bacteriophage replication and transcription. Accurate identification of bacteriophage virion proteins from bacteriophage protein sequences is significant to understand the complex virulence mechanism in host bacteria and the influence of bacteriophages on the development of antibacterial drugs. In this study, an ensemble method for bacteriophage virion protein prediction from bacteriophage protein sequences is put forward with hybrid feature spaces incorporating CTD (composition, transition and distribution), bi-profile Bayes, PseAAC (pseudo-amino acid composition) and PSSM (position-specific scoring matrix). When performing on the training dataset 10-fold cross-validation, the presented method achieves a satisfactory prediction result with a sensitivity of 0.870, a specificity of 0.830, an accuracy of 0.850 and Matthew’s correlation coefficient (MCC) of 0.701, respectively. To evaluate the prediction performance objectively, an independent testing dataset is used to evaluate the proposed method. Encouragingly, our proposed method performs better than previous studies with a sensitivity of 0.853, a specificity of 0.815, an accuracy of 0.831 and MCC of 0.662 on the independent testing dataset. These results suggest that the proposed method can be a potential candidate for bacteriophage virion protein prediction, which may provide a useful tool to find novel antibacterial drugs and to understand the relationship between bacteriophage and host bacteria. For the convenience of the vast majority of experimental scientists, a user-friendly and publicly-accessible web-server for the proposed ensemble method is established. PMID:26370987

  16. Bayesian ensemble refinement by replica simulations and reweighting.

    PubMed

    Hummer, Gerhard; Köfinger, Jürgen

    2015-12-28

    We describe different Bayesian ensemble refinement methods, examine their interrelation, and discuss their practical application. With ensemble refinement, the properties of dynamic and partially disordered (bio)molecular structures can be characterized by integrating a wide range of experimental data, including measurements of ensemble-averaged observables. We start from a Bayesian formulation in which the posterior is a functional that ranks different configuration space distributions. By maximizing this posterior, we derive an optimal Bayesian ensemble distribution. For discrete configurations, this optimal distribution is identical to that obtained by the maximum entropy "ensemble refinement of SAXS" (EROS) formulation. Bayesian replica ensemble refinement enhances the sampling of relevant configurations by imposing restraints on averages of observables in coupled replica molecular dynamics simulations. We show that the strength of the restraints should scale linearly with the number of replicas to ensure convergence to the optimal Bayesian result in the limit of infinitely many replicas. In the "Bayesian inference of ensembles" method, we combine the replica and EROS approaches to accelerate the convergence. An adaptive algorithm can be used to sample directly from the optimal ensemble, without replicas. We discuss the incorporation of single-molecule measurements and dynamic observables such as relaxation parameters. The theoretical analysis of different Bayesian ensemble refinement approaches provides a basis for practical applications and a starting point for further investigations. PMID:26723635

  17. Bayesian ensemble refinement by replica simulations and reweighting

    NASA Astrophysics Data System (ADS)

    Hummer, Gerhard; Köfinger, Jürgen

    2015-12-01

    We describe different Bayesian ensemble refinement methods, examine their interrelation, and discuss their practical application. With ensemble refinement, the properties of dynamic and partially disordered (bio)molecular structures can be characterized by integrating a wide range of experimental data, including measurements of ensemble-averaged observables. We start from a Bayesian formulation in which the posterior is a functional that ranks different configuration space distributions. By maximizing this posterior, we derive an optimal Bayesian ensemble distribution. For discrete configurations, this optimal distribution is identical to that obtained by the maximum entropy "ensemble refinement of SAXS" (EROS) formulation. Bayesian replica ensemble refinement enhances the sampling of relevant configurations by imposing restraints on averages of observables in coupled replica molecular dynamics simulations. We show that the strength of the restraints should scale linearly with the number of replicas to ensure convergence to the optimal Bayesian result in the limit of infinitely many replicas. In the "Bayesian inference of ensembles" method, we combine the replica and EROS approaches to accelerate the convergence. An adaptive algorithm can be used to sample directly from the optimal ensemble, without replicas. We discuss the incorporation of single-molecule measurements and dynamic observables such as relaxation parameters. The theoretical analysis of different Bayesian ensemble refinement approaches provides a basis for practical applications and a starting point for further investigations.

  18. Bayesian ensemble refinement by replica simulations and reweighting.

    PubMed

    Hummer, Gerhard; Köfinger, Jürgen

    2015-12-28

    We describe different Bayesian ensemble refinement methods, examine their interrelation, and discuss their practical application. With ensemble refinement, the properties of dynamic and partially disordered (bio)molecular structures can be characterized by integrating a wide range of experimental data, including measurements of ensemble-averaged observables. We start from a Bayesian formulation in which the posterior is a functional that ranks different configuration space distributions. By maximizing this posterior, we derive an optimal Bayesian ensemble distribution. For discrete configurations, this optimal distribution is identical to that obtained by the maximum entropy "ensemble refinement of SAXS" (EROS) formulation. Bayesian replica ensemble refinement enhances the sampling of relevant configurations by imposing restraints on averages of observables in coupled replica molecular dynamics simulations. We show that the strength of the restraints should scale linearly with the number of replicas to ensure convergence to the optimal Bayesian result in the limit of infinitely many replicas. In the "Bayesian inference of ensembles" method, we combine the replica and EROS approaches to accelerate the convergence. An adaptive algorithm can be used to sample directly from the optimal ensemble, without replicas. We discuss the incorporation of single-molecule measurements and dynamic observables such as relaxation parameters. The theoretical analysis of different Bayesian ensemble refinement approaches provides a basis for practical applications and a starting point for further investigations.

  19. An integrated logic system for time-resolved fluorescent "turn-on" detection of cysteine and histidine base on terbium (III) coordination polymer-copper (II) ensemble.

    PubMed

    Xue, Shi-Fan; Lu, Ling-Fei; Wang, Qi-Xian; Zhang, Shengqiang; Zhang, Min; Shi, Guoyue

    2016-09-01

    Cysteine (Cys) and histidine (His) both play indispensable roles in many important biological activities. An enhanced Cys level can result in Alzheimer's and cardiovascular diseases. Likewise, His plays a significant role in the growth and repair of tissues as well as in controlling the transmission of metal elements in biological bases. Therefore, it is meaningful to detect Cys and His simultaneously. In this work, a novel terbium (III) coordination polymer-Cu (II) ensemble (Tb(3+)/GMP-Cu(2+)) was proposed. Guanosine monophosphate (GMP) can self-assemble with Tb(3+) to form a supramolecular Tb(3+) coordination polymer (Tb(3+)/GMP), which can be suited as a time-resolved probe. The fluorescence of Tb(3+)/GMP would be quenched upon the addition of Cu(2+), and then the fluorescence of the as-prepared Tb(3+)/GMP-Cu(2+) ensemble would be restored again in the presence of Cys or His. By incorporating N-Ethylmaleimide and Ni(2+) as masking agents, Tb(3+)/GMP-Cu(2+) was further exploited as an integrated logic system and a specific time-resolved fluorescent "turn-on" assay for simultaneously sensing His and Cys was designed. Meanwhile it can also be used in plasma samples, showing great potential to meet the need of practical application. PMID:27343597

  20. Comparison of Ensemble Strategies in Online NIR for Monitoring the Extraction Process of Pericarpium Citri Reticulatae Based on Different Variable Selections.

    PubMed

    Zhou, Zheng; Li, Yang; Zhang, Qiao; Shi, Xinyuan; Wu, Zhisheng; Qiao, Yanjiang

    2016-01-01

    Different ensemble strategies were compared in online near-infrared models for monitoring active pharmaceutical ingredients of Traditional Chinese Medicine. Bagging partial least square regression and boosting partial least square regression were adopted to near-infrared models, to determine hesperidin and nobiletin content during the extraction process of Pericarpium Citri Reticulatae in a pilot scale system. Different pretreatment methods were investigated, including Savitzky-Golay smoothing, derivatives, multiplicative scatter correction, standard normal variate, normalize, and combinations of them. Two different variable selection methods, including synergy interval partial least squares and backward interval partial least squares algorithms, were performed. Based on the result of the synergy interval partial least squares algorithm, bagging partial least square regression and boosting partial least square regression were adopted into the quantitative analysis. The results demonstrated that the established approach could be applied for rapid determination and real-time monitoring of hesperidin and nobiletin in Pericarpium Citri Reticulatae (Citrus reticulata) during the extraction process. Comparing the results, the boosting partial least square regression provided a slightly better accuracy than the bagging partial least square regression. Finally, this paper provides a promising ensemble strategy on online near-infrared models in Chinese medicine. PMID:26485639

  1. Project fires. Volume 2: Protective ensemble performance standards, phase 1B

    NASA Astrophysics Data System (ADS)

    Abeles, F. J.

    1980-05-01

    The design of the prototype protective ensemble was finalized. Prototype ensembles were fabricated and then subjected to a series of qualification tests which were based upon the protective ensemble performance standards PEPS requirements. Engineering drawings and purchase specifications were prepared for the new protective ensemble.

  2. Project FIRES [Firefighters' Integrated Response Equipment System]. Volume 2: Protective Ensemble Performance Standards, Phase 1B

    NASA Technical Reports Server (NTRS)

    Abeles, F. J.

    1980-01-01

    The design of the prototype protective ensemble was finalized. Prototype ensembles were fabricated and then subjected to a series of qualification tests which were based upon the protective ensemble performance standards PEPS requirements. Engineering drawings and purchase specifications were prepared for the new protective ensemble.

  3. Protein-based tumor molecular imaging probes

    PubMed Central

    Lin, Xin; Xie, Jin

    2013-01-01

    Molecular imaging is an emerging discipline which plays critical roles in diagnosis and therapeutics. It visualizes and quantifies markers that are aberrantly expressed during the disease origin and development. Protein molecules remain to be one major class of imaging probes, and the option has been widely diversified due to the recent advances in protein engineering techniques. Antibodies are part of the immunosystem which interact with target antigens with high specificity and affinity. They have long been investigated as imaging probes and were coupled with imaging motifs such as radioisotopes for that purpose. However, the relatively large size of antibodies leads to a half-life that is too long for common imaging purposes. Besides, it may also cause a poor tissue penetration rate and thus compromise some medical applications. It is under this context that various engineered protein probes, essentially antibody fragments, protein scaffolds, and natural ligands have been developed. Compared to intact antibodies, they possess more compact size, shorter clearance time, and better tumor penetration. One major challenge of using protein probes in molecular imaging is the affected biological activity resulted from random labeling. Site-specific modification, however, allows conjugation happening in a stoichiometric fashion with little perturbation of protein activity. The present review will discuss protein-based probes with focus on their application and related site-specific conjugation strategies in tumor imaging. PMID:20232092

  4. Density based visualization for molecular simulation.

    PubMed

    Rozmanov, Dmitri; Baoukina, Svetlana; Tieleman, D Peter

    2014-01-01

    Molecular visualization of structural information obtained from computer simulations is an important part of research work flow. A good visualization technique should be capable of eliminating redundant information and highlight important effects clarifying the key phenomena in the system. Current methods of presenting structural data are mostly limited to variants of the traditional ball-and-stick representation. This approach becomes less attractive when very large biological systems are simulated at microsecond timescales, and is less effective when coarse-grained models are used. Real time rendering of such large systems becomes a difficult task; the amount of information in one single frame of a simulation trajectory is enormous given the large number of particles; at the same time, each structure contains information about one configurational point of the system and no information about statistical weight of this specific configuration. In this paper we report a novel visualization technique based on spatial particle densities. The atomic densities are sampled on a high resolution 3-dimensional grid along a relatively short molecular dynamics trajectory using hundreds of configurations. The density information is then analyzed and visualized using the open-source ParaView software. The performance and capability of the method are demonstrated on two large systems simulated with the MARTINI coarse-grained force field: a lipid nanoparticle for delivering siRNA molecules and monolayers with a complex composition under conditions that induce monolayer collapse.

  5. Dimensionality Reduction Through Classifier Ensembles

    NASA Technical Reports Server (NTRS)

    Oza, Nikunj C.; Tumer, Kagan; Norwig, Peter (Technical Monitor)

    1999-01-01

    In data mining, one often needs to analyze datasets with a very large number of attributes. Performing machine learning directly on such data sets is often impractical because of extensive run times, excessive complexity of the fitted model (often leading to overfitting), and the well-known "curse of dimensionality." In practice, to avoid such problems, feature selection and/or extraction are often used to reduce data dimensionality prior to the learning step. However, existing feature selection/extraction algorithms either evaluate features by their effectiveness across the entire data set or simply disregard class information altogether (e.g., principal component analysis). Furthermore, feature extraction algorithms such as principal components analysis create new features that are often meaningless to human users. In this article, we present input decimation, a method that provides "feature subsets" that are selected for their ability to discriminate among the classes. These features are subsequently used in ensembles of classifiers, yielding results superior to single classifiers, ensembles that use the full set of features, and ensembles based on principal component analysis on both real and synthetic datasets.

  6. Projections of Water Stress Based on an Ensemble of Socioeconomic Growth and Climate Change Scenarios: A Case Study in Asia.

    PubMed

    Fant, Charles; Schlosser, C Adam; Gao, Xiang; Strzepek, Kenneth; Reilly, John

    2016-01-01

    The sustainability of future water resources is of paramount importance and is affected by many factors, including population, wealth and climate. Inherent in current methods to estimate these factors in the future is the uncertainty of their prediction. In this study, we integrate a large ensemble of scenarios--internally consistent across economics, emissions, climate, and population--to develop a risk portfolio of water stress over a large portion of Asia that includes China, India, and Mainland Southeast Asia in a future with unconstrained emissions. We isolate the effects of socioeconomic growth from the effects of climate change in order to identify the primary drivers of stress on water resources. We find that water needs related to socioeconomic changes, which are currently small, are likely to increase considerably in the future, often overshadowing the effect of climate change on levels of water stress. As a result, there is a high risk of severe water stress in densely populated watersheds by 2050, compared to recent history. There is strong evidence to suggest that, in the absence of autonomous adaptation or societal response, a much larger portion of the region's population will live in water-stressed regions in the near future. Tools and studies such as these can effectively investigate large-scale system sensitivities and can be useful in engaging and informing decision makers.

  7. Projections of Water Stress Based on an Ensemble of Socioeconomic Growth and Climate Change Scenarios: A Case Study in Asia.

    PubMed

    Fant, Charles; Schlosser, C Adam; Gao, Xiang; Strzepek, Kenneth; Reilly, John

    2016-01-01

    The sustainability of future water resources is of paramount importance and is affected by many factors, including population, wealth and climate. Inherent in current methods to estimate these factors in the future is the uncertainty of their prediction. In this study, we integrate a large ensemble of scenarios--internally consistent across economics, emissions, climate, and population--to develop a risk portfolio of water stress over a large portion of Asia that includes China, India, and Mainland Southeast Asia in a future with unconstrained emissions. We isolate the effects of socioeconomic growth from the effects of climate change in order to identify the primary drivers of stress on water resources. We find that water needs related to socioeconomic changes, which are currently small, are likely to increase considerably in the future, often overshadowing the effect of climate change on levels of water stress. As a result, there is a high risk of severe water stress in densely populated watersheds by 2050, compared to recent history. There is strong evidence to suggest that, in the absence of autonomous adaptation or societal response, a much larger portion of the region's population will live in water-stressed regions in the near future. Tools and studies such as these can effectively investigate large-scale system sensitivities and can be useful in engaging and informing decision makers. PMID:27028871

  8. Six-month lead downscaling prediction of winter-spring drought in South Korea based on multi-model ensemble

    NASA Astrophysics Data System (ADS)

    Sohn, Soo-Jin; Ahn, Joong-Bae; Tam, Chi-Yung

    2013-04-01

    Given the changing climate, advance information on hydrological extremes such as droughts will help in planning for disaster mitigation and facilitate better decision making for water availability management. A deficit of precipitation for long-term time scales beyond 6 months has impacts on the hydrological sectors such as ground water, streamflow, and reservoir storage. The potential of using a dynamical-statistical method for long-lead drought prediction was investigated. In particular, the APEC Climate Center (APCC) 1-Tier multi-model ensemble (MME) was downscaled for predicting the standardized precipitation evapotranspiration index (SPEI) over 60 stations in South Korea. SPEI depends on both of precipitation and temperature, and can incorporate the impact of global warming on the balance between precipitation and evapotranspiration. It was found that 1-Tier MME has difficulties in capturing the local temperature and rainfall variations over extratropical land areas, and has no skill in predicting SPEI during boreal winter and spring. On the other hand, temperature and precipitation predictions were substantially improved in the downscaled MME (DMME). In conjunction with variance inflation, DMME can give reasonably skillful six-month-lead forecasts of SPEI for the winter-to-spring period. The results could potentially improve hydrological extreme predictions using meteorological forecasts for policymaker and stakeholders in water management sector for better climate adaption.

  9. Six month-lead downscaling prediction of winter to spring drought in South Korea based on a multimodel ensemble

    NASA Astrophysics Data System (ADS)

