Science.gov

Sample records for molecular ensemble based

  1. 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.

  2. Molecular dynamics ensemble, equation of state, and ergodicity.

    PubMed

    Wood, W W; Erpenbeck, J J; Baker, G A; Johnson, J D

    2001-01-01

    The variant of the NVE ensemble known as the molecular dynamics ensemble was recently redefined by Ray and Zhang [Phys. Rev. E 59, 4781 (1999)] to include the specification of a time invariant G (a function of phase and, explicitly, the time) in addition to the total linear momentum M. We reformulate this ensemble slightly as the NVEMR ensemble, in which R/N is the center-of-mass position, and consider the equation of state of the hard-sphere system in this ensemble through both the virial function and the Boltzmann entropy. We test the quasiergodic hypothesis by a comparison of old molecular dynamics and Monte Carlo results for the compressibility factor of the 12-particle, hard-disk systems. The virial approach, which had previously been found to support the hypothesis in the NVEM ensemble, remains unchanged in the NVEMR ensemble. The entropy S approach depends on whether S is defined through the phase integral over the energy sphere or the energy shell, the parameter straight theta being 0 or 1, respectively. The ergodic hypothesis is found to be supported for straight theta=0 but not for straight theta=1. PMID:11304233

  3. Molecular dynamics ensemble, equation of state, and ergodicity

    SciTech Connect

    Wood, William W.; Erpenbeck, Jerome J.; Baker, George A.; Johnson, J. D.

    2001-01-01

    The variant of the NVE ensemble known as the molecular dynamics ensemble was recently redefined by Ray and Zhang [Phys. Rev. E 59, 4781 (1999)] to include the specification of a time invariant G (a function of phase and, explicitly, the time) in addition to the total linear momentum M. We reformulate this ensemble slightly as the NVEMR ensemble, in which R/N is the center-of-mass position, and consider the equation of state of the hard-sphere system in this ensemble through both the virial function and the Boltzmann entropy. We test the quasiergodic hypothesis by a comparison of old molecular dynamics and Monte Carlo results for the compressibility factor of the 12-particle, hard-disk systems. The virial approach, which had previously been found to support the hypothesis in the NVEM ensemble, remains unchanged in the NVEMR ensemble. The entropy S approach depends on whether S is defined through the phase integral over the energy sphere or the energy shell, the parameter {theta} being 0 or 1, respectively. The ergodic hypothesis is found to be supported for {theta}=0 but not for {theta}=1.

  4. The Ensemble/Legacy Chimera extension: standardized user and programmer interface to molecular Ensemble data and Legacy modeling programs.

    PubMed

    Konerding, D E

    2000-01-01

    Ensemble/Legacy is a toolkit extension of the Object Technology Framework (OTF) that exposes an object oriented interface for accessing and manipulating ensembles (collections of molecular conformations that share a common chemical topology) and driving Legacy programs (such as MSMS, AMBER, X-PLOR, CORMA/MARDIGRAS, Dials and Windows, and CURVES). Ensemble/Legacy provides a natural programming interface for running Legacy programs on ensembles of molecules and accessing the resulting data. Using the OTF reduces the time cost of developing a new library to store and manipulate molecular data and also allows Ensemble/Legacy to integrate into the Chimera visualization program. The extension to Chimera exposes the Legacy functionality using a graphical user interface that greatly simplifies the process of modeling and analyzing conformational ensembles. Furthermore, all the C++ functionality of the Ensemble/Legacy toolkit is "wrapped" for use in the Python programming language. More detailed documentation on using Ensemble/Legacy is available online (http:¿picasso.nmr.ucsf.edu/dek/ensemble. html). PMID:10902174

  5. 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.

  6. 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.

  7. Argumentation Based Joint Learning: A Novel Ensemble Learning Approach

    PubMed Central

    Xu, Junyi; Yao, Li; Li, Le

    2015-01-01

    Recently, ensemble learning methods have been widely used to improve classification performance in machine learning. In this paper, we present a novel ensemble learning method: argumentation based multi-agent joint learning (AMAJL), which integrates ideas from multi-agent argumentation, ensemble learning, and association rule mining. In AMAJL, argumentation technology is introduced as an ensemble strategy to integrate multiple base classifiers and generate a high performance ensemble classifier. We design an argumentation framework named Arena as a communication platform for knowledge integration. Through argumentation based joint learning, high quality individual knowledge can be extracted, and thus a refined global knowledge base can be generated and used independently for classification. We perform numerous experiments on multiple public datasets using AMAJL and other benchmark methods. The results demonstrate that our method can effectively extract high quality knowledge for ensemble classifier and improve the performance of classification. PMID:25966359

  8. Rydberg ensemble based CNOTN gates using STIRAP

    NASA Astrophysics Data System (ADS)

    Gujarati, Tanvi; Duan, Luming

    2016-05-01

    Schemes for implementation of CNOT gates in atomic ensembles are important for realization of quantum computing. We present here a theoretical scheme of a CNOTN gate with an ensemble of three-level atoms in the lambda configuration and a single two-level control atom. We work in the regime of Rydberg blockade for the ensemble atoms due to excitation of the Rydberg control atom. It is shown that using STIRAP, atoms from one ground state of the ensemble can be adiabatically transferred to the other ground state, depending on the state of the control atom. A thorough analysis of adiabatic conditions for this scheme and the influence of the radiative decay is provided. We show that the CNOTN process is immune to the decay rate of the excited level in ensemble atoms. This work is supported by the ARL, the IARPA LogiQ program, and the AFOSR MURI program.

  9. Identifying ultrasound and clinical features of breast cancer molecular subtypes by ensemble decision.

    PubMed

    Zhang, Lei; Li, Jing; Xiao, Yun; Cui, Hao; Du, Guoqing; Wang, Ying; Li, Ziyao; Wu, Tong; Li, Xia; Tian, Jiawei

    2015-01-01

    Breast cancer is molecularly heterogeneous and categorized into four molecular subtypes: Luminal-A, Luminal-B, HER2-amplified and Triple-negative. In this study, we aimed to apply an ensemble decision approach to identify the ultrasound and clinical features related to the molecular subtypes. We collected ultrasound and clinical features from 1,000 breast cancer patients and performed immunohistochemistry on these samples. We used the ensemble decision approach to select unique features and to construct decision models. The decision model for Luminal-A subtype was constructed based on the presence of an echogenic halo and post-acoustic shadowing or indifference. The decision model for Luminal-B subtype was constructed based on the absence of an echogenic halo and vascularity. The decision model for HER2-amplified subtype was constructed based on the presence of post-acoustic enhancement, calcification, vascularity and advanced age. The model for Triple-negative subtype followed two rules. One was based on irregular shape, lobulate margin contour, the absence of calcification and hypovascularity, whereas the other was based on oval shape, hypovascularity and micro-lobulate margin contour. The accuracies of the models were 83.8%, 77.4%, 87.9% and 92.7%, respectively. We identified specific features of each molecular subtype and expanded the scope of ultrasound for making diagnoses using these decision models. PMID:26046791

  10. Protection of densely populated excited triplet state ensembles against deactivation by molecular oxygen.

    PubMed

    Filatov, Mikhail A; Baluschev, Stanislav; Landfester, Katharina

    2016-08-22

    This critical review discusses different approaches towards protection of photoactive materials based on triplet excited state ensembles against deactivation by molecular oxygen though quenching and photooxidation mechanisms. Passive protection, based on the application of barrier materials for packaging, sealing, or encapsulation of the active substances, which prevent oxygen molecules from penetration and physical contact with excited states and active protection, based on the application of oxygen scavenging species are compared. Efficiencies of different approaches together with examples and prospects of their applications are outlined. PMID:27277068

  11. 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. PMID:25810748

  12. 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

  13. 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

  14. An ensemble of SVM classifiers based on gene pairs.

    PubMed

    Tong, Muchenxuan; Liu, Kun-Hong; Xu, Chungui; Ju, Wenbin

    2013-07-01

    In this paper, a genetic algorithm (GA) based ensemble support vector machine (SVM) classifier built on gene pairs (GA-ESP) is proposed. The SVMs (base classifiers of the ensemble system) are trained on different informative gene pairs. These gene pairs are selected by the top scoring pair (TSP) criterion. Each of these pairs projects the original microarray expression onto a 2-D space. Extensive permutation of gene pairs may reveal more useful information and potentially lead to an ensemble classifier with satisfactory accuracy and interpretability. GA is further applied to select an optimized combination of base classifiers. The effectiveness of the GA-ESP classifier is evaluated on both binary-class and multi-class datasets. PMID:23668348

  15. 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.

  16. Reaction ensemble molecular dynamics: Direct simulation of the dynamic equilibrium properties of chemically reacting mixtures

    NASA Astrophysics Data System (ADS)

    Brennan, John K.; Lísal, Martin; Gubbins, Keith E.; Rice, Betsy M.

    2004-12-01

    A molecular simulation method to study the dynamics of chemically reacting mixtures is presented. The method uses a combination of stochastic and dynamic simulation steps, allowing for the simulation of both thermodynamic and transport properties. The method couples a molecular dynamics simulation cell (termed dynamic cell) to a reaction mixture simulation cell (termed control cell) that is formulated upon the reaction ensemble Monte Carlo (RxMC) method, hence the term reaction ensemble molecular dynamics. Thermodynamic and transport properties are calculated in the dynamic cell by using a constant-temperature molecular dynamics simulation method. RxMC forward and reverse reaction steps are performed in the control cell only, while molecular dynamics steps are performed in both the dynamic cell and the control cell. The control cell, which acts as a sink and source reservoir, is maintained at reaction equilibrium conditions via the RxMC algorithm. The reaction ensemble molecular dynamics method is analogous to the grand canonical ensemble molecular dynamics technique, while using some elements of the osmotic molecular dynamics method, and so simulates conditions that directly relate to real, open systems. The accuracy and stability of the method is assessed by considering the ammonia synthesis reaction N2+3H2⇔2NH3 . It is shown to be a viable method for predicting the effects of nonideal environments on the dynamic properties (particularly diffusion) as well as reaction equilibria for chemically reacting mixtures.

  17. 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

  18. 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.

  19. 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

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

    PubMed

    Swift, Robert V; Jusoh, Siti A; Offutt, Tavina L; Li, Eric S; Amaro, Rommie E

    2016-05-23

    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(2(N)). A recursive approximation to the optimal solution scales as O(N(2)), 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

  1. 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

  2. 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.

  3. Wiring and Molecular Features of Prefrontal Ensembles Representing Distinct Experiences.

    PubMed

    Ye, Li; Allen, William E; Thompson, Kimberly R; Tian, Qiyuan; Hsueh, Brian; Ramakrishnan, Charu; Wang, Ai-Chi; Jennings, Joshua H; Adhikari, Avishek; Halpern, Casey H; Witten, Ilana B; Barth, Alison L; Luo, Liqun; McNab, Jennifer A; Deisseroth, Karl

    2016-06-16

    A major challenge in understanding the cellular diversity of the brain has been linking activity during behavior with standard cellular typology. For example, it has not been possible to determine whether principal neurons in prefrontal cortex active during distinct experiences represent separable cell types, and it is not known whether these differentially active cells exert distinct causal influences on behavior. Here, we develop quantitative hydrogel-based technologies to connect activity in cells reporting on behavioral experience with measures for both brain-wide wiring and molecular phenotype. We find that positive and negative-valence experiences in prefrontal cortex are represented by cell populations that differ in their causal impact on behavior, long-range wiring, and gene expression profiles, with the major discriminant being expression of the adaptation-linked gene NPAS4. These findings illuminate cellular logic of prefrontal cortex information processing and natural adaptive behavior and may point the way to cell-type-specific understanding and treatment of disease-associated states. PMID:27238022

  4. 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

  5. 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

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. 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

  11. 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

  12. Grand canonical ensemble molecular dynamics simulations: Reformulation of extended system dynamics approaches

    NASA Astrophysics Data System (ADS)

    Lynch, Gillian C.; Pettitt, B. Montgomery

    1997-11-01

    The extended system Hamiltonian for carrying out grand canonical ensemble molecular dynamics simulations is reformulated. This new Hamiltonian includes a generalized treatment of the reference state partition function of the total chemical potential that reproduces the ideal gas behavior and various previous partitionings of ideal and excess terms. Initial calculations are performed on a system of Lennard-Jones particles near the triple point and on liquid water at room temperature.

  13. Denoising ECG signal based on ensemble empirical mode decomposition

    NASA Astrophysics Data System (ADS)

    Zhi-dong, Zhao; Liu, Juan; Wang, Sheng-tao

    2011-10-01

    The electrocardiogram (ECG) has been used extensively for detection of heart disease. Frequently the signal is corrupted by various kinds of noise such as muscle noise, electromyogram (EMG) interference, instrument noise etc. In this paper, a new ECG denoising method is proposed based on the recently developed ensemble empirical mode decomposition (EEMD). Noisy ECG signal is decomposed into a series of intrinsic mode functions (IMFs). The statistically significant information content is build by the empirical energy model of IMFs. Noisy ECG signal collected from clinic recording is processed using the method. The results show that on contrast with traditional methods, the novel denoising method can achieve the optimal denoising of the ECG signal.

  14. GECC: Gene Expression Based Ensemble Classification of Colon Samples.

    PubMed

    Rathore, Saima; Hussain, Mutawarra; Khan, Asifullah

    2014-01-01

    Gene expression deviates from its normal composition in case a patient has cancer. This variation can be used as an effective tool to find cancer. In this study, we propose a novel gene expressions based colon classification scheme (GECC) that exploits the variations in gene expressions for classifying colon gene samples into normal and malignant classes. Novelty of GECC is in two complementary ways. First, to cater overwhelmingly larger size of gene based data sets, various feature extraction strategies, like, chi-square, F-Score, principal component analysis (PCA) and minimum redundancy and maximum relevancy (mRMR) have been employed, which select discriminative genes amongst a set of genes. Second, a majority voting based ensemble of support vector machine (SVM) has been proposed to classify the given gene based samples. Previously, individual SVM models have been used for colon classification, however, their performance is limited. In this research study, we propose an SVM-ensemble based new approach for gene based classification of colon, wherein the individual SVM models are constructed through the learning of different SVM kernels, like, linear, polynomial, radial basis function (RBF), and sigmoid. The predicted results of individual models are combined through majority voting. In this way, the combined decision space becomes more discriminative. The proposed technique has been tested on four colon, and several other binary-class gene expression data sets, and improved performance has been achieved compared to previously reported gene based colon cancer detection techniques. The computational time required for the training and testing of 208 × 5,851 data set has been 591.01 and 0.019 s, respectively. PMID:26357050

  15. Grand canonical ensemble molecular dynamics simulation of water solubility in polyamide-6,6.

    PubMed

    Eslami, Hossein; Mehdipour, Nargess

    2011-01-14

    Grand canonical ensemble molecular dynamics simulation is employed to calculate the solubility of water in polyamide-6,6. It is shown that performing two separate simulations, one in the polymeric phase and one in the gaseous phase, is sufficient to find the phase coexistence point. In this method, the chemical potential of water in the polymer phase is expanded as a first-order Taylor series in terms of pressure. Knowing the chemical potential of water in the polymer phase in terms of pressure, another simulation for water in the gaseous phase, in the grand canonical ensemble, is done in which the target chemical potential is set in terms of pressure in the gas phase. The phase coexistence point can easily be calculated from the results of these two independent simulations. Our calculated sorption isotherms and solubility coefficients of water in polyamide-6,6, over a wide range of temperatures and pressures, agree with experimental data. PMID:21031194

  16. Uncertainty Visualization in HARDI based on Ensembles of ODFs

    PubMed Central

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

    2013-01-01

    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. PMID:24466504

  17. 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.