    Sohn, Soo-Jin; Ahn, Joong-Bae; Tam, Chi-Yung

    2013-02-01

    Abstract The potential of using a dynamical-statistical method for long-lead drought prediction was investigated. In particular, the APEC Climate Center one-tier multimodel <span class="hlt">ensemble</span> (MME) was downscaled for predicting the standardized precipitation evapotranspiration index (SPEI) over 60 stations in South Korea. SPEI depends on both precipitation and temperature, and can incorporate the effect of global warming on the balance between precipitation and evapotranspiration. It was found that the one-tier MME has difficulty in capturing the local temperature and rainfall variations over extratropical land areas, and has no skill in predicting SPEI during boreal winter and spring. On the other hand, temperature and precipitation predictions were substantially improved in the downscaled MME. In conjunction with variance inflation, downscaled MME can give reasonably skillful 6 month-lead forecasts of SPEI for the winter to spring period. Our results could lead to more reliable hydrological extreme predictions for policymakers and stakeholders in the water management sector, and for better mitigation and climate adaptations.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016NatSR...629962J','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016NatSR...629962J"><span id="translatedtitle">Predictability of Precipitation Over the Conterminous U.S. <span class="hlt">Based</span> on the CMIP5 Multi-Model <span class="hlt">Ensemble</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Jiang, Mingkai; Felzer, Benjamin S.; Sahagian, Dork</p> <p>2016-07-01</p> <p>Characterizing precipitation seasonality and variability in the face of future uncertainty is important for a well-informed climate change adaptation strategy. Using the Colwell index of predictability and monthly normalized precipitation data from the Coupled Model Intercomparison Project Phase 5 (CMIP5) multi-model <span class="hlt">ensembles</span>, this study identifies spatial hotspots of changes in precipitation predictability in the United States under various climate scenarios. Over the historic period (1950–2005), the recurrent pattern of precipitation is highly predictable in the East and along the coastal Northwest, and is less so in the arid Southwest. Comparing the future (2040–2095) to the historic period, larger changes in precipitation predictability are observed under Representative Concentration Pathways (RCP) 8.5 than those under RCP 4.5. Finally, there are region-specific hotspots of future changes in precipitation predictability, and these hotspots often coincide with regions of little projected change in total precipitation, with exceptions along the wetter East and parts of the drier central West. Therefore, decision-makers are advised to not rely on future total precipitation as an indicator of water resources. Changes in precipitation predictability and the subsequent changes on seasonality and variability are equally, if not more, important factors to be included in future regional environmental assessment.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/26173218','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/26173218"><span id="translatedtitle">Single-Channel EMG Classification With <span class="hlt">Ensemble-Empirical-Mode-Decomposition-Based</span> ICA for Diagnosing Neuromuscular Disorders.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Naik, Ganesh R; Selvan, S Easter; Nguyen, Hung T</p> <p>2016-07-01</p> <p>An accurate and computationally efficient quantitative analysis of electromyography (EMG) signals plays an inevitable role in the diagnosis of neuromuscular disorders, prosthesis, and several related applications. Since it is often the case that the measured signals are the mixtures of electric potentials that emanate from surrounding muscles (sources), many EMG signal processing approaches rely on linear source separation techniques such as the independent component analysis (ICA). Nevertheless, naive implementations of ICA algorithms do not comply with the task of extracting the underlying sources from a single-channel EMG measurement. In this respect, the present work focuses on a classification method for neuromuscular disorders that deals with the data recorded using a single-channel EMG sensor. The <span class="hlt">ensemble</span> empirical mode decomposition algorithm decomposes the single-channel EMG signal into a set of noise-canceled intrinsic mode functions, which in turn are separated by the FastICA algorithm. A reduced set of five time domain features extracted from the separated components are classified using the linear discriminant analysis, and the classification results are fine-tuned with a majority voting scheme. The performance of the proposed method has been validated with a clinical EMG database, which reports a higher classification accuracy (98%). The outcome of this study encourages possible extension of this approach to real settings to assist the clinicians in making correct diagnosis of neuromuscular disorders. PMID:26173218</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4947969','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4947969"><span id="translatedtitle">Predictability of Precipitation Over the Conterminous U.S. <span class="hlt">Based</span> on the CMIP5 Multi-Model <span class="hlt">Ensemble</span></span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Jiang, Mingkai; Felzer, Benjamin S.; Sahagian, Dork</p> <p>2016-01-01</p> <p>Characterizing precipitation seasonality and variability in the face of future uncertainty is important for a well-informed climate change adaptation strategy. Using the Colwell index of predictability and monthly normalized precipitation data from the Coupled Model Intercomparison Project Phase 5 (CMIP5) multi-model <span class="hlt">ensembles</span>, this study identifies spatial hotspots of changes in precipitation predictability in the United States under various climate scenarios. Over the historic period (1950–2005), the recurrent pattern of precipitation is highly predictable in the East and along the coastal Northwest, and is less so in the arid Southwest. Comparing the future (2040–2095) to the historic period, larger changes in precipitation predictability are observed under Representative Concentration Pathways (RCP) 8.5 than those under RCP 4.5. Finally, there are region-specific hotspots of future changes in precipitation predictability, and these hotspots often coincide with regions of little projected change in total precipitation, with exceptions along the wetter East and parts of the drier central West. Therefore, decision-makers are advised to not rely on future total precipitation as an indicator of water resources. Changes in precipitation predictability and the subsequent changes on seasonality and variability are equally, if not more, important factors to be included in future regional environmental assessment. PMID:27425819</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4814075','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4814075"><span id="translatedtitle">Projections of Water Stress <span class="hlt">Based</span> on an <span class="hlt">Ensemble</span> of Socioeconomic Growth and Climate Change Scenarios: A Case Study in Asia</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Fant, Charles; Schlosser, C. Adam; Gao, Xiang; Strzepek, Kenneth; Reilly, John</p> <p>2016-01-01</p> <p>The sustainability of future water resources is of paramount importance and is affected by many factors, including population, wealth and climate. Inherent in current methods to estimate these factors in the future is the uncertainty of their prediction. In this study, we integrate a large <span class="hlt">ensemble</span> of scenarios—internally consistent across economics, emissions, climate, and population—to develop a risk portfolio of water stress over a large portion of Asia that includes China, India, and Mainland Southeast Asia in a future with unconstrained emissions. We isolate the effects of socioeconomic growth from the effects of climate change in order to identify the primary drivers of stress on water resources. We find that water needs related to socioeconomic changes, which are currently small, are likely to increase considerably in the future, often overshadowing the effect of climate change on levels of water stress. As a result, there is a high risk of severe water stress in densely populated watersheds by 2050, compared to recent history. There is strong evidence to suggest that, in the absence of autonomous adaptation or societal response, a much larger portion of the region’s population will live in water-stressed regions in the near future. Tools and studies such as these can effectively investigate large-scale system sensitivities and can be useful in engaging and informing decision makers. PMID:27028871</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/pages/biblio/1255116-projections-water-stress-based-ensemble-socioeconomic-growth-climate-change-scenarios-case-study-asia','SCIGOV-DOEP'); return false;" href="http://www.osti.gov/pages/biblio/1255116-projections-water-stress-based-ensemble-socioeconomic-growth-climate-change-scenarios-case-study-asia"><span id="translatedtitle">Projections of water stress <span class="hlt">based</span> on an <span class="hlt">ensemble</span> of socioeconomic growth and climate change scenarios: A case study in Asia</span></a></p> <p><a target="_blank" href="http://www.osti.gov/pages">DOE PAGESBeta</a></p> <p>Fant, Charles; Schlosser, C. Adam; Gao, Xiang; Strzepek, Kenneth; Reilly, John; Ebi, Kristie L.</p> <p>2016-03-30</p> <p>The sustainability of future water resources is of paramount importance and is affected by many factors, including population, wealth and climate. Inherent in current methods to estimate these factors in the future is the uncertainty of their prediction. In this study, we integrate a large <span class="hlt">ensemble</span> of scenarios—internally consistent across economics, emissions, climate, and population—to develop a risk portfolio of water stress over a large portion of Asia that includes China, India, and Mainland Southeast Asia in a future with unconstrained emissions. We isolate the effects of socioeconomic growth from the effects of climate change in order to identify themore » primary drivers of stress on water resources. We find that water needs related to socioeconomic changes, which are currently small, are likely to increase considerably in the future, often overshadowing the effect of climate change on levels of water stress. As a result, there is a high risk of severe water stress in densely populated watersheds by 2050, compared to recent history. There is strong evidence to suggest that, in the absence of autonomous adaptation or societal response, a much larger portion of the region’s population will live in water-stressed regions in the near future. Lastly, tools and studies such as these can effectively investigate large-scale system sensitivities and can be useful in engaging and informing decision makers.« less</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/27425819','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/27425819"><span id="translatedtitle">Predictability of Precipitation Over the Conterminous U.S. <span class="hlt">Based</span> on the CMIP5 Multi-Model <span class="hlt">Ensemble</span>.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Jiang, Mingkai; Felzer, Benjamin S; Sahagian, Dork</p> <p>2016-07-18</p> <p>Characterizing precipitation seasonality and variability in the face of future uncertainty is important for a well-informed climate change adaptation strategy. Using the Colwell index of predictability and monthly normalized precipitation data from the Coupled Model Intercomparison Project Phase 5 (CMIP5) multi-model <span class="hlt">ensembles</span>, this study identifies spatial hotspots of changes in precipitation predictability in the United States under various climate scenarios. Over the historic period (1950-2005), the recurrent pattern of precipitation is highly predictable in the East and along the coastal Northwest, and is less so in the arid Southwest. Comparing the future (2040-2095) to the historic period, larger changes in precipitation predictability are observed under Representative Concentration Pathways (RCP) 8.5 than those under RCP 4.5. Finally, there are region-specific hotspots of future changes in precipitation predictability, and these hotspots often coincide with regions of little projected change in total precipitation, with exceptions along the wetter East and parts of the drier central West. Therefore, decision-makers are advised to not rely on future total precipitation as an indicator of water resources. Changes in precipitation predictability and the subsequent changes on seasonality and variability are equally, if not more, important factors to be included in future regional environmental assessment.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.H52F..07P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.H52F..07P"><span id="translatedtitle">History matching and parameter estimation of surface deformation data for a CO2 sequestration field project using <span class="hlt">ensemble-based</span> algorithm</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ping, J.; Tavakoli, R.; Min, B.; Srinivasan, S.; Wheeler, M. F.</p> <p>2015-12-01</p> <p>Optimal management of subsurface processes requires the characterization of the uncertainty in reservoir description and reservoir performance prediction. The application of <span class="hlt">ensemble-based</span> algorithms for history matching reservoir models has been steadily increasing over the past decade. However, the majority of implementations in the reservoir engineering have dealt only with production history matching. During geologic sequestration, the injection of large quantities of CO2 into the subsurface may alter the stress/strain field which in turn can lead to surface uplift or subsidence. Therefore, it is essential to couple multiphase flow and geomechanical response in order to predict and quantify the uncertainty of CO2 plume movement for long-term, large-scale CO2 sequestration projects. In this work, we simulate and estimate the properties of a reservoir that is being used to store CO2 as part of the In Salah Capture and Storage project in Algeria. The CO2 is separated from produced natural gas and is re-injected into downdip aquifer portion of the field from three long horizontal wells. The field observation data includes ground surface deformations (uplift) measured using satellite-<span class="hlt">based</span> radar (InSAR), injection well locations and CO2 injection rate histories provided by the operators. We implement <span class="hlt">ensemble-based</span> algorithms for assimilating both injection rate data as well as geomechanical observations (surface uplift) into reservoir model. The preliminary estimation results of horizontal permeability and material properties such as Young Modulus and Poisson Ratio are consistent with available measurements and previous studies in this field. Moreover, the existence of high-permeability channels/fractures within the reservoir; especially in the regions around the injection wells are confirmed. This estimation results can be used to accurately and efficiently predict and monitor the movement of CO2 plume.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3099348','PMC'); return false;" href="http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3099348"><span id="translatedtitle">Disease and Phenotype Data at <span class="hlt">Ensembl</span></span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Spudich, Giulietta M.; Fernández-Suárez, Xosè M.</p> <p>2011-01-01</p> <p>Biological databases are an important resource for the life sciences community. Accessing the hundreds of databases supporting <span class="hlt">molecular</span> biology and related fields is a daunting and time-consuming task. Integrating this information into one access point is a necessity for the life sciences community, which includes researchers focusing on human disease. Here we discuss the <span class="hlt">Ensembl</span> genome browser, which acts as a single entry point with Graphical User Interface to data from multiple projects, including OMIM, dbSNP, and the NHGRI GWAS catalog. <span class="hlt">Ensembl</span> provides a comprehensive source of annotation for the human genome, along with other species of biomedical interest. In this unit, we explore how to use the <span class="hlt">Ensembl</span> genome browser in example queries related to human genetic diseases. Support protocols demonstrate quick sequence export using the BioMart tool. PMID:21400687</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/servlets/purl/1231194','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/servlets/purl/1231194"><span id="translatedtitle">Matlab Cluster <span class="hlt">Ensemble</span> Toolbox</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Sapio, Vincent De; Kegelmeyer, Philip</p> <p>2009-04-27</p> <p>This is a Matlab toolbox for investigating the application of cluster <span class="hlt">ensembles</span> to data classification, with the objective of improving the accuracy and/or speed of clustering. The toolbox divides the cluster <span class="hlt">ensemble</span> problem into four areas, providing functionality for each. These include, (1) synthetic data generation, (2) clustering to generate individual data partitions and similarity matrices, (3) consensus function generation and final clustering to generate <span class="hlt">ensemble</span> data partitioning, and (4) implementation of accuracy metrics. With regard to data generation, Gaussian data of arbitrary dimension can be generated. The kcenters algorithm can then be used to generate individual data partitions by either, (a) subsampling the data and clustering each subsample, or by (b) randomly initializing the algorithm and generating a clustering for each initialization. In either case an overall similarity matrix can be computed using a consensus function operating on the individual similarity matrices. A final clustering can be performed and performance metrics are provided for evaluation purposes.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016GML...tmp...23S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016GML...tmp...23S"><span id="translatedtitle">Effects of chemical dispersants on oil spill drift paths in the German Bight—probabilistic assessment <span class="hlt">based</span> on numerical <span class="hlt">ensemble</span> simulations</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Schwichtenberg, Fabian; Callies, Ulrich; Groll, Nikolaus; Maßmann, Silvia</p> <p>2016-06-01</p> <p>Oil dispersed in the water column remains sheltered from wind forcing, so that an altered drift path is a key consequence of using chemical dispersants. In this study, <span class="hlt">ensemble</span> simulations were conducted <span class="hlt">based</span> on 7 years of simulated atmospheric and marine conditions, evaluating 2,190 hypothetical spills from each of 636 cells of a regular grid covering the inner German Bight (SE North Sea). Each simulation compares two idealized setups assuming either undispersed or fully dispersed oil. Differences are summarized in a spatial map of probabilities that chemical dispersant applications would help prevent oil pollution from entering intertidal coastal areas of the Wadden Sea. High probabilities of success overlap strongly with coastal regions between 10 m and 20 m water depth, where the use of chemical dispersants for oil spill response is a particularly contentious topic. The present study prepares the ground for a more detailed net environmental benefit analysis (NEBA) accounting also for toxic effects.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/24490961','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/24490961"><span id="translatedtitle">WExplore: hierarchical exploration of high-dimensional spaces using the weighted <span class="hlt">ensemble</span> algorithm.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Dickson, Alex; Brooks, Charles L</p> <p>2014-04-01</p> <p>As most relevant motions in biomolecular systems are inaccessible to conventional <span class="hlt">molecular</span> dynamics simulations, algorithms that enhance sampling of rare events are indispensable. Increasing interest in intrinsically disordered systems and the desire to target <span class="hlt">ensembles</span> of protein conformations (rather than single structures) in drug development motivate the need for enhanced sampling algorithms that are not limited to "two-basin" problems, and can efficiently determine structural <span class="hlt">ensembles</span>. For systems that are not well-studied, this must often be done with little or no information about the dynamics of interest. Here we present a novel strategy to determine structural <span class="hlt">ensembles</span> that uses dynamically defined sampling regions that are organized in a hierarchical framework. It is <span class="hlt">based</span> on the weighted <span class="hlt">ensemble</span> algorithm, where an <span class="hlt">ensemble</span> of copies of the system ("replicas") is directed to new regions of configuration space through merging and cloning operations. The sampling hierarchy allows for a large number of regions to be defined, while using only a small number of replicas that can be balanced over multiple length scales. We demonstrate this algorithm on two model systems that are analytically solvable and examine the 10-residue peptide chignolin in explicit solvent. The latter system is analyzed using a configuration space network, and novel hydrogen bonds are found that facilitate folding.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li class="active"><span>19</span></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_19 --> <div id="page_20" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li class="active"><span>20</span></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="381"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011WRR....47.0G02S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011WRR....47.0G02S"><span id="translatedtitle">Hydroclimatic projections for the Murray-Darling Basin <span class="hlt">based</span> on an <span class="hlt">ensemble</span> derived from Intergovernmental Panel on Climate Change AR4 climate models</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Sun, Fubao; Roderick, Michael L.; Lim, Wee Ho; Farquhar, Graham D.</p> <p>2011-12-01</p> <p>We assess hydroclimatic projections for the Murray-Darling Basin (MDB) using an <span class="hlt">ensemble</span> of 39 Intergovernmental Panel on Climate Change AR4 climate model runs <span class="hlt">based</span> on the A1B emissions scenario. The raw model output for precipitation, P, was adjusted using a quantile-<span class="hlt">based</span> bias correction approach. We found that the projected change, ΔP, between two 30 year periods (2070-2099 less 1970-1999) was little affected by bias correction. The range for ΔP among models was large (˜±150 mm yr-1) with all-model run and all-model <span class="hlt">ensemble</span> averages (4.9 and -8.1 mm yr-1) near zero, against a background climatological P of ˜500 mm yr-1. We found that the time series of actually observed annual P over the MDB was indistinguishable from that generated by a purely random process. Importantly, nearly all the model runs showed similar behavior. We used these facts to develop a new approach to understanding variability in projections of ΔP. By plotting ΔP versus the variance of the time series, we could easily identify model runs with projections for ΔP that were beyond the bounds expected from purely random variations. For the MDB, we anticipate that a purely random process could lead to differences of ±57 mm yr-1 (95% confidence) between successive 30 year periods. This is equivalent to ±11% of the climatological P and translates into variations in runoff of around ±29%. This sets a baseline for gauging modeled and/or observed changes.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/27516599','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/27516599"><span id="translatedtitle">Imprinting and recalling cortical <span class="hlt">ensembles</span>.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Carrillo-Reid, Luis; Yang, Weijian; Bando, Yuki; Peterka, Darcy S; Yuste, Rafael</p> <p>2016-08-12</p> <p>Neuronal <span class="hlt">ensembles</span> are coactive groups of neurons that may represent building blocks of cortical circuits. These <span class="hlt">ensembles</span> could be formed by Hebbian plasticity, whereby synapses between coactive neurons are strengthened. Here we report that repetitive activation with two-photon optogenetics of neuronal populations from <span class="hlt">ensembles</span> in the visual cortex of awake mice builds neuronal <span class="hlt">ensembles</span> that recur spontaneously after being imprinted and do not disrupt preexisting ones. Moreover, imprinted <span class="hlt">ensembles</span> can be recalled by single- cell stimulation and remain coactive on consecutive days. Our results demonstrate the persistent reconfiguration of cortical circuits by two-photon optogenetics into neuronal <span class="hlt">ensembles</span> that can perform pattern completion. PMID:27516599</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4271150','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4271150"><span id="translatedtitle">The <span class="hlt">Ensembl</span> REST API: <span class="hlt">Ensembl</span> Data for Any Language</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Yates, Andrew; Beal, Kathryn; Keenan, Stephen; McLaren, William; Pignatelli, Miguel; Ritchie, Graham R. S.; Ruffier, Magali; Taylor, Kieron; Vullo, Alessandro; Flicek, Paul</p> <p>2015-01-01</p> <p>Motivation: We present a Web service to access <span class="hlt">Ensembl</span> data using Representational State Transfer (REST). The <span class="hlt">Ensembl</span> REST server enables the easy retrieval of a wide range of <span class="hlt">Ensembl</span> data by most programming languages, using standard formats such as JSON and FASTA while minimizing client work. We also introduce bindings to the popular <span class="hlt">Ensembl</span> Variant Effect Predictor tool permitting large-scale programmatic variant analysis independent of any specific programming language. Availability and implementation: The <span class="hlt">Ensembl</span> REST API can be accessed at http://rest.<span class="hlt">ensembl</span>.org and source code is freely available under an Apache 2.0 license from http://github.com/<span class="hlt">Ensembl/ensembl</span>-rest. Contact: ayates@ebi.ac.uk or flicek@ebi.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online. PMID:25236461</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012AGUFM.H41J..03B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AGUFM.H41J..03B"><span id="translatedtitle"><span class="hlt">Ensemble</span> postprocessing for probabilistic quantitative precipitation forecasts</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bentzien, S.; Friederichs, P.