  18. 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

  19. 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

  20. 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

  1. 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)

  2. 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

  3. Non-Hamiltonian molecular dynamics implementation of the Gibbs ensemble method. II. Molecular liquid-vapor results for carbon dioxide.

    PubMed

    Bratschi, Christoph; Huber, Hanspeter; Searles, Debra J

    2007-04-28

    The Gibbs ensemble molecular dynamics algorithm introduced in the preceding paper (paper I) [C. Bratschi and H. Huber, J. Chem. Phys. v126, 164104 (2007)] is applied to two recently published CO2 ab initio pair potentials, the Bock-Bich-Vogel and symmetry-adapted perturbation theory site-site potentials. The critical properties of these potentials are calculated for the first time. Critical values and points in the single and two-phase zones are compared with Monte Carlo results to demonstrate the accuracy of the molecular dynamics algorithm, and are compared with experiment to test the accuracy of the potentials. Pressure calculations in the liquid, gas, and supercritical states are carried out and are used to explain potential-related effects and systematic discrepancies. The best ab initio potential yields results in good agreement with experiment. PMID:17477587

  4. Non-Hamiltonian molecular dynamics implementation of the Gibbs ensemble method. II. Molecular liquid-vapor results for carbon dioxide

    NASA Astrophysics Data System (ADS)

    Bratschi, Christoph; Huber, Hanspeter; Searles, Debra J.

    2007-04-01

    The Gibbs ensemble molecular dynamics algorithm introduced in the preceding paper (paper I) [C. Bratschi and H. Huber, J. Chem. Phys. v126, 164104 (2007)] is applied to two recently published CO2 ab initio pair potentials, the Bock-Bich-Vogel and symmetry-adapted perturbation theory site-site potentials. The critical properties of these potentials are calculated for the first time. Critical values and points in the single and two-phase zones are compared with Monte Carlo results to demonstrate the accuracy of the molecular dynamics algorithm, and are compared with experiment to test the accuracy of the potentials. Pressure calculations in the liquid, gas, and supercritical states are carried out and are used to explain potential-related effects and systematic discrepancies. The best ab initio potential yields results in good agreement with experiment.

  5. Hydrological Ensemble Simulation in Huaihe Catchment Based on VIC Model

    NASA Astrophysics Data System (ADS)

    Sun, R.; Yuan, H.

    2013-12-01

    Huaihe catchment plays a very important role in the political, economic, and cultural development in China. However, hydrological disasters frequently occur in Huaihe catchment, and thus hydrological simulation in this area has very important significance. The Variable Infiltration Capacity(VIC)model, a macroscale distributed hydrological model is applied to the upper Huaihe Catchment, to simulate the discharge of the basin outlet Bengbu station from 1970 to 1999. The uncertainty in the calibration of VIC model parameters has been analyzed, and the best set of parameters in the training period of 1970~1993 is achieved using the Generalized Likelihood Uncertainty Estimation (GLUE) method. The study also addresses the influence of different likelihood functions for the parameter sensitivity as well as the uncertainty of discharge simulation. Results show that among the six chosen parameters, the soil thickness of the second layer (d2) is the most sensitive one, followed by the saturation capacity curve shape parameter (B). Moreover, the parameter selection is sensitive to different likelihood functions. For example, the soil thickness of the third layer (d3) is sensitive when using Nash coefficient as the likelihood function, while d3 is not sensitive when using relative error as the likelihood function. With the 95% confidence interval, the coverage rate of the simulated discharge versus the observed discharge is small (around 0.4), indicating that the uncertainty in the model is large. The coverage rate of selecting relative error as the likelihood function is bigger than that of selecting Nash coefficient. Based on the calibration and sensitivity studies, hydrological ensemble forecasts have been established using multiple parameter sets. The ensemble mean forecasts show better simulations than the control forecast (i.e. the simulation using the best set of parameters) for the long-term trend of discharge, while the control forecast is better in the simulation of

  6. 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).

  7. 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.

  8. 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.

  9. 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. PMID:26450299

  10. Ensemble-based methods for forecasting census in hospital units

    PubMed Central

    2013-01-01

    Background The ability to accurately forecast census counts in hospital departments has considerable implications for hospital resource allocation. In recent years several different methods have been proposed forecasting census counts, however many of these approaches do not use available patient-specific information. Methods In this paper we present an ensemble-based methodology for forecasting the census under a framework that simultaneously incorporates both (i) arrival trends over time and (ii) patient-specific baseline and time-varying information. The proposed model for predicting census has three components, namely: current census count, number of daily arrivals and number of daily departures. To model the number of daily arrivals, we use a seasonality adjusted Poisson Autoregressive (PAR) model where the parameter estimates are obtained via conditional maximum likelihood. The number of daily departures is predicted by modeling the probability of departure from the census using logistic regression models that are adjusted for the amount of time spent in the census and incorporate both patient-specific baseline and time varying patient-specific covariate information. We illustrate our approach using neonatal intensive care unit (NICU) data collected at Women & Infants Hospital, Providence RI, which consists of 1001 consecutive NICU admissions between April 1st 2008 and March 31st 2009. Results Our results demonstrate statistically significant improved prediction accuracy for 3, 5, and 7 day census forecasts and increased precision of our forecasting model compared to a forecasting approach that ignores patient-specific information. Conclusions Forecasting models that utilize patient-specific baseline and time-varying information make the most of data typically available and have the capacity to substantially improve census forecasts. PMID:23721123

  11. Model structure identification based on ensemble model evaluation

    NASA Astrophysics Data System (ADS)

    Van Hoey, S.; van der Kwast, J.; Nopens, I.; Seuntjens, P.; Pereira, F.

    2012-04-01

    Identifying the most appropriate hydrological model for a given problem is more than fitting the parameters of a fixed model structure to reproduce the measured hydrograph. Defining the most appropriate model structure is dependent of the modeling objective, the characteristics of the system under investigation and the available data. To be able to adapt to the different conditions and to propose different hypotheses of the underlying system, a flexible model structure is preferred in combination with a rejectionist analysis based on different diagnostics supporting the model objective. By confronting the model structures with the model diagnostics, an identification of the dominant processes is attempted. In the presented work, a set of 24 model structures was constructed, by combining interchangeable components representing different hypotheses of the system under study, the Nete catchment in Belgium. To address the effect of different model diagnostics on the performance of the selected model structures, an optimization of the model structures was performed to identify the parameter sets minimizing specific objective functions, focusing on low or high flow conditions. Furthermore, the different model structures are compared simultaneously within the Generalized Likelihood Uncertainty Estimation (GLUE) approach. The rejection of inadequate model structures by specifying limits of acceptance and weighting of the accepted ones is the basis of the GLUE approach. Multiple measures are combined to give guidance about the suitability of the different structures and information about the identifiability and uncertainty of the parameters is extracted from the ensemble of selected structures. The results of the optimization demonstrate the relationship between the selected objective function and the behaviour of the model structures, but also the compensation for structural differences by different parameter values resulting in similar performance. The optimization gives

  12. Nonlinear stability and ergodicity of ensemble based Kalman filters

    NASA Astrophysics Data System (ADS)

    Tong, Xin T.; Majda, Andrew J.; Kelly, David

    2016-02-01

    The ensemble Kalman filter (EnKF) and ensemble square root filter (ESRF) are data assimilation methods used to combine high dimensional, nonlinear dynamical models with observed data. Despite their widespread usage in climate science and oil reservoir simulation, very little is known about the long-time behavior of these methods and why they are effective when applied with modest ensemble sizes in large dimensional turbulent dynamical systems. By following the basic principles of energy dissipation and controllability of filters, this paper establishes a simple, systematic and rigorous framework for the nonlinear analysis of EnKF and ESRF with arbitrary ensemble size, focusing on the dynamical properties of boundedness and geometric ergodicity. The time uniform boundedness guarantees that the filter estimate will not diverge to machine infinity in finite time, which is a potential threat for EnKF and ESQF known as the catastrophic filter divergence. Geometric ergodicity ensures in addition that the filter has a unique invariant measure and that initialization errors will dissipate exponentially in time. We establish these results by introducing a natural notion of observable energy dissipation. The time uniform bound is achieved through a simple Lyapunov function argument, this result applies to systems with complete observations and strong kinetic energy dissipation, but also to concrete examples with incomplete observations. With the Lyapunov function argument established, the geometric ergodicity is obtained by verifying the controllability of the filter processes; in particular, such analysis for ESQF relies on a careful multivariate perturbation analysis of the covariance eigen-structure.

  13. Force-momentum-based self-guided Langevin dynamics: A rapid sampling method that approaches the canonical ensemble

    NASA Astrophysics Data System (ADS)

    Wu, Xiongwu; Brooks, Bernard R.

    2011-11-01

    The self-guided Langevin dynamics (SGLD) is a method to accelerate conformational searching. This method is unique in the way that it selectively enhances and suppresses molecular motions based on their frequency to accelerate conformational searching without modifying energy surfaces or raising temperatures. It has been applied to studies of many long time scale events, such as protein folding. Recent progress in the understanding of the conformational distribution in SGLD simulations makes SGLD also an accurate method for quantitative studies. The SGLD partition function provides a way to convert the SGLD conformational distribution to the canonical ensemble distribution and to calculate ensemble average properties through reweighting. Based on the SGLD partition function, this work presents a force-momentum-based self-guided Langevin dynamics (SGLDfp) simulation method to directly sample the canonical ensemble. This method includes interaction forces in its guiding force to compensate the perturbation caused by the momentum-based guiding force so that it can approximately sample the canonical ensemble. Using several example systems, we demonstrate that SGLDfp simulations can approximately maintain the canonical ensemble distribution and significantly accelerate conformational searching. With optimal parameters, SGLDfp and SGLD simulations can cross energy barriers of more than 15 kT and 20 kT, respectively, at similar rates for LD simulations to cross energy barriers of 10 kT. The SGLDfp method is size extensive and works well for large systems. For studies where preserving accessible conformational space is critical, such as free energy calculations and protein folding studies, SGLDfp is an efficient approach to search and sample the conformational space.

  14. 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.

  15. Log-normal distribution based Ensemble Model Output Statistics models for probabilistic wind-speed forecasting

    NASA Astrophysics Data System (ADS)

    Baran, Sándor; Lerch, Sebastian

    2015-07-01

    Ensembles of forecasts are obtained from multiple runs of numerical weather forecasting models with different initial conditions and typically employed to account for forecast uncertainties. However, biases and dispersion errors often occur in forecast ensembles, they are usually under-dispersive and uncalibrated and require statistical post-processing. We present an Ensemble Model Output Statistics (EMOS) method for calibration of wind speed forecasts based on the log-normal (LN) distribution, and we also show a regime-switching extension of the model which combines the previously studied truncated normal (TN) distribution with the LN. Both presented models are applied to wind speed forecasts of the eight-member University of Washington mesoscale ensemble, of the fifty-member ECMWF ensemble and of the eleven-member ALADIN-HUNEPS ensemble of the Hungarian Meteorological Service, and their predictive performances are compared to those of the TN and general extreme value (GEV) distribution based EMOS methods and to the TN-GEV mixture model. The results indicate improved calibration of probabilistic and accuracy of point forecasts in comparison to the raw ensemble and to climatological forecasts. Further, the TN-LN mixture model outperforms the traditional TN method and its predictive performance is able to keep up with the models utilizing the GEV distribution without assigning mass to negative values.

  16. 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. PMID:27078633

  17. 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

  18. An integrated uncertainty and ensemble-based data assimilation approach for improved operational streamflow predictions

    NASA Astrophysics Data System (ADS)

    He, M.; Hogue, T. S.; Margulis, S. A.; Franz, K. J.

    2012-03-01

    The current study proposes an integrated uncertainty and ensemble-based data assimilation framework (ICEA) and evaluates its viability in providing operational streamflow predictions via assimilating snow water equivalent (SWE) data. This step-wise framework applies a parameter uncertainty analysis algorithm (ISURF) to identify the uncertainty structure of sensitive model parameters, which is subsequently formulated into an Ensemble Kalman Filter (EnKF) to generate updated snow states for streamflow prediction. The framework is coupled to the US National Weather Service (NWS) snow and rainfall-runoff models. Its applicability is demonstrated for an operational basin of a western River Forecast Center (RFC) of the NWS. Performance of the framework is evaluated against existing operational baseline (RFC predictions), the stand-alone ISURF and the stand-alone EnKF. Results indicate that the ensemble-mean prediction of ICEA considerably outperforms predictions from the other three scenarios investigated, particularly in the context of predicting high flows (top 5th percentile). The ICEA streamflow ensemble predictions capture the variability of the observed streamflow well, however the ensemble is not wide enough to consistently contain the range of streamflow observations in the study basin. Our findings indicate that the ICEA has the potential to supplement the current operational (deterministic) forecasting method in terms of providing improved single-valued (e.g., ensemble mean) streamflow predictions as well as meaningful ensemble predictions.

  19. An integrated uncertainty and ensemble-based data assimilation approach for improved operational streamflow predictions

    NASA Astrophysics Data System (ADS)

    He, M.; Hogue, T. S.; Margulis, S. A.; Franz, K. J.

    2011-08-01

    The current study proposes an integrated uncertainty and ensemble-based data assimilation framework (ICEA) and evaluates its viability in providing operational streamflow predictions via assimilating snow water equivalent (SWE) data. This step-wise framework applies a parameter uncertainty analysis algorithm (ISURF) to identify the uncertainty structure of sensitive model parameters, which is subsequently formulated into an Ensemble Kalman Filter (EnKF) to generate updated snow states for streamflow prediction. The framework is coupled to the US National Weather Service (NWS) snow and rainfall-runoff models. Its applicability is demonstrated for an operational basin of a western River Forecast Center (RFC) of the NWS. Performance of the framework is evaluated against existing operational baseline (RFC predictions), the stand-alone ISURF, and the stand-alone EnKF. Results indicate that the ensemble-mean prediction of ICEA considerably outperforms predictions from the other three scenarios investigated, particularly in the context of predicting high flows (top 5th percentile). The ICEA streamflow ensemble predictions capture the variability of the observed streamflow well, however the ensemble is not wide enough to consistently contain the range of streamflow observations in the study basin. Our findings indicate that the ICEA has the potential to supplement the current operational (deterministic) forecasting method in terms of providing improved single-valued (e.g., ensemble mean) streamflow predictions as well as meaningful ensemble predictions.

  20. Coherent Spin Control at the Quantum Level in an Ensemble-Based Optical Memory

    NASA Astrophysics Data System (ADS)

    Jobez, Pierre; Laplane, Cyril; Timoney, Nuala; Gisin, Nicolas; Ferrier, Alban; Goldner, Philippe; Afzelius, Mikael

    2015-06-01

    Long-lived quantum memories are essential components of a long-standing goal of remote distribution of entanglement in quantum networks. These can be realized by storing the quantum states of light as single-spin excitations in atomic ensembles. However, spin states are often subjected to different dephasing processes that limit the storage time, which in principle could be overcome using spin-echo techniques. Theoretical studies suggest this to be challenging due to unavoidable spontaneous emission noise in ensemble-based quantum memories. Here, we demonstrate spin-echo manipulation of a mean spin excitation of 1 in a large solid-state ensemble, generated through storage of a weak optical pulse. After a storage time of about 1 ms we optically read-out the spin excitation with a high signal-to-noise ratio. Our results pave the way for long-duration optical quantum storage using spin-echo techniques for any ensemble-based memory.

  1. Dynamic conformational ensembles regulate casein kinase-1 isoforms: Insights from molecular dynamics and molecular docking studies.

    PubMed

    Singh, Surya Pratap; Gupta, Dwijendra K

    2016-04-01

    Casein kinase-1 (CK1) isoforms actively participate in the down-regulation of canonical Wnt signaling pathway; however recent studies have shown their active roles in oncogenesis of various tissues through this pathway. Functional loss of two isoforms (CK1-α/ε) has been shown to activate the carcinogenic pathway which involves the stabilization of of cytoplasmic β-catenin. Development of anticancer therapeutics is very laborious task and depends upon the structural and conformational details of the target. This study focuses on, how the structural dynamics and conformational changes of two CK1 isoforms are synchronized in carcinogenic pathway. The conformational dynamics in kinases is the responsible for their action as has been supported by the molecular docking experiments. PMID:26788877

  2. 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

  3. Force sensor based tool condition monitoring using a heterogeneous ensemble learning model.

    PubMed

    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

  4. 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

  5. Classification of lung cancer using ensemble-based feature selection and machine learning methods.

    PubMed

    Cai, Zhihua; Xu, Dong; Zhang, Qing; Zhang, Jiexia; Ngai, Sai-Ming; Shao, Jianlin

    2015-03-01

    Lung cancer is one of the leading causes of death worldwide. There are three major types of lung cancers, non-small cell lung cancer (NSCLC), small cell lung cancer (SCLC) and carcinoid. NSCLC is further classified into lung adenocarcinoma (LADC), squamous cell lung cancer (SQCLC) as well as large cell lung cancer. Many previous studies demonstrated that DNA methylation has emerged as potential lung cancer-specific biomarkers. However, whether there exists a set of DNA methylation markers simultaneously distinguishing such three types of lung cancers remains elusive. In the present study, ROC (Receiving Operating Curve), RFs (Random Forests) and mRMR (Maximum Relevancy and Minimum Redundancy) were proposed to capture the unbiased, informative as well as compact molecular signatures followed by machine learning methods to classify LADC, SQCLC and SCLC. As a result, a panel of 16 DNA methylation markers exhibits an ideal classification power with an accuracy of 86.54%, 84.6% and a recall 84.37%, 85.5% in the leave-one-out cross-validation (LOOCV) and independent data set test experiments, respectively. Besides, comparison results indicate that ensemble-based feature selection methods outperform individual ones when combined with the incremental feature selection (IFS) strategy in terms of the informative and compact property of features. Taken together, results obtained suggest the effectiveness of the ensemble-based feature selection approach and the possible existence of a common panel of DNA methylation markers among such three types of lung cancer tissue, which would facilitate clinical diagnosis and treatment. PMID:25512221

  6. 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

  7. 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

  8. 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.

  9. 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.

  10. 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.

  11. 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

  12. Targeted and Highly Multiplexed Detection of Microorganisms by Employing an Ensemble of Molecular Probes

    PubMed Central

    Xu, Weihong; Krishnakumar, Sujatha; Miranda, Molly; Jensen, Michael A.; Fukushima, Marilyn; Palm, Curtis; Fung, Eula; Davis, Ronald W.; St.Onge, Robert P.

    2014-01-01

    The vast majority of microscopic life on earth consists of microbes that do not grow in laboratory culture. To profile the microbial diversity in environmental and clinical samples, we have devised and employed molecular probe technology, which detects and identifies bacteria that do and do not grow in culture. The only requirement is a short sequence of contiguous bases (currently 60 bases) unique to the genome of the organism of interest. The procedure is relatively fast, inexpensive, customizable, robust, and culture independent and uses commercially available reagents and instruments. In this communication, we report improving the specificity of the molecular probes substantially and increasing the complexity of the molecular probe set by over an order of magnitude (>1,200 probes) and introduce a new final readout method based upon Illumina sequencing. In addition, we employed molecular probes to identify the bacteria from vaginal swabs and demonstrate how a deliberate selection of molecular probes can identify less abundant bacteria even in the presence of much more abundant species. PMID:24795371

  13. Dissimilarity-Based Ensembles for Multiple Instance Learning.

    PubMed

    Cheplygina, Veronika; Tax, David M J; Loog, Marco

    2016-06-01

    In multiple instance learning, objects are sets (bags) of feature vectors (instances) rather than individual feature vectors. In this paper, we address the problem of how these bags can best be represented. Two standard approaches are to use (dis)similarities between bags and prototype bags, or between bags and prototype instances. The first approach results in a relatively low-dimensional representation, determined by the number of training bags, whereas the second approach results in a relatively high-dimensional representation, determined by the total number of instances in the training set. However, an advantage of the latter representation is that the informativeness of the prototype instances can be inferred. In this paper, a third, intermediate approach is proposed, which links the two approaches and combines their strengths. Our classifier is inspired by a random subspace ensemble, and considers subspaces of the dissimilarity space, defined by subsets of instances, as prototypes. We provide insight into the structure of some popular multiple instance problems and show state-of-the-art performances on these data sets. PMID:27214351

  14. 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.

  15. GACEM: Genetic Algorithm Based Classifier Ensemble in a Multi-sensor System

    PubMed Central

    Xu, Rongwu; He, Lin

    2008-01-01

    Multi-sensor systems (MSS) have been increasingly applied in pattern classification while searching for the optimal classification framework is still an open problem. The development of the classifier ensemble seems to provide a promising solution. The classifier ensemble is a learning paradigm where many classifiers are jointly used to solve a problem, which has been proven an effective method for enhancing the classification ability. In this paper, by introducing the concept of Meta-feature (MF) and Trans-function (TF) for describing the relationship between the nature and the measurement of the observed phenomenon, classification in a multi-sensor system can be unified in the classifier ensemble framework. Then an approach called Genetic Algorithm based Classifier Ensemble in Multi-sensor system (GACEM) is presented, where a genetic algorithm is utilized for optimization of both the selection of features subset and the decision combination simultaneously. GACEM trains a number of classifiers based on different combinations of feature vectors at first and then selects the classifiers whose weight is higher than the pre-set threshold to make up the ensemble. An empirical study shows that, compared with the conventional feature-level voting and decision-level voting, not only can GACEM achieve better and more robust performance, but also simplify the system markedly.

  16. 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.

  17. 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.

  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. 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

  20. An ensemble approach for phenotype classification based on fuzzy partitioning of gene expression data.

    PubMed

    Dragomir, A; Maraziotis, I; Bezerianos, A

    2006-01-01

    We focus on developing a pattern recognition method suitable for performing supervised analysis tasks on molecular data resulting from microarray experiments. Molecular characterization of tissue samples using microarray gene expression profiling is expected to uncover fundamental aspects related to cancer diagnosis and drug discovery. There is therefore a need for reliable, accurate classification methods. With this study we propose a framework for constructing an ensemble of individually trained SVM classifiers, each of them specialized on subsets of the input space. The fuzzy approach used for partitioning the data produces overlapping subsets of the input space that facilitates subsequent classification tasks. PMID:17946338

  1. Ensembles of neural networks based on the alteration of input feature values.

    PubMed

    Akhand, M A H; Murase, K

    2012-02-01

    An ensemble performs well when the component classifiers are diverse yet accurate, so that the failure of one is compensated for by others. A number of methods have been investigated for constructing ensemble in which some of them train classifiers with the generated patterns. This study investigates a new technique of training pattern generation. The method alters input feature values of some patterns using the values of other patterns to generate different patterns for different classifiers. The effectiveness of neural network ensemble based on the proposed technique was evaluated using a suite of 25 benchmark classification problems, and was found to achieve performance better than or competitive with related conventional methods. Experimental investigation of different input values alteration techniques finds that alteration with pattern values in the same class is better for generalization, although other alteration techniques may offer more diversity. PMID:22262526

  2. Cavity QED Based on Collective Magnetic Dipole Coupling: Spin Ensembles as Hybrid Two-Level Systems

    NASA Astrophysics Data System (ADS)

    Imamoǧlu, Atac

    2009-02-01

    We analyze the magnetic dipole coupling of an ensemble of spins to a superconducting microwave stripline structure, incorporating a Josephson junction based transmon qubit. We show that this system is described by an embedded Jaynes-Cummings model: in the strong coupling regime, collective spin-wave excitations of the ensemble of spins pick up the nonlinearity of the cavity mode, such that the two lowest eigenstates of the coupled spin wave-microwave cavity-Josephson junction system define a hybrid two-level system. The proposal described here enables new avenues for nonlinear optics using optical photons coupled to spin ensembles via Raman transitions. The possibility of strong coupling cavity QED with magnetic dipole transitions also opens up the possibility of extending quantum information processing protocols to spins in silicon or graphene, without the need for single-spin confinement.

  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. An Ensemble Learning Based Framework for Traditional Chinese Medicine Data Analysis with ICD-10 Labels

    PubMed Central

    Zhang, Gang; Huang, Yonghui; Zhong, Ling; Ou, Shanxing; Zhang, Yi; Li, Ziping

    2015-01-01

    Objective. This study aims to establish a model to analyze clinical experience of TCM veteran doctors. We propose an ensemble learning based framework to analyze clinical records with ICD-10 labels information for effective diagnosis and acupoints recommendation. Methods. We propose an ensemble learning framework for the analysis task. A set of base learners composed of decision tree (DT) and support vector machine (SVM) are trained by bootstrapping the training dataset. The base learners are sorted by accuracy and diversity through nondominated sort (NDS) algorithm and combined through a deep ensemble learning strategy. Results. We evaluate the proposed method with comparison to two currently successful methods on a clinical diagnosis dataset with manually labeled ICD-10 information. ICD-10 label annotation and acupoints recommendation are evaluated for three methods. The proposed method achieves an accuracy rate of 88.2%  ±  2.8% measured by zero-one loss for the first evaluation session and 79.6%  ±  3.6% measured by Hamming loss, which are superior to the other two methods. Conclusion. The proposed ensemble model can effectively model the implied knowledge and experience in historic clinical data records. The computational cost of training a set of base learners is relatively low. PMID:26504897

  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. PMID:26273876

  7. 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. PMID:17148474

  8. Coherent Spin Control at the Quantum Level in an Ensemble-Based Optical Memory.

    PubMed

    Jobez, Pierre; Laplane, Cyril; Timoney, Nuala; Gisin, Nicolas; Ferrier, Alban; Goldner, Philippe; Afzelius, Mikael

    2015-06-12

    Long-lived quantum memories are essential components of a long-standing goal of remote distribution of entanglement in quantum networks. These can be realized by storing the quantum states of light as single-spin excitations in atomic ensembles. However, spin states are often subjected to different dephasing processes that limit the storage time, which in principle could be overcome using spin-echo techniques. Theoretical studies suggest this to be challenging due to unavoidable spontaneous emission noise in ensemble-based quantum memories. Here, we demonstrate spin-echo manipulation of a mean spin excitation of 1 in a large solid-state ensemble, generated through storage of a weak optical pulse. After a storage time of about 1 ms we optically read-out the spin excitation with a high signal-to-noise ratio. Our results pave the way for long-duration optical quantum storage using spin-echo techniques for any ensemble-based memory. PMID:26196785

  9. An ensemble of dissimilarity based classifiers for Mackerel gender determination

    NASA Astrophysics Data System (ADS)

    Blanco, A.; Rodriguez, R.; Martinez-Maranon, I.

    2014-03-01

    Mackerel is an infravalored fish captured by European fishing vessels. A manner to add value to this specie can be achieved by trying to classify it attending to its sex. Colour measurements were performed on Mackerel females and males (fresh and defrozen) extracted gonads to obtain differences between sexes. Several linear and non linear classifiers such as Support Vector Machines (SVM), k Nearest Neighbors (k-NN) or Diagonal Linear Discriminant Analysis (DLDA) can been applied to this problem. However, theyare usually based on Euclidean distances that fail to reflect accurately the sample proximities. Classifiers based on non-Euclidean dissimilarities misclassify a different set of patterns. We combine different kind of dissimilarity based classifiers. The diversity is induced considering a set of complementary dissimilarities for each model. The experimental results suggest that our algorithm helps to improve classifiers based on a single dissimilarity.