</p> <p>2012-12-01</p> <p>Precipitation is one of the most difficult weather variables to predict in hydrometeorological applications. In order to assess the uncertainty inherent in deterministic numerical weather prediction (NWP), meteorological services around the globe develop <span class="hlt">ensemble</span> prediction systems (EPS) <span class="hlt">based</span> on high-resolution NWP systems. With non-hydrostatic model dynamics and without parameterization of deep moist convection, high-resolution NWP models are able to describe convective processes in more detail and provide more realistic mesoscale structures. However, precipitation forecasts are still affected by displacement errors, systematic biases and fast error growth on small scales. Probabilistic guidance can be achieved from an <span class="hlt">ensemble</span> setup which accounts for model error and uncertainty of initial and boundary conditions. The German Meteorological Service (Deutscher Wetterdienst, DWD) provides such an <span class="hlt">ensemble</span> system <span class="hlt">based</span> on the German-focused limited-area model COSMO-DE. With a horizontal grid-spacing of 2.8 km, COSMO-DE is the convection-permitting high-resolution part of the operational model chain at DWD. The COSMO-DE-EPS consists of 20 realizations of COSMO-DE, driven by initial and boundary conditions derived from 4 global models and 5 perturbations of model physics. <span class="hlt">Ensemble</span> systems like COSMO-DE-EPS are often limited with respect to <span class="hlt">ensemble</span> size due to the immense computational costs. As a consequence, they can be biased and exhibit insufficient <span class="hlt">ensemble</span> spread, and probabilistic forecasts may be not well calibrated. In this study, probabilistic quantitative precipitation forecasts are derived from COSMO-DE-EPS and evaluated at more than 1000 rain gauges located all over Germany. COSMO-DE-EPS is a frequently updated <span class="hlt">ensemble</span> system, initialized 8 times a day. We use the time-lagged approach to inexpensively increase <span class="hlt">ensemble</span> spread, which results in more reliable forecasts especially for extreme precipitation events. Moreover, we will show that statistical</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://eric.ed.gov/?q=crescendo&id=ED254263','ERIC'); return false;" href="http://eric.ed.gov/?q=crescendo&id=ED254263"><span id="translatedtitle">Music <span class="hlt">Ensemble</span>: Course Proposal.</span></a></p> <p><a target="_blank" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p>Kovach, Brian</p> <p></p> <p>A proposal is presented for a Music <span class="hlt">Ensemble</span> course to be offered at the Community College of Philadelphia for music students who have had previous vocal or instrumental training. A standardized course proposal cover form is followed by a statement of purpose for the course, a list of major course goals, a course outline, and a bibliography. Next,…</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.H51G1460X','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.H51G1460X"><span id="translatedtitle">Prediction of Turbulent Heat Fluxes by Assimilation of Remotely Sensed Land Surface Temperature and Soil Moisture Data into an <span class="hlt">Ensemble-Based</span> Data Assimilation Framework</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Xu, T.; Bateni, S. M.; Liu, S.</p> <p>2015-12-01</p> <p>Accurate estimation of turbulent heat fluxes is important for water resources planning and management, irrigation scheduling, and weather forecast. Land surface models (LSMs) can be used to simulate turbulent heat fluxes over large-scale domains. However, the application of LSMs is hindered due to the high uncertainty in model parameters and state variables. In this study, a dual-pass <span class="hlt">ensemble-based</span> data assimilation (DA) approach is developed to estimate turbulent heat fluxes. Initially, the common land model (CoLM) is used as the LSM (open-loop), and thereafter the <span class="hlt">ensemble</span> Kalman filter is employed to optimize the CoLM parameters and variables. The first pass of the DA scheme optimizes vegetation parameters of CoLM (which are related to the leaf stomatal conductance) on a weekly-basis by assimilating the MODIS land surface temperature (LST) data. The second pass optimizes the soil moisture state of CoLM on a daily-basis by assimilating soil moisture observations from Cosmic-ray instrument. The ultimate goal is to improve turbulent heat fluxes estimates from CoLM by optimizing its vegetation parameters and soil moisture state via assimilation of LST and soil moisture data into the proposed DA system. The DA approach is tested over a wet and densely vegetated site, called Daman in northwest of China. Results indicate that the CoLM (open-loop) model typically underestimates latent heat flux and overestimates sensible heat flux. By assimilation of LST in the first pass, the turbulent heat fluxes are improved compared to those of the open-loop. These fluxes become even more accurate by assimilation of soil moisture in the second pass of the DA approach. These findings illustrate that the introduced DA approach can successfully extract information in LST and soil moisture data to optimize the CoLM parameters and states and improve the turbulent heat fluxes estimates.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016PhRvE..94b1301S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016PhRvE..94b1301S"><span id="translatedtitle">First-order phase transitions in the real microcanonical <span class="hlt">ensemble</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Schierz, Philipp; Zierenberg, Johannes; Janke, Wolfhard</p> <p>2016-08-01</p> <p>We present a simulation and data analysis technique to investigate first-order phase transitions and the associated transition barriers. The simulation technique is <span class="hlt">based</span> on the real microcanonical <span class="hlt">ensemble</span> where the sum of kinetic and potential energy is kept constant. The method is tested for the droplet condensation-evaporation transition in a Lennard-Jones system with up to 2048 particles at fixed density, using simple Metropolis-like sampling combined with a replica-exchange scheme. Our investigation of the microcanonical <span class="hlt">ensemble</span> properties reveals that the associated transition barrier is significantly lower than in the canonical counterpart. Along the line of investigating the microcanonical <span class="hlt">ensemble</span> behavior, we develop a framework for general <span class="hlt">ensemble</span> evaluations. This framework is <span class="hlt">based</span> on a clear separation between system-related and <span class="hlt">ensemble</span>-related properties, which can be exploited to specifically tailor artificial <span class="hlt">ensembles</span> suitable for first-order phase transitions.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/27627238','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/27627238"><span id="translatedtitle">First-order phase transitions in the real microcanonical <span class="hlt">ensemble</span>.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Schierz, Philipp; Zierenberg, Johannes; Janke, Wolfhard</p> <p>2016-08-01</p> <p>We present a simulation and data analysis technique to investigate first-order phase transitions and the associated transition barriers. The simulation technique is <span class="hlt">based</span> on the real microcanonical <span class="hlt">ensemble</span> where the sum of kinetic and potential energy is kept constant. The method is tested for the droplet condensation-evaporation transition in a Lennard-Jones system with up to 2048 particles at fixed density, using simple Metropolis-like sampling combined with a replica-exchange scheme. Our investigation of the microcanonical <span class="hlt">ensemble</span> properties reveals that the associated transition barrier is significantly lower than in the canonical counterpart. Along the line of investigating the microcanonical <span class="hlt">ensemble</span> behavior, we develop a framework for general <span class="hlt">ensemble</span> evaluations. This framework is <span class="hlt">based</span> on a clear separation between system-related and <span class="hlt">ensemble</span>-related properties, which can be exploited to specifically tailor artificial <span class="hlt">ensembles</span> suitable for first-order phase transitions. PMID:27627238</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.H52D..05E','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.H52D..05E"><span id="translatedtitle">Variance-<span class="hlt">based</span> Sensitivity Analysis of Large-scale Hydrological Model to Prepare an <span class="hlt">Ensemble-based</span> SWOT-like Data Assimilation Experiments</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Emery, C. M.; Biancamaria, S.; Boone, A. A.; Ricci, S. M.; Garambois, P. A.; Decharme, B.; Rochoux, M. C.</p> <p>2015-12-01</p> <p> discharge is more affected by parameters from the whole upstream drainage area. Understanding model output variance behavior will have a direct impact on the design and performance of the <span class="hlt">ensemble-based</span> data assimilation platform, for which uncertainties are also modeled by variances. It will help to select more objectively RRM parameters to correct.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014NHESD...2.3289R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014NHESD...2.3289R"><span id="translatedtitle">Towards predictive data-driven simulations of wildfire spread - Part I: Reduced-cost <span class="hlt">Ensemble</span> Kalman Filter <span class="hlt">based</span> on a Polynomial Chaos surrogate model for parameter estimation</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Rochoux, M. C.; Ricci, S.; Lucor, D.; Cuenot, B.; Trouvé, A.</p> <p>2014-05-01</p> <p>This paper is the first part in a series of two articles and presents a data-driven wildfire simulator for forecasting wildfire spread scenarios, at a reduced computational cost that is consistent with operational systems. The prototype simulator features the following components: a level-set-<span class="hlt">based</span> fire propagation solver FIREFLY that adopts a regional-scale modeling viewpoint, treats wildfires as surface propagating fronts, and uses a description of the local rate of fire spread (ROS) as a function of environmental conditions <span class="hlt">based</span> on Rothermel's model; a series of airborne-like observations of the fire front positions; and a data assimilation algorithm <span class="hlt">based</span> on an <span class="hlt">ensemble</span> Kalman filter (EnKF) for parameter estimation. This stochastic algorithm partly accounts for the non-linearities between the input parameters of the semi-empirical ROS model and the fire front position, and is sequentially applied to provide a spatially-uniform correction to wind and biomass fuel parameters as observations become available. A wildfire spread simulator combined with an <span class="hlt">ensemble-based</span> data assimilation algorithm is therefore a promising approach to reduce uncertainties in the forecast position of the fire front and to introduce a paradigm-shift in the wildfire emergency response. In order to reduce the computational cost of the EnKF algorithm, a surrogate model <span class="hlt">based</span> on a polynomial chaos (PC) expansion is used in place of the forward model FIREFLY in the resulting hybrid PC-EnKF algorithm. The performance of EnKF and PC-EnKF is assessed on synthetically-generated simple configurations of fire spread to provide valuable information and insight on the benefits of the PC-EnKF approach as well as on a controlled grassland fire experiment. The results indicate that the proposed PC-EnKF algorithm features similar performance to the standard EnKF algorithm, but at a much reduced computational cost. In particular, the re-analysis and forecast skills of data assimilation strongly relate</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17.9176V','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.9176V"><span id="translatedtitle">A probabilistic approach to forecast the uncertainty with <span class="hlt">ensemble</span> spread</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Van Schaeybroeck, Bert; Vannitsem, Stéphane</p> <p>2015-04-01</p> <p>For most purposes the information gathered from an <span class="hlt">ensemble</span> forecast is the <span class="hlt">ensemble</span> mean and its uncertainty. The <span class="hlt">ensemble</span> spread is commonly used as a measure of the uncertainty. We propose a method to assess whether the <span class="hlt">ensemble</span> spread is a good measure of uncertainty and to bring forward an underlying spread-skill relationship. Forecasting the uncertainty should be probabilistic of nature. This implies that, if only the <span class="hlt">ensemble</span> spread is available, a probability density function (PDF) for the uncertainty forecast must be reconstructed <span class="hlt">based</span> on one parameter. Different models are introduced for the composition of such PDFs and evaluated for different spread-error metrics. The uncertainty forecast can then be verified <span class="hlt">based</span> on probabilistic skill scores. For a perfectly reliable forecast the spread-error relationship is strongly heteroscedastic since the error can take a wide range of values, proportional to the <span class="hlt">ensemble</span> spread. This makes a proper statistical assessment of the spread-skill relation intricate. However, it is shown that a logarithmic transformation of both spread and error allows for alleviating the heteroscedasticity. A linear regression analysis can then be performed to check whether the flow-dependent spread is a realistic indicator of the uncertainty and to what extent <span class="hlt">ensemble</span> underdispersion or overdispersion depends on the <span class="hlt">ensemble</span> spread. The methods are tested on the <span class="hlt">ensemble</span> forecast of wind and geopotential height of the European Centre of Medium-range forecasts (ECMWF) over Europe and Africa. A comparison is also made with spread-skill analysis <span class="hlt">based</span> on binning methods.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014NHESS..14.2951R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014NHESS..14.2951R"><span id="translatedtitle">Towards predictive data-driven simulations of wildfire spread - Part I: Reduced-cost <span class="hlt">Ensemble</span> Kalman Filter <span class="hlt">based</span> on a Polynomial Chaos surrogate model for parameter estimation</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Rochoux, M. C.; Ricci, S.; Lucor, D.; Cuenot, B.; Trouvé, A.</p> <p>2014-11-01</p> <p>This paper is the first part in a series of two articles and presents a data-driven wildfire simulator for forecasting wildfire spread scenarios, at a reduced computational cost that is consistent with operational systems. The prototype simulator features the following components: an Eulerian front propagation solver FIREFLY that adopts a regional-scale modeling viewpoint, treats wildfires as surface propagating fronts, and uses a description of the local rate of fire spread (ROS) as a function of environmental conditions <span class="hlt">based</span> on Rothermel's model; a series of airborne-like observations of the fire front positions; and a data assimilation (DA) algorithm <span class="hlt">based</span> on an <span class="hlt">ensemble</span> Kalman filter (EnKF) for parameter estimation. This stochastic algorithm partly accounts for the nonlinearities between the input parameters of the semi-empirical ROS model and the fire front position, and is sequentially applied to provide a spatially uniform correction to wind and biomass fuel parameters as observations become available. A wildfire spread simulator combined with an <span class="hlt">ensemble-based</span> DA algorithm is therefore a promising approach to reduce uncertainties in the forecast position of the fire front and to introduce a paradigm-shift in the wildfire emergency response. In order to reduce the computational cost of the EnKF algorithm, a surrogate model <span class="hlt">based</span> on a polynomial chaos (PC) expansion is used in place of the forward model FIREFLY in the resulting hybrid PC-EnKF algorithm. The performance of EnKF and PC-EnKF is assessed on synthetically generated simple configurations of fire spread to provide valuable information and insight on the benefits of the PC-EnKF approach, as well as on a controlled grassland fire experiment. The results indicate that the proposed PC-EnKF algorithm features similar performance to the standard EnKF algorithm, but at a much reduced computational cost. In particular, the re-analysis and forecast skills of DA strongly relate to the spatial and temporal</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012JChPh.137l4306D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012JChPh.137l4306D"><span id="translatedtitle">Symmetry interplay in Brownian photomotors: From a single-molecule device to <span class="hlt">ensemble</span> transport</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Dekhtyar, Marina L.; Rozenbaum, Viktor M.</p> <p>2012-09-01</p> <p>Unlike most of Brownian motor models in which the state of a point particle is described by a single scalar fluctuating parameter, we consider light-induced dichotomic fluctuations of electron density distributions in an extended molecule moving in the electrostatic periodic potential of a polar substrate. This model implies that the potential energy profiles of two motor states differ substantially and their symmetry is dictated by the interplay between the symmetries of the substrate potential and of <span class="hlt">molecular</span> electronic states. As shown, a necessary condition for the occurrence of directed motion, the asymmetry of the potential energy profiles, is satisfied for (i) symmetric electron density distributions in molecules on asymmetric substrates and (ii) asymmetric electron density distributions in molecules on symmetric substrates. In the former case, the average velocity of directed motion is independent of <span class="hlt">molecular</span> orientations and the <span class="hlt">ensemble</span> of molecules moves as a whole, whereas in the latter case, oppositely oriented molecules move counterdirectionally thus causing the <span class="hlt">ensemble</span> to diffuse. Using quantum chemical data for a specific organic-<span class="hlt">based</span> photomotor as an example, we demonstrate that the behavior of <span class="hlt">molecular</span> <span class="hlt">ensembles</span> is controllable by switching on/off resonance laser radiation: they can be transported as a whole or separated into differently oriented molecules depending on the ratio of symmetric and antisymmetric contributions to the substrate electrostatic potential and to the <span class="hlt">molecular</span> electron density distributions.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/23020330','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/23020330"><span id="translatedtitle">Symmetry interplay in Brownian photomotors: From a single-molecule device to <span class="hlt">ensemble</span> transport.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Dekhtyar, Marina L; Rozenbaum, Viktor M</p> <p>2012-09-28</p> <p>Unlike most of Brownian motor models in which the state of a point particle is described by a single scalar fluctuating parameter, we consider light-induced dichotomic fluctuations of electron density distributions in an extended molecule moving in the electrostatic periodic potential of a polar substrate. This model implies that the potential energy profiles of two motor states differ substantially and their symmetry is dictated by the interplay between the symmetries of the substrate potential and of <span class="hlt">molecular</span> electronic states. As shown, a necessary condition for the occurrence of directed motion, the asymmetry of the potential energy profiles, is satisfied for (i) symmetric electron density distributions in molecules on asymmetric substrates and (ii) asymmetric electron density distributions in molecules on symmetric substrates. In the former case, the average velocity of directed motion is independent of <span class="hlt">molecular</span> orientations and the <span class="hlt">ensemble</span> of molecules moves as a whole, whereas in the latter case, oppositely oriented molecules move counterdirectionally thus causing the <span class="hlt">ensemble</span> to diffuse. Using quantum chemical data for a specific organic-<span class="hlt">based</span> photomotor as an example, we demonstrate that the behavior of <span class="hlt">molecular</span> <span class="hlt">ensembles</span> is controllable by switching on/off resonance laser radiation: they can be transported as a whole or separated into differently oriented molecules depending on the ratio of symmetric and antisymmetric contributions to the substrate electrostatic potential and to the <span class="hlt">molecular</span> electron density distributions. PMID:23020330</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2014ClDy...43.1303A&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2014ClDy...43.1303A&link_type=ABSTRACT"><span id="translatedtitle">Development of an artificial neural network <span class="hlt">based</span> multi-model <span class="hlt">ensemble</span> to estimate the northeast monsoon rainfall over south peninsular India: an application of extreme learning machine</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Acharya, Nachiketa; Shrivastava, Nitin Anand; Panigrahi, B. K.; Mohanty, U. C.</p> <p>2014-09-01</p> <p>The south peninsular part of India gets maximum amount of rainfall during the northeast monsoon (NEM) season [October to November (OND)] which is the primary source of water for the agricultural activities in this region. A nonlinear method viz., Extreme learning machine (ELM) has been employed on general circulation model (GCM) products to make the multi-model <span class="hlt">ensemble</span> (MME) <span class="hlt">based</span> estimation of NEM rainfall (NEMR). The ELM is basically is an improved learning algorithm for the single feed-forward neural network (SLFN) architecture. The 27 year (1982-2008) lead-1 (using initial conditions of September for forecasting the mean rainfall of OND) hindcast runs (1982-2008) from seven GCM has been used to make MME. The improvement of the proposed method with respect to other regular MME (simple arithmetic mean of GCMs (EM) and singular value decomposition <span class="hlt">based</span> multiple linear regressions <span class="hlt">based</span> MME) has been assessed through several skill metrics like Spread distribution, multiplicative bias, prediction errors, the yield of prediction, Pearson's and Kendal's correlation coefficient and Wilmort's index of agreement. The efficiency of ELM estimated rainfall is established by all the stated skill scores. The performance of ELM in extreme NEMR years, out of which 4 years are characterized by deficit rainfall and 5 years are identified as excess, is also examined. It is found that the ELM could expeditiously capture these extremes reasonably well as compared to the other MME approaches.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014JAG...111..102L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014JAG...111..102L"><span id="translatedtitle">A robust method for analyzing the instantaneous attributes of seismic data: The instantaneous frequency estimation <span class="hlt">based</span> on <span class="hlt">ensemble</span> empirical mode decomposition</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Li, Xiangfang; Chen, Wenchao; Zhou, Yanhui</p> <p>2014-12-01</p> <p>The Hilbert-Huang transform (HHT) includes two procedures. First, empirical mode decomposition (EMD) is used to decompose signals into several intrinsic mode functions (IMFs) before the Hilbert transform (HT) of these IMFs are calculated. Compared to the conventional Hilbert transform (HT), HHT is more sensitive to thickness variations of seismic beds. However, random noise in seismic signal may cause the mixture of the modes from HHT. The recent <span class="hlt">ensemble</span> empirical mode decomposition (EEMD) presents the advantages in decreasing mode mixture and has the promising potential in seismic signal analysis. Currently, EEMD is mainly used in seismic spectral decomposition and noise attenuation. We extend the application of EEMD <span class="hlt">based</span> instantaneous frequency to the analysis of bed thickness. The tests on complex Marmousi2 model and a 2D field data show that EEMD is more effective in weakening mode mixtures contained in the IMFs, compared with that calculated by EMD. Furthermore, the EEMD <span class="hlt">based</span> instantaneous frequency is more sensitive to the seismic thickness variation than that <span class="hlt">based</span> on EMD and more consistent with the stratigraphic structure, which means that E-IFPs are more advantageous in characterizing reservoirs.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/25927892','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/25927892"><span id="translatedtitle">A Bayesian <span class="hlt">ensemble</span> approach for epidemiological projections.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Lindström, Tom; Tildesley, Michael; Webb, Colleen</p> <p>2015-04-01</p> <p>Mathematical models are powerful tools for epidemiology and can be used to compare control actions. However, different models and model parameterizations may provide different prediction of outcomes. In other fields of research, <span class="hlt">ensemble</span> modeling has been used to combine multiple projections. We explore the possibility of applying such methods to epidemiology by adapting Bayesian techniques developed for climate forecasting. We exemplify the implementation with single model <span class="hlt">ensembles</span> <span class="hlt">based</span> on different parameterizations of the Warwick model run for the 2001 United Kingdom foot and mouth disease outbreak and compare the efficacy of different control actions. This allows us to investigate the effect that discrepancy among projections <span class="hlt">based</span> on different modeling assumptions has on the <span class="hlt">ensemble</span> prediction. A sensitivity analysis showed that the choice of prior can have a pronounced effect on the posterior estimates of quantities of interest, in particular for <span class="hlt">ensembles</span> with large discrepancy among projections. However, by using a hierarchical extension of the method we show that prior sensitivity can be circumvented. We further extend the method to include a priori beliefs about different modeling assumptions and demonstrate that the effect of this can have different consequences depending on the discrepancy among projections. We propose that the method is a promising analytical tool for <span class="hlt">ensemble</span> modeling of disease outbreaks.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2002AGUFM.H12G..03W&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2002AGUFM.H12G..03W&link_type=ABSTRACT"><span id="translatedtitle">Streamflow <span class="hlt">Ensemble</span> Generation using Climate Forecasts</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Watkins, D. W.; O'Connell, S.; Wei, W.; Nykanen, D.; Mahmoud, M.</p> <p>2002-12-01</p> <p>Although significant progress has been made in understanding the correlation between large-scale atmospheric circulation patterns and regional streamflow anomalies, there is a general perception that seasonal climate forecasts are not being used to the fullest extent possible for optimal water resources management. Possible contributing factors are limited knowledge and understanding of climate processes and prediction capabilities, noise in climate signals and inaccuracies in forecasts, and hesitancy on the part of water managers to apply new information or methods that could expose them to greater liability. This work involves a decision support model <span class="hlt">based</span> on streamflow <span class="hlt">ensembles</span> developed for the Lower Colorado River Authority in Central Texas. Predicative skill is added to <span class="hlt">ensemble</span> forecasts that are <span class="hlt">based</span> on climatology by conditioning the <span class="hlt">ensembles</span> on observable climate indicators, including streamflow (persistence), soil moisture, land surface temperatures, and large-scale recurrent patterns such as the El Ni¤o-Southern Oscillation, Pacific Decadal Oscillation, and the North Atlantic Oscillation. A Bayesian procedure for updating <span class="hlt">ensemble</span> probabilities is outlined, and various skill scores are reviewed for evaluating forecast performance. Verification of the <span class="hlt">ensemble</span> forecasts using a resampling procedure indicates a small but potentially significant improvement in forecast skill that could be exploited in seasonal water management decisions. The ultimate goal of this work will be explicit incorporation of climate forecasts in reservoir operating rules and estimation of the value of the forecasts.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/22978601','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/22978601"><span id="translatedtitle">Multiscale macromolecular simulation: role of evolving <span class="hlt">ensembles</span>.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Singharoy, A; Joshi, H; Ortoleva, P J</p> <p>2012-10-22</p> <p>Multiscale analysis provides an algorithm for the efficient simulation of macromolecular assemblies. This algorithm involves the coevolution of a quasiequilibrium probability density of atomic configurations and the Langevin dynamics of spatial coarse-grained variables denoted order parameters (OPs) characterizing nanoscale system features. In practice, implementation of the probability density involves the generation of constant OP <span class="hlt">ensembles</span> of atomic configurations. Such <span class="hlt">ensembles</span> are used to construct thermal forces and diffusion factors that mediate the stochastic OP dynamics. Generation of all-atom <span class="hlt">ensembles</span> at every Langevin time step is computationally expensive. Here, multiscale computation for macromolecular systems is made more efficient by a method that self-consistently folds in <span class="hlt">ensembles</span> of all-atom configurations constructed in an earlier step, history, of the Langevin evolution. This procedure accounts for the temporal evolution of these <span class="hlt">ensembles</span>, accurately providing thermal forces and diffusions. It is shown that efficiency and accuracy of the OP-<span class="hlt">based</span> simulations is increased via the integration of this historical information. Accuracy improves with the square root of the number of historical timesteps included in the calculation. As a result, CPU usage can be decreased by a factor of 3-8 without loss of accuracy. The algorithm is implemented into our existing force-field <span class="hlt">based</span> multiscale simulation platform and demonstrated via the structural dynamics of viral capsomers.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFM.H41E0866E','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFM.H41E0866E"><span id="translatedtitle">A Novel approach for monitoring cyanobacterial blooms using an <span class="hlt">ensemble</span> <span class="hlt">based</span> system from MODIS imagery downscaled to 250 metres spatial resolution</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>El Alem, A.; Chokmani, K.; Laurion, I.; El-Adlouni, S. E.</p> <p>2014-12-01</p> <p>In reason of inland freshwaters sensitivity to Harmful algae blooms (HAB) development and the limits coverage of standards monitoring programs, remote sensing data have become increasingly used for monitoring HAB extension. Usually, HAB monitoring using remote sensing data is <span class="hlt">based</span> on empirical and semi-empirical models. Development of such models requires a great number of continuous in situ measurements to reach an acceptable accuracy. However, Ministries and water management organizations often use two thresholds, established by the World Health Organization, to determine water quality. Consequently, the available data are ordinal «semi-qualitative» and they are mostly unexploited. Use of such databases with remote sensing data and statistical classification algorithms can produce hazard management maps linked to the presence of cyanobacteria. Unlike standard classification algorithms, which are generally unstable, classifiers <span class="hlt">based</span> on <span class="hlt">ensemble</span> systems are more general and stable. In the present study, an <span class="hlt">ensemble</span> <span class="hlt">based</span> classifier was developed and compared to a standard classification method called CART (Classification and Regression Tree) in a context of HAB monitoring in freshwaters using MODIS images downscaled to 250 spatial resolution and ordinal in situ data. Calibration and validation data on cyanobacteria densities were collected by the Ministère du Développement durable, de l'Environnement et de la Lutte contre les changements climatiques on 22 waters bodies between 2000 and 2010. These data comprise three density classes: waters poorly (< 20,000 cells mL-1), moderately (20,000 - 100,000 cells mL-1), and highly (> 100,000 cells mL-1) loaded in cyanobacteria. Results were very interesting and highlighted that inland waters exhibit different spectral response allowing them to be classified into the three above classes for water quality monitoring. On the other, even if the accuracy (Kappa-index = 0.86) of the proposed approach is relatively lower</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li class="active"><span>20</span></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_20 --> <div id="page_21" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li class="active"><span>21</span></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="401"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/servlets/purl/919456','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/servlets/purl/919456"><span id="translatedtitle">Marking up lattice QCD configurations and <span class="hlt">ensembles</span></span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>P.Coddington; B.Joo; C.M.Maynard; D.Pleiter; T.Yoshie</p> <p>2007-10-01</p> <p>QCDml is an XML-<span class="hlt">based</span> markup language designed for sharing QCD configurations and <span class="hlt">ensembles</span> world-wide via the International Lattice Data Grid (ILDG). <span class="hlt">Based</span> on the latest release, we present key ingredients of the QCDml in order to provide some starting points for colleagues in this community to markup valuable configurations and submit them to the ILDG.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014PhDT........20M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014PhDT........20M"><span id="translatedtitle">Primary structure and solution conditions determine conformational <span class="hlt">ensemble</span> properties of intrinsically disordered proteins</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Mao, Hsuan-Han Alberto</p> <p></p> <p>Intrinsically disordered proteins (IDPs) are a class of proteins that do not exhibit well-defined three-dimensional structures. The absence of structure is intrinsic to their amino acid sequences, which are characterized by low hydrophobicity and high net charge per residue compared to folded proteins. Contradicting the classic structure-function paradigm, IDPs are capable of interacting with high specificity and affinity, often acquiring order in complex with protein and nucleic acid binding partners. This phenomenon is evident during cellular activities involving IDPs, which include transcriptional and translational regulation, cell cycle control, signal transduction, <span class="hlt">molecular</span> assembly, and <span class="hlt">molecular</span> recognition. Although approximately 30% of eukaryotic proteomes are intrinsically disordered, the nature of IDP conformational <span class="hlt">ensembles</span> remains unclear. In this dissertation, we describe relationships connecting characteristics of IDP conformational <span class="hlt">ensembles</span> to their primary structures and solution conditions. Using <span class="hlt">molecular</span> simulations and fluorescence experiments on a set of <span class="hlt">base</span>-rich IDPs, we find that net charge per residue segregates conformational <span class="hlt">ensembles</span> along a globule-to-coil transition. Speculatively generalizing this result, we propose a phase diagram that predicts an IDP's average size and shape <span class="hlt">based</span> on sequence composition and use it to generate hypotheses for a broad set of intrinsically disordered regions (IDRs). Simulations reveal that acid-rich IDRs, unlike their oppositely charged <span class="hlt">base</span>-rich counterparts, exhibit disordered globular <span class="hlt">ensembles</span> despite intra-chain repulsive electrostatic interactions. This apparent asymmetry is sensitive to simulation parameters for representing alkali and halide salt ions, suggesting that solution conditions modulate IDP conformational <span class="hlt">ensembles</span>. We refine the ion parameters using a calibration procedure that relies exclusively on crystal lattice properties. Simulations with these parameters recover swollen</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/22458519','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/22458519"><span id="translatedtitle"><span class="hlt">Molecular</span> and genetic <span class="hlt">bases</span> of pancreatic cancer.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Vaccaro, Vanja; Gelibter, Alain; Bria, Emilio; Iapicca, Pierluigi; Cappello, Paola; Di Modugno, Francesca; Pino, Maria Simona; Nuzzo, Carmen; Cognetti, Francesco; Novelli, Francesco; Nistico, Paola; Milella, Michele</p> <p>2012-06-01</p> <p>Pancreatic cancer remains a formidable challenge for oncologists and patients alike. Despite intensive efforts, attempts at improving survival in the past 15 years, particularly in advanced disease, have failed. This is true even with the introduction of <span class="hlt">molecularly</span> targeted agents, chosen on the basis of their action on pathways that were supposedly important in pancreatic cancer development and progression: indeed, with the notable exception of the epidermal growth factor receptor (EGFR) inhibitor erlotinib, that has provided a minimal survival improvement when added to gemcitabine, other agents targeting EGFR, matrix metallo-proteases, farnesyl transferase, or vascular endothelial growth factor have not succeeded in improving outcomes over standard gemcitabine monotherapy for a variety of different reasons. However, recent developments in the <span class="hlt">molecular</span> epidemiology of pancreatic cancer and an ever evolving understanding of the <span class="hlt">molecular</span> mechanisms underlying pancreatic cancer initiation and progression raise renewed hope to find novel, relevant therapeutic targets that could be pursued in the clinical setting. In this review we focus on <span class="hlt">molecular</span> epidemiology of pancreatic cancer, epithelial-to-mesenchymal transition and its influence on sensitivity to EGFR-targeted approaches, apoptotic pathways, hypoxia-related pathways, developmental pathways (such as the hedgehog and Notch pathways), and proteomic analysis as keys to a better understanding of pancreatic cancer biology and, most importantly, as a source of novel <span class="hlt">molecular</span> targets to be exploited therapeutically.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AcASn..57..326Y','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AcASn..57..326Y"><span id="translatedtitle"><span class="hlt">Ensemble</span> Pulsar Time Scale</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Yin, D. S.; Gao, Y. P.; Zhao, S. H.</p> <p>2016-05-01</p> <p>Millisecond pulsars can generate another type of time scale that is totally independent of the atomic time scale, because the physical mechanisms of the pulsar time scale and the atomic time scale are quite different from each other. Usually the pulsar timing observational data are not evenly sampled, and the internals between data points range from several hours to more than half a month. What's more, these data sets are sparse. And all these make it difficult to generate an <span class="hlt">ensemble</span> pulsar time scale. Hence, a new algorithm to calculate the <span class="hlt">ensemble</span> pulsar time scale is proposed. Firstly, we use cubic spline interpolation to densify the data set, and make the intervals between data points even. Then, we employ the Vondrak filter to smooth the data set, and get rid of high-frequency noise, finally adopt the weighted average method to generate the <span class="hlt">ensemble</span> pulsar time scale. The pulsar timing residuals represent clock difference between the pulsar time and atomic time, and the high precision pulsar timing data mean the clock difference measurement between the pulsar time and atomic time with a high signal to noise ratio, which is fundamental to generate pulsar time. We use the latest released NANOGRAV (North American Nanohertz Observatory for Gravitational Waves) 9-year data set to generate the <span class="hlt">ensemble</span> pulsar time scale. This data set is from the newest NANOGRAV data release, which includes 9-year observational data of 37 millisecond pulsars using the 100-meter Green Bank telescope and 305-meter Arecibo telescope. We find that the algorithm used in this paper can lower the influence caused by noises in timing residuals, and improve long-term stability of pulsar time. Results show that the long-term (> 1 yr) frequency stability of the pulsar time is better than 3.4×10-15.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/23536714','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/23536714"><span id="translatedtitle">State-<span class="hlt">based</span> decoding of hand and finger kinematics using neuronal <span class="hlt">ensemble</span> and LFP activity during dexterous reach-to-grasp movements.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Aggarwal, Vikram; Mollazadeh, Mohsen; Davidson, Adam G; Schieber, Marc H; Thakor, Nitish V</p> <p>2013-06-01</p> <p>The performance of brain-machine interfaces (BMIs) that continuously control upper limb neuroprostheses may benefit from distinguishing periods of posture and movement so as to prevent inappropriate movement of the prosthesis. Few studies, however, have investigated how decoding behavioral states and detecting the transitions between posture and movement could be used autonomously to trigger a kinematic decoder. We recorded simultaneous neuronal <span class="hlt">ensemble</span> and local field potential (LFP) activity from microelectrode arrays in primary motor cortex (M1) and dorsal (PMd) and ventral (PMv) premotor areas of two male rhesus monkeys performing a center-out reach-and-grasp task, while upper limb kinematics were tracked with a motion capture system with markers on the dorsal aspect of the forearm, hand, and fingers. A state decoder was trained to distinguish four behavioral states (baseline, reaction, movement, hold), while a kinematic decoder was trained to continuously decode hand end point position and 18 joint angles of the wrist and fingers. LFP amplitude most accurately predicted transition into the reaction (62%) and movement (73%) states, while spikes most accurately decoded arm, hand, and finger kinematics during movement. Using an LFP-<span class="hlt">based</span> state decoder to trigger a spike-<span class="hlt">based</span> kinematic decoder [r = 0.72, root mean squared error (RMSE) = 0.15] significantly improved decoding of reach-to-grasp movements from baseline to final hold, compared with either a spike-<span class="hlt">based</span> state decoder combined with a spike-<span class="hlt">based</span> kinematic decoder (r = 0.70, RMSE = 0.17) or a spike-<span class="hlt">based</span> kinematic decoder alone (r = 0.67, RMSE = 0.17). Combining LFP-<span class="hlt">based</span> state decoding with spike-<span class="hlt">based</span> kinematic decoding may be a valuable step toward the realization of BMI control of a multifingered neuroprosthesis performing dexterous manipulation.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/22159069','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/22159069"><span id="translatedtitle">Optical materials <span class="hlt">based</span> on <span class="hlt">molecular</span> nanoparticles.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Patra, A; Chandaluri, Ch G; Radhakrishnan, T P</p> <p>2012-01-21</p> <p>A major part of contemporary nanomaterials research is focused on metal and semiconductor nanoparticles, constituted of extended lattices of atoms or ions. <span class="hlt">Molecular</span> nanoparticles assembled from small molecules through non-covalent interactions are relatively less explored but equally fascinating materials. Their unique and versatile characteristics have attracted considerable attention in recent years, establishing their identity and status as a novel class of nanomaterials. Optical characteristics of <span class="hlt">molecular</span> nanoparticles capture the essence of their nanoscale features and form the basis of a variety of applications. This review describes the advances made in the field of fabrication of <span class="hlt">molecular</span> nanoparticles, the wide spectrum of their optical and nonlinear optical characteristics and explorations of the potential applications that exploit their unique optical attributes.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/biblio/1231194-matlab-cluster-ensemble-toolbox','SCIGOV-ESTSC'); return false;" href="http://www.osti.gov/scitech/biblio/1231194-matlab-cluster-ensemble-toolbox"><span id="translatedtitle">Matlab Cluster <span class="hlt">Ensemble</span> Toolbox</span></a></p> <p><a target="_blank" href=""></a></p> <p></p> <p>2009-04-27</p> <p>This is a Matlab toolbox for investigating the application of cluster <span class="hlt">ensembles</span> to data classification, with the objective of improving the accuracy and/or speed of clustering. The toolbox divides the cluster <span class="hlt">ensemble</span> problem into four areas, providing functionality for each. These include, (1) synthetic data generation, (2) clustering to generate individual data partitions and similarity matrices, (3) consensus function generation and final clustering to generate <span class="hlt">ensemble</span> data partitioning, and (4) implementation of accuracy metrics. Withmore » regard to data generation, Gaussian data of arbitrary dimension can be generated. The kcenters algorithm can then be used to generate individual data partitions by either, (a) subsampling the data and clustering each subsample, or by (b) randomly initializing the algorithm and generating a clustering for each initialization. In either case an overall similarity matrix can be computed using a consensus function operating on the individual similarity matrices. A final clustering can be performed and performance metrics are provided for evaluation purposes.« less</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=2143983','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=2143983"><span id="translatedtitle">Flexible ligand docking using conformational <span class="hlt">ensembles</span>.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Lorber, D. M.; Shoichet, B. K.</p> <p>1998-01-01</p> <p><span class="hlt">Molecular</span> docking algorithms suggest possible structures for <span class="hlt">molecular</span> complexes. They are used to model biological function and to discover potential ligands. A present challenge for docking algorithms is the treatment of <span class="hlt">molecular</span> flexibility. Here, the rigid body program, DOCK, is modified to allow it to rapidly fit multiple conformations of ligands. Conformations of a given molecule are pre-calculated in the same frame of reference, so that each conformer shares a common rigid fragment with all other conformations. The ligand conformers are then docked together, as an <span class="hlt">ensemble</span>, into a receptor binding site. This takes advantage of the redundancy present in differing conformers of the same molecule. The algorithm was tested using three organic ligand protein systems and two protein-protein systems. Both the bound and unbound conformations of the receptors were used. The ligand <span class="hlt">ensemble</span> method found conformations that resembled those determined in X-ray crystal structures (RMS values typically less than 1.5 A). To test the method's usefulness for inhibitor discovery, multi-compound and multi-conformer databases were screened for compounds known to bind to dihydrofolate reductase and compounds known to bind to thymidylate synthase. In both cases, known inhibitors and substrates were identified in conformations resembling those observed experimentally. The ligand <span class="hlt">ensemble</span> method was 100-fold faster than docking a single conformation at a time and was able to screen a database of over 34 million conformations from 117,000 molecules in one to four CPU days on a workstation. PMID:9568900</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/22706892','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/22706892"><span id="translatedtitle">An improved structural characterisation of reduced French bean plastocyanin <span class="hlt">based</span> on NMR data and local-elevation <span class="hlt">molecular</span> dynamics simulation.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Steiner, Denise; van Gunsteren, Wilfred F</p> <p>2012-07-01</p> <p>Deriving structural information about a protein from NMR experimental data is still a non-trivial challenge to computational biochemistry. This is because of the low ratio of the number of independent observables to the number of <span class="hlt">molecular</span> degrees of freedom, the approximations involved in the different relationships between particular observable quantities and <span class="hlt">molecular</span> conformation, and the averaged character of the experimental data. For example, protein (3)J-coupling data are seldom used for structure refinement because of the multiple-valuedness and limited accuracy of the Karplus relationship linking a (3)J-coupling to a torsional angle. Moreover, sampling of the large conformational space is still problematic. Using the 99-residue protein plastocyanin as an example we investigated whether use of a thermodynamically calibrated force field, inclusion of solvent degrees of freedom, and application of adaptive local-elevation sampling that accounts for conformational averaging produces a more realistic representation of the <span class="hlt">ensemble</span> of protein conformations than standard single-structure refinement in a non-explicit solvent using restraints that do not account for averaging and are partly <span class="hlt">based</span> on non-observed data. Yielding better agreement with observed experimental data, the protein conformational <span class="hlt">ensemble</span> is less restricted than when using standard single-structure refinement techniques, which are likely to yield a picture of the protein which is too rigid.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2015AMTD....8..759L&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2015AMTD....8..759L&link_type=ABSTRACT"><span id="translatedtitle">Quantifying residual ionospheric errors in GNSS radio occultation bending angles <span class="hlt">based</span> on <span class="hlt">ensembles</span> of profiles from end-to-end simulations</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Liu, C. L.; Kirchengast, G.; Zhang, K.; Norman, R.; Li, Y.; Zhang, S. C.; Fritzer, J.; Schwaerz, M.; Wu, S. Q.; Tan, Z. X.</p> <p>2015-01-01</p> <p>The radio occultation (RO) technique using signals from the Global Navigation Satellite System (GNSS), in particular from the Global Positioning System (GPS) so far, is meanwhile widely used to observe the atmosphere for applications such as numerical weather prediction and global climate monitoring. The ionosphere is a major error source in RO measurements at stratospheric altitudes and a linear ionospheric correction of dual-frequency RO bending angles is commonly used to remove the first-order ionospheric effect. However, the residual ionopheric error (RIE) can still be significant so that it needs to be further mitigated for high accuracy applications, especially above about 30 km altitude where the RIE is most relevant compared to the magnitude of the neutral atmospheric bending angle. Quantification and careful analyses for better understanding of the RIE is therefore important towards enabling benchmark-quality stratospheric RO retrievals. Here we present such an analysis of bending angle RIEs covering the stratosphere and mesosphere, using quasi-realistic end-to-end simulations for a full-day <span class="hlt">ensemble</span> of RO events. <span class="hlt">Based</span> on the <span class="hlt">ensemble</span> simulations we assessed the variation of bending angle RIEs, both biases and SDs, with solar activity, latitudinal region, and with or without the assumption of ionospheric spherical symmetry and of co-existing observing system errors. We find that the bending angle RIE biases in the upper stratosphere and mesosphere, and in all latitudinal zones from low- to high-latitudes, have a clear negative tendency and a magnitude increasing with solar activity, in line with recent empirical studies <span class="hlt">based</span> on real RO data. The maximum RIE biases are found at low latitudes during daytime, where they amount to with in -0.03 to -0.05 μrad, the smallest at high latitudes (0 to -0.01 μrad; quiet space weather and winter conditions). Ionospheric spherical symmetry or asymmetries about the RO event location have only a minor influence on</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2015AMT.....8.2999L&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2015AMT.....8.2999L&link_type=ABSTRACT"><span id="translatedtitle">Quantifying residual ionospheric errors in GNSS radio occultation bending angles <span class="hlt">based</span> on <span class="hlt">ensembles</span> of profiles from end-to-end simulations</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Liu, C. L.; Kirchengast, G.; Zhang, K.; Norman, R.; Li, Y.; Zhang, S. C.; Fritzer, J.; Schwaerz, M.; Wu, S. Q.; Tan, Z. X.