  10. Temperature rating prediction of Tibetan robe ensemble based on different wearing ways.

    PubMed

    Li, Jun; Guo, Xiaofang; Wang, Yunyi

    2012-09-01

    Each piece of Western clothing has a unique temperature rating (TR); however, based on different wearing ways, one Tibetan robe ensemble can be used in various environments of the Tibetan plateau. To explain this environmental adaptation, thermal insulations and TR values of Tibetan robe ensembles in three typical wearing ways were measured by manikin testing and wearing trials, respectively. The TR prediction models for Tibetan robe ensembles were built in this research. The results showed that the thermal insulations of Tibetan robe ensembles changed from 0.26 clo to 0.91 clo; the corresponding TRs ranged from 9.90 °C to 16.86 °C because of different wearing ways. Not only the thermal insulation, but also the ways of wearing Tibetan robes was important to determining their TR values. The three TR models and a triangle area for each piece of Tibetan clothing explained its positive adaptation into the environment; this was different from the current TR models for Western clothing. PMID:22321946

  11. 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. PMID:26794929

  12. Ensemble-Based Virtual Screening Led to the Discovery of New Classes of Potent Tyrosinase Inhibitors.

    PubMed

    Choi, Joonhyeok; Choi, Kwang-Eun; Park, Sung Jean; Kim, Sun Yeou; Jee, Jun-Goo

    2016-02-22

    In this study, we report new classes of potent tyrosinase inhibitors identified by enhanced structure-based virtual screening prediction; the enzyme and melanin content assays were also confirmed. Tyrosinase, a type-3 copper protein, participates in two distinct reactions, hydroxylation of tyrosine to DOPA and conversion of DOPA to dopaquinone, in melanin biosynthesis. Although numerous inhibitors of this reaction have been reported, there is a lag in the discovery of the new functional moieties. In order to improve the performance of virtual screening, we first produced an ensemble of 10,000 structures using molecular dynamics simulation. Quantum mechanical calculation was used to determine the partial charges of catalytic copper ions based on the met and deoxy states. Second, we selected a structure showing an optimal receiver operating characteristic (ROC) curve with known direct binders and their physicochemically matched decoys. The structure revealed more than 10-fold higher enrichment at 1% of the ROC curve than those observed in X-ray structures. Third, high-throughput virtual screening with DOCK 3.6 was performed using a library consisting of approximately 400,000 small molecules derived from the ZINC database. Fourth, we obtained the top 60 molecules and tested their inhibition of mushroom tyrosinase. The extended assays included 21 analogs of the 21 initial hits to test their inhibition properties. Here, the moieties of tetrazole and triazole were identified as new binding cores interacting with the dicopper catalytic center. All 42 inhibitors showed inhibitory constant, Ki, values ranging from 11.1 nM and 33.4 μM, with a tetrazole compound exhibiting the strongest activity. Among the 42 molecules, five displayed more than 30% reduction in melanin production when treated in B16F10 melanoma cells; cell viability was >90% at 20 μM. Particularly, a thiosemicarbazone-containing compound reduced melanin content by 55%. PMID:26750991

  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. PMID:26405970

  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. PMID:19171524

  15. 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.

  16. Profiles and Majority Voting-Based Ensemble Method for Protein Secondary Structure Prediction

    PubMed Central

    Bouziane, Hafida; Messabih, Belhadri; Chouarfia, Abdallah

    2011-01-01

    Machine learning techniques have been widely applied to solve the problem of predicting protein secondary structure from the amino acid sequence. They have gained substantial success in this research area. Many methods have been used including k-Nearest Neighbors (k-NNs), Hidden Markov Models (HMMs), Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs), which have attracted attention recently. Today, the main goal remains to improve the prediction quality of the secondary structure elements. The prediction accuracy has been continuously improved over the years, especially by using hybrid or ensemble methods and incorporating evolutionary information in the form of profiles extracted from alignments of multiple homologous sequences. In this paper, we investigate how best to combine k-NNs, ANNs and Multi-class SVMs (M-SVMs) to improve secondary structure prediction of globular proteins. An ensemble method which combines the outputs of two feed-forward ANNs, k-NN and three M-SVM classifiers has been applied. Ensemble members are combined using two variants of majority voting rule. An heuristic based filter has also been applied to refine the prediction. To investigate how much improvement the general ensemble method can give rather than the individual classifiers that make up the ensemble, we have experimented with the proposed system on the two widely used benchmark datasets RS126 and CB513 using cross-validation tests by including PSI-BLAST position-specific scoring matrix (PSSM) profiles as inputs. The experimental results reveal that the proposed system yields significant performance gains when compared with the best individual classifier. PMID:22058650

  17. Comparison of two ways for representation of the forecast probability density function in ensemble-based sequential data assimilation

    NASA Astrophysics Data System (ADS)

    Nakano, Shinya

    2013-04-01

    In the ensemble-based sequential data assimilation, the probability density function (PDF) at each time step is represented by ensemble members. These ensemble members are usually assumed to be Monte Carlo samples drawn from the PDF, and the probability density is associated with the concentration of the ensemble members. On the basis of the Monte Carlo approximation, the forecast ensemble, which is obtained by applying the dynamical model to each ensemble member, provides an approximation of the forecast PDF on the basis of the Chapman-Kolmogorov integral. In practical cases, however, the ensemble size is limited by available computational resources, and it is typically much less than the system dimension. In such situations, the Monte Carlo approximation would not well work. When the ensemble size is less than the system dimension, the ensemble would form a simplex in a subspace. The simplex can not represent the third or higher-order moments of the PDF, but it can represent only the Gaussian features of the PDF. As noted by Wang et al. (2004), the forecast ensemble, which is obtained by applying the dynamical model to each member of the simplex ensemble, provides an approximation of the mean and covariance of the forecast PDF where the Taylor expansion of the dynamical model up to the second-order is considered except that the uncertainties which can not represented by the ensemble members are ignored. Since the third and higher-order nonlinearity is discarded, the forecast ensemble would provide some bias to the forecast. Using a small nonlinear model, the Lorenz 63 model, we also performed the experiment of the state estimation with both the simplex representation and the Monte Carlo representation, which corresponds to the limited-sized ensemble case and the large-sized ensemble case, respectively. If we use the simplex representation, it is found that the estimates tend to have some bias which is likely to be caused by the nonlinearity of the system rather

  18. 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

  19. Clustering-Based Ensemble Learning for Activity Recognition in Smart Homes

    PubMed Central

    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. PMID:25014095

  20. 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. PMID:25014095

  1. 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

  2. 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.

  3. Random feature subspace ensemble based Extreme Learning Machine for liver tumor detection and segmentation.

    PubMed

    Huang, Weimin; Yang, Yongzhong; Lin, Zhiping; Huang, Guang-Bin; Zhou, Jiayin; Duan, Yuping; Xiong, Wei

    2014-01-01

    This paper presents a new approach to detect and segment liver tumors. The detection and segmentation of liver tumors can be formulized as novelty detection or two-class classification problem. Each voxel is characterized by a rich feature vector, and a classifier using random feature subspace ensemble is trained to classify the voxels. Since Extreme Learning Machine (ELM) has advantages of very fast learning speed and good generalization ability, it is chosen to be the base classifier in the ensemble. Besides, majority voting is incorporated for fusion of classification results from the ensemble of base classifiers. In order to further increase testing accuracy, ELM autoencoder is implemented as a pre-training step. In automatic liver tumor detection, ELM is trained as a one-class classifier with only healthy liver samples, and the performance is compared with two-class ELM. In liver tumor segmentation, a semi-automatic approach is adopted by selecting samples in 3D space to train the classifier. The proposed method is tested and evaluated on a group of patients' CT data and experiment show promising results. PMID:25571035

  4. 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.

  5. 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

  6. 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

  7. 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

  8. Ensembl 2012.

    PubMed

    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

  9. 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

  10. Performance and reliability of multimodel hydrological ensemble simulations based on seventeen lumped models and a thousand catchments

    NASA Astrophysics Data System (ADS)

    Velázquez, J. A.; Anctil, F.; Perrin, C.

    2010-06-01

    This work investigates the added value of ensembles constructed from seventeen lumped hydrological models against their simple average counterparts. It is thus hypothesized that there is more information provided by all the outputs of these models than by their single aggregated predictors. For all available 1061 catchments, results showed that the mean continuous ranked probability score of the ensemble simulations were better than the mean average error of the aggregated simulations, confirming the added value of retaining all the components of the model outputs. Reliability of the simulation ensembles is also achieved for about 30% of the catchments, as assessed by rank histograms and reliability plots. Nonetheless this imperfection, the ensemble simulations were shown to have better skills than the deterministic simulations at discriminating between events and non-events, as confirmed by relative operating characteristic scores especially for larger streamflows. From 7 to 10 models are deemed sufficient to construct ensembles with improved performance, based on a genetic algorithm search optimizing the continuous ranked probability score. In fact, many model subsets were found improving the performance of the reference ensemble. This is thus not essential to implement as much as seventeen lumped hydrological models. The gain in performance of the optimized subsets is accompanied by some improvement of the ensemble reliability in most cases. Nonetheless, a calibration of the predictive distribution is still needed for many catchments.

  11. Performance and reliability of multimodel hydrological ensemble simulations based on seventeen lumped models and a thousand catchments

    NASA Astrophysics Data System (ADS)

    Velázquez, J. A.; Anctil, F.; Perrin, C.

    2010-11-01

    This work investigates the added value of ensembles constructed from seventeen lumped hydrological models against their simple average counterparts. It is thus hypothesized that there is more information provided by all the outputs of these models than by their single aggregated predictors. For all available 1061 catchments, results showed that the mean continuous ranked probability score of the ensemble simulations were better than the mean average error of the aggregated simulations, confirming the added value of retaining all the components of the model outputs. Reliability of the simulation ensembles is also achieved for about 30% of the catchments, as assessed by rank histograms and reliability plots. Nonetheless this imperfection, the ensemble simulations were shown to have better skills than the deterministic simulations at discriminating between events and non-events, as confirmed by relative operating characteristic scores especially for larger streamflows. From 7 to 10 models are deemed sufficient to construct ensembles with improved performance, based on a genetic algorithm search optimizing the continuous ranked probability score. In fact, many model subsets were found improving the performance of the reference ensemble. This is thus not essential to implement as much as seventeen lumped hydrological models. The gain in performance of the optimized subsets is accompanied by some improvement of the ensemble reliability in most cases. Nonetheless, a calibration of the predictive distribution is still needed for many catchments.

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

    PubMed

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

    2014-03-01

    This article investigates an ensemble-based technique called Bayesian Model Averaging (BMA) to improve the performance of protein amino acid pKa predictions. Structure-based pKa 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 pKa prediction, ranging from empirical statistical models to ab initio quantum mechanical approaches. However, each of these methods are based on a set of conceptual assumptions that can effect a model's accuracy and generalizability for pKa 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 García-Moreno lab. Our cross-validation study demonstrates that the aggregated estimate obtained from BMA outperforms all individual prediction methods with improvements ranging from 45 to 73% over other method classes. This study also compares BMA's predictive performance to other ensemble-based techniques and demonstrates that BMA can outperform these approaches with improvements ranging from 27 to 60%. This work illustrates a new possible mechanism for improving the accuracy of pKa prediction and lays the foundation for future work on aggregate models that balance computational cost with prediction accuracy. PMID:23946048

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

    NASA Astrophysics Data System (ADS)

    Okumura, Hisashi

    2015-12-01

    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.

  14. 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.

  15. 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.

  16. Optimal control of light storage in atomic ensemble based on photon echoes

    NASA Astrophysics Data System (ADS)

    Wu, Tingwan; Chen, Qinzhi

    2009-11-01

    This paper presents a simple quantum memory method for efficient storage and retrieve of light. The technique is based on the principle of controlled reversible inhomogeneous broadening for which the information of the quantum state light is imprinted in a two-level atoms ensemble and recalled by flipping the external nonuniform electric field. In present work, the induced Stark shift varied linearly with position, and a numerical analysis for this protocol has been studied. It shows that the storage efficiency can nearly reach 100% with a large enough optical depth, and the optimal broadening for a given pulse width is also analyzed.

  17. 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

  18. Ensemble-Based Network Aggregation Improves the Accuracy of Gene Network Reconstruction

    PubMed Central

    Xiao, Guanghua; Xie, Yang

    2014-01-01

    Reverse engineering approaches to constructing gene regulatory networks (GRNs) based on genome-wide mRNA expression data have led to significant biological findings, such as the discovery of novel drug targets. However, the reliability of the reconstructed GRNs needs to be improved. Here, we propose an ensemble-based network aggregation approach to improving the accuracy of network topologies constructed from mRNA expression data. To evaluate the performances of different approaches, we created dozens of simulated networks from combinations of gene-set sizes and sample sizes and also tested our methods on three Escherichia coli datasets. We demonstrate that the ensemble-based network aggregation approach can be used to effectively integrate GRNs constructed from different studies – producing more accurate networks. We also apply this approach to building a network from epithelial mesenchymal transition (EMT) signature microarray data and identify hub genes that might be potential drug targets. The R code used to perform all of the analyses is available in an R package entitled “ENA”, accessible on CRAN (http://cran.r-project.org/web/packages/ENA/). PMID:25390635

  19. 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.

  20. A neural network based ensemble approach for improving the accuracy of meteorological fields used for regional air quality modeling.

    PubMed

    Cheng, Shuiyuan; Li, Li; Chen, Dongsheng; Li, Jianbing

    2012-12-15

    A neural network based ensemble methodology was presented in this study to improve the accuracy of meteorological input fields for regional air quality modeling. Through nonlinear integration of simulation results from two meteorological models (MM5 and WRF), the ensemble approach focused on the optimization of meteorological variable values (temperature, surface air pressure, and wind field) in the vertical layer near ground. To illustrate the proposed approach, a case study in northern China during two selected air pollution events, in 2006, was conducted. The performances of the MM5, the WRF, and the ensemble approach were assessed using different statistical measures. The results indicated that the ensemble approach had a higher simulation accuracy than the MM5 and the WRF model. Performance was improved by more than 12.9% for temperature, 18.7% for surface air pressure field, and 17.7% for wind field. The atmospheric PM(10) concentrations in the study region were also simulated by coupling the air quality model CMAQ with the MM5 model, the WRF model, and the ensemble model. It was found that the modeling accuracy of the ensemble-CMAQ model was improved by more than 7.0% and 17.8% when compared to the MM5-CMAQ and the WRF-CMAQ models, respectively. The proposed neural network based meteorological modeling approach holds great potential for improving the performance of regional air quality modeling. PMID:23000477

  1. Toward a global ocean data assimilation system based on ensemble optimum interpolation: altimetry data assimilation experiment

    NASA Astrophysics Data System (ADS)

    Fu, Weiwei; Zhu, Jiang; Yan, Changxiang; Liu, Hailong

    2009-08-01

    A global ocean data assimilation system based on the ensemble optimum interpolation (EnOI) has been under development as the Chinese contribution to the Global Ocean Data Assimilation Experiment. The system uses a global ocean general circulation model, which is eddy permitting, developed by the Institute of Atmospheric Physics of the Chinese Academy of Sciences. In this paper, the implementation of the system is described in detail. We describe the sampling strategy to generate the stationary ensembles for EnOI. In addition, technical methods are introduced to deal with the requirement of massive memory space to hold the stationary ensembles of the global ocean. The system can assimilate observations such as satellite altimetry, sea surface temperature (SST), in situ temperature and salinity from Argo, XBT, Tropical Atmosphere Ocean (TAO), and other sources in a straightforward way. As a first step, an assimilation experiment from 1997 to 2001 is carried out by assimilating the sea level anomaly (SLA) data from TOPEX/Poseidon. We evaluate the performance of the system by comparing the results with various types of observations. We find that SLA assimilation shows very positive impact on the modeled fields. The SST and sea surface height fields are clearly improved in terms of both the standard deviation and the root mean square difference. In addition, the assimilation produces some improvements in regions where mesoscale processes cannot be resolved with the horizontal resolution of this model. Comparisons with TAO profiles in the Pacific show that the temperature and salinity fields have been improved to varying degrees in the upper ocean. The biases with respect to the independent TAO profiles are reduced with a maximum magnitude of about 0.25°C and 0.1 psu for the time-averaged temperature and salinity. The improvements on temperature and salinity also lead to positive impact on the subsurface currents. The equatorial under current is enhanced in the Pacific

  2. 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.

  3. Single-molecule imaging of non-equilibrium molecular ensembles on the millisecond timescale.

    PubMed

    Juette, Manuel F; Terry, Daniel S; Wasserman, Michael R; Altman, Roger B; Zhou, Zhou; Zhao, Hong; Blanchard, Scott C

    2016-04-01

    Single-molecule fluorescence microscopy is uniquely suited for detecting transient molecular recognition events, yet achieving the time resolution and statistics needed to realize this potential has proven challenging. Here we present a single-molecule imaging and analysis platform using scientific complementary metal-oxide semiconductor (sCMOS) detectors that enables imaging of 15,000 individual molecules simultaneously at millisecond rates. This system enabled the detection of previously obscured processes relevant to the fidelity mechanism in protein synthesis. PMID:26878382

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

    NASA Astrophysics Data System (ADS)

    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.

  5. Ensemble learning for spatial interpolation of soil potassium content based on environmental information.

    PubMed

    Liu, Wei; Du, Peijun; Wang, Dongchen

    2015-01-01

    One important method to obtain the continuous surfaces of soil properties from point samples is spatial interpolation. In this paper, we propose a method that combines ensemble learning with ancillary environmental information for improved interpolation of soil properties (hereafter, EL-SP). First, we calculated the trend value for soil potassium contents at the Qinghai Lake region in China based on measured values. Then, based on soil types, geology types, land use types, and slope data, the remaining residual was simulated with the ensemble learning model. Next, the EL-SP method was applied to interpolate soil potassium contents at the study site. To evaluate the utility of the EL-SP method, we compared its performance with other interpolation methods including universal kriging, inverse distance weighting, ordinary kriging, and ordinary kriging combined geographic information. Results show that EL-SP had a lower mean absolute error and root mean square error than the data produced by the other models tested in this paper. Notably, the EL-SP maps can describe more locally detailed information and more accurate spatial patterns for soil potassium content than the other methods because of the combined use of different types of environmental information; these maps are capable of showing abrupt boundary information for soil potassium content. Furthermore, the EL-SP method not only reduces prediction errors, but it also compliments other environmental information, which makes the spatial interpolation of soil potassium content more reasonable and useful. PMID:25928138

  6. Ensemble Learning for Spatial Interpolation of Soil Potassium Content Based on Environmental Information

    PubMed Central

    Liu, Wei; Du, Peijun; Wang, Dongchen

    2015-01-01

    One important method to obtain the continuous surfaces of soil properties from point samples is spatial interpolation. In this paper, we propose a method that combines ensemble learning with ancillary environmental information for improved interpolation of soil properties (hereafter, EL-SP). First, we calculated the trend value for soil potassium contents at the Qinghai Lake region in China based on measured values. Then, based on soil types, geology types, land use types, and slope data, the remaining residual was simulated with the ensemble learning model. Next, the EL-SP method was applied to interpolate soil potassium contents at the study site. To evaluate the utility of the EL-SP method, we compared its performance with other interpolation methods including universal kriging, inverse distance weighting, ordinary kriging, and ordinary kriging combined geographic information. Results show that EL-SP had a lower mean absolute error and root mean square error than the data produced by the other models tested in this paper. Notably, the EL-SP maps can describe more locally detailed information and more accurate spatial patterns for soil potassium content than the other methods because of the combined use of different types of environmental information; these maps are capable of showing abrupt boundary information for soil potassium content. Furthermore, the EL-SP method not only reduces prediction errors, but it also compliments other environmental information, which makes the spatial interpolation of soil potassium content more reasonable and useful. PMID:25928138

  7. Assessing the predictive capability of randomized tree-based ensembles in streamflow modelling

    NASA Astrophysics Data System (ADS)

    Galelli, S.; Castelletti, A.

    2013-07-01

    Combining randomization methods with ensemble prediction is emerging as an effective option to balance accuracy and computational efficiency in data-driven modelling. In this paper, we investigate the prediction capability of extremely randomized trees (Extra-Trees), in terms of accuracy, explanation ability and computational efficiency, in a streamflow modelling exercise. Extra-Trees are a totally randomized tree-based ensemble method that (i) alleviates the poor generalisation property and tendency to overfitting of traditional standalone decision trees (e.g. CART); (ii) is computationally efficient; and, (iii) allows to infer the relative importance of the input variables, which might help in the ex-post physical interpretation of the model. The Extra-Trees potential is analysed on two real-world case studies - Marina catchment (Singapore) and Canning River (Western Australia) - representing two different morphoclimatic contexts. The evaluation is performed against other tree-based methods (CART and M5) and parametric data-driven approaches (ANNs and multiple linear regression). Results show that Extra-Trees perform comparatively well to the best of the benchmarks (i.e. M5) in both the watersheds, while outperforming the other approaches in terms of computational requirement when adopted on large datasets. In addition, the ranking of the input variable provided can be given a physically meaningful interpretation.

  8. Assessing the predictive capability of randomized tree-based ensembles in streamflow modelling

    NASA Astrophysics Data System (ADS)

    Galelli, S.; Castelletti, A.

    2013-02-01

    Combining randomization methods with ensemble prediction is emerging as an effective option to balance accuracy and computational efficiency in data-driven modeling. In this paper we investigate the prediction capability of extremely randomized trees (Extra-Trees), in terms of accuracy, explanation ability and computational efficiency, in a streamflow modeling exercise. Extra-Trees are a totally randomized tree-based ensemble method that (i) alleviates the poor generalization property and tendency to overfitting of traditional standalone decision trees (e.g. CART); (ii) is computationally very efficient; and, (iii) allows to infer the relative importance of the input variables, which might help in the ex-post physical interpretation of the model. The Extra-Trees potential is analyzed on two real-world case studies (Marina catchment (Singapore) and Canning River (Western Australia)) representing two different morphoclimatic contexts comparatively with other tree-based methods (CART and M5) and parametric data-driven approaches (ANNs and multiple linear regression). Results show that Extra-Trees perform comparatively well to the best of the benchmarks (i.e. M5) in both the watersheds, while outperforming the other approaches in terms of computational requirement when adopted on large datasets. In addition, the ranking of the input variable provided can be given a physically meaningful interpretation.

  9. Regional climate change projections over South America based on the CLARIS-LPB RCM ensemble

    NASA Astrophysics Data System (ADS)

    Samuelsson, Patrick; Solman, Silvina; Sanchez, Enrique; Rocha, Rosmeri; Li, Laurent; Marengo, José; Remedio, Armelle; Berbery, Hugo

    2013-04-01

    CLARIS-LPB was an EU FP7 financed Europe-South America Network for Climate Change Assessment and Impact Studies in La Plata Basin. CLARIS-LPB has created the first ensemble ever of RCM downscalings over South America. Here we present the climate change scenarios for a near future period (2011-2040) and for a far future period (2071-2100). The ensemble is based on seven RCMs driven by three CMIP3 GCMs for emission scenario SRES A1B. The RCM model domains cover all of South America, with a horizontal resolution of approximately 50 km, but project focus has been on results over the La Plata Basin. The ensemble mean for temperature change shows more warming over tropical South America than over the southern part of the continent. During summer (DJF) the Low-Parana and Uruguay regions show less warming than the surrounding regions. For the ensemble mean of precipitation changes the patterns are almost the same for near and far future but with larger values for far future. Thus overall trends do not change with time. The near future shows in general small changes over large areas (less than ±10%). For JJA a dry tendency is seen over eastern Brazil that becomes stronger and extends geographically with time. In near future most models show a drying trend over this area. In far future almost all models agree on the drying. For DJF a wet tendency is seen over the La Plata basin area which becomes stronger with time. In near future almost all downscalings agree on this wet tendency and in far future all downscalings agree on the sign. The RCM ensemble is unbalanced with respect to forcing GCMs. 6 out of 11(10) simulations use ECHAM5 for the near(far) future period while 4(3) use HadCM3 and only one IPSL. Thus, all ensemble mean values will be tilted towards ECHAM5. It is of course possible to compensate for this imbalance among GCMs by some weighting but no such weighting has been applied for the current analysis. The north-south gradient in warming is in general stronger in

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

    SciTech Connect

    Singh, Kunwar P. Gupta, Shikha

    2014-03-15

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

  11. Structural refinement from restrained-ensemble simulations based on EPR/DEER data: application to T4 lysozyme.

    PubMed

    Islam, Shahidul M; Stein, Richard A; McHaourab, Hassane S; Roux, Benoît

    2013-05-01

    DEER (double electron-electron resonance) is a powerful pulsed ESR (electron spin resonance) technique allowing the determination of distance histograms between pairs of nitroxide spin-labels linked to a protein in a native-like solution environment. However, exploiting the huge amount of information provided by ESR/DEER histograms to refine structural models is extremely challenging. In this study, a restrained ensemble (RE) molecular dynamics (MD) simulation methodology is developed to address this issue. In RE simulation, the spin-spin distance distribution histograms calculated from a multiple-copy MD simulation are enforced, via a global ensemble-based energy restraint, to match those obtained from ESR/DEER experiments. The RE simulation is applied to 51 ESR/DEER distance histogram data from spin-labels inserted at 37 different positions in T4 lysozyme (T4L). The rotamer population distribution along the five dihedral angles connecting the nitroxide ring to the protein backbone is determined and shown to be consistent with available information from X-ray crystallography. For the purpose of structural refinement, the concept of a simplified nitroxide dummy spin-label is designed and parametrized on the basis of these all-atom RE simulations with explicit solvent. It is demonstrated that RE simulations with the dummy nitroxide spin-labels imposing the ESR/DEER experimental distance distribution data are able to systematically correct and refine a series of distorted T4L structures, while simple harmonic distance restraints are unsuccessful. This computationally efficient approach allows experimental restraints from DEER experiments to be incorporated into RE simulations for efficient structural refinement. PMID:23510103

  12. 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.

  13. Nucleic acid based molecular devices.

    PubMed

    Krishnan, Yamuna; Simmel, Friedrich C

    2011-03-28

    In biology, nucleic acids are carriers of molecular information: DNA's base sequence stores and imparts genetic instructions, while RNA's sequence plays the role of a messenger and a regulator of gene expression. As biopolymers, nucleic acids also have exciting physicochemical properties, which can be rationally influenced by the base sequence in myriad ways. Consequently, in recent years nucleic acids have also become important building blocks for bottom-up nanotechnology: as molecules for the self-assembly of molecular nanostructures and also as a material for building machinelike nanodevices. In this Review we will cover the most important developments in this growing field of nucleic acid nanodevices. We also provide an overview of the biochemical and biophysical background of this field and the major "historical" influences that shaped its development. Particular emphasis is laid on DNA molecular motors, molecular robotics, molecular information processing, and applications of nucleic acid nanodevices in biology. PMID:21432950

  14. Bayesian Model Aggregation for Ensemble-Based Estimates of Protein pKa Values

    PubMed Central

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

    2013-01-01

    This paper investigates an ensemble-based technique called Bayesian Model Averaging (BMA) to improve the performance of protein amino acid pKa predictions. Structure-based pKa 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 pKa prediction, ranging from empirical statistical models to ab initio quantum mechanical approaches. However, each of these methods are based on a set of conceptual assumptions that can effect a model’s accuracy and generalizability for pKa 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 García-Moreno lab. Our cross-validation study demonstrates that the aggregated estimate obtained from BMA outperforms all individual prediction methods with improvements ranging from 45-73% over other method classes. This study also compares BMA’s predictive performance to other ensemble-based techniques and demonstrates that BMA can outperform these approaches with improvements ranging from 27-60%. This work illustrates a new possible mechanism for improving the accuracy of pKa prediction and lays the foundation for future work on aggregate models that balance computational cost with prediction accuracy. PMID:23946048

  15. The Ensemble Canon

    NASA Technical Reports Server (NTRS)

    MIittman, David S

    2011-01-01

    Ensemble is an open architecture for the development, integration, and deployment of mission operations software. Fundamentally, it is an adaptation of the Eclipse Rich Client Platform (RCP), a widespread, stable, and supported framework for component-based application development. By capitalizing on the maturity and availability of the Eclipse RCP, Ensemble offers a low-risk, politically neutral path towards a tighter integration of operations tools. The Ensemble project is a highly successful, ongoing collaboration among NASA Centers. Since 2004, the Ensemble project has supported the development of mission operations software for NASA's Exploration Systems, Science, and Space Operations Directorates.

  16. 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.