</p> <p>2015-07-01</p> <p>The radio occultation (RO) technique using signals from the Global Navigation Satellite System (GNSS), in particular from the Global Positioning System (GPS) so far, is currently widely used to observe the atmosphere for applications such as numerical weather prediction and global climate monitoring. The ionosphere is a major error source in RO measurements at stratospheric altitudes, and a linear ionospheric correction of dual-frequency RO bending angles is commonly used to remove the first-order ionospheric effect. However, the residual ionospheric error (RIE) can still be significant so that it needs to be further mitigated for high-accuracy applications, especially above about 30 km altitude where the RIE is most relevant compared to the magnitude of the neutral atmospheric bending angle. Quantification and careful analyses for better understanding of the RIE is therefore important for enabling benchmark-quality stratospheric RO retrievals. Here we present such an analysis of bending angle RIEs covering the stratosphere and mesosphere, using quasi-realistic end-to-end simulations for a full-day <span class="hlt">ensemble</span> of RO events. <span class="hlt">Based</span> on the <span class="hlt">ensemble</span> simulations we assessed the variation of bending angle RIEs, both biases and standard deviations, with solar activity, latitudinal region and with or without the assumption of ionospheric spherical symmetry and co-existing observing system errors. We find that the bending angle RIE biases in the upper stratosphere and mesosphere, and in all latitudinal zones from low to high latitudes, have a clear negative tendency and a magnitude increasing with solar activity, which is in line with recent empirical studies <span class="hlt">based</span> on real RO data although we find smaller bias magnitudes, deserving further study in the future. The maximum RIE biases are found at low latitudes during daytime, where they amount to within -0.03 to -0.05 μrad, the smallest at high latitudes (0 to -0.01 μrad; quiet space weather and winter conditions</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AdWR...83..421S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AdWR...83..421S"><span id="translatedtitle">Parameter estimation of a physically-<span class="hlt">based</span> land surface hydrologic model using an <span class="hlt">ensemble</span> Kalman filter: A multivariate real-data experiment</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Shi, Yuning; Davis, Kenneth J.; Zhang, Fuqing; Duffy, Christopher J.; Yu, Xuan</p> <p>2015-09-01</p> <p>The capability of an <span class="hlt">ensemble</span> Kalman filter (EnKF) to simultaneously estimate multiple parameters in a physically-<span class="hlt">based</span> land surface hydrologic model using multivariate field observations is tested at a small watershed (0.08 km2). Multivariate, high temporal resolution, in situ measurements of discharge, water table depth, soil moisture, and sensible and latent heat fluxes encompassing five months of 2009 are assimilated. It is found that, for five out of the six parameters, the EnKF estimated parameter values from different test cases converge strongly, and the estimates after convergence are close to the manually calibrated parameter values. The EnKF estimated parameters and manually calibrated parameters yield similar model performance, but the EnKF sequential method significantly decreases the time and labor required for calibration. The results demonstrate that, given a limited number of multi-state, site-specific observations, an automated sequential calibration method (EnKF) can be used to optimize physically-<span class="hlt">based</span> land surface hydrologic models.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3640596','PMC'); return false;" href="http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3640596"><span id="translatedtitle">Accurate <span class="hlt">Molecular</span> Polarizabilities <span class="hlt">Based</span> on Continuum Electrostatics</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Truchon, Jean-François; Nicholls, Anthony; Iftimie, Radu I.; Roux, Benoît; Bayly, Christopher I.</p> <p>2013-01-01</p> <p>A novel approach for representing the intramolecular polarizability as a continuum dielectric is introduced to account for <span class="hlt">molecular</span> electronic polarization. It is shown, using a finite-difference solution to the Poisson equation, that the Electronic Polarization from Internal Continuum (EPIC) model yields accurate gas-phase <span class="hlt">molecular</span> polarizability tensors for a test set of 98 challenging molecules composed of heteroaromatics, alkanes and diatomics. The electronic polarization originates from a high intramolecular dielectric that produces polarizabilities consistent with B3LYP/aug-cc-pVTZ and experimental values when surrounded by vacuum dielectric. In contrast to other approaches to model electronic polarization, this simple model avoids the polarizability catastrophe and accurately calculates <span class="hlt">molecular</span> anisotropy with the use of very few fitted parameters and without resorting to auxiliary sites or anisotropic atomic centers. On average, the unsigned error in the average polarizability and anisotropy compared to B3LYP are 2% and 5%, respectively. The correlation between the polarizability components from B3LYP and this approach lead to a R2 of 0.990 and a slope of 0.999. Even the F2 anisotropy, shown to be a difficult case for existing polarizability models, can be reproduced within 2% error. In addition to providing new parameters for a rapid method directly applicable to the calculation of polarizabilities, this work extends the widely used Poisson equation to areas where accurate <span class="hlt">molecular</span> polarizabilities matter. PMID:23646034</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/10602240','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/10602240"><span id="translatedtitle"><span class="hlt">Molecular</span> Nanocapsules <span class="hlt">Based</span> on Amphiphilic Hyperbranched Polyglycerols.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Sunder; Krämer; Hanselmann; Mülhaupt; Frey</p> <p>1999-12-01</p> <p>Polar dyes can be solubilized in apolar media-<span class="hlt">molecular</span> nanocapsules with hydrophilic interiors have been prepared (see schematic representation) using polyglycerols with narrow polydispersity and simple esterification with fatty acids. These unimolecular micelles offer attractive potential for a variety of applications ranging from controlled drug release to the design of microreactors and catalysts.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/20136746','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/20136746"><span id="translatedtitle"><span class="hlt">Ensemble</span> habitat mapping of invasive plant species.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Stohlgren, Thomas J; Ma, Peter; Kumar, Sunil; Rocca, Monique; Morisette, Jeffrey T; Jarnevich, Catherine S; Benson, Nate</p> <p>2010-02-01</p> <p><span class="hlt">Ensemble</span> species distribution models combine the strengths of several species environmental matching models, while minimizing the weakness of any one model. <span class="hlt">Ensemble</span> models may be particularly useful in risk analysis of recently arrived, harmful invasive species because species may not yet have spread to all suitable habitats, leaving species-environment relationships difficult to determine. We tested five individual models (logistic regression, boosted regression trees, random forest, multivariate adaptive regression splines (MARS), and maximum entropy model or Maxent) and <span class="hlt">ensemble</span> modeling for selected nonnative plant species in Yellowstone and Grand Teton National Parks, Wyoming; Sequoia and Kings Canyon National Parks, California, and areas of interior Alaska. The models are <span class="hlt">based</span> on field data provided by the park staffs, combined with topographic, climatic, and vegetation predictors derived from satellite data. For the four invasive plant species tested, <span class="hlt">ensemble</span> models were the only models that ranked in the top three models for both field validation and test data. <span class="hlt">Ensemble</span> models may be more robust than individual species-environment matching models for risk analysis.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://pubs.er.usgs.gov/publication/70035550','USGSPUBS'); return false;" href="http://pubs.er.usgs.gov/publication/70035550"><span id="translatedtitle"><span class="hlt">Ensemble</span> habitat mapping of invasive plant species</span></a></p> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Stohlgren, T.J.; Ma, P.; Kumar, S.; Rocca, M.; Morisette, J.T.; Jarnevich, C.S.; Benson, N.</p> <p>2010-01-01</p> <p><span class="hlt">Ensemble</span> species distribution models combine the strengths of several species environmental matching models, while minimizing the weakness of any one model. <span class="hlt">Ensemble</span> models may be particularly useful in risk analysis of recently arrived, harmful invasive species because species may not yet have spread to all suitable habitats, leaving species-environment relationships difficult to determine. We tested five individual models (logistic regression, boosted regression trees, random forest, multivariate adaptive regression splines (MARS), and maximum entropy model or Maxent) and <span class="hlt">ensemble</span> modeling for selected nonnative plant species in Yellowstone and Grand Teton National Parks, Wyoming; Sequoia and Kings Canyon National Parks, California, and areas of interior Alaska. The models are <span class="hlt">based</span> on field data provided by the park staffs, combined with topographic, climatic, and vegetation predictors derived from satellite data. For the four invasive plant species tested, <span class="hlt">ensemble</span> models were the only models that ranked in the top three models for both field validation and test data. <span class="hlt">Ensemble</span> models may be more robust than individual species-environment matching models for risk analysis. ?? 2010 Society for Risk Analysis.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2015PhRvE..92d3310G&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2015PhRvE..92d3310G&link_type=ABSTRACT"><span id="translatedtitle">Simulations in generalized <span class="hlt">ensembles</span> through noninstantaneous switches</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Giovannelli, Edoardo; Cardini, Gianni; Chelli, Riccardo</p> <p>2015-10-01</p> <p>Generalized-<span class="hlt">ensemble</span> simulations, such as replica exchange and serial generalized-<span class="hlt">ensemble</span> methods, are powerful simulation tools to enhance sampling of free energy landscapes in systems with high energy barriers. In these methods, sampling is enhanced through instantaneous transitions of replicas, i.e., copies of the system, between different <span class="hlt">ensembles</span> characterized by some control parameter associated with thermodynamical variables (e.g., temperature or pressure) or collective mechanical variables (e.g., interatomic distances or torsional angles). An interesting evolution of these methodologies has been proposed by replacing the conventional instantaneous (trial) switches of replicas with noninstantaneous switches, realized by varying the control parameter in a finite time and accepting the final replica configuration with a Metropolis-like criterion <span class="hlt">based</span> on the Crooks nonequilibrium work (CNW) theorem. Here we revise these techniques focusing on their correlation with the CNW theorem in the framework of Markovian processes. An outcome of this report is the derivation of the acceptance probability for noninstantaneous switches in serial generalized-<span class="hlt">ensemble</span> simulations, where we show that explicit knowledge of the time dependence of the weight factors entering such simulations is not necessary. A generalized relationship of the CNW theorem is also provided in terms of the underlying equilibrium probability distribution at a fixed control parameter. Illustrative calculations on a toy model are performed with serial generalized-<span class="hlt">ensemble</span> simulations, especially focusing on the different behavior of instantaneous and noninstantaneous replica transition schemes.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/20136746','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/20136746"><span id="translatedtitle"><span class="hlt">Ensemble</span> habitat mapping of invasive plant species.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Stohlgren, Thomas J; Ma, Peter; Kumar, Sunil; Rocca, Monique; Morisette, Jeffrey T; Jarnevich, Catherine S; Benson, Nate</p> <p>2010-02-01</p> <p><span class="hlt">Ensemble</span> species distribution models combine the strengths of several species environmental matching models, while minimizing the weakness of any one model. <span class="hlt">Ensemble</span> models may be particularly useful in risk analysis of recently arrived, harmful invasive species because species may not yet have spread to all suitable habitats, leaving species-environment relationships difficult to determine. We tested five individual models (logistic regression, boosted regression trees, random forest, multivariate adaptive regression splines (MARS), and maximum entropy model or Maxent) and <span class="hlt">ensemble</span> modeling for selected nonnative plant species in Yellowstone and Grand Teton National Parks, Wyoming; Sequoia and Kings Canyon National Parks, California, and areas of interior Alaska. The models are <span class="hlt">based</span> on field data provided by the park staffs, combined with topographic, climatic, and vegetation predictors derived from satellite data. For the four invasive plant species tested, <span class="hlt">ensemble</span> models were the only models that ranked in the top three models for both field validation and test data. <span class="hlt">Ensemble</span> models may be more robust than individual species-environment matching models for risk analysis. PMID:20136746</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/servlets/purl/15006172','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/servlets/purl/15006172"><span id="translatedtitle">Creating <span class="hlt">Ensembles</span> of Decision Trees Through Sampling</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Kamath,C; Cantu-Paz, E</p> <p>2001-07-26</p> <p>Recent work in classification indicates that significant improvements in accuracy can be obtained by growing an <span class="hlt">ensemble</span> of classifiers and having them vote for the most popular class. This paper focuses on <span class="hlt">ensembles</span> of decision trees that are created with a randomized procedure <span class="hlt">based</span> on sampling. Randomization can be introduced by using random samples of the training data (as in bagging or boosting) and running a conventional tree-building algorithm, or by randomizing the induction algorithm itself. The objective of this paper is to describe the first experiences with a novel randomized tree induction method that uses a sub-sample of instances at a node to determine the split. The empirical results show that <span class="hlt">ensembles</span> generated using this approach yield results that are competitive in accuracy and superior in computational cost to boosting and bagging.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015SPIE.9534E..15R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015SPIE.9534E..15R"><span id="translatedtitle"><span class="hlt">Ensemble</span> approach for differentiation of malignant melanoma</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Rastgoo, Mojdeh; Morel, Olivier; Marzani, Franck; Garcia, Rafael</p> <p>2015-04-01</p> <p>Melanoma is the deadliest type of skin cancer, yet it is the most treatable kind depending on its early diagnosis. The early prognosis of melanoma is a challenging task for both clinicians and dermatologists. Due to the importance of early diagnosis and in order to assist the dermatologists, we propose an automated framework <span class="hlt">based</span> on <span class="hlt">ensemble</span> learning methods and dermoscopy images to differentiate melanoma from dysplastic and benign lesions. The evaluation of our framework on the recent and public dermoscopy benchmark (PH2 dataset) indicates the potential of proposed method. Our evaluation, using only global features, revealed that <span class="hlt">ensembles</span> such as random forest perform better than single learner. Using random forest <span class="hlt">ensemble</span> and combination of color and texture features, our framework achieved the highest sensitivity of 94% and specificity of 92%.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li class="active"><span>21</span></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_21 --> <div id="page_22" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li class="active"><span>22</span></li> <li><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li><a href="#" onclick='return showDiv("page_24");'>24</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="421"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://eric.ed.gov/?q=Artificial+AND+Neural+AND+Networks&pg=5&id=ED518411','ERIC'); return false;" href="http://eric.ed.gov/?q=Artificial+AND+Neural+AND+Networks&pg=5&id=ED518411"><span id="translatedtitle">Competitive Learning Neural Network <span class="hlt">Ensemble</span> Weighted by Predicted Performance</span></a></p> <p><a target="_blank" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p>Ye, Qiang</p> <p>2010-01-01</p> <p><span class="hlt">Ensemble</span> approaches have been shown to enhance classification by combining the outputs from a set of voting classifiers. Diversity in error patterns among <span class="hlt">base</span> classifiers promotes <span class="hlt">ensemble</span> performance. Multi-task learning is an important characteristic for Neural Network classifiers. Introducing a secondary output unit that receives different…</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/biblio/20857669','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/biblio/20857669"><span id="translatedtitle">Quantum measurement of a mesoscopic spin <span class="hlt">ensemble</span></span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Giedke, G.; Taylor, J. M.; Lukin, M. D.; D'Alessandro, D.; Imamoglu, A.</p> <p>2006-09-15</p> <p>We describe a method for precise estimation of the polarization of a mesoscopic spin <span class="hlt">ensemble</span> by using its coupling to a single two-level system. Our approach requires a minimal number of measurements on the two-level system for a given measurement precision. We consider the application of this method to the case of nuclear-spin <span class="hlt">ensemble</span> defined by a single electron-charged quantum dot: we show that decreasing the electron spin dephasing due to nuclei and increasing the fidelity of nuclear-spin-<span class="hlt">based</span> quantum memory could be within the reach of present day experiments.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/biblio/5206885','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/biblio/5206885"><span id="translatedtitle">Forecast of iceberg <span class="hlt">ensemble</span> drift</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>El-Tahan, M.S.; El-Tahan, H.W.; Venkatesh, S.</p> <p>1983-05-01</p> <p>The objectives of the study are to gain a better understanding of the characteristics of iceberg motion and the factors controlling iceberg drift, and to develop an iceberg <span class="hlt">ensemble</span> drift forecast system to be operated by the Canadian Atmospheric Environment Service. An extensive review of field and theoretical studies on iceberg behaviour, and the factors controlling iceberg motion has been carried out. Long term and short term behaviour of icebergs are critically examined. A quantitative assessment of the effects of the factors controlling iceberg motion is presented. The study indicated that wind and currents are the primary driving forces. Coriolis Force and ocean surface slope also have significant effects. As for waves, only the higher waves have a significant effect. Iceberg drift is also affected by iceberg size characteristics. <span class="hlt">Based</span> on the findings of the study a comprehensive computerized forecast system to predict the drift of iceberg <span class="hlt">ensembles</span> off Canada's east coast has been designed. The expected accuracy of the forecast system is discussed and recommendations are made for future improvements to the system.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/servlets/purl/862384','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/servlets/purl/862384"><span id="translatedtitle">Quantitative <span class="hlt">Molecular</span> Thermochemistry <span class="hlt">Based</span> on Path Integrals</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Glaesemann, K R; Fried, L E</p> <p>2005-03-14</p> <p>The calculation of thermochemical data requires accurate <span class="hlt">molecular</span> energies and heat capacities. Traditional methods rely upon the standard harmonic normal mode analysis to calculate the vibrational and rotational contributions. We utilize path integral Monte Carlo (PIMC) for going beyond the harmonic analysis, to calculate the vibrational and rotational contributions to ab initio energies. This is an application and extension of a method previously developed in our group.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/7571065','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/7571065"><span id="translatedtitle">[Department of the <span class="hlt">molecular</span> <span class="hlt">bases</span> of semiotics].</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Ternovyĭ, K S</p> <p>1995-01-01</p> <p>Department of <span class="hlt">molecular</span> basis of semiotics was organized in 1986. The main task of the department was to work out new approaches in estimation of the state of immune and blood system at the tissue, cell and <span class="hlt">molecular</span> levels, using biochemical, biophysical and <span class="hlt">molecular</span> biology techniques. There are several main directions of scientific investigations at the department. Most informational methods were collected in "immunological portrait" for differential diagnostic and complex investigation of the immune system of autoimmune patients. This group of techniques was used to study changes in the immune system of Kievites after the Chernobyl disaster. A decrease of complement and thymic serum activity was detected. Antibodies against nuclear components appeared in 20% of donors. And a higher of circulating immune complex of low <span class="hlt">molecular</span> weight was observed. Low level of thymic serum activity in blood of autoimmune patients with rheumatoid arthritis, lupus erythematosus, diabetes, herpes and other depends on the appearance of zinc-independent timuline inhibitor less then 2000 D. Another kind of thymic hormone inhibitors was detected in thymectomized adult mice. Its effect disappears when zinc added in blood rather due to competition for lymphocyte surface receptors timuline and its inactive analogue than other mechanism. Therapeutic effect of UV irradiation of patients' blood was shown to be closely connected with the changes in thymic serum activity in respect to stabilization of thymic hormone/inhibitor ratio. The immunochemical techniques were used to detect and investigate tumor-associated chromatin antigens in human and animal tumor cells. Antigens not found in normal tissues were detected when using rabbit antibodies against chromatin of rat hepatocarcinoma and human colon and carcinoma.(ABSTRACT TRUNCATED AT 250 WORDS)</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/25935576','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/25935576"><span id="translatedtitle">Future projections of labor hours <span class="hlt">based</span> on WBGT for Tokyo and Osaka, Japan, using multi-period <span class="hlt">ensemble</span> dynamical downscale simulations.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Suzuki-Parker, Asuka; Kusaka, Hiroyuki</p> <p>2016-02-01</p> <p>Following the heatstroke prevention guideline by the Ministry of Health, Labor, and Welfare of Japan, "safe hours" for heavy and light labor are estimated <span class="hlt">based</span> on hourly wet-bulb globe temperature (WBGT) obtained from the three-member <span class="hlt">ensemble</span> multi-period (the 2000s, 2030s, 2050s, 2070s, and 2090s) climate projections using dynamical downscaling approach. Our target cities are Tokyo and Osaka, Japan. The results show that most of the current climate daytime hours are "light labor safe,", but these hours are projected to decrease by 30-40% by the end of the twenty-first century. A 60-80% reduction is projected for heavy labor hours, resulting in less than 2 hours available for safe performance of heavy labor. The number of "heavy labor restricted days" (days with minimum daytime WBGT exceeding the safe level threshold for heavy labor) is projected to increase from ~5 days in the 2000s to nearly two-thirds of the days in August in the 2090s.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/25935576','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/25935576"><span id="translatedtitle">Future projections of labor hours <span class="hlt">based</span> on WBGT for Tokyo and Osaka, Japan, using multi-period <span class="hlt">ensemble</span> dynamical downscale simulations.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Suzuki-Parker, Asuka; Kusaka, Hiroyuki</p> <p>2016-02-01</p> <p>Following the heatstroke prevention guideline by the Ministry of Health, Labor, and Welfare of Japan, "safe hours" for heavy and light labor are estimated <span class="hlt">based</span> on hourly wet-bulb globe temperature (WBGT) obtained from the three-member <span class="hlt">ensemble</span> multi-period (the 2000s, 2030s, 2050s, 2070s, and 2090s) climate projections using dynamical downscaling approach. Our target cities are Tokyo and Osaka, Japan. The results show that most of the current climate daytime hours are "light labor safe,", but these hours are projected to decrease by 30-40% by the end of the twenty-first century. A 60-80% reduction is projected for heavy labor hours, resulting in less than 2 hours available for safe performance of heavy labor. The number of "heavy labor restricted days" (days with minimum daytime WBGT exceeding the safe level threshold for heavy labor) is projected to increase from ~5 days in the 2000s to nearly two-thirds of the days in August in the 2090s. PMID:25935576</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/26552095','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/26552095"><span id="translatedtitle">Direct Extraction of Tumor Response <span class="hlt">Based</span> on <span class="hlt">Ensemble</span> Empirical Mode Decomposition for Image Reconstruction of Early Breast Cancer Detection by UWB.