  17. 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

  18. An empirical study of ensemble-based semi-supervised learning approaches for imbalanced splice site datasets

    PubMed Central

    2015-01-01

    Background Recent biochemical advances have led to inexpensive, time-efficient production of massive volumes of raw genomic data. Traditional machine learning approaches to genome annotation typically rely on large amounts of labeled data. The process of labeling data can be expensive, as it requires domain knowledge and expert involvement. Semi-supervised learning approaches that can make use of unlabeled data, in addition to small amounts of labeled data, can help reduce the costs associated with labeling. In this context, we focus on the problem of predicting splice sites in a genome using semi-supervised learning approaches. This is a challenging problem, due to the highly imbalanced distribution of the data, i.e., small number of splice sites as compared to the number of non-splice sites. To address this challenge, we propose to use ensembles of semi-supervised classifiers, specifically self-training and co-training classifiers. Results Our experiments on five highly imbalanced splice site datasets, with positive to negative ratios of 1-to-99, showed that the ensemble-based semi-supervised approaches represent a good choice, even when the amount of labeled data consists of less than 1% of all training data. In particular, we found that ensembles of co-training and self-training classifiers that dynamically balance the set of labeled instances during the semi-supervised iterations show improvements over the corresponding supervised ensemble baselines. Conclusions In the presence of limited amounts of labeled data, ensemble-based semi-supervised approaches can successfully leverage the unlabeled data to enhance supervised ensembles learned from highly imbalanced data distributions. Given that such distributions are common for many biological sequence classification problems, our work can be seen as a stepping stone towards more sophisticated ensemble-based approaches to biological sequence annotation in a semi-supervised framework. PMID:26356316

  19. 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

  20. 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

  1. 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.

  2. 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

  3. Elucidating Molecular Motion through Structural and Dynamic Filters of Energy-Minimized Conformer Ensembles

    PubMed Central

    2015-01-01

    Complex RNA structures are constructed from helical segments connected by flexible loops that move spontaneously and in response to binding of small molecule ligands and proteins. Understanding the conformational variability of RNA requires the characterization of the coupled time evolution of interconnected flexible domains. To elucidate the collective molecular motions and explore the conformational landscape of the HIV-1 TAR RNA, we describe a new methodology that utilizes energy-minimized structures generated by the program “Fragment Assembly of RNA with Full-Atom Refinement (FARFAR)”. We apply structural filters in the form of experimental residual dipolar couplings (RDCs) to select a subset of discrete energy-minimized conformers and carry out principal component analyses (PCA) to corroborate the choice of the filtered subset. We use this subset of structures to calculate solution T1 and T1ρ relaxation times for 13C spins in multiple residues in different domains of the molecule using two simulation protocols that we previously published. We match the experimental T1 times to within 2% and the T1ρ times to within less than 10% for helical residues. These results introduce a protocol to construct viable dynamic trajectories for RNA molecules that accord well with experimental NMR data and support the notion that the motions of the helical portions of this small RNA can be described by a relatively small number of discrete conformations exchanging over time scales longer than 1 μs. PMID:24479561

  4. Protein docking using an ensemble of spin labels optimized by intra-molecular paramagnetic relaxation enhancement.

    PubMed

    Schilder, Jesika; Liu, Wei-Min; Kumar, Pravin; Overhand, Mark; Huber, Martina; Ubbink, Marcellus

    2016-02-17

    Paramagnetic NMR is a useful technique to study proteins and protein complexes and the use of paramagnetic relaxation enhancement (PRE) for this purpose has become wide-spread. PREs are commonly generated using paramagnetic spin labels (SLs) that contain an unpaired electron in the form of a nitroxide radical, with 1-oxyl-2,2,5,5-tetramethyl-2,5-dihydropyrrol-3-ylmethyl methane thiosulfonate (MTSL) being the most popular tag. The inherent flexibility of the SL causes sampling of several conformations in solution, which can be problematic as over- or underestimation of the spatial distribution of the unpaired electron in structural calculations will lead to errors in the distance restraints. We investigated the effect of this mobility on the accuracy of protein-protein docking calculations using intermolecular PRE data by comparing MTSL and the less mobile 3-methanesulfonilthiomethyl-4-(pyridin-3-yl)-2,2,5,5-tetramethyl-2,5-dihydro-1H-pyrrol-1-yloxyl (pyMTSL) on the dynamic complex of cytochrome c and cytochrome c peroxidase. No significant differences were found between the two SLs. Docking was performed using either single or multiple conformers and either fixed or flexible SLs. It was found that mobility of the SLs is the limiting factor for obtaining accurate solutions. Optimization of SL conformer orientations using intra-molecular PRE improves the accuracy of docking. PMID:26356049

  5. 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

  6. 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

  7. Aquifer characterization and uncertainty assessment: an ensemble-based pattern matching inverse method

    NASA Astrophysics Data System (ADS)

    Li, L.; Srinivasan, S.; Zhou, H.; Gómez-Hernández, J.

    2013-12-01

    In complex geological systems such as fluvial aquifers, carbonate systems and naturally fractured aquifers, multiple-point statistics (MPS) based modeling methods are required to characterize complex, curvilinear features. History matching with MPS calls for an effective inverse method that can not only honor the observed dynamic data, but also preserve the curvilinear geologic features that impact the aquifer remediation. We introduce a novel pattern matching based approach to history matching that uses an ensemble of prior of models capturing the prior uncertainty in geology. In the developed method, multiple point pattern-search is implemented not only to identify the pattern of conductivity variability in the neighborhood of a simulation node but also the pattern of state (for example, piezometric head) variables. The unknown parameter and state values are simultaneously and sequentially simulated by pattern searching through an ensemble of realizations rather than by optimizing an objective function. In order to accelerate the computational efficiency, pattern-search is applied only at the predefined pilot point locations. Subsequently, a fast MPS method is employed to extrapolate the spatial patterns away from the pilot points. The pattern search algorithm also utilizes a flexible search radius that can be optimized for the estimation of either large-scale or short-scale structures. The algorithm is evaluated for both categorical and continuous conductivity fields by continuous conditioning to the observed dynamic data. The results show that the measured conductivity and head data can be updated in a continuous fashion as dynamic data becomes available and flow predictions are more accurate. Furthermore, curvilinear geologic structures are preserved after data integration. The significant advantages of this method are: (1) parameter and state variable do not have to be modeled using the multi-Gaussian distribution; (2) the relationship between parameters and

  8. 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.

  9. 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

  10. 3-D 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.

    2015-02-01

    We present the application of interactive 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 campaign, a method to predict 3-D probabilities of the spatial occurrence of warm conveyor belts has been developed. Probabilities are derived from Lagrangian particle trajectories computed on the forecast wind fields of the 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 forecast wind field resolution. 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 (three to seven days before take-off).

  11. 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

  12. 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

  13. 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.

  14. 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

  15. 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

  16. 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

  17. 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

  18. Ensemble Manifold Rank Preserving for Acceleration-Based Human Activity Recognition.

    PubMed

    Tao, Dapeng; Jin, Lianwen; Yuan, Yuan; Xue, Yang

    2016-06-01

    With the rapid development of mobile devices and pervasive computing technologies, acceleration-based human activity recognition, a difficult yet essential problem in mobile apps, has received intensive attention recently. Different acceleration signals for representing different activities or even a same activity have different attributes, which causes troubles in normalizing the signals. We thus cannot directly compare these signals with each other, because they are embedded in a nonmetric space. Therefore, we present a nonmetric scheme that retains discriminative and robust frequency domain information by developing a novel ensemble manifold rank preserving (EMRP) algorithm. EMRP simultaneously considers three aspects: 1) it encodes the local geometry using the ranking order information of intraclass samples distributed on local patches; 2) it keeps the discriminative information by maximizing the margin between samples of different classes; and 3) it finds the optimal linear combination of the alignment matrices to approximate the intrinsic manifold lied in the data. Experiments are conducted on the South China University of Technology naturalistic 3-D acceleration-based activity dataset and the naturalistic mobile-devices based human activity dataset to demonstrate the robustness and effectiveness of the new nonmetric scheme for acceleration-based human activity recognition. PMID:25265635

  19. 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

  20. 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: ...

  1. Ensembl 2005

    PubMed Central

    Hubbard, T.; Andrews, D.; Caccamo, M.; Cameron, G.; Chen, Y.; Clamp, M.; Clarke, L.; Coates, G.; Cox, T.; Cunningham, F.; Curwen, V.; Cutts, T.; Down, T.; Durbin, R.; Fernandez-Suarez, X. M.; Gilbert, J.; Hammond, M.; Herrero, J.; Hotz, H.; Howe, K.; Iyer, V.; Jekosch, K.; Kahari, A.; Kasprzyk, A.; Keefe, D.; Keenan, S.; Kokocinsci, F.; London, D.; Longden, I.; McVicker, G.; Melsopp, C.; Meidl, P.; Potter, S.; Proctor, G.; Rae, M.; Rios, D.; Schuster, M.; Searle, S.; Severin, J.; Slater, G.; Smedley, D.; Smith, J.; Spooner, W.; Stabenau, A.; Stalker, J.; Storey, R.; Trevanion, S.; Ureta-Vidal, A.; Vogel, J.; White, S.; Woodwark, C.; Birney, E.

    2005-01-01

    The Ensembl (http://www.ensembl.org/) project provides a comprehensive and integrated source of annotation of large genome sequences. Over the last year the number of genomes available from the Ensembl site has increased by 7 to 16, with the addition of the six vertebrate genomes of chimpanzee, dog, cow, chicken, tetraodon and frog and the insect genome of honeybee. The majority have been annotated automatically using the Ensembl gene build system, showing its flexibility to reliably annotate a wide variety of genomes. With the increased number of vertebrate genomes, the comparative analysis provided to users has been greatly improved, with new website interfaces allowing annotation of different genomes to be directly compared. The Ensembl software system is being increasingly widely reused in different projects showing the benefits of a completely open approach to software development and distribution. PMID:15608235

  2. Identifying climate analogues for precipitation extremes for Denmark based on RCM simulations from the ENSEMBLES database.

    PubMed

    Arnbjerg-Nielsen, K; Funder, S G; Madsen, H

    2015-01-01

    Climate analogues, also denoted Space-For-Time, may be used to identify regions where the present climatic conditions resemble conditions of a past or future state of another location or region based on robust climate variable statistics in combination with projections of how these statistics change over time. The study focuses on assessing climate analogues for Denmark based on current climate data set (E-OBS) observations as well as the ENSEMBLES database of future climates with the aim of projecting future precipitation extremes. The local present precipitation extremes are assessed by means of intensity-duration-frequency curves for urban drainage design for the relevant locations being France, the Netherlands, Belgium, Germany, the United Kingdom, and Denmark. Based on this approach projected increases of extreme precipitation by 2100 of 9 and 21% are expected for 2 and 10 year return periods, respectively. The results should be interpreted with caution as the best region to represent future conditions for Denmark is the coastal areas of Northern France, for which only little information is available with respect to present precipitation extremes. PMID:25714642

  3. An Approach for Identifying Cytokines Based on a Novel Ensemble Classifier

    PubMed Central

    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. PMID:24027761

  4. 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

  5. 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. PMID:20457550

  6. 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.

  7. 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

  8. MRF-Based Spatial Expert Tracking of the Multi-Model Ensemble

    NASA Astrophysics Data System (ADS)

    McQuade, S.; Monteleoni, C.

    2013-12-01

    We consider the problem of adaptively combining the 'multi-model ensemble' of General Circulation Models (GCMs) that inform the Intergovernmental Panel on Climate Change (IPCC), drawn from major laboratories around the world. This problem can be treated as an expert tracking problem in the online setting, where an algorithm maintains a set of weights over the experts (here the GCMs are the experts). At each time interval these weights are used to make a combined projection, and then the weights can be updated based on the performance of experts. In this work we focus on tracking the GCMs at different geographic locations and effectively incorporating spatial influence and correlations between these locations. We approach this multi-model ensemble problem using a pairwise Markov Random Field (MRF), where the state of each hidden variable is the identity of the best GCM at a specific location. Our MRF takes the form of a lattice over the Earth, with links between neighboring locations. To establish reasonable energy functions for the MRF, we first show that the Fixed-Share algorithm for expert tracking over time can be expressed as a simple MRF. By expressing Fixed-Share as an MRF, we identify the energy function that corresponds to the switching dynamics (how the best expert switches over time). Since an MRF is an undirected graph, this 'switching' energy function can be naturally applied to spatial links between variables as well. To calculate the marginal probabilities of the hidden variables (i.e. our new beliefs over GCMs), we apply Loopy Belief Propagation (LBP) to the MRF. In LBP, each node sends messages to neighboring nodes about the sender's 'belief' of the neighbor's state. Figure 1 shows our initial results from an online evaluation of GCM temperature hindcasts from the IPCC Phase 3 Coupled Model Intercomparison Project (CMIP3) archive. The red line shows the mean loss of our method versus the spatial switching rate. The right-most point on the graph

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

    PubMed

    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

  10. A global perspective of the limits of prediction skill based on the ECMWF ensemble

    NASA Astrophysics Data System (ADS)

    Zagar, Nedjeljka

    2016-04-01

    In this talk presents a new model of the global forecast error growth applied to the forecast errors simulated by the ensemble prediction system (ENS) of the ECMWF. The proxy for forecast errors is the total spread of the ECMWF operational ensemble forecasts obtained by the decomposition of the wind and geopotential fields in the normal-mode functions. In this way, the ensemble spread can be quantified separately for the balanced and inertio-gravity (IG) modes for every forecast range. Ensemble reliability is defined for the balanced and IG modes comparing the ensemble spread with the control analysis in each scale. The results show that initial uncertainties in the ECMWF ENS are largest in the tropical large-scale modes and their spatial distribution is similar to the distribution of the short-range forecast errors. Initially the ensemble spread grows most in the smallest scales and in the synoptic range of the IG modes but the overall growth is dominated by the increase of spread in balanced modes in synoptic and planetary scales in the midlatitudes. During the forecasts, the distribution of spread in the balanced and IG modes grows towards the climatological spread distribution characteristic of the analyses. The ENS system is found to be somewhat under-dispersive which is associated with the lack of tropical variability, primarily the Kelvin waves. The new model of the forecast error growth has three fitting parameters to parameterize the initial fast growth and a more slow exponential error growth later on. The asymptotic values of forecast errors are independent of the exponential growth rate. It is found that the asymptotic values of the errors due to unbalanced dynamics are around 10 days while the balanced and total errors saturate in 3 to 4 weeks. Reference: Žagar, N., R. Buizza, and J. Tribbia, 2015: A three-dimensional multivariate modal analysis of atmospheric predictability with application to the ECMWF ensemble. J. Atmos. Sci., 72, 4423-4444.

  11. 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.

  12. A new ensemble-based consistency test for the Community Earth System Model

    NASA Astrophysics Data System (ADS)

    Baker, A. H.; Hammerling, D. M.; Levy, M. N.; Xu, H.; Dennis, J. M.; Eaton, B. E.; Edwards, J.; Hannay, C.; Mickelson, S. A.; Neale, R. B.; Nychka, D.; Shollenberger, J.; Tribbia, J.; Vertenstein, M.; Williamson, D.

    2015-05-01

    Climate simulations codes, such as the Community Earth System Model (CESM), are especially complex and continually evolving. Their on-going state of development requires frequent software verification in the form of quality assurance to both preserve the quality of the code and instill model confidence. To formalize and simplify this previously subjective and computationally-expensive aspect of the verification process, we have developed a new tool for evaluating climate consistency. Because an ensemble of simulations allows us to gauge the natural variability of the model's climate, our new tool uses an ensemble approach for consistency testing. In particular, an ensemble of CESM climate runs is created, from which we obtain a statistical distribution that can be used to determine whether a new climate run is statistically distinguishable from the original ensemble. The CESM Ensemble Consistency Test, referred to as CESM-ECT, is objective in nature and accessible to CESM developers and users. The tool has proven its utility in detecting errors in software and hardware environments and providing rapid feedback to model developers.

  13. 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.

  14. 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.

  15. Ensemble framework based real-time respiratory motion prediction for adaptive radiotherapy applications.

    PubMed

    Tatinati, Sivanagaraja; Nazarpour, Kianoush; Tech Ang, Wei; Veluvolu, Kalyana C

    2016-08-01

    Successful treatment of tumors with motion-adaptive radiotherapy requires accurate prediction of respiratory motion, ideally with a prediction horizon larger than the latency in radiotherapy system. Accurate prediction of respiratory motion is however a non-trivial task due to the presence of irregularities and intra-trace variabilities, such as baseline drift and temporal changes in fundamental frequency pattern. In this paper, to enhance the accuracy of the respiratory motion prediction, we propose a stacked regression ensemble framework that integrates heterogeneous respiratory motion prediction algorithms. We further address two crucial issues for developing a successful ensemble framework: (1) selection of appropriate prediction methods to ensemble (level-0 methods) among the best existing prediction methods; and (2) finding a suitable generalization approach that can successfully exploit the relative advantages of the chosen level-0 methods. The efficacy of the developed ensemble framework is assessed with real respiratory motion traces acquired from 31 patients undergoing treatment. Results show that the developed ensemble framework improves the prediction performance significantly compared to the best existing methods. PMID:27238760

  16. 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.

  17. 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

  18. 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. PMID:26259520

  19. A novel signal compression method based on optimal ensemble empirical mode decomposition for bearing vibration signals

    NASA Astrophysics Data System (ADS)

    Guo, Wei; Tse, Peter W.

    2013-01-01

    Today, remote machine condition monitoring is popular due to the continuous advancement in wireless communication. Bearing is the most frequently and easily failed component in many rotating machines. To accurately identify the type of bearing fault, large amounts of vibration data need to be collected. However, the volume of transmitted data cannot be too high because the bandwidth of wireless communication is limited. To solve this problem, the data are usually compressed before transmitting to a remote maintenance center. This paper proposes a novel signal compression method that can substantially reduce the amount of data that need to be transmitted without sacrificing the accuracy of fault identification. The proposed signal compression method is based on ensemble empirical mode decomposition (EEMD), which is an effective method for adaptively decomposing the vibration signal into different bands of signal components, termed intrinsic mode functions (IMFs). An optimization method was designed to automatically select appropriate EEMD parameters for the analyzed signal, and in particular to select the appropriate level of the added white noise in the EEMD method. An index termed the relative root-mean-square error was used to evaluate the decomposition performances under different noise levels to find the optimal level. After applying the optimal EEMD method to a vibration signal, the IMF relating to the bearing fault can be extracted from the original vibration signal. Compressing this signal component obtains a much smaller proportion of data samples to be retained for transmission and further reconstruction. The proposed compression method were also compared with the popular wavelet compression method. Experimental results demonstrate that the optimization of EEMD parameters can automatically find appropriate EEMD parameters for the analyzed signals, and the IMF-based compression method provides a higher compression ratio, while retaining the bearing defect

  20. 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.

    2016-02-01

    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.

  1. 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

  2. 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

  3. Highly selective ensembles for D-fructose based on fluorescent method in aqueous solution

    NASA Astrophysics Data System (ADS)

    Wang, Zhijun; Lei, Haiying; Zhou, Chengyong; Wang, Guofeng; Feng, Liheng

    2012-06-01

    Three highly sensitive and selective switches for monosaccharides were composed by anionic polyelectrolyte PPPSO3Na and cationic viologen quencheres BBVs. The sensing processes of three ensembles (PPPSO3Na/o-BBV, PPPSO3Na/m-BBV and PPPSO3Na/p-BBV) to common seven monosaccharides have been determined by fluorescence spectra at pH 7.4 buffer solution. The results show that the three sensing ensembles all embody higher selectivity and sensitivity for D-fructose with reversible "on-off-on" fluorescence response. The research results can provide a new mode for developing highly selective probes.

  4. Probabilistic regional wind power forecasts based on calibrated Numerical Weather Forecast ensembles

    NASA Astrophysics Data System (ADS)

    Späth, Stephan; von Bremen, Lueder; Junk, Constantin; Heinemann, Detlev

    2014-05-01

    With increasing shares of installed wind power in Germany, accurate forecasts of wind speed and power get increasingly important for the grid integration of Renewable Energies. Applications like grid management and trading also benefit from uncertainty information. This uncertainty information can be provided by ensemble forecasts. These forecasts often exhibit systematic errors such as biases and spread deficiencies. The errors can be reduced by statistical post-processing. We use forecast data from the regional Numerical Weather Prediction model COSMO-DE EPS as input to regional wind power forecasts. In order to enhance the power forecast, we first calibrate the wind speed forecasts against the model analysis, so some of the model's systematic errors can be removed. Wind measurements at every grid point are usually not available and as we want to conduct grid zone forecasts, the model analysis is the best target for calibration. We use forecasts from the COSMO-DE EPS, a high-resolution ensemble prediction system with 20 forecast members. The model covers the region of Germany and surroundings with a vertical resolution of 50 model levels and a horizontal resolution of 0.025 degrees (approximately 2.8 km). The forecast range is 21 hours with model output available on an hourly basis. Thus, we use it for shortest-term wind power forecasts. The COSMO-DE EPS was originally designed with a focus on forecasts of convective precipitation. The COSMO-DE EPS wind speed forecasts at hub height were post-processed by nonhomogenous Gaussian regression (NGR; Thorarinsdottir and Gneiting, 2010), a calibration method that fits a truncated normal distribution to the ensemble wind speed forecasts. As calibration target, the model analysis was used. The calibration is able to remove some deficits of the COSMO-DE EPS. In contrast to the raw ensemble members, the calibrated ensemble members do not show anymore the strong correlations with each other and the spread-skill relationship

  5. [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. PMID:23487919

  6. The Performance and Feasibility of Ensemble Forecast Sensitivity to Observations-based Proactive Quality Control Scheme

    NASA Astrophysics Data System (ADS)

    Chen, T. C.; Hotta, D.; Kalnay, E.

    2015-12-01

    Operational numerical weather prediction (NWP) systems occasionally exhibit "forecast skill dropouts" in which the forecast skill drops to an abnormally low level, due in part to the assimilation of flawed observational data. Recent studies have shown that a diagnostic technique called Ensemble Forecast Sensitivity to Observations (EFSO) can detect such observations (Kalnay et.al 2012; Ota et al. 2013, Tellus A). Based on this technique, a new Quality Control (QC) scheme called Proactive QC (PQC) has been proposed which detects "flawed" observations using EFSO after just 6 hours forecast, when the analysis at the next cycle becomes available for verification and then repeats the analysis and forecast without using the detected observations (Hotta 2014). In Hotta (2014), it was shown using the JCSDA S4 Testbed that the 6hr PQC reduces the 24-hour forecast errors from the detected skill dropout events. With such encouraging results we are performing preliminary experiments towards operational implementation. First, we show that offline PQC correction can significantly reduce forecast errors up to 5 days, and that the reduction and improved areal coverage can grow with synoptic weather disturbances for several days. Second, with online PQC cycle experiment the reduction of forecast error is shown to be even larger than in the offline version, since the effect could accumulate over each time we perform a PQC correction. Finally, the operational center imposes very tight schedule in order to deliver the products on time, thus the computational cost has to be minimized in order for PQC to be implemented. To avoid performing the analysis twice, which is the most expensive part of PQC, we test the accuracy of constant-K approximation, which assumes the Kalman gain K doesn't change much given the fact that only a small subset of observation is rejected. In this presentation, we will demonstrate the performance and feasibility of PQC implementation in real-time operational

  7. 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

  8. 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…

  9. 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.

  10. 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.

  11. Exploring the PDE5 H-pocket by ensemble docking and structure-based design and synthesis of novel β-carboline derivatives

    PubMed Central

    Ahmed, Nermin S.; Ali, Amal H.; El-Nashar, Shreen M.; Gary, Bernard D.; Fajardo, Alexandra M.; Tinsley, Heather N.; Piazza, Gary A.; Negri, Matthias; Abadi, Ashraf H.

    2016-01-01

    By studying the co-crystal information of interactions between PDE5 and its inhibitors, forty new tetrahydro-β-carbolines based-analogues were synthesized, and tested for their PDE5 inhibition. Some compounds were as active as tadalafil in inhibiting PDE5 and of better selectivity profile particularly versus PDE11A, the nature of the terminal ring and its nitrogen substituent are the main determinants of selectivity. Ensemble docking confirmed the role of H-loop closed conformer in activity versus its occluded and open forms. Conformational studies showed the effect of bulkiness of the terminal ring N-alkyl substituent on the formation of stable enzyme ligands conformers. The difference in potencies of hydantoin and piperazinedione analogues, together with the necessity of C-5/C-6 R-absolute configuration has been revealed through molecular docking. PMID:23117589

  12. 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.

  13. 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. PMID:26896847

  14. 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

  15. 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

  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. Assessment of probability density function based on POD reduced-order model for ensemble-based data assimilation

    NASA Astrophysics Data System (ADS)

    Kikuchi, Ryota; Misaka, Takashi; Obayashi, Shigeru

    2015-10-01

    An integrated method of a proper orthogonal decomposition based reduced-order model (ROM) and data assimilation is proposed for the real-time prediction of an unsteady flow field. In this paper, a particle filter (PF) and an ensemble Kalman filter (EnKF) are compared for data assimilation and the difference in the predicted flow fields is evaluated focusing on the probability density function (PDF) of the model variables. The proposed method is demonstrated using identical twin experiments of an unsteady flow field around a circular cylinder at the Reynolds number of 1000. The PF and EnKF are employed to estimate temporal coefficients of the ROM based on the observed velocity components in the wake of the circular cylinder. The prediction accuracy of ROM-PF is significantly better than that of ROM-EnKF due to the flexibility of PF for representing a PDF compared to EnKF. Furthermore, the proposed method reproduces the unsteady flow field several orders faster than the reference numerical simulation based on the Navier-Stokes equations.

  18. 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

  19. 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.

  20. 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.

  1. 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.

  2. 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.

  3. 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

  4. Parameter Estimation of a Physically-Based Land Surface Hydrologic Model Using the Ensemble Kalman Filter

    NASA Astrophysics Data System (ADS)

    Shi, Y.; Davis, K. J.; Zhang, F.; Duffy, C.

    2012-12-01

    A fully-coupled physically-based land surface hydrologic model, Flux-PIHM, is developed by incorporating a land-surface scheme into the Penn State Integrated Hydrologic Model (PIHM). The land-surface scheme is mainly adapted from the Noah LSM, which is widely used in mesoscale atmospheric models and has undergone extensive testing. Because PIHM is capable of simulating lateral water flow and deep groundwater, Flux-PIHM is able to represent both the link between groundwater and the surface energy balance, as well as some of the land surface heterogeneities caused by topography. Flux-PIHM has been implemented and manually calibrated at the Shale Hills watershed (0.08 km2) in central Pennsylvania. Model predictions of discharge, soil moisture, water table depth, sensible and latent heat fluxes, and soil temperature show good agreement with observations. The discharge prediction is significantly better than state-of-the-art conceptual models implemented at similar watersheds. The ensemble Kalman filter (EnKF) provides a promising approach for physically-based land surface hydrologic model calibration. A Flux-PHIM data assimilation system is developed by incorporating EnKF into Flux-PIHM for model parameter and state estimation. This is the first parameter estimation using EnKF for a physically-based hydrologic model. Both synthetic and real data experiments are performed at the Shale Hills watershed to test the capability of EnKF in parameter estimation. Six model parameters selected from a model parameter sensitivity test are estimated. In the synthetic experiments, synthetic observations of discharge, water table depth, soil moisture, land surface temperature, sensible and latent heat fluxes, and transpiration are assimilated into the system. Observations are assimilated every 72 hours in wet periods, and every 144 hours in dry periods. Results show that EnKF is capable of accurately estimating model parameter values for Flux-PIHM. In the first set of experiments

  5. 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

  6. 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.

  7. Evaluation and Sensitivity Analysis of An Ensemble-based Coupled Flash Flood and Landslide Modelling System Using Remote Sensing Forcing

    NASA Astrophysics Data System (ADS)

    Zhang, K.; Hong, Y.; Gourley, J. J.; Xue, X.; He, X.

    2015-12-01

    Heavy rainfall-triggered landslides are often associated with flood events and cause additional loss of life and property. It is pertinent to build a robust coupled flash flood and landslide disaster early warning system for disaster preparedness and hazard management based. In this study, we built an ensemble-based coupled flash flood and landslide disaster early warning system, which is aimed for operational use by the US National Weather Service, by integrating the Coupled Routing and Excess STorage (CREST) model and Sacramento Soil Moisture Accounting Model (SAC-SMA) with the physically based SLope-Infiltration-Distributed Equilibrium (SLIDE) landslide prediction model. We further evaluated this ensemble-based prototype warning system by conducting multi-year simulations driven by the Multi-Radar Multi-Sensor (MRMS) rainfall estimates in North Carolina and Oregon. We comprehensively evaluated the predictive capabilities of this system against observed and reported flood and landslides events. We then evaluated the sensitivity of the coupled system to the simulated hydrological processes. Our results show that the system is generally capable of making accurate predictions of flash flood and landslide events in terms of their locations and time of occurrence. The occurrence of predicted landslides show high sensitivity to total infiltration and soil water content, highlighting the importance of accurately simulating the hydrological processes on the accurate forecasting of rainfall triggered landslide events.

  8. 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

  9. Combining NMR ensembles and molecular dynamics simulations provides more realistic models of protein structures in solution and leads to better chemical shift prediction.