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Li, Qinwei; Xiao, Xia; Wang, Liang; Song, Hang; Kono, Hayato; Liu, Peifang; Lu, Hong; Kikkawa, Takamaro</p> <p>2015-10-01</p> <p>A direct extraction method of tumor response <span class="hlt">based</span> on <span class="hlt">ensemble</span> empirical mode decomposition (EEMD) is proposed for early breast cancer detection by ultra-wide band (UWB) microwave imaging. With this approach, the image reconstruction for the tumor detection can be realized with only extracted signals from as-detected waveforms. The calibration process executed in the previous research for obtaining reference waveforms which stand for signals detected from the tumor-free model is not required. The correctness of the method is testified by successfully detecting a 4 mm tumor located inside the glandular region in one breast model and by the model located at the interface between the gland and the fat, respectively. The reliability of the method is checked by distinguishing a tumor buried in the glandular tissue whose dielectric constant is 35. The feasibility of the method is confirmed by showing the correct tumor information in both simulation results and experimental results for the realistic 3-D printed breast phantom.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016IJBm...60..307S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016IJBm...60..307S"><span id="translatedtitle">Future projections of labor hours <span class="hlt">based</span> on WBGT for Tokyo and Osaka, Japan, using multi-period <span class="hlt">ensemble</span> dynamical downscale simulations</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Suzuki-Parker, Asuka; Kusaka, Hiroyuki</p> <p>2016-02-01</p> <p>Following the heatstroke prevention guideline by the Ministry of Health, Labor, and Welfare of Japan, "safe hours" for heavy and light labor are estimated <span class="hlt">based</span> on hourly wet-bulb globe temperature (WBGT) obtained from the three-member <span class="hlt">ensemble</span> multi-period (the 2000s, 2030s, 2050s, 2070s, and 2090s) climate projections using dynamical downscaling approach. Our target cities are Tokyo and Osaka, Japan. The results show that most of the current climate daytime hours are "light labor safe,", but these hours are projected to decrease by 30-40 % by the end of the twenty-first century. A 60-80 % reduction is projected for heavy labor hours, resulting in less than 2 hours available for safe performance of heavy labor. The number of "heavy labor restricted days" (days with minimum daytime WBGT exceeding the safe level threshold for heavy labor) is projected to increase from ~5 days in the 2000s to nearly two-thirds of the days in August in the 2090s.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=2712746','PMC'); return false;" href="http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=2712746"><span id="translatedtitle">sigReannot: an oligo-set re-annotation pipeline <span class="hlt">based</span> on similarities with the <span class="hlt">Ensembl</span> transcripts and Unigene clusters</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Casel, Pierrot; Moreews, François; Lagarrigue, Sandrine; Klopp, Christophe</p> <p>2009-01-01</p> <p>Background Microarray is a powerful technology enabling to monitor tens of thousands of genes in a single experiment. Most microarrays are now using oligo-sets. The design of the oligo-nucleotides is time consuming and error prone. Genome wide microarray oligo-sets are designed using as large a set of transcripts as possible in order to monitor as many genes as possible. Depending on the genome sequencing state and on the assembly state the knowledge of the existing transcripts can be very different. This knowledge evolves with the different genome builds and gene builds. Once the design is done the microarrays are often used for several years. The biologists working in EADGENE expressed the need of up-to-dated annotation files for the oligo-sets they share including information about the orthologous genes of model species, the Gene Ontology, the corresponding pathways and the chromosomal location. Results The results of SigReannot on a chicken micro-array used in the EADGENE project compared to the initial annotations show that 23% of the oligo-nucleotide gene annotations were not confirmed, 2% were modified and 1% were added. The interest of this up-to-date annotation procedure is demonstrated through the analysis of real data previously published. Conclusion SigReannot uses the oligo-nucleotide design procedure criteria to validate the probe-gene link and the <span class="hlt">Ensembl</span> transcripts as reference for annotation. It therefore produces a high quality annotation <span class="hlt">based</span> on reference gene sets. PMID:19615116</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3159309','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3159309"><span id="translatedtitle"><span class="hlt">Molecular</span> <span class="hlt">Bases</span> of Cutaneous and Uveal Melanomas</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Gaudi, Sudeep; Messina, Jane L.</p> <p>2011-01-01</p> <p>Intensive research in recent years has begun to unlock the mysteries surrounding the <span class="hlt">molecular</span> pathogenesis of melanoma, the deadliest of skin cancers. The high-penetrance, low-frequency susceptibility gene CDKN2A produces tumor suppressor proteins that function in concert with p53 and retinoblastoma protein to thwart melanomagenesis. Aberrant CDKN2A gene products have been implicated in a great many cases of familial cutaneous melanoma. Sporadic cases, on the other hand, often involve constitutive signal transduction along the mitogen-activated protein kinase (MAPK) pathway, with particular focus falling upon mutated RAS and RAF protooncogenes. The proliferative effects of the MAPK pathway may be complemented by the antiapoptotic signals of the PI3K/AKT pathway. After skin, melanoma most commonly affects the eye. Data for the constitutive activation of the MAPK pathway in uveal melanoma exists as well, however, not through mutations of RAS and RAF. Rather, evidence implicates the proto-oncogene GNAQ. In the following discussion, we review the major <span class="hlt">molecular</span> pathways implicated in both familial and sporadic cutaneous melanomagenesis, the former accounting for approximately 10% of cases. Additionally, we discuss the <span class="hlt">molecular</span> pathways for which preliminary evidence suggests a role in uveal melanomagenesis. PMID:21876842</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/27462766','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/27462766"><span id="translatedtitle"><span class="hlt">Ensemble</span> Rule-<span class="hlt">Based</span> Classification of Substrates of the Human ABC-Transporter ABCB1 Using Simple Physicochemical Descriptors.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Demel, Michael A; Kraemer, Oliver; Ettmayer, Peter; Haaksma, Eric; Ecker, Gerhard F</p> <p>2010-03-15</p> <p>Within the last decades, the detailed knowledge on the impact of membrane bound drug efflux transporters of the ATP binding cassette (ABC) protein family on the pharmacological profile of drugs has enormously increased. Especially, ABCB1 (P-glycoprotein, P-gp, MDR1) has attracted particular interest in medicinal chemistry, since it determines the clinical efficacy, side effects and toxicity risks of drug candidates. <span class="hlt">Based</span> on this, the development of in silico models that provide rapid and cost-effective screening tools for the classification of substrates and nonsubstrates of ABCB1 is an urgent need in contemporary ADMET profiling. A characteristic hallmark feature of this transporter is its polyspecific ligand recognition pattern. In this study we describe a method for classifying ABCB1 ligands in terms of simple, conjunctive rules (RuleFit) <span class="hlt">based</span> on interpretable ADMET features. The retrieved results showed that models <span class="hlt">based</span> on large, very diverse data sets gave better classification performance than models <span class="hlt">based</span> on smaller, more homogenous training sets. The best model achieved gave a correct classification rate of 0.90 for an external validation set. Furthermore, from the interpretation of the best performing model it could be concluded that in comparison to nonsubstrates ABCB1 substrates generally show a higher number of hydrogen-bond acceptors, are more flexible and exhibit higher logP values.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/27575979','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/27575979"><span id="translatedtitle">Beyond <span class="hlt">Molecular</span> Wires: Design <span class="hlt">Molecular</span> Electronic Functions <span class="hlt">Based</span> on Dipolar Effect.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Lo, Wai-Yip; Zhang, Na; Cai, Zhengxu; Li, Lianwei; Yu, Luping</p> <p>2016-09-20</p> <p>As the semiconductor companies officially abandoned the pursuit of Moore's law, the limitation of silicone-<span class="hlt">based</span> semiconductor electronic devices is approaching. Single <span class="hlt">molecular</span> devices are considered as a potential solution to overcome the physical barriers caused by quantum interferences because the intermolecular interactions are mainly through weak van der Waals force between <span class="hlt">molecular</span> building blocks. In this bottom-up approach, components are built from atoms up, allowing great control over the <span class="hlt">molecular</span> properties. Moreover, single <span class="hlt">molecular</span> devices are powerful tools to understand quantum physics, reaction mechanism, and electron and charge transfer processes in organic semiconductors and molecules. So far, a great deal of effort is focused on understanding charge transport through organic single-<span class="hlt">molecular</span> wires. However, to control charge transport, <span class="hlt">molecular</span> diodes, switches, transistors, and memories are crucial. Significant progress in these topics has been achieved in the past years. The introduction and advances of scanning tunneling microscope break-junction (STM-BJ) techniques have led to more detailed characterization of new <span class="hlt">molecular</span> structures. The modern organic chemistry provides an efficient access to a variety of functional moieties in single <span class="hlt">molecular</span> device. These moieties have the potential to be incorporated in miniature circuits or incorporated as parts in <span class="hlt">molecular</span> machines, bioelectronics devices, and bottom-up <span class="hlt">molecular</span> devices. In this Account, we discuss progress mainly made in our lab in designing and characterizing organic single-<span class="hlt">molecular</span> electronic components beyond <span class="hlt">molecular</span> wires and with varied functions. We have synthesized and demonstrated <span class="hlt">molecular</span> diodes with p-n junction structures through various scanning probe microscopy techniques. The assembly of the <span class="hlt">molecular</span> diodes was achieved by using Langmuir-Blodgett technique or thiol/gold self-assembly chemistry with orthogonal protecting groups. We have thoroughly</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/27575979','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/27575979"><span id="translatedtitle">Beyond <span class="hlt">Molecular</span> Wires: Design <span class="hlt">Molecular</span> Electronic Functions <span class="hlt">Based</span> on Dipolar Effect.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Lo, Wai-Yip; Zhang, Na; Cai, Zhengxu; Li, Lianwei; Yu, Luping</p> <p>2016-09-20</p> <p>As the semiconductor companies officially abandoned the pursuit of Moore's law, the limitation of silicone-<span class="hlt">based</span> semiconductor electronic devices is approaching. Single <span class="hlt">molecular</span> devices are considered as a potential solution to overcome the physical barriers caused by quantum interferences because the intermolecular interactions are mainly through weak van der Waals force between <span class="hlt">molecular</span> building blocks. In this bottom-up approach, components are built from atoms up, allowing great control over the <span class="hlt">molecular</span> properties. Moreover, single <span class="hlt">molecular</span> devices are powerful tools to understand quantum physics, reaction mechanism, and electron and charge transfer processes in organic semiconductors and molecules. So far, a great deal of effort is focused on understanding charge transport through organic single-<span class="hlt">molecular</span> wires. However, to control charge transport, <span class="hlt">molecular</span> diodes, switches, transistors, and memories are crucial. Significant progress in these topics has been achieved in the past years. The introduction and advances of scanning tunneling microscope break-junction (STM-BJ) techniques have led to more detailed characterization of new <span class="hlt">molecular</span> structures. The modern organic chemistry provides an efficient access to a variety of functional moieties in single <span class="hlt">molecular</span> device. These moieties have the potential to be incorporated in miniature circuits or incorporated as parts in <span class="hlt">molecular</span> machines, bioelectronics devices, and bottom-up <span class="hlt">molecular</span> devices. In this Account, we discuss progress mainly made in our lab in designing and characterizing organic single-<span class="hlt">molecular</span> electronic components beyond <span class="hlt">molecular</span> wires and with varied functions. We have synthesized and demonstrated <span class="hlt">molecular</span> diodes with p-n junction structures through various scanning probe microscopy techniques. The assembly of the <span class="hlt">molecular</span> diodes was achieved by using Langmuir-Blodgett technique or thiol/gold self-assembly chemistry with orthogonal protecting groups. We have thoroughly</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/pages/biblio/1302921-multilevel-ensemble-kalman-filtering','SCIGOV-DOEP'); return false;" href="http://www.osti.gov/pages/biblio/1302921-multilevel-ensemble-kalman-filtering"><span id="translatedtitle">Multilevel <span class="hlt">ensemble</span> Kalman filtering</span></a></p> <p><a target="_blank" href="http://www.osti.gov/pages">DOE PAGESBeta</a></p> <p>Hoel, Hakon; Law, Kody J. H.; Tempone, Raul</p> <p>2016-06-14</p> <p>This study embeds a multilevel Monte Carlo sampling strategy into the Monte Carlo step of the <span class="hlt">ensemble</span> Kalman filter (EnKF) in the setting of finite dimensional signal evolution and noisy discrete-time observations. The signal dynamics is assumed to be governed by a stochastic differential equation (SDE), and a hierarchy of time grids is introduced for multilevel numerical integration of that SDE. Finally, the resulting multilevel EnKF is proved to asymptotically outperform EnKF in terms of computational cost versus approximation accuracy. The theoretical results are illustrated numerically.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/servlets/purl/799409','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/servlets/purl/799409"><span id="translatedtitle"><span class="hlt">Ensemble</span> Atmospheric Dispersion Modeling</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Addis, R.P.</p> <p>2002-06-24</p> <p>Prognostic atmospheric dispersion models are used to generate consequence assessments, which assist decision-makers in the event of a release from a nuclear facility. Differences in the forecast wind fields generated by various meteorological agencies, differences in the transport and diffusion models, as well as differences in the way these models treat the release source term, result in differences in the resulting plumes. Even dispersion models using the same wind fields may produce substantially different plumes. This talk will address how <span class="hlt">ensemble</span> techniques may be used to enable atmospheric modelers to provide decision-makers with a more realistic understanding of how both the atmosphere and the models behave.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4256725','PMC'); return false;" href="http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4256725"><span id="translatedtitle">Current and emerging opportunities for <span class="hlt">molecular</span> simulations in structure-<span class="hlt">based</span> drug design</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Michel, Julien</p> <p>2014-01-01</p> <p>An overview of the current capabilities and limitations of <span class="hlt">molecular</span> simulation of biomolecular complexes in the context of computer-aided drug design is provided. Steady improvements in computer hardware coupled with more refined representations of energetics are leading to a new appreciation of the driving forces of <span class="hlt">molecular</span> recognition. <span class="hlt">Molecular</span> simulations are poised to more frequently guide the interpretation of biophysical measurements of biomolecular complexes. Ligand design strategies emerge from detailed analyses of computed structural <span class="hlt">ensembles</span>. The feasibility of routine applications to ligand optimization problems hinges upon successful extensive large scale validation studies and the development of protocols to intelligently automate computations. PMID:24469595</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=2151381','PMC'); return false;" href="http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=2151381"><span id="translatedtitle">Assistive technology and robotic control using motor cortex <span class="hlt">ensemble-based</span> neural interface systems in humans with tetraplegia</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Donoghue, John P; Nurmikko, Arto; Black, Michael; Hochberg, Leigh R</p> <p>2007-01-01</p> <p>This review describes the rationale, early stage development, and initial human application of neural interface systems (NISs) for humans with paralysis. NISs are emerging medical devices designed to allow persons with paralysis to operate assistive technologies or to reanimate muscles <span class="hlt">based</span> upon a command signal that is obtained directly from the brain. Such systems require the development of sensors to detect brain signals, decoders to transform neural activity signals into a useful command, and an interface for the user. We review initial pilot trial results of an NIS that is <span class="hlt">based</span> on an intracortical microelectrode sensor that derives control signals from the motor cortex. We review recent findings showing, first, that neurons engaged by movement intentions persist in motor cortex years after injury or disease to the motor system, and second, that signals derived from motor cortex can be used by persons with paralysis to operate a range of devices. We suggest that, with further development, this form of NIS holds promise as a useful new neurotechnology for those with limited motor function or communication. We also discuss the additional potential for neural sensors to be used in the diagnosis and management of various neurological conditions and as a new way to learn about human brain function. PMID:17272345</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/21910639','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/21910639"><span id="translatedtitle"><span class="hlt">Molecular</span> <span class="hlt">bases</span> of plant resistance to arthropods.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Smith, C Michael; Clement, Stephen L</p> <p>2012-01-01</p> <p>Arthropod-resistant crops provide significant ecological and economic benefits to global agriculture. Incompatible interactions involving resistant plants and avirulent pest arthropods are mediated by constitutively produced and arthropod-induced plant proteins and defense allelochemicals synthesized by resistance gene products. Cloning and <span class="hlt">molecular</span> mapping have identified the Mi-1.2 and Vat arthropod resistance genes as CC-NBS-LRR (coiled coil-nucleotide binding site-leucine rich repeat) subfamily NBS-LRR resistance proteins, as well as several resistance gene analogs. Genetic linkage mapping has identified more than 100 plant resistance gene loci and linked <span class="hlt">molecular</span> markers used in cultivar development. Rice and sorghum arthropod-resistant cultivars and, to a lesser extent, raspberry and wheat cultivars are components of integrated pest management (IPM) programs in Asia, Australia, Europe, and North America. Nevertheless, arthropod resistance in most food and fiber crops has not been integrated due primarily to the application of synthetic insecticides. Plant and arthropod genomics provide many opportunities to more efficiently develop arthropod-resistant plants, but integration of resistant cultivars into IPM programs will succeed only through interdisciplinary collaboration.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/21910639','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/21910639"><span id="translatedtitle"><span class="hlt">Molecular</span> <span class="hlt">bases</span> of plant resistance to arthropods.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Smith, C Michael; Clement, Stephen L</p> <p>2012-01-01</p> <p>Arthropod-resistant crops provide significant ecological and economic benefits to global agriculture. Incompatible interactions involving resistant plants and avirulent pest arthropods are mediated by constitutively produced and arthropod-induced plant proteins and defense allelochemicals synthesized by resistance gene products. Cloning and <span class="hlt">molecular</span> mapping have identified the Mi-1.2 and Vat arthropod resistance genes as CC-NBS-LRR (coiled coil-nucleotide binding site-leucine rich repeat) subfamily NBS-LRR resistance proteins, as well as several resistance gene analogs. Genetic linkage mapping has identified more than 100 plant resistance gene loci and linked <span class="hlt">molecular</span> markers used in cultivar development. Rice and sorghum arthropod-resistant cultivars and, to a lesser extent, raspberry and wheat cultivars are components of integrated pest management (IPM) programs in Asia, Australia, Europe, and North America. Nevertheless, arthropod resistance in most food and fiber crops has not been integrated due primarily to the application of synthetic insecticides. Plant and arthropod genomics provide many opportunities to more efficiently develop arthropod-resistant plants, but integration of resistant cultivars into IPM programs will succeed only through interdisciplinary collaboration. PMID:21910639</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li class="active"><span>22</span></li> <li><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li><a href="#" onclick='return showDiv("page_24");'>24</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_22 --> <div id="page_23" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li class="active"><span>23</span></li> <li><a href="#" onclick='return showDiv("page_24");'>24</a></li> <li><a href="#" onclick='return showDiv("page_25");'>25</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="441"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/25099914','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/25099914"><span id="translatedtitle">A dual input DNA-<span class="hlt">based</span> <span class="hlt">molecular</span> switch.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Nesterova, Irina V; Elsiddieg, Siddieg O; Nesterov, Evgueni E</p> <p>2014-11-01</p> <p>We have designed and characterized a DNA-<span class="hlt">based</span> <span class="hlt">molecular</span> switch which processes two physiologically relevant inputs: pH (i.e. alkalinisation) and enzymatic activity, and generates a chemical output (in situ synthesized oligonucleotide). The design, <span class="hlt">based</span> on allosteric interactions between i-motif and hairpin stem within the DNA molecule, addresses such critical physiological system parameters as <span class="hlt">molecular</span> simplicity, tunability, orthogonality of the two input sensing domains, and compatibility with intracellular operation/delivery. PMID:25099914</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015ERL....10h4013Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015ERL....10h4013Z"><span id="translatedtitle">Estimating heat stress from climate-<span class="hlt">based</span> indicators: present-day biases and future spreads in the CMIP5 global climate model <span class="hlt">ensemble</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Zhao, Y.; Ducharne, A.; Sultan, B.; Braconnot, P.; Vautard, R.</p> <p>2015-08-01</p> <p>The increased exposure of human populations to heat stress is one of the likely consequences of global warming, and it has detrimental effects on health and labor capacity. Here, we consider the evolution of heat stress under climate change using 21 general circulation models (GCMs). Three heat stress indicators, <span class="hlt">based</span> on both temperature and humidity conditions, are used to investigate present-day model biases and spreads in future climate projections. Present day estimates of heat stress indicators from observational data shows that humid tropical areas tend to experience more frequent heat stress than other regions do, with a total frequency of heat stress 250-300 d yr-1. The most severe heat stress is found in the Sahel and south India. Present-day GCM simulations tend to underestimate heat stress over the tropics due to dry and cold model biases. The model <span class="hlt">based</span> estimates are in better agreement with observation in mid to high latitudes, but this is due to compensating errors in humidity and temperature. The severity of heat stress is projected to increase by the end of the century under climate change scenario RCP8.5, reaching unprecedented levels in some regions compared with observations. An analysis of the different factors contributing to the total spread of projected heat stress shows that spread is primarily driven by the choice of GCMs rather than the choice of indicators, even when the simulated indicators are bias-corrected. This supports the utility of the multi-model <span class="hlt">ensemble</span> approach to assess the impacts of climate change on heat stress.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/10636037','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/10636037"><span id="translatedtitle">The cell as the smallest DNA-<span class="hlt">based</span> <span class="hlt">molecular</span> computer.