    PubMed

    Lehtivarjo, Juuso; Tuppurainen, Kari; Hassinen, Tommi; Laatikainen, Reino; Peräkylä, Mikael

    2012-03-01

    While chemical shifts are invaluable for obtaining structural information from proteins, they also offer one of the rare ways to obtain information about protein dynamics. A necessary tool in transforming chemical shifts into structural and dynamic information is chemical shift prediction. In our previous work we developed a method for 4D prediction of protein (1)H chemical shifts in which molecular motions, the 4th dimension, were modeled using molecular dynamics (MD) simulations. Although the approach clearly improved the prediction, the X-ray structures and single NMR conformers used in the model cannot be considered fully realistic models of protein in solution. In this work, NMR ensembles (NMRE) were used to expand the conformational space of proteins (e.g. side chains, flexible loops, termini), followed by MD simulations for each conformer to map the local fluctuations. Compared with the non-dynamic model, the NMRE+MD model gave 6-17% lower root-mean-square (RMS) errors for different backbone nuclei. The improved prediction indicates that NMR ensembles with MD simulations can be used to obtain a more realistic picture of protein structures in solutions and moreover underlines the importance of short and long time-scale dynamics for the prediction. The RMS errors of the NMRE+MD model were 0.24, 0.43, 0.98, 1.03, 1.16 and 2.39 ppm for (1)Hα, (1)HN, (13)Cα, (13)Cβ, (13)CO and backbone (15)N chemical shifts, respectively. The model is implemented in the prediction program 4DSPOT, available at http://www.uef.fi/4dspot. PMID:22314705

  10. 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.

  11. The diagnostics of diabetes mellitus based on ensemble modeling and hair/urine element level analysis.

    PubMed

    Chen, Hui; Tan, Chao; Lin, Zan; Wu, Tong

    2014-07-01

    The aim of the present work focuses on exploring the feasibility of analyzing the relationship between diabetes mellitus and several element levels in hair/urine specimens by chemometrics. A dataset involving 211 specimens and eight element concentrations was used. The control group was divided into three age subsets in order to analyze the influence of age. It was found that the most obvious difference was the effect of age on the level of zinc and iron. The decline of iron concentration with age in hair was exactly consistent with the opposite trend in urine. Principal component analysis (PCA) was used as a tool for a preliminary evaluation of the data. Both ensemble and single support vector machine (SVM) algorithms were used as the classification tools. On average, the accuracy, sensitivity and specificity of ensemble SVM models were 99%, 100%, 99% and 97%, 89%, 99% for hair and urine samples, respectively. The findings indicate that hair samples are superior to urine samples. Even so, it can provide more valuable information for prevention, diagnostics, treatment and research of diabetes by simultaneously analyzing the hair and urine samples. PMID:24835087

  12. Quantification of Transport Errors in regional CO2 inversions using a physics-based ensemble of WRF-Chem simulations

    NASA Astrophysics Data System (ADS)

    Diaz Isaac, L. I.; Davis, K. J.; Lauvaux, T.; Miles, N. L.; Richardson, S.; Andrews, A. E.

    2013-12-01

    Atmospheric inversions can be used to assess biosphere-atmosphere CO2 surface exchanges, but variability among inverse flux estimates at regional scales remains significant. Atmospheric transport model errors are presumed to be one of the main contributors to this variability, but have not been quantified thoroughly. Our study aims to evaluate and quantify the transport errors in the Weather Research and Forecasting (WRF) mesoscale model, recently used to produce inverse flux estimates at the regional scale over the NACP Mid-Continental Intensive (MCI) domain. We evaluate transport errors with an ensemble of WRF simulations using different physical parameterizations (e.g., atmospheric boundary layer (ABL) schemes, land surface models (LSMs), and cumulus parameterizations (CP)). Modeled meteorological variables and atmospheric CO2 mixing ratios are compared to observations (e.g., radiosondes, wind profilers, AmeriFlux sites, and CO2 mixing ratio towers) available in the MCI region for summer of 2008. Comparisons to date include simulations using two different land surface models (Noah and Rapid Update Cycle (RUC)), three different ABL schemes (YSU, MYJ and MYNN) and two different cumulus parameterizations (Kain-Fritsch and Grell-3D). We examine using the ensemble as a proxy for the observed model-data mismatch. Then we present a study of the sensitivity of atmospheric conditions to the choice of physical parameterization, to identify the parameterization driving the model-to-model variability in atmospheric CO2 concentrations at the mesoscale over the MCI domain. For example, we show that, whereas the ABL depth is highly influenced by the choice of ABL scheme and LSM, the mean horizontal wind speed is mainly influenced by the LSM only. Finally, we evaluate the variability in space and time of transport errors and their impact in atmospheric CO2 concentrations. Future work will be to describe transport errors in the MCI regional atmospheric inversion based on the

  13. 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.

  14. 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. PMID:26744898

  15. 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. PMID:27232449

  16. 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.

  17. 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.

  18. 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

  19. 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.

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

    NASA Astrophysics Data System (ADS)

    Zhu, Chaoyuan

    2016-04-01

    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.

  1. 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.

  2. 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

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

    NASA Astrophysics Data System (ADS)

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

    2013-10-01

    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.

  4. 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.

  5. 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.

  6. Bioconjugate-Based Molecular Umbrellas

    PubMed Central

    Janout, Vaclav; Regen, Steven L.

    2009-01-01

    Molecular umbrellas are “amphomorphic” compounds that can produce a hydrophobic or a hydrophilic exterior when exposed to a hydrophobic or hydrophilic microenvironment, respectively. Such molecules are composed of two or more facial amphiphiles that are connected to a central scaffold. Molecular umbrellas that have been synthesized to date, using bile acids as umbrella “walls”, polyamines such as spermidine and spermine as scaffold material, and L-lysine as “branches”, have been found capable of transporting certain hydrophilic peptides, nucleotides, and oligonucleotides across liposomal membranes by passive diffusion. They have also have been shown to increase in the water solubility and hydrolytic stability of a hydrophobic drug, and to exhibit significant antiviral activity. The ability of a fluorescently-labeled molecular umbrella to readily enter live HeLa cells suggests that such conjugates could find use as drug carriers. PMID:19053303

  7. 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

  8. 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. PMID:27222203

  9. A retrospective assessment of National Centers for Environmental Prediction climate model-based ensemble hydrologic forecasting in the western United States

    NASA Astrophysics Data System (ADS)

    Wood, Andrew W.; Kumar, Arun; Lettenmaier, Dennis P.

    2005-02-01

    We assess the potential forecast skill of a climate model-based approach for seasonal ensemble hydrologic and streamflow forecasting for the western United States. By using climate model ensemble forecasts and ensembles formed via the resampling of observations, we distinguish hydrologic forecast skill resulting from the predictable evolution of initial hydrologic conditions from that derived from the climate model forecasts. Monthly climate model ensembles of precipitation and temperature produced by the National Centers for Environmental prediction global spectral model (GSM) are downscaled for use as forcings of the variable infiltration capacity (VIC) hydrologic model. VIC then simulates ensembles of streamflow and spatially distributed hydrologic variables such as snowpack, soil moisture, and runoff. The regional averages of the ensemble forcings and derived hydrologic variables were evaluated over five regions: the Pacific Northwest, California, the Great Basin, the Colorado River basin, and the upper Rio Grande River basin. The skill assessment focuses on a retrospective 21-year period (1979-1999) during which GSM retrospective forecast ensembles (termed hindcasts), created using similar procedures to GSM real-time forecasts, are available. The observational verification data set for the hindcasts was a retrospective hydroclimatology at 1/8°-1/4° consisting of gridded observations of temperature and precipitation and gridded hydrologic simulation results (for hydrologic variables and streamflow) based on the observed meteorological inputs. The GSM hindcast skill was assessed relative to that of a naive ensemble climatology forecast and to that of ensemble streamflow prediction (ESP) hindcasts, a forecast baseline sharing the same initial condition information as the GSM-based hindcasts. We found that the unconditional (all years) GSM hindcasts for regionally averaged variables provided practically no skill improvement over the ESP hindcasts and did not

  10. 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

  11. 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.

  12. 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.

  13. 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

  14. 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

  15. 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

  16. 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

  17. 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.

  18. 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.

  19. 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. PMID:26320594

  20. Encompassing receptor flexibility in virtual screening using ensemble docking-based hybrid QSAR: discovery of novel phytochemicals for BACE1 inhibition.

    PubMed

    Chakraborty, Sandipan; Ramachandran, Balaji; Basu, Soumalee

    2014-10-01

    Mimicking receptor flexibility during receptor-ligand binding is a challenging task in computational drug design since it is associated with a large increase in the conformational search space. In the present study, we have devised an in silico design strategy incorporating receptor flexibility in virtual screening to identify potential lead compounds as inhibitors for flexible proteins. We have considered BACE1 (β-secretase), a key target protease from a therapeutic perspective for Alzheimer's disease, as the highly flexible receptor. The protein undergoes significant conformational transitions from open to closed form upon ligand binding, which makes it a difficult target for inhibitor design. We have designed a hybrid structure-activity model containing both ligand based descriptors and energetic descriptors obtained from molecular docking based on a dataset of structurally diverse BACE1 inhibitors. An ensemble of receptor conformations have been used in the docking study, further improving the prediction ability of the model. The designed model that shows significant prediction ability judged by several statistical parameters has been used to screen an in house developed 3-D structural library of 731 phytochemicals. 24 highly potent, novel BACE1 inhibitors with predicted activity (Ki) ≤ 50 nM have been identified. Detailed analysis reveals pharmacophoric features of these novel inhibitors required to inhibit BACE1. PMID:25088750

  1. Predictive Skill of Meteorological Drought Based on Multi-Model Ensemble Forecasts: A Real-Time Assessment

    NASA Astrophysics Data System (ADS)

    Chen, L. C.; Mo, K. C.; Zhang, Q.; Huang, J.

    2014-12-01

    Drought prediction from monthly to seasonal time scales is of critical importance to disaster mitigation, agricultural planning, and multi-purpose reservoir management. Starting in December 2012, NOAA Climate Prediction Center (CPC) has been providing operational Standardized Precipitation Index (SPI) Outlooks using the North American Multi-Model Ensemble (NMME) forecasts, to support CPC's monthly drought outlooks and briefing activities. The current NMME system consists of six model forecasts from U.S. and Canada modeling centers, including the CFSv2, CM2.1, GEOS-5, CCSM3.0, CanCM3, and CanCM4 models. In this study, we conduct an assessment of the predictive skill of meteorological drought using real-time NMME forecasts for the period from May 2012 to May 2014. The ensemble SPI forecasts are the equally weighted mean of the six model forecasts. Two performance measures, the anomaly correlation coefficient and root-mean-square errors against the observations, are used to evaluate forecast skill.Similar to the assessment based on NMME retrospective forecasts, predictive skill of monthly-mean precipitation (P) forecasts is generally low after the second month and errors vary among models. Although P forecast skill is not large, SPI predictive skill is high and the differences among models are small. The skill mainly comes from the P observations appended to the model forecasts. This factor also contributes to the similarity of SPI prediction among the six models. Still, NMME SPI ensemble forecasts have higher skill than those based on individual models or persistence, and the 6-month SPI forecasts are skillful out to four months. The three major drought events occurred during the 2012-2014 period, the 2012 Central Great Plains drought, the 2013 Upper Midwest flash drought, and 2013-2014 California drought, are used as examples to illustrate the system's strength and limitations. For precipitation-driven drought events, such as the 2012 Central Great Plains drought

  2. [Molecular bases of cancer immunology].

    PubMed

    Barrera-Rodríguez, R; Peralta-Zaragoza, O; Madrid-Marina, V

    1995-01-01

    The immune system is a tight network of different types of cells and molecules. The coordinated action of these elements mounts a precise immune response against tumor cells. However, these cells present several escape mechanisms, leading to tumor progression. This paper shows several cellular and molecular events involved in the regulation of the immune response against tumor cells. The interaction of several molecules such as MHC, TcR, adhesins, tumor antigens and cytokines are discussed, as well as the most recent knowledge about escape mechanisms and immunotherapy. PMID:7502157

  3. Dynamic neuronal ensembles: Issues in representing structure change in object-oriented, biologically-based brain models

    SciTech Connect

    Vahie, S.; Zeigler, B.P.; Cho, H.

    1996-12-31

    This paper describes the structure of dynamic neuronal ensembles (DNEs). DNEs represent a new paradigm for learning, based on biological neural networks that use variable structures. We present a computational neural element that demonstrates biological neuron functionality such as neurotransmitter feedback absolute refractory period and multiple output potentials. More specifically, we will develop a network of neural elements that have the ability to dynamically strengthen, weaken, add and remove interconnections. We demonstrate that the DNE is capable of performing dynamic modifications to neuron connections and exhibiting biological neuron functionality. In addition to its applications for learning, DNEs provide an excellent environment for testing and analysis of biological neural systems. An example of habituation and hyper-sensitization in biological systems, using a neural circuit from a snail is presented and discussed. This paper provides an insight into the DNE paradigm using models developed and simulated in DEVS.

  4. 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.

  5. 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. PMID:26210398

  6. 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

  7. 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.

  8. Improving cross-resolution face matching using ensemble-based co-transfer learning.

    PubMed

    Bhatt, Himanshu S; Singh, Richa; Vatsa, Mayank; Ratha, Nalini K

    2014-12-01

    Face recognition algorithms are generally trained for matching high-resolution images and they perform well for similar resolution test data. However, the performance of such systems degrades when a low-resolution face image captured in unconstrained settings, such as videos from cameras in a surveillance scenario, are matched with high-resolution gallery images. The primary challenge, here, is to extract discriminating features from limited biometric content in low-resolution images and match it to information rich high-resolution face images. The problem of cross-resolution face matching is further alleviated when there is limited labeled positive data for training face recognition algorithms. In this paper, the problem of cross-resolution face matching is addressed where low-resolution images are matched with high-resolution gallery. A co-transfer learning framework is proposed, which is a cross-pollination of transfer learning and co-training paradigms and is applied for cross-resolution face matching. The transfer learning component transfers the knowledge that is learnt while matching high-resolution face images during training to match low-resolution probe images with high-resolution gallery during testing. On the other hand, co-training component facilitates this transfer of knowledge by assigning pseudolabels to unlabeled probe instances in the target domain. Amalgamation of these two paradigms in the proposed ensemble framework enhances the performance of cross-resolution face recognition. Experiments on multiple face databases show the efficacy of the proposed algorithm and compare with some existing algorithms and a commercial system. In addition, several high profile real-world cases have been used to demonstrate the usefulness of the proposed approach in addressing the tough challenges. PMID:25314702

  9. 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. PMID:25488024

  10. 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.