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Ji, S</p> <p>1999-10-01</p> <p>The pioneering work of Adleman (1994) demonstrated that DNA molecules in test tubes can be manipulated to perform a certain type of mathematical computation. This has stimulated a theoretical interest in the possibility of constructing DNA-<span class="hlt">based</span> <span class="hlt">molecular</span> computers. To gauge the practicality of realizing such microscopic computers, it was thought necessary to learn as much as possible from the biology of the living cell--presently the only known DNA-<span class="hlt">based</span> <span class="hlt">molecular</span> computer in existence. Here the recently developed theoretical model of the living cell (the Bhopalator) and its associated theories (e.g. cell language), principles, laws and concepts (e.g. conformons, IDS's) are briefly reviewed and summarized in the form of a set of five laws of '<span class="hlt">molecular</span> semiotics' (synonyms include 'microsemiotics', 'cellular semiotics', or 'cytosemiotics') the study of signs mediating measurement, computation, and communication on the cellular and <span class="hlt">molecular</span> levels. Hopefully, these laws will find practical applications in designing DNA-<span class="hlt">based</span> computing systems.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://eric.ed.gov/?q=computer+AND+databases&id=EJ938354','ERIC'); return false;" href="http://eric.ed.gov/?q=computer+AND+databases&id=EJ938354"><span id="translatedtitle">Inquiry-<span class="hlt">Based</span> Learning of <span class="hlt">Molecular</span> Phylogenetics</span></a></p> <p><a target="_blank" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p>Campo, Daniel; Garcia-Vazquez, Eva</p> <p>2008-01-01</p> <p>Reconstructing phylogenies from nucleotide sequences is a challenge for students because it strongly depends on evolutionary models and computer tools that are frequently updated. We present here an inquiry-<span class="hlt">based</span> course aimed at learning how to trace a phylogeny <span class="hlt">based</span> on sequences existing in public databases. Computer tools are freely available…</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/16913810','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/16913810"><span id="translatedtitle">Quantifying polypeptide conformational space: sensitivity to conformation and <span class="hlt">ensemble</span> definition.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Sullivan, David C; Lim, Carmay</p> <p>2006-08-24</p> <p>Quantifying the density of conformations over phase space (the conformational distribution) is needed to model important macromolecular processes such as protein folding. In this work, we quantify the conformational distribution for a simple polypeptide (N-mer polyalanine) using the cumulative distribution function (CDF), which gives the probability that two randomly selected conformations are separated by less than a "conformational" distance and whose inverse gives conformation counts as a function of conformational radius. An important finding is that the conformation counts obtained by the CDF inverse depend critically on the assignment of a conformation's distance span and the <span class="hlt">ensemble</span> (e.g., unfolded state model): varying <span class="hlt">ensemble</span> and conformation definition (1 --> 2 A) varies the CDF-<span class="hlt">based</span> conformation counts for Ala(50) from 10(11) to 10(69). In particular, relatively short <span class="hlt">molecular</span> dynamics (MD) relaxation of Ala(50)'s random-walk <span class="hlt">ensemble</span> reduces the number of conformers from 10(55) to 10(14) (using a 1 A root-mean-square-deviation radius conformation definition) pointing to potential disconnections in comparing the results from simplified models of unfolded proteins with those from all-atom MD simulations. Explicit waters are found to roughen the landscape considerably. Under some common conformation definitions, the results herein provide (i) an upper limit to the number of accessible conformations that compose unfolded states of proteins, (ii) the optimal clustering radius/conformation radius for counting conformations for a given energy and solvent model, (iii) a means of comparing various studies, and (iv) an assessment of the applicability of random search in protein folding.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2008JChPh.128w4908P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2008JChPh.128w4908P"><span id="translatedtitle">Stochastic dynamics of bionanosystems: Multiscale analysis and specialized <span class="hlt">ensembles</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Pankavich, S.; Miao, Y.; Ortoleva, J.; Shreif, Z.; Ortoleva, P.</p> <p>2008-06-01</p> <p>An approach for simulating bionanosystems such as viruses and ribosomes is presented. This calibration-free approach is <span class="hlt">based</span> on an all-atom description for bionanosystems, a universal interatomic force field, and a multiscale perspective. The supramillion-atom nature of these bionanosystems prohibits the use of a direct <span class="hlt">molecular</span> dynamics approach for phenomena such as viral structural transitions or self-assembly that develop over milliseconds or longer. A key element of these multiscale systems is the cross-talk between, and consequent strong coupling of processes over many scales in space and time. Thus, overall nanoscale features of these systems control the relative probability of atomistic fluctuations, while the latter mediate the average forces and diffusion coefficients that induce the dynamics of these nanoscale features. This feedback loop is overlooked in typical coarse-grained methods. We elucidate the role of interscale cross-talk and overcome bionanosystem simulation difficulties with (1) automated construction of order parameters (OPs) describing suprananometer scale structural features, (2) construction of OP-dependent <span class="hlt">ensembles</span> describing the statistical properties of atomistic variables that ultimately contribute to the entropies driving the dynamics of the OPs, and (3) the derivation of a rigorous equation for the stochastic dynamics of the OPs. As the OPs capture hydrodynamic modes in the host medium, ``long-time tails'' in the correlation functions yielding the generalized diffusion coefficients do not emerge. Since the atomic-scale features of the system are treated statistically, several <span class="hlt">ensembles</span> are constructed that reflect various experimental conditions. Attention is paid to the proper use of the Gibbs hypothesized equivalence of long-time and <span class="hlt">ensemble</span> averages to accommodate the varying experimental conditions. The theory provides a basis for a practical, quantitative bionanosystem modeling approach that preserves the cross</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..18.4328V','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..18.4328V"><span id="translatedtitle">The role of <span class="hlt">ensemble</span> post-processing for modeling the <span class="hlt">ensemble</span> tail</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Van De Vyver, Hans; Van Schaeybroeck, Bert; Vannitsem, Stéphane</p> <p>2016-04-01</p> <p>The past decades the numerical weather prediction community has witnessed a paradigm shift from deterministic to probabilistic forecast and state estimation (Buizza and Leutbecher, 2015; Buizza et al., 2008), in an attempt to quantify the uncertainties associated with initial-condition and model errors. An important benefit of a probabilistic framework is the improved prediction of extreme events. However, one may ask to what extent such model estimates contain information on the occurrence probability of extreme events and how this information can be optimally extracted. Different approaches have been proposed and applied on real-world systems which, <span class="hlt">based</span> on extreme value theory, allow the estimation of extreme-event probabilities conditional on forecasts and state estimates (Ferro, 2007; Friederichs, 2010). Using <span class="hlt">ensemble</span> predictions generated with a model of low dimensionality, a thorough investigation is presented quantifying the change of predictability of extreme events associated with <span class="hlt">ensemble</span> post-processing and other influencing factors including the finite <span class="hlt">ensemble</span> size, lead time and model assumption and the use of different covariates (<span class="hlt">ensemble</span> mean, maximum, spread...) for modeling the tail distribution. Tail modeling is performed by deriving extreme-quantile estimates using peak-over-threshold representation (generalized Pareto distribution) or quantile regression. Common <span class="hlt">ensemble</span> post-processing methods aim to improve mostly the <span class="hlt">ensemble</span> mean and spread of a raw forecast (Van Schaeybroeck and Vannitsem, 2015). Conditional tail modeling, on the other hand, is a post-processing in itself, focusing on the tails only. Therefore, it is unclear how applying <span class="hlt">ensemble</span> post-processing prior to conditional tail modeling impacts the skill of extreme-event predictions. This work is investigating this question in details. Buizza, Leutbecher, and Isaksen, 2008: Potential use of an <span class="hlt">ensemble</span> of analyses in the ECMWF <span class="hlt">Ensemble</span> Prediction System, Q. J. R. Meteorol</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012TePhL..38..687S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012TePhL..38..687S"><span id="translatedtitle">Reconstruction of the coupling architecture in an <span class="hlt">ensemble</span> of coupled time-delay systems</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Sysoev, I. V.; Ponomarenko, V. I.; Prokhorov, M. D.</p> <p>2012-08-01</p> <p>A method for reconstructing the coupling architecture and values in an <span class="hlt">ensemble</span> of time-delay interacting systems with an arbitrary number of couplings between <span class="hlt">ensemble</span> elements is proposed. This method is <span class="hlt">based</span> on reconstruction of the model equations of <span class="hlt">ensemble</span> elements and diagnostics of the coupling significance by successive trial exclusion or adding coupling coefficients to the model.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://eric.ed.gov/?q=drum&id=EJ832826','ERIC'); return false;" href="http://eric.ed.gov/?q=drum&id=EJ832826"><span id="translatedtitle">The Oral Tradition in the Sankofa Drum and Dance <span class="hlt">Ensemble</span>: Student Perceptions</span></a></p> <p><a target="_blank" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p>Hess, Juliet</p> <p>2009-01-01</p> <p>The Sankofa Drum and Dance <span class="hlt">Ensemble</span> is a Ghanaian drum and dance <span class="hlt">ensemble</span> that focusses on music in the Ewe tradition. It is <span class="hlt">based</span> in an elementary school in the Greater Toronto Area and consists of students in Grade 4 through Grade 8. Students in the <span class="hlt">ensemble</span> study Ghanaian traditional Ewe drumming and dancing in the oral tradition. Nine students…</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/servlets/purl/15005930','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/servlets/purl/15005930"><span id="translatedtitle">Approximate Splitting for <span class="hlt">Ensembles</span> of Trees using Histograms</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Kamath, C; Cantu-Paz, E; Littau, D</p> <p>2001-09-28</p> <p>Recent work in classification indicates that significant improvements in accuracy can be obtained by growing an <span class="hlt">ensemble</span> of classifiers and having them vote for the most popular class. Implicit in many of these techniques is the concept of randomization that generates different classifiers. In this paper, they focus on <span class="hlt">ensembles</span> of decision trees that are created using a randomized procedure <span class="hlt">based</span> on histograms. Techniques, such as histograms, that discretize continuous variables, have long been used in classification to convert the data into a form suitable for processing and to reduce the compute time. The approach combines the ideas behind discretization through histograms and randomization in <span class="hlt">ensembles</span> to create decision trees by randomly selecting a split point in an interval around the best bin boundary in the histogram. The experimental results with public domain data show that <span class="hlt">ensembles</span> generated using this approach are competitive in accuracy and superior in computational cost to other <span class="hlt">ensembles</span> techniques such as boosting and bagging.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/27383269','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/27383269"><span id="translatedtitle"><span class="hlt">Molecular</span> dynamics-<span class="hlt">based</span> refinement and validation for sub-5 Å cryo-electron microscopy maps.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Singharoy, Abhishek; Teo, Ivan; McGreevy, Ryan; Stone, John E; Zhao, Jianhua; Schulten, Klaus</p> <p>2016-01-01</p> <p>Two structure determination methods, <span class="hlt">based</span> on the <span class="hlt">molecular</span> dynamics flexible fitting (MDFF) paradigm, are presented that resolve sub-5 Å cryo-electron microscopy (EM) maps with either single structures or <span class="hlt">ensembles</span> of such structures. The methods, denoted cascade MDFF and resolution exchange MDFF, sequentially re-refine a search model against a series of maps of progressively higher resolutions, which ends with the original experimental resolution. Application of sequential re-refinement enables MDFF to achieve a radius of convergence of ~25 Å demonstrated with the accurate modeling of β-galactosidase and TRPV1 proteins at 3.2 Å and 3.4 Å resolution, respectively. The MDFF refinements uniquely offer map-model validation and B-factor determination criteria <span class="hlt">based</span> on the inherent dynamics of the macromolecules studied, captured by means of local root mean square fluctuations. The MDFF tools described are available to researchers through an easy-to-use and cost-effective cloud computing resource on Amazon Web Services. PMID:27383269</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4990421','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4990421"><span id="translatedtitle"><span class="hlt">Molecular</span> dynamics-<span class="hlt">based</span> refinement and validation for sub-5 Å cryo-electron microscopy maps</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Singharoy, Abhishek; Teo, Ivan; McGreevy, Ryan; Stone, John E; Zhao, Jianhua; Schulten, Klaus</p> <p>2016-01-01</p> <p>Two structure determination methods, <span class="hlt">based</span> on the <span class="hlt">molecular</span> dynamics flexible fitting (MDFF) paradigm, are presented that resolve sub-5 Å cryo-electron microscopy (EM) maps with either single structures or <span class="hlt">ensembles</span> of such structures. The methods, denoted cascade MDFF and resolution exchange MDFF, sequentially re-refine a search model against a series of maps of progressively higher resolutions, which ends with the original experimental resolution. Application of sequential re-refinement enables MDFF to achieve a radius of convergence of ~25 Å demonstrated with the accurate modeling of β-galactosidase and TRPV1 proteins at 3.2 Å and 3.4 Å resolution, respectively. The MDFF refinements uniquely offer map-model validation and B-factor determination criteria <span class="hlt">based</span> on the inherent dynamics of the macromolecules studied, captured by means of local root mean square fluctuations. The MDFF tools described are available to researchers through an easy-to-use and cost-effective cloud computing resource on Amazon Web Services. DOI: http://dx.doi.org/10.7554/eLife.16105.001 PMID:27383269</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/27383269','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/27383269"><span id="translatedtitle"><span class="hlt">Molecular</span> dynamics-<span class="hlt">based</span> refinement and validation for sub-5 Å cryo-electron microscopy maps.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Singharoy, Abhishek; Teo, Ivan; McGreevy, Ryan; Stone, John E; Zhao, Jianhua; Schulten, Klaus</p> <p>2016-07-07</p> <p>Two structure determination methods, <span class="hlt">based</span> on the <span class="hlt">molecular</span> dynamics flexible fitting (MDFF) paradigm, are presented that resolve sub-5 Å cryo-electron microscopy (EM) maps with either single structures or <span class="hlt">ensembles</span> of such structures. The methods, denoted cascade MDFF and resolution exchange MDFF, sequentially re-refine a search model against a series of maps of progressively higher resolutions, which ends with the original experimental resolution. Application of sequential re-refinement enables MDFF to achieve a radius of convergence of ~25 Å demonstrated with the accurate modeling of β-galactosidase and TRPV1 proteins at 3.2 Å and 3.4 Å resolution, respectively. The MDFF refinements uniquely offer map-model validation and B-factor determination criteria <span class="hlt">based</span> on the inherent dynamics of the macromolecules studied, captured by means of local root mean square fluctuations. The MDFF tools described are available to researchers through an easy-to-use and cost-effective cloud computing resource on Amazon Web Services.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/24089348','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/24089348"><span id="translatedtitle">Visual-size <span class="hlt">molecular</span> recognition <span class="hlt">based</span> on gels.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Tu, Tao; Fang, Weiwei; Sun, Zheming</p> <p>2013-10-01</p> <p>Since their discovery, stimuli-responsive organogels have garnered considerable and increasing attention from a broad range of research fields. In consideration of an one-dimensional ordered relay in anisotropic phase, the assembled gel networks can amplify various properties of the functional moieties possessed by the gelator molecules. Recently, substantial efforts have been focused on the development of facile, straightforward, and low-cost <span class="hlt">molecular</span> recognition approaches by using nanostructured gel matrices as visual sensing platforms. In this research news, the recent progresses in macroscopic or visual-size <span class="hlt">molecular</span> recognition for a number of homologues, isomers, and anions, as well as extremely challenging chiral enantiomers, using polymer and <span class="hlt">molecular</span> gels are reviewed. Several strategies--including guest <span class="hlt">molecular</span> competition, hydrogen-bonding blocking, and metal-coordination--for visual discrimination are included. Finally, the future trends and potential application in facile visual-size <span class="hlt">molecular</span> recognition <span class="hlt">based</span> on organogel matrices are highlighted. PMID:24089348</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/26797600','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/26797600"><span id="translatedtitle">iPPBS-Opt: A Sequence-<span class="hlt">Based</span> <span class="hlt">Ensemble</span> Classifier for Identifying Protein-Protein Binding Sites by Optimizing Imbalanced Training Datasets.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Jia, Jianhua; Liu, Zi; Xiao, Xuan; Liu, Bingxiang; Chou, Kuo-Chen</p> <p>2016-01-19</p> <p>Knowledge of protein-protein interactions and their binding sites is indispensable for in-depth understanding of the networks in living cells. With the avalanche of protein sequences generated in the postgenomic age, it is critical to develop computational methods for identifying in a timely fashion the protein-protein binding sites (PPBSs) <span class="hlt">based</span> on the sequence information alone because the information obtained by this way can be used for both biomedical research and drug development. To address such a challenge, we have proposed a new predictor, called iPPBS-Opt, in which we have used: (1) the K-Nearest Neighbors Cleaning (KNNC) and Inserting Hypothetical Training Samples (IHTS) treatments to optimize the training dataset; (2) the <span class="hlt">ensemble</span> voting approach to select the most relevant features; and (3) the stationary wavelet transform to formulate the statistical samples. Cross-validation tests by targeting the experiment-confirmed results have demonstrated that the new predictor is very promising, implying that the aforementioned practices are indeed very effective. Particularly, the approach of using the wavelets to express protein/peptide sequences might be the key in grasping the problem's essence, fully consistent with the findings that many important biological functions of proteins can be elucidated with their low-frequency internal motions. To maximize the convenience of most experimental scientists, we have provided a step-by-step guide on how to use the predictor's web server (http://www.jci-bioinfo.cn/iPPBS-Opt) to get the desired results without the need to go through the complicated mathematical equations involved.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010ems..confE.129F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010ems..confE.129F"><span id="translatedtitle">Can multimodel <span class="hlt">ensembles</span> improve medium range precipitation forecasts?</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Flowerdew, J.</p> <p>2010-09-01</p> <p><span class="hlt">Ensemble</span> forecasts aim to improve decision-making by predicting the full distribution of possible outcomes. Forecasts from a single <span class="hlt">ensemble</span> may suffer from systematic errors due to the model and assimilation processes used. By combining results from multiple <span class="hlt">ensembles</span> which have different systematic errors and are more skilful in different situations, it should be possible to produce a composite forecast superior to that from any individual <span class="hlt">ensemble</span>. The value of <span class="hlt">ensemble</span> combination has been demonstrated by several authors for variables such as surface temperature, but there has been relatively little investigation of the potential benefit for the key forecast variable of precipitation. This work uses a subset of the TIGGE dataset to investigate the benefit of multimodel <span class="hlt">ensembles</span> over single <span class="hlt">ensembles</span> for medium range forecasts of precipitation. Early results verifying raw output against short range control forecasts show noticeable benefit across a range of score measures and thresholds, particularly for the reliability component of the Brier Skill Score. We plan to extend this verification to radar- and gauge-<span class="hlt">based</span> observations, examining the nature and independence of the forecast errors to understand how multimodel combination might be beneficial. Further work would consider appropriate methods of calibration, along with the question of whether the multimodel advantage survives against calibrated single <span class="hlt">ensembles</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/servlets/purl/650344','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/servlets/purl/650344"><span id="translatedtitle">Optimal separable <span class="hlt">bases</span> and <span class="hlt">molecular</span> collisions</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Poirier, L W</p> <p>1997-12-01</p> <p>A new methodology is proposed for the efficient determination of Green`s functions and eigenstates for quantum systems of two or more dimensions. For a given Hamiltonian, the best possible separable approximation is obtained from the set of all Hilbert space operators. It is shown that this determination itself, as well as the solution of the resultant approximation, are problems of reduced dimensionality for most systems of physical interest. Moreover, the approximate eigenstates constitute the optimal separable basis, in the sense of self-consistent field theory. These distorted waves give rise to a Born series with optimized convergence properties. Analytical results are presented for an application of the method to the two-dimensional shifted harmonic oscillator system. The primary interest however, is quantum reactive scattering in <span class="hlt">molecular</span> systems. For numerical calculations, the use of distorted waves corresponds to numerical preconditioning. The new methodology therefore gives rise to an optimized preconditioning scheme for the efficient calculation of reactive and inelastic scattering amplitudes, especially at intermediate energies. This scheme is particularly suited to discrete variable representations (DVR`s) and iterative sparse matrix methods commonly employed in such calculations. State to state and cumulative reactive scattering results obtained via the optimized preconditioner are presented for the two-dimensional collinear H + H{sub 2} {yields} H{sub 2} + H system. Computational time and memory requirements for this system are drastically reduced in comparison with other methods, and results are obtained for previously prohibitive energy regimes.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://eric.ed.gov/?q=Chamber+AND+Music&pg=2&id=EJ594156','ERIC'); return false;" href="http://eric.ed.gov/?q=Chamber+AND+Music&pg=2&id=EJ594156"><span id="translatedtitle">The Importance of Bass <span class="hlt">Ensemble</span>.</span></a></p> <p><a target="_blank" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p>Bitz, Michael</p> <p>1997-01-01</p> <p>States that bass players should be allowed to play chamber music because it is an essential component to all string students' musical development. Expounds that bassists can successfully enjoy chamber music through participation in a bass <span class="hlt">ensemble</span>. Gives suggestions on how to form a bass <span class="hlt">ensemble</span> and on the repertoire of music. (CMK)</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4912128','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4912128"><span id="translatedtitle">AUC-Maximizing <span class="hlt">Ensembles</span> through Metalearning</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>LeDell, Erin; van der Laan, Mark J.; Peterson, Maya</p> <p>2016-01-01</p> <p>Area Under the ROC Curve (AUC) is often used to measure the performance of an estimator in binary classification problems. An AUC-maximizing classifier can have significant advantages in cases where ranking correctness is valued or if the outcome is rare. In a Super Learner <span class="hlt">ensemble</span>, maximization of the AUC can be achieved by the use of an AUC-maximining metalearning algorithm. We discuss an implementation of an AUC-maximization technique that is formulated as a nonlinear optimization problem. We also evaluate the effectiveness of a large number of different nonlinear optimization algorithms to maximize the cross-validated AUC of the <span class="hlt">ensemble</span> fit. The results provide evidence that AUC-maximizing metalearners can, and often do, out-perform non-AUC-maximizing metalearning methods, with respect to <span class="hlt">ensemble</span> AUC. The results also demonstrate that as the level of imbalance in the training data increases, the Super Learner <span class="hlt">ensemble</span> outperforms the top <span class="hlt">base</span> algorithm by a larger degree. PMID:27227721</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/27227721','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/27227721"><span id="translatedtitle">AUC-Maximizing <span class="hlt">Ensembles</span> through Metalearning.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>LeDell, Erin; van der Laan, Mark J; Peterson, Maya</p> <p>2016-05-01</p> <p>Area Under the ROC Curve (AUC) is often used to measure the performance of an estimator in binary classification problems. An AUC-maximizing classifier can have significant advantages in cases where ranking correctness is valued or if the outcome is rare. In a Super Learner <span class="hlt">ensemble</span>, maximization of the AUC can be achieved by the use of an AUC-maximining metalearning algorithm. We discuss an implementation of an AUC-maximization technique that is formulated as a nonlinear optimization problem. We also evaluate the effectiveness of a large number of different nonlinear optimization algorithms to maximize the cross-validated AUC of the <span class="hlt">ensemble</span> fit. The results provide evidence that AUC-maximizing metalearners can, and often do, out-perform non-AUC-maximizing metalearning methods, with respect to <span class="hlt">ensemble</span> AUC. The results also demonstrate that as the level of imbalance in the training data increases, the Super Learner <span class="hlt">ensemble</span> outperforms the top <span class="hlt">base</span> algorithm by a larger degree. PMID:27227721</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li class="active"><span>23</span></li> <li><a href="#" onclick='return showDiv("page_24");'>24</a></li> <li><a href="#" onclick='return showDiv("page_25");'>25</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_23 --> <div id="page_24" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li class="active"><span>24</span></li> <li><a href="#" onclick='return showDiv("page_25");'>25</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="461"> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/22012256','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/22012256"><span id="translatedtitle">Supramolecular polymers constructed by crown ether-<span class="hlt">based</span> <span class="hlt">molecular</span> recognition.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Zheng, Bo; Wang, Feng; Dong, Shengyi; Huang, Feihe</p> <p>2012-03-01</p> <p>Supramolecular polymers, polymeric systems beyond the molecule, have attracted more and more attention from scientists due to their applications in various fields, including stimuli-responsive materials, healable materials, and drug delivery. Due to their good selectivity and convenient enviro-responsiveness, crown ether-<span class="hlt">based</span> <span class="hlt">molecular</span> recognition motifs have been actively employed to fabricate supramolecular polymers with interesting properties and novel applications in recent years. In this tutorial review, we classify supramolecular polymers <span class="hlt">based</span> on their differences in topology and cover recent advances in the marriage between crown ether-<span class="hlt">based</span> <span class="hlt">molecular</span> recognition and polymer science.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.H43H1635L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.H43H1635L"><span id="translatedtitle">Downscaling Satellite Data for Predicting Catchment-scale Root Zone Soil Moisture with Ground-<span class="hlt">based</span> Sensors and an <span class="hlt">Ensemble</span> Kalman Filter</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lin, H.; Baldwin, D. C.; Smithwick, E. A. H.</p> <p>2015-12-01</p> <p>Predicting root zone (0-100 cm) soil moisture (RZSM) content at a catchment-scale is essential for drought and flood predictions, irrigation planning, weather forecasting, and many other applications. Satellites, such as the NASA Soil Moisture Active Passive (SMAP), can estimate near-surface (0-5 cm) soil moisture content globally at coarse spatial resolutions. We develop a hierarchical <span class="hlt">Ensemble</span> Kalman Filter (EnKF) data assimilation modeling system to downscale satellite-<span class="hlt">based</span> near-surface soil moisture and to estimate RZSM content across the Shale Hills Critical Zone Observatory at a 1-m resolution in combination with ground-<span class="hlt">based</span> soil moisture sensor data. In this example, a simple infiltration model within the EnKF-model has been parameterized for 6 soil-terrain units to forecast daily RZSM content in the catchment from 2009 - 2012 <span class="hlt">based</span> on AMSRE. LiDAR-derived terrain variables define intra-unit RZSM variability using a novel covariance localization technique. This method also allows the mapping of uncertainty with our RZSM estimates for each time-step. A catchment-wide satellite-to-surface downscaling parameter, which nudges the satellite measurement closer to in situ near-surface data, is also calculated for each time-step. We find significant differences in predicted root zone moisture storage for different terrain units across the experimental time-period. Root mean square error from a cross-validation analysis of RZSM predictions using an independent dataset of catchment-wide in situ Time-Domain Reflectometry (TDR) measurements ranges from 0.060-0.096 cm3 cm-3, and the RZSM predictions are significantly (p < 0.05) correlated with TDR measurements [r = 0.47-0.68]. The predictive skill of this data assimilation system is similar to the Penn State Integrated Hydrologic Modeling (PIHM) system. Uncertainty estimates are significantly (p < 0.05) correlated to cross validation error during wet and dry conditions, but more so in dry summer seasons. Developing an</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015ChPhB..24f0202G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015ChPhB..24f0202G"><span id="translatedtitle">Reweighted <span class="hlt">ensemble</span> dynamics simulations: Theory, improvement, and application</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Gong, Lin-Chen; Zhou, Xin; Ouyang, Zhong-Can</p> <p>2015-06-01</p> <p><span class="hlt">Based</span> on multiple parallel short <span class="hlt">molecular</span> dynamics simulation trajectories, we designed the reweighted <span class="hlt">ensemble</span> dynamics (RED) method to more efficiently sample complex (biopolymer) systems, and to explore their hierarchical metastable states. Here we further present an improvement to depress statistical errors of the RED and we discuss a few keys in practical application of the RED, provide schemes on selection of basis functions, and determination of the free parameter in the RED. We illustrate the application of the improvements in two toy models and in the solvated alanine dipeptide. The results show the RED enables us to capture the topology of multiple-state transition networks, to detect the diffusion-like dynamical behavior in an entropy-dominated system, and to identify solvent effects in the solvated peptides. The illustrations serve as general applications of the RED in more complex biopolymer systems. Project supported by the National Natural Science Foundation of China (Grant No. 11175250).</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3540257','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3540257"><span id="translatedtitle">Optimal Superpositioning of Flexible Molecule <span class="hlt">Ensembles</span></span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Gapsys, Vytautas; de Groot, Bert L.</p> <p>2013-01-01</p> <p>Analysis of the internal dynamics of a biological molecule requires the successful removal of overall translation and rotation. Particularly for flexible or intrinsically disordered peptides, this is a challenging task due to the absence of a well-defined reference structure that could be used for superpositioning. In this work, we started the analysis with a widely known formulation of an objective for the problem of superimposing a set of multiple molecules as variance minimization over an <span class="hlt">ensemble</span>. A negative effect of this superpositioning method is the introduction of ambiguous rotations, where different rotation matrices may be applied to structurally similar molecules. We developed two algorithms to resolve the suboptimal rotations. The first approach minimizes the variance together with the distance of a structure to a preceding molecule in the <span class="hlt">ensemble</span>. The second algorithm seeks for minimal variance together with the distance to the nearest neighbors of each structure. The newly developed methods were applied to <span class="hlt">molecular</span>-dynamics trajectories and normal-mode <span class="hlt">ensembles</span> of the Aβ peptide, RS peptide, and lysozyme. These new (to our knowledge) superpositioning methods combine the benefits of variance and distance between nearest-neighbor(s) minimization, providing a solution for the analysis of intrinsic motions of flexible molecules and resolving ambiguous rotations. PMID:23332072</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/26244742','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/26244742"><span id="translatedtitle">Retinal Conformation Changes Rhodopsin's Dynamic <span class="hlt">Ensemble</span>.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Leioatts, Nicholas; Romo, Tod D; Danial, Shairy Azmy; Grossfield, Alan</p> <p>2015-08-01</p> <p>G protein-coupled receptors are vital membrane proteins that allosterically transduce biomolecular signals across the cell membrane. However, the process by which ligand binding induces protein conformation changes is not well understood biophysically. Rhodopsin, the mammalian dim-light receptor, is a unique test case for understanding these processes because of its switch-like activity; the ligand, retinal, is bound throughout the activation cycle, switching from inverse agonist to agonist after absorbing a photon. By contrast, the ligand-free opsin is outside the activation cycle and may behave differently. We find that retinal influences rhodopsin dynamics using an <span class="hlt">ensemble</span> of all-atom <span class="hlt">molecular</span> dynamics simulations that in aggregate contain 100 μs of sampling. Active retinal destabilizes the inactive state of the receptor, whereas the active <span class="hlt">ensemble</span> was more structurally homogenous. By contrast, simulations of an active-like receptor without retinal present were much more heterogeneous than those containing retinal. These results suggest allosteric processes are more complicated than a ligand inducing protein conformational changes or simply capturing a shifted <span class="hlt">ensemble</span> as outlined in classic models of allostery.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EPJB...89..191Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EPJB...89..191Z"><span id="translatedtitle">Electronic transport properties of a quinone-<span class="hlt">based</span> <span class="hlt">molecular</span> switch</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Zheng, Ya-Peng; Bian, Bao-An; Yuan, Pei-Pei</p> <p>2016-09-01</p> <p>In this paper, we carried out first-principles calculations <span class="hlt">based</span> on density functional theory and non-equilibrium Green's function to investigate the electronic transport properties of a quinone-<span class="hlt">based</span> molecule sandwiched between two Au electrodes. The <span class="hlt">molecular</span> switch can be reversibly switched between the reduced hydroquinone (HQ) and oxidized quinone (Q) states via redox reactions. The switching behavior of two forms is analyzed through their I- V curves, transmission spectra and <span class="hlt">molecular</span> projected self-consistent Hamiltonian at zero bias. Then we discuss the transmission spectra of the HQ and Q forms at different bias, and explain the oscillation of current according to the transmission eigenstates of LUMO energy level for Q form. The results suggest that this kind of a quinone-<span class="hlt">based</span> molecule is usable as one of the good candidates for redox-controlled <span class="hlt">molecular</span> switches.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/26306428','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/26306428"><span id="translatedtitle">Modulating RNA Alignment Using Directional Dynamic Kinks: Application in Determining an Atomic-Resolution <span class="hlt">Ensemble</span> for a Hairpin using NMR Residual Dipolar Couplings.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Salmon, Loïc; Giambaşu, George M; Nikolova, Evgenia N; Petzold, Katja; Bhattacharya, Akash; Case, David A; Al-Hashimi, Hashim M</p> <p>2015-10-14</p> <p>Approaches that combine experimental data and computational <span class="hlt">molecular</span> dynamics (MD) to determine atomic resolution <span class="hlt">ensembles</span> of biomolecules require the measurement of abundant experimental data. NMR residual dipolar couplings (RDCs) carry rich dynamics information, however, difficulties in modulating overall alignment of nucleic acids have limited the ability to fully extract this information. We present a strategy for modulating RNA alignment that is <span class="hlt">based</span> on introducing variable dynamic kinks in terminal helices. With this strategy, we measured seven sets of RDCs in a cUUCGg apical loop and used this rich data set to test the accuracy of an 0.8 μs MD simulation computed using the Amber ff10 force field as well as to determine an atomic resolution <span class="hlt">ensemble</span>. The MD-generated <span class="hlt">ensemble</span> quantitatively reproduces the measured RDCs, but selection of a sub-<span class="hlt">ensemble</span> was required to satisfy the RDCs within error. The largest discrepancies between the RDC-selected and MD-generated <span class="hlt">ensembles</span> are observed for the most flexible loop residues and backbone angles connecting the loop to the helix, with the RDC-selected <span class="hlt">ensemble</span> resulting in more uniform dynamics. Comparison of the RDC-selected <span class="hlt">ensemble</span> with NMR spin relaxation data suggests that the dynamics occurs on the ps-ns time scales as verified by measurements of R(1ρ) relaxation-dispersion data. The RDC-satisfying <span class="hlt">ensemble</span> samples many conformations adopted by the hairpin in crystal structures indicating that intrinsic plasticity may play important roles in conformational adaptation. The approach presented here can be applied to test nucleic acid force fields and to characterize dynamics in diverse RNA motifs at atomic resolution.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/17057842','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/17057842"><span id="translatedtitle">Artificial nanomachines <span class="hlt">based</span> on interlocked <span class="hlt">molecular</span> species: recent advances.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Balzani, Vincenzo; Credi, Alberto; Silvi, Serena; Venturi, Margherita</p> <p>2006-11-01</p> <p>The bottom-up construction and operation of nanoscale machines and motors, that is, supramolecular systems wherein the <span class="hlt">molecular</span> components can be set in motion in a controlled manner for ultimately accomplishing a function, is a topic of great interest in nanoscience and a fascinating challenge of nanotechnology. The field of artificial <span class="hlt">molecular</span> machines and motors is growing at an astonishing rate and is attracting a great deal of interest. Research in the last decade has shown that species made of interlocked <span class="hlt">molecular</span> components like rotaxanes, catenanes and related systems are most attractive candidates. In recent times, the evolution of the structural and functional design of such systems has led to the construction and operation of complex <span class="hlt">molecular</span> machines that, in some cases, are able to do specific tasks. This tutorial review is intended to discuss the design principles for nanomachines <span class="hlt">based</span> on interlocked molecules, and to provide a timely overview on representative prototype systems.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2007AGUFM.H32C..05P&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2007AGUFM.H32C..05P&link_type=ABSTRACT"><span id="translatedtitle">Application of a Multi-Scheme <span class="hlt">Ensemble</span> Prediction System and an <span class="hlt">Ensemble</span> Classification Method to Streamflow Forecasting</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Pahlow, M.; Moehrlen, C.; Joergensen, J.; Hundecha, Y.</p> <p>2007-12-01</p> <p>Europe has experienced a number of unusually long-lasting and intense rainfall events in the last decade, resulting in severe floods in most European countries. <span class="hlt">Ensemble</span> forecasts emerged as a valuable resource to provide decision makers in case of emergency with adequate information to protect downstream areas. However, forecasts should not only provide a best guess of the state of the stream network, but also an estimate of the range of possible outcomes. <span class="hlt">Ensemble</span> forecast techniques are a suitable tool to obtain the required information. Furthermore a wide range of uncertainty that may impact hydrological forecasts can be accounted for using an <span class="hlt">ensemble</span> of forecasts. The forecasting system used in this study is <span class="hlt">based</span> on a multi-scheme <span class="hlt">ensemble</span> prediction method and forecasts the meteorological uncertainty on synoptic scales as well as the resulting forecast error in weather derived products. Statistical methods are used to directly transform raw weather output to derived products and thereby utilize the statistical capabilities of each <span class="hlt">ensemble</span> forecast. The forecasting system MS-EPS (Multi-Scheme <span class="hlt">Ensemble</span> Prediction System) used in this study is a limited area <span class="hlt">ensemble</span> prediction system using 75 different numerical weather prediction (NWP) model parameterisations. These individual 'schemes' each differ in their formulation of the fast meteorological processes. The MS-EPS forecasts are used as input for a hydrological model (HBV) to generate an <span class="hlt">ensemble</span> of streamflow forecasts. Determining the most probable forecast from an <span class="hlt">ensemble</span> of forecasts requires suitable statistical tools. They must enable a forecaster to interpret the model output, to condense the information and to provide the desired product. For this purpose, a probabilistic multi-trend filter (pmt-filter) for statistical post-processing of the hydrological <span class="hlt">ensemble</span> forecasts is used in this study. An application of the forecasting system is shown for a watershed located in the eastern part of</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016CPL...652...40N','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016CPL...652...40N"><span id="translatedtitle"><span class="hlt">Molecular</span> partitioning <span class="hlt">based</span> on the kinetic energy density</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Noorizadeh, Siamak</p> <p>2016-05-01</p> <p><span class="hlt">Molecular</span> partitioning <span class="hlt">based</span> on the kinetic energy density is performed to a number of chemical species, which show non-nuclear attractors (NNA) in their gradient maps of the electron density. It is found that NNAs are removed using this <span class="hlt">molecular</span> partitioning and although the virial theorem is not valid for all of the basins obtained in the being used AIM, all of the atoms obtained using the new approach obey this theorem. A comparison is also made between some atomic topological parameters which are obtained from the new partitioning approach and those calculated <span class="hlt">based</span> on the electron density partitioning.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AIPC.1691c0008H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AIPC.1691c0008H"><span id="translatedtitle">Improving <span class="hlt">ensemble</span> decision tree performance using Adaboost and Bagging</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hasan, Md. Rajib; Siraj, Fadzilah; Sainin, Mohd Shamrie</p> <p>2015-12-01</p> <p><span class="hlt">Ensemble</span> classifier systems are considered as one of the most promising in medical data classification and the performance of deceision tree classifier can be increased by the <span class="hlt">ensemble</span> method as it is proven to be better than single classifiers. However, in a <span class="hlt">ensemble</span> settings the performance depends on the selection of suitable <span class="hlt">base</span> classifier. This research employed two prominent esemble s namely Adaboost and Bagging with <span class="hlt">base</span> classifiers such as Random Forest, Random Tree, j48, j48grafts and Logistic Model Regression (LMT) that have been selected independently. The empirical study shows that the performance varries when different <span class="hlt">base</span> classifiers are selected and even some places overfitting issue also been noted. The evidence shows that <span class="hlt">ensemble</span> decision tree classfiers using Adaboost and Bagging improves the performance of selected medical data sets.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20150003243','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20150003243"><span id="translatedtitle">Device and Method for Gathering <span class="hlt">Ensemble</span> Data Sets</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Racette, Paul E. (Inventor)</p> <p>2014-01-01</p> <p>An <span class="hlt">ensemble</span> detector uses calibrated noise references to produce <span class="hlt">ensemble</span> sets of data from which properties of non-stationary processes may be extracted. The <span class="hlt">ensemble</span> detector comprising: a receiver; a switching device coupled to the receiver, the switching device configured to selectively connect each of a plurality of reference noise signals to the receiver; and a gain modulation circuit coupled to the receiver and configured to vary a gain of the receiver <span class="hlt">based</span> on a forcing signal; whereby the switching device selectively connects each of the plurality of reference noise signals to the receiver to produce an output signal derived from the plurality of reference noise signals and the forcing signal.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5048093','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5048093"><span id="translatedtitle"><span class="hlt">Ensemble</span> Deep Learning for Biomedical Time Series Classification</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p></p> <p>2016-01-01</p> <p><span class="hlt">Ensemble</span> learning has been proved to improve the generalization ability effectively in both theory and practice. In this paper, we briefly outline the current status of research on it first. Then, a new deep neural network-<span class="hlt">based</span> <span class="hlt">ensemble</span> method that integrates filtering views, local views, distorted views, explicit training, implicit training, subview prediction, and Simple Average is proposed for biomedical time series classification. Finally, we validate its effectiveness on the Chinese Cardiovascular Disease Database containing a large number of electrocardiogram recordings. The experimental results show that the proposed method has certain advantages compared to some well-known <span class="hlt">ensemble</span> methods, such as Bagging and AdaBoost. PMID:27725828</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/23934896','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/23934896"><span id="translatedtitle">An automated approach to network features of protein structure <span class="hlt">ensembles</span>.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Bhattacharyya, Moitrayee; Bhat, Chanda R; Vishveshwara, Saraswathi</p> <p>2013-10-01</p> <p>Network theory applied to protein structures provides insights into numerous problems of biological relevance. The explosion in structural data available from PDB and simulations establishes a need to introduce a standalone-efficient program that assembles network concepts/parameters under one hood in an automated manner. Herein, we discuss the development/application of an exhaustive, user-friendly, standalone program package named PSN-<span class="hlt">Ensemble</span>, which can handle structural <span class="hlt">ensembles</span> generated through <span class="hlt">molecular</span> dynamics (MD) simulation/NMR studies or from multiple X-ray structures. The novelty in network construction lies in the explicit consideration of side-chain interactions among amino acids. The program evaluates network parameters dealing with topological organization and long-range allosteric communication. The introduction of a flexible weighing scheme in terms of residue pairwise cross-correlation/interaction energy in PSN-<span class="hlt">Ensemble</span> brings in dynamical/chemical knowledge into the network representation. Also, the results are mapped on a graphical display of the structure, allowing an easy access of network analysis to a general biological community. The potential of PSN-<span class="hlt">Ensemble</span> toward examining structural <span class="hlt">ensemble</span> is exemplified using MD trajectories of an ubiquitin-conjugating enzyme (UbcH5b). Furthermore, insights derived from network parameters evaluated using PSN-<span class="hlt">Ensemble</span> for single-static structures of active/inactive states of β2-adrenergic receptor and the ternary tRNA complexes of tyrosyl tRNA synthetases (from organisms across kingdoms) are discussed. PSN-<span class="hlt">Ensemble</span> is freely available from http://vishgraph.mbu.iisc.ernet.in/PSN-<span class="hlt">Ensemble</span>/psn_index.html.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3299414','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3299414"><span id="translatedtitle">Plasmonic-<span class="hlt">Based</span> Electrochemical Impedance Spectroscopy: Application to <span class="hlt">Molecular</span> Binding</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Lu, Jin; Wang, Wei; Wang, Shaopeng; Shan, Xiaonan; Li, Jinghong; Tao, Nongjian</p> <p>2012-01-01</p> <p>Plasmonic-<span class="hlt">based</span> electrochemical impedance spectroscopy (P-EIS) is developed to investigate <span class="hlt">molecular</span> binding on surfaces. Its basic principle relies on the sensitive dependence of surface plasmon resonance (SPR) signal on surface charge density, which is modulated by applying an AC potential to a SPR chip surface. The AC component of the SPR response gives the electrochemical impedance, and the DC component provides the conventional SPR detection. The plasmonic-<span class="hlt">based</span> impedance measured over a range of frequency is in quantitative agreement with the conventional electrochemical impedance. Compared to the conventional SPR detection, P-EIS is sensitive to <span class="hlt">molecular</span> binding taking place on the chip surface, and less sensitive to bulk refractive index changes or non-specific binding. Moreover, this new approach al